arXiv subjects

Subin Kim

Publications and source records attributed to Subin Kim.

33 records · Page 2Linked to original sources

arXiv2023

Ultrafast spin dynamics in the proximate quantum spin liquid α-RuCl3

α-RuCl3 is a Kitaev material suggested to be a proximate quantum spin liquid in a certain temperature and magnetic field range. Nonequilibrium measurements of transient dynamics have been proposed to detect fractionalized particles that emerge in the spin liquid and to possibly drive the system into novel photoinduced magnetic states that cannot be accessed by conventional equilibrium probes. Here we study ultrafast spin dynamics of photoinduced excitations in α-RuCl3 using pump-probe transient grating spectroscopy. In the real part of the complex transient reflectance change ΔR/R, we observe the long-range antiferromagnetic correlation near the Néel temperature. Most intriguingly, above the Néel temperature in the Kitaev paramagnetic phase, we reveal a photoexcitation component sensitive to the in-plane magnetic field in the imaginary part of ΔR/R. This component exhibits two distinct lifetimes of about tens of picoseconds. This photoexcitation component may be connected to novel photoexcited states in the Kitaev quantum spin liquid, and its lifetimes likely reflect the dynamics of unconventional spin excitations in the Kitaev model.

cond-mat.str-el↗

arXiv2023

Structural Transition and Magnetic Anisotropy in $α$-RuCl$_{3}$

We report X-ray diffraction and magnetic susceptibility studies of the structural phase transition in $α$-RuCl$_{3}$. By utilizing a single crystal sample with predominantly single twin domain, we show that $α$-RuCl$_{3}$ goes from high-temperature C2/m structure to a rhombohedral structure with R$\bar{3}$ symmetry at low temperature. While the defining feature of the structural transition is changing the stacking direction from the monoclinic a-axis to the b-axis, bond-anisotropy disappears when the structural change occurs, indicating that the local $C_3$ symmetry is restored within the honeycomb layer. The symmetry change is corroborated by the vanishing magnetic anisotropy in the low-temperature structure. Our study demonstrates that magnetic interaction is extremely sensitive to structural details in $α$-RuCl$_{3}$, which could explain the sample dependence found in this material.

cond-mat.str-el↗

arXiv2023

Learning Large-scale Neural Fields via Context Pruned Meta-Learning

We introduce an efficient optimization-based meta-learning technique for large-scale neural field training by realizing significant memory savings through automated online context point selection. This is achieved by focusing each learning step on the subset of data with the highest expected immediate improvement in model quality, resulting in the almost instantaneous modeling of global structure and subsequent refinement of high-frequency details. We further improve the quality of our meta-learned initialization by introducing a bootstrap correction resulting in the minimization of any error introduced by reduced context sets while simultaneously mitigating the well-known myopia of optimization-based meta-learning. Finally, we show how gradient re-scaling at meta-test time allows the learning of extremely high-quality neural fields in significantly shortened optimization procedures. Our framework is model-agnostic, intuitive, straightforward to implement, and shows significant reconstruction improvements for a wide range of signals. We provide an extensive empirical evaluation on nine datasets across multiple multiple modalities, demonstrating state-of-the-art results while providing additional insight through careful analysis of the algorithmic components constituting our method. Code is available at https://github.com/jihoontack/GradNCP

cs.LG↗

arXiv2023

Non-local features of the spin-orbit exciton in Kitaev materials

A comparative resonant inelastic x-ray scattering (RIXS) study of three well-known Kitaev materials is presented: $α$-Li$_2$IrO$_3$, Na$_2$IrO$_3$, and $α$-RuCl$_3$. Despite similar low-energy physics, these materials show distinct electronic properties, such as the large difference in the size of the charge gap. The RIXS spectra of the spin-orbit exciton for these materials show remarkably similar three-peak features, including sharp low energy peak (peak A) as well as transitions between $j_{\text{eff}}=1/2$ and $j_{\text{eff}}=3/2$ states. Comparison of experimental spectra with cluster calculations reveals that the observed three-peak structure reflects the significant role that non-local physics plays in the electronic structure of these materials. In particular, the low-energy peak A arises from a holon-doublon pair rather than a conventional particle-hole exciton as proposed earlier. Our study suggests that while spin-orbit assisted Mott insulator is still the best description for these materials, electron itinerancy cannot be ignored when formulating low-energy Hamiltonian of these materials.

cond-mat.str-el↗

arXiv2023

Stacking disorder in $α$-RuCl$_3$ via x-ray three-dimensional difference pair distribution function analysis

The van der Waals layered magnet $α$-RuCl$_3$ offers tantalizing prospects for the realization of Majorana quasiparticles. Efforts to understand this are, however, hampered by inconsistent magnetic and thermal transport properties likely coming from the formation of structural disorder during crystal growth, postgrowth processing, or upon cooling through the first order structural transition. Here, we investigate structural disorder in $α$-RuCl$_3$ using x-ray diffuse scattering and three-dimensional difference pair distribution function (3D-$Δ$PDF) analysis. We develop a quantitative model that describes disorder in $α$-RuCl$_3$ in terms of rotational twinning and intermixing of the high and low-temperature structural layer stacking. This disorder may be important to consider when investigating the detailed magnetic and electronic properties of this widely studied material.

cond-mat.str-el↗

arXiv2023

Collaborative Score Distillation for Consistent Visual Synthesis

Generative priors of large-scale text-to-image diffusion models enable a wide range of new generation and editing applications on diverse visual modalities. However, when adapting these priors to complex visual modalities, often represented as multiple images (e.g., video), achieving consistency across a set of images is challenging. In this paper, we address this challenge with a novel method, Collaborative Score Distillation (CSD). CSD is based on the Stein Variational Gradient Descent (SVGD). Specifically, we propose to consider multiple samples as "particles" in the SVGD update and combine their score functions to distill generative priors over a set of images synchronously. Thus, CSD facilitates seamless integration of information across 2D images, leading to a consistent visual synthesis across multiple samples. We show the effectiveness of CSD in a variety of tasks, encompassing the visual editing of panorama images, videos, and 3D scenes. Our results underline the competency of CSD as a versatile method for enhancing inter-sample consistency, thereby broadening the applicability of text-to-image diffusion models.

cs.CV↗

arXiv2023

Video Probabilistic Diffusion Models in Projected Latent Space

Despite the remarkable progress in deep generative models, synthesizing high-resolution and temporally coherent videos still remains a challenge due to their high-dimensionality and complex temporal dynamics along with large spatial variations. Recent works on diffusion models have shown their potential to solve this challenge, yet they suffer from severe computation- and memory-inefficiency that limit the scalability. To handle this issue, we propose a novel generative model for videos, coined projected latent video diffusion models (PVDM), a probabilistic diffusion model which learns a video distribution in a low-dimensional latent space and thus can be efficiently trained with high-resolution videos under limited resources. Specifically, PVDM is composed of two components: (a) an autoencoder that projects a given video as 2D-shaped latent vectors that factorize the complex cubic structure of video pixels and (b) a diffusion model architecture specialized for our new factorized latent space and the training/sampling procedure to synthesize videos of arbitrary length with a single model. Experiments on popular video generation datasets demonstrate the superiority of PVDM compared with previous video synthesis methods; e.g., PVDM obtains the FVD score of 639.7 on the UCF-101 long video (128 frames) generation benchmark, which improves 1773.4 of the prior state-of-the-art.

cs.CV↗

arXiv2023

Oscillations in the magnetothermal conductivity of $\boldsymbolα$-RuCl$_3$: Evidence of transition anomalies

The 2D layered insulator $α$-RuCl$_3$ is a candidate material for a quantum spin-liquid state, which may be realized when a magnetic field suppresses the antiferromagnetic order present at low temperature. Oscillations in the field dependence of the thermal conductivity, observed for an in-plane magnetic field $B$ up to a critical field $B^{\star}$, have been attributed to exotic charge-neutral fermions, viewed as evidence of a quantum spin-liquid state between the critical field $B_{c}$ $\simeq 7$ T at which the antiferromagnetic phase ends and $B^{\star}$. Here we report measurements of the thermal conductivity of $α$-RuCl$_3$ as a function of magnetic field up to 15 T applied in two distinct in-plane directions: parallel and perpendicular to the Ru-Ru bond. We find that the number of oscillations between $B_{c}$ and $B^{\star}$ is the same for the two field directions even though the field interval between $B_{c}$ and $B^{\star}$ is different. In other words, the period of the oscillations is controlled by the transition fields $B_{c}$ and $B^{\star}$. We conclude that these are not true oscillations -- coming from putative fermions in a spin-liquid state -- but anomalies associated with a sequence of magnetic transitions.

cond-mat.str-el↗

arXiv2022

Scalable Neural Video Representations with Learnable Positional Features

Succinct representation of complex signals using coordinate-based neural representations (CNRs) has seen great progress, and several recent efforts focus on extending them for handling videos. Here, the main challenge is how to (a) alleviate a compute-inefficiency in training CNRs to (b) achieve high-quality video encoding while (c) maintaining the parameter-efficiency. To meet all requirements (a), (b), and (c) simultaneously, we propose neural video representations with learnable positional features (NVP), a novel CNR by introducing "learnable positional features" that effectively amortize a video as latent codes. Specifically, we first present a CNR architecture based on designing 2D latent keyframes to learn the common video contents across each spatio-temporal axis, which dramatically improves all of those three requirements. Then, we propose to utilize existing powerful image and video codecs as a compute-/memory-efficient compression procedure of latent codes. We demonstrate the superiority of NVP on the popular UVG benchmark; compared with prior arts, NVP not only trains 2 times faster (less than 5 minutes) but also exceeds their encoding quality as 34.07$\rightarrow$34.57 (measured with the PSNR metric), even using $>$8 times fewer parameters. We also show intriguing properties of NVP, e.g., video inpainting, video frame interpolation, etc.

cs.CV↗

arXiv2022

Transformer Network-based Reinforcement Learning Method for Power Distribution Network (PDN) Optimization of High Bandwidth Memory (HBM)

In this article, for the first time, we propose a transformer network-based reinforcement learning (RL) method for power distribution network (PDN) optimization of high bandwidth memory (HBM). The proposed method can provide an optimal decoupling capacitor (decap) design to maximize the reduction of PDN self- and transfer impedance seen at multiple ports. An attention-based transformer network is implemented to directly parameterize decap optimization policy. The optimality performance is significantly improved since the attention mechanism has powerful expression to explore massive combinatorial space for decap assignments. Moreover, it can capture sequential relationships between the decap assignments. The computing time for optimization is dramatically reduced due to the reusable network on positions of probing ports and decap assignment candidates. This is because the transformer network has a context embedding process to capture meta-features including probing ports positions. In addition, the network is trained with randomly generated data sets. Therefore, without additional training, the trained network can solve new decap optimization problems. The computing time for training and data cost are critically decreased due to the scalability of the network. Thanks to its shared weight property, the network can adapt to a larger scale of problems without additional training. For verification, we compare the results with conventional genetic algorithm (GA), random search (RS), and all the previous RL-based methods. As a result, the proposed method outperforms in all the following aspects: optimality performance, computing time, and data efficiency.

cs.LG↗

arXiv2022

Acoustic phonon dispersion of $α$-RuCl$_3$

Acoustic phonons have recently been posited as playing an integral role in explaining the half-quantized thermal Hall effect in $α$-RuCl$_3$. Therefore, we present much needed inelastic x-ray scattering measurements of its acoustic phonon dispersion, along with calculations using the frozen-phonon method. We also discuss a temperature study which conclusively shows a first-order structural transition to a non-$C2/m$ space group at low temperature. Together these results are an important backbone for future theoretical and experimental studies of $α$-RuCl$_3$.

cond-mat.str-el↗

arXiv2022

Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection

As large-scale time-series data can easily be found in real-world applications, multivariate time-series anomaly detection has played an essential role in diverse industries. It enables productivity improvement and maintenance cost reduction by preventing malfunctions and detecting anomalies based on time-series data. However, multivariate time-series anomaly detection is challenging because real-world time-series data exhibit complex temporal dependencies. For this task, it is crucial to learn a rich representation that effectively contains the nonlinear temporal dynamics of normal behavior. In this study, we propose an unsupervised multivariate time-series anomaly detection model named RAE-MEPC which learns informative normal representations based on multi-resolution ensemble and predictive coding. We introduce multi-resolution ensemble encoding to capture the multi-scale dependency from the input time series. The encoder hierarchically aggregates the temporal features extracted from the sub-encoders with different encoding lengths. From these encoded features, the reconstruction decoder reconstructs the input time series based on multi-resolution ensemble decoding where lower-resolution information helps to decode sub-decoders with higher-resolution outputs. Predictive coding is further introduced to encourage the model to learn the temporal dependencies of the time series. Experiments on real-world benchmark datasets show that the proposed model outperforms the benchmark models for multivariate time-series anomaly detection.

cs.LG↗

arXiv2020

Robust Long Range Magnetic Correlation across Anti-phase Domain Boundaries in Sr$_2$CrReO$_6$

Anti-site disorder is one of the most important issues that arises in synthesis of double perovskite for spintronic applications. Although it is known that anti-site disorder leads to a proliferation of structural defects, known as the anti-phase boundaries that separate ordered anti-phase domains in the sample, little is known about the magnetic correlation across these anti-phase boundaries on a microscopic level. Motivated by this, we report resonant elastic X-ray scattering study of room temperature magnetic and structural correlation in a thin-film sample of Sr$_2$CrReO$_6$, which has one of the highest $\mathrm{T_C}$ among double perovskites. Structurally, we discovered existence of anti-phase nanodomains of $\sim$15~nm in the sample. Magnetically, the ordered moments are shown to lie perpendicular to the $c$ direction. Most remarkably, we found that the magnetic correlation length far exceeds the size of individual anti-phase nanodomains. Our results therefore provide conclusive proof for existence of robust magnetic correlation across the anti-phase boundaries in Sr$_2$CrReO$_6$.

cond-mat.mes-hall↗

arXiv2019

Ferromagnetic Kitaev interaction and the origin of large magnetic anisotropy in $α$-RuCl$_3$

$α$-RuCl$_3$ is drawing much attention as a promising candidate Kitaev quantum spin liquid. However, despite intensive research efforts, controversy remains about the form of the basic interactions governing the physics of this material. Even the sign of the Kitaev interaction (the bond-dependent anisotropic interaction responsible for Kitaev physics) is still under debate, with conflicting results from theoretical and experimental studies. The significance of the symmetric off-diagonal exchange interaction (referred to as the $Γ$ term) is another contentious question. Here, we present resonant elastic x-ray scattering data that provides unambiguous experimental constraints to the two leading terms in the magnetic interaction Hamiltonian. We show that the Kitaev interaction ($K$) is ferromagnetic, and that the $Γ$ term is antiferromagnetic and comparable in size to the Kitaev interaction. Our findings also provide a natural explanation for the large anisotropy of the magnetic susceptibility in $α$-RuCl$_3$ as arising from the large $Γ$ term. We therefore provide a crucial foundation for understanding the interactions underpinning the exotic magnetic behaviours observed in $α$-RuCl$_3$.

cond-mat.str-el↗

arXiv2019

Resonant inelastic x-ray scattering study of $α$-RuCl$_3$: a progress report

Ru M$_3$-edge resonant inelastic x-ray scattering (RIXS) measurements of RuCl$_3$ with 27 meV resolution reveals a spin-orbit exciton without noticeable splitting. We extract values for the spin-orbit coupling constant ($λ=154\pm2$ meV) and trigonal distortion field energy ($\left|Δ\right|<65$ meV) which support the $j_{\rm eff}=1/2$ nature of RuCl$_3$. We demonstrate the feasibility of M-edge RIXS for $4d$ systems, which allows ultra high-resolution RIXS of $4d$ systems until instrumentation for L-edge RIXS improves.

cond-mat.str-el↗