arXiv subjects

Youjin Lee

Publications and source records attributed to Youjin Lee.

At least 19 recordsLinked to original sources

arXiv2026

ARNAI: Artifact Removal Network based on Autoencoding and Inpainting for Robust Spinal Image Segmentation and Measurement

Purpose: This study aims to develop an AI framework applicable for postoperative imaging for automated measurement of spinopelvic parameters on radiographs with robustness to the presence of spinal implants. Materials and Methods: We retrospectively reviewed lateral lumbar spine radiographs from two institutions (Internal: January 2017--December 2024; External: October 2021--September 2025). We developed the Restore, Segment, and Measure (RSM) framework, incorporating a novel Artifact Removal Network based on Autoencoding and Inpainting (ARNAI) to mitigate implant-related artifacts in postoperative radiographs. Segmentation and spinopelvic parameter (PT, LL, SS, SCA) measurement performance were assessed using Wilcoxon signed-rank tests and intraclass correlation coefficients. Results: When ARNAI was added to a recent Transformer-based segmentation model, FCBFormer, the mean DSC increased to 0.870 from 0.814, with marked gains at L3--L5 and smaller improvements at L1--L2. On 91 radiographs with implants, the mean L4--L5 segmental Cobb angle error decreased to 4.7 {\deg} from 15.6--16.2 {\deg}, an average error reduction of 70%. The ICC for L4--L5 segmental Cobb angle improved to 0.54 (Rater 1) and 0.59 (Rater 2) from 0.18, and ICCs for pelvic tilt, lumbar lordosis, and sacral slope all exceeded 0.70. The improvement in L4--L5 segmental Cobb angle error was statistically significant in the internal implant-containing cohort after correction for multiple comparisons. Conclusion: The proposed RSM framework improved automated spinopelvic parameter measurement in implant-containing postoperative radiographs. By mitigating implant-related artifacts, ARNAI improved segmentation and downstream measurement accuracy, with the greatest benefit observed for L4--L5 segmental Cobb angle estimation, where the mean error was reduced by approximately 70%.

cs.CV↗

arXiv2026

Small Samples and Short Panels: Evaluating Policy Evaluation Methods with Realistic Data

Methods for estimating causal effects in longitudinal, quasi-experimental settings are widely used in economics, public health, political science, and other fields. However, studies evaluating effects of health policies often rely on limited sample sizes, both in terms of study units and time periods analyzed. Synthetic difference-in-differences (SDiD) and the augmented synthetic control method (ASCM) are recently developed methods for evaluating the effects of policy interventions. Although SDiD and ASCM generally rely on weaker assumptions than both DiD and SCM, they lack theoretical performance guarantees with respect to bias or coverage in relevant settings with small sample sizes or short panel lengths. To evaluate the performance of SDiD and ASCM in realistic, small-sample settings, we employ a calibrated simulation strategy that allows the injection of a known treatment effect into existing data in a setting of interest. Drawing on findings from these empirical investigations, we offer practical guidance for researchers and policymakers on when SDiD and ASCM are likely to yield reliable estimates and inferences under realistic scenarios.

stat.ME↗

arXiv2026

Unconventional and Fragile Magnetic Exciton in a van der Waals Quantum Magnet

The recently discovered magnetic exciton in the van der Waals (vdW) antiferromagnet NiPS3 exemplifies these phenomena, exhibiting several distinctive characteristics. Despite extensive investigation, much of its physics remains unresolved, with key questions about why the NiPS3 magnetic exciton is so sharp and optically bright despite the nominally spin-forbidden transition, posing significant challenges to a proper understanding and practical manipulation of the exciton. An urgent question is to what extent it is due to chemical disorder, magnetic weakening, lattice modification, or intrinsic instability of the bright exciton itself: answers to which will put stringent constraints on possible theoretical models. Here we address these questions using hydrostatic pressure as a clean, continuous, reversible, and in-situ tuning parameter. We find that the sharp photoluminescence peak is drastically suppressed by as little as 0.4 GPa and completely quenched by 1.5 GPa, with demonstrating its reversibility. Crucially, this bright-to-dark conversion occurs without magnetic, crystallographic, or electronic reconstruction despite an increase in the Neel temperature, as established by Raman, X-ray absorption, nuclear magnetic resonance spectroscopy, and first-principles many-body calculations. Our results demonstrate that the optical brightness of the magnetic exciton is independent of chemical disorder, lattice expansion, and weakening of magnetic order, indicating that a higher-order correlated mechanism governs the bright exciton. We further propose experimentally constrained microscopic scenarios involving exciton pairing, crystal-field-controlled spin-orbit mixing, and symmetry breaking, providing a framework for future tests of entangled magnetic exciton in correlated quantum magnets.

cond-mat.mtrl-sci↗

arXiv2026

Microscopic evidence for a Zhang-Rice triplet state in the van der Waals antiferromagnet, NiPS$_3$

Quantum-entangled states underpin many emergent phenomena in quantum materials, yet their direct experimental identification remains a challenge. NiPS$_3$, a van der Waals antiferromagnet exhibiting a resolution-limited magnetic exciton in its ordered phase, has been proposed to host a many-body entangled Zhang-Rice triplet state. Here, using $^{33}$S nuclear magnetic resonance (NMR) on $^{33}$S-enriched NiPS$_3$ single crystals, we provide microscopic evidence for this charge-transfer state. The $^{33}$S and $^{31}$P Knight shifts as a function of temperature reveal a unified spin-triplet configuration arising from strong hybridization between a self-doped hole in the S $3p$ orbitals and a hole in Ni $3d$ orbitals. Furthermore, the $^{33}$S nuclear spin-lattice relaxation rate exhibits a power-law divergence as it approaches the N\'eel temperature $T_N=155$ K, indicating critical slowing down of collective charge fluctuations consistent with spin-nematic correlations. These results reveal a spin-charge-intertwined ground state and establish the microscopic foundation for the exceptional coherence of the magnetic exciton in NiPS$_3$.

cond-mat.str-el↗

arXiv2026

Causal Inference for Unobservable Multivariate Outcomes, with Applications to Brain Effective Connectivity

Evaluating the causal effect of an intervention on multivariate outcomes is challenging when the outcomes are interdependent and derived rather than directly observed. Effective connectivity, which summarizes the directional neural communication between brain regions, is one such derived relational outcome. Estimating how external interventions affect effective connectivity introduces two layers of causal inference problems: identifying directional relationships among brain regions from high-dimensional neuroimaging time series and estimating the causal effect of the intervention on these derived relationships. Each layer introduces distinct biases. The first arises from within-outcome dependencies unrelated to the intervention; to address this, we propose a sample-splitting method for estimating meaningful, and potentially causally informative, effective connectivity measures. The second arises from confounding between the intervention and the derived outcomes; to address this, we apply inverse probability weighting methods and incorporate multiple testing when causal effects on multiple components of the outcomes are of interest. We demonstrate, through theoretical results and simulations, that the proposed methods are asymptotically valid under certain conditions with effective type-I and familywise error control. Finally, we apply the proposed methods to examine the causal effect of amyloid on effective connectivity using the resting-state fMRI data from the Alzheimer's Disease Neuroimaging Initiative database.

stat.ME↗

arXiv2026

A Bayesian framework for cost-effectiveness analysis with time-varying treatment decisions

Cost-effectiveness analyses (CEAs) compare the costs and health outcomes of treatment regimes to inform medical decisions. With observational claims data, CEAs must address nonrandom treatment assignment, administrative censoring, and irregularly spaced medical visits that reflect the continuous timing of care and treatment initiation. In high-risk, early-stage endometrial cancer (HR-EC), adjuvant radiation is initiated at patient-specific times following hysterectomy, causing confounding between treatment and outcomes that can evolve with post-surgical recovery and clinical course. Most existing CEA methods use point-treatment or discrete-time models. However, point-treatment approaches break down with time-varying confounding, while discrete-time models bin continuous time, expand the data into a person-period format, and can induce zero-inflation by creating many intervals with no cost-accruing events. We propose a Bayesian framework for CEAs with sequential decision-making that jointly models costs and event times in continuous time, accounts for administrative censoring, and supports dynamic treatment regimes with minimal parametric assumptions. We use Bayesian g-computation to estimate causally interpretable cost-effectiveness measures, including net monetary benefit, and to compare regimes through posterior contrasts. We evaluate the finite-sample performance of the proposed method in simulations across censoring levels and compare it against discrete-time and fully parametric alternatives. We then use SEER-Medicare data to assess the cost-effectiveness of initiating adjuvant radiation therapy within six months following hysterectomy among HR-EC patients.

stat.ME↗

arXiv2025

Magneto-Optical Study of Chiral Magnetic Modes in NiI$_{2}$: Direct Evidence for Kitaev Interactions

Bond-dependent magnetic interactions, particularly those described by the Kitaev model, have emerged as a key pathway toward realizing unconventional magnetic states such as quantum spin liquids and topologically nontrivial excitations, including skyrmions. These interactions frustrate conventional magnetic order and give rise to rich collective behavior that continues to challenge both theory and experiment. While Kitaev physics has been extensively explored in the context of honeycomb magnets, direct evidence for its role in real materials remains scarce. Magnetic van der Waals (vdW) materials have emerged as a versatile platform for exploring low-dimensional electrical, magnetic, and correlated electronic phenomena, and provide a fertile ground for potential applications ranging from spintronics to multiferroic devices and quantum information technologies. Here, we demonstrate, through magneto-transmission, Faraday angle rotation, and magnetic circular dichroism measurements, that the magnetic excitation spectrum of NiI$_2$, a van der Waals multiferroic material, is more accurately captured by a Kitaev-based spin model than by the previously invoked helical spin framework.

cond-mat.str-el↗

arXiv2025

Disentangling network dependence among multiple variables

When two variables depend on the same or similar underlying network, their shared network dependence structure can lead to spurious associations. While statistical associations between two variables sampled from interconnected subjects are a common inferential goal across various fields, little research has focused on how to disentangle shared dependence for valid statistical inference. We revisit two different approaches from distinct fields that may address shared network dependence: the pre-whitening approach, commonly used in time series analysis to remove the shared temporal dependence, and the network autocorrelation model, widely used in network analysis often to examine or account for autocorrelation of the outcome variable. We demonstrate how each approach implicitly entails assumptions about how a variable of interest propagates among nodes via network ties given the network structure. We further propose adaptations of existing pre-whitening methods to the network setting by explicitly reflecting underlying assumptions about "level of interaction" that induce network dependence, while accounting for its unique complexities. Our simulation studies demonstrate the effectiveness of the two approaches in reducing spurious associations due to shared network dependence when their respective assumptions hold. However, the results also show the sensitivity to assumption violations, underscoring the importance of correctly specifying the shared dependence structure based on available network information and prior knowledge about the interactions driving dependence.

stat.ME↗

arXiv2025

Current-driven collective control of helical spin texture in van der Waals antiferromagnet

Electrical control of quantum magnetic states is essential in spintronic science. Initial studies on the ferromagnetic state control were extended to collinear antiferromagnets and, more recently, noncollinear antiferromagnets. However, electrical control mechanisms of such exotic magnetic states remain poorly understood. Here, we report the first experimental and theoretical example of the current control of helical antiferromagnets, arising from the competition between collinear antiferromagnetic exchange and interlayer Dzyaloshinskii-Moriya interaction in new van-der-Waals (vdW) material Ni1/3NbS2. Due to the intrinsic broken inversion symmetry, an in-plane current generates spin-orbit torque that, in turn, interacts directly with the helical antiferromagnetic order. Our theoretical analyses indicate that a weak ferromagnetic order coexists due to the Dzyaloshinskii-Moriya interaction, mediating the spin-orbit torque to collectively rotate the helical antiferromagnetic order. Our Ni1/3NbS2 nanodevice experiments produce current-dependent resistance change consistent with the theoretical prediction. This work widens our understanding of the electrical control of helical antiferromagnets and promotes vdW quantum magnets as interesting material platforms for electrical control.

cond-mat.mtrl-sci↗

arXiv2025

Kitaev interaction and proximate higher-order skyrmion crystal in the triangular lattice van der Waals antiferromagnet NiI2

Topological spin textures, such as magnetic skyrmions, are a spectacular manifestation of magnetic frustration and anisotropy. Most known skyrmion systems are restricted to a topological charge of one, require an external magnetic field for stabilization, and are only reported in a few materials. Here, we investigate the possibility that the Kitaev anisotropic-exchange interaction stabilizes a higher-order skyrmion crystal in the insulating van der Waals magnet NiI2. We unveil and explain the incommensurate static and dynamic magnetic correlations across three temperature-driven magnetic phases of this compound using neutron scattering measurements, simulations, and modeling. Our parameter optimisation yields a minimal Kitaev-Heisenberg Hamiltonian for NiI2 which reproduces the experimentally observed magnetic excitations. Monte Carlo simulations for this model predict the emergence of the higher-order skyrmion crystal but neutron diffraction and optical experiments in the candidate intermediate temperature regime are inconclusive. We discuss possible deviations from the Kitaev-Heisenberg model that explains our results and conclude that NiI2, in addition to multiferroic properties in the bulk and few-layer limits, is a Kitaev bulk material proximate to the finite temperature higher-order skyrmion crystal phase.

cond-mat.mtrl-sci↗

arXiv2025

Bayesian shrinkage priors for penalized synthetic control estimators in the presence of spillovers

Synthetic control (SC) methods are widely used to estimate the effects of policy interventions, especially those targeting specific geographic regions, referred to as units. These methods construct a weighted combination of untreated units, forming a "synthetic" control that approximates the counterfactual outcomes of the treated unit had the intervention not occurred. Although neighboring areas are often selected as controls due to their similarity in observed and unobserved characteristics, their proximity can lead to spillover effects, where the intervention indirectly impacts control units, potentially biasing causal estimates. To address this challenge, we introduce a Bayesian SC framework with utility-based shrinkage priors. Our approach extends traditional penalization techniques (i.e., horseshoe, spike-and-slab) by incorporating a utility function that combines covariate similarity and spatial distance. This provides a metric that guides the data-driven selection of control units based on their relevance and spillover risk, which is assumed to increase with spatial proximity. Rather than outright excluding neighboring units, the method balances bias and variance by reducing the importance of potentially contaminated controls by spillovers. We evaluate the proposed method through simulation studies at varying spillover levels and apply it to assess the impact of Philadelphia's 2017 beverage tax on the sales of sugar-sweetened and artificially sweetened beverages in mass merchandise stores.

stat.ME↗

arXiv2025

Magnetoelectric effect in van der Waals magnets

The magnetoelectric (ME) effect is a fundamental concept in modern condensed matter physics and represents the electrical control of magnetic polarisations or vice versa. Two-dimensional (2D) van-der-Waals (vdW) magnets have emerged as a new class of materials and exhibit novel ME effects with diverse manifestations. This review emphasizes some important recent discoveries unique to vdW magnets: multiferroicity on two dimensions, spin-charge correlation, atomic ME effect and current-induced intrinsic spin-orbit torque, and electrical gating control and magnetic control of their electronic properties. We also highlight the promising route of utilizing quantum magnetic hetero- or homo-structures to engineer the ME effect and corresponding spintronic and optoelectronic device applications. Due to the intrinsic two-dimensionality, vdW magnets with those ME effects are expected to form a new, exciting research direction.

cond-mat.mtrl-sci↗

arXiv2024

A Causal Framework for Evaluating Drivers of Policy Effect Heterogeneity Using Difference-in-Differences

Policymakers and researchers often seek to understand how a policy differentially affects a population and the pathways driving this heterogeneity. For example, when studying an excise tax on sweetened beverages, researchers might assess the roles of cross-border shopping, economic competition, and store-level price changes on beverage sales trends. However, traditional policy evaluation tools, like the difference-in-differences (DiD) approach, primarily target average effects of the observed intervention rather than the underlying drivers of effect heterogeneity. Common approaches to evaluate sources of heterogeneity often lack a causal framework, making it difficult to determine whether observed outcome differences are truly driven by the proposed source of heterogeneity or by other confounding factors. In this paper, we present a framework for evaluating such policy drivers by representing questions of effect heterogeneity under hypothetical interventions and use it to evaluate drivers of the Philadelphia sweetened beverage tax policy effects. Building on recent advancements in estimating causal effect curves under DiD designs, we provide tools to assess policy effect heterogeneity while addressing practical challenges including confounding and neighborhood dynamics.

stat.ME↗

arXiv2024

Difference-in-Differences for Health Policy and Practice: A Review of Modern Methods

Difference-in-differences (DiD) is the most popular observational causal inference method in health policy, employed to evaluate the real-world impact of policies and programs. To estimate treatment effects, DiD relies on the "parallel trends assumption", that on average treatment and comparison groups would have had parallel trajectories in the absence of an intervention. Historically, DiD has been considered broadly applicable and straightforward to implement, but recent years have seen rapid advancements in DiD methods. This paper reviews and synthesizes these innovations for medical and health policy researchers. We focus on four topics: (1) assessing the parallel trends assumption in health policy contexts; (2) relaxing the parallel trends assumption when appropriate; (3) employing estimators to account for staggered treatment timing; and (4) conducting robust inference for analyses in which normal-based clustered standard errors are inappropriate. For each, we explain challenges and common pitfalls in traditional DiD and modern methods available to address these issues.

stat.AP↗

arXiv2024

Statistical and Causal Robustness for Causal Null Hypothesis Tests

Prior work applying semiparametric theory to causal inference has primarily focused on deriving estimators that exhibit statistical robustness under a prespecified causal model that permits identification of a desired causal parameter. However, a fundamental challenge is correct specification of such a model, which usually involves making untestable assumptions. Evidence factors is an approach to combining hypothesis tests of a common causal null hypothesis under two or more candidate causal models. Under certain conditions, this yields a test that is valid if at least one of the underlying models is correct, which is a form of causal robustness. We propose a method of combining semiparametric theory with evidence factors. We develop a causal null hypothesis test based on joint asymptotic normality of K asymptotically linear semiparametric estimators, where each estimator is based on a distinct identifying functional derived from each of K candidate causal models. We show that this test provides both statistical and causal robustness in the sense that it is valid if at least one of the K proposed causal models is correct, while also allowing for slower than parametric rates of convergence in estimating nuisance functions. We demonstrate the effectiveness of our method via simulations and applications to the Framingham Heart Study and Wisconsin Longitudinal Study.

stat.ME↗

arXiv2024

Imaging thermally fluctuating Nèel vectors in van der Waals antiferromagnet NiPS3

Studying antiferromagnetic domains is essential for fundamental physics and potential spintronics applications. Despite its importance, few systematic studies have been performed on van der Waals (vdW) antiferromagnets (AFMs) domains with high spatial resolutions, and direct probing of the Nèel vectors remains challenging. In this work, we found a multidomain in vdW AFM NiPS3, a material extensively investigated for its exotic magnetic exciton. We employed photoemission electron microscopy combined with the X-ray magnetic linear dichroism (XMLD-PEEM) to image the NiPS3's magnetic structure. The nanometer-spatial resolution of XMLD-PEEM allows us to determine local Nèel vector orientations and discover thermally fluctuating Néel vectors that are independent of the crystal symmetry even at 65 K, well below TN of 155 K. We demonstrate a Ni ions' small in-plane orbital moment anisotropy is responsible for the weak magneto-crystalline anisotropy. The observed multidomain's thermal fluctuations may explain the broadening of magnetic exciton peaks at higher temperatures.

cond-mat.mtrl-sci↗

arXiv2024

Giant Linear Dichroism Controlled by Magnetic Field in FePS$_3$

Magnetic-field control of fundamental optical properties is a crucial challenge in the engineering of multifunctional microdevices. Van der Waals (vdW) magnets retaining a magnetic order even in atomically thin layers, offer a promising platform for hosting exotic magneto-optical functionalities owing to their strong spin-charge coupling. Here, we demonstrate that a giant optical anisotropy can be controlled by magnetic fields in the vdW magnet FePS$_3$. The giant linear dichroism ($\sim$11%), observed below $T_{\text{N}}\!\sim\!120$ K, is nearly fully suppressed in a wide energy range from 1.6 to 2.0 eV, following the collapse of the zigzag magnetic order above 40 T. This remarkable phenomenon can be explained as a result of symmetry changes due to the spin order, enabling minority electrons of Fe$^{2+}$ to hop in a honeycomb lattice. The modification of spin-order symmetry by external fields provides a novel route for controllable anisotropic optical micro-devices.

cond-mat.mtrl-sci↗

arXiv2024

Multiply Robust Difference-in-Differences Estimation of Causal Effect Curves for Continuous Exposures

Researchers commonly use difference-in-differences (DiD) designs to evaluate public policy interventions. While methods exist for estimating effects in the context of binary interventions, policies often result in varied exposures across regions implementing the policy. Yet, existing approaches for incorporating continuous exposures face substantial limitations in addressing confounding variables associated with intervention status, exposure levels, and outcome trends. These limitations significantly constrain policymakers' ability to fully comprehend policy impacts and design future interventions. In this work, we propose new estimators for causal effect curves within the DiD framework, accounting for multiple sources of confounding. Our approach accommodates misspecification of a subset of treatment, exposure, and outcome models while avoiding any parametric assumptions on the effect curve. We present the statistical properties of the proposed methods and illustrate their application through simulations and a study investigating the heterogeneous effects of a nutritional excise tax under different levels of accessibility to cross-border shopping.

stat.ME↗