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.

Recent advances in FPGA technology make them an attractive platform for accelerating scientific computing applications. We present a novel hardware accelerator for Quantum Monte Carlo simulations in N-body systems. The design is deeply pipelined and exploits the inherent fine-grained parallelism available using an FPGA for all calculations. The design is implemented on a Xilinx Virtex II Pro XC2VP30 device and preliminary results indicate a maximum operating frequency of 100MHz. A single instance of our design offers an estimated speedup of 20x and accuracy comparable to the serial code running on a 2.8GHz Intel Pentium 4 processor. This architecture performs all computations with fixed-point representation and delivers accuracy on the order of or better than double-precision floating point. After deploying a single instance on the present FPGA platform, targeting our design on the Cray XD1 platform with a high gate-density FPGA will allow us to operate multiple cores in parallel.

Reconfigurable accelerator for quantum Monte Carlo simulations in N-body systems

This paper presents continuous heartbeat monitoring using evolvable block-based neural networks (BbNNs). An evolutionary algorithm is used to optimize the structure and weights of BbNN simultaneously. A BbNN, trained with the Hermite transform coefficients and a time interval between the two neighboring R peaks of ECG signal, promises a patient-specific heartbeat monitoring system. BbNNs reconfigure the structure and internal weights to cope with individual differences and the changes in physical conditions. Simulation results using the MIT-BIH Arrhythmia database demonstrate a high accuracy of 98.7% on average for the classification of ventricular ectopic beats (VEBs), being a substantial improvement over conventional techniques.

Continuous Heartbeat Monitoring Using Evolvable Block-based Neural Networks

Exploiting computational precision can improve performance significantly without losing accuracy in many applications. To enable this, we propose an innovative arithmetic logic unit (ALU) architecture that supports true dynamic precision operations on the fly. The proposed architecture targets both fixed-point and floating-point ALUs, but in this paper we focus mainly on the precision-controlling mechanism and the corresponding implementations for fixed-point adders and multipliers. We implemented the architecture on Xilinx Virtex-5 XC5VLX110T FPGAs, and the results show that the area and latency overheads are 1% ~ 24% depending on the structure and configuration. This implies the overhead can be minimized if the ALU structure and configuration are chosen carefully for specific applications. As a case study, we apply this architecture to binary cascade iterative refinement (BCIR). 4X speedup is observed in this case study.

ALU Architecture with Dynamic Precision Support

A popular research topic as of late has been dynamic partial FPGA (Field Programmable Gate Array) reconfiguration. This concept allows on-the-fly reconfiguration of digital systems where only parts of the circuit change, providing application acceleration and allowing static modules to continue processing unaffected by the dynamic elements. Design characteristics which benefit from this progressive approach include increased system flexibility, increased performance, and a reduction in circuit complexity. One characteristic receiving limited focus thus far has been the increased security that could result from these changing circuits. This benefit is innate to the design and makes reverse engineering of the system a much more ambitious task. In an effort to further enhance this passive security feature, a new partial reconfiguration technique has been proposed that changes connectivity between generic modules. This extended abstract introduces the method, model, and design flow for this dynamic partial FPGA reconfiguration technique and addresses the security implications of such a design.

Secure processing using dynamic partial reconfiguration

Through computational methods, biologists are able learn more about molecular biology by building accurate models. These models represent and predict the reactions among species populations within a system. One popular method to develop predictive models is to use a stochastic, Monte Carlo method developed by Gillespie called the stochastic simulation algorithm (SSA). Since this algorithm is based on stochastic principles, large numbers of simulations are needed to provide quality statistical models of the species and their interactions, giving way to long runtimes for large systems. In this paper, we provide an implementation of SSA onto NIVIDA graphics processing units using CUDA to parallelize ensembles of simulations. With this implementation we are able to see up to 41.9x speedup over the best-known serial implementations.

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Gregory D. Peterson

Papers

Reconfigurable accelerator for quantum Monte Carlo simulations in N-body systems

Continuous Heartbeat Monitoring Using Evolvable Block-based Neural Networks

ALU Architecture with Dynamic Precision Support

Secure processing using dynamic partial reconfiguration

GPU Accelerated Stochastic Simulation