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arXiv 2025年

作者： Zhao, Xinxin Li, Haoyang Zhang, Jing Huang, Xinmei Zhang, Tieying Chen, Jianjun Shi, Rui Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China ByteDance China

Index recommendation is essential for improving query performance in database management systems (DBMSs) through creating an optimal set of indexes under specific constraints. Traditional methods, such as heuristic and learning-based approaches, are effective but face challenges like lengthy recommendation time, resource-intensive training, and poor generalization across different workloads and database schemas. To address these issues, we propose LLMIdxAdvis, a resource-efficient index advisor that uses large language models (LLMs) without extensive fine-tuning. LLMIdxAdvis frames index recommendation as a sequence-to-sequence task, taking target workload, storage constraint, and corresponding database environment as input, and directly outputting recommended indexes. It constructs a high-quality demonstration pool offline, using GPT-4-Turbo to synthesize diverse SQL queries and applying integrated heuristic methods to collect both default and refined labels. During recommendation, these demonstrations are ranked to inject database expertise via in-context learning. Additionally, LLMIdxAdvis extracts workload features involving specific column statistical information to strengthen LLM’s understanding, and introduces a novel inference scaling strategy combining vertical scaling (via "Index-Guided Major Voting" and Best-of-N) and horizontal scaling (through iterative "self-optimization" with database feedback) to enhance reliability. Experiments on 3 OLAP and 2 real-world benchmarks reveal that LLMIdxAdvis delivers competitive index recommendation with reduced runtime, and generalizes effectively across different workloads and database schemas. Copyright © 2025, The Authors. All rights reserved.

关键词： Query languages

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Mining User–Item Interactions via knowledge Graph for Recommendation

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ACM Transactions on Recommender Systems 2025年第3期3卷 1-19页

作者： Shenghao Liu Lingyun Lu Bang Wang Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Smart Internet Technology School of Electronic Information and Communications Huazhong University of Science and Technology (HUST) Wuhan China

Introducing a knowledge Graph (KG) to facilitate a recommender system has become a tendency in recent years. Many existing methods leverage KGs to obtain side information of items to promote item representation learning for enhancing recommendation performance. However, they ignore that KGs also may contribute to better user representation learning. To solve this issue, we propose a novel algorithm, the KIGR (knowledge-aware Interaction Graph for Recommendation), to mine user–item interactions via KGs for assisting user representation learning. Specifically, a user–item interaction is encoded by attentively summing up the relation embedding about the item in the KG. Then, an unsupervised learning method is used to group the user–item interactions into different latent types. Further, a user–item interaction graph is divided into several subgraphs, which is referred to as a knowledge-aware Interaction Graph, making each subgraph only contain one latent type of interaction. Finally, user representation is the fusion of user interest embedding, which is learned on the knowledge-aware interaction graph, whereas item representation is learned on the KG. Experimental results on MovieLens, LastFM and Amazon-Book validate that the proposed KIGR has a superior performance compared with the state-of-the-art algorithms.

关键词： Recommendation system knowledge graph graph neural network

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Real-Time Proportional-Integral-Derivative(PID)Tuning Based on Back Propagation(BP)Neural Network for Intelligent Vehicle Motion Control

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Computers, Materials & Continua 2025年第5期83卷 2375-2401页

作者： Liang Zhou Qiyao Hu Xianlin Peng Qianlong Liu School of Information Science and Technology Northwest UniversityXi’an710127China State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services Northwest UniversityXi’an710127China Generative Artificial Intelligence and Mixed Reality Key Laboratory of Higher Education Institutions in Shaanxi Province Xi’an710127China Shaanxi Silk Road Cultural Heritage Digital Protection and Inheritance Collaborative Innovation Center Xi’an710127China School of Art Northwest UniversityXi’an710127China Network and Data Center Northwest UniversityXi’an710127China

Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control *** modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and ***,deficiencies in these systems can lead to significant operational *** paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter *** paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and *** BP neural network is first trained to capture the nonlinear dynamic characteristics of the *** network is then combined with the PID controller to forma hybrid control *** output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight *** experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 ***,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.

关键词： PID control backpropagation neural network hybrid control nonlinear dynamic processes edge intelligence

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A review on global spatial distribution,sources and toxicity of perfluoroalkyl acid and prospect in the cryosphere

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Journal of Mountain Science 2025年第1期22卷 167-183页

作者： LI Bowen Du Wentao KANG Shichang CHEN Jizu JIANG Youyan YIN Lichen LU Changsheng LIU Hongyi State Key Laboratory of Cryospheric Science and frozen soil engineering Northwest Institute of Eco-Environment and ResourcesChinese Academy of Sciences(CAS)Lanzhou 730000China University of Chinese Academy of Sciences Beijing 100049China Gansu Provincial Industry Technology Center of Intelligent Equipment and Big Data for Disaster Prevention Northwest Institute of Eco-Environment and ResourcesChinese Academy of Sciences(CAS)Lanzhou 730000China Lanzhou Regional Climate Center Lanzhou 730000China School of Earth Sciences and Spatial Information Engineering Hunan University of Science and TechnologyXiangtan 411201China School of Energy and Power Engineering Lanzhou University of TechnologyLanzhou 730000China Zhangye Meteorological Service Zhangye 734000China

Perfluoroalkyl acid analogs(PFAAs)are a class of chemically stable environmentally persistent organic pollutants(POPs)that are difficult to degrade and have a strong capacity to accumulate in the human *** have been found to be biotoxic to humans and have been detected in various environmental media,especially in the cryosphere at trace *** cryosphere,sensitively responds to climate change,plays a crucial role in the global water,carbon and energy ***,researches on cryosphere PFAAs especially in Tibetan Plateau(TP)is ***,we summarize the physicochemical properties,physiological toxicity,spatiotemporal distribution,sources,diffusion and migration pathways,as well as analysis and removal methods of PFAAs in the cryosphere *** results show that PFAAs pollutants are mainly produced and distributed in the more economically developed countries in Europe and the United States,as well as in East Asia,and PFAAs can be transported by atmospheric circulation and water cycle to remote regions including cryosphere *** current detection methods for PFAAs in cryosphere need to be further refined for increased accuracy and *** is also a need to develop more effective removal methods that will reduce the environmental and human threats posed by these ***,we propose key scientific questions for future research in cryosphere including PFAAs redistribution influenced by cryosphere changes,human activities,and the interaction of other spheres.

关键词： Perfluoroalkyl acid Cryosphere Physiological toxicity Analytical method Removal method

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CMD: A Cache-Assisted GPU Memory Deduplication Architecture

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Zhao, Wei Feng, Dan Tong, Wei Wei, Xueliang Wu, Bing Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of data storage systems and Technology China

Massive off-chip accesses in GPUs are the main performance bottleneck. We find that many writes are duplicate, and the duplication can be inter-dup and intra-dup. While inter-dup means different memory blocks are identical, and intra-dup means all the 4B elements in a line are the same. In this work, we propose a cache-assisted GPU memory deduplication architecture named CMD to reduce the off-chip accesses via utilizing the data duplication in GPU applications. CMD includes three key design contributions which aim to reduce the three kinds of accesses: (1) A novel GPU memory deduplication architecture that removes the intra-dup and inter-dup lines. We design several techniques to manage duplicate blocks, reducing massive off-chip writes. (2) We propose a cache-assisted read scheme to reduce the reads to duplicate data. When an L2 cache miss wants to read the duplicate block, if the reference block has been fetched to L2 and it is clean, we can copy it to the L2 missed block without accessing off-chip DRAM. As for the reads to intra-dup data, CMD uses the on-chip metadata cache to get the data. (3) When a cache line is evicted, the clean sectors in the line are invalidated while the dirty sectors are written back. However, most read-only victims are re-referenced from DRAM more than twice. Therefore, we add a full-associate FIFO to accommodate the read-only (it is also clean) victims to reduce the re-reference counts. Experiments show that CMD can decrease the off-chip accesses by 31.01%, reduce the energy by 32.78% and improve performance by 42.53%. Besides, CMD can improve the performance of memory-intensive workloads by 57.56%. © 2025 IEEE.

关键词： Cache memory

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OmniSQL: Synthesizing High-quality Text-to-SQL data at Scale

arXiv

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arXiv 2025年

作者： Li, Haoyang Wu, Shang Zhang, Xiaokang Huang, Xinmei Zhang, Jing Jiang, Fuxin Wang, Shuai Zhang, Tieying Chen, Jianjun Shi, Rui Chen, Hong Li, Cuiping Engineering Research Center of Database and Business Intelligence MOE China School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China ByteDance Inc China

Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements in large language models (LLMs) have significantly enhanced text-to-SQL performance, existing approaches face notable limitations in real-world text-to-SQL applications. Prompting-based methods often depend on closed-source LLMs, which are expensive, raise privacy concerns, and lack customization. Fine-tuning-based methods, on the other hand, suffer from poor generalizability due to the limited coverage of publicly available training data. To overcome these challenges, we propose a novel and scalable text-to-SQL data synthesis framework for automatically synthesizing large-scale, high-quality, and diverse datasets without extensive human intervention. Using this framework, we introduce SynSQL-2.5M, the first million-scale text-to-SQL dataset, containing 2.5 million samples spanning over 16,000 synthetic databases. Each sample includes a database, SQL query, natural language question, and chain-of-thought (CoT) solution. Leveraging SynSQL-2.5M, we develop OmniSQL, a powerful open-source text-to-SQL model available in three sizes: 7B, 14B, and 32B. Extensive evaluations across nine datasets demonstrate that OmniSQL achieves state-of-the-art performance, matching or surpassing leading closed-source and open-source LLMs, including GPT-4o and DeepSeek-V3, despite its smaller size. We release all code, datasets, and models to support further research. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

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Temperature distribution and damage of thrombus induced by pulsed laser

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Infrared Physics and technology 2025年 148卷

作者： Zhao, Xiaoli Qiu, Pengtianyu Xie, Enci Zhang, Chenxi Wang, Guangyu Fu, Bo Key Laboratory of Precision Opto-Mechatronics Technology of Education Ministry School of Instrumentation and Optoelectronic Engineering Beihang University Beijing100191 China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology School of Engineering Medicine Beihang University Beijing100191 China

In the process of laser thermal ablation, real-time observation of temperature distribution and damage is helpful to guide the laser thrombolysis process. However, in actual operation, it is difficult to predict the effect of laser parameters on the thermal effect of thrombus. It is of great significance to use finite element simulation method to study the influence of different laser parameters, such as power, time, spot size, pulse width, pulse interval, absorption coefficient, and laser mode on the surface temperature and damage of thrombus. The influence of power and time on temperature and damage in the simulation are verified by experiment, and the trend of experimental variation is consistent with the simulation results. The simulation results will provide theoretical guidance for selecting the optimal laser parameters in subsequent experiments. © 2025 Elsevier B.V.

关键词： Temperature distribution

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VIPNet: Combining Viewpoint information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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Experimental and Modeling Study on Void Fraction using Thermal Distribution Sensor in Horizontal Annular Flow

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IEEE Sensors Journal 2025年第10期25卷 17640-17649页

作者： Zhao, Ning Chen, Xiangyi Sun, Mingcong Song, Yajing Fang, Lide Li, Zhibin Hebei University College of Quality and Technical Supervision Hebei Baoding071000 China National and Local Joint Engineering Research Center of Metrology Instrument and System Hebei Baoding071002 China Chengdu University of Information Technology School of Software Engineering Chengdu610225 China Sichuan University of Arts and Science Dazhou Key Laboratory of Government Data Security Sichuan Dazhou635000 China Chinese Academy of Sciences Xinjiang Technical Institute of Physics & Chemistry Urumqi830011 China

The void fraction is an important parameter to characterize the characteristics of annular flow, which is an important precondition for calculating the flow velocity, average density, pressure gradient and analyzing the flow status. Based on the principle of convective heat transfer, the effects of the void fraction α and the liquid Froude number Frl on the heat transfer coefficient h of annular flow are analyzed. The thermal distribution sensor of constant power type is designed in this paper. Air-water annular flow experiments have been conducted in a stainless-steel tubular test section having an inside diameter 17.0 mm on the high-precision small-diameter two-phase flow experimental device of Hebei University. The 75 sets of temperature T at four angles (0°, 90°, 180°, 270°) of pipe circumference and entrainment E under different flow conditions were collected. Utilizing the S. L. Smith correlation, the void fraction are calculated. By introducing dimensionless heat transfer coefficient h+, the prediction correlation of void fraction α which could acceptably handle the data is proposed. laboratory results indicate that the relative error of 99% the data are within ± 3% error band, the mean absolute percentage error (MAPE) of the correlation is 0.63%, and the root mean square error (RMSE) is 0.76%. © 2001-2012 IEEE.

关键词： Void fraction

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data security and access control method of blockchain with revocable attribute encryption in steel supply chain

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Peer-to-Peer Networking and Applications 2025年第4期18卷 1-28页

作者： Gao, Jie Zhang, Xiaohong Zhong, Shaojiang School of Information Engineering Jiangxi Province Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Ganzhou China College of Modern Economics and Management Jiangxi University of Finance and Economics Gongqingcheng China Department of Computer Science Purdue University West Lafayette USA

With the rapid development of the steel supply chain, the volume of data generated has increased dramatically, revealing potential risks associated with traditional coarse-grained data access control mechanisms and centralized architectures. To address these challenges, a blockchain-based data access control method utilizing attribute revocation ciphertext policies is proposed. This method enhances existing schemes by introducing a pre-decryption process and integrating an attribute revocation list, thereby enabling real-time attribute revocation. In terms of security, this approach is validated based on the Decisional Bilinear Diffie-Hellman (DBDH) hypothesis, which is a difficult problem under asymmetric groups, ensuring data security. By leveraging the decentralized nature of blockchain technology, this method mitigates the risks associated with single points of failure in central certification institutions. The token request mechanism introduced enables users to request multiple resources simultaneously, thereby reducing the frequency of operations. Compliant requests are authenticated, granted permissions, and subsequently returned to the user via a token. Additionally, this method integrates the Interplanetary File System (IPFS) and employs both chain-up and chain-down storage modes, effectively addressing issues related to insufficient blockchain capacity and system efficiency. The research validates the efficacy of this approach in enhancing efficiency and resolving challenges within the steel supply chain, providing substantial support for the industry’s digital transformation. Experimental results indicate that when handling large-scale attribute sets, both pre-decryption and decryption times remain stable at approximately 100 milliseconds, thereby achieving efficient and fine-grained access control to blockchain data. Overall, the scheme demonstrates excellent performance in terms of feature richness, practicality, and high efficiency, effectively mitigat

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