Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

In many fields of science, generalized likelihood ratio tests are established tools for statistical inference. At the same time, it has become increasingly common that a simulator (or generative model) is used to describe complex processes that tie parameters $\theta$ of an underlying theory and measurement apparatus to high-dimensional observations $\mathbf{x}\in \mathbb{R}^p$. However, simulator often do not provide a way to evaluate the likelihood function for a given observation $\mathbf{x}$, which motivates a new class of likelihood-free inference algorithms. In this paper, we show that likelihood ratios are invariant under a specific class of dimensionality reduction maps $\mathbb{R}^p \mapsto \mathbb{R}$. As a direct consequence, we show that discriminative classifiers can be used to approximate the generalized likelihood ratio statistic when only a generative model for the data is available. This leads to a new machine learning-based approach to likelihood-free inference that is complementary to Approximate Bayesian Computation, and which does not require a prior on the model parameters. Experimental results on artificial problems with known exact likelihoods illustrate the potential of the proposed method.Comment: 35 pages, 5 figure

Distributed Hypothesis Testing, Attention Shifts and Transmitter Dynatmics During the Self-Organization of Brain Recognition Codes

BP (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175, 90-0128); Army Research Office (DAAL-03-88-K0088

Sample positioning in neutron diffraction experiments using a multi-material fiducial marker

An alternative sample positioning method is reported for use in conjunction with sample positioning and experiment planning software systems deployed on some neutron diffraction strain scanners. In this approach, the spherical fiducial markers and location trackers used with optical metrology hardware are replaced with a specifically designed multi-material fiducial marker that requires one diffraction measurement. In a blind setting, the marker position can be determined within an accuracy of ±164 µm with respect to the instrument gauge volume. The scheme is based on a pre-determined relationship that links the diffracted peak intensity to the absolute positioning of the fiducial marker with respect to the instrument gauge volume. Two methods for establishing the linking relationship are presented, respectively based on fitting multi-dimensional quadratic functions and a cross-correlation artificial neural network

Changepoint Detection over Graphs with the Spectral Scan Statistic

We consider the change-point detection problem of deciding, based on noisy measurements, whether an unknown signal over a given graph is constant or is instead piecewise constant over two connected induced subgraphs of relatively low cut size. We analyze the corresponding generalized likelihood ratio (GLR) statistics and relate it to the problem of finding a sparsest cut in a graph. We develop a tractable relaxation of the GLR statistic based on the combinatorial Laplacian of the graph, which we call the spectral scan statistic, and analyze its properties. We show how its performance as a testing procedure depends directly on the spectrum of the graph, and use this result to explicitly derive its asymptotic properties on few significant graph topologies. Finally, we demonstrate both theoretically and by simulations that the spectral scan statistic can outperform naive testing procedures based on edge thresholding and $\chi^2$ testing

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Pilot fatigue detection using aircraft state variables

Pilot fatigue has been proven to be the cause of many aviation accidents. Fatigue introduces error into the pilot\u27s inputs, which can potentially lead to accidents. To date, fatigue has been widely researched through physiological variables and sleep studies. Often, systems monitoring physiological variables would require constant physical contact with the pilot during flight. This arrangement could be cumbersome to pilots, and may hinder their flying ability even more. These systems will also add unnecessary weight to the aircraft, which could lead to increases in fuel consumption. Sleep studies have been investigated in an attempt to determine causes of pilot fatigue based on the amount and quality of sleep they have received pre-flight, but they only serve for fatigue prevention purposes.;The main objective of this research effort is to show that separation between \u27rested\u27 and \u27tired\u27 pilot conditions can be put into evidence using parameters based on aircraft state and control variables and to design a fatigue detection scheme to determine the \u27on-line\u27 state of the pilot for a set of typical maneuvers.;Five pilots were instructed to fly a 6 degrees-of-freedom flight simulator through a given flight scenario under \u27rested\u27 and \u27tired\u27 conditions. State and control variables such as aircraft roll rate, angle of attack, elevator deflection, and others were recorded during flight. The desired values of these variables were determined depending on what maneuver the pilot was trying to accomplish. Steady state flight conditions and doublet inputs in still air and turbulence were considered in this study. Tracking errors were defined as the difference between the actual variable value and the desired value. Standard deviation and mean of the tracking errors were considered as candidate fatigue detectors and their performance was analyzed. The most promising detectors were then used to define composite detection parameters as weighted sums.;Two detection schemes were designed to determine the \u27rested\u27 or \u27tired\u27 state of the pilot based on comparing the composite parameter values to a threshold. The first scheme used heuristic and binary logic to define a series of rules hard coded through \u27if else\u27 statements capable of determining the pilot\u27s condition. The second detection scheme relied on fuzzy logic to make a \u27rested\u27 or \u27tired\u27 determination. Results showed that both schemes were capable of correctly classifying the condition of the pilot for many maneuvers. The detection schemes performed the best for the maneuvers performed in still air, but the detection rate was reduced when severe turbulence was present. A third approach of fatigue detection was investigated through implementation of a fuzzy neural network, and positive preliminary results deemed this method worthy of further exploration.;The analysis in this study presented compelling evidence that fatigue detection can be accomplished through the monitoring of aircraft state variables. Further research into using these detection schemes in conjunction with a flight compensation system may prove to be a viable, cost-effective intervention for reducing the number of accidents attributed to pilot fatigue