Information-Based Approaches of Noninvasive Transcranial Brain Stimulation

Progress in cognitive neuroscience relies on methodological developments to increase the specificity of knowledge obtained regarding brain function. For example, in functional neuroimaging the current trend is to study the type of information carried by brain regions rather than simply compare activation levels induced by task manipulations. In this context noninvasive transcranial brain stimulation (NTBS) in the study of cognitive functions may appear coarse and old fashioned in its conventional uses. However, in their multitude of parameters, and by coupling them with behavioral manipulations, NTBS protocols can reach the specificity of imaging techniques. Here we review the different paradigms that have aimed to accomplish this in both basic science and clinical settings and follow the general philosophy of information-based approache

Comparing sensor-based and camera-based approaches to recognizing the occupancy status of the load handling device of forklift trucks

Real-time data of key performance enablers in logistics warehouses are of growing importance as they permit decision-makers to instantaneously react to alerts, deviations and damages. Several technologies appear as adequate data sources to collect the information required in order to achieve the goal. In the present re-search paper, the load status of the fork of a forklift is to be recognized with the help of a sensor-based and a camera-based solution approach. The comparison of initial experimentation results yields a statement about which direction to pursue for promising further research.

Detection of mesoscale thermal fronts from 4km data using smoothing techniques: Gradient-based fronts classification and basin scale application

In order to optimize frontal detection in sea surface temperature fields at 4 km resolution, a combined statistical and expert-based approach is applied to test different spatial smoothing of the data prior to the detection process. Fronts are usually detected at 1 km resolution using the histogram-based, single image edge detection (SIED) algorithm developed by Cayula and Cornillon in 1992, with a standard preliminary smoothing using a median filter and a 3 × 3 pixel kernel. Here, detections are performed in three study regions (off Morocco, the Mozambique Channel, and north-western Australia) and across the Indian Ocean basin using the combination of multiple windows (CMW) method developed by Nieto, Demarcq and McClatchie in 2012 which improves on the original Cayula and Cornillon algorithm. Detections at 4 km and 1 km of resolution are compared. Fronts are divided in two intensity classes (“weak” and “strong”) according to their thermal gradient. A preliminary smoothing is applied prior to the detection using different convolutions: three type of filters (median, average and Gaussian) combined with four kernel sizes (3 × 3, 5 × 5, 7 × 7, and 9 × 9 pixels) and three detection window sizes (16 × 16, 24 × 24 and 32 × 32 pixels) to test the effect of these smoothing combinations on reducing the background noise of the data and therefore on improving the frontal detection. The performance of the combinations on 4 km data are evaluated using two criteria: detection efficiency and front length. We find that the optimal combination of preliminary smoothing parameters in enhancing detection efficiency and preserving front length includes a median filter, a 16 × 16 pixel window size, and a 5 × 5 pixel kernel for strong fronts and a 7 × 7 pixel kernel for weak fronts. Results show an improvement in detection performance (from largest to smallest window size) of 71% for strong fronts and 120% for weak fronts. Despite the small window used (16 × 16 pixels), the length of the fronts has been preserved relative to that found with 1 km data. This optimal preliminary smoothing and the CMW detection algorithm on 4 km sea surface temperature data are then used to describe the spatial distribution of the monthly frequencies of occurrence for both strong and weak fronts across the Indian Ocean basin. In general strong fronts are observed in coastal areas whereas weak fronts, with some seasonal exceptions, are mainly located in the open ocean. This study shows that adequate noise reduction done by a preliminary smoothing of the data considerably improves the frontal detection efficiency as well as the global quality of the results. Consequently, the use of 4 km data enables frontal detections similar to 1 km data (using a standard median 3 × 3 convolution) in terms of detectability, length and location. This method, using 4 km data is easily applicable to large regions or at the global scale with far less constraints of data manipulation and processing time relative to 1 km data.

School-based approaches to pre-service primary science teacher education resulting in gains in confidence

The quality of science education has been the focus of a number of research projects nationally and internationally, including concerns about primary teachers’ lack of science knowledge and confidence to teach science. In addition, the effectiveness of traditional approaches to teacher education have been questioned. The Science Teacher Partnerships with Schools (STEPS) responds to these concerns by investigating the effectiveness of school-based approaches to pre-service primary science teacher education. It considers established, innovative and successful practices at five universities to develop and promote a framework supporting school-based approaches to pre-service teacher education. An analysis of the five models was conducted in 2013 involving interviews with teacher educators, pre-service teachers, and school principals and teachers. Pre-service teachers at these universities also engaged in pre- and post- online surveys generating data on their expectations and experiences associated with these experiences. This paper reports on the analysis of the survey data, which shows that there are statistically significant gains in pre-service teachers’ responses to several items relating to their confidence to teach science. Analysis of the data also shows interesting differences between universities noted in different confidence items. The school based experience was shown to provide these pre-service teachers with an authentic engagement with the teaching of science while being supported by their university tutors. While raising confidence at university does not automatically translate to confident early career teachers, the gains in confidence are an important step in assisting prospective teachers to approach the teaching of science more positively than they might otherwise. Implications for teacher education and the role that university-school partnerships can play in preparing confident teachers of science will be discussed.

Re-presenting teachers’ experiences of using game based approaches through poetic transcription

This paper provides an overview of poetic transcription and how I used it to re-present physical education teachers’ experiences of teaching games using a game based approach (GBA). Composite narratives derived from a study exploring teachers’ experiences of GBA teaching provided the initial storying of experience with three separate teacher capacities for experience identified; that of a Learner, a Collaborator, and a Catalyst. These narratives were then re-storied as found poems. Discussion within this paper comments on the reflexive action I engaged with to transform interview transcripts into poetic form with specific comment offered as to my rationale for use of poetic transcription as well as the process I undertook to re-see teachers’ experiences of GBA experience. Comment stemming from a comparison of poems is also offered along with what experimentation with poetic transcription enabled me to “do” with my understanding of the experience of GBA teaching.

Recent trends in research literature on game-based approaches to teaching and coaching games

As suggested by Curry and Light in chapter nine, the expanding output of research on games-based approaches (GBAs) over the past decade has not been reflected in expanding utilisation of GBAs in school-based physical education programmes and club-based sport coaching environments. Reasons for this lack of ‘uptake’ are varied and range from a lack of exposure to effective GBA professional development opportunities to the prolonged acceptance of a performative culture often embedded within physical education and youth sport programmes (Harvey and Jarrett, 2012; Dismore and Bailey, 2010). The literature on games teaching published since Oslin and Mitchell’s review of GBAs in 2006 continues to acknowledge the many benefits of using GBAs, but also acknowledges, and to a lesser extent addresses, the key challenges associated with the employment of learner-centred and GBA pedagogies. This chapter provides an overview of post-2005 research trends in the GBA literature to identify and discuss the prominent themes that arose from this meta-analysis.

Urban codes : abstraction and case-based approaches to algorithmic design and implications for the design of contemporary cities

This paper reports on a comparative study that evaluates two approaches to support the learning and use of algorithmic design in architecture, and extrapolates from this to consider applications for the algorithmic design of cities. The study explored two methods to reduce the barriers of using programming and potentially improve design performance. The first is the reuse of abstract algorithmic ‘patterns’. The second approach is the reuse of algorithmic solutions from specific design cases (case-based design). Reflecting on this research we outline how our findings discussed in relation to alternate thinking on the use of pattern, might inform a hybrid approach to the algorithmic design of cities.

Using elicitation interview within a phenomenographic framework : developing the breadth of research designs associated with game based approaches

This paper describes how elicitation interview technique was used within a phenomenographic research design to explore physical education teachers’ experiences of teaching games using a Game Based Approach (GBA). Participants taught in one of two different international contexts, Australia or England, and all had some experience of using a GBA to teach games. The focus of the paper is the presentation and discussion of the unique research design used to generate understanding about GBA teaching experiences as well as extending the examination of GBAs from different philosophical viewpoints. Authors’ reflections on the utilised research design are presented with concluding discussion identifying further research opportunities relating to GBAs in teaching and coaching contexts.

Similar, but not the same: comparing the game based approaches of TGfU and game sense

The development and use of game based approaches (GBAs) across a range of global teaching and coaching settings has expanded significantly over the last two decades. And with each GBA underpinned by similar theories of learning, distinctions between each approach can often be blurred. Arguably, this can lead to teachers' and coaches' blended conceptualisations of different pedagogical approaches. Thus, although similar there is a need for teachers and coaches to recognise that not all GBAs are the same with each model or approach chosen impacting significantly upon learner experiences. Through analysis of literature and presentation of teaching/coaching lesson/session outlines, this paper presents similarities and differences of two game based instructional pedagogies - TGfU and Game Sense - and discusses the need for teachers and coaches to recognise and respond to the contextual differences of each when considering their use.

An Examination of the merits of a human rights-based approach to higher education teaching and learning, compared with welfare and market-based approaches.

The thesis concludes that a human rights-based approach to higher education will produce better teaching and learning outcomes than welfare state or market-based approaches. It is intended that this research might influence an improvement in policy-making, identify a ‘feasible utopia’ for higher education, and contribute to discussion about the public interest role of higher education.

On Convergence Properties of the EM Algorithm for Gaussian Mixtures

"Expectation-Maximization'' (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix $P$, and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of $P$ and provide new results analyzing the effect that $P$ has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages and disadvantages of EM and other algorithms for the learning of Gaussian mixture models.

Efficient accommodation of local minima in watershed model calibration

The Gauss-Marquardt-Levenberg (GML) method of computer-based parameter estimation, in common with other gradient-based approaches, suffers from the drawback that it may become trapped in local objective function minima, and thus report optimized parameter values that are not, in fact, optimized at all. This can seriously degrade its utility in the calibration of watershed models where local optima abound. Nevertheless, the method also has advantages, chief among these being its model-run efficiency, and its ability to report useful information on parameter sensitivities and covariances as a by-product of its use. It is also easily adapted to maintain this efficiency in the face of potential numerical problems (that adversely affect all parameter estimation methodologies) caused by parameter insensitivity and/or parameter correlation. The present paper presents two algorithmic enhancements to the GML method that retain its strengths, but which overcome its weaknesses in the face of local optima. Using the first of these methods an intelligent search for better parameter sets is conducted in parameter subspaces of decreasing dimensionality when progress of the parameter estimation process is slowed either by numerical instability incurred through problem ill-posedness, or when a local objective function minimum is encountered. The second methodology minimizes the chance of successive GML parameter estimation runs finding the same objective function minimum by starting successive runs at points that are maximally removed from previous parameter trajectories. As well as enhancing the ability of a GML-based method to find the global objective function minimum, the latter technique can also be used to find the locations of many non-global optima (should they exist) in parameter space. This can provide a useful means of inquiring into the well-posedness of a parameter estimation problem, and for detecting the presence of bimodal parameter and predictive probability distributions. The new methodologies are demonstrated by calibrating a Hydrological Simulation Program-FORTRAN (HSPF) model against a time series of daily flows. Comparison with the SCE-UA method in this calibration context demonstrates a high level of comparative model run efficiency for the new method. (c) 2006 Elsevier B.V. All rights reserved.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.