arXiv subjects

Junho Choi

Publications and source records attributed to Junho Choi.

At least 19 recordsLinked to original sources

arXiv2026

Bulk and surface excitons in the van der Waals magnet CrSBr: Magneto-optical studies to 55 tesla

In thin layers of the 2D magnetic semiconductor CrSBr, very recent studies identified two distinct band-edge optical resonances, believed to arise from distinguishable bulk and surface excitons. This behavior reportedly originates from the highly anisotropic nature of CrSBr -- particularly in its antiferromagnetic state -- where excitons are effectively confined within individual monolayers, such that excitons in the two surface layers "see" a different local dielectric environment and have a lower resonance energy. To explore this scenario, here we investigate optical absorption properties of few-layer CrSBr in magnetic fields. In addition to the fundamental exciton resonance at ~1.36eV, we observe an absorption resonance ~20 meV lower in energy. Compared to the fundamental transition, this resonance redshifts only half as much in small magnetic fields that induce ferromagnetic order, while in high fields to 55T it exhibits a smaller diamagnetic shift. Both behaviors point to distinguishable populations of bulk and surface excitons in CrSBr.

cond-mat.mes-hall↗

arXiv2026

Band Renormalization in Monolayer MoS2 Induced by Multipole Screening

Dielectric screening plays a crucial role in shaping the electronic structure of two-dimensional (2D) materials. In 2D semiconductors, screened Coulomb interactions arising from the surrounding dielectric environment are known to induce band renormalization, which is typically understood as a rigid shift of the electronic bands. Here, we experimentally demonstrate that dielectric screening can also give rise to non-rigid, momentum-dependent band renormalization. Using temperature-dependent angle-resolved photoemission spectroscopy (ARPES), we observe pronounced changes in the electronic band structure of monolayer MoS2 on a highly oriented pyrolytic graphite (HOPG) substrate. The results indicate that temperature-driven variations in the effective interlayer separation modulate the dielectric screening experienced by monolayer MoS2. At room temperature, the screening behavior is well described by a momentum-independent monopole approximation, whereas at liquid-helium temperatures the screening evolves into a multipole-like regime, leading to momentum-dependent band shifts.

cond-mat.str-el↗

arXiv2026

Modulating nonlinear optical responses in 3R-MoS$_2$ Fabry-P\'erot microcavities

Rhombohedrally stacked transition metal dichalcogenides such as 3R-MoS$_2$ offer an exceptional platform for nonlinear optics, naturally forming Fabry-P\'erot (FP) microcavities due to their giant dielectric contrast with the surrounding media. However, rigorously tracking the evolution of multiple harmonic fields within these unpatterned monolithic crystals remains a fundamental challenge. Here, we establish a self-consistent framework, spanning from linear broadband reflectance to second- and third-harmonic generation (SHG and THG), to systematically decode these nonlinear behaviors. Moving beyond conventional models, we demonstrate that the nonlinear emission is dictated by a delicate interplay among the intrinsic material absorption, the FP effects at the fundamental frequency, as well as those at the harmonic frequencies. When harmonic photons lie below the bandgap, weak absorption allows the nonlinear spectra to exhibit a complex modulation driven by the synergistic contribution of FP effects from both fundamental and harmonic waves. In stark contrast, severe intrinsic absorption of higher-energy photons heavily damps the FP effects of the harmonic fields, reducing the nonlinear response to an absorption-limited regime modulated almost exclusively by the FP effects at the fundamental frequency. By successfully decoupling these geometric and material contributions across different harmonic orders, our findings provide a precise design paradigm for engineering next-generation van der Waals photonic architectures.

physics.optics↗

arXiv2026

Why Look at It at All?: Vision-Free Multifingered Blind Grasping Using Uniaxial Fingertip Force Sensing

Grasping under limited sensing remains a fundamental challenge for real-world robotic manipulation, as vision and high-resolution tactile sensors often introduce cost, fragility, and integration complexity. This work demonstrates that reliable multifingered grasping can be achieved under extremely minimal sensing by relying solely on uniaxial fingertip force feedback and joint proprioception, without vision or multi-axis/tactile sensing. To enable such blind grasping, we employ an efficient teacher-student training pipeline in which a reinforcement-learned teacher exploits privileged simulation-only observations to generate demonstrations for distilling a transformer-based student policy operating under partial observation. The student policy is trained to act using only sensing modalities available at real-world deployment. We validate the proposed approach on real hardware across 18 objects, including both in-distribution and out-of-distribution cases, achieving a 98.3~$\%$ overall grasp success rate. These results demonstrate strong robustness and generalization beyond the simulation training distribution, while significantly reducing sensing requirements for real-world grasping systems.

cs.RO↗

arXiv2025

A data free neural operator enabling fast inference of 2D and 3D Navier Stokes equations

Ensemble simulations of high-dimensional flow models (e.g., Navier Stokes type PDEs) are computationally prohibitive for real time applications. Neural operators enable fast inference but are limited by costly data requirements and poor generalization to 3D flows. We present a data-free operator network for the Navier Stokes equations that eliminates the need for paired solution data and enables robust, real time inference for large ensemble forecasting. The physics-grounded architecture takes initial and boundary conditions as well as forcing functions, yielding solutions robust to high variability and perturbations. Across 2D benchmarks and 3D test cases, the method surpasses prior neural operators in accuracy and, for ensembles, achieves greater efficiency than conventional numerical solvers. Notably, it delivers accurate solutions of the three dimensional Navier Stokes equations, a regime not previously demonstrated for data free neural operators. By uniting a numerically grounded architecture with the scalability of machine learning, this approach establishes a practical pathway toward data free, high fidelity PDE surrogates for end to end scientific simulation and prediction.

cs.LG↗

arXiv2025

On the role of the design phase in a linear regression

The "design phase" refers to a stage in observational studies, during which a researcher constructs a subsample that achieves a better balance in covariate distributions between the treated and untreated units. In this paper, we study the role of this preliminary phase in the context of linear regression, offering a justification for its utility. To that end, we first formalize the design phase as a process of estimand adjustment via selecting a subsample. Then, we show that covariate balance of a subsample is indeed a justifiable criterion for guiding the selection: it informs on the maximum degree of model misspecification that can be allowed for a subsample, when a researcher wishes to restrict the bias of the estimand for the parameter of interest within a target level of precision. In this sense, the pursuit of a balanced subsample in the design phase is interpreted as identifying an estimand that is less susceptible to bias in the presence of model misspecification. Also, we demonstrate that covariate imbalance can serve as a sensitivity measure in regression analysis, and illustrate how it can structure a communication between a researcher and the readers of her report.

econ.EM↗

arXiv2025

SaWa-ML: Structure-Aware Pose Correction and Weight Adaptation-Based Robust Multi-Robot Localization

Multi-robot localization is a crucial task for implementing multi-robot systems. Numerous researchers have proposed optimization-based multi-robot localization methods that use camera, IMU, and UWB sensors. Nevertheless, characteristics of individual robot odometry estimates and distance measurements between robots used in the optimization are not sufficiently considered. In addition, previous researches were heavily influenced by the odometry accuracy that is estimated from individual robots. Consequently, long-term drift error caused by error accumulation is potentially inevitable. In this paper, we propose a novel visual-inertial-range-based multi-robot localization method, named SaWa-ML, which enables geometric structure-aware pose correction and weight adaptation-based robust multi-robot localization. Our contributions are twofold: (i) we leverage UWB sensor data, whose range error does not accumulate over time, to first estimate the relative positions between robots and then correct the positions of each robot, thus reducing long-term drift errors, (ii) we design adaptive weights for robot pose correction by considering the characteristics of the sensor data and visual-inertial odometry estimates. The proposed method has been validated in real-world experiments, showing a substantial performance increase compared with state-of-the-art algorithms.

cs.RO↗

arXiv2025

Explainable AI-Based Interface System for Weather Forecasting Model

Machine learning (ML) is becoming increasingly popular in meteorological decision-making. Although the literature on explainable artificial intelligence (XAI) is growing steadily, user-centered XAI studies have not extend to this domain yet. This study defines three requirements for explanations of black-box models in meteorology through user studies: statistical model performance for different rainfall scenarios to identify model bias, model reasoning, and the confidence of model outputs. Appropriate XAI methods are mapped to each requirement, and the generated explanations are tested quantitatively and qualitatively. An XAI interface system is designed based on user feedback. The results indicate that the explanations increase decision utility and user trust. Users prefer intuitive explanations over those based on XAI algorithms even for potentially easy-to-recognize examples. These findings can provide evidence for future research on user-centered XAI algorithms, as well as a basis to improve the usability of AI systems in practice.

cs.AI↗

arXiv2025

Example-Based Concept Analysis Framework for Deep Weather Forecast Models

To improve the trustworthiness of an AI model, finding consistent, understandable representations of its inference process is essential. This understanding is particularly important in high-stakes operations such as weather forecasting, where the identification of underlying meteorological mechanisms is as critical as the accuracy of the predictions. Despite the growing literature that addresses this issue through explainable AI, the applicability of their solutions is often limited due to their AI-centric development. To fill this gap, we follow a user-centric process to develop an example-based concept analysis framework, which identifies cases that follow a similar inference process as the target instance in a target model and presents them in a user-comprehensible format. Our framework provides the users with visually and conceptually analogous examples, including the probability of concept assignment to resolve ambiguities in weather mechanisms. To bridge the gap between vector representations identified from models and human-understandable explanations, we compile a human-annotated concept dataset and implement a user interface to assist domain experts involved in the the framework development.

cs.AI↗

arXiv2024

Single-gate electro-optic beam switching metasurfaces

Electro-optic active metasurfaces have attracted attention due to their ability to electronically control optical wavefront with unprecedented spatiotemporal resolutions. In most studies, such devices require gate arrays composed of a large number of independently-controllable local gate electrodes that address local scattering response of individual metaatoms. Although this approach in principle enables arbitrary wavefront control, the complicated driving mechanism and low optical efficiency have been hindering its practical applications. In this work, we demonstrate an active beam switching device that provides high directivity, uniform efficiency across diffraction orders, and a wide field of view while operating with only a single-gate bias. Experimentally, the metasurface achieves 57{\deg} of active beam switching from the 0th to the -1st order diffraction, with efficiencies of 0.084 and 0.078 and directivities of 0.765 and 0.836, respectively. Furthermore, an analytical framework using nonlocal quasinormal mode expansion provides deeper insight into the operating mechanism of active beam switching. Finally, we discuss the performance limitations of this design platform and provide insights into potential improvements.

physics.optics↗

arXiv2024

Singularity formation of hydromagnetic waves in cold plasma

We study $C^1$ blow-up of the compressible fluid model introduced by Gardner and Morikawa, which describes the dynamics of a magnetized cold plasma. We propose sufficient conditions that lead to $C^1$ blow-up. In particular, we find that smooth solutions can break down in finite time even if the gradient of initial velocity is identically zero. The density and the gradient of the velocity become unbounded as time approaches the lifespan of the smooth solution. The Lagrangian formulation reduces the singularity formation problem to finding a zero of the associated second-order ODE.

math.AP↗

arXiv2024

Latent group structure in linear panel data models with endogenous regressors

This paper concerns the estimation of linear panel data models with endogenous regressors and a latent group structure in the coefficients. We consider instrumental variables estimation of the group-specific coefficient vector. We show that direct application of the Kmeans algorithm to the generalized method of moments objective function does not yield unique estimates. We newly develop and theoretically justify two-stage estimation methods that apply the Kmeans algorithm to a regression of the dependent variable on predicted values of the endogenous regressors. The results of Monte Carlo simulations demonstrate that two-stage estimation with the first stage modeled using a latent group structure achieves good classification accuracy, even if the true first-stage regression is fully heterogeneous. We apply our estimation methods to revisiting the relationship between income and democracy.

econ.EM↗

arXiv2023

Spectral operator learning for parametric PDEs without data reliance

In this paper, we introduce the Spectral Coefficient Learning via Operator Network (SCLON), a novel operator learning-based approach for solving parametric partial differential equations (PDEs) without the need for data harnessing. The cornerstone of our method is the spectral methodology that employs expansions using orthogonal functions, such as Fourier series and Legendre polynomials, enabling accurate PDE solutions with fewer grid points. By merging the merits of spectral methods - encompassing high accuracy, efficiency, generalization, and the exact fulfillment of boundary conditions - with the prowess of deep neural networks, SCLON offers a transformative strategy. Our approach not only eliminates the need for paired input-output training data, which typically requires extensive numerical computations, but also effectively learns and predicts solutions of complex parametric PDEs, ranging from singularly perturbed convection-diffusion equations to the Navier-Stokes equations. The proposed framework demonstrates superior performance compared to existing scientific machine learning techniques, offering solutions for multiple instances of parametric PDEs without harnessing data. The mathematical framework is robust and reliable, with a well-developed loss function derived from the weak formulation, ensuring accurate approximation of solutions while exactly satisfying boundary conditions. The method's efficacy is further illustrated through its ability to accurately predict intricate natural behaviors like the Kolmogorov flow and boundary layers. In essence, our work pioneers a compelling avenue for parametric PDE solutions, serving as a bridge between traditional numerical methodologies and cutting-edge machine learning techniques in the realm of scientific computation.

cs.LG↗

arXiv2022

Unsupervised Legendre-Galerkin Neural Network for Singularly Perturbed Partial Differential Equations

Machine learning methods have been lately used to solve partial differential equations (PDEs) and dynamical systems. These approaches have been developed into a novel research field known as scientific machine learning in which techniques such as deep neural networks and statistical learning are applied to classical problems of applied mathematics. In this paper, we develop a novel numerical algorithm that incorporates machine learning and artificial intelligence to solve PDEs. Based on the Legendre-Galerkin framework, we propose the {\it unsupervised machine learning} algorithm to learn {\it multiple instances} of the solutions for different types of PDEs. Our approach overcomes the limitations of data-driven and physics-based methods. The proposed neural network is applied to general 1D and 2D PDEs with various boundary conditions as well as convection-dominated {\it singularly perturbed PDEs} that exhibit strong boundary layer behavior.

cs.LG↗

arXiv2022

Asymmetric magnetic proximity interactions in MoSe$_{2}$/CrBr$_{3}$ van der Waals heterostructures

Magnetic proximity interactions (MPIs) between atomically-thin semiconductors and two-dimensional magnets provide a means to manipulate spin and valley degrees of freedom in nonmagnetic monolayers, without the use of applied magnetic fields. In such van der Waals (vdW) heterostructures, MPIs originate in the nanometer-scale coupling between the spin-dependent electronic wavefunctions in the two materials, and typically their overall effect is regarded as an effective magnetic field acting on the semiconductor monolayer. Here we demonstrate that this picture, while appealing, is incomplete: The effects of MPIs in vdW heterostructures can be markedly asymmetric, in contrast to that from an applied magnetic field. Valley-resolved optical reflection spectroscopy of MoSe$_{2}$/CrBr$_{3}$ vdW structures reveals strikingly different energy shifts in the $K$ and $K'$ valleys of the MoSe$_2$, due to ferromagnetism in the CrBr$_3$ layer. Strong asymmetry is observed at both the A- and B-exciton resonances. Density-functional calculations indicate that valley-asymmetric MPIs depend sensitively on the spin-dependent hybridization of overlapping bands, and as such are likely a general feature of such hybrid vdW structures. These studies suggest routes to selectively control \textit{specific} spin and valley states in monolayer semiconductors.

cond-mat.mes-hall↗

arXiv2022

Dative epitaxy of commensurate monocrystalline covalent-van der Waals moir\'e supercrystal

Realizing van der Waals (vdW) epitaxy in the 80s represents a breakthrough that circumvents the stringent lattice matching and processing compatibility requirements in conventional covalent heteroepitaxy. However, due to the weak vdW interactions, there is little control over film qualities by the substrate. Typically, discrete domains with a spread of misorientation angles are formed, limiting the applicability of vdW epitaxy. Here we report the epitaxial growth of monocrystalline, covalent Cr5Te8 2D crystals on monolayer vdW WSe2 by chemical vapor deposition, driven by interfacial dative bond formation. The lattice of Cr5Te8, with a lateral dimension of a few ten microns, is fully commensurate with that of WSe2 via 3 x 3 (Cr5Te8)-7 x 7 (WSe2) supercell matching, forming a single crystalline moire superlattice. Our work has established a conceptually distinct paradigm of thin film epitaxy termed dative epitaxy, which takes full advantage of covalent epitaxy with chemical bonding for fixing the atomic registry and crystal orientation, while circumventing its stringent lattice matching and processing compatibility requirements; conversely, it ensures the full flexibility of vdW epitaxy, while avoiding its poor orientation control. Cr5Te8 2D crystals grown by dative epitaxy exhibit square magnetic hysteresis, suggesting minimized interfacial defects that can serve as pinning sites.

cond-mat.mtrl-sci↗

arXiv2022

Proximity Induced Chiral Quantum Light Generation in Strain-Engineered WSe2/NiPS3 Heterostructures

Quantum light emitters (QEs) capable of generating single photons of well-defined circular polarization could enable non-reciprocal single photon devices and deterministic spin-photon interfaces critical for realizing complex quantum networks. To date, emission of such chiral quantum light has been achieved via the application of intense external magnetic field electrical/optical injection of spin polarized carriers/excitons, or coupling with complex photonic/meta-structures. Here we report free-space generation of highly chiral single photons from QEs created in monolayer WSe2 - NiPS3 heterostructures at zero external magnetic field. These QEs emit in the 760-800 nm range with a degree of circular polarization and single photon purity as high as 0.71 and 80% respectively, independent of pump laser polarization. QEs are deterministically created by pressing a scanning probe microscope tip into a two-dimensional heterostructure comprising a WSe2 monolayer and a ~50 nm thick layer of the antiferromagnetic (AFM) insulator NiPS3. Temperature dependent magneto-photoluminescence studies indicate that the chiral quantum light emission arises from magnetic proximity interactions between localized excitons in the WSe2 monolayer and the out-of-plane magnetization of AFM defects in NiPS3, both of which are co-localized by the strain field arising from the nanoscale indentations.

cond-mat.mes-hall↗

arXiv2022

Many-body exciton and inter-valley correlations in heavily electron-doped WSe$_2$ monolayers

In monolayer transition-metal dichalcogenide semiconductors, many-body correlations can manifest in optical spectra when photoexcited electron-hole pairs (excitons) are introduced into a 2D Fermi sea of mobile carriers. At low carrier densities, the formation of positively and negatively charged excitons ($X^\pm$) is well documented. However, in WSe$_2$ monolayers, an additional absorption resonance, often called $X^{-\prime}$, emerges at high electron density. Its origin is not understood. Here we investigate the $X^{-\prime}$ state via polarized absorption spectroscopy of electrostatically-gated WSe$_2$ monolayers in high magnetic fields to 60~T. Field-induced filling and emptying of the lowest optically-active Landau level in the $K'$ valley causes repeated quenching of the corresponding optical absorption. Surprisingly, however, these quenchings are accompanied by absorption changes to higher-lying Landau levels in both $K'$ and $K$ valleys, which are unoccupied. These results cannot be reconciled within a single-particle picture, and demonstrate the many-body nature and inter-valley correlations of the $X^{-\prime}$ quasiparticle state.

cond-mat.mes-hall↗