Prob \begin{align} \dot{x} & = \sigma(y-x) \\
\dot{y} & = \rho x - y - xz \\
\dot{z} & = -\beta z + xy \end{align}
$$\alpha = 1000$$
import matplotlib import numpy as np import matplotlib.pyplot as plt %matplotlib inline /usr/local/lib/python3.6/site-packages/matplotlib/font_manager.py:278: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment. 'Matplotlib is building the font cache using fc-list. ' x = np.linspace(0, 3*np.

# Active Matter

I first caught wind of Active Matter through the work of one of my favorite researchers, Tamas Vicsek, whose intellectual bling includes a fractal bearing his name.
My understanding of this sprawling sub-domain of physics is that we can study the activity of individuals agents and systems as though studying something like hydrodynamics, kinematics, and non-equilbrium statistical phsyics. Crowds become streams. Flocks become rivers. Networks of agents become oceans.

# Network Topology

What are the advantages of various topogical structures? How can they be combined? Mutate over time? How do swarm systems develop communication topologies?
First, let’s get acclimated with the different structures - their definitions and some useful examples of their use in practice.
Ring This is a bus topology in a closed loop, with data traveling around the ring in one direction, passing through each node in turn until it arrives where its going.

# Outlier Detection

PSO Approach A paper was brought to my attention today (Particle Swarm Optimization for Outlier Detection) presenting a novel application of PSOs in outlier detection, and I wanted to write about it to see if I can find my way to some context in operations intelligence (i.e. possibly anomaly detection in log files?).
As the authors established the outlier problem, I realized that I carry with me a distance measurement bias when I think about classifying outliers.