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SSRN 2025年

作者： Liu, Shaopeng Wang, Kai Wu, Xiaohang Xu, Fabao Zhou, Ying Wang, Xinpeng Wang, Qinkang Cen, Haoran Guo, Jianhua Lu, Xu School of Computer Science Guangdong Polytechnic Normal University Guangzhou510665 China Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology Sun Yat-Sen University Guangdong Guangzhou China Qilu Hospital Shandong University Jinan China Shenzhen Institute of Information Technology Shenzhen China Guangdong Provincial Key Laboratory of Intellectual Property & Big Data Guangzhou510665 China Pazhou Lab Guangzhou510330 China

The development of fundus imaging techniques has greatly supported the diagnosis of ophthalmic *** advancements, poor imaging quality, such as artifacts or insufficient illumination, poses challenges for disease *** clinical, it is often difficult to obtain paired data for fundus image enhancement. For unpaired data, CycleGAN-based methods are commonly used to learn the style transfer from low-quality images to high-quality images for enhancement, improving the aforementioned transfer process through brightness loss or regularization ***, fundus images are quite complex, and high-quality images can also exhibit uneven *** methods are not efficient in modeling the characteristics of fundus *** wavelet analysis, we found that the texture information represented by vessels is expressed in the high-frequency components of the ***, we designed a high-frequency information guided cycle-consistent generative adversarial network for unpaired fundus image enhancement, termed *** introduces a novel high-frequency guided generator (HFGen) and a corresponding frequency-aware discriminator (FADis).Specifically, HG uses the proposed high-frequency feature extraction module (HFM) to extract high-frequency features and inject them into the decoding process, outside of the main branches of the *** concatenate the input image with the frequency components obtained from wavelet transformation and input them into *** embedded channel-wise attention blocks of discriminator enable HFCycleGAN to focus on the frequency information differences between low-quality and high-quality fundus *** experiments on two real-world datasets and one synthetic dataset verify that HFcycleGAN outperforms existing *** conclusion, the HFcycleGAN effectively enhances important structures such as vessels in fundus images, significantly impacting fundus image analysis. © 2025, The Author

关键词： Image segmentation

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DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and networking 2025年

作者： Zhang, Zheng Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Letaief, Khaled B. Jiangnan University School of Internet of Things Engineering Wuxi214122 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering State Key laboratory of Space Network and Communications Tsinghua University Beijing100084 China Xidian University State Key Lab. of ISN School of Telecom-munications Engineering Xi'an710071 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Department of Electrical and Computer Engineer-ing Hong Kong

To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Introducing the new metric Age of Information (AoI) provides a more comprehensive evaluation of communication system. Additionally, to ensure service quality, user terminals need to possess high computational capabilities, which may lead to increased energy consumption, necessitating a trade-off between communication energy consumption and effectiveness. Given the complexity and dynamics of communication systems, Deep Reinforcement Learning (DRL) serves as an intelligent learning method capable of learning optimal strategies in dynamic environments. Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL. Finally, through comparative simulations with baseline methods, the proposed approach demonstrates its advances in terms of energy consumption and AoI. © 2017 IEEE.

关键词： Deep reinforcement learning

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PUGS: Zero-shot Physical Understanding with Gaussian Splatting

arXiv

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arXiv 2025年

作者： Shuai, Yinghao Yu, Ran Chen, Yuantao Jiang, Zijian Song, Xiaowei Wang, Nan Zheng, Jv Ma, Jianzhu Yang, Meng Wang, Zhicheng Ding, Wenbo Zhao, Hao School of Computer Science and Technology Tongji University China Shenzhen Ubiquitous Data Enabling Key Lab Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The Chinese University of Hong Kong Shenzhen China MGI Tech Shenzhen518083 China Chulalongkorn University Bangkok10330 Thailand Tsinghua University China China Lightwheel AI

Current robotic systems can understand the categories and poses of objects well. But understanding physical properties like mass, friction, and hardness, in the wild, remains challenging. We propose a new method that reconstructs 3D objects using the Gaussian splatting representation and predicts various physical properties in a zero-shot manner. We propose two techniques during the reconstruction phase: a geometry-aware regularization loss function to improve the shape quality and a region-aware feature contrastive loss function to promote region affinity. Two other new techniques are designed during inference: a feature-based property propagation module and a volume integration module tailored for the Gaussian representation. Our framework is named as zero-shot physical understanding with Gaussian splatting, or PUGS. PUGS achieves new state-of-the-art results on the standard benchmark of ABO-500 mass prediction. We provide extensive quantitative ablations and qualitative visualization to demonstrate the mechanism of our designs. We show the proposed methodology can help address challenging real-world grasping tasks. Our codes, data, and models are available at https://***/EverNorif/PUGS © 2025, CC BY.

关键词： Gaussian distribution

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A survey on multimodal large language models

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National science Review 2024年第12期11卷 277-296页

作者： Shukang Yin Chaoyou Fu Sirui Zhao Ke Li Xing Sun Tong Xu Enhong Chen School of Artificial Intelligence and Data Science University of Science and Technology of China State Key Laboratory for Novel Software Technology Nanjing University School of Intelligence Science and Technology Nanjing University Tencent YouTu Lab

Recently, the multimodal large language model(MLLM) represented by GPT-4V has been a new rising research hotspot, which uses powerful large language models(LLMs) as a brain to perform multimodal tasks. The surprising emergent capabilities of the MLLM, such as writing stories based on images and optical character recognition–free math reasoning, are rare in traditional multimodal methods, suggesting a potential path to artificial general intelligence. To this end, both academia and industry have endeavored to develop MLLMs that can compete with or even outperform GPT-4V, pushing the limit of research at a surprising speed. In this paper, we aim to trace and summarize the recent progress of MLLMs. First, we present the basic formulation of the MLLM and delineate its related concepts, including architecture,training strategy and data, as well as evaluation. Then, we introduce research topics about how MLLMs can be extended to support more granularity, modalities, languages and scenarios. We continue with multimodal hallucination and extended techniques, including multimodal in-context learning, multimodal chain of thought and LLM-aided visual reasoning. To conclude the paper, we discuss existing challenges and point out promising research directions.

关键词： multimodal large language model vision language model large language model

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Edge-Enhanced Cascaded MRF for SAR Image Segmentation

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Liu, Mengmeng Shang, Ronghua Liu, Kang Feng, Jie Wang, Chao Xu, Songhua Li, Yangyang Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Shaanxi Xi’an710071 China Xidian University School of Computer Science and Technology Shaanxi Xi’an710071 China Research Center for Data Hub and Security Zhejiang Lab Hangzhou311100 China The Second Affiliated Hospital of Xi’an Jiaotong University Department of Health Management Institute of Medical Artificial Intelligence Xi’an710004 China

Markov random fields (MRFs) effectively capture local contextual information by modeling the spatial dependencies between pixels, which helps highlight details and enhances segmentation smoothness. To fully exploit MRF for synthetic aperture radar (SAR) image segmentation, we propose a novel edge-enhanced cascaded MRF (ECMRF) approach. Specifically, we introduce multiple edge-constrained filters to emphasize SAR image boundaries and provide relatively clean features. Building on this, we present a cascaded MRF framework that sequentially integrates region-level and pixel-level segmentations with feature perturbation and fusion to generate the final segmentation output. The framework comprises four key components: 1) a region-level MRF (RMRF), regulated by edge features, to achieve precise region segmentation;2) a pixel-level MRF (PMRF) with selective label smoothing to refine edges and reduce noise clusters;3) equal-channel feature perturbation (EC-FP) to increase feature diversity;and 4) a random probability-based feature fusion (RP-FF) scheme to merge the input features. The experimental results demonstrate that our ECMRF outperforms six state-of-the-art comparable methods, underscoring its competitive performance. © 1980-2012 IEEE.

关键词： Image segmentation

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Sparse word embeddings using l1 regularized online learning 25

Sparse word embeddings using l1 regularized online learning

引用

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Sun, Fei Guo, Jiafeng Lan, Yanyan Xu, Jun Xueqi, Cheng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

Recently, Word2Vec tool has attracted a lot of interest for its promising performances in a variety of natural language processing (NLP) tasks. However, a critical issue is that the dense word representations learned in Word2Vec are lacking of interpretability. It is natural to ask if one could improve their interpretability while keeping their performances. Inspired by the success of sparse models in enhancing interpretability, we propose to introduce sparse constraint into Word2Vec. Specifically, we take the Continuous Bag of Words (CBOW) model as an example in our study and add the l regularizer into its learning objective. One challenge of optimization lies in that stochastic gradient descent (SGD) cannot directly produce sparse solutions with l regularizer in online training. To solve this problem, we employ the Regularized Dual Averaging (RDA) method, an online optimization algorithm for regularized stochastic learning. In this way, the learning process is very efficient and our model can scale up to very large corpus to derive sparse word representations. The proposed model is evaluated on both expressive power and interpretability. The results show that, compared with the original CBOW model, the proposed model can obtain state-of-the-art results with better interpretability using less than 10% non-zero elements.

关键词： Optimization

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Analysis and improvement of a provable secure fuzzy identity-based signature scheme

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science China(Information sciences) 2014年第9期57卷 156-160页

作者： XIONG Hu CHEN YaNan ZHU GuoBin QIN ZhiGuang School of Computer Science and Engineering University of Electronic Science and Technology of China Chongqing Key Lab of Computer Network and Communication Technology Key Lab of Network Security and Cryptology Fujian Normal University School of Traffic and Transportation Beijing Jiaotong University

To enjoy the property of error tolerance in the biometric identities extraction,the fuzzy identitybased signature has been initialized to issue a signature on behalf of a descriptive attributes setωinstead of a single *** attributes setω′can be used to verify the validity of the signature in case the distance betweenωandω′is less than a pre-defined ***,Wang re-formalized the notion of fuzzy identitybased signature and proposed a concrete pairing-based construction along with the efficiency analysis and formal security ***,by giving concrete attack,we indicate that Wang’s scheme is not secure against forgery *** also present an improved scheme to prevent this attack.

关键词： identity-based cryptosystem fuzzy identity-based signature forgery attack cryptanalysis bilinear pairings

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Virtual node based adaptive routing in wireless ad hoc networks

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中国高等学校学术文摘·计算机科学 2009年第4期3卷 535-542页

作者： Gang LI Hongmei SUN Key Lab of Network Science and Technology Institute of Computing TechnologyChinese Academy of SciencesBeijing 100080China Communications Technology Lab Intel China Research CenterBeijing 100080China

It is a challenge to make the routes quickly adapt to the changed network topology when nodes fail in a wireless ad hoc *** this paper,we propose an adaptive routing protocol,which groups the network nodes into virtual nodes according to their data transfer capabilities and creates virtual-node-based *** protocol can accommodate the routes to node failures by adaptively updating the virtual nodes and just-in-time using available nodes during data *** simulations indicate that the proposed protocol can keep the routes failed-node-free when the available virtual node members cover the failed nodes scattering area.

关键词： wireless ad hoc networks mobile ad hoc networks route protocols adaptability route repair

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PRAD: Periapical Radiograph Analysis dataset and Benchmark Model Development

arXiv

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arXiv 2025年

作者： Zhou, Zhenhuan Zhang, Yuchen Xu, Ruihong Zhao, Xuansen Li, Tao College of Computer Science Nankai University Tianjin300350 China Key Laboratory of Data and Intelligent System Security Ministry of Education China Department of stomatology Tianjin Union Medical Center The First Affiliated Hospital of Nankai University Tianjin300121 China College of Mechanical Engineering Taiyuan University of Technology Shanxi 030000 China Haihe Lab of ITAI Tianjin300459 China

Deep learning (DL), a pivotal technology in artificial intelligence, has recently gained substantial traction in the domain of dental auxiliary diagnosis. However, its application has predominantly been confined to imaging modalities such as panoramic radiographs and Cone Beam Computed Tomography, with limited focus on auxiliary analysis specifically targeting Periapical Radiographs (PR). PR are the most extensively utilized imaging modality in endodontics and periodontics due to their capability to capture detailed local lesions at a low cost. Nevertheless, challenges such as resolution limitations and artifacts complicate the annotation and recognition of PR, leading to a scarcity of publicly available, large-scale, high-quality PR analysis datasets. This scarcity has somewhat impeded the advancement of DL applications in PR analysis. In this paper, we present PRAD-10K, a dataset for PR analysis. PRAD-10K comprises 10,000 clinical periapical radiograph images, with pixel-level annotations provided by professional dentists for nine distinct anatomical structures, lesions, and artificial restorations or medical devices, We also include classification labels for images with typical conditions or lesions. Furthermore, we introduce a DL network named PRNet to establish benchmarks for PR segmentation tasks. Experimental results demonstrate that PRNet surpasses previous state-of-the-art medical image segmentation models on the PRAD-10K dataset. The codes and dataset will be made publicly available. © 2025, CC BY-NC-ND.

关键词： Dental orthoses

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Hot spot temperature optimization of micro-channel heat sinks enabled by designing vortex generators and realizing optimal flow pattern

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International Journal of Heat and Mass Transfer 2025年 247卷

作者： Yu, Lingfeng Wu, Xiaoling Song, Mengxuan Chen, Kai Key Laboratory of Enhanced Heat Transfer and Energy Conservation of the Ministry of Education School of Chemistry and Chemical Engineering South China University of Technology Guangdong Guangzhou510640 China Lab of Applied Biocatalysis School of Food Science and Engineering South China University of Technology Guangdong Guangzhou510640 China School of Energy and Materials Shanghai Key Laboratory of Engineering Materials Application and Evaluation Shanghai Polytechnic University Shanghai 201209 China Shanghai Thermophysical Properties Big Data Professional Technical Service Platform Shanghai Engineering Research Center of Advanced Thermal Functional Materials Shanghai 201209 China

Reduction of hot spot temperature is critical for electronic devices under high heat flux conditions. Micro-channel heat sinks (MCHSs) are commonly used in electronic cooling, with vortex generators introduced to enhance the heat transfer. However, existing convective heat transfer optimization equations are not consistent with the aim of hot spot temperature minimization, which are not efficient for design of vortex generators in MCHSs. To address this issue, the optimization equations with minimizing the hot spot temperature of MCHSs are developed, and a novel method is proposed to efficiently design vortex generators in MCHSs. The heat convection optimization equations aiming at hot spot temperature minimization of MCHSs under fixed power consumption are derived using variational method. The equations enable us to achieve the optimal flow field that guarantees a lower hot spot temperature than those obtained based on equations that aim at entropy generation minimization or entransy dissipation extremum in previous studies. The results reveal that the mechanism of hot spot temperature minimization in the rectangular channel is to induce four longitudinal vortices at the cross-section with specific rotations that drive more fluid to the heating surface. Furthermore, vortex generators composed of the inclined triangular-prism ribs and plates are inserted into the channel, which successfully constructs the optimal flow patterns matching the derived optimization equations. The generated similar longitudinal vortices are evidenced by numerical results. With mass flow rate at 0.1 g/s, the hot spot temperature of the MCHSs with the designed vortex generators is reduced by 32.7 K, and the maximum temperature rising decreases by 48 %, and the performance evaluation criterion increases by 60 % when compared to the channel without vortex generators. Moreover, the designed vortex generator increases the performance evaluation criterion by more than 55 % compared to the one in

关键词： Electronic cooling

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