arXiv subjects

Seunghyun Moon

Publications and source records attributed to Seunghyun Moon.

8 recordsLinked to original sources

arXiv2026

A Korean Macroeconomic Database for Data-Rich Policy Analysis and U.S.--Korea Dependence

We introduce KRED (Korea Research Economic Database), a FRED-MD-compatible monthly macroeconomic database for Korea designed for data-rich policy analysis and cross-country comparison. KRED contains 125 monthly series from ECOS, KOSIS, and administrative labor-market sources, with coverage back to 1960. Using a balanced panel of 104 series over 2009:06--2025:12, principal-components analysis extracts four factors that explain about 30% of total variation. These factors correspond to financial conditions, real activity, housing and real-estate credit, and labor-market and price pressures, and their diffusion indices summarize major Korean macroeconomic episodes. We then use KRED in two empirical applications. First, factor-augmented VARs show that U.S. monetary tightening transmits strongly to Korea and that factor augmentation yields a more coherent inflation response than a low-dimensional VAR. Second, a grouped U.S.--Korea tensor autoregression shows that cross-country dependence is concentrated in financially oriented blocks, with stronger transmission from the U.S. financial block to Korea than in the reverse direction, while spillovers in real activity and housing are much weaker. KRED thus provides a transparent public database for Korean macroeconomic research and a useful building block for comparative work on macro-financial dependence in Asia.

econ.EM↗

arXiv2026

Ultrasensitive Nanoplastics Detection Leveraging Shrinking Surface Plasmonic Bubble

Nanoplastics pose serious environmental and health risks due to their widespread presence in aquatic systems. Detecting trace amounts of nanoplastics is a challenging task, which currently requires sophisticated equipment and tedious sample preparation (e.g., ultrafiltration). In this work, we demonstrate an ultra-sensitive Shrinking Surface Bubble Deposition (SSBD) technique for nanoplastics detection. SSBD leverages plasmonic photothermal effects to generate a surface bubble and the resulting Marangoni flow to concentrate sparsely suspended nanoplastics onto the bubble surface. The collected nanoplastic particles are subsequently deposited on the substrate after the bubble shrinks and vanishes. To quantify the detection limit of SSBD for nanoplastics in water, core-shell gold plasmonic nanoparticles are mixed with the aqueous sample to enable photothermal bubble generation, while also supporting surface-enhanced Raman spectroscopy (SERS) for signal enhancement. Results show that the limits of detection are 10 ng/mL, 10-1 ng/mL and 10-3 ng/mL for polystyrene (PS) particles with diameters of 500 nm, 200 nm and 30 nm, respectively. We further used SSBD to detect plastics particles in real drinking water (e.g., bottled and fountain water) and found polyamides (PA) and polypropylene (PP) micro/nanoplastics, demonstrating the potential of the SSBD-SERS technique as a versatile and sensitive platform for detecting trace-level nanoplastic contamination and assessing human exposure risk.

physics.optics↗

arXiv2025

Operon: Incremental Construction of Ragged Data via Named Dimensions

Modern data processing workflows frequently encounter ragged data: collections with variable-length elements that arise naturally in domains like natural language processing, scientific measurements, and autonomous AI agents. Existing workflow engines lack native support for tracking the shapes and dependencies inherent to ragged data, forcing users to manage complex indexing and dependency bookkeeping manually. We present Operon, a Rust-based workflow engine that addresses these challenges through a novel formalism of named dimensions with explicit dependency relations. Operon provides a domain-specific language where users declare pipelines with dimension annotations that are statically verified for correctness, while the runtime system dynamically schedules tasks as data shapes are incrementally discovered during execution. We formalize the mathematical foundation for reasoning about partial shapes and prove that Operon's incremental construction algorithm guarantees deterministic and confluent execution in parallel settings. The system's explicit modeling of partially-known states enables robust persistence and recovery mechanisms, while its per-task multi-queue architecture achieves efficient parallelism across heterogeneous task types. Empirical evaluation demonstrates that Operon outperforms an existing workflow engine with 14.94x baseline overhead reduction while maintaining near-linear end-to-end output rates as workloads scale, making it particularly suitable for large-scale data generation pipelines in machine learning applications.

cs.PL↗

arXiv2025

Spacer: Towards Engineered Scientific Inspiration

Recent advances in LLMs have made automated scientific research the next frontline in the path to artificial superintelligence. However, these systems are bound either to tasks of narrow scope or the limited creative capabilities of LLMs. We propose Spacer, a scientific discovery system that develops creative and factually grounded concepts without external intervention. Spacer attempts to achieve this via 'deliberate decontextualization,' an approach that disassembles information into atomic units - keywords - and draws creativity from unexplored connections between them. Spacer consists of (i) Nuri, an inspiration engine that builds keyword sets, and (ii) the Manifesting Pipeline that refines these sets into elaborate scientific statements. Nuri extracts novel, high-potential keyword sets from a keyword graph built with 180,000 academic publications in biological fields. The Manifesting Pipeline finds links between keywords, analyzes their logical structure, validates their plausibility, and ultimately drafts original scientific concepts. According to our experiments, the evaluation metric of Nuri accurately classifies high-impact publications with an AUROC score of 0.737. Our Manifesting Pipeline also successfully reconstructs core concepts from the latest top-journal articles solely from their keyword sets. An LLM-based scoring system estimates that this reconstruction was sound for over 85% of the cases. Finally, our embedding space analysis shows that outputs from Spacer are significantly more similar to leading publications compared with those from SOTA LLMs.

cs.AI↗

arXiv2025

T-REX: A 68-567 μs/token, 0.41-3.95 μJ/token Transformer Accelerator with Reduced External Memory Access and Enhanced Hardware Utilization in 16nm FinFET

This work introduces novel training and post-training compression schemes to reduce external memory access during transformer model inference. Additionally, a new control flow mechanism, called dynamic batching, and a novel buffer architecture, termed a two-direction accessible register file, further reduce external memory access while improving hardware utilization.

cs.AR↗

arXiv2022

Nanoparticle Photo-Ejection from Liquid via Excited Plasmonic Supercavitation

The ability to separate miniscule solid particles (e.g., nanoparticles, NPs) from liquid is important to a wide range of applications, such as water purification, material deposition, and biomedical engineering. Such separation is usually achieved by displacing liquid via filtration or distillation. However, directly moving small particles out of liquid is difficult, especially when their sizes approach the nanometer scale, as the capillary force on the NP at the liquid surface is too large for common body forces (e.g., optical or magnetic) to overcome. Here, we demonstrate the ability to eject metallic NPs out of liquid with a laser excitation at their surface plasmon resonance (SPR) wavelength. The laser applies an optical force on the NPs to drive them toward the liquid surface. In the meantime, the laser can also intensely heat the NP to form a nanobubble encapsulating the NP (i.e., supercavitation), which achieves the liquid-NP separation and thus eliminates the capillary force on the NP at the liquid free surface. We show that such a mechanism can expel NPs out of liquid as observed using a transient scattering experiment, which is further confirmed by molecular dynamics simulations. We also demonstrate depositing the NPs on a solid surface not in contact with the liquid. This study reveals an interesting mechanism to separate NPs from liquid and could potentially benefit separation, nanomaterials and biomedical applications.

physics.optics↗

arXiv2021

Impact of Surface and Pore Characteristics on Fatigue Life of Laser Powder Bed Fusion Ti-6Al-4V Alloy Described by Neural Network Models

In this study, the effects of surface roughness and pore characteristics on fatigue lives of laser powder bed fusion (LPBF) Ti-6Al-4V parts were investigated. The 197 fatigue bars were printed using the same laser power but with varied scanning speeds. These actions led to variations in the geometries of microscale pores, and such variations were characterized using micro-computed tomography. To generate differences in surface roughness in fatigue bars, half of the samples were grit-blasted and the other half machined. Fatigue behaviors were analyzed with respect to surface roughness and statistics of the pores. For the grit-blasted samples, the contour laser scan in the LPBF strategy led to a pore-depletion zone isolating surface and internal pores with different features. For the machined samples, where surface pores resemble internal pores, the fatigue life was highly correlated with the average pore size and projected pore area in the plane perpendicular to the stress direction. Finally, a machine learning model using a drop-out neural network (DONN) was employed to establish a link between surface and pore features to the fatigue data (logN), and good prediction accuracy was demonstrated. Besides predicting fatigue lives, the DONN can also estimate the prediction uncertainty.

cond-mat.mtrl-sci↗

arXiv2021

Plasmonic Nanobubbles: A Perspective

The field of plasmonic nanobubbles, referring to bubbles generated around nanoparticles due to plasmonic heating, is growing rapidly in recent years. Theoretical, simulation and experimental studies have been reported to reveal the fundamental physics related to this nanoscale multi-physics phenomenon. Using plasmonic nanobubbles for applications is in the early stage but progressing. In this article, we briefly review the current state of this research field and give our perspectives on the research needs in the theoretical, simulation and experimental fronts. We also give our perspectives on how the fundamental understanding can be applied to more practical applications.

physics.optics↗