Based on a reflection on presentism, this text is interested in the uses of memory in Spain at the beginning of the 21st century, in particular around the so-called ‘recovery of historical memory’, which encompasses the uses of the past, memorial discourses and political and judicial claims carried out by the victims of Franco's dictatorship and their descendants. The analysis of these uses takes into account the ‘emotional turn’ in the social sciences and proposes a new interpretation of the presence of the past in the political debates and social practices of contemporary Spain as a reenactment, which not only brings the past into the present but also transforms its meaning, with undeniable political consequences.
Language models have led to a leap forward in web automation. The current web automation approaches take the current web state, history actions, and language instruction as inputs to predict the next action, overlooking the importance of history states. However, the highly verbose nature of web page states can result in long input sequences and sparse information, hampering the effective utilization of history states. In this paper, we propose a novel web history compressor approach to turbocharge web automation using history states. Our approach employs a history compressor module that distills the most task-relevant information from each history state into a fixed-length short representation, mitigating the challenges posed by the highly verbose history states. Experiments are conducted on the Mind2Web and WebLINX datasets to evaluate the effectiveness of our approach. Results show that our approach obtains 1.2-5.4% absolute accuracy improvements compared to the baseline approach without history inputs.
This paper explores the potential of contextualized word embeddings (CWEs) as a new tool in the history, philosophy, and sociology of science (HPSS) for studying contextual and evolving meanings of scientific concepts. Using the term "Planck" as a test case, I evaluate five BERT-based models with varying degrees of domain-specific pretraining, including my custom model Astro-HEP-BERT, trained on the Astro-HEP Corpus, a dataset containing 21.84 million paragraphs from 600,000 articles in astrophysics and high-energy physics. For this analysis, I compiled two labeled datasets: (1) the Astro-HEP-Planck Corpus, consisting of 2,900 labeled occurrences of "Planck" sampled from 1,500 paragraphs in the Astro-HEP Corpus, and (2) a physics-related Wikipedia dataset comprising 1,186 labeled occurrences of "Planck" across 885 paragraphs. Results demonstrate that the domain-adapted models outperform the general-purpose ones in disambiguating the target term, predicting its known meanings, and generating high-quality sense clusters, as measured by a novel purity indicator I developed. Additionally, this approach reveals semantic shifts in the target term over three decades in the unlabeled Astro-HEP Corpus, highlighting the emergence of the Planck space mission as a dominant sense. The study underscores the importance of domain-specific pretraining for analyzing scientific language and demonstrates the cost-effectiveness of adapting pretrained models for HPSS research. By offering a scalable and transferable method for modeling the meanings of scientific concepts, CWEs open up new avenues for investigating the socio-historical dynamics of scientific discourses.
Understanding how cooperation emerges in public goods games is crucial for addressing societal challenges. While optional participation can establish cooperation without identifying cooperators, it relies on specific assumptions -- that individuals abstain and receive a non-negative payoff, or that non-participants cause damage to public goods -- which limits our understanding of its broader role. We generalize this mechanism by considering non-participants' payoffs and their potential direct influence on public goods, allowing us to examine how various strategic motives for non-participation affect cooperation. Using replicator dynamics, we find that cooperation thrives only when non-participants are motivated by individualistic or prosocial values, with individualistic motivations yielding optimal cooperation. These findings are robust to mutation, which slightly enlarges the region where cooperation can be maintained through cyclic dominance among strategies. Our results suggest that while optional participation can benefit cooperation, its effectiveness is limited and highlights the limitations of bottom-up schemes in supporting public goods.
Ellen Vorstenbosch, Ariadna Rodríguez-Liron, Enric Vicens-Pons
et al.
Abstract Background Prison suicide is a complex phenomenon that may be influenced by individual, clinical, social and environmental factors. In Spain, few studies have explored the relationship with institutional, prison-related variables. The aim of this study is to examine correlates of suicide in a sample of male incarcerated individuals from 5 Spanish penitentiary centers. Methods This present study entails a secondary data analysis, using data from the Prevalence of mental disorders in prisons study. This is a cross-sectional multicenter study conducted in 2007–2008 across 5 penitentiary centers in Spain. The Spanish version of the Plutchik suicide risk scale was used to assess the risk of suicide (those scoring ≥ 6 were considered to be at risk of suicide). Sociodemographic, clinical, criminological and prison-related data were collected via face-to face interviews and criminological data were confirmed using penitentiary records. Results The final sample included 707 male incarcerated individuals (mean age 36.79 years ± 9.90 years). Several significant correlates associated with higher risk of suicide were identified including criminological factors (having committed a violent offense, being a recidivist), clinical factors (family history of mental disorders, the presence of mental disorders, having physical conditions, contact with a mental health specialist, medication treatment in the last 12 months), and prison-related determinants (workshop/training course participation) was significantly associated with lower suicide risk. Conclusions Several correlates within a comprehensive range of sociodemographic, criminological, clinical and prison-related variables were identified. This information is primordial for preventing suicide and reducing the existing risk. The findings may contribute to developing effective suicide prevention programs within Spanish prison services. Importantly, future research must continue to investigate the nature of suicidal outcomes among incarcerated individuals.
In this paper, we revisit Johanna Drucker's question, "Is there a digital art history?" -- posed exactly a decade ago -- in the light of the emergence of large-scale, transformer-based vision models. While more traditional types of neural networks have long been part of digital art history, and digital humanities projects have recently begun to use transformer models, their epistemic implications and methodological affordances have not yet been systematically analyzed. We focus our analysis on two main aspects that, together, seem to suggest a coming paradigm shift towards a "digital" art history in Drucker's sense. On the one hand, the visual-cultural repertoire newly encoded in large-scale vision models has an outsized effect on digital art history. The inclusion of significant numbers of non-photographic images allows for the extraction and automation of different forms of visual logics. Large-scale vision models have "seen" large parts of the Western visual canon mediated by Net visual culture, and they continuously solidify and concretize this canon through their already widespread application in all aspects of digital life. On the other hand, based on two technical case studies of utilizing a contemporary large-scale visual model to investigate basic questions from the fields of art history and urbanism, we suggest that such systems require a new critical methodology that takes into account the epistemic entanglement of a model and its applications. This new methodology reads its corpora through a neural model's training data, and vice versa: the visual ideologies of research datasets and training datasets become entangled.
Neural Language Models (LMs) offer an exciting solution for general-purpose embodied control. However, a key technical issue arises when using an LM-based controller: environment observations must be converted to text, which coupled with history, results in long and verbose textual prompts. As a result, prior work in LM agents is limited to restricted domains with small observation size as well as minimal needs for interaction history or instruction tuning. In this paper, we introduce diff history, a simple and highly effective solution to these issues. By applying the Unix diff command on consecutive text observations in the interaction histories used to prompt LM policies, we can both abstract away redundant information and focus the content of textual inputs on the salient changes in the environment. On NetHack, an unsolved video game that requires long-horizon reasoning for decision-making, LMs tuned with diff history match state-of-the-art performance for neural agents while needing 1800x fewer training examples compared to prior work. Even on the simpler BabyAI-Text environment with concise text observations, we find that although diff history increases the length of prompts, the representation it provides offers a 25% improvement in the efficiency of low-sample instruction tuning. Further, we show that diff history scales favorably across different tuning dataset sizes. We open-source our code and data to https://diffhistory.github.io.
Since the beginning of the 20th century, a continuous evolution and perfection of what we today call the standard cosmological model has been produced, although some authors like to distinguish separate periods within this evolution. A possible historical division of the development of cosmology into six periods is: (1) the initial period (1917-1927); (2) the period of development (1927-1945); (3) the period of consolidation (1945-1965); (4) the period of acceptance (1965-1980); (5) the period of enlargement (1980-1998); and (6) the period of high-precision experimental cosmology (1998-now). The last period started with a epistemological optimism that has declined with time, and the expression "crisis in cosmology" is now stubbornly reverberating in the media. The initial expectation of removing the pending minor problems arising from the increased accuracy of measurements has backfired: the higher the precision with which the standard model tries to fit the data, the greater the number of tensions that arise, the problems proliferating rather than diminishing.
La Revolució russa tingué inicialment un fort ressò al moviment obrer ibèric i en particular entre els anarquistes de la CNT catalana. Resulta paradoxal doncs que el comunisme nasqués a començaments dels anys 20 a Espanya com un moviment fràgil i minoritari. Aquesta feblesa era especialment palesa a Catalunya. Aquí, tanmateix, sorgí als anys 1921-24 un petit corrent de partidaris del bolxevisme al si de la CNT, que desenvoluparen una política sui generis, vinculada a altres grups anàlegs a altres països, i orientada a atreure la militància anarcosindicalista per refundar el comunisme espanyol des de Barcelona, romanent independents del PCE. En un context de depressió per a l’obrerisme a Catalunya i arreu d’Espanya, l’estratègia dels anomenats comunistes-sindicalistes fracassà i, aïllats i afeblits, acabaren integrant-se al PCE a contracor a finals de 1924. Mantingueren però un esperit independent que en gran mesura ajuda a explicar la integració de la majoria d’aquests militants a les files del comunisme heterodox i catalanista del Bloc Obrer i Camperol el 1931. Aquest article rastreja la història d’aquests pioners del comunisme a Catalunya als anys 1921-24 utilitzant fonts noves dels arxius soviètics, que permeten reconstruir els orígens del grup i entendre la seva evolució posterior.
History (General) and history of Europe, History of Spain
As the History of Science Society, which is based in America, holds its annual meeting in Utrecht, one of the key academic centers on the European continent, one may surmise that the field has returned home. Yet, this hardly reflects how today's world of scholarship is constituted: in the historiography of science, 'provincializing Europe' has become an important theme, while the field itself, as is the case across the world of academia, is centered around a predominantly American literature. At the same time, ever since historians of science have emancipated themselves from the sciences a long time ago, they often have appeared, in the public eye, to question rather than to seek to bolster the authority of the sciences. How has this situation come about, and what does it tell us about the world we live in today? What insight is sought and what public benefit is gained by the historical study of science? As we try to answer these questions, we will follow a number of key mid-twentieth century historians--Eduard Dijksterhuis, Thomas Kuhn and Martin Klein--in their Atlantic crossings. Their answers to debates on the constitution of the early modern scientific revolution or the novelty of the work of Max Planck will illustrate how notions of 'center' and 'periphery' have shifted--and what that may tell us about being 'in Europe' today.
Gábor Timár, Rui A. da Costa, Sergey N. Dorogovtsev
et al.
The structure of an evolving network contains information about its past. Extracting this information efficiently, however, is, in general, a difficult challenge. We formulate a fast and efficient method to estimate the most likely history of growing trees, based on exact results on root finding. We show that our linear-time algorithm produces the exact stepwise most probable history in a broad class of tree growth models. Our formulation is able to treat very large trees and therefore allows us to make reliable numerical observations regarding the possibility of root inference and history reconstruction in growing trees. We obtain the general formula $\langle \ln \mathcal{N} \rangle \cong N \ln N - cN$ for the size-dependence of the mean logarithmic number of possible histories of a given tree, a quantity that largely determines the reconstructability of tree histories. We also reveal an uncertainty principle: a relationship between the inferrability of the root and that of the complete history, indicating that there is a tradeoff between the two tasks; the root and the complete history cannot both be inferred with high accuracy at the same time.
The concept of indistinguishable particles in quantum theory is fundamental to questions of ontology. All ordinary matter is made of electrons, protons, neutrons, and photons and they are all indistinguishable particles. Yet the concept itself has proved elusive, in part because of the interpretational difficulties that afflict quantum theory quite generally, and in part because the concept was so central to the discovery of the quantum itself, by Planck in 1900; it came encumbered with revolution. I offer a deflationary reading of the concept "indistinguishable" that is identical to the Gibbs concept of "generic phase", save that it is defined for state spaces with only finitely-many states of bounded volume and energy (finitely-many orthogonal states, in quantum mechanics). That, and that alone, makes for the difference between the quantum and Gibbs concepts of indistinguishability. This claim is heretical on several counts, but here we consider only the content of the claim itself, and its bearing on the early history of quantum theory rather than in relation to contemporary debates about particle indistinguishability and permutation symmetry. It powerfully illuminates that history.
In this paper we provide a mathematical programming based decision tool to optimally reallocate and share equipments between different units in emergency situations under lack of resources. The approach is motivated by the COVID-19 pandemic in which many Heath National Systems were not able to satisfy the demand of ventilators, sanitary individual protection equipments or different human resources. Our tool is based in two main principles: (1) Part of the stock of equipments at a unit that is not needed (in near future) could be shared to other units; and (2) extra stock to be shared among the units in a region can be efficiently distributed taking into account the demand of the units. The decisions are taken with the aim of minimizing certain measures of the non-covered demand in a region where a given network structured set of units is given. The mathematical programming models that we provide are stochastic and multiperiod and we provide different robust objective functions. Since the proposed models are computationally hard to solve, we provide a divide-et-conquer math-heuristic approach. We report the results of applying our approach to the data of the COVID-19 case in different regions of Spain, highlighting some interesting conclusions of our analysis, such as the great increase of treated patients if the proposed redistribution tool is applied.