Improving productivity - Opening the black box

Hourly productivity levels in the UK still remain behind those in some competitor countries. The government devotes much policy attention to enhancing productivity and continues to emphasise its five drivers - investment, innovation, skills, enterprise, and competition. This article argues that it is investment broadly defined that is the key to sustained productivity improvement. The emphasis should be on improving productivity simultaneously with improving the quality of production. Only thus will the gains be widely shared. In achieving these aims there are two prerequisites for policy-makers. The first is to ensure better coordination of policy than appears to be currently achieved by the present departmental structures in Whitehall. The second is to recognize fully the long and complex chain of causation that can be triggered by pulling on one policy lever. Such complexity can only be fully understood by more research on what actually goes on inside the black box of the organization. © 2006 Oxford University Press.

Clustering user data for user modelling in the GUIDE multi-modal set-top box

Chapter 20 Clustering User Data for User Modelling in the GUIDE Multi-modal Set- top Box PM Langdon and P. Biswas 20.1 ... It utilises advanced user modelling and simulation in conjunction with a single layer interface that permits a ...

The unidirectional emptying box

A theoretical description of the turbulent mixing within and the draining of a dense fluid layer from a box connected to a uniform density, quiescent environment through openings in the top and the base of the box is presented in this paper. This is an extension of the draining model developed by Linden et al. (Annu. Rev. Fluid Mech. vol. 31, 1990, pp. 201-238) and includes terms that describe localized mixing within the emptying box at the density interface. Mixing is induced by a turbulent flow of replacement fluid into the box and as a consequence we predict, and observe in complementary experiments, the development of a three-layer stratification. Based on the data collated from previous researchers, three distinct formulations for entrainment fluxes across density interfaces are used to account for this localized mixing. The model was then solved numerically for the three mixing formulations. Analytical solutions were developed for one formulation directly and for a second on assuming that localized mixing is relatively weak though still significant in redistributing buoyancy on the timescale of the draining process. Comparisons between our theoretical predictions and the experimental data, which we have collected on the developing layer depths and their densities show good agreement. The differences in predictions between the three mixing formulations suggest that the normalized flux turbulently entrained across a density interface tends to a constant value for large values of a Froude number FrT, based on conditions of the inflow through the top of the box, and scales as the cube of FrT for small values of FrT. The upper limit on the rate of entrainment into the mixed layer results in a minimum time (tD) to remove the original dense layer. Using our analytical solutions, we bound this time and show that 0.2tE ≈tD tE, i.e. the original dense layer may be depleted up to five times more rapidly than when there is no internal mixing and the box empties in a time tE. © 2010 Cambridge University Press.

Enzyme-free glucose biosensor based on low density CNT forest grown directly on a Si/SiO2 substrate

A highly sensitive nonenzymatic amperometric glucose sensor was fabricated by using Ni nanoparticles homogeneously dispersed within and on the top of a vertically aligned CNT forest (CNT/Ni nanocomposite sensor), which was directly grown on a Si/SiO2 substrate. The surface morphology and elemental analysis were characterized using scanning electron microscopy and energy dispersive spectroscopy, respectively. Cyclic voltammetry and chronoamperometry were used to evaluate the catalytic activities of CNT/Ni electrode. The CNT/Ni nanocomposite sensor exhibited a great enhancement of anodic peak current after adding 5 mM glucose in alkaline solution. The sensor can also be applied to the quantification of glucose content with a linear range covering from 5 μM to 7 mM, a high sensitivity of 1433 μA mM-1 cm-2, and a low detection limit of 2 μM. The CNT/Ni nanocomposite sensor exhibits good reproducibility and long-term stability, moreover, it was also relatively insensitive to commonly interfering species, such as uric acid, ascorbic acid, acetaminophen, sucrose and d-fructose. © 2013 Elsevier B.V.

High contrast holograms using nanotube forest

Nanotube forest behaves as highly absorbent material when they are randomly placed in sub-wavelength scales. Furthermore, it is possible to create diffractive structures when these bulks are patterned in a substrate. Here, we introduce an alternative to fabricate intensity holograms by patterning fringes of nanotube forest on a substrate. The result is an efficient intensity hologram that is not restricted to sub-wavelength patterning. Both the theoretical and experimental analysis was performed with good agreement. The produced holograms show a uniform behaviour throughout the visible spectra. © 2013 AIP Publishing LLC.

Unconstrained monocular 3D human pose estimation by action detection and cross-modality regression forest

This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts. © 2013 IEEE.

The Random Forest Kernel and other kernels for big data from random partitions

We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, we propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, we show how the form of these kernels lend themselves to a natural approximation that is appropriate for certain big data problems, allowing $O(N)$ inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.

Predictive entropy search for efficient global optimization of black-box functions

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.