Cultural Event Recognition with Visual ConvNets and Temporal Models

This paper presents our contribution to the ChaLearn Challenge 2015 on Cultural Event Classification. The challenge in this task is to automatically classify images from 50 different cultural events. Our solution is based on the combination of visual features extracted from convolutional neural networks with temporal information using a hierarchical classifier scheme. We extract visual features from the last three fully connected layers of both CaffeNet (pretrained with ImageNet) and our fine tuned version for the ChaLearn challenge. We propose a late fusion strategy that trains a separate low-level SVM on each of the extracted neural codes. The class predictions of the low-level SVMs form the input to a higher level SVM, which gives the final event scores. We achieve our best result by adding a temporal refinement step into our classification scheme, which is applied directly to the output of each low-level SVM. Our approach penalizes high classification scores based on visual features when their time stamp does not match well an event-specific temporal distribution learned from the training and validation data. Our system achieved the second best result in the ChaLearn Challenge 2015 on Cultural Event Classification with a mean average precision of 0.767 on the test set.Comment: Initial version of the paper accepted at the CVPR Workshop ChaLearn Looking at People 201

Expression of the blood-group-related glycosyltransferase B4galnt2 influences the intestinal microbiota in mice

Glycans on mucosal surfaces have an important role in host–microbe interactions. The locus encoding the blood-group-related glycosyltransferase β-1,4-N-acetylgalactosaminyltransferase 2 (B4galnt2) is subject to strong selective forces in natural house-mouse populations that contain a common allelic variant that confers loss of B4galnt2 gene expression in the gastrointestinal (GI) tract. We reasoned that altered glycan-dependent intestinal host–microbe interactions may underlie these signatures of selection. To determine whether B4galnt2 influences the intestinal microbial ecology, we profiled the microbiota of wild-type and B4galnt2-deficient siblings throughout the GI tract using 16S rRNA gene pyrosequencing. This revealed both distinct communities at different anatomic sites and significant changes in composition with respect to genotype, indicating a previously unappreciated role of B4galnt2 in host–microbial homeostasis. Among the numerous B4galnt2-dependent differences identified in the abundance of specific bacterial taxa, we unexpectedly detected a difference in the pathogenic genus, Helicobacter, suggesting Helicobacter spp. also interact with B4galnt2 glycans. In contrast to other glycosyltransferases, we found that the host intestinal B4galnt2 expression is not dependent on presence of the microbiota. Given the long-term maintenance of alleles influencing B4galnt2 expression by natural selection and the GI phenotypes presented here, we suggest that variation in B4galnt2 GI expression may alter susceptibility to GI diseases such as infectious gastroenteritis

Afinació d'una xarxa convolucional per a reconeixer esdeveniments culturals

Participation in the challenge "Cultural event classification" from ChaLearn. Teh description of teh challenge is the following: More than 10,000 images corresponding to 50 different cultural event categories will be considered. In all the categories, garments, human poses, objects and context will be possible cues to be exploited for recognizing the events, while preserving the inherent inter- and intra-class variability of this type of images. Examples of cultural events will be Carnival, Oktoberfest, San Fermin, Maha-Kumbh-Mela and Aoi-Matsuri, among others. MoreThis thesis explores good practices for improving the performance of an existing convnet trained with a dataset of clean data when an additional dataset of noisy data is available. We develop techniques to clean the noisy data with the help of the clean one, a family of solutions that we will refer to as denoising, and then we explore the best sorting of the clean and noisy datasets during the fine-tuning of a convnet. Then we study strategies to select the subset of images of the clean data that will improve the classification performance, a practice we will efer to as fracking. Next, we determine how many layers are actually better to fine-tune in our convnet, given our amount of data. And finally, we compare the classic convnet architecture where a single network is fine-tuned to solve a multi-class problem with the case of fine-tuning a convnet for binary classification for each considered class.Esta tesis explora varias prácticas para mejorar el rendimiento de una convnet entrenada con un dataset que contiene datos limpios, cuando tenemos disponible un dataset adicional con datos ruidosos. Desarrollamos técnicas para limpiar los datos ruidosos con ayuda de los limpios, una familia de soluciones a las que nos referiremos como denoising, y después exploramos la mejor manera de ordenar el dataset limpio y el ruidoso durante la afinación de una convnet. Después, estudiamos estrategias para seleccionar un conjunto de imágenes del dataset limpio con tal de mejorar el rendimiento de la convnet, una práctica a la que nos referiremos como fracking. A continuación, determinamos cuantas capas es mejor modificar durante la afinación en nuestra red, dada nuestra cantidad de imágenes. Finalmente, comparamos la estructura clásica de una convnet, donde una red es afinada para resolver un problema de varias clases, con el caso donde afinamos una red para hacer una clasificación binaria para cada clase.Aquesta tesis explora diverses pràctiques per millorar el rendiment d'una convnet entrenada amb un dataset que conté dades netes, quan tenim disponible un dataset addicional amb dades sorolloses. Desenvolupem tècniques per netejar les dades sorolloses amb l'ajuda de les netes, una família de solucions a les que ens referirem com denoising, i desprès explorem la millor manera d'ordenar el dataset net i el sorollós durant l'afinació d'una convnet. Desprès, estudiem estratègies per seleccionar un conjunt d'imatges del dataset net per tal de millorar el rendiment de la convnet, una pràctica a la que ens referirem com a fracking. A continuació, determinem quantes capes és millor modificar durant l'afinació en la nostre xarxa, donada la nostre quantitat d'imatges. I finalment, comparem l'estructura clàssica d'una convnet, on una xarxa es afinada per a resoldre un problema de varies classes, amb el cas on afinem una xarxa per fer una classificació binaria per cada classe

Afinació d'una xarxa convolucional per a reconeixer esdeveniments culturals

Participation in the challenge "Cultural event classification" from ChaLearn. Teh description of teh challenge is the following: More than 10,000 images corresponding to 50 different cultural event categories will be considered. In all the categories, garments, human poses, objects and context will be possible cues to be exploited for recognizing the events, while preserving the inherent inter- and intra-class variability of this type of images. Examples of cultural events will be Carnival, Oktoberfest, San Fermin, Maha-Kumbh-Mela and Aoi-Matsuri, among others. MoreThis thesis explores good practices for improving the performance of an existing convnet trained with a dataset of clean data when an additional dataset of noisy data is available. We develop techniques to clean the noisy data with the help of the clean one, a family of solutions that we will refer to as denoising, and then we explore the best sorting of the clean and noisy datasets during the fine-tuning of a convnet. Then we study strategies to select the subset of images of the clean data that will improve the classification performance, a practice we will efer to as fracking. Next, we determine how many layers are actually better to fine-tune in our convnet, given our amount of data. And finally, we compare the classic convnet architecture where a single network is fine-tuned to solve a multi-class problem with the case of fine-tuning a convnet for binary classification for each considered class.Esta tesis explora varias prácticas para mejorar el rendimiento de una convnet entrenada con un dataset que contiene datos limpios, cuando tenemos disponible un dataset adicional con datos ruidosos. Desarrollamos técnicas para limpiar los datos ruidosos con ayuda de los limpios, una familia de soluciones a las que nos referiremos como denoising, y después exploramos la mejor manera de ordenar el dataset limpio y el ruidoso durante la afinación de una convnet. Después, estudiamos estrategias para seleccionar un conjunto de imágenes del dataset limpio con tal de mejorar el rendimiento de la convnet, una práctica a la que nos referiremos como fracking. A continuación, determinamos cuantas capas es mejor modificar durante la afinación en nuestra red, dada nuestra cantidad de imágenes. Finalmente, comparamos la estructura clásica de una convnet, donde una red es afinada para resolver un problema de varias clases, con el caso donde afinamos una red para hacer una clasificación binaria para cada clase.Aquesta tesis explora diverses pràctiques per millorar el rendiment d'una convnet entrenada amb un dataset que conté dades netes, quan tenim disponible un dataset addicional amb dades sorolloses. Desenvolupem tècniques per netejar les dades sorolloses amb l'ajuda de les netes, una família de solucions a les que ens referirem com denoising, i desprès explorem la millor manera d'ordenar el dataset net i el sorollós durant l'afinació d'una convnet. Desprès, estudiem estratègies per seleccionar un conjunt d'imatges del dataset net per tal de millorar el rendiment de la convnet, una pràctica a la que ens referirem com a fracking. A continuació, determinem quantes capes és millor modificar durant l'afinació en la nostre xarxa, donada la nostre quantitat d'imatges. I finalment, comparem l'estructura clàssica d'una convnet, on una xarxa es afinada per a resoldre un problema de varies classes, amb el cas on afinem una xarxa per fer una classificació binaria per cada classe

Reshaping the Hexagone: the genetic landscape of modern France

Poster presented at the Human Evolution, held between 30th Octuber and 1st November in 2019, in Cambridge (UK).Unlike other European countries, Metropolitan France is surprisingly understudied. We have combined newly genotyped samples from various zones in France with publicly available data and applied both allele frequency and haplotype-based methods in order to describe the internal structure of this country, taking advantage of the Human Origins SNP array, specifically designed for human population genetics studies. We found out that French Basques are genetically distinct from all other populations in the Hexagone and that the populations from southwest France (namely the Franco-Cantabrian region) are intermediate between Basques and other populations. Moreover, Bretons slightly separated from the rest of the groups and a link with the historical gene flow from the British Isles has been found. Results from the allele frequency analyses point to a general background that appears to be a mixture of two components, one closer to Southern Italy and the other to Ireland. This combination may be the result of a contact that happened in two different moments: in the Early Neolithic, and then Ireland would be a proxy for the continental pathway for the Neolithic wave of advance and South Italy for the coastal penetration, or the Iron Age, when the Celtic and the Mediterranean worlds met in France. On the other hand, results from the haplotype-based methods describe a more structured landscape, highlighting the presence of areas characterized by differential links with the neighboring populations, possibly reflecting a more recent history

Reshaping the Hexagone: the genetic landscape of modern France

Poster presented at the Human Evolution, held between 30th Octuber and 1st November in 2019, in Cambridge (UK).Unlike other European countries, Metropolitan France is surprisingly understudied. We have combined newly genotyped samples from various zones in France with publicly available data and applied both allele frequency and haplotype-based methods in order to describe the internal structure of this country, taking advantage of the Human Origins SNP array, specifically designed for human population genetics studies. We found out that French Basques are genetically distinct from all other populations in the Hexagone and that the populations from southwest France (namely the Franco-Cantabrian region) are intermediate between Basques and other populations. Moreover, Bretons slightly separated from the rest of the groups and a link with the historical gene flow from the British Isles has been found. Results from the allele frequency analyses point to a general background that appears to be a mixture of two components, one closer to Southern Italy and the other to Ireland. This combination may be the result of a contact that happened in two different moments: in the Early Neolithic, and then Ireland would be a proxy for the continental pathway for the Neolithic wave of advance and South Italy for the coastal penetration, or the Iron Age, when the Celtic and the Mediterranean worlds met in France. On the other hand, results from the haplotype-based methods describe a more structured landscape, highlighting the presence of areas characterized by differential links with the neighboring populations, possibly reflecting a more recent history

Cultural event recognition with visual ConvNets and temporal models

This paper presents our contribution to the ChaLearn Challenge 2015 on Cultural Event Classification. The challenge in this task is to automatically classify images from 50 different cultural events. Our solution is based on the combination of visual features extracted from convolutional neural networks with temporal information using a hierarchical classifier scheme. We extract visual features from the last three fully connected layers of both CaffeNet (pretrained with ImageNet) and our fine tuned version for the ChaLearn challenge. We propose a late fusion strategy that trains a separate low-level SVM on each of the extracted neural codes. The class predictions of the low-level SVMs form the input to a higher level SVM, which gives the final event scores. We achieve our best result by adding a temporal refinement step into our classification scheme, which is applied directly to the output of each low-level SVM. Our approach penalizes high classification scores based on visual features when their time stamp does not match well an event-specific temporal distribution learned from the training and validation data. Our system achieved the second best result in the ChaLearn Challenge 2015 on Cultural Event Classification with a mean average precision of 0.767 on the test set.Peer Reviewe

People from Ibiza: an unexpected isolate in the Western Mediterranean

In this study, we seek to understand and to correlate the genetic patterns observed in the population of the island of Ibiza in the Western Mediterranean basin with past events. Genome-wide genotypes of 189 samples representing 13 of 17 regions in Spain have been analyzed, in addition to 105 samples from the Levant, 157 samples from North Africa, and one ancient sample from the Phoenician Cas Molí site in Ibiza. Before the Catalans conquered the island in 1235 CE, Ibiza (Eivissa) had already been influenced by several cultures, starting with the Phoenicians, then the Carthaginians, followed by the Umayyads. The impact of these various cultures on the genetic structure of the islanders is still unexplored. Our results show a clear distinction between Ibiza and the rest of Spain. To investigate whether this was due to the Phoenician colonization or to more recent events, we compared the genomes of current Ibizans to that of an ancient Phoenician sample from Ibiza and to both modern Levantine and North African genomes. We did not identify any trace of Phoenician ancestry in the current Ibizans. Interestingly, the analysis of runs of homozygosity and changes in the effective population size through time support the idea that drift has shaped the genetic structure of current Ibizans. In addition to the small carrying capacity of the island, Ibiza experienced a series of dramatic demographic changes due to several instances of famine, war, malaria and plague that could have significantly contributed to its current genetic differentiation.Funding was provided by the Agencia Estatal de Investigación and Fondo Europeo de Desarollo Regional (FEDER) (grant CGL2016-75389-P), Agència de Gestió d’Ajuts Universitaris i de la Recerca (Generalitat de Catalunya) grant 2014 SGR 866, and “Unidad de Excelencia María de Maeztu”, funded by the MINECO (ref: MDM-2014-0370). SAB was supported by the Agencia Estatal de Investigación FPI grant BES-2014-06922

People from Ibiza: an unexpected isolate in the Western Mediterranean

In this study, we seek to understand and to correlate the genetic patterns observed in the population of the island of Ibiza in the Western Mediterranean basin with past events. Genome-wide genotypes of 189 samples representing 13 of 17 regions in Spain have been analyzed, in addition to 105 samples from the Levant, 157 samples from North Africa, and one ancient sample from the Phoenician Cas Molí site in Ibiza. Before the Catalans conquered the island in 1235 CE, Ibiza (Eivissa) had already been influenced by several cultures, starting with the Phoenicians, then the Carthaginians, followed by the Umayyads. The impact of these various cultures on the genetic structure of the islanders is still unexplored. Our results show a clear distinction between Ibiza and the rest of Spain. To investigate whether this was due to the Phoenician colonization or to more recent events, we compared the genomes of current Ibizans to that of an ancient Phoenician sample from Ibiza and to both modern Levantine and North African genomes. We did not identify any trace of Phoenician ancestry in the current Ibizans. Interestingly, the analysis of runs of homozygosity and changes in the effective population size through time support the idea that drift has shaped the genetic structure of current Ibizans. In addition to the small carrying capacity of the island, Ibiza experienced a series of dramatic demographic changes due to several instances of famine, war, malaria and plague that could have significantly contributed to its current genetic differentiation.Funding was provided by the Agencia Estatal de Investigación and Fondo Europeo de Desarollo Regional (FEDER) (grant CGL2016-75389-P), Agència de Gestió d’Ajuts Universitaris i de la Recerca (Generalitat de Catalunya) grant 2014 SGR 866, and “Unidad de Excelencia María de Maeztu”, funded by the MINECO (ref: MDM-2014-0370). SAB was supported by the Agencia Estatal de Investigación FPI grant BES-2014-06922

Ancient DNA of Phoenician remains indicates discontinuity in the settlement history of Ibiza

Ibiza was permanently settled around the 7th century BCE by founders arriving from west Phoenicia. The founding population grew significantly and reached its height during the 4th century BCE. We obtained nine complete mitochondrial genomes from skeletal remains from two Punic necropoli in Ibiza and a Bronze Age site from Formentara. We also obtained low coverage (0.47X average depth) of the genome of one individual, directly dated to 361-178 cal BCE, from the Cas Molí site on Ibiza. We analysed and compared ancient DNA results with 18 new mitochondrial genomes from modern Ibizans to determine the ancestry of the founders of Ibiza. The mitochondrial results indicate a predominantly recent European maternal ancestry for the current Ibizan population while the whole genome data suggest a significant Eastern Mediterranean component. Our mitochondrial results suggest a genetic discontinuity between the early Phoenician settlers and the island's modern inhabitants. Our data, while limited, suggest that the Eastern or North African influence in the Punic population of Ibiza was primarily male dominated