arXiv subjects

Tao Ye

Publications and source records attributed to Tao Ye.

At least 19 recordsLinked to original sources

arXiv2026

An on-chip programmable mechano-quantum transducer

Solid-state spin defects encode local perturbations as measurable shifts in spin-transition frequencies, but mechanical actuation and quantum readout remain physically separated, resulting in a discrete measurement setup. Integrating these functions requires an on-site mechano-quantum interface that programs the lattice state of a defect host and quantitatively maps it onto the spin Hamiltonian. Here we first report an on-chip programmable mechano-quantum transducer (OCPMQT) that integrates voltage-defined micromechanical actuation with in situ spin-frequency readout in a two-dimensional van der Waals quantum-defect host. Mechanically programmed lattice states are encoded as shifts in the axial zero-field splitting parameter and resolved by optically detected magnetic resonance (ODMR) spectroscopy. Within a chip volume of 2.05*10^-2 cm^3, the transducer accesses ODMR-inferred strains as low as 0.0080% and delivers a volumetric force density of approximately 2.6*10^4 N*m^-3. A micromechanical-to-spin-Hamiltonian framework links on-chip electromechanics, interfacial strain transfer, and strain-spin coupling, enabling the electrical control micromechanical input to be measured directly as spin-frequency response.

cond-mat.mes-hall↗

arXiv2026

Multi-modality Image Fusion under Adverse Weather: Mask-Guided Feature Restoration and Interaction

Multi-modality image fusion (MMIF) enhances scene representation by exploiting complementary cues from different modalities. Adverse weather, however, causes significant image degradation, disrupting feature representation and requiring simultaneous feature restoration and cross-modal complementarity. Existing methods often struggle with effective representation learning under such conditions, limiting their practical performance. To address these challenges, we propose a mask-guided MMIF method that integrates feature restoration and interaction. We first introduce "Pseudo Ground Truth" to simplify training, promoting faster and more effective feature learning. Then, we design a mask generation mechanism based on the mapping relationship between the fused result and the source images, quantifying the relative contribution of each modality during the fusion process. By incorporating the proposed mask-guided cross-modal cross-attention mechanism, the network is encouraged to selectively attend to informative features during modality interaction, mitigating the risk of overfitting to the static distribution of the "Pseudo Ground Truth". Additionally, we propose a mask-guided learning strategy and a task-coupled degradation-aware learning strategy to balance feature restoration and interaction. Extensive experiments on synthetic and real-world datasets demonstrate that our method surpasses state-of-the-art approaches in visual quality, quantitative metrics, and downstream tasks. The source code is available at https://github.com/ixilai/AMG-Fuse.

cs.CV↗

arXiv2026

Generation of fission yield covariance matrices and its application in uncertainty analysis of decay heat

The uncertainties and covariance matrices of fission yield are important in the uncertainty analysis of decay heat. At present, there are no covariance matrixes of fission yield given in the evaluated nuclear data library, although they have provided the uncertainties with good estimates. In this work, the generalized least squares (GLS) updating approach was adopted to evaluate the fission yield covariances with the constraints from basic physical conservation equation and chain yield data, using the nuclear data files from ENDF/B-VIII.0, JENDL-5 and JEFF-3.3. Based on these original and updated data, summation calculation was performed for fission pulse decay heat of thermal neutron-induced fission of $^{235}$U. The uncertainties of decay heat were obtained through generalized perturbation theory, including the uncertainties propagated from fission yield, decay energy, decay constant and branching ratio. The original uncorrelated yield data contributes a $\sim 4 \%$ uncertainty at all times and dominates the decay heat uncertainty at cooling times longer than \SI{100}{s}. With the generated covariance matrixes, the uncertainty of calculated decay heat is strongly reduced and decay energy data makes a major contribution in general. The relative uncertainties at cooling time \SI{0.1}{\second} are $\sim$10$\%$ for ENDF/V-VIII.0 and JEFF-3.3 and $\sim$5$\%$ for JENDL-5 and those at cooling time 10$^{5}$ s are about 1$\%$ for three libraries. The influence of the GLS updating procedure on the contributions of important fission products to decay heat and their sensitive coefficients was also discussed.

nucl-th↗

arXiv2026

MAVFusion: Efficient Infrared and Visible Video Fusion via Motion-Aware Sparse Interaction

Infrared and visible video fusion combines the object saliency from infrared images with the texture details from visible images to produce semantically rich fusion results. However, most existing methods are designed for static image fusion and cannot effectively handle frame-to-frame motion in videos. Current video fusion methods improve temporal consistency by introducing interactions across frames, but they often require high computational cost. To mitigate these challenges, we propose MAVFusion, an end-to-end video fusion framework featuring a motion-aware sparse interaction mechanism that enhances efficiency while maintaining superior fusion quality. Specifically, we leverage optical flow to identify dynamic regions in multi-modal sequences, adaptively allocating computationally intensive cross-modal attention to these sparse areas to capture salient transitions and facilitate inter-modal information exchange. For static background regions, a lightweight weak interaction module is employed to maintain structural and appearance integrity. By decoupling the processing of dynamic and static regions, MAVFusion simultaneously preserves temporal consistency and fine-grained details while significantly accelerating inference. Extensive experiments demonstrate that MAVFusion achieves state-of-the-art performance on multiple infrared and visible video benchmarks, achieving a speed of 14.16 FPS at $640 \times 480$ resolution. The source code will be available at https://github.com/ixilai/MAVFusion.

cs.CV↗

arXiv2026

CAWM-Mamba: A unified model for infrared-visible image fusion and compound adverse weather restoration

Multimodal Image Fusion (MMIF) integrates complementary information from various modalities to produce clearer and more informative fused images. MMIF under adverse weather is particularly crucial in autonomous driving and UAV monitoring applications. However, existing adverse weather fusion methods generally only tackle single types of degradation such as haze, rain, or snow, and fail when multiple degradations coexist (e.g., haze+rain, rain+snow). To address this challenge, we propose Compound Adverse Weather Mamba (CAWM-Mamba), the first end-to-end framework that jointly performs image fusion and compound weather restoration with unified shared weights. Our network contains three key components: (1) a Weather-Aware Preprocess Module (WAPM) to enhance degraded visible features and extracts global weather embeddings; (2) a Cross-modal Feature Interaction Module (CFIM) to facilitate the alignment of heterogeneous modalities and exchange of complementary features across modalities; and (3) a Wavelet Space State Block (WSSB) that leverages wavelet-domain decomposition to decouple multi-frequency degradations. WSSB includes Freq-SSM, a module that models anisotropic high-frequency degradation without redundancy, and a unified degradation representation mechanism to further improve generalization across complex compound weather conditions. Extensive experiments on the AWMM-100K benchmark and three standard fusion datasets demonstrate that CAWM-Mamba consistently outperforms state-of-the-art methods in both compound and single-weather scenarios. In addition, our fusion results excel in downstream tasks covering semantic segmentation and object detection, confirming the practical value in real-world adverse weather perception. The source code will be available at https://github.com/Feecuin/CAWM-Mamba.

cs.CV↗

arXiv2025

JDPNet: A Network Based on Joint Degradation Processing for Underwater Image Enhancement

Given the complexity of underwater environments and the variability of water as a medium, underwater images are inevitably subject to various types of degradation. The degradations present nonlinear coupling rather than simple superposition, which renders the effective processing of such coupled degradations particularly challenging. Most existing methods focus on designing specific branches, modules, or strategies for specific degradations, with little attention paid to the potential information embedded in their coupling. Consequently, they struggle to effectively capture and process the nonlinear interactions of multiple degradations from a bottom-up perspective. To address this issue, we propose JDPNet, a joint degradation processing network, that mines and unifies the potential information inherent in coupled degradations within a unified framework. Specifically, we introduce a joint feature-mining module, along with a probabilistic bootstrap distribution strategy, to facilitate effective mining and unified adjustment of coupled degradation features. Furthermore, to balance color, clarity, and contrast, we design a novel AquaBalanceLoss to guide the network in learning from multiple coupled degradation losses. Experiments on six publicly available underwater datasets, as well as two new datasets constructed in this study, show that JDPNet exhibits state-of-the-art performance while offering a better tradeoff between performance, parameter size, and computational cost.

cs.CV↗

arXiv2025

A Luminance-Aware Multi-Scale Network for Polarization Image Fusion with a Multi-Scene Dataset

Polarization image fusion combines S0 and DOLP images to reveal surface roughness and material properties through complementary texture features, which has important applications in camouflage recognition, tissue pathology analysis, surface defect detection and other fields. To intergrate coL-Splementary information from different polarized images in complex luminance environment, we propose a luminance-aware multi-scale network (MLSN). In the encoder stage, we propose a multi-scale spatial weight matrix through a brightness-branch , which dynamically weighted inject the luminance into the feature maps, solving the problem of inherent contrast difference in polarized images. The global-local feature fusion mechanism is designed at the bottleneck layer to perform windowed self-attention computation, to balance the global context and local details through residual linking in the feature dimension restructuring stage. In the decoder stage, to further improve the adaptability to complex lighting, we propose a Brightness-Enhancement module, establishing the mapping relationship between luminance distribution and texture features, realizing the nonlinear luminance correction of the fusion result. We also present MSP, an 1000 pairs of polarized images that covers 17 types of indoor and outdoor complex lighting scenes. MSP provides four-direction polarization raw maps, solving the scarcity of high-quality datasets in polarization image fusion. Extensive experiment on MSP, PIF and GAND datasets verify that the proposed MLSN outperms the state-of-the-art methods in subjective and objective evaluations, and the MS-SSIM and SD metircs are higher than the average values of other methods by 8.57%, 60.64%, 10.26%, 63.53%, 22.21%, and 54.31%, respectively. The source code and dataset is avalable at https://github.com/1hzf/MLS-UNet.

cs.CV↗

arXiv2025

First-Principle Modeling Framework of Boost Converter Dynamics for Precise Energy Conversions in Space

Boost converters are essential for modern electrification and intelligent technologies. However, conventional Boost converter models relying on steady-state assumptions fail to accurately predict transient behaviors during input voltage and load fluctuations, which cause significant output voltage overshoots and instability, resulting in failures of electrical systems, thereby restricting their use in space. This study introduces a first-principle modeling framework that derives precise dynamic equations for Boost converters by incorporating non-ideal component coupling. As compared to the most accurate existing Boost converter model, the proposed models reduce steady-state and dynamic-state errors between experimental and simulated output voltages by factors of 11.0 (from 20.9% to 1.9%) and 15.4 (from 77.1% to 5.0%) under input voltage variations, and by factors of 10.2 (from 15.3% to 1.5%) and 35.1 (from 42.1% to 1.2%) under load changes, respectively. Consequently, a reliable Boost converter is accordingly designed and on-orbit deployed for precise energy conversions.

eess.SY↗

arXiv2025

AWM-Fuse: Multi-Modality Image Fusion for Adverse Weather via Global and Local Text Perception

Multi-modality image fusion (MMIF) in adverse weather aims to address the loss of visual information caused by weather-related degradations, providing clearer scene representations. Although less studies have attempted to incorporate textual information to improve semantic perception, they often lack effective categorization and thorough analysis of textual content. In response, we propose AWM-Fuse, a novel fusion method for adverse weather conditions, designed to handle multiple degradations through global and local text perception within a unified, shared weight architecture. In particular, a global feature perception module leverages BLIP-produced captions to extract overall scene features and identify primary degradation types, thus promoting generalization across various adverse weather conditions. Complementing this, the local module employs detailed scene descriptions produced by ChatGPT to concentrate on specific degradation effects through concrete textual cues, thereby capturing finer details. Furthermore, textual descriptions are used to constrain the generation of fusion images, effectively steering the network learning process toward better alignment with real semantic labels, thereby promoting the learning of more meaningful visual features. Extensive experiments demonstrate that AWM-Fuse outperforms current state-of-the-art methods in complex weather conditions and downstream tasks. Our code is available at https://github.com/Feecuin/AWM-Fuse.

cs.CV↗

arXiv2025

Deriving effective electrode-ion interactions from free-energy profiles at electrochemical interfaces

Understanding ion adsorption at electrified metal-electrolyte interfaces is essential for accurate modeling of electrochemical systems. Here, we systematically investigate the free energy profiles of Na$^+$, Cl$^-$, and F$^-$ ions at the Au(111)-water interface using enhanced sampling molecular dynamics with both classical force fields and machine-learned interatomic potentials (MLIPs). Our classical metadynamics results reveal a strong dependence of predicted ion adsorption on the Lennard-Jones parameters, highlighting that -- without due care -- standard mixing rules can lead to qualitatively incorrect descriptions of ion-metal interactions. We present a systematic methodology for tuning the cross-term LJ parameters to control adsorption energetics in agreement with more accurate models. As a surrogate for an ab initio model, we employed the recently released Universal Models for Atoms (UMA) MLIP, which validates classical trends and displays strong specific adsorption for chloride, weak adsorption for fluoride, and no specific adsorption for sodium, in agreement with experimental and theoretical expectations. By integrating molecular-level adsorption free energies into continuum models of the electric double layer, we show that specific ion adsorption substantially alters the interfacial ion population, the potential of zero charge, and the differential capacitance of the system. Our results underscore the critical importance of force field parameterization and advanced interatomic potentials for the predictive modeling of ion-specific effects at electrified interfaces and provide a robust framework for bridging molecular simulations and continuum electrochemical models.

physics.chem-ph↗

arXiv2025

Enhancing Learned Knowledge in LoRA Adapters Through Efficient Contrastive Decoding on Ascend NPUs

Huawei Cloud users leverage LoRA (Low-Rank Adaptation) as an efficient and scalable method to fine-tune and customize large language models (LLMs) for application-specific needs. However, tasks that require complex reasoning or deep contextual understanding are often hindered by biases or interference from the base model when using typical decoding methods like greedy or beam search. These biases can lead to generic or task-agnostic responses from the base model instead of leveraging the LoRA-specific adaptations. In this paper, we introduce Contrastive LoRA Decoding (CoLD), a novel decoding framework designed to maximize the use of task-specific knowledge in LoRA-adapted models, resulting in better downstream performance. CoLD uses contrastive decoding by scoring candidate tokens based on the divergence between the probability distributions of a LoRA-adapted expert model and the corresponding base model. This approach prioritizes tokens that better align with the LoRA's learned representations, enhancing performance for specialized tasks. While effective, a naive implementation of CoLD is computationally expensive because each decoding step requires evaluating multiple token candidates across both models. To address this, we developed an optimized kernel for Huawei's Ascend NPU. CoLD achieves up to a 5.54% increase in task accuracy while reducing end-to-end latency by 28% compared to greedy decoding. This work provides practical and efficient decoding strategies for fine-tuned LLMs in resource-constrained environments and has broad implications for applied data science in both cloud and on-premises settings.

cs.LG↗

arXiv2025

A Unified Model for Cardinality Estimation by Learning from Data and Queries via Sum-Product Networks

Cardinality estimation is a fundamental component in database systems, crucial for generating efficient execution plans. Despite advancements in learning-based cardinality estimation, existing methods may struggle to simultaneously optimize the key criteria: estimation accuracy, inference time, and storage overhead, limiting their practical applicability in real-world database environments. This paper introduces QSPN, a unified model that integrates both data distribution and query workload. QSPN achieves high estimation accuracy by modeling data distribution using the simple yet effective Sum-Product Network (SPN) structure. To ensure low inference time and reduce storage overhead, QSPN further partitions columns based on query access patterns. We formalize QSPN as a tree-based structure that extends SPNs by introducing two new node types: QProduct and QSplit. This paper studies the research challenges of developing efficient algorithms for the offline construction and online computation of QSPN. We conduct extensive experiments to evaluate QSPN in both single-table and multi-table cardinality estimation settings. The experimental results have demonstrated that QSPN achieves superior and robust performance on the three key criteria, compared with state-of-the-art approaches.

cs.DB↗

arXiv2024

MMA-UNet: A Multi-Modal Asymmetric UNet Architecture for Infrared and Visible Image Fusion

Multi-modal image fusion (MMIF) maps useful information from various modalities into the same representation space, thereby producing an informative fused image. However, the existing fusion algorithms tend to symmetrically fuse the multi-modal images, causing the loss of shallow information or bias towards a single modality in certain regions of the fusion results. In this study, we analyzed the spatial distribution differences of information in different modalities and proved that encoding features within the same network is not conducive to achieving simultaneous deep feature space alignment for multi-modal images. To overcome this issue, a Multi-Modal Asymmetric UNet (MMA-UNet) was proposed. We separately trained specialized feature encoders for different modal and implemented a cross-scale fusion strategy to maintain the features from different modalities within the same representation space, ensuring a balanced information fusion process. Furthermore, extensive fusion and downstream task experiments were conducted to demonstrate the efficiency of MMA-UNet in fusing infrared and visible image information, producing visually natural and semantically rich fusion results. Its performance surpasses that of the state-of-the-art comparison fusion methods.

cs.CV↗

arXiv2024

UMCFuse: A Unified Multiple Complex Scenes Infrared and Visible Image Fusion Framework

Infrared and visible image fusion has emerged as a prominent research area in computer vision. However, little attention has been paid to the fusion task in complex scenes, leading to sub-optimal results under interference. To fill this gap, we propose a unified framework for infrared and visible images fusion in complex scenes, termed UMCFuse. Specifically, we classify the pixels of visible images from the degree of scattering of light transmission, allowing us to separate fine details from overall intensity. Maintaining a balance between interference removal and detail preservation is essential for the generalization capacity of the proposed method. Therefore, we propose an adaptive denoising strategy for the fusion of detail layers. Meanwhile, we fuse the energy features from different modalities by analyzing them from multiple directions. Extensive fusion experiments on real and synthetic complex scenes datasets cover adverse weather conditions, noise, blur, overexposure, fire, as well as downstream tasks including semantic segmentation, object detection, salient object detection, and depth estimation, consistently indicate the superiority of the proposed method compared with the recent representative methods. Our code is available at https://github.com/ixilai/UMCFuse.

cs.CV↗

arXiv2024

Bridging the Gap between Multi-focus and Multi-modal: A Focused Integration Framework for Multi-modal Image Fusion

Multi-modal image fusion (MMIF) integrates valuable information from different modality images into a fused one. However, the fusion of multiple visible images with different focal regions and infrared images is a unprecedented challenge in real MMIF applications. This is because of the limited depth of the focus of visible optical lenses, which impedes the simultaneous capture of the focal information within the same scene. To address this issue, in this paper, we propose a MMIF framework for joint focused integration and modalities information extraction. Specifically, a semi-sparsity-based smoothing filter is introduced to decompose the images into structure and texture components. Subsequently, a novel multi-scale operator is proposed to fuse the texture components, capable of detecting significant information by considering the pixel focus attributes and relevant data from various modal images. Additionally, to achieve an effective capture of scene luminance and reasonable contrast maintenance, we consider the distribution of energy information in the structural components in terms of multi-directional frequency variance and information entropy. Extensive experiments on existing MMIF datasets, as well as the object detection and depth estimation tasks, consistently demonstrate that the proposed algorithm can surpass the state-of-the-art methods in visual perception and quantitative evaluation. The code is available at https://github.com/ixilai/MFIF-MMIF.

cs.CV↗

arXiv2023

Multiphoton fusion of light nuclei in intense laser fields

We investigate the fusion cross sections of light nuclei in the presence of linearly polarized intense laser fields. By combining the Coulomb-Volkov solutions with the complex spherical square-well nuclear potential, we derive an explicit formulation of the multiphoton cross section in a self-consistent manner. Our analysis is specifically focused on deuteron-triton (DT) and proton-boron (p$^{11}{\rm B}$) fusion reactions, both of which have garnered widespread attention. The theoretical results reveal that, under conditions of longer laser wavelengths and lower incident particle kinetic energies, a few thousands of photons can participate in the fusion reactions, resulting in a substantial enhancement of fusion cross sections by almost ten orders of magnitude. We elucidate the multiphoton mechanism underlying these findings and discuss their implications.

nucl-th↗

arXiv2022

Application of the Lagrange mesh method in continuum-discretized coupled-channels calculations

We apply the Lagrange-mesh method to discretize continuum states of weakly bound nuclei for continuum-discretized coupled-channel (CDCC) calculations of three-body breakup reactions. This discretization method is compared with the bin method, which is regarded as the standard continuum discretization method, for the $d$ and $^6$Li induced reactions. An improved Numerov algorithm is used to solve the coupled channels equations, which permits a fast integration of equations and a convenient treatment of the closed channels. A new CDCC model code named \emph{CDCC-R} is developed. In all cases, the combination of the Lagrange-mesh method and the Numerov algorithm shows high efficiency and accuracy for the CDCC calculations of the elastic scattering and breakup reactions. Especially, various numerical and physical aspects are discussed for $^6$Li induced reactions. The coupling effects of the continuum states with high orbital angular momentum and closed channels are discussed. Moderate effects are found in the calculations for elastic scattering and breakup reaction when $F$- and $G$-wave continuum states are included in CDCC model space for $^6$Li induced reactions at the incident energies well above the Coulomb barrier. The closed channel effect is found to be indispensable for $^6$Li breakup reaction calculation when the incident energy is around the Coulomb barrier.

nucl-th↗

arXiv2020

Music Classification in MIDI Format based on LSTM Mdel

Music classification between music made by AI or human composers can be done by deep learning networks. We first transformed music samples in midi format to natural language sequences, then classified these samples by mLSTM (multiplicative Long Short Term Memory) + logistic regression. The accuracy of the result evaluated by 10-fold cross validation can reach 90%. Our work indicates that music generated by AI and human composers do have different characteristics, which can be learned by deep learning networks.

cs.SD↗