We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans. Given a set of 3D point correspondences, we build a deep neural network to address the following two challenges: (i) classification of the point correspondences into inliers/outliers, and (ii) regression of the motion parameters that align the scans into a common reference frame. With regard to regression, we present two alternative approaches: (i) a Deep Neural Network (DNN) registration and (ii) a Procrustes approach using SVD to estimate the transformation. Our correspondence-based approach achieves a higher speedup compared to competing baselines. We further propose the use of a refinement network, which consists of a smaller 3DRegNet as a refinement to improve the accuracy of the registration. Extensive experiments on two challenging datasets demonstrate that we outperform other methods and achieve state-of-the-art results. The code is available at https://github.com/3DVisionISR/ 3DRegNet.

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Ecological dynamics of decision-making in the sport of sailing exemplifies emergent, conditionally coupled, co-adaptive behaviours. In this study, observation of the coupling dynamics of paired boats during competitive sailing showed that decision-making can be modelled as a self-sustained, co-adapting system of informationally coupled oscillators (boats). Bytracing the spatial-temporal displacements of the boats, time series analyses (autocorrelations, periodograms and running correlations) revealed that trajectories of match racing boats are coupled more than 88% of the time during a pre-start race, via continuous, competing co-adaptions between boats. Results showed that both the continuously selected trajectories of the sailors (12 years of age) and their categorical starting point locations were examples of emergent decisions. In this dynamical conception of decision-making behaviours, strategic positioning (categorical) and continuous displacement of a boat over the course in match-race sailing emerged as a function of interacting task, personal and environmental constraints. Results suggest how key interacting constraints could be manipulated in practice to enhance sailors' perceptual attunement to them in competition.

The authors present a comparison of the classification accuracy of 5 pattern detection methods in the performance of golf putting. The detection of the position of the golf club was performed using a computer vision technique followed by the estimation algorithm Darwinian particle swarm optimization to obtain a kinematical model of each trial. The estimated parameters of the models were subsequently used as sample of five classification algorithms: (a) linear discriminant analysis, (b) quadratic discriminant analysis, (c) naive Bayes with normal distribution, (d) naive Bayes with kernel smoothing density estimate, and (e) least squares support vector machines. Beyond testing the performance of each classification method, it was also possible to identify a putting signature that characterized each golf player. It may be concluded that these methods can be applied to the study of coordination and motor control on the putting performance, allowing for the analysis of the intra- and interpersonal variability of motor behavior in performance contexts.

The performance of football players within game context can be analyzed based on their ability to break or (re)balance the attacker-defender dyad. In this context, the analysis of each sub-phase (e.g., 1v1, 2v2) presents a feature that needs to be taken into account in sports analysis. This study aims to investigate the interpersonal dynamics dyad formed by the attacker and the defender in 1v1 situations with a goalkeeper. A sample of 11 football male players (age: 17.91 ± 1.04 years) with 8.6 ± 1.52 years of practice experience participated in the study. Analyzing the 1v1 sub-phase, results show that the distance, speed and angular amplitude between the attacker and the defender increases, especially when the attacker attempts to overtake the defender (i.e., score a goal). We conclude that decision-making emerges from the perception that players draw from the action, actively and consistently interacting to find solutions to emerging problems within the game context.

This paper discusses how an ecological dynamics framework can be implemented to interpret data, design practice tasks and interpret athletic performance in collective sports, exemplified here by research ideas within the Augmented peRCeption ANalysis framEwork for Football (ARCANE) project promoting an augmented perception of football teams for scientists and practitioners. An ecological dynamics rationale can provide an interpretation of athletes' positional and physiological data during performance, using new methods to assess athletes' behaviours in real-time and, to some extent, predict health and performance outcomes. The proposed approach signals practical applications for coaches, sports analysts, exercise physiologists and practitioners through merging a large volume of data into a smaller set of variables, resulting in a deeper analysis than typical measures of performance outcomes of competitive games.

Purpose This study aims to investigate whether, discounting the effect of the relative wealth of countries, it is possible to observe the relevance of policies for e-government development. Design/methodology/approach The deviations of countries' results from what could be expected, considering their relative wealth is calculated by using the residuals of a linear regression using the Gross Domestic Product per capita as the independent variable and the UN E-Government Development Index as the dependent variable. The countries that achieve better and worse results than expected are then identified and their cases are analyzed by resorting to secondary sources, namely, published research referring to their cases. Those research documents were identified by successively searching the Scopus database, the Google Scholar database and the Web of Science. Findings The existence of formal e-government strategies and plans and the capacity to implement them can make a difference, allowing countries to achieve better results than expected or, in their absence, to perform worse than expected. Research limitations/implications The proposed methodology can be useful to e-government researchers, particularly as a basis for deeper and more detailed studies. Practical implications Countries should invest in well-developed and focused strategies and continuity of public policies and their capacity to deliver results. For that purpose, political commitment and high-level coordination are key factors. For low-income countries, long-lasting cooperation with external experienced partners is crucial. For high-income countries, innovative thinking is a key enabler. Originality/value This study uses an innovative method to look beyond the effect of the relative wealth of countries and investigate the relevance of public policies for e-government development.

A LTMEX intervention improves physical and mental HRQoL in older adults with T2D, and also anthropometric, hemodynamic profile, and cardiorespiratory fitness.