From the Publisher:
A valuable reference for the novice as well as for the expert who needs a wider scope of coverage within the area of cryptography, this book provides easy and rapid access of information and includes more than 200 algorithms and protocols; more than 200 tables and figures; more than 1,000 numbered definitions, facts, examples, notes, and remarks; and over 1,250 significant references, including brief comments on each paper.

Handbook of Applied Cryptography

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

/pdf/gaussian-processes-for-machine-learning-40fp9c190d.pdf

Gaussian Processes for Machine Learning

This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order tensors (i.e., $N$-way arrays with $N \geq 3$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.

/pdf/tensor-decompositions-and-applications-46vl2ih5gn.pdf

Tensor Decompositions and Applications

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

1 This report is available by anonymous ftp from ftp.esat.kuleuven.ac.be in the directory pub/SISTA/moreau/reports/. The missing gures, which have been excluded because they are diicult to print, are available separately in the le chua gures.ps. Abstract. Composition methods are methods arising from diierential geometry for the integration of ordinary diierential equations. We apply them here to arrays of Chua circuits. In these methods, we split the vector eld of the array of Chua circuits into its linear part and its nonlinear part. We then solve the elementary diierential equation for each part separately, and recombine these contributions in a sequence of compositions. This gives rise to simple integration rules for arrays of Chua circuits, which we compare to more classical approaches: the xed time-step explicit Euler and adaptive fourth-order Runge-Kutta methods.

Simulation of Arrays of Chua Circuits by Composition Methods

In this paper we show the use of the quotient singular value decomposition QSVD in the analysis of aspects of multivariable systems relative to one another. The LQ-∞-norm is introduced as a measure for the largest ratio of the output signals of 2 multiple input-multiple output systems. It is shown how the LQ-∞-norm can be computed. Using the QSVD the direction of the worst case input signal can be found.

The quotient SVD and H ∞ norms

It is acknowledged worldwide over the past several decades that the basic education of circuits and systems (CAS) is not in stable water. A first reason is that smart phones, iPads, the Internet and computers have created expectations of instant gratification among young people. This has reduced the attention span of students, so that they are not prepared for the lengthy lectures that traditionally used to dominate the conceptual elements of CAS education. Another issue is the fact that, in EE departments, basic CAS courses are often assigned to uninterested or unmotivated teachers. Even research in CAS is not in the mainstream at many universities anymore. However, the role of CAS is still central in basic EE education, with numerous concepts that are essential for every EE undergraduate student: transfer functions, impulse response, time constant, Bode diagrams, impedance, ?

Yannis Tsividis As a Role Model for Reversing the Negative Spiral of Basic Circuits and Systems Education

A globally convergent algorithm for solving harmonic balance equations is discussed. The method is a variable-dimension algorithm that exploits the structure in the equations by processing the contributions at each frequency consecutively. A convergence condition is proved. >

A variable dimension algorithm for solving harmonic balance equations

We present sufficient conditions for global asymptotic stability and I/O stability (finite L/sub 2/-gain) of multilayer recurrent neural networks, with parametric uncertainties upon the interconnection matrices. This is done by considering perturbed NL/sub q/ systems. NL/sub q/s are discrete time nonlinear dynamical systems in state space form, containing q layers with alternating linear and static nonlinear operators that satisfy a sector condition. Within the present framework, linear fractional transformations with real diagonal uncertainty block are interpreted as perturbed NL/sub q/s with q=1. While in /spl mu/ robust control theory uncertainties upon nominal linear models are investigated, uncertainties upon nominal nonlinear models can be studied in NL/sub q/ neural control theory.

Joos Vandewalle

Papers

Simulation of Arrays of Chua Circuits by Composition Methods

The quotient SVD and H ∞ norms

Yannis Tsividis As a Role Model for Reversing the Negative Spiral of Basic Circuits and Systems Education

A variable dimension algorithm for solving harmonic balance equations

Robust NL/sub q/ neural control theory