Individual differences in human path integration abilities correlate with gray matter volume in retrosplenial cortex, hippocampus, and medial prefrontal cortex

Humans differ in their individual navigational abilities. These individual differences may exist in part because successful navigation relies on several disparate abilities, which rely on different brain structures. One such navigational capability is path integration, the updating of position and orientation, in which navigators track distances, directions, and locations in space during movement. Although structural differences related to landmark-based navigation have been examined, gray matter volume related to path integration ability has not yet been tested. Here, we examined individual differences in two path integration paradigms: (1) a location tracking task and (2) a task tracking translational and rotational self-motion. Using voxel-based morphometry, we related differences in performance in these path integration tasks to variation in brain morphology in 26 healthy young adults. Performance in the location tracking task positively correlated with individual differences in gray matter volume in three areas critical for path integration: the hippocampus, the retrosplenial cortex, and the medial prefrontal cortex. These regions are consistent with the path integration system known from computational and animal models and provide novel evidence that morphological variability in retrosplenial and medial prefrontal cortices underlies individual differences in human path integration ability. The results for tracking rotational self-motion-but not translation or location-demonstrated that cerebellum gray matter volume correlated with individual performance. Our findings also suggest that these three aspects of path integration are largely independent. Together, the results of this study provide a link between individual abilities and the functional correlates, computational models, and animal models of path integration

High Frequency Data Acquisition System for Modelling the Impact of Visitors on the Thermo-Hygrometric Conditions of Archaeological Sites: A Casa di Diana (Ostia Antica, Italy) Case Study

[EN] The characterization of the microclimatic conditions is fundamental for the preventive conservation of archaeological sites. In this context, the identification of the factors that influence the thermo-hygrometric equilibrium is key to determine the causes of cultural heritage deterioration. In this work, a characterization of the thermo-hygrometric conditions of Casa di Diana (Ostia Antica, Italy) is carried out analyzing the data of temperature and relative humidity recorded by a system of sensors with high monitoring frequency. Sensors are installed in parallel, calibrated and synchronized with a microcontroller. A data set of 793,620 data, arranged in a matrix with 66,135 rows and 12 columns, was used. Furthermore, the influence of human impact (visitors) is evaluated through a multiple linear regression model and a logistic regression model. The visitors do not affect the environmental humidity as it is very high and constant all the year. The results show a significant influence of the visitors in the upset of the thermal balance. When a tourist guide takes place, the probability that the hourly temperature variation reaches values higher than its monthly average is 10.64 times higher than it remains equal or less to its monthly average. The analysis of the regression residuals shows the influence of outdoor climatic variables in the thermal balance, such as solar radiation or ventilation.The authors would like to thank the staff of the archaeological area of Ostia Antica for the permission to work in this house. This work is partially supported by the projects HAR2013-47895-C2-1-P and HAR2013-47895-C2-2-P from MINECO.Merello Giménez, P.; García Diego, FJ.; Beltrán Medina, P.; Scatigno, C. (2018). High Frequency Data Acquisition System for Modelling the Impact of Visitors on the Thermo-Hygrometric Conditions of Archaeological Sites: A Casa di Diana (Ostia Antica, Italy) Case Study. Sensors. 18(2):1-15. https://doi.org/10.3390/s18020348S11518

Haptic Perception with a Robot Hand: Requirements and Realization

This paper first discusses briefly some of the recent ideas of perceptual psychology on the human haptic system particularly those of J.J. Gibson and Klatzky and Lederman. Following this introduction, we present some of the requirements of robotic haptic sensing and the results of experiments using a Utah/MIT dexterous robot hand to derive geometric object information using active sensing

A Visual Approach To Exploratory Data Mining

As the first step upon commencing an in-depth data mining analysis, students should become intimately acquainted with the data under study.  In this paper, we present a methodology and set of custom tools that we have designed and developed for use in our data mining courses that allows students to efficiently and effectively accomplish this task.  The tools create interactive visual presentations of the data, encouraging students to explore the data in search of patterns or relationships that would then be investigated in subsequent steps using sophisticated statistical and machine learning tools

An investigation to determine the kinematic variables associated with the production of topspin in the tennis groundstrokes

The ability to impart topspin to the ball when playing forehand and backhand groundstrokes can give a tennis player a tactical advantage in a rally. Recent developments in racket technology and tactical approaches to the game have increased the prevalence of topspin strokes. However, there is a limited scientific knowledge base for players and coaches to draw upon when seeking to improve this aspect of the game. Many of the kinematic analyses into tennis groundstrokes were conducted more than ten years ago, with measurement techniques that may not have accurately measured the anatomical rotations important for generating racket velocity. It has only recently been possible to measure the spin rate of a ball, and this has not been investigated in relation to the kinematics of a player. This study aimed to make an important contribution to the knowledge of tennis professionals by establishing which kinematic variables are related to the production of high ball spin rates resulting from topspin strokes. In order to achieve this aim, consideration was given to the accurate measurement of the joint rotations of the player in all planes of movement and the quantification of the ball spin rate. This information was used to answer three further questions; what are the kinematic differences between flat and topspin groundstrokes, how do these differences relate to the spin rate of the ball and how do these findings relate to individual players? Joint rotations were calculated based on three-dimensional data captured from twenty participants playing flat and topspin forehand and backhand strokes. The resulting ball spin rate was captured using a high-speed camera. The participants produced larger ball spin rates when playing the topspin strokes, indicating that they were able to produce spin if required. Analysis of the joint rotations revealed that there were adaptations in the stroke in order to achieve the higher spin rates. The adaptations were not uniform among participants, but did produce similar alterations in racket trajectory, inclination and velocity for the topspin strokes. It was these measures that were found to be the strongest predictors of ball spin rates, accounting for over 60 % of the variation in ball spin rate in the forehand stroke and over 70% in the backhand. Case study analyses confirmed the importance of the optimal racket kinematics at impact and provided models of technique throughout the forward swing of each stroke. This study has made a contribution to the knowledge of generating topspin in the tennis groundstrokes by establishing the parameters that predict high spin rates and applying them to analyses of individual players. In doing so, this investigation has also demonstrated methodology that is capable of accurately measuring the joint rotations associated with tennis strokes, and suggested a method by which the spin rate of the ball can be calculated

Detection and classification of small impacts on vehicles based on deep learning algorithms

Dissertação de mestrado integrado em Informatics EngineeringThis thesis explores the detection of impacts that cause damage based on data retrieved by an accelerometer placed inside a vehicle and subsequently classified by deep learning algorithms. The real world application of this work inserts itself in the car sharing market, by providing an automated service that allows constant monitoring on the vehicle status. The proposed solution was set as an alternative to the current machine learning algorithms in use. Previous research showed that deep learning algorithms are achieving better performance results when compared to non deep learning algorithms. We use data retrieved from two types of events: Normal driving and damage causing situations to test if the models are capable of generalising damage events. The approach to achieve this objective consisted in exploring and testing different algorithms: Multi Layer Perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Results revealed promising performance, with the MLP reaching a 82% true positive rate. Despite not matching the result obtained by the current non deep learning algorithm allows us to assess that deep learning is a strong alternative in the long term as more data is collected.O principal objectivo desta tese foi a exploração e detecção de impactos que causam danos com base em dados recolhidos por um acelerómetro colocado no interior um veículo e posteriormente classificados por algoritmos de deep learning. A aplicação deste trabalho no mundo real insere-se no mercado de partilha de veículos, ao fornecer um serviço automático que permite uma monitorização constante do estado do veículo. A solução proposta foi definida como uma alternativa aos actuais algoritmos de machine learning em uso. A revisão de literatura revelou que algoritmos de deep learning estão a alcançar melhores resultados de desempenho quando comparados com algoritmos de machine learning. Utilizamos dados recolhidos de dois tipos de eventos: Condução normal e situações que causam dano e testar se os modelos são capazes de generalizar os eventos de danos. A abordagem para alcançar este objectivo consistiu em explorar e testar diferentes algoritmos: MLP, CNN e RNN. Os resultados revelaram um desempenho promissor, com a MLP a atingir uma taxa de 82% de verdadeiros positivos. Apesar de não corresponder ao melhor resultado obtido pelo actual algoritmo de machine learning em uso permite-nos avaliar que deep learning é uma forte alternativa a longo prazo à medida que mais dados forem recolhidos

Layout Optimization of Microsatellite Components Using Genetic Algorithm

The placement of satellite components usually belongs to non-deterministic polynomial-time hard (NP-hard) problems that in terms of computational complexity is very difficult to solve. This problem is normally known as layout optimization problem (LOP). In this study the layout of microsatellite components has to meet the requirements set by mission payloads, launcher and spacecraft attitude control. The novel scheme is to find the various possibilities of optimal layout using genetic algorithms combined with order-based positioning technique (OPT). Each component has a given index and then placed in a container based on specific order of placements in accordance with a bottom-left (BL) algorithm that is already established. The placement order is generated by the genetic algorithm which explore various possibilities to obtain a sequence that brings the best solution

RECONCILING KNOWLEGDE MANAGEMENT AND E-COLLABORATION SYSTEMS: THE INFORMATION-DRIVEN KNOWLEDGE MANAGEMENT FRAMEWORK

In recent years, e-collaboration systems have emerged as an essential enabler of communication and collaboration between enterprises. Current trends in the area of e-collaboration emphasize the importance of effective collaborative knowledge management support in e-collaboration systems. Our research aims at proposing an intelligent infrastructure for the reconciliation of knowledge management and e-collaboration systems. The objective of the paper is to introduce a conceptual framework for designing and building the new infrastructure that supports specific characteristics of collaborative knowledge management in e-collaboration systems. The paper articulates how this framework enables efficient knowledge exploration and exploitation, before concluding with implications and recommendations for future developments in this area