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arXiv 2025年

作者： Lei, Mengqi Wu, Yihong Li, Siqi Zheng, Xinhu Wang, Juan Gao, Yue Du, Shaoyi BNRist THUIBCS BLBCI School of Software Tsinghua University Beijing100084 China Department of Mechanical Engineering Taiyuan University of Technology Taiyuan030024 China Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ultrasound the Second Affiliated Hospital of Xi’an Jiaotong University Xi’an China Department of Ultrasound the Second Affiliated Hospital Xi’an Jiaotong University China State Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China

Visual recognition relies on understanding both the semantics of image tokens and the complex interactions among them. Mainstream self-attention methods, while effective at modeling global pair-wise relations, fail to capture high-order associations inherent in real-world scenes and often suffer from redundant computation. Hypergraphs extend conventional graphs by modeling high-order interactions and offer a promising framework for addressing these limitations. However, existing hypergraph neural networks typically rely on static and hard hyperedge assignments, leading to excessive and redundant hyperedges with hard binary vertex memberships that overlook the continuity of visual semantics. To overcome these issues, we present Soft Hypergraph Neural Networks (SoftHGNNs), which extend the methodology of hypergraph computation, to make it truly efficient and versatile in visual recognition tasks. Our framework introduces the concept of soft hyperedges, where each vertex is associated with hyperedges via continuous participation weights rather than hard binary assignments. This dynamic and differentiable association is achieved by using the learnable hyperedge prototype. Through similarity measurements between token features and the prototype, the model generates semantically rich soft hyperedges. By using these soft hyperedges as the medium for message aggregation and dissemination, SoftHGNN models various high-order semantic relationships within visual features. To further enhance efficiency when scaling up the number of soft hyperedges, we incorporate a sparse hyperedge selection mechanism that activates only the top-k important hyperedges, along with a load-balancing regularizer to ensure adequate and balanced hyperedge utilization. Experimental results across three tasks on five datasets demonstrate that SoftHGNN efficiently captures high-order associations in visual scenes, achieving significant performance improvements. The source code of our framework is availa

关键词： computer vision

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A framework and resource for global collaboration in non-human primate neuroscience

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Current research in Neurobiology 2023年 4卷 100079页

作者： Hartig, Renée Klink, P. Christiaan Polyakova, Zlata Dehaqani, Mohammad-Reza A. Bondar, Igor Merchant, Hugo Vanduffel, Wim Roe, Anna Wang Nambu, Atsushi Thirumala, M. Shmuel, Amir Kapoor, Vishal Gothard, Katalin M. Evrard, Henry C. Basso, Michele A. Petkov, Christopher I. Mitchell, Anna S. Max Planck Institute for Biological Cybernetics Max-Planck-Ring 11 Tübingen 72076 Germany Functional and Comparative Neuroanatomy Laboratory Werner Reichardt Centre for Integrative Neuroscience Eberhard Karls University of Tübingen Otfried-Müller-Straße 25 Tübingen 72076 Germany Department of Psychiatry and Psychotherapy University Medical Center Johannes Gutenberg-University Mainz 55131 Germany Nathan S. Kline Institute for Psychiatric Research Center for Biomedical Imaging and Neuromodulation Orangeburg New York 10962 United States Department of Vision and Cognition Netherlands Institute for Neuroscience Royal Netherlands Academy of Arts and Sciences Meibergdreef 47 Amsterdam 1105 BA Netherlands Laboratory of Visual Brain Therapy Sorbonne Université Institut National de la Santé et de la Recherche Médicale Centre National de la Recherche Scientifique Institut de la Vision Paris F-75012 France Experimental Psychology Helmholtz institute Utrecht University Heidelberglaan 1 Utrecht 3584 CS Netherlands Center for Human Nature Artificial Intelligence and Neuroscience Hokkaido University Sapporo 060-0812 Japan School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran Department of Brain and Cognitive Sciences Cell Science Research Center Royan Institute for Stem Cell Biology and Technology ACECR Tehran Iran Institute of Higher Nervous Activity and Neurophysiology Russian Academy of Sciences Moscow Russian Federation Institute of Neurobiology Universidad Nacional Autónoma de México Campus Juriquilla Department of Neurophysiology Querétaro 76230 Mexico Laboratory for Neuro- and Psychophysiology Department of Neurosciences KU Leuven Medical School Leuven 3000 Belgium Leuven Brain Institute KU Leuven Leuven 3000 Belgium Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlest

As science and technology evolve, there is an increasing need for promotion of international scientific exchange. Collaborations, while offering substantial opportunities for scientists and benefit to society, also present challenges for those working with animal models, such as non-human primates (NHPs). Diversity in regulation of animal research is sometimes mistaken for the absence of common international welfare standards. Here, the ethical and regulatory protocols for 13 countries that have guidelines in place for biomedical research involving NHPs were assessed with a focus on neuroscience. Review of the variability and similarity in trans-national NHP welfare regulations extended to countries in Asia, Europe and North America. A tabulated resource was established to advance solution-oriented discussions and scientific collaborations across borders. Our aim is to better inform the public and other stakeholders. Through cooperative efforts to identify and analyze information with reference to evidence-based discussion, the proposed key ingredients may help to shape and support a more informed, open framework. This framework and resource can be expanded further for biomedical research in other countries. © 2023

关键词： Ethics Macaque Marmoset Regulations Requirements Welfare

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Large-scale video compression： recent advances and challenges

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Frontiers of computer science 2018年第5期12卷 825-839页

作者： Tao TIAN Hanli WANG Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Shanghai Engineering Research Center of Industrial Vision Perception & Intelligent Computing Shanghai 200092 China

The evolution of social network and multimedia technologies encourage more and more people to generate and upload visual information, which leads to the generation of large-scale video data. Therefore, preeminent compression technologies are highly desired to facilitate the storage and transmission of these tremendous video data for a wide variety of applications. In this paper, a systematic review of the recent advances for large-scale video compression （LSVC） is presented. Specifically, fast video coding algorithms and effective models to improve video compression efficiency are introduced in detail, since coding complexity and compression efficiency are two important factors to evaluate video coding approaches. Finally, the challenges and fu- ture research trends for LSVC are discussed.

关键词： large-scale video compression fast video coding compression efficiency

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Audio-visual model distillation using acoustic images

arXiv

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arXiv 2019年

作者： Pérez, Andres F. Sanguineti, Valentina Morerio, Pietro Murino, Vittorio Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Università degli Studi di Genova Italy Computer Science Department - Università di Verona Italy Huawei Technologies Ltd. Ireland Research Center

In this paper, we investigate how to learn rich and robust feature representations for audio classification from visual data and acoustic images, a novel audio data modality. Former models learn audio representations from raw signals or spectral data acquired by a single microphone, with remarkable results in classification and retrieval. However, such representations are not so robust towards variable environmental sound conditions. We tackle this drawback by exploiting a new multimodal labeled action recognition dataset acquired by a hybrid audio-visual sensor that provides RGB video, raw audio signals, and spatialized acoustic data, also known as acoustic images, where the visual and acoustic images are aligned in space and synchronized in time. Using this richer information, we train audio deep learning models in a teacher-student fashion. In particular, we distill knowledge into audio networks from both visual and acoustic image teachers. Our experiments suggest that the learned representations are more powerful and have better generalization capabilities than the features learned from models trained using just single-microphone audio data. Copyright © 2019, The Authors. All rights reserved.

关键词： Microphones

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A survey on recent advancements for AI enabled radiomics in neuro-oncology

arXiv

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arXiv 2019年

作者： Anwar, Syed Muhammad Altaf, Tooba Rafique, Khola RaviPrakash, Harish Mohy-Ud-Din, Hassan Bagci, Ulas Center for Research in Computer Vision University of Central Florida OrlandoFL32816 United States Department of Computer Science University of Wah Wah Pakistan Department of Software Engineering University of Engineering and Technology Taxila47050 Pakistan Lahore Pakistan

Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology. Radiomics provide assistance in diagnosis of cancer, planning of treatment strategy, and prediction of survival. Radiomics in neuro-oncology has progressed significantly in the recent past. Deep learning has outperformed conventional machine learning methods in most image-based applications. Convolutional neural networks (CNNs) have seen some popularity in radiomics, since they do not require hand-crafted features and can automatically extract features during the learning process. In this regard, it is observed that CNN based radiomics could provide state-of-the-art results in neuro-oncology, similar to the recent success of such methods in a wide spectrum of medical image analysis applications. Herein we present a review of the most recent best practices and establish the future trends for AI enabled radiomics in neuro-oncology. Copyright © 2019, The Authors. All rights reserved.

关键词： Diagnosis

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Fungal electronics

arXiv

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arXiv 2021年

作者： Adamatzky, Andrew Ayres, Phil Beasley, Alexander E. Chiolerio, Alessandro Dehshibi, Mohammad M. Gandia, Antoni Albergati, Elena Mayne, Richard Nikolaidou, Anna Roberts, Nic Tegelaar, Martin Tsompanas, Michail-Antisthenis Phillips, Neil Wösten, Han A.B. Unconventional Computing Laboratory UWE Bristol United Kingdom The Centre for Information Technology and Architecture Royal Danish Academy Copenhagen Denmark Centre for Engineering Research University of Hertfordshire United Kingdom Center for Bioinspired Soft Robotics Istituto Italiano di Tecnologia Via Morego 30 Genova10163 Italy Department of Computer Science Multimedia and Telecommunications Universitat Oberta de Catalunya Barcelona Spain Institute for Plant Molecular and Cell Biology CSIC-UPV Valencia Spain Department of Design Politecnico di Milano Milan Italy MOGU S.r.l. Inarzo Italy Microbiology Department of Biology University of Utrecht Utrecht Netherlands

Fungal electronics is a family of living electronic devices made of mycelium bound composites or pure mycelium. Fungal electronic devices are capable of changing their impedance and generating spikes of electrical potential in response to external control parameters. Fungal electronics can be embedded into fungal materials and wearables or used as stand alone sensing and computing devices. Copyright © 2021, The Authors. All rights reserved.

关键词： Fungi

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Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks

arXiv

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arXiv 2021年

作者： Grandits, Thomas Pezzuto, Simone Costabal, Francisco Sahli Perdikaris, Paris Pock, Thomas Plank, Gernot Krause, Rolf Institute of Computer Graphics and Vision TU Graz Graz Austria BioTechMed-Graz Graz Austria Center for Computational Medicine in Cardiology Institute of Computational Science Università della Svizzera italiana Lugano Switzerland Department of Mechanical and Metallurgical Engineering School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Institute for Biological and Medical Engineering Schools of Engineering Medicine and Biological Sciences Pontificia Universidad Católica de Chile Santiago Chile Millennium Nucleus for Cardiovascular Magnetic Resonance Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA United States Gottfried Schatz Research Center Division of Biophysics Medical University of Graz Graz Austria

Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information can be extracted from the available data. The fibers in cardiac tissue conduct the electrical wave faster, and their direction could be inferred from activation times. In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation. In particular, we train the neural network to weakly satisfy the anisotropic eikonal equation and to predict the measured activation times. We use a local basis for the anisotropic conductivity tensor, which encodes the fiber orientation. The methodology is tested both in a synthetic example and for patient data. Our approach shows good agreement in both cases, with an RMSE of 2.2ms on the in-silico data and outperforming a state of the art method on the patient data. The results show a first step towards learning the fiber orientations from electroanatomical maps with physics-informed neural networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Tensors

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DeepADNet: A CNN-LSTM model for the multi-class classification of Alzheimer's disease using multichannel EEG

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Alzheimer's & dementia : the journal of the Alzheimer's Association 2021年第S7期17卷 e057573-e057573页

作者： Thi Kieu Khanh Ho YoungHoon Jeon Eunchan Na Zahid Ullah Byeong C Kim Kun Ho Lee Jong-In Song Jeonghwan Gwak Department of Software Korea National University of Transportation Republic of South Korea School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Republic of South KoreaGwangju South Korea Department of Neurology Chonnam National University Medical School Republic of South KoreaGwangju South Korea Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center Chosun University Republic of South KoreaGwangju South Korea Aging Neuroscience Research Group Korea Brain Research Institute Republic of South KoreaDaegu South Korea Department of Biomedical Science Chosun University Republic of South KoreaGwangju South Korea Department of IT & Energy Convergence (BK21 FOUR) Korea National University of Transportation Republic of South Korea Department of AI Robotics Engineering Korea National University of Transportation Republic of South Korea Department of Biomedical Engineering Korea National University of Transportation Republic of South Korea

BACKGROUND: Early and accurate diagnosis of Alzheimer's disease (AD) - an incurable and progressive brain disorder, along with a sharp upward trend in the incidence rate, is significantly important for patients to take prevention and appropriate treatments. However, the pathophysiological mechanism at the levels of AD severity is still poorly understood in spite of the worldwide financial and research efforts. Using an inexpensive and noninvasive modality, such as Electroencephalography (EEG) coupled with Deep Learning (DL)-derived diagnostic tool to retain the accuracy and versatility, therefore, has gained much attention in AD multi-class classification. METHODS: The present study proposes DL model, named DeepADNet, for the AD multi-class classification using multichannel EEG. First, we extracted the event-related spectral perturbation (ERSP) features measuring the average dynamic changes in amplitude of three main EEG frequency bands relative to a specific experimental event. Second, the hybrid deep Convolutional Neural Network (CNN) - Long Short-Term Memory Network (LSTM) was built to exploit the wide range of ERSP patterns on a time-frequency domain and to generate discriminant features to classify healthy controls (HC) with two AD subject groups. To tackle the imbalance problem and improve the classification accuracy, we also applied different over-sampling techniques RESULTS: The EEG data were collected from the Oddball - a cognitive ability test with 63 subjects (23 HC, 17 Pre-symptomatic AD (aAD) and 23 Prodromal AD (pAD) subjects) and the N-back - a memory ability test with 36 subjects (13 HC, 11 aAD and 12 pAD subjects). We demonstrated that the ERSP patterns displayed significant differences among three subject groups during the two experimental stages. Classification results also reveal that a CNN-LSTM model could overcome the existing methods utilizing the hand-engineered features which demand on prior knowledge of AD analysis and achieved the highest

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A review of uncertainty quantification in deep learning: Techniques, applications and challenges

arXiv

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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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Improving the multi-class classification of Alzheimer's disease with machine learning-based techniques: An EEG-fNIRS hybridization study

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Alzheimer's & dementia : the journal of the Alzheimer's Association 2021年第S7期17卷 e057565-e057565页

作者： Thi Kieu Khanh Ho Minhee Kim YoungHoon Jeon Eunchan Na Zahid Ullah Byeong C Kim Kun Ho Lee Jong-In Song Jae Gwan Kim Jeonghwan Gwak Department of Software Korea National University of Transportation Republic of South Korea Department of Biomedical Science and Engineering Gwangju Institute of Science and Technology Republic of South KoreaGwangju South Korea School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Republic of South KoreaGwangju South Korea Department of Neurology Chonnam National University Medical School Republic of South KoreaGwangju South Korea Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center Chosun University Republic of South KoreaGwangju South Korea Aging Neuroscience Research Group Korea Brain Research Institute Republic of South KoreaDaegu South Korea Department of Biomedical Science Chosun University Republic of South KoreaGwangju South Korea Department of IT & Energy Convergence (BK21 FOUR) Korea National University of Transportation Republic of South Korea Department of AI Robotics Engineering Korea National University of Transportation Republic of South Korea Department of Biomedical Engineering Korea National University of Transportation Republic of South Korea

BACKGROUND: As an incurable neurodegenerative disorder, Alzheimer's Disease (AD) is being projected to become one of the most expensive diseases for understanding the pathophysiological mechanism. Exploring alterations in the AD brain network is therefore of central importance for early and accurate diagnosis of cognitive deficits levels, yet diagnostic uncertainties exist merely confirmed by postmortem brain examination. The hybrid approach of two modalities, Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) reveals an ideal technique-derived diagnosis due to the inexpensive and noninvasive experiments coupled with the reliability and versatility that make it highly desirable for AD multi-class classification tasks. METHODS: In this study, the hybrid EEG-fNIRS was employed in constructing the classification machine learning (ML)-based models to categorize four subject groups, including healthy controls (HC) and three AD patient classes. First, a concurrent EEG-fNIRS setup was utilized to record data from 41 subjects during the Oddball - a cognitive ability test and 1-back - a memory ability test. Second, while fNIRS features were computed and extracted, the event-related spectral perturbation (ERSP) features measuring the average dynamic changes in amplitude of three main EEG frequency bands were extracted. Third, a conventional neural network and a hybrid deep Convolutional Neural Network (CNN) - Long Short-Term Memory Network (LSTM) was built for the multi-class classification using the fNIRS and EEG features, respectively. To boost the classification accuracy, the output of different models was combined by majority voting methods. RESULTS: The hybrid EEG-fNIRS feature set was able to achieve a higher accuracy (71.01% ± 0.004) by combing their complementary properties, compared to using EEG (66.35% ± 0.016) or fNIRS (67.15% ± 0.017) alone. In addition, fNIRS and ERSP feature patterns displayed a significant difference between subject

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