PlateUNLP is a semi-automatic software tool designed to assist in the processing of digitized spectral plate images. It automates key stages such as spectra detection, spectral extraction, wavelength calibration, and metadata generation. These tasks, traditionally performed manually, are significantly accelerated while still allowing user intervention when necessary. This work focuses on the integration of automation for the detection of spectra in images using deep learning techniques, as well as the extraction of one-dimensional spectra based on the image segments corresponding to each spectrum, and their subsequent wavelength calibration. The tool is being developed in collaboration with expert astronomers, demonstrating high reliability and practical utility in the recovery of historical astronomical data.

In this study, we examine the potential of using high-resolution satellite imagery and machine learning techniques to create income maps with a high level of geographic detail. We trained a convolutional neural network with satellite images from the Metropolitan Area of Buenos Aires (Argentina) and 2010 census data to estimate per capita income at a 50x50 meter resolution for 2013, 2018, and 2022. This outperformed the resolution and frequency of available census information. Based on the MobileNetV3 architecture, the model achieved high accuracy in predicting household incomes (R2= 0.77), surpassing the spatial resolution and model performance of other methods used in the existing literature. This approach presents new opportunities for the generation of highly disaggregated data, enabling the assessment of public policies at a local scale, providing tools for better targeting of social programs, and reducing the information gap in areas where data is not collected.

As the presence of artificial intelligence has increased in a variety of different areas, the use of machine learning and deep learning techniques for creative purposes has also risen significantly in recent years. Works of this kind within the area of natural language processing (NLP) are typically neural models used for fiction or lyrics generation. Those works are in most cases in English and adapting them to other languages is not feasible. In this work, we develop a Spanish text generator system for the rap sub-genre known as freestyle. Freestyle songs present unique challenges for text generation given that performers compete with one another in a lyric improvisation contest. Given the low availability of freestyle text, especially in Spanish, we collected two separate datasets, one with freestyle lyrics and the other, larger, with rap lyrics, which are more readily available. The rap dataset can be used for pretraining, and the freestyle dataset for finetuning on the generation task. Furthermore, we design a neural network-based generation model that takes into account both the structure of freestyle and the low data availability. The model was able to generate realistic freestyle verses in Spanish.