检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Zhang, Yichen Gao, Yuxiang Gao, Xue-Jian Lei, Shiming Ni, Zhuoliang Oh, Ji Seop Huang, Jianwei Yue, Ziqin Zonno, Marta Gorovikov, Sergey Hashimoto, Makoto Lu, Donghui Denlinger, Jonathan D. Birgeneau, Robert J. Kono, Junichiro Wu, Liang Law, Kam Tuen Morosan, Emilia Yi, Ming Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Physics Hong Kong University of Science and Technology Clear Water Bay Hong Kong Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Department of Physics University of California BerkeleyCA94720 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Canadian Light Source Inc. 44 Innovation Boulevard SaskatoonSKS7N 2V3 Canada Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Advanced Light Source Lawrence Berekeley National Laboratory BerkeleyCA94720 United States Materials Science Division Lawrence Berekeley National Laboratory BerkeleyCA94720 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Materials Science and Nanoengineering Rice University HoustonTX77005 United States

Kramers nodal lines (KNLs) have recently been proposed theoretically as a special type of Weyl line degeneracy connecting time-reversal invariant momenta. KNLs are robust to spin orbit coupling and are inherent to all non-centrosymmetric achiral crystal structures, leading to unusual spin, magneto-electric, and optical properties. However, their existence in in real quantum materials has not been experimentally established. Here we gather the experimental evidence pointing at the presence of KNLs in SmAlSi, a non-centrosymmetric metal that develops incommensurate spin density wave order at low temperature. Using angle-resolved photoemission spectroscopy, density functional theory calculations, and magneto-transport methods, we provide evidence suggesting the presence of KNLs, together with observing Weyl fermions under the broken inversion symmetry in the paramagnetic phase of SmAlSi. We discuss the nesting possibilities regarding the emergent magnetic orders in SmAlSi. Our results provide a solid basis of experimental observations for exploring correlated topology in SmAlSi. © 2022, CC BY-NC-SA.

关键词： Temperature

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Erratum to: The real cost of sequencing: scaling computation to keep pace with data generation

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Genome biology 2016年第1期17卷 78页

作者： Paul Muir Shantao Li Shaoke Lou Daifeng Wang Daniel J Spakowicz Leonidas Salichos Jing Zhang George M Weinstock Farren Isaacs Joel Rozowsky Mark Gerstein Department of Molecular Cellular and Developmental Biology Yale University New Haven CT 06520 USA. Systems Biology Institute Yale University West Haven CT 06516 USA. Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. The Jackson Laboratory for Genomic Medicine Farmington CT 06032 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. mark@***. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. mark@***. Department of Computer Science Yale University New Haven CT 06520 USA. mark@***.

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Identification of gene expression signatures for psoriasis classification using machine learning techniques

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Medicine in Omics 2021年 1卷

作者： Nguyen Quoc Khanh Le Duyen Thi Do Trinh-Trung-Duong Nguyen Ngan Thi Kim Nguyen Truong Nguyen Khanh Hung Nguyen Thi Thu Trang Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei City 106 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City 106 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City 106 Taiwan Department of Computer Science and Engineering Yuan Ze University Chung-Li 32003 Taiwan School of Nutrition and Health Sciences Taipei Medical University Taipei City 110 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan Orthopedic and Trauma Department Cho Ray Hospital Ho Chi Minh City Viet Nam International PhD Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

关键词： Psoriasis classification Random forest Gene expression Feature selection Machine learning FABP5 Dermatology

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THE NO FRAME CONCEPT - ITS IMPACT ON SHIPYARD COST

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NAVAL ENGINEERS JOURNAL 1984年第3期96卷 218-232页

作者： NAPPI, NS WALZ, RW WIERNICKI, CJ Natale S. Nappi:graduated from City College of New York in 1954 with a B.S. degree in civil engineering and received his M.S. in civil engineering from the Polytechnic Institute of Brooklyn in 1959. He began his professional career in 1954 at the New York Naval Shipyard as a naval architect (structures) performing detail structural design and fabrication studies for CVAs LPDs DDs and CGs and eventually became a supervisory naval architect (structures). From 1965 to 1973 he was a member of the staff of the Computer-Aided Design Division at the David Taylor Naval Ship Research and Development Center (DTNSRDC). As such he was involved in the development of the computer structural design tool the SSDP (in association with Frank M. Lev) and automated detail design programs (CASDOS). His current position is Senior Naval Architect Consultant in the structural integrity group of the Ship Structures Division Structures Department DTNSRDC. Mr. Nappi is the author and co-author of numerous technical papers and reports covering a wide spectrum of topics such as automated structural design process design for producibility and survivability material weight and cost trade-off studies and structural weight determination for high performance ships (i.e. SES SWATH HYSWAS). He has lectured on the subjects of design for survivability and ship structures at the Naval Post Graduate School and MIT. He is a member of ASNE ASCE U.S. Naval Institute Sigma Xi and is a registered Professional Engineer in the State of New York. Mr. Nappi was a member of the NAVSEA working commitee for the computer supported design planning effort and is currently a member of the DTNSRDC ASSET Advisory Committee. Ronald W. Walz:graduated in 1974 from Pennsylvania State University with a B.S. degree in civil engineering. He began his professional career in 1974 at the David Taylor Naval Ship Research and Development Center as a structural engineer in the structural design concepts group of the Ship Structures Division Structures Department.

A proposed cost effective alternative to current U.S. Navy structurally configured hulls is presented in this paper. This proposed design for producibility concept involves the elimination of structural stanchions and transverse web frames. The potential impact of this “no frame” concept on structural design, weight and construction and material costs for naval surface frigates and destroyers is reflected in 1) reduced costs for the installation of distributive systems and 2) a reduced number and complexity of structural details providing a more reliable and less costly structure. This study was performed in three parts: 1) Determine the most feasible length between bulkheads without frames; 2) Using this length perform detail weight studies and construction and material costs analysis comparison on a 72-foot long hull module, with and without frames, for a FFG-7, and 3) Estimate the saving in man hours of labor on the installation of distributive systems and shipfitting for an FFG-7. For the feasible length studies on the “no frame” structural configuration, thirty-seven strength, weight and vertical center of gravity studies were performed on two ship classes; twenty-two on the FFG-7 class and fifteen on the DD-963 class. The detailed weight studies and construction and material cost analyses were conducted for FFG-7 “no frame” and “as built” modules. Results indicating the “no frame” concept module was 6.8% heavier and 14.8% less costly than the “as built” module. For the impact of an FFG-7 “no frame” structurally configured hull on the cost of labor required for the installation of distributive systems and for other functional work such as ship fitting, welding, and electrical, this study indicated a reduction of 169,206 labor hours per ship, representing 7.12% of the total required man hours to fabricate an FFG-7 class ship. With the employment of the “no frame” concept, certain areas of significant concern and potential risk were addressed. These include: 1) t

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THz generation by exchange-coupled spintronic emitters

npj Spintronics

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npj Spintronics 2024年第1期2卷 1-8页

作者： Roman Adam Derang Cao Daniel E. Bürgler Sarah Heidtfeld Fangzhou Wang Christian Greb Markus Büscher Claus M. Schneider Jing Cheng Debamitra Chakraborty Ivan Komissarov Roman Sobolewski Martin Mikulics Hilde Hardtdegen Peter Grünberg Institute Research Centre Jülich Jülich Germany College of Physics Qingdao University Qingdao China Faculty of Physics University Duisburg-Essen Duisburg Germany Institut für Laser- und Plasmaphysik Heinrich-Heine Universität Düsseldorf Düsseldorf Germany Materials Science Graduate Program University of Rochester Rochester NY USA Laboratory for Laser Energetics University of Rochester Rochester NY USA Department of Electrical and Computer Engineering University of Rochester Rochester NY USA Ernst Ruska Centre Research Centre Jülich Jülich Germany

The mechanism of THz generation in ferromagnet/metal (F/M) bilayers has been typically ascribed to the inverse spin Hall effect (ISHE). Here, we fabricated Pt/Fe/Cr/Fe/Pt multilayers containing two back-to-back spintronic THz emitters separated by a thin (tCr≤ 3nm) wedge-shaped Cr spacer. In such an arrangement, magnetization alignment of the two Fe films can be controlled by the interplay between Cr-mediated interlayer exchange coupling (IEC) and an external magnetic field. This in turn results in a strong variation of the THz amplitudeA, withA↑↓reaching up to 14 timesA↑↑(arrows indicate the relative alignment of the magnetization of the two magnetic layers). This observed functionality is ascribed to the interference of THz transients generated by two closely spaced THz emitters. Moreover, the magnetic field dependenceA(H) shows a strong asymmetry that points to an additional performance modulation of the THz emitter via IEC and multilayer design.

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Influence of human engineering on manning levels and human performance on ships

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NAVAL ENGINEERS JOURNAL 1997年第5期109卷 67-76页

作者： Anderson, DE Oberman, FR Malone, TB Baker, CC David E. Anderson:has a bachelor of science degree in environmental engineering from Florida Technological University and a master's degree in environmental engineering from the University of Central Florida. He is a graduate of the Naval Sea Systems Command's Engineer-In-Training (EIT) Program. Mr. Anderson was instrumental in introducing the collective protection system (CPS) in the U.S. Navy developing the initial forward-fit package for the USS Gunston Hall and the engineering change proposal (ECP) for the USS Wasp. In 1990 he joined the Human Systems Integration (HSI) Division (SEA 55W5) where he was task leader for auxiliary ships. He is currently the HSI manager for the future technology variant of the SeaLift ship and the future carrier. Association of Scientists and Engineers 33rdAnnual Technical Symposium 26 April 1996. Fred R. Oberman:has B.A. and M.A. degrees from the University of Chicago and Loyola University (experimental psychology) and an M.S. degree in industrial engineering and operations research from Virginia Polytechnic Institute (VPI). He has more than 30 years experience in HSI management planning research analysis design and testing in government and private sector positions. He is currently responsible for NavSea HSI generic research and tool development efforts. He is responsible for Human Engineering Specifications and Standards (Commercial Hypertext) and is the NavSea 03D7 representative on HSI in Performance Specifications and for integration of HSI within Integrated Logistic Support (ILS). He has served as DoD HSI SubTAG chair and member of the Simulation and Modeling Test and Evaluation Display and Control Systems Human Computer Interaction Specifications and Standards and Systems Design Sub Tags as a member of the Society of Naval Architects and Marine Engineers (SNAME) Systems Safety Panel and a member of NATO RSG 14 on man-machine analysis. Thomas B. Malone: CHFEP received a Ph.D. in experimental psychology from Fordham University in 1964. He is president of Carlow

The objectives of Human engineering (HE) are generally viewed as increasing human performance, reducing human error, enhancing personnel and equipment safety, and reducing training and related personnel costs. There are other benefits that are thoroughly consistent with the direction of the Navy of the future, chief among these is reduction of required numbers of personnel to operate and maintain Navy ships. The Naval Research Advisory Committee (NRAC) report on Man-Machine Technology in the Navy estimated that one of the benefits from increased application of man-machine technology to Navy ship design is personnel reduction as well as improving system availability, effectiveness, and safety The objective of this paper is to discuss aspects of the human engineering design of ships and systems that affect manning requirements, and impact human-performance and safety The paper will also discuss how the application of human engineering leads to improved performance, and crew safety, and reduced workload, all of which influence manning levels. Finally, the paper presents a discussion of tools and case studies of good human engineering design practices which reduce manning.

关键词： Human engineering

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Mitigating errors in mobile-based dietary assessments: Effects of a data modification process on the validity of an image-assisted food and nutrition app

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Nutrition 2023年 116卷 112212-112212页

作者： Ho, Dang Khanh Ngan Chiu, Wan-Chun Kao, Jing-Wen Tseng, Hsiang-Tung Yao, Chih-Yuan Su, Hsiu-Yueh Wei, Pin-Hui Le, Nguyen Quoc Khanh Nguyen, Hung Trong Chang, Jung-Su School of Nutrition and Health Sciences College of Nutrition Taipei Medical University Taipei Taiwan Department of Nutrition Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Dietetics Taipei Medical University Hospital Taipei Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei Taiwan Department of Adult Nutrition Counselling National Institute of Nutrition Hanoi Viet Nam Department of Clinical Nutrition and Dietetics National Hospital of Endocrinology Hanoi Viet Nam Graduate Institute of Metabolism and Obesity Sciences College of Nutrition Taipei Medical University Taipei Taiwan Nutrition Research Center Taipei Medical University Hospital Taipei Taiwan Chinese Taipei Society for the Study of Obesity (CTSSO) Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

Objective: Mobile nutrition applications (apps) provide a simple way for individuals to record their diet, but the validity and inherent errors need to be carefully evaluated. The aim of this study was to assess the validity and clarify the sources of measurement errors of image-assisted mobile nutrition apps. Methods: This was a cross-sectional study with 98 students recruited from School of Nutrition and Health Sciences, Taipei Medical University. A 3-d nutrient intake record by Formosa Food and Nutrient Recording App (FoodApp) was compared with a 24-h dietary recall (24-HDR). A two-stage data modification process, manual data cleaning, and reanalyzing of prepackaged foods were employed to address inherent errors. Nutrient intake levels obtained by the two methods were compared with the recommended daily intake (DRI), Taiwan. Paired t test, Spearman's correlation coefficients, and Bland–Altman plots were used to assess agreement between the FoodApp and 24-HDR. Results: Manual data cleaning identified 166 food coding errors (12%;stage 1), and 426 food codes with missing micronutrients (32%) were reanalyzed (stage 2). Positive linear trends were observed for total energy and micronutrient intake (all P_trend < 0.05) after the two stages of data modification, but not for dietary fat, carbohydrates, or vitamin D. There were no statistical differences in mean energy and macronutrient intake between the FoodApp and 24-HDR, and this agreement was confirmed by Bland–Altman plots. Spearman's correlation analyses showed strong to moderate correlations (r = 0.834 ∼ 0.386) between the two methods. Participants’ nutrient intake tended to be lower than the DRI, but no differences in proportions of adequacy/inadequacy for DRI values were observed between the two methods. Conclusions: Mitigating errors significantly improved the accuracy of the Formosa FoodApp, indicating its validity and reliability as a self-reporting mobile-based dietary assessment tool. Dietitians a

关键词： Food records Mobile application Mobile health Smartphone application

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Quantum computing hardware for HEP algorithms and sensing

arXiv

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arXiv 2022年

作者： Alam, M. Sohaib Belomestnykh, Sergey Bornman, Nicholas Cancelo, Gustavo Chao, Yu-Chiu Checchin, Mattia Dinh, Vinh San Grassellino, Anna Gustafson, Erik J. Harnik, Roni McRae, Corey Rae Harrington Huang, Ziwen Kapoor, Keshav Kim, Taeyoon Kowalkowski, James B. Kramer, Matthew J. Krasnikova, Yulia Kumar, Prem Kurkcuoglu, Doga Murat Lamm, Henry Lyon, Adam L. Milathianaki, Despina Murthy, Akshay Mutus, Josh Nekrashevich, Ivan Oh, JinSu Özgüler, A. Barış Perdue, Gabriel Nathan Reagor, Matthew Romanenko, Alexander Sauls, James A. Stefanazzi, Leandro Tubman, Norm M. Venturelli, Davide Wang, Changqing You, Xinyuan van Zanten, David M.T. Zhou, Lin Zhu, Shaojiang Zorzetti, Silvia NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Scientific Computing Division Fermi National Accelerator Laboratory BataviaIL60510 United States Northwestern-Fermilab Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Theory Division Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO80305 United States National Institute of Standards and Technology Boulder CO80305 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Ames Laboratory U.S. Department of Energy AmesIA50011 United States Center for Photonic Communication and Computing ECE Department Northwestern University EvanstonIL60208 United States Rigetti Computing Inc. BerkeleyCA94710 United States NASA Ames Research Center Moffett FieldCA94035 United States

Quantum information science harnesses the principles of quantum mechanics to realize computational algorithms with complexities vastly intractable by current computer platforms. Typical applications range from quantum chemistry to optimization problems and also include simulations for high energy physics. The recent maturing of quantum hardware has triggered preliminary explorations by several institutions (including Fermilab) of quantum hardware capable of demonstrating quantum advantage in multiple domains, from quantum computing to communications, to sensing. The Superconducting Quantum Materials and Systems (SQMS) Center, led by Fermilab, is dedicated to providing breakthroughs in quantum computing and sensing, mediating quantum engineering and HEP based material science. The main goal of the Center is to deploy quantum systems with superior performance tailored to the algorithms used in high energy physics. In this Snowmass paper, we discuss the two most promising superconducting quantum architectures for HEP algorithms, i.e. three-level systems (qutrits) supported by transmon devices coupled to planar devices and multi-level systems (qudits with arbitrary N energy levels) supported by superconducting 3D cavities. For each architecture, we demonstrate exemplary HEP algorithms and identify the current challenges, ongoing work and future opportunities. Furthermore, we discuss the prospects and complexities of interconnecting the different architectures and individual computational nodes. Finally, we review several different strategies of error protection and correction and discuss their potential to improve the performance of the two architectures. This whitepaper seeks to reach out to the HEP community and drive progress in both HEP research and QIS hardware. Copyright © 2022, The Authors. All rights reserved.

关键词： High energy physics

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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