arXiv subjects

Junghoon Lee

Publications and source records attributed to Junghoon Lee.

At least 19 recordsLinked to original sources

arXiv2026

Energy spectra and cascade in the spin turbulence of a driven spinor Bose-Einstein condensate

We investigate the spin-interaction energy spectrum of spin turbulence in a driven spinor Bose-Einstein condensate. Continuous spin driving of a spin-1 condensate produces a nonequilibrium steady state with spatially fluctuating magnetization. We observe a power-law scaling consistent with the $-7/3$ exponent predicted for spin-wave turbulence, which persists across our full range of drive strengths despite substantial changes in the spectral anisotropy. After switching off the drive, we track the free-decay evolution and find evidence consistent with a direct cascade of spin-interaction energy toward higher wavenumbers. These results establish an energy-spectral hallmark of spin turbulence and enable quantitative studies of cascade dynamics in spinor superfluids.

cond-mat.quant-gas↗

arXiv2026

Molecular Origin of UV-Induced Irreversible Phase Changes in a Chromonic Liquid Crystal

Aqueous solutions of disodium cromoglycate (DSCG), a representative model system for chromonic liquid crystals, exhibit temperature- and concentration-dependent phase behaviors spanning isotropic, nematic, and columnar phases, as well as their coexistence regions. Nastishin et al. (2018) reported that UV irradiation can alter the phase diagram, transforming a nematic phase into a nematic-isotropic biphasic state due to weakened molecular attractions, accompanied by a slow post-irradiation relaxation. Here, we revisit this phenomenon and elucidate the molecular origin of this phase diagram shift: the UV-induced photodegradation of DSCG into specific photodegradation products, which we identify using liquid chromatography-mass spectrometry. Through an integrated approach combining in situ X-ray scattering and polarized optical microscopy, we demonstrate that these degradation products disrupt the self-assembly of DSCG aggregates, thereby expanding the isotropic and biphasic regions in the phase diagram. These findings demonstrate that chromonic assemblies and their phase behaviors are highly sensitive to minor chemical alterations, providing a potential route toward light-controlled self assembly of soft matter.

cond-mat.soft↗

arXiv2026

TriGen: NPU Architecture for End-to-End Acceleration of Large Language Models based on SW-HW Co-Design

Recent studies have extensively explored NPU architectures for accelerating AI inference in on-device environments, which are inherently resource-constrained. Meanwhile, transformer-based large language models (LLMs) have become dominant, with rapidly increasing model sizes but low degree of parameter reuse compared to conventional CNNs, making end-to-end execution on resource-limited devices extremely challenging. To address these challenges, we propose TriGen, a novel NPU architecture tailored for resource-constrained environments through software-hardware co-design. Firstly, TriGen adopts low-precision computation using microscaling (MX) to enable additional optimization opportunities while preserving accuracy, and resolves the issues that arise by employing such precision. Secondly, to jointly optimize both nonlinear and linear operations, TriGen eliminates the need for specialized hardware for essential nonlinear operations by using fast and accurate LUT, thereby maximizing performance gains and reducing hardware-cost in on-device environments, and finally, by taking practical hardware constraints into account, further employs scheduling techniques to maximize computational utilization even under limited on-chip memory capacity. We evaluate the performance of TriGen on various LLMs and show that TriGen achieves an average 2.73x performance speedup and 52% less memory transfer over the baseline NPU design with negligible accuracy loss.

cs.AR↗

arXiv2025

Magnetically controlled double-twist director configuration of lyotropic chromonic liquid crystals in cylinders: Energetics, topological defects, and instability

We study experimentally how the double-twist (DT) configuration of cylindrically confined lyotropic chromonic liquid crystals (LCLCs) responds to axial magnetic fields. Our director field model unveils the energetics behind the magnetic field-induced transition in the twist profile of the DT configuration. Additionally, we catalog three different types of topological defects -- residing between the DT domains of opposite handedness -- before and after the field application, and propose a new director field model for the defect with a ring disclination. Lastly, we report a symmetry-breaking instability occurring when the field strength exceeds a critical value, suggesting an eccentric DT director field model that reproduces a helix-like optical texture. Our systematic investigation not only enhances our understanding of LCLC energetics but also provides potential for precise control over DT configurations.

cond-mat.soft↗

arXiv2025

Enhancement of damping in a turbulent atomic Bose-Einstein condensate

Turbulence enhances momentum transport in classical fluids, effectively increasing their viscosity. We investigate an analogous effect in a superfluid by measuring the damping of collective oscillations in an atomic Bose-Einstein condensate (BEC) containing stationary spin-superflow turbulence. Using continuous spin driving to maintain turbulence in a spin-1 $^{23}$Na BEC, we excite its quadrupole mode and measure the damping rate over a range of temperatures. The damping consistently exceeds the Landau-damping rate expected for an equilibrium, non-turbulent BEC. The enhancement likely originates from two complementary processes: direct energy transfer from the mode to turbulent condensate fluctuations and turbulence-induced modification of the thermal cloud that amplifies Landau damping. These results establish collective-mode damping as a sensitive probe of momentum transport in superfluid turbulence.

cond-mat.quant-gas↗

arXiv2024

Chaos-assisted Turbulence in Spinor Bose-Einstein Condensates

We present a turbulence-sustaining mechanism in a spinor Bose-Einstein condensate, which is based on the chaotic nature of internal spin dynamics. Magnetic driving induces a complete chaotic evolution of the local spin state, thereby continuously randomizing the spin texture of the condensate to maintain the turbulent state. We experimentally demonstrate the onset of turbulence in the driven condensate as the driving frequency changes and show that it is consistent with the regular-to-chaotic transition of the local spin dynamics. This chaos-assisted turbulence establishes the spin-driven spinor condensate as an intriguing platform for exploring quantum chaos and related superfluid turbulence phenomena.

cond-mat.quant-gas↗

arXiv2023

Random spin textures in turbulent spinor Bose-Einstein condensates

We numerically investigate the stationary turbulent states of spin-1 Bose-Einstein condensates under continuous spin driving. We analyze the entanglement entropy and magnetization correlation function to demonstrate the isotropic nature of the intricate spin texture that is generated in the nonequilibrium steady state. We observe a $-7/3$ power-law behavior in the spin-dependent interaction energy spectrum. To gain further insight into the statistical properties of the spin texture, we introduce a spin state ensemble obtained through position projection, revealing its close resemblance to the Haar random ensemble for spin-1 systems. We also present the probability distribution of the spin vector magnitude in the turbulent condensate, which can be tested in experiments. Our numerical study highlights the characteristics of stationary turbulence in the spinor BEC system and confirms previous experimental findings by Hong et al. [Phys. Rev. A 108, 013318 (2023)].

cond-mat.quant-gas↗

arXiv2023

Spin-driven stationary turbulence in spinor Bose-Einstein condensates

We report the observation of stationary turbulence in antiferromagnetic spin-1 Bose-Einstein condensates driven by a radio-frequency magnetic field. The magnetic driving injects energy into the system by spin rotation and the energy is dissipated via dynamic instability, resulting in the emergence of an irregular spin texture in the condensate. Under continuous driving, the spinor condensate evolves into a nonequilibrium steady state with characteristic spin turbulence, while the low energy scale of spin excitations ensures that the sample's lifetime is minimally affected. When the driving strength is on par with the system's spin interaction energy and the quadratic Zeeman energy, remarkably, the stationary turbulent state exhibits spin-isotropic features in spin composition and spatial spin texture. We numerically show that ambient field fluctuations play a crucial role in sustaining the turbulent state within the system. These results open up new avenues for exploring quantum turbulence in spinor superfluid systems.

cond-mat.quant-gas↗

arXiv2021

Back-Translated Task Adaptive Pretraining: Improving Accuracy and Robustness on Text Classification

Language models (LMs) pretrained on a large text corpus and fine-tuned on a downstream text corpus and fine-tuned on a downstream task becomes a de facto training strategy for several natural language processing (NLP) tasks. Recently, an adaptive pretraining method retraining the pretrained language model with task-relevant data has shown significant performance improvements. However, current adaptive pretraining methods suffer from underfitting on the task distribution owing to a relatively small amount of data to re-pretrain the LM. To completely use the concept of adaptive pretraining, we propose a back-translated task-adaptive pretraining (BT-TAPT) method that increases the amount of task-specific data for LM re-pretraining by augmenting the task data using back-translation to generalize the LM to the target task domain. The experimental results show that the proposed BT-TAPT yields improved classification accuracy on both low- and high-resource data and better robustness to noise than the conventional adaptive pretraining method.

cs.CL↗

arXiv2020

Finding novelty with uncertainty

Medical images are often used to detect and characterize pathology and disease; however, automatically identifying and segmenting pathology in medical images is challenging because the appearance of pathology across diseases varies widely. To address this challenge, we propose a Bayesian deep learning method that learns to translate healthy computed tomography images to magnetic resonance images and simultaneously calculates voxel-wise uncertainty. Since high uncertainty occurs in pathological regions of the image, this uncertainty can be used for unsupervised anomaly segmentation. We show encouraging experimental results on an unsupervised anomaly segmentation task by combining two types of uncertainty into a novel quantity we call scibilic uncertainty.

eess.IV↗

arXiv2020

Validating uncertainty in medical image translation

Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians. However, standard deep neural networks do not provide a reliable measure of uncertainty in those quantitative values. Recent work has shown that using dropout during training and testing can provide estimates of uncertainty. In this work, we investigate using dropout to estimate epistemic and aleatoric uncertainty in a CT-to-MR image translation task. We show that both types of uncertainty are captured, as defined, providing confidence in the output uncertainty estimates.

eess.IV↗

arXiv2018

Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN

The cycleGAN is becoming an influential method in medical image synthesis. However, due to a lack of direct constraints between input and synthetic images, the cycleGAN cannot guarantee structural consistency between these two images, and such consistency is of extreme importance in medical imaging. To overcome this, we propose a structure-constrained cycleGAN for brain MR-to-CT synthesis using unpaired data that defines an extra structure-consistency loss based on the modality independent neighborhood descriptor to constrain structural consistency. Additionally, we use a position-based selection strategy for selecting training images instead of a completely random selection scheme. Experimental results on synthesizing CT images from brain MR images demonstrate that our method is better than the conventional cycleGAN and approximates the cycleGAN trained with paired data.

cs.CV↗

arXiv2018

Laplace Functional Ordering of Point Processes in Large-scale Wireless Networks

Stochastic orders on point processes are partial orders which capture notions like being larger or more variable. Laplace functional ordering of point processes is a useful stochastic order for comparing spatial deployments of wireless networks. It is shown that the ordering of point processes is preserved under independent operations such as marking, thinning, clustering, superposition, and random translation. Laplace functional ordering can be used to establish comparisons of several performance metrics such as coverage probability, achievable rate, and resource allocation even when closed form expressions of such metrics are unavailable. Applications in several network scenarios are also provided where tradeoffs between coverage and interference as well as fairness and peakyness are studied. Monte-Carlo simulations are used to supplement our analytical results.

cs.IT↗

arXiv2014

Modification of particle-laden near-wall turbulence: effect of Stokes number

Turbulent channel flows laden with particles are investigated using direct numerical simulation with a point-force approximation for small, heavy particles with a diameter smaller than the Kolmogorov length scale of the fluid. The Stokes numbers based on the wall units considered in our study are $St^+=0.5, 5, 35$ and 125. The main purpose of this study is to examine the effect of Stokes number on turbulence modification in a channel. We found that particles with $St^+=0.5$ enhance turbulence by increasing the occurrence of quasistreamwise vortices, while larger-Stokes-number particles attenuate turbulence. When $St^+=0.5$, kinetic energy is transferred from the particles to streamwise fluid velocity fluctuations in the high-speed regions and low-speed streaks, which may increase the instability of the low-speed streaks responsible for the birth of new quasistreamwise vortices. On the other hand, the preferential concentration of larger-Stokes-number particles in low-speed streaks is responsible for turbulence attenuation, and the slow response of the particles to the fluid produces feedback against the fluid velocity associated with quasistreamwise vortices.

physics.flu-dyn↗

arXiv2013

Distributed Detection in Coexisting Large-scale Sensor Networks

This paper considers signal detection in coexisting wireless sensor networks (WSNs). We characterize the aggregate signal and interference from a Poisson random field of nodes and define a binary hypothesis testing problem to detect a signal in the presence of interference. For the testing problem, we introduce the maximum likelihood (ML) detector and simpler alternatives. The proposed mixed-fractional lower order moment (FLOM) detector is computationally simple and close to the ML performance, and robust to estimation errors in system parameters. We also derived asymptotic theoretical performances for the proposed simple detectors. Monte-Carlo simulations are used to supplement our analytical results and compare the performance of the receivers.

cs.IT↗

arXiv2012

Stochastic Ordering of Interferences in Large-scale Wireless Networks

Stochastic orders are binary relations defined on probability distributions which capture intuitive notions like being larger or being more variable. This paper introduces stochastic ordering of interference distributions in large-scale networks modeled as point process. Interference is the main performance-limiting factor in most wireless networks, thus it is important to understand its statistics. Since closed-form results for the distribution of interference for such networks are only available in limited cases, interference of networks are compared using stochastic orders, even when closed form expressions for interferences are not tractable. We show that the interference from a large-scale network depends on the fading distributions with respect to the stochastic Laplace transform order. The condition for path-loss models is also established to have stochastic ordering between interferences. The stochastic ordering of interferences between different networks are also shown. Monte-Carlo simulations are used to supplement our analytical results.

cs.IT↗

arXiv2011

Space-Time Coding over Fading Channels with Stable Noise

This paper addresses the performance of space-time coding over fading channels with impulsive noise which is known to accurately capture network interference. We use the symmetric alpha stable noise distribution and adopt two models which assume dependent and independent noise components across receive antennas. We derive pairwise error probability (PEP) of orthogonal space-time block codes (STBC) with a benchmark genie-aided receiver (GAR), or the minimum distance receiver (MDR) which is optimal in the Gaussian case. For general space-time codes we propose a maximum-likelihood (ML) receiver, and its approximation at high signal-to-noise ratio (SNR). The resulting asymptotically optimal receiver (AOR) does not depend on noise parameters and is computationally simple. Monte-Carlo simulations are used to supplement our analytical results and compare the performance of the receivers.

cs.IT↗

arXiv2010

Reversible Metal-Semiconductor Transition of ssDNA-Decorated Single-Walled Carbon Nanotubes

A field effect transistor (FET) measurement of a SWNT shows a transition from a metallic one to a p-type semiconductor after helical wrapping of DNA. Water is found to be critical to activate this metal-semiconductor transition in the SWNT-ssDNA hybrid. Raman spectroscopy confirms the same change in electrical behavior. According to our ab initio calculations, a band gap can open up in a metallic SWNT with wrapped ssDNA in the presence of water molecules due to charge transfer.

cond-mat.mes-hall↗