Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.

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The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update

Stochastic chemical kinetics describes the time evolution of a wellstirred chemically reacting system in a way that takes into account the fact that molecules come in whole numbers and exhibit some degree of randomness in their dynamical behavior. Researchers are increasingly using this approach to chemical kinetics in the analysis of cellular systems in biology, where the small molecular populations of only a few reactant species can lead to deviations from the predictions of the deterministic differential equations of classical chemical kinetics. After reviewing the supporting theory of stochastic chemical kinetics, I discuss some recent advances in methods for using that theory to make numerical simulations. These include improvements to the exact stochastic simulation algorithm (SSA) and the approximate explicit tau-leaping procedure, as well as the development of two approximate strategies for simulating systems that are dynamically stiff: implicit tau-leaping and the slow-scale SSA.

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Stochastic Simulation of Chemical Kinetics

In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.

https://www.mdpi.com/1424-8220/16/5/707/pdf

Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances.

Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.

Data Center Energy Consumption Modeling: A Survey

No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading molecular biology of the gene is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.

Molecular Biology of the Gene.

A low sampling rate approach for recovering ultra wide band (UWB) signals is proposed, using Distributed Amplifiers (DAs) and low speed Analog-to-Digital Converters (ADCs) and based on the theory of compressed sensing. A microwave circuit consisting of a bank of DAs, followed by a bank of ADCs, is designed to implement analog compressing, where the elements of measurement matrix are realized by picosecond delay tap and flexible gain coefficients in DAs. Numerical simulation shows that a bank of eight DAs and ADCs with 500MHz sampling rate can almost perfectly recover a 100ps-resolution UWB echo signal in the noiseless case. For recovering the UWB signals in a real-time way, issues in field programmable gate array (FPGA) implementation are discussed.

Compressed sensing based UWB receiver: Hardware compressing and FPGA reconstruction

This paper presents a hardware implementation approach for block-based neural networks (BbNNs) on a Programmable System-On-Chip. This is an intrinsic online evolution system that can be genetically evolved and adapted to changes in input data patterns dynamically without any need for multiple FPGA reconfigurations to accommodate various network structure/parameter changes. This removes a considerable bottleneck for performance. The research presented here is a first step towards an evolvable system that can be implemented as an embedded system.

FPGA Implementation of Evolvable Block-based Neural Networks

Improved performance is a major motivation for using parallel computation. However, performance models are frequently used only to predict an algorithm's execution time, not to accurately evaluate how the choices of architecture, operating system, interprocessor communication protocol, and programming language also dramatically affect parallel performance. We have developed an analytic model for synchronous iterative algorithms running on distributed-memory MIMD machines, and refined it for disrete-event simulation. The model describes the execution time of a single run in terms of application parameters such as the number of iterations and the required computation in each, and architectural parameters such as the number of processors, processor speed, and communication time. Our experience has shown us that an analytic model can not only accurately predict an algorithm's performance but can also match the algorithm to an appropriate architecture, identify ways to improve the algorithm's performance, quantify the performance effects of algorithmic or architectural changes, and provide a better understanding of how the algorithm works. >

Beyond execution time: expanding the use of performance models

Compressive sensing based sub-mm accuracy UWB positioning systems: A space-time approach

A compact sequential sampling scheme using a high sampling rate analog digital converter (ADC) and direct digital synthesis (DDS) technology has been proposed for the millimeter accuracy UWB positioning system, which can achieve the equivalent of a 100GHz sampling rate. The analog bandwidth, frequency resolution, and time jitter of the sampler for UWB signal acquisitions are detailed. Based on the sampled UWB signal, a modified correlation algorithm for time difference estimation is proposed to further improve positioning accuracy and reduce the computational burden. Simulation results based on IEEE802.15.4a channel models and experiments utilizing the proposed sub-sampler show that our UWB positioning system can achieve up to millimeter accuracy.

Gregory D. Peterson

Papers

Compressed sensing based UWB receiver: Hardware compressing and FPGA reconstruction

FPGA Implementation of Evolvable Block-based Neural Networks

Beyond execution time: expanding the use of performance models

Compressive sensing based sub-mm accuracy UWB positioning systems: A space-time approach

Millimeter accuracy UWB positioning system using sequential sub-sampler and time difference estimation algorithm