arXiv subjects

Joohyun Lee

Publications and source records attributed to Joohyun Lee.

At least 19 recordsLinked to original sources

arXiv2026

Double-Peaked Ly$α$ Emission during Reionization Requires Nearby Voids and a Favorable Local Ionizing Background

Several Lyman-alpha (Ly$α$) emitters deep into the reionization era exhibit double-peaked Ly$α$ emission profiles, raising the question of how the intergalactic medium can transmit photons blueward of the Ly$α$ resonance at such high redshifts. To investigate this, we compute Ly$α$ transmission along sightlines originating from galaxies in the Cosmic Dawn III simulation and identify cases that closely reproduce the observed double-peaked emission. In these cases, the sightlines intersect highly underdense voids located a few comoving megaparsecs from the source galaxy. These voids allow photons emitted blueward of Ly$α$ to redshift through resonance without scattering while traversing them. The low opacity arises because the neutral hydrogen density scales with the square of the underlying gas density under ionization equilibrium, making sufficiently underdense regions with $\lesssim30~\%$ of cosmic mean density highly transmissive. Such voids naturally occur in the fluctuating cosmic density field, even in the vicinity of galaxies, and can also be associated with transmissive spikes in the Ly$α$ forest. We find that the global probability of observing double-peaked emission is $\sim0.5~\%$ during reionization at an 80\% global ionization fraction, while no cases are found at 60\% ionization. We also find that this probability depends sensitively on the local ionizing background intensity, increasing by a factor of $\sim10^3$ for a tenfold increase in intensity. Recent accretion episodes of supermassive black holes can further increase this probability. These results suggest that double-peaked Ly$α$ emission in high-$z$ galaxies can serve as a sensitive probe of the ionizing background during the late stages of cosmic reionization.

astro-ph.CO↗

arXiv2026

Hierarchical Reranking for Scalable Financial RAG System

Analyzing financial documents such as 10-K filings, tabular disclosures, and macroeconomic reports demands expert reasoning and extensive time. However, existing Retrieval-Augmented Generation systems often struggle to process hybrid text-table structures or the massive scale of financial documents. To address these challenges, we propose Hierarchical Reranker, a RAG framework designed to improve retrieval performance and generative reliability across large-scale financial datasets. The system integrates three key innovations: Pre-Retrieval Optimization, enhancing query clarity and search efficiency through normalization, keyword expansion, and table transformation; Hierarchical Reranker Architecture, improving retrieval precision through a two-stage ranking mechanism; and Long-Context Management, preserving reasoning accuracy through adaptive input partitioning and fusion under extensive contexts. Across multiple benchmarks, including FinQA, FinanceBench, and ConvFinQA, the proposed system achieved an NDCG@20 score of 0.7918 and demonstrated superior factual consistency. Its robustness was further validated by achieving second place in the ACM-ICAIF '24 FinanceRAG Challenge. This work presents a deployable, domain-optimized RAG pipeline that enhances both the accuracy and scalability of financial reasoning, paving the way for automated audit reporting and quantitative investment analysis. The source code will be made publicly available on GitHub upon acceptance.

cs.IR↗

arXiv2025

Personalized Auto-Grading and Feedback System for Constructive Geometry Tasks Using Large Language Models on an Online Math Platform

As personalized learning gains increasing attention in mathematics education, there is a growing demand for intelligent systems that can assess complex student responses and provide individualized feedback in real time. In this study, we present a personalized auto-grading and feedback system for constructive geometry tasks, developed using large language models (LLMs) and deployed on the Algeomath platform, a Korean online tool designed for interactive geometric constructions. The proposed system evaluates student-submitted geometric constructions by analyzing their procedural accuracy and conceptual understanding. It employs a prompt-based grading mechanism using GPT-4, where student answers and model solutions are compared through a few-shot learning approach. Feedback is generated based on teacher-authored examples built from anticipated student responses, and it dynamically adapts to the student's problem-solving history, allowing up to four iterative attempts per question. The system was piloted with 79 middle-school students, where LLM-generated grades and feedback were benchmarked against teacher judgments. Grading closely aligned with teachers, and feedback helped many students revise errors and complete multi-step geometry tasks. While short-term corrections were frequent, longer-term transfer effects were less clear. Overall, the study highlights the potential of LLMs to support scalable, teacher-aligned formative assessment in mathematics, while pointing to improvements needed in terminology handling and feedback design.

cs.CY↗

arXiv2025

Dust-UV offsets in high-redshift galaxies in the Cosmic Dawn III simulation

Recent observations have revealed puzzling spatial disparities between ALMA dust continuum and UV emission as seen by HST and JWST in galaxies at $z=5-7$ (e.g. ALPINE and REBELS surveys), compelling us to propose a physical interpretation of such offsets. We investigate these offsets using the Cosmic Dawn III (CoDa III) simulation, a state-of-the-art fully coupled radiation-hydrodynamics cosmological simulation, which incorporates a dynamical dust model. First of all, we find that our simulated dust masses, while calibrated to match observed ones, yield unrealistically large UV attenuations. In fact, the bright-end galaxy UV Luminosity function is best reproduced using only 7.5\% of the dust content of CoDa III galaxies. With this recalibration, we obtain populations of massive galaxies matching ALPINE and REBELS magnitudes and UV slopes, but with smaller dust masses than observed. In this framework, we also find significant dust-UV offsets in massive, UV-bright galaxies ($\mathrm{M}_\mathrm{DM}> 10^{11.5}$ M$_\odot$, M$_*>10^{10}$ M$_\odot$, M$_{\rm AB1600}<-21.5$), reaching up to $\sim 2$ pkpc for the most massive systems. Our analysis reveals that these offsets primarily result from severe dust extinction in galactic centers rather than a misalignment between dust and stellar mass distributions. At the spatial resolution of CoDa III (1.65 pkpc at z=6), the dust remains in majority well-aligned with the bulk stellar component, and we predict the dust continuum should therefore align well with the stellar rest-frame NIR component, less affected by dust attenuation. This study highlights the importance of dust in shaping the appearance of early galaxies at UV wavelengths, even as early as in the Epoch of Reionization.

astro-ph.GA↗

arXiv2025

Line Intensity Mapping Prediction from the Cosmic Dawn (CoDa) III Simulation for H$α$ from Galaxies and the Intergalactic Medium during the Epoch of Reionization

The evolution of large-scale structure, galaxies and the intergalactic medium (IGM) during the Epoch of Reionization (EoR) can be probed by upcoming Line Intensity Mapping (LIM) experiments, which sample in redshift and direction without needing to resolve individual galaxies. We predict the intensity and sources of hydrogen H$α$ emission, dominated by radiative recombination following ionization by UV from the same massive stars that caused reionization, down to redshift 4.6, using the largest fully-coupled, radiation-hydro simulation of galaxy formation and reionization to date, Cosmic Dawn (CoDa) III. We compute the mean intensity and Voxel Intensity Distribution (VID) vs. redshift, including the relative contributions of galaxies and IGM. This will provide mock data to guide and interpret LIM experiments such as NASA's SPHEREx and proposed Cosmic Dawn Intensity Mapper (CDIM).

astro-ph.GA↗

arXiv2024

Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge

As Large Language Models (LLMs) increasingly address domain-specific problems, their application in the financial sector has expanded rapidly. Tasks that are both highly valuable and time-consuming, such as analyzing financial statements, disclosures, and related documents, are now being effectively tackled using LLMs. This paper details the development of a high-performance, finance-specific Retrieval-Augmented Generation (RAG) system for the ACM-ICAIF '24 FinanceRAG competition. We optimized performance through ablation studies on query expansion and corpus refinement during the pre-retrieval phase. To enhance retrieval accuracy, we employed multiple reranker models. Notably, we introduced an efficient method for managing long context sizes during the generation phase, significantly improving response quality without sacrificing performance. We ultimately achieve 2nd place in the FinanceRAG Challenge. Our key contributions include: (1) pre-retrieval ablation analysis, (2) an enhanced retrieval algorithm, and (3) a novel approach for long-context management. This work demonstrates the potential of LLMs in effectively processing and analyzing complex financial data to generate accurate and valuable insights. The source code and further details are available at https://github.com/cv-lee/FinanceRAG.

cs.CL↗

arXiv2024

Dynamic Anchor Selection and Real-Time Pose Prediction for Ultra-wideband Tagless Gate

Ultra-wideband (UWB) is emerging as a promising solution that can realize proximity services, such as UWB tagless gate (UTG), thanks to centimeter-level localization accuracy based on two different ranging methods such as downlink time-difference of arrival (DL-TDoA) and double-sided two-way ranging (DS-TWR). The UTG is a UWB-based proximity service that provides a seamless gate pass system without requiring real-time mobile device (MD) tapping. The location of MD is calculated using DL-TDoA, and the MD communicates with the nearest UTG using DS-TWR to open the gate. Therefore, the knowledge about the exact location of MD is the main challenge of UTG, and hence we provide the solutions for both DL-TDoA and DS-TWR. In this paper, we propose dynamic anchor selection for extremely accurate DL-TDoA localization and pose prediction for DS-TWR, called DynaPose. The pose is defined as the actual location of MD on the human body, which affects the localization accuracy. DynaPose is based on line-of-sight (LOS) and non-LOS (NLOS) classification using deep learning for anchor selection and pose prediction. Deep learning models use the UWB channel impulse response and the inertial measurement unit embedded in the smartphone. DynaPose is implemented on Samsung Galaxy Note20 Ultra and Qorvo UWB board to show the feasibility and applicability. DynaPose achieves a LOS/NLOS classification accuracy of 0.984, 62% higher DL-TDoA localization accuracy, and ultimately detects four different poses with an accuracy of 0.961 in real-time.

eess.SP↗

arXiv2024

Power-Efficient Indoor Localization Using Adaptive Channel-aware Ultra-wideband DL-TDOA

Among the various Ultra-wideband (UWB) ranging methods, the absence of uplink communication or centralized computation makes downlink time-difference-of-arrival (DL-TDOA) localization the most suitable for large-scale industrial deployments. However, temporary or permanent obstacles in the deployment region often lead to non-line-of-sight (NLOS) channel path and signal outage effects, which result in localization errors. Prior research has addressed this problem by increasing the ranging frequency, which leads to a heavy increase in the user device power consumption. It also does not contribute to any increase in localization accuracy under line-of-sight (LOS) conditions. In this paper, we propose and implement a novel low-power channel-aware dynamic frequency DL-TDOA ranging algorithm. It comprises NLOS probability predictor based on a convolutional neural network (CNN), a dynamic ranging frequency control module, and an IMU sensor-based ranging filter. Based on the conducted experiments, we show that the proposed algorithm achieves 50% higher accuracy in NLOS conditions while having 46% lower power consumption in LOS conditions compared to baseline methods from prior research.

eess.SP↗

arXiv2024

Multiple Beads-on-a-string: Dark Matter-Deficient Galaxy Formation in a Mini-bullet Satellite-satellite Galaxy Collision

Dark matter-deficient galaxies (DMDGs) discovered in the survey of ultra-diffuse galaxies (UDGs), in apparent conflict with standard CDM, may be produced by high-velocity galaxy-galaxy collisions, the $\textit{Mini-bullet}$ scenario. Recent observations of an aligned trail of $7-11$ UDGs near NGC1052, including DMDGs DF2 and DF4, suggesting a common formation event, $\sim8.9\pm1.5$ Gyr ago, provide a test. Hydro/N-body simulations, supplemented by galaxy orbit integrations, demonstrate that satellite-satellite collisions outside the host-galaxy virial radius can reproduce the observed UDGs in the NGC1052 group. A trail of $\sim10$ DMDGs is shown to form, including two massive ones that replicate the observed motions of DF2 and DF4. The linear relation, $v=Ax+v_{0}$, conjectured previously to relate positions ($x$) and velocities ($v$) of the aligned DMDGs as a signature of the collision event, is approximately obeyed, but individual DMDGs can deviate significantly from it. The progenitors whose collision spawned the trail of DMDGs survive the collision without, themselves, becoming DMDGs. We predict one progenitor is located at the end of the trail, testable by observing the difference between its stars, formed pre-collision, from those of the DMDGs, formed post-collision. By contrast, stellar ages and metallicities of the DMDGs are nearly identical. We further offer a hint that the tidal field of host NGC1052 may contribute to making DMDGs diffuse. $Λ$CDM simulation in a 100 cMpc box finds our required initial conditions $\sim10$ times at $z<3$. These results indicate current observations are consistent with the $\textit{Mini-bullet}$ scenario.

astro-ph.GA↗

arXiv2021

Dark Matter Deficient Galaxies And Their Member Star Clusters Form Simultaneously During High-velocity Galaxy Collisions In 1.25 pc Resolution Simulations

How diffuse dwarf galaxies that are deficient in dark matter -- such as NGC1052-DF2 and NGC1052-DF4 -- formed remains a mystery. Along with their luminous member globular clusters (GCs), the so-called dark matter deficient galaxies (DMDGs) have challenged observers and theorists alike. Here we report a suite of galaxy collision simulations using the adaptive mesh refinement code ENZO with 1.25 pc resolution, which demonstrates that high-velocity galaxy collisions induce the formation of DMDGs and their star clusters (SCs) simultaneously. With a numerical resolution significantly better than our previous study (Shin et al. 2020), we resolve the dynamical structure of the produced DMDGs and the detailed formation history of their SCs which are possible progenitors of the DMDG's member GCs. In particular, we show that a galaxy collision with a high relative velocity of $\sim 300\;{\rm km\;s}^{-1}$, invoking severe shock compression, spawns multiple massive SCs ($\gtrsim 10^{6}\;{\rm M}_{\odot}$) within 150 Myr after the collision. At the end of our ~ 800 Myr fiducial run, the resulting DMDG of $M_{\star} \simeq 3.5\times 10^{8}\;{M}_{\odot}$ hosts 10 luminous ($M_{\rm V} \lesssim -8.5\;{\rm mag}$), gravitational bound SCs with a line-of-sight velocity dispersion $11.2\;{\rm km\;s}^{-1}$. Our study suggests that DMDGs and their luminous member SCs could form simultaneously in high-velocity galaxy collisions while being in line with the key observed properties of NGC1052-DF2 and NGC1052-DF4.

astro-ph.GA↗

arXiv2020

A Faster FPTAS for Knapsack Problem With Cardinality Constraint

We study the $K$-item knapsack problem (i.e., $1.5$-dimensional KP), which is a generalization of the famous 0-1 knapsack problem (i.e., $1$-dimensional KP) in which an upper bound $K$ is imposed on the number of items selected. This problem is of fundamental importance and is known to have a broad range of applications in various fields. It is well known that, there is no FPTAS for the $d$-dimensional knapsack problem when $d\geq 2$, unless P $=$ NP. While the $K$-item knapsack problem is known to admit an FPTAS, the complexity of all existing FPTASs have a high dependency on the cardinality bound $K$ and approximation error $\varepsilon$, which could result in inefficiencies especially when $K$ and $\varepsilon^{-1}$ increase. The current best results are due to Mastrolilli and Hutter (2006), in which two schemes are presented exhibiting a space-time tradeoff--one scheme with time complexity $O(n+Kz^{2}/\varepsilon^{2})$ and space complexity $O(n+z^{3}/\varepsilon)$, while another scheme requires $O(n+(Kz^{2}+z^{4})/\varepsilon^{2})$ run-time but only needs $O(n+z^{2}/\varepsilon)$ space, where $z=\min\{K,1/\varepsilon\}$. In this paper we close the space-time tradeoff exhibited in the state-of-the-art by designing a new FPTAS with a run-time of $\widetilde{O}(n+z^{2}/\varepsilon^{2})$, while simultaneously reaching the $O(n+z^{2}/\varepsilon)$ space bound. Our scheme provides $\widetilde{O}(K)$ and $O(z)$ improvements on the state-of-the-art algorithms in time and space complexity respectively, and is the first scheme that achieves a run-time that is independent of cardinality bound $K$ (up to logarithmic factors) under fixed $\varepsilon$. Another salient feature of our scheme is that it is the first FPTAS that achieves better time and space complexity bounds than the very first standard FPTAS over all parameter regimes.

cs.DS↗

arXiv2020

Dark Matter Deficient Galaxies Produced Via High-velocity Galaxy Collisions In High-resolution Numerical Simulations

The recent discovery of diffuse dwarf galaxies that are deficient in dark matter appears to challenge the current paradigm of structure formation in our Universe. We describe the numerical experiments to determine if the so-called dark matter deficient galaxies (DMDGs) could be produced when two gas-rich, dwarf-sized galaxies collide with a high relative velocity of $\sim 300\,{\rm kms^{-1}}$. Using idealized high-resolution simulations with both mesh-based and particle-based gravito-hydrodynamics codes, we find that DMDGs can form as high-velocity galaxy collisions separate dark matter from the warm disk gas which subsequently is compressed by shock and tidal interaction to form stars. Then using a large simulated universe IllustrisTNG, we discover a number of high-velocity galaxy collision events in which DMDGs are expected to form. However, we did not find evidence that these types of collisions actually produced DMDGs in the TNG100-1 run. We argue that the resolution of the numerical experiment is critical to realize the "collision-induced" DMDG formation scenario. Our results demonstrate one of many routes in which galaxies could form with unconventional dark matter fractions.

astro-ph.GA↗

arXiv2020

Q-greedyUCB: a New Exploration Policy for Adaptive and Resource-efficient Scheduling

This paper proposes a learning algorithm to find a scheduling policy that achieves an optimal delay-power trade-off in communication systems. Reinforcement learning (RL) is used to minimize the expected latency for a given energy constraint where the environments such as traffic arrival rates or channel conditions can change over time. For this purpose, this problem is formulated as an infinite-horizon Markov Decision Process (MDP) with constraints. To handle the constrained optimization problem, we adopt the Lagrangian relaxation technique to solve it. Then, we propose a variant of Q-learning, Q-greedyUCB that combines Q-learning for \emph{average} reward algorithm and Upper Confidence Bound (UCB) policy to solve this decision-making problem. We prove that the Q-greedyUCB algorithm is convergent through mathematical analysis. Simulation results show that Q-greedyUCB finds an optimal scheduling strategy, and is more efficient than Q-learning with the $\varepsilon$-greedy and Average-payoff RL algorithm in terms of the cumulative reward (i.e., the weighted sum of delay and energy) and the convergence speed. We also show that our algorithm can reduce the regret by up to 12% compared to the Q-learning with the $\varepsilon$-greedy and Average-payoff RL algorithm.

eess.SY↗

arXiv2019

An Attention-Based Speaker Naming Method for Online Adaptation in Non-Fixed Scenarios

A speaker naming task, which finds and identifies the active speaker in a certain movie or drama scene, is crucial for dealing with high-level video analysis applications such as automatic subtitle labeling and video summarization. Modern approaches have usually exploited biometric features with a gradient-based method instead of rule-based algorithms. In a certain situation, however, a naive gradient-based method does not work efficiently. For example, when new characters are added to the target identification list, the neural network needs to be frequently retrained to identify new people and it causes delays in model preparation. In this paper, we present an attention-based method which reduces the model setup time by updating the newly added data via online adaptation without a gradient update process. We comparatively analyzed with three evaluation metrics(accuracy, memory usage, setup time) of the attention-based method and existing gradient-based methods under various controlled settings of speaker naming. Also, we applied existing speaker naming models and the attention-based model to real video to prove that our approach shows comparable accuracy to the existing state-of-the-art models and even higher accuracy in some cases.

cs.MM↗

arXiv2019

Delay-Optimal and Energy-Efficient Communications with Markovian Arrivals

In this paper, delay-optimal and energy-efficient communication is studied for a single link under Markov random arrivals. We present the optimal tradeoff between delay and power over Additive White Gaussian Noise (AWGN) channels and extend the optimal tradeoff for block fading channels. Under time-correlated traffic arrivals, we develop a cross-layer solution that jointly considers the arrival rate, the queue length, and the channel state in order to minimize the average delay subject to a power constraint. For this purpose, we formulate the average delay and power problem as a Constrained Markov Decision Process (CMDP). Based on steady-state analysis for the CMDP, a Linear Programming (LP) problem is formulated to obtain the optimal delay-power tradeoff. We further show the optimal transmission strategy using a Lagrangian relaxation technique. Specifically, the optimal adaptive transmission is shown to have a threshold type of structure, where the thresholds on the queue length are presented for different transmission rates under the given arrival rates and channel states. By exploiting the result, we develop a threshold-based algorithm to efficiently obtain the optimal delay-power tradeoff. We show how a trajectory-sampling version of the proposed algorithm can be developed without the prior need of arrival statistics.

cs.IT↗

arXiv2017

A Change-Detection based Framework for Piecewise-stationary Multi-Armed Bandit Problem

The multi-armed bandit problem has been extensively studied under the stationary assumption. However in reality, this assumption often does not hold because the distributions of rewards themselves may change over time. In this paper, we propose a change-detection (CD) based framework for multi-armed bandit problems under the piecewise-stationary setting, and study a class of change-detection based UCB (Upper Confidence Bound) policies, CD-UCB, that actively detects change points and restarts the UCB indices. We then develop CUSUM-UCB and PHT-UCB, that belong to the CD-UCB class and use cumulative sum (CUSUM) and Page-Hinkley Test (PHT) to detect changes. We show that CUSUM-UCB obtains the best known regret upper bound under mild assumptions. We also demonstrate the regret reduction of the CD-UCB policies over arbitrary Bernoulli rewards and Yahoo! datasets of webpage click-through rates.

cs.LG↗

arXiv2017

Delay-Optimal Probabilistic Scheduling with Arbitrary Arrival and Adaptive Transmission

In this paper, we aim to obtain the optimal delay-power tradeoff and the corresponding optimal scheduling policy for an arbitrary i.i.d. arrival process and adaptive transmissions. The number of backlogged packets at the transmitter is known to a scheduler, who has to determine how many backlogged packets to transmit during each time slot. The power consumption is assumed to be convex in transmission rates. Hence, if the scheduler transmits faster, the delay will be reduced but with higher power consumption. To obtain the optimal delay-power tradeoff and the corresponding optimal policy, we model the problem as a Constrained Markov Decision Process (CMDP), where we minimize the average delay given an average power constraint. By steady-state analysis and Lagrangian relaxation, we can show that the optimal tradeoff curve is decreasing, convex, and piecewise linear, and the optimal policy is threshold-based. Based on the revealed properties of the optimal policy, we develop an algorithm to efficiently obtain the optimal tradeoff curve and the optimal policy with full information of the system. The complexity of our proposed algorithm is much lower than a general algorithm based on Linear Programming. However, usually the distribution of the arrival process is unknown to the scheduler, therefore we proposed a reinforcement learning algorithm to efficiently obtain the optimal policy under this circumstance. We also analyse in details about how the system parameters affect the optimal policy and the system performance. In the final, we use simulations to validate the derived results and the proposed algorithms.

cs.IT↗

arXiv2016

Delay-Optimal Buffer-Aware Scheduling with Adaptive Transmission

In this work, we aim to obtain the optimal tradeoff between the average delay and the average power consumption in a communication system. In our system, the arrivals occur at each timeslot according to a Bernoulli arrival process and are buffered at the transmitter. The transmitter determines the scheduling policy of how many packets to transmit under an average power constraint. The power is assumed to be an increasing and convex function of the number of packets transmitted in each timeslot to capture the realism in communication systems. We also consider a finite buffer and allow the scheduling decision to depend on the buffer occupancy. This problem is modelled as a Constrained Markov Decision Process (CMDP). We first prove that the optimal policy of the (Lagrangian) relaxation of the CMDP is deterministic and threshold-based. We then show that the optimal delay-power tradeoff curve is convex and piecewise linear, where each of the vertices are obtained by the optimal solution to the relaxed problem. This allows us to show the optimal policies of the CMDP are threshold-based, and hence can be implemented by a proposed efficient algorithm. The theoretical results and the algorithm are validated by Linear Programming and simulations.

cs.IT↗