Developing a compiler for the XeonPhi (TR-2014-341)

The XeonPhi is a highly parallel x86 architecture chip made by Intel. It has a number of novel features which make it a particularly challenging target for the compiler writer. This paper describes the techniques used to port the Glasgow Vector Pascal Compiler (VPC) to this architecture and assess its performance by comparisons of the XeonPhi with 3 other machines running the same algorithms

Comments on the 'China model'

This paper reviews the articles by Pan and by Zhu on the China Model. The review of Pan is critical, that of Zhu sympathetic. Pan is criticised for taking an unquestioning attitude towards state supporting ideologies and failing to adequately account for the effects of changes in family structure and class structure in China over the past 50 years. The reviewer broadly agrees with Zhu's comments about a future steady state economy. The article provides statistical data from the recent economic and demographic histories of China and Japan to back up the general conclusions drawn by Zhu

Profit rates: Their dispersion and long term determination

This chapter introduces Marx's theory of the determination of profit rates. It contrasts this theory with what happened in the late nineteenth century to British profit rates with a detailed statistical account. It identifies missing features in the standard presentation and contrasts these with the overaccumulation hypothesis that he presents elsewhere. A formal mathematical model using the overaccumulation hypothesis is then given and tested against modern empirical data

A Biologically Motivated Software Retina for Robotic Vision Applications

We present work in progress to address current limitations in image analysis by Deep Convolutional Neural Networks. By applying structural constraints based on known properties of the human visual system we propose to facilitate learning simple scale and rotation transformations, which contribute to large computational demands for training and opaqueness of the learned structure. We propose to apply a version of the retino-cortical transform to reduce the dimensionality of the input image space by a factor of e×100, and map this spatially to transform rotations and scale changes into spatial shifts. By reducing the input image size accordingly, and therefore learning requirements, we aim to develop a compact and lightweight robot vision sensor using a smartphone as the target platform. We also consider the visual processing architectural issues that must be addressed to integrate the mobile phone based front-end within a larger robot cognitive vision system