The RTA Betatron-Node Experiment: Limiting Cumulative BBU Growth In A Linear Periodic System

The successful operation of a Two-Beam accelerator based on extended relativistic klystrons hinges upon decreasing the cumulative dipole BBU growth from an exponential to a more manageable linear growth rate. We describe the theoretical scheme to achieve this, and a new experiment to test this concept. The experiment utilizes a 1-MeV, 600-Amp, 200-ns electron beam and a short beamline of periodically-spaced rf dipole-mode pillbox cavities and solenoid magnets for transport. Descriptions of the beamline are presented, followed by theoretical studies of the beam transport and dipole-mode growth.Comment: 3 pages, 3 figures. Submitted to XX Int'l. LINAC Conferenc

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table