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Semantic Prior Distillation with Vision Foundation Model for Enhanced Rapid Bone Scintigraphy Image Restoration

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arXiv 2025年

作者： Liang, Pengchen Shi, Leijun Yao, Huiping Pu, Bin Chen, Jianguo Zhao, Lei Huang, Haishan Chen, Zhuangzhuang Xu, Zhaozhao Xu, Lite Chang, Qing Li, Yiwei Department of Nuclear Medicine Shanghai Children’s Hospital School of Medicine Shanghai Jiao Tong University Shanghai200062 China School of Microelectronics Shanghai University Shanghai201800 China Department of Ophthalmology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai201800 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong School of Software Engineering Sun Yat-sen University Guangdong Province Zhuhai519000 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Computer Science and Technology Henan Polytechnic University Henan Jiaozuo454000 China Department Shanghai Key Laboratory of Gastric Neoplasms Department of Surgery Shanghai Institute of Digestive Surgery Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai201800 China

Rapid bone scintigraphy is an essential tool for diagnosing skeletal diseases and tumor metastasis in pediatric patients, as it reduces scan time and minimizes patient discomfort. However, rapid scans often result in poor image quality, potentially affecting diagnosis due to reduced resolution and detail, which make it challenging to identify and evaluate finer anatomical structures. To address this issue, we propose the first application of SAM-based semantic priors for medical image restoration, leveraging the Segment Anything Model (SAM) to enhance rapid bone scintigraphy images in pediatric populations. Our method comprises two cascaded networks, fIR1 and fIR2, augmented by three key modules: a Semantic Prior Integration (SPI) module, a Semantic Knowledge Distillation (SKD) module, and a Semantic Consistency Module (SCM). The SPI and SKD modules incorporate domain-specific semantic information from a fine-tuned SAM, while the SCM maintains consistent semantic feature representation throughout the cascaded networks. In addition, we will release a novel Rapid Bone Scintigraphy dataset called RBS, the first dataset dedicated to rapid bone scintigraphy image restoration in pediatric patients. RBS consists of 137 pediatric patients aged between 0.5 and 16 years who underwent both standard and rapid bone scans. The dataset includes scans performed at 20 cm/min (standard) and 40 cm/min (rapid), representing a 2× acceleration. We conducted extensive experiments on both the publicly available endoscopic dataset and RBS. The results demonstrate that our method outperforms all existing methods across various metrics, including PSNR, SSIM, FID, and LPIPS. Copyright © 2025, The Authors. All rights reserved.

关键词： Pediatrics

Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems

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arXiv 2022年

作者： Deng, Yanchen Kong, Shufeng Liu, Caihua An, Bo School of Software Engineering Sun Yat-sen University Guangdong China Department of Computer Science and Engineering Nanyang Technological University Singapore School of Artificial Intelligence Guilin University of Electronic Technology Guangxi China

Belief Propagation (BP) is an important message-passing algorithm for various reasoning tasks over graphical models, including solving the Constraint Optimization Problems (COPs). It has been shown that BP can achieve state-of-the-art performance on various benchmarks by mixing old and new messages before sending the new one, i.e., damping. However, existing methods of tuning a static damping factor for BP not only are laborious but also harm their performance. Moreover, existing BP algorithms treat each variable node’s neighbors equally when composing a new message, which also limits their exploration ability. To address these issues, we seamlessly integrate BP, Gated Recurrent Units (GRUs), and Graph Attention Networks (GATs) within the message-passing framework to reason about dynamic weights and damping factors for composing new BP messages. Our model, Deep Attentive Belief Propagation (DABP), takes the factor graph and the BP messages in each iteration as the input and infers the optimal weights and damping factors through GRUs and GATs, followed by a multi-head attention layer. Furthermore, unlike existing neural-based BP variants, we propose a novel self-supervised learning algorithm for DABP with a smoothed solution cost, which does not require expensive training labels and also avoids the common out-of-distribution issue through efficient online learning. Extensive experiments show that our model significantly outperforms state-of-the-art baselines. © 2022, CC BY.

关键词： Belief propagation

Self-representation-based Generative Graph Neural Networks for End-to-End Link Prediction

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IEEE Transactions on Big Data 2025年

作者： Xian, Xingping Wu, Tao Qiao, Shaojie Wang, Chao Yuan, Lin Liu, Yanbing School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computer and Information Science Chongqing Normal University Chongqing China Chongqing Engineering Laboratory of Internet and Information Security Chongqing University of Posts and Telecommunications Chongqing400065 China

Recently, deep neural networks have revolutionized the field of link prediction, and the state-of-the-art works are typically subgraph-based discriminative methods, which construct features of local subgraphs firstly and predicting potential links via deep learning based binary subgraph classification. However, the discriminative link prediction methods always fail to automatically learn features and perform link prediction, and the performance of them depends on the construction of enclosing subgraphs and the manually-designed features for the subgraphs. To address these issues, we leverage the idea of graph disentangling and propose a novel self-representation-based generative graph neural network framework (GraphLP) for end-to-end link prediction, which learns to extract the latent patterns, i.e., recurring subgraphs, from input graphs via self-supervised learning and reconstruct graphs for link prediction using the subgraphs as structural basis. GraphLP consists of three components: self-representation-based collaborative inference, high-order connectivity computation, and multi-scale pattern fusion. The key idea is to utilize the correlations between the extracted recurring subgraphs on different scales to effectively assist link inference. GraphLP also can effectively exploit the hierarchical organization patterns and incorporate them within the representation procedure, producing robust and accurate results. Compared with traditional methods and state-of-the-art methods, experimental results on public benchmark datasets demonstrate that GraphLP achieves promising performance. Different from the discriminative methods, GraphLP provides a new paradigm for generative neural-network-based link prediction. © 2015 IEEE. All rights reserved.

关键词： Self-supervised learning

AngleSizer: Enhancing Spatial Scale Perception for the Visually Impaired with an Interactive Smartphone Assistant

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arXiv 2024年

作者： Jing, Xiaoqing Yu, Chun Yue, Kun Lu, Liangyou Gao, Nan Shi, Weinan Zhang, Mingshan Wang, Ruolin Shi, Yuanchun Department of Computer Science and Technology Tsinghua University Beijing China Key Laboratory of Pervasive Computing Ministry of Education Beijing China School of Software Tsinghua University Beijing China University of California Los Angeles Los AngelesCA United States Qinghai University Qinghai Province Xining China

Spatial perception, particularly at small and medium scales, is an essential human sense but poses a significant challenge for the blind and visually impaired (BVI). Traditional learning methods for BVI individuals are often constrained by the limited availability of suitable learning environments and high associated costs. To tackle these barriers, we conducted comprehensive studies to delve into the real-world challenges faced by the BVI community. We have identified several key factors hindering their spatial perception, including the high social cost of seeking assistance, inefficient methods of information intake, cognitive and behavioral disconnects, and a lack of opportunities for hands-on exploration. As a result, we developed AngleSizer, an innovative teaching assistant that leverages smartphone technology. AngleSizer is designed to enable BVI individuals to use natural interaction gestures to try, feel, understand, and learn about sizes and angles effectively. This tool incorporates dual vibration-audio feedback, carefully crafted teaching processes, and specialized learning modules to enhance the learning experience. Extensive user experiments validated its efficacy and applicability with diverse abilities and visual conditions. Ultimately, our research not only expands the understanding of BVI behavioral patterns but also greatly improves their spatial perception capabilities, in a way that is both cost-effective and allows for independent learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Smartphones

Model-enhanced vector index 23

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Model-enhanced vector index

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

作者： Hailin Zhang Yujing Wang Qi Chen Ruiheng Chang Ting Zhang Ziming Miao Yingyan Hou Yang Ding Xupeng Miao Haonan Wang Bochen Pang Yuefeng Zhan Hao Sun Weiwei Deng Qi Zhang Fan Yang Xing Xie Mao Yang Bin Cui School of Computer Science & Key Lab of High Confidence Software Technologies Peking University and Microsoft Microsoft Microsoft and Aerospace Information Research Institute & Key Laboratory of Target Cognition and Application Technology Chinese Academy of Sciences Microsoft and Institute of Information Engineering Chinese Academy of Sciences Carnegie Mellon University National University of Singapore School of Computer Science & Key Lab of High Confidence Software Technologies Peking University and National Engineering Laboratory for Big Data Analysis and Applications Peking University

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with end-to-end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Model-enhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embedding-based models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions.

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Teaching Reform and Practice on Course Construction of Discrete Mathematics

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计算机教育 2022年第12期 158-162页

作者： Yahui Li Jiangyuan Yao Weiwei Xing Institute for Network Sciences and Cyberspace Tsinghua UniversityBeijing100084China College of Computer Science and Technology Hainan UniversityHainan570100China School of Software Engineering Beijing Jiaotong UniversityBeijing100091China

Discrete mathematics studies the respective structures,laws and interrelationships of different discrete quantities,which has a wide range of applications in computer science and software *** has the characteristics of research,practice and *** teaching goals of the course of discrete mathematics include not only the understanding and mastery of knowledge,but also the study of ***,current teaching mechanism has limitations in outdated course contents,rare real project examples and nonscientific scoring *** paper attempts to think about the course construction of discrete mathematics to improve the teaching effects and cultivate students’practical ability.

关键词： Discrete mathematics Computational thinking Programming practice Teaching reform

An efficient iterative graph data processing framework based on bulk synchronous parallel model

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An efficient iterative graph data processing framework based...

作者： Liu, Chao Zeng, Deze Yao, Hong Yan, Xuesong Yu, Linchen Fu, Zhangjie School of Computer Science Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

Graph data processing has been widely applied in a variety of domains such as industry, science, social network, and so on. It therefore has stimulated many efforts devoted to this area. To embrace the fast development trend of big graph data, graph data processing based on Pregel-like systems has been regarded as one of the most promising ways and has widely attracted the attention of researchers. However, it still remains in its early stage and there still exist many challenges. In Pregel, the superstep synchronization is time consuming as the graph data iteration operation requires multiple synchronizations. Furthermore, the graph data partition strategy adopted by Pregel fails to support load balancing, therefore causing the increase of network I/O overhead as the scale of graph data grows. To address these issues, this paper presents an efficient computational framework for graph data processing based on the bulk synchronous parallel model. The global synchronization control mechanism is improved by determining the start time of the next round of superstep through counting the number of global message files. Furthermore, an improved graph data partition mechanism based on a balanced hash method is proposed to reduce the communication overhead between different partitions of sub-graph computational tasks. We also re-design the PageRank algorithm to verify the effectiveness of the proposed framework. Experimental results on different real-world datasets verify the efficiency of our proposed framework as it outperforms Giraph (an open source Pregel-like system) by 58%−69%, and achieves 10×−17× performance improvement over Hadoop. © 2018 John Wiley & Sons, Ltd.

关键词： MapReduce

LRASGen: LLM-based RESTful API Specification Generation

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arXiv 2025年

作者： Deng, Sida Huang, Rubing Zhang, Man Cui, Chenhui Towey, Dave Wang, Rongcun School of Computer Science and Engineering Macau University of Science and Technology China Macau University of Science and Technology Zhuhai MUST Science and Technology Research Institute China State Key Laboratory of Complex & Critical Software Environment Beihang University China School of Computer Science University of Nottingham Ningbo China China School of Computer Science and Technology China University of Mining and Technology China

REpresentation State Transfer (REST) is an architectural style for designing web applications that enable scalable, stateless communication between clients and servers via common HTTP techniques. Web APIs that employ the REST style are known as RESTful (or REST) APIs. When using or testing a RESTful API, developers may need to employ its specification, which is often defined by open-source standards such as the OpenAPI Specification (OAS). However, it can be very time-consuming and error-prone to write and update these specifications, which may negatively impact the use of RESTful APIs, especially when the software requirements change. Many tools and methods have been proposed to solve this problem, such as Respector and Swagger Core. OAS generation can be regarded as a common text-generation task that creates a formal description of API endpoints derived from the source code. A potential solution for this may involve using Large Language Models (LLMs), which have strong capabilities in both code understanding and text generation. Motivated by this, we propose a novel approach for generating the OASs of RESTful APIs using LLMs: LLM-based RESTful API-Specification Generation (LRASGen). To the best of our knowledge, this is the first use of LLMs and API source code to generate OASs for RESTful APIs. Compared with existing tools and methods, LRASGen can generate the OASs, even when the implementation is incomplete (with partial code, and/or missing annotations/comments, etc.). To evaluate the LRASGen performance, we conducted a series of empirical studies on 20 real-world RESTful APIs: These studies examined the precision, recall, and F1-score when identifying the four main entities, i.e., endpoint methods and parameters, parameter constraints, and endpoint responses. The results show that two LLMs (GPT-4o mini and DeepSeek V3) can both support LARSGen to generate accurate specifications, and LRASGen-generated specifications cover an average of 48.85% more missed entit

关键词： Specifications

A method based on spatial and temporal multi grained information for gait recognition 9

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A method based on spatial and temporal multi grained informa...

9th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2020

作者： Xu, Huanhuan Li, Hongmei Sun, Xuemei Li, Fuyu Tiangong University School of Computer Science and Technology Tianjin China Northwestern Polytechnical University School of Software Xi'an China Limited Shanghai China

Gait recognition is one of the new biometric techniques. However, gait recognition is easily affected by external factors, resulting in a decrease in the accuracy. Therefore, this paper proposes a gait recognition method based on spatial and temporal multi grained information. This method uses hierarchical network to obtain different receptive fields, focusing on both shallow and deep features of gait. In addition, more spatial and temporal information can be saved to improve the accuracy of gait recognition. Finally, the proposed method is experimentally verified on the CASIA-B dataset. the experimental results show that the accuracy of the proposed method is greatly improved under normal walking, carrying bags and wearing coats. © 2020 IEEE.

关键词： Gait analysis