Background Rejection in Atmospheric Cherenkov Telescopes using Recurrent Convolutional Neural Networks

In this work, we present a new, high performance algorithm for background rejection in imaging atmospheric Cherenkov telescopes. We build on the already popular machine-learning techniques used in gamma-ray astronomy by the application of the latest techniques in machine learning, namely recurrent and convolutional neural networks, to the background rejection problem. Use of these machine-learning techniques addresses some of the key challenges encountered in the currently implemented algorithms and helps to significantly increase the background rejection performance at all energies. We apply these machine learning techniques to the H.E.S.S. telescope array, first testing their performance on simulated data and then applying the analysis to two well known gamma-ray sources. With real observational data we find significantly improved performance over the current standard methods, with a 20-25\% reduction in the background rate when applying the recurrent neural network analysis. Importantly, we also find that the convolutional neural network results are strongly dependent on the sky brightness in the source region which has important implications for the future implementation of this method in Cherenkov telescope analysis.Comment: 11 pages, 7 figures. To be submitted to The European Physical Journal

A two-stage stochastic programming with recourse model for determining robust planting plans in horticulture

A two-stage stochastic programming with recourse model for the problem of determining optimal planting plans for a vegetable crop is presented in this paper. Uncertainty caused by factors such as weather on yields is a major influence on many systems arising in horticulture. Traditional linear programming models are generally unsatisfactory in dealing with the uncertainty and produce solutions that are considered to involve an unacceptable level of risk. The first stage of the model relates to finding a planting plan which is common to all scenarios and the second stage is concerned with deriving a harvesting schedule for each scenario. Solutions are obtained for a range of risk aversion factors that not only result in greater expected profit compared to the corresponding deterministic model, but also are more robust

Waveform distortion in an FM/FM telemetry system

Waveform distortion in FM/FM telemetry syste

Synthesis of empty bacterial microcompartments, directed organelle protein incorporation, and evidence of filament-associated organelle movement

Compartmentalization is an important process, since it allows the segregation of metabolic activities and, in the era of synthetic biology, represents an important tool by which defined microenvironments can be created for specific metabolic functions. Indeed, some bacteria make specialized proteinaceous metabolic compartments called bacterial microcompartments (BMCs) or metabolosomes. Here we demonstrate that the shell of the metabolosome (representing an empty BMC) can be produced within E. coil cells by the coordinated expression of genes encoding structural proteins. A plethora of diverse structures can be generated by changing the expression profile of these genes, including the formation of large axial filaments that interfere with septation. Fusing GFP to PduC, PduD, or PduV, none of which are shell proteins, allows regiospecific targeting of the reporter group to the empty BMC. Live cell imaging provides unexpected evidence of filament-associated BMC movement within the cell in the presence of Pdu

Self-reported pain severity is associated with a history of coronary heart disease

This study was funded by Arthritis Research UK (grant number: 17292).Peer reviewedPublisher PD