Localized Photonic jets from flat 3D dielectric cuboids in the reflection mode

A photonic jet (terajet at THz frequencies) commonly denotes a specific spatially localized region in the near-field at the front side of a dielectric particle with diameter comparable with wavelength illuminated with a plane wave from its backside (i.e., the jet emerges from the shadow surface of a dielectric particle). In this paper the formation of photonic is demonstrated using the recently proposed 3D dielectric cuboids working in reflection mode when the specific spatially localized region is localized towards the direction of incidence wavefront. The results of simulations based on Finite Integration Technique are discussed. All dimensions are given in wavelength units so that all results can be scaled any frequency of interest including optical frequencies, simplifying the fabrication process compared with spherical dielectrics. The results here presented may be of interest for novel applications including microscopy techniques and sensors.Comment: 5 page

Regions in the global knowledge economy

Two bodies of literature converge to explain regions in the global knowledge economy and to identify the factors that lead to competitiveness and innovation of a local economic system. The first section of this statement summarizes the progress in regional studies from a purely locational approach to the focus on clusters and industrial districts. The second part shows how advances in the economics of innovation lead to a renewed and different interest to regions and local systems of innovation. The third section concludes showing how the two trends of the literature just mentioned are instrumental to explain regions in a context where competition becomes global and increasingly based on knowledge goods and services. The focus on the “glocal” exchange of outputs of the knowledge economy is useful to explain the factors behind the rise and fall of new centers of production and growth. In this statement glocalization is defined as the phenomenon that leads to the competition, on a global market, of products and services whose successful development from the conceptualization of an idea to the actual commercial application requires enabling factors (such as institutions, entrepreneurship, knowledge, skills…) that are embedded in a specific local environment. The study of this phenomenon justifies the convergence of regional economics and the economics of innovation. The goal of this statement is to present the literature which might be used in two classes on regional development in the knowledge economy and glocalization of production, that could be taught in a planning, business or public policy department.

Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing

Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring a large integration step to impute over all possible hidden events. Recent work has successfully applied generating function techniques to computing transition probabilities for linear multitype branching processes. While these techniques often require significantly fewer computations than matrix exponentiation, they also become prohibitive in applications with large populations. We propose a compressed sensing framework that significantly accelerates the generating function method, decreasing computational cost up to a logarithmic factor by only assuming the probability mass of transitions is sparse. We demonstrate accurate and efficient transition probability computations in branching process models for hematopoiesis and transposable element evolution.Comment: 18 pages, 4 figures, 2 table

Phylogenetic Stochastic Mapping without Matrix Exponentiation

Phylogenetic stochastic mapping is a method for reconstructing the history of trait changes on a phylogenetic tree relating species/organisms carrying the trait. State-of-the-art methods assume that the trait evolves according to a continuous-time Markov chain (CTMC) and work well for small state spaces. The computations slow down considerably for larger state spaces (e.g. space of codons), because current methodology relies on exponentiating CTMC infinitesimal rate matrices -- an operation whose computational complexity grows as the size of the CTMC state space cubed. In this work, we introduce a new approach, based on a CTMC technique called uniformization, that does not use matrix exponentiation for phylogenetic stochastic mapping. Our method is based on a new Markov chain Monte Carlo (MCMC) algorithm that targets the distribution of trait histories conditional on the trait data observed at the tips of the tree. The computational complexity of our MCMC method grows as the size of the CTMC state space squared. Moreover, in contrast to competing matrix exponentiation methods, if the rate matrix is sparse, we can leverage this sparsity and increase the computational efficiency of our algorithm further. Using simulated data, we illustrate advantages of our MCMC algorithm and investigate how large the state space needs to be for our method to outperform matrix exponentiation approaches. We show that even on the moderately large state space of codons our MCMC method can be significantly faster than currently used matrix exponentiation methods.Comment: 33 pages, including appendice

Locally adaptive smoothing with Markov random fields and shrinkage priors

We present a locally adaptive nonparametric curve fitting method that operates within a fully Bayesian framework. This method uses shrinkage priors to induce sparsity in order-k differences in the latent trend function, providing a combination of local adaptation and global control. Using a scale mixture of normals representation of shrinkage priors, we make explicit connections between our method and kth order Gaussian Markov random field smoothing. We call the resulting processes shrinkage prior Markov random fields (SPMRFs). We use Hamiltonian Monte Carlo to approximate the posterior distribution of model parameters because this method provides superior performance in the presence of the high dimensionality and strong parameter correlations exhibited by our models. We compare the performance of three prior formulations using simulated data and find the horseshoe prior provides the best compromise between bias and precision. We apply SPMRF models to two benchmark data examples frequently used to test nonparametric methods. We find that this method is flexible enough to accommodate a variety of data generating models and offers the adaptive properties and computational tractability to make it a useful addition to the Bayesian nonparametric toolbox.Comment: 38 pages, to appear in Bayesian Analysi

Why is strategic R&D (still) homebound in a globalized industry? The case of leading firms in wireless telecom

This paper looks at internationalization of R&D in the wireless telecommunications industry. We compare the international distribution of strategic R&D activities related to the development of wireless standards to other (non standard related) projects. While there is evidence that leading companies in this industry are sourcing globally their know how, still more strategic R&D projects remain homebound. This finding is further elaborated through conversations with R&D and IP managers at Ericsson, Motorola, Nokia, and Qualcomm. These semi-structured interviews suggested that a closer look at the internationalization of R&D investment requires scholars to consider maturation and decentralization of R&D and R&D management.

rbrothers: R Package for Bayesian Multiple Change-Point Recombination Detection.

Phylogenetic recombination detection is a fundamental task in bioinformatics and evolutionary biology. Most of the computational tools developed to attack this important problem are not integrated into the growing suite of R packages for statistical analysis of molecular sequences. Here, we present an R package, rbrothers, that makes a Bayesian multiple change-point model, one of the most sophisticated model-based phylogenetic recombination tools, available to R users. Moreover, we equip the Bayesian change-point model with a set of pre- and post- processing routines that will broaden the application domain of this recombination detection framework. Specifically, we implement an algorithm that forms the set of input trees required by multiple change-point models. We also provide functionality for checking Markov chain Monte Carlo convergence and creating estimation result summaries and graphics. Using rbrothers, we perform a comparative analysis of two Salmonella enterica genes, fimA and fimH, that encode major and adhesive subunits of the type 1 fimbriae, respectively. We believe that rbrothers, available at R-Forge: http://evolmod.r-forge.r-project.org/, will allow researchers to incorporate recombination detection into phylogenetic workflows already implemented in R

Through the eyes of industrial researchers: how new “Connect & Develop” practices change the role of human resources in the lab

An intense debate is going on about more “open” strategies that are supposedly diffusing in industrial R&D. We here discuss the relationship between such practices and Human Resources Management (HRM) in industrial R&D Labs. The paper in fact aims at representing an original attempt of looking at the linkage between R&D strategy and HRM in some Italian high-tech firms. In particular, we identify, select and discuss a set of variables related to the management of HR in R&D that fit with the reconceptualization of innovation proposed by Chesbrough in the “Open Innovation” (OI) paradigm and inspired by the example of P&G’s model of Connect and Develop (C&D). More precisely, our objective is that of investigating the role of HRM in the shift towards “Open Innovation” through the bottom-up lenses of industrial researchers’ characteristics, feelings and behaviours. What we here suggest is that by observing behaviour and expectations of R&D workers, we can investigate the acceptance and implementation of new R&D management practices. Our empirical base is represented by 330 questionnaires completed by R&D personnel and collected through an online survey. The results have been discussed with the HR managers of each company, in order to also gain a “top-down” perspective on the observed dynamics. The research is carried out around three main groups of issues: HR characteristics (e.g., demographic parameters, productivity, time horizons, satisfaction, expectations, mobility, education), job organization aspects (e.g., teamwork vs. individual research, flexibility, decisional centres, work time allocation, type of relationships, communication flows), and HRM tools (e.g., talent attraction, training, evaluation methods, goal definition, roles, leadership, responsibility, incentives, career systems, problem sources). According to Chesbrough, firms fitting the OI model present characteristics related to the R&D structure itself. Nonetheless, even if this model has been widely enthusiastically discussed and sometimes criticized by both practitioners and researchers, we still lack a comprehensive understanding of how such changes effect dynamics and daily operations of an R&D lab. Our empirical analysis ultimately aims at understanding to what extent the shift towards an extended definition of R&D, which includes the new concept of C&D, can be considered as one of the main potential factors of change in HR organization. Beyond the relevance of our findings for the debate among scholars, we argue that managerial implications may derive from a better knowledge of individual perceptions and behaviours of R&D personnel. In fact, the changing pattern of innovation processes implies parallel changes in the organization of R&D labs, where the role of the most important component, i. e. researchers themselves, is not always adequately considered. This paper is a first attempt to explore these relationships. Through a convenience sample we first attempted to test various strategies to best collect data, provide timely valuable feedbacks to our industrial partners and better define our framework, matching early results with existing theories. Further research will aim at making the sample representative of the Italian industrial R&D system.Open Innovation Human Resources Management