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The HIFI data processing pipeline was developed to systematically process diagnostic, calibration and astronomical observations taken with the HIFI science instrumentas part of the Herschel mission. The HIFI pipeline processed data from all…
Multi-modal large language models (MLLMs) have rapidly advanced in visual tasks, yet their spatial understanding remains limited to single images, leaving them ill-suited for physical-world applications that require multi-frame reasoning.…
Many scientific areas are faced with the challenge of extracting information from large, complex, and highly structured data sets. A great deal of modern statistical work focuses on developing tools for handling such data. This paper…
Developing efficient parallel applications is critical to advancing scientific development but requires significant performance analysis and optimization. Performance analysis tools help developers manage the increasing complexity and scale…
Explaining complex or seemingly simple machine learning models is an important practical problem. We want to explain individual predictions from a complex machine learning model by learning simple, interpretable explanations. Shapley values…
The medical adoption of NLP tools requires interpretability by end users, yet traditional explainable AI (XAI) methods are misaligned with clinical reasoning and lack clinician input. We introduce CHiRPE (Clinical High-Risk Prediction with…
Feature selection of high-dimensional labeled data with limited observations is critical for making powerful predictive modeling accessible, scalable, and interpretable for domain experts. Spectroscopy data, which records the interaction…
Background:Technical systems are growing in complexity with more components and functions across various disciplines. Model-Driven Engineering (MDE) helps manage this complexity by using models as key artifacts. Domain-Specific Languages…
Analyzing large-scale scientific datasets presents substantial challenges due to their sheer volume, structural complexity, and the need for specialized domain knowledge. Automation tools, such as PandasAI, typically require full data…
The amount of multidimensional data published on the semantic web (SW) is constantly increasing, due to initiatives such as Open Data and Open Government Data, among other ones. Models, languages, and tools, that allow to obtain valuable…
Spatial dataflow architectures like the Cerebras Wafer-Scale Engine deliver exceptional performance in AI and scientific computing by distributing scratchpad memory across hundreds of thousands of processing elements (PEs). Yet programming…
ASTRA is a Python package that provides a modular, instrument-independent interface for working with high-resolution stellar spectra. Designed to support data from multiple spectrographs, including ESPRESSO (Pepe et al., 2021), HARPS (Mayor…
In recent years two sets of planar (2D) shape attributes, provided with an intuitive physical meaning, were proposed to the remote sensing community by, respectively, Nagao & Matsuyama and Shackelford & Davis in their seminal works on the…
Written in Python and utilising ParselTongue to interface with the Astronomical Image Processing System (AIPS), the e-MERLIN data reduction pipeline is intended to automate the procedures required in processing and calibrating radio…
For efficiency reasons, the software system designers' will is to use an integrated set of methods and tools to describe specifications and designs, and also to perform analyses such as dependability, schedulability and performance. AADL…
Iris is an extensible application that provides astronomers with a user-friendly interface capable of ingesting broad-band data from many different sources in order to build, explore, and model spectral energy distributions (SEDs). Iris…
We present a science-driven discovery portal for all the ESA Astronomy Missions called ESA Sky that allow users to explore the multi-wavelength sky and to seamlessly retrieve science-ready data in all ESA Astronomy mission archives from a…
We describe a new code for the deep analysis of stellar fields, designed for Adaptive Optics Nyquist-sampled images with high and low Strehl ratio. The Point Spread Function is extracted directly from the image frame, to take into account…
Purpose: Multicriteria decision analysis (MCDA) has become increasingly essential for decision-making in complex environments. In response to this need, the pyDecision library, implemented in Python and available at https://bit.ly/3tLFGtH,…
Multilevel compositional data, such as data sampled over time that are non-negative and sum to a constant value, are common in various fields. However, there is currently no software specifically built to model compositional data in a…