arXiv subjects

Sujin Park

Publications and source records attributed to Sujin Park.

5 recordsLinked to original sources

arXiv2026

Exploiting Per-Core Leakage: Electromagnetic Side-Channel Monitoring of Multicore Architectures

Multicore processors are increasingly adopted in embedded systems to meet growing performance demands. However, physical side-channel analysis of multicore architectures remains underexplored, as obtaining usable leakage is inherently challenging. Consequently, side-channel security research on such systems has lagged far behind, leaving a critical security gap. To address this gap, we reveal the electromagnetic leakage mechanisms in multicore architectures and, for the first time, demonstrate per-core leakage exploitation, thereby enabling physical side-channel analysis for these systems. As a practical extension, we present a non-intrusive side-channel monitoring method that achieves per-core granularity. To validate its feasibility and practicality, we implement a prototype on a heterogeneous SoC platform with an RF front-end, and evaluate on a commercial off-the-shelf quad-core embedded system, the Raspberry Pi 4B with ARM Cortex-A72 cores.

cs.CR↗

arXiv2026

Programmable spatial coherence tomography: diffraction-limited three-dimensional reflection imaging under modulated monochromatic illumination

Depth sectioning in reflection microscopy has predominantly relied on temporal coherence gating. Here we show that volumetric reflection tomography at diffraction-limited resolution can be achieved under monochromatic illumination by engineering spatial, rather than temporal, coherence. In programmable spatial coherence tomography (PSCT), a sequence of pupil-coded illumination patterns with angular-spectrum diversity generates measurement redundancy enabling the system to calibrate itself, jointly retrieving aberrations, illumination profiles, and sample motion without guide stars or modal priors. We demonstrate label-free volumetric imaging of thick human tissues, organoids, frequency-resolved dynamic contrast, and high-resolution in vivo brain imaging through a cranial window. These results position PSCT as an alternative to temporal coherence based reflection imaging in complex biological systems.

physics.optics↗

arXiv2025

Soft Inductive Bias Approach via Explicit Reasoning Perspectives in Inappropriate Utterance Detection Using Large Language Models

Recent incidents in certain online games and communities, where anonymity is guaranteed, show that unchecked inappropriate remarks frequently escalate into verbal abuse and even criminal behavior, raising significant social concerns. Consequently, there is a growing need for research on techniques that can detect inappropriate utterances within conversational texts to help build a safer communication environment. Although large-scale language models trained on Korean corpora and chain-of-thought reasoning have recently gained attention, research applying these approaches to inappropriate utterance detection remains limited. In this study, we propose a soft inductive bias approach that explicitly defines reasoning perspectives to guide the inference process, thereby promoting rational decision-making and preventing errors that may arise during reasoning. We fine-tune a Korean large language model using the proposed method and conduct both quantitative performance comparisons and qualitative evaluations across different training strategies. Experimental results show that the Kanana-1.5 model achieves an average accuracy of 87.0046, improving by approximately 3.89 percent over standard supervised learning. These findings indicate that the proposed method goes beyond simple knowledge imitation by large language models and enables more precise and consistent judgments through constrained reasoning perspectives, demonstrating its effectiveness for inappropriate utterance detection.

cs.CL↗

arXiv2025

Oxygen vacancy formation in ZnSeTe blue quantum dot light-emitting diodes

Recent advancements have led to the development of bright and heavy metal-free blue-emitting quantum dot light-emitting diodes (QLEDs). However, consensus understanding of their distinct photophysical and electroluminescent dynamics remains elusive. This work correlates the chemical and electronic changes occurring in a QLED during operation using depth-resolved and operando techniques. The results indicate that oxygen vacancy forms in the ZnMgO layer during operation, with important implications on the charge injection and electrochemical dynamics. Taken together, the results suggest a causal relationship between oxygen vacancy formation and operational degradation of the blue-emitting ZnSeTe-based QLEDs.

physics.chem-ph↗

arXiv2025

Aligning Reasoning LLMs for Materials Discovery with Physics-aware Rejection Sampling

AI-driven materials discovery that couples automated experimentation with algorithmic decision-making requires process aware recipe to property predictors that are accurate, calibrated, and physically admissible. We approach this as a reasoning problem with large reasoning models (LRMs). To instill reasoning capability into language models, we curate reasoning traces from a teacher model to train a student model. However, most training pipelines select reasoning traces using binary correctness or learned preference signals that poorly reflect physical admissibility. We introduce Physics-aware Rejection Sampling (PaRS), a training-time trace selection scheme that favors traces consistent with fundamental physics and numerically close to targets, with lightweight halting to control compute. We instantiate our framework with a large student model fine-tuned on traces synthesized by a larger teacher model, and evaluate under matched token budgets against various rejection sampling baselines. Our method improves accuracy and calibration, reduces physics-violation rates, and lowers sampling cost relative to baselines. These results indicate that modest, domain-aware constraints combined with trace-level selection provide a practical path toward reliable, efficient LRMs for process-aware property prediction and closed-loop materials design.

cs.AI↗