Showing 1–50 of 65 results for author: Du, M

CVEvolve: Autonomous Algorithm Discovery for Unstructured Scientific Data Processing

Authors: Ming Du, Xiangyu Yin, Yanqi Luo, Dishant Beniwal, Songyuan Tang, Hemant Sharma, Mathew J. Cherukara

Abstract: Scientific data processing often requires task-specific algorithms or AI models, creating a barrier for domain scientists who need to analyze their data but may not have extensive computing or image-processing expertise. This barrier is especially pronounced when data are noisy, have a high dynamic range, are sparsely labeled, or are only loosely specified. We introduce CVEvolve, an autonomous age… ▽ More Scientific data processing often requires task-specific algorithms or AI models, creating a barrier for domain scientists who need to analyze their data but may not have extensive computing or image-processing expertise. This barrier is especially pronounced when data are noisy, have a high dynamic range, are sparsely labeled, or are only loosely specified. We introduce CVEvolve, an autonomous agentic harness with a zero-code interface for scientific data-processing algorithm discovery. CVEvolve combines a multi-round search strategy with tools for code execution, evaluation implementation, history management, holdout testing, and optional inspection of scientific data and visual outputs. The search alternates between discovery and improvement actions, and uses lineage-aware stochastic candidate sampling to balance exploration and exploitation. We demonstrate CVEvolve on X-ray fluorescence microscopy image registration, Bragg peak detection, high-energy diffraction microscopy image segmentation, and hybrid analytical-learning-based affine registration. Across these tasks, CVEvolve discovers algorithms that improve over baseline methods, while holdout test tracking helps identify candidates that generalize better than later over-optimized alternatives. These results show that zero-code, autonomous LLM-powered algorithm development can help domain scientists turn unstructured scientific image data into practical algorithms and downstream scientific discoveries. △ Less

Submitted 1 June, 2026; v1 submitted 11 May, 2026; originally announced May 2026.

MSC Class: 68T42 ACM Class: I.2.2

Physics informed operator learning of parameter dependent spectra

Authors: Haohao Gu, Sensen He, Hanlin Song, Bo Liang, Zhenwei Lyu, Xiaoguang Hu, Minghui Du, Peng Xu, Bo-Qiang Ma

Abstract: Spectral problems governed by differential operators underpin a wide range of physical systems, yet remain computationally challenging because their spectra depend sensitively on continuous parameters and often demand repeated evaluations across parameter space. Here we present $\texttt{DeepOPiraKAN}$, an open source physics informed neural network architecture for spectral analysis. By combining… ▽ More Spectral problems governed by differential operators underpin a wide range of physical systems, yet remain computationally challenging because their spectra depend sensitively on continuous parameters and often demand repeated evaluations across parameter space. Here we present $\texttt{DeepOPiraKAN}$, an open source physics informed neural network architecture for spectral analysis. By combining operator learning with enhanced optimization stability, it captures the underlying parameter-to-spectrum mapping in a single model, avoiding repeated spectral solutions at isolated points in parameter space. As a representative and stringent benchmark, we apply this framework to the computation of quasinormal modes of Kerr black holes. A single trained network accurately resolves modes with $(\ell,m)\in \{(2,0),(2,1)\}$ and overtones up to $n=7$ across the full spin range, achieving relative errors of $\mathcal{O}(10^{-6})$ for the fundamental mode and gradually increasing to $\mathcal{O}(10^{-4})$ for higher overtones, benchmarked against the Leaver's method. This level of accuracy is already significant for black hole spectroscopy and practical ringdown modelling for current and future observatories. More broadly, these results highlight the potential of $\texttt{DeepOPiraKAN}$ as a general and scalable framework for parameter dependent spectral problems across complex physical systems. △ Less

Submitted 26 April, 2026; originally announced April 2026.

High-Precision Ground Characterization of Test-Mass Magnetic Properties for the Taiji Gravitational Wave Mission via a Physics-Informed Neural Framework

Authors: Chang Liu, Qiong Deng, Huadong Li, Liwei Yang, Xiaodong Peng, Ziren Luo, Yuzhu Zhang, Chen Gao, Xiaotong Wei, Minghui Du, Zihao Xiao, Peng Xu, Bo Liang, Zhi Wang, Li-e Qiang

Abstract: Taiji is a gravitational wave detection mission in space initiated by the Chinese Academy of Sciences, which will open the millihertz window through a heliocentric triangular constellation of three drag-free spacecraft. Its ultimate sensitivity is determined partly by the residual acceleration noise of the gravitational reference sensors (GRS), within which the coupling between the test-mass and t… ▽ More Taiji is a gravitational wave detection mission in space initiated by the Chinese Academy of Sciences, which will open the millihertz window through a heliocentric triangular constellation of three drag-free spacecraft. Its ultimate sensitivity is determined partly by the residual acceleration noise of the gravitational reference sensors (GRS), within which the coupling between the test-mass and the fluctuating environmental magnetic field constitutes one of the key stray-force contributions. Following the path established by the LISA and TianQin teams, high-precision ground characterization of remanent magnetic moment $\vec{m}_r$ and volume susceptibility $χ$ of the test masses is a central step in the Taiji pre-launch test program. A persistent challenge for this characterization is the non-stationary, colored background noise inherent to torsion-pendulum facilities, which systematically biases classical Ordinary Least Squares (OLS) and Kalman filter (KF) estimators. We propose an AI-enhanced Differentiable Weighted Least Squares (AI-WLS) framework that fuses a dilated one-dimensional residual network, acting as a dynamic noise evaluator, with a fully differentiable analytical physical solver. This architecture preserves the exact linear mapping from the magnetic parameters to the torque response while autonomously identifying and suppressing contaminated data segments. Validated on real measured noise from the Changchun Institute of Optics, Fine Mechanics and Physics torsion-pendulum facility developed for Taiji, which achieves a torque sensitivity of order $10^{-13}\,\mathrm{N\cdot m\,Hz^{-1/2}}$, the AI-WLS framework bounds the maximum absolute estimation errors at $4.46\times 10^{-10}\,\mathrm{A\cdot m^2}$ for $\vec{m}_r$ and $7.8\times 10^{-8}$ for $χ$, satisfying Taiji's ground-test requirements on all these parameters simultaneously. △ Less

Submitted 25 April, 2026; originally announced April 2026.

FluxMC: Rapid and High-Fidelity Inference for Space-Based Gravitational-Wave Observations

Authors: Bo Liang, Chang Liu, Hanlin Song, Tianyu Zhao, Minghui Du, He Wang, Haohao Gu, Sensen He, Yuxiang Xu, Wei-Liang Qian, Li-e Qiang, Peng Xu, Ziren Luo, Mingming Sun

Abstract: Bayesian inference in the physical sciences faces a fundamental challenge: the imperative for high-fidelity physical modeling often clashes with the intrinsic limitations of stochastic sampling algorithms. Complex, high-dimensional parameter spaces expose the universal vulnerability of conventional methods, e.g., Markov Chain Monte Carlo (MCMC), which struggle with the prohibitive costs of likelih… ▽ More Bayesian inference in the physical sciences faces a fundamental challenge: the imperative for high-fidelity physical modeling often clashes with the intrinsic limitations of stochastic sampling algorithms. Complex, high-dimensional parameter spaces expose the universal vulnerability of conventional methods, e.g., Markov Chain Monte Carlo (MCMC), which struggle with the prohibitive costs of likelihood evaluations and the risk of entrapment in local optima. To resolve this impasse, we introduce FluxMC (Flow-guided Unbiased eXploration Monte Carlo), a machine learning-enhanced framework designed to shift the inference paradigm from blind local search to globally guided transport. It integrates Flow Matching with Parallel Tempering MCMC, effectively combining the global foresight of generative AI with the rigorous asymptotic convergence and local robustness of temperature-based sampling. We showcase the efficacy of this framework through the lens of space-based gravitational-wave (GW) astronomy -- a field representing the frontier of challenging parameter inversion. In the analysis of massive black hole binaries using high-fidelity waveforms (IMRPhenomHM), FluxMC achieves robust convergence in under five hours, whereas traditional Parallel Tempering MCMC fails to converge even after hundreds of hours, yielding high Jensen-Shannon divergences (JSD) of $O(10^{-1})$. Our method reduces the distributional error by two to three orders of magnitude. Furthermore, for computationally efficient models (IMRPhenomD), it eliminates systematic biases caused by local-optima entrapment. Ultimately, FluxMC removes the necessity to compromise between model accuracy and analysis speed, establishing a new computational foundation where scientific discovery is limited only by observational data quality, not by algorithmic capacity. △ Less

Submitted 8 April, 2026; v1 submitted 3 April, 2026; originally announced April 2026.

Rigorous Quantum Thermodynamics from Entropic Path Integral Coarse-Graining

Authors: Jing Shen, Ziyan Ye, Ming-Zheng Du, Shi-Yu He, Dong H. Zhang, Jia-Xi Zeng, Venkat Kapil, Wei Fang

Abstract: Nuclear quantum effects (NQEs) remain a major challenge for molecular simulations, as rigorous treatment requires imaginary-time path-integral methods with heavy computational overhead. Neglecting NQEs leads to systematic errors in thermodynamic properties and failures in predicting isotope effects, quantum tunnelling, and anharmonic zero-point motion. Here, we introduce entropic path-integral coa… ▽ More Nuclear quantum effects (NQEs) remain a major challenge for molecular simulations, as rigorous treatment requires imaginary-time path-integral methods with heavy computational overhead. Neglecting NQEs leads to systematic errors in thermodynamic properties and failures in predicting isotope effects, quantum tunnelling, and anharmonic zero-point motion. Here, we introduce entropic path-integral coarse-graining (EPIGS), which enables rigorous quantum thermodynamics at the cost of classical simulations by training size- and temperature-transferable effective potentials utilising absolute centroid free energy and entropy. Central to EPIGS is an instanton-based free-energy perturbation scheme that enables efficient and accurate evaluation of the centroid free energy and entropy for large systems, making construction of the EPIGS training dataset practical. Benchmarks against full path-integral simulations on representative hydrogen-bonded systems, including liquid water, show that EPIGS reproduces quantum free energies and enthalpies within 0.2 meV/atom at near-classical computational cost. EPIGS provides a highly accurate, scalable and low-cost framework for quantum thermodynamic simulations of complex systems across temperatures. △ Less

Submitted 13 April, 2026; v1 submitted 21 February, 2026; originally announced February 2026.

On the role of nuclear quantum effects on the stability of peptides

Authors: Jing Shen, Ming-Zheng Du, Dong H. Zhang, Venkat Kapil, Wei Fang

Abstract: Nuclear quantum effects (NQEs) arising from the light mass of hydrogen can influence the structure and stability of hydrogen-bonded biomolecules, yet their role in determining peptide and protein folding remains unclear. Experiments show that substituting H$_2$O with D$_2$O often stabilizes folded states, but the microscopic mechanism associated with this phenomena remains unresolved. Through ab i… ▽ More Nuclear quantum effects (NQEs) arising from the light mass of hydrogen can influence the structure and stability of hydrogen-bonded biomolecules, yet their role in determining peptide and protein folding remains unclear. Experiments show that substituting H$_2$O with D$_2$O often stabilizes folded states, but the microscopic mechanism associated with this phenomena remains unresolved. Through ab initio-level path-integral molecular dynamics simulations enabled by machine-learning interatomic potentials, we address the fundamental question of the role of NQEs in peptides by investigating both their overall impact and isotope substitution effects. Overall, NQEs systematically destabilize compact three-dimensional structures across peptide systems, independent of secondary structure type or side-chain interactions. Contrary to the conventional picture that places central importance on hydrogen bonds, we find that the dominant destabilization instead arises from the quantum C-H vibrations. In addition, we reveal microscopic insights into the stabilization of folded peptides upon H$_2$O to D$_2$O substitution, showing that the H/D isotope substitution of active peptide hydrogens, previously considered unimportant, produces free-energy changes within the range of experimentally observed shifts. These findings provide a new interpretation of isotope effects in biological systems, indicating that seemingly small H$\to$D substitutions within peptides can be as important as, or even outweigh, solvent contributions. △ Less

Submitted 5 December, 2025; originally announced December 2025.

Pty-Chi: A PyTorch-based modern ptychographic data analysis package

Authors: Ming Du, Hanna Ruth, Steven Henke, Yi Jiang, Viktor Nikitin, Ashish Tripathi, Junjing Deng, Jeffrey Klug, Peco Myint, Tao Zhou, Nicholas Schwarz, Mathew Cherukara, Alec Sandy, Stefan Vogt

Abstract: Ptychography has become an indispensable tool for high-resolution, non-destructive imaging using coherent light sources. The processing of ptychographic data critically depends on robust, efficient, and flexible computational reconstruction software. We introduce Pty-Chi, an open-source ptychographic reconstruction package built on PyTorch that unifies state-of-the-art analytical algorithms with a… ▽ More Ptychography has become an indispensable tool for high-resolution, non-destructive imaging using coherent light sources. The processing of ptychographic data critically depends on robust, efficient, and flexible computational reconstruction software. We introduce Pty-Chi, an open-source ptychographic reconstruction package built on PyTorch that unifies state-of-the-art analytical algorithms with automatic differentiation methods. Pty-Chi provides a comprehensive suite of reconstruction algorithms while supporting advanced experimental parameter corrections such as orthogonal probe relaxation and multislice modeling. Leveraging PyTorch as the computational backend ensures vendor-agnostic GPU acceleration, multi-device parallelization, and seamless access to modern optimizers. An object-oriented, modular design makes Pty-Chi highly extendable, enabling researchers to prototype new imaging models, integrate machine learning approaches, or build entirely new workflows on top of its core components. We demonstrate Pty-Chi's capabilities through challenging case studies that involve limited coherence, low overlap, and unstable illumination during scanning, which highlight its accuracy, versatility, and extensibility. With community-driven development and open contribution, Pty-Chi offers a modern, maintainable platform for advancing computational ptychography and for enabling innovative imaging algorithms at synchrotron facilities and beyond. △ Less

Submitted 23 October, 2025; originally announced October 2025.

MSC Class: 78A45 ACM Class: G.1.6

Fidelity-preserving enhancement of ptychography with foundational text-to-image models

Authors: Ming Du, Volker Rose, Junjing Deng, Dileep Singh, Si Chen, Mathew J. Cherukara

Abstract: Ptychographic phase retrieval enables high-resolution imaging of complex samples but often suffers from artifacts such as grid pathology and multislice crosstalk, which degrade reconstructed images. We propose a plug-and-play (PnP) framework that integrates physics model-based phase retrieval with text-guided image editing using foundational diffusion models. By employing the alternating direction… ▽ More Ptychographic phase retrieval enables high-resolution imaging of complex samples but often suffers from artifacts such as grid pathology and multislice crosstalk, which degrade reconstructed images. We propose a plug-and-play (PnP) framework that integrates physics model-based phase retrieval with text-guided image editing using foundational diffusion models. By employing the alternating direction method of multipliers (ADMM), our approach ensures consensus between data fidelity and artifact removal subproblems, maintaining physics consistency while enhancing image quality. Artifact removal is achieved using a text-guided diffusion image editing method (LEDITS++) with a pre-trained foundational diffusion model, allowing users to specify artifacts for removal in natural language. Demonstrations on simulated and experimental datasets show significant improvements in artifact suppression and structural fidelity, validated by metrics such as peak signal-to-noise ratio (PSNR) and diffraction pattern consistency. This work highlights the combination of text-guided generative models and model-based phase retrieval algorithms as a transferable and fidelity-preserving method for high-quality diffraction imaging. △ Less

Submitted 2 September, 2025; originally announced September 2025.

MSC Class: 65Kxx

Toward Efficient and Accurate EMRI Parameter Estimation: A Machine Learning-Enhanced MCMC Framework

Authors: Bo Liang, Chang Liu, Hanlin Song, Zhenwei Lyu, Minghui Du, Peng Xu, Ziren Luo, Sensen He, Haohao Gu, Tianyu Zhao, Manjia Liang, Yuxiang Xu, Li-e Qiang, Mingming Sun, Wei-Liang Qian

Abstract: The detection of gravitational waves from extreme-mass-ratio inspirals (EMRIs) in space-based antennas like Taiji and Laser Interferometer Space Antenna promises deep insights into strong-field gravity and black hole physics. However, the complex, highly degenerate, and nonconvex likelihood landscapes characteristic of EMRI parameter spaces pose severe challenges for conventional Markov chain Mont… ▽ More The detection of gravitational waves from extreme-mass-ratio inspirals (EMRIs) in space-based antennas like Taiji and Laser Interferometer Space Antenna promises deep insights into strong-field gravity and black hole physics. However, the complex, highly degenerate, and nonconvex likelihood landscapes characteristic of EMRI parameter spaces pose severe challenges for conventional Markov chain Monte Carlo (MCMC) methods. Under realistic instrumental noise and broad priors, these methods demand impractical computational costs but are prone to becoming trapped in local maxima, leading to biased and unreliable parameter estimates. To address these challenges, we introduce flow-matching MCMC (FM-MCMC), a novel Bayesian framework that integrates continuous normalizing flows (CNFs) with parallel tempering MCMC (PTMCMC). By generating high-likelihood regions via CNFs and refining them through PTMCMC, FM-MCMC enables robust exploration of the nontrivial parameter spaces, achieves orders-of-magnitude improvement in computational efficiency, and, more importantly, ensures statistically unbiased inference. By enabling real-time, unbiased parameter inference, FM-MCMC could unlock the full scientific potential of EMRI observations, and would serve as a scalable pipeline for precision gravitational-wave astronomy. △ Less

Submitted 18 August, 2026; v1 submitted 1 August, 2025; originally announced August 2025.

Journal ref: Research 9, 1055 (2026)

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks

Authors: Hao Xu, Yuntian Chen, Rui Cao, Tianning Tang, Mengge Du, Jian Li, Adrian H. Callaghan, Dongxiao Zhang

Abstract: Data driven discovery of partial differential equations (PDEs) is a promising approach for uncovering the underlying laws governing complex systems. However, purely data driven techniques face the dilemma of balancing search space with optimization efficiency. This study introduces a knowledge guided approach that incorporates existing PDEs documented in a mathematical handbook to facilitate the d… ▽ More Data driven discovery of partial differential equations (PDEs) is a promising approach for uncovering the underlying laws governing complex systems. However, purely data driven techniques face the dilemma of balancing search space with optimization efficiency. This study introduces a knowledge guided approach that incorporates existing PDEs documented in a mathematical handbook to facilitate the discovery process. These PDEs are encoded as sentence like structures composed of operators and basic terms, and used to train a generative model, called EqGPT, which enables the generation of free form PDEs. A loop of generation evaluation optimization is constructed to autonomously identify the most suitable PDE. Experimental results demonstrate that this framework can recover a variety of PDE forms with high accuracy and computational efficiency, particularly in cases involving complex temporal derivatives or intricate spatial terms, which are often beyond the reach of conventional methods. The approach also exhibits generalizability to irregular spatial domains and higher dimensional settings. Notably, it succeeds in discovering a previously unreported PDE governing strongly nonlinear surface gravity waves propagating toward breaking, based on real world experimental data, highlighting its applicability to practical scenarios and its potential to support scientific discovery. △ Less

Submitted 9 May, 2025; originally announced May 2025.

Nonperiodic dynamic CT reconstruction using backward-warping INR with regularization of diffeomorphism (BIRD)

Authors: Muge Du, Zhuozhao Zheng, Wenying Wang, Guotao Quan, Wuliang Shi, Le Shen, Li Zhang, Liang Li, Yinong Liu, Yuxiang Xing

Abstract: Dynamic computed tomography (CT) reconstruction faces significant challenges in addressing motion artifacts, particularly for nonperiodic rapid movements such as cardiac imaging with fast heart rates. Traditional methods struggle with the extreme limited-angle problems inherent in nonperiodic cases. Deep learning methods have improved performance but face generalization challenges. Recent implicit… ▽ More Dynamic computed tomography (CT) reconstruction faces significant challenges in addressing motion artifacts, particularly for nonperiodic rapid movements such as cardiac imaging with fast heart rates. Traditional methods struggle with the extreme limited-angle problems inherent in nonperiodic cases. Deep learning methods have improved performance but face generalization challenges. Recent implicit neural representation (INR) techniques show promise through self-supervised deep learning, but have critical limitations: computational inefficiency due to forward-warping modeling, difficulty balancing DVF complexity with anatomical plausibility, and challenges in preserving fine details without additional patient-specific pre-scans. This paper presents a novel INR-based framework, BIRD, for nonperiodic dynamic CT reconstruction. It addresses these challenges through four key contributions: (1) backward-warping deformation that enables direct computation of each dynamic voxel with significantly reduced computational cost, (2) diffeomorphism-based DVF regularization that ensures anatomically plausible deformations while maintaining representational capacity, (3) motion-compensated analytical reconstruction that enhances fine details without requiring additional pre-scans, and (4) dimensional-reduction design for efficient 4D coordinate encoding. Through various simulations and practical studies, including digital and physical phantoms and retrospective patient data, we demonstrate the effectiveness of our approach for nonperiodic dynamic CT reconstruction with enhanced details and reduced motion artifacts. The proposed framework enables more accurate dynamic CT reconstruction with potential clinical applications, such as one-beat cardiac reconstruction, cinematic image sequences for functional imaging, and motion artifact reduction in conventional CT scans. △ Less

Submitted 6 May, 2025; originally announced May 2025.

Demonstration of an AI-driven workflow for dynamic x-ray spectroscopy

Authors: Ming Du, Mark Wolfman, Chengjun Sun, Shelly D. Kelly, Mathew J. Cherukara

Abstract: X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but requires collecting data at many energy points which can be time-consuming. While adaptive sampling methods exist for efficiently collecting spectroscopic data, they often lack domain-specific knowledge about XANES spectra… ▽ More X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but requires collecting data at many energy points which can be time-consuming. While adaptive sampling methods exist for efficiently collecting spectroscopic data, they often lack domain-specific knowledge about XANES spectra structure. Here we demonstrate a knowledge-injected Bayesian optimization approach for adaptive XANES data collection that incorporates understanding of spectral features like absorption edges and pre-edge peaks. We show this method accurately reconstructs the absorption edge of XANES spectra using only 15-20% of the measurement points typically needed for conventional sampling, while maintaining the ability to determine the x-ray energy of the sharp peak after absorption edge with errors less than 0.03 eV, the absorption edge with errors less than 0.1 eV; and overall root-mean-square errors less than 0.005 compared to compared to traditionally sampled spectra. Our experiments on battery materials and catalysts demonstrate the method's effectiveness for both static and dynamic XANES measurements, improving data collection efficiency and enabling better time resolution for tracking chemical changes. This approach advances the degree of automation in XANES experiments reducing the common errors of under- or over-sampling points in near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time. △ Less

Submitted 23 April, 2025; originally announced April 2025.

Comprehensive Study of Phonon Chirality under Symmetry Constraints

Authors: Shuai Zhang, Zhiheng Huang, Muchen Du, Tianping Ying, Luojun Du, Tiantian Zhang

Abstract: Phonons are quanta of lattice vibrations, and their modes (linear, circular, or stationary) are symmetry-determined. Circularly polarized phonons, possessing nonzero angular momentum (AM), have drawn widespread attention recently. Despite widespread use of pseudo-angular momentum (PAM) and circularly polarized light polarization flips to identify chiral phonons in Raman scattering, their reliabili… ▽ More Phonons are quanta of lattice vibrations, and their modes (linear, circular, or stationary) are symmetry-determined. Circularly polarized phonons, possessing nonzero angular momentum (AM), have drawn widespread attention recently. Despite widespread use of pseudo-angular momentum (PAM) and circularly polarized light polarization flips to identify chiral phonons in Raman scattering, their reliability is debated due to symmetry dependence, and experimental verification standards remain lacking. Here, we systematically study phonon chirality and associated phenomena across magnetic point groups. We establish that the AM-PAM correlation is governed by both crystalline symmetry and Wyckoff positions, dictating conditions where nonzero AM manifests in PAM signatures. Crucially, phonons belonging to distinct irreducible representations exhibit distinct experimental benchmarks, enabling direct determination of crystalline chirality and symmetry classification. Furthermore, we report the discovery of a signature for symmetry-induced phenomena, notably a half-wave plate-analogous effect induced by mirror-odd phonons. Meanwhile, we conducted five experiments to validate our theory. △ Less

Submitted 13 August, 2025; v1 submitted 28 March, 2025; originally announced March 2025.

Accelerating Stochastic Gravitational Wave Backgrounds Parameter Estimation in Pulsar Timing Arrays with Flow Matching

Authors: Bo Liang, Chang Liu, Tianyu Zhao, Minghui Du, Manjia Liang, Ruijun Shi, Hong Guo, Yuxiang Xu, Li-e Qiang, Peng Xu, Wei-Liang Qian, Ziren Luo

Abstract: Pulsar timing arrays (PTAs) are essential tools for detecting the stochastic gravitational wave background (SGWB), but their analysis faces significant computational challenges. Traditional methods like Markov-chain Monte Carlo (MCMC) struggle with high-dimensional parameter spaces where noise parameters often dominate, while existing deep learning approaches fail to model the Hellings-Downs (HD)… ▽ More Pulsar timing arrays (PTAs) are essential tools for detecting the stochastic gravitational wave background (SGWB), but their analysis faces significant computational challenges. Traditional methods like Markov-chain Monte Carlo (MCMC) struggle with high-dimensional parameter spaces where noise parameters often dominate, while existing deep learning approaches fail to model the Hellings-Downs (HD) correlation or are validated only on synthetic datasets. We propose a flow-matching-based continuous normalizing flow (CNF) for efficient and accurate PTA parameter estimation. By focusing on the 10 most contributive pulsars from the NANOGrav 15-year dataset, our method achieves posteriors consistent with MCMC, with a Jensen-Shannon divergence below \(10^{-2}\) nat, while reducing sampling time from 50 hours to 4 minutes. Powered by a versatile embedding network and a reweighting loss function, our approach prioritizes the SGWB parameters and scales effectively for future datasets. It enables precise reconstruction of SGWB and opens new avenues for exploring vast observational data and uncovering potential new physics, offering a transformative tool for advancing gravitational wave astronomy. △ Less

Submitted 26 December, 2024; originally announced December 2024.

Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning

Authors: Bo Liang, Hong Guo, Tianyu Zhao, He wang, Herik Evangelinelis, Yuxiang Xu, Chang liu, Manjia Liang, Xiaotong Wei, Yong Yuan, Peng Xu, Minghui Du, Wei-Liang Qian, Ziren Luo

Abstract: Extreme-mass-ratio inspiral (EMRI) signals pose significant challenges in gravitational wave (GW) astronomy owing to their low-frequency nature and highly complex waveforms, which occupy a high-dimensional parameter space with numerous variables. Given their extended inspiral timescales and low signal-to-noise ratios, EMRI signals warrant prolonged observation periods. Parameter estimation becomes… ▽ More Extreme-mass-ratio inspiral (EMRI) signals pose significant challenges in gravitational wave (GW) astronomy owing to their low-frequency nature and highly complex waveforms, which occupy a high-dimensional parameter space with numerous variables. Given their extended inspiral timescales and low signal-to-noise ratios, EMRI signals warrant prolonged observation periods. Parameter estimation becomes particularly challenging due to non-local parameter degeneracies, arising from multiple local maxima, as well as flat regions and ridges inherent in the likelihood function. These factors lead to exceptionally high time complexity for parameter analysis while employing traditional matched filtering and random sampling methods. To address these challenges, the present study applies machine learning to Bayesian posterior estimation of EMRI signals, leveraging the recently developed flow matching technique based on ODE neural networks. Our approach demonstrates computational efficiency several orders of magnitude faster than the traditional Markov Chain Monte Carlo (MCMC) methods, while preserving the unbiasedness of parameter estimation. We show that machine learning technology has the potential to efficiently handle the vast parameter space, involving up to seventeen parameters, associated with EMRI signals. Furthermore, to our knowledge, this is the first instance of applying machine learning, specifically the Continuous Normalizing Flows (CNFs), to EMRI signal analysis. Our findings highlight the promising potential of machine learning in EMRI waveform analysis, offering new perspectives for the advancement of space-based GW detection and GW astronomy. △ Less

Submitted 12 September, 2024; originally announced September 2024.

In-depth Understanding of the Band Alignment and Interface States Scenario in Bi$_2$O$_2$Se/SrTiO$_3$ Ultrathin Heterojunction

Authors: Ke Zhang, Yusen Feng, Lei Hao, Jing Mi, Miao Du, Minghui Xu, Yan Zhao, Jianping Meng, Liang Qiao

Abstract: Bismuth oxyselenide (Bi$_2$O$_2$Se), a novel quasi-2D charge-carrying semiconductor, is hailed as one of the best emerging platforms for the next generation semiconductor devices. Recent efforts on developing diverse Bi$_2$O$_2$Se heterojunctions have produced extensive potential applications in electronics and optoelectronics. In-depth understanding of the band alignment and especially interface… ▽ More Bismuth oxyselenide (Bi$_2$O$_2$Se), a novel quasi-2D charge-carrying semiconductor, is hailed as one of the best emerging platforms for the next generation semiconductor devices. Recent efforts on developing diverse Bi$_2$O$_2$Se heterojunctions have produced extensive potential applications in electronics and optoelectronics. In-depth understanding of the band alignment and especially interface dynamics is, however, still challenging. In this work, a comprehensive experimental investigation on the band alignment is performed by a high-resolution X-ray photoelectron spectrometer (HRXPS), and the properties of interface states are also fully discussed. The results show that the ultrathin film Bi$_2$O$_2$Se grown on SrTiO$_3$ (TiO$_2$ (001) termination) exhibits Type-I (straddling gap) band alignment with a valence band offset (VBO) of about 1.77\pm0.04 eV and conduction band offset (CBO) of about 0.68\pm0.04 eV. However, further considering the contribution of the interface states, the bands on the interface present a herringbone configuration due to sizable build-in electric fields, which is significantly different from the conventional band alignment. In this sense, our results provide an insightful guidance to the development of high-efficiency electronic and optoelectronic devices, specifically of the devices where the charge transfer is highly sensitive to interface states. △ Less

Submitted 4 August, 2024; originally announced August 2024.

Discovery of Green's function based on symbolic regression with physical hard constraints

Authors: Jianghang Gu, Mengge Du, Yuntian Chen, Shiyi Chen

Abstract: The Green's function, serving as a kernel function that delineates the interaction relationships of physical quantities within a field, holds significant research implications across various disciplines. It forms the foundational basis for the renowned Biot-Savart formula in fluid dynamics, the theoretical solution of the pressure Poisson equation, and et al. Despite their importance, the theoreti… ▽ More The Green's function, serving as a kernel function that delineates the interaction relationships of physical quantities within a field, holds significant research implications across various disciplines. It forms the foundational basis for the renowned Biot-Savart formula in fluid dynamics, the theoretical solution of the pressure Poisson equation, and et al. Despite their importance, the theoretical derivation of the Green's function is both time-consuming and labor-intensive. In this study, we employed DISCOVER, an advanced symbolic regression method leveraging symbolic binary trees and reinforcement learning, to identify unknown Green's functions for several elementary partial differential operators, including Laplace operators, Helmholtz operators, and second-order differential operators with jump conditions. The Laplace and Helmholtz operators are particularly vital for resolving the pressure Poisson equation, while second-order differential operators with jump conditions are essential for analyzing multiphase flows and shock waves. By incorporating physical hard constraints, specifically symmetry properties inherent to these self-adjoint operators, we significantly enhanced the performance of the DISCOVER framework, potentially doubling its efficacy. Notably, the Green's functions discovered for the Laplace and Helmholtz operators precisely matched the true Green's functions. Furthermore, for operators without known exact Green's functions, such as the periodic Helmholtz operator and second-order differential operators with jump conditions, we identified potential Green's functions with solution error on the order of 10^(-10). This application of symbolic regression to the discovery of Green's functions represents a pivotal advancement in leveraging artificial intelligence to accelerate scientific discoveries, particularly in fluid dynamics and related fields. △ Less

Submitted 1 August, 2024; originally announced August 2024.

Comments: arXiv admin note: text overlap with arXiv:2210.16016 by other authors

Machine Learning Boosted Entropy-Engineered Synthesis of CuCo Nanometric Solid Solution Alloys for Near-100% Nitrate-to-Ammonia Selectivity

Authors: Yao Hu, Haihui Lan, Bo Hu, Jiaxuan Gong, Donghui Wang, Wen-Da Zhang, Mo Yan, Huicong Xia, Mingde Yao, Mingliang Du

Abstract: Nanometric solid solution alloys are utilized in a broad range of fields, including catalysis, energy storage, medical application, and sensor technology. Unfortunately, the synthesis of these alloys becomes increasingly challenging as the disparity between the metal elements grows, due to differences in atomic sizes, melting points, and chemical affinities. This study utilized a data-driven appro… ▽ More Nanometric solid solution alloys are utilized in a broad range of fields, including catalysis, energy storage, medical application, and sensor technology. Unfortunately, the synthesis of these alloys becomes increasingly challenging as the disparity between the metal elements grows, due to differences in atomic sizes, melting points, and chemical affinities. This study utilized a data-driven approach incorporating sample balancing enhancement techniques and multilayer perceptron (MLP) algorithms to improve the model's ability to handle imbalanced data, significantly boosting the efficiency of experimental parameter optimization. Building on this enhanced data processing framework, we developed an entropy-engineered synthesis approach specifically designed to produce stable, nanometric copper and cobalt (CuCo) solid solution alloys. Under conditions of -0.425 V (vs. RHE), the CuCo alloy exhibited nearly 100% Faraday efficiency (FE) and a high ammonia production rate of 232.17 mg h-1 mg-1. Stability tests in a simulated industrial environment showed that the catalyst maintained over 80% FE and an ammonia production rate exceeding 170 mg h-1 mg-1 over a testing period of 120 hours, outperforming most reported catalysts. To delve deeper into the synergistic interaction mechanisms between Cu and Co, in situ Raman spectroscopy was utilized for realtime monitoring, and density functional theory (DFT) calculations further substantiated our findings. These results not only highlight the exceptional catalytic performance of the CuCo alloy but also reflect the effective electronic and energy interactions between the two metals. △ Less

Submitted 17 October, 2024; v1 submitted 31 July, 2024; originally announced August 2024.

Comments: We found some mistakes and revisions are needed. It will take a long time

Study of the decay and production properties of $D_{s1}(2536)$ and $D_{s2}^*(2573)$

Authors: M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann , et al. (645 additional authors not shown)

Abstract: The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be… ▽ More The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be $(35.9\pm 4.8\pm 3.5)\%$ and $(37.4\pm 3.1\pm 4.6)\%$, respectively. The measurements are in tension with predictions based on the assumption that the $D_{s1}(2536)$ and $D_{s2}^*(2573)$ are dominated by a bare $c\bar{s}$ component. The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ cross sections are measured, and a resonant structure at around 4.6~GeV with a width of 50~MeV is observed for the first time with a statistical significance of $15σ$ in the $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ process. It could be the $Y(4626)$ found by the Belle collaboration in the $D_s^+D_{s1}(2536)^{-}$ final state, since they have similar masses and widths. There is also evidence for a structure at around 4.75~GeV in both processes. △ Less

Submitted 10 July, 2024; originally announced July 2024.

Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using Continuous Normalizing Flows

Authors: Bo Liang, Minghui Du, He Wang, Yuxiang Xu, Chang Liu, Xiaotong Wei, Peng Xu, Li-e Qiang, Ziren Luo

Abstract: Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as LISA, Taiji, and Tianqin. The fast and accurate parameter estimation of merging MBHBs is of great significance for the global fitting of all resolvable sources, as well as the astrophysical interpretation of gravitational wave signals. However, su… ▽ More Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as LISA, Taiji, and Tianqin. The fast and accurate parameter estimation of merging MBHBs is of great significance for the global fitting of all resolvable sources, as well as the astrophysical interpretation of gravitational wave signals. However, such analyses usually entail significant computational costs. To address these challenges, inspired by the latest progress in generative models, we explore the application of continuous normalizing flows (CNFs) on the parameter estimation of MBHBs. Specifically, we employ linear interpolation and trig interpolation methods to construct transport paths for training CNFs. Additionally, we creatively introduce a parameter transformation method based on the symmetry in the detector's response function. This transformation is integrated within CNFs, allowing us to train the model using a simplified dataset, and then perform parameter estimation on more general data, hence also acting as a crucial factor in improving the training speed. In conclusion, for the first time, within a comprehensive and reasonable parameter range, we have achieved a complete and unbiased 11-dimensional rapid inference for MBHBs in the presence of astrophysical confusion noise using CNFs. In the experiments based on simulated data, our model produces posterior distributions comparable to those obtained by nested sampling. △ Less

Submitted 5 December, 2024; v1 submitted 9 July, 2024; originally announced July 2024.

Predicting ptychography probe positions using single-shot phase retrieval neural network

Authors: Ming Du, Tao Zhou, Junjing Deng, Daniel J. Ching, Steven Henke, Mathew J. Cherukara

Abstract: Ptychography is a powerful imaging technique that is used in a variety of fields, including materials science, biology, and nanotechnology. However, the accuracy of the reconstructed ptychography image is highly dependent on the accuracy of the recorded probe positions which often contain errors. These errors are typically corrected jointly with phase retrieval through numerical optimization appro… ▽ More Ptychography is a powerful imaging technique that is used in a variety of fields, including materials science, biology, and nanotechnology. However, the accuracy of the reconstructed ptychography image is highly dependent on the accuracy of the recorded probe positions which often contain errors. These errors are typically corrected jointly with phase retrieval through numerical optimization approaches. When the error accumulates along the scan path or when the error magnitude is large, these approaches may not converge with satisfactory result. We propose a fundamentally new approach for ptychography probe position prediction for data with large position errors, where a neural network is used to make single-shot phase retrieval on individual diffraction patterns, yielding the object image at each scan point. The pairwise offsets among these images are then found using a robust image registration method, and the results are combined to yield the complete scan path by constructing and solving a linear equation. We show that our method can achieve good position prediction accuracy for data with large and accumulating errors on the order of $10^2$ pixels, a magnitude that often makes optimization-based algorithms fail to converge. For ptychography instruments without sophisticated position control equipment such as interferometers, our method is of significant practical potential. △ Less

Submitted 31 May, 2024; originally announced May 2024.

MSC Class: 94A08 ACM Class: I.4.0

Global-local Fourier Neural Operator for Accelerating Coronal Magnetic Field Model

Authors: Yutao Du, Qin Li, Raghav Gnanasambandam, Mengnan Du, Haimin Wang, Bo Shen

Abstract: Exploring the outer atmosphere of the sun has remained a significant bottleneck in astrophysics, given the intricate magnetic formations that significantly influence diverse solar events. Magnetohydrodynamics (MHD) simulations allow us to model the complex interactions between the sun's plasma, magnetic fields, and the surrounding environment. However, MHD simulation is extremely time-consuming, t… ▽ More Exploring the outer atmosphere of the sun has remained a significant bottleneck in astrophysics, given the intricate magnetic formations that significantly influence diverse solar events. Magnetohydrodynamics (MHD) simulations allow us to model the complex interactions between the sun's plasma, magnetic fields, and the surrounding environment. However, MHD simulation is extremely time-consuming, taking days or weeks for simulation. The goal of this study is to accelerate coronal magnetic field simulation using deep learning, specifically, the Fourier Neural Operator (FNO). FNO has been proven to be an ideal tool for scientific computing and discovery in the literature. In this paper, we proposed a global-local Fourier Neural Operator (GL-FNO) that contains two branches of FNOs: the global FNO branch takes downsampled input to reconstruct global features while the local FNO branch takes original resolution input to capture fine details. The performance of the GLFNO is compared with state-of-the-art deep learning methods, including FNO, U-NO, U-FNO, Vision Transformer, CNN-RNN, and CNN-LSTM, to demonstrate its accuracy, computational efficiency, and scalability. Furthermore, physics analysis from domain experts is also performed to demonstrate the reliability of GL-FNO. The results demonstrate that GL-FNO not only accelerates the MHD simulation (a few seconds for prediction, more than \times 20,000 speed up) but also provides reliable prediction capabilities, thus greatly contributing to the understanding of space weather dynamics. Our code implementation is available at https://github.com/Yutao-0718/GL-FNO △ Less

Submitted 8 September, 2024; v1 submitted 21 May, 2024; originally announced May 2024.

Comments: 10 pages

Entropy Engineered Middle-In Synthesis of Dual Single-Atom Compounds for Nitrate Reduction Reaction

Authors: Yao Hu, Haihui Lan, Junjun He, Wenjing Fang, Wen-Da Zhang, Shuanglong Lu, Fang Duan, Mingliang Du

Abstract: Despite the immense potential of Dual Single-Atom Compounds (DSACs), the challenges in their synthesis process, including complexity, stability, purity, and scalability, remain primary concerns in current research. Here, we present a general strategy, termed "Entropy-Engineered Middle-In Synthesis of Dual Single-Atom Compounds" (EEMIS-DSAC), which is meticulously crafted to produce a diverse range… ▽ More Despite the immense potential of Dual Single-Atom Compounds (DSACs), the challenges in their synthesis process, including complexity, stability, purity, and scalability, remain primary concerns in current research. Here, we present a general strategy, termed "Entropy-Engineered Middle-In Synthesis of Dual Single-Atom Compounds" (EEMIS-DSAC), which is meticulously crafted to produce a diverse range of DSACs, effectively addressing the aforementioned issues. Our strategy integrates the advantages of both bottom-up and top-down paradigms, proposing a new insight to optimize the catalyst structure. The as-fabricated DSACs exhibited excellent activity and stability in the nitrate reduction reaction (NO3RR). In a significant advancement, our prototypical CuNi DSACs demonstrated outstanding performance under conditions reminiscent of industrial wastewater. Specifically, under a NO3- concentration of 2000 ppm, it yielded a Faradaic efficiency (FE) for NH3 of 96.97 %, coupled with a mass productivity of 131.47 mg h-1 mg-1 and an area productivity of 10.06 mg h-1 cm-2. Impressively, even under a heightened NO3- concentration of 0.5 M, the FE for NH3 peaked at 90.61 %, with mass productivity reaching 1024.50 mg h-1 mg-1 and an area productivity of 78.41 mg h-1 cm-2. This work underpins the potential of the EEMIS-DSAC approach, signaling a promising frontier for high-performing DSACs. △ Less

Submitted 7 April, 2024; originally announced April 2024.

Gravitational Wave Signal Extraction Against Non-Stationary Instrumental Noises with Deep Neural Network

Authors: Yuxiang Xu, Minghui Du, Peng Xu, Bo Liang, He Wang

Abstract: Sapce-borne gravitational wave antennas, such as LISA and LISA-like mission (Taiji and Tianqin), will offer novel perspectives for exploring our Universe while introduce new challenges, especially in data analysis. Aside from the known challenges like high parameter space dimension, superposition of large number of signals etc., gravitational wave detections in space would be more seriously affect… ▽ More Sapce-borne gravitational wave antennas, such as LISA and LISA-like mission (Taiji and Tianqin), will offer novel perspectives for exploring our Universe while introduce new challenges, especially in data analysis. Aside from the known challenges like high parameter space dimension, superposition of large number of signals etc., gravitational wave detections in space would be more seriously affected by anomalies or non-stationarities in the science measurements. Considering the three types of foreseeable non-stationarities including data gaps, transients (glitches), and time-varying noise auto-correlations, which may come from routine maintenance or unexpected disturbances during science operations, we developed a deep learning model for accurate signal extractions confronted with such anomalous scenarios. Our model exhibits the same performance as the current state-of-the-art models do for the ideal and anomaly free scenario, while shows remarkable adaptability in extractions of coalescing massive black hole binary signal against all three types of non-stationarities and even their mixtures. This also provide new explorations into the robustness studies of deep learning models for data processing in space-borne gravitational wave missions. △ Less

Submitted 16 September, 2024; v1 submitted 20 February, 2024; originally announced February 2024.

Comments: 13 pages, 11 figures, 6 tables

Journal ref: Published in Physics Letters B, Volume 858, November 2024, 139016

Advancing Space-Based Gravitational Wave Astronomy: Rapid Parameter Estimation via Normalizing Flows

Authors: Minghui Du, Bo Liang, He Wang, Peng Xu, Ziren Luo, Yueliang Wu

Abstract: Gravitational wave (GW) astronomy is witnessing a transformative shift from terrestrial to space-based detection, with missions like Taiji at the forefront. While the transition brings unprecedented opportunities for exploring massive black hole binaries (MBHBs), it also imposes complex challenges in data analysis, particularly in parameter estimation amidst confusion noise. Addressing this gap, w… ▽ More Gravitational wave (GW) astronomy is witnessing a transformative shift from terrestrial to space-based detection, with missions like Taiji at the forefront. While the transition brings unprecedented opportunities for exploring massive black hole binaries (MBHBs), it also imposes complex challenges in data analysis, particularly in parameter estimation amidst confusion noise. Addressing this gap, we utilize scalable normalizing flow models to achieve rapid and accurate inference within the Taiji environment. Innovatively, our approach simplifies the data's complexity, employs a transformation mapping to overcome the year-period time-dependent response function, and unveils additional multimodality in the arrival time parameter. Our method estimates MBHBs several orders of magnitude faster than conventional techniques, maintaining high accuracy even in complex backgrounds. These findings significantly enhance the efficiency of GW data analysis, paving the way for rapid detection and alerting systems and enriching our ability to explore the universe through space-based GW observation. △ Less

Submitted 20 February, 2024; v1 submitted 10 August, 2023; originally announced August 2023.

Comments: 14 pages, 7 figures. Published version

Journal ref: SCIENCE CHINA Physics, Mechanics & Astronomy, Volume 67, Issue 3: 230412 (2024)

Block definition design for stretchable metamaterials: enabling configurable sensitivity to deformation

Authors: Sihong Chen, Taisong Pan, Zhengcheng Mou, Mingde Du, Tianxiang Wang, Bing-Zhong Wang, and Yuan Lin

Abstract: The sensitivity to deformation plays a key role in determining the applicability of stretchable metamaterials (MMs) to be used for conformal integration or mechanical reconfiguration. Typically, different unit designs are required to achieve the desired sensitivity, but this article proposes a block definition design for stretchable MMs that enables regulation of the MMs' response to deformation b… ▽ More The sensitivity to deformation plays a key role in determining the applicability of stretchable metamaterials (MMs) to be used for conformal integration or mechanical reconfiguration. Typically, different unit designs are required to achieve the desired sensitivity, but this article proposes a block definition design for stretchable MMs that enables regulation of the MMs' response to deformation by defining various block arrangements with the same precursor structure. The article demonstrates a stretchable MM that employs the block definition design to show the mechanical reconfigurability of resonant frequency. Different block definitions result in modulation ranges of resonant frequency ranging from 39\% to 85\% when applying a 20\% tensile strain. Additionally, the proposed design is also used to realize another MM with contradictory sensitivity to the deformation and electromagnetically induced transparency (EIT) MMs with configurable transmission bandwidth to the deformation, indicating its potential for broader applications. △ Less

Submitted 22 May, 2023; originally announced June 2023.

Many-Body Anderson Metal-Insulator Transition using Kicked Quantum Gases

Authors: Jun Hui See Toh, Mengxin Du, Xinxin Tang, Ying Su, Tristan Rojo, Carson O. Patterson, Nicolas R. Williams, Chuanwei Zhang, Subhadeep Gupta

Abstract: Understanding the interplay of interactions and disorder in quantum transport poses long-standing scientific challenges, with many-body quantum transport phenomena in high-dimensional disordered systems remaining largely unexplored experimentally. We utilize a momentum space lattice platform using quasi-periodically kicked ultracold atomic gases to experimentally investigate many-body effects on t… ▽ More Understanding the interplay of interactions and disorder in quantum transport poses long-standing scientific challenges, with many-body quantum transport phenomena in high-dimensional disordered systems remaining largely unexplored experimentally. We utilize a momentum space lattice platform using quasi-periodically kicked ultracold atomic gases to experimentally investigate many-body effects on the three-dimensional Anderson metal-insulator transition. We observe interaction-driven sub-diffusion and a divergence of delocalization onset time on approaching the many-body phase boundary. Mean-field numerical simulations are in qualitative agreement with experimental observations. △ Less

Submitted 18 July, 2023; v1 submitted 24 May, 2023; originally announced May 2023.

Comments: 14 pages, 11 figures, 2 tables, including supplementary materials

Journal ref: Phys Rev Lett. 133, 076301 (2024)

Thermodynamic coupling of reactions via few-molecule vibrational polaritons

Authors: Arghadip Koner, Matthew Du, Sindhana Pannir-Sivajothi, Randall H. Goldsmith, Joel Yuen-Zhou

Abstract: Interaction between light and molecular vibrations leads to hybrid light-matter states called vibrational polaritons. Even though many intriguing phenomena have been predicted for single-molecule vibrational strong coupling (VSC), several studies suggest that these effects tend to be diminished in the many-molecule regime due to the presence of dark states. Achieving single or few-molecule vibrati… ▽ More Interaction between light and molecular vibrations leads to hybrid light-matter states called vibrational polaritons. Even though many intriguing phenomena have been predicted for single-molecule vibrational strong coupling (VSC), several studies suggest that these effects tend to be diminished in the many-molecule regime due to the presence of dark states. Achieving single or few-molecule vibrational polaritons has been constrained by the need for fabricating extremely small mode volume infrared cavities. In this work, we propose an alternative strategy to achieve single-molecule VSC in a cavity-enhanced Raman spectroscopy (CERS) setup, based on the physics of cavity optomechanics. We then present a scheme harnessing few-molecule VSC to thermodynamically couple two reactions, such that a spontaneous electron transfer can now fuel a thermodynamically uphill reaction that was non-spontaneous outside the cavity. △ Less

Submitted 7 February, 2023; originally announced February 2023.

Comments: preprint

PtyLab.m/py/jl: a cross-platform, open-source inverse modeling toolbox for conventional and Fourier ptychography

Authors: Lars Loetgering, Mengqi Du, Dirk Boonzajer Flaes, Tomas Aidukas, Felix Wechsler, Daniel S. Penagos Molina, Max Rose, Antonios Pelekanidis, Wilhelm Eschen, Jürgen Hess, Thomas Wilhein, Rainer Heintzmann, Jan Rothhardt, Stefan Witte

Abstract: Conventional (CP) and Fourier (FP) ptychography have emerged as versatile quantitative phase imaging techniques. While the main application cases for each technique are different, namely lens-less short wavelength imaging for CP and lens-based visible light imaging for FP, both methods share a common algorithmic ground. CP and FP have in part independently evolved to include experimentally robust… ▽ More Conventional (CP) and Fourier (FP) ptychography have emerged as versatile quantitative phase imaging techniques. While the main application cases for each technique are different, namely lens-less short wavelength imaging for CP and lens-based visible light imaging for FP, both methods share a common algorithmic ground. CP and FP have in part independently evolved to include experimentally robust forward models and inversion techniques. This separation has resulted in a plethora of algorithmic extensions, some of which have not crossed the boundary from one modality to the other. Here, we present an open source, cross-platform software, called PtyLab, enabling both CP and FP data analysis in a unified framework. With this framework, we aim to facilitate and accelerate cross-pollination between the two techniques. Moreover, the availability in Matlab, Python, and Julia will set a low barrier to enter each field. △ Less

Submitted 16 January, 2023; originally announced January 2023.

Non-volatile Electric Control of Magnetic and Topological Properties of MnBi2Te4 Thin Films

Authors: Wei Luo, Mao-Hua Du, Fernando A. Reboredo, Mina Yoon

Abstract: In this letter, we propose a mechanism to control the magnetic properties of topological quantum material (TQM) by using magnetoelectric coupling: this mechanism uses a heterostructure of TQM with two-dimensional (2D) ferroelectric material, which can dynamically control the magnetic order by changing the polarization of the ferroelectric material and induce possible topological phase transitions.… ▽ More In this letter, we propose a mechanism to control the magnetic properties of topological quantum material (TQM) by using magnetoelectric coupling: this mechanism uses a heterostructure of TQM with two-dimensional (2D) ferroelectric material, which can dynamically control the magnetic order by changing the polarization of the ferroelectric material and induce possible topological phase transitions. This concept is demonstrated using the example of the bilayer MnBi2Te4 on ferroelectric In2Se3 or In2Te3, where the polarization direction of the 2D ferroelectrics determines the interfacial band alignment and consequently the direction of the charge transfer. This charge transfer, in turn, enhances the stability of the ferromagnetic state of MnBi2Te4 and leads to a possible topological phase transition between the quantum anomalous Hall (QAH) effect and the zero plateau QAH. Our work provides a route to dynamically alter the magnetic ordering of TQMs and could lead to the discovery of new multifunctional topological heterostructures. △ Less

Submitted 29 December, 2022; originally announced December 2022.

Comments: 22 pages, 5 figures

Journal ref: 2D materials (2023)

Swinging between shine and shadow: Theoretical advances on thermally-activated vibropolaritonic chemistry (a perspective)

Authors: Jorge A. Campos-Gonzalez-Angulo, Yong Rui Poh, Matthew Du, Joel Yuen-Zhou

Abstract: Polariton chemistry has emerged as an appealing branch of synthetic chemistry that promises mode selectivity and a cleaner approach to kinetic control. Of particular interest are the numerous experiments in which reactivity has been modified by virtue of performing the reaction inside infrared optical microcavities in the absence of optical pumping; this effort is known as "vibropolaritonic chemis… ▽ More Polariton chemistry has emerged as an appealing branch of synthetic chemistry that promises mode selectivity and a cleaner approach to kinetic control. Of particular interest are the numerous experiments in which reactivity has been modified by virtue of performing the reaction inside infrared optical microcavities in the absence of optical pumping; this effort is known as "vibropolaritonic chemistry." The optimal conditions for these observations are (1) resonance between cavity and reactive modes at normal incidence ($k=0$), and (2) monotonic increase of the effect with the concentration of emitters in the sample. Importantly, vibropolaritonic chemistry has only been experimentally demonstrated in the so-called "collective" strong coupling regime, where there is a macroscopic number of molecules (rather than a single molecule) coupled to each photon mode of the microcavity. Strikingly, efforts to understand this phenomenon from a conceptual standpoint have encountered several roadblocks and no single, unifying theory has surfaced thus far. This perspective documents the most relevant approaches taken by theorists, laying out the contributions and unresolved challenges from each work. We expect this manuscript to not only serve as a primer for experimentalists and theorists alike, but also inform future endeavors in the quest for the ultimate formalism of vibropolaritonic chemical kinetics. △ Less

Submitted 20 January, 2023; v1 submitted 7 December, 2022; originally announced December 2022.

Comments: 18 pages, 5 figures

Journal ref: The Journal of Chemical Physics 2023 158 (23), 230901

Vibropolaritonic Reaction Rates in the Collective Strong Coupling Regime: Pollak-Grabert-Hänggi Theory

Authors: Matthew Du, Yong Rui Poh, Joel Yuen-Zhou

Abstract: Following experimental evidence that vibrational polaritons, formed from collective vibrational strong coupling (VSC) in optical microcavities, can modify ground-state reaction rates, a spate of theoretical explanations relying on cavity-induced frictions has been proposed through the Pollak-Grabert-Hänggi (PGH) theory, which goes beyond transition state theory (TST). However, by considering only… ▽ More Following experimental evidence that vibrational polaritons, formed from collective vibrational strong coupling (VSC) in optical microcavities, can modify ground-state reaction rates, a spate of theoretical explanations relying on cavity-induced frictions has been proposed through the Pollak-Grabert-Hänggi (PGH) theory, which goes beyond transition state theory (TST). However, by considering only a single reacting molecule coupled to light, these works do not capture the ensemble effects present in experiments. Moreover, the relevant light-matter coupling should have been $\sqrt{N}$ times smaller than those used by preceding works, where $N\approx10^{6}-10^{12}$ is the ensemble size. In this work, we explain why this distinction is significant and can nullify effects from these cavity-induced frictions. By analytically extending the cavity PGH model to realistic values of $N$, we show how this model succumbs to the polariton "large $N$ problem", that is, the situation whereby the single reacting molecule feels only a tiny $1/N$ part of the collective light-matter interaction intensity, where $N$ is large. △ Less

Submitted 9 January, 2023; v1 submitted 10 November, 2022; originally announced November 2022.

Comments: 9 pages, 4 figures

Journal ref: The Journal of Physical Chemistry C 2023 127 (11), 5230-5237

Pressure-induced high-temperature superconductivity in ternary Y-Zr-H compounds

Authors: Wendi Zhao, Defang Duan, Hao Song, Mingyang Du, Qiwen Jiang, Tiancheng Ma, Ming Xu, Tian Cui

Abstract: Compressed hydrogen-rich compounds have received extensive attention as appealing contenders for superconductors, and further challenges are maintaining the stability and superconductivity of hydrides at lower pressures. In this work, we found several novel hydrides YZrH6, YZrH8 and YZrH12 with excellent superconductivity in the Y-Zr-H ternary system. Interestingly, YZrH6 with an A15-type structur… ▽ More Compressed hydrogen-rich compounds have received extensive attention as appealing contenders for superconductors, and further challenges are maintaining the stability and superconductivity of hydrides at lower pressures. In this work, we found several novel hydrides YZrH6, YZrH8 and YZrH12 with excellent superconductivity in the Y-Zr-H ternary system. Interestingly, YZrH6 with an A15-type structure can maintain dynamic stability down to 0.01 GPa and still with a critical temperature (Tc) of 16 K. YZrH8 and YZrH12 have high Tc of 70 K and 183 K at 200 GPa and 160 GPa, respectively. The phonon modes associated with H atoms contribute significantly to the electron-phonon coupling, and the H-driven electronic density of states play an important role in superconductivity. These findings highlight relationship between the H-driven electronic density of states, electron-phonon coupling and the superconductivity in a distinct class of hydrides, opening new avenues for designing and optimizing new hydrogen-rich high temperature superconductors. △ Less

Submitted 9 September, 2022; originally announced September 2022.

Comments: 15 pages, 8 figures

Probing Supermassive Black Hole Binaries with Orbital Resonances of Laser-Ranged Satellite

Authors: Minghui Du, Qiong Deng, Yifan Bian, Ziren Luo, Peng Xu

Abstract: Coalescing supermassive black hole binaries (SMBHBs) are the primary source candidates for low frequency gravitational wave (GW) detections, which could bring us deep insights into galaxy evolutions over cosmic time and violent processes of spacetime dynamics. Promising candidates had been found based on optical and X-ray observations, which claims for new and ready-to-use GW detection approaches… ▽ More Coalescing supermassive black hole binaries (SMBHBs) are the primary source candidates for low frequency gravitational wave (GW) detections, which could bring us deep insights into galaxy evolutions over cosmic time and violent processes of spacetime dynamics. Promising candidates had been found based on optical and X-ray observations, which claims for new and ready-to-use GW detection approaches before the operations of space-borne antennas. We show that, satellite laser ranging (SLR) missions could serve as probes of coalescing SMBHBs through the GW-induced resonant effects. Lasting and characteristic imprints caused by such resonances in the residual distances or accelerations from SLR measurements are studied, and the detection SNR is analyzed with both the current and future improved ranging precisions. Within redshift $z \sim 1$, the threshold SNR=5 requires 1-2 years of accumulated data for the current precision and months of data for improved precision, which are workable for the data processing of SLR missions. Meanwhile, joint detections with multiple SLR missions could further improve the total SNR and the confidence level. Such a detection scheme could fulfill the requirement of a tentative SMBHB probe during the preparing stage of LISA and Taiji, and it requires no further investment to any new and advanced facilities. It is also worthwhile to look back and re-process the archived data from the past decades, in where resonant signals from SMBHBs might be hidden. △ Less

Submitted 2 July, 2023; v1 submitted 3 July, 2022; originally announced July 2022.

Comments: 12 pages, 10 figures

Journal ref: Eur. Phys. J. C, 83:554, (2023)

Suppressing data anomalies of gravitational reference sensors with time delay interferometry combinations

Authors: Pengzhan Wu, Minghui Du, Peng Xu

Abstract: For the LISA and Taiji missions, both transient and continuous data anomalies would pose significant challenges to the detection, estimation, and subsequent scientific interpretation of gravitational wave signals. As is indicated by the experiences of LISA PathFinder and Taiji-1, these anomalies may originate from the disturbances of the gravitational reference sensors due to routine maintenances… ▽ More For the LISA and Taiji missions, both transient and continuous data anomalies would pose significant challenges to the detection, estimation, and subsequent scientific interpretation of gravitational wave signals. As is indicated by the experiences of LISA PathFinder and Taiji-1, these anomalies may originate from the disturbances of the gravitational reference sensors due to routine maintenances and unexpected environmental or instrumental issues. To effectively mitigate such anomalies and thereby enhance the robustness and reliability of the scientific outputs, we suggest to employ the ``position noise suppressing'' time delay interferometry channels. Through analytical derivations and numerical simulations, we demonstrate that these time delay interferometry channels can suppress data anomalies by more than 2 orders of magnitude within the sensitive band of 0.1 mHz - 0.05 Hz, while still remaining sensitive to most of the target signals. Compared with existing researches that focus on reconstructing and subtracting data anomalies, our method does not rely on the prior knowledge about the models of anomalies. Furthermore, the potential application scenarios of these channels have also been explored. △ Less

Submitted 10 November, 2024; v1 submitted 21 June, 2022; originally announced June 2022.

Comments: 15 pages, 6 figures

Journal ref: Published in Optics Express (November 2024)

AutoKE: An automatic knowledge embedding framework for scientific machine learning

Authors: Mengge Du, Yuntian Chen, Dongxiao Zhang

Abstract: Imposing physical constraints on neural networks as a method of knowledge embedding has achieved great progress in solving physical problems described by governing equations. However, for many engineering problems, governing equations often have complex forms, including complex partial derivatives or stochastic physical fields, which results in significant inconveniences from the perspective of im… ▽ More Imposing physical constraints on neural networks as a method of knowledge embedding has achieved great progress in solving physical problems described by governing equations. However, for many engineering problems, governing equations often have complex forms, including complex partial derivatives or stochastic physical fields, which results in significant inconveniences from the perspective of implementation. In this paper, a scientific machine learning framework, called AutoKE, is proposed, and a reservoir flow problem is taken as an instance to demonstrate that this framework can effectively automate the process of embedding physical knowledge. In AutoKE, an emulator comprised of deep neural networks (DNNs) is built for predicting the physical variables of interest. An arbitrarily complex equation can be parsed and automatically converted into a computational graph through the equation parser module, and the fitness of the emulator to the governing equation is evaluated via automatic differentiation. Furthermore, the fixed weights in the loss function are substituted with adaptive weights by incorporating the Lagrangian dual method. Neural architecture search (NAS) is also introduced into the AutoKE to select an optimal network architecture of the emulator according to the specific problem. Finally, we apply transfer learning to enhance the scalability of the emulator. In experiments, the framework is verified by a series of physical problems in which it can automatically embed physical knowledge into an emulator without heavy hand-coding. The results demonstrate that the emulator can not only make accurate predictions, but also be applied to similar problems with high efficiency via transfer learning. △ Less

Submitted 11 May, 2022; originally announced May 2022.

Organic metallic epsilon-near-zero materials with large ultrafast optical nonlinearity

Authors: Qili Hu, Xinlan Yu, Hongqi Liu, Jiahuan Qiu, Wei Tang, Sen Liang, Linjun Li, Miao Du, Junjun Jia, Hui Ye

Abstract: Epsilon-near-zero (ENZ) materials have shown significant potential for nonlinear optical applications due to their ultrafast hot carriers and consequent optical nonlinearity enhancement. Modified poly(3,4-ethylenedioxythiophene) (PEDOT) films show metallic characteristics and a resultant ENZ wavelength near 1550nm through polar solvent treatment and annealing. The metallic PEDOT film exhibits an i… ▽ More Epsilon-near-zero (ENZ) materials have shown significant potential for nonlinear optical applications due to their ultrafast hot carriers and consequent optical nonlinearity enhancement. Modified poly(3,4-ethylenedioxythiophene) (PEDOT) films show metallic characteristics and a resultant ENZ wavelength near 1550nm through polar solvent treatment and annealing. The metallic PEDOT film exhibits an intrinsic optical nonlinear response that is comparable to gold and 100-fold higher than typical inorganic semiconductor ENZ materials due to π-conjugated delocalized electrons. Hot carriers generate a 22-fold increase in the optical nonlinearity coefficient of metallic PEDOT films at 1550 nm. Hot holes in metallic PEDOT films have a smaller enhancement multiple of carrier temperature and a longer relaxation time than hot electrons in inorganic ENZ materials due to the larger imaginary permittivity and hot-phonon bottleneck for carrier cooling. Our findings suggest that π-conjugated ENZ polymer may have unique ultrafast and nonlinear optical properties compared to inorganic ENZ materials, enabling new possibilities in on-chip nanophotonic devices, nonlinear optics, and plasmonics. △ Less

Submitted 5 October, 2022; v1 submitted 12 April, 2022; originally announced April 2022.

Comments: There is a mistake in our simulation part of two-temperature model. Thus we revise the parameter and reconstruct our idea. The new content is very different to this old one. Thus, we want to withdraw this paper and submit another new one

New source for tuning the effective Rabi frequency discovered in multiphoton ionization

Authors: Wankai Li, Yue Lei, Xing Li, Tao Yang, Mei Du, Ying Jiang, Jialong Li, Aihua Liu, Lanhai He, Pan Ma, Sizuo Luo, Dongdong Zhang, Dajun Ding

Abstract: The Autler-Townes effect due to near resonance transition between 4s-4p states in potassium atoms is mapped out in the photo-electron-momentum distribution and manifests itself as a splitting in the photo-electron kinetic energy spectra. The energy splitting fits well with the calculated Rabi frequency at low laser intensities and shows clear deviation at laser intensities above 1.5x10^11 W/cm^2.… ▽ More The Autler-Townes effect due to near resonance transition between 4s-4p states in potassium atoms is mapped out in the photo-electron-momentum distribution and manifests itself as a splitting in the photo-electron kinetic energy spectra. The energy splitting fits well with the calculated Rabi frequency at low laser intensities and shows clear deviation at laser intensities above 1.5x10^11 W/cm^2. An effective Rabi frequency formulae including the ionization process explains the observed results. Our results reveal the possibility to tune the effective coupling strength with the cost of the number of level-populations. △ Less

Submitted 24 December, 2021; originally announced December 2021.

Lonely individuals process the world in idiosyncratic ways

Authors: Elisa C. Baek, Ryan Hyon, Karina López, Meng Du, Mason A. Porter, Carolyn Parkinson

Abstract: Loneliness is detrimental to well-being and is often accompanied by self-reported feelings of not being understood by others. What contributes to such feelings in lonely people? We used functional magnetic resonance imaging (fMRI) of 66 participants to unobtrusively measure the relative alignment of people's mental processing of naturalistic stimuli and tested whether or not lonely people actually… ▽ More Loneliness is detrimental to well-being and is often accompanied by self-reported feelings of not being understood by others. What contributes to such feelings in lonely people? We used functional magnetic resonance imaging (fMRI) of 66 participants to unobtrusively measure the relative alignment of people's mental processing of naturalistic stimuli and tested whether or not lonely people actually process the world in idiosyncratic ways. We found evidence for such idiosyncrasy: lonely individuals' neural responses were dissimilar to their peers, particularly in regions of the default-mode network in which similar responses have been associated with shared perspectives and subjective understanding. These relationships persisted when controlling for demographic similarities, objective social isolation, and participants' friendships with each other. Our findings suggest the possibility that being surrounded by people who see the world differently from oneself, even if one is friends with them, may be a risk factor for loneliness. △ Less

Submitted 16 August, 2022; v1 submitted 2 July, 2021; originally announced July 2021.

Comments: revised version. arXiv admin note: text overlap with arXiv:2106.02726

Adorym: A multi-platform generic x-ray image reconstruction framework based on automatic differentiation

Authors: Ming Du, Saugat Kandel, Junjing Deng, Xiaojing Huang, Arnaud Demortiere, Tuan Tu Nguyen, Remi Tucoulou, Vincent De Andrade, Qiaoling Jin, Chris Jacobsen

Abstract: We describe and demonstrate an optimization-based x-ray image reconstruction framework called Adorym. Our framework provides a generic forward model, allowing one code framework to be used for a wide range of imaging methods ranging from near-field holography to and fly-scan ptychographic tomography. By using automatic differentiation for optimization, Adorym has the flexibility to refine experime… ▽ More We describe and demonstrate an optimization-based x-ray image reconstruction framework called Adorym. Our framework provides a generic forward model, allowing one code framework to be used for a wide range of imaging methods ranging from near-field holography to and fly-scan ptychographic tomography. By using automatic differentiation for optimization, Adorym has the flexibility to refine experimental parameters including probe positions, multiple hologram alignment, and object tilts. It is written with strong support for parallel processing, allowing large datasets to be processed on high-performance computing systems. We demonstrate its use on several experimental datasets to show improved image quality through parameter refinement. △ Less

Submitted 22 December, 2020; originally announced December 2020.

MSC Class: 78-04

Relative merits and limiting factors for x-ray and electron microscopy of thick, hydrated organic materials (revised)

Authors: Ming Du, Chris Jacobsen

Abstract: Electron and x-ray microscopes allow one to image the entire, unlabeled structure of hydrated materials at a resolution well beyond what visible light microscopes can achieve. However, both approaches involve ionizing radiation, so that radiation damage must be considered as one of the limits to imaging. Drawing upon earlier work, we describe here a unified approach to estimating the image contras… ▽ More Electron and x-ray microscopes allow one to image the entire, unlabeled structure of hydrated materials at a resolution well beyond what visible light microscopes can achieve. However, both approaches involve ionizing radiation, so that radiation damage must be considered as one of the limits to imaging. Drawing upon earlier work, we describe here a unified approach to estimating the image contrast (and thus the required exposure and corresponding radiation dose) in both x-ray and electron microscopy. This approach accounts for factors such as plural and inelastic scattering, and (in electron microscopy) the use of energy filters to obtain so-called "zero loss" images. As expected, it shows that electron microscopy offers lower dose for specimens thinner than about 1 micron (such as for studies of macromolecules, viruses, bacteria and archaebacteria, and thin sectioned material), while x-ray microscopy offers superior characteristics for imaging thicker specimen such as whole eukaryotic cells, thick-sectioned tissues, and organs. The required radiation dose scales strongly as a function of the desired spatial resolution, allowing one to understand the limits of live and frozen hydrated specimen imaging. Finally, we consider the factors limiting x-ray microscopy of thicker materials, suggesting that specimens as thick as a whole mouse brain can be imaged with x-ray microscopes without significant image degradation should appropriate image reconstruction methods be identified. The as-published article [Ultramicroscopy 184, 293--309 (2018); doi:10.1016/j.ultramic.2017.10.003] had some minor mistakes that we correct here, with all changes from the as-published article shown in blue. △ Less

Submitted 21 April, 2020; originally announced April 2020.

Comments: This is a revised full manuscript from the previously published version [Ultramicroscopy 184, 293--309 (2018); doi:10.1016/j.ultramic.2017.10.003]. An erratum is submitted to the journal, but we also wish to publish the full revised paper for readers' convenience

Journal ref: Ultramicroscopy 184, 293-309 (2018). (Reference to erratum will be updated once published)

RobustECD: Enhancement of Network Structure for Robust Community Detection

Authors: Jiajun Zhou, Zhi Chen, Min Du, Lihong Chen, Shanqing Yu, Guanrong Chen, Qi Xuan

Abstract: Community detection, which focuses on clustering vertex interactions, plays a significant role in network analysis. However, it also faces numerous challenges like missing data and adversarial attack. How to further improve the performance and robustness of community detection for real-world networks has raised great concerns. In this paper, we explore robust community detection by enhancing netwo… ▽ More Community detection, which focuses on clustering vertex interactions, plays a significant role in network analysis. However, it also faces numerous challenges like missing data and adversarial attack. How to further improve the performance and robustness of community detection for real-world networks has raised great concerns. In this paper, we explore robust community detection by enhancing network structure, with two generic algorithms presented: one is named robust community detection via genetic algorithm (RobustECD-GA), in which the modularity and the number of clusters are combined in a fitness function to find the optimal structure enhancement scheme; the other is called robust community detection via similarity ensemble (RobustECD-SE), integrating multiple information of community structures captured by various vertex similarities, which scales well on large-scale networks. Comprehensive experiments on real-world networks demonstrate, by comparing with two traditional enhancement strategies, that the new methods help six representative community detection algorithms achieve more significant performance improvement. Moreover, experiments on the corresponding adversarial networks indicate that the new methods could also optimize the network structure to a certain extent, achieving stronger robustness against adversarial attack. The source code of this paper is released on https://github.com/jjzhou012/RobustECD. △ Less

Submitted 1 July, 2021; v1 submitted 5 November, 2019; originally announced November 2019.

Comments: Under review. 21 pages, 14 figures

Journal ref: in IEEE Transactions on Knowledge & Data Engineering, vol. , no. 01, pp. 1-1, 5555

Deciphering the Effect of Traps on Electronic Charge Transport Properties of Methylammonium Lead Tribromide

Authors: Artem Musiienko, Jindrich Pipek, Petr Praus, Mykola Brynza, Eduard Belas, Bogdan Dryzhakov, Mao-Hua Du, Mahshid Ahmadi, Roman Grill

Abstract: Organometallic halide perovskites (OMHPs) have undergone remarkable developments as highly efficient optoelectronic materials for a variety of applications. Several studies indicated the critical role of defects on the performance of OMHP devices. Yet, the parameters of defects and their interplay with free charge carriers remain unclear. In this study we explore the dynamics of free holes in meth… ▽ More Organometallic halide perovskites (OMHPs) have undergone remarkable developments as highly efficient optoelectronic materials for a variety of applications. Several studies indicated the critical role of defects on the performance of OMHP devices. Yet, the parameters of defects and their interplay with free charge carriers remain unclear. In this study we explore the dynamics of free holes in methylammonium lead tribromide (MAPbBr3) single crystals using the time of flight (ToF) current spectroscopy. By combining the current waveform (CWF) ToF spectroscopy and the Monte Carlo (MC) simulation, three energy states were detected in the band gap of MAPbBr3. Additionally, we found the trapping and detrapping rates of free holes ranging from a few us to hundreds of us and, contrary to previous studies, a strong detrapping activity was revealed. It was shown that these traps have a significant impact on the transport properties of MAPbBr3 single crystal devices, including drift mobility and mobility-lifetime product. To demonstrate the impact of traps on the delay of free carriers, we developed a new model of the effective mobility valid for the case of multiple traps in a semiconductor. Our results provide a new insight on charge transport properties of OMHP semiconductors, which is required for further development of this class of optoelectronic devices. △ Less

Submitted 10 December, 2019; v1 submitted 23 September, 2019; originally announced September 2019.

Comments: 21 page

Ultrafast long-range energy transport via light-matter coupling in organic semiconductor films

Authors: Raj Pandya, Richard Y. S. Chen, Qifei Gu, Jooyoung Sung, Christoph Schnedermann, Oluwafemi S. Ojambati, Rohit Chikkaraddy, Jeffrey Gorman, Gianni Jacucci, Olimpia D. Onelli, Tom Willhammar, Duncan N. Johnstone, Sean M. Collins, Paul A. Midgley, Florian Auras, Tomi Baikie, Rahul Jayaprakash, Fabrice Mathevet, Richard Soucek, Matthew Du, Silvia Vignolini, David G Lidzey, Jeremy J. Baumberg, Richard H. Friend, Thierry Barisien , et al. (7 additional authors not shown)

Abstract: The formation of exciton-polaritons allows the transport of energy over hundreds of nanometres at velocities up to 10^6 m s^-1 in organic semiconductors films in the absence of external cavity structures. The formation of exciton-polaritons allows the transport of energy over hundreds of nanometres at velocities up to 10^6 m s^-1 in organic semiconductors films in the absence of external cavity structures. △ Less

Submitted 7 September, 2019; originally announced September 2019.

Near, far, wherever you are: simulations on the dose efficiency of holographic and ptychographic coherent imaging

Authors: Ming Du, Doga Gursoy, Chris Jacobsen

Abstract: Different studies in x-ray microscopy have arrived at conflicting conclusions about the dose efficiency of imaging modes involving the recording of intensity distributions in the near (Fresnel regime) or far (Fraunhofer regime) field downstream of a specimen. We present here a numerical study on the dose efficiency of near-field holography (NFH), near-field ptychography (NFP), and far-field ptycho… ▽ More Different studies in x-ray microscopy have arrived at conflicting conclusions about the dose efficiency of imaging modes involving the recording of intensity distributions in the near (Fresnel regime) or far (Fraunhofer regime) field downstream of a specimen. We present here a numerical study on the dose efficiency of near-field holography (NFH), near-field ptychography (NFP), and far-field ptychography (FFP), where ptychography involves multiple overlapping finite-sized illumination positions. Unlike what has been reported for coherent diffraction imaging (CDI), which involves recording a single far-field diffraction pattern, we find that all three methods offer similar image quality when using the same fluence on the specimen, with far-field ptychography offering slightly better spatial resolution and lower mean error. These results support the concept that (if the experiment and image reconstruction are done properly) the sample can be near, or far; wherever you are, photon fluence on the specimen sets one limit to spatial resolution. △ Less

Submitted 11 March, 2020; v1 submitted 16 August, 2019; originally announced August 2019.

Journal ref: Journal of Applied Crystallography. 53, 748-759 (2020)

Three dimensions, two microscopes, one code: automatic differentiation for x-ray nanotomography beyond the depth of focus limit

Authors: Ming Du, Youssef S. G. Nashed, Saugat Kandel, Doga Gursoy, Chris Jacobsen

Abstract: Conventional tomographic reconstruction algorithms assume that one has obtained pure projection images, involving no within-specimen diffraction effects nor multiple scattering. Advances in x-ray nanotomography are leading towards the violation of these assumptions, by combining the high penetration power of x-rays which enables thick specimens to be imaged, with improved spatial resolution which… ▽ More Conventional tomographic reconstruction algorithms assume that one has obtained pure projection images, involving no within-specimen diffraction effects nor multiple scattering. Advances in x-ray nanotomography are leading towards the violation of these assumptions, by combining the high penetration power of x-rays which enables thick specimens to be imaged, with improved spatial resolution which decreases the depth of focus of the imaging system. We describe a reconstruction method where multiple scattering and diffraction effects in thick samples are modeled by multislice propagation, and the 3D object function is retrieved through iterative optimization. We show that the same proposed method works for both full-field microscopy, and for coherent scanning techniques like ptychography. Our implementation utilizes the optimization toolbox and the automatic differentiation capability of the open-source deep learning package TensorFlow, which demonstrates a much straightforward way to solve optimization problems in computational imaging, and endows our program great flexibility and portability. △ Less

Submitted 24 May, 2019; originally announced May 2019.

Journal ref: Science Advances. 6, eaay3700 (2020)

Verification of the Herschel-Bulkley Model by a Visualization Experiment of a Elastoviscoplastic Yield-Stress Fluid Flow in Straight and Bended Tubes

Authors: Mingjun Li, Miaorong Du, Hiroshi Yamaguchi, Xiao-Dong Niu, Hiroki Nakakoji

Abstract: The elastoviscoplastic yield-stress fluid flows in a horizontal straight tube and a bended tube have been investigated using hydrogen bubble visualization method. The experimental results are used to verify the empirical Herschel-Bulkley model. Both experimental and theoretical investigations well predict the yield-stress fluid flow behaviors. It is found that the significant factors influencing o… ▽ More The elastoviscoplastic yield-stress fluid flows in a horizontal straight tube and a bended tube have been investigated using hydrogen bubble visualization method. The experimental results are used to verify the empirical Herschel-Bulkley model. Both experimental and theoretical investigations well predict the yield-stress fluid flow behaviors. It is found that the significant factors influencing on the predictions of the Herschel-Bulkley model are the yield stress, viscosity and viscoelasticity. For the yield-stress flows in the bended tube, a more delicate constitutive model with consideration of the viscoelastic effects is expected for accurately predicting the flow behaviors. △ Less

Submitted 25 March, 2019; originally announced March 2019.

Complementarity Assessment of South Greenland Katabatic Flows and West Europe Wind Regimes

Authors: David Radu, Mathias Berger, Raphaël Fonteneau, Simon Hardy, Xavier Fettweis, Marc Le Du, Patrick Panciatici, Lucian Balea, Damien Ernst

Abstract: Current global environmental challenges require vigorous and diverse actions in the energy sector. One solution that has recently attracted interest consists in harnessing high-quality variable renewable energy resources in remote locations, while using transmission links to transport the power to end users. In this context, a comparison of western European and Greenland wind regimes is proposed.… ▽ More Current global environmental challenges require vigorous and diverse actions in the energy sector. One solution that has recently attracted interest consists in harnessing high-quality variable renewable energy resources in remote locations, while using transmission links to transport the power to end users. In this context, a comparison of western European and Greenland wind regimes is proposed. By leveraging a regional atmospheric model specifically designed to accurately capture polar phenomena, local climatic features of southern Greenland are identified to be particularly conducive to extensive renewable electricity generation from wind. A methodology to assess how connecting remote locations to major demand centres would benefit the latter from a resource availability standpoint is introduced and applied to the aforementioned Europe-Greenland case study, showing superior and complementary wind generation potential in the considered region of Greenland with respect to selected European sites. △ Less

Submitted 2 April, 2019; v1 submitted 5 December, 2018; originally announced December 2018.

Comments: Published in Elsevier Energy

Critical Time Windows for Renewable Resource Complementarity Assessment

Authors: Mathias Berger, David Radu, Raphael Fonteneau, Robin Henry, Mevludin Glavic, Xavier Fettweis, Marc Le Du, Patrick Panciatici, Lucian Balea, Damien Ernst

Abstract: This paper proposes a systematic framework to assess the complementarity of renewable resources over arbitrary geographical scopes and temporal scales which is particularly well-suited to exploit very large data sets of climatological data. The concept of critical time windows is introduced, and a spatio-temporal criticality indicator is proposed, consisting in a parametrised family of scalar indi… ▽ More This paper proposes a systematic framework to assess the complementarity of renewable resources over arbitrary geographical scopes and temporal scales which is particularly well-suited to exploit very large data sets of climatological data. The concept of critical time windows is introduced, and a spatio-temporal criticality indicator is proposed, consisting in a parametrised family of scalar indicators quantifying the complementarity between renewable resources in both space and time. The criticality indicator is leveraged to devise a family of optimisation problems identifying sets of locations with maximum complementarity under arbitrary geographical deployment constraints. The applicability of the framework is shown in a case study investigating the complementarity between the wind regimes in continental western Europe and southern Greenland, and its usefulness in a power system planning context is demonstrated. Besides showing that the occurrence of low wind power production events can be significantly reduced on a regional scale by exploiting diversity in local wind patterns, results highlight the fact that aggregating wind power production sites located on different continents may result in a lower occurrence of system-wide low wind power production events and indicate potential benefits of intercontinental electrical interconnections. △ Less

Submitted 5 December, 2018; originally announced December 2018.

Remote control of chemistry in optical cavities

Authors: Matthew Du, Raphael F. Ribeiro, Joel Yuen-Zhou

Abstract: Manipulation of chemical reactivity often involves changing reagents or environmental conditions. Alternatively, strong coupling between light and matter offers a way to tunably hybridize their physicochemical properties and thereby change reaction dynamics without synthetic modifications to the starting material. Here, we theoretically design a polaritonic (hybrid photonic-molecular) device that… ▽ More Manipulation of chemical reactivity often involves changing reagents or environmental conditions. Alternatively, strong coupling between light and matter offers a way to tunably hybridize their physicochemical properties and thereby change reaction dynamics without synthetic modifications to the starting material. Here, we theoretically design a polaritonic (hybrid photonic-molecular) device that supports ultrafast tuning of reaction yields even when the catalyst and its reactant are spatially separated across several optical wavelengths. We demonstrate how photoexcitation of a `remote catalyst' in an optical microcavity can control photochemistry of a reactant in another microcavity. Harnessing delocalization across the spatially separated compounds that arises from strong cavity-molecule coupling, this intriguing phenomenon is shown for the infrared-induced \textit{cis} $\rightarrow$ \textit{trans} conformational isomerization of nitrous acid (HONO). Indeed, increasing the excited-state population of the remote catalyst can enhance the isomerization efficiency by an order of magnitude. The theoretical proposal reported herein is generalizable to other reactions and thus introduces a versatile tool to control photochemistry. △ Less

Submitted 15 November, 2018; v1 submitted 23 October, 2018; originally announced October 2018.