LSinev

Resource for answer in https://tex.stackexchange.com/questions/109467/footnote-in-tabular-environment .

Packages to be tested:

footnote credit to https://tex.stackexchange.com/a/109471/250119
footnotehyper package --- direct replacement for footnote package.
tablefootnote credit to https://tex.stackexchange.com/a/35327/250119 or https://tex.stackexchange.com/a/35328/250119 or https://tex.stackexchange.com/a/35327/250119
longtable package. (it has native footnote support. As mentioned in the documentation, \footnote will not work in the head or foot sections of the table.)
ftnxtra package credit to https://tex.stackexchange.com/a/35276/250119
threeparttable package credit to https://tex.stackexchange.com/a/327385/250119 or https://tex.stackexchange.com/a/1590/250119

Reproducibility

ML experiments may be very hard to reproduce. You have a lot of hyperparameters, different dataset splits, different ways to preprocess your data, bugs, etc. Ideally, you should log data split (already preprocessed), all hyperparameters (including learning rate scheduling), the initial state of your model and optimizer, random seeds used for initialization, dataset shuffling and all of your code. Your GPU is also should be in deterministic mode (which is not the default mode). For every single model run. This is a very hard task. Different random seed can significantly change your metrics and even GPU-induced randomness can be important. We're not solving all of these problems, but we need to address at least what we can handle.

For every result you report in the paper you need (at least) to:

Track your model and optimizer hyperparameters (including learning rate schedule)
Save final model parameters
Report all of the parameters in the pap

How to get Inbound Liquidity on the Lightning Network

There are several ways you can find inbound liquidity on LN.

1. Wait

If your node is up 24x7 and you have some outgoing channels, the network will connect to you if you simply wait.

However, it might take a couple of weeks to get a significant amount of incoming liquidity, and ideally you want your outgoing liquidity to match too.

	# Convert GPT-2 huggingface transformer model to ggml format
	#
	# Load the model using GPT2Model.
	# Iterate over all variables and write them to a binary file.
	#
	# For each variable, write the following:
	# - Number of dimensions (int)
	# - Name length (int)
	# - Dimensions (int[n_dims])
	# - Name (char[name_length])

	from typing import Union, List
	from dataclasses import dataclass


	class Validations:
	def __post_init__(self):
	"""Run validation methods if declared.

	The validation method can be a simple check
	that raises ValueError or a transformation to

	def load_graph(frozen_graph_filename):
	with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
	graph_def = tf.GraphDef()
	graph_def.ParseFromString(f.read())

	with tf.Graph().as_default() as graph:
	tf.import_graph_def(graph_def)
	return graph

	restored_graph = load_graph("frozen_graph.pb")

	from tensorflow.python.framework.graph_util import convert_variables_to_constants
	from tensorflow.python.tools.optimize_for_inference_lib import optimize_for_inference

	def freeze_keras_model(model, export_path=None, clear_devices=True):
	"""
	Freezes the state of a session into a pruned computation graph.

	@param model The Keras model to be optimized for inference.
	@param clear_devices Remove the device directives from the graph for better portability.
	@return The frozen graph definition.

	def f1_loss(y_true:torch.Tensor, y_pred:torch.Tensor, is_training=False) -> torch.Tensor:
	'''Calculate F1 score. Can work with gpu tensors

	The original implmentation is written by Michal Haltuf on Kaggle.

	Returns
	-------
	torch.Tensor
	`ndim` == 1. 0 <= val <= 1

	#!/bin/bash

	# Anonymization of source code for blind review paper submission
	# using sed to replace words with XXX. It also removes the .git
	# directory in the anonymized project to avoid exposing any
	# sensitive information about the author(s).
	#
	# Usage: anonymize.sh /path/to/project words_to_anonymize.txt
	# words_to_anonymize.txt contains a word in each line
	#

	#!/bin/bash

	NVIDIA_DRIVER=/tmp/NVIDIA-Linux-x86_64-410.78.run
	DRIVER_URL=http://us.download.nvidia.com/XFree86/Linux-x86_64/410.78/NVIDIA-Linux-x86_64-410.78.run
	BLACKLIST_FILE=/etc/modprobe.d/blacklist-nvidia-nouveau.conf

	if [ ! -f $BLACKLIST_FILE ]; then
	echo "Disabling nouveau driver and then rebooting..."
	bash -c "echo blacklist nouveau > /etc/modprobe.d/blacklist-nvidia-nouveau.conf"
	bash -c "echo options nouveau modeset=0 >> /etc/modprobe.d/blacklist-nvidia-nouveau.conf"