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arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Chen, Geng Xu, Yong Hu, Ying Shao, Ling Fu, Huazhu Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Terminus Group China Institute of High Performance Computing A*STAR Singapore138632 Singapore

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Task Offloading for End-Edge-Cloud Orchestrated computing in Mobile Networks

Task Offloading for End-Edge-Cloud Orchestrated Computing in...

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IEEE Conference on Wireless Communications and Networking

作者： Chuan Sun L. Hui Xiuhua Li Junhao We Qingyu Xiongl Xiaofei Wang Victor C.M. Leun School of Big Data Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

Recently, mobile edge computing has received widespread attention, which provides computing infrastructure via pushing cloud computing, network control, and storage to the network edges. To improve the resource utilization and Quality of Service, we investigate the issue of task offloading for End-EdgeCloud orchestrated computing in mobile networks. Particularly, we jointly optimize the server selection and resource allocation to minimize the weighted sum of the average cost. A cost minimization problem is formulated underjoint the constraints of cache resource and communication/computation resource of edge servers. The resultant problem is a Mixed-Integer Non-linear Programming, which is NP-hard. To tackle this problem, we decompose it into simpler subproblems for server selection and resource allocation, respectively. We propose a low-complexity hierarchical heuristic approach to achieve server selection, and a Cauchy-Schwards Inequality based closed-form approach to efficiently determine resource allocation. Finally, simulation results demonstrate the superior performance of the proposed scheme on reducing the weighted sum of the average cost in the network.

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Lidar Mapping Optimization Based on Lightweight Semantic Segmentation

Lidar Mapping Optimization Based on Lightweight Semantic Seg...

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作者： Zhao, Zhihao Zhang, Wenquan Gu, Jianfeng Yang, Junjie Huang, Kai Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Data and Computer Science Sun Yat-sen University Guangzhou510245 China UISEE Technology Inc. ShenZhen518000 China

Lidar Simultaneous Localization and Mapping (LiDAR-SLAM) algorithm with semantic information is an open research question and it is such a time consuming task. The related work that focus on real-time LiDAR-SLAM has poor accuracy. To solve these problems, a lightweight semantic segmentation network to assist in localization and mapping is proposed in this paper. The method uses the lidar point clouds generated by the simulator and annotated manually in real world as the original input. Then, the semantic cloud is segmented by the semantic segmentation network to obtain the semantic information. Finally, the semantic information obtained by the segmentation is used to assist the localization and mapping. © 2016 IEEE.

关键词： Optical radar

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Corrigendum to “Random projection ensemble conformal prediction for high-dimensional classification” [Chemometr. Intell. Lab. Syst. 253 (2024) 1–10, 105225]

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Chemometrics and Intelligent Laboratory Systems 2024年 254卷

作者： Xiaoyu Qian Jinru Wu Ligong Wei Youwu Lin School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin 541002 PR China Guangxi Academy of Sciences High-tech Group Co. LTD Nanning 530007 PR China Center for Applied Mathematics of Guangxi (GUET) Guilin 541002 PR China Guanghua School of Management Peking University Beijing PR China

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Markerless tracking enables distinction between strategic compensation and functional recovery after spinal cord injury

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Experimental Neurology 2022年 354卷 114085页

作者： Mah, Kar Men Badillo-Martinez, Abdiel Jann, Victor Bixby, John L. Lemmon, Vance P. Miami Project to Cure Paralysis University of Miami Miller School of Medicine Miami 33136 FL United States Dept. of Neurological Surgery University of Miami Miller School of Medicine Miami 33136 FL United States Institute for Data Science and Computing University of Miami Miller School of Medicine Miami 33136 FL United States Dept. of Molecular and Cellular Pharmacology University of Miami Miller School of Medicine Miami 33136 FL United States Sylvester Comprehensive Cancer Center University of Miami Miller School of Medicine Miami 33136 FL United States Neuroscience Graduate Program University of Miami Miller School of Medicine Miami 33136 FL United States University of California Berkeley United States

Injuries to the cervical spinal cord represent around 60% of all spinal cord injuries (SCIs). A major priority for patients with cervical SCIs is the recovery of any hand or arm function. The similarities between human and rodent “reach-to-eat movements” indicate that analyzing mouse forelimb reaching behavior may be a method of identifying clinically relevant treatments for people with cervical SCIs. One popular behavioral measure of forelimb functional recovery comprises the Single Pellet Retrieval Task (SPRT). The most common outcome measure for this task, however (percentage of pellets successfully retrieved), cannot readily distinguish between recovery of pre-injury motor patterns and strategic compensation. Our objective was to establish outcome measures for the SPRT that are readily adopted by different investigators and capable of measuring recovery of limb function after SCI. We used a simple semi-automated approach to high-speed tracking of mouse forepaw movements during pellet retrieval. DeepLabCut™, a machine learning based computer vision software package, was used to track individual features of the mouse forepaw, allowing a more detailed assessment of reaching behavior after SCI. Interestingly, kinematic analysis of movements pre- and post-injury illuminated persistent deficits in specific features of the reaching motor patterns, namely pronation and paw trajectory, that were poorly correlated with recovery of the ability to successfully retrieve pellets. Thus, we have developed an inexpensive method for detailed analysis of mouse reach-to-eat behavior following SCI. Further, our results suggest that binary success/fail outcome measures primarily assess an animal's ability to compensate rather than a restoration of normal function in the injured pathways and networks. © 2022 Elsevier Inc.

关键词： DeepLabCut Forelimb Machine learning Mouse kinematics Pronation Reaching Spinal cord injury

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Resolvability of classical-quantum channels

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IEEE Transactions on Information Theory 2025年

作者： Hayashi, Masahito Cheng, Hao-Chung Gao, Li Chinese University of Hong Kong School of Data Science Shenzhen Longgang District Shenzhen518172 China Nagoya University Graduate School of Mathematics Nagoya464-8602 Japan National Taiwan University Graduate Institute of Communication Engineering Department of Mathematics Institute of Applied Mathematical Sciences Center for Quantum Science and Engineering epartment of Electrical Engineering Taipei10617 Taiwan National Center for Theoretical Sciences Physics Division Taipei10617 Taiwan Quantum Computing Center New Taipei City236 Taiwan Wuhan University School of Mathematics and Statistics Wuhan430072 China

Channel resolvability concerns the minimum resolution for approximating the channel output. We study the resolvability of classical-quantum channels in two settings, for the channel output generated from the worst input, and from the fixed independent and identically distributed (i.i.d.) input. The direct part of the worst-input setting is derived from sequential hypothesis testing as it involves non-i.i.d. inputs. The strong converse of the worst-input setting is obtained via the connection to identification codes. For the fixed-input setting, while the direct part follows from the known quantum soft covering result, we exploit the recent alternative quantum Sanov theorem to prove the strong converse. © 1963-2012 IEEE.

关键词： Quantum channel

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Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis

arXiv

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arXiv 2023年

作者： Reynaud, Hadrien Qiao, Mengyun Dombrowski, Mischa Day, Thomas Razavi, Reza Gomez, Alberto Leeson, Paul Kainz, Bernhard UKRI CDT in AI for Healthcare Imperial College London London United Kingdom Department of Computing Imperial College London London United Kingdom Department of Brain Sciences Data Science Institute Imperial College London London United Kingdom Friedrich–Alexander University Erlangen Nürnberg Germany School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Department of Congenital Cardiology Evelina London Children’s Healthcare Guy’s and St Thomas’ NHS Foundation Trust London United Kingdom Ultromics Ltd Oxford United Kingdom John Radcliffe Hospital Cardiovascular Clinical Research Facility Oxford United Kingdom

Image synthesis is expected to provide value for the translation of machine learning methods into clinical practice. Fundamental problems like model robustness, domain transfer, causal modelling, and operator training become approachable through synthetic data. Especially, heavily operator-dependant modalities like Ultrasound imaging require robust frameworks for image and video generation. So far, video generation has only been possible by providing input data that is as rich as the output data, e.g., image sequence plus conditioning in → video out. However, clinical documentation is usually scarce and only single images are reported and stored, thus retrospective patient-specific analysis or the generation of rich training data becomes impossible with current approaches. In this paper, we extend elucidated diffusion models for video modelling to generate plausible video sequences from single images and arbitrary conditioning with clinical parameters. We explore this idea within the context of echocardiograms by looking into the variation of the Left Ventricle Ejection Fraction, the most essential clinical metric gained from these examinations. We use the publicly available EchoNet-Dynamic dataset for all our experiments. Our image to sequence approach achieves an R2 score of 93%, which is 38 points higher than recently proposed sequence to sequence generation methods. Code and models will be available at: https://***/HReynaud/ EchoDiffusion. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Assertion-based approaches to auditing complex elections, with application to party-list proportional elections

arXiv

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arXiv 2021年

作者： Blom, Michelle Budurushi, Jurlind Rivest, Ronald L. Stark, Philip B. Stuckey, Peter J. Teague, Vanessa Vukcevic, Damjan School of Computing and Information Systems University of Melbourne Parkville Australia Cloudical Deutschland GmbH Berlin Germany Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Statistics University of California BerkeleyCA United States Department of Data Science and AI Monash University Clayton Australia Thinking Cybersecurity Pty. Ltd. Melbourne Australia School of Mathematics and Statistics University of Melbourne Parkville Australia Melbourne Integrative Genomics University of Melbourne Parkville Australia

Risk-limiting audits (RLAs), an ingredient in evidence-based elections, are increasingly common. They are a rigorous statistical means of ensuring that electoral results are correct, usually without having to perform an expensive full recount - at the cost of some controlled probability of error. A recently developed approach for conducting RLAs, SHANGRLA, provides a flexible framework that can encompass a wide variety of social choice functions and audit strategies. Its flexibility comes from reducing sufficient conditions for outcomes to be correct to canonical 'assertions' that have a simple mathematical form. Assertions have been developed for auditing various social choice functions including plurality, multi-winner plurality, super-majority, Hamiltonian methods, and instant runoff voting. However, there is no systematic approach to building assertions. Here, we show that assertions with linear dependence on transformations of the votes can easily be transformed to canonical form for SHANGRLA. We illustrate the approach by constructing assertions for party-list elections such as Hamiltonian free list elections and elections using the D'Hondt method, expanding the set of social choice functions to which SHANGRLA applies directly. Copyright © 2021, The Authors. All rights reserved.

关键词： Hamiltonians

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Anomaly detection on attributed networks via contrastive self-supervised learning

arXiv

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arXiv 2021年

作者： Liu, Yixin Li, Zhao Pan, Shirui Gong, Chen Zhou, Chuan Karypis, George The Department of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia The Alibaba Group Hangzhou310000 China The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing The Hong Kong Polytechnic University Hong Kong The Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100093 China The Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States

Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this paper, we present a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA for abbreviation). Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multi-round predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the graph neural network module is batches of instance pairs instead of the full network, our framework can adapt to large networks flexibly. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all seven benchmark datasets. © 2021, CC BY.

关键词： Anomaly detection

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Improving Automated GUI Exploration of Android Apps via Static Dependency Analysis

Improving Automated GUI Exploration of Android Apps via Stat...

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International Conference on Software Maintenance (ICSM)

作者： Wunan Guo Liwei Shen Ting Su Xin Peng Weiyang Xie School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China School of Software Engineering East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China

ISBN: (数字)9781728156194

ISBN: (纸本)9781728156200

Exploring GUIs of Android apps plays a key role in many important scenarios such as functional testing (e.g., finding crash errors), security analysis (e.g., identifying malicious behav-iors) and competitive analysis (e.g., storyboarding app features). To automate GUI exploration, existing techniques often try to visit as many GUI pages as possible via specific strategies, e.g., random (like Monkey) or heuristic (like Stoat, A 3 E). However, their effectiveness is still unclear and much under-explored. To this end, we conducted the first study in this paper to understand and characterize their limitations by carefully analyzing the coverage reports from a set of real-world, open-source apps. Through this study, we identified three key limitations due to the lack of dependency knowledge during exploration, i.e., widget-page dependency, widget-widget dependency and system-event dependency. To overcome them, we introduce dependency-informed exploration, an automated approach that leverages static dependency analysis to effectively improve GUI exploration performance. Given an app, our approach first constructs a GUI page transition model that captures the dependencies between GUI widgets, and then guides GUI exploration during a depth-first traversal. We realized our approach as a tool named Gesda, and evaluated it on 70 open-source Android apps. The results show Gesda outperforms existing state-of-the-art GUI exploration techniques, i.e., Monkey and Stoat. Additionally, Gesda uncovers 4 previously unknown crashes in 4 apps as a by-product of GUI exploration due to the benefit of dependency knowledge, while Monkey and Stoat have not discovered them.

关键词： Software maintenance Tools Computer crashes Security Open source software Graphical user interfaces Testing

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