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

Third generation sequencing platforms produce longer reads with higher error rates than second generation sequencing technologies. While the improved read length can provide useful information for downstream analysis, underlying algorithms are challenged by the high error rate. Error correction methods in which accurate short reads are used to correct noisy long reads appear to be attractive to generate high-quality long reads. Methods that align short reads to long reads do not optimally use the information contained in the second generation data, and suffer from large runtimes. Recently, a new hybrid error correcting method has been proposed, where the second generation data is first assembled into a de Bruijn graph, on which the long reads are then aligned. In this context we present Jabba, a hybrid method to correct long third generation reads by mapping them on a corrected de Bruijn graph that was constructed from second generation data. Unique to our method is that this mapping is constructed with a seed and extend methodology, using maximal exact matches as seeds. In addition to benchmark results, certain theoretical results concerning the possibilities and limitations of the use of maximal exact matches in the context of third generation reads are presented.

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Jabba: Hybrid Error Correction for Long Sequencing Reads Using Maximal Exact Matches

There has been a large global effort to innovate and design optical access technologies that can accommodate the requirements emerging from a colossal increase in data rates. Currently TWDM-PONs and WDM-PONs have been foreseen as the main candidates for next-generation access systems. Due to current business modeling trends and possible regulatory obligations, these networks should also support open access, which refers to the sharing of a network infrastructure among different network entities in a non-discriminatory way. By sharing the (bottleneck) infrastructure facility, open access reduces the entry barrier for a network entity. This opens doors for a multi-provider scenario, which leads to competition among network players and can significantly reduce the price of services. Opening up the network, however, entails new architectures. In this article we propose novel architectures to support open access at the fiber and wavelength level for WDM-PON and TWDM-PON. These architectures differ significantly in terms of their cost (capital and operational expenditures). We compare the proposed architectures with regard to their cost and analyze the impact of adoption levels (percentage of users subscribed) and customer churn rate (how often the customers change network provider) on the cost of the architectures.

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Fiber and wavelength open access in WDM and TWDM passive optical networks

Wireless sensor networks consist of embedded devices (sensor nodes), equipped with a low-power radio. They are used for many applications: from wireless building automation to e-health applications. However, due to the limited capabilities of sensor nodes, designing network protocols for these constrained devices is currently very challenging. Therefore, this paper presents the IDRA platform: an information driven architecture designed to support next-generation applications on resource constrained networked objects. IDRA supports simple but useful optimizations at an architectural level. These include support for cross-protocol interactions, energy efficiency optimizations, QoS optimizations (packet priorities, dynamic protocol selection), mobility support and heterogeneous network support. The paper shows how the development of protocols is improved by using an architecture which delegates specific tasks to a central system, decreasing the memory requirements of associated network protocols. A thorough experimental performance analysis demonstrates that IDRA is much more scalable in terms of memory requirements, energy requirements and processing overhead than traditional system architectures. Finally, the paper discusses how the optimizations presented in this paper can be used for the clean-slate design of architectures for other wireless or wired network types.

IDRA: A flexible system architecture for next generation wireless sensor networks

Both bandwidth demand and energy consumption of ICT and communication networks is increasing and optical networks are regarded to provide high bandwidth solutions while enabling more energy efficiency. In this article we give an overview of energy consumption in access and core networks with a focus on optical technologies. Also, possible strategies to enable power reductions are discussed.

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Optical networks: How much power do they consume and how can we optimize this?

Time and wavelength division multiplexed passive optical networks (TWDM-PONs) have been widely considered as one of the next evolutionary steps of optical access networks. A variety of algorithms exists that explore the problem of scheduling and wavelength assignment in TWDM-PONs. These algorithms, however, allow unlimited switching of wavelengths. In reality, wavelength switching increases guard bands due to the tuning and the switching time of components, limiting channel utilization and increasing packet delays. We propose a novel dynamic bandwidth allocation (DBA) algorithm for TWDM-PON that minimizes the performance degradation due to excessive wavelength switching.

/pdf/dynamic-bandwidth-allocation-with-optimal-wavelength-3iy5x9ng1p.pdf

Piet Demeester

Papers

Jabba: Hybrid Error Correction for Long Sequencing Reads Using Maximal Exact Matches

Fiber and wavelength open access in WDM and TWDM passive optical networks

IDRA: A flexible system architecture for next generation wireless sensor networks

Optical networks: How much power do they consume and how can we optimize this?

Dynamic bandwidth allocation with optimal wavelength switching in TWDM-PONs