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arXiv 2025年

作者： Wong, Hiu-Tung Lee, Darrick Yan, Hong Centre for Intelligent Multidimensional Data Analysis Science and Technology Park Hong Kong School of Mathematics University of Edinburgh United Kingdom Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong

Latent space matching, which consists of matching distributions of features in latent space, is a crucial component for tasks such as adversarial attacks and defenses, domain adaptation, and generative modelling. Metrics for probability measures, such as Wasserstein and maximum mean discrepancy, are commonly used to quantify the differences between such distributions. However, these are often costly to compute, or do not appropriately take the geometric and topological features of the distributions into consideration. Persistent homology is a tool from topological data analysis which quantifies the multi-scale topological structure of point clouds, and has recently been used as a topological regularizer in learning tasks. However, computation costs preclude larger scale computations, and discontinuities in the gradient lead to unstable training behavior such as in adversarial tasks. We propose the use of principal persistence measures, based on computing the persistent homology of a large number of small subsamples, as a topological regularizer. We provide a parallelized GPU implementation of this regularizer, and prove that gradients are continuous for smooth densities. Furthermore, we demonstrate the efficacy of this regularizer on shape matching, image generation, and semi-supervised learning tasks, opening the door towards a scalable regularizer for topological features. © 2025, CC BY.

关键词： Probability distributions

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LLM Knows Geometry Better than Algebra: Numerical Understanding of LLM-Based Agents in A Trading Arena

arXiv

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arXiv 2025年

作者： Ma, Tianmi Du, Jiawei Huang, Wenxin Wang, Wenjie Xie, Liang Zhong, Xian Zhou, Joey Tianyi Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology China Hubei Key Laboratory of Big Data Intelligent Analysis and Application Hubei University China Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Computing National University of Singapore Singapore School of Science Wuhan University of Technology China

Recent advancements in large language models (LLMs) have significantly improved performance in natural language processing tasks. However, their ability to generalize to dynamic, unseen tasks, particularly in numerical reasoning, remains a challenge. Existing benchmarks mainly evaluate LLMs on problems with predefined optimal solutions, which may not align with real-world scenarios where clear answers are absent. To bridge this gap, we design the Agent Trading Arena, a virtual numerical game simulating complex economic systems through zero-sum games, where agents invest in stock portfolios. Our experiments reveal that LLMs, including GPT-4o, struggle with algebraic reasoning when dealing with plain-text stock data, often focusing on local details rather than global trends. In contrast, LLMs perform significantly better with geometric reasoning when presented with visual data, such as scatter plots or K-line charts, suggesting that visual representations enhance numerical reasoning. This capability is further improved by incorporating the reflection module, which aids in the analysis and interpretation of complex data. We validate our findings on NASDAQ STOCK dataset, where LLMs demonstrate stronger reasoning with visual data compared to text. Our code and data are publicly available at https://***/wekjsdvnm/***. © 2025, CC BY.

关键词： Natural language processing systems

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A secure and trustworthy blockchain-assisted edge computing architecture for industrial internet of things

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Scientific Reports 2025年第1期15卷 1-17页

作者： Asaithambi, Sasikumar Nallusamy, Senthilkumar Yang, Jing Prajapat, Sunil Kumar, Gyanendra Rathore, Pramod Singh Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of Science & Technology Tamil Nadu Kattankulathur 603203 India Centre for Applied Nanomaterials Chennai Institute of Technology Tamil Nadu Chennai 600069 India Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia Department of Mathematics Central University of Himachal Pradesh Dharamshala 176215 India Department of IoT and Intelligent Systems Manipal University Jaipur Jaipur 303007 India Department of Computer and Communication Engineering Manipal University Jaipur Jaipur 303007 India

The Industrial Internet of Things (IIoT) seeks to improve smart factory productivity by leveraging automation and scalability. For automation in industry, optimization, collaboration and protection, and scalability, the IoTs paradigm, technology for communication and information, and intelligent systems are integrated as a single organism. This article presents a blockchain-assisted safe data-sharing mechanism that provides security requirements in the industry using IoT. End-to-end authentication is developed based on the blockchain’s reputation, and the smart contract is used to validate nodes’ security measures. Through integrity verification and categorization in node terminals and industry, the blockchain paradigm manages data collection and dissemination. An efficient proof of authentication (PoAh) consensus mechanism is created using the blockchain network to build a collaborative network to preserve logs and verification data in the industrial IoT. It accomplishes trustworthy authentication and endpoint activity tracing, and edge computing is used in blockchain nodes to offer device authentication processes that utilize smart contracts and PoAh. According to an experimental study, the proposed architecture lowers the authentication time and obtains a high response rate. The proposed system’s service time shows the efficiency of PoAh-based blockchain architecture for industrial IoT compared with existing works. Finally, we evaluated various block sizes to ensure an efficient transaction rate. The outcomes demonstrate the practicality of the suggested and put-into-practice architecture, distinguished by enhanced data audibility, device data ownership, security, and privacy while utilizing decentralized storage. © The Author(s) 2025.

关键词： Authentication Blockchain Edge computing Industrial internet of things PoAh

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Experimental evaluation of an advanced predictive control technique for variable-speed wind turbine systems

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International Journal of Electrical Power and Energy Systems 2025年 168卷

作者： Echiheb, Farah Majout, Btissam Kafazi, Ismail EL Bossoufi, Badre Rabhi, Abdelhamid Bizon, Nicu Zhilenkov, Anton Mobayen, Saleh LIMAS Laboratory Faculty of Sciences Dhar El Mahraz Sidi Mohammed Ben Abdellah University Fez Morocco SMARTilab Laboratory Moroccan School of Engineering Sciences Rabat Morocco MIS Laboratory University of Picardie Jules Verne Amiens80000 France The National University of Science and Technology Politechnica Bucharest Pitești University Centre Pitesti110040 Romania Department of Cyber-Physical Systems St. Petersburg State Marine Technical University Saint-Petersburg190121 Russia of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou640301 Taiwan Energy Systems Research Center Khazar university Mahasti str. 41 BakuAZ1096 Azerbaijan

Wind energy control plays a crucial role in optimizing the performance of Doubly Fed Induction Generators (DFIGs) by maximizing power extraction while ensuring stable grid integration. To achieve this, a Maximum Power Point Tracking (MPPT) strategy is employed to determine the optimal mechanical speed and reference power, enabling efficient wind energy conversion. However, maintaining precise control over the active and reactive power exchange remains a challenge, especially under varying operating conditions. This paper presents an experimental study on the application of deadbeat predictive control to a DFIG-based wind energy system, integrating MPPT for optimal power tracking. The study, conducted using a DSPACE DS1104 test bench, includes the development of a comprehensive mathematical model, an analysis of the deadbeat control strategy, and its implementation in MATLAB/Simulink. Experimental validation demonstrates that the proposed control method achieves faster response time (0.0504 s), reduced Total Harmonic Distortion (THD) to 0.5 %, and enhanced robustness against parameter variations, ensuring both maximum power extraction and high-quality power injection into the grid. These results confirm the superiority of the MPPT-integrated deadbeat predictive control over conventional methods in terms of efficiency, power quality, and system stability. However, while this method shows promising results, its implementation in real-world, large-scale systems requires further investigation to address challenges such as grid stability under fluctuating conditions and the scalability of the control strategy. In terms of practical implications, the proposed control method offers potential for improving the performance and efficiency of DFIG-based wind energy systems, contributing to more sustainable and reliable energy production. The research also holds social implications by advancing renewable energy technologies, which are essential for reducing dependency on fossil

关键词： MATLAB

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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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Delving into Invisible Semantics for Generalized One-shot Neural Human Rendering

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IEEE Transactions on Visualization and Computer Graphics 2025年

作者： Lin, Yihong Xu, Xuemiao Zhang, Huaidong Xu, Cheng Li, Weijie Xie, Yi Qin, Jing He, Shengfeng South China University of Technology Guangdong China Guangdong Engineering Center for Large Model and GenAI Technology State Key Laboratory of Subtropical Building and Urban Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China South China University of Technology China Hong Kong Polytechnic University Centre for Smart Health Hong Kong CAS-Hong Kong Joint Laboratory for Multimodal Medical Molecular Imaging Hong Kong Singapore Management University School of Computing and Information Systems Singapore

Traditional human neural radiance fields often overlook crucial body semantics, resulting in ambiguous reconstructions, particularly in occluded regions. To address this problem, we propose the Super-Semantic Disentangled Neural Renderer (SSD-NeRF), which employs rich regional semantic priors to enhance human rendering accuracy. This approach initiates with a Visible-Invisible Semantic Propagation module, ensuring coherent semantic assignment to occluded parts based on visible body segments. Furthermore, a Region-Wise Texture Propagation module independently extends textures from visible to occluded areas within semantic regions, thereby avoiding irrelevant texture mixtures and preserving semantic consistency. Additionally, a view-aware curricular learning approach is integrated to bolster the model's robustness and output quality across different viewpoints. Extensive evaluations confirm that SSD-NeRF surpasses leading methods, particularly in generating quality and structurally semantic reconstructions of unseen or occluded views and poses. © 2025 IEEE.

关键词： Semantics

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Physical information-enhanced graph neural network for predicting phase separation

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Chinese Physics B 2024年第7期33卷 278-283页

作者：张亚强王煦文王雅楠郑文 Institute of Public-Safety and Big Data College of Computer Science and Technology(College of Data Science)Taiyuan University of TechnologyJinzhong 030600China Shanxi Engineering Research Centre for Intelligent Data Assisted Treatment Changzhi Medical CollegeChangzhi 046000China Innovation Academy for Microsatellites of Chinese Academy of Sciences Shanghai 200050China

Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here,we present a physical information-enhanced graph neural network(PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we design an order parameter based on local density to measure the evolution of phase separation and analyze the systematic changes with different repulsion coefficients and different Schmidt *** results demonstrate that our model can achieve long-term accurate predictions of order parameters without requiring complex handcrafted features. These results prove that graph neural networks can become new tools and methods for predicting the structure and properties of complex physical systems.

关键词： graph neural network phase separation machine learning dissipative particle dynamics

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Optimization of fracture reduction robot controller based on improved sparrow algorithm

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Biomimetic Intelligence & Robotics 2023年第4期3卷 16-28页

作者： Baichuan An Jianwen Chen Hao Sun Minghuan Yin Zicheng Song Chao Zhuang Cheng Chang Minghe Liu School of Artificial Intelligence and Data Science Hebei University of TechnologyTianjin 300401China Limb Deformity Correction and Function Reconstruction Department Southern University of Science and Technology HospitalShenzhen 518000China Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology Ministry of EducationTianjin 300401China

The accuracy of a fracture reduction robot(FRR)is critical for ensuring the safety of *** the repositioning accuracy of a FRR,reducing the error,and realizing a safer and more stable folding motion is *** achieve this,a sparrow search algorithm(SSA)based on the Levy flight operator was proposed in this study for self-tuning the robot controller *** inverse kinematic analysis of the FRR was also *** robot dynamics model was established using Simulink,and the inverse dynamics controller for the fracture reduction mechanism was designed using the computed torque control *** simulation and physical experiments were also *** actual motion trajectory of the actuator drive rod and its error with a desired trajectory was obtained through *** optimized Levy-sparrow search algorithm(Levy-SSA)crack reduction robot controller demonstrated an overall reduction of two orders of magnitude in the reduction error,with an average error reduction of 98.74%compared with the traditional unoptimized *** Levy-SSA increased the convergence of the crack reduction robot control system to the optimal solution,improved the accuracy of the motion trajectory,and exhibited important implications for robot controller optimization.

关键词： Fracture reduction robot The sparrow search algorithm Levy flight Reduction accuracy

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Programming Variational Quantum Circuits with Quantum-Train Agent

Programming Variational Quantum Circuits with Quantum-Train ...

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Chen-Yu Liu Samuel Yen-Chi Chen Kuan-Cheng Chen Wei-Jia Huang Yen-Jui Chang Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Hon Hai (Foxconn) Research Institute Taipei Taiwan Wells Fargo New York NY USA Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

This study introduces the Quantum-Train Quantum Fast Weight Programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates for the classical slow programmer controlling the fast programmer VQC. By optimizing quantum and classical parameter management, QT-QFWP significantly reduces parameters (by 70–90%) compared to Quantum Long Short-Term Memory (QLSTM) and Quantum Fast Weight Programmer (QFWP) while maintaining accuracy. Benchmarking on time-series tasks—including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW)—demonstrates superior efficiency and predictive accuracy. QT-QFWP is particularly advantageous for near-term quantum systems, addressing qubit and gate fidelity constraints, enhancing VQC deployment in time-sensitive applications, and expanding quantum computing’s role in machine learning.

关键词： Quantum system Accuracy Computational modeling Qubit Neural networks Programming Predictive models Quantum circuit Physics Long short term memory

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Research on the psychological classification of violent crime based on a convolutional neural network

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16397-16408页

作者： Li, Hui Gao, Guandong Xiao, Ke Song, Shengzun College of Information Science and Technology Hebei Agricultural University Hebei Baoding071001 China Department of Information Management The National Police University for Criminal Justice Hebei Baoding071001 China The Centre of Data Science and Intelligent Correction Technology The National Police University for Criminal Justice Hebei Baoding071001 China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural University Hebei Baoding071001 China Department of Penology The National Police University for Criminal Justice Hebei Baoding071001 China

This paper uses a text classification algorithm in the context of criminal psychology to scientifically evaluate the correctional effects among offenders in the hopes of preventing recidivism. The proposed algorithm uses an improved convolutional neural network (CNN) and feature fusion to carry out the psychological attribution of violent crime based on criminal fact texts, which are divided into three types: the premeditated type, the impulsive type, and the pathological type. First, a text set of criminal facts with labels was constructed by criminal psychology experts. Then, the numerical features are extracted by the word count and a keyword extraction method based on the criminal feature dictionaries, which are established by criminal psychology experts. The improved CNN model is used to extract the text features by replacing a fully connected layer with the global average pooling layer. Finally, after fusing the numerical features and the text features, softmax function is used to calculate the probability of each category and divide the attribution type. To verify the effect of the algorithm, the proposed model is evaluated and compared with the original CNN, the SVM based on TF-IDF and word count, random forest, and some integration methods. Experimental results show that the proposed algorithm is better in accuracy, recall, precision, and F1 in the micro and macro scopes, with the highest accuracy of 98%. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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