Regulating Workplace Risks: A Comparative Study of Inspection Regimes in Times of Change

Regulating Workplace Risks is a study of regulatory inspection of occupational health and safety (OHS) and its management in five countries – Australia, Canada (Québec), France, Sweden and the UK – during a time of major change. It examines the implications of the shift from specification to process based regulation, in which attention has been increasingly directed to the means of managing OHS more systematically at a time in which a major restructuring of work has occurred in response to the globalised economy. These changes provide both the context and material for a wider discussion of the nature of regulation and regulatory inspection and their role in protecting the health, safety and well-being of workers in advanced market economies.

The entrepreneurial break-up : disengaging from the start-up phase

Research on firm exit has grown considerably in volume and sophistication in recent years, leading to new insights and strengthened research-based evidence. However, no framework explicitly explains nascent disengagement, i.e., termination of start-up efforts before the firm has reached an operational stage. Further, prior research has had limited success at explaining nascent entrepreneurial behaviour using theories based on logics of resource availability and economic rationality. In response, this chapter approaches nascent stage disengagement unconventionally by proposing to analogously apply Sternberg’s (1986) Triangular Theory of Love, arguing that founders are less likely to give up the start-up effort if they create strong, almost loving relations to their businesses. Nascent entrepreneurs who terminate the start-up process are proposed to lack one or more of the components – intimacy, passion, and commitment – which are essential according to Sternberg’s theory.

Sydney's media cluster: Continuity and change in film and television

A significant media city globally , Sydney is the production and design centre for the Australian media system and a subsidiary node of larger international systems principally headquartered in Los Angeles and London. Its media cluster is undergoing transformations to improve its position internationally by increasing capabilities and ties to other Australian and international production clusters. Sydney’s media cluster is a collection of suburbs forming an “arc” along major transport corridors stretching from Macquarie Park in the north to Sydney airport in the south. As a dispersed rather than tightly bound cluster, it is defined by the functional proximity provided by automobile and telecommunication networks Sydney’s media cluster is considered here along two dimensions—that of Sydney’s place within the ecology of Australian and international media and that of its internal organization within the geographical space of metropolitan Sydney. The first examines Sydney’s media cluster at the level of the metropolitan area of Sydney within its state, national and international contexts; while the second digs below this level to explore its working out in urban space.

The cluster effect in China: Real or imagined?

Policy makers, urban planners and economic geographers readily acknowledge the potential value of industrial clustering. Clusters attract policy makers’ interest because it is widely held that they are a way of connecting agglomeration to innovation and human capital to investment. Urban planners view clustering as a way of enticing creative human capital, the so-called ‘creative class’, that is, creative people are predisposed to live where there is a range of cultural infrastructure and amenities. Economists and geographers have contrived to promote clustering as a solution to stalled regional development. In the People’s Republic of China, over the past decade the cluster has become the default setting of the cultural and creative industries, the latter a composite term applied to the quantifiable outputs of artists, designers and media workers as well as related service sectors such as tourism, advertising and management. The thinking behind many cluster projects is to ‘pick winners’. In this sense the rapid expansion in the number of cultural and creative clusters in China over the past decade is not so very different from the early 1990s, a period that saw an outbreak of innovation parks, most of which inevitably failed to deliver measurable innovation and ultimately served as revenue-generating sources for district governments via real estate speculation. Since the early years of the first decade of the new millennium the cluster model has been pressed into the service of cultural development.

Intellectual property, innovation, and the environment (2014) edited by Peter Menell and Sarah Tran: A review

Peter S. Menell and Sarah M. Tran (ed.), Intellectual Property, Innovation and the Environment, Cheltenham (UK) and Northampton (MA): Edward Elgar, 2014, 756 pp Hardback 978 1 78195 160 6, http://www.e-elgar.com/bookentry_main.lasso?id=15063 There has been a longstanding deadlock over intellectual property and clean technologies in international climate talks. The United States — and other developed countries such as Japan, Denmark Germany, the United Kingdom, Australia, and New Zealand — have pushed for stronger and longer protection of intellectual property rights related to clean technologies. BASIC countries — such as Brazil, South Africa, India, and China — have pushed for greater flexibilities in respect of intellectual property for the purpose of addressing climate change and global warming. Small island states, least developed countries, and nations vulnerable to climate change have called for climate-adaptation and climate-mitigation technologies to be available in the public domain. In the lead-up to the United Nations Climate Summit in New York on the 23rd September 2014, it is timely to consider the debate over intellectual property, innovation, the environment, and climate change.

Nascent Entrepreneurship

Research on nascent entrepreneurship concerns itself with the emergence of new business ventures. The research aims to capture the pre-operational stage, from first idea or action to the point where the process ends either in the establishment of a viable new business or in termination of the start-up attempt. Although the label “nascent entrepreneur” is commonly used, it should be noted that it is really the venture that is nascent. The founder(s) may or may not have prior entrepreneurial experience.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

Streaming covariance selection with applications to adaptive querying in sensor networks

Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

Filtering via approximate Bayesian computation

Approximate Bayesian computation (ABC) has become a popular technique to facilitate Bayesian inference from complex models. In this article we present an ABC approximation designed to perform biased filtering for a Hidden Markov Model when the likelihood function is intractable. We use a sequential Monte Carlo (SMC) algorithm to both fit and sample from our ABC approximation of the target probability density. This approach is shown to, empirically, be more accurate w.r.t.~the original filter than competing methods. The theoretical bias of our method is investigated; it is shown that the bias goes to zero at the expense of increased computational effort. Our approach is illustrated on a constrained sequential lasso for portfolio allocation to 15 constituents of the FTSE 100 share index.

The Cryo-EM Structure of a Complete 30S Translation Initiation Complex from Escherichia coli

11 p.

Mathematical study of complex networks : brain, internet, and power grid

The dissertation is concerned with the mathematical study of various network problems. First, three real-world networks are considered: (i) the human brain network (ii) communication networks, (iii) electric power networks. Although these networks perform very different tasks, they share similar mathematical foundations. The high-level goal is to analyze and/or synthesis each of these systems from a “control and optimization” point of view. After studying these three real-world networks, two abstract network problems are also explored, which are motivated by power systems. The first one is “flow optimization over a flow network” and the second one is “nonlinear optimization over a generalized weighted graph”. The results derived in this dissertation are summarized below.

Brain Networks: Neuroimaging data reveals the coordinated activity of spatially distinct brain regions, which may be represented mathematically as a network of nodes (brain regions) and links (interdependencies). To obtain the brain connectivity network, the graphs associated with the correlation matrix and the inverse covariance matrix—describing marginal and conditional dependencies between brain regions—have been proposed in the literature. A question arises as to whether any of these graphs provides useful information about the brain connectivity. Due to the electrical properties of the brain, this problem will be investigated in the context of electrical circuits. First, we consider an electric circuit model and show that the inverse covariance matrix of the node voltages reveals the topology of the circuit. Second, we study the problem of finding the topology of the circuit based on only measurement. In this case, by assuming that the circuit is hidden inside a black box and only the nodal signals are available for measurement, the aim is to find the topology of the circuit when a limited number of samples are available. For this purpose, we deploy the graphical lasso technique to estimate a sparse inverse covariance matrix. It is shown that the graphical lasso may find most of the circuit topology if the exact covariance matrix is well-conditioned. However, it may fail to work well when this matrix is ill-conditioned. To deal with ill-conditioned matrices, we propose a small modification to the graphical lasso algorithm and demonstrate its performance. Finally, the technique developed in this work will be applied to the resting-state fMRI data of a number of healthy subjects.

Communication Networks: Congestion control techniques aim to adjust the transmission rates of competing users in the Internet in such a way that the network resources are shared efficiently. Despite the progress in the analysis and synthesis of the Internet congestion control, almost all existing fluid models of congestion control assume that every link in the path of a flow observes the original source rate. To address this issue, a more accurate model is derived in this work for the behavior of the network under an arbitrary congestion controller, which takes into account of the effect of buffering (queueing) on data flows. Using this model, it is proved that the well-known Internet congestion control algorithms may no longer be stable for the common pricing schemes, unless a sufficient condition is satisfied. It is also shown that these algorithms are guaranteed to be stable if a new pricing mechanism is used.

Electrical Power Networks: Optimal power flow (OPF) has been one of the most studied problems for power systems since its introduction by Carpentier in 1962. This problem is concerned with finding an optimal operating point of a power network minimizing the total power generation cost subject to network and physical constraints. It is well known that OPF is computationally hard to solve due to the nonlinear interrelation among the optimization variables. The objective is to identify a large class of networks over which every OPF problem can be solved in polynomial time. To this end, a convex relaxation is proposed, which solves the OPF problem exactly for every radial network and every meshed network with a sufficient number of phase shifters, provided power over-delivery is allowed. The concept of “power over-delivery” is equivalent to relaxing the power balance equations to inequality constraints.

Flow Networks: In this part of the dissertation, the minimum-cost flow problem over an arbitrary flow network is considered. In this problem, each node is associated with some possibly unknown injection, each line has two unknown flows at its ends related to each other via a nonlinear function, and all injections and flows need to satisfy certain box constraints. This problem, named generalized network flow (GNF), is highly non-convex due to its nonlinear equality constraints. Under the assumption of monotonicity and convexity of the flow and cost functions, a convex relaxation is proposed, which always finds the optimal injections. A primary application of this work is in the OPF problem. The results of this work on GNF prove that the relaxation on power balance equations (i.e., load over-delivery) is not needed in practice under a very mild angle assumption.

Generalized Weighted Graphs: Motivated by power optimizations, this part aims to find a global optimization technique for a nonlinear optimization defined over a generalized weighted graph. Every edge of this type of graph is associated with a weight set corresponding to the known parameters of the optimization (e.g., the coefficients). The motivation behind this problem is to investigate how the (hidden) structure of a given real/complex valued optimization makes the problem easy to solve, and indeed the generalized weighted graph is introduced to capture the structure of an optimization. Various sufficient conditions are derived, which relate the polynomial-time solvability of different classes of optimization problems to weak properties of the generalized weighted graph such as its topology and the sign definiteness of its weight sets. As an application, it is proved that a broad class of real and complex optimizations over power networks are polynomial-time solvable due to the passivity of transmission lines and transformers.

Convex Relaxation for Low-Dimensional Representation: Phase Transitions and Limitations

There is a growing interest in taking advantage of possible patterns and structures in data so as to extract the desired information and overcome the curse of dimensionality. In a wide range of applications, including computer vision, machine learning, medical imaging, and social networks, the signal that gives rise to the observations can be modeled to be approximately sparse and exploiting this fact can be very beneficial. This has led to an immense interest in the problem of efficiently reconstructing a sparse signal from limited linear observations. More recently, low-rank approximation techniques have become prominent tools to approach problems arising in machine learning, system identification and quantum tomography.

In sparse and low-rank estimation problems, the challenge is the inherent intractability of the objective function, and one needs efficient methods to capture the low-dimensionality of these models. Convex optimization is often a promising tool to attack such problems. An intractable problem with a combinatorial objective can often be "relaxed" to obtain a tractable but almost as powerful convex optimization problem. This dissertation studies convex optimization techniques that can take advantage of low-dimensional representations of the underlying high-dimensional data. We provide provable guarantees that ensure that the proposed algorithms will succeed under reasonable conditions, and answer questions of the following flavor:

More specifically, i) Focusing on linear inverse problems, we generalize the classical error bounds known for the least-squares technique to the lasso formulation, which incorporates the signal model. ii) We show that intuitive convex approaches do not perform as well as expected when it comes to signals that have multiple low-dimensional structures simultaneously. iii) Finally, we propose convex relaxations for the graph clustering problem and give sharp performance guarantees for a family of graphs arising from the so-called stochastic block model. We pay particular attention to the following aspects. For i) and ii), we aim to provide a general geometric framework, in which the results on sparse and low-rank estimation can be obtained as special cases. For i) and iii), we investigate the precise performance characterization, which yields the right constants in our bounds and the true dependence between the problem parameters.

Segregation: theoretical approach. an empirical analysis of gender segregation in undergraduate studies at the University of the Basque Country.

The aim of this study is to analyze the gender segregation in undergraduate studies in the University from Basque Country (UPV/EHU). We use data from UPV/EHU for the period 2003-2013. We focus on the period from 2003 to 2013 to analyze the changes in the segregation over ten years. We analyze the tendencies of the men and the women inside undergraduate studies. Undergraduate studies are decomposed into five fields: Legal and social sciences, experimental sciences, engineering, arts and humanities, and health sciences. We draw segregation curves and compute the Gini segregation index within the Lorenz approach. Our results show that the gender segregation in undergraduate studies in the UPV/EHU has decreased from 2003 to 2013.

Gender segregation in technological undergraduate studies in the University of the Basque Country (UPV/EHU)

In this work we study the gender segregation in technological undergraduate studies in the University of The Basque Country (UPV/EHU). For this study we use the data of new admissions at the UPV/EHU. They are from the time period of the years 2003-2013. We focus on the first and last year to check if the segregation has changed over these ten years. We build segregation curves within the Lorenz approach. Our results show that the gender segregation in technological undergraduate studies in the University of the Basque Country has increased over the last ten years. We also show that the distribution between men and women has changed.