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arXiv 2025年

作者： Ding, Kuiyuan Guo, Caili Yang, Yang Hu, Wuxia Eldar, Yonina C. Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Beijing Laboratory of Advanced Information Net works School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

The next generation of wireless communications seeks to deeply integrate artificial intelligence (AI) with user-centric communication networks, with the goal of developing AI-native networks that more accurately address user requirements. The rapid development of large language models (LLMs) offers significant potential in realizing these goals. However, existing efforts that leverage LLMs for wireless communication often overlook the considerable gap between human natural language and the intricacies of real-world communication systems, thus failing to fully exploit the capabilities of LLMs. To address this gap, we propose a novel LLM-driven paradigm for wireless communication that innovatively incorporates the nature language to structured query language (NL2SQL) tool. Specifically, in this paradigm, user personal requirements is the primary focus. Upon receiving a user request, LLMs first analyze the user intent in terms of relevant communication metrics and system parameters. Subsequently, a structured query language (SQL) statement is generated to retrieve the specific parameter values from a high-performance real-time database. We further utilize LLMs to formulate and solve an optimization problem based on the user request and the retrieved parameters. The solution to this optimization problem then drives adjustments in the communication system to fulfill the user’s requirements. To validate the feasibility of the proposed paradigm, we present a prototype system. In this prototype, we consider user-request centric semantic communication (URC-SC) system in which a dynamic semantic representation network at the physical layer adapts its encoding depth to meet user requirements, such as improved data transmission quality or reduced data transmission latency. Additionally, two LLMs are employed to analyze user requests and generate SQL statements, respectively. Simulation results demonstrate the effectiveness of the prototype in personalizing and addressing user r

关键词： Structured Query Language

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Multi-Objective Optimization Algorithms for Energy Management System in Microgrids Including Control Strategy

Multi-Objective Optimization Algorithms for Energy Managemen...

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Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES Companion), IEEE Symposia on

作者： Saiful Islam Sanaz Mostaghim Michael Hartmann Research Associate Otto von Guericke University Magdeburg SRH University of Applied Sciences Germany Chair of Computational Intelligence Faculty of Computer Science Otto von Guericke University Magdeburg Director of the Fraunhofer Institute for Transportation and Infrastructure Systems IVI Germany Dean of Berlin School of Technology SRH University of Applied Sciences Germany

ISBN: (数字)9798331519681

ISBN: (纸本)9798331519698

This paper focuses on multi-objective optimization methods to address control strategies in microgrid systems. We introduce an unique problem formulation capable of processing real-time data and propose a new methodology that integrates multiple energy management constraints within a multi-objective optimization framework. Additionally, we propose a set of objective functions tailored for this optimization approach. The goal of this paper is to implement several multi-objective optimization algorithms within this strategy and use a dataset to evaluate them. This approach is applied to optimize microgrid design based on energy requirements, while conducting optimal sizing analysis accompanied by sensitivity analysis. An Energy Management System has been developed with technical functionalities that incorporate various constraints. Our findings demonstrate that the NSGA-II algorithm delivers highly promising results, achieving significant reductions in energy costs and total net present value, while maximizing energy production from sustainable sources.

关键词： Renewable energy sources Costs Sensitivity analysis Heuristic algorithms Microgrids Production Reliability engineering Real-time systems Energy management systems Optimization

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Data Heterogeneity Challenge in Consumer Electronics: A Federated Learning Solution

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IEEE Transactions on Consumer Electronics 2025年

作者： Liu, Zhenyu Wu, Yunting Ge, Huanhuan Wang, Jinlong Lyu, Zhihan Qingdao University of Technology School of Information and Control Engineering Shandong Qingdao China School of Information and Control Engineering Innovation Institute for Sustainable Maritime Architecture Research and Technology Shandong Qingdao China School of Computer Science and Technology Xidian University Xi’an 710126 China

Federated learning(FL) enables collaborative model training across consumer electronic devices while preserving data privacy. However, the non-independent and identically distributed nature of data in real-world scenarios significantly impacts personalization performance. To address this challenge, we propose pFedPKD, in this framework, we first employ model pruning to simplify the global model, thereby enhancing its adaptability and improving the efficiency of knowledge transfer between different electronics devices. Moreover, we introduce an attention-based knowledge transfer mechanism, which leverages knowledge distillation to transfer the attention map and predictions of the pruned global model to the local models, thus improving their generalization capability and performance. Extensive experiments on three datasets demonstrate that pFedPKD consistently outperforms state-of-the-art methods, achieving higher personalization accuracy and robustness under heterogeneous data distributions. This work provides an efficient solution for applying FL in consumer electronics, offering improved adaptability and resource efficiency. © 1975-2011 IEEE.

关键词： consumer electronics Federated learning knowledge distillation Non-IID

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Automatic tuning of sparse matrix-vector multiplication on multicore clusters

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science China(Information sciences) 2015年第9期58卷 17-30页

作者： LI ShiGang HU ChangJun ZHANG JunChao ZHANG YunQuan State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences School of Computer and Communication Engineering University of Science and Technology Beijing Department of Computer Science University of Illinois at Urbana-Champaign

To have good performance and scalability, parallel applications should be sophisticatedly optimized to exploit intra-node parallelism and reduce inter-node communication on multicore clusters. This paper investigates the automatic tuning of the sparse matrix-vector(Sp MV) multiplication kernel implemented in a partitioned global address space language, which supports a hybrid thread- and process-based communication layer for multicore systems. One-sided communication is used for inter-node data exchange, while intra-node communication uses a mix of process shared memory and multithreading. We develop performance models to facilitate selecting the best configuration of threads and processes hybridization as well as the best communication pattern for Sp MV. As a result, our tuned Sp MV in the hybrid runtime environment consumes less memory and reduces inter-node communication volume, without damaging the data locality. Experiments are conducted on 12 real sparse matrices. On 16-node Xeon and 8-node Opteron clusters, our tuned Sp MV kernel gets on average 1.4X and 1.5X improvement in performance over the well-optimized process-based message-passing implementation, respectively.

关键词： Sp MV PGAS hybridization model-driven multicore clusters

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Coupled Matrix Factorization for Question Similarity

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Chinese Journal of Electronics 2016年第4期25卷 665-671页

作者： WEI Chuyuan LI Fangfang ZHAN Qiang FAN Xiongzhong School of Computer Science and Technology Beijing Institute of Technology Department of Computer Beijing University of Civil Engineering and Architecture Advanced Analytics Institute University of TechnologySydneyAustralia

Community question answering(CQA)has provided an increasingly popular service where users ask and answer questions and access historical questionanswer *** a fundamental task in CQA,question similarity measure is to compute the similarity between the queried question and the historical questions which have been solved by other *** mine and use the most important semantic features as the semantic representation of questions,and try to incorporate the couplings of semantic features into vector space *** propose Coupled question similarity(CQS) model,and compute the similarity in matrix factorization *** conducted on real CQA data sets demonstrate that with the incorporation of such couplings,the performance of sentence similarity is improved compared to a variety of baseline methods significantly.

关键词： Sentence similarity Community question answering(CQA) Matrix factorization(MF) Coupled question similarity(CQS)

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Multimodal Large Models Are Effective Action Anticipators

arXiv

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arXiv 2025年

作者： Wang, Binglu Tian, Yao Wang, Shunzhou Yang, Le College of Information and Control Engineering Xi'an University of Architecture and Technology Xi'An710055 China The School of Astronautics Northwestern Polytechnical University Xi'An710072 China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University Shenzhen518055 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China The School of Information Science and Technology University of Science and Technology of China Hefei230022 China

The task of long-term action anticipation demands solutions that can effectively model temporal dynamics over extended periods while deeply understanding the inherent semantics of actions. Traditional approaches, which primarily rely on recurrent units or Transformer layers to capture long-term dependencies, often fall short in addressing these challenges. Large Language Models (LLMs), with their robust sequential modeling capabilities and extensive commonsense knowledge, present new opportunities for long-term action anticipation. In this work, we introduce the ActionLLM framework, a novel approach that treats video sequences as successive tokens, leveraging LLMs to anticipate future actions. Our baseline model simplifies the LLM architecture by setting future tokens, incorporating an action tuning module, and reducing the textual decoder layer to a linear layer, enabling straightforward action prediction without the need for complex instructions or redundant descriptions. To further harness the commonsense reasoning of LLMs, we predict action categories for observed frames and use sequential textual clues to guide semantic understanding. In addition, we introduce a Cross-Modality Interaction Block, designed to explore the specificity within each modality and capture interactions between vision and textual modalities, thereby enhancing multimodal tuning. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed ActionLLM framework, encouraging a promising direction to explore LLMs in the context of action anticipation. Code is available at https://***/2tianyao1/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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A METHOD TO PARALLELIZE TRIDIAGONAL SOLVERS

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PARALLEL COMPUTING 1991年第2-3期17卷 181-188页

作者： MULLER, SM SCHEERER, D Institute for Computer Architecture Computer Science Department (Bau 36) University of Saarland D-6600 Saarbrücken 11 Germany

A general method to parallelize any tridiagonal solver is presented. The method, based ona suitable decomposition of the numerical problem, is rather simple but efficient. The communication overhead stays small and with respect to stability and vectorizability the parallel algorithms have nearly the same qualities as their sequential counterparts. When applied to standard sequential solvers the method yields some well-known as well as many new parallel algorithms.

关键词： PARALLEL TRIDIAGONAL SOLVERS WANG DECOMPOSITION PARALLEL LU-FACTORIZATION

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Runtime Engine for Dynamic Profile Guided Stride Prefetching

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Journal of computer science & Technology 2008年第4期23卷 633-643页

作者：邹琼李晓峰章隆兵 Department of Computer Science University of Science and Technology of China Intel China Research Center Key Laboratorg of Computer System and Architecture Institute of Computing TechnologyChinese Academy of Sciences

Stride prefetching is recognized as an important technique to improve memory access performance. The prior work usually profiles and/or analyzes the program behavior offline, and uses the identified stride patterns to guide the compilation process by injecting the prefetch instructions at appropriate places. There are some researches trying to enable stride prefetching in runtime systems with online profiling, but they either cannot discover cross-procedural prefetch opportunity, or require special supports in hardware or garbage collection. In this paper, we present a prefetch engine for JVM （Java Virtual Machine）. It firstly identifies the candidate load operations during just-in-time （JIT） compilation, and then instruments the compiled code to profile the addresses of those loads. The runtime profile is collected in a trace buffer, which triggers a prefetch controller upon a protection fault. The prefetch controller analyzes the trace to discover any stride patterns, then modifies the compiled code to inject the prefetch instructions in place of the instrumentations. One of the major advantages of this engine is that, it can detect striding loads in any virtual code places for both regular and irregular code, not being limited with plain loop or procedure scopes. Actually we found the cross-procedural patterns take about 30% of all the prefetchings in the representative Java benchmarks. Another major advantage of the engine is that it has runtime overhead much smaller （the maximal is less than 4.0%） than the benefits it brings. Our evaluation with Apache Harmony JVM shows that the engine can achieve an average 6.2% speed-up with SPECJVM98 and DaCapo on Intel Pentium 4 platform, in spite of the runtime overhead.

关键词： stride prefetching dynamic profiling runtime system

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From hand sketches to daylight performance: a mixed-input neural prediction framework

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Journal of Building Performance Simulation 2025年

作者： Le, Thanh-Luan Chong, HeeGun Le, Binh M. Nguyen-Xuan, H. Kim, Sung-Ah Department of Global Smart City Sungkyunkwan University Suwon Korea Republic of Department of Computer Science & Engineering Sungkyunkwan University Suwon Korea Republic of CIRTech Institute Hutech University Ho Chi Minh City Viet Nam Department of Architecture Sungkyunkwan University Suwon Korea Republic of

Deep learning can accelerate daylight analysis, but existing methods require multiple tools and complex coding. This paper proposes a streamlined framework enabling daylight predictions from architectural hand-sketches with real-time 3D visualization. The method is implemented based on three main modules: (1) hand-sketch recognition and conversion, (2) mixed-input neural network (MINN), and (3) mixed-input pix2pix (MIpix2pix). Three modules were integrated into a concept application, allowing a comprehensive daylight prediction from a hand-sketched floor plan. Training data were generated using Rhino, Grasshopper, PlanFinder, Ladybug, and Honeybee. The MINN achieved a coefficient of determination above 0.92 for spatial daylight autonomy and 0.959 for annual sunlight exposure. The MIpix2pix2 model generate useful daylight illuminance images with SSIM values exceeding 0.93, closely aligning with simulations. This high-accuracy, fully integrated approach streamlines daylight analysis from concept to evaluation. By simplifying AI-based predictions, the framework offers a practical, efficient alternative to existing workflows. © 2025 International Building Performance Simulation Association (IBPSA).

关键词： Augmented reality

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Quantifying Indoor Air Quality Determinants in Green-Certified Buildings Using a Hybrid Machine Learning Method: A Case Study in Florida

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Indoor Air 2025年第1期2025卷

作者： Zhang, He Srinivasan, Ravi Yang, Xu Ganesan, Vikram Zhang, Junxue Zhang, Han Institute of Creativity and Innovation Xiamen University Fujian Xiamen China UrbSys Laboratory M.E. Rinker Sr. School of Construction Management University of Florida GainesvilleFL United States School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an China Lennox GainesvilleFL United States School of Civil Engineering and Architecture Jiangsu University of Science and Technology Jiangsu Zhenjiang China School of Mathematical Sciences University of Electronic Science and Technology of China Sichuan Chengdu China

This study investigates the indoor air quality (IAQ) conditions in green-certified buildings and examines the factors influencing them. An integrated IoT sensing system was implemented indoors and outdoors to assess the levels of particulate matter, nitrogen dioxide, and ozone at five Leadership in Energy and Environmental Design (LEED)-certified and five non-LEED educational buildings in Central Florida. Building-related characteristics were collected through walk-through surveys, BACnet systems, and construction drawings. An algorithm model based on support vector machine (SVM) and nonnegative matrix factorization (NMF) was developed to analyze the features of pollutants and the relative contribution of different influencing factors. The findings reveal that concentrations of target pollutants are generally lower in LEED buildings compared to non-LEED buildings. Although IAQ influencing factors are generally similar between LEED and non-LEED buildings, the weighted contribution ratios of specific factors, particularly for indoor nitrogen dioxide and ozone, vary significantly. The concentration of pollutants in non-LEED buildings is more susceptible to adverse environmental factors. The SVM-NMF model demonstrates significant advantages in nonlinear feature extraction and handling multicollinearity issues. It surpasses multiple linear regression and backpropagation neural network models in analyzing multidimensional indoor air data by 26.9% and 18% (p Copyright © 2025 He Zhang et al. Indoor Air published by John Wiley & Sons Ltd.

关键词： Indoor air pollution

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