Inventors and entrepreneurs in academia: What types of skills and experience matter?

This paper aims to improve our understanding of the attributes of academic researchers that influence the capacity to contribute to technical advance, by adding to the pool of technological opportunities available to industry or engaging in the exploitation of entrepreneurial opportunities. We investigate a number of factors associated with the skills developed by academic researchers. We find that contributions to the pool of technological opportunities and exploitation of entrepreneurial opportunities involve different sets of skills and expertise of scientists. Our results show that the former is driven by academic scientists research excellence and discovery of earlier technological opportunities and the latter is driven by previous collaboration with industry partners, scientific breadth and experience of technological discovery. © 2011 Elsevier Ltd. All rights reserved.

Which diagrams and when? Health workers' choice and usage of different diagram types for service improvement

Diagrammatic representations, such as process mapping and care pathways, have been often used for service evaluation and improvement in healthcare. While a broad range of diagrammatic representations exist, their application in healthcare has been very limited. There is a lack of understanding about how and which diagrams could be usable and useful to health workers. In this study, ten mental health workers were asked to discuss positive and negative issues around their service delivery using one or two diagrams of their choice out of seven different diagrams representing their service: care pathway diagram; organisation diagram; communication diagram; service blueprint; patient state transition diagram; free form diagram; geographic map. Their interactions with diagrams were video-taped for analysis. The patient state transition diagram was the most popular choice in spite of relatively low previous familiarity. The overall findings provided insight into a better use of diagrams in healthcare. © 2012 Springer-Verlag.

Management and Analysis of Unstructured Construction Data Types

Compared with structured data sources that are usually stored and analyzed in spreadsheets, relational databases, and single data tables, unstructured construction data sources such as text documents, site images, web pages, and project schedules have been less intensively studied due to additional challenges in data preparation, representation, and analysis. In this paper, our vision for data management and mining addressing such challenges are presented, together with related research results from previous work, as well as our recent developments of data mining on text-based, web-based, image-based, and network-based construction databases.

A neurophysiologically plausible population code model for feature integration explains visual crowding.

An object in the peripheral visual field is more difficult to recognize when surrounded by other objects. This phenomenon is called "crowding". Crowding places a fundamental constraint on human vision that limits performance on numerous tasks. It has been suggested that crowding results from spatial feature integration necessary for object recognition. However, in the absence of convincing models, this theory has remained controversial. Here, we present a quantitative and physiologically plausible model for spatial integration of orientation signals, based on the principles of population coding. Using simulations, we demonstrate that this model coherently accounts for fundamental properties of crowding, including critical spacing, "compulsory averaging", and a foveal-peripheral anisotropy. Moreover, we show that the model predicts increased responses to correlated visual stimuli. Altogether, these results suggest that crowding has little immediate bearing on object recognition but is a by-product of a general, elementary integration mechanism in early vision aimed at improving signal quality.

A Maximum-Likelihood Interpretation for Slow Feature Analysis

The brain extracts useful features from a maelstrom of sensory information, and a fundamental goal of theoretical neuroscience is to work out how it does so. One proposed feature extraction strategy is motivated by the observation that the meaning of sensory data, such as the identity of a moving visual object, is often more persistent than the activation of any single sensory receptor. This notion is embodied in the slow feature analysis (SFA) algorithm, which uses “slowness” as an heuristic by which to extract semantic information from multi-dimensional time-series. Here, we develop a probabilistic interpretation of this algorithm showing that inference and learning in the limiting case of a suitable probabilistic model yield exactly the results of SFA. Similar equivalences have proved useful in interpreting and extending comparable algorithms such as independent component analysis. For SFA, we use the equivalent probabilistic model as a conceptual spring-board, with which to motivate several novel extensions to the algorithm.

Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data

We present a nonparametric Bayesian method for disease subtype discovery in multi-dimensional cancer data. Our method can simultaneously analyse a wide range of data types, allowing for both agreement and disagreement between their underlying clustering structure. It includes feature selection and infers the most likely number of disease subtypes, given the data. We apply the method to 277 glioblastoma samples from The Cancer Genome Atlas, for which there are gene expression, copy number variation, methylation and microRNA data. We identify 8 distinct consensus subtypes and study their prognostic value for death, new tumour events, progression and recurrence. The consensus subtypes are prognostic of tumour recurrence (log-rank p-value of $3.6 \times 10^{-4}$ after correction for multiple hypothesis tests). This is driven principally by the methylation data (log-rank p-value of $2.0 \times 10^{-3}$) but the effect is strengthened by the other 3 data types, demonstrating the value of integrating multiple data types. Of particular note is a subtype of 47 patients characterised by very low levels of methylation. This subtype has very low rates of tumour recurrence and no new events in 10 years of follow up. We also identify a small gene expression subtype of 6 patients that shows particularly poor survival outcomes. Additionally, we note a consensus subtype that showly a highly distinctive data signature and suggest that it is therefore a biologically distinct subtype of glioblastoma. The code is available from https://sites.google.com/site/multipledatafusion/

On the relation of slow feature analysis and Laplacian eigenmaps

The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised learning, and for providing efficient state representations for reinforcement learning. Here, we show that LEMs are closely related to slow feature analysis (SFA), a biologically inspired, unsupervised learning algorithm originally designed for learning invariant visual representations. We show that SFA can be interpreted as a function approximation of LEMs, where the topological neighborhoods required for LEMs are implicitly defined by the temporal structure of the data. Based on this relation, we propose a generalization of SFA to arbitrary neighborhood relations and demonstrate its applicability for spectral clustering. Finally, we review previous work with the goal of providing a unifying view on SFA and LEMs. © 2011 Massachusetts Institute of Technology.

A theory of slow feature analysis for transformation-based input signals with an application to complex cells

We develop a group-theoretical analysis of slow feature analysis for the case where the input data are generated by applying a set of continuous transformations to static templates. As an application of the theory, we analytically derive nonlinear visual receptive fields and show that their optimal stimuli, as well as the orientation and frequency tuning, are in good agreement with previous simulations of complex cells in primary visual cortex (Berkes and Wiskott, 2005). The theory suggests that side and end stopping can be interpreted as a weak breaking of translation invariance. Direction selectivity is also discussed. © 2011 Massachusetts Institute of Technology.

The interaction of riblets with wall-bounded turbulence

The purpose of this thesis is to give answer to the question: why do riblets stop working for a certain size? Riblets are small surface grooves aligned in the mean direction of an overlying turbulent flow, designed specifically to reduce the friction between the flow and the surface. They were inspired by biological surfaces, like the oriented denticles in the skin of fastswimming sharks, and were the focus of a significant amount of research in the late eighties and nineties. Although it was found that the drag reduction depends on the riblet size scaled in wall units, the physical mechanisms implicated have not been completely understood up to now. It has been explained how riblets of vanishing size interact with the turbulent flow, producing a change in the drag proportional to their size, but that is not the regime of practical interest. The optimum performance is achieved for larger sizes, once that linear behavior has broken down, but before riblets begin adopting the character of regular roughness and increasing drag. This regime, which is the most relevant from a technological perspective, was precisely the less understood, so we have focused on it. Our efforts have followed three basic directions. First, we have re-assessed the available experimental data, seeking to identify common characteristics in the optimum regime across the different existing riblet geometries. This study has led to the proposal of a new length scale, the square root of the groove crosssection, to substitute the traditional peak-to-peak spacing. Scaling the riblet dimension with this length, the size of breakdown of the linear behavior becomes roughly universal. This suggests that the onset of the breakdown is related to a certain, fixed value of the cross-section of the groove. Second, we have conducted a set of direct numerical simulations of the turbulent flow over riblets, for sizes spanning the full drag reduction range. We have thus been able to reproduce the gradual transition between the different regimes. The spectral analysis of the flows has proven particularly fruitful, since it has made possible to identify spanwise rollers immediately above the riblets, which begin to appear when the riblet size is close to the optimum. This is a quite surprising feature of the flow, not because of the uniqueness of the phenomenon, which had been reported before for other types of complex and porous surfaces, but because most previous studies had focused on the detail of the flow above each riblet as a unit. Our novel approach has provided the adequate tools to capture coherent structures with an extended spanwise support, which interact with the riblets not individually, but collectively. We have also proven that those spanwise structures are responsible for the increase in drag past the viscous breakdown. Finally, we have analyzed the stability of the flow with a simplified model that connects the appearance of rollers to a Kelvin–Helmholtz-like instability, as is the case also for the flow over plant canopies and porous surfaces. In spite of the model emulating the presence of riblets only in an averaged, general fashion, it succeeds to capture the essential attributes of the breakdown, and provides a theoretical justification for the scaling with the groove cross-section.

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data. Our model allows simultaneous inference of the number of binary latent variables, and their values. The latent variables preserve neighbourhood structure of the data in a sense that objects in the same semantic concept have similar latent values, and objects in different concepts have dissimilar latent values. We formulate the supervised infinite latent variable problem based on an intuitive principle of pulling objects together if they are of the same type, and pushing them apart if they are not. We then combine this principle with a flexible Indian Buffet Process prior on the latent variables. We show that the inferred supervised latent variables can be directly used to perform a nearest neighbour search for the purpose of retrieval. We introduce a new application of dynamically extending hash codes, and show how to effectively couple the structure of the hash codes with continuously growing structure of the neighbourhood preserving infinite latent feature space.