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The article argues that the reproduction prices introduced in the first chapter of Sraffa's book are not necessarily compatible with the profit equalising prices that form the substance of the book. It uses probabilistic arguments about how probable it is that reproduction prices will approximate to profit equalising prices. By use of random matrix techniques, it shows that the solutions space associated with prices of production is similar to that associated with classical labour values. In the latter part of the article, a random sample of reproduction schemes is simulated over time, under assumptions of capital movement, to see whether such systems dynamically converge on profit equalising prices. It is found that some converge and some fail to converge.

A key issue in designing robotics systems is the cost of an integrated camera sensor that meets the bandwidth/processing requirement for many advanced robotics applications, especially lightweight robotics applications, such as visual surveillance or SLAM in autonomous aerial vehicles. There is currently much work going on to adapt smartphones to provide complete robot vision systems, as the smartphone is so exquisitely integrated by having camera(s), inertial sensing, sound I/O and excellent wireless connectivity. Mass market production makes this a very low-cost platform and manufacturers from quadrotor drone suppliers to children’s toys, such as the Meccanoid robot [5], employ a smartphone to provide a vision system/control system [7,8].\ud \ud Accordingly, many research groups are attempting to optimise image analysis, computer vision and machine learning libraries for the smartphone platform. However current approaches to robot vision remain highly demanding for mobile processors such as the ARM, and while a number of algorithms have been developed, these are very stripped down, i.e. highly compromised in function or performance. For example, the semi-dense visual odometry implementation of [1] operates on images of only 320x240pixels.\ud \ud In our research we have been developing biologically motivated foveated vision algorithms based on a model of the mammalian retina [2], potentially 100 times more efficient than their conventional counterparts. Accordingly, vision systems based on the foveated architectures found in mammals have also the potential to reduce bandwidth and processing requirements by about x100 - it has been estimated that our brains would weigh ~60Kg if we were to process all our visual input at uniform high resolution. We have reported a foveated visual architecture [2,3,4] that implements a functional model of the retina-visual cortex to produce feature vectors that can be matched/classified using conventional methods, or indeed could be adapted to employ Deep Convolutional Neural Nets for the classification/interpretation stage. Given the above processing/bandwidth limitations, a viable way forward would be to perform off-line learning and implement the forward recognition path on the mobile platform, returning simple object labels, or sparse hierarchical feature symbols, and gaze control commands to the host robot vision system and controller. \ud \ud We are now at the early stages of investigating how best to port our foveated architecture onto an ARM-based smartphone platform. To achieve the required levels of performance we propose to port and optimise our retina model to the mobile ARM processor architecture in conjunction with their integrated GPUs. We will then be in the position to provide a foveated smart vision system on a smartphone with the advantage of processing speed gains and bandwidth optimisations. Our approach will be to develop efficient parallelising compilers and perhaps propose new processor architectural features to support this approach to computer vision, e.g. efficient processing of hexagonally sampled foveated images.\ud \ud Our current goal is to have a foveated system running in real-time on at least a 1080p input video stream to serve as a front-end robot sensor for tasks such as general purpose object recognition and reliable dense SLAM using a commercial off-the-shelf smartphone. Initially this system would communicate a symbol stream to conventional hardware performing back-end visual classification/interpretation, although simple object detection and recognition tasks should be possible on-board the device. We propose that, as in Nature, foveated vision is the key to achieving the necessary data reduction to be able to implement complete visual recognition and learning processes on the smartphone itself.

The presentation describes an architecture for logic simulation that takes advantages of the features of multi-core SIMD architectures. It uses neither explicit locks nor queues, using instead oblivious simulation. Data structures are targeted to efficient SIMD and multi-core cache operation. We demonstrate high levels of parallelisation on Xeon Phi and AMD multi-core machines. Performance on a Xeon Phi is comparable to or better than on a 1000 core Blue Gene machine.

Electron tomography (ET) is an increasingly important technique for the study of the three-dimensional morphologies of nanostructures. ET involves the acquisition of a set of two-dimensional projection images, followed by the reconstruction into a volumetric image by solving an inverse problem. However, due to limitations in the acquisition process, this inverse problem is ill-posed (i.e., a unique solution may not exist). Furthermore, reconstruction usually suffers from missing wedge artifacts (e.g., star, fan, blurring, and elongation artifacts). Recently, compressed sensing (CS) has been applied to ET and showed promising results for reducing missing wedge artifacts. This uses image sparsity as a priori knowledge to improve the accuracy of reconstruction, and can require fewer projections than other reconstruction techniques. The performance of CS relies heavily on the degree of sparsity in the selected transform domain and this depends essentially on the choice of sparsifying transform. We propose a new image reconstruction algorithm for ET that learns the sparsifying transform adaptively using a dictionary-based approach. We demonstrate quantitatively using simulations from complex phantoms that this new approach reconstructs the morphology with higher fidelity than either analytically based CS reconstruction algorithms or traditional weighted back projection from the same dataset.

I shall argue two main points. The first is that although Marx is\ud conventionally taken to have formulated two different theories of\ud price in the the three volumes of Capital, labour values in volume\ud I and prices of production in volume III, there is actually a third\ud theory, hidden inside the reproduction schemes of volume II. This\ud theory is not explicit, but can be logically deduced from the constraints\ud that he presents on simple reproduction. It is not a theory of individual\ud prices, but a theory of relative sectoral prices. \ud \ud I will go on to argue that this theory of sectoral prices allows us\ud to make probabilistic arguments about the relative likely-hood that\ud either production prices or labour values will operate at the level\ud of reproduction schemes. This paper provides a measure on the configuration\ud space associated with Marxian prices of production and labour values.\ud By use of random matrix techniques it shows that the solutions space\ud associated with prices of production is similar to that associated\ud with classical labour values.\ud \ud In the latter part of the paper, a sample of reproduction schemes\ud is simulated over time, under assumptions of capital movement,\ud to see whether such systems dynamically converge on prices of production.\ud It is found that some converge, and some fail to converge.

We present work in progress to develop a low-cost highly\ud integrated camera sensor for egocentric and robotic vision. Our underlying\ud approach is to address current limitations to image analysis by Deep\ud Convolutional Neural Networks, such as the requirement to learn simple\ud scale and rotation transformations, which contribute to the large computational\ud demands for training and opaqueness of the learned structure,\ud by applying structural constraints based on known properties of the human\ud visual system. We propose to apply a version of the retino-cortical\ud transform to reduce the dimensionality of the input image space by a\ud factor of ex100, and map this spatially to transform rotations and scale\ud changes into spatial shifts. By reducing the input image size accordingly,\ud and therefore learning requirements, we aim to develop compact and\ud lightweight egocentric and robot vision sensor using a smartphone as the\ud target platform

Presents a critical analysis of Hayek in the light of modern computability and economic computability theory.

Persistent object oriented architectures have been researched for many years, deriving initially from the Manchester University Atlas machine. In reality however, few actual implementations of persistent architectures exist. In the first half of this paper an examination of four well known designs is examined, namely the SYSTEM/38, MONADS, MUTABOR, and the Rekursiv. Each machineÕs object management model is explained, along with an analysis of the design decisions made. Following this, a discussion concerning the ideal persistent architecture is presented, suggesting design decisions which should be considered in any future persistent architecture.

The location of foundations in a demolished building can be accomplished by undertaking a Ground Penetrating Radar (GPR) survey and then to use the GPR data to generate 3D isosurfaces of what was beneath the soil surface using image reconstruction. The SIMCA ('SIMulated Correlation Algorithm') algorithm is a technique based on a comparison between the trace that would be returned by an ideal point reflector in the soil conditions at the site and the actual trace. During an initialization phase, SIMCA carries out radar simulation using the design parameters of the radar and the soil properties. The trace which would be returned by a target under these conditions is then used to form a kernel. Then SIMCA takes the raw data as the radar is scanned over the ground and removes clutter using a clutter removal technique. The system correlates the kernel with the data by carrying out volume correlation and produces 3D images of the surface of subterranean objects detected. The 3D isosurfaces are generated using MATLAB software. The validation of the algorithm has been accomplished by comparing the 3D isosurfaces produced by the SIMCA algorithm, Scheers algorithm and REFLEXW commercial software. Then the depth and the position in the x and y directions as obtained using MATLAB software for each of the cases are compared with the corresponding values approximately obtained from original Architect's drawings of the buildings.