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

Multi-layer traffic engineering (MTE) dynamically reconfigures the logical network as needed by the changing traffic pattern offered to the network: e.g., in the case of an IP-over-WDM network, the MTE strategy reconfigures the logical IP network. However, only a limited amount of physical resources is available in the underlying flexible network: e.g., each WDM system is able to multiplex a limited number of wavelength channels onto the same fiber. The paper investigates the impact this physical capacity constraint may impose on the performance of such MTE strategies.

Multi-layer traffic engineering: performance evaluation under a physical capacity constraint

Massively Online Virtual Environments (MOVEs) have been gaining popularity for several years. Today, these complex networked applications are serving thousands of clients simultaneously. However, these MOVEs are typically hosted on specialized server clusters and rely on internal knowledge of the services to optimize the load balancing. This makes running MOVEs an expensive undertaking as it cannot be outsourced to third party hosting providers. This paper details two Integer Linear Programming approaches to optimize the MOVE deployment through load balancing and minimizing the delay experienced by the end-users. Optimization includes assigning MOVE components to resources and replication of components to increase the scalability. One approach assuming full application knowledge of a dedicated MOVE and one with no internal knowledge and geared toward a generic MOVE hosting platform. For both cases an optimizing heuristic is evaluated and the obtained results are compared.

/pdf/autonomic-service-hosting-for-large-scale-distributed-move-50mkzos0vl.pdf

Autonomic service hosting for large-scale distributed MOVE-services

Energy footprint of ICT: future outlook and challenges

Compact routing schemes address the fundamental tradeoff between the memory space required to store the routing table entries and the length of the routing paths they produce. This paper introduces a dynamic compact routing scheme that allows the distribution of traffic from any source to any set of leaf nodes along a point-to-multipoint routing path referred to as multicast (traffic) distribution tree. By means of the proposed scheme, a multicast distribution tree can dynamically evolve according to the arrival of leaf-initiated join/leave requests. We evaluate by means of simulation the performance of the proposed scheme and analyze the tradeoffs between the stretch of the produced routing paths, the size and the number of routing table entries, and the communication cost.

https://hal.inria.fr/inria-00588106/document

Performance Analysis of a Dynamic Compact Multicast Routing Scheme

Transparent optical networking promises a cost-efficient solution for future core and metro networks because of the efficacy of switching high-granularity trunk traffic without opto-electronic conversion. Network availability is an important performance parameter for network operators, who are incorporating protection and restoration mechanisms in the network to achieve competitive advantages. This paper focuses on the reduction in Capital Expenditures (CapEx) expected from implementing sharing of backup resources in path-protected transparent networks. We dimension a nationwide network topology for different protection mechanisms using transparent and opaque architectures. We investigate the CapEx reductions obtained through protection sharing on a population of 1000 randomly generated biconnected planar topologies with 14 nodes. We show that the gain for transparent networks is heavily dependent on the offered load, with almost no relative gain for low load (no required parallel line systems). We also show that for opaque networks the CapEx reduction through protection sharing is independent of the traffic load and shows only a small dependency on the number of links in the network. The node CapEx reduction for high load (relative to the number of channels in a line system) is comparable to the CapEx reduction in opaque OTN systems. This is rather surprising as in OTN systems the number of transceivers and linecards and the size of the OTN switching matrix all decrease, while in transparent networks only the degree of the ROADM (number and size of WSSs in the node) decreases while the number of transponders remains the same.

/pdf/analysis-of-resource-sharing-in-transparent-networks-4hrsq1w3n0.pdf

Piet Demeester

Papers

Multi-layer traffic engineering: performance evaluation under a physical capacity constraint

Autonomic service hosting for large-scale distributed MOVE-services

Energy footprint of ICT: future outlook and challenges

Performance Analysis of a Dynamic Compact Multicast Routing Scheme

Analysis of resource sharing in transparent networks