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View ncollide2d-crash.txt
thread 'main' panicked at 'Matrix index out of bounds.', /Users/darth/.cargo/registry/src/github.com-1ecc6299db9ec823/ncollide2d-0.26.1/src/query/ray/ray_aabb.rs:257:13
stack backtrace:
0: 0x103a862b4 - std::backtrace_rs::backtrace::libunwind::trace::h9b9011e5262e4f26
at /rustc/0edce6f4bbb4514482537f569f0b8ef48e71e0a0/library/std/src/../../backtrace/src/backtrace/libunwind.rs:90:5
1: 0x103a862b4 - std::backtrace_rs::backtrace::trace_unsynchronized::hdb5ec51860531ffc
at /rustc/0edce6f4bbb4514482537f569f0b8ef48e71e0a0/library/std/src/../../backtrace/src/backtrace/mod.rs:66:5
2: 0x103a862b4 - std::sys_common::backtrace::_print_fmt::h8851f8ff0b9f14ac
at /rustc/0edce6f4bbb4514482537f569f0b8ef48e71e0a0/library/std/src/sys_common/backtrace.rs:67:5
3: 0x103a862b4 - <std::sys_common::backtrace::_print::DisplayBacktrace as core::fmt::Display>::fmt::h8afa911bc5282e85
View DarkFlame.shader
// Made with Amplify Shader Editor
// Available at the Unity Asset Store - http://u3d.as/y3X
Shader "DarkFlame"
{
Properties
{
_Color("Color", Color) = (0.6226415,0.2907618,0.2907618,0)
}
View BehaviourTree.json
{
"type": "NodeCanvas.BehaviourTrees.BehaviourTree",
"nodes": [
{
"_position": {
"x": 717.0,
"y": 139.0
},
"$type": "NodeCanvas.BehaviourTrees.Sequencer",
"$id": "0"
View autotools.nix
pkgs: attrs:
with pkgs;
let defaultAttrs = {
builder = "${bash}/bin/bash";
args = [ ./builder.sh ];
baseInputs = [ gnutar gzip gnumake gcc binutils-unwrapped coreutils gawk gnused gnugrep findutils patchelf ];
buildInputs = [];
system = builtins.currentSystem;
};
in
View github-plugins.md
  • Codacy
  • Coveralls
  • Travis
  • Lean Board
  • Starhub
  • Code Dog Auto Merge
  • Greenkeeper
  • Dependabot preview
  • Hound
  • Imgbot
View summ-papers.md
  • Rush, A. M., Chopra, S., and Weston, J. (2015). A neural attention model for abstractive sentence summarization.
  • Chopra, S., Auli, M., and Rush, A. M. (2016). Abstractive sentence summarization with attentive recurrent neural networks.
  • Nallapati, R., Zhou, B., and Ma, M. (2016). Classify or select: Neural architectures for extractive document summarization.
  • Nallapati, R., Zhou, B., dos Santos, C. N., Gülçehre, Ç., and Xiang, B. (2016). Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond.
  • Nallapati, R., Zhai, F., and Zhou, B. (2017). Summarunner: A recurrent neural network based sequence model for extractive summarization of documents.
  • See, A., Liu, P. J., and Manning, C. D. (2017). Get to the point: Summarization with pointer-generator networks.
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need.
  • Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz K
View vycislitelnost.tex
\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage{todonotes}
\usepackage{amsmath}
\usepackage{amsthm}
\usepackage{amssymb}
\newcommand{\inlinecode}{\texttt}
\title{Státnice - Vyčíslitelnost}
View glow-layers.txt
QuantizeImage/Forward/ : x=[8, 48, 48, 3] z=[None] logdet=[8]
SqueezingLayer/Forward/Scale1 : x=[8, 24, 24, 12] z=[None] logdet=[8]
ActnormBiasLayer/Forward/ : x=[8, 24, 24, 12] z=[None] logdet=[8]
ActnormScaleLayer/Forward/ : x=[8, 24, 24, 12] z=[None] logdet=[8]
ChainLayer/Forward/ : x=[8, 24, 24, 12] z=[None] logdet=[8]
ActnormLayer/Forward/ : x=[8, 24, 24, 12] z=[None] logdet=[8]
InvertibleConv1x1Layer/Forward/ : x=[8, 24, 24, 12] z=[None] logdet=[8]
AffineCouplingLayer/Forward/ : x=[8, 24, 24, 12] z=[None] logdet=[8]
ChainLayer/Forward/Step1 : x=[8, 24, 24, 12] z=[None] logdet=[8]
ActnormBiasLayer/Forward/ : x=[8, 24, 24, 12] z=[None] logdet=[8]
View jitchol.py
import numpy as np
import numpy.linalg as linalg
import logging
def jitchol(A, maxtries=6):
A = np.ascontiguousarray(A)
diagA = np.diag(A)
if np.any(diagA <= 0.):
View config.yml
# This file is a template for a new experiment.
# It specifies how the experiment is to be created,
# but does not hold its state.
# Name of the experiment
name: {{experiment_name}}
# A List of hyperparamters to be tuned.
hyperparameters:
# Each hyperparameter needs to specify: