A Recurrent Deep Neural Network Model to measure Sentence Complexity for the Italian Language

Text simplification (TS) is a natural language processing task devoted to the modification of a text in such a way that the grammar and structure of the phrases is greatly simplified, preserving the underlying meaning and information contents. In this paper we give a contribution to the TS field presenting a deep neural network model able to detect the complexity of italian sentences. In particular, the system gives a score to an input text that identifies the confidence level during the decision making process and that could be interpreted as a measure of the sentence complexity. Experiments have been carried out on one public corpus of Italian texts created specifically for the task of TS. We have also provided a comparison of our model with a state of the art method used for the same purpos

Neural networks as building blocks for the design of efficient learned indexes

The new area of Learned Data Structures consists of mixing Machine Learning techniques with those specific to Data Structures, with the purpose to achieve time/space gains in the performance of those latter. The perceived paradigm shift in computer architectures, that would favor the employment of graphics/tensor units over traditional central processing units, is one of the driving forces behind this new area. The advent of the corresponding branch-free programming paradigm would then favor the adoption of Neural Networks as the fundamental units of Classic Data Structures. This is the case of Learned Bloom Filters. The equally important field of Learned Indexes does not appear to make use of Neural Networks at all. In this paper, we offer a comparative experimental investigation regarding the potential uses of Neural Networks as a fundamental building block of Learned Indexes. Our results provide a solid and much-needed evaluation of the role Neural Networks can play in Learned Indexing. Based on our findings, we highlight the need for the creation of highly specialised Neural Networks customised to Learned Indexes. Because of the methodological significance of our findings and application of Learned Indexes in strategic domains, such as Computer Networks and Databases, care has been taken to make the presentation of our results accessible to the general audience of scientists and engineers working in Neural Networks and with no background about Learned Indexing

Immersive Virtual Reality for Cultural Heritage Exploration

Virtual Reality can provide an immersive experience that allows cultural heritage to be experienced in a more realistic and immersive way than traditional showcasing techniques. The objective of this paper is to provide a software pipeline that can be adopted for the realization of immersive experiences in cultural heritage sites. This work has been realized within the 3DLab-Sicilia project, which includes the realization of immersive virtual tours of UNESCO World Heritage sites located in the Sicily area (Italy)

Learned Sorted Table Search and Static Indexes in Small-Space Data Models †

Machine-learning techniques, properly combined with data structures, have resulted in Learned Static Indexes, innovative and powerful tools that speed up Binary Searches with the use of additional space with respect to the table being searched into. Such space is devoted to the machine-learning models. Although in their infancy, these are methodologically and practically important, due to the pervasiveness of Sorted Table Search procedures. In modern applications, model space is a key factor, and a major open question concerning this area is to assess to what extent one can enjoy the speeding up of Binary Searches achieved by Learned Indexes while using constant or nearly constant-space models. In this paper, we investigate the mentioned question by (a) introducing two new models, i.e., the Learned k-ary Search Model and the Synoptic Recursive Model Index; and (b) systematically exploring the time–space trade-offs of a hierarchy of existing models, i.e., the ones in the reference software platform Searching on Sorted Data, together with the new ones proposed here. We document a novel and rather complex time–space trade-off picture, which is informative for users as well as designers of Learned Indexing data structures. By adhering to and extending the current benchmarking methodology, we experimentally show that the Learned k-ary Search Model is competitive in time with respect to Binary Search in constant additional space. Our second model, together with the bi-criteria Piece-wise Geometric Model Index, can achieve speeding up of Binary Search with a model space of (Formula presented.) more than the one taken by the table, thereby, being competitive in terms of the time–space trade-off with existing proposals. The Synoptic Recursive Model Index and the bi-criteria Piece-wise Geometric Model complement each other quite well across the various levels of the internal memory hierarchy. Finally, our findings stimulate research in this area since they highlight the need for further studies regarding the time–space relation in Learned Indexes

Towards a deep-learning-based methodology for supporting satire detection

This paper describes an approach for supporting automatic satire detection through effective deep learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection problems. We both trained and tested the system exploiting articles derived from two important satiric blogs, Lercio and IlFattoQuotidiano, and significant Italian newspapers

Metric Learning in Histopathological Image Classification: Opening the Black Box

The application of machine learning techniques to histopathology images enables advances in the field, providing valuable tools that can speed up and facilitate the diagnosis process. The classification of these images is a relevant aid for physicians who have to process a large number of images in long and repetitive tasks. This work proposes the adoption of metric learning that, beyond the task of classifying images, can provide additional information able to support the decision of the classification system. In particular, triplet networks have been employed to create a representation in the embedding space that gathers together images of the same class while tending to separate images with different labels. The obtained representation shows an evident separation of the classes with the possibility of evaluating the similarity and the dissimilarity among input images according to distance criteria. The model has been tested on the BreakHis dataset, a reference and largely used dataset that collects breast cancer images with eight pathology labels and four magnification levels. Our proposed classification model achieves relevant performance on the patient level, with the advantage of providing interpretable information for the obtained results, which represent a specific feature missed by the all the recent methodologies proposed for the same purpose

Extracellular vesicle microRNAs contribute to the osteogenic inhibition of mesenchymal stem cells in multiple myeloma

Osteolytic bone disease is the major complication associated with the progression of multiple myeloma (MM). Recently, extracellular vesicles (EVs) have emerged as mediators of MM-associated bone disease by inhibiting the osteogenic differentiation of human mesenchymal stem cells (hMSCs). Here, we investigated a correlation between the EV-mediated osteogenic inhibition and MM vesicle content, focusing on miRNAs. By the use of a MicroRNA Card, we identified a pool of miRNAs, highly expressed in EVs, from MM cell line (MM1.S EVs), expression of which was confirmed in EVs from bone marrow (BM) plasma of patients affected by smoldering myeloma (SMM) and MM. Notably,we found that miR-129-5p, which targets different osteoblast (OBs) differentiation markers, is enriched in MM-EVs compared to SMM-EVs, thus suggesting a selective packaging correlated with pathological grade. We found that miR-129-5p can be transported to hMSCs by MM-EVs and, by the use of miRNA mimics, we investigated its role in recipient cells. Our data demonstrated that the increase of miR-129-5p levels in hMSCs under osteoblastic differentiation stimuli inhibited the expression of the transcription factor Sp1, previously described as a positive modulator of osteoblastic differentiation, and of its target the Alkaline phosphatase (ALPL), thus identifying miR-129-5p among the players of vesicle-mediated bone disease

Spread of tomato brown rugose fruit virus in sicily and evaluation of the spatiotemporal dispersion in experimental conditions

Tomato brown rugose fruit virus (ToBRFV) is an emerging pathogen that causes severe disease in tomato (Solanum lycopersicum L.) crops. The first ToBRFV outbreak in Italy occurred in 2018 in several Sicilian provinces, representing a serious threat for tomato production. In the present work, the spatiotemporal displacement of ToBRFV in Sicily was evaluated, analyzing a total of 590 lots of tomato seed, 982 lots of plantlets from nurseries and 100 commercial greenhouses. Furthermore, we investigated the ToBRFV spreading dynamic in a greenhouse under experimental conditions. Results showed several aspects related to ToBRFV dispersion in protected tomato crops. In detail, an important decrease of the ToBRFV-infected seed and plantlet lots was detected. Regarding the examined commercial greenhouses, ToBRFV still appears to be present in Sicily, although there has been a decrease during monitoring. In experimental conditions, it was demonstrated that the presence of few infected plants are sufficient to damage the entire crop in a short time, reaching almost 100% of infection

A New Dissimilarity Measure for Clustering Seismic Signals

Hypocenter and focal mechanism of an earthquake can be determined by the analysis of signals, named waveforms, related to the wave field produced and recorded by a seismic network. Assuming that waveform similarity implies the similarity of focal parameters, the analysis of those signals characterized by very similar shapes can be used to give important details about the physical phenomena which have generated an earthquake. Recent works have shown the effectiveness of cross-correlation and/or cross-spectral dissimilarities to identify clusters of seismic events. In this work we propose a new dissimilarity measure between seismic signals whose reliability has been tested on real seismic data by computing external and internal validation indices on the obtained clustering. Results show its superior quality in terms of cluster homogeneity and computational time with respect to the largely adopted cross correlation dissimilarit

Molecular mimicry in the post-COVID-19 signs and symptoms of neurovegetative disorders?

Many individuals who have severe forms of COVID-19 experience a suite of neurovegetative signs and symptoms (eg, tachycardia) after their recovery, suggesting that the imbalance of the sympathetic-parasympathetic activity of the autonomic nervous system1 could continue for many weeks or months after respiratory symptoms stop. Moreover, a reduction of the parasympathetic tone could have a role in restricting the cholinergic anti-inflammatory pathway, thus favouring hyperinflammation and cytokine storm in the most severe phases of the disease. As reported by Guglielmo Lucchese in The Lancet Microbe,2 SARS-CoV-2 can damage the nervous system via an indirect mechanism, resulting in a high prevalence of autoantibodies, mainly against unknown autoantigens in the brain, in cerebrospinal fluid from patients with neurological complications.2 The cause of low vagal tone and SARS-CoV-2 has not yet been investigated sufficiently and here we would like to share some original data supporting the putative role of molecular mimicry as the culprit of COVID-19 pathogenesis, including the post-COVID-19 neurovegetative syndrome.2, 3, 4, 5 Using methods that have been previously described,3 we looked specifically at the human proteins expressed in vagal nuclei and ganglia. As shown in the appendix (pp 1–2), we found that 22 of these proteins share peptides that could putatively generate a T-cell or B-cell driven autoimmune response. The location and function of these proteins are described in the appendix (pp 3–24). Fibres of the vagal nerve originate from four nuclei located in the medulla oblongata—ie, the dorsal motor nucleus, the nucleus ambiguus, the solitary nucleus, and, to a lesser extent, the spinal trigeminal nucleus. These fibres contribute to the somatic and visceral motricity, somatic and visceral sensibility, and the sense of taste. The visceral motor inputs originate specifically from the dorsal motor nucleus and nucleus ambiguus and are directed towards the heart, the airways, and the gastrointestinal system. Moreover, the vagal visceral innervation includes two sensory ganglia of the peripheral nervous system—the nodose ganglion and the jugular ganglion. In particular, peripheral fibres of the neurons of the nodose ganglion not only innervate the taste buds on the epiglottis, the chemoreceptors of the aortic bodies, and baroreceptors in the aortic arch, but they also provide sensory innervation to the circulatory, respiratory, and gastrointestinal systems. An impairment of the vagal innervation of the heart can lead to tachycardia at rest, which is often seen by clinicians during physical examination of patients who have recovered from a severe form of COVID-19.1 We found that the dorsal motor nucleus, nucleus ambiguus, nodose ganglion, and jugular ganglion can all host neurons presenting proteins with epitopes in common with SARS-CoV-2 proteins, and the peptide TGRLQSL is embedded in one immunoreactive linear epitope that has already been experimentally validated in the human host (Immune Epitope Database and Analysis Resource identification number 36724) to be able to generate an autoimmune response. We share our findings to prompt further studies assessing whether severe forms of COVID-19 could produce transitory or permanent damage in some vagal structure and whether this can, in turn, be responsible for the low vagal tone and the related clinical signs and symptoms