arXiv subjects

Li Ma

Publications and source records attributed to Li Ma.

At least 19 recordsLinked to original sources

arXiv2026

On isomorphisms of tetravalent Cayley graphs over generalized quaternion groups

Let $G$ be a finite group and $S$ be a subset of $G$ with $1\not\in S$. $S$ is called a $CI$-subset of $G$ if for any $T\subset G$, whenever $Cay (G,S)\cong Cay (G,T)$, there is an element $σ\in Aut(G)$ such that $S^σ=T$. For a positive integer $m$, $G$ is called an $m$-$CI$-group if all subsets $S$ of $G \setminus \{1\}$ with $|S|\leq m$ and $S^{-1}=S$ are $CI$-subsets. In this paper, we show that the generalized quaternion group of order $4n$ is a $4$-$CI$-group if and only if $n$ is odd.

math.GR↗

arXiv2026

Two-sample comparison through additive tree models for density ratios

The ratio of two densities provides a direct characterization of their differences. We consider the two-sample comparison problem by estimating this ratio given i.i.d. observations from two distributions. To this end, we propose additive tree models for density ratio estimation along with efficient algorithms using a new loss function, the balancing loss. The loss allows tree-based models to be trained using several algorithms originally designed for supervised learning, such as forward-stagewise optimization and gradient boosting. Moreover, the balancing loss resembles an exponential family kernel, and it can serve as a pseudo-likelihood with conjugate priors. This property enables generalized Bayesian inference on the density ratio using backfitting samplers designed for Bayesian additive regression trees (BART). Our Bayesian strategy provides uncertainty quantification for the inferred density ratio, which is critical for applications involving high-dimensional and data-limited distributions with potentially substantial uncertainty. We further show connections of the balancing loss to the exponential loss in binary classification and to the variational form of f-divergence, particularly the squared Hellinger distance. Numerical experiments demonstrate that the proposed boosting algorithms achieve high accuracy at low computational cost, and that the Bayesian additive tree is competitive in accuracy while additionally providing uncertainty quantification, which the compared estimators do not offer. Finally, we demonstrate its application to assessing the quality of generative models for microbiome compositional data.

stat.ME↗

arXiv2026

Positive solutions of Yamabe-type equations with general potentials

Let $(M^n,g)$ be a smooth closed connected Riemannian manifold of dimension $n\ge3$, and let $ψ\in C^\infty(M,\mathbb R)$. We prove that the equation $$ -\frac{4(n-1)}{n-2}Δ_g u+ψu=u^{\frac{n+2}{n-2}} $$ has a smooth positive solution if and only if its linear part has positive first eigenvalue. In dimensions $n\ge6$, the proof combines an energy estimate under exact local mass constraints with an intersection argument for finite cycles of atomic measures; dimensions $3\le n\le5$ follow from the theorem of Bahri and Brezis.

math.AP↗

arXiv2026

Complete conformal metrics of constant positive scalar curvature with prescribed isolated singularities

Let $(M^n,g)$ be a smooth closed connected Riemannian manifold of dimension $n\ge3$ with positive Yamabe constant. For any prescribed finite set $Λ\subset M$ containing at least two points, we construct a complete metric on $M\setminusΛ$, conformal to $g$, with scalar curvature $n(n-1)$. At each singularity, the local geometric term in the reduced action comes from the first nonzero homogeneous metric term of degree at most $(n-2)/2$ in finite conformal normal coordinates, or from the mass of the Green function metric when all such terms vanish. We completely solve Schoen's conjecture about singular Yamabe problem.

math.DG↗

arXiv2026

On global Chern-Calabi flows on compact complex surfaces

In this paper, we propose the Chern-Calabi Flow for extremal metrics in conformal Hermitian geometry. After the existence of extremal metric, we consider the existence of global flows to the Chern-Calabi flows for extremal metrics on compact complex surfaces with Gauduchon metrics. To achieve this goal, we use the Adams inequality and the Gronwall inequality method to obtain fourth order derivative norm estimates. We also study the convergence of the global solution to the Chern-Calabi flow and its limit is the solution of the Chern-Calabi extremal metric problem. We propose a fourth order elliptic problem and derive the Bochner formula relevant to the elliptic estimates for the Chern-Calabi heat flow.

math.DG↗

arXiv2026

Continuation and convergence for the higher dimensional conformal Chern--Calabi flow

We study the volume-normalized gradient flow of the squared Chern scalar curvature on compact Hermitian manifolds of complex dimension $n\ge3$. The flow extends smoothly past a finite time if the spatial supremum of Chern scalar curvature belongs to $L^2$ in time. A uniform curvature bound in the evolving $L^p$ norm, for one fixed $p>n$, gives global existence and smooth convergence of the whole trajectory to a constrained critical metric. On Chern scalar-flat backgrounds, small initial $L^p$ curvature gives exponential convergence to the background. A corresponding condition on the trace-free first Chern--Ricci form gives exponential convergence on Chern--Ricci-flat Hermitian and positive Kähler--Einstein backgrounds. The smallness thresholds are independent of the initial higher derivatives. We also construct constant Chern scalar curvature backgrounds of either sign with arbitrarily small smooth perturbations whose solutions, if global, cannot converge back to the background.

math.CV↗

arXiv2026

The Eighth-Order Evolution Model for Closed Planar Curves

We consider the $L^2(ds)$ gradient flow of $\frac12\int k_{ss}^2\,ds$ for smooth closed immersed planar curves. The stationary curves are precisely the multiply covered circles. We give an explicit sufficient condition under which the elastic energy is non-increasing and for each nonzero turning number, explicit small initial scale-invariant energy yields a global smooth solution with bounded positive length and exponential convergence to a fixed multiply covered circle, after reparametrisation. We note that that the qualitative rigidity and convergence results are already contained in the generalised ideal flow theory of McCoy and Wheeler. The present paper gives quantitative measure of small initial data, corrected evolution identities, and a complete argument in the case of the curvature energy $\frac12\int k_{ss}^2\,ds$.

math.AP↗

arXiv2026

Seeing is Believing: Benchmarking and Mitigating the Interference of Language Priors on Visual Perception in MLLMs

Language priors in MLLMs can override visual evidence, impairing visual perception. Existing benchmarks assume shared priors across models and rely solely on accuracy. However, cross-model prior agreement falls below 80%, and over 15% of items can be answered correctly without any image, causing accuracy to overestimate prior resistance. To address these limitations, we introduce ViSiP-Bench, a benchmark grounded in rare-but-real scenarios across six prior categories, with a model-aware dynamic prior induction protocol and three metrics that more precisely and continuously measure prior resistance than accuracy alone. Evaluating 24 models reveals that prior adherence is pervasive, does not improve with scale, and larger models tend to either strongly override their priors or firmly adhere to them. We therefore propose MAPS, which identifies each model's strongest priors and fine-tunes on factually grounded knowledge to soften excessive prior reliance without disrupting general capabilities.

cs.CV↗

arXiv2026

Maximum index of unbalanced signed graphs without a positive odd cycle

Let $\mathcal{C}_{r}^+$ be the set of all balanced signed graphs with underlying graphs $C_r$. A signed graph is called $\mathcal{C}_{r}^+$-free if it contains no subgraph isomorphic to any member of $\mathcal{C}_{r}^+$. In this paper, we characterize the extremal graphs that achieve the maximum index among all $\mathcal{C}_{2k+1}^+$-free unbalanced signed graphs of order $n$ when $k\ge 2$ and $n\ge 187k$.

math.CO↗

arXiv2026

A Residual Tree Gaussian Process Modeling Framework for High-Dimensional Data

With the advance of measurement technologies and increasing computing power, large spatial data with heterogeneous structures are often collected over high-dimensional domains. Existing Gaussian process (GP) models and computational strategies are often inadequate for analyzing such datasets in multi-dimensional domains. To address these challenges, we develop a Bayesian residual tree GP methodology called ResTGP for large spatial data with potentially heterogeneous structures in multi-dimensional domains. The key idea is to decompose a Gaussian process at a cascade of resolutions along a dyadic tree through iteratively computing predictive and residual processes so that the residual process on each tree node, both interior and leaf, becomes sufficient for the finer-level dependency within that node. This allows characterization of the underlying covariance structure in a flexible, multi-scale manner while achieving divide-and-conquer on the data domain, which leads to computational efficiency. To allow efficient tree inference, we introduce a computational strategy for Bayesian inference based on recursive message passing, which scales linearly with the sample size given the tree. This paper also proves posterior consistency of the model for estimating continuous functions in a nonparametric regression framework. Extensive numerical examples and the storm surge application confirm the advantages of the proposed method.

stat.ME↗

arXiv2026

Switching Anomalous Hall and Nernst Responses by Nonmagnetic N Occupation at Fixed Noncoplanar Mn Antiferromagnetic Order

Nonmagnetic atomic occupation can control anomalous transverse transport by modifying magnetic symmetry without changing the underlying magnetic order. We demonstrate this effect using controlled $γ$-Mn, Mn$_4$N, and MnN reference states with the same lattice constant and identical noncoplanar all-in-all-out Mn magnetic configurations, while varying only the occupation of the N sublattice. The anomalous Hall and anomalous Nernst responses exhibit a pronounced zero--finite--zero evolution across the series despite the unchanged Mn spin order. In $γ$-Mn and MnN, the high magnetic symmetry enforces complete cancellation of the Brillouin-zone-integrated Berry curvature. In Mn$_4$N, N occupation lowers the magnetic symmetry while preserving inversion and breaking the relevant twofold rotational symmetries, thereby lifting the cancellation constraint and permitting an uncompensated Berry-curvature contribution along the [111] direction. The resulting finite anomalous Hall conductivity reaches $-126$~S/cm near the Fermi level. These results establish nonmagnetic sublattice occupation as a symmetry-control parameter for Berry-curvature-driven transport in compensated antiferromagnets, independent of changes in the magnetic order.

cond-mat.mtrl-sci↗

arXiv2026

Bond-selective modulation of scalar spin chirality in strained Mn4N

Scalar spin chirality (SSC) underlies a variety of Berry-phase-driven transport phenomena in noncoplanar magnets. Mn4N is a unique ferrimagnetic system in which both collinear and noncoplanar magnetic configurations have been reported at different lattice parameters, featuring vanishing and finite SSC, respectively. However, the microscopic mechanism governing the competition between these magnetic states and the associated modulation of SSC remains unclear. This issue is particularly important for Mn4N because its high magnetic ordering temperature (TN = 740 K) provides an attractive platform for exploring chiral magnetic states and their associated topological functionalities at elevated temperatures. Here, using first-principles calculations, we investigate the strain-driven evolution of the magnetic ground state and SSC in Mn4N and uncover the microscopic origin of the collinear-to-noncoplanar magnetic transition. We demonstrate that tensile strain continuously stabilizes the noncoplanar configuration and enhances SSC, as quantified by the magnitude of the chirality-order vector. Orbital-resolved crystal orbital Hamilton population and charge-density-difference analyses reveal that strain selectively weakens the Mn3c-N hybridization while preserving direct Mn3c-Mn3c interactions, leading simultaneously to the activation of the Mn3c in-plane magnetic moment and the suppression of N-mediated ferromagnetic superexchange between nearest-neighbor Mn3c atoms. This bond-selective electronic response governs the magnetic ground state and consequently controls the emergence and enhancement of SSC in Mn4N. Our work establishes bond selectivity as an effective strategy for engineering SSC in high-temperature ferrimagnetic materials.

cond-mat.mtrl-sci↗

arXiv2026

WPBench: A Comprehensive Benchmark for Wind Power Forecasting

Accurate, reliable, and deployable wind power forecasting is critical for power system dispatch, renewable energy integration, and electricity market operations. Progress in this field hinges on the ability to empirically and comprehensively benchmark forecasting methods. Yet existing benchmarks fall short of supporting systematic evaluation in four key aspects: 1) limited coverage of wind power scenarios across turbine scale, variable composition, and spatial structure; 2) incomplete coverage of forecasting model families; 3) evaluation metrics misaligned with wind power requirements; and 4) limited structure-aware diagnostics beyond individual temporal patterns. To address these limitations, we propose WPBench, a comprehensive, fair, and extensible benchmark for wind power forecasting. WPBench integrates 26 public datasets organized by turbine scale and variable composition, spanning single-turbine, multi-turbine, univariate, and multivariate settings. Under unified processing, training, and evaluation protocols, it benchmarks 19 representative models covering traditional methods, deep temporal models, spatio-temporal models, and foundation models. Beyond point-wise errors, WPBench assesses forecast-curve fidelity and computational efficiency, and delivers structure-aware diagnostics across temporal, variable-dependency, and spatial-dependency perspectives. Together, these capabilities enable systematic model comparison across diverse wind scenarios and provide a reusable platform for future research.

cs.LG↗

arXiv2026

Predictive Likelihood Ratios for Language Model Watermark Detection

Keyed watermark detection tests dependence between observed tokens and pseudorandom variables reconstructed from a secret key. Building on the pivotal framework of Li et al. (2025), we construct predictive likelihood ratios that average over uncertain probability deficits and residual-tail distributions. The aim is robust detection power across alternative specifications without requiring a single signal-strength tuning. A mixture prior combines tail shape and effective width; hierarchical extensions allow within-document variation in deficit or width. The test maximizes prior-averaged power at a fixed size, but is not generally uniformly most powerful or minimax. Under the exact conditional pivot null, normalized predictive alternatives selected before each observation yield a Bayes factor that is also a test martingale: Type I error control is unaffected by alternative misspecification and remains valid under optional stopping. This guarantee does not cover violations of the conditional null, and the interpolated implementation has no certified anytime guarantee. Gumbel marginal likelihoods are evaluated by fixed quadrature. Across the evaluated tail-shape and tail-width alternatives and three horizons, the union-tail mixture has maximum observed Type II error regret .0080, compared with .0962 for the equal-tail mixture, relative to the best tested rule. On temperature-matched outputs from two open models, it improves AUC over the equal-tail baseline in all eight non-saturated model-temperature cells, although the leading reference score generally has higher AUC. Supplementary experiments show retained power under independent null-like replacement and smaller changes from hierarchical dependence modeling. The evidence supports robustness across the evaluated alternatives, not uniform power guarantees or resistance to arbitrary text edits.

stat.ML↗

arXiv2026

ALIGN-HOLD: Experience Alignment for Real-Time Hold Control in Large-Scale Ride-Hailing Matching at DiDi

Real-time hold control is a high-leverage mechanism in large-scale ride-hailing systems: by selectively deferring driver-order pairs, the platform can wait for better matching opportunities and improve end-to-end passenger-driver experience. Existing production systems such as EXHOLD learn bandit-based hold policies from handcrafted combinations of trip completion, cancellations, waiting time, and driver effort. However, designing such rewards becomes increasingly difficult as marketplace preferences are heterogeneous and observed passenger-driver behavior can be sparse, noisy, and affected by dynamic supply-demand conditions. We present ALIGN-HOLD, a production-scale experience alignment framework that learns hold policy from implicit marketplace preferences. ALIGN-HOLD constructs complementary preference pairs from order trajectories, driver trajectories, and contemporaneous local matching graphs, and trains an experience Reward Model (RM) using balanced multi-view sampling and model-adaptive hard preference sampling. During simulator-based policy learning, the frozen RM provides a dense, context-dependent reward and supports label-free filtering of low-identifiability interactions whose behavioral feedback is difficult to attribute to matching quality. We deploy ALIGN-HOLD on DiDi's ride-hailing platform and evaluate it in a 28-day randomized A/B experiment, covering approximately 100,000 passenger requests per day. Compared with the deployed production policy, ALIGN-HOLD achieves statistically significant improvements in trip completion rate and driver income, while significantly reducing passenger cancellations before and after driver acceptance. Complementary ablations, RM diagnostics, and behavioral analyses validate the contributions of the proposed components. ALIGN-HOLD has been fully ramped up and is currently serving DiDi's Brazil marketplace.

cs.LG↗

arXiv2026

DiffHDR: Re-Exposing LDR Videos with Video Diffusion Models

Most digital videos are stored in 8-bit low dynamic range (LDR) formats, where much of the original high dynamic range (HDR) scene radiance is lost due to saturation and quantization. This loss of highlight and shadow detail precludes mapping accurate luminance to HDR displays and limits meaningful re-exposure in post-production workflows. Although techniques have been proposed to convert LDR images to HDR through dynamic range expansion, they struggle to restore realistic detail in the over- and underexposed regions. To address this, we present DiffHDR, a framework that formulates LDR-to-HDR conversion as a generative radiance inpainting task within the latent space of a video diffusion model. By operating in Log-Gamma color space, DiffHDR leverages spatio-temporal generative priors from a pretrained video diffusion model to synthesize plausible HDR radiance in over- and underexposed regions while recovering the continuous scene radiance of the quantized pixels. Our framework further enables controllable LDR-to-HDR video conversion guided by text prompts or reference images. To address the scarcity of paired HDR video data, we develop a pipeline that synthesizes high-quality HDR video training data from static HDRI maps. Extensive experiments demonstrate that DiffHDR significantly outperforms state-of-the-art approaches in radiance fidelity and temporal stability, producing realistic HDR videos with considerable latitude for re-exposure.

cs.CV↗

arXiv2026

GenMatch: An End-to-End Generative Matching Framework for Micro-View Order-Dispatching in Ride-Hailing

Micro-View Order-Dispatching assigns available drivers to passenger orders within each dispatch batch and is critical to the service quality and operational efficiency of ride-hailing platforms. Mainstream industrial solutions follow a multi-stage paradigm of model prediction, value calculation, and dispatch matching. Although dispatch quality is determined by the final batch-level assignment, these stages optimize different intermediate objectives. This cross-stage objective inconsistency means that improving a single stage does not necessarily improve the overall dispatch result. We therefore formulate Micro-View Order-Dispatching as a generative matching problem and propose GenMatch, an end-to-end Generative Matching framework and the first such framework deployed in a real-world production environment. Applying generative modeling to this problem introduces three challenges. First, each dispatch batch forms a dynamic sparse bipartite graph, requiring efficient structured batch-level encoding. Second, replacing the hand-crafted value function requires learning unified business utility from heterogeneous feedback. Third, directly generating an assignment requires tracking the evolving matching state because each selected order-driver pair changes the remaining feasible candidates. GenMatch addresses these challenges with a Context-Aware Bipartite Encoder, a Business-Aware Utility Learner, and a State-Aware Pointer Decoder. Extensive offline evaluations and online A/B tests in five cities across DiDi's international ride-hailing markets show consistent improvements over competitive baselines, confirming the effectiveness and practicality of GenMatch for industrial order-dispatching.

cs.AI↗

arXiv2026

ID-V2V: Identity-Preserving Video Restylization

In visual storytelling, human performances are central to creative intent and narrative meaning. However, preserving human identity and performance while enabling flexible visual edits remains challenging for generative video models. We formalize this challenge as identity-preserving video restylization, which propagates scene, lighting, and style changes specified by an edited keyframe across a source video, while preserving facial likeness and performance, including expressions, eye gaze, and lip synchronization. A key obstacle is the absence of paired training data, as identity-preserving restylized video pairs are rare in real-world settings. To address this, we propose a decoupling of source-grounded identity preservation and edit-driven video synthesis. Our key insight is that facial appearance and expression should remain invariant, with illumination being the primary permissible variation. We therefore cast identity preservation as a video relighting problem, while modeling visual edit propagation as controlled video synthesis guided by the edited keyframe. Building on this formulation, we introduce ID-V2V, a video-to-video generative framework integrating complementary control signals: relit facial regions and facial normal maps tightly constrain facial likeness and performance, while edited keyframes and depth sequences enable flexible and temporally coherent generation. This design enables constructing training pairs from a single video, eliminating the need for scarce paired data. Extensive experiments demonstrate that ID-V2V significantly outperforms existing methods in preserving facial likeness and fine-grained facial performance, supports both single- and multi-subject scenarios, and delivers high visual quality, highlighting its potential as a human-centric tool for real-world content production. The code is available at: https://github.com/Eyeline-Labs/ID-V2V.

cs.CV↗