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Hang Yang

Publications and source records attributed to Hang Yang.

At least 19 recordsLinked to original sources

Factoriality of Mixed $q$-Gaussian Algebras at the Boundary

We determine the center and characterize factoriality for mixed $q$-Gaussian von Neumann algebras associated with real symmetric matrices $Q=(q_{ij})_{i,j\in I}$ satisfying $|q_{ij}|\le1$. We prove that every central element is supported on the singleton components of the strict graph of $Q$ and on its two-vertex components $\{a,b\}$ with $q_{aa}=q_{bb}=-1$ in terms of the graph-theoretic language. We also determine the spectral laws of some central elements.

math.OA↗

A quantitative Robin Pólya--Szegő theorem for tangential polygons

We extend the quantitative Dirichlet theorem of Gui, Hu, and Li \cite{GuiHuLi2026} to Robin torsional rigidity. For every fixed $β>0$, we prove that the regular $N$-gon uniquely maximizes Robin torsional rigidity among tangential $N$-gons of prescribed area. Our explicit perimeter deficit bound contains the Dirichlet deficit obtained by Gui, Hu, and Li \cite{GuiHuLi2026} and an additional Robin term that is positive for nonregular tangential polygons. For triangles, we answer the fixed-area optimization problem recorded as open by Keady and Wiwatanapataphee \cite[Appendix A.1.2]{KeadyWiwatanapataphee2016}. We also establish strict monotonicity along equal-area regular polygons as the number of sides increases, with an explicit bound for each consecutive gap. The proof combines mixed Robin--Neumann triangular cells, spectral estimates for angular concavity, and a quantitative scaling estimate.

math.AP↗

History-Conditioned Flow Matching for Probabilistic Dynamics of Tendon-Driven Continuum Robots

Deterministic dynamics modeling of tendon-driven continuum robots remains challenging owing to uncertainties in material behavior, tendon transmission, friction, and contact. Measured joint configurations and nominal tendon commands do not fully characterize these internal mechanical factors, leaving uncertainty in the subsequent motion. We therefore develop a history-conditioned, physics-informed flow-matching framework for probabilistic dynamics prediction, using motion and actuation histories to predict the distribution of the next complete joint configuration. By recursively sampling next-step configurations under prescribed commands, the model predicts distributions of future whole-body motions. In simulation, scenario-specific models achieve five-second trajectory Energy Scores (lower is better) of 12.05 mm under internal friction variation and 9.29 mm under unobserved actuation disturbances. Relative to the conditional variational autoencoder and diffusion baselines, Flow attains lower Energy Scores and coverage closer to the nominal level in both scenarios. Ablations support history and structural conditioning in both scenarios. On the physical robot, predictions under two tendon-command profiles excluded from training capture the principal motion sequences, with five-second Energy Scores of 11.91 and 11.42 mm, lower than the compared baselines. The predicted-to-measured spread ratios are 1.65 and 1.22 (closer to 1 is better). These results support history-conditioned probabilistic dynamics prediction under incomplete mechanical observations.

cs.RO↗

Real-Time Whole-Body Safe Motion Generation for Multi-Segment Tendon-Driven Continuum Robots

Real-time motion generation for tendon-driven continuum robots requires accurate modeling of nonuniform bending and whole-body collision avoidance. This paper presents a unified actuation-space framework for planar multi-segment tendon-driven continuum robots. An energy-based variable-curvature model captures spatially varying tendon spacing and bending stiffness and provides analytical Jacobians for differential inverse kinematics and safety monitoring. A multipoint CBF-QP enforces backbone clearance under obstacle motion and actuation-velocity bounds, while its decision dimension depends only on the number of independently actuated segments. The model closely agrees with GVS references, with a maximum curvature error of $5.223 \times 10^{-2}\,\mathrm{m}^{-1}$. Over 100 MuJoCo trials, the proposed method achieves collision-free success rates of 96% and 100% in static and dynamic scenarios, respectively, compared with approximately 60% and 80% without CBF constraints. Hole-traversal tests further demonstrate safe motion in constrained environments. With 600 backbone monitoring points, the mean control-step time is 6.66 ms, demonstrating real-time whole-body safe motion generation.

cs.RO↗

Lightweight Dynamic Modeling of Cable-Driven Continuum Robots Based on Actuation-Space Energy Formulation

Cable-driven continuum robots (CDCRs) require accurate, real-time dynamic models for high-speed dynamics prediction or model-based control, making such capability an urgent need. In this paper, we propose the Lightweight Actuation-Space Energy Modeling (LASEM) framework for CDCRs, which formulates actuation potential energy directly in actuation space to enable lightweight yet accurate dynamic modeling. Through a unified variational derivation, the governing dynamics reduce to a single partial differential equation (PDE), requiring only the Euler moment balance while implicitly incorporating the Newton force balance. By also avoiding explicit computation of cable-backbone contact forces, the formulation simplifies the model structure and improves computational efficiency while preserving geometric accuracy and physical consistency. Importantly, the proposed framework for dynamic modeling natively supports both force-input and displacement-input actuation modes, a capability seldom achieved in existing dynamic formulations. Leveraging this lightweight structure, a Galerkin space-time modal discretization with analytical time-domain derivatives of the reduced state further enables an average 62.3% computational speedup over state-of-the-art real-time dynamic modeling approaches.

cs.RO↗

SARTM: Segment Any RGB Thermal Model with Language aided Distillation

The recent Segment Anything Model (SAM) demonstrates strong instance segmentation performance across various downstream tasks. However, SAM is trained solely on RGB data, limiting its direct applicability to RGB-thermal (RGB-T) semantic segmentation. Given that RGB-T provides a robust solution for scene understanding in adverse weather and lighting conditions, such as low light and overexposure, we propose a novel framework, SARTM, which customizes the powerful SAM for RGB-T semantic segmentation. Our key idea is to unleash the potential of SAM while introduce semantic understanding modules for RGB-T data pairs. Specifically, our framework first involves fine-tuning the original SAM by adding extra LoRA layers, aiming at preserving SAM's strong generalization and segmentation capabilities for downstream tasks. Secondly, we introduce language information as guidance for training our SARTM. To address cross-modal inconsistencies, we introduce a Cross-Modal Knowledge Distillation(CMKD) module that effectively achieves modality adaptation while maintaining its generalization capabilities. This semantic module enables the minimization of modality gaps and alleviates semantic ambiguity, facilitating the combination of any modality under any visual conditions. Furthermore, we enhance the segmentation performance by adjusting the segmentation head of SAM and incorporating an auxiliary semantic segmentation head, which integrates multi-scale features for effective fusion. Extensive experiments are conducted across three multi-modal RGBT semantic segmentation benchmarks: MFNET, PST900, and FMB. Both quantitative and qualitative results consistently demonstrate that the proposed SARTM significantly outperforms state-of-the-art approaches across a variety of conditions. Code and pre-trained weights can be found at https://github.com/wahaha-debug/SARTM.

cs.CV↗

Not All Synthetic Data Are Equal: Expert-Committee Audit Screening for Imbalanced Crash-Injury-Severity Prediction in Automated Driving Systems

Automated driving systems (ADSs) are increasingly operating on public roads, raising safety concerns, yet reliable prediction of crash injury severity remains difficult because crash reports are limited, severe outcomes are rare, and injury classes are highly imbalanced. Existing augmentation methods mainly increase minority-class sample size but rarely assess whether generated samples are credible for safety-critical prediction. This study proposes Expert-Committee Audit Screening (ECAS), a credibility-aware sample acceptance framework for ADS crash injury severity prediction under data imbalance. Using 1,477 incident-level ADS crashes from the National Highway Traffic Safety Administration Standing General Order records, ECAS audits generated minority samples through a real-data-only expert committee based on label support, boundary separation, committee agreement, and local plausibility. Within-class percentile normalization and Pareto non-dominated sorting select accepted samples without manually assigned evidence weights. With a fixed backbone combining normalizing flow augmentation and a Tabular Prior-data Fitted Network (TabPFN) classifier, the best ECAS configuration achieved the highest balanced accuracy, macro-F1, and minor-injury recall among all evidence configurations. Local neighborhood analysis showed that ECAS-accepted samples were better supported by nearby real minority crashes than unscreened retained samples. Shapley additive explanations and partial dependence plots further indicated that lower injury severity classes were mainly associated with crash counterpart and pre-crash movement, whereas moderate-plus injuries were more sensitive to posted speed limit and operating context. These findings support a shift from quantity-oriented augmentation to credibility-aware sample acceptance for ADS safety prediction and risk governance.

cs.LG↗

The capture of halo material by orbiting subhaloes

When a dark matter halo falls into a more massive object and becomes a subhalo, it typically loses much of its mass through tidal stripping. The reverse process is also possible in principle. The subhalo may gravitationally capture material from its host. If sufficiently efficient, this process could make an initially starless subhalo visible. We use high-resolution N-body simulations to estimate the efficiency of capture. We find that after an extended period orbiting within its host, at most $\sim 10^{-4}$ of a subhalo's remaining mass has been acquired since infall. This captured material is less concentrated to subhalo centre than material retained from before infall. It is also very much less abundant than host material that is instantaneously passing through the subhalo on almost unperturbed orbits. Captured stars are not sufficiently spatially concentrated to be distinguished from the dominant background of "field" stars, and their concentration in velocity space is no greater than that of typical stellar streams in the halo. Unfortunately, stellar capture is not efficient enough to allow initially starless low-mass subhaloes to be detected.

astro-ph.GA↗

VADER: Adaptive Debiasing for Hallucination Mitigation in Video Large Language Models

Large vision-language models (LVLMs) have demonstrated strong performance in open-ended video understanding, yet they remain prone to fluent responses unsupported by video evidence. Existing training-free methods typically apply a globally fixed visual intervention or construct a contrastive branch through input perturbation. The former cannot accommodate video-dependent fusion paths, while the latter can be compensated by cross-frame redundancy. We therefore propose Video-Adaptive Debiasing via Evidence Reweighting (VADER), a training-free framework with two complementary modules. Visual Focus Reallocation (VFR) automatically instantiates an intervention policy for each video-question input: it diagnoses layer-wise visual-to-text evidence flow, determines where to intervene, and derives how strongly to reallocate pre-softmax attention from system-token to video-token blocks. Selective Evidence Erasure (SEE) independently masks high-importance visual tokens in every frame, constructing a prior-biased branch that is difficult to compensate through neighboring frames. Contrastive decoding then down-weights predictions that remain confident after selective evidence erasure. Across multiple VideoLLMs, VADER yields substantial improvements on event-level grounding and temporal consistency; on LLaVA-Video-7B, it reaches 72.60% accuracy on EventHallusion.

cs.CV↗

GIFT: Geometry-Invariant Fine-Tuning for Non-Lambertian Monocular Depth Estimation

Monocular depth foundation models, benefiting from large-scale synthetic training data, have demonstrated strong generalization. However, they often hallucinate depth on non-Lambertian surfaces, estimating reflected content in mirrors or transmitted content behind glass rather than the physical surface itself. Adapting these models with real-world data is challenging because conventional depth sensors are also unreliable in such regions. We observe that while the appearance of a non-Lambertian surface varies with its reflected or transmitted environment, its underlying geometry remains unchanged. Based on this observation, we propose GIFT (Geometry-Invariant Fine-Tuning), a parameter-efficient post-training framework that requires no measured depth labels. We collect groups of RGB images under controlled appearance changes while keeping the camera and target geometry fixed. GIFT exploits geometric invariance across these observations to suppress non-Lambertian depth hallucinations while retaining general depth estimation capability. We further construct a controlled benchmark that evaluates non-Lambertian depth recovery, robustness to appearance changes, and performance retention in other regions. Experiments on our benchmark and an independent real-world dataset demonstrate that GIFT improves depth prediction for mirrors and transparent objects while largely preserving the base model's performance, providing a practical and low-cost approach for adapting monocular depth foundation models to non-Lambertian scenes.

cs.CV↗

d-Spectral Bitopological Spaces

We introduce and study the category of \emph{d-spectral spaces}, a bitopological analogue of the classical spectral spaces of Stone and Hochster. A d-spectral space is a compact, d-sober bitopological space such that both open set lattices are coherent frames, where d-sobriety is the bitopological notion of sobriety due to Jung and Moshier. We show that the category of spectral spaces embeds into the category of d-spectral spaces as a simultaneously reflective and coreflective full subcategory. Moreover, we prove that d-spectral spaces are precisely the spectra of d-lattices. Key to this result is the d-lattice of compact open sets associated to a d-spectral space and the spectrum construction for d-lattices. We also show that the patch space of a d-spectral space is d-Boolean and that the de Groot dual of a d-spectral space is again d-spectral, mirroring the corresponding classical properties of spectral spaces. Our results demonstrate that d-spectral spaces form a natural and well-behaved bitopological extension of the spectral space framework.

math.GN↗

Serrin's Problem under Dirichlet Perturbations: Geometric Compactness and Sharp Planar Stability

In earlier work [21], we posed a stability question for Serrin's overdetermined problem under Dirichlet perturbations and proved that the answer is negative in dimensions $n\ge3$. Here we resolve the question in the planar convex class and obtain a sharp quantitative theory without any a priori geometric nondegeneracy. Let $u_Ω$ solve \[ -Δu_Ω=1\ \text{in }Ω,\qquad \partial_νu_Ω=-\frac{|Ω|}{P(Ω)}\ \text{on }\partialΩ, \qquad \int_{\partialΩ}u_Ω\,dσ=0, \] and set $O(Ω):=\text{osc}_{\partial Ω}u_Ω$. We construct fixed-area annuli with $O(Ω_k)\to0$ that remain far from every disk, showing that convexity is essential in dimension two. By contrast, if $Ω_k\subset\mathbb R^2$ are convex, $|Ω_k|=π$, and $O(Ω_k)\to0$, then, up to translations, $Ω_k$ converges in Hausdorff distance to the unit disk. Moreover, \[ R_Ω-r_Ω+\inf_{z\in\mathbb R^2}d_H(Ω,B_1(z)) \le C\,O(Ω) \] for all planar convex $Ω$ with $|Ω|=π$ and sufficiently small $O(Ω)$, and the linear order is optimal. The proof combines a new mechanism excluding long-thin degeneration, the rough-domain Serrin rigidity theorem of Figalli--Zhang, new tangential-gradient and linear boundary-growth estimates, a boundary $P$-function estimate, and the reverse-Serrin identity of Magnanini--Molinarolo--Poggesi. We also study the weaker deficit \[ A(Ω):=\frac1{P(Ω)}\int_{\partialΩ}u_Ω,dσ-\min_{\partialΩ}u_Ω. \] In the planar convex class, $A(Ω_k)\to0$ still forces convergence to a disk, and \[ R_Ω-r_Ω+\inf_z d_H(Ω,B_1(z)) \le C A(Ω)^{2/3} \] for $|Ω|=π$ and sufficiently small $A(Ω)$.

math.AP↗

Diophantine analysis and the Braid group ${\bf B}_3$

Given a finite dimensional representation $π$ of a finitely generated group $G=\langle g_1, \ldots, g_n\rangle$, the associated characteristic polynomial is defined as $Q_π(z):=\det(z_0I+z_1π(g_1)+\cdots +z_nπ(g_n))$, and it is known to contain a good amount of structural information about $G$ and $π$. This paper is a part of an ongoing project to investigate the number-theoretic properties of the algebraic varieties (called {\em eigensurfaces}) $\{z\in \mathbb{C}^{n+1}: Q_π(z)=0\}$. Its focus is the distribution of prime triples in the eigensurface $S:=\{z\in \mathbb{C}^3: (z_0+z_1+z_2)^2+z_0z_1=0\}$ associated with the braid group ${\bf B}_3$ and its reduced Burau representation. We prove that such triples occur with higher frequency on $S$ than in the ambient lattice, revealing an unexpected connection between group representation theory and analytic number theory.

math.NT↗

Autonomous End-to-End SOH Prediction Services for Battery Systems via Temporal-Contrastive Representation Learning

Accurate state of health (SOH) estimation is a critical diagnostic service for lithium-ion battery management. However, reliance on labor-intensive manual feature engineering and opaque black-box models hinders scalable industrial deployment. To address this, we introduce TC-SOH: a modular, plug-and-play service architecture for autonomous, end-to-end SOH prediction. TC-SOH employs a temporal-contrastive mechanism and a cross-window prediction pretext task to extract degradation-relevant representations directly from raw operational data. To improve transparency, we connect model efficacy with representation diagnostics: visualization, sensitivity analysis, redundancy analysis, bidirectional probing, future-SOH probing, and temporal shuffling show that learned features overlap with selected expert descriptors while retaining additional SOH-relevant variation, and that ordered temporal context improves subsequent-SOH prediction. Across four public datasets, TC-SOH outperforms the considered physics-informed and data-driven baselines, reducing MAPE by 1.91 times and RMSE by 2.13 times.

cs.LG↗

Large Language Model-Powered Query-Driven Event Timeline Summarization in Industrial Search

Understanding how events evolve over time is essential for search engines handling queries about trending news. We present QDET (Query-Driven Event Timeline Summarization), a production system deployed on Baidu Search that constructs focused event timelines to explain specific query events. Unlike traditional topic-centric approaches that aim for comprehensive coverage, QDET identifies and organizes sub-events closely relevant to the query from noisy candidate sets formed by millions of documents retrieved daily. QDET incorporates two key innovations: (1) multi-task supervised fine-tuning with three auxiliary tasks-temporal ordering, causal judgment, and timeline completion-that enable compact models to match the performance of much larger general-purpose models in specialized domains; (2) reinforcement learning-based event concise summarization that enforces strict length constraints while maintaining semantic quality, achieving 88.2% length compliance and outperforming 671B-scale models by 7.7 points in constraint satisfaction. Our fine-tuned 7B parameter model achieves 76.2% F1 score on timeline summarization, slightly surpassing the zero-shot performance of DeepSeek-R1-671B (76.1% F1) while using only 1% of its parameters-demonstrating that domain-specific optimization enables production-ready models with comparable quality at drastically reduced computational costs. Online A/B tests on Baidu Search validate real-world effectiveness, showing 5.5% CTR improvement, 4.6% longer dwell time, and 4.4% deeper exploration compared to single-task baselines. We further demonstrate that timeline understanding transfers to heat prediction, confirming effective knowledge transfer to downstream tasks.

cs.CL↗

Microwave photonic radar jamming and target detection integration based on advanced waveform editing, forwarding, and self-squaring reception

The integrated radar and jamming (IRAJ) system provides a promising solution that meets the demands for miniaturization, integration, and multifunctionality in complex warfare environments. However, traditional electronic-domain IRAJ systems face limitations in operating frequency and bandwidth. In this paper, we propose and experimentally demonstrate a microwave photonic IRAJ system based on pseudo-random binary phase modulation and segmented frequency shifting. By modulating pseudo-random binary coding sequence and frequency-shifting signals onto linearly frequency-modulated (LFM) pulses, an IRAJ waveform is generated to achieve noise-like jamming against the adversary radar. To overcome the random π-phase jumps introduced by pseudo-random binary modulation in the de-chirped signal, a time-domain squaring operation is implemented during de-chirped reception, restoring the radar detection ability of our system and enabling accurate target sensing without prior knowledge of the coding sequence. Experimental results demonstrate that the system can generate IRAJ waveforms with a bandwidth of up to 4 GHz, covering both 10-28 GHz. The proposed system achieves effective jamming against adversary radars employing either de-chirped reception or pulse compression, with the generated jamming results exhibiting an irregular and random distribution of false targets. Meanwhile, the system maintains radar performance with a ranging error of around 5 cm and a radial velocity measurement error below 4 cm/s.

physics.optics↗

Cross-Modal RGB-D Fusion Transformer for 6D Pose Estimation of Non-Cooperative Spacecraft with Stereo-Derived Depth

On-orbit servicing and active debris removal involving non-cooperative spacecraft require reliable pose estimation to supply accurate position and orientation data for autonomous visual navigation. Learning-based monocular methods have seen widespread adoption in spacecraft pose estimation, yet they suffer from an intrinsic depth ambiguity problem and tend to fail under the harsh illumination conditions routinely encountered in orbit. Active depth sensors could in principle address the geometric ambiguity, but their power and mass requirements make them poorly suited to most spacecraft platforms. This work addresses these issues through a passive stereo vision framework for six-degree-of-freedom (6-DOF) pose estimation of non-cooperative spacecraft. A binocular stereo matching network called TSCA-Stereo is developed to cope with weak-texture surfaces, specular highlights, and severe lighting variations typical of space imagery. A cross-modal fusion Transformer is introduced to combine RGB appearance information with stereo depth features in an adaptive manner, supporting reliable pose recovery. A synthetic binocular multimodal dataset is also built for the experiments, covering stereo disparity maps and 6-DOF pose annotations across a range of illumination scenarios, attitude configurations, and noise levels. Experimental results show that TSCA-Stereo outperforms the baseline across every evaluated metric on this space-specific dataset. The full pose estimation pipeline achieves a mean translation error of 0.0419 m and a mean orientation error of 0.8632° under varied imaging conditions, confirming that the passive stereo approach is both effective and resilient when operating under the demanding visual conditions of the space environment.

cs.CV↗