## Searching (again!?) for the SS Central America

On Tuesday, September 8th 1857, the steamboat SS Central America left Havana at 9 AM for New York, carrying about 600 passengers and crew members. Inside of this vessel, there was stowed a very precious cargo: a set of manuscripts by John James Audubon, and three tons of gold bars and coins. The manuscripts documented an expedition through the yet uncharted southwestern United States and California, and contained 200 sketches and paintings of its wildlife. The gold, fruit of many years of prospecting and mining during the California Gold Rush, was meant to start anew the lives of many of the passengers aboard.

On the 9th, the vessel ran into a storm which developed into a hurricane. The steamboat endured four hard days at sea, and by Saturday morning the ship was doomed. The captain arranged to have women and children taken off to the brig Marine, which offered them assistance at about noon. In spite of the efforts of the remaining crew and passengers to save the ship, the inevitable happened at about 8 PM that same day. The wreck claimed the lives of 425 men, and carried the valuable cargo to the bottom of the sea.

It was not until late 1980s that technology allowed recovery of shipwrecks at deep sea. But no technology would be of any help without an accurate location of the site. In the following paragraphs we would like to illustrate the power of the `scipy` stack by performing a simple simulation, that ultimately creates a dataset of possible locations for the wreck of the SS Central America, and mines the data to attempt to pinpoint the most probable target.

We simulate several possible paths of the steamboat (say 10,000 randomly generated possibilities), between 7:00 AM on Saturday, and 13 hours later, at 8:00 pm on Sunday. At 7:00 AM on that Saturday the ship’s captain, William Herndon, took a celestial fix and verbally relayed the position to the schooner *El Dorado*. The fix was 31º25′ North, 77º10′ West. Because the ship was not operative at that point—no engine, no sails—, for the next thirteen hours its course was solely subjected to the effect of ocean current and winds. With enough information, it is possible to model the drift and leeway on different possible paths.

## Advanced Problem #18

Another fun problem, as requested by Qinfeng Li in MA598R: Measure Theory

Let so that too.

Show that and

I got this problem by picking some strictly positive value and breaking the integral as follows:

Let us examine now the factors and above:

We have thus proven that with At this point, all you have to do is pick (provided the denominator is not zero) and you are done.

## Book presentation at the USC Python Users Group

## Areas of Mathematics

For one of my upcoming talks I am trying to include an exhaustive mindmap showing the different areas of Mathematics, and somehow, how they relate to each other. Most of the information I am using has been processed from years of exposure in the field, and a bit of help from Wikipedia.

But I am not entirely happy with what I see: my lack of training in the area of Combinatorics results in a rather dry treatment of that part of the mindmap, for example. I am afraid that the same could be told about other parts of the diagram. Any help from the reader to clarify and polish this information will be very much appreciated.

And as a bonus, I included a script to generate the diagram with the aid of the `tikz` libraries.

\tikzstyle{level 2 concept}+=[sibling angle=40] \begin{tikzpicture}[scale=0.49, transform shape] \path[mindmap,concept color=black,text=white] node[concept] {Pure Mathematics} [clockwise from=45] child[concept color=DeepSkyBlue4]{ node[concept] {Analysis} [clockwise from=180] child { node[concept] {Multivariate \& Vector Calculus} [clockwise from=120] child {node[concept] {ODEs}}} child { node[concept] {Functional Analysis}} child { node[concept] {Measure Theory}} child { node[concept] {Calculus of Variations}} child { node[concept] {Harmonic Analysis}} child { node[concept] {Complex Analysis}} child { node[concept] {Stochastic Analysis}} child { node[concept] {Geometric Analysis} [clockwise from=-40] child {node[concept] {PDEs}}}} child[concept color=black!50!green, grow=-40]{ node[concept] {Combinatorics} [clockwise from=10] child {node[concept] {Enumerative}} child {node[concept] {Extremal}} child {node[concept] {Graph Theory}}} child[concept color=black!25!red, grow=-90]{ node[concept] {Geometry} [clockwise from=-30] child {node[concept] {Convex Geometry}} child {node[concept] {Differential Geometry}} child {node[concept] {Manifolds}} child {node[concept,color=black!50!green!50!red,text=white] {Discrete Geometry}} child { node[concept] {Topology} [clockwise from=-150] child {node [concept,color=black!25!red!50!brown,text=white] {Algebraic Topology}}}} child[concept color=brown,grow=140]{ node[concept] {Algebra} [counterclockwise from=70] child {node[concept] {Elementary}} child {node[concept] {Number Theory}} child {node[concept] {Abstract} [clockwise from=180] child {node[concept,color=red!25!brown,text=white] {Algebraic Geometry}}} child {node[concept] {Linear}}} node[extra concept,concept color=black] at (200:5) {Applied Mathematics} child[grow=145,concept color=black!50!yellow] { node[concept] {Probability} [clockwise from=180] child {node[concept] {Stochastic Processes}}} child[grow=175,concept color=black!50!yellow] {node[concept] {Statistics}} child[grow=205,concept color=black!50!yellow] {node[concept] {Numerical Analysis}} child[grow=235,concept color=black!50!yellow] {node[concept] {Symbolic Computation}}; \end{tikzpicture}