Ubicar la indignación: reflexiones y escenarios de acción

La indignación no es simplemente ira o rabia individual, sino una experiencia que requiere consciencia social y un horizonte compartido. Cuando nos indignamos, estamos expresando nuestra concepción de cómo debería ser el mundo, ubicando las injusticias en un orden común donde reconocemos qué es lo justo. Este libro explora precisamente esa dimensión social de la indignación como brújula moral que orienta la construcción de sociedades más justas. Recopilando estudios de investigadores internacionales, la obra se organiza en dos bloques: "Reflexiones", que analiza cómo se constituye la indignación, su concepto y función social; y "Escenarios de indignación", que examina su presencia en nuestro contexto cultural. Con este recorrido, la obra busca desarrollar una topología del orden social que nos ayude a comprender mejor cómo habitamos el mundo común y cómo podemos enfrentar juntos los desafíos que compartimos. Así, ofrece un espacio de reflexión para indignaciones pasadas, presentes y futuras.BLOQUE I: Reflexiones. -- La significación filosófica de la indignación en el pensamiento de Aristóteles / Ignacio García Peña (pp. 15-34). -- Indignación y política republicana en Maquiavelo / Guido Frilli (pp. 35-48). -- El grito indignado de los Calas: Voltaire y la justicia del corazón / Marco Menin (pp. 49-62). -- Ira, indignación y política: un enfoque cognitivo / Giulio Sacco (pp. 63-76). -- Indignación y agotamiento vis-à-vis: si el agotado agota, ¿también el indignado “indigna”? Indignarse ante la vida (indignación ontológica) / Marina Christodoulou (pp. 77-94). -- Perdón y Ética de la Indignación: Un Ensayo / M. Hadi Fazeli (pp. 95-106). -- El cuerpo en duelo de “la izquierda”: un acercamiento al paradigma actual de los movimientos sociales autónomos / Andrea Acosta Landín (pp. 107-120). -- Más allá de la inmovilidad: ¿por qué la indignación no conduce a la acción? / Grégori de Souza (pp. 121-132). -- Crisis ambiental y cultura: una ecosofía de la indignación / Luca Valera (pp. 133-146). -- BLOQUE II: Escenarios de indignación. -- ¡Contra el cambio! Platón sobre el conservadurismo egipcio y la posibilidad de innovación en las artes / Tomás N. Castro (pp. 149-162). -- La indignación como anagnórisis y catarsis en el Λόγος σωκρατικός / Álvaro R. Maestro Ibarzábal (pp. 163-176). -- Escribir la indignación. Literatura y crítica ético-política en Isabelle de Charrière / Francesco Boccolari (pp. 177-190). -- Wollstonecraft y el poder movilizador de la indignación / Alexandra Abranches (pp. 191-202). -- Experiencia y mutismo: sobre la indignación en Walter Benjamin y Simone Weil / César Adolfo Arceo-Arévalo (pp. 203-216). -- El desafío de Hayek para una filosofía de la indignación / Diego Colomés (pp. 217-230). -- Capitalismo e indignación social. Una reconfiguración emancipatoria desde Nancy Fraser / Jorge Ojeda-Cabrera (pp. 231-242). -- El eco de la catástrofe. Basinski, Deleuze y la resistencia sonora del arte / Sofía Cortez-Maciel (pp. 243-256). -- Vergüenza e indignación en el horizonte del nihilismo contemporáneo / Dune Valle Jiménez (pp. 257-270). -- Valencias de la indignación / Guillermo García Santos (pp. 271-284). -- Estética do lixo: el cine frente a realidades insoportables / Ananda Cinti-Romero (pp. 285-298). -- Acampe: la resistencia del arraigo en clave vegetal / Claudia Donoso (pp. 299-308)

Calibration and uncertainty quantification for deep learning-based drought detection

Droughts are hydrometeorological extreme events influenced by highly intricate land–atmosphere feedback mechanisms and climate variability. Deep learning models have recently succeeded in detecting extreme climate events and promise to uncover and understand droughts further. There are two main challenges of reliability and trustworthiness limiting their applications: miscalibration and inherent uncertainty. However, they remain rarely explored because deep learning models are overparameterized and seldom tractable. To address this shortcoming, we introduce methodologies for model calibration and entropy-based uncertainty quantification for deep learning-based drought detection. The calibration algorithm can deal with calibration errors by reducing distributional shifts and alleviating overconfident predictions. The uncertainty framework, in turn, decomposes and quantifies the total uncertainty according to several components: data uncertainty, procedural variability, parametric variability, and latent variability. Thus, our method identifies uncertain predictions and supports robust evaluations, benefiting the credibility of the decision-making process. Empirical evidence of performance in a wide range of European drought events is given, justifying the effectiveness of our approach. The calibration methodology yields the lowest expected calibration error (0.31%) and the precision of the uncertainty-based decision-making is improved from 72.27% to 74.06% and 76.59%, based on ensemble predictions and rejecting the predictions for the top 20% uncertain negative samples, respectively. In summary, our approach significantly enhances drought detection’s reliability and classification accuracy, constituting a key step toward more trustworthy and actionable climate decision-making.M Z appreciates the financial support from the China Scholarship Council (CSC) through the State Scholarship Fund for Overseas Study (No. 202106710031). All authors acknowledge the support from the European Research Council (ERC) under the ERC Synergy Grant USMILE (grant agreement 855187), the European Union‘s Horizon 2020 research and innovation program within the project ‘XAIDA: Extreme Events - Artificial Intelligence for Detection and Attribution,’ (GA 101003469), ‘AI4PEX: Artificial Intelligence and Machine Learning for Enhanced Representation of Processes and Extremes in Earth System Models’ (GA 101137682) and the computer resources provided by the Jülich Supercomputing Centre (JSC) (Project No. PRACE-DEV-2022D01-048), the computer resources provided by Artemisa (funded by the European Union ERDF and Comunitat Valenciana), as well as the technical support provided by the Instituto de Física Corpuscular, IFIC (CSIC-UV)

Ensuring Semantic Consistency in SysML v2 Models Through Metamodel-Driven Validation

Model-Based Systems Engineering (MBSE) relies on formal models for system lifecycle management, supporting model coherence and efficient reuse of components. Modelling languages, particularly SysML v2, provide a standardized framework for complex system modelling, overcoming key limitations of earlier versions. Unlike SysML v1, which was a UML profile and inherited UML"s complexities, SysML v2 is designed to better handle multi-disciplinary, large-scale, and emergent system behaviours. However, the validation of SysML v2 models is still an emerging area, and while some methods are beginning to surface, comprehensive and standardized validation approaches are not yet fully established. This may pose challenges to ensuring correctness and reliability in engineering workflows. This paper presents a systematic, metamodel-based method for validating SysML v2 models, utilising the SysML v2 metamodel as a formal specification. By defining validation rules derived from this metamodel, the method facilitates automated detection of structural and semantic inconsistencies. A practical case study validates the method on SysML v2 models of aerospace, automotive, and software development domains, demonstrating the systematic identification and resolution of errors. This research advances SysML v2 model validation, contributing to broader MBSE objectives by ensuring correct SysML v2 models for complex system development in multi-disciplinary environments.This work is part of the University Carlos III of Madrid (UC3M) research project CICLONES: Collaboration and smart continuous integration in Software and Systems Engineering with MBSE and DevOps. 10.13039/501100006318-Universidad Carlos III de Madrid CRUE-Madroño (Grant Number: 2025

Explainable Earth Surface Forecasting Under Extreme Events

With climate change-related extreme events on the rise, high-dimensional Earth observation data present a unique opportunity for forecasting and understanding impacts on ecosystems. This is, however, impeded by the complexity of processing, visualizing, modeling, and explaining this data. We train a convolutional long short-term memory-based architecture on the novel DeepExtremeCubes data set to showcase how this challenge can be met. DeepExtremeCubes includes around 40,000 long-term Sentinel-2 minicubes (January 2016–October 2022) worldwide, along with labeled extreme events, meteorological data, vegetation land cover, and a topography map, sampled from locations affected by extreme climate events and surrounding areas. When predicting future reflectances and vegetation impacts through the kernel normalized difference vegetation index, the model achieved an R2 score of 0.9055 in the test set. Explainable artificial intelligence was used to analyze the model's predictions during the October 2020 Central South America compound heatwave and drought event. We chose the same area exactly 1 year before the event as a counterfactual, finding that the average temperature and surface pressure are generally the most important predictors. In contrast, minimum evaporation anomalies play a leading role during the event. We also found the anomalies of the reflectances in the timestep before the extreme event to be critical predictors of its impact on vegetation. The code to replicate all experiments and figures in this paper is publicly available at https://github.com/DeepExtremes/txyXAI.This work was supported by the ESA AI4Science project Multi-Hazards, Compounds and Cascade events: DeepExtremes, 2022-2024; the European Union's Horizon 2020 research and innovation project XAIDA: Extreme Events Artificial Intelligence for Detection and Attribution, 2021-2024 (Grant agreement No 101003469); and the European Union's Horizon 2022 project ELIAS: European Lighthouse of AI for Sustainability, 2023-2027 (Grant agreement No. 101120237). The authors acknowledge the support from the computer resources provided by Artemisa (funded by the European Union ERDF and Comunitat Valenciana), as well as the technical support provided by the Instituto de Física Corpuscular, IFIC (CSIC-UV)

Reprogrammable mechanical metamaterials via passive and active magnetic interactions

This study experimentally demonstrates the reprogrammability of a rotating-squares-based mechanical metamaterial with an embedded array of permanent magnets. How the orientation, residual magnetization, and stiffness of the magnets influence both the static and dynamic responses of the metamaterial is systematically investigated. It is showed that by carefully tuning the magnet orientation within the metamaterial, notable tunability of the metamaterial response can be achieved across static and dynamic regimes. More complex magnetic node configurations can optimize specific structural responses by decoupling the tunability of quasi-static stress-strain behavior from energy absorption under impact loading. Additionally, reprogrammability can be further enhanced by an external magnetic field, which modulates magnetic interactions within the structure. This work paves the way for developing engineered structural components with adaptable mechanical responses, reprogrammable through either the redistribution of magnetic elements or the application of an external magnetic field.The authors acknowledge support from Ministerio de Ciencia e Innovacion MCIN/AEI/10.13039/501100011033 under Grant number PID2020- 117894GA-I00, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 947723, project: 4D-BIOMAP), and the Catedra UC3M–NAVANTIA– MONODON: DEEPTECH. G.R. acknowledges the Swiss National Science Foundation for funding the project. Project: Metamaterials with reprogrammable shape and stiffness (grant no.: 217901)

Adversarial dynamics in centralized versus decentralized intelligent systems

This article is part of the topic "Building the Socio-Cognitive Architecture of COHUMAIN: Collective Human-Machine Intelligence," Cleotilde Gonzalez, Henny Admoni, Scott Brown and Anita Williams Woolley (Topic Editors).Artificial intelligence (AI) is often used to predict human behavior, thus potentially posing limitations to individuals' and collectives' freedom to act. AI's most controversial and contested applications range from targeted advertisements to crime prevention, including the suppression of civil disorder. Scholars and civil society watchdogs are discussing the oppressive dangers of AI being used by centralized institutions, like governments or private corporations. Some suggest that AI gives asymmetrical power to governments, compared to their citizens. On the other hand, civil protests often rely on distributed networks of activists without centralized leadership or planning. Civil protests create an adversarial tension between centralized and decentralized intelligence, opening the question of how distributed human networks can collectively adapt and outperform a hostile centralized AI trying to anticipate and control their activities. This paper leverages multi-agent reinforcement learning to simulate dynamics within a human-machine hybrid society. We ask how decentralized intelligent agents can collectively adapt when competing with a centralized predictive algorithm, wherein prediction involves suppressing coordination. In particular, we investigate an adversarial game between a collective of individual learners and a central predictive algorithm, each trained through deep Q-learning. We compare different predictive architectures and showcase conditions in which the adversarial nature of this dynamic pushes each intelligence to increase its behavioral complexity to outperform its counterpart. We further show that a shared predictive algorithm drives decentralized agents to align their behavior. This work sheds light on the totalitarian danger posed by AI and provides evidence that decentrally organized humans can overcome its risks by developing increasingly complex coordination strategies.Manuel Cebrian was partially supported by the Ministry of Universities of the Government of Spain, under the program "Convocatoria de Ayudas para la recualificación del sistema universitario español para 2021-2023, de la Universidad Carlos III de Madrid, de 1 de Julio de 2021."Open access funding enabled and organized by Projekt DEAL

Influence of coupling symmetries and noise on the critical dynamics of synchronizing oscillator lattices

Recent work has shown that the synchronization process in lattices of self-sustained (phase and limit-cycle) oscillators displays universal scale-invariant behavior previously studied in the physics of surface kinetic roughening. The type of dynamic scaling ansatz which is verified depends on the randomness that occurs in the system, whether it is columnar disorder (quenched noise given by the random assignment of natural frequencies), leading to anomalous scaling, or else time-dependent noise, inducing the more standard Family-Vicsek dynamic scaling ansatz, as in equilibrium critical dynamics. The specific universality class also depends on the coupling function: for a sine function (as in the celebrated Kuramoto model) the critical behavior is that of the Edwards-Wilkinson equation for the corresponding type of randomness, with Gaussian fluctuations around the average growth. In all the other cases investigated, Tracy–Widom fluctuations ensue, associated with the celebrated Kardar–Parisi–Zhang equation for rough interfaces. Two questions remain to be addressed in order to complete the picture, however: (1) Is the atypical scaling displayed by the sine coupling preserved if other coupling functions satisfying the same (odd) symmetry are employed (as suggested by continuum approximations and symmetry arguments)? and (2) how does the competition between both types of randomness (which are expected to coexist in experimental settings) affect the nonequilibrium behavior? We address the latter question by numerically characterizing the crossover between thermal-noise and columnar-disorder criticality, and the former by providing evidence confirming that it is the symmetry of the coupling function that sets apart the sine coupling, among other odd-symmetric couplings, due to the absence of Kardar–Parisi–Zhang fluctuations.This work has been partially supported by Ministerio de Ciencia e Innovación (Spain), by Agencia Estatal de Investigación, Spain (AEI, Spain, 10.13039/501100011033), and by European Regional Development Fund, (ERDF, A way of making Europe) through Grants No. PID2021-123969NB-I00 and No. PID2021-128970OA-I00

In situ synthesis of biocompatible NaY1−xGdxF4 :Yb/Er nanoparticles for cell labeling and temperature sensing

Biocompatible, up-converting NaY1-xGdxF4:Yb/Er nanoparticles have been successfully obtained in situ by chitosan assisted solvothermal synthesis, and were further characterized to check their potential for optical thermometry. The temperature dependent change in the green emission intensity, originating from the thermalization between 4S3/2 and 2H11/2 levels, implied maximum relative sensitivity of 1.3 %K-1 in the physiologically interesting temperature range. Presence of chitosan ligands at the nanoparticles surface promotes their biocompatibility. The nanoparticles concentration ranging from 10 to 50 µg/mL yielded viability higher than 80 % for HS-5 fibroblast and SCC-25 oral cancer cells. Efficient visualization of alfaNaY0.65Gd0.15F4:Yb0.18Er0.02 nanoparticles in cytoplasmic region of cells, under excitation at 976 nm, validates their potential to be used for cell labeling and temperature sensing in tissue.The research was supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia on the research programs of Institute of Physics Belgrade and Institute of Technical Sciences of SASA (Grant No. 451-03-136/2025-03/200175)