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This paper provides a critical reflection on teacher education and professional development in Northern Ireland between 2003 and 2018. It argues that whilst there is much about teacher education in Northern Ireland that is to be commended, the unique socio-historical, political and cultural context within which it operates may be considered as a 'Wicked Problem' which currently mitigates the potential to develop the teaching profession to best effect. Two key issues which highlight the Wicked Problem are addressed: the extended process of teacher education review; and a focus on the infrastructure of the system. The paper concludes by suggesting that future policy priorities need to value and promote existing strengths and cohere around the new approach to 'Shared Education' and the 'Learning Leaders Strategy' in order to build a mature system of teacher education to meet the challenges of a changing Northern Ireland.

The purpose of this paper is to consider how a small Catholic institute of higher education (HE) attempts to balance the broad demands of globalisation with the need to adhere to, promote and develop its foundation as a Catholic college. The paper begins with a consideration of the changes which have taken place in HE in the UK over the past 50 years and goes on to consider the response of the Church to this with regard to "Ex Corde Ecclesiae". Using an interpretive research paradigm, a threefold data collection was conducted in the case study institution. Data emerged from: observation; documentary analysis; and interviews. Despite the obvious limitations of the study, the data indicated that the Catholic ethos is clearly evident in the day-to-day practice of the college and is central to its success both as a Catholic institution and as a key provider of higher learning.

This study considers the effectiveness of initial teacher education in one university college in Northern Ireland in preparing students for teaching in an increasingly diverse society and promoting a culture of tolerance. A cohort of 168 students in their final year were surveyed and this was followed by three focus group interviews. The findings showed that the student population came from a largely similar sociocultural background, with little prior experience of diverse contexts. Although students viewed the accommodation of diversity as an important issue for teachers, a limited number felt comfortable with and prepared for dealing with diversity in the classroom. The paper proposes that alternative strategies need to be employed to ensure the more effective preparation of beginning teachers.

Objectives: To determine the feasibility of using a deep learning (DL) algorithm to assess the quality of focused assessment with sonography in trauma (FAST) exams. Methods: Our dataset consists of 441 FAST exams, classified as good‐quality or poor‐quality, with 3161 videos. We first used convolutional neural networks (CNNs), pretrained on the Imagenet dataset and fine‐tuned on the FAST dataset. Second, we trained a CNN autoencoder to compress FAST images, with a 20–1 compression ratio. The compressed codes were input to a two‐layer classifier network. To train the networks, each video was labeled with the quality of the exam, and the frames were labeled with the quality of the video. For inference, a video was classified as poor‐quality if half the frames were classified as poor‐quality by the network, and an exam was classified as poor‐quality if half the videos were classified as poor‐quality. Results: The results with the encoder‐classifier networks were much better than the transfer learning results with CNNs. This was primarily because the Imagenet dataset is not a good match for the ultrasound quality assessment problem. The DL models produced video sensitivities and specificities of 99% and 98% on held‐out test sets. Conclusions: Using an autoencoder to compress FAST images is a very effective way to obtain features that can be used to predict exam quality. These features are more suitable than those obtained from CNNs pretrained on Imagenet. [ABSTRACT FROM AUTHOR]

This paper gives a detailed analysis of the error surfaces of certain recurrent networks and explains some difficulties encountered in training recurrent networks. We show that these error surfaces contain many spurious valleys, and we analyze the mechanisms that cause the valleys to appear. We demonstrate that the principle mechanism can be understood through the analysis of the roots of random polynomials. This paper also provides suggestions for improvements in batch training procedures that can help avoid the difficulties caused by spurious valleys, thereby improving training speed and reliability. Index Terms--Backpropagation, error surface, recurrent neural networks, spurious minima, spurious valleys, training.

This paper introduces a general framework for describing dynamic neural networks--the layered digital dynamic network (LDDN). This framework allows the development of two general algorithms for computing the gradients and Jacobians for these dynamic networks: backpropagation-through-time (BPTT) and real-time recurrent learning (RTRL). The structure of the LDDN framework enables an efficient implementation of both algorithms for arbitrary dynamic networks. This paper demonstrates that the BPTT algorithm is more efficient for gradient calculations, but the RTRL algorithm is more efficient for Jacobian calculations. Index Terms--Backpropagation through time (BPTT), dynamic neural networks, gradient, Jacobian, layered digital dynamic network (LDDN), real-time recurrent learning (RTRL), recurrent neural networks.

This paper analyzes the effect of momentum on steepest descent training for quadratic performance functions. We demonstrate that there always exists a momentum coefficient that will stabilize the steepest descent algorithm, regardless of the value of the learning rate. We also demonstrate how the value of the momentum coefficient changes the convergence properties of the algorithm. Index Terms--Convergence speed, gradient descent, momentum, stability.

The technique of multireference adaptive noise canceling (MRANC) is applied to enhance transient nonstationarities in the electroencephalogram (EEG), with the adaptation implemented by means of a multilayer-perceptron artificial neural network (ANN). The method was applied to recorded EEG segments and the performance on documented nonstationarities recorded. The results show that the neural network (nonlinear) gives an improvement in performance (i.e., signal-to-noise ratio (SNR) of the nonstationarities) compared to a linear implementation of MRANC. In both cases an improvement in the SNR was obtained. The advantage of the spatial filtering aspect of MRANC is highlighted when the performance of MRANC is compared to that of the inverse auto-regressive filtering of the EEG, a purely temporal filter. Index Terms - Adaptive filters, electroencephalography, neural network applications, nonlinear filters.

This study considers the role of the practicum in supporting professional identity development in the initial stage of teacher education. Employing an interpretative paradigm and a qualitative methodological approach (Lincoln & Guba, 2000), data were collected from a purposive sample of student teachers (n = 6) in the first year of study, using individual semi-structured interviews and episodic documents. The findings show that emerging teacher identity was informed but also challenged by the participants’ engagement with pupils, other teachers and visiting tutors. Collectively, these influences effected shifts in the participants’ constructs on teaching and prompted change in their perception of themselves and others as teachers. The study provides for an improved understanding of teacher growth in the first stage of career development. By so doing, it makes a valid contribution to the discourse on initial teacher education to better inform teacher educators and policy makers in relation to teacher professional learning. [ABSTRACT FROM AUTHOR]

The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks. The algorithm is tested on several function approximation problems, and is compared with a conjugate gradient algorithm and a variable learning rate algorithm. It is found that the Marquardt algorithm is much more efficient than either of the other techniques when the network contains no more than a few hundred weights.

A linear time series structural model developed to represent multiperiodicity and nonlinearity involves fewer parameters than other bilinear models. Testing of three time series samples helps validate the model's performance. The model is useful for general applications and can be varied periodically or seasonally.

Networking large numbers of manned flight simulators over wide geographical areas has become feasible because of the great advances in the technologies of microprocessors, and local area and long haul networks. One of the main concerns in developing networked simulators is the effect of intersimulator time delay on the performance of these networks as training devices. Previous studies on the effects of time delays in single simulators provide guidance in analyzing this problem. This paper first introduces the networked simulator problem. Next, it reviews the basic Optimal Control Model (OCM), one of the most useful methods for the mathematical modeling of pilots. Finally, it describes how the OCM concept can be used in the analysis of some networked flight simulator experiments. A simplified formation flight task is carefully analyzed, and a number of interesting phenomena are described.

The neural network (NN) procedure to interpolate ab initio data for the purpose of molecular dynamics (MD) simulations has been tested on the SiO2 system. Unlike other similar NN studies, here, we studied the dissociation of SiO2 without the initial use of any empirical potential. During the dissociation of SiO2 into Si+O or Si+O2, the spin multiplicity of the system changes from singlet to triplet in the first reaction and from singlet to pentet in the second. This paper employs four potential surfaces. The first is a NN fit [NN(STP)] to a database comprising the lowest of the singlet, triplet, and pentet energies obtained from density functional calculations in 6673 nuclear configurations. The other three potential surfaces are obtained from NN fits to the singlet, triplet, and pentet-state energies. The dissociation dynamics on the singlet-state and NN(STP) surfaces are reported. The results obtained using the singlet surface correspond to those expected if the reaction were to occur adiabatically. The dynamics on the NN(STP) surface represent those expected if the reaction follows a minimum-energy pathway. This study on a small system demonstrates the application of NNs for MD studies using ab initio data when the spin multiplicity of the system changes during the dissociation process. [ABSTRACT FROM AUTHOR]

A new approach involving neural networks combined with molecular dynamics has been used for the determination of reaction probabilities as a function of various input parameters for the reactions associated with the chemical-vapor deposition of carbon dimers on a diamond (100) surface. The data generated by the simulations have been used to train and test neural networks. The probabilities of chemisorption, scattering, and desorption as a function of input parameters, such as rotational energy, translational energy, and direction of the incident velocity vector of the carbon dimer, have been considered. The very good agreement obtained between the predictions of neural networks and those provided by molecular dynamics and the fact that, after training the network, the determination of the interpolated probabilities as a function of various input parameters involves only the evaluation of simple analytical expressions rather than computationally intensive algorithms show that neural networks are extremely powerful tools for interpolating the probabilities and rates of chemical reactions. We also find that a neural network fits the underlying trends in the data rather than the statistical variations present in the molecular-dynamics results. Consequently, neural networks can also provide a computationally convenient means of averaging the statistical variations inherent in molecular-dynamics calculations. In the present case the application of this method is found to reduce the statistical uncertainty in the molecular-dynamics results by about a factor of 3.5. [ABSTRACT FROM AUTHOR]

This paper presents a new fuzzy-logic-control based filter with the ability to remove impulsive noise and smooth Gaussian noise, while, simultaneously, preserving edges and image details efficiently. To achieve these three image enhancement goals, we first develop filters that have excellent edge-preserving capability but do not perform well in smoothing Gaussian noise. Next, we modify the filters so that they perform all three image enhancement tasks. These filters are based on the idea that individual pixels should not be uniformly fired by each of the fuzzy rules. To demonstrate the capability of our filtering approach, it was tested on several different image enhancement problems. These experimental results demonstrate the speed, filtering quality, and image sharpening ability of the new filter. Index Terms--Edge enhancement, fuzzy logic, image processing.