Value innovation modelling: Design thinking as a tool for business analysis and strategy

This paper explores the use of multiple perspective problem framing (English 2008) as a tool to reveal hidden value and commercial opportunity for business. Creative thinking involves the interrelationship of parameters held open and fluid within the cognitive span of the creative mind. The recognition of new associations can create new value that can lead to innovation in designed products, intellectual property and business strategy. The ‘Ideas-lab’ process is based on the proposition that a company’s capacity for innovation is dependent on the way the business is able to see its problems and opportunities. In this process the attributes of a company and the experience of the researchers are considered as the parameters of a design problem. It is therefore important to acknowledge the commercial experience of the project researchers, all of whom have a proven track record in helping businesses develop, exploit and protect their know how. Semi structured interviews were carried out with key individuals in 34 companies. The resulting data was assessed on a company-by-company basis through a process of multiple perspective problem framing, enabling key nodes, patterns and relationships to be identified and explored. A ‘Cornerstones of Innovation’ report was prepared to inform each company of the observations made by the researchers. The paper describes the methods adopted and summarises the feedback from participating companies. Case studies are highlighted to demonstrate ways in which the process influenced the actions of particular businesses, and the commercial outcomes that resulted. Finally the researchers reflect on the structure of the Ideas-lab process

Investigations of high altitude geomagnetic phenomena using rubidium vapour systems

Imperial Users onl

Bias and Sensitivity Analysis When Estimating Treatment Effects from the Cox Model with Omitted Covariates

Omission of relevant covariates can lead to bias when estimating treatment or exposure effects from survival data in both randomized controlled trials and observational studies. This paper presents a general approach to assessing bias when covariates are omitted from the Cox model. The proposed method is applicable to both randomized and non‐randomized studies. We distinguish between the effects of three possible sources of bias: omission of a balanced covariate, data censoring and unmeasured confounding. Asymptotic formulae for determining the bias are derived from the large sample properties of the maximum likelihood estimator. A simulation study is used to demonstrate the validity of the bias formulae and to characterize the influence of the different sources of bias. It is shown that the bias converges to fixed limits as the effect of the omitted covariate increases, irrespective of the degree of confounding. The bias formulae are used as the basis for developing a new method of sensitivity analysis to assess the impact of omitted covariates on estimates of treatment or exposure effects. In simulation studies, the proposed method gave unbiased treatment estimates and confidence intervals with good coverage when the true sensitivity parameters were known. We describe application of the method to a randomized controlled trial and a non‐randomized study

The geography of entrepreneurship in the New York metropolitan area

This article was presented at a conference organized by the Federal Reserve Bank of New York in April 2005, "Urban Dynamics in New York City." The goal of the conference was threefold: to examine the historical transformations of the engine-of-growth industries in New York and distill the main determinants of the city's historical dominance as well as the challenges to its continued success; to study the nature and evolution of immigration flows into New York; and to analyze recent trends in a range of socioeconomic outcomes, both for the general population and recent immigrants more specifically.Business enterprises - New York (N.Y.) ; Economic conditions - New York (N.Y.) ; Federal Reserve District, 2nd ; Urban economics

Pathways to Economic Mobility: Key Indicators

Outlines how indicators of social, human, and financial capital affect an individual's chances of moving up or down the economic ladder. Analyzes data on family structure, community, education, race/ethnicity, health, home ownership, and other factors

Chemical and forensic analysis of JFK assassination bullet lots: Is a second shooter possible?

The assassination of President John Fitzgerald Kennedy (JFK) traumatized the nation. In this paper we show that evidence used to rule out a second assassin is fundamentally flawed. This paper discusses new compositional analyses of bullets reportedly to have been derived from the same batch as those used in the assassination. The new analyses show that the bullet fragments involved in the assassination are not nearly as rare as previously reported. In particular, the new test results are compared to key bullet composition testimony presented before the House Select Committee on Assassinations (HSCA). Matches of bullets within the same box of bullets are shown to be much more likely than indicated in the House Select Committee on Assassinations' testimony. Additionally, we show that one of the ten test bullets is considered a match to one or more assassination fragments. This finding means that the bullet fragments from the assassination that match could have come from three or more separate bullets. Finally, this paper presents a case for reanalyzing the assassination bullet fragments and conducting the necessary supporting scientific studies. These analyses will shed light on whether the five bullet fragments constitute three or more separate bullets. If the assassination fragments are derived from three or more separate bullets, then a second assassin is likely, as the additional bullet would not easily be attributable to the main suspect, Mr. Oswald, under widely accepted shooting scenarios [see Posner (1993), Case Closed, Bantam, New York].Comment: Published in at http://dx.doi.org/10.1214/07-AOAS119 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Adaptive Image Restoration: Perception Based Neural Nework Models and Algorithms.

Abstract This thesis describes research into the field of image restoration. Restoration is a process by which an image suffering some form of distortion or degradation can be recovered to its original form. Two primary concepts within this field have been investigated. The first concept is the use of a Hopfield neural network to implement the constrained least square error method of image restoration. In this thesis, the author reviews previous neural network restoration algorithms in the literature and builds on these algorithms to develop a new faster version of the Hopfield neural network algorithm for image restoration. The versatility of the neural network approach is then extended by the author to deal with the cases of spatially variant distortion and adaptive regularisation. It is found that using the Hopfield-based neural network approach, an image suffering spatially variant degradation can be accurately restored without a substantial penalty in restoration time. In addition, the adaptive regularisation restoration technique presented in this thesis is shown to produce superior results when compared to non-adaptive techniques and is particularly effective when applied to the difficult, yet important, problem of semi-blind deconvolution. The second concept investigated in this thesis, is the difficult problem of incorporating concepts involved in human visual perception into image restoration techniques. In this thesis, the author develops a novel image error measure which compares two images based on the differences between local regional statistics rather than pixel level differences. This measure more closely corresponds to the way humans perceive the differences between two images. Two restoration algorithms are developed by the author based on versions of the novel image error measure. It is shown that the algorithms which utilise this error measure have improved performance and produce visually more pleasing images in the cases of colour and grayscale images under high noise conditions. Most importantly, the perception based algorithms are shown to be extremely tolerant of faults in the restoration algorithm and hence are very robust. A number of experiments have been performed to demonstrate the performance of the various algorithms presented

Towards an Information Theoretic Framework for Evolutionary Learning

The vital essence of evolutionary learning consists of information flows between the environment and the entities differentially surviving and reproducing therein. Gain or loss of information in individuals and populations due to evolutionary steps should be considered in evolutionary algorithm theory and practice. Information theory has rarely been applied to evolutionary computation - a lacuna that this dissertation addresses, with an emphasis on objectively and explicitly evaluating the ensemble models implicit in evolutionary learning. Information theoretic functionals can provide objective, justifiable, general, computable, commensurate measures of fitness and diversity. We identify information transmission channels implicit in evolutionary learning. We define information distance metrics and indices for ensembles. We extend Price\u27s Theorem to non-random mating, give it an effective fitness interpretation and decompose it to show the key factors influencing heritability and evolvability. We argue that heritability and evolvability of our information theoretic indicators are high. We illustrate use of our indices for reproductive and survival selection. We develop algorithms to estimate information theoretic quantities on mixed continuous and discrete data via the empirical copula and information dimension. We extend statistical resampling. We present experimental and real world application results: chaotic time series prediction; parity; complex continuous functions; industrial process control; and small sample social science data. We formalize conjectures regarding evolutionary learning and information geometry