highfestiva

#!/usr/bin/env python3

import argparse
from math import ceil, floor


sign = lambda amt: -1 if amt<0 else (1 if amt>0 else 0)


class dobj:

johncantrell97

Today (April 16th 2019 at noon) the first major clues to discover key #1 was set to be released in a few cities. A QR code with the words 'orbital' were found at these locations and looked like this: (https://imgur.com/a/6rNmz7T). If you read the QR code with your phone you will be directed to this url: https://satoshistreasure.xyz/k1

At this URL you are prompted to input a passphrase to decrypt the first shard. An obvious first guess was to try the word 'orbital' from the QR code. Not suprisingly this worked! This reveals a congratulations page and presents the first key shard:

ST-0001-a36e904f9431ff6b18079881a20af2b3403b86b4a6bace5f3a6a47e945b95cce937c415bedaad6c86bb86b59f0b1d137442537a8.

Now, we were supposed to wait until April 17th to get clues from the other cities for keys #2 and #3 but that wouldn't stop me from digging around with all the new information we had. All that time "playing" notpron (http://notpron.org/notpron/) years ago was going to help me here.

The first thing I noticed was

pquentin

#!/usr/bin/env python3

import asyncio
import time

import aiohttp

START = time.monotonic()

Nikhil-Kasukurthi

import cv2
import numpy as np
import torch
from torchvision import models, transforms
from torch.autograd import Variable
import torch
import torch.nn as nn
import pickle
import os
import argparse

fortinmike

// NOTE: Copied and cleaned-up from Unity/Libraries/VuforiaNativeRendererController.mm
// REASON: The .mm file did not expose an interface and categorically overrode the Unity app delegate subclass
// using `IMPL_APP_CONTROLLER_SUBCLASS(VuforiaNativeRendererController)`, preventing us from providing our own
// subclass of `VuforiaNativeRendererController` to serve as the final app delegate. Diff this file with the
// source file (specified above) to view the applied changes.

#import "CustomVuforiaNativeRendererController.h"
#import "VuforiaRenderDelegate.h"

// Unity native rendering callback plugin mechanism is only supported

lassoan

import SampleData
sampleDataLogic = SampleData.SampleDataLogic()
masterVolumeNode = sampleDataLogic.downloadMRHead()

# Create segmentation
segmentationNode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLSegmentationNode")
segmentationNode.CreateDefaultDisplayNodes() # only needed for display
segmentationNode.SetReferenceImageGeometryParameterFromVolumeNode(masterVolumeNode)
addedSegmentID = segmentationNode.GetSegmentation().AddEmptySegment("skin")

Geoyi

How to install virtualenv:

Install pip first

sudo apt-get install python3-pip

Then install virtualenv using pip3

sudo pip3 install virtualenv 

Thimira

'''
Using Bottleneck Features for Multi-Class Classification in Keras

We use this technique to build powerful (high accuracy without overfitting) Image Classification systems with small
amount of training data.

The full tutorial to get this code working can be found at the "Codes of Interest" Blog at the following link,
https://www.codesofinterest.com/2017/08/bottleneck-features-multi-class-classification-keras.html

Please go through the tutorial before attempting to run this code, as it explains how to setup your training data.

mingrui

using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using UnityEditor;

public class CleanupMissingScriptsHelper{
    [MenuItem("Edit/Cleanup Missing Scripts")]
    static void CleanupMissingScripts() {
        for (int i = 0; i < Selection.gameObjects.Length; i++) {
            var gameObject = Selection.gameObjects[i];

tomokishii

Modern Gradient Boosting models - how to use GridSearchCV

My current environment is...

• Python 3.5.2
• Scikit-learn 0.18.1
• XGBoost (python-package) 0.6
• LightGBM (python-package) v2.0