sangohan

import it.unimi.dsi.fastutil.objects.*;
public static void main(String[] args) {
		BufferedReader stdin = new BufferedReader(new InputStreamReader(System.in));
		Object2IntMap h = new Object2IntMap();
		String l;
		int max = 0;
		try {
			while ((l = stdin.readLine()) != null) {
				int x = 1;
				if (h.containsKey(l)) {

sangohan

data:text/html, <style type="text/css">#e{position:absolute;top:0;right:0;bottom:0;left:0;}</style><div id="e"></div><script src="http://d1n0x3qji82z53.cloudfront.net/src-min-noconflict/ace.js" type="text/javascript" charset="utf-8"></script><script>var e=ace.edit("e");e.setTheme("ace/theme/monokai");e.getSession().setMode("ace/mode/ruby");</script>

sangohan

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 * 
 *      http://www.apache.org/licenses/LICENSE-2.0
 * 

sangohan

<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0
                             http://maven.apache.org/maven-v4_0_0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>com.darkflame</groupId>
    <artifactId>supersimplesemantics</artifactId>
    <packaging>jar</packaging>
    <version>1.0-SNAPSHOT</version>
    <name>super simple semantics</name>

sangohan

1. General Background and Overview

• 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.
• Models and Issues in Data Stream Systems
• 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
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

sangohan

abstract ArrayRead<T>(Array<T>) from Array<T> {
    @:arrayAccess inline function get(i:Int):T return this[i];
    public var length(get,never):Int;
    inline function get_length() return this.length;
}

sangohan

import haxe.macro.Context;
import haxe.macro.Expr;

using haxe.macro.ExprTools;

class Main
{
    inline static var QUOTED_FIELD_PREFIX = "@$__hx__";

    static function main()

sangohan

    // Live video stream management for HTML5 video. Uses FFMPEG to connect to H.264 camera stream, 
    // Camera stream is remuxed to a MP4 stream for HTML5 video compatibility and segments are recorded for later playback
var liveStream = function (req, resp) {                                            // handle each client request by instantiating a new FFMPEG instance
    // For live streaming, create a fragmented MP4 file with empty moov (no seeking possible).

    var reqUrl = url.parse(req.url, true)
    var cameraName = typeof reqUrl.pathname === "string" ? reqUrl.pathname.substring(1) : undefined;
    if (cameraName) {
        try {
            cameraName = decodeURIComponent(cameraName);

sangohan

//frag shader
precision mediump float;
uniform vec4 I;

vec3 Z(vec3 p,float a){
    return vec3(cos(a)*p.y+sin(a)*p.x,cos(a)*p.x-sin(a)*p.y,p.z);
}


float F(vec3 P) {

sangohan

#!/bin/sh

# Alot of these configs have been taken from the various places
# on the web, most from here
# https://github.com/mathiasbynens/dotfiles/blob/master/.osx

# Set the colours you can use
black='\033[0;30m'
white='\033[0;37m'
red='\033[0;31m'