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arXiv 2022年

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao The Chien-Shiung Wu College Southeast University Nanjing210096 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Mathematical Science Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Systems Engineering National University of Defense Technology China The 52nd Research Institute of China Electronics Technology Group China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://***/Chongjie-Si/HOMI. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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Global pesticide use and trade database (GloPUT): New estimates show pesticide use trends in low-income countries substantially underestimated

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Global Environmental Change 2023年第1期81卷

作者： Shattuck, Annie Werner, Marion Mempel, Finn Dunivin, Zackary Galt, Ryan Department of Geography Indiana University Bloomington 701 E. Kirkwood Ave. 120 Student Building Bloomington 47405 IN United States Sustainable Food Systems Sciences Initiative Indiana University Bloomington United States Department of Geography State University of New York Buffalo United States Institute of Environmental Science and Technology of the Universitat Autònoma de Barcelona (ICTA-UAB) Spain Center for Complex Networks and Systems Research Luddy School of Informatics Computer Science Engineering Indiana University Bloomington United States Department of Sociology Indiana University Bloomington United States Agricultural Sustainability Institute University of California Davis United States

Assessments of pesticide impacts globally and holistic policies to address them require accurate pesticide use data, but good use data are difficult to find. For comparable estimates across countries, researchers and policymakers depend upon pesticide use data collected by the UN Food and Agriculture Organization (FAO). We analyze the FAO database and find declines in data reporting and data quality since 2007. We present a novel method that uses bilateral paired mirror trade statistics and an index of reporter reliability to add, update and/or replace data for 137 countries. The resulting Global Pesticide Use and Trade (GloPUT) database shows pesticide use in low and lower-middle income countries has been substantially underestimated. Over the last decade, global pesticide use grew 20% by volume;use in low-income countries grew by 153% over the same period. GloPUT estimates more accurately reflect social science findings on recent agrichemical supply chain restructuring and agrarian development, which indicate substantial increases in pesticide use. Significant issues with data reporting and quality mean that the impacts of recent changes in pesticide production, availability and adoption were not reflected in the FAO database, and, as a result, neither are they reflected in high profile environmental assessments. © 2023 Elsevier Ltd

关键词： Agricultural development Environmental health Human-environment geography Pesticide trade Pesticides

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RACE: Reinforced cooperative autonomous vehicle collision avoidance

arXiv

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arXiv 2020年

作者： Yuan, Yali Tasik, Robert Adhatarao, Sripriya Srikant Yuan, Yachao Liu, Zheli Fu, Xiaoming Computer Networking Group Institute of Computer Science University of Goettingen Goettingen37077 Germany Smart Mobility Research Group Institute of Information Systems University of Goettingen Goettingen37075 Germany college of computer and control engineering Nankai University Tianjin China

—With the rapid development of autonomous driving, collision avoidance has attracted attention from both academia and industry. Many collision avoidance strategies have emerged in recent years, but the dynamic and complex nature of driving environment poses a challenge to develop robust collision avoidance algorithms. Therefore, in this paper, we propose a decentralized framework named RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE. Leveraging a hierarchical architecture we develop an algorithm named Co-DDPG to efficiently train autonomous vehicles. Through a security abiding channel, the autonomous vehicles distribute their driving policies. We use the relative distances obtained by the opponent sensors to build the VANET instead of locations, which ensures the vehicle’s location privacy. With a leader-follower architecture and parameter distribution, RACE accelerates the learning of optimal policies and efficiently utilizes the remaining resources. We implement the RACE framework in the widely used TORCS simulator and conduct various experiments to measure the performance of RACE. Evaluations show that RACE quickly learns optimal driving policies and effectively avoids collisions. Moreover, RACE also scales smoothly with varying number of participating vehicles. We further compared RACE with existing autonomous driving systems and show that RACE outperforms them by experiencing 65% less collisions in the training process and exhibits improved performance under varying vehicle density. Copyright © 2020, The Authors. All rights reserved.

关键词： Autonomous vehicles

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You Can Wash Hands Better: Accurate Daily Handwashing Assessment with Smartwatches

arXiv

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arXiv 2021年

作者： Wang, Fei Wu, Xilei Zhang, Tingting Wang, Xin Wang, Pengcheng Ding, Han Shi, Jingang Han, Jinsong Huang, Dong School of Software Engineering Xi’an Jiaotong University Xi’an China Local Life Research and Development Center Kuaishou Technology Inc. Beijing China School of Computer Science and Technology Xi’an Jiaotong University Xi’an China College of Computer Science and Technology Zhejiang University Hangzhou China Robotics Institute Carnegie Mellon University Pittsburgh United States

Hand hygiene is among the most effective daily practices for preventing infectious diseases such as influenza, malaria, and skin infections. While professional guidelines emphasize proper handwashing to reduce the risk of viral infections, surveys reveal that adherence to these recommendations remains low. To address this gap, we propose UWash, a wearable solution leveraging smartwatches to evaluate handwashing procedures, aiming to raise awareness and cultivate high-quality handwashing habits. We frame the task of handwashing assessment as an action segmentation problem, similar to those in computer vision, and introduce a simple yet efficient two-stream UNet-like network to achieve this goal. Experiments involving 51 subjects demonstrate that UWash achieves 92.27% accuracy in handwashing gesture recognition, an error of Copyright © 2021, The Authors. All rights reserved.

关键词： Diseases

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CircRNAFisher: a systematic computational approach for de novo circular RNA identification

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中国药理学报（英文版） 2019年第1期40卷 55-63页

作者： Guo-yi Jia Duo-lin Wang Meng-zhu Xue Yu-wei Liu Yu-chen Pei Ying-qun Yang Jing-mei Xu Yan-chun Liang Peng Wang School of Life Sciences Shanghai University Shanghai 200444 China Laboratory of Systems Biology Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China Laboratory of Systems Biology Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210 China Laboratory of Systems Biology Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210 China School of Life Science and Technology Shanghai Tech University Shanghai 201210 China

Circular RNAs (circRNAs) are emerging species of mRNA splicing products with largely unknown *** several computational pipelines for circRNA identification have been developed,these methods strictly rely on uniquely mapped reads overlapping back-splice junctions (BSJs) and lack approaches to model the statistical significance of the identified ***,we reported a systematic computational approach to identify circRNAs by simultaneously utilizing BSJ overlapping reads and discordant BSJ spanning reads to identify ***,we developed a novel procedure to estimate the P-values of the identified circRNAs.A computational cross-validation and experimental validations demonstrated that our method performed favorably compared to existing circRNA detection *** created a standalone tool,CircRNAFisher,to implement the method,which might be valuable to computational and experimental scientists studying circRNAs.

关键词： circRNA RNA-Seq alternative splicing pipeline

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Correlation between Cation Order/Disorder and the Electrocaloric Effect in Relaxor Ferroelectrics

SSRN

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SSRN 2022年

作者： Yan, Yongke Geng, Liwei D. Cheng, Li-Qian Li, Xiaotian Leng, Haoyang Wang, Ke Poudel, Bed Nozariasbmarz, Amin Sanghadasa, Mohan Trolier-McKinstry, Susan Zhang, Qi-Ming Wang, Yu U. Priya, Shashank Department of Materials Science and Engineering Pennsylvania State University University ParkPA16802 United States Department of Materials Science and Engineering Michigan Technological University HoughtonMI49931 United States Materials Research Institute Pennsylvania State University University ParkPA16802 United States School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA16802 United States U.S. Army Combat Capabilities Development Command Aviation & Missile Center Redstone ArsenalAL35898 United States

The physical properties (dielectric, ferroelectric, piezoelectric, etc.) of relaxor ferroelectric complex perovskites depend on the degree of order/disorder and the scale of the ordered domains. In this study, the electrocaloric (EC) properties of three representative relaxors,Pb(Mg1/3Nb2/3)O3-8PbTiO3 (PMN-8PT), 1mol% Sm-doped Pb(Mg1/3Nb2/3)O3-8PbTiO3 (1SPMN-8PT) and Pb(Sc1/2Ta1/2)O3 (PST) are evaluated. Multi-layer ceramic capacitors (MLCCs) with identical structural configurations were fabricated for these three compounds, and their EC properties were characterized by direct measurement using a thermocouple. The EC temperature change ∆T of PMN-8PT, 1S-PMN-8PT and PST MLCCs under 20 V μm−1 at room temperature were found to be 1.42 K, 1.54 K, and 3.10 K, respectively. X-ray diffraction and high-resolution transmission electron microscopy data suggests that the high EC performance of PST is related to the ordering of B-site cations (Sc3+ and Ta5+) with the ordering parameter S = 0.82 and a long coherence length of ~100 nm, such that the sample transitioned from a relaxor ferroelectric to a normal ferroelectric. These results provide pathway towards design of high performance EC materials required for solid state refrigeration and air-conditioning technologies. © 2022, The Authors. All rights reserved.

关键词： Perovskite

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Rate-Splitting Multiple Access for Uplink Massive MIMO With Electromagnetic Exposure Constraints

arXiv

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arXiv 2022年

作者： Jiang, Hanyu You, Li Elzanaty, Ahmed Wang, Jue Wang, Wenjin Gao, Xiqi Alouini, Mohamed-Slim The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211100 China The 5GIC & 6GIC Institute for Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom School of Information Science and Technology Nantong University Nantong226019 China Nantong Research Institute for Advanced Communication Technologies Nantong226019 China The Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Makkah Province Thuwal Saudi Arabia

Over the past few years, the prevalence of wireless devices has become one of the essential sources of electromagnetic (EM) radiation to the public. Facing with the swift development of wireless communications, people are skeptical about the risks of long-term exposure to EM radiation. As EM exposure is required to be restricted at user terminals, it is inefficient to blindly decrease the transmit power, which leads to limited spectral efficiency and energy efficiency (EE). Recently, rate-splitting multiple access (RSMA) has been proposed as an effective way to provide higher wireless transmission performance, which is a promising technology for future wireless communications. To this end, we propose using RSMA to increase the EE of massive MIMO uplink while limiting the EM exposure of users. In particularly, we investigate the optimization of the transmit covariance matrices and decoding order using statistical channel state information (CSI). The problem is formulated as non-convex mixed integer program, which is in general difficult to handle. We first propose a modified water-filling scheme to obtain the transmit covariance matrices with fixed decoding order. Then, a greedy approach is proposed to obtain the decoding permutation. Numerical results verify the effectiveness of the proposed EM exposure-aware EE maximization scheme for uplink RSMA. © 2022, CC BY-NC-ND.

关键词： Energy efficiency

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Multimodal foundation models for material property prediction and discovery

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Newton 2025年第1期1卷

作者： Viggo Moro Charlotte Loh Rumen Dangovski Ali Ghorashi Andrew Ma Zhuo Chen Samuel Kim Peter Y. Lu Thomas Christensen Marin Soljačić Department of Physics Massachusetts Institute of Technology Cambridge MA 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02139 USA Research and Exploratory Development John Hopkins University Applied Physics Laboratory Laurel MD 20723 USA Data Science Institute University of Chicago Chicago IL 60637 USA Department of Electrical and Photonics Engineering Technical University of Denmark 2800 Kongens Lyngby Denmark

Artificial intelligence is transforming computational materials science by improving property prediction and accelerating the discovery of novel materials. Recently, publicly available material data repositories have grown rapidly, encompassing not only more materials but also a greater variety and quantity of their associated properties. Existing machine-learning efforts in materials science focus primarily on single-modality tasks, i.e., relationships between materials and a single physical property, thus not taking advantage of the rich multimodal data available. Here, we introduce multimodal learning for materials (MultiMat), a framework enabling self-supervised multimodal training of foundation models for materials. Using the Materials Project database, we demonstrate the potential of MultiMat by: (1) achieving state-of-the-art performance for challenging material property prediction tasks; (2) enabling novel and accurate material discovery via latent-space similarity, allowing screening for stable materials with desired properties; and (3) encoding emergent features that correlate with material properties and may provide novel scientific insights.

关键词： multimodal learning foundation models machine learning for materials science material property prediction material discovery contrastive learning representation learning self-supervised learning

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Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss

arXiv

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arXiv 2023年

作者： Zhu, Jiarui Chen, Weixing Sun, Hongfei Zhi, Shaohua Zhao, Mayang Lap, Lam Yu Zhou, Zihan Peng, Tao Cai, Jing Ren, Ge Department of Health Technology and Informatics The Hong Kong Polytechnic University 999077 Hong Kong School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Department of Radiation Oncology Xijing Hospital Fourth Military Medical University Xian710032 China Department of Clinical Oncology Queen Mary Hospital 999077 Hong Kong Department of Radiation Oncology Jinshazhou Hospital Guangzhou University of Chinese Medicine Guangzhou510168 China School of Future Science and Engineering Soochow University Suzhou215299 China Research Institute for Intelligent Wearable Systems The Hong Kong Polytechnic University 999077 Hong Kong

Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy (IGRT) to provide updated patient anatomy information for cancer treatments. However, CBCT images often suffer from streaking artifacts and noise caused by under-rate sampling projections and low-dose exposure, resulting in low clarity and information loss. While recent deep learning-based CBCT enhancement methods have shown promising results in suppressing artifacts, they have limited performance on preserving anatomical details since conventional pixel-to-pixel loss functions are incapable of describing detailed anatomy. To address this issue, we propose a novel feature-oriented deep learning framework that translates low-quality CBCT images into high-quality CT-like imaging via a multi-task customized feature-to-feature perceptual loss function. The framework comprises two main components: a multi-task learning feature-selection network(MTFS-Net) for customizing the perceptual loss function;and a CBCT-to-CT translation network guided by feature-to-feature perceptual loss, which uses advanced generative models such as U-Net, GAN and CycleGAN. Our experiments showed that the proposed framework can generate synthesized CT (sCT) images for the lung that achieved a high similarity to CT images, with an average SSIM index of 0.9869 and an average PSNR index of 39.9621. The sCT images also achieved visually pleasing performance with effective artifacts suppression, noise reduction, and distinctive anatomical details preservation. Functional imaging tests further demonstrated the pulmonary texture correction performance of the sCT images. Comparison experiments with pixel-to-pixel loss also showed that the proposed perceptual loss significantly enhances the performance of involved generative models. Our experiment results indicate that the proposed framework outperforms the state-of-the-art models for pulmonary CBCT enhancement. This framework holds great promise for generat

关键词： Pixels

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Multi-objective Optimal Control of Dynamic Integrated Model of Climate and Economy: Evolution in Action

arXiv

引用

arXiv 2020年

作者： Heris, Mostapha Kalami Rahnamayan, Shahryar Intelligent Systems Lab. Research and Development Department FaraDars Online Education Organization Tehran Iran Lab Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

One of the widely used models for studying economics of climate change is the Dynamic Integrated model of Climate and Economy (DICE), which has been developed by Professor William Nordhaus, one of the laureates of the 2018 Nobel Memorial Prize in Economic Sciences. Originally a single-objective optimal control problem has been defined on DICE dynamics, which is aimed to maximize the social welfare. In this paper, a bi-objective optimal control problem defined on DICE model, objectives of which are maximizing social welfare and minimizing the temperature deviation of atmosphere. This multi-objective optimal control problem solved using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) also it is compared to previous works on single-objective version of the problem. The resulting Pareto front rediscovers the previous results and generalizes to a wide range of non-dominant solutions to minimize the global temperature deviation while optimizing the economic welfare. The previously used single-objective approach is unable to create such a variety of possibilities, hence, its offered solution is limited in vision and reachable performance. Beside this, resulting Pareto-optimal set reveals the fact that temperature deviation cannot go below a certain lower limit, unless we have significant technology advancement or positive change in global conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Climate change

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