Path planning of robots in noisy workspaces using learning automata

The problem of a manipulator operating in a noisy workspace and required to move from an initial fixed position P0 to a final position Pf is considered. However, Pf is corrupted by noise, giving rise to Pˆf, which may be obtained by sensors. The use of learning automata is proposed to tackle this problem. An automaton is placed at each joint of the manipulator which moves according to the action chosen by the automaton (forward, backward, stationary) at each instant. The simultaneous reward or penalty of the automata enables avoiding any inverse kinematics computations that would be necessary if the distance of each joint from the final position had to be calculated. Three variable-structure learning algorithms are used, i.e., the discretized linear reward-penalty (DLR-P, the linear reward-penalty (LR-P ) and a nonlinear scheme. Each algorithm is separately tested with two (forward, backward) and three forward, backward, stationary) actions.

Second language influence on first language motion event encoding and categorization in Spanish-speaking children learning L2 English

Studies show cross-linguistic differences in motion event encoding, such that English speakers preferentially encode manner of motion more than Spanish speakers, who preferentially encode path of motion. Focusing on native Spanish speaking children (aged 5;00-9;00) learning L2 English, we studied path and manner verb preferences during descriptions of motion stimuli, and tested the linguistic relativity hypothesis by investigating categorization preferences in a non-verbal similarity judgement task of motion clip triads. Results revealed L2 influence on L1 motion event encoding, such that bilinguals used more manner verbs and fewer path verbs in their L1, under the influence of English. We found no effects of linguistic structure on non-verbal similarity judgements, and demonstrate for the first time effects of L2 on L1 lexicalization in child L2 learners in the domain of motion events. This pattern of verbal behaviour supports theories of bilingual semantic representation that postulate a merged lexico-semantic system in early bilinguals.

Two-Pass Hexagonal algorithm with improved Hashtable structure for motion estimation

This paper presents an improved Two-Pass Hexagonal (TPA) algorithm constituted by Linear Hashtable Motion Estimation Algorithm (LHMEA) and Hexagonal Search (HEXBS) for motion estimation. In the TPA, Motion Vectors (MV) are generated from the first-pass LHMEA and are used as predictors for second-pass HEXBS motion estimation, which only searches a small number of Macroblocks (MBs). The hashtable structure of LHMEA is improved compared to the original TPA and LHMEA. The evaluation of the algorithm considers the three important metrics being processing time, compression rate and PSNR. The performance of the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms.

The role of statistical learning in the acquisition of motion event construal in a second language

Learning to talk about motion in a second language is very difficult because it involves restructuring deeply entrenched patterns from the first language (Slobin 1996). In this paper we argue that statistical learning (Saffran et al. 1997) can explain why L2 learners are only partially successful in restructuring their second language grammars. We explore to what extent L2 learners make use of two mechanisms of statistical learning, entrenchment and pre-emption (Boyd and Goldberg 2011) to acquire target-like expressions of motion and retreat from overgeneralisation in this domain. Paying attention to the frequency of existing patterns in the input can help learners to adjust the frequency with which they use path and manner verbs in French but is insufficient to acquire the boundary crossing constraint (Slobin and Hoiting 1994) and learn what not to say. We also look at the role of language proficiency and exposure to French in explaining the findings.

Non-radial solar wind flows induced by the motion of interplanetary coronal mass ejections

A survey of the non-radial flows (NRFs) during nearly five years of interplanetary observations revealed the average non-radial speed of the solar wind flows to be �30 km/s, with approximately one-half of the large (>100 km/s) NRFs associated with ICMEs. Conversely, the average non-radial flow speed upstream of all ICMEs is �100 km/s, with just over one-third preceded by large NRFs. These upstream flow deflections are analysed in the context of the large-scale structure of the driving ICME. We chose 5 magnetic clouds with relatively uncomplicated upstream flow deflections. Using variance analysis it was possible to infer the local axis orientation, and to qualitatively estimate the point of interception of the spacecraft with the ICME. For all 5 events the observed upstream flows were in agreement with the point of interception predicted by variance analysis. Thus we conclude that the upstream flow deflections in these events are in accord with the current concept of the large scale structure of an ICME: a curved axial loop connected to the Sun, bounded by a curved (though not necessarily circular)cross section.

Learning across business sectors: Knowledge sharing between aerospace and construction

This report addresses the extent that managerial practices can be shared between the aerospace and construction sectors. Current recipes for learning from other industries tend to be oversimplistic and often fail to recognise the embedded and contextual nature of managerial knowledge. Knowledge sharing between business sectors is best understood as an essential source of innovation. The process of comparison challenges assumptions and better equips managers to cope with future change. Comparisons between the aerospace and construction sectors are especially useful because they are so different. The two sectors differ hugely in terms of their institutional context, structure and technological intensity. The aerospace sector has experienced extensive consolidation and is dominated by a small number of global companies. Aerospace companies operate within complex networks of global interdependency such that collaborative working is a commercial imperative. In contrast, the construction sector remains highly fragmented and is characterised by a continued reliance on small firms. The vast majority of construction firms compete within localised markets that are too often characterised by opportunistic behaviour. Comparing construction to aerospace highlights the unique characteristics of both sectors and helps explain how managerial practices are mediated by context. Detailed comparisons between the two sectors are made in a range of areas and guidance is provided for the implementation of knowledge sharing strategies within and across organisations. The commonly accepted notion of ‘best practice’ is exposed as a myth. Indeed, universal models of best practice can be detrimental to performance by deflecting from the need to adapt continuously to changing circumstances. Competitiveness in the construction sector too often rests on efficiency in managing contracts, with a particular emphasis on the allocation of risk. Innovation in construction tends to be problem-driven and is rarely shared from project to project. In aerospace, the dominant model of competitiveness means that firms have little choice other than to invest in continuous innovation, despite difficult trading conditions. Research and development (R&D) expenditure in aerospace continues to rise as a percentage of turnovers. A sustained capacity for innovation within the aerospace sector depends crucially upon stability and continuity of work. In the construction sector, the emergence of the ‘hollowed-out’ firm has undermined the industry’s capacity for innovation. Integrated procurement contexts such as prime contracting in construction potentially provide a more supportive climate for an innovation-based model of competitiveness. However, investment in new ways of working depends upon a shift in thinking not only amongst construction contractors, but also amongst the industry’s major clients.

Training conditional and cumulative risk judgements: the role of frequencies, problem-structure and Einstellung

Two experiments implement and evaluate a training scheme for learning to apply frequency formats to probability judgements couched in terms of percentages. Results indicate that both conditional and cumulative probability judgements can be improved in this manner, however the scheme is insufficient to promote any deeper understanding of the problem structure. In both experiments, training on one problem type only (either conditional or cumulative risk judgements) resulted in an inappropriate transfer of a learned method at test. The obstacles facing a frequency-based training programme for teaching appropriate use of probability data are discussed. Copyright (c) 2006 John Wiley & Sons, Ltd.

Location, location, location: development of spatiotemporal sequence learning in infancy

We investigated infants' sensitivity to spatiotemporal structure. In Experiment 1, circles appeared in a statistically defined spatial pattern. At test 11-month-olds, but not 8-month-olds, looked longer at a novel spatial sequence. Experiment 2 presented different color/shape stimuli, but only the location sequence was violated during test; 8-month-olds preferred the novel spatial structure, but 5-month-olds did not. In Experiment 3, the locations but not color/shape pairings were constant at test; 5-month-olds showed a novelty preference. Experiment 4 examined "online learning": We recorded eye movements of 8-month-olds watching a spatiotemporal sequence. Saccade latencies to predictable locations decreased. We argue that temporal order statistics involving informative spatial relations become available to infants during the first year after birth, assisted by multiple cues.

The use of social and salience cues in early word learning

This article explores young infants' ability to learn new words in situations providing tightly controlled social and salience cues to their reference. Four experiments investigated whether, given two potential referents, 15-month-olds would attach novel labels to (a) an image toward which a digital recording of a face turned and gazed, (b) a moving image versus a stationary image, (c) a moving image toward which the face gazed, and (d) a gazed-on image versus a moving image. Infants successfully used the recorded gaze cue to form new word-referent associations and also showed learning in the salience condition. However, their behavior in the salience condition and in the experiments that followed suggests that, rather than basing their judgments of the words' reference on the mere presence or absence of the referent's motion, infants were strongly biased to attend to the consistency with which potential referents moved when a word was heard. (c) 2006 Elsevier Inc. All rights reserved.

Humans ignore motion and stereo cues in favor of a fictional stable world

As we move through the world, our eyes acquire a sequence of images. The information from this sequence is sufficient to determine the structure of a three-dimensional scene, up to a scale factor determined by the distance that the eyes have moved [1, 2]. Previous evidence shows that the human visual system accounts for the distance the observer has walked [3,4] and the separation of the eyes [5-8] when judging the scale, shape, and distance of objects. However, in an immersive virtual-reality environment, observers failed to notice when a scene expanded or contracted, despite having consistent information about scale from both distance walked and binocular vision. This failure led to large errors in judging the size of objects. The pattern of errors cannot be explained by assuming a visual reconstruction of the scene with an incorrect estimate of interocular separation or distance walked. Instead, it is consistent with a Bayesian model of cue integration in which the efficacy of motion and disparity cues is greater at near viewing distances. Our results imply that observers are more willing to adjust their estimate of interocular separation or distance walked than to accept that the scene has changed in size.

Video activity extraction and reporting with incremental unsupervised learning

The present work presents a new method for activity extraction and reporting from video based on the aggregation of fuzzy relations. Trajectory clustering is first employed mainly to discover the points of entry and exit of mobiles appearing in the scene. In a second step, proximity relations between resulting clusters of detected mobiles and contextual elements from the scene are modeled employing fuzzy relations. These can then be aggregated employing typical soft-computing algebra. A clustering algorithm based on the transitive closure calculation of the fuzzy relations allows building the structure of the scene and characterises the ongoing different activities of the scene. Discovered activity zones can be reported as activity maps with different granularities thanks to the analysis of the transitive closure matrix. Taking advantage of the soft relation properties, activity zones and related activities can be labeled in a more human-like language. We present results obtained on real videos corresponding to apron monitoring in the Toulouse airport in France.

Nonlinear model structure design and construction using orthogonal least squares and D-optimality design

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model robustness and adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the model subset selection cost function includes a D-optimality design criterion that maximizes the determinant of the design matrix of the subset to ensure the model robustness, adequacy, and parsimony of the final model. The proposed approach is based on the forward orthogonal least square (OLS) algorithm, such that new D-optimality-based cost function is constructed based on the orthogonalization process to gain computational advantages and hence to maintain the inherent advantage of computational efficiency associated with the conventional forward OLS approach. Illustrative examples are included to demonstrate the effectiveness of the new approach.

Nonlinear model structure detection using optimum experimental design and orthogonal least squares

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the subset selection cost function includes an A-optimality design criterion to minimize the variance of the parameter estimates that ensures the adequacy and parsimony of the final model. An illustrative example is included to demonstrate the effectiveness of the new approach.