Semantically-driven automatic creation of training sets for object recognition

In the object recognition community, much effort has been spent on devising expressive object representations and powerful learning strategies for designing effective classifiers, capable of achieving high accuracy and generalization. In this scenario, the focus on the training sets has been historically weak; by and large, training sets have been generated with a substantial human intervention, requiring considerable time. In this paper, we present a strategy for automatic training set generation. The strategy uses semantic knowledge coming from WordNet, coupled with the statistical power provided by Google Ngram, to select a set of meaningful text strings related to the text class-label (e.g., \u201ccat\u201d), that are subsequently fed into the Google Images search engine, producing sets of images with high training value. Focusing on the classes of different object recognition benchmarks (PASCAL VOC 2012, Caltech-256, ImageNet, GRAZ and OxfordPet), our approach collects novel training images, compared to the ones obtained by exploiting Google Images with the simple text class-label. In particular, we show that the gathered images are better able to capture the different visual facets of a concept, thus encoding in a more successful manner the intra-class variance. As a consequence, training standard classifiers with this data produces performances not too distant from those obtained from the classical hand-crafted training sets. In addition, our datasets generalize well and are stable, that is, they provide similar performances on diverse test datasets. This process does not require manual intervention and is completed in a few hours

Campagna Oceanografica Sismica Magnetica Elettrica Ischia (COSMEI)

La Campagna Oceanografica Sismica Magnetica Elettrica Ischia (denominata "COSMEI") è nata a seguito dell'evento sismico verificatosi alle 20:57 del 21 agosto 2017 Mw 3.9 con epicentro nell'area di Casamicciola, con lo scopo di fornire ulteriori contributi nell'individuazione di strutture vulcano-tettoniche attive nel settore nord marino dell'isola d'Ischia. Il CNR-DTA nell'ambito di tali attività ed in concomitanza delle attività del Centro per la Microzonazione Sismica e delle sue applicazioni (centro MS), ha disposto un piano di indagini a mare necessarie per la ricostruzione di strutture tettoniche e vulcaniche potenzialmente origine di eventi sismici. La campagna oceanografica COSMEI dell'IAMC-CNR di Napoli, ha predisposto rilievi geofisici di tipo sismico multicanale, magnetico differenziale, di resistività elettrica, Chirp e Multibeam nel settore marino nord-orientale dell'isola d'Ischia. Le attività di acquisizione sono state condotte utilizzando la N/O Minerva Uno del Consiglio Nazionale delle Ricerche (CNR). L'obiettivo finale di tale studio è quello di fornire nuovi elementi geofisici finalizzati a migliorare la conoscenza dell'evoluzione geologica di questo settore dell'isola. In tale contesto, l'interpretazione geologico-strutturale dei profili sismici multicanale combinata col dato magnetometrico e di resistività elettrica ha lo scopo di migliorare la conoscenza dell’area nord-orientale dell'isola che risulta controllata da complessi vulcano-tettonici

Comment on “The 21 August 2017 M d 4.0 Casamicciola Earthquake: First Evidence of Coseismic Normal Surface Faulting at the Ischia Volcanic Island” by

We are writing this comment because many aspects of the analysis presented by Nappi et al. (2018) are debatable. In particular, a major issue is relevant to the conclusion suggested by Nappi et al. (2018) about a seismogenic normal fault with northward dip. This finding is not well‐founded because the authors do not really present a causative source model. In addition, their statement is clearly not consistent with the Differential Interferometric Synthetic Aperture Radar (DInSAR), Global Positioning System (GPS) and seismological measurements presented in the article previously published by De Novellis et al. (2018). Moreover, we also report an evident error in the geologic map proposed by Nappi et al. (2018, their fig. 3).Published313-3156V. Pericolosità vulcanica e contributi alla stima del rischioJCR Journa

The 21 August 2017 Ischia (Italy) Earthquake Source Model Inferred From Seismological, GPS, and DInSAR Measurements

The causative source of the first damaging earthquake instrumentally recorded in the Island of Ischia, occurred on 21 August 2017, has been studied through a multiparametric geophysical approach. In order to investigate the source geometry and kinematics we exploit seismological, Global Positioning System, and Sentinel-1 and COSMO-SkyMed differential interferometric synthetic aperture radar coseismic measurements. Our results indicate that the retrieved solutions from the geodetic data modeling and the seismological data are plausible; in particular, the best fit solution consists of an E-W striking, south dipping normal fault, with its center located at a depth of 800 m. Moreover, the retrieved causative fault is consistent with the rheological stratification of the crust in this zone. This study allows us to improve the knowledge of the volcano-tectonic processes occurring on the Island, which is crucial for a better assessment of the seismic risk in the area.Published2193-22023T. Sorgente sismicaJCR Journa

Semantically-driven automatic creation of training sets for object recognition

In the object recognition community, much effort has been spent on devising expressive object representations and powerful learning strategies for design-ing effective classifiers, capable of achieving high accuracy and generalization. In this scenario, the focus on the training sets has been historically weak; by and large, training sets have been generated with a substantial human in-tervention, requiring considerable time. In this paper, we present a strategy for automatic training set generation. The strategy uses semantic knowledge coming from WordNet, coupled with the statistical power provided by Google Ngram, to select a set of meaningful text strings related to the text class-label (e.g., “cat”), that are subsequently fed into the Google Images search engine, producing sets of images with high training value. Focusing on the classes of different object recognition benchmarks (PASCAL VOC 2012, Caltech-256, ImageNet, GRAZ and OxfordPet), our approach collects novel training im-ages, compared to the ones obtained by exploiting Google Images with the simple text class-label. In particular, we show that the gathered images are better able to capture the different visual facets of a concept, thus encod-ing in a more successful manner the intra-class variance. As a consequence, training standard classifiers with this data produces performances not too distant from those obtained from the classical hand-crafted training sets. In addition, our datasets generalize well and are stable, that is, they provide similar performances on diverse test datasets. This process does not require manual intervention and is completed in a few hours

Biological Effects and Safety in Magnetic Resonance Imaging: A Review

Since the introduction of Magnetic Resonance Imaging (MRI) as a diagnostic technique, the number of people exposed to electromagnetic fields (EMF) has increased dramatically. In this review, based on the results of a pioneer study showing in vitro and in vivo genotoxic effects of MRI scans, we report an updated survey about the effects of non-ionizing EMF employed in MRI, relevant for patients’ and workers’ safety. While the whole data does not confirm a risk hypothesis, it suggests a need for further studies and prudent use in order to avoid unnecessary examinations, according to the precautionary principle

Serological response and breakthrough infection after COVID-19 vaccination in patients with cirrhosis and post-liver transplant

BACKGROUND: Vaccine hesitancy and lack of access remain major issues in disseminating COVID-19 vaccination to liver patients globally. Factors predicting poor response to vaccination and risk of breakthrough infection are important data to target booster vaccine programs. The primary aim of the current study was to measure humoral responses to 2 doses of COVID-19 vaccine. Secondary aims included the determination of factors predicting breakthrough infection. METHODS: COVID-19 vaccination and Biomarkers in cirrhosis And post-Liver Transplantation is a prospective, multicenter, observational case-control study. Participants were recruited at 4-10 weeks following first and second vaccine doses in cirrhosis [n = 325; 94% messenger RNA (mRNA) and 6% viral vaccine], autoimmune liver disease (AILD) (n = 120; 77% mRNA and 23% viral vaccine), post-liver transplant (LT) (n = 146; 96% mRNA and 3% viral vaccine), and healthy controls (n = 51; 72% mRNA, 24% viral and 4% heterologous combination). Serological end points were measured, and data regarding breakthrough SARS-CoV-2 infection were collected. RESULTS: After adjusting by age, sex, and time of sample collection, anti-Spike IgG levels were the lowest in post-LT patients compared to cirrhosis (p < 0.0001), AILD (p < 0.0001), and control (p = 0.002). Factors predicting reduced responses included older age, Child-Turcotte-Pugh B/C, and elevated IL-6 in cirrhosis; non-mRNA vaccine in AILD; and coronary artery disease, use of mycophenolate and dysregulated B-call activating factor, and lymphotoxin-α levels in LT. Incident infection occurred in 6.6%, 10.6%, 7.4%, and 15.6% of cirrhosis, AILD, post-LT, and control, respectively. The only independent factor predicting infection in cirrhosis was low albumin level. CONCLUSIONS: LT patients present the lowest response to the SARS-CoV-2 vaccine. In cirrhosis, the reduced response is associated with older age, stage of liver disease and systemic inflammation, and breakthrough infection with low albumin level

Improving the Quality of Conceptual Models with NLP Tools: An Experiment

Conceptual models are used in a variety of areas within Computer Science, including Software Engineering, Databases and AI. A major bottleneck in broadening their applicability is the time it takes to build a conceptual model for a new application. Not surprisingly, a variety of tools and techniques have been proposed for reusing conceptual models (e.g., ontologies), or for building them semi-automatically from natural language descriptions. What has been left largely unexplored is the impact of such tools on the quality of the models that are being created. This paper presents the results of an experiment designed to assess the extent to which a Natural Language Processing (NLP) tool improves the quality of conceptual models, specifically objectoriented ones. Our main experimental hypothesis is that the quality of a domain class model is higher if its development is supported by a NLP system. The tool used for the experiment -- named NL-OOPS -- extracts classes and associations from a knowledge base realized by a deep semantic analysis of a sample text. Specifically, NL-OOPS produces class models at different levels of detail by exploiting class hierarchies in the knowledge base of a NLP system and marks ambiguities in the text. In our experiments, we had groups working with/without the tool, and then compared and evaluated the final class models they produced

NLP-Based Requirements Modeling: Experiments on the Quality of the models

Conceptual models are used in a variety of areas within Computer Science, including Software Engineering, Databases and AI. A major bottleneck in broadening their applicability is the time it takes to build a conceptual model for a new application. Not surprisingly, a variety of tools and techniques have been proposed for reusing conceptual models, e.g. ontologies, or for building them semi-automatically from natural language (NL) descriptions. What has been left largely unexplored is the impact of such tools on the quality of the models that are being created. This paper presents the results of three experiments designed to assess the extent to which a Natural-Language Processing (NLP) tool improves the quality of conceptual models, specifically object-oriented ones. Our main experimental hypothesis is that the quality of a domain class model is higher if its development is supported by a NLP system. The tool used for the experiment – named NL-OOPS – extracts classes and associations from a knowledge base realized by a deep semantic analysis of a sample text. Specifically, NL-OOPS produces class models at different levels of detail by exploiting class hierarchies in the knowledge base of a NLP system and marks ambiguities in the text. In our experiments, we had groups working with and without the tool, and then compared and evaluated the final class models they produced. The results of the experiments – the first on this topic – give insights on the state of the art of linguistics-based Computer Aided Software Engineering (CASE) tools and allow identifying important guidelines to improve their performance. In particular it was possible to highlight which of the linguistic tasks are more critical to effectively support conceptual modelling

Ambiguity Identification and Measurement in Natural Language Texts

Text ambiguity is one of the most interesting phenomenon in human communication and a difficult problem in Natural Language Processing (NLP). Identification of text ambiguities is an important task for evaluating the quality of text and uncovering its vulnerable points. There exist several types of ambiguity. In the present work we review and compare different approaches to ambiguity identification task. We also propose our own approach to this problem. Moreover, we present the prototype of a tool for ambiguity identification and measurement in natural language text. The tool is intended to support the process of writing high quality documents