University of California, Irvine

About: University of California, Irvine is a education organization based out in Irvine, California, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 47031 authors who have published 113602 publications receiving 5521832 citations. The organization is also known as: UC Irvine & UCI.

Searching for exotic particles in high-energy physics with deep learning

Abstract: Collisions at high-energy particle colliders are a traditionally fruitful source of exotic particle discoveries. Finding these rare particles requires solving difficult signal-versus-background classification problems, hence machine-learning approaches are often used. Standard approaches have relied on 'shallow' machine-learning models that have a limited capacity to learn complex nonlinear functions of the inputs, and rely on a painstaking search through manually constructed nonlinear features. Progress on this problem has slowed, as a variety of techniques have shown equivalent performance. Recent advances in the field of deep learning make it possible to learn more complex functions and better discriminate between signal and background classes. Here, using benchmark data sets, we show that deep-learning methods need no manually constructed inputs and yet improve the classification metric by as much as 8% over the best current approaches. This demonstrates that deep-learning approaches can improve the power of collider searches for exotic particles.

Temperature and soil organic matter decomposition rates – synthesis of current knowledge and a way forward

Abstract: The response of soil organic matter (OM) decomposition to increasing temperature is a critical aspect of ecosystem responses to global change The impacts of climate warming on decomposition dynamics have not been resolved due to apparently contradictory results from field and lab experiments, most of which has focused on labile carbon with short turnover times But the majority of total soil carbon stocks are comprised of organic carbon with turnover times of decades to centuries Understanding the response of these carbon pools to climate change is essential for forecasting longer-term changes in soil carbon storage Herein, we briefly synthesize information from recent studies that have been conducted using a wide variety of approaches In our effort to understand research to-date, we derive a new conceptual model that explicitly identifies the processes controlling soil OM availability for decomposition and allows a more explicit description of the factors regulating OM decomposition under different circumstances It explicitly defines resistance of soil OM to decomposition as being due either to its chemical conformation (quality )o r its physico-chemical protection from decomposition The former is embodied in the depolymerization process, the latter by adsorption/desorption and aggregate turnover We hypothesize a strong role for variation in temperature sensitivity as a function of reaction rates for both We conclude that important advances in understanding the temperature response of the processes that control substrate availability, depolymerization, microbial efficiency, and enzyme production will be needed to predict the fate of soil carbon stocks in a warmer world

Beta-adrenergic activation and memory for emotional events.

Abstract: Substantial evidence from animal studies suggests that enhanced memory associated with emotional arousal results from an activation of beta-adrenergic stress hormone systems during and after an emotional experience. To examine this implication in human subjects, we investigated the effect of the beta-adrenergic receptor antagonist propranolol hydrochloride on long-term memory for an emotionally arousing short story, or a closely matched but more emotionally neutral story. We report here that propranolol significantly impaired memory of the emotionally arousing story but did not affect memory of the emotionally neutral story. The impairing effect of propranolol on memory of the emotional story was not due either to reduced emotional responsiveness or to nonspecific sedative or attentional effects. The results support the hypothesis that enhanced memory associated with emotional experiences involves activation of the beta-adrenergic system.

Learning Collaborative Information Filters

Abstract: Predicting items a user would like on the basis of other users’ ratings for these items has become a well-established strategy adopted by many recommendation services on the Internet. Although this can be seen as a classification problem, algorithms proposed thus far do not draw on results from the machine learning literature. We propose a representation for collaborative filtering tasks that allows the application of virtually any machine learning algorithm. We identify the shortcomings of current collaborative filtering techniques and propose the use of learning algorithms paired with feature extraction techniques that specifically address the limitations of previous approaches. Our best-performing algorithm is based on the singular value decomposition of an initial matrix of user ratings, exploiting latent structure that essentially eliminates the need for users to rate common items in order to become predictors for one another's preferences. We evaluate the proposed algorithm on a large database of user ratings for motion pictures and find that our approach significantly outperforms current collaborative filtering algorithms.