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arXiv 2023年

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

Graph Structure learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023, CC BY.

关键词： Benchmarking

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SE(3)-equivariant prediction of molecular wavefunctions and electronic densities 21

SE(3)-equivariant prediction of molecular wavefunctions and ...

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Proceedings of the 35th International Conference on Neural Information Processing systems

作者： Oliver T. Unke Mihail Bogojeski Michael Gastegger Mario Geiger Tess Smidt Klaus-Robert Müller Machine Learning Group Technische Universität Berlin Berlin Germany and DFG Cluster of Excellence "Unifying Systems in Catalysis" (UniSysCat) Technische Universität Berlin Berlin Germany and BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany and Google Research Brain Team Berlin Germany Machine Learning Group Technische Universität Berlin Berlin Germany and BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Machine Learning Group Technische Universität Berlin Berlin Germany and DFG Cluster of Excellence "Unifying Systems in Catalysis" (UniSysCat) Technische Universität Berlin Berlin Germany and BASLEARN – TU Berlin/BASF Joint Lab for Machine Learning Technische Universität Berlin Berlin Germany Institute of Physics École Polytechnique Fédérale de Lausanne Lausanne Switzerland Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA and Center for Advanced Mathematics for Energy Research Applications (CAMERA) Lawrence Berkeley National Laboratory Berkeley CA Machine Learning Group Technische Universität Berlin Berlin Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul Korea and Max Planck Institute for Informatics Stuhlsatzenhausweg 66123 Saarbrücken Germany and BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany and Google Research Brain Team Berlin Germany

ISBN: (纸本)9781713845393

machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Instead of training on a fixed set of properties, more recent approaches attempt to learn the electronic wavefunction (or density) as a central quantity of atomistic systems, from which all other observables can be derived. This is complicated by the fact that wavefunctions transform non-trivially under molecular rotations, which makes them a challenging prediction target. To solve this issue, we introduce general SE(3)-equivariant operations and building blocks for constructing deep learning architectures for geometric point cloud data and apply them to reconstruct wavefunctions of atomistic systems with unprecedented accuracy. Our model achieves speedups of over three orders of magnitude compared to ab initio methods and reduces prediction errors by up to two orders of magnitude compared to the previous state-of-the-art. This accuracy makes it possible to derive properties such as energies and forces directly from the wavefunction in an end-to-end manner. We demonstrate the potential of our approach in a transfer learning application, where a model trained on low accuracy reference wavefunctions implicitly learns to correct for electronic many-body interactions from observables computed at a higher level of theory. Such machine-learned wavefunction surrogates pave the way towards novel semi-empirical methods, offering resolution at an electronic level while drastically decreasing computational cost. Additionally, the predicted wavefunctions can serve as initial guess in conventional ab initio methods, decreasing the number of iterations required to arrive at a converged solution, thus leading to significant speedups without any loss of accuracy or robustness. While we focus on physics applications in this contribution, the proposed equivariant framework for deep learning on point clouds is p

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Optimal Wildfire Escape Route Planning for Drones under Dynamic Fire and Smoke

Optimal Wildfire Escape Route Planning for Drones under Dyna...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Chang Liu Tamas Sziranyi Department of Networked Systems and Services Budapest University of Technology and Economics Műegyetem rkp. 3H-1111 Budapest Hungary Machine Perception Research Laboratory of HUN-REN Institute for Computer Science and Control (SZTAKI) H-1111 Budapest Kende u. 13-17 Hungary Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics (BME-KJK) Műegyetem rkp. 3 Budapest Hungary

In recent years, the increasing prevalence and intensity of wildfires have posed significant challenges to emergency response teams. The utilization of unmanned aerial vehicles (UAVs), commonly known as drones, has shown promise in aiding wildfire management efforts. This work focuses on the development of an optimal wildfire escape route planning system specifically designed for drones, considering dynamic fire and smoke models. First, the location of the source of the wildfire can be well located by information fusion between UAV and satellite, and the road conditions in the vicinity of the fire can be assessed and analyzed using multichannel remote sensing data. Second, the road network can be extracted and segmented in real time using UAV vision technology, and each road in the road network map can be given priority based on the results of road condition classification. Third, the spread model of dynamic fires calculates the new location of the fire source based on the fire intensity, wind speed and direction, and the radius increases as the wildfire spreads. Smoke is generated around the fire source to create a visual representation of a burning fire. Finally, based on the improved A* algorithm, which considers all the above factors, the UAV can quickly plan an escape route based on the starting and destination locations that avoid the location of the fire source and the area where it is spreading. By considering dynamic fire and smoke models, the proposed system enhances the safety and efficiency of drone operations in wildfire environments.

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Pulmonary Tuberculosis Detection Using an Ensemble of ConvNeXts

Pulmonary Tuberculosis Detection Using an Ensemble of ConvNe...

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International Conference on Biomedical Engineering (IBIOMED)

作者： Seng Hansun Ahmadreza Argha Hamid Alinejad-Rokny Siaw-Teng Liaw Branko G. Celler Guy B. Marks South West Sydney (SWS) School of Clinical Medicine University of New South Wales Woolcock Institute of Medical Research Sydney Australia Universitas Multimedia Nusantara Tangerang Indonesia Graduate School of Biomedical Engineering Tyree Institute of Health Engineering (IHealthE) Ageing Future Institute (AFI) University of New South Wales Sydney Australia BioMedical Machine Learning Lab Graduate School of Biomedical Engineering Tyree Institute of Health Engineering (IHealthE) Ageing Future Institute (AFI) University of New South Wales Sydney Australia School of Population Health University of New South Wales Sydney Australia Biomedical Systems Research Laboratory School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia

ISBN: (数字)9798350389265

ISBN: (纸本)9798350389272

This study evaluates the effectiveness of a novel ensemble transfer learning (TL) method for tuberculosis (TB) detection. We employed the recently introduced ConvNeXt convolutional-based deep learning architecture as the backbone model. Two TL approaches were applied at intermediate layers of the pre-trained model (PTM), specifically ConvNeXt-Tiny, Small, and Base variants: Hard Cut, where features from an intermediate layer are directly passed to the classifier, and Soft Cut, where features from an intermediate layer are fine-tuned before classification. Experimental results on two widely used TB datasets-the Montgomery County (MC) and the Shenzhen (SZ) datasets demonstrate that the proposed ensemble TL method enhances detection performance. The Soft Cut approach yielded accuracy improvements of approximately 11.46% (Tiny), 8.71% (Small), 3.58% (Base) for MC, and 11.18% (Tiny), 10.27% (Small), 6.65% (Base) for SZ compared to the direct method, compared to the direct method.

关键词： Visualization Accuracy Tuberculosis Explainable AI Lungs Transfer learning Deep architecture Biomedical engineering

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A sensory signature of unaffected biological parents predicts the risk of autism in their offspring

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Psychiatry and Clinical Neurosciences 2024年第1期78卷 60-68页

作者： Chen, Chenyi Cheng, Yawei Wu, Chien-Te Chiang, Chung-Hsin Wong, Ching-Ching Huang, Chih-Mao Martínez, Róger Marcelo Tzeng, Ovid J. L. Fan, Yang-Teng Graduate Institute of Injury Prevention and Control College of Public Health Taipei Medical University Taipei Taiwan Research Center of Brain and Consciousness Shuang-Ho Hospital Taipei Medical University New Taipei City Taiwan Graduate Institute of Mind Brain and Consciousness College of Humanities and Social Sciences Taipei Medical University Taipei Taiwan Psychiatric Research Center Wan Fang Hospital Taipei Medical University Taipei Taiwan Institute of Neuroscience and Brain Research Center National Yang Ming Chiao Tung University Taipei Taiwan Department of Physical Medicine and Rehabilitation National Yang Ming Chiao Tung University Hospital Yilan Taiwan International Research Center for Neurointelligence (WPI-IRCN) The University of Tokyo Institutes for Advanced Study (UTIAS) The University of Tokyo Tokyo Japan Department of Psychology and Research Center for Mind Brain and Learning National Chengchi University Taipei Taiwan Child Developmental Assessment & Intervention Center Department of Child & Adolescent Psychiatry Taipei City Hospital Taipei Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu Taiwan School of Psychological Sciences National Autonomous University of Honduras Tegucigalpa Honduras Cognitive Neuroscience Laboratory Institute of Linguistics Academia Sinica Taipei Taiwan College of Humanities and Social Sciences Taipei Medical University Taipei Taiwan Department of Educational Psychology and Counseling National Taiwan Normal University Taipei Taiwan Graduate Institute of Medicine Yuan Ze University Taoyuan Taiwan

Aim: Despite the emphasis on sensory dysfunction phenotypes in the revised diagnostic criteria for autism spectrum disorder (ASD), there has been limited research, particularly in the field of neurobiology, investigating the concordance in sensory features between individuals with ASD and their genetic relatives. Therefore, our objective was to examine whether neurobehavioral sensory patterns could serve as endophenotypic markers for ASD. Methods: We combined questionnaire- and lab-based sensory evaluations with sensory fMRI measures to examine the patterns of sensory responsivity in 30 clinically diagnosed with ASD, 26 matched controls (CON), and 48 biological parents for both groups (27 parents of individuals with ASD [P-ASD] and 21 for individuals with CON [P-CON]). Results: The ASD and P-ASD groups had higher sensory responsivity and rated sensory stimuli as more unpleasant than the CON and P-CON groups, respectively. They also exhibited greater hemodynamic responses within the sensory cortices. Overlapping activations were observed within these sensory cortices in the ASD and P-ASD groups. Using a machine learning approach with robust prediction models across cohorts, we demonstrated that the sensory profile of biological parents accurately predicted the likelihood of their offspring having ASD, achieving a prediction accuracy of 71.4%. Conclusions: These findings provide support for the hereditary basis of sensory alterations in ASD and suggest a potential avenue to improve ASD diagnosis by utilizing the sensory signature of biological parents, especially in families with a high risk of ASD. This approach holds promising prospects for early detection, even before the birth of the offspring. © 2023 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.

关键词： autism spectrum disorder endophenotype machine learning parent–child dyads sensory responsivity

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Investigation of electrical and thermal behavior of modified epoxy structures for DC and high frequency applications

Investigation of electrical and thermal behavior of modified...

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22nd International Symposium on High Voltage Engineering (ISH 2021)

作者： M. Awais C. Dai F. B. Meng A. Paramane X. Wang X. Chen Zhejiang Provincial Key Laboratory of Electrical Machine Systems College of Electrical Engineering Zhejiang University Hangzhou China Zhejiang Key Laboratory of High-efficient Energy-saving and Pollutant Control Technology for Thermal Power Generation Zhejiang Energy Group Research Institute Co. Ltd Hangzhou China

Epoxy resin insulations are considered as a potential candidate for various applications like transformers, cable terminations, power electronic device packaging, and bushings. This has promoted a great interest in their use as high voltage insulation materials for high frequency applications. This work presents the effect of different curing agent concentrations on the electrical and thermal properties of epoxy resin for DC and high frequency applications. Differential scanning calorimetry is used to characterize the thermal performance of the materials. The insulation properties such as breakdown measurements under DC and high frequency stress, DC resistivity, and space charge distributions at different temperatures, and dielectric spectroscopy at different frequencies and temperatures are performed. Results reveal that high curing agent concentrations optimize the thermal and electrical performance of epoxy resin insulation. However, high frequency stress severely degrades the breakdown and dielectric properties of all the prepared materials. By the control of resin stoichiometry, epoxy resin with suitable electrical and thermal properties can be realized for high frequency applications.

关键词： curing dielectric materials differential scanning calorimetry electric breakdown epoxy insulation epoxy insulators filled polymers insulating materials resins space charge stoichiometry

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FCFR-Net: Feature fusion based coarse-to-fine residual learning for depth completion

arXiv

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arXiv 2020年

作者： Liu, Lina Song, Xibin Lyu, Xiaoyang Diao, Junwei Wang, Mengmeng Liu, Yong Zhang, Liangjun Institute of Cyber-Systems and Control Zhejiang University China Baidu Research China National Engineering Laboratory of Deep Learning Technology and Application China

Depth completion aims to recover a dense depth map from a sparse depth map with the corresponding color image as input. Recent approaches mainly formulate depth completion as a one-stage end-to-end learning task, which outputs dense depth maps directly. However, the feature extraction and supervision in one-stage frameworks are insufficient, limiting the performance of these approaches. To address this problem, we propose a novel end-to-end residual learning framework, which formulates the depth completion as a two-stage learning task, i.e., a sparse-to-coarse stage and a coarse-to-fine stage. First, a coarse dense depth map is obtained by a simple CNN framework. Then, a refined depth map is further obtained using a residual learning strategy in the coarse-to-fine stage with a coarse depth map and color image as input. Specially, in the coarse-to-fine stage, a channel shuffle extraction operation is utilized to extract more representative features from the color image and coarse depth map, and an energy based fusion operation is exploited to effectively fuse these features obtained by channel shuffle operation, thus leading to more accurate and refined depth maps. We achieve SoTA performance in RMSE on KITTI benchmark. Extensive experiments on other datasets future demonstrate the superiority of our approach over current state-of-the-art depth completion approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

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Laser speckle contrast imaging with principal component and entropy analysis: a novel approach for depth-independent blood flow assessment

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Frontiers of Optoelectronics 2025年第1期 6-24页

作者： Yu.Surkov P.Timoshina I.Serebryakova D.Stavtcev I.Kozlov G.Piavchenko I.Meglinski A.Konovalov D.Telyshev S.Kuznetcov E.Genina V.Tuchin Institution of Physics Saratov State University Scientific Medical Center Saratov State University Laboratory of Laser Molecular Imaging and Machine Learning Tomsk State University Institute for Bionic Technologies and Engineering I.M.Sechenov First Moscow State Medical University Institute of Biomedical Systems National Research University of Electronic Technology Department of Human Anatomy and Histology Cytology and Embryology Institute of Clinical Medicine N.V.Sklifosovsky I.M.Sechenov First Moscow State Medical University Aston Institute of Materials Research School of Engineering and Applied Science Aston University Burdenko Neurosurgery Institute

Current study presents an advanced method for improving the visualization of subsurface blood vessels using laser speckle contrast imaging(LSCI), enhanced through principal component analysis(PCA) filtering. By combining LSCI and laser speckle entropy imaging with PCA filtering, the method effectively separates static and dynamic components of the speckle signal, significantly improving the accuracy of blood flow assessments, even in the presence of static scattering layers located above and below the vessel. Experiments conducted on optical phantoms, with the vessel depths ranging from 0.6 to 2 mm,and in vivo studies on a laboratory mouse ear demonstrate substantial improvements in image contrast and resolution. The method's sensitivity to blood flow velocity within the physiologic range(0.98–19.66 mm/s) is significantly enhanced, while its sensitivity to vessel depth is minimized. These results highlight the method's ability to assess blood flow velocity independently of vessel depth, overcoming a major limitation of conventional LSCI techniques. The proposed approach holds great potential for non-invasive biomedical imaging, offering improved diagnostic accuracy and contrast in vascular *** findings may be particularly valuable for advancing the use of LSCI in clinical diagnostics and biomedical research,where high precision in blood flow monitoring is essential.

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Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside"

arXiv

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arXiv 2024年

作者： Santina, Cosimo Della Corbato, Carlos Hernandez Sisman, Burak Leiva, Luis A. Arapakis, Ioannis Vakalellis, Michalis Vanderdonckt, Jean D’Haro, LuisFernando Manzi, Guido Becchio, Cristina Elamrani, Aïda Alirezaei, Mohsen Castellano, Ginevra Dimarogonas, Dimos V. Ghosh, Arabinda Haesaert, Sofie Soudjani, Sadegh Stroeve, Sybert Verschure, Paul Bacciu, Davide Deroy, Ophelia Bahrami, Bahador Gallicchio, Claudio Hauert, Sabine Sanz, Ricardo Lanillos, Pablo Iacca, Giovanni Sigg, Stephan Gasulla, Manel Steels, Luc Sierra, Carles Department of Cognitive Robotics Delft University of Technology Delft Netherlands Oberpfaffenhofen Germany Department of Computer Science University of Luxembourg Luxembourg 4 Telefónica I+D Spain AEGIS IT Research GmbH Germany Université catholique de Louvain Belgium Speech Technology and Machine Learning Group ETSI de Telecomunicación Universidad Politécnica de Madrid Madrid Spain Alien Venture Studio Alien Technology Transfer Rome Italy Hamburg Germany Institut Jean Nicod École Normale Supérieure Paris France Research Technology and Development Department Digital Industry Software Helmond Netherlands Department of Information Technology Uppsala University Uppsala Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Max Planck Institute for Software Systems Kaiserslautern Germany Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Amsterdam Netherlands Donders Centre for Neuroscience Radboud University Nijmegen Netherlands Computer Science Department University of Pisa Pisa Italy Faculty of Philosophy Philosophy of Science The Study of Religion Ludwig Maximilian University of Munich Munich Germany School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Autonomous Systems Laboratory Universidad Politécnica de Madrid Spain Madrid Spain Department of Information Engineering and Computer Science University of Trento Trento Italy Department of Information and Communications Engineering Aalto University Espoo Finland Department of Electronic Engineering Universitat Politècnica de Catalunya Barcelona Spain Studio Stelluti Brussels Belgium Barcelona Spain

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices that are connected with projects funded by the EIC Pathfinder Challenge called "Awareness Inside", a nonrecurring call for proposals within Horizon Europe that was designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems. © 2024, CC BY.

关键词： Robotics

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Role of strategy update rules in the spatial memory-based mixed strategy games

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The European Physical Journal B 2021年第1期94卷 1-11页

作者： Zhang, Fan Wang, Juan Gao, Hongyu Li, Xiaopeng Xia, Chengyi Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Tianjin University of Technology Tianjin People’s Republic of China School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin People’s Republic of China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin People’s Republic of China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin People’s Republic of China

Based on a continuous mixed strategy game model considering the memory mechanism and the uncertainty of individual decision-making, we explore the influence of several different update rules on the spatial cooperation behavior. Here, we mainly investigate the role of the Moran-like process, Fermi rule and replicator dynamics in the evolution of cooperation. Meanwhile, we further compare the evolution of cooperation when the memory mechanism and individual uncertainty are taken into account. In particular, memory length M and strategy adjustment factor $$\delta $$ can promote the cooperation behavior in different ways. Extensive Monte Carlo simulations indicate that the Moran-like process can generally drive the expansion of cooperative clusters faster and finally achieve the highest frequency of cooperation although the Fermi update rule performs better under the same condition. Moreover, the replicator dynamics create the worst scenario as far as the evolution of cooperation is concerned. Therefore, the level of cooperation strongly depends on the strategy update rules; these findings may be helpful to understand and analyze the evolutionary process of cooperation in many real-world natural and social systems.

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