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11th International Conference on Learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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Active Grammatical Inference for Non-Markovian Planning 32

Active Grammatical Inference for Non-Markovian Planning

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32nd International Conference on Automated Planning and Scheduling, ICAPS 2022

作者： Topper, Noah Atia, George Trivedi, Ashutosh Velasquez, Alvaro Department of Computer Science University of Central Florida United States Department of Electrical and Computer Engineering University of Central Florida United States Department of Computer Science University of Colorado Boulder United States Information Directorate Air Force Research Laboratory United States

ISBN: (纸本)9781577358749

Planning in finite stochastic environments is canonically posed as a Markov decision process where the transition and reward structures are explicitly known. Reinforcement learning (RL) lifts the explicitness assumption by working with sampling models instead. Further, with the advent of reward machines, we can relax the Markovian assumption on the reward. Angluin's active grammatical inference algorithm L∗ has found novel application in explicating reward machines for non-Markovian RL. We propose maintaining the assumption of explicit transition dynamics, but with an implicit non-Markovian reward signal, which must be inferred from experiments. We call this setting non-Markovian planning, as opposed to non-Markovian RL. The proposed approach leverages L∗ to explicate an automaton structure for the underlying planning objective. We exploit the environment model to learn an automaton faster and integrate it with value iteration to accelerate the planning. We compare against recent non-Markovian RL solutions which leverage grammatical inference, and establish complexity results that illustrate the difference in runtime between grammatical inference in planning and RL settings. © 2022, Association for the Advancement of Artificial Intelligence.

关键词： Reinforcement learning

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Multi-agent Cooperative Games Using Belief Map Assisted Training

arXiv

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arXiv 2024年

作者： Luo, Chen Huang, Qinwei Wu, Alex B. Khan, Simon Li, Hai Qiu, Qinru Department of Electrical Engineering & Computer Science Syracuse University United States Fayetteville-Manlius High School United States Air Force Research Laboratory United States Department of Electrical Engineering & Computer Science Duke University United States

In a multi-agent system, agents share their local observations to gain global situational awareness for decision making and collaboration using a message passing system. When to send a message, how to encode a message, and how to leverage the received messages directly affect the effectiveness of the collaboration among agents. When training a multi-agent cooperative game using reinforcement learning (RL), the message passing system needs to be optimized together with the agent policies. This consequently increases the model’s complexity and poses significant challenges to the convergence and performance of learning. To address this issue, we propose the Belief-map Assisted Multi-agent System (BAMS), which leverages a neuro-symbolic belief map to enhance training. The belief map decodes the agent’s hidden state to provide a symbolic representation of the agent’s understanding of the environment and other agents’ status. The simplicity of symbolic representation allows the gathering and comparison of the ground truth information with the belief, which provides an additional channel of feedback for the learning. Compared to the sporadic and delayed feedback coming from the reward in RL, the feedback from the belief map is more consistent and reliable. Agents using BAMS can learn a more effective message passing network to better understand each other, resulting in better performance in the game. We evaluate BAMS’s performance in a cooperative predator and prey game with varying levels of map complexity and compare it to previous multi-agent message passing models. The simulation results showed that BAMS reduced training epochs by 66%, and agents who apply the BAMS model completed the game with 34.62% fewer steps on average. © 2024, CC BY.

关键词： Multi agent systems

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Surface-patterned chalcogenide glasses with high-aspect-ratio microstructures for long-wave infrared metalenses

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Opto-Electronic science 2024年第10期3卷 1-13页

作者： Zhaofeng Gu Yixiao Gao Kongsi Zhou Junyang Ge Chen Xu Lei Xu Mohsen Rahmani Ran Jiang Yimin Chen Zijun Liu Chenjie Gu Yaoguang Ma Jianrong Qiu Xiang Shen Laboratory of Infrared Materials and Devices Research Institute of Advanced TechnologiesNingbo UniversityNingbo 315211China Zhejiang Key Laboratory of Advanced Optical Functional Materials and Devices Ningbo 315211China Advanced Optics&Photonics Laboratory Department of EngineeringSchool of Science and TechnologyNottingham Trent UniversityNottingham NG118NSUnited Kingdom Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational ResearchCenter for Advanced PhotonicsZhejiang UniversityHangzhou 310027China Ningbo Institute of Oceanography Ningbo 315832China

Multidimensional-engineering chalcogenide glasses is widely explored to construct various infrared photonic devices,with their surface as a key dimension for wavefront ***,we demonstrate direct patterning high-aspect-ratio microstructures on the surface of chalcogenide glasses offers an efficient and robust method to manipulate longwave infrared *** chalcogenide glass being considered soft in terms of its mechanical properties,we successfully fabricate high-aspect-ratio micropillars with a height of 8μm using optimized deep etching process,and we demonstrate a 2-mm-diameter all-chalcogenide metalens with a numerical aperture of 0.45 on the surface of a 1.5-mm-thick As2Se3 *** the exceptional longwave infrared(LWIR)transparency and moderate refractive index of As2Se3 glass,the all-chalcogenide metalens produces a focal spot size of~1.39λ0 with a focusing efficiency of 47%at the wavelength of 9.78μm,while also exhibiting high-resolution imaging *** work provides a promising route to realize easy-to-fabricate,mass-producible planar infrared optics for compact,light-weight LWIR imaging systems.

关键词： chalcogenide glasses long wave infrared metalens

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Enhancing Multi-Label ECG Classification via Task-Guided Lead Correlations in Internet of Medical Things

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IEEE Internet of Things Journal 2025年第12期12卷 20544-20555页

作者： Yuan, Xiaoyan Wang, Wei Chen, Junxin Fang, Kai Bashir, Ali Kashif Mondal, Tapas Hu, Xiping Deen, M. Jamal Beijing Institute of Technology School of Medical Technology Beijing100081 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Guangdong Shenzhen518172 China Dalian University of Technology School of Software Dalian116621 China Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Chitkara University Institute of Engineering and Technology Centre for Research Impact and Outcome Chitkara University Punjab Rajpura140401 India McMaster University Faculty of Health Science and the Department of Pediatrics Hamilton Canada McMaster University McMaster School of Biomedical Engineering Hamilton Canada McMaster University Electrical and Computer Engineering Department Hamilton Canada

With the rise of the Internet of Things (IoT), wearable devices have enabled real-time health monitoring, particularly through physiological signals like electrocardiograms (ECG). The standard 12-lead ECG records the electrical activity of the heart from multiple perspectives, providing valuable insights into cardiac health. However, existing 12-lead ECG analysis methods often treat leads as channel-level arrangements or rely on spatial adjacency to predefine lead connections, limiting their ability to capture the complex spatial and functional relationships between leads fully. To address this limitation, we propose TGLLNet, a task-driven model that automatically learns inter-lead relationships to improve multi-label ECG classification. TGLLNet adaptively learns lead connectivity patterns and relational strengths, enhancing ECG representation and improving model generalizability across tasks. Specifically, TGLLNet employs a Temporal Graph Construction (ETGC) module to convert ECG signals into temporal graphs and uses a Residual Pyramid Graph Convolution (RPG) module for multi-level graph embeddings, utilizing a Graph Convolutional Network (GCN) with independently learnable adjacency matrices. Combined with a Temporal Context Convolution (TCC) module, TGLLNet captures spatio-temporal dependencies, significantly improving ECG representation. Experimental results on 7 tasks from PTB-XL and CPSC2018 datasets demonstrate that TGLLNet outperforms existing methods, showing superior generalizability across different tasks. Our code is available at https://***/rosemary333/TGLLnet. © 2014 IEEE.

关键词： Graph embeddings

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Study on indoor pollutants emission in Akure, Ondo State, Nigeria

Arab Gulf Journal of Scientific Research

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Arab Gulf Journal of Scientific research 2024年第4期42卷 1643-1663页

作者： Abulude, Francis Olawale Suriano, Domenico Oluwagbayide, Samuel Dare Akinnusotu, Akinyinka Abulude, Ifeoluwa Ayodeji Awogbindin, Emmanuel Environmental and Sustainable Research Group Science and Education Development Institute Akure Nigeria Department of Sustainable Development Research Center of Brindisi ENEA-Italian National Agency for New Technologies Energy and Environment Brindisi Italy Department of Agricultural and Bio-Environmental Engineering Federal Polytechnic Idah Nigeria Department of Science Laboratory Technology Rufus Giwa Polytechnic Owo Nigeria Department of Agricultural Economics Bioeconomy and Food Systems Justus – Liebig University (JLU) Giessen Germany Department of Computer Science Bamidele Olumilua University of Education Science and Technology Ekiti Ikere Nigeria

Purpose: This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2), as well as particulate matter (PM) (PM1, PM... 详细信息

Purpose: This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO₂), ozone (O₃), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), as well as particulate matter (PM) (PM₁, PM_2.5 and PM₁₀) in Akure, Nigeria, as well as the relationship between the parameters’ concentrations. Design/methodology/approach: The evaluation, which lasted four months, used a low-cost air sensor that was positioned two meters above the ground. All sensor procedures were correctly carried out. Findings: CO₂ (430.34 ppm), NO₂ (93.31 ppb), O₃ (19.94 ppb), SO₂ (40.87 ppb), PM₁ (29.31 µg/m3), PM_2.5 (43.56 µg/m³), PM₁₀ (50.70 µg/m³), temperature (32.4°C) and relative humidity (50.53%) were the average values obtained. The Pearson correlation depicted the relationships between the pollutants and weather factors. With the exception of April, which had significant SO₂ (18%) and low PM₁₀ (49%) contributions, NO₂ and PM₁₀ were the most common pollutants in all of the months. The mean air quality index (AQI) for NO₂indicated that the AQI was “moderate” (51–100). In contrast to SO₂, whose AQI ranged from “moderate” to “very unhealthy,” O₃'s AQI ranged from “good” (50) to “unhealthy” (151–200). Since PM₁, PM_2.5 and PM₁₀ made up the majority of PC1’s contribution, both PM_2.5 and PM₁₀ were deemed “hazardous.” Practical implications: The practical implication of indoor air pollution is long-term health effects, including heart disease, lung cancer and respiratory diseases such as emphysema. Indoor air pollution can also cause long-term damage to people’s nerves, brain, kidneys, liver and other organs. Originality/value: Lack of literature in terms of indoor air qu

关键词： Air quality index Indoor air quality Nigeria Pollutants United States environmental protection agency (USEPA) World health organization (WHO)

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A Novel Inherited Modeling Structure of Automatic Brain Tumor Segmentation from MRI

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computers, Materials & Continua 2022年第11期73卷 3983-4002页

作者： Abdullah AAsiri Tariq Ali Ahmad Shaf Muhammad Aamir Muhammad Shoaib Muhammad Irfan Hassan A.Alshamrani Fawaz F.Alqahtani Osama M.Alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Informatics and Systems University of Management and TechnologyLahore54000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia

Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to *** resonance imaging methods capture skull images that contain healthy and affected *** checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic ***,auto segmentation of the affected part is needed to facilitate ***,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for the auto-segmented brain tumor *** CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region *** proposed method was evaluated on a publicly available BraTS2020 *** statistical parameters used in this work for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice ***,our method achieved an accuracy value of 0.98,which is most prominent than existing ***,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.

关键词： Brain tumor support vector machine convolutional neural network BraTS classification

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Recycled silicon solar cell-derived nanostructured p-black silicon device for high performance NO₂ gas sensor applications

RSC Applied Interfaces

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RSC Applied Interfaces 2024年第1期2卷 220-229页

作者： Aleem, Mahaboobbatcha Vishnuraj, Ramakrishnan Pullithadathil, Biji Nanosensors & Clean Energy Laboratory Department of Chemistry & Nanoscience and Technology PSG Institute of Advanced Studies Coimbatore 641 India Advanced Self-Powered Systems of Integrated Sensors and Technology Center Department of Electrical and Computer Engineering North Carolina State University Raleigh 27695 NC United States Center of Excellence in Advanced Materials and Green Technologies Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Chemical Engineering and Materials Science Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

Nitrogen dioxide (NO2) is a toxic gas that can cause respiratory problems, and sensing its presence is crucial for environmental monitoring and industrial safety. This investigation presents a novel approach towards s...

Nitrogen dioxide (NO₂) is a toxic gas that can cause respiratory problems, and sensing its presence is crucial for environmental monitoring and industrial safety. This investigation presents a novel approach towards sensing NO₂ gas by utilizing partially completed/recycled silicon solar cells employing a metal-assisted etching process to fabricate a high-performance p-black-silicon based sensor. Structural and morphological analyses using X-ray diffraction patterns, Raman spectroscopy and cross sectional FESEM characterization confirm the integrity of the p-B-silicon sensor. By combining recycling techniques with advanced fabrication methods, the resulting sensor exhibits exceptional sensitivity, a low detection limit of 1 ppm, and rapid response times (12-14 s) when exposed to NO₂ gas concentrations ranging from 1 to 5 ppm. The enhanced sensitivity is attributed to the unique nanostructured comb-like morphology of the sensor material, which facilitates fast charge transport mechanisms, and a plausible sensing mechanism has been proposed and explained using a depletion model diagram and energy model diagram. This eco-friendly and cost-effective solution not only addresses electronic waste concerns but also highlights the potential of sustainable practices in scientific research. The findings emphasize on the importance of environmental consciousness and innovation, showcasing a promising future for gas sensing technology. By utilizing recycled materials and advanced fabrication techniques, this study contributes to the development of efficient, eco-friendly sensors for environmental monitoring applications, paving the way for a more sustainable and technologically advanced future in the field of gas sensors. © 2025 RSC.

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Personalized Multimedia Recommendation systems Using Higher-Order Tensor Singular-Value-Decomposition

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IEEE Transactions on Broadcasting 2024年第1期70卷 148-160页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Huang, Scott Chih-Hao Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Department of Information and Telecommunication Guilin541004 China Northwestern State University Department of Engineering Technology NatchitochesLA71497 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Communications Research Centre Canada OttawaK2H 8S2 Canada

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE. © 1963-12012 IEEE.

关键词： Singular value decomposition

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Longitudinal velocity control of autonomous driving based on extended state observer

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 36-46页

作者： Hongbo Gao Hanqing Yang Xiaoyu Zhang Xiangyun Ren Fenghua Liang Ruidong Yan Qingchao Liu Mingmao Hu Fang Zhang Jiabing Gao Siyu Bao Keqiang Li Deyi Li Danwei Wang Department of Automation University of Science and Technology of ChinaHefeiChina State Key Laboratory of Intelligent Vehicle Safety Technology ChongqingChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Traffic and Transportation Beijing Jiaotong UniversityBeijingChina Automotive Engineering Research Institute Jiangsu UniversityZhenjiangChina School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore School of Automotive Engineering Hubei University of Automotive TechnologyShiyanChina Beijing Idriverplus Technology Co. Ltd.BeijingChina Chery Automobile Co. Ltd.WuhuChina State Key Laboratory of Automotive Safety and Energy Tsinghua UniversityBeijingChina Department of Computer Science and Technology Tsinghua UniversityBeijingChina

Active Disturbance Rejection Control(ADRC)possesses robust disturbance rejection capabilities,making it well-suited for longitudinal velocity ***,the conventional Extended State Observer(ESO)in ADRC fails to fully exploit feedback from first-order and higher-order estimation errors and tracking error simultaneously,thereby diminishing the control performance of *** address this limitation,an enhanced car-following algorithm utilising ADRC is proposed,which integrates the improved ESO with a feedback *** comparison to the conventional ESO,the enhanced version effectively utilises multi-order estimation and tracking ***,it enhances convergence rates by incorporating feedback from higher-order estimation errors and ensures the estimated value converges to the reference value by utilising tracking error *** improved ESO significantly enhances the disturbance rejection performance of ***,the effectiveness of the proposed algorithm is validated through the Lyapunov approach and experiments.

关键词： active disturbance rejection control(ADRC) autonomous vehicle extended state observer(ESO) intelligent control longitudinal velocity control

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