检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Vogt, Ryan H. Strikwerda, Sarah Department of Mathematics North Carolina State University RaleighNC27695 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States

In this paper we study numerically solving optimal control problems with bang-bang control functions. We present a formal Lagrangian approach for solving the optimal control problem, and address difficulties encountered when numerically solving the state and adjoint equations by using the immersed interface method. We note that our numerical approach does not approximate the discontinuous control function with smooth functions, instead we solve the true bang-bang optimal control problem. Our approach for solving the optimal control problem uses an adjoint-based gradient. We use the gradient in our first-order trust-region method to generate a local minimizing control. We present detailed numerical results to demonstrate the effectiveness of our method. © 2021, CC BY.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis

Predicting Internet of Things Data Traffic Through LSTM and ...

引用

IEEE Symposium on Network Operations and Management

作者： Yuhong Li Di Jin Bailin Wang Xiang Su Jukka Riekki Chao Sun Hanyu Wei Hao Wang Lei Han State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Computer Science University of Helsinki Helsinki Finland Center for Ubiquitous Computing University of Oulu Oulu Finland Network Technology Laboratory Huawei Technologies Co. Ltd Nanjing China

ISBN: (数字)9781728149738

ISBN: (纸本)9781728149745

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM's performance deterioration with the increase of sequence length. A sliding window enables predicting the long¬term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Proceedings of the 16th Annual UT-KBRIN Bioinformatics Summit 2016: bioinformatics

引用

BMC Bioinformatics 2017年第9期18卷 1-9页

作者： Eric C. Rouchka Juw Won Park Julia H. Chariker David A. Tieri Shreedharkumar Rajurkar Vikas Singh Nishchal K. Verma Yan Cui Mark Farman Bradford Condon Jolanta Jaromczyk Neil Moore Jerzy Jaromczyk Daniel Harris Patrick Calie Eun Kyong Shin Robert L. Davis Arash Shaban-Nejad Joshua M. Mitchell Hunter N. B. Moseley Robert M. Flight Qing Jun Wang Richard M. Higashi Teresa W-M Fan Andrew N. Lane Liangqun Lu Bernie J Daigle Jr. Xi Chen Andrey Smelter Jerzy W. Jaromczyk Bailey K. Phan Nathaniel J. Serpico Ethan G. Toney Li Chen Caroline E. Melton Jennifer R. Mandel Bernie J. Daigle Jr. Hao Chen Kazi I. Zaman Ramin Homayouni Patrick J. Trainor Andrew P. DeFilippis Samantha M. Carlisle Shesh N. Rai Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville KY 40292 USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core 522 East Gray Street Louisville KY 40292 USA Department of Psychological and Brain Sciences University of Louisville Louisville KY 40292 USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville KY 40292 USA Department of Electrical Engineering IIT Kanpur India Department of Microbiology Immunology and Biochemistry University of Tennessee Health Science Center Memphis TN 38103 USA Center for Integrative and Translational Genomics University of Tennessee Health Science Center Memphis TN 38103 USA Department of Plant Pathology University of Kentucky Lexington KY 40546 USA UK Healthcare Genomics University of Kentucky Lexington KY 40536 USA Department of Entomology & Plant Pathology University of Tennessee Knoxville TN 37996 USA Department of Forestry University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40508 USA Department of Biological Sciences Eastern Kentucky University Richmond KY 40475 USA University of Tennessee Health Science Center – Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics Department of Pediatrics Memphis TN 38103 USA Department of Molecular & Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Markey Cancer Center University of Kentucky Lexington KY 40536 USA Center for Environmental and Systems Biochemistry University of Kentucky Lexington KY 40536 USA Institute for Biomedical Informatics University of Kentucky Lexington KY 40536 USA Department of Molecular and Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Department of Statistics University of Kentucky Louisville KY

来源：评论

学校读者我要写书评

暂无评论

Dual Bidirectional Copy-Paste with Shape Constraint for Semi-Supervised Image Segmentation

Dual Bidirectional Copy-Paste with Shape Constraint for Semi...

引用

IEEE International Symposium on Biomedical Imaging

作者： Bowei Shen Xiaoquan Huang Yonghong Shi Shiyao Chen School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China Department of Gastroenterology and Hepatology Zhongshan Hospital Fudan University Shanghai China Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Semi-supervised learning utilizes unlabeled data and a small amount of pixel-level labeled data to achieve image semantic segmentation. However, its performance largely depends on the semantic learning of the boundary details of the object in the image, as the uncertainty of the boundary region can reduce the segmentation accuracy. To this end, this paper proposes a novel semi-supervised learning method based on the student-teacher model, which is constrained by the dual bidirectional copy-paste and the signed distance map of the object shape. The method is carried out through two stages: fully supervised pre-training and semi-supervised re-training. The former uses labeled data to train and learn the initial student model, and its parameters are used to initialize and update the latter’s teacher model, which is used to segment unlabeled data to obtain its pseudo labels. At each stage, the dual bidirectional copy-paste between ‘labeled data’ enhances the data, while the similar copy-paste performed on the corresponding ‘label’ supervises the student model. Copy-paste allows the network to focus on the entire object, while the signed distance map on the label data helps to learn the complex shape changes of the object boundary. Especially in the re-training stage, the above calculation mainly occurs between labeled data and unlabeled data with 'pseudo label', which effectively bridges the empirical distribution mismatch between labeled data and unlabeled data, enabling the network to learn a more generalized data distribution. Our method was validated on two datasets, LA Heart and ACDC. Compared with other semi-supervised learning methods, our method outperforms previous works at 5% and 10% labeled data volumes, achieving the SOTA results.

关键词： Bridges Heart Uncertainty Shape Biological system modeling Semantic segmentation Semantics

来源：评论

学校读者我要写书评

暂无评论

Few-Shot Small Vessel Segmentation Using a Detail-Preserving Network Enhanced by Discriminator

SSRN

引用

SSRN 2024年

作者： Huang, Yan Yang, Jinzhu Sun, Qi Yuan, Yuliang Hou, Yang Shang, Jin Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Computer Science and Engineering Northeastern University Liaoning Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang China Department of Radiology Shengjing Hospital of China Medical University Liaoning Shenyang China

Accurate segmentation of small vessels, such as coronary and pulmonary arteries, is crucial for early detection and treatment of vascular diseases. However, challenges persist due to the vessel's small size, complex structures, morphological variations, and limited annotated data. To address these challenges, we propose a detail-preserving network enhanced by a discriminator to improve the few-shot small vessel segmentation performance. The detail-preserving network constructs a complex module with multi-residual hybrid dilated convolution, which can enhance the network's receptive field while preserving the image's full detail features, enabling it to better capture the small vessel's structural features. Simultaneously, discriminator enhancement is incorporated into the training process through adversarial learning, effectively utilizing large amounts of unlabeled data to boost the generalization and robustness of the segmentation model. We validate the proposed method on in-house and public coronary artery datasets and public pulmonary artery datasets. Experimental results demonstrate that the proposed method significantly improves segmentation accuracy, particularly for small vessels. Compared with other state-of-the-art methods, the proposed method achieves higher accuracy, a lower false positive rate, and superior generalization capability, effectively assisting the clinical diagnosis of vessel diseases. © 2024, The Authors. All rights reserved.

关键词： Semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Hierarchical background subtraction using local pixel clustering

Hierarchical background subtraction using local pixel cluste...

引用

International Conference on Pattern Recognition

作者： Bineng Zhong Hongxun Yao Shiguang Shan Xilin Chen Wen Gao Department of Computer Science and Engineering Harbin Institute of Technology Digital Media Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China Digital Media Institute Peking University Beijing China

ISBN: (纸本)9781424421749

We propose a robust hierarchical background subtraction technique which takes the spatial relations of neighboring pixels in a local region into account to detect objects in difficult conditions. Our algorithm combines a per-pixel with a per-region background model in a hierarchical manner, which accentuates the advantages of each. This is a natural combination because the two models have complementary strengths. The per-pixel background model is achieved by mixture of Gaussians models (GMM) with RGB feature. Although precisely describing background change in high resolution, it suffers from the sensitivity to quick variations in dynamic environment. To tolerate these quick variations, we further develop a novel GMM based per-region background model, which is updated by the cluster centers obtained from a k-means clustering of the pixelspsila RGB feature in the region. Numerical and qualitative experimental results on challenging videos demonstrate the robustness of the proposed method.

关键词： Autoregressive processes Robustness Object detection Videos Layout Smart pixels Content addressable storage Gaussian processes Lighting Cameras

来源：评论

学校读者我要写书评

暂无评论

Drum-aware ensemble architecture for improved joint musical beat and downbeat tracking

arXiv

引用

arXiv 2021年

作者： Chiu, Ching-Yu Su, Alvin Wen-Yu Yang, Yi-Hsuan The Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan The Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan

This paper presents a novel system architecture that integrates blind source separation with joint beat and downbeat tracking in musical audio signals. The source separation module segregates the percussive and non-percussive components of the input signal, over which beat and downbeat tracking are performed separately and then the results are aggregated with a learnable fusion mechanism. This way, the system can adaptively determine how much the tracking result for an input signal should depend on the input's percussive or non-percussive components. Evaluation on four testing sets that feature different levels of presence of drum sounds shows that the new architecture consistently outperforms the widely-adopted baseline architecture that does not employ source separation. Copyright © 2021, The Authors. All rights reserved.

关键词： Blind source separation

来源：评论

学校读者我要写书评

暂无评论

Deep Kernel Embedded Clustering Network

SSRN

引用

SSRN 2024年

作者： Ren, Lina Huang, Ruizhang Chen, Yanping Lin, Chuan Qin, Yongbin State Key Laboratory of Public Big Data Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang550025 China Department of Information Engineering Guizhou Light Industry Technical College Guiyang550025 China

In this paper, we propose a deep kernel embedded clustering network, namely DKEC, which learns data partitions with kernelized semantic embeddings of data samples via a self-supervised deep neural network. A kernelized semantic embedding learner is designed to learn high quality data representations, which not only capture the data semantic characteristics but also show good structural separable characteristics. Other than directly apply traditional predefined kernel mapping methods, the kernelized semantic embedding learner, investigated by a deep variational Gaussian process, is able to learn the kernel mapping according to the nature of various datasets automatically, while maintain reasonable computational complexity. The DKEC, which is an end-to-end deep clustering network, is invested based on the kernelized semantic embedding learner. A triplet self-supervised optimization objective is designed in the DKEC model to guide the clustering partition, the separable factor of the kernelized semantic embedding, and the data reconstruction factor of the kernelized semantic embedding, simultaneously. We conducted extensive experiments on $ 7 $ real-world datasets. The results unequivocally showcase the exceptional clustering performance of the proposed DKEC. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Synchronizing the Timestamps of Concurrent Events in Traces of Hybrid MPI/OpenMP Applications

Synchronizing the Timestamps of Concurrent Events in Traces ...

引用

IEEE International Conference on Cluster computing

作者： Daniel Becker Markus Geimer Rolf Rabenseifner Felix Wolf Department of Computer Science RWTH Aachen University Aachen Germany German Research School for Simulation Sciences Laboratory for Parallel Programming Aachen Germany Forschungszentrum Jülich Jülich Supercomputing Centre Jülich Germany University of Stuttgart High Performance Computing-Center Stuttgart Germany

Event traces are helpful in understanding the performance behavior of parallel applications since they allow the in-depth analysis of communication and synchronization patterns. However, the absence of synchronized clocks on most cluster systems may render the analysis ineffective because inaccurate relative event timings may misrepresent the logical event order and lead to errors when quantifying the impact of certain behaviors or confuse the users of time-line visualization tools by showing messages flowing backward in time. In our earlier work, we have developed a scalable algorithm that eliminates inconsistent inter-process timings postmortem in traces of pure MPI applications. Since hybrid programming, the combination of MPI and OpenMP in a single application, is becoming more popular on clusters in response to rising numbers of cores per chip and widening shared-memory nodes, we present an extended version of the algorithm that in addition to message-passing event semantics also preserves and restores shared-memory event semantics.

关键词： Clocks Message systems Semantics Synchronization Algorithm design and analysis Interpolation

来源：评论

学校读者我要写书评

暂无评论

Variational learning algorithms for quantum query complexity

arXiv

引用

arXiv 2022年

作者： Wu, Zipeng Hou, Shi-Yao Zhang, Chao Li, Lvzhou Zeng, Bei Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong College of Physics and Electronic Engineering Center for Computational Sciences Sichuan Normal University Chengdu610068 China Shenzhen SpinQ Technology Co. Ltd. Shenzhen China Institute of Quantum Computing and Computer Theory School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

Quantum query complexity is pivotal in the analysis of quantum algorithms, encompassing wellknown examples like search and period-finding algorithms. These algorithms typically involve a sequence of unitary operations and oracle calls dependent on an input variable. In this study, we introduce a variational learning approach to explore quantum query complexity. Our method employs an efficient parameterization of the unitary operations and utilizes a loss function derived from the algorithm's error probability. We apply this technique to various quantum query complexities, notably devising a new algorithm that resolves the 5-bit Hamming modulo problem with four queries, addressing an open question from arXiv:2112.14682. This finding is corroborated by a Semidefinite Programming (SDP) approach. Our numerical method exhibits superior memory efficiency compared to SDP and can identify quantum query algorithms that require a smaller workspace register dimension, an aspect not optimized by SDP. These advancements present a significant step forward in the practical application and understanding of quantum query algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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：