O artigo discute a judicialização da violência contra as mulheres, com foco nos processos de vitimização que emergem em casos de feminicídio no Estado de Santa Catarina. As reflexões baseiam-se em pesquisa etnográfica realizada em 2019, no âmbito de um doutorado em antropologia social. A metodologia envolveu a observação de audiências judiciais nas quais eram julgados fatos tipificados pela “Lei Maria da Penha” (Lei 11.340/06) e pela Lei do Feminicídio (Lei 13.104/15). A partir do relato de um caso, o artigo analisa as disputas em torno da vitimização (Barthe, 2019) e a construção do feminicídio como um intolerável social (Fassin; Bourdelais; Dozon, 2005) no campo judicial. As análises evidenciam a mobilização de discursos que hierarquizam vítimas (entre “boas” e “más”), revelando uma tensão específica nos casos de feminicídio, que, embora recebam maior investimento do aparato judicial, não se traduzem necessariamente em avanços nas políticas públicas voltadas à violência contra as mulheres.
El exemplum XXVI del Libro del conde Lucanor (1335) merece que nos detengamos en el motivo del árbol, tercer personaje por derecho propio de esta fábula alegórica ‒junto con las personificaciones de la Verdad y la Mentira, dos conceptos clave en la obra manuelina–. El simbolismo del árbol, y el de cada una de sus partes (ramas, hojas, flores, frutos, tronco, sombra, raíces), es explotado por don Juan Manuel de forma muy sugerente, traspasando los límites de la simple comparación: ello le permite abrir el sentido del relato a lecturas complementarias y, a veces, incluso en apariencia contradictorias. Este tupido bosque de metáforas e interpretaciones sirve tanto al propósito narrativo como a la intención del autor, pero también permite acercarse al corpus de textos conocidos por el noble castellano y estudiar el modo en que se apropia de ellos para crear una obra altamente original.
Is it possible to bring into conversation two different traumatic legacies that occurred in the twentieth century in Europe? How can we engage in productive conversation about two totalitarian systems that repressed, incarcerated, dehumanized, and murdered people deemed enemies of the state or unworthy of living? These were some of the challenging questions addressed in the roundtable symposium “Holocaust***Gulag: Repressing, Rescuing, and Regulating Recalcitrant Legacies.” The symposium aimed at addressing specific aspects of the difficult and painful histories of the Holocaust and the Gulag, and to probe how these long-lasting legacies intrude into contemporary society, culture, religion, and politics.
Inverse problems for a diffusion equation containing a generalized fractional derivative are studied. The equation holds in a time interval $(0,T)$ and it is assumed that a state $u$ (solution of diffusion equation) and a source $f$ are known for $t\in (t_0,T)$ where $t_0$ is some number in $(0,T)$. Provided that $f$ satisfies certain restrictions, it is proved that product of a kernel of the derivative with an elliptic operator as well as the history of $f$ for $t\in (0,t_0)$ are uniquely recovered. In case of less restrictions on $f$ the uniqueness of the kernel and the history of $f$ is shown. Moreover, in a case when a functional of $u$ for $t\in (t_0,T)$ is given the uniqueness of the kernel is proved under unknown history of $f$.
Hagit Attiya, Michael A. Bender, Martin Farach-Colton
et al.
A data structure is called history independent if its internal memory representation does not reveal the history of operations applied to it, only its current state. In this paper we study history independence for concurrent data structures, and establish foundational possibility and impossibility results. We show that a large class of concurrent objects cannot be implemented from smaller base objects in a manner that is both wait-free and history independent; but if we settle for either lock-freedom instead of wait-freedom or for a weak notion of history independence, then at least one object in the class, multi-valued single-reader single-writer registers, can be implemented from smaller base objects, binary registers. On the other hand, using large base objects, we give a strong possibility result in the form of a universal construction: an object with $s$ possible states can be implemented in a wait-free, history-independent manner from compare-and-swap base objects that each have $O(s + 2^n)$ possible memory states, where $n$ is the number of processes in the system.
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.
Dynamic Stochastic General Equilibrium (DSGE) models are nowadays a crucial quantitative tool for policy-makers. However, they did not emerge spontaneously. They are built upon previously established ideas in Economics and relatively recent advancements in Mathematics. This essay provides a comprehensive coverage of their history, starting from the pioneering Neoclassical general equilibrium theories and eventually reaching the New Neoclassical Synthesis (NNS). In addition, the mathematical tools involved in formulating a DSGE model are thoroughly presented. I argue that this history has a mixed nature rather than an absolutist or relativist one, that the NNS may have emerged due to the complementary nature of New Classical and New Keynesian theories, and that the recent adoption and development of DSGE models by central banks from different countries has entailed a departure from the goal of building a universally valid theory that Economics has always had. The latter means that DSGE modeling has landed not without loss of generality.
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.
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.
Ali Yeganeh, Ahmad Kamrani Far, Mohammad Reza Gholi Zadeh
Abstract:Although by the end of the first century AD the Mediterranean Sea had become a purely Roman sea, in later periods following the rise of the Sassanid, as they sought to reclaim the lands of their ancestors, this empire's confrontation with Byzantium became inevitable. By developing and consolidating their dominance over the political-commercial sphere of the Persian Gulf, Sassanid held an important part of the main transportation and trade axes of the ancient world. This was while on the borders of the other two areas, namely from Mesopotami to the eastern shores of the Mediterranean and the general area of the Caucasus, with their aggressive policy, they had a serious competition with the Byzantines, which was to get the main home, the conflicting ligaments.This research intends to deal with the main causes and factors of the Iran-Byzantine conflict between the Mediterranean Sea and the Persian Gulf by descriptive-analytical method. In order to answer this question, the geopolitical and commercial-economic importance of the disputed areas is discussed. It will also show how the geopolitical position of these lands led the two great powers of that time into a series of land and sea battles.
History and principles of religions, History of Asia
Damian Clarke, Manuel Llorca Jaña, Daniel Pailañir
Quantile regression and quantile treatment effect methods are powerful econometric tools for considering economic impacts of events or variables of interest beyond the mean. The use of quantile methods allows for an examination of impacts of some independent variable over the entire distribution of continuous dependent variables. Measurement in many quantative settings in economic history have as a key input continuous outcome variables of interest. Among many other cases, human height and demographics, economic growth, earnings and wages, and crop production are generally recorded as continuous measures, and are collected and studied by economic historians. In this paper we describe and discuss the broad utility of quantile regression for use in research in economic history, review recent quantitive literature in the field, and provide an illustrative example of the use of these methods based on 20,000 records of human height measured across 50-plus years in the 19th and 20th centuries. We suggest that there is considerably more room in the literature on economic history to convincingly and productively apply quantile regression methods.
The Jeffreys-Lindley paradox exposes a rift between Bayesian and frequentist hypothesis testing that strikes at the heart of statistical inference. Contrary to what most current literature suggests, the paradox was central to the Bayesian testing methodology developed by Sir Harold Jeffreys in the late 1930s. Jeffreys showed that the evidence against a point-null hypothesis $\mathcal{H}_0$ scales with $\sqrt{n}$ and repeatedly argued that it would therefore be mistaken to set a threshold for rejecting $\mathcal{H}_0$ at a constant multiple of the standard error. Here we summarize Jeffreys's early work on the paradox and clarify his reasons for including the $\sqrt{n}$ term. The prior distribution is seen to play a crucial role; by implicitly correcting for selection, small parameter values are identified as relatively surprising under $\mathcal{H}_1$. We highlight the general nature of the paradox by presenting both a fully frequentist and a fully Bayesian version. We also demonstrate that the paradox does not depend on assigning prior mass to a point hypothesis, as is commonly believed.
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.
Conversational search is an emerging topic in the information retrieval community. One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question. Existing methods either prepend history turns to the current question or use complicated attention mechanisms to model the history. We propose a conceptually simple yet highly effective approach referred to as history answer embedding. It enables seamless integration of conversation history into a conversational question answering (ConvQA) model built on BERT (Bidirectional Encoder Representations from Transformers). We first explain our view that ConvQA is a simplified but concrete setting of conversational search, and then we provide a general framework to solve ConvQA. We further demonstrate the effectiveness of our approach under this framework. Finally, we analyze the impact of different numbers of history turns under different settings to provide new insights into conversation history modeling in ConvQA.
While extracting a subset of a commit history, specifying the necessary portion is a time-consuming task for developers. Several commit-based history slicing techniques have been proposed to identify dependencies between commits and to extract a related set of commits using a specific commit as a slicing criterion. However, the resulting subset of commits become large if commits for systematic edits whose changes do not depend on each other exist. We empirically investigated the impact of systematic edits on history slicing. In this study, commits in which systematic edits were detected are split between each file so that unnecessary dependencies between commits are eliminated. In several histories of open source systems, the size of history slices was reduced by 13.3-57.2% on average after splitting the commits for systematic edits.