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The arrival of Modern Humans (MHs) in Europe between 50 ka and 36 ka coincides with significant changes in human behaviour, regarding the production of tools, the exploitation of resources and the systematic use of ornaments and colouring substances. The emergence of the so-called modern behaviours is usually associated with MHs, although in these last decades findings relating to symbolic thinking of pre-Sapiens groups have been claimed. In this paper we present a synthesis of the Italian evidence concerning bone manufacturing and the use of ornaments and pigments in the time span encompassing the demise of Neandertals and their replacement by MHs. Current data show that Mousterian bone tools are mostly obtained from bone fragments used as is. Conversely an organized production of fine shaped bone tools is characteristic of the Uluzzian and the Protoaurignacian, when the complexity inherent in the manufacturing processes suggests that bone artefacts are not to be considered as expedient resources. Some traces of symbolic activities are associated to Neandertals in Northern Italy. Ornaments (mostly tusk shells) and pigments used for decorative purposes are well recorded during the Uluzzian. Their features and distribution witness to an intriguing cultural homogeneity within this technocomplex. The Protoaurignacian is characterized by a wider archaeological evidence, consisting of personal ornaments (mostly pierced gastropods), pigments and artistic items.

Graph-based semantic parsing aims to represent textual meaning through directed graphs. As one of the most promising general-purpose meaning representations, these structures and their parsing have gained a significant interest momentum during recent years, with several diverse formalisms being proposed. Yet, owing to this very heterogeneity, most of the research effort has focused mainly on solutions specific to a given formalism. In this work, instead, we reframe semantic parsing towards multiple formalisms as Multilingual Neural Machine Translation (MNMT), and propose SGL, a many-to-many seq2seq architecture trained with an MNMT objective. Backed by several experiments, we show that this framework is indeed effective once the learning procedure is enhanced with large parallel corpora coming from Machine Translation: we report competitive performances on AMR and UCCA parsing, especially once paired with pre-trained architectures. Furthermore, we find that models trained under this configuration scale remarkably well to tasks such as cross-lingual AMR parsing: SGL outperforms all its competitors by a large margin without even explicitly seeing non-English to AMR examples at training time and, once these examples are included as well, sets an unprecedented state of the art in this task. We release our code and our models for research purposes at https://github.com/SapienzaNLP/sgl.

AbstractThe archaeological site of Lepenski Vir is widely known after its remarkable stone art sculptures that represent a unique and unprecedented case of Holocene hunter-gatherer creativity. These artworks were found largely associated with equally unique trapezoidal limestone building floors around their centrally located rectangular stone-lined hearths. A debate has raged since the discovery of the site about the chronological place of various discovered features. While over years different views from that of the excavator about the stratigraphy and chronology of the site have been put forward, some major disagreements about the chronological position of the features that make this site a key point of reference in European Prehistory persist. Despite challenges of re-analyzing the site’s stratigraphy from the original excavation records, taphonomic problems, and issues of reservoir offsets when providing radiocarbon measurements on human and dog bones, our targeted AMS (Accelerator Mass Spectrometry) dating of various contexts from this site with the application of Bayesian statistical modelling allows us to propose with confidence a new and sound chronological framework and provide formal estimates for several key developments represented in the archaeological record of Lepenski Vir that help us in understanding the transition of last foragers to first farmers in southeast Europe as a whole.

In Text-to-AMR parsing, current state-of-the-art semantic parsers use cumbersome pipelines integrating several different modules or components, and exploit graph recategorization, i.e., a set of content-specific heuristics that are developed on the basis of the training set. However, the generalizability of graph recategorization in an out-of-distribution setting is unclear. In contrast, state-of-the-art AMR-to-Text generation, which can be seen as the inverse to parsing, is based on simpler seq2seq. In this paper, we cast Text-to-AMR and AMR-to-Text as a symmetric transduction task and show that by devising a careful graph linearization and extending a pretrained encoder-decoder model, it is possible to obtain state-of-the-art performances in both tasks using the very same seq2seq approach, i.e., SPRING (Symmetric PaRsIng aNd Generation). Our model does not require complex pipelines, nor heuristics built on heavy assumptions. In fact, we drop the need for graph recategorization, showing that this technique is actually harmful outside of the standard benchmark. Finally, we outperform the previous state of the art on the English AMR 2.0 dataset by a large margin: on Text-to-AMR we obtain an improvement of 3.6 Smatch points, while on AMR-to-Text we outperform the state of the art by 11.2 BLEU points. We release the software at github.com/SapienzaNLP/spring.

The role of task has received special attention in visual cognition research because it can provide causal explanations of goal-directed eye-movement responses. The dependency between visual attention and task suggests that eye-movements can be used to classify the task being performed. A recent study by Greene, Liu, and Wolfe (2012), however, fails to achieve accurate classification of visual tasks based on eye-movement features. In the present study, we hypothesize that tasks can be successfully classified when they differ with the respect to the involvement of other cognitive domains such as language processing. We extract the eye-movement features used by Greene et. al., as well as additional features, from the data of three different tasks: visual search, object naming, and scene description. First, we demonstrate that eye-movement responses make it possible to characterize the goals of these tasks. Then, we train three different types of classifiers and predict the task participants performed with an accuracy well above chance (a maximum of 88% for visual search). An analysis of the relative importance of features for classification accuracy reveals that just one feature, i.e., initiation time, is sufficient for above-chance performance (a maximum of 79% accuracy in object naming). Crucially, this feature is independent of task duration, which differs systematically across the three tasks we investigate. Overall, the best task classification performance is obtained with a set of seven features that include both spatial information (e.g., entropy of attention allocation) and temporal components (e.g., total fixation on objects) of the eye-movement record. This result confirms the taskdependent allocation of visual attention, and extends previous work by showing that task classification is possible when tasks differ in the cognitive processes involved (purely visual tasks such as search vs. communicative tasks such as scene description). Keywords: Task classification; eye-movement features; active vision; visual attention; communicative tasks.

Large sense-annotated datasets are increasingly necessary for training deep supervised systems in Word Sense Disambiguation. However, gathering high-quality sense-annotated data for as many instances as possible is a laborious and expensive task. This has led to the proliferation of automatic and semi-automatic methods for overcoming the so-called knowledge-acquisition bottleneck. In this short survey we present an overview of sense-annotated corpora, annotated either manually- or (semi)automatically, that are currently available for different languages and featuring distinct lexical resources as inventory of senses, i.e. WordNet, Wikipedia, BabelNet. Furthermore, we provide the reader with general statistics of each dataset and an analysis of their specific features. 7 pages, 1 figure, 1 table

The Future Circular Collider (FCC-hh) is being designed as a 100 km ring that should collide 50 TeV proton beams. At 8.3 GJ, its stored beam energy will be a factor 28 higher than what has been achieved in the Large Hadron Collider, which has the highest stored beam energy among the colliders built so far. This puts unprecedented demands on the control of beam losses and collimation, since even a tiny beam loss risks quenching superconducting magnets. We present in this article the design of the FCC-hh collimation system and study the beam cleaning through simulations of tracking, energy deposition, and thermo-mechanical response. We investigate the collimation performance for design beam loss scenarios and potential bottlenecks are highlighted. Proceedings of the 10th Int. Particle Accelerator Conf., IPAC2019, Melbourne, Australia

The knowledge acquisition bottleneck strongly affects the creation of multilingual sense-annotated data, hence limiting the power of supervised systems when applied to multilingual Word Sense Disambiguation. In this paper, we propose a semi-supervised approach based upon a novel label propagation scheme, which, by jointly leveraging contextualized word embeddings and the multilingual information enclosed in a knowledge base, projects sense labels from a high-resource language, i.e., English, to lower-resourced ones. Backed by several experiments, we provide empirical evidence that our automatically created datasets are of a higher quality than those generated by other competitors and lead a supervised model to achieve state-of-the-art performances in all multilingual Word Sense Disambiguation tasks. We make our datasets available for research purposes at https://github.com/SapienzaNLP/mulan.

European cultural heritage (CH) is at risk, threatened by environmental processes strengthened by climate change and anthropogenic pressure. In particular, the slow (landslides, subsidence) and seismic (earthquakes) movements of the soil have a strong impact on the structural stability of our cultural heritage (CH). The actions to be carried out to protect and safeguard CH are in continuous development and this is where the STABLE (STructural stABiLity risk assEssment) project fits. STABLE concerns the design and development of a thematic platform, which combines structural stability models, simulation and damage assessment tools, advanced remote sensing, in situ monitoring technologies, geotechnical and cadastral data sets with the WebGIS application for mapping and long-term monitoring of the CH. The thematic platform, which is the final objective of the project, will therefore support the authorities responsible for the conservation of cultural heritage in the design and implementation of policies for monitoring, preserving and safeguarding our heritage. This will allow effective monitoring and management of CH to prevent or at least reduce the possible irreparable damages. STABLE will coordinate existing skills and research in a synergistic plan of collaborations and staff exchanges to offer a complete transfer of knowledge and training to researchers in the specific area under study. The development of the platform will be the strategy that scientists will have to follow to share and improve CH safeguard methods. It will serve professionals to apply the most advanced technologies in their fields.