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arXiv 2019年

作者： Noé, Frank Tkatchenko, Alexandre Müller, Klaus-Robert Clementi, Cecilia Freie Universität Berlin Department of Mathematics and Computer Science Arnimallee 6 Berlin14195 Germany Freie Universität Berlin Department of Physics Arnimallee 6 Berlin14195 Germany Rice University Department of Chemistry HoustonTX77005 United States Rice University Center for Theoretical Biological Physics HoustonTX77005 United States Rice University Department of Physics HoustonTX77005 United States Physics and Materials Science Research Unit University of Luxembourg LuxembourgL-1511 Luxembourg Technical University Berlin Department of Computer Science Marchstr. 23 Berlin10587 Germany Max-Planck-Institut für Informatik Saarbrücken Germany Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for a machine learning revolution and have already been profoundly impacted by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, coarse-grained molecular dynamics, the extraction of free energy surfaces and kinetics and generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into machine learning structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum theory

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Lack of effects of four-week theta burst stimulation on white matter macro/microstructure in children and adolescents with autism

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NeuroImage. Clinical 2023年 37卷 103324页

作者： Hsing-Chang Ni Yi-Ping Chao Rung-Yu Tseng Chen-Te Wu Luca Cocchi Tai-Li Chou Rou-Shayn Chen Susan Shur-Fen Gau Chun-Hung Yeh Hsiang-Yuan Lin Department of Psychiatry Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan. Deparment of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Otorhinolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan. Department of Psychiatry Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Institute for Radiological Research Chang Gung University Taoyuan Taiwan. Department of Medical Imaging and Intervention Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan. Clinical Brain Networks Group QIMR Berghofer Medical Research Institute Brisbane Queensland Australia. Department of Psychology National Taiwan University Taipei Taiwan Graduate Institute of Brain and Mind Sciences College of Medicine National Taiwan University Taipei Taiwan Neurobiology and Cognitive Science Center National Taiwan University Taipei Taiwan. Department of Neurology Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan. Department of Psychiatry National Taiwan University Hospital and College of Medicine Taipei Taiwan. Institute for Radiological Research Chang Gung University Taoyuan Taiwan. Electronic address: chunhung.yeh@mail.cgu.edu.tw. Azrieli Adult Neurodevelopmental Centre Campbell Family Mental Health Research Institute Centre for Addiction and Mental Health Toronto Ontario Canada Department of Psychiatry Temerty Faculty of Medicine University of Toronto Toronto Ontario Canada.

Following the published behavioral and cognitive results of this single-blind parallel sham-controlled randomized clinical trial, the current study aimed to explore the impact of intermittent theta burst stimulation (iTBS), a variant of excitatory transcranial magnetic stimulation, over the bilateral posterior superior temporal sulci (pSTS) on white matter macro/microstructure in intellectually able children and adolescents with autism. Participants were randomized and blindly received active or sham iTBS for 4 weeks (the single-blind sham-controlled phase). Then, all participants continued to receive active iTBS for another 4 weeks (the open-label phase). The clinical results were published elsewhere. Here, we present diffusion magnetic resonance imaging data on potential changes in white matter measures after iTBS. Twenty-two participants in Active-Active group and 27 participants in Sham-Active group underwent multi-shell high angular resolution diffusion imaging (64-direction for b = 2000 & 1000 s/mm, respectively) at baseline, week 4, and week 8. With longitudinal fixel-based analysis, we found no white matter changes following iTBS from baseline to week 4 (a null treatment by time interaction and a null within-group paired comparison in the Active-Active group), nor from baseline to week 8 (null within-group paired comparisons in both Active-Active and Sham-Active groups). As for the brain-symptoms relationship, we did not find baseline white matter metrics associated with symptom changes at week 4 in either group. Our results raise the question of what the minimal cumulative stimulation dose required to induce the white matter plasticity is.

关键词： Autism Diffusion MRI Posterior superior temporal sulcus Randomized controlled trial Theta burst stimulation Transcranial magnetic stimulation

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Peri-Saccadic Remapping Accounts for Visual Stability

Peri-Saccadic Remapping Accounts for Visual Stability

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IEEE International Conference on cognitive Informatics

作者： Xiao Wang Mingsha Zhang Si Wu State Key Laboratory of Cognitive Neuroscience and Learning IDG/McGovern Institute for Brain Research Beijing Normal University Beijing China School of Electronic Engineering and Computer Science Peking University Beijing China

ISBN: (纸本)9781538633618

Our brain makes saccadic eye movements up to 3-5 times per second, which disrupt visual images on the retina, yet we perceive the world to be stable. It has been suggested that this perceptual stability is achieved via peri-saccadic remapping of neuronal receptive fields (RFs), that is, before a saccade, neurons start to respond to visual stimuli at their future RFs to be brought up by the impending eye movement. The recent experimental study further unveiled that the remapping of neuronal RF in Lateral Intraparietal Area (LIP) is an elongation process along the saccadic trajectory. Despite that peri-saccadic remapping is known to be closely associated to visual stability, a deep understanding of how it works remains largely unknown. Here, to address this question, we formulate visual stability as an optimization problem, and explore, from a computational point of view, how the response property of a LIP neuron should vary during a saccade, in order to predict the locations of visual images disrupted by the saccade. Through building a biologically plausible network and training it for the corresponding prediction task, we find that the optimal recurrent connections between LIP neurons are asymmetric, biased to the opposite direction of the saccade, such that when combined with the corollary discharge signal responsible for the saccade, a visual stimulus triggers a cortical wave in LIP propagating in the opposite direction of the saccade, manifesting the elongated remapping of neuronal RF. Our model reproduces the experimental data on peri-saccadic remapping, and demonstrates that peri-saccadic remapping accounts for visual stability.

关键词： Lips Handheld computers Visualization Radio frequency Artificial neural networks cognitive informatics

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Dora the explorer: Directed outreaching reinforcement action-selection

arXiv

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arXiv 2018年

作者： Choshen, Leshem Fox, Lior Loewenstein, Yonatan School of Computer Science and Engineering Department of Cognitive Sciences Hebrew University of Jerusalem Edmond and Lily Safra Center for Brain Sciences Hebrew University of Jerusalem Edmond and Lily Safra Center for Brain Sciences Departments of Neurobiology and Cognitive Sciences Federmann Center for the Study of Rationality Hebrew University of Jerusalem

Exploration is a fundamental aspect of Reinforcement Learning, typically implemented using stochastic action-selection. Exploration, however, can be more efficient if directed toward gaining new world knowledge. Visit-counters have been proven useful both in practice and in theory for directed exploration. However, a major limitation of counters is their locality. While there are a few model-based solutions to this shortcoming, a model-free approach is still missing. We propose E-values, a generalization of counters that can be used to evaluate the propagating exploratory value over state-action trajectories. We compare our approach to commonly used RL techniques, and show that using E-values improves learning and performance over traditional counters. We also show how our method can be implemented with function approximation to efficiently learn continuous MDPs. We demonstrate this by showing that our approach surpasses state of the art performance in the Freeway Atari 2600 game. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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Affordable on-line dialogue policy learning

Affordable on-line dialogue policy learning

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2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017

作者： Chang, Cheng Yang, Runzhe Chen, Lu Zhou, Xiang Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Eng. SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781945626838

The key to building an evolvable dialogue system in real-world scenarios is to ensure an affordable on-line dialogue policy learning, which requires the on-line learning process to be safe, efficient and economical. But in reality, due to the scarcity of real interaction data, the dialogue system usually grows slowly. Besides, the poor initial dialogue policy easily leads to bad user experience and incurs a failure of attracting users to contribute training data, so that the learning process is unsustainable. To accurately depict this, two quantitative metrics are proposed to assess safety and efficiency issues. For solving the unsustainable learning problem, we proposed a complete companion teaching framework incorporating the guidance from the human teacher. Since the human teaching is expensive, we compared various teaching schemes answering the question how and when to teach, to economically utilize teaching budget, so that make the online learning process affordable. © 2017 Association for Computational Linguistics.

关键词： Learning systems

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Agent-aware dropout DQN for safe and efficient on-line dialogue policy learning

Agent-aware dropout DQN for safe and efficient on-line dialo...

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2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017

作者： Chen, Lu Zhou, Xiang Chang, Cheng Yang, Runzhe Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Eng. SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781945626838

Hand-crafted rules and reinforcement learning (RL) are two popular choices to obtain dialogue policy. The rule-based policy is often reliable within predefined scope but not self-adaptable, whereas RL is evolvable with data but often suffers from a bad initial performance. We employ a companion learning framework to integrate the two approaches for on-line dialogue policy learning, in which a predefined rule-based policy acts as a teacher and guides a data-driven RL system by giving example actions as well as additional rewards. A novel agent-aware dropout Deep Q-Network (AAD-DQN) is proposed to address the problem of when to consult the teacher and how to learn from the teacher’s experiences. AAD-DQN, as a data-driven student policy, provides (1) two separate experience memories for student and teacher, (2) an uncertainty estimated by dropout to control the timing of consultation and learning. Simulation experiments showed that the proposed approach can significantly improve both safety and efficiency of on-line policy optimization compared to other companion learning approaches as well as supervised pre-training using static dialogue corpus. © 2017 Association for Computational Linguistics.

关键词： Reinforcement learning

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On-line dialogue policy learning with companion teaching 15

On-line dialogue policy learning with companion teaching

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15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017

作者： Chen, Lu Runzhe, Yang Cheng, Chang Ye, Zihao Zhou, Xiang Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Eng. SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781510838604

On-line dialogue policy learning is the key for building evolvable conversational agent in real world scenarios. Poor initial policy can easily lead to bad user experience and consequently fail to attract sufficient real users for policy training. We propose a novel framework, companion teaching, to include a human teacher in the on-line dialogue policy training loop to address the cold start problem. Here, dialogue policy is trained using not only user's reward but also teacher's example action as well as estimated immediate reward at turn level. Simulation experiments showed that, with a small number of human teaching dialogues, the proposed approach can effectively improve user experience at the beginning and smoothly lead to good performance with more user interaction data. © 2017 Association for Computational Linguistics.

关键词： Personnel training

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Wearable Sensors: Large‐Scale Patterning of Reactive Surfaces for Wearable and Environmentally Deployable Sensors (Adv. Mater. 28/2020)

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Advanced Materials 2020年第28期32卷

作者： Giusy Matzeu Laia Mogas‐Soldevila Wenyi Li Arin Naidu Trent H. Turner Roger Gu Patricia R. Blumeris Patrick Song Daniel G. Pascal Giulia Guidetti Meng Li Fiorenzo G. Omenetto Silklab Department of Biomedical Engineering Tufts University Medford MA 02155 USA Center for Applied Brain and Cognitive Science Tufts University Medford MA 02155 USA Department of Electrical and Computer Engineering Tufts University Medford MA 02155 USA Department of Physics Tufts University Medford MA 02155 USA Laboratory for Living Devices Tufts University Medford MA 02155 USA

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On the role of theory and modeling in neuroscience

arXiv

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arXiv 2020年

作者： Levenstein, Daniel Alvarez, Veronica A. Amarasingham, Asohan Azab, Habiba Chen, Zhe S. Gerkin, Richard C. Hasenstaub, Andrea Iyer, Ramakrishnan Jolivet, Renaud B. Marzen, Sarah Monaco, Joseph D. Prinz, Astrid A. Quraishi, Salma Santamaria, Fidel Shivkumar, Sabyasachi Singh, Matthew F. Traub, Roger Rotstein, Horacio G. Nadim, Farzan Redish, A. David Montreal Neurological Institute McGill University MontrealQC Canada Laboratory on Neurobiology of Compulsive Behaviors National Institute on Alcohol Abuse and Alcoholism NIH BethesdaMD United States Department of Neuroscience Center for Magnetic Resonance Research University of Minnesota MinneapolisMN United States School of Life Sciences Arizona State University TempeAZ United States Department of Otolaryngology - Head and Neck Surgery University of California San Francisco San FranciscoCA United States Allen Institute for Brain Science SeattleWA United States Maastricht University Maastricht Netherlands Department of Biomedical Engineering Johns Hopkins University School of Medicine BaltimoreMD United States Department of Biology University of Texas at San Antonio San AntonioTX United States Department of Psychological & Brain Sciences Department of Electrical & Systems Engineering Washington University in St. Louis St. LouisMO United States IBM T.J. Watson Research Center AI Foundations Yorktown HeightsNY United States W. M. Keck Science Department Pitzer Scripps and Claremont McKenna Colleges ClaremontCA United States Department of Biology Emory University AtlantaGA United States Department of Mathematics City College of New York United States Departments of Biology Computer Science and Psychology The Graduate Center City University of New York United States Brain and Cognitive Sciences University of Rochester RochesterNY United States Federated Department of Biological Sciences New Jersey Institute of Technology Rutgers University Institute for Brain and Neuroscience Research New Jersey Institute of Technology NewarkNJ United States Department of Neuroscience University of Minnesota MinneapolisMN United States Department of Psychiatry Neuroscience & Physiology New York University School of Medicine United States Mila - Quebec AI Institute MontrealQC Canada

In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena that cross a wide range of scales and often require consideration at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative models and theories each play a distinct role in defining and bridging levels of abstraction will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment. © 2020, CC BY.

关键词： Computation theory

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