Procedural embodiment and magic in linear equations

How do students think about algebra? Here we consider a theoretical framework which builds from natural human functioning in terms of embodiment – perceiving the world, acting on it and reflecting on the effect of the actions – to shift to the use of symbolism to solve linear equations. In the main, the students involved in this study do not encapsulate algebraic expressions from process to object, they do not solve ‘evaluation equations’ such as by ‘undoing’ the operations on the left, they do not find such equations easier to solve than , and they do not use general principles of ‘do the same thing to both sides.’ Instead they build their own ways of working based on the embodied actions they perform on the symbols, mentally picking them up and moving them around, with the added ‘magic’ of rules such as ‘change sides, change signs.’ We consider the need for a theoretical framework that includes both embodiment and process-object encapsulation of symbolism and the need for communication of theoretical insights to address the practical problems of teachers and students

Neural Mechanisms for Information Compression by Multiple Alignment, Unification and Search

This article describes how an abstract framework for perception and cognition may be realised in terms of neural mechanisms and neural processing. This framework — called information compression by multiple alignment, unification and search (ICMAUS) — has been developed in previous research as a generalized model of any system for processing information, either natural or artificial. It has a range of applications including the analysis and production of natural language, unsupervised inductive learning, recognition of objects and patterns, probabilistic reasoning, and others. The proposals in this article may be seen as an extension and development of Hebb’s (1949) concept of a ‘cell assembly’. The article describes how the concept of ‘pattern’ in the ICMAUS framework may be mapped onto a version of the cell assembly concept and the way in which neural mechanisms may achieve the effect of ‘multiple alignment’ in the ICMAUS framework. By contrast with the Hebbian concept of a cell assembly, it is proposed here that any one neuron can belong in one assembly and only one assembly. A key feature of present proposals, which is not part of the Hebbian concept, is that any cell assembly may contain ‘references’ or ‘codes’ that serve to identify one or more other cell assemblies. This mechanism allows information to be stored in a compressed form, it provides a robust mechanism by which assemblies may be connected to form hierarchies and other kinds of structure, it means that assemblies can express abstract concepts, and it provides solutions to some of the other problems associated with cell assemblies. Drawing on insights derived from the ICMAUS framework, the article also describes how learning may be achieved with neural mechanisms. This concept of learning is significantly different from the Hebbian concept and appears to provide a better account of what we know about human learning

Learning visual representations with deep neural networks for intelligent transportation systems problems

Esta tesis se centra en dos grandes problemas en el área de los sistemas de transportes inteligentes (STI): el conteo de vehículos en escenas de congestión de tráfico; y la detección y estimación del punto de vista, de forma simultánea, de los objetos en una escena. Respecto al problema del conteo, este trabajo se centra primero en el diseño de arquitecturas de redes neuronales profundas que tengan la capacidad de aprender representaciones multi-escala profundas, capaces de estimar de forma precisa la cuenta de objetos, mediante mapas de densidad. Se trata también el problema de la escala de los objetos introducida por la gran perspectiva típicamente presente en el área de recuento de objetos. Además, con el éxito de las redes hourglass profundas en el campo del conteo de objetos, este trabajo propone un nuevo tipo de red hourglass profunda con conexiones de corto circuito auto-gestionadas. Los modelos propuestos se evalúan en las bases de datos públicas más utilizadas y logran los resultados iguales o superiores al estado del arte en el momento en que fueron publicadas. Para la segunda parte, se realiza un estudio comparativo completo del problema de detección de objetos y la estimación de la pose de forma simultánea. Se expone el compromiso existente entre la localización del objeto y la estimación de su pose. Un detector necesita idealmente una representación que sea invariable al punto de vista, mientras que un estimador de poses necesita ser discriminatorio. Por lo tanto, se proponen tres nuevas arquitecturas de redes neurales profundas en las que el problema de la detección de objetos y la estimación de la pose se van desacoplando progresivamente. Además, se aborda la cuestión de si la pose debe expresarse como un valor discreto o continuo. A pesar de ofrecer un rendimiento similar, los resultados muestran que los enfoques continuos son más sensibles al sesgo del punto de vista principal de la categoría del objeto. Se realiza un análisis comparativo detallado en las dos bases de datos principales, es decir, PASCAL3D+ y ObjectNet3D. Se logran resultados competitivos con todos los modelos propuestos en ambos conjuntos de datos

Mathematical difficulties as decoupling of expectation and developmental trajectories

Recent years have seen an increase in research articles and reviews exploring mathematical difficulties (MD). Many of these articles have set out to explain the etiology of the problems, the possibility of different subtypes, and potential brain regions that underlie many of the observable behaviors. These articles are very valuable in a research field, which many have noted, falls behind that of reading and language disabilities. Here will provide a perspective on the current understanding of MD from a different angle, by outlining the school curriculum of England and the US and connecting these to the skills needed at different stages of mathematical understanding. We will extend this to explore the cognitive skills which most likely underpin these different stages and whose impairment may thus lead to mathematics difficulties at all stages of mathematics development. To conclude we will briefly explore interventions that are currently available, indicating whether these can be used to aid the different children at different stages of their mathematical development and what their current limitations may be. The principal aim of this review is to establish an explicit connection between the academic discourse, with its research base and concepts, and the developmental trajectory of abstract mathematical skills that is expected (and somewhat dictated) in formal education. This will possibly help to highlight and make sense of the gap between the complexity of the MD range in real life and the state of its academic science

Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices

Internet of Things(IoT) devices, mobile phones, and robotic systems are often denied the power of deep learning algorithms due to their limited computing power. However, to provide time-critical services such as emergency response, home assistance, surveillance, etc, these devices often need real-time analysis of their camera data. This paper strives to offer a viable approach to integrate high-performance deep learning-based computer vision algorithms with low-resource and low-power devices by leveraging the computing power of the cloud. By offloading the computation work to the cloud, no dedicated hardware is needed to enable deep neural networks on existing low computing power devices. A Raspberry Pi based robot, Cloud Chaser, is built to demonstrate the power of using cloud computing to perform real-time vision tasks. Furthermore, to reduce latency and improve real-time performance, compression algorithms are proposed and evaluated for streaming real-time video frames to the cloud.Comment: Accepted to The 11th International Conference on Machine Vision (ICMV 2018). Project site: https://zhengyiluo.github.io/projects/cloudchaser