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Low-dose Dynamic Cerebral Perfusion CT Reconstruction Method Based on Voxel-level TAC Correction

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Low-dose Dynamic Cerebral Perfusion CT Reconstruction Method...

第十七届中国体视学与图像分析学术会议

作者： Zixiang Chen Ying Huang Zhenxing Huang Guotao Quan Xiang Li Xin Liu Hairong Zheng Dong Liang Hu Zhanli Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Chinese Academy of Sciences Key Laboratory of Health Informatics Department of CT Physics & Algorithms United Imaging Healthcare Group

Background Dynamic cerebral perfusion computed tomography(DCP-CT) is a advanced imaging technique that help in clinical diagnosis of cerebrovascular ***,the radiation dose deposition during the repeated CT scans serious choke off its clinical *** this study,we proposed a DCP-CT images reconstruction method that is able to reconstruct images from low-dose scan *** Raw DCP-CT data were acquired by uCT-960+(United Imaging Healthcare Inc.),and data corresponding to the middle slice of the detector were used in our ***-CT images were reconstructed from raw data using a least square-based iterative method accompanied with voxel-level time-attenuation curve(TAC) correction(LS-VC).Low-dose CT imaging protocol was simulated by data down-sampling(representing the mode of sparse-view) and adding gaussian noise to the down-sampled sinograms(representing the mode of reduced kVp).During the reconstruction iterations,dynamic image noise was eliminated from the temporal domain via the application of the theory of third-order Hermite interpolation(THI).Quantitative evaluations of the reconstructed images were carried out by using the peak signal-to-noise ratio(PSNR) and structural similarity(SSIM) referred to normal-dose DCPCT *** maps were calculated from the reconstructed images for imaging *** conventional FBP and the famous PICCS methods were involved for ***:Superior performance of the proposed PSVC method is demonstrated by the remarkable visual effect of image denoising and the better PSNR and SSIM values compared to several state-of-the-art ***,LSVC yields the best hemodynamic maps among all the compared ***:The proposed LSVC method has very advanced performance in DCP-CT image reconstruction with raw data acquired with a low-dose scan *** may effectively promote the clinical application of DCP-CT.

关键词： Low-dose dynamic cerebral perfusion CT Iterative reconstruction Voxel-level TAC correction Thirdorder Hermite interpolation Hemodynamic maps

Xinyu: An Efficient LLM-based System for Commentary Generation

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arXiv 2024年

作者： Wu, Yiquan Tang, Bo Xi, Chenyang Yu, Yu Wang, Pengyu Liu, Yifei Kuang, Kun Deng, Haiying Li, Zhiyu Xiong, Feiyu Hu, Jie Cheng, Peng Wang, Zhonghao Wang, Yi Luo, Yi Yang, Mingchuan Zhejiang University Hangzhou China University of Science and Technology of China Hefei China Institute for Advanced Algorithms Research Shanghai China Northeastern University Shenyang China State Key Laboratory of Media Convergence Production Technology and Systems Beijing China Research Institute of China Telecom Beijing China

Commentary provides readers with a deep understanding of events by presenting diverse arguments and evidence. However, creating commentary is a time-consuming task, even for skilled commentators. Large language models (LLMs) have simplified the process of natural language generation, but their direct application in commentary creation still faces challenges due to unique task requirements. These requirements can be categorized into two levels: 1) fundamental requirements, which include creating well-structured and logically consistent narratives, and 2) advanced requirements, which involve generating quality arguments and providing convincing evidence. In this paper, we introduce Xinyu, an efficient LLM-based system designed to assist commentators in generating Chinese commentaries. To meet the fundamental requirements, we deconstruct the generation process into sequential steps, proposing targeted strategies and supervised fine-tuning (SFT) for each step. To address the advanced requirements, we present an argument ranking model for arguments and establish a comprehensive evidence database that includes up-to-date events and classic books, thereby strengthening the substantiation of the evidence with retrieval augmented generation (RAG) technology. To evaluate the generated commentaries more fairly, corresponding to the two-level requirements, we introduce a comprehensive evaluation metric that considers five distinct perspectives in commentary generation. Our experiments confirm the effectiveness of our proposed system. We also observe a significant increase in the efficiency of commentators in real-world scenarios, with the average time spent on creating a commentary dropping from 4 hours to 20 minutes. Importantly, such an increase in efficiency does not compromise the quality of the commentaries. © 2024, CC BY.

关键词： Economic and social effects

Distributional property testing in a quantum world

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arXiv 2019年

作者： Gilyén, András Liy, Tongyang QuSoft CWI University of Amsterdam Netherlands Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland U.S. Department of Energy Office of Science Office of Advanced Scientific Computing Research Quantum Algorithms Teams Program

A fundamental problem in statistics and learning theory is to test properties of distributions. We show that quantum computers can solve such problems with significant speed-ups. In particular, we give fast quantum algorithms for testing closeness between unknown distributions, testing independence between two distributions, and estimating the Shannon / von Neumann entropy of distributions. The distributions can be either classical or quantum, however our quantum algorithms require coherent quantum access to a process preparing the samples. Our results build on the recent technique of quantum singular value transformation, combined with more standard tricks such as divide-and-conquer. The presented approach is a natural fit for distributional property testing both in the classical and the quantum case, demonstrating the first speed-ups for testing properties of density operators that can be accessed coherently rather than only via sampling;for classical distributions our algorithms significantly improve the precision dependence of some earlier results. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum theory

BIM: Improving Graph Neural Networks with Balanced Influence Maximization

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BIM: Improving Graph Neural Networks with Balanced Influence...

International Conference on Data Engineering

作者： Wentao Zhang Xinyi Gao Ling Yang Meng Cao Ping Huang Jiulong Shan Hongzhi Yin Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications The University of Queensland Australia Key Lab of High Confidence Software Technologies Peking University Apple Inc. Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The imbalanced data classification problem has aroused lots of concerns from both academia and industry since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) expose a unique source of imbalance from the influenced nodes of different classes of labeled nodes, i.e., labeled nodes are imbalanced in terms of the number of nodes they influenced during the influence propagation in GNNs. To tackle this previously unexplored influence-imbalance issue, we connect social influence maximization with the imbalanced node classification problem and propose balanced influence maximization (BIM). Specifically, BIM greedily assigns the pseudo label to the node which can maximize the number of influenced nodes in GNN training while making the influence of each class more balance. Experimental results on five public datasets demonstrate the effectiveness of our method in relieving the influence-imbalance issue. For example, when training a GCN with an imbalance ratio of 0.1, BIM significantly outperforms the most competitive baseline by 0.6% -9.8% in five public datasets in terms of the F1 score.

关键词： Training Industries Data engineering Graph neural networks

Unveiling Large Language Models Generated Texts: A Multi-Level Fine-Grained Detection Framework

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arXiv 2024年

作者： Tao, Zhen Li, Zhiyu Chen, Runyu Xi, Dinghao Xu, Wei School of Information Renmin University of China Beijing China Institute for Advanced Algorithms Research Shanghai China School of Information Technology and Management University of International Business and Economics China Department of Digital Economics Shanghai University of Finance and Economics Shanghai China

Large language models (LLMs) have transformed human writing by enhancing grammar correction, content expansion, and stylistic refinement. However, their widespread use raises concerns about authorship, originality, and ethics, even potentially threatening scholarly integrity. Existing detection methods, which mainly rely on single-feature analysis and binary classification, often fail to effectively identify LLM-generated text in academic contexts. To address these challenges, we propose a novel Multi-level Fine-grained Detection (MFD) framework that detects LLM-generated text by integrating low-level structural, high-level semantic, and deep-level linguistic features, while conducting sentence-level evaluations of lexicon, grammar, and syntax for comprehensive analysis. To improve detection of subtle differences in LLM-generated text and enhance robustness against paraphrasing, we apply two mainstream evasion techniques to rewrite the text. These variations, along with original texts, are used to train a text encoder via contrastive learning, extracting high-level semantic features of sentence to boost detection generalization. Furthermore, we leverage advanced LLM to analyze the entire text and extract deep-level linguistic features, enhancing the model’s ability to capture complex patterns and nuances while effectively incorporating contextual information. Extensive experiments on public datasets show that the MFD model outperforms existing methods, achieving an MAE of 0.1346 and an accuracy of 88.56%. Our research provides institutions and publishers with an effective mechanism to detect LLM-generated text, mitigating risks of compromised authorship. Educators and editors can use the model’s predictions to refine verification and plagiarism prevention protocols, ensuring adherence to standards. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

Quantum-inspired classical sublinear-time algorithm for solving low-rank semidefinite programming via sampling approaches

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arXiv 2019年

作者： Chia, Nai-Hui Li, Tongyang Lin, Han-Hsuan Wang, Chunhao Department of Computer Science University of Texas Austin United States Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland U.S. Department of Energy Office of Science Office of Advanced Scientific Computing Research Quantum Algorithms Teams program

Semidefinite programming (SDP) is a central topic in mathematical optimization with extensive studies on its efficient solvers. Recently, quantum algorithms with superpolynomial speedups for solving SDPs have been proposed assuming access to its constraint matrices in quantum superposition. Mutually inspired by both classical and quantum SDP solvers, in this paper we present a sublinear classical algorithm for solving low-rank SDPs which is asymptotically as good as existing quantum algorithms. Specifically, given an SDP with m constraint matrices, each of dimension n and rank poly(log n), our algorithm gives a succinct description and any entry of the solution matrix in time O(m · poly(log n, 1/Ε)) given access to a sample-based low-overhead data structure of the constraint matrices, where Ε is the precision of the solution. In addition, we apply our algorithm to a quantum state learning task as an application. Technically, our approach aligns with both the SDP solvers based on the matrix multiplicative weight (MMW) framework and the recent studies of quantum-inspired machine learning algorithms. The cost of solving SDPs by MMW mainly comes from the exponentiation of Hermitian matrices, and we propose two new technical ingredients (compared to previous sample-based algorithms) for this task that may be of independent interest: • Weighted sampling: assuming sampling access to each individual constraint matrix A1,..., Aτ, we propose a procedure that gives a good approximation of A = A1 + ··· + Aτ. • Symmetric approximation: we propose a sampling procedure that gives low-rank spectral decomposition of a Hermitian matrix A. This improves upon previous sampling procedures that only give low-rank singular value decompositions, losing the signs of eigenvalues. Copyright © 2019, The Authors. All rights reserved.

关键词： Singular value decomposition

Finite-Agent Stochastic Differential Games on Large Graphs: I. The Linear-Quadratic Case

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arXiv 2024年

作者： Hu, Ruimeng Long, Jihao Zhou, Haosheng Department of Mathematics Department of Statistics and Applied Probability University of California Santa BarbaraCA93106-3080 United States Institute for Advanced Algorithms Research Shanghai China Department of Statistics and Applied Probability University of California Santa BarbaraCA93106-3110 United States

In this paper, we study finite-agent linear-quadratic games on graphs. Specifically, we propose a comprehensive framework that extends the existing literature by incorporating heterogeneous and interpretable player interactions. Compared to previous works, our model offers a more realistic depiction of strategic decision-making processes. For general graphs, we establish the convergence of fictitious play, a widely-used iterative solution method for determining the Nash equilibrium of our proposed game model. Notably, under appropriate conditions, this convergence holds true irrespective of the number of players involved. For vertex-transitive graphs, we develop a semi-explicit characterization of the Nash equilibrium. Through rigorous analysis, we demonstrate the well-posedness of this characterization under certain conditions. We present numerical experiments that validate our theoretical results and provide insights into the intricate relationship between various game dynamics and the underlying graph structure. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

NC-ALG: Graph-Based Active Learning Under Noisy Crowd

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NC-ALG: Graph-Based Active Learning Under Noisy Crowd

International Conference on Data Engineering

作者： Wentao Zhang Yexin Wang Zhenbang You Yang Li Gang Cao Zhi Yang Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications Key Lab of High Confidence Software Technologies Peking University Department of Data Platform TEG Tencent Inc. Beijing Academy of Artificial Intelligence Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed.

关键词： Crowdsourcing Costs Noise Predictive models Graph neural networks Labeling Noise measurement

Deployment of digital vehicle/highway technology for safety enhancement

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Deployment of digital vehicle/highway technology for safety ...

IEEE Symposium on Intelligent Vehicle

作者： Fei-Yue Wang Guanpi Lai P. Mirchandani The Key Laboratory for Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences Beijing China University of Arizona Tucson Tucson AZ USA Institute of Automation Chinese Academy and Sciences Beijing China Advanced Traflic and Logistics Algorithms and Systems Research Center University of Arizona Tucson Tucson AZ USA

Although currently seatbelts and airbags are world widely used, there are still more than 40,000 people killed annually in traffic accidents. Referred to David Breed's "Road to Zero Fatalities" (RtZF/spl reg/) concept, this paper mainly discusses how to develop and deploy a safety warning system using high precision digital road maps and a combination of various vehicle status sensory techniques, without or with a minimum requirement of additional road infrastructures. This system will help to avoid accidents and enhance safety by warning drivers of possible hazardous situations in advance.

关键词： Vehicle safety Road safety Road vehicles Road transportation Road accidents Vehicle driving Alarm systems Laboratories Intelligent systems Automation