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10th IEEE Joint International information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Lai, Yanming Li, Qishen Huang, Hua Li, Qiufeng School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Nanchang Hangkong University Ministry of Education Key Laboratory of Nondestructive Testing Jiangxi China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Single image rain removal is an important research direction in the field of computer vision. In this paper, the Multi-scale Features Fusion Network (MFFN) is presented for rain removal. MFFN is mainly composed of Multi-features Fusion Module (MFM) and Dual Attention Module (DAM). In the MFM, we make the improved dense block and the dilation convolutions to form the feature extraction branches, which is conducive to improve the receptive fields of network. Subsequently, the bottom-up connection method is adopted between branches to help different branches make full use of image information. Then, feature branches are merged to fusion different features. DAM is consisted of purposed SA Block and standard SE Block. The purpose of DAM is to reduce the non-rain features which is extracted by the network. Experiments show that MFFN can obtain the better result of rain removal than several advanced methods. © 2022 IEEE.

关键词： Computer vision

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Addressing Imbalance for Class Incremental Learning in Medical Image Classification

arXiv

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arXiv 2024年

作者： Hao, Xuze Ni, Wenqian Jiang, Xuhao Tan, Weimin Yan, Bo Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

Deep convolutional neural networks have made significant breakthroughs in medical image classification, under the assumption that training samples from all classes are simultaneously available. However, in real-world medical scenarios, there’s a common need to continuously learn about new diseases, leading to the emerging field of class incremental learning (CIL) in the medical domain. Typically, CIL suffers from catastrophic forgetting when trained on new classes. This phenomenon is mainly caused by the imbalance between old and new classes, and it becomes even more challenging with imbalanced medical datasets. In this work, we introduce two simple yet effective plug-in methods to mitigate the adverse effects of the imbalance. First, we propose a CIL-balanced classification loss to mitigate the classifier bias toward majority classes via logit adjustment. Second, we propose a distribution margin loss that not only alleviates the inter-class overlap in embedding space but also enforces the intra-class compactness. We evaluate the effectiveness of our method with extensive experiments on three benchmark datasets (CCH5000, HAM10000, and EyePACS). The results demonstrate that our approach outperforms state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Adaptive Neural Consensus Tracking Control for Nonlinear Multiagent Systems with Dead Zone and Sensor Faults 42

Adaptive Neural Consensus Tracking Control for Nonlinear Mul...

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42nd Chinese Control Conference, CCC 2023

作者： Huang, Chengjie Chen, Ci Liu, Zhi Li, Zhenni Guangdong University of Technology Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Ministry of Education Guangzhou510006 China

ISBN: (纸本)9789887581543

In this paper, an adaptive neural tracking control design is investigated for uncertain multiagent systems (MASs) with actuator dead zone and sensor faults. The adaptive control technique based on neural networks is employed to handle the uncertainty in the systems, which, however, results in a bounded tracking error from most existing works. To achieve the asymptotic tracking performance, we proposed a direct adaptive strategy via the tuning function method. Technically, a Lyapunov function with a series of smooth functions is designed, which benefits proposing of an asymptotic controller. Then, under the framework of direct adaptive control, an inverse compensation scheme, as well as a fault-tolerant mechanism, are considered for disposing of the actuator dead zone and sensor failures. As a result, user-preset tracking performance is achieved and the system error in the MASs converges to a predefined range. An example in the simulation section illustrates the effectiveness of the proposed method. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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AN ACCELERATED MANN-TYPE METHOD FOR SPLIT COMMON FIXED POINT PROBLEM IN HILBERT SPACES

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第1期86卷 3-16页

作者： Yang, Lan Zhu, Li-Jun Postolache, Mihai School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province North Minzu University Yinchuan750021 China Bucharest060042 Romania Health Big Data Research Institute of North Minzu University Yinchuan750021 China Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest050711 Romania

In this paper, we introduce a new method to solve split common fixed point problem of demicontractive operators in Hilbert spaces, which is based on the Wang method, inertial method and Mann method. Strong convergence result of the suggested algorithm is proved, and our results are utilized to study split feasibility problem and split variational inequality problem in Hilbert spaces. Finally, we compare the convergence speed of our algorithm with the Wang algorithm by a numerical example. © 2024, Politechnica university of Bucharest. All rights reserved.

关键词： Hilbert spaces

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IF-Font: Ideographic Description Sequence-Following Font Generation 38

IF-Font: Ideographic Description Sequence-Following Font Gen...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Chen, Xinping Ke, Xiao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Few-shot font generation (FFG) aims to learn the target style from a limited number of reference glyphs and generate the remaining glyphs in the target font. Previous works focus on disentangling the content and style features of glyphs, combining the content features of the source glyph with the style features of the reference glyph to generate new glyphs. However, the disentanglement is challenging due to the complexity of glyphs, often resulting in glyphs that are influenced by the style of the source glyph and prone to artifacts. We propose IF-Font, a novel paradigm which incorporates Ideographic Description Sequence (IDS) instead of the source glyph to control the semantics of generated glyphs. To achieve this, we quantize the reference glyphs into tokens, and model the token distribution of target glyphs using corresponding IDS and reference tokens. The proposed method excels in synthesizing glyphs with neat and correct strokes, and enables the creation of new glyphs based on provided IDS. Extensive experiments demonstrate that our method greatly outperforms state-of-the-art methods in both one-shot and few-shot settings, particularly when the target styles differ significantly from the training font styles. The code is available at https://***/Stareven233/IF-Font. © 2024 Neural information processing systems foundation. All rights reserved.

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Acute Observational Stimulus of Restrained Mice Induced Anxiolytic Effects in Observer Mice

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Neuroscience Bulletin 2024年第10期40卷 1545-1550页

作者： Chun-Hui Jia Fang-Qi Shu Pak-Ming Lau Hao Wang CAS Key Laboratory of Brain Function and Disease Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei 230026China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science CenterHefei 230088China Interdisciplinary Center for Brain Information The Brain Cognition and Brain Disease InstituteShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China National Engineering Laboratory for Brain-inspired Intelligence Technology and Application School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China

Dear Editor,Ensuring an appropriate response to external stimuli is essential for animals'survival and *** indicates that such stimuli can affect animals'physiological state and hormone levels,including corticosterone(CORT)[1,2].The impact of these stimuli on animals'performance depends on their density and duration,as they can either facilitate or hinder *** chronic and intense stressors can have detrimental effects on animals'physiological health and behavioral performance,leading to psychiatric disorders such as depression,anxiety,and schizophrenia[3],the effects of acute and mild observational stimuli are still unclear.

关键词： evolution. Observation acute

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Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning 31

Unifying Dual-Space Embedding for Entity Alignment via Contr...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Cunda Wang, Weihua Liang, Qiuyu Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Entity alignment (EA) aims to match identical entities across different knowledge graphs (KGs). Graph neural network-based entity alignment methods have achieved promising results in Euclidean space. However, KGs often contain complex local and hierarchical structures, which are hard to represent in a single space. In this paper, we propose a novel method named as UniEA, which unifies dual-space embedding to preserve the intrinsic structure of KGs. Specifically, we simultaneously learn graph structure embeddings in both Euclidean and hyperbolic spaces to maximize the consistency between embeddings in the two spaces. Moreover, we employ contrastive learning to mitigate the misalignment issues caused by similar entities, where embeddings of similar neighboring entities become too close. Extensive experiments on benchmark datasets demonstrate that our method achieves state-of-the-art performance in structure-based EA methods. Our code is available at https://***/wonderCS1213/UniEA. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation 31

Distance-Adaptive Quaternion Knowledge Graph Embedding with ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Weihua Liang, Qiuyu Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet through either semantic matching or geometric distance scoring functions. However, it appears that semantic matching diminishes the separability of entities, while the distance scoring function weakens the semantics of entities. To address this issue, we propose a novel quaternion knowledge graph embedding model. Our model combines semantic matching with the geometric distance of entities to better measure the plausibility of triplets. Specifically, in the quaternion space, we perform a right rotation on head entity and a reverse rotation on tail entity to learn rich semantic features. We then utilize distance-adaptive translations to learn geometric distance between entities. Furthermore, we provide mathematical proofs to demonstrate our model can handle complex logical relationships. Extensive experimental results and analyses show our model significantly outperforms previous models on well-known knowledge graph completion benchmark datasets. Our code is available at https://***/llqy123/DaBR. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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C3LRSO: A Chinese Corpus for Complex Logical Reasoning in Sentence Ordering 31

C3LRSO: A Chinese Corpus for Complex Logical Reasoning in Se...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Xiaotao Li, Jiang Su, Xiangdong Zhang, Fujun College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Sentence ordering is the task of rearranging a set of unordered sentences into a coherent and logically consistent sequence. Recent work has primarily used pre-trained language models, achieving significant success in the task. However, existing sentence ordering corpora are predominantly in English, and comprehensive benchmark datasets for non-English languages are unavailable. Meanwhile, current datasets often insert specific markers into paragraphs, inadvertently making the logical sequence between sentences more apparent and reducing the models' ability to handle genuinely unordered sentences in real applications. To address these limitations, we develop C3LRSO, a high-quality Chinese sentence ordering dataset that overcomes the aforementioned shortcomings by providing genuinely unordered sentences without artificial segmentation cues. Furthermore, given the outstanding performance of large language models on NLP tasks, we evaluate these models on our dataset for this task. Additionally, we propose a simple yet effective parameter-free approach that outperforms existing methods on this task. Experiments demonstrate the challenging nature of the dataset and the strong performance of our proposed method. These findings highlight the potential for further research in sentence ordering and the development of more robust language models. Our dataset is freely available at https://***/JasonGuo1/C3LRSO. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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ChatGPT: potential, prospects, and limitations

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Frontiers of information Technology & Electronic Engineering 2024年第1期25卷 6-11页

作者： Jie ZHOU Pei KE Xipeng QIU Minlie HUANG Junping ZHANG School of Computer Science Fudan UniversityShanghai 200433China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shanghai Key Laboratory of Intelligent Information Processing Shanghai 200433China

Recently,OpenAI released Chat Generative Pre-trained Transformer(ChatGPT)(Schulman et al.,2022)(https://***),which has attracted considerable attention from the industry and academia because of its impressive *** is the first time that such a variety of open tasks can be well solved within one large language *** better understand ChatGPT,we briefly introduce its history,discuss its advantages and disadvantages,and point out several potential ***,we analyze its impact on the development of trustworthy artificial intelligence,conversational search engine,and artificial general intelligence.

关键词： artificial limitations intelligence

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