Trees of Fusion Systems

We define a `tree of fusion systems' and give a sufficient condition for its completion to be saturated. We apply this result to enlarge an arbitrary fusion system by extending the automorphism groups of certain of its subgroups

Analysis of lexical semantic changes in corpora with the diachronic engine

With the growing availability of digitized diachronic corpora, the need for tools capable of taking into account the diachronic component of corpora becomes ever more pressing. Recent works on diachronic embeddings show that computational approaches to the diachronic analysis of language seem to be promising, but they are not user friendly for people without a technical background. This paper presents the Diachronic Engine, a system for the diachronic analysis of corpora lexical features. Diachronic Engine computes word frequency, concordances and collocations taking into account the temporal dimension. It is also able to compute temporal word embeddings and time-series that can be exploited for lexical semantic change detection

A comparative study of approaches for the diachronic analysis of the Italian language

In recent years, there has been a significant increase in interest in lexical semantic change detection. Many are the existing approaches, data used, and evaluation strategies to detect semantic drift. Most of those approaches rely on diachronic word embeddings. Some of them are created as post-processing of static word embeddings, while others produce dynamic word embeddings where vectors share the same geometric space for all time slices. The large majority of the methods use English as the target language for the diachronic analysis, while other languages remain under-explored. In this work, we compare state-of-the-art approaches in computational historical linguistics to evaluate the pros and cons of each model, and we present the results of an in-depth analysis conducted using an Italian diachronic corpus. Specifically, several approaches based on both static embeddings and dynamic ones are implemented and evaluated by using the Kronos-It dataset. We train all word embeddings on the Italian Google n-gram corpus. The main result of the evaluation is that all approaches fail to significantly reduce the number of false-positive change points, which confirms that lexical semantic change is still a challenging task

A general aspect-term-extraction model for multi-criteria recommendations

In recent years, increasingly large quantities of user reviews have been made available by several e-commerce platforms. This content is very useful for recommender systems (RSs), since it reflects the users' opinion of the items regarding several aspects. In fact, they are especially valuable for RSs that are able to exploit multi-faceted user ratings. However, extracting aspect-based ratings from unstructured text is not a trivial task. Deep Learning models for aspect extraction have proven to be effective, but they need to be trained on large quantities of domain-specific data, which are not always available. In this paper, we explore the possibility of transferring knowledge across domains for automatically extracting aspects from user reviews, and its implications in terms of recommendation accuracy. We performed different experiments with several Deep Learning-based Aspect Term Extraction (ATE) techniques and Multi-Criteria recommendation algorithms. Results show that our framework is able to improve recommendation accuracy compared to several baselines based on single-criteria recommendation, despite the fact that no labeled data in the target domain was used when training the ATE model

Resolvent Analysis: With or Without Eddy Viscosity?

International audienceIn this study, estimations of the spatio-temporal power cross-spectral density based on the resolvent operator are compared to those obtained by direct numerical simulation (DNS) in the turbulent plane channel flow at Re τ = 1007 by analysing separately the contribution of each temporal frequency ω. The comparison is performed for spatial scales characteristic of buffer-layer and large-scale motions. Good agreement between the resolvent-based estimates and the statistics obtained by DNS is found when the resolvent operator is based on a linear model which includes the effect of an eddy-viscosity modelling the effect of turbulent Reynolds stresses. The agreement is further improved when a colored noise matching the measures is used instead of white noise in the forcing modelling. Such a good agreement is not observed when the eddy-viscosity terms are not included in the linear model. In this case, the estimation based on the resolvent is unable to select the right peak frequency and wall-normal location of buffer-layer motions

A techno-economic approach for decision-making in metal additive manufacturing: metal extrusion versus single and multiple laser powder bed fusion

This work presents a decision-making methodology that allows the merging of quantitative and qualitative decision variables for selecting the optimal metal Additive Manufacturing (AM) technology. The approach is applied on two competing technologies in the field of metal AM industry, i.e., the metal extrusion AM process (metal FFF) and the Laser Powder Bed Fusion process (LPBF) with single and multiple lasers, which represent the benchmark solution currently on the market. A comprehensive techno-economical comparison is presented where the two processes are analysed in terms of process capabilities (quality, easiness of use, setup time, range of possible materials, etc.) and costs, considering two different production scenarios and different parts’ geometries. In the first scenario, the AM system is assumed to be dedicated to one single part production while in this second scenario, the AM system is assumed to be saturated, as devoted to producing a wide mix of part types. For each scenario, two different part types made of 17–4 PH stainless steel are considered as a reference to investigate the effect of shape complexity, part size and production times to select the best technology when metal FFF and LPBF must be considered. The first part type refers to an extrusion die, to represent typical shapes of interest in the tooling industry, while the second part type is an impeller which can be used in many different industrial sectors, ranging from oil and gas to aerospace. In order to include quantitative and qualitative criteria, a decision-making model based on Analytic Hierarchy Process (AHP) is proposed as the enabler tool for decision making. The proposed approach allows to determine the most effective solution depending on the different production configurations and part types and can be used as a guideline and extended to include other technologies in the field of metal AM. On the other side, the critical discussion of the criteria selected, and the results achieved allow to highlight the pros and cons of the competing technologies, thus defining the existing limits to define directions for future research

A deep learning model for the analysis of medical reports in ICD-10 clinical coding task

The practice of assigning a uniquely identifiable and easily traceable code to pathology from medical diagnoses is an added value to the current modality of archiving health data collected to build the clinical history of each of us. Unfortunately, the enormous amount of possible pathologies and medical conditions has led to the realization of extremely wide international codifications that are difficult to consult even for a human being. This difficulty makes the practice of annotation of diagnoses with ICD-10 codes very cumbersome and rarely performed. In order to support this operation, a classification model was proposed, able to analyze medical diagnoses written in natural language and automatically assign one or more international reference codes. The model has been evaluated on a dataset released in the Spanish language for the eHealth challenge (CodiEsp) of the international conference CLEF 2020, but it could be extended to any language with latin characters. We proposed a model based on a two-step classification process based on BERT and BiLSTM. Although still far from an accuracy sufficient to do without a licensed physician opinion, the results obtained show the feasibility of the task and are a starting point for future studies in this direction

Extracting relations from Italian wikipedia using self-training

In this paper, we describe a supervised approach for extracting relations from Wikipedia. In particular, we exploit a self-training strategy for enriching a small number of manually labeled triples with new self-labeled examples. We integrate the supervised stage in WikiOIE, an existing framework for unsupervised extraction of relations from Wikipedia. We rely on WikiOIE and its unsupervised pipeline for extracting the initial set of unlabelled triples. An evaluation involving different algorithms and parameters proves that self-training helps to improve performance. Finally, we provide a dataset of about three million triples extracted from the Italian version of Wikipedia and perform a preliminary evaluation conducted on a sample dataset, obtaining promising results