arXiv subjects

Daeun Jung

Publications and source records attributed to Daeun Jung.

3 recordsLinked to original sources

arXiv2026

Characterization of the 20-inch Photomultiplier Tubes for RENE Detector

To address the Reactor Antineutrino Anomaly (RAA) observed in neutrino experiments, the Reactor Experiment for Neutrino and Exotics (RENE) has been initiated using a liquid scintillation detector. In this study, we investigate the characteristics of two 20-inch Hamamatsu R12860 photomultiplier tubes (PMTs) intended for installation in the RENE detector. The charge and timing responses of the PMTs were evaluated at both the nominal and target gains expected during actual operation. In particular, gain non-uniformity arising from the large-diameter photocathode with a box-and-line type dynode structure was examined, and the maximum gain variation was measured. The occurrence rate, timing, and charge distributions of late pulses and afterpulses were also investigated to characterize the specific response features of the R12860 PMT. The results reported in this study will aid in the interpretation of signals from the RENE detector and serve as a reference for estimating potential systematic uncertainties in RENE data. Furthermore, these findings are expected to provide valuable information for other experiments employing the same type of PMTs.

physics.ins-det↗

arXiv2026

FedMOA: Federated GRPO for Personalized Reasoning LLMs under Heterogeneous Rewards

Group Relative Policy Optimization (GRPO) has recently emerged as an effective approach for improving the reasoning capabilities of large language models through online multi-objective reinforcement learning. While personalization on private data is increasingly vital, traditional Reinforcement Learning (RL) alignment is often memory-prohibitive for on-device federated learning due to the overhead of maintaining a separate critic network. GRPO's critic-free architecture enables feasible on-device training, yet transitioning to a federated setting introduces systemic challenges: heterogeneous reward definitions, imbalanced multi-objective optimization, and high training costs. We propose FedMOA, a federated GRPO framework for multi-objective alignment under heterogeneous rewards. FedMOA stabilizes local training through an online adaptive weighting mechanism via hypergradient descent, which prioritizes primary reasoning as auxiliary objectives saturate. On the server side, it utilizes a task- and accuracy-aware aggregation strategy to prioritize high-quality updates. Experiments on mathematical reasoning and code generation benchmarks demonstrate that FedMOA consistently outperforms federated averaging, achieving accuracy gains of up to 2.2% while improving global performance, personalization, and multi-objective balance.

cs.LG↗

arXiv2025

MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer Analysis

Computed tomography (CT) and clinical numeric data are essential modalities for cancer evaluation, but building large-scale multimodal training datasets for developing medical foundation models remains challenging due to the structural complexity of multi-slice CT data and high cost of expert annotation. In this study, we propose MEDFORM, a multimodal pre-training strategy that guides CT image representation learning using complementary information from clinical data for medical foundation model development. MEDFORM efficiently processes CT slice through multiple instance learning (MIL) and adopts a dual pre-training strategy: first pretraining the CT slice feature extractor using SimCLR-based self-supervised learning, then aligning CT and clinical modalities through cross-modal contrastive learning. Our model was pre-trained on three different cancer types: lung cancer (141,171 slices), breast cancer (8,100 slices), colorectal cancer (10,393 slices). The experimental results demonstrated that this dual pre-training strategy improves cancer classification performance and maintains robust performance in few-shot learning scenarios. Code available at https://github.com/DigitalHealthcareLab/25MultiModalFoundationModel.git

cs.CV↗