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arXiv 2019年

作者： Canuto, Clebeson Moreno, Plinio Samatelo, Jorge Vassallo, Raquel Santos-Victor, José CT-II Department of Electrical Engineering Federal University Espírito Santo Av. Fernando Ferrari 514 Goiabeiras VitóriaES29075-910 Brazil North Tower Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Av. Rovisco Pais 1 Lisbon1049-001 Portugal

For effectively interacting with humans in collaborative environments, machines need to be able to predict (i.e. anticipate) future events, in order to execute actions in a timely manner. However, the observation of the human limbs movements may not be sufficient to anticipate their actions in an unambiguous manner. In this work we consider two additional sources of information (i.e. context) over time, gaze movements and object information, and study how these additional contextual cues improve the action anticipation performance. We address action anticipation as a classification task, where the model takes the available information as the input, and predicts the most likely action. We propose to use the uncertainty about each prediction as an online decision-making criterion for action anticipation. Uncertainty is modeled as a stochastic process applied to a time-based neural network architecture, which improves the conventional class-likelihood (i.e. deterministic) criterion. The main contributions of this paper are three-fold: (i) we propose a deep architecture that outperforms previous results in the action anticipation task, when using the Acticipate collaborative dataset;(ii) we show that contextual information is important do disambiguate the interpretation of similar actions;(iii) we propose the minimization of uncertainty as a more effective criterion for action anticipation, when compared with the maximization of class probability. Our results on the Acticipate dataset showed the importance of contextual information and the uncertainty criterion for action anticipation. We achieve an average accuracy of 98.75% in the anticipation task using only an average of 25% of observations. In addition, considering that a good anticipation model should also perform well in the action recognition task, we achieve an average accuracy of 100% in action recognition on the Acticipate dataset, when the entire observation set is used. Copyright © 2019, The Authors. All ri

关键词： Forecasting

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IROS 2019 lifelong robotic vision challenge lifelong object recognition report

arXiv

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arXiv 2020年

作者： She, Qi Feng, Fan Liu, Qi Chan, Rosa H.M. Hao, Xinyue Lan, Chuanlin Yang, Qihan Lomonaco, Vincenzo Parisi, German I. Ma, Liang Wu, Jianwen Zhong, Qiaoyong Xie, Di Pu, Shiliang Graffieti, Gabriele Pellegrini, Lorenzo Lomonaco, Vincenzo Maltoni, Davide Wang, Zhengwei Brophy, Eoin Ward, Tomás E. Zhou, Liguang Yang, Lin Goel, Vidit Sheet, Debdoot Kumar, Somesh Song, Soonyong Bae, Heechul Han, Hyonyoung Son, Youngsung Piyasena, Duvindu Kanagarajah, Sathursan Lam, Siew-Kei Wu, Meiqing Department of Electrical Engineering City University of Hong Kong Hong Kong Department of Electronic Engineering Tsinghua University Beijing University of Posts and Telecommunications Beijing China School of Electronic Information Wuhan University Wuhan China ContinualAI Research University of Bologna Italy ContinualAI Research University of Hamburg Germany Intel Labs and ContinualAI Research Hikvision Research Institute Hangzhou China University of Bologna Bologna Italy V-SENSE School of Computer Science and Statistics Trinity College Dublin Dublin Ireland Inisht Centre for Data Analytics School of Computing Dublin City University Dublin Ireland Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Peking University Beijing China Shandong University Qingdao China Beijing Film Academy Beijing China Indian Institute of Technology Kharagpur India Korea Republic of Nanyang Technological University Singapore Singapore

This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top 8 finalists (out of over 150 teams). The competition dataset (L)ifel(O)ng (R)obotic V(IS)ion (OpenLORIS) - Object Recognition (OpenLORIS-object) is designed for driving lifelong/continual learning research and application in robotic vision domain, with everyday objects in home, office, campus, and mall scenarios. The dataset explicitly quantifies the variants of illumination, object occlusion, object size, camera-object distance/angles, and clutter information. Rules are designed to quantify the learning capability of the robotic vision system when faced with the objects appearing in the dynamic environments in the contest. Individual reports, dataset information, rules, and released source code can be found at the project homepage. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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Corrigendum to "PSFHS challenge report: pubic symphysis and fetal head segmentation from intrapartum ultrasound images" [Medical Image Analysis 99 (2025),103353]

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Medical image analysis 2025年 104卷 103651页

作者： Jieyun Bai Zihao Zhou Zhanhong Ou Gregor Koehler Raphael Stock Klaus Maier-Hein Marawan Elbatel Robert Martí Xiaomeng Li Yaoyang Qiu Panjie Gou Gongping Chen Lei Zhao Jianxun Zhang Yu Dai Fangyijie Wang Guénolé Silvestre Kathleen Curran Hongkun Sun Jing Xu Pengzhou Cai Lu Jiang Libin Lan Dong Ni Mei Zhong Gaowen Chen Víctor M Campello Yaosheng Lu Karim Lekadir Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China Auckland Bioengineering Institute The University of Auckland Auckland New Zealand. Electronic address: jbai996@aucklanduni.ac.nz. Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Computer Science and Engineering The Chinese University of Hong Kong China. Computer Vision and Robotics Group University of Girona Girona Spain. Canon Medical Systems (China) Co. LTD Beijing China. College of Artificial Intelligence Nankai University Tianjin China. College of Computer Science and Electronic Engineering Hunan University Changsha China. School of Medicine University College Dublin Dublin Ireland. School of Computer Science University College Dublin Dublin Ireland. School of Statistics & Mathematics Zhejiang Gongshang University Hangzhou China. School of Computer Science & Engineering Chongqing University of Technology Chongqing China. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound & Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging & School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China. NanFang Hospital of Southern Medical University Guangzhou China. Zhujiang Hospital of Southern Medical University Guangzhou China. Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain. Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China. Electronic address: tluys@***. Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain.

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Contrastive functional connectivity defines neurophysiology-informed symptom dimensions in major depression

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Cell reports. Medicine 2025年 102151页

作者： Hao Zhu Xiaoyu Tong Nancy B Carlisle Hua Xie Corey J Keller Desmond J Oathes Feng Liu Charles B Nemeroff Gregory A Fonzo Yu Zhang Department of Bioengineering Lehigh University Bethlehem PA USA. Department of Psychology Lehigh University Bethlehem PA USA. Center for Neuroscience Research Children's National Hospital Washington DC USA. Wu Tsai Neuroscience Institute Stanford University Stanford CA USA Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford CA USA Veterans Affairs Palo Alto Healthcare System and the Sierra Pacific Mental Illness Research Education and Clinical Center (MIRECC) Palo Alto CA USA. Center for Brain Imaging and Stimulation Department of Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia PA USA Center for Neuromodulation in Depression and Stress Department of Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia PA USA Penn Brain Science Translation Innovation and Modulation Center University of Pennsylvania Perelman School of Medicine Philadelphia PA USA Departments of Neurology Neurosurgery Bioengineering and Neuroscience University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Systems and Enterprises Stevens Institute of Technology Hoboken NJ USA. Center for Psychedelic Research and Therapy Department of Psychiatry and Behavioral Sciences Dell Medical School The University of Texas at Austin Austin TX USA. Department of Bioengineering Lehigh University Bethlehem PA USA Department of Electrical and Computer Engineering Lehigh University Bethlehem PA USA. Electronic address: yuzi20@lehigh.edu.

Major depressive disorder (MDD) is highly heterogeneous, posing challenges for effective treatment due to complex interactions between clinical symptoms and neurobiological features. To address this, we apply contrastive principal-component analysis to fMRI-based resting-state functional connectivity, isolating disorder-specific variations by contrasting data from 233 MDD patients and 285 healthy controls. Subsequently, we use sparse canonical correlation analysis to identify two significant dimensions linking distinct brain circuits with clinical profiles. One dimension relates to an internalizing-externalizing symptom spectrum involving visual and limbic networks and is associated with cognitive task reaction times. The other dimension, linked to personality traits protective against depression (e.g., extraversion), is driven by dorsal attention network connections and correlates with cognitive control and psychomotor performance. This approach illuminates stable symptom dimensions and their neurophysiological underpinnings, aiding in precision phenotyping for MDD and supporting the development of targeted, individualized therapeutic strategies for mental health care.

关键词： brain-symptom dimension contrastive machine learning disease heterogeneity fMRI functional connectivity major depressive disorder

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A hybrid agent-based secondary control for microgrids with increased fault-tolerance needs

A hybrid agent-based secondary control for microgrids with i...

引用

Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2018

作者： Bintoudi, Angelina D. Bintoudi, Angelina D. Zyglakis, Lampros Apostolos C. Tsolakis Ioannidis, Dimosthenis Al-Agtash, S. Al-Agtash, S. Martinez-Ramos, Jose L. Onen, Ahmet Azzopardi, Brian Hadjidemetriou, Lenos Martensen, Nis Mounir, Khiat Borg, Nicholas Fragale, Nunziatina Demoulias, Charis Tzovaras, Dimitrios Information Technologies Institute Center for Research and Technology Hellas Thessaloniki Greece Department of Computer Engineering German Jordanian University Amman Jordan Department of Computer Engineering Santa Clara University CA United States Department of Electrical Engineering Universidad de Sevilla Seville Spain Department of Electrical and Electronics Engineering Abdullah Gul University Kayseri Turkey MCAST Triq Kordin Malta KIOS Research Center of Excellence University of Cyprus Nicosia Cyprus Energynautics GmbH Darmstadt Germany Département de Génie Électrique Laboratoire SCAMRE Ecole Nationale Polytechnique d'Oran Algeria Electronic Systems Design Ltd Malta GeoSYS Ltd San Gwann Malta Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9781839531330

This paper proposes a hybrid secondary control architecture for microgrids with AC-coupled droop-controlled units, based on both centralised and distributed control principles. The proposed secondary control is based on a multi-agent system (MAS), complemented by a microgrid centralised controller (MGCC). The system is able to adjust the droop curves dynamically in order to achieve voltage/frequency restoration as well as active/reactive power optimal allocation, based on the actual status of the controllable units, in particular, the state-of-charge of batteries and maximum power point of photovoltaics. The distributed nature of the agents is also fully exploited because the proposed framework retains operability even under fault on secondary MGCC. To evaluate the proposed framework, a scenario-based performance analysis has been tested over a simulated AC islanded microgrid, where communication from the MGCC is suddenly interrupted and the MAS is required to reconfigure in order to maintain the same control objectives. MATLAB/Simulink simulations have been realised using detailed physical form models for a small-scale microgrid, while the implementation of the MGCC and MAS is accomplished through Java Agent Development (JADE) framework. © 2018 Institution of engineering and Technology. All rights reserved.

关键词： Multi agent systems

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Convergence of the expectation-maximization algorithm through discrete-time Lyapunov stability theory

arXiv

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arXiv 2018年

作者： Romero, Orlando Chatterjee, Sarthak Pequito, Sérgio Department of Industrial and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

In this paper, we propose a dynamical systems perspective of the Expectation-Maximization (EM) algorithm. More precisely, we can analyze the EM algorithm as a nonlinear state-space dynamical system. The EM algorithm is widely adopted for data clustering and density estimation in statistics, control systems, and machine learning. This algorithm belongs to a large class of iterative algorithms known as proximal point methods. In particular, we re-interpret limit points of the EM algorithm and other local maximizers of the likelihood function it seeks to optimize as equilibria in its dynamical system representation. Furthermore, we propose to assess its convergence as asymptotic stability in the sense of Lyapunov. As a consequence, we proceed by leveraging recent results regarding discrete-time Lyapunov stability theory in order to establish asymptotic stability (and thus, convergence) in the dynamical system representation of the EM algorithm. Copyright © 2018, The Authors. All rights reserved.

关键词： Dynamical systems

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On landscape of lagrangian function and stochastic search for constrained nonconvex optimization

arXiv

引用

arXiv 2018年

作者： Chen, Zhehui Li, Xingguo Yang, Lin F. Haupt, Jarvis Zhao, Tuo School of Industrial and Systems Engineering Georgia Institute of Technology Department of Electrical and Computer Engineering University of Minnesota Operation Research and Financial Engineering Department Princeton University

We study constrained nonconvex optimization problems in machine learning, signal processing, and stochastic control. It is well-known that these problems can be rewritten to a minimax problem in a Lagrangian form. However, due to the lack of convexity, their landscape is not well understood and how to find the stable equilibria of the Lagrangian function is still unknown. To bridge the gap, we study the landscape of the Lagrangian function. Further, we define a special class of Lagrangian functions. They enjoy two properties: *** are either stable or unstable (Formal definition in Section 2);*** equilibria correspond to the global optima of the original problem. We show that a generalized eigenvalue (GEV) problem, including canonical correlation analysis and other problems, belongs to the class. Specifically, we characterize its stable and unstable equilibria by leveraging an invariant group and symmetric property (more details in Section 3). Motivated by these neat geometric structures, we propose a simple, efficient, and stochastic primal-dual algorithm solving the online GEV problem. Theoretically, we provide sufficient conditions, based on whichwe establish an asymptotic convergence rate and obtain the first sample complexity result for the online GEV problem by diffusion approximations, which are widely used in applied probability and stochastic control. Numerical results are provided to support our theory. Copyright © 2018, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Single photonic perceptron based on a soliton crystal Kerr microcomb for high-speed, scalable, optical neural networks

arXiv

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arXiv 2020年

作者： Xu, Xingyuan Tan, Mengxi Corcoran, Bill Wu, Jiayang Nguyen, Thach G. Boes, Andreas Chu, Sai T. Little, Brent E. Morandotti, Roberto Mitchell, Arnan Hicks, Damien G. Moss, David J. Optical Sciences Centre for Micro-Photonics Swinburne University of Technology HawthornVIC3122 Australia Department of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering RMIT University MelbourneVIC3001 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Hong Kong Xi’an Institute of Optics and Precision Mechanics Precision Mechanics of CAS Xi’an China INRS-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X 1S2 Canada Bioinformatics Division Walter & Eliza Hall Institute of Medical Research ParkvilleVIC3052 Australia

Optical artificial neural networks (ONNs) — analog computing hardware tailored for machine learning [1, 2] — have significant potential for ultra-high computing speed and energy efficiency [3]. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources [4] that is programmable, highly scalable and capable of reaching ultra-high speeds. We experimentally demonstrate the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths of a micro-comb to achieve a high single-unit throughput of 11.9 Giga-FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps. We test the perceptron on simple standard benchmark datasets — handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. This performance is a direct result of the record small wavelength spacing (49GHz) for a coherent integrated microcomb source, which results in an unprecedented number of wavelengths for neuromorphic optics. Finally, we propose an approach to scaling the perceptron to a deep learning network using the same single micro-comb device and standard off-the-shelf telecommunications technology, for high-throughput operation involving full matrix multiplication for applications such as real-time massive data processing for unmanned vehicle and aircraft tracking. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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Stochastic Search Methods for Mobile Manipulators ⁎

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Procedia Manufacturing 2018年 17卷 976-984页

作者： Amoako-Frimpong Samuel Yaw Matthew Messina Henry Medeiros Jeremy Marvel Roger Bostelman Department of Electrical and Computer Engineering Marquette University Milwaukee WI 53233 USA Intelligent Systems Division National Institute of Standards and Technology Gaithersburg MD 20899

Mobile manipulators are a potential solution to the increasing need for additional flexibility and mobility in industrial applications. However, they tend to lack the accuracy and precision achieved by fixed manipulators, especially in scenarios where both the manipulator and the autonomous vehicle move simultaneously. This paper analyzes the problem of dynamically evaluating the positioning error of mobile manipulators. In particular, it investigates the use of Bayesian methods to predict the position of the end-effector in the presence of uncertainty propagated from the mobile platform. The precision of the mobile manipulator is evaluated through its ability to intercept retroreflective markers using a photoelectric sensor attached to the end-effector. Compared to a deterministic search approach, we observed improved robustness with comparable search times, thereby enabling effective calibration of the mobile manipulator.

关键词： mobile manipulators calibration search algorithms

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Dealing with state estimation in fractional-order systems under artifacts

arXiv

引用

arXiv 2018年

作者： Chatterjee, Sarthak Pequito, Sérgio Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Industrial and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Fractional-order dynamical systems are used to describe processes that exhibit long-term memory with power-law dependence. Notable examples include complex neurophysiological signals such as electroencephalogram (EEG) and blood-oxygen-level dependent (BOLD) signals. When analyzing different neurophysiological signals and other signals with different origin (for example, biological systems), we often find the presence of artifacts, that is, recorded activity that is due to external causes and does not have its origins in the system of interest. In this paper, we consider the problem of estimating the states of a discrete-time fractional-order dynamical system when there are artifacts present in some of the sensor measurements. Specifically, we provide necessary and sufficient conditions that ensure we can retrieve the system states even in the presence of artifacts. We provide a state estimation algorithm that can estimate the states of the system in the presence of artifacts. Finally, we present illustrative examples of our main results using real EEG data. Copyright © 2018, The Authors. All rights reserved.

关键词： Neurophysiology

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