The effect of microaggregation on regression results: an application to Spanish innovation data

Microaggregation is a technique for masking confidential data by aggregation. The aim of this paper is to analyze the extent to which microaggregated data can be used for rigorous empirical research. In doing this, I adopt an empirical perspective. I use data from the Technological Innovation Panel (PITEC) and compare regression results using both original and anonymized data. PITEC is a new firm-level panel data base for innovative activities of Spanish firms based on CIS data. I find that the microaggregation procedure used has a slight effect on the coefficient estimates and their estimated standard errors, especially when estimating linear models.Microaggregation; Individual ranking; Bias; Innovation data

Berkeley's Three dialogues

A review from the book: Stefan Storrie (ed.) (2018). Berkeley’s Three Dialogues: New Essays. Oxford (UK): Oxford University Press. 240 pp

A new algorithm for computing branching rules and Clebsch-Gordan coefficients of unitary representations of compact groups

A numerical algorithm that computes the decomposition of any finite-dimen\-sio\-nal unitary reducible representation of a compact Lie group is presented. The algorithm, which does not rely on an algebraic insight on the group structure, is inspired by quantum mechanical notions. After generating two adapted states (these objects will be conveniently defined in {\bf Def.\,II.1}) and after appropriate algebraic manipulations, the algorithm returns the block matrix structure of the representation in terms of its irreducible components. It also provides an adapted orthonormal basis. The algorithm can be used to compute the Clebsch--Gordan coefficients of the tensor product of irreducible representations of a given compact Lie group. The performance of the algorithm is tested on various examples: the decomposition of the regular representation of two finite groups and the computation of Clebsch--Gordan coefficients of two examples of tensor products of representations of $SU(2)$.Comment: Updated paper. arXiv admin note: text overlap with arXiv:1512.0824

Improvement of the IT-PES-PS Section Services Statistics Page

Project Specification: The IT-PES-PS service managers gather a lot of statistics for the services they run. These statistics are currently displayed by SLS (Service Level Status) or Lemon pages. They also use the Web interface provided with OpenTSDB, a DB optimised for time series. And while these various pages give very useful and technical information, they do not always emphasise the important figures. Having different statistics pages makes it difficult to see the relevant numbers at once. The goal of this project was to build homogeneous dashboards with interactive plots to better reflect the activity and resources of each service, showing relevant figures at first sight in a single website. Abstract: There is a need in the IT-PES-PS section to improve the current situation of having to consult the relevant information from several heterogeneous websites by introducing interactive homogeneous dashboards, accessible from a single web application where the information needed can be quickly accessed. The goal of this openlab Summer Student project was to create a website with homogeneous and interactive dashboards. Its architecture had to allow the creation of new dashboards easily

Learning the hidden human knowledge of UAV pilots when navigating in a cluttered environment for improving path planning

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksWe propose in this work a new model of how the hidden human knowledge (HHK) of UAV pilots can be incorporated in the UAVs path planning generation. We intuitively know that human’s pilots barely manage or even attempt to drive the UAV through a path that is optimal attending to some criteria as an optimal planner would suggest. Although human pilots might get close but not reach the optimal path proposed by some planner that optimizes over time or distance, the final effect of this differentiation could be not only surprisingly better, but also desirable. In the best scenario for optimality, the path that human pilots generate would deviate from the optimal path as much as the hidden knowledge that its perceives is injected into the path. The aim of our work is to use real human pilot paths to learn the hidden knowledge using repulsion fields and to incorporate this knowledge afterwards in the environment obstacles as cause of the deviation from optimality. We present a strategy of learning this knowledge based on attractor and repulsors, the learning method and a modified RRT* that can use this knowledge for path planning. Finally we do real-life tests and we compare the resulting paths with and without this knowledge.Accepted versio