arXiv subjects

Xing Guo

Publications and source records attributed to Xing Guo.

6 recordsLinked to original sources

arXiv2026

A Systematic Analysis of Automatic Differentiation versus Discretization-based Constraints for Physics-Informed PDE Solvers

Physics-informed neural networks (PINNs) represent a growing frontier in using artificial intelligence to solve partial differential equations (PDEs). Automatic differentiation (AD) plays a central role in this paradigm, which is mesh-free and replaces traditional iterative solvers with gradient-based optimization in continuous space. However, the inherent limitations of AD, particularly in handling higher-order derivatives and discontinuous solutions, pose significant challenges for complex problems. This has motivated a growing number of researchers to explore discretization-based constraints as an alternative path. Yet, the respective applicability of these two paradigms remains largely unexplored. In this work, we conduct systematic experiments across a wide spectrum of problems, from simple linear Poisson to high-Mach hypersonic flows with strong discontinuities. Through a rigorous decomposition of approximation, optimization, and truncation errors, we systematically elucidate the fundamental trade-offs and error-governing mechanisms of both paradigms, as well as two representative network architectures: multi-layer perceptron (MLP) and graph neural network (GNN). Our results reveal a consistent trend: as nonlinearity strengthens, the accuracy advantage of discretization-based constraints becomes increasingly pronounced, with smaller optimization errors compensating for the truncation errors. Moreover, the more complex the nonlinearity and boundary conditions, the greater the advantage of GNN over MLP. These insights offer a robust practical guideline for configuring neural PDE solvers in demanding engineering applications. Our source data and code are available at https://github.com/guoxing0809/neuropde_analysis.

math.NA↗

arXiv2024

Research on Personalized Compression Algorithm for Pre-trained Models Based on Homomorphic Entropy Increase

In this article, we explore the challenges and evolution of two key technologies in the current field of AI: Vision Transformer model and Large Language Model (LLM). Vision Transformer captures global information by splitting images into small pieces and leveraging Transformer's multi-head attention mechanism, but its high reference count and compute overhead limit deployment on mobile devices. At the same time, the rapid development of LLM has revolutionized natural language processing, but it also faces huge deployment challenges. To address these issues, we investigate model pruning techniques, with a particular focus on how to reduce redundant parameters without losing accuracy to accommodate personalized data and resource-constrained environments. In this paper, a new layered pruning strategy is proposed to distinguish the personalized layer from the common layer by compressed sensing and random sampling, thus significantly reducing the model parameters. Our experimental results show that the introduced step buffering mechanism further improves the accuracy of the model after pruning, providing new directions and possibilities for the deployment of efficient and personalized AI models on mobile devices in the future.

cs.LG↗

arXiv2024

Dimeric Drug Polymeric Micelles with Acid-Active Tumor Targeting and FRET-indicated Drug Release

Trans-activating transcriptional activator (TAT), a cell-penetrating peptide, has been extensively used for facilitating cellular uptake and nuclear targeting of drug delivery systems. However, the positively charged TAT peptide usually strongly interacts with serum components and undergoes substantial phagocytosis by the reticuloendothelial system, causing a short blood circulation in vivo. In this work, an acid-active tumor targeting nanoplatform DA-TAT-PECL was developed to effectively inhibit the nonspecific interactions of TAT in the bloodstream. 2,3-dimethylmaleic anhydride (DA) was first used to convert the TAT amines to carboxylic acid, the resulting DA-TAT was further conjugated to get DA-TAT-PECL. After self-assembly into polymeric micelles, they were capable of circulating in the physiological condition for a long time and promoting cell penetration upon accumulation at the tumor site and de-shielding the DA group. Moreover, camptothecin (CPT) was used as the anticancer drug and modified into a dimer (CPT)2-ss-Mal, in which two CPT molecules were connected by a reduction-labile maleimide thioether bond. The FRET signal between CPT and maleimide thioether bond was monitored to visualize the drug release process and effective targeted delivery of antitumor drugs was demonstrated. This pH/reduction dual-responsive micelle system provides a new platform for high fidelity cancer therapy.

q-bio.TO↗

arXiv2024

Domain Adaptation for Industrial Time-series Forecasting via Counterfactual Inference

Industrial time-series, as a structural data responds to production process information, can be utilized to perform data-driven decision-making for effective monitoring of industrial production process. However, there are some challenges for time-series forecasting in industry, e.g., predicting few-shot caused by data shortage, and decision-confusing caused by unknown treatment policy. To cope with the problems, we propose a novel causal domain adaptation framework, Causal Domain Adaptation (CDA) forecaster to improve the performance on the interested domain with limited data (target). Firstly, we analyze the causality existing along with treatments, and thus ensure the shared causality over time. Subsequently, we propose an answer-based attention mechanism to achieve domain-invariant representation by the shared causality in both domains. Then, a novel domain-adaptation is built to model treatments and outcomes jointly training on source and target domain. The main insights are that our designed answer-based attention mechanism allows the target domain to leverage the existed causality in source time-series even with different treatments, and our forecaster can predict the counterfactual outcome of industrial time-series, meaning a guidance in production process. Compared with commonly baselines, our method on real-world and synthetic oilfield datasets demonstrates the effectiveness in across-domain prediction and the practicality in guiding production process

cs.LG↗

arXiv2023

Ensemble Interpretation: A Unified Method for Interpretable Machine Learning

To address the issues of stability and fidelity in interpretable learning, a novel interpretable methodology, ensemble interpretation, is presented in this paper which integrates multi-perspective explanation of various interpretation methods. On one hand, we define a unified paradigm to describe the common mechanism of different interpretation methods, and then integrate the multiple interpretation results to achieve more stable explanation. On the other hand, a supervised evaluation method based on prior knowledge is proposed to evaluate the explaining performance of an interpretation method. The experiment results show that the ensemble interpretation is more stable and more consistent with human experience and cognition. As an application, we use the ensemble interpretation for feature selection, and then the generalization performance of the corresponding learning model is significantly improved.

cs.LG↗

arXiv2018

Topological thermal Hall effect driven by fluctuation of spin chirality in frustrated antiferromagnets

By revealing an underlying relation between the Dzyaloshinskii-Moriya interaction (DMI) and the scalar spin chirality, we develop the theory of magnon thermal Hall effects in antiferromagnetic systems. The dynamic fluctuation of the scalar chirality is shown to directly respond to the nontrivial topology of magnon bands. In materials such as the jarosites compounds KFe$_3$(OH)$_6$(SO$_4$)$_2$ and veseignite BaCu$_3$V$_2$O$_8$(OH)$_2$ in the presence of in-plane DMI, the time-reversal symmetry can be broken by the fluctuations of scalar chirality even in the case of coplanar $\mathbf{q}=0$ magnetic configuration. The spin-wave Hamiltonian is influenced by a fictitious magnetic flux determined by the in-plane DMI. Topological magnon bands and corresponding nonzero Chern numbers are presented without the need of a canted non-coplanar magnetic ordering. The canting angle dependence of thermal Hall conductivity is discussed in detail as well. These results offer a clear principle of chirality-driven topological effects in antiferromagnetically coupled systems.

cond-mat.str-el↗