Primordial black hole production during preheating in a chaotic inflationary model

In this paper we review the production of primordial black holes (PBHs) during preheating after a chaotic inflationary model. All relevant equations of motion are solved numerically in a modified version of HLattice, and we then calculate the mass variance to determine structure formation during preheating. It is found that production of PBHs can be a generic result of the model, even though the results seem to be sensitive to the values of the smoothing scale. We consider a constraint for overproduction of PBHs that could uncover some stress between inflation-preheating models and observations.Comment: 6 pages, 5 figures. Prepared for the conference proceedings of the 9th Mexican School on Gravitation and Mathematical Physics : Cosmology for the XXI Century: Inflation, Dark Matter and Dark Energ

S4ND: Single-Shot Single-Scale Lung Nodule Detection

The state of the art lung nodule detection studies rely on computationally expensive multi-stage frameworks to detect nodules from CT scans. To address this computational challenge and provide better performance, in this paper we propose S4ND, a new deep learning based method for lung nodule detection. Our approach uses a single feed forward pass of a single network for detection and provides better performance when compared to the current literature. The whole detection pipeline is designed as a single $3D$ Convolutional Neural Network (CNN) with dense connections, trained in an end-to-end manner. S4ND does not require any further post-processing or user guidance to refine detection results. Experimentally, we compared our network with the current state-of-the-art object detection network (SSD) in computer vision as well as the state-of-the-art published method for lung nodule detection (3D DCNN). We used publically available $888$ CT scans from LUNA challenge dataset and showed that the proposed method outperforms the current literature both in terms of efficiency and accuracy by achieving an average FROC-score of $0.897$. We also provide an in-depth analysis of our proposed network to shed light on the unclear paradigms of tiny object detection.Comment: Accepted for publication at MICCAI 2018 (21st International Conference on Medical Image Computing and Computer Assisted Intervention

GPCALMA: a Grid Approach to Mammographic Screening

The next generation of High Energy Physics experiments requires a GRID approach to a distributed computing system and the associated data management: the key concept is the "Virtual Organisation" (VO), a group of geographycally distributed users with a common goal and the will to share their resources. A similar approach is being applied to a group of Hospitals which joined the GPCALMA project (Grid Platform for Computer Assisted Library for MAmmography), which will allow common screening programs for early diagnosis of breast and, in the future, lung cancer. HEP techniques come into play in writing the application code, which makes use of neural networks for the image analysis and shows performances similar to radiologists in the diagnosis. GRID technologies will allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays presently associated to screening programs.Comment: 4 pages, 3 figures; to appear in the Proceedings of Frontier Detectors For Frontier Physics, 9th Pisa Meeting on Advanced Detectors, 25-31 May 2003, La Biodola, Isola d'Elba, Ital

Computer-aided detection systems to improve lung cancer early diagnosis: state-of-the-art and challenges

Lung cancer is one of the most lethal types of cancer, because its early diagnosis is not good enough. In fact, the detection of pulmonary nodule, potential lung cancers, in Computed Tomography scans is a very challenging and time-consuming task for radiologists. To support radiologists, researchers have developed Computer-Aided Diagnosis (CAD) systems for the automated detection of pulmonary nodules in chest Computed Tomography scans. Despite the high level of technological developments and the proved benefits on the overall detection performance, the usage of Computer-Aided Diagnosis in clinical practice is far from being a common procedure. In this paper we investigate the causes underlying this discrepancy and present a solution to tackle it: the M5L WEB- and Cloud-based on-demand Computer- Aided Diagnosis. In addition, we prove how the combination of traditional imaging processing techniques with state-of-art advanced classification algorithms allows to build a system whose performance could be much larger than any Computer-Aided Diagnosis developed so far. This outcome opens the possibility to use the CAD as clinical decision support for radiologists

Resiliencia, ansiedad y hábitos alimentarios de la población amazónica sur-oriente antes y durante la pandemia: Resilience, anxiety and eating habits of the south-east amazon population before and during the pandemic

Objective: To determine the relationship between resilience, anxiety and eating habits of the south-eastern Amazonian population before and during the pandemic in 2020. Methods: Relational, quantitative and qualitative descriptive, the sample consisted of 150 inhabitants aged 30 to 45 years. The instrument used was the STAI: state-trait anxiety inventory, the data were processed with SPSS-25, analysis of significance and difference of means was performed, non-parametric test and post-test significant differences (Wilcoxon test for related samples ). Results: In adults, State-Trait Anxiety was greater than 65%, a bilateral significance of p = 0.01 was obtained, indicating that anxiety was related to resilience. Food consumption before the pandemic was classified: natural 60%, processed 40% and ultra-processed 0%; During confinement, the consumption of processed foods was 53.33% and ultra-processed 20%. The level of food consumption before and during the pandemic shows a difference, being significant of p <5%. The level of anxiety before and during the pandemic, obtaining a significant of p <5%. Conclusions: State-Trait Anxiety was greater than 65% in adults. Anxiety was related to resilience. There is a significant difference in the type of food consumed, in anxiety levels, before and during the Covid-19 pandemic, in the inhabitants of the South-Eastern Amazon. The consumption of processed and ultra-processed foods, before the pandemic, was 40% and during the pandemic this consumption has increased to 73%, therefore the consumption of food was high in fat and sugar content.Objetivo: Determinar la relación entre la resiliencia, la ansiedad y los hábitos alimentarios de la población amazónica sur-oriente antes y durante la pandemia en el año 2020. Métodos: Descriptivo relacional, cuantitativo y cualitativo, la muestra fue de 150 pobladores amazónicos adultos cuyas edades son de 30 a 45 años. El instrumento utilizado fue el STAI: Inventario de ansiedad estad-rasgo, los datos fueron procesados con SPSS- 25, se realizo análisis de significancia y diferencia de medias, prueba no paramétrica de test y post test diferencias significativas (Prueba de Wilcoxon para muestras relacionadas). Resultados: En los adultos la Ansiedad Estado-Rasgo fue mayor al 65%, se obtuvo una significancia bilateral de p=0,01, indicando que la ansiedad estuvo relacionada con la resiliencia. El consumo de alimentos antes de la pandemia, estuvo clasificado: natural 60%, procesado 40% y ultra procesado 0%; durante el confinamiento, el consumo de alimentos procesados fue 53,33% y ultraprocesados 20%.El nivel consumo de alimentos antes y durante la pandemia, muestra una diferencia, siendo significativa de p <5%. El nivel de ansiedad antes y durante la pandemia, obteniendo una significativa de p <5%. Conclusiones: La Ansiedad Estado-Rasgo fue mayor al 65% en adultos. La ansiedad estuvo relacionada con la capacidad resilente. Existe diferencia significativa en el tipo de alimentos consumidos, en los niveles de ansiedad, antes y durante la pandemia Covid-19, en los pobladores de la Amazonia Sur-Oriente. El consumo de alimentos procesados y ultra procesados, antes de la pandemia, fue del 40% y durante la pandemia este consumo se ha incrementado al 73%, por tanto, el consumo de alimentos se elevó en alto contenido de grasas y azucares

Multi-objective Optimal Test Suite Computation for Software Product Line Pairwise Testing

Lopez-Herrejon, R. E., Chicano F., Ferrer J., Egyed A., & Alba E. (2013). Multi-objective Optimal Test Suite Computation for Software Product Line Pairwise Testing. 2013 IEEE International Conference on Software Maintenance, Eindhoven, The Netherlands, September 22-28, 2013. 404–407.Software Product Lines (SPLs) are families of related software products, which usually provide a large number of feature combinations, a fact that poses a unique set of challenges for software testing. Recently, many SPL testing approaches have been proposed, among them pair wise combinatorial techniques that aim at selecting products to test based on the pairs of feature combinations such products provide. These approaches regard SPL testing as an optimization problem where either coverage (maximize) or test suite size (minimize) are considered as the main optimization objective. Instead, we take a multi-objective view where the two objectives are equally important. In this exploratory paper we propose a zero-one mathematical linear program for solving the multi-objective problem and present an algorithm to compute the true Pareto front, hence an optimal solution, from the feature model of a SPL. The evaluation with 118 feature models revealed an interesting trade-off between reducing the number of constraints in the linear program and the runtime which opens up several venues for future research.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Austrian Science Fund (FWF) project P21321-N15 and Lise Meitner Fellowship M1421-N15. Spanish Ministry of Economy and Competitiveness and FEDER under contract TIN2011-28194 and fellowship BES-2012-055967

Comparative analysis of classical multi-objective evolutionary algorithms and seeding strategies for pairwise testing of Software Product Lines

Lopez-Herrejon, R. Erick, Ferrer J., Chicano F., Egyed A., & Alba E. (2014). Comparative analysis of classical multi-objective evolutionary algorithms and seeding strategies for pairwise testing of Software Product Lines. Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, China, July 6-11, 2014. 387–396.Software Product Lines (SPLs) are families of related software products, each with its own set of feature combinations. Their commonly large number of products poses a unique set of challenges for software testing as it might not be technologically or economically feasible to test of all them individually. SPL pairwise testing aims at selecting a set of products to test such that all possible combinations of two features are covered by at least one selected product. Most approaches for SPL pairwise testing have focused on achieving full coverage of all pairwise feature combinations with the minimum number of products to test. Though useful in many contexts, this single-objective perspective does not reflect the prevailing scenario where software engineers do face trade-offs between the objectives of maximizing the coverage or minimizing the number of products to test. In contrast and to address this need, our work is the first to propose a classical multi-objective formalisation where both objectives are equally important. In this paper, we study the application to SPL pairwise testing of four classical multi-objective evolutionary algorithms. We developed three seeding strategies — techniques that leverage problem domain knowledge — and measured their performance impact on a large and diverse corpus of case studies using two well-known multi-objective quality measures. Our study identifies the performance differences among the algorithms and corroborates that the more domain knowledge leveraged the better the search results. Our findings enable software engineers to select not just one solution (as in the case of single-objective techniques) but instead to select from an array of test suite possibilities the one that best matches the economical and technological constraints of their testing context.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Austrian Science Fund (FWF) project P25289- N15 and Lise Meitner Fellowship M1421-N15. Spanish Ministry of Economy and Competitiveness and FEDER under contract TIN2011-28194 and fellowship BES-2012-055967. Project 8.06/5.47.4142 in collaboration with the VSB-Tech. Univ. of Ostrava and Universidad de Málaga, Andalucía Tech

Enzymatic extraction of hydroxycinnamic acids from coffee pulp

Ferulic, caffeic, p-coumaric and chlorogenic acids are classified as hydroxycinnamic acids, presenting anticarcinogenic, anti-inflammatory and antioxidant properties. In this work, enzymatic extraction has been studied in order to extract high value-added products like hydroxycinnamic acids from coffee pulp. A commercial pectinase and enzyme extract produced by Rhizomucor pusillus strain 23aIV in solid-state fermentation using olive oil or coffee pulp (CP) as an inducer of the feruloyl esterase activity were evaluated separately and mixed. The total content (covalently linked and free) of ferulic, caffeic, p-coumaric and chlorogenic acids was 5276 mg per kg of coffee pulp. Distribution was as follows (in %): chlorogenic acid 58.7, caffeic acid 37.6, ferulic acid 2.1 and p-coumaric acid 1.5. Most of the hydroxycinnamic acids were covalently bound to the cell wall (in %): p-coumaric acid 97.2, caffeic acid 94.4, chlorogenic acid 76.9 and ferulic acid 73.4. The content of covalently linked hydroxycinnamic acid was used to calculate the enzyme extraction yield. The maximum carbon dioxide rate for the solid-state fermentation using olive oil as an inducer was higher and it was reached in a short cultivation time. Nevertheless, the feruloyl esterase (FAE) activity (units per mg of protein) obtained in the fermentation using CP as an inducer was 31.8 % higher in comparison with that obtained in the fermentation using olive oil as the inducer. To our knowledge, this is the first report indicating the composition of both esterified and free ferulic, caffeic, p-coumaric and chlorogenic acids in coffee pulp. The highest yield of extraction of hydroxycinnamic acids was obtained by mixing the produced enzyme extract using coffee pulp as an inducer and a commercial pectinase. Extraction yields were as follows (in %): chlorogenic acid 54.4, ferulic acid 19.8, p-coumaric acid 7.2 and caffeic acid 2.3. An important increase in the added value of coffee pulp was mainly due to the extraction of chlorogenic acid