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Minjae Kim

Minjae Kim contributes to research discovery and scholarly infrastructure.

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Machine Learning Artificial Intelligence Computer Vision cond-mat.str-el astro-ph.EP cond-mat.mtrl-sci eess.IV Populations and Evolution

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preprint2026arXiv

AdaTKG: Adaptive Memory for Temporal Knowledge Graph Reasoning

Temporal knowledge graphs (TKGs) represent time-stamped relational facts and support a wide range of reasoning tasks over evolving events. However, existing methods produce entity representations that are static at the entity level, in that each representation is a function of learned parameters only and retains no trace of the interactions in which the entity has participated. In this paper, we depart from this static view and propose that each entity be modeled as an adaptive process whose representation is refined every time the entity participates in a fact. To this end, we propose AdaTKG, which maintains a per-entity memory that is updated with every observed interaction, with the memory accumulating online and predictions improving as more interactions arrive. Specifically, we instantiate the memory update as a learnable exponential moving average governed by a single shared scalar instead of using learnable parameters for each entity, enabling AdaTKG to handle entities unseen during training. Extensive experiments confirm consistent gains over TKG baselines, demonstrating the effectiveness of adaptive memory. Code is publicly available at: https://github.com/seunghan96/AdaTKG.

preprint2026arXiv

FinSTaR: Towards Financial Reasoning with Time Series Reasoning Models

Time series (TS) reasoning models (TSRMs) have shown promising capabilities in general domains, yet they consistently fail on financial domain, which exhibit unique characteristics. We propose a general 2x2 capability taxonomy for TSRMs by crossing 1) single-entity vs. multi-entity analysis with 2) assessment of the current state vs. prediction of future behavior. We instantiate this taxonomy in the financial domain -- where the distinction between deterministic assessment and stochastic prediction is particularly critical -- as ten financial reasoning tasks, forming the FinTSR-Bench benchmark based on S&P stocks. To this end, we propose FinSTaR (Financial Time Series Thinking and Reasoning), trained on FinTSR-Bench with distinct chain-of-thought (CoT) strategies tailored to each category. For assessment, which is deterministic (i.e., computable from observable data), we employ Compute-in-CoT, a programmatic CoT that enables models to derive answers directly from raw prices. For prediction, which is inherently stochastic (i.e., subject to unobservable factors), we adopt Scenario-Aware CoT, which generates diverse scenarios before making a judgment, mirroring how financial analysts reason under uncertainty. The proposed method achieves 78.9% average accuracy on FinTSR-Bench, substantially outperforming LLM and TSRM baselines. Furthermore, we show that the four capability categories are complementary and mutually reinforcing through joint training, and that Scenario-Aware CoT consistently improves prediction accuracy over standard CoT. Code is publicly available at: https://github.com/seunghan96/FinSTaR.

preprint2026arXiv

Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment

TabPFN has recently gained attention as a foundation model for tabular datasets, achieving strong performance by leveraging in-context learning on synthetic data. However, we find that TabPFN is vulnerable to label shift, often overfitting to the majority class in the training dataset. To address this limitation, we propose DistPFN, the first test-time posterior adjustment method designed for tabular foundation models. DistPFN rescales predicted class probabilities by downweighting the influence of the training prior (i.e., the class distribution of the context) and emphasizing the contribution of the model's predicted posterior, without architectural modification or additional training. We further introduce DistPFN-T, which incorporates temperature scaling to adaptively control the adjustment strength based on the discrepancy between prior and posterior. We evaluate our methods on over 250 OpenML datasets, demonstrating substantial improvements for various TabPFN-based models in classification tasks under label shift, while maintaining strong performance in standard settings without label shift. Code is available at this repository: https://github.com/seunghan96/DistPFN.

preprint2026arXiv

Protoplanetary disk cavities with JWST-MIRI: a dichotomy in molecular emission

The evolution of planet-forming regions in protoplanetary disks is of fundamental importance to understanding planet formation. Disks with a central deficit in dust emission, a "cavity", have long attracted interest as potential evidence for advanced disk clearing by protoplanets and/or winds. Before JWST, infrared spectra showed that these disks typically lack the strong molecular emission observed in full disks. In this work, we combine a sample of 12 disks with millimeter cavities of a range of sizes ($\sim2$-70 au) and different levels of millimeter and infrared continuum deficits. We analyze their molecular spectra as observed with MIRI on JWST, homogeneously reduced with the new JDISCS pipeline. This analysis demonstrates a stark dichotomy in molecular emission where "molecule-rich" (MR) cavities follow global trends between water, CO, and OH luminosity and accretion luminosity as in full disks, while "molecule-poor" (MP) cavities are significantly sub-luminous in all molecules except sometimes OH. Disk cavities generally show sub-luminous organic emission, higher OH/H$_2$O ratios, and suggest a lower water column density. The sub-thermal excitation of CO and water vibrational lines suggests a decreased gas density in the emitting layer in all cavities, supporting model expectations for C$_2$H$_2$ photodissociation. We discover a bifurcation in infrared index (lower in MR cavities) suggesting that the molecular dichotomy is linked to residual $μ$m-size dust within millimeter disk cavities. Put together, these results suggest a feedback process between dust depletion, gas density decrease, and molecule dissociation. Disk cavities may have a common evolutionary sequence where MR switch into MP over time.

preprint2025arXiv

Low-dimensionality-induced tunable ferromagnetism in SrRuO$_3$ ultrathin films

Quantum materials near electronic or magnetic phase boundaries exhibit enhanced tunability, as their emergent properties become highly sensitive to external perturbations. Here, we demonstrate precise control of ferromagnetism in a SrRuO$_3$ ultrathin film, where a high density of states (DOS), arising from low-dimensional quantum states, places the system at the crossover between a non-magnetic and bulk ferromagnetic state. Using spin- and angle-resolved photoemission spectroscopy (SRPES/ARPES), transport measurements, and theoretical calculations, we systematically tune the Fermi level via electron doping across the high-DOS point. We directly visualize the spin-split band structure and reveal its influence on both magnetic and transport properties. Our findings provide compelling evidence that magnetism can be engineered through DOS control at a phase crossover, establishing a pathway for the rational design of tunable quantum materials.

preprint2022arXiv

Social norms in indirect reciprocity with ternary reputations

Indirect reciprocity is a key mechanism that promotes cooperation in social dilemmas by means of reputation. Although it has been a common practice to represent reputations by binary values, either `good' or `bad', such a dichotomy is a crude approximation considering the complexity of reality. In this work, we studied norms with three different reputations, i.e., `good', `neutral', and `bad'. Through massive supercomputing for handling more than thirty billion possibilities, we fully identified which norms achieve cooperation and possess evolutionary stability against behavioural mutants. By systematically categorizing all these norms according to their behaviours, we found similarities and dissimilarities to their binary-reputation counterpart, the leading eight. We obtained four rules that should be satisfied by the successful norms, and the behaviour of the leading eight can be understood as a special case of these rules. A couple of norms that show counter-intuitive behaviours are also presented. We believe the findings are also useful for designing successful norms with more general reputation systems.

preprint2022arXiv

StyLandGAN: A StyleGAN based Landscape Image Synthesis using Depth-map

Despite recent success in conditional image synthesis, prevalent input conditions such as semantics and edges are not clear enough to express `Linear (Ridges)' and `Planar (Scale)' representations. To address this problem, we propose a novel framework StyLandGAN, which synthesizes desired landscape images using a depth map which has higher expressive power. Our StyleLandGAN is extended from the unconditional generation model to accept input conditions. We also propose a '2-phase inference' pipeline which generates diverse depth maps and shifts local parts so that it can easily reflect user's intend. As a comparison, we modified the existing semantic image synthesis models to accept a depth map as well. Experimental results show that our method is superior to existing methods in quality, diversity, and depth-accuracy.

preprint2020arXiv

U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner. The attention module guides our model to focus on more important regions distinguishing between source and target domains based on the attention map obtained by the auxiliary classifier. Unlike previous attention-based method which cannot handle the geometric changes between domains, our model can translate both images requiring holistic changes and images requiring large shape changes. Moreover, our new AdaLIN (Adaptive Layer-Instance Normalization) function helps our attention-guided model to flexibly control the amount of change in shape and texture by learned parameters depending on datasets. Experimental results show the superiority of the proposed method compared to the existing state-of-the-art models with a fixed network architecture and hyper-parameters. Our code and datasets are available at https://github.com/taki0112/UGATIT or https://github.com/znxlwm/UGATIT-pytorch.

preprint2019arXiv

Photoemission and Dynamical Mean Field Theory Study of Electronic Correlation in a $t_{2g}^{5}$ Metal of SrRhO$_{3}$ Thin Film

Perovskite rhodates are characterized by intermediate strengths of both electronic correlation as well as spin-orbit coupling (SOC) and usually behave as moderately correlated metals. A recent publication (Phys. Rev. B 95, 245121(2017)) on epitaxial SrRhO$_3$ thin films unexpectedly reported a bad-metallic behavior and suggested the occurrence of antiferromagnetism below 100 K. We studied this SrRhO$_3$ thin film by hard x-ray photoemission spectroscopy and found a very small density of states (DOS) at Fermi level, which is consistent with the reported bad-metallic behavior. However, this negligible DOS persists up to room temperature, which contradicts with the explanation of antiferromagnetic transition at around 100 K. We also employed electronic structure calculations within the framework of density functional theory and dynamical mean-field theory. In contrast to the experimental results, our calculations indicate metallic behavior of both bulk SrRhO$_3$ and the SrRhO$_3$ thin film. The thin film exhibits stronger correlation effects than the bulk, but the correlation effects are not sufficient to drive a transition to an insulating state. The calculated uniform magnetic susceptibility is substantially larger in the thin film than that in the bulk. The role of SOC was also investigated and only a moderate modulation of the electronic structure was observed. Hence SOC is not expected to play an important role for electronic correlation in SrRhO$_3$.

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