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arXiv 2025年

作者： Zhao, Changyuan Zhang, Ruichen Wang, Jiacheng Niyato, Dusit Sun, Geng Du, Hongyang Li, Zan Jamalipour, Abbas Kim, Dong In College of Computing and Data Science Nanyang Technological University Singapore CNRS@CREATE 1 Create Way 08-01 Create Tower 138602 Singapore College of Computer Science and Technology Jilin University 130012 China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China School of Electrical and Computer Engineering University of Sydney Australia Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

This paper introduces a two-stage generative AI (GenAI) framework tailored for temporal spectrum cartography in low-altitude economy networks (LAENets). LAENets, characterized by diverse aerial devices such as UAVs, rely heavily on wireless communication technologies while facing challenges, including spectrum congestion and dynamic environmental interference. Traditional spectrum cartography methods have limitations in handling the temporal and spatial complexities inherent to these networks. Addressing these challenges, the proposed framework first employs a Reconstructive Masked Autoencoder (RecMAE) capable of accurately reconstructing spectrum maps from sparse and temporally varying sensor data using a novel dual-mask mechanism. This approach significantly enhances the precision of reconstructed radio frequency (RF) power maps. In the second stage, the Multi-agent Diffusion Policy (MADP) method integrates diffusion-based reinforcement learning to optimize the trajectories of dynamic UAV sensors. By leveraging temporal-attention encoding, this method effectively manages spatial exploration and exploitation to minimize cumulative reconstruction errors. Extensive numerical experiments validate that this integrated GenAI framework outperforms traditional interpolation methods and deep learning baselines by achieving 57.35% and 88.68% reconstruction error reduction, respectively. The proposed trajectory planner substantially improves spectrum map accuracy, reconstruction stability, and sensor deployment efficiency in dynamically evolving low-altitude environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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Adaptive interaction modeling via graph operations search

arXiv

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arXiv 2020年

作者： Li, Haoxin Zheng, Wei-Shi Tao, Yu Hu, Haifeng Lai, Jian-Huang School of Electronics and Information Technology Sun Yat-sen University China School of Data and Computer Science Sun Yat-sen University China Peng Cheng Laboratory Shenzhen518005 China Accuvision Technology Co. Ltd Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Interaction modeling is important for video action analysis. Recently, several works design specific structures to model interactions in videos. However, their structures are manually designed and non-Adaptive, which require structures design efforts and more importantly could not model interactions adaptively. In this paper, we automate the process of structures design to learn adaptive structures for interaction modeling. We propose to search the network structures with differentiable architecture search mechanism, which learns to construct adaptive structures for different videos to facilitate adaptive interaction modeling. To this end, we first design the search space with several basic graph operations that explicitly capture different relations in videos. We experimentally demonstrate that our architecture search framework learns to construct adaptive interaction modeling structures, which provides more understanding about the relations between the structures and some interaction characteristics, and also releases the requirement of structures design efforts. Additionally, we show that the designed basic graph operations in the search space are able to model different interactions in videos. The experiments on two interaction datasets show that our method achieves competitive performance with state-of-The-Arts. Copyright © 2020, The Authors. All rights reserved.

关键词： Network architecture

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BuildSenSys: Reusing building sensing data for traffic prediction with cross-domain learning

arXiv

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arXiv 2020年

作者： Fan, Xiaochen Xiang, Chaocan Chen, Chao Yang, Panlong Gong, Liangyi Song, Xudong Nanda, Priyadarsi He, Xiangjian School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China College of Computer Science Chongqing University Chongqing400044 China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui230026 China School of Software BNRist Tsinghua University Beijing100084 China

With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outperf

关键词： Internet of things

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data-driven analysis of electron relaxation times in PbTe-type thermoelectric materials (vol 20, pg 511, 2019)

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science AND TECHNOLOGY OF ADVANCED MATERIALS 2019年第1期20卷 589-589页

作者： Katsura, Y. Kumagai, M. Kodani, T. Department of Advanced Materials Science Graduate School of Frontier Sciences SAKURA Research Center The University of Tokyo Chiba Japan Research and Services Division of Materials Data and Integrated System (MaDIS) National Institute for Materials Science (NIMS) Tsukuba Ibaraki Japan Molecular Informatics Team Center for Advanced Intelligence Project RIKEN Tokyo Japan SAKURA Research Center SAKURA Internet Inc. Osaka Japan Scientific Computing Division X-Ability Co. Ltd Tokyo Japan

data mining from published papers can generate large experimental datasets that have been overlooked in computational materials informatics. We developed an open web system Starrydata2 to accelerate a comprehensive digitization of data of materials from as-reported plot images in published papers, without sample selection based on performance. By plotting results obtained from our dataset on experimental thermoelectric properties of 434 samples of rock-salt-type (PbTe-type) thermoelectric materials, we revealed differences in electronic structure of parent compounds PbTe, PbSe, PbS, and SnTe from just experimental data. We observed that the calculated Seebeck coefficients were fairly consistent with experimental data for n-type PbTe but not for p-type PbTe, indicating possible modifications in its valence-band electronic structure. We evaluated the electron relaxation time τel from 207 reported samples of n-type PbTe by combining calculations and experimental data. We found that τel is not a constant but varies by at least two orders of magnitude. Achieving long τel was suggested to be critical in increasing the thermoelectric figure of merit ZT.

关键词： Materials informatics database data curation thermoelectric materials first-principles calculation electron relaxation time

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PC-RGNN: Point cloud completion and graph neural network for 3D object detection

arXiv

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arXiv 2020年

作者： Zhang, Yanan Huang, Di Wang, Yunhong Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

LiDAR-based 3D object detection is an important task for autonomous driving and current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions. On the one hand, we introduce a point cloud completion module to recover high-quality proposals of dense points and entire views with original structures preserved. On the other hand, a graph neural network module is designed, which comprehensively captures relations among points through a local-global attention mechanism as well as multi-scale graph based context aggregation, substantially strengthening encoded features. Extensive experiments on the KITTI benchmark show that the proposed approach outperforms the previous state-of-the-art baselines by remarkable margins, highlighting its effectiveness. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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Lifelong property price prediction: A case study for the toronto real estate market

arXiv

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arXiv 2020年

作者： Peng, Hao Li, Jianxin Wang, Zheng Yang, Renyu Liu, Mingzhe Zhang, Mingming Yu, Philip S. He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China School of Computing University of Leeds LeedsLS2 9JT United Kingdom UrBrain Technology Canada Department of Computer Science University of Illinois at Chicago Chicago60607 United States Department of Computer Science and Engineering Lehigh University BethlehemPA18015 United States

We present LUCE, the first life-long predictive model for automated property valuation. LUCE addresses two critical issues of property valuation: the lack of recent sold prices and the sparsity of house data. It is designed to operate on a limited volume of recent house transaction data. As a departure from prior work, LUCE organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation. We employ a Graph Convolutional Network (GCN) to extract the spatial information from the HIN for house-related data like geographical locations, and then use a Long Short Term Memory (LSTM) network to model the temporal dependencies for house transaction data over time. Unlike prior work, LUCE can make effective use of the limited house transactions data in the past few months to update valuation information for all house entities within the HIN. By providing a complete and up-to-date house valuation dataset, LUCE thus massively simplifies the downstream valuation task for the targeting properties. We demonstrate the benefit of LUCE by applying it to large, real-life datasets obtained from the Toronto real estate market. Extensive experimental results show that LUCE not only significantly outperforms prior property valuation methods but also often reaches and sometimes exceeds the valuation accuracy given by independent experts when using the actual realization price as the ground truth. Copyright © 2020, The Authors. All rights reserved.

关键词： Long short-term memory

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Cross-lingual machine reading comprehension with language branch knowledge distillation

arXiv

引用

arXiv 2020年

作者： Liu, Junhao Shou, Linjun Pei, Jian Gong, Ming Yang, Min Jiang, Daxin Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Microsoft STCA NLP Group Beijing China School of Computing Science Simon Fraser University Canada

Cross-lingual Machine Reading Comprehension (CLMRC) remains a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese. Many previous approaches use translation data by translating from a rich-source language, such as English, to low-source languages as auxiliary supervision. However, how to effectively leverage translation data and reduce the impact of noise introduced by translation remains onerous. In this paper, we tackle this challenge and enhance the cross-lingual transferring performance by a novel augmentation approach named Language Branch Machine Reading Comprehension (LBMRC). A language branch is a group of passages in one single language paired with questions in all target languages. We train multiple machine reading comprehension (MRC) models proficient in individual language based on LBMRC. Then, we devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages. Combining the LBMRC and multilingual distillation can be more robust to the data noises, therefore, improving the model’s cross-lingual ability. Meanwhile, the produced single multilingual model is applicable to all target languages, which saves the cost of training, inference, and maintenance for multiple models. Extensive experiments on two CLMRC benchmarks clearly show the effectiveness of our proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

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The association between bone density of lumbar spines and different daily protein intake in different renal function

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Renal Failure 2024年第1期46卷 2298080页

作者： Lee, Chia-Lin Chen, Kun-Hui Liu, Wei‑Ju Chen, Ching-Hsien Tsai, Shang-Feng Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan School of Medicine National Yang-Ming University Taipei Taiwan Division of Endocrinology and Metabolism Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan Intelligent data mining laboratory Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Public Health College of Public Health China Medical University Taichung Taiwan Department of Orthopedic Surgery Taichung Veterans General Hospital Taichung Taiwan Department of Computer Science & Information Engineering College of Computing and Informatics Providence University Taichung Taiwan Divisions of Nephrology and Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine University of California at Davis Davis CA United States Division of Nephrology Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Life Science Tunghai University Taichung Taiwan

Background: Low protein intake (LPI) has been suggested as a treatment for chronic kidney disease (CKD). However, protein intake is essential for bone health. Methods: We studied the database of the National Health and Nutrition Examination Survey, 2005–2010. Basic variables, metabolic diseases, and bone density of different femoral areas were stratified into four subgroups according to different protein intake (DPI) (that is, <0.8, 0.8–1.0, 1.0–1.2, and >1.2 g/kg/day). Results: Significant differences were found among all lumbar area bone mineral density (BMD) and T-scores (p < 0.0001). There was an apparent trend between a decreasing BMD in the CKD groups with increasing DPI in all single lumbar spines (L1, L2, L3, and L4) and all L spines (L1-L4). Compared with DPI (0.8–1.0 g/day/kg), higher risks of osteoporosis were noticed in the subgroup of >1.2 g/day/kg over L2 (relative risk (RR)=1.326, 95% confidence interval (CI)=1.062–1.656), subgroup >1.2 g/day/kg over L3 (RR = 1.31, 95%CI = 1.057–1.622), subgroup <0.8 g/day/kg over L4 (RR = 1.276, 95%CI = 1.015–1.605), subgroup <0.8 g/day/kg over all L spines (RR = 11.275, 95%CI = 1.051–1.548), and subgroup >1.2 g/day/kg over all L spines (RR = 0.333, 95%CI = 1.098–1.618). However, a higher risk of osteoporosis was observed only in the non-CKD group. There was an apparent trend of higher DPI coexisting with lower BMD and T scores in patients with CKD. For osteoporosis (reference:0.8–1.0 g/day/kg), lower (<0.8 g/day/kg) or higher DPI (>1.2 g/day/kg) was associated with higher risks in the non-CKD group, but not in the CKD group. Conclusions: In the CKD group, LPI for renal protection was safe without threatening L spine bone density and without causing a higher risk of osteoporosis. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Bone density chronic kidney disease lumbar spine osteoporosis protein diet

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Accelerated Sparse Recovery via Gradient Descent with Nonlinear Conjugate Gradient Momentum

arXiv

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arXiv 2022年

作者： Hu, Mengqi Lou, Yifei Wang, Bao Yan, Ming Yang, Xiu Ye, Qiang Department of Industrial and Systems Engineering Lehigh University 200 West Packer Avenue BethlehemPA18015 United States Department of Mathematical Sciences The University of Texas at Dallas 800 W. Campbell Rd RichardsonTX75080 United States Department of Mathematics and Scientific Computing Imaging Institute The University of Utah 72 Central Campus Dr Salt Lake CityUT84102 United States School of Data Science The Chinese University of Hong Kong Shenzhen 2001 Longxiang Blvd Guangdong Shenzhen China Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University 428 South Shaw Lane East LansingMI48824 United States Department of Mathematics University of Kentucky LexingtonKY40513 United States

This paper applies an idea of adaptive momentum for the nonlinear conjugate gradient to accelerate optimization problems in sparse recovery. Specifically, we consider two types of minimization problems: a (single) differentiable function and the sum of a non-smooth function and a differentiable function. In the first case, we adopt a fixed step size to avoid the traditional line search and establish the convergence analysis of the proposed algorithm for a quadratic problem. This acceleration is further incorporated with an operator splitting technique to deal with the non-smooth function in the second case. We use the convex 1 and the nonconvex 1 − 2 functionals as two case studies to demonstrate the efficiency of the proposed approaches over traditional methods. © 2022, CC BY-SA.

关键词： Momentum

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Adaptive Handover Decision Inspired By Biological Mechanism in Vehicle Ad-hoc Networks

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Computers, Materials & Continua 2019年第9期61卷 1117-1128页

作者： Xuting Duan Jingyi Wei Daxin Tian Jianshan Zhou Haiying Xia Xin Li Kunxian Zheng Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems&SafetyControl Beijing100191China School of Transportation Science and Engineering Beihang UniversityBeijing100191China Department of Engineering and Design University of SussexBrightonBN19RHUK Research Institute of Highway Ministry Transport Beijing100088China Department of Computer Science and Technology Tsinghua UniversityHaidian DistrictBeijing100088China

In vehicle ad-hoc networks(VANETs),the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes *** by the self-adaptive paradigm of cellular attractors,this paper regards an individual communication as a cell,so that we can apply the revised attractor selection model to induce each connected *** at improving the Quality of Service(QoS),we presented the bio-inspired handover decision-making *** addition,we employ the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)for any vehicle to choose an access *** paper proposes a novel framework where the bio-inspired mechanism is combined with *** a dynamic and random mobility environment,our method achieves the coordination of performance of heterogeneous networks by guaranteeing the efficient utilization and fair distribution of network resources in a global *** experimental results confirm that the proposed method performs better when compared with conventional schemes.

关键词： Revised attraction selection model TOPSIS VANETs QoS

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