Authentication of Amadeo de Souza-Cardoso Paintings and Drawings With Deep Learning

Art forgery has a long-standing history that can be traced back to the Roman period and has become more rampant as the art market continues prospering. Reports disclosed that uncountable artworks circulating on the art market could be fake. Even some principal art museums and galleries could be exhibiting a good percentage of fake artworks. It is therefore substantially important to conserve cultural heritage, safeguard the interest of both the art market and the artists, as well as the integrity of artists’ legacies. As a result, art authentication has been one of the most researched and well-documented fields due to the ever-growing commercial art market in the past decades. Over the past years, the employment of computer science in the art world has flourished as it continues to stimulate interest in both the art world and the artificial intelligence arena. In particular, the implementation of Artificial Intelligence, namely Deep Learning algorithms and Neural Networks, has proved to be of significance for specialised image analysis. This research encompassed multidisciplinary studies on chemistry, physics, art and computer science. More specifically, the work presents a solution to the problem of authentication of heritage artwork by Amadeo de Souza-Cardoso, namely paintings, through the use of artificial intelligence algorithms. First, an authenticity estimation is obtained based on processing of images through a deep learning model that analyses the brushstroke features of a painting. Iterative, multi-scale analysis of the images is used to cover the entire painting and produce an overall indication of authenticity. Second, a mixed input, deep learning model is proposed to analyse pigments in a painting. This solves the image colour segmentation and pigment classification problem using hyperspectral imagery. The result is used to provide an indication of authenticity based on pigment classification and correlation with chemical data obtained via XRF analysis. Further algorithms developed include a deep learning model that tackles the pigment unmixing problem based on hyperspectral data. Another algorithm is a deep learning model that estimates hyperspectral images from sRGB images. Based on the established algorithms and results obtained, two applications were developed. First, an Augmented Reality mobile application specifically for the visualisation of pigments in the artworks by Amadeo. The mobile application targets the general public, i.e., art enthusiasts, museum visitors, art lovers or art experts. And second, a desktop application with multiple purposes, such as the visualisation of pigments and hyperspectral data. This application is designed for art specialists, i.e., conservators and restorers. Due to the special circumstances of the pandemic, trials on the usage of these applications were only performed within the Department of Conservation and Restoration at NOVA University Lisbon, where both applications received positive feedback.A falsificação de arte tem uma história de longa data que remonta ao período romano e tornou-se mais desenfreada à medida que o mercado de arte continua a prosperar. Relatórios revelaram que inúmeras obras de arte que circulam no mercado de arte podem ser falsas. Mesmo alguns dos principais museus e galerias de arte poderiam estar exibindo uma boa porcentagem de obras de arte falsas. Por conseguinte, é extremamente importante conservar o património cultural, salvaguardar os interesses do mercado da arte e dos artis- tas, bem como a integridade dos legados dos artistas. Como resultado, a autenticação de arte tem sido um dos campos mais pesquisados e bem documentados devido ao crescente mercado de arte comercial nas últimas décadas.Nos últimos anos, o emprego da ciência da computação no mundo da arte floresceu à medida que continua a estimular o interesse no mundo da arte e na arena da inteligência artificial. Em particular, a implementação da Inteligência Artificial, nomeadamente algoritmos de aprendizagem profunda (ou Deep Learning) e Redes Neuronais, tem-se revelado importante para a análise especializada de imagens.Esta investigação abrangeu estudos multidisciplinares em química, física, arte e informática. Mais especificamente, o trabalho apresenta uma solução para o problema da autenticação de obras de arte patrimoniais de Amadeo de Souza-Cardoso, nomeadamente pinturas, através da utilização de algoritmos de inteligência artificial. Primeiro, uma esti- mativa de autenticidade é obtida com base no processamento de imagens através de um modelo de aprendizagem profunda que analisa as características de pincelada de uma pintura. A análise iterativa e multiescala das imagens é usada para cobrir toda a pintura e produzir uma indicação geral de autenticidade. Em segundo lugar, um modelo misto de entrada e aprendizagem profunda é proposto para analisar pigmentos em uma pintura. Isso resolve o problema de segmentação de cores de imagem e classificação de pigmentos usando imagens hiperespectrais. O resultado é usado para fornecer uma indicação de autenticidade com base na classificação do pigmento e correlação com dados químicos obtidos através da análise XRF. Outros algoritmos desenvolvidos incluem um modelo de aprendizagem profunda que aborda o problema da desmistura de pigmentos com base em dados hiperespectrais. Outro algoritmo é um modelo de aprendizagem profunda estabelecidos e nos resultados obtidos, foram desenvolvidas duas aplicações. Primeiro, uma aplicação móvel de Realidade Aumentada especificamente para a visualização de pigmentos nas obras de Amadeo. A aplicação móvel destina-se ao público em geral, ou seja, entusiastas da arte, visitantes de museus, amantes da arte ou especialistas em arte. E, em segundo lugar, uma aplicação de ambiente de trabalho com múltiplas finalidades, como a visualização de pigmentos e dados hiperespectrais. Esta aplicação é projetada para especialistas em arte, ou seja, conservadores e restauradores. Devido às circunstâncias especiais da pandemia, os ensaios sobre a utilização destas aplicações só foram realizados no âmbito do Departamento de Conservação e Restauro da Universidade NOVA de Lisboa, onde ambas as candidaturas receberam feedback positivo

Assignments of ΛQ\Lambda_Q and ΞQ\Xi_Q baryons in the heavy quark-light diquark picture

We apply a new mass formula which is derived analytically in the relativistic flux tube model to the mass spectra of $\Lambda_Q$ and $\Xi_Q$ (\emph{Q} = \emph{c} or \emph{b} quark) baryons. To this end, the heavy quark-light diquark picture is employed. We find that all masses of the available $\Lambda_Q$ and $\Xi_Q$ states can be understood well. The assignments to these states do not appear to contradict the strong decay properties. $\Lambda_c(2760)^+$ and $\Xi_c(2980)$ are assigned to the first radial excitations with $J^P = 1/2^+$. $\Lambda_c(2940)^+$ and $\Xi_c(3123)$ might be the 2\emph{P} states. The $\Lambda_c(2880)^+$ and $\Xi_c(3080)$ are the good 1\emph{D} candidates with $J^P = 5/2^+$. $\Xi_c(3055)$ is likely to be a 1\emph{D} state with $J^P = 3/2^+$. $\Lambda_b(5912)^0$ and $\Lambda_b(5920)^0$ favor the 1\emph{P} assignments with $J^P = 1/2^-$ and $3/2^-$, respectively. We propose a search for the $\tilde{\Lambda}_{c2}(5/2^-)$ state which can help to distinguish the diquark and three-body schemes.Comment: 9 tables, more discussions and references adde

We're Not Even Allowed to Ask for Help Debunking the Myth of the Model Minority

Asian Pacific Americans are the fastest growing population in New York City. Over one million constitute 13% of the population and nearly 14% of the city's public school students. Education experts concur that the public school system faces significant challenges in effectively serving the growing Asian Pacific American community in New York City. The Model Minority Myth homogenizes the diversity of cultures, languages, economics, and unique histories of Asian Pacific American communities. This stereotype trivializes the academic and developmental needs of Asian Pacific American children. While mainstream media focuses on the Asian Pacific American students who attend New York City's specialized high schools, "We're Not Even Allowed to Ask for Help": Debunking the Myth of the Model Minority focuses on the other 95% of Asian Pacific American students. "We're Not Even Allowed to Ask for Help" addresses the issues faced by Asian Pacific American students striving and struggling to get an education in New York City public schools. This report provides data about the challenges in school climate that Asian Pacific American students are facing as well as the effects of poverty on Asian Pacific American students' education

Hyperspectral image reconstruction of heritage artwork using RGB images and deep neural networks

The application of our research is in the art world where the scarcity of available analytical data from a particular artist or physical access for its acquisition is restricted. This poses a fundamental problem for the purpose of conservation, restoration or authentication of historical artworks. We address part of this problem by providing a practical method to generate hyperspectral data from readily available RGB imagery of artwork by means of a two-step process using deep neural networks. The particularities of our approach include the generation of learnable colour mixtures and reflectances from a reduced collection of prior data for the mapping and reconstruction of hyperspectral features on new images. Further analysis and correction of the prediction are achieved by a second network that reduces the error by producing results akin to those obtained by a hyperspectral camera. Our method has been used to study a collection of paintings by Amadeo de Souza-Cardoso where successful results were obtained. CCS CONCEPTS • Computing methodologies → Neural networks; Artificial intelligence; • Applied computing → Arts and humanities.info:eu-repo/semantics/publishedVersio