arXiv subjects

Minseok Choi

Publications and source records attributed to Minseok Choi.

At least 19 recordsLinked to original sources

arXiv2026

SePArate: Segmenting Patterns from Defects in Wafer Manufacturing Using Weak Supervision

In semiconductor manufacturing, defect analysis is essential, but manual inspection cannot scale. However, existing automated inspection methods remain insufficient for root-cause analysis and process optimization. To this end, we present SePArate, a weakly supervised wafer defect segmentation method. SePArate enables pixel-level separation of patterns by leveraging only image-level annotations. It consists of a three-phase training: encoder pretraining, knowledge transfer to learn spatial cues, and training on synthetic mixed-defect data for accurate segmentation. Experiments demonstrate that SePArate outperforms the baselines.

cs.CV↗

arXiv2026

Density functional study of native point defects in CaO

We investigate the structural, electronic, and optical properties of native point defects in CaO using first-principles density-functional calculations. Oxygen vacancies are favored under O-poor conditions, whereas calcium vacancies dominate under O-rich conditions. Calculated migration barriers and binding energies indicate that vacancy complexes are thermodynamically stable and can survive high-temperature annealing. Optical transition energies, evaluated using the Franck-Condon framework, suggest that several experimentally observed absorption and emission peaks can be attributed to negatively charged vacancy complexes as well as isolated oxygen vacancies.

cond-mat.mtrl-sci↗

arXiv2026

Membrane: A Self-Evolving Contrastive Safety Memory for LLM Agent Defense

Despite advances in safety alignment, large language models remain vulnerable to continuously evolving jailbreaks. Existing fine-tuned safety classifiers cannot adapt to these evolving attacks, while adaptive memory-based guardrails tend to over-refuse benign queries that resemble stored attacks. We propose Membrane, a self-evolving guardrail built on Contrastive Safety Memory (CSM): each cell pairs the conditions for blocking a harmful query with those for permitting a superficially similar benign request. Without retraining, Membrane evolves CSM by distilling each harmful interaction and its benign counterpart into a contrastive cell indexed by the underlying attack strategy, so that one cell generalizes across topical variants of the same mechanism. At inference, retrieved cells serve as grounding context for precise safety decisions. Across model-level safety on HarmBench and agent-level safety on AgentHarm, Membrane achieves the highest F1 on all six modern jailbreak attacks. Notably, benign refusal on AgentHarm stays at 7-14%, well below the 28-85% range of prior guards. Memory cells also retain 87-88% F1 under cross-attack transfer and remain stable under memory poisoning.

cs.CR↗

arXiv2026

Native defects and erbium impurities in CaWO4

We perform hybrid density functional calculation to study the energetics, electronic properties, optical transitions, and migration barriers of native defects in CaWO$_4$. Oxygen and calcium vacancies are most likely to form in the absence of doping, but interstitials could also incorporate. Tungsten-related defects are unlikely to be present. The positively charged $V_{\rm O}$ and the negatively charged $V_{\rm Ca}$ are likely to form complexes. Calculated optical transition levels indicate that experimentally observed absorption and emission peaks can be attributed mainly to oxygen-related defects. Calculations of migration barriers allow us to conclude that Ca$_i^{2+}$, $V_{\rm O}^{2+}$ and O$_i^{2-}$ are highly mobile, even below room temperature. We have also examined Er dopants, finding that erbium easily substitutes on the Ca site in a positive charge state. Erbium can form complexes with $V_{\rm Ca}$ and O$_i$, which would deactivate the Er. If Er is introduced by implantation, Er interstitials are likely present, which will produce emission that is prone to spectral diffusion and blinking. Our calculated properties of Er$_i$ explain why annealing at modest temperatures allows the interstitials to move into substitutional sites and point defects to move away, resulting in stable emission.

cond-mat.mtrl-sci↗

arXiv2026

Bond-Strength-Based Understanding of Oxygen Vacancy Migration Barriers in Rutile Oxides

We carry out bond-strength based analysis for the migration barrier ($E_{\rm B}$) of oxygen vacancies in rutile-type 3$d$ transition-metal dioxides by combining density-functional theory (DFT) and the bond-valence model. The covalent and ionic contributions to chemical bonding are explicitly decomposed and quantified by the sum of the integrated crystal orbital Hamilton population ($S_c$) and the Madelung energy ($S_i$), respectively. Both $S_c$ and $S_i$ exhibit strong correlations with the $E_{\rm B}$ from DFT ($E_{\rm B}^{\rm DFT}$), and their average $\bar{S}$ provides a reasonable estimate of $E_{\rm B}^{\rm DFT}$ across the oxide series. Inspired by the bond-valence model, two parameters are extracted by fitting to a large dataset of 3$d$ transition-metal dioxides. Our results show that using these parameters, $E_{\rm B}$ of oxygen vacancies can be efficiently estimated.

cond-mat.mtrl-sci↗

arXiv2026

ExpGuard: LLM Content Moderation in Specialized Domains

With the growing deployment of large language models (LLMs) in real-world applications, establishing robust safety guardrails to moderate their inputs and outputs has become essential to ensure adherence to safety policies. Current guardrail models predominantly address general human-LLM interactions, rendering LLMs vulnerable to harmful and adversarial content within domain-specific contexts, particularly those rich in technical jargon and specialized concepts. To address this limitation, we introduce ExpGuard, a robust and specialized guardrail model designed to protect against harmful prompts and responses across financial, medical, and legal domains. In addition, we present ExpGuardMix, a meticulously curated dataset comprising 58,928 labeled prompts paired with corresponding refusal and compliant responses, from these specific sectors. This dataset is divided into two subsets: ExpGuardTrain, for model training, and ExpGuardTest, a high-quality test set annotated by domain experts to evaluate model robustness against technical and domain-specific content. Comprehensive evaluations conducted on ExpGuardTest and eight established public benchmarks reveal that ExpGuard delivers competitive performance across the board while demonstrating exceptional resilience to domain-specific adversarial attacks, surpassing state-of-the-art models such as WildGuard by up to 8.9% in prompt classification and 15.3% in response classification. To encourage further research and development, we open-source our code, data, and model, enabling adaptation to additional domains and supporting the creation of increasingly robust guardrail models.

cs.CL↗

arXiv2026

Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations

Neural operators have emerged as fast surrogate solvers for parametric partial differential equations (PDEs). However, purely data-driven models often require extensive training data and can generalize poorly, especially in small-data regimes and under unseen (out-of-distribution) input functions that are not represented in the training data. To address these limitations, we propose the Physics-Informed Laplace Neural Operator (PILNO), which enhances the Laplace Neural Operator (LNO) by embedding governing physics into training through PDE, boundary condition, and initial condition residuals. To improve expressivity, we first introduce an Advanced LNO (ALNO) backbone that retains a pole-residue transient representation while replacing the steady-state branch with an FNO-style Fourier multiplier. To make physics-informed training both data-efficient and robust, PILNO further leverages (i) virtual inputs: an unlabeled ensemble of input functions spanning a broad spectral range that provides abundant physics-only supervision and explicitly targets out-of-distribution (OOD) regimes; and (ii) temporal-causality weighting: a time-decaying reweighting of the physics residual that prioritizes early-time dynamics and stabilizes optimization for time-dependent PDEs. Across four representative benchmarks -- Burgers' equation, Darcy flow, a reaction-diffusion system, and a forced KdV equation -- PILNO consistently improves accuracy in small-data settings (e.g., N_train <= 27), reduces run-to-run variability across random seeds, and achieves stronger OOD generalization with respect to input function statistics than purely data-driven baselines.

cs.LG↗

arXiv2026

NavFormer: IGRF Forecasting in Moving Coordinate Frames

Triad magnetometer components change with sensor attitude even when the IGRF total intensity target stays invariant. NavFormer forecasts this invariant target with rotation invariant scalar features and a Canonical SPD module that stabilizes the spectrum of window level second moments of the triads without sign discontinuities. The module builds a canonical frame from a Gram matrix per window and applies state dependent spectral scaling in the original coordinates. Experiments across five flights show lower error than strong baselines in standard training, few shot training, and zero shot transfer. The code is available at: https://anonymous.4open.science/r/NavFormer-Robust-IGRF-Forecasting-for-Autonomous-Navigators-0765

cs.LG↗

arXiv2026

Portable Single-Beam Atomic Total-Field Magnetometer for Stand-off Magnetic Sensing

Optically pumped atomic magnetometers (OPAMs) offer high sensitivity at room temperature and are increasingly considered for portable magnetic sensing in geomagnetic-field environments. Here we report a handheld-scale, single-beam scalar $^{87}$Rb OPAM with a sensor-head volume of approximately 110~mL. The device operates in an all-optical Bell-Bloom configuration and uses digital lock-in, dispersive tracking of the $^{87}$Rb Larmor resonance, implemented with a hybrid electronics stack that combines in-house control hardware with commercial modules. A single frequency-modulated laser beam performs both pumping and probing without RF coils. All signal processing is realized in Python on a single-board computer paired with a commercial off-the-shelf (COTS) data-acquisition module, enabling immediate deployment without dedicated signal-processing hardware. The magnetometer has an intrinsic in-band field sensitivity of approximately 21~pT/$\sqrt{\mathrm{Hz}}$, estimated from the lock-in dispersion slope, over a 0.1--30~Hz closed-loop in-band region with a digital-output rate of 200~samples/s. In an unshielded Earth-field deployment, we detect repeatable transient magnetic signatures from a controlled elevator motion sequence and quantify standoff observability over sensor-elevator distances from 1.25~m to 10~m. These results show that compact scalar OPAMs can provide bandwidth and range-resolved event sensitivity suitable for field-deployable magnetic anomaly detection and infrastructure monitoring in realistic geomagnetic environments.

physics.atom-ph↗

arXiv2025

Improving 2D-ness to enhance thermopower in oxide superlattices

The transport dynamics of itinerant charge carriers and their interactions with the environment. For two-dimensional oxide thermoelectrics, predominantly represented by doped SrTiO3-based superlattices, reduced spatial dimensions and increased effective mass are known to enhance thermopower (S). However, because of their large effective Bohr radius resulting from their high dielectric constant, SrTiO3-based systems have limitations in exhibiting the 2D characteristic. Here, we focus on EuTiO3 as an alternative perovskite platform in which fractional LaxEu1-xTiO3/EuTiO3 artificial superlattices demonstrate the improvement in 2D nature for the dimensionality-induced improvement of S. We observed a quasi-2D thermopower S2D of -950 uV K-1 and S2D/S3D of ~20 resulting from the improved 2D confinement. Thermopower measurements, combined with hybrid density functional theory calculations, show the enhanced S originates from the confinement of Ti 3dxy-states within the LaxEu1-xTiO3 layers and the associated increase in the 2D density of states. In detail, a smaller effective Bohr radius and modified electronic band structures, in conjunction with the presence of the Eu 4f-states in EuTiO3 which modified the local electronic potential and strengthened the spatial confinement of Ti 3d-states. This approach to improving the dimensional confinement establishes a small effective Bohr radius and Eu 4f-state assisted 2D confinement provides valuable insights into the design of high-performance applications in artificial oxide superlattices.

cond-mat.mtrl-sci↗

arXiv2025

Real-time physics-informed reconstruction of transient fields using sensor guidance and higher-order time differentiation

This study proposes FTI-PBSM (Fixed-Time-Increment Physics-informed neural network-Based Surrogate Model), a novel physics-informed surrogate modeling framework designed for real-time reconstruction of transient responses in time-dependent Partial Differential Equations (PDEs) using only sparse, time-dependent sensor measurements. Unlike conventional Physics-Informed Neural Network (PINN)-based models that rely on Automatic Differentiation (AD) over both spatial and temporal domains and require dedicated causal network architectures to impose temporal causality, the proposed approach entirely removes AD in the time direction. Instead, it leverages higher-order numerical differentiation methods, such as the Central Difference, Adams-Bashforth, and Backward Differentiation Formula, to explicitly impose temporal causality. This leads to a simplified model architecture with improved training stability, computational efficiency, and extrapolation capability. Furthermore, FTI-PBSM is trained on sparse sensor measurements from multiple PDE cases generated by varying PDE coefficients, with the sensor data serving as model input. This enables the model to learn a parametric PDE family and generalize to unseen physical cases, accurately reconstructing full-field transient solutions in real time. The proposed model is validated on four representative PDE problems-the convection equation, diffusion-reaction dynamics, Korteweg-de Vries (KdV) equation, and Allen-Cahn equation-and demonstrates superior prediction accuracy and generalization performance compared to a causal PBSM, which is used as the baseline model, in both interpolation and extrapolation tasks. It also shows strong robustness to sensor noise and variations in training data size, while significantly reducing training time.

physics.comp-ph↗

arXiv2025

Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport

Instruction-following large language models (LLMs), such as ChatGPT, have become widely popular among everyday users. However, these models inadvertently disclose private, sensitive information to their users, underscoring the need for machine unlearning techniques to remove selective information from the models. While prior work has focused on forgetting small, random subsets of training data at the instance-level, we argue that real-world scenarios often require the removal of an entire user data, which may require a more careful maneuver. In this study, we explore entity-level unlearning, which aims to erase all knowledge related to a target entity while preserving the remaining model capabilities. To address this, we introduce Opt-Out, an optimal transport-based unlearning method that utilizes the Wasserstein distance from the model's initial parameters to achieve more effective and fine-grained unlearning. We also present the first Entity-Level Unlearning Dataset (ELUDe) designed to evaluate entity-level unlearning. Our empirical results demonstrate that Opt-Out surpasses existing methods, establishing a new standard for secure and adaptable LLMs that can accommodate user data removal requests without the need for full retraining.

cs.CL↗

arXiv2024

Breaking Chains: Unraveling the Links in Multi-Hop Knowledge Unlearning

Large language models (LLMs) serve as giant information stores, often including personal or copyrighted data, and retraining them from scratch is not a viable option. This has led to the development of various fast, approximate unlearning techniques to selectively remove knowledge from LLMs. Prior research has largely focused on minimizing the probabilities of specific token sequences by reversing the language modeling objective. However, these methods still leave LLMs vulnerable to adversarial attacks that exploit indirect references. In this work, we examine the limitations of current unlearning techniques in effectively erasing a particular type of indirect prompt: multi-hop queries. Our findings reveal that existing methods fail to completely remove multi-hop knowledge when one of the intermediate hops is unlearned. To address this issue, we propose MUNCH, a simple uncertainty-based approach that breaks down multi-hop queries into subquestions and leverages the uncertainty of the unlearned model in final decision-making. Empirical results demonstrate the effectiveness of our framework, and MUNCH can be easily integrated with existing unlearning techniques, making it a flexible and useful solution for enhancing unlearning processes.

cs.CL↗

arXiv2024

Cross-Lingual Unlearning of Selective Knowledge in Multilingual Language Models

Pretrained language models memorize vast amounts of information, including private and copyrighted data, raising significant safety concerns. Retraining these models after excluding sensitive data is prohibitively expensive, making machine unlearning a viable, cost-effective alternative. Previous research has focused on machine unlearning for monolingual models, but we find that unlearning in one language does not necessarily transfer to others. This vulnerability makes models susceptible to low-resource language attacks, where sensitive information remains accessible in less dominant languages. This paper presents a pioneering approach to machine unlearning for multilingual language models, selectively erasing information across different languages while maintaining overall performance. Specifically, our method employs an adaptive unlearning scheme that assigns language-dependent weights to address different language performances of multilingual language models. Empirical results demonstrate the effectiveness of our framework compared to existing unlearning baselines, setting a new standard for secure and adaptable multilingual language models.

cs.CL↗

arXiv2024

T-matrix representation of optical scattering response: Suggestion for a data format

The transition matrix, frequently abbreviated as T-matrix, contains the complete information in a linear approximation of how a spatially localized object scatters an incident field. The T-matrix is used to study the scattering response of an isolated object and describes the optical response of complex photonic materials made from ensembles of individual objects. T-matrices of certain common structures, potentially, have been repeatedly calculated all over the world again and again. This is not necessary and constitutes a major challenge for various reasons. First, the resources spent on their computation represent an unsustainable financial and ecological burden. Second, with the onset of machine learning, data is the gold of our era, and it should be freely available to everybody to address novel scientific challenges. Finally, the possibility of reproducing simulations could tremendously improve if the considered T-matrices could be shared. To address these challenges, we found it important to agree on a common data format for T-matrices and to enable their collection from different sources and distribution. This document aims to develop the specifications for storing T-matrices and associated metadata. The specifications should allow maximum freedom to accommodate as many use cases as possible without introducing any ambiguity in the stored data. The common format will assist in setting up a public database of T-matrices.

physics.optics↗

arXiv2024

Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival

Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL). Although the limited (radio and computational) resources are widely acknowledged, two critical yet often ignored aspects are (a) client devices can only dedicate a small chunk of their limited storage for the FL task and (b) new training samples may arrive continually in many practical wireless applications. Therefore, we propose a new FL algorithm, online-score-aided federated learning (OSAFL), specifically designed for tasks with continual data arrival in resource-constrained environments. We first theoretically show how the convergence bound is affected by continual data distribution shifts, uncertain client participation, gradient quantization errors, and noise from stochastic gradients and statistical data heterogeneity across clients. We then show how to (sub-optimally) minimize these errors by choosing appropriate aggregation weights at the CS during global update. Our extensive simulation results across three popular image classification datasets and three ML models with different numbers of trainable parameters validate the effectiveness of the proposed OSAFL algorithm compared to (modified) state-of-the art FL baselines.

cs.LG↗

arXiv2024

PairEval: Open-domain Dialogue Evaluation with Pairwise Comparison

Building a reliable and automated evaluation metric is a necessary but challenging problem for open-domain dialogue systems. Recent studies proposed evaluation metrics that assess generated responses by considering their relevance to previous dialogue histories. Although effective, these metrics evaluate individual responses directly rather than considering their relative quality compared to other responses. To handle this, we propose PairEval, a novel dialogue evaluation metric for assessing responses by comparing their quality against responses in different conversations. PairEval is built on top of open-sourced and moderate-size language models, and we make them specialized in pairwise comparison between dialogue responses. Extensive experiments on multiple benchmarks demonstrate that our metric exhibits a higher correlation with human judgments than baseline metrics. We also find that the proposed comparative metric is more robust in detecting common failures from open-domain dialogue systems, including repetition and speaker insensitivity.

cs.CL↗

arXiv2024

Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models

Although language models (LMs) demonstrate exceptional capabilities on various tasks, they are potentially vulnerable to extraction attacks, which represent a significant privacy risk. To mitigate the privacy concerns of LMs, machine unlearning has emerged as an important research area, which is utilized to induce the LM to selectively forget about some of its training data. While completely retraining the model will guarantee successful unlearning and privacy assurance, it is impractical for LMs, as it would be time-consuming and resource-intensive. Prior works efficiently unlearn the target token sequences, but upon subsequent iterations, the LM displays significant degradation in performance. In this work, we propose Privacy Protection via Optimal Parameters (POP), a novel unlearning method that effectively forgets the target token sequences from the pretrained LM by applying optimal gradient updates to the parameters. Inspired by the gradient derivation of complete retraining, we approximate the optimal training objective that successfully unlearns the target sequence while retaining the knowledge from the rest of the training data. Experimental results demonstrate that POP exhibits remarkable retention performance post-unlearning across 9 classification and 4 dialogue benchmarks, outperforming the state-of-the-art by a large margin. Furthermore, we introduce Remnant Memorization Accuracy that quantifies privacy risks based on token likelihood and validate its effectiveness through both qualitative and quantitative analyses.

cs.CL↗