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Ping Li

Publications and source records attributed to Ping Li.

At least 19 recordsLinked to original sources
arXiv2026

Eclipse Properties and Superhump Evolution in the SU UMa-Type Dwarf Nova Z Cha

The advent of large-scale time-domain surveys provides both opportunities and challenges for understanding accretion disk evolution in cataclysmic variables (CVs). Using high-cadence photometry from the Transiting Exoplanet Survey Satellite (TESS), we investigate the eclipsing SU UMa-type dwarf nova Z Cha. Leveraging eclipses as a natural probe, we examine the evolution of the accretion disk through variations in eclipse depth, O--C of eclipse minima, and positive superhump (PSH) amplitude. During superoutbursts, all three quantities exhibit quasi-periodic modulations with a common period of $\sim$2 days, consistent with the precession period of an eccentric disk. We interpret these correlated variations as evidence of an eccentric, precessing disk: O--C traces the periodic shift of the system's brightness center, while eclipse depth and PSH amplitude vary with the orientation of the disk bulge relative to the line of sight. In quiescence (Sectors 13 and 93), PSHs with periods of $\sim$0.0762 days show linearly decreasing amplitudes and periods, indicating gradual shrinkage of the eccentric disk and a slowing precession. Remarkably, a coherent signal with a period of $\sim$0.0729~days ($\epsilon^{-}\approx-0.02$) appears in the same quiescent intervals. This signal may represent negative superhumps (NSHs) coexisting with PSHs, although an orbital sideband of the PSH cannot presently be excluded with the available data. If confirmed as NSHs, their coexistence with PSHs would challenge the classical tilted-disk model, and could be explained by retrograde apsidal precession of an eccentric disk, where the inner disk precesses retrogradely (NSHs) and the outer disk progradely (PSHs); this interpretation remains to be tested by further observations.

astro-ph.SR↗
arXiv2026

TDFNet: Tri-projection Deformable Fusion Network for Panoramic Salient Object Detection

Recent years have witnessed the growing potential of panoramic salient object detection in robotic vision, virtual reality, and related applications. However, projecting spherical scenes onto 2D planes inevitably introduces geometric distortions, which fundamentally limit the effectiveness of existing projection-based methods. Specifically, Equirectangular Projection (ERP) suffers from severe polar stretching distortions, while cube map projection introduces discontinuities across cube-face boundaries, resulting in degraded feature discriminability and compromised geometric consistency. To address these limitations, we propose TDFNet, the first Tri-projection Deformable Fusion Network for panoramic salient object detection, exploiting complementary projection representations to alleviate geometric distortions and improve detection performance.Specifically, we design a cross-projection deformable attention (CDA) module that leverages spatial correspondences between different projections to construct geometry-aware sampling locations, guiding deformable attention for cross-projection contextual aggregation and enhancing robustness against projection-induced deformations. Furthermore, we introduce a latitude-guided fusion module, which utilizes spherical latitude priors to construct geometric confidence weights for adaptively balancing ERP and CMP features. Meanwhile, LGF incorporates distortion-reduced semantic references from Tangent Projection to achieve cross-projection feature refinement and spatial alignment.By constructing a three-branch encoding architecture based on ERP, CMP, and Tangent Projection, TDFNet simultaneously preserves global spatial continuity, local geometric details, and fine-grained boundary information.

cs.CV↗
arXiv2026

Topological obstructions to geometric positivity and negativity on Calabi-Yau manifolds

We study whether the topology underlying a Calabi-Yau manifold can support natural geometric positivity or negativity structures. In even complex dimension $n \geq 4$ (assuming $b_2=1$ when $n \geq 6$), we strengthen a theorem of Oguiso-Peternell by proving that a Calabi-Yau manifold is not homeomorphic to a weak Fano $n$-fold. The same obstruction applies to K\"{a}hler manifolds with quasi-positive holomorphic sectional curvature. A transformation-group analogue excludes, in particular, symplectic manifolds admitting Hamiltonian circle actions with isolated fixed points. On the negative side, we show that the fundamental group of a Calabi-Yau manifold is not isomorphic to that of a K\"{a}hler hyperbolic manifold. Taken together, these results exhibit a common topological rigidity separating Calabi-Yau manifolds from several fundamental classes governed by geometric positivity or negativity.

math.AG↗
arXiv2026

Channel-wise Dynamic Knowledge Distillation via Adaptive Sample Generation for Action Recognition

Knowledge Distillation (KD) offers a promising yet underexplored path for compressing large action recognition models. However, existing KD methods suffer from two key limitations: 1) reliance on fixed input samples leads to suboptimal feature alignment between the frozen teacher (larger model) and the learnable student (smaller model), and 2) applying a uniform distillation strength for all channels fails to account for their varying importance in capturing distinct knowledge (e.g., motion tempo or magnitude) across training epochs. This motivates us to develop an Adaptive Sample-aware Channel-wise Dynamic (ASCD) KD approach, which operates in two stages. First, we use an adaptive sample generation module to create updated samples by incorporating semantics from sample gradients, which are derived by minimizing a feature loss weighted by channel centroid frequency differences at each layer. Meanwhile, crucial motion-related details are preserved by applying a Gaussian mask to frequency features. Second, we employ a channel-wise dynamic distillation module to train student on these generated samples, guided by sample gradients and feature frequencies. For efficiency, samples are updated periodically rather than per epoch. Extensive experiments on three video benchmarks (UCF101, Kinetics-400, Something-Something-v2) and two image datasets (CIFAR-100, ImageNet) demonstrate the state-of-the-art performance of our method. Code is available at https://github.com/mlvccn/ASCD_KD_Action.

cs.CV↗
arXiv2026

Coordinate Independence of the Schwarzschild Black Hole Accretion Vlasov Gas Model

This paper presents a detailed study of the coordinate dependence of Vlasov gas accretion onto a Schwarzschild black hole. Asymptotic results at infinity and near horizon are obtained via Taylor expansions for three different statistical distributions within the framework of the most general stationary spherically symmetric spacetime. Our findings demonstrate that the particle number density, energy density, radial and tangential pressures, and accretion rates are independent of the coordinate choice, even though individual components such as the particle current density and the stress-energy tensor explicitly depend on the coordinate system. Consequently, the accretion theory can be formulated without reference to any particular coordinate system. We also show that the mean energy of the accreted particles is $m_0+k_BT$, lower than the mean energy $m_0+\frac{3}{2}k_BT$ of the Maxwell-Boltzmann system in the classical limit. And the specific entropy of the accreted particles is lower than the global average by $\frac{3}{2}k_B$. This is because particles of lower energy are more easily accreted, while particles of higher energy are more readily scattered. We also present numerical results at finite radii for the relevant physical quantities.

gr-qc↗
arXiv2026

KIC 6464285: A Solar-type Eclipsing Binary in a Hierarchical Triple System with Quasi-periodic Out-of-eclipse Modulations

We present the first detailed analysis of the solar-type triple system KIC 6464285. Combining long-term, high-precision photometry from Kepler, TESS, and ZTF with low-resolution spectra from LAMOST and near-infrared high-resolution spectra from SDSS/APOGEE, we performed a joint analysis of the light curves, eclipse timing variations (ETVs), and radial velocities. Spectroscopic analysis reveals the system to be triple-lined, with the inner binary's primary being a G-type main-sequence star and a mass ratio of $0.627(7)$. Light curve modeling indicates that the inner binary is detached, with filling factors of approximately 26\% and 11\% for the primary and secondary, respectively, and a tertiary light contribution of about 27\%. ETV analysis shows a significant light-travel-time effect (LTTE), consistent with the presence of the tertiary companion, whose minimum mass is estimated as $M_{3,\rm min}=0.74(1)~M_\odot$. The light curve exhibits a pronounced O'Connell effect and quasi-periodic variations, indicative of starspot activity modulating the photometry on a $\sim$131-day timescale. Kepler observations further reveal 30 superflares, each with total energies exceeding $10^{34}$ erg. This study presents detailed observational constraints on the orbital configuration, stellar properties, and magnetic activity of KIC 6464285, providing a benchmark for studies of hierarchical triple systems.

astro-ph.SR↗
arXiv2026

Heterogeneous and Adept Snapshot Distillation for 3D Semantic Segmentation

Multi-modal fusion and multi-model ensembling are prevalent in enhancing the performance of 3D semantic segmentation. Despite the impressive performance, these methods either rely on auxiliary input signals or suffer from costly computational expense. To efficaciously enhance the segmentation performance without introducing intolerable costs, we propose to transfer the rich knowledge from the multi-modal model (i.e., point clouds and images) and multiple model experts to the point-cloudbased network through knowledge distillation. Specifically, we present Information-oriented Heterogeneous Distillation (IHD) to help the uni-modal model absorb the complementary knowledge from the multi-modal teacher. We design the Information-Oriented Filtering (IOF) strategy to select informative images from the continuous image sequence for multi-modal fusion. This practice can boost the performance of the multi-modal teacher, thus benefiting the learning of the student. Besides, as opposed to vanilla model ensembling that requires the separate training of each expert, we propose Adept Snapshot Distillation (ASD). ASD treats the freely available model snapshots generated during the training phase as multiple experts, which significantly reduces the training cost for model ensembling. For each expert teacher, it only provides supervision to the student in the class where it is adept. The resulting Heterogeneous and Adept Snapshot Knowledge Distillation, dubbed HAS-KD, attains state-of-the-art results on ScanNetV2 and S3DIS datasets. HAS-KD can be seamlessly integrated into contemporary 3D segmentation algorithms and bring considerable gains without introducing extra inference burdens. The code will be made publicly available upon publication.

cs.CV↗
arXiv2026

Sliding ferroelectricity tunable conventional and anomalous spin Hall effects in bilayer 1T'-WTe2

The spin Hall effect, recognized for its high-speed, low-power, and highly controllable characteristics, is a key enabler for next-generation memory and logic devices. However, a primary challenge lies in achieving 180$^{\circ}$ magnetization switching without an external magnetic field in spin-orbit torque devices. Here, we propose a method to tune the conventional and anomalous spin Hall effects by the intrinsic sliding ferroelectricity. Importantly, the anomalous spin Hall effect can enable the field-free switching of perpendicular magnetization. We find a substantial anomalous spin Hall conductivity of $\sigma_{xy}^{y}$ = 45.62 ($\hbar$/e)S/cm and $\sigma_{yx}^{y}$ = 56.84 ($\hbar$/e)S/cm in monolayer 1T'-WTe$_2$. These values are significantly enhanced to $\sigma_{xy}^{y}$ = -96.77 ($\hbar$/e)S/cm and $\sigma_{yx}^{y}$ = 104.03 ($\hbar$/e)S/cm in the bilayer 1T'-WTe$_2$. More interestingly, the sliding ferroelectricity enables reversible switching of the signs and magnitudes for both the conventional and anomalous spin Hall conductivities. This originates from the fact that the sliding ferroelectric markedly shifts the relative spin Berry curvature contributions from the valence and conduction bands around the $\Gamma$-X path. Our findings not only reveal a strong coupling between sliding ferroelectricity and spin transport, but also propose a strategy for the nonvolatile electrical control of spintronic devices.

cond-mat.mtrl-sci↗
arXiv2026

Construction of Historical Knowledge Graphs Based on BERT and Graph Neural Networks

Through digital humanities research and scale-up historical data analysis, a significant amount of traditional historical text is converted into structured knowledge graphs. This paper provides a high-level architecture that combines bidirectional encoder representations of transformers (BERT) and graph neural networks (GNN) to extract the entities and relationships from various types of historical texts. The texts of traditional history resolve linguistic ambiguities, references limited by context, and a lack of established grammatical norms in a systematic way. This study develops a new image retrieval system based on FastRQNet and pre-trained vision-language model Vilt-qaformer+RoBInet in accordance with the aforementioned recommendations. The experiments make full use of a comprehensive collection of municipal records, parliamentary documents, and historical correspondence. When compared to conventional rule-based techniques and other popular deep-learning baselines, the joint BERT-GNN system obtains greater Precision, Recall, and F1-score (Table 2). Complex nested structures and implicit reference issues can be handled by this structure with sufficient accuracy and thoroughness when creating knowledge graphs. The aforementioned experiments show that combining relational graph learning algorithms with context-sensitive semantic representation techniques can automatically extract historical data to add accumulated wisdom to the knowledge repository.

cs.CL↗
arXiv2026

Non-Relativistic Spin-Orbit Interaction in Triplet Superconductors: Edelstein Effect and Spin Pumping by Electric Fields

Non-relativistic momentum-dependent spin splitting, as observed in collinear altermagnets and non-collinear $p$-wave magnets, provides exciting avenues for controlling spin dynamics. Here, we reveal a distinct form of non-relativistic ``spin-orbit coupling" in triplet superconductors by demonstrating that the triplet order parameter induces a wave-vector-dependent spin texture of Bogoliubov quasiparticles, thereby entangling their orbital and spin motions. Even in the absence of relativistic spin-orbit coupling, this intertwining of spin and orbital motion allows an electric field to generate spin polarization in a $p$-wave superconductor -- that is, an Edelstein effect. Building on this mechanism, we propose an efficient scheme for the nonlinear generation of a DC spin current via electric near fields, driven by AC spin polarization and electron velocity. This general principle offers a powerful route for generating and manipulating spin currents in unconventional superconductors.

cond-mat.supr-con↗
arXiv2026

DebiasRAG: A Tuning-Free Path to Fair Generation in Large Language Models through Retrieval-Augmented Generation

Large language models (LLMs) have achieved unprecedented success due to their exceptional generative capabilities. However, because they depend on knowledge encapsulated from training corpora, they may produce hallucinations, stereotypes, and socially biased content. In particular, LLMs are prone to prejudiced responses involving race, gender, and age, which are collectively referred to as social biases. Prior studies have used fine-tuning and prompt engineering to mitigate such biases in LLMs, but these methods require additional training resources or domain knowledge to design the framework. Moreover, they may degrade the original capabilities of LLMs and often overlook the need for dynamic debiasing contexts for fairer inference. In this paper, we propose DebiasRAG, a novel tuning-free and dynamic query-specific debiasing framework based on retrieval-augmented generation (RAG). DebiasRAG improves fairness while preserving the intrinsic properties of LLMs, such as representation ability. DebiasRAG consists of three stages: (1) query-specific debiasing candidate generation; (2) context candidate pool construction; and (3) gradient-updated debiasing-guided context piece reranking. First, DebiasRAG leverages self-diagnosed bias contexts relevant to the query through regular retrieval, where the bias contexts are prepared offline by the DebiasRAG provider. Given the query-specific bias contexts, DebiasRAG reversely produces debiasing contexts, which are provided as additional fairness constraints for LLM outputs. Second, a regular RAG retrieval process produces query-related contexts from the regular RAG document database, such as a chunked Wikipedia dataset.

cs.CL↗
arXiv2026

Near-Optimal Last-Iterate Convergence for Zero-Sum Games with Bandit Feedback and Opponent Actions

Last-iterate convergence of learning dynamics in games has attracted significant recent attention. In two-player zero-sum games with bandit feedback, where only the loss of the selected action pair is observed, Fiegel et al. (2025) show a separation between average-iterate and last-iterate convergence in duality gap: while the optimal t^(-1/2) rate after t rounds is achievable for the former via standard no-regret algorithms, the latter cannot converge faster than t^(-1/3) in expectation or t^(-1/4) with high probability. However, in many practical settings, such as preference learning, the players observe not only their loss but also the opponent's action. This raises a natural question: can such additional information enable faster last-iterate convergence? We answer this question affirmatively, showing that t^(-1/2) last-iterate convergence is achievable with high probability in this setting, via an efficient algorithm that updates its strategy infrequently by solving an estimated log-barrier-regularized game. We identify fundamental obstacles preventing standard analysis for multi-armed bandits, the single-player case, from generalizing to games, and develop a novel analysis to overcome them. Experiments confirm that our algorithm indeed converges faster than naive baselines and prior methods that do not exploit opponent-action feedback. Finally, we note that our results also improve those for dueling bandits, a special case with skew-symmetric game matrices.

cs.LG↗
arXiv2026

Research on Vision-Language Question Answering Models for Industrial Robots

A hierarchical cross-modal fusion model is proposed for vision-language question answering (VLQA) in industrial robotics, targeting the challenges of semantic ambiguity, complex environmental layouts, and domain-specific terminology common in modern manufacturing. The framework integrates advanced object detection, multi-scale visual encoding, syntactic parsing, and task-aware semantic attention to unite vision and language signals into a joint reasoning space. Region-based deep networks extract visual features, weighted embeddings aggregate, and recurrent neural parsing encodes sentence structures. Through fine-grained semantic alignment driven by adaptive fusion and cross-attention mechanisms, the system can handle operational queries, instruction steps, and anomaly detection with higher reliability. Compared to the existing VLQA benchmarks, validation experiments conducted on the IVQA and RIF benchmarks indicate improvements in semantic alignment, Top-1 accuracy, and robustness to ambiguous or procedural task queries. Ablation studies further quantify the impact of each architectural module, confirming the necessity of multi-level feature integration and context-driven gating for dependable industrial deployment. The technical advancements reported here provide core methodologies to improve the interpretability and operational effectiveness of industrial robots faced with diverse human-robot interaction tasks.

cs.CV↗
arXiv2026

Analysis of Electromagnetic Scattering from Semiconductor Nanostructures by Solving Coupled Volume Integral and Two-fluid Hydrodynamic Equations

Semiconductor-based plasmonic nanostructures support localized surface plasmon modes in the infrared region. Unlike metallic nanostructures, they support both free electrons and holes, requiring a two-fluid hydrodynamic Drude equation (HDE) to accurately capture spatial dispersion effects and low-frequency acoustic plasmon modes that cannot be described by single-fluid models. In this work, a volume integral equation (VIE)-based solver is proposed for the analysis of electromagnetic scattering from semiconductor nanostructures. The proposed approach couples the VIE, formulated in terms of the electric flux density and the free-electron and hole polarization currents, with the two-fluid HDE. The coupled system is discretized using a tetrahedral mesh and solved efficiently using a two-level iterative solver. In contrast to finite-element-based methods, the proposed VIE-based approach does not require domain-wide meshing and inherently satisfies the radiation condition, thereby eliminating artificial absorbing boundaries. Numerical results for InSb-type semiconductor nanostructures demonstrate the accuracy and efficiency of the proposed VIE-based solver and its ability to capture unique optical phenomena, such as acoustic plasmon resonances and the blueshift of localized surface plasmon resonances, that cannot be described by the single-fluid HDE or classical Drude-based models.

physics.optics↗
arXiv2026

Applications of the Transformer Architecture in AI-Assisted English Reading Comprehension

This paper studies interpretable and fair artificial intelligence architectures for understanding English reading. Introduced transformer-based models, integrating advanced attention mechanisms and gradient-based feature attribution. The model's lack of interpretability, reduction of algorithmic bias, and unreliable performance in learning environments are the current issues faced in natural language teaching. A unified technical pipeline has been constructed, including adversarial bias correction methods, token-level attribution analysis, and multi-head attention heatmap visualization. Experimental validation was conducted using a large-scale labeled English reading comprehension dataset, and the data partitioning scheme and parameter optimization procedures have been determined. The method significantly outperforms the state-of-the-art models for this task in terms of accuracy and macro-average F1 score; in some aspects, it even surpasses or closely matches the results of human evaluations. In multi-week user experiments, the explainable transformer improved teachers' trust and operability in feedback-based assessments within the scoring system. The proposed method aims to ensure high prediction accuracy and fairness for different learners. This indicates that it is a real-world educational application based on artificial intelligence with a focus on interpretation. Improve the user experience in AI-assisted reading comprehension systems, counteract biases, and enhance the details explained by transformers.

cs.CL↗
arXiv2026

Pumping of spin supercurrent in unitary triplet superconductors

One efficient mechanism for generating a charge supercurrent is Andreev reflection, in which the electric current injected from a normal metal into a conventional superconductor is converted into a supercurrent, thereby preserving charge conservation. We here propose a general principle for generating spin supercurrents in triplet superconductors by analogy with such charge transport, i.e., assuming spin conservation. We find a spin torque that is proportional to the triplet superconducting order parameter and, in the spin-conservation scenario, converts the particle spin to that of Cooper pairs. Based on this general principle, we propose an implementation to efficiently generate a spin supercurrent in unitary triplet superconductors, even though Cooper pairs carry no spin polarization at equilibrium, by the magnetization dynamics ${\bf M}(t)$ of a proximity magnetic nanostructure. The efficiency of this spin pumping is not solely limited to the $d{\bf M}/dt\times {\bf M}$ due to the emergent particle-hole symmetry, thereby going beyond the conventional spin pumping of electrons. This general principle provides an efficient approach to generating and manipulating dissipationless spin currents in many unconventional superconductors.

cond-mat.supr-con↗
arXiv2026

The $\chi_y$-genus, Chern number inequalities and signature

This article has two parts. In the first part we introduce two positivity conditions for the modified $\chi_y$-genus on almost-complex manifolds and show that each of them implies a family of optimal Chern number inequalities. It turns out that many important K\"{a}hler and symplectic manifolds satisfy either of the two positivity conditions, and hence these Chern number inequalities hold true on them. In the second part we focus on the signature, a special value of the $\chi_y$-genus, of symplectic manifolds equipped with symplectic circle actions and give applications. Our results in this part unify and generalize various related results in the existing literature.

math.DG↗
arXiv2026

Gradient Descent with Projection Finds Over-Parameterized Neural Networks for Learning Low-Degree Polynomials with Nearly Minimax Optimal Rate

We study the problem of learning a low-degree spherical polynomial of degree $k_0 = \Theta(1) \ge 1$ defined on the unit sphere in $\RR^d$ by training an over-parameterized two-layer neural network with augmented feature in this paper. Our main result is the significantly improved sample complexity for learning such low-degree polynomials. We show that, for any regression risk $\eps \in (0, \Theta(d^{-k_0})]$, an over-parameterized two-layer neural network trained by a novel Gradient Descent with Projection (GDP) requires a sample complexity of $n \asymp \Theta( \log(4/\delta) \cdot d^{k_0}/\eps)$ with probability $1-\delta$ for $\delta \in (0,1)$, in contrast with the representative sample complexity $\Theta(d^{k_0} \max\set{\eps^{-2},\log d})$. Moreover, such sample complexity is nearly unimprovable since the trained network renders a nearly optimal rate of the nonparametric regression risk of the order $\log({4}/{\delta}) \cdot \Theta(d^{k_0}/{n})$ with probability at least $1-\delta$. On the other hand, the minimax optimal rate for the regression risk with a kernel of rank $\Theta(d^{k_0})$ is $\Theta(d^{k_0}/{n})$, so that the rate of the nonparametric regression risk of the network trained by GDP is nearly minimax optimal. In the case that the ground truth degree $k_0$ is unknown, we present a novel and provable adaptive degree selection algorithm which identifies the true degree and achieves the same nearly optimal regression rate. To the best of our knowledge, this is the first time that a nearly optimal risk bound is obtained by training an over-parameterized neural network with a popular activation function (ReLU) and algorithmic guarantee for learning low-degree spherical polynomials. Due to the feature learning capability of GDP, our results are beyond the regular Neural Tangent Kernel (NTK) limit.

stat.ML↗