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Minjae Kim

Minjae Kim appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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cond-mat.str-elMachine LearningArtificial Intelligencecond-mat.supr-conComputer Visionastro-ph.EPComputationcond-mat.mtrl-scieess.IVMethodologyPopulations and Evolutionquant-ph

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Published work

18 published item(s)

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.

0 citations0 reviewsNo code linkedOpen access
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.

0 citations0 reviewsNo code linkedOpen access
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.

0 citations0 reviewsNo code linkedOpen access
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.

0 citations0 reviewsNo code linkedOpen access
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.

0 citations0 reviewsNo code linkedOpen access
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.

0 citations0 reviewsNo code linkedOpen access
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.

0 citations0 reviewsNo code linkedOpen access
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.

0 citations0 reviewsNo code linkedOpen access
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$_3$.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Enhancing superconductivity in A3C60 fullerides

Motivated by the recent experimental report of a possible light-induced superconductivity in A3C60 at high temperature [Mitrano et al., Nature 530, 451 (2016)], we investigate theoretical mechanisms for enhanced superconductivity in A3C60 fullerenes. We find that an `interaction imbalance' corresponding to a smaller value of the Coulomb matrix element for two of the molecular orbitals in comparison to the third one, efficiently enhances superconductivity. Furthermore, we perform first-principle calculations of the changes in the electronic structure and in the screened Coulomb matrix elements of A3C60, brought in by the deformation associated with the pumped T1u intra-molecular mode. We find that an interaction imbalance is indeed induced, with a favorable sign and magnitude for superconductivity enhancement. The physical mechanism responsible for this enhancement consists in a stabilisation of the intra-molecular states containing a singlet pair, while preserving the orbital fluctuations allowing for a coherent inter-orbital delocalization of the pair. Other perturbations have also been considered and found to be detrimental to superconductivity. The light-induced deformation and ensuing interaction imbalance is shown to bring superconductivity further into the strong-coupling regime.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Particle in a box with a time-dependent $δ$-function potential

In quantum information processing, one often considers inserting a barrier into a box containing a particle to generate one bit of Shannon entropy. We formulate this problem as a one-dimensional Schrödinger equation with a time-dependent $δ$-function potential. It is a natural generalization of the particle in a box, a canonical example of quantum mechanics, and we present analytic and numerical investigations on this problem. After deriving an exact Volterra-type integral equation, composed of an infinite sum of modes, we show that approximate formulas with the lowest-frequency modes correctly capture the qualitative behavior of the wave function. If we take into account hundreds of modes, our numerical calculation shows that the quantum adiabatic theorem actually gives a very good approximation even if the barrier height diverges within finite time, as long as it is sufficiently longer than the characteristic time scale of the particle. In particular, if the barrier is slowly inserted at an asymmetric position, the particle is localized by the insertion itself, in accordance with a prediction of the adiabatic theorem. On the other hand, when the barrier is inserted quickly, the wave function becomes rugged after the insertion because of the energy transfer to the particle. Regardless of the position of the barrier, the fast insertion leaves the particle unlocalized so that we can obtain meaningful information by a which-side measurement. Our numerical procedure provides a precise way to calculate the wave function throughout the process, from which one can estimate the amount of this information for an arbitrary insertion protocol.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Spectral M-estimation with Applications to Hidden Markov Models

Method of moment estimators exhibit appealing statistical properties, such as asymptotic unbiasedness, for nonconvex problems. However, they typically require a large number of samples and are extremely sensitive to model misspecification. In this paper, we apply the framework of M-estimation to develop both a generalized method of moments procedure and a principled method for regularization. Our proposed M-estimator obtains optimal sample efficiency rates (in the class of moment-based estimators) and the same well-known rates on prediction accuracy as other spectral estimators. It also makes it straightforward to incorporate regularization into the sample moment conditions. We demonstrate empirically the gains in sample efficiency from our approach on hidden Markov models.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

The Insulating Nature of Na2IrO3: Mott-type or Slater-type?

We have investigated temperature-dependent electronic structures of Na2IrO3 to unravel its insulating nature. Employing the combined scheme of the density-functional theory (DFT) and the dynamical mean-field theory (DMFT), we have shown that the insulating state persists even above the Neel temperature (T_{N}), which reveals that Na2IrO3 is classified into a Mott-type insulator. The measured photoemission spectrum in the paramagnetic (PM) state is well described by the electronic structure obtained from the DFT+DMFT for the insulating state above T_{N}. The analysis of optical conductivity, however, suggests that the non-local correlation effect is also important in Na2IrO3. Therefore, Na2IrO3 is not to be a standard Mott insulator in that the extended nature and the non-local correlation effect of Ir 5d electrons are important as well in describing its electronic and magnetic properties.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

The nature of Itinerant Ferromagnetism of SrRuO3 : A DFT+DMFT Study

We have investigated the temperature (T)-dependent evolution of electronic structures and magnetic properties of an itinerant ferromagnet SrRuO3, employing the combined scheme of the density functional theory and the dynamical mean-field theory (DFT+DMFT). The inclusion of finite dynamical correlation effects beyond the DFT well describes not only the incoherent hump structure observed in the photoemission experiment but also the T-dependent magnetic properties in accordance with experiments. We have shown that the magnetization of SrRuO3 evolves with the Stoner behavior below the Curie temperature (Tc), reflecting the weak itinerant ferromagnetic behavior, but the local residual magnetic moment persists even above Tc, indicating the local magnetic moment behavior. We suggest that the ferromagnetism of SrRuO3 has dual nature of both weak and local moment limits, even though the magnetism of SrRuO3 is more itinerant than that of Fe.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Correlated Electronic Structures and the Phase Diagram of Hydrocarbon-based Superconductors

We have investigated correlated electronic structures and the phase diagram of electron-doped hydrocarbon molecular solids, based on the dynamical mean-field theory. We have found that the ground state of hydrocarbon-based superconductors such as electron-doped picene and coronene is a multi-band Fermi liquid, while that of non-superconducting electron-doped pentacene is a single-band Fermi liquid in the proximity of the metal-insulator transition. The size of the molecular orbital energy level splitting plays a key role in producing the superconductivity of electron-doped hydrocarbon solids. The multi-band nature of hydrocarbon solids would boost the superconductivity through the enhanced density of states at the Fermi level.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Density functional calculations of the electronic structure and magnetic properties of the hydrocarbon K3picene superconductor near the metal-insulator transition

We have investigated the electronic structures and magnetic properties of of K3picene, which is a first hydrocarbon superconductor with high transition temperature T_c=18K. We have shown that the metal-insulator transition (MIT) is driven in K3picene by 5% volume enhancement with a formation of local magnetic moment. Active bands for superconductivity near the Fermi level E_F are found to have hybridized character of LUMO and LUMO+1 picene molecular orbitals. Fermi surfaces of K3picene manifest neither prominent nesting feature nor marked two-dimensional behavior. By estimating the ratio of the Coulomb interaction U and the band width W of the active bands near E_F, U/W, we have demonstrated that K3picene is located in the vicinity of the Mott transition.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Mott physics in $2p$ electron dioxygenyl magnet : O$_{2}$$M$F$_{6}$ ($M$=Sb, Pt)

We have investigated electronic structures and magnetic properties of O$_{2}$$M$F$_{6}$ ($M$=Sb, Pt), which are composed of two building blocks of strongly correlated electrons: O$_{2}^{+}$ dioxygenyls and $M$F$_{6}^{-}$ octahedra, by employing the first-principles electronic structure band method. For O$_{2}$SbF$_{6}$, as a reference system of O$_{2}$PtF$_{6}$, we have shown that the Coulomb correlation of O(2$p$) electrons drives the Mott insulating state. For O$_{2}$PtF$_{6}$, we have demonstrated that the Mott insulating state is induced by the combined effects of the Coulomb correlation of O(2$p$) and Pt(5$d$) electrons and the spin-orbit (SO) interaction of Pt(5$d$) states. The role of the SO interaction in forming the Mott insulating state of O$_{2}$PtF$_{6}$ is similar to the case of Sr$_{2}$IrO$_{4}$ that is a prototype of a SO induced Mott system with J$_{eff}=1/2$.

0 citations0 reviewsNo code linkedOpen access
preprint2009arXiv

Antiferromagnetic and structural transitions in the superoxide KO2 from first principles: A 2p-electron system with spin-orbital-lattice coupling

KO2 exhibits concomitant antiferromagnetic (AFM) and structural transitions, both of which originate from the open-shell 2p electrons of O$_{2}^{-}$ molecules. The structural transition is accompanied by the coherent tilting of O$_{2}^{-}$ molecular axes. The interplay among the spin-orbital-lattice degrees of freedom in KO2 is investigated by employing the first-principles electronic structure theory and the kinetic-exchange interaction scheme. We have shown that the insulating nature of the high symmetry phase of KO2 at high temperature (T) arises from the combined effect of the spin-orbit coupling and the strong Coulomb correlation of O 2p electrons. In contrast, for the low symmetry phase of KO2 at low T with the tilted O$_{2}^{-}$ molecular axes, the band gap and the orbital ordering are driven by the combined effects of the crystal-field and the strong Coulomb correlation. We have verified that the emergence of the O 2p ferro-orbital ordering is essential to achieve the observed AFM structure for KO2.

0 citations0 reviewsNo code linkedOpen access