cotsog

sudo: required
script:
  - sudo unlink /usr/bin/gcc && sudo ln -s /usr/bin/gcc-5 /usr/bin/gcc
  - gcc --version
addons:
  apt:
    sources:
    - ubuntu-toolchain-r-test
    packages:
    - gcc-5

manuelmenzella

#ifndef PRIORITY_QUEUE_H_
#define PRIORITY_QUEUE_H_

#include <iostream>

#include <vector>
#include <unordered_map>

#include <utility>
#include <functional>

steve-jansen

This script enables you stop and start Symantec Endpoint Protection on OS X

Installation

sudo curl https://gist.githubusercontent.com/steve-jansen/61a189b6ab961a517f68/raw/sep -o /usr/local/bin/sep
sudo chmod 755 /usr/local/bin/sep
sudo chown root:staff /usr/local/bin/sep

fmela

#include <vector>
#include <cassert>

// Call |f| with every length-K combination of { 1, 2, ..., N }, in ascending
// lexicographic order.
template<typename F>
void enumerate_combinations(size_t N, size_t K, const F& f) {
	assert(K <= N);
	std::vector<size_t> position;
	// Initialize with initial position.

fmela

int f(int x, int negate) {
    return (x ^ -negate) + negate;
}

fmela

SOURCES = $(wildcard *.cxx)
HEADERS = $(wildcard *.hxx)

OBJECTS = $(SOURCES:%.cxx=%.o)
PROGRAM = $(shell basename `pwd`)

CC := $(shell which clang || which gcc)
CFLAGS = -Wall -W -O -fno-exceptions -fno-rtti
LIBS = stdc++ m
LDFLAGS = $(LIBS:%=-l%)

fmela

SOURCES = $(wildcard *.c)
HEADERS = $(wildcard *.h)
 
OBJECTS = $(SOURCES:%.c=%.o)
PROGRAM = $(shell basename `pwd`)
 
CC := $(shell which clang || which gcc)
CFLAGS = -Wall -W -O
LIBS =
LDFLAGS = $(LIBS:%=-l%)

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

shanselman

{
{I have|I've} been {surfing|browsing} online more than {three|3|2|4} hours today, yet I never found any interesting article like yours. {It's|It
is} pretty worth enough for me. {In my opinion|Personally|In my view}, if all {webmasters|site owners|website owners|web owners} and bloggers made good content as
you did, the {internet|net|web} will be {much more|a lot more}
useful than ever before.|
I {couldn't|could not} {resist|refrain from} commenting. {Very well|Perfectly|Well|Exceptionally well} written!|
{I will|I'll} {right away|immediately} {take hold of|grab|clutch|grasp|seize|snatch}
your {rss|rss feed} as I {can not|can't} {in finding|find|to find} your {email|e-mail} subscription {link|hyperlink} or {newsletter|e-newsletter} service. Do {you have|you've} any?
{Please|Kindly} {allow|permit|let} me {realize|recognize|understand|recognise|know} {so that|in order that} I {may just|may|could} subscribe.
Thanks.|

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD