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Sumi Lee

Publications and source records attributed to Sumi Lee.

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Size-Dependent Band-Tail Localization in Oxide Semiconductors Revealed by Direct Density-of-States Mapping

Disorder-induced localization is expected to become increasingly important as amorphous oxide semiconductor transistors are scaled toward low-dimensional channels, yet the electronic states responsible for this transport regime remain difficult to resolve experimentally. Here, we use a lock-in-based electric-field penetration technique to directly map the effective density of states (DOS) in In-based oxide semiconductor thin-film transistors (TFTs). The extracted quantum capacitance, carrier density, and chemical potential reveal a disorder-dominated transport regime in which band-tail states are not merely passive traps, but become screening-active and partially transport-active. Geometry-dependent DOS mapping shows an exponential suppression of the effective DOS with channel length, demonstrating size-dependent band-tail localization and providing a microscopic origin for a distinct localization-induced threshold-voltage roll-off mechanism. Temperature-dependent measurements show that the disorder-dominated DOS is strongly suppressed at low temperatures, while extended diffusive states remain nearly unchanged, confirming the localization origin. By tuning film thickness, O2 annealing, and In/Ga/Zn composition, we further demonstrate systematic suppression of disorder and effective-DOS localization. This work establishes direct DOS mapping as a device-level probe of localization physics and provides a pathway for engineering disorder in low-dimensional oxide semiconductor electronics.

cond-mat.mes-hall

Role of Dependency Distance in Text Simplification: A Human vs ChatGPT Simplification Comparison

This study investigates human and ChatGPT text simplification and its relationship to dependency distance. A set of 220 sentences, with increasing grammatical difficulty as measured in a prior user study, were simplified by a human expert and using ChatGPT. We found that the three sentence sets all differed in mean dependency distances: the highest in the original sentence set, followed by ChatGPT simplified sentences, and the human simplified sentences showed the lowest mean dependency distance.

cs.CL

Building Korean Sign Language Augmentation (KoSLA) Corpus with Data Augmentation Technique

We present an efficient framework of corpus for sign language translation. Aided with a simple but dramatic data augmentation technique, our method converts text into annotated forms with minimum information loss. Sign languages are composed of manual signals, non-manual signals, and iconic features. According to professional sign language interpreters, non-manual signals such as facial expressions and gestures play an important role in conveying exact meaning. By considering the linguistic features of sign language, our proposed framework is a first and unique attempt to build a multimodal sign language augmentation corpus (hereinafter referred to as the KoSLA corpus) containing both manual and non-manual modalities. The corpus we built demonstrates confident results in the hospital context, showing improved performance with augmented datasets. To overcome data scarcity, we resorted to data augmentation techniques such as synonym replacement to boost the efficiency of our translation model and available data, while maintaining grammatical and semantic structures of sign language. For the experimental support, we verify the effectiveness of data augmentation technique and usefulness of our corpus by performing a translation task between normal sentences and sign language annotations on two tokenizers. The result was convincing, proving that the BLEU scores with the KoSLA corpus were significant.

cs.CL