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

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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.

Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

In a future 'internet of things', an increasing number of every-day objects are connected with each other. Nowadays, connectivity between these devices is supported by assigning each device to an existing (wireless) network. However, these networks do not take into account the individual needs of these devices, even though all these devices are very different in terms of application requirements and hardware capabilities. Moreover, multiple existing networks are often configured independent from each other without any interaction. As an alternative, this paper proposes and discusses a methodology that more efficiently supports network cooperation between heterogeneous devices. The paper argues for autonomously created communities of similar devices, that are able to negotiate with different co-located communities to further optimize their network performance. Different communities engage in cooperation by activating network service, but only when the end result is beneficial for all involved communities. In this paper, the concepts and advantages of this approach are discussed. In addition, a methodology is explored that is able to realize these concepts. Finally, based on this methodology, possible network solutions are presented, remaining challenges are listed and future research opportunities are identified.

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Exploring a Boundary-Less Cooperation Approach for Heterogeneous Co-Located Networks

We address the design of a generic architecture for the management of residential services. The architecture consists of components both at the customers' side as well as at the service provider's side. The key features of the architecture are service modularity, the concept of service sessions, service packaging and subscription. The architecture allows service providers and telecom operators to rapidly provide new integrated value-added services to their customers. Layer-based design ensures that the architecture is independent of the particular service and service realization technology. The architecture provides generic access session management, service session management, subscription management and billing. Its implementation is based on J2EE (Java 2 Enterprise Edition). The various components of the architecture are discussed, together with the implementation issues.

/pdf/design-and-implementation-of-a-generic-software-architecture-3mdjxgxisq.pdf

Design and implementation of a generic software architecture for the management of next-generation residential services

This work presents a review of optical network research activities in Europe, based on projects funded by the EU and national governments. Research networks and new initiatives are discussed.

Optical networking research: a European perspective

Quantative and theoretical analysis of recovery convergence in IP networks.

The integration of GaAs MESFET circuits with InP components using epitaxial lift-off (ELO) technology is discussed. Specific ELO problems (film-substrate isolation, alignment) and solutions as well as the various ways to integrate MESFETs on InP are described. The fabrication of a long wavelength OEIC, in which a GaAs ELO MESFET is integrated with an InP 2*2 buried waveguide optical switch (fully interconnected oil chip), is presented. >

Piet Demeester

Papers

Exploring a Boundary-Less Cooperation Approach for Heterogeneous Co-Located Networks

Design and implementation of a generic software architecture for the management of next-generation residential services

Optical networking research: a European perspective

Quantative and theoretical analysis of recovery convergence in IP networks.

Fabrication of long wavelength QEICs using GaAs on InP epitaxial lift-off technology