Jasa Konsultasi Disertasi Keuangan (1) dan Perbankan S3, hubungi WA 0821 2230 7021: The 4th International Conference on Digital Arts, Media and Technology

The 4th International Conference on Digital Arts, Media and Technology

and 2nd ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering

A Machine Learning Approach for Detecting

Distributed Denial of Service Attacks

Tanaphon Roempluk *† and Olarik Surinta *

* Multi-agent Intelligent Simulation Laboratory (MISL)

Department of Information Technology, Faculty of Informatics

Mahasarakham University

Maha Sarakham, Thailand

{tanaphon.roe, olarik.s}@msu.ac.th

†Master student

Abstract—This research aims to present the method for identifying distributed denial of service (DDoS) attacks. Two benchmark dataset, including KDD CUP 1999 and NSL-KDD, were used. The dataset were checked and deleted duplicate data. After the process, the amount of records of KDD Cup 1999 dataset were decreased from 4,898,431 records to 529,655 records, and the amount of records of NSL-KDD dataset were decreased from 125,373 to only 12,354 records. The reduction of the records always happened because of the characteristics of DDoS attacks which send repeated data to the victims’ server. The researchers converted alphabet data to numeric data, then training by K-nearest neighbor (KNN), multi-layer perceptron and support vector machine. The result showed that KNN was the best method to identify the DDoS attacks.

Index Terms—Distributed denial of service (DDoS) attack, K-nearest neighbor (KNN), Multi-layer percetron (MLP), Support vector machine (SVM)

I. INTRODUCTION

Nowadays, security concerns from the use of the internet and computer system is one of problems. The security has been attacked in different ways. Distributed Denial of Service (DDoS) [1] is one of the most common attacks on the internet due to the limitation on the attacked device such as memory or bandwidth. The victims require to open the system to allow users to connect. The cyber attackers use these channels to make the victims’ resources reach their peak until they cannot be used. Then, the victim’ devices are out of service and cannot serve the users. Classification o f abnormalities in computer network can be classified b y u sing computer network traffic l ogs, w hich i nclude n ormal d ata a nd various network attack data. In each attack features, there are different characteristics which can be used to detect abnormalities when occurring in the system. In [2]–[5] artificial n eural network (ANN) were used in order to classify attack data. The accuracy is higher than 90%.

Related work: In the research [2], researchers specified the numbers of hidden layers of the network from 30-55 layers in order to to classify the DDoS attack data from 4,986 records. Records were classified into 4 groups including, DNS DDoS attack, CharGen DDoS attack, UDP DDoS attack and Normal. The result found that ANN with total 50 hidden layers can

identify the DDoS data with a 95.6% accuracy rate. While Hsieh and Chan [3] used neural network and Apache Spark framework, which has been used to manage large-scale data (Big Data) and work as a cluster for DDos detection. In the experiment of series ARPA 2000 LLDOS 1.0 which has 7 special features including, Number of Packets, Average of Packet Size, Time Interval Variance, Packet Size Variance, Number of Bytes, Packet Rate, and Bit Rate. All data were classified into two categories: normal data and attack data. There were 51,040 normal data and 74,480 attack data. All data samples were separated into 2 parts, 30% for the learning series, and another 70% for the test Data. It was found 94% accuracy rate.

In the research of Devaraju and Ramakrishnan [4] tested three artificial neural networks including 3 methods. There were feed forward neural network (FFNN), probabilistic neural network (PNN) and radial basis neural network (RBNN). The methods has been used to test the effectiveness of the Intrusion Detection System by tested with the KDD Cup 1999 dataset [6], containing 41 special features. There are four types of attacks including, 1) Denial of Service (DoS) containing back, land, neptune, pod, smurf and teardrop, 2) Remote to Local (R2L), containing ftp write, guess passwd, imap, multihop, phf, spy, warezclient and warezmaster, 3) User-to-Root (U2R), containing buffer overflow, loadmodule, perl and rootkit, 4) probing, containing ipsweep, nmap, portsweep and satan. The data were divided into 7 classes. There were normal class, smurf class, neptune class, saint class, mail bomb class, Apache class and satan class. The experiment was divided into training set and test set. Each set contains 700 data. The experimental data showed that the PNN network was the best. The PNN, FFNN and RBNN neural networks performed accuracy rate at 97.5%, 94.3% and 65%, respectively.

Researchers also use different machine learning techniques e.g. In [5], they classified network attack information by using ANN, SVM, and ANN+SVM techniques and using dataset NSL KDD [7] . In this experiment, the attack were divided into 2 classes including, 58,630 attack class and 67,343 normal classes. The accuracy rate were 79.56, 79.27 and 79.71%, respectively.

In [8] offers intrusion detection method by using decision

The 4th International Conference on Digital Arts, Media and Technology

and 2nd ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering

tree ID3 in order to reduce the number of special features. From the KDD Cup 1999 dataset which reduced 41 attributes to 18 attributes. The information used in this test were divided into four intrusion categories. There are Denial of Service (DoS), Remote to Local (R2L), User to Root (U2R) and Probe. The 26,167 data sets are divided into two equal parts for training and testing. Special feature information were trained through K-nearest neighbor and genetic algorithm (KNN-GA) techniques in order to categorize the information and compare to KNN methods and support vector machine (SVM). The experiments showed that the KNN-GA method was the most accurate. The accuracy rate was 98%.

In [9] presented a method for improving the detection and classification process using na¨ıve bayes, bayesian networks (NB-Tree) and AD-Tree which tested with NSL-KDD 99 dataset. The test data values were converted to the rage 0-1 by min-max normalization method. Then, the special features were selected from the data by correlation-based feature selection (CFS). The results showed that NB-Tree had the best performance. NB-Tree, AD-Tree and na¨ıve bayes are effective at 99.87%, 98.49% and 90.38%, consequently.

Kushwaha et al. [10] presented a method for selecting the best special features with the Mutual Information (MI) for detecting abnormalities. 10% of KDD Cup 1999 data were tested. The data contains 494,021 training data and 311,029 test data. The data were divided into 2 classes including, attack data and normal data. When using MI, 30 special features were selected and applied to further training with na¨ıve, random forest, OneR, SVM, Adaboost, Bagging, KNN (k=5), KNN (k=10) and SVM+Nave Bayes. The accuracy rates were 92.73%, 99.89%, 95.58%, 99.91%, 95.05%, 99.79%, 99.77%, 99.69%, and 60.40% respectively. The results showed that the SVM performs the best accuracy rate at 99.91%. In [11] presented the method for identifying abnormal data in computer networks. The KDD Cup 1999 and NSD-KDD dataset were tested with J48 graft and na¨ıve bayes method. Cross validation were conducted as an effective evaluator. Given K = 10, from the test, the J48 graft method has 99.435% accuracy rate. the J48 got the best effectiveness. The na¨ıve bayes method got 92.715% accuracy rate.

Contribution: This research presents a method for classi-fying DDoS attack data by using computer network security information with machine learning. The KNN, SVM and MLP method are introduced. The grid search method are used for finding the suitable parameters for the KDD Cup 1999 and NSL-KDD dataset in order to make a performance comparison.

Paper outline: The remaining parts of the paper is or-ganized as follows: In Section II, the machine learning ap-proaches are described. In Section III data processing is presented. In Section IV experimental settings and the results are presented. Finally, a conclusion and future work is given in Section V.

II. MACHINE LEARNING APPROACHES A. K-Nearest Neighbors Algorithm (KNN)

KNN which is the method to find the nearest member in dataset. It is a technique of machine learning that does not require modeling for data classification, but all data will be calculated to find distance value in order to compare the distance between the data that need to be classified y and all data Xi [12]. Therefore, the data with the smallest distance, the amount of data is k , is taken to be considered. In the total k, if any of members in the group Ci has the highest values of k, that data which needs to be categorized y will be categorized at that group. Therefore, given k = 3 means 3 values with minimum distance value will be taken into the account. If three data, containing the distance value, are in the group d = (C1, C1, C3), the data which need to be categorized will be set as C1 because there are the most appearances. Euclidean distance calculations can be calculated as Eq. 1.

(xi - yi)2 (1)

where N is the number of special features (Dimensions) of the data. x, y is the data in the training set, and y is the data which needs to be classified. Then, calculate all the distance values d(x, y) for voting by majority vote method. Therefore, Ck dataset which most appears is determined as the result of KNN.

B. Support Vector Machine (SVM)

SVM [13], [14] is the powerful and accurate categorization method, so researchers always use in classifcation problems. The SVM algorithm find the optimal hyperplane with the maximum margin between training point and hyperplane. The training point that approach the hyperplane line are called support vectors. Initially, SVM was designed to work with the special two-class classification, using a linear equation for segmenting feature vector data (Eq. 2).

f (x) = sign(wT x + b) (2)

where w is weight vector and B is bias.

C. Multi-Layer Perceptron (MLP)

MLP is artificial neural network with a multi-layer structure [15]. It consists of an input layer and passes from one layer to another hidden layer. It has a function for calculating when receiving an output from the node in the previous layer. The function called activation function. Each layer does not need to be the same function. The function converts incoming data to distinguish using a single line called linearly separable. Before the data has been sent to the output layer, it is sometimes necessary to use more than one hidden layer in order to convert the data into Linearly Separable until it reach the output layer.

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III. DATA PROCESSING

A. Data Analysis

KDD CUP 1999 dataset and NSL-KDD dataset were di-vided into normal class and 4 features of attack class including,

1) Denial of Service (DOS) attacks is an attack that send a large number of packets to the target victims which cause the service to become failed.

2) Remote to Local (R2L) attacks is an attempt to access the targeted system without permission to access.

3) User to Root (U2R) attacks is an attempt to access unauthorized function in order to reach the Supper-user (root).

4) Probing attacks are the data validation on the network.

Then, trying to find the vulnerability of the target in order to use in the attacks. The example of common types are Nmap or port scanning. In the dataset of this research, there are 41 special features which are selected only normal data and DDos attacks.

B. Data Pre-Processing

The KDD CUP 1999 dataset and NSL-KDD dataset are composed of duplicate data, numbers, and alphabets. Before sending the data to machine learning, the data required to be processed as follows.

1) Removed the DDos data which is duplicate data. A lot of duplicate data were sent to the system during attacks. Deleting the duplicate data result as the data could be different in one row. Deleting method is done by diagnosis the duplicate special attributes and classes (Class).

2) Convert the alphabet values of the special feature to numeric values.

3) From normal dataset and DDoS dataset (Total 526,655 record), the dataset were divided into 3 series which different classes as follows:

a) Series 1 has 2 classes: normal data and DDoS attack which converted from 6 classes to 1 class which is attacks.

b) Series 2 has 6 classes: the dataset that get rid of normal data. The remaining data are DDoS attacks. There are Neptune, Pod, Smurf, Teardrop, Land and Back.

c) Series 3 has 7 classes: the dataset contains Nep-tune, Pod, Smurf, Teardrop, Land, Back and Nor-mal.

IV. EXPERIMENTAL SETTING AND RESULTS

This research is a research on the security of computer networks for identifying DDoS attacks using the 4,898,431 KDD Cup 1999 dataset and 125,373 NSL-KDD dataset. Those dataset are normal data and computer network intrusion data.

A. Parameter Tuning

Classification on information of machine learning were conducted by modeling both sets of data for DDoS attacks classification and identification. Training data and testing data were divided by cross validation method (given, k = 2). Then, both dataset were classified by MLP, SVM and KNN. The Parameter Tuning were as follows:

1) KNN is the identification of information by distance detection K position which get majority vote or nearest distance value. Given K = 1, 3, 5, 7, 9 to find the parameter for the test that aim to give the highest accuracy.

2) SVM [12,13] is a discriminative classifier formally de-fined by a separating hyperplane. The data classifica-tion which presented in this research apply grid search method to find suitable parameter for SVM. The testing parameter were as follows. Kernel function were set as RBF and linear. Gamma 7 were set as between 1e - 3 and 1000. C were set as between 1e - 3 and 1000 to calculate the best parameter.

3) MLP applying grid search method to find the parameter. The test were set hidden layer as 10, 50, 100, 150, 200, 500, 1000. alpha were set as 1e-05, 1000, ... , 0.0001, 0.00001 and use the same methods as Adam [16] which works great when the data is large.

B. Experimental Results

There are three machine learning methods of classifying DDoS attacks in this research including, 1) K-Nearest Neigh-bor (KNN), 2) Support Vector Machine (SVM), and 3) Multi-Layer Perceptron (MLP). The accuracy equation [16] is as follows.

(TP + TN) ACC = (3)

(TP + TN + FP + FN)

where True Positive (TP): refer to predictions is true and confirm that is true.

True Negative (TN): refer to predictions is not true and confirm that is not true.

False Positive (FP): refer to predictions is true and confirm that is not true.

False Negative (FN): refer to predictions is not true and confirm that is true.

TABLE I: Accuracy Results of the KDD Dataset

Methods Parameters Setting Accuracy (%)

KDD 2-Class+SVM

KDD 2-Class+KNN

KDD 2-Class+MLP rbf kernel, C = 8, -y =

K = 3

Hidden layer = 150 16 98.946± 0.022

99.983± 0.003

98.833± 0.131

KDD 6-Class+SVM

KDD 6-Class+KNN

KDD 6-Class+MLP rbf kernel, C = 8, -y =

K = 3

Hidden layer = 20 32 98.781± 0.020

99.998± 0.002

99.981± 0.131

KDD 7-Class+SVM

KDD 7-Class+KNN

KDD 7-Class+MLP rbf kernel, C = 4, -y =

K = 3

Hidden layer = 500 32 99.096± 0.027

99.984± 0.002

99.944± 0.019

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TABLE II: Accuracy Results of the NSL-KDD Dataset

Parameters Setting

NSL-KDD 2-Class+SVM

NSL-KDD 2-Class+KNN

NSL-KDD 2-Class+MLP rbf kernel, C = 1, -y = 32

K = 3

Hidden layer = 200 91.171± 0.194

99.191± 0.044

98.091± 0.265

NSL-KDD 6-Class+SVM rbf kernel, C = 4, -y = 16 95.364± 0.603

NSL-KDD 6-Class+KNN K = 3 99.951± 0.026

NSL-KDD 6-Class+MLP Hidden layer = 150 98.730± 1.200

NSL-KDD 7-Class+SVM rbf kernel, C = 1, -y = 16 91.182± 0.183

NSL-KDD 7-Class+KNN K = 3 99.087± 0.076

NSL-KDD 7-Class+MLP Hidden layer = 100 98.066± 0.137

The accuracy results of the DDoS attack classification test with KDD CUP 1999 dataset and NSL-KDD dataset are showed at Table I and Table II, respectively.

In this paper, the data divided by cross validation method. The data were divided into two parts (k = 2) and tested 10 iterations. Starting with the KDD CUP 1999 dataset, the results found KNN had the best performance for all three subsets (2 Classes, 6 Classes, and 7 Classes). The accuracy rate were 99.98%, 99.99% and 99.98%, respectively which is very effective when compared to SVM and MLP. Then, testing with the NSL-KDD dataset which were also divided into three subsets. The results found that the KNN method had better performance than MLP and SVM with the three subsets data. The accuracy rate were 99.19% for NSL-KDD (2 Classes), 99.95% for NSL-KDD (6 Classes), and 99.08% for NSL-KDD (7 Classes).

V. CONCLUSION

In this research, machine Learning including; KNN, MLP and SVM were used to identify DDoS attacks. Two benchmark dataset which is 529,655 records of KDD CUP 1999 dataset and 12,354 records of NSL-KDD dataset. Those two dataset were divided into three subordinate dataset. There are 2, 6 and 7 classes for accuracy test of classification of DDoS attacks. When testing were conducted for 10 iterations, the result showed that KKN method obtained the best performance when compare with MLP and SVM with the accuracy rate 99.99%. In the future, researchers have planned to find a special feature that could possible to reduce the number of features. At the same time, it must not reduce the accuracy rate. Then, experiment with other types of attacks.

ACKNOWLEDGMENT

This research was supported by the Rajamanjala University of Technology Isan, Surin Campus, Thailand.

REFERENCES

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[2] D. Perakovi´c, M. Peri˘sa, I. Cviti´c, and S. Husnjak, “Artificial neuron network implementation in detection and classification of DDoS traffic,” in 24th Telecommunications Forum (TELFOR), Nov 2016, pp. 1–4.

[3] C. Hsieh and T. Chan, “Detection DDoS attacks based on neural-network using apache spark,” in Applied System Innovation (ICASI), International Conference on, May 2016, pp. 1–4.

[4] S. Devaraju and S. Ramakrishnan, “Performance analysis of intrusion detection system using various neural network classifiers,” in Recent Trends in Information Technology (ICRTIT, International Conference on), Jun 2011, pp. 1033–1038.

[5] T. Omrani, A. Dallali, B. C. Rhaimi, and J. Fattahi, “Fusion of ANN and SVM classifiers for network attack detection,” CoRR, vol. abs/1801.02746, pp. 1–5, Jan 2018. [Online]. Available: http://arxiv.org/abs/1801.02746

[6] Information and Computer Science, “KDD cup

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http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

[7] S. Yuanyuan, W. Yongming, G. Lili, M. Zhongsong, and J. Shan, “The comparison of optimizing SVM by GA and grid search,” in Electronic Measurement Instruments (ICEMI), 13th IEEE International Conference on, Oct 2017, pp. 354–360.

[8] P. Singh and A. Tiwari, “An efficient approach for intrusion detection in reduced features of KDD99 using ID3 and classification with KNNGA,” in Advances in Computing and Communication Engineering ICACCE, 2nd International Conference on, May 2015, pp. 445–452.

[9] D. H. Deshmukh, T. Ghorpade, and P. Padiya, “Intrusion detection system by improved preprocessing methods and na¨ıve bayes classifier using NSL-KDD 99 dataset,” in Electronics and Communication Systems (ICECS), International Conference on, Feb 2014, pp. 1–7.

[10] P. Kushwaha, H. Buckchash, and B. Raman, “Anomaly based intrusion detection using filter based feature selection on KDD-CUP 99,” in IEEE Region 10 Conference TENCON, Nov 2017, pp. 839–844.

[11] G. Meena and R. R. Choudhary, “A review paper on IDS classification using KDD 99 and NSL KDD dataset in WEKA,” in Computer, Communications and Electronics (Comptelix), International Conference on, Jul 2017, pp. 553–558.

[12] O. Surinta, M. F. Karaaba, L. R. Schomaker, and M. A. Wiering, “Recognition of handwritten characters using local gradient feature descriptors,” Engineering Applications of Artificial Intelligence, vol. 45, pp. 405 – 414, 2015.

[13] V. N. Vapnik, Statistical Learning Theory. Wiley, 1998.

[14] A. G. Gedam and S. G. Shikalpure, “Direct kernel method for machine learning with support vector machine,” in Intelligent Computing, Instru¬mentation and Control Technologies (ICICICT), International Confer¬ence on, Jul 2017, pp. 1772–1775.

[15] J. Wu, X. Wang, X. Lee, and B. Yan, “Detecting DDoS attack towards DNS server using a neural network classifier,” in Artificial Neural Networks (ICANN), 20th International Conference on, K. Diamantaras, W. Duch, and L. S. Iliadis, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 118–123.

[16] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. Elsevier, 2011.

149

Neuromorphic Learning VLSI Systems: A Survey

Gert Cauwenberghs

Electrical and Computer Engineering

Johns Hopkins University, Baltimore, MD 21218

E-mail: gert@bach.ece.jhu.edu

Abstract

This [chapter] reviews advances in hardware learning and adaptation in synthetic neural systems. Over the last decade, research in the field has intensified, drawing inspiration across several science and engineering disciplines. This review briefly covers neural models, implementation technology, architectural constraints, and system applications of learning in hardware.

1 Introduction

Carver Mead introduced "neuromorphic engineering" [1] as an interdisciplinary approach to the design of biologically inspired neural information processing systems, whereby neurophys-iological models of perception and information processing in biological systems are mapped onto analog VLSI systems that not only emulate their functions but also resemble their struc¬ture [2]. The motivation for emulating neural function and structure in analog VLSI is the realization that challenging tasks of perception, classification, association and control success¬fully performed by living organisms can only be accomplished in artificial systems by using an implementation medium that matches their structure and organization.

Essential to neuromorphic systems are mechanisms of adaptation and learning, modeled after the "plasticity" of synapses and neural structure in biological systems [3],[4]. Learning can be broadly defined as a special case of adaptation whereby past experience is used effectively in readjusting the system response to previously unseen, although similar, stimuli. Based on the nature and availability of a training feedback signal, learning algorithms for artificial neural networks fall under three broad categories: unsupervised, supervised and reward/punishment (reinforcement). Physiological experiments have revealed plasticity mechanisms in biology that correspond to Hebbian unsupervised learning [5], and classical (pavlovian) conditioning [6],[7] characteristic of reinforcement learning.

Mechanisms of adaptation and learning also provide a means to compensate for analog imperfections in the physical implementation of neuromorphic systems, and fluctuations and uncertainties in the environment in which it operates. To this end, it is crucial that the learning be continuously performed on the system in operation. This enables the system to be func¬tionally self-contained, and to adapt continuously to the environment in which they operate.

For neuromorphic systems which involve a large number of parameters such as synapses in a densely connected neural network, it is imperative that the learning functions be an integral part of the hardware, implemented locally and interfacing directly with the synaptic functions.

Practical limits of integrated implementation of learning functions are imposed by the degree of locality implied by the learning rule, and the available memory bandwidth and fanout provided by the technology. This is an important point to consider in the design, and determines whether an electronic, optical, or hybrid implementation is most suited for the targeted application. A very important consideration as well is the need for locally storing the analog or digital parameter values, to retain the information being extracted during learning. Not surprisingly, technological issues of adaptation and memory are directly related, and both need to be addressed concurrently.

A vast research literature is dedicated to various styles of neural hardware implementations with provisions for learning, some of it with integrated learning functions. A selection of the literature (which is bound to be incomplete even at the time of printing!) is included in the list of references below. Some examples of early implementations of neural systems with integrated adaptation and learning functions can be found in edited volumes such as [8], [9] and [10], in conference proceedings such as NIPS, IJCNN (ICNN/WCNN) and ISCAS, and in special and regular issues of journals such as IEEE Transactions on Neural Networks (May 1002 and 1003 [12]), IEEE Micro (Micro-Neuro special issues) and Kluwer's International Journal of Analog Integrated Circuits and Signal Processing [13, 14]. The exposition below will serve as a brief description of a limited cross-section of research in the field over the last decade (mainly focusing on analog VLSI systems), as well as a general coverage of the important issues.

2 Adaptation and Learning

Definitions for the terms adaptation and learning come in several varieties, differing with the particular discipline in which it is formulated, such as cognitive science, neuroscience, neural computation, artificial intelligence, information theory, and control theory.

From a system level perspective, a general framework for adaptation and learning is de¬picted in Figure 1 [17]. A system with adjustable parameters pi (vector p) interacts with the environment through sensory inputs and activation outputs. An adaptive element, either internal or external to the system, adjusts the parameters of the system to "optimize" some performance index that is either defined or implied in relation to the system and its interaction with the environment. In most models of learning and adaptation, the measure of performance is quantified either as an error index E(p) which needs to be minimized:

p = argmin E(p) (1)

or, equivalently, a quality index which needs to be maximized. The optimization is subject to suitable constraints that have to do with physical limits on the system as well as other requirements on the system and the way it interacts with the environment.

SYSTEM

AAAA

AAA

{pi}

AAA

 (p)

ADAPTIVE

ELEMENT

Figure 1: Adaptation and learning in an information processing system by adjusting the analog system parameters pi to optimize a performance index ~7(p). The system interacts with the environment through its sensory inputs and activation outputs.

What distinguishes learning from more general forms of adaptation is the way in which the system uses past experience in trying to respond effectively to previously unseen, although similar, input stimuli. The distinct objective in learning is to generalize beyond the specifics of the presented input samples, and minimize the expected value of ~7(p) from the underlying statistics of the training samples:

p = argmin E(~7 (p)) (2)

Based on the nature and availability of a training feedback signal in the formulation of E(~7(p)), learning algorithms for artificial neural networks (ANNs) fall under three broad categories: supervised [19], unsupervised [26], and reward/punishment (reinforcement) [33].

Supervised Learning [18]-[23] assumes that a "teacher" is continuously available to produce target values ytarget

k (t) for the outputs yk(t), whereby the (instantaneous) error index is

quantified as the distance between actual and target outputs

~7(p; t) = X

k lytarget k (t) - yk(t)l ; (3)

using a distance metric with norm v > 0. Supervised learning is in a sense the easiest case of learning to implement, since the learning task is well defined and the performance index, directly quantified in terms of the target training signals, can be evaluated and optimized on-line. The most popular of all learning algorithms is backpropagation [20], which is effectively the chain rule of differentiation applied to gradient descent of (3) on a multilayer feedforward ANN, and which can be extended to more general feedforward structures [19], and to more complex structures with recurrent dynamics in the state variables [22, 23]. A system example of supervised learning in VLSI with recurrent dynamics is presented in [the next chapter].

Unsupervised learning [24]-[29] does not assume any feedback from an external teacher, and attempts to classify inputs based on the underlying statistics of the data. Classifiers

of this type are intended for tasks which inherently require some form of data com¬pression or an otherwise more suitable data representation for subsequent information processing. The criterion for adjusting the boundaries between classes can be expressed in information-theoretic terms, either as to maximize the mutual information between the analog inputs and the discrete output classes [28], or (equivalently) to minimize the average description length of the output classes [30]. Typical unsupervised learning tech¬niques include Hebbian learning [24] in a self-organizing neural network, auto-associative memories [26, 25], k-means clustering in a vector quantizer [27], and adaptive resonance theory [29]. A VLSI learning binary vector quantizer is described in [one of the following chapters].

Reinforcement learning [32]-[38] assumes the available external feedback on system per-formance is limited to discrete-time, delayed rewards and punishments, without a target specified for the system outputs. The difficulty in this type of learning is the assignment of proper credit to responsible actions in the past leading to the system failure or success indicated by the penalty or reward. Algorithms of the reinforcement learning type use internal mechanisms of credit assignment which make no prior assumptions on the causal relationships of the system and the enviroment in which it operates. Closely related to models of learning in artificial intelligence [31, 39], they include "time difference learning" or TD(A) [35] as a special case of [33], Q-learning [38] using a value function on the state space for optimal policy iteration, and "advanced heuristic dynamic programming" [36] using vectorial gradient information for increased speed of convergence. Details on rein¬forcement learning system in analog VLSI and a system example are given in [the next chapter].

Hybrids: Unsupervised and supervised learning approaches can be combined in many ways with various networks architectures to generate internally self-organizing adap¬tive hetero-associative systems. This synthesis reaches beyond neural nets in the re¬stricted sense of what is conventionally known as ANNs, and includes fuzzy neural sys¬tems [40, 41, 42] as well as "hierarchical mixture of experts" models trained with the expectation-maximization algorithm [43]. In both cases, internal structure is learned using unsupervised clustering techniques based on the input statistics, and the output structure is trained through (gradient-based and other) supervised learning.

3 Technology

Biological neural systems are built out of "wetware" components in an implementation medium which is necessarily different from technologies available to the implementation of artificial computing systems, such as semiconductors and optical propagation media. The neuromor-phic engineering approach extends the functionality and structure of biological systems to artificial systems built with components and architectures that closely resemble their biologi¬cal counterparts at all levels, transparent to differences in technology. Still, the physical limits

on size, density and connectivity depend strongly on the technology used.

Most neural hardware implementations use VLSI technology, which is functionally highly versatile but mostly restricted to two dimensions. The planar nature of VLSI technology is not necessarily a restriction for neural implementations since neural structures such as in the cerebral cortex are mostly two-dimensional as well- after all the brain is itself a folded 2-D structure. Optical free-space interconnects, on the other hand, allow synaptic densities presently unavailable in state-of-the-art VLSI technology. Hybrid opto-electronic systems com¬bine the technological advantages of both worlds, with functionally rich local VLSI processing and global optical interconnects.

For learning and adaptation, a central issue in all implementation technologies is the local storage of synaptic parameters. This issue, together with the means of incrementally adapting the stored parameters, is addressed below in particular detail. For brevity, the exposition focuses mainly on electronic implementations in analog VLSI technology.

3.1 VLSI Subthreshold MOS Technology

MOS transistors operating in the subthreshold region [46] are attractive for use in medium-speed, medium-accuracy analog VLSI processing, because of the low current levels and the exponential current-voltage characteristics that span a wide dynamic range of currents [47] (roughly from 100 fA to 100 nA for a square device in 2µ.m CMOS technology at room temperature). Subthreshold MOS transistors provide a clear "neuromorph" [1], since their exponential I-V characteristics closely resemble the carrier transport though cell membranes in biological neural systems, as governed by the same Boltzman statistics [45]. The expo¬nential characteristics provide a variety of subthreshold MOS circuit topologies that serve as useful computational primitives (such as nonlinear conductances, sigmoid nonlinearities, etc.) for compact analog VLSI implementation of neural systems [2]. Of particular interest are translinear subthreshold MOS circuits, derived from similar bipolar circuits [47]. They are based on the exponential nature of current-voltage relationships, and offer attractive compact implementations of product and division operations in VLSI.

3.2 Adaptation and Memory

Learning in analog VLSI systems is inherently coupled with the problem of storage of analog information, since after learning it is most often desirable to retain the learned weights for an extended period of time. The same is true for biological neural systems, and mechanisms of plasticity for short-term and long-term synaptic storage are not yet clearly understood.

In VLSI, analog weights are conveniently stored as charge or voltage on a capacitor. A capacitive memory is generically depicted in Figure 2. The stored weight charge is preserved when brought in contact with the gate of an MOS transistor, which serves as a buffer between weight storage and the implementation of the synaptic function. An adaptive element in

Figure 2: Adaptation and memory in analog VLSI: storage cell with charge buffer. contact with the capacitor updates the stored weight in the form of discrete charge increments

Vstored(t + ~t) = Vstored(t) + C AQadapt(t) (4)

or, equivalently, a continuous current supplying a derivative

d 1

Vstored(t) = C Iadapt(t) (5)

where AQadapt(t) = Rt+t

t Iadapt(t~)dt~.

On itself, a floating gate capacitor is a near-perfect memory. How ev er,leakage and spon-taneous decay of the weights result when the capacitor is in volatile contact with the adaptive element, suc has through drain or source terminals of MOS transistors. This distinguishes v olatile from non-volatile storage VLSI tec hnology. An excellent review of analog memories for neural computation is given in [48].

Non-volatile Memories [49 ]-[60] contain adaptive elements that interface with the floating gate capacitor by capacitive coupling across an insulating oxide. In standard CMOS VLSI technologies, charge transport through the oxide is typically controlled by tunneling [84, 134, 49 , 51], hot electron injection [59] or UV-activated conduction [179, 52 , 56, 74]. Flash memories offer fast adaptation rates (msecs) and long retention times (y ears) without the need for high programming voltages or UV light, but are not standardly av ailable in CMOS processes.

Volatile Memories [49 ],[61 ]-[66 ]offer fast adaptation rates and instantaneous reprogram¬ming of the parameter values, using a voltage-controlled ohmic connection to the capaci¬tor in the form of MOS switches and switched current sources. A leakage current results from the reverse diode formed betw eensource and drain diffusions and bulk connection of a switch transistor. The leakage typically resticts the retention time of the memory to the msec range, adequate for short-term storage. An active refresh mechanism is required for long-term storage [49],[62]-[64]. An adaptive element which combines active refresh storage and incremental adaptation, and which allows a random-access read and write digital interface, is described in [the next chapter].

Other implementations frequently use local or external digital storage of the parameters, combined with either local or multiplexed D/A conversion. This solution is less attractive for large-scale neural processors with local learning functions that require incremental adaptation of the parameters, since then the increment would need to be performed in digital as well. Both volatile and non-volatile analog memories allow incremental updates in direct analog format, according to (4) or (5).

The non-volatile solution is more attractive than volatile alternatives when long-term stor¬age is a more pressing concern than speed of adaptation and flexibility of programming. The volatile scheme is particularly useful in multiplexed hardware implementations for multi¬purpose applications or to realize virtual larger-scale systems, requiring frequent reloading of large blocks of partial weight matrices. This could be done with an external digital cache memory and an array of A/D/A converters for bi-directional digital read and write access to the synaptic array [65]. Random-access memory addressing in digital format is on itself a valuable feature for system-level interfacing.

3.3 Emerging Technologies

Innovation and continued progress in information technology benefits the design of learning neural systems of larger size and better performance, as it benefits other information pro¬cessing systems. Some relatively new developments in VLSI include micro-electromechanical systems (MEMS) [67], wafer-scale integration [141, 143], chip-scale packaging [68], and silicon-on-insulator (SOI) integrated circuit fabrication [60, 70]. The latter is of special interest to analog storage, because significant reduction of leakage currents due to bulk reverse diodes in MOS switches allows longer retention times of capacitive memories.

Continued technology developments in optical and optoelectronic information processing in combination with mature VLSI technology hold the potential for significant performance improvements in artificial neural information processing systems [150]-[158], promising massive inter-chip connectivity as needed for larger size neural networks. High-density optical storage and adaptation for integrated learning could be achieved in 3-D optical media such as photo-refractive crystals.

4 Architecture

Learning algorithms that are efficiently implemented on general-purpose digital computers do not necessarily map efficiently onto analog VLSI hardware. The good news is that the converse is also true, as it is well known that special-purpose processors tuned to a given task easily outperform most general-purpose computing engines, on that particular task. From the perspective of computational efficiency, it is therefore important to closely coordinate the design of algorithms and corresponding VLSI architecture to ensure an optimal match.

Important guidelines in efficiency of computation dictate the usual principles commonly taught in modern VLSI design: locality, scalability, and parallelism. The principle of locality

confines intensive computations to the cell level, and restricts global operations to nearest-neighbor interactions. In addition, certain scalar global operations which can be easily per¬formed with a single common wire in analog VLSI technology are allowed, such as global summing of currents or charges, and global communication of voltage-coded variables. Scal-ability implies that the implemented algorithms cannot scale stronger than second order in a linear parameter such as the number of neurons, since nothing more complex than a 2-D array can be implemented on an extended scale in planar VLSI technology. Parallelism in this context implies that the number of operations performed concurrently at any given time scales linearly with the number of cells.

Even if the learning algorithm supports a parallel and scalable architecture suitable for analog VLSI implementation, inaccuracies in the implementation of the learning functions may significantly affect the performance of the trained system. Neuromorphic principles call for a distributed architecture not only for the network of neurons but also to implement the learning functions, robust to localized errors in the implementation.

4.1 Incremental Outer-Product Learning in Distributed Systems For networks with distributed neurons such as linear and multilayer perceptrons [20]

xi = f(X pijxj) (6)

gradient descent of an LMS error functional E defined on the output neurons xout

k gives rise to

incremental outer-product learning rules of the form

~pij = xjei (7)

where the backpropagation of the error variables ei is derived by application of the chain rule for differentiation as [19]

eout @E

k = fk 0 (8)

@xout

Xej=fj 0 pijei

where fj0 denotes the derivative of the function f(:) evaluated at its argument in (6). Outer-product rules of the form (7) are local: synaptic updates are constructed from intersecting variables at the location of the synapses. The general class of learning algorithms of the incremental outer-product type include

Supervised Learning: the delta rule [18] and backpropagation [20] for supervised learning in linear or multilayer feedforward perceptrons with a functional (3). Also included, with stochastic rather than deterministic neurons, are Boltzman learning in networks of stochastic neurons [21, 71], and pulse firing neural nets [89].

Unsupervised Learning: hebbian learning [24], where ei = fi'xi corresponding to a func-tional E - - Pi xi2. The k-means clustering algorithm for learning vector quantization (LVQ) [27] is a special case of the latter, where the nonlinearity in the output layer fk se¬lects a single winner across all outputs k. Kohonen topology-preserving maps [26] further include the neighbors of the winner k ± 1 into the learning updates. Learning in ART networks [29] also fits in this category although it is slightly more involved. Learning in Hopfield networks [25] is hebbian in slightly modified form.

Hybrids and Variants: fuzzy maps, hetero-associative neural networks, radial basis

networks, etc. which conform to the general structure of Eqns. (6)-(0) and their variants and combinations.

Reinforcement Learning: The reinforcement learning updates for both the action network and the adaptive critic in [33] are of the general incremental outer-product form (7), al¬though modulated with a global (common) reinforcement signal, and low-pass filtered for credit assignment back in time. See [the next chapter] for more details on the equivalent gradient-descent outer-product formulation. An outer-product VLSI implementation is described in [116].

Since all of the above learning algorithm share essentially the same incremental outer-product learning rule, they can be cast into the same general VLSI architecture depicted in Figure 3. Clearly, this architecture exhibits the desirable properties of locality, parallelism and scalability. Forward and backward signal paths xj and ei traverse in horizontal and vertical directions through the array of synapse cells pij. The neuron nonlinearity f(.) and its derivative are implemented at the output periphery the array. Several layers of this structure can be cascaded in alternating horizontal and vertical directions to form multi-layer perceptrons. The array architecture of Figure 3 (b) forms the basis for many of the implemented VLSI learning systems [71]-[103]. One example, described in [74], arguably contains the densest VLSI array for general outer-product learning developed to date, using only two transistors for synapse and learning operations per cell. An array of single-transistor learning synapses for certain classes of learning is presented in [59].

Digital VLSI implementations [130]-[140] differ from the analog architecture mainly in that contributions to the summations in (6) and (0) cannot be accumulated onto a single line. Global summations are most commonly implemented using a systolic array architecture.

4.2 Localized Outer-Product Learning in Cellular Neural Systems

Notice that the fully interconnected architecture of Figure 3 (b) becomes inefficient when the network that it implements has sparse connectivity. A limiting case of sparsely interconnected networks are cellular neural networks [105], in which neurons only interact with their immediate neighbors, conveniently arranged on a 2-D grid. Since the synaptic connections in networks of this type are only peripheral, the implementation architecture is determined directly by the topology of the neurons in relation with their neighbors. The synapse and learning functions

Figure 3: Incremental outer-product learning. (a) F eedforward and backpropagation model; (b) Simplified VLSI architecture.

are integrated at the neuron level, rather than distributed over an array as in Figure 3 (b). Other than that, the same principles hold, and rules of the outer-product type as illustrated in Figure 3 (a) are implemented locally at the neuron inter-cell level [104]-[107].

4.3 Model-Free Learning Approaches

Although model-based approaches for learning suc has the outerpro duct learning models de-scribed abov e are fairly robust to mismatches in the implemen tation of the learning functions owing to theirdistributed arc hitecture [118, 119 , 122 , 123], the same can not be said a priori of more general classes of learning which do not fit the outerpro duct type. This is particularly so for recurrent neural netw orks with hidden internal dynamics for which learning complexity rises sharply with the number of parameters [22, 23 ],or for more complex systems of which a model is difficult to derive or unknown to the learning element. Model-free approaches to learning [124] do not assume a particular model for the system nor the environment in which it operates, and derive parameter updates Api b y ph ysically probingthe dependency of the performance index E on the parameters pi through perturbations -/ri on the parameters.

The term "model-free" pertains to the learning, and not necessarily to the structure of the system itself being adapted, which can be anything and which clearly is parametric. The main advantage of model-free learning is that it leav es tremendous freedom in configuring the system, which is allo w edto change structurally on-line as learning progresses, without the need to compile models. This is particularly useful for training reconfigurable architectures [135, 111]. The insensitivity of learning performance to inaccuracies in the implemented system, and the abilit y to learn systems with intractible models, are direct benefits of model-free learning. An additional benefit of stochastic perturbative learning approaches seems to be that the synaptic noise thus introduced improves generalization performance of the learned system [120].

V ariants on perturbative model-free learning use some limited model information to train feedforward multila yer ANNs more effectively [128, 131, 133]. The question of ho w muc h

model information can be reliably used is important, although truly model-free approaches are most generally applicable and expandable, and their performance does not significantly suffer from the lack of complete gradient information on the error E as some asymptotic theory establishes [126].

The model-free nature of learning applies to general learning tasks beyond the traditionally supervised and unsupervised, and can be extended to reinforcement learning. An extensive study of model-free supervised and reinforcement learning architectures with examples of ana¬log VLSI systems is the subject of [the next chapter].

5 Systems

Several examples of adaptive and/or learning VLSI systems with applications in vision, speech, signal processing, pattern recognition, communications, control and physics are included in the references [170]-[202]. This list is by no means complete, and the spectrum of applications will likely expand as the new application areas are discovered and research advances create new ways of using adaptation and learning in the design of intelligent neuromorphic information processing systems.

Covering such diverse range of disciplines across neurobiology, artificial intelligence, cogni¬tive science, information theory, etc., research on learning systems is bound to develop further as different concepts and experimental evidence combine to bridge the gap between bottom-up and top-down modeling approaches, towards the engineering of truly intelligent autonomous learning systems, and towards a better understanding of learning mechanisms in biological neural systems at different levels of abstraction.

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6.2 Edited Book Volumes, Journal Issues and Reviews

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6.3 Learning Models

6.3.1 Supervised Learning

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6.3.2 Unsupervised Learning

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6.3.4 Hybrid Learning Approaches

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6.4 Technology

6.4.1 Subthreshold MOS Operation

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6.4.2 Analog Storage

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Volatile Analog Storage and Refresh

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6.4.3 Emerging VLSI Technologies

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6.5 Architecture

6.5.1 Outer-Product Supervised Learning

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6.5.2 Outer-Product Unsupervised Learning

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[91] B.J. Maundy and E.I. Elmasry, "A Self-Organizing Switched-Capacitor Neural Network," IEEE T. Circ. Syst., vol. 38 (12), pp 1556-1563, Dec. 1991.

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[93] J. Donald and L. Akers, "An Adaptive Neural Processor Node," IEEE Transactions on Neural Networks, vol. 4 (3), pp 413-426, 1993.

[94] Y. He and U. Cilingiroglu, "A Charge-Based On-Chip Adaptation Kohonen Neural Network," IEEE Transactions on Neural Networks, vol. 4 (3), pp 462-469, 1993.

[95] D. Macq, M.Verleysen, P. Jespers and J.D. Legat, "Analog Implementation of a Kohonen Map with On-Chip Learning," IEEE T. Neural Networks, vol. 4 (3), pp 456-461, May 1993.

[96] B. Linares-Barranco, E. Sanchez-Sinencio, A. Rodriguez-Vazquez, and J.L. Huertas, "A CMOS Analog Adaptive BAM with On-Chip Learning and Weight Refreshing," IEEE Transactions on Neural Networks, vol. 4 (3), pp 445-455, 1993.

[97] P. Heim and E.A. Vittoz, "Precise Analog Synapse for Kohonen Feature Maps," IEEE J. Solid-State Circuits, vol. 29 (8), pp 982-985, Aug. 1994.

[98] G. Cauwenberghs and V. Pedroni,"A charge-based CMOS parallel analog vector quantizer," in Advances in Neural Information Processing Systems, Cambridge, MA: MIT Press, vol. 7, pp 779¬786, 1995.

[99] T. Shibata, H. Kosaka, H. Ishii and T. Ohmi, "A Neuron-MOS Neural-Network Using Self-Learning-Compatible Synapse Circuits," IEEE J. Solid-State Circuits, vol. 30 (8), pp 913-922, Aug. 1995.

[100] R.Y. Liu, C.Y. Wu and I.C. Jou, "A CMOS Current-Mode Design of Modified Learning-Vector-Quantization Neural Networks," Int. J. Analog Integ. Circ. Signal Proc., vol. 8 (2), pp 157-181, Sept. 1995.

[101] C.Y. Wu and J.F. Lan, "CMOS Current-Mode Neural Associative Memory Design with On-Chip Learning," IEEE T. Neural Networks, vol. 7 (1), pp 167-181, Jan. 1996.

[102] K. Hosono, K. Tsuji, K. Shibao, E. Io, H. Yonezu et al., "Fundamental Device and Circuits for Synaptic Connections in Self-Organizing Neural Networks," IEICE T. Electronics, vol. E79C (4), pp 560-567, Apr. 1996.

[103] T. Serrano-Gotarredona and B. Linares-Barranco, "A Real-Time Clustering Microchip Neural Engine," IEEE T. VLSI Systems, vol. 4 (2), pp 195-209, June 1996.

6.5.3 Adaptive Cellular Neural Networks

[104] P. Tzionas, P. Tsalides and A. Thanailakis, "Design and VLSI Implementation of a Pattern Classifier Using Pseudo-2D Cellular Automata," IEE Proc G, vol. 139 (6), pp 661-668, Dec. 1992.

[105] T. Roska and L.O. Chua, "The CNN Universal Machine - an Analogic Array Computer," IEEE T. Circ. Syst. II, vol. 40 (3), pp 163-173, March 1993.

[106] Y. Miyanaga and K. Tochinai, "Parallel VLSI Architecture for Multilayer Self-Organizing Cellular Network," IEICE T. Electronics, vol. E76C (7), pp 1174-1181, July 1993.

[107] S. Espejo, R. Carmona, R. Dominguez-Castro and A. Rodriguez-Vazquez, "A CNN Universal Chip in CMOS Technology," Int J. Circuit Theory Appl., vol. 24 (1), pp 93-109, Jan-Febr. 1996.

6.5.4 Adaptive Fuzzy Classifiers

[108] J.W. Fattaruso, S.S. Mahant-Shetti, and J.B. Barton, "A Fuzzy Logic Inference Processor," IEEE Journal of Solid-State Circuits, vol. 29 (4), pp. 397-401, 1994.

[109] Z. Tang, Y. Kobayashi, O. Ishizuka and K. Tanno, "A Learning Fuzzy Network and Its Appli¬cations to Inverted Pendulum System," IEICE T. Fund. El. Comm. Comp. Sci., vol. E78A (6), pp 701-707, June 1995.

[110] F. Vidal-Verdu and A. Rodriguez-Vazquez, "Using Building Blocks to Design Analog Neuro-Fuzzy Controllers," IEEE Micro, vol. 15 (4), pp 49-57, Aug. 1995.

[111] W. Pedrycz, C.H. Poskar and P.J. Czezowski, "A Reconfigurable Fuzzy Neural-Network with In-Situ Learning," IEEE Micro, vol. 15 (4), pp 19-30, Aug. 1995.

[112] T. Yamakawa, "Silicon Implementation of a Fuzzy Neuron," IEEE Fuz Sy, vol. 4 (4), pp 488-501, Nov. 1996.

6.5.5 Reinforcement Learning

[113] C. Schneider and H. Card, "Analog CMOS Synaptic Learning Circuits Adapted from Invertebrate Biology," IEEE T. Circ. Syst., vol. 38 (12), pp 1430-1438, Dec. 1991.

[114] T.G. Clarkson, C.K. Ng and Y. Guan, "The pRAM: An Adaptive VLSI Chip," IEEE Trans. on Neural Networks, vol. 4 (3), pp 408-412, 1993.

[115] A.F. Murray, S. Churcher, A. Hamilton, A.J. Holmes, G.B. Jackson et al., "Pulse Stream VLSI Neural Networks," IEEE Micro, vol. 14 (3), pp 29-39, June 1994.

[116] G. Cauwenberghs, "Reinforcement Learning in a Nonlinear Noise Shaping Oversampled A/D Converter," to appear in Proc. Int. Symp. Circuits and Systems, Hong Kong, June 1997.

6.5.6 Nonidealities and Error Models

[117] M.J.S. Smith, "An Analog Integrated Neural Network Capable of Learning the Feigenbaum Lo¬gistic Map," IEEE Transactions on Circuits and Systems, vol. 37 (6), pp 841-844, 1990.

[118] R.C. Frye, E.A. Rietman, and C.C. Wong, "Back-Propagation Learning and Nonidealities in Analog Neural Network Hardware," IEEE Transactions on Neural Networks, vol. 2 (1), pp 110¬117, 1991.

[119] L.M. Reyneri and E. Filippi, "An Analysis on the Performance of Silicon Implementations of Backpropagation Algorithms for Artificial Neural Networks," IEEE Comput, vol. 40 (12), pp 1380¬1389, 1991.

[120] A. Murray and P.J. Edwards, "Synaptic Noise During MLP Training Enhances Fault-Tolerance, Generalization and Learning Trajectory," in Advances in Neural Information Processing Systems, San Mateo, CA: Morgan Kaufman, vol. 5, pp 491-498, 1993.

[121] P. Thiran and M. Hasler, "Self-Organization of a One-Dimensional Kohonen Network with Quan¬tized Weights and Inputs," Neural Networks, vol. 7 (9), pp 1427-1439, 1994.

[122] G. Cairns and L. Tarassenko, "Precision Issues for Learning with Analog VLSI Multilayer Per-ceptrons," IEEE Micro, vol. 15 (3), pp 54-56, June 1995.

[123] B.K. Dolenko and H.C. Card, "Tolerance to Analog Hardware of On-Chip Learning in Backprop-agation Networks," IEEE T. Neural Networks, vol. 6 (5), pp 1045-1052, Sept. 1995.

6.5.7 Model-Free Learning

[124] A. Dembo and T. Kailath, "Model-Free Distributed Learning," IEEE Transactions on Neural Networks, vol. 1 (1), pp 58-70, 1990.

[125] M. Jabri and B. Flower, "Weight Perturbation: An Optimal Architecture and Learning Tech-nique for Analog VLSI Feedforward and Recurrent Multilayered Networks," IEEE Transactions on Neural Networks, vol. 3 (1), pp 154-157, 1992.

[126] G. Cauwenberghs, "A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Op¬timization," in Advances in Neural Information Processing Systems, San Mateo, CA: Morgan Kaufman, vol. 5, pp 244-251, 1993.

[127] J. Alspector, R. Meir, B. Yuhas, and A. Jayakumar, "A Parallel Gradient Descent Method for Learning in Analog VLSI Neural Networks," in Advances in Neural Information Processing Sys¬tems, San Mateo, CA: Morgan Kaufman, vol. 5, pp 836-844, 1993.

[128] B. Flower and M. Jabri, "Summed Weight Neuron Perturbation: An O(n) Improvement over Weight Perturbation," in Advances in Neural Information Processing Systems, San Mateo, CA: Morgan Kaufman, vol. 5, pp 212-219, 1993.

[129] D. Kirk, D. Kerns, K. Fleischer, and A. Barr, "Analog VLSI Implementation of Gradient Descent," in Advances in Neural Information Processing Systems, San Mateo, CA: Morgan Kaufman, vol. 5, pp 789-796, 1993.

[130] G. Cauwenberghs, \A Learning Analog Neural Network Chip with Continuous-Recurrent Dynam¬ics", in Advances in Neural Information Processing Systems, San Mateo, CA: Morgan Kaufman, vol. 6, pp 858-865, 1994.

[131] P.W. Hollis and J.J. Paulos, "A Neural-Network Learning Algorithm Tailored for VLSI Imple¬mentation," IEEE T. Neural Networks, vol. 5 (5), pp 784-791, Sept. 1994.

[132] G. Cauwenberghs, "An Analog VLSI Recurrent Neural Network Learning a Continuous-Time Trajectory," IEEE Transactions on Neural Networks, vol. 7 (2), March 1996.

[133] A.J. Montalvo, R.S. Gyurcsik and J.J. Paulos, "Toward a General-Purpose Analog VLSI Neural-Network with On-Chip Learning," IEEE T. Neural Networks, vol. 8 (2), pp 413-423, March 1997.

6.5.8 Chip-in-the-Loop Training

[134] M. Holler, S. Tam, H. Castro and R. Benson, "An Electrically Trainable Artificial Neural Net-work (ETANN) with 10240 Floating Gate Synapses," in Proc. Int. Joint Conf. Neural Networks, Washington DC, pp 191-196, 1989.

[135] S. Satyanarayana, Y. Tsividis and H.P. Graf, "A Reconfigurable Analog VLSI Neural Network Chip," in Advances in Neural Information Processing Systems, San Mateo, CA: Morgan Kaufman, vol. 2, pp 758-768, 1990.

[136] E. Sackinger, B.E. Boser and L.D. Jackel, "A Neurocomputer Board Based on the ANNA Neural Network Chip," in Advances in Neural Information Processing Systems, San Mateo, CA: Morgan Kaufman, vol. 4, pp 773-780, 1992.

[137] J.A. Lansner, "An Experimental Hardware Neural-Network Using a Cascadable, Analog Chipset," Int J Elect, vol. 78 (4), pp 679-690, Apr. 1995.

[138] J.O. Klein, H. Pujol and P. Garda, "Chip-In-The-Loop Learning Algorithm for Boltzmann Ma¬chine," Electronics Letters, vol. 31 (12), pp 986-988, Jun 8, 1995.

6.5.9 Digital Implementations

[139] A. Johannet, L. Personnaz, G. Dreyfus, J.D. Gascuel and M. Weinfeld, "Specification and Im-plementation of a Digital Hopfield-Type Associative Memory with On-Chip Training," IEEE T. Neural Networks, vol. 3 (4), pp 529-539, July 1992.

[140] T. Shima, T. Kimura, Y. Kamatani, T. Itakura, Y. Fujita et al., "Neurochips with On-Chip Backpropagation and/or Hebbian Learning," IEEE J. Solid-State Circuits, vol. 27 (12), pp 1868¬1876, Dec. 1992.

[141] M. Yasunaga, N. Masuda, M. Yagyu, M. Asai, K. Shibata et al., "A Self-Learning Digital Neural Network Using Wafer-Scale LSI," IEEE J. Solid-State Circuits, vol. 28 (2), pp 106-114, Febr. 1993.

[142] C. Lehmann, M. Viredaz and F. Blayo, "A Generic Systolic Array Building-Block for Neural Networks with On-Chip Learning," IEEE T. Neural Networks, vol. 4 (3), pp 400-407, May 1993.

[143] M. Fujita, Y. Kobayashi, K. Shiozawa, T. Takahashi, F. Mizuno et al., "Development and Fab-rication of Digital Neural-Network WSIs," IEICE T. Electronics, vol. E76C (7), pp 1182-1190, July 1993.

[144] P. Murtagh, A.C. Tsoi and N. Bergmann, "Bit-Serial Systolic Array Implementation of a Multi-layer Perceptron," IEE Proc E, vol. 140 (5), pp 277-288, Sept. 1993.

[145] T. Morishita and I. Teramoto, "Neural-Network Multiprocessors Applied with Dynamically Reconfigurable Pipeline Architecture," IEICE T. Electronics, vol. E77C (12), pp 1937-1943, Dec. 1994.

[146] Z. Tang and O. Ishizuka, "Design and Implementations of a Learning T-Mo del Neural-Network," IEICE T. Fund. El. Comm. Comp. Sci., vol. E78A (2), pp 259-263, Febr. 1995.

[147] M.P. Perrone and L.N. Cooper, "The NI1000: High Speed Parallel VLSI for Implementing Multi-layer Perceptrons," in Advances in Neural Information Processing Systems, Cambridge, MA: MIT Press, vol. 7, pp 747-754, 1995.

[148] J. Wawrzynek, et al., "SPERT-II: A Vector Microprocessor System and its Application to Large Problems in Backpropagation Training," in Advances in Neural Information Processing Systems, Cambridge, MA: MIT Press, vol. 8, pp 619-625, 1996.

[149] S. Rehfuss and D. Hammerstrom, "Model Matching and SFMD Computation," in Advances in Neural Information Processing Systems, Cambridge, MA: MIT Press, vol. 8, pp 713-719, 1996.

6.5.10 Optical and Optoelectronic Implementations

[150] J. Ohta, Y. Nitta and K. Kyuma, "Dynamic Optical Neurochip Using Variable-Sensitivity Pho¬to diodes," Optics Lett, vol. 16 (10), pp 744-746, 1991.

[151] D.Z. Anderson, C. Benkert, V. Hebler, J.-S. Jang, D, Montgomery and M. Saffman, "Optical Implementation of a Self-Organizing Feature Extractor," in Advances in Neural Information Pro¬cessing Systems, San Mateo, CA: Morgan Kaufman, vol. 4, pp 821-828, 1992.

[152] Y. Nitta, J. Ohta, S. Tai and K. Kyuma, "Optical Learning Neurochip with Internal Analog Memory," Appl Optics, vol. 32 (8), pp 1264-1274, March 10, 1993.

[153] K. Wagner and T.M. Slagle, "Optical Competitive Learning with VLSI Liquid-Crystal Winner-Take-All Modulators," Appl Optics, vol. 32 (8), pp 1408-1435, March 10, 1993.

[154] M. Oita, Y. Nitta, S. Tai and K. Kyuma, "Optical Associative Memory Using Optoelectronic Neurochips for Image-Processing," IEICE T. Electronics, vol. E77C (1), pp 56-62, Jan. 1994.

[155] E. Lange, Y. Nitta and K. Kyuma, "Optical Neural Chips," IEEE Micro, vol. 14 (6), pp 29-41, Dec. 1994.

[156] A.J. Waddie and J.F. Snowdon, "A Smart-Pixel Optical Neural-Network Design Using Cus-tomized Error Propagation," Inst. Phys. Conf. Series, vol. 139, pp 511-514, 1995.

[157] K. Tsuji, H. Yonezu, K. Hosono, K. Shibao, N. Ohshima et al., "An Optical Adaptive Device and Its Application to a Competitive Learning Circuit," Japan JAP 1, vol. 34 (2B), pp 1056-1060, Febr. 1995.

[158] W.E. Foor and M.A. Neifeld, "Adaptive, Optical, Radial Basis Function Neural-Network for Handwritten Digit Recognition," Appl Optics, vol. 34 (32), pp 7545-7555, Nov. 10, 1995.

6.5.11 Architectural Novelties

[159] J. Alspector, J.W. Gannett, S. Haber, M.B. Parker and R. Chu, "A VLSI-Efficient Technique for Generating Multiple Uncorrelated Noise Sources and Its Application to Stochastic Neural Networks," IEEE T. Circ. Syst., vol. 38 (1), pp 109-123, 1991.

[160] P.A. Shoemaker, M.J. Carlin and R.L. Shimabukuro, "Back Propagation Learning with Trinary Quantization of Weight Updates," Neural Networks, vol. 4 (2), pp 231-241, 1991.

[161] Y.H. Pao and W. Hafez, "Analog Computational Models of Concept-Formation," Int. J. Analog Integ. Circ. Signal Proc., vol. 2 (4), pp 265-272, Nov. 1992.

[162] T. Morie and Y. Amemiya, "Deterministic Boltzmann Machine Learning Improved for Analog LSI Implementation," IEICE T. Electronics, vol. E76C (7), pp 1167-1173, July 1993.

[163] S.P. Deweerth and D.M. Wilson, "Fixed-Ratio Adaptive Thresholding Using CMOS Circuits," Electronics Letters, vol. 31 (10), pp 788-789, May 11, 1995.

[164] M. Vandaalen, J. Zhao and J. Shawetaylor, "Real-Time Output Derivatives for on Chip Learning Using Digital Stochastic Bit Stream Neurons," Electronics Letters, vol. 30 (21), pp 1775-1777, Oct. 13, 1994.

[165] V. Petridis and K. Paraschidis, "On the Properties of the Feedforward Method - a Simple Training Law for On-Chip Learning," IEEE T. Neural Networks, vol. 6 (6), pp 1536-1541, Nov. 1995.

[166] H. Singh, H.S. Bawa and L. Anneb erg, "Boolean Neural-Network Realization of an Adder Sub-tractor Cell," Microel Rel, vol. 36 (3), pp 367-369, March 1996.

[167] T. Lehmann, E. Bruun and C. Dietrich, "Mixed Analog-Digital Matrix-Vector Multiplier for Neural-Network Synapses," Int. J. Analog Integ. Circ. Signal Proc., vol. 9 (1), pp 55-63, Jan. 1996.

[168] T. Serrano-Gotarredona and B. Linares-Barranco, "A Modified ART-1 Algorithm More Suitable for VLSI Implementations," Neural Networks, vol. 9 (6), pp 1025-1043, Aug. 1996.

[169] M.L. Marchesi, F. Piazza and A. Uncini, "Backpropagation Without Multiplier for Multilayer Neural Networks," IEE P. Circ., vol. 143 (4), pp 229-232, Aug. 1996.

6.6 Systems Applications of Learning

6.6.1 General Purpose Neural Emulators

[170] P. Mueller, J. Van der Spiegel, D. Blackman, T. Chiu, T. Clare, C. Donham, T.P. Hsieh and M. Lionaz, "Design and Fabrication of VLSI Components for a General Purpose Analog Neural Computer," in Analog VLSI Implementation of Neural Systems, Norwell, MA: Kluwer, pp 135-169, 1989.

6.6.2 Blind Signal Processing

[171] E. Vittoz and X. Arreguit, "CMOS Integration of Herault-Jutten Cells for Separation of Sources," in Analog VLSI Implementation of Neural Systems, Norwell, MA: Kluwer, pp 57-83, 1989.

[172] M.H. Cohen and A.G. Andreou, "Current-Mode Subthreshold MOS Implementation of the Jutten-Herault Autoadaptive Network," IEEE J. Solid-State Circuits, vol. 27 (5), pp 714-727, 1992.

[173] R.P. Mackey, J.J. Rodriguez, J.D. Carothers and S.B.K. Vrudhula, "Asynchronous VLSI Archi¬tecture for Adaptive Echo Cancellation," Electronics Letters, vol. 32 (8), pp 710-711, Apr. 11, 1996.

6.6.3 Biomedical Adaptive Signal Processing

[174] R. Coggings, M. Jabri, M. Flower and S. Pickard, "ICEG Morphology Classification Using an Analogue VLSI Neural Network," in Advances in Neural Information Processing Systems, Cam¬bridge, MA: MIT Press, vol. 7, pp 731-738, 1995.

6.6.4 Speech Research

[175] J. Wawrzynek, et al., "SPERT-II: A Vector Microprocessor System and its Application to Large Problems in Backpropagation Training," in Advances in Neural Information Processing Systems, Cambridge, MA: MIT Press, vol. 8, pp 619-625, 1996.

[176] J. Lazzaro, "Temporal Adaptation in a Silicon Auditory Nerve," in Advances in Neural Informa¬tion Processing Systems, San Mateo, CA: Morgan Kaufman, vol. 4, pp 813-820, 1992.

6.6.5 Olfactory Sensory Processing

[177] P.A. Shoemaker, C.G. Hutchens and S.B. Patil, "A Hierarchical-Clustering Network Based on a Model of Olfactory Processing," Int. J. Analog Integ. Circ. Signal Proc., vol. 2 (4), pp 297-311, Nov. 1992.

6.6.6 Focal-Plane Sensors and Adaptive Vision Systems

[178] J. Tanner and C.A. Mead, "An Integrated Analog Optical Motion Sensor," in VLSI Signal Pro¬cessing II, S.Y. Kung, Ed., New York: IEEE Press, 1986, pp 59-76.

[179] C.A. Mead, "Adaptive Retina," in Analog VLSI Implementation of Neural Systems, C. Mead and M. Ismail, Eds., Norwell, MA: Kluwer Academic Pub., 1989, pp 239-246.

[180] M. Mahowald, An Analog VLSI Stereoscopic Vision System, Boston, MA: Kluwer Academic, 1994.

[181] T. Delbruck, "Silicon Retina with Correlation-Based, Velocity-Tuned Pixels," IEEE Transactions on Neural Networks, vol. 4, pp 529-541, 1993.

[182] J.C. Lee, B.J. Sheu, and W.C. Fang, "VLSI Neuroprocessors for Video Motion Detection," IEEE Transactions on Neural Networks, vol. 4 (2), pp 78-191, 1993.

[183] R. Etienne-Cummings, J. Van der Spiegel, P. Mueller, "VLSI Model of Primate Visual Smooth Pursuit," in Advances in Neural Information Processing Systems, Cambridge, MA: MIT Press, vol. 8, pp 707-712, 1996.

[184] R. Sarpeshkar, J. Kramer, G. Indiveri and C. Koch, "Analog VLSI Architectures for Motion Processing - from Fundamental Limits to System Applications," P IEEE, vol. 84 (7), pp 969-987, July 1996.

[185] K. Boahen, "A Retinomorphic Vision System," IEEE Micro, vol. 16 (5), pp 30-39, October 1996.

[186] S.C. Liu and C. Mead, "Continuous-Time Adaptive Delay System," IEEE T. Circ. Syst. II, vol. 43 (11), pp 744-751, Nov. 1996.

6.6.7 Optical Character Recognition

[187] B.Y. Chen, M.W. Mao and J.B. Kuo, "Coded Block Neural Network VLSI System Using an Adaptive Learning-Rate Technique to Train Chinese Character Patterns," Electronics Letters, vol. 28 (21), pp 1941-1942, Oct 8, 1992.

[188] C.S. Miou, T.M. Shieh, G.H. Chang, B.S. Chien, M.W. Chang et al., "Optical Chinese Character-Recognition System Using a New Pipelined Matching and Sorting VLSI," Opt Eng, vol. 32 (7), pp 1623-1632, July 1993.

[189] S. Maruno, T. Kohda, H. Nakahira, S. Sakiyama and M. Maruyama, "Quantizer Neuron Model and Neuroprocessor-Named Quantizer Neuron Chip," IEEE J. Sel. Areas Comm., vol. 12 (9), pp 1503-1509, Dec. 1994.

6.6.8 Image Compression

[190] W.C. Fang, B.J. Sheu, O.T.C. Chen and J. Choi, "A VLSI Neural Processor for Image Data-Compression Using Self-Organization Networks," IEEE Transactions on Neural Networks, vol. 3 (3), pp 506-518, 1992.

6.6.9 Communications and Decoding

[191] J.G. Choi, S.H. Bang and B.J. Sheu, "A Programmable Analog VLSI Neural-Network Processor for Communication Receivers," IEEE T. Neural Networks, vol. 4 (3), pp 484-495, May 1993.

[192] M.I. Chan, W.T. Lee, M.C. Lin and L.G. Chen, "IC Design of an Adaptive Viterbi Decoder," IEEE T. Cons. El., vol. 42 (1), pp 52-62, Febr. 1996.

[193] R. Mittal, K.C. Bracken, L.R. Carley and D.J. Allstot, "A Low-Power Backward Equalizer for DFE Read-Channel Applications," IEEE J. Solid-State Circuits, vol. 32 (2), pp 270-273, Febr. 1997.

[194] B.C. Rothenberg, J.E.C. Brown, P.J. Hurst and S.H. Lewis, "A Mixed-Signal RAM Decision-Feedback Equalizer for Disk Drives," IEEE J. Solid-State Circuits, vol. 32 (5), pp 713-721, 1997.

6.6.10 Clock Skew Timing Control

[195] W.D. Grover, J. Brown, T. Friesen and S. Marsh, "All-Digital Multipoint Adaptive Delay Com¬pensation Circuit for Low Skew Clock Distribution," Electronics Letters, vol. 31 (23), pp 1996¬1998, Nov 9, 1995.

[196] M. Mizuno, M. Yamashina, K. Furuta, H. Igura, H. Abiko et al., "A GHz MOS Adaptive Pipeline Technique Using MOS Current-Mode Logic," IEEE J. Solid-State Circuits, vol. 31 (6), pp 784-791, June 1996.

[197] E.W. Justh and F.J. Kub, "Analog CMOS Continuous-Time Tapped Delay-Line Circuit," Elec¬tronics Letters, vol. 31 (21), pp 1793-1794, Oct. 12, 1995.

6.6.11 Control and Autonomous Systems

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6.6.12 High-Energy Physics

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Employment law—looking ahead to 2017

03/01/2017

Employment analysis: Our panel of experts considers what 2017 might have in store for employment lawyers.

The experts

Elspeth Beatty, associate at rabners

Sarah Chilton, artner at CM Murray

Hayley Robinson, artner at Macfarlanes

Gerwyn Davies, labour mar et adviser at t e CIPD

Legal developments and practical impact

What are likely going to be the most important cases in 2017, and why?

Elspeth Beatty (EB): There have been a number of challenges recently concerning ‘gig economy’ working.

It as unanimously eld at t e reliminary earing in Aslam & Others v Uber BV & Others E Case o 2202550/2015

others), that the two ‘test’ claimants were not self-employed, but were ‘employed’ as ‘workers’ for the purposes of claims under t e ational Minimum age Regulations 2015, SI 2015/621, and t e or ing ime Regulations 1998, SI 1998/1833. Ho ever, t e tribunal ualified its reasoning in Aslam (para [97] of the reserved judgment) saying that Uber’s business model for organising eer-to- eer mar et activity for freelance work ‘fails to achieve that aim’. In a competitive and difficult economic mar et t at calls for innovative or ing, t ere may yet be some breat ing room for businesses see ing to utilise gig economy latforms.

et er t is is vie ed as a victory for workplace protections or a merely barrier to ‘gig economy’ entrepreneurship, the

tribunal’s reasoning in Aslam, ile sub ect to any a eal, as s urred t e Inde endent or ers nion of reat ritain to t reaten legal roceedings against Deliveroo if they refuse their riders’ requests for union recognition and employment rig ts. e can e ect t at t ese sorts of cases ill become more revalent in 2017.

Sarah Chilton (SC): I t in t e focus on em loyee and or er status is going to continue, and e ill see more cases in

t is area. Self-em loyed eo le no re resent 15 of t e or force and t ere as been a 60 rise in t e last five

years. e la in t is area as been under t e s otlig t recently in t e case broug t by t e ber drivers, in ic t e Em loyment ribunal found t at t e drivers ere or ers and t erefore entitled to certain rig ts including:

• to be aid t e national minimum age

• to aid annual leave, and

• to rest brea s

I understand t at ber are a ealing t e case so t e outcome of t e a eal ill be an interesting case for ne t year.

not er case on t e ori on in t is area is t e Em loyment ribunal a lication by a grou of Deliveroo drivers in Camden o are see ing union recognition as or ers.

e outcome of t ese ill be significant because t e issue affects a large and gro ing sector, and t e ay in ic businesses in the ‘gig economy’ operate. These cases always turn on the individual facts so the judgments may not offer any clarity on t e la , but t e ider financial and ractical im act on ber, t e drivers, and ot er entities o erating in t is ay is li ely to be significant.

I fre uently advise on istle-blowing matters and the Court of Appeal’s judgment in Chesterton Global Ltd and another v Nurmohamed (due to be heard in June 2017) will be significant. It deals with the meaning of ‘public interest’ under the

Public Interest Disclosure ct 1998, as amended in 2013, and ill be t e first Court of eal guidance on at

constitutes ‘public interest’. The Employment Appeal Tribunal has already given its view on the meaning ‘public interest’

saying t at somet ing could be in t e ublic interest if it as an issue affecting 100 em loyees, em loyed by t e same employer, because they are a ‘section of the public’.

Hayley Robinson (HR): or er status is li ely to be t e most tal ed-about em loyment la to ic in t e coming mont s. The Employment Appeal Tribunal (EAT) will hear Uber’s appeal against the finding that its drivers are workers—so

entitled to or ing time rotection, t e national minimum age, istleblo ing rotection and a range of ot er rig ts—and

a number of ot er com anies are also facing litigation by individuals claiming eit er em loyee or or er status. ose cases clearly ave a direct im act on com anies in t e gig economy, but are also im ortant for em loyment la more idely.

e government and Parliament are e amining et er our current em loyment la frame or fits t e ne ays of

or ing and roviding services t at so many com anies and individuals no o erate. ose models ose c allenges for

t e ta base and societal co esion, not ust for em loyment la yers.

e also antici ate t e gro ing focus on t e ider diversity agenda to continue. On t e litigation front, t e most ig -

rofile cases are t o claims in t e Euro ean Court of ustice from rance and elgium in ic female Muslim em loyees ave been refused t e rig t to ear a veil at or . e o inions from t e dvocates- eneral in eac case reached radically different views on the extent to which the employees’ freedom of conscience should be balanced against their employers’ right to run a business in a neutral way. Whether the court is able to find a commercially

racticable ay to balance t ose com eting freedoms remains to be seen.

What are likely to be the most significant legislative and regulatory developments, and why?

EB: e a rentices i levy is due to ta e effect on 6 ril 2017 and ill re uire all em loyers it annual age bills

of more t an 3m to ay 0.5 of t eir annual age bill to ards t e cost of a rentices i training. e ur ose of t e levy is to encourage em loyers to invest in a rentices i rogrammes and to raise additional funds to im rove t e

uality and uantity of a rentices i s. lt oug lans for t e a rentices i levy ere under ay before t e referendum result, t is is a significant develo ment, as t e need for investment in s ills ill be even more urgent and im ortant for t e

en it is outside t e E .

unding for a rentices i s de ends on t e economy being strong and businesses being rofitable. ollo ing t e

referendum result t e is entering into a eriod of economic uncertainty, and business confidence as been noc ed,

it some businesses calling for t e a rentices i levy to be delayed.

SC: There isn’t much in the pipeline for employment law this year compared to usual.

One of t e significant legislative develo ments ill be t e introduction of t e gender ay ga re orting regulations,

e ected to come into force in Marc 2017. e gender ay ga as 18.1 in ril 2016 and t e government ants to

close that gap although it’s estimated that it could take until 2069 to do so.

e draft regulations a ly to businesses it 250 or more relevant em loyees so ill only end u a lying to 34 of em loyees because t e ma ority of em loyees or in micro-business not caug t by t e draft regulations. Em loyers to

om t e draft regulations a ly ill ave to analyse t eir gender ay and gender bonus ga s, as ell as t e ro ortion of male and female em loyees in eac ay uartile, every ril and ublis a re ort every year. Ho ever, t e draft regulations lac any sanctions for non-compliance but it’s expected that employers will be incentivised to com ly for ot er reasons, including: to avoid negative ublicity, im rove staff retention, reduce t e ris of e ual ay litigation alt oug if t eir ga is large t at could ave t e o osite effect and maintain or im rove t eir re utation.

s an area ri e for revie , em loyment status may also see some develo ments. In res onse to t e gro ing gig-economy, t e Prime Minister as commissioned Matt e aylor, t e C ief E ecutive of t e Royal Society of rts, to loo at o em loyment ractices need to c ange in order to ee ace it modern business models. e De artment for

usiness, Energy and Industrial Strategy formerly IS is also going to launc a researc ro ect into t e scale of t e gig economy.

I’m also qualified in Scotland, so I’m keeping an eye on the tribunal reform consultation in Scotland. The proposal to bring t e Em loyment ribunal it in t e broader irst-tier ribunal in Scotland could ave a big im act on t e s ecialist nature of Scottis em loyment tribunals.

HR: ay from litigation, t e diversity agenda encom asses a number of increasingly im ortant strands: from trans issues, to recruitment that takes account of candidates’ backgrounds, to promoting a greater range of senior leaders. Em loyment la yers are being as ed to become more and more involved it t ose initiatives, or ing alongside HR and ot er rofessionals to assist clients embrace often uite difficult c anges.

e ey legislative c ange in t e diversity field is t e introduction of t e gender ay ga re orting regime. is is a ide

ranging measure designed to increase trans arency over ay, and com el businesses to communicate more o enly it staff and stakeholders about their pay and promotion structures. The government’s recent green paper on cor orate governance as s a number of ot er uestions about ay trans arency and o best to ensure em loyee artici ation in e ecutive decisions, and t at ill also be a significant area to atc .

e ot er big s a e u for t e financial sector is t e s read of t e senior managers and conduct regimes. an s and larger institutions ave been sub ect to t e regimes since Marc 2016, but t e model is e ected to be rolled out across t e regulated environment by 2018. e recise form of t e ne regulatory frame or ill be a ey issue for clients in t at sector.

How is Brexit likely to affect these?

EB: It is far from clear at re it ill actually mean in ractice.

The Great Repeal Bill will make provision for the safeguarding of workers’ rights derived from E legislation after t e

it dra al of t e from t e E , but greater uncertainty surrounds t e im lications of re it for secondary legislation, in

ic muc em loyment la is contained.

Safeguarding workers’ rights has not been at the top of the government’s agenda when it comes to Brexit. However, we should not expect the government to announce any erosion of workers’ rights contained in secondary legislation any time soon. o do so may rove to ic to t e c allenging re it negotiations t at lie a ead. In addition, ard- ressed or ers were among those who voted leave in the referendum, and it is those large groups of ‘gig economy’ workers who consider themselves to be ‘exploited’ when it comes to employment protections, who are making the headlines.

SC: I don’t think Brexit will affect these proposed changes. Equal pay has been such a big issue over the last decade, or longer, that I believe the government’s commitment to reducing the gender pay gap will continue. Where Brexit is more li ely to affect em loyment la is in areas suc as t e ransfer of nderta ings Protection of Em loyment Regulations

2006, SI 2006/246 PE and or ing time. e la in relation to or ing time and in articular oliday ay as become

more com le over t e last fe years and derives from E la so t e government may revie t at area, follo ing re it.

HR: e do not antici ate a direct im act on em loyment la from re it, at least in t e s ort to medium term. Most of t e

develo ments outlined above are ome gro n, alt oug many ave E dimensions. e government as re eatedly announced a desire to maintain t e e isting em loyment la frame or for t e time being, alt oug e sus ect t at t ere is li ely to be ressure from some lobby grou s for as ects of t e or ing time regime and rules governing agency or ers among ot er t ings to be re ealed in t e longer term.

re it ill ave its clearest effect on data rotection and immigration. e eneral Data Protection Regulation E

2016/679 ill come into effect in t e in 2018, in all li eli ood very s ortly before e leave t e E . Clients ill be een

to understand t eir data rotection obligations ost- re it as soon as ossible. Similarly, clients it significant need for

or ers from outside t e ill ant clarity on the government’s future EU and non-E immigration structures as a matter of urgency so t at t ey can sensibly lan investment and labour usage. Some businesses may, of course, need to restructure or even relocate—and e ave already seen instructions from clients in t at osition.

Clients and business developments

How do you think the practice of employment law is going to develop in 2017?

EB: e Court of eal is sc eduled to ear R (UNISON) v Lord Chancellor in Marc 2017. or no t oug , fees

remain, and ill continue to ave a significant im act on t e volume of litigation in t e em loyment tribunals. Ho ever disputes have not gone away. There has merely been a ‘cultural’ shift in the way employers and employees resolve dis utes in t e or lace. Re resentatives ill need to develo t eir or ing ractices to ada t to t is s ift.

SC: De ending on t e timing of re it, and t e outcome of t e Su reme Court case on triggering rticle 50, I t in e ill

be as ed more about t e im act of re it for em loyers. e or it a lot of S em loyers, o I e ect ill be loo ing

for more guidance in t is area as t e re it rocess continues.

I also e ect to be as ed to advise on t e structuring of em loyment relations i s, ere businesses mig t be trying to

ensure t eir or force are genuinely self-em loyed and not or ers or em loyees.

HR: e ave seen a gro ing trend for em loyment la yers to move outside t e traditionally core elements of unfair

dismissal, em loyment contracts, discrimination and settlement agreements, to assist clients it ne em loyment models, data rotection, diversity and recruitment, regulatory investigations and immigration. e e ect t at trend to continue. Clients increasingly e ect to deal it a single adviser, and it is u to e ternal la yers to or collaboratively across s ecialisms to ensure client service is co esive and delivered in an efficient and effective ay.

What do you think the key challenges are going to be?

EB: Pro osals to revent lo s illed immigrants from t e E entering t e could cause s ills s ortages on varying

levels, and problems for businesses who rely on their labour. Uncertainty over the government’s immigration policy will be a ey c allenge for businesses in lanning for t e future.

SC: Res onding to develo ments ic ill arise as a result of re it and continuing to rovide commercially valuable

advice to clients in t e face of a c anging legal and economic landsca e.

I also t in tribunal fees ill continue to resent a c allenge to claimants and to ractitioners, as a result of t e dro in

claims since t eir introduction. It ill be interesting to see at a ens in Scotland if t e government t ere delivers on

t eir romise to abolis fees, and whether that will have any impact at Westminster. There is also UNISON’s challenge to

fees, due to be eard by t e Su reme Court in Marc ne t year, ic could ave a big im act for ractitioners if fees are

abolis ed.

HR: at desire for a single oint of contact drives im rovement in our ro ect management and communication s ills.

ose remain c allenges for most firms. Clients continue rig tly to ant more for less. Clients increasingly ant to see

demonstrable value for money, and ant t eir advisers to be innovative in finding tec nological solutions ere t at is

more cost-effective. e advance of artificial intelligence in la as been e tensively covered. So far t e main im act of

I as been felt in due diligence and disclosure e ercises, but t is is already beginning to affect all ractice areas and

or ty es. a yers o fail to embrace t e ossibilities t at tec nology offers ill be increasingly left be ind.

A view from the labour market

What are your predictions for 2017?

Gerwyn Davies (GD): CIPD/Adecco Group’s research for their latest quarterly report abour Mar et Outloo s o s t at,

ile em loyment gro t loo s set to continue in t e , t ere are signs t at t is gro t is beginning to slo and real

ages are li ely to fall during 2017 for many em loyees. Concerns are also emerging over t e im lications of re it for employers’ access to migrant labour and a reduction in employer investment intentions.

The report points to the UK economy beginning to face some likely headwinds following the UK’s decision to leave the EU. The impact of potential restrictions to migrant labour will certainly be exacerbated by the fall we’re seeing in business investment intentions. In fact, t e gro t in t e number of E nationals in em loyment as slo ed to a tric le during t e past quarter, following previously strong growth, which could be the first tentative sign that the UK’s vote to leave the EU is already starting to stem t e su ly of E migrant labour, before any formal restrictions are introduced as a result of t e UK’s eventual exit from the EU. Given the current level of uncertainty and the projected increase in costs as a result of the

ea er ound, it’s not surprising that employers aren’t currently persuaded to respond to likely controls on migration by investing more in s ills.

All this will put more pressure on the UK’s productivity growth potential, which is critical to employers’ ability to afford more generous ay increases. Pay e ectations are already ea , and as inflation moves u e can e ect a eriod of lo or negative real wage growth for the ‘squeezed middle’— artly because a ea er ound ill increase costs for many

businesses and artly because of a rise in em loyment costs as a result of t e introduction of t e national living age, ension auto-enrolment and t e fort coming a rentices i levy.

gainst t is bac dro of uncertainty, no is t e rig t time for organisations to revie t eir strategic riorities and align investment in s ills and ysical ca ital to el t em overcome a range of related c allenges. e recent cut in interest rates s ould offer em loyers a timely reminder t at borro ing costs for business investment are istorically very lo , ic can el offset t e increased ris of s ills s ortages and lo er roductivity gro t .

How is Brexit likely to affect these?

GD: It seems t at fe em loyers ant or are ready for a ard re it outcome, ic t e current olitical commentary

seems to be pointing towards. However, uncertainty over the UK’s future arrangements with the EU is no excuse for inaction. rom all t e information e ave, it’s inevitable that there will be restrictions on EU migrant labour after the UK leaves t e E and em loyers must be re ared for t is. Em loyers need to res ond no by investing more in s ills and revie ing t eir resourcing c annels and retention met ods to offset t e ris of looming labour s ortages.

It’s vital that the UK government considers making intermediate arrangements when introducing changes to immigration

olicy. is ill ensure em loyers t at ave come to de end on E migrants to deal it recruitment difficulties or s ills s ortages ave time to revie t eir recruitment and training and develo ment strategies a ead of re it.

Interviewed by Kate Beaumont.

The views expressed by our Legal Analysis interviewees are not necessarily those of the proprietor

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Writing a Master Thesis

within the Research Project

„German Airport Performance„

The German Airport Performance (GAP) project is a joint re-search project between different German universities (University of Applied Sciences Bremen, Berlin School of Economics and Law and the International University of Applied Sciences).

The project is funded by the federal ministry of education and re-search and postgraduate studies are currently working for the pro-ject. The goal of the project is to study changes in the institutional structure and performance of airports, the effect of commercializa-tion and the competitive environment on them, and to consider fu-ture requirements with regards to financial and ecological regula-tion. Moreover, the goal is to carry out a nation- and Europe-wide airport benchmarking study, thus providing airports with more transparency regarding their current market position as well as showing them best practices and individual weaknesses.

For our GAP-project we are currently looking for:

Interested students who have finished their basic

studies and are now looking for their Master Thesis top-ic.

The main research areas are:

• Airport Charges, Regulation and Airport / Airline Relationship

• Regulation of airport charges

• Impact of different pricing schemes and trends in price level structures 1995-2007

• Risk sharing between airlines and airports

• Benchmarking

• Efficiency and capacity measurement in airport sector

• Effects of privatization, regulation and restructuring on airport performance

• Production function and econometric approaches for productivity

• Financial Analysis

• Cost and revenue analysis, financial performance of the airports

• Financing structure

• Organization of the airports value chain, especially for ground handling services

• EU liberalization and its effects on vertical integration and labour markets

• Non-Aviation

• Understanding the composition of non-aviation activities (Retail, Catering, Parking, Real Estate, etc.) and its growing importance

• Entry and Exit of Airports in Europe

• Analysing changes in market structure, barriers to entry, competition between air¬ports

You may propose your own topic or work directly with one of the teams and then benefit from our database and contacts in the aviation industry. If the topic fits with the Gap pro-ject, financial assistance may also be provided.

Additional support is offered from the Erich-Becker-Stiftung (Fraport AG). The society offers up to 3000 Euro financing for the students writing their scientific papers on the air-lines and airports related topics.

If you are interested, welcome to our project!

Please send your complete application (CV, motivation letter, transcripts, references and topic you are interested in) to Prof. Dr. Jürgen Müller (Project Manager): jmueller@hwr-berlin.de.

For more details about the GAP-project please visit www.gap-projekt.de

Mehdi Khamassi

Tenured Research Scientist (CR1 CNRS)

Institute of Intelligent Systems and Robotics (ISIR)

Université Pierre et Marie Curie (UPMC), Paris, France

mehdi.khamassi (AT) upmc.fr; people.isir.upmc.fr/khamassi

Research interests

Computational, behavioral & brain mechanisms for the online adaptive coordination of parallel learning processes in animals and robots.

Approaches

Combining computational modelling, experimentation in cognitive robotics, model-based analyses of neurobiological data, design of new experimental protocols to test model predictions, and (formerly) extracellular multi-unit recordings in behaving animals.

Affiliations

Since 2016 Visiting researcher, National Polytechnical University of Athens, Greece

Since 2014 Tenured research scientist (CR1 CNRS), ISIR, UPMC, Paris, France

2013-2015 Visiting researcher, Center for Mind/Brain Sciences, University of Trento, Italy

2010-2014 Tenured research scientist (CR2 CNRS), ISIR, UPMC, Paris, France.

2008-2010 Post-doctoral fellow, INSERM - Stem-cells and Brain Research Institute, Lyon, France

2008 (3m.) Visiting researcher, Neural Computation Unit, Okinawa Inst. of Science & Tech, Japan

Other current academic responsibilities

Since 2015 Director of studies and pedagogical council member for the CogMaster, École Normale Supérieure / École des Hautes Études en Sciences Sociales / Univ. Paris 5.

Since 2012 Co-animator of the “GT8 Robotics & Neuroscience” working group, CNRS National Robotics Network called “Groupement De Recherche” (GDR).

Since 2012 Member of the executive committee of the SMART Labex, gathering eight institutes/laboratories related to UPMC: ISIR, LIP6, LIF, LJLL, LTCI, LUTIN, L2E & STMS.

Education

2014 HDR (Habilitation to Direct Researches), UPMC, Paris, France

2003-2007 Ph.D. in Cognitive Neuroscience (summa cum laude), UPMC, Paris, France

2002-2003 M.Sc. in Cognitive Sciences – CogMaster (summa cum laude, major), UPMC / École Normale Supérieure Ulm / École Polytechnique / EHESS, Paris, France

2000-2003 M.Eng. in Computer Science, École Nationale Supérieure d’Informatique pour l’Industrie et l’Entreprise, Conservatoire National des Arts et Métiers / Université

d’Évry, France. Specialties: Artificial Intelligence & Statistical Modelling.

1998-2000 Maths Sup. / Maths Spé. (MP), Lycée Charlemagne, Paris, France. 2 years of intensive Maths/Physics preparing the competitive entrance to French “Grandes Écoles”.

July 2005 Okinawa Computational Neuroscience Course, Okinawa, Japan

Nov. 2004 Okinawa Computational Neuroscience Course, Okinawa, Japan

Aug. 2003 Integrative and Computational Neuroscience Summer School, Concarneau, France

Awards, honors and fellowships

2012 Best paper award at the International Conference on Simulation of Adaptive Behavior, with Jean Bellot & Olivier Sigaud.

2011 Best 2010 Paper in Neuroscience, "La Recherche" Price, with Karim Benchenane, Sidney Wiener, Francesco Battaglia, Adrien Peyrache, Patrick Tierney & Yves Gioanni.

2010 1st rank at national concourse for a tenured research position at the Centre

National de la Recherche Scientifique (CNRS). Interdisciplinary commission (CID) 44.

2007 National Qualification for university-level teaching both in Computer Science and in Neuroscience by the Conseil National des Universités (CNU).

2005 Initial Research Project Award from Okinawa Institute of Science and Technology (OIST) to attend the Okinawa Computational Neuroscience Course (OCNC), Japan.

2004 Initial Research Project Award from Okinawa Institute of Science and Technology (OIST) to attend the Okinawa Computational Neuroscience Course (OCNC), Japan.

2003 French Research Ministry Award to attend the Integrative and Computational Neuroscience Summer School in Concarneau, France.

2003 French Research Ministry PhD fellowship (MENRT), Université Pierre et Marie Curie, “Brain, Cognition & Behavior” Doctoral School, 1st rank.

Funding (since tenure)

2016-2019 ANR-NSF Collaborative Research in Computational Neuroscience – “Neurobehavioral assessment of a computational model of reward learning” (role: co-PI with Matt R. Roesch (PI), Alain Marchand) – Total: 670 K$ (123 K$ for the team)

2016-2018 Royal Society International Exchanges Scheme (inc CNRS) “Highly stochastic analytic meta-learning: the Braitenberg vehicles case study” (role: co-PI with Inaki Rano (PI)) – Total: 10 K£ (half for the team).

2015-2018 European Union H2020-ICT-2014 – “DREAM: Deferred Restructuring of Experience in Autonomous Machines” (role: participant with Stéphane Doncieux (PI) et al.) – Total: 2784 K€ (758 K€ for the team)

2015-2016 Sorbonne-Universités ANR-11-IDEX-0004-02 Idex SUPER SU-15-R-PERSU-14 PERSU – “ROBOT PARALLEARNING, Neuro-inspired coordination of parallel learning processes in robots” (role: PI) – Total direct costs: 70 K€ (for the team)

2013-2017 Agence Nationale de la Recherche ANR-11-LABX-65 Labex SMART – “Online Budgeted Learning” (role: co-PI with Ludovic Denoyer (PI), Patrick Gallinari, Benoît Girard) – Total direct costs: 285 K€ (half for the team)

2013-2016 Agence Nationale de la Recherche ANR-12-CORD-0030 (CONTINT) – “ROBOERGOSUM, Robot Self-Awareness” (role: co-PI with Rachid Alami, Benoît Girard, Raja Chatila (PI)) – Total direct costs: 422 K€ (258 K€ for the team)

2012-2013 CNRS PEPS Program – “GoHaL, Computational and neurophysiological bases of goal-directed and habit learning” (role: co-PI with Andrea Brovelli (PI), Francesca Sargolini) – Total direct costs: 44 K€ (10 K€ for the team)

2011-2015 Agence Nationale de la Recherche ANR-11-BSV4-006 – “Learning Under Uncertainty” (role: co-PI with Paul Apicella, Etienne Coutureau, Benoît Girard, Alain Marchand, Emmanuel Procyk (PI)) – Total direct costs: 616 K€ (73 K€ for the team)

2011-2014 Ville de Paris Emergence(s) Program – “HABOT, From flexible to habitual behaviors: neuroinspired learning for humanoid robots” (role: co-PI with Raja Chatila, Benoît Girard (PI)) – Total direct costs: 285 K€ (for the team)

2011-2012 CNRS PEPII Program – “IMAVO, Interactions between learning modules in a volatile environment” (role: co-PI with Etienne Coutureau, Alain Dutech, Benoît Girard, Alain Marchand (PI), Nicolas Rougier) – Total direct costs: 27 K€ (6 K€ for the team)

Publications in international peer-reviewed journals

22. Viejo, G., Girard, B. and Khamassi, M. (2016). [Re] Speed/accuracy trade-off between the habitual and the goal-directed process. ReScience, 2(1).

21. Viejo, G., Khamassi, M., Brovelli, A. and Girard, B. (2015). Modelling choice and reaction time during instrumental learning through the coordination of adaptive working memory and reinforcement learning. Frontiers in Behavioral Neuroscience, 9:225.

20. Palminteri, S., Khamassi, M., Joffily, M. and Coricelli, G. (2015). Contextual modulation of value signals in reward and punishment learning. Nature Communications, 6:8096.

19. Lesaint, F., Sigaud, O., Clark, J.J., Flagel, S.B. and Khamassi, M. (2015). Experimental predictions drawn from a computational model of sign-trackers and goal-trackers. Journal of Physiology – Paris, 109(1-3):78-86.

18. Khamassi, M., Quilodran, R., Enel, P., Dominey, P.F. and Procyk, E. (2015). Behavioral regulation and the modulation of information coding in the lateral prefrontal and cingulate cortex. Cerebral Cortex, 25(9):3197-218.

17. Lesaint, F., Sigaud, O. and Khamassi, M. (2014). Accounting for negative automaintenance in pigeons: A dual learning systems approach and factored representations. PLoS ONE, 9(10):e111050.

16. Lesaint, F., Sigaud, O., Flagel, S.B., Robinson, T.E. and Khamassi, M. (2014). Modelling individual differences observed in Pavlovian autoshaping in rats using a dual learning systems approach and factored representations. PLoS Computational Biology, 10(2):e1003466.

15. Arleo, A., Déjean, C., Allegraud, P., Khamassi, M., Zugaro, M.B. and Wiener, S.I. (2013). Optic flow stimuli update anterodorsal head direction neuronal activity in rats. Journal of Neuroscience, 33(42):16790-5.

14. Cos, I., Khamassi, M.*, Girard, B. (2013). Modelling the learning of biomechanics and visual planning for decision-making of motor actions. Journal of Physiology – Paris, 107(5):399-408. (* corresponding author)

13. Khamassi, M., Enel, P., Dominey, P.F. and Procyk, E. (2013). Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters. Progress in Brain Research, 202:441-464.

12. Humphries, M.D., Khamassi, M. and Gurney, K. (2012). Dopaminergic control of the exploration-exploitation trade-off via the basal ganglia. Frontiers in Neuroscience, 6:9.

11. Caluwaerts, K., Staffa, M., N'Guyen, S., Grand, C., Dollé, L., Favre-Félix, A., Girard, B. and Khamassi, M. (2012). A biologically inspired meta-control navigation system for the Psikharpax rat robot. Bioinspiration & Biomimetics, 7(2):025009.

10. Khamassi, M. and Humphries, M.D. (2012), Integrating cortico-limbic-basal ganglia architectures for learning model-based and model-free navigation strategies. Frontiers in Behavioral Neuroscience, 6-79.

9. Khamassi, M., Lallée, S., Enel, P., Procyk, E. and Dominey P.F. (2011). Robot cognitive control with a neurophysiologically inspired reinforcement learning model. Frontiers in Neurorobotics, 5:1.

8. Benchenane, K., Peyrache, A., Khamassi, M., Wiener, S.I. and Battaglia, F.P. (2010). Coherent theta oscillations and reorganization of spike timing in the hippocampal-prefrontal network upon learning. Neuron, 66(6):921-936.

7. Peyrache, A., Benchenane, K., Khamassi, M., Wiener, S.I. and Battaglia, F.P. (2010). Sequential reinstatement of neocortical activity during slow oscillations depends on cells’ intrinsic excitability. Frontiers in Systems Neuroscience, 3:18.

6. Peyrache, A., Benchenane, K., Khamassi, M., Wiener, S.I. and Battaglia, F.P. (2010). Principal component analysis of ensemble recordings reveals cell assemblies at high temporal resolution. Journal of Computational Neuroscience, 29(1-2):309-325.

5. Peyrache, A., Khamassi, M., Benchenane, K., Wiener, S.I. and Battaglia, F.P. (2009). Replay of rule-learning related neural patterns in the prefrontal cortex during sleep. Nature Neuroscience, 12(7):919-926.

4. Khamassi, M.*, Mulder, A.B. *, Tabuchi, E., Douchamps, V. and Wiener S.I. (2008). Anticipatory reward signals in ventral striatal neurons of behaving rats. European Journal of Neuroscience, 28(9):1849-1866. (* equally contributing authors)

3. Khamassi, M., Lachèze, L., Girard, B., Berthoz, A. and Guillot, A. (2005). Actor-critic models of reinforcement learning in the basal ganglia: From natural to artificial rats. Adaptive Behavior, 13(2):131-148.

2. Meyer, J.-A., Guillot, A., Girard, B., Khamassi, M., Pirim, P., and Berthoz, A. (2005). The Psikharpax project: Towards building an artificial rat. Robotics and Autonomous Systems, 50(4):211-223.

1. Zugaro, M. B.*, Arleo, A.*, Déjean, C., Burguière, E., Khamassi, M. and Wiener, S. I. (2004). Rat anterodorsal thalamic head direction neurons depend upon dynamic visual signals to select anchoring landmark cues. European Journal of Neuroscience, 20(2):530-536. (* equally contributing authors)

Journal papers in preparation, submitted or in revision

8. Khamassi, M., Velentzas, G., Tsitsimis, T. and Tzafestas, C. (in preparation). Active exploration through meta-learning in parameterized reinforcement learning applied to human-robot interaction.

7. Khamassi, M., Lebas, N., Peyrache, A., Benchenane, K., Hopkins, A., Douchamps, V., Battaglia, F.P. and Wiener, S.I. (in preparation). Rat prelimbic cortex neurons encode both task rule changes and spontaneous behavioral strategy shifts.

6. Griessinger, T., Coricelli, G.* and Khamassi, M.* (in preparation). A behavioral investigation of inter-individual differences in learning during repeated strategic interactions. (* equally contributing authors)

5. Cinotti, F., Fresno, V., Aklil, N., Coutureau, E., Girard, B., Marchand, A.* and Khamassi, M.* (in preparation). Striatal dopamine controls exploration in a non-stationary probabilistic multi-armed bandit task. (* equally contributing authors)

4. Wydoodt, P., Sescousse, G., Domenech, P., Barbalat, G., Khamassi, M. and Dreher, J.-C. (in preparation). Gambler’s fallacy and hot hand fallacy in pathological gamblers.

3. Renaudo, E., Girard, B., Devin, S., Alami, R., Clodic, A., Chatila, C. and Khamassi, M. (submitted). Can robots learn behavioral habits? Coordination of model-based and model-free reinforcement learning in a robot neuro-inspired cognitive architecture.

2. Dollé, L., Chavarriaga, R., Khamassi, M. and Guillot, A. (submitted). Interactions between spatial strategies producing generalization gradient and blocking: a computational approach.

1. Bellot, J., Sigaud, O., Roesch, M.R., Schoenbaum, G., Girard, B. and Khamassi, M. (submitted). Dopamine neurons phasic activity does not encode the reward prediction error that behavioral adaptation would predict.

Book chapters

4. Alexandre, F., Dominey, P.F., Gaussier, P., Girard, B., Khamassi, M. and Rougier, N. (2017). When Artificial Intelligence and Computational Neuroscience meet. In editors (Eds.), Book title, Heidelberg: Springer-Verlag.

3. Pacherie, E. and Khamassi, M. (2017). Action. In Andler, D., Collins, T. and Tallon-Baudry, C. (Eds.), Manuel de Sciences Cognitives, Paris, France: Gallimard.

2. Khamassi, M., Wilson, C., Rothé, R., Quilodran, R., Dominey, P.F. and Procyk, E. (2011). Meta-learning, cognitive control, and physiological interactions between medial and lateral prefrontal cortex. In Mars, R., Sallet, J., Rushworth, M. and Yeung, N. (Eds.), Neural Bases of Motivational and Cognitive Control, Cambridge, MA: MIT Press.

1. Battaglia, F.P., Peyrache, A., Khamassi, M. and Wiener, S.I. (2008). Spatial decisions and neuronal activity in hippocampal projection zones in prefrontal cortex and striatum. In Mizumori S. (Ed.) Hippocampal place fields: Relevance to learning and memory, Oxford, UK: Oxford University Press.

Peer-reviewed international conferences

15. Velentzas, G., Tzafestas, C. and Khamassi, M., (2017). Bio-inspired meta-learning for active exploration during non-stationary multi-armed bandit tasks. Proceedings of IEEE Intelligent Systems Conference 2017. London, UK.

14. Khamassi, M., Velentzas, G., Tsitsimis, T. and Tzafestas, C. (2017). Active exploration and parameterized reinforcement learning applied to a simulated human-robot interaction task. Proceedings of IEEE Robotic Computing 2017. Taipei, Taiwan.

13. Aklil, N., Girard, B., Khamassi, M. and Denoyer, L. (2017). Sequential Action Selection for Budgeted Localization in Robots. Proceedings of IEEE Robotic Computing 2017. Taipei, Taiwan.

12. Pasala, S.K., Khamassi, M. and Pammi, V.S.C. (2016). Variation in Intuitive Geometric Construct of Spatial Perception during Navigation. Proceedings of the International Conference of the Academy of Neuroscience for Architecture (ANFA 2016). Salk Institute, La Jolla, CA, USA.

11. Renaudo, E., Girard, B., Chatila, C. and Khamassi, M. (2015). Respective advantages and disadvantages of model-based and model-free reinforcement learning in a robotics neuro-inspired cognitive architecture. 6th International Conference on Biologically Inspired Cognitive Architectures, Lyon, France/ open-access Elsevier journal "Procedia Computer Science".

10. Renaudo, E., Girard, B., Chatila, C. and Khamassi, M. (2015). Which criteria for autonomously shifting between goal-directed and habitual behaviors in robots? 5th International Conference on Development and Learning and on Epigenetic Robotics, Providence, RI, USA.

9. Renaudo, E., Girard, B., Chatila, C. and Khamassi, M. (2014). Design of a control architecture for habit learning in robots. 3rd Living Machines Conference, Lecture Notes in Artificial Intelligence, Springer, Publisher.

8. Bellot, J., Sigaud, O. and Khamassi, M. (2012). Which Temporal Difference Learning algorithm best reproduces dopamine activity in a multi-choice task? From Animals to Animats 12: SAB Conference, Lecture Note in Computer Science 7426, Springer Verlag, Publisher, pp. 289-298. BEST PAPER AWARD.

7. Caluwaerts, K., Favre-Félix, A., Staffa, M., N’Guyen, S., Grand, C., Girard, B. and Khamassi, M. (2012). Neuro-inspired navigation strategies shifting for robots: Integration of a multiple landmark taxon strategy. 1st Living Machines Conference, Lecture Notes in Artificial Intelligence 7375, Prescott, T.J. et al. (Eds.), Springer, Publisher, pp. 62-73.

6. Khamassi, M., Quilodran, R., Enel, P., Procyk, E. and Dominey P.F. (2010). A computational model of integration between reinforcement learning and task monitoring in the prefrontal cortex. From Animals to Animats 11: SAB Conference, Lecture Note in Computer Science 6226, Springer Verlag, Publisher, pp. 424-434.

5. Dollé, L., Khamassi, M., Girard, B., Guillot, A. and Chavarriaga, R. (2008) Analyzing interactions between navigation strategies using a computational model of action selection. Spatial Cognition Conference, Lecture Notes in Computer Science 5248, Springer, Publisher, pp. 71-86.

4. Battaglia, F.P., Benchenane, K., Khamassi, M., Peyrache, A. and Wiener, S.I. (2007) Neural ensembles and local field potentials in the hippocampo-prefrontal cortex system during spatial learning and strategy. 1st Int Conference on Cognitive Neurodynamics (ICCN), Springer, Publisher, pp. 1-4.

3. Khamassi, M., Martinet, L.-E. and Guillot, A. (2006). Combining self-organizing maps with mixture of experts: Application to an actor-critic model of reinforcement learning in the basal ganglia. From Animals to Animats 9, SAB Conference, Lecture Notes in Computer Science 4095, Springer Verlag, Publisher, pp. 394-405.

2. Filliat, D., Girard, B., Guillot, A., Khamassi, M., Lachèze, L. and Meyer, J.-A. (2004) State of the artificial rat Psikharpax. From Animals to Animats 8, SAB Conference, MIT Press, Publisher, pp. 3-12.

1. Khamassi, M., Girard, B., Guillot, A. and Berthoz, A. (2004) Comparing three Critic models of reinforcement learning on the basal ganglia connected to a detailed actor in a S-R task. 8th Int Conference on Intelligent autonomous systems, IOS Press, Publisher, pp. 430-437.

National peer-reviewed journals

4. Girard, B. and Khamassi, M. (2016). Coopération de systèmes d’apprentissage par renforcement multiples. Techniques de l’Ingénieur, to appear. (In French)

3. Khamassi, M. and Doncieux, S. (2016). Nouvelles Approches en Robotique Cognitive. Intellectica, vol. 2016/1, num. 65, pp. 7-25. (In French)

2. Khamassi, M., Girard, B., Clodic, A., Devin, S., Renaudo, E., Pacherie, E., Alami, R. and Chatila, R. (2016). Integraton of action, joint action and learning in robot cognitive architectures. Intellectica, vol. 2016/1, num. 65, pp. 169-203. (In English)

1. de Loor, P., Mille, A. and Khamassi, M. (2015). Intelligence artificielle : l’apport des paradigmes incarnés. Intellectica, vol. 2015/2, num. 64, pp. 27-52. (In French)

Theses

3. Khamassi, M. (2014). Coordination of parallel learning processes in animals and robots. HDR Thesis, Université Pierre et Marie Curie – Paris 6, France.

2. Khamassi, M. (2007). Complementary roles of the rat prefrontal cortex and striatum in reward-based learning and shifting navigation strategies. PhD Thesis, UPMC – Paris 6, France.

1. Khamassi, M. (2003). Une architecture de contrôle de la sélection de l’action dans les ganglions de la base pour le rat artificiel Psikharpax. Master Thesis, UPMC – Paris 6, France.

Vulgarization articles

3. Khamassi, M. and Decremps, F. (2016). De l’art de conjuguer esprit critique et démarche scientifique. The Conversation. Contributions from Marie Pinhas & Fabrice Rousselot. (In French)

2. Khamassi, M. and Chatila, R. (2015). La conscience d’une machine. Pour la Science. (In French)

1. Khamassi, M. (2011). Psikharpax, le robot-rat intelligent. Futura-sciences.com. (In both French &

English)

Papers, abstracts and posters in journals, conferences or workshops with minimal review

55. Renaudo, E., Girard, B., Chatila, R. and Khamassi, M. (2016). Bio-inspired habit learning in a robotic architecture. NIPS Workshop on Neurorobotics at NIPS 2016 Conference.

54. Marchand, A., Coutureau, E., Khamassi, M. and Roesch, M.R. (2016). Neurobehavioral assessment of a computational model of reward learning. Poster at the Collaborative Research in Computational Neuroscience Conference, Paris, France.

53. Cinotti, F., Fresno, V., Aklil, N., Coutureau, E., Girard, B., Marchand, A.* and Khamassi, M.* (2016). Dopamine blockade affects exploration and learning rate in a non-stationary 3-armed bandit task. Poster at the Collaborative Research in Computational Neuroscience Conf., Paris, France.

52. Bault, N., Larsen, T., Khamassi, M., Polonio, L., Vostroknutov, A. and Coricelli, G. (2016). Influence of others' choice behavior on observational learning. Poster at the 14th Annual Meeting of the Society for NeuroEconomics, Berlin, Germany.

51. Cinotti, F., Fresno, V., Aklil, N., Coutureau, E., Girard, B., Marchand, A.* and Khamassi, M.* (2016). Dopamine blockade affects exploration and learning rate in a non-stationary 3-armed bandit task. Poster at the 6th International Symposium on Biology of Decision-Making, Paris, France.

50. Larsen, T., Palminteri, S., Vidal, J.R., Khamassi, M., Joffily, M. and Coricelli, G. (2015). Context can induce seeking behaviour in punishment conditions. Poster at the 13th Annual Meeting of the Society for NeuroEconomics, Miami, U.S.A.

49. Renaudo, E., Devin, S., Girard, B., Chatila, R., Alami, R., Khamassi, M. and Clodic, A. (2015). Learning to interact with humans using goal-directed and habitual behaviors. Workshop on Learning for Human-Robot Collaboration at RO-MAN 2015 Conference.

48. Wydoodt, P., Sescousse, G., Domenech, P., Barbalat, G., Khamassi, M. and Dreher, J.-C. (2015). Gambler’s fallacy and hot hand fallacy in pathological gamblers. Poster at the 5th International Symposium on Biology of Decision-Making, Paris, France.

47. Marchand, A., Fresno, V., Aklil, N., Cinotti, F., Girard, B., Khamassi, M. and Coutureau, E. (2015). Striatal dopamine controls exploration in a probabilistic task. Poster at the 5th International Symposium on Biology of Decision-Making, Paris, France.

46. Griessinger, T., Khamassi, M. and Coricelli, G. (2015). A behavioral investigation of inter-individual differences in learning during repeated strategic interactions. Poster at the 5th International Symposium on Biology of Decision-Making, Paris, France.

45. Girard, B., Aklil, N., Cinotti, F., Fresno, V., Denoyer, L., Coutureau, E., Khamassi, M. and Marchand, A. (2015). Modelling rat learning behavior under uncertainty in a non-stationary multi-armed bandit task. Poster at the Colloque de la Société des Neurosciences Françaises, Montpellier, France.

44. Viejo, G., Khamassi, M., Brovelli, A. and Girard, B. (2015). Modelling choice and reaction time during instrumental learning through the coordination of adaptive working-memory and reinforcement learning. Poster at the Colloque de la Société des Neurosciences Françaises, Montpellier, France.

43. Liénard, J., Bellot, J., Cos, I., Khamassi, M. and Girard, B. (2015). Transmission delays in the basal ganglia proper are sufficient to explain beta-band oscillations in Parkinson’s disease: mean-field and reduced models. iCODE-SynchNeuro Workshop on neural population dynamics, France.

42. Lesaint, F., Sigaud, O. and Khamassi, M. (2014). A model of negative automaintenance in pigeons:

Dual learning and factored representations. Society for Neurosci Abstracts, Washington, USA. 41. Bellot, J., Liénard, J., Khamassi, M. and Girard, B. (2014). A biological plausible D1/D2 basal

ganglia model. Society for Neurosci Abstracts, Washington, USA.

40. Lesaint, F., Sigaud, O. and Khamassi, M. (2014). Accounting for negative automaintenance in pigeons: A dual learning systems approach and factored representations. Poster at the 4th International Symposium on Biology of Decision-Making, Paris, France.

39. Aklil, N., Marchand, A., Fresno, V., Coutureau, E., Denoyer, L., Girard, B. and Khamassi, M. (2014). Modelling rat learning behavior under uncertainty in a non-stationary multi-armed bandit task. Poster at the 4th International Symposium on Biology of Decision-Making, Paris, France.

38. Viejo, V., Khamassi, M., Brovelli, A. and Girard, B. (2014). Modelling choice and reaction time during instrumental learning through the coordination of adaptive working-memory and reinforcement learning. Poster at the 4th International Symposium on Biology of Decision-Making, Paris, France.

37. Viejo, V., Khamassi, M., Brovelli, A. and Girard, B. (2014). Coordination of adaptive working-memory and reinforcement learning systems explaining choice and reaction time during a human experiment.Poster at the 23rd Computational Neuroscience Society meeting (CNS 2014).

36. Marchand, A., Fresno, V., Khamassi, M. and Coutureau, E. (2014). Dopaminergic modulation of the exploration level in a non-stationary probabilistic task. FENS Abstract, Milan, Italy.

35. Palminteri, S., Khamassi, M., Joffily, M. and Coricelli, G. (2013). Reinforcement learning and counterfactual outcomes: evidence for context-value dependent adjustment of action values. Poster at the Society for Neuroeconomics Annual Meeting, Lausanne, Switzerland.

34. Lesaint, F., Sigaud, O., Flagel, S.B., Robinson, T.E. and Khamassi, M. (2013). Modelling individual differences in rats using a dual learning systems approach and factored representations. Poster at the 5th International Symposium on Motivational and Cognitive Control, ICM, Paris, France.

33. Humphries, M.D., Khamassi, M. and Gurney, K. (2013). Dopaminergic control of the exploration-exploitation trade-off via the basal ganglia. Poster at the 3rd International Symposium on Biology of Decision-Making, Paris, France.

32. Lesaint, F., Sigaud, O. and Khamassi, M. (2013). Modelling individual differences in rats using a dual learning systems approach and factored representations. Poster at the 3rd International Symposium on Biology of Decision-Making, Paris, France.

31. Bellot, J., Sigaud, O., Girard, B. and Khamassi, M. (2013). Which Temporal Difference Learning algorithm best reproduces dopamine activity in a multi-choice task? Poster at the 3rd International Symposium on Biology of Decision-Making, Paris, France.

30. Lesaint, F., Sigaud, O., Flagel, S.B., Robinson, T.E. and Khamassi, M. (2013). Modelling individual differences observed in Pavlovian autoshaping in rats using a dual learning systems approach and factored representations. Poster at the 1st International Conference on Reinforcement Learning and Decision Making (RLDM), Princeton Univ., USA.

29. Bellot, J., Khamassi, M., Sigaud, O. and Girard, B. (2013). Which Temporal Difference Learning algorithm best reproduces dopamine activity in a multi-choice task? Poster at the 22nd Computational Neuroscience Society meeting (CNS 2013), Paris, France.

28. Khamassi, M., Bellot, J., Sigaud, O. and Girard, B. (2013). Which Temporal Difference Learning algorithm best reproduces dopamine activity in a multi-choice task? Poster at the Colloquium of the French Neuroscience Society, Lyon, France.

27. Bellot, J., Sigaud, O. and Khamassi, M. (2012). Which Temporal Difference Learning algorithm best reproduces dopamine activity in a multi-choice task? Poster at the 4th Robotics and Neuroscience Days, Paris, France.

26. Caluwaerts, K., Staffa, M., N'Guyen, S., Grand, C., Dollé, L., Favre-Félix, A., Girard, B. and Khamassi, M. (2012). A biologically inspired meta-control navigation system for the Psikharpax rat robot. Poster at the 2nd International Symposium on Biology of Decision-Making, Paris, France.

25. Bellot, J., Sigaud, O. and Khamassi, M. (2012). Which Temporal Difference Learning algorithm best reproduces dopamine activity in a multi-choice task? Poster at the 2nd International Symposium on Biology of Decision-Making, Paris, France.

24. Khamassi, M., Lallée, S., Enel, P., Procyk, E. and Dominey P.F. (2012). Robot cognitive control with a neurophysiologically inspired reinforcement learning model. Poster at the 2nd International Symposium on Biology of Decision-Making, Paris, France.

23. Bellot, J., Sigaud, O., Roesch, M.R., Schoenbaum, G., Girard, B. and Khamassi, M. (2012). Dopamine neurons activity in a multi-choice task: reward prediction error or value function? Full paper at the French Computational Neuroscience NeuroComp / KEOpS 12 workshop, pp. 1-7, Bordeaux, France.

22. Humphries, M.D., Khamassi, M. and Gurney, K. (2012). Dopaminergic control of the exploration-exploitation trade-off via the basal ganglia. Poster at FENS Forum, Barcelona, Spain.

21. Khamassi, M., Lallée, S., Enel, P., Procyk, E. and Dominey, P.F. (2011). Human-Robot Interaction with the iCub Humanoid Robot using a Neuro-Inspired Model of Reinforcement Learning. Full paper + poster at International workshop on bio-inspired robots, Nantes, France.

20. Caluwaerts, K., Grand, C., N'Guyen, S., Dollé, L., Guillot, A. and Khamassi, M. (2011). Design of a biologically inspired navigation system for the Psikharpax rodent robot. Full paper + poster at International workshop on bio-inspired robots, Nantes, France.

19. Khamassi, M., Quilodran, R., Enel, P., Dominey P.F. and Procyk, E. (2010). Role of the frontal cortex in solving the exploration-exploitation trade-off. Poster at the 4th International Symposium on Motivational and Cognitive Control, Oxford, UK.

18. Khamassi, M., Quilodran, R., Enel, P., Dominey P.F. and Procyk, E. (2010). Role of the frontal cortex in solving the exploration-exploitation trade-off. Full paper at the 5th French Neurocomp Conference, Lyon, France.

17. Enel, P., Khamassi, M., Procyk, E. and Dominey P.F. (2010). Reinforcement learning model in probabilistically rewarded task. Poster at the 5th Neurocomp Conference, Lyon, France.

16. Benchenane, K., Peyrache, A., Khamassi, M., Wiener, S.I. and Battaglia, F.P. (2010). Coherent oscillations and learning-related reorganization of spike timing. Poster at the 4th International Conference on Cognitive Systems, CogSys10. January 27 & 28, 2010, ETH Zurich, Switzerland.

15. Benchenane, K., Peyrache, A., Khamassi, M., Wiener, S.I. and Battaglia, F.P. (2009). Coherence of Theta Rhythm between Hippocampus and Medial Prefrontal Cortex Modulates Prefrontal Network Activity During Learning in Rats. Conference abstract in Frontiers in Systems Neuroscience. 12th Meeting of the Hungarian Neuroscience Society, doi: 10.3389/ conf.neuro.01.2009.04.132.

14. Khamassi, M., Quilodran, R., Procyk, E. and Dominey P.F. (2009). Anterior Cingulate Cortex integrates reinforcement learning and task-monitoring: evidence from computational modelling, neural network simulation and primate neurophysiology. Society for Neuroscience Abstracts, Chicago, USA.

13. Benchenane, K., Peyrache, A., Khamassi, M., Wiener, S.I. and Battaglia, F.P. (2008). Theta Band LFP Coherence Between Hippocampus And Prefrontal Cortex and Reorganization of Ensemble Cell Activity During Learning. Conference Abstract in Neuropsychobiology, 58(3-4):233-233.

12. Khamassi, M., Mulder, A.B., Tabuchi, E., Douchamps, V. and Wiener S.I. (2007). Actor-Critic models of reward prediction signals in the rat ventral striatum require multiple input modules. Society for Neuroscience Abstracts, San Diego, USA.

11. Peyrache, A., Benchenane, K., Khamassi, M., Douchamps, V., Tierney, P.L., Battaglia, F.P. and Wiener, S.I. (2007). Rat medial prefrontal cortex neurons are modulated by both hippocampal theta rhythm and sharp waveripple events. Society for Neuroscience Abstracts, San Diego, USA.

10. Benchenane, K., Peyrache, A., Khamassi, M., Tierney, P.L., Douchamps, V., Battaglia, F.P. and Wiener, S.I. (2007). Increased firing rate and theta modulation in medial prefrontal neurons during episodes of high coherence in the theta band of hippocampal/prefrontal local field potentials (LFP) in behaving rats. Society for Neuroscience Abstracts, San Diego, USA.

9. Battaglia, F.P., Peyrache, A., Benchenane, K., Khamassi, M., Douchamps, V., Tierney, P.L. and Wiener, S.I. (2007). Rat medial prefrontal cortex neurons are modulated by both hippocampal theta rhythm and sharp waveripple events. Society for Neuroscience Abstracts, San Diego, USA.

8. Khamassi, M., Battaglia, F.P., Peyrache, A., Douchamps, V., Tierney, P. and Wiener S.I. (2007). Transitions in behaviorally correlated activity in medial prefrontal neurons of rats acquiring and switching strategies in a y-maze. Poster presented at the Okinawa Computational Neuroscience Workshop, Okinawa, Japan.

7. Battaglia, F.P., Khamassi, M., Peyrache, A., Douchamps, V., Tierney, P. and Wiener, S.I. (2006). Spatial and reward correlates in medial prefrontal neurons of rats acquiring and switching strategies in a y-maze. Society for Neuroscience Abstracts, Atlanta, USA.

6. Wiener, S.I., Khamassi, M., Peyrache, A., Douchamps, V., Tierney, P. and Battaglia, F.P. (2006). Transitions in behaviorally correlated activity in medial prefrontal neurons of rats acquiring and switching strategies in a y-maze. Society for Neuroscience Abstracts, Atlanta, USA.

5. Battaglia, F.P., Khamassi, M., Douchamps, V., Tierney, P.L. and Wiener, S.I. (2005). EEG correlations between hippocampus and prefrontal portex in rats performing a decision-making spatial task. Society for Neuroscience Abstracts, Washington DC, USA.

4. Mulder, A.B., Tabuchi, E., Khamassi, M. and Wiener S.I. (2005). Reward site associated activity in the ventral striatum of behaving rats. Society for Neuroscience Abstracts, Washington DC, USA.

3. Arleo, A., Déjean, C., Boucheny, C., Khamassi, M., Zugaro, M.B. and Wiener, S.I. (2004). Optic field flow signals update the activity of head direction cells in the rat anterodorsal thalamus. Abstract in Journal of Vestibular Research, 14(2/3):P095.

2. Wiener, S.I., Arleo, A., Déjean, C., Boucheny, C., Khamassi, M. and Zugaro, M.B. (2004). Optic field flow signals update the activity of head direction cells in the rat anterodorsal thalamus. Society for Neuroscience Abstracts, San Diego, USA.

1. Khamassi, M., Girard, B., Guillot, A. and Berthoz, A. (2003). Mécanismes neuromimétiques d'apprentissage par renforcement dans l'architecture de contrôle du rat artificiel Psikharpax. Poster presented at the French Conference on Artificial learning (CAp) within the frame of the AFIA platform, Laval, France.

Invited talks and seminars

2017 Panel at the 50th Winter Conference on Brain Research, Big Sky, USA

2017 Symposium at the French Neuroscience Society Colloquium, Bordeaux, France

2016 Dept. Electrical & Computer Engineering, Tarbiat Modares University, Teheran, Iran

2016 Department of Experimental Psychology, University of Oxford, UK

2016 “Addiction, in theory” meeting, Gatsby Unit, University College London, UK

2016 6th International Symposium on Motivational and Cognitive Control, St Andrews, UK

2016 Inst. Com. & Computer Systems, National Polytechnical University of Athens, Greece

2016 6th International Symposium on Biology of Decision-Making, Paris, France

2016 5th International Meeting on Comput. Properties of Prefrontal Cortex, Lyon, France

2016 15th National Forum of Cognitive Sciences, Univ. Paris Descartes 5, Paris, France

2016 Symposium at the National GDR Neurosciences de la Mémoire, Bordeaux, France

2015 3rd International Conf. on Cognition, Brain & Comput. (Plenary), Ahmedabad, India

2015 International Conf. on Computational Intelligence (Keynote), Visakhapatnam, India

2015 International Conf. on Cognition in Smart Cities (Keynote), Vizag, India

2015 International NeuroBridges Workshop, Univ. Paris Descartes, Paris, France

2015 1st Computational Neuroscience Symposium at UPMC, Paris, France

2015 Computational Neuroscience Seminars, Ecole Normale Supérieure Ulm, Paris, France

2014 Symposium at International Cognitive Neuroscience Conference, Brisbane, Australia

2013 Brain & Language Research Institute, CNRS, Avignon, France

2013 5th International Symposium on Motivational and Cognitive Control, Paris, France

2013 Center for Mind/Brain Sciences, University of Trento, Italy

2013 Centre de Neurosciences Cognitives, CNRS, Lyon, France

2012 GDR Colloquium, Institut des Neurosciences de la Timone, CNRS, Marseille, France

2012 Basal Ganglia Days, Institut du Cerveau et de la Moëlle Epinière, Paris, France

2012 Neuromorphic Engineering Workshop/Summerschool, Telluride, USA

2011 International Conference on Decision Making, Allahabad, India

2010 Institut des Neurosciences Cognitives de la Méditerranée CNRS, Marseille, France

2010 Laboratoire de Recherche en Informatique, Univ. Paris-Sud 11, Orsay, France

2010 Computational Neuroscience Day, Ecole Normale Supérieure Ulm, Paris, France

2010 Centre de Recherche en Neurosciences de Lyon, INSERM, Lyon, France

2009 Laboratoire de Neurosciences Cognitives, Ecole Normale Supérieure, Paris, France

2009 Laboratoire de Robotique GREYC, Université de Caen, France

2008 Symposium at the National GDR Neurosciences de la Mémoire, Aussois, France

2008 Institut Cellules Souches et Cerveau, INSERM, Lyon, France

2008 Okinawa Institute of Science and Technology, Okinawa, Japan

2007 Third day in Computational Neuroscience, Collège de France, Paris, France

2006 “ICEA” FP6 European project workshop, Derby, UK

2004 Graduate School of Medecine, University of Toyama, Toyama, Japan

2004 Ecole Supérieure de Physique et Chimie Industrielles, Paris, France

Scientific events organized

2012-2016 2nd - 6th International Symposia on Biology of Decision-Making, Paris, France. Co-organizers: T. Boraud, S. Bourgeois-Gironde, K. Doya, E. Koechlin and M. Pessiglione.

200 participants, 30 speakers, 80 posters. Top-level meeting in the field.

2012-2016 6 “Robotics and Neuroscience” days, Paris area, France, for the French Neuroscience and Robotics communities, in the framework of the CNRS Groupements de Recherches (GDRs) Robotics and Neuroscience of Memory. Co-organizers: Benoît Girard, Ghilès Mostafaoui, Alex Pitti, Olivier Sigaud, Philippe Souères. 40 participants on average. (11/12,11/12,03/14,10/15,04/16,06/16)

2016 From Artificial Intelligence to Neuroscience, and back workshop at the Collaborative Research in Computational Neuroscience Conference, Paris, France.

Co-organizers: S. Gershman and B. Gutkin. 50 participants, 4 speakers.

2015 3rd Orbitofrontal Cortex and Cognition Meeting, Paris, France. Co-organizers: Jay Gottfried, Elisabeth Murray, Mathias Pessiglione, Geoffrey Schoenbaum. 150 participants, 30 speakers, 50 posters. Top-level meeting in the field.

2015 Scientific Day around Intellectica journal Issue #61, co-organized with Alexandre Monnin and Gunnar Declerck, CNRS-ISCC, Paris, France. 20 participants.

2013 Symposium at the Colloquium of the French Neuroscience Society, Lyon, France. Theme: Neural dynamics of spatial navigation: electrophysiological data and computational models. Co-organizers: Francesca Sargolini, Bruno Poucet. 50 particip.

2013 Interdisciplinary day (Philosophy, Robotics, Biology) on Cognition, Adaptation and Complexity: From Living Beings to Robots, Paris-Sorbonne University, France, April 11. Principal organizer: Thomas Pradeu. 50 participants.

2005 National Cognitive Science Forum gathering laboratories, companies and students, concerned with cognitive science. Co-organizers: board members of Cognivence association.

2004 Paris-area Regional Cognitive Science Forum gathering laboratories, companies and students, concerned with cognitive science. Co-organizers: Vincent Jacob, board members of Cognivence association.

2004 Regional workshop for the French « États Généraux de la Recherche » (CloEG Paris-Centre; June 21, 27 and 28, 2004). Co-organizers: Marie-Pierre Junier, Catherine Dargemont.

Student supervision

Current PhD students

2016- Fran;ois Cinotti (graduate student), co-supervised with Benoît Girard (ISIR Université Pierre et Marie Curie - Paris 6). Neuroscience Program.

2013- Nassim Aklil (graduate student), co-supervised with Ludovic Denoyer (LIP6 Université Pierre et Marie Curie - Paris 6). Robotics Program.

2013- Thibaud Griessinger (graduate student), co-supervised with Giorgio Coricelli (Ecole Normale Supérieure, Université Pierre et Marie Curie - Paris 6). Neurosci. Program.

Past PhD students

2013-2016 Dr. Guillaume Viejo, co-supervised with Benoît Girard (ISIR Université Pierre et Marie Curie - Paris 6). Neuroscience Program. Now post-doc at McGill University.

2012-2016 Dr. Erwan Renaudo, co-supervised with Raja Chatila (ISIR Université Pierre et Marie Curie - Paris 6). Robotics Program. Now post-doc in the lab.

2011-2015 Dr. Jean Bellot, co-supervised with Benoît Girard (ISIR Université Pierre et Marie Curie - Paris 6). Neuroscience Program. Now working at HEURITECH.

2011-2014 Dr. Florian Lesaint, co-supervised with Olivier Sigaud (ISIR Université Pierre et Marie Curie - Univ. Paris 6). Neuroscience Program. Now working at DEEZER.

Past Postdoctoral fellows

2012-2013 Dr. Ignasi Cos, co-supervised with Benoît Girard (ISIR Université Pierre et Marie Curie - Paris 6). Now Marie-Curie research fellow at Pompeu Fabra University, Barcelona.

Current & Past undergraduate students

2016 Avel Guénin--Carlut (M1 master student ENS). Now Cogmaster student.

2016 Anne Chadoeuf (M2 Cogmaster ENS) co-supervised with Benoît Girard (ISIR Université Pierre et Marie Curie - Paris 6).

2015 Pierre Luce-Vayrac (M2 Androide UPMC) co-supervised with Raja Chatila (ISIR Université Pierre et Marie Curie - Paris 6). Now PhD student in the team.

2015 Fran;ois Cinotti (M2 Cogmaster ENS) co-supervised with Benoît Girard (ISIR Université Pierre et Marie Curie - Paris 6). Now PhD student in the team.

2015 Rémi Dromnelle (M1 student in Bio-informatics at University Denis Diderot – Paris 7). Now Master student at University Denis Diderot.

2013 Timothée Dubuc (M2 student), co-supervised with Arthur Leblois and Olivier Sigaud (ISIR Univ Paris 6). Now PhD student at the University of Reading, UK.

2013 Omar Islas-Ramirez (M2 master student), co-supervised with Benoît Girard (ISIR UMR7222, Université Paris 6). Now PhD student at UPMC.

2013 Sana Bahri (engineering student), co-supervised with Benoît Girard (ISIR Univ. Paris 6). Now still studying at her engineering school.

2012 Erwan Renaudo (M2 master student), co-supervised with Benoît Girard (6 months internship, ISIR UMR7222, Universite Paris 6). Now PhD student in the team.

2011 Sélim Khamassi (engineering student), co-supervised with Benoît Girard (2 months internship, ISIR, Univ. Paris 6). Now studying at Ecole Centrale d’Electronique.

2011 Valère Pique (licence student), co-supervised with Benoît Girard (2 months internship, ISIR, Univ. Paris 6). Now still studying at his university (IUT).

2011 Antoine Favre-Félix (engineering student), co-supervised with Benoît Girard (2 months internship, ISIR Univ. Paris 6). Now studying at Ecole Centrale Nantes.

2011 Mariacarla Staffa (graduate student), PhD 4m. internship co-supervised with Agnes Guillot (ISIR Univ. Paris 6). Now post-doc fellow at Univ. Naples Federico II.

2010 Ken Caluwaerts (M2 master student), co-supervised with Agnes Guillot and Christophe Grand (ISIR Univ. Paris 6). Now Post-doc fellow at NASA, USA.

2009 Pierre Enel (M2 master student), co-supervised with Emmanuel Procyk and Peter F. Dominey (INSERM - Univ. Lyon 1). Now PhD student at Univ Lyon 1.

2007 Manuel Rolland (engineering student), co-supervised with Agnes Guillot (, ISIR, Univ. Paris 6). Now works at Aldebaran Robotics.

2006 Laurent Dollé (M2 master student), co-supervised with Agnes Guillot (LIP6, Univ. Paris 6). Now post-doc fellow at EDF R&D and College de France.

2006 Anthony Truchet (M2 master student), co-supervised with Agnes Guillot (LIP6, Univ. Paris 6). Then PhD student at ENSTA. Now working at CRITEO.

2005 Louis-Emmanuel Martinet (engineering student) co-supervised with Agnes Guillot (LIP6, Univ. Paris 6). Now post-doc fellow at Boston University.

2005 Laurent Dollé (M1 master student), co-supervised with Agnes Guillot (LIP6, Univ. Paris 6). Now post-doc fellow at EDF R&D and College de France.

2005 Vincent Douchamps (M2 master student), co-supervised with Sidney Wiener (LPPA, College de France). Now post-doc fellow at Durham University.

2004 Paul Simard (M2 master student), co-supervised with Agnes Guillot (LIP6, Univ. Paris 6). Now R&D engineer at Dassault Systems.

PhD & HDR theses evaluation committees (HDR = Habilitation to Direct Researches)

2017 Examiner for the jury of Gabriel Sulem's PhD thesis (supervisor: Etienne Koechlin), Universite Pierre et Marie Curie, Paris, France.

2016 Reviewer for the jury of Emilio Cartoni's PhD thesis (supervisor: Gianluca Baldassarre), Università degli Studi di Roma " La Sapienza", Rome, Italy.

2016 Examiner for the jury of Steve Didienne's PhD thesis (supervisor: Philippe Faure), Universite Pierre et Marie Curie, Paris, France.

2016 Reviewer for the jury of Maxime Carrere's PhD thesis (supervisor: Frederic Alexandre), Universite de Bordeaux, France.

2016 President of the jury of Ralph Bourdoukan's PhD thesis (supervisor: Sophie Deneve), Universite Pierre et Marie Curie, Paris, France.

2015 Examiner for Virginie Oberto's 2nd PhD mid-term evaluation committee (supervisor: Sidney I. Wiener), Universite Pierre et Marie Curie, Paris, France.

2015 Examiner for the jury of Vassilisa Skvortsova's PhD thesis (supervisors: Mathias Pessiglione and Hilke Plassmann) entitled: Neural mechanisms of instrumental learning: Neuroimaging, pharmacological and stimulation studies in humans, Universite Pierre et Marie Curie, Paris, France.

2015 Reviewer for the jury of Céline Amiez’s HDR thesis entitled: Functional neuroimaging of cognitive adaptation in primates, Universite Claude Bernard – Lyon 1, France.

2015 Reviewer for the jury of Emmanuel Breysse’s PhD thesis (supervisor: Christelle Baunez), Universite Aix-Marseille, France.

2015 Examiner for the Virginie Oberto's PhD mid-term evaluation committee (supervisor: Sidney I. Wiener), Universite Pierre et Marie Curie, Paris, France.

2014 Reviewer for the jury of Simon Gay’s PhD thesis (supervisors: Olivier Georgeon & Alain Mille) entitled: Intrinsically motivated developmental learning mechanisms in Artificial Intelligence: A study of environmental space integration mechanisms, Universite Claude Bernard – Lyon 1, France.

2014 Examiner for the jury of Raphaël Le Bouc's PhD thesis (supervisor: Mathias Pessiglione) entitled: La Motivation comme un compromis recompense-effort : neuro-imagerie, modelisation, manipulations pharmacologiques et investigations cliniques, Universite Pierre et Marie Curie, Paris, France.

2014 Examiner for Flora Bouchacourt's PhD mid-term evaluation committee (supervisors: Srdjan Ostojic & Boris Gutkin), Paris, France.

2013 Examiner for Alexandre Salvador's PhD mid-term evaluation committee (supervisors: Raphaël Gaillard & Stefano Palminteri), Paris, France.

2012 Examiner for the jury of Stefano Palminteri's PhD thesis (supervisor: Mathias Pessiglione) entitled: Neural underpinnings of reinforcement based learning and decision making in humans, Universite Pierre et Marie Curie, Paris, France.

2012 Examiner for Vasilisa Skvortsova's PhD mid-term evaluation committee (supervisor: Mathias Pessiglione), Universite Pierre et Marie Curie, Paris, France.

2012 Examiner for Éléonore Duvelle's PhD mid-term evaluation committee (supervisors: Angelo Arleo & Etienne Save), Universite Pierre et Marie Curie, Paris, France.

2008 Examiner for the jury of Mathieu Bertin's PhD thesis (supervisors: Kenji Doya & Agnès Guillot) entitled: Timing and discounting of rewards in reinforcement learning models of the basal ganglia, Universite Pierre et Marie Curie, Paris, France.

Teaching (CS: Computer Science; NE: Neuroscience; RO: Robotics; CO: Cognitive Science)

Creation of two new courses currently taught

2015- École Normale Supérieure Ulm - Paris, CogMaster (Co-coordinator with B. Girard) CO: Robotic modelling approaches to Cognitive Sciences (Lectures: 12h).

2014- UPMC, License 1 (all disciplines; Co-coordinator with Frederic Decremps) CO: Role of science in society and critical thinking (Lectures: 20h + Lab: 20h).

Other current lectures

2014- Univ. Pierre & Marie Curie (UPMC), Master 2 of Computer Science (ANDROIDE) CS: Neuro-inspired reinforcement learning (Lecture: 2h).

2013- Ecole X Polytechnique, Palaiseau, Engineering school

NE: Reinforcement Learning and Brain related mechanisms (Lecture: 2h).

2012- École Normale Supérieure Ulm - Paris, Master 1 of Neuroscience NE: Decision-making: elements of modelling (Lecture: 3h).

2011- UPMC, Master 2 of Mechatronics Sys. for Rehabilitation

RO: Bio-inspired action selection and learning (Lecture: 2h).

Past lectures

2015 Univ. Orsay – Paris 11, Master 2 of Robotics

CS: Neuro-inspired reinforcement learning (Lecture: 3h).

2014 École Normale Supérieure Ulm / PSL - Paris, Master 1 of Cognitive Engineering RO: Reinforcement Learning, Neuroscience & Robotics applications (Lecture: 2h).

2013 Harvard Summer Program in Trento, Italy

NE: Reinforcement Learning models (Lecture: 1h30 + Tutorial: 2h).

2013 Univ. Pierre & Marie Curie (UPMC), Master 2 Integrative Neuroscience

NE: Comput. approach to parallel memory systems for navigation (Lecture: 3h).

2013-2014 Université Claude Bernard - Lyon 1, License 1 and 2 (all disciplines)

CO: Analyzing the influence of advertising on brain and behavior (Lecture: 1h).

2012 Telluride Neuromorphic Engineering Summerschool, USA

CS: Model-free and model-based reinforcement learning (Lecture: 1h + Tutorial: 4h).

2012-2015 Polytech UPMC – Paris 6, 5th year of Engineering School

RO: Reinforcement Learning and Decision-Making (Lecture: 2h + Lab: 4h).

2010-2014 Université Claude Bernard - Lyon 1, Master 2 of Integrative Neuroscience NE: Decision-making: elements of modelling (Lecture: 1h30 + Debate: 2h).

2008-2014 UPMC, Master 2 of Artificial Intelligence and Decision

CS: Neuro-inspired reinforcement learning (Lecture: 2h + Project supervision: 4h).

Past practical/laboratory courses

2006 ESIEA, Laval, CS: Artificial and cognitive life in M1 (18h).

2006 La Sorbonne, Paris, CS: Computer Science in L2 (18h).

2006 Université Paris 6, CS: Computed Science in L2 (38h).

2006 ESIEA, Laval, CS: Artificial and cognitive life in M1 (12h).

2005 La Sorbonne, Paris, CS: Computer Science in L2 (36h).

2004 ENSIIE-CNAM, Evry CS: Computer Science in L3 (20h).

2003 La Sorbonne, Paris, CS: Computer Science in L2 (36h).

2003 Université d'Évry, France CS: Bio-Informatics in M2 (12h).

Editorial activity

2015 Co-Editor of a special issue with Stéphane Doncieux on New Approaches to Cognitive Robotics to be published in 2016 in the Intellectica journal.

2015- Associate Editor for Frontiers in Neurorobotics. Specialty Chief Editor: Alois C Knoll. Assistant Specialty Chief Editor: Florian Röhrbein.

2014- Review Editor for Frontiers in Behavioral Neuroscience. Editors-in-Chief: Carmen Sandi and Nuno Sousa and Frontiers in Decision Neuroscience (since 2016).

2013- Editorial Board Member for Intellectica. Editor-in-Chief: Olivier Gapenne.

2008-2014 Review Editor for Frontiers in Neurorobotics. Specialty Chief Editor: Frederic Kaplan.

Ad-hoc reviewer

Journals Behavioral Neuroscience, Biological Cybernetics, Brain Research, Cerebral Cortex, Connection Science, Frontiers in Cognitive Science, Frontiers in Evolutionary (alphabetical Psychology and Neuroscience, Frontiers in Neurorobotics, Frontiers in Systems

order) Neuroscience, IEEE Transactions on Autonomous Mental Development, IEEE Transactions on Neural Networks and Learning Systems, Intellectica, International Journal of Social Robotics, Journal of Neuroscience, Neural Computation, Neurosignals, PLoS Computational Biology, PLoS One, Progress in Brain Research, ReScience, Review of Philosophy and Psychology, Scientific Reports.

Grants Human Frontiers Science Program, IFREMER, UK’s Economic and Social Research Council.

Conferences

/ workshops Behavior Adaptation, Interaction and Learning for Assistive Robotics (BAILAR), Biologically Inspired Cognitive Architectures (BICA), International Conference on Development and Learning – Epigenetic Robotics (ICDL-EPIROB), Living Machines (LM), Orbitofrontal Cortex Meeting (OFC), Simulation of Adaptive Behavior (SAB), Symposium on Biology of Decision-Making (SBDM), IFAC Symposium on System, Structure and Control (SSSC).

Program Committee member for international conferences / workshops

(chronological Conferences: SAB 2010, SBDM 2012, SBDM 2013, Living Machines 2013, SBDM

order) 2014, SBDM 2015, BICA 2015, OFC 2015, SBDM 2016, SBDM 2017. Workshops: Navigation workshop (Arleo, Chavarriaga) at SAB 2006, “Behavior Adaptation,

Interaction and Learning for Assistive Robotics” workshop (Rossi, Siciliano, Staffa) at RO-MAN 2016, “From Artificial Intelligence to Neuroscience, and back” workshop (Gershman, Gutkin, Khamassi) at CRCNS 2016.

Other academic professional activities

2016 Evaluation comity member for an associate professor position recruitment in Robot cognitive architectures at Université Pierre et Marie Curie (UPMC).

2015 Member of the SMART Labex Doctoral Committee for evaluating 20 applications for PhD funding at UPMC.

2014 Evaluation comity member for an associate professor position recruitment in Neurorobotics at Université de Cergy-Pontoise / ENSEA.

2013-2016 Evaluation jury for 40 Master 2 students’ research projects, CogMaster, ENS Paris.

2013 Evaluation comity member for an associate professor position recruitment in Computational Neuroscience at Université de Lorraine / INRIA LORIA.

2013 Co-organized with Vincent Hayward ISIR’s tutorial on preparation for the writing of individual research grant applications (20 participants).

2010-2012 Member of the “platform committee” of ISIR, managing inventory, presentation, costs and grant applications of the institute’s robotic platforms.

2006-2007 PhD students representative, with Zoë Cimatti and Matthieu Lafon, at the ED3C (Brain, Cognition, Behavior) doctoral school of UPMC Univ. Paris 6.

2005-2006 Board Member of Doc’Up, the association of PhD students of UPMC Univ. Paris 6.

2002-2005 Board Member (2002-2005) and President (2003-2004) of Cognivence, association of students and young researchers in cognitive science in Paris' area.

2003-2005 Board Member and co-founder of FRESCO (2003-2005), national federation of students and young researchers in cognitive science.

2004 Vice-president of Paris-area regional committee (CloEG) for the organization of the French « États Généraux de la Recherche et de l’Enseignement Supérieur 2004 ».

2004 Member of the national committee (CIP-CloEG) for the national synthesis of texts elaborated regionally during the French « États Généraux de la Recherche et de l’Enseignement Supérieur 2004 ». The synthesized document was then submitted to the French Ministry of Research in November 2004.

Vulgarization and wide-audience presentations

2016 Featured interview in the online educative scientific video game http://www.memorya.org/ by ART’M and B2V.

2016 Wide-audience conference on scientific, historical and ethical issues of Neuromarketing at Université du Temps Libre de l’Essonne (91), Epinay-sur-Orge.

2016 Wide-audience conference on scientific, historical and ethical issues of Neuromarketing at Université du Temps Libre de l’Essonne (91), Montgeron-Draveil.

2015 Invited expert by UNICEF (Genève, Switzerland) for a report on the impact of advertising and marketing practices on children.

2015 Wide-audience talk + round table on scientific, historical and ethical issues of Neuromarketing at the Alternatiba Festival, Les Ulis, Essonne (91).

2015 Interview and scientific advisor for the project of introduction to science (TFE) of a high-school student: Victoria Peek. Theme: Influence of advert. on brain and behav.

2014 “Bar des Sciences” about Robotics and Neuroscience, Montbéliard.

2014 Interview and scientific advisor for the project of introduction to science (TFE) of a high-school student: Bertrand Raysz. Theme: Today Robotics vs. Asimov’s Robots.

2014 Invited speaker at the closing “Table ronde” of the Cognitive Science Forum.

2013 Interview and scientific advisor for the project of introduction to science (TFE) of 2 high-school students: Satnam Singh and Yanis Mendil. Theme: Influence of

advertising on brain and behavior.

2013 Invited expert at the United Nations (New York, USA) by the High Commissioner for Human Rights to write a report on the impact of advertising and marketing practices on the enjoyment of cultural rights.

2013 Scientific exhibition on Memory, organized by ART’M (Jacques Roux et al), Chambery, France. Interview, scientific advisor, and robot demonstration.

2013 Two 1-hour presentations at Marcelin Berthelot High School (Pantin) about Education to image and critical thinking towards advertising.

2013 Interview and scientific advisor for the project of introduction to science (TPE) of 2 high-school students: Florian Desrosiers et al. Theme: The Psikharpax rat robot.

2012 Wide-audience conference on Neuro-robotics. Fête de Luttes Ouvrières (93).

2011 “Cafe des Sciences” about Robotics, Mediathèque de Combs-la-Ville (93).

2011 Wide-audience conference on Neuro-robotics. Fête de Luttes Ouvrières (93).

2011 National science celebration (« Fête de la Science »). 1 day demonstration of the Psikharpax rat robot at Universite Pierre et Marie Curie.

2010 Wide-audience day on Robots and Humans (“Des Robots et des Hommes”) at Cite des Sciences, Paris. Demonstration on the Psikharpax rat robot, with Agnès Guillot, Christophe Grand, Steve N’Guyen and Mathieu Bernard.

2010 National science celebration (« Fête de la Science »). 2 days demonstration of the Psikharpax rat robot at Universite Pierre et Marie Curie.

2009 “Cafe des Sciences” about Artificial Intelligence, MJC de Combs-la-Ville (93).

2007 National science celebration (« Fête de la Science »). 1 day demonstration of the Psikharpax rat robot at Universite Pierre et Marie Curie.

Media coverage

2016 Interview in Science, 352(6290):1161, about science and diplomacy in relation to the Middle-East conflict, following the NeuroBridges workshop organized in Paris by Ahmed El Hady and Yonathan Loewenstein.

2016 Article written with Raja Chatila about consciousness in robots published on Interstices, INRIA’s webzine for scientific culture dedicated to IT and mathematics.

2016 Our work with Indian architect Sudhir K. Pasala and psychologist V.S. Chandrasekhar Pammi has been mentioned (text and figure) in an article on “How neuroscience can influence architecture” in the journal of the American institute of architects.

2016 Article written with Frederic Decremps for The Conversation on the Scientific approach and critical thinking, (contrib. from Marie Pinhas & Fabrice Rousselot).

2016 UPMC website: interview entitled “Errare scientificum est!” on the Scientific approach and critical thinking course I co-animate with Pr. Frederic Decremps.

2016 “L’âge de faire” journal (N°113). Interview about current scientific knowledge on the influence of advertising on brain activity and behavior (conditioning, reward system, and multiple types of memorizations).

2015 Politis information journal. Full-page interview about current scientific knowledge on the influence of advertising on brain activity and behavior (conditioning, reward system, and processing of urban adverts during habitual navigation).

2015 Article written with Raja Chatila about consciousness in robots for Pour La Science.

2015 Article in Navette Science written by Ombelliscience Picardie about our collaborative work with INSERM Lyon.

2014 Est Republicain newspaper article about the Robotics and Neuroscience Bar des Sciences to which I participated in Montbeliard.

2013 Radio Campus Paris. Interview about Computational Neuroscience and Neurorobotics for the “La Puce à l’Oreille” show.

2013 Politis information journal. Video interview about current scientific knowledge on

the influence of advertising on brain activity and behavior (conditioning, reward system, and processing of urban adverts during habitual navigation).

2012 Planète Robots magazine. Interview about the Psikharpax rat robot.

2012 LeMonde.fr. Article about Current estimations of nociceptive effects of advertising on the society. Co-writers: Guillaume Dumas, Karim N’Diaye, Luc Foubert, Yves Jouffe and Camille Roth.

2012 Le Monde. Interview for the article “What robots can teach us about ourselves” by Viviane Thivent.

2011 Futura-sciences.com web scientific magazine. Vulgarization article about the Psikharpax rat robot (2 versions: FR and UK).

2011 INSERM Health & Science Magazine. Interview about a neuromimetic prefrontal cortex model controlling the iCub humanoid robot.

2011 Marion Montaigne’s humouristic scientific blog. Interview about robotics research work at the Institute of Intelligent Systems and Robotics. With Benoît Girard, Stéphane Doncieux and Jean-Baptiste Mouret.

2010 Arte TV channel. Interview + robot demonstration for the “Global Mag” show.

2009 Tekiano.com, Tunisian scientific web magazine. Interview about neuro-inspired approaches to robotics.

2009 France 5 TV channel. Interview and demonstration of a robot controlled by a neuromimetic learning model for the “Magazine de la Santé” show.

2009 France 3 Rhônes-Alpes TV channel. Interview and demonstration of a robot controlled by a neuromimetic learning model for the mid-day news program.

2009 Press conference with Peter F. Dominey on the iCub humanoid robot in front of 30 journalists including AFP and Reuters at INSERM Stem-cell and Brain Research Institute, Lyon.

2008 France Culture Radio. Interview for the “Science and Consciousness” show, with Agnès Guillot and Jean-Arcady Meyer.

Scientific societies memberships

(alphabetical EU Technical Committee on Cognitive Robotics, FENS, French Society for

order) Neuroscience, French Neuroscience of Memory GDR, French Robotics GDR, Society for Neuroscience.

Scientific collaborators

France Rachid Alami, Aurélie Clodic, Sandra Devine

CNRS LAAS, Toulouse, France

Céline Amiez, Peter Dominey, Jean-Claude Dreher, Mateus Joffily, Manu Procyk

CNRS / INSERM / Univ. Lyon 1, Lyon, France

Paul Apicella, Andrea Brovelli, Kevin Marche, Francesca Sargolini

CNRS / Université Aix-Marseille, Marseille, France

Angelo Arleo, Raja Chatila, Ludovic Denoyer, Stéphane Doncieux, Jacques Droulez,

Patrick Gallinari, Benoît Girard, Olivier Sigaud

CNRS / Université Pierre et Marie Curie – Paris 6, France

Alain Berthoz, Sidney I. Wiener, Michael Zugaro

CNRS / Collège de France, Paris, France

Arnaud Blanchard, Philippe Gaussier

CNRS / Univ. Cergy-Pontoise, Cergy, France

Etienne Coutureau, Virginie Fresno, Alain Marchand

CNRS / Université Bordeaux 2, France

Elisabeth Pacherie, Stefano Palminteri

CNRS / Ecole Normale Supérieure, Paris, France

Abroad Francesco P. Battaglia, Guillaume Sescousse

Radboud Universiteit, Nijmegen, The Netherlands

Nadège Bault, Giorgio Coricelli, Tobias Larsen, David Pascucci, Massimo Turatto

Univ. of Trento, Italy

Riadh Ben Rejeb, Rym Ben Sghayer

Université de Tunis, Tunisia

V.S. Chandrasekhar Pammi, Sudhir K. Pasala

University of Allahabad / Andhra University, India

Ricardo Chavarriaga

Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Kenji Doya

Okinawa Institute of Science and Technology, Okinawa, Japan

Mark D. Humphries

University of Manchester, United Kingdom

Adrien Peyrache

McGill University, Montreal, Canada

Terry E. Robinson, Shelly B. Flagel

NIH-NIDA / Univ. Michigan, Ann Arbor, United States of America

Geoffrey Schoenbaum, Matthew R. Roesch, Donna J. Calu

NIH-NIDA / Univ. Maryland, Baltimore, United States of America

Costas Tzafestas

National Technical University of Athens, Greece

Subject Code COMP6402

Subject Title Natural Language Processing Techniques

Credit Value 3

Level 600 level

Pre-requisite / Co-requisite/ Exclusion For DALS students, Taking FH6051 (Computational Linguistics) first is preferred, but not absolutely necessary

Objectives This subject aims to achieve the following goals:

• To introduce students the challenges of empirical methods for natural language processing (NLP) applications.

• To introduce basic mathematical models and methods used in NLP applications to formulate computational solutions.

• To provide students with the knowledge on designing procedures for natural language resource annotation and the use of related tools for text analysis and hands-on experience of using such tools.

• To introduce students research and development work in information retrieval, information extraction, and knowledge discovery using different natural language resources.

• To give an overview of the major technologies in speech recognition and synthesis including tools for acoustic analysis and hands-on experience of using such tools

• To give students opportunities to sharpen their programming skills for computational linguistics applications

Intended Learning Outcomes Upon completion of the subject, students will be able to:

(a) Understanding of the fundamental mathematical models and algorithms in the field of NLP.

(b) Apply these mathematical models and algorithms in applications in software design and implementation for NLP.

(c) Understand the principles of language resource annotation and its use in machine learning applications and apply the above principles in analysis of data and acquire intended information through the use of available tools.

(d) Understand the design and implementation issues in various NLP applications such as information retrieval and information extraction.

(e) Understand the complexity of speech and the challenges facing speech engineers.

(f) Understand the principles of automatic speech recognition and

synthesis.

(g) Problem solving using systematic ways and learning independently.

Subject Synopsis/ a Introduction and overview

Indicative Syllabus a Statistical models, information theory, and vector space models of

data representation

a Formal language, automaton, and their applications in NLP

a Lexical resources, corpora, and annotations

a Statistical language models and machine learning algorithms

a Text classification and indexing

a Advanced applications such as Information retrieval, information

extraction and knowledge discovery

a Introduction to speech technology including phonetics, text-to

speech synthesis, and automatic speech recognition

Teaching/Learning The course will be taught in a combined form of seminars and lab

Methodology sessions. Readings will be assigned every week and students are expected

to participate in discussion during the seminars. Students are also

expected to participate in lab sessions and complete lab exercises on computers.

Assessment

Methods in

Alignment with Specific % Intended subject learning outcomes to

Intended Learning assessment weighting be assessed (Please tick as

Outcomes methods/tasks appropriate)

a b c d e f g

1. Homework assignments 60%      

2. Lab exercises 10%       

3.Tests 30%       

Total 100 %

Explanation of the appropriateness of the assessment methods in assessing the intended learning outcomes:

All the above assessment methods are appropriate for evaluating students’

understanding of course materials and their problem solving skills.

Individual assignments provide assessment on a regular basis, which also

serve as a means of self-monitoring for students. Lab exercises will emphasize the ability to apply knowledge to real-world problems and have hands-on experience of using certain tools. Tests will assess students’ overall understanding of the concepts and algorithms learnt in class.

Student Study Effort Required Class contact:

n Seminars 36 Hrs.

n Tutorial/Lab 6 Hrs.

Other student study effort:

n Reading 35 Hrs.

n Homework assignments and preparation for tests 40Hrs.

Total student study effort 117Hrs.

Reading List and References Text Book:

There is no single text book for this classes. Selected readings from text of

the references will be suggested, along with relevant research papers.

References:

Dan Jurafsky and James H. Martin, Speech and Language processing, 2nd Addition, Prentice Hall, 2008

Christopher Manning and Hinrich Schuetze, Foundations of Statistical

Natural Language Processing, the MIT Press, 1999

Ruslan Mitkov, The Oxford Handbook of Computational Linguistics, Oxford University Press, 2005

Christopher Manning, Prabhakar Raghavan, and Hinrich Schuetze, Introduction to Information Retrieval, Cambridge University Press, 2008

Charu C. Aggarwal and ChengXiang Zhai, Mining Text Data, Springer, 2012

Steven Bird, ewan Klein, and Edward Loper, Natural Language Processing with Python, O’Reilly Media, 2009

Hopcroft, J.E. and Ullman, J.D., Introduction to Automata, Theory and Languages, Addison-Wesley, 1979

Mark Lutz, Learning Python, O'Reilly Media; Third Edition edition (October 29, 2007)

I.J. Modern Education and Computer Science, 2014, 12, 47-54

Published Online December 2014 in MECS (http://www.mecs-press.org/)

DOI: 10.5815/ijmecs.2014.12.07

A Study of Sentiment and Trend Analysis

Techniques for Social Media Content

Asad Mehmood

Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan

Email: asadmahmood16@hotmail.com

Abdul S. Palli and M.N.A. Khan

Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan

Email: abdulsattarpalli@gmail.com, mnak2010@gmail.com

Abstract—The social media networks have evolved rapidly and people frequently use these services to communicate with others and express themselves by sharing opinions, views, ideas etc. on different topics. The social media trend analysis is generally carried out by sifting the corresponding or interlinked events discussed on social media websites such as Twitter, Facebook etc. The fundamental objective behind such analyses is to determine the level of criticality with respect to criticism or appreciation described in the comments, tweets or blogs. The trend analysis techniques can also be systematically exploited for opinion making among the masses at large. The results of such analyses show how people think, assess, orate and opine about different issues. This paper primarily focuses on the trend detection and sentiment analysis techniques and their efficacy in the contextual information. We further discuss these techniques which are used to analyze the sentiments expressed within a particular sentence, paragraph or document etc. The analysis based on sentiments can pave way for automatic trend analysis, topic recognition and opinion mining etc. Furthermore, we can fairly estimate the degree of positivity and negativity of the opinions and sentiments based on the content obtained from a particular social media.

Index Terms—Trend Analysis, Sentiment Analysis, Social Media Analysis, Semantic Web, Opinion Mining.

I. INTRODUCTION

Social media has evolved into a vibrant platform where people communicate freely with each other, share ideas and comment on various events and issues. Twitter is one of the evolving social media which, on average, hosts around 200 million tweets per day. Tweets generally correspond to infinite number of products, services, social issues, news, incidents and reviews etc. Further, people also comment and share views about tweets pertaining to various topics/issues. Twitter share these tweets in a unique way, which cannot be out rightly used to judge the essence of the tweets and topics being discussed on social media. Facebook supports five different types of post

status, video, link, image and music; and this information is very useful for trend analysis.

There is a verity of events discussed on social media (such as Twitter, Facebook) which can be grouped into two categories: planned events and unplanned events. Planned events include general elections in a country, music concert, educational or employment workshops, sports tournament etc. and unplanned events pertain to unanticipated, out of the blue and sudden incident such as earthquakes, hurricanes, bomb blasts, spot-fixing etc. Sakaki et al. [1] divide these events in two categories – social events such as large parties, sports events, exhibitions, accidents and political campaigns, and natural events such as storms, tornadoes and earthquakes.

Twitter is one of the evolving social media which, on average, hosts around 200 million tweets per day. It is an evolving social media platform that acts as communication source where the real-time information from users becomes available instantly. People express their views/comments about any event of interest and share the latest information about the particular event/incident. This can be quite useful for creating awareness and getting solution to the problem as well as ascertaining general public‘s trend on that issue. Tweets can correspond to innumerable products, services, social issues, news, incidents and reviews etc. Further, people also comment and share views about tweets pertaining to various topics/issues. Different organizations rely on such information to analyze and evaluate the customers‘ and consumers‘ views about their products or services. Twitter share tweets in a unique way, which cannot out rightly be used to judge the essence of the tweets and topics being discussed on social media. For example, TV channels use tweets as a major source of feedback about their programs and talk shows. Although a single tweet consists of maximum 140 characters, but this information becomes reasonably huge due to numerous tweets being made by thousands of users on a single issue in a shorter span of time. Eventually, analyzing such a large amount of data which is totally in textual form becomes a colossal task. According to Pohl et al. [2], social media platform can be used to manage crisis by (i) sharing useful information about a particular event or disaster before actually it happened so that people get awareness,

48 A Study of Sentiment and Trend Analysis Techniques for Social Media Content

(ii) to control effects of the event if it had happened, and

(iii) get out from the disastrous situation.

Facebook is one the most popular social media network that started evolving in 2004. People use Facebook for several purposes — such as posting reviews about products/services on dedicated pages; vote on different kinds of polls launched by users etc. Facebook offers users three types of actions ("like", "comments" and "share") against a post to express themselves. The number of "likes" for a post shows how many people viewed and liked it as a positive gesture whereas some user comment to express likes and dislikes about the post. Some people use posts frequently to update their status to share their mood, thoughts, activities, criticism, likes, dislike about any related event or situation which is associated with them. This medium not only serves as a source of expressing for large number of users, but has also become a business for some people.

The fad of microblogging is becoming popular among the Internet users. Microblogging is broadcast medium and is an underlying form of blogs. Microblog also known as micropost, has smaller size and is quite different from the traditional blogs. The microblogs are usually a miniature form of the actual content like short sentences, images or video links [3, 4]. Millions of people across the world use this medium to express their views on different events and daily-life routine matter. Though this trend has emerged recently yet a number of approaches related to sentiment analysis of microblogs have been devised and explored.

Social media such as Facebook, Twitter etc. is one of the fast evolving phenomena for sentiment analysis to know how people think about a particular event. In the present day technology driven culture, we can get opinions from different polls and advertisements placed on blogs and social media sources. In general, human beings have natural instinct to share information or give feedback about the product or items they purchase in their daily lives. And this very trait of sharing information has now moved on to social media sites like Twitter, Facebook, Linkedin and other microblogging sites. By this means, these sources are becoming very useful in identifying and analyzing diverse opinions on different topics and areas. Twitter is one of the important sources for getting opinion from microblogging data available in different languages. Such data can be obtained through the Twitter‘s "Tweet Entities" using various applications.

Two types of analyses, trend analysis and sentimental analysis, can be highly beneficial to determine how people think and get emotional on certain social, religious or political issues. One reason for trend analysis can be to detect an emergent or suspicious behavior happening on the social media platform. For example, trend analysis can be used to see how certain groups of people are using it to launch their propaganda or forging facts about certain political or religious issues. Corporate sector can also use it to get feedback about their products.

Social media trend can be broadly categorized into four types namely positive, negative, neutral and uninterested. The level of trend can further be classified into low,

medium, high which will mainly be linked to the critical comments/tweets. Sentiments are evaluated and extracted from the social media content, which can either be in positive or negative attitude. The positive attitude of a person can be conceived as being happy or pleased with the content expressed by someone on certain issue. On the other hand, negative expressions can pertain to being unhappy or angry with the content posted by someone on certain issue.

Trend analysis in a traditional sense can be defined as the frequently mentioned topics throughout the stream of user activity [5]. Hence, for generating an effective trend out of the social media content, the need for an automated classifier becomes necessary to reduce the time for analyzing the large amount of data and improving efficiency of the analysis process.

Sentiment analysis basically tries to judge different aspects of natural language which help people to find valuable information from large amount of unstructured data [6]. It is an emerging concept in which different human emotions are determined from textual content. It enables us to extract opinions and sentimental feelings of the people. To know people‘s opinion about a particular event and its future impact (commonly termed as social media trends), there is a need for an automated system that can analyze such a huge amount of data and produce desired results with certain level of accuracy so that such results can be made acceptable by the masses. On Internet, people use blog posts and forums for promoting products or services as well as discussing any topic and expressing their views. The sentiment analysis on this platform possesses very important information for security analysts to keep an eye on the activities of miscreants and terrorists etc. However, it becomes a serious challenge to perform such types of analysis on a big data. Numerous events take place regularly in our daily life, therefore, it is not possible to manually analyze every event and predict its future impact. It is really hard for the computing machines to automatically extract the meaning and tone of content as people express so many things in many different ways and styles etc. Sentiment analyses can prove very useful when we analyze search engine results, different blogs, social networks, web forums, different review of people on books, movies, sport and products etc [7]. This can help reduce the efforts required to go through large amount of documents to generate an opinion about the nature of the contents.

II. LITERATURE REVIEW

Topic identification on social media helps understand what is being discussed and it also helps users to grab the broader picture without reading all the available information. Using network topology in trend deduction method can distinguish "viral topics" (topic which spreads via peer influence) from the shared information topics (spreads via news media). In the context of social media, Twitter is ranked as the second most famous social network [8]. The tweets made on it can be used to

A Study of Sentiment and Trend Analysis Techniques for Social Media Content 49

predict the future impact of different events/issues by applying data mining techniques. The authors designed a tool that performs trend analysis for social celebrities to find most influential among them as well as tend analysis of national and international issues, and recent events. The system proposed by Lin et al. [8] has two layers: data processing layer (for data collection and applying data mining techniques for performing trend analysis) and information display layer (for representing or visualization the results). After collecting the required data which mostly pertains to message properties, it uses Term Frequency- Inverse Document Frequency (TF-IDF)and fixed keywords to analyze the tweets. The mined results are presented to the user in four sections: top news section, trending topics section, active users section and top sources section.

Huberman et al. [9] used tweets to predict revenue generated fora newly released movie. The proposed approach entails selecting and analyzing tweets, before and after the release of a movie, that match the specific keywords taken from title of the movie. Tumasjan et al. [10] suggest that Twitter can be used as a platform for political discussion, and tweets can be used to detect election results. In this regards, the party with highest rate of tweets in its favor has fair chance of winning the elections. Wegrzyn-Wolska et al. [11] designed a sentimental analysis system based on tweets for French Presidential Election held in 2012. The aim of study was to correlate what is being discussed at Twitter-sphere. By using REST API, the authors collected specific tweets matching the user supplied keywords. The system detects trend by calculating both the frequency of a searched keyword in the dataset and its sentiments on the basis of positive, negative or neutral comments made against a post.

Asur et al. [12] argue that the topic being discussed quite often on twitter during certain timeframe becomes the trend. Re-tweets on the same topic from multiple users such as news from various media sources can also be the reason of setting trend of the topic. If multiple topics being discussed on Twitter among the different user groups are divided according to the region, then it results into multiple trends in tandem. The frequently discussed topic becomes the principal trend in the trends list. Likewise, Asur et al. [13] compared SinaWeibo (Chinese social media) with Twitter and found differences in trends. On SinaWeibo, users share jokes, images, videos, and re-tweet most of the time. But on twitter people mostly amplify the news which they obtain from other media sources.

Sakaki et al. [1] propose earthquake detection and reporting system which sends email alerts to the registered users when it gets any tweet originated from Japan only about an earthquake. In the proposed system, all the Twitter users are considered as a sensor because they send sensory information. After every second, the system searches tweets which match the given keywords and applies semantic analysis to get accurate results. To know the location or area where that event has occurred, the system uses event detection algorithm. The proposed

algorithm uses search API to get the time and location of a tweet and the same is automatically attached with the tweet when it is posted via iPhone or phone that has GPS system. The other alternate it uses for finding event location is to get the registered location of the user through Kalman filter or particle filtering algorithms.

Achrekar et al. [14] designed a system to predict the ratio of flu disease in USA for a specific time period using tweets. For data collection, the authors developed a crawler using the Twitter real time search API to retrieve tweets which match the keywords and to collect patient details such as name, age, location etc. from their profiles in order to identify the affected area and the number of expected patients in that area.

In order to evaluate large volume of Twitter data, Hao et al. [15] introduced three techniques which were mainly based on visual sentiment analysis. The suggested technique include topic-based analysis that involves natural language processing to determine nature of the topic of discussion by extracting different opinion-related attributes to measure the degree of sentiments. Then a stream analysis is performed on large number of tweets to extract information of interest based on positive/negative attitude and the influencing characteristics that it possesses within the larger density of tweets. Pixel cell-based sentiment calendars and high density geomaps are used to visualize and depict large number of tweets in a single view.

Since Twitter data is one of the important sources of microblogging platform, so it has been used for sentiment analysis and Pak et al. [16] used this corpus for sentiment analysis and opinion mining. The corpus containing emotions like happy smiley ":)" or sad smiley ":(" are readily evaluated as positive or negative sentiments respectively. After corpus collection, it is analyzed to check how data has been distributed into subjective corpus (containing positive or negative set) and objective corpus (containing neutral set). The authors calculated the presence of n-gram for extracting binary feature and keyword frequency was used to obtain rest of the general information. The analytics reported by the authors showed that objective sets contained more common and proper nouns which in turn have often used personal pronouns. Similarly, the objective sets bloggers addressed themselves as third person while the subjective sets bloggers described themselves as first person or second person. Lima et al. [17] propose a classifier for Twitter messages that comprises three modules: Support Counting, Database Selection and Classification modules. Support counting module counts percentage of the tweets that contain at least one word or emotion in the tweet. Database Selection module divides data into two sets: training set and testing set. Classification module classifies data using Naïve Bayes algorithm.

Cvijikj and Michahelles [18] figured out discovering trends on Facebook using features of shared posts. They categoried topic of interest into three groups: descriptive events, popular events and daily routines. For this purpose, Graph API search feature is used to find the posts against the specific keyword after every 10 minutes.

50 A Study of Sentiment and Trend Analysis Techniques for Social Media Content

Trend deduction is a two pronged strategy which involves topic identification and cluster detection. Only the ‗status‘ attribute used with Facebook posts was used to identify trend. The methodology uses TF-IDF for assigning weights to the terms on the basis of two quanta: frequency of occurrence of a term within a single document and the number of documents in the corpus which contain the given term. Finally, the trend analysis was done using LingPipe API. Li et al. [19] applied clustering-based sentiment analysis approach on dataset which was based on the review of a movie. The dataset was divided into two primary clusters corresponding to the positive and negative user comments and remarks about that movie. For experimental purpose, the technique of TF-IDF (Term Frequency – Inverse Document Frequency) was applied. The stability of results was increased when the voting mechanism was used, and finally a symbolic technique was applied to enhance the clustering results. Symbolic technique, as described by Li et al. [19], is the technique in which each term (or word) is assigned some value based on the negative and positive connotation as well asthe intensity/criticality of that term (or word). Then, an aggregation functions is used to obtain cumulative word scores to draw conclusions about the overall negativity or positivity of the comments.

Goorha et al. [20] used entity extraction system for extracting more relevant and most occurred phrases form tweets, blogs and newspaper articles to find out people's opinion about a product or company. The proposed system used cluster streaming to identify related terms and used IF-IDF for assigning and calculating weights of terms which were useful for making cluster. Pohl et al. [2] proposed a system to detect sub-events related to a crisis situation based on YouTube and Flickr data such as picture or video. Video or picture data item has two parts: the coordinates and the terms. For extraction of the coordinates, it uses two-phase clustering approach which is based on longitude and latitude; and for the terms extraction, it uses textual metadata fields of a particular data item and calculates TF/IDF for clustering. To present results visually, it uses OpenStreetMap which is capable to display cluster data and its location.

A proximity-based sentiment analysis is proposed by Hasan et al. [7] that uses features based on word proximities within a sentence. The authors used three proximity-based features which are called proximity distribution, mutual information between proximity types, and proximity patterns. The dataset was divided into number of segments in which each segment contained over 100 words. The distance between positive and negative pair of word was calculated. Three proximity-based features were used. In Proximity Distributions, different numbers of bin were considered which returned the distribution of pair-wise distances from the proximity models. In Mutual Information between Proximity Types, the relationship between the proximity types was used to determine the polarity of the document. Then, the theoretic quantities of entropy for each sequence, was used to get mutual information between pairs of

proximity types. In Proximity Patterns, it described polarity of words which were used in the document.

Corley et al. [21] proposed a technique for finding Flu cases discussed over blogs and finding the relationship among the outcome of blog posts and data reported by Center for Disease Control and Prevention. Python script with combination of pyMPI was used for data extraction. The pyMPI is software that integrates the Message Passing Interface into the Python interpreter". Suzumura et al. [22] developed a system for processing large amount of data on-the-fly by using web services including Twitter Search Service and Twitter Streaming API, and then displayed the tweets on Google Maps using Google Maps API and Ajax components.

Karamibekr et al. [23] proposed an approach to classify the sentiments using verbs from the sentence. The approach uses the verbs as core opinion terms in social domains on the pretext that verb is considered as the main opinion term. The authors used lexical knowledge to extract the sentiment terms present in the sentence. With the help of bootstrapping process, authors collect the list of opinion verbs using an English dictionary and its synonyms in the WordNet [24]. A subject in the sentence describes action of the doer or what the predicate does, whereas the object in sentence is what or whom the verb is acting. Cai et al. [25] proposed an approach to create an effective sentiment based taxonomy that employs statistical based approach for the sentiment analysis. The sentiments expressed by the words, are measured on the scale of positive or negative. Then, the list of positive and negative words is created by using two external NLP resources. In order to score the relative sentiment between the posts which have the positive and negative words, the author characterize the degree of positive/negative sentiment that each word conveys. Koncz et al. [26] proposed a feature selection approach that used frequencies of terms in a particular document. The values are normalized in terms of total number of documents in category.

Mizumoto et al. [27] proposed the system in which the author created polarity dictionary to determine the sentimental polarities of stock market. While constructing the dictionary, the author has used the semi-supervised learning approach from which small polarity dictionary is made. Using the co-occurrence frequency with words in polarity dictionary, those new words are added to the dictionary whose polarities are unknown. For estimating the polarity of the text, the author has used sentiment analysis method. The polarity of article is determined according to the frequency of words in the polarity diction; hence, the articles are determined as positive, negative or neutral. In [28], the authors introduced text sentiment classification for the contextual information. For this purpose, the flow of the sentiments and keywords in the paragraph were taken out from the contextual information. Finally, by computing the contextual information degree (linearly combined weighted sum of contextual information), the overall sentiment of sentence was classified.

Iqbal et al. [30, 31] proposed performance metrics for

A Study of Sentiment and Trend Analysis Techniques for Social Media Content 51

software design and software project management. Process improvement methodologies are elaborated in [32, 43] and Khan et al. [33] carried out quality assurance assessment. Amir et al. [34] discussed agile software development processes. Rehman et al. [35] and Khan et al. [44] analyzed issues pertaining to requirement engineering processes. Umar and Khan [36, 37] analyzed non-functional requirements for software maintainability. Khan et al. [38, 39] proposed a machine learning approaches for post-event timeline reconstruction. Khan

[40] suggests that Bayesian techniques are more promising than other conventional machine learning techniques for timeline reconstruction. Rafique and Khan

[41] explored various methods, practices and tools being used for static and live digital forensics. In [42], Bashir and Khan discuss triaging methodologies being used for live digital forensic analysis.

III. CRITICAL ANALYSIS

Budak [5] Used Independent Trend Formation Model (ITFM) and nearest neighbor model to identify structural trends and compared them with traditional trends by analyzing tweets. However, it does not defined structural trends in generalized form so one cannot identify gap among the discussed trend types. Lin et al. [8] used natural language processing, semantic analysis, and TF-IDF to Analyze tweets of celebrities for finding trend on Twitter. Same approach can also be used to analyze tweets of common people or public to figure out public trend on national and international issues and events. Cvijikj [18] used linguistic analysis, TF-IDF, clustering by distribution, and clustering by co-occurrence to identify the topic of discussion by analyzing the `status‘ posts over Facebook. Nevertheless, selecting only the `status‘ posts does not provide such a dataset to analyze trends. One should collect other types also such as posted image or audio/video and users comments on that post.

Asur et al. [9] used linguistic analysis and sentiment analysis on tweets which matches the given keywords to analyze and predicts the revenue of a movie. The author selects tweets by matching the keywords that are present in only the title of a movie. User may include director, producer, actor/actress, or character titles in their tweets, but there is a possibility that that those tweet will be left over.

Tumasjan et al. [10] used linguistic analysis and sentiment analysis techniques to predict the election results by using twitter as a platform for political discussion as well as a data source for finding people's opinion about a particular political party. Dataset contains only those tweets which contain names of political parties and names of some well-known politicians. However, the tweets which miss the parties name can also be very useful for this analysis, since people may use polling symbols instead of a party name or people may use slogans of the parties in their tweets. Furthermore, the selected tweets only come from the same group of users instead of variety of other social media content. Asur et al.

[12] analyzed twitter data to know the reasons which are important for different stages of trend by keywords matching. There should me some mechanism for matching misspelled words, and sentimental analysis of tweets for better results. Yu et al. [13] observed trends over Chines social media and compared with Twitter trends. Research is more focused towards analyzing new tweets and retweets over SinaWeibo (Chinese social media). Trends can also be found through location as well. Sakaki et al. [1] used Kalman filter (a particle filtering algorithms) for semantic analysis of users tweets that belongs to Japan and performed semantic analysis to generate email alerts about real time event such as earthquake. There is a very less chance of getting GPS data (location of tweet) from tweet because every user could not have i-Phone to tweet. User may be able to tweet from any location other that the registered location.

Achrekar et al. [14] Used auto-regression with exogenous inputs (ARX) model to design a system to predict the ratio of flu disease in USA using tweets of a specific time period. Pohl et al. [20] used streaming clustering algorithm and TF-IDF to design a system which tells the public opinion about any product or company by extracting more relevant and most occurred phrases form tweets, blogs and newspaper‘s articles, and displays result in a user interface by plotting the clusters. It decides popularity of an entity (product or company) based on the number of tweets which discuss that particular entity. There is no any mechanism used for semantic analysis to decide the positivity and negativity of tweets about the entity. There is a great chance that tweets might be about the closing down of a company or product. Pohl et al. [2] proposed a system to detect sub-events in crisis situation over YouTube and Flickr data such as picture or video using two-phase clustering and TF-IDF approach. However, the approach lacks mechanism for filtering the duplication of data. Suzumura et al. [22] designed the system architecture for processing the large amount of data on the fly rather than store and process using probabilistic models (such as Temporal Model, Spatial Model). The authors developed a system for predicting real time events based on their designed architecture. But, such techniques used for finding location are unable to find the specific location of the posted massages. Corley et al. [21] proposed a technique for finding Flu cases discussed over blogs and to find relationship among the outcome of blog posts and data reported by Center for Disease Control and Prevention. It filters only English language words from blog and compares them with the given keywords. No procedure was defined for matching any misspelled or informal short words.

In [15], the authors primarily focused on visual opinion analysis and sentiment analysis. The technique visualized large number of Tweets to a single view that depicts the overall sentiments. This approach does not help identify opinion association. The major focus area of research in [16] was sentiment analysis in which the proposed technique automatically collected corpus to train a sentiment classifier. Syntactic structures are useful in

52 A Study of Sentiment and Trend Analysis Techniques for Social Media Content

describing emotions in the Twitter content. The approach needs to develop multilingual version of such technique or to include an auto-translator with the proposed technique. Li and Liu [19] focused on sentiment analysis and clustering. The cluster based technique was applied which had more accurate results as compared to techniques that involve human intervention. The dataset used for evaluation was small which would otherwise have produced more accurate results. Sentiment Analysis was major focus in the research and technique proposed by Lima and deCastro [17]. The proposed technique automatically trained the classifier based on Naïve Bayes algorithm to categorize datasets. However, the combination of different proposed techniques could have produced accurate results while using the neural classification.

The techniques proposed in [28] are based on sentiment analysis and security informatics which estimate sentiments that were expressed in content. It does not require labeling and collecting the document. It is a semi-supervised sentiment estimation technique which lacks multi-lingual sentiment analysis as well as the criteria/procedure that indicates when to smooth the polarity estimates. The major area of focus of the techniques proposed by Hasan and Adjeroh [7] is based on sentiment analysis and text mining. The technique used proximity-based sentiment analysis. The approach depends on the polarity of dictionary that was created from the corpus. Again, the major area of focus in [26] was Sentiment Analysis. The approach used the feature selection in comparison with Information Gain (IG) feature selection. The approach showed slightly poor results than the Information Gain (IG).

Mizumoto et al. [27] used semi-supervised learning for sentiment analysis. The technique created the polarity dictionary to estimate the polarity of stock market contextual information. This technique does not deal with the negation and adversative conjunction. The major research focus in [23] was on sentiment analysis and opinion structure. The technique used verb oriented sentiment classification approach for social domains. The technique did not use main verbs of the sentences to classify the sentiments related to sentence. Cai et al. [25] carried out sentiment analysis through Topic Detection. The technique for sentiment analysis used sentiment classification and sentiment detection scheme. The technique does not use part-of-speech and word syntactic relationships doing sentiment analysis and topic detection.

IV. CONCLUSION AND FUTURE WORK

In this study, we looked into different techniques used for trend analysis on social media such as Facebook and Twitter. We also studied that how data which is available on social media can be used in different ways to analyze and predict future trends. We observed that there is not any flexible system that has data dictionary with more appropriate keywords for predicting trends over Facebook. As most of the work is done using Twitter as a source, we have plan to design and develop a system to

predict future trends over Facebook by collecting all type of public posts and related user comments and applying sentiment analysis. We also intend to propose a model that is capable to analyze different events being discussed on social media along with detecting trends to make future prediction about the outcome of such events and issues. Since, most of the content available on Facebook and Twitter are unstructured text, therefore, we have planned to develop the system which can automatically analyze sentiments from the available content and verify the opinions that are expressed in the contextual information.

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Authors’ Profiles

Asad Mehmood has completed his MS in Computing from Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad, Pakistan. He has over 7 years of industry experience at his credit. His research interests include Business Intelligence, Big Data Analytics and Sentimental Analysis.

54 A Study of Sentiment and Trend Analysis Techniques for Social Media Content

Abdul Sattar Palli has completed his MS in Computing from Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad, Pakistan. His research interests include Data Mining and Software Engineering.

M. N. A. Khan obtained D.Phil. degree in Computer System Engineering. His research interests are in the fields of software engineering, data mining, cyber administration, digital forensic analysis and machine learning techniques.

How to cite this paper: Asad Mehmood, Abdul S. Palli, M.N.A. Khan,"A Study of Sentiment and Trend Analysis Techniques for Social Media Content", IJMECS, vol.6, no.12, pp.47-54, 2014.DOI: 10.5815/ijmecs.2014.12.07

Automatic verification of privacy properties in

the applied pi calculus*

St´ephanie Delaune', Mark Ryan', and Ben Smyth'

1 School of Computer Science, University of Birmingham, UK

{B.A.Smyth, M.D.Ryan}@cs.bham.ac.uk

2 LSV, CNRS & INRIA & ENS Cachan, France

delaune@lsv.ens-cachan.fr

Abstract. We develop a formal method verification technique for cryp¬tographic protocols. We focus on proving equivalences of the kind P  Q, where the processes P and Q have the same structure and differ only in the choice of terms. The calculus of ProVerif, a variant of the applied pi calculus, makes some progress in this direction. We expand the scope of ProVerif, to provide reasoning about further equivalences. We also provide an extension which allows modelling of protocols which require global synchronisation. Finally we develop an algorithm to enable auto¬mated reasoning. We demonstrate the practicality of our work with two case studies.

1 Introduction

Security protocols are small distributed programs that aim to provide some se-curity related objective over a public communications network like the Internet. Considering the increasing size of networks and their dependence on crypto-graphic protocols, a high level of assurance is needed in the correctness of such protocols. It is difficult to ascertain whether or not a cryptographic protocol sat-isfies its security requirements. Numerous protocols have appeared in literature and have subsequently been found to be flawed [1–4]. Typically, cryptographic protocols are expected to achieve their objectives in the presence of an attacker that is assumed to have full control of the network (sometimes called the Dolev-Yao attacker). The attacker can therefore eavesdrop, replay, modify, inject and block messages. The attacker is also able to perform cryptographic operations when in possession of the required keys. Furthermore the attacker may be in control of one or more of the protocol’s participants. With no more than the abilities listed, and irrespective of the underlying cryptographic algorithms, nu-merous protocols have been found to be vulnerable to attack. Formal verification of cryptographic protocols is therefore required to ensure that cryptographic pro-tocols can be deployed without the risk of damage and ultimately financial loss.

This work has been partly supported by the ARA SESUR project AVOT´E and the EPSRC projects Verifying anonymity and privacy properties (EP/E040829/1) & UbiVal (EP/D076625/1)

Traditionally cryptographic protocols have been required to satisfy secrecy and authentication properties [5]. These requirements have been successfully ver¬ified by modelling them as reachability problems. Current research into appli¬cations such as electronic voting, fair exchange, reputation systems and trusted computing has resulted in a plethora of new requirements which protocols must satisfy (e.g. [6–8]). Some of these properties cannot easily be expressed using tra¬ditional reachability techniques but can be written as equivalences. For example, the privacy, receipt-freeness and coercion resistance properties of electronic vot¬ing protocols can be expressed using equivalences (see [9, 10]).

We focus on proving equivalences of the kind P - Q, where the processes P and Q have the same structure and differ only in the choice of terms. For exam¬ple, the secret ballot (privacy) property of an electronic voting protocol can be expressed as

P (skva, v) | P(skvb, v) - P (skva, v) | P(skvb, v)

where P is the voter process with two parameters: its secret key (skva, skvb) and the candidate for whom he wish to cast their vote (here v, v). Histori¬cally many applications of equivalences to prove security requirements of cryp¬tographic protocols have relied upon hand written proofs [9, 10]. Such proofs are time consuming and error prone. Accordingly, we direct our attention to automated techniques. The calculus developed by Blanchet et al. makes some progress in this direction [11]. However, the method developed for proving ob¬servational equivalence is not complete and is unable to prove certain interesting equivalences.

Contribution. We build upon [11] to provide reasoning about further equiva¬lences. We also extend the syntax to allow the modelling of a new class of pro¬cesses which require global synchronisation. Finally we develop an algorithm to enable automated reasoning about security requirements. The focus of our work is to model the privacy properties increasingly found in cryptographic protocols. We demonstrate the practical application of our contribution with case studies. Using our approach we provide the first automated proof that the electronic vot¬ing protocol due to Fujioka, Okamoto & Ohta (FOO) [12] satisfies privacy. As a second case study we provide a formal methods proof that the Direct Anonymous Attestation (DAA) [8] protocol also satisfies privacy (the DAA authors provided a provable security proof). The ProVerif source code that accompanies this paper is available online at the following address http://www.cs.bham.ac.uk/˜bas/.

Related work. Kremer & Ryan [9] have previously demonstrated the electronic voting protocol FOO satisfies fairness, eligibility and privacy. The first two prop¬erties were verified automatically using ProVerif, and the third relied on a hand proof. In this paper we present the first automated proof of this protocol. The DAA protocol makes extensive use of signature proofs of knowledge. Delaune et al. [13] model zero knowledge proofs with an equational theory and prove properties of protocols which use zero knowledge using the applied pi. Backes et al. [14] model a variant of DAA and provide some proofs. However, their

model is not accurate w.r.t. DAA, because it uses the TPM endorsement key to produce a digital signature and they model zero knowledge proofs instead of sig-nature proofs of knowledge. In addition the secret f value is incorrectly formed, which would allow an attack of cross issuer linkability [15]. Nevertheless their idea of modelling synchronisation by private channel communication influenced the design of our translator algorithm.

Structure of paper. The remainder of this paper is structured as follows. Section 2 introduces the calculus of ProVerif [11] and discusses its limitations. Section 3 provides our extension to the calculus. We consider the FOO and DAA case studies in Sections 4 & 5 and we conclude in Section 6.

2 Applied pi calculus

The process calculi of Blanchet et al. [11], used by the tool ProVerif, is a variant of the applied pi calculus [16], a process calculi for formally modelling concur¬rent systems and their interactions. In this paper we use the phrase calculus of ProVerif to mean the calculus defined in [11], and ProVerif software tool to refer to the software tool developed in accompaniment of [11].

2.1 Syntax and informal semantics

The calculus assumes an infinite set of names and an infinite set of variables. It also assumes a signature Σ which consists of a finite set of function symbols each with an associated arity. A function symbol with arity 0 is used to define a constant symbol. We distinguish two categories of function symbols: construc¬tors f and destructors g. We use h to range over both constructors and destruc¬tors. We use standard notation for function application h(M,, ... , Mn) where h ranges over the functions of Σ and n is the arity of h. Destructors are partial, non-deterministic operations on terms that processes can apply. They represent primitives that can visibly succeed or fail, while constructors and the associated equational theory apply to primitives that always succeed but may sometimes return “junk”. The grammar for terms/term evaluations is given below. The meaning of the choice operator is explained later on.

M, N ::= term D ::= term evaluation

a, b, c name M term

x, y, z variable choice[D, D'] choice term eval.

choice[M, M'] choice term h(D,, ... , D) function evaluation

f (M,, ... , M) constructor

We equip the signature Σ with an equational theory, say E, i.e. a finite set of equations of the form Mi = Ni, where Mi and Ni are terms without names. The equational theory is then obtained from this set of equations by reflexive, symmetric and transitive closure, closure by substitution of terms for variables

and closure by context application. We write M =E N (resp. M 6=E N) for equality (resp. inequality) modulo E.

Processes are built up in a similar way to processes in the pi calculus, except that messages can contain terms/term evaluations (rather than just names). In the grammar described below, M and N are terms, D is a term evaluation, a is a name, x a variable and t an integer. The syntax also permits the use of comments in the form (* comment *).

P, Q, R ::= processes

null null process

P | Q parallel composition

!P replication

new a; P name restriction

let x = D in P else Q term evaluation

in(M, x); P message input

out(M, N); P message output

phase t; P weak phase

The choice operator allows us to model a pair of processes which have the same structure and differ only in the choice of terms and terms evaluations. We call such a pair of processes a biprocess. Given a biprocess P, we define two processes fst(P) and snd(P) as follows: fst(P) is obtained by replacing all occur¬rences of choice[M, M'] with M and choice[D, D'] with D in P. Similarly, snd(P) is obtained by replacing choice[M, M'] with M' and choice[D, D'] with D' in P. We define fst(D), fst(M), snd(D) and snd(M) similarly.

As usual, names and variables have scopes, which are delimited by restrictions and by inputs. We write fv(P), bv(P) (resp. fn(P) and bn(P)) for the sets of free and bound variables (resp. names) in P. A process is closed if it has no free variables (but it may contain free names). A context C[ ] is a process with a hole. We obtain C[P] as the result of filling C[ ]’s hole with P. An evaluation context C is a closed context built from [ ], C | P, P | C and new a; C. We sometimes refer to contexts without choice as plain contexts.

The major difference between the syntax of the applied pi calculus and the calculus of ProVerif, is the introduction of the choice operator. In addition there are some minor changes. For instance, communication is permitted on arbitrary terms, not just names. Function symbols are supplemented with destructors. Active substitutions are removed in favour of term evaluations. The syntax does not include the conditional “if M = N then P else Q”, which can be defined as “let x = equals(M, N) in P else Q” where x 6 fv(P) and equals is a deconstruc-tor with the equation equals(x, x) = x. We omit “else Q” when the process Q is null. Finally the calculus of ProVerif does not rely on a sort system. We believe that processes written in the calculus of ProVerif, can be mapped to semantically equivalent processes in the applied pi calculus and vice-versa, although proving this remains an open problem. This can easily be extended to biprocesses.

2.2 Operational semantics

The operational semantics of processes in the calculus of ProVerif, are defined by three relations, namely term evaluation , structural equivalence  and re¬ductions . Structural equivalence and reductions are only defined on closed processes. We write * for the reflexive and transitive closure of , and * for its union with . The operational semantics for the calculus of ProVerif differ in minor ways from the semantics of the applied pi calculus. Structural equivalence is the smallest equivalence relation on processes that is closed under application of evaluation contexts and some other standard rules such as asso¬ciativity and commutativity of the parallel operator and commutativity of the bindings. Reduction is the smallest relation on biprocesses closed under struc¬tural equivalence and application of evaluation contexts such that

RED I/O out(N, M); Q | in(N', x); P  Q | P{/} if fst(N) = fst(N') and snd(N) = snd(N')

RED FUN 1 let x = D in P else Q  P {choice[12]/}

if fst(D)  M1 and snd(D)  M2

RED FUN 2 let x = D in P else Q  Q

if there is no M1 such that fst(D)  M1 and

there is no M2 such that snd(D)  M2

RED REPL !P  P |!P

2.3 Extension to processes with weak phases

Many protocols can be broken into phases, and their security properties can be formulated in terms of these phases. Typically, for instance, if a protocol discloses a session key after the conclusion of a session, then the secrecy of the data exchanged during the session may be compromised but not its authenticity. To enable modelling of protocols with several phases the calculus of ProVerif is extended [11].

The syntax of processes is supplemented with a phase prefix “phase t; P”, where t is a non-negative integer. Intuitively, t represents a global clock, and the process “phase t; P” is active only during phase t. However, it is possible that not all instructions of a particular phase are executed prior to a phase transition. Moreover, parallel processes may only communicate if they are under the same phase.

Example 1. Let P = phase 1; out(c, a) | phase 2; out(c, b). The process P can output b without having first output a.

The semantics of processes are extended to deal with weak phases (see [11]).

2.4 Observational equivalence

In this section we establish sufficient conditions for observational equivalence in the calculus of ProVerif. We first recall the standard definition of observational equivalence for the applied pi calculus. We write P  when P emits a message on the channel M, that is, when P  C[out(M', N); R] for some evaluation context C[ ] that does not bind fn(M) and M =E M'.

Definition 1 (Observational equivalence [11]). Observational equivalence is the largest symmetric relation R on closed processes such that P R Q implies:

1. if P  then Q ;

2. if P  P' then Q  Q' and P' R Q' for some Q';

3. C[P] R C[Q] for all evaluation contexts C.

Intuitively, a context may represent an attacker, and two processes are observa-tionally equivalent if they cannot be distinguished by any attacker. Given a bipro-cess P, we say that P satisfies observational equivalence when fst(P)  snd(P).

A reduction P  Q for a biprocess P implies the corresponding processes have reductions fst(P)  fst(Q) and snd(P)  snd(Q). However, reductions in fst(P) and snd(P) do not necessarily correspond to any biprocess reduction. When such a corresponding reduction does exist the processes fst(P) and snd(P) satisfy uniformity under reduction, formally defined below.

Definition 2 (Uniformity Under Reductions (UUR)). A biprocess P sat-isfies uniformity under reduction if:

1. fst(P)  Q implies that P  Q for some biprocess Q with fst(Q)  Q, and symmetrically for snd(P)  Q.

2. For all plain evaluation contexts C, for all biprocess Q, C[P]  Q implies that Q satisfies UUR

Blanchet et al. [11] have shown that if a biprocess P satisfies uniformity under reductions then P satisfies observational equivalence. The ProVerif software au-tomatically verifies whether its input satisfies uniformity under reductions and thus enables us to prove observational equivalence in some cases.

2.5 Limitations of the calculus

There are trivial equivalences (see Example 2) which the calculus of ProVerif, is unable to prove since the definition of observational equivalence by uniformity under reductions is too strong. We overcome this problem with data swapping.

Example 2. The equivalence out(c, a) | out(c, b)  out(c, b) | out(c, a) holds triv-ially since the processes are in fact structurally equivalent. But the correspond¬ing biprocess out(c, choice[a, b]) | out(c, choice[b, a]) does not satisfy uniformity under reductions and therefore the equivalence cannot be proved by ProVerif.

Moreover, the phase semantics introduced by the calculus of ProVerif [11] are insufficient to model protocols which require synchronisation, as the phase semantics do not enforce that all instances of a phase must be completed prior to phase progression. We solve this problem with the introduction of strong phases.

Both of these problems are encountered when modelling cryptographic pro-tocols from literature. As case studies we demonstrate the suitability of our approach by modelling the privacy properties of the electronic voting protocol FOO [12] and Direct Anonymous Attestation (DAA) [8].

3 Extending the calculus

To overcome the limitations stated in the previous section, we extend the calculus with strong phases and data swapping.

3.1 Extension to processes with several strong phases

Similarly to weak phases the syntax of processes is supplemented with a strong phase prefix “strong phase t; P”, where t is a non-negative integer. A strong phase represents a global synchronisation and t represents the global clock. The process strong phase t; P is active only during strong phase t and a strong phase progression may only occur once all the instructions under the previous phase have been executed.

Example 3. Consider our earlier example (Example 1) with the use of strong phase. Now, the process strong phase 1; out(c, a)|strong phase 2; out(c, b) cannot output b without having previously output a.

3.2 Extension to processes with data swapping

Let us first consider the background to our approach. Referring back to Ex¬ample 2 we recall the biprocess Q = out(c, choice[a, b]) | out(c, choice[b, a]) which does not satisfy UUR. We note that fst(Q) = out(c, a) | out(c, b) and snd(Q) = out(c, b) | out(c, a). Since out(c, b) | out(c, a)  out(c, a) | out(c, b) it seems reasonable to rewrite snd(Q) as out(c, a) | out(c, b), enabling us to write Q as out(c, choice[a, a]) | out(c, choice[b, b]) which is semantically equiva¬lent to out(c, a) | out(c, b). Our new biprocess satisfies observational equivalence by uniformity under reductions. It therefore seems possible (under certain cir-cumstances) to swap values from the left to the right side of the parallel operator. Sometimes the swap is not done initially but instead immediately after a strong phase. To specify data swapping we introduce the special comment (**swap*) in process descriptions, which can be seen as a proof hint.

3.3 Automated reasoning with ProVerif

To allow automated reasoning we describe a translator which accepts as input processes written in our extended language. It may also include a single main process and subprocesses of the form “let P = Q”, subject to the following restrictions.

1. The commands strong phase t; and (**swap*) can only appear in a subpro¬cess defined using the let keyword (not in the main process);

2. Only one subprocess may contain strong phases and data swapping;

3. The subprocess defined using the let keyword that contain strong phases and data swapping must be instantiated precisely twice in the main process. Moreover, it must be of the form let P = α, where α is a process that is sequential until its last strong phase, at which point it is an arbitrary process. Formally α is given by the grammar below:

α := Rnew a; αin(M, x); αout(M, N); αlet x = D in αstrong phase t; α

where R is an arbitrary processes without data swapping and strong phases;

4. We further require that (**swap*) may only occur at the start of a subprocess definition or immediately after a strong phase.

The translator outputs processes in the standard language of ProVerif, which can be automatically reasoned about by the software tool. The pseudocode of our algorithm is presented in Figure 1. Step one of our translator makes the necessary modifications to subprocesses and step two handles the main process. The other parts of the translator’s input are copied to the output without changes. We demonstrate its application with several toy examples (see Section 3.4) and two case studies (see Sections 4 & 5).

3.4 Examples

Example 4. We begin by returning to our trivial observational equivalence: out(c, a) | out(c, b)  out(c, b) | out(c, a). As the definition of observational equivalence by UUR is too strong the calculus, and therefore the software tool, are unable to reason about such an equivalence. Using our data swapping syntax, the biprocess encoding the previous equivalence is given below.

let P = (**swap*) out(c, x ).

process let x = choice [ a, b ] in P| let x = choice [ b, a ] in P

Our translator gives us the following biprocess, which ProVerif can successfully prove.

let P = out(c, x ).

process let x = choice [choice [ a , b ] , choice [ b, a ] ] in P|

let x = choice [choice[ b, a ], choice [ a, b ] ] in P

Fig. 1. Translator algorithm

Example 5. We consider the observational equivalence shown below:

out(c, a); strong phase 1; out(c, d) | out(c, b); strong phase 1; null

- out(c, a); strong phase 1; null | out(c, b); strong phase 1; out(c, d)

The pair of processes are both able to output a and b. We then have a synchro¬nisation and discover the troublesome process out(c, d) | null - null | out(c, d). To allow ProVerif to prove such an equivalence we provide our translator with the following input:

let P=out ( c, x ) ; strong phase 1; i f y=ok then out ( c, d ).

process let x = a in let y = choice[ ok, ko ] in P|

let x = b in let y = choice[ ko, ok ] in P

Our translator produces the biprocess described below.

let P1 = out( c, x ) ; out ( pc, y ).

let P2 = i f y = ok then out( c, c ).

process new pc0 ; new pc 1 ; (

let x = a in let y = choice[ ok, ko ] in let pc = pc0 in P1 |

let x = b in let y = choice[ ko, ok ] in let pc = pc 1 in P1 |

in ( pc0 , y 0 ) ; in ( pc 1 , y 1 ) ; (

let y = choice [ y 0 , y 1 ] in P2 |

let y = choice [ y 1 , y 0 ] in P2 ))

Example 6. As our final example we consider the following equivalence:

out(c, a); strong phase 1; out(c, a) | out(c, b); strong phase 1; out(c, b) -out(c, a); strong phase 1; out(c, b) | out(c, b); strong phase 1; out(c, a)

This is similar to Example 5 with two outputs after the strong phase. Again, thanks to our translator, we are able to conclude on such an example. The input to our translator is shown below:

let P = out( c, x ) ; strong phase 1; out ( c, z ).

process let (x, z ) = ( a 1 , choice[ a 2, b 2 ] ) in P|

let (x, z ) = (b 1, choice [ b 2, a 2 ] ) in P

Our translator produces the following description.

let P1 = out( c, x ) ; out ( pc, z ).

let P2 = out( c, z ).

process new pc 1 ; new pc2 ; (

let (x, z ) = ( a 1 , choice [ a 2 , b 2 ] ) in let pc = pc 1 in P1 |

let (x, z ) = (b 1, choice [ b 2, a 2 ] ) in let pc = pc2 in P1 | in ( pc 1 , z 1 ) ; in ( pc2 , z 2 ) ; (

let z = choice [ z 1 , z 2 ] in P2 |

let z = choice [ z 2 , z 1 ] in P2 ))

ProVerif is able to successfully prove equivalence.

4 E-voting protocol due to Fujioka et al.

In this section, we study the privacy property of the e-voting protocol due to Fujioka et al. [12]. In [9], it is shown that this protocol provides fairness, eligi¬bility and privacy. However, the proof of privacy given in [9] is manual: ProVerif is unable to prove it directly, because its ability to prove observational equiv¬alence between processes is not complete. We now demonstrate the automatic verification of the privacy property using the approach we have developed in this paper.

4.1 Description

The protocol involves voters, an administrator and a collector. The administra¬tor is responsible for verifying that only eligible voters can cast votes and the collector handles the collecting and publishing of votes. The protocol requires three strong phases.

In the first phase, the voter gets a signature on a commitment to his vote from the administrator, i.e. m = sign(blind(commit(v, k), r), ska) where r, k are random keys and ska is the private key of the administrator. To ensure pri¬vacy, blind signatures are used: the voter blinds his commitment with a blind¬ing factor r. At the end of this first phase, the voter unblinds m and obtains y = sign(commit(v, k), ska), i.e. the signature of his commitment. The second phase of the protocol is the actual voting phase. The voter sends y to the col¬lector who checks correctness of the signature and, if the test succeeds, enters (B, x, y) onto a list as an B-th item. The last phase of the voting protocol starts, once the collector decides that he received all votes, e.g. after a fixed deadline. In this phase the voters reveal the random key k which allows the collector to open the votes and publish them. The voter verifies that his commitment is in the list and sends t, r to the collector. Hence, the collector opens the ballots. We summarise the protocol in Figure 2.

1. V A : id, sign((blind(commit(v, k), r)), skv)

2. A V : sign((blind(commit(v, k), r)), ska)

strong phase

3. V C : sign((commit(v, k)), ska)

strong phase

4. C : B, sign((commit(v, k)), ska)

5. V  C : B, k

Fig. 2. Protocol due to Fujioka et al.

4.2 Modelling privacy in applied pi

Privacy properties have been successfully studied using equivalences. In the con¬text of voting protocols, the definition of privacy is rather subtle. We recall the definition of privacy for electronic voting protocols given in [9]. A voting proto¬col guarantees ballot secrecy (privacy) whenever a process where Alice votes for candidate v and Bob votes for candidate v is observationally equivalent to a process where their votes are swapped, i.e. Alice votes v and Bob votes v. We denote their secret keys skva and skvb respectively. In [9], they rely on hand proof techniques to show privacy on FOO. Our modelling of FOO in the applied pi is similar to the one given in [9] except that we use strong phases. .

The underlying equational theory is the same as in [9] and is presented in Process 1. We model cryptography in a Dolev-Yao style as being perfect. In this model we can note that bit commitment (modelled by the functions commit and open) is identical to classical symmetric key encryption. The handling of public keys should be clear. Digital signatures are modelled as being signatures with message recovery, i.e. the signature itself contains the signed message which can be extracted using the checksign function. To model blind signatures we add the pair of functions blind and unblind. These functions are again similar to perfect symmetric key encryption and bit commitment. However, we add a second equation which permits us to extract a signature out of a blinded signature, when the blinding factor is known.

The main process given in Process 2 models the environment and specifies how the other processes are combined. To establish privacy, we do not require the authorities are honest, so we do not need to model them and we only con¬sider two voter processes in parallel. First, fresh private keys for the voters and the administrator are generated. The corresponding public keys are then made available to the attacker. We also output the secret key of the administrator. We will show that the privacy property holds even in the presence of a corrupt administrator.

fun commit/2. (* bit commitment *)

fun open/2. (* open bit commitment *)

fun sign /2. (* digital signature *)

fun checksign /2. (* open digital signature *)

fun pk / 1. (* get public key from private key *)

fun blind /2. (* blinding *)

fun unblind / 2. (* undo blinding *)

equation open (commit (m, r ) , r ) = m.

equation checksign (sign (m, sk ) , pk ( sk )) = m.

equation unblind ( blind (m, r ), r ) = m.

equation unblind ( sign (blind (m, r ) , sk ) , r ) = sign (m, sk ) .

Process 1. FOO signature and equational theory

let V =

new k ; new r ;

l e t x = commit (v, k) in

out ( c , ( pk( skv ) , sign ( blind (x , r ) , skv ) ) ) ;

in ( c,m2 ) ;

let y = unblind (m2, r ) in

i f checks ign (y, pka ) = x then

strong phase 1; 

out( c, y ) ;

strong phase 2;

in ( c , ( l , yprime ) ) ;

i f yprime = y then

out( c, ( l, k ) ).

process

new ska ; new skva ; new skvb ;

let pka = pk( ska ) in

out ( c, ( ska, pka, pk( skva ), pk( skvb ) ) ) ; (

(let ( skv, v) = ( skva , choice [ v 1 , v2 ] ) in V) |

(let ( skv, v) = ( skvb , choice [ v2, v 1 ] ) in V))

Process 2. FOO model (extended syntax)

The voter process given in Process 2 models the role of a voter. The specifica¬tion follows directly from our informal description. Note that we use the strong phase command to enforce the synchronisation of the voter processes. As men¬tioned initially in [9], the separation of the protocol into strong phases is crucial for privacy to hold. We also provide a data swapping hint to allow our translator to produce an output suitable for automatic verification using ProVerif.

4.3 Analysis

We use our translator to remove all instances of strong phases and handle data swapping. Our translator produces Process 3, which is suitable for automatic verification using ProVerif. ProVerif is able to successfully prove that attacker cannot distinguish between a process where Alice & Bob vote for candidates v, v respectively and a process where their votes are swapped, i.e. Alice votes vand Bob votes v. Hence, using our approach, we provide the first automatic proof that this protocol satisfies privacy according to the definition given in [9].

5 Direct Anonymous Attestation (DAA)

The Direct Anonymous Attestation (DAA) scheme provides a means for remotely authenticating a trusted platform whilst preserving the user’s privacy [8]. In [15], two of the authors have shown that corrupt administrators are able to violate

let V1 =

new k ; new r ;

l e t x = commit (v, k) in

out ( c , ( pk( skv ) , sign ( blind (x , r ) , skv ) ) ) ;

in ( c,m2 ) ;

let y = unblind (m2, r ) in

i f checks ign (y, pka ) = x then

out( pc, ( y, k ) ).

let V2 =

out( c, y ) ; out( pc, ( y, k ) ).

let V3 =

in ( c, ( l, yprime ) ) ;

i f yprime = y then

out( c, ( l, k ) ).

process

new ska ; new skva ; new skvb ;

let pka = pk( ska ) in

out( c, ( ska, pka, pk( skva ), pk( skvb ) ) ) ;

new pc 1 ; new pc 2 ; new pc 3 ; new pc 4 ; (

(let ( skv , v)=(skva , choice [ v 1 , v 2 ] ) in let pc=pc 1 in V1 ) |

(let ( skv , v)=(skvb , choice [ v 2 , v 1 ] ) in let pc=pc 2 in V1 ) |

( in ( pc 1 , ( y 1 , k 1 ) ) ; in ( pc 2 , ( y 2 , k 2 ) ) ; (

(let (y, k)=choice [ ( y 1, k 1 ),( y 2, k 2 ) ] in let pc=pc 3 in V2 ) |

(let (y, k)=choice [ ( y 2, k 2 ),( y 1, k 1 ) ] in let pc=pc 4 in V2 ) ) ) |

( in ( pc 3 , ( y 3 , k 3 ) ) ; in ( pc 4 , ( y 4 , k 4 ) ) ; (

(let (y, k)=(y 3, k 3 ) in V3 ) |

(let (y, k)=(y 4, k 4 ) in V3 ) ) ) )

Process 3. Translated FOO model (ProVerif syntax)

the privacy of the host. Using our extended calculus we are now able to provide a formal and automatic proof that the rectified protocol proposed in [15] satisfies its privacy requirements. We start with a short description of the protocol. For a more complete description please refer to [15, 8].

5.1 Description

The protocol can be seen as a group signature scheme without the ability to revoke anonymity and an additional mechanism to detect rogue members. In broad terms the host contacts an issuer and requests membership to a group. If the issuer wishes to accept the request, it grants the host/TPM an attestation identity credential. The host is now able to anonymously authenticate itself as a group member to a verifier with respect its credential.

The protocol is initiated when a host wishes to obtain a credential. This is known as the join protocol. The TPM creates a secret f value and a blinding factor v'. It then constructs the blind message U := blind(f, v') and NI := t;If, where t;I := hash(0llbsnI). The U and NI values are submitted to the issuer I. The issuer creates a random nonce value ne, encrypts it with the public key PKEK of the host’s TPM and returns the encrypted value. The TPM decrypts the message, revealing ne, and returns hash(Ullne). The issuer confirms that the hash is correctly formed. The issuer generates a nonce ni and sends it to the host. The host/TPM constructs a signature proof of knowledge that the messages U and NI are correctly formed. The issuer verifies the proof and generates a blind signature on the message U. It returns the signature along with a proof that a covert channel has not been used. The host verifies the signature and proof and the TPM unblinds the signature revealing a secret credential v (the signed f).

Once the host has obtained an anonymous attestation credential from the issuer it is able to produce a signature proof of knowledge of attestation on a message m. This is known as the sign/verify protocol. The verifier sends nonce n„ to the host. The host/TPM produce a signature proof of knowledge of attestation on the message (ntlln„llbllm), where nt is a nonce defined by the TPM and b is a parameter. In addition the host computes NV := t;f, where t; := hash(1llbsnV ). Intuitively if a verifier is presented with such a proof it is convinced that it is communicating with a trusted platform and the message is genuine. A message sequence diagram describing the protocol is presented in Figure 3.

1. H I : U, NI

2. I H : {ne}P KEK

3. H I : hash(Ullne)

4. I H : ni

5. H I : nt, SPK{(f, v') : U- blind(f, v') n NI- ζIf }(ntllni)

6. H I : nh

7. I H : C, SPK{(SKI) : C - sign(U, SKI)}(nh)

strong phase

8. V H : nv

9. H V : ζ, NV , nt, m, SPK{(f, v) : v -sign(f, SKI) n NV - ζf}(ntllnvllbllm)

Fig. 3. DAA protocol

5.2 Modelling privacy in applied pi

The DAA protocol satisfies privacy whenever a process where Alice interacts with the verifier is observationally equivalent to when Bob interacts with the verifier. For privacy we require that both Alice and Bob have completed the join protocol.

Signature and equational theory. The signature and equational theory can be seen in Process 4. The modelling of digital signatures, blind signatures and pub¬lic keys is the same as in FOO, we omit their presentation. The handling of en¬cryption, hash functions and exponential arithmetic should be clear. The DAA protocol makes extensive use of signature proofs of knowledge (SPK) to prove knowledge of and relations among discrete logarithms. We will discuss our for¬malism with an example. The signature proof of knowledge SPK{(α, 0) : x = g  y = h}(m) denotes a signature proof of knowledge on the message m that x, y were constructed correctly. This leads us to define function spk/3 to construct an SPK. The first argument contains a tuple of secret values known to the prover α, 0. The second argument consists of a tuple of the values on which the prover is claiming to have constructed correctly x, y, such that x = gand y = h. Finally the third argument is the message m on which the prover produces a signature on. Verifying the correctness of a SPK is specific to its construction, thus we must require a function checkspk for each SPK that the protocol uses. To verify the SPK produced in the aforementioned example the verifier must be in possession of the SPK itself and x, y, g, h, m. We define the equation: checkspk (spk ((α, 0), (g, h), m), g, h, g, h, m) = ok. A verifier can now check a SPK using an if statement. We define spk, checkspk1, checkspk2 and checkspk3 in the manner previously discussed.

fun exp / 2. (* exponential arithmetic *)

fun hash/1. (* one way hash function *)

fun enc / 2. (* public key encryption *)

fun dec / 2. (* public key decryption *)

fun spk / 3. (* signature proof of knowledge ( spk ) *) fun checkspk 1 /5. (* check spk created by DAA Join, step 4 *) fun checkspk2 / 5. (* check spk created by DAA Join, step 6 *) fun checkspk3 / 5. (* check spk created by DAASign *)

equation dec ( enc (m, pk( sk )), sk ) = m.

equation checkspk 1 ( spk (( f , v ’ ) , ( blind ( f , v ’ ) , exp ( zeta I , f )) , m) ,

blind ( f , v ’ ) , exp (zeta I , f ) , zeta I , m) = ok.

equation checkspk2 ( spk ( skI , sign (U, skI ) ,m),

sign( U, skI ) ,U, pk( skI ) ,m) = ok.

equation checkspk3 ( spk ( f , ( sign ( f , skI ) , exp (zeta , f )) , m) ,

exp (zeta , f ) , zeta , pk( skI ) ,m) = ok.

Process 4. DAA signature and equational theory

Modelling the DAA protocol. As in FOO, the main process (Process 5) models the environment and specifies how the other processes are combined. First, fresh secret keys for the TPMs, the issuer and the verifier are generated using the restriction operator. We also generate two DAASeed values. The public keys are

then sent on a public channel, i.e. they are made available to the intruder. We also output the secret key of the verifier and issuer since the privacy property should be preserved even if they are corrupt. Next we input the basenames bsn, bsn of the issuer and verifier. Then we instantiate two instances of the DAA protocol with the necessary parameters.

Our encoding of the DAA protocol (see Process 5) follows directly from our informal description. Note that we use the strong phase and data swapping commands introduced by our extension to the calculus to ensure synchronisation. The two instances of the DAA processes must first execute all instructions of DAAJoin before moving onto DAASign. The separation of the protocol into strong phases is crucial for privacy to hold.

5.3 Analysis

We use our translator to remove all instances of strong phases from our encoding (Process 5) and produce code suitable for input to ProVerif. Our translator pro¬duces Process 6 which permits the automatic verification of the privacy property using ProVerif. We are also able to detect the vulnerability in the original DAA protocol [15] and prove the optimisation presented in [15].

6 Conclusion

In this paper we have extended the class of equivalences which ProVerif is able to automatically verify. More specifically we able to reason about processes which require data swapping and/or strong phases. Using the approach developed we are to automatically verify the privacy properties of the electronic voting protocol FOO and the Direct Anonymous Attestation scheme. In the future we aim to generalise our translation algorithm and develop a software implementation.

References

1. Lowe, G.: An attack on the Needham-Schroeder public-key authentication proto¬col. Information Processing Letters 56(3) (1995) 131–133

2. Mukhamedov, A., Ryan, M.D.: Fair Multi-party Contract Signing using Private Contract Signatures. Information & Computation (2007)

3. Chadha, R., Kremer, S., Scedrov, A.: Formal Analysis of Multi-Party Fair Ex-change Protocols. In Focardi, R., ed.: 17th IEEE Computer Security Foundations Workshop, Asilomar, USA, IEEE Computer Society Press (2004) 266–279

4. Gollmann, D.: Analysing Security Protocols. In: FASec. (2002) 71–80

5. Clark, J., Jacob, J.: A Survey of Authentication Protocol Literature. http://www.cs.york.ac.uk/˜jac/papers/drareviewps.ps (1997)

6. Hirt, M., Sako, K.: Efficient receipt-free voting based on homomorphic encryption. In: Eurocrypt. Volume 1807 of Lecture Notes in Computer Science. (2000) 539–556

7. Garay, J.A., Jakobsson, M., MacKenzie, P.D.: Abuse-Free Optimistic Contract Signing. In: Crypto’99: Advances in Cryptology. Volume 1666 of Lecture Notes in Computer Science. (1999) 449–466

let DAA =

new vPrime ; (* TPM requests attestation *)

let f = hash (( DAASeed, hash ( pkI ), cnt, zero )) in

let U = blind( f, vPrime ) in

let zeta I = hash(( zero, bsnI )) in

let NI = exp ( zetaI , f ) in

out( c, (U, NI ) ) ;

in ( c, encNe ) ; (* Authentication *)

let ne = dec ( encNe, skH) in

out ( c , hash ((U, ne ) ) ) ;

in ( c, ni ) ; (* SPK on U,NI values *)

new nt ;

out( c, ( nt, spk (( f , vPrime ) ,(U, NI ) ,( ni, nt ) ) ) ) ;

new nh ; (* Receive/verify blind signature from issuer *)

out( c, nh ) ;

in ( c , ( blind Sig , spk 2 ) ) ;

i f checkspk2 ( spk 2, blind Sig ,U, pkI, nh) = ok then

let v = unblind ( blindSig , vPrime ) in

strong phase 1;

(**swap *)

i f dos ign = ok then

in ( c, ( nv ,m) ) ; (* DAASign *)

new nt ;

let b = one in

let zeta = hash (( one, bsnV )) in

let NV = exp ( zeta, f ) in

out( c, ( zeta ,NV, nt , b , spk ( f , ( v ,NV) , ( nt , nv, b ,m) ) ) ).

process

new skH 1 ; new skH 2 ; new skI ;

new DAASeed1 ; new DAASeed2 ;

let pkI = pk( skI ) in

out( c, ( pk(skH 1 ) , pk(skH 2 ) , pkI, skI ) ) ;

in ( c, ( bsnI , bsnV ) ) ; (

(let (skH, DAASeed, cnt ) = (skH 1, DAASeed1, zero) in

let dos ign = choice[ ok, ko ] in DAA ) |

(let (skH, DAASeed, cnt ) = (skH 2, DAASeed2, zero) in

let dos ign = choice[ ko, ok ] in DAA ))

Process 5. DAA model (extended syntax)

l e t DAAJoin =

new vPrime ; (* TPM requests attestation *)

let f = hash (( DAASeed, hash ( pkI ), cnt, zero )) in

let U = blind( f, vPrime ) in

let zeta I = hash(( zero, bsnI )) in

let NI = exp ( zetaI , f ) in

out( c, (U, NI ) ) ;

in ( c , encNe ) ; (* Authentication *)

let ne = dec ( encNe , skH) in

out ( c , hash ((U, ne ) ) ) ;

in ( c , ni ) ; (* SPK on U,NI values *)

new nt ;

out( c, ( nt, spk (( f , vPrime ) ,(U, NI ) ,( ni, nt ) ) ) ) ;

new nh ; (* Receive/verify blind signature from issuer *)

out( c, nh ) ;

in ( c , ( blind Sig , spk 2 ) ) ;

i f checkspk2 ( spk 2, blind Sig ,U, pkI, nh) = ok then

let v = unblind ( blindSig , vPrime ) in

out( pc, ( f , v, dosign ) ).

let DAASign =

i f dos ign = ok then

in ( c , ( nv ,m) ) ;

new nt ;

let b = one in

let zeta = hash (( one, bsnV )) in

let NV = exp ( zeta, f ) in

let spk3 = spk ( f, ( v ,NV),( nt, nv, b ,m)) in

out ( c , ( zeta ,NV, nt, b, spk3 ) ).

process

new skH 1 ; new skH 2 ; new skI ;

new DAASeed1 ; new DAASeed2 ;

let pkI = pk( skI ) in

out ( c , ( pk(skH 1 ) , pk(skH 2 ) , pkI , skI ) ) ;

in ( c , ( bsnI , bsnV ) ) ;

new pc 1 ; new pc 2 ; (

(let (skH, DAASeed, cnt )=(skH 1 , DAASeed1 , zero) in

let dos ign=choice [ ok, ko ] in let pc=pc 1 in DAAJoin ) |

(let (skH, DAASeed, cnt )=(skH 2, DAASeed2, zero) in

let dos ign=choice [ ko, ok ] in let pc=pc 2 in DAAJoin ) | ( in ( pc 1 , ( f 1 , v 1 , d 1 ) ) ; in ( pc 2 , ( f 2 , v 2 , d 2 ) ) ; (* s-phase 1*) (* swap *)( (let ( f , v , dosign )=choice [ ( f 1 , v 1 , d 1 ) ,( f 2 , v 2 , d 2 ) ] in DAASign ) | (let ( f, v, dosign )=choice [ ( f 2 , v 2 , d 2 ) ,( f 1 , v 1 , d 1 ) ] in DAASign ) ) ) )

Process 6. Translated DAA model (ProVerif syntax)

8. Brickell, E., Camenisch, J., Chen, L.: Direct Anonymous Attestation. In: CCS ’04: 11th ACM conference on Computer and communications security, New York, United States of America, ACM Press (2004) 132–145

9. Kremer, S., Ryan, M.D.: Analysis of an Electronic Voting Protocol in the Applied Pi Calculus. In: ESOP’05: Proceedings of the European Symposium on Program¬ming. Volume 3444 of Lecture Notes in Computer Science. (2005) 186–200

10. Delaune, S., Kremer, S., Ryan, M.: Coercion-Resistance and Receipt-Freeness in Electronic Voting. In: CSFW ’06: Proceedings of the 19th IEEE workshop on Computer Security Foundations, IEEE Computer Society (2006) 28–42

11. Blanchet, B., Abadi, M., Fournet, C.: Automated Verification of Selected Equiva¬lences for Security Protocols. Journal of Logic and Algebraic Programming (2007)

12. Fujioka, A., Okamoto, T., Ohta, K.: A Practical Secret Voting Scheme for Large Scale Elections. In: ASIACRYPT ’92: Proceedings of the Workshop on the Theory and Application of Cryptographic Techniques, London, Springer (1993) 244–251

13. Delaune, S., Klay, F., Kremer, S.: Sp´ecification du protocole de vote ´electronique. Technical Report 6, projet RNTL PROUV´E (November 2005) 19 pages.

14. Backes, M., Maffei, M., Unruh, D.: Zero-Knowledge in the Applied Pi-calculus and Automated Verification of the Direct Anonymous Attestation Protocol. Cryptology ePrint Archive: Report 2007/289 (July 2007)

15. Smyth, B., Ryan, M., Chen, L.: Direct Anonymous Attestation (DAA): Ensuring privacy with corrupt administrators. In: ESAS’07: Fourth European Workshop on Security and Privacy in Ad hoc and Sensor Networks. Volume 4572 of Lecture Notes in Computer Science. (2007) 218–231

16. Abadi, M., Fournet, C.: Mobile values, new names, and secure communication. In: POPL ’01: Proceedings of the 28th ACM SIGPLAN-SIGACT symposium on Principles of programming languages, New York, USA, ACM Press (2001) 104–115

IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS – PART B 1

Exploratory Undersampling for

Class-Imbalance Learning

Xu-Ying Liu, Jianxin Wu, and Zhi-Hua Zhou, Senior Member, IEEE

Abstract—Under-sampling is a popular method in deal¬ing with class-imbalance problems, which uses only a subset of the majority class and thus is very efficient. The main deficiency is that many majority class examples are ignored. We propose two algorithms to overcome this deficiency. EasyEnsemble samples several subsets from the majority class, trains a learner using each of them, and combines the outputs of those learners. BalanceCascade trains the learners sequentially, where in each step the majority class examples which are correctly classified by the current trained learners are removed from further consideration. Experimental results show that both meth¬ods have higher AUC, F-measure and G-mean values than many existing class-imbalance learning methods. Moreover, they have approximately the same training time as that of under-sampling when the same number of weak classifiers are used, which is significantly faster than other methods.

Index Terms—Data mining, machine learning, class-imbalance learning, under-sampling, ensemble learning.

I. INTRODUCTION

N many real-world problems, the data sets are typi

cally imbalanced, i.e., some classes have much more

instances than others. The level of imbalance (ratio of size of the majority class to minority class) can be as huge as 106 [41]. It is noteworthy that class-imbalance is emerging as an important issue in designing classifiers [11], [23], [37].

Imbalance has a serious impact on the performance of classifiers. Learning algorithms that do not consider class-imbalance tend to be overwhelmed by the majority class and ignore the minority class [10]. For example, in a problem with imbalance level 99, a learning algorithm that minimizes error rate could decide to classify all examples as the majority class in order to achieve a low error rate of 1%. However, all minority class examples

Manuscript received October 26, 2007; revised June 24, 2008, and accepted October 5, 2008.

X.-Y. Liu and Z.-H. Zhou are with National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China email: {liuxy, zhouzh}@lamda.nju.edu.cn

J. Wu is with School of Interactive Computing, College of Com-puting, Georgia Institute of Technology, Atlanta, GA 30332 email: wujx@cc.gatech.edu

will be wrongly classified in this case. In problems where the imbalance level is huge, class-imbalance must be carefully handled to build a good classifier.

Class-imbalance is also closely related to cost-sensitive learning, another important issue in machine learning. Misclassifying a minority class instance is usually more serious than misclassifying a majority class one. For example, approving a fraudulent credit card application is more costly than declining a credible one. Breiman et al. [7] pointed out that training set size, class priors, cost of errors in different classes, and placement of decision boundaries are all closely con¬nected. In fact, many existing methods for dealing with class-imbalance rely on connections among these four components. Sampling methods handle class-imbalance by varying the minority and majority class sizes in the training set. Cost-sensitive learning deals with class-imbalance by incurring different costs for the two classes and is considered as an important class of methods to handle class-imbalance [37]. More details about class-imbalance learning methods are presented in Section II.

In this paper we examine only binary classification problems by ensembling classifiers built from multiple under-sampled training sets. Under-sampling is an effi-cient method for class-imbalance learning. This method uses a subset of the majority class to train the classifier. Since many majority class examples are ignored, the training set becomes more balanced and the training process becomes faster. However, the main drawback of under-sampling is that potentially useful information contained in these ignored examples is neglected. The intuition of our proposed methods is then to wisely ex-plore these ignored data, while keeping the fast training speed of under-sampling.

We propose two ways to use these data. One straight-forward way is to sample several subsets independently from N (the majority class), use these subsets to train classifiers separately, and combine the trained classifiers. Another method is to use trained classifiers to guide the sampling process for subsequent classifiers. After we have trained n classifiers, examples correctly classified by them will be removed from N. Experiments on 16 UCI data sets [3] show that both methods have higher

IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS – PART B 2

AUC, F-measure and G-mean values than many existing class-imbalance learning methods.

The rest of this paper is organized as follows. Sec¬tion II reviews related methods. Section III presents EasyEnsemble and BalanceCascade. Section IV reports the experiments. Finally, Section V concludes this paper.

II. RELATED WORK

As mentioned in the previous section, many existing class-imbalance learning methods manipulate the follow-ing four components: training set size, class prior, cost matrix, and placement of decision boundary. Here we pay special attention to two classes of methods that are most widely used: sampling and cost-sensitive learning. For other methods, we refer the readers to [37] for a more complete and detailed review.

Sampling is a class of methods that alters the size of training sets. Under-sampling and over-sampling change the training sets by sampling a smaller majority training set and repeating instances in the minority training set, respectively [15]. The level of imbalance is reduced in both methods, with the hope that a more balanced training set can give better results. Both sampling meth-ods are easy to implement and have been shown to be helpful in imbalanced problems [37], [47]. Under-sampling requires shorter training time, at the cost of ignoring potentially useful data. Over-sampling increases the training set size, and thus requires longer training time. Furthermore, it tends to lead to overfitting since it repeats minority class examples [9], [15]. Besides the basic under-sampling and over-sampling methods, there are also methods that sample in more complex ways. SMOTE [9] added new synthetic minority class examples by randomly interpolating pairs of closest neighbors in the minority class. The one-sided selection procedures [25] tried to find a representative subset of majority class examples by only removing ‘borderline’ and ‘noisy’ majority examples. Some other methods combine different sampling strategies to achieve further improvement [1]. Also, researchers have studied the effect of varying the level of imbalance and how to find the best ratio when a C4.5 tree classifier was used [38].

Cost-sensitive learning [14], [16] is another important class of class-imbalance learning methods. Although many learning algorithms have been adapted to ac-commodate class-imbalance and cost-sensitive problems, variants of AdaBoost appear to be the most popular ones. Many cost-sensitive boosting algorithms have been proposed [31]. A common strategy of these variants was to intentionally increase the weights of examples

with higher misclassification cost in the boosting pro¬cess. In [30] the initial weights of high cost exam¬ples were increased. It was reported that, however, the weight differences between examples in different classes disappear quickly when the boosting process proceeds [33]. Thus, many algorithms raised high cost examples’ weights in every iteration of the boosting process, for example, AsymBoost [33], AdaCost [17], CSB [31], DataBoost [21], AdaUBoost [24], just to name a few. Another way to adapt a boosting algorithm to cost-sensitive problems is to change the weights of the weak classifiers in forming the final ensemble classifier, such as BMPM [22] and LAC [41]. Unlike the heuristic methods mentioned above, Asymmetric Boosting [28] directly minimized a cost-sensitive loss function in the statistical interpretation of boosting.

SMOTEBoost [12] is designed for class-imbalance learning, which is very similar to AsymBoost. Both methods alter the distribution for the minority class and majority class in separate ways. The only difference is how these distributions are altered. AsymBoost di¬rectly updates instance weights for the majority class and minority class differently in each iteration, while SMOTEBoost alters distribution by first updating in-stance weights for majority class and minority class equally and then using SMOTE to get new minority class instances.

Chan and Stolfo [8] introduced an approach to explore majority class examples. They split the majority class into several non-overlapping subsets, with each subset having approximately the same number of examples as the minority class. One classifier was trained from each of these subsets and the minority class. The final classifier ensembled these classifiers using stacking [40]. However, when a data set is highly imbalanced, this approach requires a much longer training time than under-sampling. Also, since the minority class examples are used by every classifier, stacking these classifiers will have a high probability of suffering from overfitting when the number of minority class examples is limited.

III. EASYENSEMBLE & BALANCECASCADE

As was shown by [15], under-sampling is an effi¬cient strategy to deal with class-imbalance. However, the drawback of under-sampling is that it throws away many potentially useful data. In this section, we pro¬pose two strategies to explore the majority class exam¬ples ignored by under-sampling: EasyEnsemble and BalanceCascade.

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A. EasyEnsemble

Given the minority training set P and the majority training set N, the under-sampling method randomly samples a subset N' from N, where |N'| < |N|.Usually we choose |N'| = |P|, and therefore have |N'| « |N| for highly imbalanced problems.

EasyEnsemble is probably the most straightforward way to further exploit the majority class examples ig-nored by under-sampling, i.e. examples in N n N'. In this method, we independently sample several subsets N1, N2, . . . , NT from N. For each subset Ni (1 <

i < T), a classifier Hi is trained using Ni and all of P. All generated classifiers are combined for the final decision. AdaBoost [29] is used to train the classifier Hi. The pseudo-code for EasyEnsemble is shown in Algorithm 1.

Algorithm 1 The EasyEnsemble algorithm.

1: {Input: A set of minority class examples P, a set of majority class examples N, |P| < |N|, the number of subsets T to sample from N, and s, the number of iterations to train an AdaBoost ensemble H}

2: i  0

3: repeat

4: i  i + 1

5: Randomly sample a subset N from N, |N| = |P|.

6: Learn H using P and N. H is an AdaBoost ensemble with s weak classifiers h and corresponding weights

αij. The ensemble’s threshold is θi, i.e.

(Ei )

H(x) = sgn =1 αijhij(x)  θi.

7: until i = T

8: Output: An ensemble:

(E Ei )

H(x) = sgn =1 αijhij(x)  ET .

=1 =1 θ

The idea behind EasyEnsemble is simple. Similar to the balanced Random Forests [13], EasyEnsemble generates T balanced sub-problems. The output of the ith sub-problem is AdaBoost classifier Hi, an ensemble with si weak classifiers {hi,j}. An alternative view of hi,j is to treat it as a feature that is extracted by the ensemble learning method and can only take binary values [41]. Hi, in this viewpoint, is simply a linear classifier built on these features. Features extracted from different subsets Ni thus contain information of different aspects of the original majority training set N. Finally, instead of counting votes from {Hi}i=1...T, we collect all the features hi,j (i = 1, 2, ... , T, j = 1, 2,.. . , si), and form an ensemble classifier from them.

The output of EasyEnsemble is a single ensem¬ble, but it looks like an ‘ensemble of ensembles’. It is known that, Boosting mainly reduces bias while Bagging mainly reduces variance. Several works [19],

[35], [36], [42] combine different ensemble strategies to achieve stronger generalization. MultiBoosting [35], [36] combines boosting with bagging/wagging [2] by using boosted ensembles as base learners. Stochastic Gradient Boosting [19] and Cocktail Ensemble [42] also combine different ensemble strategies. It is evident that EasyEnsemble has benefited from the combination of boosting and a bagging-like strategy with balanced class distribution.

Both EasyEnsemble and Balanced Random Forests try to use balanced bootstrap samples, however, the former uses the samples to generate boosted ensembles while the latter uses the samples to train decision trees randomly. Costing [43] also uses multiple samples of the original training set. Costing was initially proposed as a cost-sensitive learning method, while EasyEnsemble is proposed to deal with class-imbalance directly. Be-sides, the working style of EasyEnsemble is quite different from Costing. For example, the Costing method samples the examples with probability in proportion to their costs (Rejection Sampling). Since this is a probability-based sampling method, no positive exam¬ple will definitely appear in all the samples (in fact, the probability of a positive example appearing in all the samples is small). While in EasyEnsemble, all the positive examples will definitely appear in all the samples. When the size of minority class is very small, it is important to utilize every minority class example.

B. BalanceCascade

EasyEnsemble is an unsupervised strategy to explore N since it uses independent random sampling with replacement. Our second algorithm, BalanceCascade, explores N in a supervised manner. The idea is as follows. After H1 is trained, if an example x1  N is correctly classified to be in the majority class by H1, it is reasonable to conjecture that x1 is somewhat redundant in N, given that we already have H1. Thus, we can remove some correctly classified majority class examples from N. As in EasyEnsemble, we use AdaBoost in this method. The pseudo-code of BalanceCascade is described in Algorithm 2.

This method is called BalanceCascade since it is somewhat similar to the cascade classifier in [34]. The majority training set N is shrunk after every Hi is trained, and every node Hi is dealing with a balanced sub-problem (|Ni| = |P|). However, the final classifier is different. A cascade classifier is the conjunction of all {Hi}i=1...T, i.e. H(x) predicts positive if and only if all Hi(x) (i = 1, 2, ... , T) predict positive. Viola and Jones

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Algorithm 2 The BalanceCascade algorithm.



2: i  0, f  T 1 , f is the false positive rate (the

error rate of misclassifying a majority class example to the minority class) that H should achieve.

3: repeat

4: i  i + 1

5: Randomly sample a subset N from N, |N| = |P |.

6: Learn H using P and N. H is an AdaBoost ensemble with s weak classifiers h and corresponding weights

αij. The ensemble’s threshold is θi i.e.

i

H(x) = sgn =1 αijhij(x)  θi .

7: Adjust θi such that Hi’s false positive rate is f.

8: Remove from N all examples that are correctly classi¬fied by H.

9: until i = T

10: Output: A single ensemble:

i

H(x) = sgn =1 αijhij(x)  PT .

=1 =1 θ

[34] used the cascade classifier mainly to achieve fast testing speed. While in BalanceCascade, sequential dependency between classifiers is mainly exploited for reducing the redundant information in the majority class. This sampling strategy leads to a restricted sample space for the following under-sampling process, to explore as much useful information as possible.

BalanceCascade is similar to EasyEnsemble in their structures. The main difference between them is the line III-B and III-B of Algorithm 2. Line III-B removes the true majority class examples from N, and line III-B specifies how many majority class examples can be removed. At the beginning of the T-th iteration, N has been shrunk T 1 times, and therefore its current size is |N| • fT1 = |P|. Thus, after HT is trained and N is shrunk again, the size of N is smaller than |P|. We can stop the training process at this time.

There are other ways to combine weak classifiers in EasyEnsemble and BalanceCascade. A popular one is stacking [40]. It takes the outputs of other classi-fiers as input to train a generalizer. However, Ting [32] stated that, the use of class probabilities is crucial for the successful application of stacked generalization in classification tasks. Furthermore, since minority class examples are used to train each weak classifier, stacking these classifiers is likely to suffer from overfitting when the number of minority class examples is limited. To verify this, stacking is compared with the ensemble strategy used in the proposed methods in section IV-E.

Chan and Stolfo’s method [8] (abbreviated as

Chan) is closely related to EasyEnsemble and BalanceCascade. It splits the majority class into several non-overlapping subsets, with each subset having similar size to the the minority class. Classifiers trained from each majority class subset and the minority class are combined by stacking. The differences between Chan and the proposed methods are obvious: (1) Chan uses all majority class examples, while EasyEnsemble and BalanceCascade use only part of them. When a data set is highly imbalanced, Chan requires a much longer training time than the proposed methods. How¬ever, the experimental results reveal that it is not nec¬essary to use all majority class examples to achieve good performances. (2) Chan uses stacking to combine classifiers trained from each subset. As stated above, since the minority class is used repeatedly, stacking is likely to suffer from overfitting when the number of minority class examples is limited.

Both EasyEnsemble and BalanceCascade are very efficient. Their training time is roughly the same as that of under-sampling when the same number of weak classifiers are used. Detailed analysis of training time and empirical running time are presented in section IV-C.

IV. EXPERIMENTS

A. Evaluation Criteria

It is now well-known that error rate is not an appropri-ate evaluation criterion when there are class-imbalance or unequal costs. In this paper, we use F-measure, G-mean, and AUC (Area Under the ROC Curve) [4] as performance evaluation measures. F-measure and G-mean are functions of the confusion matrix as shown in Table I. F-measure and G-mean are then defined as follows. Here, we take minority class as positive class.

False Positive Rate (fpr)

True Positive Rate (Acc+)

True Negative Rate (Acc)

Gmean

Precision

Recall

Fmeasure

(1)

AUC has proved to be a reliable performance measure for imbalanced and cost-sensitive problems [18]. Given a binary classification problem, an ROC curve depicts the performance of a method using the (fpr,tpr) pairs, as illustrated in Figure 1. fpr is the false positive rate of the classifier, and tpr is the true positive rate (Acc+). AUC is the area below the curve (shaded region in Fig. 1). It integrates performance of the classification method over

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TABLE I

CONFUSION MATRIX.

Predicted Positive Class Predicted Negative Class

Actual Positive Class TP (True Positives) FN (False Negatives)

Actual Negative Class FP (False Positives) TN (True Negatives)

all possible values of fpr and is proved to be a reliable performance measure for imbalanced and cost-sensitive problems [18].

0.8

0.6

0.4

0.2

0 0.1 0.4 0.7 1

False Positive Rate

Fig. 1. Example of an ROC curve.

In our experiments, for ensemble classifiers in the form H(x) = sgn(~Ti=1 αihi(x) 0), we alter the value of 0 from  to . In this way we get a full range of (fpr,tpr) pairs and build an ROC curve from these data. We then use the Algorithm 3 in [18] to calculate the AUC score. Details of AUC can be found in [18].

B. Experimental Settings

We tested the proposed methods on 16 UCI data sets [3]. Information about these data sets is summarized in Table II.

TABLE II

BASIC INFORMATION OF DATA SETS. Size IS NUMBER OF

EXAMPLES. Target IS USED AS MINORITY CLASS, AND ALL

OTHERS ARE USED AS MAJORITY CLASS. IN Attribute, B: BINARY,

N: NOMINAL, C: CONTINUOUS. #min/#maj IS THE SIZE OF

MINORITY AND MAJORITY CLASS, AND Ratio IS THE SIZE OF

MAJORITY CLASS DIVIDED BY THAT OF MINORITY CLASS.

Dataset Size Attribute Target #min/#maj Ratio

abalone 4177 1N,7C Ring=7 391/3786 9.7

balance 625 4C Balance 49/576 11.8

car 1728 6N acc 384/1344 3.5

cmc 1473 3B,4N,2C class 2 333/1140 3.4

haberman 306 1N,2C class 2 81/225 2.8

housing 506 1B,12C [20, 23] 106/400 3.8

ionosphere 351 33C bad 126/225 1.8

letter 20000 16C A 789/19211 24.3

mf-morph 2000 6C class 10 200/1800 9.0

mf-zernike 2000 47C class 10 200/1800 9.0

phoneme 5404 5C class 1 1586/3818 2.4

pima 768 8C class 1 268/500 1.9

satimage 6435 36C class 4 626/5809 9.3

vehicle 846 18C opel 212/634 3.0

wdbc 569 30C malignant 212/357 1.7

wpbc 198 33C recur 47/151 3.2

For every data set, we perform a 10-fold stratified cross validation. Within each fold, the classification method is repeated 10 times considering that the sam-pling of subsets introduces randomness. The AUC, F-measure and G-mean of this cross validation process are averaged from these 10 runs. The whole cross validation process is repeated for 5 times, and the final values from this method are the averages of these 5 cross validation runs.

We compared the performance of 15 methods, includ-ing:

• CART. Classification and regression trees [7]. It uses the entire data set (P and N) to train a single classifier.

• Bagging (abbreviated as Bagg): Bagging [5] uses the entire data set (P and N). CART is used to train weak classifiers. The number of iterations is 40.

• AdaBoost (abbreviated as Ada). AdaBoost uses the entire data set (P and N). CART is used to train weak classifiers. The number of iterations is 40.

• AsymBoost (abbreviated as Asym). AsymBoost is a typical cost-sensitive variant of AdaBoost1. Let r = |N|/|P | be the imbalance level. At each iteration,

by Tr, where Tis the number of iterations [33]. the weight of every positive example is multiplied

AsymBoost uses the entire data set (P and N). CART is used to train weak classifiers. The number of iterations is 40.

• SMOTEBoost (abbreviated as SMB). SMOTE adds synthetic minority class examples [9]. For data sets having nominal attributes, we use SMOTE-NC. Details for implementing SMOTE and SMOTE-NC can be found in [9]. SMOTEBoost uses SMOTE to get new minority class examples in each iteration. CART is used to train weak classifiers. The number of iterations is 40. The k nearest neighbor parameter of SMOTE is 5. The amount of new data generated using SMOTE in each iteration is |P |.

• Under-sampling + AdaBoost (abbreviated as Under). A subset N' is sampled (without replacement) from N, |N'| = |P|. Then, AdaBoost is used to train a classifier using P and N', since the problem is balanced after under-sampling.

1It is also equivalent to the CSB2 algorithm in [31].

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CART is used to train weak classifiers. The number of iterations is 40.

• Over-sampling + AdaBoost (abbreviated as Over). A new minority training set is sampled (with re-placement) from the original minority class, |P'| = |N|. Then, AdaBoost is used to train a classifier using P' and N. CART is used to train weak classifiers. The number of iterations is 40.

• SMOTE + AdaBoost (abbreviated as SMOTE). In our experiments, we first generate P' using SMOTE, a set of synthetic minority class examples with |P'| = |P |. We sample a new majority training set N' with |N'| = 2|P| when |N| > 2|P|, and let N' = N otherwise. Then we use AdaBoost to train a classifier with P, P', and N'. CART is used to train weak classifiers. The number of iterations is 40. The settings of SMOTE are the same as SMOTEBoost (k = 5).

• Chan & Stolfo’s method + AdaBoost. (abbrevi¬ated as Chan). It splits N into L|N|/|P|] non-overlapping subsets. An AdaBoost classifier was trained from each of these subsets and P. Fisher Discriminant Analysis [20] is used as the stacking method. CART is used to train weak classifiers. AdaBoost classifiers are trained for F40|P|/|N|1 iterations when L|N|/|P |] < 40, otherwise, only one iteration is allowed.

• BalanceCascade (abbreviated as Cascade). CART is used to train weak classifiers. Number of subsets T = 4, number of rounds in each AdaBoost ensemble si = 10.

• EasyEnsemble (abbreviated as Easy). CART is used to train weak classifiers. Number of subsets T = 4, number of rounds in each AdaBoost ensemble si = 10.

• Random Forests (abbreviated as RF). Random Forests [6] uses bootstrap samples of training data to generate random trees and then form an ensem¬ble. Here, we use RandomForest in WEKA [39], in which a random tree is a variant of REPTree, using random feature selection in the tree induction process, and not pruned. RF uses the entire data set (P and N). The number of iterations is 40.

• Under-sampling + Random Forests (abbreviated as Under-RF). A subset N' is sampled (without replacement) from N, |N'| = |P|. Then, Random Forests is used to train a classifier using P and N', The number of iterations is 40.

• Over-sampling + Random Forests (abbreviated as Over-RF). A new minority training set is sampled (with replacement) from the original minority class, |P'| = |N|. Then, Random Forests is used to train a

classifier using P' and N. The number of iterations is 40.

• Balanced Random Forests (abbreviated as BRF). Balanced Random Forests is different from Random Forests in that it uses balanced bootstrap samples of training data. It is different from under-sampling + Random Forests, because the latter preprocesses the training data and then learns a Random Forests classifier. Here, we use RandomTree in WEKA to train weak classifiers, which is the same weak classifier learning method used by RandomForest in WEKA. The number of iterations is 40.

The settings of CART are the same. In CART, pruning is used, and impure nodes must have at least 10 examples to be split. CART and Ada are baseline methods. All other classifiers have 40 weak classifiers. In Chan, the amount of classifiers is also 40 since the imbalance levels of data sets in Table II are all lower than 40.

C. Analysis of Training Time

Random Forests series (RF, Under-RF, Over-RF, and BRF) use random decision trees, which train much faster than CART. Moreover, they are implemented in Java code, while the other methods are in Matlab code. Therefore, it is not fair to compare the running time of them directly. Here, we only analyze the training time of CART based methods.

Since all methods use the same weak learner and have the same amount of weak classifiers, the training time of these methods mainly depends on the number of training examples.

From the descriptions in section IV-B, Under uses the smallest number (2|P|) of examples and is the fastest among all methods. The proposed methods (Cascade and Easy) and Chan use the same number of weak clas¬sifiers as Under, and use the same number of examples as Under to train every weak classifier2. These methods require additional time to sample or split subsets of N. However, this time is negligible. Thus, the proposed methods and Chan have approximately the same training time as Under. Note that, the imbalance level of data sets used in the experiment happens to be lower than 40, so the number of weak classifiers in Chan can be the same with Cascade and Easy. However, when the data set is highly imbalanced (say the imbalance level is 1000), Chan will require extremely more training time than the proposed methods. Furthermore, Easy has a potential computational advantage since each under-sampling process can be executed in parallel.

2Although different subsets of N are used in the training process, the number of active training examples is always 2|P| at all times.

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TABLE III

RUNNING TIMES (IN SECONDS). THE ROW AVG. SHOWS THE AVERAGE RUNNING TIME OF EACH METHOD.

CART Bagg Ada Asym SMB Under Over SMOTE Chan Easy Casc

abalone 0.21 8.39 18.06 18.04 35.51 3.47 39.11 7.78 4.83 3.72 6.22

balance 0.03 0.96 2.16 2.20 2.72 0.53 3.41 0.99 0.97 0.65 0.85

car 0.09 2.50 6.42 5.71 9.70 3.18 9.10 5.14 4.20 3.10 4.79

cmc 0.25 6.64 8.64 9.01 11.54 4.42 11.44 7.85 6.06 4.51 7.33

haberman 0.03 0.71 1.35 1.15 1.32 0.84 1.26 1.11 1.26 0.80 0.86

ionosphere 0.09 2.11 2.30 2.19 2.77 1.75 2.30 2.87 2.46 1.70 2.83

letter 0.41 19.70 153.47 138.11 1120.99 3.92 549.73 9.72 5.19 3.87 5.62

phoneme 0.34 9.72 23.20 22.87 150.09 12.03 38.78 30.18 16.67 11.64 20.12

pima 0.07 2.51 3.42 3.58 4.91 2.51 3.97 4.22 4.24 2.37 2.38

sat 0.78 27.74 54.83 53.29 102.84 9.62 116.83 21.24 11.66 10.08 13.96

wdbc 0.06 2.05 2.53 2.42 3.44 1.70 2.62 3.03 2.47 1.93 2.63

wpbc 0.06 1.97 2.04 1.85 2.26 1.00 2.29 2.01 1.17 1.27 1.50

vehicle 0.13 4.54 5.82 5.67 7.63 2.90 6.58 5.90 4.28 3.17 3.69

housing 0.08 2.17 2.66 2.92 3.84 1.32 3.35 2.42 1.89 1.15 0.77

mf-morph 0.06 2.06 5.69 5.78 11.62 1.08 11.22 2.34 1.60 1.17 2.57

mf-zernike 0.50 17.47 24.74 23.28 35.65 5.01 37.47 10.26 5.39 4.91 11.81

avg. 0.20 6.95 19.83 18.63 94.18 3.45 52.47 7.31 4.65 3.50 5.50

Both Ada and Asym use |P| + |N| examples. Since |N| > |P|, these methods are slower than Under. When the imbalance level is high, these methods have much longer training time than that of Under and the proposed methods.

In our experiments, SMOTE uses either 4|P| or 2|P|+ |N| examples. SMB uses 2|P| + |N| examples. And both of them require to compute the distance between minority class examples. Thus they are much slower than Under and the proposed methods.

Over uses 2|N| examples, which has the largest training set. SMB and Over are the most expensive ones. For data sets with a large number of examples, e.g. letter, the time to train a over-sampled or SMOTEBoost classifier is too long to be practical.

CART uses |P| + |N| examples. CART trains only one classifier, so it indicates the time baseline.

Running times of these methods are recorded in Table III, on a computer with a 3.0GHz Intel Xeon CPU. It shows that Chan, Easy and Cascade are as efficient as Under. The most expensive ones are SMB and Over, followed by Ada and Asym, and then by SMOTE.

D. Results and Analyses

The average AUC of the compared methods are sum-marized in Table IV and Table V. On car, ionosphere, letter, phoneme, sat and wdbc, Ada achieves very high AUC values, which are all greater than 0.95. Applying class-imbalance learning methods on these data sets is not necessarily beneficial. On the other 10 data sets, Ada’s AUC values are not high and these data sets seem suffer from class-imbalance problem. Therefore, we divide the 16 data sets into two groups. The first group contains 6 ‘easy’ tasks, on which the AUC values of Ada are greater than 0.95. The second group contains

10 ‘hard’ tasks, on which the AUC values of Ada are lower than 0.95. The AUC results are shown separately in Table IV and V3. The results of t-test (significance level 0.05) of AUC are also shown separately in the upper and lower triangles in Table VI. The average F-measure of the compared methods are summarized in Table VII and VIII, and the t-test result is shown in Table IX. The average G-mean of the compared methods are summarized in Table X and XI, and the t-test result is shown in Table XII.

The results show that on ‘easy’ tasks, all class-imbalance learning methods have lower AUC and F-measure than Ada, except that Asym has similar AUC and F-measure to it. While on ‘hard’ tasks, class-imbalance learning methods generally have higher AUC and F-measure than Ada, including SMOTE, Chan, Cascade and Easy. We argue that for tasks on which ordinary methods can achieve high AUC (e.g.  0.95), class-imbalance learning is generally not helpful with AUC and F-measure. However, Easy and Cascade can be used to reduce the training time, while their average AUC are close to that of Ada and Asym.

We are more interested in the results on ‘hard’ tasks, where class-imbalance learning really helps. Compared with the results on ‘easy’ tasks, they reveal more proper-ties of class-imbalance learning and the proposed meth-ods.

Under is not performing well with AUC and F-measure. Its AUC and F-measure are lower than Ada and Asym on all ‘easy’ tasks, and lower than many other class-imbalance learning methods on ‘hard’ tasks. Our conjecture is that this is due to the information

3Note that the performance of Over and SMB on the data sets in the former group has not been obtained due to its large training time costs. CART gives discrete outputs, so its AUC is not available.

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TABLE IV

AUC OF THE COMPARED METHODS (PART 1). THIS TABLE SHOWS RESULTS FOR DATA SETS ON WHICH ADABOOST’S AUC IS HIGHER

THAN 0.95. FOR EACH METHOD AND EACH DATA SET, THE AVERAGE AUC IS FOLLOWED BY A STANDARD DEVIATION. THE COLUMN

AVG. SHOWS THE AVERAGE AUC OF EACH METHOD.

AUC car ionosphere letter phoneme sat wdbc avg.

Bagg .995 ±.000 .962 ±.004 .997 ±.001 .955 ±.001 .946 ±.001 .987 ±.001 .974 ±.020

Ada .998 ±.000 .978 ±.003 1.000 ±.000 .965 ±.000 .953 ± .001 .994 ±.001 .981±.018

Asym .998 ±.000 .979 ±.002 1.000 ±.000 .965 ±.001 .953 ± .001 .994 ±.000 .982 ±.018

Under .989 ±.001 .973 ±.002 1.000 ±.000 .953 ±.001 .941±.001 .993 ±.001 .975 ±.021

SMOTE .995 ±.000 .978 ±.002 1.000 ±.000 .964 ±.000 .946 ±.001 .994 ±.001 .979 ±.019

Chan .996 ±.000 .979 ±.002 1.000 ±.000 .960 ±.000 .955 ± .000 .993 ±.000 .980 ±.018

Cascade .996 ±.000 .976 ±.002 1.000 ±.000 .962 ±.000 .949 ±.001 .994 ±.000 .979 ±.019

Easy .994 ±.000 .974 ±.002 1.000 ±.000 .958 ±.000 .947 ±.000 .993 ±.000 .978 ±.020

RF .784 ±.003 .981 ±.004 1.000 ±.000 .965 ±.001 .961 ± .002 .991 ±.000 .947 ±.074

BRF .749 ±.004 .969 ±.003 .999 ±.000 .960 ±.001 .952 ± .001 .990 ±.001 .937 ±.085

Under-RF .786 ±.001 .976 ±.002 1.000 ±.000 .952 ±.001 .953 ± .000 .991 ±.001 .943 ±.072

Over-RF .785 ±.002 .981 ±.001 1.000 ±.000 .964 ±.001 .962 ± .001 .991 ±.001 .947 ±.074

TABLE V

AUC OF THE COMPARED METHODS (PART 2). THIS TABLE SHOWS RESULTS FOR DATA SETS ON WHICH ADABOOST’S AUC IS LOWER

THAN 0.95.

AUC abalone balance cmc haberman housing

Bagg .824 ± .002 .439 ±.018 .705 ±.004 .669 ±.014 .825 ±.011

Ada .811±.001 .616 ±.009 .675 ±.008 .641 ±.015 .815 ±.010

Asym .812 ± .003 .619 ±.012 .675 ±.010 .639 ±.015 .815 ±.010

SMB .818 ± .002 .599 ±.010 .687±.011 .646 ±.006 .824 ±.014

Under .830 ± .002 .617 ±.011 .671 ±.007 .646 ±.010 .805 ±.007

Over .817 ±.002 .540 ±.010 .675 ±.008 .637 ±.017 .821 ±.010

SMOTE .831±.001 .617 ±.015 .680 ±.008 .647 ±.017 .816 ±.008

Chan .850 ± .001 .652 ±.011 .696 ±.006 .638 ±.008 .811 ±.010

Cascade .828 ± .002 .637 ±.011 .686 ±.007 .653 ±.012 .808 ±.008

Easy .847 ±.002 .633 ±.008 .704 ±.008 .668 ±.011 .825 ±.008

RF .827 ±.004 .435 ±.029 .669 ±.007 .645 ±.021 .828 ±.015

BRF .853 ± .001 .558 ±.013 .683 ±.003 .677 ±.013 .798 ±.018

Under-RF .842 ± .002 .593 ±.014 .676 ±.002 .643 ±.009 .820 ±.010

Over-RF .823 ± .001 .458 ±.014 .660 ±.005 .641 ±.014 .826 ±.014

AUC mf-morph mf-zernike pima vehicle wpbc avg.

Bagg .887 ±.004 .855 ±.002 .821 ±.003 .859 ±.003 .688 ±.009 .757 ±.129

Ada .888 ± .002 .795 ±.003 .788 ±.006 .854 ±.003 .716 ±.009 .760 ±.088

Asym .888 ±.001 .801 ±.005 .788 ±.005 .853 ±.002 .721 ±.012 .761 ±.088

SMB .897 ±.002 .788 ±.007 .790 ±.003 .864 ±.003 .720 ±.013 .763 ±.092

Under .916 ±.001 .881 ±.003 .789 ±.002 .846 ±.003 .694 ±.010 .769 ±.100

Over .889 ± .002 .779 ±.007 .791 ±.004 .855 ±.003 .711 ±.010 .751±.103

SMOTE .912 ±.001 .862 ±.004 .792 ±.003 .858 ±.004 .709 ±.004 .772 ±.097

Chan .912 ± .002 .903 ±.002 .786 ±.007 .856 ±.002 .706 ±.009 .781 ±.097

Cascade .905 ± .001 .891 ±.001 .799 ±.005 .856 ±.002 .712 ±.011 .778 ±.093

Easy .918 ± .002 .904 ±.001 .809 ±.004 .859 ±.004 .707 ±.009 .787 ±.096

RF .880 ±.007 .840 ±.008 .821 ±.004 .869 ±.008 .677 ±.030 .749 ±.133

BRF .901 ± .002 .866 ±.009 .809 ±.003 .850 ±.002 .646 ±.014 .764 ±.109

Under-RF .919 ±.003 .889 ±.002 .818 ±.004 .855 ±.002 .661 ±.008 .772 ±.110

Over-RF .881 ±.004 .854 ±.003 .819 ±.004 .866 ±.003 .670 ±.010 .750 ±.130

TABLE VI

SUMMARY OF t-TEST OF AUC WITH SIGNIFICANCE LEVEL AT 0.05. THE UPPER TRIANGLE SHOWS THE RESULT OF 6 ‘EASY’ TASKS

AND THE LOWER TRIANGLE SHOWS THE RESULT OF 10 ‘HARD’ TASKS. EACH TABULAR SHOWS THE AMOUNT OF WIN-TIE-LOSE OF A

METHOD IN A ROW COMPARING WITH THE METHOD IN A COLUMN.

Bagg Ada Asym SMB Under Over SMOTE Chan Cascade Easy RF BRF Under-RF Over-RF

Bagg – 0-0-6 0-0-6 NA 3-0-3 NA 0-2-4 0-0-6 0-0-6 1-1-4 1-0-5 1-0-5 2-0-4 1-0-5

Ada 2-1-7 – 0-5-1 NA 6-0-0 NA 4-2-0 4-0-2 5-1-0 5-1-0 3-2-1 5-1-0 5-1-0 3-1-2

Asym 2-1-7 1-9-0 – NA 6-0-0 NA 4-2-0 4-1-1 5-1-0 5-1-0 3-2-1 6-0-0 6-0-0 3-1-2

SMB 4-1-5 5-3-2 5-3-2 – NA NA NA NA NA NA NA NA NA NA

Under 5-0-5 3-4-3 3-3-4 4-2-4 – NA 0-0-6 1-1-4 0-0-6 0-2-4 2-0-4 4-0-2 3-1-2 2-0-4

Over 2-1-7 3-5-2 4-4-2 0-5-5 4-2-4 – NA NA NA NA NA NA NA NA

SMOTE 5-1-4 6-4-0 6-4-0 3-5-2 4-4-2 5-5-0 – 3-1-2 2-1-3 4-1-1 2-2-2 5-0-1 5-0-1 2-1-3

Chan 5-1-4 5-4-1 6-2-2 5-0-5 6-2-2 5-3-2 4-5-1 – 3-0-3 4-1-1 2-1-3 5-1-0 5-0-1 2-1-3

Cascade 5-1-4 7-2-1 8-1-1 5-3-2 7-2-1 7-2-1 4-3-3 2-3-5 – 6-0-0 2-0-4 5-0-1 4-1-1 2-0-4

Easy 5-4-1 9-1-0 8-2-0 7-2-1 9-1-0 8-2-0 8-2-0 6-2-2 8-2-0 – 2-0-4 4-0-2 4-0-2 2-0-4

RF 1-5-4 5-2-3 5-1-4 3-3-4 3-3-4 5-1-4 3-3-4 3-2-5 3-2-5 2-2-6 – 5-1-0 4-2-0 1-3-2

BRF 5-0-5 6-0-4 6-0-4 5-1-4 5-1-4 7-0-3 3-2-5 3-0-7 3-2-5 2-1-7 6-0-4 – 1-2-3 0-1-5

Under-RF 4-0-6 5-3-2 5-3-2 4-3-3 6-1-3 5-4-1 4-4-2 4-1-5 4-1-5 1-1-8 5-3-2 6-1-3 – 0-2-4

Over-RF 2-3-5 5-1-4 5-1-4 3-3-4 3-1-6 5-1-4 3-1-6 3-1-6 3-0-7 2-1-7 1-7-2 4-0-6 2-3-5 –

IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS – PART B 9

TABLE VII

F-MEASURE OF THE COMPARED METHODS ON ‘EASY’ TASKS (PART 1).

F Measure car ionosphere letter phoneme sat wdbc avg.

CART .857±.011 .831 ±.024 .945 ±.005 .773 ±.007 .546 ±.014 .895 ±.004 .808 ±.128

Bagg .933 ±.004 .883 ±.005 .962 ±.003 .834 ± .002 .641 ±.007 .938 ±.004 .865 ±.109

Ada .967 ±.002 .907 ±.004 .988 ±.002 .850 ± .002 .664 ±.006 .956 ±.003 .889 ±.110

Asym .966 ±.002 .910 ±.004 .987 ±.001 .852 ± .002 .668 ±.004 .956 ±.003 .890 ±.109

Under .884 ±.001 .900 ±.004 .903 ±.002 .819 ±.001 .546 ±.002 .952 ±.002 .834 ±.134

SMOTE .930 ±.004 .907 ±.003 .954 ±.002 .847 ±.002 .610 ±.003 .957 ±.003 .867 ±.121

Chan .916 ±.003 .910 ±.006 .905 ±.001 .837 ±.002 .607 ±.001 .954 ±.002 .855 ±.116

Cascade .917 ±.002 .905 ±.003 .976 ±.002 .839 ± .002 .619 ±.002 .957 ±.002 .869 ±.120

Easy .880 ±.002 .901 ±.005 .910 ±.002 .821 ± .002 .554 ±.001 .951 ±.004 .836 ±.132

RF .307 ±.013 .906 ±.005 .979 ±.003 .850 ± .004 .666 ±.008 .954 ±.002 .777 ±.234

BRF .521±.001 .887 ±.004 .889 ±.016 .821 ± .003 .553 ±.001 .945 ±.005 .769 ±.168

Under-RF .513 ±.001 .895 ±.005 .895 ±.003 .813 ±.001 .557 ±.001 .948 ±.002 .770 ±.171

Over-RF .518 ±.001 .904 ±.004 .986 ±.001 .851 ± .002 .689 ±.004 .955 ±.003 .817 ±.164

TABLE VIII

F-MEASURE OF THE COMPARED METHODS ON ‘HARD’ TASKS (PART 2).

F Measure abalone balance cmc haberman housing

CART .232 ±.018 .000 ±.000 .356 ± .009 .335 ±.046 .420 ±.031

Bagg .170 ±.010 .000 ±.000 .362 ±.011 .334 ±.030 .419 ±.029

Ada .210 ±.008 .000 ±.000 .388 ±.009 .348 ±.022 .475 ±.022

Asym .222 ±.006 .000 ±.001 .400 ±.011 .360 ±.020 .485 ±.015

SMB .286 ±.008 .001±.001 .393 ±.013 .377 ±.024 .530 ±.016

Under .367 ±.001 .175 ±.009 .429 ±.007 .442 ±.017 .529 ±.006

Over .195 ±.005 .000 ±.000 .383 ±.011 .338 ±.024 .470 ±.016

SMOTE .379 ±.005 .149 ±.011 .421 ±.007 .405 ±.016 .532 ±.017

Chan .400 ±.002 .156 ±.005 .437 ±.007 .380 ±.018 .523 ±.010

Cascade .384 ±.002 .194 ±.011 .436 ±.009 .438 ±.014 .529 ±.008

Easy .382 ±.003 .184 ±.007 .454 ± .008 .466 ±.013 .543 ±.007

RF .189 ±.015 .000 ±.000 .347 ±.017 .321 ±.027 .445 ±.035

BRF .382 ±.002 .167 ±.006 .441 ±.004 .468 ±.015 .515 ±.018

Under-RF .375 ±.002 .168 ±.007 .435 ± .003 .445 ±.011 .537 ±.006

Over-RF .253 ±.004 .000 ±.000 .408 ±.008 .348 ±.015 .490 ±.025

F Measure mf-morph mf-zernike pima vehicle wpbc avg.

CART .251 ±.022 .216 ±.015 .584 ± .029 .523 ±.019 .373 ±.023 .329 ±.158

Bagg .263 ±.016 .183 ±.014 .644 ±.007 .526 ±.011 .410 ±.019 .331 ±.177

Ada .321 ±.014 .188 ±.017 .611±.007 .545 ±.010 .432 ±.014 .352 ±.173

Asym .344 ±.015 .191±.010 .613 ±.011 .561 ±.008 .444 ±.015 .362 ±.175

SMB .351 ±.013 .295 ±.018 .641 ±.006 .606 ±.012 .452 ±.011 .393 ±.175

Under .579 ±.004 .538 ±.004 .644 ± .002 .623 ±.005 .449 ±.008 .477 ±.132

Over .319 ±.012 .166 ±.011 .609 ±.009 .539 ±.017 .427 ±.010 .345 ±.175

SMOTE .560 ±.005 .538 ±.007 .627 ±.004 .615 ±.006 .459 ±.009 .468 ±.134

Chan .635 ±.001 .577 ±.002 .618 ±.006 .608 ±.003 .448 ±.018 .478 ±.140

Cascade .596 ±.006 .549 ±.004 .649 ±.007 .623 ±.012 .454 ±.007 .485 ±.128

Easy .624 ±.002 .564 ±.002 .660 ± .005 .638 ±.007 .452 ±.014 .497 ±.136

RF .261 ±.023 .144 ±.034 .641 ±.013 .544 ±.024 .393 ±.027 .328 ±.181

BRF .627 ±.003 .500 ±.013 .663 ± .005 .633 ±.007 .401 ±.006 .480 ±.140

Under-RF .602 ±.004 .530 ±.004 .668 ±.006 .633 ±.007 .419 ±.008 .481 ±.140

Over-RF .349 ±.014 .292 ±.012 .656 ± .005 .564 ±.015 .397 ±.019 .376 ±.171

TABLE IX

SUMMARY OF t-TEST OF F-MEASURE WITH SIGNIFICANCE LEVEL AT 0.05. THE UPPER TRIANGLE SHOWS THE RESULT OF 6 ‘EASY’

TASKS AND THE LOWER TRIANGLE SHOWS THE RESULT OF 10 ‘HARD’ TASKS. EACH TABULAR SHOWS THE AMOUNT OF WIN-TIE-LOSE

OF A METHOD IN A ROW COMPARING WITH THE METHOD IN A COLUMN.

Cart Bagg Ada Asym SMB Under Over SMOTE Chan Cascade Easy RF BRF Under-RF Over-RF

CART – 0-0-6 0-0-6 0-0-6 NA 1-1-4 NA 0-0-6 1-0-5 0-0-6 1-1-4 1-0-5 2-1-3 2-1-3 1-0-5

Bagg 2-6-2 – 0-0-6 0-0-6 NA 4-0-2 NA 3-0-3 3-0-3 2-0-4 4-0-2 1-0-5 4-1-1 4-0-2 1-0-5

Ada 6-2-2 7-2-1 – 0-4-2 NA 6-0-0 NA 4-2-0 4-2-0 5-1-0 6-0-0 2-4-0 6-0-0 6-0-0 3-2-1

Asym 5-4-1 7-2-1 5-5-0 – NA 6-0-0 NA 4-2-0 5-1-0 5-1-0 6-0-0 2-4-0 6-0-0 6-0-0 3-2-1

SMB 9-1-0 8-2-0 8-2-0 7-3-0 – NA NA NA NA NA NA NA NA NA NA

Under 10-0-0 9-1-0 10-0-0 9-1-0 7-3-0 – NA 0-0-6 0-1-5 0-0-6 1-2-3 1-2-3 3-2-1 5-0-1 1-0-5

Over 4-4-2 5-3-2 0-6-4 0-2-8 0-1-9 0-0-10 – NA NA NA NA NA NA NA NA

SMOTE 10-0-0 9-0-1 10-0-0 9-1-0 7-2-1 1-3-6 10-0-0 – 5-1-0 2-2-2 6-0-0 2-2-2 6-0-0 6-0-0 1-2-3

Chan 10-0-0 9-0-1 9-1-0 9-1-0 5-4-1 4-1-5 10-0-0 4-2-4 – 1-2-3 4-1-1 1-2-3 6-0-0 6-0-0 2-1-3

Cascade 10-0-0 9-1-0 10-0-0 9-1-0 8-2-0 5-5-0 10-0-0 8-2-0 4-3-3 – 6-0-0 1-2-3 6-0-0 6-0-0 2-1-3

Easy 10-0-0 10-0-0 10-0-0 9-1-0 9-1-0 9-1-0 10-0-0 8-2-0 6-1-3 7-2-1 – 1-2-3 3-3-0 4-1-1 1-0-5

RF 1-7-2 3-6-1 1-2-7 1-2-7 0-2-8 0-1-9 1-5-4 1-0-9 1-0-9 0-1-9 0-0-10 – 5-0-1 5-0-1 0-3-3

BRF 10-0-0 9-1-0 9-0-1 9-0-1 8-0-2 6-1-3 9-0-1 6-1-3 4-2-4 4-1-5 0-5-5 9-1-0 – 2-2-2 1-0-5

Under-RF 10-0-0 9-1-0 9-0-1 9-0-1 8-1-1 6-2-2 9-1-0 6-2-2 5-1-4 5-1-4 1-0-9 10-0-0 3-3-4 – 0-0-6

Over-RF 7-3-0 7-3-0 7-2-1 5-3-2 2-3-5 1-0-9 7-2-1 1-0-9 1-0-9 1-0-9 0-0-10 8-2-0 0-1-9 0-1-9 –

IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS – PART B 10

TABLE X

G-MEAN OF THE COMPARED METHODS ON ‘EASY’ TASKS (PART 1). THE ROW AVG. SHOWS THE AVERAGE G-MEAN OF EACH METHOD.

G-mean car ionosphere letter phoneme sat wdbc avg.

CART .910 ±.011 .867 ±.021 .968 ±.003 .836 ±.006 .716 ±.009 .918 ±.004 .869 ±.080

Bagg .964 ±.002 .906 ±.003 .972 ±.002 .880 ±.001 .729 ±.005 .950 ±.003 .900 ±.083

Ada .980 ±.001 .920 ± .003 .989 ±.002 .890 ±.001 .754 ±.005 .963 ±.003 .916 ±.080

Asym .981 ±.001 .922 ± .003 .988 ±.001 .892 ±.002 .761 ±.003 .963 ±.002 .918 ±.078

Under .956 ±.001 .918 ±.003 .994 ±.000 .889 ±.001 .871 ±.002 .963 ±.001 .932 ± .043

SMOTE .969 ±.002 .922 ± .002 .995 ±.001 .899 ±.001 .862 ±.003 .964 ±.003 .935 ± .046

Chan .970 ±.001 .923 ± .005 .992 ±.001 .897 ±.001 .881 ±.001 .962 ±.002 .937 ±.040

Cascade .969 ±.001 .921 ± .002 .996 ±.001 .897 ±.001 .867 ±.002 .967 ±.002 .936 ± .045

Easy .958 ±.001 .919 ±.003 .995 ±.000 .892 ±.001 .876 ±.001 .962 ±.003 .934 ± .042

RF .452 ±.013 .918 ± .005 .980 ±.002 .892 ±.003 .744 ±.006 .962 ±.003 .825 ± .183

BRF .693 ±.001 .911±.004 .989 ±.002 .893 ±.002 .881 ±.001 .957 ±.004 .887 ±.095

Under-RF .687 ±.001 .916 ±.003 .993 ±.001 .887 ±.001 .883 ±.000 .960 ±.002 .888 ±.098

Over-RF .690 ±.001 .918 ± .002 .987 ±.001 .897 ±.001 .782 ±.003 .963 ±.003 .873 ±.104

TABLE XI

G-MEAN OF THE COMPARED METHODS ON ‘HARD’ TASKS (PART 2).

G-mean abalone balance cmc haberman housing

CART .453 ±.021 .000 ±.000 .525 ±.008 .483 ± .045 .586 ±.026

Bagg .337±.011 .000 ±.000 .509 ±.010 .476 ±.036 .553 ±.032

Ada .396 ±.008 .001 ±.002 .561 ±.007 .502 ± .025 .615 ±.017

Asym .412 ±.007 .002 ±.004 .577 ±.010 .515 ± .023 .627 ±.011

SMB .511±.010 .002 ±.004 .560 ±.011 .536 ± .022 .686 ±.013

Under .765 ±.003 .560 ±.020 .623 ±.007 .592 ± .018 .725 ±.005

Over .372 ±.005 .000 ±.000 .555 ±.009 .491 ± .028 .607 ±.010

SMOTE .742 ±.006 .465 ±.027 .605 ±.006 .562 ± .016 .710 ±.014

Chan .778 ±.001 .465 ±.011 .622 ±.006 .536 ± .020 .698 ±.007

Cascade .755 ±.001 .595 ±.021 .628 ±.008 .591±.013 .721 ±.007

Easy .785 ±.004 .577 ±.015 .646 ±.007 .615 ±.012 .739 ±.006

RF .363 ±.016 .000 ±.000 .516 ±.015 .476 ±.028 .580 ±.031

BRF .790 ±.003 .548 ±.012 .634 ±.004 .618 ±.014 .718 ±.018

Under-RF .778 ±.002 .548 ±.015 .627 ±.003 .593 ±.011 .735 ±.005

Over-RF .457 ±.004 .000 ±.000 .587 ±.006 .504 ± .016 .638 ±.019

G-mean mf-morph mf-zernike pima vehicle wpbc avg.

CART .473 ±.022 .428 ±.020 .673 ±.024 .658 ± .013 .513 ±.032 .479 ±.178

Bagg .483 ±.016 .378 ±.021 .720 ±.006 .642 ± .008 .510 ±.032 .461±.187

Ada .560 ±.012 .386 ±.020 .694 ±.006 .664 ±.008 .537 ±.025 .492 ±.189

Asym .594 ±.014 .392 ±.013 .696 ±.009 .679 ±.007 .549 ±.028 .504 ±.193

SMB .605 ±.013 .524 ±.019 .719 ±.006 .728 ±.009 .584 ±.021 .545 ±.196

Under .873 ±.003 .848 ±.004 .719 ±.001 .768 ±.004 .617 ±.008 .709 ±.102

Over .559 ±.012 .358 ±.015 .692 ±.007 .657 ±.013 .527 ±.013 .482 ±.191

SMOTE .841 ±.006 .813 ±.007 .708 ±.003 .743 ±.005 .610 ±.009 .680 ±.111

Chan .920 ±.001 .854 ±.002 .700 ±.005 .738 ±.004 .585 ±.021 .690 ±.134

Cascade .874 ±.006 .820 ±.003 .725 ±.005 .760 ±.011 .623 ±.007 .709 ±.092

Easy .914 ±.001 .869 ±.003 .734 ±.004 .781 ±.005 .623 ±.014 .728 ±.107

RF .479 ±.022 .326 ±.049 .717 ±.010 .659 ± .018 .477 ±.019 .459 ±.190

BRF .918 ±.002 .831 ±.007 .735 ±.004 .780 ±.007 .567 ±.007 .714 ±.114

Under-RF .888 ±.005 .844 ±.002 .740 ±.005 .779 ±.006 .588 ±.011 .712 ±.111

Over-RF .597 ±.013 .519 ±.016 .731 ±.004 .689 ±.013 .494 ±.022 .522 ±.193

TABLE XII

SUMMARY OF t-TEST OF G-MEAN WITH SIGNIFICANCE LEVEL AT 0.05. THE UPPER TRIANGLE SHOWS THE RESULT OF 6 ‘EASY’ TASKS

AND THE LOWER TRIANGLE SHOWS THE RESULT OF 10 ‘HARD’ TASKS. EACH TABULAR SHOWS THE AMOUNT OF WIN-TIE-LOSE OF A

METHOD IN A ROW COMPARING WITH THE METHOD IN A COLUMN.

CART Bagg Ada Asym SMB Under Over SMOTE Chan Cascade Easy RF BRF Under-RF Over-RF

CART – 0-0-6 0-0-6 0-0-6 NA 0-0-6 NA 0-0-6 0-0-6 0-0-6 0-0-6 1-0-5 1-0-5 1-0-5 1-0-5

Bagg 1-7-2 – 0-0-6 0-0-6 NA 1-0-5 NA 0-0-6 0-0-6 0-0-6 1-0-5 1-0-5 1-1-4 1-0-5 1-0-5

Ada 5-3-2 7-2-1 – 0-3-3 NA 2-2-2 NA 1-2-3 1-2-3 1-1-4 1-2-3 3-3-0 3-1-2 4-0-2 3-1-2

Asym 6-2-2 7-2-1 6-4-0 – NA 3-1-2 NA 1-1-4 1-2-3 1-1-4 2-2-2 3-3-0 3-1-2 4-0-2 3-1-2

SMB 9-1-0 8-2-0 8-2-0 8-1-1 – NA NA NA NA NA NA NA NA NA NA

Under 10-0-0 9-1-0 10-0-0 10-0-0 9-1-0 – NA 1-2-3 1-1-4 1-0-5 0-3-3 3-2-1 4-0-2 5-0-1 3-2-1

Over 3-5-2 5-3-2 0-5-5 0-2-8 0-2-8 0-0-10 – NA NA NA NA NA NA NA NA

SMOTE 10-0-0 9-0-1 10-0-0 10-0-0 9-0-1 0-1-9 10-0-0 – 3-2-1 1-3-2 2-3-1 5-1-0 5-0-1 5-0-1 5-1-0

Chan 10-0-0 9-0-1 10-0-0 10-0-0 7-2-1 3-1-6 10-0-0 4-1-5 – 1-3-2 4-1-1 4-2-0 5-1-0 4-1-1 4-2-0

Cascade 10-0-0 9-1-0 10-0-0 10-0-0 10-0-0 2-5-3 10-0-0 10-0-0 7-0-3 – 4-1-1 5-1-0 5-0-1 5-0-1 5-1-0

Easy 10-0-0 10-0-0 10-0-0 10-0-0 10-0-0 9-1-0 10-0-0 10-0-0 9-0-1 8-2-0 – 3-3-0 3-1-2 4-1-1 3-2-1

RF 1-6-3 2-6-2 1-2-7 1-1-8 0-2-8 0-1-9 1-5-4 1-0-9 1-0-9 0-0-10 0-0-10 – 2-1-3 1-2-3 0-2-4

BRF 10-0-0 10-0-0 10-0-0 9-1-0 9-1-0 6-2-2 10-0-0 9-0-1 7-1-2 6-2-2 2-3-5 10-0-0 – 2-1-3 2-1-3

Under-RF 10-0-0 10-0-0 10-0-0 10-0-0 9-1-0 5-3-2 10-0-0 9-0-1 6-2-2 6-2-2 1-1-8 10-0-0 3-3-4 – 2-2-2

Over-RF 6-4-0 8-2-0 7-2-1 6-3-1 2-2-6 1-0-9 7-2-1 1-0-9 1-0-9 1-0-9 0-0-10 8-2-0 0-1-9 0-0-10 –

IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS – PART B 11

contained in the majority class which is ignored by Under. Both our proposed methods can improve upon Under, no matter on ‘easy’ tasks or ‘hard’ tasks. This result supports our argument that Easy and Cascade can effectively explore the majority class examples.

Chan uses all the majority class examples, and it generally has higher AUC and F-measure than Under. But the results show that on ‘hard’ tasks, its AUC, F-measure and G-mean are comparable to or slightly lower than Cascade, and they are lower than Easy on most of the data sets. This implies that using all majority class examples is not necessary. In particular, when the data set is highly imbalanced, Chan will consume a lot of time.

Both Easy and Cascade attain higher average AUC, F-measure and G-mean than almost all the other methods on ‘hard’ tasks, except that Cascade is comparable to Chan with AUC and F-measure, and slightly worse than BRF and Under-RF with G-mean. But Chan has much lower G-mean, and, BRF & Under-RF have much lower AUC and F-measure than many other class-imbalance learning methods. While both Easy and Cascade are very robust with different performance measures.

Easy and Cascade can not only improve the AUC scores, but also reduce the training time. They require approximately the same training time as Under, and are faster than other methods. Considering both classification performance and training time, they are better than all other compared methods.

The results on ‘hard’ tasks show that Cascade is inferior to Easy. The way Cascade explores the majority class examples might be responsible for this observation. In Cascade, the majority training set of Hi+1 is produced by Hi. Such a supervised, cascading way of sampling might suffer from overfitting. In other words, the correctly predicted majority class examples that have been filtered out may be useful [27]. In particular, some examples that are deemed redundant and discarded in earlier rounds may be helpful in some later rounds, after some other examples have been discarded. Note that there are also situations in which Cascade is preferred. From the results on “easy” tasks we can see that Cascade has higher AUC, F-measure and G-mean than Easy on almost all data sets. This suggests that Cascade can focus on more useful data. In addition, note that Cascade is more favorable than Easy on data set balance and wpbc. Both of these data sets have a very small minority class. In fact, if the number of examples in a class is very small, there is a significant chance that the examples will scatter around broadly. It is difficult to get a representative subset by using under-sampling

alone. Focusing on more informative examples may be particularly helpful in this case. Also, Cascade is more suitable for highly imbalanced problems. For example, in the face detection problem described in [41], there are 5000 positive examples and 2284 million negative ones. The independent random sampling strategy of Easy requires T, the number of subsets, to be very large in order to catch all the information in N. Furthermore, the number of subsets is hard to decide since no prior information is available. Thus, Easy is computationally infeasible for this problem. But for Cascade, it is much easier to set the iteration number since it is reasonable to set fp rate around 0.5. So, T = 20 is sufficient for the face detection problem, since log2(2.284 × 109/5000)  19 (assuming a false positive rate of 0.5).

E. Analysis of the Ensemble Strategy

As stated above, since minority class examples are used to train each weak classifier in the proposed method, stacking these classifiers may cause overfitting when the number of minority class examples is limited. To verify this, the 16 data sets in Table II were used to compare stacking with the ensemble strategy used in Easy and Cascade.

The AUC values are summarized in Table XIII. Similar to the experiments in the previous subsection, the 16 data sets are divided into groups based on the performance of AdaBoost. When Cascade is used on ‘easy’ tasks, stacking is inferior to the original ensemble strategy on 3 out of 6 data sets, while it is superior on only one data set. However, the difference between the two strategies is small. The same observation holds for Easy. On ‘hard’ tasks, the performance of Cascade dominates that of stacking on all data sets. As for Easy, there is only one data set on which stacking is bet¬ter. Generally speaking, there are significant differences between the performance of stacking and the current ensemble strategy used in our proposed methods.

Therefore, stacking is not very suitable for the case when minority class examples are used in each weak classifier. In such a case, stacking may cause overfitting. This is probably a major reason for Chan to be inferior to Easy.

F. Additional Remarks

We have the following remarks regarding the results in AUC, F-measure and G-mean on both ‘easy’ and ‘hard’ tasks:

• The proposed methods EasyEnsemble and BalanceCascade are more robust than many other class-imbalance learning methods. When

IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS – PART B 12

TABLE XIII

COMPARISON OF STACKING WITH ENSEMBLE STRATEGY IN BALANCECASCADE AND EAS YEN SEMBLE. THIS TABLE SHOWS AUC’S OF

THE COMPARED METHODS. THE FIRST GROUP DATA SETS IS ‘EASY’ TASKS, AND THE SECOND GROUP IS ‘HARD’ TASKS. THE ROW avg1.

SHOWS THE AVERAGE AUC OF EACH METHOD ON ‘EASY’ TASKS. THE ROW avg2. SHOWS THE AVERAGE AUC ON ‘HARD’ TASKS. THE

ROW avg. SHOWS THE OVERALL AVERAGE AUC. TABULAR IN BOLD DENOTES THE SUPERIOR ENSEMBLE STRATEGY BETWEEN THE

ORIGINAL ONE AND STACKING.

Data Set BalanceCascade EasyEnsemble

original stacking original stacking

car 0.996 f 0.000 0.997 f 0.000 0.994 f 0.000 0.995 f 0.000

letter 1.000 f 0.000 1.000 f 0.000 1.000 f 0.000 1.000 f 0.000

ionosphere 0.976 f 0.002 0.976 f 0.002 0.974 f 0.002 0.974 f 0.002

phoneme 0.962 f 0.000 0.960 f 0.000 0.958 f 0.000 0.957 f 0.000

sat 0.949 f 0.001 0.944 f 0.001 0.947 f 0.000 0.946 f 0.001

wdbc 0.994 f 0.000 0.992 f 0.001 0.993 f 0.000 0.992 f 0.001

avg1. 0.979 f 0.018 0.978 f 0.019 0.978 f 0.018 0.977 f 0.019

abalone 0.828 f 0.002 0.802 f 0.002 0.847 f 0.002 0.844 f 0.002

balance 0.637 f 0.011 0.631 f 0.008 0.633 f 0.008 0.640 f 0.012

cmc 0.686 f 0.007 0.679 f 0.006 0.704 f 0.008 0.698 f 0.009

haberman 0.653 f 0.012 0.637 f 0.013 0.668 f 0.011 0.647 f 0.011

housing 0.809 f 0.008 0.800 f 0.009 0.827 f 0.005 0.811 f 0.013

mf-morph 0.904 f 0.002 0.903 f 0.002 0.917 f 0.001 0.916 f 0.002

mf-zernike 0.890 f 0.002 0.864 f 0.003 0.904 f 0.002 0.901 f 0.001

pima 0.799 f 0.005 0.792 f 0.005 0.809 f 0.004 0.802 f 0.004

vehicle 0.856 f 0.002 0.848 f 0.002 0.860 f 0.001 0.857 f 0.004

wpbc 0.712 f 0.011 0.707 f 0.009 0.707 f 0.009 0.705 f 0.012

avg2. 0.778 f 0.089 0.766 f 0.087 0.788 f 0.092 0.782 f 0.092

avg. 0.853 f 0.119 0.846 f 0.122 0.859 f 0.116 0.855 f 0.119

class-imbalance is not harmful, they don’t cause serious degeneration of performance. When class-imbalance is indeed harmful, they are better than almost all other methods we have compared with.

. Class-imbalance is not harmful for some tasks and applying class-imbalance learning methods in such cases may lead to performance degeneration. A con-sequence of this observation is that, class-imbalance learning methods should only be applied to tasks which suffer from class imbalance. For this purpose, we need to develop some methods to judge whether a task suffers from class imbalance or not, before applying class-imbalance learning methods to it.

. We observed that, on tasks which do not suffer from class-imbalance, AdaBoost and Bagging can improve the performance of decision trees signif-icantly; while on tasks which suffer from class-imbalance, they could not help and sometimes even deteriorate the performance. This might give us some clues on judging whether a task suffers from class imbalance or not, which will be studied in the future.

V. CONCLUSION

This paper extends our preliminary work [26] which proposed two algorithms EasyEnsemble and BalanceCascade for class-imbalance learning. Both algorithms are designed to utilize the majority class examples ignored by under-sampling, while at the same time keeping its fast training speed. Both algorithms

sample multiple subsets of the majority class, train an ensemble from each of these subsets, and combine all weak classifiers in these ensembles into a final output. Both algorithms make better use of the majority class than under-sampling, since multiple subsets contain more information than a single one. The main difference is that EasyEnsemble samples independent subsets, while BalanceCascade uses trained classifiers to guide the sampling process for subsequent classifiers. Both algorithms have approximately the same training time as that of under-sampling when the same number of weak classifiers are used.

Empirical results suggest that for problems on which ordinary methods achieve high AUC (e.g.  0.95), class-imbalance learning is not helpful. However, the pro¬posed methods can be used to reduce training time. For problems where class-imbalance learning methods really help, both EasyEnsemble and BalanceCascade have higher AUC, F-measure and G-mean than almost all other compared methods and the former is superior than the latter. However, since BalanceCascade removes correctly classified majority class examples in each it-eration, it will be more efficient on highly imbalanced data sets. In addition, the comparison of Chan and our proposed methods reveals that, it is not necessary to use all examples in the majority class.

In the current version of the proposed methods, we use the αi,j returned by the weak learner directly. Further improvements are possible by learning αi,j, as shown in [22], [41]. Note that both EasyEnsemble and

IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS – PART B 13

BalanceCascade are ensemble methods. So, while they provide strong generalization ability, they also in-herit the weaknesses of ensemble methods. An apparent weakness is the lack of comprehensibility. Even when the base classifiers are comprehensible symbolic learners, ensembles are still black-boxes. There are some research on this problem [44]–[46] and it is possible to use those research outputs to enhance the comprehensibility of EasyEnsemble and BalanceCascade.

ACKNOWLEDGMENT

The authors want to thank the anonymous review¬ers and the associate editor for helpful comments and suggestions. This research was partially sup¬ported by the National Science Foundation of China (60635030, 60721002), the Jiangsu Science Founda¬tion (BK2008018) and the National High Technol¬ogy Research and Development Program of China (2007AA01Z169).

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[42] Y. Yu, Z.-H. Zhou, and K. M. Ting., “Cocktail ensemble for regression,” in Proceedings of the 7th IEEE International Conference on Data Mining, Omeha, NE, 2007, pp. 721–726.

[43] B. Zadrozny, J. Langford, and N. Abe, “Cost-sensitive learning by cost-proportionate example weighting,” in Proceedings of the 3rd IEEE International Conference on Data Mining, Mel¬bourne, FL, 2003, pp. 435–442.

[44] Z.-H. Zhou and Y. Jiang, “Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble,” IEEE Trans¬actions on Information Technology in Biomedicine, vol. 7, no. 1, pp. 37–42, 2003.

[45] ——, “NeC4.5: Neural ensemble based C4.5,” IEEE Transac¬tions on Knowledge and Data Engineering, vol. 16, no. 6, pp. 770–773, 2004.

[46] Z.-H. Zhou, Y. Jiang, and S.-F. Chen, “Extracting symbolic rules from trained neural network ensembles,” AI Communications, vol. 16, no. 1, pp. 3–15, 2003.

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Xu-Ying Liu received her BSc and MSc de¬gree in computer science from Nanjing Univer¬sity of Aeronautics and Astronautics, China in 2003 and Nanjing University, China in 2006, respectively. Currently she is a PhD candidate in Nanjing University and is a member of the LAMDA Group. Her research interests are in machine learning and data mining, especially in cost-sensitive and class imbalance learning.

Jianxin Wu received the BS degree and MSc degree in computer science, both from Nan¬jing University, China. He is currently a PhD candidate in Georgia Institute of Technology under the supervision of Dr. James M. Rehg. His research interests are computer vision, machine learning, and robotics.

Zhi-Hua Zhou (S’00-M’01-SM’06) received the BSc, MSc and PhD degrees in computer science from Nanjing University, China, in 1996, 1998 and 2000, respectively, all with the highest honors.

He joined the Department of Computer Sci¬ence & Technology at Nanjing University as an assistant professor in 2001, and is currently Cheung Kong Professor and Director of the LAMDA group. His research interests are in artificial intelligence, machine learning, data mining, pattern recognition, information re¬trieval, evolutionary computation, and neural computation. In these areas he has published over 60 papers in leading international journals or conference proceedings.

Dr. Zhou has won various awards/honors including the National Sci¬ence & Technology Award for Young Scholars of China (2006), the Award of National Science Fund for Distinguished Young Scholars of China (2003), the National Excellent Doctoral Dissertation Award of China (2003), the Microsoft Young Professorship Award (2006), etc. He is an Associate Editor of IEEE Transactions on Knowledge and Data Engineering, Associate Editor-in-Chief of Chinese Science Bulletin, and on the editorial boards of Artificial Intelligence in Medicine, Intelligent Data Analysis, Knowledge and Information Sys¬tems, Science in China, etc. He is/was a PAKDD Steering Committee member, Program Committee Chair/Co-Chair of PAKDD’07 and PRICAI’08, Vice Chair/Area Chair of ICDM’06, ICDM’08, etc., Program Committee member of various international conferences including AAAI, ICML, ECML, SIGKDD, ICML, ACM Multimedia, etc., and General Chair/Co-Chair or Program Committee Chair/Co-Chair of a dozen of native conferences. He is a senior member of China Computer Federation (CCF), the Vice Chair of the CCF Artificial Intelligence & Pattern Recognition Society, an Executive Committee member of Chinese Association of Artificial Intelligence (CAAI), the Chair of the CAAI Machine Learning Society, and the Chair of the IEEE Computer Society Nanjing Chapter. He is a member of AAAI and ACM, and a senior member of IEEE and IEEE Computer Society.

Mandatory Reference: N/A

Supplementary Reference: 303

File Name: ads16/30354s1.doc

GUIDE

TO USAID'S ASSISTANCE APPLICATION PROCESS

AND

TO SUBMITTING UNSOLICITED ASSISTANCE APPLICATIONS

1. Introduction

The United States Agency for International Development (USAID) is the independent government agency that provides economic development and humanitarian assistance to advance U.S. economic and political interests overseas. USAID was created under the Foreign Assistance Act of 1961 in recognition of the above objectives. More information concerning USAID's approach to sustainable development can be found under Strategies for Sustainable Development.

2. Assistance Instruments

Use of Grants and Cooperative Agreements. In each instance, USAID shall decide on the appropriate award instrument (i.e., grant or cooperative agreement). The Federal Grant and Cooperative Agreement Act (31 U.S.C. Sec. 6301-08) governs the selection of grants and cooperative agreements. A grant or cooperative agreement shall be used only when the principal purpose of a transaction is to accomplish a public purpose of support or stimulation authorized by Federal statute. The statutory criterion for choosing between grants and cooperative agreements is that for the latter, "substantial involvement is expected between the executive agency and the State, local government, or other recipient when carrying out the activity contemplated in the agreement."

Except as otherwise authorized by statute, 22 CFR 226 establishes uniform administrative requirements for grants and cooperative agreements awarded by USAID to U.S. institutions of higher education, hospitals, and other non-profit organizations, and to U.S. commercial organizations; and to subawards thereunder (see http://www.usaid.gov/policy/ads/cfr.html#22cfr226). In addition to 22 CFR 226, USAID has established an Automated Directive System (ADS) chapter to implement USAID policies and standards for awarding and administering grants and cooperative agreements (see http://www.usaid.gov/pubs/ads/300/303.pdf).

3. Assistance Applications

USAID generally undertakes direct assistance programs to benefit developing countries through COMPETITIVE grants and cooperative agreements. This ensures that all activities are concentrated on pre-defined objectives to maximize impact; and that they are consistent, mutually reinforcing and draw support from the best available sources. While unsolicited applications can be received and reviewed for funding, potential applicants should be aware that only in highly exceptional cases are such applications likely to be approved for funding. Resources available to USAID for programs must be

concentrated and focused on clear objectives which fit within program priorities. Thus, only exceptional unsolicited applications can be considered for funding on a noncompetitive basis--ones which present a unique approach, are fully supportive of USAID's development objectives, and demonstrate a unique capacity by the applicant to carry out proposed activities and where there is clear support for such activities by the recipient country government or private institutions. Further, only limited funding may be available for even the best of such applications, since most funding is reserved for development priorities already established by USAID. Accordingly, it is strongly recommended that potential applicants review USAID competitive announcements (see http://www.usaid.gov/ procurement_bus_opp/procurement/announce) as well as specific USAID bureau open announcements (e.g., humanitarian response, http://www.usaid.gov/hum_response). Applicants responding to specific announcements should follow the directions contained in that announcement. If a potential applicant still desires to submit an unsolicited application, the applicant should follow the procedures described below under Concept Paper.

4. Concept Paper Stage

USAID strongly encourages the submission of a concept paper for initial consideration as opposed to a more lengthy detailed application. An unsolicited concept paper (generally not to exceed five pages) should contain the following:

(a) Cover Page/Introduction:

1. Name and address of organization;

2. Type of organization (e.g., non-profit, university, etc.);

3. Contact point (phone and e-mail);

4. Names of other organizations (federal and non-federal as well as any other USAID offices) to whom you are/have submitted the application and/or are funding the proposed activity; and

5. Signature of authorized representative of the applicant, authorized to contractually obligate the applicant.

b) Technical Information:

1. Concise title and objective of proposed activity;

2. Discussion of the objectives, the method of approach, the amount of effort to be employed, the anticipated results, and how the work will help accomplish USAID's mission as elaborated in USAID's Strategies for Sustainable Development; and

3. Type of support the applicant requests from USAID (e.g., funds, facilities, equipment, materials, or personnel resources).

1. Proposed estimated cost;

2. Brief cost breakdown (e.g., salaries, travel, etc.);

3. Proposed duration of activity.

4. Any proposed "cost-sharing or matching" (see 22 CFR 226.23 and ADS 303.5.10); and

5. Brief description of applicant's previous work and experience, both functionally and geographically.

5. Submission of Concept Paper

Unsolicited concept papers may be submitted directly to the Program Offices within USAID/Washington Bureaus, or directly to USAID missions. The technical nature of the proposed concept paper, region or country of implementation will determine the applicable Bureau/Mission to which the paper should be submitted. Please select the most applicable USAID/Washington Bureau as listed below, or designate a specific Mission:

Bureau for Global Programs, Field Support and Research

Democracy and Governance Center

Economic Growth and Agricultural Development Center

Environment Center

Human Capacity Development Center

Population, Health and Nutrition Center

Women in Development

Bureau for Humanitarian Response

Bureau for Africa

Bureau for Latin America and the Caribbean

Bureau for Asia and the Near East

Bureau for Europe and the New Independent States

Specific Mission

Concept papers should be addressed to the attention of the "Senior Program Officer." TWO copies of the application should be submitted.

6. USAID Review of Concept Paper

The cognizant USAID Senior Program Officer or his/her designee should notify the applicant within 60 days of any further interest in the application or lack thereof. If USAID has further interest in the proposed activity, USAID may request the applicant to submit a more detailed application. Such a request is not a commitment by USAID to support the activity. The applicant's decision to submit a more detailed application is at the applicant's discretion, and any expenses associated with preparation and submittal of the application are the responsibility of the applicant.

7. Application Stage

The applicant, upon notification from the USAID technical/program office that reviewed the concept paper, should follow any specific instructions provided by them. In general, a more detailed application will involve greater elaboration of the following:

(a) Program Goals/Objectives;

(b) Background/Introduction;

(d) Milestones/Results/Time line;

(s) Monitoring/Evaluation Plan;

(f) Management Plan;

(g) Business/Cost Section:

1. SF424 (activities/line items--headquarters/field--http://www.usaid.gov/procurement_bus_opp/procurement/forms/ SF-424 for the forms, and see Attachment A for example);

2. Detailed support for cost (breakdown/basis/support --see Attachment A for example);

3. Cost Share/Other Donor Support/Program Income (see 22 CFR 226.23-24 and ADS 303.5.10); and

4. Past Performance (similar projects last 3 years)

(h) Representations/Certifications:

1. Assurance of Compliance with Laws and Regulations Governing Nondiscrimination in Federally Assisted Programs;

2. Certification Regarding Debarment, Suspension, and Other Responsibility Matters--Primary Covered Transactions, and Lower Tier Covered Transactions;

3. Certification Regarding Drug Free Work place Requirements;

4. Restrictions on Lobbying; and

5. Prohibiting Assistance to Drug Traffickers (Required as applicable--USAID Agreement Officer can advise as to applicability).

8. Evaluation

Decisions to proceed with the award of a noncompetitive grant or cooperative agreement on the basis of an unsolicited application shall be made in accord with the requirements of ADS 303.5.5d. If it is determined that the subject matter of any unsolicited application is acceptable for funding on a noncompetitive basis, the unsolicited application will serve as the basis for negotiation.

9. Award Consideration Stage

The USAID Agreement Officer upon receipt of an award recommendation with accompanying justification(s) from the USAID Cognizant Technical Office (CTO) will proceed to negotiate an award. A recommendation does not guarantee an award, nor does it mean that a successful negotiation will lead to an award within the same fiscal year that the concept paper/application was submitted. The final negotiation will typically involve clarifications and/or discussions of any remaining cost issues, and a pre-award responsibility determination that may or may not require additional information from the applicant. The Agreement Officer is required to make a pre-award responsibility determination (see ADS 303.5.9).

(a) Applicants with continuing relations with USAID:

This responsibility determination can usually be made by the Agreement Officer based on existing information in the application, and by consulting materials from other sources (e.g., A-133 audit).

(b) Applicants new to USAID:

This responsibility determination will normally require obtaining the following additional information from the applicant:

1. audited financial statements (last three years);

2. projected budget, cash flow and organization chart; and

3. applicable policies and procedures (e.g., accounting, purchasing, property management).

(c) If the Agreement Officer cannot make a positive pre-award responsibility determination, he/she will undertake a formal

selection survey that may involve a pre-award audit.

+++++ 4111-++4+4-44111.++++++-1-41-4+++++4- +++++++ ++.1- +++++ +++ +++++++++ +-Ft +++++++

5E424 & SF424a Budget Support Information

Personnel:

Project Director: $50,000 (based on 50% time)

Country Director: $80,000 (based on 100% time)

Research Assistant: $20,000 (based on 50% time)

Personnel estimates based on current salaries. Experience of staff was highlighted in our application. Salaries are based on written personnel policies, and represent current staff employed by our organization.

Fringe Benefits:

Project Director: $10,000 (based on rate of 20% applied to salary)

Country Director: $16,000 (based on rate of 20% applied to salary)

Research Assistant: $2,000 (based on rate of 10% applied to salary)

Fringe benefits based on negotiated indirect rate agreement dated xx/xx/xx with our cognizant government audit agency (USAID), and represent costs associated with FICA, leave, and retirement benefits.

Travel:

Project Director: $5,500 (1 RT from a to b 0 $3,000; $2,000 based on 10 days of per diem at $200/day; and $500 based on airport taxis/in-country travel.

Country Director: $25,000 (1 RT from a to b 0 $3,000; Living quarters allowance 0 $15,000; Regional air travel 0 $5,000 based on 10 trips at $500/trip; Regional travel per diem 0 $1,000 based on 20 days at $50/day; and $1,000 for miscellaneous in-country travel.

Research Assistant: $11,000 (2 RTs from a to b 0 $3,000/each; $4,000 based on 20 days of per diem at $200/day; and $1,000 based on airport taxis/in-country travel.

Airfare based on economy rates quoted from ABC travel agency. Per diems/Allowances based on written travel policies which follow Department of State travel regulations.

Equipment:

$4,000 (Computer 0 $2,000, Printer 0 $500, Fax 0 $500, and Copier 0 $1,000) Equipment based on quotes from ABC company, and meet U.S. source/origin/nationality requirements. Equipment needed to produce/track the extensive research efforts involved in this program, and purchase is more cost effective than leasing.

Supplies:

$1,500 (Paper, printer/fax/copier cartridges, and general office supplies)

Supplies based on prior experience with similar projects of this magnitude.

Other:

$5,000 (Communication costs 0 $3,600 based on $300/month for international phone/faxes/express mail, and $1,400 for other expenses including monitoring/evaluation regional site visit reporting, _. The above communication costs are significant based on the extensive dissemination efforts with this program.

Indirect Charges:

$69,000 (Based on 30% of total direct costs and fringe benefits in

accordance with our negotiated indirect rate agreement dated xx/xx/xx with USAID.

The above sample is for illustrative purposes to serve as one example of the type of minimal detail and rationale needed in order to assist USAID in determining cost reasonableness. Typically, the more effectively the applicant can detail, support and link costs to their proposed program, the more streamlined negotiations will proceed. USAID Agreement Officer will advise applicants on needed cost information during proposed award negotiations.

Pacey, Arnold , The Maze of Ingenuity: Ideas and Idealism in the Development of

Technology

Pacey, Arnold , The Culture of Technology

Pacey, Arnold , Technology in World Civilization

Paciorek, Michael J. , Sports and Recreation For the Disabled, 2nd ed.

Packard, Robert , Encyclopedia of American Architecture, 2nd ed.

Paddock, Stephen W. (ed.) , Confocal Microscopy: Methods and Protocols

Padmanabhan, T. , Structure Formation in the Universe

Padovano, Anthony , The Process of Sculpture

Paesler, Michael A. , Near Field Optics: Theory, Instrumentation and

Applications

Pagano, M. , Cell Cycle Control: Results and Problems in Cell Differentiation

Page, Jake , Forest

Page, Jake , Arid Lands

Page, Leigh , Introduction to Theoretical Physics, 3rd ed.

Page, Leigh , Electrodynamics

Page, R. S. (ed.) , RS Means Heavy Construction Data, 12th ed.

Page, Robert Morris , The Origin of Radar

Page, Thornton, ed. , Wanderers in the Sky: the Motions of Planets and Space

Probes

Page, Thornton, ed. , Neighbors of the Earth: Planets, Comets, and the Edbris of

Space

Page, Thornton, ed. , Beyond the Milky Way: Galaxies, Quasars, and the New

Cosmology

Page, Thornton , Telescopes: How to Make Them and Use Them

Pagels, Heinz , The Dreams of Reason: the Computer and the Rise of the Sciences

of Complexity

Pagen, Dennis , Powered Ultralight Flying

Pahl, G. , Engineering Design: a Systematic Approach, 2nd ed.

Pahl, Greg , The Complete Idiot's Guide to Saving the Environment

Pai, Shih-I , Viscous Flow Theory I.: Laminar Flow

Pai, Shih-I. , Modern Fluid Mechanics

Pain, H. J. , The Physics of Vibrations and Waves, 6th ed.

Paine, Lincoln , Shops of the World: an Historical Encyclopedia

Pais, Abraham , Niels Bohr's Times, in Physics, Philosophy, and Polity

Pais, Abraham , Inward Bound: of Matter and Forces in the Physical World

Pake, George E. , Paramagnetic Resonance

Paley, Raymond E. A. C. , Fourier Transforms in the Complex Domain

Palik, Edward D. , Handbook of Optical Constants of Solids II

Paliouras, John D. , Complex Variables For Scientists and Engineers

Palis, J. , Hyperbolicity & Sensitive Chaotic Dynamics at Homoclinic

Bifurcations

Pallas-Areny, Ramon , Sensors and Signal Conditioning

Pallu de la Barriere, R. , Optimal Control Theory: a Course in Automatic Control

Theory

Palma, Robert J., Sr. , The Complete Quide to Household Chemicals

Palmer, C. Harvey , Optics: Experiments and Demonstrations

Palmer, Rose A. , The North American Indians: an Account of the American Indians

North of Mexico, Compiled From the Original Sources

Palmquist, Roland , Answers on Blueprint Reading

Palsson, Bernhard O. , Systems Biology: Properties of Reconstructed Networks

Palsson, Bernhard O. , Tissue Engineering

Pan American Navigation Service , The Instrument Rating: a Guide to the FAA

Written Examination, 20th revised ed.

Panati, Charles , Panati's Extraordinary Origin of Ordinary Things

Panati, Charles , Browser's Book of Beginnings: Origins of Everything Under, and

Including, the Sun

Panero, Julius , Human Dimension & Interior Space: a Source Book of Design

Reference Standards

Panfilov, A. V. , Computational Biology of the Heart

Pankove, Jacques I. , Optical Processes in Semiconductors

Panofsky, Wolfgang K. H. , Classical Electricity and Magnetism, 2nd ed.

Pansimi, Anthony J. , Undergrounding Electric Lines, 2nd ed.

Pansini, Anthony J. , Basic Electrical Power Distribution, Vol. 1

Pansini, Anthony J. , Basic Electrical Power Distribution, Vol. 2

Pansini, Anthony J. , Electrical Transformers and Power Equipment

Pantell, Richard H. , Fundamentals of Quantum Electronics

Panter, Philip F. , Modulation, Noise, Ane Spectral Analysis: Applied to

Information Transmission

Panton, Ronald L. , Incompressible Flow

Papalia, Diane E. , Human Development, 5th ed.

Papanek, Victor , Deesign For the Real World: Human Ecology and Social Change

Papanek, Victor , Design For Human Scale

Papanek, Victor , How Things Don't Work

Papanicolaou, Goerge (ed.) , Hydrodynamic Behavior and Interacting Particle

Systems

Papas, Charles Herach , Theory of Electromagnetic Wave Propagation

Papoulis, Athanasios , The Fourier Integral and Its Applications

Papoulis, Athanasios , Probability, Random Variables, and Stochastic Processes

Papoulis,A. , Systems and Transforms With Applications in Optics

Pappas, Theoni , Mathematical Footprints: Discovering Mathematical Impressions

All Around Us

Pappenfus, E. W. , Single Sideband Principles and Circuits

Parasnis, D. S. , Mining Geophysics, 2nd ed.

Parasnis, D. S. , Principles of Applied Geophysics, 4th ed.

Parcel, John I. , Analysis of Statically Indeterminate Structures

Pare, Eugene G. , Computer Graphics Projects For Design and Descriptive Geometry

Paries, Jeff , The Animation Master Handbook, 2nd ed.

Parikh, Girish , Techniques of Program and System Maintenance

Parish, John Howard , Principles and Practics of Experiments With Nucleic Acids

Parisi, Giorgio , Statistical Field Theory

Park, David , Introduction to the Quantum Theory

Park, David , The How and the Why: an Essay on the Origins and Development of

Physical Theory

Park, David , Introduction to the Quantum Theory, 2nd ed.

Park, Jack , The Wind Power Book

Park, R. , Reinforced Concrete Structures

Park, Robert , The Inventor's Handbook: How to Develop, Proect & Market Your

Invention, 2nd ed.

Park, Robert L. , Solid State Physics: Surfaces

Parke, Nathan Grier, III. , Guide Tot he Literature of Mathematics and Physics

Including Related Works on Engineering Science

Parker Hannifin Motion and Control Training Department , Industrial Hydraulic

Technology, 2nd ed.

Parker Hannifin Motion and Control Training Department , Fluid Power Basics

Parker, A. P. , The Mechanics of Fracture and Fatigue

Parker, Alfred Browning , You and Architecture: a Practical Guide to the Best in

Building

Parker, C. C. , Information Sources in Science and Technology: a Practical Guide

to Traditional and Online Use, 2nd ed.

Parker, Earl , The Science of Materials Used in Advanced Technology

Parker, Greg , Introductory Semiconductor Device Physics

Parker, Harry , Simplifed Engineering For Architects and Builders, 4th ed.

Parker, Rollin J. , Permanent Magnets and Their Applications

Parker, Steve , The Random House Book of How Things Work

Parker, Steve , Medicine

Parker, Steve , The Practical Paleontologist

Parker, Sybil P. , Solid-state Physics Source Book

Parker, Sybil P. (ed.) , McGraw-Hill Encyclopedia of Physics, 2nd ed.

Parker, Sybil P. (ed.) , McGraw-Hill Encyclopedia of Engineering, 2nd ed.

Parker, Sybil P. (ed.) , McGraw-Hill Dictionary of Mechanical and Design

Engineering

Parker, Sybil P. (ed.) , Physical Chemistry Source Book

Parker, Sybil P. (ed.) , McGraw-Hill Dictionary of Physics

Parker, Sybil P. (ed.) , Synopsis and Classification of Living Organisms, Vol. 1

Parker, Sybil P. (ed.) , Synopsis and Classification of Living Organisms, Vol. 2

Parker, T. S. , Practical Numerical Algorithms For Chaotic Systems

Parker, Tim , Linus Unleashed, 3rd ed.

Parker, W. Oren , Scene Design and Stage Lighting

Parkinson, Claire L. , Breakthroughs: a Chronology of Grerat Acheivements in

Science and Mathematics 1200-1930

Parkinson, David H. , The Generation of High Magnetic Fields

Parkr, Sybil P. , Optics Source Book

Parks, Bob , Makers: All Kinds of People Making Amazing Things in Garages,

Basements, and Backyards

Parks, George K. , Physics of Space Plasmas: an Introduction

Parmakian, John , Waterhammer Analysis

Parmeggiani, Luigi , Encyclopedia of Occupational Health and Safety, 3rd Rev.

ed., Vol. 1 A-K

Parmeggiani, Luigi , Encyclopedia of Occupational Health and Safety, 3rd Rev.

ed., Vol. 2 L-Z

Parmley, Robert O. (ed.) , Standard Handbook of Fastening & Joining, 2nd ed.

Parmley, Robert O. (ed.) , Illustrated Sourcebook of Mechanical Components

Parola, Rene , Optical Art: Theory and Practice

Parr, Andrew , Hydraulics and Pneumatics: a Technician's and Engineer's Guide,

2nd. ed.

Parr, Andrew , Logic Designer's Handbook: Circuits & Systems, 2nd ed.

Parr, E. A. , Control Engineering

Parr, E. A. , Industrial Control Handbook, Volume 1: Transducers

Parr, E. A. , Industrial Control Handbook, Volume 2: Techniques

Parr, Robert G. , Quantum Theory of Molecular Electronic Structure

Parramon's Editorial Team , Essential Atlas of Technology

Parrent, Goorge B. , Physical Optics Notebook

Parrish, Lex , Space-flight Simulation Technology

Parry, Albert (ed.) , Peter Kapitsa on Life and Science: Addresses and Essays

Parsegian, V. Adrian , Van Der Waals Forces: a Handbook For Biologists,

Chemists, Engineers, and Physicists

Parsons, Cyril , The Chemist at Work

Parsons, Cyril , The Giant Molecules

Parsons, Cyril , Engineering Technology

Partain, Larry D. , Solar Cells and Their Applications

Partington, J. R. , An Advanced Treatise on Physical Chemistry, Vol. 1:

Fundamental Principles, the Properties of Gases

Partington, J. R. , An Advanced Treatise on Physical Chemistry, Vol.2: the

Properties of Liquids

Partington, J. R. , An Advanced Treatise on Physical Chemistry, Vol.3: the

Properties of Solids

Partington, J. R. , An Advanced Treatise on Physical Chemistry, Vol. 4: Physico

chemical Optics

Partington, J. R. , An Advanced Treatise on Physical Chemistry, Vol. 5:

Molecular Spectra and Structure; Dielectrics and Dipole Moments

Parton, J. E. , Applied Electromagentics, 2nd ed.

Partridge, Lloyd D. , The Nervous System: Its Function and Its Interaction With

the World

Parzen, Emanuel , Stochastic Processes

Pasachoff, Jay M. , Contemporary Astronomy, 2nd ed.

Pasachoff, Jay M. , The Cambridge Eclipse Photography Guide

Pascal, Blaise , The Provincial Letters; Pensees; Scientific Treatises

Pascoe, Robert D. , Solid-state Switching: Discrete and Integrated

Pashley, Richard M. , Applied Colloid and Surface Chemistry

Passino, Kevin M. , Stability Analysis of Discrete Event Systems

Passman, Donald , Permutation Groups

Pasternak, Charles A. , The Molecules Within Us: Our Body in Health and Disease

Pasto, Daniel J. , Organic Structure Determination

Pasztory, Esther , Thing With Things: Toward a New Vision of Art

Paterson, A. R. , A First Course in Fluid Dynamics

Paterson, W. S. B. , The Physics of Glaciers, 2nd ed.

Pathokinesiology Service and Physical Theraphy Department , Observational Gait

Analysis

Pathria, R. K. , Statistical Mechanics

Paton, David , Introduction to Ophthalmoscopy

Patrick, Dale R. , Electronic Instruments, 2nd ed.

Patrick, Dale R. , Rotating Electrical Machines and Power Systems

Patrick, Dale R. , Pneumatic Instrumentation, 3rd ed.

Patrick, Dale R. , Pneumatic Instruments, 2nd ed.

Patrick, Edward A. , Fundamentals of Pattern Recognition

Patrick, Graham L. , An Introduction to Medicinal Chemistry

Patterson, G. N. , Molecular Flow of Gases

Patterson, Gordon N. , Introduction to the Kinetic Theory of Gas Flows

Patterson, Terry L. , Construction Materials For Architects & Designers

Pattinson, Graham Day , A Guide to Professional Architectural and Industrial

Scale Model Building

Patton, Temple C. , Paint Flow and Pigment Dispersion

Paul, Caroline , Fighting Fire: a Personal Story

Paul, Clayton R. , Introduction to Electromagnetic Fields

Paul, Henry E. , Telescopes For Skygazing

Paul, John , Cell and Tissue Culture, 4th ed.

Paul, Joshua , Digital Video Hacks: Tips & Tools For Shooting, Editing, and

Sharing

Paul, Leendert C. , Adhesion Molecules in Health and Disease

Pauli, W. , Theory of Relativity

Pauli, Wolfgang , Electrodynamics (Pauli Lectures in Physics, Vol.1)

Pauli, Wolfgang , Selected Topics in Field Quantization (Pauli Lectures in

Physics, Vol. 6)

Pauli, Wolfgang , Wave Mechanics (Pauli Lectures in Physics, Vol. 5)

Pauli, Wolfgang , Statistical Mechanics (Pauli Lectures in Physics, Vol. 4)

Pauli, Wolfgang , Thermodynamics and the Kinetic Theory of Gases (Pauli Lectures

in Physics, Vol. 3)

Pauli, Wolfgang , Optics and the Theory of Electrons (Pauli Lectures in Physics,

Vol. 2)

Pauli, Wolfgang , Electrodynamics (Pauli Lectures in Physics, Vol. 1)

Pauling, Linus , The Nature of the Chemical Bond, 3rd ed.

Pauling, Linus , General Chemistry, 2nd ed.

Pauling, Linus , The Architecture of Molecules

Pauling, Linus , Introduction to Quantum Mechanics With Applications to

Chemistry

Pauls, Michael , The Travellers' Guide to Mars: Don't Leave Earth Without It

Pavella, M. , Transient Stability of Power Systems: Theory and Practice

Pavia, Donald L. , Introduction to Organic Laboratory Techniques: a Contemporary

Approach, 2nd ed.

Pavia, Donald L. , Introduction to Organic Laboratory Techniques: a Microscale

Approach, 2nd ed.

Pavia, Donald L. , Introduction to Organic Laboratory Techniques: a Small-scale

Approach

Pavlidis, Theo , Algorithms For Graphics and Image Processing

Pawsey, J. L. , Radio Astronomy

Paxton, John , Calendar of Creative Man

Paxton, Mary Jean W. , Endrocrincology: Biological and Medical Perspectives

Payne, Christopher , The Encyclopedia of Modelmaking Techniques

Payne, Vicki , The Stained Glass Classroom: Projects Using Copper Foil, Lead &

Mosaic Techniques

Payne-Gallwey, Sir Ralph , The Crossbow: Mediaeval and Modern, Military and

Sporting - Its Construction, History & Management, 2nd ed.

Paynter, Robert T. , Introductory Electronic Devices and Circuits

Paz, Mario , Structural Dynamics: Theory and Computation, 4th ed.

Paz, Mario , Microcomputer Aided Engineering: Structural Dynamics

Pazdur, Richard , Medical Oncology: a Comprehensive Review, 2nd ed.

Pazdur, Richard , Cancer Management: a Multidisciplinary Approach, 6th ed.

Peak, David , Chaos Under Control: the Art and Science of Complexity

Pearce, John Chilton , Evolution's End: Claiming the Potential of Our

Intelligence

Pearce, John M. , An Introduction to Animal Cognition

Pearce, Peter , Structure in Nature is a Strategy For Design

Pearce, Peter , Experiments in Form: a Foundation Course in Three-dimesnional

Design

Pearl, Judea , Probablistic Reasoning in Intelligent Systems: Networks of

Plausible Inference

Pearsall, Ronald , Collecting Mechanical Antiques

Pearsall, Thomas P. , Photonics Essentials: an Introduction With Experiments

Pearson, David , Earth to Spirit: in Search of Natural Architecture

Pearson, David , Circle Houses: Yurts, Tipis and Benders

Pearson, Eric B. , Technology of Instrumentation

Pearson, Greg , Technically Speaking: Why All Americans Need to Know More About

Technology

Pearson, J. M. , A Theory of Waves

Pease, Daniel C. , Histological Techniques For Electron Microscopy

Pease, Dudley A. , Basic Fluid Power

Pease, Robert A. , Troubleshooting Analog Circuits

Peaslee, D. C. , Elements of Atomic Physics

Peat, F. David , Synchronicity: the Bridge Between Matter and Mind

Peatfield, A. E. , Engineering Components and Materials

Peatfield, A. E. , Mechanical Engineering 1: Hand Tools, 2nd ed.

Peatman, John B. , Microcomputer Based Design

Peatman, John B. , Design With Microcontrollers

Peatman, John B. , Design With PIC Microcontrollers

Pechenik, Jan A. , A Short Guide to Writing About Biology, 4th ed.

Pecht, Michael G. , Guidebook For Managing Silcon Chip Reliability

Peck, Ralph B. , Foundation Engineering

Pecker, Jean-Claude , The Future of the Sun

Peckner, Donald , Handbook of Stainless Steels

Pecseli, Hans L. , Fluctuations in Physical Systems

Pecsok, Robert L. , Modern Methods of Chemical Analysis

Pedlosky, Joseph , Geophysical Fluid Dynamics

Pedoe, Dan , Geometry and the Visual Arts

Pedoe, Daniel , An Introduction to Projective Geometry

Pedoe, Dn , Circles: a Mathematical View

Pedretti, Carlo , Leonard: the Machines

Pedretti, Carlo (ed.) , Leonardo Da Vinci Nature Studies From the Royal Library

at Windsor Castle

Pedrotti, Frank L. , Introduction to Optics

Peebles, P. J. E. , The Large-Scale Structure of the Universe

Peebles, P. J. E. , Principles of Physical Cosmology

Peebles, Peyton Z. , Communication System Principles

Peerless, S. J. , Basic Fluid Mechanics

Peet, John , Energy and the Ecological Economics of Sustainability

Peet, Louise Jenison , Household Equipment, 7th ed.

Peierls, R. E. , Quantum Theory of Solids

Peierls, R. E. , The Laws of Nature

Peierls, R. E. , Surprises in Theoretical Physics

Peierls, Rudolf , More Surprises in Theoretical Physics

Peierls, Rudolf , Bird of Passage: Recollections of a Physicist

Peinke, J. , Encounter With Chaos: Self-organized Hierarchical Complexity in

Semiconductor Experiments

Peirce, W. M. , Die Casting With Zinc

Peitgen, H. -O. , The Beauty of Fractals: Images of Complex Dynamical Systems

Peitgen, Heinz-Otto , The Science of Fractal Images

Peitgen, Heinz-Otto , Chaos and Fractals: New Frontiers of Science

Peixoto, Jose P. , Physics of Climate

Pekar, S. I. , Crystal Optics and Additional Light Waves

Pelce, Pierre , New Visions of Form and Growth: Fingered Growth, Dendrites, and

Flames

Pelczar, Michael J., Jr. , Laboratory Exercises in Microbiology, 2nd ed.

Pelissier, Jaime , The Jeweler's Craft: Mastering Traditional Techniques

Pelosi, Giuseppe , Quick Finite Elements For Electromagnetic Waves

Pendergast, Mark , Mirror Mirror: a Historyof the Human Love Affair With

Reflection

Pendray, G. Edward , Men, Mirrors, and Stars

Penfield, Paul, Jr. , Electrodynamics of Moving Media

Penman, Laurie , The Clock Repairer's Handbook

Penning, F. M. , Electrical Discharges in Gases

Pennington, Ralph H. , Introductory Computer Nethods and Numerical Analysis

Penrose, R. , Quantum Concepts of Space and Time

Penrose, Roger , The Emperor's New Mind: Concerning Computers, Mind, and the

Laws of Physics

Penrose, Roger , Shadows of the Mind: a Search For the Missing Science of

Consciousness

Penrose, Roger , Shadows of the Mind: a Search For the Missing Science of

Consciousness

Penrose, Roger , The Large, the Small and the Human Mind

Penrose, W. R. , Designing Portable Computerized Instruments

Pepper, Clement S. , Build Your Own Home Lab

Pepper, J. H. , Static Electricity: With 30 Experiments

Peray, Kurt E. , The Rotary Cement Kiln, 2nd ed.

Percival, Ian , Introduction to Dynamics

Percy, John R. (ed.) , The Study of Variable Stars Using Small Telescopes

Perel, Azriel , Handbook of Mechanical Ventilatory Support

Perelson, Alan S. , Molecular Evolution on Rugged Landscapes: Proteins, RNA, and

the Immune System

Perez, Richard A. , Electronic Display Devices

Perez, Richard A. , The Complete Battery Book

Perfil'ev, B. V. , Capillary Methods of Investigating Micro-organisms

Perina, Jan , Coherence of Light

Perkins, Courtland D. , Airplane Performance, Stability, and Control

Perkins, William R. , Engineering of Dynamic Systems

Perko, Lawrence , Differential Equations and Dynamical Systems, 3rd ed.

Perkowitz, Sidney , Empire of Light: a History of Discovery in Science and Art

Perkowitz, Sidney , Universal Foam: From Cappuccino to the Cosmos

Perkowitz, Sidney , Optical Characterization of Semiconductors: Infrared, Raman,

and Photoluminescence Spectroscopy

Perlick, Volker , Ray Optics, Fermat's Principle, and Applications to General

Relativity

Perlin, John , From Space to Earth: the Story of Solar Electricity

Perozzo, James , Practical Electronics Troubleshooting

Perrin, Jacques , Winged Migration

Perrin, Noel , Life With an Electric Car

Perrow, Charles , Normal Accidents: Living With High-risk Technologies

Perry, Greg , Sams Teach Yourself Visual Basic 6 in 21 Days

Perry, Robert H. , Chemical Engineers Handbook, 5th ed.

Perry, Robert H. , Perry's Chemical Engineers' Handbook, 7th ed.

Persico, Enrico , Principles of Particle Accelerators

Persson, B. N. J. , Sliding Friction: Physical Principles and Applications

Pesce, Lark , VRML Browsing & Building Cyberspace

Pesic, Peter , Sky in a Bottle

Peskin, Edward , Transient and Steady State Analysis of Electric Networks

Peter, John , Aluminum in Modern Architecture, Vol. I Buildings

Peterlongo, Paolo , The Violin: Its Physical and Acoustic Principles

Peters, Edgar E. , Fractal Market Analysis

Peters, Edgar E. , Chaos and Order in the Capital Markets: a New View of Cycles,

Prices, and Market Volatility

Peters, Max S. , Plant Design and Economics For Chemical Engineers, 2nd ed.

Peters, Rock , Woodworker's Power Tools: an Essential Guide

Peters, T. Michael , Insects and Human Society

Peterson, Arnold P. G. , Handbook of Noise Measurement, 7th ed.

Peterson, E. L. , Statistical Analysis and Optimization of Systems

Peterson, Harold A. , Transients in Power Systems

Peterson, Ivars , Islands of Truth: a Mathematical Mystery Cruise

Peterson, Ivars , Newton's Clock: Chaos in the Solar System

Peterson, Ivars , The Jungles of Randomness: a Mathematical Safari

Peterson, Ivars , Fragments of Infinity: a Kaleidoscope of Math and Art

Peterson, Mendel , History Under the Sea: a Manual For Underwater Exploration

Peterson, Rita , Science and Society: Asource Book For Elementary and Junior

High School Teachers

Peterson, W. Wesley , Error-Correcting Codes

Petrashen, M. I. , Applications of Group Theory in Quantum Mechanics

Pétré, G. , Capillarity Today

Petroleum Extension Service - University of Texas at Austin , A Dictionary Pf

Petroleum Terms, 2nd ed.

Petroleum Extension Service - University of Texas at Austin , Fundamentals of

Petroleum, 2nd ed.

Petroski, Henry , The Book on the Bookshelf

Petroski, Henry , The Evolution of Useful Things

Petroski, Henry , Beyond Engineering

Petroski, Henry , To Engineer is Human: the Role of Failure in Successful Design

Petroski, Henry , Invention By Design

Petroski, Henry , Engineers of Dreams: Great Bridge Builders and the Spanning of

America

Petrosky, I. G. , Lectures on Partial Differential Equaitons

Pettai, Raoul , Noise in Receiving Systems

Petterssen, Sverre , Weather Analysis and Forecasting, 2nd Ed.: Vol. I Motion

and Motion Systems

Petterssen, Sverre , Introduction to Meteorology, 3rd ed.

Pettijohn, F. J. , Sedimentary Rocks, 3rd ed.

Pettijohn, F. J. , Sand and Sandstone

Pettit, Joseph Mayo , Electronic Amplifier Circuits

Pettofrezzo, Anthony J. , Matrices and Transformations

Petty, Michael C. , Langmuir-Blodgett Films: an Introduction

Petukhov, B. S. (ed.) , Heat Transfer in Turbulent Mixed Convection

Peurifoy, Robert L. , Construction Planning, Equipment and Methods, 4th ed.

Peyret, Roger , Computational Methods For Fluid Flow

Peyton, A. J. , Analog Electronics With Op Amps: a Source Book of Practical

Circuits

Pfaender, Heinz G. , Schott Guide to Glass

Pfeffer, W. , Osmotic Investigations: Studies on Cell Mechanics

Pfeuty, P. , Introduction to the Renormalization Group and Critical Phenomena

Pfleider, Eugene P. , Surface Mining

Phalen, Robert F. , Inhalation Studies: Foundations and Techniques

Phan, Trent , Practical Nuclear Pharmacy, 3rd ed.

Phan-Thien, Nhan , Microstucture of Elastic Media: Principles and Computational

Methods

Philbin, Tom , The Complete Illustrated Guide to Everything Sold in Hardware

Stores

Philco Corporation , Philco Training Manual: Synchors and Servomechanisms

Phillips, Bill , Professional Locksmithing Techniques

Phillips, Chandler Allen , Functional Electrical Rehabilitation: Technological

Restoration After Spinal Cord Injury

Phillips, Esther R. , An Introduction to Analysis and Integration Theory

Phillips, F. C. , An Introduction to Crystallography

Phillips, G. B. , Biomedical Applications of Laminar Airflow

Phillips, George C. , A Concise Introduction to Ceramics

Phillips, J. C. , Physics of High-Tc Superconductors

Phillips, James C. , Covalent Bonding in Crystals, Molecules, and Ploymers

Phillips, Joy B. , Development of Vertebrate Anatomy

Phillips, Kenneth J. H. , Guide to the Sun

Phillips, Leon F. , Electronics For Experimenters in Chemistry, Physics, and

Biology

Phillips, O. M. , The Dynamics of the Upper Ocean

Phillips, O. M. , Wave Dynamics and Radio Probing of the Ocean Surface

Phillips, Rob , Crystals, Defects, and Microstructures: Modeling Across Scales

Phillips, Roger , Sources and Applications of Ultraviolet Radiation

Phillips, Steven J. , Old House Dictionary: an Illustrated Quide to American

Domestic Architecture 1600 to 1940

Phillips, V. J. , Waveforms: a History of Early Oscillography

Phipps, Clarence A. , Fundamentals of Electrical Control

Phipps, Lloyd J. , Mechanics in Agriculture

Photovoltaic Systems Design Assistance Center , The Design of Residential

Photvoltaic Systems

Physical Science Study Committee , Physics

Pichal, M. (ed.) , Optical Methods in Dynamics of Fluids and Solids

Pickard, George L. , Descriptive Physical Oceanography, an Introduction, 4th ed.

Pickering, H. S. , The Covalent Bond

Pickover, Clifford A. , Computers, Pattern, Chaos, and Beauty: Graphics From an

Unseen World

Pickover, Clifford A. , Computers and the Imagination: Visual Adventures Beyond

the Edge

Pickover, Clifford A. , Mazes For the Mind: Computers and the Unexpected

Pickover, Clifford A. , Chaos in Wonderland: Visual Adventures in a Fractal

World

Pickover, Clifford A. , Visions of the Future: Art, Technology and Computing in

the 21st Century

Pickover, Clifford A. , Frontiers of Scientific Visualization

Piel, Gerard , Only One World: Our Own to Make and to Keep

Pielke, ROger A. , Mesoscale Meteorological Modeling

Pieper, John F. , RSX: A Guide For Users

Pierce, Allan D. , Acoustics, An Introduction to Its Physical Principles and

Applications

Pierce, J. Frank , Semiconductor Junction Devices

Pierce, J. R. , Symbols, Signals and Noise: the Nature and Process of

Communication

Pierce, John R. , An Introduction to Information Theory: Symbols, Signals, and

Noise, 2nd rev ed

Pierce, John R. , The Science of Musical Sound

Pierce, John R. , Almost All About Waves

Pierce, John R. , Quantum Electronics: the Fundamentals of Transistors and

Lasers

Pierce, John R. , Electrons, Waves and Messages

Pierce, John R. , Signals: the Science of Telecommunications

Pierce, Willis Conway , Quantitative Analysis, 2nd ed

Pierpont, James , The Theory of Functions of Real Variables, Vol. Two

Pierre, Donald A. , Optimization Theory With Applications

Pierre, Edward R. , Welding Processes and Power Sources, 2nd ed.

Pierret, Robert F. , Field Effect Devices, 2nd ed.

Pierret, Robert F. , Smeiconductor Fundamentals, 2nd ed.

Pierret, Robert F. , Advanced Semiconductor Fundamentals

Pierson, Hugh O. , Handbook of Chemical Vapor Deposition: Principles,

Technology, and Applications

Pierson, Willard J. , Practical Methods For Observing and Forecasting Ocean

Waves By Means of Wave Spectra and Statistics

Pignatoaro, Louis J. , Traffic Engineering: Theory and Practice

Pigott, Rod , The Adhesion Molecule Facts Book

Pike, Dag , Electronic Navigation For Small Craft

Pikovsky, Arkady , Synchronization: a Universal Concept in Nonlinear Science

Pilar, Frank L. , Elementary Quantum Chemistry

Pilbeam, Susan P. , Mechanical Ventilation: Physiological and Clinical

Applications

Pilkey, Walter D. , Mechanics of Structures: Variational and Computational

Methods

Pillai, S. Unnikrishna , Array Signal Processing

Pimentel, David , Food, Energy, and Society, revised ed.

Pimentel, G. C. (ed.) , Chemistry: an Experimental Science

Pimentel, George C. , The Hydrogen Bond

Pimentel, Ken , Virtual Reality: Through the New Looking Glass

Pimpinelli, Alberto , Physics of Crystal Growth

Pina, Larry , The Dead Mac Scrolls: the Macintosh Bible Guide to Saving a

Fortune on Mac Repairs

Pina, Larry , Macintosh Repair and Upgrade Secrets For Models 128K to Macintosh

Pine, Jerry , Zap! Experiments in Electrical Currents and Fields

Pine, Jerry , A Teacher's Guide to Zap! Experiments in Electrical Currents and

Fields

Pine, Stanley H. , Organic Chemistry, 4th ed.

Pines, D. , The Many Body Problem

Pines, D. , Elementary Excitations in Solids: Lectures on Phonons, Electrons,

and Plasmons

Pines, D. , The Theory of Quantum Liquids, Vol.1: Normal Fermi Liquids

Pines, Maya , Inside the Cell: the New Frontier of Medical Science

Pinker, Steven , How the Mind Works

Pioneer Electronic Corporation , Understanding High Fidelity, 6th Rd.

Pipes, Alan , Production For Graphic Designers

Pipes, Louis A. , Operational Methods in Nonlinear Mechanics

Pipkin, A. C. , Lectures on Viscoelasticity Theory, 2nd ed.

Pippard, A. B. , Response and Stability: An Introduction to the Physical Theory

Pippard, A. B. , Elements of Classical Thermodynamics

Pippard, A. B. , The Physics of Vibration (omnibus Ed.)

Pippenger, D. E> , Linear and Interface Circuits Applications, 2nd ed.

Pirani, M. , Principles of Vacuum Engineering

Pirson, Sylvain J. , Handbook of Well Log Analysis

Pirsson, Louis V. , Rocks and Rock Minerls, 3rd ed.

Pitman, George R., Jr. (ed.) , Intertial Guidance

Pitts, Donald R. , Schaum's Outline of Theory and Problems of Heat Transfer

Piwnica, Armand , Stentless Bioprostheses

Pizzutiello, Robert J., Jr. , Introduction to Medical Radiographic Imaging

Pizzuto, Joseph J. , Fabric Science, 5th ed.

Plambeck, James A. , Electroanalytical Chemistry: Basic Principles and

Applications

Planck, Max , Thermodynamics, 3rd Rev. ed.

Planck, Max , The Theory of Heat Radiation

Planck, Max , Eight Lectures on Theoretical Physics

Plaskowski, A. , Imaging Industrial Flows: Applications of Electrical Process

Tomography

Platt, Richard , Crime Scene: the Definitive Guide to the World Behind the Tape

Platzman, P. M. , Waves and Interactions in Solid State Plasmas

Pledge, H. T. , Science Since 1500: a Short History of Mathematics, Physics,

Chemistry, and Biology

Plischke, Michael , Equilibrium Statistical Mechanics

Ploe, Robert J. , Handbook of Reliability Prediction Procedures For Mechanical

Equipment

Ploe, Robert J. , Handbook of Reliability Prediction Procedures For Mechanical

Equipment

Plonsey, Robert , Bioelectricity: a Quantitative Approach

Plonsey, Robert , Bioelectric Phenomena

Plossl, George W. , Production and Inventory Control: Principles and Techniques,

2nd ed.

Plossl, George W. , Production and Inventory Control: Applications

Plossl, George W. , Effective Corporate Strategy in Manufacturing

Plum, Thomas , Reliable Data Structures in C

Plumb, Harmon H. , Temperature: Its Measurement and Contol in Science and

Industry, Vol 4, Part 1: Basic Methods, Scales and Fixed Points, Radiation

Plumb, Harmon H. , Temperature: Its Measurement and Control in Science and

Industry, Vol. 4, Part 2: Resistance, Electronic and Magnetic Thermometry

Plummer, Charles C. , Physical Geology, 4th ed.

Plummer, Charles C. , Physical Geology, 8th ed.

Plummer, James , Electric Power Strategic Issues

Pobell, Frank , Matter and Methods at Low Temperatures, 2nd ed.

Pocket Books , The Atomic Age Opens

Pocock, Lynn , The Computer Animator's Technical Handbook

Podendorf, Illa , One Hundred One Science Experiments

Podobed, V. V. , Fundamental Astrometry: Determination of Stellar Coordinates

Podolny, R. , SOmething Called Nothing: Physical Vaccum: What is It?

Pogue, David , Macworld Mac and Power Mac Secrets, 2nd ed.

Pogue, William R. , How Do You Go to the Bathroom in Space? All the Ansewrs to

All the Questions You Have About Living in Space

Pohl, R. W. , Mechanik Und Akustik

Pohlman, Ken C. , Principles of Digital Audio, 2nd ed.

Poinar, George , The Quest For Life in Amber

Poincare, Henri , Science and Hypothesis

Poincare, Henri , Science and Method

Poincare, Henri , The Value of Science

Poincare, Henri , New Methods of Celestial Mechanics: 1. Periodic and Aymptotic

Solutions

Poincare, Henri , New Methods of Celestial Mechanics: 2. Approximation By Series

Poincare, Henri , New Methods of Celestial Mechanics: 3. Integral Invariants and

Asymptotic Properties of Certain Solutions

Poirier, Jean-Paul , Creep of Crystals: High Temperature Deformation Processes

in Metals, Ceramics and Minerals

Poirier, Jean-Paul , Introduction to the Physics of the Earth's Interior

Pokock, Emil (ed.) , Beyond the Line of Sight: a History of VHF Propagation From

the Pages of QST

Pokorny, Cornel K. , Computer Graphics: the Principles Behind the Art and

Science

Pokorski, Stefan , Gauge Field Theories

Polak, E. , Notes For a First Course in Linear Systems

Polak, J. M. , Introduction to Immunochemistry, 2nd ed.

Polimeros, George , Energy Cogeneration Handbook: Criteria For Central Plant

Design

Poling, James , The Story of Tools: How They Built Our World and Shaped Man's

Life

Polkinghorne, J. C. , The Quantum World

Pollack, Daniel D. , Electrical Conduction in Solids: an Introduction

Pollack, Gerald H. , Cells, Gels, and the Engines of Life

Pollack, Gerald H. , Muscles & Molecules: Uncovering the Principles of

Biological Motion

Pollack, Herman W. , Manufacturing and Machine Tool Operations

Pollack, Robert , Readings in Mammalian Cell Culture, 2nd ed.

Pollard, H. F. , Sound Waves in Solids

Pollermann, Max , Bauelemente Der Physikalischen Technik, Zweite Auflage

Pollister, Arthur W. , Physical Techniques in Biological Research: Vol. III Part

A Cells and Tissues, 2nd ed.

Pollister, Arthur W. (ed.) , Physical Techniques in Biological Research: Vol.

III Part C Cells and Tissues, 2nd ed.

Pollister, Arthur W. (ed.) , Physical Techniques in Biological Research: Vol.

III Part B Autoradiography at the Cellular Level, 2nd ed.

Polmar, Norman , The Ships and Aircraft of the U.S. Fleet, 13th ed.

Polmear, I. J. , Light Alloys: Metallurgy of the Light Metals

Polya, G. , How to Solve It: a New Aspect of Mathematical Method, 2nd ed.

Polya, G. , Mathematics and Plausible Reasoning, Vol. 1: Induction and Analogy

in Mathematics

Polya, G. , Mathematics and Plausible Reasoning, Vol. 2: Patterns of Plausible

Inference

Polya, George , Mathematical Discovery: on Understanding, Learning, and Teaching

Problem Solving, Combined ed.

Polyakov, A. F. , Thermo- and Laser Anemometry

Polyanin, Andrei D. , Handbook of Exact Solutions For Ordinary Differential

Equations

Pomasanoff, Alex , The Invisible World

Pomerantz, Martin A. , Cosmic Rays

Pomraning, G. C. , The Equations of Radiation Hydrodynamics

Pond, Caroline M. , The Fats of Life

Pond, Stephen , Introductory Dynamical Oceanography

Ponec, Vladimir , Adsorption on Solids

Ponopmarev, L. I. , The Quantum Dice

Poole, Charles P., Jr. , Introduction to Nanotechnology

Poole, Robert M. (ed.) , The Incredible Machine

Pope, Alan , Wind Tunnel Testing

Pope, Alan , High-Speed Wind Tunnel Testing

Pope, Stephen B. , Turbulent Flows

Popov, E. P. , Mechanics of Materials, 2nd ed.

Popov, Egor P. , Engineering Mechanics of Solids

Popov, V. N. , Functional Integrals and Collective Excitations

Popovic, Branko D. , Introductory Engineering Electromagnetics

Popovic, Dejan , Control of Movement For the Physically Disabled

Popovic, R. S. , Hall Effect Devices: Magntic Sensors and Characterization of

Semiconductors

Popper, Karl R. , The Logic of Scientific Discovery

Popper, Karl R. , The Logic of Scientific Discovery, 2nd ed.

Popper, Karl R. , Quantum Theory and the Schism in Physics: From Postscropt to

the Logic of Scientific Discovery

Popper, Steven W. , New Forces at Work: Industry Views Critical Technologies

Popular Mechanics , Popular Mechanics Farm Manual

Porat, Dan I. , Introduction to Digital Techniques

Porcellino, Michael R. , Through the Telescope: a Guide For the Amateur

Astronomer

Port, Robert F. , Mind As Motion: Explorations in the Dynamics of Cognition

Porteous, I. R. , Topological Geometry

Porter, Brian , Synthesis of Dynamical Systems

Porter, David A. , Phase Transformations in Metals and Alloys

Porter, David , Integral Equations

Porter, Eliot , Nature's Chaos

Porter, Harold W. , Machine Shop Operations and Setups

Porter, M. P. , Handbook of Surfactants

Porter, Neil A. , Physicists in Conflict: From Antiquity to the New Millennium

Porter, Richard W. , The Versatile Satellite

Porter, Roger S. , Ordered Fluids and Liquid Crystals

Porter, Roy , Man Masters Nature: Twenty-five Centuries of Science

Porter, Roy , The Scientific Revolution in National Context

Porter, Tom , How Architects Visualize

Portis, Alan M. , Berkeley Physics Laboratory, 2nd ed.

Portis, Alan M. , Instructor's Manual to Accompany Berkeley Physics Laboratory,

2nd ed.

Portland Cement Association , Handbook of Concrete Culvert Pipe Hydraulics

Portland Cement Association , Laboratory and Exercise Manual on Concrete

Construction

Posner, Michael I. , Images of Mind

Post, E. J. , Formal Structure of Electromagnetics

Postgate, John , Microbes and Man, 3rd ed.

Postgate, John , The Outer Reaches of Life

Poston, Tom , Catastrophe Theory and Its Applications

Poteet, G. Howard , Complete Guide to the Use and Maintenance of Hand and Power

Tools

Potter, E. B. , Sea Power: a Naval History

Potter, Merle C. (ed.) , Fundamentals of Engineering: FE/EIT Review For All A.

M. and General P.M. Subjects, 6th ed.

Potter, Merle C. , Fluid Mechanics

Potter, Ronald W. , The Art of Measurement: Theory and Practice

Potts, Malcolm , Ever Since Adam and Eve: the Evolution of Human Sexuality

Potts, Rick , Humanity's Descent: the Consequences of Ecological Instability

Potts, W. J., Jr. , Chemical Infrared Spectroscopy

Poundstone, William , Carl Sagan: a Life in the Cosmos

Poundstone, William , Big Secrets: the Uncensored Truth About All Sorts of Stuff

You Are Never Supposed to Know

Povejsil, Donald J. , Airborn Radar

Powell, Evan , Popular Science Book of Home Heating ( and Cooling)

Powell, Evan , Complete Guide to Home Appliance Repair

Powell, James , Aircraft Radio Systems

Powell, M. J. D. , Approximation Theory and Methods

Powelson, David R. , The Recycler's Manual For Business, Government, and the

Environmental Community

Power , The Engineer's Reference Library

Powers, David L. , Boundary Value Problems, 2nd ed.

Powers, Scott K. , Exercise Physiology: Theory and Application to Fitness and

Performance, 3rd ed.

Powers, Thomas , Heisenberg's War: the Secret History of the German Bomb

Poynton, James P. , Metering Pumps: Selection and Application

Pozrikidis, C. , Boundary Integral and Singularity Methods For Linearized

Viscous Flow

Pracht, M. , Geology of Dingle Bay

Prager, William , Theory of Perfectly Plastic Solids

Prandtl, L. , The Essentials of Fluid Dynamics

Prandtl, L. , Applied Hydro- and Aeromechanics

Prandtl, L. , Fundamentals of Hydro- and Aeromechanics

Prange, Richard E. (ed) , The Quantum Hall Effect

Prasad, Paras N. , Introduction to Biophotonics

Prasad, Paras N. , Nanophotonics

Prasad, Ray P. , Surface Mount Technology: Principles and Practice

Pratt, Douglas R. , The Advanced Guide to Radio Control Sport Flying

Pratt, Douglas R. , Selecting Radio Control Systems and Components For Your

Airplane, Boat and Buggy

Pratt, William K. , Digital Image Processing, 2nd ed.

Pratt, William K. , Digital Image Processing

Prausnitz, J. M. , Molecular Thermodynamics of Fluid-phase Equilibria

Predko, Myke , Programming and Customizing the 8051 Microcontroller

Predko, Myke , Programming and Customizing PICmicro Microcontrollers

Predko, Myke , Handbook of Microcontrollers

Predko, Myke , Programming Robot Controllers

Preisendorfer, Rudolph W. , Radiative Transfer on Discrete Spaces

Preiss, Byron , The Microverse

Prendergast, Edward J. , Building Construction Related to the Fire Service, 2nd

ed.

Prentice, Geoffrey , Electrochemical Engineering Principles

Prentice, William E. , Therapeutic Modalities For Physical Therapists, 2nd ed.

Prentice, WIlliam E. , Rehabilitation Techniques in Sports Medicine

Prentice, William E. , Therapeutic Modalities in Sports Medicine

Prentiss, Stan , The Complete Book of Oscilloscopes, 2nd ed.

Prentiss, Stan , Electronic Signals and Systems

Preobrazhensky, V. , Measurements & Instrumentation in Heat Engineering, Vol. 1

Preobrazhensky, V. , Measurements & Instrumentation in Heat Engineering, Vol. 2

Preparata, Franco P. , Computational Geometry, an Introduction

Prescott, David M. (ed.) , Methods in Cell Biology, Vol. XII

Prescott, David M. (ed.) , Methods in Cell Biology, Vol. XX

Present, R. D. , Kinetic Theory of Gases

President's Council on Sustainable Development , Sustainable America: a New

Consensus For Prosperity, Opportunity, and a Healthy Environment For the Future

Press, Barry , PC Toys: 14 Cool Projects For Home, Office, and Entertainment

Press, Hans Jurgen , Simple Science Experiments

Press, William H. , Numerical Recipes in C: the Art of Scientific Computing

Press, William H. , Numerical Recipes: the Art of Scientific Computing

Preston, Daryl W. , Experiments in Physics: a Laboratory Manual For Scientists

and Engineers

Preston, Daryl W. , The Art of Experimental Physics

Preston, Diana , A Priate of Exquisite Mind:explorer, Naturalist, and Buccaneer:

the Life of William Dampier

Preston, Kendall, Jr. , Coherent Optical Computers

Preston, M. A. , Structure of the Nucleus

Preston, Richard , The Hot Zone: a Terrifying True Story

Preztak, Louis , Standard Details For Fire-resistive Building Construction

Price, Arthur , J. J. Pizzuto's Fabric Science Swatch Kit, 6th ed.

Price, Fred W. , The Planet Observer's Handbook

Price, Fred W. , The Planet Observer's Handbook, 2nd ed.

Price, G. Baley , Multivariable Analysis

Price, Neville J. , Fault and Joint Development in Brittle and Semi-brittle Rock

Price, Nicholas C. , Principles and Problems in Physical Chemistry For

Biochemists, 2nd ed.

Pridmore, Jay , Museum of Science and Industry, Chicago

Priest, Joseph , Energy: Principles, Problems, Alternative, 4th ed.

Priest, Joseph , Problems of Our Physical Environment: Energy, Transportation,

Pollution

Priest, Joseph , Energy For a Technological Society, 2nd ed.

Priestley, J. B. , Man & Time

Priestley, M. B. , Non-linear and Non-stationary Time Series Analysis

Priestley, M. B. , Spectral Analysis and Time Series: Vol. 1 Univariate Series

Vol. 2 Multivariate Series, Prediction, and Control

Prigogine, ed. I. , Introduction to Thermodynamics of Irreversible Processes, 2nd

Prigogine, ed. I. , Introduction to Thermodynamics of Irreversible Processes, 3rd

Prigogine, Prigogine, I. I. , Non-equilibrium Statistical Mechanics , From Being to Becoming

Prigogine, I. (ed.) , Transport Processes in Statistical Mechanics, Proc. of

International Symposium, Brussels, August 27-31, 1956

Prigogine, I. , Advances in Chemical Physics, Vol.26

Prigogine, I. , Advances in Chemical Physics, Vol.34

Prigogine, Ilya , The End of Certainty: Time, Chaos, and the New Laws of Nature

Prigogine, Ilya , Kinetic Theory of Vehicular Traffic

Prigogine, Ilya , Order Out of Chaos: Man's New Dialogue With Nature

Primack, Alice Lefler , Finding Answers In Science and Technology

Pringle, Laurence , Nuclear Energy: Troubled Past, Uncertain Future

Pringle, Laurence , Rivers and Lakes

Pritchard, R. L. , Electrical Characteristics of Transistors

Priyani, V. B. , A Treatise on Hydraulics and Hydraulic Machinery (fluid

Mechanics), 4th ed.

Proakis, John G. , Digital Communications

Probstein, Ronald , Physicochemical Hydrodynamics: an Introduction, 2nd ed.

Probstein, Ronald , Physicochemical Hydrodynamics: an Introduction

Probstein, Ronald F. , Synthetic Fuels

Prochazka, A. , Signal Analysis and Prediction

Prochiantz, Alain , How the Brain Evolved

Prochnow, Dave , Take This Stuff and Hack It: Transform Everyday Electronics

into Modern Techno-wonders

Prochnow, Dave , Experiments in CMOS Technology

Proctor, M. R. E. , Lectures on Solar and Planetary Dynamos

Proctor, Nick H. , Chemical Hazards of the Workplace, 2nd ed.

Prodger, Rhillip , Time Stands Still: Muybridge and the Instantaneous

Photography Movement

Proffit, William R. , Contemporary Orthodontics, 2nd ed.

Profio, A. Edward , Radiation Shielding and Dosimetry

Prokhorov, A. M. , Coherent Radiation Generation and Particle Acceleration

Prokhorov, A. M. , Laser Heating of Metals

Prokhorov, A. M. , Ferroelectric Crystals For Laser Radiation

Protter, Murray H. , Modern Mathematical Analysis

Protter, Murray H. , Maximum Principles in Differential Equations

Protter, Philip , Stochastic Integration and Differential Equations: a New

Approach

Proulx, Earl , Vinegar, Duct Tape, Milk Jugs & More: 1,001 Ingenious Ways to Use

Common Household Items to Repair, Restore, Revive, Or Replace Just About

Everything in Your Life

Prouty, Raymond W. , Helicopter Performance, Stability,a nd Control

Pruett, Nancy Jones , Scientific and Technical Libraries: Vol. 2 Special Formats

and Subject Areas

Prusinkiewicz, Przemyslaw , The Algorithmic Beauty of Plants

Prust, Z. A. , Photo-offset Lithography

Prutton, M. , Introduction to Surface Physics

Prutton, M. , Surface Physics, 2nd ed.

Psencik, Ivan , Seismic Waves in Laterally Inhomogeneous Media, Part II

Puccia, Charles J. , Qualitative Modeling of Complex Systems: an Introduction to

Loop Analysis and Time Averaging

Puchstein, A. F. , Alternating-Current Machines, 3rd ed.

Pucket, Allen E. , Guided Missile Engineering

Pucknat, A. W. , Health Impacts of Polynuclear Aromatic Hydrocarbons

Pucknell, Douglas A. , Basic VLSI Design, 3rd ed.

Pugh, Alan (ed.) , Robot Sensors, Vol. 1: Vision

Pugh, Emerson M. , Principles of Electricity and Magnetism

Pugh, Frank , Student Workbook For Switching Power Supplies Videocasette

Pugh, Stuart , Total Design: Integrated Methods For Successful Product

Engineering

Pulfrey, David L. , Photovoltaic Power Generation

Pulkrabek, Willard W. , Engineering Fundamentals of the Internal Combustion

Engine

Pullan, Wendy , Structure in Science and Art

Pullman, Bernard , The Atom in the History of Human Thought

Pullman, Bernard (ed.) , The Emergence of Complexity in Mathematics, Physics,

Chemistry and Biology

Pullman, Bernard (ed.) , Frontiers in Physicochemical Biology

Pulos, Arthur J. , The American Design Adventure

Pulyer, Yuly M. , Electromagnetic Devices For Motion Control and Signal

Processing

Pumfrey, Stephen , Lititude & the Magnetic Earth: the True Story of Queen

Elizabeth's Most Distinguished Man of Science

Purcell, Ben , Flying Kites

Purcell, Edward M. , Electricity and Magnetism (Berkeley Physics Course Vol.2)

Purdum, Jack J. , C Programming Guide, 2nd ed.

Purdum, Jack J. , C Programmer's Tollkit

Purdum, Jack J. , C Programmer's Library

Puri, A. N. , Soils: Theor Physics and Chemistry

Purves, Dale , Neuroscience, 2nd ed.

Purves, R. D. , Microelectrode Methods For Intracellular Recording and

Ionophoresis

Putley, E. H. , The Hall Effect and Semiconductor Physics

Putnis, Andrew , Introduction to Mineral Sciences

Pye, David , The Nature and Aesthetics of Design

Pye, David , Polarised Light in Science & Nature

Pyle, Gerald F. , The Diffusion of Influenza: Patterns and Paradigms

Pyne, Stephen J. , World of Fire:the Culture of Fire on Earth

Qasim, Syed R. , Wastewater Treatment Plants: Planning, Design, and Operation

Qu, Zhihua , Robust Tracking Control of Robot Manipulators

Quarmby, David (ed) , Signal Processor Chips

Quercia, Valerie , X Window System User's GUide, OSF/Motif Edition, Vol 3

Quere, Yves , Physics of Materials

Quimby, Ian M. G. (ed.) , Material Culture and the Study of American Life

Quimby, Ian M. G. , Technological Innovation and the Decorative Arts

Quine, W. V. , Philosophy of Logic, 2nd ed.

Quine, W. V. , Methods of Logic, 4th ed.

Quinley, Eva D. , Immunohematology: Prnciples and Practice

Quinn, T. J. , Temperature

Quye, Anita , Plastics: Collecting and Conserving

Raal, J. David , Phase Equilibria: Meaurement and Computation

Rabinbach, Anson , The Human Motor: Energy, Fatigue, and the Origins of

Modernity

Rabiner, L. R. , Digital Processing of Speech Signals

Rabiner, Lawrence R. , Theory and Application of Digital Signal Processing

Rabinovich, M. I. , The Dynamics of Patterns

Rabinovich, Semyon , Measurement Errors: Theory and Practice

Rabonowicz, Ernest , Friction and Wear of Materials, 2nd ed.

Rabonowicz, Ernest , Friction and Wear of Materials

Racihel, Daniel R. , The Science and Applications of Acoustics

Radarsat International , Radarsat Geology Handbook

Radarsat International , Radarsat Illuminated: Your Guide to Products and

Services

Radenmacher, Hans , The Enjoymnent of Mathematics: Selections From Mathematics

For the Amateur

Radinsky, Leonard B. , The Evolution of Vertebrate Design

Radio Corporation of America , RCA Silicon Controlled Rectifier Experimenter's

Manual

Radio Corporation of America , RCA Tunnel Diodes For Switching and Microwave

Applications

Radio Corporation of America , RCA Phototubes and Photocells

Radio Corporation of America , RCA Receivning Tube Manual: Technical Series RC

Radio Shack , Enercell Battery Book: Complete Battery Information For the User

and the Designer

Radlow, James , Understanding Finite Mathematics

Radmanesh, Matthew M. , Radio Frequency and Microwave Electronics

Radnitzky, Gerard , Centripetal Forces in the Sciences, Vol. I

Radnitzky, Gerard , Centripetal Forces in the Sciences, Vol. 2

Rae, Alastair , Quantum Physics: Illusion Or Reality

Rae, William H., Jr. , Low-Speed Wind Tunnel Testing, 2nd ed.

Raffel, M. , Particle Image Velocimetry

Ragsdale, L. A. , Building Materials Technology

Rahtjen, Don , Square Wheels and Other Easy-to-build Hands-on Science Activities

Rainer, George , Understanding Infrastructure: a Guide For Architects and

Planners

Rainey, Fred A. , Extremophiles

Rainis, Kenneth G. , Exploring With a Magnifying Glass

Rainis, Kenneth G. , Nature Projects For Young Scientists

Rainvolle, Earl D. , The Laplace Transform: an Introduction

Raizer, Yuri P. , Gas Discharge Physics

Ralston, Anthony , A First Course in Numerical Analysis

Ramanathan, N (ed.) , Collagen

Ramaswami, Rajiv , Optical Networks: a Practical Perspective

Ramey, Robert L. , Electronics and Instrumentation

Ramirez, Robert W. , The FFT: Fundamentals and Concepts

Ramirez, W. Fred , Process Simulation

Ramirez, W. Fred , Computational Methods For Process Simulation

Ramm, A. G. , The Radon Transform and Local Tomography

Ramm, Heinrich J. , Fluid Dynamics For the Study of Transonic Flow

Ramo, Simon , Fields and Waves in Communication Electronics

Ramond, P. , Field Theory: A Moder Primer

Rampaul, Hoobasar , Pipe Welding Procedures

Ramsay, J. A. , The Experimental Basis of Modern Biology

Ramsay, John G. , Folding and Fracture of Rocks

Ramsay, John G. , The Techniques of Modern Structural Geology, Vol. 1: Strain

Analysis

Ramsden, Edward , Hall Effect Sensors: Theory and Application

Ramsey, Dan , Builder's Guide to Barriers: Doors, Windows & Trim

Ramsey, Norman F. , Molecular Beams

Ramshaw, Raymond , Power Electronics: Thyristor Controlled Power For Electric

Motors

Rancourt, James D. , Optical Thin Films: Users' Handbook

Rand, D. A. , Dynamical Systems and Turbulence, Warwick 1980: Proceedings

Rand, Gary M. , Fundamentals of Aquatic Toxicology: Methods and Applications

Rand, Paul , Design, Form, and Chaos

Rand, Richard H. , Perturbation Methods, Bifurcation Theory and Computer Algebra

Randall, David , Eckert Animal Physiology: Mechanisms and Adaptations, 4th ed.

Randall, Robert H. , An Introduction to Acoustics

Randolph, Randy , Radio Control Airplanes

Ransom, Robert , Text and Document Processing in Science and Technology

Rao, Bindu R. , C++ and the OOP Paradigm

Rao, J. S. , Turbomachine Blade Vibration

Rao, M. M. , Measure Theory and Integration

Rao, N. Narayana , Elements of Engineering Electromagnetics, 4th ed.

Rao, N. Narayana , Elements of Engineering Electromagnetics

Rao, Singiresu S. , Mechanical Vibrations

Rapoport, Anatol , Prisoner's Dilemma

Rapp, Donald , Quantum Mechanics

Rappoportm Zvi , Handbook of Tables For Organic Compound Identification, 3rd ed.

Rasband, S. Neil , Dynamics

Rasband, S. Neil , Chaotic Dynamics of Nonlinear Systems

Rasch, Philip J. , Kinesiology and Appled Anatomy, 7th ed.

Rasetti, Franco , Elements of Nuclear Physics

Rasetti, Mario , Modern Methods in Equilibrium Statistical Mechanics

Rashkovich, L. N. , KDP-Family Single Crystals

Rasis, E. P. , Technical Reference Handbook

Raskhodoff, Nicholas M. , Electronic Drafting and Design, 4th ed.

Raskin, Chuck , Designing With Motion Handbook, 5th ed.

Rastogi, Pramod K. (ed.) , Optical Measurement Techniques and Applications

Ratledge, David , The Art and Science of Ccd Astronomy

Ratner, A. M. , Spectral, Spatial, and Temporal Properties of Lasers

Ratner, Buddy D. , Biomaterials Science: an Introduction to Materials in

Medicine

Ratzlaff, Kenneth L. , Introduction to Computer-assisted Experimentation

Rau, Joseph L., Jr. , Respiratory Care Pharmacology, 4th ed.

Rauschenbach, Hans S. , Solar Cell Array Design Handbook

Rauschkolb, Roy S. , Nitrogen Management in Irrigated Agriculture

Rauwendaal, Chris , Polymer Extrusion

Rawlence, Christopher (ed.) , About Time

Rawlings, A. L. , The Science of Clocks and Watches, 3rd ed.

Rawlings, A. L. , The Theory o f the Gyroscopic Compass and Its Deviations

Rawlings, Charles A. , Electrocardiography

Rawlins, Greogry J. E. , Foundations of Genetic Algorithms

Rawson, K. J. , Basic Ship Theory, Vol. 1, 4th ed.

Rawson, K. J. , Basic Ship Theory, Vol. 2, 4th ed.

Ray, John , Max OS X Tiger Unleashed

Ray, Sidney F. , High Speed Photography and Photonics

Ray, Sidney F. , Applied Photographic Optics, 2nd ed.

Ray, Slim , Swiftwater Rescue

Raychaudhuri, A. K. , Classical Mechanics: a Course of Lectures

Rayl, John , Max OS X Unleashed

Rayleigh, Lord (John William Strutt) , Scientific Papers, Vol. I (1869-1881) and

II (1881-1887)

Rayleigh, Lord (John William Strutt) , Scientific Papers, Vol. III (1887-1892)

and IV (1892-1901)

Rayleigh, Lord (John William Strutt) , Scientific Papers, Vol. IV (1902-1910)

and V (1911-1919)

Rayleigh, Lord J. W. S. , The Theory of Sound, Vol. 1

Rayleigh, Lord J. W. S. , The Theory of Sound, Vol. 2

Raymond, Eric S. , The Cathedral and the Bazaar: Musings on Linux and Open

Source By an Accidental Revolutionary

Raymond, Robert , Out of the Fiery Furnace: the Impact of Metals on the History

of Mankind

Razeghi, Manijeh , The MOCVD Challenge, Vol. 2: a Survey of GaInAsP-GaAs For

Photonic and Electronic Applications

RCA Corporation , RCA Designer's Handbook: Solid State Power Circuits

RCA Corporation , RCA Transistor Manual

RCA Corporation , RCA Solid State Hobby Circuits Manual

RCA Corporation , RCA Linear Integrated Circuit Fundamentals

RCA Corporation , Electro-Optics Handbook

Read, John W. (ed.) , Gate Arrays: Design Techniques and Applications

Read, Timothy R. C. , Goodness of Fit Statistics For Discrete Multivariate Data

Read, W. T. , Dislocations in Crystals

Reade, John B. , An Introduction to Mathematical Analysis

Reader's Digest , Home Improvements Manual: a Do-it-yourself Guide to

Renovating, Modernizing, and Adding Space to Your Home

Reader's Digest , Joy of Nature: How to Observe and Appreciate the Great

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Reader, John , Man on Earth

Readman, Mark C. , Flexible Joint Robots

Ready, John F. , Effects of High-power Radiation

Recio, Belinda , The Inventor's Handbook

Reddy, J. N. , Advanced Engineering Analysis

Redford, G. D. , Mechanical Engineering Design, 2nd ed. revised in SI Units

Redhead, P. A. , The Physical Basis of Ultrahigh Vacuum

Redish, E. F., ed. , The Conference on Computers in Physics Instruction:

Proceedings

Redlin, Michael H. , Managing Hospitality Engineering Systems

Redman, Barbara Klug , The Practice of Patient Education

Reed, David W. , Spirit of Enterprise: the 1987 Rolex Awards

Reed, James S. , Principles of Ceramic Processing, 2nd ed.

Reed, Michael , Methods of Modern Mathematical Physics, Vol. 1:functional

Analysis, revised and Enlarged ed.

Reed, Myril B. , Alternating-current Circuit Theory

Reed, Myril B. , Mathematical Methods in Eelctrical Engineering

Reed, Richard J. , North American Combustion Handbook, Vol. I, 3rd ed.

Reed, Richard P. , Materials at Low Temperatures

Reed, S. J. B. , Electron Microprobe Analysis, 2nd ed.

Reed-Hill, Robert E. , Physical Metallurgy Principles, 2nd ed.

Rees, A. R. , Proteinengineering: a Practical Approach

Rees, Martin , Just Six Numbers: the Deep Forces That Shape the Universe

Rees, Martin , Black Holes, Gravitational Waves and Cosmology

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Rees, Robin , The Way Nature Works

Rees, W. G. , Physical Principles of Remote Sensing

Rees, W. G. , Physics By Example: 200 Problems and Solutions

Reeves, E. A. (ed.) , Newnes Electrical Pocket Book

Reeves, Hubert , The Hour of Our Delight: Cosmic Evolution, Order, and

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Refi, James J. , Fiber Optic Cable - a Lightguide

Regel, Lia , Materials Science in Space: Theory, Experiments, and Technology

Regezi, Joseph A. , Oral Pathology: Clinical-pathologic Correlations

Regis, Ed , Nano: the Emerging Sicence of Nanotechnology: Remaking the World -

Molecule By Molecule

Regtien, P. P. L. , Instrumentation Electronics

Reich, Herbert J. , Functional Circuits and Oscillators

Reich, Herbert J. , Principles of Electron Tubes: Understanding and Designing

Simple Circuits

Reich, Herbert J. , Theory and Applications of Electron Tubes, 2nd ed.

Reich, Herbert J. , Principles of Electron Tubes, 2nd ed.

Reich, Herbert J. (ed.) , Very High Frequency Techniques, Vol. I

Reich, Herbert J. (ed.) , Very High Frequency Techniques, Vol. II

Reich, Jens-Georg , C Curve Fitting and Modeling For Scientists and Engineers

Reichardt, Jasia , Robots: Fact, Fiction, and Prediction

Reichenbach, Hans , The Philosophy of Space and Time

Reichenbach, Hans , THe Direction of Time

Reichl, L. E. , A Modern Course in Statistical Physics

Reichl, L. E. , The Transition to Chaos: in Conservative Classical Systems:

Quantum Manifistations

Reichl, L. E. , Instabilities, Bifurcations, and Fluctuations in Chemical

Systems

Reid, C. , Hilbert

Reid, Constance , Hilbert - Courant

Reid, Esmond , Understanding Buildings: a Multidisciplinary Approach

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Reid, Robert C. , The Properties of Gases and Liquids, 3rd ed.

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Reid,William , Weapons Through the Ages

Reif, F. , Fundamentals of Statistical and Thermal Physics

Reif,F. , Statistical Physics (Berkeley Physics Course, Vol.5)

Reilly, Joseph , Physico-Chemical Methods, Vol. 1, 5th ed.

Reilly, Joseph , Physico-Chemical Methods, Vol. II, 5th ed.

Reilly, Joseph , Physico-Chemical Methods, Vol. III (Supplementary)

Reimann, Arnold L. , Physics, Vol. III: Modern Physics

Reineck, H.-E. , Depostional Sedimentary Environments: With Refeence to

Terrigenous Clastics

Reiner, Markus , Deformation, Strain, and Flow: An Elementary Introduction to

Rheology, 2nd ed.

Reingold, Edward M. , Data Structures

Reinhard, Herve , Differential Equations: Foundations and Applications

Reintjes, John F. , Nonlinear Optical Parametric Processes in Liquids and Gases

Reis, Ronald A. , Electronic Project Design and Fabrication

Reischman, M. M. , Nonlinear Interaction Effects and Chaotic Motions: 1988

International Symposium on Flow-induced Vibration and Noise

Reiss, Howard , Methods of Thermodynamics

Reiss, Michael J. , Improving Nature? The Science and Ethics of Genetic

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Reiter, Elmar R. , Jet Streams: How Do They Affect Our Weather?

Reiter, Elmar R. , Atmospheric Transport Processes, Part I.: Energy Transfers

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Reiter, Elmar R. , Atmospheric Transport Processes, Part 2: Chemical Tracers

Reitz, Allen B. , Inositol Phosphates and Derivatives: Synthesis, Biochemistry,

and Therapeutic Potential

Reitz, John R. , Foundations of Electromagnetic Theory

Relethford, John , The Human Species: an Introduction to Biological Anthropology

Rellich, Franz , Perturbation Theory of Eigenvalue Problems

Remmey, G. Bickley, Jr. , Firing Ceramics

Remoissenet, M. , Waves Called Solitons: Concepts and Experiments

Renner, Jeff , Lightning Strikes: Staying Safe Under Stormy Skies

Rensbderger, Boyce , Life Itself: Exploring the Realm of the Living Cell

Repp, Victor E. , Metalwork Technology and Practice, 7th ed.

Resibois, P. , Classical Kinetic Theory of Fluids

Resistance Welder Manufacturers' Association , Resistance Welding Manual

Resnick, Mitchel , Turtles, Termites, and Traffic Jams: Explorations in

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Resnick, R. , Physics, Part 1.

Resnick, R. , Physics, Part 2.

Resnick, Sidney I. , Adventures in Stochastic Processes

Resnikoff, H. L. , Mathematics in Civilization

Rettinger, Michael , Acoustics: Room Design and Noise Control

Revkin, Andrew , Global Warming: Understanding the Forecast

Rex, Andrew F. , Integrated Physics and Calculus, Vol. 1

Rexnord Process Machinery Division , Nordberg Process Machinery Reference Manual

Rey, R. F. , Engineering and Operations in the Bell System, 2nd ed.

Reynold, George O. , The Brain

Reynolds, George O. , Physical Optics Notebook: Tutorials in Fourier Optics

Reynolds, W. C. , Thermodynamic Properties in SI

Reynolds, William C. , Engineering Thermodynamics

Reza, Fazlollah M. , Introduction to Information Theory

Rhein, Michael J. , Anatomy of the Lighthouse

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Technology

Rhoades, Rodney A. , Medical Physiology

Rhodes, Daniel , Stoneware and Porcelain: the Art of High-fired Pottery

Rhodes, Daniel , Clay and Glazes For the Potter, Rev. ed.

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Macromolecular Models, 3rd ed.

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Rhody, Alan , Holography Marketplace, 7th ed.

Riaz, M. , Electrical Engineering Laboratory Manual

Riaziat, M. Leonard , Introduction to High-speed Electronics and Optoelectronics

Ribbens, William B. , Understanding Automotive Electronics, 5th ed.

Ribner, H. S. , Aerodynamic Noise

Ricard-Blum, Silvia , Unconventional Collagens: Types VI, VII, VIII, IX, X, XIV,

XVI, and XIX

Rice, Dale H. , Surgical Pathology of the Head and Neck

Rice, Francis Owen , The Structure of Matter

Rice, Paul , Timesource: a Handy Compendium of Facts and Uses

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Rice, R. J. , Fundamentals of Geomorphology

Rice, Stuart A. , The Kinetic Theory of Dense Fluids

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Applications

Rich, George R. , Hydraulic Transients, 2nd Rev. and Enlarged ed.

Rich, Jack C. , The Materials and Methods of Scultpure

Rich, Jack C. , Sculpture in Wood

Richard, Adrian , Epilepsy: a New Approach

Richard, Patrick (ed.) , Atomic Physics: Accelerators

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Richards, E.G. , An Introduction to the Physical Properties of Large Molecules

in Solution

Richards, J. A. , Analysis of Periodically Time-varying Systems

Richards, Keith , Rivers: Form and Process in Alluvial Channels

Richards, P. H. (ed.) , Optical Measurements in Fluid Mechanics 1985

Richards, R. J. , An Introduction to Dynamics and Control

Richards, Whitman, ed. , Natural Computation

Richardson, E. G. , Ultrasonic Physics

Richardson, Jim (ed.) , Kilauea: the Flow to the Sea

Richardson, K. I. T. , The Gyroscope Applied

Richardson, Robert C. , Experimental Techniques in Condensed Matter Physics at

Low Temperatures

Richardson, Robert S. , The Stars & Serendipity

Richardson, Terry , Composites: a Design Guide

Richardson, Terry L. , Industrial Plastics: Theory and Application, 3rd ed.

Riches, Colin , Ship Models From Kits

Richmond,J.C. ed. , Space Simulation

Richter, Charles F. , Elementary Seismology

Richter, Herbert W. , Electrical and Electronic Drafting

Richter, Herbert , Practical Electric Wiring, 12th ed

Richter, Herbert , Practical Electric Wiring, 16th ed.

Richter, Jean Paul (ed.) , The Notebooks of Leonardo Da Vinci, Vol. II

Richter, Joel D. (ed.) , A Comparative Methods Approach to the Study of Oocytes

and Embryos

Richtmeyer, F. K. , Introduction to Modern Physics

Richtmyer, Robert D. , Principles of Advanced Mathematical Physics, Vol.1

Richtmyer, Robert D. , Principles of Advanced Mathematical Physics, Vol.2

Richtmyer, Robert D. , Difference Methods For Initial Value Problems, 2nd ed.

Ricklefs, Robert E. , Aging: a Natural History

Riddle, Alfred , Applied Electronic Engineering With Mathematica

Riddle, Robert L. , Transistor Physics and Circuits

Ridenour, Louis N. (ed.) , Modern Physics For the Engineer: Second Series

Ridenour, Louis N. , Modern Physics For the Engineer: Second Series

Ridgeway, James , Powering Civilization: the Complete Energy Reader

Ridley, B. K. , The Physical Environment

Riebsame, William E. , Atlas of the New West: Portrait of a Changing Region

Riedel, Hermann , Fracture at High Temperatures

Riederer, Stephen J. (ed.) , Medical Imaging: Selected Reprints

Rieger, Philip H. , Electrochemistry, 2nd ed.

Riehl, Nikolaus , Physics of Ice

Rieke, Fred , Spikes: Exploring the Neural Code

Rieke, G. H. , Detection of Light From the Ultraviolet to the Submillimeter

Riesz, Frigyes , Functional Analysis

Rietman, Edward , Exploring the Geometry of Nature: Computer Modeling of Chaos,

Fractals, Cellular Automata, and Neural Networks

Rigden, John S. , Rabi: Scientist and Citizen

Rigden, John S. , Hydrogen: the Essential Element

Rigden, John S. (ed.) , Macmillan Encyclopedia of Physics, Vol. 1

Rigden, John S. (ed.) , Macmillan Encyclopedia of Physics, Vol. 2

Rigden, John S. (ed.) , Macmillan Encyclopedia of Physics, Vol. 3

Rigden, John S. (ed.) , Macmillan Encyclopedia of Physics, Vol. 4

Riggs, Douglas Shepard , The Mathematical Approach to Physiological Problems: a

Critical Primer

Rigler, R. , Fluorescence Correlation Spectroscopy: Theory and Applications

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Riley, Frank J. , Assembly Automation: a Management Handbook

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Transportation Paradigm

Rinehart, John S. , Behavior of Metals Under Impulsive Loads

Rinehart, Ronald E. , Radar For Meteorologists

Rinker, Robert A. , Understanding Firearm Ballistics: Basic to Advanced

Ballistics, Simplified, Illustrated and Explained, 6th ed.

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of the Information Age

Riordan, Michael , The Shadows of Creation: Dark Matter and the Structure of the

Universe

Ripka, L. V. , Plumbing: Installation and Design, 2nd ed.

Ripley, Brian D. , Spatial Statistics

Ripley, Brian D. , Stochastic Simulation

Ripps, David L. , An Implementation Guide to Teal-time Programming

Risebero, Bill , The Story of Western Architecture

Risken, H. , The Fokker-Planck Equation: Methods of Solution and Applications,

2nd ed.

Ristic, Ljubisa (ed.) , Sensor Technology and Devices

Ristinen, Robert A. , Energy and the Environment

Ritchie, Carson I. A. , Making Scientific Toys

Ritchie, David , The Encyclopedia of Earthquakes & Volcanoes

Ritchie, George L. , Electronics Construction and Assembly

Ritchie, P. D. (ed.) , Physics of Plastics

Ritsko, Alan J. , Lighting For Location Motion Pictures

Ritter, Dale F. , Process Geomorphology

Ritter, Dale F. , Process Geomorphology, 2nd ed.

Ritter, Helge , Neural Computation and Self-organizing Maps: an Introduction

Ritter, M. A. , The Thymus

Ritterbush, Philip C. , The Art of Organic Forms

Ritvo, Harriet , The Platypus and the Mermaid and Other Figments of the

Classifying Imagination

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Manufacturing

Rivers, Beverly (ed.) , Your Guide to Creativity

Rivin, Eugene I. , Mechanical Design of Robots

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Roach, G. F. , Green's Functions, 2nd ed.

Roache, Patrick J. , Fundamentals of Computational Fluid Dynamics

Roache, Patrick J. , Computational Fluid Dynamics, revised ed.

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ed.

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Robbins, jennifer Niederst , Web Design in a Nutshell, 3rd ed.

Robbins, Maurice , The Amateur Archeologist's Handbook, 3rd ed.

Robbins-Roth, Cynthia (ed.) , Alternative Careers in Science

Roberge, James K. , Operational Amplifiers: Theory and Practice

Robert I. Sutherland Laboratory Staff , Care and Feeding of Power Grid Tubes

Roberts, A. D. , Contact Stress Analysis

Roberts, Arthur D. , Programming For Numerical Control Machines

Roberts, David D. , Cell Surface and Extracellular Glycoconjugates: Structure

and Function

Roberts, Fred S. , Applied Combinatorics

Roberts, J. E. , Meandering in Medical Physics

Roberts, J. K. , Heat and Thermodynamics, 5th ed.

Roberts, John D. , Nuclear Magnetic Resonance: Applications to Organic Chemistry

Roberts, John L. , Geological Maps and Structures

Roberts, L. E. J. , Power Generation and the Environment

Roberts, Nancy , Introduction to Computer Simulation: a System Dynamics Modeling

Approach

Roberts, Rex , Your Engineered House

Roberts, Richard W. , Ultrahigh Vacuum and Its Applications

Roberts, Royston M. , An Introduction to Modern Experimental Organic Chemistry,

2nd ed

Roberts, Royston M. , Experimental Organic Chemistry: a Miniscale Approach

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Installing Small Photovoltaic Systems

Robertson, G. Ross , Laboratory Practice of Organic Chemistry

Robertson, Harry S. , Statistical Thermophysics

Robertson, John K. , Atomic Artillery

Robichaux, Paul , Managing Microsoft Exchange Server

Robillard, Jean , Industrial Applications of Holography

Robin, Harry , The Scientific Image: From Cave to Computer

Robinson, Allan R. (ed.) , Eddies in Marine Science

Robinson, Allan R. , Oceanography and Acoustics: Prediction and Propagation

Models

Robinson, Andrew , Earth Shock: Hurricanes, Volcanoes, Earthquakes, Tornadoes

and Other Forces of Nature

Robinson, Arthur H. , Elements of Cartography, 3rd ed.

Robinson, Clark , Dynamical Systems: Stability, Symbolic Dynamics, and Chaos

Robinson, Clark Shove , Elements of Fractional Distillation, 4th ed.

Robinson, David W. , Interferogram Analysis: Digital Fringe Pattern Measurement

Techniques

Robinson, Enders A. , Statistical Communication and Detection: With Special

Reference to Digital Data Processing of Radar and Seismic Signals

Robinson, Enders A. , Multichannel Time Series Analysis With Digital Computer

Programs, 2nd ed.

Robinson, Enders A. , Multichannel Time Series Analysis With Digital Computer

Programs, Rev. ed.

Robinson, Enders A. , Digital Signal Processing and Time Series

Robinson, Enders A. , Geophysical Signal Analysis

Robinson, Eric , Partners in Science: James Watt & Joseph Black

Robinson, F. N. H. , Noise and Fluctuations in Electronic Devices and Circuits

Robinson, Howard A. (ed.) , High-polymer Physics: a Symposium

Robinson, I. S. , Satellite Oceanography

Robinson, J. W. (ed.) , Handbook of Spectroscopy, Vol. I

Robinson, J. W. (ed.) , Handbook of Spectroscopy, Vol. II

Robinson, James W. , Atomic Absorption Spectroscopy, 2nd ed.

Robinson, Joseph E. , Computer Applications in Petroleum Geology

Robinson, L. C. (ed.) , Physical Principles of Far-Infrared Radiation

Robinson, Leif J. , Outdoor Optics

Robinson, Robert L. , Complete Course in Professional Locksmithing

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Robinson, William L. , Wildlife Ecology and Management, 2nd ed.

Robson, J. D. , An Introduction to Random Vibration

Robson, Pam , Encyclopedia of Science Projects

Robyt, John F. , Biochemical Techniques: Theory and Practice

Rocard, Y. , General Dynamics of Vibrations

Rocard, Y. , Dynamic Instability: Automobiles, Aircraft, Suspension Bridges

Rochat, Caterina , Time: the Measuring of Time - From Egyptian Calendar to the

Atomic Clock

Rochow, Eugene G. , An Introduction to the Chemistry of the Silicones, 2nd ed.

Rochow, Theodore Goerge , An Introduction to Microscopy By Means of Light, X

rays, Or Ultrasound

Rock, Irvin , Perception

Rockis, Gary , Electrical Motor Controls

Rodale Press , Rodale's Complete Home Products Manual: the Best Guide For Using

and Maintaining Your Appliances, Tools, Furnishings - and More!

Rodberg, Leonard S. , Introduction to the Quantum Theory of Scattering

Rodd, Rosemary , Biology, Ethics and Animals

Roddy, Dennis , Satellite Communications, 2nd ed.

Roddy, Dennis , Electronic Communications

Rodean, Howard C. , Nuclear Explosion Seismology

Rodgers, Glen E. , Introduction to Coordination, Solid State, and Descriptive

Inorganic Chemistry

Rodolph, James Smith , Making Your Own Working Paper Clock

Rodriguez, Ferdinand , Principles of Polymer Systems, 2nd ed.

Rodriguez, Walter , The Modeling of Design Ideas: Grpahics and Visualization

Techniques For Engineers

Roe, Anne , The Making of a Scientist

Roe, Byron P. , Probability and Statistics in Experimental Physics

Roe, Byron P. , Particle Physics at the New Millennium

Roe, L. B. , Practices and Procedures in Industrial Electrical Design

Roederer, Juan G. , The Physics and Psychophysics of Music: an Introduction

Roffel, Brian , Process Dynamics, Control and Protection

Rogers, C. , Nonlinear Boundary Value Problems in Science and Engineering

Rogers, Eric M. , Physics For the Inquiring Mind

Rogers, G. F. C. , Engineering ThermodynamicsWork and Heat Transfer, 3rd ed.

Rogers, G. L. , Noncoherent Optical Processing

Rogers, John H. , The Giant Planet Jupiter

Rogers, John J. W. , A History of the Earth

Rogers, Martha E. , An Introductiuon to the Theoretical Basis of Nursing

Roggersdorf, Wilhelm , In the Realm of Chemistry: Centenary of BASF

Rohde, Ulrich L. , Communications Receivers: Principles and Design

Rohlfs, Kristen , Tools of Radio Astronomy

Rohnke, Karl , Cowstails & Cobras: a Guide to Ropes Courses, Initiative Games,

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Rohr, Rene R. J. , Sundials: History, Theory, and Practice

Rohsenow, Warren M. , Handbook of Heat Transfer

Roitt, Ivan , Essential Immunology, 5th ed.

Rojansky, Vladimir , Electromagnetic Fields and Waves

Rolf, Ida P. , ROlfing: Reestablishing the Natural Alignment and Structural

Integration of the Human Body For Vitality and Well-being

Rolfs, Claus E. , Cauldrons in the Cosmos: Nuclear Astrophysics

Romanov, V. G. , Inverse Problems of Mathematical Physics

Romberg, T. M. , Signal Processing For Industrial Diagnostics

Rome Laboratory , Reliability Toolkit: Commercial Practices Edition, a Practical

Guide For Commercial Products and Military Systems Under Acquisition Reform

Romer, Alfred , The Restless Atom

Romney, Marshall B. , Accounting Information Systems, 7th ed.

Romoser, William S. , The Science of Entomology

Ronan, Colin (ed) , Amateur Astronomy: a Comprehensive and Practical Survey

Ronchi, Vasco , Optics: the Science of Vision

Ronin, Colin A. (ed.) , Science Explained: the World of Science in Everyday Life

Rony, Peter R. , Logic and Memory Experiments Using TTL Integrated Circuits,

Book 1

Rony, Peter R. , Logic and Memory Experiments Using TTL Integrated Circuits,

Book 2

Roodyn, D. B. , Automated Enzyme Assays

Rooney, D. E. , Human Cytogenics: a Practical Approach, Vol. I: Constitutional

Analysis, 2nd ed.

Rooney, D. E. , Human Cytogenics: a Practical Approach, Vol. II. Malignancy and

Acuired Abnormalities, 3rd ed.

Roosendaal, Ton , Blender 2.3 Guide: Fee 3D Creation Suite For Modeling,

Animation, and Rendering

Roosendaal, Ton , The Official Blender Gamekit: Interactive 3-D For Artists

Root-Bernstein, Robert , Honey, Mud, Maggots, and Other Medical Marvels: the

Science Behind Folk Remedies and Old Wives' Tales

Roper, C. A. , The Complete Book of Locks and Locksmithing, 3rd ed.

Rorabaugh, Britt , Signal Processing Design Techniques

Rorabaugh, Britt , Mechanical Devices For the Electronics Experimenter

Rosaler, Robert C. , Standard Handbook of Plant Engineering

Rosandich, Ryan G. , Fundamentals of Programmable Logic Controllers

Rose, Augustus , How to work copper for fun and profit

Rose, Donald J. , Sparse Matrices and Their Applications

Rose, M. E> , Elementary Theory of Angular Momentum

Rose, Rich , Drafting Scenery For Theater, Film, and Television

Rose, Robert M. , The Structure and Properties of Materials, Vol. IV: Electronic

Properties

Rose, S. Meryl , Regeneration: Key to Understanding Normal and Abnormal Growth

and Development

Rose, Sharon , How Things Are Made: From Automobiles to Zippers

Rose, Steven , Lifelines: Biology Beyond Determinism

Rose, Steven , The Making of Memory

Rose, William K. , Advanced Stellar Astrophysics

Rose-Innes, A. C. , Low Temperature Techniques: the Use of Liquid Helium in the

Laboratory

Rose-Innes, A. C. , Introduction to Superconductivity, 2nd ed.

Rosebury, Fred , Handbook of Electron Tube and Vacuum Techniques

Rosen, Dennis , London Science: Museums, Libraries, and Places of Scientific,

Technological & Medical Interest

Rosen, Joe , Symmetry Discovered

Rosen, Joe , Symmetry in Science: an Introduction to the General Theory

Rosen, Kenneth H. , Discrete Mathematics and Its Applications, 2nd ed.

Rosen, Michael R. , Cardiac Electrophysiology: a Textbook

Rosen, Milton J. , Surfactants and Interfacial Phenomena, 2nd ed.

Rosen, Stephen L. , Fundamental Principles of Polymeric Materials, 2nd ed.

Rosen, VIcki , The Cellular and Molecular Basis of Bone Formation and Repair

Rosenberg, H. M. , Low Temperature Solid State Physics: Some Selected Topics

Rosenberg, H. M. , The Solid State, 2nd ed.

Rosenberg, Milton , Audels Programmed Basic Electricity Course, 2nd ed.

Rosenberg, Robert , Electric Motor Repair, 3rd ed.

Rosenberg, S. , The Laplacian on a Riemannian Manifold

Rosenblatt, Jack , Direct and Alternating Current Machinery

Rosenblatt, Murray , Random Processes

Rosenblum, L. , Scientific Visualization: Advances and Challenges

Rosencher, Emmanuel , Optoelectronics

Rosenfeld, A. (ed.) , Digital Picture Analysis

Rosenfeld, Azriel , Digital Picture Processing, Vol.1, 2nd ed.

Rosenfeld, Azriel , Digital Picture Processing, Vol.2, 2nd ed.

Rosenfeld, Jo Ann , Women's Health in Primary Care

Rosenhead, L. (ed) , Laminar Boundary Layers

Rosenlicht, Maxwell , Introduction to Analysis

Rosenstein, Milton , Modern Electronic Devices

Rosensweig, R. E. , Ferrohydrodynamics

Rosenthal, Jean , The Magic of Light: the Craft and Career of Jean Rosenthal,

Pioneer in Lighting For the Modern Stage

Roses, Daniel F. , Breast Cancer

Rosinski, Richard R. , The Development of Visual Perception

Rosner, Daniel E. , Transport Processes in Chemically Reacting Flow Systems

Rosner, Roy D. , Distributed Telecommunications Networks Via Satellites and

Packet Switching

Ross, Andrew , Strange Weather: Culture, Science, and Technology in the Age of

Limits

Ross, Barry , Hands-on Guide to Oscilloscopes

Ross, David , Energy From the Waves, 2nd ed.

Ross, Donald , Mechanics of Underwater Noise

Ross, Douglas A. , Optoelectronic Devices and Optical Imaging Techniques

Ross, Fred F. , OCR With a Smile

Ross, Ian M. , The Transistor: Dawn of a New Era

Ross, J. N. , The Essence of Power Electronics

Ross, John , The Complete Printmaker

Ross, Michael H. , Histology: a Text and Atals, 3rd ed.

Ross, Michael H. , Histology: a Text and Atals, 2nd ed.

Ross, N.P. ed. , The Wonders of Life on Earth

Ross, Sheldon M. , Applied Probability Models With Optimization Applications

Ross, Sheldon M. , Simulation, 2nd ed.

Ross, Sydney , Chemistry and Physics of Interfaces

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Ross, Sydney , On Physical Adsorption

Ross, Timothy J. , Fuzzy Logic With Engineering Applications

Rossell, Henry E. , Principles of Naval Architecture, Vol. Two

Rosser, J. Barkley , Space Mathematics, Part 1

Rosser, J. Barkley , Space Mathematics, Part 2

Rosser, J. Barkley , Space Mathematics, Part 3

Rosser, J. Barkley, Jr. , From Catastrophe to Chaos: a General Theory of

Economic Discontinuities

Rossi, Bruno , High-energy Physics

Rossing, Thomas D. , The Science of Sound

Rossini, Frederick D. , Thermodynamics and Physics of Matter

Rossiter, Margaret W. , Women Scientists in America: Struggles and Strategies to

1940

Rossman, Allan J. , Workshop Statistics: Discovery With Data and the Graphing

Calculator

Rossnagel, W. E. , Handbook of Rigging: For Construction and Industrial

Operations, 4th ed.

Rossotti, Hazel , Colour: Why the World Isn't Grey

Rossotti, Hazel , Fire: Technology, Symbolism, Ecology, Science, Hazard

Roth, A. , Vacuum Sealing Techniques

Roth, Charles E. , The Amateur Naturalist: Exploraitons and Investigations

Roth, G. D. (ed.) , Astronomy: a Handbook

Roth, Gunter Dietmar (ed.) , Compendium of Practical Astronomy, Vol. 1:

Instrumentation and Reduction Techniques

Roth, Gunter Dietmar (ed.) , Compendium of Practical Astronomy, Vol. 2: Earth

and the Solar System

Roth, Gunter Dietmar (ed.) , Compendium of Practical Astronomy, Vol. 3: Stars

and Stellar Systems

Roth, J. Reece , Industrial Plasma Engineering, Vol. 1: Principles

Roth, Klaus , NMR-tomography and Spectroscopy in Medicine: an Introduction

Roth, Leland , Understanding Architecture: Its Elements, History, and Meaning

Roth, Stephen (ed.) , Molecular Approahces to Supracellular Phenomena

Rothbart, Harold A. , Mechanical Design Handbook

Rothemann, Karl , Das Grosse Rezeptbuch Der Haut- Und Korperpflegemittel, 3rd

ed.

Rothenberg, Albert , Creativity and Madness

Rothenberg, David , Writing on Water

Rothenberg, Polly , The Complete Book of Creative Glass Art

Rothman, Milton A. , The Science Gap: Dispelling the Myths and Understanding the

Reality of Science

Rothman, Milton A. , Discovering the Natural Laws: the Experimental Basis of

Physics

Rothman, Stephen , Lessons From the Living Cell: the Limits of Reductionism

Rothman, Tony , Everything's Relative and Other Fables From Science and

Technology

Rothman, Tony , Frontiers of Modern Physics: New Perspectives on Cosmology,

Relativity, Black Holes and Extraterrestrial Intelligence

Rothman, Tony , A Physicist on Madison Avenue

Rothman, Tony , Instant Physics: From Aristotle to Einstein,a nd Beyond

Rothman, Tony , Frontiers of Modern Physics: New Perspectives on Cosmology,

Relativity, Black Holes and Extraterrestrial Intelligence

Rothman, Tony , Instant Physics: From Aristotle to Einstein, and Beyond

Rothrock, Jane (ed.) , Alexander's Care of the Patient in Surgery

Rothschild, Brian J. , Dynamics of Marine Fish Populations

Rothstein, Rochelle , Kaplan MCAT Comprehensive Review

Rothwell, Stuart C. , A Geography of Earth Form: Preface to Physical Geography

Rotman, Josph J. , An Introduction to the Theory of Groups, 3rd ed.

Roukes, Nicholas , Sculpture in Plastics

Rouse, H. , Elementary Mechanics of Fluids

Rouse, Hunter , Fluid Mechanics For Hydraulic Engineers

Rouse, Hunter , Elementary Mechanics of Fluids

Rouse, Hunter , Advanced Mechanics of Fluids

Rousseau, M. , Problems in Optics

Rousseau, Ronald W. , Handbook of Separation Process Technology

Routh, Edward John , Elementary Part: Dynamics of a System of Rigid Bodies, 7th

ed.

Routh, Edward John , A Treatise on Dynamics of a Particle

Routh, Edward John

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Rowan-Robinson, M. , Cosmology, 2nd ed.

Rowan-Robinson, Michael , Ripples in the Cosmos: a View Behind the Scenes of the

New Cosmology

Rowan-Robinson, Michael , The Nine Numbers of the Cosmos

Rowbottom, Derek , Miniature Dolls' Houses in 1/24th Scale: a Complete Guide to

Making and Furnishing Houses

Rowe, A. P. , One Story of Radar

Rowe, C. E. , Engineering Descriptive Geometry

Rowe, Joseph E. , Nonlinear Electron-wave Interaction Phenomena

Rowe, Peter G. , Making a Middle Landscape

Rowe, William , Machinery and Mechanical Devices: a Treasury of Nineteenth

century Cuts

Rowlinson, J. S. , The Perfect Gas

Rowlinson, J. S. , Liquids and Liquid Mixtures, 2nd ed.

Rowlinson, J. S. , Molecular Theory of Capillarity

Roy, A. E. , Orbital Motion, 3rd ed.

Roy, Archie E. , The Foundations of Astrodynamics

Roy, B. N. , Crystal Growth From Melts: Applications to Growth of Groups 1 and 2

Crystals

Roy, Steven , Sports Medicine: Prevention, Evaluation, Management, and

Rehabilitation

Royden, H. L. , Real Analysis, 2nd ed.

Roylance, David , Mechanics of Materials

Rozanov, Y. A. , Probability Theory: a Concise Course

Rozanov, Yurii A. , Introduction to Random Processes

Ruark, Arthur Edward , Atoms, Molecules, and Quanta

Rubens, Philip (ed.) , Science and Technical Writing

Rubeska, Ivan , Atomic Absorption Spectrophotometry

Rubin, Louis D. , The Weather Wizard's 5-year Weather Diary

Rubin, Louis D. , Forecasting the Weather

Rubin, Susan Goldman , Toilets, Toasters & Telephones: the How and Why of

Everyday Objects

Rubinstein, Moshe F. , Patterns in Problem Solving

Ruby, Lionel , Logic: an Introduction

Ruch, Theodore C. , Physiology and Biophysics, 19th ed.

Ruch, Theodore C. , Physiology and Biophysics, 20th Ed.: V. 4 Excitable Tissues

and Reflex Control of Muscle

Ruchlis, Hy , Bathtub Physics

Ruchlis, Hy , Orbit: a Picture Story of Force and Motion

Ruckdeschel, F. R. , Basic Scientific Subroutines, Vol. 2

Ruckdeschel, F. R. , Basic Scientific Subroutines, Vol. 1

Rucker, Rudolph v. B. , Geometry, Relativity and the Fourth Dimension

Rudawsky, O. , Mineral Economics: Development and Management of Natural

Resources

Rudd, RObert D. , Remote Sensing: a Better View

Ruddon, Raymond W. , Cancer Biology, 3rd ed.

Rudenberg, Reinhold , Transient Performance of Electric Power Systems

Rudin, Walter , Principles of Mathematical Analysis, 2nd ed.

Rüeger, J. M. , Electronic Distance Measurement: an Introduction, 3rd ed.

Ruelle, David , Statistical Mechanics: Rigorous Results

Ruelle, David , Thermodynamic Formalism: the Mathematical Structures of

Classical Equilibrium Statistical Mechanics

Ruelle, David , Chaotic Evolution and Strange Attractors

Ruelle, David , Elements of Differentiable Dynamics and Bifurcation Theory

Ruelle, David , Chance and Chaos

Ruhe, Robert V. , Geomorphology: Geomorphic Processes and Surficial Geology

Ruheman, M. , Low Temperature Physics

Ruhemann, M. , The Separation of Gases, 2nd ed.

Ruhla, Charles , The Physics of Chance

Rumelhart, David E. , Parallel Distributed Processing: Exploraitons in the

Microsctructure of Cognition, Vol. 2: Psychological and Biological Models

Rumpf, Hans , Particle Technology

Rumsey, Francis , THe Digital Interface Handbook

Rumsey, Franics , Sound and Recording: an Introduction, 3rd ed.

Runcorn, S. K. , Paleogeophysics

Runcorn, S. K. (ed.) , Methods and Techniques in Geophysics, Vol. I

Runcorn, S. K. (ed.) , Methods and Techniques in Geophysics, Vol. II

Rundle, John B. , Reduction and Predictability of Natural Disasters

Runes, Dagobert D. (ed.) , Treasury of World Science

Runowicz, Carolyn D. , The Answer to Cancer

Runstein, Robert E. , Modern Recording Techniques

Runyan, W. R. , Semiconductor Measurements and Instrumentation

Runyan, W. R. , Semiconductor Measurements and Instrumentation, 2nd ed.

Runyan, W. R. , Semiconductor Integrated Circuit Processing Technology

Runyon, Stan (ed.) , Advanced Test and Measurement Instrumentation (selected

From Electronic Design)

Ruocco, S. R. , Robot Sensors and Transducers

Rupp, William , Construction Materials For Interior Design: Principles of

Structure and Properties of Materials

Ruppel, Gregg , Manual of Pulmonary Function Testing

Ruppel, Gregg , Manual of Pulmonary Function Testing, 7th ed.

Rusanov, A. I. , Interfacial Tensiometry

Rushkoff, Douglas , Cyberia: Life in the Trenches of Hyperspace

Ruskai, M. B. , Wavelets and Their Applications

Russ, John C. , Practical Stereiology

Russ, John C. , The Image Processing Handbook, 2nd ed.

Russ, John C. , Computer-assisted Microscopy: the Measurement and Analysis of

Images

Russ, John C. , Practical Stereology

Russ, John C. , Elemental Xray Analysis of Materials: EXAM Methods

Russ, John C. , Advances in X-ray Analysis

Russ, Martin , Sound Synthesis and Sampling

Russel, W. B. , Colloidal Dispersions

Russell, Bertrand , Principles of Mathematics, 2nd ed.

Russell, Bertrand , Our Knowledge of the External World

Russell, John B. , Study Guide For Sienko and Plane Chemistry 4th ed

Russell, Lynn D. , Classical Thermodynamics

Russell, Robert John , Chaos and Complexity: Scientific Perspectives on Divine

Action

Russinovich, Mark E. , Microsoft Windows Internals: Microsoft Windows Server

2003, Windows Xp, and Windows 2000, 4th ed.

Russo, Mark F. , Automating Science and Engineering Laboratories With Visual

BASIC

Ruston, Henry , Electric Networks: Functions, Filters, Analysis

Rutherford, D. E. , Classical Mechanics, 2nd ed.

Rutherford, F. James , Project Physics Handbook

Rutishauser, Heinz , Lectures on Numerical Mathematics

Rutkowski, George B. , Handbook of Integrated-circuit Operational Amplifiers

Rutledge, David B. , The Electronics of Radio

Rutten, Harrie , Telescope Optics: Evaluation and Design

Ruvalds, J., ed. , Quantum Liquids

Ryan, Daniel L. , Computer-aided Kinetics For Machine Design

Ryan, Patrick J. , Euclidean and Non-euclidean Geometry: an Analytic Approach

Ryan, W. , Whitewares Production, Testing and Quality Control

Rybczynski, Witold , Taming the Tiger: the Struggle to Contol Technology

Rybczynski, Witold , One Good Turn: a Natural History of the Screwdriver and the

Screw

Ryder, G. H. , Mechanics of Machines

Ryder, John D. , Electronic Engineering Principles

Ryder, John D. , Networks, Lines, and Fields, 2nd ed.

Ryder, Robert T. , Tectonically Controlled Fan Delta and Submarine Fan

Sedimentation of Late Miocene Age, Southern Temblor Range, California

Rykalin, N. , Laser and Electron Beam Material Processing Handbook

Saab-Scania , The Saab-Scania Story

Saadat, Hadi , Power System Analysis

Saadawi, Tarek N. , Fundamentals of Telecommunication Networks

Saarinen, Eliel , The Search For Form in Art and Architecture

Saaty, Thomas L. , Elements of Queuing Theory With Applications

Saaty, Thomas L. , Nonlinear Mathematics

Saaty, Thomas L. , The Four-Color Problem: Assaults and Conquest

Sabersky, Rolf H. , Fluid Flow, 2nd ed.

Sabin, William E. , Single-sideband Systems & Circuits

Sabins, Floyd F. , Remote Sensing: Principles and Interpretation

Sabiston, David C. , Textbook of Surgery: the Biological Basis of Modern

Surgical Practice, 15th ed.

Sachdev, P. L. , Nonlinear Diffusive Waves

Sachs, George , Principles and Methods of Sheet-metal Fabricating

Sachs, Mendel , Relativity in Our Time: From Physics to Human Relations

Sachs, Robert G. , The Physics of Time Reversal

Sack, Robert David , Conceptions of Space in Social Thought:a Geopgraphic

Perspective

Sackett, Russell , Edge of the Sea

Sacks, Oliver , Uncle Tungsten: Memories of a Chemical Boyhood

Sadiku, Matthew N. O. , Elements of Electromagnetics, 3rd ed.

Sadiku, Matthew N. O. , Numerical Techniques in Electromagnetics

Sadovskii, L. E. , Mathematics and Sports

Sadun, Erica , Digital Photography!

Sadun, Erica , Digital Video!

SAE Committee AC-9 - Aircraft Environmental Systems , SAE Aerospace Applied

Thermodynamics Manual, 2nd ed.

Saeed, Zed , Final Cut Pro 4

Saferstein, Richard , Criminalistics: an Introduction to Forensic Science

Saff, E. B. , Fundamentals of Complex Analysis: With Applications to Engineering

and Science, 3rd ed.

Saffman, P. G. , Vortex Dynamics

Safford, Edward L., Jr. , Handbook of Advanced Robotics

Safford, Edward L., Jr. , Aviation Electronics Handbook

Safford, Edward L., Jr. , Fiberoptics and Laser Handbook, 2nd ed.

Safonov,. Michael George , Stability and Robustness of Multivariate Feedback

Systems

Safran, Samuel A. , Physics of Complex Supermolecular Fluids

Sagan, Carl , Pale Blue Dot: a Vision of the Human Future in Space

Sagan, Carl , The Demon Haunted World: Science As a Candle in the Dark

Sagan, Carl , Murmers of Earth: the Voyager Interstellar Record

Sagan, Carl , Comet

Sagan, Dorion , Garden of Microbial Delisghts: a Practical Guide to the

Subvisible World

Sagan, Leonard A. , Human and Ecological Effects of Nuclear Power Plants

Sagdeev, R. Z. , Nonlinear Plasma Theory

Sagdeev, R. Z. , Nonlinear Physics: From the Pendulum to Turbulence and Chaos

Sah, Chih-Tang , Fundamentals of Solid-state Electronics

Saha, Gopal B. , Fundamentals of Nuclear Pharmacy, 2nd ed.

Saha, Gopal B. , Physics and Radiobiology of Nuclear Medicine

Sahni, Sartaj , Concepts of Discrete Mathematics

Sakai, Jun-Ichi , Phase Conjugate Optics

Sakrison, David , Communication Theory: Transmission of Waveforms and Digital

Information

Saksaganskii, G. L. , Molecular Flow in Complex Vacuum Systems

Sakurai, J. J. , Modern Quantum Mechanics

Sakurai, J. J. , Advanced Quantum Mechanics

Salam, Abdus , Unification of Fundamental Forces: the First of the 1988 Dirac

Memorial Lectures

Salamone, Donna , Fiber Optic Lab Manual and Reference Guide

Saldin, E. L. , The Physics of Free Electron Lasers

Saleh, B. E. A. , Fundamentals of Photonics

Salem, Kenneth G. , The New Gravity

Sales, Gillian , Ultrasonic Communication By Animals

Salkind, Neil J. , Exlporing Research, 3rd ed.

Salmon, Charles G. , Steel Structures: Design and Behavior, 3rd ed.

Salmon, Charles G. , Steel Structures: Design and Behavior, 2nd ed.

Salmon, Rod , Computer Graphics: Systems and Concepts

Salter, Charles M., Associates , Acoustics: Architecture, Engineering,

Environment

Salthe, Stanley N. , Evolving Heirarchical Systems

Saltzman, Barry (ed) , Selected Papers on the Theory of Thermal Convection

Salvadori, Mario , Why Buildings Stand Up: the Strength of Architecture

Salvadori, Mario , Building: the Fight Against Gravity

Salvadori, Mario , The Art of Construction: Projects and Principles For

Beginning Engineers & Architects

Salvadori, Mario , Structure in Architecture: the Building of Buildings

Salvato, Joseph A., Jr. , Environmental Engineering and Sanitation, 2nd ed.

Salvendy, G. , ed. , Handbook of Industrial Engineering

Salyers, Abigail A. , Bacterial Pathogenesis: a Molecular Approach

Salzmann, David , Atomic Physics in Hot Plasmas

Samaras, Demetrios G. , Theory of Ion Flow Dynamics

Samet, Jonathan M. , Indoor Air Pollution: a Health Perspective

Sammis, Charles G. , Geophysics, Part A: Laboratory Measurements

Sammis, Charles G. , Geophysics, Part B: Field Measurements

Sams, Howard W. & Company , Semiconductor Cross Reference Book, 4th ed.

Samson, James A. R. , Techniques of Vacuum Ultraviolet Spectroscopy

Samuel, Andrew , Introduction to Engieering Design: Modelling, Synthesis, and

Problem Solving Strategies

Samuel, Andrew , Introduction to Engineering Design: Modelling, Synthesis, and

Problem Solving Strategies

Samuels, Jeffrey M. (ed.) , Patent, Trademark, and Copyright Laws: 1987 ed.

Samuels, L. E. , Metallographic Polishing By Mechanical Methods, 2nd ed.

Samuels, Myra L. , Statistics For the Life Sciences

Sanchez, David A. , Ordinary Differential Equations and Stability Theory: an

Introduction

Sand, L. B. , Natural Zeolites: Occurance, Properties, Use

Sandbank, C. P. (ed.) , Optical Fiber Communication Systems

Sandborn, V. A. , Resistance Temperature Transducers

Sande, Merle A. , The Meidcal Management of Aids

Sande, Theodore Anton , Industrial Archeology: a New Look at the American

Heritage

Sandefur, James T. , Discrete Dynamical Modeling

Sander, K. F. , Microwave Components and Systems

Sanders, Gordon A. , Light Building Construction

Sanders, J. A. , Averaging Methods in Nonlinear Dynamical Systems

Sanders, Jeremy K. M. , Modern NMR Spectroscopy, 2nd ed.

Sanders, Roger C. , Clinical Sonography: a Practical Guide, 3rd ed.

Sanderson,I.T. , The Continent We Live On

Sandfort, John F. , Heat Engines: Thermodynamics in Theory and Practice

Sandhu, Pavi , THe MathML Handbook

Sandia National Laboratories , Stand-alone Photovoltaic Systems: a Handbook of

Recommended Design Practices

Sandin, Paul E. , Robot Mechanisms and Mechanical Devices Illustrated

Sandin, T. R. , Essentials of Modern Physics

Sandler, Ben-Zion , Creative Machine Design: Design Innovation and the Right

Solutions

Sandler, Ben-Zion , Robotics: Designing the Mechanisms For Automated Machinery,

2nd ed.

Sandler, Ben-Zion , Robotics: Designing the Mechanisms For Automated Machinery,

2nd ed.

Sandler, Corey , Mac to VAX: a Communications Guide

Sandlin, Robert E. , Hearing Instrument Science & Fitting Practices, 2nd ed.

Sandori, Paul , The Logic of Machines and Structures

Sands, D. , Diode Lasers

Sands, Leo G. , Installing TV & FM Antennas

Sands, Leo G. , Encyclopedia of Electronic Circuits

Sanfilippo, M. J. , Solid-state Electronics Theory With Experiments

Sanford, Richard S. , Physical Networks

Sangwine, S. J. , Electronic Components and Technology, 2nd ed.

Sanks, Robert L. (ed.) , Pumping Station Design

Sansone, G. , Orthogonal Functions

Santeler, Donald J. , Vacuum Technology and Space Simulation

Santosa, Fadil , Analytical and Computational Methods in Scattering and Applied

Mathematics

Sapoval, B. , Physics of Semiconductors

Sapp, J. Philip , Contemporary Oral and Maxillofacial Pathology, 2nd ed.

Sarachik, Philip E. , Principles of Linear Systems

Sardar, Ziauddin , Introduction to Chaos

Sarewitz, Daniel , Prediction: Science, Decission Making, and the Future of

Nature

Sargent III, Murray , Laser Physics

Sargent III, Murray , Interfacing Microcomputers to the Real World

Sarma, Mulukutla S. , Electric Machines: Steady-state Theory and Dynamic

Performance

Sarpkaya, Turgut , Mechanics of Wave Forces on Offshore Structures

Sarton, George , A History of Science: Ancient Science Through the Golden Age of

Greece

Sarton, George , A History of Science: Hellenistic Science and Culture in the

Last Three Centuries B.C.

Sarton, George , The History of Science and the New Humanism

Sass, Stephen L. , Substance of Civilization: Materials and Human History From

the Stone Age to the Age of Silicon

Sasso, John , Plastics For Industrial Use: an Engineering Handbook of Meterials

and Methods

Sassower, Raphael , Knowledge Without Expertise: on the Status of Scientists

Sastry, Shankar , Nonlinear Systems: Analysis, Stability, and Control

Satinover, Jeffrey , The Quantum Brain: the Search For Freedom and the Next

Generation of Man

Sato, Jin , Jin Sato's Lego Mindstorms: the Master's Technique

Sattinger, D. H. , Branching in the Presence of Symmetry

Saucedo, Roberto , Introduction to Continuous and Digital Control Systems

Saucier, Walter J. , Principles of Meteorological Analysis

Sauer, Hans , Modern Relay Technology, 2n ed.

Sauer, Harry J., Jr. , Heat Pump Systems

Sauer, Peter W. , Power System Dynamics and Stability

Sauerbier, Charles , Marine Cargo Operations, 2nd ed.

Saunders, P. T. , An Introduction to Catastrophe Theory

Sauvageot, Henri , Radar Meteorology

Savant, C. J., Jr. , Basic Feedback Control System Design

Savant, C. J., Jr. , Fundamentals of the Laplace Transformation

Savet, Paul H. , Gyroscopes: Theory and Design

Savitch, Walter J. , An Introduction to the Art and Science of Programming:

Turbo Pascal Edition

Savitch, Walter J. , An Introduction to the Art and Science of Programming, 3rd

ed.

Savitt, Steven F. (ed.) , Time's Arrows Today: Recent Physical and Philosophical

Work on the Direction of Time

Savitzky, Stephen R. , Real Time Microprocessor Systems

Savola, Tom , Special Edition Using HTML

Sawyer, Ralph A. , Experimental Spectroscopy

Sawyer, Roger Williams , New Ideas For Science Fair Projects

Sawyer, Stanley A. , A TeX Primer For Scientists

Sawyer, W. W. , Prelude to Mathmatics

Sax, N. Irving , Dangerous Properties of Industrial Materials, 6th ed.

Saxby, Graham , Manual of Practical Holography

Saxby, Graham , Practical Holography, 2nd ed.

Saxena, S. K. (ed.) , Chemistry and Physics of Terrestrial Planets

Saxon, Richard , Atrium Buildings: Development and Design

Saxton, W. O. , Computer Techniques For Image Processing in Electron Microscopy

Sayer, F. P. , Applied Mechanics: a Modern Approach

Sayre, Cotter W. , Complete Wireless Design

Scagell, Robin , City Astronomy

Scaife, B. K. P. , Principles of Dielectrics

Scala, S. M. , Dynamics of Manned Lifting Planetary Entry

Scanlan, Craig L. , Egan's Fundamentals of Respiratory Care, 6th ed.

Scanlan, Robert H. , Aircraft Vibration and Flutter

Scarr, A. J. T. , Metrology and Precision Engineering

Scarth, Alwyn , Savage Earth

Scarth, Alwyn , Vulcan's Furty: Man Against the Volcano

Schaaf, Fred , Wonders of the Sky: Observing Rainbows, Comets, Eclipses, the

Stars, and Other Phenomena

Schaaf, Fred , Seeing the Solar System: Telescopic Projects, Activities &

Explorations in Astronomy

Schaaf, Fred , Seeing the Solar System: Telescope Projects, Activities &

Explorations in Astronomy

Schachter, Steven C. , Vagus Nerve Stimulation

Schachtman, Tom , Absolute Sero and the Conquest of Cold

Schaefer, Clemens , Einfuhrung in Die Theoretische Physik, Ester Band: Mechanik

Materieller Punkte, Mechanik Starrer Korper Und Mechanik Der Kontinua

(Elastizitat Und Hydrodynamik)

Schaefer, Clemens , Einfuhrung in Die Theoretische Physik, Zwietes Band, Erster

Teil: Theorie Der Warme, Molekular-kinetische Theorie Der Materie

Schaefer, Karl E. , Man's Defendence on the Earthly Atmosphere

Schaeffer III, Henry F. , The Electronic Structure of Atoms and Molecules: a

Survey of Rigorous Quantum Mechanical Results

Schaeffer III, Henry F. , Methods of Electronic Structure Theory

Schaeffer III, Henry F. , Applications of Electronic Structure Theory

Schaeffer, Harold F. , Microscopy For Chemists

Schaeffer, John , Solar Living Source Book, 9th ed.

Schaeffer, John , Alternative Energy Source Book, 7th ed.

Schafer, F. P., ed. , Dye Lasers

Schafer, R. Murray , Our Sonic Environment and the Soundscape: the Tuning of the

World

Schaffer, James P. , The Science and Design of Engineering Materials

Schalkoff, Robert J. , Digital Image Processing and Computer Vision: an

Introduction to Theory and Implementations

Schank, Roger C. , The Cognitive Computer: on Language, Learning, and Artifical

Intelligence

Scharf, Waldemar , Particle Accelerators and Their Uses, Part 1: Accelerator

Design

Scharf, Waldemar , Particle Accelerators and Their Uses, Part 2: Applications of

Accelerators

Scharf, Waldemar H. , Biomedical Particle Accelerators

Scharff, Robert , Workshop Math

Schatt, Stan , Linking LAN's, 2nd ed.

Schatzman, Evry , Our Expanding Universe

Schaub, George , The Amphoto Book of Film

Schaumann, Rolf , Design of Analog Filters: Passive, Active RC, and Switched

Capacitor

Schechter, R. S. , The Variational Method in Engineering

Schechter, Robert S. , Oil Well Stimulation

Scheck, Florian , Mechanics: From Newton's Laws to Deterministic Chaos

Scheffer, Victor B. , A Natural History of Marine Mammals

Scheicher, Robert , Building and Flying Model Aircraft

Scheider, Herman , Everyday Machines and How They Work

Scheifer, Robert , Genetics and Molecular Biology, 2nd ed.

Schein, L. B. , Electro-photography and Development Physics, 2nd ed.

Scheinerman, Edward R. , Invitation to Dynamical Systems

Schelling, Thomas C. , The Strategy of Conflict

Schenk, George H. , Absorption of Light and Ultraviolet Radiation, Fluorescence

and Phosphorescence Emission: an Introduction With Experiments

Schenk, Sonya , Digital Non-linear Desktop Editing

Schenkel, G. , Plastics Extrusion

Scherr, George H. (ed.) , The Best of the Journal of Irreproducible Results

Scherz, Paul , Practical Electronics For Inventors

Schetgen, Robert , Hints and Kinks For the Radio Amateur, 13th Ed,

Schetz, Joseph A. , Injection and Mixing in Turbulent Flow

Schetz, Joseph A. , Foundations of Boudnary Layer Theory For Momentum, Heat, and

Mass Transfer

Schetz, Joseph A. , Handbook of Fluid Dynamics and Fluid Machinery, Vol. 1:

Fundamentals of Fluid Dynamics

Schetz, Joseph A. , Handbook of Fluid Dynamics and Fluid Machinery, Vol. 2:

Experimental and Computational Fluid Dynamics

Schetz, Joseph A. , Handbook of Fluid Dynamics and Fluid Machinery, Vol. 3:

Applications of Fluid Dynamics

Schey, John A. , Introduction to Manufacturing Processes

Schick, Kathy D. , Making Silent Stones Speak: Human Evolution and the Dawn of

Technology

Schick, Theodore, Jr. , How to Think About Weird Things: Critical Thinking For a

New Age

Schiff, Leonard I. , Quantum Mechanics 3rd ed

Schiller, Ludwig (ed.) , Hydro- Und Aerodynamk 1. Teil: Stromungslehre Und

Algemeine Versuchstechnik

Schiller, Ludwig (ed.) , Hydro- Und Aerodynamk 2. Teil: Widerstand Und Auftrieb

Schilling, Donald K. , Electronic Circuits: Discrete and Integrated

Schilling, Donald L. , Meteor Burst Communications: Theory and Practice

Schillinger, Joseph , The Mathematical Basis of the Arts

Schimel, David S. , Theory and Application of Tracers

Schlegel, Hans G. , General Microbiology, 6th ed.

Schleher, D. Curtis , MTI and Pulsed Doppler Radar

Schleicher, Robert , Radio Control and Tabletop Model Car Racing

Schlichting, Hermann , Boundary Layer Theory, 6th ed

Schlichting, Hermann , Boundary Layer Theory, 8th ed.

Schlick, Tamar , Molecular Modeling and Simulation: an Interdisciplinary Guide

Schlosser, Gerhard , Modularity in Development and Evolution

Schlumberger , Log Interpretation: Principles/applications

Schlumberger , Log Interpretation: Vol. I Interpretation

Schlumberger , Log Interpretation: Vol. II Applications

Schlumberger , Log Interpretation Charts

Schlumberger, Anne Gruner , The Schlumberger Adventure

Schmenner, Roger W. , Production / Operations Management: Comcepts and

Situations, 3rd ed.

Schmid, E. W. , Theoretical Physics on the Personal Computer

Schmid-Hempel, Paul , Parasites in Social Insects

Schmidt, Ernst , Thermodynamics: Principles and Applications to Engineering

Schmidt, Frank W. , Introduction to Thermal Sciences: Thermodynamics, Fluid

Dynamics, Heat Transfer

Schmidt, Frank W. , Introduction to Thermal Sciences: Thermodynamics, Fluid

Dynamics, Heat Transfer, 2nd. ed.

Schmidt, Peter , May

Schmidt, Richard A. , Motor Learning & Performance: From Principles to Practice

Schmidt, Robert P. , Auto Body Repair and Refinishing

Schmidt-Nielsen, Knut , Animal Physiology: Adaptation and Environment

Schmidt-Nielsen, Knut , Scaling: Why is Animal Size So Important?

Schmitt, Neil M. , Understanding Electronic Control of Automation Systems

Schnabel, W. , Polymer Degredation: Principles and Practical Applications

Schnaser, Gene , Trade Secrets: Tips & Hints From the Pros

Schneider, Herman , Everyday Machines and How They Work

Schneider, Herman , Science Fun With Milk Cartons

Schneider, P. J. , Temperature Response Charts

Schneider, Philip J. , Geometric Tools For Computer Graphics

Schneider, Stephen H. , The Coevolution of Climate and Life

Schneider, Walter A. , Experimental Physics For Colleges, revised ed.

Schneiderman, Neil , Handbook of Research Methods in Cardiovascular Behavioral

Medicine

Schneierson, S. Stanley , Atlas of Diagnostic Microbiology

Schnell, L. , Technology of Electrical Measurements

Schnore, Leo F. , The New Urban History: Quantitative Explorations By American

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Schopf, J. William , Life's Origin: the Beginnings of Biological Evolution

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Schowengerdt, R. A. , Remote Sensing: Models and Methods For Image Processing,

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Serafini, Aldo N. , Medical Cyclotrons in Nuclear Medicine

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Sheet Metal and Air Conditioning Contractors Association , Fundamentals of Solar

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Sheffield Corporation, The , 66 Centuries of Measurement

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Shklovsky, I. S. , Supernovae

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Sigurdsson, Haraldur , Melting the Earth: the History of Ideas on Volcanic

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Sinclair, David , Human Growth After Birth, 6th ed.

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Singer, Charles , A History of Technology, Vol. II: The Mediterranean

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Singer, Jar R. (ed.) , Advances in Quantum Electronics

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Singmin, Andrew , Modern Electronics Soldering Techniques

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Sinha, Divyendu , Introduction to Computer-based Imaging Systems

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Sirovich, L. , Introduction to Applied Mathematics

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Slotnik, Morris Miller , Lessons in Seismic Computing: a Memorial to the Author

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Smart, W. M. , Text-Book on Spherical Astronomy, 5th ed.

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Sokolov, A. A. , Radiation From Relativistic Electrons

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Song, Shin-Min , Machines That Walk: the Adaptive Suspension Vehicle

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Sorenson, Harold W. (ed.) , Kalman Filtering: Theory and Application

Sorrell, Thomas N. , Interpretating Spectra of Organic Molecules

Soucek, Branko , Neural and Concurrent Real-time Systems: the Sixth Generation

Souder, William E. , Managing New Product Innovations

Souers, P. Clark , Hydrogen Properties For Fusion Energy

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Spain, Ian L. , High Pressure Technology, Vol. I: Equipment Design, Materials,

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Spalding, D. Brian (ed.) , Heat and Mass Transfer in Gasoline and Diesel Engines

Spangenberg, Karl R. , Fundamentals of Electron Devices

Spangenburg, Ray , The History of Science in the Nineteenth Century

Spangenburg, Ray , The History of Science From 1895-1945

Spanier, Jerome , An Atlas of Functions

Spanner, D. C. , Introduction to Thermodynamics

Spar, Debora L. , Ruling the Waves: Cycles of Discovery, Chaos, and Wealth From

the Compass to the Internet

Sparey, L. H. , The Mateur's Lathe, 5th ed.

Sparey, L. H. , The Amateur's Lathe, 5th ed.

Sparkes, J. J. , Semiconductor Devices, 2nd ed.

Sparks, Harvey V., Jr. , Essentials of Cardiovascular Physiology

Sparrow,C. , The Lorenz Equations: Bifurcations,Chaos and Strange Attractors

Spasov, Peter , Microcontroller Technology: the 68HC11

Speight, Charlotte F. , Make It in Clay: a Beginner's Guide to Ceramics, 2nd ed.

Spence, John C. H. , Experimental High-Resolution Electron Microscopy

Spence, John C. H. , Experimental High-Resolution Electron Microscopy, 2nd ed.

Spencer, Charles D. , Digital Design For Computer Data Acquisition

Spencer, Christian , Spencer Christian's Goegraphy Book

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Experiments of This Century As Recorded in 'Typographica' Magazine

Spencer, James , Maintenance and Servicing of Electrical Instruments

Sperisen, Francis J. , The Art of the Lapidary, Rev. ed.

Speyer, Edward , Six Roads From Newton: Great Discoveries in Physics

Spiegel, Murray R. , Theory and Problems of Advanced Calculus

Spiegel, Murray R. , Theory and Problems of Complex Variables

Spiegel, Murray R. , Theory and Problems of Vector Analysis and an Introduction

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Spiegel, Murray R. , Theory and Problems of Statistics

Spiegel, Murray R. , Applied Differential Equations, 2nd ed.

Spigel, Leonard , Reinforced Concrete Design, 3rd ed.

Spiller, Eberhard , Soft X-ray Optics

Spinar, Leo H. , College Chemistry

Spink, L. K. , Principles and Practice of Flow Meter Engineering, 9th ed.

Spinks, Michael , Microprocessor System Design: a Practical Introduction

Spiteri, Charles J. , Robotics Technology

Spitzer, David W. , Regulatory and Advanced Regulatory Controls: Application

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Spitzer, L., Jr. , Diffuse Matter in Space

Spitzer, Lyman, Jr. , Physics of Fully Ionized Gases

Spitzer, Lyman, Jr. , Physical Processes in the Interstellar Medium

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Human Body

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Splinter, R. , An Introduction to Biomedical Optics

Spoor, Jack , Heat Sink Application Handbook

Sporn, M. B. , Peptide Growth Factors and Their Receptors I

Sporn, M. B. , Peptide Growth Factors and Their Receptors II

Spotts, M. F. , Design of Machine Elements

Sprackling, M. T. , Liquids and Solids

Sprackling, Michael , Thermal Physics

Spragg, S. P. , The Physical Behavior of Macromolecules With Biological

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Sprague de Camp, L. , The Ancient Engineers

Sprawls, Perry, Jr. , Physical Principles of Medical Imaging, 2nd ed.

Sprecher, David A. , Elements of Real Analysis

Springhouse Corporation , Normal and Abnormal Heart Sounds

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Sproull, Amy (ed.) , A Breast Cancer Journey: Your Personal Guidebook

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Spudich, James A. (ed.) , Dictyostelium Discoideum: Molecular Approaches to Cell

Biology

Spurr, Daniel , Upgrading the Cruising Sailoat, 2nd ed.

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Spurr, Stephen H. , Forest Ecology, 2nd ed.

Squire, Charles F. , Waves in Physical Systems

Squire, Charles F. , Low Temperature Physics

Squire, Larry R. , Memory: From Mind to Molecules

Squires, Euan , The Mystery of the Quantum World, 2nd ed.

Squires, G. L. , Practical Physics, 3rd ed.

Srinath, M. D. , An Introduction to Statistical Signal Processing With

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Srinivasan, A. V. , Smart Structures: Analysis and Design

Srinivasan, S. K. , Point Process Models of Cavity Radiation and Detection

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Sritharan, S. S. , Invariant Manifold Theory For Hydrodynamic Transition

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Stableford, Brian , The Third Millennium: a History of the World: AD 2000-3000

Stacey, Frank D. , Physics Oft he Earth, 2nd ed.

Stacy, Ralph W. , Biological and Medical Electronics

Stadler, Wolfram , Analytical Robotics and Mechatronics

Staelin, David H. , Electromagnetic Waves

Stafford, Barbara Maria , Visual Analogy: Consciousness As the Art of Connecting

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Eclipse of Visual Education

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Stahl, Egon (ed.) , Thin Layer Chromatography: a Laboratory Handbook, 2nd ed.

Stakgold, Ivar , Green's Functions and Boundary Value Problems

Stakgold, Ivar , Boundary Value Problems of Mathematical Physics, Vol. 1

Stalkup, Fred I., Jr. , Miscible Displacement

Stallcup, James G. , Motors and Transforments: Based on the 1990 NEC

Stambler, Irwin , The Worlds of Sound

Stamnes, Jakob J. , Waves in Focal Regions

Standen, Anthony , Science is a Sacred Cow

Standing, Arthur F. , Measurment Techniques For In-orbit Testing of Satellites

Standish, Thomas A. , Data Structure Techniques

Stanford, John L. , Statsitical Methods For Physical Science

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Scientists

Staniar, William (ed.) , Plant Engineering Handbook (2nd Ed.)

Staniforth, Allan , Competition Car Suspension: Design, Construction, Tuning

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Stanley, H. Eugene , Inroduction to Phase Transitions and Critical Phenomena

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Stanley, H. Eugene (ed) , Random Fluctuations and Pattern Growth: Experiments

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Stanley, H. Eugene (ed) , Correlations and Connectivity: Geometric Aspects of

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Stanley, H. Eugene (ed.) , On Growth and Form: Fractal and Non-fractal Patterns

in Physics

Stanley, H. Eugene, ed. , Cooperative Phenomena Near Phase Transitions

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Stanley, William D. , Digital Signal Processing, 2nd ed.

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Stanton, Ralph G. , Numerical Methods For Science and Engineering

Stanton, Robert , An Introduction to Radiation Oncology Physics

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Stapleton, John F. , Essentials of Clinical Cardiology

Starck, J. -L. , Image Processing and Data Analysis: the Multiscale Approach

Stark, Harold M. , An Introduction to Number Theory

Stark, Henry (ed.) , Applications of Optical Fourier Transforms

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Staten, Vince , Did Monkeys Invent the Monkey Wrench: Hardware Stores, Hardware

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Staudenmaier, H. M. (ed.) , Physics Experiments Using PCs: a Guide For Students

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Stauffer, Dietrich , Introduction to Percolation Theory

Stauffer, Dietrich , Introduction to Percolation Theory, 2nd ed.

Stavroudis, O. N. , The Optics of Rays, Wavefronts, and Caustics

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Steeds, W. , Involute Gears

Steel, W. H. , Interferometry, 2nd ed.

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Stein, Daniel L. (ed.) , Lectures in the Sciences of Complexity

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Stephens, R. W. B. (ed.) , Underwater Acoustics

Stephens, R. W. B. , Acoustics and Vibrational Physics, 2nd ed.

Stephenson, Fred , The Dollhouse Builder's Handbook, 4th Printing

Stephenson, Revis L. , Centrifugal Compressor Engineering

Stephenson, Robert E. , Computer Simulation For Engineers

Stephenson, William K. , Concepts in Biochemistry: a Programmed Text

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Steward, E. G. , Fourier Optics: An Introduction, 2nd ed

Steward, Robert M. , Boatbuilding Manual, 4th ed.

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Stewart, Frank E. , Basic Units in Physics, Rev. ed.

Stewart, George Walter , Introductory Acoustics

Stewart, George Wlater , Acoustics: a Text on Theory and Applications

Stewart, Ian , Does God Play Dice? The Mathematics of Chaos

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Stewart, Ian , The Foundations of Mathematics

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Stewart, James E. , Infrared Spectroscopy: Experimental Methods and Techniques

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Stockmann, H.-J. , Quantum Chaos: an Introduction

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Stoelting, Robert K. , Basics of Anesthesia, 3rd ed.

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Stolarski, T. A. , Tribology in Machine Design

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Safety, 2nd ed.

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Stommel, Henry M. , An Introduction to the Coriolis Force

Stone, C. S. , Radiation and Optics: an Introduction to the Classical Theory

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Scientist

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Scientist

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Scientist

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Storer, James E. , Passive Network Synthesis

Stout, David F. , Handbook of Operational Amplifier Circuit Design

Stout, Melville B. , Basic Electrical Measurements

Stover, John C. , Optical Scattering: Meaurement and Analysis

Stowe, Donald H. (ed.) , Effective Use of Lime For Flue Gas Desulfurization

Strahan, Ronald (ed.) , The Australian Museum Complete Book of Australian

Mammals: the National Photographic Index of Australian Wildlife

Strahler, Arthur N. , Physical Geopgraphy, 3rd ed.

Strain, Pricilla , Looking at Earth

Strandh, Sigvard , The History of the Machine

Strang, Gilbert , Introduction to Applied Mathematics

Strang, Gilbert , Linear Algebra and Its Applications, 1st ed.

Strang, Gilbert , Linear Algebra and Its Applications, 2nd ed.

Strang, Gilbert , An Analysis of the Finite Element Method

Strange, Philip G. , Brain Biochemistry and Brain Disorders

Strathern, Paul , The Gig Idea: Newton and Gravity

Stratonovich, Rouslan L. , Nonlinear Nonequilibrium Thermodynamics I: Linear and

Nonlinear Fluctuation-dissipation Theorems

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Lighting, 4th ed.

Stratton, Julius A. , Electromagnetic Theory

Straub, Conrad P. , Practical Handbook of Environmental Control

Straughan, B. P. , Spectroscopy Vol. 1: Atomic, N.M.R., N.Q.R., E.S.R., and

Mossbauer Spectroscopy

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infrared, and Raman Spectroscopy, Force Constants, Group Theory, and

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Phosphorescnece, and Photoelectron Spectroscopy, Quantum Numers, Dissociation

Energies, and Astrochemistry

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Strauss, Herbert L. , Quantum Mechanics: an Introduction

Strauss, Howard J. , Handbook For Chemical Technicians

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Strawn, John (ed.) , Digital Audio Signal Processing: an Anthology

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Streeter, Victor L. , Fluid Dynamics

Streeter, Victor L. , Fluid Mechanics, 7th ed.

Streetman, Ben G. , Solid State Electronic Devices, 3rd ed.

Streitwieser, Andrew, Jr. , Molecular Orbital Theory For Organic Chemists

Strickberger, Monroe W. , Evolution, 3rd ed.

Strobel, Howard A. , Chemical Instrumentation: A Systematic Approach, 2nd ed.

Strobl, Gert , The Physics of Polymers: Concepts For Understanding Their

Structures and Behavior

Strogatz, Steven H. , Nonlinear Dynamics and Chaos

Strogatz, Steven H. , Sync: the Emerging Science of Spontaneous Order

Stroke, G. W. , Ultrasonic Imaging and Holography: Medical, Sonar; and Optical

Applications

Stroke, George W. , An Introduction to Coherent Optics and Holography

Strommen, Dennis P. , Laboratory Raman Spectroscopy

Strong, John , Concepts in Classical Optics

Strong, John , Procedures in Experimental Physics

Strong, Steven J. , The Solar Electric House

Strongin, Herb , Science on a Shoestring, 2nd ed.

Stroscio, Joseph A. , Scanning Tunneling Microscopy

Strothotte, Thomas , Computational Visualization: Graphics, Abstraction, and

Interactivity

Stroyan, K. D. , Calculus Using Mathematica

Struik, Dirk J. , A Concise History of Mathematics, 4th revised ed.

Struik, Dirk J. , Lectures on Classical Differential Geometry, 2nd ed.

Struik, Dirk J. , Yankee Science in the Making: Science and Engineering in New

England From Colonial Times Top Thje Civil War

Struik, Dirk J. , Yankee Science in the Making: Science and Engineering in New

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Stuart, A. M. , Dynamical Systems and Numerical Analysis

Studebaker, John M. , Electricity Retail Wheeling Handbook

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Studzinski, G. P. (ed.) , Cell Growth and Apoptosis: a Practical Approach

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Waters, 3rd ed.

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Stupochencko, Ye. V. , Relaxation in Shock Waves

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Styer, Daniel F. , The Strange World of Quantum Mechanics

Style Manual Committee, COuncil of Biology Editors , Scientific Style and

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Modelling in Chemical Science, Technology and Industry

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Optical Evaluation and Adjustment

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Summers, Luis H. , Environmental Systems For Buildings I, Volume 2

Summers, Luis H. , Environmental Systems For Buildings II, Volume 2

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Surovell, David A. , Programming QUickdraw

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Vol. 1

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Sussman, Gerald Jay , Structure and Interpretation of Classical Mechanics

Sussman, Janet Iris , Timeshift: the Experience of Dimensional Change

Sutherland, Herbert J. (ed.) , A Collaection of the 2003 ASME Wind Energy

Symposoum Technical Papers Presented at the 41st AIAA Aerospace Sciences

Meeeting and Exhibit

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Demonstration of Enlightenment

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Sutton, O. G. , Atmospheric Turbulence, 2nd ed.

Sutton, Richard M. , The Physics of Space

Sutton, Richard Manliffe , Demonstration Experiments in Physics

Suvorov, Vladimir , The First Manned Spaceflight: Russia's Quest For Space

Svarovsky, L. (ed.) , Solid-Liquid Separation

Sverdrup, H. U. , The Oceans: Their Physics, Chemistry, and General Biology

Svoboda, Antonin , Computing Mechanisms and Linkages

Swain, Roger B. , Earthly Pleasures: Tales From a Biologist's Garden

Swan, Frank , Basic DC Circuits

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Swanson, Carl P. , The Cell, 3rd ed.

Swanson, D. G. , Plasma Waves

Swanson, Mark , Path Integrals and Quantum Processes

Swartz, Harold M. , Biological Applications of Electron Spin Resonance

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Sweeney, R. J. , Measurement Techniques in Mechanical Engineering

Swenson, George W., Jr. , Principles of Modern Acoustics

Swing, Richard E. , An Introduction to Microdensitometry

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Turbulence, 2nd ed.

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Sydenham, P. H. , Measuring Instruments: Tools of Knowledge and Control

Sydenham, P. H. , Introduction to Measurement Science and Engineering

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Sze, S. M. (ed.) , VLSI Technology

Sze, S. M. (ed.) , Modern Semiconductor Device Physics

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Szebehely, Victor G. , Adventures in Celestial Mechanics: a First Course in the

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Szilard, J. (ed.) , Ultrasonic Testing: Non-conventional Testing Techniques

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Tang, C. L. (ed.) , Quantum Electronics, Part A.

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Tani, Tadaaki , Photographic Sensitivity

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Taylor, A. , Crystallographic Data on Metal and Alloy Structures

Taylor, Andrew M. , Guide to the Geology of Colorado

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Taylor, C. A. , The Physics of Musical Sounds

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Texas A & M Physics Department Staff , Manual of A&M Physics Laboratory

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Teyler, Timothy J. (ed.) , Altered States of Awareness: Readings From Scientific

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Thackeray, A. D. , Atronomical Spectroscopy

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The Encyclopedia of Hardware , The Encyclopedia of Hardware

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This Old House Magazine (eds.) , This Old House Sourcebook: Where to Find and

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Thomas, Henry H. , The Engineering of Large Dams, Part 1

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Thompson, Travis , Readings in Behavioral Pharmacology

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Thomson, Sir William (Lord Kelvin) , Principles of Mechanics and Dynamics, Vol.

Thomson, William Tyrrell , Introduction to Space Dynamics

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Thorpe, Trevor A. (ed.) , Plant Tissue Culture: Mehtods and Applications in

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Thuan, Trinh Xuan , The Secret Melody: and Man Created the Universe

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Time-Life Books , Small Appliances

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Toksoz, M. Nafi , Seismic Wave Attenuation

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Tolbert, N. E. , Regulation of Atmospheric CO2 and O2 By Photosynthetic Carbon

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Townes, C. H. (ed.) , Quantum Electronics and Coherent Light

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Townsend, John, Sir , Electrons in Gases

Townsend, P. D. , Colour Centers and Imperfections in Insulators and

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Townsend, P. D. , Ion Implantaion, Sputtering and Their Applications

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Trigg, George L. , Landmark Experiments in Twentieth Centruury Physics

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Trillo, Robert L. , Jane's Ocean Technology 1974-75

Trimble, Michael R. , Biological Psychiatry, 2nd ed.

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Tuckey, Bill , Sunraycer's Solar Saga

Tudbury, Chester A. , Basics of Induction Heating, Vol. 1

Tudbury, Chester A. , Basics of Induction Heating, Vol. 2

Tudge, Colin , The Engineer in the Garden: Genes and Genetics: From the Idea of

Heredity to the Creation of Life

Tufillaro, Nicholas B. , An Experimental Approach to Nonlinear Dynamics and

Chaos

Tufte, Edward R. , The Visual Display of Quantitative Information

Tufte, Edward R. , Envisioning Information

Tufte, Edward R. , Visual Explanations: Images and Quantities, Evidence and

Narrative

Tufty, Barbara , 1001 Questions Naswered About Earthquakes, Avalanches, Floods

and Other Natural Disasters

Tuinenga, Paul W. , SPICE: a Guide to Circuit Simulation & Analysis Using PSpice

Tuma, Jan J. , Handbook of Numerical Calculations in Engineering

Tuma, Jan J. , Handbook of Physical Calculations

Tung, Wu-Ki , Group Theory in Physics

Tuniz, Claudio , Accelrator Mass Spectrometry: Ultrasensitive Analysis For

Global Science

Tunnard, Christopher , Man-made in America Chaos Or Control? An Inquiry into

Selected Problems of Design in the Urbanized Landscape

Tupper, Eric , Introduction to Naval Architecture, 3rd ed.

Turcotte, Donald L. , Fractals and Chaos in Geology and Geophysics, 2nd ed.

Turcotte, Donald L. , Fractals and Chaos in Geology and Geophysics

Turcotte, Donald L. , Space Propulsion

Turdgill, Stephen T. , Soil and Vegetation Systems, 2nd ed.

Turekian, Karl K. , Oceans

Turi, Edith A. , Thermal Characterization of Polymeric Materials

Turnbull, A. H. , An Introduction to Vacuum Technique

Turnbull, Colin M. , The Forest People: a Study of the Pygmies of the Congo

Turner, A. J. (ed.) , Time

Turner, A. Richard , Inventing Leonardo

Turner, Charles , The Chemistry of Fire and Hazardous Materials

Turner, Gerard L'E , Scientific Intruments 1500-1900: an Introduction

Turner, J. D. , Instrumentation For Engineers

Turner, J. S. , Buoyancy Effects in Fluids

Turner, J. Scott , The Extended Organism: the Physiology of Animal-built

Structures

Turner, J. Scott , The Tinkerer's Accomplice: How Design Emerges From Life

Itself

Turner, James E. , Atoms, Radiation, and Radiation Protection

Turner, Martin J. L. , Rocket and Spacecraft Propulsion: Principles, Practice,

and New Developments

Turner, Martin J. , Fractal Geometry in Digital Imaging

Turner, Rufus P. , Waveform Measurements

Turner, Rufus P. , Basic Electronic Test Instruments: Their Operation and Use

Turner, Rufus P. , 125 One Transistor Projects

Turner, Wayne C. , Energy Management Handbook

Turton, Richard , The Quantum Dot: a Journey into the Future of Microelectronics

Tuskey, Goerge A. , Transport Phenomena in Biological Systems

Tuszynski, J. A. , Biomedical Applications of Introductory Physics

Tuszynski, Jack A. , Introduction to Molecular Biophysics

Tuthill, G. Steven , Knowledge Engineering: Concepts and Practices For

Knowledge-based Systems

Twenhofel, William H. , Invertabrate Paleontology

Twitchell, Mary , Wood Energy: a Practical Guide to Heating With Wood

Twyman, F. , Prism and Lens Making, 2nd ed.

Twyman, F. , Optical Glassworking

Tyagi, M. S. , Introduction to Semiconductor Materials and Devices

Tyler, E. J. , The Craft of the Clockmaker

Tymony, Cy , Sneaky Uses For Everyday Things

Tyndall, John , Fragements of Science: a Series of Detached Essays, Addresses,

and Reviews, Vol. I

Tyndall, John , Fragements of Science: a Series of Detached Essays, Addresses,

and Reviews, Vol. II

Tyndall, John , Forms of Water

Tyndall, John , The Science of Sound

Tyrrell, H.J.V. , Diffusion and Heat Flow in Liquids

Tyson, Robert K. , Principles of Adaptive Optics

Jasa Konsultasi Disertasi Keuangan (1) dan Perbankan S3, hubungi WA 0821 2230 7021

Selasa, 09 Agustus 2022

The 4th International Conference on Digital Arts, Media and Technology

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