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arXiv 2024年

作者： Wu, Yinyu Zhang, Xuhui Ren, Jinke Shen, Yanyan Yang, Bo Wang, Shuqiang Guan, Xinping Niyato, Dusit The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China The University of Chinese Academy of Sciences Beijing100049 China The Shenzhen Future Network of Intelligence Institute The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen University of Advanced Technology Guangdong518055 China The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency. However, the resource competition among users will lead to uneven allocation, increased latency, and lower throughput. Fortunately, the rate-splitting multiple access (RSMA) technique has emerged as a promising solution for managing interference and optimizing resource allocation in MEC systems. This paper studies an IRS-assisted MEC system with RSMA, aiming to jointly optimize the passive beamforming of the IRS, the active beamforming of the base station, the task offloading allocation, the transmit power of users, the ratios of public and private information allocation, and the decoding order of the RSMA to minimize the average delay from a novel uplink transmission perspective. Since the formulated problem is non-convex and the optimization variables are highly coupled, we propose a hierarchical deep reinforcement learning-based algorithm to optimize both continuous and discrete variables of the problem. Additionally, to better extract channel features, we design a novel network architecture within the policy and evaluation networks of the proposed algorithm, combining convolutional neural networks and densely connected convolutional network for feature extraction. Simulation results indicate that the proposed algorithm not only exhibits excellent convergence performance but also outperforms various benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Bayesian forward regularization replacing ridge in online randomized neural networks with multiple output layers

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Pattern Recognition 2025年

作者： Junda Wang Minghui Hu Ning Li Ponnuthurai Nagaratnam Suganthan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore KINDI Computing Research Center College of Engineering Qatar University Doha P.O. Box 2713 Qatar

Forward regularization (-F) with unsupervised knowledge was advocated to replace canonical Ridge regularization (-R) in online linear learners, as it achieved a lower relative regret boundary. However, we observe that -F cannot perform as expected in practice, even possibly losing to -R for online tasks. We identify two main causes for this: (1) inappropriate intervened regularization, and (2) non-i.i.d. nature and data distribution changes in online learning (OL), both of which result in unstable posterior distribution and optima offset of the learner. To improve these, we first introduce the adjustable forward regularization (- k F), a more general -F with controllable knowledge intervention. We also derive - k F’s incremental updates with variable learning rate, and study relative regret and boundary in OL. Inspired by the regret analysis, to curb unstable penalties, we further propose - k F-Bayes style with k synchronously self-adapted to revise the intractable tuning of - k F by considering parametric posterior distribution changes in non-i.i.d. online data streams. Additionally, we integrate the - k F and - k F-Bayes into a multi-layer ensemble deep random vector functional link (edRVFL) and present two practical algorithms for batch learning, avoiding past replay and catastrophic forgetting. In experiments, we conducted tests on numerical simulation, tabular, and image datasets, where - k F-Bayes surpassed traditional -R and -F, highlighting the efficacy of ready-to-work - k F-Bayes and the great potentials of edRVFL- k F-Bayes in OL and continual learning (CL) scenarios.

关键词： Randomized neural network Forward regression Random vector functional link Online learning Continual learning Multiple output layers

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Reinforcement Learning Algorithms for Online Single-Machine Scheduling

Reinforcement Learning Algorithms for Online Single-Machine ...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Yuanyuan Li Edoardo Fadda Daniele Manerba Roberto Tadei Olivier Terzo LINKS Foundation-Advanced Computing and Applications Torino Italy Department of Control and Computer Engineering Politecnico di Torino Torino Italy Department of Information Engineering University of Brescia Brescia Italy

ISBN: (数字)9788395541674

ISBN: (纸本)9781728141473

Online scheduling has been an attractive field of research for over three decades. Some recent developments suggest that Reinforcement Learning (RL) techniques have the potential to deal with online scheduling issues effectively. Driven by an industrial application, in this paper we apply four of the most important RL techniques, namely Q-learning, Sarsa, Watkins's Q(λ), and Sarsa(λ), to the online single-machine scheduling problem. Our main goal is to provide insights on how such techniques perform. The numerical results show that Watkins's Q(λ) performs best in minimizing the total tardiness of the scheduling process.

关键词： field surveys Learning learning (artificial intelligence) machine scheduling scheduling process

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Chest area segmentation in depth images of sleeping patients

arXiv

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arXiv 2020年

作者： Goldstein, Yoav Schätz, Martin Avigal, Mireille Department of Mathematics and Computer Science Open University of Israel Raanana Israel University of Chemistry and Technology in Prague Department of Computing and Control Engineering Czech Republic

Although the field of sleep study has greatly developed over the recent years, the most common and efficient way to detect sleep issues remains a sleep examination performed in a sleep laboratory, in a procedure called Polysomnography (PSG). This examination measures several vital signals during a full night’s sleep using multiple sensors connected to the patient’s body. Yet, despite being the golden standard, the connection of the sensors and the unfamiliar environment inevitably impact the quality of the patient’s sleep and the examination itself. Therefore, with the novel development of more accurate and affordable 3D sensing devices, new approaches for non-contact sleep study emerged. These methods utilize different techniques with the purpose to extract the same sleep parameters, but remotely, eliminating the need of any physical connections to the patient’s body. However, in order to enable reliable remote extraction, these methods require accurate identification of the basic Region of Interest (ROI) i.e. the chest area of the patient, a task that is currently holding back the development process, as it is performed manually for each patient. In this study, we propose an automatic chest area segmentation algorithm, that given an input set of 3D frames of a sleeping patient, outputs a segmentation image with the pixels that correspond to the chest area, and can then be used as an input to subsequent sleep analysis algorithms. Except for significantly speeding up the development process of the non-contact methods, accurate automatic segmentation can also enable a more precise feature extraction and it is shown it is already improving sensitivity of prior solutions on average 46.9% better compared to manual ROI selection. All mentioned will place the extraction algorithms of the non-contact methods as a leading candidate to replace the existing traditional methods used today. Copyright © 2020, The Authors. All rights reserved.

关键词： Sleep research

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Learning hierarchical behavior and motion planning for autonomous driving

arXiv

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arXiv 2020年

作者： Wang, Jingke Wang, Yue Zhang, Dongkun Yang, Yezhou Xiong, Rong State Key Laboratory of Industrial Control and Technology Zhejiang University Hangzhou China School of Computing Informatics and Decision Systems Engineering Arizona State University

Learning-based driving solution, a new branch for autonomous driving, is expected to simplify the modeling of driving by learning the underlying mechanisms from data. To improve the tactical decision-making for learning-based driving solution, we introduce hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learningbased solution. Due to the coupled action space of behavior and motion, it is challenging to solve HBMP problem using reinforcement learning (RL) for long-horizon driving tasks. We transform HBMP problem by integrating a classical samplingbased motion planner, of which the optimal cost is regarded as the rewards for high-level behavior learning. As a result, this formulation reduces action space and diversifies the rewards without losing the optimality of HBMP. In addition, we propose a sharable representation for input sensory data across simulation platforms and real-world environment, so that models trained in a fast event-based simulator, SUMO, can be used to initialize and accelerate the RL training in a dynamics based simulator, CARLA. Experimental results demonstrate the effectiveness of the method. Besides, the model is successfully transferred to the real-world, validating the generalization capability. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion planning

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Exponential Sensitivity Revival of Noisy non-Hermitian Quantum Sensing with Two-photon Drives

arXiv

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arXiv 2023年

作者： Bao, Liying Qi, Bo Nori, Franco Dong, Daoyi Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Civil Aviation University of China Tianjin300300 China Theoretical Quantum Physics Laboratory RIKEN Saitama351-0198 Japan Quantum Computing Center RIKEN Saitama351-0198 Japan Physics Department The University of Michigan Ann ArborMI48109 United States School of Engineering Australian National University CanberraACT2601 Australia

Unique properties of multimode non-Hermitian lattice dynamics can be utilized to construct exponentially sensitive sensors. However, the impact of noise remains unclear, which may severely degrade their sensitivity. We analytically characterize and highlight the impact of loss and gain on the sensitivity revival and stability of non-Hermitian sensors. Defying the general belief that the superiority of quantum sensing will vanish in the presence of loss, we find that by proactively tuning the loss, the exponential sensitivity can be surprisingly regained when the sensing dynamics is stable. Furthermore, we prove that gain is crucial to fully revive the ideally exponential sensitivity and to ensure the stability of non-Hermitian sensing by making a balanced loss and gain. Our work opens a new way to significantly enhance the sensitivity by proactively tuning the loss and gain, which may promote future quantum sensing and quantum engineering. Copyright © 2023, The Authors. All rights reserved.

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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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The Use of Various Neurostimulators in Therapy of Neurodegenerative Disorders – A Survey

The Use of Various Neurostimulators in Therapy of Neurodegen...

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Progress in Applied Electrical engineering (PAEE)

作者： Aleksandra Kawala-Sterniuk Agata Gajos Mateusz Pawłowski Witold Libionka Władysław Piotr Duch Bartosz Pańczyszak Monika Stomal-Słowińska Michał Podpora Waldemar Bauer Adam Sudoł Mariusz Pelc Jarosław Zygarlicki Piotr Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Department of Neurosurgery "Vital Medic" Hospital Kluczbork Poland Department of Extrapyramidal Diseases Medical University of Łódź Łódź Poland Faculty of Health Sciences Wroclaw Medical University Wrocław Poland Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland Faculty of Natural Sciences and Technology University of Opole Opole Poland School Of Computing and Mathematical Sciences University of Greenwich London United Kindgom Center for Technology Transfer Opole University of Technology Opole Poland

ISBN: (数字)9781665455152

ISBN: (纸本)9781665455169

The use of neurostimulators in the treatment and therapy of neurodegenerative diseases is an alternative to pharmacological treatment. Unfortunately, most of these disorders are incurable, however, the use of neurostimulators can delay the onset of symptoms, the course of the disease and significantly improve the quality of life. Treatment with the use of neurostimulators also allows you to avoid side effects which usually accompany pharmacotherapy, or in order to replace it when prescribed medication is not working properly or not working at all. This paper focuses mainly on vagus nerve and deep brain stimulation.

关键词： Electrical engineering Deep brain stimulation Medical treatment Delays Diseases

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Persulfate-based advanced oxidation processes: The new hope brought by nanocatalyst immobilization

Environmental Functional Materials

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Environmental Functional Materials 2022年第1期1卷 67-91页

作者： Ruonan Guo Beidou Xi Changsheng Guo Xiuwen Cheng Ningqing Lv Wen Liu Alistair G.L. Borthwick Jian Xu State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Beijing 100012 PR China School of Environment Tsinghua University Beijing 100084 PR China Key Laboratory for Environmental Pollution Prediction and Control College of Earth and Environmental Sciences Lanzhou University Lanzhou Gansu Province 730000 PR China College of Environmental Sciences and Engineering The Key Laboratory of Water and Sediment Sciences (Ministry of Education) Peking University Beijing 100871 PR China Institute for Infrastructure and Environment School of Engineering The University of Edinburgh Edinburgh EH9 3FG UK School of Engineering Computing and Mathematics University of Plymouth Plymouth PL4 8AA UK

Persulfate-based advanced oxidation processes (persulfate-AOPs) offer great promise for environmental remediation, with heterogeneous catalysts providing the backbone of many wastewater purification technologies. Unlike conventional nanocatalyst heterogeneous systems, the immobilized-catalyst system can bypass the separation problem to reduce scour and prevent aggregation by anchoring nanoparticles onto porous or large-particle carriers. This review presents the state-of-the-art of knowledge concerning immobilization methodologies and reactors, reaction mechanisms, and activation performance. Immobilization techniques onto supports are summarized and discussed, including membrane-based reaction systems (immersion mode, and filtration mode), electrocatalytic auxiliary systems, and alternative supports (metallic glasses, aerogels, hydrogels, and specific materials). Key scientific problems and important prospects for the further development of immobilized catalysts are outlined.

关键词： Advanced oxidation process Nanocatalyst immobilization Persulfate Wastewater treatment

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COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images

arXiv

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arXiv 2020年

作者： Hemdan, Ezz El-Din Shouman, Marwa A. Karar, Mohamed Esmail Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Egypt Department of Computer Engineering College of Computing and Information Technology Shaqra University Saudi Arabia

Background and Purpose: Coronaviruses (CoV) are perilous viruses that may cause Severe Acute Respiratory Syndrome (SARS-CoV), Middle East Respiratory Syndrome (MERS-CoV). The novel 2019 Coronavirus disease (COVID-19) was discovered as a novel disease pneumonia in the city of Wuhan, China at the end of 2019. Now, it becomes a Coronavirus outbreak around the world, the number of infected people and deaths are increasing rapidly every day according to the updated reports of the World Health Organization (WHO). Therefore, the aim of this article is to introduce a new deep learning framework;namely COVIDX-Net to assist radiologists to automatically diagnose COVID-19 in X-ray images. Materials and Methods: Due to the lack of public COVID-19 datasets, the study is validated on 50 Chest X-ray images with 25 confirmed positive COVID-19 cases. The COVIDX-Net includes seven different architectures of deep convolutional neural network models, such as modified Visual Geometry Group Network (VGG19) and the second version of Google MobileNet. Each deep neural network model is able to analyze the normalized intensities of the X-ray image to classify the patient status either negative or positive COVID-19 case. Results: Experiments and evaluation of the COVIDX-Net have been successfully done based on 80-20% of X-ray images for the model training and testing phases, respectively. The VGG19 and Dense Convolutional Network (DenseNet) models showed a good and similar performance of automated COVID-19 classification with f1-scores of 0.89 and 0.91 for normal and COVID-19, respectively. The worst classification performance is obtained for the InceptionV3 model with f1-scores of 0.67 for normal cases and 0.00 for COVID-19 cases. Conclusions: This study demonstrated the useful application of deep learning models to classify COVID-19 in X-ray images based on the proposed COVIDX-Net framework. Clinical studies are the next milestone of this research work. Copyright © 2020, The Authors. All ri

关键词： Coronavirus

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