Chen Yu cwhy

My Ubuntu theme config

A Unity theme inspired by OSX Yosemite based on Ambiance.

sudo apt-add-repository ppa:bsundman/themes
sudo apt-get update
sudo apt-get install -y yosembiance-theme

sudo apt install build-essential zlib1g-dev libssl-dev openssl sqlite libsqlite3-dev libbz2-dev tk-dev libffi-dev
mkdir $HOME/local
cd $HOME/local
mkdir bin
mkdir lib
mkdir include

Create a file in $HOME/local/set_env.py

This file shows how to set up a minimal web font-end stack with `Yarn`, `webpack` and `Typescript`

Install newest NodeJS

curl -sL https://deb.nodesource.com/setup_8.x | sudo -E bash -
sudo apt-get install -y nodejs

[1]

Install yarn

(Tested on 16.04.3)

System Config

Choose closest update server location
Disable activity record in Security & Privacy
Change Firefox download settings
Enable workspace in Appearance/Behavior
[Optional] Dual screen setup (remove sticky edges)
[Optional] Add printers

Learning Active Learning from Data

In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection procedure as a regression problem we are not restricted to working with existing AL heuristics; instead, we learn strategies based on experience from previous AL outcomes. We show that a strategy can be learnt either from simple synthetic 2D datasets or from a subset of domain-specific data. Our method yields strategies that work well on

	# By CWhy
	# [email protected]
	import numpy as np
	import tensorflow as tf

	# Generate equation for mixed moments
	def moments_eqn(_l, order):
	if order == 1:
	return _l
	r = []

	letsencrypt certonly --manual
	# And follow the instructions