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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 German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Wellcome Sanger Institute Wellcome Genome Campus Hinxton Cambridge CB10 1SA UK Korea Advanced Institute of Science and Technology Daejeon 34141 South Korea Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Korea Advanced Institute of Science and Technology Daejeon South Korea Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 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 Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston MA USA Ludwig Center at Harvard Medical School Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Broad Institute of MIT and Harvard

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Fault adaptive routing in metasurface controller networks

arXiv

引用

arXiv 2018年

作者： Saeed, Taqwa Skitsas, Constantinos Kouzapas, Dimitrios Lestas, Marios Soteriou, Vassos Philippou, Anna Abadal, Sergi Liaskos, Christos Petrou, Loukas Georgiou, Julius Pitsillides, Andreas Department of Electrical Engineering Frederick University Cyprus Nicosia Cyprus Department of Computer Science University of Cyprus Nicosia Cyprus Dept. of Electrical Eng. Computer Eng. and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Architecture Universitat Politècnica de Catalunya Barcelona Spain Institute of Computer Science Foundation of Research and Technology Hellas Heraklion Greece Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

HyperSurfaces are a merge of structurally reconfigurable metasurfaces whose electromagnetic properties can be changed via a software interface, using an embedded miniaturized network of controllers, thus enabling novel capabilities in wireless communications. Resource constraints associated with the development of a hardware testbed of this breakthrough technology necessitate network controller architectures different from traditional regular Network-on-Chip architectures. The Manhattan-like topology chosen to realize the controller network in the testbed under development is irregular, with restricted local path selection options, operating in an asynchronous fashion. These characteristics render traditional fault-tolerant routing mechanisms inadequate. In this paper, we present work in progress towards the development of fault-tolerant routing mechanisms for the chosen architecture. We present two XY-based approaches which have been developed aiming to offer reliable data delivery in the presence of faults. The first approach aims to avoid loops while the second one attempts to maximize the success delivery probabilities. Their effectiveness is demonstrated via simulations conducted on a custom developed simulator. Copyright © 2018, The Authors. All rights reserved.

关键词： Network routing

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Fault Adaptive Routing in Metasurface Controller Networks

Fault Adaptive Routing in Metasurface Controller Networks

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International Workshop on Network on Chip Architectures, NoCArc

作者： Taqwa Saeed Constantinos Skitsas Dimitrios Kouzapas Marios Lestas Vassos Soteriou Anna Philippou Sergi Abadal Christos Liaskos Loukas Petrou Julius Georgiou Andreas Pitsillides Department of Computer Science University of Cyprus Nicosia Cyprus Department of Electrical Engineering Frederick University Cyprus Nicosia Cyprus Dept. of Electrical Eng. Computer Eng. and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Architecture Universitat Politècnica de Catalunya Barcelona Spain Institute of Computer Science Foundation of Research and Technology Hellas Heraklion Greece Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

关键词： Routing Topology System recovery Pins Network topology Logic gates Fault tolerance

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Emotion Recognition from Human Behaviors Using Attention Model

Emotion Recognition from Human Behaviors Using Attention Mod...

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Artificial Intelligence and Knowledge engineering (AIKE)

作者： James Jie Deng Clement Ho Cheung Leung Paolo Mengoni Yuanxi Li Hong Kong Applied Science and Technology Research Institute Hong Kong China Centre for Applied Informatics Victoria University Melbourne Victoria Australia Dept. of Mathematics and Computer Science University of Florence Florence Italy Dept. of Computer Science Hong Kong Baptist University Hong Kong China

ISBN: (纸本)9781538695562;9781538660843

Human behaviors and the emotional states that they convey have been studied by psychologist and sociologists. The tracking of behaviors and emotions is becoming more pervasive with the advent of the Internet of Things (IoT), where small and always connected sensors can continuously capture information about human gestures, movements and postures. The captured information about readable behaviors conveys signicant information that can be represented as time series. Few studies in emotion recognition and affective computing have explored the connection between the time series sensors data and the emotional behavior they conveys. In this paper, an innovative approach is proposed to study the emotions and behaviors connected to the sensors time series data. A deep learning attention-based bidirectional LSTM is introduced to represent the correlations between data and emotions. The attention-based mechanism is then used to focus on the most significant information. The advantage of this model is that it can represent various human emotions by exploiting the data captured by sensors. The experimental results show that the proposed deep learning method outperforms shallow models and achieves a high degree of accuracy for modelling human behaviors and emotions.

关键词： Emotion recognition Sensors Computational modeling Accelerometers Training Time series analysis

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Search for a light charged Higgs boson in t → H ^± b decays, with H ^± → cb, in the lepton+jets final state in proton-proton collisions at √s = 13 TeV with the ATLAS detector

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Journal of High Energy Physics 2023年第9期2023卷 4页

作者： Aad, G. Abbott, B. Abbott, D.C. Abed Abud, A. Abeling, K. Abhayasinghe, D.K. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Abusleme Hoffman, A.C. Acharya, B.S. Achkar, B. Adam, L. Adam Bourdarios, C. Adamczyk, L. Adamek, L. Addepalli, S.V. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Aizenberg, I. Akatsuka, S. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Alconada Verzini, M.J. Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M.M. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alunno Camelia, E. Alvarez Estevez, M. Alviggi, M.G. Amaral Coutinho, Y. Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Amor Dos Santos, S.P. Amoroso, S. Amos, K.R. Amrouche, C.S. Ananiev, V. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Aparisi Pozo, J.A. Aparo, M.A. Aperio Bella, L. Aranzabal, N. Araujo Ferraz, V. Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armstrong, A. Arnaez, O. Arnold, H. Arrubarrena Tame, Z.P. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Asimakopoulou, E.M. Asquith, L. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Austrup, V.A. Avner, G. Avolio, G. Ayoub, M.K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Bakshi Gupta, D. Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Balli, F Department of Physics University of Adelaide Adelaide Australia Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Univ. Savoie Mont Blanc CNRS/IN2P3 Annecy France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics University of Texas at Arlington Arlington TX United States Physics Department National and Kapodistrian University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics University of Texas at Austin Austin TX United States Bahcesehir University Faculty of Engineering and Natural Sciences Istanbul Turkey Istanbul Bilgi University Faculty of Engineering and Natural Sciences Istanbul Turkey Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institute of High Energy Physics Chinese Academy of Sciences Beijing China Physics Department Tsinghua University Beijing China Department of Physics Nanjing University Nanjing China University of Chinese Academy of Science (UCAS) Beijing China Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Facultad de Ciencias y Centro de Investigaciónes Unive

A search for a charged Higgs boson, H ±, produced in top-quark decays, t → H ± b, is presented. The search targets H ± decays into a bottom and a charm quark, H ± → cb. The analysis focuses o... 详细信息

A search for a charged Higgs boson, H ^±, produced in top-quark decays, t → H ^± b, is presented. The search targets H ^± decays into a bottom and a charm quark, H ^± → cb. The analysis focuses on a selection enriched in top-quark pair production, where one top quark decays into a leptonically decaying W boson and a bottom quark, and the other top quark decays into a charged Higgs boson and a bottom quark. This topology leads to a lepton-plus-jets final state, characterised by an isolated electron or muon and at least four jets. The search exploits the high multiplicity of jets containing b-hadrons, and deploys a neural network classifier that uses the kinematic differences between the signal and the background. The search uses a dataset of proton-proton collisions collected at a centre-of-mass energy s = 13 TeV between 2015 and 2018 with the ATLAS detector at CERN’s Large Hadron Collider, amounting to an integrated luminosity of 139 fb⁻¹. Observed (expected) 95% confidence-level upper limits between 0.15% (0.09%) and 0.42% (0.25%) are derived for the product of branching fractions B (t → H ^± b) × B(H ^± → cb) for charged Higgs boson masses between 60 and 160 GeV, assuming the SM production of the top-quark pairs. [Figure not available: see fulltext.]. © 2023, The Author(s).

关键词： Hadron-Hadron Scattering

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Search for high-mass Wγ and Zγ resonances using hadronic W/Z boson decays from 139 fb⁻¹ of pp collisions at √s = 13 TeV with the ATLAS detector

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Journal of High Energy Physics 2023年第7期2023卷 125页

作者： Aad, G. Abbott, B. Abbott, D.C. Abed Abud, A. Abeling, K. Abhayasinghe, D.K. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Abusleme Hoffman, A.C. Acharya, B.S. Achkar, B. Adam, L. Adam Bourdarios, C. Adamczyk, L. Adamek, L. Addepalli, S.V. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Aizenberg, I. Akatsuka, S. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Alconada Verzini, M.J. Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M.M. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alunno Camelia, E. Alvarez Estevez, M. Alviggi, M.G. Amaral Coutinho, Y. Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Amor Dos Santos, S.P. Amoroso, S. Amos, K.R. Amrouche, C.S. Ananiev, V. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Aparisi Pozo, J.A. Aparo, M.A. Aperio Bella, L. Aranzabal, N. Araujo Ferraz, V. Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armstrong, A. Arnaez, O. Arnold, H. Arrubarrena Tame, Z.P. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Asimakopoulou, E.M. Asquith, L. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Austrup, V.A. Avner, G. Avolio, G. Ayoub, M.K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Bakshi Gupta, D. Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Balli, F Department of Physics University of Adelaide Adelaide Australia Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Université Savoie Mont Blanc CNRS/IN2P3 Annecy France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics University of Texas at Arlington Arlington TX United States Physics Department National and Kapodistrian University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics University of Texas at Austin Austin TX United States Bahcesehir University Faculty of Engineering and Natural Sciences Istanbul Turkey Istanbul Bilgi University Faculty of Engineering and Natural Sciences Istanbul Turkey Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institute of High Energy Physics Chinese Academy of Sciences Beijing China Physics Department Tsinghua University Beijing China Department of Physics Nanjing University Nanjing China University of Chinese Academy of Science (UCAS) Beijing China Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Facultad de Ciencias y Centro de Investigaciónes

A search for high-mass charged and neutral bosons decaying to Wγ and Zγ final states is presented in this paper. The analysis uses a data sample of s = 13 TeV proton-proton collisions with an integrated luminosity of 139 fb⁻¹ collected by the ATLAS detector during LHC Run 2 operation. The sensitivity of the search is determined using models of the production and decay of spin-1 charged bosons and spin-0/2 neutral bosons. The range of resonance masses explored extends from 1.0 TeV to 6.8 TeV. At these high resonance masses, it is beneficial to target the hadronic decays of the W and Z bosons because of their large branching fractions. The decay products of the high-momentum W/Z bosons are strongly collimated and boosted-boson tagging techniques are employed to improve the sensitivity. No evidence of a signal above the Standard Model backgrounds is observed, and upper limits on the production cross-sections of these bosons times their branching fractions to Wγ and Zγ are derived for various boson production models. [Figure not available: see fulltext.]. © 2023, The Author(s).

关键词： Hadron-Hadron Scattering

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Considering discrepancy when calibrating a mechanistic electrophysiology model

arXiv

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arXiv 2020年

作者： Lei, Chon Lok Ghosh, Sanmitra Whittaker, Dominic G. Aboelkassem, Yasser Beattie, Kylie A. Cantwell, Chris D. Delhaas, Tammo Houston, Charles Novaes, Gustavo Montes Panfilov, Alexander V. Pathmanathan, Pras Riabiz, Marina Weber dos Santos, Rodrigo Worden, Keith Mirams, Gary R. Wilkinson, Richard D. Computational Biology & Health Informatics Dept. of Computer Science University of Oxford United Kingdom MRC Biostatistics Unit University of Cambridge United Kingdom Centre for Mathematical Medicine & Biology School of Mathematical Sciences University of Nottingham United Kingdom Department of Bioengineering University of California San Diego United States Systems Modeling and Translational Biology GlaxoSmithKline R&D Stevenage United Kingdom ElectroCardioMaths Programme Centre for Cardiac Engineering Imperial College London United Kingdom CARIM School for Cardiovascular Diseases Maastricht University Netherlands Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil Department of Physics and Astronomy Ghent University Belgium Laboratory of Computational Biology and Medicine Ural Federal University Ekaterinburg Russia U.S. Food and Drug Administration Center for Devices and Radiological Health Office of Science and Engineering Laboratories United States Department of Biomedical Engineering King’s College London Alan Turing Institute United Kingdom Dynamics Research Group Department of Mechanical Engineering University of Sheffield United Kingdom School of Mathematics and Statistics University of Sheffield United Kingdom

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions — that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Beyond Low Earth Orbit: Biological research, Artificial Intelligence, and Self-Driving Labs

arXiv

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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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Renewable Mobility in Smart Cities: Cloud-Based Services

Renewable Mobility in Smart Cities: Cloud-Based Services

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IEEE Symposium on computers and Communications

作者： Damianos Gavalas Dionisis Kehagias Damianos Kypriadis Kalliopi Giannakopoulou Charalampos Konstantopoulos Grammati Pantziou Christos Zaroliagis Vlasios Kasapakis Spyros Kontogiannis Andreas Paraskevopoulos Dept of Product & Systems Design Engineering University of the Aegean Syros Greece & CTI Patras Greece Dept of Computer Engineering & Informatics University of Patras Patras Greece & CTI Patras Greece Dept of Cultural Technology & Communication University of the Aegean Mytilene Greece & CTI Patras Greece Centre for Research and Technology Hellas/Information Technologies Institute (CERTH/ITI) Thessaloniki Greece Dept of Informatics University of Piraeus Piraeus Greece & CTI Patras Greece Dept of Computer Science & Engineering University of Ioannina Ioannina Greece & CTI Patras Greece Dept of Informatics Technological Educational Institution of Athens Athens Greece & CTI Patras Greece

Providing efficient, sustainable and personalized mobility services in urban environments that combine a spectrum of transport modes (e.g., public transport, electric vehicles, vehicle sharing, low energy and/or emission routes) constitutes a great challenge. In this work, we present MOVESMART, a holistic approach (and integrated platform) for the provision of renewable personal mobility services, leveraging crowd-sourcing data, tools for collecting real-time information by multimodal travelers, and traffic prediction mechanisms. MOVESMART guarantees real-time responses to renewable (on-demand) mobility queries for efficient multi-modal route planning that are time-dependent as well as sensitive to aperiodic incidents and traffic prediction forecasts. This paper focuses on the cloud-based (backend) services of the MOVESMART platform.

关键词： Sustainable mobility Renewable mobility Multimodal route planning Crowdsourcing Vehicle sharing Algorithm engineering

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Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

arXiv

引用

arXiv 2018年

作者： Cheplygina, Veronika De Bruijne, Marleen Pluim, Josien P.W. Biomedical Imaging Group Rotterdam Depts. Radiology and Medical Informatics Erasmus Medical Center Rotterdam Medical Image Analysis Dept. Biomedical Engineering Eindhoven University of Technology Eindhoven Image Sciences Institute University Medical Center Utrecht Utrecht Image Section Dept. Computer Science University of Copenhagen Copenhagen Denmark

Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods that can learn with less/other types of supervision, have been proposed. We give an overview of semi-supervised, multiple instance, and transfer learning in medical imaging, both in diagnosis or segmentation tasks. We also discuss connections between these learning scenarios, and opportunities for future research. Copyright © 2018, The Authors. All rights reserved.

关键词： Medical imaging

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