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High-Quality Thermal Gibbs Sampling with Quantum Annealing Hardware

Physical Review Applied 2022年第4期17卷 044046-044046页

作者： Jon Nelson Marc Vuffray Andrey Y. Lokhov Tameem Albash Carleton Coffrin Center for Nonlinear Studies Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Advanced Network Science Initiative Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Theoretical Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico 87131 USA Department of Physics and Astronomy and Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131 USA

Quantum annealing (QA) was originally intended for accelerating the solution of combinatorial optimization tasks that have natural encodings as Ising models. However, recent experiments on QA-hardware platforms have demonstrated that, in the operating regime corresponding to weak interactions, the QA hardware behaves like a noisy Gibbs sampler at a hardware-specific effective temperature. This work builds on those insights and identifies a class of small hardware-native Ising models that are robust to noise effects and proposes a procedure for executing these models on QA hardware to maximize Gibbs-sampling performance. The experimental results indicate that the proposed protocol results in high-quality Gibbs samples from a hardware-specific effective temperature. Furthermore, we show that this effective temperature can be adjusted by modulating the annealing time and energy scale. The procedure proposed in this work provides an approach to using QA hardware for Ising-model sampling presenting potential opportunities for applications in machine learning and physics simulation.

关键词： Adiabatic quantum optimization Machine learning Quantum benchmarking Quantum fluctuations & noise Quantum information architectures & platforms Ising model Simulated annealing

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A Zeroth-Order Momentum Method for Risk-Averse Online Convex Games

arXiv

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arXiv 2022年

作者： Wang, Zifan Shen, Yi Bell, Zachary I. Nivison, Scott Zavlanos, Michael M. Johansson, Karl H. Division of Decision and Control Systems School of Electrical Enginnering and Computer Science KTH Royal Institute of Technology StockholmSE-10044 Sweden The Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States The Air Force Research Laboratory Eglin AFB FL United States

We consider risk-averse learning in repeated unknown games where the goal of the agents is to minimize their individual risk of incurring significantly high cost. Specifically, the agents use the conditional value at risk (CVaR) as a risk measure and rely on bandit feedback in the form of the cost values of the selected actions at every episode to estimate their CVaR values and update their actions. A major challenge in using bandit feedback to estimate CVaR is that the agents can only access their own cost values, which, however, depend on the actions of all agents. To address this challenge, we propose a new risk-averse learning algorithm with momentum that utilizes the full historical information on the cost values. We show that this algorithm achieves sub-linear regret and matches the best known algorithms in the literature. We provide numerical experiments for a Cournot game that show that our method outperforms existing methods. © 2022, CC BY.

关键词： Learning algorithms

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Algal Bloom Front Tracking Using an Unmanned Surface Vehicle: Numerical Experiments Based on Baltic Sea Data

Algal Bloom Front Tracking Using an Unmanned Surface Vehicle...

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OCEANS

作者： Joana Fonseca Miguel Aguiar João Borges de Sousa Karl H. Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Underwater Systems and Technology Laboratory (LSTS) University of Porto Portugal

ISBN: (纸本)9781665427883

We consider the problem of tracking moving algal bloom fronts using an unmanned surface vehicle (USV) equipped with a sensor that measures the concentration of chlorophyll a. Chlorophyll a is a green pigment found in plants, and its concentration is an indicator of phytoplankton abundance. Our algal bloom front tracking mission consists of three stages: deployment, data collection, and front tracking. At the deployment stage, a satellite collects an image of the sea from which the location of the front, the reference value for the concentration at this front and, consequently, the appropriate initial position for the USV are determined. At the data collection stage, the USV collects data points to estimate the local algal gradient as it crosses the front. Finally, at the front tracking stage, an adaptive algorithm based on recursive least squares fitting using recent past sensor measures is executed. We evaluate the performance of the algorithm and its sensitivity to measurement noise through MATLAB simulations. We also present an implementation of the algorithm on the DUNE onboard software platform for marine robots and validate it using simulations with satellite model forecasts from Baltic sea data.

关键词： Water Temperature measurement Satellites Software algorithms Sea measurements Predictive models Robot sensing systems

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Interpretable classification of epileptic EEG signals using ALIF decomposition and attention-augmented cascaded deep neural networks

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Applied Soft Computing 2025年 180卷

作者： Zeng, Wei Zhang, Minglin Shan, Liangmin Chen, Yang Li, Zuoyong Du, Shaoyi School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China School of Mechanical Engineering and Automation Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China

Epilepsy is a chronic neurological disorder characterized by recurrent seizures. Accurate diagnosis and effective monitoring require the precise classification of electroencephalogram (EEG) signals. In this study, we introduce a novel approach that combines Adaptive Local Iterative Filtering (ALIF) for signal decomposition with an attention-enhanced cascaded deep neural network (CDNN) architecture. The ALIF algorithm decomposes EEG signals into intrinsic mode functions (IMFs) that capture inherent oscillatory components. These IMFs are processed by the CDNN, which operates in two stages: a feature extraction module and a classification module. In the feature extraction stage, a SEblock channel attention mechanism dynamically prioritizes significant features within the IMFs. The classification stage employs a hybrid CNN-LSTM architecture that effectively captures both spatial and temporal dependencies. To enhance interpretability, the SHapley Additive exPlanations (SHAP) framework is incorporated to provide insights into the model's decision-making process, while Gradient-weighted Class Activation Mapping (Grad-CAM) visualizes the most discriminative regions in the input data. Rigorously validated using 10-fold cross-validation on the Bonn and EEG Epilepsy databases, the proposed methodology achieved an exceptional classification accuracy of 100%, with sensitivity, specificity, and F1-scores exceeding 99% across various scenarios. The integration of SHAP and Grad-CAM not only elucidates the model's decision processes but also contributes to a more interpretable and reliable system for epileptic EEG signal classification. This synergistic combination of advanced signal processing, deep learning, and interpretability techniques holds significant potential to enhance epilepsy diagnosis and strengthen trust in clinical decision support systems. © 2025

关键词： Deep neural networks

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Consensus of linear multi-agent systems with a smart leader

Consensus of linear multi-agent systems with a smart leader

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Chinese Intelligent Systems Conference, CISC 2018

作者： Li, Yangbo Liu, Hui Liu, Zhongxin Chen, Zengqiang College of Computer and Control Engineering Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (纸本)9789811322877

This paper addresses the consensus problem with linear systems via a smart leader under directed topology. The smart leader tracks a given reference model and a control function is constructed to control the smart leader. The smart leader can utilize its neighboring followers’ feedback information when the control function meet certain condition, which can effectively reduce the leader’s controller cost and the tracking error among the leader and followers. By utilizing the relative output message of neighboring agents, a reduced-order observer is adopted under the assumption that the directed topology have a directed spanning tree. A sufficient condition is given to guarantee that the leader-following system can achieve consensus. Finally, simulation examples are given to demonstrate the effectiveness of the obtained results. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Linear systems

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Multi-hop reasoning over sparse knowledge graphs with deep reinforcement learning

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Expert Systems with Applications 2025年 290卷

作者： Liu, Yiyang Fu, Lejun Fu, Yukai Wu, Tianxing Bai, Hongfei State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China School of Computer Science and Engineering Southeast University Nanjing211189 China

In recent years, knowledge reasoning has demonstrated substantial progress in inferring missing information, effectively alleviating the limitations caused by the incompleteness of knowledge graphs (KGs). Dealing with entities that have sufficient paths between them, multi-hop reasoning methods have achieved notable success and demonstrated interpretability, but they still exhibit deficiencies when dealing with sparse KGs. To overcome the insufficiency of edges in sparse KGs and the absence of effective guidance in multi-hop reasoning methods, a hybrid multi-hop collaborative Multi-Agent Sparse Knowledge Graph Reasoning model (MASKGR) is proposed. Attention-based dynamic completion (ADC) and variable rule guidance(VRG) are used to enhance the reasoning capability of deep reinforcement learning (RL) in sparse KGs. ADC focuses on high-value relations based on attention weights and combines embedding-based methods to predict the tail entities of these relations, expanding the original action space and improving the exploration capability of agents. Moreover, VRG greatly improves the ability of MASKGR to use rule-guided agent learning by utilizing the variable rule ratio and the variable loss reshaping. ADC can be applied to both single-agent and multi-agent in other models, while VRG is specifically designed for multi-agent. Comprehensive experiments have been conducted on four datasets: FB15K-237-10 %, FB15K-237-50 %, NELL23K, and WD-singer. Specifically, MASKGR achieves relative improvements of 1.3 % in Mean Reciprocal Rank (MRR) and 1.1 % in the proportion of correct tail entity rankings in the top 3 (Hit@3) on the FB15K-237-10 %, 2.4 % in MRR and 1.3 % in Hit@3 on the NELL23K, and 2.7 % in MRR and 3.6 % in Hit@3 on the WD-singer. The results indicate that MASKGR outperforms state-of-the-art multi-hop reasoning models based on reinforcement learning. © 2025

关键词： Deep reinforcement learning

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An efficient and adaptable multimedia system for converting PAL to VGA in real-time video processing

An efficient and adaptable multimedia system for converting ...

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作者： Jain, Deepak Kumar Jacob, Sunil Alzubi, Jafar Menon, Varun Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Center for Robotics SCMS School of Engineering and Technology Kerala683576 India School of Engineering Al-Balqa Applied University Salt19117 Jordan Department of Computer Science and Engineering SCMS School of Engineering and Technology Kerala683576 India

Real-time video processing has found its range of applications from defense to consumer electronics for surveillance, video conferencing, etc. With the advent of Field Programmable Gate Arrays (FPGAs), flexible real-time video processing systems which can meet hard real-time constraints are easily realized with short development time. Most of the existing solutions have high utilization of system resources and are not quite flexible with many applications. Here we propose a hardware–software co-design for an FPGA-based real-time video processing system to convert video in standard Phase Alternating Line (PAL) 576i format to standard video of Video Graphics Array (VGA)/Super Video Graphics Array (SVGA) format with little utilization of resources. Switching between multiple video streams, character/text overlaying, and skin color detection are also incorporated with the system. The system is also adaptable for rugged applications. VHSIC Hardware Description Language (VHDL) codes for the architecture were synthesized using Altera Quartus II and targeted for Altera Stratix I FPGA. Results achieved confirm that the proposed system performs efficient conversion with very less resource utilization compared to the existing solutions. Since the proposed system is also flexible, many other applications can be incorporated in the future. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Field programmable gate arrays (FPGA)

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Joint beamforming and reflecting design in reconfigurable intelligent surface-aided multi-user communication systems

arXiv

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arXiv 2021年

作者： Ma, Xiaoyan Guo, Shuaishuai Zhang, Haixia Fang, Yuguang Yuan, Dongfeng Shandong Provincial Key Laboratory of Wireless Communication Technologies School of Information Science and Engineering Shandong University Qingdao266237 China Shandong Provincial Key Laboratory of Wireless Communication Technologies School of Control Science and Engineering Shandong University Jinan250061 China Computer Electrical and Mathematical Science and Engineering Division Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States

Reconfigurable intelligent surface (RIS) provides a promising way to build the programmable wireless transmission environments in the future. Owing to the large number of reflecting elements used at the RIS, joint optimization for the active beamforming at the transmitter and the passive reflector at the RIS is usually complicated and time-consuming. To address this problem, this paper proposes a low-complexity joint beamforming and reflecting algorithm based on fractional programing (FP). Specifically, we first consider a RIS-aided multi-user communication system with perfect channel state information (CSI) and formulate an optimization problem to maximize the sum rate of all users. Since the problem is nonconvex, we decompose the original problem into three disjoint subproblems. By introducing favorable auxiliary variables, we derive the closed-form expressions of the beamforming vectors and reflecting matrix in each subproblem, leading to a joint beamforming and reflecting algorithm with low complexity. We then extend our approach to handle the case when transmitter-RIS and RIS-receiver channels are not perfect and develop corresponding low-complexity joint beamforming and reflecting algorithm with practical channel estimation. Simulation results have verified the effectiveness of the proposed algorithms as compared to various benchmark schemes. © 2021, CC BY.

关键词： Channel state information

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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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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