arXiv subjects

Ao Wang

Publications and source records attributed to Ao Wang.

At least 19 recordsLinked to original sources

arXiv2026

Quantum Compressed Sensing CT Reconstruction Algorithm Based on Penalized Weighted Least Squares and Guided Total Variation

Objective. Existing quadratic unconstrained binary optimization (QUBO)-based sparse-view computed tomography (CT) reconstruction neglects photon-counting statistics and anatomical heterogeneity. We address both limitations within the QUBO framework.Approach. We propose a quantum compressed-sensing CT method combining penalized weighted least squares (PWLS) and guided total variation (GTV). PWLS weights projection residuals by photon-count reliability, whereas GTV uses gradients from a prior image reconstructed by the simultaneous algebraic reconstruction technique (SART) to preserve edges and suppress noise in homogeneous regions. After binary encoding, both terms form a unified QUBO model. Experiments used four 40 times 40 CT images under a 10-view fan-beam geometry with Poisson noise. Comparisons included conventional reconstruction methods, QUBO variants, gradient descent, simulated annealing, and a D-Wave hybrid quantum-classical solver.Main results. PWLS-GTV achieved the best reconstruction quality across all cases. In the representative chest case, it reached a peak signal-to-noise ratio (PSNR) of 36.64 dB, compared with 22.48 dB for SART, the best conventional baseline. GTV consistently outperformed conventional total variation. Simulated annealing and the D-Wave hybrid solver produced similar reconstructions, whereas gradient descent was ineffective. Repeated hybrid-solver runs showed stable performance.Significance. The framework incorporates photon-statistical weighting and structure-guided regularization into QUBO-based CT reconstruction without changing its quadratic form, providing a proof of concept for quantum-assisted sparse-view CT reconstruction.

cs.CV↗

arXiv2026

Quantum CT via Dynamic Interval Encoding and Prior-Balanced QUBO Reconstruction

Quadratic unconstrained binary optimization (QUBO)-based quantum computed tomography (CT) casts reconstruction as a binary quadratic problem for quantum annealing and hybrid quantum--classical solvers. For grayscale CT, however, image encoding is constrained by the binary-variable budget: fixed global bit-plane encodings increase QUBO size and coupling complexity as gray-level precision improves, whereas low-bit encodings introduce quantization error. We propose a QUBO-based grayscale CT reconstruction framework that combines dynamic interval encoding with prior-balanced optimization. Each refinement round encodes active pixels only within local gray-level intervals around the current estimate, and a boundary-hit-guided update rule adaptively switches between search expansion and local refinement. To improve optimization stability, the method balances projection-domain data consistency and an edge-preserving quadratic prior before forming the final QUBO. Sparse-view and limited-angle fan-beam CT experiments show that the proposed method recovers structures and gray-level distributions more faithfully than the evaluated analytic, iterative, variational, and representation-based baselines. Expressivity analysis and ablation studies further indicate that the improvement mainly arises from effective gray-level representation through dynamic local encoding and more stable data-fidelity--prior coupling. Experiments on the D-Wave hybrid binary quadratic model (BQM) solver further demonstrate that the formulation is executable on a hardware-backed hybrid quantum--classical backend.

cs.CV↗

arXiv2026

Projection-Volume Fidelity Divergence: Diagnosing and Controlling Optimization Drift in Sparse-View 3D Gaussian Tomography

Sparse-view computed tomography is a severely ill-posed inverse problem, where recent 3D Gaussian Splatting methods offer an efficient explicit representation for tomographic reconstruction. However, we find that projection-domain optimization can be misleading in this setting: the rendered projections may continue to improve while the reconstructed volume deteriorates. We identify this failure mode as Projection-Volume Fidelity Divergence (PVFD), a representation-level optimization drift caused by anisotropic Gaussian deformation and view-specific primitive co-adaptation under sparse Radon constraints. To characterize this behavior, we introduce geometry- and volume-level diagnostics that measure needle-like Gaussian degeneration and the stability of the voxelized density field. Based on these observations, we propose LADES, a ground-truth-free optimization controller for sparse-view Gaussian tomography. LADES combines Linearly Annealed Dropout, which applies strong stochastic masking in early training to disrupt premature primitive co-adaptation and gradually restores full capacity for structural consolidation, with Structure-Aware Early Stopping, which terminates densification according to the saturation of Gaussian population growth rather than validation PSNR. Experiments on sparse-view CT reconstruction show that LADES improves volumetric fidelity, suppresses structural degeneration, and substantially reduces training time while maintaining competitive projection accuracy. These results suggest that robust Gaussian-based tomography requires monitoring and controlling volumetric structure, rather than optimizing projection fit alone.

cs.CV↗

arXiv2026

Joint Multi-Period Fermi-LAT and LHAASO Constraints on Axion-Like Particles from Mrk 421 Using Profile Likelihood with Gaussian Copula Correlation

We propose a joint multi-epoch profile-likelihood analysis of axion-like particles (ALPs) using five sets of simultaneous Fermi-LAT and LHAASO observations of the TeV blazar Mrk 421. Photon-ALP oscillations are calculated self-consistently together with EBL absorption for two representative jet emission models: a two-zone hybrid model and a single-zone hadronic model. To account for weak correlations among different observational epochs, we introduce a Gaussian copula with a conservative correlation coefficient $\rho = 0.03$ and perform a global optimization of nuisance parameters under the no-ALP hypothesis before profiling the ALP parameters. In the low-mass regime relevant to CAST ($m_a \lesssim 1$ neV), we obtain a 95\% CL upper limit of $g_{a\gamma} = 7.46 \times 10^{-13}\,\mathrm{GeV}^{-1}$. Over the full mass range $0.1$--$500$ neV, the most conservative 95\% CL upper limits are $g_{a\gamma} < 6.50 \times 10^{-12}\,\mathrm{GeV}^{-1}$ (two-zone) and $g_{a\gamma} < 7.34 \times 10^{-12}\,\mathrm{GeV}^{-1}$ (single-zone). These constraints benefit from the broadband VHE coverage and long-term monitoring provided by LHAASO. The analysis framework developed here offers a statistically consistent approach for future ALP searches with multi-messenger gamma-ray data.

astro-ph.HE↗

arXiv2026

FastOCR: Dynamic Visual Fixation via KV Cache Pruning for Efficient Document Parsing

Vision-Language Models (VLMs) have shown strong promise on Optical Character Recognition (OCR), yet the sheer number of visual tokens required to encode dense documents incurs prohibitive inference cost. Existing pruning methods rely on physical eviction, e.g., permanently discarding visual tokens during the prefill stage. While effective for natural images, this strategy fundamentally breaks down on OCR, where virtually every visual token may correspond to a character or structural element, and any irreversible loss leads to catastrophic accuracy degradation. We observe that, although document images appear globally dense and seemingly unprunable, the model's attention to them is in fact temporally sparse: at each decoding step it concentrates on a small region that shifts gradually across steps, much as a human reader fixates on successive words rather than perceiving an entire page at once. Motivated by this Dynamic Visual Fixation phenomenon, we recast the intractable global pruning problem as a tractable local, dynamic one and propose FastOCR, a training-free framework with two complementary modules. Specifically, Focal-Guided Pruning identifies a small set of focal layers and selects the most task-relevant visual tokens from them at each step, while Cross-Step Fixation Reuse exploits the gradual shift of fixation to warm-start each step from the previous one. By dynamically adjusting which tokens are attended rather than evicting any from the cache, FastOCR avoids permanent information loss. Extensive experiments show that FastOCR serves as a plug-and-play acceleration module, generalizing consistently across five VLMs of varying sizes and architectures. On Qwen2.5-VL, FastOCR retains 98% of the unpruned model's accuracy while attending to only 5% of the visual tokens per decoding step, reducing attention latency by 3.0$\times$.

cs.CV↗

arXiv2026

Re-acceleration of Energetic Ions via Small-Scale Reconnection in Magnetic Fusion Plasmas

We report the first observation on the EXL-50U spherical torus that energetic particles injected by neutral beam injection (NBI) can be stably accelerated to significantly higher energies - reaching up to 2.5 times the injection energy, occurring without significant large-scale magnetohydrodynamic (MHD) bursts. Simulations based on EXL-50U parameters indicate that small-scale magnetic reconnection, mediated by multiple magnetic islands, fails to accelerate bulk thermal ions but efficiently energizes seed fast ions. Unlike global MHD events, such small-scale reconnection is ubiquitous in magnetic confinement devices and does not degrade core confinement. This mechanism offers a novel and potentially universal channel for auxiliary ion heating in future fusion reactors.

physics.plasm-ph↗

arXiv2026

Invisible Adversaries: A Systematic Study of Session Manipulation Attacks on VPNs

Virtual Private Networks (VPNs) are widely used for censorship evasion and traffic protection. VPN users expect to be provided with adequate security protection, and at the same time not be affected by other users connected to the same VPN server, which can be illustrated as the non-interference property. However, in this paper, we have identified several vulnerabilities that violate this property, specifically within the connection tracking frameworks of VPN servers, stemming from shared resource misuse and insufficient validation of session state transitions. We present three session manipulation attacks targeting TCP and UDP traffic tunneled through VPNs. The attacker who only connects to the same VPN server can launch denial-of-service attacks, hijack TCP connections of other clients, or inject forged DNS responses into their queries. We evaluate these attacks against five popular connection tracking frameworks across different OSes and nine major commercial VPN providers. Experimental results reveal that all frameworks and eight providers are vulnerable to at least one of the attacks. We have responsibly disclosed our findings with countermeasures, resulting in 19 assigned CVEs/CNVDs and acknowledgments from the communities and providers.

cs.CR↗

arXiv2026

A Unified Origin of Primordial Black Hole Dark Matter and Nanohertz Gravitational Waves

Recent high-cadence observations by Subaru-HSC have identified a population of ultrashort-timescale microlensing events, providing a compelling window for planet-mass primordial black holes (PBHs) to constitute the entirety of dark matter. In this Letter, we demonstrate that this PBH population and the nanohertz stochastic gravitational-wave (GW) background reported by pulsar timing arrays (PTAs) can be naturally unified by a single primordial origin: a broad, nearly-flat enhancement of the curvature power spectrum with an amplitude of $O(10^{-2})$. The resulting PBH mass function spans the planet-to-solar mass range, while remaining consistent with all current observational constraints. This unified PBH--induced-GW framework makes concrete multi-messenger predictions, which can be decisively scrutinized by forthcoming microlensing surveys, next-generation PTAs, space-borne interferometers, precision astrometry, and laser ranging experiments.

astro-ph.CO↗

arXiv2026

DALL: Data Labeling via Data Programming and Active Learning Enhanced by Large Language Models

Deep learning models for natural language processing rely heavily on high-quality labeled datasets. However, existing labeling approaches often struggle to balance label quality with labeling cost. To address this challenge, we propose DALL, a text labeling framework that integrates data programming, active learning, and large language models. DALL introduces a structured specification that allows users and large language models to define labeling functions via configuration, rather than code. Active learning identifies informative instances for review, and the large language model analyzes these instances to help users correct labels and to refine or suggest labeling functions. We implement DALL as an interactive labeling system for text labeling tasks. Comparative, ablation, and usability studies demonstrate DALL's efficiency, the effectiveness of its modules, and its usability.

cs.HC↗

arXiv2026

Symmetry-engineered and electrically tunable in-plane anomalous Hall effect in oxide heterostructures

The family of Hall effects has long served as a premier probe of how symmetry, magnetic order, and topology intertwine in solids. Recently, the in-plane anomalous Hall effect (IP-AHE), a transverse Hall response driven by in-plane magnetization, has emerged as a distinct member of this family, offering innovative spintronic functionalities and illuminating intricate interplay between mirror-symmetry breaking and in-plane magnetic order. However, practical routes to deterministically and reversibly control IP-AHE remain limited. Here, we establish a symmetry-engineered IP-AHE platform, CaRuO3/La2/3Ca1/3MnO3/CaRuO3 heterostructure on NdGaO3(110), that turns strict mirror-symmetry breaking constraints into effective tuning knobs. IP-AHE in these epitaxial trilayers unambiguously couples to the CaRuO3-buffer-induced mirror-symmetry breaking and faithfully reproduces the ferromagnetic hysteresis. Ionic liquid gating further enables reversible reconfigurations of the symmetry breaking, thereby achieving electrical modulation and ON/OFF switching of IP-AHE. This highly tunable IP-AHE platform opens pathways for exploring nontrivial magnetic order and developing programmable Hall functionalities in planar geometries.

cond-mat.str-el↗

arXiv2026

Constraining the Primordial Black Hole Abundance with Space-Based Detectors

Overdense regions can collapse into primordial black holes (PBHs) in the early universe, which are a compelling candidate for dark matter. Current constraints leave the asteroid-mass window the only possible one for PBH to account for all the dark matter, which can only be probed indirectly by the scalar-induced gravitational waves (GWs) sourced by the curvature perturbation which forms PBH. In this work, we explore the capabilities of future space-based gravitational wave detectors, including LISA, Taiji, and TianQin, to constrain such induced GWs as well as the PBH abundance. We systematically account for the width of the primordial curvature power spectrum, and find that the asteroid-mass window can be fully probed by all three space-based interferometers. If PBHs constitute the majority of dark matter, the induced GW leaves a strong signal in the mHz band with a signal-to-noise ratio of $10^3$--$10^4$.

astro-ph.CO↗

arXiv2026

FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Experts

Spatial-Temporal Graph (STG) forecasting on large-scale networks has garnered significant attention. However, existing models predominantly focus on short-horizon predictions and suffer from notorious computational costs and memory consumption when scaling to long-horizon predictions and large graphs. Targeting the above challenges, we present FaST, an effective and efficient framework based on heterogeneity-aware Mixture-of-Experts (MoEs) for long-horizon and large-scale STG forecasting, which unlocks one-week-ahead (672 steps at a 15-minute granularity) prediction with thousands of nodes. FaST is underpinned by two key innovations. First, an adaptive graph agent attention mechanism is proposed to alleviate the computational burden inherent in conventional graph convolution and self-attention modules when applied to large-scale graphs. Second, we propose a new parallel MoE module that replaces traditional feed-forward networks with Gated Linear Units (GLUs), enabling an efficient and scalable parallel structure. Extensive experiments on real-world datasets demonstrate that FaST not only delivers superior long-horizon predictive accuracy but also achieves remarkable computational efficiency compared to state-of-the-art baselines. Our source code is available at: https://github.com/yijizhao/FaST.

cs.LG↗

arXiv2025

Observable Gravitational Wave Strain at Second Order

There is currently no rigorous definition of gravitational wave strain at second order in cosmological perturbation theory. The usual association of gravitational waves with transverse and traceless fluctuations of the metric on spatial hypersurfaces becomes ambiguous at second order, as it inherently depends on the spacetime slicing. While this poses no practical issues in linearized gravity, it presents a fundamental problem for secondary gravitational waves, especially notorious for gravitational waves induced by primordial fluctuations. We compute, for the first time, the observable gravitational wave strain at second order, as measured by geodesic observers that emit and receive electromagnetic signals, thereby settling the debate on gauge ambiguities. Working in a gauge invariant fashion, we find that the measured gravitational wave strain coincides with the transverse-traceless components in the Newton gauge.

gr-qc↗

arXiv2025

Opposite impact of thermal expansion and phonon anharmonicity on the phonon-limited resistivity of elemental metals from first principles

Understanding electrical resistivity in metals remains a central challenge in quantifying charge transport at finite temperature. Current first-principles calculations based on the Boltzmann transport equation often match experiments, yet they almost always neglect the effect of thermal expansion and phonon anharmonicity. We show that both effects exert an opposite impact on electron-phonon coupling and on electrical resistivity. Thermal expansion enhances the coupling and leads to overestimation of resistivity, whereas anharmonic effects reduce it. By explicitly incorporating both effects, we establish a more complete description of resistivity in elemental metals, demonstrated here for Pb, Nb, and Al.

cond-mat.mtrl-sci↗

arXiv2025

DevPiolt: Operation Recommendation for IoT Devices at Xiaomi Home

Operation recommendation for IoT devices refers to generating personalized device operations for users based on their context, such as historical operations, environment information, and device status. This task is crucial for enhancing user satisfaction and corporate profits. Existing recommendation models struggle with complex operation logic, diverse user preferences, and sensitive to suboptimal suggestions, limiting their applicability to IoT device operations. To address these issues, we propose DevPiolt, a LLM-based recommendation model for IoT device operations. Specifically, we first equip the LLM with fundamental domain knowledge of IoT operations via continual pre-training and multi-task fine-tuning. Then, we employ direct preference optimization to align the fine-tuned LLM with specific user preferences. Finally, we design a confidence-based exposure control mechanism to avoid negative user experiences from low-quality recommendations. Extensive experiments show that DevPiolt significantly outperforms baselines on all datasets, with an average improvement of 69.5% across all metrics. DevPiolt has been practically deployed in Xiaomi Home app for one quarter, providing daily operation recommendations to 255,000 users. Online experiment results indicate a 21.6% increase in unique visitor device coverage and a 29.1% increase in page view acceptance rates.

cs.AI↗

arXiv2025

Wavelet-Optimized Motion Artifact Correction in 3D MRI Using Pre-trained 2D Score Priors

Motion artifacts in magnetic resonance imaging (MRI) remain a major challenge, as they degrade image quality and compromise diagnostic reliability. Score-based generative models (SGMs) have recently shown promise for artifact removal. However, existing 3D SGM-based approaches are limited in two key aspects: (1) their strong dependence on known forward operators makes them ineffective for correcting MRI motion artifacts, and (2) their slow inference speed hinders clinical translation. To overcome these challenges, we propose a wavelet-optimized end-to-end framework for 3D MRI motion correct using pre-trained 2D score priors (3D-WMoCo). Specifically, two orthogonal 2D score priors are leveraged to guide the 3D distribution prior, while a mean-reverting stochastic differential equation (SDE) is employed to model the restoration process of motion-corrupted 3D volumes to motion-free 3D distribution. Furthermore, wavelet diffusion is introduced to accelerate inference, and wavelet convolution is applied to enhance feature extraction. We validate the effectiveness of our approach through both simulated motion artifact experiments and real-world clinical motion artifact correction tests. The proposed method achieves robust performance improvements over existing techniques. Implementation details and source code are available at: https://github.com/ZG-yuan/3D-WMoCo.

cs.CE↗

arXiv2025

Revisiting Very High Energy Gamma-Ray Absorption in Cosmic Propagation under the Combined Effects of Axion-Like Particles and Lorentz Invariance Violation

Very-high-energy (VHE; $E \gtrsim 100$ GeV) gamma rays are expected to experience strong attenuation during cosmological propagation due to electron-positron pair production on the extragalactic background light (EBL). Recent observations of GRB 221009A (z = 0.151), including photons up to $\sim 18$ detected by LHAASO and a $\sim 300\ \mathrm{TeV}$ event reported by Carpet-3, suggest a higher-than-expected transparency of the Universe at extreme energies. These observations cannot be explained by standard EBL absorption alone; moreover, neither Lorentz invariance violation (LIV) nor photon-axion-like particle (ALP) oscillations, when considered in isolation, appear sufficient to account for the survival of such photons over cosmological distances. In this work, we propose a joint propagation scenario that incorporates photon-ALP mixing in astrophysical magnetic fields together with subluminal quadratic LIV corrections to the $\gamma\gamma$ pair-production threshold. Applying this framework to the broadband gamma-ray spectrum of GRB 221009A, we show that ALPs with coupling ($g_{a\gamma} = 1.685 \times 10^{-10}\mathrm{GeV}^{-1}$ ) and mass ($m_a = 9.545 \times 10^{-8}\mathrm{eV}$), combined with a quadratic LIV energy scale ($E_{\rm LIV,2} = 1.30 \times 10^{-7} E_{\rm Pl}$) adopted from the literature, can significantly enhance the photon survival probability in the energy range (10\text{-}300) TeV. The resulting enhancement exceeds that obtained from either ALP mixing or LIV effects alone. These results indicate that a combined ALP-LIV scenario may provide a viable interpretation of the extreme-energy gamma-ray observations of GRB 221009A and highlight the potential of VHE gamma-ray measurements as probes of physics beyond the Standard Model.

astro-ph.HE↗

arXiv2025

AgentMental: An Interactive Multi-Agent Framework for Explainable and Adaptive Mental Health Assessment

Mental health assessment is crucial for early intervention and effective treatment, yet traditional clinician-based approaches are limited by the shortage of qualified professionals. Recent advances in artificial intelligence have sparked growing interest in automated psychological assessment, yet most existing approaches are constrained by their reliance on static text analysis, limiting their ability to capture deeper and more informative insights that emerge through dynamic interaction and iterative questioning. Therefore, in this paper, we propose a multi-agent framework for mental health evaluation that simulates clinical doctor-patient dialogues, with specialized agents assigned to questioning, adequacy evaluation, scoring, and updating. We introduce an adaptive questioning mechanism in which an evaluation agent assesses the adequacy of user responses to determine the necessity of generating targeted follow-up queries to address ambiguity and missing information. Additionally, we employ a tree-structured memory in which the root node encodes the user's basic information, while child nodes (e.g., topic and statement) organize key information according to distinct symptom categories and interaction turns. This memory is dynamically updated throughout the interaction to reduce redundant questioning and further enhance the information extraction and contextual tracking capabilities. Experimental results on the DAIC-WOZ dataset illustrate the effectiveness of our proposed method, which achieves better performance than existing approaches.

cs.CL↗