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Efficient Restoration for Binary Neural Networks

Tien Tzu Hsueh Pao/Acta Electronica Sinica 2025年第2期53卷 568-580页

作者： Zeng, Kai Wan, Zi-Xin Wang, Ming-Tao Shen, Tao Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Yunnan Kunming650500 China

Restoring the weight distribution, activation distribution, and gradient to the original full precision network data as much as possible can greatly improve the inference ability of the binary network. However, existing methods directly apply the restoration operation in forward propagation to binary data, and the gradient approximation functions for back-propagation are fixed or manually determined, resulting in the need for improvement in the restoration efficiency of binary networks. To address this problem, the efficient restoration method is investigated for binary neural networks. Firstly, a distribution recovery method for maximizing information entropy is proposed. By shifting the original full precision weight mean and scaling the modulus, the quantized binary weight directly has the characteristic of maximum distribution restoration. At the same time, a simple statistical translation and scaling factor is used to greatly improve the restoration efficiency of weight and activation. Furthermore, it is proposed a gradient function based on adaptive distribution approximation, which dynamically determines the update range of the current gradient in the P-percentile according to the actual distribution of the current full precision data. It adaptively changes the shape of the approximation function to efficiently update the gradient during the training process, thereby improving the convergence ability of the model. On the premise of ensuring the improvement of execution efficiency, theoretical analysis has confirmed that the method proposed in this paper can achieve maximum restoration of binary data. Compared with the existing advanced binary network models, the experimental results of our method show excellent performance, with a 60% and 67% reduction in computational time for the distribution restoration operation quantization of ResNet-18 and ResNet-20, respectively. An accuracy of 93.0% is achieved for VGG-Small binary quantization on the CIFAR-10 dataset

关键词： Restoration

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Research on Magnetic Anisotropy in Amorphous Materials with Soft Magnetic Properties

Research on Magnetic Anisotropy in Amorphous Materials with ...

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作者： Stambula, S. Jeż, K. Major, M. Said, R.M. Vizureanu, P. Nabialek, M. Jeż, B. Department of Physics Faculty of Production Engineering and Materials Technology Czestochowa University of Technology al. Armii Krajowej 19 Częstochowa42-200 Poland Faculty of Civil Engineering Czestochowa University of Technology Akademicka 3 Częstochowa42-200 Poland Center of Excellence Geopolymer and Green Technology Universiti Malaysia Perlis Taman Muhibbah Perlis Arau02600 Malaysia Faculty of Material Science and Engineering Gheorghe Asachi Technical University 64 Dumitru Mangeron Blvd Iasi700050 Romania Department of Technology and Automation Faculty of Mechanical Engineering and Computer Science Czestochowa University of Technology al. Armii Krajowej 19c Częstochowa42-200 Poland

During the magnetization process, the increase in magnetization is blocked by the increase in the magnetic field strength. The reason for these magnetic delays may be the magnetic anisotropy of the tested material. In crystalline materials, this phenomenon is quite well described and is related to the parameters of the crystal lattice. For amorphous alloys, which do not have long-range atomic order, this phenomenon has been much less studied. The paper presents the results of studies on the primary magnetization curves for two different alloys showing soft magnetic properties measured in three different directions: parallel to the casting direction, perpendicular to this direction, and perpendicular to the alloy surface. As indicated by the research, in amorphous materials, one can distinguish the direction of diffIcult magnetization and two easier ones, which means that the phenomenon of magnetic anisotropy occurs in them. © 2025 Polska Akademia Nauk. All rights reserved.

关键词： Amorphous alloys

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Value Stream Mapping for Smart Pharmaceutical Management in a Thai Hospital

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Procedia computer Science 2025年 253卷 495-504页

作者： Ganda Boonsothonsatit Rardchawadee Silapunt Supachai Vongbunyong Kamol Kaemarungsi Wirachchaya Chanpuypetch Nilubon Chonsawat Graduate School of Management and Innovation King Mongkut’s University of technology Thonburi Thailand Electronic and Telecommunication Engineering King Mongkut’s University of Technology Thonburi Thailand Hospital Automation Research Center Institute of Field Robotics King Mongkut’s University of Technology Thonburi Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Faculty of Agro-Industry Chiang Mai University Thailand Department of Agricultural Economy and Developement Faculty of Agriculture Chiang Mai University Thailand

The evolution of healthcare systems worldwide necessitates continual improvement in hospital management practices, particularly pharmaceutical management. This paper explores transforming traditional pharmaceutical management systems in large and medium-sized hospitals in Thailand into smart systems using Value Stream Mapping (VSM) as a guiding tool. Traditional systems often face challenges such as inefficiencies and medication errors. VSM, rooted in lean management principles, offers a structured approach to visualize and optimize processes. Additionally, smart technologies like automated inventory systems and electronic health records enhance pharmaceutical management effectiveness. Through a comprehensive research methodology encompassing case studies, data collection, and VSM analysis, this study identifies inefficiencies and proposes evidence-based strategies for improvement. Results from a case study highlight the benefits of VSM implementation in enhancing pharmacy operations, emphasizing improved patient care and operational efficiency. The implementation of a smart medication dispensing system shows promising results in reducing workload and accommodating increased patient arrivals. Continued evaluation and refinement are crucial for maximizing benefits and addressing emerging challenges effectively.

关键词： lean management smart medication dispensing smart pharmaceutical smart technologies Value Stream Mapping

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on Control of Network Systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Next-generation pediatric care:nanotechnology-based and AI-driven solutions for cardiovascular,respiratory,and gastrointestinal disorders

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World Journal of Pediatrics 2025年第1期21卷 8-28页

作者： Waldenice de Alencar Morais Lima Jackson G.de Souza Fátima García-Villén Julia Lira Loureiro Fernanda Nervo Raffin Marcelo A.C.Fernandes Eliana B.Souto Patricia Severino Raquel de M.Barbosa Laboratory of Galenic Pharmacy Department of PharmacyFederal University of Rio Grande Do NorteNatal59012-570Brazil InovAI Lab nPITI/IMDFederal University of Rio Grande Do NorteNatalRN59078-970Brazil Department of Pharmacy and Pharmaceutical Technology School of PharmacyUniversity of GranadaCampus of Cartuja18071GranadaSpain Department of Computer Engineering and Automation Federal University of Rio Grande Do NorteNatalRN59078-970Brazil Laboratory of Pharmaceutical Technology Faculty of PharmacyUniversity of PortoRua Jorge de Viterbo Ferreira2284050-313PortoPortugal Industrial Biotechnology Program University of Tiradentes(UNIT)AracajuSergipe49032-490Brazil Department of Pharmacy and Pharmaceutical Technology School of PharmacyUniversity of SevilleC/Professor García González241012SevilleSpain

Background Global pediatric healthcare reveals significant morbidity and mortality rates linked to respiratory,cardiac,and gastrointestinal disorders in children and newborns,mostly due to the complexity of therapeutic management in pediatrics and neonatology,owing to the lack of suitable dosage forms for these patients,often rendering them“therapeutic orphans”.The development and application of pediatric drug formulations encounter numerous challenges,including physiological heterogeneity within age groups,limited profitability for the pharmaceutical industry,and ethical and clinical *** drugs are used unlicensed or off-label,posing a high risk of toxicity and reduced *** these circumstances,some regulatory changes are being performed,thus thrusting research innovation in this *** sources Up-to-date peer-reviewed journal articles,books,government and institutional reports,data repositories and databases were used as main data *** Among the main strategies proposed to address the current pediatric care situation,nanotechnology is specially promising for pediatric respiratory diseases since they offer a non-invasive,versatile,tunable,site-specific drug *** engineering is in the spotlight as strategy to address pediatric cardiac diseases,together with theragnostic *** integration of nanotechnology and theragnostic stands poised to refine and propel nanomedicine approaches,ushering in an era of innovative and personalized drug delivery for pediatric ***,the intersection of drug repurposing and artificial intelligence tools in pediatric healthcare holds great *** promises not only to enhance efficiency in drug development in general,but also in the pediatric field,hopefully boosting clinical trials for this *** Despite the long road ahead,the deepening of nanotechnology,the evolution of tissue engineering,and the combination of traditional techniques with artificia

关键词： Artificial intelligence Conventional therapy Nanotechnology Pediatrics Theragnostics

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Unmanned aerial vehicle based multi-person detection via deep neural network models

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Frontiers in Neurorobotics 2025年 19卷 1582995页

作者： Alshehri, Mohammed Zahoor, Laiba AlQahtani, Yahya Alshahrani, Abdulmonem AlHammadi, Dina Abdulaziz Jalal, Ahmad Liu, Hui Department of Computer Science King Khalid University Abha Saudi Arabia Faculty of Computer Science Air University Islamabad Pakistan Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science and Engineering College of Informatics Korea University Seoul Korea Republic of Cognitive Systems Lab University of Bremen Bremen Germany Guodian Nanjing Automation Company Ltd. Nanjing China Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and Technology Nanjing China

Introduction: Understanding human actions in complex environments is crucial for advancing applications in areas such as surveillance, robotics, and autonomous systems. Identifying actions from UAV-recorded videos becomes more challenging as the task presents unique challenges, including motion blur, dynamic background, lighting variations, and varying viewpoints. The presented work develops a deep learning system that recognizes multi-person behaviors from data gathered by UAVs. The proposed system provides higher recognition accuracy while maintaining robustness along with dynamic environmental adaptability through the integration of different features and neural network models. The study supports the wider development of neural network systems utilized in complicated contexts while creating intelligent UAV applications utilizing neural networks. Method: The proposed study uses deep learning and feature extraction approaches to create a novel method to recognize various actions in UAV-recorded video. The proposed model improves identification capacities and system robustness by addressing motion dynamic problems and intricate environmental constraints, encouraging advancements in UAV-based neural network systems. Results: We proposed a deep learning-based framework with feature extraction approaches that may effectively increase the accuracy and robustness of multi-person action recognition in the challenging scenarios. Compared to the existing approaches, our system achieved 91.50% on MOD20 dataset and 89.71% on Okutama-Action. These results do, in fact, show how useful neural network-based methods are for managing the limitations of UAV-based application. Discussion: Results how that the proposed framework is indeed effective at multi-person action recognition under difficult UAV conditions. Copyright © 2025 Alshehri, Zahoor, AlQahtani, Alshahrani, AlHammadi, Jalal and Liu.

关键词： Drones

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OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering

arXiv

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arXiv 2025年

作者： Ding, Guanhua Xia, Yuxuan Guan, Runwei Wu, Qinchen Huang, Tao Ding, Weiping Sun, Jinping Mao, Guoqiang School of Electronic Information Engineering Beihang University Beijing100191 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou511455 China College of Science and Engineering James Cook University SmithfieldQLD4878 Australia School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China Faculty of Data Science City University of Macau 999078 China Research Laboratory of Smart Driving and Intelligent Transportation Systems Southeast University Nanjing210096 China

Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional model-based trackers typically rely on random vector-based Bayesian filters within the tracking-by-detection (TBD) framework but face limitations due to heuristic data association and track management schemes. In contrast, random finite set (RFS)-based Bayesian filtering handles object birth, survival, and death in a theoretically sound manner, facilitating interpretability and parameter tuning. In this paper, we present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli (PMB) filter while incorporating several key innovative designs within the TBD framework. Specifically, we propose a measurement-driven hybrid adaptive birth model for improved track initialization, employ adaptive detection probability parameters to effectively maintain tracks for occluded objects, and optimize density pruning and track extraction modules to further enhance overall tracking performance. Extensive evaluations on nuScenes and KITTI datasets show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods, thereby establishing a new benchmark for model-based 3D MOT and offering valuable insights for future research on RFS-based trackers in autonomous driving. Copyright © 2025, The Authors. All rights reserved.

关键词： Wiener filtering

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The In uence of Structural Relaxation on the Holstein-Primakoff Paraprocess

The In uence of Structural Relaxation on the Holstein-Primak...

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作者： Nabiaek, M. Postawa, P. Pietrusiewicz, P. Razak, K.A. Walters, S. Stambuaa, S. Jez, B. Department of Physics Faculty of Production Engineering and Materials Technology Czestochowa University of Technology al. Armii Krajowej 19 Czestochowa42-200 Poland Department of Technology and Automation Faculty of Mechanical Engineering and Computer Science Czestochowa University of Technology al. Armii Krajowej 19c Czestochowa42-200 Poland Center of Excellence Geopolymer and Green Technology Universiti Malaysia Perlis TamanMuhibbah Arau Perlis02600 Malaysia Advanced Engineering Centre University of Brighton BrightonBN2 4GJ United Kingdom

Structural relaxations play a large role in forming the properties of magnetic materials. In amorphous alloys, which are metastable materials, obtaining a relatively stable structure requires a properly designed thermal treatment process. Samples should be annealed above the Curie temperature and below the crystallization temperature. With the process designed in such a way, the domain structure is not interfered with and the formation of crystal grains does not occur. By analyzing the so-called Holstein-Primakoff paraprocess, it is possible to determine the in uence of magnetic atoms on the magnetization process in strong magnetic elds above the eld of effective anisotropy. © 2025 Polska Akademia Nauk. All rights reserved.

关键词： Amorphous alloys

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Kernel contextual fuzzy rule model based on conditional input space partitioning driven by data reconstruction in autoencoder and randomization-based neural networks

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Knowledge-Based Systems 2025年 320卷

作者： Zhang, Congcong Oh, Sung-Kwun Fu, Zunwei Pedrycz, Witold School of Mathematics and Statistics Linyi University Shandong Linyi276005 China Shandong Data Open Innovative Application Laboratory Linyi276005 China School of Automation and Electrical Engineering Linyi University Shandong Linyi276005 China School of Electrical & Electronic Engineering The University of Suwon Gyeonggi-do Hwaseong-si18323 Korea Republic of Department of Electronic Engineering Seokyeong University Seoul 02713 Korea Republic of Department of Computer Science The University of Suwon Gyeonggi-do Hwaseong-si18323 Korea Republic of Department of Electrical and Computer Engineering University of Alberta EdmontonAB T6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw 00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer/Istanbul Turkey

Fuzzy Rule-based Models (FRMs) have attracted significant interest in the field of machine learning owing to their modular architecture, robust design methodologies, and sound interpretability. This study introduces a novel Kernel Contextual Fuzzy Rule Model (KCFRM) designed to cope with regression tasks. A Kernel Contextual Fuzzy Clustering (KCFC) algorithm is proposed for conditional partitioning of the input space. Specifically, we incorporate the Mercer kernel into context fuzzy clustering to enhance the abilities of the model to distinguish, extract, and amplify useful features via nonlinear mapping, thereby generating more suitable information granules. Additionally, we employ an autoencoder's "encoding-decoding" mechanism to extract differences between data patterns and subsequently transform these into KCFC contexts via a conversion function, therefore leading to the creation of high-quality fuzzy sets. In the conclusion portion of fuzzy rules, conventional numerical or linear functions struggle to adequately describe the complex behavior present in local fuzzy regions. To mitigate this, we incorporate a Randomization-based Neural Network (RANN), capable of providing superior approximation capabilities and substantial computational efficiency. RANN overcomes traditional method constraints in representing complex behaviors within the fuzzy region. The inclusion of RANN results in more accurate and efficient conclusions for fuzzy rules. The uniqueness of this study lies in its holistic approach to designing fuzzy models with KCFC and RANN, improving the expressiveness of the rules and enhancing the generalization of the models. KCFRM's performance is assessed using various publicly available machine learning datasets, with experimental results underscoring its effectiveness and performance enhancements. © 2025

关键词： Fuzzy rules

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