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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

Minimum information and guidelines for reporting a Multiplexed Assay of Variant Effect

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arXiv 2023年

作者： Claussnitzer, Melina Parikh, Victoria N. Wagner, Alex H. Arbesfeld, Jeremy A. Bult, Carol J. Firth, Helen V. Muffley, Lara A. Ba, Alex N. Nguyen Riehle, Kevin Roth, Frederick P. Tabet, Daniel Bolognesi, Benedetta Glazer, Andrew M. Rubin, Alan F. The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard CambridgeMA02142 United States Center for Genomic Medicine Massachusetts General Hospital Harvard Medical School CambridgeMA02142 United States Stanford Center for Inherited Cardiovascular Disease Stanford University School of Medicine StanfordCA94305 United States The Steve and Cindy Rasmussen Institute for Genomic Medicine Nationwide Children's Hospital ColumbusOH43215 United States Department of Pediatrics The Ohio State University College of Medicine ColumbusOH43210 United States Department of Biomedical Informatics The Ohio State University ColumbusOH43210 United States The Jackson Laboratory Bar HarborME04609 United States Wellcome Sanger Institute Hinxton Cambridge United Kingdom Dept of Medical Genetics Cambridge University Hospitals NHS Trust Cambridge United Kingdom Department of Genome Sciences University of Washington SeattleWA98105 United States Department of Biology University of Toronto at Mississauga MississaugaON Canada Department of Molecular and Human Genetics Baylor College of Medicine HoustonTX77030 United States Donnelly Centre University of Toronto TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Lunenfeld-Tanenbaum Research Institute Sinai Health TorontoON Canada The Barcelona Institute of Science and Technology Barcelona Spain Vanderbilt University Medical Center NashvilleTN37232 United States Bioinformatics Division WEHI ParkvilleVIC Australia Department of Medical Biology University of Melbourne ParkvilleVIC Australia

Multiplexed Assays of Variant Effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines has led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs. © 2023, CC BY.

关键词： Genome

The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

Dependency or span, end-to-end uniform semantic role labeling

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arXiv 2019年

作者： Li, Zuchao He, Shexia Zhao, Hai Zhang, Yiqing Zhang, Zhuosheng Zhou, Xi Zhou, Xiang Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China CloudWalk Technology Shanghai China

Semantic role labeling (SRL) aims to discover the predicate-argument structure of a sentence. End-to-end SRL without syntactic input has received great attention. However, most of them focus on either span-based or dependency-based semantic representation form and only show specific model optimization respectively. Meanwhile, handling these two SRL tasks uniformly was less successful. This paper presents an end-to-end model for both dependency and span SRL with a unified argument representation to deal with two different types of argument annotations in a uniform fashion. Furthermore, we jointly predict all predicates and arguments, especially including long-term ignored predicate identification subtask. Our single model achieves new state-of-the-art results on both span (CoNLL 2005, 2012) and dependency (CoNLL 2008, 2009) SRL benchmarks. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

Seq2seq dependency parsing 27

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Seq2seq dependency parsing

27th International Conference on Computational Linguistics, COLING 2018

作者： Li, Zuchao Cai, Jiaxun He, Shexia Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781948087506

This paper presents a sequence to sequence (seq2seq) dependency parser by directly predicting the relative position of head for each given word, which therefore results in a truly end-to-end seq2seq dependency parser for the first time. Enjoying the advantage of seq2seq modeling, we enrich a series of embedding enhancement, including firstly introduced subword and node2vec augmentation. Meanwhile, we propose a beam search decoder with tree constraint and sub-root decomposition over the sequence to furthermore enhance our seq2seq parser. Our parser is evaluated on benchmark treebanks, being on par with the state-of-the-art parsers by achieving 94.11% UAS on PTB and 88.78% UAS on CTB, respectively. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

Challenges of Artificial Intelligence in Space Medicine

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空间科学与技术（英文） 2023年第2期3卷 141-147页

作者： Ethan Waisberg Joshua Ong Phani Paladugu Sharif Amit Kamran Nasif Zaman Andrew G.Lee Alireza Tavakkoli University College Dublin School of Medicine BelfieldDublinIreland Michigan Medicine University of MichiganAnn ArborMichiganUSA Brigham and Women's Hospital Harvard Medical SchoolBostonMassachusettsUSA Human-Machine Perception Laboratory Department of Computer Science and EngineeringUniversity of NevadaRenoRenoNevadaUSA Center for Space Medicine Baylor College of MedicineHoustonTexasUSA Department of Ophthalmology Blanton Eye InstituteHouston Methodist HospitalHoustonTexasUSA

The human body undergoes many changes during long-duration spaceflight including musculoskeletal,visual,and behavioral *** of these microgravity-induced effects serve as potential barriers to future exploration *** advent of artificial intelligence(AI)in medicine has progressed rapidly and has many promising applications for maintaining and monitoring astronaut health during ***,the austere environment and unique nature of spaceflight present with challenges in successfully training and deploying successful systems for upholding astronaut health and mission *** this article,the dynamic barriers facing AI development in space medicine are *** diverse challenges range from limited astronaut data for algorithm training to ethical/legal considerations in deploying automated diagnostic systems in the setting of the medically limited space *** to address these challenges is then discussed and future directions for this emerging field of research.

关键词： intelligence medicine space artificial challenges

A conceptual framework of cognitive-affective theory of mind: towards a precision identification of mental disorders

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npj Mental Health Research 2023年第1期2卷 12页

作者： Zhou, Peng Ma, Huimin Zou, Bochao Zhang, Xiaowen Zhao, Shuyan Lin, Yuxin Wang, Yidong Feng, Lei Wang, Gang School of International Studies Zhejiang University Hangzhou China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Foreign Languages and Literatures Tsinghua University Beijing China Department of Electronic Engineering Tsinghua University Beijing China National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Advanced Innovation Center for Human Brain Protection Capital Medical University Beijing China

To explore the minds of others, which is traditionally referred to as Theory of Mind (ToM), is perhaps the most fundamental ability of humans as social beings. Impairments in ToM could lead to difficulties or even deficits in social interaction. The present study focuses on two core components of ToM, the ability to infer others’ beliefs and the ability to infer others’ emotions, which we refer to as cognitive and affective ToM respectively. Charting both typical and atypical trajectories underlying the cognitive-affective ToM promises to shed light on the precision identification of mental disorders, such as depressive disorders (DD) and autism spectrum disorder (ASD). However, most prior studies failed to capture the underlying processes involved in the cognitive-affective ToM in a fine-grained manner. To address this problem, we propose an innovative conceptual framework, referred to as visual theory of mind (V-ToM), by constructing visual scenes with emotional and cognitive meanings and by depicting explicitly a four-stage process of how humans make inferences about the beliefs and emotions of others. Through recording individuals’ eye movements while looking at the visual scenes, our model enables us to accurately measure each stage involved in the computation of cognitive-affective ToM, thereby allowing us to infer about potential difficulties that might occur in each stage. Our model is based on a large sample size (n > 700) and a novel audio-visual paradigm using visual scenes containing cognitive-emotional meanings. Here we report the obtained differential features among healthy controls, DD and ASD individuals that overcome the subjectivity of conventional questionnaire-based assessment, and therefore could serve as valuable references for mental health applications based on AI-aided digital medicine. © The Author(s) 2023.

关键词： human behaviour Diagnostic markers Psychiatric disorders

Joint learning of POS and dependencies for multilingual universal dependency parsing

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Joint learning of POS and dependencies for multilingual univ...

2018 SIGNLL Conference on Computational Natural Language Learning, CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, CoNLL 2018

作者： Li, Zuchao He, Shexia Zhang, Zhuosheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781948087827

This paper describes the system of team LeisureX in the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system predicts the part-of-speech tag and dependency tree jointly. For the basic tasks, including tokenization, lemmatization and morphology prediction, we employ the official baseline model (UDPipe). To train the low-resource languages, we adopt a sampling method based on other rich-resource languages. Our system achieves a macro-average of 68.31% LAS F1 score, with an improvement of 2.51% compared with the UDPipe. © 2018 Association for Computational Linguistics

关键词： Computational linguistics

Improving marker-based tracking for augmented reality in underwater environments

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Improving marker-based tracking for augmented reality in und...

2018 Eurographics Workshop on Graphics and Cultural Heritage, GCH 2018

作者： Cejka, Jan Marek, U. Agrafiotis, Panagiotis Skarlatos, Dimitrios Bruno, Fabio Liarokapis, Fotis Human Computer Interaction Laboratory Faculty of Informatics Masaryk University Czech Republic Photogrammetric Vision Laboratory Department of Civil Engineering and Geomatics Cyprus University of Technology Cyprus Cyprus Research S.r.l. - University of Calabria Italy

ISBN: (纸本)9783038680574

Underwater sites are a challenging environment for augmented reality. Images taken under water are degraded in several ways, most importantly they are affected by unbalanced colors due to uneven absorption of light in each color channel, blurring and desaturation caused by turbidity, or noise due to the presence of larger organisms, plants, or bubbles. In this paper, we introduce a new method based on white balancing that enhances underwater images to improve the results of detection of markers. We compare this method with several image enhancement methods, and evaluate their performance when applied to the problem of detecting markers under water. Our results show that our method improves the detection in underwater environments while keeping the computation time low. © 2018 GCH 2018 - Eurographics Workshop on Graphics and Cultural Heritage. All rights reserved.

关键词： Augmented reality