348 research outputs found
Effect of mixing and spatial dimension on the glass transition
We study the influence of composition changes on the glass transition of
binary hard disc and hard sphere mixtures in the framework of mode coupling
theory. We derive a general expression for the slope of a glass transition
line. Applied to the binary mixture in the low concentration limits, this new
method allows a fast prediction of some properties of the glass transition
lines. The glass transition diagram we find for binary hard discs strongly
resembles the random close packing diagram. Compared to 3D from previous
studies, the extension of the glass regime due to mixing is much more
pronounced in 2D where plasticization only sets in at larger size disparities.
For small size disparities we find a stabilization of the glass phase quadratic
in the deviation of the size disparity from unity.Comment: 13 pages, 8 figures, Phys. Rev. E (in print
Generalising Deep Learning MRI Reconstruction across Different Domains
We look into robustness of deep learning based MRI reconstruction when tested
on unseen contrasts and organs. We then propose to generalise the network by
training with large publicly-available natural image datasets with synthesised
phase information to achieve high cross-domain reconstruction performance which
is competitive with domain-specific training. To explain its generalisation
mechanism, we have also analysed patch sets for different training datasets.Comment: Accepted for ISBI2019 as a 1-page abstrac
Fractality of Body Movements Predicts Perception of Affordances: Evidence From Stand-On-Ability Judgments About Slopes
We recorded head motion with one wireless marker attached to the back of the head during quiet stance as participants visually inspected a sloped ramp in order to perceive whether they might be able to stand on the surface. Participants responded with yes or no without attempting to stand on the ramp. As has been found in dynamic touch (Palatinus, Kelty-Stephen, Kinsella-Shaw, Carello, & Turvey, 2014), we hypothesized that multiscale fluctuation patterns in bodily movement during visual observation would predict perceptual judgments. Mixed-effects logistic regression predicted binary affordance judgments as a function of geographical slant angle, head-motion standar deviation, and multifractal spectrum width (Ihlen, 2012). Multifractal spectrum width was the strongest predictor of affordance judgments. Specifically, increased spectrum width predicted decreased odds of a yes answer. Interestingly, standard deviation was not a significant predictor, reinforcing our prediction that traditional measures of variability fail to account for what fractal measures of multiscale interactions can predict about information pickup in perception-action systems
Complex diffusion-weighted image estimation via matrix recovery under general noise models
We propose a patch-based singular value shrinkage method for diffusion
magnetic resonance image estimation targeted at low signal to noise ratio and
accelerated acquisitions. It operates on the complex data resulting from a
sensitivity encoding reconstruction, where asymptotically optimal signal
recovery guarantees can be attained by modeling the noise propagation in the
reconstruction and subsequently simulating or calculating the limit singular
value spectrum. Simple strategies are presented to deal with phase
inconsistencies and optimize patch construction. The pertinence of our
contributions is quantitatively validated on synthetic data, an in vivo adult
example, and challenging neonatal and fetal cohorts. Our methodology is
compared with related approaches, which generally operate on magnitude-only
data and use data-based noise level estimation and singular value truncation.
Visual examples are provided to illustrate effectiveness in generating denoised
and debiased diffusion estimates with well preserved spatial and diffusion
detail.Comment: 26 pages, 9 figure
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