arXiv subjects

Chunyang Wang

Publications and source records attributed to Chunyang Wang.

At least 19 recordsLinked to original sources

arXiv2026

A Counting Lov\'asz Local Lemma

We establish a counting analogue of the Lov\'asz Local Lemma: we give polynomial-time algorithms for approximately counting satisfying assignments of general constraint satisfaction problems (CSPs) in the local lemma regime $$ 4 \mathrm{e}\cdot p\cdot (D+1)^2\leq 1, $$ where $p$ is the maximum constraint violation probability and $D$ is the maximum dependency degree. This condition is tight up to constant factors, matching known lower bounds $pD^2\gtrsim 1$ for approximate counting in natural subclasses of CSPs. The core of our approach is a novel $2$-tree expansion for constraint marginal probabilities, which captures the decay of correlations in the local lemma regime.

cs.DS↗

arXiv2026

Approximate Colorwise Tensorization of Entropy and Optimal Mixing of the Wang-Swendsen-Koteck\'y Dynamics

We study the mixing time of Wang-Swendsen-Koteck\'{y} (WSK) dynamics for uniformly sampling proper $q$-colorings. The WSK dynamics is widely used in statistical physics for sampling from the antiferromagnetic Potts model and can be considered a global counterpart of the flip dynamics, which currently yields the state-of-the-art bounds for sampling colorings in general graphs (Carlson and Vigoda, SODA 2025). However, despite its importance, the tools for analyzing such dynamics remain limited. We develop new tools that enable us to analyze the mixing time of the WSK dynamics through the lens of relative entropy contraction. We introduce new criteria for multi-spin distributions: approximate colorwise tensorization of entropy (ACTE) and approximate colorwise subadditivity of entropy (ACSE). These criteria provide a colorwise counterpart to standard vertex-wise entropy factorization principles, and expose a form of color symmetry beyond coordinate-wise analyses. We also develop new inductive approaches for establishing such criteria on specific types of graphs, which can be viewed as local-to-global arguments for proving high-dimensional functional inequalities in a graph-theoretic sense. As concrete applications, we establish an optimal $O_q(\log n)$ mixing time for the WSK dynamics on chordal and outerplanar graphs, down to the optimal number of colors. Because trees and line graphs of trees are chordal, the result covers both vertex and edge colorings of trees. Our results work in a regime that bypasses the irreducibility threshold for Glauber dynamics while also improving the best known mixing time bounds (Carlson, Chen, Feng and Vigoda, SODA 2025).

cs.DS↗

arXiv2026

oxo-call: Documentation-grounded Skill Augmentation for Accurate Bioinformatics Command-line Generation with Large Language Models

Command-line bioinformatics tools remain essential for genomic analysis, yet their diversity in syntax and parameterization presents a persistent barrier to productive research. We present oxo-call, a Rust-based command-line assistant that translates natural-language task descriptions into accurate tool invocations through two complementary strategies: documentation-first grounding, which provides the large language model (LLM) with the complete, version-specific help text of each target tool, and curated skill augmentation, which primes the model with domain-expert concepts, common pitfalls, and worked examples. oxo-call (v0.10) ships >150 built-in skills covering 44 analytical categories, from variant calling and genome assembly to single-cell transcriptomics, compiled into a single, statically linked binary. Every generated command is logged with provenance metadata to support reproducible research. oxo-call also provides a DAG-based workflow engine, extensibility through user-defined and community skills via the Model Context Protocol, and support for local LLM inference to address data-privacy requirements. oxo-call is freely available for academic use at https://traitome.github.io/oxo-call/.

q-bio.GN↗

arXiv2026

Learning Pore-scale Multiphase Flow from 4D Velocimetry

Multiphase flow in porous media underpins subsurface energy and environmental technologies, including geological CO$_2$ storage and underground hydrogen storage, yet pore-scale dynamics in realistic three-dimensional materials remain difficult to characterize and predict. Here we introduce a multimodal learning framework that infers multiphase pore-scale flow directly from time-resolved four-dimensional (4D) micro-velocimetry measurements. The model couples a graph network simulator for Lagrangian tracer-particle motion with a 3D U-Net for voxelized interface evolution. The imaged pore geometry serves as a boundary constraint to the flow velocity and the multiphase interface predictions, which are coupled and updated iteratively at each time step. Trained autoregressively on experimental sequences in capillary-dominated conditions ($Ca\approx10^{-6}$), the learned surrogate captures transient, nonlocal flow perturbations and abrupt interface rearrangements (Haines jumps) over rollouts spanning seconds of physical time, while reducing hour-to-day--scale direct numerical simulations to seconds of inference. By providing rapid, experimentally informed predictions, the framework opens a route to ''digital experiments'' to replicate pore-scale physics observed in multiphase flow experiments, offering an efficient tool for exploring injection conditions and pore-geometry effects relevant to subsurface carbon and hydrogen storage.

cs.LG↗

arXiv2025

Screen, Cache, and Match: A Training-Free Causality-Consistent Reference Frame Framework for Human Animation

Human animation aims to generate temporally coherent and visually consistent videos over long sequences, yet modeling long-range dependencies while preserving frame quality remains challenging. Inspired by the human ability to leverage past observations for interpreting ongoing actions, we propose FrameCache, a training-free, causality-consistent reference frame framework. FrameCache explicitly converts historical generation results into causal guidance through two complementary mechanisms. First, at the reference level, a novel Screen-Cache-Match (SCM) strategy constructs a dynamic, high-quality reference memory, ensuring motion-consistent appearance guidance to reduce identity drift. Second, at the generative level, a Trajectory-Aware Autoregressive Generation (TAAG) mechanism aligns denoising trajectories across adjacent video chunks. This is achieved through an overlap-aware latent propagation and a dual-domain fusion strategy that seamlessly blends low-frequency structural layouts with high-frequency textural details. Extensive experiments on standard benchmarks demonstrate that FrameCache consistently improves temporal coherence and visual stability while integrating seamlessly with diverse diffusion baselines. Code will be made publicly available.

cs.GR↗

arXiv2025

Adaptive Dual-Weighted Gravitational Point Cloud Denoising Method

High-quality point cloud data is a critical foundation for tasks such as autonomous driving and 3D reconstruction. However, LiDAR-based point cloud acquisition is often affected by various disturbances, resulting in a large number of noise points that degrade the accuracy of subsequent point cloud object detection and recognition. Moreover, existing point cloud denoising methods typically sacrifice computational efficiency in pursuit of higher denoising accuracy, or, conversely, improve processing speed at the expense of preserving object boundaries and fine structural details, making it difficult to simultaneously achieve high denoising accuracy, strong edge preservation, and real-time performance. To address these limitations, this paper proposes an adaptive dualweight gravitational-based point cloud denoising method. First, an octree is employed to perform spatial partitioning of the global point cloud, enabling parallel acceleration. Then, within each leaf node, adaptive voxel-based occupancy statistics and k-nearest neighbor (kNN) density estimation are applied to rapidly remove clearly isolated and low-density noise points, thereby reducing the effective candidate set. Finally, a gravitational scoring function that combines density weights with adaptive distance weights is constructed to finely distinguish noise points from object points. Experiments conducted on the Stanford 3D Scanning Repository, the Canadian Adverse Driving Conditions (CADC) dataset, and in-house RUBY PLUS LiDAR point clouds acquired in our laboratory demonstrate that, compared with existing methods, the proposed approach achieves consistent improvements in F1, PSNR, and Chamfer Distance (CD) across various noise conditions while reducing the single-frame processing time, thereby validating its high accuracy, robustness, and real-time performance in multi-noise scenarios.

cs.CV↗

arXiv2025

DM3D: Dynamic Mamba via Offset-Guided Feature Resampling for Point Cloud Understanding

State Space Models (SSMs) model long token sequences of point cloud with linear complexity, but require an unordered point cloud to be serialized. Existing methods mainly address this requirement by designing or learning a better token order. Even a well-constructed order, however, cannot preserve every local relation on an irregular 3D surface: a fixed sequence may still mix points that are close in index but distant in 3D or belong to different object parts. We propose DM3D, a dynamic Mamba architecture that preserves the base token order while adapting local feature support and state propagation. First, according to local feature context, DM3D learns spatial and sequence offsets without constructing a global permutation. Then, spatial offsets adjust the sampling anchors in 3D space, whereas sequence offsets guide feature resampling within a local sequence window, which lets different slots draw from overlapping local supports while retaining their identities. This design preserves the global prior of the original traversal, allowing each token to aggregate a more suitable local context. Second, the state update is modulated by the 3D distance between points at adjacent sequence positions, thereby reducing information propagation when these points are spatially far apart. DM3D reaches 95.2\% accuracy on the ModelNet40, 93.3\% accuracy on the PB\_T50\_RS split of ScanObjectNN, and 84.8\% class mIoU on ShapeNetPart. Extensive experiments on benchmark datasets show that DM3D achieves strong and competitive performance, validating the effectiveness of local feature adaptation for point cloud understanding.

cs.CV↗

arXiv2025

A matrix form solution of the multi-dimensional generalized Langevin equation in the quadratic potential

In this research paper, we present an exact matrix form analytical solution of the multi-dimensional generalized Langevin equation with quadratic potentials. Our investigation provides detailed expressions for the two-dimensional probability distribution and extends the understanding of the dynamics governed by harmonic potentials. By utilizing the inverse Laplace transformation, we offer a precise method to solve these equations, corroborated by specific examples. This study contributes to the fundamental understanding of stochastic processes in multi-dimensional systems with harmonic potentials and clarifies the limitations of our approach. While the findings are specific to quadratic potentials, they provide a robust framework for exploring related phenomena within this context.

cond-mat.stat-mech↗

arXiv2025

Exploring the Tradeoff Between Diversity and Discrimination for Continuous Category Discovery

Continuous category discovery (CCD) aims to automatically discover novel categories in continuously arriving unlabeled data. This is a challenging problem considering that there is no number of categories and labels in the newly arrived data, while also needing to mitigate catastrophic forgetting. Most CCD methods cannot handle the contradiction between novel class discovery and classification well. They are also prone to accumulate errors in the process of gradually discovering novel classes. Moreover, most of them use knowledge distillation and data replay to prevent forgetting, occupying more storage space. To address these limitations, we propose Independence-based Diversity and Orthogonality-based Discrimination (IDOD). IDOD mainly includes independent enrichment of diversity module, joint discovery of novelty module, and continuous increment by orthogonality module. In independent enrichment, the backbone is trained separately using contrastive loss to avoid it focusing only on features for classification. Joint discovery transforms multi-stage novel class discovery into single-stage, reducing error accumulation impact. Continuous increment by orthogonality module generates mutually orthogonal prototypes for classification and prevents forgetting with lower space overhead via representative representation replay. Experimental results show that on challenging fine-grained datasets, our method outperforms the state-of-the-art methods.

cs.CV↗

arXiv2025

SM3D: Mitigating Spectral Bias and Semantic Dilution in Point Cloud State Space Models

Point clouds are a fundamental 3D data representation that underpins various computer vision tasks. Recently, Mamba has demonstrated strong potential for 3D point cloud understanding. However, existing approaches primarily focus on point serialization, overlooking a more fundamental limitation: State Space Models (SSMs) inherently exhibit a spectral low-pass bias arising from their recursive formulation. In serialized point clouds, this bias is particularly detrimental, as it suppresses high-frequency geometric structures and progressively dilutes semantic discriminability across deep layers. To address these limitations, we propose SM3D, a spectral-aware framework designed to jointly preserve geometric fidelity and semantic consistency. First, a Geometric Spectral Compensator (GSC) is introduced to counteract the low-pass bias by explicitly injecting graph-guided high-frequency components through local Laplacian analysis, thereby restoring structural sensitivity. Second, we design a Semantic Coherence Refiner (SCR) to rectify semantic drift through frequency-aware channel recalibration. To balance theoretical precision and computational efficiency, SCR is instantiated via two pathways: an exact Laplacian eigendecomposition (SCR-L) and a linear-complexity Chebyshev polynomial approximation (SCR-C). Extensive experiments demonstrate that SM3D achieves state-of-the-art performance, including 96.0% accuracy on ModelNet40 and 86.5% mIoU on ShapeNetPart, validating its effectiveness in mitigating spectral low-pass bias and semantic dilution (Code: https://github.com/L1277471578/SM3D).

cs.CV↗

arXiv2025

Bizard: A Community-Driven Platform for Accelerating and Enhancing Biomedical Data Visualization

Biomedical research increasingly relies on heterogeneous, high-dimensional datasets, yet effective visualization remains hindered by fragmented code resources, steep programming barriers, and limited domain-specific guidance. Bizard is an open-source visualization code repository engineered to streamline data analysis in biomedical research. It aggregates a diverse array of executable visualization scripts, empowering researchers to select and tailor optimal graphical methods for their specific investigative demands. The platform features an intuitive interface equipped with sophisticated browsing and filtering capabilities, exhaustive tutorials, and interactive discussion forums that foster knowledge dissemination. Through its community-driven paradigm, Bizard promotes continual refinement and functional expansion, establishing itself as an essential resource for elevating biomedical data visualization and analytical standards. By harnessing Bizard's infrastructure, researchers can augment their visualization proficiency, propel methodological progress, and enhance interpretive rigor, ultimately accelerating precision medicine and personalized therapeutics. Bizard is freely accessible at https://openbiox.github.io/Bizard/.

q-bio.GN↗

arXiv2025

Local Gibbs sampling beyond local uniformity

Local samplers are algorithms that generate random samples based on local queries to high-dimensional distributions, ensuring the samples follow the correct induced distributions while maintaining time complexity that scales locally with the query size. These samplers have broad applications, including deterministic approximate counting [He, Wang, Yin, SODA '23; Feng et.al., FOCS '23], sampling from infinite or high-dimensional Gibbs distributions [Anand, Jerrum, SICOMP '22; He, Wang, Yin, FOCS '22], and providing local access to large random objects [Biswas, Rubinfield, Yodpinyanee, ITCS '20]. In this work, we present local samplers for Gibbs distributions of spin systems. Specifically, we design linear-time local samplers for: - permissive spin systems, including the first local sampler for the Ising model in near-critical regimes; - truly repulsive spin systems, represented by the first local sampler for uniform proper $q$-colorings, with $q=O(\Delta)$ colors on graphs with maximum degree $\Delta$. These local samplers are efficient beyond the "local uniformity" threshold, which imposes unconditional marginal lower bounds -- a key assumption required by all prior local samplers. Our results show that, in general, local sampling is not significantly harder than global sampling for spin systems. As an application, our results also imply local algorithms for probabilistic inference in the same near-critical regimes.

cs.DS↗

arXiv2025

Sink-free orientations: a local sampler with applications

For sink-free orientations in graphs of minimum degree at least $3$, we show that there is a deterministic approximate counting algorithm that runs in time $O((n^{73}/\varepsilon^{72})\log(n/\varepsilon))$, a near-linear time sampling algorithm, and a randomised approximate counting algorithm that runs in time $O((n/\varepsilon)^2\log(n/\varepsilon))$, where $n$ denotes the number of vertices of the input graph and $0<\varepsilon<1$ is the desired accuracy. All three algorithms are based on a local implementation of the sink popping method (Cohn, Pemantle, and Propp, 2002) under the partial rejection sampling framework (Guo, Jerrum, and Liu, 2019).

cs.DS↗

arXiv2024

AeroDiT: Diffusion Transformers for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows

Real-time and accurate prediction of aerodynamic flow fields around airfoils is crucial for flow control and aerodynamic optimization. However, achieving this remains challenging due to the high computational costs and the non-linear nature of flow physics. Traditional Computational Fluid Dynamics (CFD) methods face limitations in balancing computational efficiency and accuracy, hindering their application in real-time scenarios. To address these challenges, this study presents AeroDiT, a novel surrogate model that integrates scalable diffusion models with transformer architectures to address these challenges. Trained on Reynolds-Averaged Navier-Stokes (RANS) simulation data for high Reynolds-number airfoil flows, AeroDiT accurately captures complex flow patterns while enabling real-time predictions. The model demonstrates impressive performance, with mean relative $L_2$ errors of 0.1, 0.025, and 0.050 for pressure $p$ and velocity components $u_x, u_y$, confirming its reliability. To further enhance physical consistency, we incorporate explicit physics-informed losses based on RANS residuals, including mass and momentum conservation constraints. The transformer-based structure allows for real-time predictions within seconds, enabling efficient aerodynamic simulations. This work underscores the potential of generative machine learning techniques to advance computational fluid dynamics, offering potential solutions to challenges in simulating high-fidelity aerodynamic flows.

physics.flu-dyn↗

arXiv2024

Phase Transitions via Complex Extensions of Markov Chains

We study algebraic properties of partition functions, particularly the location of zeros, through the lens of rapidly mixing Markov chains. The classical Lee-Yang program initiated the study of phase transitions via locating complex zeros of partition functions. Markov chains, besides serving as algorithms, have also been used to model physical processes tending to equilibrium. In many scenarios, rapid mixing of Markov chains coincides with the absence of phase transitions (complex zeros). Prior works have shown that the absence of phase transitions implies rapid mixing of Markov chains. We reveal a converse connection by lifting probabilistic tools for the analysis of Markov chains to study complex zeros of partition functions. Our motivating example is the independence polynomial on $k$-uniform hypergraphs, where the best-known zero-free regime has been significantly lagging behind the regime where we have rapidly mixing Markov chains for the underlying hypergraph independent sets. Specifically, the Glauber dynamics is known to mix rapidly on independent sets in a $k$-uniform hypergraph of maximum degree $\Delta$ provided that $\Delta \lesssim 2^{k/2}$. On the other hand, the best-known zero-freeness around the point $1$ of the independence polynomial on $k$-uniform hypergraphs requires $\Delta \le 5$, the same bound as on a graph. By introducing a complex extension of Markov chains, we lift an existing percolation argument to the complex plane, and show that if $\Delta \lesssim 2^{k/2}$, the Markov chain converges in a complex neighborhood, and the independence polynomial itself does not vanish in the same neighborhood. In the same regime, our result also implies central limit theorems for the size of a uniformly random independent set, and deterministic approximation algorithms for the number of hypergraph independent sets of size $k \le \alpha n$ for some constant $\alpha$.

cs.DS↗

arXiv2024

Counting random $k$-SAT near the satisfiability threshold

We present efficient counting and sampling algorithms for random $k$-SAT when the clause density satisfies $\alpha \le \frac{2^k}{\mathrm{poly}(k)}.$ In particular, the exponential term $2^k$ matches the satisfiability threshold $\Theta(2^k)$ for the existence of a solution and the (conjectured) algorithmic threshold $2^k (\ln k) / k$ for efficiently finding a solution. Previously, the best-known counting and sampling algorithms required far more restricted densities $\alpha\lesssim 2^{k/3}$ [He, Wu, Yang, SODA '23]. Notably, our result goes beyond the lower bound $d\gtrsim 2^{k/2}$ for worst-case $k$-SAT with bounded-degree $d$ [Bez\'akov\'a et al, SICOMP '19], showing that for counting and sampling, the average-case random $k$-SAT model is computationally much easier than the worst-case model. At the heart of our approach is a new refined analysis of the recent novel coupling procedure by [Wang, Yin, FOCS '24], utilizing the structural properties of random constraint satisfaction problems (CSPs). Crucially, our analysis avoids reliance on the $2$-tree structure used in prior works, which cannot extend beyond the worst-case threshold $2^{k/2}$. Instead, we employ a witness tree similar to that used in the analysis of the Moser-Tardos algorithm [Moser, Tardos, JACM '10] for the Lov\'{a}sz Local lemma, which may be of independent interest. Our new analysis provides a universal framework for efficient counting and sampling for random atomic CSPs, including, for example, random hypergraph colorings. At the same time, it immediately implies as corollaries several structural and probabilistic properties of random CSPs that have been widely studied but rarely justified, including replica symmetry and non-reconstruction.

cs.DS↗

arXiv2024

Towards Universal Mesh Movement Networks

Solving complex Partial Differential Equations (PDEs) accurately and efficiently is an essential and challenging problem in all scientific and engineering disciplines. Mesh movement methods provide the capability to improve the accuracy of the numerical solution without increasing the overall mesh degree of freedom count. Conventional sophisticated mesh movement methods are extremely expensive and struggle to handle scenarios with complex boundary geometries. However, existing learning-based methods require re-training from scratch given a different PDE type or boundary geometry, which limits their applicability, and also often suffer from robustness issues in the form of inverted elements. In this paper, we introduce the Universal Mesh Movement Network (UM2N), which -- once trained -- can be applied in a non-intrusive, zero-shot manner to move meshes with different size distributions and structures, for solvers applicable to different PDE types and boundary geometries. UM2N consists of a Graph Transformer (GT) encoder for extracting features and a Graph Attention Network (GAT) based decoder for moving the mesh. We evaluate our method on advection and Navier-Stokes based examples, as well as a real-world tsunami simulation case. Our method outperforms existing learning-based mesh movement methods in terms of the benchmarks described above. In comparison to the conventional sophisticated Monge-Amp\`ere PDE-solver based method, our approach not only significantly accelerates mesh movement, but also proves effective in scenarios where the conventional method fails. Our project page is at https://erizmr.github.io/UM2N/.

math.NA↗

arXiv2024

Spatio-Temporal Bi-directional Cross-frame Memory for Distractor Filtering Point Cloud Single Object Tracking

3D single object tracking within LIDAR point clouds is a pivotal task in computer vision, with profound implications for autonomous driving and robotics. However, existing methods, which depend solely on appearance matching via Siamese networks or utilize motion information from successive frames, encounter significant challenges. Issues such as similar objects nearby or occlusions can result in tracker drift. To mitigate these challenges, we design an innovative spatio-temporal bi-directional cross-frame distractor filtering tracker, named STMD-Tracker. Our first step involves the creation of a 4D multi-frame spatio-temporal graph convolution backbone. This design separates KNN graph spatial embedding and incorporates 1D temporal convolution, effectively capturing temporal fluctuations and spatio-temporal information. Subsequently, we devise a novel bi-directional cross-frame memory procedure. This integrates future and synthetic past frame memory to enhance the current memory, thereby improving the accuracy of iteration-based tracking. This iterative memory update mechanism allows our tracker to dynamically compensate for information in the current frame, effectively reducing tracker drift. Lastly, we construct spatially reliable Gaussian masks on the fused features to eliminate distractor points. This is further supplemented by an object-aware sampling strategy, which bolsters the efficiency and precision of object localization, thereby reducing tracking errors caused by distractors. Our extensive experiments on KITTI, NuScenes and Waymo datasets demonstrate that our approach significantly surpasses the current state-of-the-art methods.

cs.CV↗