Applied Measure Theory for Probabilistic Modeling

Probabilistic programming and statistical computing are vibrant areas in the development of the Julia programming language, but the underlying infrastructure dramatically predates recent developments. The goal of MeasureTheory.jl is to provide Julia with the right vocabulary and tools for these tasks. In the package we introduce a well-chosen set of notions from the foundations of probability together with powerful combinators and transforms, giving a gentle introduction to the concepts in this article. The task is foremost achieved by recognizing measure as the central object. This enables us to develop a proper concept of densities as objects relating measures with each others. As densities provide local perspective on measures, they are the key to efficient implementations. The need to preserve this computationally so important locality leads to the new notion of locally-dominated measure solving the so-called base measure problem and making work with densities and distributions in Julia easier and more flexible

Applying Machine Learning to Neutron-Gamma Ray Discrimination from Scintillator Readout Using Wavelength Shifting Fibers

Advances in machine learning have found wide applications including radiation detection. In this work, machine learning is applied to neutron-gamma ray discrimination of an organic liquid scintillator (OLS) readout using wavelength shifting (WLS) fibers. The objective of using WLS fiber is to enable the transfer of the light signal from the scintillation medium, with almost any active volume geometry, to a low-profile photomultiplier. This is a common practice in high-energy physics research and has proven to be very effective for such applications. The drawback of this approach is the light pulses carried to the photomultiplier through the WLS fibers do not perfectly replicate the original OLS light pulses’ intensities or timing. This drawback causes traditional pulse shape discrimination algorithms applied to the degraded light pulses to fail to discriminate between neutron and gamma ray events. However, differences in the degraded light pulses for neutrons and gamma rays still exist and various machine learning algorithms can be applied to identify these differences. An experimental system was constructed to simultaneously capture part of the scintillation medium signal and the corresponding signal through the WLS fibers. Using the known neutron-gamma ray discrimination characteristics directly measured in the scintillation medium to provide the ground truth, supervised machine learning algorithms were applied to the corresponding light pulses carried to the photomultiplier through the WLS fibers. The results indicate that this approach will enable enhanced recovery of neutron-gamma ray discrimination information. This research effort will focus on two aspects of the OLS-WLS system: 1) developing an experimental system to create machine learning training data and 2) applying and evaluating various machine learning algorithms

Statistical Detection of Atypical Aircraft Flights

A computational method and software to implement the method have been developed to sift through vast quantities of digital flight data to alert human analysts to aircraft flights that are statistically atypical in ways that signify that safety may be adversely affected. On a typical day, there are tens of thousands of flights in the United States and several times that number throughout the world. Depending on the specific aircraft design, the volume of data collected by sensors and flight recorders can range from a few dozen to several thousand parameters per second during a flight. Whereas these data have long been utilized in investigating crashes, the present method is oriented toward helping to prevent crashes by enabling routine monitoring of flight operations to identify portions of flights that may be of interest with respect to safety issues

Hashing Strategies for the Cray XMT

Abstract—Two of the most commonly used hashing strategies—linear probing and hashing with chaining—are adapted for efficient execution on a Cray XMT. These strategies are designed to minimize memory contention. Datasets that follow a power law distribution cause significant performance challenges to shared memory parallel hashing implementations. Experimental results show good scalability up to 128 processors on two power law datasets with different data types: integer and string. These implementations can be used in a wide range of applications. I

TuringLang/AdvancedHMC.jl: v0.6.0

<h2>AdvancedHMC v0.6.0</h2> <p><a href="https://github.com/TuringLang/AdvancedHMC.jl/compare/v0.5.5...v0.6.0">Diff since v0.5.5</a></p> <p><strong>Merged pull requests:</strong></p> <ul> <li>fix: arg order (#349) (@xukai92)</li> <li>CompatHelper: bump compat for AbstractMCMC to 5, (keep existing compat) (#352) (@github-actions[bot])</li> <li>Deprecate <code>init_params</code> which is no longer in AbstractMCMC (#353) (@torfjelde)</li> <li>CompatHelper: add new compat entry for Statistics at version 1, (keep existing compat) (#354) (@github-actions[bot])</li> <li>Removed deprecation of init_params + bump minor version (#355) (@torfjelde)</li> <li>Fix some tests. (#356) (@yebai)</li> <li>Fix docs CI (#357) (@yebai)</li> </ul> <p><strong>Closed issues:</strong></p> <ul> <li>Doc string error for NUTS (#346)</li> </ul&gt