University of Notre Dame

About: University of Notre Dame is a education organization based out in Notre Dame, Indiana, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 22238 authors who have published 55201 publications receiving 2032925 citations. The organization is also known as: University of Notre Dame du Lac & University of Notre Dame, South Bend.

Scenarios of freshwater fish extinctions from climate change and water withdrawal

Abstract: Reductions in river discharge (water availability) like those from climate change or increased water withdrawal, reduce freshwater biodiversity. We combined two scenarios from the Intergovernmental Panel for Climate Change with a global hydrological model to build global scenarios of future losses in river discharge from climate change and increased water withdrawal. Applying these results to known relationships between fish species and discharge, we build scenarios of losses (at equilibrium) of riverine fish richness. In rivers with reduced discharge, up to 75% (quartile range 4–22%) of local fish biodiversity would be headed toward extinction by 2070 because of combined changes in climate and water consumption. Fish loss in the scenarios fell disproportionately on poor countries. Reductions in water consumption could prevent many of the extinctions in these scenarios.

Neural networks for control systems

Abstract: A description is given of 11 papers from the April 1990 special issue on neural networks in control systems of IEEE Control Systems Magazine. The emphasis was on presenting as varied and current a picture as possible of the use of neural networks in control. The papers described cover: the design of associative memories using feedback neural networks; a method to use neural networks to control highly nonlinear systems; the modeling of nonlinear chemical systems using neural networks; the identification of dynamical systems; the comparison of conventional adaptive controllers and neural-network-based controllers; a method to provide adaptive control for nonlinear systems; neural networks and back-propagation; the back-propagation algorithm; the use of trained neural networks to regulate the pitch attitude of an underwater telerobot; the control of mobile robots; and the issues involved in integrating neural networks and knowledge-based systems. >

Building solidarity through generalized exchange: A theory of reciprocity

Abstract: Despite the prevalence of generalized exchange in social life, few studies have investigated the classic anthropological prediction that generalized (indirect) exchange produces greater social solidarity than restricted (direct) exchange. Building on recent work comparing negotiated and reciprocal forms of direct exchange, the authors develop a theory of reciprocity in exchange. The theory argues that two structural characteristics of reciprocity distinguish among all three forms of exchange and affect the emergence of social solidarity through three causal mechanisms. Experimental results provide strong support for the causal logic of the theory and for the predicted ordering of subjective dimensions of solidarity across the three forms of exchange, with generalized indirect exchange > reciprocal direct exchange > negotiated direct exchange.

Pseudo Market Timing and the Long-Run Underperformance of IPOs

Abstract: Numerous studies document long-run underperformance by firms following equity offerings. This paper shows that underperformance is very likely to be observed ex-post in an efficient market. The premise is that more firms issue equity at higher stock prices even though they cannot predict future returns. Ex-post, issuers seem to time the market because offerings cluster at market peaks. Simulations based on 1973 through 1997 data reveal that when ex-ante expected abnormal returns are zero, median ex-post underperformance for equity issuers will be significantly negative in event-time. Using calendar-time returns solves the problem.

Burstiness and memory in complex systems

Abstract: The dynamics of a wide range of real systems, from email patterns to earthquakes, display a bursty, intermittent nature, characterized by short timeframes of intense activity followed by long times of no or reduced activity. The understanding of the origin of such bursty patterns is hindered by the lack of tools to compare different systems using a common framework. Here we propose to characterize the bursty nature of real signals using orthogonal measures quantifying two distinct mechanisms leading to burstiness: the interevent time distribution and the memory. We find that while the burstiness of natural phenomena is rooted in both the interevent time distribution and memory, for human dynamics memory is weak, and the bursty character is due to the changes in the interevent time distribution. Finally, we show that current models lack in their ability to reproduce the activity pattern observed in real systems, opening up avenues for future work. Copyright c �EPLA, 2008