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Author Correction: Sub-femtonewton force sensing in solution by super-resolved photonic force microscopy

Nature Photonics 2024年 998页

作者： Xuchen Shan Dajing Wang Jinlong Shi Hongyan Zhu Xiaolan Zhong Baolei Liu Weichang Hao Fan Wang Lei Ding Qian Peter Su Shihui Wen Dayong Jin Lei Jiang Chaohao Chen Yang Wang Zhaocun Huang Lingqian Chang Shen S. J. Wang Peter John Reece Wei Ren Xunyu Lu Jie Lu Lingdong Sun School of Physics Beihang University Beijing People’s Republic of China Science and Technology Center for Quantum Biology National Institute of Extremely-Weak Magnetic Field Infrastructure Hangzhou People’s Republic of China School of Biomedical Engineering Faculty of Engineering and Information Technology University of Technology Sydney Sydney New South Wales Australia Centre for Atomaterials and Nanomanufacturing School of Science RMIT University Melbourne Victoria Australia Institute for Biomedical Materials and Devices (IBMD) School of Mathematical and Physical Sciences Faculty of Science University of Technology Sydney Sydney New South Wales Australia Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo People’s Republic of China Key Laboratory of Bio-Inspired Materials and Interfaces Sciences Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing People’s Republic of China Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems Department of Electronic Materials Engineering Research School of Physics The Australian National University Canberra Australian Capital Territory Australia Key Laboratory of Biomechanics and Mechanobiology (Ministry of Education) Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing People’s Republic of China Computing and Information Systems Department Melbourne School of Engineering University of Melbourne Melbourne Victoria Australia School of Physics The University of New South Wales Sydney New South Wales Australia School of Chemistry & Chemical Engineering Shaanxi Normal University Xi’an People’s Republic of China Particles and Catalysis Research Laboratory School of Chemical Engineering The University of New South Wales Sydney New South Wales Australia Australian Artificial Intelligence Institute Faculty of Engineering and IT University of Technology

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An Efficient Truth Discovery Mechanism for Crowdsensing Tasks With Temporal and Spatial Correlations

An Efficient Truth Discovery Mechanism for Crowdsensing Task...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Runzhi Wang Yu-E Sun He Huang Le Lu Yang Du Danlei Huang School of Computer Science and Technology Soochow University China School of Rail Transportation Soochow University China Department of Computer and Information of Science and Engineering University of Florida US The Suzhou Institute for Advanced Study University of Science and Technology of China China

Crowdsensing is a promising sensing paradigm to efficiently collect and monitor the physical world by using the embedded sensors in mobile devices. However, the observations (sensory data) submitted by mobile device users may not be reliable. For the same sensing task, users with different reliabilities may submit conflicting information. Thus, we need to estimate the truth based on the submitted observations. Temporal and spatial correlations among tasks are widely observed in crowdsensing applications. However, most of the existing truth discovery mechanisms assume that the tasks are independent, which is not suitable for all crowdsensing applications. To solve this problem, we propose an efficient truth discovery mechanism for crowdsensing tasks with temporal and spatial correlations. To improve the reliability of the estimated truth, we first filter the outliers based on the temporal correlations among tasks, then estimate the truth based on the weighted observations, and finally refine the estimated truth based on the spatial correlations among tasks. Our experiments on a real transportation dataset show the efficiency of the proposed mechanism.

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CPDM: An Efficient Crowdsensing-Based Pothole Detection and Measurement System Design

CPDM: An Efficient Crowdsensing-Based Pothole Detection and ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xiaofeng Zhao Xiaocan Wu Yu-E Sun He Huang Yang Du Zhen Cao School of Rail Transportation Soochow University China School of Computer Science and Technology Soochow University China Department of Computer and Information of Science and Engineering University of Florida US The Suzhou Institute for Advanced Study University of Science and Technology of China China

Real-time road condition monitoring, especially of severe road damage, is critical for driving safety. In this work, we propose a crowdsensing-based pothole detection and measurement system, called CPDM, which can accurately measure the depth and length of detected potholes in urban roads. CPDM learns road surface information by collecting the sensor data from the smartphones belonging to the passing drivers and passengers. The proposed system can detect potholes by observing the vibration signal when the vehicle passes over the pothole, learn the vehicle parameters by fitting the underdamping vibration signal with stochastic gradient descent, and estimate the depth and length of the pothole. We have implemented a prototype system to prove the efficiency and adaptability of the proposed CPDM. The experimental results based on real traces show that CPDM has a high detection accuracy at different vehicle speeds.

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Quality-Aware Pricing for Mobile Crowdsensing

Quality-Aware Pricing for Mobile Crowdsensing

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作者： Han, Kai Huang, He Luo, Jun School of Computer Science and Technology Suzhou Institute for Advanced Study University of Science and Technology of China Hefei230026 China School of Computer Science and Technology Soochow University Suzhou215000 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Mobile crowdsensing has been considered as a promising approach for large scale urban data collection, but has also posed new challenging problems, such as incentivization and quality control. Among the other incentivization approaches, posted pricing has been widely adopted by commercial systems due to the reason that it naturally achieves truthfulness and fairness and is easy to be implemented. However, the fundamental problem of how to set the 'right' posted prices in crowdsensing systems remains largely open. In this paper, we study a quality-aware pricing problem for mobile crowdsensing, and our goal is to choose an appropriate posted price to recruit a group of participants with reasonable sensing qualities for robust crowdsensing, while the total expected payment is minimized. We show that our problem is NP-hard and has close ties with the well-known Poisson binomial distributions (PBDs). To tackle our problem, we first discover some non-trivial submodular properties of PBD, which have not been reported before, and then propose a novel 'ironing method' that transforms our problem from a non-submodular optimization problem into a submodular one by leveraging the newly discovered properties of PBD. Finally, with the ironing method, several approximation algorithms with provable performance ratios are provided, and we also conduct extensive numerical experiments to demonstrate the effectiveness of our approach. © 1993-2012 IEEE.

关键词： Approximation algorithms

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Quantum criticality tuned by magnetic field in optimally electron-doped cuprate thin films

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Physical Review B 2021年第1期103卷 014517-014517页

作者： Xu Zhang Heshan Yu Qihong Chen Runqiu Yang Ge He Ziquan Lin Qian Li Jie Yuan Beiyi Zhu Liang Li Yi-feng Yang Tao Xiang Rong-Gen Cai Anna Kusmartseva F. V. Kusmartsev Jun-Feng Wang Kui Jin Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing 100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China Department of Materials Science and Engineering University of Maryland College Park Maryland 20742 USA Quantum Universe Center Korea Institute for Advanced Study Seoul 130-722 Korea Center for Joint Quantum Studies and Department of Physics School of Science Tianjin University Yaguan Road 135 Jinnan District Tianjin 300350 China Wuhan National High Magnetic Field Center Huazhong University of Science and Technology Wuhan 430074 China Songshan Lake Materials Laboratory Dongguan Guangdong 523808 China CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China Department of Physics Loughborough University Loughborough LE11 3TU United Kingdom College of Art and Science Khalifa University P.O. Box 127788 Abu Dhabi United Arab Emirates

Antiferromagnetic (AF) spin fluctuations are commonly believed to play a key role in electron pairing of cuprate superconductors. In electron-doped cuprates, a paradox still exists about the interplay among different electronic states in quantum perturbations, especially between superconducting and magnetic states. Here, we report a systematic transport study of cation-optimized La2−xCexCuO4±δ (x=0.10) thin films in high magnetic fields. We find an AF quantum phase transition near 60 T, where the Hall number jumps from nH=−x to nH=1−x, resembling the change in nH at the AF boundary (xAF=0.14) tuned by Ce doping. In the AF region a spin-dependent state manifesting anomalous positive magnetoresistance is observed, which is closely related to superconductivity. Once the AF state is suppressed by magnetic field, a polarized ferromagnetic state is predicted, reminiscent of the recently reported ferromagnetic state at the quantum end point of the superconducting dome by Ce doping. The magnetic field that drives phase transitions in a manner similar to but distinct from doping thereby provides a unique perspective to understand the quantum criticality of electron-doped cuprates.

关键词： Antiferromagnetism Hall effect Magnetoresistance Quantum phase transitions Superconductivity Cuprates Thin films

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Report on Reproducibility in Condensed Matter Physics

arXiv

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arXiv 2025年

作者： Akrap, A. Bordelon, D. Chatterjee, S. Dahlberg, E.D. Devaty, R.P. Frolov, S.M. Gould, C. Greene, L.H. Guchhait, S. Hamlin, J.J. Hunt, B.M. Jardine, M.J.A. Kayyalha, M. Kurchin, R.C. Kozii, V. Legg, H.F. Mazin, I.I. Mourik, V. Özgüler, A.B. Peñuela-Parra, J. Seradjeh, B. Skinner, B. Quader, K.F. Zwolak, J.P. Department of Physics Faculty of Science University of Zagreb ZagrebHR-10000 Croatia Pittsburgh Supercomputing Center PittsburghPA15213 United States Department of Physics Carnegie Mellon University PittsburghPA15213 United States School of Physics and Astronomy The University of Minnesota MinneapolisMN55455 United States Department of Physics and Astronomy University of Pittsburgh PittsburghPA15260 United States Universitat Würzburg Am Hubland Würzburg Germany Institute for Topological Insulators Am Hubland Würzburg Germany National High Magnetic Field Laboratory Florida State University FL32310 United States Department of Physics Florida State University FL32306 United States Department of Physics and Astronomy Howard University WashingtonDC20059 United States Department of Physics University of Florida GainesvilleFL32611 United States Department of Materials Science and Engineering Carnegie Mellon University PittsburghPA15213 United States Department of Electrical Engineering The Pennsylvania State University University ParkPA16802 United States Materials Research Institute The Pennsylvania State University University ParkPA16802 United States Department of Physics University of Basel Basel4056 Switzerland SUPA School of Physics and Astronomy University of St. Andrews St AndrewsKY16 9SS United Kingdom Department of Physics and Astronomy George Mason University FairfaxVA22030 United States Center for Quantum Science and Engineering George Mason University FairfaxVA22030 United States Forschungszentrum Jülich GmbH Aachen52074 Germany Department of Mechanical and Materials Science University of Pittsburgh PittsburghPA15261 United States Haas School of Business University of California BerkeleyCA94720 United States Department of Physics Indiana University BloomingtonIN47405 United States Indiana University Center for Spacetime Symmetries BloomingtonIN47405 United States Quantum Science and Engineering Center

We present recommendations for how to improve reproducibility in the field of condensed matter physics. This area of physics has consistently produced both fundamental insights into the functioning of matter as well as transformative inventions. Our recommendations result from a collaboration that includes researchers in academia and government laboratories, scientific journalists, legal professionals, representatives of publishers, professional societies, and other experts. The group met in person in May 2024 at a conference at the University of Pittsburgh to discuss the growing challenges related to research reproducibility in condensed matter physics. We discuss best practices and policies at all stages of the scientific process to safeguard the value condensed matter research brings to society. We look forward to comments and suggestions, especially regarding subfield-specific recommendations, and will incorporate them into the next version of the report. © 2025, CC BY.

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Non-clairvoyant precedence constrained scheduling

arXiv

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arXiv 2019年

作者： Garg, Naveen Gupta, Anupam Kumar, Amit Singla, Sahil Computer Science and Engineering Department Indian Institute of Technology Delhi India Computer Science Department Carnegie Mellon University Department of Computer Science Princeton University School of Mathematics Institute for Advanced Study Princeton United States

We consider the online problem of scheduling jobs on identical machines, where jobs have precedence constraints. We are interested in the demanding *** where the jobs sizes are not known upfront, but are revealed only upon completion (the non-clairvoyant ***). Such precedence-constrained scheduling problems routinely arise inmap-reduce and large-scale optimization. In this paper, wemake progress on this problem. For the objective of total weighted completion time, we give a constantcompetitive algorithm. And for total weighted flow-time, we give an O(1/ϵ2)-competitive algorithm under (1 + ϵ)-speed augmentation and a natural "no-surprises" assumption on release dates of jobs (which we show is necessary in this context). Our algorithm proceeds by assigning virtual rates to all the waiting jobs, including the ones which are dependent on other uncompleted jobs, and then use these virtual rates to decide on the actual rates of minimal jobs (i.e., jobs which do not have dependencies and hence are eligible to run). Interestingly, the virtual rates are obtained by allocating time in a fair manner, using a Eisenberg-Gale-type convex program (which we can also solve optimally using a primal-dual scheme). .e optimality condition of this convex program allows us to show *** proofs more easily, without having to guess and hand-cra. the duals. We feel that this idea of using fair virtual rates should have broader applicability in scheduling problems. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

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Hidden frustration in double-perovskite CaFeTi2O6

arXiv

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arXiv 2021年

作者： Liu, Di Wu, Si Long, Xuanyu Wang, Yao Li, Hai-Feng Yang, Ming Wang, Junfeng Liu, Zheng Li, Xiang Wan, Yuan Li, Shiliang Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100190 China Institute of Applied Physics and Materials Engineering University of Macau Avenida da Universidade Taipa China Institute for Advanced Study Tsinghua University Beijing100084 China Wuhan National High Magnetic Field Center School of Physics Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China School of Physics Beijing Institute of Technology Beijing100081 China Songshan Lake Materials Laboratory Guangdong Dongguan523808 China

We study the magnetic properties of CaFeTi2O6 (CFTO) by high-field magnetization and specific heat measurements. While the magnetic susceptibility data yield a vanishingly small Curie-Weiss temperature, the magnetic moments are not fully polarized in magnetic field up to 60 T, which reveals a large spin exchange energy scale. Yet, the system shows no long range magnetic order but a spin-glass-like state below 5.5 K in zero field, indicating strong magnetic frustration in this system. Applying magnetic field gradually suppresses the spin-glass-like state and gives rise to a potential quantum spin liquid state whose low-temperature specific heat exhibits a T1.6 power-law. Crucially, conventional mechanisms for frustration do not apply to this system as it possesses neither apparent geometrical frustration nor exchange frustration. We suggest that the orbital modulation of exchange interaction is likely the source of hidden frustration in CFTO, and its full characterization may open a new route in the quest for quantum spin liquids. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic fields

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On scientific understanding with artificial intelligence

arXiv

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arXiv 2022年

作者： Krenn, Mario Pollice, Robert Guo, Si Yue Aldeghi, Matteo Cervera-Lierta, Alba Friederich, Pascal dos Passos Gomes, Gabriel Häse, Florian Jinich, Adrian Nigam, AkshatKumar Yao, Zhenpeng Aspuru-Guzik, Alán Erlangen Germany Chemical Physics Theory Group Department of Chemistry University of Toronto Canada Department of Computer Science University of Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada Institute of Nanotechnology Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Department of Chemistry and Chemical Biology Harvard University Cambridge United States Division of Infectious Diseases Weill Department of Medicine Weill-Cornell Medical College New York United States Center of Hydrogen Science Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The State Key Laboratory of Metal Matrix Composites School of Materials Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Innovation Center for Future Materials Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University 429 Zhangheng Road Shanghai201203 China Toronto Canada

Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein. Such an oracle would revolutionize science and technology as we know them. However, as scientists, we would not be satisfied with the oracle itself. We want more. We want to comprehend how the oracle conceived these predictions. This feat, denoted as scientific understanding, has frequently been recognized as the essential aim of science. Now, the ever-growing power of computers and artificial intelligence poses one ultimate question: How can advanced artificial systems contribute to scientific understanding or achieve it autonomously? We are convinced that this is not a mere technical question but lies at the core of science. Therefore, here we set out to answer where we are and where we can go from here. We first seek advice from the philosophy of science to understand scientific understanding. Then we review the current state of the art, both from literature and by collecting dozens of anecdotes from scientists about how they acquired new conceptual understanding with the help of computers. Those combined insights help us to define three dimensions of android-assisted scientific understanding: The android as a I) computational microscope, II) resource of inspiration and the ultimate, not yet existent III) agent of understanding. For each dimension, we explain new avenues to push beyond the status quo and unleash the full power of artificial intelligence’s contribution to the central aim of science. We hope our perspective inspires and focuses research towards androids that get new scientific understanding and ultimately bring us closer to true artificial scientists. © 2022, CC BY.

关键词： Artificial intelligence

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