An ELECTRA-Based Model for Neural Coreference Resolution

In last years, coreference resolution has received a sensibly performance boost exploiting different pre-trained Neural Language Models, from BERT to SpanBERT until Longformer. This work is aimed at assessing, for the rst time, the impact of ELECTRA model on this task, moved by the experimental evidence of an improved contextual representation and better performance on different downstream tasks. In particular, ELECTRA has been employed as representation layer in an assessed neural coreference architecture able to determine entity mentions among spans of text and to best cluster them. The architecture itself has been optimized: i) by simplifying the modality of representation of spans of text but still considering both the context they appear and their entire content, ii) by maximizing both the number and length of input textual segments to exploit better the improved contextual representation power of ELECTRA, iii) by maximizing the number of spans of text to be processed, since potentially representing mentions, preserving computational ef ciency. Experimental results on the OntoNotes dataset have shown the effectiveness of this solution from both a quantitative and qualitative perspective, and also with respect to other state-of-the-art models, thanks to a more pro cient token and span representation. The results also hint at the possible use of this solution also for low-resource languages, simply requiring a pre-trained version of ELECTRA instead of language-speci c models trained to handle either spans of text or long documents

A Semantic Index for Linked Open Data and Big Data Applications

This work proposes a new approach to index multidimensional data based on kd-trees and proposes also a novel approach to query processing. The indexing data structure is distributed across a network of "peers", where each one hosts a part of the tree and uses message passing for communication among nodes. The advantages of this kind of approach are mainly two: it is possible to i) handle a larger number of nodes and points than a single peer based architecture and ii) to run in an efficient way the elaboration of multiple queries. In particular, we propose a novel version of the k-nearest neighbor algorithm that is able to start a query in a randomly chosen peer. Furthrmore, it returns the results without traverse the peer containing the root. Preliminary experiments demonstrated that on average in about 65% of cases a query starting in a random node, does not involve the peer containing the root of the tree. Also, on average in about 98% of cases, it returns the results without involving the root peer. This work also proposes an approach to cope with textual data and provides a way to perform semantic query over the text

Swarm intelligence in evacuation problems: A review

In this paper authors introduce swarm intelligence’s algorithms (ACO and PSO) to determine the optimum path during an evacuation process. Different PSO algorithms are compared when applied to an evacuation process and results reveal important aspects, as following detaile

Apoptotic cell death in canine hair follicle

Apoptotic cell death is an essential homeostatic mechanism involved in the control of cellular turnover in a variety of adult tissues. Cytoplasmic and nuclear condensation morphologically define this process whose biochemical hallmark is extensive DNA fragmentation into discrete oligonucleosomic units. Hair follicle growth and regression has been shown to be correlated with apoptosis in humans, mice, rats and guinea pigs. The present study was carried out to evaluate its implication in canine hair biology in order to define the spatio-temporal relationship between apoptosis and the hair cycle in dogs. As assessed by terminal deoxy-nucleotidyl transferase-mediated d-UTP nick-end-labelling (TUNEL) and by basic histological and ultrastructural assays, apoptotic cells appeared both in the growing and in the regressing follicle epithelium showing the well characterized morphological features described in the previous relevant literature

Decision Tree-Based Multiple Classifier Systems: An FPGA Perspective

Combining a hardware approach with a multiple classifier method can deeply improve system performance, since the multiple classifier system can successfully enhance the classification accuracy with respect to a single classifier, and a hardware implementation would lead to systems able to classify samples with high throughput and with a short latency. To the best of our knowledge, no paper in the literature takes into account the multiple classifier scheme as additional design parameter, mainly because of lack of efficient hardware combiner architecture. In order to fill this gap, in this paper we will first propose a novel approach for an efficient hardware implementation of the majority voting combining rule. Then, we will illustrate a design methodology to suitably embed in a digital device a multiple classifier system having Decision Trees as base classifiers and a majority voting rule as combiner. Bagging, Boosting and Random Forests will be taken into account. We will prove the effectiveness of the proposed approach on two real case studies related to Big Data issues

Finding unexplained human behaviors in Social Networks

Detection of human behavior in On-line Social Networks (OSNs) has become a very important challenge for a wide range of appli- cations, such as security, marketing, parent controls and so on, opening a wide range of novel research areas, which have not been fully addressed yet. In this paper, we present a two-stage method for finding unexplained (and potentially anomalous) behaviors in social networks. First, we use Markov chains to automatically learn from the social network graph a number of models of human behaviors (normal behaviors); the second stage applies an activity detection framework based on the concept of possible words to detect all unexplained activities with respect to the well-known behaviors. Some preliminary experiments using Facebook data show the approach efficiency and effectiveness. Copyright © (2014) by Universita Reggio Calabria & Centro di Competenza (ICT-SUD) All rights reserved

An Efficient Decentralized Multidimensional Data Index: A Proposal

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The Role of Optical Coherence Tomography in an Atypical Case of Oculocutaneous Albinism: A Case Report

Background: Oculocutaneous albinism is a group of autosomal recessive disorders featuring hypopigmentation of the hair, skin and eyes. Ocular signs associated with the disease are nystagmus, decreased visual acuity, hypopigmentation of the retina, foveal hypoplasia, translucency of the iris, macular transparency, photophobia and abnormal decussation of nerve fibers at the chiasm. Case Report: An 8-year-old Caucasian girl presented to our clinic ‘Referral Center for Hereditary Retinopathies’ of the Second University of Naples with a diagnosis of Stargardt disease and a progressive reduction in visual acuity in both eyes. She underwent a complete ophthalmic examination including standard electroretinography and optical coherence tomography (OCT). A molecular analysis was also performed. Best-corrected visual acuity was 20/30 in the right eye and 20/40 in the left eye. Biomicroscopy of the anterior segment revealed a transparent cornea, in situ and transparent lens and normally pigmented iris. A mild diffuse depigmentation and macular dystrophy were observed at fundus examination. Standard electroretinography showed normal scotopic and photopic responses. OCT revealed high reflectivity across the fovea without depression. The typical OCT pattern led us to direct the molecular analysis towards the genes involved in oculocutaneous albinism. The molecular analysis identified mutations in the TYR gene. Conclusion: In this case, the role of OCT was crucial in guiding the molecular analysis for the diagnosis of albinism. OCT is therefore instrumental in similar cases that do not present typical characteristics of a disease. The case also proves the relevance of molecular analysis to confirm clinical diagnoses in hereditary retinal diseases

A polydnavirus-encoded ANK protein has a negative impact on steroidogenesis and development

Polydnaviruses (PDV) are viral symbionts associated with ichneumonid and braconid wasps parasitizing moth larvae, which are able to disrupt the host immune response and development, as well as a number of other physiological pathways. The immunosuppressive role of PDV has been more intensely investigated, while very little is known about the PDV-encoded factors disrupting host development. Here we address this research issue by further expanding the functional analysis of ankyrin genes encoded by the bracovirus associated with Toxoneuron nigriceps (Hymenoptera, Braconidae). In a previous study, using Drosophila melanogaster as experimental model system, we demonstrated the negative impact of TnBVank1 impairing the ecdysone biosynthesis by altering endocytic traffic in prothoracic gland cells. With a similar approach here we demonstrate that another member of the viral ank gene family, TnBVank3, does also contribute to the disruption of ecdysone biosynthesis, but with a completely different mechanism. We show that its expression in Drosophila prothoracic gland (PG) blocks the larval-pupal transition by impairing the expression of steroidogenic genes. Furthermore, we found that TnBVank3 affects the expression of genes involved in the insulin/TOR signaling and the constitutive activation of the insulin pathway in the PG rescues the pupariation impairment. Collectively, our data demonstrate that TnBVANK3 acts as a virulence factor by exerting a synergistic and non-overlapping function with TnBVANK1 to disrupt the ecdysone biosynthesis