arXiv subjects

Jeongmin Park

Publications and source records attributed to Jeongmin Park.

7 recordsLinked to original sources

arXiv2026

SpokenUS: A Spoken User Simulator for Task-Oriented Dialogue

Robust voice agents require exposure to the full diversity of how people interact through speech. However, obtaining enough spoken interactions is prohibitively expensive. Building spoken user simulators that address this requires large-scale spoken task-oriented dialogue (TOD) data encompassing spoken user behaviors, yet existing datasets are limited in scale and domain coverage, with no systematic pipeline for augmenting them. To address this, we introduce SpokenTOD, a spoken TOD dataset of 52,390 dialogues and 1,034 hours of speech augmented with four spoken user behaviors---cross-turn slots, barge-in, disfluency, and emotional prosody---across diverse speakers and domains. Building on SpokenTOD, we present SpokenUS, a spoken user simulator grounded in TOD that decides when to speak through a dedicated turn-taking head. SpokenUS achieves comparable goal coverage to much larger models while substantially outperforming all baselines in human MOS, disclosing slot values gradually across the dialogue as humans do rather than front-loading them. Further analysis confirms that SpokenUS's spoken behaviors pose meaningful challenges to voice agents, making it a practical tool for evaluating more robust spoken dialogue systems. Our code is available at https://github.com/holi-lab/SpokenUS.

cs.CL↗

arXiv2025

Bayesian Bootstrap based Gaussian Copula Model for Mixed Data with High Missing Rates

Missing data is a common issue in various fields such as medicine, social sciences, and natural sciences, and it poses significant challenges for accurate statistical analysis. Although numerous imputation methods have been proposed to address this issue, many of them fail to adequately capture the complex dependency structure among variables. To overcome this limitation, models based on the Gaussian copula framework have been introduced. However, most existing copula-based approaches do not account for the uncertainty in the marginal distributions, which can lead to biased marginal estimates and degraded performance, especially under high missingness rates. In this study, we propose a Bayesian bootstrap-based Gaussian Copula model (BBGC) that explicitly incorporates uncertainty in the marginal distributions of each variable. The proposed BBGC combines the flexible dependency modeling capability of the Gaussian copula with the Bayesian uncertainty quantification of marginal cumulative distribution functions (CDFs) via the Bayesian bootstrap. Furthermore, it is extended to handle mixed data types by incorporating methods for ordinal variable modeling. Through simulation studies and experiments on real-world datasets from the UCI repository, we demonstrate that the proposed BBGC outperforms existing imputation methods across various missing rates and mechanisms (MCAR, MAR). Additionally, the proposed model shows superior performance on real semiconductor manufacturing process data compared to conventional imputation approaches.

stat.ME↗

arXiv2023

CODAG: Characterizing and Optimizing Decompression Algorithms for GPUs

Data compression and decompression have become vital components of big-data applications to manage the exponential growth in the amount of data collected and stored. Furthermore, big-data applications have increasingly adopted GPUs due to their high compute throughput and memory bandwidth. Prior works presume that decompression is memory-bound and have dedicated most of the GPU's threads to data movement and adopted complex software techniques to hide memory latency for reading compressed data and writing uncompressed data. This paper shows that these techniques lead to poor GPU resource utilization as most threads end up waiting for the few decoding threads, exposing compute and synchronization latencies. Based on this observation, we propose CODAG, a novel and simple kernel architecture for high throughput decompression on GPUs. CODAG eliminates the use of specialized groups of threads, frees up compute resources to increase the number of parallel decompression streams, and leverages the ample compute activities and the GPU's hardware scheduler to tolerate synchronization, compute, and memory latencies. Furthermore, CODAG provides a framework for users to easily incorporate new decompression algorithms without being burdened with implementing complex optimizations to hide memory latency. We validate our proposed architecture with three different encoding techniques, RLE v1, RLE v2, and Deflate, and a wide range of large datasets from different domains. We show that CODAG provides 13.46x, 5.69x, and 1.18x speed up for RLE v1, RLE v2, and Deflate, respectively, when compared to the state-of-the-art decompressors from NVIDIA RAPIDS.

cs.DC↗

arXiv2023

GPU-Initiated On-Demand High-Throughput Storage Access in the BaM System Architecture

Graphics Processing Units (GPUs) have traditionally relied on the host CPU to initiate access to the data storage. This approach is well-suited for GPU applications with known data access patterns that enable partitioning of their dataset to be processed in a pipelined fashion in the GPU. However, emerging applications such as graph and data analytics, recommender systems, or graph neural networks, require fine-grained, data-dependent access to storage. CPU initiation of storage access is unsuitable for these applications due to high CPU-GPU synchronization overheads, I/O traffic amplification, and long CPU processing latencies. GPU-initiated storage removes these overheads from the storage control path and, thus, can potentially support these applications at much higher speed. However, there is a lack of systems architecture and software stack that enable efficient GPU-initiated storage access. This work presents a novel system architecture, BaM, that fills this gap. BaM features a fine-grained software cache to coalesce data storage requests while minimizing I/O traffic amplification. This software cache communicates with the storage system via high-throughput queues that enable the massive number of concurrent threads in modern GPUs to make I/O requests at a high rate to fully utilize the storage devices and the system interconnect. Experimental results show that BaM delivers 1.0x and 1.49x end-to-end speed up for BFS and CC graph analytics benchmarks while reducing hardware costs by up to 21.7x over accessing the graph data from the host memory. Furthermore, BaM speeds up data-analytics workloads by 5.3x over CPU-initiated storage access on the same hardware.

cs.DC↗

arXiv2019

Synergetic Behavior in 2D Layered Material/Complex Oxide Heterostructures

The marriage between a two-dimensional layered material (2DLM) and a complex transition metal oxide (TMO) results in a variety of physical and chemical phenomena that would not have been achieved in either material alone. Interesting recent discoveries in systems such as graphene/SrTiO3, graphene/LaAlO3/SrTiO3, graphene/ferroelectric oxide, MoS2/SrTiO3, and FeSe/SrTiO3 heterostructures include voltage scaling in field-effect transistors, charge state coupling across an interface, quantum conductance probing of the electrochemical activity, novel memory functions based on charge traps, and greatly enhanced superconductivity. In this progress report, we review various properties and functionalities appearing in numerous different 2DLM/TMO heterostructure systems. The results imply that the multidimensional heterostructure approach based on the disparate material systems leads to an entirely new platform for the study of condensed matter physics and materials science. The heterostructures are also highly relevant technologically, as each constituent material is a promising candidate for next-generation opto-electronic devices.

cond-mat.mtrl-sci↗

arXiv2017

Quantum Conductance Probing of Oxygen Vacancies in SrTiO3 Epitaxial Thin Film Using Graphene

The quantum Hall conductance in monolayer graphene on an epitaxial SrTiO3 (STO) thin film is studied to understand the role of oxygen vacancies in determining the dielectric properties of STO. As the gate voltage sweep range is gradually increased in our device, we observe systematic generation and annihilation of oxygen vacancies evidenced from the hysteretic conductance behavior in graphene. Furthermore, based on the experimentally observed linear scaling relation between the effective capacitance and the voltage sweep range, a simple model is constructed to manifest the relationship among the dielectric properties of STO with oxygen vacancies. The inherent quantum Hall conductance in graphene can be considered as a sensitive, robust, and non-invasive probe for understanding the electronic and ionic phenomena in complex transition metal oxides without impairing the oxide layer underneath.

cond-mat.mtrl-sci↗

arXiv2016

Voltage Scaling of Graphene Device on SrTiO3 Epitaxial Thin Film

Electrical transport in monolayer graphene on SrTiO3 (STO) thin film is examined in order to promote gate-voltage scaling using a high-k dielectric material. The atomically flat surface of thin STO layer epitaxially grown on Nb-doped STO single-crystal substrate offers good adhesion between the high-k film and graphene, resulting in nonhysteretic conductance as a function of gate voltage at all temperatures down to 2 K. The two-terminal conductance quantization under magnetic fields corresponding to quantum Hall states survives up to 200 K at a magnetic field of 14 T. In addition, the substantial shift of charge neutrality point in graphene seems to correlate with the temperature-dependent dielectric constant of the STO thin film, and its effective dielectric properties could be deduced from the universality of quantum phenomena in graphene. Our experimental data prove that the operating voltage reduction can be successfully realized due to the underlying high-k STO thin film, without any noticeable degradation of graphene device performance.

cond-mat.mtrl-sci↗