forax

import static java.lang.invoke.MethodHandles.constant;
import static java.lang.invoke.MethodHandles.dropArguments;
import static java.lang.invoke.MethodHandles.exactInvoker;
import static java.lang.invoke.MethodHandles.foldArguments;
import static java.lang.invoke.MethodHandles.lookup;
import static java.lang.invoke.MethodType.methodType;

import java.lang.invoke.MethodHandle;
import java.lang.invoke.MethodHandles;
import java.lang.invoke.MethodHandles.Lookup;

sortie

#include <sys/socket.h>

#include <err.h>
#include <errno.h>
#include <netinet/in.h>
#include <stdio.h>
#include <string.h>

int main(void)
{

apangin

_hashCode                               java/lang/Object.hashCode()I
_getClass                               java/lang/Object.getClass()Ljava/lang/Class;
_clone                                  java/lang/Object.clone()Ljava/lang/Object;
_notify                                 java/lang/Object.notify()V
_notifyAll                              java/lang/Object.notifyAll()V
_dabs                                   java/lang/Math.abs(D)D
_dsin                                   java/lang/Math.sin(D)D
_dcos                                   java/lang/Math.cos(D)D
_dtan                                   java/lang/Math.tan(D)D
_datan2                                 java/lang/Math.atan2(DD)D

elizarov

import kotlin.coroutines.experimental.*
import kotlin.coroutines.experimental.intrinsics.COROUTINE_SUSPENDED
import kotlin.coroutines.experimental.intrinsics.suspendCoroutineOrReturn

@RestrictsSuspension
abstract class StackComputation {
    abstract suspend fun <T> push(block: suspend StackComputation.() -> T): T
}

fun <T> compute(block: suspend StackComputation.() -> T): T =

viktorklang

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