Search Results for "code name m-horse"

EBF, which stands for Efficient Binary Format, is a binary file format for reading and writing binary data easily. Reading writing routines are currently available in C,C++,Fortran,Java, Python, IDL, MATLAB. A program called ebftkpy which has a set of utility functions to work with the .ebf files , e.g., viewing the contents and getting a summary, is also provided. The EBF specification is designed to be concise and easy to understand to make it easier for others to write their own code...

The Matlab/Octave rotations library is a collection of functions, bundled as m-scripts, that address computations and numerical handling of rotations in 3D Euclidean space. The rotation representations that are supported are rotation matrices (Rotmat), Quaternions (Quat), intrinsic ZYX Euler angles (Euler), fused angles (Fused) and tilt angles (Tilt). Operations such as composition, inversion, ZYX yaw extraction, fused yaw extraction, random generation, equality detection, vector rotation...

This site provides the source code of two approaches for density-ratio based clustering, used for discovering clusters with varying densities. One approach is to modify a density-based clustering algorithm to do density-ratio based clustering by using its density estimator to compute density-ratio. The other approach involves rescaling the given dataset only. An existing density-based clustering algorithm, which is applied to the rescaled dataset, can find all clusters with varying...

Functional Mechanism is a differentially private method designed for a large class of optimization-based analyses. The main idea is to enforce epsilon-differential privacy by perturbing the objective function of the optimization problem, rather than its results. Please cite the following paper if you choose to use this code: J. Zhang, Z. Zhang, X. Xiao, Y. Yang, and M. Winslett. Functional Mechanism: Regression Analysis under Differential Privacy. PVLDB 5(11):1364-1375, 2012.

Line-scanning particle image velocimetry (LS-PIV) is an analytical method to quantify blood velocity data generated by many confocal and two-photon excited fluorescence microscopes. We hope that this method will be of use to the research community! The associated files include Matlab code for LS-PIV and example data-sets of measured blood flow in the cerebral cortex of mouse. For additional information, please see the manuscript "Line-Scanning Particle Image Velocimetry: an Optical Approach...