Notes

Epigenetic and somaclonal divergence within Dendrocalamus farinosus regarding biological development along with lignin wreckage.
Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Throughout the years the paradigm has shifted from local, pixel-level decision to various forms of discrete and continuous optimization to data-driven, learning-based methods. Recently, the rise of machine learning and the rapid proliferation of deep learning enhanced stereo matching with new exciting trends and applications unthinkable until a few years ago. Interestingly, the relationship between these two worlds is two-way. While machine, and especially deep, learning advanced the state-of-the-art in stereo matching, stereo itself enabled new ground-breaking methodologies such as self-supervised monocular depth estimation based on deep networks. In this paper, we review recent research in the field of learning-based depth estimation from single and binocular images highlighting the synergies, the successes achieved so far and the open challenges the community is going to face in the immediate future.We consider predicting the user's head motion in 360 videos, with 2 modalities only the past user's position and the video content (not knowing other users' traces). We make two main contributions. First, we re-examine existing deep-learning approaches for this problem and identify hidden flaws from a thorough root-cause analysis. Second, from the results of this analysis, we design a new proposal establishing state-of-the-art performance. First, re-assessing the existing methods using both modalities, we obtain the surprising result that they all perform worse than baselines using the user's trajectory only. A root-cause analysis shows particularly that (i) the content can inform the prediction for horizons longer than 2 to 3s (existing methods consider shorter horizons), and that (ii) to compete with the baselines, it is necessary to have a recurrent unit dedicated to process the positions, but this is not sufficient. Second, from a re-examination of the problem supported with the concept of Structural-RNN, we design a new deep neural architecture, named TRACK. TRACK achieves state-of-the-art performance on all considered datasets and prediction horizons, outperforming competitors by up to 20% on focus-type videos and horizons 2-5 seconds. The entire framework is online and received an ACM reproducibility badge.Standard supervised learning frameworks for image restoration require a set of noisy measurement and clean image pairs for which a distance between the output of the restoration model and the ground truth images is minimized. The ground truth images, however, are often unavailable or very expensive to acquire in real-world applications. We circumvent this problem by proposing a class of structured denoisers that can be decomposed as the sum of a nonlinear image-dependent mapping, a linear noise-dependent term and a small residual term. We show that these denoisers can be trained with only noisy images under the condition that the noise has zero mean and known variance. The exact distribution of the noise, however, is not assumed to be known. We show the superiority of our approach for image denoising, and demonstrate its extension to solving other restoration problems such as blind deblurring where the ground truth is not available. Our method outperforms some recent unsupervised and self-supervised deep denoising models that do not require clean images for their training. For blind deblurring problems, the method, using only one noisy and blurry observation per image, reaches a quality not far away from its fully supervised counterparts on a benchmark dataset.
To extend closed-loop modeling of the heart-rate reflex (HRR) by including the dynamic effects of concurrent changes in blood CO2 tension. This extended dynamic model can be used to generate physio-markers of baroreflex gain (BRG) and chemoreflex gain (CRG) that allow quantitative assessment of the possible impact of pathologies upon HRR. Mild Cognitive Impairment (MCI) is used as an example.

The proposed data-based closed-loop modeling methodology estimates the forward and reverse dynamic components of the model via Laguerre kernel expansions of two open-loop models using spontaneous time-series data collected in 45 MCI patients and 15 controls. The BRG and CRG physio-markers are subsequently computed for each subject via simulation of the obtained closed-loop model for unit-step change of arterial pressure or blood CO2 tension, respectively.

Both open-loop and closed-loop HRR modeling revealed that MCI patients exhibit significantly smaller CRG relative to controls (p<0.001), but not significantly different BRG. Furthermore, the closed-loop model captured the dynamic effect of sympathetic activity as resonant peak around 0.1 Hz (Mayer wave) in the chemoreflex and baroreflex transfer functions (not captured via open-loop modeling). This may prove valuable in advancing our understanding of how sympathetic activity impacts HRR in various pathologies.

The extended HRR model, incorporating the dynamic effects of concurrent changes of blood CO2 tension, revealed significantly reduced chemoreflex gain (but not baroreflex gain) in MCI patients. Furthermore, the closed-loop model captured the sympathetic influence around 0.1 Hz.

Multivariate closed-loop dynamic modeling is valuable for understanding physiological autoregulation.
Multivariate closed-loop dynamic modeling is valuable for understanding physiological autoregulation.
Longitudinal neuroimaging data have been widely used to predict clinical scores for automatic diagnosis of Alzheimer's Disease (AD) in recent years. However, incomplete temporal neuroimaging records of the patients pose a major challenge to use these data for accurately diagnosing AD. In this paper, we propose a novel method to learn an enriched representation for imaging biomarkers, which simultaneously captures the information conveyed by both the baseline neuroimaging records of all the participants in a studied cohort and the progressive variations of the available follow-up records of every individual participant.

Taking into account that different participants usually take different numbers of medical records at different time points, we develop a robust learning objective that minimizes the summations of a number of not-squared L2-norm distances, which, though, is difficult to efficiently solve in general. NSC 663284 molecular weight Thus we derive a new efficient iterative algorithm with rigorously proved convergence.

We have conducted extensive experiments using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
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