检索结果-内蒙古大学图书馆

CLEO: QELS_Fundamental science, QELS 2022

作者： Wang, Hanfeng Trusheim, Matthew E. Kim, Laura Englund, Dirk Research Laboratory of Electronics M.I.T. CambridgeMA02139 United States Department of Electrical Engineering and Computer Science M.I.T. CambridgeMA02139 United States U.S. Army Research Laboratory Sensors and Electron Devices Directorate AdelphiMD20783 United States

We introduce an electric-field programmable spin array (FPSA) for scalable quantum repeaters. We analyze the FPSA scheme for arrays of nitrogen vacancy color centers in a slow-light diamond waveguide. © Optica Pu... 详细信息

ISBN: (纸本)9781557528209

关键词： Electric fields

来源：评论

学校读者我要写书评

暂无评论

Procrustean entanglement concentration in quantum-classical networking

引用

Physical Review Applied 2024年第4期21卷 044027-044027页

作者： Hsuan-Hao Lu Muneer Alshowkan Jude Alnas Joseph M. Lukens Nicholas A. Peters Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona 85287 USA

The success of a future quantum internet will rest in part on the ability of quantum and classical signals to coexist in the same optical fiber infrastructure, a challenging endeavor given the orders of magnitude differences in flux of single-photon-level quantum fields and bright classical traffic. We theoretically describe and experimentally implement Procrustean entanglement concentration for polarization-entangled states contaminated with classical light, showing significant mitigation of crosstalk noise in dense wavelength-division multiplexing. Our approach leverages a pair of polarization-dependent loss emulators to attenuate highly polarized crosstalk that results from imperfect isolation of conventional signals copropagating on shared fiber links. We demonstrate our technique both on the tabletop and over a deployed quantum local area network, finding a substantial improvement of two-qubit entangled state fidelity from approximately 75% to over 92%. This local filtering technique could be used as a preliminary step to reduce asymmetric errors, potentially improving the overall efficiency when combined with more complex error-mitigation techniques in future quantum repeater networks.

关键词： Entanglement manipulation Optical quantum information processing Quantum entanglement Quantum networks Quantum optics Quantum repeaters Internet Multilayer & multiplex networks

来源：评论

学校读者我要写书评

暂无评论

Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes

引用

Breast Disease 2023年第1期42卷 59-66页

作者： Ferre, Romuald Elst, Janne Senthilnathan, Seanthan Lagree, Andrew Tabbarah, Sami Lu, Fang-I Sadeghi-Naini, Ali Tran, William T. Curpen, Belinda Department of Medical Imaging Sunnybrook Health Sciences Centre Toronto ON Canada Department of Radiation Oncology Sunnybrook Health Sciences Centre Toronto ON Canada Department of Laboratory Medicine and Molecular Diagnostics Sunnybrook Health Sciences Centre Toronto ON Canada Department of Electrical Engineering and Computer Science York University Toronto ON Canada Department of Radiation Oncology University of Toronto Toronto ON Canada Temerty Centre for Ai Research and Education University of Toronto ON Canada Department of Medical Imaging University of Toronto Toronto ON Canada

OBJECTIVES: Early diagnosis of triple-negative (TN) and human epidermal growth factor receptor 2 positive (HER2+) breast cancer is important due to its increased risk of micrometastatic spread necessitating early treatment and for guiding targeted therapies. This study aimed to evaluate the diagnostic performance of machine learning (ML) classification of newly diagnosed breast masses into TN versus non-TN (NTN) and HER2+ versus HER2 negative (HER2-) breast cancer, using radiomic features extracted from grayscale ultrasound (US) b-mode images. MATERIALS AND METHODS: A retrospective chart review identified 88 female patients who underwent diagnostic breast US imaging, had confirmation of invasive malignancy on pathology and receptor status determined on immunohistochemistry available. The patients were classified as TN, NTN, HER2+ or HER2- for ground-truth labelling. For image analysis, breast masses were manually segmented by a breast radiologist. Radiomic features were extracted per image and used for predictive modelling. Supervised ML classifiers included: logistic regression, k-nearest neighbour, and Naïve Bayes. Classification performance measures were calculated on an independent (unseen) test set. The area under the receiver operating characteristic curve (AUC), sensitivity (%), and specificity (%) were reported for each classifier. RESULTS: The logistic regression classifier demonstrated the highest AUC: 0.824 (sensitivity: 81.8%, specificity: 74.2%) for the TN sub-group and 0.778 (sensitivity: 71.4%, specificity: 71.6%) for the HER2 sub-group. CONCLUSION: ML classifiers demonstrate high diagnostic accuracy in classifying TN versus NTN and HER2+ versus HER2- breast cancers using US images. Identification of more aggressive breast cancer subtypes early in the diagnostic process could help achieve better prognoses by prioritizing clinical referral and prompting adequate early treatment. © 2023 - IOS Press. All rights reserved.

关键词： HER2+ breast cancer Machine learning triple negative breast cancer ultrasound

来源：评论

学校读者我要写书评

暂无评论

Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

引用

IEEE International Symposium on Biomedical Imaging

作者： Eugenia Mylona Dimitrios I. Zaridis Charalampos N. Kalantzopoulos Vasileios C. Pezoulas Nikolaos S. Tachos George K. Matsopoulos Daniele Regge Nikolaos Papanikolaou Manolis Tsiknakis Kostas Marias Dimitrios I. Fotiadis Biomedical Research Institute FORTH Ioannina Greece Unit of Medical Technology Intelligent Information Systems University of Ioannina Greece Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Greece Institute of Computer Science FORTH Heraklion Greece Department of Radiology Candiolo Cancer Institute FPO-IRCCS Strada Provinciale Candiolo Italy Computational Clinical Imaging Group Champalimaud Foundation Portugal Computational Biomedicine Laboratory Institute of Computer Science FORTH Heraklion Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability.

关键词： Deep learning Support vector machines Analytical models Biological system modeling Feature extraction Transformers Lesions Prostate cancer Radiomics Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

City-Scale Point Cloud Stitching Using 2D/3D Registration for Large Geographical Coverage 25th

City-Scale Point Cloud Stitching Using 2D/3D Registration fo...

引用

25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Yao, Shizeng AliAkbarpour, Hadi Seetharaman, Guna Palaniappan, Kannappan Department of Electrical Engineering and Computer Science University of Missouri - Columbia ColumbiaMO United States US Naval Research Laboratory WashingtonDC United States

ISBN: (纸本)9783030687922

3D city-scale point cloud stitching is a critical component for large data collection, environment change detection, in which massive amounts of 3D data are captured under different times and conditions. This paper proposes a novel point cloud stitching approach, that automatically and accurately stitches multiple city-scale point clouds, which only share relatively small overlapping areas, into one single model for a larger geographical coverage. The proposed method firstly employs 2D image mosaicking techniques to estimate 3D overlapping areas among multiple point clouds, then applies 3D point cloud registration techniques to estimate the most accurate transformation matrix for 3D stitching. The proposed method is quantitatively evaluated on city-scale reconstructed point cloud dataset and real-world city LiDAR dataset, in which, our method outperforms other competing methods with significant margins and achieved the highest precision score, recall score, and F-score. Our method makes an important step towards automatic and accurate city-scale point cloud data stitching, which could be used in a variety of applications. © 2021, Springer Nature Switzerland AG.

关键词： Linear transformations

来源：评论

学校读者我要写书评

暂无评论

Critical hybridization of skin modes in coupled non-Hermitian chains

引用

Physical Review research 2022年第1期4卷 013243-013243页

作者： S. M. Rafi-Ul-Islam Zhuo Bin Siu Haydar Sahin Ching Hua Lee Mansoor B. A. Jalil Department of Electrical and Computer Engineering National University of Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Department of Physics National University of Singapore Singapore

Non-Hermitian topological systems exhibit a plethora of unusual topological phenomena including the extreme localization of eigenstates, known as the non-Hermitian skin effect (NHSE), which occurs in open chains. However, the non-Hermitian characteristics of coupled non-Hermitian chains remain largely unexplored. Here, we report on the eigenstate localization in coupled non-Hermitian chains with dissimilar inverse skin lengths in which the NHSE can be switched on and off by modulating the interchain coupling strength. In the limit of small interchain strength, the NHSE is present at both ends of the coupled system because of the weak hybridization of the eigenstates of the individual chains. The eigenspectrum under open boundary conditions (OBC) exhibits a discontinuous jump known as the critical NHSE (CNHSE) as the chain length increases. However, when the interchain coupling strength and hence, the hybridization between eigenstates become significant, the NHSE and CNHSE vanish. Instead, a peculiar “half-half skin localization” occurs in composite chains with opposite signs of inverse skin lengths, where half of the eigenstates are exponentially localized at one chain and the remainder of the eigenstates on the other chain. Our results provide new insights into the non-Hermitian phenomena in coupled systems.

关键词： Electronic structure Exotic phases of matter Skin effect Topological insulators Topological phase transition Topological phases of matter 1-dimensional systems Topological materials Band structure methods Bloch wave theory Lattice models in condensed matter Tight-binding model

来源：评论

学校读者我要写书评

暂无评论

Multi-agent Collaboration for Vehicular Task Offloading Using Federated Deep Reinforcement Learning

引用

IEEE Transactions on Mobile computing 2025年

作者： Chen, Xing Xiao, Bohuai Lin, Xinyu Chen, Zheyi Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile Edge computing (MEC) distributes resources such as computing, storage, and bandwidth to the side close to users, which can provide low-latency services to in-vehicle users, thus promising a more efficient and safer driving environment. However, due to the dynamic scale of vehicle and the variability of resource requirements, it is a significant challenge to quickly obtain effective task offloading in large-scale vehicle scenarios. The existing studies generally adopt the centralized decision-making method, with long decision-making time and high computational overhead, which cannot effectively achieve good offloading decisions in large-scale scenarios. To address these problems, we propose a Multi-agent Collaborative Method for vehicular task offloading using Federated Deep Reinforcement Learning called MCM-FDRL. First, each vehicle as an agent, independently makes offloading decisions based on local information. Next, the offloading decision model of each vehicle is obtained through federated reinforcement learning training. At runtime, an effective vehicle offloading plan can be gradually developed through multi-agent collaboration. Using two real-world datasets, experiments show that the MCM-FDRL has good adaptability and scalability. Moreover, compared to the state-of-the-art methods, the task's average response time of the MCM-FDRL is reduced by 9.75%-64.90%, respectively. © 2002-2012 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Efficient Error-Bounded Lossy Compression for CPU Architectures

Efficient Error-Bounded Lossy Compression for CPU Architectu...

引用

International Symposium on Modeling, Analysis and Simulation of computer and Telecommunication Systems (MASCOTS)

作者： Griffin Dube Jiannan Tian Sheng Di Dingwen Tao Jon C. Calhoun Franck Cappello Department of Computer Science Northwestern University Evanston IL Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN Holcombe Department of Electrical and Computing Engineering Clemson University Clemson SC Mathematics and Computer Science Division Argonne National Laboratory Lemont IL

Modern HPC applications produce increasingly large amounts of data, which limits the performance of current extreme-scale systems. Lossy compression, helps to mitigate this issue by decreasing the size of data generated by these applications. SZ, a current state-of-the-art lossy compressor, is able to achieve high compression ratios, but its prediction/quantization methods contain RAW dependencies that prevent parallelizing this step of the compression. Recent work proposes a parallel dual prediction/quantization algorithm for GPUs which removes these dependencies. However, some HPC systems and applications do not use GPUs, and could still benefit from the fine-grained parallelism of this method. Using the dual-quantization technique, we implement and optimize a SIMD vectorized CPU version of SZ (vecSZ), and create a heuristic for selecting the optimal block size and vector length. We propose a novel block padding algorithm to decrease the number of unpredictable values along compression block borders and find it reduces the number of prediction outliers by up to 100%. We measure performance of our vecSZ against an CPU version of SZ using dual-quantization, pSZ, as well as SZ-1.4. Using real-world scientific datasets, we evaluate vecSZ on the Intel Skylake and AMD Rome architectures. vecSZ results in up to 32% improvement in rate-distortion and up to 15× speedup over SZ-1.4, achieving a prediction and quantization bandwidth in excess of 3.4 GB/s.

关键词： Analytical models Quantization (signal) Computational modeling Rate-distortion computer architecture Bandwidth Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

arXiv

引用

arXiv 2024年

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado SpringsCO United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China JinYinHu Laboratory Wuhan China

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

来源：评论

学校读者我要写书评

暂无评论

Maximizing the Thermal Performance of Microheaters for Non-Volatile Phase Change Photonics: A Comparative Study of Pulse Width Parameter Effects 23

Maximizing the Thermal Performance of Microheaters for Non-V...

引用

23rd IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITherm 2024

作者： F., Vasquez-Aza H., Sun C., Lian Y.-S., Huang S.A., Vitale I., Takeuchi J., Hu N., Youngblood C.A.R., Ocampo G., Pavlidis University of Connecticut School of Mechanical Aerospace and Manufacturing Engineering StorrsCT06269 United States University of Maryland Department of Materials Science and Engineering College ParkMD20742 United States University of Maryland Institute for Research in Electronics and Applied Physics College ParkMD20742 United States Mit Lincoln Laboratory Advanced Materials and Microsystems Group LexingtonMA02139 United States University of Maryland Quantum Materials Center Department of Physics College ParkMD20742 United States Massachusetts Institute of Technology Department of Materials & Science Engineering CambridgeMA02139 United States The University of Pittsburgh Electrical & Computer Engineering Department PittsburghPA15213 United States

ISBN: (数字)9798350364330

ISBN: (纸本)9798350364330

Guiding, manipulating, and detecting light is integral to modern photonic systems that can be designed for displays, memory, data transfer, and sensors. The prospect of realizing programmable yet zero-static power photonics has recently gained attention thanks to the rise of optical phase change materials (PCMs). PCMs have a unique set of properties that make them desirable for this task: nonvolatile retention and reversible switching between their amorphous and crystalline states, which, in addition, display distinct optical and electronic properties. Nonvolatile photonic devices have primarily relied on either electrical or optical switching of PCMs. electrical switching offers effortless CMOS integration;however, it is predominantly limited to binary output. In contrast, optical switching enables accurate multilevel control but is limited in scalability. This paper studies an alternative electro-thermal approach to nonvolatile photonics by controlling the PCM phase via doped silicon microheaters. The results highlight the significance of precise spatial and temporal temperature control to ensure optimal modulation of the PCM. Specifically, this paper investigates the impact of the electric pulse width (0.2 μs to 10 μs) on the maximum temperature for two different microheater designs. High resolution (≈410 nm) transient thermoreflectance imaging (TTI) is used to measure and analyze the surface temperature distribution across each microheater under pulsed biasing. To assess the impact of varying pulse widths, this study systematically examines their effects on maximum temperature, response time, and maximum power. Shorter pulse widths ( 1 μs) provide greater thermal stability but exhibit reduced maximum temperatures due to limited current levels. The experimental results demonstrate a nonlinear relationship between the peak temperature and the power supplied at sub-steady state thermalization time scales. For the specific microheater geometry, the optimal pulse wid

关键词： Photonic integration technology

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：