Conflictes i educació física a la llum de la praxeologia motriu. Estudi de cas d’un centre educatiu de primària

Aquesta tesi té com a objecte d’estudi l’observació dels conflictes sorgits en les classes d’educació física d’un centre educatiu de primària. L’estudi pren com a marc teòric de referència la praxeologia motriu i les principals aportacions teòriques sobre els conflictes. Des d’aquesta perspectiva es pot justificar que l’educació física origina diferents escenaris de relació motriu que poden desencadenar possibles conflictes motors entre els seus protagonistes

Enseñanza - color - País Vasco: claves de una reflexión (ávida de esperanza)

This article revolves around the initial components of the title -a professional situation, a topic being studied and a socio cultural context with the Basque language as its outstanding protagonist - and it aims, faced with the vigour of the 'universalist' (or Western mentality's) outline of perception, comprehension and articulation of the world of colour which tends to build itself up as the main (if not the only) reference guideline, claiming responsibility for its cultural dimension by raising awareness of the Basque situation, with particular reference to schools

Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression

We demonstrate the applicability of model-agnostic algorithms for meta-learning, specifically Reptile, to GNN models in molecular regression tasks. Using meta-learning we are able to learn new chemical prediction tasks with only a few model updates, as compared to using randomly initialized GNNs which require learning each regression task from scratch. We experimentally show that GNN layer expressivity is correlated to improved meta-learning. Additionally, we also experiment with GNN emsembles which yield best performance and rapid convergence for k-shot learning

Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries

The inductive bias of a graph neural network (GNN) is largely encoded in its specified graph. Latent graph inference relies on latent geometric representations to dynamically rewire or infer a GNN's graph to maximize the GNN's predictive downstream performance, but it lacks solid theoretical foundations in terms of embedding-based representation guarantees. This paper addresses this issue by introducing a trainable deep learning architecture, coined neural snowflake, that can adaptively implement fractal-like metrics on $\mathbb{R}^d$. We prove that any given finite weights graph can be isometrically embedded by a standard MLP encoder. Furthermore, when the latent graph can be represented in the feature space of a sufficiently regular kernel, we show that the combined neural snowflake and MLP encoder do not succumb to the curse of dimensionality by using only a low-degree polynomial number of parameters in the number of nodes. This implementation enables a low-dimensional isometric embedding of the latent graph. We conduct synthetic experiments to demonstrate the superior metric learning capabilities of neural snowflakes when compared to more familiar spaces like Euclidean space. Additionally, we carry out latent graph inference experiments on graph benchmarks. Consistently, the neural snowflake model achieves predictive performance that either matches or surpasses that of the state-of-the-art latent graph inference models. Importantly, this performance improvement is achieved without requiring random search for optimal latent geometry. Instead, the neural snowflake model achieves this enhancement in a differentiable manner.Comment: 9 Pages + Appendix, 2 Figures, 9 Table

Teaching - colour - Basque Country: keys to reflection (keen for hope)

This article revolves around the initial components of the title -a professional situation, a topic being studied and a socio cultural context with the Basque language as its outstanding protagonist - and it aims, faced with the vigour of the 'universalist' (or Western mentality's) outline of perception, comprehension and articulation of the world of colour which tends to build itself up as the main (if not the only) reference guideline, claiming responsibility for its cultural dimension by raising awareness of the Basque situation, with particular reference to schools

Study of Conflicts in Games Played during Primary School physical Education Classes

Este estudio investigó los conflictos motores (CM) surgidos en los juegos realizados en clases de educación física en un centro de educación primaria. Se examinaron los conflictos que se originaron en cuatro clases de juegos (psicomotores, cooperación, oposición y cooperación-oposición). Se trata de un estudio de caso único ramificado multisujeto (n = 43 estudiantes, 21 niños y 22 niñas, de entre 8 y 11 años de edad). El profesor compartió el rol de investigador participante y de docente. Se aplicaron 255 juegos que originaron 747 CM. El análisis estadístico inferencial (modelo de regresión logística univariable y multivariable) permitió investigar el origen, la respuesta y su relación en los diferentes CM. El estudio ha confirmado el alto nivel conflictivo del alumnado También se ha observado que los CM están directamente relacionados con la familia de juegos motores en las que emergenThis study investigated the types of motor conflict arising in four kinds of games (psychomotor, cooperation, opposition and cooperation-opposition) played in the context of primary school physical education classes. This was a multi-subject, single-case study (n = 43 students, 21 boys and 22 girls aged between 8 and 11 years). The class teacher had a dual role as participant researcher and educator. A total of 255 games were studied, in which 747 motor conflicts arose. An inferential statistical analysis (univariate and multivariate logistic regression) was used to investigate the origin of and response to each motor conflict, as well as the relationship between these two aspects. The results revealed high levels of conflict among these students. The different types of conflict were also found to be related to the family of motor games in which they aros

Capacity Bounds for Hyperbolic Neural Network Representations of Latent Tree Structures

We study the representation capacity of deep hyperbolic neural networks (HNNs) with a ReLU activation function. We establish the first proof that HNNs can $\varepsilon$-isometrically embed any finite weighted tree into a hyperbolic space of dimension $d$ at least equal to $2$ with prescribed sectional curvature $\kappa 1$ (where $\varepsilon=1$ being optimal). We establish rigorous upper bounds for the network complexity on an HNN implementing the embedding. We find that the network complexity of HNN implementing the graph representation is independent of the representation fidelity/distortion. We contrast this result against our lower bounds on distortion which any ReLU multi-layer perceptron (MLP) must exert when embedding a tree with $L>2^d$ leaves into a $d$-dimensional Euclidean space, which we show at least $\Omega(L^{1/d})$; independently of the depth, width, and (possibly discontinuous) activation function defining the MLP.Comment: 22 Pages + References, 1 Table, 4 Figure

Els conflictes en clubs esportius amb esportistes adolescents

En detectar-se que en els mitjans de comunicació apareixien sovint notícies, en la secció d’esports, sobre els conflictes que es generaven als clubs esportius entre pares, entrenadores, jugadors i directius, ens plantegem conèixer de prop el problema i estudiar la percepció que tenen els entrenadors sobre els conflictes dels nois i noies adolescents que dirigeixen. En els resultats es destaca que els entrenadors no perceben que hi hagi gaires conflictes als seus clubs, però al contrari sí que els observen en altres equips. Cal destacar també que mentre que els entrenadors es queixen sempre dels pares, els conflictes els perceben en les relacions que estableixen els seus jugadors. Finalment, és necessari constatar que als clubs estudiats no hi ha un sistema de resolució de conflictes, sinó un sistema d’arbitratge d’un suposat reglament de règim intern

Los conflictos en clubes deportivos con deportistas adolescentes

Al detectarse que en los medios de comunicación aparecían a menudo noticias, en la sección de deportes, sobre los conflictos que se generaban en los clubes deportivos entre padres, entrenadores, jugadores y directivos, nos planteamos conocer de cerca el problema y estudiar la percepción que tienen los entrenadores sobre los conflictos de los chicos y chicas adolescentes que dirigen. En los resultados se destaca que los entrenadores no perciben que haya muchos conflictos en sus clubes, pero por el contrario sí los observan en otros equipos. Es de destacar también que mientras los entrenadores se quejan siempre de los padres, los conflictos los perciben en las relaciones que establecen sus jugadores. Finalmente, es necesario constatar que en los clubes estudiados no existe un sistema de resolución de conflictos, sino un sistema de arbitraje de un supuesto reglamento de régimen interior.Al detectarse que en los medios de comunicación aparecían a menudo noticias, en la sección de deportes, sobre los conflictos que se generaban en los clubes deportivos entre padres, entrenadores, jugadores y directivos, nos planteamos conocer de cerca el problema y estudiar la percepción que tienen los entrenadores sobre los conflictos de los chicos y chicas adolescentes que dirigen. En los resultados se destaca que los entrenadores no perciben que haya muchos conflictos en sus clubes, pero por el contrario sí los observan en otros equipos. Es de destacar también que mientras los entrenadores se quejan siempre de los padres, los conflictos los perciben en las relaciones que establecen sus jugadores. Finalmente, es necesario constatar que en los clubes estudiados no existe un sistema de resolución de conflictos, sino un sistema de arbitraje de un supuesto reglamento de régimen interior

Closed-Form Diffusion Models

Score-based generative models (SGMs) sample from a target distribution by iteratively transforming noise using the score function of the perturbed target. For any finite training set, this score function can be evaluated in closed form, but the resulting SGM memorizes its training data and does not generate novel samples. In practice, one approximates the score by training a neural network via score-matching. The error in this approximation promotes generalization, but neural SGMs are costly to train and sample, and the effective regularization this error provides is not well-understood theoretically. In this work, we instead explicitly smooth the closed-form score to obtain an SGM that generates novel samples without training. We analyze our model and propose an efficient nearest-neighbor-based estimator of its score function. Using this estimator, our method achieves sampling times competitive with neural SGMs while running on consumer-grade CPUs.Comment: Under revie