Globalization of R&D: Leveraging Offshoring for Innovative Capability and Organizational Flexibility

Within the realm of globalization of R&D, offshoring is a relatively recent and still emerging phenomenon. Rooted in the notion of comparative advantage, offshoring of R&D involves disaggregation and global distribution of the firm’s R&D value chain activities to leverage innovation capacity of low-cost countries. Characteristically different from market- and technology-seeking globalization of R&D, offshoring is motivated by the intertwining competitive needs to gain efficiency and access knowledge resources. This study represents a systematic, grounds-up attempt to explore the terrain of the phenomenon of offshoring of R&D and its influence on the competitive advantage of firms. Specifically, going beyond structural cost savings, the research examines the link between offshoring of R&D and the firm’s innovative capability and organizational flexibility—the two most important organizational capabilities of high technology firms. Employing an interpretive approach, the research includes multiple case studies of intra-firm and inter-firm offshoring of software R&D across a range of industries. The study demonstrates that by strategically organizing and managing offshoring of R&D, firms can significantly enhance their innovative capability and organizational flexibility. The findings suggest that offshoring of R&D is a new global organizational form that not only serves as an adaptive device but also allows firms to achieve ambidexterity

Parallel software tools at Langley Research Center

This document gives a brief overview of parallel software tools available on the Intel iPSC/860 parallel computer at Langley Research Center. It is intended to provide a source of information that is somewhat more concise than vendor-supplied material on the purpose and use of various tools. Each of the chapters on tools is organized in a similar manner covering an overview of the functionality, access information, how to effectively use the tool, observations about the tool and how it compares to similar software, known problems or shortfalls with the software, and reference documentation. It is primarily intended for users of the iPSC/860 at Langley Research Center and is appropriate for both the experienced and novice user

On the role of a new type of correlated disorder in extended electronic states in the Thue-Morse lattice

A new type of correlated disorder is shown to be responsible for the appearance of extended electronic states in one-dimensional aperiodic systems like the Thue-Morse lattice. Our analysis leads to an understanding of the underlying reason for the extended states in this system, for which only numerical evidence is available in the literature so far. The present work also sheds light on the restrictive conditions under which the extended states are supported by this lattice.Comment: 11 pages, LaTeX V2.09, 1 figure (available on request), to appear in Physical Review Letter

Learning to Fix Build Errors with Graph2Diff Neural Networks

Professional software developers spend a significant amount of time fixing builds, but this has received little attention as a problem in automatic program repair. We present a new deep learning architecture, called Graph2Diff, for automatically localizing and fixing build errors. We represent source code, build configuration files, and compiler diagnostic messages as a graph, and then use a Graph Neural Network model to predict a diff. A diff specifies how to modify the code’s abstract syntax tree, represented in the neural network as a sequence of tokens and of pointers to code locations. Our network is an instance of a more general abstraction which we call Graph2Tocopo, which is potentially useful in any development tool for predicting source code changes. We evaluate the model on a dataset of over 500k real build errors and their resolutions from professional developers. Compared to the approach of DeepDelta [23], our approach tackles the harder task of predicting a more precise diff but still achieves over double the accuracy

Learning to Fix Build Errors with Graph2Diff Neural Networks

Professional software developers spend a significant amount of time fixing builds, but this has received little attention as a problem in automatic program repair. We present a new deep learning architecture, called Graph2Diff, for automatically localizing and fixing build errors. We represent source code, build configuration files, and compiler diagnostic messages as a graph, and then use a Graph Neural Network model to predict a diff. A diff specifies how to modify the code’s abstract syntax tree, represented in the neural network as a sequence of tokens and of pointers to code locations. Our network is an instance of a more general abstraction which we call Graph2Tocopo, which is potentially useful in any development tool for predicting source code changes. We evaluate the model on a dataset of over 500k real build errors and their resolutions from professional developers. Compared to the approach of DeepDelta [23], our approach tackles the harder task of predicting a more precise diff but still achieves over double the accuracy

The assessment of present-moment awareness and acceptance: the Philadelphia Mindfulness Scale

AssessmentThe purpose of this project was to develop a bi-dimensional measure of mindfulness to assess its two key components: present-moment awareness and acceptance. The development and psychometric validation of the Philadelphia Mindfulness Scale (PHLMS) is described, and data are reported from expert raters, two nonclinical samples (n = 204 and 559), and three clinical samples including mixed psychiatric outpatients (n = 52), eating disorder inpatients (n = 30), and student counseling center outpatients (n = 78). Exploratory and confirmatory factor analyses support a two-factor solution, corresponding to the two constituent components of the construct. Good internal consistency was demonstrated, and relationships with other constructs were largely as expected. As predicted, significant differences were found between the nonclinical and clinical samples in levels of awareness and acceptance. The awareness and acceptance subscales were not correlated, suggesting that these two constructs can be examined independently. Potential theoretical and applied uses of the measure are discussed

Extended states in 1D lattices: application to quasiperiodic copper-mean chain

The question of the conditions under which 1D systems support extended electronic eigenstates is addressed in a very general context. Using real space renormalisation group arguments we discuss the precise criteria for determining the entire spertrum of extended eigenstates and the corresponding eigenfunctions in disordered as well as quasiperiodic systems. For purposes of illustration we calculate a few selected eigenvalues and the corresponding extended eigenfunctions for the quasiperiodic copper-mean chain. So far, for the infinite copper-mean chain, only a single energy has been numerically shown to support an extended eigenstate [ You et al. (1991)] : we show analytically that there is in fact an infinite number of extended eigenstates in this lattice which form fragmented minibands.Comment: 10 pages + 2 figures available on request; LaTeX version 2.0