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Bad polarization in structurally cohesive communities
Full text linkCommunities frequently experience belief polarization, even in the face of overwhelming scientific evidence supporting one side of the debate. Current explanations for this phenomenon, which we define as bad polarization, attribute its emergence to the influence of social incentives on belief formation. However, these explanations presuppose the existence of a fragmented community in which opposing groups develop different beliefs. Here, we provide a model of belief formation in which social incentives bring about bad polarization even in structurally cohesive communities. We assume agents to distribute a finite amount of social support among their like-minded neighbors and to sample evidence partially in order to form the belief that grants them the highest possible support. Accordingly, we show that bad polarization emerges more frequently when communities are highly connected, and that bad believers, individuals holding unsupported beliefs, are consistently a minority. Bad polarization is driven by the competition for social support: bad believers form a minority because this allows them to gain a higher amount of support than if they adhered to the majority view.Open Access funding enabled and organized by Projekt DEAL. Daniele Vilone was partially supported by project SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union NextGenerationEU. Eugenia Polizzi was partially supported by the EU H2020 ICT48 project ‘Humane AI Net’ under contract n 952026
Towards 6G Architecture: Key Concepts, Challenges, and Building Blocks
Full text linkWe are entering the standardization phase for the 6th generation (6G) of wireless technologies. While valuable lessons have been learned from the design, deployment, and operation of 5G and Beyond in Europe, new requirements, emerging technologies, and evolving business models must be natively integrated into the next-generation mobile network architecture. This white paper presents a comprehensive snapshot of the current architectural considerations explored by the Smart Networks and Services Joint Undertaking (SNS-JU) projects. It aims to discuss the rationale for novel architectural components, the ongoing design efforts, and the future outlook for 6G. We analyse the blueprint for next-generation mobile networks, building on past experiences while integrating cutting-edge advancements. The structure follows the IMT-2030 framework, categorizing insights into the key usage scenarios and overarching architectural aspects. - Usage Scenarios: We analyse the evolution of IMT-2030 paradigms, focusing on Immersive, Massive, and Hyper-Reliable Low-Latency Communications. We also explore novel 6G enablers, such as Ubiquitous Connectivity, AI-driven Communication, and Integrated Sensing & Communication. - Overarching Aspects: The next-generation architecture must natively embed Security, Privacy & Trustworthiness, Sustainability, and Network Exposure Capabilities¿ensuring these critical aspects are foundationally considered rather than retroactively incorporated. Finally, we conclude by outlining the essential building blocks shaping the next-generation mobile network architecture, highlighting the most promising research paths identified in this white paper.6G4SOCIETY project has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101139070. This work has received funding from the Swiss State Secretariat for Education, Research and Innovation (SERI
Linking birth experience and perinatal depression symptoms to neuroanatomical changes in hippocampus and amygdala
Full text linkChildbirth is a life-changing event in a mother's life. While the transition to motherhood has recently been recognized as one of the most neuroplastic periods in adulthood, no study has yet explored whether the hippocampus and amygdala change during the peripartum in relation to childbirth experience and perinatal depression symptoms. In this longitudinal neuroimaging study, we assessed 88 first-time gestational mothers in late pregnancy and early postpartum and 30 noneiparous control women. We used optimized high-resolution MRI scans to quantify volumetric changes in the hippocampus and amygdala, along with their substructures. We found that increases in depression symptoms during the peripartum were positively correlated with changes in the right amygdala. A more challenging birth experience was associated with bilateral increases in hippocampal volume. These findings show that studying the neuroanatomical changes during the transition to motherhood can inform not only about adaptive processes but also about potential vulnerabilities, highlighting the importance of tracking perinatal experiences to enhance women's health.This work has been funded by Instituto de Salud Carlos III (ISCIII) through the project “PI22/01365” and cofunded by the European Union, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 883069) and the Centro Nacional de Investigaciones Cardiovasculares (CNIC). The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia, Innovación y Universidades (MICIU), and the Pro CNIC Foundation and is a Severo Ochoa Center of Excellence (grant CEX2020-001041-S funded by MICIU/AEI/10.13039/501100011033). C.B. was funded by the grant Intramural Programme of IiSGM for the Promotion of R&D&I 2023, subprogram “Pre-doctoral training contract.” S.C. was funded by Miguel Servet Type II research contract CPII21/00016 and co-funded by the European Social Fund “Investing in your future.
IA, diritto all'infromazione e le nuove forme di partecipazione: l'esperienza spagnola
Full text link[EN] This paper examines Spain's pioneering approach to regulating algorithmic management and artificial intelligence, with the aim of assessing how workers' participation-particularly the right to information-has evolved following this legal reform. The main objective is not only to evaluate the effectiveness of the measures introduced to promote transparency, but also to highlight good practices that could be replicated in other legal systems. To achieve this aim, the article is divided as follows. Following this introduction, the regulatory legal framework is outlined. This is then followed by an analysis of developments arising from collective bargaining. Finally, the paper presents some recommendations for improvement and potential areas for further development in the conclusions.[IT] Il presente lavoro esamina l'approccio pionieristico della Spagna alla regolamentazione della gestione algoritmica e dell'intelligenza artificiale, con l'obiettivo di valutare come la partecipazione dei lavoratori, in particolare il diritto all'informazione, si sia evoluta in seguito a questa riforma giuridica. L'obiettivo principale non è solo quello di valutare l'efficacia delle misure introdotte per promuovere la trasparenza, ma anche di evidenziare le buone pratiche che potrebbero essere replicate in altri sistemi giuridici. Per raggiungere questo obiettivo, l'articolo è suddiviso come segue. Dopo questa introduzione, viene delineato il quadro giuridico normativo. Segue poi un'analisi degli sviluppi derivanti dalla contrattazione collettiva. Infine, l'articolo presenta alcune raccomandazioni per il miglioramento e potenziali aree di ulteriore sviluppo nelle conclusioni.This article is part of the research projects "Technological change and the transformation of labour sources: Law and collective bargaining in the face of digital disruption" (RTI2018-094547-B-C21), "Algorithmic management and equal opportunities in the enterprise (AlgoEquality)" (TED2021-130325A-I00) and "Internacionalización y europeización del Derecho del Trabajo: desafíos en un contexto globalizado de los instrumentos de flexibilidad" (UCM-PR12/24-31563
Evaluation of traditional machine learning algorithms for featuring educational exercises
No full textArtificial intelligence (AI) algorithms are important in educational environments, and the use of machine learning algorithms to evaluate and improve the quality of education. Previous studies have individually analyzed algorithms to estimate item characteristics, such as grade, number of attempts, and time from student interactions. By contrast, this study integrated all three characteristics to discern the relationships between attempts, time, and performance in educational exercises. We analyzed 15 educational assessments using different machine learning algorithms, specifically 12 for regression and eight for classification, with different hyperparameters. This study used real student interaction data from Zenodo.org, encompassing over 150 interactions per exercise, to predict grades and to improve our understanding of student performance. The results show that, in regression, the Bayesian ridge regression and random forest regression algorithms obtained the best results, and for the classification algorithms, Random Forest and Nearest Neighbors stood out. Most exercises in both scenarios involved more than 150 student interactions. Furthermore, the absence of a pattern in the variables contributes to suboptimal outcomes in some exercises. The information provided makes it more efficient to enhance the design of educational exercises.This work was supported by FEDER / Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación through grants PID2023-146692OB-C31 (GENIE Learn project) funded by MICIU/AEI/10.13039/501100011033 and by ERDF/UE and PID2020-112584RB-C31 (H2O Learn project) and by the UNESCO Chair of “Scalable Digital Education for All” at UC3M and by grant RED2022-134284-T funded by MICIU/AEI/10.13039/501100011033 and by Universidad Carlos III de Madrid (UC3M) through the Grants for the Research Activity of Young Doctors of the UC3M’s Own Research and Transfer Program (ASESOR-IA project)
DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts
No full textWith climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models. Despite recent progress in deep learning to ecosystem monitoring, there is a need for datasets specifically designed to analyse compound heatwave and drought extreme impact. Here, we introduce the DeepExtremeCubes database, tailored to map around these extremes, focusing on persistent natural vegetation. It comprises over 40,000 globally sampled small data cubes (i.e. minicubes), with a spatial coverage of 2.5 by 2.5 km. Each minicube includes (i) Sentinel-2 L2A images, (ii) ERA5-Land variables and generated extreme event cube covering 2016 to 2022, and (iii) ancillary land cover and topography maps. The paper aims to (1) streamline data accessibility, structuring, pre-processing, and enhance scientific reproducibility, and (2) facilitate biosphere dynamics forecasting in response to compound extremes.The authors acknowledge the support from the ESA AI4Science project “Multi-Hazards, Compounds and Cascade events: DeepExtremes,” 2022-2024, and the European Union’s Horizon 2020 research and innovation program within the project “XAIDA: Extreme Events - Artificial Intelligence for Detection and Attribution” - [101003469]. K.M. acknowledges funding by the Saxon State Ministry for Science, Culture and Tourism (SMWK) - [3-7304/35/6-2021/48880]. C.J. and M.M. acknowledge funding from the German Federal Ministry for Economic Affairs and Energy (BMWK) for the project “TEE Cube - Time-varying AI-based Mapping of Ecosystem Conditions and Extents Using Multi-source Earth Observation Data Cubes” - [50EE2413] and the ML4Earth project [50EE2201B]
Non-recyclable municipal solid waste characterization and pyrolysis for energy recovery
Full text linkEuropean regulations require that by 2030 waste suitable for recycling, material recovery, or energy recovery will no longer be allowed to end up in landfills. Material composition in non-recyclable MSW bins dictates which valorization measures could be implemented. This study examines 32 non-recyclable MSW bins in the Getafe municipality (Spain). The bulk non-recyclable MSW bin is separated into 15 residue materials along with non-combustible materials. Merely 18.1 % of the non-recyclable MSW bins occupy non-recyclable waste. This indicates inadequate separation at source. MSW samples are grouped into six clusters with similar properties using the K-nearest neighbor methodology. Representative sample from each cluster is pyrolyzed at 520 °C. The main product of pyrolysis is liquid, which makes up 57.9 wt%, while solid and gas fractions are 16.4 and 16.5 wt%, respectively. Liquid fraction is a blend of aromatic, aliphatic, oxygenated, and nitrogenated compounds, while CO2 is the main gas compound.This study was funded by the research contract “Contrato de suministro de 4 lotes vinculados a sistemas de evaluación de calidad: Lote 3. Reducción del residuo orgánico urbano en vertedero y posterior valorización”. Project partners: UC3M & Limpieza y Medio Ambiente de Getafe S.A., Municipal (LYMA). Funding for APC: Universidad Carlos III de Madrid (Agreement CRUE-Madroñoo 2024)
For the love of art: work and gender in the jazz scene
Full text link[ES] La teorización sobre el concepto de trabajo ocupa un espacio menor en la sociología cuando se hace referencia a las profesiones artísticas. Este artículo explora las relaciones entre ese concepto y el desarrollo de trayectorias profesionales en el jazz a través de los resultados obtenidos en cuarenta y dos entrevistas en profundidad con mujeres artistas. Los resultados muestran que el trabajo artístico-musical está directamente relacionado con la idea de trabajar «por amor al arte», pero es determinante que el jazz sea un campo profesional predominantemente masculinizado. Una difícil separación de las esferas personal y profesional, el «estilo de vida bohemio o alternativo» y la deslegitimación profesional afecta directamente a las artistas. Se concluye que la experiencia profesional en el jazz presenta mayores dificultades para las mujeres desde una perspectiva comparativa internacional inusualmente explorada.[EN] Sociology has traditionally analyzed the concept of work, although when we refer to artistic professions it has been done to a lesser extent. This article explores the relationship between that concept and the development of professional trajectories in jazz, through the results obtained in 42 in-depth interviews with women artists. The main findings reveal that the musical-artistic work is related to the idea of working without the need for adequate remuneration, but the masculinization of jazz is relevant. A difficult separation of the personal and professional spheres, as well as the bohemian and alternative lifestyle, make the female artists' experience more difficult. Furthermore, professional delegitimization directly affects women. From an uncommon international comparative perspective, this article concludes that professional experiences in the jazz scene are particularly difficult for women
Fast k-medoids and q-Fold Fast k-medoids: New distance-based clustering algorithms for large mixed-type data
Full text linkIn this work new robust efficient clustering algorithms for large datasets of mixedtype data are proposed and implemented in a new Python package called FastKmedoids. Their performance is analyzed through an extensive simulation study, and compared to a wide range of existing clustering alternatives in terms of both predictive power and computational efficiency. MDS is used to visualize clustering results