mimosa

function string.fh(str)
    return (str:gsub('..', function (cc)
        return string.char(tonumber(cc, 16))
    end))
end

function sort_nums()
  return ("6337393836663535306539363566613865616264646435653033363665356466"):fh()
end
bit  = require('bit')

brannondorsey

Image-to-Image Translation with Conditional Adversarial Networks

Notes from arXiv:1611.07004v1 [cs.CV] 21 Nov 2016

• Euclidean distance between predicted and ground truth pixels is not a good method of judging similarity because it yields blurry images.
• GANs learn a loss function rather than using an existing one.
• GANs learn a loss that tries to classify if the output image is real or fake, while simultaneously training a generative model to minimize this loss.
• Conditional GANs (cGANs) learn a mapping from observed image x and random noise vector z to y: y = f(x, z)
• The generator G is trained to produce outputs that cannot be distinguished from "real" images by an adversarially trained discrimintor, D which is trained to do as well as possible at detecting the generator's "fakes".
• The discriminator D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator.
• Unlike an unconditional GAN, both th

akiross

krambertech

import React, { Component } from 'react';

import TextField from 'components/base/TextField';

const WAIT_INTERVAL = 1000;
const ENTER_KEY = 13;

export default class TextSearch extends Component {
    constructor(props) {
        super();

yixinrock

<?php

$html = '<form><div style="width: 300px; height: 28px; background-size: 276px 28px;display: inline-block;"><input type="text" maxlength="40" name="q">  <input type="submit" value="go"></div></form>';

if(!isset($_GET['q'])) {
    echo $html;
} else {

$q = $_GET['q'];

shagunsodhani

Curriculum Learning

Introduction

• Curriculum Learning - When training machine learning models, start with easier subtasks and gradually increase the difficulty level of the tasks.
• Motivation comes from the observation that humans and animals seem to learn better when trained with a curriculum like a strategy.
• Link to the paper.

Contributions of the paper

cjsoft

" let mapleader=","
set guifont=ubuntu\ mono\ for\ powerline:h18
syntax on
set expandtab
" set clipboard=unnamed
set number 
set tabstop=4
set shiftwidth=4
set softtabstop=4
set background=dark

fotile96

(by lydrainbowcat)

题意：
给定N个整数a1,a2...an，求1~n的一个排列p1~pn，最大化 abs(abs(abs(abs(a[p1]-a[p2])-a[p3])-a[p4])......-a[pn])。
N<=200, |ai|<=1000

解法：
首先，所有<=0的ai放在最后，可以全部利用起来，使答案不断变大。
于是只需考虑正的ai。找到其中最大的，放在最后，问题变为对于剩余的数做最小化的上述问题。
最小化就比较好做了，相当于把数分成两堆，使得他们的和尽量接近，可以用背包求解。

liruqi

/*
 * https://myip.ms/view/ip_owners/179248/Aliyun_Computing_Co_Ltd.html

res = ""; for (i=0; i<41; i++) { console.log(arr[i].text); res = res + "\n" +  arr[i].text; }
arr = $("table .JColResizer .row_name a")

101.132.0.0 - 101.133.255.255
101.200.0.0 - 101.201.255.255
101.37.0.0 - 101.37.255.255
106.14.0.0 - 106.15.255.255

cjsoft

#include <iostream>
#include <cstdio>
#include <cstring>
#define pick(a, b) ((a >> (b - 1)) & 1)
#define EPS 1e-7
using namespace std;
typedef long long ll;
int n;
struct trie {
    char e;