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arXiv 2018年

作者： Jiang, Linxing Stocco, Andrea Losey, Darby M. Abernethy, Justin A. Prat, Chantel S. Rao, Rajesh P.N. University of Washington Paul G. Allen School of Computer Science & Engineering SeattleWA98195 United States University of Washington Department of Psychology SeattleWA98195 United States University of Washington Institute for Learning and Brain Sciences SeattleWA98195 United States Carnegie Mellon University Joint Program in Neural Computation & Machine Learning PittsburghPA15213 United States Carnegie Mellon University Center for the Neural Basis of Cognition PittsburghPA15213 United States University of Washington Institute for Neuroengineering SeattleWA98195 United States University of Washington Center for Neurotechnology SeattleWA98195 United States

We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as "Senders" whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender’s decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders’ decisions are transmitted via the Internet to the brain of a third subject, the "Receiver," who cannot see the game screen. The Senders’ decisions are delivered to the Receiver’s brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and uses an EEG interface to make a decision about either turning the block or keeping it in the same orientation. A second round of the game provides an additional chance for the Senders to evaluate the Receiver’s decision and send feedback to the Receiver’s brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game, (2) True/False positive rates of subjects’ decisions, and (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the collaborative task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender’s signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that like conventional social networks, BrainN

关键词： Electrophysiology

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Exact entanglement cost of quantum states and channels under positive-partial-transpose-preserving operations

arXiv

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arXiv 2018年

作者： Wang, Xin Wilde, Mark M. Institute for Quantum Computing Baidu Research Beijing100093 China Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States Hearne Institute for Theoretical Physics Department of Physics and Astronomy Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

This paper establishes single-letter formulas for the exact entanglement cost of simulating quantum channels under free quantum operations that completely preserve positivity of the partial transpose (PPT). First, we introduce the κ-entanglement measure for point-to-point quantum channels, based on the idea of the κ-entanglement of bipartite states, and we establish several fundamental properties for it, including amortization collapse, monotonicity under PPT superchannels, additivity, normalization, faithfulness, and non-convexity. Second, we introduce and solve the exact entanglement cost for simulating quantum channels in both the parallel and sequential settings, along with the assistance of free PPT-preserving operations. In particular, we establish that the entanglement cost in both cases is given by the same single-letter formula, the κ-entanglement measure of a quantum channel. We further show that this cost is equal to the largest κ-entanglement that can be shared or generated by the sender and receiver of the channel. This formula is calculable by a semidefinite program, thus allowing for an efficiently computable solution for general quantum channels. Noting that the sequential regime is more powerful than the parallel regime, another notable implication of our result is that both regimes have the same power for exact quantum channel simulation, when PPT superchannels are free. For several basic Gaussian quantum channels, we show that the exact entanglement cost is given by the Holevo-Werner formula [Holevo and Werner, Phys. Rev. A 63, 032312 (2001)], giving an operational meaning of the Holevo-Werner quantity for these channels. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum entanglement

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Plasmonic coaxial waveguides: Cavity-based devices and slitbased couplers

Plasmonic coaxial waveguides: Cavity-based devices and slitb...

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Advanced Photonics, IPRSN 2017

作者： Mahigir, Amirreza Dastmalchi, Pouya Shin, Wonseok Fan, Shanhui Veronis, Georgios School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States Department of Electrical Engineering Stanford University StanfordCA93405 United States

We investigate 3D plasmonic coaxial waveguide devices. Optical response of stub resonators coupled to a coaxial waveguide is investigated. Also, slit-based structures for coupling free space light into plasmonic coaxi... 详细信息

ISBN: (纸本)9781943580309

关键词： Plasmonics

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Cryptanalysis and improvement of quantum broadcast communication and authentication protocol with a quantum one-time pad

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Chinese Physics B 2016年第8期25卷 59-64页

作者：刘志昊陈汉武 School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Center for Quantum Computation and Intelligent Systems Faculty of Engineering and Information TechnologyUniversity of Technology Sydney

The security of quantum broadcast communication（QBC） and authentication protocol based on Greenberger–Horne–Zeilinger（GHZ） state and quantum one-time pad is analyzed. It is shown that there are some security issues in this ***, an external eavesdropper can take the intercept–measure–resend attack strategy to eavesdrop on 0.369 bit of every bit of the identity string of each receiver without being detected. Meanwhile, 0.524 bit of every bit of the secret message can be eavesdropped on without being detected. Secondly, an inner receiver can take the intercept–measure–resend attack strategy to eavesdrop on half of the identity string of the other＇s definitely without being checked. In addition, an alternative attack called the CNOT-operation attack is discussed. As for the multi-party QBC protocol, the attack efficiency increases with the increase of the number of users. Finally, the QBC protocol is improved to a secure one.

关键词： cryptanalysis quantum broadcast communication information leakage intercept-measure-resendattack CNOT-operation attack

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An environment for sustainable research software in Germany and beyond: Current state, open challenges, and call for action

arXiv

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arXiv 2020年

作者： Anzt, Hartwig Bach, Felix Druskat, Stephan Löffler, Frank Loewe, Axel Renard, Bernhard Y. Seemann, Gunnar Struck, Alexander Achhammer, Elke Aggarwal, Piush Appel, Franziska Bader, Michael Brusch, Lutz Busse, Christian Chourdakis, Gerasimos Dabrowski, Piotr W. Ebert, Peter Flemisch, Bernd Friedl, Sven Fritzsch, Bernadette Funk, Maximilian D. Gast, Volker Goth, Florian Grad, Jean-Noël Hermann, Sibylle Hohmann, Florian Janosch, Stephan Kutra, Dominik Linxweiler, Jan Muth, Thilo Peters-Kottig, Wolfgang Rack, Fabian Raters, Fabian H.C. Rave, Stephan Reina, Guido Reißig, Malte Ropinski, Timo Schaarschmidt, Joerg Seibold, Heidi Thiele, Jan P. Uekerman, Benjamin Unger, Stefan Weeber, Rudolf Germany Innovative Computing Lab University of Tennessee KnoxvilleTN United States Department of English Studies Friedrich Schiller University Jena Germany Germany Department of Computer Science Humboldt-Universität zu Berlin Germany Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany Michael Stifel Center Jena Germany Center for Computation and Technology Louisiana State University Baton Rouge United States Germany Robert Koch Institute Berlin Germany Hasso Plattner Institute Faculty of Digital Engineering University of Potsdam Germany Institute for Experimental Cardiovascular Medicine University Heart Centre Freiburg Bad Krozingen Germany Faculty of Medicine University of Freiburg Freiburg Germany Matters of Activity. Image Space Material. Cluster of Excellence at Humboldt-Universität zu Berlin Germany Technische Universität München Germany Language Technology Lab Universität Duisburg-Essen Germany Germany Department of Informatics Technical University of Munich Germany Technische Universität Dresden Germany Deutsches Krebsforschungszentrum Heidelberg Germany Scientific Computing in Computer Science Technical University Munich Germany School of Computing Communication and Business Hochschule für Technik und Wirtschaft Berlin Germany Center for Bioinformatics Saar Saarland Informatics Campus Germany Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Germany Berlin Institute of Health Germany Scientific Computing Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven Germany Max-Planck-Gesellschaft e.V. Institut für Theoretische Physik und Astrophysik Universität Würzburg Germany Institut für Computerphysik University of Stuttgart Germany University Library University of Stuttgart Germany Universität Bremen Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany European Molecular Biology Laboratory Heidelb

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. Copyright © 2020, The Authors. All rights reserved.

关键词： Software design

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Abstracts

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JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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Social and moral psychology of COVID-19 across 69 countries

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Scientific data 2023年第1期10卷 272页

作者： Flavio Azevedo Tomislav Pavlović Gabriel G Rêgo F Ceren Ay Biljana Gjoneska Tom W Etienne Robert M Ross Philipp Schönegger Julián C Riaño-Moreno Aleksandra Cichocka Valerio Capraro Luca Cian Chiara Longoni Ho Fai Chan Jay J Van Bavel Hallgeir Sjåstad John B Nezlek Mark Alfano Michele J Gelfand Michèle D Birtel Aleksandra Cislak Patricia L Lockwood Koen Abts Elena Agadullina John Jamir Benzon Aruta Sahba Nomvula Besharati Alexander Bor Becky L Choma Charles David Crabtree William A Cunningham Koustav De Waqas Ejaz Christian T Elbaek Andrej Findor Daniel Flichtentrei Renata Franc June Gruber Estrella Gualda Yusaku Horiuchi Toan Luu Duc Huynh Agustin Ibanez Mostak Ahamed Imran Jacob Israelashvili Katarzyna Jasko Jaroslaw Kantorowicz Elena Kantorowicz-Reznichenko André Krouwel Michael Laakasuo Claus Lamm Caroline Leygue Ming-Jen Lin Mohammad Sabbir Mansoor Antoine Marie Lewend Mayiwar Honorata Mazepus Cillian McHugh John Paul Minda Panagiotis Mitkidis Andreas Olsson Tobias Otterbring Dominic J Packer Anat Perry Michael Bang Petersen Arathy Puthillam Tobias Rothmund Hernando Santamaría-García Petra C Schmid Drozdstoy Stoyanov Shruti Tewari Bojan Todosijević Manos Tsakiris Hans H Tung Radu G Umbres Edmunds Vanags Madalina Vlasceanu Andrew Vonasch Meltem Yucel Yucheng Zhang Mohcine Abad Eli Adler Narin Akrawi Hamza Alaoui Mdarhri Hanane Amara David M Amodio Benedict G Antazo Matthew Apps Mouhamadou Hady Ba Sergio Barbosa Brock Bastian Anton Berg Maria P Bernal-Zárate Michael Bernstein Michał Białek Ennio Bilancini Natalia Bogatyreva Leonardo Boncinelli Jonathan E Booth Sylvie Borau Ondrej Buchel C Daryl Cameron Chrissie F Carvalho Tatiana Celadin Chiara Cerami Hom Nath Chalise Xiaojun Cheng Kate Cockcroft Jane Conway Mateo Andres Córdoba-Delgado Chiara Crespi Marie Crouzevialle Jo Cutler Marzena Cypryańska Justyna Dabrowska Michael A Daniels Victoria H Davis Pamala N Dayley Sylvain Delouvée Ognjan Denkovski Guillaume Dezecache Nathan A Dhaliwal Alelie B Diato Roberto Di Paolo Marianna Drosinou Uwe Dulleck Jānis Ekmanis Department of Psychology University of Cambridge Cambridge England. fa441@cam.ac.uk. Institute of Communication Science Friedrich-Schiller University Jena Jena Germany. fa441@cam.ac.uk. Institute of Social Sciences Ivo Pilar Zagreb Croatia. Social and Cognitive Neuroscience Laboratory Mackenzie Presbyterian University São Paulo Brazil. Department of Economics Norwegian School of Economics Bergen Norway. Telenor Research Oslo Norway. Macedonian Academy of Sciences and Arts Skopje Republic of North Macedonia. Kieskompas - Election Compass Amsterdam Netherlands. Department of Political Science & Annenberg School for Communication University of Pennsylvania Philadelphia PA USA. Department of Psychology Macquarie University Sydney NSW Australia. Department of Philosophy University of St Andrews St Andrews Scotland. School of Economics and Finance University of St Andrews St Andrews Scotland. Medicine Faculty Cooperative University of Colombia Villavicencio Colombia. Department of Bioethics El Bosque University Bogotá Colombia. School of Psychology University of Kent Canterbury England. Department of Economics Middlesex University London London England. Darden School of Business University of Virginia Charlottesville VA USA. Questrom School of Business Boston University Boston MA USA. School of Economics and Finance Queensland University of Technology Brisbane QLD Australia. Center for Behavioural Economics Society and Technology Queensland University of Technology Brisbane QLD Australia. Department of Psychology and Neural Science New York University New York NY USA. Department of Strategy and Management Norwegian School of Economics Bergen Norway. SWPS University of Social Sciences and Humanities Warsaw Poland. Department of Psychological Sciences College of William and Mary Williamsburg VA USA. Department of Philosophy Macquarie University Sydney NSW Australia. Stanford Graduate School of Business Stanford University Stanford CA USA.

The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.

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Reasoning and predicting POMDP planning complexity via covering numbers

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Frontiers of computer science 2016年第4期10卷 726-740页

作者： Zongzhang ZHANG Qiming FU Xiaofang ZHANG Quan LIU School of Computer Science and Technology Soochow University Suzhou 215000 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210000 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou 215000 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

Partially observable Markov decision processes （POMDPs） provide a rich mathematical framework for planning tasks in partially observable stochastic environments. The notion of the covering number, a metric of capturing the search space size of a POMDP planning problem, has been proposed as a complexity measure of approximate POMDP planning. Existing theoretical results are based on POMDPs with finite and discrete state spaces and measured in the l1- metric space. When considering heuristics, they are assumed to be always admissible. This paper extends the theoretical results on the covering numbers of different search spaces, including the newly defined space reachable under inadmissible heuristics, to the ln-metric spaces. We provide a simple but scalable algorithm for estimating covering numbers. Experimentally, we provide estimated covering numbers of the search spaces reachable by following different policies on several benchmark problems, and analyze their abilities to predict the runtime of POMDP planning algorithms.

关键词： POMDP planning complexity theory covering number search space

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Gaussian lower bound for the information bottleneck limit

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2017年第1期18卷

作者： Amichai Painsky Naftali Tishby School of Computer Science and Engineering and The Interdisciplinary Center for Neural Computation The Hebrew University of Jerusalem Jerusalem Israel

The Information Bottleneck (IB) is a conceptual method for extracting the most compact, yet informative, representation of a set of variables, with respect to the target. It generalizes the notion of minimal sufficient statistics from classical parametric statistics to a broader information-theoretic sense. The IB curve defines the optimal trade-off between representation complexity and its predictive power. Specifically, it is achieved by minimizing the level of mutual information (MI) between the representation and the original variables, subject to a minimal level of MI between the representation and the target. This problem is shown to be in general NP hard. One important exception is the multivariate Gaussian case, for which the Gaussian IB (GIB) is known to obtain an analytical closed form solution, similar to Canonical Correlation Analysis (CCA). In this work we introduce a Gaussian lower bound to the IB curve; we find an embedding of the data which maximizes its "Gaussian part", on which we apply the GIB. This embedding provides an efficient (and practical) representation of any arbitrary data-set (in the IB sense), which in addition holds the favorable properties of a Gaussian distribution. Importantly, we show that the optimal Gaussian embedding is bounded from above by non-linear CCA. This allows a fundamental limit for our ability to Gaussianize arbitrary data-sets and solve complex problems by linear methods.

关键词： ACE canonical correlations gaussianization infomax information bottleneck mutual information maximization

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GiGA: Giraph-based genome assembler for gigabase scale genomes 8

GiGA: Giraph-based genome assembler for gigabase scale genom...

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8th International Conference on Bioinformatics and computational Biology, BICOB 2016

作者： Kondikoppa, Praveen Kumar Das, Arghya Kusum Goswami, Sayan Platania, Richard Park, Seung-Jong Center for Computation and Technology School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70808 United States

ISBN: (纸本)9781943436033

Managing prodigious volumes of NGS input data in a cost effective way forces a growing number of sequence analytic applications to run on scaled out clusters of low cost commodity hardware. Large-scale de novo genome assembly is no exception. However, traditional MPI-based assembly software cannot scale well with the huge volumes of data unless sophisticated and costly compute resources are provided, which are unavailable to most researchers. The model of underlying computation should be changed significantly to address this critical need. In this work, we develop GiGA, a parallel Giraph-based Genome Assembler that uses the de Bruijn graph approach for the assembly. GiGA uses recent big data analytic software, Hadoop and Giraph, which carefully consider data locality, thus automatically scale with terabytes of data on low cost commodity clusters. Our benchmark-evaluation over GAGE data sets shows that GiGA achieved significantly higher scalability, substantially lower misassembly and competitive NG50 compared to other assembly software. GiGA performs almost 1.5x faster than Contrail, a Hadoop-based genome assembler developed for commodity cluster. To demonstrate the capability of GiGA to assemble large-scale vertebrate genomes over hundreds of cores, we assemble a human genome data set (SRA000271) of size 452 gigabyte and almost 2 billion reads with 512 cores in almost 8.5 hours. Copyright ISCA.

关键词： Big data

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