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Detecting the chiral magnetic effect via deep learning

Physical Review C 2022年第5期106卷 L051901-L051901页

作者： Yuan-Sheng Zhao Lingxiao Wang Kai Zhou Xu-Guang Huang Physics Department and Center for Particle Physics and Field Theory Fudan University Shanghai 200433 China Frankfurt Institute for Advanced Studies Ruth Moufang Strasse 1 D-60438 Frankfurt am Main Germany Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Fudan University Shanghai 200433 China

The search for chiral magnetic effect (CME) in heavy-ion collisions has attracted long-term attention. Multiple observables are proposed, but all suffer large background contaminations. In this study, we construct an end-to-end CME-meter based on a deep convolutional neural network. After being trained over a dataset generated by a multiphase transport model, the CME-meter shows high accuracy in recognizing the CME-featured charge separation from the final-state pion spectra. It also exhibits remarkable robustness to diverse conditions including different collision energies, centralities, and elliptic flow backgrounds. In extrapolation tests, the CME-meter is validated in isobaric collisions and different model simulation, showing robust generalization performance. Moreover, based on variational approaches, we utilize the DeepDream method to derive the most responsive CME-spectrum that demonstrates the physical contents the machine learned.

关键词： Quantum chromodynamics Relativistic heavy-ion collisions Artificial neural networks Chiral symmetry

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Preparation and Microstructural Characterization of High-Strength PEKK (Polyether Ketone Ketone)-Based Ternary Composites for FDM Fabrication

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Polymers for advanced Technologies 2025年第5期36卷

作者： Wen, Pin Shang, Tong Rao, Wei Zhao, Wei Chen, Fei Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics School of Science Wuhan University of Technology Wuhan China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing International School of Materials Science and Engineering Wuhan University of Technology Wuhan China School of Mathematics Statistics and Mechanics Beijing University of Technology Beijing China Beijing China

High strength Polyether ketone ketone (PEKK) composites and its complex structures made by Fused Deposition Modeling (FDM) exhibit wide application in aerospace and medical fields. However, FDM of PEKK-carbon fiber (CF) composites is hampered by poor interfacial compatibility and weak adhesion between PEKK and CF. This severely degrades the mechanical properties and printability of composite filaments, thus impeding their widespread application. To tackle this crucial scientific problem, this study devises a novel strategy by constructing a ternary composite system consisting of CF, trisilanolphenyl-polyhedral oligomeric silsesquioxane (POSS), and PEKK. Specifically, benzene derivatives derived from POSS pyrolysis are deliberately introduced to form conjugate interactions with PEKK molecular chains, thereby strengthening the interfacial bond between CF and the PEKK matrix. This innovative approach not only improves interfacial compatibility but also significantly enhances the mechanical properties of the composite. Employing the FDM process, we successfully fabricated high-precision components with intricate geometries. Comprehensive mechanical assessments revealed that the CF/POSS/PEKK ternary composite exhibits a 33% increase in tensile strength and a 27% improvement in interlayer bonding strength compared to pure PEKK. Moreover, the printed components achieved excellent dimensional accuracy and surface quality, fully meeting the strict requirements of aerospace applications. This study not only offers a groundbreaking method for developing high-performance PEKK-based composite filaments but also establishes a solid theoretical and experimental foundation for their broader use in FDM-based additive manufacturing. © 2025 John Wiley & Sons Ltd.

关键词： Tensile strength

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A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction

arXiv

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arXiv 2022年

作者： Dang, Yuchen Chen, Ziqi Li, Heng Shu, Hai Center for Data Science New York University New YorkNY United States Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Shanghai China Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Province Shenzhen China Department of Biostatistics School of Global Public Health New York University New YorkNY United States

Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-deep learning models, four popular deep learning models, and their five ensemble models in forecasting sunspot numbers. In particular, we propose an ensemble model called XGBoost-DL, which uses XGBoost as a two-level nonlinear ensemble method to combine the deep learning models. Our XGBoost-DL achieves the best forecasting performance (RMSE = 25.70 and MAE = 19.82) in the comparison, outperforming the best non-deep learning model SARIMA (RMSE = 54.11 and MAE = 45.51), the best deep learning model Informer (RMSE = 29.90 and MAE = 22.35) and the NASA's forecast (RMSE = 48.38 and MAE = 38.45). Our XGBoost-DL forecasts a peak sunspot number of 133.47 in May 2025 for Solar Cycle 25 and 164.62 in November 2035 for Solar Cycle 26, similar to but later than the NASA's at 137.7 in October 2024 and 161.2 in December 2034. An open-source Python package of our XGBoost-DL for the sunspot number prediction is available at https://***/yd1008/ts_ensemble_sunspot. Copyright © 2022, The Authors. All rights reserved.

关键词： Solar energy

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Semiparametric quantile-difference estimation for length-biased and right-censored data

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Science China Mathematics 2019年第9期62卷 1823-1838页

作者： Yutao Liu Shucong Zhang Yong Zhou School of Statistics and Mathematics Central University of Finance and EconomicsBeijing100081China School of Statistics and Management Shanghai University of Finance and EconomicsShanghai200433China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) Institute of Statistics and Interdisciplinary Sciences and School of StatisticsEast China Normal UniversityShanghai200241China

Prevalent cohort studies frequently involve length-biased and right-censored data, a fact that has drawn considerable attention in survival analysis. In this article, we consider survival data arising from lengthbiased sampling, and propose a new semiparametric-model-based approach to estimate quantile differences of failure time. We establish the asymptotic properties of our new estimators theoretically under mild technical conditions, and propose a resampling method for estimating their asymptotic variance. We then conduct simulations to evaluate the empirical performance and efficiency of the proposed estimators, and demonstrate their application by a real data analysis.

关键词： quantile differences length-biased sampling right-censored proportional hazards model

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A Double Varying-coefficient Modeling Approach for Analyzing Longitudinal Observations

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Acta Mathematicae Applicatae Sinica 2019年第3期35卷 671-688页

作者： Qun-fang XU Rui LI School of Business Ningbo UniversityNingbo 315211China School of Statistics and Information Shanghai University of International Business and EconomicsShanghai 201620China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal UniversityMinistry of EducationShanghai 200062China

The identification of within-subject dependence is important for constructing efficient estimation in longitudinal data *** this paper,we proposed a flexible way to study this dependence by using nonparametric regression ***,we considered the estimation of varying coefficient longitudinal data model with non-stationary varying coefficient autoregressive error process over observational time *** on spline approximation and local polynomial techniques,we proposed a two-stage nonparametric estimation for unknown functional coefficients and didn’t not drop any observations in a hybrid least square loss ***,we showed that the estimated coefficient functions are asymptotically normal and derived the asymptotic biases and variances *** Carlo studies and two real applications were conducted for illustrating the performance of our proposed methods.

关键词： autoregressive process double varying coefficient irregular time quantum locally linear

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How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing

arXiv

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arXiv 2024年

作者： Huang, Keke Wang, Yu Guang Li, Ming Liò, Pietro School of Computing National University of Singapore Singapore Institute of Natural Sciences School of Mathematical Sciences Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai China Shanghai AI Laboratory Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Zhejiang Institute of Optoelectronics Jinhua China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science and Technology Cambridge University Cambridge United Kingdom

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert prohibitive computations, numerous polynomial filters have been proposed. However, polynomials in the majority of these filters are predefined and remain fixed across different graphs, failing to accommodate the varying degrees of heterophily. Addressing this gap, we demystify the intrinsic correlation between the spectral property of desired polynomial bases and the heterophily degrees via thorough theoretical analyses. Subsequently, we develop a novel adaptive heterophily basis wherein the basis vectors mutually form angles reflecting the heterophily degree of the graph. We integrate this heterophily basis with the homophily basis to construct a universal polynomial basis UniBasis, which devises a polynomial filter-based graph neural network – UniFilter. It optimizes the convolution and propagation in GNN, thus effectively limiting over-smoothing and alleviating over-squashing. Our extensive experiments, conducted on datasets with a diverse range of heterophily, support the superiority of UniBasis in the universality but also its proficiency in graph explanation. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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Novel View Synthesis on Unpaired Data by Conditional Deformable Variational Auto-Encoder

arXiv

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arXiv 2020年

作者： Yin, Mingyu Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing China Key Laboratory of Advanced Theory and Application in Statistics & Data Science East China Normal University Shanghai200241 China

Novel view synthesis often needs the paired data from both the source and target views. This paper proposes a view translation model under cVAE-GAN framework without requiring the paired data. We design a conditional deformable module (CDM) which uses the view condition vectors as the filters to convolve the feature maps of the main branch in VAE. It generates several pairs of displacement maps to deform the features, like the 2D optical flows. The results are fed into the deformed feature based normalization module (DFNM), which scales and offsets the main branch feature, given its deformed one as the input from the side branch. Taking the advantage of the CDM and DFNM, the encoder outputs a view-irrelevant posterior, while the decoder takes the code drawn from it to synthesize the reconstructed and the view-translated images. To further ensure the disentanglement between the views and other factors, we add adversarial training on the code. The results and ablation studies on MultiPIE and 3D chair datasets validate the effectiveness of the framework in cVAE and the designed module. https://***/MingyuY/deformable-view-synthesis Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

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How universal polynomial bases enhance spectral graph neural networks: heterophily, over-smoothing, and over-squashing 24

How universal polynomial bases enhance spectral graph neural...

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Proceedings of the 41st International Conference on Machine Learning

作者： Keke Huang Yu Guang Wang Ming Li Pietro Liò School of Computing National University of Singapore Singapore Institute of Natural Sciences School of Mathematical Sciences Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai China and Shanghai AI Laboratory Shanghai China and School of Mathematics and Statistics University of New South Wales Sydney Australia Zhejiang Institute of Optoelectronics Jinhua China and Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science and Technology Cambridge University Cambridge UK

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert prohibitive computations, numerous polynomial filters have been proposed. However, polynomials in the majority of these filters are predefined and remain fixed across different graphs, failing to accommodate the varying degrees of heterophily. Addressing this gap, we demystify the intrinsic correlation between the spectral property of desired polynomial bases and the heterophily degrees via thorough theoretical analyses. Subsequently, we develop a novel adaptive heterophily basis wherein the basis vectors mutually form angles reflecting the heterophily degree of the graph. We integrate this heterophily basis with the homophily basis to construct a universal polynomial basis UniBasis, which devises a polynomial filter-based graph neural network - UniFilter. It optimizes the convolution and propagation in GNN, thus effectively limiting over-smoothing and alleviating over-squashing. Our extensive experiments, conducted on datasets with a diverse range of heterophily, support the superiority of UniBasis in the universality but also its proficiency in graph explanation.

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High conductive graphene assembled films with porous micro-structure for freestanding and ultra-low power strain sensors

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Science Bulletin 2020年第16期65卷 1363-1370,M0004页

作者： Zhe Wang Peng Li Rongguo Song Wei Qian Huang Zhou Qianlong Wang Yong Wang Xianci Zeng Lin Ren Shilin Yan Shichun Mu Daping He Hubei Engineering Research Center of RF-Microwave Technology and Application.School of Science Wuhan University of TechnologyWuhan 430070China Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics School of ScienceWuhan University of TechnologyWuhan 430070China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China

Graphene emerges as an ideal material for constructing high-performance strain sensors,due to its superior mechanical property and high ***,in the process of assembling graphene into macroscopic materials,its conductivity decreases ***,tedious fabrication process hinders the application of graphene-based strain *** this work,we report a freestanding graphene assembled film(GAF)with high conductivity((2.32±0.08)×105 S m-1).For the sensitive materials of strain sensors,it is higher than most of reported carbon nanotube and graphene *** advantages enable the GAF to be an ultra-low power consumption strain sensor for detecting airflow and vocal *** resistance of the GAF remains unchanged with increasing temperature(20-100℃),exhibiting a good thermal ***,the GAF can be used as a strain sensor directly without any flexible substrates,which greatly simplifies the fabrication process in comparison with most reported strain ***,the GAF used as a pressure sensor with only^4.7μW power is *** work provides a new direction for the preparation of advanced sensors with ultra-low power consumption,and the development of flexible and energy-saving electronic devices.

关键词： Strain sensor High conductivity Graphene assembled film Freestanding Ultra-low power consumption

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Enhanced high-temperature energy storage density in P-type NiOx quantum dots and P(VDF-HFP) composite dielectrics

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Composites Science and Technology 2025年 269卷

作者： Fang, Cheng Zhang, Ziyi Wang, Jian Ye, Zhanwen Li, Kaiyuan Wu, Luoqi Feng, Xiaobin Duan, Bo Dong, Lijie Zhou, Ling Shen, Zhonghui Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics School of Physics and Mechanics Wuhan University of Technology Wuhan430070 China Center for Smart Materials and Devices State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Hubei Wuhan430070 China

The harsh application environment poses higher requirements for the operating temperature of dielectric energy storage capacitors. Suppressing carrier transport at high temperatures is a key method to increase the breakdown field strength (Eb) and the operating temperature of dielectrics. In this paper, low-concentration P-type semiconductor nickel oxide quantum dots (NiOx QDs) were surface modified with polydopamine (PDA) and introduced into the polymer matrix, with the aim to capture hot carriers by constructing hole traps, thereby increasing the high-temperature Eb and energy storage density (Udis) of the polymer. The results confirmed the efficiency of the strategy. At 90 °C, the Eb of the NiOx@PDA/P(VDF-HFP) film reached 2242.78 kV/cm, and the Udis reached 2.94 J/cm3, which were 42.41 % and 164.86 % higher than those of the pure P(VDF-HFP) film. Thermal and mechanical analyses indicated that the enhanced Eb and Udis were mainly attributed to the restriction of the Poole-Frenkel emission within the dielectrics. Together with the enhanced compatibility of the organic-inorganic composite interface, the macro defects of the composites were further suppressed and electrical properties were further increased. This work offers a straightforward and efficient approach for manufacturing high-temperature energy storage dielectrics. © 2025 Elsevier Ltd

关键词： Carrier transport

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