From Castile to Kristallnacht: The Similarities in the Events Preceding the Spanish Inquisition and the Nazi Holocaust

Numerous scholars and historians have cautioned against the comparison of the Holocaust with other humanitarian tragedies and genocides. In particular, historians of Jewish cultures have debated both the connection between events of Anti-Jewish and Anti-Semitic violence and the trajectory of development connecting medieval and early-modern Anti-Jewish violence with twentieth-century history. Without rejecting the caution against linking the Holocaust to earlier Anti-Jewish events, this thesis proposes to compare the social and intellectual conditions that led up to the Holocaust with those of another iconic tragedy in Jewish history, the persecution of the Jews by the Spanish Inquisition and the Expulsion of the Jews from Spain in 1492. In particular, it compares four phenomena from each respective period: the rise of social Anti-Jewish movements, the emergence of Anti-Jewish legislation, the lack of a powerful central government, and the active participation of common civilians. In comparing these four categories from fifteenth-century Castile and Aragon and 1930s Germany, this thesis argues that while the events of the Holocaust and the Inquisition/Expulsion themselves cannot be compared directly or equated meaningfully, the conditions that gave rise to them bear striking similarities. The thesis concludes by exploring why, in light of these similar conditions, the events turned out so differently in the two cases.Judaic Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/85257/4/ejcohen.pd

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc

Obesity, inflammatory and thrombotic markers, and major clinical outcomes in critically ill patients with COVID‐19 in the US

Objective This study aimed to determine whether obesity is independently associated with major adverse clinical outcomes and inflammatory and thrombotic markers in critically ill patients with COVID‐19. Methods The primary outcome was in‐hospital mortality in adults with COVID‐19 admitted to intensive care units across the US. Secondary outcomes were acute respiratory distress syndrome (ARDS), acute kidney injury requiring renal replacement therapy (AKI‐RRT), thrombotic events, and seven blood markers of inflammation and thrombosis. Unadjusted and multivariable‐adjusted models were used. Results Among the 4,908 study patients, mean (SD) age was 60.9 (14.7) years, 3,095 (62.8%) were male, and 2,552 (52.0%) had obesity. In multivariable models, BMI was not associated with mortality. Higher BMI beginning at 25 kg/m2 was associated with a greater risk of ARDS and AKI‐RRT but not thrombosis. There was no clinically significant association between BMI and inflammatory or thrombotic markers. Conclusions In critically ill patients with COVID‐19, higher BMI was not associated with death or thrombotic events but was associated with a greater risk of ARDS and AKI‐RRT. The lack of an association between BMI and circulating biomarkers calls into question the paradigm that obesity contributes to poor outcomes in critically ill patients with COVID‐19 by upregulating systemic inflammatory and prothrombotic pathways

Search for intermediate-mass black hole binaries in the third observing run of Advanced LIGO and Advanced Virgo

International audienceIntermediate-mass black holes (IMBHs) span the approximate mass range 100−105 M⊙, between black holes (BHs) that formed by stellar collapse and the supermassive BHs at the centers of galaxies. Mergers of IMBH binaries are the most energetic gravitational-wave sources accessible by the terrestrial detector network. Searches of the first two observing runs of Advanced LIGO and Advanced Virgo did not yield any significant IMBH binary signals. In the third observing run (O3), the increased network sensitivity enabled the detection of GW190521, a signal consistent with a binary merger of mass ∼150 M⊙ providing direct evidence of IMBH formation. Here, we report on a dedicated search of O3 data for further IMBH binary mergers, combining both modeled (matched filter) and model-independent search methods. We find some marginal candidates, but none are sufficiently significant to indicate detection of further IMBH mergers. We quantify the sensitivity of the individual search methods and of the combined search using a suite of IMBH binary signals obtained via numerical relativity, including the effects of spins misaligned with the binary orbital axis, and present the resulting upper limits on astrophysical merger rates. Our most stringent limit is for equal mass and aligned spin BH binary of total mass 200 M⊙ and effective aligned spin 0.8 at 0.056 Gpc−3 yr−1 (90% confidence), a factor of 3.5 more constraining than previous LIGO-Virgo limits. We also update the estimated rate of mergers similar to GW190521 to 0.08 Gpc−3 yr−1.Key words: gravitational waves / stars: black holes / black hole physicsCorresponding author: W. Del Pozzo, e-mail: [email protected]† Deceased, August 2020

Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo

Advanced LIGO and Advanced Virgo are monitoring the sky and collecting gravitational-wave strain data with sufficient sensitivity to detect signals routinely. In this paper we describe the data recorded by these instruments during their first and second observing runs. The main data products are gravitational-wave strain time series sampled at 16384 Hz. The datasets that include this strain measurement can be freely accessed through the Gravitational Wave Open Science Center at http://gw-openscience.org, together with data-quality information essential for the analysis of LIGO and Virgo data, documentation, tutorials, and supporting software