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Almost entirely overlooked throughout the 20th century, neglected by contemporary medical manuals, the clitoris has gradually returned centre stage thanks to Western feminism.

Web accessibility has only recently begun to be considered as a key component in the task of the web localiser and, crucially, in the assessment of localisation quality. The ALMA research project (Approaching Localisation by Means of Accessibility) seeks to address this gap by gradually but comprehensively introducing accessibility awareness, issues and perspectives in the principles and procedures of localisation. One of the approaches of ALMA focuses on localiser education and aims at both integrating web accessibility as content to be transferred in the process of localisation and as a methodological way of rethinking website analysis and interlingual, intercultural, intersemiotic transformation. This would allow localisation students to observe the interrelation between the different semiotic, temporal, spatial or ergodic elements coded in the product, with the aim of being perceived, understood and operated by users through different modalities, senses, capacities and technologies. In this chapter, the specific example of culture and heritage websites is used to illustrate how the social and technological dimensions of multimodal translation, localisation and accessibility converge. By exploring the interrelation of web accessibility, localiser education, Universal Design for Learning, and culture and heritage websites, we conclude that such combination can provide a critical opportunity to enhance accessibility and learning at various levels: as an outcome of localisation training (more accessible multilingual culture and heritage websites), as a motivational driver for all students to access and be engaged in education, as an accessibility-aware mindset and methodology (better and deeper access to training materials), as well as an excellent interdisciplinary tool.

A recent trend in health-related machine learning proposes the use of Graph Neural Networks (GNN’s) to model biomedical data. This is justified due to the complexity of healthcare data and the modelling power of graph abstractions. Thus, GNN’s emerge as the natural choice to learn from increasing amounts of healthcare data. While formulating the problem, however, there are usually multiple design choices and decisions that can affect the final performance. In this work, we focus on Clinical Trial (CT) protocols consisting of hierarchical documents, containing free text as well as medical codes and terms, and design a classifier to predict each CT protocol termination risk as “low” or “high”. We show that while using GNN’s to solve this classification task is very successful, the way the graph is constructed is also of importance and one can benefit from making a priori useful information more explicit. While a natural choice is to consider each CT protocol as an independent graph and pose the problem as a graph classification, consistent performance improvements can be achieved by considering them as super-nodes in one unified graph and connecting them according to some metadata, like similar medical condition or intervention, and finally approaching the problem as a node classification task rather than graph classification. We validate this hypothesis experimentally on a large-scale manually labeled CT database. This provides useful insights on the flexibility of graph-based modeling for machine learning in the healthcare domain.

The topicality of cases of infanticide invites us to question the historical origin of the madness of motherhood.

Disseminated by biblical and classical sources (and by Aristotle in particular), the image of an uncultivated, barbarian north shaped medieval socio-geography. Based on climatic considerations, it seemed obvious that these cold zones did not allow for the establishment of civilization and rationality. At the end of the Middle Ages, however, Scottish intellectuals who were embarrassed by this prevailing image started to defy it. In order to inscribe themselves into the realm of European civilization, they presented, on the one hand, a sometimes quite creative reshaping of the climatic scheme. On the other hand, they reinforced an alternative explanation for cultural differences and varying states of rationality, resorting to a conception of different natures within the human species. After introducing two exponents of these developments in the 16th century ( John Mair and Hector Boece), this paper turns to the Scottish Enlightenment of the late 18 th century. Focusing on David Hume and Adam Ferguson, it describes how these same two arguments, based on either climate or inherent nature, still governed the discussion about different states of civilization in this period, the latter alternative having now been developed into a full-fledged theory of race.

We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of hierarchical documents using the permutation invariant message passing operations, we show that we can gain extra performance improvements using our proposed selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents. We applied our model to classify clinical trial (CT) protocols into completed and terminated categories. We use bag-of-words based, as well as pre-trained transformer-based embeddings to featurize the graph nodes, achieving f1-scores around 0.85 on a publicly available large scale CT registry of around 360K protocols. We further demonstrate how the selective pooling can add insights into the CT termination status prediction. We make the source code and dataset splits accessible.

The discoveries at Ounjougou (Mali), an open-air site in the Dogon Country, shed new light on the “early Neolithic” in Africa. The stratigraphic sequence and a cluster of absolute dates established a terminus ante quem of 9400 cal bc for ceramic sherds associated with a small bifacial lithic industry. The emergence of this typo-technical complex corresponds to one of the wet phases of the Pleistocene–Holocene transition in West Africa, most probably that of the climatic upturn at the beginning of the Holocene, between 10,200 and 9,400 cal bc. Paleoenvironmental results, particularly archaeobotanical ones, indicate that the landscape was in a state of change and that, for several millennia, the surfaces covered by desert overlapped an open steppe with grasses, some of which were edible. This environmental situation allowed the dispersion of prehistoric groups over the continent and probably encouraged a new behavior: the practice of intensive selective gathering (i.e., the targeted and rational harvesting of wild grasses for their seeds). However, not only must seeds be kept dry and protected from rodents, they must also be processed through cooking or fermentation. This process helps the human body to assimilate the starch, as the digestive enzymes necessary for its digestion are not naturally present. Ceramics would have been particularly useful in this process. Ceramics emerged in sub-Saharan Africa and seem to have spread toward the central Sahara during the early Holocene at the end of the 10th and the beginning of the 9th millennium cal bc, while the desert zone became increasingly greener. It has yet to be understood whether the Nile Valley was an important corridor for the diffusion of this technology or if ceramics appeared as the result of a second independent process of innovation.

Almost entirely overlooked throughout the 20th century, neglected by contemporary medical manuals, the clitoris has gradually returned centre stage thanks to Western feminism.