Learning perceptual schemas to avoid the utility problem

This paper describes principles for representing and organising planning knowledge in a machine learning architecture. One of the difficulties with learning about tasks requiring planning is the utility problem: as more knowledge is acquired by the learner, the utilisation of that knowledge takes on a complexity which overwhelms the mechanisms of the original task. This problem does not, however, occur with human learners: on the contrary, it is usually the case that, the more knowledgeable the learner, the greater the efficiency and accuracy in locating a solution. The reason for this lies in the types of knowledge acquired by the human learner and its organisation. We describe the basic representations which underlie the superior abilities of human experts, and describe algorithms for using equivalent representations in a machine learning architecture

DEVELOPMENT OF MORAL VALUES AND CONSTRUCTIVISM THROUGH THE BILINGUAL LEARNING MODEL WITH A BCCT APPROACH (BEYOND CENTER AND CIRCLE TIME) IN EARLYCHILDHOOD EDUCATION IN SEMARANG 1

This research aims to develop moral values and constructivism through a bilingual learning model with a BCCT approach. The theory of an English learning model through a BCCT approach can be used byteachers of early childhood education not only to improve the communication skills of students but also tounleash all the potentials of children by promoting freedom of choice, stimulation of creativity and charactergrowth. The study begins with a preliminary study to map out the implementation of bilingual learningwith a BCCT approach based on moral values and constructivism in early childhood education whichconsists of two phases, namely, the study of literature and field studies. It is followed by the stage ofplanning based on the analysis of needs so as to make the design of bilingual learning model with a BCCTapproach based on moral values and constructivism. The analysis and interpretation of data as a result of reflection and evaluation of the developmenof the learning model are used as a reference guide to produce a bilingual learning model with a BCCTapproach based on constructivism and moral values that can be used by early childhood education teachersin their respective schools

A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction

The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from undersampled data using a deep cascade of convolutional neural networks to accelerate the data acquisition process. We show that for Cartesian undersampling of 2D cardiac MR images, the proposed method outperforms the state-of-the-art compressed sensing approaches, such as dictionary learning-based MRI (DLMRI) reconstruction, in terms of reconstruction error, perceptual quality and reconstruction speed for both 3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the method proposed is approximately twice as small, allowing to preserve anatomical structures more faithfully. Using our method, each image can be reconstructed in 23 ms, which is fast enough to enable real-time applications