arXiv subjects

Jong-Min Lee

Publications and source records attributed to Jong-Min Lee.

7 recordsLinked to original sources

arXiv2026

Bioinspired Microactuators Fabricated via One-step Meniscus-guided 3D Nanoprinting

We introduce a bioinspired microactuator fabricated via a one-step meniscus guided 3D nanoprinting technique. This technique directly produces a freestanding three dimensional composite architecture by integrating a rigid nanoparticle framework with a hygroscopic polymer matrix in a single, assembly free process. Inspired by the functional integration of rigid and compliant components in insect exoskeletal joint, our design synergistically combines load-bearing strength and humidity-driven swelling in a single microscale pillar. Comprehensive experiments, including comparative control structures and microscopic analysis, elucidated the actuation mechanism: the nanoparticle scaffold provides mechanical support while the polymeric phase provides volumetric expansion, yielding large reversible elongation under humidity with preserved structural integrity. The resulting AuNP PVP microactuator shows humidity responsive actuation at the microscale, with representative demonstrations of repeated humidity cycling and load lifting up to approximately 800 times its estimated own weight under the tested conditions. Variant geometries such as hinged pillars demonstrate how axial swelling can be converted into bending motion, and an optical configuration shows how actuation can modulate reflected light signals. This nanoprinting approach provides a simple strategy for constructing bioinspired soft microactuators with humidity-responsive deformation and potential applicability in microscale sensing, optical modulation, and adaptive microdevices.

physics.optics↗

arXiv2026

Engineering Disordered Many-particle Plasmonic Nanoclusters for Wafer-scale Uniform and Giant Electromagnetic Field Enhancement

Scalable plasmonic technologies face a critical trade-off: few-body architectures offer high enhancement but are sensitive to fabrication flaws, while scalable methods like solid-state dewetting yield large, low-enhancement gaps. We introduce a paradigm shift using a many-body plasmonic architecture inspired by statistical mechanics. By moving toward the continuum limit (N>>1), local geometric variations are statistically averaged out, effectively decoupling optical performance from microscopic disorder. We implement this concept via a lithography- and etching-free, multi-step dewetting strategy, creating wafer-scale nanoclusters. This process strategically forms a robust many-body system by introducing numerous small satellite nanoparticles between larger particles. Crucially, this design achieves a high collective enhancement that surpasses even optimized few-body systems, despite having larger individual gaps. Under optimized conditions, these substrates exhibit a surface-enhanced Raman scattering enhancement factor approaching 4 x 108 with unprecedented reproducibility (RSD of ~10%). This scalable, low-cost concept establishes a practical route toward reproducible wafer-scale nanophotonic platforms for sensing, spectroscopy, and quantum technologies.

physics.optics↗

arXiv2026

Spatially Uniform and Defect-Tolerant Plasmonic Responses in 3D printed Gold Nanoparticle Assemblies

Three-dimensional (3D) assemblies of gold nanoparticles (AuNPs) offer a rich platform for plasmonic coupling and near-field engineering, yet their optical behavior is often complex due to structural disorder and fabrication-induced variability. Here, we present a systematic optical investigation of large-scale 3D AuNP assemblies fabricated via meniscus-guided assembly, focusing on the reproducibility and spatial uniformity. Spatially-resolved dark-field scattering measurements reveal that high-aspect-ratio AuNP pillars exhibit uniform scattering spectra along their height and across different pillars, despite variations in geometry and structure. Electromagnetic simulations suggest that this robustness arises from an ensemble-averaged plasmonic response governed by many local coupling regions within a finite plasmon delocalization length. Simulated near-field and surface charge distributions suggest that the broad ensemble response remains spatially distributed under representative structural perturbations, consistent with volumetric averaging. Building on this robust platform, we introduce compositional modulation through a core-satellite architecture by incorporating smaller AuNPs. This yields a composition-dependent spectral redistribution, including an additional long-wavelength spectral feature in the core-satellite assemblies. Wavelength-dependent surface-enhanced Raman scattering measurements reveal contrasting responses under 633 and 785 nm excitation, reflecting redistribution of local plasmonic coupling pathways. These results establish design principles for robust 3D plasmonic nanoparticle assemblies with ensemble-averaged and composition-tunable optical responses.

physics.optics↗

arXiv2026

Fully Guided Neural Schrödinger bridge for Brain MR image synthesis

Multi-modal brain MRI provides essential complementary information for clinical diagnosis. However, acquiring all modalities in practice is often constrained by time and cost. To address this, various methods have been proposed to generate missing modalities from available ones. Existing approaches can be broadly categorized into two types: paired and unpaired methods. While paired methods achieve high synthesis accuracy, obtaining large-scale paired datasets is typically impractical. In contrast, unpaired methods, though more scalable, often fail to preserve critical anatomical features, such as lesions. In this paper, we propose Fully Guided Schrödinger Bridge (FGSB), a novel framework designed to overcome these limitations by enabling high-fidelity generation with extremely limited paired data. When lesion-specific information, such as expert annotations or segmentation masks, is available, FGSB preserves clinically relevant lesions during missing modality synthesis. Our model comprises two stages: (1) a generation stage that iteratively refines synthetic images using paired source images and Gaussian noise, and (2) a training stage that learns optimal transformation pathways by modeling intermediate states to ensure consistent, high-fidelity synthesis. Experimental results across multiple datasets demonstrate that FGSB achieves reliable synthesis performance across diverse imaging resolutions and data acquisition environments. In addition, incorporating lesion-specific priors further enhances the preservation of clinically relevant features.

eess.IV↗

arXiv2023

Self-Supervised Learning from Non-Object Centric Images with a Geometric Transformation Sensitive Architecture

Most invariance-based self-supervised methods rely on single object-centric images (e.g., ImageNet images) for pretraining, learning features that invariant to geometric transformation. However, when images are not object-centric, the semantics of the image can be significantly altered due to cropping. Furthermore, as the model becomes insensitive to geometric transformations, it may struggle to capture location information. For this reason, we propose a Geometric Transformation Sensitive Architecture designed to be sensitive to geometric transformations, specifically focusing on four-fold rotation, random crop, and multi-crop. Our method encourages the student to be sensitive by predicting rotation and using targets that vary with those transformations through pooling and rotating the teacher feature map. Additionally, we use patch correspondence loss to encourage correspondence between patches with similar features. This approach allows us to capture long-term dependencies in a more appropriate way than capturing long-term dependencies by encouraging local-to-global correspondence, which occurs when learning to be insensitive to multi-crop. Our approach demonstrates improved performance when using non-object-centric images as pretraining data compared to other methods that train the model to be insensitive to geometric transformation. We surpass DINO[Caron et al.[2021b]] baseline in tasks including image classification, semantic segmentation, detection, and instance segmentation with improvements of 4.9 $Top-1 Acc$, 3.3 $mIoU$, 3.4 $AP^b$, and 2.7 $AP^m$. Code and pretrained models are publicly available at: https://github.com/bok3948/GTSA

cs.CV↗

arXiv2022

Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning

Few-shot object detection (FSOD) aims to classify and detect few images of novel categories. Existing meta-learning methods insufficiently exploit features between support and query images owing to structural limitations. We propose a hierarchical attention network with sequentially large receptive fields to fully exploit the query and support images. In addition, meta-learning does not distinguish the categories well because it determines whether the support and query images match. In other words, metric-based learning for classification is ineffective because it does not work directly. Thus, we propose a contrastive learning method called meta-contrastive learning, which directly helps achieve the purpose of the meta-learning strategy. Finally, we establish a new state-of-the-art network, by realizing significant margins. Our method brings 2.3, 1.0, 1.3, 3.4 and 2.4% AP improvements for 1-30 shots object detection on COCO dataset. Our code is available at: https://github.com/infinity7428/hANMCL

cs.CV↗

arXiv2022

Boosting Graph Neural Networks by Injecting Pooling in Message Passing

There has been tremendous success in the field of graph neural networks (GNNs) as a result of the development of the message-passing (MP) layer, which updates the representation of a node by combining it with its neighbors to address variable-size and unordered graphs. Despite the fruitful progress of MP GNNs, their performance can suffer from over-smoothing, when node representations become too similar and even indistinguishable from one another. Furthermore, it has been reported that intrinsic graph structures are smoothed out as the GNN layer increases. Inspired by the edge-preserving bilateral filters used in image processing, we propose a new, adaptable, and powerful MP framework to prevent over-smoothing. Our bilateral-MP estimates a pairwise modular gradient by utilizing the class information of nodes, and further preserves the global graph structure by using the gradient when the aggregating function is applied. Our proposed scheme can be generalized to all ordinary MP GNNs. Experiments on five medium-size benchmark datasets using four state-of-the-art MP GNNs indicate that the bilateral-MP improves performance by alleviating over-smoothing. By inspecting quantitative measurements, we additionally validate the effectiveness of the proposed mechanism in preventing the over-smoothing issue.

cs.LG↗