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arXiv 2019年

作者： Snyder, Michael P. Lin, Shin Posgai, Amanda Atkinson, Mark Regev, Aviv Rood, Jennifer Rozenblatt-Rosen, Orit Gaffney, Leslie Hupalowska, Anna Satija, Rahul Gehlenborg, Nils Shendure, Jay Laskin, Julia Harbury, Pehr Nystrom, Nicholas A. Bar-Joseph, Ziv Zhang, Kun Börner, Katy Lin, Yiing Conroy, Richard Procaccini, Dena Roy, Ananda L. Pillai, Ajay Brown, Marishka Galis, Zorina S. Cai, Long Shendure, Jay Trapnell, Cole Lin, Shin Jackson, Dana Snyder, Michael P. Nolan, Garry Greenleaf, William James Lin, Yiing Plevritis, Sylvia Ahadi, Sara Lee, Hayan Nevins, Stephanie Esplin, Ed Horning, Aaron Bahmani, Amir Zhang, Kun Sun, Xin Jain, Sanjay Hagood, James Kharchenko, Peter Atkinson, Mark Bodenmiller, Bernd Brusko, Todd Clare-Salzler, Michael Nick, Harry Otto, Kevin Posgai, Amanda Wasserfall, Clive Maffioletti, Sergio Caprioli, Richard M. Spraggins, Jeffrey M. Gutierrez, Danielle Patterson, Nathan Heath Neumann, Elizabeth K. Harris, Raymond deCaestecker, Mark de Plas, Raf Van Lau, Ken Cai, Long Yuan, Guo-Cheng Zhu, Qian Dries, Ruben Yin, Peng Saka, Sinem K. Kishi, Jocelyn Y. Wang, Yu Goldaracena, Isabel Laskin, Julia Ye, DongHye Burnum-Johnson, Kristin E. Piehowski, Paul D. Ansong, Charles Zhu, Ying Harbury, Pehr Desai, Tushar Mulye, Jay Chou, Peter Nagendran, Monica Bar-Joseph, Ziv Teichmann, Sarah A. Paten, Benedict Murphy, Robert F. Ma, Jian Kiselev, Vladimir Yu Kingsford, Carl Ricarte, Allyson Ruffalo, Matthew Gehlenborg, Nils Kharchenko, Peter Vella, Margaret McCallum, Chuck Börner, Katy Cross, Leonard E. Friedman, Samuel H. Heiland, Randy Herr, Bruce Macklin, Paul Quardokus, Ellen M. Record, Lisel Sluka, James P. weber, Griffin M. Nystrom, Nicholas A. Silverstein, Jonathan C. Blood, Philip D. Ropelewski, Alexander J. Shirey, William E. Mabee, Paula Lenhardt, W. Christopher Robasky, Kimberly Michailidis, Stavros Satija, Rahul Marioni, John Regev, Aviv Butler, Andrew Stuart, Tim Fisher, Eyal Ghazanfar, Shila Rood, Jennifer Gaffney, Leslie Ersalan, Gokcen Biancalani, Tommaso Conroy, Richard Procaccini, Dena Roy, Anan School of Medicine Stanford University 300 Pasteur Drive United States Department of Genetics Stanford School of Medicine StanfordCA94305 United States Department of Medicine University of Washington SeattleWA98195 United States Department of Pathology University of Florida Diabetes Institute GainesvilleFL32610 United States Klarman Cell Observatory Broad Institute of MIT and Harvard CambridgeMA02142 United States Howard Hughes Medical Institute Koch Institute of Integrative Cancer Research Department of Biology Massachusetts Institute of Technology CambridgeMA02140 United States New York Genome Center New York CityNY10013 United States New York University New YorkNY10012 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Genome Sciences University of Washington SeattleWA98105 United States Department of Chemistry Purdue University West LafayetteIN47907 United States Department of Biochemistry Stanford University School of Medicine StanfordCA94305 United States Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States Department of Computational Biology School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Department of Bioengineering University of California San Diego La JollaCA92093 United States Department of Intelligent Systems Engineering School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Surgery Washington University School of Medicine St LouisMO63110 United States Division of Program Coordination Planning and Strategic Initiatives National Institutes of Health BethesdaMD20892 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Caltech-UW TMC Departmen

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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Electrolyte‐Gated Transistors: Ink‐Jet Printable, Self‐Assembled, and Chemically Crosslinked Ion‐Gel as Electrolyte for Thin Film, Printable Transistors (Adv. Mater. Interfaces 21/2019)

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Advanced Materials Interfaces 2019年第21期6卷

作者： Jaehoon Jeong Gabriel Cadilha Marques Xiaowei Feng Dominic Boll Surya Abhishek Singaraju Jasmin Aghassi‐Hagmann Horst Hahn Ben Breitung Institute of Nanotechnology (INT) Karlsruhe Institute of Technology (KIT) 76344 Eggenstein‐Leopoldshafen Germany Joint Research Laboratory Nanomaterials ‐ Technische Universität Darmstadt and Karlsruhe Institute of Technology (KIT) Otto‐Berndt‐Str. 3 64206 Darmstadt Germany Chair of Dependable Nano Computing (CDNC) Department of Computer Science (ITEC) Karlsruhe Institute of Technology (KIT) 76131 Karlsruhe Germany Department of Electrical Engineering and Information Technology Offenburg University of Applied Sciences 77652 Offenburg Germany Helmholtz Institute Ulm for Electrochemical Energy Storage Helmholtzstr. 11 89081 Ulm Germany Karlsruhe Nano Micro Facility Karlsruhe Institute of Technology Hermann‐von‐Helmholtz‐Platz 1 76344 Eggenstein‐Leopoldshafen Germany

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Accelerated quantum walk, two-particle entanglement generation and localization

arXiv

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arXiv 2018年

作者： Singh, Shivani Balu, Radhakrishnan Laflamme, Raymond Chandrashekar, C.M. Institute of Mathematical Sciences C. I. T. Campus Taramani Chennai600113 India Homi Bhabha National Institute Training School Complex Anushakti Nagar Mumbai400094 India U.S. Army Research Laboratory Computational and Information Sciences Directorate AdelphiMD20783 United States Computer Science and Electrical Engineering University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada

We present a scheme to describe the dynamics of accelerating discrete-time quantum walk for one- and two-particle in position space. We show the effect of acceleration in enhancing the entanglement between the particle and position space in one-particle quantum walk and in generation of entanglement between the two unentangled particle in two-particle quantum walk. By introducing the disorder in the form of phase operator we study the transition from localization to delocalization as a function of acceleration. These inter-winding connection between acceleration, entanglement generation and localization along with well established connection of quantum walks with Dirac equation can be used to probe further in the direction of understanding the connection between acceleration, mass and entanglement in relativistic quantum mechanics and quantum field theory. Expansion of operational tools for quantum simulations and for modelling quantum dynamics of accelerated particle using quantum walks is an other direction where these results can play an important role. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum chemistry

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GB-RC4: Effective brute force attacks on RC4 algorithm using GPU 7

GB-RC4: Effective brute force attacks on RC4 algorithm using...

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7th International Green and Sustainable computing Conference, IGSC 2016

作者： Xue, Pei Li, Tao Dong, Han Liu, Chunbo Ma, Wenjing Pei, Songwen College of Computer and Control Engineering Nankai University Tianjin300071 China Information Security Evaluation Center Civil Aviation University of China Tianjin300300 China Laboratory of Parallel Software and Computational Science State Key Laboratory of Computing Science Institute of Software CAS Beijing China Shanghai Key Lab of Modern Optical Systems Department of Computer Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781509051175

Encryption algorithms are applied to a variety of fields and the security of encryption algorithms depends heavily on the computational infeasibility of exhaustive key-space search. RC4 algorithm has an extensive application for stream encryption, however, the disadvantages of traditional RC4 serial algorithm are large computational quantity and slow computation speed, which means a great challenge for throughput of computing system. Graphics processors recently have been widely used as accelerators for high throughput computing. In this paper, GPU is used to improve the efficiency and performance of attacking RC4 algorithm. First, we revised RC4 algorithm to make it easier to parallelize on a GPU. Then we take advantage of constant memory and shared memory in GPU to search the key space of RC4 algorithm efficiently. Besides, we solved the bank conflict problem in shared memory to further improve the performance. Our approach was evaluated in the whole key space of 40-bit, and the results show it can complete searching of the whole key space within 12.8 hours. © 2016 IEEE.

关键词： Graphics processing unit

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René Labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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Middle East and North African Health Informatics Association (MENAHIA): Building Sustainable Collaboration

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Yearbook of Medical Informatics 2018年第1期27卷 286-291页

作者： Najeeb Al-Shorbaji Mowafa Househ Adel Taweel Abdullah Alanizi Bennani Othmani Mohammed Haitham Abaza Hala Bawadi Hamayon Rasuly Khalid Alyafei Luis Fernandez-Luque Mohamed Shouman Osama El-Hassan Rada Hussein Riyad Alshammari Salah Mandil Sarah Shouman Shahrad Taheri Tamer Emara Wasmiya Dalhem Zaid Al-Hamdan Zineb Serhier eHealth and Knowledge Management Consultant Retired Director of Knowledge Ethics and Research Department WHO/HQ Amman Jordan Department of Health Informatics College of Public Health and Health Informatics King Saud Bin Abdulaziz University for Health Sciences Ministry of National Guard-Health Affairs Riyadh Saudi Arabia Department of Computer Science BirZeit University Ramallah Palestine Head of Medical Informatics Unit 20 August Hospital Ibn Rochd University Hospital Casablanca Head of Casablanca Medical Informatics Laboratory Medical Faculty of Casablanca Morocco Department of Biomedical Engineering Misr University for Science & Technology Cairo Egypt Associate Professor College of Nursing University of Jordan Amman Jordan eHealth Program Coordinator Bamyan Provincial Hospital Bamyan City Afghanistan Chief Medical Informatics Officer (CMIO) - SIDRA Assistant Professor Clinical Pediatrics at Weill Cornell Medicine Qatar Qatar Computing Research Institute Hamad Bin Khalifa University Doha Qatar Faculty of Medicine Ain Shams University Cairo Egypt Vice-Chair of Emirates Health Informatics Society Head of eHealth Section at Dubai Health Authority Dubai United Arab Emirates Founder and Former Director of the Biomedical Informatics Center of Excellence (BMICoE) Information Technology Institute (ITI) Ministry of Communications and Information Technology (MCIT) Egypt President of Saudi Association for Health Informatics Riyadh Saudi Arabia Senior Consultant on eStrategies and eHealth to WHO and ITU Geneva Switzerland Department of Medicine Weill Cornell Medical College in Qatar Qatar Foundation Doha Qatar Executive Director of Nursing Informatics Hamad Medical Corporation Qatar Associate Professor of Nursing Management Faculty of Nursing Jordan University of Science and Technology Irbid Jordan Professor at Medical Informatics Laboratory Faculty of Medicine and Pharmacy Hassan II University Casablanca Morocco

There has been a growing interest in Health Informatics applications, research, and education within the Middle East and North African Region over the past twenty years. People of this region share similar cultural and religious values, primarily speak the Arabic language, and have similar health care related issues, which are in dire need of being addressed. Health Informatics efforts, organizations, and initiatives within the region have been largely under-represented within, but not ignored by, the International Medical Informatics Association (IMIA). Attempts to create bonds and collaboration between the different organizations of the region have remained scattered, and often, resulted in failure despite the fact that the need for a united health informatics collaborative within the region has never been more crucial than today. During the 2017 MEDINFO, held in Hangzhou, China, a new organization, the Middle East and North African Health Informatics Association (MENAHIA) was conceived as a regional non-governmental organization to promote and facilitate health informatics uptake within the region endorsing health informatics research and educational initiatives of the 22 countries represented within the region. This paper provides an overview of the collaboration and efforts to date in forming MENAHIA and displays the variety of initiatives that are already occurring within the MENAHIA region, which MENAHIA will help, endorse, support, share, and improve within the international forum of health informatics.

关键词： eHealth Middle East and North Africa Health Informatics collaboration MENAHIA Universal Health Coverage Bioinformatics International Medical Informatics Association

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Learning with only multiple instance positive bags

Learning with only multiple instance positive bags

引用

2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Wang, Zhigang Zhao, Zengshun Zhang, Changshui Computer and Communication Engineering School Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education China College of Electronics Communication and Physics Shandong University of Science and Technology China Automation Department Tsinghua University State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

ISBN: (纸本)9781509006199

In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag - positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative bags. Contrary to our expectation, nearly all existing MIL methods require enough negative bags besides positive ones. In this paper we propose an algorithm called 'Positive Multiple Instance' (PMI), which learns a classifier given only a set of positive bags. So the annotation of negative bags becomes unnecessary in our method. PMI is constructed based on the assumption that the unknown positive instances in positive bags be similar each other and constitute one compact cluster in feature space and the negative instances locate outside this cluster. The experimental results demonstrate that PMI achieves the performances close to or a little worse than those of the traditional MIL algorithms on benchmark and real data sets. However, the number of training bags in PMI is reduced significantly compared with traditional MIL algorithms. © 2016 IEEE.

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Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network

arXiv

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arXiv 2017年

作者： Jiang, Dongsheng Dou, Weiqiang Vosters, Luc Xu, Xiayu Sun, Yue Tan, Tao Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer-Assisted Intervention Shanghai China Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen Netherlands Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Xian Jiaotong University Xian710049 China Key Laboratory of Biomedical Information Engineering of Ministry of Education School of Life Science and Technology Xian Jiaotong Universty Xian710049 China Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands ScreenPoint Medical Nijmegen Netherlands

The denoising of magnetic resonance (MR) images is a task of great importance for improving the acquired image quality. Many methods have been proposed in the literature to retrieve noise free images with good performances. Howerever, the state-of-the-art denoising methods, all needs a time-consuming optimization processes and their performance strongly depend on the estimated noise level parameter. Within this manuscript we propose the idea of denoising MRI Rician noise using a convolutional neural network. The advantage of the proposed methodology is that the learning based model can be directly used in the denosing process without optimization and even without the noise level parameter. Specifically, a ten convolutional layers neural network combined with residual learning and multi-channel strategy was proposed. Two training ways: training on a specific noise level and training on a general level were conducted to demonstrate the capability of our methods. Experimental results over synthetic and real 3D MR data demonstrate our proposed network can achieve superior performance compared with other methods in term of both of the peak signal to noise ratio and the global of structure similarity index. Without noise level parameter, our general noise-applicable model is also better than the other compared methods in two datasets. Furthermore, our training model show good general applicability. Copyright © 2017, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Thread-aware adaptive prefetcher on multicore systems: Improving the performance for multithreaded workloads

Thread-aware adaptive prefetcher on multicore systems: Impro...

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作者： Liu, Peng Yu, Jiyang Huang, Michael C. College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi214125 China Department of Shannon Lab. Huawei Technologies Co. Ltd. Hangzhou310051 China Department of Electrical and Computer Engineering University of Rochester RochesterNY14627-0231 United States

Most processors employ hardware data prefetching techniques to hide memory access latencies. However, the prefetching requests from different threads on a multicore processor can cause severe interference with prefetching and/or demand requests of others. The data prefetching can lead to significant performance degradation due to shared resource contention on shared memory multicore systems. This article proposes a thread-aware data prefetching mechanism based on low-overhead runtime information to tune prefetching modes and aggressiveness, mitigating the resource contention in the memory system. Our solution has three new components: (1) a self-tuning prefetcher that uses runtime feedback to dynamically adjust data prefetching modes and arguments of each thread, (2) a filtering mechanism that informs the hardware about which prefetching request can cause shared data invalidation and should be discarded, and (3) a limiter thread acceleration mechanism to estimate and accelerate the critical thread which has the longest completion time in the parallel region of execution. On a set of multithreaded parallel benchmarks, our thread-aware data prefetchingmechanism improves the overall performance of 64-core system by 13% over a multimode prefetch baseline system with two-level cache organization and conventional modified, exclusive, shared, and invalid-based directory coherence *** compare our approach with the feedback directed prefetching technique and find that it provides 9% performance improvement on multicore systems, while saving the memory bandwidth consumption.

关键词： Benchmarking

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An image compression method based on block truncation coding and linear regression

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Journal of information Hiding and Multimedia Signal Processing 2016年第1期7卷 198-215页

作者： Lyu, Wan-Li Chang, Chin-Chen Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230039 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Rd Seatwen Taichung40724 Taiwan Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan

This paper describes an image compression method based on block truncation coding (BTC) and linear regression coding (LRC) hybrid strategy. BTC is simple and easy to use, but only two representative values can not adequately represent the visual feature of image blocks. However, LRC can obtain different values of points with the line function by recording the coefficient of the regression line while the data distribution shows a linear relationship. The proposed method designs a hybrid strategy to combine the advantages of the two methods to improve image quality. In the paper, first, models are designed to rearrange the data of image blocks to satisfy a linear distribution, and then these models are used to code the image blocks. The experimental results indicated that the proposed method is superior to the BTC scheme in terms of image quality, and it can be used for image compression applications. © 2016.

关键词： Linear regression

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