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Sub-femtonewton force sensing in solution by super-resolved photonic force microscopy (Jun, 10.1038/s41566-024-01462-7, 2024)

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NATURE PHOTONICS 2024年第9期18卷 998-998页

作者： Shan, Xuchen Ding, Lei Wang, Dajing Wen, Shihui Shi, Jinlong Chen, Chaohao Wang, Yang Zhu, Hongyan Huang, Zhaocun Wang, Shen S. J. Zhong, Xiaolan Liu, Baolei Reece, Peter John Ren, Wei Hao, Weichang Lu, Xunyu Lu, Jie Su, Qian Peter Chang, Lingqian Sun, Lingdong Jin, Dayong Jiang, Lei Wang, Fan Beihang University School of Physics Beijing People’s Republic of China (GRID:grid.64939.31) (ISNI:0000 0000 9999 1211) University of Technology Sydney School of Biomedical Engineering Faculty of Engineering and Information Technology Sydney Australia (GRID:grid.117476.2) (ISNI:0000 0004 1936 7611) RMIT University Centre for Atomaterials and Nanomanufacturing School of Science Melbourne Australia (GRID:grid.1017.7) (ISNI:0000 0001 2163 3550) University of Technology Sydney Institute for Biomedical Materials and Devices (IBMD) School of Mathematical and Physical Sciences Faculty of Science Sydney Australia (GRID:grid.117476.2) (ISNI:0000 0004 1936 7611) The Australian National University Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems Department of Electronic Materials Engineering Research School of Physics Canberra Australia (GRID:grid.1001.0) (ISNI:0000 0001 2180 7477) Beihang University Key Laboratory of Biomechanics and Mechanobiology (Ministry of Education) Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beijing People’s Republic of China (GRID:grid.64939.31) (ISNI:0000 0000 9999 1211) University of Melbourne Computing and Information Systems Department Melbourne School of Engineering Melbourne Australia (GRID:grid.1008.9) (ISNI:0000 0001 2179 088X) The University of New South Wales School of Physics Sydney Australia (GRID:grid.1005.4) (ISNI:0000 0004 4902 0432) Shaanxi Normal University School of Chemistry & Chemical Engineering Xi’an People’s Republic of China (GRID:grid.412498.2) (ISNI:0000 0004 1759 8395) The University of New South Wales Particles and Catalysis Research Laboratory School of Chemical Engineering Sydney Australia (GRID:grid.1005.4) (ISNI:0000 0004 4902 0432) University of Technology Sydney Australian Artificial Intelligence Institute Faculty of Engineering and IT Sydney Australia (GRID:grid.117476.2) (ISNI:0000 0004 1936 7611) University of

Precise force measurement is critical to probe biological events and physics processes, spanning from molecular motor’s motion to the Casimir effect, as well as the detection of gravitational waves. Yet, despite extensive technological developments, the three-dimensional nanoscale measurement of weak forces in aqueous solutions still faces major challenges. Techniques that rely on optically trapped nanoprobes are of significant potential but are beset with limitations, including probe heating induced by high trapping power, undetectable scattering signals and localization errors. Here we report the measurement of the long-distance interaction force in aqueous solutions with a minimum detected force value of 108.2 ± 510.0 attonewton. To achieve this, we develop a super-resolved photonic force microscope based on optically trapped lanthanide-doped nanoparticles coupled with nanoscale three-dimensional tracking-based force sensing. The tracking method leverages neural-network-empowered super-resolution localization, where the position of the force probe is extracted from the optical-astigmatism-modified point spread function. We achieve a force sensitivity down to 1.8 fN Hz–1/2, which approaches the nanoscale thermal limit. We experimentally measure electrophoresis forces acting on single nanoparticles as well as the surface-induced interaction force on a single nanoparticle. This work opens the avenue of nanoscale thermally limited force sensing and offers new opportunities for detecting sub-femtonewton forces over long distances and biomechanical forces at the single-molecule ***-resolved photonic force microscopy employs the fluorescence of lanthanide-doped nanoparticles as a force probe, enabling the measurement of sub-femtonewton forces with a sensitivity of 1.8 fN Hz–1/2, approaching the thermal limit.

关键词： Electrophoresis

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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Ya'nan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China Eth Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech Advanced Research Center KKCompany Taiwan ByteDance China Mgtv China Fuzhou University China Shopee Information Technology Co. Ltd. S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China Insa Rennes Cnrs Ietr - Umr 6164 Rennes France National Higher School of Telecommunications and Ict Oran Algeria Buaa Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798350302493

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. © 2023 IEEE.

关键词： Image enhancement

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Does Thermal Really Always Matter for RGB-T Salient Object Detection?

arXiv

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arXiv 2022年

作者： Cong, Runmin Zhang, Kepu Zhang, Chen Zheng, Feng Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong China The Department of Computer Science and Technology Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by introducing thermal image. However, most of the existing RGBT SOD models focus on how to perform cross-modality feature fusion, ignoring whether thermal image is really always matter in SOD task. Starting from the definition and nature of this task, this paper rethinks the connotation of thermal modality, and proposes a network named TNet to solve the RGB-T SOD task. In this paper, we introduce a global illumination estimation module to predict the global illuminance score of the image, so as to regulate the role played by the two modalities. In addition, considering the role of thermal modality, we set up different cross-modality interaction mechanisms in the encoding phase and the decoding phase. On the one hand, we introduce a semantic constraint provider to enrich the semantics of thermal images in the encoding phase, which makes thermal modality more suitable for the SOD task. On the other hand, we introduce a two-stage localization and complementation module in the decoding phase to transfer object localization cue and internal integrity cue in thermal features to the RGB modality. Extensive experiments on three datasets show that the proposed TNet achieves competitive performance compared with 20 state-of-the-art methods. The code and results can be found from the link of https://***/proj_***. © 2022, CC BY-NC-SA.

关键词： Semantics

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A Review on Wireless Charging Methods, Coil Design, Applications, Optimization Techniques and Challenges of Electric Vehicle

A Review on Wireless Charging Methods, Coil Design, Applicat...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Bilal Alam Saad Mekhilef Mehdi Seyedmahmoudian Alex Stojcevski Ben Horan M. Rawa Mohamed Becherif Ahmad Department of Electrical Engineering Power Electronics and Renewable Energy Research Laboratory University of Malaya Kuala Lumpur Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn VIC Australia School of Engineering Deakin University Geelong VIC Australia Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia FEMTO-ST Institute Univ. Bourgogne Franche-Comté UTBM CNRS Rue Ernest Thierry Mieg Belfort France Department of Electrical Engineering Aligarh Muslim University India

This article provides an up-to-date overview of wireless charging technologies, coupler pad design, standards, and optimization strategies. First, the key qualities of an electric vehicle (EV) are explained. Wireless charging approaches and the kind of charging system that depends on vehicle location during charging have been described in detail. The many varieties of coupler pad designs are presented using a graphic and a table. The topics of application and standards are covered. Following that, some of the most often utilized optimization approaches for EV charging station sizing and location are examined. A comparative analysis of the optimization of various optimization techniques has been outlined in tabular form. The paper gave researchers and scholars a thorough understanding of the fundamentals of WPT and its mechanism of operation, with a focus on its explicit application in EVs.

关键词： Wireless communication Graphics Renewable energy sources Inductive charging Hydrogen Transportation Couplers

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Evaluation of the geochemical background of soil in a hyper-arid zone using a multivariate statistical methodology: The case of the city of Antofagasta in the Atacama Desert

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Chemosphere 2024年 366卷 143472页

作者： Keith, Brian F. Lam, Elizabeth J. Montofré, Ítalo L. Zetola, Vicente Urrutia, Javier Herrera, Christian Bech, Jaume Department of Computing & Systems Engineering Universidad Católica del Norte Antofagasta1270709 Chile Chemical Engineering Department Universidad Católica del Norte Antofagasta1270709 Chile Mining and Metallurgical Engineering Department Universidad Católica del Norte Antofagasta1270709 Chile Mining Business School ENM Universidad Católica del Norte Antofagasta Chile Construction Management Department Universidad Católica del Norte Antofagasta1270709 Chile Center for Research and Development of Water Ecosystems Universidad Bernardo O'Higgins Santiago Chile Departamento de Ciencias Geológicas Universidad Católica del Norte Antofagasta1270709 Chile Soil Science Laboratory Faculty of Biology Universidad de Barcelona Barcelona Spain

This study investigates the geochemical background and factors influencing the variability of 19 environmentally relevant elements in the soils of Antofagasta, Chile, a region known for its extensive mining activities. Employing robust multivariate statistical techniques on a dataset of 94 soil samples, we identified four main factors explaining 70% of the total variance in elemental concentrations. These four factors reflect the influence of Jurassic volcanic rocks, intrusive rocks, marine sediments, and mafic to intermediate intrusive rocks. Cluster analysis revealed three distinct geochemical populations, each reflecting a unique combination of natural and anthropogenic influences. We established background concentrations for each element within these clusters using robust statistical methods. Geostatistical analysis, employing inverse distance weighted interpolation, produced factor distribution maps that, when integrated with geological data, provided insights into lithological and anthropogenic influences on soil geochemistry. Our findings highlight the complex interplay between natural geological processes, the region's unique arid climate, and anthropogenic activities in shaping the geochemical landscape of Antofagasta. This study contributes to the understanding of geochemical backgrounds in mining-intensive, arid regions and provides a methodological framework applicable to similar environments worldwide. © 2024 Elsevier Ltd

关键词： Jurassic

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AI exposure predicts unemployment risk

arXiv

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arXiv 2023年

作者： Frank, Morgan R. Ahn, Yong-Yeol Moro, Esteban Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15216 United States Digital Economy Lab Institute for Human-Centered Artificial Intelligence Stanford University StanfordCA94305 United States Media Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Connection Science Massachusetts Institute of Technology CambridgeMA United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Indiana University Network Science Institute Indiana University BloomingtonIN United States Department of Mathematics & GISC Universidad Carlos III de Madrid Leganes28911 Spain Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States

Is artificial intelligence (AI) disrupting jobs and creating unemployment? Despite many attempts to quantify occupations' exposure to AI, inconsistent validation obfuscates the relative benefits of each approach. A lack of disaggregated labor outcome data, including unemployment data, further exacerbates the issue. Here, we assess which models of AI exposure predict job separations and unemployment risk using new occupation-level unemployment data by occupation from each US state's unemployment insurance office spanning 2010 through 2020. Although these AI exposure scores have been used by governments and industry, we find that individual AI exposure models are not predictive of unemployment rates, unemployment risk, or job separation rates. However, an ensemble of those models exhibits substantial predictive power suggesting that competing models may capture different aspects of AI exposure that collectively account for AI's variable impact across occupations, regions, and time. Our results also call for dynamic, context-aware, and validated methods for assessing AI exposure. Interactive visualizations for this study are available at https://***/~mrfrank/uiRiskDemo/. © 2023, CC BY.

关键词： Visualization

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A Review of Barren Plateaus in Variational Quantum computing

arXiv

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arXiv 2024年

作者： Larocca, Martín Thanasilp, Supanut Wang, Samson Sharma, Kunal Biamonte, Jacob Coles, Patrick J. Cincio, Lukasz McClean, Jarrod R. Holmes, Zoë Cerezo, M. Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States Center for Non-Linear Studies Los Alamos National Laboratory NM87545 United States LausanneCH-1015 Switzerland Chula Intelligent and Complex Systems Department of Physics Faculty of Science Chulalongkorn University Bangkok10330 Thailand Institute for Quantum Information and Matter Caltech PasadenaCA91125 United States IBM Quantum IBM T.J. Watson Research Center Yorktown HeightsNY10598 United States Quantum Artificial Intelligence Laboratory NASA Ames Research Center Moffett Federal Airfield Mountain ViewCA94035 United States Simons Institute for the Theory of Computing University of California BerkeleyCA94720-2190 United States Normal Computing Corporation New YorkNY United States Quantum Science Center Oak RidgeTN37931 United States Google Quantum AI VeniceCA90291 United States Information Sciences Los Alamos National Laboratory NM87545 United States

Variational quantum computing offers a flexible computational paradigm with applications in diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) phenomenon. When a model exhibits a BP, its parameter optimization landscape becomes exponentially flat and featureless as the problem size increases. Importantly, all the moving pieces of an algorithm—choices of ansatz, initial state, observable, loss function and hardware noise—can lead to BPs when ill-suited. Due to the significant impact of BPs on trainability, researchers have dedicated considerable effort to develop theoretical and heuristic methods to understand and mitigate their effects. As a result, the study of BPs has become a thriving area of research, influencing and cross-fertilizing other fields such as quantum optimal control, tensor networks, and learning theory. This article provides a comprehensive review of the current understanding of the BP phenomenon. Copyright © 2024, The Authors. All rights reserved.

关键词： Heuristic methods

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Spatial computing: Concept, Applications, Challenges and Future Directions

arXiv

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arXiv 2024年

作者： Yenduri, Gokul Ramalingam, M. Maddikunta, Praveen Kumar Reddy Gadekallu, Thippa Reddy Jhaveri, Rutvij H. Bandi, Ajay Chen, Junxin Wang, Wei Shirawalmath, Adarsh Arunkumar Ravishankar, Raghav Wang, Weizheng School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology TamilNadu 632014 India Zhongda Group Haiyan County Zhejiang Province Jiaxing City314312 China College of Information Science and Engineering Jiaxing University Jiaxing314001 China Division of Research and Development Lovely Professional University Phagwara India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gandhinagar India School of Computer Science and Information Systems Northwest Missouri State University 800 University Dr MaryvilleMO64468 United States School of Software Dalian University of Technology Dalian116621 China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Computer Science and Engineering Vellore Institute of Technology TamilNadu 632014 India Department of Computer Science City University of Hong Kong Hong Kong

Spatial computing is a technological advancement that facilitates the seamless integration of devices into the physical environment, resulting in a more natural and intuitive digital world user experience. Spatial computing has the potential to become a significant advancement in the field of computing. From GPS and location-based services to healthcare, spatial computing technologies have influenced and improved our interactions with the digital world. The use of spatial computing in creating interactive digital environments has become increasingly popular and effective. This is explained by its increasing significance among researchers and industrial organisations, which motivated us to conduct this review. This review provides a detailed overview of spatial computing, including its enabling technologies and its impact on various applications. Projects related to spatial computing are also discussed. In this review, we also explored the potential challenges and limitations of spatial computing. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of spatial computing, its enabling technologies, their impact on various applications, emerging challenges, and potential solutions. © 2024, CC BY.

关键词： Augmented reality

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Cosmology with fast radio bursts in the era of SKA

arXiv

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arXiv 2023年

作者： Zhang, Ji-Guo Zhao, Ze-Wei Li, Yichao Zhang, Jing-Fei Li, Di Zhang, Xin Department of Physics College of Sciences Northeastern University Shenyang110819 China Northeastern University Shenyang110819 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China

We present a forecast of the cosmological parameter estimation using fast radio bursts (FRBs) from the upcoming Square Kilometre Array (SKA), focusing on the issues of dark energy, the Hubble constant, and baryon density. We simulate 105 and 106 localized FRBs from a 10-year SKA observation, and find that: (i) using 106 FRB data alone can tightly constrain dark-energy equation of state parameters better than CMB+BAO+SNe, providing an independent cosmological probe to explore dark energy;(ii) combining the FRB data with gravitational-wave standard siren data from 10-year observation with the Einstein Telescope, the Hubble constant can be constrained to a sub-percent level, serving as a powerful low-redshift probe;(iii) using 106 FRB data can constrain the baryon density Ωbh to a precision of ∼ 0.1%. Our results indicate that SKA-era FRBs will provide precise cosmological measurements to shed light on both dark energy and the missing baryon problem, and help resolve the Hubble tension. Copyright © 2023, The Authors. All rights reserved.

关键词： Cosmology

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Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining

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Journal of Computer science & Technology 2020年第2期35卷 305-319页

作者： Fu-Zhen Zhuang Ying-Min Zhou Hao-Chao Ying Fu-Zheng Zhang Xiang Ao Xing Xie Qing He Hui Xiong Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Public Health Zhejiang University School of MedicineHangzhou 310027China Meituan-Dianping Group Beijing 100102China Microsoft Research Asia Beijing 100080China Department of Management Science and Information Systems Rutgers UniversityNew Jersey 07102U.S.A.

Transfer learning has attracted a large amount of interest and research in last decades, and some effort has been made to build more precise recommendation systems. Most previous transfer recommendation systems assume that the target domain shares the same/similar rating patterns with the auxiliary source domain, which is used to improve the recommendation performance. However, almost all existing transfer learning work does not consider the characteristics of sequential data. In this paper, we study the new cross-domain recommendation scenario by mining novelty-seeking trait. Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior, which has a profound business impact on online recommendation. Previous work performed on only one single target domain may not fully characterize users' novelty-seeking trait well due to the data scarcity and sparsity, leading to the poor recommendation performance. Along this line, we propose a new cross-domain novelty-seeking trait mining model (CDNST for short) to improve the sequential recommendation performance by transferring the knowledge from auxiliary source domain. We conduct systematic experiments on three domain datasets crawled from Douban to demonstrate the effectiveness of our proposed model. Moreover, we analyze the directed influence of the temporal property at the source and target domains in detail.

关键词： sequential recommendation novelty-seeking trait transfer learning

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