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The Gravitational-wave physics Ⅱ: Progress

science China(Physics,Mechanics & Astronomy) 2021年第12期64卷 18-67页

作者： Ligong Bian Rong-Gen Cai Shuo Cao Zhoujian Cao He Gao Zong-Kuan Guo Kejia Lee Di Li Jing Liu Youjun Lu Shi Pi Jian-Min Wang Shao-Jiang Wang Yan Wang Tao Yang Xing-Yu Yang Shenghua Yu Xin Zhang CAS Key Laboratory of Theoretical Physics Institute of Theoretical PhysicsChinese Academy of SciencesBeijing 100190China Department of Astronomy Beijing Normal UniversityBeijing 100875China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Kavli Institute for Astronomy and Astrophysics Peking UniversityBeijing 100080China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Astronomy and Space Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory for Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study(HIAS)University of Chinese Academy of SciencesHangzhou 310024China MOE Key Laboratory of Fundamental Physical Quantities Measurements Hubei Key Laboratory of Gravitation and Quantum PhysicsNational Precise Gravity Measurement Facility(PGMF)Department of Astronomy and School of PhysicsHuazhong University of Science and TechnologyWuhan 430074China Asia Pacific Center for Theoretical Physics Pohang 37673Korea Department of Physics College of SciencesNortheastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China Department of Physics Chongqing UniversityChongqing 401331China

It has been a half-decade since the first direct detection of gravitational waves, which signifies the coming of the era of the gravitational-wave astronomy and gravitational-wave cosmology. The increasing number of the detected gravitational-wave events has revealed the promising capability of constraining various aspects of cosmology, astronomy, and gravity. Due to the limited space in this review article, we will briefly summarize the recent progress over the past five years, but with a special focus on some of our own work for the Key Project "Physics associated with the gravitational waves" supported by the National Natural science Foundation of China. In particular,(1) we have presented the mechanism of the gravitational-wave production during some physical processes of the early Universe, such as inflation, preheating and phase transition, and the cosmological implications of gravitational-wave measurements;(2) we have put constraints on the neutron star maximum mass according to GW170817 observations;(3) we have developed a numerical relativity algorithm based on the finite element method and a waveform model for the binary black hole coalescence along an eccentric orbit.

关键词： gravitational wave gravitational-wave cosmology gravitational-wave astronomy numerical relativity

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Impact of metabolic health and weight transition on COVID-19 risk: A population-based cohort study

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Public health 2025年 245卷 105777页

作者： Yujin Park Su Hwan Kim Ahnul Ha Young Kook Kim Hyung-Jin Yoon Jae-Heon Kang So-Eun Kim Yoon Ji Kim Ye Seul Bae Healthcare Data Center Kangbuk Samsung Hospital Sungkyunkwan University School of Medicine Seoul 03181 Republic of Korea. Department of Information Statistics Gyeongsang National University Jinju-si Gyeongsangnam-do 52828 Republic of Korea. Department of Ophthalmology Seoul National University College of Medicine Seoul 03080 Republic of Korea Department of Ophthalmology Jeju National University Hospital Jeju-si Jeju-do 63241 Republic of Korea Department of Ophthalmology Jeju National University College of Medicine Jeju-si Jeju-do 63241 Republic of Korea. Department of Ophthalmology Seoul National University Hospital Seoul 03080 Republic of Korea EyeLight Data Science Laboratory Seoul National University College of Medicine Seoul 03080 Republic of Korea. Medical Big Data Research Center Seoul National University College of Medicine Seoul 03080 Republic of Korea. Department of Family Medicine Kangbuk Samsung Hospital Sungkyunkwan University School of Medicine Seoul 03181 Republic of Korea Institute for Clinical Nutrition Sungkyunkwan University School of Medicine Seoul 16419 Republic of Korea Institute for Future Healthcare Sungkyunkwan University School of Medicine Seoul 16419 Republic of Korea. Division of Healthcare Planning Big Data Research Center Kangbuk Samsung Hospital Sungkyunkwan University School of Medicine Seoul 03181 Republic of Korea. Division of Healthcare Planning Big Data Research Center Kangbuk Samsung Hospital Sungkyunkwan University School of Medicine Seoul 03181 Republic of Korea. Electronic address: byeye1313@***.

OBJECTIVES:Obesity and metabolic health are known factors influencing COVID-19 severity. However, the effect of changes in metabolic health and weight status on COVID-19 outcomes remains unclear. To investigate the association between changes in metabolic health and weight status over time and COVID-19-related risks. STUDY DESIGN:Retrospective population-based cohort study. METHODS:This retrospective population-based cohort study used data from South Korea's National Health Information Service collected between January 2014 and December 2021. A total of 284,719 participants who underwent health screening both in 2014-2015 and 2016-2017 were divided into the metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO) groups. RESULTS:Among initial MHNO participants, those who became MHO (hazard ratio (HR) 1·082, 95 % confidence intervals 1·01-1·16) and MUO (1·18, 1·11-1·26) had elevated risk for COVID-19 compared with those who remained MHNO. Conversely, participants who transitioned from MUO to MUNO (0·89, 0·84-0·95) were associated with a lower risk of COVID-19. Improvements in metabolic health did not mitigate COVID-19-related risks. Transitioning from MHNO to MHO (3·57, 1·38-9·26) was associated with a significantly higher risk of death due to COVID-19. CONCLUSIONS:Changes in metabolic health and obesity status significantly influence COVID-19 outcomes. Addressing obesity and promoting metabolic health are critical for mitigating the impact of COVID-19.

关键词： COVID-19 related outcomes High blood pressure Hypercholesterolemia Hyperglycemia Hypertension Obesity

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Phase diagram of compressed sensing with $\ell_0$-norm regularization

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Journal of Statistical Mechanics: Theory and Experiment 2025年第6期2025卷 063402-063402页

作者： Damien Barbier Carlo Lucibello Luca Saglietti Florent Krzakala Lenka Zdeborová École Polytechnique Fédérale de Lausanne Information Learning and Physics Lab and Statistical Physics of Computation Lab Lausanne CH-1015 Switzerland Institute for Data Science and Analytics Bocconi University Milan Italy Department of Computing Sciences Bocconi University Milan Italy École Polytechnique Fédérale de Lausanne Statistical Physics of Computation Laboratory CH-1015 Lausanne Switzerland

Noiseless compressive sensing is a two-step setting that allows for undersampling a sparse signal and then reconstructing it without loss of information. The LASSO algorithm, based on $\ell_1$ $ℓ_{1}$ regularization, provides an efficient and robust way to address this problem, but it fails in the regime of very high compression rate. Here we present two algorithms, based on $\ell_0$

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Hybrid Projection Methods for Large-scale Inverse Problems with Mixed Gaussian Priors

arXiv

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arXiv 2020年

作者： Cho, Taewon Chung, Julianne Jiang, Jiahua Department of Mathematics Virginia Tech BlacksburgVA United States Department of Mathematics Computational Modeling and Data Analytics Division Academy of Integrated Science Virginia Tech BlacksburgVA United States Department of Mathematics Virginia Tech BlacksburgVA United States

When solving ill-posed inverse problems, a good choice of the prior is critical for the computation of a reasonable solution. A common approach is to include a Gaussian prior, which is defined by a mean vector and a symmetric and positive definite covariance matrix, and to use iterative projection methods to solve the corresponding regularized problem. However, a main challenge for many of these iterative methods is that the prior covariance matrix must be known and fixed (up to a constant) before starting the solution process. In this paper, we develop hybrid projection methods for inverse problems with mixed Gaussian priors where the prior covariance matrix is a convex combination of matrices and the mixing parameter and the regularization parameter do not need to be known in advance. Such scenarios may arise when data is used to generate a sample prior covariance matrix (e.g., in data assimilation) or when different priors are needed to capture different qualities of the solution. The proposed hybrid methods are based on a mixed Golub-Kahan process, which is an extension of the generalized Golub-Kahan bidiagonalization, and a distinctive feature of the proposed approach is that both the regularization parameter and the weighting parameter for the covariance matrix can be estimated automatically during the iterative process. Furthermore, for problems where training data are available, various data-driven covariance matrices (including those based on learned covariance kernels) can be easily incorporated. Numerical examples from tomographic reconstruction demonstrate the potential for these methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Differential equations

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Automatic detection of single-electron regime of quantum dots and definition of virtual gates using U-Net and clustering

arXiv

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arXiv 2025年

作者： Muto, Yui Zielewski, Michael R. Shinozaki, Motoya Noro, Kosuke Otsuka, Tomohiro Research Institute of Electrical Communication Tohoku University 2-1-1 Katahira Aoba-ku Sendai980-8577 Japan Department of Electronic Engineering Graduate School of Engineering Tohoku University Aoba 6-6-05 Aramaki Aoba-Ku Sendai980-8579 Japan Graduate School of Information Sciences Tohoku University 6-3-09 Aramaki-aza-Aoba Aoba-ku Sendai980-8579 Japan Unprecedented-scale Data Analytics Center Tohoku University 468-1 Aramaki-aza-Aoba Aoba-ku Sendai980-8572 Japan WPI Advanced Institute for Materials Research Tohoku University 2-1-1 Katahira Aoba-ku Sendai980-8577 Japan Center for Science and Innovation in Spintronics Tohoku University 2-1-1 Katahira Aoba-ku Sendai980-8577 Japan Center for Emergent Matter Science RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan

To realize practical quantum computers, a large number of quantum bits (qubits) will be required. Semiconductor spin qubits offer advantages such as high scalability and compatibility with existing semiconductor technologies. However, as the number of qubits increases, manual qubit tuning becomes infeasible, motivating automated tuning approaches. In this study, we use U-Net, a neural network method for object detection, to identify charge transition lines in experimental charge stability diagrams. The extracted charge transition lines are analyzed using the Hough transform to determine their positions and angles. Based on this analysis, we obtain the transformation matrix to virtual gates. Furthermore, we identify the single-electron regime by clustering the Hough transform outputs. We also show the single-electron regime within the virtual gate space. These sequential processes are performed automatically. This approach will advance automated control technologies for large-scale quantum devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Hough transforms

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Low-Temperature Growing Anatase TiO2/SnO2 Multi-dimensional Heterojunctions at MXene Conductive Network for High-Efficient Perovskite Solar Cells

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Nano-Micro Letters 2020年第3期12卷 199-217页

作者： Linsheng Huang Xiaowen Zhou Rui Xue Pengfei Xu Siliang Wang Chao Xu Wei Zeng Yi Xiong Hongqian Sang Dong Liang National Engineering Research Center for Agro‑Ecological Big Data Analysis and Application School of Electronics and Information EngineeringAnhui UniversityNo.111 Jiulong RoadHefei 230601People’s Republic of China Science and Technology Institute Hubei Key Laboratory of Biomass Fibers and Eco‑Dyeing and FinishingWuhan Textile UniversityWuhan 430073People’s Republic of China School of Physics and Technology MOE Key Laboratory of Artificial Micro‑and Nano‑Structures and Center for Electron MicroscopyWuhan UniversityWuhan 430072People’s Republic of China Department of Physics King’s College LondonThe StrandLondon WC2R 2LSUK

A multi-dimensional conductive heterojunction structure,composited by TiO2,SnO2,and Ti3C2TX MXene,is facilely designed and applied as electron transport layer in efficient and stable planar perovskite solar *** on an oxygen vacancy scramble effect,the zero-dimensional anatase TiO2 quantum dots,surrounding on two-dimensional conductive Ti3C2TX sheets,are in situ rooted on three-dimensional SnO2 nanoparticles,constructing nanoscale TiO2/SnO2 *** fabrication is implemented in a controlled lowtemperature anneal method in air and then in N2 *** the optimal MXene content,the optical property,the crystallinity of perovskite layer,and internal interfaces are all facilitated,contributing more amount of carrier with effective and rapid transferring in *** champion power conversion efficiency of resultant perovskite solar cells achieves 19.14%,yet that of counterpart is just 16.83%.In addition,it can also maintain almost 85%of its initial performance for more than 45 days in 30–40%humidity air;comparatively,the counterpart declines to just below 75%of its initial performance.

关键词： In situ fabrication Multi-dimensional heterojunction Oxygen vacancy scramble effect Electron transport layer Perovskite solar cells

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On-edge multi-task transfer learning: Model and practice with data-driven task allocation

arXiv

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arXiv 2021年

作者： Zheng, Zimu Chen, Qiong Hu, Chuang Wang, Dan Liu, Fangming The National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China The Edge Cloud Innovation Lab. Technical Innovation Department Cloud BU Huawei Technologies Co. Ltd. Shenzhen China The Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Hong Kong

On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes heavy computation burden to the resource-constrained edge devices. Existing task allocation works usually assume all submitted tasks are equally important, leading to inefficient resource allocation at a task level when directly applied in Multi-task Transfer Learning (MTL). To address these issues, we first reveal that it is crucial to measure the impact of tasks on overall decision performance improvement and quantify task importance. We then show that task allocation with task importance for MTL (TATIM) is a variant of NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we propose a data-driven Cooperative Task Allocation (DCTA) approach. Finally, we evaluate the performance of DCTA by not only a trace-driven simulation, but also a new comprehensive real-world AIOps case study which bridges model and practice via a new architecture and main components design within AIOps system. Extensive experiments show that our DCTA reduces 3.24 times of processing time, and saves 48.4% energy consumption compared with the state-of-the-art when solving TATIM. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy utilization

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Enhancing the Prediction of Breast Cancer Progression Through Multi-modal data Transformation

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Cognitive Computation 2025年第3期17卷

作者： Abdullakutty, Faseela Akbari, Younes Al-Maadeed, Somaya Bouridane, Ahmed Rifat Hamoudi, Rifat Department of Computer Science and Engineering Qatar University Doha Qatar Center for Data Analytics and Cybernetics University of Sharjah Sharjah United Arab Emirates Research Institute for Medical and Health Sciences University of Sharjah Sharjah United Arab Emirates Clinical Sciences Department College of Medicine University of Sharjah Sharjah United Arab Emirates BIMAI-Lab Biomedically Informed Artificial Intelligence Laboratory University of Sharjah Sharjah United Arab Emirates

The ability to predict breast cancer metastases is essential for making effective clinical decisions and managing patients. Traditional models predominantly rely on structured clinical data, which often lacks essential contextual details, limiting their predictive accuracy. In order to address this limitation, a multi-modal approach is introduced in which structured data is transformed into unstructured text, while contextual richness is preserved. Using this text, synthetic images are generated across three key diagnostic modalities, histopathology, mammography, and ultrasound, to enhance predictive capabilities. Based on converted text data, a pre-trained diffusion model was used to generate synthetic medical images in histopathology, mammography, and ultrasound modalities. The impact of a variety of text description variants on image quality and metastasis prediction was assessed. Comprehensive tumor descriptions or a combination of histological type and differentiation status were the most effective generation strategies. A comparison was conducted between three prediction approaches: a unimodal approach, an early fusion approach based on concatenation, and the Multi Co-Guided Attention (MCGA) approach. Through mutual attention, MCGA enhances feature alignment by addressing inter- and intra-modal heterogeneity and capturing complex relationships. Unimodal and multi-modal methods were evaluated with the application of SMOTE to mitigate the impact of data imbalance. Multi-modal fusion significantly outperforms unimodal methods, especially when class imbalances are mitigated by using SMOTE. When SMOTE was applied, Ultrasound+BERT achieved the highest level of accuracy (0.90), followed by Histopathology+BERT (0.88), and Mammogram+BERT (0.88). As compared to early fusion, MCGA demonstrated better class balance and improved minority class detection. Incorporating unstructured text with synthetic imaging modalities improves the accuracy of metastasis prediction by pres

关键词： Modal analysis

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Dynamic disruption index across citation and cited references windows: Recommendations for thresholds in research evaluation

arXiv

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arXiv 2025年

作者： Chen, Hongkan Bornmann, Lutz Bu, Yi Department of Information Management Peking University 5 Yiheyuan Road Haidian District Beijing100871 China Science Policy and Strategy Department Administrative Headquarters of the Max Planck Society Hofgartenstr. 8 Munich80539 Germany Center for Informationalization and Information Management Peking University 5 Yiheyuan Road Haidian District Beijing100871 China Peking University Chongqing Research Institute of Big Data Building 10 Science Valley High-Tech Zone Chongqing401332 China

The temporal dimension of citation accumulation poses fundamental challenges for quantitative research evaluations, particularly in assessing disruptive and consolidating research through the disruption index (D). While prior studies emphasize minimum citation windows (mostly 3–5 years) for reliable citation impact measurements, the time-sensitive nature of D—which quantifies a paper’s capacity to eclipse prior knowledge—remains underexplored. This study addresses two critical gaps: (1) determining the temporal thresholds required for publications to meet citation/reference prerequisites, and (2) identifying "optimal" citation windows that balance early predictability and longitudinal validity. By analyzing millions of publications across four fields with varying citation dynamics, we employ some metrics to track D stabilization patterns. Key findings reveal that a 10-year window achieves >80% agreement with final D classifications, while shorter windows (3 years) exhibit instability. Publications with ≥30 references stabilize 1–3 years faster, and extreme cases (top/bottom 5% D values) become identifiable within 5 years—enabling early detection of 60–80% of highly disruptive and consolidating works. The findings offer significant implications for scholarly evaluation and science policy, emphasizing the need for careful consideration of citation window length in research assessment (based on D). © 2025, CC BY.

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Active legibility in multiagent reinforcement learning

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Artificial Intelligence 2025年 346卷

作者： Liu, Yanyu Pan, Yinghui Zeng, Yifeng Ma, Biyang Doshi, Prashant School of Automation Central South University No. 605 South Lushan Road Hunan Changsha410083 China School of Artificial Intelligence & National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen China Department of Computer and Information Sciences Northumbria University Newcastle United Kingdom School of Computer Science Minnan Normal University Fujian Zhangzhou China Department of Computer Science University of Georgia AthensGA United States

A multiagent sequential decision problem has been seen in many critical applications including urban transportation, autonomous driving cars, military operations, etc. Its widely known solution, namely multiagent reinforcement learning, has evolved tremendously in recent years. Among them, the solution paradigm of modeling other agents attracts our interest, which is different from traditional value decomposition or communication mechanisms. It enables agents to understand and anticipate others' behaviors and facilitates their collaboration. Inspired by recent research on the legibility that allows agents to reveal their intentions through their behavior, we propose a multiagent active legibility framework to improve their performance. The legibility-oriented framework drives agents to conduct legible actions so as to help others optimize their behaviors. In addition, we design a series of problem domains that emulate a common legibility-needed scenario and effectively characterize the legibility in multiagent reinforcement learning. The experimental results demonstrate that the new framework is more efficient and requires less training time compared to several multiagent reinforcement learning algorithms. © 2025 The Author(s)

关键词： Urban transportation

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