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

The potential technological advantages of growing GaAs on Si, such as the development of a new generation of OEIC's, has attracted a large research effort during recent years. Many problems encountered in the hetero-epitaxxal growth such as lattice ndsmatch, anti-phase boundaries, pre-growth cleaning and difference in thermal expansion coefficients, nave been extensively studied In particular the thermal mismatch between the two materials involved is still a severe problem, since it induces a high tensile stress within the GaAs layer on cooling down from the epitaxial growth temperature. These stresses cause the wafer to bend, which, harriers lithography and further processing. Above a certain strain level, this means thicker epilayers, nucrocracks will appear in the GaAs along its cleaving directions, reducing the material stresses. These arbitrarily occurlng microoracks form isolated regions In the layerstructure which may cause improper device operation In this paper selective growth has been used In an original way to solve the problem of random crack formation In thick GaAs on Si layers [ 1 ]. The hetero-epitaxial growth was carried out in a small MOVPE system working at atmospheric pressure. The sources used are TMG and a 5% mbcture of AsEL in

Crack Formation and Selective Growth in MOVPE-GaAs on Si and its Application to OEICs

Multilayer traffic engineering (MLTE) allows coping with ever-increasing and varying traffic demands in IP-over-Optical multilayer networks. It utilizes cross-layer TE (Traffic Engineering) techniques to provision optical lightpath capacity to the IP/MPLS (Internet Protocol/ Multi-Protocol Label Switching) logical topology on-demand. Such provisioning however causes optical connection arrival rates that pose strong performance requirements to Routing and Wavelength Assignment (RWA) strategies. Collecting up-to-date network information for the RWA with rapidly changing network states can be quite difficult. Exposing optical layer state information to the IP layer in the overlay model, or transforming this optical layer information in a workable representation in an integrated control plane is similarly problematic. Prediction-Based Routing (PBR) has been proposed as a RWA mechanism for optical transport networks; it bases routing not on possibly inaccurate or outdated network state, but instead on previous connections set-up. In this article, we propose to implement PBR as the RWA mechanism in the optical layer of a multilayer network, and use the predictive capabilities of PBR to expose dynamic optical network information into the multilayer traffic engineering algorithm with minimal control plane overhead. Some simulations show the benefits of using the PBR in the optical layer for MLTE purposes.

/pdf/prediction-based-routing-as-rwa-in-multilayer-traffic-14ls2qamf2.pdf

Prediction-based routing as RWA in multilayer traffic engineering

Recent advances in optical networking technology have moved the state-of-the-art from manually installed fiber connections to fully automatic switched lightpaths. IP-over-Optical networks carry Internet traffic between IP routers, using lightpaths (optical connections) with multi Gbit/s bandwidth as transport method. Multilayer Traffic Engineering (MTE) in an IP-over-Optical network allows to leverage rapid lightpath setup/teardown as a cross-layer traffic engineering technique. It enables on-the-fly reconfiguration of the IP layer logical topology (creating connectivity between IP routers) and up/downgrade of the capacity of IP links (by adding or removing lightpaths between IP routers). Together with classical IP layer routing techniques, MTE intelligently solves problems such as IP layer congestion and packet loss and it may optimize optical layer capacity usage and total network throughput. We present some example MTE techniques and discuss how they impact perceived network performance.

https://biblio.ugent.be/publication/333232/file/564264.pdf

Multilayer traffic engineering in IP-over-Optical Networks

Special Issue on Converged Optical Networks

In this paper we present an architecture for a robust commercial content delivery service, implemented on top of a Content Distribution Network (CDN). The focus will be on the automated management and flexibility of the system, while optimizing its performance in terms of Quality of Service (QoS) and availability. In the proposed content delivery architecture, distributed replication of streaming videos, multicasting and traffic engineering will be combined in order to tackle specific problems such as congested network parts, overloaded servers and the occurrence of flash crowds. Simulations will validate the usefulness of each individual technique and show how much the overall network usage and experienced QoS can be increased.

Piet Demeester

Papers

Crack Formation and Selective Growth in MOVPE-GaAs on Si and its Application to OEICs

Prediction-based routing as RWA in multilayer traffic engineering

Multilayer traffic engineering in IP-over-Optical Networks

Special Issue on Converged Optical Networks

An architecture for delivery of streaming media content based on network monitoring