On a curious bias arising when the 2/ scaling prescription is first applied to a sub-sample of the individual resultsAs it is well known, the standard deviation of a weighted average depends only on the individual standard deviations, but not on the dispersion of the values around the mean. This property leads…Giulio D'Agostini·Jan 17, 2020SaveLearn
Correlations between Background Radiation inside a Multilayer Interleaving Structure, Geomagnetic Activity, and Cosmic Radiation: A Fourth Order Cumulant-based Correlation AnalysisIn this work, we analyzed time-series of background radiation inside a multilayer interleaving structure, geomagnetic activity and cosmic-ray activity using the Pearson correlation coefficient and a…M. E. Iglesias-Martínez, J. C. Castro-Palacio, F. Scholkmann et al.·Jan 16, 2020SaveLearn
Fully Bayesian Unfolding with RegularizationFully Bayesian Unfolding differs from other unfolding methods by providing the full posterior probability of unfolded spectra for each bin. We extended the method for the feature of regularization…Petr Baron·Jan 15, 2020SaveLearn
Measures of spike train synchrony and directionalityMeasures of spike train synchrony have become important tools in both experimental and theoretical neuroscience. Three time-resolved measures called the ISI-distance, the SPIKE-distance, and…Eero Satuvuori, Irene Malvestio, Thomas Kreuz·Jan 13, 2020SaveLearn
ABCNet: An attention-based method for particle taggingIn high energy physics, graph-based implementations have the advantage of treating the input data sets in a similar way as they are collected by collider experiments. To expand on this concept, we…Vinicius Mikuni, Florencia Canelli·Jan 13, 2020SaveLearn
Spatial Deconvolution of Aerial Radiometric Survey and its application to the Fallout from a Radiological Dispersal DeviceMapping radioactive contamination using aerial survey measurements is an area under active investigation today. The radiometric aerial survey technique has been extensively applied following reactor…Laurel E. Sinclair, Richard Fortin·Jan 10, 2020SaveLearn
Skeptical combination of experimental results using JAGS/rjags with application to the K mass determinationThe question of how to combine experimental results that `appear' to be in mutual disagreement, treated in detail years ago in a previous paper, is revisited. The first novelty of the present note is…Giulio D'Agostini·Jan 9, 2020SaveLearn
A new approach on estimating the fluid temperature in a multiphase flow system using particle filter methodFluid temperature is important for the analysis of the heat transfers in thermal hydraulics. An accurate measurement or estimation of the fluid temperature in multiphase flows is challenging. This is…Zhuoran Dang·Jan 6, 2020SaveLearn
How neural networks find generalizable solutions: Self-tuned annealing in deep learningDespite the tremendous success of Stochastic Gradient Descent (SGD) algorithm in deep learning, little is known about how SGD finds generalizable solutions in the high-dimensional weight space. By…Yu Feng, Yuhai Tu·Jan 6, 2020SaveLearn
Intrinsic regularization effect in Bayesian nonlinear regression scaled by observed dataOccam's razor is a guiding principle that models should be simple enough to describe observed data. While Bayesian model selection (BMS) embodies it by the intrinsic regularization effect (IRE), how…Satoru Tokuda, Kenji Nagata, Masato Okada·Jan 5, 2020SaveLearn
Estimation of roughness measurement bias originating from background subtractionWhen measuring the roughness of rough surfaces, the limited sizes of scanned areas lead to its systematic underestimation. Levelling by polynomials and other filtering used in real-world processing…David Nečas, Petr Klapetek, Miroslav Valtr·Jan 2, 2020SaveLearn
SquidLab -- a user-friendly program for background subtraction and fitting of magnetization dataWe present an open-source program free to download for academic use with full user-friendly graphical interface for performing flexible and robust background subtraction and dipole fitting on…Matthew J. Coak, Cheng Liu, David M. Jarvis et al.·Jan 2, 2020SaveLearn
Machine learning technique to improve anti-neutrino detection efficiency for the ISMRAN experimentThe Indian Scintillator Matrix for Reactor Anti-Neutrino detection - ISMRAN experiment aims to detect electron anti-neutrinos (e) emitted from a reactor via inverse beta decay reaction…D. Mulmule, P. K. Netrakanti, L. M. Pant et al.·Jan 2, 2020SaveLearn
Interpretable Conservation Law Estimation by Deriving the Symmetries of Dynamics from Trained Deep Neural NetworksUnderstanding complex systems with their reduced model is one of the central roles in scientific activities. Although physics has greatly been developed with the physical insights of physicists, it…Yoh-ichi Mototake·Dec 31, 2019SaveLearn
Data blinding for the nEDM experiment at PSIPsychological bias towards, or away from, a prior measurement or a theory prediction is an intrinsic threat to any data analysis. While various methods can be used to avoid the bias, e.g. actively…N. J. Ayres, G. Ban, G. Bison et al.·Dec 19, 2019SaveLearn
Uncertainty Analysis of Stray Field Measurements by Quantitative Magnetic Force MicroscopyMagnetic force microscopy (MFM) measurements generally provide phase images which represent the signature of domain structures on the surface of nanomaterials. To quantitatively determine magnetic…Xiukun Hu, Gaoliang Dai, Sibylle Sievers et al.·Dec 18, 2019SaveLearn
Machine and Deep Learning Applications in Particle PhysicsThe many ways in which machine and deep learning are transforming the analysis and simulation of data in particle physics are reviewed. The main methods based on boosted decision trees and various…Dimitri Bourilkov·Dec 17, 2019SaveLearn
Human search in a fitness landscape: How to assess the difficulty of a search problemComputational modeling is widely used to study how humans and organizations search and solve problems in fields such as economics, management, cultural evolution, and computer science. We argue that…Oana Vuculescu, Mads Kock Pedersen, Jacob F. Sherson et al.·Dec 17, 2019SaveLearn
Rapid Identification of X-ray Diffraction Spectra Based on Very Limited Data by Interpretable Convolutional Neural NetworksLarge volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was…Hong Wang, Yunchao Xie, Dawei Li et al.·Dec 16, 2019SaveLearn
A deep neural network for simultaneous estimation of b jet energy and resolutionWe describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of s = 13 TeV at the CERN LHC.…CMS Collaboration·Dec 12, 2019SaveLearn
KLT Picker: Particle Picking Using Data-Driven Optimal TemplatesParticle picking is currently a critical step in the cryo-EM single particle reconstruction pipeline. Despite extensive work on this problem, for many data sets it is still challenging, especially…Amitay Eldar, Boris Landa, Yoel Shkolnisky·Dec 12, 2019SaveLearn
A Sheaf Theoretical Approach to Uncertainty Quantification of Heterogeneous Geolocation InformationIntegration of heterogeneous sensors is a challenging problem across a range of applications. Prominent among these are multi-target tracking, where one must combine observations from different…Cliff Joslyn, Lauren Charles, Chris DePerno et al.·Dec 11, 2019SaveLearn
Pore Network and Medial Axis simultaneous extraction through Maximal Ball AlgorithmNetwork Extraction algorithms from X-ray microcomputed tomography have become a routine method to obtain pore connectivity and pore morphology information from porous media. The main approaches for…Mariane Barsi-Andreeta, Everton Lucas-Oliveira, Arthur Gustavo de Araujo-Ferreira et al.·Dec 10, 2019SaveLearn
Gradient Profile Estimation Using Exponential Cubic Spline Smoothing in a Bayesian FrameworkAttaining reliable profile gradients is of utmost relevance for many physical systems. In most situations, the estimation of gradient can be inaccurate due to noise. It is common practice to first…Kushani De Silva, Carlo Cafaro, Adom Giffin·Dec 9, 2019SaveLearn
The Probabilistic Backbone of Data-Driven Complex Networks: An example in ClimateCorrelation Networks (CNs) inherently suffer from redundant information in their network topology. Bayesian Networks (BNs), on the other hand, include only non-redundant information (from a…Catharina Graafland, José M. Gutiérrez, Juan M. López et al.·Dec 8, 2019SaveLearn