Recent progress in the transition radiation detector techniques

A list of some of the major experimental achievements involving charged particles in the relativistic region are presented. With the emphasis mainly directed to the X-ray region, certain modes of application of the transition radiation for the identification and separation of relativistic charged particles are discussed. Some recent developments in detection techniques and improvements in detector performances are presented. Experiments were also carried out to detect the dynamic radiation, but no evidence of such an effect was observed

Maximum Likelihood Method for Predicting Environmental Conditions from Assemblage Composition: The R Package bio.infer

This paper provides a brief introduction to the R package bio.infer, a set of scripts that facilitates the use of maximum likelihood (ML) methods for predicting environmental conditions from assemblage composition. Environmental conditions can often be inferred from only biological data, and these inferences are useful when other sources of data are unavailable. ML prediction methods are statistically rigorous and applicable to a broader set of problems than more commonly used weighted averaging techniques. However, ML methods require a substantially greater investment of time to program algorithms and to perform computations. This package is designed to reduce the effort required to apply ML prediction methods.

Comment on 'Secure Communication using mesoscopic coherent states', Barbosa et al, Phys Rev Lett 90, 227901 (2003)

In a recent letter, Barbosa et al [PRL 90, 227901(2003)] claim that secure communication is possible with bright coherent pulses, by using quantum noise to hide the data from an eavesdropper. We show here that the secrecy in the scheme of Barbosa et al is unrelated to quantum noise, but rather derives from the secret key that sender and receiver share beforehand

Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning

The majority of current studies on autonomous vehicle control via deep reinforcement learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which includes acceleration delay and acceleration command dynamics. The acceleration delay, which results from sensing and actuation delays, results in delayed execution of the control inputs. The acceleration command dynamics dictates that the actual vehicle acceleration does not rise up to the desired command acceleration instantaneously due to dynamics. In this work, we investigate the feasibility of applying DRL controllers trained using vehicle kinematic models to more realistic driving control with vehicle dynamics. We consider a particular longitudinal car-following control, i.e., Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass kinematic model. When such a controller is applied to car following with vehicle dynamics, we observe significantly degraded car-following performance. Therefore, we redesign the DRL framework to accommodate the acceleration delay and acceleration command dynamics by adding the delayed control inputs and the actual vehicle acceleration to the reinforcement learning environment state, respectively. The training results show that the redesigned DRL controller results in near-optimal control performance of car following with vehicle dynamics considered when compared with dynamic programming solutions.Comment: Accepted to 2019 IEEE Intelligent Transportation Systems Conferenc