arXiv subjects

Pengfei Zhu

Publications and source records attributed to Pengfei Zhu.

At least 37 records · Page 2Linked to original sources

arXiv2026

DynCur-Geo: Dynamic Curiosity Reward Shaping for Multimodal Active Geo-Localization

Active geo-localization enables low-altitude UAVs to search for specified targets from limited local aerial observations, supporting time-sensitive applications such as search and rescue and emergency inspection. However, multimodal target cues, restricted views, and sparse feedback make it difficult to balance exploration with target convergence. Existing curiosity-driven methods assign a fixed intrinsic-reward weight throughout search, which can continue rewarding novelty after the agent nears the target and induce detours. We propose DynCur-Geo, a dynamic curiosity framework that adjusts prediction-error intrinsic reward according to remaining target distance. A distance-aware gate encourages early exploration and shifts the policy toward goal-directed behavior near the target, while potential-based reward shaping supplies dense progress guidance. Experiments across multimodal, cross-scene, disaster-affected, and long-range settings show consistent gains over active geo-localization baselines.

cs.CV↗

arXiv2026

SelfDRSC++: Self-Supervised Dual Reversed Rolling Shutter Correction via Video Interpolation

Modern consumer cameras often use rolling shutter, capturing scenes row-by-row and causing distortion in dynamic scenes. Existing correction methods rely on supervised learning with high-frame-rate global shutter images as ground truth. We propose SelfDRSC++, a self-supervised framework for RS distortion correction {from simultaneously captured top-to-bottom and bottom-to-top RS images}. A lightweight network with a bidirectional correlation matching block jointly optimizes optical flows and corrected RS features, improving performance with fewer parameters. A self-supervised strategy enforces {a physically constrained RS--GS--RS cycle} between input and reconstructed dual reversed RS images. RS reconstruction is formulated as a specialized video frame interpolation task, enabling feasible one-stage training. Extensive experiments on synthetic and real-world data show that SelfDRSC++ achieves competitive quantitative performance, improves perceptual quality, and produces high-frame-rate GS sequences with better temporal consistency.

cs.CV↗

arXiv2026

Electronic excitation of ultrafast collective amorphous-amorphous transitions in glassy phase-change material

The intrinsic nature of glass states and glass transitions remain a fundamental open question in condensed-matter physics and materials science. The key to solving the glass transition problem lies in achieving a complete understanding of the physics governing the structural relaxation. Nonetheless, directly probing dynamic atomic-scale structural changes in order to identify the precise local structural motifs and establish quantitative structure-property relationships remains an outstanding challenge. By combining femtosecond electron diffraction with time-dependent density-functional theory molecular dynamics simulations, we directly capture ultrafast amorphous-amorphous transitions indicated by collective bond stretching (0.2 ps) and angle bending (0.5-2 ps) in glassy phase-change material GeTe. The ultrafast bond stretching is accompanied by localized oscillation modes with the frequency of 3.10 THz, unambiguously signaling the local Peierls-like bonding structure and the flexibility of these polarized bonds. These ultrafast collective atomic motions, captured across timescales ranging from femtoseconds to picoseconds, directly reveals the structural origin of the boson peak and provide compelling evidence for many-body interactions in amorphous materials. Furthermore, the ultrafast amorphous-amorphous transitions induce a drastic insulator-metal transition, directly revealing both the underlying switching mechanism and the fundamental speed limit of the ovonic threshold switch. These insights establish a fundamental framework for rationally engineering relaxation pathways and phase-change/threshold switch in amorphous materials. Femtosecond electron diffraction provides a powerful novel approach to deciphering the structural complexity and functional mechanisms of amorphous materials by resolving collective atomic motions from random diffusion dynamics in the time domain.

cond-mat.mtrl-sci↗

arXiv2026

Sub-angstrom many-body localization driven by phononic flat bands in real quantum materials

Defects, fluctuations, degenerate states and correlated interactions facilitate the emergence of exotic properties in condensed matter systems while also inducing atomic-scale local correlated structures that deviate from the average long-range order. Establishing the structure-property relationship from the perspective of these atomic-scale local correlated structures remains ambiguous and controversial due to the lack of direct methods for identifying such local correlated structures. In this work, based on the photoexcited ultrafast structural response, we propose a Bragg scattering phase breaking regime to identify sub-angstrom local correlated structures in quantum materials. With this regime, we unambiguously identify the many-body-interaction driven local correlated structures in the low temperature ground state of AgCrSe2, characterized by static off-center displacements of Ag atoms ranging from 0 to 0.5 angstrom. The competition between Ag-Ag Coulomb correlations and potential wells induced by CrSe2 layers, leading to phononic flat bands and driving the system into a many body localization (MBL) regime. As temperature rising, these static local correlated structures transform to a dynamic state where the thermal fluctuations overwhelm the multiple localized states. These distinctive local correlated structures constitute the first experimental observation of MBL with vortex-like topological characteristic in a real material system. Emergent vibrational modes arising from MBL have been confirmed and show excellent agreement with inelastic neutron scattering experiments. Our work not only offers a universal approach to characterize sub-angstrom local correlated structures across a wide range of quantum materials but also deepens our understanding of the fundamental mechanism behind exotic properties from the perspective of atomic-scale local correlated structures.

cond-mat.mtrl-sci↗

arXiv2026

CAP: A Scalable Benchmark for Evaluating Cross-Site Browser Agents with Complex Actions and Perception

Large language models are increasingly deployed as autonomous agents that interact with the web through browsers. While recent progress has been driven by benchmarks that evaluate end-to-end task success, these evaluations largely overlook two fundamental sources of difficulty in real web browsing: complex actions over rich user interfaces and visual perception of dynamically rendered content, especially in workflows that span multiple websites. We introduce CAP, a scalable benchmark for evaluating browser agents on cross-site, human-like web tasks that require non-trivial UI interactions and visual understanding. Specifically, we adopt a decomposition-and-recomposition pipeline that first abstracts each website into a structured site card capturing user-facing functions, complex execution operations, and perceptual requirements, and then recomposes these components into realistic cross-site workflows. Each task is therefore grounded in multiple specific operations on each website, enabling fine-grained diagnosis. Built on this framework, we construct 420 tasks across 108 real-world websites and 24 domains under careful quality control. Experiments on state-of-the-art browser agents using our verifiable agent-as-a-judge evaluation framework show low success rates and reveal that perception-heavy interactions remain a major bottleneck, exposing substantial gaps between current agents and real-world web browsing demands.

cs.AI↗

arXiv2026

Towards a new paradigm of scientific discovery with socialized artificial intelligence

Scientific discovery has advanced through successive transformations in the organization of knowledge. Observation and experimentation established the empirical foundations of science. Theory made it possible to derive general principles from particular phenomena. Computation extended inquiry into systems beyond direct observation, while data-intensive methods opened new spaces of pattern and prediction. Science now confronts a different frontier. The central challenge is no longer simply to produce more information, but to organize expanding knowledge, reasoning, and evidence into a coherent process of discovery. Here, we introduce Bridging Literature, Agents, and Zero-gap Experimentation (BLAZE), a paradigm of socialized scientific intelligence. BLAZE conceives AI not as an assistant for isolated research tasks, but as an organizational infrastructure for scientific discovery. It connects persistent knowledge, collective reasoning, empirical validation, and human judgment within a continuous research lifecycle, transforming fragmented activities into a cumulative process of inquiry, criticism, and revision. The central premise of BLAZE is that scientific intelligence does not arise from computation alone. It emerges from the sustained interaction among knowledge, hypotheses, experiments, and collective verification. By organizing humans and machines within a shared scientific process, BLAZE makes discovery more traceable, reproducible, and cumulative while preserving human creativity, judgment, and responsibility. Socialized scientific intelligence may provide a foundation for the next era of science. Its purpose is not to replace human discovery, but to extend the scale, depth, and continuity of collective scientific inquiry.

cs.AI↗

arXiv2026

Quantitative Infrared Thermographic Assessment of Hand Cooling Dynamics During Controlled Contact with Metal Plates

This study investigates the spatiotemporal thermal response of human hands during controlled contact cooling using short wave infrared (SWIR), mid wave infrared (MWIR), and long wave infrared (LWIR) thermography. Three participants simultaneously placed one hand on a cooling metal plate and the contralateral hand on a reference plate maintained near room temperature. Temperature evolution was analyzed in five anatomical regions, including the distal finger, proximal finger, vessel associated region, non vessel region, and forearm. Quantitative metrics, including temperature variation, bilateral temperature difference, initial cooling rate, and frequency-domain amplitude, were extracted from the thermal image sequences. The results showed that the finger regions exhibited the largest temperature reductions and highest cooling rates, indicating greater sensitivity to thermal stimulation than the dorsal hand and forearm. MWIR and LWIR measurements revealed highly consistent cooling dynamics, while LWIR imaging provided enhanced thermal contrast and sensitivity. Frequency-domain analysis demonstrated that the dominant thermal response was concentrated in the low frequency range below 0.05 Hz. Furthermore, pixel-wise cooling rate maps highlighted substantial spatial heterogeneity across the hand surface. Numerical bioheat simulations confirmed that blood perfusion and skin plate contact conductance are key factors governing the cooling response. These findings demonstrate the potential of dynamic infrared thermography as a non-contact tool for assessing peripheral thermoregulation and vascular function during controlled cooling experiments.

physics.app-ph↗

arXiv2026

Structured Illumination Scanning Thermography (SISTER)

Conventional non-invasive photothermal imaging techniques are fundamentally constrained by the diffusive nature of heat transport, which causes severe energy dissipation during subsurface reconstruction. Although modulation-based approaches partially mitigate this limitation by encoding depth information into phase delay and amplitude attenuation, they remain inherently restricted by repeated temporal excitation, long acquisition times, and stitching artifacts in large-area inspection. In this work, we propose a structured illumination scanning thermography (SISTER) framework that replaces conventional temporal modulation with continuous spatial scanning under static structured illumination. The key theoretical insight is that heat diffusion is governed by a Markov semigroup, while sample motion transforms static spatial illumination into an equivalent temporal excitation through a Galilean coordinate transformation. This formulation enables dynamic-to-static reconstruction without repeated temporal modulation and provides a unified interpretation of spatial scanning and conventional signal modulation. A scanning system is integrated to implement the proposed framework together with a dynamic-to-static reconstruction algorithm for continuous subsurface defect inspection. Both numerical simulations and experimental results demonstrate that the proposed method significantly improves spatial continuity, signal-to-noise ratio, and detection capability while effectively eliminating stitching artifacts and reducing acquisition complexity. The proposed SISTER framework establishes a unified theoretical foundation for scanning photothermal imaging and provides a practical paradigm for high-efficiency, large-scale industrial non-destructive testing.

physics.app-ph↗

arXiv2026

Rethinking Air-Ground Collaboration: A Progressive Cross-Task Benchmark and Socialized Learning Framework

Air-ground collaborative perception is crucial for robust visual understanding in real-world dynamic environments. However, existing studies typically formulate collaboration as single-task cross-view fusion, overlooking the functional dependencies among localization, target association, and fine-grained parsing. In addition, the heterogeneous nature of aerial and ground views introduces substantial geometric, scale, and occlusion discrepancies, making uniform feature sharing vulnerable to negative transfer. To tackle these issues, we model air-ground perception as a progressive cross-task collaboration task and construct the Air-Ground Progressive Collaboration (AGPC) benchmark, a spatio-temporally aligned benchmark comprising more than 745K raw video frames. Built upon this benchmark, we propose Socialized Co-Perception (SCP), a coarse-to-fine framework that organizes collaboration progressively from aerial global localization to ground target association and identity-aware parsing. Its core module, the Dual-Layer Router (DLR), decouples input-side multi-scale expert selection from output-side task-conditioned modulation, enabling selective cross-view and cross-task interaction while suppressing harmful interference. Extensive experiments demonstrate the effectiveness of SCP. It achieves a 3.73\% coevolutionary gain and a 7.86\% improvement in average downstream performance. These results show that task-conditioned collaboration is more effective than uniform fusion for heterogeneous air-ground perception. The code is available at https://github.com/g1136639260-spec/AGSCP.

cs.CV↗

arXiv2026

Generalized Virtual-Wave Theory for Photothermal Coherence Tomography under Arbitrary Excitation Toward Non-Contact Industrial Inspection of Composite Materials

Photothermal imaging is a powerful noncontact and nondestructive technique for subsurface inspection of composite materials, yet its performance is fundamentally limited by the diffusive and irreversible nature of heat transport, leading to severe image blurring and ambiguous depth interpretation. The concept of virtual waves provides a route to overcome this limitation by linking diffusion fields to propagating wave fields, but existing approaches are largely restricted to idealized impulsive excitation. Here, we propose a generalized virtual-wave photothermal tomography framework that extends the diffusion-to-wave transformation to arbitrary boundary excitations, including pulsed, harmonic, and chirped waveforms. Starting from the heat equation with a general source term, we derive a Fredholm integral mapping between the measured diffusion field and a virtual wave field governed by a wave equation, explicitly enforcing causality and thermodynamic irreversibility. The resulting ill-posed inverse problem is solved using ADMM or truncated SVD, depending on the excitation characteristics. Numerical and experimental results demonstrate that the proposed method converts blurred thermal responses into wave-like fields with clear wavefronts and reflections, enabling improved depth localization and tomographic reconstruction. Experiments on carbon fiber reinforced polymer samples with embedded defects show enhanced contrast, sharper boundaries, and more reliable depth interpretation compared with conventional thermographic techniques. This work establishes a unified and physically grounded framework for wave-based photothermal tomography under realistic excitation conditions.

physics.app-ph↗

arXiv2026

A Unified Framework for Virtual Wave Transform: From Generalized Formulation to Excitation-Specific Projection

We present a unified theoretical framework for the mapping between diffusive and wave-like dynamics, formulated as a spectral integral operator acting on temporal fields. By introducing an analytic continuation in the complex frequency plane, we establish an explicit correspondence between thermal diffusion and a virtual wave field governed by a hyperbolic equation. This mapping is shown to define a causal, compact Fredholm operator that acts as a nonstationary low-pass filter, thereby revealing the intrinsic information loss of diffusive processes and the fundamental ill-posedness of the inverse reconstruction. Within this operator framework, we demonstrate that commonly used excitation schemes-including pulse, lock-in, chirped, and coded excitations-emerge as distinct projections onto subspaces of a single underlying transformation, corresponding to different sampling strategies of its spectral structure. This unifies previously disparate virtual wave formulations and provides a systematic interpretation of excitation design in terms of operator sampling and information encoding. The framework further generalizes to matrix-valued systems and suggests a spectral-geometric interpretation of temporal evolution across diffusive and propagative regimes.

math-ph↗

arXiv2026

Analytic Continuation Between Real- and Imaginary-Time Quantum Dynamics and the Fundamental Instability of Inverse Reconstruction

We develop a unified spectral-semigroup framework that connects real-time and imaginary-time quantum dynamics through analytic continuation. Within this formulation, evolution is expressed as an exponential reweighting of spectral components generated by a single operator $\mathcal{G}$, placing unitary and dissipative dynamics on equal footing within a common spectral structure. The mapping naturally induces a nonlocal fractional operator in time, giving rise to a contractive semigroup governed by a square-root spectral deformation and identifying imaginary-time evolution as an effective fractional low-pass filter. While exponential attenuation suppresses high-frequency components, the inverse transformation remains systematically controllable within a well-defined spectral window. In this regime, stable reconstruction of low-energy and coarse-grained dynamical features is achieved, establishing a predictive relation between imaginary-time evolution and recoverable information. This leads to a quantitative description of a bandwidth-resolved asymmetry between forward propagation and inverse recovery. Across systems with continuous and discrete spectra, few-level coherence, and non-Hermitian generators, we demonstrate that spectral structure governs reconstruction fidelity in a unified manner. In particular, non-Hermitian and open-system settings reveal that irreversibility emerges as a geometry- and scale-dependent feature of the spectrum, tied to both damping and eigenstate non-orthogonality. These results recast analytic continuation as a structured, scale-dependent filtering process with quantifiable and systematically accessible reconstruction limits, providing a unified perspective on the interplay between dynamics, spectral geometry, and information recovery.

physics.app-ph↗

arXiv2026

Non-Hermitian Exceptional Dynamics in First-Order Heat Transport

Heat transport exhibits distinct regimes ranging from ballistic propagation to diffusive relaxation, traditionally described by disparate theoretical frameworks. Here, we introduce a unified first-order operator formulation in which temperature and heat flux are treated as a coupled state vector, yielding a minimal dynamical closure of heat transport. The resulting generator is intrinsically non-Hermitian and gives rise to a spectral structure governed by an exceptional point that separates overdamped diffusion from underdamped wave-like propagation. In this framework, Fourier law emerges as a singular limit of a hyperbolic dissipative system, while the Cattaneo equation arises naturally as the minimal hydrodynamic closure of kinetic theory. We show that the exceptional point induces nonanalytic spectral transitions, nonmodal transient dynamics, and a breakdown of conventional modal decomposition. The theory further generalizes to anisotropic media, where direction-dependent exceptional surfaces enable intrinsic steering of heat flow. Our results establish a unified non-Hermitian dynamical framework for heat transport and reveal exceptional-point physics as a fundamental organizing principle underlying thermal dynamics across scales.

physics.optics↗

arXiv2026

The Spectral Domain Snell Law in Diffusion-Wave Fields

Snell law is traditionally regarded as a hallmark of phase-propagating phenomena such as optical, acoustic, elastic, electromagnetic, and quantum waves. In contrast, purely diffusive processes, such as Fourier heat conduction and chemical diffusion, are generally considered incapable of exhibiting refractive/reflective behavior. In this letter, we demonstrate that although diffusion waves including thermal diffusion, mass diffusion, Lindblad quantum diffusion, and electromagnetic diffusion do not follow Snell law in either time or frequency-domain, nevertheless they obey a spectral form of Snell law which reveals a hidden analog of wave refraction/reflection within the mathematical structure of diffusion dynamics. Remarkably, the spectral refraction ratio is governed not by the diffusion coefficient itself but by the constitutive relations of the media across the interface, establishing a new physical paradigm for diffusion-wave fields. Importantly, while each spectral eigenmode satisfies a rigorous Snell-type refraction relation, the inverse Fourier-Laplace transformation mixes these modes and suppresses any persistent real-space refraction angle, thereby reconciling the modal-level directionality with the long-standing absence of geometric refraction in diffusive systems.

physics.optics↗

arXiv2026

Generalized virtual wave reconstruction for vibrothermography: Overcoming the wavefront-free behavior and quantification challenges in the diffusion-wave field

Wavefront-free behavior and the resulting quantification difficulties are intrinsic limitations of vibrothermography due to the diffusive nature of thermal fields. This work proposes a generalized virtual wave reconstruction framework to address the absence of propagation features in thermal diffusion-wave fields and its impact on quantitative defect characterization. Unlike conventional virtual wave formulations restricted to Dirac-type excitations, the proposed approach establishes a rigorous spatiotemporal mapping between diffusion-wave fields and virtual wave fields under arbitrary heat-generation conditions without simplifying assumptions on thermo-mechanical coupling. The resulting ill-posed inversion problem is solved using truncated singular value decomposition (T-SVD) and the alternating direction method of multipliers (ADMM) to enhance numerical stability and suppress noise amplification. Numerical simulations demonstrate that the reconstructed virtual wave fields recover propagation characteristics absent in temperature distributions, leading to improved defect boundary definition, contrast enhancement, and depth-resolved analysis. Experiments on CFRP laminates validate the robustness of the approach and show significantly improved signal-to-noise ratio, spatial clarity, and defect size estimation compared with conventional thermographic processing methods.

physics.app-ph↗

arXiv2026

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion

Image fusion synthesizes complementary information from multiple sources, mitigating the inherent limitations of unimodal imaging systems. Accurate image registration is essential for effective multi-source data fusion. However, existing registration methods, often based on image translation in Euclidean space, fail to handle cross-modal misalignment effectively, resulting in suboptimal alignment and fusion quality. To overcome this limitation, we explore image alignment in non-Euclidean space and propose a Hyperbolic Cycle Alignment Network (Hy-CycleAlign). To the best of our knowledge, Hy-CycleAlign is the first image registration method based on hyperbolic space. It introduces a dual-path cross-modal cyclic registration framework, in which a forward registration network aligns cross-modal inputs, while a backward registration network reconstructs the original image, forming a closed-loop registration structure with geometric consistency. Additionally, we design a Hyperbolic Hierarchy Contrastive Alignment (H$^{2}$CA) module, which maps images into hyperbolic space and imposes registration constraints, effectively reducing interference caused by modality discrepancies. We further analyze image registration in both Euclidean and hyperbolic spaces, demonstrating that hyperbolic space enables more sensitive and effective multi-modal image registration. Extensive experiments on misaligned multi-modal images demonstrate that our method significantly outperforms existing approaches in both image alignment and fusion. Our code will be publicly available.

cs.CV↗

arXiv2026

Geometric Mixture-of-Experts with Curvature-Guided Adaptive Routing for Graph Representation Learning

Graph-structured data typically exhibits complex topological heterogeneity, making it difficult to model accurately within a single Riemannian manifold. While emerging mixed-curvature methods attempt to capture such diversity, they often rely on implicit, task-driven routing that lacks fundamental geometric grounding. To address this challenge, we propose a Geometric Mixture-of-Experts framework (GeoMoE) that adaptively fuses node representations across diverse Riemannian spaces to better accommodate multi-scale topological structures. At its core, GeoMoE leverages Ollivier-Ricci Curvature (ORC) as an intrinsic geometric prior to orchestrate the collaboration of specialized experts. Specifically, we design a graph-aware gating network that assigns node-specific fusion weights, regularized by a curvature-guided alignment loss to ensure interpretable and geometry-consistent routing. Additionally, we introduce a curvature-aware contrastive objective that promotes geometric discriminability by constructing positive and negative pairs according to curvature consistency. Extensive experiments on six benchmark datasets demonstrate that GeoMoE outperforms state-of-the-art baselines across diverse graph types.

cs.LG↗

arXiv2026

Principal Component Analysis-Based Terahertz Self-Supervised Denoising and Deblurring Deep Neural Networks

Terahertz (THz) systems inherently introduce frequency-dependent degradation effects, resulting in low-frequency blurring and high-frequency noise in amplitude images. Conventional image processing techniques cannot simultaneously address both issues, and manual intervention is often required due to the unknown boundary between denoising and deblurring. To tackle this challenge, we propose a principal component analysis (PCA)-based THz self-supervised denoising and deblurring network (THz-SSDD). The network employs a Recorrupted-to-Recorrupted self-supervised learning strategy to capture the intrinsic features of noise by exploiting invariance under repeated corruption. PCA decomposition and reconstruction are then applied to restore images across both low and high frequencies. The performance of the THz-SSDD network was evaluated on four types of samples. Training requires only a small set of unlabeled noisy images, and testing across samples with different material properties and measurement modes demonstrates effective denoising and deblurring. Quantitative analysis further validates the network feasibility, showing improvements in image quality while preserving the physical characteristics of the original signals.

cs.CV↗