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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

Science China(Information Sciences) 2025年

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering research center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

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Decision making module based on stochastic magnetic tunnel junctions

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 202-209页

作者： Yifan Miao Li Zhao Yajun Zhang Zhe Yuan center for Advanced Quantum Studies and Department of Physics Beijing Normal University Institute for Nanoelectric Devices and Quantum computing Fudan University Interdisciplinary center for Theoretical Physics and Information Sciences Fudan University

In biological neural systems, noise is ubiquitous but does not affect the correct decisions made in the complex cognitive ***-making in biological neural system is typically achieved by accumulating input information over a period of *** by recent developments in neurosciences, we design a decision-making module based on spintronic devices, utilizing superparamagnetic tunnel junctions as artificial neurons. The feasibility of this decision-making module is verified through circuit simulations. Taking a multi-layer perceptron as an example, the module significantly improves the accuracy of the perceptron in the handwritten digit recognition task. Furthermore, the spintronic decision-making module offers advantages over the conventional pooling methods, such as adaptive decision time, high performance and the absence of analog-to-digital conversion. The decision-making module is flexible to be integrated into artificial neural networks and provides a general yet effective solution to enhance performance against device noise.

关键词： neuromorphic computing spintronics magnetic tunnel junction neural networks

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Modification of Maxwell model for conductivity prediction of carbon nanotubes-filled polymer composites with tunneling effect

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Applied Mathematics and Mechanics(English Edition) 2025年第1期46卷 25-36页

作者： Jue ZHU Longyuan LI Ningtao ZHU Zhejiang Provincial Engineering research center for the Safety of Pressure Vessel and Pipeline Key Laboratory of Impact and Safety Engineering (Ningbo University) Ministry of Education School of Engineering Computing and Mathematics University of Plymouth Xi'an Zhanshi Testing & Engineering Co.Ltd.

Carbon nanotubes(CNTs) have garnered great attention in recent years due to their outstanding electrical, thermal, and mechanical properties. The incorporation of small amounts of CNTs in polymers can substantially improve the sensitivity of the polymer's electrical conductivity. This paper presents a modified Maxwell model to evaluate the electrical conductivity of CNTs-filled polymer composites by introducing a transition zone to account for the tunneling effect. In this modified Maxwell model, the CNTs-filled polymer composite is modeled as a three-phase composite, consisting of a matrix(polymer), inclusions(CNTs), and a transition zone(tunneling zone). The effective electrical conductivity(EEC) of the composite is calculated based on the volume fractions and electrical conductivities of the matrix, inclusions, and transition zone. The model's validity is confirmed through the use of available test data, which demonstrates its capability to accurately capture the nonlinear conductivity behavior observed in CNTs-polymer composites. This study offers valuable insights into the design of high-performance conductive polymer nanocomposites, and enhances the understanding of electrical conduction mechanisms in CNT-dispersed polymer composites.

关键词： Nanoclay

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Boosting cationic and anionic redox activity of Li-rich layered oxide cathodes via Li/Ni disordered regulation

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Journal of Energy Chemistry 2025年第1期100卷 533-543页

作者： Zewen Liu Zhen Wu Hao Wang Xudong Zhang Yuanzhen Chen Yongning Liu Shengwu Guo Shenghua Chen Yanli Nan Yan Liu State Key Laboratory for Mechanical Behavior of Materials Xi'an Jiaotong University School of Materials Science and Engineering Xi'an University of Architecture and Technology Hefei Advanced computing center Operation Management Corp Ltd Network Information center Xi'an Jiaotong University

Lithium-rich layered oxides （LLOs） are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries （LIBs）.However,they suffer from voltage decay and low initial Coulombic efficiency （ICE） due to severe structural degradation caused by irreversible O ***,we introduce a three-in-one strategy of increasing Ni and Mn content,along with Li/Ni disordering and TM–O covalency regulation to boost cationic and anionic redox activity simultaneously and thus enhance the electrochemical activity of *** target material,Li1.2Ni0.168Mn0.558Co0.074O2（L1）,exhibits an improved ICE of 87.2%and specific capacity of 293.2 mA h g-1and minimal voltage decay of less than 0.53 m V cycle-1over 300 cycles at 1C,compared to Li1.2Ni0.13Mn0.54Co0.13O2（Ls）(274.4 mA h g-1for initial capacity,73.8%for ICE and voltage decay of 0.84 mV/cycle over 300 cycles at 1C).Theoretical calculations reveal that the density of states （DOS） area near the Fermi energy level for L1 is larger than that of Ls,indicating higher anionic and cationic redox reactivity than ***,L1 exhibits increased O-vacancy formation energy due to higher Li/Ni disordering of 4.76%（quantified by X-ray diffraction Rietveld refinement） and enhanced TM–O covalency,making lattice O release more difficult and thus improving electrochemical *** increased Li/Ni disordering also leads to more Ni2+presence in the Li layer,which acts as a pillar during Li+de-embedding,improving structural *** research not only presents a viable approach to designing low-Co LLOs with enhanced capacity and ICE but also contributes significantly to the fundamental understanding of structural regulation in high-performance LIB cathodes.

关键词： Ice

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Detection using mask adaptive transformers in unmanned aerial vehicle imagery

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Optoelectronics Letters 2025年第2期21卷 113-120页

作者： YE Huibiao FAN Weiming GUO Yuping WANG Xuna ZHOU Dalin China Telecommunication Corporation Zhejiang Branch Innovation center for Smart Medical Technologies & Devices Binjiang Institute of Zhejiang University School of computing University of Portsmouth

Drone photography is an essential building block of intelligent transportation,enabling wide-ranging monitoring,precise positioning,and rapid ***,the high computational cost of transformer-based methods in object detection tasks hinders real-time result transmission in drone target detection ***,we propose mask adaptive transformer (MAT) tailored for such ***,we introduce a structure that supports collaborative token sparsification in support windows,enhancing fault tolerance and reducing computational *** structure comprises two modules:a binary mask strategy and adaptive window self-attention (A-WSA).The binary mask strategy focuses on significant objects in various complex *** A-WSA mechanism is employed to self-attend for balance perfomance and computational cost to select objects and isolate all contextual *** experiments on the challenging CarPK and VisDrone datasets demonstrate the effectiveness and superiority of the proposed ***,it achieves a mean average precision (mAP@0.5) improvement of 1.25%over car detector based on you only look once version 5 (CD-YOLOv5) on the CarPK dataset and a 3.75%average precision(AP@0.5) improvement over cascaded zoom-in detector (CZ Det) on the VisDrone dataset.

关键词： Drones

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Review:Data center network architecture in cloud computing:review, taxonomy, and open research issues

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第9期15卷 776-793页

作者： Han QI Muhammad SHIRAZ Jie-yao LIU Abdullah GANI Zulkanain ABDUL RAHMAN Torki A.ALTAMEEM Mobile Cloud computing research Lab Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur 50603 Malaysia Department of History Faculty of Arts and Social Sciences University of MalayaKuala Lumpur 50603 Malaysia Department of Computer Science Riyadh Community College King Saud UniversityRiyadh 11533 Saudi Arabia

The data center network(DCN), which is an important component of data centers, consists of a large number of hosted servers and switches connected with high speed communication links. A DCN enables the deployment of resources centralization and on-demand access of the information and services of data centers to users. In recent years, the scale of the DCN has constantly increased with the widespread use of cloud-based services and the unprecedented amount of data delivery in/between data centers, whereas the traditional DCN architecture lacks aggregate bandwidth, scalability, and cost effectiveness for coping with the increasing demands of tenants in accessing the services of cloud data centers. Therefore, the design of a novel DCN architecture with the features of scalability, low cost, robustness, and energy conservation is required. This paper reviews the recent research findings and technologies of DCN architectures to identify the issues in the existing DCN architectures for cloud computing. We develop a taxonomy for the classification of the current DCN architectures, and also qualitatively analyze the traditional and contemporary DCN architectures. Moreover, the DCN architectures are compared on the basis of the significant characteristics, such as bandwidth, fault tolerance, scalability, overhead, and deployment cost. Finally, we put forward open research issues in the deployment of scalable, low-cost, robust, and energy-efficient DCN architecture, for data centers in computational clouds.

关键词： Data center network Cloud computing Architecture Network topology

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Engineering nanoparticle structure at synergistic Ru-Na interface for integrated CO2 capture and hydrogenation

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Journal of Energy Chemistry 2025年第1期100卷 779-791页

作者： Hanzi Liu Ling Cen Xinlin Xie Lei Liu Zhao Sun Zhiqiang Sun Hunan Engineering research center of Clean and Low-Carbon Energy Technology School of Energy Science and Engineering Central South University

The development of dual functional material for cyclic CO2capture and hydrogenation is of great significance for converting diluted CO2into valuable fuels,but suffers from kinetic limitation and deactivation of adsorbent and ***,we engineered a series of RuNa/γ-Al2O3materials,varying the size of ruthenium from single atoms to clusters/*** coordination environment and structure sensitivity of ruthenium were quantitatively investigated at atomic *** findings reveal that the reduced Ru nanoparticles,approximately 7.1 nm in diameter with a Ru-Ru coordination number of 5.9,exhibit high methane formation activity and selectivity at 340°*** Ru-Na interfacial sites facilitate CO2migration through a deoxygenation pathway,involving carbonate dissociation,carbonyl formation,and ***-situ experiments and theoretical calculations show that stable carbonyl intermediates on metallic Ru nanoparticles facilitate heterolytic C–O scission and C–H bonding,significantly lowering the energy barrier for activating stored CO2.

关键词： Carbon capture and utilization

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LiDAR-Visual SLAM with Integrated Semantic and Texture Information for Enhanced Ecological Monitoring Vehicle Localization

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Computers, Materials & Continua 2025年第1期82卷 1401-1416页

作者： Yiqing Lu Liutao Zhao Qiankun Zhao Foreign Environmental Cooperation center Ministry of Ecology and Environment of PRCBeijing100035China Beijing computing center Co. Ltd.Beijing Academy of Science and TechnologyBeijing100083China Beijing Guyu Interactive Artificial Intelligence Application Co. Ltd.Beijing100024China

Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental *** vehicles are crucial in various fields,including environmental science research,ecological and environmental monitoring projects,disaster response,and emergency management.A key method employed in these vehicles for achieving high-precision positioning is LiDAR(lightlaser detection and ranging)-Visual Simultaneous Localization and Mapping(SLAM).However,maintaining highprecision localization in complex scenarios,such as degraded environments or when dynamic objects are present,remains a significant *** address this issue,we integrate both semantic and texture information from LiDAR and cameras to enhance the robustness and efficiency of data ***,semantic information simplifies the modeling of scene elements,reducing the reliance on dense point clouds,which can be less ***,visual texture information complements LiDAR-Visual localization by providing additional contextual *** incorporating semantic and texture details frompaired images and point clouds,we significantly improve the quality of data association,thereby increasing the success rate of *** approach not only enhances the operational capabilities of ecological monitoring vehicles in complex environments but also contributes to improving the overall efficiency and effectiveness of ecological monitoring and environmental protection efforts.

关键词： LiDAR-Visual simultaneous localization and mapping integrated semantic texture information

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Software-defined process-near-memory architecture using 3D hybrid bonding integration

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Science China(Information Sciences) 2025年第1期68卷 366-378页

作者： Anlin XU Chenchen DENG Jianfeng ZHU Yao WANG Shaojun WEI Leibo LIU School of Integrated Circuits Tsinghua University Beijing Institution of Tracking and Communication Technology Beijing National research center for Information Science and Technology Tsinghua University

With the unprecedented explosive growing amount of global data, the development of computing chips, which encounter bottlenecks such as power wall and memory wall, cannot satisfy the demanding requirement. This work proposes a software-defined process-near-memory(SDPNM) computing architecture implemented using 3D hybrid bonding *** software-defined chip architecture, featuring spatial computations and dynamic reconfiguration, innovates in a top-down manner to achieve high energy efficiency while maintaining flexibility after fabrication. The process-near-memory integration further advances the SDNPM chip in a bottom-up way to reduce the energy consumption of data movement while improving the bandwidth. Utilizing a relatively mature fabrication and bonding process can result in feasible solutions for both data-intensive and compute-intensive applications including digital signal processing and artificial intelligence. The logic die is fabricated in the SMIC 40 nm process and the DRAM die is fabricated in the PSMC 25 nm process. The hybrid bonding is implemented by XMC. The experimental results show that the energy efficiency of the proposed SDPNM chip is 33.1× better than the state-of-the-art FPGA ranging from 8.2× to 104.1×.

关键词： software-defined chips process near memory energy efficiency dynamic reconfiguration domain specific

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Recent Advances in Artificial Sensory Neurons:Biological Fundamentals,Devices,Applications,and Challenges

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Nano-Micro Letters 2025年第3期17卷 168-216页

作者： Shuai Zhong Lirou Su Mingkun Xu Desmond Loke Bin Yu Yishu Zhang Rong Zhao Guangdong Institute of Intelligence Science and Technology HengqinZhuhai 519031People’s Republic of China College of Integrated Circuits Zhejiang UniversityHangzhou 3112000People’s Republic of China ZJU-Hangzhou Global Scientific and Technological Innovation center Hangzhou 310027People’s Republic of China Department of Science Mathematics and TechnologySingapore University of Technology and DesignSingapore 487372Singapore Department of Precision Instruments Tsinghua UniversityBeijing 100084People’s Republic of China center for Brain‑Inspired computing research Tsinghua UniversityBeijing 100084People’s Republic of China IDG/McGovern Institute for Brain research Tsinghua UniversityBeijing 100084People’s Republic of China

Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power *** fully leverage its advantages,convertingthe external analog signals to spikes is an essential *** including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area *** efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory *** can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory *** review summarizesand benchmarks the recent progress of artificial sensory *** starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial ***,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also ***,we highlight the challenges and perspectives for the future development of artificial sensory neurons.

关键词： Artificial intelligence Emerging devices Artificial sensory neurons Spiking neural networks Neuromorphic sensing

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