This article examines Cuba’s involvement on the side of the Algerian National Liberation Front between 1959 and 1962, from its stance at the United Nations to the material and military assistance provided to the Provisional Government of the Algerian Republic (GPRA). Drawing on French and U.S. diplomatic archives, UN debates, and Cuban and Algerian sources, it argues that Havana went far beyond rhetorical support and turned the Algerian struggle into an early testing ground for its state-led internationalism. The shift from Batista’s pro-French alignment to Fidel Castro’s open backing of Algerian self-determination, the recognition of the GPRA, consistent pro-independence voting at the UN, and the dispatch of arms and wounded fighters reveal a principled choice, maintained despite U.S. pressure and the risk of damaging relations with Paris. The study highlights the diplomatic shock this reversal produced in France, as well as the way in which Algerian-Cuban convergence came to embody, in Washington’s eyes, the threat of an emerging anti-colonial and anti-imperialist front within the Third World.
The paper presents an approach for the recognition of toxic habits named entities in Spanish clinical texts. The approach was developed for the ToxHabits Shared Task. Our team participated in subtask 1, which aims to detect substance use and abuse mentions in clinical case reports and classify them in four categories (Tobacco, Alcohol, Cannabis, and Drug). We explored various methods of utilizing LLMs for the task, including zero-shot, few-shot, and prompt optimization, and found that GPT-4.1's few-shot prompting performed the best in our experiments. Our method achieved an F1 score of 0.65 on the test set, demonstrating a promising result for recognizing named entities in languages other than English.
Germán Capdehourat, Isabel Amigo, Brian Lorenzo
et al.
Grading is a time-consuming and laborious task that educators must face. It is an important task since it provides feedback signals to learners, and it has been demonstrated that timely feedback improves the learning process. In recent years, the irruption of LLMs has shed light on the effectiveness of automatic grading. In this paper, we explore the performance of different LLMs and prompting techniques in automatically grading short-text answers to open-ended questions. Unlike most of the literature, our study focuses on a use case where the questions, answers, and prompts are all in Spanish. Experimental results comparing automatic scores to those of human-expert evaluators show good outcomes in terms of accuracy, precision and consistency for advanced LLMs, both open and proprietary. Results are notably sensitive to prompt styles, suggesting biases toward certain words or content in the prompt. However, the best combinations of models and prompt strategies, consistently surpasses an accuracy of 95% in a three-level grading task, which even rises up to more than 98% when the it is simplified to a binary right or wrong rating problem, which demonstrates the potential that LLMs have to implement this type of automation in education applications.
This paper investigates the potentials of Large Language Models (LLMs) as adaptive tutors in the context of second-language learning. In particular, we evaluate whether system prompting can reliably constrain LLMs to generate only text appropriate to the student's competence level. We simulate full teacher-student dialogues in Spanish using instruction-tuned, open-source LLMs ranging in size from 7B to 12B parameters. Dialogues are generated by having an LLM alternate between tutor and student roles with separate chat histories. The output from the tutor model is then used to evaluate the effectiveness of CEFR-based prompting to control text difficulty across three proficiency levels (A1, B1, C1). Our findings suggest that while system prompting can be used to constrain model outputs, prompting alone is too brittle for sustained, long-term interactional contexts - a phenomenon we term alignment drift. Our results provide insights into the feasibility of LLMs for personalized, proficiency-aligned adaptive tutors and provide a scalable method for low-cost evaluation of model performance without human participants.
Matteo Boschini, Davide Gerosa, Alessandro Crespi
et al.
Latin Hypercube Sampling (LHS) is a prominent tool in simulation design, with a variety of applications in high-dimensional and computationally expensive problems. LHS allows for various optimization strategies, most notably to ensure space-filling properties. However, LHS is a single-stage algorithm that requires a priori knowledge of the targeted sample size. In this work, we present LHS in LHS, a new expansion algorithm for LHS that enables the addition of new samples to an existing LHS-distributed set while (approximately) preserving its properties. In summary, the algorithm identifies regions of the parameter space that are far from the initial set, draws a new LHS within those regions, and then merges it with the original samples. As a by-product, we introduce a new metric, the LHS degree, which quantifies the deviation of a given design from an LHS distribution. Our public implementation is distributed via the Python package expandLHS.
The Amazon Basin and the Latin America and Caribbean (LAC) region stands at a critical juncture, grappling with pressing environmental challenges while holding immense potential for transformative change through innovative solutions. This report illuminates the diverse landscape of social-ecological issues, technological advancements, community-led initiatives, and strategic actions that could help foster biosphere-based sustainability and resilience across the region.
ALEJANDRO BUITRAGO LOPEZ, Javier Pastor-Galindo, José Antonio Ruipérez-Valiente
In the digital era, the internet and social media have transformed communication but have also facilitated the spread of hate speech and disinformation, leading to radicalization, polarization, and toxicity. This is especially concerning for media outlets due to their significant role in shaping public discourse. This study examines the topics, sentiments, and hate prevalence in 337,807 response messages (website comments and tweets) to news from five Spanish media outlets (La Vanguardia, ABC, El País, El Mundo, and 20 Minutos) in January 2021. These public reactions were originally labeled as distinct types of hate by experts following an original procedure, and they are now classified into three sentiment values (negative, neutral, or positive) and main topics. The BERTopic unsupervised framework was used to extract 81 topics, manually named with the help of Large Language Models (LLMs) and grouped into nine primary categories. Results show social issues (22.22%), expressions and slang (20.35%), and political issues (11.80%) as the most discussed. Content is mainly negative (62.7%) and neutral (28.57%), with low positivity (8.73%). Toxic narratives relate to conversation expressions, gender, feminism, and COVID-19. Despite low levels of hate speech (3.98%), the study confirms high toxicity in online responses to social and political topics.
El capítulo analiza los principales cambios en el uso del suelo en el extremo sur de Hispanoamérica desde finales del siglo XVII. El proceso de ocupación del espacio colonial por parte de España tuvo un impacto demográfico que acompañó al establecimiento de los nuevos cultivos, de la expansión de la cría de especies de animales foráneas al continente y de la implementación de varias formas de producción basada mayormente en la extracción de los recursos naurales. En su conjunto, el proceso colonial afectó a los entornos naturales del sur de Hispanoamérica de múltiples maneras. Junto a severos cambios poblacionales, los europeos introdujeron cambios en los ambientes naturales que serían no solamente profundos y decisivos en su momento, sino que también serían cambios acumulativos e irreversibles. Esos cambios en el uso del suelo colonial, por lo tanto, sentaron las bases para procesos de larga duración con efectos que actualmente son una parte importante de las consideraciones a que da lugar el concepto de Antropoceno.
Lucas S. Maia, Martín Rocamora, Luiz W. P. Biscainho
et al.
Beat and downbeat tracking models have improved significantly in recent years with the introduction of deep learning methods. However, despite these improvements, several challenges remain. Particularly, the adaptation of available models to underrepresented music traditions in MIR is usually synonymous with collecting and annotating large amounts of data, which is impractical and time-consuming. Transfer learning, data augmentation, and fine-tuning techniques have been used quite successfully in related tasks and are known to alleviate this bottleneck. Furthermore, when studying these music traditions, models are not required to generalize to multiple mainstream music genres but to perform well in more constrained, homogeneous conditions. In this work, we investigate simple yet effective strategies to adapt beat and downbeat tracking models to two different Latin American music traditions and analyze the feasibility of these adaptations in real-world applications concerning the data and computational requirements. Contrary to common belief, our findings show it is possible to achieve good performance by spending just a few minutes annotating a portion of the data and training a model in a standard CPU machine, with the precise amount of resources needed depending on the task and the complexity of the dataset.
Generative adversarial networks (GANs) are a method based on the training of two neural networks, one called generator and the other discriminator, competing with each other to generate new instances that resemble those of the probability distribution of the training data. GANs have a wide range of applications in fields such as computer vision, semantic segmentation, time series synthesis, image editing, natural language processing, and image generation from text, among others. Generative models model the probability distribution of a data set, but instead of providing a probability value, they generate new instances that are close to the original distribution. GANs use a learning scheme that allows the defining attributes of the probability distribution to be encoded in a neural network, allowing instances to be generated that resemble the original probability distribution. This article presents the theoretical foundations of this type of network as well as the basic architecture schemes and some of its applications. This article is in Spanish to facilitate the arrival of this scientific knowledge to the Spanish-speaking community.
This study devised a physics-informed neural network (PINN) framework to solve the wave equation for acoustic resonance analysis. The proposed analytical model, ResoNet, minimizes the loss function for periodic solutions and conventional PINN loss functions, thereby effectively using the function approximation capability of neural networks while performing resonance analysis. Additionally, it can be easily applied to inverse problems. The resonance in a one-dimensional acoustic tube, and the effectiveness of the proposed method was validated through the forward and inverse analyses of the wave equation with energy-loss terms. In the forward analysis, the applicability of PINN to the resonance problem was evaluated via comparison with the finite-difference method. The inverse analysis, which included identifying the energy loss term in the wave equation and design optimization of the acoustic tube, was performed with good accuracy.
Diffusion-based generative models are a design framework that allows generating new images from processes analogous to those found in non-equilibrium thermodynamics. These models model the reversal of a physical diffusion process in which two miscible liquids of different colors progressively mix until they form a homogeneous mixture. Diffusion models can be applied to signals of a different nature, such as audio and image signals. In the image case, a progressive pixel corruption process is carried out by applying random noise, and a neural network is trained to revert each one of the corruption steps. For the reconstruction process to be reversible, it is necessary to carry out the corruption very progressively. If the training of the neural network is successful, it will be possible to generate an image from random noise by chaining a number of steps similar to those used for image deconstruction at training time. In this article we present the theoretical foundations on which this method is based as well as some of its applications. This article is in Spanish to facilitate the arrival of this scientific knowledge to the Spanish-speaking community.
Este trabajo plantea que las revistas montoneras representaron el golpe de Estado chileno como una coyuntura sangrienta que dejó una lección política y le otorgó trascendencia a la muerte de Allende. A partir del análisis de El Descamisado, El Peronista lucha por la liberación, La Causa Peronista y Evita Montonera, se infiere que la figura de Allende fue representada como un combatiente heroico que sacrificó su vida por la causa solidaria latinoamericana. Asimismo, la experiencia chilena adquirió una lectura asimilada a la experiencia dictatorial argentina que percibió a la “vía chilena al socialismo” como un contramodelo.
Latin America. Spanish America, Social history and conditions. Social problems. Social reform
Enrique M. Padilla, Birgit L. Emberger, Manuel Diez-Minguito
In 1926 Albert Einstein gave a clear explanation of the physical processes involved in the meander formation and evolution in open channels (Einstein, 1926). Although this work is far from being recognized as one of his greatest achievements, such as his annus mirabilis papers in 1905, he shows a truly remarkable didactic skills that make it easy to understand even to the non-specialist. In particular, a brilliant explanation of the tea leaf paradox can be found in this paper of 1926, presented as a simple experiment for clarifying the role of Earth rotation and flow curvature in the differential river banks erosion. This work deserves to be considered as a pioneering work that has laid a basic knowledge in currently very active research fields in fluvial geomorphology, estuarine physics, and hydraulic engineering. In response to the curiosity aroused and transmitted to the authors over the years by undergraduates and MSc. students, and also due to its historical and scientific significance, we present here the Spanish translation of Einstein's original work published in German in 1926 in Die Naturwissenschaften (Einstein, 1926). Einstein's drawings have not been interpreted, but just updated preserving their original spirit.
In order to be applicable in real-world scenario, Boundary Attacks (BAs) were proposed and ensured one hundred percent attack success rate with only decision information. However, existing BA methods craft adversarial examples by leveraging a simple random sampling (SRS) to estimate the gradient, consuming a large number of model queries. To overcome the drawback of SRS, this paper proposes a Latin Hypercube Sampling based Boundary Attack (LHS-BA) to save query budget. Compared with SRS, LHS has better uniformity under the same limited number of random samples. Therefore, the average on these random samples is closer to the true gradient than that estimated by SRS. Various experiments are conducted on benchmark datasets including MNIST, CIFAR, and ImageNet-1K. Experimental results demonstrate the superiority of the proposed LHS-BA over the state-of-the-art BA methods in terms of query efficiency. The source codes are publicly available at https://github.com/GZHU-DVL/LHS-BA.
Javier de la Rosa, Eduardo G. Ponferrada, Paulo Villegas
et al.
The pre-training of large language models usually requires massive amounts of resources, both in terms of computation and data. Frequently used web sources such as Common Crawl might contain enough noise to make this pre-training sub-optimal. In this work, we experiment with different sampling methods from the Spanish version of mC4, and present a novel data-centric technique which we name $\textit{perplexity sampling}$ that enables the pre-training of language models in roughly half the amount of steps and using one fifth of the data. The resulting models are comparable to the current state-of-the-art, and even achieve better results for certain tasks. Our work is proof of the versatility of Transformers, and paves the way for small teams to train their models on a limited budget. Our models are available at this $\href{https://huggingface.co/bertin-project}{URL}$.