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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.

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

AbstractThis paper provides results from a suite of analyses made on human dental material from the Late Palaeolithic to Neolithic strata of the cave site of Grotta Continenza situated in the Fucino Basin of the Abruzzo region of central Italy. The available human remains from this site provide a unique possibility to study ways in which forager versus farmer lifeways affected human odonto-skeletal remains. The main aim of our study is to understand palaeodietary patterns and their changes over time as reflected in teeth. These analyses involve a review of metrics and oral pathologies, micro-fossils preserved in the mineralized dental plaque, macrowear, and buccal microwear. Our results suggest that these complementary approaches support the assumption about a critical change in dental conditions and status with the introduction of Neolithic foodstuff and habits. However, we warn that different methodologies applied here provide data at different scales of resolution for detecting such changes and a multipronged approach to the study of dental collections is needed for a more comprehensive and nuanced understanding of diachronic changes.

Object detection and identification are fundamental to human vision, and there is mounting evidence that objects guide the allocation of visual attention. However, the role of objects in tasks involving multiple modalities is less clear. To address this question, we investigate object naming, a task in which participants have to verbally identify objects they see in photorealistic scenes. We report an eye-tracking study that investigates which features (attentional, visual, and linguistic) influence object naming. We need that the amount of visual attention directed towards an object, its position and saliency, along with linguistic factors such as word frequency, animacy, and semantic proximity, significantly influence whether the object will be named or not. We then ask how features from different modalities are combined during naming, and significant interactions between saliency and position, saliency and linguistic features,and attention and position. We conclude that when the cognitive systemperforms tasks such as object naming, it uses input from one modalityto constraint or enhance the processing of other modalities, rather thanprocessing each input modality independently.

The lexicons of human languages organize their units at two distinct levels. At a first combinatorial level, meaningless forms (typically referred to as phonemes) are combined into meaningful units (typically referred to as morphemes). Thanks to this, many morphemes can be obtained by relatively simple combinations of a small number of phonemes. At a second compositional level of the lexicon, morphemes are composed into larger lexical units, the meaning of which is related to the individual meanings of the composing morphemes. This duality of patterning is not a necessity for lexicons and the question remains wide open regarding how a population of individuals is able to bootstrap such a structure and the evolutionary advantages of its emergence. Here we address this question in the framework of a multi-agents model, where a population of individuals plays simple naming games in a conceptual environment modeled as a graph. We demonstrate that errors in communication as well as a blending repair strategy, which crucially exploits a shared conceptual representation of the environment, are sufficient conditions for the emergence of duality of patterning, that can thus be explained in a pure cultural way. Compositional lexicons turn out to be faster to lead to successful communication than purely combinatorial lexicons, suggesting that meaning played a crucial role in the evolution of language.