arXiv subjects

Sehyeon Park

Publications and source records attributed to Sehyeon Park.

3 recordsLinked to original sources

arXiv2026

Imaging-system-aware color routers optimized for imaging information

Conventional nanophotonic color routers are typically optimized under idealized, normal plane waves. However, this standard assumption fails in real-world imaging-system environments, where structures are illuminated by converging light cones and field-dependent chief-ray angles. Here, we present an imaging-system-aware, end-to-end inverse-design framework that directly maximizes the mutual imaging information $\Iimg$ preserved by a single-layer silicon nitride color router under realistic pupil illumination. By analytically embedding the optimal reconstruction decoder directly inside the gradient loop, we co-design the optical nanostructures and the digital recovery pipeline. To scale this approach across a full sensor, we exploit the $D_4$ symmetry of the square pixel lattice, tiling $48$ distinct sensor-field regions using only six unique lithographic masks. Our optimized router is predicted to collect $2.8\times$ more photoelectrons than a conventional color-filter array. Consequently, under low-light conditions, below a green-site signal-to-noise ratio of $13.7$~dB, the color router preserves superior image information compared to the color-filter array; evaluated from its measured routing fractions together with the modeled throughput, the fabricated device reproduces this crossover at $11.1^{+2.1}_{-2.3}$~dB. This marks the first experimental demonstration, from measured routing and a modeled throughput, of a single-layer nanophotonic color router achieving a performance crossover against the color-filter array. These results establish that next-generation flat optics must shift from isolated device efficiency toward system-level co-design optimized under physical imaging-system-pupil geometry.

physics.optics↗

arXiv2026

FOCUS & RePAIR: Mitigating Text Degeneration via Token-Level Guidance for Pruned Large Language Models

Pruning is a practical approach to compress large language models (LLMs), but it can amplify text degeneration, especially repetition loops, even when perplexity and task accuracy remain largely unchanged. In this work, we present a token-level analysis of this failure mode by viewing decoding as a dynamical process that enters and persists in a small set of recurrent contexts. Our analysis decomposes degeneration into loop entry risk and loop persistence, and shows that persistence is controlled by the escape mass assigned to plausible alternatives within the token sampling set. Motivated by these findings, we propose two token-level guidance objectives for post-pruning fine-tuning. FOCUS reweights distillation toward high-confidence teacher regions to suppress leakage, while RePAIR uses onset-centered positive/negative continuation pairs with a margin loss to promote plausible alternatives and prevent early commitment to repetition loops. Experiments on open-ended continuation and instruction-based generation show that both methods consistently reduce repetition and improve generation quality.

cs.CL↗

arXiv2025

Evaluation of Thermal Control Based on Spatial Thermal Comfort with Reconstructed Environmental Data

Achieving thermal comfort while maintaining energy efficiency is a critical objective in building system control. Conventional thermal comfort models, such as the Predicted Mean Vote (PMV), rely on both environmental and personal variables. However, the use of fixed-location sensors limits the ability to capture spatial variability, which reduces the accuracy of occupant-specific comfort estimation. To address this limitation, this study proposes a new PMV estimation method that incorporates spatial environmental data reconstructed using the Gappy Proper Orthogonal Decomposition (Gappy POD) algorithm. In addition, a group PMV-based control framework is developed to account for the thermal comfort of multiple occupants. The Gappy POD method enables fast and accurate reconstruction of indoor temperature fields from sparse sensor measurements. Using these reconstructed fields and occupant location data, spatially resolved PMV values are calculated. Group-level thermal conditions are then derived through statistical aggregation methods and used to control indoor temperature in a multi-occupant living lab environment. Experimental results show that the Gappy POD algorithm achieves an average relative error below 3\% in temperature reconstruction. PMV distributions varied by up to 1.26 scale units depending on occupant location. Moreover, thermal satisfaction outcomes varied depending on the group PMV method employed. These findings underscore the importance for adaptive thermal control strategies that incorporate both spatial and individual variability, offering valuable insights for future occupant-centric building operations.

cs.CE↗