An analysis of relational complexity in an air traffic control conflict detection task

Theoretical analyses of air traffic complexity were carried out using the Method for the Analysis of Relational Complexity. Twenty-two air traffic controllers examined static air traffic displays and were required to detect and resolve conflicts. Objective measures of performance included conflict detection time and accuracy. Subjective perceptions of mental workload were assessed by a complexity-sorting task and subjective ratings of the difficulty of different aspects of the task. A metric quantifying the complexity of pair-wise relations among aircraft was able to account for a substantial portion of the variance in the perceived complexity and difficulty of conflict detection problems, as well as reaction time. Other variables that influenced performance included the mean minimum separation between aircraft pairs and the amount of time that aircraft spent in conflict.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Convergence analysis of a discrete-time single-unit gradient ICA algorithm

We revisit the one-unit gradient ICA algorithm derived from the kurtosis function. By carefully studying properties of the stationary points of the discrete-time one-unit gradient ICA algorithm, with suitable condition on the learning rate, convergence can be proved. The condition on the learning rate helps alleviate the guesswork that accompanies the problem of choosing suitable learning rate in practical computation. These results may be useful to extract independent source signals on-line.

A class of self-stabilizing MCA learning algorithms

In this letter, we propose a class of self-stabilizing learning algorithms for minor component analysis (MCA), which includes a few well-known MCA learning algorithms. Self-stabilizing means that the sign of the weight vector length change is independent of the presented input vector. For these algorithms, rigorous global convergence proof is given and the convergence rate is also discussed. By combining the positive properties of these algorithms, a new learning algorithm is proposed which can improve the performance. Simulations are employed to confirm our theoretical results.

The Seduction of Sarah: travel memoirs and intercultural learning

The paper explores the nexus between intercultural storytelling and intercultural learning. Noting the wide appeal of the travel memoir set in France, it takes as a case study a book that, while positioned within that genre, attempts to shift some predictable patterns: Sarah Turnbull’s best-selling Almost French. Analysis shows that the book in fact participates in a subtle play of genres, whereby the lure of the travel memoir is used to entice readers towards a position where they read the book as a guide to French culture. The particular form of hybridity attempted is, however, a delicate enterprise, as the reception of the book demonstrates, in that the intercultural lessons on offer risk being overshadowed by the expectations readers bring to the genre of the travel memoir. The paper examines the competing seductions operating throughout the text and relates the conditions for taking up the opportunity for intercultural learning to questions of genre. It offers a pedagogical uptake of the textual analysis, thus bridging disciplines in a way that mirrors Turnbull’s bridging of genres

A corpus-based analysis of transfer effects and connected speech processes in Vietnamese English

This paper presents a corpus-based descriptive analysis of the most prevalent transfer effects and connected speech processes observed in a comparison of 11 Vietnamese English speakers (6 females, 5 males) and 12 Australian English speakers (6 males, 6 females) over 24 grammatical paraphrase items. The phonetic processes are segmentally labelled in terms of IPA diacritic features using the EMU speech database system with the aim of labelling departures from native-speaker pronunciation. An analysis of prosodic features was made using ToBI framework. The results show many phonetic and prosodic processes which make non-native speakers’ speech distinct from native ones. The corpusbased methodology of analysing foreign accent may have implications for the evaluation of non-native accent, accented speech recognition and computer assisted pronunciation- learning.

The effects of domain-specific avoid and prove goal orientations on performance: A multilevel analysis

Recently, goal orientation, a mental framework for understanding how individuals approach learning and achievement situadons, has emerged as an important predictor of performance. This study addressed the effects of domain-specific avoid and prove orientations on performance from the betweenand within-person levels of analysis. One hundred and three participants performed thirty trials of an airtraffic control task. Domain-specific avoid and prove orientations were measured before each trial to assess the effects of changes in goal orientadon on changes in performance (i.e. within-person relationships). Average levels of avoid and prove orientations were calculated to assess the effect of goal orientation on overall performance (i.e. between-person relationships). Findings from the between-person level of analysis revealed that high prove-orientated individuals performed better than low proveorientated individuals. Results also revealed that average goal orientation levels moderated the withinperson relationships. The effect of changes in avoid orientation on changes in performance was stronger for low versus high avoid-oriented individuals while the effect of changes in prove orientadon on changes in performances was stronger for low versus highprove oriented individuals. Implications of these findings are considered.

Teaching principles of genetics through SNP analysis

An understanding of inheritance requires comprehension of genetic processes at all levels, from molecules to populations. Frequently genetics courses are separated into molecular and organismal genetics and students may fail to see the relationships between them. This is particularly true with human genetics, because of the difficulties in designing experimental approaches which are consistent with ethical restrictions, student abilities and background knowledge, and available time and materials. During 2005 we used analysis of single nucleotide polymorphisms (SNPs) in two genetic regions to enhance student learning and provide a practical experience in human genetics. Students scanned databases to discover SNPs in a gene of interest, used software to design PCR primers and a restriction enzyme based assay for the alleles, and carried out an analysis of the SNP on anonymous individual and family DNAs. The project occupied eight to ten hours per week for one semester, with some time spent in the laboratory and some spent in database searching, reading and writing the report. In completing their projects, students acquired a knowledge of Mendel’s first law (through looking at inheritance patterns), Mendel’s second law and the exceptions (the concepts of linkage and linkage disequilibrium), DNA structure (primer design and restriction enzyme analysis) and function (SNPs in coding and non-coding regions), population genetics and the statistical analysis of allele frequencies, genomics, bioinformatics and the ethical issues associated with the use of human samples. They also developed skills in presentation of results by publication and conference participation. Deficiencies in their understanding (for example of inheritance patterns, gene structure, statistical approaches and report writing) were detected and guidance given during the project. SNP analysis was found to be a powerful approach to enhance and integrate student understanding of genetic concepts.

A learning approach for performance evaluation of local network

In this paper, a novel approach is developed to evaluate the overall performance of a local area network as well as to monitor some possible intrusion detections. The data is obtained via system utility 'ping' and huge data is analyzed via statistical methods. Finally, an overall performance index is defined and simulation experiments in three months proved the effectiveness of the proposed performance index. A software package is developed based on these ideas.

Adaptive back-propagation in on-line learning of multilayer networks

An adaptive back-propagation algorithm is studied and compared with gradient descent (standard back-propagation) for on-line learning in two-layer neural networks with an arbitrary number of hidden units. Within a statistical mechanics framework, both numerical studies and a rigorous analysis show that the adaptive back-propagation method results in faster training by breaking the symmetry between hidden units more efficiently and by providing faster convergence to optimal generalization than gradient descent.

Dynamics of on-line learning in radial basis function networks

On-line learning is examined for the radial basis function network, an important and practical type of neural network. The evolution of generalization error is calculated within a framework which allows the phenomena of the learning process, such as the specialization of the hidden units, to be analyzed. The distinct stages of training are elucidated, and the role of the learning rate described. The three most important stages of training, the symmetric phase, the symmetry-breaking phase, and the convergence phase, are analyzed in detail; the convergence phase analysis allows derivation of maximal and optimal learning rates. As well as finding the evolution of the mean system parameters, the variances of these parameters are derived and shown to be typically small. Finally, the analytic results are strongly confirmed by simulations.

Upper and lower bounds on the learning curve for Gaussian processes

In this paper we introduce and illustrate non-trivial upper and lower bounds on the learning curves for one-dimensional Gaussian Processes. The analysis is carried out emphasising the effects induced on the bounds by the smoothness of the random process described by the Modified Bessel and the Squared Exponential covariance functions. We present an explanation of the early, linearly-decreasing behavior of the learning curves and the bounds as well as a study of the asymptotic behavior of the curves. The effects of the noise level and the lengthscale on the tightness of the bounds are also discussed.