Resumen del trabajo presentado a la Conferencia Bio Diversity Next, celebrada en Leiden (Países Bajos) del 22 al 25 de octubre de 2019. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 777435.
Phytoplankton form the basis of the marine food web and are an indicator for the overall status of the marine ecosystem. Changes in this community may impact a wide range of species (Capuzzo et al. 2018) ranging from zooplankton and fish to seabirds and marine mammals. Efficient monitoring of the phytoplankton community is therefore essential (Edwards et al. 2002). Traditional monitoring techniques are highly time intensive and involve taxonomists identifying and counting numerous specimens under the light microscope. With the recent development of automated sampling devices, image analysis technologies and learning algorithms, the rate of counting and identification of phytoplankton can be increased significantly (Thyssen et al. 2015). The FlowCAM (Álvarez et al. 2013) is an imaging particle analysis system for the identification and classification of phytoplankton. Within the Belgian Lifewatch observatory, monthly phytoplankton samples are taken at nine stations in the Belgian part of the North Sea. These samples are run through the FlowCAM and each particle is photographed. Next, the particles are identified based on their morphology (and fluorescence) using state-of-the-art Convolutional Neural Networks (CNNs) for computer vision. This procedure requires learning sets of expert validated images. The CNNs are specifically designed to take advantage of the two dimensional structure of these images by finding local patterns, being easier to train and having many fewer parameters than a fully connected network with the same number of hidden units. In this work we present our approach to the use of CNNs for the identification and classification of phytoplankton, testing it on several benchmarks and comparing with previous classification techniques. The network architecture used is ResNet50 (He et al. 2016). The framework is fully written in Python using the TensorFlow (Abadi, M. et al. 2016) module for Deep Learning. Deployment and exploitation of the current framework is supported by the recently started European Union Horizon 2020 programme funded project DEEP-Hybrid-Datacloud (Grant Agreement number 777435), which supports the expensive training of the system needed to develop the application and provides the necessary computational resources to the users.
The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too. The software devel- opment in this field is fast paced with a large number of open-source software coming from the academy, industry, start-ups or wider open-source commu- nities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Arti- ficial Intelligence software. It also provides an overview of massive parallelism support that is capable of scaling computation effectively and efficiently in the era of Big Data. ; Álvaro López García, Ignacio Heredia, Giang Nguyen, Viet Tran, Stefan Dlugolinsky, Martin Bobák, and Ladislav Hluchý are supported by the project DEEP-HybridDataCloud "Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud" that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 777435. Giang Nguyen, Viet Tran, Stefan Dlugolinsky, Martin Bobák, and Ladislav Hluchý are also supported by the Project VEGA 2/0167/16 "Methods and algorithms for the semantic processing of Big Data in distributed computing environment". The authors would like to thanks to all colleagues, especially for Ján Astaloš for knowledge sharing and teamwork. ; Peer reviewed
The Fourier coeffcients v2and v3characterizing the anisotropy of the azimuthal distribution of charged particles produced in PbPb collisions at √sNN= 5.02 TeV are measured with data collected by the CMS experiment. The measurements cover a broad transverse momentum range, 1 10 GeV/c range, where anisotropic azimuthal distributions should reflect the path-length dependence of parton energy loss in the created medium. Results are presented in several bins of PbPb collision centrality, spanning the 60% most central events. The v2coeffcient is measured with the scalar product and the multiparticle cumulant methods, which have different sensitivities to initial-state fluctuations. The values from both methods remain positive up to pT∼ 60–80 GeV/c, in all examined centrality classes. The v3coeffcient, only measured with the scalar product method, tends to zero for pT>~ 20 GeV/c. Comparisons between theoretical calculations and data provide new constraints on the path-length dependence of parton energy loss in heavy ion collisions and highlight the importance of the initial-state fluctuations. ; Individuals have received support from the Marie-Curie program and the European Research Council and EPLANET (European Union); the Leventis Foundation; the A.P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Council of Science and Industrial Research, India; the HOMING PLUS program of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus program of the Ministry of Science and Higher Education, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities Research Program by Qatar National Research Fund; the Programa Clarín-COFUND del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); and the Welch Foundation, contract C-1845. ; Peer Reviewed