arXiv subjects

Nguyen Phan

Publications and source records attributed to Nguyen Phan.

6 recordsLinked to original sources

arXiv2026

Interactive Exploration of Large-scale Streamlines of Vector Fields via a Curve Segment Neighborhood Graph

Streamlines have been widely used to represent and analyze various steady vector fields. To sufficiently represent important features in complex vector fields (like flow), a large number of streamlines are required. Due to the lack of a rigorous definition of features or patterns in streamlines, user interaction and exploration are required to achieve effective interpretation. Existing approaches based on clustering or pattern search, while valuable for specific analysis tasks, often face challenges in supporting interactive and level-of-detail exploration of large-scale curve-based data, particularly when real-time parameter adjustment and iterative refinement are needed. To address this, we design and implement an interactive web-based system. Our system utilizes a Curve Segment Neighborhood Graph (CSNG) to encode the neighboring relationships between curve segments. CSNG enables us to adapt a fast community detection algorithm to identify coherent flow structures and spatial groupings in the streamlines interactively. CSNG also supports a multi-level exploration through an enhanced force-directed layout. Furthermore, our system integrates an adjacency matrix representation to reveal detailed inter-relations among segments. To achieve real-time performance within a web browser, our system employs matrix compression for memory-efficient CSNG storage and parallel processing. We have applied our system to analyze and interpret complex patterns in several streamline datasets. Our experiments show that we achieve real-time performance on datasets with hundreds of thousands of segments.

cs.CG↗

arXiv2025

Evaluate Neighbor Search for Curve-based Vector Field Processing

Curve-based representations, particularly integral curves, are often used to represent large-scale computational fluid dynamic simulations. Processing and analyzing curve-based vector field data sets often involves searching for neighboring segments given a query point or curve segment. However, because the original flow behavior may not be fully represented by the set of integral curves and the input integral curves may not be evenly distributed in space, popular neighbor search strategies often return skewed and redundant neighboring segments. Yet, there is a lack of systematic and comprehensive research on how different configurations of neighboring segments returned by specific neighbor search strategies affect subsequent tasks. To fill this gap, this study evaluates the performance of two popular neighbor search strategies combined with different distance metrics on a point-based vector field reconstruction task and a segment saliency estimation using input integral curves. A large number of reconstruction tests and saliency calculations are conducted for the study. To characterize the configurations of neighboring segments for an effective comparison of different search strategies, a number of measures, like average neighbor distance and uniformity, are proposed. Our study leads to a few observations that partially confirm our expectations about the ideal configurations of a neighborhood while revealing additional findings that were overlooked by the community.

cs.GR↗

arXiv2024

Curve Segment Neighborhood-based Vector Field Exploration

Integral curves have been widely used to represent and analyze various vector fields. In this paper, we propose a Curve Segment Neighborhood Graph (CSNG) to capture the relationships between neighboring curve segments. This graph representation enables us to adapt the fast community detection algorithm, i.e., the Louvain algorithm, to identify individual graph communities from CSNG. Our results show that these communities often correspond to the features of the flow. To achieve a multi-level interactive exploration of the detected communities, we adapt a force-directed layout that allows users to refine and re-group communities based on their domain knowledge. We incorporate the proposed techniques into an interactive system to enable effective analysis and interpretation of complex patterns in large-scale integral curve datasets.

cs.SI↗

arXiv2023

ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development

Existing medical text datasets usually take the form of question and answer pairs that support the task of natural language generation, but lacking the composite annotations of the medical terms. In this study, we publish a Vietnamese dataset of medical questions from patients with sentence-level and entity-level annotations for the Intent Classification and Named Entity Recognition tasks. The tag sets for two tasks are in medical domain and can facilitate the development of task-oriented healthcare chatbots with better comprehension of queries from patients. We train baseline models for the two tasks and propose a simple self-supervised training strategy with span-noise modelling that substantially improves the performance. Dataset and code will be published at https://github.com/tadeephuy/ViMQ

cs.CL↗

arXiv2015

Reducing DRIFT Backgrounds with a Submicron Aluminized-Mylar Cathode

Background events in the DRIFT-IId dark matter detector, mimicking potential WIMP signals, are predominantly caused by alpha decays on the central cathode in which the alpha particle is completely or partially absorbed by the cathode material. We installed a 0.9 micron thick aluminized-mylar cathode as a way to reduce the probability of producing these backgrounds. We study three generations of cathode (wire, thin-film, and radiologically clean thin-film) with a focus on the ratio of background events to alpha decays. Two independent methods of measuring the absolute alpha decay rate are used to ensure an accurate result, and agree to within $10\%$. Using alpha range spectroscopy, we measure the radiologically cleanest cathode version to have a contamination of $3.3\pm0.1$ ppt $^{234}$U and $73\pm2$ ppb $^{238}$U. This cathode reduces the probability of producing an RPR from an alpha decay by a factor of $70\pm20$ compared to the original stainless steel wire cathode. First results are presented from a texturized version of the cathode, intended to be even more transparent to alpha particles. These efforts, along with other background reduction measures, have resulted in a drop in the observed background rate from 500/day to 1/day. With the recent implementation of full-volume fiducialization, these remaining background events are identified, allowing for background-free operation.

physics.ins-det↗

arXiv2014

Background Assay and Rejection in DRIFT

The DRIFT-IId dark matter detector is a m$^3$-scale low-pressure TPC with directional sensitivity to WIMP-induced nuclear recoils. Its primary backgrounds were due to alpha decays from contamination on the central cathode. Efforts to reduce these backgrounds led to replacing the 20 μm wire central cathode with one constructed from 0.9 μm aluminized mylar, which is almost totally transparent to alpha particles. Detailed modeling of the nature and origin of the remaining backgrounds led to an in-situ, ppt-sensitive assay of alpha decay backgrounds from the central cathode. This led to further improvements in the thin-film cathode resulting in over 2 orders of magnitude reduction in backgrounds compared to the wire cathode. Finally, the addition of O$_2$ to CS$_2$ gas was found to produce multiple species of electronegative charge carriers, providing a method to determine the absolute position of nuclear recoils and reject all known remaining backgrounds while retaining a high efficiency for nuclear recoil detection.

physics.ins-det↗