silky

Below I collected relevant links and papers more or less pertaining to the subject of tetrahedral meshes. 
It's an ever-growing list.

------------------------------
Relevant links: 

http://en.wikipedia.org/wiki/Types_of_mesh
http://en.wikipedia.org/wiki/Tetrahedron
http://en.wikipedia.org/wiki/Simplicial_complex

silky

{- 
    Zachary Weaver <zaw6@pitt.edu>
    JSONParser.hs
    Version 0.1.1
    
    A simple example of parsing JSON with Parsec in haskell.  Note that 
    since the primary point of this excersize is demonstration, 
    Text.Parsec.Token was avoided to expose more of the grammar.  Also,
    complicated optimizations and shorcuts were avoided (mostly).

silky

do androids dream of cooking?

The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.

The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training.

It seems to be in a format intended to be read by a program called Meal-Master, therefore you will see those lines repeated all over:

silky

(ns repl-test.mud-experiment
  (:use [overtone.live])
  (:use [overtone.studio.scope])
  (:use mud.core)
  (:require [mud.timing :as timing]))

(defn bpm->rate-of-16 [bpm]
  (* (/ bpm 60) 4.))

(ctl timing/root-s :rate (bpm->rate-of-16 127))

silky

Direct links to free Springer books (pdf versions)

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

silky

{
  "type": "object",
  "properties": {
    "AWSEBDockerrunVersion": {
      "type": "integer",
      "minimum": 1,
      "maximum": 2
    },
    "authentication": {
      "type": "object",

silky

#! /usr/bin/env python

""" Convert values between RGB hex codes and xterm-256 color codes.

Nice long listing of all 256 colors and their codes. Useful for
developing console color themes, or even script output schemes.

Resources:
* http://en.wikipedia.org/wiki/8-bit_color
* http://en.wikipedia.org/wiki/ANSI_escape_code

silky

Colours in terminal

It's a common confusion about terminal colours... Actually we have this:

• plain ascii
• ansi escape codes (16 colour codes with bold/italic and background)
• 256 colour palette (216 colours + 16 ansi + 24 gray) (colors are 24bit)
• 24bit true colour ("888" colours (aka 16 milion))

printf "\x1b[${bg};2;${red};${green};${blue}m\n"

silky

Experiments

• http://absulit.com/portfolio/demo/webvr/001/
• https://www.clicktorelease.com/code/polygon-shredder/vr/ #positional
• https://www.clicktorelease.com/tmp/threejs/webvr-physics/ #positional #input #haptic
• https://janusweb.metacade.com/ https://github.com/jbaicoianu/janusweb/
• https://jzitelli.github.io/poolvr/
• https://kuva.io/block-background/ #positional #input
• https://www.shadertoy.com/results?filter=vr
• https://vr-batted-ball-vis.herokuapp.com/index.html

silky

# Initialize placeholders for feeding in to the queue
pl_queue_screens = tf.placeholder(tf.float32, shape=[config.seq_length, config.image_size, config.image_size, config.input_channels], name="queue_inputs")
pl_queue_targets = tf.placeholder(tf.uint8, shape=[config.seq_length], name="queue_targets_cnt")

#  ...

capacity = config.min_after_dequeue + 10 * (config.num_gpus*config.batch_size)
q = tf.RandomShuffleQueue(