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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Sifan Long Zhen Zhao Jimin Pi Shengsheng Wang Jingdong Wang College of Computer Science and Technology Jilin University Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin China Baidu VIS University of Sydney

Vision transformers have achieved significant improvements on various vision tasks but their quadratic interactions between tokens significantly reduce computational efficiency. Many pruning methods have been proposed to remove redundant tokens for efficient vision transformers recently. However, existing studies mainly focus on the token importance to preserve local attentive tokens but completely ignore the global token diversity. In this paper, we emphasize the cruciality of diverse global semantics and propose an efficient token decoupling and merging method that can jointly consider the token importance and diversity for token pruning. According to the class token attention, we decouple the attentive and inattentive tokens. In addition to preserving the most discriminative local tokens, we merge similar inattentive tokens and match homogeneous attentive tokens to maximize the token diversity. Despite its simplicity, our method obtains a promising trade-off between model complexity and classification accuracy. On DeiT-S, our method reduces the FLOPs by 35% with only a 0.2% accuracy drop. Notably, benefiting from maintaining the token diversity, our method can even improve the accuracy of DeiT-T by 0.1% after reducing its FLOPs by 40%.

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Do We Need an Encoder-Decoder to Model Dynamical Systems on Networks?

arXiv

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arXiv 2023年

作者： Liu, Bing Luo, Wei Li, Gang Huang, Jing Yang, Bo College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China School of Information Technology Deakin University Geelong Australia

As deep learning gains popularity in modelling dynamical systems, we expose an underappreciated misunderstanding relevant to modelling dynamics on networks. Strongly influenced by graph neural networks, latent vertex embeddings are naturally adopted in many neural dynamical network models. However, we show that embeddings tend to induce a model that fits observations well but simultaneously has incorrect dynamical behaviours. Recognising that previous studies narrowly focus on short-term predictions during the transient phase of a flow, we propose three tests for correct long-term behaviour, and illustrate how an embedding-based dynamical model fails these tests, and analyse the causes, particularly through the lens of topological conjugacy. In doing so, we show that the difficulties can be avoided by not using embedding. We propose a simple embedding-free alternative based on parametrising two additive vector-field components. Through extensive experiments, we verify that the proposed model can reliably recover a broad class of dynamics on different network topologies from time series data. © 2023, CC BY.

关键词： Dynamical systems

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An Improved Pigeon-Inspired Optimization for Multi-focus Noisy Image Fusion

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Journal of Bionic engineering 2021年第6期18卷 1452-1462页

作者： Yingda Lyu Yunqi Zhang Haipeng Chen Public Computer Education and Research Center Jilin UniversityChangchun130012China College of Software Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China

Image fusion technology is the basis of computer vision task,but information is easily affected by noise during *** this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-focus noisy image fusion by combining with the boundary handling of the convolutional sparse *** two-scale image decomposition,the input image is decomposed into base layer and detail *** the base layer,IPIO algorithm is used to obtain the optimized weights for fusion,whose value range is gained by fusing the edge ***,the global information entropy is used as the fitness index of the IPIO,which has high efficiency especially for discrete optimization *** the detail layer,the fusion of its coefficients is completed by performing boundary processing when solving the convolution sparse representation in the frequency *** sum of the above base and detail layers is as the final fused *** results show that the proposed algorithm has a better fusion effect compared with the recent algorithms.

关键词： Improved pigeon-inspired optimization Convolutional sparse representation Noisy image fusion Bionic algorithm

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NON-RADIATING ELASTIC SOURCES IN INHOMOGENEOUS ELASTIC MEDIA AT CORNERS WITH APPLICATIONS

arXiv

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arXiv 2025年

作者： Diao, Huaian Geng, Yueran Tang, Ruixiang School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China School of Mathematics Jilin University Changchun130012 China Department of Mathematics City University of Hong Kong Kowloon Hong Kong

This paper is concerned with non-radiating elastic sources in inhomogeneous elastic media. We demonstrate that the value of non-radiating elastic sources must vanish at convex corners of their support, provided the sources exhibit Hölder continuous regularity near the corner. Additionally, their gradient must satisfy intricate algebraic relationships with the angles defining the underlying corners, assuming the sources have C1,α regularity with α ∈ (0, 1) in the neighborhood of the corners. To perform microlocal analysis around the corners, we employ the so-called complex geometrical optics (CGO) solutions as test functions within a partial differential system. These characterizations of non-radiating elastic sources in inhomogeneous elastic media at corners enable us to establish the unique identifiability results for determining the position and shape of radiating elastic sources by a single far-field measurement, both locally and globally. The uniqueness result by a single far-field measurement is a challenging problem with a colorful history in inverse scattering. Indeed, when the support of a radiating elastic source is a convex polygon, we can simultaneously determine the shape of the elastic source and its values at the corners, provided the source is Hölder continuous at the corner. Furthermore, when the source function exhibits C1,α regularity in the neighborhood of a corner, the gradient of the source function at that corner can also be generically *** Codes 35Q74, 35R30, 74B05, 86A22 © 2025, CC BY.

关键词： Geometrical optics

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MP: Endowing Large Language Models with Lateral Thinking 39

MP: Endowing Large Language Models with Lateral Thinking

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Bai, Tian Cao, Yongwang Ge, Yan Yu, Haitao College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China Graduate School of Comprehensive Human Sciences University of Tsukuba Japan Institute of Library Information and Media Science University of Tsukuba Japan

ISBN: (纸本)157735897X

The recent studies show that Large Language Models (LLMs) often fall short in tasks demanding creative, lateral thinking due to lacking a clear awareness of their own reasoning processes. To cope with this issue, we propose a novel metacognitive prompting method (titled as MP) by mimicking human metacognition. Through integrating metacognitive principles, MP endows LLMs with lateral thinking ability, thereby enhancing their abilities to strategize, monitor, and reflect on their responses when dealing with creative tasks. The experimental results with five base LLMs across three lateral thinking datasets demonstrate that: All LLMs armed with MP consistently outperform the representative baseline methods. For example, MP demonstrates superior performance over CoT prompting across Sentence Puzzle (+5.00%), Word Puzzle (+10.07%), BiRdQA (+6.48%), and RiddleSense (+2.65%) with GPT-3.5-turbo model. In particular, the deployment of MP with GPT-4 achieves significant performance improvements that even surpass human performance on BRAINTEASER benchmark, demonstrating the transformative potential of MP in enhancing the creative problem-solving abilities of LLMs. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Automating Discussion Structure Re-Organization for Github Issues

SSRN

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SSRN 2023年

作者： Bai, Shuotong Liu, Lei Meng, Chenkun Liu, Huaxiao College of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun130012 China

As a popular social code hosting platform, GitHub encourages developers to discuss and leave opinions on issues for better repository development and closer team collaboration. However, popular issues can be bloated over the time, in particular, the linear format of GitHub issue discussions makes it difficult for developers to organize and extract useful information. For a better understanding of GitHub issue discussions, we first conduct an empirical study. Among the 14 most-starred repositories, we notice that 16,740 issues contain more than 10 comments. Then, more commented issue discussions refer to additional repository contributions and draw more developer attention. Hence, we narrow our perspective to the issue discussions with more than 10 comments. For 50.29% of these popular discussions, the topics of content are subject to change explicitly, and more than 36% of consecutive comment pairs do not host response relationships. In addition, just 40% of the comments on GitHub use the @ or quoting functions when commenting on other people's comments, but these functions are not only used for responding, but also for referencing, informing others, etc. Based on these results, these popular discussions suffer from intertwined content of various topics and ambiguous response linkage. To mitigate the situation, we propose a new approach IRA to automatically re-organize GitHub issue discussions, aiming at converting an issue discussion with the linear structure into a discussion tree with key information. The experimental results show that our approach outperforms other baselines, and achieves an average improvement of 19.97%, 14,25% on metrics of ACC and F1-score in the task of predicting response relationships, as well as gets 15.78%, 51.72%, 26.92%, 21.03%, 22.08%, 25.59% improvement in terms of parent accuracy, Variation of Information, One-to-One Overlap, and all Exact Match metrics in the re-organizing task. To investigate human perspectives on our re-organized

关键词： Deep learning

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GraCEImpute: A novel graph clustering autoencoder approach for imputation of single-cell RNA-seq data

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Computers in Biology and Medicine 2025年 184卷 109400-109400页

作者： Wang, Yueying Li, Kewei Zhang, Ruochi Fan, Yusi Huang, Lan Zhou, Fengfeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Artificial Intelligence Jilin University Changchun130012 China School of Biology and Engineering Guizhou Medical University Guizhou Guiyang550025 China

Single-cell RNA sequencing (scRNA-seq) technology establishes a unique view for elucidating cellular heterogeneity in various biological systems. Yet the scRNA-seq data is compromised by a high dropout rate due to the technological limitation, and the substantial data loss poses computational challenges on subsequent analyses. This study introduces a novel graph clustering autoencoder (GCAE)-based imputation approach (GraCEImpute) to address the challenge of missing data in scRNA-seq data. Our comprehensive evaluation demonstrates that the GraCEImpute model outperforms existing approaches in accurately imputing dropout zeros within scRNA-seq data. The proposed GraCEImpute model also demonstrates the significantly enhanced quality of downstream scRNA-seq data analyses, including clustering, differential gene expression (DEG) analysis, and cell trajectory inference. These improvements underscore the GraCEImpute model's potential to facilitate a deeper understanding of cellular processes and heterogeneity through the scRNA-seq data analyses. The source code is released at https://***/supp/. © 2024 Elsevier Ltd

关键词： Gene expression

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Spatiotemporal Transformer for Data Inference and Long Prediction in Sparse Mobile CrowdSensing

Spatiotemporal Transformer for Data Inference and Long Predi...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： En Wang Weiting Liu Wenbin Liu Chaocan Xiang Bo Yang Yongjian Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science Chongqing University China

Mobile CrowdSensing (MCS) is a data sensing paradigm that recruits users carrying mobile terminals to collect data. As its variant, Sparse MCS has been further proposed for large-scale and fine-grained sensing task with the advantage of collecting only a few data to infer unsensed data. However, in many real-world scenarios, such as early prevention of epidemic, people are interested in not only the data at the current, but also in the future or even long-term future, and the latter may be more important. Long-term prediction not only reduces sensing cost, but also identifies trends or other characteristics of the data. In this paper, we propose a spatiotemporal model based on Transformer to infer and predict the data with sparse sensed data by utilizing spatiotemporal relationships. We design a spatiotemporal feature embedding to embed the prior spatiotemporal information of sensing map into the model to guide model learning. Moreover, we also design a novel multi-head spatiotemporal attention mechanism to dynamically capture spatiotemporal relationships among data. Extensive experiments have been conducted on three types of typical urban sensing tasks, which verify the effectiveness of our proposed algorithms in improving the inference and long-term prediction accuracy with the sparse sensed data.

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A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection 29

A Coarse-to-fine Cascaded Evidence-Distillation Neural Netwo...

引用

29th International Conference on computational Linguistics, COLING 2022

作者： Yang, Zhiwei Ma, Jing Chen, Hechang Lin, Hongzhan Luo, Ziyang Chang, Yi College of Computer Science and Technology Jilin University Changchun China Department of Computer Science Hong Kong Baptist University Hong Kong School of Artificial Intelligence International Center of Future Science Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education

Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances. However, they often tailor automated solutions on manual fact-checked reports, suffering from limited news coverage and debunking delays. When a piece of news has not yet been fact-checked or debunked, certain amounts of relevant raw reports are usually disseminated on various media outlets, containing the wisdom of crowds to verify the news claim and explain its verdict. In this paper, we propose a novel Coarse-to-fine Cascaded Evidence-Distillation (CofCED) neural network for explainable fake news detection based on such raw reports, alleviating the dependency on fact-checked ones. Specifically, we first utilize a hierarchical encoder for web text representation, and then develop two cascaded selectors to select the most explainable sentences for verdicts on top of the selected top-K reports in a coarse-to-fine manner. Besides, we construct two explainable fake news datasets, which is publicly available. Experimental results demonstrate that our model significantly outperforms state-of-the-art detection baselines and generates high-quality explanations from diverse evaluation perspectives. © 2022 Proceedings - International Conference on computational Linguistics, COLING. All rights reserved.

关键词： Distillation

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Fast Converging Anytime Model Counting

arXiv

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arXiv 2022年

作者： Lai, Yong Meel, Kuldeep S. Yap, Roland H.C. Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China School of Computing National University of Singapore Singapore

Model counting is a fundamental problem which has been influential in many applications, from artificial intelligence to formal verification. Due to the intrinsic hardness of model counting, approximate techniques have been developed to solve real-world instances of model counting. This paper designs a new anytime approach called PartialKC for approximate model counting. The idea is a form of partial knowledge compilation to provide an unbiased estimate of the model count which can converge to the exact count. Our empirical analysis demonstrates that PartialKC achieves significant scalability and accuracy over prior state-of-the-art approximate counters, including satss and STS. Interestingly, the empirical results show that PartialKC reaches convergence for many instances and therefore provides exact model counting performance comparable to state-of-the-art exact counters. Copyright © 2022, The Authors. All rights reserved.

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