arXiv subjects

Wonjae Lee

Publications and source records attributed to Wonjae Lee.

9 recordsLinked to original sources

arXiv2026

Cross-National Information Attacks: A Two-Decade Analysis of Troll Behavior in Korea

Coordinated foreign influence operations pose a growing threat to online platforms, but detecting state-linked troll activity and tracking its evolution remain challenging. This paper presents an explainable machine learning framework for theory-guided detection and longitudinal analysis of suspected trolling within Korean online news comment sections. Our hierarchical model classifies comments along three dimensions central to influence campaigns: foreign origin, moral-emotional framing, and target country. To support explainability, it also extracts brief span-level textual evidence that provides human-interpretable rationales. We apply the approach to 112M South Korean news comments authored by 4M users over nearly 20 years, identifying 23,998 accounts exhibiting behavior consistent with coordinated manipulation. Analyzing these accounts, we find that they predominantly rely on morally condemning rhetoric rather than direct promotion of foreign-aligned narratives; this rhetoric receives significantly higher user engagement. Among the highest-engagement comments, the moral condemnation most frequently targets domestic political figures (e.g., presidents or party leaders) on both the left and the right, potentially amplifying polarization. Our framework supports transparent platform governance through explainable, evidence-based moderation. These observed rhetorical and engagement patterns can inform how platforms and observatories prioritize defenses and intervene before harmful narrative-target combinations achieve widespread reach.

cs.SI↗

arXiv2026

Two-grid Penalty Approximation Scheme for Doubly Reflected BSDEs

We study penalization coupled with time discretization for decoupled Markovian doubly reflected BSDEs with obstacles $p_b(t,X_t)\le Y_t\le p_w(t,X_t)$. The DRBSDE is approximated by a penalized BSDE with parameter $\lambda$ and discretized by an implicit Euler scheme with step $\Delta t$. A key difficulty is that the forward approximation used to evaluate the obstacles generates an error term that is amplified by $\lambda$. In the single-obstacle case this amplification can be removed by the shift $Y-p_b(t,X)$, but no analogous transformation eliminates both obstacles simultaneously; this motivates simulating the forward SDE on a finer grid $\tilde{\Delta t}$ and projecting onto the backward grid (two-grid scheme). Under structural assumptions motivated by financial barriers we sharpen penalization rates and obtain a uniform $O(\lambda^{-1})$ bound for the value process. We derive an explicit error bound in $(\Delta t,\tilde{\Delta t},\lambda)$ and tuning rules; for $Z$-independent drivers, $\lambda\asymp \Delta t^{-1/2}$ with $\tilde{\Delta t}=O(\Delta t/\lambda^2)$ yields the target $O(\Delta t^{1/2})$ rate. Nonsmooth barriers/payoffs are handled via a multivariate It\^o--Tanaka and local-time-on-surfaces argument. We also provide numerical experiments for a one-dimensional game put under the Black--Scholes model. The observed grid-refinement errors are consistent with the predicted $O(n^{-1/2})$ behavior, while the penalty sweep indicates that the tested regime remains pre-asymptotic with respect to the penalty parameter.

math.PR↗

arXiv2026

Moral Outrage Shapes Commitments Beyond Attention: Multimodal Moral Emotions on YouTube in Korea and the US

Understanding how media rhetoric shapes audience engagement is crucial in the attention economy. This study examines how moral emotional framing by mainstream news channels on YouTube influences user behavior across Korea and the United States. To capture the platform's multimodal nature, combining thumbnail images and video titles, we develop a multimodal moral emotion classifier by fine tuning a vision language model. The model is trained on human annotated multimodal datasets in both languages and applied to approximately 400,000 videos from major news outlets. We analyze engagement levels including views, likes, and comments, representing increasing degrees of commitment. The results show that other condemning rhetoric expressions of moral outrage that criticize others morally consistently increase all forms of engagement across cultures, with effects ranging from passive viewing to active commenting. These findings suggest that moral outrage is a particularly effective emotional strategy, attracting not only attention but also active participation. We discuss concerns about the potential misuse of other condemning rhetoric, as such practices may deepen polarization by reinforcing in group and out group divisions. To facilitate future research and ensure reproducibility, we publicly release our Korean and English multimodal moral emotion classifiers.

cs.CY↗

arXiv2025

Fourier Neural Operators for Non-Markovian Processes:Approximation Theorems and Experiments

This paper introduces an operator-based neural network, the mirror-padded Fourier neural operator (MFNO), designed to learn the dynamics of stochastic systems. MFNO extends the standard Fourier neural operator (FNO) by incorporating mirror padding, enabling it to handle non-periodic inputs. We rigorously prove that MFNOs can approximate solutions of path-dependent stochastic differential equations and Lipschitz transformations of fractional Brownian motions to an arbitrary degree of accuracy. Our theoretical analysis builds on Wong--Zakai type theorems and various approximation techniques. Empirically, the MFNO exhibits strong resolution generalization--a property rarely seen in standard architectures such as LSTMs, TCNs, and DeepONet. Furthermore, our model achieves performance that is comparable or superior to these baselines while offering significantly faster sample path generation than classical numerical schemes.

cs.LG↗

arXiv2025

Adaptive Migration Decision for Multi-Tenant Memory Systems

Tiered memory systems consisting of fast small memory and slow large memory have emerged to provide high capacity memory in a cost-effective way. The effectiveness of tiered memory systems relies on how many memory accesses can be absorbed by the fast first-tier memory by page migration. The recent studies proposed several different ways of detecting hot pages and migrating them efficiently. However, our investigation shows that page migration is not always beneficial as it has the associated cost of detecting and migrating hot pages. When an application is unfriendly to migration, it is often better not to migrate pages at all. Based on the observation on migration friendliness, this paper proposes a migration control framework for multi-tenant tiered memory systems. First, it proposes a detection mechanism for migration friendliness, using per-page ping-pong status. Ping-pong pages which are promoted and demoted repeatedly in a short period of time tells migration effectiveness. Based on their change behaviors, migration is stopped or continued. After the page migration is stopped, the second mechanism detects changes of memory access patterns in a low cost way to determine whether migration needs to be resumed. Finally, as each application has a different behavior, our framework provides per-process migration control to selectively stop and start migration depending on application characteristics. We implement the framework in the Linux kernel. The evaluation with a commercial CXL-based tiered memory system shows that it effectively controls migration in single and multi-tenant environments.

cs.OS↗

arXiv2025

Pulsed Vector Atomic Magnetometer Using an Alternating Fast-Rotating Field

We introduce a vector atomic magnetometer that employs a fast-rotating magnetic field applied to a pulsed $^{87}$Rb scalar atomic magnetometer. This approach enables simultaneous measurements of the total magnetic field and its two polar angles relative to the rotation plane. Operating in gradiometer mode, the magnetometer achieves a total field gradient sensitivity of 35 $\mathrm{fT/\sqrt{Hz}}$ (0.7 parts per billion) and angular resolutions of 6 $\mathrm{nrad/\sqrt{Hz}}$ at a 50 $μ$T Earth field strength. The noise spectra remain flat down to 1 Hz and 0.1 Hz, respectively. Here we show that this method overcomes several metrological challenges commonly faced by vector magnetometers and gradiometers. We propose a unique peak-altering modulation technique to mitigate systematic effects, including a newly identified dynamic heading error. Additionally, we establish the fundamental sensitivity limits of the sensor, demonstrating that its vector sensitivity approaches scalar sensitivity while preserving the inherent accuracy and calibration benefits of scalar sensors. This high-dynamic-range, ultrahigh-resolution magnetometer offers exceptional versatility for diverse applications.

quant-ph↗

arXiv2024

Investigating the diversity and stylization of contemporary user generated visual arts in the complexity entropy plane

The advent of computational and numerical methods in recent times has provided new avenues for analyzing art historiographical narratives and tracing the evolution of art styles therein. Here, we investigate an evolutionary process underpinning the emergence and stylization of contemporary user-generated visual art styles using the complexity-entropy (C-H) plane, which quantifies local structures in paintings. Informatizing 149,780 images curated in DeviantArt and Behance platforms from 2010 to 2020, we analyze the relationship between local information of the C-H space and multi-level image features generated by a deep neural network and a feature extraction algorithm. The results reveal significant statistical relationships between the C-H information of visual artistic styles and the dissimilarities of the multi-level image features over time within groups of artworks. By disclosing a particular C-H region where the diversity of image representations is noticeably manifested, our analyses reveal an empirical condition of emerging styles that are both novel in the C-H plane and characterized by greater stylistic diversity. Our research shows that visual art analyses combined with physics-inspired methodologies and machine learning, can provide macroscopic insights into quantitatively mapping relevant characteristics of an evolutionary process underpinning the creative stylization of uncharted visual arts of given groups and time.

cs.CV↗

arXiv2024

Intrinsic high-fidelity spin polarization of charged vacancies in hexagonal boron nitride

The negatively charged boron vacancy ($\mathrm{V}_{\mathrm{B}}^-$) in hexagonal boron nitride (hBN) has garnered significant attention among defects in two-dimensional materials. This owes, in part, to its deterministic generation, well-characterized atomic structure, and optical polarizability at room temperature. We investigate the latter through extensive measurements probing both the ground and excited state polarization dynamics. We develop a semiclassical model based on these measurements that predicts a near-unity degree of spin polarization, surpassing other solid-state spin defects under ambient conditions. Building upon our model, we include the presence of nuclear spin degrees of freedom adjacent to the $\mathrm{V}_{\mathrm{B}}^-$ and perform a comprehensive set of Lindbladian numerics to investigate the hyperfine-induced polarization of the nuclear spins. Our simulations predict a number of important features that emerge as a function of magnetic field which are borne out by experiment.

quant-ph↗

arXiv2020

Risk Communication in Asian Countries: COVID-19 Discourse on Twitter

COVID-19 has become one of the most widely talked about topics on social media. This research characterizes risk communication patterns by analyzing the public discourse on the novel coronavirus from four Asian countries: South Korea, Iran, Vietnam, and India, which suffered the outbreak to different degrees. The temporal analysis shows that the official epidemic phases issued by governments do not match well with the online attention on COVID-19. This finding calls for a need to analyze the public discourse by new measures, such as topical dynamics. Here, we propose an automatic method to detect topical phase transitions and compare similarities in major topics across these countries over time. We examine the time lag difference between social media attention and confirmed patient counts. For dynamics, we find an inverse relationship between the tweet count and topical diversity.

cs.SI↗