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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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TDiffSal: Text-Guided Diffusion Saliency Prediction Model for Images 27th

TDiffSal: Text-Guided Diffusion Saliency Prediction Model fo...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Nana Xiong, Min Zhu, Dandan Zhu, Kun Zhai, Guangtao School of Computer Science and Technology Donghua University No. 2999 Renmin North Road Songjiang District Shanghai201620 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China Institute of AI Education Shanghai East China Normal University No. 3663 Zhongshan North Road Putuo District Shanghai200333 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University No. 4800 Cao’an Highway Jiading District Shanghai201804 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9783031781858

Existing visual saliency prediction methods mainly focus on single-modal visual saliency prediction, while ignoring the significant impact of text on visual saliency. To more comprehensively explore the influence of text on human attention in images, we propose a text-guided diffusion saliency prediction model, named TDiffSal. In specific, recent studies on stable diffusion models have shown promising performance in unifying tasks due to their inherent generalization ability. Inspired by this, a novel diffusion model for generalized visual-text saliency prediction is proposed, which formulates the prediction issue as a conditional generative task of the saliency map by employing input visual and text as the conditions. Meanwhile, we introduce a multi-head fusion module to effectively integrate text features and image features, which can efficiently guide the image denoising process and progressively refine the generated saliency map to make it semantically relevant to the text. Additionally, we employ an efficient pre-training strategy to enhance the robustness and generalization of the proposed model. We conduct extensive experiments on benchmark datasets to demonstrate its superior performance compared to other state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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SatCooper: Enhancing Cooperative Inference Analytics for Satellite Service via Multi-exit DNNs

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Qiyang Wang, Shangguang Guan, Jinglong Donta, Praveen Kumar Ma, Xiao Prasad, RangaRao Venkatesha Dustdar, Schahram Liu, Xuanzhe Peking University School of Computer Science Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Stockholm University Department of Computer and Systems Sciences Kista16455 Sweden Beiyou Shenzhen Institute Shenzhen China Netherlands TU Wien Distributed System Group Wien1040 Austria UPF Barcelona Spain

As a key technology of intelligent satellite-enabled services in B5G or 6G networks, deploying Deep Neural Networks (DNN) models on satellites has been a notable trend, catering to the daily demand for extensive computing-intensive and latency-sensitive tasks. The computing resources are strategically deployed on satellites where sensor data is generated or collected, facilitating the fine-grained computational inference of DNN-based tasks. However, no prior study has comprehensively explored the crucial inference challenges-e.g., the trade-off between the number of tasks completed and accuracy and partitioning models in multi-exit models-in the resource-constrained space environment. Effective scheduling frameworks cater to various streams of inference tasks are scarce because inference performance may deviate from the ideal situation due to changes in task system status, such as task profiles and network state. To this end, we first formulate a gain-aware in-orbit computing inference problem to strike a proper trade-off between inference latency and the number of tasks completed by dynamically selecting optimal early exit points and model partitioning points. We propose an offline dynamic programming-based algorithm that provides an effective solution when comprehensive system details are to be predicted. We have developed an online learning-based method to schedule inference tasks with uncertain and dynamic system statuses in real-world situations. Our evaluation shows that, compared to baseline methods, the online learning-based algorithm can improve task gain by an average of 87.3% across various tasks. © 2002-2012 IEEE.

关键词： Dynamic programming

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SimK:A Large-Scale Parallel Simulation Engine

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Journal of computer Science & Technology 2009年第6期24卷 1048-1060页

作者：许建卫陈明宇郑规曹政吕慧伟孙凝晖 Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Simulation is an important method to evaluate future computer systems. Currently microprocessor architecture has switched to parallel, but almost all simulators remained at sequential stage, and the advantages brought by multi-core or many-core processors cannot be utilized. This paper presents a parallel simulator engine （SimK） towards the prevalent SMP/CMP platform, aiming at large-scale fine-grained computer system simulation. In this paper, highly efficient synchronization, communication and buffer management policies used in SimK are introduced, and a novel lock-free scheduling mechanism that avoids using any atomic instructions is presented. To deal with the load fluctuation at light load case, a cooperated dynamic task migration scheme is proposed. Based on SimK, we have developed large-scale parallel simulators HppSim and HppNetSim, which simulate a full supercomputer system and its interconnection network respectively. Results show that HppSim and HppNetSim both gain sound speedup with multiple processors, and the best normalized speedup reaches 14.95X on a two-way quad-core server.

关键词： large scale system simulation fine-grained synchronization simulation framework lock-free synchronization

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Improved High-Speed Thermal Analysis Based on Two-Step Simulation for High-Frequency Transformers

Improved High-Speed Thermal Analysis Based on Two-Step Simul...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Zheyuan Yi Kai Sun Qiang Li Zengyang Liu Department of Electrical Engineering State Key Laboratory of Power System Tsinghua University Beijing China Delta Electronics Shanghai Co. Ltd. Shanghai China The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State Center for Power Electronics Systems University Blacksburg Virginia USA

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Thermal analysis is a crucial and time-consuming step in the optimization design process of high-frequency transformers (HFTs). This paper proposes an improved thermal analysis method based on heat dissipation-conduction two-step simulation, significantly reducing the computation time while ensuring the accuracy of thermal analysis. Simulation results validate the accuracy and of the proposed method and demonstrate that the proposed method saves 82% of computation time for a single thermal analysis case compared to direct CFD. Experimental results further verify the accuracy of the proposed method. An exemplary application of the proposed method in the optimization design of the HFT illustrates a 98% reduction in computation time for thermal analysis during the optimization process.

关键词： Heating systems Temperature distribution Accuracy Simulation Computational modeling Thermal analysis High-frequency transformers Optimization Surface treatment Heat transfer

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Predicting on the Extreme Precipitation in East and Southeast Asia during Summer 2022 from the Antecedent Soil Temperature Anomaly over the Tibetan Plateau

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Journal of Climate 2025年第9期38卷 2109-2128页

作者： Kong, Xianghui Wang, Aihui Xue, Yongkang Wei, Nan Zhang, He Hu, Qin He, Juanxiong Bi, Xunqiang Chen, Yue Nansen-Zhu International Research Centre Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing China Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information Technology Sichuan Province Chengdu China University of California Los AngelesCA United States School of Atmospheric Sciences Sun Yat-sen University Zhuhai China International Center for Climate and Environment Sciences Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China School of Atmospheric Sciences Chengdu University of Information Technology Sichuan Province Chengdu China Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China National Super Computer Center in Tianjin Tianjin China State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China

It has demonstrated that May land surface temperature (LST) and subsurface temperature (SUBT) anomalies on the Tibetan Plateau (TP) can serve as a predictor of June precipitation in downstream *** summer 2022, there was heavy precipitation (severe drought) in June (August) over southern China, coinciding with high May (July) LST/SUBT anomalies in *** the framework of the Impact of Initialized Land Temperature and Snowpack on Subseasonal to Seasonal Prediction (LS4P), this study explores the causality between 2022 May (July) TP LST/SUBT and June (August) precipitation using an ensemble of land–atmosphere coupled model *** show that model prediction with imposed strongly positive May TP LST/SUBT (i.e., a triple) anomaly could produce 67% (46%) of June precipitation anomalies in South (northeast) China, while sea surface temperature (SST) effects account for 5% (51%).The atmosphere condensation heating and surface sensible heating due to TP LST/SUBT anomaly induce anomalous South Asian high, which moves eastward along with the Rossby ***, the above process enhances upward motion, convective instability, and water vapor transport, eventually be favorable for rainfall formation in South China and northeast *** Southeast Asia, it makes a downward movement, decrease in atmospheric water vapor, and unfavorable to the occurrence of ***, July TP LST/SUBT contributes to the prediction skill for August record-breaking drought in southern *** to SST effects, it is found that TP LST/SUBT effects significantly enhance the prediction skill for downstream region, suggesting that the TP land contributes an important source for subseasonal climate prediction. © 2025 American Meteorological Society.

关键词： Rivers

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MQADet: A Plug-and-Play Paradigm for Enhancing Open-Vocabulary Object Detection via Multimodal Question Answering

arXiv

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arXiv 2025年

作者： Li, Caixiong Zhao, Xiongwei Zhang, Jinhang Zhang, Xing Sun, Qihao Wu, Zhou School of Computer and Information Science Qinghai Institute of Technology China School of Information Science and Technology Harbin Institute of Technology Shen Zhen China The State Key Laboratory of Robotics and System Harbin Institute of Technology China School of Computer Science and Technology Qinghai University China Eryuan Digital Technology Co. Ltd. China

Open-vocabulary detection (OVD) is a challenging task to detect and classify objects from an unrestricted set of categories, including those unseen during training. Existing open-vocabulary detectors are limited by complex visual-textual misalignment and long-tailed category imbalances, leading to suboptimal performance in challenging scenarios. To address these limitations, we introduce MQADet, a universal paradigm for enhancing existing open-vocabulary detectors by leveraging the cross-modal reasoning capabilities of multimodal large language models (MLLMs). MQADet functions as a plug-and-play solution that integrates seamlessly with pre-trained object detectors without substantial additional training costs. Specifically, we design a novel three-stage Multimodal Question Answering (MQA) pipeline to guide the MLLMs to precisely localize complex textual and visual targets while effectively enhancing the focus of existing object detectors on relevant objects. To validate our approach, we present a new benchmark for evaluating our paradigm on four challenging open-vocabulary datasets, employing three state-of-the-art object detectors as baselines. Experimental results demonstrate that our proposed paradigm significantly improves the performance of existing detectors, particularly in unseen complex categories, across diverse and challenging scenarios. To facilitate future research, we will publicly release our code. © 2025, CC BY.

关键词： Object detection

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PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing

arXiv

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arXiv 2025年

作者： Zhang, Yuwei Jin, Zhi Xing, Ying Li, Ge Liu, Fang Zhu, Jiaxin Dou, Wensheng Wei, Jun Affiliated with Nanjing Institute of Software Technology University of Chinese Academy of Sciences Nanjing China Key Laboratory of System Software Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China School of Computer Science Wuhan University Wuhan China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Beihang University Beijing China

Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs. Copyright © 2025, The Authors. All rights reserved.

关键词： computer software maintenance

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Mesh2Brep: B-rep Reconstruction via Robust Primitive Fitting and Intersection-aware Constraints

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Shen, Zeyu Zhao, Mingyang Yan, Dong-Ming Wang, Wencheng Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China The University of Chinese Academy of Sciences Beijing100049 China CAIR Hong Kong Institute of Science & Innovation Chinese Academy of Sciences China MAIS Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

In boundary representation (B-rep) reconstruction for computer aided design (CAD) applications, it is still challenging with existing methods to distinguish primitives in the smoothly blended regions reasonably. Thus, intensive manual post-processing is always required for correcting the primitives and their neighboring relationships to obtain a valid B-rep solid, seriously preventing the efficiency. In this paper, we address these challenges by presenting two novel techniques. The first is to robustly extract primitives by iteratively estimating the probability distribution of the noise to eliminate outliers. The second is to present intersection-aware constraints, like tangency and collinearity constraints, to correctly obtain intersections between primitives, which have not been explored in existing methods to our knowledge. Therefore, we can effectively extract primitives, especially those blended smoothly, and obtain high-quality relationships between them. As a result, a valid B-rep model can be constructed without a lot of manual post-processing on topology correction, while not with existing methods. As a benefit, with our constructed B-rep models, their corresponding meshes can be intuitively and conveniently edited, which is quite useful in CAD applications. Experimental results show that our proposed B-rep construction method outperforms both classical and recent learning-based methods in terms of reconstruction efficiency and accuracy. © 2025 IEEE.

关键词： Mesh generation

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Hafnium oxide-based nonvolatile ferroelectric memcapacitor array for high energy-efficiency neuromorphic computing

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Nano Energy 2025年 140卷

作者： Wang, Xuepei Ye, Sheng Cui, Boyao Li, Yu-Chun Wei, Ye Xiao, Yu Liu, Jinhao Huang, Zi-Ying Wu, Yishan Wen, Yichen Wang, Ziming Wu, Maokun Ren, Pengpeng Fang, Hui Lu, Hong-Liang Wang, Runsheng Ji, Zhigang Huang, Ru National Key Laboratory of Science and Technology on Micro/Nano Fabrication Department of Micro/Nano Electronics Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of ASIC and System School of Microelectronics Shanghai Institute of Intelligent Electronics and Systems Fudan University Shanghai200433 China School of Integrated Circuits Peking University Beijing100871 China Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom

Recent advancements in neuromorphic computing hardware have led to significant progress in image classification, speech recognition, and fuzzy computing, outperforming traditional von Neumann computing paradigm. However, the widely-investigated memristor-based neuromorphic computing hardware still suffers high writing/reading currents and serious variability issue as well as sneak path challenges, leading to high power consumption and peripheral circuit design complication. Memcapacitor-based neuromorphic computing is expected to alleviate these problems, while the limited memory windows and endurance hindered the practical applications. Here, we present a hafnium oxide-based ferroelectric memcapacitor developed through work function engineering. The memcapacitor demonstrates an overall excellent performance in memory windows (∼7.8 fF/μm2), endurance (>109 cycles), retention (>10 years), dynamic energy consumption (31 fJ/inference), and near-zero standby static power consumption. The fabricated memcapacitor array shows high linearity and device-to-device variations, and can perform complete multiplication-accumulation (MAC) operation. The constructed artificial neural network (ANN) achieves 96.68 % accuracy on the MNIST data set after 200 epochs. Our findings underscore the potential of ferroelectric memcapacitor device as a robust candidate for high energy-efficiency neuromorphic computing applications in intelligent terminals. © 2025 Elsevier Ltd

关键词： Speech recognition

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