arXiv subjects

Jihyun Kim

Publications and source records attributed to Jihyun Kim.

At least 19 recordsLinked to original sources

arXiv2026

Recent Findings from the Telescope Array Experiment

The Telescope Array (TA) is the largest ultra-high energy cosmic ray (UHECR) observatory in the Northern Hemisphere. Together with its extensions, TA Low Energy (TALE), TALE infill, and the TA$\times$4 array, it measures extensive air showers (EAS) initiated by UHECRs across an energy range spanning from $10^{15}$ eV to beyond $10^{20}$ eV. All components of the experiment employ a hybrid detection approach, combining plastic scintillator arrays that sample the EAS footprint at ground level with telescopes that record fluorescence and Cherenkov light from shower development in the atmosphere. The ongoing construction of TA$\times$4 will significantly increase statistics at the highest energies by expanding the surface detector area by a factor of four. In addition, the recently deployed TALE infill array further lowers the hybrid energy threshold of TALE. This presentation summarizes the current status of the TA experiment and highlights recent findings on the energy spectrum, mass composition, and anisotropy.

astro-ph.HE↗

arXiv2026

MentalBench: A DSM-Grounded Benchmark for Evaluating Psychiatric Diagnostic Capability of Large Language Models

Large language models (LLMs) have attracted growing interest as supportive tools for psychiatric assessment and clinical decision support. However, existing mental health benchmarks largely rely on social media data or supportive dialogue settings, limiting their ability to assess whether models can apply formal diagnostic criteria and differential diagnostic rules. In this paper, we introduce MentalBench, a benchmark for evaluating whether LLMs can make DSM-grounded psychiatric diagnostic decisions under varying levels of clinical ambiguity. At the core of MentalBench is MentalKG, a psychiatrist-built and validated knowledge graph encoding DSM-5 diagnostic criteria and differential diagnostic rules for 23 psychiatric disorders. Using MentalKG as an expert-curated logical backbone, we generate 24,750 synthetic clinical cases that systematically vary in information completeness and diagnostic complexity, enabling DSM-grounded evaluation. Our experiments show that although state-of-the-art LLMs perform well on noise-free queries that probe DSM-5 knowledge, they struggle to calibrate their confidence when distinguishing between disorders with overlapping symptoms. These findings raise concerns about the reliability of LLMs as psychiatric decision-support tools and highlight the need for more evaluation that reflects the diverse challenges in real-world psychiatric diagnosis.

cs.CL↗

arXiv2026

Robust Cauchy-Based Methods for Predictive Regressions

This paper develops robust inference methods for predictive regressions that address key challenges posed by endogenously persistent or heavy-tailed regressors, as well as persistent volatility in errors. Building on the Cauchy estimation framework, we propose two novel tests: one based on $t$-statistic group inference and the other employing a hybrid approach that combines Cauchy and OLS estimation. These methods effectively mitigate size distortions that commonly arise in standard inference procedures under endogeneity, near nonstationarity, heavy tails, and persistent volatility. The proposed tests are simple to implement and applicable to both continuous- and discrete-time models. Extensive simulation experiments demonstrate favorable finite-sample performance across a range of realistic settings. An empirical application examines the predictability of excess stock returns using the dividend-price and earnings-price ratios as predictors. The results suggest that the dividend-price ratio possesses predictive power, whereas the earnings-price ratio does not significantly forecast returns.

econ.EM↗

arXiv2026

SynSym: A Synthetic Data Generation Framework for Psychiatric Symptom Identification

Psychiatric symptom identification on social media aims to infer fine-grained mental health symptoms from user-generated posts, allowing a detailed understanding of users' mental states. However, the construction of large-scale symptom-level datasets remains challenging due to the resource-intensive nature of expert labeling and the lack of standardized annotation guidelines, which in turn limits the generalizability of models to identify diverse symptom expressions from user-generated text. To address these issues, we propose SynSym, a synthetic data generation framework for constructing generalizable datasets for symptom identification. Leveraging large language models (LLMs), SynSym constructs high-quality training samples by (1) expanding each symptom into sub-concepts to enhance the diversity of generated expressions, (2) producing synthetic expressions that reflect psychiatric symptoms in diverse linguistic styles, and (3) composing realistic multi-symptom expressions, informed by clinical co-occurrence patterns. We validate SynSym on three benchmark datasets covering different styles of depressive symptom expression. Experimental results demonstrate that models trained solely on the synthetic data generated by SynSym perform comparably to those trained on real data, and benefit further from additional fine-tuning with real data. These findings underscore the potential of synthetic data as an alternative resource to real-world annotations in psychiatric symptom modeling, and SynSym serves as a practical framework for generating clinically relevant and realistic symptom expressions.

cs.CL↗

arXiv2025

Strain Engineering of van Hove Singularity and Coupled Itinerant Ferromagnetism in Quasi-2D Oxide Superlattices

Engineering van Hove singularities (vHss) near the Fermi level, if feasible, offers a powerful route to control exotic quantum phases in electronic and magnetic behaviors. However, conventional approaches, which rely primarily on chemical and electrical doping, focus mainly on local electrical or optical measurements, limiting their applicability to coupled functionalities. In this study, a vHs-induced insulator-metal transition coupled with a ferromagnetic phase transition was empirically achieved in atomically designed quasi-2D SrRuO3 (SRO) superlattices via epitaxial strain engineering, which has not been observed in conventional 3D SRO systems. Theoretical calculations revealed that epitaxial strain effectively modulates the strength and energy positions of vHs of specific Ru orbitals, driving correlated phase transitions in the electronic and magnetic ground states. X-ray absorption spectroscopy confirmed the anisotropic electronic structure of quasi-2D SRO modulated by epitaxial strain. Magneto-optic Kerr effect and electrical transport measurements demonstrated modulated magnetic and electronic phases. Furthermore, magneto-electrical measurements detected significant anomalous Hall effect signals and ferromagnetic magnetoresistance, indicating the presence of magnetically coupled charge carriers in the 2D metallic regime. This study establishes strain engineering as a promising platform for tuning vHss and resultant itinerant ferromagnetism of low-dimensional correlated quantum systems.

cond-mat.mtrl-sci↗

arXiv2025

Features in the Cosmic Ray Energy Spectrum Observed with Telescope Array Surface Detectors

Ultra-high energy cosmic rays (UHECRs) are extremely energetic charged particles that originate from outer space. The Telescope Array (TA) experiment, the largest UHECR observatory in the Northern Hemisphere, has provided high-precision measurements of the cosmic ray energy spectrum due to its stable operation and efficient data collection. These measurements have revealed three significant spectral features: the ankle, shoulder, and cutoff. Analyzing these features is crucial for understanding the origin and propagation of UHECRs. In this talk, we will present the latest energy spectrum measured by the TA surface detectors and discuss the observed differences in the UHECR energy spectrum between the northern and southern skies.

astro-ph.HE↗

arXiv2025

Medium-scale anisotropies measured by Telescope Array surface detectors

The Telescope Array (TA) experiment, the largest observatory for ultra-high energy cosmic rays in the Northern Hemisphere, has identified two medium-scale anisotropies: the TA Hotspot near the constellation Ursa Major and an excess in the direction of the Perseus-Pisces supercluster. Studying these medium-scale anisotropies may provide insights into the origins of ultra-high energy cosmic rays. This presentation will explore an oversampling analysis of TA surface detector data to evaluate these medium-scale event excesses and will present the latest findings on the TA Hotspot and the Perseus-Pisces supercluster excess.

astro-ph.HE↗

arXiv2025

SAGE-LD: Towards Scalable and Generalizable End-to-End Language Diarization via Simulated Data Augmentation

In this paper, we present a neural spoken language diarization model that supports an unconstrained span of languages within a single framework. Our approach integrates a learnable query-based architecture grounded in multilingual awareness, with large-scale pretraining on simulated code-switching data. By jointly leveraging these two components, our method overcomes the limitations of conventional approaches in data scarcity and architecture optimization, and generalizes effectively to real-world multilingual settings across diverse environments. Experimental results demonstrate that our approach achieves state-of-the-art performance on several language diarization benchmarks, with a relative performance improvement of 23% to 52% over previous methods. We believe that this work not only advances research in language diarization but also establishes a foundational framework for code-switching speech technologies.

cs.CL↗

arXiv2025

Hybridization gap and $f$-electron effect evolutions with Cd- and Sn-doping in CeCoIn$_5$ via infrared spectroscopy

We investigated hole (Cd)- and electron (Sn)-doped CeCoIn$_5$ (CeCo(In$_{1-x}T_x$)$_5$ ($T$ = Cd or Sn)) using infrared spectroscopy. Doping-dependent hybridization gap distribution functions were obtained from the optical conductivity spectra based on the periodic Anderson model formalism. The hybridization gap distribution exhibits two components: in-plane and out-of-plane hybridization gaps. The doping-dependent evolution of the two gaps indicated that the out-of-plane gap was more sensitive to doping. Furthermore, the magnetic optical resistivity exhibited a doping-dependent evolution of the $f$-electron amplitude. The two dopant types exhibited different physical properties depending on the level of doping. The Sn dopant increases the $f$-electron amplitude, whereas the Cd dopant does not affect the $f$-electron amplitude. Doping-dependent effective mass is peaked at pure (or undoped) CeCoIn$_5$. Our spectroscopic results may help understand the doping-dependent electronic evolution of one of the canonical heavy fermion systems, CeCoIn$_5$.

cond-mat.str-el↗

arXiv2025

Updated comparison of the UHECR energy spectra measured by the Pierre Auger Observatory and the Telescope Array

The Pierre Auger and Telescope Array joint Working Group on the UHECR energy spectrum was established in 2012 to analyze energy scale uncertainties in both experiments and to in vestigate their systematic differences, particularly in the spectral shape of the flux measurements. Previous studies have indeed shown that, within systematic uncertainties, the energy s pectra measured by the two observatories are consistent below~$10\,\mathrm{EeV}$. However, at higher energies, a significant difference remains. In this work, we re-examine this discrepan cy in greater detail and explore its possible origins. We consider systematic and statistical uncertainties, including the conversion from directly measured observables to energy and the calculation of exposures. We present an updated energy scale comparison between the two experiments and updated flux measurements in the common declination band.

astro-ph.HE↗

arXiv2025

Region-Aware Multimodal Large Language Model via SlowFast Tokenization and Pseudo-Mask Guidance for 3D CT Report Generation

Current CT report generation frameworks predominantly rely on global feature representations, often failing to capture region-specific details and potentially missing certain abnormalities. To overcome this limitation, we propose MedRegion-CT, a region-focused multimodal large language model framework featuring three key innovations. First, we revisit the SlowFast strategy to jointly model global and fine-grained information and adapt it to the medical domain via a Region-based SlowFast Tokenizer that extracts tokens guided by clinically meaningful regions. Second, generated pseudo-masks guide the model to attend to diagnostically important anatomical regions, facilitating a systematic understanding of the overall scan context. Third, quantitative lesion information, including size, diameter, and spatial location, is encoded as structured textual prompts, enabling context-aware and clinically informed report generation. To enable rigorous evaluation, we validate our framework on multi-institutional structured report generation benchmarks. Experimental results demonstrate that MedRegion-CT achieves state-of-the-art performance, outperforming existing approaches in both linguistic quality and clinical accuracy. All code is publicly available at: https://github.com/babbu3682/MedRegion-CT.

eess.IV↗

arXiv2025

MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports

Computed Tomography (CT) plays a crucial role in clinical diagnosis, but the growing demand for CT examinations has raised concerns about diagnostic errors. While Multimodal Large Language Models (MLLMs) demonstrate promising comprehension of medical knowledge, their tendency to produce inaccurate information highlights the need for rigorous validation. However, existing medical visual question answering (VQA) benchmarks primarily focus on simple visual recognition tasks, lacking clinical relevance and failing to assess expert-level knowledge. We introduce MedErr-CT, a novel benchmark for evaluating medical MLLMs' ability to identify and correct errors in CT reports through a VQA framework. The benchmark includes six error categories - four vision-centric errors (Omission, Insertion, Direction, Size) and two lexical error types (Unit, Typo) - and is organized into three task levels: classification, detection, and correction. Using this benchmark, we quantitatively assess the performance of state-of-the-art 3D medical MLLMs, revealing substantial variation in their capabilities across different error types. Our benchmark contributes to the development of more reliable and clinically applicable MLLMs, ultimately helping reduce diagnostic errors and improve accuracy in clinical practice. The code and datasets are available at https://github.com/babbu3682/MedErr-CT.

cs.CV↗

arXiv2025

Programmable-Room: Interactive Textured 3D Room Meshes Generation Empowered by Large Language Models

We present Programmable-Room, a framework which interactively generates and edits a 3D room mesh, given natural language instructions. For precise control of a room's each attribute, we decompose the challenging task into simpler steps such as creating plausible 3D coordinates for room meshes, generating panorama images for the texture, constructing 3D meshes by integrating the coordinates and panorama texture images, and arranging furniture. To support the various decomposed tasks with a unified framework, we incorporate visual programming (VP). VP is a method that utilizes a large language model (LLM) to write a Python-like program which is an ordered list of necessary modules for the various tasks given in natural language. We develop most of the modules. Especially, for the texture generating module, we utilize a pretrained large-scale diffusion model to generate panorama images conditioned on text and visual prompts (i.e., layout, depth, and semantic map) simultaneously. Specifically, we enhance the panorama image generation quality by optimizing the training objective with a 1D representation of a panorama scene obtained from bidirectional LSTM. We demonstrate Programmable-Room's flexibility in generating and editing 3D room meshes, and prove our framework's superiority to an existing model quantitatively and qualitatively. Project page is available in https://jihyun0510.github.io/Programmable_Room_Page/.

cs.CV↗

arXiv2025

Evidence of surface $p$-wave superconductivity and higher-order topology in MoTe$_2$

Exploration of nontrivial superconductivity and electronic band topology is at the core of condensed matter physics and applications to quantum information. The transition-metal dichalcogenide (TMDC) MoTe$_2$ has been proposed as an ideal candidate to explore the interplay between topology and superconductivity, but their studies remain limited regarding the required high-pressure environments. Here, we observe proximity-induced surface $p$-wave superconductivity, and investigate the higher-order topological nature of MoTe$_2$ in its 1T$'$ phase, which emerges from the T$_d$ phase through a high-pressure-induced topological phase transition. Using surface-sensitive soft-point-contact Andreev reflection spectroscopy, we confirm the emergence of surface $s+p$-wave superconductivity via the BTK model as well as a zero-bias conductance peak. Such surface $p$-wave superconductivity emerges via the proximity effect between an $s$-wave superconducting band and a second-order topological band, which is protected by the time-reversal and inversion symmetries. The temperature dependence of the surface $p$-wave superconducting gap shows a correlation with that of the bulk $s$-wave gap, as well as its suppression by an external magnetic field or a reduction in pressure, implying its proximity-induced origin. Moreover, we suggest that the topological hinge states, derived from second-order topological bands, evolve into zero-energy Majorana corner states in this proximity-effect-induced third-order topological superconducting phase. These results demonstrate the potential realization of topological superconductivity in MoTe$_2$, thus opening a pathway for studying various topological natures of TMDC materials.

cond-mat.supr-con↗

arXiv2024

An Adversarial Learning Approach to Irregular Time-Series Forecasting

Forecasting irregular time series presents significant challenges due to two key issues: the vulnerability of models to mean regression, driven by the noisy and complex nature of the data, and the limitations of traditional error-based evaluation metrics, which fail to capture meaningful patterns and penalize unrealistic forecasts. These problems result in forecasts that often misalign with human intuition. To tackle these challenges, we propose an adversarial learning framework with a deep analysis of adversarial components. Specifically, we emphasize the importance of balancing the modeling of global distribution (overall patterns) and transition dynamics (localized temporal changes) to better capture the nuances of irregular time series. Overall, this research provides practical insights for improving models and evaluation metrics, and pioneers the application of adversarial learning in the domian of irregular time-series forecasting.

cs.LG↗

arXiv2024

Enhanced Deep Speech Separation in Clustered Ad Hoc Distributed Microphone Environments

Ad-hoc distributed microphone environments, where microphone locations and numbers are unpredictable, present a challenge to traditional deep learning models, which typically require fixed architectures. To tailor deep learning models to accommodate arbitrary array configurations, the Transform-Average-Concatenate (TAC) layer was previously introduced. In this work, we integrate TAC layers with dual-path transformers for speech separation from two simultaneous talkers in realistic settings. However, the distributed nature makes it hard to fuse information across microphones efficiently. Therefore, we explore the efficacy of blindly clustering microphones around sources of interest prior to enhancement. Experimental results show that this deep cluster-informed approach significantly improves the system's capacity to cope with the inherent variability observed in ad-hoc distributed microphone environments.

eess.AS↗

arXiv2024

Diffusion-driven GAN Inversion for Multi-Modal Face Image Generation

We present a new multi-modal face image generation method that converts a text prompt and a visual input, such as a semantic mask or scribble map, into a photo-realistic face image. To do this, we combine the strengths of Generative Adversarial networks (GANs) and diffusion models (DMs) by employing the multi-modal features in the DM into the latent space of the pre-trained GANs. We present a simple mapping and a style modulation network to link two models and convert meaningful representations in feature maps and attention maps into latent codes. With GAN inversion, the estimated latent codes can be used to generate 2D or 3D-aware facial images. We further present a multi-step training strategy that reflects textual and structural representations into the generated image. Our proposed network produces realistic 2D, multi-view, and stylized face images, which align well with inputs. We validate our method by using pre-trained 2D and 3D GANs, and our results outperform existing methods. Our project page is available at https://github.com/1211sh/Diffusion-driven_GAN-Inversion/.

cs.CV↗

arXiv2023

Exotic magnetic anisotropy near digitized dimensional Mott boundary

The magnetic anisotropy of low-dimensional Mott systems exhibits unexpected magnetotransport behavior useful for spin-based quantum electronics. Yet, the anisotropy of natural materials is inherently determined by the crystal structure, highly limiting its engineering. We demonstrate the magnetic anisotropy modulation near a digitized dimensional Mott boundary in artificial superlattices composed of a correlated magnetic monolayer SrRuO3 and nonmagnetic SrTiO3. The magnetic anisotropy is initially engineered by modulating the interlayer coupling strength between the magnetic monolayers. Interestingly, when the interlayer coupling strength is maximized, a nearly degenerate state is realized, in which the anisotropic magnetotransport is strongly influenced by both the thermal and magnetic energy scales. Our results offer a new digitized control for magnetic anisotropy in low-dimensional Mott systems, inspiring promising integration of Mottronics and spintronics.

cond-mat.str-el↗