Adaptive learning, endogenous inattention, and changes in monetary policy

This paper develops an adaptive learning formulation of an extension to the Ball, Mankiw, and Reis (2005) sticky information model that incorporates endogenous inattention. We show that, following an exogenous increase in the policymaker’s preferences for price vs. output stability, the learning process can converge to a new equilibrium in which both output and price volatility are lower.Monetary policy ; Information theory

The Impact of Augmented Reality (AR) on the Academic Performance of High School Students

New technologies currently play a fundamental role in the educational context. As a result, augmented reality (AR) has recently gained a presence in educational centres. However, this educational technology has not been explored in depth at the secondary education level. Therefore, this research aims to analyse the impact of augmented reality on the academic performance of secondary education students, considering gender and the students’ attitudes toward this technology. In this mixed-method research based on convenient sampling, 321 students aged 14 to 17 years from the same secondary education school were assigned to an experimental group (n = 159) and a control group (n = 162). The control group used a traditional methodology in a slide-based learning environment, while the experimental group worked with an AR mobile application (ComputAR) designed with the same concepts. The data collection instruments used comprised a pre-test/post-test in both groups and semi-structured interviews in the experimental group. The results showed that the students who used augmented reality achieved better grades, highlighting the potential benefits of integrating this technology into the teaching process. No significant differences were observed regarding the gender of the students. In conclusion, this study provides findings that encourage the use of augmented reality in secondary schools

Influence of motivation and academic performance in the use of Augmented Reality in education. A systematic review

The recent technologies rise today as a tool of significant importance today, especially in the educational context. In this sense, Augmented Reality (AR) is a technology that is achieving a greater presence in educational centers in the last decade. However, Augmented Reality has not been explored in depth at the Secondary Education stage. Due to this, it is essential to analyze and concentrate the scientific research developed around this educational technology at that stage. Therefore, the aim of this research is to describe the influence that Augmented Reality shows on the motivation and academic performance of students in the Secondary Education stage. In relation to the methodology, a systematic review of the literature has been conducted using the Kitchenham protocol, where several factors have been analyzed, such as subjects, activities, and electronic implementation devices, together with the effects on motivation and student's academic performance. The Scopus and Web of Science (WoS) databases have been used to search for scientific papers, with a total of 344 investigations being analyzed between 2012 and 2022. The methodological stages considered were the formulation of research questions, the choice of data sources, search strategies, inclusion and exclusion criteria and quality assessment, and finally, data extraction and synthesis. The results obtained have shown that the use of AR in the classroom provides higher levels of motivation, reflected by factors such as attention, relevance, confidence, and satisfaction, and reflects better results in the tests carried out on the experimental groups compared to the control groups, which means an improvement in the academic performance of students. These results supply a fundamental theoretical basis, where the different teachers should be supported for the incorporation of AR in the classroom, since how this educational technology has been shown offers great opportunities. Likewise, the development of research in areas not so addressed can further clarify the generality of AR based on its influence on learning. In addition, the fields of natural sciences and logical-mathematical have been the most addressed, managing to implement their contents through object modeling. In short, this research highlights the importance of incorporating Augmented Reality into all areas and educational stages, since it is a significant improvement in the teaching and learning process

Motorcycle Classification in Urban Scenarios using Convolutional Neural Networks for Feature Extraction

This paper has been presented at 8th International Conference of Pattern Recognition Systems.This paper presents a motorcycle classification system for urban scenarios using Convolutional Neural Network (CNN). Significant results on image classification has been achieved using CNNs at the expense of a high computational cost for training with thousands or even millions of examples. Nevertheless, features can be extracted from CNNs already trained. In this work AlexNet, included in the framework CaffeNet, is used to extract features from frames taken on a real urban scenario. The extracted features from the CNN are used to train a support vector machine (SVM) classifier to discriminate motorcycles from other road users. The obtained results show a mean accuracy of 99.40% and 99.29% on a classification task of three and five classes respectively. Further experiments are performed on a validation set of images showing a satisfactory classification.S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santande

Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN

This paper has been presented at: 9th International Conference on Pattern Recognition Systems (ICPRS-18)This paper introduces a Deep Learning Convolutional Neutral Network model based on Faster-RCNN for motorcycle detection and classification on urban environments. The model is evaluated in occluded scenarios where more than 60% of the vehicles present a degree of occlusion. For training and evaluation, we introduce a new dataset of 7500 annotated images, captured under real traffic scenes, using a drone mounted camera. Several tests were carried out to design the network, achieving promising results of 75% in average precision (AP), even with the high number of occluded motorbikes, the low angle of capture and the moving camera. The model is also evaluated on low occlusions datasets, reaching results of up to 92% in AP.S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research. The data and code used for this work is available upon request from the authors

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area

Automatic determination of the Atterberg limits with machine learning

In this study, we determine the liquid limit (Wt), plasticity index (PI), and plastic limit (Wp) of several natural fine-grained soil samples with the help of machine-learning and statistical methods. This enables us to locate each soil type analysed in the Casagrande plasticity chart with a single measure in pressure-membrane extractors. These machine-learning models showed adjustments in the determination of the liquid limit for design purposes when compared with standardised methods. Similar adjustments were achieved in the determination of the plasticity index, whereas the plastic limit determinations were applicable for control works. Because the best techniques were based in Multiple Linear Regression and Support Vector Machines Regression, they provide explainable plasticity models. In this sense, (Equation presented), and (Equation presented). So that, we propose an alternative, automatic, multi-sample, and static method to address current issues on Atterberg limits determination with standardised tests.En este estudio, determinamos el límite líquido (), el índice de plasticidad (PI) y el límite plástico () de suelos naturales finos con ayuda de machine-learning y métodos estadísticos. Ello permite localizarlos en la Carta de Plasticidad de Casagrande con una sola medida en extractores de presión-membrana. Los modelos de machine-learning mostraron ajustes en la determinación de apropiados para propósitos de diseño, comparados con métodos estandarizados. Ajustes similares se alcanzaron en la determinación de PI, mientras que las determinaciones de permiten ajustes apropiados para trabajos de control. Debido a que las técnicas más apropiadas se basaron en Regresión Lineal Múltiple y Máquinas de Soporte de Vectores, aportaron modelos de plasticidad explicables. En este sentido, =(9.94±4.2)+(2.25±0.3)∙4.2,=(−20.47±5.6)+(1.48±0.3)∙4.2+(0.21±0.1)∙y=(23.32±3.5)+(0.60±0.2)∙4.2−(0.13±0.04)∙. Por consiguiente, proponemos un método alternativo, automático, estático y multimuestra para enfrentar problemas frecuentes en la determinación de los Límites de Atterberg con ensayos normalizados

Detection and Tracking of Motorcycles in Congested Urban Environments Using Deep Learning and Markov Decision Processes

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11524) pp 139-148 Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 11524)Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 11524)This research describes “EspiNet”, a Deep Learning Convolutional Neural Network model, in conjunction with a Markov Decision Process (MDP) tracker for detection and tracking of occluded motorcycles in urban environments. The model is trained and evaluated, using a new public dataset with up to 10,000 annotated images, created for this research, and captured in real urban traffic scenes. Images were captured using a moving camera mounted in a drone, where more than 60% of the motorcycles are affected by occlusions. The network design involves many tests, where a promising result of 88.84% in average precision (AP) is achieved, despite the considerable number of occluded vehicles, the movement of the camera and the low angle used for capture. The model predictions are used as input to an MDP tracker, reaching results up to 85.2% in Multiple Object Tracking Accuracy (MOTA). The proposed network architecture outperforms state of the art YOLO (You Look Only Once) v3.0 and Faster R-CNN (VGG16 based) detection models, producing also better tracking results in comparison with the use of the other two models as detector base for the MDP tracker.This work was partially supported by COLCIENCIAS project: Reduccion de Emisiones Vehiculares Mediante el Modelado y Gestion Optima de Trafico en Areas Metropolitanas - Caso Medellin - Area Metropolitana del Valle de Aburra, codigo 111874558167, CT 049-2017. Universidad Nacional de Colombia. Proyecto HERMES 25374. The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of GPUs used for this research

Ignition and combustion of bulk metals under elevated, normal and reduced gravity conditions

This research effort is aimed at providing further insight into this multi-variable dependent phenomena by looking at the effects of gravity on the ignition and combustion behavior of metals. Since spacecraft are subjected to higher-than-1g gravity loads during launch and reentry and to zero-gravity environments while in orbit, the study of ignition and combustion of bulk metals at different gravitational potentials is of great practical concern. From the scientific standpoint, studies conducted under microgravity conditions provide simplified boundary conditions since buoyancy is removed, and make possible the identification of fundamental ignition mechanisms. The effect of microgravity on the combustion of bulk metals has been investigated by Steinberg, et al. on a drop tower simulator. However, no detailed quantitative work has been done on ignition phenomena of bulk metals at lower or higher-than-normal gravitational fields or on the combustion characteristics of metals at elevated gravity. The primary objective of this investigation is the development of an experimental system capable of providing fundamental physical and chemical information on the ignition of bulk metals under different gravity levels. The metals used in the study, iron (Fe), titanium (Ti), zirconium (Zr), magnesium (Mg), zinc (Zn), and copper (Cu) were selected because of their importance as elements of structural metals and their simple chemical composition (pure metals instead of multi-component alloys to avoid complication in morphology and spectroscopic studies). These samples were also chosen to study the two different combustion modes experienced by metals: heterogeneous or surface oxidation, and homogeneous or gas-phase reaction. The experimental approach provides surface temperature profiles, spectroscopic measurements, surface morphology, x-ray spectrometry of metals specimens and their combustion products, and high-speed cinematography of the heating, ignition and combustion stages of the metal specimen. This paper summarizes the results obtained to date from experiments conducted under normal and high-gravity conditions

Caracterización de tejido cerebral artificial utilizando Inverse-FEM para simular indentación y comprensión

The realistic simulation of tool-tissue interactions is necessary for the development of surgical simulators and one of the key element for it realism is accurate bio-mechanical tissue models. In this paper, we determined the mechanical properties of soft tissue by minimizing the difference between experimental measurements and the analytical or simulated solution of the deformation. Then, we selected the best model parameters that fit the experimental data to simulate a bonded compression and a needle indentation with a flat-tip. We show that the inverse FEM allows accurate material property estimation. We also validated our results using multiple tool-tissue interactions over the same specimen.La simulación realista de las interacciones herramienta-tejido es necesaria para el desarrollo de simuladores quirúrgicos y uno de los elementos clave para su realismo son los modelos precisos de tejido biomecánico. En este artículo, determinamos las propiedades mecánicas del tejido blando minimizando la diferencia entre las mediciones experimentales y la solución analítica o simulada de la deformación. Luego, seleccionamos los mejores parámetros del modelo que se ajustan a los datos experimentales para simular una compresión unida y una sangría de aguja con una punta plana. Mostramos que el FEM inverso permite una estimación precisa de la propiedad del material. También validamos nuestros resultados usando múltiples interacciones herramienta-tejido sobre la misma muestra