检索结果-内蒙古大学图书馆

IEEE International Conference on data Mining (ICDM)

作者： Shenghao Liu Guoyang Wu Xianjun Deng Hongwei Lu Bang Wang Laurence Yang James J. Park Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Smart Internet Technology School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea

Discrimination against different user groups has received growing attention in the recommendation field. To address this problem, existing works typically remove sensitive attributes that may cause discrimination through adversary learning to achieve fair recommendations. However, these approaches leverage all available interactions for learning user representations and overlook the fact that different interactions have varying relevance to users’ sensitive attributes. Ignoring this issue may weaken the effectiveness of adversary learning in removing sensitive attributes. To tackle this challenge, we propose a novel model called GS-FairRec, which distinguishes between user interactions to achieve better removal of sensitive attributes. The model consists of three modules: graph sampling-based representation learning, pseudo-user representation learning, and adversarial learning. Firstly, the graph sampling-based representation learning module removes some irrelevant neighbors from a user-item bipartite graph and employs a graph convolutional network (GCN) to learn user/item representations. Next, items that are relevant to a user’s sensitive information but do not match their preferences are defined as the user’s pseudo-interest items, which are leveraged to learn the pseudo-user representation. In the adversarial learning module, the user’s two kinds of representations are fused for adversarial learning to remove sensitive information. Additionally, we design a new metric to measure the model’s ability to remove sensitive attributes based on how a generated recommendation list discloses the user’s sensitive attributes. Finally, we conduct experiments on two real-world datasets, and our results demonstrate the superiority of our proposed model in fairness tasks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Digital Over-the-Air Federated Learning in Multi-Antenna systems

arXiv

引用

arXiv 2023年

作者： Wang, Sihua Chen, Mingzhe Shen, Cong Yin, Changchuan Brinton, Christopher G. The Beijing Laboratory of Advanced Information Network The Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States The School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

来源：评论

学校读者我要写书评

暂无评论

K-Space-Aware Cross-Modality Score for Quality Assessment of Synthesized Neuroimages

arXiv

引用

arXiv 2023年

作者： Xie, Guoyang Wang, Jinbao Huang, Yawen Lyu, Jiayi Zheng, Feng Zheng, Yefeng Jin, Yaochu Southern University of Science and Technology Department of Computer Science Shenzhen518055 China CATL Department of Intelligent Manufacturing Ningde352000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Tencent Youtu Lab Jarvis Research Center Shenzhen510310 China University of Chinese Academy of Sciences the School of Engineering Science Beijing100049 China Westlake University the School of Engineering Hangzhou310030 China

The problem of how to assess cross-modality medical image synthesis has been largely unexplored. The most used measures like PSNR and SSIM focus on analyzing the structural features but neglect the crucial lesion location and fundamental k-space speciality of medical images. To overcome this problem, we propose a new metric K-CROSS to spur progress on this challenging problem. Specifically, K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location, together with a tumor encoder for representing features, such as texture details and brightness intensities. To further reflect the frequency-specific information from the magnetic resonance imaging principles, both k-space features and vision features are obtained and employed in our comprehensive encoders with a frequency reconstruction penalty. The structure-shared encoders are designed and constrained with a similarity loss to capture the intrinsic common structural information for both modalities. As a consequence, the features learned from lesion regions, k-space, and anatomical structures are all captured, which serve as our quality evaluators. We evaluate the performance by constructing a large-scale cross-modality neuroimaging perceptual similarity (NIRPS) dataset with 6,000 radiologist judgments. Extensive experiments demonstrate that the proposed method outperforms other metrics, especially in comparison with the radiologists on NIRPS. To forster reproducibility and accessibility, the source codeis uploaded to the website: https://***/M-3LAB/K-CROSS © 2023, CC BY.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Cognitive protection and brain entropy changes from omega-3 polyunsaturated fatty acids supplement in late-life depression: A 52-week randomized controlled trial

引用

Journal of Affective Disorders 2024年 351卷 15-23页

作者： Lin, Chemin Lee, Shwu-Hua Huang, Chih-Mao Wu, Yu-Wen Chang, You-Xun Liu, Ho-Ling Ng, Shu-Hang Cheng, Ying-Chih Chiu, Chih-Chiang Wu, Shun-Chi Department of Psychiatry Keelung Chang Gung Memorial Hospital Keelung City Taiwan College of Medicine Chang Gung University Taoyuan County Taiwan Community Medicine Research Center Chang Gung Memorial Hospital Keelung Keelung Taiwan Department of Psychiatry Linkou Chang Gung Memorial Hospital Taoyuan County Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu Taiwan Department of Imaging Physics University of Texas MD Anderson Cancer Center Houston TX United States Department of Head and Neck Oncology Group Linkou Chang Gung Memorial Hospital and Chang Gung University Taoyuan Taiwan Department of Diagnostic Radiology Linkou Chang Gung Memorial Hospital and Chang Gung University Taoyuan Taiwan Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan Department of Psychiatry China Medical University Hsinchu Hospital China Medical University Hsinchu Taiwan Research Center of Big Data and Meta-analysis Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Psychiatry Taipei City Psychiatric Center Taipei City Hospital Taipei Taiwan Department of Psychiatry Taipei Medical University Taipei Taiwan

Background: Late-life depression (LLD) is associated with risk of dementia, yet intervention of LLD provides an opportunity to attenuate subsequent cognitive decline. Omega-3 polyunsaturated fatty acids (PUFAs) supplement is a potential intervention due to their beneficial effect in depressive symptoms and cognitive function. To explore the underlying neural mechanism, we used resting-state functional MRI (rs-fMRI) before and after omega-3 PUFAs supplement in older adults with LLD. Methods: A 52-week double-blind randomized controlled trial was conducted. We used multi-scale sample entropy to analyze rs-fMRI data. Comprehensive cognitive tests and inflammatory markers were collected to correlate with brain entropy changes. Results: A total of 20 patients completed the trial with 11 under omega-3 PUFAs and nine under placebo. While no significant global cognitive improvement was observed, a marginal enhancement in processing speed was noted in the omega-3 PUFAs group. Importantly, participants receiving omega-3 PUFAs exhibited decreased brain entropy in left posterior cingulate gyrus (PCG), multiple visual areas, the orbital part of the right middle frontal gyrus, and the left Rolandic operculum. The brain entropy changes of the PCG in the omega-3 PUFAs group correlated with improvement of language function and attenuation of interleukin-6 levels. Limitations: Sample size is small with only marginal clinical effect. Conclusion: These findings suggest that omega-3 PUFAs supplement may mitigate cognitive decline in LLD through anti-inflammatory mechanisms and modulation of brain entropy. Larger clinical trials are warranted to validate the potential therapeutic implications of omega-3 PUFAs for deterring cognitive decline in patients with late-life depression. © 2024

关键词： Cognitive decline Fish oil Late-life depression Multi-scale entropy Omega-3 PUFAs Resting-state fMRI

来源：评论

学校读者我要写书评

暂无评论

Mass flow rates effect on the performance of PV/T bi-fluid hybrid collector (single and simultaneous modes) 2nd

Mass flow rates effect on the performance of PV/T bi-fluid h...

引用

2nd International Conference on Electronic engineering and Renewable Energy, ICEERE 2020

作者： El Manssouri, Oussama El Fouas, Chaimae Hajji, Bekkay Rabhi, Abdelhamid Tina, Giuseppe Marco Gagliano, Antonio Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco Modelization Information and Systems Laboratory Picardie Jules Verne University Amiens France DIEEI: Department of Electric Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9789811562587

A hybrid photovoltaic/thermal collector (PV/T) is used to produce simultaneously electrical and thermal energy from absorbed solar irradiation. The research to date has tended to focus on either bi-fluids (water and air) as the working fluid to supply energy needs for different applications. The purpose of this work is to test the performances of PV/T at different operating modes of fluid e.g. the air mode, the water mode, and the simultaneous mode (water& air). Furthermore the effect of mass flow rate as a key parameter for better electrical and thermal performances has been investigated. The PV/T performances were assessed based on a dynamic numerical model. An energy balance equations have been established for each layer, then implemented in MATLAB software. The results show that thermal efficiency in the simultaneous mode (air & water) is better compared to others modes. The thermal efficiencies for independently fluid condition have ranged from approximately 20 to 48%, and increased to a maximum efficiency of near to 68% for the case of the simultaneously fluids. This result indicates that the optimum mass flow rates for air and water are 0.035 kg/s and 0.007 kg/s respectively. Therefore, the theoretical model developed of the independently and simultaneously operational modes is validated, evidencing a good fit between simulation results and the experimental data available in literature experimental. © Springer Nature Singapore Pte Ltd. 2021.

关键词： Air

来源：评论

学校读者我要写书评

暂无评论

F-HMTC: Detecting financial events for investment decisions based on neural hierarchical multi-label text classification 29

F-HMTC: Detecting financial events for investment decisions ...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Liang, Xin Cheng, Dawei Yang, Fangzhou Luo, Yifeng Qian, Weining Zhou, Aoying Shanghai Engineering Research Center on Big Data Management System Shanghai China School of Data Science and Engineering East China Normal University Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China SeekData Inc. Shanghai China

ISBN: (纸本)9780999241165

The share prices of listed companies in the stock trading market are prone to be influenced by various events. Performing event detection could help people to timely identify investment risks and opportunities accompanying these events. The financial events inherently present hierarchical structures, which could be represented as tree-structured schemes in real-life applications, and detecting events could be modeled as a hierarchical multi-label text classification problem, where an event is designated to a tree node with a sequence of hierarchical event category labels. Conventional hierarchical multi-label text classification methods usually ignore the hierarchical relationships existing in the event classification scheme, and treat the hierarchical labels associated with an event as uniform labels, where correct or wrong label predictions are assigned with equal rewards or penalties. In this paper, we propose a neural hierarchical multi-label text classification method, namely F-HMTC, for a financial application scenario with massive event category labels. F-HMTC learns the latent features based on bidirectional encoder representations from transformers, and directly maps them to hierarchical labels with a delicate hierarchy-based loss layer. We conduct extensive experiments on a private financial dataset with elaborately-annotated labels, and F-HMTC consistently outperforms state-of-art baselines by substantial margins. We will release both the source codes and dataset on a public repository1,. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Forestry

来源：评论

学校读者我要写书评

暂无评论

Physics-informed deep-learning model for mitigating spatiotemporal imbalances in FLUXNET2015 global evapotranspiration data

Research Square

引用

Research Square 2024年

作者： Wang, Jiancheng Xu, Tongren Bateni, Sayed M. Liu, Shaomin Jun, Changhyun Kim, Dongkyun Li, Xiaoyan Li, Xin Yang, Xiaofan Xu, Ziwei Zhang, Gangqiang Ming, Wenting State Key Laboratory of Earth Surface Processes and Resource Ecology School of Natural Resources Faculty of Geographical Science Beijing Normal University China Department of Civil Environmental and Construction Engineering Water Resources Research Center University of Hawaii Manoa United States School of Civil Environmental and Architectural Engineering College of Engineering Korea University Korea Republic of Department of Civil Engineering Hongik University Korea Republic of National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth System Environment and Resources Institute of Tibetan Plateau Research Chinese Academy of Sciences China

As a key component of the water cycle, evapotranspiration (ET) plays a critical role in agricultural management and climate prediction. While numerous long-term observation sites have been established in Europe and North America (data-rich regions), fewer short-term observation sites exist in South America and, particularly, in Africa (data-poor regions). Several machine learning approaches have been developed for ET estimations. However, most existing studies in this field have used training and testing data from the same region, potentially leading to poor extrapolation in unseen areas. This paper proposes a physics-informed deep-learning model that considers external environmental variables, enabling more accurate identification of different underlying surfaces. Our results demonstrate that the proposed model effectively transfers the knowledge acquired from its training on data-rich regions to data-poor regions, thereby mitigating spatiotemporal imbalances in global in-situ ET observations. Overall, this approach can support the sustainable development of data-deficient regions or countries. © 2024, CC BY.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

引用

Chinese Journal of Chemical engineering 2019年第8期27卷 1888-1894页

作者： Kexin Bi Tong Qiu Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data System and Application Beijing 100084China

Properties prediction of crude oil remains an essential issue for refineries. In this communication, an exhaustive and extendable support vector machine(SVM) intelligent prediction process has been proposed to solve this problem. A novel hybrid genetic algorithm-particle swarm optimization(GA-PSO)method was applied to optimize the SVM model. The optimization process and result demonstrated that the newly proposed GA-PSO-SVM method was more accurate and time-saving than the classical GA or PSO method. Compared with the classical Grid-search SVM, the combined GA-PSO-SVM model appeared to be more applicable for the properties prediction task. The TBP distillation curve fitting was exampled to evaluate the performance of the developed model. The regression result demonstrated the high accuracy and efficiency of the proposed process. The model can be applied in the Industrial Internet as a plugin, and the adaptability and flexibility is demonstrated by the implement of crude oil molecular reconstruction employing the intelligent prediction process.

关键词： Intelligent properties prediction Support vector machine Hybrid GA-PSO TBP distillation curve fitting

来源：评论

学校读者我要写书评

暂无评论

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

引用

IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

来源：评论

学校读者我要写书评

暂无评论

Rate-Splitting for Cell-Free Massive MIMO: Performance Analysis and Generative AI Approach

arXiv

引用

arXiv 2024年

作者： Zheng, Jiakang Zhang, Jiayi Du, Hongyang Zhang, Ruichen Niyato, Dusit Dobre, Octavia A. Ai, Bo The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The College of Computing and Data Science Nanyang Technological University Singapore Faculty of Engineering and Applied Science Memorial University Canada The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

Cell-free (CF) massive multiple-input multiple-output (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Rate-splitting (RS) effectively extracts data by decoding interference, yet its effectiveness is limited by the weakest UE. In this paper, we investigate an RS-based CF massive MIMO system, which combines strengths and mitigates weaknesses of both approaches. Considering imperfect channel state information (CSI) resulting from both pilot contamination and noise, we derive a closed-form expression for the sum spectral efficiency (SE) of the RS-based CF massive MIMO system under a spatially correlated Rician channel. Moreover, we propose low-complexity heuristic algorithms based on statistical CSI for power-splitting of common messages and power-control of private messages, and genetic algorithm is adopted as a solution for upper bound performance. Furthermore, we formulate a joint optimization problem, aiming to maximize the sum SE of the RS-based CF massive MIMO system by optimizing the power-splitting factor and power-control coefficient. Importantly, we improve a generative AI (GAI) algorithm to address this complex and non-convexity problem by using a diffusion model to obtain solutions. Simulation results demonstrate its effectiveness and practicality in mitigating interference, especially in dynamic environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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：