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Researcher profile

Dongwon Kim

Dongwon Kim contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
astro-ph.GAComputer VisionMachine LearningArtificial Intelligenceastro-ph.SR

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Published work

8 published item(s)

preprint2026arXiv

Personalized Federated Learning for Gradient Alignment

Personalized federated learning (pFL) aims to adapt models to client specific data distributions, yet it often fails to reliably preserve personalized information. Local training is hindered by high variance gradients induced by limited and heterogeneous client data, while aggregation further distorts client specific optimization directions. To address these challenges, we propose pFLAlign, a gradient alignment framework to maintain client specific information during both local training and aggregation. pFLAlign consists of two complementary mechanisms: one adapts local gradient directions to reduce variance during client side optimization, and the other mitigates aggregation induced distortion by realigning the global model with each client's personalized direction. Theoretically, we derive pFLAlign from a PAC Bayesian analysis, which reveals how personalized gradient alignment preserves client specific information. Our experiments and ablation studies show that pFLAlign consistently improves personalization performance and training stability, achieving state of the art results.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Detailed chemical abundances of stars in the outskirts of the Tucana II ultra-faint dwarf galaxy

We present chemical abundances and velocities of five stars between 0.3 kpc to 1.1 kpc from the center of the Tucana II ultra-faint dwarf galaxy (UFD) from high-resolution Magellan/MIKE spectroscopy. We find that every star is deficient in metals (-3.6 < [Fe/H] < -1.9) and in neutron-capture elements as is characteristic of UFD stars, unambiguously confirming their association with Tucana II. Other chemical abundances (e.g., C, iron-peak) largely follow UFD trends and suggest that faint core-collapse supernovae (SNe) dominated the early evolution of Tucana II. We see a downturn in [$α$/Fe] at [Fe/H] $\approx -2.8$, indicating the onset of Type Ia SN enrichment and somewhat extended chemical evolution. The most metal-rich star has strikingly low [Sc/Fe] = $-1.29 \pm 0.48$ and [Mn/Fe] = $-1.33 \pm 0.33$, implying significant enrichment by a sub-Chandrasekhar mass Type Ia SN. We do not detect a radial velocity gradient in Tucana II ($\text{d}v_{\text{helio}}/\text{d}θ_1=-2.6^{+3.0}_{-2.9}$ km s$^{-1}$ kpc$^{-1}$) reflecting a lack of evidence for tidal disruption, and derive a dynamical mass of $M_{1/2} (r_h) = 1.6^{+1.1}_{-0.7}\times 10^6$ M$_{\odot}$. We revisit formation scenarios of the extended component of Tucana II in light of its stellar chemical abundances. We find no evidence that Tucana II had abnormally energetic SNe, suggesting that if SNe drove in-situ stellar halo formation then other UFDs should show similar such features. Although not a unique explanation, the decline in [$α$/Fe] is consistent with an early galactic merger triggering later star formation. Future observations may disentangle such formation channels of UFD outskirts.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ReSTR: Convolution-free Referring Image Segmentation Using Transformers

Referring image segmentation is an advanced semantic segmentation task where target is not a predefined class but is described in natural language. Most of existing methods for this task rely heavily on convolutional neural networks, which however have trouble capturing long-range dependencies between entities in the language expression and are not flexible enough for modeling interactions between the two different modalities. To address these issues, we present the first convolution-free model for referring image segmentation using transformers, dubbed ReSTR. Since it extracts features of both modalities through transformer encoders, it can capture long-range dependencies between entities within each modality. Also, ReSTR fuses features of the two modalities by a self-attention encoder, which enables flexible and adaptive interactions between the two modalities in the fusion process. The fused features are fed to a segmentation module, which works adaptively according to the image and language expression in hand. ReSTR is evaluated and compared with previous work on all public benchmarks, where it outperforms all existing models.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Self-Taught Metric Learning without Labels

We present a novel self-taught framework for unsupervised metric learning, which alternates between predicting class-equivalence relations between data through a moving average of an embedding model and learning the model with the predicted relations as pseudo labels. At the heart of our framework lies an algorithm that investigates contexts of data on the embedding space to predict their class-equivalence relations as pseudo labels. The algorithm enables efficient end-to-end training since it demands no off-the-shelf module for pseudo labeling. Also, the class-equivalence relations provide rich supervisory signals for learning an embedding space. On standard benchmarks for metric learning, it clearly outperforms existing unsupervised learning methods and sometimes even beats supervised learning models using the same backbone network. It is also applied to semi-supervised metric learning as a way of exploiting additional unlabeled data, and achieves the state of the art by boosting performance of supervised learning substantially.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

An extended halo around an ancient dwarf galaxy

The Milky Way is surrounded by dozens of ultra-faint (< $10^5$ solar luminosities) dwarf satellite galaxies. They are the surviving remnants of the earliest galaxies, as confirmed by their ancient (~13 billion years old) and chemically primitive stars. Simulations suggest that these systems formed within extended dark matter halos and experienced early galaxy mergers and supernova feedback. However, the signatures of these events would lie outside their core regions (>2 half-light radii), which are spectroscopically unstudied due to the sparseness of their distant stars. Here we identify members of the Tucana II ultra-faint dwarf galaxy in its outer region (up to 9 half-light radii), demonstrating the system to be dramatically more spatially extended and chemically primitive than previously found. These distant stars are extremely metal-poor (<[Fe/H]>=-3.02; less than ~1/1000th of the solar iron abundance), affirming Tucana II as the most metal-poor known galaxy. We observationally establish, for the first time, an extended dark matter halo surrounding an ultra-faint dwarf galaxy out to one kiloparsec, with a total mass of >$10^7$ solar masses. This measurement is consistent with the expected ~2x$10^7$ solar masses using a generalized NFW density profile. The extended nature of Tucana II suggests that it may have undergone strong bursty feedback or been the product of an early galactic merger. We demonstrate that spatially extended stellar populations, which other ultra-faint dwarfs hint at hosting as well, are observable in principle and open the possibility for detailed studies of the stellar halos of relic galaxies.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

The 3-D Kinematics of the Orion Nebula Cluster: NIRSPEC-AO Radial Velocities of the Core Population

The kinematics and dynamics of stellar and substellar populations within young, still-forming clusters provides valuable information for constraining theories of formation mechanisms. Using Keck II NIRSPEC+AO data, we have measured radial velocities for 56 low-mass sources within 4&#39; of the core of the Orion Nebula Cluster (ONC). We also re-measure radial velocities for 172 sources observed with SDSS/APOGEE. These data are combined with proper motions measured using $HST$ ACS/WFPC2/WFC3IR and Keck II NIRC2, creating a sample of 135 sources with all three velocity components. The velocities measured are consistent with a normal distribution in all three components. We measure intrinsic velocity dispersions of ($σ_{v_α}$, $σ_{v_δ}$, $σ_{v_r}$) = ($1.64\pm0.12$, $2.03\pm0.13$, $2.56^{+0.16}_{-0.17}$) km s$^{-1}$. Our computed intrinsic velocity dispersion profiles are consistent with the dynamical equilibrium models from Da Rio et al. (2014) in the tangential direction, but not in the line of sight direction, possibly indicating that the core of the ONC is not yet virialized, and may require a non-spherical potential to explain the observed velocity dispersion profiles. We also observe a slight elongation along the north-south direction following the filament, which has been well studied in previous literature, and an elongation in the line of sight to tangential velocity direction. These 3-D kinematics will help in the development of realistic models of the formation and early evolution of massive clusters.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Proxy Anchor Loss for Deep Metric Learning

Existing metric learning losses can be categorized into two classes: pair-based and proxy-based losses. The former class can leverage fine-grained semantic relations between data points, but slows convergence in general due to its high training complexity. In contrast, the latter class enables fast and reliable convergence, but cannot consider the rich data-to-data relations. This paper presents a new proxy-based loss that takes advantages of both pair- and proxy-based methods and overcomes their limitations. Thanks to the use of proxies, our loss boosts the speed of convergence and is robust against noisy labels and outliers. At the same time, it allows embedding vectors of data to interact with each other in its gradients to exploit data-to-data relations. Our method is evaluated on four public benchmarks, where a standard network trained with our loss achieves state-of-the-art performance and most quickly converges.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Stellar metallicities from SkyMapper photometry I: A study of the Tucana II ultra-faint dwarf galaxy

We present a study of the ultra-faint Milky Way dwarf satellite galaxy Tucana II using deep photometry from the 1.3m SkyMapper telescope at Siding Spring Observatory, Australia. The SkyMapper filter-set contains a metallicity-sensitive intermediate-band $v$ filter covering the prominent Ca II K feature at 3933.7A. When combined with photometry from the SkyMapper $u, g$, and $i$ filters, we demonstrate that $v$ band photometry can be used to obtain stellar metallicities with a precision of $\sim0.20$dex when [Fe/H] $> -2.5$, and $\sim0.34$dex when [Fe/H] $< -2.5$. Since the $u$ and $v$ filters bracket the Balmer Jump at 3646A, we also find that the filter-set can be used to derive surface gravities. We thus derive photometric metallicities and surface gravities for all stars down to a magnitude of $g\sim20$ within $\sim$75 arcminutes of Tucana II. Photometric metallicity and surface gravity cuts remove nearly all foreground contamination. By incorporating Gaia proper motions, we derive quantitative membership probabilities which recover all known members on the red giant branch of Tucana II. Additionally, we identify multiple likely new members in the center of the system and candidate members several half-light radii from the center of the system. Finally, we present a metallicity distribution function derived from the photometric metallicities of likely Tucana II members. This result demonstrates the utility of wide-field imaging with the SkyMapper filter-set in studying UFDs, and in general, low surface brightness populations of metal-poor stars. Upcoming work will clarify the membership status of several distant stars identified as candidate members of Tucana II.

0 citations0 reviewsNo code linkedOpen access