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A transnational flow of capital exchange during the 19th and early 20th centuries brought planning ideas and modernity into China. Since European countries and America used violence to place China ...

AbstractScientific writings, as one essential part of human culture, have evolved over centuries into their current form. Knowing how scientific writings evolved is particularly helpful in understanding how trends in scientific culture developed. It also allows us to better understand how scientific culture was interwoven with human culture generally. The availability of massive digitized texts and the progress in computational technologies today provide us with a convenient and credible way to discern the evolutionary patterns in scientific writings by examining the diachronic linguistic changes. The linguistic changes in scientific writings reflect the genre shifts that took place with historical changes in science and scientific writings. This study investigates a general evolutionary linguistic pattern in scientific writings. It does so by merging two credible computational methods: relative entropy; word-embedding concreteness and imageability. It thus creates a novel quantitative methodology and applies this to the examination of diachronic changes in the Philosophical Transactions of Royal Society (PTRS, 1665–1869). The data from two computational approaches can be well mapped to support the argument that this journal followed the evolutionary trend of increasing professionalization and specialization. But it also shows that language use in this journal was greatly influenced by historical events and other socio-cultural factors. This study, as a “culturomic” approach, demonstrates that the linguistic evolutionary patterns in scientific discourse have been interrupted by external factors even though this scientific discourse would likely have cumulatively developed into a professional and specialized genre. The approaches proposed by this study can make a great contribution to full-text analysis in scientometrics.

Marine phytoplankton are believed to account for more than 45% of photosynthetic net primary production on Earth, and hence are at the base of marine food webs and have an enormous impact on the entire Earth system. Their members are found across many of the major clades of the tree of life, including bacteria (cyanobacteria) and multiple eukaryotic lineages that acquired photosynthesis through the process of endosymbiosis. Our understanding of their distribution in marine ecosystems and their contribution to biogeochemical cycles have increased since they were first described in the 18th century. Here, we review historical milestones in marine phytoplankton research and how their roles were gradually understood, with a particular focus on insights derived from large-scale ocean exploration. We start from the first observations made by explorers and naturalists, review the initial identification of the main phytoplankton groups and the appreciation of their function in the influential Kiel and Plymouth schools that established biological oceanography, to finally outline the contribution of modern large-scale initiatives to understand this fundamental biological component of the ocean. Fil: Pierella Karlusich, Juan José. Centre National de la Recherche Scientifique. Ecole Normale Supérieure; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Ibarbalz, Federico Matias. Centre National de la Recherche Scientifique. Ecole Normale Supérieure; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Bowler, Chris. Centre National de la Recherche Scientifique. Ecole Normale Supérieure; Francia. Centre National de la Recherche Scientifique; Francia

AbstractMany believe that the quality of a scientific publication is as good as the science it cites. However, quantifications of how features of reference lists affect citations remain sparse. We examined seven numerical characteristics of reference lists of 50,878 research articles published in 17 ecological journals between 1997 and 2017. Over this period, significant changes occurred in reference lists’ features. On average, more recent papers have longer reference lists and cite more high Impact Factor papers and fewer non-journal publications. We also show that highly cited articles across the ecological literature have longer reference lists, cite more recent and impactful references, and include more self-citations. Conversely, the proportion of ‘classic’ papers and non-journal publications cited, as well as the temporal span of the reference list, have no significant influence on articles’ citations. From this analysis, we distill a recipe for crafting impactful reference lists, at least in ecology.

The last glacial period is characterized by a number of millennial climate events that have been identified in both Greenland and Antarctic ice cores and that are abrupt in Greenland climate records. The mechanisms governing this climate variability remain a puzzle that requires a precise synchronization of ice cores from the two hemispheres to be resolved. Previously, Greenland and Antarctic ice cores have been synchronized primarily via their common records of gas concentrations or isotopes from the trapped air and via cosmogenic isotopes measured on the ice. In this work, we apply ice core volcanic proxies and annual layer counting to identify large volcanic eruptions that have left a signature in both Greenland and Antarctica. Generally, no tephra is associated with those eruptions in the ice cores, so the source of the eruptions cannot be identified. Instead, we identify and match sequences of volcanic eruptions with bipolar distribution of sulfate, i.e. unique patterns of volcanic events separated by the same number of years at the two poles. Using this approach, we pinpoint 82 large bipolar volcanic eruptions throughout the second half of the last glacial period (12–60 ka). This improved ice core synchronization is applied to determine the bipolar phasing of abrupt climate change events at decadal-scale precision. In response to Greenland abrupt climatic transitions, we find a response in the Antarctic water isotope signals (δ18O and deuterium excess) that is both more immediate and more abrupt than that found with previous gas-based interpolar synchronizations, providing additional support for our volcanic framework. On average, the Antarctic bipolar seesaw climate response lags the midpoint of Greenland abrupt δ18O transitions by 122±24 years. The time difference between Antarctic signals in deuterium excess and δ18O, which likewise informs the time needed to propagate the signal as described by the theory of the bipolar seesaw but is less sensitive to synchronization errors, suggests an Antarctic δ18O lag behind Greenland of 152±37 years. These estimates are shorter than the 200 years suggested by earlier gas-based synchronizations. As before, we find variations in the timing and duration between the response at different sites and for different events suggesting an interaction of oceanic and atmospheric teleconnection patterns as well as internal climate variability.

The automatic extraction of a patient’s natural history from Electronic Health Records (EHRs) is a critical step towards building intelligent systems that can reason about clinical variables and support decision making. Although EHRs contain a large amount of valuable information about the patient’s medical care, this information can only be fully understood when analyzed in a temporal context. Any intelligent system should then be able to extract medical concepts, date expressions, temporal relations and the temporal ordering of medical events from the free texts of EHRs; yet, this task is hard to tackle, due to the domain specific nature of EHRs, writing quality and lack of structure of these texts, and more generally the presence of redundant information. In this paper, we introduce a new Natural Language Processing (NLP) framework, capable of extracting the aforementioned elements from EHRs written in Spanish using rule-based methods. We focus on building medical timelines, which include disease diagnosis and its progression over time. By using a large dataset of EHRs comprising information about patients suffering from lung cancer, we show that our framework has an adequate level of performance by correctly building the timeline for 843 patients from a pool of 989 patients, achieving a correct result in 85% of instances.

Massively multilingual models for neural machine translation (NMT) are theoretically attractive, but often underperform bilingual models and deliver poor zero-shot translations. In this paper, we explore ways to improve them. We argue that multilingual NMT requires stronger modeling capacity to support language pairs with varying typological characteristics, and overcome this bottleneck via language-specific components and deepening NMT architectures. We identify the off-target translation issue (i.e. translating into a wrong target language) as the major source of the inferior zero-shot performance, and propose random online backtranslation to enforce the translation of unseen training language pairs. Experiments on OPUS-100 (a novel multilingual dataset with 100 languages) show that our approach substantially narrows the performance gap with bilingual models in both one-to-many and many-to-many settings, and improves zero-shot performance by ~10 BLEU, approaching conventional pivot-based methods. Comment: ACL2020

RésumésL’objectif de cet article est de proposer un examen détaillé des apports et des limites de la modélisation en histoire à partir du cas de la Shoah. Il s’appuie sur une enquête qui a permis de reconstituer les « trajectoires de persécution » des 992 Juifs de Lens pendant la Seconde Guerre mondiale, dont 527 seulement ont survécu. 491 ont été arrêtés, 468 ont été déportés et 449 ont été exterminés. Les données prosopographiques sont utilisées ici pour répondre à une question simple : est-il possible de modéliser la persécution ? En d’autres termes, est-il possible de construire une représentation simplifiée mais heuristique des processus causaux complexes qui ont déterminé les chances de survie face à la persécution nazie à partir de données standardisées sur un nombre relativement important d’individus ? L’article discute les apports et les limites d’une succession de méthodes quantifiées : celles qui s’inscrivent dans ce qu’Andrew Abbott appelle le « programme standard » des sciences sociales, ainsi que l’analyse des réseaux et l’analyse séquentielle. Pour chacune d’entre elles, sont plus particulièrement discutées les manières de rendre compte des interactions entre les individus, de l’historicité des comportements et des processus déterminant ces chances de survie. Les tentatives de modélisation à partir de données historiennes apportent ainsi de véritables renouvellements de connaissances, notamment lorsqu’elles sont menées de manière cumulative sur une même enquête. En passant d’une logique de propriétés individuelles à une logique de trajectoires interconnectées, ces approches permettent de mieux comprendre les interactions sociales et locales, et offrent ainsi des perspectives stimulantes pour la microhistoire de l’Holocauste.