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function [ D ] = hoeffdingsD( x, y )
%Compute's Hoeffding's D measure of dependence between x and y
%   inputs x and y are both N x 1 arrays
%   output D is a scalar
%     The formula for Hoeffding's D is taken from
%     http://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#procstat_corr_sect016.htm
% Below is demonstration code for several types of dependencies.
% Implementation by Jascha https://stackoverflow.com/a/9322657
% 
% % this case should be 0 - there are no dependencies

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{
  "name": "ds000031 example",
  "description": "An example of connectivity analysis using ds000031",
  "input":
    {
 	"task": "rest"
	},
  "blocks": [
	{
	"level": "run",

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function [cca_rho cca_v cca_cv] = sample_canonical_correlation(X,Y,varargin)
    % SAMPLE_CANONICAL_CORRELATION
    % 
    % Usage: [rho] = sample_canonical_correlation(X,Y, R_X, R_Y)
    % 
    % Inputs: 
    %   - X is the test set data matrix of n_samples x p features
    %   - Y is the test set data matrix of n_samples x r features
    %   - options.W_X is the linear projection matrix for X
    %   - options.W_Y is a linear projection matrix for Y

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import sys
import numpy as np

from skopt.space import Real, Categorical, Integer
from skopt import BayesSearchCV

from sklearn.grid_search import GridSearchCV
from sklearn.datasets import make_sparse_spd_matrix
from sklearn.model_selection import train_test_split
from sklearn.metrics import make_scorer

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Introduction

Here is an example of single-subject time-series during R pMFG stimulation.

Break down of global time-series by wavelet scales

Measuring total variance in the signal at each ROI could be very useful to do, particularly across scales 1 to 4.

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Additional Algorithm for Two Sample Differences in Populations of Graphical Models

Consider two graphical models case

$$\mathbf{X}^{(1,i)} \sim \mathcal{N}(0,\boldsymbol\Sigma^{(1,i)})$$ $$\mathbf{X}^{(2,i)} \sim \mathcal{N}(0,\boldsymbol\Sigma^{(2,i)})$$

Idea:

1. Create pooled sample covariance matrix given by $\frac{\widehat{\boldsymbol\Sigma^{1}} + \widehat{\boldsymbol\Sigma^{2}}}{2}$
2. Obtain multiple estimates of $\Delta^{(1,i)}, i=1,\ldots, n_g$ and similarly for $g = 2$ using the sparse log-likelihood ratio

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Here is an improvement over a nice answer from @herbert that took advantage of pstricks-add and xelatex.

The earlier answer however did not create a proper grid for every cell in the matrix.It only displayed the grid structure for values set to 1. This enables a more generic solution beyond [0,1] valued matrices.

What we would like to see however is something like this —

[![Alternative color scheme, highlights grid over all cells][2]][2]

Here is a code snippet to achieve this:

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\documentclass{article}

\usepackage{booktabs}
\usepackage{geometry}
\usepackage{tabularx}
\geometry{lmargin=72pt,tmargin=72pt,textwidth=350pt,marginparwidth=144pt}
\usepackage{tufte-sidenotes}

\begin{document}
Here is demo of the \verb#tufte-sidenotes# style.

mnarayan

How to backup data from sherlock to google drive

1. Download and install rclone to your $HOME/bin directory. Setup up rclone to connect to your google drive. Suppose you call your remote google drive drive.

2. Create a bash script called backup_gdrive.sh with the following information

#!/bin/sh

STUDY_DIR=STUDY
LOCAL_DIR=${SCRATCH}/${STUDY_DIR}