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If the dark matter halos of galaxies contain large numbers of subhalos as predicted by the $\Lambda$CDM model, these subhalos are expected to appear in strong galaxy--galaxy lens systems as small--scale perturbations in individual images.…
Vision-Language-Action (VLA) models built on pretrained Vision-Language Models (VLMs) show strong potential but are limited in practicality due to their large parameter counts. To mitigate this issue, using a lightweight VLM has been…
Nowadays medium-large size astronomical projects have to face the management of a large amount of information and data. Dedicated data centres manage the collection of raw and processed data and consequently make them accessible, typically…
ALMA is revolutionizing the way we study and understand the astrophysics of galaxies, both as a whole and individually. By exploiting its unique sensitivity and resolution to make spatially and spectrally resolved images of the gas and dust…
A fast, efficient and comprehensive monitoring system is a vital part of any HEP experiment. This paper describes the software framework that will be used during ATLAS data taking to monitor the state of the data acquisition and the quality…
We present the first data release of the ALMA-IMF Large Program, which covers the 12m-array continuum calibration and imaging. The ALMA-IMF Large Program is a survey of fifteen dense molecular cloud regions spanning a range of evolutionary…
We have developed a Java-based teaching tool, "Appreciating Hubble at Hyper-speed" ($\textit{AHaH}$), intended for use by students and instructors in beginning astronomy and cosmology courses, which we have made available online. This tool…
We present TinyLlama, a compact 1.1B language model pretrained on around 1 trillion tokens for approximately 3 epochs. Building on the architecture and tokenizer of Llama 2, TinyLlama leverages various advances contributed by the…
We present the various science cases for building Band 1 receivers as part of ALMA's ongoing Development Program. We describe the new frequency range for Band 1 of 35-52 GHz, a range chosen to maximize the receiver suite's scientific…
General-purpose LLMs pose misinformation risks for development and policy experts, lacking epistemic humility for verifiable outputs. We present AVA (AI + Verified Analysis), a GenAI platform built on a curated library of over 4,000 World…
Computer-using agents (CUAs) must plan task workflows across diverse and evolving applications, yet progress is limited by the lack of large-scale, high-quality training data. Existing datasets are narrow, static, and costly to annotate,…
Test-Time Optimization enables models to adapt to new data during inference by updating parameters on-the-fly. Recent advances in Vision-Language Models (VLMs) have explored learning prompts at test time to improve performance in downstream…
Stream-based runtime monitors are used in safety-critical applications such as Unmanned Aerial Systems (UAS) to compute comprehensive statistics and logical assessments of system health that provide the human operator with critical…
The vast portion of workloads employing LLMs involves answering questions grounded on PDF or scan content. We introduce the Arctic-TILT achieving accuracy on par with models 1000$\times$ its size on these use cases. It can be fine-tuned and…
TOPCAT is a desktop application for interactive analysis of tabular data, especially source catalogues. Along with its command-line counterpart STILTS, it has been under more or less continuous development for the past 15 years and is now…
Large Language Models (LLMs) exhibit strong capabilities in text processing, and recent research has augmented SQL and DataFrame with LLM-powered semantic operators for data analysis. However, LLM-based data processing is hindered by slower…
The ALMACAL project leverages ALMA maps of calibrator-centered fields to conduct deep mm/sub-mm surveys, enabling the detection of extragalactic sources with flux densities orders of magnitude fainter than achievable with other instruments.…
Graphical User Interface (GUI) agents offer cross-platform solutions for automating complex digital tasks, with significant potential to transform productivity workflows. However, their performance is often constrained by the scarcity of…
We propose a novel framework that leverages Visual Question Answering (VQA) models to automate the evaluation of LLM-generated data visualizations. Traditional evaluation methods often rely on human judgment, which is costly and unscalable,…
We present the Virtual Research Assistant (VRA) of the ATLAS sky survey which performs preliminary eyeballing on our clean transient data stream. The VRA uses Histogram Based Gradient Boosted Decision Tree Classifiers trained on real data…