Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Quality in Blended Learning in Higher Education. A proposal for an Evaluation Model.

This paper presents an analysis of different models used to assess the quality of formative actions, considering classroom learning and distance education courses. Taking as starting point one of the analyzed models, the paper sets out the necessity of developing a new model that could measure the quality of a blended formation process, by selecting the applicable indicators and proposing some new. The model is composed of seven different categories, which include a sum of thirty five indicators. They will be used to represent courses quality level in Kiviat?s diagrams. This model is currently being put into practice in a real university environment.

Secundary school profile in science and technology versus the first year at the Technical University of Madrid. Cause of failure or quitting?

Se analiza el absentismo, el fallo y el abandono de los estudiantes en los primeros semestres del grado sobre la base de su formación en la educación secundaria.

Applying concept mapping strategies to improve students’ lifelong learning in physical chemistry

Este artículo ofrece una reflexión sobre el papel de los mapas conceptuales en el actual escenario de la educación In the present paper, we carry out the application of concept mapping strategies to learning Physical Chemistry, in particular, of all aspect of Corrosion. This strategy is an alternative method to supplement examinations: it can show the teacher how much the students knew and how much they didn´t know; and the students can evaluate their own learning. Before giving tile matter on Corrosion, the teachers evaluated the previous knowledge of the students in the field and explained to the students how create the conceptual maps with Cmap tools. When the subject is finished, teachers are assessed the conceptual maps developed by students and therefore also the level of the students learning. Teachers verified that the concept mapping is quite suitable for complicated theorics as Corrosion and it is an appropriate tool for the consolidation of educational experiences and for improvement affective lifelong learning. By using this method we demonstrated that the set of concepts accumulated in the cognitive structure of every student in unique and every student has therefore arranged the concepts from top to bottom in the mapping field in different ways with different linking" phrases, although these are involved in the same learning task.

Managing team learning in a Spanish commercial bank

Purpose – The purpose of this paper is to analyze how team management affects team-learning activities. Design/methodology/approach – The authors empirically study 68 teams as they operate in the natural business context of a major Spanish bank. Quantitative research utilizing multiple regression analyses is used to test hypotheses. Findings – The leadership behaviour (consideration, initiation of structure) displayed by the team leader plays a key role in facilitating team learning. Team leader behaviour characterised by consideration and in particular by initiation of structure are both positively related to team-learning activities. Cross-training of team members also contributes to team-learning behaviour. Research limitations/implications – A specific setting may limit the generalizability of findings. Further research may accordingly investigate to what extent these results can be generalized to other settings or other aspects of team learning. Practical implications – The leadership style adopted by the team leader, as well as cross-training of members, affect team-learning activities. These results link leadership theory to collective learning in teams and organizations, and suggest ways leaders can contribute to improved learning. Originality/value – The study provides new insight into how management of teams facilitates team-learning activities. While consideration is somewhat related to team learning, initiation of structure as well as cross-training appear as key variables.

Blended learning in the design of collective protections in building works

The design of collective protections of construction works is a topic that is integrated within one of the MBTI subjects. This paper presents an experience for teaching the design of collective protection in building works. Teaching has been done in a bended way. This subject is taught as part of the content of a wider subject included in the Master "Building Technological Innovation" (MBTI) of the Universidad Politécnica de Madrid (UPM). This Master's degree is a postgraduate of research nature. Graduating in MBTI implies that students are in a position to begin their PhD thesis. At this stage, often students are not able to follow the rigour established by the schedule of classes. For this reason it is interesting that part of the teaching is developed in a non-classroom based way.

Proposal for a semantic hierarchy of terminological metaphors in science and technology

Cognitive linguistics is considered as one of the most appropriate approaches to the study of scientific and technical language formation and development, where metaphor is accepted to play an essential role. This paper, based on the Cognitive Theory of Metaphor, takes as the starting point the terminological metaphors established in the research project METACITEC(Note 1), which was developed with the purpose of unfolding constitutive metaphors and their function in the language of science and technology. After the analysis of metaphorical terms and using a mixed corpus from the fields of Agriculture, Geology, Mining, Metallurgy, and other related technical fields, this study presents a proposal for a hierarchy of the selected metaphors underlying the scientific conceptual system, based on the semantic distance found in the projection from the source domain to the target domain. We argue that this semantic distance can be considered as an important parameter to take into account in order to establish the metaphoricity of science and technology metaphorical terms. The findings contribute to expand on the CTM stance that metaphor is a matter of cognition by reviewing the abstract-concrete conceptual relationship between the target and source domains, and to determine the role of human creativity and imagination in the language of science and technology configuration

The Annual of scientific discovery: or, Year-book of facts in science and art.

1866-1867

The Annual of scientific discovery: or, Year-book of facts in science and art.

1870

The Annual of scientific discovery: or, Year-book of facts in science and art.

1859

The Annual of scientific discovery: or, Year-book of facts in science and art.

1863

Low-status monkeys “play dumb” when learning in mixed social groups

Many primates, including humans, live in complex hierarchical societies where social context and status affect daily life. Nevertheless, primate learning studies typically test single animals in limited laboratory settings where the important effects of social interactions and relationships cannot be studied. To investigate the impact of sociality on associative learning, we compared the individual performances of group-tested rhesus monkeys (Macaca mulatta) across various social contexts. We used a traditional discrimination paradigm that measures an animal’s ability to form associations between cues and the obtaining of food in choice situations; but we adapted the task for group testing. After training a 55-member colony to separate on command into two subgroups, composed of either high- or low-status families, we exposed animals to two color discrimination problems, one with all monkeys present (combined condition), the other in their “dominant” and “subordinate” cohorts (split condition). Next, we manipulated learning history by testing animals on the same problems, but with the social contexts reversed. Monkeys from dominant families excelled in all conditions, but subordinates performed well in the split condition only, regardless of learning history. Subordinate animals had learned the associations, but expressed their knowledge only when segregated from higher-ranking animals. Because aggressive behavior was rare, performance deficits probably reflected voluntary inhibition. This experimental evidence of rank-related, social modulation of performance calls for greater consideration of social factors when assessing learning and may also have relevance for the evaluation of human scholastic achievement.

Material matters for learning in virtual networks : A case study of a professional learning programme hosted in a Google+ online community

Date of Acceptance: 13/07/2015

Perceptual learning in motion discrimination that generalizes across motion directions

When human subjects discriminate motion directions of two visual stimuli, their discrimination improves with practice. This improved performance has been found to be specific to the practiced directions and does not transfer to new motion directions. Indeed, such stimulus-specific learning has become a trademark finding in almost all perceptual learning studies and has been used to infer the loci of learning in the brain. For example, learning in motion discrimination has been inferred to occur in the visual area MT (medial temporal cortex) of primates, where neurons are selectively tuned to motion directions. However, such motion discrimination task is extremely difficult, as is typical of most perceptual learning tasks. When the difficulty is moderately reduced, learning transfers to new motion directions. This result challenges the idea of using simple visual stimuli to infer the locus of learning in low-level visual processes and suggests that higher-level processing is essential even in “simple” perceptual learning tasks.

Effect of reduced myristoylated alanine-rich C kinase substrate expression on hippocampal mossy fiber development and spatial learning in mutant mice: Transgenic rescue and interactions with gene background

The myristoylated alanine-rich C kinase substrate (MARCKS) is a prominent protein kinase C (PKC) substrate in brain that is expressed highly in hippocampal granule cells and their axons, the mossy fibers. Here, we examined hippocampal infrapyramidal mossy fiber (IP-MF) limb length and spatial learning in heterozygous Macs mutant mice that exhibit an ≈50% reduction in MARCKS expression relative to wild-type controls. On a 129B6(N3) background, the Macs mutation produced IP-MF hyperplasia, a significant increase in hippocampal PKCɛ expression, and proficient spatial learning relative to wild-type controls. However, wild-type 129B6(N3) mice exhibited phenotypic characteristics resembling inbred 129Sv mice, including IP-MF hypoplasia relative to inbred C57BL/6J mice and impaired spatial-reversal learning, suggesting a significant contribution of 129Sv background genes to wild-type and possibly mutant phenotypes. Indeed, when these mice were backcrossed with inbred C57BL/6J mice for nine generations to reduce 129Sv background genes, the Macs mutation did not effect IP-MF length or hippocampal PKCɛ expression and impaired spatial learning relative to wild-type controls, which now showed proficient spatial learning. Moreover, in a different strain (B6SJL(N1), the Macs mutation also produced a significant impairment in spatial learning that was reversed by transgenic expression of MARCKS. Collectively, these data indicate that the heterozygous Macs mutation modifies the expression of linked 129Sv gene(s), affecting hippocampal mossy fiber development and spatial learning performance, and that MARCKS plays a significant role in spatial learning processes.