Shadowing experiences and the extension of communities of practice: A case study of women education managers

Recent developments in workplace learning have focused on relational and social network views of learning that introduce practitioners to the norms, values and assumptions of the workplace as well as the learning processes through which knowledge is acquired. This article reports on a qualitative study of a mentoring programme designed to assist women education managers gain promotion by broadening their networks and stimulating insights into the senior management positions for which they were being prepared. The findings are that members reflexively assess and reassess goals and values to demystify knowledge and resolved cognitive dissonance in these processes. Moreover, this article shows that women participants learn from the networks, and that the networks learn from the participant in a reciprocal and informal way. The article concludes that organizational learning programmes must focus on enabling such networks to flourish.

GABAergic excitation in the basolateral amygdala

GABA-containing interneurons are a diverse population of cells whose primary mode of action in the mature nervous system is inhibition of postsynaptic target neurons. Using paired recordings from parvalbumin-positive interneurons in the basolateral amygdala, we show that, in a subpopulation of interneurons, single action potentials in one interneuron evoke in the postsynaptic interneuron a monosynaptic inhibitory synaptic current, followed by a disynaptic excitatory glutamatergic synaptic current. Interneuron-evoked glutamatergic events were blocked by antagonists of either AMPA/kainate or GABA(A) receptors, and could be seen concurrently in both presynaptic and postsynaptic interneurons. These results show that single action potentials in a GABAergic interneuron can drive glutamatergic principal neurons to threshold, resulting in both feedforward and feedback excitation. In interneuron pairs that both receive glutamatergic inputs after an interneuron spike, electrical coupling and bidirectional GABAergic connections occur with a higher probability relative to other interneuron pairs. We propose that this form of GABAergic excitation provides a means for the reliable and specific recruitment of homogeneous interneuron networks in the basal amygdala.

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.

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

Extreme learning machine for predicting HLA-peptide binding

Machine learning techniques have been recognized as powerful tools for learning from data. One of the most popular learning techniques, the Back-Propagation (BP) Artificial Neural Networks, can be used as a computer model to predict peptides binding to the Human Leukocyte Antigens (HLA). The major advantage of computational screening is that it reduces the number of wet-lab experiments that need to be performed, significantly reducing the cost and time. A recently developed method, Extreme Learning Machine (ELM), which has superior properties over BP has been investigated to accomplish such tasks. In our work, we found that the ELM is as good as, if not better than, the BP in term of time complexity, accuracy deviations across experiments, and most importantly - prevention from over-fitting for prediction of peptide binding to HLA.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.