arXiv subjects

Lingxiao Wang

Publications and source records attributed to Lingxiao Wang.

At least 37 records · Page 2Linked to original sources

arXiv2025

High-resolution ensemble retrieval of cloud properties for all-day based on geostationary satellite

Clouds play a critical role in Earth's hydrological and energy cycles, and accurately representing their properties is essential for effective numerical modeling and weather forecasting. Machine learning methods have been widely used for cloud property retrieval; however, most existing techniques are deterministic and do not incorporate uncertainty quantification. Generative machine learning has made significant advances in various domains, including natural language processing, image generation, and notably weather forecasting, where it has enabled ensemble predictions and the quantification of forecast uncertainty. This ability to quantify uncertainty offers valuable opportunities for cloud remote sensing. In this study, we propose a novel cloud property retrieval method, CloudDiff, based on a generative diffusion model. By leveraging thermal infrared observations from the Himawari-8 Advanced Himawari Imager (AHI), CloudDiff generates high spatiotemporal resolution cloud properties for both daytime and nighttime conditions, increasing the resolution of Himawari-8/AHI cloud retrievals from 2 km to 1 km. Unlike deterministic retrieval methods, CloudDiff generates multiple samples from the underlying probability distribution, allowing for a diverse range of plausible retrievals and taking steps towards providing uncertainty assessment. Additionally, CloudDiff produces sharper samples and better captures fine local features, enhancing the precision of cloud property retrieval. By averaging over the ensemble of generated samples, we demonstrate that both the accuracy and reliability of the retrievals are significantly improved. These high-resolution cloud properties have been successfully applied to analyze extreme weather events, such as typhoons, providing potentially valuable insights into atmospheric processes.

physics.ao-ph↗

arXiv2025

Latent Representation Learning in Heavy-Ion Collisions with MaskPoint Transformer

A central challenge in high-energy nuclear physics is to extract informative features from the high-dimensional final-state data of heavy-ion collisions (HIC) in order to enable reliable downstream analyses. Traditional approaches often rely on selected observables, which may miss subtle but physically relevant structures in the data. To address this, we introduce a Transformer-based autoencoder trained with a two-stage paradigm: self-supervised pre-training followed by supervised fine-tuning. The pretrained encoder learns latent representations directly from unlabeled HIC data, providing a compact and information-rich feature space that can be adapted to diverse physics tasks. As a case study, we apply the method to distinguish between large and small collision systems, where it achieves significantly higher classification accuracy than PointNet. Principal component analysis and SHAP interpretation further demonstrate that the autoencoder captures complex nonlinear correlations beyond individual observables, yielding features with strong discriminative and explanatory power. These results establish our two-stage framework as a general and robust foundation for feature learning in HIC, opening the door to more powerful analyses of quark--gluon plasma properties and other emergent phenomena. The implementation is publicly available at https://github.com/Giovanni-Sforza/MaskPoint-AMPT.

hep-ph↗

arXiv2025

Combining complex Langevin dynamics with score-based and energy-based diffusion models

Theories with a sign problem due to a complex action or Boltzmann weight can sometimes be numerically solved using a stochastic process in the complexified configuration space. However, the probability distribution effectively sampled by this complex Langevin process is not known a priori and notoriously hard to understand. In generative AI, diffusion models can learn distributions, or their log derivatives, from data. We explore the ability of diffusion models to learn the distributions sampled by a complex Langevin process, comparing score-based and energy-based diffusion models, and speculate about possible applications.

hep-lat↗

arXiv2025

Aligning Core Aspects: Improving Vulnerability Proof-of-Concepts via Cross-Source Insights

For vulnerabilities, Proof-of-Concept (PoC) plays an irreplaceable role in demonstrating the exploitability. PoC reports may include critical information such as specific usage, test platforms, and more, providing essential insights for researchers. However, in reality, due to various PoC templates across PoC platforms, PoC reports extensively suffer from information deficiency, leading the suboptimal quality and limited usefulness. Fortunately, we found that information deficiency of PoC reports could be mitigated by the completion from multiple sources given the same referred vulnerability. In this paper, we conduct the first study on the deficiency of information in PoC reports across public platforms. We began by collecting 173,170 PoC reports from 4 different platforms and defined 8 key aspects that PoCs should contain. By integrating rule-based matching and a fine-tuned BERT-NER model for extraction of key aspects, we discovered that all PoC reports available on public platforms have at least one missing key aspect. Subsequently, we developed a multi-source information fusion method to complete the missing aspect information in PoC reports by leveraging CVE entries and related PoC reports from different sources. Finally, we successfully completed 69,583 PoC reports (40.18% of all reports).

cs.CR↗

arXiv2025

Neural network extraction of chromo-electric and chromo-magnetic gluon masses

We present a neural network-based quasi-particle model to separate the contributions of chromo-electric and chromo-magnetic gluons. Using dual residual networks, we extract temperature-dependent masses from SU(3) lattice thermodynamic data of pressure and trace anomaly. After incorporating physics regularizations, the trained models reproduce lattice results with high accuracy over $T/T_c \in [1,10]$, capturing both the crossover behavior near $T_c$ and linear scaling at high temperatures. The extracted masses exhibit a physically reasonable behavior: they decrease sharply around $T_c$ and increase linearly thereafter. We find significant differences between thermal and screening masses near $T_c$, reflecting non-perturbative dynamics, while they converge at $T \gtrsim 2T_c$.

hep-ph↗

arXiv2025

Gradient-Boosted Pseudo-Weighting: Methods for Population Inference from Nonprobability samples

Nonprobability samples have rapidly emerged to address time-sensitive priority topics in a variety of fields. While these data are timely, they are prone to selection bias. To mitigate selection bias, a large number of survey research literature has explored the use of propensity score (PS) adjustment methods to enhance population representativeness of nonprobability samples, using probability-based survey samples as external references. A recent advancement, the 2-step PS-based pseudo-weighting adjustment method (2PS, Li 2024), has been shown to improve upon recent developments with respect to mean squared error. However, the effectiveness of these methods in reducing bias critically depends on the ability of the underlying propensity model to accurately reflect the true selection process, which is challenging with parametric regression. In this study, we propose a set of pseudo-weight construction methods, which utilize gradient boosting methods (GBM) to estimate PSs in 2PS to construct pseudo-weights, offering greater flexibility compared to logistic regression-based methods. We compare the proposed GBM-based pseudo-weights with existing methods, including 2PS. The population mean estimators are evaluated via Monte Carlo simulation studies. We also evaluated prevalence of various health outcomes, including 15-year mortality, using 1988 ~ 1994 NHANES III as a nonprobability sample and the 1994 NHIS as the reference survey.

stat.ME↗

arXiv2025

Towards constraining QCD phase transitions in neutron star interiors: Bayesian Inference with TOV linear response analysis

The potential hadron-to-quark phase transition in neutron stars has not been fully understood as the property of cold, dense, and strongly interacting matter cannot be theoretically described by the first-principle perturbative calculations, nor have they been systematically measured through terrestrial low-to-intermediate energy heavy-ion experiments. Given the Tolman--Oppenheimer--Volkoff (TOV) equations, the equation of state (EoS) of the neutron star (NS) matter can be constrained by the observations of NS mass, radius, and tidal deformability. However, large observational uncertainties and the limited number of observations currently make it challenging to strictly reconstruct the EoS, especially to identify interesting features such as a strong first-order phase transition. In this work, we study the dependency of reconstruction quality of the phase transition on the number of NS observations of mass and radius as well as their uncertainty, based on a fiducial EoS. We conquer this challenging problem by constructing a neural network, which allows one to parametrize the EoS with minimum model-dependency, and by devising an algorithm of parameter optimization based on the analytical linear response analysis of the TOV equations. This work may pave the way for the understanding of the phase transition features in NSs using future $x$-ray and gravitational wave measurements.

nucl-th↗

arXiv2025

Personalized Federated Training of Diffusion Models with Privacy Guarantees

The scarcity of accessible, compliant, and ethically sourced data presents a considerable challenge to the adoption of artificial intelligence (AI) in sensitive fields like healthcare, finance, and biomedical research. Furthermore, access to unrestricted public datasets is increasingly constrained due to rising concerns over privacy, copyright, and competition. Synthetic data has emerged as a promising alternative, and diffusion models -- a cutting-edge generative AI technology -- provide an effective solution for generating high-quality and diverse synthetic data. In this paper, we introduce a novel federated learning framework for training diffusion models on decentralized private datasets. Our framework leverages personalization and the inherent noise in the forward diffusion process to produce high-quality samples while ensuring robust differential privacy guarantees. Our experiments show that our framework outperforms non-collaborative training methods, particularly in settings with high data heterogeneity, and effectively reduces biases and imbalances in synthetic data, resulting in fairer downstream models.

cs.LG↗

arXiv2025

On learning higher-order cumulants in diffusion models

To analyse how diffusion models learn correlations beyond Gaussian ones, we study the behaviour of higher-order cumulants, or connected n-point functions, under both the forward and backward process. We derive explicit expressions for the moment- and cumulant-generating functionals, in terms of the distribution of the initial data and properties of forward process. It is shown analytically that during the forward process higher-order cumulants are conserved in models without a drift, such as the variance-expanding scheme, and that therefore the endpoint of the forward process maintains nontrivial correlations. We demonstrate that since these correlations are encoded in the score function, higher-order cumulants are learnt in the backward process, also when starting from a normal prior. We confirm our analytical results in an exactly solvable toy model with nonzero cumulants and in scalar lattice field theory.

hep-lat↗

arXiv2025

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

Due to the high cost of communication, federated learning (FL) systems need to sample a subset of clients that are involved in each round of training. As a result, client sampling plays an important role in FL systems as it affects the convergence rate of optimization algorithms used to train machine learning models. Despite its importance, there is limited work on how to sample clients effectively. In this paper, we cast client sampling as an online learning task with bandit feedback, which we solve with an online stochastic mirror descent (OSMD) algorithm designed to minimize the sampling variance. We then theoretically show how our sampling method can improve the convergence speed of federated optimization algorithms over the widely used uniform sampling. Through both simulated and real data experiments, we empirically illustrate the advantages of the proposed client sampling algorithm over uniform sampling and existing online learning-based sampling strategies. The proposed adaptive sampling procedure is applicable beyond the FL problem studied here and can be used to improve the performance of stochastic optimization procedures such as stochastic gradient descent and stochastic coordinate descent.

cs.LG↗

arXiv2025

Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics

The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from complex data sets. This is particularly relevant for quantum chromodynamics (QCD), the theory of strong interactions, with its inherent limitations in observational data and demanding computational approaches. This perspective highlights advances and potential of physics-driven learning methods, focusing on predictions of physical quantities towards QCD physics, and drawing connections to machine learning(ML). It is shown that the fusion of ML and physics can lead to more efficient and reliable problem-solving strategies. Key ideas of ML, methodology of embedding physics priors, and generative models as inverse modelling of physical probability distributions are introduced. Specific applications cover first-principle lattice calculations, and QCD physics of hadrons, neutron stars, and heavy-ion collisions. These examples provide a structured and concise overview of how incorporating prior knowledge such as symmetry, continuity and equations into deep learning designs can address diverse inverse problems across different physical sciences.

hep-lat↗

arXiv2025

Learning Hadron Emitting Sources with Deep Neural Networks

The correlation function observed in high-energy collision experiments encodes critical information about the emitted source and hadronic interactions. While the proton-proton interaction potential is well constrained by nucleon-nucleon scattering data, these measurements offer a unique avenue to investigate the proton-emitting source, reflecting the dynamical properties of the collisions. In this Letter, we present an unbiased approach to reconstruct proton-emitting sources from experimental correlation functions. Within an automatic differentiation framework, we parameterize the source functions with deep neural networks, to compute correlation functions. This approach achieves a lower chi-squared value compared to conventional Gaussian source functions and captures the long-tail behavior, in qualitative agreement with simulation predictions.

nucl-th↗

arXiv2024

Deep learning for exploring hadron-hadron interactions

In this proceeding, we introduce deep learning technologies for studying hadron-hadron interactions. To extract parameterized hadron interaction potentials from collision experiments, we employ a supervised learning approach using Femtoscopy data. The deep neural networks (DNNs) are trained to learn the inverse mapping from observations to potentials. To link between experiments and first-principles simulations, we further investigate hadronic interactions in Lattice QCD simulations from the HAL QCD method perspective. Using an unsupervised learning approach, we construct a model-free potential function with symmetric DNNs, aiming to learn hadron interactions directly from simulated correlation functions (equal-time Nambu-Bethe-Salpeter amplitudes). On both fronts, deep learning methods show great promise in advancing our understanding of hadron interactions.

nucl-th↗

arXiv2024

Diffusion models and stochastic quantisation in lattice field theory

Diffusion models are currently the leading generative AI approach used for image generation in e.g. DALL-E and Stable Diffusion. In this talk we relate diffusion models to stochastic quantisation in field theory and employ it to generate configurations for scalar fields on a two-dimensional lattice. We end with some speculations on possible applications.

hep-lat↗

arXiv2024

Diffusion models learn distributions generated by complex Langevin dynamics

The probability distribution effectively sampled by a complex Langevin process for theories with a sign problem is not known a priori and notoriously hard to understand. Diffusion models, a class of generative AI, can learn distributions from data. In this contribution, we explore the ability of diffusion models to learn the distributions created by a complex Langevin process.

hep-lat↗

arXiv2024

Diffusion models for lattice gauge field simulations

We develop diffusion models for lattice gauge theories which build on the concept of stochastic quantization. This framework is applied to $U(1)$ gauge theory in $1+1$ dimensions. We show that a model trained at one small inverse coupling can be effectively transferred to larger inverse coupling without encountering issues related to topological freezing, i.e., the model can generate configurations corresponding to different couplings by introducing the Boltzmann factors as physics conditions, while maintaining the correct physical distributions without any additional training. This demonstrates the potential of physics-conditioned diffusion models for efficient and flexible lattice gauge theory simulations.

hep-lat↗

arXiv2024

Bridging the Sim-to-Real Gap from the Information Bottleneck Perspective

Reinforcement Learning (RL) has recently achieved remarkable success in robotic control. However, most works in RL operate in simulated environments where privileged knowledge (e.g., dynamics, surroundings, terrains) is readily available. Conversely, in real-world scenarios, robot agents usually rely solely on local states (e.g., proprioceptive feedback of robot joints) to select actions, leading to a significant sim-to-real gap. Existing methods address this gap by either gradually reducing the reliance on privileged knowledge or performing a two-stage policy imitation. However, we argue that these methods are limited in their ability to fully leverage the available privileged knowledge, resulting in suboptimal performance. In this paper, we formulate the sim-to-real gap as an information bottleneck problem and therefore propose a novel privileged knowledge distillation method called the Historical Information Bottleneck (HIB). In particular, HIB learns a privileged knowledge representation from historical trajectories by capturing the underlying changeable dynamic information. Theoretical analysis shows that the learned privileged knowledge representation helps reduce the value discrepancy between the oracle and learned policies. Empirical experiments on both simulated and real-world tasks demonstrate that HIB yields improved generalizability compared to previous methods. Videos of real-world experiments are available at https://sites.google.com/view/history-ib .

cs.LG↗

arXiv2024

Building imaginary-time thermal field theory with artificial neural networks

In this study, we introduce a novel approach in quantum field theories to estimate the action using the artificial neural networks (ANNs). The estimation is achieved by learning on system configurations governed by the Boltzmann factor, $e^{-S}$ at different temperatures within the imaginary time formalism of thermal field theory. We focus on 0+1 dimensional quantum field with kink/anti-kink configurations to demonstrate the feasibility of the method. The continuous-mixture autoregressive networks (CANs) enable the construction of accurate effective actions with tractable probability density estimation. Our numerical results demonstrate that this methodology not only facilitates the construction of effective actions at specified temperatures but also adeptly estimates the action at intermediate temperatures using data from both lower and higher temperature ensembles. This capability is especially valuable for the detailed exploration of phase diagrams.

hep-lat↗