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USNisan.H
 

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    <p>You can reach me at <code>hanisan</code> on gmail.</p> <p>Though my education is formally in Physics, I would say my greatest traits are the ability to learn any skill necessary to complete a task, and the inability to comprehend the meaning of 'impossible'. I enpossible. To date, I have learnt, dealt with, or simply meddled in the following:</p> <p>Math:</p> <ul> <li>Stats</li> <li>Multivariable Calculus, Partial and Ordinary Different Equations</li> <li>Nonlinear Dynamics, Chaos</li> <li>Signal and Coding Theory</li> <li>Wave theory, and its application to Signal and Coding Theory as well as novel computational methods</li> <li>Graph theory</li> <li>Perspective transforms, tracers, collision and multi-body gravitational modelling</li> <li>Clustering, networks, graphs, and novel approaches for single-pass massively-dimensional dense cluster analysis (D > 100, N > 1E+10)</li> <li>Trending, parallel trending, and time-weighted expectation value analysis for dynamic optimization.</li> <li>Matching and scoring algorithms for both massive and small object sets, using both single and multi-interaction models.</li> </ul> <p>Physics:</p> <ul> <li>Mechanics</li> <li>Thermodynamics</li> <li>Quantum Mechanics</li> <li>Statistical Mechanics</li> <li>Positioning techniques using both standard triangulation and novel experimental techniques</li> <li>Radiation Biophysics (in relation to cancer treatments in particular)</li> <li>Custom hardware design and construction</li> </ul> <p>Hardware &amp; Programming:</p> <ul> <li>Assembler, custom hardware design and construction</li> <li>Python (my favourite language so far)</li> <li>C#</li> <li>JS (In particular in conjunction with HTML5 Canvas in animation and perspective rendering of 3D objects)</li> <li>FORTRAN</li> <li>Matlab, Maple, Mathematica, Gnuplot and many other scientific programming tools (that I generally no longer use, because I can do it all in Python)</li> <li>Android (custom automation tools for load and crash testing, with device disambiguation and abstraction layers)</li> </ul> <p>Databases:</p> <ul> <li>Efficient, database-specific algorithms that tailor the database engine's strengths and weaknesses to the functionality in question</li> <li>DB managed persistency and synchronization for asynchronous client-bound applications</li> <li>Custom low-level DB engine enhancements (in environments that allow this), such as a vector-specific aggregate operations and n-length bitmask overlay element selection on a table or column. </li> <li>Analytics, and in particular low-level in-DB recursive functionality with crash handling, pausing, unpausing and resuming from stopped states capabilities (e.g. cluster analysis inside the DB)</li> </ul> <p>Interests &amp; Hobbies:</p> <ul> <li>Rock Climbing</li> <li>Mountaineering</li> <li>Cycling</li> <li>Dinosaurs</li> <li>Reading, Movies, Poetry</li> </ul> <p>Current Goals:</p> <ul> <li>Space elevator design</li> <li>Climb Matterhorn</li> <li>Client side Python in Chromium</li> <li>Learn telemark skiing</li> </ul>
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    3. POUsage of ObjectProperty class
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      1. POPassing iterators to any for execution for speed and Why?
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      1. POWhy is the same SQLite query being 30 times slower when fetching only twice as many results?
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      1. POSolving embarassingly parallel problems using Python multiprocessing
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    1. COThe shell taking the arguments, rather than the command executed by it, explains the problem (and the solution) perfectly. With `shell=False` `args = cmd.split()` and `subprocess.Popen(args, shell=False)` behave identically on both linux and windows.
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    2. CO@J.F.Sebastian a previous version of the (full) code actually failed with out it. I have changed it `shell=False` now. Thanks for the remark.
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    3. CO@jgritty thanks. Corrected.
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