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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Evaluating and Comparing Game-controller based Virtual Locomotion Techniques 27

Evaluating and Comparing Game-controller based Virtual Locom...

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27th International Conference on Artificial Reality and Telexistence, ICAT 2017 and the 22nd Eurographics Symposium on Virtual Environments, EGVE 2017

作者： Sarupuriy, Bhuvaneswari Hoermann, Simon Whitton, Mary C. Lindeman, Robert W. Human Interface Technology Laboratory New Zealand University of Canterbury New Zealand Department of Computer Science University of North Carolina at Chapel Hill NC United States

ISBN: (纸本)9783038680383

The incremental hardware costs of virtual locomotion are minimized when the technique uses interaction capabilities available in controllers and devices that are already part of the VE system, e.g., gamepads, keyboards, and multi-function controllers. We used a different locomotion technique for each of these three devices: gamepad thumb-stick (joystick walking), a customized hybrid keyboard for gaming (speedpad walking), and an innovative technique that uses the orientation and triggers of the HTC Vive controllers (TriggerWalking). We explored the efficacy of locomotion techniques using these three devices in a hide and seek task in an indoor environment. We measured task performance, simulator sickness, system usability, perceived workload, and preference. We found that users had a strong preference for TriggerWalking, which also had the least increase in simulator sickness, the highest performance score, and highest perceived usability. However, participants using TriggerWalking also had the most object and wall-collisions. Overall we found that TriggerWalking is an effective locomotion technique and that is has significant and important benefits. Future research will explore if TriggerWalking can be used with equal benefits in other virtual-environments, on different tasks, and types of movement. © 2017 The Author(s).

关键词： Controllers

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Catalog of Invisibility Requirements for UbiComp and IoT Applications

Catalog of Invisibility Requirements for UbiComp and IoT App...

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IEEE International Conference on Requirements Engineering

作者： Rainara M. Carvalho Rossana M.C. Andrade Káthia M. Oliveira Christophe Kolski Computer Science Department (DC) Federal University of Ceará Fortaleza Brazil Universidade Federal do Ceara Fortaleza CE BR Laboratory of Industrial and Human Automation control Université Polytechnique Hauts-de-France Valenciennes France

A new set of Non-Functional Requirements (NFRs) have appeared with the advent of Ubiquitous Computing (UbiComp) and more recently Internet of Things (IoT). Invisibility is one of these NFRs that means the ability to hide technology from users. Although invisibility is long seen as an essential characteristic for achieving the goals of UbiComp, it has not been cataloged regarding its subcharacteristics and solutions, making its design and requirements specification in such applications a challenging task. Considering the Softgoal Interdependency Graph (SIG), which is a well-known format to catalog NFRs, this work aims at capturing subcharacteristics and solutions for Invisibility and cataloguing them in a SIG. Since there is no systematic approach on how to build SIGs, we also propose to systematize the definition of Invisibility SIG using the following well-defined research methods: snowballing, database search, grounded theory and questionnaires. As a result, we got an Invisibility SIG composed of two main subcharacteristics, twelve subsubcharacteristics, ten general solutions and fifty-six specific solutions. This organized body of knowledge is useful for supporting software engineers to specify requirements and practical solutions for UbiComp and IoT applications. Furthermore, the proposed methodology used to capture and catalog requirements in a SIG can be reused for other NFRs.

关键词： Software Internet of Things Systematics Bibliographies Ubiquitous computing Task analysis

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Music, Computing, and Health: A Roadmap for the Current and Future Roles of Music Technology for Health Care and Well-Being

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Music and science 2021年 4卷

作者： Agres, Kat R. Schaefer, Rebecca S. Volk, Anja van Hooren, Susan Holzapfel, Andre Dalla Bella, Simone Müller, Meinard de Witte, Martina Herremans, Dorien Ramirez Melendez, Rafael Neerincx, Mark Ruiz, Sebastian Meredith, David Dimitriadis, Theo Magee, Wendy L. Yong Siew Toh Conservatory of Music National University of Singapore Singapore Social and Cognitive Computing Department Institute of High Performance Computing A*STAR Singapore Institute for Psychology Health Medical Neuropsychology Unit Leiden University Leiden Netherlands Leiden Institute for Brain and Cognition Leiden University Leiden Netherlands Academy of Creative and Performing Arts Leiden University Leiden Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Faculty of Health care Department of Arts Therapies Zuyd University of Applied Sciences Heerlen Netherlands KenVaK Research Centre for the Arts Therapies and Psychomotricity Heerlen Netherlands Faculty of Psychology Open University of The Netherlands Heerlen Netherlands Division of Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden International Laboratory for Brain Music and Sound Research (BRAMS) Outremont QC Canada Department of Psychology University of Montreal Montreal QC Canada Centre for Research on Brain Language and Music (CRBLM) Montreal QC Canada University of Economics and Human Sciences in Warsaw Warsaw Poland International Audio Laboratories Erlangen Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany HAN University of Applied Sciences Department of Arts Therapies and Psychological Studies Nijmegen Netherlands University of Amsterdam Research Institute of Child Development and Education Amsterdam Netherlands Treatment Centre for People with Mild Intellectual Disabilities and Psychiatric and Behavioral Disorders Gennep Netherlands Information Systems Technology and Design Singapore University of Technology and Design Singapore Music and Machine Learning Lab Music Technology Group Universitat Pompeu Fabra Barcelona Spain Faculty of EEMCS Interactive Intelligence Group Delft University of Technology Delft Netherlands Centre for Digital Music School of Electronic Engineerin

The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop “Music, Computing, and Health” was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field. © The Author(s) 2021.

关键词： health care interdisciplinarity MedTech music information retrieval (MIR) music neuroscience Music psychology music technology music therapy well-being

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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Technology for underserved communities;personal fabrication: Expert-curated guides to the best of CS research

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Queue 2017年第2期15卷

作者： Bailis, Peter Dillahunt, Tawanna Mueller, Stefanie Baudisch, Patrick Department of Computer Science Stanford University United States University of Michigan's School of Information United States MIT's Electrical Engineering Computer Science Department United States Department of Computer Science Hasso Plattner Institute Potsdam University Germany Department of the Human Computer Interaction Lab. United States

Research for Practice combines the resources of the ACM Digital Library, the largest collection of computer science research in the world, with the expertise of the ACM membership. In every RfP column two experts share a short curated selection of papers on a concentrated, practically oriented topic. Copyright © 2017 held by owner/author. Publication rights licensed to ACM.

关键词： Digital libraries

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Evolvable dialogue state tracking for statistical dialogue management

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Frontiers of computer science 2016年第2期10卷 201-215页

作者： Kai YU Lu CHEN Kai SUN Qizhe XIE Su ZHU Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive EngineeringSpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Computer Science Cornell University Ithaca NY 14853 USA

Statistical dialogue management is the core of cognitive spoken dialogue systems （SDS） and has attracted great research interest. In recent years, SDS with the ability of evolution is of particular interest and becomes the cuttingedge of SDS research. Dialogue state tracking （DST） is a process to estimate the distribution of the dialogue states at each dialogue turn, given the previous interaction history. It plays an important role in statistical dialogue management. To provide a common testbed for advancing the research of DST, international DST challenges （DSTC） have been organised and well-attended by major SDS groups in the world. This paper reviews recent progresses on rule-based and statistical approaches during the challenges. In particular, this paper is focused on evolvable DST approaches for dialogue domain extension. The two primary aspects for evolution, semantic parsing and tracker, are discussed. Semantic enhancement and a DST framework which bridges rule-based and statistical models are introduced in detail. By effectively incorporating prior knowledge of dialogue state transition and the ability of being data-driven, the new framework supports reliable domain extension with little data and can continuously improve with more data available. This makes it excellent candidate for DST evolution. Experiments show that the evolvable DST approaches can achieve the state-of-the-art performance and outperform all previously submitted trackers in the third DSTC.

关键词： dialogue management domain extension,evolvable dialogue state tracking parser tracker

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Building a Chinese discourse topic corpus with a micro-topic scheme based on theme-rheme theory

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Big Data Analytics 2017年第1期2卷 1-14页

作者： Xi, Xue-feng Zhou, Guodong Department of Computer Science and Technology Suzhou University of Science and Technology Suzhou China School of Computer Science and Technology Soochow University Suzhou China Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou Suzhou China

How to build a suitable discourse topic structure is an important issue in discourse topic analysis, which is the core of natural language understanding. Not only is it the key basic unit to implement automatic computing, but also the key to realize the transformation from unstructured data to structured data during the process of big data analytics. Although the discourse topic structure has wide potential for application in discourse analysis and related tasks, the research on constructing such discourse resources is quite limited in Chinese language. In this paper, we propose a micro-topic scheme (MTS) to represent the discourse topic structure in the Chinese language according to theme-rheme theory, with elementary discourse topic unit(EDTU) as the node and referent of theme-rheme as link. In particular, thematic progression is employed to directly represent the development of the discourse topic structure. Guided by the MTS, we manually annotate a Chinese Discourse Topic Corpus (CDTC) of 500 documents. Moreover, we get 89.9 and 72.15 F1 value in two identification preliminary experiments, respectively, which show that the proposed representation can perform good automatic computation. The lack of the formal representation system and related corpus resources for Chinese discourse topic structure has greatly restricted the study of discourse topic analysis in natural language, and further affected the development of natural language understanding. To address the above issues, a micro-topic scheme(MTS) representation is proposed based on functional grammar theory, and the corresponding corpus resources(i.e., CDTC) are constructed. Our preliminary evaluation justifies the appropriateness of the MTS for Chinese discourse analysis and the usefulness of our CDTC.

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Emotion and Memory Model for a Robotic Tutor in a Learning Environment 7

Emotion and Memory Model for a Robotic Tutor in a Learning E...

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7th ISCA Workshop on Speech and Language Technology in Education, SLaTE 2017

作者： Ahmad, Muneeb Imtiaz Mubin, Omar Shahid, Suleman Orlando, Joanne MARCS Institute Human-Machine Interaction Group Western Sydney University Australia School of Computing Engineering and Mathematics Western Sydney University Australia Department of Computer Science Lahore University of Management and Sciences Pakistan School of Education Western Sydney University Australia

In this paper, we present an emotion and memory model for a social robot. The model allowed the robot to create a memory account of a child’s emotional events over 4 individual sessions. The robot then adapted its behaviour based on the developed memory. We tested our model through using the NAO robot. The robot was programmed to teach vocabulary to children during the popular game’Snakes and Ladders’. We conducted a between-subject study with 24 children at a primary school to check the validity of our model. We also evaluated the impact of robot’s positive, negative, and neutral emotional feedback of the NAO robot on children vocabulary learning. Three groups of children (8/group) interacted with the robot for four different times during three weeks. Our results showed that the condition where the robot displayed positive emotional responses had a significant effect on the child’s learning performance as compared to the two other conditions: negative feedback and neutral feedback. In addition, our model helped children in improving their vocabulary. © 2017 7th ISCA Workshop on Speech and Language Technology in Education, SLaTE 2017. All rights reserved.

关键词： computer aided instruction

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Interpretable brain age prediction using linear latent variable models of functional connectivity

arXiv

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arXiv 2019年

作者： Monti, Ricardo Pio Gibberd, Alex Roy, Sandipan Nunes, Matt Lorenz, Romy Leech, Robert Ogawa, Takeshi Kawanabe, Motoaki Hyvarinenj, Aapo Gatsby Computational Neuroscience Unit University College London London United Kingdom Department of Mathematics and Statistics Lancaster University Bailrigg United Kingdom Department of Mathematics University of Bath Bath United Kingdom Mrc Cognition and Brain Sciences Unit University of Cambridge Cambridge United Kingdom Max-Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Centre for Neuroimaging Science Kings College London London United Kingdom Advanced Telecommunications Research Institute International Cognitive Mechanisms Laboratories Kyoto Japan Brain Information Communication Research Laboratory Group Kyoto Japan Department of Computer Science and Hiit University of Helsinki Helsinki Finland Paris-Saclay University INRIA Paris France

Neuroimaging-driven prediction of brain age, defined as the predicted biological age of a subject using only brain imaging data, is an exciting avenue of research. In this work we seek to build models of brain age based on functional connectivity while prioritizing model interpretability and understanding. This way, the models serve to both provide accurate estimates of brain age as well as allow us to investigate changes in functional connectivity which occur during the ageing process. The methods proposed in this work consist of a two-step procedure: First, linear latent variable models, such as PCA and its extensions, are employed to learn reproducible functional connectivity networks present across a cohort of subjects. The activity within each network is subsequently employed as a feature in a linear regression model to predict brain age. The proposed framework is employed on the data from the CamCAN repository and the inferred brain age models are further demonstrated to generalize using data from two open-access repositories: The human Connectome Project and the ATR Wide-Age-Range. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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