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An Alternative Algorithm for the Symmetry Classification of Ordinary Differential Equations

Sound & Vibration 2022年第1期56卷 65-76页

作者： Yi Tian Jing Pang College of Data Science and Application Inner Mongolia University of TechnologyHohhot010080China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080China College of Science Inner Mongolia University of TechnologyHohhot010051China Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Inner Mongolia University of TechnologyHohhot010051China

This is the first paper on symmetry classification for ordinary differential equations(ODEs)based on Wu’s *** carry out symmetry classification of two ODEs,named the generalizations of the Kummer-Schwarz equations which involving arbitrary ***,Lie algorithm is used to give the determining equations of symmetry for the given equations,which involving arbitrary ***,differential form Wu’s method is used to decompose determining equations into a union of a series of zero sets of differential characteristic sets,which are easy to be solved *** branch of the decomposition yields a class of symmetries and associated *** algorithm makes the classification become direct and *** Dimitrov Bozhkov,and Pammela Ramos da Conceição have used the Lie algorithm to give the symmetry classifications of the equations talked in this paper in *** this paper,we can find that the differential form Wu’s method for symmetry classification of ODEs with arbitrary function(parameter)is effective,and is an alternative method.

关键词： Kummer-Schwarz equation ordinary differential equations(ODEs) differential form Wu’s method

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Legal Judgment Prediction with Multiple Perspectives on Civil Cases 1st

Legal Judgment Prediction with Multiple Perspectives on Civi...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Zhao, Lili Yue, Linan An, Yanqing Liu, Ye Zhang, Kai He, Weidong Chen, Yanmin Yuan, Senchao Liu, Qi Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science and School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9783030930455

Legal Judgment Prediction, which aims at predicting the judgment result based on case materials, is an essential task in Legal Intelligence. Most existing studies have analyzed and modeled criminal cases as a whole, while a small part of the research focuses on civil cases which are also significant in the legal system. In these studies, most model on a certain type of civil causes and make the judgment prediction based on the ascertained fact from the perspective of the court. However, in real-world scenarios, there are different civil cases on various causes, and every case is judged from multiple perspectives (i.e., plaintiff, defendant, and the court). It is difficult to make judgment predictions on various civil causes. To address the above challenges, in this paper, we propose a novel Civil Case Judgment (CCJudge) prediction method by simulating the logic of judges. Following this logic, we construct an external knowledge base that contains the explanations and applications of every cause, it helps make the judgment on various causes. Furthermore, a special encoder layer and interaction layer are designed for learning linguistic semantics from multiple perspectives. We conduct extensive experiments on a real-world dataset. The experimental results demonstrate the effectiveness of our method. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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CoCGAN: Contrastive Learning for Adversarial Category Text Generation 29

CoCGAN: Contrastive Learning for Adversarial Category Text G...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Sheng, Xin Xu, Linli Xu, Yinlong Bao, Changcun Chen, Huang Ren, Bo Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Tencent Youtu Lab China

The task of generating texts of different categories has attracted more and more attention in the area of natural language generation recently. Meanwhile, generative adversarial net (GAN) has demonstrated its effectiveness on text generation, and is further applied to category text generation in later works. Different from existing methods, which mainly consider the pairwise relations between the text embedding and the corresponding fixed one-hot class label (data-to-class relations), this paper proposes a novel Contrastive Category Generative Adversarial Net (CoCGAN) to incorporate contrastive learning into adversarial category text generation, considering more flexible data-to-class relations as well as relations between the multiple text embeddings in the same batch (data-to-data relations). The discriminator of CoCGAN discriminates the authenticity of given samples and optimizes a contrastive learning objective to capture both more flexible data-to-class relations and data-to-data relations among training samples. Accordingly, the generator tries to produce more realistic samples which can confuse the discriminator. Experimental results on both synthetic and real category text generation datasets demonstrate that CoCGAN can achieve significant improvements over the baseline category text generation models. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Embeddings

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A-Map: Interactive Visual Exploration of Intercity Accessibility Dynamics Based on Railway Network data

A-Map: Interactive Visual Exploration of Intercity Accessibi...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Kaichen Nie Hanning Shao Yuchu Luo Min Tian Hao Wu Wei Zeng Xin Fu Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) School of AI Peking University Hong Kong University of Science and Technology Guangzhou Chang’an University National Engineering Laboratory for Big Data Analysis and Application Peking University

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

Railway transportation is closely linked to everyday lives while also aiding domain experts in analyzing national or regional development. However, discrepancies between travel time reduced by railways and actual distance pose challenges in visualizing the accessibility. Existing methods either struggle to simultaneously depict the accessibility relationships among all cities or disregard genuine geographical positions, leading to spatial cognitive confusion. In this work, we propose a novel approach to balance between the geographical positions and travel time between cities. We construct linear cartograms to represent accessibility while preserving better local railway network structures compared with existing methods. We further propose the A-Map system, enabling users to interactively explore the extensive development of China’s railway system over decades. We validate the effectiveness of our proposed layout algorithm from qualitative metric evaluation and illustrate the applicability of our system with results.

关键词： Measurement Visualization Heuristic algorithms Urban areas Layout Transportation data visualization

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Lightweight Transformer Network and Self-supervised Task for Kinship Verification 8

Lightweight Transformer Network and Self-supervised Task for...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Li, Yunwei Li, Danyang Dong, Lingyun Chen, Xiaopan School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results. © 2022 IEEE.

关键词： Image recognition

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A bounded ability estimation for computerized adaptive testing 23

A bounded ability estimation for computerized adaptive testi...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yan Zhuang Qi Liu GuanHao Zhao Zhenya Huang Weizhe Huang Zachary A. Pardos Enhong Chen Jinze Wu Xin Li Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence University of California Berkeley State Key Laboratory of Cognitive Intelligence and iFLYTEK Co. Ltd

Computerized adaptive testing (CAT), as a tool that can efficiently measure student's ability, has been widely used in various standardized tests (e.g., GMAT and GRE). The adaptivity of CAT refers to the selection of the most informative questions for each student, reducing test length. Existing CAT methods do not explicitly target ability estimation accuracy since there is no student's true ability as ground truth; therefore, these methods cannot be guaranteed to make the estimate converge to the true with such limited responses. In this paper, we analyze the statistical properties of estimation and find a theoretical approximation of the true ability: the ability estimated by full responses to question bank. Based on this, a Bounded Ability Estimation framework for CAT (BECAT) is proposed in a data-summary manner, which selects a question subset that closely matches the gradient of the full responses. Thus, we develop an expected gradient difference approximation to design a simple greedy selection algorithm, and show the rigorous theoretical and error upper-bound guarantees of its ability estimate. Experiments on both real-world and synthetic datasets, show that it can reach the same estimation accuracy using 15% less questions on average, significantly reducing test length.

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Point-Level and Set-Level Deep Representation Learning for Cross-Modal Person Re-identification 8

Point-Level and Set-Level Deep Representation Learning for C...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Hu, Jihui Ye, Pengfei Li, Danyang Dong, Lingyun Chen, Xiaopan Zhu, Xiaoke School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China

ISBN: (纸本)9781665450515

In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Infrared imaging

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Semi-supervised Segmentation and Quantization Algorithm for Infarct Size Measurement of Rat Heart Sections 8

Semi-supervised Segmentation and Quantization Algorithm for ...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Cai, Yulong Chen, Xiaopan Hu, Qi Dong, Lingyun Yuan, Caihong School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Segmentation of rat heart section images and infarct area quantization are essential components of infarct size measurement in processing data from animal experiments on ischemic heart disease. Previously, the task of measuring the infarct size of rat heart sections was performed manually by experienced researchers, which was very costly and time-consuming. In this paper, we propose a two-stage semi-supervised approach to solve the task of ischemic heart infarct size calculation in images of rat section images. The approach uses a semi-supervised semantic segmentation model and a semi-automated quantization algorithm to separate the differently stained regions of rat heart sections. We demonstrate the results of the approach on a rat heart section dataset, and experiments on this dataset emphasize the effectiveness of our approach compared to other methods. © 2022 IEEE.

关键词： Semantic Segmentation

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Inverse Markov Process Based Constrained Dynamic Graph Layout

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Journal of Computer Science & Technology 2021年第3期36卷 707-718页

作者： Shi-Ying Sheng Sheng-Tao Chen Xiao-Ju Dong Chun-Yuan Wu Xiao-Ru Yuan ParisTech Elite Institute of Technology Shanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Machine Perception(Ministry of Education) National Engineering Laboratory for Big Data Analysis and ApplicationPeking UniversityBeijing 100080China

In online dynamic graph drawing,constraints over nodes and node pairs help preserve a coherent mental map in a sequence of *** the constraints is challenging due to the requirements of both preserving mental map and satisfying the visual aesthetics of a graph *** existing algorithms basically depend on local changes but fail to do proper evaluations on the global propagation when setting *** solve this problem,we introduce a heuristic model derived from PageRank which simulates the node movement as an inverse Markov process hence to give a global analysis of the layout's change,according to which different constraints can be *** constraints,along with stress function,generate layouts maintaining spatial positions and shapes of relatively stable substructures between adjacent *** demonstrate that our method preserves both structure and position similarity to help users track graph changes visually.

关键词： graph drawing data stream dynamic graph layout

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Status and trends in the stability of the three largest ice shelves in Antarctica

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Science China Earth Sciences 2024年第8期67卷 2615-2632页

作者： Rongxing LI Guojun LI Lu AN Menglian XIA Da LV Yuan CHENG Aiguo ZHAO Tian CHANG Xiangbin CUI Shengkai ZHANG Shijie LIU Yixiang TIAN Weian WANG Center for Spatial Information Science and Sustainable Development Applications Tongji UniversityShanghai 200092China College of Surveying and Geo-informatics Tongji UniversityShanghai 200092China Postdoctoral Research Station of Shanghai Surveying and Mapping Institute Shanghai 200063China Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Shanghai 200063China Institute for Conservation of Cultural Heritage School of Cultural Heritage and Information ManagementShanghai UniversityShanghai 200444China Polar Research Institute of China Shanghai 200136China Chinese Antarctic Center of Surveying and Mapping Wuhan UniversityWuhan 430079China

The Ross,Filchner-Ronne,and Amery ice shelves are the three largest ice shelves in Antarctica,playing a crucial role in supporting the Antarctic ice ***,current studies on the stability of the three largest ice shelves primarily focus on singular or limited factors,lacking a comprehensive assessment of multiple *** systematically and in-depth study the stability and trend of the three largest ice shelves,we comprehensively collected and analyzed key parameters,including elevation changes,basal melting,surface meltwater,major rifts propagation rate,suture zones,ice front area change rate,grounding lines,ice velocity,and mass ***,we selected the collapsed Larsen B Ice Shelf(LBIS),the rapidly changing and structurally weakened Pine Island Ice Shelf(PIIS),and the accelerating Totten Ice Shelf(TIS)as reference ice *** comparing and analyzing the key parameters between these reference ice shelves and the three largest ice shelves,we find the status and trends in the stability of the *** findings reveal that most key parameters of the three largest ice shelves present relatively minor variations compared to those of the reference ice ***,50%of the parameters are smaller than those of the accelerating TIS,88%are smaller than those of the rapidly changing PIIS,and all parameters are smaller than those of the collapsed ***,after analyzing parameters that are not smaller than those of the TIS,it is observed that they remain in a stable ***,the three largest ice shelves are currently undergoing natural changes that do not threaten their stability in the short ***,the evolution of the ice shelves under global climate change remains uncertain,making long-term observation and monitoring essential to assess their impact on sea level rise.

关键词： Three largest ice shelves in Antarctica key parameters Remote sensing monitoring Ice shelf stability Sea level change

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