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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2021年 12856 LNCS卷 v页

作者： Hernández-Leo, Davinia Hishiyama, Reiko Zurita, Gustavo Weyers, Benjamin Nolte, Alexander Ogata, Hiroaki Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Department of Management Control and Information Systems University of Chile Santiago Chile Institute of Computer Science University of Tartu Tartu Estonia School of Creative Science Waseda University Tokyo Japan Human-Computer Interaction Group University of Trier Trier Germany Academic Center for Computing and Media Studies Kyoto University Kyoto Japan

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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Unsupervised Person Re-Identification with Iterative Self-Supervised Domain Adaptation

Unsupervised Person Re-Identification with Iterative Self-Su...

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IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops

作者： Haotian Tang Yiru Zhao Hongtao Lu Key Lab of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

In real applications, person re-identification (re-id) is an inherently domain adaptive computer vision task which often requires the model trained on a group of people to perform well on an unlabeled dataset consisting of another group of pedestrians without supervised fine-tuning. Furthermore, there are typically a large number of classes (people) with small number of samples belonging to each class. Based on the characteristics of person re-id and general assumptions related to domain adaptation, we put forward a novel algorithm for cross-dataset person re-id. Our idea is simple yet effective: first, we preprocess the source dataset with style transfer GAN and train a baseline on it in a supervised learning manner, then we assign pseudo labels to unlabeled samples in target dataset based on the model trained on labeled source dataset;finally, we train on the target dataset with pseudo labels in traditional supervised learning manner. We adopt the idea of co-training in the training process to make the pseudo labels more reliable. We show the superiority of our model over all state-of-the-art methods through extensive experiments.

关键词： Persons Iterative Dataset Pseudo Semi-supervised Learning Domain Pedestrians

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Experiencing an Invisible World War i Battlefield Through Narrative-Driven Redirected Walking in Virtual Reality 25

Experiencing an Invisible World War i Battlefield Through Na...

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25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018

作者： Yu, Run Duer, Zachary Ogle, Todd Bowman, Doug A. Tucker, Thomas Hicks, David Choi, Dongsoo Bush, Zach Ngo, Huy Nguyen, Phat Liu, Xindi Virginia Tech Center for Human-Computer Interaction BlacksburgVA United States Department of Computer Science United States School of Visual Arts United States University Libraries United States School of Education United States

ISBN: (纸本)9781538633656

Redirected walking techniques have the potential to provide natural locomotion while users experience large virtual environments. However, when using redirected walking in small physical workspaces, disruptive overt resets are often required. We describe the design of an educational virtual reality experience in which users physically walk through virtual tunnels representative of the World War I battle of Vauquois. Walking in only a 15-by 5-foot tracked space, users are redirected through subtle, narrative-driven resets to walk through a tunnel nearly 50 feet in length. This work contributes approaches and lessons that can be used to provide a seamless and natural virtual reality walking experience in highly constrained physical spaces. © 2018 IEEE.

关键词： Virtual reality

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Natural Language Processing Markers for Psychosis and Other Psychiatric Disorders: Emerging Themes and Research Agenda From a Cross-Linguistic Workshop

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SCHIZOPHRENIA BULLETIN 2023年第SUPP2期49卷 S86-S92页

作者： Corona Hernandez, Hugo Corcoran, Cheryl Achim, Amelie M. de Boer, Janna N. Boerma, Tessel Brederoo, Sanne G. Cecchi, Guillermo A. Ciampelli, Silvia Elvevag, Brita Fusaroli, Riccardo Giordano, Silvia Hauglid, Mathias van Hessen, Arjan Hinzen, Wolfram Homan, Philipp de Kloet, Sybren F. Koops, Sanne Kuperberg, Gina R. Maheshwari, Kritika Mota, Natalia B. Parola, Alberto Rocca, Roberta Sommer, Iris E. C. Truong, Khiet Voppel, Alban E. van Vugt, Marieke Wijnen, Frank Palaniyappan, Lena Department of Biomedical Sciences of Cells and Systems University Medical Center Groningen University of Groningen Groningen Netherlands Department of Psychiatry Icahn School of Medicine at Mount Sinai NY USA Département de Psychiatrie et Neurosciences VITAM Centre de Recherche en Santé Durable Cervo Brain Research Centre Université Laval Québec Canada Department of Psychiatry University Medical Center Utrecht Utrecht University Utrecht Netherlands Department of Languages Literature and Communication Institute for Language Sciences Utrecht University Utrecht Netherlands University Center of Psychiatry University Medical Center Groningen Groningen Netherlands IBM T. J. Watson Research Center Yorktown Heights NY USA Department of Clinical Medicine University of Tromsø—the Arctic University of Norway Tromsø Norway Department of Linguistics Cognitive Science and Semiotics School of Communication and Culture Aarhus University Aarhus Denmark Department of Culture Interacting Minds Center Cognition and Computation Communication School of Culture and Society Aarhus University Aarhus Denmark Linguistic Data Consortium University of Pennsylvania PA USA Department of Innovative Technologies University of Applied Sciences and Arts of Southern Switzerland Lugano Switzerland Faculty of Law University of Tromsø—the Arctic University of Norway Tromsø Norway Department of Human Media Interaction University of Twente Enschede Netherlands Institució Catalana de Recerca i Estudis Avançats (ICREA) Universitat Pompeu Fabra Barcelona Spain Department of Psychiatry Psychiatric Hospital of the University of Zurich Psychotherapy and Psychosomatics Zurich Switzerland Neurocast BV Amsterdam Netherlands Department of Psychology Tufts University Medford MA USA Department of Psychiatry Harvard Medical School Boston MA USA The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School Boston MA USA Department of Genetics U

This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis. Of note, a CDSS is meant to enhance decision-making by clinicians by providing additional relevant information primarily through software (although CDSSs are not without risks). In psychiatry, a field that relies on subjective clinical ratings that condense rich temporal behavioral information, the inclusion of computational quantitative NLP markers can plausibly lead to operationalized decision models in place of idiosyncratic ones, although ethical issues must always be paramount.

关键词： speech technology implementation digital markers psychiatric practice pathophysiology

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Climate, biogeography, and human resilience in the demographic history of the Canary Islands during the Amazigh period

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Scientific Reports 2025年第1期15卷 1-17页

作者： Santana, Jonathan del Pino Curbelo, Miguel Iriarte, Eneko Morales, Jacob Caro, José L. Fregel, Rosa Hagenblad, Jenny García González, Rebeca Rodríguez Rodríguez, Amelia Tarha Research Group Department of Historical Sciences Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Spain Laboratory of Human Evolution-IsoTOPIK Lab Department of History Universidad de Burgos Burgos Spain Department of Languages and Computer Science University of Málaga Málaga Spain Evolution Paleogenomics and Population Genetics Group Department of Biochemistry Microbiology Cell Biology and Genetics Universidad de La Laguna Santa Cruz de Tenerife San Cristóbal de La Laguna Spain IFM Biology Linköping University Linköping Sweden

This study explores the dynamic interplay between biogeography, climate variability, and human agency in shaping the population trajectories of Amazigh communities in the Canary Islands (1st to fifteenth centuries cal CE). Using radiocarbon dating as a proxy for population size, this research suggests potential links between demographic trends and environmental factors, highlighting how climatic phases influence agricultural productivity and settlement patterns. Favorable conditions during the Roman Warm Period (RWP) facilitated population expansion, whereas climatic stress during positive phases of the North Atlantic oscillation (NAO) (700–800 cal CE) led to significant demographic declines, particularly on smaller and more arid islands. Larger and ecologically more diverse islands, such as Gran Canaria and Tenerife, showed resilience due to their ecological diversity, agricultural innovations, and food security strategies, which supported sustained growth even during challenging periods such as the Early Medieval Climate Anomaly (MCA, 800–1150 cal CE). From 1150 to 1350 cal CE, cooler sea surface temperatures and a prevailing negative NAO phase increased marine productivity, enabling demographic recovery across islands. However, the arrival of Europeans in the fourteenth–fifteenth centuries introduced external disruptions, including slave raids, novel pathogens, and land seizures, leading to societal collapse. Overall, this study highlights the critical role of environmental diversity and agricultural adaptability in supporting human populations through climatic change and offers valuable perspectives on the relationships among climate, biogeography and human societies. © The Author(s) 2025.

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Structured word embedding for low memory neural network language model 19

Structured word embedding for low memory neural network lang...

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19th Annual Conference of the International Speech Communication, INTERSPEECH 2018

作者： Shi, Kaiyu Yu, Kai Key Lab of Shanghai Education Commission Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Neural network language model (NN LM), such as long short term memory (LSTM) LM, has been increasingly popular due to its promising performance. However, the model size of an uncompressed NN LM is still too large to be used in embedded or portable devices. The dominant part of memory consumption of NN LM is the word embedding matrix. Directly compressing the word embedding matrix usually leads to performance degradation. In this paper, a product quantization based structured embedding approach is proposed to significantly reduce memory consumption of word embeddings without hurting LM performance. Here, each word embedding vector is cut into partial embedding vectors which are then quantized separately. Word embedding matrix can then be represented by an index vector and a code-book tensor of the quantized partial embedding vectors. Experiments show that the proposed approach can achieve 10 to 20 times embedding parameter reduction rate with negligible performance loss. © 2018 International Speech Communication Association. All rights reserved.

关键词： Embeddings

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Roman Abramovich and his principles of management

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Culture, Sport, Society 2020年第9期23卷 1546-1559页

作者： Sergey Altukov Boris Morozov Hongxin Li John Nauright Sport Management Centre of Lomonosov Moscow State University Moscow Russia Department of Business and Computer Science Lock Haven University of Pennsylvania Pennsylvania United States International Sport and Event Management (iSEM) Lab at the University of North Texas Texas United States Stephen Poorman College of Business and Human Services Lock Haven University of Pennsylvania Pennsylvania United States

Almost one in three of the 92 Premier and Football League clubs are now substantially owned and incorporated internationally, including in offshore tax havens. Such ownership structure leads to the English football leagues being accused of allowing ownership structures of clubs that could be used for tax avoidance. What is ignored is the analysis of owners’ management strategies and tactics. This articles uses the example of Roman Abromovish and his Chelsea F.C. for analysis of application of the classic and neoclassic principles of management. It identifies the relative consistency and relevancy of traditional managerial principles in increasingly cross-sectoral and transnational enterprises such as Premier League Football Clubs.

关键词： Roman Abramovich Chelsea FC International Sports Management Management Principles

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Revisiting metric learning for few-shot image classification

arXiv

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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

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Concurrent parsing of constituency and dependency

arXiv

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arXiv 2019年

作者： Zhou, Junru Zhang, Shuailiang Zhao, Hai Department of Computer Science and Engineering Key Lab of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Constituent and dependency representation for syntactic structure share a lot of linguistic and computational characteristics, this paper thus makes the first attempt by introducing a new model that is capable of parsing constituent and dependency at the same time, so that lets either of the parsers enhance each other. Especially, we evaluate the effect of different shared network components and empirically verify that dependency parsing may be much more beneficial from constituent parsing structure. The proposed parser achieves new stateof-the-art performance for both parsing tasks, constituent and dependency on PTB and CTB benchmarks. Copyright © 2019, The Authors. All rights reserved.

关键词： Syntactics

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