arXiv subjects

Inhyuk Park

Publications and source records attributed to Inhyuk Park.

5 recordsLinked to original sources

arXiv2026

Tidal Grinding of Dwarf Galaxies in Cluster Environments

Dwarf elliptical galaxies (dEs) dominate galaxy clusters and provide key constraints on environmentally driven galaxy evolution. Here we examine whether the projected shapes of dEs retain information about their accretion and transformation histories using a homogeneous sample of 1,108 bright (m_g < 19 mag) dEs in the Virgo cluster. Based on the axis-ratio (b/a), we define flat (< 0.70) and round (> 0.74) subsamples and compare their spatial and kinematic properties. We find that flat dEs are distributed more uniformly across the cluster, whereas round dEs preferentially occupy regions of stronger tidal fields around massive (M_* > 10^{10} M_sun) galaxies. Within the central 5^\circ x 5^\circ region around the Virgo central galaxy (M87), 149 dEs have spectroscopic radial velocities compiled from public archives. In this region, the two shape classes also exhibit clear kinematic segregation: flat dEs have systematically larger line-of-sight velocity offsets from the cluster mean (median $\Delta v = 654 km/s$), whereas round dEs have smaller offsets (median $\Delta v = 414 km/s$), as expected for a more dynamically relaxed population. Flat dEs are consistent with a population that has experienced weaker tidal processing and consequently retains more flattened morphologies. By contrast, round dEs are consistent with prolonged tidal processing (``tidal grinding'') that may have transformed initially flattened systems into rounder spheroids. However, projection contamination of the round subsample may have introduced some uncertainty in the interpretation of intrinsic galaxy shapes.

astro-ph.GA↗

arXiv2026

Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation

Noisy labels are common in large-scale medical imaging datasets due to inter-observer variability and ambiguous cases. We propose a statistically grounded and task-agnostic framework, Standardized Loss Aggregation (SLA), for detecting noisy labels at the sample level. SLA quantifies label reliability by aggregating standardized fold-level validation losses across repeated cross-validation runs. This formulation generalizes discrete hard-counting schemes into a continuous estimator that captures both the frequency and magnitude of performance deviations, yielding interpretable and statistically stable noisiness scores. Experiments on a public fundus dataset demonstrate that SLA consistently outperforms the hard-counting baseline across all noise levels and converges substantially faster, especially under low noise ratios where subtle loss variations are informative. Samples with high SLA scores indicate potentially ambiguous or mislabeled cases, guiding efficient re-annotation and improving dataset reliability for any classification task.

cs.CV↗

arXiv2026

When Prompts Mislead: Textual Dominance and Diagnostic Bias in MLLMs

Multimodal large language models (MLLMs) are increasingly being evaluated for medical applications, where computational constraints often make prompting strategies the only practical alternative to fine-tuning. Such strategies are generally assumed to support diagnostic reasoning, yet their potential failure modes in medical MLLMs remain poorly characterized. We analyze FundusExpert-1B, an open-source ophthalmology MLLM, on a hemorrhage versus drusen discrimination task using the public BRSET dataset, adopted here as a controlled testbed for our analysis. (i) A controlled probe with artificially injected markers confirms that the model retains coarse, region-level spatial grounding. (ii) Compared with zero-shot inference, one-shot textual prompts bias predictions toward the prompted finding. (iii) When an overlaid lesion contour is paired with an inconsistent textual claim, the textual prompt overrides the correct visual cue: overall accuracy drops from 75% to 46% relative to the visual-only condition, and Chain-of-Thought (CoT) reasoning is associated with further degradation rather than self-correction. Although limited to a single model and dataset, our findings suggest that prompting strategies alone may be insufficient for the safe clinical deployment of medical MLLMs.

cs.HC↗

arXiv2026

SN Ia Population Machine. I. A Unified Cosmological Simulation-Binary Synthesis Framework Establishing Non-universal Delay-time Distributions and Cosmic Progenitor-channel Dominance Crossover

We present a forward-modeling framework for synthesizing Type Ia supernova (SN Ia) populations by coupling cosmological hydrodynamic simulations to binary population synthesis (BPS). Using IllustrisTNG star particles as simple stellar populations, we generate binaries and evolve them with COMPAS to produce synthetic SNe Ia tagged with explosion times and progenitor channels (single- and double-degenerate; SD and DD). This cosmology-BPS pipeline enables self-consistent, end-to-end tracking of SN Ia populations from individual galaxies to cosmic scales. The model reproduces key SN-related observables, including host-galaxy demographics, delay-time distributions (DTDs), SN-rate trends with host properties and redshift, and a progenitor-age 'step' implicated by the mass step in Hubble residuals. Our main findings are as follows. (1) Contrary to the standard assumption, DTDs appear intrinsically non-universal: their form depends on progenitor channel and metallicity, and thus varies systematically across hosts and with redshift. The commonly adopted DTD is therefore best regarded as a population-averaged approximation rather than a fundamental kernel. (2) We predict that the dominant SN Ia progenitor population shifts from SD to DD with cosmic time, with a demographic crossover near z = 0.5 (~5.2 Gyr ago). This non-monolithic SN Ia population with a redshift-dependent SD/DD mixture weakens the universality implicit in a single globally calibrated standardization. Taken together, evolution in both the DTD and the channel mixture can imprint redshift-dependent systematics on SN Ia luminosities, strengthening the case for jointly inferring progenitor/host-driven effects alongside cosmic acceleration. The full catalogue and analysis scripts are available via Zenodo.

astro-ph.GA↗

arXiv2023

Robust Asymmetric Loss for Multi-Label Long-Tailed Learning

In real medical data, training samples typically show long-tailed distributions with multiple labels. Class distribution of the medical data has a long-tailed shape, in which the incidence of different diseases is quite varied, and at the same time, it is not unusual for images taken from symptomatic patients to be multi-label diseases. Therefore, in this paper, we concurrently address these two issues by putting forth a robust asymmetric loss on the polynomial function. Since our loss tackles both long-tailed and multi-label classification problems simultaneously, it leads to a complex design of the loss function with a large number of hyper-parameters. Although a model can be highly fine-tuned due to a large number of hyper-parameters, it is difficult to optimize all hyper-parameters at the same time, and there might be a risk of overfitting a model. Therefore, we regularize the loss function using the Hill loss approach, which is beneficial to be less sensitive against the numerous hyper-parameters so that it reduces the risk of overfitting the model. For this reason, the proposed loss is a generic method that can be applied to most medical image classification tasks and does not make the training process more time-consuming. We demonstrate that the proposed robust asymmetric loss performs favorably against the long-tailed with multi-label medical image classification in addition to the various long-tailed single-label datasets. Notably, our method achieves Top-5 results on the CXR-LT dataset of the ICCV CVAMD 2023 competition. We opensource our implementation of the robust asymmetric loss in the public repository: https://github.com/kalelpark/RAL.

cs.CV↗