arXiv subjects

Seungjun Lee

Publications and source records attributed to Seungjun Lee.

At least 55 records · Page 3Linked to original sources

arXiv2025

Generalizable Implicit Neural Representations via Parameterized Latent Dynamics for Baroclinic Ocean Forecasting

Mesoscale ocean dynamics play a critical role in climate systems, governing heat transport, hurricane genesis, and drought patterns. However, simulating these processes at high resolution remains computationally prohibitive due to their nonlinear, multiscale nature and vast spatiotemporal domains. Implicit neural representations (INRs) reduce the computational costs as resolution-independent surrogates but fail in many-query scenarios (inverse modeling) requiring rapid evaluations across diverse parameters. We present PINROD, a novel framework combining dynamics-aware implicit neural representations with parameterized neural ordinary differential equations to address these limitations. By integrating parametric dependencies into latent dynamics, our method efficiently captures nonlinear oceanic behavior across varying boundary conditions and physical parameters. Experiments on ocean mesoscale activity data show superior accuracy over existing baselines and improved computational efficiency compared to standard numerical simulations.

cs.LG↗

arXiv2025

Coexistence of unconventional spin Hall effect and antisymmetric planar Hall effect in IrO$_2$

Crystal symmetry plays an important role in the Hall effects. Unconventional spin Hall effect (USHE), characterized by Dresselhaus and out-of-plane spins, has been observed in materials with low crystal symmetry. Recently, antisymmetric planar Hall effect (APHE) was discovered in rutile RuO2 and IrO2 (101) thin films, which also exhibit low crystal symmetry. In this study, we report the observation of both USHE and APHE in IrO2 (111) films, using spin-torque ferromagnetic resonance (ST-FMR) and harmonic Hall measurements, respectively. Notably, the unconventional spin torque efficiency from Dresselhaus spin was more than double that of a previous report. Additionally, the temperature dependence of APHE suggests that it arises from the Lorentz force, constrained by crystal symmetry. Symmetry analysis supports the coexistence of USHE and APHE and demonstrates that both originate from the crystal symmetry of IrO2 (111), paving the way for a deeper understanding of Hall effects and related physical phenomena.

cond-mat.mes-hall↗

arXiv2025

Metallicity and Anomalous Hall Effect in Epitaxially-Strained, Atomically-thin RuO2 Films

The anomalous Hall effect (AHE), a hallmark of time-reversal symmetry breaking, has been reported in rutile RuO2, a debated metallic altermagnetic candidate. Previously, AHE in RuO2 was observed only in strain-relaxed thick films under extremely high magnetic fields (~50 T). Yet, in ultrathin strained films with distinctive anisotropic electronic structures, there are no reports, likely due to disorder and defects suppressing metallicity thus hindering its detection. Here, we demonstrate that ultrathin, fully-strained 2 nm TiO2/t nm RuO2/TiO2 (110) heterostructures, grown by hybrid molecular beam epitaxy, retain metallicity and exhibit a sizeable AHE at a significantly lower magnetic field (< 9 T). Density functional theory calculations reveal that epitaxial strain stabilizes a non-compensated magnetic ground state and reconfigures magnetic ordering in RuO2 (110) thin films. These findings establish ultrathin RuO2 as a platform for strain-engineered magnetism and underscore the transformative potential of epitaxial design in advancing spintronic technologies.

cond-mat.mtrl-sci↗

arXiv2024

ImagePiece: Content-aware Re-tokenization for Efficient Image Recognition

Vision Transformers (ViTs) have achieved remarkable success in various computer vision tasks. However, ViTs have a huge computational cost due to their inherent reliance on multi-head self-attention (MHSA), prompting efforts to accelerate ViTs for practical applications. To this end, recent works aim to reduce the number of tokens, mainly focusing on how to effectively prune or merge them. Nevertheless, since ViT tokens are generated from non-overlapping grid patches, they usually do not convey sufficient semantics, making it incompatible with efficient ViTs. To address this, we propose ImagePiece, a novel re-tokenization strategy for Vision Transformers. Following the MaxMatch strategy of NLP tokenization, ImagePiece groups semantically insufficient yet locally coherent tokens until they convey meaning. This simple retokenization is highly compatible with previous token reduction methods, being able to drastically narrow down relevant tokens, enhancing the inference speed of DeiT-S by 54% (nearly 1.5$\times$ faster) while achieving a 0.39% improvement in ImageNet classification accuracy. For hyper-speed inference scenarios (with 251% acceleration), our approach surpasses other baselines by an accuracy over 8%.

cs.CV↗

arXiv2024

Solvated Electrons and Hydroxyl Radicals at the Plasma-Liquid Interface

While hydroxyl radicals ($\cdot$OH) play an important role as potent oxidizing agents in various plasma applications, their high reactivity confines them to a thin layer at the plasma-liquid interface, posing challenges in comprehending the intricate generation and transport processes. Similarly, solvated electrons ($\mathrm{e_{aq}}$), highly reactive reducing agents, are expected to exhibit distribution beneath the liquid surface and interact with $\cdot$OH in the thin layer. In this study, we have determined the penetration depth and concentration of ($\mathrm{e_{aq}}$) at the interface between an atmospheric argon plasma plume and an electrolyte anode via a lock-in amplification absorbance measurement. With bias voltages from 1000 to 2500 V, the penetration depth remains approximately 10 nm, and the peak concentration near the surface reaches 1 mM. Diffusion is the primary mechanism for $\cdot$OH generation in the electrolyte, with most $\cdot$OH reacting with ($\mathrm{e_{aq}}$) at the interface, thus influencing the ($\mathrm{e_{aq}}$) distribution. In contrast, the electrolyte cathode significantly boosts $\cdot$OH generation, leading to rapid recombination into hydrogen peroxide.

physics.plasm-ph↗

arXiv2024

Giant spin Hall effect with multi-directional spin components in Ni4W

Spin-orbit torque (SOT) can be used to efficiently manipulate the magnetic state of magnetic materials, which is an essential element for memory and logic applications. Due to symmetry constraints, only in-plane spins can be injected into the ferromagnet from the underlying SOT layer for conventional SOT materials such as heavy metals and topological materials. Through the use of materials with low symmetries, or other symmetry breaking approaches, unconventional spin currents with out-of-plane polarization has been demonstrated and enabled field-free deterministic switching of perpendicular magnetization. Despite this progress, the SOT efficiency of these materials has typically remained low. Here, we report a giant SOT efficiency of 0.85 in sputtered Ni4W/CoFeB heterostructure at room temperature, as evaluated by second harmonic Hall measurements. In addition, due to the low crystal symmetry of Ni4W, unconventional out-of-plane and Dresselhaus-like spin components were observed. Macro-spin simulation suggests our spin Hall tensor to provide about an order of magnitude improvement in the magnetization switching efficiency, thus broadening the path towards energy efficient spintronic devices using low-symmetry materials.

cond-mat.mes-hall↗

arXiv2024

Translation of Multifaceted Data without Re-Training of Machine Translation Systems

Translating major language resources to build minor language resources becomes a widely-used approach. Particularly in translating complex data points composed of multiple components, it is common to translate each component separately. However, we argue that this practice often overlooks the interrelation between components within the same data point. To address this limitation, we propose a novel MT pipeline that considers the intra-data relation in implementing MT for training data. In our MT pipeline, all the components in a data point are concatenated to form a single translation sequence and subsequently reconstructed to the data components after translation. We introduce a Catalyst Statement (CS) to enhance the intra-data relation, and Indicator Token (IT) to assist the decomposition of a translated sequence into its respective data components. Through our approach, we have achieved a considerable improvement in translation quality itself, along with its effectiveness as training data. Compared with the conventional approach that translates each data component separately, our method yields better training data that enhances the performance of the trained model by 2.690 points for the web page ranking (WPR) task, and 0.845 for the question generation (QG) task in the XGLUE benchmark.

cs.CL↗

arXiv2024

Moiré Kramers-Weyl Fermions from Structural Chirality with Ideal Radial Spin Texture

We demonstrate that two-dimensional Kramers-Weyl fermions can be engineered in spin-orbit coupled twisted bilayers, where the chiral structure of these moiré systems breaks all mirror symmetries, confining Kramers-Weyl fermions to high-symmetry points in the Brillouin zone under time reversal symmetry. Our theoretical analysis reveals a symmetry-enforced Weyl-like interlayer moiré coupling that universally ensures an ideal radial spin-texture at arbitrary twist angles, under $C_{nz}$ symmetry with n>2. First principles density functional calculation confirm the realization of these fermions in twisted $α$-In$_2$Se$_3$ bilayers, where flat bands and out-of-plane ferroelectric polarization in each layer guarantee two-dimensional Kramers-Weyl physics with perfectly ideal radial spin textures.

cond-mat.mes-hall↗

arXiv2024

Pairwise Distance Distillation for Unsupervised Real-World Image Super-Resolution

Standard single-image super-resolution creates paired training data from high-resolution images through fixed downsampling kernels. However, real-world super-resolution (RWSR) faces unknown degradations in the low-resolution inputs, all the while lacking paired training data. Existing methods approach this problem by learning blind general models through complex synthetic augmentations on training inputs; they sacrifice the performance on specific degradation for broader generalization to many possible ones. We address the unsupervised RWSR for a targeted real-world degradation. We study from a distillation perspective and introduce a novel pairwise distance distillation framework. Through our framework, a model specialized in synthetic degradation adapts to target real-world degradations by distilling intra- and inter-model distances across the specialized model and an auxiliary generalized model. Experiments on diverse datasets demonstrate that our method significantly enhances fidelity and perceptual quality, surpassing state-of-the-art approaches in RWSR. The source code is available at https://github.com/Yuehan717/PDD.

eess.IV↗

arXiv2024

Control of charge-spin interconversion in van der Waals heterostructures with chiral charge density waves

A charge density wave (CDW) represents an exotic state in which electrons are arranged in a long range ordered pattern in low-dimensional materials. Although our understanding of the fundamental character of CDW has been enriched after extensive studies, its relationship with functional phenomena remains relatively limited. Here, we show an unprecedented demonstration of a tunable charge-spin interconversion (CSI) in graphene/1T-TaS$_2$ van der Waals heterostructures by manipulating the distinct CDW phases in 1T-TaS$_2$. Whereas CSI from spins polarized in all three directions are observed in the heterostructure when the CDW phase does not show commensurability, the output of one of the components disappears and the other two are enhanced when the CDW phase becomes commensurate. The experimental observation is supported by first-principles calculations, which evidence that chiral CDW multidomains are at the origin of the switching of CSI. Our results uncover a new approach for on-demand CSI in low-dimensional systems, paving the way for advanced spin-orbitronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Moiré polar vortex, flat bands and Lieb lattice in twisted bilayer BaTiO$_3$

Advances in material fabrication techniques and growth methods have opened up a new chapter for twistronics, in the form of twisted freestanding three-dimensional material membranes. Through first-principles calculations based on density functional theory, we investigate the crystal and electronic structures of twisted bilayer BaTiO$_3$. Our findings reveal that large stacking fault energy leads to chiral in-plane vortex pattern that was recently observed in experiments. Moreover, we also found non-zero out-of-plane local dipole moments, indicating that the strong interlayer interaction might offer promising strategy to stabilize ferroelectric order in the two-dimensional limit. Remarkably, the vortex pattern in the twisted BaTiO$_3$ bilayer support localized electronic states with quasi-flat bands, associated with the interlayer hybridization of oxygen $p_z$ orbitals. We found that the associated band width reaches a minimum at $\sim$19$^{\circ}$ twisting, configuring the largest magic angle in moiré systems reported so far. Further, the moiré vortex pattern bears a striking resemblance to two interpenetrating Lieb lattices and corresponding tight-binding model provides a comprehensive description of the evolution the moiré bands with twist angle and reveals the topological nature of these states.

cond-mat.mtrl-sci↗

arXiv2024

Segment Any 3D Object with Language

In this paper, we investigate Open-Vocabulary 3D Instance Segmentation (OV-3DIS) with free-form language instructions. Earlier works that rely on only annotated base categories for training suffer from limited generalization to unseen novel categories. Recent works mitigate poor generalizability to novel categories by generating class-agnostic masks or projecting generalized masks from 2D to 3D, but disregard semantic or geometry information, leading to sub-optimal performance. Instead, generating generalizable but semantic-related masks directly from 3D point clouds would result in superior outcomes. In this paper, we introduce Segment any 3D Object with LanguagE (SOLE), which is a semantic and geometric-aware visual-language learning framework with strong generalizability by generating semantic-related masks directly from 3D point clouds. Specifically, we propose a multimodal fusion network to incorporate multimodal semantics in both backbone and decoder. In addition, to align the 3D segmentation model with various language instructions and enhance the mask quality, we introduce three types of multimodal associations as supervision. Our SOLE outperforms previous methods by a large margin on ScanNetv2, ScanNet200, and Replica benchmarks, and the results are even close to the fully-supervised counterpart despite the absence of class annotations in the training. Furthermore, extensive qualitative results demonstrate the versatility of our SOLE to language instructions.

cs.CV↗

arXiv2024

Giant piezoelectricity in group IV monochalcogenides with ferroelectric AA layer stacking

The piezoelectricity of group IV monochalcogenides (MXs, with M = Ge, Sn and X = S, Se) has attracted much attention due to their substantially higher piezoelectric coefficients compared to other 2D materials. However, with increasing layer number, their piezoelectricity rapidly disappears due to the antiferroelectric stacking order, severely limiting their practical applications. Using first-principles calculations, we investigated the piezoelectricity of MXs with the ferroelectric AA stacking configuration, which has recently been stabilized in experiments. We found that AA-stacked MXs have a ferroelectric ground state with the smallest lattice constant among other stacking configurations, resulting in a giant piezoelectric coefficient, which is the first demonstration of a strategy where the piezoelectric coefficients can increase with the number of layers. This can be attributed to a strong negative correlation between the lattice constant along the armchair direction and the piezoelectric coefficient, and spontaneous compressive strain stabilized in ferroelectric AA stacking configuration.

cond-mat.mtrl-sci↗

arXiv2023

Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection

For object detection task with noisy labels, it is important to consider not only categorization noise, as in image classification, but also localization noise, missing annotations, and bogus bounding boxes. However, previous studies have only addressed certain types of noise (e.g., localization or categorization). In this paper, we propose Universal-Noise Annotation (UNA), a more practical setting that encompasses all types of noise that can occur in object detection, and analyze how UNA affects the performance of the detector. We analyzed the development direction of previous works of detection algorithms and examined the factors that impact the robustness of detection model learning method. We open-source the code for injecting UNA into the dataset and all the training log and weight are also shared.

cs.CV↗

arXiv2023

Inducing Point Operator Transformer: A Flexible and Scalable Architecture for Solving PDEs

Solving partial differential equations (PDEs) by learning the solution operators has emerged as an attractive alternative to traditional numerical methods. However, implementing such architectures presents two main challenges: flexibility in handling irregular and arbitrary input and output formats and scalability to large discretizations. Most existing architectures are limited by their desired structure or infeasible to scale large inputs and outputs. To address these issues, we introduce an attention-based model called an inducing-point operator transformer (IPOT). Inspired by inducing points methods, IPOT is designed to handle any input function and output query while capturing global interactions in a computationally efficient way. By detaching the inputs/outputs discretizations from the processor with a smaller latent bottleneck, IPOT offers flexibility in processing arbitrary discretizations and scales linearly with the size of inputs/outputs. Our experimental results demonstrate that IPOT achieves strong performances with manageable computational complexity on an extensive range of PDE benchmarks and real-world weather forecasting scenarios, compared to state-of-the-art methods.

cs.LG↗

arXiv2023

Ferroelectric Semimetals with $α$-Bi/SnSe van der Waals heterostructures and its Topological Currents

We show that proximity effects can be utilized to engineer van der Waals heterostructures (vd- WHs) displaying spin-ferroelectricity locking, where ferroelectricity and spin states are confined to different layers, but are correlated by means of proximity effects. Our findings are supported by first principles calculations in $α$-Bi/SnSe bilayers. We show that such systems support ferroelectrically switchable non-linear anomalous Hall effect originating from large Berry curvature dipoles as well as direct and inverse spin Hall effects with giant bulk spin-charge interconversion efficiencies. The giant efficiencies are consequences of the proximity-induced semimetallic nature of low energy electron states, which are shown to behave as two-dimensional pseudo-Weyl fermions by means of symmetry analysis, first principles calculations as well as direct angle-resolved photoemission spectroscopy measurements.

cond-mat.mtrl-sci↗

arXiv2023

Establishing Epitaxial Connectedness in Multi-Stacking: The Survival of Thru-Holes in Thru-Hole Epitaxy

Thru-hole epitaxy has recently been reported to be able to grow readily detachable domains crystallographically aligned with the underlying substrate over 2D mask material transferred onto a substrate. [Jang \textit{et al.}, \textit{Adv. Mater. Interfaces}, \textbf{2023} \textit{10}, 4 2201406] While the experimental demonstration of thru-hole epitaxy of GaN over multiple stacks of $h$-BN was evident, the detailed mechanism of how small holes in each stack of $h$-BN survived as thru-holes during multiple stacking of $h$-BN was not intuitively clear. Here, we use Monte Carlo simulations to investigate the conditions under which holes in each stack of 2D mask layers can survive as thru-holes during multiple stacking. If holes are highly anisotropic in shape by connecting smaller holes in a particular direction, thru-holes can be maintained with a high survival rate per stack, establishing more epitaxial connectedness. Our work verifies and supports that thru-hole epitaxy is attributed to the epitaxial connectedness established by thru-holes surviving even through multiple stacks.

cond-mat.mtrl-sci↗

arXiv2023

Enhanced Spin Hall Response From Aligned Kramers-Weyl Points in High Chern Number Semimetals

We propose a spin Hall effect (SHE) enhancement mechanism due to Kramers-Weyl point (KWP) alignment in chiral topological semimetals with high Chern numbers (CNs). Through model Hamiltonian calculations, we identify enhancements in the intrinsic spin Hall conductivity (SHC) and the spin Hall angle (SHA). Such enhancements, attributed to a unique high CN KWP energetic alignment and a high degree of SOC-induced band nesting, strongly depend on orbital-orbital interactions. This represents a novel mechanism to enhance SHE, differing from the spin-orbit induced anticrossing mechanism in gapped systems. Guided by this principle, we corroborate our results by means of first-principles calculations and reveal multiple realistic materials with large intrinsic SHCs and even larger SHAs than Pt.

cond-mat.mes-hall↗