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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Lin, Zebing Lv, Jingjie Li, Pingping Song, Linqi Liang, Hui Hou, Hanxu School of Computer Science and Technology Dongguan University of Technology China School of Electrical Engineering & Intelligentization Dongguan University of Technology China Shenzhen Institute for Advanced Study University of Electronic Science & Technology of China China City University of Hong Kong Department of Computer Science Hong Kong City University of Hong Kong Shenzhen Research Institute Hong Kong

ISBN: (纸本)9798350390643

Reed-Solomon (RS) codes are widely applied in storage systems. The main bottleneck of RS codes is that the high decoding complexity defined as the number of multiplications and additions. In this paper, we propose a fast decoding algorithm for RS codes that requires fewer number of multiplications. Our fast decoding algorithm is designed by a decomposition of Vandermonde matrices. We show that our decoding algorithm requires fewer number of multiplications than the existing best known algorithm when the number of parities T is large. When the codeword length N=16, our decoding algorithm reduces the number of multiplications by 7% and 29% when T=4 and T=7, respectively, compared with the best known decoding algorithm. © 2024 IEEE.

关键词： Reed-Solomon codes

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Dual-GTF: Enhancing Community Detection with Dual-Scale Game Theoretic Framework

Dual-GTF: Enhancing Community Detection with Dual-Scale Game...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Shi, Haodong Kang, Zhao Yan, Ke Liu, Sicong Xin, Yichen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study Uestc Shenzhen China Kashi Institute of Electronics and Information Industry Kashi China

ISBN: (纸本)9781665410205

Community detection is a crucial task in complex network analysis, and existing game-theoretic-based community detection methods struggle to achieve a balance between local and global competition and cooperation. This paper introduce a Dual-scale Game theoretical Framework (Dual-GTF) for community detection. First, a new modularity function is proposed, enabling each node to incorporate additional expansion information at the community scale by collaborating closely with the individual scale modularity function. Subsequently, leveraging cooperative and competitive strategies informed by realistic empiricism, this paper integrate community-scale information to facilitate effective role division within communities. Evaluations of real-world datasets demonstrate that this approach, Dual-GTF, achieves superior convergence and performance results in most cases. © 2024 IEEE.

关键词： Complex networks

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A Correlation And Order-Aware Rule Learning Method For Knowledge Graph Reasoning 29

A Correlation And Order-Aware Rule Learning Method For Knowl...

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

作者： He, Yuefeng He, Yang Zheng, Xu Hui, Bei Tian, Bin University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Naval University of Engineering Wuhan China

ISBN: (纸本)9798350330717

Mining high-quality logical rules is crucial as they can provide beneficial interpretability for predictions. Recent methods that incorporate logical rules into learning tasks have been proven to yield high-quality logical rules successfully. However, existing methods either rely on the rule instances observed to support rule mining, or simply embed the rule head and the rule body to learn from them. Additionally, they can not fully utilize the rich semantic information contained in logical rules and overlook the intrinsic correlations between all relations within the domain. In this paper, we propose a model called Correlation and order-Aware Rule Learning (CARL) that captures deeper semantic information in rules by allowing relations to be co-aware of each other and paying attention to logical sequence sensitivity. CARL utilizes semantic consistency between the rule body and rule head as its learning objective, continuously introducing more semantic information and logically simplifying the rule body while considering logical sequence sensitivity. We explored the internal correlations between domain relations and used the thought of knowledge distillation to simplify modules so that relations in CARL can share or perceive each other's information or state efficiently. Experiments on link prediction tasks have demonstrated that CARL can learn higher-quality rules and yield state-of-the-art results on four popular public datasets. https://***/burning5112/CARL © 2023 IEEE.

关键词： knowledge graph reasoning rule learning

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Cross-view Contrastive Unification Guides Generative Pretraining for Molecular Property Prediction 24

Cross-view Contrastive Unification Guides Generative Pretrai...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Lin, Junyu Zheng, Yan Chen, Xinyue Ren, Yazhou Pu, Xiaorong He, Jing Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Medicine Biomedical Sciences The University of Queensland BrisbaneQLD Australia

ISBN: (纸本)9798400706868

Multi-view based molecular properties prediction learning has received widely attention in recent years in terms of its potential for the downstream tasks in the field of drug discovery. However, the consistency of different molecular view representations and the full utilization of complementary information among them in existing multi-view molecular property prediction methods remain to be further explored. Furthermore, most current methods focus on generating global level representations at the graph level with information from different molecular views (e.g., 2D and 3D views) assuming that the information can be corresponded to each other. In fact it is not unusual that for example the conformation change or computational errors may lead to discrepancies between views. To addressing these issues, we propose a new Cross-View contrastive unification guides Generative Molcular pre-trained model, call MolCVG. We first focus on common and private information extraction from 2D graph views and 3D geometric views of molecules, Minimizing the impact of noise in private information on subsequent strategies. To exploit both types of information in a more refined way, we propose a cross-view contrastive unification strategy to learn cross-view global information and guide the reconstruction of masked nodes, thus effectively optimizing global features and local descriptions. Extensive experiments on real-world molecular data sets demonstrate the effectiveness of our approach for molecular property prediction task. © 2024 ACM.

关键词： Contrastive Learning

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On Which Nodes Does GCN Fail? Enhancing GCN From the Node Perspective 41

On Which Nodes Does GCN Fail? Enhancing GCN From the Node Pe...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Jincheng Shen, Jialie Shi, Xiaoshuang Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science City University of London London United Kingdom Sichuan Artificial Intelligence Research Institute Yibin China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

The label smoothness assumption is at the core of Graph Convolutional Networks (GCNs): nodes in a local region have similar labels. Thus, GCN performs local feature smoothing operation to adhere to this assumption. However, there exist some nodes whose labels obtained by feature smoothing conflict with the label smoothness assumption. We find that the label smoothness assumption and the process of feature smoothing are both problematic on these nodes, and call these nodes out of GCN's control (OOC nodes). In this paper, first, we design the corresponding algorithm to locate the OOC nodes, then we summarize the characteristics of OOC nodes that affect their representation learning, and based on their characteristics, we present DaGCN, an efficient framework that can facilitate the OOC nodes. Extensive experiments verify the superiority of the proposed method and demonstrate that current advanced GCNs are improvements specifically on OOC nodes;the remaining nodes under GCN's control (UC nodes) are already optimally represented by vanilla GCN on most datasets. Copyright 2024 by the author(s)

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A Fast Algorithm for Finding the Roots of Polynomials over Finite fields 15

A Fast Algorithm for Finding the Roots of Polynomials over F...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Li, Pingping Yu, Leilei Song, Linqi Hou, Hanxu Dongguan University of Technology School of Computer Science & Technology Dongguan China University of Electronic Science & Technology of China Shenzhen Institute for Advanced Study China City University of Hong Kong Department of Computer Science Hong Kong Dongguan University of Technology School of Electrical Engineering & Intelligentization Dongguan China

ISBN: (纸本)9798350324662

Finding all roots of polynomials over finite fields is a crucial problem in algebraic coding techniques, such as decoding BCH codes and Reed-Solomon codes. The current commonly used methods are Chien search or fast Fourier transform (FFT), which evaluates all elements in a finite field and results in field multiplications whose number increases linearly with the field size. In this paper, we propose a new approach that enables the multiplicative complexity to grow sub-linearly with the field size. To our knowledge, this is the currently lowest multiplicative complexity for this problem. Additionally, the proposed approach maintains the additive complexity similar to that of FFT-based method. © 2023 IEEE.

关键词： error-correction codes error-locator polynomial fast Fourier transforms (FFT) Polynomial evaluation

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Fix the Tests: Augmenting LLMs to Repair Test Cases with Static Collector and Neural Reranker 35

Fix the Tests: Augmenting LLMs to Repair Test Cases with Sta...

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35th IEEE International Symposium on Software Reliability engineering, ISSRE 2024

作者： Liu, Jun Yan, Jiwei Xie, Yuanyuan Yan, Jun Zhang, Jian State Key Laboratory of Computer Science Institute of Software China Chinese Academy of Sciences Technology Center of Software Engineering Institute of Software China China UCAS School of Intelligent Science and Technology Hangzhou Institute for Advanced Study China

ISBN: (纸本)9798350353884

During software evolution, it is advocated that test code should co-evolve with production code. In real development scenarios, test updating may lag behind production code changing, which may cause compilation failure or bring other troubles. Existing techniques based on pre-trained language models can be directly adopted to repair obsolete tests caused by such unsynchronized code changes, especially syntactic-related ones. However, the lack of task-oriented contextual information affects the repair accuracy on large-scale projects. Starting from an obsolete test, the key challenging task is precisely identifying and constructing Test-Repair-Oriented Contexts (TROCtxs) from the whole repository within a limited token *** this paper, we propose Synter (SYNtactic-breaking- changes-induced TEst Repair), a novel approach based on LLMs to automatically repair obsolete test cases via precise and concise TROCtxs construction. Inspired by developers' programming practices, we design three types of TROCtx: class context, usage context, and environment context. Given an obsolete test case to repair, Synter firstly collects the related code information for each type of TROCtx through static analysis techniques automatically. Then, it generates reranking queries to identify the most relevant TROCtxs, which will be taken as the repair-required key contexts and be input to the large language model for the final test *** evaluate the effectiveness of Synter, we construct a benchmark dataset that contains a set of obsolete tests caused by syntactic breaking changes. The experimental results show that Synter outperforms baseline approaches both on textual- and intent-matching metrics. With the augmentation of constructed TROCtxs, hallucinations are reduced by 57.1%. © 2024 IEEE.

关键词： Syntactics

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Mask R-CNN Network for Back Recognition on Adam's Bending Images 21

Mask R-CNN Network for Back Recognition on Adam's Bending Im...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Yuxin, Pu Hongbin, Cai Jipeng, Fan Xin, Zheng School of Computer Science and Engineering University of Electronic Science and Technology of China China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China Chengdu Chengdian Goldisc HealthData Technology Co. Ltd. China School of Artificial Intelligence and Big Data Sichuan University of Arts and Science China

ISBN: (纸本)9798331519254

Back recognition is critical for adolescent idiopathic scoliosis screening. Existing methods mainly rely on manual work which is time-consuming and laborious and may introduce errors and mismatching impacting the accuracy of subsequent screening outcomes. In this work, we propose to utilize the Mask R-CNN network as the framework for auto-extraction of the back region on Adam's bending images. However, due to the inherent limit receptive fields, initial Mask R-CNN can hardly learn long-range dependencies which are crucial for object detection and segmentation. To this end, we introduce the explicit visual center module into Mask R-CNN. In the EVC module, a lightweight MLP is devised to extract global long-range dependencies. To compensate for MLP's lack of ability to capture features of local corner regions, a learnable visual center is incorporated. By aggregating global long-range dependencies and discriminative feature representation of local key regions, our Mask R-CNN can achieve better performance on Adam's bending images. Moreover, we collect and annotate an Adam's bending image dataset for back recognition. Experimental results on our annotated dataset demonstrate that our improved Mask R-CNN considerably improves the average precision in detection and segmentation of Mask R-CNN from 78.6% to 83.6% and 85.8% to 87.0%, respectively. © 2024 IEEE.

关键词： Object detection

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SMART: A Decision-Making Framework with Multi-modality Fusion for Autonomous Driving Based on Reinforcement Learning 28th

SMART: A Decision-Making Framework with Multi-modality Fusi...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Xia, Yuyang Liu, Shuncheng Hu, Rui Yu, Quanlin Feng, Xiushi Zheng, Kai Su, Han School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9783031306778

Decision-making in autonomous driving is an emerging technology that has rapid progress over the last decade. In single-lane scenarios, autonomous vehicles should simultaneously optimize their velocity decisions and steering angle decisions to achieve safety, efficiency, comfort, small impacts on rear vehicles, and small offsets to the lane center line. Previous studies, however, have typically optimized these two decisions separately, ignoring the potential relationship between them. In this work, we propose a decision-making framework, named SMART (deciSion-Making frAmework based on ReinforcemenT learning), to optimize the velocity and steering angle of the autonomous vehicle in parallel. In order for the autonomous vehicle to effectively perceive the curvature of the lane and interactions with other vehicles, we adopt a graph attention mechanism to extract and fuse the features from different modalities (i.e., sensor-collected vehicle states and camera-collected lane information). Then a hybrid reward function takes into account aspects of safety, efficiency, comfort, impact, and lane centering to instruct the autonomous vehicle to make optimal decisions. Furthermore, our framework enables the autonomous vehicle to adaptively choose the duration of an action, which helps the autonomous vehicle pursue higher reward values. Extensive experiments evidence that SMART significantly outperforms the existing methods in multiple metrics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Emergence of composite many-body exciton states in WS2 and MoSe2 monolayers

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Physical Review B 2024年第4期109卷 L041304-L041304页

作者： J. Choi J. Li D. Van Tuan H. Dery S. A. Crooker National High Magnetic Field Laboratory Los Alamos New Mexico 87545 USA Advanced Instrumentation Institute Korea Research Institute of Standards and Science Daejeon 34113 Korea Wuhan National High Magnetic Field Center and School of Physics Huazhong University of Science and Technology Hubei 430074 China Department of Electrical and Computer Engineering University of Rochester Rochester New York 14627 USA Department of Physics and Astronomy University of Rochester Rochester New York 14627 USA

When doped with a high density of mobile charge carriers, monolayer transition-metal dichalcogenide (TMD) semiconductors can host new types of composite many-particle exciton states that do not exist in conventional semiconductors. Such multiparticle bound states arise when a photoexcited electron-hole pair couples not to just a single Fermi sea that is quantum-mechanically distinguishable (as in the case of conventional charged excitons or trions), but rather couples simultaneously to multiple Fermi seas, each having distinct spin and valley quantum numbers. Composite six-particle “hexciton” states were recently identified in electron-doped WSe2 monolayers, but under suitable conditions they should also form in all other members of the monolayer TMD family. Here we present spectroscopic evidence demonstrating the emergence of many-body hexcitons in charge-tunable WS2 monolayers (at the A-exciton) and MoSe2 monolayers (at the B-exciton). The roles of distinguishability and carrier screening on the stability of hexcitons are discussed.

关键词： Excitons Trions Transition metal dichalcogenides Two-dimensional electron system

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