Ion beam analysis of microcrystalline quartz artifacts from the Reed Mound Site, Delaware County, Oklahoma

Ion beam analysis (IBA) has been a powerful, non-destructive tool for archaeological research worldwide for over four decades, yet its full potential is seldom realized in North American archaeology. Herein the potential of particle induced X-ray emission spectrometry (PIXE) as a tool for future Ozarks chert provenance studies is evaluated based on its ability to facilitate (1) discrimination of Ozarks chert materials from different geological formations and (2) identification of discrete groups of artifacts from the same geological formation. In addition, PIXE was also used to evaluate the elemental heterogeneity of Ozarks chert materials. Thirty chert (microcrystalline quartz) artifacts were visually sorted and classified according to macroscopic features characteristic of certain chert resources from particular Ozarks geological formations. The elemental concentrations obtained from PIXE analysis underwent multivariate statistical analyses in order to gain insight from the data. The results indicate that PIXE could be a useful tool for assigning Ozarks chert materials to their respective geological formations, and possibly for determining regional or sub-regional provenance

Math empowerment: a multidisciplinary example to engage primary school students in learning mathematics

This paper describes an educational project conducted in a primary school in Italy (Scuola Primaria Alessandro Manzoni at Mulazzano, near to Milan). The school requested our collaboration to help improve upon the results achieved on the National Tests for Mathematics, in which students, aged 7, registered performances lower than the national average the past year. From January to June, 2016, we supported teachers, providing them with information, tools and methods to increase their pupils’ curiosity and passion for mathematics. Mixing our different experiences and competences (instructional design and gamification, information technologies and psychology) we have tried to provide a broader spectrum of parameters, tools and keys to understand how to achieve an inclusive approach that is ‘personalised’ to each student. This collaboration with teachers and students allowed us to draw interesting observations about learning styles, pointing out the negative impact that standardized processes and instruments can have on the self‐esteem and, consequently, on student performance. The goal of this programme was to find the right learning levers to intrigue and excite students in mathematical concepts and their applications. Our hypothesis is that, by considering the learning of mathematics as a continuous process, in which students develop freely through their own experiments, observations, involvement and curiosity, students can achieve improved results on the National Tests (INVALSI). This paper includes results of a survey conducted by children ‐’About Me and Mathematics‘

Learning Robust Object Recognition Using Composed Scenes from Generative Models

Recurrent feedback connections in the mammalian visual system have been hypothesized to play a role in synthesizing input in the theoretical framework of analysis by synthesis. The comparison of internally synthesized representation with that of the input provides a validation mechanism during perceptual inference and learning. Inspired by these ideas, we proposed that the synthesis machinery can compose new, unobserved images by imagination to train the network itself so as to increase the robustness of the system in novel scenarios. As a proof of concept, we investigated whether images composed by imagination could help an object recognition system to deal with occlusion, which is challenging for the current state-of-the-art deep convolutional neural networks. We fine-tuned a network on images containing objects in various occlusion scenarios, that are imagined or self-generated through a deep generator network. Trained on imagined occluded scenarios under the object persistence constraint, our network discovered more subtle and localized image features that were neglected by the original network for object classification, obtaining better separability of different object classes in the feature space. This leads to significant improvement of object recognition under occlusion for our network relative to the original network trained only on un-occluded images. In addition to providing practical benefits in object recognition under occlusion, this work demonstrates the use of self-generated composition of visual scenes through the synthesis loop, combined with the object persistence constraint, can provide opportunities for neural networks to discover new relevant patterns in the data, and become more flexible in dealing with novel situations.Comment: Accepted by 14th Conference on Computer and Robot Visio

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers