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arXiv 2024年

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Stability of diverse dodecagonal quasicrystals in T-shaped liquid crystalline molecules

arXiv

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arXiv 2024年

作者： Wang, Xin Shi, An-Chang Zhang, Pingwen Jiang, Kai Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China Department of Physics and Astronomy McMaster University HamiltonONL8S4M1 Canada School of Mathematics and Statistics Wuhan University Wuhan430072 China

Quasicrystals are intriguing ordered structures characterized by the lack of translational symmetry and the existence of rotational symmetry. The tiling of different geometric units such as triangles and squares in two-dimensional space can result in a great variety of quasicrystals that could be realized by the self-assembly of liquid crystalline molecules. In this study, we introduce three self-similar dodecagonal tilings, including a novel Diamond-Square-Triangle pattern, composed of triangular and quadrangular tiles and examine their thermodynamic stability by using the self-consistent field theory applied to T-shaped liquid crystalline molecules. Specifically, we detail the inflation rules for the construction of these dodecagonal tilings and analyze their self-similarity, and show that these tilings can be viewed as projections of higher-dimensional periodic lattice points with projection windows. Using these dodecagonal tilings as initial configurations of the SCFT results in solutions corresponding to quasicrystals that could form from the T-shaped liquid crystalline molecules. The relative stability of these aperiodic phases is analyzed to obtain design rules that could stabilize quasicrystals. Meanwhile, we provide two criteria for distinguishing three dodecagonal quasicrystals and their approximants by analyzing their diffraction peaks. These findings shed new lighten on the discovery of new quasicrystals in soft materials. Copyright © 2024, The Authors. All rights reserved.

关键词： Quasicrystals

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Spatiotemporal Action Detection Based on Fine-Grained

SSRN

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SSRN 2024年

作者： Cheng, Yan Mike, Jack Fang, Chengxing Huang, Jiawen Wen, Yu School of Software Jiangxi Normal University Nanchang330022 China School of Computer Information Engineering Jiangxi Normal University Nanchang330022 China Business College of Jiangxi Normal University Nanchang330022 China Key Laboratory of Intelligent Information Processing and Emotional Computing in Jiangxi Province Nanchang330022 China Shanxi Datong University China

In the field of computer vision, spatiotemporal action detection is a challenging task. Traditional methods such as YOWO have achieved certain results in real-time and accuracy, but still have shortcomings in dealing with missing detail information, capturing long-distance dependencies, and target overlap problems. In response to these issues, this article proposes a spatiotemporal action detection method based on fine-grained enhancement. Firstly, a dual path fine-grained enhancement module is designed to enhance the ability to extract fine-grained features;Secondly, the self-attention&convolution module was introduced, which can better capture long-distance dependencies and cross level feature relationships;Finally, in order to address the issue of target overlap, the SIoU loss function is introduced to more comprehensively evaluate the similarity between predicted boxes and real boxes. The experimental results show that the proposed method achieves 86.4% F-mAP and 52.6% V-mAP on the UCF101-24 dataset, 20.5% F-mAP on the AVA dataset, and only 44.0 GFlops with an FPS of 34, ensuring the real-time performance of the proposed method with higher accuracy than SOTA model. In addition, the ablation experiment further demonstrated the effectiveness of each module in this paper. Overall, this method significantly improves the accuracy of spatiotemporal action detection while maintaining high efficiency, providing an effective solution for real-time action detection. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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A posteriori error estimation for an interior penalty virtual element method of Kirchhoff plates

arXiv

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arXiv 2024年

作者： Feng, Fang Hu, Yuming Yu, Yue Zhao, Jikun School of Mathematics and Statistics Nanjing University of Science and Technology Nanjing China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China School of Mathematics and Statistics Zhengzhou University Zhengzhou450001 China

In this paper, we develop a residual-type a posteriori error estimation for an interior penalty virtual element method (IPVEM) for the Kirchhoff plate bending problem. Building on the work in [18], we adopt a modified discrete variational formulation that incorporates the H1-elliptic projector in the jump and average terms. This allows us to simplify the numerical implementation by including the H1-elliptic projector in the computable error estimators. We derive the reliability and efficiency of the a posteriori error bound by constructing an enriching operator and establishing some related error estimates that align with C0-continuous interior penalty finite element methods. As observed in the a priori analysis, the interior penalty virtual elements exhibit similar behaviors to C0continuous elements despite its discontinuity. This observation extends to the a posteriori estimate since we do not need to account for the jumps of the function itself in the discrete scheme and the error estimators. As an outcome of the error estimator, an adaptive VEM is introduced by means of the mesh refinement strategy with the one-hanging-node rule. Numerical results from several benchmark tests confirm the robustness of the proposed error estimators and show the efficiency of the resulting adaptive VEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Discrete element methods

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Existence of Solutions for the Continuous Algebraic Riccati Equation via Polynomial Optimization

arXiv

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arXiv 2024年

作者： Zhang, Juan Zhao, Wenjie Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Hunan Key Laboratory for Computation and Simulation in Science and Engineering School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China School of Mathematics and Statistics Hunan First Normal University Hunan Changsha410205 China School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

This paper studies the solution existence of the continuous-time algebraic Riccati equation (CARE). We formulate the CARE as two constrained polynomial optimization problems, and then use Lasserre's hierarchy of semi-definite relaxations to solve them. Compared to the existing work, our approaches can obtain the exact positive semi-definite solution of the CARE when the coefficient matrices are symmetric. Moreover, our methods can detect the nonexistence of the positive semi-definite solution for the CARE. Numerical examples show that our approaches are effective compared to the existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Riccati equations

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Variable Rate Syndrome-Trellis Codes for Steganography on Bursty Channels 19th

Variable Rate Syndrome-Trellis Codes for Steganography on Bu...

引用

19th International Workshop on Digital Forensics and Watermarking, IWDW 2020

作者： Feng, Bingwen Liu, Zhiquan Wei, Kaimin Lu, Wei Lin, Yuchun College of Information Science and Technology Jinan University Guangzhou510632 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen518060 China School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Guangzhou Institute of Science and Technology Guangzhou510006 China

ISBN: (纸本)9783030694487

This paper presents a type of variable rate syndrome-trellis codes (VR-STC) for bursty channels. It can embed message bits with two different embedding rates. In the embedding, a cover vector is sliced into segments, and the embedding rates for each segment depend on the local channel distribution. The parities of stego segments are exploited to indicate the selected embedding rates according to a mapping function between parities and embedding rates. The core of the VR-STC is the parity-aware encoder, which can simultaneously output two candidate stego segments with different embedding rates and opposite parities, either of which can be used to constitute the final stego vector. A Viterbi algorithm is also suggested to find the closed stego segments. Besides, the mapping function between parities and embedding rates is designed by minimizing the embedding costs on a down sampled version of the cover vector. It can further improve the undetectability of the VR-STC. Experimental results on artificial signals and binary images suggest that the proposed VR-STC can provide high success rate of embedding and reduce the embedding cost on bursty channels. © 2021, Springer Nature Switzerland AG.

关键词： Steganography

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection 39

Look Inside for More: Internal Spatial Modality Perception f...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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Fine-grained pseudo-code generation method via code feature extraction and transformer

arXiv

引用

arXiv 2021年

作者： Yang, Guang Zhou, Yanlin Chen, Xiang Yu, Chi School of Information Science and Technology Nantong University China Ministry of Industry and Information Technology China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University China

Pseudo-code written by natural language is helpful for novice developers’ program comprehension. However, writing such pseudo-code is time-consuming and laborious. Motivated by the research advancements of sequence-to-sequence learning and code semantic learning, we propose a novel deep pseudo-code generation method DeepPseudo via code feature extraction and Transformer. In particular, DeepPseudo utilizes a Transformer encoder to perform encoding for source code and then use a code feature extractor to learn the knowledge of local features. Finally, it uses a pseudo-code generator to perform decoding, which can generate the corresponding pseudo-code. We choose two corpora (i.e., Django and SPoC) from real-world large-scale projects as our empirical subjects. We first compare DeepPseudo with seven state-of-the-art baselines from pseudo-code generation and neural machine translation domains in terms of four performance measures. Results show the competitiveness of DeepPseudo. Moreover, we also analyze the rationality of the component settings in DeepPseudo. Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

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Dynamics-Based Algorithm-Level Privacy Preservation for Push-Sum Average Consensus

arXiv

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arXiv 2023年

作者： Cheng, Huqiang Xie, Mengying Yang, Xiaowei Lü, Qingguo Li, Huaqing Key Laboratory of Dependable Services Computing in Cyber Physical Society-Ministry of Education College of Computer Science Chongqing University Chongqing401331 China School of Software Engineering South China University of Technology Guangzhou510006 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China

In the intricate dance of multi-agent systems, achieving average consensus is not just vital–it is the backbone of their functionality. In conventional average consensus algorithms, all agents reach an agreement by individual calculations and sharing information with their respective neighbors. Nevertheless, the information interactions that occur in the communication network may make sensitive information be revealed. In this paper, we develop a new privacy-preserving average consensus method on unbalanced directed networks. Specifically, we ensure privacy preservation by carefully embedding randomness in mixing weights to confuse communications and introducing an extra auxiliary parameter to mask the state-updated rule in initial several iterations. In parallel, we exploit the intrinsic robustness of consensus dynamics to guarantee that the average consensus is precisely achieved. Theoretical results demonstrate that the designed algorithms can converge linearly to the exact average consensus value and can guarantee privacy preservation of agents against both honest-but-curious and eavesdropping attacks. © 2023, CC BY.

关键词： Consensus algorithm

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Extract Features from Lithium-Ion Battery Electrochemical Impedance Spectra and Estimate State of Health Based on Improved Convolutional Autoencoder-Temporal Convolutional Network

SSRN

引用

SSRN 2024年

作者： Wang, Kai Lou, Cheng Wang, Shi Li, Zhaoting School of Electrical Engineering Weihai Innovation Research Institute Qingdao University Qingdao266000 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Engineering Brown University ProvidenceRI02912 United States Shandong Suoxiang Intelligent Technology Co. Ltd Weifang261101 China

Predicting the State of Health (SOH) of batteries is crucial for understanding their remaining lifespan and formulating more effective maintenance and management strategies. Utilizing electrochemical impedance spectroscopy (EIS) to assess the health status of batteries offers advantages such as high precision, rapid response, non-invasiveness, and reliability for accurately forecasting the remaining lifespan of batteries. This study introduces an innovative deep learning technique that integrates an improved convolutional autoencoder (ICAE) with a temporal convolutional network (TCN) for extracting unsupervised features from 2D imagery derived from EIS data. This approach effectively captures battery feature information closely associated with its operational state. The unsupervised feature extraction process proposed in this work outperforms traditional supervised learning methods, enabling more accurate SOH predictions without reliance on pre-labeled data. Experimental results indicate that the deep learning methodology, employing the unsupervised feature extraction process, achieved a root mean square error (RMSE) of less than 2% in the SOH prediction task, and exhibited a prediction performance enhancement of 55.6% compared to the existing baseline model. © 2024, The Authors. All rights reserved.

关键词： Electrochemical impedance spectroscopy

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