Workload modeling using time windows and utilization in an air traffic control task

In this paper, we show how to assess human workload for continuous tasks and describe how operator performance is affected by variations in break-work intervals and by different utilizations. A study was conducted examining the effects of different break-work intervals and utilization as a factor in a mental workload model. We investigated the impact of operator performance on operational error while performing continuous event-driven air traffic control tasks with multiple aircraft. To this end we have developed a simple air traffic control (ATC) model aimed at distributing breaks to form different configurations with the same utilization. The presented approach extends prior concepts of workload and utilization, which are based on a simple average utilization, and considers the specific patterns of break-work intervals. Copyright 2011 by Human Factors and Ergonomics Society, Inc. All rights reserved

End-to-end Neural Coreference Resolution

We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a head-finding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources.Comment: Accepted to EMNLP 201

Bayesian spline method for assessing extreme loads on wind turbines

This study presents a Bayesian parametric model for the purpose of estimating the extreme load on a wind turbine. The extreme load is the highest stress level imposed on a turbine structure that the turbine would experience during its service lifetime. A wind turbine should be designed to resist such a high load to avoid catastrophic structural failures. To assess the extreme load, turbine structural responses are evaluated by conducting field measurement campaigns or performing aeroelastic simulation studies. In general, data obtained in either case are not sufficient to represent various loading responses under all possible weather conditions. An appropriate extrapolation is necessary to characterize the structural loads in a turbine's service life. This study devises a Bayesian spline method for this extrapolation purpose, using load data collected in a period much shorter than a turbine's service life. The spline method is applied to three sets of turbine's load response data to estimate the corresponding extreme loads at the roots of the turbine blades. Compared to the current industry practice, the spline method appears to provide better extreme load assessment.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS670 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Bayesian Restricted Likelihood Methods: Conditioning on Insufficient Statistics in Bayesian Regression

Bayesian methods have proven themselves to be successful across a wide range of scientific problems and have many well-documented advantages over competing methods. However, these methods run into difficulties for two major and prevalent classes of problems: handling data sets with outliers and dealing with model misspecification. We outline the drawbacks of previous solutions to both of these problems and propose a new method as an alternative. When working with the new method, the data is summarized through a set of insufficient statistics, targeting inferential quantities of interest, and the prior distribution is updated with the summary statistics rather than the complete data. By careful choice of conditioning statistics, we retain the main benefits of Bayesian methods while reducing the sensitivity of the analysis to features of the data not captured by the conditioning statistics. For reducing sensitivity to outliers, classical robust estimators (e.g., M-estimators) are natural choices for conditioning statistics. A major contribution of this work is the development of a data augmented Markov chain Monte Carlo (MCMC) algorithm for the linear model and a large class of summary statistics. We demonstrate the method on simulated and real data sets containing outliers and subject to model misspecification. Success is manifested in better predictive performance for data points of interest as compared to competing methods

Action-Conditional Video Prediction using Deep Networks in Atari Games

Motivated by vision-based reinforcement learning (RL) problems, in particular Atari games from the recent benchmark Aracade Learning Environment (ALE), we consider spatio-temporal prediction problems where future (image-)frames are dependent on control variables or actions as well as previous frames. While not composed of natural scenes, frames in Atari games are high-dimensional in size, can involve tens of objects with one or more objects being controlled by the actions directly and many other objects being influenced indirectly, can involve entry and departure of objects, and can involve deep partial observability. We propose and evaluate two deep neural network architectures that consist of encoding, action-conditional transformation, and decoding layers based on convolutional neural networks and recurrent neural networks. Experimental results show that the proposed architectures are able to generate visually-realistic frames that are also useful for control over approximately 100-step action-conditional futures in some games. To the best of our knowledge, this paper is the first to make and evaluate long-term predictions on high-dimensional video conditioned by control inputs.Comment: Published at NIPS 2015 (Advances in Neural Information Processing Systems 28

Global consensus Monte Carlo

To conduct Bayesian inference with large data sets, it is often convenient or necessary to distribute the data across multiple machines. We consider a likelihood function expressed as a product of terms, each associated with a subset of the data. Inspired by global variable consensus optimisation, we introduce an instrumental hierarchical model associating auxiliary statistical parameters with each term, which are conditionally independent given the top-level parameters. One of these top-level parameters controls the unconditional strength of association between the auxiliary parameters. This model leads to a distributed MCMC algorithm on an extended state space yielding approximations of posterior expectations. A trade-off between computational tractability and fidelity to the original model can be controlled by changing the association strength in the instrumental model. We further propose the use of a SMC sampler with a sequence of association strengths, allowing both the automatic determination of appropriate strengths and for a bias correction technique to be applied. In contrast to similar distributed Monte Carlo algorithms, this approach requires few distributional assumptions. The performance of the algorithms is illustrated with a number of simulated examples

A Newly Identified Hantavirus: The Development of Immunologic Diagnostic Assays and Phylogenetic Analysis for Detection and Characterization

Hantaviruses are the etiologic agents of hemorrhagic fever with renal syndrome (HFRS) in Europe and Asia and hantavirus cardiopulmonary syndrome (HCPS) in the Americas. As of July 2005, 396 HCPS cases in 30 U.S. states with a 36% mortality rate have been confirmed since reporting began in 1993. The primary rodent host of numerous U.S. hantaviruses in Peromyscus maniculatus (deer mouse) although other strains have been found in association with a district rodent host and geographical region. Reservoir hosts are asymptomatically, persistently infected and shed virus particles intermittently in urine, saliva and feces. Thus, the primary transmission route for hantavirus from infected small mammals to humans is inhalation of aerosolized excreta. Between September 13, 2000 and December 1, 2000; November 28 and 29, 2001; May 27, 2002 through July 24, 2002; and May 1, 2004 through May 8, 2004 trapping was conducted in the Great Smoky Mountains National Park (GSMNP). A total of 310 rodents and 35 insectivores (a total of 345 animals) were captured. Blood samples were obtained from 305 animals and subsequently tested for anti-hantavirus antibodies with enzyme linked immunosorbent assays (ELISAs) and immunofluorescent assay (IFA). We performed RT-PCR, PCR and sequencing analysis encoding the immunodominant region of the nucleocapsid (N) protein contained within the S gene. A 59 amino acid peptide was synthesized based on the deduced amino acid sequence of the 59 residue epitope region of the N protein. This 59-mer was applied as a serodiagnostic antigen in an ELISA for the detection of anti-hantavirus antibody in rodent and insectivore sera. The sensitivity and specificity of the ELISA were comparable to those of an IFA using virus infected cells. The only seropositive animals detected were deer mice and white-footed mice (Peromyscus leucopus). The overall estimated seroprevalance in GSMNP was 8.9% (27/305). In our study, we also developed a one-step real-time detection-PCR (RTD-PCR) assay based on Superscript III reverse-transcriptase-Platinum Taq polymerase enzyme mixture. PCR amplicons were detected in real time with the use of a 5’hybridization probe. Results were compared to RT-PCR/nested PCR amplification products. We dectected our target genome sequence in one Sorex fumeus (smoky shrew), one Clethrionomys gapperi (Southern red-backed vole) and 16 mice of Peromyscus spp. by RTD-PCR. The overall estimated viral prevalence in GSMNP was 5.2% (18/345). Sequence analysis of the amplicon detected in the smoky shrew was identical to that previously taken from a deer mouse. This is the first reported case of a New World hantavirus being detected in a shrew and the first evidence of a hantavirus detected in a Clethrionomys spp. Lastly, we sequenced G1 and G2 segments of the M gene and performed phylogenetic analysis with these segments and the N segment. We have designated this hantavirus strain Newfound Gap virus (NGV). The G1 and G2 segments demonstrated homology to New York virus, a pathogenic strain maintained in white-footed mice while the N segment demonstrated greater homology to Monongahela and Sin Nombre viruses, also pathogenic strains maintained in deer mice. The incongruous homologies of the NGV S and M segments to other closely related hantaviruses suggest that genetic reassortment resulting in a hybrid virus may have occurred. NGV possesses unique characteristics and is closely related to pathogenic strains that have resulted in HCPS case fatalities in this region