THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.

/pdf/evolving-artificial-neural-networks-4kzrbu7pkn.pdf

Evolving artificial neural networks

List of Figures. List of Tables. Preface. Contributing Authors. Series Foreword. Part I: Foundations. 1. An Introduction to Evolutionary Algorithms J.A. Lozano. 2. An Introduction to Probabilistic Graphical Models P. Larranaga. 3. A Review on Estimation of Distribution Algorithms P. Larranaga. 4. Benefits of Data Clustering in Multimodal Function Optimization via EDAs J.M. Pena, et al. 5. Parallel Estimation of Distribution Algorithms J.A. Lozano, et al. 6. Mathematical Modeling of Discrete Estimation of Distribution Algorithms C. Gonzalez, et al. Part II: Optimization. 7. An Empiricial Comparison of Discrete Estimation of Distribution Algorithms R. Blanco., J.A. Lozano. 8. Results in Function Optimization with EDAs in Continuous Domain E. Bengoetxea, et al. 9. Solving the 0-1 Knapsack Problem with EDAs R. Sagarna, P. Larranaga. 10. Solving the Traveling Salesman Problem with EDAs V. Robles, et al. 11. EDAs Applied to the Job Shop Scheduling Problem J.A. Lozano, A. Mendiburu. 12. Solving Graph Matching with EDAs Using a Permutation-Based Representation E. Bengoetxea, et al. Part III: Machine Learning. 13. Feature Subset Selection by Estimation of Distribution Algorithms I. Inza, et al. 14. Feature Weighting for Nearest Neighbor by EDAs I. Inza, et al. 15. Rule Induction by Estimation of Distribution Algorithms B. Sierra, et al. 16. Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs L.M. de Campos, et al.17. Comparing K-Means, GAs and EDAs in Partitional Clustering J. Roure, et al. 18. Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms C. Cotta, et al. Index.

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

In this work we compare some of the freely available parallel Toolboxes for MATLAB, which differ in purpose and implementation details: while DP-Toolbox and MultiMATLAB offer a higher-level parallel environment, the goals of PVMTB and MPITB, developed by us [7], are to closely adhere to the PVM system and MPI standard, respectively. DP-Toolbox is also based on PVM, and MultiMATLAB on MPI. These Toolboxes allow the user to build a parallel application under the rapid-prototyping MATLAB environment. The differences between them are illustrated by means of a performance test and a simple case study frequently found in the literature. Thus, depending on the preferred message-passing software and the performance requirements of the application, the user can either choose a higher-level Toolbox and benefit from easier coding, or directly interface the message-passing routines and benefit from greater control and performance.

Performance of message-passing MATLAB Toolboxes

This paper presents a novel network context monitoring system, known as the Context Monitoring System (CMS), that is designed to accommodate the rapidly changing network context requirements and network context availability in dynamically (de) composing Ambient Networks (ANs). CMS is designed to support dynamic deployment, activation, and (re) configuration of Context Sensors in ANs in an efficient and scalable way, and to locate available distributed network context in a scalable and distributed manner once Context Sensors are deployed, in order to support subsequent efficient and scalable network context retrieval.

Towards a Context Monitoring System for Ambient Networks

The availability of multi-core processors and programmable NICs (Network Interface Cards), such as TOEs (TCP/IP Offloading Engines), provides new opportunities for designing efficient network interfaces to cope with the gap between the improvement rates of link bandwidths and microprocessor performance. This gap poses important challenges related with the high computational requirements associated to the traffic volumes and wider functionality that the network interface has to support. An opportunity to reach these goals comes from the exploitation of the parallelism in the communication path by distributing the protocol processing work across processors available in the computer, i.e. multi-core microprocessors and programmable NICs. Thus, alternatives such as offloading and onloading try to release host CPU cycles by this approach. Nevertheless, whereas onloading uses another general-purpose processor, either included in a multi-core microprocessor or in a symmetric multiprocessor (SMP), offloading takes advantage of processors in programmable network interface cards (NICs). Some experimental results demonstrate that the relative improvement on peak throughput offered by offloading and onloading depends on the rate of application workload to communication overhead, the message sizes, and on the characteristics of system architecture, more specifically the bandwidth of the buses and the way the NIC is connected to the system processor and memory. Thus, in this paper we propose a network interface that takes the advantages of both offloading and onloading approaches while avoids their respective drawbacks. The performance analyses done by using a full-system simulator, shows that, in the benchmarks and application used for the experiments, our hybrid interface improves the latency and bandwidth behavior of the onloading and offloading approaches.

A New Offloaded/Onloaded Network Interface for High Performance Communication

Bio-Inspired Systems: Computational and Ambient Intelligence

We introduce A-GAP, a protocol for continuous monitoring of network state variables with configurable accuracy. Network state variables are computed from device counters using aggregation functions, such as SUM, AVERAGE and MAX. In A-GAP, the accuracy is expressed in terms of the average error and is controlled by dynamically configuring filters in the management nodes. The protocol follows the push approach to monitoring and uses the concept of incremental aggregation on a self-stabilizing spanning tree. A-GAP is decentralized and asynchronous to achieve robustness and scalability. We provide some results from evaluating the protocol for an ISP topology (Abovenet) in several scenarios through simulation. The results show that we can effectively control the fundamental trade-off between accuracy and overhead. The protocol overhead can be reduced significantly by allowing only small error objectives.

/pdf/distributed-real-time-monitoring-with-accuracy-objectives-1hoaiw16ax.pdf

Alberto Prieto

Papers

Performance of message-passing MATLAB Toolboxes

Towards a Context Monitoring System for Ambient Networks

A New Offloaded/Onloaded Network Interface for High Performance Communication

Bio-Inspired Systems: Computational and Ambient Intelligence

Distributed real-time monitoring with accuracy objectives