检索结果-内蒙古大学图书馆

Deep and Localized Transcranial Magnetic Stimulation in Rat Using Conventional Round Coil 34

学校读者我要写书评

暂无评论

Deep and Localized Transcranial Magnetic Stimulation in Rat ...

34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021

作者： Lu, Mai. Ueno, Shoogo Lanzhou Jiaotong University Key Lab. of Opt-Electronic Technology and Intelligent Control of Ministry of Education Lanzhou730070 China Graduate School of Engineering Kyushu University Department of Applied Quantum Physics Fukuoka812-8581 Japan

ISBN: (纸本)9789463968027

Transcranial magnetic stimulation (TMS) shows significant values in both brain research and therapeutic applications in the treatment of various psychiatric and neurological disorders. Animal studies of TMS plays an important role in understanding of the cellular and molecular mechanisms underlying TMS induced effects in the brain. This work investigated TMS in rat brain by employing conventional round coil with varied coil positions. Three dimensional distributions of induced electric field in realistic rat model were calculated by 3-D impedance method. Simulations results indicated that the conventional round coil can be applied to rat TMS with improved focality while also keeping high stimulation intensities in deep brain regions. © 2021 URSI.

关键词： Rats

Disentangled Noisy Correspondence Learning

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Dang, Zhuohang Luo, Minnan Wang, Jihong Jia, Chengyou Han, Haochen Wan, Herun Dai, Guang Chang, Xiaojun Wang, Jingdong The School of Computer Science and Technology The Ministry of Education Key Laboratory of Intelligent Networks and Network Security The Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi'An710049 China SGIT AI Lab State Grid Shaanxi Electric Power Company Limited State Grid Corporation of China Shaanxi China The School of Information Science and Technology University of Science and Technology China United Arab Emirates The Baidu Inc China

Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impractical as real-world data inevitably involves imperfect alignments, i.e., noisy correspondences. Although some works explore similarity-based strategies to address such noise, they suffer from sub-optimal similarity predictions influenced by modality-exclusive information (MEI), e.g., background noise in images and abstract definitions in texts. This issue arises as MEI is not shared across modalities, thus aligning it in training can markedly mislead similarity predictions. Moreover, although intuitive, directly applying previous cross-modal disentanglement methods suffers from limited noise tolerance and disentanglement efficacy. Inspired by the robustness of information bottlenecks against noise, we introduce DisNCL, a novel information-theoretic framework for feature Disentanglement in Noisy Correspondence Learning, to adaptively balance the extraction of MII and MEI with certifiable optimal cross-modal disentanglement efficacy. DisNCL then enhances similarity predictions in modality-invariant subspace, thereby greatly boosting similarity-based alleviation strategy for noisy correspondences. Furthermore, DisNCL introduces soft matching targets to model noisy many-to-many relationships inherent in multi-modal input for noise-robust and accurate cross-modal alignment. Extensive experiments confirm DisNCL's efficacy by 2% average recall improvement. Mutual information estimation and visualization results show that DisNCL learns meaningful MII/MEI subspaces, validating our theoretical analyses. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

Developing a new red band- SEVI-blue band (RSB) enhancement method for recognition the extra-high-voltage transmission line corridor in green mountains

学校读者我要写书评

暂无评论

International Journal of Digital Earth 2023年第1期16卷 806-824页

作者： Hong Jiang Yong Zhang Jinglan Lin Xiaogan Zheng Hui Yue Yunzhi Chen Key Laboratory of Spatial Data Mining&Information Sharing of Ministry of Education National&Local Joint Engineering Research Center of Satellite Geospatial Information TechnologyAcademy of Digital China(Fujian)Fuzhou UniversityFuzhouPeople’s Republic of China Fujian Soil and Water Conservation Experimental Station FuzhouPeople’s Republic of China Lab of Live Working Tech for Power Transmission Fuzhou Power Supply Company of State Grid Fujian Electric Power Co.Ltd.FuzhouPeople’s Republic of China Fujian(Changting)Soil and Water Conservation Research Center Soil and Water Conservation Center of Changting CountyLongyanPeople’s Republic of China

Monitoring the extra-high-voltage transmission line corridor(EHVTLC)in mountains is critical for safe smart-grid ***,the transmission lines are so narrow that they are difficult to recognize using multispectral satellite images with a spatial resolution of 10 *** this study,we developed a new method using the red band–shadow-eliminated vegetation index(SEVI)–blue band(RSB)composite image to enhance the EHVTLC in green mountains(named RSB-enhancement method).Using this method,the EHVTLC becomes evident in the false-color synthesis of the RSB composite of the Sentinel-2 ***,we recognized and extracted approximately 342.45 km of the EHVTLC in a mountainous region of Fuzhou City,China,including a 46.73 km three-parallel-lane segment of 1000 kV and a 295.72 km two-parallel-lane segment of 500 *** analysis shows that the SEVI mean difference between the EHVTLC and the buffer zone reaches approximately 10%,and three landslides and 2.66 km^(2) soil erosion reside in the buffer zone which area is approximately 73.67 km^(2).Finally,the RSB-enhancement method can be used in other satellite images with spatial resolutions of greater than 10 m for enhancement and recognition the transmission line corridors in green mountains.

关键词： Enhancement method shadow-eliminated vegetation index(SEVI) transmission line lane(TLL) green mountains soil erosion

The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018

学校读者我要写书评

暂无评论

Big Earth data 2023年第3期7卷 860-885页

作者： Rongrong Zhang Virgílio A.Bento Junyu Qi Feng Xu Jianjun Wu Jianxiu Qiu Jianwei Li Wei Shui Qianfeng Wang Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection/College of Environmental&Safety Engineering Fuzhou UniversityFuzhouChina Instituto Dom Luiz Faculdade de CiênciasUniversidade de LisboaLisboaPortugal Earth System Science Interdisciplinary Center University of MarylandCollege ParkMDUSA State Key Laboratory of Earth Surface Processes and Resource Ecology/Faculty of Geographical Science Beijing Normal UniversityBeijingChina Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and PlanningSun Yat-sen UniversityGuangzhouChina College of Physics and Information Engineering Fuzhou UniversityFuzhouChina Key Lab of Spatial Data Mining&Information Sharing Ministry of Education of ChinaFuzhouChina

Standardized Precipitation Index(SPI)and Standardized Precipitation Evapotranspiration Index(SPEI),traditionally derived at a monthly scale,are widely used drought *** overcome temporalresolution limitations,we have previously developed and published a well-validated daily SPI/SPEI in situ *** having a high temporal resolution,this in situ dataset presents low spatial resolution due to the scarcity of ***,based on the China Meteorological Forcing dataset,which is composed of data from more than 1,000 ground-based observation sites and multiple remote sensing grid meteorological dataset,we present the first high spatiotemporal-resolution daily SPI/SPEI raster datasets over *** spans from 1979 to 2018,with a spatial resolution of 0.1°×0.1°,a temporal resolution of 1-day,and the timescales of 30-,90-,and *** show that the spatial distributions of drought event characteristics detected by the daily SPI/SPEI are consistent with the monthly SPI/*** correlation between the daily value of the 12-month scale and the monthly value of SPI/SPEI is the strongest,with linear correlation,Nash-Sutcliffe coefficient,and normalized root mean square error of 0.98,0.97,and 0.04,*** daily SPI/SPEI is shown to be more sensitive to flash drought than the monthly SPI/*** improved SPI/SPEI shows high accuracy and credibility,presenting enhanced results when compared to the monthly SPI/*** total data volume is up to 150 GB,compressed to 91 GB in Network Common data Form(NetCDF).It can be availab.e from Figshare(https://***/10.6084/***.c.5823533)and ScienceDB(https://***/10.57760/sciencedb.j00076.00103).

关键词： Drought index climate change climatic disaster Generalized Extreme Value

Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Mei, Fang Kang, Jiawen Du, Hongyang Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn36830 United States

As a form of artificial intelligence (AI) technology based on interactive learning, deep reinforcement learning (DRL) has been widely applied across various fields and has achieved remarkable accomplishments. However, DRL faces certain limitations, including low sample efficiency and poor generalization. Therefore, we present how to leverage generative AI (GAI) to address these issues above and enhance the performance of DRL algorithms in this paper. We first introduce several classic GAI and DRL algorithms and demonstrate the applications of GAI-enhanced DRL algorithms. Then, we discuss how to use GAI to improve DRL algorithms from the data and policy perspectives. Subsequently, we introduce a framework that demonstrates an actual and novel integration of GAI with DRL, i.e., GAI-enhanced DRL. Additionally, we provide a case study of the framework on UAV-assisted integrated near-field/far-field communication to validate the performance of the proposed framework. Moreover, we present several future directions. Finally, the related code is availab.e at: https://***/GAI-enhanced-DRL. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

Detection of ChatGPT Fake Science with the xFakeSci Learning Algorithm

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Hamed, Ahmed Abdeen Wu, Xindong Complex Adaptive Systems and Computational Intelligence Laboratory State University of New York at Binghamton BinghamtonNY13902 United States Sano Centre for Computational Medicine Clinical Data Science Cracow30-072 Poland Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230009 China China Electronics Data Industry Group Shenzhen518057 China

Generative AI tools exemplified by ChatGPT are becoming a new reality. This study is motivated by the premise that "AI generated content may exhibit a distinctive behavior that can be separated from scientific articles". In this study, we show how articles can be generated using means of prompt engineering for various diseases and conditions. We then show how we tested this premise in two phases and prove its validity. Subsequently, we introduce xFakeSci, a novel learning algorithm, that is capable of distinguishing ChatGPT-generated articles from publications produced by scientists. The algorithm is trained using network models driven from both sources. To mitigate overfitting issues, we incorporated a calibration step that is built upon data-driven heuristics, including proximity and ratios. Specifically, from a total of a 3952 fake articles for three different medical conditions, the algorithm was trained using only 100 articles, but calibrated using folds of 100 articles. As for the classification step, it was performed using 300 articles per condition. The actual lab.l steps took place against an equal mix of 50 generated articles and 50 authentic PubMed abstracts. The testing also spanned publication periods from 2010 to 2024 and encompassed research on three distinct diseases: cancer, depression, and Alzheimer’s. Further, we evaluated the accuracy of the xFakeSci algorithm against some of the classical data mining algorithms (e.g., Support Vector Machines, Regression, and Naive Bayes). The xFakeSci algorithm achieved F1 scores ranging from 80% to 94%, outperforming common data mining algorithms, which scored F1 values between 38% and 52%. We attribute the noticeable difference to the introduction of calibration and a proximity distance heuristic, which underscores this promising performance. Indeed, the prediction of fake science generated by ChatGPT presents a considerable challenge. Nonetheless, the introduction of the xFakeSci algorithm is a significant st

关键词： Learning algorithms

Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wu, Xuan Wang, Di Wen, Lijie Xiao, Yubin Wu, Chunguo Wu, Yuesong Yu, Chaoyu Maskell, Douglas L. Zhou, You Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin university Changchun130012 China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore School of Software Tsinghua University Beijing100084 China College of Computing and Data Science Nanyang Technological University 639798 Singapore

Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically designed to solve Vehicle Routing Problems (VRPs) have been conducted, they did not cover the state-of-the-art (SOTA) NCO solvers emerged recently. More importantly, to establish a comprehensive and up-to-date taxonomy of NCO solvers, we systematically review relevant publications and preprints, categorizing them into four distinct types, namely Learning to Construct, Learning to Improve, Learning to Predict-Once, and Learning to Predict-Multiplicity solvers. Subsequently, we present the inadequacies of the SOTA solvers, including poor generalization, incapability to solve large-scale VRPs, inability to address most types of VRP variants simultaneously, and difficulty in comparing these NCO solvers with the conventional Operations Research algorithms. Simultaneously, we discuss on-going efforts, identify open inadequacies, as well as propose promising and viable directions to overcome these inadequacies. Notably, existing efforts focus on only one or two of these inadequacies, with none attempting to address all of them concurrently. In addition, we compare the performance of representative NCO solvers from the Reinforcement, Supervised, and Unsupervised Learning paradigms across VRPs of varying scales. Finally, following the proposed taxonomy, we provide an accompanying web page as a live repository for NCO solvers. Through this survey and the live repository, we aim to foster further advancements in the NCO community. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

Influence of COVID-19 Face Mask Additives on the High-Temperature and Fatigue Performance of Asphalt Binder 4

学校读者我要写书评

暂无评论

Influence of COVID-19 Face Mask Additives on the High-Temper...

4th International Conference on Green Energy, Environment and Sustainable Development, GEESD 2023

作者： Li, Liang Shen, Erbu Zhang, Saibang Sun, Mutian Lin, Hongwei Ningbo Rail Transit Group Co. Ltd. Zhejiang Ningbo315000 China Cccc WuHan Harbour Engineering Design and Research Co. Ltd. Hubei Wuhan430000 China Cccc Shec Wuhan Harbour New Materials Co. Ltd. Hubei Wuhan430000 China Hubei Key Lab. of Adv. Mat. and Reinforcement Technology Research for Marine Environment Structures Hubei Wuhan430071 China Wenshan Communications Investment Group Engineering Quality Testing Co. Ltd. Wenshan Zhuang and Miao Autonomous Prefecture Yunnan663000 China Key Laboratory of Road and Traffic Engineering of Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9781643684260

In order to alleviate the environmental impact of mask waste, 5%, 10%, and 15% shredded face masks (SFM) were added to 70# base asphalt, respectively, and the feasibility of applying masks in asphalt modification was evaluated by temperature sweep, frequency sweep, MSCR, and time scan tests. The complex modulus, rutting factor, and average percent recovery were all improved by SFM modification, while the non-recoverable creep compliance and phase angle were also reduced. Effective enhancements were made to resistance to rutting and creeping. Additionally, 5% SFM-modified asphalt had a 5.4% increment in fatigue life compared to the original binder, whereas 10% and 15% SFM-modified asphalt had a decrease in fatigue life of 11.5% and 29.0%, respectively. The test results provide a reference for the engineering application of waste mask modified asphalt and have potential environmental benefits. © 2023 IOS Press BV. All rights reserved.

关键词： COVID-19

Resource Scheduling in URLLC and eMBB Coexistence Based on Dynamic Selection Numerology

学校读者我要写书评

暂无评论

Wireless Communications and Mobile Computing 2024年第1期2024卷

作者： Wang, Lei Tao, Sijie Zhao, Lindong Zhou, Dengyou Liu, Zhe Sun, Yanbing School of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China Key Lab. of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China School of Foreign Studies Jiangsu Open University Nanjing210036 China

This paper focuses on the resource allocation problem of multiplexing two different service scenarios, enhanced mobile broadband (eMBB) and ultrareliable low latency (URLLC) in 5G New Radio, based on dynamic numerology structure, mini-time slot scheduling, and puncturing to achieve optimal resource allocation. To obtain the optimal channel resource allocation under URLLC user constraints, this paper establishes a relevant channel model divided into two convex optimization problems: (a) eMBB resource allocation and (b) URLLC scheduling. We also determine the numerology values at the beginning of each time slot with the help of deep reinforcement learning to achieve flexible resource scheduling. The proposed algorithm is verified in simulation software, and the simulation results show that the dynamic selection of numerologies proposed in this paper can better improve the data transmission rate of eMBB users and reduce the latency of URLLC services compared with the fixed numerology scheme for the same URLLC packet arrival, while the reasonable resource allocation ensures the reliability of URLLC and eMBB communication. © 2024 Lei Wang et al.

关键词： Resource allocation