arXiv subjects

Julia Chen

Publications and source records attributed to Julia Chen.

4 recordsLinked to original sources

arXiv2026

The 2026 Singapore Consensus on Global AI Safety Research Priorities

Frontier AI capabilities and autonomy are advancing rapidly. A growing number of real-world incidents make a trusted AI ecosystem essential to embracing AI with confidence. The 2026 Singapore Consensus is an outcome of the second International Scientific Exchange on AI Safety, bringing together over 100 contributors spanning 13 countries from frontier developers, government safety institutes, academia, and civil society. Building on the 2025 report, it presents a global understanding of technical AI safety research problems of top priority, now with a dedicated focus on societal resilience and on managing the risks of increasingly autonomous AI agents.

cs.CY↗

arXiv2024

Semi-supervised variational autoencoder for cell feature extraction in multiplexed immunofluorescence images

Advancements in digital imaging technologies have sparked increased interest in using multiplexed immunofluorescence (mIF) images to visualise and identify the interactions between specific immunophenotypes with the tumour microenvironment at the cellular level. Current state-of-the-art multiplexed immunofluorescence image analysis pipelines depend on cell feature representations characterised by morphological and stain intensity-based metrics generated using simple statistical and machine learning-based tools. However, these methods are not capable of generating complex representations of cells. We propose a deep learning-based cell feature extraction model using a variational autoencoder with supervision using a latent subspace to extract cell features in mIF images. We perform cell phenotype classification using a cohort of more than 44,000 multiplexed immunofluorescence cell image patches extracted across 1,093 tissue microarray cores of breast cancer patients, to demonstrate the success of our model against current and alternative methods.

eess.IV↗

arXiv2024

Multi-User Chat Assistant (MUCA): a Framework Using LLMs to Facilitate Group Conversations

Recent advancements in large language models (LLMs) have provided a new avenue for chatbot development. Most existing research, however, has primarily centered on single-user chatbots that determine "What" to answer. This paper highlights the complexity of multi-user chatbots, introducing the 3W design dimensions: "What" to say, "When" to respond, and "Who" to answer. Additionally, we proposed Multi-User Chat Assistant (MUCA), an LLM-based framework tailored for group discussions. MUCA consists of three main modules: Sub-topic Generator, Dialog Analyzer, and Conversational Strategies Arbitrator. These modules jointly determine suitable response contents, timings, and appropriate addressees. This paper further proposes an LLM-based Multi-User Simulator (MUS) to ease MUCA's optimization, enabling faster simulation of conversations between the chatbot and simulated users, and speeding up MUCA's early development. In goal-oriented conversations with a small to medium number of participants, MUCA demonstrates effectiveness in tasks like chiming in at appropriate timings, generating relevant content, and improving user engagement, as shown by case studies and user studies.

cs.CL↗

arXiv2008

Starburst99 for Windows

We describe a Windows compatible version of the evolutionary synthesis code Starburst99. Starburst99 for Windows was developed from the public UNIX based version at STScI. We converted the original Fortran77 source code into a version for a Win32 environment with an Absoft Fortran Pro x86 compiler. Extensive testing showed no significant numerical differences in comparison with the previous UNIX version. The software application consists of the source code, executable, and a number of auxiliary files. The package installs on any PC running Windows 2000, XP, or Vista and can be obtained as freeware at http://www.stsci.edu/science/starburst/PCStarburst99.html. We give an overview of the different running modes and provide instructions for getting started with the initial set-up.

astro-ph↗