Alex Wang KeAWang

"Scar Tissues Make Relationships Wear Out"

04/26/2103. From a lecture by Professor John Ousterhout at Stanford, class CS142.

This is my most touchy-feely thought for the weekend. Here’s the basic idea: It’s really hard to build relationships that last for a long time. If you haven’t discovered this, you will discover this sooner or later. And it's hard both for personal relationships and for business relationships. And to me, it's pretty amazing that two people can stay married for 25 years without killing each other.

[Laughter]

> But honestly, most professional relationships don't last anywhere near that long. The best bands always seem to break up after 2 or 3 years. And business partnerships fall apart, and there's all these problems in these relationships that just don't last. So, why is that? Well, in my view, it’s relationships don't fail because there some single catastrophic event to destroy them, although often there is a single catastrophic event around the the end of the relation

Save these files as ~/.config/systemd/user/some-service-name.*

Run this now and after any modifications: systemctl --user daemon-reload

Try out the service (oneshot): systemctl --user start some-service-name

Check logs if something is wrong: journalctl -u --user-unit some-service-name

Start the timer after this user logs in: systemctl --user enable --now some-service-name.timer

Setup:

Add this code to your ~/.bash_aliases file.

function gdrive_download () {
  CONFIRM=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate "https://docs.google.com/uc?export=download&id=$1" -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')
  wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$CONFIRM&id=$1" -O $2
  rm -rf /tmp/cookies.txt
}

	import numpy as np
	import pylab as pl
	from numpy import fft

	def fourierExtrapolation(x, n_predict):
	n = x.size
	n_harm = 10 # number of harmonics in model
	t = np.arange(0, n)
	p = np.polyfit(t, x, 1) # find linear trend in x
	x_notrend = x - p[0] * t # detrended x

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

 """
 Simple example of building your own context manager.
 Resources:
 - http://preshing.com/20110920/the-python-with-statement-by-example/
 - https://docs.python.org/3/library/contextlib.html
 - PEP 343 -- the "with" statement: https://www.python.org/dev/peps/pep-0343/
 """

	"""
	Author: Awni Hannun

	This is an example CTC decoder written in Python. The code is
	intended to be a simple example and is not designed to be
	especially efficient.

	The algorithm is a prefix beam search for a model trained
	with the CTC loss function.

	import math
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	from torch.nn.modules.utils import _pair, _quadruple


	class MedianPool2d(nn.Module):
	""" Median pool (usable as median filter when stride=1) module.

	import torch
	from torch import LongTensor
	from torch.nn import Embedding, LSTM
	from torch.autograd import Variable
	from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

	## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
	#
	# Step 1: Construct Vocabulary
	# Step 2: Load indexed data (list of instances, where each instance is list of character indices)

	import numpy as np
	import os
	import time
	import warnings
	import pickle
	# from accimage import Image
	from PIL import Image
	import io

	try: