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An incremental turn-taking model for task-oriented dialog systems

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arXiv 2019年

作者： Coman, Andrei C. Yoshino, Koichiro Murase, Yukitoshi Nakamura, Satoshi Riccardi, Giuseppe Signals and Interactive Systems Lab Department of Information Engineering and Computer Science University of Trento Italy Augmented Human Communication Laboratory Graduate School of Information Science Nara Institute of Science and Technology Japan

In a human-machine dialog scenario, deciding the appropriate time for the machine to take the turn is an open research problem. In contrast, humans engaged in conversations are able to timely decide when to interrupt the speaker for competitive or non-competitive reasons. In state-of-the-art turn-by-turn dialog systems the decision on the next dialog action is taken at the end of the utterance. In this paper, we propose a token-by-token prediction of the dialog state from incremental transcriptions of the user utterance. To identify the point of maximal understanding in an ongoing utterance, we a) implement an incremental Dialog State Tracker which is updated on a token basis (iDST) b) re-label the Dialog State Tracking Challenge 2 (DSTC2) dataset and c) adapt it to the incremental turn-taking experimental scenario. The re-labeling consists of assigning a binary value to each token in the user utterance that allows to identify the appropriate point for taking the turn. Finally, we implement an incremental Turn Taking Decider (iTTD) that is trained on these new labels for the turn-taking decision. We show that the proposed model can achieve a better performance compared to a deterministic handcrafted turn-taking algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

Older adults’ reasons to participate in digital skills learning: An interdisciplinary, multiple case study from Austria, Finland, and Germany

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Studies in Adult Education 2023年第1期55卷

作者： Kaisa Pihlainen Anja Ehlers Rebekka Rohner Katerina Cerna Eija Kärnä Moritz Hess Lisa Hengl Lotta Aavikko Susanne Frewer-Graumann Vera Gallistl Claudia Müller a School of Educational Sciences and Psychology University of Eastern Finland Joensuu Finland b Institute of Gerontology at Technical University Dortmund Research Association for Gerontology Technical University Dortmund Dortmund Germany c Department of General Health Studies Karl Landsteiner University Krems an der Donau Austria d Division of Human-Computer Interaction University of Gothenburg Gothenburg Sweden e Hochschule Niederrhein Krefeld Germany f FOM University of Applied Science Münster Germany g Institute for Information Systems University of Siegen Siegen Germany

The rapid development of digital technologies and their increasing application in many areas of everyday life challenge all citizens to continuously learn digital skills. This also applies to older adults, among whom digital literacy is on average less well-developed than among younger adults. This article investigates why retired older adults participate in opportunities to learn digital skills. Multiple case design with both qualitative and quantitative methods was used to include the views of older adults from Austria, Finland, and Germany. The results of this interdisciplinary study indicated individual, social and technical reasons for their participation in digital skills training. Practical implications and recommendations for future studies are suggested.

关键词： Older adults participation digital skills multiple case study

Identifying latent disease factors differently expressed in patient subgroups using group factor analysis

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arXiv 2024年

作者： Ferreira, Fabio S. Ashburner, John Bouzigues, Arabella Suksasilp, Chatrin Russell, Lucy L. Foster, Phoebe H. Ferry-Bolder, Eve van Swieten, John C. Jiskoot, Lize C. Seelaar, Harro Sanchez-Valle, Raquel Laforce, Robert Graff, Caroline Galimberti, Daniela Vandenberghe, Rik de Mendonça, Alexandre Tiraboschi, Pietro Santana, Isabel Gerhard, Alexander Levin, Johannes Sorbi, Sandro Otto, Markus Pasquier, Florence Ducharme, Simon Butler, Chris R. Le Ber, Isabelle Finger, Elizabeth Tartaglia, Maria Carmela Masellis, Mario Rowe, James B. Synofzik, Matthis Moreno, Fermin Borroni, Barbara Kaski, Samuel Rohrer, Jonathan D. Mourão-Miranda, Janaina Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Max Planck University College London Centre for Computational Psychiatry and Ageing Research University College London United Kingdom Wellcome Centre for Human Neuroimaging University College London London United Kingdom Dementia Research Centre Department of Neurodegenerative Disease UCL Queen Square Institute of Neurology London United Kingdom Institute of Cognitive Neuroscience University College London London United Kingdom Department of Neurology Erasmus Medical Centre Rotterdam Netherlands Alzheimer's Disease and Other Cognitive Disorders Unit Neurology Service Hospital Clinic Institut d'Investigacions Biomediques August Pi I Sunyer University of Barcelona Barcelona Spain Clinique Interdisciplinaire de Memoire Departement des Sciences Neurologiques CHU de Quebec Faculte de Medecine Universite Laval QC Canada Department of Neurobiology Care Sciences and Society Center for Alzheimer Research Division of Neurogeriatrics Bioclinicum Karolinska Institutet Solna Sweden Fondazione Ca Granda IRCCS Ospedale Policlinico Milan Italy Laboratory for Cognitive Neurology Department of Neurosciences KU Leuven Leuven Belgium Faculty of Medicine University of Lisbon Lisbon Portugal Fondazione IRCCS Istituto Neurologico Carlo Besta Milano Italy Neurology Service Faculty of Medicine University of Coimbra Coimbra Portugal Division of Psychology Communication and Human Neuroscience Wolfson Molecular Imaging Centre University of Manchester Manchester United Kingdom Department of Neurology Ludwig-Maximilians Universitat Munchen Munich Germany Department of Neurofarba University of Florence Italy Department of Neurology University of Ulm Germany Univ Lille France Douglas Mental Health University Institute Department of Psychiatry McGill University Montreal Canada Nuffield Department of Clinical Neurosciences Medical Sciences Division University of

In this study, we propose a novel approach to uncover subgroup-specific and subgroup-common latent factors addressing the challenges posed by the heterogeneity of neurological and mental disorders, which hinder disease understanding, treatment development, and outcome prediction. The proposed approach, sparse Group Factor Analysis (GFA) with regularised horseshoe priors, was implemented with probabilistic programming and can uncover associations (or latent factors) among multiple data modalities differentially expressed in sample subgroups. Synthetic data experiments showed the robustness of our sparse GFA by correctly inferring latent factors and model parameters. When applied to the Genetic Frontotemporal Dementia Initiative (GENFI) dataset, which comprises patients with frontotemporal dementia (FTD) with genetically defined subgroups, the sparse GFA identified latent disease factors differentially expressed across the subgroups, distinguishing between "subgroup-specific" latent factors within homogeneous groups and "subgroup common" latent factors shared across subgroups. The latent disease factors captured associations between brain structure and non-imaging variables (i.e., questionnaires assessing behaviour and disease severity, neuropsychological tasks and medical assessments of disease severity) across the different genetic subgroups, offering insights into disease profiles. Importantly, two latent factors were more pronounced in the two more homogeneous FTD patient subgroups (progranulin (GRN) and microtubule-associated protein tau (MAPT) mutation), showcasing the method's ability to reveal subgroup-specific characteristics. These findings underscore the potential of sparse GFA for integrating multiple data modalities and identifying interpretable latent disease factors that can improve the characterization and stratification of patients with neurological and mental health disorders. © 2024, CC BY-NC-ND.

关键词： Brain mapping

SenseMag: Enabling low-cost traffic monitoring using non-invasive magnetic sensing

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arXiv 2021年

作者： Wang, Kafeng Xiong, Haoyi Zhang, Jie Chen, Hongyang Dou, Dejing Xu, Cheng-Zhong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Shenzhen China Big Data Lab Baidu Inc. Haidian Beijing China Key Laboratory of High Confidence Software Technologies Peking University Haidian Beijing China Research Center for Intelligent Network Zhejiang Lab Zhejiang Hangzhou China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau Tapia Macau China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

The operation and management of intelligent transportation systems (ITS), such as traffic monitoring, relies on real-time data aggregation of vehicular traffic information, including vehicular types (e.g., cars, trucks, and buses), in the critical roads and highways. While traditional approaches based on vehicular-embedded GPS sensors or camera networks would either invade drivers’ privacy or require high deployment cost, this paper introduces a low-cost method, namely SenseMag, to recognize the vehicular type using a pair of non-invasive magnetic sensors deployed on the straight road section. SenseMag filters out noises and segments received magnetic signals by the exact time points that the vehicle arrives or departs from every sensor node. Further, SenseMag adopts a hierarchical recognition model to first estimate the speed/velocity, then identify the length of vehicle using the predicted speed, sampling cycles, and the distance between the sensor nodes. With the vehicle length identified and the temporal/spectral features extracted from the magnetic signals, SenseMag classify the types of vehicles accordingly. Some semi-automated learning techniques have been adopted for the design of filters, features, and the choice of hyper-parameters. Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i.e., 7 types by SenseMag versus 4 types by the existing work in comparisons). To be specific, our field experiment results validate that SenseMag is with at least 90% vehicle type classification accuracy and less than 5% vehicle length classification error. Copyright © 2021, The Authors. All rights reserved.

关键词： Sensor nodes

Outcomes from a Workshop on a National Center for Quantum Education

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arXiv 2024年

作者： Barnes, Edwin Bennett, Michael B. Boltasseva, Alexandra Borish, Victoria Brown, Bennett Carr, Lincoln D. Ceballos, Russell R. Dukes, Faith Easton, Emily W. Economou, Sophia E. Edwards, E.E. Finkelstein, Noah D. Fracchiolla, C. Franklin, Diana Freericks, J.K. Goss, Valerie Hannum, Mark Holincheck, Nancy Kelly, Angela M. Lanes, Olivia Lewandowski, H.J. Matsler, Karen Jo Mercurio, Emily Montaño, Inès Murdock, Maajida Peltz, Kiera Perron, Justin K. Richardson, Christopher J.K. Rosenberg, Jessica L. Ross, Richard S. Ryu, Minjung Samuel, Raymond E. Schrode, Nicole Schwamberger, Susan Searles, Thomas A. Singh, Chandralekha Tingle, Alexandra Zwickl, Benjamin M. Department of Physics Virginia Tech Center for Quantum Information Science and Engineering Virginia Tech 850 West Campus Drive BlacksburgVA24061 United States Q-SEnSE NSF QLCI University of Colorado 440 UCB BoulderCO80301 United States Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University 610 Purdue Mall West LafayetteIN47907 United States Quantum Science Center A National Quantum Information Science Research Center of The U.S. Department of Energy Oak Ridge National Laboratory 1 Bethel Valley Road Oak RidgeTN37830 United States JILA National Institute of Standards and Technology the University of Colorado BoulderCO80309 United States Department of Physics University of Colorado 390 UCB BoulderCO80309 United States QuSTEAM 510 Ronalds St. Iowa CityIA52245 United States Quantum Engineering Program Department of Physics Colorado School of Mines 1523 Illinois St. GoldenCO80401 United States QuSTEAM Non-Profit Wilbur Wright College City Colleges of Chicago 4300 N Narragansett Ave ChicagoIL60634 United States Government and Community Relations Office - K-12 Programs Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA United States Chicago Quantum Exchange 5235 S. Harper Court ChicagoIL60615 United States Department of Physics Virginia Tech 850 West Campus Drive BlacksburgVA24060 United States Department of Electrical and Computer Engineering Duke Quantum Center Duke University Duke University 130 Hudson Hall DurhamNC27708 United States American Physical Society 555 12th St. NW WashingtonDC20004 United States Computer Science University of Chicago 5730 S. Ellis Ave. ChicagoIL60637 United States Department of Physics Georgetown University 552 Reiss Science Building WashingtonDC20057 United States Chemistry Physics and Engineering Studies Chicago State University 9501 S. King Drive WSC 309 ChicagoIL606

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science. © 2024, CC BY.

关键词： Students

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Editorial

Procedia computer science 2018年 154卷 1-3页

作者： Kountchev, P.R. Nakamatsu, P.K. Sung, P.D.W.-T. Zhou, P.D.X.-W. Ari, P.A. Faculty of Telecommunications Department of Radio Communications and Video Technologies Technical University of Sofia Bulgaria School of Human Science and Environment University of Hyogo Japan Electrical Engineering National Chin-Yi University of Technology Taiwan Beijing Key Laboratory of Information Service Engineering Beijing China Department of Computer and Information Sciences Faculty of Computer and Information Sciences Sojo University Japan

Misspecification-robust likelihood-free inference in high dimensions

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arXiv 2020年

作者： Thomas, Owen Sá-Leão, Raquel de Lencastre, Hermínia Kaski, Samuel Corander, Jukka Pesonen, Henri Oslo Centre for Biostatistics and Epidemiology University of Oslo Oslo Norway Laboratory of Molecular Microbiology of Human Pathogens Instituto de Tecnologia Química e Biológica António Xavier Universidade Nova de Lisboa Oeiras Portugal Laboratory of Molecular Genetics Instituto de Tecnologia Química e Biológica António Xavier Universidade Nova de Lisboa Oeiras Portugal Laboratory of Microbiology and Infectious Diseases The Rockefeller University New York United States Helsinki Institute of Information Technology Department of Computer Science Aalto University Finland Department of Computer Science University of Manchester United Kingdom Helsinki Institute of Information Technology Department of Mathematics and Statistics University of Helsinki Finland Parasites and Microbes Wellcome Sanger Institute United Kingdom Oslo Centre for Biostatistics and Epidemiology Oslo University Hospital Oslo Norway

Likelihood-free inference for simulator-based statistical models has developed rapidly from its infancy to a useful tool for practitioners. However, models with more than a handful of parameters still generally remain a challenge for the Approximate Bayesian Computation (ABC) based inference. To advance the possibilities for performing likelihood-free inference in higher dimensional parameter spaces, we introduce an extension of the popular Bayesian optimisation based approach to approximate discrepancy functions in a probabilistic manner which lends itself to an efficient exploration of the parameter space. Our approach achieves computational scalability for higher dimensional parameter spaces by using separate acquisition functions and discrepancies for subsets of the parameters. The efficient additive acquisition structure is combined with exponentiated loss-likelihood to provide a misspecification-robust characterisation of posterior distributions for subsets of model parameters. The method successfully performs computationally efficient inference in a moderately sized parameter space and compares favourably to existing modularised ABC methods. We further illustrate the potential of this approach by fitting a bacterial transmission dynamics model to a real data set, which provides biologically coherent results on strain competition in a 30-dimensional parameter space. Copyright © 2020, The Authors. All rights reserved.

关键词： Digital elevation model

Revisiting metric learning for few-shot image classification

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arXiv 2019年

作者： Li, Xiaomeng Yu, Lequan Fu, Chi-Wing Fang, Meng Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Tencent AI Lab United States

The goal of few-shot learning is to recognize new visual concepts with just a few amount of labeled samples in each class. Recent effective metric-based few-shot approaches employ neural networks to learn a feature similarity comparison between query and support examples. However, the importance of feature embedding, i.e., exploring the relationship among training samples, is neglected. In this work, we present a simple yet powerful baseline for few-shot classification by emphasizing the importance of feature embedding. Specifically, we revisit the classical triplet network from deep metric learning, and extend it into a deep K-tuplet network for few-shot learning, utilizing the relationship among the input samples to learn a general representation learning via episode-training. Once trained, our network is able to extract discriminative features for unseen novel categories and can be seamlessly incorporated with a non-linear distance metric function to facilitate the few-shot classification. Our result on the miniImageNet benchmark outperforms other metric-based few-shot classification methods. More importantly, when evaluated on completely different datasets (Caltech-101, CUB-200, Stanford Dogs and Cars) using the model trained with miniImageNet, our method significantly outperforms prior methods, demonstrating its superior capability to generalize to unseen classes. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

Genomic Surveillance of SARS-CoV-2 in Taiwan: A Perspective on Evolutionary Data Interpretation and Sequencing Issues

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Biomedical journal 2024年 100820页

作者： Yu-Nong Gong Nai-Yu Kuo Ting-Syuan Yeh Shin-Ru Shih Guang-Wu Chen Research Center for Emerging Viral Infections College of Medicine Chang Gung University Taoyuan Taiwan International Master Degree Program for Molecular Medicine in Emerging Viral Infections College of Medicine Chang Gung University Taoyuan Taiwan Department of Laboratory Medicine Linkou Chang Gung Memorial Hospital Taoyuan Taiwan. Medical Education Department Linkou Chang Gung Memorial Hospital Taoyuan Taiwan. National Institute of Infectious Diseases and Vaccinology National Health Research Institutes Taiwan. Department of Laboratory Medicine Linkou Chang Gung Memorial Hospital Taoyuan Taiwan Department of Medical Biotechnology and Laboratory Science College of Medicine Chang Gung University Taoyuan Taiwan Research Center for Chinese Herbal Medicine Research Center for Food and Cosmetic Safety and Graduate Institute of Health Industry Technology College of Human Ecology Chang Gung University of Science and Technology Taoyuan Taiwan. Department of Computer Science and Information Engineering College of Engineering Chang Gung University Taoyuan Taiwan. Electronic address: gwchen@mail.cgu.edu.tw.

This review presents a comprehensive perspective on the genomic surveillance of SARS-CoV-2 in Taiwan, with a focus on next-generation sequencing and phylogenetic interpretation. This article aimed to explore how Taiwan has utilized genomic sequencing technologies and surveillance to monitor and mitigate the spread of COVID-19. We examined databases and sources of genomic sequences and highlighted the role of data science methodologies in the explanation and analyses of evolutionary data. This review addressed the challenges and limitations inherent in genomic surveillance, such as concerns regarding data quality and the necessity for interdisciplinary expertise for accurate data interpretation. Special attention was given to the unique challenges faced by Taiwan, including its high population density and major transit destination for international travelers. We underscored the far-reaching implications of genomic surveillance data for public health policy, particularly in influencing decisions regarding travel restrictions, vaccine administration, and public health decision-making. Studies were examined to demonstrate the effectiveness of using genomic data to implement public health measures. Future research should prioritize the integration of methodologies and technologies in evolutionary data science, particularly focusing on phylodynamic analytics. This integration is crucial to enhance the precision and applicability of genomic data. Overall, we have provided an overview of the significance of genomic surveillance in tracking SARS-CoV-2 variants globally and the pivotal role of data science methodologies in interpreting these data for effective public health interventions.

关键词： SARS-CoV-2 data science genomic surveillance next-generation sequencing phylodynamics