K S Kim

Sumin Lee, Kyeonghun Kim, Subeen Lee et al.

On the recent EyeBench benchmark, predicting reading comprehension from eye movements exposes a stark gap: text-aware models using pretrained language models reach 56--63% AUROC, while gaze-only models operate at chance. We ask how far a gaze-only model can be pushed by lightweight, language-model-free conditioning. Building on the EyeBench AhnCNN baseline, LEXIC-Base, we propose two mechanisms to inject three precomputed word-level difficulty signals, GPT-2 surprisal, word frequency, and word length, into the per-fixation input: direct concatenation, LEXIC-Concat, and a residual mechanism, LEXIC-Res, where a small head predicts typical-reader gaze response and the encoder is conditioned on the deviation. On the OneStop reading comprehension task, with K=5 seed-ensemble training across ten folds, both mechanisms produce statistically consistent AUROC gains on Unseen Text, +1.8 to +2.2 percentage points, Wilcoxon p <= 0.065. LEXIC-Concat additionally lifts Unseen Reader by +2.9 percentage points, p = 0.010. We trace an architectural boundary in LEXIC-Res on Unseen Reader, +1.8 percentage points, p = 0.19, to the prediction head being calibrated to training readers, transferring imperfectly to out-of-distribution readers.

Anna Jung, Kyeonghun Kim, Youngung Han et al.

Whole slide images (WSIs) provide rich diagnostic information for computational pathology, but their gigapixel scale, stain variation, scanner differences, tissue artifacts, and limited expert annotation make robust model training challenging. This paper presents a multi-source Masked Autoencoder (MAE) framework, named ProsMAE, for histopathology representation learning. Tiles from Prostate cANcer graDe Assessment (PANDA), CAncer MEtastases in LYmph nOdes challeNge 2017 (CAMELYON17), and BReAst Carcinoma Subtyping (BRACS) are used for ProsMAE pretraining to expose the encoder to diverse tissue morphology and acquisition conditions. The learned encoder is transferred for International Society of Urological Pathology (ISUP) grade classification through ProsCLS, using a frozen encoder and a linear classification head. ProsMAE achieved a higher mean validation quadratic weighted kappa (QWK) than the vanilla MAE frozen linear-probe baseline under the evaluated disjoint PANDA split. Repeated-split evaluation remains necessary to further establish robustness across split compositions.

Stephen Arinze Obi, Ki-Hyun Kim, Jae-Jung Jung

Virtual admittance (VA) is widely used in cascaded voltage-control and current-control (VC-CC) grid-forming inverters (GFMIs) because it shapes the converter terminal behavior while preserving the current-regulation path required for current shaping and limiting. However, the achievable VC-loop bandwidth remains strongly coupled to the CC-loop bandwidth and to the VA parameters. Voltage-feedback decoupling (VFD) is commonly used to relax this coupling, but in VA-based control its benefit is not unconditional. This paper shows that unity-gain VFD, which represents the full-decoupling condition, removes the low-frequency restoring term associated with the filter capacitor and drives the voltage loop toward a delay-sensitive double-integrator structure. This internal-stability limitation is referred to here as the VFD trap. To address this trap without attenuating VFD, a proportional active-damping (AD) path is proposed, implemented as negative capacitor-voltage feedback in the current-reference path. The proposed path restores the missing low-frequency support while retaining unity VFD and introduces an additional AD-based degree of freedom for VC-loop tuning. A minimum support condition, a delay-aware phase-margin expression, and compact forward/inverse design equations are derived for operating-point selection. Standalone and grid-connected experiments on a 3-kVA prototype verify the analysis, showing that the proposed path recovers stable unity-VFD operation, reduces the voltage-step settling time from approximately 9~ms to 3~ms, and maintains stable power injection.