arXiv subjects

Jiwon Lee

Publications and source records attributed to Jiwon Lee.

At least 19 recordsLinked to original sources

arXiv2026

Search for Axion Dark Matter around \texorpdfstring{$15.6\,\mu\text{eV}$}{15.6 microeV} with a Broadly Tunable High-Temperature Superconducting Cavity

We report an axion dark matter search in the mass range of $15.30\text{--}15.85\,\mu\text{eV}$ using a broadly tunable high-temperature superconducting (HTS) haloscope. By soldering substrate-stripped rare-earth barium copper oxide (REBCO) films onto a copper shell, we achieved a quality factor 3--4 times higher than in copper cavities across its tuning band in an 8\text{-T} magnetic field. We set robust Frequentist 90\%~confidence-level exclusion limits on the axion--photon coupling down to 1.3 times the KSVZ coupling, while a complementary Bayesian analysis achieves KSVZ-level sensitivity.

hep-ex↗

arXiv2026

Multi-tesla operation of high-temperature superconducting cavities for accelerated axion dark matter searches

Axion haloscopes use radio-frequency cavities immersed in a magnetic field to search for dark-matter axions, which could resolve two central puzzles in fundamental physics: the strong charge-parity problem in quantum chromodynamics and the nature of dark matter. Multi-tesla fields trigger axion-to-photon conversion but induce severe vortex dissipation in standard superconductors, whereas copper cavities are limited by the anomalous skin effect ($Q \lesssim 10^5$). Here, we overcome these barriers by introducing a pole-to-pole 3-dimensional cavity architecture constructed from strain-controlled, mechanically delaminated rare-earth barium copper oxide (REBCO) tapes. By selectively stripping the lossy metallic substrate while utilizing the copper stabilizer as a "conductive backing" we convert the longitudinal assembly gaps into waveguides below cutoff, effectively suppressing cross-seam RF leakage. Employing a two-track strategy, we first unveiled the intrinsic high-field potential of the material with a 5.4 GHz resonant cavity, achieving an unloaded quality-factor ($Q_0$) of $1.4 \times 10^7$ in an 8 T magnetic field, exceeding conventional copper baselines by two orders of magnitude. Second, prioritizing practical haloscope integration, we engineered a tunable, volume-maximized 2.3 GHz pathfinder cavity. Deployed in the Pilot Axion Cavity Experiment at the Center for Axion and Precision Physics Research (CAPP-PACE), this system achieved a 180 mK noise temperature and a 5-fold $Q$ enhancement over copper, cumulatively delivering a $\sim$8.4-fold scan-rate acceleration.

hep-ex↗

arXiv2026

LoViF 2026 The First Challenge on Unified Removal of Raindrops and Reflections: Methods and Results

This workshop paper comprehensively reviews the First Challenge on Unified Removal of Raindrops and Reflections. The challenge aims to address a frequently encountered practical problem in the field of autonomous driving, i.e., raindrop-reflection composite degradation on rainy days. This competition attracted 149 registered participants and received 12 valid final submissions with corresponding fact sheets, significantly contributing to the progress of unified removal of raindrops and reflections. All the methods are developed and evaluated on our real-shot RainDrop and ReFlection (RDRF) dataset. A detailed analysis of the submitted methods and corresponding results is provided in this report, which highlights effective approaches and provides interesting insights for future research.

cs.CV↗

arXiv2026

Diversity is Not Ambiguity: Toward Accurate and Efficient Ambiguity Detection for Open-Domain QA

How can question answering (QA) systems determine whether a query is ambiguous? Ambiguity detection is essential in open-domain QA, as misclassification leads to answering the wrong interpretation or unnecessary clarification. However, existing methods conflate answer diversity with ambiguity, leading to inaccurate predictions. They also process queries uniformly, resulting in wasteful computation. We propose ARCHIVE (Ambiguity Recognition via Cascaded Hypothesis Inspection and Conflict Verification), an accurate and efficient framework that detects ambiguity via logical conflict: a query is ambiguous when its valid answers cannot all be true under a single interpretation. ARCHIVE combines a lightweight early-exit encoder for surface-detectable cases with a conflict reasoning module that models logical relations among answers, reinforced by an invariance objective for robustness to noisy answer sets. We present QuireQA, a 4,703-query benchmark spanning factoid, non-factoid, and ill-formed queries. Experiments show ARCHIVE outperforms competitors, improving F1-amb by up to 10.4% and F1-unamb by up to 21.6%, while operating 16$\times$ faster than the best competitor.

cs.AI↗

arXiv2026

CBF-based Probabilistic Safe Navigation under Unknown Nonlinear Obstacle Dynamics

Safe navigation for an ego vehicle in uncertain environments characterized by dynamic obstacles with unknown nonlinear dynamics is a challenging problem of significant practical interest. Existing approaches in the literature either lack formal safety guarantees, require full model knowledge, or fail to account for the risk associated with the vehicle's exact body geometry and the temporal evolution of uncertainty between sampling instants. In this paper, we propose a data-driven observer for the unknown obstacle dynamics that generates an alpha-confidence set flow, which is exactly transformed into a Control Barrier Function (CBF) to enforce (1-alpha)-probability safety. The proposed framework accommodates nonlinear ego vehicle dynamics of arbitrary relative degree, as demonstrated through case studies involving first- and second-order dynamics of an unmanned surface vehicle.

eess.SY↗

arXiv2026

Extended Haloscope Search and Exclusion of a Candidate Signal near 1.036 GHz

We report a follow-up axion haloscope search near 1.036 GHz that completes and extends our previous work [Phys. Rev. X 14, 031023 (2024)], in which a portion of the HEMT-based data could not be analyzed due to unrecorded experimental information. While recovering this dataset, we identified an excess near 1.036 GHz that satisfied our candidate-selection criteria, motivating dedicated validation studies, including independent cross-checks and re-examination with the original apparatus. The excess did not persist under these investigations and was not confirmed as an axion dark-matter signal. We subsequently extended the search over a 20-MHz band surrounding the candidate using a quantum-noise-limited amplifier, achieving sensitivity close to the Dine-Fischler-Srednicki-Zhitnitsky benchmark. In the absence of a confirmed signal, we set improved 90% confidence-level upper limits on the axion-photon coupling over the frequency range 1.026-1.045 GHz. This work highlights the importance of robust candidate-validation strategies as haloscope searches approach discovery-level sensitivity.

hep-ex↗

arXiv2026

Comparison of Image Processing Models in Quark Gluon Jet Classification

We present a comprehensive comparison of convolutional and transformer-based models for distinguishing quark and gluon jets using simulated jet images from Pythia 8. By encoding jet substructure into a three-channel representation of particle kinematics, we evaluate the performance of convolutional neural networks (CNNs), Vision Transformers (ViTs), and Swin Transformers (Swin-Tiny) under both supervised and self-supervised learning setups. Our results show that fine-tuning only the final two transformer blocks of the Swin-Tiny model achieves the best trade-off between efficiency and accuracy, reaching 81.4% accuracy and an AUC (area under the ROC curve) of 88.9%. Self-supervised pretraining with Momentum Contrast (MoCo) further enhances feature robustness and reduces the number of trainable parameters. These findings highlight the potential of hierarchical attention-based models for jet substructure studies and for domain transfer to real collision data.

physics.data-an↗

arXiv2025

Fast and Precise Track Fitting with Machine Learning

Efficient and accurate particle tracking is crucial for measuring Standard Model parameters and searching for new physics. This task consists of two major computational steps: track finding, the identification of a subset of all hits that are due to a single particle; and track fitting, the extraction of crucial parameters such as direction and momenta. Novel solutions to track finding via machine learning have recently been developed. However, track fitting, which traditionally requires searching for the best global solutions across a parameter volume plagued with local minima, has received comparatively little attention. Here, we propose a novel machine learning solution to track fitting. The per-track optimization task of traditional fitting is transformed into a single learning task optimized in advance to provide constant-time track fitting via direct parameter regression. This approach allows us to optimize directly for the true targets, i.e., the precise and unbiased estimates of the track parameters. This is in contrast to traditional fitting, which optimizes a proxy based on the distance between the track and the hits. In addition, our approach removes the requirement of making simplifying assumptions about the nature of the noise model. Most crucially, in the simulated setting described here, it provides more precise parameter estimates at a computational cost over 1,000 times smaller, which leads directly to improvements in particle momentum estimation, vertex finding, jet substructure, and anomaly detection.

hep-ex↗

arXiv2024

Unified Domain Generalization and Adaptation for Multi-View 3D Object Detection

Recent advances in 3D object detection leveraging multi-view cameras have demonstrated their practical and economical value in various challenging vision tasks. However, typical supervised learning approaches face challenges in achieving satisfactory adaptation toward unseen and unlabeled target datasets (\ie, direct transfer) due to the inevitable geometric misalignment between the source and target domains. In practice, we also encounter constraints on resources for training models and collecting annotations for the successful deployment of 3D object detectors. In this paper, we propose Unified Domain Generalization and Adaptation (UDGA), a practical solution to mitigate those drawbacks. We first propose Multi-view Overlap Depth Constraint that leverages the strong association between multi-view, significantly alleviating geometric gaps due to perspective view changes. Then, we present a Label-Efficient Domain Adaptation approach to handle unfamiliar targets with significantly fewer amounts of labels (\ie, 1$\%$ and 5$\%)$, while preserving well-defined source knowledge for training efficiency. Overall, UDGA framework enables stable detection performance in both source and target domains, effectively bridging inevitable domain gaps, while demanding fewer annotations. We demonstrate the robustness of UDGA with large-scale benchmarks: nuScenes, Lyft, and Waymo, where our framework outperforms the current state-of-the-art methods.

cs.CV↗

arXiv2024

Extensive search for axion dark matter over 1\,GHz with CAPP's Main Axion eXperiment

We report an extensive high-sensitivity search for axion dark matter above 1\,GHz at the Center for Axion and Precision Physics Research (CAPP). The cavity resonant search, exploiting the coupling between axions and photons, explored the frequency (mass) range of 1.025\,GHz (4.24\,$μ$eV) to 1.185\,GHz (4.91\,$μ$eV). We have introduced a number of innovations in this field, demonstrating the practical approach of optimizing all the relevant parameters of axion haloscopes, extending presently available technology. The CAPP 12\,T magnet with an aperture of 320\,mm made of Nb$_3$Sn and NbTi superconductors surrounding a 37-liter ultralight-weight copper cavity is expected to convert DFSZ axions into approximately $10^2$ microwave photons per second. A powerful dilution refrigerator, capable of keeping the core system below 40\,mK, combined with quantum-noise limited readout electronics, achieved a total system noise of about 200\,mK or below, which corresponds to a background of roughly $4\times 10^3$ photons per second within the axion bandwidth. The combination of all those improvements provides unprecedented search performance, imposing the most stringent exclusion limits on axion--photon coupling in this frequency range to date. These results also suggest an experimental capability suitable for highly-sensitive searches for axion dark matter above 1\,GHz.

hep-ex↗

arXiv2023

Contextual quantum metrology

Quantum metrology promises higher precision measurements than classical methods. Entanglement has been identified as one of quantum resources to enhance metrological precision. However, generating entangled states with high fidelity presents considerable challenges, and thus attaining metrological enhancement through entanglement is generally difficult. Here, we show that contextuality of measurement selection can enhance metrological precision, and this enhancement is attainable with a simple linear optical experiment. We call our methodology "contextual quantum metrology" (coQM). Contextuality is a nonclassical property known as a resource for various quantum information processing tasks. Until now, it has remained an open question whether contextuality can be a resource for quantum metrology. We answer this question in the affirmative by showing that the coQM can elevate precision of an optical polarimetry by a factor of 1.4 to 6.0, much higher than the one by quantum Fisher information, known as the limit of conventional quantum metrology. We achieve the contextuality-enabled enhancement with two polarization measurements which are mutually complementary, whereas, in the conventional method, some optimal measurements to achieve the precision limit are either theoretically difficult to find or experimentally infeasible. These results highlight that the contextuality of measurement selection is applicable in practice for quantum metrology.

quant-ph↗

arXiv2023

Metrological power of incompatible measurements

We show that measurement incompatibility is a necessary resource to enhance the precision of quantum metrology. To utilize incompatible measurements, we propose a probabilistic method of operational quasiprobability (OQ) consisting of the measuring averages. OQ becomes positive semidefinite for some quantum states. We prove that Fisher information (FI), based on positive OQ, can be larger than the conventional quantum FI. Applying the proof, we show that FI of OQ can be extremely larger than quantum FI, when estimating a parameter encoded onto a qubit state with two mutually unbiased measurements. By adopting maximum likelihood estimator and linear error propagation methods, we illustrate that they achieve the high precision that our model predicts. This approach is expected to be applicable to improve quantum sensors.

quant-ph↗

arXiv2023

A Learnable Counter-condition Analysis Framework for Functional Connectivity-based Neurological Disorder Diagnosis

To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages. In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individual-specific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the inter-network relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed counter-condition analysis. We simulated the FC that reverses the diagnostic information (i.e., counter-condition FC): converting a normal brain to be abnormal and vice versa. We validated the effectiveness of our framework by using two large resting-state functional magnetic resonance imaging (fMRI) datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and demonstrated that our framework outperforms other competing methods for disease identification. Furthermore, we analyzed the disease-related neurological patterns based on counter-condition analysis.

cs.LG↗

arXiv2023

A Forensic Methodology for Detecting Image Manipulations

By applying artificial intelligence to image editing technology, it has become possible to generate high-quality images with minimal traces of manipulation. However, since these technologies can be misused for criminal activities such as dissemination of false information, destruction of evidence, and denial of facts, it is crucial to implement strong countermeasures. In this study, image file and mobile forensic artifacts analysis were conducted for detecting image manipulation. Image file analysis involves parsing the metadata of manipulated images (e.g., Exif, DQT, and Filename Signature) and comparing them with a Reference DB to detect manipulation. The Reference DB is a database that collects manipulation-related traces left in image metadata, which serves as a criterion for detecting image manipulation. In the mobile forensic artifacts analysis, packages related to image editing tools were extracted and analyzed to aid the detection of image manipulation. The proposed methodology overcomes the limitations of existing graphic feature-based analysis and combines with image processing techniques, providing the advantage of reducing false positives. The research results demonstrate the significant role of such methodology in digital forensic investigation and analysis. Additionally, We provide the code for parsing image metadata and the Reference DB along with the dataset of manipulated images, aiming to contribute to related research.

cs.MM↗

arXiv2023

Search for the Sagittarius Tidal Stream of Axion Dark Matter around 4.55 $μ$eV

We report the first search for the Sagittarius tidal stream of axion dark matter around 4.55 $μ$eV using CAPP-12TB haloscope data acquired in March of 2022. Our result excluded the Sagittarius tidal stream of Dine-Fischler-Srednicki-Zhitnitskii and Kim-Shifman-Vainshtein-Zakharov axion dark matter densities of $ρ_a\gtrsim0.184$ and $\gtrsim0.025$ GeV/cm$^{3}$, respectively, over a mass range from 4.51 to 4.59 $μ$eV at a 90% confidence level.

hep-ex↗

arXiv2023

Axion Dark Matter Search around 4.55 $μ$eV with Dine-Fischler-Srednicki-Zhitnitskii Sensitivity

We report an axion dark matter search at Dine-Fischler-Srednicki-Zhitnitskii sensitivity with the CAPP-12TB haloscope, assuming axions contribute 100\% of the local dark matter density. The search excluded the axion--photon coupling $g_{aγγ}$ down to about $6.2\times10^{-16}$ GeV$^{-1}$ over the axion mass range between 4.51 and 4.59 $μ$eV at a 90\% confidence level. The achieved experimental sensitivity can also exclude Kim-Shifman-Vainshtein-Zakharov axion dark matter that makes up just 13\% of the local dark matter density. The CAPP-12TB haloscope will continue the search over a wide range of axion masses.

hep-ex↗

arXiv2022

GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks

Graph convolutional neural networks (GCNs) have emerged as a key technology in various application domains where the input data is relational. A unique property of GCNs is that its two primary execution stages, aggregation and combination, exhibit drastically different dataflows. Consequently, prior GCN accelerators tackle this research space by casting the aggregation and combination stages as a series of sparse-dense matrix multiplication. However, prior work frequently suffers from inefficient data movements, leaving significant performance left on the table. We present GROW, a GCN accelerator based on Gustavson's algorithm to architect a row-wise product based sparse-dense GEMM accelerator. GROW co-designs the software/hardware that strikes a balance in locality and parallelism for GCNs, achieving significant energy-efficiency improvements vs. state-of-the-art GCN accelerators.

cs.AR↗

arXiv2022

LOSTIN: Logic Optimization via Spatio-Temporal Information with Hybrid Graph Models

Despite the stride made by machine learning (ML) based performance modeling, two major concerns that may impede production-ready ML applications in EDA are stringent accuracy requirements and generalization capability. To this end, we propose hybrid graph neural network (GNN) based approaches towards highly accurate quality-of-result (QoR) estimations with great generalization capability, specifically targeting logic synthesis optimization. The key idea is to simultaneously leverage spatio-temporal information from hardware designs and logic synthesis flows to forecast performance (i.e., delay/area) of various synthesis flows on different designs. The structural characteristics inside hardware designs are distilled and represented by GNNs; the temporal knowledge (i.e., relative ordering of logic transformations) in synthesis flows can be imposed on hardware designs by combining a virtually added supernode or a sequence processing model with conventional GNN models. Evaluation on 3.3 million data points shows that the testing mean absolute percentage error (MAPE) on designs seen and unseen during training are no more than 1.2% and 3.1%, respectively, which are 7-15X lower than existing studies.

cs.LG↗