Object Frequency and Predictability Effects on Eye Fixation Durations in Real-World Scene Viewing

During text reading, the durations of eye fixations decrease with greater frequency and predictability of the currently fixated word (Rayner, 1998; 2009). However, it has not been tested whether those results also apply to scene viewing. We computed object frequency and predictability from both linguistic and visual scene analysis (LabelMe, Russell et al., 2008), and Latent Semantic Analysis (Landauer et al., 1998) was applied to estimate predictability. In a scene-viewing experiment, we found that, for small objects, linguistics-based frequency, but not scene-based frequency, had effects on first fixation duration, gaze duration, and total time. Both linguistic and scene-based predictability affected total time. Similar to reading, fixation duration decreased with higher frequency and predictability. For large objects, we found the direction of effects to be the inverse of those found in reading studies. These results suggest that the recognition of small objects in scene viewing shares some characteristics with the recognition of words in reading

Reduced basis method for source mask optimization

Image modeling and simulation are critical to extending the limits of leading edge lithography technologies used for IC making. Simultaneous source mask optimization (SMO) has become an important objective in the field of computational lithography. SMO is considered essential to extending immersion lithography beyond the 45nm node. However, SMO is computationally extremely challenging and time-consuming. The key challenges are due to run time vs. accuracy tradeoffs of the imaging models used for the computational lithography. We present a new technique to be incorporated in the SMO flow. This new approach is based on the reduced basis method (RBM) applied to the simulation of light transmission through the lithography masks. It provides a rigorous approximation to the exact lithographical problem, based on fully vectorial Maxwell's equations. Using the reduced basis method, the optimization process is divided into an offline and an online steps. In the offline step, a RBM model with variable geometrical parameters is built self-adaptively and using a Finite Element (FEM) based solver. In the online step, the RBM model can be solved very fast for arbitrary illumination and geometrical parameters, such as dimensions of OPC features, line widths, etc. This approach dramatically reduces computational costs of the optimization procedure while providing accuracy superior to the approaches involving simplified mask models. RBM furthermore provides rigorous error estimators, which assure the quality and reliability of the reduced basis solutions. We apply the reduced basis method to a 3D SMO example. We quantify performance, computational costs and accuracy of our method.Comment: BACUS Photomask Technology 201

Eye movement control during visual pursuit in Parkinson's disease

BACKGROUND: Prior studies of oculomotor function in Parkinson’s disease (PD) have either focused on saccades without considering smooth pursuit, or tested smooth pursuit while excluding saccades. The present study investigated the control of saccadic eye movements during pursuit tasksand assessed the quality of binocular coordinationas potential sensitive markers of PD. METHODS: Observers fixated on a central cross while a target moved toward it. Once the target reached the fixation cross, observers began to pursue the moving target. To further investigate binocular coordination, the moving target was presented on both eyes (binocular condition), or on one eye only (dichoptic condition). RESULTS: The PD group made more saccades than age-matched normal control adults (NC) both during fixation and pursuit. The difference between left and right gaze positions increased over time during the pursuit period for PD but not for NC. The findings were not related to age, as NC and young-adult control group (YC) performed similarly on most of the eye movement measures, and were not correlated with classical measures of PD severity (e.g., Unified Parkinson’s Disease Rating Scale (UPDRS) score). DISCUSSION: Our results suggest that PD may be associated with impairment not only in saccade inhibition, but also in binocular coordination during pursuit, and these aspects of dysfunction may be useful in PD diagnosis or tracking of disease course.This work was supported in part by grants from the National Science Foundation (NSF SBE-0354378 to Arash Yazdanbakhsh and Bo Cao) and Office of Naval Research (ONR N00014-11-1-0535 to Bo Cao, Chia-Chien Wu, and Arash Yazdanbakhsh). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. (SBE-0354378 - National Science Foundation (NSF); ONR N00014-11-1-0535 - Office of Naval Research)Published versio

Uncertainty Principle for Control of Ensembles of Oscillators Driven by Common Noise

We discuss control techniques for noisy self-sustained oscillators with a focus on reliability, stability of the response to noisy driving, and oscillation coherence understood in the sense of constancy of oscillation frequency. For any kind of linear feedback control--single and multiple delay feedback, linear frequency filter, etc.--the phase diffusion constant, quantifying coherence, and the Lyapunov exponent, quantifying reliability, can be efficiently controlled but their ratio remains constant. Thus, an "uncertainty principle" can be formulated: the loss of reliability occurs when coherence is enhanced and, vice versa, coherence is weakened when reliability is enhanced. Treatment of this principle for ensembles of oscillators synchronized by common noise or global coupling reveals a substantial difference between the cases of slightly non-identical oscillators and identical ones with intrinsic noise.Comment: 10 pages, 5 figure

Graphene-based modulation-doped superlattice structures

The electronic transport properties of graphene-based superlattice structures are investigated. A graphene-based modulation-doped superlattice structure geometry is proposed and consist of periodically arranged alternate layers: InAs/graphene/GaAs/graphene/GaSb. Undoped graphene/GaAs/graphene structure displays relatively high conductance and enhanced mobilities at elevated temperatures unlike modulation-doped superlattice structure more steady and less sensitive to temperature and robust electrical tunable control on the screening length scale. Thermionic current density exhibits enhanced behaviour due to presence of metallic (graphene) mono-layers in superlattice structure. The proposed superlattice structure might become of great use for new types of wide-band energy gap quantum devices.Comment: 5 figure

The Effect of Auditory Distraction on the Useful Field of View in Hearing Impaired Individuals and its implications for driving

This study assessed whether the increased demand of listening in hearing impaired individuals exacerbates the detrimental impact of auditory distraction on a visual task (useful field of view test), relative to normally hearing listeners. Auditory distraction negatively affects this visual task, which is linked with various driving performance outcomes. Hearing impaired and normally hearing participants performed useful field of view testing with and without a simultaneous listening task. They also undertook a cognitive test battery. For all participants, performing the visual and auditory tasks together reduced performance on each respective test. For a number of subtests, hearing impaired participants showed poorer visual task performance, though not to a statistically significant extent. Hearing impaired participants were significantly poorer at a reading span task than normally hearing participants and tended to score lower on the most visually complex subtest of the visual task in the absence of auditory task engagement. Useful field of view performance is negatively affected by auditory distraction, and hearing loss may present further problems, given the reductions in visual and cognitive task performance suggested in this study. Suggestions are made for future work to extend this study, given the practical importance of the findings

Super-resolution reconstruction of brain magnetic resonance images via lightweight autoencoder

Magnetic Resonance Imaging (MRI) is useful to provide detailed anatomical information such as images of tissues and organs within the body that are vital for quantitative image analysis. However, typically the MR images acquired lacks adequate resolution because of the constraints such as patients’ comfort and long sampling duration. Processing the low resolution MRI may lead to an incorrect diagnosis. Therefore, there is a need for super resolution techniques to obtain high resolution MRI images. Single image super resolution (SR) is one of the popular techniques to enhance image quality. Reconstruction based SR technique is a category of single image SR that can reconstruct the low resolution MRI images to high resolution images. Inspired by the advanced deep learning based SR techniques, in this paper we propose an autoencoder based MRI image super resolution technique that performs reconstruction of the high resolution MRI images from low resolution MRI images. Experimental results on synthetic and real brain MRI images show that our autoencoder based SR technique surpasses other state-of-the-art techniques in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Information Fidelity Criterion (IFC), and computational time

Automated sleep stage classification in sleep apnoea using convolutional neural networks

A sleep disorder is a condition that adversely impacts one\u27s ability to sleep well on a regular schedule. It also occurs as a consequence of numerous neurological sicknesses. These types of disorders can be investigated using laboratory-based polysomnography (PSG) signals. The detection of neurological disorders is exact and efficient thanks to the automated monitoring of sleep relegation stages. This automation method publicly presents a flexible deep learning model and machine learning approach utilizing raw electroencephalogram (EEG) signals. The deep learning model is a Deep Convolutional Neural Network (CNN) that analyses invariant time capacities and frequency actualities and collects assessment adaptations. It also captures the inviolate and long brief length setting conditions between the epochs and the degree of sleep stage relegation. This method uses an innovative function to calculate data loss and misclassified errors found while training the network for the sleep stage, considering the restrictions found in the publicly available sleep datasets. It is used in conjunction with machine learning techniques to forecast the best approach for the process. Its effectiveness is determined by using two open-source, public databases available from PhysioNet: two recordings with 5402 epoch counts. The technique used in this approach achieves an accuracy of 90.70%, precision of 90.50%, recall of 92.70%, and F-measure of 90.60%. The proposed method is more significant than existing models like AlexNet, ResNet, VGGNet, and LeNet. The comparative study of the models could be adopted for clinical use and modified based on the requirements

Route planning with transportation network maps: an eye-tracking study.

Planning routes using transportation network maps is a common task that has received little attention in the literature. Here, we present a novel eye-tracking paradigm to investigate psychological processes and mechanisms involved in such a route planning. In the experiment, participants were first presented with an origin and destination pair before we presented them with fictitious public transportation maps. Their task was to find the connecting route that required the minimum number of transfers. Based on participants' gaze behaviour, each trial was split into two phases: (1) the search for origin and destination phase, i.e., the initial phase of the trial until participants gazed at both origin and destination at least once and (2) the route planning and selection phase. Comparisons of other eye-tracking measures between these phases and the time to complete them, which depended on the complexity of the planning task, suggest that these two phases are indeed distinct and supported by different cognitive processes. For example, participants spent more time attending the centre of the map during the initial search phase, before directing their attention to connecting stations, where transitions between lines were possible. Our results provide novel insights into the psychological processes involved in route planning from maps. The findings are discussed in relation to the current theories of route planning

Does oculomotor inhibition of return influence fixation probability during scene search?

Oculomotor inhibition of return (IOR) is believed to facilitate scene scanning by decreasing the probability that gaze will return to a previously fixated location. This “foraging” hypothesis was tested during scene search and in response to sudden-onset probes at the immediately previous (one-back) fixation location. The latencies of saccades landing within 1º of the previous fixation location were elevated, consistent with oculomotor IOR. However, there was no decrease in the likelihood that the previous location would be fixated relative to distance-matched controls or an a priori baseline. Saccades exhibit an overall forward bias, but this is due to a general bias to move in the same direction and for the same distance as the last saccade (saccadic momentum) rather than to a spatially specific tendency to avoid previously fixated locations. We find no evidence that oculomotor IOR has a significant impact on return probability during scene search