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SSRN 2025年

作者： Ye, Jialu Tan, Chaogui Xia, Yizhang Hou, Zhanglu Liu, Yuan Zou, Juan Hunan Engineering Research Center of intelligent System Optimization and Security Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Hunan National Applied Mathematics Center China Xiangtan University China

Population co-evolution strategies are widely used to handle constrained multi-objective optimization problems (CMOPs). However, existing coevolutionary algorithms oversimplify population collaboration and are rigid in evolving the auxiliary population, ignoring the importance of maintaining population diversity, especially for CMOPs with irregular feasible regions. To tackle this issue, we propose a new dual-population evolutionary algorithm, denoted as AACMO, which attempts to mine the relational features between the unconstrained Pareto front (UPF) and the constrained Pareto front (CPF) to better adapt to CMOPs with various constraints. Specifically, the method is categorized into two phases: a learning phase and an evolving phase. First, the main population (MP) aims to quickly learn the relationships between the UPF and the CPF by leveraging the advantages of different operators, namely, genetic algorithm operators and differential evolution operators, during the learning phase. Secondly, two auxiliary strategies are designed to collaborate with the MP during the evolving phase. These strategies are capable of employing a more effective co-evolution method to assist the MP in maintaining diversity, based on the degree of alignment between the learned UPF and CPF at the end of the learning phase. The effectiveness of AACMO is confirmed through comparative analysis with eight state-of-the-art algorithms across four CMOP benchmark suites. © 2025, The Authors. All rights reserved.

关键词： Optimization algorithms

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HighlightRemover: Spatially Valid Pixel Learning for Image Specular Highlight Removal 24

HighlightRemover: Spatially Valid Pixel Learning for Image S...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Ling Ma, Yidong Jiang, Zhi He, Weilei Bao, Zhongyun Fu, Gang Xu, Wenju Xiao, Chunxia School of Computer Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial Systems Wuhan University of Science and Technology Wuhan China School of Computer Science Wuhan University Wuhan China Department of Computing Hong Kong Polytechnic University Hong Kong Amazon Palo Alto United States

ISBN: (纸本)9798400706868

Recently, learning-based methods have made significant progress for image specular highlight removal. However, many of these approaches treat all the image pixels uniformly, overlooking the negative impact of invalid pixels on feature reconstruction. This oversight often leads to undesirable outcomes, such as color distortion or residual highlights. In this paper, we propose a novel image specular highlight removal network called HighlightRNet, which utilizes valid pixels as references to reconstruct the highlight-free image. To achieve this, we introduce a context-aware fusion block (CFBlock) that aggregates information in four directions, effectively capturing global contextual information. Additionally, we introduce a location-aware feature transformation module (LFTModule) to adaptively learn the valid pixels for feature reconstruction, thereby avoiding information errors caused by invalid pixels. With these modules, our method can produce high-quality highlight-free results without color distortion and highlight residual. Furthermore, we develop a multiple light image-capturing system to construct a large-scale highlight dataset called NSH, which exhibits minimal misalignment in image pairs and minimal brightness variation in non-highlight regions. Experimental results on various datasets demonstrate the superiority of our method over state-of-the-art methods, both qualitatively and quantitatively. © 2024 ACM.

关键词： Pixels

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Meibomian glands segmentation in infrared images with limited annotation

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International Journal of Ophthalmology(English edition) 2024年第3期17卷 401-407页

作者： Jia-Wen Lin Ling-Jie Lin Feng Lu Tai-Chen Lai Jing Zou Lin-Ling Guo Zhi-Ming Lin Li Li College of Computer and Data Science Fuzhou UniversityFuzhou 350108Fujian ProvinceChina Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou UniversityFuzhou 350108Fujian ProvinceChina Shengli Clinical Medical College of Fujian Medical University Fuzhou 350001Fujian ProvinceChina Department of Ophthalmology Fujian Provincial Hospital South BranchFujian Provincial HospitalFuzhou 350001Fujian ProvinceChina

●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble *** viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,***,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for *** model maintains an exceptionally high level of segmentation accuracy while substantially reducing training *** holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.

关键词： infrared meibomian glands images meibomian gland dysfunction meibomian glands segmentation weak supervision scribbled annotation

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TFGDA: Exploring Topology and Feature Alignment in Semi-supervised Graph Domain Adaptation through Robust Clustering 38

TFGDA: Exploring Topology and Feature Alignment in Semi-supe...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Dan, Jun Liu, Weiming Xie, Chunfeng Yu, Hua Dong, Shunjie Tan, Yanchao Zhejiang University China Queen Mary University of London United Kingdom Dalian University of Technology China Shanghai Jiao Tong University China Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China Fujian Key Laboratory of Network Computing and Intelligent Information Processing China

Semi-supervised graph domain adaptation, as a branch of graph transfer learning, aims to annotate unlabeled target graph nodes by utilizing transferable knowledge learned from a label-scarce source graph. However, most existing studies primarily concentrate on aligning feature distributions directly to extract domain-invariant features, while ignoring the utilization of the intrinsic structure information in graphs. Inspired by the significance of data structure information in enhancing models' generalization performance, this paper aims to investigate how to leverage the structure information to assist graph transfer learning. To this end, we propose an innovative framework called TFGDA. Specially, TFGDA employs a structure alignment strategy named STSA to encode graphs' topological structure information into the latent space, greatly facilitating the learning of transferable features. To achieve a stable alignment of feature distributions, we also introduce a SDA strategy to mitigate domain discrepancy on the sphere. Moreover, to address the overfitting issue caused by label scarcity, a simple but effective RNC strategy is devised to guide the discriminative clustering of unlabeled nodes. Experiments on various benchmarks demonstrate the superiority of TFGDA over SOTA methods. © 2024 Neural information processing systems foundation. All rights reserved.

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Memory-Augmented Transformer for Efficient End-to-End Video Grounding

Memory-Augmented Transformer for Efficient End-to-End Video ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yuanwu Xu Mohan Chen Yuejie Zhang Rui Feng Tao Zhang Shang Gao School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics School of Information Technology Deakin University

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Video grounding aims to localize a specific segment corresponding to a text query in an untrimmed video. Due to the tremendous computational cost required to process the video frames, the de facto paradigm of video grounding is to extract video features using pretrained video encoders. The parameters of the video encoders are fixed during training, which limits the performance of the localization model. To solve this problem, we propose a Memory-Augmented Transformer (MAT) model. Specifically, each video is split into non-overlapping clips, and our MAT processes videos in a clip-by-clip manner while caching video features into FIFO cached memory queues. By enabling early return, our MAT outperforms previous methods with only less than 60% frames seen. Extensive experimental results on three public benchmark datasets demonstrate that our MAT can achieve competitive performance while being much more efficient than currently prevailing two-stage methods. Code is available at https://***/xuyw1997/MAT.

关键词： Training Location awareness Codes Grounding Computational modeling Streaming media Predictive models

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Deep Reinforcement Learning based intelligent Routing for Multi-service Traffic SDN

Deep Reinforcement Learning based Intelligent Routing for Mu...

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Science and Technology Innovation (IAECST), International Academic Exchange Conference on

作者： Lingjiao Wang Jiachen Li Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiang Tan University School of Automation and Electronic Information Xiang Tan University

The development of network has imposed higher requirements on QoS (Quality of Service) for routing, and the emergence of traffic classification techniques, software defined networking (SDN), and programmable network devices has made it possible to quickly identify user needs and route for service traffic requirements. Deep reinforcement learning routing algorithms for existing QoS compliant routing algorithms perform poorly in terms of generalizability. A deep graph reinforcement learning intelligent routing algorithm DDGL applied to SDN scenarios is proposed, which uses GNN to extract the link feature information of SDN data plane for deep reinforcement learning routing decision making, and comprehensively takes into account the performance metrics such as latency, packet loss, throughput, and bandwidth, to improve the algorithm generalization ability and enhance the SDN routing performance.

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High Contrast Ratio 4-to-2 Plasmonic Encoder Utilizing Cascaded Ring Resonators

High Contrast Ratio 4-to-2 Plasmonic Encoder Utilizing Casca...

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International Conference on Electronic Engineering and Informatics (EEI)

作者： Yan Chen Yiyuan Xie Junxiong Chai Yichen Ye College of Electronic and Information Engineering Southwest University Chongqing China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing Chongqing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

In this paper, a novel plasmonic 4-to-2 encoder comprising cascaded ring resonators coated with graphene layers and two metal-insulator-metal (MIM) waveguides. The encoding function operates at 850 nm by modulating the intensity of pump beam focused on the graphene layer to manipulate the state of the plasmonic resonators. The performance of the encoder is analyzed utilizing the finite-difference time-domain method, demonstrating a high contrast ratio of 15.1 dB. Thus, this efficient 4-to-2 optical encoder is well-suited for integration into optical integrated computing devices.

关键词： Integrated optics Optical switches Graphene Plasmons Optical pumping Encoding Optical modulation Optical ring resonators Optical devices Finite difference methods

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BTC-Shadow: an analysis and visualization system for exposing implicit behaviors in Bitcoin transaction graphs

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Frontiers of Computer Science 2023年第6期17卷 167-169页

作者： Ding BAO Wei REN Yuexin XIANG Weimao LIU Tianqing ZHU Yi REN Kim-Kwang Raymond CHOO School of Computer Science China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan 430074China NSFOCUS Technologies Group Co. Ltd.Beijing 100089China School of Computing Sciences University of East AngliaNorwich NR47TJUK Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX 78249-0631USA

1 Introduction A Bitcoin ledger comprises a sizable number of transaction records,which can be utilized to make it easier to track and analyze the traits and patterns of cryptocurrency-related *** facilitate the visual analysis of Bitcoin,numerous tools with various aims have been *** example,MiningVis[1]and SuPoolVisor[2]are the analytics systems for Bitcoin mining pools,as well as[3-5]focus on the Bitcoin transaction graphs ***,due to our previous research requirements for Bitcoin transaction graphs,none of the available tools can provide exploring the features of connection related to the address and observe significant visual ***,using these tools is challenging to navigate to abnormal node clusters effortlessly from large node groups and then analyze the local interlink characteristics between transaction nodes with interactive analytics.

关键词： analytic implicit utilized

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Integrating Multi-scale Contextualized information for Byte-based Neural Machine Translation

arXiv

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arXiv 2024年

作者： Huang, Langlin Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of AI Safety Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Subword tokenization is a common method for vocabulary building in Neural Machine Translation (NMT) models. However, increasingly complex tasks have revealed its disadvantages. First, a vocabulary cannot be modified once it is learned, making it hard to adapt to new words. Second, in multilingual translation, the imbalance in data volumes across different languages spreads to the vocabulary, exacerbating translations involving low-resource languages. While byte-based tokenization addresses these issues, byte-based models struggle with the low information density inherent in UTF-8 byte sequences. Previous works enhance token semantics through local contextualization but fail to select an appropriate contextualizing scope based on the input. Consequently, we propose the Multi-Scale Contextualization (MSC) method, which learns contextualized information of varying scales across different hidden state dimensions. It then leverages the attention module to dynamically integrate the multi-scale contextualized information. Experiments show that MSC significantly outperforms subword-based and other byte-based methods in both multilingual and out-of-domain scenarios. We have uploaded the code to github1 © 2024, CC BY-NC-ND.

关键词： Neural machine translation

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An Efficient Saddle Search Method for Ordered Phase Transitions Involving Translational Invariance

SSRN

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SSRN 2024年

作者： Cui, Gang Jiang, Kai Zhou, Tiejun 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

In this work, we propose an efficient nullspace-preserving saddle search (NPSS) method for a class of phase transitions involving translational invariance, where the critical states are often degenerate. The NPSS method includes two stages, escaping from the basin and searching for the index-1 generalized saddle point. The NPSS method climbs upward from the generalized local minimum in segments to overcome the challenges of degeneracy. In each segment, an effective ascent direction is ensured by keeping this direction orthogonal to the nullspace of the initial state in this segment. This method can escape the basin quickly and converge to the transition states efficiently. We apply the NPSS method to the phase transitions between crystals, and between crystal and quasicrystal, based on the Landau-Brazovskii and Lifshitz-Petrich free energy functionals. Numerical results show a good performance of the NPSS method. © 2024, The Authors. All rights reserved.

关键词： Free energy

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