arXiv subjects

Hao Qin

Publications and source records attributed to Hao Qin.

At least 19 recordsLinked to original sources

arXiv2026

NGS-Marker: Robust Native Watermarking for 3D Gaussian Splatting

With the rapid development and adoption of 3D Gaussian Splatting (3DGS), the need for effective copyright protection has become increasingly critical. Existing watermarking techniques for 3DGS mainly focus on protecting rendered images via pre-trained decoders, leaving the underlying 3D Gaussian primitives vulnerable to misuse. In particular, they are ineffective against Partial Infringement, where an adversary extracts and reuses only a subset of Gaussians. In this paper, we propose NGS-Marker, a novel native watermarking framework for 3DGS. It integrates a jointly trained watermark injector and message decoder, and employs a gradientbased progressive injection strategy to ensure full-scene coverage. This enables robust ownership decoding from any local region. We further extend NGS-Marker with hybrid protection (combining native and indirect watermarks) and support for multimodal watermarking. Extensive experiments demonstrate that NGS-Marker effectively defends against partial infringement while offering practical flexibility for real-world deployment.

cs.CV↗

arXiv2026

Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

We study the contextual combinatorial semi-bandit (CCSB) problem with general reward function approximation. At each round, the learner observes a context, selects a combinatorial action consisting of a subset of basic arms, and receives the reward of each selected arm; the goal is to maximize the cumulative reward over time. We propose SquareCB.Comb, a computationally efficient algorithm that, at each round, solves a convex optimization problem to sample a combinatorial action that balances exploration and exploitation. SquareCB.Comb scales to large arm sets and imposes no structural assumptions on the action set beyond a cardinality bound of $m$ on each combinatorial action. We prove that SquareCB.Comb achieves a minimax optimal regret bound of $O(\sqrt{m A T \log |\mathcal{F}|})$, where $A$ is the number of arms, $m$ is the maximum number of arms in a combinatorial action, $T$ is the time horizon, and $\mathcal{F}$ is the reward function class. In the realizable setting, this bound matches the state-of-the-art regret guarantees achieved by policy search-based algorithms in the more restricted slate recommendation settings, while simultaneously generalizing to arbitrary combinatorial action structures and general reward function approximation.

cs.LG↗

arXiv2026

Arbitrage-Free Multi-Maturity Risk-Neutral Marginals

Many quantitative finance methods and applications are formulated in terms of option-implied risk-neutral marginals rather than directly in terms of option prices. Representative examples include martingale optimal transport, Bass local-volatility calibration, scenario analysis, and option-implied tail-risk measurement. The desired risk-neutral marginals should define a genuine probability law on the entire support, reproduce the input arbitrage-free option prices exactly, be free of butterfly and calendar arbitrage, and admit efficient evaluation of the density, distribution function, and quantiles, as well as Monte Carlo sampling. Existing methods typically optimize only a subset of these properties, depending on their intended purpose. This leaves a gap between upstream arbitrage-free option prices and the readily usable risk-neutral marginals required by downstream applications. We propose an explicit construction of risk-neutral marginals from discrete arbitrage-free option prices. On the observed strike range, probability mass is assigned interval by interval to exactly reproduce the input option prices. Outside the observed range, closed-form power-law tails complete the distribution by satisfying price and slope boundary conditions and allocating the remaining probability mass. Butterfly- and calendar-arbitrage-freeness are guaranteed by construction. The construction is feasible by design and computationally efficient. The resulting marginal laws admit closed-form densities, distribution functions, quantiles, and efficient Monte Carlo sampling. Numerical experiments on synthetic SSVI data and S\&P~500 market data demonstrate that the proposed construction efficiently and robustly produces marginals satisfying all of these properties in practice.

q-fin.CP↗

arXiv2026

Spatio-Sequential Recurrent Network for 3-D Tunnel Propagation Modeling

Fine-mesh parabolic wave equation (PWE) simulations are high-fidelity but time-consuming, which limits real-time tunnel propagation analysis and motivates coarse-to-fine reconstruction. Existing machine learning (ML)-assisted tunnel models typically provide only one-dimensional (1-D) longitudinal refinement or two-dimensional (2-D) cross-sectional refinement, rather than joint 3-D enhancement. Motivated by this gap, this letter proposes a U-shaped gated spatio-sequential recurrent neural network (UG-SSRNN), a spatio-sequential reconstruction model for tunnel electromagnetic fields. UG-SSRNN jointly super-resolves transverse slices and models longitudinal evolution. It uses sliding-window context encoding and a K-layer convolutional recurrent backbone with a shared propagation-context state and diagonal feedback. A prediction-aware upsampling head leverages the previous prediction to improve slice-to-slice consistency. Experiments on four tunnel cross sections, unseen-material and unseen-frequency tests, and validation in the Massif Central tunnel show close agreement with fine-mesh PWE references. The proposed approach significantly reduces tunnel electromagnetic modeling time.

eess.SP↗

arXiv2026

Real-Space Imaging of Guided Exciton Polaritons in Free-standing Monolayer WSe2

Monolayers of transition metal dichalcogenides (TMDCs), known for their strong excitonic states with high binding energies in the visible spectrum at room temperature, offer great potential for polariton-driven devices. While polariton guided modes in bulk TMDCs have been reported the real space experimental observation of 2D exciton-polariton guided modes in a monolayer remains challenging due to various mode cut-off conditions that arise as the TMDC layer becomes thinner, including cut-off frequency, mode confinement and boundary conditions. Here using scanning near-field optical microscopy (s-SNOM), we directly visualized the real-space propagation of these guided modes for the first time in an angstrom-thick, suspended monolayer of WSe2. Through numerical simulations we have also validated that the guided mode can only exist in a monolayer WSe2 when symmetric cladding conditions are closely applied. By tuning the excitation laser energy and analysing the guided mode distribution, we observed a pronounced back-bending dispersion around the A exciton, indicating strong light-matter interactions, and confirmed the existence of the fundamental TE0 exciton polariton (EP) propagation mode. The unique dispersion characteristics of these modes were further validated through theoretical modelling of the mode in free-standing monolayer WSe2. Our findings provide crucial experimental evidence of guided mode EPs in atomically thin TMDCs, opening new possibilities for nanoscale photonic applications.

physics.optics↗

arXiv2026

AgentGate: A Lightweight Structured Routing Engine for the Internet of Agents

The rapid development of AI agent systems is leading to an emerging Internet of Agents, where specialized agents operate across local devices, edge nodes, private services, and cloud platforms. Although recent efforts have improved agent naming, discovery, and interaction, efficient request dispatch remains an open systems problem under latency, privacy, and cost constraints. In this paper, we present AgentGate, a lightweight structured routing engine for candidate-aware agent dispatch. Instead of treating routing as unrestricted text generation, AgentGate formulates it as a constrained decision problem and decomposes it into two stages: action decision and structural grounding. The first stage determines whether a query should trigger single-agent invocation, multi-agent planning, direct response, or safe escalation, while the second stage instantiates the selected action into executable outputs such as target agents, structured arguments, or multi-step plans. To adapt compact models to this setting, we further develop a routing-oriented fine-tuning scheme with candidate-aware supervision and hard negative examples. Experiments on a curated routing benchmark with several 3B--7B open-weight models show that compact models can provide competitive routing performance in constrained settings, and that model differences are mainly reflected in action prediction, candidate selection, and structured grounding quality. These results indicate that structured routing is a feasible design point for efficient and privacy-aware agent systems, especially when routing decisions must be made under resource-constrained deployment conditions.

cs.AI↗

arXiv2026

MSSSeg: Learning Multi-Scale Structural Complexity for Self-Supervised Segmentation

Self-supervised semantic segmentation methods often suffer from structural errors, including merging distinct objects or fragmenting coherent regions, because they rely primarily on low-level appearance cues such as color and texture. These cues lack structural discriminability: they carry no information about the structural organization of a region, making it difficult to distinguish boundaries between similar-looking objects or maintain coherence within internally varying regions. Recent approaches attempt to address this by incorporating depth priors, yet remain limited by not explicitly modeling structural complexity that persists even when appearance cues are ambiguous. To bridge this gap, we present MSSSeg, a framework that explicitly learns multi-scale structural complexity from both semantic and depth domains, via three coupled components: (1) a Differentiable Box-Counting (DBC) module that captures and aligns multi-scale structural complexity features with semantic features; (2) a Learnable Structural Augmentation (StructAug) that corrupts pixel-intensity patterns, forcing the network to rely on structural complexity features from DBC; and (3) a Persistent Homology Loss (PHLoss) that directly supervises the structural complexity of predicted segmentations. Extensive experiments demonstrate that MSSSeg achieves new state-of-the-art performance on COCO-Stuff-27, Cityscapes, and Potsdam without excessive computational overhead, validating that explicit structural complexity learning is crucial for self-supervised segmentation.

cs.CV↗

arXiv2026

Variation-aware Flexible 3D Gaussian Editing

Indirect editing methods for 3D Gaussian Splatting (3DGS) have recently witnessed significant advancements. These approaches operate by first applying edits in the rendered 2D space and subsequently projecting the modifications back into 3D. However, this paradigm inevitably introduces cross-view inconsistencies and constrains both the flexibility and efficiency of the editing process. To address these challenges, we present VF-Editor, which enables native editing of Gaussian primitives by predicting attribute variations in a feedforward manner. To accurately and efficiently estimate these variations, we design a novel variation predictor distilled from 2D editing knowledge. The predictor encodes the input to generate a variation field and employs two learnable, parallel decoding functions to iteratively infer attribute changes for each 3D Gaussian. Thanks to its unified design, VF-Editor can seamlessly distill editing knowledge from diverse 2D editors and strategies into a single predictor, allowing for flexible and effective knowledge transfer into the 3D domain. Extensive experiments on both public and private datasets reveal the inherent limitations of indirect editing pipelines and validate the effectiveness and flexibility of our approach.

cs.GR↗

arXiv2026

On Dual-Fed Pinching Antenna Systems with In-Waveguide Attenuation

Pinching antenna systems (PAS) have recently emerged as a promising architecture for flexible and reconfigurable wireless communications. However, their performance is fundamentally constrained by in-waveguide attenuation, which is non-negligible in practical dielectric waveguides and can severely degrade the achievable data rate, particularly for long waveguides. To overcome this limitation, we propose a dual-fed PAS (DF-PAS), in which each waveguide is equipped with two feed points located at the two ends, enabling dynamic feed-point selection based on user locations. This design effectively shortens the in-waveguide propagation distance and mitigates attenuation-induced power loss without modifying the waveguide structure or the PA actuation mechanism. We investigate the DF-PAS in both single- and multi-waveguide scenarios. For the single-waveguide case, we derive closed-form high-SNR approximations of the ergodic rate and obtain closed-form solutions for the optimal PA position and feed-point selection under time-division multiple access (TDMA). We then extend DF-PAS to a multi-waveguide scenario, where we first derive closed-form high-SNR approximations of the ergodic rate and then formulate a joint optimization problem over feed-point selection, PA placement, and beamforming under general orthogonal multiple access (OMA). To solve this problem efficiently, we develop a two-phase optimization framework that integrates greedy feed-point switching, gradient-based PA placement, and WMMSE-based beamforming. Simulation results demonstrate that the proposed DF-PAS consistently outperforms conventional single-fed PAS (SF-PAS) across various network configurations, validating its effectiveness as a practical and scalable solution for mitigating in-waveguide attenuation in PAS-enabled wireless networks.

eess.SP↗

arXiv2026

Uncertainty Quantification of Radio Wave Propagation over Irregular Terrains Using Adaptive Polynomial Chaos Expansion

Accurate modeling of radio wave propagation over irregular terrains is crucial for designing reliable wireless communication systems in such environments, yet uncertainties in the antenna configuration are not quantified within deterministic models. In this paper, we present, to the best of our knowledge, the first uncertainty quantification (UQ) study of realistic antenna configurations for irregular-terrain propagation. An adaptive polynomial chaos expansion (APCE) method is improved and coupled with a two-way parabolic wave equation (PWE) method to address this problem efficiently. The polynomial basis is extended according to variance contributions and terminated by a composite criterion combining validation error and sample-to-basis ratio, enabling stable coefficient estimations via least-square regression without additional regularization. Convergence analysis shows a monotonic error decay with increasing training samples, producing compact, low-interaction models and improved accuracy and robustness over the previous APCE methods. For two realistic terrain profiles, the proposed method accurately predicts the mean and the 5th-95th percentile range of the path loss, matching Monte Carlo (MC) references using only 30 PWE simulations. Using a fixed sampling budget, APCE outperforms standard and sparse PCE, with the largest gains observed for the 5th and 95th percentile estimates; as the sample size increases, APCE maintains low errors with reduced trial-to-trial variability.

eess.SP↗

arXiv2026

Physics-Informed Parametric Bandits for Beam Alignment in mmWave Communications

In millimeter wave (mmWave) communications, beam alignment and tracking are crucial to combat the significant path loss. As scanning the entire directional space is inefficient, designing an efficient and robust method to identify the optimal beam directions is essential. Since traditional bandit algorithms require a long time horizon to converge under large beam spaces, many existing works propose efficient bandit algorithms for beam alignment by relying on unimodality or multimodality assumptions on the reward function's structure. However, such assumptions often do not hold (or cannot be strictly satisfied) in practice, which causes such algorithms to converge to choosing suboptimal beams. In this work, we propose two physics-informed bandit algorithms \textit{pretc} and \textit{prgreedy} that exploit the sparse multipath property of mmWave channels - a generic but realistic assumption - which is connected to the Phase Retrieval Bandit problem. Our algorithms treat the parameters of each path as black boxes and maintain optimal estimates of them based on sampled historical rewards. \textit{pretc} starts with a random exploration phase and then commits to the optimal beam under the estimated reward function. \textit{prgreedy} performs such estimation in an online manner and chooses the best beam under current estimates. Our algorithms can also be easily adapted to beam tracking in the mobile setting. Through experiments using both the synthetic DeepMIMO dataset and the real-world DeepSense6G dataset, we demonstrate that both algorithms outperform existing approaches in a wide range of scenarios across diverse channel environments, showing their generalizability and robustness.

cs.LG↗

arXiv2026

Dynamic Antenna Placement for Mobile Users in Urban Micro Pinching-Antenna Systems

The pinching-antenna systems (PASS) enable blockage mitigation in urban micro (UMi) networks through flexible antenna placement. However, the joint optimization of antenna positions and beamforming precoding is inherently nonconvex and becomes significantly more challenging under user mobility. To address this issue, we propose a bilevel optimization framework for dynamic antenna positioning and beamforming precoding design. In the outer level, a soft actor-critic (SAC) agent learns a continuous control policy for real-time antenna positioning, while in the inner level, zero-forcing (ZF) precoding is applied based on the instantaneous effective channel. Numerical results demonstrate that the proposed framework significantly improves spectral efficiency (SE) and enhances robustness against user mobility and random blockages.

eess.SP↗

arXiv2026

Fairness-Oriented Optimization of NOMA-Enabled Pinching-Antenna Systems Under Blockage and Imperfect CSI

The pinching-antenna system (PASS) has been proposed as a promising solution for mitigating line-of-sight (LoS) blockages by dynamically repositioning pinching antennas (PAs) along a dielectric waveguide. This paper develops a fairness-oriented downlink design for a non-orthogonal multiple access (NOMA)-enabled PASS, where the longitudinal placement of PAs and the NOMA power allocation coefficients are jointly optimized to maximize the minimum user signal-to-interference-plus-noise ratio (SINR) across all users under transmit power and waveguide constraints. A soft-blockage channel model incorporating waveguide attenuation and imperfect channel state information (CSI) is developed. To ensure the feasibility of successive interference cancellation under CSI uncertainty, a conservative SINR evaluation framework is proposed. The resulting non-convex max-min SINR optimization problem is efficiently solved using a tailored particle swarm optimization (PSO) algorithm. Numerical results demonstrate that the proposed design improves the minimum user SINR by approximately 7-10 dB compared with fixed-antenna systems and non-robust optimization baselines under moderate blockage and imperfect CSI.

eess.SP↗

arXiv2026

Taming the Monster Every Context: Complexity Measure and Unified Framework for Offline-Oracle Efficient Contextual Bandits

We propose an algorithmic framework, Offline Estimation to Decisions (OE2D), that efficiently reduces contextual bandit learning with general reward function approximation to offline regression. The framework allows near-optimal regret for contextual bandits with large action spaces with $O(\log T)$ calls to an offline regression oracle over $T$ rounds, and makes $O(\log\log T)$ calls when $T$ is known. The design of OE2D algorithm generalizes Falcon~\citep{simchi2022bypassing} and its linear reward version~\citep[][Section 4]{xu2020upper} in that it finds an action distribution that we term ``exploitative F-design'' that simultaneously guarantees low regret and good coverage, striking a balance between exploration and exploitation. Central to our regret analysis is a new complexity measure, the Decision-Offline Estimation Coefficient (DOEC), which we show is small in many settings, including bounded Eluder dimension per-context and the smoothed regret setting. We also establish a relationship between DOEC and Decision Estimation Coefficient (DEC)~\citep{foster2021statistical}, bridging the design principles of offline- and online-oracle efficient contextual bandit algorithms for the first time.

cs.LG↗

arXiv2026

Co-PLNet: A Collaborative Point-Line Network for Prompt-Guided Wireframe Parsing

Wireframe parsing aims to recover line segments and their junctions to form a structured geometric representation useful for downstream tasks such as Simultaneous Localization and Mapping (SLAM). Existing methods predict lines and junctions separately and reconcile them post-hoc, causing mismatches and reduced robustness. We present Co-PLNet, a point-line collaborative framework that exchanges spatial cues between the two tasks, where early detections are converted into spatial prompts via a Point-Line Prompt Encoder (PLP-Encoder), which encodes geometric attributes into compact and spatially aligned maps. A Cross-Guidance Line Decoder (CGL-Decoder) then refines predictions with sparse attention conditioned on complementary prompts, enforcing point-line consistency and efficiency. Experiments on Wireframe and YorkUrban show consistent improvements in accuracy and robustness, together with favorable real-time efficiency, demonstrating our effectiveness for structured geometry perception. Our code is available at https://github.com/GalacticHogrider/Co-PLNet.

cs.CV↗

arXiv2026

Physics-Informed Deep Recurrent Back-Projection Network for Tunnel Propagation Modeling

Accurate and efficient modeling of radio wave propagation in railway tunnels is is critical for ensuring reliable communication-based train control (CBTC) systems. Fine-grid parabolic wave equation (PWE) solvers provide high-fidelity field predictions but are computationally expensive for large-scale tunnels, whereas coarse-grid models lose essential modal and geometric details. To address this challenge, we propose a physics-informed recurrent back-projection propagation network (PRBPN) that reconstructs fine-resolution received-signal-strength (RSS) fields from coarse PWE slices. The network integrates multi-slice temporal fusion with an iterative projection/back-projection mechanism that enforces physical consistency and avoids any pre-upsampling stage, resulting in strong data efficiency and improved generalization. Simulations across four tunnel cross-section geometries and four frequencies show that the proposed PRBPN closely tracks fine-mesh PWE references. Engineering-level validation on the Massif Central tunnel in France further confirms robustness in data-scarce scenarios, trained with only a few paired coarse/fine RSS. These results indicate that the proposed PRBPN can substantially reduce reliance on computationally intensive fine-grid solvers while maintaining high-fidelity tunnel propagation predictions.

cs.ET↗

arXiv2025

Physics-Constrained Inc-GAN for Tunnel Propagation Modeling from Sparse Line Measurements

High-speed railway tunnel communication systems require reliable radio wave propagation prediction to ensure operational safety. However, conventional simulation methods face challenges of high computational complexity and inability to effectively process sparse measurement data collected during actual railway operations. This letter proposes an inception-enhanced generative adversarial network (Inc-GAN) that can reconstruct complete electric field distributions across tunnel cross-sections using sparse value lines measured during actual train operations as input. This directly addresses practical railway measurement constraints. Through an inception-based generator architecture and progressive training strategy, the method achieves robust reconstruction from single measurement signal lines to complete field distributions. Numerical simulation validation demonstrates that Inc-GAN can accurately predict electric fields based on measured data collected during actual train operations, with significantly improved computational efficiency compared to traditional methods, providing a novel solution for railway communication system optimization based on real operational data.

eess.SP↗

arXiv2025

Intelligent Optimization of Wireless Access Point Deployment for Communication-Based Train Control Systems Using Deep Reinforcement Learning

Urban railway systems increasingly rely on communication based train control (CBTC) systems, where optimal deployment of access points (APs) in tunnels is critical for robust wireless coverage. Traditional methods, such as empirical model-based optimization algorithms, are hindered by excessive measurement requirements and suboptimal solutions, while machine learning (ML) approaches often struggle with complex tunnel environments. This paper proposes a deep reinforcement learning (DRL) driven framework that integrates parabolic wave equation (PWE) channel modeling, conditional generative adversarial network (cGAN) based data augmentation, and a dueling deep Q network (Dueling DQN) for AP placement optimization. The PWE method generates high-fidelity path loss distributions for a subset of AP positions, which are then expanded by the cGAN to create high resolution path loss maps for all candidate positions, significantly reducing simulation costs while maintaining physical accuracy. In the DRL framework, the state space captures AP positions and coverage, the action space defines AP adjustments, and the reward function encourages signal improvement while penalizing deployment costs. The dueling DQN enhances convergence speed and exploration exploitation balance, increasing the likelihood of reaching optimal configurations. Comparative experiments show that the proposed method outperforms a conventional Hooke Jeeves optimizer and traditional DQN, delivering AP configurations with higher average received power, better worst-case coverage, and improved computational efficiency. This work integrates high-fidelity electromagnetic simulation, generative modeling, and AI-driven optimization, offering a scalable and data-efficient solution for next-generation CBTC systems in complex tunnel environments.

eess.SP↗