dualbus

alienino              3511c0997339cfcccccca39f214322eb22e8fc43
all'equipaggio        14555555ec8dcf1400db9b375c5b8ca836362d8b
Anacyclus             182c40fc4df5b4d997feeeeee22c4dbf059a95d4
bajar                 1352a687f6840df8801aaaaaadf71de84b816f86
bandwagon's           f6e6deb71111110839bc14dd9cbab6eb7b16f09c
barbihecho            bc45de24f03f2a086666668e2a0812a5f270c8cb
calcitrant            86d4ffffff9aae00ace440e93c1d87bb4ec8b56c
cornetti              000000f636f0d7cbc963a62f3a1bc87c9c985a04
crépir                a21303cfa9b7c6f0cccccc19cc59556a188ccac7
cyclosporin's         b5baaaaaa744f480586a905f692cdec2fa0a1919

lelandbatey

Description

This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.

The script is here:

#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"

Results

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

maxlapshin

-module(crc17).

-export([calc/1]).

calc(List) ->  calc(List,16#FFFF).

calc(<<>>,CRC) ->
   CRC;
calc(<<Value:8,Rest/binary>>,CRC) ->
  Index = (CRC bxor Value) band 255,

robertoaloi

Start an instance of the Erlang run-time system

$ erl

Show information about processes

> i().

Spawn a new process which sleeps for 10 seconds

jlong

var parser = document.createElement('a');
parser.href = "http://example.com:3000/pathname/?search=test#hash";

parser.protocol; // => "http:"
parser.hostname; // => "example.com"
parser.port;     // => "3000"
parser.pathname; // => "/pathname/"
parser.search;   // => "?search=test"
parser.hash;     // => "#hash"
parser.host;     // => "example.com:3000"

dreid

#!/bin/sh

OTP_VERSION=$(erl -noinput \
                  -noshell \
                  -eval 'io:fwrite(erlang:system_info(otp_release)).' \
                  -s init stop)

PLT_DIR="$HOME/.dialyzer/${OTP_VERSION}"

CURRENT_APP=$(basename $(pwd));

antirez

diff --git a/iPhoneTrackingAppDelegate.m b/iPhoneTrackingAppDelegate.m
index 1d22ecb..4af2bcf 100644
--- a/iPhoneTrackingAppDelegate.m
+++ b/iPhoneTrackingAppDelegate.m
@@ -146,7 +146,7 @@
     return NO;
   }

-  const float precision = 100;
+  const float precision = 10000;

jdmaturen

import logging

from math import exp
from random import random
from time import sleep
from time import time
from uuid import uuid1

from redis.exceptions import WatchError

tompaton

#!/usr/bin/env python

# kd-tree index and nearest neighbour search
# includes doctests, run with: python -m doctest kdtree.py

class KDTree(object):
    """
    kd-tree spatial index and nearest neighbour search
    http://en.wikipedia.org/wiki/Kd-tree
    """