skip to main content


Search for: All records

Creators/Authors contains: "Kwon, E."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Gero, JS (Ed.)
    Recent developments in using Large Language Models (LLMs) to predict and align with neural representations of language can be applied to achieving a future vision of design tools that enable detection and reconstruction of designers’ mental representations of ideas. Prior work has largely explored this relationship during passive language tasks only, e.g., reading or listening. In this work, the relationship between brain activation data (functional imaging, fMRI) during appropriate and novel word association generation and LLM (Llama-2 7b) word representations is tested using Representational Similarity Analysis (RSA). Findings suggest that LLM word representations align with brain activity captured during novel word association, but not when forming appropriate associates. Association formation is one cognitive process central to design. By demonstrating that brain activity during this task can align with LLM word representations, insights from this work encourage further investigation into this relationship during more complex design ideation processes. 
    more » « less
    Free, publicly-accessible full text available September 28, 2025
  2. Free, publicly-accessible full text available February 19, 2025
  3. Abstract

    Neutrinos from very nearby supernovae, such as Betelgeuse, are expected to generate more than ten million events over 10 s in Super-Kamokande (SK). At such large event rates, the buffers of the SK analog-to-digital conversion board (QBEE) will overflow, causing random loss of data that are critical for understanding the dynamics of the supernova explosion mechanism. In order to solve this problem, two new data-acquisition (DAQ) modules were developed to aid in the observation of very nearby supernovae. The first of these, the SN module, is designed to save only the number of hit photomultiplier tubes during a supernova burst and the second, the Veto module, prescales the high-rate neutrino events to prevent the QBEE from overflowing based on information from the SN module. In the event of a very nearby supernova, these modules allow SK to reconstruct the time evolution of the neutrino event rate from beginning to end using both QBEE and SN module data. This paper presents the development and testing of these modules together with an analysis of supernova-like data generated with a flashing laser diode. We demonstrate that the Veto module successfully prevents DAQ overflows for Betelgeuse-like supernovae as well as the long-term stability of the new modules. During normal running the Veto module is found to issue DAQ vetos a few times per month resulting in a total dead-time less than 1 ms, and does not influence ordinary operations. Additionally, using simulation data we find that supernovae closer than 800 pc will trigger the Veto module, resulting in a prescaling of the observed neutrino data.

     
    more » « less