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Hojun Lee

Publications and source records attributed to Hojun Lee.

At least 19 recordsLinked to original sources
arXiv2026

Emergence of the magnetic octupole Rashba-Edelstein effect from spin-orbit entanglement

Magnetic multipoles have attracted growing interest as order parameters and dynamical degrees of freedom in unconventional magnets, yet it remains unclear how broadly they can emerge as active electronic degrees of freedom. Here, we show that spin-orbit-entangled multiorbital states can host magnetic octupole (MO) degrees of freedom. In Rashba systems, this gives rise to an MO Rashba texture accompanying the J-Rashba texture. Remarkably, spin-orbital entanglement can realize a pure-MO limit in which the spin and orbital-angular-momentum textures vanish while the MO texture remains finite. An applied electric field converts this texture into a nonequilibrium MO polarization through an MO Rashba-Edelstein effect. Around the pure-MO regime, the MO Edelstein response dominates over the conventional spin response, showing that multipolar responses need not be small corrections to dipolar spin physics. Our results establish an interface-based route for electrically generating nonequilibrium multipolar magnetic polarization in spin-orbit-coupled systems.

cond-mat.mes-hall↗
arXiv2026

Aharonov-Casher-induced electric quadrupole of charge-neutral particles

For charged particles, their orbital angular momentum (OAM) in solid have a direct magnetic manifestation as an orbital magnetization. For charge-neutral particles, however, the physical manifestation of the OAM in solid remains unclear. Here, we show that a charge-neutral particle carrying a magnetic moment couples to electric-field gradient through the Aharonov-Casher (AC) effect, thereby exhibiting the electric quadrupole in crystalline solids. This AC-induced electric quadrupole (AC-EQ) contains the scalar, toroidal dipole, and reduced quadrupole components, which are conjugate to the divergence, circulation, and shear of the electric field, respectively. As representative examples, we calculate the AC-EQ of magnons in two magnetic systems. In ferromagnetic pyrochlore, Dzyaloshinskii-Moriya interaction (DMI) induces a sizable AC-EQ, whereas in helical Fe langasite the AC-EQ emerges from the helical spin configuration even in the absence of DMI. In both systems, the unit-cell-integrated AC-EQ components reach magnitudes comparable to typical nuclear electric quadrupole moments.

cond-mat.mes-hall↗
arXiv2026

Modern Approach to Orbital Hall Effect Based on Wannier Picture of Solids

The orbital Hall effect (OHE) and its key figure of merit, the orbital Hall conductivity (OHC), are central to the emerging field of orbitronics. The OHC is conventionally computed via Kubo linear response theory, where the orbital angular momentum (OAM) operator is approximated through atom-centered projections that retain only local contributions, discarding physically important itinerant components. Here, we present a rigorous framework for the OAM operator grounded in the modern theory of orbital magnetization, formulated in a Wannier function basis. Critically, this naturally introduces a hierarchy of non-local contributions to the OAM operator. In turn, this gives rise to an additional non-Kubo contribution to the OHC of purely geometric origin, absent in all atom-centered treatments. First-principles calculations across a diverse set of materials demonstrate that this term can constitute a significant fraction of the total OHC, with its omission leading to qualitatively incorrect predictions. Our framework provides a unified and numerically tractable approach to orbital transport and improves our understanding of the OAM in solids, allowing for a more precise estimation of various orbital effects in complex materials.

cond-mat.mes-hall↗
arXiv2026

Anatomy of the modern theory of orbital magnetism from first-principles: term-by-term analysis in the gauge-covariant formalism

We present an in-depth analysis of the orbital magnetism by means of the so-called modern theory based on the Berry phase across distinct classes of materials-d transition metals, sp metals, and transition metal dichalcogenides-highlighting the microscopic nature of band structure characteristics. We adopt a gauge-covariant formulation of the modern theory proposed in [Lopez et al. Phys. Rev. B 85, 014435 (2012)], which enables the calculation of orbital magnetism in a controlled manner in any chosen gauge of Wannier functions and gives the total contribution as a gauge-invariant measurable. This captures consistently the contributions due to the anomalous position, velocity, and orbital angular momentum of Wannier basis, as well as the contributions due to Hamiltonian such that their sum is gauge-invariant. For d transition metals, we find that the atom-centered approximation captures the majority of the total contribution given by modern theory, which we attribute to localized nature of d electrons. However, 5d metals tend to exhibit larger deviation between the two methods than 3d metals do, as 5d electrons are more delocalized than 3d electrons. On the other hand, sp metals exhibit a strong deviation between the two methods, where large kinetic energy of sp electrons is important. Finally, in 1H-MoS2, we find that the valley orbital moment far exceeds the atomic limit of d electrons due to coherent hybridization between valence and conduction bands in direct band gaps. Our work elucidates the interplay of the chemical nature of electronic orbitals and the effect of band structures in a consistent manner and highlights the role of Berry phase in orbital magnetism. The results suggest a promising direction of orbitronics beyond controlling atomic orbitals, in which the orbital magnetism can be greatly enhanced by exploiting Berry phase.

cond-mat.mes-hall↗
arXiv2026

SyNeT: Synthetic Negatives for Traversability Learning

Reliable traversability estimation is crucial for autonomous robots to navigate complex outdoor environments safely. Existing self-supervised learning frameworks primarily rely on positive and unlabeled data; however, the lack of explicit negative data remains a critical limitation, hindering the model's ability to accurately identify diverse non-traversable regions. To address this issue, we introduce a method to explicitly construct synthetic negatives, representing plausible but non-traversable, and integrate them into vision-based traversability learning. Our approach is formulated as a training strategy that can be seamlessly integrated into both Positive-Unlabeled (PU) and Positive-Negative (PN) frameworks without modifying inference architectures. Complementing standard pixel-wise metrics, we introduce an object-centric FPR evaluation approach that analyzes predictions in regions where synthetic negatives are inserted. This evaluation provides an indirect measure of the model's ability to consistently identify non-traversable regions without additional manual labeling. Extensive experiments on both public and self-collected datasets demonstrate that our approach significantly enhances robustness and generalization across diverse environments. The source code and demonstration videos will be publicly available.

cs.RO↗
arXiv2025

Targeted Data Protection for Diffusion Model by Matching Training Trajectory

Recent advancements in diffusion models have made fine-tuning text-to-image models for personalization increasingly accessible, but have also raised significant concerns regarding unauthorized data usage and privacy infringement. Current protection methods are limited to passively degrading image quality, failing to achieve stable control. While Targeted Data Protection (TDP) offers a promising paradigm for active redirection toward user-specified target concepts, existing TDP attempts suffer from poor controllability due to snapshot-matching approaches that fail to account for complete learning dynamics. We introduce TAFAP (Trajectory Alignment via Fine-tuning with Adversarial Perturbations), the first method to successfully achieve effective TDP by controlling the entire training trajectory. Unlike snapshot-based methods whose protective influence is easily diluted as training progresses, TAFAP employs trajectory-matching inspired by dataset distillation to enforce persistent, verifiable transformations throughout fine-tuning. We validate our method through extensive experiments, demonstrating the first successful targeted transformation in diffusion models with simultaneous control over both identity and visual patterns. TAFAP significantly outperforms existing TDP attempts, achieving robust redirection toward target concepts while maintaining high image quality. This work enables verifiable safeguards and provides a new framework for controlling and tracing alterations in diffusion model outputs.

cs.AI↗
arXiv2025

Dual-wavelength Fourier Ptychographic Topography

We introduce a dual-wavelength Fourier ptychographic topography (FPT) method that extends the lambda/2 height-range limit of single-wavelength FPT. By reconstructing complex fields at two illumination wavelengths and exploiting their phase difference, the method achieves an effective synthetic wavelength lambda_s and an unambiguous range of lambda_s/2 without reducing lateral resolution. A noise-robust wrapped-number search is used to select per-pixel integer pairs (k1, k2), and a global refinement with circular TV regularization and soft bounds improves stability and preserves height discontinuities. The approach is validated through rigorous scattering-model-based simulations and experiments on structured silicon samples, demonstrating accurate height recovery in regimes where single-wavelength FPT exhibits phase wrapping. We analyze the limits of the FPT forward model and identify aspect ratio (AR) and phase modulation transfer function (ph-MTF) as key predictors of reconstruction fidelity. Simulations and experiments show that increasing AR beyond a practical threshold causes loss of high-frequency phase transfer and destabilizes dual-wavelength unwrapping. Within this AR range, dual-wavelength FPT provides robust, high-resolution topography suitable for semiconductor and industrial metrology.

physics.optics↗
arXiv2025

IMPACT: Industrial Machine Perception via Acoustic Cognitive Transformer

Acoustic signals from industrial machines offer valuable insights for anomaly detection, predictive maintenance, and operational efficiency enhancement. However, existing task-specific, supervised learning methods often scale poorly and fail to generalize across diverse industrial scenarios, whose acoustic characteristics are distinct from general audio. Furthermore, the scarcity of accessible, large-scale datasets and pretrained models tailored for industrial audio impedes community-driven research and benchmarking. To address these challenges, we introduce DINOS (Diverse INdustrial Operation Sounds), a large-scale open-access dataset. DINOS comprises over 74,149 audio samples (exceeding 1,093 hours) collected from various industrial acoustic scenarios. We also present IMPACT (Industrial Machine Perception via Acoustic Cognitive Transformer), a novel foundation model for industrial machine sound analysis. IMPACT is pretrained on DINOS in a self-supervised manner. By jointly optimizing utterance and frame-level losses, it captures both global semantics and fine-grained temporal structures. This makes its representations suitable for efficient fine-tuning on various industrial downstream tasks with minimal labeled data. Comprehensive benchmarking across 30 distinct downstream tasks (spanning four machine types) demonstrates that IMPACT outperforms existing models on 24 tasks, establishing its superior effectiveness and robustness, while providing a new performance benchmark for future research.

cs.SD↗
arXiv2024

Enhanced transverse electron transport via disordered composite formation

Transverse electron transport in magnetic materials - manifested in effects such as the anomalous Hall and Nernst effects - holds promise for spintronic and thermoelectric applications. While recent advances have focused on enhancing such transport through topological single crystals via intrinsic mechanisms linked to Berry curvature, practical limitations remain due to their mechanical fragility and narrow material scope. Here, we demonstrate a distinct approach for transverse transport enhancement based on composite formation. Using both theoretical modeling and experiments, we show that disordered mixtures of two ferromagnetic materials can exhibit significantly stronger transverse electron deflection than either constituent alone. This enhancement originates from meandering electron pathways created by the disordered mixture of two materials and does not rely on long-range crystalline order. The identified requirements for this mechanism can be broadly satisfied across different material systems, offering a universal and tunable strategy to engineer large transverse responses in structurally robust platforms.

cond-mat.mtrl-sci↗
arXiv2024

Feature Map for Quantum Data in Classification

The kernel trick in supervised learning signifies transformations of an inner product by a feature map, which then restructures training data in a larger Hilbert space according to an endowed inner product. A quantum feature map corresponds to an instance with a Hilbert space of quantum states by fueling quantum resources to machine learning algorithms. In this work, we point out that the quantum state space is specific such that a measurement postulate characterizes an inner product and that manipulation of quantum states prepared from classical data cannot enhance the distinguishability of data points. We present a feature map for quantum data as a probabilistic manipulation of quantum states to improve supervised learning algorithms.

quant-ph↗
arXiv2024

Coreset Selection for Object Detection

Coreset selection is a method for selecting a small, representative subset of an entire dataset. It has been primarily researched in image classification, assuming there is only one object per image. However, coreset selection for object detection is more challenging as an image can contain multiple objects. As a result, much research has yet to be done on this topic. Therefore, we introduce a new approach, Coreset Selection for Object Detection (CSOD). CSOD generates imagewise and classwise representative feature vectors for multiple objects of the same class within each image. Subsequently, we adopt submodular optimization for considering both representativeness and diversity and utilize the representative vectors in the submodular optimization process to select a subset. When we evaluated CSOD on the Pascal VOC dataset, CSOD outperformed random selection by +6.4%p in AP$_{50}$ when selecting 200 images.

cs.CV↗
arXiv2024

ARC-NeRF: Area Ray Casting for Broader Unseen View Coverage in Few-shot Object Rendering

Recent advancements in the Neural Radiance Field (NeRF) have enhanced its capabilities for novel view synthesis, yet its reliance on dense multi-view training images poses a practical challenge, often leading to artifacts and a lack of fine object details. Addressing this, we propose ARC-NeRF, an effective regularization-based approach with a novel Area Ray Casting strategy. While the previous ray augmentation methods are limited to covering only a single unseen view per extra ray, our proposed Area Ray covers a broader range of unseen views with just a single ray and enables an adaptive high-frequency regularization based on target pixel photo-consistency. Moreover, we propose luminance consistency regularization, which enhances the consistency of relative luminance between the original and Area Ray, leading to more accurate object textures. The relative luminance, as a free lunch extra data easily derived from RGB images, can be effectively utilized in few-shot scenarios where available training data is limited. Our ARC-NeRF outperforms its baseline and achieves competitive results on multiple benchmarks with sharply rendered fine details.

cs.CV↗
arXiv2023

Tradeoff of generalization error in unsupervised learning

Finding the optimal model complexity that minimizes the generalization error (GE) is a key issue of machine learning. For the conventional supervised learning, this task typically involves the bias-variance tradeoff: lowering the bias by making the model more complex entails an increase in the variance. Meanwhile, little has been studied about whether the same tradeoff exists for unsupervised learning. In this study, we propose that unsupervised learning generally exhibits a two-component tradeoff of the GE, namely the model error and the data error -- using a more complex model reduces the model error at the cost of the data error, with the data error playing a more significant role for a smaller training dataset. This is corroborated by training the restricted Boltzmann machine to generate the configurations of the two-dimensional Ising model at a given temperature and the totally asymmetric simple exclusion process with given entry and exit rates. Our results also indicate that the optimal model tends to be more complex when the data to be learned are more complex.

cond-mat.stat-mech↗
arXiv2023

End-to-End Multi-Object Detection with a Regularized Mixture Model

Recent end-to-end multi-object detectors simplify the inference pipeline by removing hand-crafted processes such as non-maximum suppression (NMS). However, during training, they still heavily rely on heuristics and hand-crafted processes which deteriorate the reliability of the predicted confidence score. In this paper, we propose a novel framework to train an end-to-end multi-object detector consisting of only two terms: negative log-likelihood (NLL) and a regularization term. In doing so, the multi-object detection problem is treated as density estimation of the ground truth bounding boxes utilizing a regularized mixture density model. The proposed \textit{end-to-end multi-object Detection with a Regularized Mixture Model} (D-RMM) is trained by minimizing the NLL with the proposed regularization term, maximum component maximization (MCM) loss, preventing duplicate predictions. Our method reduces the heuristics of the training process and improves the reliability of the predicted confidence score. Moreover, our D-RMM outperforms the previous end-to-end detectors on MS COCO dataset.

cs.CV↗
arXiv2023

Tracing multiple scattering trajectories for deep optical imaging in scattering media

Multiple light scattering hampers imaging objects in complex scattering media. Approaches used in real practices mainly aim to filter out multiple scattering obscuring the ballistic waves that travel straight through the scattering medium. Here, we propose a method that makes the deterministic use of multiple scattering for microscopic imaging of an object embedded deep within scattering media. The proposed method finds a stack of multiple complex phase plates that generate similar light trajectories as the original scattering medium. By implementing the inverse scattering using the identified phase plates, our method rectifies multiple scattering and amplifies ballistic waves by almost 600 times, which leads to a substantial increase in imaging depth. Our study marks an important milestone in solving the longstanding high-order inverse scattering problems.

physics.optics↗
arXiv2021

Visualization of reaction chemistry in W-KClO4-BaCrO4 delay mixtures via a Sestak-Berggren model based isoconversional method

The combustion delay mixture of tungsten (W), potassium perchlorate (KClO4), and barium chromate (BaCrO4), also known as the WKB mixture, has long been considered to be an integral part of military-grade ammunition. Despite its long history, however, their progressive reaction dynamics remains a question mark, especially due to the complex nature of their combustion reaction. As opposed to a one-step oxidation commonly observed in conventional combustions, the WKB mixture is associated with a multibody reaction between its solid-state components. To this end, the emergence of three combustion peaks, which we corresponded with disparate chemical reactions, was observed using thermogravimetric analysis on two separate WKB mixtures with differing mixture ratios. We applied the stepwise isoconversional method on each of the peaks to match the combustion chemistry it represents to the Sestak-Berggren model and computed the conceptual activation energy. Further plotting the logarithmic pre-exponential factor as a function of the reaction progress, we demonstrate a method of using the plot as an intuitive tool to understand the dynamics of individual reactions that compose multi-step chemical reactions. Our study provides a systematic approach in visualizing the reaction chemistry, thereby strengthening the analytical arsenal against reaction dynamics of combustion compounds in general.

cond-mat.mtrl-sci↗
arXiv2021

Few-Shot Object Detection by Attending to Per-Sample-Prototype

Few-shot object detection aims to detect instances of specific categories in a query image with only a handful of support samples. Although this takes less effort than obtaining enough annotated images for supervised object detection, it results in a far inferior performance compared to the conventional object detection methods. In this paper, we propose a meta-learning-based approach that considers the unique characteristics of each support sample. Rather than simply averaging the information of the support samples to generate a single prototype per category, our method can better utilize the information of each support sample by treating each support sample as an individual prototype. Specifically, we introduce two types of attention mechanisms for aggregating the query and support feature maps. The first is to refine the information of few-shot samples by extracting shared information between the support samples through attention. Second, each support sample is used as a class code to leverage the information by comparing similarities between each support feature and query features. Our proposed method is complementary to the previous methods, making it easy to plug and play for further improvement. We have evaluated our method on PASCAL VOC and COCO benchmarks, and the results verify the effectiveness of our method. In particular, the advantages of our method are maximized when there is more diversity among support data.

cs.CV↗
arXiv2021

High-throughput volumetric adaptive optical imaging using compressed time-reversal matrix

Deep-tissue optical imaging suffers from the reduction of resolving power due to tissue-induced optical aberrations and multiple scattering noise. Reflection matrix approaches recording the maps of backscattered waves for all the possible orthogonal input channels have provided formidable solutions for removing severe aberrations and recovering the ideal diffraction-limited spatial resolution without relying on fluorescence labeling and guide stars. However, measuring the full input-output response of the tissue specimen is time-consuming, making the real-time image acquisition difficult. Here, we present the use of a time-reversal matrix, instead of the reflection matrix, for fast high-resolution volumetric imaging of a mouse brain. The time-reversal matrix reduces two-way problem to one-way problem, which effectively relieves the requirement for the coverage of input channels. Using a newly developed aberration correction algorithm designed for the time-reversal matrix, we demonstrated the correction of complex aberrations using as small as 2 % of the complete basis while maintaining the image reconstruction fidelity comparable to the fully sampled reflection matrix. Due to nearly 100-fold reduction in the matrix recording time, we could achieve real-time aberration-correction imaging for a field of view of 40 x 40 microns (176 x 176 pixels) at a frame rate of 80 Hz. Furthermore, we demonstrated high-throughput volumetric adaptive optical imaging of a mouse brain by recording a volume of 128 x 128 x 125 microns (568 x 568 x 125 voxels) in 3.58 s, correcting tissue aberrations at each and every 1-micron depth section, and visualizing myelinated axons with a lateral resolution of 0.45 microns and an axial resolution of 2 microns.

physics.optics↗