arXiv subjects

Jihun Kim

Publications and source records attributed to Jihun Kim.

At least 37 records · Page 2Linked to original sources

arXiv2025

Real-time prediction of breast cancer sites using deformation-aware graph neural network

Early diagnosis of breast cancer is crucial, enabling the establishment of appropriate treatment plans and markedly enhancing patient prognosis. While direct magnetic resonance imaging-guided biopsy demonstrates promising performance in detecting cancer lesions, its practical application is limited by prolonged procedure times and high costs. To overcome these issues, an indirect MRI-guided biopsy that allows the procedure to be performed outside of the MRI room has been proposed, but it still faces challenges in creating an accurate real-time deformable breast model. In our study, we tackled this issue by developing a graph neural network (GNN)-based model capable of accurately predicting deformed breast cancer sites in real time during biopsy procedures. An individual-specific finite element (FE) model was developed by incorporating magnetic resonance (MR) image-derived structural information of the breast and tumor to simulate deformation behaviors. A GNN model was then employed, designed to process surface displacement and distance-based graph data, enabling accurate prediction of overall tissue displacement, including the deformation of the tumor region. The model was validated using phantom and real patient datasets, achieving an accuracy within 0.2 millimeters (mm) for cancer node displacement (RMSE) and a dice similarity coefficient (DSC) of 0.977 for spatial overlap with actual cancerous regions. Additionally, the model enabled real-time inference and achieved a speed-up of over 4,000 times in computational cost compared to conventional FE simulations. The proposed deformation-aware GNN model offers a promising solution for real-time tumor displacement prediction in breast biopsy, with high accuracy and real-time capability. Its integration with clinical procedures could significantly enhance the precision and efficiency of breast cancer diagnosis.

cs.LG↗

arXiv2025

Dual-Polarization SHG Interferometry for Imaging Antiparallel Domains and Stacking Angles of 2D Heterocrystals

Optical second-harmonic generation (SHG) enables orientational polarimetry for crystallographic analysis and domain imaging of various materials. However, conventional intensity polarimetry, which neglects phase information, fails to resolve antiparallel domains and to describe two-dimensional heterostructures, which represent a new class of van der Waals-bound composite crystals. In this work, we report dual-polarization spectral phase interferometry (DP-SPI) and establish a generalized SHG superposition model that incorporates the observables of DP-SPI. Antiparallel domains of monolayer transition metal dichalcogenides (TMDs) were successfully imaged with distinction, validating the interferometric polarimetry. From DP interferograms of TMD heterobilayers, the orientation of each layer could be determined, enabling layer-resolved probing. By employing the superposition model, we also demonstrate the photonic design and fabrication of ternary TMD heterostructures for circularly polarized SHG. These methods, providing comprehensive SHG measurements and theoretical description, can be extended to heterostructures consisting of more than two constituent layers and are not limited to TMDs or 2D materials.

physics.optics↗

arXiv2025

Balancing Efficiency and Quality: MoEISR for Arbitrary-Scale Image Super-Resolution

Arbitrary-scale image super-resolution employing implicit neural functions has gained significant attention lately due to its capability to upscale images across diverse scales utilizing only a single model. Nevertheless, these methodologies have imposed substantial computational demands as they involve querying every target pixel to a single resource-intensive decoder. In this paper, we introduce a novel and efficient framework, the Mixture-of-Experts Implicit Super-Resolution (MoEISR), which enables super-resolution at arbitrary scales with significantly increased computational efficiency without sacrificing reconstruction quality. MoEISR dynamically allocates the most suitable decoding expert to each pixel using a lightweight mapper module, allowing experts with varying capacities to reconstruct pixels across regions with diverse complexities. Our experiments demonstrate that MoEISR successfully reduces significant amount of floating point operations (FLOPs) while delivering comparable or superior peak signal-to-noise ratio (PSNR).

cs.CV↗

arXiv2025

K-DRIFT: Unveiling New Imagery of the Hidden Universe

Low-surface-brightness (LSB) structures play a crucial role in understanding galaxy evolution by providing significant insights into galaxy interactions, the histories of mass assembly, and the distribution of dark matter. Nevertheless, their inherently faint nature, coupled with observational difficulties such as stray light interference and variations in the sky background, has significantly impeded comprehensive studies of LSB features. The KASI Deep Rolling Imaging Fast Telescope (K-DRIFT) project aims to address these observational challenges by developing off-axis freeform three-mirror telescopes and observational strategies specifically designed for LSB imaging surveys. The first generation of the K-DRIFT (K-DRIFT G1) has been successfully completed, and the forthcoming survey, scheduled to commence shortly, is expected to yield novel insights into the LSB universe. This paper outlines the scientific motivations of the project, discusses the technical challenges encountered, highlights the innovative solutions devised, and describes the future trajectory of the K-DRIFT.

astro-ph.GA↗

arXiv2025

Subgradient Method for System Identification with Non-Smooth Objectives

This paper investigates a subgradient-based algorithm to solve the system identification problem for linear time-invariant systems with non-smooth objectives. This is essential for robust system identification in safety-critical applications. While existing work provides theoretical exact recovery guarantees using optimization solvers, the design of fast learning algorithms with convergence guarantees for practical use remains unexplored. We analyze the subgradient method in this setting, where the optimization problems to be solved evolve over time as new measurements are collected, and we establish linear convergence to the ground-truth system for both the best and Polyak step sizes after a burn-in period. We further characterize sublinear convergence of the iterates under constant and diminishing step sizes, which require only minimal information and thus offer broad applicability. Finally, we compare the time complexity of standard solvers with the subgradient algorithm and support our findings with experimental results. This is the first work to analyze subgradient algorithms for system identification with non-smooth objectives.

math.OC↗

arXiv2025

Higher order obstructions to Riccati-type equations

We develop new techniques in order to deal with Riccati-type equations, subject to a further algebraic constraint, on Riemannian manifolds $(M^3,g)$. We find that the obstruction to solve the aforementioned equation has order $4$ in the metric coefficients and is fully described by an homogeneous polynomial in $\mathrm{Sym}^{16}TM$. Techniques from real algebraic geometry, reminiscent of those used for the "PositiveStellen-Satz " problem, allow determining the geometry in terms of the connection coefficients and a class of Hessian-type equations. Analysis of the latter shows flatness for the metric $g$; in particular we complete the classification of asymptotically harmonic manifolds of dimension $3$, establishing those are either flat or real hyperbolic spaces.

math.DG↗

arXiv2025

Bridging Batch and Streaming Estimations to System Identification under Adversarial Attacks

System identification in modern engineering systems faces emerging challenges from unanticipated adversarial attacks beyond existing detection mechanisms. In this work, we obtain a provably accurate estimate of the Markov parameter matrix of order $k$ to identify partially observed linear systems, in which the probability of having an attack at each time is $O(1/k)$. We show that given the batch data accumulated up to time $T^*$, the $\ell_2$-norm estimator achieves an error decaying exponentially as $k$ grows. We then propose a stochastic projected subgradient descent algorithm on streaming data that produces an estimate at each time $t<T^*$, in which case the expected estimation error proves to be the larger of $O(k/\sqrt{t})$ and an exponentially decaying term in $k$. This stochastic approach illustrates how non-smooth estimators can leverage first-order methods despite lacking recursive formulas. Finally, we integrate batch and streaming estimations to recover the Hankel matrix using the appropriate estimates of the Markov parameter matrix, which enables the synthesis of a robust adaptive controller based on the estimated balanced truncated model under adversarial attacks.

math.OC↗

arXiv2025

System Identification from Partial Observations under Adversarial Attacks

This paper is concerned with the partially observed linear system identification, where the goal is to obtain reasonably accurate estimation of the balanced truncation of the true system up to order $k$ from output measurements. We consider the challenging case of system identification under adversarial attacks, where the probability of having an attack at each time is $Θ(1/k)$ while the value of the attack is arbitrary. We first show that the $\ell_1$-norm estimator exactly identifies the true Markov parameter matrix for nilpotent systems under any type of attack. We then build on this result to extend it to general systems and show that the estimation error exponentially decays as $k$ grows. The estimated balanced truncation model accordingly shows an exponentially decaying error for the identification of the true system up to a similarity transformation. This work is the first to provide the input-output analysis of the system with partial observations under arbitrary attacks.

math.OC↗

arXiv2025

Artificial Intelligence for Pediatric Height Prediction Using Large-Scale Longitudinal Body Composition Data

This study developed an accurate artificial intelligence model for predicting future height in children and adolescents using anthropometric and body composition data from the GP Cohort Study (588,546 measurements from 96,485 children aged 7-18). The model incorporated anthropometric measures, body composition, standard deviation scores, and growth velocity parameters, with performance evaluated using RMSE, MAE, and MAPE. Results showed high accuracy with males achieving average RMSE, MAE, and MAPE of 2.51 cm, 1.74 cm, and 1.14%, and females showing 2.28 cm, 1.68 cm, and 1.13%, respectively. Explainable AI approaches identified height SDS, height velocity, and soft lean mass velocity as crucial predictors. The model generated personalized growth curves by estimating individual-specific height trajectories, offering a robust tool for clinical decision support, early identification of growth disorders, and optimization of growth outcomes.

q-bio.QM↗

arXiv2025

Prevailing against Adversarial Noncentral Disturbances: Exact Recovery of Linear Systems with the $l_1$-norm Estimator

This paper studies the linear system identification problem in the general case where the disturbance is sub-Gaussian, correlated, and possibly adversarial. First, we consider the case with noncentral (nonzero-mean) disturbances for which the ordinary least-squares (OLS) method fails to correctly identify the system. We prove that the $l_1$-norm estimator accurately identifies the system under the condition that each disturbance has equal probabilities of being positive or negative. This condition restricts the sign of each disturbance but allows its magnitude to be arbitrary. Second, we consider the case where each disturbance is adversarial with the model that the attack times happen occasionally but the distributions of the attack values are arbitrary. We show that when the probability of having an attack at a given time is less than 0.5 and each attack spans the entire space in expectation, the $l_1$-norm estimator prevails against any adversarial noncentral disturbances and the exact recovery is achieved within a finite time. These results pave the way to effectively defend against arbitrarily large noncentral attacks in safety-critical systems.

math.OC↗

arXiv2024

Weakly Einstein hypersurfaces in space forms

A Riemannian manifold $(M,g)$ is called \emph{weakly Einstein} if the tensor $R_{iabc}R_{j}^{~~abc}$ is a scalar multiple of the metric tensor $g_{ij}$. We give a complete classification of weakly Einstein hypersurfaces in the spaces of nonzero constant curvature (the classification in a Euclidean space has been previously known). The main result states that such a hypersurface can only be the product of two spaces of constant curvature or a rotation hypersurface.

math.DG↗

arXiv2024

TALoS: Enhancing Semantic Scene Completion via Test-time Adaptation on the Line of Sight

Semantic Scene Completion (SSC) aims to perform geometric completion and semantic segmentation simultaneously. Despite the promising results achieved by existing studies, the inherently ill-posed nature of the task presents significant challenges in diverse driving scenarios. This paper introduces TALoS, a novel test-time adaptation approach for SSC that excavates the information available in driving environments. Specifically, we focus on that observations made at a certain moment can serve as Ground Truth (GT) for scene completion at another moment. Given the characteristics of the LiDAR sensor, an observation of an object at a certain location confirms both 1) the occupation of that location and 2) the absence of obstacles along the line of sight from the LiDAR to that point. TALoS utilizes these observations to obtain self-supervision about occupancy and emptiness, guiding the model to adapt to the scene in test time. In a similar manner, we aggregate reliable SSC predictions among multiple moments and leverage them as semantic pseudo-GT for adaptation. Further, to leverage future observations that are not accessible at the current time, we present a dual optimization scheme using the model in which the update is delayed until the future observation is available. Evaluations on the SemanticKITTI validation and test sets demonstrate that TALoS significantly improves the performance of the pre-trained SSC model. Our code is available at https://github.com/blue-531/TALoS.

cs.CV↗

arXiv2024

Online Bandit Nonlinear Control with Dynamic Batch Length and Adaptive Learning Rate

This paper is concerned with the online bandit nonlinear control, which aims to learn the best stabilizing controller from a pool of stabilizing and destabilizing controllers of unknown types for a given nonlinear dynamical system. We develop an algorithm, named Dynamic Batch length and Adaptive learning Rate (DBAR), and study its stability and regret. Unlike the existing Exp3 algorithm requiring an exponentially stabilizing controller, DBAR only needs a significantly weaker notion of controller stability, in which case substantial time may be required to certify the system stability. Dynamic batch length in DBAR effectively addresses this issue and enables the system to attain asymptotic stability, where the algorithm behaves as if there were no destabilizing controllers. Moreover, adaptive learning rate in DBAR only uses the state norm information to achieve a tight regret bound even when none of the stabilizing controllers in the pool are exponentially stabilizing.

eess.SY↗

arXiv2024

The landscape of deterministic and stochastic optimal control problems: One-shot Optimization versus Dynamic Programming

Optimal control problems can be solved via a one-shot (single) optimization or a sequence of optimization using dynamic programming (DP). However, the computation of their global optima often faces NP-hardness, and thus only locally optimal solutions may be obtained at best. In this work, we consider the discrete-time finite-horizon optimal control problem in both deterministic and stochastic cases and study the optimization landscapes associated with two different approaches: one-shot and DP. In the deterministic case, we prove that each local minimizer of the one-shot optimization corresponds to some control input induced by a locally minimum control policy of DP, and vice versa. However, with a parameterized policy approach, we prove that deterministic and stochastic cases both exhibit the desirable property that each local minimizer of DP corresponds to some local minimizer of the one-shot optimization, but the converse does not necessarily hold. Nonetheless, under different technical assumptions for deterministic and stochastic cases, if there exists only a single locally minimum control policy, one-shot and DP turn out to capture the same local solution. These results pave the way to understand the performance and stability of local search methods in optimal control.

math.OC↗

arXiv2024

UniHENN: Designing Faster and More Versatile Homomorphic Encryption-based CNNs without im2col

Homomorphic encryption (HE) enables privacy-preserving deep learning by allowing computations on encrypted data without decryption. However, deploying convolutional neural networks (CNNs) with HE is challenging due to the need to convert input data into a two-dimensional matrix for convolution using the im2col technique, which rearranges the input for efficient computation. This restricts the types of CNN models that can be used since the encrypted data structure must be compatible with the specific model. UniHENN is a novel HE-based CNN architecture that eliminates the need for im2col, enhancing its versatility and compatibility with a broader range of CNN models. UniHENN flattens input data to one dimension without using im2col. The kernel performs convolutions by traversing the image, using incremental rotations and structured multiplication on the flattened input, with results spaced by the stride interval. Experimental results show that UniHENN significantly outperforms the state-of-the-art 2D CNN inference architecture named PyCrCNN in terms of inference time. For example, on the LeNet-1 model, UniHENN achieves an average inference time of 30.089 seconds, about 26.6 times faster than PyCrCNN's 800.591 seconds. Furthermore, UniHENN outperforms TenSEAL, an im2col-optimized CNN model, in concurrent image processing. For ten samples, UniHENN (16.247 seconds) was about 3.9 times faster than TenSEAL (63.706 seconds), owing to its support for batch processing of up to 10 samples. We demonstrate UniHENN's adaptability to various CNN architectures, including a 1D CNN and six 2D CNNs, highlighting its flexibility and efficiency for privacy-preserving cloud-based CNN services.

cs.CR↗

arXiv2024

Long-Tailed Recognition on Binary Networks by Calibrating A Pre-trained Model

Deploying deep models in real-world scenarios entails a number of challenges, including computational efficiency and real-world (e.g., long-tailed) data distributions. We address the combined challenge of learning long-tailed distributions using highly resource-efficient binary neural networks as backbones. Specifically, we propose a calibrate-and-distill framework that uses off-the-shelf pretrained full-precision models trained on balanced datasets to use as teachers for distillation when learning binary networks on long-tailed datasets. To better generalize to various datasets, we further propose a novel adversarial balancing among the terms in the objective function and an efficient multiresolution learning scheme. We conducted the largest empirical study in the literature using 15 datasets, including newly derived long-tailed datasets from existing balanced datasets, and show that our proposed method outperforms prior art by large margins (>14.33% on average).

cs.CV↗

arXiv2023

On weakly Einstein submanifolds in space forms satisfying certain equalities

We classify weakly Einstein submanifolds in space forms that satisfy Chen's equality. We also give a classification of weakly Einstein hypersurfaces in space forms that satisfy the semisymmetric condition. In addition, we discuss some characterizations of weakly Einstein submanifolds in space forms whose normal connection is flat.

math.DG↗

arXiv2023

Exciton-Sensitized Second-Harmonic Generation in 2D Heterostructures

The efficient optical second-harmonic generation (SHG) of two-dimensional (2D) crystals, coupled with their atomic thickness that circumvents the phase-match problem, has garnered considerable attention. While various 2D heterostructures have shown promising applications in photodetectors, switching electronics, and photovoltaics, the modulation of nonlinear optical properties in such hetero-systems remains unexplored. In this study, we investigate exciton sensitized SHG in heterobilayers of transition metal dichalcogenides (TMDs), where photoexcitation of one donor layer enhances the SHG response of the other as an acceptor. We utilize polarization-resolved interferometry to detect the SHG intensity and phase of each individual layer, revealing the energetic match between the excitonic resonances of donors and the SHG enhancement of acceptors for four TMD combinations. Our results also uncover the dynamic nature of interlayer coupling, as evidenced by the dependence of sensitization on interlayer gap spacing and the average power of the fundamental beam. This work provides insights into how interlayer coupling of two different layers can modify nonlinear optical phenomena in 2D heterostructures.

cond-mat.mtrl-sci↗