检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Yang, Xian Wang, Shuo Xing, Yuting Li, Ling Xu, Richard Yi Da Friston, Karl J. Guo, Yike Department of Computer Science Hong Kong Baptist University Hong Kong Special Administrative Region Hong Kong Data Science Institute Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China School of Computing University of Kent United Kingdom Faculty of Engineering and Information Technology University of Technology Sydney Australia Institute of Neurology University College London United Kingdom

The evolution of epidemiological parameters, such as instantaneous reproduction number Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to Rt estimation, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt;the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes;the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in revealing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for accurate and timely estimating transmission dynamics from reported data. Copyright © 2020, The Authors. All rights reserved.

关键词： Cell proliferation

来源：评论

学校读者我要写书评

暂无评论

TagSheet: Sleeping Posture Recognition with an Unobtrusive Passive Tag Matrix

TagSheet: Sleeping Posture Recognition with an Unobtrusive P...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Jia Liu Xingyu Chen Shigang Chen Xiulong Liu Yanyan Wang Lijun Chen State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer & Information Science & Engineering University of Florida USA Department of Computing The Hong Kong Polytechnic University China

Sleep monitoring plays an important role in many medical applications, including SIDS prevention, care of patients with pressure ulcers, and assistance to patients with sleep apnea, where studies have shown that autonomous and continuous monitoring of sleep postures provides useful information for lowering health risk. Existing systems are designed based on electrocardiogram, cameras and pressure sensors, which are expensive to deploy, intrusive to privacy, or uncomfortable to use. This paper presents TagSheet, the first sleep monitoring system based on passive RFID tags, which provides a convenient, non-intrusive, and comfortable way of monitoring the sleeping postures. It does not require attaching any tag directly to a patient's body. Tags are taped under a bed sheet. With a combination of hierarchical recognition, image processing and polynomial fitting, the proposed system identifies body postures based on the observed variation caused by the patient body to the backscattered signals from tags. The system does not require any personalized data training, making it plug-n-play in use. One additional advantage is that the system can also estimate the patient's respiration rate. This is particularly helpful in assisting patients with sleep apnea. We have implemented a prototype system, and experiments show that the system performs posture identification with an accuracy up to 96.7% and in the meantime it measures the respiration rate with a small error of about 0.7 bpm (breath per minute).

关键词： Sleep apnea Monitoring Diversity reception Cameras Pressure sensors Training Pediatrics

来源：评论

学校读者我要写书评

暂无评论

Generalized WKB theory for electron tunneling in gapped α−T3 lattices

引用

Physical Review B 2021年第16期103卷 165429-165429页

作者： Nicholas Weekes Andrii Iurov Liubov Zhemchuzhna Godfrey Gumbs Danhong Huang Department of Physics and Computer Science Medgar Evers College of City University of New York Brooklyn New York 11225 USA Department of Information Systems and Cybersecurity Grove School of Engineering The City College of New York 275 Convent Avenue New York New York 10031 USA Department of Physics and Astronomy Hunter College of the City University of New York 695 Park Avenue New York New York 10065 USA Donostia International Physics Center (DIPC) P de Manuel Lardizabal 4 20018 San Sebastian Basque Country Spain Space Vehicles Directorate US Air Force Research Laboratory Kirtland Air Force Base New Mexico 87117 USA Center for High Technology Materials University of New Mexico 1313 Goddard SE Albuquerque New Mexico 87106 USA

We generalize Wentzel-Kramers-Brillouin (WKB) semiclassical equations for pseudospin-1 α−T3 materials with arbitrary hopping parameter 0<α<1, which includes the dice lattice and graphene as two limiting cases. In conjunction with a series-expansion method in powers of Planck constant ℏ, we acquired and solved a system of recurrent differential equations for semiclassical electron wave functions in α−T3. Making use of these obtained wave functions, we analyzed the physics-related mechanism and quantified the transmission of pseudospin-1 Dirac electrons across nonrectangular potential barriers in α−T3 materials with both zero and finite band gaps. Our studies reveal several unique features, including the way in which the electron transmission depends on the energy gap, the slope of the potential barrier profile and the transverse momentum of incoming electrons. Specifically, we have found a strong dependence of the obtained transmission amplitude on the geometry-phase ϕ=tan−1α of α−T3 lattices. We believe our current findings can be applied to Dirac cone-based tunneling transistors in ultrafast analog RF devices, as well as to tunneling-current control by a potential barrier through a one-dimensional array of scatters.

关键词： Ballistic transport Electronic structure Transport phenomena Semiclassical methods

来源：评论

学校读者我要写书评

暂无评论

Interactive Protocols for Classically-Verifiable Quantum Advantage

arXiv

引用

arXiv 2021年

作者： Zhu, Daiwei Kahanamoku-Meyer, Gregory D. Lewis, Laura Noel, Crystal Katz, Or Harraz, Bahaa Wang, Qingfeng Risinger, Andrew Feng, Lei Biswas, Debopriyo Egan, Laird Gheorghiu, Alexandru Nam, Yunseong Vidick, Thomas Vazirani, Umesh Yao, Norman Y. Cetina, Marko Monroe, Christopher Joint Quantum Institute Departments of Physics and Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Department of Physics University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Computing and Mathematical Sciences California Institute of Technology CA91125 United States Division of Physics Mathematics and Astronomy California Institute of Technology CA91125-0001 United States Duke Quantum Center Department of Physics Duke University DurhamNC27708 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States IonQ Inc. College ParkMD20740 United States Institute for Theoretical Studies ETH Zürich CH 8001 Switzerland Chemical Physics Program Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Departments of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States

Interaction is a powerful resource for quantum computation. It can be utilized in applications ranging from the verification of quantum algorithms all the way to verifying quantum mechanics itself. Here, we present the first implementation of interactive protocols for proofs of quantumness — which when suitably scaled promise the efficient verification of quantum computational advantage. The key feature that distinguishes these protocols from existing demonstrations of quantum advantage is that the classical verification is efficient and scales polynomially rather than exponentially with the number of qubits. The experimental implementation of such interactive protocols requires the ability to independently measure subsets of qubits in the middle of a quantum circuit and to continue coherent evolution afterwards. This is achieved by spatially isolating target qubits via shuttling, and opens the door to a range of quantum interactive protocols as well as new information processing architectures. Copyright © 2021, The Authors. All rights reserved.

关键词： Qubits

来源：评论

学校读者我要写书评

暂无评论

Fusion of metadata and dermoscopic images for melanoma detection: Deep learning and feature importance analysis

引用

information Fusion 2025年 124卷

作者： Misbah Ahmad Imran Ahmed Abdellah Chehri Gwangill Jeon Centre for Machine Vision Bristol Robotics Laboratory University of West of England Frenchy Campus Bristol BS16 1QY Bristol UK Department of Animal and Agriculture Hartpury University Hartpury House Gloucester GL19 3BE Gloucestershire UK School of Computing and Information Science Anglia Ruskin University East Road Cambridge CB1 1PT Cambridgeshire UK Department of Mathematics and Computer Science Royal Military College of Canada (RMC) P.O. Box 17000 Station Forces Kingston K7K 7B4 Ontario Canada Department of Embedded Systems Engineering Incheon National University 119 Academy-ro Incheon 22012 Incheon Republic of Korea

In the era of smart healthcare, integrating multimodal data is essential for improving diagnostic accuracy and enabling personalized care. This study presented a deep learning-based multimodal approach for melanoma detection, leveraging both dermoscopic images and clinical metadata to enhance classification performance. The proposed model integrated a multi-layer convolutional neural network (CNN) to extract image features and combined them with structured metadata, including patient age, gender, and lesion location, through feature-level fusion. The fusion process occurred at the final CNN layer, where high-dimensional image feature vectors were concatenated with processed metadata. The metadata was handled separately through a fully connected neural network comprising multiple dense layers. The final fused representation was passed through additional dense layers, culminating in a classification layer that outputted the probability of melanoma presence. The model was trained end-to-end using the SIIM-ISIC dataset, allowing it to learn a joint representation of image and metadata features for optimal classification. Various data augmentation techniques were applied to dermoscopic images to mitigate class imbalance and improve model robustness. Additionally, exploratory data analysis (EDA) and feature importance analysis were conducted to assess the contribution of each metadata feature to the overall classification. Our fusion-based deep learning architecture outperformed single-modality models, boosting classification accuracy. The presented model achieved an accuracy of 94.5% and an overall F1-score of 0.94, validating its effectiveness in melanoma detection. This study aims to highlight the potential of deep learning-based multimodal fusion in enhancing diagnostic precision, offering a scalable and reliable solution for improved melanoma detection in smart healthcare systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

LEARN++: Recurrent dual-domain reconstruction network for compressed sensing CT

arXiv

引用

arXiv 2020年

作者： Zhang, Yi Chen, Hu Xia, Wenjun Chen, Yang Liu, Baodong Liu, Yan Sun, Huaiqiang Zhou, Jiliu College of Computer Science Sichuan University Chengdu610065 China Laboratory of Image Science and Technology Southeast University Nanjing210096 China Beijing Engineering Research Center of Radiographic Techniques and Equipment Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China School of Electrical Engineering Information Sichuan University Chengdu610065 China Department of Radiology West China Hospital of Sichuan University Chengdu610041 China

Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis and interior tomography. Traditional compressed sensing focuses on the design of handcrafted prior regularizers, which are usually image-dependent and time-consuming. Inspired by recently proposed deep learning-based CT reconstruction models, we extend the state-of-the-art LEARN model to a dual-domain version, dubbed LEARN++. Different from existing iteration unrolling methods, which only involve projection data in the data consistency layer, the proposed LEARN++ model integrates two parallel and interactive subnetworks to perform image restoration and sinogram inpainting operations on both the image and projection domains simultaneously, which can fully explore the latent relations between projection data and reconstructed images. The experimental results demonstrate that the proposed LEARN++ model achieves competitive qualitative and quantitative results compared to several state-of-the-art methods in terms of both artifact reduction and detail preservation. © 2020, CC BY.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy

arXiv

引用

arXiv 2024年

作者： Iyer, Kartheik G. Yunus, Mikaeel O’Neill, Charles Ye, Christine Hyk, Alina McCormick, Kiera Ciucă, Ioana Wu, John F. Accomazzi, Alberto Astarita, Simone Chakrabarty, Rishabh Cranney, Jesse Field, Anjalie Ghosal, Tirthankar Ginolfi, Michele Huertas-Company, Marc Jablońska, Maja Kruk, Sandor Liu, Huiling Marchidan, Gabriel Mistry, Rohit Naiman, J.P. Peek, J.E.G. Polimera, Mugdha Rodríguez Méndez, Sergio J. Schawinski, Kevin Sharma, Sanjib Smith, Michael J. Ting, Yuan-Sen Walmsley, Mike Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States Department of Physics and Astronomy Johns Hopkins University 3400 N. Charles Street BaltimoreMD21218 United States School of Computing TheAustralian National University 108 North Rd ActonACT2601 Australia Stanford University 450 Jane Stanford Way StanfordCA94305 United States School of Electrical Engineering and Computer Science Oregon State University 1500 SW Jefferson Way CorvallisOR97331 United States Department of Engineering Loyola University Maryland 4501 North Charles Street BaltimoreMD21210 United States Research School of Astronomy & Astrophysics Australian National University Cotter Rd. WestonACT2611 Australia School of Computing Australian National University ActonACT2601 Australia Kavli Institute for Particle Astrophysics and Cosmology Department of Physics Stanford University StanfordCA94305 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States Camino Bajo del Castillo s/n Villanueva de la Cañada Madrid28692 Spain Paris France Astralis-AITC - Stromlo RSAA Australian National University Cotter Road WestonACT2600 Australia Department of Computer Science Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Dipartimento di Fisica e Astronomia Universita di Firenze Via G. Sansone 1 Sesto Fiorentino Firenze50019 Italy INAF - Osservatorio Astrofisico di Arcetri Largo E. Fermi 5 FirenzeI-50125 Italy Instituto de Astrofisica de Canarias C. Via Lactea 1 Tenerife La LagunaE-38205 Spain Universidad de la Laguna dept. Astrofisica Tenerife La LagunaE-38206 Spain Universite Paris-Cite LERMA - Observatoire de Paris PSL Paris France SCIPP University of

The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with key-words. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use key-words or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool’s versatility through case studies, showcasing its application in various research scenarios. The system’s performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature. © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A smart pain management system using big data computing 1st

引用

1st International Conference on Infrastructure, Technologies and Applications, SCITA 2017

作者： Al Shehri, Waleed Mehmood, Rashid Alayyaf, Hassan Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia High Performance Computing Center King Abdulaziz University Jeddah Saudi Arabia Al Hada Military Hospital Taif Saudi Arabia

ISBN: (纸本)9783319941790

Pain is a universal experience and there is hardly a human being who has never experienced pain at one time or another. Managing pain is of high priority for healthcare organizations given the increasing incidence of pain among patients and the costs associated with it. The current standards and practices in pain management are limited to mostly manual processes hindering innovations in this area. This paper proposes a smart pain management system based on big data computing technologies. The system devises pain management strategies based on the relevant standards and patients’ data, and these strategies are identified, applied, and monitored by the system in real-time. A perpetual feedback loop is created among the system components and the outcomes are communicated to the stakeholders to enable reflections and continuous improvements in the pain management standards, strategies, and processes. The system architecture and its architectural components are described. A preliminary analysis is provided using handwritten and digital pain management related data. © 2018, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Generating ultradense pair beams using 400 GeV/c protons

引用

Physical Review Research 2021年第2期3卷 023103-023103页

作者： C. D. Arrowsmith N. Shukla N. Charitonidis R. Boni H. Chen T. Davenne A. Dyson D. H. Froula J. T. Gudmundsson B. T. Huffman Y. Kadi B. Reville S. Richardson S. Sarkar J. L. Shaw L. O. Silva P. Simon R. M. G. M. Trines R. Bingham G. Gregori Department of Physics University of Oxford Parks Road Oxford OX1 3PU United Kingdom CINECA High-Performance Computing Department Via Magnanelli 6/3 40033 Casalecchio di Reno - Bologna Italy European Organization for Nuclear Research (CERN) CH-1211 Geneva 23 Switzerland University of Rochester Laboratory for Laser Energetics Rochester New York 14623 USA Lawrence Livermore National Laboratory 7000 East Avenue Livermore California 94550 USA Rutherford Appleton Laboratory Chilton Didcot OX11 0QX United Kingdom Science Institute University of Iceland Dunhaga 3 IS-107 Reykjavik Iceland Department of Space and Plasma Physics School of Electrical Engineering and Computer Science KTH Royal Institute of Technology SE-100 44 Stockholm Sweden Max-Planck-Institut für Kernphysik Saupfercheckweg 1 D-69117 Heidelberg Germany Atomic Weapons Establishment Aldermaston Reading Berkshire RG7 4PR United Kingdom GoLP/Instituto de Plasmas e Fusão Nuclear Instituto Superior Técnico Universidade de Lisboa 1049-001 Lisboa Portugal Department of Physics University of Strathclyde Glasgow G4 0NG United Kingdom

An experimental scheme is presented for generating low-divergence, ultradense, relativistic, electron-positron beams using 400 GeV/c protons available at facilities such as HiRadMat and AWAKE at CERN. Preliminary Monte Carlo and particle-in-cell simulations demonstrate the possibility of generating beams containing 1013–1014 electron-positron pairs at sufficiently high densities to drive collisionless beam-plasma instabilities, which are expected to play an important role in magnetic field generation and the related radiation signatures of relativistic astrophysical phenomena. The pair beams are quasineutral, with size exceeding several skin depths in all dimensions, allowing the examination of the effect of competition between transverse and longitudinal instability modes on the growth of magnetic fields. Furthermore, the presented scheme allows for the possibility of controlling the relative density of hadrons to electron-positron pairs in the beam, making it possible to explore the parameter spaces for different astrophysical environments.

关键词： Beam instabilities Cosmic ray acceleration Cosmic ray composition & spectra Cosmic ray sources Gamma ray astronomy Gamma ray bursts high-energy-density plasmas laboratory studies of space & astrophysical plasmas Optical, UV, & IR astronomy Plasma instabilities Plasma-beam interactions Relativistic multiple-particle dynamics

来源：评论

学校读者我要写书评

暂无评论

Preface

Advances in Intelligent Systems and Computing

引用

Advances in Intelligent Systems and computing 2021年 1262卷 xv-xvi页

作者： Giri, Debasis Buyya, Rajkumar Ponnusamy, S. De, Debashis Adamatzky, Andrew Hussien, Jemal Department of Information Technology Maulana Abul Kalam Azad University of Technology HaringhataWest Bengal India School of Computing and Information Systems University of Melbourne MelbourneVIC Australia Department of Mathematics Indian Institute of Technology Madras Chennai India Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology HaringhataWest Bengal India Unconventional Computing Laboratory Department of Computer Science and Creative Technologies University of the West of England Bristol United Kingdom Faculty of Science Engineering and Built Environment Deakin University Geelong GeelongVIC Australia

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：