arXiv subjects

Mingzhe Liu

Publications and source records attributed to Mingzhe Liu.

At least 19 recordsLinked to original sources

arXiv2026

Ranking Infrared-Visible Fusion the Way Humans Do: A Learned Pairwise Preference Measure

Human pairwise comparison provides a direct basis for perceptual infrared-visible image fusion assessment, but dense annotation becomes costly as method pools grow. We present the Learned Perceptual Image Fusion Measure (LPIFM), among the earliest learned fusion assessors trained directly on dense human A/B/Tie comparisons. LPIFM jointly examines both source images and both fused candidates, combining a shared hierarchical encoder, triadic interaction, and a tie-aware objective to predict comparative preference and perceptual indifference. We construct and publicly release all 6,300 unordered comparisons among 25 methods on 21 VIFB scenes, collected through blinded, randomized annotation and expert adjudication. Across four VIFB evaluation settings, LPIFM achieves 79.2-84.0% agreement with human pairwise judgments and Spearman correlations of 0.941-0.977 with human-derived method rankings. On full method pools, accuracy exceeds the strongest of 19 conventional metrics by 16.3-21.1 pp. Consistency diagnostics show 99.98-100% candidate-swap agreement and no observed decisive preference cycles. External experiments on EVAFusion further demonstrate rapid adaptation to a different fusion-evaluation preference protocol. After only three epochs of fine-tuning, LPIFM surpasses all 19 conventional metrics in accuracy, macro-F1, and ranking correlation. LPIFM provides a scalable instrument for human-aligned fusion assessment, with the preference corpus, model weights, and code publicly available.

cs.CV↗

arXiv2026

Statistical Energy Budget of the Solar Wind with Parker Solar Probe and Solar Orbiter : 1 - Unbalanced Total Energy Radial Evolution

The magneto-hydrodynamic (MHD) description of the solar wind has long been regarded as one of the most successful frameworks for investigating solar wind heating and acceleration. Under a set of simplifying assumptions, it provides a reduced large-scale description that is widely used to study the energetic role of electromagnetic fluctuations in the solar wind energy budget. In practice, applying simplification to MHD can result in the loss of some physics compared to an exact set of equations. However, an observational energy budget can help us to verify whether any energetic information has been lost in the process of simplification. To do so, we directly test the validity of the large-scale non-linear ideal MHD energy budget with solar wind observations from Parker Solar Probe (PSP) and Solar Orbiter (SO). After verifying the reliability of the measurements through comparison with previous studies of similar solar wind properties, we test the conservation of the total energy predicted by the MHD theory. We find that the average total energy increases with radial distance by $56\%$ ($\pm9\%$) between 14 and 203 solar radii. The increase in kinetic energy is not sufficiently compensated by the decrease of the thermal and electromagnetic contributions. We investigate the major sources of observational uncertainty and find that they cannot account for the observed energy increase. These results suggest that the large-scale theoretical description may neglect an energetically significant contribution to solar wind acceleration. Assuming that such a contribution exists, we predict that this extra energy would scale with radial distance as $r^{-0.54\pm0.38}$.

astro-ph.SR↗

arXiv2026

HazeSpikeMamba: Coupling Spiking-Inspired and State-Space Features for Self-Supervised Real-World Dehazing

Dehazing networks are commonly trained on synthetic hazy-clear pairs, but their performance often drops on real photographs. Synthetic haze generated using the atmospheric scattering model does not fully capture the variability of real haze, and paired real hazy-clear images are scarce. In this work, we propose HazeSpikeMamba, a compact dehazing framework that combines a spiking-inspired local path and an attentive state-space global path in a multi-scale U-Net. The local path uses TPCNNSpike, a new spike-emission scheme inspired by the neighborhood coupling of Pulse-Coupled Neural Network (PCNN). Unlike grouped directional scanning, TPCNNSpike updates all neurons in parallel using the previous firing states of their Gaussian-weighted neighborhoods. The global path adapts the Attentive State-Space Module of MambaIRv2, retaining semantic prompting and sequence reordering while removing the window self-attention branch. Its state-space processing models long-range dependencies with complexity linear in sequence length. For target-domain adaptation, a frozen degradation network, pretrained on paired NH-HAZE data, re-synthesizes haze from the dehazed prediction. The reconstruction error updates only the final restoration layers of HazeSpikeMamba without haze-free labels during adaptation. A shared checkpoint is adapted once on each complete unlabeled target set, making the evaluation dataset-level and transductive rather than zero-shot or per-image optimization. The forward network contains 2.02M active parameters and requires 13.27G nominal MACs (measured with thop at 256x256 input). This adaptation consistently improves BRISQUE and NIMA on RTTS, URHI, and HSTS. On RTTS, BRISQUE decreases from 30.13 to 27.72 and NIMA increases from 4.13 to 4.87. Under this transductive protocol, the adapted model also achieves the best BRISQUE and NIMA on URHI and HSTS among the compared methods.

cs.CV↗

arXiv2026

RegisterBridgeMM: A Register-Centric Framework for RGB-Infrared Object Detection

RGB-infrared (RGB-IR) object detection benefits from complementary visible and thermal cues, but effective fusion remains challenging under illumination changes, weather variation, and cluttered scenes. Existing RGB-IR fusion methods often trade expressive patch-level interaction for lighter but more constrained adaptation mechanisms. We empirically observe that pretrained register tokens contain both modality-shared and modality-specific information on paired RGB-IR inputs, suggesting that they can serve as a compact substrate for cross-modal communication. Building on this observation, we propose RegisterBridgeMM, a register-mediated fusion framework organized as a three-stage register lifecycle. Aggregate preserves per-modality register summarization inherited from pretraining; Bridge performs bidirectional register-to-patch reading with consensus-residual regulation; and Project translates the resulting register summary into spatially adaptive calibration of patch features. This register pathway avoids dense patch-to-patch cross-modal interaction while preserving the pretrained patch representation. With both backbone streams frozen, RegisterBridgeMM achieves the highest mAP50-95 among the evaluated methods on all four benchmarks: LLVIP, M3FD, DroneVehicle, and FLIR-Aligned.

cs.CV↗

arXiv2026

Spherically Polarized Alfvén Waves and the Gosling Boost

Alfvén waves are thought to play critical roles in solar wind acceleration and plasma heating in the solar corona and inner heliosphere. Parker Solar Probe (PSP) has highlighted the role of large amplitude Spherically Polarized Alfvén Waves (SPAWs), where the locally constant magnetic field magnitude $|\mathbf{B}|$ together with outward propagation explains the observed one sided radial velocity enhancement - the Gosling boost. Starting from the MHD equations, we derive the modified wave pressure and Poynting flux under the SPAW condition, and demonstrate both are governed solely by the transverse magnetic fluctuations. Using PSP data from Encounters 6--25, we define an unperturbed velocity baseline from the lower 10th-percentile running average and statistically characterize the radial evolution of Alfvénic fluctuations. The background solar wind velocity shows clear radial acceleration, while the velocity perturbation amplitude $δv$ decreases with heliocentric distance. This decay is anisotropic between the radial and perpendicular directions, which is a direct consequence of the growing magnetic deflection angle related to the spherical polarization. Our results demonstrate that radial velocity enhancements in the young solar wind arise naturally from SPAWs rather than from localized velocity jets, and provide direct observational evidence for the anisotropic radial evolution of SPAWs in the inner heliosphere.

astro-ph.SR↗

arXiv2026

Molecular chiral discrimination through symmetry-breaking spin dynamics

Molecular chirality plays a crucial role in physics, chemistry, life sciences and pharmacology. Nowadays, the chiral discrimination and control at the single-molecule level is urgently needed to reveal the origin of the chirality-relevant phenomena by recovering the information disturbed by the ensemble averaging. The method of magnetic resonance (MR), as one of powerful tools for structure analysis, is blind to the molecular chirality in the absence of a chiral reagent. Here we propose and experimentally demonstrate a direct MR-based method for determining the chirality at the single-molecule level through constructing the symmetry-breaking dynamics of nearby nuclear spins. In principle, the mirror asymmetry of two enantiomers in real space is manifested by breaking the joint symmetry of the mirror reflection and time reversal in spin space under spin dynamics. Experimentally, two enantiomers are indistinguishable from the dynamics of strongly-coupled but unpolarized nuclear spins, but diverge evidently in the dynamical results that break the field-inversion symmetry after spins are polarized. Our method and results will benefit the study of chirality-induced properties in the fields of chemistry and biology.

quant-ph↗

arXiv2026

Wave Activity at MHD-ion Scales Associated with Switchbacks

Magnetic switchbacks (SB) -- the localized magnetic structures with magnetic field direction inclined at an angle $θ$ relative to the background $B_0$ -- in the young solar wind have been associated with enhanced ion-scale wave activity and local plasma heating. It remains debated whether the apparent wave-power increase is intrinsic or mainly caused by sampling geometry. In this work, we analyze magnetic and electric field fluctuations measured by Parker Solar Probe, focusing on the 0.1--3~$f_{cp}$ frequency band that spans the transition from the MHD inertial range to ion-kinetic scales. By decomposing magnetic fluctuations into field-aligned and transverse components and comparing SB and non-SB intervals at the same local magnetic field angle, we test whether SBs sample an anisotropic cascade from different viewing angles or host intrinsically amplified wave activity. We find that the transverse magnetic power $δB_{\perp}$ is systematically enhanced inside switchbacks across a wide range of magnetic field rotation angles $θ$. The enhancement persists even at small and intermediate deflections, where geometric projection alone predicts weak power, indicating an intrinsic origin beyond sampling geometry. The inertial-range spectral indices also remain similar between SB and non-SB intervals despite the enhanced wave power inside SBs, suggesting that the underlying turbulence cascade is largely preserved. This excess $δB_{\perp}$ coincides with elevated proton temperatures and enhanced electric-field fluctuations, supporting the interpretation that SBs act as localized sites of cross-scale energy transfer and ion-scale dissipation in the near-Sun solar wind.

physics.space-ph↗

arXiv2026

SFN-YOLO: Towards Free-Range Poultry Detection via Scale-aware Fusion Networks

Detecting and localizing poultry is essential for advancing smart poultry farming. Despite the progress of detection-centric methods, challenges persist in free-range settings due to multiscale targets, obstructions, and complex or dynamic backgrounds. To tackle these challenges, we introduce an innovative poultry detection approach named SFN-YOLO that utilizes scale-aware fusion. This approach combines detailed local features with broader global context to improve detection in intricate environments. Furthermore, we have developed a new expansive dataset (M-SCOPE) tailored for varied free-range conditions. Comprehensive experiments demonstrate our model achieves an mAP of 80.7% with just 7.2M parameters, which is 35.1% fewer than the benchmark, while retaining strong generalization capability across different domains. The efficient and real-time detection capabilities of SFN-YOLO support automated smart poultry farming.

cs.CV↗

arXiv2026

DCVD: Dual-Channel Cross-Modal Fusion for Joint Vulnerability Detection and Localization

Software vulnerability detection plays a critical role in ensuring system security, where real-world auditing requires not only determining whether a function is vulnerable but also pinpointing the specific lines responsible. However, existing approaches either rely on a single information source -- sequential, structural, or semantic -- failing to jointly exploit the complementary strengths across modalities, or treat statement-level localization merely as a byproduct of function-level detection without explicit line-level supervision. To address these limitations, we propose DCVD (Dual-Channel Cross-Modal Vulnerability Detection), a unified framework that performs joint function-level detection and statement-level localization. DCVD extracts control-dependency and semantic features through two parallel branches and integrates them via contrastive alignment coupled with bidirectional cross-attention, effectively bridging the cross-modal representation gap. It further introduces explicit supervision signals at both the function and statement levels, enabling collaborative optimization across the two granularities. Extensive experiments on a large-scale real-world vulnerability benchmark demonstrate that DCVD consistently outperforms state-of-the-art methods on both function-level detection and statement-level localization. Our code is available at https://github.com/vinsontang1/DCVD.

cs.CR↗

arXiv2026

On the Radial Evolution of the Solar Wind : The Source Alignment Method Applied to Parker Solar Probe and Solar Orbiter Observations

The properties of the solar wind, as measured in situ throughout the heliosphere, depend both on the characteristics of its coronal source and on the intrinsic processes governing its interplanetary evolution. Recently, radial and Parker spiral alignment techniques have been applied to Parker Solar Probe (PSP) and Solar Orbiter (SO) observations to investigate the radial evolution of the same solar wind parcel. These studies have shown that the solar wind can undergo significant acceleration even beyond its primary acceleration region (i.e., above 15 solar radii). However, such radial and Parker spiral alignments are rare in practice, which limits the statistical significance and general applicability of the results. We introduce a new source alignment technique designed to overcome these limitations. Using magnetic backmapping, we associate similar solar wind streams observed by the two spacecraft based on the proximity of their photospheric footpoints, combined with additional in-situ stream similarity criteria. Applying the source alignment method to PSP and SO observations, we identify a total of 548 alignment intervals, each lasting 30 minutes. By constructing statistics over all alignments, we find that the solar wind speed increases by an average of 45% per radial decade (approximately 147 km/s) between the two probes. This result demonstrates that solar wind acceleration in the inner heliosphere remains significant compared to that occurring below 15 solar radii. Among the different studied plasma parameters, the radial evolution of the electron temperature and plasma density, show the strongest anti-correlation with the increase in bulk velocity.

astro-ph.SR↗

arXiv2026

Parker Solar Probe Observations of Compound Reconnection Exhaust Boundaries and Mirror-Mode Structures in the Near-Sun Heliospheric Current Sheet

Magnetic reconnection is a fundamental physical process that can drive rapid conversion of magnetic energy into plasma bulk flows, thermal heating, and particle acceleration in space and astrophysical plasmas. Classical reconnection theory predicts that the Alfvenic reconnection exhausts are bounded by pairs of slow-mode shocks. However, identifying and characterizing these shocks through in situ spacecraft observations remains a challenge. Here we report Parker Solar Probe (PSP) observations of a reconnection exhaust embedded in the heliospheric current sheet (HCS) at a heliocentric distance of 12.2 R_O. The reconnection exhaust is bounded on both boundaries by compound magnetic structures rather than a pair of pure slow shocks. Each boundary consists of a rapidly evolving, steep inner slow shock, whose Mach numbers and shock-normal angles change significantly within several minutes, and an outer, gradual compound structure which comprises a slow shock and a rotational discontinuity. These slow shocks are quasi-perpendicular and are accompanied by enhanced proton perpendicular heating. Deep within the reconnection exhaust, high perpendicular temperature together with large plasma beta trigger mirror instability and generate mirror-mode structures. These observations provide new insights into the structure of reconnection exhaust boundaries and their role in energy conversion in the near-Sun plasma.

astro-ph.SR↗

arXiv2026

Pulse Shape Discrimination Algorithms: Survey and Benchmark

This review presents a comprehensive survey and benchmark of pulse shape discrimination (PSD) algorithms for radiation detection, classifying nearly sixty methods into statistical (time-domain, frequency-domain, neural network-based) and prior-knowledge (machine learning, deep learning) paradigms. We implement and evaluate all algorithms on two standardized datasets: an unlabeled set from a 241Am-9Be source and a time-of-flight labeled set from a 238Pu-9Be source, using metrics including Figure of Merit (FOM), F1-score, ROC-AUC, and inter-method correlations. Our analysis reveals that deep learning models, particularly Multi-Layer Perceptrons (MLPs) and hybrid approaches combining statistical features with neural regression, often outperform traditional methods. We discuss architectural suitabilities, the limitations of FOM, alternative evaluation metrics, and performance across energy thresholds. Accompanying this work, we release an open-source toolbox in Python and MATLAB, along with the datasets, to promote reproducibility and advance PSD research.

cs.LG↗

arXiv2026

GasLiteAA: Optimizing ERC-4337 for Efficient and Secure Gas Sponsorship

ERC-4337, the Ethereum account abstraction standard, simplifies account management and transaction fee payment in decentralized applications by introducing programmable smart contract wallets and gas sponsorship via paymasters. However, its heavy reliance on on-chain validation and frequent state updates incurs substantial gas overhead, leading to performance bottlenecks and limiting scalability in large-scale deployments. To mitigate these issues, we propose GasLiteAA, a framework that optimize ERC-4337 by offloading paymaster logic to Trusted Execution Environments (TEE). GasLiteAA delegates the secure execution of stateful gas sponsorship logic and user quota management to TEE, enforcing validation rules off-chain while anchoring their integrity on-chain via lightweight cryptographic attestations. This verifiable offloading architecture significantly reduces on-chain computation and storage costs without sacrificing verifiability or decentralization. Experimental results demonstrate that GasLiteAA substantially lowers transaction fees, while remaining fully compatible with Ethereum Layer 1. By balancing security, efficiency, and deployability, GasLiteAA provides a practical and scalable approach to gas sponsorship for account-abstraction-based decentralized applications.

cs.CE↗

arXiv2026

Oxygen-vacancy quantum spin defects in silicon carbide

Optically addressable spin defects in wide-bandgap semiconductors are promising building blocks for quantum sensing and quantum networks. Establishing their atomic structure is essential for understanding functionality and enabling controlled engineering. In 4H-SiC, the PL5 and PL6 centers have long been recognized for their exceptional charge stability and room-temperature optically detected magnetic resonance (ODMR) performance, but their structural origin has remained elusive for over a decade. Here, we provide direct evidence for their oxygen-vacancy (${\rm O_C V_{Si}}$) origins through a combined chemical and isotopic control strategy. Under oxygen ion implantation, we observe over tenfold enhancement in the yield of PL5 and PL6 compared to nitrogen ion implantation. Furthermore, implantation with $^{17}{\rm O}$ ions produces PL5 and PL6 defects that exhibit a characteristic six-fold $^{17}{\rm O}$ hyperfine splitting in their ODMR spectra. These results affirm PL6 as the ${\rm O_C V_{Si}}$ defect in the $hh$ configuration. For PL5, the oxygen-related evidence, together with \textit{ab initio} calculations and additional measurements of the zero-field splitting and hyperfine structure, establishes it as the ${\rm O_C V_{Si}}$ defect in the $kh$ configuration. This unambiguous structural identification, achieved through materials-level chemical control, provides the microscopic foundation for deterministic engineering of these defects, paving the way for scalable photonic devices and high-sensitivity ensemble quantum sensors based on oxygen-vacancy centers.

cond-mat.mtrl-sci↗

arXiv2025

U-Net-Like Spiking Neural Networks for Single Image Dehazing

Image dehazing is a critical challenge in computer vision, essential for enhancing image clarity in hazy conditions. Traditional methods often rely on atmospheric scattering models, while recent deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Transformers, have improved performance by effectively analyzing image features. However, CNNs struggle with long-range dependencies, and Transformers demand significant computational resources. To address these limitations, we propose DehazeSNN, an innovative architecture that integrates a U-Net-like design with Spiking Neural Networks (SNNs). DehazeSNN captures multi-scale image features while efficiently managing local and long-range dependencies. The introduction of the Orthogonal Leaky-Integrate-and-Fire Block (OLIFBlock) enhances cross-channel communication, resulting in superior dehazing performance with reduced computational burden. Our extensive experiments show that DehazeSNN is highly competitive to state-of-the-art methods on benchmark datasets, delivering high-quality haze-free images with a smaller model size and less multiply-accumulate operations. The proposed dehazing method is publicly available at https://github.com/HaoranLiu507/DehazeSNN.

cs.CV↗

arXiv2025

Behavior Tokens Speak Louder: Disentangled Explainable Recommendation with Behavior Vocabulary

Recent advances in explainable recommendations have explored the integration of language models to analyze natural language rationales for user-item interactions. Despite their potential, existing methods often rely on ID-based representations that obscure semantic meaning and impose structural constraints on language models, thereby limiting their applicability in open-ended scenarios. These challenges are intensified by the complex nature of real-world interactions, where diverse user intents are entangled and collaborative signals rarely align with linguistic semantics. To overcome these limitations, we propose BEAT, a unified and transferable framework that tokenizes user and item behaviors into discrete, interpretable sequences. We construct a behavior vocabulary via a vector-quantized autoencoding process that disentangles macro-level interests and micro-level intentions from graph-based representations. We then introduce multi-level semantic supervision to bridge the gap between behavioral signals and language space. A semantic alignment regularization mechanism is designed to embed behavior tokens directly into the input space of frozen language models. Experiments on three public datasets show that BEAT improves zero-shot recommendation performance while generating coherent and informative explanations. Further analysis demonstrates that our behavior tokens capture fine-grained semantics and offer a plug-and-play interface for integrating complex behavior patterns into large language models.

cs.LG↗

arXiv2025

In situ Evidence of 5-minute Oscillations from Parker Solar Probe

The Sun's surface vibrates in characteristic 5-minute oscillations, known as p-modes, generated by sound waves trapped within the convection zone. Although these oscillations have long been hypothesized to reach into the solar wind, direct in situ evidence has remained elusive, even during previous close encounters by Parker Solar Probe (PSP). Here, we present the first promising in situ detection of 5-minute oscillations in the upper solar corona, based on observations from PSP's three closest perihelia. In two events at 9.9 solar radii, we identify statistically significant ($\sim$ 6 $σ$) 3.1-3.2 mHz peaks in the magnetic field power spectrum, each appearing as a large-amplitude, spherically polarized Alfvénic wave train lasting approximately 35 minutes. These results demonstrate that global solar oscillations can reach and potentially influence the solar wind.

astro-ph.SR↗

arXiv2025

MELDAE: A Framework for Micro-Expression Spotting, Detection, and Automatic Evaluation in In-the-Wild Conversational Scenes

Accurately analyzing spontaneous, unconscious micro-expressions is crucial for revealing true human emotions, but this task remains challenging in wild scenarios, such as natural conversation. Existing research largely relies on datasets from controlled laboratory environments, and their performance degrades dramatically in the real world. To address this issue, we propose three contributions: the first micro-expression dataset focused on conversational-in-the-wild scenarios; an end-to-end localization and detection framework, MELDAE; and a novel boundary-aware loss function that improves temporal accuracy by penalizing onset and offset errors. Extensive experiments demonstrate that our framework achieves state-of-the-art results on the WDMD dataset, improving the key F1_{DR} localization metric by 17.72% over the strongest baseline, while also demonstrating excellent generalization capabilities on existing benchmarks.

cs.CV↗