Incorporating Prediction in Models for Two-Dimensional Smooth Pursuit

A predictive component can contribute to the command signal for smooth pursuit. This is readily demonstrated by the fact that low frequency sinusoidal target motion can be tracked with zero time delay or even with a small lead. The objective of this study was to characterize the predictive contributions to pursuit tracking more precisely by developing analytical models for predictive smooth pursuit. Subjects tracked a small target moving in two dimensions. In the simplest case, the periodic target motion was composed of the sums of two sinusoidal motions (SS), along both the horizontal and the vertical axes. Motions following the same or similar paths, but having a richer spectral composition, were produced by having the target follow the same path but at a constant speed (CS), and by combining the horizontal SS velocity with the vertical CS velocity and vice versa. Several different quantitative models were evaluated. The predictive contribution to the eye tracking command signal could be modeled as a low-pass filtered target acceleration signal with a time delay. This predictive signal, when combined with retinal image velocity at the same time delay, as in classical models for the initiation of pursuit, gave a good fit to the data. The weighting of the predictive acceleration component was different in different experimental conditions, being largest when target motion was simplest, following the SS velocity profiles

Developing a Series of AI Challenges for the United States Department of the Air Force

Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure American leadership in AI. These broad strategy documents have influenced organizations such as the United States Department of the Air Force (DAF). The DAF-MIT AI Accelerator is an initiative between the DAF and MIT to bridge the gap between AI researchers and DAF mission requirements. Several projects supported by the DAF-MIT AI Accelerator are developing public challenge problems that address numerous Federal AI research priorities. These challenges target priorities by making large, AI-ready datasets publicly available, incentivizing open-source solutions, and creating a demand signal for dual use technologies that can stimulate further research. In this article, we describe these public challenges being developed and how their application contributes to scientific advances

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Perceived impact of the COVID-19 pandemic on postgraduate residents

Introduction: COVID-19 has led to an increase in burden on health-care workers. Postgraduate residents are at the forefront of the pandemic. Understanding its impact and the perspectives of postgraduate residents could provide better solutions to their problems. Objectives: 1. To assess the psychological impact of the COVID-19 pandemic on postgraduate doctors. 2. To assess the perception of postgraduate doctors regarding the medical profession and its association to the psychological impact of the COVID-19 pandemic. Methodology: This cross-sectional study recruited 65 postgraduate medical resident doctors working in teaching hospitals involved in the care of COVID-19 patients. They were requested to complete the questionnaire consisting of qualitative as well as quantitative questions. For an objective assessment of anxiety, the Generalized Anxiety Disorder-7 (GAD-7) scale was used. Relevant statistical methods were employed and statistical analysis was done using IBM SPSS software version 20.0. Results: The qualitative data showed themes of concern regarding social awareness about COVID-19 (hygiene, masks, and social distancing), working conditions of doctors, and increased stress and burnout. 47.54% (n = 29) of respondents said that they would reconsider their profession/specialty, out of which 21 said that they would switch to another profession. The resident doctors who would reconsider their choice of profession/specialty had significantly higher (P < 0.05) scores for anxiety (GAD-7). Conclusion: Owing to the significant association between anxiety and desire to change their profession altogether, greater psychological support for doctors appears to be a pressing need. It is important that the concerns of the postgraduate residents are addressed promptly and holistically

EEG alpha and pupil diameter reflect endogenous auditory attention switching and listening effort

Everyday environments often contain distracting competing talkers and background noise, requiring listeners to focus their attention on one acoustic source and reject others. During this auditory attention task, listeners may naturally interrupt their sustained attention and switch attended sources. The effort required to perform this attention switch has not been well studied in the context of competing continuous speech. In this work, we developed two variants of endogenous attention switching and a sustained attention control. We characterized these three experimental conditions under the context of decoding auditory attention, while simultaneously evaluating listening effort and neural markers of spatial‐audio cues. A least‐squares, electroencephalography (EEG)‐based, attention decoding algorithm was implemented across all conditions. It achieved an accuracy of 69.4% and 64.0% when computed over nonoverlapping 10 and 5‐s correlation windows, respectively. Both decoders illustrated smooth transitions in the attended talker prediction through switches at approximately half of the analysis window size (e.g., the mean lag taken across the two switch conditions was 2.2 s when the 5‐s correlation window was used). Expended listening effort, as measured by simultaneous EEG and pupillometry, was also a strong indicator of whether the listeners sustained attention or performed an endogenous attention switch (peak pupil diameter measure [ [Formula: see text]] and minimum parietal alpha power measure [ [Formula: see text]]). We additionally found evidence of talker spatial cues in the form of centrotemporal alpha power lateralization ( [Formula: see text]). These results suggest that listener effort and spatial cues may be promising features to pursue in a decoding context, in addition to speech‐based features

Interactions between horizontal and vertical eye velocities.

<p>The panels show the average difference (±1 SD) in pursuit velocity for two stimulus conditions in the same format as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012574#pone-0012574-g004" target="_blank">Fig. 4</a>, but for path 2. Intervals during the difference in pursuit velocity differed significantly from 0 are indicated above the velocity trace in each panel. Those intervals were sparse.</p

Effect of predictive target acceleration on the frequency response of pursuit eye movements.

<p>Traces depict the gain and phase of the response for three values of the weighting of the predictive acceleration signal, as indicated by the different symbols. Model predictions can be compared to the experimental data in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012574#pone-0012574-g003" target="_blank">Fig. 3</a>.</p

Fit of the predictive model to experimental data for another path (5) and subject (6).

<p>The results are plotted in the same format as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012574#pone-0012574-g006" target="_blank">Fig. 6</a>.</p