Seeing the invisible: The scope and limits of unconscious processing in binocular rivalry

When an image is presented to one eye and a very different image is presented to the corresponding location of the other eye, they compete for conscious representation, such that only one image is visible at a time while the other is suppressed. Called binocular rivalry, this phenomenon and its deviants have been extensively exploited to study the mechanism and neural correlates of consciousness. In this paper, we propose a framework, the unconscious binding hypothesis, to distinguish unconscious processing from conscious processing. According to this framework, the unconscious mind not only encodes individual features but also temporally binds distributed features to give rise to cortical representation, but unlike conscious binding, such unconscious binding is fragile. Under this framework, we review evidence from psychophysical and neuroimaging studies, which suggests that: (1) for invisible low level features, prolonged exposure to visual pattern and simple translational motion can alter the appearance of subsequent visible features (i.e. adaptation); for invisible high level features, although complex spiral motion cannot produce adaptation, nor can objects/words enhance subsequent processing of related stimuli (i.e. priming), images of tools can nevertheless activate the dorsal pathway; and (2) although invisible central cues cannot orient attention, invisible erotic pictures in the periphery can nevertheless guide attention, likely through emotional arousal; reciprocally, the processing of invisible information can be modulated by attention at perceptual and neural levels

Preparation and Characterization of High-Temperature Thermally Stable Alumina Composite Membrane

A crack- and pinhole-free composite membrane consisting of an α-alumina support and a modified γ-alumina top layer which is thermally stable up to 1100°C was prepared by the sol–gel method. The supported thermally stable top layer was made by dipcoating the support with a boehmite sol doped with lanthanum nitrate. The temperature effects on the microstructure of the (supported and unsupported) La-doped top layers were compared with those of a common γ-alumina membrane (without doping with lanthanum), using the gas permeability and nitrogen adsorption porosimetry data. After sintering at 1100°C for 30 h, the average pore diameter of the La-doped alumina top layer was 17 nm, compared to 109 nm for the common alumina top layer. Addition of poly(vinyl alcohol) to the colloid boehmite precursor solution prevented formation of defects in the γ-alumina top layer. After sintering at temperatures higher than 900°C, the common alumina top layer with addition of poly(vinyl alcohol) exhibits a bimodal pore distribution. The La-doped alumina top layer (also with addition of poly(vinyl alcohol)) retains a monopore distribution after sintering at 1200°C

A Dilated Inception Network for Visual Saliency Prediction

Recently, with the advent of deep convolutional neural networks (DCNN), the improvements in visual saliency prediction research are impressive. One possible direction to approach the next improvement is to fully characterize the multi-scale saliency-influential factors with a computationally-friendly module in DCNN architectures. In this work, we proposed an end-to-end dilated inception network (DINet) for visual saliency prediction. It captures multi-scale contextual features effectively with very limited extra parameters. Instead of utilizing parallel standard convolutions with different kernel sizes as the existing inception module, our proposed dilated inception module (DIM) uses parallel dilated convolutions with different dilation rates which can significantly reduce the computation load while enriching the diversity of receptive fields in feature maps. Moreover, the performance of our saliency model is further improved by using a set of linear normalization-based probability distribution distance metrics as loss functions. As such, we can formulate saliency prediction as a probability distribution prediction task for global saliency inference instead of a typical pixel-wise regression problem. Experimental results on several challenging saliency benchmark datasets demonstrate that our DINet with proposed loss functions can achieve state-of-the-art performance with shorter inference time.Comment: Accepted by IEEE Transactions on Multimedia. The source codes are available at https://github.com/ysyscool/DINe

Distance versus energy fluctuations and electron transfer in single protein molecules

Stochastic nature due to distance and energy fluctuations of single protein molecules involved in electron-transfer (ET) reactions is studied. Distance fluctuations have been assumed previously for causing the slow fluctuations in the ET rates between a donor-acceptor pair constrained to a native protein. Although the observed t–1/2 power law can be derived using Langevin dynamics with a simple chain model, some discrepancies exist. The friction coefficient and the Rouse segment time constant deduced from experimental data are several orders of magnitude too large, even though the extracted force constant is reasonable. Therefore, questions are raised about the distance-fluctuation mechanism and the activationless ET hypothesis. As an alternative mechanism, we considered fluctuations in activation energy and analyzed the data from two different single protein experiments to determine spectral distribution of energy fluctuations

Analysis of binary spatial data by quasi-likelihood estimating equations

The goal of this paper is to describe the application of quasi-likelihood estimating equations for spatially correlated binary data. In this paper, a logistic function is used to model the marginal probability of binary responses in terms of parameters of interest. With mild assumptions on the correlations, the Leonov-Shiryaev formula combined with a comparison of characteristic functions can be used to establish asymptotic normality for linear combinations of the binary responses. The consistency and asymptotic normality for quasi-likelihood estimates can then be derived. By modeling spatial correlation with a variogram, we apply these asymptotic results to test independence of two spatially correlated binary outcomes and illustrate the concepts with a well-known example based on data from Lansing Woods. The comparison of generalized estimating equations and the proposed approach is also discussed.Comment: Published at http://dx.doi.org/10.1214/009053605000000057 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org