Rewarding Innovation: Improving Federal Tax Support for Business R&D in Canada

Business innovation is viewed by many as a solution to Canada’s ailing productivity performance. One of the more troubling aspects of Canada’s innovation track record is that businesses spend relatively little on research and development (R&D) despite having access to some of the world’s most generous R&D tax incentives. Canada’s low levels of business R&D have called into question the effectiveness of Canada’s generous R&D tax incentives, particularly the flagship federal Scientific Research and Experimental Development (SR&ED) program. A deeper analysis, however, reveals that tax incentives are effective in stimulating more R&D – that is, Canada would have lower levels of business R&D in the absence of these inducements. Instead, the root cause of Canada’s business R&D deficit appears to stem from structural aspects of the economy and, more importantly, a lack of demand-related pressure to pursue innovation.Fiscal and Tax Competitiveness, Canada, research and development (R&D) incentives, Scientific Research and Experimental Development (SR&ED) program

Competing edge networks

We introduce a model for a pair of nonlinear evolving networks, defined over a common set of vertices, sub ject to edgewise competition. Each network may grow new edges spontaneously or through triad closure. Both networks inhibit the other’s growth and encourage the other’s demise. These nonlinear stochastic competition equations yield to a mean field analysis resulting in a nonlinear deterministic system. There may be multiple equilibria; and bifurcations of different types are shown to occur within a reduced parameter space. This situation models competitive peer-to-peer communication networks such as BlackBerry Messenger displacing SMS; or instant messaging displacing emails

Response by Bivard et al to letter regarding article, "Impact of Computed Tomography Perfusion Imaging on the Response to Tenecteplase in Ischemic Stroke: Analysis of 2 Randomized Controlled Trials"

No abstract available

Knowing me, knowing you: perspectives on awareness in autism

Purpose: This paper raises important questions from the different perspectives on autism research that arose from a seminar on autism and technology, held as part of an ESRC-funded series on innovative technologies for autism. Design/methodology/approach: The paper focuses on the roles of technology in understanding questions about different perspectives on autism: how do people on the spectrum see neurotypicals (people without autism) and vice versa?; how do we use eye-gaze differently from each other?; how might technology influence what is looked at and how we measure this?; what differences might there be in how people use imitation of others?; and finally, how should we study and treat any differences? Findings: We synthesise common themes from invited talks and responses. The audience discussions highlighted the ways in which we take account of human variation, how we can understand the perspective of another, particularly across third-person and second-person approaches in research, and how researchers and stakeholders engage with each other. Originality/value: We argue that the question of perspectives is important for considering how people with autism and neurotypical people interact in everyday contexts, and how researchers frame their research questions and methods. We propose that stakeholders and researchers can fruitfully engage directly in discussions of research, in ways that benefit both research and practice

Bistability through triadic closure

We propose and analyse a class of evolving network models suitable for describing a dynamic topological structure. Applications include telecommunication, on-line social behaviour and information processing in neuroscience. We model the evolving network as a discrete time Markov chain, and study a very general framework where, conditioned on the current state, edges appear or disappear independently at the next timestep. We show how to exploit symmetries in the microscopic, localized rules in order to obtain conjugate classes of random graphs that simplify analysis and calibration of a model. Further, we develop a mean field theory for describing network evolution. For a simple but realistic scenario incorporating the triadic closure effect that has been empirically observed by social scientists (friends of friends tend to become friends), the mean field theory predicts bistable dynamics, and computational results confirm this prediction. We also discuss the calibration issue for a set of real cell phone data, and find support for a stratified model, where individuals are assigned to one of two distinct groups having different within-group and across-group dynamics

Science Bots: a Model for the Future of Scientific Computation?

As a response to the trends of the increasing importance of computational approaches and the accelerating pace in science, I propose in this position paper to establish the concept of "science bots" that autonomously perform programmed tasks on input data they encounter and immediately publish the results. We can let such bots participate in a reputation system together with human users, meaning that bots and humans get positive or negative feedback by other participants. Positive reputation given to these bots would also shine on their owners, motivating them to contribute to this system, while negative reputation will allow us to filter out low-quality data, which is inevitable in an open and decentralized system.Comment: WWW 2015 Companion, May 18-22, 2015, Florence, Ital