Motor task variation induces structural learning.

When we have learned a motor skill, such as cycling or ice-skating, we can rapidly generalize to novel tasks, such as motorcycling or rollerblading [1-8]. Such facilitation of learning could arise through two distinct mechanisms by which the motor system might adjust its control parameters. First, fast learning could simply be a consequence of the proximity of the original and final settings of the control parameters. Second, by structural learning [9-14], the motor system could constrain the parameter adjustments to conform to the control parameters' covariance structure. Thus, facilitation of learning would rely on the novel task parameters' lying on the structure of a lower-dimensional subspace that can be explored more efficiently. To test between these two hypotheses, we exposed subjects to randomly varying visuomotor tasks of fixed structure. Although such randomly varying tasks are thought to prevent learning, we show that when subsequently presented with novel tasks, subjects exhibit three key features of structural learning: facilitated learning of tasks with the same structure, strong reduction in interference normally observed when switching between tasks that require opposite control strategies, and preferential exploration along the learned structure. These results suggest that skill generalization relies on task variation and structural learning.

Facilitation of learning induced by both random and gradual visuomotor task variation.

Motor task variation has been shown to be a key ingredient in skill transfer, retention, and structural learning. However, many studies only compare training of randomly varying tasks to either blocked or null training, and it is not clear how experiencing different nonrandom temporal orderings of tasks might affect the learning process. Here we study learning in human subjects who experience the same set of visuomotor rotations, evenly spaced between -60° and +60°, either in a random order or in an order in which the rotation angle changed gradually. We compared subsequent learning of three test blocks of +30°→-30°→+30° rotations. The groups that underwent either random or gradual training showed significant (P < 0.01) facilitation of learning in the test blocks compared with a control group who had not experienced any visuomotor rotations before. We also found that movement initiation times in the random group during the test blocks were significantly (P < 0.05) lower than for the gradual or the control group. When we fit a state-space model with fast and slow learning processes to our data, we found that the differences in performance in the test block were consistent with the gradual or random task variation changing the learning and retention rates of only the fast learning process. Such adaptation of learning rates may be a key feature of ongoing meta-learning processes. Our results therefore suggest that both gradual and random task variation can induce meta-learning and that random learning has an advantage in terms of shorter initiation times, suggesting less reliance on cognitive processes.

Kernelized structural classification for 3D dogs body parts detection

Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the welfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition, we propose a system for recognizing body parts of dogs kept in pens. We decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy.

Optimization and Machine Learning Frameworks for Complex Network Analysis

Thesis (Ph.D.)--University of Washington, 2016-06

Estudo Comparativo de Métricas de Pontuação para Aprendizagem Estrutural de Redes Bayesianas

Bayesian networks are powerful tools as they represent probability distributions as graphs. They work with uncertainties of real systems. Since last decade there is a special interest in learning network structures from data. However learning the best network structure is a NP-Hard problem, so many heuristics algorithms to generate network structures from data were created. Many of these algorithms use score metrics to generate the network model. This thesis compare three of most used score metrics. The K-2 algorithm and two pattern benchmarks, ASIA and ALARM, were used to carry out the comparison. Results show that score metrics with hyperparameters that strength the tendency to select simpler network structures are better than score metrics with weaker tendency to select simpler network structures for both metrics (Heckerman-Geiger and modified MDL). Heckerman-Geiger Bayesian score metric works better than MDL with large datasets and MDL works better than Heckerman-Geiger with small datasets. The modified MDL gives similar results to Heckerman-Geiger for large datasets and close results to MDL for small datasets with stronger tendency to select simpler network structures

Use of advanced computer graphics and data visualisation technologies in teaching and learning of steel structural design

A teaching and learning development project is currently under way at Queens-land University of Technology to develop advanced technology videotapes for use with the delivery of structural engineering courses. These tapes consist of integrated computer and laboratory simulations of important concepts, and behaviour of structures and their components for a number of structural engineering subjects. They will be used as part of the regular lectures and thus will not only improve the quality of lectures and learning environment, but also will be able to replace the ever-dwindling laboratory teaching in these subjects. The use of these videotapes, developed using advanced computer graphics, data visualization and video technologies, will enrich the learning process of the current diverse engineering student body. This paper presents the details of this new method, the methodology used, the results and evaluation in relation to one of the structural engineering subjects, steel structures.

Improving the teaching and learning of steel structural design

Engineering students are best able to understand theory when one explains it in relation to realistic problems and its practical applications. Teaching theory in isolation has led to lower levels of comprehension and motivation and a correspondingly higher rate of failure. At Queensland University of Technology, a number of new methods have been introduced recently to improve the teaching and learning of steel structural design at undergradt1ate level. In the basic steel structures subject a project-based teaching method was introduced in which the students were required to analyse, design and build the lightest I most efficient steel columns for a given target capacity. A design assignment involving simple, but real structures was also introduced in the basic steel structures subject. Both these exercises simulated realistic engineering problems from the early years of the course and produced a range of benefits. Improvements to the teaching and learning was also made through integration of a number of related structural engineering subjects and by the introduction of animated computer models and laboratory models. This paper presents the details of all these innovative methods which improved greatly the students' understanding of the steel structures design process.

Older Australians: Structural barriers to learning in later life

Learning in older age is associated with a wide range of benefits including increases in skills, social interactions, self-satisfaction, coping ability, enjoyment, and resilience to age-related changes in the brain. It is also recognized as being a fundamental component of active ageing and if active ageing objectives are to be met for the growing ageing population, barriers to learning for this group need to be fully understood so that they can be properly addressed. This paper reports on findings from a study aimed at determining the degree that structural factors deter older people aged 55 years and older from engaging in learning activities relative to other factors, based on survey (n=421) and interview (n=40) data. Quantitative and qualitative analyses revealed that factors related to educational institutions as well as infrastructure were commonly cited as barriers to participation in learning. The implications of these and other findings are discussed.

Learning Structural Descriptions from Examples

The research here described centers on how a machine can recognize concepts and learn concepts to be recognized. Explanations are found in computer programs that build and manipulate abstract descriptions of scenes such as those children construct from toy blocks. One program uses sample scenes to create models of simple configurations like the three-brick arch. Another uses the resulting models in making identifications. Throughout emphasis is given to the importance of using good descriptions when exploring how machines can come to perceive and understand the visual environment.