Competitions of multi-agent systems for teaching artificial intelligence

This paper presents an approach based on competitions of multiagent systems as the basis for teaching advanced topics in Artificial Intelligence. The method was applied in the Cognitive Robotics course with students of the 5th-year in Computer Science from the University of Mumbai in India, in the domain of Soccer. The championships are played between different teams to allow students to assess and compare the results. The motivation that is reached is fundamental for creating interest in the study of Artificial Intelligence techniques and in research. The developed experiences are described, as well as an analysis of the method and its impact for the academy and the research

Corrosion in 316L porous prostheses obtained by gelcasting

Gelcasting (GC) process, usually used for ceramic moulding, is adapted for producing spongy or porous metal osteosynthesis components destined to bone void filling. The main objective of the interconnected porosity is to improve the osteoconductive of metal matrix by ingrowth of bone. Further, porosity reduces metal density and Young module, which causes bone resorption, leading to implant failure, phenomenon known as stress shielding. The employed GC is based on the formulation of AISI 316L stainless steel powder suspension in an aqueous solution of organic polymers. This suspension is cast into porous ceramic shells, like those used in lost wax technique, wherein the polymer crosslinking is induced by heating. The shells, containing the resulting hydrogel–metal composite, are subjected to thermal cycle in order to dry, burn the organic phase, sinter the metal particles at 1200 °C, and cool down to room temperature under dry hydrogen permanent flow. The susceptibility to corrosion of 50-60 % porous pieces was analyzed. The results indicated that the lower relation between the open porosity and the total porosity, the lower the corrosion rate.International Congress of Science and Technology of Metallurgy and Materials, SAM – CONAMET 201

Corrosión en prótesis porosas de AISI 316L obtenidas por Gelcasting

El Proceso gelcasting (GC), que generalmente se usa para el moldeo de cerámica, es adaptado para la producción de componentes de metal de osteosíntesis esponjosos porosos o prótesis, destinada llenado de defectos óseos. El objetivo principal de la porosidad interconectada es mejorar la osteoconductividad de matriz metálica y el crecimiento de hueso en su interior. Además, la porosidad disminuye la densidad de metal y los módulos de Young, que causan la resorción ósea, lo que lleva al fracaso del implante, fenómeno conocido apantallamiento de tensiones. El GC empleada se basa en la formulación de una suspensión de polvo de acero inoxidable AISI 316L en una solución acuosa de monómeros y polímeros orgánicos. Esta es colada en cáscara cerámicas porosas, como los utilizados en la técnica de la cera perdida, en la que el entrecruzado polimérico es inducido por calentamiento. Las cáscaras, que contienen el compuesto de metal de hidrogel resultante, se someten a ciclo térmico con el fin de secar, quemar la fase orgánica, sinterizar las partículas de metal a 1200 º C, y enfriar a temperatura ambiente bajo hidrógeno flujo permanente seco. La susceptibilidad a la corrosión fue analizada en piezas con porosidades entre 50 a 60%.. Los resultados indicaron que cuanto menor es la relación entre la porosidad abierta y la porosidad total, menor es la velocidad de corrosión.Gelcasting (GC) process, usually used for ceramic moulding, is adapted for producing spongy or porous metal osteosynthesis components destined to bone void filling. The main objective of the interconnected porosity is to improve the osteoconductive of metal matrix by ingrowth of bone. Further, porosity reduces metal density and Young module, which cause bone resorption, leading to implant failure, phenomenon known as stress shielding. The employed GC is based on the formulation of AISI 316L stainless steel powder suspension in an aqueous solution of organic polymers. This suspension is cast into porous ceramic shells, like those used in lost wax technique, wherein the polymer crosslinking is induced by heating. The shells, containing the resulting hydrogel–metal composite, are subjected to thermal cycle in order to dry, burn the organic phase, sinter the metal particles at 1200 °C, and cool down to room temperature under dry hydrogen permanent flow. The susceptibility to corrosion of 50-60 % porous pieces was analyzed. The results indicated that the lower relation between the open porosity and the total porosity, the lower the corrosion rate

Visualizing the Feature Importance for Black Box Models

In recent years, a large amount of model-agnostic methods to improve the transparency, trustability and interpretability of machine learning models have been developed. We introduce local feature importance as a local version of a recent model-agnostic global feature importance method. Based on local feature importance, we propose two visual tools: partial importance (PI) and individual conditional importance (ICI) plots which visualize how changes in a feature affect the model performance on average, as well as for individual observations. Our proposed methods are related to partial dependence (PD) and individual conditional expectation (ICE) plots, but visualize the expected (conditional) feature importance instead of the expected (conditional) prediction. Furthermore, we show that averaging ICI curves across observations yields a PI curve, and integrating the PI curve with respect to the distribution of the considered feature results in the global feature importance. Another contribution of our paper is the Shapley feature importance, which fairly distributes the overall performance of a model among the features according to the marginal contributions and which can be used to compare the feature importance across different models.Comment: To Appear in Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10 to 14, 2018, Proceedings, Part

Segmentation of sales for a mobile phone service through CART classification tree algorithm

The work consisted of detailing the CRISP-DM method in order to identify optimal groups of customers who are more likely to migrate from a prepaid to postpaid option in order to formulate an improvement plan for in call management by sorting the database. Classification models were applied to analyze the characteristics generated by the purchase of the different services. The CART Classification Tree algorithm. As a result, groups differentiated by probabilities of sales success (migrate from a prepaid to postpaid plan) were found, segments that reflect particular needs and characteristics to design marketing actions focused on the objective of increasing the effectiveness rate, contact information, and sales increase

Survey on retinopathy of prematurity (ROP) in Italy

This study aims to investigate the incidence and the relative risk factors of retinopathy of prematurity (ROP) and posterior-ROP (P-ROP): ROP in Zone I and posterior Zone II, as well as to analyze the occurrence of surgical treatment of ROP and to evaluate the short term outcome of the disease in Italy

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

La falsificazione epigrafica. Questioni di metodo e casi di studio

This paper aims to reconsider the manuscript by Jacopo Valvasone (1499-1570), formerly owned by the Earl of Leicester (now British Library, Additional MS 49369), which Theodor Mommsen borrowed and inspected in 1876, just before the publication of the second part of CIL V. In the letter that he wrote to thank the Vicar and Librarian of Halkham Hall, Mommsen declared that Valvasone joined \u201cthe the long list of forgers\u201d. The analysis of forgeries in Valvasone\u2019s manuscript could show whether Mommsen was right in his opinion

A methodology for exploring biomarker – phenotype associations: application to flow cytometry data and systemic sclerosis clinical manifestations

BACKGROUND: This work seeks to develop a methodology for identifying reliable biomarkers of disease activity, progression and outcome through the identification of significant associations between high-throughput flow cytometry (FC) data and interstitial lung disease (ILD) - a systemic sclerosis (SSc, or scleroderma) clinical phenotype which is the leading cause of morbidity and mortality in SSc. A specific aim of the work involves developing a clinically useful screening tool that could yield accurate assessments of disease state such as the risk or presence of SSc-ILD, the activity of lung involvement and the likelihood to respond to therapeutic intervention. Ultimately this instrument could facilitate a refined stratification of SSc patients into clinically relevant subsets at the time of diagnosis and subsequently during the course of the disease and thus help in preventing bad outcomes from disease progression or unnecessary treatment side effects. The methods utilized in the work involve: (1) clinical and peripheral blood flow cytometry data (Immune Response In Scleroderma, IRIS) from consented patients followed at the Johns Hopkins Scleroderma Center. (2) machine learning (Conditional Random Forests - CRF) coupled with Gene Set Enrichment Analysis (GSEA) to identify subsets of FC variables that are highly effective in classifying ILD patients; and (3) stochastic simulation to design, train and validate ILD risk screening tools. RESULTS: Our hybrid analysis approach (CRF-GSEA) proved successful in predicting SSc patient ILD status with a high degree of success (>82 % correct classification in validation; 79 patients in the training data set, 40 patients in the validation data set). CONCLUSIONS: IRIS flow cytometry data provides useful information in assessing the ILD status of SSc patients. Our new approach combining Conditional Random Forests and Gene Set Enrichment Analysis was successful in identifying a subset of flow cytometry variables to create a screening tool that proved effective in correctly identifying ILD patients in the training and validation data sets. From a somewhat broader perspective, the identification of subsets of flow cytometry variables that exhibit coordinated movement (i.e., multi-variable up or down regulation) may lead to insights into possible effector pathways and thereby improve the state of knowledge of systemic sclerosis pathogenesis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0722-x) contains supplementary material, which is available to authorized users