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Discover mouse gene coexpression landscape using dictionary learning and sparse coding 1

1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2016

作者： Li, Yujie Chen, Hanbo Jiang, Xi Li, Xiang Lv, Jinglei Peng, Hanchuan Tsien, Joe Z. Liu, Tianming Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States School of Automation Northwestern Polytechnical University Xi’an China Allen Institute for Brain Science SeattleWA United States Brain and Behavior Discovery Institute Medical College of Georgia at Augusta University AugustaGA United States

ISBN: (数字)9783319467207

ISBN: (纸本)9783319467191

Gene coexpression patterns carry rich information of complex brain structures and functions. Characterization of these patterns in an unbiased and integrated manner will illuminate the higher order transcriptome organization and offer molecular foundations of functional circuitry. Here we demonstrate a data-driven method that can effectively extract coexpression networks from transcriptome profiles using the Allen Mouse Brain Atlas dataset. For each of the obtained networks,both genetic compositions and spatial distributions in brain volume are learned. A simultaneous knowledge of precise spatial distributions of specific gene as well as the networks the gene plays in and the weights it carries can bring insights into the molecular mechanism of brain formation and functions. Gene ontologies and the comparisons with published data reveal interesting functions of the identified coexpression networks,including major cell types,biological functions,brain regions,and/or brain diseases. © Springer International Publishing AG 2016.

关键词： Genes

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Local energy micro-storage systems in smart communities with active, reactive and harmonic control

Local energy micro-storage systems in smart communities with...

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International Conference on Environment and Electrical Engineering (EEEIC)

作者： María-Isabel Milanés-Montero Eva González- Romera Fermín Barrero-González Enrique Romero-Cadaval Antonio Moreno-Munoz Universidad de Extremadura Caceres Extremadura ES Electrical Electronic and Automation Engineering Dept. University of Extremadura Badajoz Spain Computer Architecture Electronics and Electronic Technology Department Universidad de Córdoba Córdoba Spain

ISBN: (纸本)9781509023219

This paper aims to provide control strategies for distributed micro-storage energy systems at the residential level to contribute to smart grid goals. The operation of local Energy Storage Systems (ESS) at homes in a Smart Community with distributed generation based of renewable energies is analyzed by simulation. Each individual home receive the active and reactive power set-points from the Smart Community Energy Management System (SCEMS), acting as an aggregator of resources in the community according to the expected demand and production. Besides, in case the ESS has got available capacity, once the SCEMS requirements are satisfied, it is used to inject the harmonic current components demanded by the household circuitry. It allows a local improvement in the power quality of the demanded current, and so contributes to the global power quality consumption of the community. Simulation results showing the operation of the local ESS are presented to validate the proposed control strategies.

关键词： Harmonic analysis Reactive power Simulation Power quality Batteries Power system harmonics

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Application of proper orthogonal decomposition and radial basis functions for crack size estimation using particle swarm optimization

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Journal of Physics: Conference Series 2017年第1期842卷

作者： B Benaissa M Köppen M Abdel Wahab S Khatir LEMI Department of Mechanical Engineering University M'hamed Bougara of Boumerdes. Algeria. Faculty of Computer Science and Systems Engineering Department of Creative Informatics Kyushu Institute of Technology. Japan. Department of Electrical Energy Systems and Automation Faculty of Engineering and Architecture Ghent University. Belgium.

Complex engineering problems require simulations, which are computationally expensive in cases of inverse identification tasks since they commonly requires hundreds of thousands of simulations. This paper propose a method based on model reduction for crack size estimation, combining the proper orthogonal decomposition method with radial basis functions. The reduced model is validated by comparing the obtained boundary displacements with the corresponding results from a finite element model. This inverse procedure is formulated as the minimization of the difference between the measured and computed values of displacement at selected boundary nodes, called sensor points, using particle swarm optimization algorithm. Convex and a non-convex specimens have been considered for investigations of crack presence, and identification of its size, different crack sizes have been tested to demonstrate the efficiency of the proposed approach.

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Group-wise sparse representation of brain states reveal network abnormalities in mild traumatic brain injury

Group-wise sparse representation of brain states reveal netw...

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IEEE International Symposium on Biomedical Imaging

作者： Jinglei Lv Armin Iraji Hanbo Chen Fangfei Ge Lei Guo Xin Zhang Zhifeng Kou Tianming Liu School of Automation Northwestern Polytechnical University Xi'an China Biomedical Engineering and Radiology Wayne State University Detroit Michigan Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia Athens GA

ISBN: (纸本)9781479923519

Mild traumatic brain injury (mTBI) is a leading public health care burden. Recent research has shown that the functional impairment in mTBI patients could be captured by resting state fMRI (rsfMRI) at network level. Moreover exploring brain response to mTBI over time at large scale network level can help physicians better diagnose brain injury and order appropriate rehabilitation plan. Therefore, there is a need for methodological innovation that could assess brain impairment in rsfMRI data and further define biomarkers for network changes. In this paper, we propose a novel group-wise sparse representation of brain states (GSRBS) approach, based on rsfMRI data, to explore the effect of mTBI on functional networks across different groups and longitudinal stages. Specifically, a dictionary of brain networks is learned from the volumes of rsfMRI data, and at each time point these networks are linearly and sparsely combined to realize a brain state. Our results showed that group-wise statistical difference on the network composition of brain states could be found between healthy controls and mTBI patients at two different temporal stages.

关键词： Dictionaries Encoding Brain injuries Learning systems Training Biomedical imaging

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System level exploration of a STT-MRAM based level 1 data-cache

System level exploration of a STT-MRAM based level 1 data-ca...

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2015 Design, automation and Test in Europe Conference and Exhibition, DATE 2015

作者： Komalan, Manu Perumkunnil Tenllado, Christian Perez, Jose Ignacio Gomez Fernandez, Francisco Tirado Catthoor, Francky Department of Computer Architecture and Automation Universidad Complutense de Madrid Spain KU Leuven Leuven3000 Belgium IMEC Leuven3001 Belgium

ISBN: (纸本)9783981537048

Since Non-Volatile Memory (NVM) technologies are being explored extensively nowadays as viable replacements for SRAM based memories in LLCs and even L2 caches, we try to take stock of their potential as level 1 (L1) data caches. These NVMs like Spin Torque Transfer RAM(STT-MRAM), Resistive-RAM(ReRAM) and Phase Change RAM (PRAM) are not subject to leakage problems with technology scaling. They also show significant area gains and lower dynamic power consumption. A direct drop-in replacement of SRAM by NVMs is, however, still not feasible due to a number of shortcomings with latency (write or read) and/or endurance/reliability among them being the major issues. STT-MRAM is increasingly becoming the NVM of choice for high performance and general purpose embedded platforms due to characteristics like low access latency, low power and long lifetime. With advancements in cell technology, and taking into account the stringent reliability and performance requirements for advanced technology nodes, the major bottleneck to the use of STT-MRAM in high level caches has become read latency (instead of write latency as previously believed). The main focus of this paper is the exploration of read penalty issues in a NVM based L1 data cache (D-cache) for an ARM like single core general purpose system. We propose a design method for the STT-MRAM based D-cache in such a platform. This design addresses the adverse effects due to the STT-MRAM read penalty issues by means of micro-architectural modifications along with code transformations. According to our simulations, the appropriate tuning of selective architecture parameters in our proposal and suitable optimizations can reduce the performance penalty introduced by the NVM (initially ∼54%) to extremely tolerable levels (∼8%). © 2015 EDAA.

关键词： Static random access storage

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An improved genetic algorithm based on the shortest path problem

An improved genetic algorithm based on the shortest path pro...

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International Conference on Information and automation (ICIA)

作者： Zhendong Liu Yawei Kong Bin Su Department of Automation Tsinghua University Beijing China School of Computer Science and Technology Shandong Jianzhu University Jinan Shundong Province China Co-Innovation Center of Green Building School of Architecture and Urban Planning Shandong Jianzhu University Jinan Shandong Province China School of Architecture Unitec Institute of Technology Auckland New Zealand

ISBN: (纸本)9781509041039

The problem of dynamic stochastic shortest path is NP-hard. The transportation network of the city is dynamic and stochastic, the optimal problem of path is widely used in the fields of transportation, communication and computer network. The paper investigates the shortest path problem based on the genetic algorithm principle, an improved self adaptive genetic algorithm is proposed by encoding the chromosomal mode. We improved genetic algorithm by adjusting the encoding parameters. The experiments indicate that the improved genetic algorithm DRSP-GA could obtain the better solutions which adapt to new transportation rapidly in global optimization than A* algorithm and Dijkstra algorithm in the shortest path problem.

关键词： Biological cells Genetic algorithms Heuristic algorithms Transportation Shortest path problem Encoding Sociology

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Fault detection by backwards analysis in coloured workflow nets 5

Fault detection by backwards analysis in coloured workflow n...

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5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2015

作者： Ganishev, Vasilii Fengler, Olga Fengler, Wolfgang Department of Applied Mathematics National Research University Moscow Power Engineering Institute Moscow Russia Faculty of Computer Science and Automation Computer Architecture Group TU Ilmenau Ilmenau Germany

ISBN: (纸本)9789897581205

The increasing complexity of the business processes requires automated methods for trouble-shooting and debugging of the process model in operation. This paper proposes mechanism of fault detection based on the concept of backwards reachability for the coloured workflow nets. The formal verification methods defined for coloured Petri nets such as state space method or place invariants declaration suffer from fast-growing computational complexity. The article offers the set of firing rules for backwards token-play on coloured workflow net. This method helps to detect resource-related failures of the two most common types: "the missing arc" fault (the resource was not initialized) and "wrong expression fault" (there is no proper resource).

关键词： Fault detection

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Effects of varying diagnostic coverage on functional safety

Effects of varying diagnostic coverage on functional safety

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2014 International Symposium on Fundamentals of Electrical Engineering, ISFEE 2014

作者： Üstoʇlu, Ilker Kaymakçi, Özgür T. Börcsök, Josef Department of Control and Automation Engineering Yildiz Technical University Esenler Istanbul34220 Turkey Department of Computer Architecture and System Programming University of Kassel Kassel34121 Germany

ISBN: (纸本)9781479968213

Diagnostics is one of the significant parameters of functional safety which concerns itself with revealing dangerous incidents that might impede the operation of safety systems. Diagnostic coverage factor (DC) is defined as a metric for diagnostics in that DC is one of the most important design parameters to measure the effectiveness of safety protection systems. This study addresses how diagnostics impacts proof test interval (TI) when the system is halted completely;a thorough maintenance and changes in parts are performed. Besides this, the relationship between average probability of failure on demand (PFDavg), DC, TI and mean time to failure (MTTF) are presented for 1oo2 and 1oo2D system architectures. © 2014 IEEE.

关键词： Safety factor

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HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI)

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Brain Informatics 2015年第4期2卷 225-238页

作者： Makkie, Milad Zhao, Shijie Jiang, Xi Lv, Jinglei Zhao, Yu Ge, Bao Li, Xiang Han, Junwei Liu, Tianming Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States School of Automation Northwestern Polytechnical University Xi’an China School of Physics & Information Technology Shaanxi Normal University Xi’an China

Tremendous efforts have thus been devoted on the establishment of functional MRI informatics systems that recruit a comprehensive collection of statistical/computational approaches for fMRI data analysis. However, the state-of-the-art fMRI informatics systems are especially designed for specific fMRI sessions or studies of which the data size is not really big, and thus has difficulty in handling fMRI ‘big data.’ Given the size of fMRI data are growing explosively recently due to the advancement of neuroimaging technologies, an effective and efficient fMRI informatics system which can process and analyze fMRI big data is much needed. To address this challenge, in this work, we introduce our newly developed informatics platform, namely, ‘HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI).’ HELPNI implements our recently developed computational framework of sparse representation of whole-brain fMRI signals which is called holistic atlases of functional networks and interactions (HAFNI) for fMRI data analysis. HELPNI provides integrated solutions to archive and process large-scale fMRI data automatically and structurally, to extract and visualize meaningful results information from raw fMRI data, and to share open-access processed and raw data with other collaborators through web. We tested the proposed HELPNI platform using publicly available 1000 Functional Connectomes dataset including over 1200 subjects. We identified consistent and meaningful functional brain networks across individuals and populations based on resting state fMRI (rsfMRI) big data. Using efficient sampling module, the experimental results demonstrate that our HELPNI system has superior performance than other systems for large-scale fMRI data in terms of processing and storing the data and associated results much faster. © 2015, The Author(s).

关键词： Big data

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Memory bandwidth optimization of SpMV on GPGPUs

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Frontiers of computer Science 2015年第3期9卷 431-441页

作者： Chenggang Clarence YAN Hui YU Weizhi XU Yingping ZHANG Bochuan CHEN Zhu TIAN Yuxuan WANG Jian YIN Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Institute of Microelectronics Tsinghua University Beijing 100084 China Automation Department Tsinghua University Beijing 100084 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China State Grid Information & Communication Company of Hunan EPC Changsha 410007 China Department of Computer Shandong University Weihai 250101 China

It is an important task to improve performance for sparse matrix vector multiplication （SpMV）, and it is a difficult task because of its irregular memory access. Gen- eral purpose GPU （GPGPU） provides high computing abil- ity and substantial bandwidth that cannot be fully exploited by SpMV due to its irregularity. In this paper, we propose two novel methods to optimize the memory bandwidth for SpMV on GPGPU. First, a new storage format is proposed to exploit memory bandwidth of GPU architecture more effi- ciently. The new storage format can ensure that there are as many non-zeros as possible in the format which is suitable to exploit the memory bandwidth of the GPU. Second, we pro- pose a cache blocking method to improve the performance of SpMV on GPU architecture. The sparse matrix is partitioned into sub-blocks that are stored in CSR format. With the block- ing method, the corresponding part of vector x can be reused in the GPU cache, so the time to access the global memory for vector x is reduced heavily. Experiments are carried out on three GPU platforms, GeForce 9800 GX2, GeForce GTX 480, and Tesla K40. Experimental results show that both new methods can efficiently improve the utilization of GPU mem- ory bandwidth and the performance of the GPU.

关键词： GPGPU performance tuning SpMV cacheblocking memory bandwidth

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