Lehen Hezkuntzako ingeleseko testuliburuak. Euskal Autonomia Erkidegoan erabiltzen diren materialen analisia

[eus] Ikerketa honen bidez, Euskal Autonomia Erkidegoan erabiltzen diren Lehen Hezkuntzako lehenengo zikloko ingeleseko testuliburuak hizkuntzen ikaskuntzaren teoriei, Europako Erreferentzia Marko Bateratuari eta Curriculum dokumentuei egokitzen zaizkien baieztatu nahi izan da. Horretarako, aipatutako teorietan eta dokumentuetan oinarrituta, sei aldagai diseinatu eta, horiek erabiliz, Euskal Autonomia Erkidegoan erabiltzen diren sei testuliburu aztertu dira. Ikerketatik ondorioztatzen da aztertutako testuliburuak ez datozela bat legediaren ikuspegi komunikatiboarekin ezta atazetan oinarritutako ikaskuntzarekin ere. Hala ere, hizkuntzaren inputa testuinguruan ematen dute eta, Curriculumean eskatzen den bezala, aspektu kulturalak lantzen dituzte.

Avaliação de usabilidade em jogo eletrônico: estudo de caso

Esta pesquisa tem como objetivo documentar o processo de redução de riscos e incertezas de um jogo eletrônico em desenvolvimento por meio da aplicação de métodos de avaliação de Usabilidade. Foi realizado um estudo de caso da utilização de métodos e técnicas de avaliação de Usabilidade durante a produção do jogo eletrônico Dungeonland, conduzido entre 2010 a 2013 ao longo de diversas iterações do produto, da pré-produção ao lançamento. Foram utilizados os métodos de observação direta baseada em problemas, avaliação cooperativa, questionário e entrevista semi-estruturada. Os dados coletados demonstram a evolução do design do jogo, as diferentes metodologias empregadas em cada estágio de desenvolvimento, e o impacto da avaliação no projeto. Apesar de problemas e limitações no emprego dos testes de Usabilidade no produto em questão, o impacto da avaliação foi visto como muito grande e muito positivo pelos desenvolvedores - através de dados qualitativos como protocolos verbais e de gameplay de usuários, e de dados quantitativos sobre suas experiências com o produto que possam ser comparados estatisticamente, os desenvolvedores de jogos têm à sua disposição poderosas ferramentas para estabelecer processos de Design claros, centrados no usuário, e que ofereçam um ambiente onde problemas são rapidamente identificados e soluções são validadas com usuários reais.

The use of role-play games in teaching: The International Climate Negotiation Game

This paper presents a role-play game designed by the authors, which focuses on international climate negotiations. The game has been used at a university with students all drawn from the same course and at summer schools with students from different levels (undergraduate, master’s and doctoral students and post-doctoral researchers) and different knowledge areas (economics, law, engineering, architecture, biology and others). We discuss how the game fits into the process of competence-based learning, and what benefits games, and role-play games in particular, have for teaching. In the game, students take on the role of representatives of national institutions and experience at first hand a detailed process of international negotiation concerned with climate change.

Poroelastic indentation analysis for hydrated biological tissues

Indentation techniques are employed for the measurement of mechanical properties of a wide range of materials. In particular, techniques focused at small length-scales, such as nanoindentation and AFM indentation, allow for local characterization of material properties in heterogeneous materials including natural tissues and biomimetic materials. Typical elastic analysis for spherical indentation is applicable in the absence of time-dependent deformation, but is inappropriate for materials with time-dependent responses. Recent analyses for the viscoelastic indentation problem, based on elastic-viscoelastic correspondence, have begun to address the issue of time-dependent deformation during an indentation test. The viscoelastic analysis has been shown to fit experimental indentation data well, and has been demonstrated as useful for characterization of viscoelasticity in polymeric materials and in hydrated mineralized tissues. However, a viscoelastic analysis is not necessarily sufficient for multi-phase materials with fluid flow. In the current work, a poroelastic analysis-based on fluid motion through a porous elastic network-is used to examine spherical indentation creep responses of hydrated biological materials. Both analytical and finite element approaches are considered for the poroelastic Hertzian indentation problem. Modeling results are compared with experimental data from nanoindentation of hydrated bone immersed in water and polar solvents (ethanol, methanol, acetone). Baseline (water-immersed) bone responses are characterized using the poroelastic model and numerical results are compared with altered hydration states due to polar solvents. © 2007 Materials Research Society.

Functional requirements for reward-modulated spike-timing-dependent plasticity.

Recent experiments have shown that spike-timing-dependent plasticity is influenced by neuromodulation. We derive theoretical conditions for successful learning of reward-related behavior for a large class of learning rules where Hebbian synaptic plasticity is conditioned on a global modulatory factor signaling reward. We show that all learning rules in this class can be separated into a term that captures the covariance of neuronal firing and reward and a second term that presents the influence of unsupervised learning. The unsupervised term, which is, in general, detrimental for reward-based learning, can be suppressed if the neuromodulatory signal encodes the difference between the reward and the expected reward-but only if the expected reward is calculated for each task and stimulus separately. If several tasks are to be learned simultaneously, the nervous system needs an internal critic that is able to predict the expected reward for arbitrary stimuli. We show that, with a critic, reward-modulated spike-timing-dependent plasticity is capable of learning motor trajectories with a temporal resolution of tens of milliseconds. The relation to temporal difference learning, the relevance of block-based learning paradigms, and the limitations of learning with a critic are discussed.

Slowness: an objective for spike-timing-dependent plasticity?

Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this "temporal stability" or "slowness" approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing-dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the "trace rule." The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons.

Linear regression under fixed-rank constraints: A Riemannian approach

In this paper, we tackle the problem of learning a linear regression model whose parameter is a fixed-rank matrix. We study the Riemannian manifold geometry of the set of fixed-rank matrices and develop efficient line-search algorithms. The proposed algorithms have many applications, scale to high-dimensional problems, enjoy local convergence properties and confer a geometric basis to recent contributions on learning fixed-rank matrices. Numerical experiments on benchmarks suggest that the proposed algorithms compete with the state-of-the-art, and that manifold optimization offers a versatile framework for the design of rank-constrained machine learning algorithms. Copyright 2011 by the author(s)/owner(s).

Regression on fixed-rank positive semidefinite matrices: A Riemannian approach

The paper addresses the problem of learning a regression model parameterized by a fixed-rank positive semidefinite matrix. The focus is on the nonlinear nature of the search space and on scalability to high-dimensional problems. The mathematical developments rely on the theory of gradient descent algorithms adapted to the Riemannian geometry that underlies the set of fixedrank positive semidefinite matrices. In contrast with previous contributions in the literature, no restrictions are imposed on the range space of the learned matrix. The resulting algorithms maintain a linear complexity in the problem size and enjoy important invariance properties. We apply the proposed algorithms to the problem of learning a distance function parameterized by a positive semidefinite matrix. Good performance is observed on classical benchmarks. © 2011 Gilles Meyer, Silvere Bonnabel and Rodolphe Sepulchre.

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data. Our model allows simultaneous inference of the number of binary latent variables, and their values. The latent variables preserve neighbourhood structure of the data in a sense that objects in the same semantic concept have similar latent values, and objects in different concepts have dissimilar latent values. We formulate the supervised infinite latent variable problem based on an intuitive principle of pulling objects together if they are of the same type, and pushing them apart if they are not. We then combine this principle with a flexible Indian Buffet Process prior on the latent variables. We show that the inferred supervised latent variables can be directly used to perform a nearest neighbour search for the purpose of retrieval. We introduce a new application of dynamically extending hash codes, and show how to effectively couple the structure of the hash codes with continuously growing structure of the neighbourhood preserving infinite latent feature space.

Fixed-rank matrix factorizations and Riemannian low-rank optimization

Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function defined on the set of fixed-rank matrices. We adopt the geometric framework of optimization on Riemannian quotient manifolds. We study the underlying geometries of several well-known fixed-rank matrix factorizations and then exploit the Riemannian quotient geometry of the search space in the design of a class of gradient descent and trust-region algorithms. The proposed algorithms generalize our previous results on fixed-rank symmetric positive semidefinite matrices, apply to a broad range of applications, scale to high-dimensional problems, and confer a geometric basis to recent contributions on the learning of fixed-rank non-symmetric matrices. We make connections with existing algorithms in the context of low-rank matrix completion and discuss the usefulness of the proposed framework. Numerical experiments suggest that the proposed algorithms compete with state-of-the-art algorithms and that manifold optimization offers an effective and versatile framework for the design of machine learning algorithms that learn a fixed-rank matrix. © 2013 Springer-Verlag Berlin Heidelberg.

基于局部子图匹配的SLAM方法

针对现有的SLAM解决方法在机器人被"绑架"时失效的问题,提出了基于局部子图匹配的方法。该方法对现有的SLAM解决构架进行了改进,提出交点最优匹配的特征相关算法,并且将奇异值分解方法引入机器人定位。最后,在结构化环境下将本方法和基于扩展卡尔曼滤波器的方法进行比较,讨论了基于局部子图匹配的方法在结构化环境中解决机器人"绑架"问题的有效性和可行性。

基于QNX实时系统的行为辅助机器人控制系统

论文设计研制了行为辅助机器人验证平台,以此平台为基础可以针对行为辅助机器人进行力控制研究。针对机器人力控制的实际要求,设计了行为辅助机器人柔性关节结构,辨识了系统参数。基于QNX实时操作系统设计了控制系统软件。机器人控制系统软件主要包括传感器数据采集和控制算法两部分,可以满足柔性关节控制需求。

机器人化纳米操作系统驱动器驱动与探针定位研究

对基于AFM的机器人化纳米操作系统而言,一个关键问题是如何实现纳米操作时探针的高精度驱动与定位。对此,本文基于对压电陶瓷驱动器的迟滞/非线性特性及现有驱动方法的详细分析,提出"基于复现扫描轨迹的驱动方法"来对操作时的驱动器进行驱动;另外,还对管式驱动器弯曲运动所产生的运动学耦合误差、探针悬臂变形所引起的针尖偏移误差进行了定量分析与补偿。采用上述驱动方法及进行误差补偿后,可以大大提高探针的定位精度,从而使纳米操作与装配得以高精度进行。纳米刻画实验验证了该新型驱动方法及误差补偿的有效性。

具有环境自适应能力的多机器人编队系统研究

对多机器人的体系结构进行了研究 .采用时空表和时间控制器相结合的方法 ,解决多机器人间的协调协作问题 .针对编队问题的具体特性 ,提出了基于环境的记忆学习方法 ,使多机器人编队系统具有较强的环境自适应能力 .最后 ,通过仿真实验实现了整个多机器人系统 ,进一步验证了各个算法的可行性和有效性 .

Del informe de laboratorio a la escritura del artículo científico: resultados de su implantación durante dos años en asignaturas básicas en Biomedicina

En los últimos años se han introducido el Aprendizaje Basado en Problemas (ABP) y de los métodos de Inquiry-Based learning (IBL) o Aprendizaje Basado en la Experimentación en los programas de asignaturas de ciencias experimentales. Estos protocolos pretenden cambiar el modelo actual de evaluación de trabajo de laboratorio basado en el informe de laboratorio o el examen final teórico de las prácticas. En este trabajo presentamos los resultados de la aplicación de un protocolo de IBL para el trabajo de laboratorio de dos asignaturas básicas del Departamento de Ciencias Biomédicas de la Universidad Europea de Madrid. Tanto los resultados de evaluación de los alumnos, como el grado de satisfacción de los mismos con este método ha sido muy satisfactorio. En nuestra opinión, la metodología IBL constituye un buen método que permite trabajar competencias como el trabajo en equipo o el razonamiento crítico, esenciales en la enseñanza de las ciencias. Esta metodología es además aplicable a cualquier curso general que tenga como parte de su programa una pequeña investigación.