DOCUMENT CONTROL OF NATIONAL MAPPING AND METRIC STUDIES CHARACTERIZING NATIONAL IDENTITY AND INDIVIDUAL. I STAGE

This is a project of the School of Library, Documentation and Information of the National University, is performed to support the initiative of UNESCO to build the Memory of the World (UNESCO, 1995) and to help provide universal access to documentation. To this end, the School of Library Science students has promoted the realization of final graduation work on documentary control of domestic production. This project has the following objectives:Objectives1. Conduct mapping national documentary through the identification, analysis, organization and access to documentary heritage of Costa Rica, to contribute to the Memory of the World.2. Perform bibliometric analysis of documentary records contained in the integrated databases.This project seeks to bring undergraduate students graduating from the school, in making final graduation work on document control. Students have the opportunity to make final graduation work on the documentary production of Costa Rica on a specific theme or on a country's geographical area.Desk audits aimed at identifying the document using access points and indicate its contents to allow recovery by the user.The result is the production of a new document, other than the original, a secondary document: the bibliography. The records in the database each control documentation completed work will be integrated into a single database to be placed on the website of EBDI, for consultation of researchers and interested users.

Rank metric convolutional codes

In this contribution, we propose a first general definition of rank-metric convolutional codes for multi-shot network coding. To this aim, we introduce a suitable concept of distance and we establish a generalized Singleton bound for this class of codes.

Linear non-local diffusion problems in metric measure spaces

The aim of this paper is to provide a comprehensive study of some linear non-local diffusion problems in metric measure spaces. These include, for example, open subsets in ℝN, graphs, manifolds, multi-structures and some fractal sets. For this, we study regularity, compactness, positivity and the spectrum of the stationary non-local operator. We then study the solutions of linear evolution non-local diffusion problems, with emphasis on similarities and differences with the standard heat equation in smooth domains. In particular, we prove weak and strong maximum principles and describe the asymptotic behaviour using spectral methods.

Evaluation of Information Entropy from Acoustic Emission Waveforms as a Fatigue Damage Metric for Al7075-T6

Information entropy measured from acoustic emission (AE) waveforms is shown to be an indicator of fatigue damage in a high-strength aluminum alloy. Several tension-tension fatigue experiments were performed with dogbone samples of aluminum alloy, Al7075-T6, a commonly used material in aerospace structures. Unlike previous studies in which fatigue damage is simply measured based on visible crack growth, this work investigated fatigue damage prior to crack initiation through the use of instantaneous elastic modulus degradation. Three methods of measuring the AE information entropy, regarded as a direct measure of microstructural disorder, are proposed and compared with traditional damage-related AE features. Results show that one of the three entropy measurement methods appears to better assess damage than the traditional AE features, while the other two entropies have unique trends that can differentiate between small and large cracks.

From the traditional verification approach to the spatial methods: a study of applicability of the SAL metric in the framework of the WMO project MesoVICT

Increasing in resolution of numerical weather prediction models has allowed more and more realistic forecasts of atmospheric parameters. Due to the growing variability into predicted fields the traditional verification methods are not always able to describe the model ability because they are based on a grid-point-by-grid-point matching between observation and prediction. Recently, new spatial verification methods have been developed with the aim of show the benefit associated to the high resolution forecast. Nested in among of the MesoVICT international project, the initially aim of this work is to compare the newly tecniques remarking advantages and disadvantages. First of all, the MesoVICT basic examples, represented by synthetic precipitation fields, have been examined. Giving an error evaluation in terms of structure, amplitude and localization of the precipitation fields, the SAL method has been studied more thoroughly respect to the others approaches with its implementation in the core cases of the project. The verification procedure has concerned precipitation fields over central Europe: comparisons between the forecasts performed by the 00z COSMO-2 model and the VERA (Vienna Enhanced Resolution Analysis) have been done. The study of these cases has shown some weaknesses of the methodology examined; in particular has been highlighted the presence of a correlation between the optimal domain size and the extention of the precipitation systems. In order to increase ability of SAL, a subdivision of the original domain in three subdomains has been done and the method has been applied again. Some limits have been found in cases in which at least one of the two domains does not show precipitation. The overall results for the subdomains have been summarized on scatter plots. With the aim to identify systematic errors of the model the variability of the three parameters has been studied for each subdomain.

Measured Time: Imposing a Temporal Metric toClassificatory Structures

Describes three units of time helpful for understanding and evaluating classificatory structures: long time (versions and states of classification schemes), short time (the act of indexing as repeated ritual or form), and micro-time (where stages of the interpretation process of indexing are separated out and inventoried). Concludes with a short discussion of how time and the impermanence of classification also conjures up an artistic conceptualization of indexing, and briefly uses that to question the seemingly dominant understanding of classification practice as outcome of scientific management and assembly line thought.

Collocative Integrity and Our Many Varied Subjects: What the Metric of Alignment between Classification Scheme and Indexer Tells Us About Langridge’s Theory of Indexing

As the universe of knowledge and subjects change over time, indexing languages like classification schemes, accommodate that change by restructuring. Restructuring indexing languages affects indexer and cataloguer work. Subjects may split or lump together. They may disappear only to reappear later. And new subjects may emerge that were assumed to be already present, but not clearly articulated (Miksa, 1998). In this context we have the complex relationship between the indexing language, the text being described, and the already described collection (Tennis, 2007). It is possible to imagine indexers placing a document into an outdated class, because it is the one they have already used for their collection. However, doing this erases the semantics in the present indexing language. Given this range of choice in the context of indexing language change, the question arises, what does this look like in practice? How often does this occur? Further, what does this phenomenon tell us about subjects in indexing languages? Does the practice we observe in the reaction to indexing language change provide us evidence of conceptual models of subjects and subject creation? If it is incomplete, but gets us close, what evidence do we still require?

Heavy-Light Mesons in Minkowski Space

Following up on earlier work on the $q\bar{q}$-bound-state problem using a covariant, chiral-symmetric formalism based upon the Covariant Spectator Theory, we study the heavy–light case for both pseudoscalar and vector mesons. Derived directly in Minkowski space, our approach approximates the full Bethe–Salpeter-equation, taking into account, effectively, the contributions of both ladder and crossed ladder diagrams in the kernel. Results for several mass spectra using a relativistic covariant generalization of a Cornell plus a constant potential to model the interquark interaction are given and discussed.

Sperimentazione di Deep Metric Loss per Self-Supervised Information Retrieval Systems su CORD19.

Dopo lo sviluppo dei primi casi di Covid-19 in Cina nell’autunno del 2019, ad inizio 2020 l’intero pianeta è precipitato in una pandemia globale che ha stravolto le nostre vite con conseguenze che non si vivevano dall’influenza spagnola. La grandissima quantità di paper scientifici in continua pubblicazione sul coronavirus e virus ad esso affini ha portato alla creazione di un unico dataset dinamico chiamato CORD19 e distribuito gratuitamente. Poter reperire informazioni utili in questa mole di dati ha ulteriormente acceso i riflettori sugli information retrieval systems, capaci di recuperare in maniera rapida ed efficace informazioni preziose rispetto a una domanda dell'utente detta query. Di particolare rilievo è stata la TREC-COVID Challenge, competizione per lo sviluppo di un sistema di IR addestrato e testato sul dataset CORD19. Il problema principale è dato dal fatto che la grande mole di documenti è totalmente non etichettata e risulta dunque impossibile addestrare modelli di reti neurali direttamente su di essi. Per aggirare il problema abbiamo messo a punto nuove soluzioni self-supervised, a cui abbiamo applicato lo stato dell'arte del deep metric learning e dell'NLP. Il deep metric learning, che sta avendo un enorme successo soprattuto nella computer vision, addestra il modello ad "avvicinare" tra loro immagini simili e "allontanare" immagini differenti. Dato che sia le immagini che il testo vengono rappresentati attraverso vettori di numeri reali (embeddings) si possano utilizzare le stesse tecniche per "avvicinare" tra loro elementi testuali pertinenti (e.g. una query e un paragrafo) e "allontanare" elementi non pertinenti. Abbiamo dunque addestrato un modello SciBERT con varie loss, che ad oggi rappresentano lo stato dell'arte del deep metric learning, in maniera completamente self-supervised direttamente e unicamente sul dataset CORD19, valutandolo poi sul set formale TREC-COVID attraverso un sistema di IR e ottenendo risultati interessanti.

Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models

Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic search or search with meaning can refer to an understanding of the query, instead of simply finding words matches and, in general, it represents knowledge in a way suitable for retrieval. We chose to investigate a new self-supervised strategy to handle the training of unlabeled data based on the creation of pairs of ’artificial’ queries and the respective positive passages. We claim that by removing the reliance on labeled data, we may use the large volume of unlabeled material on the web without being limited to languages or domains where labeled data is abundant.

End-to-end Deep Metric Learning con Vision-Language Model per il Fashion Image Captioning

L'image captioning è un task di machine learning che consiste nella generazione di una didascalia, o caption, che descriva le caratteristiche di un'immagine data in input. Questo può essere applicato, ad esempio, per descrivere in dettaglio i prodotti in vendita su un sito di e-commerce, migliorando l'accessibilità del sito web e permettendo un acquisto più consapevole ai clienti con difficoltà visive. La generazione di descrizioni accurate per gli articoli di moda online è importante non solo per migliorare le esperienze di acquisto dei clienti, ma anche per aumentare le vendite online. Oltre alla necessità di presentare correttamente gli attributi degli articoli, infatti, descrivere i propri prodotti con il giusto linguaggio può contribuire a catturare l'attenzione dei clienti. In questa tesi, ci poniamo l'obiettivo di sviluppare un sistema in grado di generare una caption che descriva in modo dettagliato l'immagine di un prodotto dell'industria della moda dato in input, sia esso un capo di vestiario o un qualche tipo di accessorio. A questo proposito, negli ultimi anni molti studi hanno proposto soluzioni basate su reti convoluzionali e LSTM. In questo progetto proponiamo invece un'architettura encoder-decoder, che utilizza il modello Vision Transformer per la codifica delle immagini e GPT-2 per la generazione dei testi. Studiamo inoltre come tecniche di deep metric learning applicate in end-to-end durante l'addestramento influenzino le metriche e la qualità delle caption generate dal nostro modello.

Potential theory on metric spaces

This work revolves around potential theory in metric spaces, focusing on applications of dyadic potential theory to general problems associated to functional analysis and harmonic analysis. In the first part of this work we consider the weighted dual dyadic Hardy's inequality over dyadic trees and we use the Bellman function method to characterize the weights for which the inequality holds, and find the optimal constant for which our statement holds. We also show that our Bellman function is the solution to a stochastic optimal control problem. In the second part of this work we consider the problem of quasi-additivity formulas for the Riesz capacity in metric spaces and we prove formulas of quasi-additivity in the setting of the tree boundaries and in the setting of Ahlfors-regular spaces. We also consider a proper Harmonic extension to one additional variable of Riesz potentials of functions on a compact Ahlfors-regular space and we use our quasi-additivity formula to prove a result of tangential convergence of the harmonic extension of the Riesz potential up to an exceptional set of null measure

Self-supervised Information Retrieval basato su deep metric learning

La tesi ha lo scopo di ricercare, esaminare ed implementare un sistema di Machine Learning, un Recommendation Systems per precisione, che permetta la racommandazione di documenti di natura giuridica, i quali sono già stati analizzati e categorizzati appropriatamente, in maniera ottimale, il cui scopo sarebbe quello di accompagnare un sistema già implementato di Information Retrieval, istanziato sopra una web application, che permette di ricercare i documenti giuridici appena menzionati.

Bacterial Diversity Assessment In Soil Of An Active Brazilian Copper Mine Using High-throughput Sequencing Of 16s Rdna Amplicons.

Mining activities pose severe environmental risks worldwide, generating extreme pH conditions and high concentrations of heavy metals, which can have major impacts on the survival of organisms. In this work, pyrosequencing of the V3 region of the 16S rDNA was used to analyze the bacterial communities in soil samples from a Brazilian copper mine. For the analysis, soil samples were collected from the slopes (geotechnical structures) and the surrounding drainage of the Sossego mine (comprising the Sossego and Sequeirinho deposits). The results revealed complex bacterial diversity, and there was no influence of deposit geographic location on the composition of the communities. However, the environment type played an important role in bacterial community divergence; the composition and frequency of OTUs in the slope samples were different from those of the surrounding drainage samples, and Acidobacteria, Chloroflexi, Firmicutes, and Gammaproteobacteria were responsible for the observed difference. Chemical analysis indicated that both types of sample presented a high metal content, while the amounts of organic matter and water were higher in the surrounding drainage samples. Non-metric multidimensional scaling (N-MDS) analysis identified organic matter and water as important distinguishing factors between the bacterial communities from the two types of mine environment. Although habitat-specific OTUs were found in both environments, they were more abundant in the surrounding drainage samples (around 50 %), and contributed to the higher bacterial diversity found in this habitat. The slope samples were dominated by a smaller number of phyla, especially Firmicutes. The bacterial communities from the slope and surrounding drainage samples were different in structure and composition, and the organic matter and water present in these environments contributed to the observed differences.