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arXiv 2025年

作者： Huang, Mingyu Guan, Ji Fang, Wang Ying, Mingsheng Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing101408 China School of Informatics University of Edinburgh EdinburghEH8 9UR United Kingdom Centre for Quantum Software and Information University of Technology Sydney UltimoNSW2007 Australia

In the current NISQ (Noisy Intermediate-Scale Quantum) era, simulating and verifying noisy quantum circuits is crucial but faces challenges such as quantum state explosion and complex noise representations, constraining simulation, and equivalence checking to circuits with a limited number of qubits. This paper introduces an approximation algorithm for simulating and assessing the equivalence of noisy quantum circuits, specifically designed to improve scalability under low-noise conditions. The approach utilizes a novel tensor network diagram combined with singular value decomposition to approximate the tensors of quantum noises. The implementation is based on Google’s TensorNetwork Python package for contraction. Experimental results on realistic quantum circuits with realistic hardware noise models indicate that our algorithm can simulate and check the equivalence of QAOA (Quantum Approximate Optimization Algorithm) circuits with around 200 qubits, surpassing the state-of-the-art methods by noise number and execution time. © 2025, CC BY.

关键词： Qubits

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MPC Implementation for Optimal Active Power Control of Large-Scale Wind-PV-Battery Hybrid system

MPC Implementation for Optimal Active Power Control of Large...

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Chinese Control Conference (CCC)

作者： Zihan Zhang Zhongwei Lin Ze Zhao Jiawei Zhou Chenzhi Qu State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing P. R. China

This paper investigates the operational strategy for real-time optimal active power control of large-scale wind-PV-battery hybrid system. The MPC strategy is established with objective, forecast models and constraints for active power control of wind-PV-battery hybrid system, which simultaneously maximizes tracking accuracy and minimizes settling time. To guarantee the industrial reliability, the corresponding optimization solver and control architecture have been specifically developed and designed. Hardware-in-loop (HIL) experiments are conducted using the real-time digital simulator (RTDS) and programmable logic controllers (PLCs). Under different operating scenarios, HIL-RTDS testing results demonstrate that the MPC strategy significantly improves the tracking speed and accuracy compared to the conventional strategy in both normal and abnormal operating scenarios, which also sustain the feasibility of industrial implementation.

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A New Paradigm of User-Centric Wireless Communication Driven by Large Language Models

arXiv

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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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Deep Common Feature Mining for Efficient Video Semantic Segmentation

arXiv

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arXiv 2024年

作者： Zheng, Yaoyan Yang, Hongyu Huang, Di State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Shanghai Artificial Intelligence Laboratory Shanghai201112 China Zhejiang Industrial Big Data and Robot Intelligent System Key Laboratory Hangzhou Innovation Institute Beihang University Hangzhou310051 China

Recent advancements in video semantic segmentation have made substantial progress by exploiting temporal correlations. Nevertheless, persistent challenges, including redundant computation and the reliability of the feature propagation process, underscore the need for further innovation. In response, we present Deep Common Feature Mining (DCFM), a novel approach strategically designed to address these challenges by leveraging the concept of feature sharing. DCFM explicitly decomposes features into two complementary components. The common representation extracted from a key-frame furnishes essential high-level information to neighboring non-key frames, allowing for direct re-utilization without feature propagation. Simultaneously, the independent feature, derived from each video frame, captures rapidly changing information, providing frame-specific clues crucial for segmentation. To achieve such decomposition, we employ a symmetric training strategy tailored for sparsely annotated data, empowering the backbone to learn a robust high-level representation enriched with common information. Additionally, we incorporate a self-supervised loss function to reinforce intra-class feature similarity and enhance temporal consistency. Experimental evaluations on the VSPW and Cityscapes datasets demonstrate the effectiveness of our method, showing a superior balance between accuracy and efficiency. The implementation is available at https://***/BUAAHugeGun/DCFM. © 2024, CC BY-NC-SA.

关键词： Semantic Segmentation

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Experimental Investigation on the Adiabatic Film Effectiveness for Counter-Inclined Simple and Laid-Back Film-Holes of Leading Edge

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Journal of Thermal Science 2020年第3期29卷 772-783页

作者： YE Lin LIU Cunliang XU Zhipeng ZHU Huiren LIU Haiyong ZHAI Yingni School of Power and Energy Northwestern Polytechnical UniversityXi'an 710072China Shaanxi Key Laboratory of Thermal Sciences in Aero-engine System Xi'an 710129China State Key Laboratory of Green Building in Western China Xi'an University of Architecture and TechnologyXi'an 710055China School of Mechanical&Electrical Engineering Xi'an University of Architecture and TechnologyXi'an 710055China

The adiabatic film effectivenessηof the counter-inclined film-holes fed by varying internal coolant intake on the turbine vane leading edge model was experimentally investigated.A semi-cylinder model was adopted to model the vane leading edge which was arranged with two-row holes,which located at±15°on both *** four Leading edge model with the combinations of hole-shape(simple holes and laid-back holes)and intake structure(plenum and impingement)were tested under four blowing ratios M of 0.5,1.0,1.5,and ***ηcontours were obtained by the transient measurement technique based on double thermochromic *** results present that theηis sensitive to the M for the four studied leading edge *** addition of impingement enhances theηfor the two studied *** film jets make the coolant-flow closed to the target surface,resulting in higherηunder lower *** core with higherηappears in the downstream area of ***ηenhancement can be provided to almost the identical level by adding the impingement-holes and improving the hole-exit shaping in most *** increasing M,the jets with stronger exit normal momentum penetrate into the *** impingement addition may be a more effective program to upgrade theηrelatively to the exit shaping under larger ***,the laid-back holes with impingement case produce the highest film cooling performance among the four cases,providing great potential in the leading edge especially under larger M.

关键词： turbine blade leading edge adiabatic film effectiveness counter-inclined holes impingement thermochromic liquid crystal transient measurement

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Consistency-based Semi-supervised Learning Framework for Power Line Insulators Detection

Consistency-based Semi-supervised Learning Framework for Pow...

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2022 International Conference on Electrical, Power and Grid systems, ICEPGS 2022

作者： Yao, Xu Lei, Longwu Zheng, Zhou Mo, Wenhao Wu, Han Li, Shufang Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Haidian District Beijing100876 China State Grid Fujian Electric Power Research Institute Fujian Province Fuzhou350007 China China Electric Power Research Institute or2 Haidian District Beijing100192 China

It is commonly believed that fast and accurate object detection is the prerequisite for power line insulators automatic fault diagnosis. While the insulators detection task requires a huge number of labeled images to guarantee its performance, placing bounding boxes for every object in each image is time-consuming and requires professional knowledge. To alleviate this problem, we propose CASD, a two-stage semi-supervised learning framework for power line insulators detection based on consistency regularization and data augmentation. Firstly, we train a teacher model on labeled images, then generate pseudo labels for unlabeled images using the trained model, and the confidence-based thresholding is introduced to control the quality of pseudo labels. In the second stage, we apply strong data augmentation to unlabeled images and use both the labeled and unlabeled images to train the CASD. Consistency regularization is introduced in the calculation of unsupervised loss to improve the accuracy and robustness of the model. Finally, we compare CASD with supervised learning, data augmentation and self-training methods on insulators aerial image dataset. The results show that the proposed method performs better than all the three baseline methods, and the detection accuracy of CASD is far better than the supervised learning method in the case of less available images. The comparison results further prove the efficacy of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Characterizing and Detecting Python Version Incompatibilities Caused by Inconsistent Version Specifications

SSRN

引用

SSRN 2024年

作者： Li, Shuo Gao, Haocheng Chen, Wei Li, Yi Tian, Haoxiang Liu, Chengwei Ye, Dan Institute of Software University of Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Nanyang Technological University Singapore Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Nanjing Institute of Software Technology Nanjing China

Python interpreter serves as the foundation for developing Python projects. Configuring compatible Python version is essential for packaging and reusing third-partylibraries (TPLs) in the Python ecosystem. Python version incompatibilities caused by inconsistent version specification frequently occur across software systems, leading to installation failures and run-time errors. We refer to such an error as a Python distribution version specification error (PDSpecErr). However, PDSpecErrs are not studied adequately and there still lacks a method to guarantee the correctness of Python version *** combat PDSpecErrs and mitigate their adverse impacts on downstream applications, we conducted the first exploratory study on 3,000 real-world TPLs to investigate PDSpecErrs about their prevalence, symptom categories, and diagnostic patterns. Based on the comprehensive study, we designed and implemented \textbf{PyChecker}, a tool to automatically detect PDSpecErrs in TPLs, locate root causes, and generate fixing solutions. We evaluated PyChecker on 3,000 TPL releases and PyChecker detected 842 PDSpecErrs, reporting their root causes and recommending fixing solutions. We have reported 52 PDSpecErrs to the concerned developers. So far, 32 issues have been confirmed and fixed according to our suggestions. © 2024, The Authors. All rights reserved.

关键词： Python

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Towards Query-Efficient Decision-Based Adversarial Attacks Through Frequency Domain

Towards Query-Efficient Decision-Based Adversarial Attacks T...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jianhao Fu Xiang Ling Yaguan Qian Changjiang Li Tianyue Luo Jingzheng Wu Intelligent Software Research Center Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Nanjing Nanjing Institute of Software Technology Key Laboratory of System Software (Chinese Academy of Sciences) State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Zhejiang University of Science and Technology Stony Brook University

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Deep neural networks are vulnerable to adversarial examples, where decision-based attacks can generate adversarial examples based solely on the predicted labels. However, these attacks typically require excessive queries to attack one example. Considering this challenge, we propose FBA (Frequency based Boundary Attack), a decision-based attack against the limitation of query efficiency. FBA incorporates a novel search process, utilizing high-frequency based importance sampling for efficient gradient estimation. Empirical results confirm the superior query efficiency of our method. Specifically, FBA surpasses SOTA attacks by achieving a 54% average improvement in query efficiency, quantified by the reduction in perturbation size within the same number of queries.

关键词： Technological innovation Monte Carlo methods Frequency-domain analysis Perturbation methods Estimation Artificial neural networks

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Rethinking Causal Relationships Learning in Graph Neural Networks

arXiv

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arXiv 2023年

作者： Gao, Hang Yao, Chengyu Li, Jiangmeng Si, Lingyu Jin, Yifan Wu, Fengge Zheng, Changwen Liu, Huaping Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China State Key Laboratory of Intelligent Game China Department of Computer Science and Technology Tsinghua University China

Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustness of GNNs, it becomes exceptionally crucial to bolster their ability to capture causal relationships. However, despite recent advancements that have indeed strengthened GNNs from a causal learning perspective, conducting an in-depth analysis specifically targeting the causal modeling prowess of GNNs remains an unresolved issue. In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels. The rationality of the generated data is further ensured through theoretical foundations. Drawing insights from analyses conducted using our dataset, we introduce a lightweight and highly adaptable GNN module designed to strengthen GNNs' causal learning capabilities across a diverse range of tasks. Through a series of experiments conducted on both synthetic datasets and other real-world datasets, we empirically validate the effectiveness of the proposed module. The codes are available at https://***/yaoyao-yaoyao-cell/CRCG. © 2023, CC BY.

关键词： Graph neural networks

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UHVDC Islanded Operation system Ultralow-Frequency Oscillation and Its Countermeasures

IEEE Canadian Journal of Electrical and Computer Engineering

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IEEE Canadian Journal of Electrical and computer Engineering 2021年第2期44卷 110-117页

作者： Li, Wei Gole, Aniruddha M. Kaffashan, Iman Xiao, Xiangning The Department of Electrical and Computer Engineering University of Manitoba ManitobaMBR3T 5V6 Canada The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China

There exists serious frequency instability in the UHVDC islanded operation system consisting of a large capacity UHVDC and its corresponding power source with hydraulic power generators. Ultralowfrequency oscillation (ULFO) is one of the frequency instable problems, which will happen because of the inconsistent frequency control features between UHVDC and hydraulic power generators. Through the frequency-domain analysis, there exists a frequency band with negative damping in the hydraulic turbine control system, and UHVDC is negative damping for frequency deviation with constant power control mode. There are four negative damping contributing factors as hydrowater starting time constant, hydrogenerator governor forward gain, islanded system total inertia, and UHVDC power. These four factors are analyzed in this article in the frequency domain. Based on the detailed ULFO damping analysis, effective frequency control strategies for UHVDC islanded system are proposed in this article by withdrawing a part of primary frequency control functions of generators or coordinating the frequency controls between hydraulic generator governors and UHVDC frequency limitation controller (FLC). Through the proposed frequency strategies, islanded system frequency is mainly controlled by UHVDC FLC, which can successfully suppress the ULFO. Validation simulations are performed on the RTDS platform, and the simulation results prove the proposed strategies. © 2021 IEEE.

关键词： Frequency domain analysis

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