The effects of entry on incumbent innovation and productivity

How does firm entry affect innovation incentives and productivity growth in incumbent firms? Micro-data suggests that there is heterogeneity across industries--incumbents in technologically advanced industries react positively to foreign firm entry, but not in laggard industries. To explain this pattern, we introduce entry into a Schumpeterian growth model with multiple sectors which differ by their distance to the technological frontier. We show that technologically advanced entry threat spurs innovation incentives in sectors close to the technological frontier--successful innovation allows incumbents to prevent entry. In laggard sectors it discourages innovation--increased entry threat reduces incumbents' expected rents from innovating. We find that the empirical patterns hold using rich micro-level productivity growth and patent panel data for the UK, and controlling for the endogeneity of entry by exploiting the large number of policy reforms undertaken during the Thatcher era

Analysis of terrestrial conditions and dynamics

Land spectral reflectance properties for selected locations, including the Goddard Space Flight Center, the Wallops Flight Facility, a MLA test site in Cambridge, Maryland, and an acid test site in Burlington, Vermont, were measured. Methods to simulate the bidirectional reflectance properties of vegetated landscapes and a data base for spatial resolution were developed. North American vegetation patterns observed with the Advanced Very High Resolution Radiometer were assessed. Data and methods needed to model large-scale vegetation activity with remotely sensed observations and climate data were compiled

Modeling the Temporal Nature of Human Behavior for Demographics Prediction

Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce. Basic demographic information is however often absent from mobile phone datasets, limiting the operational impact of the datasets. For these reasons, there has been a growing interest in predicting demographic information from mobile phone metadata. Previous work focused on creating increasingly advanced features to be modeled with standard machine learning algorithms. We here instead model the raw mobile phone metadata directly using deep learning, exploiting the temporal nature of the patterns in the data. From high-level assumptions we design a data representation and convolutional network architecture for modeling patterns within a week. We then examine three strategies for aggregating patterns across weeks and show that our method reaches state-of-the-art accuracy on both age and gender prediction using only the temporal modality in mobile metadata. We finally validate our method on low activity users and evaluate the modeling assumptions.Comment: Accepted at ECML 2017. A previous version of this paper was titled 'Using Deep Learning to Predict Demographics from Mobile Phone Metadata' and was accepted at the ICLR 2016 worksho

The Effects of Entry on Incumbent Innovation and Productivity

How does firm entry affect innovation incentives and productivity growth in incumbent firms? Micro-data suggests that there is heterogeneity across industries--incumbents in technologically advanced industries react positively to foreign firm entry, but not in laggard industries. To explain this pattern, we introduce entry into a Schumpeterian growth model with multiple sectors which differ by their distance to the technological frontier. We show that technologically advanced entry threat spurs innovation incentives in sectors close to the technological frontier--successful innovation allows incumbents to prevent entry. In laggard sectors it discourages innovation--increased entry threat reduces incumbents' expected rents from innovating. We find that the empirical patterns hold using rich micro-level productivity growth and patent panel data for the UK, and controlling for the endogeneity of entry by exploiting the large number of policy reforms undertaken during the Thatcher era.

My heart is racing! Psychophysiological dynamics of skilled racecar drivers

Our purpose was to test the multi-action plan (MAP) model assumptions in which athletes’ psychophysiological patterns differ among optimal and suboptimal performance experiences. Nine professional drivers competing in premier race categories (e.g., Formula 3, Porsche GT3 Cup Challenge) completed the study. Data collection involved monitoring the drivers’ perceived hedonic tone, accuracy on core components of action, posture, skin temperature, respiration rate, and heart rate responses during a 40-lap simulated race. Time marks, gathered at three standardized sectors, served as the performance variable. The A1GP racing simulator (Allinsport, Modena) established a realistic race platform. Specifically, the Barcelona track was chosen due to its inherently difficult nature characterized by intermittent deceleration points. Idiosyncratic analyses showed large individual differences in the drivers’ psychophysiological profile, as well as distinct patterns in regards to optimal and suboptimal performance experiences. Limitations and future research avenues are discussed. Action (e.g., attentional control) and emotion (e.g., biofeedback training) centered applied sport psychology implications are advanced

Event Recognition Using Signal Spectrograms in Long Pulse Experiments

As discharge duration increases, real-time complex analysis of the signal becomes more important. In this context, data acquisition and processing systems must provide models for designing experiments which use event oriented plasma control. One example of advanced data analysis is signal classification. The off-line statistical analysis of a large number of discharges provides information to develop algorithms for the determination of the plasma parameters from measurements of magnetohydrodinamic waves, for example, to detect density fluctuations induced by the Alfvén cascades using morphological patterns. The need to apply different algorithms to the signals and to address different processing algorithms using the previous results necessitates the use of an event-based experiment. The Intelligent Test and Measurement System platform is an example of architecture designed to implement distributed data acquisition and real-time processing systems. The processing algorithm sequence is modeled using an event-based paradigm. The adaptive capacity of this model is based on the logic defined by the use of state machines in SCXML. The Intelligent Test and Measurement System platform mixes a local multiprocessing model with a distributed deployment of services based on Jini