检索结果-内蒙古大学图书馆

DeepSeq2Drug: An expandable ensemble end-to-end anti-viral drug repurposing benchmark framework by multi-modal embeddings and transfer learning

引用

computers in Biology and Medicine 2024年 175卷 108487-108487页

作者： Xie, Weidun Yu, Jixiang Huang, Lei For, Lek Shyuen Zheng, Zetian Chen, Xingjian Wang, Yuchen Liu, Zhichao Peng, Chengbin Wong, Ka-Chun Department of Computer Science City University of Hong Kong Hong Kong SAR Kowloon Tong China Cutaneous Biology Research Center Massachusetts General Hospital Harvard Medical School BostonMA United States Sir William Dunn School of Pathology University of Oxford United Kingdom College of Information Science and Engineering Ningbo University Ningbo China Shenzhen Research Institute City University of Hong Kong Shenzhen China Hong Kong Institute for Data Science City University of Hong Kong Hong Kong SAR Kowloon Tong China

Drug repurposing is promising in multiple scenarios, such as emerging viral outbreak controls and cost reductions of drug discovery. Traditional graph-based drug repurposing methods are limited to fast, large-scale virtual screens, as they constrain the counts for drugs and targets and fail to predict novel viruses or drugs. Moreover, though deep learning has been proposed for drug repurposing, only a few methods have been used, including a group of pre-trained deep learning models for embedding generation and transfer learning. Hence, we propose DeepSeq2Drug to tackle the shortcomings of previous methods. We leverage multi-modal embeddings and an ensemble strategy to complement the numbers of drugs and viruses and to guarantee the novel prediction. This framework (including the expanded version) involves four modal types: six NLP models, four CV models, four graph models, and two sequence models. In detail, we first make a pipeline and calculate the predictive performance of each pair of viral and drug embeddings. Then, we select the best embedding pairs and apply an ensemble strategy to conduct anti-viral drug repurposing. To validate the effect of the proposed ensemble model, a monkeypox virus (MPV) case study is conducted to reflect the potential predictive capability. This framework could be a benchmark method for further pre-trained deep learning optimization and anti-viral drug repurposing tasks. We also build software further to make the proposed model easier to reuse. The code and software are freely available at http://***. © 2024

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Federated Edge Learning via Integrated Sensing, Computation, and Communication

Federated Edge Learning via Integrated Sensing, Computation,...

引用

IEEE International Conference on Communications (ICC)

作者： Peixi Liu Guangxu Zhu Shuai Wang Miaowen Wen Wu Luo H. Vincent Poor Shuguang Cui State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Shenzhen Research Institute of Big Data Chinese Academy of Sciences Shenzhen Institute of Advanced Technology School of Electronic and Information Engineering South China University of Technology Department of Electrical and Computer Engineering Princeton University the Guangdong Provincial Key Laboratory of Future Networks of Intelligence School of Science and Engineering (SSE) Future Network of Intelligence Institute (FNii) the Chinese University of Hong Kong Shenzhen

Sensing, computation, and communication (SC 2 ) are highly coupled processes in federated edge learning (FEEL) and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC 2 resources jointly affect the FEEL performance. In this paper, we address the problem of joint SC 2 resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Specifically, the joint SC 2 resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time and energy supply of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determining a joint sensing and communication resource allocation that maximizes the total number of samples sensed during the entire training process; the second one concerns the partition of the total number of sensed samples over communication rounds to determine the batch size at each round for convergence speed maximization. Finally, extensive simulation results are provided to validate the superiority of the proposed scheme over several baseline schemes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Distributed Privacy Preservation Approach for Big Data in Public Health Emergencies Using Smart Contract and SGX

引用

computers, Materials & Continua 2020年第10期65卷 723-741页

作者： Jun Li Jieren Cheng Naixue Xiong Lougao Zhan Yuan Zhang Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570228China School of Compute Science and Cyberspace Security Hainan UniversityHaikou570228China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464USA Key Laboratory of Internet Information Retrieval of Hainan Province Hainan UniversityHaikou570228China

Security and privacy issues have become a rapidly growing problem with the fast development of big data in public ***,big data faces many ongoing serious challenges in the process of collection,storage,and *** them,data security and privacy problems have attracted extensive *** an effort to overcome this challenge,this article aims to present a distributed privacy preservation approach based on smart contracts and Intel Software Guard Extensions(SGX).First of all,we define SGX as a trusted edge computing node,design data access module,data protection module,and data integrity check module,to achieve hardware-enhanced data privacy ***,we design a smart contract framework to realize distributed data access control management in a big data *** crucial role of the smart contract was revealed by designing multiple access control contracts,register contracts,and history *** control contracts provide access control methods for different users and enable static access verification and dynamic access verification by checking the user’s properties and history *** contract contains user property information,edge computing node information,the access control and history smart contract information,and provides functions such as registration,update,and *** contract records the historical behavior information of malicious users,receives the report information of malicious requestors from the access control contract,implements a misbehavior check method to determines whether the requestor has misbehavior,and returns the corresponding ***,we design decentralized system architecture,prove the security properties,and analysis to verify the feasibility of the *** demonstrate that our method can effectively improve the timeliness of data,reduce network latency,and ensure the security,reliability,and traceability of data.

关键词： SGX big data privacy protection smart contract access control

来源：评论

学校读者我要写书评

暂无评论

An Improved Online Multiple Pedestrian Tracking Based on Head and Body Detection

An Improved Online Multiple Pedestrian Tracking Based on Hea...

引用

International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Zhihong Sun Jun Chen Mithun Mukherjee Haihui Wang Dang Zhang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Technology Wuhan Institute of Technology Wuhan Hubei China School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9781665406697

Multiple Object Tracking (MOT) is an important computer vision task which has gained increasing attention due to its academic and commercial potential. Although many researchers have proposed effective method, they failed in crowd scene. The reason is that body detection and tracking is used in existing MOT methods. In crowd scene, many detections are missed detect and many overlap body bounding boxes decrease the quality of data association. To handle this issue, this paper propsed an online novel multiple pedestrians tracking, which is based on head detection. We first fuse the head and body detection to improve the detection result. Then, we use the head detection bounding box to replace the body detection bounding box for tracking. Finally, the experimental results demonstrate the effectiveness of our proposed method and achieve best performance with the state-of-the-art MOT trackers.

关键词： computer vision Fuses Benchmark testing Sensors Object tracking Task analysis

来源：评论

学校读者我要写书评

暂无评论

Modeling spatio-temporal app usage for a large user population

引用

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019年第1期3卷 1–23页

作者： Wang, Huandong Li, Yong Zeng, Sihan Wang, Gang Zhang, Pengyu Hui, Pan Jin, Depeng Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University China Department of Computer Science Virginia Tech. United States Department of Computer Science Stanford University United States Department of Computer Science and Engineering University of Helsinki Finland

With the wide adoption of mobile devices, it becomes increasingly important to understand how users use mobile apps. Knowing when and where certain apps are used is instrumental for app developers to improve app usability and for Internet service providers (ISPs) to optimize their network services. However, modeling spatio-temporal patterns of app usage has been a challenging problem due to the complicated usage behavior and the very limited personal data. In this paper, we propose a Bayesian mixture model to capture when, where and what apps are used and predict future app usage. To solve the challenge of data sparsity, we apply a hierarchical Dirichlet process to leverage the shared spatio-temporal patterns to accurately model users with insufficient data. We then evaluate our model using a large dataset of app usage traces involving 1.7 million users over 3503 apps. Our analysis shows a clear correlation between the user's location and the apps being used. Extensive evaluations show that our model can accurately predict users' future locations and app usage, outperforming the state-of-the-art algorithms by 11.7% and 11.1%, respectively. In addition, our model can be used to synthesize app usage traces that do not leak user privacy while preserving the key data statistical properties. © 2019 Association for Computing Machinery.

关键词： Large dataset

来源：评论

学校读者我要写书评

暂无评论

All-optical measurement-device-free feedforward enabling ultra-fast quantum informationprocessing

arXiv

引用

arXiv 2024年

作者： Yamashima, Taichi Kashiwazaki, Takahiro Suzuki, Takumi Nehra, Rajveer Nakamura, Tomohiro Inoue, Asuka Umeki, Takeshi Takase, Kan Asavanant, Warit Endo, Mamoru Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Atsugi Kanagawa243-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Wako Saitama351-0198 Japan

Utilizing feedforward to perform adaptive quantum operations on one entangled state according to the measurement result of the other state enables measurement-based quantum information processing (QIP). Until now, the bandwidth of feedforward in optical QIP has been limited to around 100 MHz by measurement with electronics. A potential alternative is the utilization of an optical parametric amplifier (OPA). This optical device eliminates the need for electronic measuring devices and enables all-optical broadband feedforward. In this paper, we demonstrate a variable squeezing gate with an operation bandwidth of 1.3 THz by all-optical measurement-device-free feedforward. We utilize a periodically poled lithium niobate waveguide as a broadband OPA and perform continuous phase locking in our optical system. Experimental results demonstrate that our all-optical QIP operates at a THz clock frequency, representing a major step toward the realization of an ultra-fast quantum computer. © 2024, CC BY.

关键词： Parametric amplifiers

来源：评论

学校读者我要写书评

暂无评论

network percolation reveals adaptive bridges of the mobility network response to COVID-19

arXiv

引用

arXiv 2021年

作者： Deng, Hengfang Gao, Jianxi Wang, Qi Department of Civil and Environmental Engineering Northeastern University BostonMA02115 United States Department of Computer Science and Center for Network Science and Technology Rensselaer Polytechnic Institute TroyNY12180 United States

Human mobility is crucial to understand the transmission pattern of COVID-19 on spatially embedded geographic networks. This pattern seems unpredictable, and the propagation appears unstoppable, resulting in over 350,000 death tolls in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county mobility network using 10 TB (Terabytes) trajectory data of 30 million smart devices in the U.S. in the first six months of 2020. We investigate its bound percolation by removing the weakly connected edges. The mobility network becomes vulnerable and prone to reach its criticality and thus experience surprisingly abrupt phase transitions. Despite the complex behaviors of the mobility network, we devised a novel approach to identify a small, manageable set of recurrent critical bridges, connecting the giant component and the second-largest component. These adaptive links, located across the United States, played a key role as valves connecting components in divisions and regions during the pandemic. Beyond, our numerical results unveil that network characteristics determine the critical thresholds and the bridge locations. The findings provide new insights into managing and controlling the connectivity of mobility networks during unprecedented disruptions. The work can also potentially offer practical future infectious diseases both globally and locally. © 2021, CC BY-NC-SA.

关键词： Solvents

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Constellation-based Modulation Classification under Multipath Fading Channels 11

Deep Learning for Constellation-based Modulation Classificat...

引用

11th International Conference on information and Communication Technology Convergence, ICTC 2020

作者： Huynh-The, Thien Hua, Cam-Hao Doan, Van-Sang Pham, Quoc-Viet Nguyen, Toan-Van Kim, Dong-Seong Kumoh National Institute of Technology Ict Convergence Research Center Korea Republic of Kyung Hee University Department of Computer Science and Engineering Korea Republic of Pusan National University Research Institute of Computer Information and Communication Korea Republic of Hongik University Dept. of Electronics and Computer Engineering in Graduate School Korea Republic of

ISBN: (纸本)9781728167589

Modulation classification, an intermediate step between signal detection and demodulation, is commonly deployed in many modern wireless communication systems. Although many approaches have been introduced in the last decades for identifying the modulation format of the incoming signal, they have the obstacle of mining radio characteristics for most traditional machine learning algorithms. To effectively handle this limitation, we propose an accurate modulation classification method by exploiting deep learning for being compatible with constellation diagram. A convolutional neural network (CNN), namely CRNet, is developed to proficiently learn the most relevant radio characteristics from transformed gray-scale constellation image by cross-residual connection, a novel structure for associating the intrinsic information between two processing flows specified by regular and grouped convolutional layers. Based on the experimental evaluation, CRNet achieves the classification rate of approximately 90% at +10 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel and further performs more accurately than some existing deep models for constellation-based modulation classification. © 2020 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Distinguishing severe sleep apnea from habitual snoring using a neck-wearable piezoelectric sensor and deep learning: A pilot study

引用

computers in Biology and Medicine 2025年 190卷 110070-110070页

作者： Chao, Yi-Ping Chuang, Hai-Hua Lee, Zong-Han Huang, Shu-Yi Zhan, Wan-Ting Shyu, Liang-Yu Lo, Yu-Lun Lee, Guo-She Li, Hsueh-Yu Lee, Li-Ang Department of Computer Science and Information Engineering Chang Gung University Taoyuan33302 Taiwan Department of Otorhinolaryngology Head and Neck Surgery Sleep Center Linkou Medical Center Chang Gung Memorial Hospital Chang Gung University Taoyuan33305 Taiwan Department of Community Medicine Cathay General Hospital Taipei10630 Taiwan School of Medicine College of Life Science and Medicine National Tsing Hua University Hsinchu300044 Taiwan Department of Industrial Engineering and Management National Taipei University of Technology Taipei10608 Taiwan Department of Biomedical Engineering Chung Yuan Christian University Taoyuan320314 Taiwan Department of Pulmonary and Critical Care Medicine Linkou Main Branch Chang Gung Memorial Hospital Chang Gung University Taoyuan33305 Taiwan Faculty of Medicine National Yang Ming Chiao Tung University Taipei112304 Taiwan Department of Otolaryngology Taipei City Hospital Ren-Ai Branch Taipei106243 Taiwan

This study explores the development of a deep learning model using a neck-wearable piezoelectric sensor to accurately distinguish severe sleep apnea syndrome (SAS) from habitual snoring, addressing the underdiagnosis of SAS in adults. From 2018 to 2020, 60 adult habitual snorers underwent polysomnography while wearing a neck piezoelectric sensor that recorded snoring vibrations (70–250 Hz) and carotid artery pulsations (0.01–1.5 Hz). The initial dataset comprised 1167 silence, 1304 snoring, and 399 noise samples from 20 participants. Using a hybrid deep learning model comprising a one-dimensional convolutional neural network and gated-recurrent unit, the model identified snoring and apnea/hypopnea events, with sleep phases detected via pulse wave variability criteria. The model's efficacy in predicting severe SAS was assessed in the remaining 40 participants, achieving snoring detection rates of 0.88, 0.86, and 0.92, with respective loss rates of 0.39, 0.90, and 0.23. Classification accuracy for severe SAS improved from 0.85 for total sleep time to 0.90 for partial sleep time, excluding the first sleep phase, demonstrating precision of 0.84, recall of 1.00, and an F1 score of 0.91. This innovative approach of combining a hybrid deep learning model with a neck-wearable piezoelectric sensor suggests a promising route for early and precise differentiation of severe SAS from habitual snoring, aiding guiding further standard diagnostic evaluations and timely patient management. Future studies should focus on expanding the sample size, diversifying the patient population, and external validations in real-world settings to enhance the robustness and applicability of the findings. © 2025 The Authors

关键词： Piezoelectric transducers

来源：评论

学校读者我要写书评

暂无评论

Domain-guided machine learning for remotely sensed in-season crop growth estimation

arXiv

引用

arXiv 2021年

作者： Worrall, George Rangarajan, Anand Judge, Jasmeet Center for Remote Sensing Department of Agricultural and Biological Engineering University of Florida GainesvilleFL United States Department of Computer & Information Science & Engineering University of Florida GainesvilleFL United States

Advanced machine learning techniques have been used in remote sensing (RS) applications such as crop mapping and yield prediction, but remain under-utilized for tracking crop progress. In this study, we demonstrate the use of agronomic knowledge of crop growth drivers in a Long Short-Term Memory-based, domain-guided neural network (DgNN) for in-season crop progress estimation. The DgNN uses a branched structure and attention to separate independent crop growth drivers and capture their varying importance throughout the growing season. The DgNN is implemented for corn, using RS data in Iowa for the period 2003-2019, with USDA crop progress reports used as ground truth. State-wide DgNN performance shows significant improvement over sequential and dense-only NN structures, and a widely-used Hidden Markov Model method. The DgNN had a 4.0% higher Nash-Sutfliffe efficiency over all growth stages and 39% more weeks with highest cosine similarity than the next best NN during test years. The DgNN and Sequential NN were more robust during periods of abnormal crop progress, though estimating the Silking-Grainfill transition was difficult for all methods. Finally, Uniform Manifold Approximation and Projection visualizations of layer activations showed how LSTM-based NNs separate crop growth time-series differently from a dense-only structure. Results from this study exhibit both the viability of NNs in crop growth stage estimation (CGSE) and the benefits of using domain knowledge. The DgNN methodology presented here can be extended to provide near-real time CGSE of other crops. Copyright © 2021, The Authors. All rights reserved.

关键词： Long short-term memory

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：