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Practical approach for detecting k-nonseparability of multipartite quantum states

science China(Physics,Mechanics & Astronomy) 2025年第1期68卷 28-44页

作者： Jie Guo Jinchuan Hou Xiaofei Qi Kan He College of Mathematics Taiyuan University of TechnologyTaiyuan 030006China School of Mathematics and Statistics Shanxi UniversityTaiyuan 030006China Key Laboratory of Complex Systems and Data Science of Ministry of Education Shanxi UniversityTaiyuan 030006China

The instantaneous detection of entanglement in quantum states poses a significant challenge in the fields of quantum computation and quantum information,and there are no practical methods or tools to complete this task satisfactorily so *** propose a sufficient and necessary criterion of k-nonseparability for any n-partite finite or infinite dimensional systems,2≤k≤*** criterion serves as the foundation for a practical scheme designed to detect entanglement and k-nonseparability across multipar-tite finite-dimensional *** exemplify the application of our scheme,we have developed a software tool that facilitates swift and precise identification of entanglement,k-nonseparability,and genuine entanglement within n-qubit systems,specifically tailored for systems with 2≤n≤4.

关键词： multipartite systems entanglement states entanglement witnesses k-nonseparability

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AdaptHM: A Fully Adaptive data Migration Strategy for Hybrid Memory Systems

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 923-936页

作者： Peng, Zhouxuan Feng, Dan Chen, Jianxi Hu, Jing Liu, Yachun Hu, Jinlei Zhang, Jintong Wan, Tianyu Chen, Zuoning 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 MoE of China Wuhan430074 China Chinese Academy of Engineering Beijing100088 China

data migration strategies (DMSs) improve the overall performance of hybrid memory systems by migrating frequently accessed (hot) data to faster memory. However, designing an efficient DMS is challenging since the key metrics of DMS - hot data selection, migration granularity, and migration frequency - are sensitive to access patterns of workloads. Most existing strategies focus on only one of these metrics and often overlook the crucial impact of access patterns, resulting in suboptimal performance and unnecessary migration traffic. In this article, we propose AdaptHM, a fully access-pattern-aware Adaptive DMS for Hybrid Memory systems. AdaptHM achieves adaptability on all three metrics through its unique multilevel data framework. First, AdaptHM adopts a group-level competition policy to select hot blocks, which responds faster to access patterns than threshold-based policies. Second, AdaptHM enables segment-level dynamic migration granularity by decoupling migration from remapping, which shows better-access pattern resilience than existing schemes with fixed-size global migration granularity. Third, AdaptHM adjusts the migration frequency at set-level by periodically assessing the migration benefit, avoiding unnecessary migrations. Experimental results demonstrate that AdaptHM improves the performance by an average of 12.78% and reduces energy consumption by up to 37.24% compared to the state-of-the-art scheme. © 1982-2012 IEEE.

关键词： Frequency modulation

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Boosting Text-to-SQL through Multi-grained Error Identification 31

Boosting Text-to-SQL through Multi-grained Error Identificat...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Xu, Bo Li, Shufei Jing, Hongyu Du, Ming Song, Hui Wang, Hongya Xiao, Yanghua School of Computer Science and Technology Donghua University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

ISBN: (纸本)9798891761964

Text-to-SQL is a technology that converts natural language questions into executable SQL queries, allowing users to query and manage relational databases more easily. In recent years, large language models have significantly advanced the development of text-to-SQL. However, existing methods often overlook validation of the generated results during the SQL generation process. Current error identification methods are mainly divided into self-correction approaches based on large models and feedback methods based on SQL execution, both of which have limitations. We categorize SQL errors into three main types: system errors, skeleton errors, and value errors, and propose a multi-grained error identification method. Experimental results demonstrate that this method can be integrated as a plugin into various methods, providing effective error identification and correction capabilities. © 2025 Association for Computational Linguistics.

关键词： Structured Query Language

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Enhancing Multimodal Named Entity Recognition through Adaptive Mixup Image Augmentation 31

Enhancing Multimodal Named Entity Recognition through Adapti...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Xu, Bo Jiang, Haiqi Wei, Jie Jing, Hongyu Du, Ming Song, Hui Wang, Hongya Xiao, Yanghua School of Computer Science and Technology Donghua University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

ISBN: (纸本)9798891761964

Multimodal named entity recognition (MNER) extends traditional named entity recognition (NER) by integrating visual and textual information. However, current methods still face significant challenges due to the text-image mismatch problem. Recent advancements in text-to-image synthesis provide promising solutions, as synthesized images can introduce additional visual context to enhance MNER model performance. To fully leverage the benefits of both original and synthesized images, we propose an adaptive mixup image augmentation method. This method generates augmented images by determining the mixing ratio based on the matching score between the text and image, utilizing a triplet loss-based Gaussian Mixture Model (TL-GMM). Our approach is highly adaptable and can be seamlessly integrated into existing MNER models. Extensive experiments demonstrate consistent performance improvements, and detailed ablation studies and case studies confirm the effectiveness of our method. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Threshold Huffman-Based Covert Timing Channel Detection

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International Journal of Network Security 2025年第1期27卷 61-71页

作者： Lin, Yuwei Chen, Yonghong Zhuang, Xiaolong Huang, Xuwen School of Computer Science and Technology Huaqiao University Fujian Xiamen361000 China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Fujian Xiamen361000 China

Network covert timing channels in computer networks evade effective detection by traditional security mechanisms such as firewalls, posing a significant threat to network security. However, most existing methods struggle to detect such channels when the window size is small. Additionally, the emergence of the new ϵ-κlibur channel has resulted in a decline in performance for detection methods such as GAS, SnapCatch, and DNN when detecting this particular channel. In this paper, we investigate a Huffman tree-based approach for detecting anomalous nodes in traffic and achieving the classification of channels through the design of weight intervals. Our method achieves an average detection rate of over 95% for each channel. Additionally, compared to GAS, SnapCatch, and DNN, our method improves the detection rates for the ϵ-κlibur channel by 11.75%, 8.46%, and 4.81%, respectively. The experimental results demonstrate that our proposed approach successfully detects ϵ-κlibur and achieves detection in a small window. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

关键词： Computer system firewalls

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Experimental and mechanistic study of dispersed micrometer-sized particle resuspension in a square straight duct with rough walls

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Particuology 2023年第12期83卷 101-114页

作者： Qi Sun Suyuan Yu Wei Peng Institute of Nuclear and New Energy Technology Cooperation Innovation Center of Advanced Nuclear Energy TechnologyKey Laboratory of Advanced Reactor Engineering and Safety of Ministry of EducationTsinghua UniversityBeijing100084China Center for Combustion Energy Key Laboratory for Thermal Science and Power Engineering of the Ministry of EducationDepartment of Energy and Power EngineeringTsinghua UniversityBeijing100084China

The resuspension of graphite dust is an important phenomenon in the release of radioactivity and the safety of nuclear reactors during severe *** this study,a visualization experimental platform is constructed to study effects of particle size,flow velocity,and wall roughness on the resuspension characteristics of graphite particles.A statistical model of particle resuspension applicable to monolayer dispersed particles is developed based on the moment equilibrium of the particles and the flow field characteristics,as calculated by the large-eddy simulation *** results show that particle resuspension can be divided into short-and long-term resuspension *** particle resuspension occurs during the short-term *** increases in flow velocity and particle diameter,the aerodynamic or adhesion force acting on the particles increases,and corresponding particle resuspension fraction *** influence of rough walls on particle resuspension is related to both the force on the particles and the arm ratio between the wall morphology and the particle diameter.A comparison with the experimental results demonstrates that the particle resuspension model developed in this study accurately predicts the impact of flow velocity,particle size,and wall roughness on particle resuspension.

关键词： Particle resuspension Statistical model Moment equilibrium High temperature gas-cooled reactor(HTGR)

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Multi‐task framework of precipitation nowcasting

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1350-1363页

作者： Zheng Zhang Chuyao Luo Baoquan Zhang Hao Jiang Bowen Zhang Department of Computer Science Shenzhen Key Laboratory of Internet Information CollaborationHarbin Institute of TechnologyShenzhenChina College of Big Data and Internet Shenzhen Technology UniversityShenzhenChina

Precipitation forecasting plays an important role in disaster warning,agricultural production,and other *** solve this issue,some deep learning methods are proposed to forecast future radar echo images and convert them into rainfall *** spatiotemporal sequence prediction methods are usually based on a ConvRNN structure that combines a Convolutional Neural Network and Recurrent Neural ***,these existing methods ignore the image change prediction,which causes the coherence of the predicted image has ***,these approaches mainly focus on complicating model structure to exploit more historical spatiotemporal ***,they ignore introducing other valuable information to improve *** tackle these two issues,we propose GCMT‐ConvRNN,a multi‐ask framework of *** for precipitation nowcasting as the main task,it combines the motion field estimation and sub‐regression as auxiliary *** this framework,the motion field estimation task can provide motion information,and the sub‐regression task offers future ***,to reduce the negative transfer between the auxiliary tasks and the main task,we propose a new loss function based on the correlation of gradients in different *** experiments show that all models applied in our framework achieve stable and effective improvement.

关键词： deep neural networks motion estimation

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Inductive Link Prediction in N-ary Knowledge Graphs 31

Inductive Link Prediction in N-ary Knowledge Graphs

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wei, Jiyao Guan, Saiping Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798891761964

N-ary Knowledge Graphs (NKGs), where a fact can involve more than two entities, have gained increasing attention. Link Prediction in NKGs (LPN) aims to predict missing elements in facts to facilitate the completion of NKGs. Current LPN methods implicitly operate under a closed-world assumption, meaning that the sets of entities and roles are fixed. These methods focus on predicting missing elements within facts composed of entities and roles seen during training. However, in reality, new facts involving unseen entities and roles frequently emerge, requiring completing these facts. Thus, this paper proposes a new task, Inductive Link Prediction in NKGs (ILPN), which aims to predict missing elements in facts involving unseen entities and roles in emerging NKGs. To address this task, we propose a Meta-learning-based Nary knowledge Inductive Reasoner (MetaNIR), which employs a graph neural network with meta-learning mechanisms to embed unseen entities and roles adaptively. The obtained embeddings are used to predict missing elements in facts involving unseen elements. Since no existing dataset supports this task, three datasets are constructed to evaluate the effectiveness of MetaNIR. Extensive experimental results demonstrate that MetaNIR consistently outperforms representative models across all datasets. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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Recent progress of in-situ characterization of LiNi_(1−x−y)Co_(x)Mn_(y)O_(2) cathodes for lithium metal batteries: A mini review

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Nano Research 2024年第3期17卷 1384-1401页

作者： Huanzhu Lv Xiaoqi Zhu Jun Mei Yuanhua Xia Bin Wang Institute of Fundamental and Frontier Science University of Electronic Science and Technology of ChinaChengdu 611731China Chengdu Development Center of Science and Technology China Academy of Engineering PhysicsChengdu 610200China Key Laboratory of Neutron Physics and Institute of Nuclear Physics and Chemistry China Academy of Engineering Physics(CAEP)Mianyang 621999China

In the context of the gradual popularity of electric vehicles(EVs),the development of lithium battery systems with high energy density and power density is regarded as the foremost way to improve the range of ***_(1-x-y)Co_(x)Mn_(y)O_(2)(NCM)cathodes have been the focus of researchers due to their high energy density,excellent power performance,and low-temperature ***,the elaboration of the decay mechanism of NCM cathode based on lithium metal batteries(LMBs)is still being restricted to the primary *** the past decades,the development and application of advanced in-situ characterization tools have facilitated researchers'understanding of the internal operation mechanism of batteries during charging and *** this minireview,the latest progress of in-situ observation of the NCM cathode by X-ray diffraction(XRD),fourier transform infrared(FT-IR)spectroscopy,Raman spectroscopy,atomic force microscopy(AFM),transmission electron microscope(TEM),optical microscope,and other characterization tools is *** mechanisms of structural degradation,cathode-electrolyte interfaces(CEIs)composition,and dynamic changes of NCM,electrolyte breakdown,and gas production are ***,based on the existing research progress,the opportunities and challenges for future in-situ characterization technology in the study of the mechanism of LMBs are discussed in ***,the purpose of this minireview is to summarize recent work that focuses on the outstanding application of in-situ characterization techniques in the mechanistic study of LMBs,and pointing the way to the future development of high energy density and power density LMBs systems.

关键词： lithium metal batteries(LMBs) In-situ characterization LiNi_(1-x-y)Co_(x)Mn_(y)O_(2)(NCM)

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Large Language Model-Based Event Relation Extraction with Rationales 31

Large Language Model-Based Event Relation Extraction with Ra...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Hu, Zhilei Li, Zixuan Jin, Xiaolong Bai, Long Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesSchool of Computer Science and Technology University of Chinese Academy of Sciences

ISBN: (纸本)9798891761964

Event Relation Extraction (ERE) aims to extract various types of relations between different events within texts. Although Large Language Models (LLMs) have demonstrated impressive capabilities in many natural language processing tasks, existing ERE methods based on LLMs still face three key challenges: (1) Time Inefficiency: The existing pairwise method of combining events and determining their relations is time-consuming for LLMs. (2) Low Coverage: When dealing with numerous events in a document, the limited generation length of fine-tuned LLMs restricts the coverage of their extraction results. (3) Lack of Rationale: Essential rationales concerning the results that could enhance the reasoning ability of the model are overlooked. To address these challenges, we propose LLMERE, an LLM-based approach with rationales for the ERE task. LLMERE transforms ERE into a question-and-answer task that may have multiple answers. By extracting all events related to a specified event at once, LLMERE reduces time complexity from O(n2) to O(n), compared to the pairwise method. Subsequently, LLMERE enhances the coverage of extraction results by employing a partitioning strategy that highlights only a portion of the events in the document at a time. In addition to the extracted results, LLMERE is also required to generate corresponding rationales/reasons behind them, in terms of event coreference information or transitive chains of event relations. Experimental results on three widely used datasets show that LLMERE achieves significant improvements over baseline methods. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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