检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Huang, Yuhao Chang, Ao Dou, Haoran Tao, Xing Zhou, Xinrui Cao, Yan Huang, Ruobing Frangi, Alejandro F. Bao, Lingyun Yang, Xin Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China School of Computing University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd Shenzhen China Division of Informatics Imaging and Data Science School of Health Sciences University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering University of Manchester Manchester United Kingdom Department of Electrical Engineering Department of Cardiovascular Sciences KU Leuven Belgium Alan Turing Institute London United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom Department of Ultrasound Affiliated Hangzhou First People’s Hospital School of Medicine Westlake University China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: 1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. 2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. 3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

VulDeeLocator: A deep learning-based fine-grained vulnerability detector

arXiv

引用

arXiv 2020年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Chen, Zhaoxuan Zhu, Yawei Jin, Hai The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Cyber Security and Computer Hebei University Baoding071002 China The Department of Computer Science University of Colorado Colorado Springs CO80918 United States University of Texas San Antonio United States

Automatically detecting software vulnerabilities is an important problem that has attracted much attention from the academic research community. However, existing vulnerability detectors still cannot achieve the vulnerability detection capability and the locating precision that would warrant their adoption for real-world use. In this paper, we present a vulnerability detector that can simultaneously achieve a high detection capability and a high locating precision, dubbed Vulnerability Deep learning-based Locator (VulDeeLocator). In the course of designing VulDeeLocator, we encounter difficulties including how to accommodate semantic relations between the definitions of types as well as macros and their uses across files, how to accommodate accurate control flows and variable define-use relations, and how to achieve high locating precision. We solve these difficulties by using two innovative ideas: (i) leveraging intermediate code to accommodate extra semantic information, and (ii) using the notion of granularity refinement to pin down locations of vulnerabilities. When applied to 200 files randomly selected from three real-world software products, VulDeeLocator detects 18 confirmed vulnerabilities (i.e., true-positives). Among them, 16 vulnerabilities correspond to known vulnerabilities;the other two are not reported in the National Vulnerability database (NVD) but have been "silently" patched by the vendor of Libav when releasing newer versions. Copyright © 2020, The Authors. All rights reserved.

关键词： Location

来源：评论

学校读者我要写书评

暂无评论

Generative Adversarial Stacked Autoencoders for Facial Pose Normalization and Emotion Recognition

arXiv

引用

arXiv 2020年

作者： Ruiz-Garcia, Ariel Palade, Vasile Elshaw, Mark Awad, Mariette School of Computing Electronics and Mathematics Coventry University Coventry United Kingdom Research Centre for Data Science Coventry University Coventry United Kingdom Humans and Machines Lab American University of Beirut Beirut Lebanon

In this work, we propose a novel Generative Adversarial Stacked Autoencoder that learns to map facial expressions with up to ±60 degrees to an illumination invariant facial representation of 0 degrees. We accomplish this by using a novel convolutional layer that exploits both local and global spatial information, and a convolutional layer with a reduced number of parameters that exploits facial symmetry. Furthermore, we introduce a generative adversarial gradual greedy layer-wise learning algorithm designed to train Adversarial Autoencoders in an efficient and incremental manner. We demonstrate the efficiency of our method and report state-of-the-art performance on several facial emotion recognition corpora, including one collected in the wild. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Optimization from Structured Samples for Coverage Functions

arXiv

引用

arXiv 2020年

作者： Chen, Wei Sun, Xiaoming Zhang, Jialin Zhang, Zhijie Microsoft Research Asia Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

We revisit the optimization from samples (OPS) model, which studies the problem of optimizing objective functions directly from the sample data. Previous results showed that we cannot obtain a constant approximation ratio for the maximum coverage problem using polynomially many independent samples of the form {Si, f (Si)}ti=1 (Balkanski et al., 2017), even if coverage functions are (1 − ǫ)-PMAC learnable using these samples (Badanidiyuru et al., 2012), which means most of the function values can be approximately learned very well with high probability. In this work, to circumvent the impossibility result of OPS, we propose a stronger model called optimization from structured samples (OPSS) for coverage functions, where the data samples encode the structural information of the functions. We show that under three general assumptions on the sample distributions, we can design efficient OPSS algorithms that achieve a constant approximation for the maximum coverage problem. We further prove a constant lower bound under these assumptions, which is tight when not considering computational efficiency. Moreover, we also show that if we remove any one of the three assumptions, OPSS for the maximum coverage problem has no constant approximation. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history (vol 29, pg 761, 2022)

引用

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2022年第9期29卷 1657-1657页

作者： Maurits, Marc P. Korsunsky, Ilya Raychaudhuri, Soumya Murphy, Shawn N. Smoller, JordanW Weiss, Scott T. Petukhova, Lynn M. Weng, Chunhua Wei, Wei-Qi Huizinga, Thomas W. J. Reinders, Marcel J. T. Karlson, Elizabeth W. van den Akker, Erik B. Knevel, Rachel Department of Rheumatology Leiden University Medical Center Leiden The Netherlands Leiden Computational Biology Center Leiden University Medical Center Leiden The Netherlands Center for Data Sciences Brigham and Women's Hospital Harvard Medical School Boston MA USA Research Information Science and Computing Mass General Brigham Boston MA USA Center for Precision Psychiatry Department of Psychiatry Massachusetts General Hospital Boston MA USA Psychiatric and Neurodevelopmental Genetics Unit Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Channing Division of Network Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA Lynn M. Petukhova Department of Dermatology at NewYork-Presbyterian/Columbia University Medical Center (CUMC) Chunhua Weng Biomedical Informatics - Columbia University Wei-Qi Wei Biomedical Informatics in the School of Medicine at Vanderbilt University Wei The Delft Bioinformatics Lab Delft University of Technology Delft The Netherlands Division of Rheumatology Inflammation and Immunity Brigham and Women's Hospital Harvard Medical School Boston MA USA Section of Molecular Epidemiology Leiden University Medical Center Leiden The Netherlands

OBJECTIVE:To facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects. MATERIAL AND METHODS:We used 1872 billing codes in EHRs of 102 880 patients from 12 healthcare systems. Using tools borrowed from single-cell omics, we mitigated center-specific batch effects and performed clustering to identify patients with highly similar medical history patterns across the various centers. Our visualization method (PheSpec) depicts the phenotypic profile of clusters, applies a novel filtering of noninformative codes (Ranked Scope Pervasion), and indicates the most distinguishing features. RESULTS:We observed 114 clinically meaningful profiles, for example, linking prostate hyperplasia with cancer and diabetes with cardiovascular problems and grouping pediatric developmental disorders. Our framework identified disease subsets, exemplified by 6 "other headache" clusters, where phenotypic profiles suggested different underlying mechanisms: migraine, convulsion, injury, eye problems, joint pain, and pituitary gland disorders. Phenotypic patterns replicated well, with high correlations of ≥0.75 to an average of 6 (2-8) of the 12 different cohorts, demonstrating the consistency with which our method discovers disease history profiles. DISCUSSION:Costly clinical research ventures should be based on solid hypotheses. We repurpose methods from single-cell omics to build these hypotheses from observational EHR data, distilling useful information from complex data. CONCLUSION:We establish a generalizable pipeline for the identification and replication of clinically meaningful (sub)phenotypes from widely available high-dimensional billing codes. This approach overcomes datatype problems and produces comprehensive visualizations of validation-ready phenotypes.

关键词： electronic health records clustering electronic medical records ICD PhenoGraph eMERGE

来源：评论

学校读者我要写书评

暂无评论

Anomaly Detection under Coordinate Transformations

arXiv

引用

arXiv 2022年

作者： Kasieczka, Gregor Mastandrea, Radha Mikuni, Vinicius Nachman, Benjamin Pettee, Mariel Shih, David Institut für Experimentalphysik Universität Hamburg Hamburg22761 Germany Department of Physics University of California BerkeleyCA94720 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States New High Energy Theory Center Rutgers University PiscatawayNJ08854 United States

There is a growing need for machine learning-based anomaly detection strategies to broaden the search for Beyond-the-Standard-Model (BSM) physics at the Large Hadron Collider (LHC) and elsewhere. The first step of any anomaly detection approach is to specify observables and then use them to decide on a set of anomalous events. One common choice is to select events that have low probability density. It is a well-known fact that probability densities are not invariant under coordinate transformations, so the sensitivity can depend on the initial choice of coordinates. The broader machine learning community has recently connected coordinate sensitivity with anomaly detection and our goal is to bring awareness of this issue to the growing high energy physics literature on anomaly detection. In addition to analytical explanations, we provide numerical examples from simple random variables and from the LHC Olympics dataset that show how using probability density as an anomaly score can lead to events being classified as anomalous or not depending on the coordinate frame. © 2022, CC BY.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Sledge: Towards Efficient Live Migration of Docker Containers

Sledge: Towards Efficient Live Migration of Docker Container...

引用

IEEE International Conference on Cloud computing, CLOUD

作者： Bo Xu Song Wu Jiang Xiao Hai Jin Yingxi Zhang Guoqiang Shi Tingyu Lin Jia Rao Li Yi Jizhong Jiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Intelligent Manufacturing System Technology Beijing China The University of Texas at Arlington Arlington TX USA Alibaba Cloud Computing Co. Ltd. Hangzhou China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Modern large-scale cloud platforms require live migration technique on Docker containers with stateful workload to support load balancing, host maintenance, and Quality of Service (QoS) improvement. Efficient and scalable Docker live migration is expected to guarantee the component-integrity (image, runtime, and management context) with negligible downtime. In this paper, we present a highly efficient live migration system called Sledge, which ensures the component-integrity by integrating both images and management context during runtime migration. The key insight is that the layered image can be leveraged to reduce the migration overhead, and appropriately selective migration of management context will effectively improve QoS with negligible downtime. To achieve good scalability, a lightweight container registry mechanism for end-to-end image migration is designed to avoid the redundant layers transmission. In addition, a dynamic context loading scheme is proposed to precisely load the management context into the running daemon, which can significantly reduce downtime. Experiments show that, compared with the state-of-the-art, Sledge reduces 57% of total migration time, 55% of image migration time, and 70% downtime.

关键词： Cloud computing Runtime Scalability Loading Quality of service Containers Maintenance engineering

来源：评论

学校读者我要写书评

暂无评论

Discovery of Four Pulsars in a Pilot Survey at Intermediate Galactic Latitudes with FAST

arXiv

引用

arXiv 2023年

作者： Zhi, Q.J. Bai, J.T. Dai, S. Xu, X. Dang, S.J. Shang, L.H. Zhao, R.S. Li, D. Zhu, W.W. Wang, N. Yuan, J.P. Wang, P. Zhang, L. Feng, Y. Wang, J.B. Wang, S.Q. Wu, Q.D. Dong, A.J. Yang, H. Tian, J. Zhong, W.Q. Luo, X.H. Filipović, Miroslav D. Qiao, G.J. School of Physics and Electronic Science Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China Xinjiang Astronomical Observatory Chinese Academy of Sciences Xinjiang Urumqi830011 China School of Science Western Sydney University Locked Bag 1797 Penrith South DCNSW2751 Australia School of Mathematical Sciences Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Key Laboratory of Radio Astronomy Chinese Academy of Sciences Xinjiang Urumqi830011 China Xinjiang Key Laboratory of Radio Astrophysics 150 Science1-Street Xinjiang Urumqi830011 China Zhejiang Lab Zhejiang Hangzhou311121 China Institute of Optoelectronic Technology Lishui University Lishui323000 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing100049 China School of Physics Peking University Beijing100871 China

We present the discovery and timing results of four pulsars discovered in a pilot survey at intermediate Galactic latitudes with the Five-hundred Aperture Spherical Telescope (FAST). Among these pulsars, two belong to the category of millisecond pulsars (MSPs) with spin periods of less than 20 ms. The other two fall under the classification of "mildly recycled" pulsars, with massive white dwarfs as companions. Remarkably, this small survey, covering an area of 4.7 deg2, led to the discovery of four recycled pulsars. Such success underscores the immense potential of future surveys at intermediate Galactic latitudes. In order to assess the potential yield of MSPs, we conducted population simulations and found that both FAST and Parkes new phased array feed surveys, focusing on intermediate Galactic latitudes, have the capacity to uncover several hundred new MSPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Pulsars

来源：评论

学校读者我要写书评

暂无评论

Context aware 3d unet for brain tumor segmentation

arXiv

引用

arXiv 2020年

作者： Ahmad, Parvez Qamar, Saqib Shen, Linlin Saeed, Adnan National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China School of Hydropower and Information Technology Huazhong University of Science and Technology Wuhan430074 China

Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-contexual information from image data. The multi-scaled features play an essential role in brain tumor segmentation. However, the limited use of features can degrade the performance of the UNet approach for segmentation. In this paper, we propose a modified UNet architecture for brain tumor segmentation. In the proposed architecture, we used densely connected blocks in both encoder and decoder paths to extract multi-contexual information from the concept of feature reusability. The proposed residual inception blocks (RIB) are used to extract local and global information by merging features of different kernel sizes. We validate the proposed architecture on the multimodal brain tumor segmentation challenges (BRATS) 2020 testing dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancement tumor (ET) are 89.12%, 84.74%, and 79.12%, respectively. Our proposed work is in the top ten methods based on the dice scores of the testing dataset. © 2020, CC-BY.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Spatial resolution for forest carbon maps

引用

science (New York, N.Y.) 2025年第6732期387卷 370-371页

作者： Laura Duncanson Neha Hunka Tommaso Jucker John Armston Nancy Harris Lola Fatoyinbo Christopher A Williams Jeff W Atkins Brett Raczka Shawn Serbin Michael Keller Ralph Dubayah Chad Babcock Mark A Cochrane Andrew Hudak George C Hurtt Paul M Montesano L Monika Moskal Taejin Park Sassan Saatchi Carlos A Silva Hao Tang Rodrigo Vargas Aaron Weiskittel Konrad Wessels Scott J Goetz Geographical Sciences University of Maryland College Park MD USA. School of Biological Sciences University of Bristol Bristol UK. World Resources Institute Washington DC USA. NASA Goddard Space Flight Center Greenbelt MD USA. Graduate School of Geography Clark University Worcester MA USA. US Department of Agriculture (USDA) Forest Service Southern Research Station New Ellenton SC USA. Department of Biology Virginia Commonwealth University Richmond VA USA. Computational and Information Systems Lab Data Assimilation Research Section National Center for Atmospheric Research Boulder CO USA. International Institute of Tropical Forestry USDA Forest Service Rio Piedras PR USA. Forest Resources Department University of Minnesota St. Paul MN USA. University of Maryland Center for Environmental Science Frostburg MD USA. Rocky Mountain Research Station USDA Forest Service Moscow ID USA. NASA Goddard Space Flight Center ADNET Systems Greenbelt MD USA. School of Environmental and Forest Sciences University of Washington WA USA. Bay Area Environmental Research Institute Moffett Field CA USA. NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA. CTrees Pasadena CA USA. Forest Biometrics Remote Sensing and Artificial Intelligence Lab (SilvaLab) School of Forest Fisheries and Geomatics Science University of Florida Gainesville FL USA. Department of Geography National University of Singapore Singapore. Department of Plant and Soil Sciences University of Delaware Newark DE USA. Center for Research on Sustainable Forests University of Maine Orono ME USA. Geography and Geoinformation Science George Mason University Fairfax VA USA. School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA.

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：