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

With the advent of cloud computing and remote execution of interactive applications, there is a need for evaluating the Quality of Experience (QoE) and the influence on this QoE of network condition variations, media encoding parameter settings and related optimization algorithms. However, current QoE assessment focuses mainly on audiovisual quality in non-interactive applications, such as video-on-demand services. On the other hand, where experiments aim to quantify interactive quality, the focus is typically targeted at games, using an ad-hoc test setup to assess the impact of network variations on the playing experience. In this paper, we present a novel platform enabling the assessment of a broad range of interactive applications (e.g., thin client remote desktop systems, remotely rendered engineering applications, games). Dynamic reconfiguration of media encoding and decoding is built into the system, to allow dynamic adaptation of the media encoding to the network conditions and the application characteristics. Evaluating the influence of these automatic adaptations is a key asset of our approach. A range of possible use cases is discussed, as well as a performance study of our implementation, showing that the platform we built is capable of highly controllable subjective user assessment. Furthermore, we present results obtained by applying the platform for a subjective evaluation of an interactive multimedia application. Specifically, the influence of visual quality and frame rate on interactive QoE has been assessed for a remotely executed race game.

/pdf/platform-for-real-time-subjective-assessment-of-interactive-3pws8fjybl.pdf

Platform for real-time subjective assessment of interactive multimedia applications

https://biblio.ugent.be/publication/430776/file/793283.pdf

On the costs of operating a next-generation access network

Substrate integrated waveguide (SIW) cavity-backed slot antenna topologies are promising candidates to adress the specific design challenges posed by the Internet of Things (IoT). In this contribution, we demonstrate their potential by discussing two designs on two different, application-specific, innovative substrate materials. First, a compact, ultra-wideband three-element array with very low mutual coupling is presented for integration into furniture. In the second design, the half-mode SIW technique is applied to obtain a miniaturized ultra-wideband design, enabling invisible integration into cork floor and wall tiles. The compactness, integrability, and stable, high performance of both designs in different operating conditions, make them ideal candidates for IoT applications.

/pdf/siw-cavity-backed-slot-multi-antenna-systems-for-the-next-1hqlrpse97.pdf

SIW cavity-backed slot (multi-)antenna systems for the next generation IoT applications

Several dynamic bandwidth allocation (DBA) algorithms have been proposed for long-reach passive optical networks (LR-PONs). The gains in the current protocols, however, are incremental and limited to a specific range of loads. Using an analytical framework, we first examine the main sources of performance degradation specific to LR-PONs. From this, we conclude that there is a large scope for improvement in the performance by using an optimal cycle length, which is not influenced by reach, and which polls users according to their reported load while providing opportunities for pre-granting bandwidth at the optical network units. The fraction of bandwidth that is pre-granted should be decreased with load. To tailor these optimal design concepts in a DBA algorithm, we propose Synergized-Adaptive Multi-GATE polling with Void-filling (S-AMGAV), which reduces both the delay bound and packet loss rate by at least 50% when compared to the state-of-the-art DBAs like interleaved polling with adaptive cycle time (IPACT), IPACT with grant estimation (IPACT-GE), multi-thread polling (MTP), double phase polling (DPP), gate driven (GD), and AMGAV. S-AMGAV also maintains simplicity of the protocol while removing bandwidth inefficiencies prevailing in the existing protocols. We verify the usefulness of the algorithm through both analytical modeling and simulations.

Synergized-adaptive multi-GATE polling with void filling: Overcoming performance degradation in LR-PONs

All optical switching has been proposed as a candidate to allow high capacity networking in the future. Currently, Optical Circuit Switching has been widely deployed, although this approach is potentially bandwidth-inefficient for small granularity flows and needs relatively long set-up times. Research is being done into Optical Packet Switching (OPS) and Optical Burst Switching (OBS), but these kinds of architectures are not yet able to be deployed due to the large network cost they impose. This paper tries to combine the benefits from OCS and OBS, by investigating a hybrid switching technique called burst-over-circuit switching (BoCS) where both OBS and OCS are used at a specific part in the network. First, a short summary will be provided, categorising existing hybrid ideas. Then, a simulation study will evaluate how well BoCS performs compared to pure OBS or OCS and finally the feasibility of the proposed method will be investigated by a network cost analysis.

/pdf/cost-effective-burst-over-circuit-switching-in-a-hybrid-4stlh5lbl2.pdf

Piet Demeester

Papers

Platform for real-time subjective assessment of interactive multimedia applications

On the costs of operating a next-generation access network

SIW cavity-backed slot (multi-)antenna systems for the next generation IoT applications

Synergized-adaptive multi-GATE polling with void filling: Overcoming performance degradation in LR-PONs

Cost-Effective Burst-Over-Circuit-Switching in a Hybrid Optical Network