Linear regression models have been successfully used to function estimation and model selection in high-dimensional data analysis. However, most existing methods are built on least squares with the mean square error (MSE) criterion, which are sensitive to outliers and their performance may be degraded for heavy-tailed noise. In this paper, we go beyond this criterion by investigating the regularized modal regression from a statistical learning viewpoint. A new regularized modal regression model is proposed for estimation and variable selection, which is robust to outliers, heavy-tailed noise, and skewed noise. On the theoretical side, we establish the approximation estimate for learning the conditional mode function, the sparsity analysis for variable selection, and the robustness characterization. On the application side, we applied our model to successfully improve the cognitive impairment prediction using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort data.

V ox2Cortex: Fast explicit reconstruction of cortical surfaces from 3D MRI scans with geometric deep neural networks.

However, existing contrastive learning methods primarily focus on one single data level, which fails to fully exploit the intricate nature of medical time series.

Note, the direct FC link and SC detour form a cyclic loop inherited from both SC and FC topology.

Disease progression models infer group-level temporal trajectories of change in patients' features as a chronic degenerative condition plays out.

For these systems, a common issue is the difficulty of collecting sufficiently refined timescale data.

Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience.

If taking a closer look at the MedDRA classification on the system organ level on its website, we can find a claim of "System Organ Classes (SOCs) which are groupings by aetiology (e.g. However, as claimed in the original paper, "It should be noted that we did not perform any preprocessing of our datasets, such as Tab. These datasets appear in MoleculeNet as well. As mentioned in the introduction in the main paper, there are also issues with inconsistent representations and undefined stereochemistry. We list an example for each in Figure 1 and Figure 1.