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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Symbolic Execution of MPI Programs with One-Sided Communications

Symbolic Execution of MPI Programs with One-Sided Communicat...

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Asia-Pacific Conference on Software Engineering

作者： Nenghui Hu Zheng Bian Ziqi Shuai Zhenbang Chen Yufeng Zhang College of Computer National University of Defense Technology China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China State Key Laboratory of High Performance Computing National University of Defense Technology China College of Computer Science and Electronic Engineering Hunan University Changsha China

Message-passing interface (MPI) programs are non-deterministic and challenging to ensure correctness. The introduction of one-sided communications makes the problem of non-determinism more severe for MPI programs. This paper reports our in-progress work of symbolic execution for the MPI programs with one-sided communications. Our approach can cover the non-determinism caused by the inputs, one-sided communication, and message- passing operations of MPI programs. The preliminary evaluation's results indicate the promising of our approach.

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Unsatisfiable Core Based Constraint Solving Cache in Symbolic Execution

Unsatisfiable Core Based Constraint Solving Cache in Symboli...

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Asia-Pacific Conference on Software Engineering

作者： Ziqi Shuai Zhenbang Chen Yufeng Zhang Hengbiao Yu Ji Wang College of Computer National University of Defense Technology China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China State Key Laboratory of High Performance Computing National University of Defense Technology China College of Computer Science and Electronic Engineering Hunan University Changsha China

Constraint solving stands out as a significant bot-tleneck in symbolic execution. Caching is a commonly adopted approach to alleviate this bottleneck. However, the cutting-edge caching technique targeting unsatisfiable constraints, known as unsatisfiable core caching, primarily involves checking whether the constraint being solved contains an unsatisfiable core that has been previously collected. Such straightforward reuse frequently proves less effective in numerous scenarios. In this paper, we present a novel method to enhance the utilization of unsatisfiable cores. By excavating unsatisfiable cores, our method can compute an easily solvable over-approximation that tends to be unsatis-fiable for each constraint, which facilitates the determination of the satisfiability of the original constraint. We implemented our method on KLEE symbolic executor. The evaluation results on 27 real-world programs are encouraging.

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Symbolic Verification of Fuzzy Logic Models

Symbolic Verification of Fuzzy Logic Models

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Siang Zhao Zhongyang Li Zhenbang Chen Ji Wang College of Computer National University of Defense Technology China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China State Key Laboratory of High Performance Computing National University of Defense Technology China

Fuzzy logic is widely applied in various applications. However, verifying the correctness of fuzzy logic models can be difficult. This extended abstract presents our ongoing work on verifying fuzzy logic models. We treat a fuzzy logic model as a program and propose a verification method based on symbolic execution for fuzzy logic models. We have developed and implemented the environment models for the common functions and the inference rules in fuzzy logic models. Our preliminary evaluation shows the potential of our verification method.

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An Investigation of Blockchain-Based Sharding 7th

An Investigation of Blockchain-Based Sharding

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Xiao, Jiahong Liang, Wei Cai, Jiahong Zhu, Hangyu Li, Xiong Xie, Songyou School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9783031281235

Nowadays, blockchain distributed ledger technology is becoming more and more prominent, and its decentralization, anonymization, and tampering obvious features have been widely recognized. These excellent technical features of blockchain have also made it a hot issue for global research. With the wide application of blockchain technology in various industries, some defects are gradually exposed, and more prominently, the blockchain system is unable to meet the current demand of explosive growth of data volume and frequent data interaction. As one of the key technologies to solve this problem, sharding technology is gaining attention. This article introduces common blockchain scaling schemes and focuses on an overview of blockchain sharding. Sharding technology is introduced from two perspectives of intra-slice consensus and inter-slice consensus. The current mainstream slicing technology is summarized according to three different slicing methods: network sharding, transaction sharding, and state sharding. Finally, the challenges faced by current blockchain sharding technology are analyzed and the full text is summarized. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Efficient SVV stabilized triangular spectral element methods for incompressible flows of high Reynolds numbers

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Advances in Aerodynamics 2022年第1期4卷 58-77页

作者： Lizhen Chen Tao Tang Chuanju Xu Beijing Computational Science Research Center Beijing100193People’s Republic of China Division of Science and Technology BNU-HKBU United International CollegeZhuhaiGuangdongPeople’s Republic of China Guangdong Provincial Key Laboratory of Computational Science and Material Design Southern University of Science and TechnologyShenzhenPeople’s Republic of China School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific ComputingXiamen UniversityXiamen361005People’s Republic of China

In this paper,we propose a spectral vanishing viscosity method for the triangular spectral element computation of high Reynolds number incompressible *** can be regarded as an extension of a similar stabilization technique for the standard spectral element *** difficulty of this extension lies in the fact that a suitable definition of spectral vanishing viscosity operator in non-structured elements does not exist,and it is not clear that if a suitably defined spectral vanishing viscosity provides desirable dissipation for the artificially accumulated *** main contribution of the paper includes:1)a well-defined spectral vanishing viscosity operator is proposed for non-standard spectral element methods for the Navier-Stokes equations based on triangular or tetrahedron partitions;2)an evaluation technique is introduced to efficiently implement the stabilization term without extra computational cost;3)the accuracy and efficiency of the proposed method is carefully examined through several numerical *** numerical results show that the proposed method not only preserves the exponential convergence,but also produces improved accuracy when applied to the unsteady Navier-Stokes equations having smooth ***,the stabilized triangular spectral element method efficiently stabilizes the simulation of high Reynolds incompressible flows.

关键词： Navier-Stokes equations Triangular spectral element method Spectral vanishing viscosity

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FRCNet: Frequency and Region Consistency for Semi-supervised Medical Image Segmentation

arXiv

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arXiv 2024年

作者： He, Along Li, Tao Wu, Yanlin Zou, Ke Fu, Huazhu Tianjin Key Laboratory of Network and Data Security Technology College of Computer Science Nankai University Tianjin China Haihe Lab of ITAI China National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Sichuan Chengdu China Institute of High Performance Computing Agency for Science Technology and Research Singapore

Limited labeled data hinder the application of deep learning in medical domain. In clinical practice, there are sufficient unlabeled data that are not effectively used, and semi-supervised learning (SSL) is a promising way for leveraging these unlabeled data. However, existing SSL methods ignore frequency domain and region-level information and it is important for lesion regions located at low frequencies and with significant scale changes. In this paper, we introduce two consistency regularization strategies for semi-supervised medical image segmentation, including frequency domain consistency (FDC) to assist the feature learning in frequency domain and multi-granularity region similarity consistency (MRSC) to perform multi-scale region-level local context information feature learning. With the help of the proposed FDC and MRSC, we can leverage the powerful feature representation capability of them in an effective and efficient way. We perform comprehensive experiments on two datasets, and the results show that our method achieves large performance gains and exceeds other state-of-the-art methods. © 2024, CC BY.

关键词： Deep learning

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A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking

A Novel Deep Learning Approach Featuring Graph-Based Algorit...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Keliang Niyogisubizo, Jovial Xiao, Linxia Pan, Yi Wei, Dongqing Rosiyadi, Didi Wei, Yanjie Chinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Shenzhen518055 China Shanghai Jiao Tong University State Key Laboratory of Microbial Metabolism School of Life Sciences and Biotechnology Shanghai200240 China National Research and Innovation Agency Research Center for Artificial Intelligence and Cybersecurity Bandung Indonesia

ISBN: (纸本)9798350337488

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs. © 2023 IEEE.

关键词： cell segmentation cell tracking deep learning Deeplabv3+ instance segmentation iPS cell reprogramming

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Detecting human-object interaction with multi-level pairwise feature network

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Computational Visual Media 2021年第2期7卷 229-239页

作者： Hanchao Liu Tai-Jiang Mu Xiaolei Huan Key Laboratory of Pervasive Computing Ministry of EducationBNRistDepartment of Computer Science and TechnologyTsinghua UniversityBeijing 100084China College of Information Sciences and Technology Pennsylvania State UniversityUniversity ParkPA 16802USA.

Human–object interaction(HOI)detection is crucial for human-centric image understanding which aims to infer human,action,object triplets within an *** studies often exploit visual features and the spatial configuration of a human–object pair in order to learn the action linking the human and object in the *** argue that such a paradigm of pairwise feature extraction and action inference can be applied not only at the whole human and object instance level,but also at the part level at which a body part interacts with an object,and at the semantic level by considering the semantic label of an object along with human appearance and human–object spatial configuration,to infer the *** thus propose a multi-level pairwise feature network(PFNet)for detecting human–object *** network consists of three parallel streams to characterize HOI utilizing pairwise features at the above three levels;the three streams are finally fused to give the action *** experiments show that our proposed PFNet outperforms other state-of-the-art methods on the VCOCO dataset and achieves comparable results to the state-of-the-art on the HICO-DET dataset.

关键词： human–object interaction detection pairwise feature network deep learning multi-level object instance

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An improved reconfigurable logic in resistive random access memory

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Integration 2022年 87卷 169-175页

作者： Zhao, Yi Chen, Hui Liu, Peng Wu, Jigang Luo, Dongxiang The Center for High Performance Computing and Data Science School of Computer Science and Technology Guangdong University of Technology Guangdong510006 China School of Chemistry and Chemical Engineering/Institute of Clean Energy and Materials/Guangzhou Key Laboratory for Clean Energy and Materials/Huangpu Hydrogen Innovation Center Guangzhou University Guangzhou510006 China Institute of Semiconductors South China Normal University Guangzhou510631 China

Emerging non-volatile devices have shown great potential in computing in-memory (CIM). This work proposes a logic design method based on the complementary resistance switching (CRS) structure, which is connected by two anti-serially memristors. Three logics including AND, OR, and NIMP can be implemented in one step. Moreover, XOR logic can be implemented in two steps. Based on the proposed design, a 1-bit full adder is implemented with the fewer number of memristors and operation steps compared with existing works. The feasibility and correctness of our design are verified by the SPICE simulations with the Voltage Threshold Adaptive Memristor (VTEAM) model. In addition, we evaluate the ISCAS’85 benchmark circuit based on the proposed logic. Compared with existing methods, the performance and area overhead have been improved by the proposed method. © 2022 Elsevier B.V.

关键词： Memristors

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