2,206 research outputs found

    The effect of supervision on Ph.D. duration, publications and job outcomes

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    The empirical literature on the market for PH.D. graduates is generally focused on individual characteristics and their effect on scientific achievement, career prospects and/or expected earnings. In this paper, we take a closer look at the context in which graduate training takes place. Using data on 650 Ph.D. graduates from the INRA (the French National Institute of Agronomic Research) we were able to show that supervision (described by characteristics of the Ph.D. lab) strongly affects the number (and quality) of articles published during the Ph.D., as well as its overall duration. Supervision also has a significant influence on job outcomes after the ph.D. has been completed.Doctoral training;Skills acquisition;Entry on the labour market

    Characteristics of teaching institutions and students’ performance : new empirical evidence from OECD data

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    A whole branch of the economic literature suggests that institutional differences between and inside educational systems may have a larger influence on students performance than the amount of resources devoted to schooling. In this paper, we use the PISA 2000 international OECD data to evaluate the impacts of organizational and institutional factors on students performance. We estimate an education production function with country fixed-effect and school random-effect. We find that, alongside individual characteristics, school autonomy in decisions regarding the recruitment of new personnel as well as pedagogical training strongly affect students performance. On the contrary, measures of school resources and standardised evaluation of students have no consistent effect.human capital formation; individual performance; school resources; school autonomy; institutional arrangements

    Innovation Strategy and Total Factor Productivity Growth : Micro Evidence from Taiwanese Manufacturing Firms

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    This paper investigates the relationship between firms’ innovation practices and performance in Taiwan. Using a panel of 4000 firms, we examine the effects of importing technology (versus doing R&D) on Total Factor Porductivity (TFP) growth. The relationship between these two innovation strategies is also explored. We find that R&D strongly contributes to the growth of TFP, whereas the importation of technology is only weakly significant, which makes it difficult to qualify the type of relationship (complementarity or substitutability) that exists between the two innovation strategies.Importation of technology;Newly industrializzed countries;Productivity growth;Firm-level panel data; Manufacturing industries

    Doing R&D and Importing Technology : an Empirical Investigation on Taiwan’s manufacturing firms

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    The objective of this paper is to identify the determinants of the decision to innovate in Taiwan. Three “innovation strategies” are considered : doing R&D only, importing technology only, and combining both. We estimate a Bivariate Probit on a panel of more than 27000 Taiwanese manufacturing firms observed from 1992 to 1995. Results suggest that the decision to R&D over the period was influenced by the prior changes in exportations at the industry level, whereas the decision to import technology is affected by the current changes. We identify a non-linear relationship between firm size and innovation. Moreover, older firms tend to innovate less, whereas market structure doesn’t affect the decision to innovate. These two results change when only high-tech industries are considered : the effect of firm’s age becomes insignificant, whereas a more concentrated market structure is shown to increase the probability to innovate.R&D;importation of technology;market structure;technological opportunities;high-tech industries;panel data;bivariate Probit.

    Nine Quick Tips for Analyzing Network Data

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    These tips provide a quick and concentrated guide for beginners in the analysis of network data

    A statistical approach for array CGH data analysis

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    BACKGROUND: Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise : to determine which parameters are affected by the abrupt changes (the mean and the variance, or the mean only), and the selection of the number of segments in the profile. RESULTS: We demonstrate that existing methods for estimating the number of segments are not well adapted in the case of array CGH data, and we propose an adaptive criterion that detects previously mapped chromosomal aberrations. The performances of this method are discussed based on simulations and publicly available data sets. Then we discuss the choice of modeling for array CGH data and show that the model with a homogeneous variance is adapted to this context. CONCLUSIONS: Array CGH data analysis is an emerging field that needs appropriate statistical tools. Process segmentation and model selection provide a theoretical framework that allows precise biological interpretations. Adaptive methods for model selection give promising results concerning the estimation of the number of altered regions on the genome

    High-availability displacement sensing with multi-channel self mixing interferometry

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    Laser self-mixing is in principle a simple and robust general purpose interferometric method, with the additional expressivity which results from nonlinearity. However, it is rather sensitive to unwanted changes in target reflectivity, which often hinders applications with non-cooperative targets. Here we analyze experimentally a multi-channel sensor based on three independent self-mixing signals processed by a small neural network. We show that it provides high-availability motion sensing, robust not only to measurement noise but also to complete loss of signal in some channels. As a form of hybrid sensing based on nonlinear photonics and neural networks, it also opens perspectives for fully multimodal complex photonics sensing

    Classification and estimation in the Stochastic Blockmodel based on the empirical degrees

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    International audienceThe Stochastic Blockmodel [16] is a mixture model for heterogeneous network data. Unlike the usual statistical framework, new nodes give additional information about the previous ones in this model. Thereby the distribution of the degrees concentrates in points conditionally on the node class. We show under a mild assumption that classification, estimation and model selection can actually be achieved with no more than the empirical degree data. We provide an algorithm able to process very large networks and consistent estimators based on it. In particular, we prove a bound of the probability of misclassification of at least one node, including when the number of classes grows

    Modelling the influence of dimerisation sequence dissimilarities on the auxin signalling network

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    International audienceBackground: Auxin is a major phytohormone involved in many developmental processes by controlling gene expression through a network of transcriptional regulators. In Arabidopsis thaliana, the auxin signalling network is made of 52 potentially interacting transcriptional regulators, activating or repressing gene expression. All the possible interactions were tested in two-way yeast-2-hybrid experiments. Our objective was to characterise this auxin signalling network and to quantify the influence of the dimerisation sequence dissimilarities on the interaction between transcriptional regulators.Results: We applied model-based graph clustering methods relying on connectivity profiles between transcriptional regulators. Incorporating dimerisation sequence dissimilarities as explanatory variables, we modelled their influence on the auxin network topology using mixture of linear models for random graphs. Our results provide evidence that the network can be simplified into four groups, three of them being closely related to biological groups. We found that these groups behave differently, depending on their dimerisation sequence dissimilarities, and that the two dimerisation sub-domains might play different roles.Conclusions: We propose here the first pipeline of statistical methods combining yeast-2-hybrid data and protein sequence dissimilarities for analysing protein-protein interactions. We unveil using this pipeline of analysis the transcriptional regulator interaction modes

    Data-driven Modeling of Building Consumption Profile for Optimal Flexibility: Application to Energy Intensive Industry

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    International audienceDespite the vast research on the flexibility of buildings consumption, current literature is more about predicting the impacts of energy flexibility than fo-cusing on its modeling. In this paper, a methodology is provided to go from data-driven modeling of the load consumption to an optimization problem with a Mixed-Integer Linear Programming (MILP) formulation. Illustrated on an Energy Intensive Industries (EII) with an economic point of view, the methodology is suitable for any consumption site, allowing optimal energy planning studies at the district scale. Thus, it facilitates the definition of flexibility strategies to exploit the complementary of uses of the districts