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Xiang Zhou

Publications and source records attributed to Xiang Zhou.

At least 19 recordsLinked to original sources
arXiv2026

Geometry-adaptive Ambisonic encoding for sparse microphone arrays of variable topology using physics-informed diffusion

Ambisonics delivers compact scene based spatial audio representation, yet higher order Ambisonic encoding poses difficulties for wearables and embedded hardware. Their microphone arrays are often sparse, irregular, and constrained by device specific boundary conditions. These factors make the spherical-harmonic (SH) domain encoding ill conditioned: inverse filtering amplifies noise, while deterministic neural encoders may overfit to array-specific responses or smooth ambiguous higher-order components. This paper presents DiffM2A, a geometry-adaptive conditional diffusion framework for robust Ambisonic encoding from sparse MAs with variable topologies. Its Geometry-Adaptive Spherical Harmonic Projection (GASHP) front-end constructs boundary-aware SH steering functions and applies an energy-normalized modal projection, mapping array-dependent observations to a common modal representation without explicit pseudo-inverse computation. A dual-branch Elucidated Diffusion Model then estimates complex Ambisonic coefficients, conditioned on both the raw microphone spectra and GASHP features. Sound intensity and rotational equivariance losses further enhance inter-channel phase consistency and structured behavior across SH subspaces. Evaluations on both first- and second-order Ambisonic encoding tasks, using simulated room-acoustics and real-world LOCATA recordings, demonstrate that DiffM2A outperforms conventional and neural baseline methods on signal fidelity, spectral accuracy, spatial coherence, and binaural cue preservation. Additional experiments show that these gains are largely retained across unseen five-microphone layouts and under mismatched open-array and rigid-sphere boundary models.

eess.AS↗
arXiv2026

From coherence to mixedness: a driver of barren plateaus in variational quantum algorithms

Variational quantum algorithms (VQAs) are a leading approach for near-term quantum advantage. However, their training is often hindered by barren plateaus (BPs). We present a framework based on observational entropy. The framework separates the coherent part of a quantum state from its incoherent part. We define the coherence fraction $η$ as the ratio of coherent to total contribution. This quantity measures how much of the coherence capacity is in a usable form. Using a 4-qubit Ising model and a hardware-efficient ansatz, we show that $η$ decreases monotonically during optimization, while the coherence capacity remains nearly constant. Our results show that $η$ provides an early indication of gradient collapse. It outperforms conventional diagnostics such as the gradient norm and entanglement entropy. This work offers a resource-theoretic explanation for BPs. It also provides a basis for real-time monitoring of coherence loss on noisy devices.

quant-ph↗
arXiv2026

PILOT: Privileged Imitation Learning for End-to-End Motion Planning of Autonomous UAVs under Partial Observability

Autonomous navigation in cluttered environments is hampered by partial observability and dynamic constraints. This paper presents PILOT, a constraint-aware privileged imitation learning framework for vision-based end-to-end UAV motion planning under partial observability. The framework distills planning strategies from a computationally intensive optimal control expert into a student policy regularized toward safety and dynamic requirements via a dual-objective loss function. To mitigate partial observability, a spatiotemporal perception fusion module using a Temporal Convolutional Network (TCN) is developed to integrate historical depth images and odometry. This module infers task-relevant latent context from historical observations, enhancing spatial awareness beyond the instantaneous FOV without maintaining persistent map memory. A trajectory parameterization layer mapping network outputs to a structured trajectory, while enabling explicit continuity, dynamic-consistency, and obstacle soft penalties during training, encouraging constraint satisfaction for unseen observations without formal guarantees. Simulations on quadrotor and fixed-wing aircraft demonstrate that PILOT achieves performance comparable to the privileged expert while reducing computational overhead by over 80\%. Successful indoor and outdoor zero-shot deployment confirms the practical feasibility and cross-domain generalization of the planner.

cs.RO↗
arXiv2026

Taming the Loss Landscape of PINNs with Noisy Feynman-Kac Supervision: Operator Preconditioning and Non-Asymptotic Error Bounds

Physics-Informed Neural Networks (PINNs) often train slowly or fail to converge on challenging partial differential equations (PDEs), a behavior recently linked to severely ill-conditioned loss landscapes inherited from the underlying differential operator. We study PINNs augmented with a pointwise data-fidelity term, added at a few points in the domain to the standard residual and boundary losses. We show that this supervision term acts as an operator-level preconditioner: for suitable weights, our comparison bounds guarantee a substantially smaller condition number than under the standard PINN loss, independently of how the pointwise labels are obtained. For a broad class of PDEs admitting a Feynman-Kac (FK) representation, we generate such labels by Monte Carlo averages of the FK functional, resulting in what we call ``FK-PINNs", and using the excess risk decomposition approach, we derive non-asymptotic $L^2(Ω)$-error bounds for FK-PINNs with $\tanh$ activation trained by finitely many steps of gradient descent. Along the way, we establish pseudo-dimension bounds for first- and second-order derivatives of $\tanh$ neural networks, which are of independent interest and, to the best of our knowledge, new. Numerical experiments on Poisson, Schrödinger, mean exit time, and committor problems corroborate the theory, and show that FK-PINNs can successfully solve PDEs for which standard PINNs exhibit severe failure modes.

stat.ML↗
arXiv2026

TCellAlign: Cross-study T-cell Populations Alignment with Nomenclature-Guided Multi-Agent Workflow

Cell type standardization plays a central role in integrating biological knowledge across single-cell studies. While standardized resources (e.g., Cell Ontology, Nomenclature Frameworks) provide unified vocabularies of cell populations, scientific publications and public datasets continue to use heterogeneous study-specific labels, making cross-study comparison difficult even when biologically equivalent cell populations are described. In this work, we are the first to formulate this challenge as an evidence-grounded cell population alignment problem and propose TCellAlign, a multi-agent framework that includes literature retrieval, information extraction, nomenclature-guided label alignment, and evidence-based adjudication. This modular design preserves the original terminology and supporting evidence reported by each study while producing standardized labels that can be compared across studies. We further construct a manually validated benchmark dataset linking study-specific labels, CZ CELLxGENE annotations, and standardized T-cell nomenclature across 44 manually curated, published studies (including over seven million cells) spanning four biological categories: healthy, cancer, infectious disease and inflammatory diseases. Across the evaluated tasks, TCellAlign achieves stronger semantic agreement than ontology-based baselines and maintains transcriptomic coherence with both open-source and closed-source large language models (LLM) backbones. By connecting literature, datasets, and expert's nomenclature, TCellAlign enables consistent interpretation of T-cell subtypes and states across studies, facilitating biological knowledge integration and the development of future foundation models built upon standardized cellular representations.

q-bio.QM↗
arXiv2026

An eightfold equivalence-preserving speedup of the JUNO OMILREC vertex and energy reconstruction

The Jiangmen Underground Neutrino Observatory (JUNO) reconstructs each event's vertex and energy with OMILREC, a maximum-likelihood fit that scans all $17{,}612$ large photomultiplier tubes (LPMTs) in every Minuit function evaluation, about $470$ times per event. This inner loop dominates the reconstruction CPU cost. Profiling shows that the production algorithm is latency-bound, sustaining only $9.9%$ of scalar floating-point peak because of virtual-function dispatch, ROOT-histogram pointer chasing, and repeated computation. We apply staged \emph{equivalence-preserving} optimizations: flattened data layouts, vectorizable geometry, hoisting of Minuit-invariant work, per-event precomputation, fit-phase loop splitting and indexing, and reduced-precision fast paths. Each stage is checked against a frozen reference from the unmodified code. The optimized implementation achieves single-thread speedups of $8.06\times$ ($1524.8 \rightarrow 189.2$~ms/event) on an Intel Xeon Platinum~8358P and $5.22\times$ ($705.1 \rightarrow 134.9$~ms/event) on an AMD~EPYC~9654, increasing to $8.6\times$ ($177.7$~ms/event) after further optimization. The likelihood remains bit-identical through the first seven releases and later agrees within a relative drift of $1.3\times10^{-14}$, below the $10^{-13}$ contract. For typical events, reconstructed vertex and energy agree with the baseline within $4$~mm and $7$~keV; a few boundary cases reach different valid minima owing to an improved minimizer seed. An eight-metric physics-acceptance test also passes on about $861{,}000$ $^{68}$Ge calibration events. Developed with assistance from an AI coding agent operating under these verification gates, this workflow offers a transferable template for accelerating likelihood-based reconstruction in large neutrino and collider detectors without changing physics output.

physics.ins-det↗
arXiv2026

LegoQ: Density-Matrix Representation Learning with Spectral-Spatial State Transitions for Hyperspectral Classification

Hyperspectral image classification is complicated by mixed pixels, spectral ambiguity, class imbalance, and limited annotations. Most current classifiers encode a pixel or patch as a deterministic vector and apply a linear or multilayer softmax head. Although effective for discrimination, this representation does not directly expose how mixed or uncertain a sample is. This paper presents \method, a classical density-matrix representation learning framework for hyperspectral images. The spectral bands are divided into groups and each group is mapped to a positive semi-definite, Hermitian, trace-normalized matrix state. A composable stack of spectral, spatial, and inter-group transitions then updates the states while repeatedly projecting them back to the valid state set. Instead of flattening the final features, \method\ aggregates the group states and compares them with learnable class-prototype density matrices through Uhlmann fidelity. The normalized eigenspectrum, von Neumann entropy, purity, and prototype fidelity provide sample-level diagnostics that are unavailable from a conventional vector head. On Indian Pines, ten runs yield an overall accuracy of $96.20\pm0.70\%$, an average accuracy of $95.57\pm1.29\%$, and a kappa coefficient of $95.66\pm0.80\%$. On WHU-Hi-LongKou, the best of ten runs reaches $97.52\%$ overall accuracy. Classification maps and feature projections show that the transition stack produces compact and better separated class structures. The results support constrained matrix-state learning as a practical alternative to vector-only hyperspectral classification without requiring quantum hardware.

cs.CV↗
arXiv2026

Generating Samples of Stationary Distributions of Weakly Interacting Diffusion Models Without Finite Particle Truncation: A Weak Generative Approach

Computing the stationary probability density and generating corresponding samples for the mean-field model of an infinite number of weakly interacting diffusion particles pose significant numerical challenges, particularly in the phase transition regime where the interchangeability of infinite-time and infinite-particle limits breaks down. Traditional approaches, such as direct simulation of finite-particle systems, often fail to accurately pinpoint multiple stationary distributions in the mean-field meta-stable setting. On the other hand, solving the high-dimensional McKean-Vlasov partial differential equation using neural networks typically yields only the density function, limiting its utility for estimating statistical quantities from generating samples. In this work, we propose a novel generative framework based on the weak PDE formulation of the mean-field model to address these challenges. Our approach simultaneously computes the stationary distributions of McKean-Vlasov processes and generates independent and identically distributed samples that satisfy these distributions. This integrated approach not only reveals the true stationary distributions without the random perturbation of finite particle truncation, but also offers deeper insight into the system's behavior in the mean-field limit. Extensive numerical experiments demonstrate the effectiveness of the proposed method, showcasing its ability to accurately approximate stationary distributions, capture intricate phase transitions, and handle high-dimensional complex systems.

physics.comp-ph↗
arXiv2026

Boundary-Adapted PINNs for Elliptic Dirichlet Problems: $H^2(Ω)$ A Priori Error Bounds with Application to Mean Escape Time Computation

Motivated by the numerical computation of the Mean Escape Time (MET) $τ:Ω\to\mathbb{R}$ of a stochastic process from a bounded domain $Ω\subseteq\mathbb{R}^d$, we study elliptic Dirichlet boundary value problems (BVPs) using boundary-enforced Physics-Informed Neural Networks (PINNs), in which the Dirichlet condition is imposed exactly by multiplying the network output with a predefined distance-to-boundary approximation $ρ$. Combining approximation-theoretic and statistical-learning arguments for Rectified Quadratic Unit (ReQU) and hyperbolic tangent (tanh) networks, we derive a priori error bounds that make explicit the dependence on $ρ$. In particular, we show that exact boundary enforcement alone is not enough for $H^2(Ω)$ error bounds, and that a sufficient and essentially necessary condition is for $ρ$ to be a smooth distance approximation $\textit{normalized to first order}$, of the kind constructed in arXiv:2104.08426 [math.NA]. We thereby identify this subclass of $\textit{boundary-adapted}$ PINNs as the appropriate neural network ansatz for solving Dirichlet BVPs. Numerical experiments support the theory, showing that appropriate choices of $ρ$ improve accuracy and convergence, while poorly chosen distance functions can substantially degrade the solution. Our proof also yields new VC-dimension bounds for hypothesis spaces of higher-order derivatives of ReQU and tanh networks, together with new approximation bounds for shallow ReQU networks in higher-order Sobolev norms, all of which are of important independent interest.

math.NA↗
arXiv2026

GPU-accelerated spectrum reweighting for new-physics searches in solar neutrino--electron scattering

Precision measurements of neutrino--electron elastic scattering provide low-energy tests of weak interactions and beyond-the-Standard-Model effects. Non-standard interactions (NSIs) and an anomalous neutrino magnetic moment modify the differential cross section through different kinematic terms, but both can alter the normalization and shape of the recoil-electron spectrum. Likelihood tests are computationally costly when each parameter point requires the recoil spectrum to be propagated through a detector response obtained from Monte Carlo (MC) simulation. We present a GPU-accelerated spectrum-reweighting framework that avoids regenerating detector MC samples for each new-physics parameter point. Bin-to-bin weights are applied at the recoil-spectrum level and folded with a fixed two-dimensional response model in recoil and reconstructed energy. This keeps the detector response inside the likelihood calculation while reducing each parameter update to operations on precomputed spectra and response kernels. The implementation uses NVIDIA Thrust transformation--reduction primitives and is compiled from a common source for CUDA and OpenMP back ends. In the benchmarks considered here, one likelihood evaluation takes ${\sim}87$ ms on an NVIDIA RTX 3080Ti and ${\sim}52$ ms on an NVIDIA A30X; the latter gives a $58\times$ speedup over a single CPU thread and ${\sim}2.5\times$ over a fully loaded 64-thread CPU. The consumer-GPU result demonstrates that interactive parameter scans are feasible on a single workstation. The main acceleration, however, comes from avoiding detector-MC regeneration at each parameter point rather than from GPU execution alone. The framework applies to neutrino--electron scattering analyses in which the new-physics dependence can be represented by reweighting an existing recoil spectrum, including flavor NSI and magnetic-moment cases studied here.

hep-ex↗
arXiv2026

Dynamics of Coupled Stochastic van der Pol Oscillators: Bifurcations, Synchronization and Chaos

This work presents a comprehensive analysis of coupled stochastic van der Pol oscillators, a paradigm for understanding synchronization, bifurcations, and chaos in nonlinear systems subject to random fluctuations. The system comprises two or more oscillators with nonlinear damping, linear diffusive coupling, and additive Gaussian white noise. We develop a unified framework that systematically connects global bifurcations, synchronization phenomena, and chaotic dynamics within a single coherent stochastic model. We explore the stochastic dynamics of coupled van der Pol oscillators by seamlessly blending theoretical principles with in-depth numerical simulations. This integrated approach forms a robust framework for analysis, with essential phenomena clearly depicted in the accompanying figures. We then extend this framework to a comprehensive investigation of large networks, focusing on their continuum limit, emergent pattern formation, the role of noise, and the onset of collective chaos.

nlin.CD↗
arXiv2026

Spatially Selective Self-Training for Unsupervised Building Change Detection

Unsupervised building change detection aims to learn building-change masks from unlabeled bi-temporal remote sensing images. Existing label-free methods often follow a discrepancy-to-mask paradigm, directly using temporal differences, frozen foundation-model responses, prompt-based outputs, or post-processing results as final change maps. Although these strategies provide annotation-free cues, they do not learn a task-specific building-change detector and remain vulnerable to the gap between generic temporal discrepancies and building-defined structural changes. In practice, such discrepancies are often noisy and task-irrelevant, as appearance shifts, registration errors, and non-building modifications can produce strong but misleading responses. To address this problem, we propose SST-CD, a spatially selective self-training framework that reformulates fully label-free building change detection as end-to-end detector learning under noisy pseudo supervision. SST-CD uses temporal discrepancies as candidate pseudo labels and trains the detector only on spatially reliable pixels, whose reliability is estimated by a local consistency criterion that filters inconsistent regions from supervision. To further stabilize noisy self-training, a lightweight feature adapter recalibrates bi-temporal features, while a prototype-based decoder produces compact change and no-change representations. Experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show that SST-CD achieves F1 scores of 83.08%, 91.69%, and 86.60%, respectively, outperforming existing unsupervised and label-free baselines.

cs.CV↗
arXiv2026

SWE-IF: Aligning Code Evaluation with Human Preference

Large Language Models (LLMs) have catalyzed vibe coding, where users leverage LLMs to generate and iteratively refine code through natural language interactions until it passes their vibe check. Vibe check reflects human preference and goes beyond functionality: the solution should feel right, read cleanly, preserve intent, and remain correct. However, current code evaluation remains anchored to pass@k and captures only functional correctness, overlooking non-functional instructions that users routinely apply. In this paper, we hypothesize that instruction following is the missing piece underlying vibe check besides functional correctness. To quantify models' code instruction-following capabilities with measurable signals, we present VeriCode, a taxonomy of 30 verifiable code instructions together with deterministic verifiers. We use the taxonomy to augment established evaluation suites, resulting in SWE-IF, a testbed to assess both instruction following and functional correctness. Evaluating 31 LLMs, we show that even the strongest models struggle to comply with multiple instructions and exhibit functional regression. Most importantly, a composite score of functional correctness and instruction following correlates best with human preference, with instruction following emerging as the primary differentiator among LLMs. Our code, data, and taxonomy are available at https://github.com/maszhongming/SWE-IF.

cs.CL↗
arXiv2026

Cooperative Circumnavigation for Multiple Unmanned Surface Vehicles Without External Localization

This paper proposes a cooperative target circumnavigation framework for multiple unmanned surface vehicles (USVs) operating without external localization. The objective is to maintain a uniform circular formation of a specified radius around a target using only limited onboard sensing. The framework adopts a heterogeneous perception strategy that distinguishes between the asymmetric sensing relationships with the target and among the USVs. Specifically, the USVs obtain relative range and displacement measurements through active perception and inter-vehicle communication, while bearing measurements to a non-cooperative target are acquired via passive sensors. To estimate relative positions--both among USVs and between each USV and the target--we employ a Maximum Correntropy Kalman Filter and a Pseudo-Linear Kalman Filter, respectively. A coupled oscillator-based formation controller is designed to ensure system observability while achieving circumnavigation. Theoretical analysis demonstrates that the controller ensures the relative motions between the USVs, as well as that between each USV and the target, satisfy the persistent excitation condition, thereby guaranteeing observability of the Kalman-based filters. The effectiveness of the proposed approach is validated through numerical simulations.

cs.RO↗
arXiv2026

Revealing the Two-Fold Ambiguity: Tau Momentum Reconstruction and Its Impact on Entanglement Observables

The neutrinos produced in $τ$ decays cannot be directly detected, making the reconstruction of $τ$ kinematics challenging and affecting measurements of quantum correlations such as spin entanglement. For the process $e^+e^- \to τ^+τ^- \to π^+ \barν_τπ^-ν_τ$, the kinematic constraints allow the $τ$ momenta to be reconstructed up to a well-known two-fold ambiguity, regardless of the presence of an intermediate resonance state. In this paper, we present a geometric interpretation of this ambiguity and propose a numerical reconstruction method based on singular value decomposition (SVD). Using only the information from visible final-state particles and decay kinematics, the method reconstructs the two possible solutions for the $τ^+τ^-$ pair. The reconstruction performance is validated with Monte Carlo simulations in typical collider environments. We further investigate the impact of the spurious solution on spin-entanglement measurements and show that reliable entanglement signals can still be extracted even when the true and spurious solutions cannot be experimentally distinguished. This work provides a practical approach for $τ$-lepton kinematic reconstruction and spin-entanglement measurements in $e^+e^-$ collider experiments.

hep-ph↗
arXiv2026

Super-fast Rates of Convergence for Neural Network Classifiers under the Hard Margin Condition

We study the classical binary classification problem for hypothesis spaces of Deep Neural Networks (DNNs) under Tsybakov's low-noise condition with exponent $q>0$, as well as its limit case $q=\infty$, which we refer to as the \emph{hard margin condition}. We demonstrate that, for a wide range of commonly used activation functions (including but not limited to ReLU, LeakyReLU, ELU, CELU, SELU, Softplus, GELU, SiLU, Swish, Mish, and Softmax), DNN solutions to the empirical risk minimization (ERM) problem with square loss surrogate and $\ell_p$ penalty on the weights $(0 1$ under the hard-margin condition, provided that the Bayes regression function $η$ satisfies a \emph{distribution-adapted smoothness} condition relative to the marginal data distribution $ρ_{X}$. Furthermore, when the activation function is chosen as $\tanh$ or sigmoid, we show that the same rates follow from the standard assumption that $η\in \mathcal{C}^s$. Finally, we establish minimax lower bounds, showing that these rates cannot be improved upon whenever $q\ge2$. Our proof relies on a novel decomposition of the excess risk for general ERM-based classifiers which might be of independent interest.

cs.LG↗
arXiv2026

Transition Path Theory For Lévy-Type Processes: SDE Representation and Statistics

This paper establishes a Transition Path Theory (TPT) for Lévy-type processes, addressing a critical gap in the study of the transition mechanism between meta-stabile states in non-Gaussian stochastic systems. A key contribution is the rigorous derivation of the stochastic differential equation (SDE) representation for transition path processes, which share the same distributional properties as transition trajectories, along with a proof of its well-posedness. This result provides a solid theoretical foundation for sampling transition trajectories. The paper also investigates the statistical properties of transition trajectories, including their probability distribution, probability current, and rate of occurrence.

math.PR↗
arXiv2026

Lower-bounding entanglement with nonlocality in a general Bell's scenario

Understanding the quantitative relation between entanglement and Bell nonlocality is a long-standing open problem of fundamental and practical interest. Here, we tackle this problem in a general Bell scenario. {We observe that lying in the center of quantifying these properties are two minimal distances: one from a state to separable states (entanglement), and the other from a correlation to local correlations (nonlocality).} We find that these two distances can be related to each other -- the minimal correlation distance provides a lower bound for the minimal state distance, which allows us to derive nontrivial bounds on many entanglement measures with an arbitrary nonlocal correlation. Moreover, with the on-hand structural knowledge of entanglement and nonlocality in the $(n, 2, 2)$ Bell scenario, we refine our estimate significantly.

quant-ph↗