arXiv subjects

Shan Yin

Publications and source records attributed to Shan Yin.

At least 19 recordsLinked to original sources

arXiv2026

MILP-Evo: Closed-Loop Fully Automatic Design of MILP Solvers

Machine learning methods have shown that data-driven policies can accelerate mixed-integer linear programming (MILP) solvers, but many such approaches remain difficult to inspect, adapt, and deploy because the learned policy is represented as an external predictor or other opaque model. By contrast, explicit solver logic is easier to understand and integrate, but is usually hand-designed rather than learned from solver feedback. We study whether the automatic design of MILP solver logic can instead be cast as LLM-guided closed-loop search over executable white-box components evaluated directly by end-to-end solver behavior. To this end, we propose a closed-loop program evolution framework for MILP solver auto-design, implemented through PySCIPOpt, and instantiate it on the joint design of a cut selector and a branching rule. Candidate programs are iteratively generated, loaded into SCIP, and evaluated by direct execution on MILP instances, with the resulting feedback guiding performance-based selection, targeted repair, diagnostic reflection, and diversity-aware population maintenance. The method outputs explicit solver components that can be inspected, modified, and deployed within standard solver workflows. Across four benchmark families, we find that LLM-guided program evolution can discover competitive domain-specialized policies in several settings.

cs.AI↗

arXiv2026

Fast Model-guided Instance-wise Adaptation Framework for Real-world Pansharpening with Fidelity Constraints

Pansharpening aims to generate high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) and high-resolution panchromatic (PAN) images while preserving both spectral and spatial information. Although deep learning (DL)-based pansharpening methods achieve impressive performance, they require high training cost and large datasets, and often degrade when the test distribution differs from training, limiting generalization. Recent zero-shot methods, trained on a single PAN/LRMS pair, offer strong generalization but suffer from limited fusion quality, high computational overhead, and slow convergence. To address these issues, we propose FMG-Pan, a fast and generalizable model-guided instance-wise adaptation framework for real-world pansharpening, achieving both cross-sensor generality and rapid training-inference. The framework leverages a pretrained model to guide a lightweight adaptive network through joint optimization with spectral and physical fidelity constraints. We further design a novel physical fidelity term to enhance spatial detail preservation. Extensive experiments on real-world datasets under both intra- and cross-sensor settings demonstrate state-of-the-art performance. On the WorldView-3 dataset, FMG-Pan completes training and inference for a 512x512x8 image within 3 seconds on an RTX 3090 GPU, significantly faster than existing zero-shot methods, making it suitable for practical deployment.

cs.CV↗

arXiv2026

G-ZAP: A Generalizable Zero-Shot Framework for Arbitrary-Scale Pansharpening

Pansharpening aims to fuse a high-resolution panchromatic (PAN) image and a low-resolution multispectral (LRMS) image to produce a high-resolution multispectral (HRMS) image. Recent deep models have achieved strong performance, yet they typically rely on large-scale pretraining and often generalize poorly to unseen real-world image pairs. Prior zero-shot approaches improve real-scene generalization but require per-image optimization, hindering weight reuse, and the above methods are usually limited to a fixed scale. To address this issue, we propose G-ZAP, a generalizable zero-shot framework for arbitrary-scale pansharpening, designed to handle cross-resolution, cross-scene, and cross-sensor generalization. G-ZAP adopts a feature-based implicit neural representation (INR) fusion network as the backbone and introduces a multi-scale, semi-supervised training scheme to enable robust generalization. Extensive experiments on multiple real-world datasets show that G-ZAP achieves state-of-the-art results under PAN-scale fusion in both visual quality and quantitative metrics. Notably, G-ZAP supports weight reuse across image pairs while maintaining competitiveness with per-pair retraining, demonstrating strong potential for efficient real-world deployment.

cs.CV↗

arXiv2025

Towards Instance-wise Personalized Federated Learning via Semi-Implicit Bayesian Prompt Tuning

Federated learning (FL) is a privacy-preserving machine learning paradigm that enables collaborative model training across multiple distributed clients without disclosing their raw data. Personalized federated learning (pFL) has gained increasing attention for its ability to address data heterogeneity. However, most existing pFL methods assume that each client's data follows a single distribution and learn one client-level personalized model for each client. This assumption often fails in practice, where a single client may possess data from multiple sources or domains, resulting in significant intra-client heterogeneity and suboptimal performance. To tackle this challenge, we propose pFedBayesPT, a fine-grained instance-wise pFL framework based on visual prompt tuning. Specifically, we formulate instance-wise prompt generation from a Bayesian perspective and model the prompt posterior as an implicit distribution to capture diverse visual semantics. We derive a variational training objective under the semi-implicit variational inference framework. Extensive experiments on benchmark datasets demonstrate that pFedBayesPT consistently outperforms existing pFL methods under both feature and label heterogeneity settings.

cs.LG↗

arXiv2025

Training and Inference within 1 Second -- Tackle Cross-Sensor Degradation of Real-World Pansharpening with Efficient Residual Feature Tailoring

Deep learning methods for pansharpening have advanced rapidly, yet models pretrained on data from a specific sensor often generalize poorly to data from other sensors. Existing methods to tackle such cross-sensor degradation include retraining model or zero-shot methods, but they are highly time-consuming or even need extra training data. To address these challenges, our method first performs modular decomposition on deep learning-based pansharpening models, revealing a general yet critical interface where high-dimensional fused features begin mapping to the channel space of the final image. % may need revisement A Feature Tailor is then integrated at this interface to address cross-sensor degradation at the feature level, and is trained efficiently with physics-aware unsupervised losses. Moreover, our method operates in a patch-wise manner, training on partial patches and performing parallel inference on all patches to boost efficiency. Our method offers two key advantages: (1) $\textit{Improved Generalization Ability}$: it significantly enhance performance in cross-sensor cases. (2) $\textit{Low Generalization Cost}$: it achieves sub-second training and inference, requiring only partial test inputs and no external data, whereas prior methods often take minutes or even hours. Experiments on the real-world data from multiple datasets demonstrate that our method achieves state-of-the-art quality and efficiency in tackling cross-sensor degradation. For example, training and inference of $512\times512\times8$ image within $\textit{0.2 seconds}$ and $4000\times4000\times8$ image within $\textit{3 seconds}$ at the fastest setting on a commonly used RTX 3090 GPU, which is over 100 times faster than zero-shot methods.

cs.CV↗

arXiv2025

HieraEdgeNet: A Multi-Scale Edge-Enhanced Framework for Automated Pollen Recognition

Automated pollen recognition is vital to paleoclimatology, biodiversity monitoring, and public health, yet conventional methods are hampered by inefficiency and subjectivity. Existing deep learning models often struggle to achieve the requisite localization accuracy for microscopic targets like pollen, which are characterized by their minute size, indistinct edges, and complex backgrounds. To overcome this limitation, we introduce HieraEdgeNet, a multi-scale edge-enhancement framework. The framework's core innovation is the introduction of three synergistic modules: the Hierarchical Edge Module (HEM), which explicitly extracts a multi-scale pyramid of edge features that corresponds to the semantic hierarchy at early network stages; the Synergistic Edge Fusion (SEF) module, for deeply fusing these edge priors with semantic information at each respective scale; and the Cross Stage Partial Omni-Kernel Module (CSPOKM), which maximally refines the most detail-rich feature layers using an Omni-Kernel operator - comprising anisotropic large-kernel convolutions and mixed-domain attention - all within a computationally efficient Cross-Stage Partial (CSP) framework. On a large-scale dataset comprising 120 pollen classes, HieraEdgeNet achieves a mean Average Precision (mAP@.5) of 0.9501, significantly outperforming state-of-the-art baseline models such as YOLOv12n and RT-DETR. Furthermore, qualitative analysis confirms that our approach generates feature representations that are more precisely focused on object boundaries. By systematically integrating edge information, HieraEdgeNet provides a robust and powerful solution for high-precision, high-efficiency automated detection of microscopic objects.

cs.CV↗

arXiv2025

CAT: A Conditional Adaptation Tailor for Efficient and Effective Instance-Specific Pansharpening on Real-World Data

Pansharpening is a crucial remote sensing technique that fuses low-resolution multispectral (LRMS) images with high-resolution panchromatic (PAN) images to generate high-resolution multispectral (HRMS) imagery. Although deep learning techniques have significantly advanced pansharpening, many existing methods suffer from limited cross-sensor generalization and high computational overhead, restricting their real-time applications. To address these challenges, we propose an efficient framework that quickly adapts to a specific input instance, completing both training and inference in a short time. Our framework splits the input image into multiple patches, selects a subset for unsupervised CAT training, and then performs inference on all patches, stitching them into the final output. The CAT module, integrated between the feature extraction and channel transformation stages of a pre-trained network, tailors the fused features and fixes the parameters for efficient inference, generating improved results. Our approach offers two key advantages: (1) $\textit{Improved Generalization Ability}$: by mitigating cross-sensor degradation, our model--although pre-trained on a specific dataset--achieves superior performance on datasets captured by other sensors; (2) $\textit{Enhanced Computational Efficiency}$: the CAT-enhanced network can swiftly adapt to the test sample using the single LRMS-PAN pair input, without requiring extensive large-scale data retraining. Experiments on the real-world data from WorldView-3 and WorldView-2 datasets demonstrate that our method achieves state-of-the-art performance on cross-sensor real-world data, while achieving both training and inference of $512\times512$ image within $\textit{0.4 seconds}$ and $4000\times4000$ image within $\textit{3 seconds}$ at the fastest setting on a commonly used RTX 3090 GPU.

cs.CV↗

arXiv2025

VIRGOS: Secure Graph Convolutional Network on Vertically Split Data from Sparse Matrix Decomposition

Securely computing graph convolutional networks (GCNs) is critical for applying their analytical capabilities to privacy-sensitive data like social/credit networks. Multiplying a sparse yet large adjacency matrix of a graph in GCN--a core operation in training/inference--poses a performance bottleneck in secure GCNs. Consider a GCN with $|V|$ nodes and $|E|$ edges; it incurs a large $O(|V|^2)$ communication overhead. Modeling bipartite graphs and leveraging the monotonicity of non-zero entry locations, we propose a co-design harmonizing secure multi-party computation (MPC) with matrix sparsity. Our sparse matrix decomposition transforms an arbitrary sparse matrix into a product of structured matrices. Specialized MPC protocols for oblivious permutation and selection multiplication are then tailored, enabling our secure sparse matrix multiplication ($(SM)^2$) protocol, optimized for secure multiplication of these structured matrices. Together, these techniques take $O(|E|)$ communication in constant rounds. Supported by $(SM)^2$, we present Virgos, a secure 2-party framework that is communication-efficient and memory-friendly on standard vertically-partitioned graph datasets. Performance of Virgos has been empirically validated across diverse network conditions.

cs.CR↗

arXiv2023

Deep Learning Enabled Design of Terahertz High-Q Metamaterials

Metamaterials open up a new way to manipulate electromagnetic waves and realize various functional devices. Metamaterials with high-quality (Q) resonance responses are widely employed in sensing, detection, and other applications. Traditional design of metamaterials involves laborious simulation-optimization and limits the efficiency. The high-Q metamaterials with abrupt spectral change are even harder to reverse design on-demand. In this paper, we propose novel solutions for designing terahertz high-Q metamaterials based on deep learning, including the forward prediction of spectral responses and the inverse design of structural parameters. For the forward prediction, we develop the Electromagnetic Response Transformer (ERT) model to establish the complex mapping relations between the highly sensitive structural parameters and the abrupt spectra, and realize precise prediction of the high-Q resonance in terahertz spectra from given structural parameters. For the inverse design, we introduce the Visual Attention Network (VAN) model with a large model capability to attentively learn the abrupt shifts in spectral resonances, which can efficiently reduce errors and achieve highly accurate inverse design of structural parameters according to the expected high-Q resonance responses. Both models exhibit outstanding performance, and the accuracy is improved one or two orders higher compared to the traditional machine learning methods. Besides, our ERT model can be 4000 times faster than the conventional full wave simulations in computation time. Our work provides new avenues for the deep learning enabled design of terahertz high-Q metamaterials, which holds potential applications in various fields, such as terahertz communication, sensing, imaging, and functional devices.

physics.optics↗

arXiv2023

Analogue of collectively induced transparency in metamaterials

Most recently, a brand new optical phenomenon, collectively induced transparency (CIT) has already been proposed in the cavity quantum electrodynamics system, which comes from the coupling between the cavity and ions and the quantum interference of collective ions. In this paper, we propose the CIT in terahertz (THz) metamaterial device by employing the coupling between bright mode and interference of dark modes for the first time. We give the theoretical analysis, analytical calculations and simulations to present the transmission spectrum of CIT metamaterials. Furthermore, we can observe the tendency of CIT's transmission spectrum by experiments which well verify our idea. Ideal CIT metamaterial device can produce a very high Q peak in the middle of transmission spectrum of Electromagnetically induced transparency (EIT), which can be useful for highly sensitive metamaterial sensors, optical switches and photo-memory.

physics.optics↗

arXiv2022

Morse-STF: Improved Protocols for Privacy-Preserving Machine Learning

Secure multi-party computation enables multiple mutually distrusting parties to perform computations on data without revealing the data itself, and has become one of the core technologies behind privacy-preserving machine learning. In this work, we present several improved privacy-preserving protocols for both linear and non-linear layers in machine learning. For linear layers, we present an extended beaver triple protocol for bilinear maps that significantly reduces communication of convolution layer. For non-linear layers, we introduce novel protocols for computing the sigmoid and softmax function. Both functions are essential building blocks for machine learning training of classification tasks. Our protocols are both more scalable and robust than prior constructions, and improves runtime performance by 3-17x. Finally, we introduce Morse-STF, an end-to-end privacy-preserving system for machine learning training that leverages all these improved protocols. Our system achieves a 1.8x speedup on logistic regression and 3.9-4.9x speedup on convolutional neural networks compared to prior state-of-the-art systems.

cs.CR↗

arXiv2022

Approximation-free control based on the bioinspired reference model for suspension systems with uncertainty and unknown nonlinearity

Uncertainty and unknown nonlinearity are often inevitable in the suspension systems, which were often solved using fuzzy logic system (FLS) or neural networks (NNs). However, these methods are restricted by the structural complexity of the controller and the huge computing cost. Meanwhile, the estimation error of such approximators is affected by adopted adaptive laws and learning gains. Thus, in view of the above problem, this paper proposes the approximation-free control based on the bioinspired reference model for a class of uncertain suspension systems with unknown nonlinearity. The proposed method integrates the superior vibration suppression of the bioinspired reference model and the structural advantage of the prescribed performance function (PPF) in approximation-free control. Then, the vibration suppression performance is improved, the calculation burden is relieved, and the transient performance is improved, which is analyzed theoretically in this paper. Finally, the simulation results validate the approach, and the comparisons show the advantages of the proposed control method in terms of good vibration suppression, fast convergence, and less calculation burden.

eess.SY↗

arXiv2021

Coupling-assisted quasi-bound states in the continuum in heterogeneous metasurfaces

In this paper, we present a Bound states in the continuum (BIC) metamaterial in heterogeneous structures based on the universal coupled mode theory. We find the more general physical parameters to represent BIC, which are the resonant frequencies and corresponding phases of metamaterial structures. Therefore, BIC metamaterial comes from the equal value of the resonant frequencies and phases of metamaterial structures which are not only for homogeneous structures. Meanwhile if slightly vary one of metamaterial structure's resonant frequency and phase by varying geometry, we can obtain the quasi-BIC instead of broken symmetry of homogeneous structures. In this paper, we provide the BIC and quasi-BIC with one example of two heterogeneous structures which are cut wire (CW) and Split-Ring Resonator (SRR), to widely extends the metamaterial BIC beyond common sense. Furthermore, we demonstrate the simulation results and experimental results to proof our idea.

physics.optics↗

arXiv2021

Adiabatic following of terahertz surface plasmon-polaritons coupler via two waveguides structure

Most recently, two remarkable papers [New J. Phys. 21, 113004 (2019); IEEE J. Sel. Top. Quantum Electron 27, 1 (2020)] propose broadband complete transfer terahertz (THz) surface plasmon polaritons (SPPs) waveguide coupler by applying coherent quantum control -- Stimulated Raman adiabatic passage (STIRAP). However, previous researches request three SPPs waveguides coupler. In this paper, we propose a new design of a broadband complete transfer THz SPPs coupler with an innovative structure of two waveguides by employing two state adiabatic following. In order to realize this design, we introduce the detuning parameter into the coupling equation of SPPs waveguides for the first time. We believe that this finding will improve the THz communication domain.

physics.optics↗

arXiv2021

Universal coupled theory for metamaterial Bound states in the continuum

In this paper, we present a novel universal coupled theory for metamaterial Bound states in the continuum (BIC) or quasi-Bound states in the continuum (quasi-BIC) which provides ultra-high Q resonance for metamaterial devices. Our theory analytically calculates the coupling of two bright modes with phase. Our method has much more accuracy for ultra-strong coupling comparing with the previous theory (the coupling of one bright mode and one dark mode). Therefore, our theory is much more suitable for BIC or quasi-BIC and we can accurately predict the transmission spectrum of metamaterial BIC or quasi-BIC for the first time.

physics.optics↗

arXiv2021

MPC Protocol for G-module and its Application in Secure Compare and ReLU

Secure comparison and secure selection are two fundamental MPC (secure Multi-Party Computation) protocols. One important application of these protocols is the secure ReLU and DReLU computation in privacy preserving deep learning. In this paper, we introduce G-module, a mathematics tool, to re-design such protocols. In mathematics, given a group G, a G-module is an abelian group M on which G acts compatibly with the abelian group structure on M. We design three secure protocols for three G-module operations. i.e. "G-module action", "Cross G-module action" and "G-module recover". As far as we know, this is the first work on secure G-module operations. Based on them, we design secure comparison, selection, ReLU and DReLU protocols, which improve communication efficiency by 2X to 10X compared with state of arts. Our protocols are very computation efficient too. They do not require public key operations or any other expensive operations.

cs.CR↗

arXiv2021

Broadband terahertz half-wave plate with multi-layers metamaterial via quantum engineering

In this paper, we employ the novel design of the metamaterial half-wave plate by using the multiple layers of the metamaterials with some specific rotation angles. The rotation angles are given by composite pulse control which is the well-known quantum control technique. A big advantage of this method can analytically calculate the rotation angles and can be easily extended to the multiple layers. The more layers of the metamaterials can present more bandwidth of half-wave plate. In this paper, we present 1, 3, 5, 7 configurations of layers of a metamaterial half-wave plate and we demonstrate that our device increasingly enhance the bandwidth of performance.

physics.optics↗

arXiv2021

Electronic controllable broadband and robust terahertz surface plasmon-polaritons switch based on hybrid ITO waveguide coupler

The surface plasmon-polaritons (SPPs) switch is the key element of the integrated devices in optical computation and terahertz (THz) communications. In this paper, we propose a novel design of THz SPPs switch based on quantum engineering. Due to the robustness of coherent quantum control technique, our switch is very robust against with perturbations of geometrical parameters and presents a good performance at on-state (and off-state) from 0.5 THz to 0.7 THz. The on-state and off-state of our device can be controlled by the external voltage. We believe this finding will be the great improvement for the integrated optical computing and THz communications.

physics.optics↗