arXiv subjects

Hyunsoo Kim

Publications and source records attributed to Hyunsoo Kim.

At least 37 records · Page 2Linked to original sources

arXiv2025

AC magnetic measurements with a self-oscillating LC circuit and its application to university education

Understanding the magnetic properties of matter plays a key role in materials physics. However, university education on fundamental magnetism is limited to a theoretical survey because of the lack of appropriate apparatus that can be applied for laboratory courses at the undergraduate level. In this work, we introduce an AC magnetometer based on the Colpitts self-oscillator with an inductor coil as a probe. We show that this type of self-oscillator can be adopted in a typical university laboratory course to learn the principles of magnetic measurement and to understand the fundamental magnetism of matter. We demonstrate the exceptional stability of the circuit with a working frequency range of ~10 kHz to 10 MHz and excellent performance to detect the diamagnetic signal from a superconductor at cryogenic temperature.

physics.ed-ph↗

arXiv2025

When Model Knowledge meets Diffusion Model: Diffusion-assisted Data-free Image Synthesis with Alignment of Domain and Class

Open-source pre-trained models hold great potential for diverse applications, but their utility declines when their training data is unavailable. Data-Free Image Synthesis (DFIS) aims to generate images that approximate the learned data distribution of a pre-trained model without accessing the original data. However, existing DFIS meth ods produce samples that deviate from the training data distribution due to the lack of prior knowl edge about natural images. To overcome this limitation, we propose DDIS, the first Diffusion-assisted Data-free Image Synthesis method that leverages a text-to-image diffusion model as a powerful image prior, improving synthetic image quality. DDIS extracts knowledge about the learned distribution from the given model and uses it to guide the diffusion model, enabling the generation of images that accurately align with the training data distribution. To achieve this, we introduce Domain Alignment Guidance (DAG) that aligns the synthetic data domain with the training data domain during the diffusion sampling process. Furthermore, we optimize a single Class Alignment Token (CAT) embedding to effectively capture class-specific attributes in the training dataset. Experiments on PACS and Ima geNet demonstrate that DDIS outperforms prior DFIS methods by generating samples that better reflect the training data distribution, achieving SOTA performance in data-free applications.

cs.CV↗

arXiv2025

Difference Inversion: Interpolate and Isolate the Difference with Token Consistency for Image Analogy Generation

How can we generate an image B' that satisfies A:A'::B:B', given the input images A,A' and B? Recent works have tackled this challenge through approaches like visual in-context learning or visual instruction. However, these methods are typically limited to specific models (e.g. InstructPix2Pix. Inpainting models) rather than general diffusion models (e.g. Stable Diffusion, SDXL). This dependency may lead to inherited biases or lower editing capabilities. In this paper, we propose Difference Inversion, a method that isolates only the difference from A and A' and applies it to B to generate a plausible B'. To address model dependency, it is crucial to structure prompts in the form of a "Full Prompt" suitable for input to stable diffusion models, rather than using an "Instruction Prompt". To this end, we accurately extract the Difference between A and A' and combine it with the prompt of B, enabling a plug-and-play application of the difference. To extract a precise difference, we first identify it through 1) Delta Interpolation. Additionally, to ensure accurate training, we propose the 2) Token Consistency Loss and 3) Zero Initialization of Token Embeddings. Our extensive experiments demonstrate that Difference Inversion outperforms existing baselines both quantitatively and qualitatively, indicating its ability to generate more feasible B' in a model-agnostic manner.

cs.CV↗

arXiv2025

Rapid low-temperature synthesis of graphene-coated SiC substrates for remote and van der Waals epitaxy

Non-conventional epitaxial techniques, such as van der Waals epitaxy (vdWE) and remote epitaxy, have attracted substantial attention in the semiconductor research community for their capability to repeatedly produce high-quality free-standing films from a single mother wafer. Successful implementation of these epitaxial techniques depends on creating a robust, uniform two-dimensional (2D) material surface. The conventional method for fabricating graphene on silicon carbide (SiC) is high-temperature graphitization. However, the extremely high temperature required for silicon sublimation (typically above 1500 °C) causes step-bunching of the SiC surface, forming non-uniform multilayer graphene stripes and an unfavorable surface morphology for epitaxial growth. Here, we developed a wafer-scale graphitization technique that allows fast synthesis of single-crystalline graphene at ultra-low temperatures by metal-assisted graphitization (MAG). We found annealing conditions that enable SiC dissociation while avoiding silicide formation, producing uniform single-crystalline graphene while maintaining the surface morphology of the substrate. The graphene thickness can be controlled by varying the metal thickness or annealing temperature, enabling remote epitaxy or vdWE. We successfully produced freestanding single-crystalline III-N (AlN, GaN) films on graphene/SiC via the 2D material-based layer transfer technique. Our results show that low-temperature graphene synthesis via MAG offers a promising route to producing large-scale ultra-wide bandgap free-standing crystalline membranes.

cond-mat.mtrl-sci↗

arXiv2024

Topological singularity-induced Mott-like self-energy and its impact on Kondo cloud formation

We discovered that abnormal Mott physics can emerge even in weakly correlated 4f fermions through their interplay with topological singularity. Employing ab initio many-body perturbation theory combined with dynamical mean field theory, we show that 4f electrons near the topological singular point experience strong effective Coulomb repulsion, as the hybridization channels are blocked near the singularity. As a result, Mott-like self-energy emerges, forbidding the coexistence of 4f quasiparticles and the topological singularity at the same energy level in HoPtBi, PrPtBi, and PrAlGe. The formation of 4f quasiparticles is highly dependent on the energy of topological singularity relative to the Fermi level. This effect is suggested to be responsible for the selective quantum phenomena observed between heavy fermion behavior from Kondo resonance and anomalous transport from nontrivial topological states.

cond-mat.str-el↗

arXiv2024

Ultra-reliable urban air mobility networks

Recently, urban air mobility (UAM) has attracted attention as an emerging technology that will bring innovation to urban transportation and aviation systems. Since the UAM systems pursue fully autonomous flight without a pilot, wireless communication is a key function not only for flight control signals, but also for navigation and safety information. The essential information is called a command and control (C2) message, and the UAM networks must be configured so that the UAM can receive the C2 message by securing a continuous link stability without any interruptions. Nevertheless, a lot of prior works have focused only on improving the average performance without solving the low-reliability in the cell edges and coverage holes of urban areas. In this dissertation, we identify the factors that hinder the communication link reliability in considering three-dimensional (3D) urban environments, and propose a antenna configuration, resource utilization, and transmission strategy to enable UAM receiving C2 messages regardless of time and space. First, through stochastic geometry modeling, we analyze the signal blockage effects caused by the urban buildings. The blockage probability is calculated according to the shape, height, and density of the buildings, and the coverage probability of the received signal is derived by reflecting the blockage events. Furthermore, the low-reliability area is identified by analyzing the coverage performance according to the positions of the UAMs. To overcome the low-reliability region, we propose three methods for UAM network operation: i) optimization of antennas elevation tilting, ii) frequency reuse with multi-layered narrow beam, and iii) assistive transmissions by the master UAM.

cs.NI↗

arXiv2024

Data-driven Viscosity Solver for Fluid Simulation

We propose a data-driven viscosity solver based on U-shaped convolutional neural network to predict velocity changes due to viscosity. Our solver takes velocity derivatives, fluid volume, and solid indicator quantities as input. The traditional marker-and-cell (MAC) grid stores velocities at the edges of the grid, causing the dimensions of the velocity field vary from axis to axis. In our work, we suggest a symmetric MAC grid that maintains consistent dimensions across axes without interpolation or symmetry breaking. The proposed grid effectively transfers spatial fluid quantities such as partial derivatives of velocity, enabling networks to generate accurate predictions. Additionally, we introduce a physics-based loss inspired by the variational formulation of viscosity to enhance the network's generalization for a wide range of viscosity coefficients. We demonstrate various fluid simulation results, including 2D and 3D fluid-rigid body scenes and a scene exhibiting the buckling effect. Our code is available at \url{https://github.com/SSTDV-Project/python-fluid-simulation.}

cs.GR↗

arXiv2024

Controlling structural phases of Sn through lattice engineering

Topology and superconductivity, two distinct phenomena offer unique insight into quantum properties and their applications in quantum technologies, spintronics, and sustainable energy technologies if system can be found where they coexist. Tin (Sn) plays a pivotal role here as an element due to its two structural phases, $α$-Sn and $β$-Sn, exhibiting topological characteristics ($α$-Sn) and superconductivity ($β$-Sn). In this study we show how precise control of $α$ and $β$ phases of Sn thin films can be achieved by using molecular beam epitaxy grown buffer layers with systematic control over the lattice parameter. The resulting Sn films showed either $β$-Sn or $α$-Sn phases as the lattice constant of the buffer layer was varied from 6.10 A to 6.48 A, covering the range between GaSb (closely matched to InAs) and InSb. The crystal structures of the $α$- and $β$-Sn films were characterized by x-ray diffraction and confirmed by Raman spectroscopy and scanning transmission electron microscopy. The smooth and continuous surface morphology of the Sn films was validated using atomic force microscopy. The characteristics of $α$- and $β$-Sn phases were further verified using electrical transport measurements by observing resistance drop near 3.7 K for superconductivity of the $β$-Sn phase and Shubnikov-de Haas oscillations for the $α$-Sn phase. Density functional theory calculations showed that the stability of the Sn phases is highly dependent on lattice strain, with $α$-Sn being more stable under tensile strain and $β$-Sn becoming favorable under compressive strain, which is in good agreement with experimental observations. Hence, this study sheds light on controlling Sn phases through lattice engineering, enabling innovative applications in quantum technologies and beyond.

cond-mat.mtrl-sci↗

arXiv2024

MARS: Matching Attribute-aware Representations for Text-based Sequential Recommendation

Sequential recommendation aims to predict the next item a user is likely to prefer based on their sequential interaction history. Recently, text-based sequential recommendation has emerged as a promising paradigm that uses pre-trained language models to exploit textual item features to enhance performance and facilitate knowledge transfer to unseen datasets. However, existing text-based recommender models still struggle with two key challenges: (i) representing users and items with multiple attributes, and (ii) matching items with complex user interests. To address these challenges, we propose a novel model, Matching Attribute-aware Representations for Text-based Sequential Recommendation (MARS). MARS extracts detailed user and item representations through attribute-aware text encoding, capturing diverse user intents with multiple attribute-aware representations. It then computes user-item scores via attribute-wise interaction matching, effectively capturing attribute-level user preferences. Our extensive experiments demonstrate that MARS significantly outperforms existing sequential models, achieving improvements of up to 24.43% and 29.26% in Recall@10 and NDCG@10 across five benchmark datasets. Code is available at https://github.com/junieberry/MARS

cs.IR↗

arXiv2024

Radon Concentration Measurement with a High-Sensitivity Radon Detector at the Yemilab

The radiation emitted from radon is a critical background in rare event search experiments conducted at the Yemi Underground Laboratory (Yemilab) in Jeongseon, Korea. A Radon Reduction System(RRS) has been developed and installed in Yemilab to reduce radon concentration in the air. The RRS primarily provides a purified air of 50 m3/h to the cleanroom used to assemble crystal detectors in the AMoRE, a neutrinoless double beta decay search experiment. RRS can reduce the radon level by a factor of 300, so a high-sensitivity radon detector was required. A highly sensitive radon detector was constructed using a 70 L chamber with a large PIN photodiode to measure radon concentration in the purified air. The radon detector shows an excellent resolution of 72 keV (FWHM) for 6.003 MeV alphas from 218Po decay and a sensitivity down to 23.8 +- 2.1 mBq/m3 with a boil-off N2 gas sample. The radon concentration level from the RRS measured by the radon detector was below 0.29 Bq/m3 with a reduction factor of about 300.

physics.ins-det↗

arXiv2024

Symmetry-breaking normal state response and surface superconductivity in topological semimetal YPtBi

Most of the half-Heusler RPtBi compounds (R=rare earth) host various surface states due to spin-orbit coupling driven topological band structure. While recent ARPES measurements ubiquitously reported the existence of surface states in RPtBi, their evidence by other experimental techniques remains elusive. Here we report the angle-dependent magnetic field response of electrical transport properties of YPtBi in both the normal and superconducting states. The angle dependence of both magnetoresistance and the superconducting upper critical field breaks the rotational symmetry of the cubic crystal structure, and the angle between the applied magnetic field and the measurement plane of a plate-like sample prevails. Furthermore, the measured upper critical field is notably higher than the bulk response for an in-plane magnetic field configuration, suggesting the presence of quasi-2D superconductivity. Our work suggests the transport properties cannot be explained solely by the bulk carrier response, requiring robust normal and superconducting surface states to flourish in YPtBi.

cond-mat.supr-con↗

arXiv2024

Crystallographic defects in Weyl semimetal LaAlGe

Crystallographic defects in a topological semimetal can result in charge doping, and the scattering due to the defects may mask its exotic transport properties. Here, we investigate the possible crystallographic defects including vacancy and antisite in Weyl semimetal LaAlGe using hybrid-density-functional theory calculations. We show that a considerable concentration of Al- and Ge-related defects naturally form during growth due to their low formation enthalpy. Specifically, Al can be easily replaced by Ge in the $I4_1md$ phase of LaAlGe, forming the Ge-on-Al antisite, Ge$_{\rm Al}$. The counterpart, Al-on-Ge (Al$_{\rm Ge}$), is also probable. The most abundant defect Ge$_{\rm Al}$ is donor-like, effectively electron-doping, and these defects are therefore not only scattering centers in the electronic transport process but may also induce the substantial vertical shift of the chemical potential. The results imply that the naturally occurring defects hinder both spectroscopic and transport features arising from the Weyl physics in LaAlGe. Our work can be applied to the $R$AlGe family ($R$=rare earth) and help improve the quality of single-crystal magnetic Weyl semimetal.

cond-mat.mtrl-sci↗

arXiv2023

Tuning a magnetic energy scale with pressure in UTe$_2$

A fragile ordered state can be easily tuned by various external parameters. When the ordered state is suppressed to zero temperature, a quantum phase transition occurs, which is often marked by the appearance of unconventional superconductivity. While the quantum critical point can be hidden, the influence of the quantum criticality extends to fairly high temperatures, manifesting the non-Fermi liquid behavior in the wide range of the $p$-$H$-$T$ phase space. Here, we report the tuning of a magnetic energy scale in the heavy-fermion superconductor UTe$_2$, previously identified as a peak in the $c$-axis electrical transport, with applied hydrostatic pressure and magnetic field along the $a$-axis as complementary (and opposing) tuning parameters. Upon increasing pressure, the characteristic $c$-axis peak moves to a lower temperature before vanishing near the critical pressure of about 15 kbar. The application of a magnetic field broadens the peak under all studied pressure values. The observed Fermi-liquid behavior at ambient pressure is violated near the critical pressure, exhibiting nearly linear resistivity in temperature and an enhanced pre-factor. Our results provide a clear picture of energy scale evolution relevant to magnetic quantum criticality in UTe$_2$.

cond-mat.supr-con↗

arXiv2023

Node Embedding for Homophilous Graphs with ARGEW: Augmentation of Random walks by Graph Edge Weights

Representing nodes in a network as dense vectors node embeddings is important for understanding a given network and solving many downstream tasks. In particular, for weighted homophilous graphs where similar nodes are connected with larger edge weights, we desire node embeddings where node pairs with strong weights have closer embeddings. Although random walk based node embedding methods like node2vec and node2vec+ do work for weighted networks via including edge weights in the walk transition probabilities, our experiments show that the embedding result does not adequately reflect edge weights. In this paper, we propose ARGEW (Augmentation of Random walks by Graph Edge Weights), a novel augmentation method for random walks that expands the corpus in such a way that nodes with larger edge weights end up with closer embeddings. ARGEW can work with any random walk based node embedding method, because it is independent of the random sampling strategy itself and works on top of the already-performed walks. With several real-world networks, we demonstrate that with ARGEW, compared to not using it, the desired pattern that node pairs with larger edge weights have closer embeddings is much clearer. We also examine ARGEW's performance in node classification: node2vec with ARGEW outperforms pure node2vec and is not sensitive to hyperparameters (i.e. consistently good). In fact, it achieves similarly good results as supervised GCN, even without any node feature or label information during training. Finally, we explain why ARGEW works consistently well by exploring the coappearance distributions using a synthetic graph with clear structural roles.

cs.SI↗

arXiv2023

Unconventional nodal superconductivity in miassite Rh$_{17}$S$_{15}$

Unconventional superconductivity has long been believed to arise from a lab-grown correlated electronic system. Here we report compelling evidence of unconventional nodal superconductivity in a mineral superconductor \rhs. We investigated the temperature-dependent London penetration depth $Δλ(T)$ and disorder evolution of the critical temperature $T_c$ and upper critical field $H_{c2}(T)$ in synthetic miassite \rhs. We found a power-law behavior of $Δλ(T)\sim T^n$ with $n\approx 1.1$ at low temperatures below $0.3T_c$ ($T_c$ = 5.4 K), which is consistent with the presence of lines of the node in the superconducting gap of \rhs. The nodal character of the superconducting state in \rhs~was supported by the observed pairbreaking effect in $T_c$ and $H_{c2}(T)$ in samples with the controlled disorder that was introduced by low-temperature electron irradiation. We propose a nodal sign-changing superconducting gap in the $A_{1g}$ irreducible representation, which preserves the cubic symmetry of the crystal and is in excellent agreement with the superfluid density, $λ^2(0)/λ^2(T)$.

cond-mat.supr-con↗

arXiv2023

Dual Nature of Magnetism Driven by Momentum Dependent f-d Kondo Hybridization

Intricate nature of magnetism in uranium-based Kondo lattices is a consequence of correlations between U-5$f$ and conduction electrons. Using linearized quasiparticle self-consistent GW plus dynamical mean-field theory, we demonstrate a crossover from incoherent to coherent $f$-$d$ Kondo cloud in the paramagnetic phase of UTe$_2$ with reduced volumes, USbTe and USbSe. As the transition occurs, we observe an augmented $f$-$d$ coherence and Pauli-like magnetic susceptibility, with a substantial frozen magnetic moment of U-5$f$ persisting. We show that momentum dependent $f$-$d$ hybridization is responsible for the magnetic moments arising from the renormalized $f$ electrons' van Hove singularity. Our findings provide a unique perspective to explain the dual nature of magnetism and the long-range magnetic ordering induced by pressure in UTe$_2$.

cond-mat.str-el↗

arXiv2022

$c$-axis transport in UTe$_{2}$: Evidence of Three Dimensional Conductivity Component

We study the temperature dependence of electrical resistivity for currents directed along all crystallographic axes of the spin-triplet superconductor UTe$_{2}$. We focus particularly on an accurate determination of the resistivity along the $c$-axis ($ρ_c$) by using a generalized Montgomery technique that allows extraction of crystallographic resistivity components from a single sample. In contrast to expectations from the observed highly anisotropic band structure, our measurement of the absolute values of resistivities in all current directions reveals a surprisingly nearly isotropic transport behavior at temperatures above Kondo coherence, with $ρ_c \sim ρ_b \sim 2ρ_a$, that evolves to reveal qualitatively distinct behaviors on cooling. The temperature dependence of $ρ_c$ exhibits a peak at a temperature much lower than the onset of Kondo coherence observed in $ρ_a$ and $ρ_b$, consistent with features in magnetotransport and magnetization that point to a magnetic origin. A comparison to the temperature-dependent evolution of the scattering rate observed in angle-resolved photoemission spectroscopy experiments provides important insights into the underlying electronic structure necessary for building a microscopic model of superconductivity in UTe$_{2}$.

cond-mat.str-el↗

arXiv2022

Impacts of f-d Kondo cloud on superconductivity of nickelates

The discovery of superconducting nickelates reignited hope for elucidating the high-$T_{\textrm{c}}$ superconductivity mechanism in the isostructural cuprates. While in the cuprates, the superconducting gap opens up on a single-band of the quasi-2D Fermi surface, the nickelates are known to have 3D nature of electronic structure with multi-band. This raises a serious question about the role of 2D nature for the high-$T_{\textrm{c}}$ superconductivity. Here, employing dynamical mean field theory combined with GW method, we found the Kondo effect driven by the strong correlation of Nd-4$f$ and Ni-3$d$ electrons emerging at low temperature. The Kondo effect modifies the topology of the Fermi surface leading to 3D multi-band nature. Remarkably, the Kondo effect is easily destroyed by lattice modulation, leading to the quasi-2D nature. Our findings clearly explain the inconsistent occurrence of superconductivity and distinct electrical resistivity behavior between NdNiO$_{2}$ bulk and films.

cond-mat.str-el↗