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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Guangjin Wang, Bao Xu, Fuyong Zhu, Zhenfang Wang, Peipei Wang, Ru Liu, Peiyu School of Information Science and Engineering Shandong Normal University Jinan China School of Information Science and Electrical Engineering Shandong Jiao Tong University Jinan China Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal sarcasm detection aims to identify whether utterances express sarcastic intentions contrary to their literal meaning based on multimodal information. However, existing methods fail to explore the model's "ability to understand" the semantics expressed by sentences in the image context from semantic diversity perspectives. In this paper, we propose a multi-view semantic awareness method, which concretizes semantics from multiple perspectives to improve the model's ability to capture different semantic features. Specifically, two learnable prefixes are attached to the text representation respectively to construct semantic representations from both the literal meaning and sarcastic intention perspectives. Then, image-text information is further fused through cross-attention to guide the semantic representation of different perspectives in the image context. Finally, the semantics expressed by prefixes are strengthened through KL divergence, thereby encouraging the model to capture two distinctive semantic features. Experiments on benchmark datasets demonstrate the effectiveness of our method. © 2025 IEEE.

关键词： KL divergence multi-view semantic representation multimodal sarcasm detection semantic representation enhancement

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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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Propagation of transverse photonic orbital angular momentum through few-mode fiber

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Advanced Photonics 2023年第3期5卷 101-107页

作者： Qian Cao Zhuo Chen Chong Zhang Andy Chong Qiwen Zhan University of Shanghai for Science and Technology School of Optical-Electrical and Computer EngineeringShanghaiChina Zhangjiang Laboratory ShanghaiChina University of Shanghai for Science and Technology Shanghai Key Laboratory of Modern Optical SystemShanghaiChina Pusan National University Department of PhysicsBusanRepublic of Korea

Spatiotemporal optical vortex(STOV)pulses can carry transverse orbital angular momentum(OAM)that is perpendicular to the direction of pulse *** a STOV pulse,its spatiotemporal profile can be significantly distorted due to unbalanced dispersive and diffractive *** may limit its use in many research applications,where a long interaction length and a tight confinement of the pulse are *** first demonstration of STOV pulse propagation through a few-mode optical fiber is *** numerical and experimental analysis on the propagation of STOV pulse through a commercially available SMF-28 standard telecommunication fiber is *** spatiotemporal phase feature of the pulse can be well kept after the pulse propagates a few-meter length through the fiber even with *** propagation of the pulse will result in a breakup of its spatiotemporal spiral phase structure due to an excessive amount of modal group delay *** stable and robust transmission of transverse photonic OAM through optical fiber may open new opportunities for transverse photonic OAM studies in telecommunications,OAM lasers,and nonlinear fiber-optical research.

关键词： photonic orbital angular momentum spatiotemporal optical vortices multimode fiber

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3D printed-electrospun PCL/hydroxyapatite/MWCNTs scaffolds for the repair of subchondral bone

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Regenerative Biomaterials 2023年第1期10卷 294-305页

作者： Yanyan Cao Lei Sun Zixian Liu Zhizhong Shen Wendan Jia Peiyi Hou Shengbo Sang College of Information Science and Engineering Hebei North UniversityZhangjiakou 075000China Shanxi Key Laboratory of Micro Nano Sensors&Artificial Intelligence Perception College of Information and ComputerTaiyuan University of TechnologyTaiyuan 030024China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030031China

Osteochondral defect caused by trauma or osteoarthritis exhibits a major challenge in clinical treatment with limited symptomatic effects at *** regeneration and remodeling of subchondral bone play a positive effect on cartilage regeneration and further promotes the repair of osteochondral *** use of the strengths of each preparation method,the combination of 3D printing and electrospinning is a promising method for designing and constructing multi-scale scaffolds that mimic the complexity and hierarchical structure of subchondral bone at the microscale and nanoscale,*** this study,the 3D printed-electrospun poly(ɛ-caprolactone)/nano-hydroxyapatites/multi-walled carbon nanotubes(PCL/nHA/MWCNTs)scaffolds were successfully constructed by the combination of electrospinning and layer-by-layer 3D *** resulting dual-scale scaffold consisted of a dense layer of disordered nanospun fibers and a porous microscale 3D scaffold layer to support and promote the ingrowth of subchondral ***,the biomimetic PCL/nHA/MWCNTs scaffolds enhanced cell seeding efficiency and allowed for higher cell-cell interactions that supported the adhesion,proliferation,activity,morphology and subsequently improved the osteogenic differentiation of bone marrow mesenchymal stem cells in ***,this study elucidates that the construction of 3D printed-electrospun PCL/nHA/MWCNTs scaffolds provides an alternative strategy for the regeneration of subchondral bone and lays a foundation for subsequent in vivo studies.

关键词： additive manufacturing 3D printing electrospinning subchondral bone

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SC-GNN: A Communication-Efficient Semantic Compression for Distributed Training of GNNs 24

SC-GNN: A Communication-Efficient Semantic Compression for D...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wang, Jihe Wu, Ying Wang, Danghui School of Computer Science Northwestern Polytechnical University Xi'an China Engineering Research Center of Embedded System Integration Ministry of Education Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798400706011

Training big graph neural networks (GNNs) in distributed systems is quite time-consuming mainly because of the ubiquitous aggregate operations that involve a large amount of cross-partition communication for collecting embeddings/gradients during the forward and backward propagation. To reduce the volume of the communication, some recent approaches focused on decaying each of connections via sampling, quantifying, or delaying until satisfactory trade-off are obtained between volume and accuracy. However, when applied to popular GNNs, those approaches are found to be bounded by a common volume/accuracy Pareto frontier which shows that the decaying for individual connection cannot further accelerate the aggregate of training. In this work, SC-GNN, a semantic compression of the cross-partition communication, is proposed to concentrate a group of connections as a high-level semantics and transmit to a target partition. Since carrying the overall intent of a group, the semantics can keep transferring the interactions, i.e., embeddings/gradients, between a pair of remote partitions until GNN models converge. In addition, a connection-pattern based differential optimization is proposed to further prune those weak connections, while guaranteeing the training accuracy. The results show that, for multi-field datasets, the compression rate of SC-GNN is 40.8 × higher than SOTA methods and the epoch time is reduced to 31.8% on average. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graph neural networks

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Research on Causal Model Construction and Fault Diagnosis of Control systems 7th

Research on Causal Model Construction and Fault Diagnosis of...

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7th International Conference on Computing, Control and Industrial engineering, CCIE 2023

作者： Zhu, Qun Chen, Zhi Liu, Jie Xu, Zhuoran Wen, Juan Yang, Jinghua Jian, Yifan School of Computer Science University of South China Hengyang421200 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610064 China School of Electrical Engineering University of South China Hengyang421200 China

ISBN: (纸本)9789819927296

Fault diagnosis of complex control systems is a difficult and hot topic of research at this stage. In order to explore the internal operation characteristics of complex control systems and fault propagation methods to achieve efficient and accurate fault diagnosis, this paper studies control system fault diagnosis methods based on cause-effect relationships, analyzes the role relationship between system components from the perspective of control equation structure, proposes the method of constructing cause-effect diagram models with control equations, and uses the algorithm of way to implement, and then combine the information flow to quantify the causal effect quantifiably, when the system steady state is disrupted, the information flow produces a jump, so as to extend the fault diagnosis reasoning method. The fault diagnosis is verified by simulation of the pressure control system of the nuclear power plant regulator, and the experimental results verify the effectiveness of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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Electrical resistivity-temperature characteristics enhancement of insulating cross-linked polyethylene composites by incorporating positive temperature coefficient particles with different Curie temperatures

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High Voltage 2023年第1期8卷 1-11页

作者： Guimin Jiang Yuanxiang Zhou Chenyuan Teng Yunxiao Zhang Xin Huang Lei Xue Luming Zhou The Wind Solar Storage Division of State Key Laboratory of Control and Simulation of Power System and Generation Equipment Xinjiang UniversityUrumqiChina State Key Laboratory of Power System and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijingChina College of Information Engineering Zhejiang University of TechnologyHangzhouChina School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Incorporating positive temperature coefficient(PTC)ceramic particles into polymers provides a prospective alternative solution for suppressing severe electric field distortions caused by negative temperature coefficient(NTC)of electrical resistivity within polymer insulation in high voltage direct current *** effect of the Curie temperature of PTC particles on the inhibition of the NTC effect of cross-linked polyethylene(XLPE)is investigated in this *** temperature coefficient particles with varied Curie temperatures are surface-modified and melt blending with *** modified particles are dispersed on averagely in the *** electrical resistivity,space charge behaviour,and direct current(DC)electrical breakdown strength of the samples are investigated at different temperatures,exploring the effect of the Curie temperature of PTC particles on suppressing the NTC effect of *** is demonstrated that the reduction in Curie temperature can further suppress the NTC effect of XLPE,enhance the DC strength at elevated temperatures,and inhibit the internal space charge *** reduction in the Curie temperatures means that the initiation of the PTC effect advances,exhibiting a higher potential barrier and inhibiting the NTC effect more *** work may give a reference for improving the temperature stability of DC properties for XLPE cable insulation.

关键词： coefficient resistivity insulation

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Targeted Pareto Optimization for Subset Selection With Monotone Objective Function and Cardinality Constraint

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Shang, Ke Wu, Guotong Pang, Lie Meng Ishibuchi, Hisao National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Subset selection, a fundamental problem in various domains, is to choose a subset of elements from a large candidate set under a given objective or multiple objectives. Pareto optimization for subset selection (POSS) has emerged as a powerful paradigm for addressing subset selection problems. Recently, some POSS variants have been proposed to further improve its performance. In this paper, we propose a new POSS variant, named Targeted Pareto Optimization for Subset Selection (TPOSS). TPOSS differs from POSS in four aspects: problem formulation, population initialization, mutation, and environmental selection. The main idea of TPOSS is to focus the search on the target region of subset selection with respect to the subset cardinality in order to improve the search efficiency. We conduct comprehensive experiments to compare TPOSS with six state-of-the-art algorithms on three subset selection tasks (i.e., sparse regression, unsupervised feature selection, and hypervolume subset selection) where the size of the candidate sets ranges from 20 to 400. Experimental results show that with respect to the objective value of the best feasible subset, TPOSS outperforms the other algorithms on all the three tasks, which suggests the potential of TPOSS to enhance subset selection in various domains. IEEE

关键词： Linear programming

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Gesture Recognition of sEMG Based on Res-LSTM 17th

Gesture Recognition of sEMG Based on Res-LSTM

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17th International Conference on Intelligent Robotics and Applications, ICIRA 2024

作者： Zhao, Yujia Zou, Chunlong Yun, Juntong Jiang, Du Huang, Li Liu, Ying Jiang, Guozhang Xie, Yuanmin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset;and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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Unsupervised Graph-Sequence Anomaly Detection for 5G Core Network Control Plane Traffic 29

Unsupervised Graph-Sequence Anomaly Detection for 5G Core Ne...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Wang, Min Li, Peng Cheng, Zhang Liu, Wenmao Nie, Lei Bao, Haizhou Liu, Qin Zhang, Kai Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China Nsfocus Technologies Group Co. Ltd China Wuhan University School of Cyber Science and Engineering China

ISBN: (纸本)9798350330717

5G Core network (5GC) employs a Service Based Architecture (SBA). This architecture decomposes the control plane into multiple independent Network Functions (NFs). NFs open interfaces to provide services to other NFs, which makes the control plane more susceptible to external malicious attacks. However, existing anomaly detection methods focus more on traffic statistics features and are difficult to apply to the 5GC control plane. In this paper, we proposed GSAD, a Graph-Sequence analysis-based Anomaly Detection method for 5GC control plane traffic. We model control plane traffic as a directed graph to depict topological and NF interaction information. Further, we use the normalizing flows with temporal dependencies to mine the sequential information in the traffic. GSAD combines the topological and sequential information to provide fine-grained detection. We evaluate our proposed framework on the 5GC testbed using Free5GC and UERANSIM in various scenarios. Experimental results demonstrate that our framework outperforms the baselines significantly in terms of detection performance. © 2023 IEEE.

关键词： 5G core network anomaly detection control plane graph neural network normalizing flows

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