检索结果-内蒙古大学图书馆

Large Scale Deterministic Creation of Single Photon Emitters in Silicon Nitride Nanopillars

学校读者我要写书评

暂无评论

Large Scale Deterministic Creation of Single Photon Emitters...

Conference on Lasers and Electro-Optics (CLEO)

作者： Samuel Peana Omer Yesilyurt Vahagn Mkhitaryan Alexander Senichev Zachariah O. Martin Alexei S. Lagutchev Alexandra Boltasseva Vladimir M. Shalaev Birck Nanotechnology Center Purdue University West Lafayette IN USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Purdue Quantum Science and Engineering Institute Purdue University West Lafayette IN USA a National Quantum Information Science Research Center of the U.S. Department of Energy Quantum Science Center Oak Ridge TN USA

We demonstrated large scale deterministic creation of single photon emitters in annealed silicon nitride on silicon oxide pillars. The estimated single photon emitter yield is approximately 50% with a lateral accuracy... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Laser theory Stimulated emission Optical device fabrication Silicon nitride Plasmons Silicon Nanostructures

A Survey on Large Language Models for Communication, network, and Service Management: Application Insights, Challenges, and Future Directions

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Boateng, Gordon Owusu Sami, Hani Alagha, Ahmed Elmekki, Hanae Hammoud, Ahmad Mizouni, Rabeb Mourad, Azzam Otrok, Hadi Bentahar, Jamal Muhaidat, Sami Talhi, Chamseddine Dziong, Zbigniew Guizani, Mohsen Department of Software and IT Engineering Ecole de Technologie Superieure Montreal Canada Artificial Intelligence & Cyber Systems Research Center Department of CSM Lebanese American University Beirut Lebanon Department of Electrical Engineering Ecole de Technologie Superieure Montreal Canada University Beirut Lebanon Department of CS Khalifa University Abu Dhabi United Arab Emirates KU 6G Research Center Department of CS Khalifa University United Kingdom Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada KU 6G Research Center Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Department of ML Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

The rapid evolution of communication networks in recent decades has intensified the need for advanced network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM. © 2024, CC BY-NC-SA.

关键词： Mobile telecommunication systems

Benchmarks for a Global Extraction of information from Deeply Virtual Exclusive Scattering

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Almaeen, Manal Grigsby, Jake Hoskins, Joshua Kriesten, Brandon Li, Yaohang Lin, Huey-Wen Liuti, Simonetta Department of Computer Science Old Dominion University NorfolkVA23529 United States Department of Computer Science College of Computer and Information Sciences Jouf University Aljouf Sakaka72341 Saudi Arabia Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Physics University of Virginia CharlottesvilleVA22904 United States Center for Nuclear Femtography WashingtonDC20005 United States Department of Physics Michigan State University East LansingMI48824 United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States

We develop a framework to establish benchmarks for machine learning and deep neural networks analyses of exclusive scattering cross sections (FemtoNet). Within this framework we present an extraction of Compton form factors for deeply virtual Compton scattering from an unpolarized proton target. Critical to this effort is a study of the effects of physics constraints built into machine learning (ML) algorithms. We use the Bethe-Heitler process, which is the QED radiative background to deeply virtual Compton scattering, to test our ML models and, in particular, their ability to generalize information extracted from data. We then use these techniques on the full cross section and compare the results to analytic model calculations. We propose a quantification technique, the random targets method, to begin understanding the separation of aleatoric and epistemic uncertainties as they are manifest in exclusive scattering analyses. We propose a set of both physics driven and machine learning based benchmarks providing a stepping stone towards applying explainable machine learning techniques with controllable uncertainties in a wide range of deeply virtual exclusive processes. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

Self-Supervised Multi-LiDAR Object View Generation Using Single LiDAR

学校读者我要写书评

暂无评论

Self-Supervised Multi-LiDAR Object View Generation Using Sin...

IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

作者： Yi-Hung Kuo Chi-Sheng Shih Hsiang-Jui Lin Shih-Hao Hung Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan High Performance and Scientific Computing Center National Taiwan University Taipei Taiwan Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi

3D object detection using point clouds has been widely used for self-driving vehicles, roadside vehicle detection, and tracking. However, the detection accuracy using single-LiDAR-scanned point clouds suffers from occlusion to have accurate bounding boxes. Several previous works proposed compensating for the point cloud to improve 3D detection. This work designed a self-labeling method that exploits the characteristics of multiple LiDAR-scanned point clouds collected on roadside units. This labeling method allows the point completion model to be trained without human-labeled bounding boxes. The point completion model uses the single-LiDAR-scanned point cloud as input and uses the extracted object points from the fused point cloud as the training target. The voxel IoU and recall of the point-completed point clouds increased by 17% and 22% compared to that of the raw point clouds. Additionally, the detection performance of DBSCAN, i.e., 3D AP@IoU=0.25 increased by 20% after applying the point completion algorithm.

关键词：

Cross-Domain Dual-Functional OFDM Waveform Design for Accurate Sensing/Positioning

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhang, Fan Mao, Tianqi Liu, Ruiqi Han, Zhu Chen, Sheng Wang, Zhaocheng Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China Tsinghua Shenzhen International Graduate School Shenzhen518055 China The State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China The MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing100081 China The Wireless and Computing Research Institute ZTE Corporation Beijing100029 China The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Orthogonal frequency division multiplexing (OFDM) has been widely recognized as the representative waveform for 5G wireless networks, which can directly support sensing/positioning with existing infrastructure. To guarantee superior sensing/positioning accuracy while supporting high-speed communications simultaneously, the dual functions tend to be assigned with different resource elements (REs) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for effective convergence of OFDM-based communications and sensing/positioning, following communication- and sensing-centric criteria, respectively. For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communications according to prior knowledge of the communication channel. The remaining REs are then employed for sensing/positioning, where the sidelobe level and peak-to-average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics for sensing performance improvement. For the sensing-centric design, a 'locally' perfect auto-correlation property is ensured for accurate sensing and positioning by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed waveform designs. Copyright © 2023, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

Trap Characterization of InGaN/GaN Blue Light Emitting Diode Grown on Si Substrate

学校读者我要写书评

暂无评论

Trap Characterization of InGaN/GaN Blue Light Emitting Diode...

2021 Asia Communications and Photonics Conference, ACP 2021

作者： Chen, Jiaxiang Qu, Haolan Qi, Longheng Liu, Yaying Zhang, Xu Lau, Kei May Zou, Xinbo School of Information Science and Technology ShanghaiTech University Shanghai201210 China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China University of Chinese Academy of Sciences Beijing100049 China Shanghai Engineering Research Center of Energy Efficient and Custom AI IC Shanghai200031 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Three hole traps and one electron trap were revealed for InGaN/GaN blue LEDs grown on Si by a combination of DLTS and ICTS. Time-resolved thermal-enhanced emission process of deep hole trap was investigated. © Op... 详细信息

ISBN: (纸本)9781957171005

关键词： III-V semiconductors

Virtual Z gates and symmetric gate compilation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Vezvaee, Arian Tripathi, Vinay Kowsari, Daria Levenson-Falk, Eli Lidar, Daniel A. Department of Electrical & Computer Engineering University of Southern California Los AngelesCA90089 United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Department of Physics & Astronomy University of Southern California Los AngelesCA90089 United States Department of Chemistry University of Southern California Los AngelesCA90089 United States

The virtual Z gate has been established as an important tool for performing quantum gates on various platforms, including but not limited to superconducting systems. Many such platforms offer a limited set of calibrated gates and compile other gates, such as the Y gate, using combinations of X and virtual Z gates. Here, we show that the method of compilation has important consequences in an open quantum system setting. Specifically, we experimentally demonstrate that it is crucial to choose a compilation that is symmetric with respect to virtual Z rotations. This is particularly pronounced in dynamical decoupling (DD) sequences, where improper gate decomposition can result in unintended effects such as the implementation of the wrong sequence. Our findings indicate that in many cases the performance of DD is adversely affected by the incorrect use of virtual Z gates, compounding other coherent pulse errors. In addition, we identify another source of coherent errors: interference between consecutive pulses that follow each other too closely. This work provides insights into improving general quantum gate performance and optimizing DD sequences in particular. © 2024, CC BY.

关键词： Quantum optics

Efficient Chromatic-Number-Based Multi-Qubit Decoherence and Crosstalk Suppression

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Brown, Amy F. Lidar, Daniel A. Department of Physics & Astronomy University of Southern California Los AngelesCA90089 United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Department of Electrical & Computer Engineering University of Southern California Los AngelesCA90089 United States Department of Chemistry University of Southern California Los AngelesCA90089 United States

The performance of quantum computers is hindered by decoherence and crosstalk, which cause errors and limit the ability to perform long computations. Dynamical decoupling is a technique that alleviates these issues by applying carefully timed pulses to individual qubits, effectively suppressing unwanted interactions. However, as quantum devices grow in size, it becomes increasingly important to minimize the time required to implement dynamical decoupling across the entire system. Here, we present "Chromatic-Hadamard Dynamical Decoupling" (CHaDD), an approach that efficiently schedules dynamical decoupling pulses for quantum devices with arbitrary qubit connectivity. By leveraging Hadamard matrices, CHaDD achieves a circuit depth that scales linearly with the chromatic number of the connectivity graph for general two-qubit interactions, assuming instantaneous pulses. This includes ZZ crosstalk, which is prevalent in superconducting QPUs. CHaDD’s scaling represents an exponential improvement over all previous multi-qubit decoupling schemes for devices with connectivity graphs whose chromatic number grows at most polylogarithmically with the number of qubits. For graphs with a constant chromatic number, CHaDD’s scaling is independent of the number of qubits. We report on experiments we have conducted using IBM QPUs that confirm the advantage conferred by CHaDD. Our results suggest that CHaDD can become a useful tool for enhancing the performance and scalability of quantum computers by efficiently suppressing decoherence and crosstalk across large qubit arrays. © 2024, CC BY.

关键词： Qubits

CM-HTNet: CNN-Mamba-based Framework for Predicting Hemorrhagic Transformation Risk of AIS Patients using Sequence Relationship Modeling and Multi-Modal Cross Attention

学校读者我要写书评

暂无评论

CM-HTNet: CNN-Mamba-based Framework for Predicting Hemorrhag...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuxiang Huang Huanhuan Ren Yongmei Li Jingfeng Jiang Haodong Xu Jinwang Feng Shaoguo Cui College of Computer and Information Science Chongqing Normal University China Department of Radiology Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital Chongqing University China Department of Radiology the First Affiliated Hospital of Chongqing Medical University China Center for Biocomputing and Digital Health Institute of Computing and Cybernetics and Health Research Institute Michigan Technological University USA Department of Biomedical Engineering Michigan Technological University USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Hemorrhagic transformation (HT) is a severe complication of acute ischemic stroke (AIS) that can lead to disability or death. Accurate and timely risk assessment of HT is essential for clinicians to design effective treatment strategies. Previous studies in HT prediction have largely relied on machine learning and radiomics, which demand extensive manual data preprocessing by physicians. While some HT prediction models based on convolutional neural network (CNN) have been developed, they are limited in their ability to capture the sequential relationships between image slices and often lack the focus on crucial information. This study collected non-contrast computed tomography (NCCT) images and clinical data from 512 AIS patients across six hospitals to create a multi-center dataset. Based on the dataset, we propose CM-HTNet, a novel deep learning framework designed to predict HT risk in AIS patients following intravenous thrombolysis (IVT). CM-HTNet mimics the clinical process of reviewing NCCT images by focusing on key slices and integrating information from adjacent slices. It leverages CNNs to extract features from each NCCT slice and utilizes the Selective State-Space Model (SSM) within the Mamba framework to model sequential relationships between slices while prioritizing features relevant to HT prediction. Additionally, CM-HTNet incorporates the Neighborhood Rough Set (KRS) algorithm for clinical feature selection and cross-attention mechanisms to integrate clinical and imaging data for multimodal HT risk prediction. In testing on an external dataset from independent centers, CM-HTNet achieved a prediction accuracy of 88.85% and an AUC of 95.17%, showcasing its strong performance and generalization capabilities.

关键词： Accuracy Computed tomography Computational modeling Biological system modeling Predictive models Feature extraction Data models Convolutional neural networks Hemorrhaging Testing