retronym

trait Inv[A] { self: Contra[A] => } 
trait Contra[-A] extends Inv[A @uncheckedVariance]

implicit val C1: Contra[Any] = new Contra[Any] {}
implicit val C2: Contra[Int] = new Contra[Int] {}

assert(implicitly[Inv[Any]] eq C1)                                                                                                                                        
assert(implicitly[Contra[Any]] eq C1)

assert(implicitly[Inv[Int]] eq C2)

pksunkara

[user]
	name = Pavan Kumar Sunkara
	email = [email protected]
	username = pksunkara
[init]
	defaultBranch = master
[core]
	editor = nvim
	whitespace = fix,-indent-with-non-tab,trailing-space,cr-at-eol
	pager = delta

virtualstaticvoid

# Modify this file accordingly for your specific requirement.
# http://www.thegeekstuff.com
# 1. Delete all existing rules
iptables -F

# 2. Set default chain policies
iptables -P INPUT DROP
iptables -P FORWARD DROP
iptables -P OUTPUT DROP

johntyree

#!/usr/bin/env sh

# Download lists, unpack and filter, write to stdout
curl -s https://www.iblocklist.com/lists.php \
  | sed -n "s/.*value='\(http:.*=bt_.*\)'.*/\1/p" \
  | xargs wget -O - \
  | gunzip \
  | egrep -v '^#'

bwhite

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np

kevinwright

import java.util.concurrent.ConcurrentLinkedQueue
import java.util.concurrent.atomic.AtomicInteger
import scala.concurrent.ExecutionContext
import scala.util.control.NonFatal
import scala.annotation.tailrec

//inspired by https://gist.github.com/viktorklang/4552423

object ThrottledExecutionContext {
  def apply(maxConcurrents: Int)(implicit context: ExecutionContext): ExecutionContext = {

apurvam

/* Instructions on compilation and execution
 * =========================================
 *
 * Compile this program with pthreads:
 *
 *   g++ -Wall -lpthread -o graphdb-simulator graphdb-simulator.cpp
 *
 * Before you run this program, you need to create the following
 * directories:
 *

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

tsiege

ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!






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davidfowl

$/
  artifacts/
  build/
  docs/
  lib/
  packages/
  samples/
  src/
 tests/