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19th international workshop on algorithms in Bioinformatics, WABI 2019

作者： Cauer, Alexandra Gesine Yardımcı, Gürkan Vert, Jean-Philippe Varoquaux, Nelle Noble, William Stafford Department of Genome Sciences University of Washington SeattleWA United States Google Brain Paris France Centre for Computational Biology MINES ParisTech PSL University Paris France Department of Statistics UC BerkeleyCA United States Department of Genome Sciences University of Washington SeattleWA United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States

ISBN: (纸本)9783959771238

the 3D organization of the genome plays a key role in many cellular processes, such as gene regulation, differentiation, and replication. Assays like Hi-C measure DNA-DNA contacts in a high-throughput fashion, and inferring accurate 3D models of chromosomes can yield insights hidden in the raw data. For example, structural inference can account for noise in the data, disambiguate the distinct structures of homologous chromosomes, orient genomic regions relative to nuclear landmarks, and serve as a framework for integrating other data types. Although many methods exist to infer the 3D structure of haploid genomes, inferring a diploid structure from Hi-C data is still an open problem. Indeed, the diploid case is very challenging, because Hi-C data typically does not distinguish between homologous chromosomes. We propose a method to infer 3D diploid genomes from Hi-C data. We demonstrate the accuracy of the method on simulated data, and we also use the method to infer 3D structures for mouse chromosome X, confirming that the active homolog exhibits a bipartite structure, whereas the active homolog does not. © Alexandra Gesine Cauer, Gürkan Yardımcı, Jean-Philippe Vert, Nelle Varoquaux, and William Stafford Noble

关键词： Genes

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Spatial-Temporal Transformation of Matrix and Multilayer algorithms of Binary Number Multiplication

Spatial-Temporal Transformation of Matrix and Multilayer Alg...

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IEEE international Conference on Intelligent data Acquisition and Advanced Computing Systems

作者： Volodymyr Gryga Igor Kogut Victor Holota Roman Kochan Stanislaw Rajba Tomasz Gancarczyk Uliana Iatsykovska Vasyl Stefanyk Precarpathian National University University of Bielsko-Biala

ISBN: (纸本)9781728140704

the paper conducted analysis of matrix and multilayer structures of algorithms for multiplication of binary numbers and determined their advantages and disadvantages. the hardware and time characteristics of matrix and multilayer binary number multipliers with the use of improved structures of single-digit full and half adders are investigated. Using theory of spatio-temporal graphs recursive multistep structures of the multipliers are developed. the analytical expressions for evaluation of the complexity of developed multiplier structures are obtained. It is determined that the algorithmic and conveyor structures of the multipliers have high speed and require significant hardware complexity, in contrast to the proposed multi-cycle structures that have significantly lower performance, but the hardware complexity and the corresponding occupied area on the crystal are minimal. Synthesis of developed multiplier structures on the Xilinx FPGA is performed and the convergence of theoretical and practical results of researches is shown.

关键词： Matrix and multilayer multiplier Algorithm Multistep multiplier Flow and spatiak-temporal graph FPGA Binary digits Multiplying circuits algorithms Field programmable gate arrays matrices Computer hardware Multiplication matrix multilayer structures

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A yield criterion for cubic single crystals

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international JOURNAL OF SOLIDS AND structures 2018年 151卷 9-19页

作者： Cazacu, Oana Revil-Baudard, Benoit Chandola, Nitin Univ Florida Dept Mech & Aerosp Engn REEF 1350 Poquito Rd Shalimar FL 32579 USA

In this paper a three-dimensional analytical criterion for description of the onset of plastic deformation in cubic single crystals is presented. the criterion is pressure-insensitive and form-invariant to any transformation belonging to the symmetries of the material. Specialization of this criterion for each class of the cubic system is presented. For most metallic single crystals, the criterion involves five independent parameters, which can be determined based on the yield stresses in different crystal orientations. Comparisons with single crystal data show that the criterion can successfully describe the difference in yielding anisotropy between FCC crystals. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Yield criterion of single crystals theory of representation FCC single crystal data

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A Two-Stage Multi-loss Super-Resolution Network for Arterial Spin Labeling Magnetic Resonance Imaging 1

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10th international workshop on Machine Learning in Medical Imaging (MLMI) / 22nd international Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Li, Zheng Liu, Qingping Li, Yiran Ge, Qiu Shang, Yuanqi Song, Donghui Wang, Ze Shi, Jun Shanghai Univ Sch Commun & Informat Engn Shanghai Inst Adv Commun & Data Sci Key Lab Specialty Fiber Opt & Opt Access Networks Shanghai Peoples R China Temple Univ Dept Elect & Comp Engn Philadelphia PA 19122 USA Hangzhou Normal Univ Ctr Cognit & Brain Disorders Hangzhou Peoples R China Univ Maryland Sch Med Dept Diagnost Radiol & Nucl Med Baltimore MD 21201 USA

ISBN: (数字)9783030322489

ISBN: (纸本)9783030322489;9783030322472

Arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) is a non-invasive technique for quantifying cerebral blood flow (CBF). Limited by the T1 decay rate of the labeled spins, very short time is available for data acquisition after one spin labeling cycle, resulting in a low spatial resolution. the traditional strategy to achieve high spatial resolution in ASL MRI is to add more labeling cycles. However, the total acquisition time is exponentially prolonged, making it highly sensitive to motions. Moreover, signal-to-noise-ratio (SNR) drops as spatial resolution increases. there needs an alternative approach to improve spatial resolution and SNR for ASL MRI without increasing scan time. therefore, we propose a novel two-stage multi-loss super-resolution (SR) network (TSMLSRNet) for reconstruction of high resolution ASL images. Specifically, the first stage network uses the mean squared error (MSE) loss function to produce a first SR estimate, while the second stage network adopts the gradient sensitive (GS) loss function to further improve high-frequency details for the output SR image. the multi-loss joint training strategy is finally used to preserve both the low-frequency and high-frequency information of the ASL images. Moreover, the noise in ASL images is simultaneously reduced. Validation results using in-vivo data clearly show the effectiveness of the proposed ASL SR algorithm that outperforms state-of-the-art image reconstruction algorithms.

关键词： Arterial spin labeling perfusion MRI Super-resolution Denoising Two-stage multi-loss joint training DenseNet

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Monte Carlo Tree Search for Cross-Stratum Optimization of Survivable Inter-data Center Elastic Optical Network 10

Monte Carlo Tree Search for Cross-Stratum Optimization of Su...

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10th international workshop on Resilient Networks Design and Modeling (RNDM)

作者： Aibin, Michal Walkowiak, Krzysztof British Columbia Inst Technol Dept Comp Vancouver BC Canada Wroclaw Univ Sci & Technol Fac Elect Dept Syst & Comp Networks Wroclaw Poland

ISBN: (纸本)9781538670309

In last few years, cloud computing and other services based on data centers have evolved from an emerging technology to a recognized approach that is gaining broad acceptance and deployment. therefore, there is a significant need to provide efficient and reliable operation of inter-data center networks based on optical technologies. In this article, we focus on cross stratum optimization of an inter-data center elastic optical network with additional survivability requirements. We propose a novel optimization approach that employs machine learning Monte Carlo Tree Search (MCTS) algorithm for simulation of future traffic to improve the performance of the network regarding blocking probability and operational cost. We evaluate the performance of the proposed method and other reference algorithms under various network scenarios, using representative topologies and real data provided by Amazon Web Services. the main conclusion is that the approach based on the MCTS algorithm enables better coordination of resource allocation in both strata, which results in lower blocking of requests and lower cost taking into account the survivability requirements, in comparison to other algorithms.

关键词： elastic optical networks dynamic routing cloud services path protection traffic prediction

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Identify Hierarchical structures from Task-Based fMRI data via Hybrid Spatiotemporal Neural Architecture Search Net 1

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10th international workshop on Machine Learning in Medical Imaging (MLMI) / 22nd international Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Zhang, Wei Zhao, Lin Li, Qing Zhao, Shijie Dong, Qinglin Jiang, Xi Zhang, Tuo Liu, Tianming Univ Georgia Dept Comp Sci Athens GA 30605 USA Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China Northwestern Polytech Univ Dept Automat Xian 710072 Peoples R China Univ Elect Sci & Technol China Sch Life Sci & Technol MOE Key Lab Neuroinformat Chengdu Peoples R China

ISBN: (数字)9783030322489

ISBN: (纸本)9783030322489;9783030322472

the hierarchical organization of brain function has been an established concept in the neuroscience field. Recently, these hierarchical organizations have been extensively investigated in terms of how such hierarchical functional networks are constructed in the human brain via a variety of deep learning models. However, a key problem of how to determine the optimal neural architecture (NA), e.g., hyper parameters, of deep model has not been solved yet. To address this question, in this work, a novel Hybrid Spatiotemporal Neural Architecture Search Net (HS-NASNet) is proposed by jointly using Evolutionary Optimizer, Deep Belief Networks (DBN) and Deep LASSO to reasonably determine the NA, thus revealing the latent hierarchical spatiotemporal features based on the Human Connectome Project (HCP) 900 fMRI datasets. Briefly, this HS-NASNet can automatically search the global optimal NA of DBN given the search space, and then the optimized DBN can extract the weights between two adjacent layers of the optimal NA, which are then treated as the hierarchical temporal dictionaries for Deep LASSO to identify the corresponding hierarchical spatial maps. Our results demonstrate that the optimized deep model can achieve accurate fMRI signal reconstruction and identify spatiotemporal functional networks exhibiting multiscale properties that can be well characterized and interpreted based on current neuroscience knowledge.

关键词： Hierarchical organization Neural architecture search Evolutionary optimization Deep Belief Network Task-based fMRI LASSO

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A New Fuzzy Belonging-based Multi-view K-means Clustering Algorithm

A New Fuzzy Belonging-based Multi-view K-means Clustering Al...

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international Conference and workshop on Computing and Communication (IEMCON)

作者： Junda Zhao Weiling Cai Yizhen Ge Lijuan Zhang Department of Computer Science University of Toronto Toronto Canada College of Computer Science and Technology Nanjing Normal University Nanjing China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781728125312

Recently, a variety of data acquisition methods leads to the description of the same thing from different angles, which induces a large number of multi-view data. Clustering multi-view data in large-scale data analysis is of great significance and very challenging. In this paper, we present a new robust clustering algorithm, the Multi-view Fuzzy K-means clustering (MFKC) algorithm. In MFKC, a fuzzy membership method is used and optimized to describe the probability of a sample belonging to certain clusters. Compared with the hard partition based multi-view clustering algorithms, MFKC can effectively describe the fuzzy relationship between multi-view data and all clusters, and has better interpretability for the clustering results. In addition, MFKC also sets different weights for different views. By optimizing the multi-view clustering objective function, we obtain the optimal weigh of each view to reflect the importance of the view. the feasibility and effectiveness of the proposed algorithm is validated through various experimental comparisons.

关键词： Clustering algorithms Partitioning algorithms Time complexity data analysis Computer science Clustering methods Optimization

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An Improved Differential Privacy-Preserving Truth Discovery approach In Healthcare

An Improved Differential Privacy-Preserving Truth Discovery ...

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international Conference and workshop on Computing and Communication (IEMCON)

作者： Adilakshmi Vadavalli R Subhashini School of Computing Sathyabama Institute of Science and Technology Chennai India

Nowadays, the occurrence of breast cancer amongst women has been increasing tremendously. To predict the breast cancer occurrence, physicians are in need of computer assisted way of feature extraction and classification approaches. In this scenario, there is even a greater need to maintain the privacy of patients personalized details. Many algorithms exist in literature that are used for data anonymization. the fundamental disadvantage of all these algorithms lies in limiting the ability to derive better value and insights from the data. In this proposed approach a differential privacy preserving algorithm is employed. this algorithm alters particulars of patients vital information in the data set. thus, it not only improves the privacy of patient details compared to other existing approaches but also helps in getting accurate results. the advantage is that it will have the least impact on the accuracy of the truth discovery approach as the appropriate features for cancer prediction data set are not altered. For predicting the truth discovery, this proposed system employs Expectation- Maximization Algorithm. this proposed approach is first of its kind in combining the differential privacy preservation technique with truth discovery strategy. Finally, the performance evaluation was estimated in terms of accuracy, precision, recall, and F1-score and the outcomes are compared to that of the existing approaches to prove the effectiveness of the proposed mechanism.

关键词： Big data Differential privacy Reliability Privacy Medical services

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Memory Aware thread Aggregation Framework for Dynamic Parallelism in GPUs 10

Memory Aware Thread Aggregation Framework for Dynamic Parall...

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10th international Conference on Advanced Computing (ICoAC)

作者： Balaiah, thanasekhar Parthasarathi, Ranjani Anna Univ Dept Comp Technol Chennai Tamil Nadu India Anna Univ Dept Informat Sci & Technol Chennai Tamil Nadu India

ISBN: (纸本)9781728103532

Problems using matrices and vectors exhibit regular data-level parallelism, which yields maximum throughput when executed in GPUs. But problems which use data in the form of hierarchical data structures such as adaptive grids, sparse graphs and trees, and applications using recursion, may not have regular parallelism. the parallelism in algorithms based on these data structures are discovered dynamically as they are nested. Depending on the data being processed, each task may need to perform a variable amount of computation. the amount of parallelism available at any point of execution cannot be determined apriori as it is data dependent and evolves with the computation itself. this non-determinism is due to the existence of control flow divergence, diverse memory access patterns and data dependent parallelism in the problems, which do not fit into GPU architectures as the natural choice and result in much lesser utilization of GPU resources. A three-level thread aggregation is proposed in this paper for enhancing the resource utilization and occupancy. threads are executed in the appropriate level after aggregation into larger grids. Programs from the Rodinia benchmark and NVIDIA SDK are used to test the framework. the occupancy improves from 2X to 15X and the performance improvement up to 50% in some cases.

关键词： Dynamic Parallelism irregular parallelism data dependent parallelism GPU memory hierarchy thread aggregation

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Finger-vein recognition using deep fully convolutional neural semantic segmentation networks: the impact of training data 10

Finger-vein recognition using deep fully convolutional neura...

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10th IEEE international workshop on Information Forensics and Security, WIFS 2018

作者： Jalilian, Ehsaneddin Uhl, Andreas Department of Computer Science University of Salzburg Jakob-Haringer-Str.2 Salzburg Austria

ISBN: (纸本)9781538665367

We propose a novel approach for finger-vein recognition, focused on direct extraction of actual finger-vein patterns from NIR finger images without any specific pre- or post-processing, using semantic segmentation convolutional neural networks (CNNs). We utilize three network architectures and besides identifying efficient training and configuration settings for these networks, using manually annotated training data, we present a training model based on automatically generated labels to improve the networks' performance. Based on our experimental results, the proposed model can achieve superior performance over traditional finger-vein recognition algorithms. As further contribution, we also release human annotated ground-truth vein pixel labels (required for training the networks) for a subset of two well known finger-vein databases used in this work, and a corresponding tool for further annotations. © 2018 IEEE.

关键词： Semantic Segmentation

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