Robustness of Device Independent Dimension Witnesses

Device independent dimension witnesses provide a lower bound on the dimensionality of classical and quantum systems in a "black box" scenario where only correlations between preparations, measurements and outcomes are considered. We address the problem of the robustness of dimension witnesses, namely that to witness the dimension of a system or to discriminate between its quantum or classical nature, even in the presence of loss. We consider the case when shared randomness is allowed between preparations and measurements and we provide a threshold in the detection efficiency such that dimension witnessing can still be performed.Comment: 8 pages, 5 figures, published versio

CheckIT!: A Corpus of Expert Fact-checked Claims for Italian

This paper introduces CheckIT!, a resource of expert fact-checked claims, filling a gap for the development of fact-checking pipelines in Italian. We further investigate the use of three state-of-the-art generative text models to create variations of claims in zero-shot settings as a data-augmentation strategy for the identification of previously fact-checked claims. Our results indicate that models struggles in varying the surface forms of the claims

EVALITA 2020: Overview of the 7th evaluation campaign of natural language processing and speech tools for Italian

The Evaluation Campaign of Natural Language Processing and Speech Tools for Italian (EVALITA) is the biennial initiative aimed at promoting the development of language and speech technologies for the Italian language. EVALITA is promoted by the Italian Association of Computational Linguistics (AILC) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA) and the Italian Association for Speech Sciences (AISV). EVALITA provides a shared framework where different systems and approaches can be scientifically evaluated and compared with each other with respect to a large variety of tasks, suggested and organized by the Italian research community. The proposed tasks represent scientific challenges where methods, resources, and systems can be tested against shared benchmarks representing linguistic open issues or real world applications, possibly in a multilingual and/or multi-modal perspective. The collected data sets provide big opportunities for scientists to explore old and new problems concerning NLP in Italian as well as to develop solutions and to discuss the NLP-related issues within the community. Some tasks are traditionally present in the evaluation campaign, while others are completely new. This paper introduces the tasks proposed at EVALITA 2020 and provides an overview to the participants and systems whose descriptions and obtained results are reported in these Proceedings

Multi-neuronal refractory period adapts centrally generated behaviour to reward

Oscillating neuronal circuits, known as central pattern generators (CPGs), are responsible for generating rhythmic behaviours such as walking, breathing and chewing. The CPG model alone however does not account for the ability of animals to adapt their future behaviour to changes in the sensory environment that signal reward. Here, using multi-electrode array (MEA) recording in an established experimental model of centrally generated rhythmic behaviour we show that the feeding CPG of Lymnaea stagnalis is itself associated with another, and hitherto unidentified, oscillating neuronal population. This extra-CPG oscillator is characterised by high population-wide activity alternating with population-wide quiescence. During the quiescent periods the CPG is refractory to activation by food-associated stimuli. Furthermore, the duration of the refractory period predicts the timing of the next activation of the CPG, which may be minutes into the future. Rewarding food stimuli and dopamine accelerate the frequency of the extra-CPG oscillator and reduce the duration of its quiescent periods. These findings indicate that dopamine adapts future feeding behaviour to the availability of food by significantly reducing the refractory period of the brain's feeding circuitry

Dataset for multimodal fake news detection and verification tasks

The proliferation of online disinformation and fake news, particularly in the context of breaking news events, demands the development of effective detection mechanisms. While textual content remains the predominant medium for disseminating misleading information, the contribution of other modalities is increasingly emerging within online outlets and social media platforms. However, multimodal datasets, which incorporate diverse modalities such as texts and images, are not very common yet, especially in low-resource languages. This study addresses this gap by releasing a dataset tailored for multimodal fake news detection in the Italian language. This dataset was originally employed in a shared task on the Italian language. The dataset is divided into two data subsets, each corresponding to a distinct sub-task. In sub-task 1, the goal is to assess the effectiveness of multimodal fake news detection systems. Sub-task 2 aims to delve into the interplay between text and images, specifically analyzing how these modalities mutually influence the interpretation of content when distinguishing between fake and real news. Both sub-tasks were managed as classification problems. The dataset consists of social media posts and news articles. After collecting it, it was labeled via crowdsourcing. Annotators were provided with external knowledge about the topic of the news to be labeled, enhancing their ability to discriminate between fake and real news. The data subsets for sub-task 1 and sub-task 2 consist of 913 and 1350 items, respectively, encompassing newspaper articles and tweets

Evaluating Pre-Trained Transformers on Italian Administrative Texts

In recent years, Transformer-based models have been widely used in NLP for various downstream tasks and in different domains. However, a language model explicitly built for the Italian administrative language is still lacking. Therefore, in this paper, we decided to compare the performance of five different Transformer models, pre-trained on general purpose texts, on two main tasks in the Italian administrative domain: Name Entity Recognition and multi-label document classification on Public Administration (PA) documents. We evaluate the performance of each model on both tasks to identify the best model in this particular domain. We also discuss the effect of model size and pre-training data on the performances on domain data. Our evaluation identifies UmBERTo as the best-performing model, with an accuracy of 0.71, an F1 score of 0.89 for multi-label document classification, and an F1 score of 0.87 for NER-PA

Bioengineering of Humanized Bone Marrow Microenvironments in Mouse and Their Visualization by Live Imaging

Human hematopoietic stem cells (HSCs) reside in the bone marrow (BM) niche, an intricate, multifactorial network of components producing cytokines, growth factors, and extracellular matrix. The ability of HSCs to remain quiescent, self-renew or differentiate, and acquire mutations and become malignant depends upon the complex interactions they establish with different stromal components. To observe the crosstalk between human HSCs and the human BM niche in physiological and pathological conditions, we designed a protocol to ectopically model and image a humanized BM niche in immunodeficient mice. We show that the use of different cellular components allows for the formation of humanized structures and the opportunity to sustain long-term human hematopoietic engraftment. Using two-photon microscopy, we can live-image these structures in situ at the single-cell resolution, providing a powerful new tool for the functional characterization of the human BM microenvironment and its role in regulating normal and malignant hematopoiesis

Caratteristiche sismostratigrafiche di strutture sedimentarie diagnostiche dicorrenti di fondo nell\u2019off-shore del Golfo di Taranto.

L\u2019analisi integrata di dati multibeam e sismici ad altissima risoluzione (Chirp Sub-Bottom), acquisiti in un settore del Golfo di Taranto (Mare Ionio), ha permesso di identificare e classificare strutture sedimentarie diagnostiche di correnti di fondo (conturiti), formatesi nel tardo Quaternario. La classificazione proposta \ue8 basata sia su criteri sismo-stratigrafici che sulla comparazione con strutture analoge documentate da precedenti autori nei bacini oceanici [Faugeres et al., 1999 con rif.]. Nell\u2019area in esame sono stati identificati quattro settori (Alto dell\u2019Amendolara, Bacino di Corigliano, Bacino dell\u2019Amendolara ed Alto di Rossano-Cariati) caratterizzati da morfologia, pendenza e profondit\ue0 differente. Le strutture conturitiche sono state riconosciute prevalentemente nei settori NW e SE dell\u2019alto dell\u2019Amendolara, ad una profondit\ue0 compresa tra 130 m e 400 m e le geometrie interne ed esterne mostrano caratteristiche deposizionali ed erosive. Sono state classificate come sheeted drift le strutture sviluppate subparallelamente al profilo batimetrico, infill drift ed elongated drift le strutture caratterizzate da fosse ben sviluppate ed elementi erosivi quali fosse ed abraded surface. Sono stati osservati inoltre osservati sediment waves nel settore SW dell\u2019alto strutturale. Dall\u2019analisi integrata dei nuovi dati con quelli disponibili in letteratura \ue8 possibile ipotizzare che i fattori che hanno maggiormente influenzato tipologia, distribuzione areale e batimetrica dei depositi conturitici e degli elementi erosivi sono: a) morfologia del fondo marino; b) caratteristiche dei sedimenti (es. tessitura); c) variazione della velocit\ue0 della \u201cLevantine Intermediate Water come conseguenza delle d) variazioni eustatiche. Sono stati inoltre applicati in cascata i modelli bidimensionali CMS- Wave [Lin et al, 2006] per la propagazione dello spettro d\u2019onda, e CMS- Flow [Buttolph et al, 2006] per la circolazione interna, prendendo in considerazione i dati meteo marini forniti dall\u2019ECMWF nel punto di coordinate 39,5\ub0N, 17\ub0E, in modo da valutare le condizioni idrodinamiche in prossimit\ue0 del paraggio in esame. L\u2019integrazione dei dati indicati geologici/geofisici e dei risultati del codice di calcolo numerico ha permesso di ipotizzare un modello di circolazione della corrente \u201cLevantine Intermediate Water\u201d e valutare l\u2019influenza delle morfostrutture sulla circolazione delle acque profonde durante l\u2019ultima fase di abbassamento e stazionamento basso del livello del mare. Bibliografia Buttolph, A.,D., Reed, C.W., Kraus N., Wamsley, T.V., Ono, N., Larson, M.,Camenen, B., Hanson, H. Zundel, A.K., (2006). Two-Dimensional Depth-Averaged Circulation Model CMS-M2D: Version 3.0, Report 2, Sediment Transport and Morphology Change. ERDC/CHL TR-06-9 Vicksburg, MS: U.S. Army Engineer Research and Development Center. Lin, L., H. Mase, F. Yamada, and Z. Demirbilek. (2006). Wave-action balance equation diffraction (WABED) model: Tests of wave diffraction and reflection at inlets. Coastal and Hydraulics Engineering Technical Note ERDC/CHL CHETN-III-73. Vicksburg, MS: U.S. Army Engineer Research and Development Center. Faug\ue8res, J.C., Stow, D.A.V., Imbert, P., Viana, A.R. (1999). Seismic feature diagnostic of contourite drifts. Marine Geology 162, pp. 1-38

Challenging specialized transformers on zero-shot classification

This paper investigates the feasibility of employing basic prompting systems for domain-specific language models. The study focuses on bureaucratic language and uses the recently introduced BureauBERTo model for experimentation. The experiments reveal that while further pre-trained models exhibit reduced robustness concerning general knowledge, they display greater adaptability in modeling domain-specific tasks, even under a zero-shot paradigm. This demonstrates the potential of leveraging simple prompting systems in specialized contexts, providing valuable insights both for research and industry