Exploring compression techniques for ROOT IO

ROOT provides an flexible format used throughout the HEP community. The number of use cases - from an archival data format to end-stage analysis - has required a number of tradeoffs to be exposed to the user. For example, a high "compression level" in the traditional DEFLATE algorithm will result in a smaller file (saving disk space) at the cost of slower decompression (costing CPU time when read). At the scale of the LHC experiment, poor design choices can result in terabytes of wasted space or wasted CPU time. We explore and attempt to quantify some of these tradeoffs. Specifically, we explore: the use of alternate compressing algorithms to optimize for read performance; an alternate method of compressing individual events to allow efficient random access; and a new approach to whole-file compression. Quantitative results are given, as well as guidance on how to make compression decisions for different use cases.Comment: Proceedings for 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016

The Goldman symplectic form on the PGL(V)-Hitchin component

This article is the second of a pair of articles about the Goldman symplectic form on the PGL(V )-Hitchin component. We show that any ideal triangulation on a closed connected surface of genus at least 2, and any compatible bridge system determine a symplectic trivialization of the tangent bundle to the Hitchin component. Using this, we prove that a large class of flows defined in the companion paper [SWZ17] are Hamiltonian. We also construct an explicit collection of Hamiltonian vector fields on the Hitchin component that give a symplectic basis at every point. These are used to show that the global coordinate system on the Hitchin component defined iin the companion paper is a global Darboux coordinate system.Comment: 95 pages, 24 figures, Citations update

A Hybrid Neural Network Framework and Application to Radar Automatic Target Recognition

Deep neural networks (DNNs) have found applications in diverse signal processing (SP) problems. Most efforts either directly adopt the DNN as a black-box approach to perform certain SP tasks without taking into account of any known properties of the signal models, or insert a pre-defined SP operator into a DNN as an add-on data processing stage. This paper presents a novel hybrid-NN framework in which one or more SP layers are inserted into the DNN architecture in a coherent manner to enhance the network capability and efficiency in feature extraction. These SP layers are properly designed to make good use of the available models and properties of the data. The network training algorithm of hybrid-NN is designed to actively involve the SP layers in the learning goal, by simultaneously optimizing both the weights of the DNN and the unknown tuning parameters of the SP operators. The proposed hybrid-NN is tested on a radar automatic target recognition (ATR) problem. It achieves high validation accuracy of 96\% with 5,000 training images in radar ATR. Compared with ordinary DNN, hybrid-NN can markedly reduce the required amount of training data and improve the learning performance