Understanding the boosted decision tree methods with the weak-learner approximationTwo popular boosted decsion tree (BDT) methods, Adaptive BDT (AdaBDT) and Gradient BDT (GradBDT) are studied in the classification problem of separating signal from background assuming all trees are…Li-Gang Xia·Nov 12, 2018SaveLearn
Gaining insight from large data volumes with easeEfficient handling of large data-volumes becomes a necessity in today's world. It is driven by the desire to get more insight from the data and to gain a better understanding of user trends which…Valentin Kuznetsov·Nov 9, 2018SaveLearn
Anomaly Detection in Paleoclimate Records using Permutation EntropyPermutation entropy techniques can be useful in identifying anomalies in paleoclimate data records, including noise, outliers, and post-processing issues. We demonstrate this using weighted and…Joshua Garland, Tyler R. Jones, Michael Neuder et al.·Nov 3, 2018SaveLearn
Unfolding with Gaussian ProcessesA method to perform unfolding with Gaussian processes (GPs) is presented. Using Bayesian regression, we define an estimator for the underlying truth distribution as the mode of the posterior. We show…Adam Bozson, Glen Cowan, Francesco Spanò·Nov 3, 2018SaveLearn
Beyond single-threshold searches: the Event Stacking TestWe present a new statistical test that examines the consistency of the tails of two empirical distributions at multiple thresholds. Such distributions are often encountered in counting experiments,…Ryan Lynch, Salvatore Vitale, Erik Katsavounidis·Nov 3, 2018SaveLearn
Manifold Learning for Organizing Unstructured Sets of Process ObservationsData mining is routinely used to organize ensembles of short temporal observations so as to reconstruct useful, low-dimensional realizations of an underlying dynamical system. In this paper, we use…Felix Dietrich, Mahdi Kooshkbaghi, Erik M. Bollt et al.·Oct 30, 2018SaveLearn
Discovering state-parameter mappings in subsurface models using generative adversarial networksA fundamental problem in geophysical modeling is related to the identification and approximation of causal structures among physical processes. However, resolving the bidirectional mappings between…Alexander Y. Sun·Oct 30, 2018SaveLearn
Managing Many Simultaneous Systematic UncertaintiesRecent statistical evaluations for High-Energy Physics measurements, in particular those at the Large Hadron Collider, require careful evaluation of many sources of systematic uncertainties at the…Luca Lista, Agostino De Iorio, Alberto Orso Maria Iorio·Oct 26, 2018SaveLearn
QBDT, a new boosting decision tree method with systematic uncertainties into training for High Energy PhysicsA new boosting decision tree (BDT) method, QBDT, is proposed for the classification problem in the field of high energy physics (HEP). In many HEP researches, great efforts are made to increase the…Li-Gang Xia·Oct 19, 2018SaveLearn
Recovery of Saturated γ Signal Waveforms by Artificial Neural NetworksParticle may sometimes have energy outside the range of radiation detection hardware so that the signal is saturated and useful information is lost. We have therefore investigated the possibility of…Yu Liu, Jing-Jun Zhu, Neil Roberts et al.·Oct 18, 2018SaveLearn
Bayesim: a tool for adaptive grid model fitting with Bayesian inferenceBayesian inference is a widely used and powerful analytical technique in fields such as astronomy and particle physics but has historically been underutilized in some other disciplines including…Rachel C. Kurchin, Giuseppe Romano, Tonio Buonassisi·Oct 10, 2018SaveLearn
The stepping-stone sampling algorithm for calculating the evidence of gravitational wave modelsBayesian statistical inference has become increasingly important for the analysis of observations from the Advanced LIGO and Advanced Virgo gravitational-wave detectors. To this end, iterative…Patricio Maturana Russel, Renate Meyer, John Veitch et al.·Oct 10, 2018SaveLearn
Detecting Directed Interactions of Networks by Random Variable ResettingWe propose a novel method of detecting directed interactions of a general dynamic network from measured data. By repeating random state variable resetting of a target node and appropriately averaging…Rundong Shi, Changbao Deng, Shihong Wang·Oct 10, 2018SaveLearn
Galerkin Approximation of Dynamical Quantities using Trajectory DataUnderstanding chemical mechanisms requires estimating dynamical statistics such as expected hitting times, reaction rates, and committors. Here, we present a general framework for calculating these…Erik H. Thiede, Dimitrios Giannakis, Aaron R. Dinner et al.·Oct 3, 2018SaveLearn
Three-Cornered Hat and Groslambert Covariance: A first attempt to assess the uncertainty domainsThe three-cornered hat method and the Groslambert Covariance are very often used to estimate the frequency stability of each individual oscillator in a set of three oscillators by comparing them in…François Vernotte, Éric Lantz·Oct 2, 2018SaveLearn
Biased bootstrap sampling for efficient two-sample testingThe so-called 'energy test' is a frequentist technique used in experimental particle physics to decide whether two samples are drawn from the same distribution. Its usage requires a good…Thomas P. S. Gillam, Christopher G. Lester·Sep 30, 2018SaveLearn
Contamination Source Detection in Water Distribution Networks using Belief PropagationWe present a Bayesian approach for the Contamination Source Detection problem in Water Distribution Networks. Given an observation of contaminants in one or more nodes in the network, we try to give…Ernesto Ortega, Alfredo Braunstein, Alejandro Lage-Castellanos·Sep 27, 2018SaveLearn
Inference of the Kinetic Ising Model with Heterogeneous Missing DataWe consider the problem of inferring a causality structure from multiple binary time series by using the Kinetic Ising Model in datasets where a fraction of observations is missing. We take our steps…Carlo Campajola, Fabrizio Lillo, Daniele Tantari·Sep 24, 2018SaveLearn
Analysis of Daily Streamflow Complexity by Kolmogorov Measures and Lyapunov ExponentAnalysis of daily streamflow variability in space and time is important for water resources planning, development, and management. The natural variability of streamflow is being complicated by…Dragutin T. Mihailović, Emilija Nikolić-Đorić, Ilija Arsenić et al.·Sep 23, 2018SaveLearn
ReSyst: a novel technique to Reduce the Systematic uncertainty for precision measurementsWe are in an era of precision measurements at the Large Hadron Collider. The precision that can be achieved on some of those is limited however due to large systematic uncertainties. This paper…Petra Van Mulders·Sep 17, 2018SaveLearn
DeepEfficiency - optimal efficiency inversion in higher dimensions at the LHCWe introduce a new high dimensional algorithm for efficiency corrected, maximally Monte Carlo event generator independent fiducial measurements at the LHC and beyond. The approach is driven…Mikael Mieskolainen·Sep 17, 2018SaveLearn
Statistical Models with Uncertain Error ParametersIn a statistical analysis in Particle Physics, nuisance parameters can be introduced to take into account various types of systematic uncertainties. The best estimate of such a parameter is often…Glen Cowan·Sep 15, 2018SaveLearn
Image registration and super resolution from first principlesImage registration is the inference of transformations relating noisy and distorted images. It is fundamental in computer vision, experimental physics, and medical imaging. Many algorithms and…Colin B. Clement, Matthew Bierbaum, James P. Sethna·Sep 14, 2018SaveLearn
On Micromechanical Parameter Identification With Integrated DIC and the Role of Accuracy in Kinematic Boundary ConditionsIntegrated Digital Image Correlation (IDIC) is nowadays a well established full-field experimental procedure for reliable and accurate identification of material parameters. It is based on the…O. Rokoš, J. P. M. Hoefnagels, R. H. J. Peerlings et al.·Sep 14, 2018SaveLearn
Effect of centrality bin width corrections on two-particle number and transverse momentum differential correlation functionsTwo-particle number and transverse momentum differential correlation functions are powerful tools for unveiling the detailed dynamics and particle production mechanisms involved in relativistic…Victor Gonzalez, Ana Marin, Pedro Ladron de Guevara et al.·Sep 12, 2018SaveLearn