检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Tian, Chunwei Xu, Yong Zuo, Wangmeng Lin, Chia-Wen Zhang, David The Bio-Computing Research Center Harbin Institute of Technology ShenzhenShenzhenGuangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China The School of Computer Science and Technology Harbin Institute of Technology HarbinHeilongjiang150001 China The Peng Cheng Laboratory ShenzhenGuangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen Guangdong518055 China The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan ShenzhenGuangdong518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. According to nature of different applications, designing appropriate CNN architectures is developed. However, customized architectures gather different features via treating all pixel points as equal to improve the performance of given application, which ignores the effects of local power pixel points and results in low training efficiency. In this paper, we propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a memory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution. The AB utilizes one-dimensional asymmetric convolutions to intensify the square convolution kernels in horizontal and vertical directions for promoting the influences of local salient features for SISR. The MEB fuses all hierarchical low-frequency features from the AB via residual learning (RL) technique to resolve the long-term dependency problem and transforms obtained low-frequency features into high-frequency features. The HFFEB exploits low- and high-frequency features to obtain more robust super-resolution features and address excessive feature enhancement problem. Additionally, it also takes charge of reconstructing a high-resolution (HR) image. Extensive experiments show that our ACNet can effectively address single image super-resolution (SISR), blind SISR and blind SISR of blind noise problems. The code of the ACNet is shown at https://***/hellloxiaotian/ACNet. © 2021, CC BY-NC-ND.

关键词： Image enhancement

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

引用

The Journal of Machine Learning research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Fast perception, planning, and execution for a robotic butler: Wheeled humanoid m-hubo

arXiv

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arXiv 2020年

作者： Lee, Moonyoung Heo, Yujin Park, Jinyong Yang, Hyun-Dae Jang, Ho-Deok Benz, Philipp Park, Hyunsub Kweon, In So Oh, Jun-Ho Humanoid Robot Research Center Department of Mechanical Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Robotics and Computer Vision Laboratory Department of Electrical Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of

As the aging population grows at a rapid rate, there is an ever growing need for service robot platforms that can provide daily assistance at practical speed with reliable performance. In order to assist with daily tasks such as fetching a beverage, a service robot must be able to perceive its environment and generate corresponding motion trajectories. This becomes a challenging and computationally complex problem when the environment is unknown and thus the path planner must sample numerous trajectories that often are sub-optimal, extending the execution time. To address this issue, we propose a unique strategy of integrating a 3D object detection pipeline with a kinematically optimal manipulation planner to significantly increase speed performance at runtime. In addition, we develop a new robotic butler system for a wheeled humanoid that is capable of fetching requested objects at 24% of the speed a human needs to fulfill the same task. The proposed system was evaluated and demonstrated in a real-world environment setup as well as in public exhibition. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots

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F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

arXiv

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arXiv 2020年

作者： Li, Wenhao Jin, Bo Wang, Xiangfeng Yan, Junchi Zha, Hongyuan School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201804 China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai200062 China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a flexible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multiagent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

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A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks

引用

Simulation Modelling Practice and Theory 2025年 143卷

作者： Khoshvaght, Parisa Haider, Amir Rahmani, Amir Masoud Gharehchopogh, Farhad Soleimanian Anka, Ferzat Lansky, Jan Hosseinzadeh, Mehdi Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Engineering & Technology Duy Tan University Da Nang Viet Nam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of AI and Robotics Sejong University Seoul05006 Korea Republic of Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Fatih Sultan Mehmet Vakif University Istanbul Turkey Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Korea Republic of

The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a Deep Reinforcement Learning (DRL) multi-objective algorithm, called a Double Deep Q-Learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 % improvement in energy efficiency, and a 20 % decrease in the deadline-miss ratio compared to benchmark algorithms. © 2025 Elsevier B.V.

关键词： Resource allocation

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A registration-aided domain adaptation network for 3D Point cloud based place recognition

arXiv

引用

arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

arXiv

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arXiv 2021年

作者： Lalande, Alain Chen, Zhihao Pommier, Thibaut Decourselle, Thomas Qayyum, Abdul Salomon, Michel Ginhac, Dominique Skandarani, Youssef Boucher, Arnaud Brahim, Khawla de Bruijne, Marleen Camarasa, Robin Correia, Teresa M. Feng, Xue Girum, Kibrom B. Hennemuth, Anja Huellebrand, Markus Hussain, Raabid Ivantsits, Matthias Ma, Jun Meyer, Craig Sharma, Rishabh Shi, Jixi Tsekos, Nikolaos V. Varela, Marta Wang, Xiyue Yang, Sen Zhang, Hannu Zhang, Yichi Zhou, Yuncheng Zhuang, Xiahai Couturier, Raphael Meriaudeau, Fabrice ImViA laboratory University of Burgundy Dijon France MRI department University Hospital of Dijon Dijon France Femto-ST laboratory University of Franche-Comté Belfort France Cardiology department University Hospital of Dijon Dijon France CASIS Company Quetigny France National Engineering School of Sousse University of Sousse Sousse Tunisia LASEE laboratory National Engineering School of Monastir University of Monastir Monastir Tunisia Biomedical Imaging Group Rotterdam Erasmus MC Rotterdam Netherlands Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Centre of Marine Sciences University of Algarve Faro Portugal School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Department of Biomedical Engineering University of Virginia Charlottesville United States Charité – Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Bremen Germany German Centre for Cardiovascular Research Berlin Germany Department of Mathematics Nanjing University of Science and Technology Nanjing China Data Analysis and Intelligent Systems Lab Department of Computer Science University of Houston Houston United States Medical Robotics and Imaging Lab Department of Computer Science University of Houston Houston United States National Heart and Lung Institute Imperial College London London United Kingdom College of Computer Science Sichuan University Chengdu China College of Biomedical Engineering Sichuan University Chengdu China School of Biological Science and Medical Engineering Beihang University Beijing China School of Data Science Fudan University Shanghai China

A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed several minutes after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge’s main objectives were twofold. First, to evaluate if deep learning methods can distinguish between normal and pathological cases. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures. © 2021, CC BY-NC-SA.

关键词： Cardiology

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SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition

arXiv

引用

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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Active interactions between animals and technology: biohybrid approaches for animal behaviour research

引用

Animal Behaviour 2025年 224卷

作者： Papadopoulou, M. Ball, M. Bartashevich, P. Burns, A.L.J. Chiara, V. Clark, M.A. Costelloe, B.R. Fele, M. French, F. Hauert, S. Heinrich, M.K. Herbert-Read, J.E. Hoitt, J. Ioannou, C.C. Landgraf, T. Matchette, S.R. Polverino, G. Sankey, D.W.E. Scott, D.M. Sridhar, V.H. Strömbom, D. Trianni, V. Vo-Doan, T.T. King, A.J. Department of Biosciences Faculty of Science and Engineering Swansea University Swansea United Kingdom Department of Biology Lafayette College Easton PA United States Institute for Theoretical Biology Department of Biology Humboldt-Universität zu Berlin Berlin Germany Cluster of Excellence ‘Science of Intelligence’ Berlin Germany Department Fish Biology Fisheries and Aquaculture Leibniz Institute of Freshwater Ecology and Inland Fisheries Berlin Germany Faculty of Life Sciences Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences Humboldt-Universität zu Berlin Berlin Germany Museum and Institute of Zoology Polish Academy of Science Warszawa Poland School of Biological Sciences University of Bristol Bristol United Kingdom School of Natural Sciences Macquarie University Sydney Australia Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany Department of Biology University of Konstanz Konstanz Germany Department of Collective Behavior Max Planck Institute of Animal Behavior Konstanz Germany School of Computing and Digital Media London Metropolitan University London United Kingdom Bristol Robotics Laboratory University of Bristol Bristol United Kingdom IRIDIA Université Libre de Bruxelles Brussels Belgium Department of Zoology University of Cambridge Cambridge United Kingdom Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Department of Ecological and Biological Sciences University of Tuscia Viterbo Italy School of Natural and Environmental Science Newcastle University Newcastle upon Tyne United Kingdom Centre for Ecology and Conservation Faculty of Environment Science and Economy University of Exeter Penryn Campus Cornwall United Kingdom School of Animal Rural and Environmental Sciences Nottingham Trent University Nottingham United Kingdom Department for the Ecology of Animal Societies Max Planck Institute of Animal Behavior Konstanz Germany Ins

Biohybrid approaches (where living and engineered components are combined) provide new opportunities for advancing animal behaviour research and its applications. This review article and accompanying special issue explores how different types of novel technologies can be used in the field of animal behaviour from three perspectives: (1) comprehension, (2) application and (3) integration. Under the perspective of ‘comprehension,’ we present examples of how technologies like virtual animals or robots can be used in experimental settings to interact with living animals in a standardized manner. Such interactions can advance our understanding of fundamental topics such as mate choice, social learning and collective behaviour. Under ‘application,’ we investigate the potential for technologies to monitor, react and interact with animals in a variety of scenarios. For example, we discuss how drones can be used to keep large herbivores away from valuable crops and robotic predators to deter invasive species. Under ‘integration,’ we discuss possibilities for the coexistence of engineered and biological systems, augmenting the capacity or resilience of either or both components. Integration can be physical, for example, livestock can have sensors sit in their inner body for temperature monitoring, or within the environment, where sensors or robots monitor and interact with animals, such as a short-term earthquake forecasting method. Based upon these three themes, we discuss and classify existing biohybrid animal behaviour research, including the four articles included in our special issue. We also consider the ethics of this emerging field, highlight the advantages and potential issues associated with using technologies to create biohybrid systems and emphasize how such technologies can support the advancement of animal behaviour research. © 2025 The Association for the Study of Animal Behaviour

关键词： behavioural control behavioural monitoring biomimetic robotics ethorobotics interspecies interaction sensory integration

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Defining The Role Of In-Transit Parauterine Lymphovascular And Lateral Paracervical Lymphatic Tissue Disease In Cervical Cancer: Insights From An International Expert Survey To Enhance Clinical Practice

引用

International Journal of Gynecological Cancer 2025年第2期35卷

作者： Matteo Pavone Nicolò Bizzarri Agnieszka Rychlik Jan Persson Anna Fagotti Francesco Fanfani Giovanni Scambia Denis Querleu UOC Ginecologia Oncologica Dipartimento di Scienze per la salute della Donna e del Bambino e di Sanità Pubblica Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy IHU Strasbourg Institute of Image-Guided surgery Strasbourg Italy ICube Laboratory of Engineering Computer Science and Imaging Department of Robotics Imaging Teledetection and Healthcare Technologies University of Strasbourg CNRS UMR 7357 Strasbourg France Research Institute against Digestive Cancer IRCAD Strasbourg Strasbourg France Department of Gynecological Oncology Maria Sklodowska-Curie National Research Institute of Oncology Warsaw Poland Department of Obstetrics and Gynaecology Skåne University Hospital Lund Sweden

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