arXiv subjects

Jieun Lee

Publications and source records attributed to Jieun Lee.

At least 19 recordsLinked to original sources

arXiv2026

Physics-Informed Basis Functions for Nonlinear Response Curve Decomposition: A Parsimonious Alternative to Splines

Curve fitting for physical, biological, and engineering data typically forces a choice between interpretable but rigid parametric forms and flexible but physically opaque smoothers. This paper introduces the Growth-Decay Curve (GDC), a physics-informed basis derived as the product of a lognormal growth cumulative distribution function and an exponential decay term, each traceable to a governing differential equation. GDC's parameters correspond directly to growth and decay timescales, yielding dimensionless ratios and shape descriptors that connect the fit back to the underlying dynamics. Across simulated differential-equation solutions and applications spanning physical, biological, and economic data, GDC matches the fit quality of splines, generalized additive models, radial basis function networks, and Fourier regression, while remaining substantially more parsimonious and physically interpretable.

stat.AP↗

arXiv2026

Double/Debiased Machine Learning for Functional-Form-Robust Spatial Autoregression

Spatial autoregressive inference is typically conditional on the spatial weights matrix, W, even though the underlying interaction structure is often unknown and empirical conclusions can be sensitive to its specification. This paper develops double/debiased machine learning inference for low-dimensional SAR parameters when the spatial interaction operator is learned flexibly from potentially endogenous characteristics. Within a maintained admissible support, interaction strength is generated by an unknown function of geographic and socioeconomic characteristics, making inference robust to functional form specification of the weights within that support. Endogeneity in the characteristics generating W is addressed through a nonlinear control function based on locally relevant first-stage residual information. Because the learned operator enters both the spatial lag and spatially transformed instruments, treating the estimated W as known generally leaves a first-order generated-W effect. I construct an operator-orthogonal SAR-IV/GMM score that removes this leading sensitivity and combine it with buffered spatial cross-fitting that separates evaluation-score footprints from nuisance-training observations. Under near epoch dependence on a spatially mixing innovation field and target-relevant nuisance rate and regularity conditions, the estimator is asymptotically linear and root-n normal. Monte Carlo simulations show improved finite-sample inference relative to nonorthogonal alternatives when the interaction function is misspecified, weight generating characteristics are endogenous, and observations are spatially dependent. In a U.S. application, diabetes estimates vary with the choice of W, showing the sensitivity of SAR inference to the interaction structure. Even for the same learned W, results differ across inferential methods, highlighting the importance of inference when W is learned.

econ.EM↗

arXiv2026

Risk-Optimal Curvature Selection for Finite-Sample Cressie-Read Moment Estimation

We propose a finite-sample risk-optimal selection criterion for Cressie-Read power divergence (CRPD) estimation in overidentified moment-based models. The CRPD family, dual to generalized empirical likelihood, is indexed by the power parameter $γ$. Although $γ$ is conventionally fixed at a researcher-chosen value, we argue that it should be interpreted as a data-tunable curvature parameter governing the finite-sample behavior of the CRPD objective. Through implied probability weights and associated Lagrange multipliers, $γ$ affects how the empirical distribution is reweighted to enforce the moment restrictions, even when population identification is unchanged. The proposed criterion selects $γ$ by minimizing an estimation- and system-oriented risk measure. It combines a structural component, which measures finite-sample distortion in the estimate of the structural parameter relative to a first-order GMM benchmark, with a multiplier-stability component, which measures the cost of moment enforcement in the full estimator-multiplier system. A researcher-specified weight determines the relative importance of the two components, allowing the selection rule to prioritize structural accuracy, multiplier stability, or a balance between them. The resulting selector is designed to reduce second-order finite-sample distortion while discouraging unstable multipliers, concentrated implied weights, and proximity to the feasible-probability boundary. Simulations show that the selected CRPD estimator remains approximately centered around the structural parameter while improving finite-sample stability. An empirical illustration using Owen's dairy-cow data shows that similar point estimates can correspond to different implied weighting schemes, highlighting the practical role of $γ$ as a curvature parameter in moment-based estimation.

econ.EM↗

arXiv2026

Strain-programmable exciton diffusion in moiré heterostructures

Moiré superlattices in van der Waals heterostructures have recently gained significant attention as an intriguing platform for studying correlated electronic systems and exotic excitonic properties. Previous reports, however, focused on creating and modulating moiré heterostructures through interlayer twisting or lattice constant mismatches, limiting controls on symmetry of heterostructures. In this work, we show that strain significantly alters the geometry of moiré superlattices by breaking the C3 rotational symmetry. We realize strain-induced moiré superlattices by intentionally regulating interlayer strain in WSe2-MoSe2 heterostructures, which is manifested by linearly polarized interlayer exciton emission coupled to the strain direction. Furthermore, interlayer exciton diffusion was preferentially guided along the stretched moiré superlattice orientations over a wide spatial range, reflecting the strain-modified moiré potentials. Our work highlights strain tuning as a versatile tool for designing moiré superlattices and programming excitonic transport, which opens pathways for van der Waals logic and information processing devices.

cond-mat.mes-hall↗

arXiv2026

Position: AI Lock-In Is in Progress, and We Must Be Prepared

AI safety research has mainly focused on two areas: technical alignment (ensuring AI systems produce human-aligned outputs) and the regulation of generative AI's societal impacts (including unemployment risk and labor market disruption). However, an equally important dimension remains underexplored: the risk inherent in dependence on AI systems themselves. In this position paper, we argue that AI safety research should address AI Lock-In, the phenomenon whereby excessive reliance on AI systems leads to human deskilling, diminishes human capacity for independent functioning, and creates systemic vulnerabilities when AI systems become unavailable or compromised. We highlight that AI Lock-In is a systemic threat that is already emerging at individual, societal, and national levels, one that could be dramatically amplified by AI service disruptions or geopolitical conflicts. Drawing on detailed scenarios, we investigate how AI Lock-In emerges and escalates across multiple levels, ranging from individual skill atrophy to national-scale infrastructure failures. To address this, we provide guidance on how such risks can be mitigated and prepared for at each level. We contend that proactively addressing AI Lock-In before such dependencies become entrenched, or even irreversible, is essential for preserving individual autonomy and national security.

cs.AI↗

arXiv2026

Informativeness under Model Uncertainty: Shadow Prices and Ridge Penalties

We develop inference under model uncertainty due to weak, noisy, multiple candidate restrictions and theories, and nuisance control covariates. A unified framework is given with degrees of misspecification and corresponding shadow prices, based on a Lagrangian constrained optimization approach, and a data$-$driven tolerance parameter selected via a Stein$-$type (shrinkage) risk criterion. A debiasing step is based on Karush$-$Kuhn$-$Tucker conditions. We introduce individual shadow prices (ISP) for different restrictions to measure empirical relevance and propose a plateau rule to separate signal from noise. We establish consistency and asymptotic normality of the estimators and characterize the ISP. Simulations and an application to a Solow growth model illustrate the method$^{\prime}$s practical usefulness.

econ.EM↗

arXiv2026

Vertical Nb Josephson junctions fabricated by direct metal deposition on both surfaces of freestanding graphene layers

Vertical integration of superconducting electronics requires fabrication strategies that preserve pristine interfaces while accommodating oxidation-sensitive elemental superconductors. However, existing van der Waals-based vertical Josephson junctions largely rely on transfer-based assembly schemes that are incompatible with elemental materials such as niobium (Nb). Here, we introduce a freestanding van der Waals membrane architecture that enables deposition-based fabrication of vertical Josephson junctions through double-sided processing of a single suspended two-dimensional layer. Using multilayer graphene suspended across lithographically defined through-holes in a SiNx membrane, we realize vertical Nb/multilayer graphene/Nb Josephson junctions without ambient exposure of buried interfaces. The resulting devices exhibit clear Josephson coupling, including reproducible supercurrents and a temperature dependence of the critical current consistent with short-junction behaviour. Well-defined magnetic interference patterns governed by the membrane-defined aperture geometry, together with sub-gap features that track a Bardeen-Cooper-Schrieffer (BCS)-like superconducting gap, further confirm the junction quality. This platform establishes a scalable route to vertical superconducting devices based on oxidation-sensitive elemental superconductors and van der Waals materials.

cond-mat.supr-con↗

arXiv2026

LatentTune: Efficient Tuning of High Dimensional Database Parameters via Latent Representation Learning

As data volumes continue to grow, optimizing database performance has become increasingly critical, making the implementation of effective tuning methods essential. Among various approaches, database parameter tuning has proven to be a highly effective means of enhancing performance. Recent studies have shown that machine learning techniques can successfully optimize database parameters, leading to significant performance improvements. However, existing methods still face several limitations. First, they require substantial time to generate large training datasets. Second, to cope with the challenges of highdimensional optimization, they typically optimize only a subset of parameters rather than the full configuration space. Third, they often rely on information from similar workloads instead of directly leveraging information from the target workload. To address these limitations, we propose LatentTune, a novel approach that differs fundamentally from traditional methods. To reduce the time required for data generation, LatentTune incorporates a data augmentation strategy. Furthermore, it constructs a latent space that compresses information from all database parameters, enabling the optimization of the full configuration space. In addition, LatentTune integrates external metric information into the latent space, allowing for precise tuning tailored to the actual target workload. Experimental results demonstrate that LatentTune outperforms baseline models across four workloads on MySQL and RocksDB, achieving up to 1332% improvement for RocksDB and 11.82% throughput gain with 46.01% latency reduction for MySQL.

cs.DB↗

arXiv2026

LitE-SQL: A Lightweight and Efficient Text-to-SQL Framework with Vector-based Schema Linking and Execution-Guided Self-Correction

The Text-to-SQL task translates natural language questions into SQL queries, enabling intuitive database interaction for non-experts. While recent methods leveraging Large Language Models (LLMs) achieve strong performance, their reliance on proprietary models raise concerns about deployment feasibility and data privacy. In this work, we introduce LitE-SQL, a Lightweight and Efficient framework with two components: (i) a Schema Retriever that performs efficient schema linking using a vector database of pre-computed schema embeddings, optimized with a hard-negative supervised contrastive objective to distinguish semantically similar but functionally irrelevant columns, and (ii) a SQL Generator fine-tuned in two stages-supervised fine-tuning followed by execution-guided reinforcement-enabling execution-guided self-correction without multi-candidate sampling, which is commonly required by prior LLM-based approaches. On BIRD, LitE-SQL achieves 72.10% execution accuracy, and on Spider 1.0 it reaches 88.45%, demonstrating comparable or superior performance to LLM-based methods despite using 2x to 30x fewer parameters. Our findings demonstrate that high-quality Text-to-SQL generation is feasible with lightweight models, offering a practical solution for privacy-sensitive and resource-constrained settings.

cs.CL↗

arXiv2026

Mi:dm 2.0 Korea-centric Bilingual Language Models

We introduce Mi:dm 2.0, a bilingual large language model (LLM) specifically engineered to advance Korea-centric AI. This model goes beyond Korean text processing by integrating the values, reasoning patterns, and commonsense knowledge inherent to Korean society, enabling nuanced understanding of cultural contexts, emotional subtleties, and real-world scenarios to generate reliable and culturally appropriate responses. To address limitations of existing LLMs, often caused by insufficient or low-quality Korean data and lack of cultural alignment, Mi:dm 2.0 emphasizes robust data quality through a comprehensive pipeline that includes proprietary data cleansing, high-quality synthetic data generation, strategic data mixing with curriculum learning, and a custom Korean-optimized tokenizer to improve efficiency and coverage. To realize this vision, we offer two complementary configurations: Mi:dm 2.0 Base (11.5B parameters), built with a depth-up scaling strategy for general-purpose use, and Mi:dm 2.0 Mini (2.3B parameters), optimized for resource-constrained environments and specialized tasks. Mi:dm 2.0 achieves state-of-the-art performance on Korean-specific benchmarks, with top-tier zero-shot results on KMMLU and strong internal evaluation results across language, humanities, and social science tasks. The Mi:dm 2.0 lineup is released under the MIT license to support extensive research and commercial use. By offering accessible and high-performance Korea-centric LLMs, KT aims to accelerate AI adoption across Korean industries, public services, and education, strengthen the Korean AI developer community, and lay the groundwork for the broader vision of K-intelligence. Our models are available at https://huggingface.co/K-intelligence. For technical inquiries, please contact midm-llm@kt.com.

cs.CL↗

arXiv2026

Absence of magnetic order in epitaxial RuO2 revealed by X-ray linear dichroism

Recently, the topic of altermagnetism has attracted tremendous attention and RuO2 have been demonstrated to be one of the most promising altermagnetic candidates. However, disputes still remain on the existence of magnetic order in RuO2. Here in this work, we employ X-ray linear dichroism (XLD), a widely utilized technique for characterizing antiferromagnets, in conjunction with photoemission electron microscopy and multiple scattering calculation to provide clear evidence of the absence of magnetic order in epitaxial RuO2 films. The observed XLD signal is nearly invariant with temperature and independent on cooling field direction, in stark contrast to the substantial magnetic order-related XLD signal predicted by multiple scattering calculation. This finding strongly suggests a nonmagnetic origin for RuO2. Furthermore, we observed significantly distinct XLD signals at the Ru M3 and O K edges in RuO2 films grown on TiO2 substrate with different surface orientations, which can be attributed to the low-symmetry crystal field. These results unequivocally demonstrate the absence of magnetic order in RuO2 and establishes XLD measurement as a robust technique for probing the low-symmetry magnetic materials.

cond-mat.mtrl-sci↗

arXiv2025

Beyond Competitive Gaming: How Casual Players Evaluate and Respond to Teammate Performance

Teammate performance evaluation fundamentally shapes intervention design in video games. However, our current understanding stems primarily from competitive E-Sports contexts where individual performance directly impacts outcomes. This research addresses whether performance evaluation mechanisms and behavioural responses identified in competitive games generalize to casual cooperative games. We investigated how casual players evaluate teammate competence and respond behaviourally in a controlled between-subjects experiment (N=23). We manipulated confederate performance in Overcooked 2, combining observations, NASA TLX self-reports, and interviews. We present two key findings. (1) Observations revealed frustration behaviours completely absent in self-report data. Thus, these instruments assess fundamentally distinct constructs. (2) Participants consistently evaluated teammate performance through relative comparison rather than absolute metrics. This contradicts task-performance operationalizations dominant in competitive gaming research. Hence, performance evaluation frameworks from competitive contexts cannot be directly applied to casual cooperative games. We provide empirical evidence that performance evaluation in casual games requires a comparative operationalization.

cs.HC↗

arXiv2025

Localization Meets Uncertainty: Uncertainty-Aware Multi-Modal Localization

Reliable localization is critical for robot navigation in complex indoor environments. In this paper, we propose an uncertainty-aware localization method that enhances the reliability of localization outputs without modifying the prediction model itself. This study introduces a percentile-based rejection strategy that filters out unreliable 3-DoF pose predictions based on aleatoric and epistemic uncertainties the network estimates. We apply this approach to a multi-modal end-to-end localization that fuses RGB images and 2D LiDAR data, and we evaluate it across three real-world datasets collected using a commercialized serving robot. Experimental results show that applying stricter uncertainty thresholds consistently improves pose accuracy. Specifically, the mean position error is reduced by 41.0%, 56.7%, and 69.4%, and the mean orientation error by 55.6%, 65.7%, and 73.3%, when applying 90%, 80%, and 70% thresholds, respectively. Furthermore, the rejection strategy effectively removes extreme outliers, resulting in better alignment with ground truth trajectories. To the best of our knowledge, this is the first study to quantitatively demonstrate the benefits of percentile-based uncertainty rejection in multi-modal end-to-end localization tasks. Our approach provides a practical means to enhance the reliability and accuracy of localization systems in real-world deployments.

cs.RO↗

arXiv2025

Advancing 3D Gaussian Splatting Editing with Complementary and Consensus Information

We present a novel framework for enhancing the visual fidelity and consistency of text-guided 3D Gaussian Splatting (3DGS) editing. Existing editing approaches face two critical challenges: inconsistent geometric reconstructions across multiple viewpoints, particularly in challenging camera positions, and ineffective utilization of depth information during image manipulation, resulting in over-texture artifacts and degraded object boundaries. To address these limitations, we introduce: 1) A complementary information mutual learning network that enhances depth map estimation from 3DGS, enabling precise depth-conditioned 3D editing while preserving geometric structures. 2) A wavelet consensus attention mechanism that effectively aligns latent codes during the diffusion denoising process, ensuring multi-view consistency in the edited results. Through extensive experimentation, our method demonstrates superior performance in rendering quality and view consistency compared to state-of-the-art approaches. The results validate our framework as an effective solution for text-guided editing of 3D scenes.

cs.CV↗

arXiv2025

Room temperature quantum emitters in van der Waals α-MoO3

Quantum emitters in solid-state materials are highly promising building blocks for quantum information processing and communication science. Recently, single-photon emission from van der Waals materials has been reported in transition metal dichalcogenides and hexagonal boron nitride, exhibiting the potential to realize photonic quantum technologies in two-dimensional materials. Here, we report the generation of room temperature single-photon emission from exfoliated and thermally annealed single crystals of van der Waals α-MoO3. The second-order correlation function measurement displays a clear photon antibunching, while the luminescence intensity exceeds 0.4 Mcts/s and remains stable under laser excitation. The theoretical calculation suggests that an oxygen vacancy defect is a possible candidate for the observed emitters. Together with photostability and brightness, quantum emitters in α-MoO3 provide a new avenue to realize photon-based quantum information science in van der Waals materials.

cond-mat.mtrl-sci↗

arXiv2024

Electrically pumped h-BN single-photon emission in van der Waals heterostructure

Atomic defects in solids offer a versatile basis to study and realize quantum phenomena and information science in various integrated systems. All-electrical pumping of single defects to create quantum light emission has been realized in several platforms including color centers in diamond and silicon carbide, which could lead to the circuit network of electrically triggered single-photon sources. However, a wide conduction channel which reduces the carrier injection per defect site has been a major obstacle. Here, we realize a device concept to construct electrically pumped single-photon emission using a van der Waals stacked structure with atomic plane precision. Defect-induced tunneling currents across graphene and NbSe2 electrodes sandwiching an atomically thin h-BN layer allow robust and persistent generation of non-classical light from h-BN. The collected emission photon energies range between 1.4 and 2.9 eV, revealing the electrical excitation of a variety of atomic defects. By analyzing the dipole axis of observed emitters, we further confirm that emitters are crystallographic defect structures of h-BN crystal. Our work facilitates implementing efficient and miniaturized single-photon devices in van der Waals platforms toward applications in quantum optoelectronics.

quant-ph↗

arXiv2024

Software-Defined Cryptography: A Design Feature of Cryptographic Agility

Given the widespread use of cryptography in Enterprise IT, migration to post-quantum cryptography (PQC) is not drop-in replacement at all. Cryptographic agility, or crypto-agility, is a design feature that enables seamless updates to new cryptographic algorithms and standards without the need to modify or replace the surrounding infrastructure. This paper introduces a notion of software-defined cryptography as the desired design feature for crypto-agility, emphasizing the role of software in providing centralized governance for cryptography and automated enforcement of cryptographic policies, such as migration to PQC.

cs.CR↗

arXiv2024

HyperCLOVA X Technical Report

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs.

cs.CL↗