Functional Skills Support Programme: Developing functional skills in geography

This booklet is part of "... a series of 11 booklets which helps schools to implement functional skills across the curriculum. The booklets illustrate how functional skills can be applied and developed in different subjects and contexts, supporting achievement at Key Stage 3 and Key Stage 4. Each booklet contains an introduction to functional skills for subject teachers, three practical planning examples with links to related websites and resources, a process for planning and a list of additional resources to support the teaching and learning of functional skills." - The National Strategies website

Task adapted reconstruction for inverse problems

The paper considers the problem of performing a task defined on a model parameter that is only observed indirectly through noisy data in an ill-posed inverse problem. A key aspect is to formalize the steps of reconstruction and task as appropriate estimators (non-randomized decision rules) in statistical estimation problems. The implementation makes use of (deep) neural networks to provide a differentiable parametrization of the family of estimators for both steps. These networks are combined and jointly trained against suitable supervised training data in order to minimize a joint differentiable loss function, resulting in an end-to-end task adapted reconstruction method. The suggested framework is generic, yet adaptable, with a plug-and-play structure for adjusting both the inverse problem and the task at hand. More precisely, the data model (forward operator and statistical model of the noise) associated with the inverse problem is exchangeable, e.g., by using neural network architecture given by a learned iterative method. Furthermore, any task that is encodable as a trainable neural network can be used. The approach is demonstrated on joint tomographic image reconstruction, classification and joint tomographic image reconstruction segmentation

Efficient Image Processing Via Compressive Sensing Of Integrate-And-Fire Neuronal Network Dynamics

Integrate-and-fire (I&F) neuronal networks are ubiquitous in diverse image processing applications, including image segmentation and visual perception. While conventional I&F network image processing requires the number of nodes composing the network to be equal to the number of image pixels driving the network, we determine whether I&F dynamics can accurately transmit image information when there are significantly fewer nodes than network input-signal components. Although compressive sensing (CS) theory facilitates the recovery of images using very few samples through linear signal processing, it does not address whether similar signal recovery techniques facilitate reconstructions through measurement of the nonlinear dynamics of an I&F network. In this paper, we present a new framework for recovering sparse inputs of nonlinear neuronal networks via compressive sensing. By recovering both one-dimensional inputs and two-dimensional images, resembling natural stimuli, we demonstrate that input information can be well-preserved through nonlinear I&F network dynamics even when the number of network-output measurements is significantly smaller than the number of input-signal components. This work suggests an important extension of CS theory potentially useful in improving the processing of medical or natural images through I&F network dynamics and understanding the transmission of stimulus information across the visual system

Efficiency and Accuracy of Simulated Microstructure Images Generated by Machine Learning

Computer image analysis is a well-known method in material science, mechanical engineering and other branches of science and engineering. Application of the machine learning method in image processing delivers promising results, especially in images with a high level of noise and low contrast. Employing automatic classification, predictive models, simulation models deliver a huge benefit in research. The former model of science-based mainly on experiments slowly passing away. It is still an important part of research but, using advanced computer methods save time and allow significantly reduces the cost of studies. It begins new branch of material science and new research challenges, interdisciplinary filed of materials informatics, incorporating scientists exploring, mathematics, informatics and material science

Using Intelligent Prefetching to Reduce the Energy Consumption of a Large-scale Storage System

Many high performance large-scale storage systems will experience significant workload increases as their user base and content availability grow over time. The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) center hosts one such system that has recently undergone a period of rapid growth as its user population grew nearly 400% in just about three years. When administrators of these massive storage systems face the challenge of meeting the demands of an ever increasing number of requests, the easiest solution is to integrate more advanced hardware to existing systems. However, additional investment in hardware may significantly increase the system cost as well as daily power consumption. In this paper, we present evidence that well-selected software level optimization is capable of achieving comparable levels of performance without the cost and power consumption overhead caused by physically expanding the system. Specifically, we develop intelligent prefetching algorithms that are suitable for the unique workloads and user behaviors of the world\u27s largest satellite images distribution system managed by USGS EROS. Our experimental results, derived from real-world traces with over five million requests sent by users around the globe, show that the EROS hybrid storage system could maintain the same performance with over 30% of energy savings by utilizing our proposed prefetching algorithms, compared to the alternative solution of doubling the size of the current FTP server farm

Exploiting popular culture : exploring pedagogical and motivational approaches for design and technology education

This paper describes a case study of pedagogical developments carried out with teachers and secondary school students in response to new curriculum content in Product Design courses presented in Scottish secondary schools. The pedagogy attempts to challenge the anti-commercial manufacturing attitude that prevails among teachers and students and is based on motivational principles. It makes explicit use of the language and tools of popular media culture, specifically 'ask the audience' interaction and investigative forensic science. An electronic voting system is incorporated as an introduction to detailed product evaluation and technical analysis collaborative activities. It examines the educational potential of such ICT systems to help students explore emotional response, product semantics and value judgements and make connections to commercial manufacturing detail design