text:
@InProceedings{wong13,
  pdf = {http://jmlr.org/proceedings/papers/v28/wong13.pdf},
  title = {Adaptive Sparsity in {G}aussian Graphical Models},
  author = {Wong, Eleanor and Awate, Suyash and Fletcher, P. Thomas},
  number = {1},
  section = {cycle-1},
  pages = {311-319},
  abstract = {An effective approach to structure learning and parameter estimation for Gaussian graphical models is to impose a sparsity prior, such as a Laplace prior, on the entries of the precision matrix. Such an approach involves a hyperparameter that must be tuned to control the amount of sparsity. In this paper, we introduce a parameter-free method for estimating a precision matrix with sparsity that adapts to the data automatically. We achieve this by formulating a hierarchical Bayesian model of the precision matrix with a non-informative Jeffreys' hyperprior. We also naturally enforce the symmetry and positive-definiteness constraints on the precision matrix by parameterizing it with the Cholesky decomposition. Experiments on simulated and real (cell signaling) data demonstrate that the proposed approach not only automatically adapts the sparsity of the model, but it also results in improved estimates of the precision matrix compared to the Laplace prior model with sparsity parameter chosen by cross-validation.},
}

license: MIT

repository: mreid/papersite

license_type: permissive

repo_name: mreid/papersite

branch_name: refs/heads/master

gha_license_id: MIT

gha_language: TeX

extension: bib


text:
@inproceedings{barreto2018transfer,
  title={Transfer in deep reinforcement learning using successor features and generalised policy improvement},
  author={Barreto, Andr{\'e} and Borsa, Diana and Quan, John and Schaul, Tom and Silver, David and Hessel, Matteo and Mankowitz, Daniel and Zidek, Augustin and Munos, Remi},
  booktitle={International Conference on Machine Learning},
  pages={510--519},
  year={2018}
}

@InProceedings{ganin2014unsupervised,
  title = 	 {Unsupervised Domain Adaptation by Backpropagation},
  author = 	 {Yaroslav Ganin and Victor Lempitsky},
  booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
  pages = 	 {1180--1189},
  year = 	 {2015},
  editor = 	 {Francis Bach and David Blei},
  volume = 	 {37},
  series = 	 {Proceedings of Machine Learning Research},
  publisher = 	 {PMLR},
}
@article{levine2016end,
  title={End-to-end training of deep visuomotor policies},
  author={Levine, Sergey and Finn, Chelsea and Darrell, Trevor and Abbeel, Pieter},
  journal={The Journal of Machine Learning Research},
  volume={17},
  number={1},
  pages={1334--1373},
  year={2016},
  publisher={JMLR. org}
}
@inproceedings{mao2016resource,
  title={Resource management with deep reinforcement learning},
  author={Mao, Hongzi and Alizadeh, Mohammad and Menache, Ishai and Kandula, Srikanth},
  booktitle={Proceedings of the 15th ACM Workshop on Hot Topics in Networks},
  pages={50--56},
  year={2016},
  organization={ACM}
}

@inproceedings{ballard1987modular,
  title={Modular Learning in Neural Networks.},
  author={Ballard, Dana H},
  booktitle={AAAI},
  pages={279--284},
  year={1987}
}
@inproceedings{bengio2012deep,
  title={Deep learning of representations for unsupervised and transfer learning},
  author={Bengio, Yoshua},
  booktitle={Proceedings of ICML Workshop on Unsupervised and Transfer Learning},
  pages={17--36},
  year={2012}
}

@article{williams1992simple,
  title={Simple statistical gradient-following algorithms for connectionist reinforcement learning},
  author={Williams, Ronald J},
  journal={Machine learning},
  volume={8},
  number={3-4},
  pages={229--256},
  year={1992},
  publisher={Springer}
}

@inproceedings{wang2013learning,
  title={Learning a deep compact image representation for visual tracking},
  author={Wang, Naiyan and Yeung, Dit-Yan},
  booktitle={Advances in neural information processing systems},
  pages={809--817},
  year={2013}
}

@inproceedings{ap2014autoencoder,
  title={An autoencoder approach to learning bilingual word representations},
  author={AP, Sarath Chandar and Lauly, Stanislas and Larochelle, Hugo and Khapra, Mitesh and Ravindran, Balaraman and Raykar, Vikas C and Saha, Amrita},
  booktitle={Advances in Neural Information Processing Systems},
  pages={1853--1861},
  year={2014}
}

@inproceedings{silberer2014learning,
  title={Learning grounded meaning representations with autoencoders},
  author={Silberer, Carina and Lapata, Mirella},
  booktitle={Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  volume={1},
  pages={721--732},
  year={2014}
}

@article{silver2016mastering,
  title={Mastering the game of Go with deep neural networks and tree search},
  author={Silver, David and Huang, Aja and Maddison, Chris J and Guez, Arthur and Sifre, Laurent and Van Den Driessche, George and Schrittwieser, Julian and Antonoglou, Ioannis and Panneershelvam, Veda and Lanctot, Marc and others},
  journal={nature},
  volume={529},
  number={7587},
  pages={484},
  year={2016},
  publisher={Nature Publishing Group}
}
@article{DQN,
  author    = {Volodymyr Mnih and
               Koray Kavukcuoglu and
               David Silver and
               Alex Graves and
               Ioannis Antonoglou and
               Daan Wierstra and
               Martin A. Riedmiller},
  title     = {Playing Atari with Deep Reinforcement Learning},
  journal   = {CoRR},
  year      = {2013}
}
@article{DDQN,
  author    = {Hado van Hasselt and
               Arthur Guez and
               David Silver},
  title     = {Deep Reinforcement Learning with Double Q-learning},
  journal   = {CoRR},
  year      = {2015}
}
@article{DuelingDQN,
  author    = {Ziyu Wang and
               Nando de Freitas and
               Marc Lanctot},
  title     = {Dueling Network Architectures for Deep Reinforcement Learning},
  journal   = {CoRR},
  year      = {2015}
}
@article{prioritized_memory,
  author    = {Tom Schaul and
               John Quan and
               Ioannis Antonoglou and
               David Silver},
  title     = {Prioritized Experience Replay},
  journal   = {CoRR},
  year      = {2015}
}
@article{rainbow,
  author    = {Matteo Hessel and
               Joseph Modayil and
               Hado van Hasselt and
               Tom Schaul and
               Georg Ostrovski and
               Will Dabney and
               Daniel Horgan and
               Bilal Piot and
               Mohammad Gheshlaghi Azar and
               David Silver},
  title     = {Rainbow: Combining Improvements in Deep Reinforcement Learning},
  journal   = {CoRR},
  year      = {2017}
}
@misc{gym,
  Author = {Greg Brockman and Vicki Cheung and Ludwig Pettersson and Jonas Schneider and John Schulman and Jie Tang and Wojciech Zaremba},
  Title = {OpenAI Gym},
  Year = {2016}
}
@article{deep_learning_development,
author = {LeCun, Yann and Bengio, Y and Hinton, Geoffrey},
year = {2015},
month = {05},
pages = {436-44},
title = {Deep Learning},
volume = {521},
journal = {Nature}
}

@article{replay_memory_oc,
author = {Lin, Long-Ji},
year = {1993},
month = {01},
pages = {},
title = {Reinforcement learning for robots using neural networks}
}

@article{hierarchical_dqn,
  author    = {Tejas D. Kulkarni and
               Karthik Narasimhan and
               Ardavan Saeedi and
               Joshua B. Tenenbaum},
  title     = {Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction
               and Intrinsic Motivation},
  journal   = {CoRR},
  year      = {2016}
}
@article{distributional_dqn,
  author    = {Will Dabney and
               Mark Rowland and
               Marc G. Bellemare and
               R{\'{e}}mi Munos},
  title     = {Distributional Reinforcement Learning with Quantile Regression},
  journal   = {CoRR},
  year      = {2017}
}

@article{noisy_dqn,
  author    = {Meire Fortunato and
               Mohammad Gheshlaghi Azar and
               Bilal Piot and
               Jacob Menick and
               Ian Osband and
               Alex Graves and
               Vlad Mnih and
               R{\'{e}}mi Munos and
               Demis Hassabis and
               Olivier Pietquin and
               Charles Blundell and
               Shane Legg},
  title     = {Noisy Networks for Exploration},
  journal   = {CoRR},
  year      = {2017}
}

@inproceedings{taylor2007cross,
 author = {Taylor, Matthew E. and Stone, Peter},
 title = {Cross-domain Transfer for Reinforcement Learning},
 booktitle = {Proceedings of the 24th International Conference on Machine Learning},
 series = {ICML '07},
 year = {2007},
 pages = {879--886},
 numpages = {8},
 publisher = {ACM}
}

@inproceedings{taylor2008autonomous,
 author = {Taylor, Matthew E. and Kuhlmann, Gregory and Stone, Peter},
 title = {Autonomous Transfer for Reinforcement Learning},
 booktitle = {Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1},
 series = {AAMAS '08},
 year = {2008},
 pages = {283--290},
 numpages = {8},
 publisher = {International Foundation for Autonomous Agents and Multiagent Systems}
}

@article{gupta2017learning,
  title={Learning invariant feature spaces to transfer skills with reinforcement learning},
  author={Gupta, Abhishek and Devin, Coline and Liu, YuXuan and Abbeel, Pieter and Levine, Sergey},
  journal={arXiv preprint arXiv:1703.02949},
  year={2017}
}

@article{MAML,
  author    = {Chelsea Finn and
               Pieter Abbeel and
               Sergey Levine},
  title     = {Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks},
  journal   = {CoRR},
  year      = {2017},
}

@article{MAESN,
    author    = {Abhishek Gupta and
    Russell Mendonca and
    Yuxuan Liu and
    Pieter Abbeel and
    Sergey Levine},
    title     = {Meta-Reinforcement Learning of Structured Exploration Strategies},
    journal   = {CoRR},
    year      = {2018}
}

@article{parisotto2015actor,
  author    = {Emilio Parisotto and
               Lei Jimmy Ba and
               Ruslan Salakhutdinov},
  title     = {Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning},
  journal   = {CoRR},
  year      = {2015},
}

@inproceedings{mnih2016asynchronous,
  title={Asynchronous methods for deep reinforcement learning},
  author={Mnih, Volodymyr and Badia, Adria Puigdomenech and Mirza, Mehdi and Graves, Alex and Lillicrap, Timothy and Harley, Tim and Silver, David and Kavukcuoglu, Koray},
  booktitle={International conference on machine learning},
  pages={1928--1937},
  year={2016}
}

@inproceedings{andrychowicz2017hindsight,
  title={Hindsight experience replay},
  author={Andrychowicz, Marcin and Wolski, Filip and Ray, Alex and Schneider, Jonas and Fong, Rachel and Welinder, Peter and McGrew, Bob and Tobin, Josh and Abbeel, OpenAI Pieter and Zaremba, Wojciech},
  booktitle={Advances in Neural Information Processing Systems},
  pages={5048--5058},
  year={2017}
}

@inproceedings{ng1999policy,
  title={Policy invariance under reward transformations: Theory and application to reward shaping},
  author={Ng, Andrew Y and Harada, Daishi and Russell, Stuart},
  booktitle={ICML},
  volume={99},
  pages={278--287},
  year={1999}
}

@article{popov2017data,
  author    = {Ivaylo Popov and
               Nicolas Heess and
               Timothy P. Lillicrap and
               Roland Hafner and
               Gabriel Barth{-}Maron and
               Matej Vecerik and
               Thomas Lampe and
               Yuval Tassa and
               Tom Erez and
               Martin A. Riedmiller},
  title     = {Data-efficient Deep Reinforcement Learning for Dexterous Manipulation},
  journal   = {CoRR},
  year      = {2017},
}

@book{goldberg2017neural,
 author = {Goldberg, Yoav and Hirst, Graeme},
 title = {Neural Network Methods in Natural Language Processing},
 year = {2017},
 publisher = {Morgan \& Claypool Publishers}
}

@book{goodfellow2016deep,
    title={Deep Learning},
    author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
    publisher={MIT Press},
    year={2016}
}

@article{hinton2006reducing,
  title={Reducing the dimensionality of data with neural networks},
  author={Hinton, Geoffrey E and Salakhutdinov, Ruslan R},
  journal={science},
  volume={313},
  number={5786},
  pages={504--507},
  year={2006},
  publisher={American Association for the Advancement of Science}
}

@article{deep-learning-methods-and-applications,
 author = {Deng, Li and Yu, Dong},
 title = {Deep Learning: Methods and Applications},
 journal = {Found. Trends Signal Process.},
 issue_date = {June 2014},
 volume = {7},
 month = jun,
 year = {2014},
 issn = {1932-8346},
 pages = {197--387},
 numpages = {191},
 publisher = {Now Publishers Inc.}
}

@article{zhou2017optimizing,
author = {Zhou, Zhenpeng and Li, Xiaocheng and Zare, Richard N.},
title = {Optimizing Chemical Reactions with Deep Reinforcement Learning},
journal = {ACS Central Science},
volume = {3},
number = {12},
pages = {1337-1344},
year = {2017},
}

@article{silver2017mastering,
author = {Silver, David and Schrittwieser, Julian and Simonyan, Karen and Antonoglou, Ioannis and Huang, Aja and Guez, Arthur and Hubert, Thomas and Baker, Lucas and Lai, Matthew and Bolton, Adrian and Chen, Yutian and Lillicrap, Timothy and Hui, Fan and Sifre, Laurent and Driessche, George van den and Graepel, Thore and Hassabis, Demis},
  year = {2017},
  title = {Mastering the game of Go without human knowledge},
  journal = {Nature},
  publisher = {Nature Publishing Group},
  volume = {550},
  month = {10},
  pages = {354},
  number = {7676},
}

@article{russell2016research,
  author    = {Stuart J. Russell and
               Daniel Dewey and
               Max Tegmark},
  title     = {Research Priorities for Robust and Beneficial Artificial Intelligence},
  journal   = {CoRR},
  year      = {2016},
}

@article {Hinton504,
    author = {Hinton, G. E. and Salakhutdinov, R. R.},
    title = {Reducing the Dimensionality of Data with Neural Networks},
    volume = {313},
    number = {5786},
    pages = {504--507},
    year = {2006},
    publisher = {American Association for the Advancement of Science},
    journal = {Science}
}

@article{2016arXiv160605579H,
  title={Early visual concept learning with unsupervised deep learning},
  author={Higgins, Irina and Matthey, Loic and Glorot, Xavier and Pal, Arka and Uria, Benigno and Blundell, Charles and Mohamed, Shakir and Lerchner, Alexander},
  journal={arXiv preprint arXiv:1606.05579},
  year={2016}
}

@misc{openaigym,
  Author = {Greg Brockman and Vicki Cheung and Ludwig Pettersson and Jonas Schneider and John Schulman and Jie Tang and Wojciech Zaremba},
  Title = {OpenAI Gym},
  Year = {2016},
  Eprint = {arXiv:1606.01540}
}

@article{hessel2017rainbow,
  author    = {Matteo Hessel and
               Joseph Modayil and
               Hado van Hasselt and
               Tom Schaul and
               Georg Ostrovski and
               Will Dabney and
               Daniel Horgan and
               Bilal Piot and
               Mohammad Gheshlaghi Azar and
               David Silver},
  title     = {Rainbow: Combining Improvements in Deep Reinforcement Learning},
  journal   = {CoRR},
  year      = {2017},
}

@article{tsne,
  title={Visualizing data using t-SNE},
  author={Maaten, Laurens van der and Hinton, Geoffrey},
  journal={Journal of machine learning research},
  volume={9},
  number={Nov},
  pages={2579--2605},
  year={2008}
}

@ARTICLE{vae,
       author = {{Kingma}, Diederik P and {Welling}, Max},
        title = "{Auto-Encoding Variational Bayes}",
      journal = {arXiv e-prints},
         year = 2013,
        month = Dec,
          eid = {arXiv:1312.6114},
        pages = {arXiv:1312.6114},
archivePrefix = {arXiv},
       eprint = {1312.6114},
}

repository: weidler/RLaSpa

repo_name: weidler/RLaSpa


text:
@online{beardsley_scientific_nodate,
	title = {Scientific American: Profile: Thereza Imanishi-Kari: November 1996},
	url = {http://www.gatewaycoalition.org/files/gateway_project_moshe_kam/resource/DBCre/imk.html},
	abstract = {Excerpt: "For someone whose lack of organization has become a topic of conversation throughout academe and beyond, Thereza Imanishi-Kari has a strikingly tidy office. The Brazilian-born scientist was this past summer cleared of all charges of scientific misconduct arising from a tangled, decade-old controversy that reached into the halls of Congress and forced Nobelist David Baltimore, one of Imanishi-Kari's co-authors in a disputed scientific study, to resign as president of the Rockefeller University. Because of his indignant defense of Imanishi-Kari, the case became known as the "Baltimore affair," even though she was the only one of six collaborators to be accused of wrongdoing. Intense news coverage turned the saga into the most sensational case of alleged research fraud in U.S. history: three books about it are now in progress. Recently reinstated as an assistant professor in the pathology department at the Tufts University School of Medicine, Imanishi-Kari, currently in her early fifties, seems remarkably unbitter. Casually dressed and in an ebullient mood in her small room at the top of a cramped laboratory building in the New England Medical Center, she displays no anger toward her accusers, concluding that they should look to their consciences: "We all have to live with our mistakes." She finds it "very sad," however, that some scientists, notably Mark Ptashne of Harvard University, publicly sided with her accusers without ever discussing the evidence with her."},
	author = {Beardsley, Tim},
	urldate = {2011-11-16}
}

repository: pmean/build-website

repo_name: pmean/build-website


text:
@article{yuan14,
  author = {Yuan, Ding and Luo, Yu and Zhuang, Xin and Guilherme, Renna and Rodrigues, Xu and Zhao, Yongle and U Zhang, Pranay and Jain, Michael and Stumm, Michael},
  title  = {Simple Testing Can Prevent Most Critical Failures --- {A}n Analysis of Production Failures in Distributed Data-intensive Systems},
  year   = {2014},
  month  = {10},
  pages  = {},
  doi    = {10.13140/2.1.2044.2889}
}

@inproceedings{alvaro15,
  author = {Alvaro, Peter and Rosen, Joshua and Hellerstein, Joseph M.},
  title = {Lineage-driven Fault Injection},
  booktitle = {Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data},
  series = {SIGMOD '15},
  year = 2015,
  isbn = {978-1-4503-2758-9},
  location = {Melbourne, Victoria, Australia},
  pages = {331--346},
  numpages = 16,
  url = {http://doi.acm.org/10.1145/2723372.2723711},
  doi = {10.1145/2723372.2723711},
  acmid = 2723711,
  publisher = {ACM},
  address = {New York, NY, USA},
  keywords = {fault-tolerance, provenance, verification},
}

gha_license_id: NOASSERTION

gha_language: Haskell


text:
DESCRIPTION = "LDAP client API for Python, C wrapper module around OpenLDAP 2.x with an object-oriented API"
SECTION = "devel/python"
LICENSE = "Python-style"
LIC_FILES_CHKSUM = "file://LICENCE;md5=a41c82edffa04912007cae1d20cac555"
HOMEPAGE = "http://www.python-ldap.org/"
DEPENDS = "python openldap"

SRC_URI = "http://pypi.python.org/packages/source/p/${PN}/${PN}-${PV}.tar.gz \
           file://setup.cfg.patch"
SRC_URI[md5sum] = "a9f9f16338288d118a1ae6266c993247"
SRC_URI[sha256sum] = "62f75b21c5ee744408c9d8b59878328b3bdf47899d30e8abf0c09b3ffb893ed4"

inherit setuptools

repository: ExorEmbedded/exor-workspace

repo_name: ExorEmbedded/exor-workspace

gha_language: Python

extension: bb


text:
# Copyright (C) 2009-2016, O.S. Systems Software Ltda. All Rights Reserved
# Released under the MIT license (see packages/COPYING)

require firefox-l10n.inc

SRC_URI[md5sum] = "f1296a64feebeddba87c755c0b9ccbcd"
SRC_URI[sha256sum] = "bb8abdef553c446d822262db8268beb9962e8085847e1f4735a6838b1faedc98"

repository: hufan/meta-web

repo_name: hufan/meta-web


text:
@PhdThesis{Marchand2020,
  author   = {Marchand, Pierre},
  school   = {{Sorbonne Universit{\'e}}},
  title    = {{Schwarz methods and boundary integral equations}},
  year     = {2020},
  month    = Jan,
  type     = {Theses},
  keywords = {Hierarchical matrices ; Two-level preconditioners ; Boundary element method ; Boundary integral equations ; Schwarz methods ; Domain decomposition methods ; Numerical analysis ; Equations int{\'e}grales de fronti{\`e}re ; M{\'e}thodes de Schwarz ; M{\'e}thodes de d{\'e}composition de domaine ; Analyse num{\'e}rique ; M{\'e}thode des {\'e}l{\'e}ments finis de fronti{\`e}re ; Pr{\'e}conditionneurs {\`a} deux niveaux ; Matrices hi{\'e}rarchiques},
  pdf      = {https://hal.archives-ouvertes.fr/tel-02922455/file/thesis.pdf},
  url      = {https://hal.archives-ouvertes.fr/tel-02922455},
}


text:
@article{wentz_visibility_2002,
	title = {Visibility of research: FUTON bias},
	volume = {360},
	issn = {0140-6736},
	url = {http://www.sciencedirect.com/science/article/pii/S0140673602112645},
	doi = {10.1016/S0140-6736(02)11264-5},
	shorttitle = {Visibility of research},
	pages = {1256},
	number = {9341},
	journaltitle = {The Lancet},
	shortjournal = {The Lancet},
	author = {Wentz, Reinhard},
	year = {2002},
	urldate = {2013-02-05},
	date = {2002-10-19},
	tags = {Systematic overviews}
}


text:
@article{Harper2011fl,
 abstract = {The much vaunted Web 2.0 sees once static pages evolving into hybrid applications. Content that was once simple to surf is now becoming increasingly complicated due to the many updating components 'dotted' throughout the page. In previous studies, we have shown that unlike younger users, older users have more varied interaction patterns when using dynamic content. In addition, some older users are not aware of what to expect when interacting with dynamic content and show signs of hesitancy when completing tasks. Therefore, a tool was developed to assist older users as they interacted with these kinds of pages. The tool used simple language and video demonstrations to explain how the dynamic content operated and what users could expect to happen as they were interacting with it. We found that: older users tend to deny the need for assistance technology even when non-subjective measures suggest they do use it; technology assists but cannot replace initial human training; Assistance Tools are seen as a 'security blanket' incase things go wrong; and that the actual needs of users are not that well understood by those users.},
 address = {Amsterdam, The Netherlands, The Netherlands},
 author = {Lunn, Darren and Harper, Simon$\backslash$indexLunn, D.$\backslash$indexHarper, S},
 doi = {http://dx.doi.org/10.1016/j.chb.2011.06.004},
 issn = {0747-5632},
 journal = {Computers in Human Behavior},
 keywords = {Assistance,Dynamic content,Older users,SCWeb2,Web widgets},
 mendeley-groups = {UX},
 month = {jul},
 number = {6},
 pages = {2098--2107},
 publisher = {Elsevier Science Publishers B. V.},
 title = {Providing assistance to older users of dynamic Web content},
 url = {http://dx.doi.org/10.1016/j.chb.2011.06.004 http://www.simonharper.info/publications/Harper2011fl.pdf},
 volume = {27},
 year = {2011}
}

repository: sharpic/academic-kickstart

repo_name: sharpic/academic-kickstart


text:
@misc{guzey2023dexterity,
              title={Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play},
              author={Irmak Guzey and Ben Evans and Soumith Chintala and Lerrel Pinto},
              year={2023},
              eprint={2303.12076},
              archivePrefix={arXiv},
              primaryClass={cs.RO}
        }


text:
# Copyright (c) 2018, Arm Limited or its affiliates. All rights reserved.
# SPDX-License-Identifier : Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#  http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# yocto-bsp-filename {{=example_recipe_name}}_0.1.bb
#
# This file was derived from the 'Hello World!' example recipe in the
# Yocto Project Development Manual.
#

SUMMARY = "SDEI ACS commandline application"
SECTION = "meta-luv"
LICENSE = "Apache-2.0"
LIC_FILES_CHKSUM = "file://${COMMON_LICENSE_DIR}/Apache-2.0;md5=89aea4e17d99a7cacdbeed46a0096b10"

SRC_URI = "file://sdei_app_main.c \
	   file://sdei_test_intf.c \
           file://include/sdei_app_main.h \
           file://include/sdei_test_intf.h \
           "

S = "${WORKDIR}"

do_compile() {
	     ${CC} sdei_app_main.c sdei_test_intf.c -o sdei
}

do_install() {
	     install -d ${D}${bindir}
	     install -m 0755 sdei ${D}${bindir}
}

repository: ARM-software/arm-enterprise-acs

repo_name: ARM-software/arm-enterprise-acs

gha_language: C


text:
require didiwiki.inc

SRC_URI[md5sum] = "94d5fb06d091804b31658481f23b120f"
SRC_URI[sha256sum] = "31e8e536f5efd7d7d1d5f4e4458b42aa9cd7910acf3da933cb7fa3507cf7f752"

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty

branch_name: HEAD


text:
@inproceedings{kendall2015,
  title = {{{PoseNet}}: {{A Convolutional Network}} for {{Real}}-{{Time}} 6-{{DOF Camera Relocalization}}},
  booktitle = {Proceedings of the {{IEEE International Conference}} on {{Computer Vision}}},
  author = {Kendall, Alex and Grimes, Matthew and Cipolla, Roberto},
  year = {2015},
  pages = {2938--2946}
}

url: https://raw.githubusercontent.com/retorquere/bibtex-parser/05fd68b215b96634dea5a7e6ea5e1be187caf3ed/test/better-bibtex/export/No booktitle field when exporting references from conference proceedings #1069.bibtex

repository: retorquere/bibtex-parser

repo_name: retorquere/bibtex-parser

extension: bibtex


text:
@book{evansIntro,
  title={Introduction to Computing: Explorations in Language, Logic, and Machines},
  author={EVANS, D.},
  url={http://computingbook.org},
  year={2011},
  publisher={University of Virginia}
}

repository: dacanizares/IntroCS-ES

repo_name: dacanizares/IntroCS-ES


text:
DESCRIPTION = "Convenient scripts to be used with openmoko toolchain."

SRCREV = "4913"
PV = "0.0+svnr${SRCPV}"
PE = "1"
PR = "r1"

SRC_URI = "svn://svn.openmoko.org/trunk/src/host;module=toolchain-scripts;proto=http"

S = "${WORKDIR}/toolchain-scripts"

do_install () {
        install -d ${D}/${prefix}
        cp -dr ${S}/* ${D}/${prefix}
        find ${D} -name ".svn" | xargs rm -rf
        rm -rf ${D}/${prefix}/patches
}

PACKAGE_ARCH = "all"

FILES_${PN} = "/*"

inherit sdk


text:
@charset 'UTF-8';
/* ==========================================================================
   Default settings
   ========================================================================== */

html, input, select, textarea, button {
    font:14px/1.428 Arial, "Geneva CY", sans-serif;
    color:#222;
}
html,body {
    height: 100%;
    overflow: hidden;
}
a {
  color: #0088cc;
  text-decoration: none;
  -webkit-tap-highlight-color: transparent;
}
a:hover,
a:focus,
a:active {
  color: #005580;
  text-decoration: underline;
}
a:visited {}
a:link {-webkit-tap-highlight-color: #fcd700;}

::-moz-selection {
    background: #b3d4fc;
    text-shadow: none;
}

::selection {
    background: #b3d4fc;
    text-shadow: none;
}

/* ==========================================================================
   Global styles
   ========================================================================== */
.btn {

}
.btn:hover, .btn:focus {

}
.btn:active, .btnActive {

}
/*default styles*/
input[type="text"],input[type="password"],input[type="date"],input[type="datetime"],input[type="datetime-local"],input[type="month"],input[type="week"],input[type="email"],input[type="number"],input[type="search"],input[type="tel"],input[type="time"],input[type="url"],input[type="color"]
,textarea {
    -webkit-box-sizing: border-box;
    -moz-box-sizing: border-box;
    box-sizing: border-box;
}
/*hover styles*/
input[type="text"]:hover,input[type="password"]:hover,input[type="date"]:hover,input[type="datetime"]:hover,input[type="datetime-local"]:hover,input[type="month"]:hover,input[type="week"]:hover,input[type="email"]:hover,input[type="number"]:hover,input[type="search"]:hover,input[type="tel"]:hover,input[type="time"]:hover,input[type="url"]:hover,input[type="color"]:hover
,textarea:hover {

}
/*focus styles*/
input[type="text"]:focus,input[type="password"]:focus,input[type="date"]:focus,input[type="datetime"]:focus,input[type="datetime-local"]:focus,input[type="month"]:focus,input[type="week"]:focus,input[type="email"]:focus,input[type="number"]:focus,input[type="search"]:focus,input[type="tel"]:focus,input[type="time"]:focus,input[type="url"]:focus,input[type="color"]:focus
,textarea:focus {

}
/*disables, readonly styles*/
input[type="text"][disabled], input[type="text"]:disabled, input[type="text"][readonly], fieldset[disabled] input[type="text"],input[type="password"][disabled], input[type="password"]:disabled, input[type="password"][readonly], fieldset[disabled] input[type="password"],input[type="date"][disabled], input[type="date"]:disabled, input[type="date"][readonly], fieldset[disabled] input[type="date"],input[type="datetime"][disabled], input[type="datetime"]:disabled, input[type="datetime"][readonly], fieldset[disabled] input[type="datetime"],input[type="datetime-local"][disabled], input[type="datetime-local"]:disabled, input[type="datetime-local"][readonly], fieldset[disabled] input[type="datetime-local"],input[type="month"][disabled], input[type="month"]:disabled, input[type="month"][readonly], fieldset[disabled] input[type="month"],input[type="week"][disabled], input[type="week"]:disabled, input[type="week"][readonly], fieldset[disabled] input[type="week"],input[type="email"][disabled], input[type="email"]:disabled, input[type="email"][readonly], fieldset[disabled] input[type="email"],input[type="number"][disabled], input[type="number"]:disabled, input[type="number"][readonly], fieldset[disabled] input[type="number"],input[type="search"][disabled], input[type="search"]:disabled, input[type="search"][readonly], fieldset[disabled] input[type="search"],input[type="tel"][disabled], input[type="tel"]:disabled, input[type="tel"][readonly], fieldset[disabled] input[type="tel"],input[type="time"][disabled], input[type="time"]:disabled, input[type="time"][readonly], fieldset[disabled] input[type="time"],input[type="url"][disabled], input[type="url"]:disabled, input[type="url"][readonly], fieldset[disabled] input[type="url"],input[type="color"][disabled], input[type="color"]:disabled, input[type="color"][readonly], fieldset[disabled] input[type="color"]
,textarea[disabled], textarea:disabled, textarea[readonly], fieldset[disabled] textarea {

}

textarea {
    padding:4px;
    resize: vertical;
    -webkit-box-sizing: border-box;
    -moz-box-sizing: border-box;
    box-sizing: border-box
}

select {
    -webkit-box-sizing: border-box;
    -moz-box-sizing: border-box;
    box-sizing: border-box;
}
/*from sanitize*/
/*select{-moz-appearance:none;-webkit-appearance:none}
select::-ms-expand{display:none}
select::-ms-value{color:currentColor}*/

.valid {

}
.invalid {

}
hr {
  border-color: #ccc;
}
/* ==========================================================================
   Site styles
   ========================================================================== */
#app {
  height: 100vh;
  overflow: hidden;
  position: relative;
  background-color: #ccc;
}
.scene {
  width: 100%;
  height: 100%;
  position: absolute;left: 0;top: 0;
  display: none;
  background-color: #fff;
}
.scene-menu {
  height: 50px;
  line-height: 50px;
  padding: 0 10px;
  background: red;
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
.scene-body {
  height: calc(100vh - 50px);
  overflow: auto;
}

.anim-out .scene {
  animation-direction: reverse !important;
}
.anim-fade .scene--to {
  z-index: 1;
  animation: fade 250ms ease-in-out;
}

.anim-slideUp .scene--to {
  z-index: 1;
  animation: slideUp 300ms ease-in-out;
}
.anim-slideDown .scene--to {
  z-index: 1;
  animation: slideDown 300ms ease-in-out;
}
.anim-slideLeft .scene--to {
  z-index: 1;
  animation: slideLeft 300ms ease-in-out;
}
.anim-slideRight .scene--to {
  z-index: 1;
  animation: slideRight 300ms ease-in-out;
}
.anim-scale .scene--to {
  z-index: 1;
  animation: scale 300ms ease-in-out;
}
.anim-scaleOut .scene--to {
  z-index: 1;
  animation: scaleOut 300ms ease-in-out;
}

@keyframes fade {
  0%
  {
    opacity: 0;
  }

  100%
  {
    opacity: 1;
  }
}

@keyframes slideUp {
  0%
  {
    opacity: 0;
    transform: translate3d(0,50%,0);
  }

  100%
  {
    opacity: 1;
    transform: translate3d(0,0,0);
  }
}

@keyframes slideDown {
  0%
  {
    opacity: 0;
    transform: translate3d(0,-50%,0);
  }

  100%
  {
    opacity: 1;
    transform: translate3d(0,0,0);
  }
}

@keyframes slideLeft {
  0%
  {
    opacity: 0;
    transform: translate3d(-50%,0,0);
  }

  100%
  {
    opacity: 1;
    transform: translate3d(0,0,0);
  }
}

@keyframes slideRight {
  0%
  {
    opacity: 0;
    transform: translate3d(50%,0,0);
  }

  100%
  {
    opacity: 1;
    transform: translate3d(0,0,0);
  }
}

@keyframes scale {
  0%
  {
    opacity: 0;
    transform:scale(0.5);
  }

  100%
  {
    opacity: 1;
    transform:scale(1);
  }
}

@keyframes scaleOut {
  0%
  {
    opacity: 0;
    transform:scale(2);
  }
  100%
  {
    opacity: 1;
    transform:scale(1);
  }
}

repository: Nejik/sceneManager

repo_name: Nejik/sceneManager

extension: css


text:
---
---
@article{gangwar2022semantic,
  abbr={Preprint},
  bibtex_show={true},
  title={Semantic Representations of Mathematical Expressions in a Continuous Vector Space},
  author={Neeraj Gangwar and Nickvash Kani},
  year={2022},
  eprint={2211.08142},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  selected=true,
  arxiv={2211.08142},
  journal={arXiv preprint arXiv:2211.08142},
}

@article{gangwar2022highlighting,
  abbr={Preprint},
  bibtex_show={true},
  title={Highlighting Named Entities in Input for Auto-Formulation of Optimization Problems},
  author={Gangwar, Neeraj and Kani, Nickvash},
  journal={arXiv preprint arXiv:2212.13201},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  selected=true,
  arxiv={2212.13201},
  eprint={2212.13201},
  year={2022},
}

gha_language: JavaScript


text:
package connect

import (
	"context"
	"errors"

	"github.com/aerogear/charmil-host-example/internal/build"
	"github.com/aerogear/charmil-host-example/pkg/cluster"
	"github.com/aerogear/charmil-host-example/pkg/cmd/factory"
	"github.com/aerogear/charmil-host-example/pkg/config"
	"github.com/aerogear/charmil-host-example/pkg/connection"
	"github.com/aerogear/charmil-host-example/pkg/kafka"
	"github.com/aerogear/charmil/core/utils/iostreams"
	"github.com/aerogear/charmil/core/utils/localize"
	"github.com/spf13/cobra"

	"github.com/aerogear/charmil/core/utils/logging"
)

type Options struct {
	CfgHandler *config.CfgHandler
	Connection func(connectionCfg *connection.Config) (connection.Connection, error)
	Logger     func() (logging.Logger, error)
	IO         *iostreams.IOStreams
	localizer  localize.Localizer

	kubeconfigLocation string
	namespace          string

	offlineAccessToken      string
	forceCreationWithoutAsk bool
	ignoreContext           bool
	selectedKafka           string
}

func NewConnectCommand(f *factory.Factory) *cobra.Command {
	opts := &Options{
		CfgHandler: f.CfgHandler,
		Connection: f.Connection,
		Logger:     f.Logger,
		IO:         f.IOStreams,
		localizer:  f.Localizer,
	}

	cmd := &cobra.Command{
		Use:     opts.localizer.LocalizeByID("cluster.connect.cmd.use"),
		Short:   opts.localizer.LocalizeByID("cluster.connect.cmd.shortDescription"),
		Long:    opts.localizer.LocalizeByID("cluster.connect.cmd.longDescription"),
		Example: opts.localizer.LocalizeByID("cluster.connect.cmd.example"),
		Args:    cobra.NoArgs,
		RunE: func(cmd *cobra.Command, _ []string) error {
			if opts.ignoreContext == true && !opts.IO.CanPrompt() {
				return errors.New(opts.localizer.LocalizeByID("flag.error.requiredWhenNonInteractive", localize.NewEntry("Flag", "ignore-context")))
			}
			return runConnect(opts)
		},
	}

	cmd.Flags().StringVarP(&opts.kubeconfigLocation, "kubeconfig", "", "", opts.localizer.LocalizeByID("cluster.common.flag.kubeconfig.description"))
	cmd.Flags().StringVarP(&opts.offlineAccessToken, "token", "", "", opts.localizer.LocalizeByID("cluster.common.flag.offline.token.description", localize.NewEntry("OfflineTokenURL", build.OfflineTokenURL)))
	cmd.Flags().StringVarP(&opts.namespace, "namespace", "n", "", opts.localizer.LocalizeByID("cluster.common.flag.namespace.description"))
	cmd.Flags().BoolVarP(&opts.forceCreationWithoutAsk, "yes", "y", false, opts.localizer.LocalizeByID("cluster.common.flag.yes.description"))
	cmd.Flags().BoolVarP(&opts.ignoreContext, "ignore-context", "", false, opts.localizer.LocalizeByID("cluster.common.flag.ignoreContext.description"))

	return cmd
}

func runConnect(opts *Options) error {
	connection, err := opts.Connection(connection.DefaultConfigSkipMasAuth)
	if err != nil {
		return err
	}

	logger, err := opts.Logger()
	if err != nil {
		return err
	}

	clusterConn, err := cluster.NewKubernetesClusterConnection(connection, opts.CfgHandler, logger, opts.kubeconfigLocation, opts.IO, opts.localizer)
	if err != nil {
		return err
	}

	// In future config will include Id's of other services
	if opts.CfgHandler.Cfg.Services.Kafka == nil || opts.ignoreContext {
		// nolint
		selectedKafka, err := kafka.InteractiveSelect(connection, logger)
		if err != nil {
			return err
		}
		if selectedKafka == nil {
			return nil
		}
		opts.selectedKafka = selectedKafka.GetId()
	} else {
		opts.selectedKafka = opts.CfgHandler.Cfg.Services.Kafka.ClusterID
	}

	arguments := &cluster.ConnectArguments{
		OfflineAccessToken:      opts.offlineAccessToken,
		ForceCreationWithoutAsk: opts.forceCreationWithoutAsk,
		IgnoreContext:           opts.ignoreContext,
		SelectedKafka:           opts.selectedKafka,
		Namespace:               opts.namespace,
	}

	err = clusterConn.Connect(context.Background(), arguments)
	if err != nil {
		return err
	}

	return nil
}

repository: aerogear/charmil-host-example

repo_name: aerogear/charmil-host-example

branch_name: refs/heads/main

gha_language: Go

extension: go


text:
@inproceedings{10.1145/212433.212436,
author = {Papadimitriou, Christos H.},
title = {Database Metatheory: Asking the Big Queries},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212436},
doi = {10.1145/212433.212436},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {1–10},
numpages = {10},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212437,
author = {Consens, Mariano P. and Milo, Tova},
title = {Algebras for Querying Text Regions (Extended Abstract)},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212437},
doi = {10.1145/212433.212437},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {11–22},
numpages = {12},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212440,
author = {Mecca, Giansalvatore and Bonner, Anthony J.},
title = {Sequences, Datalog and Transducers},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212440},
doi = {10.1145/212433.212440},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {23–35},
numpages = {13},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212444,
author = {Jagadish, H. V. and Mendelzon, Alberto O. and Milo, Tova},
title = {Similarity-Based Queries},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212444},
doi = {10.1145/212433.212444},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {36–45},
numpages = {10},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212447,
author = {Kanellakis, Paris},
title = {Constraint Programming and Database Languages: A Tutorial},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212447},
doi = {10.1145/212433.212447},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {46–53},
numpages = {8},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212449,
author = {Brodsky, Alexander and Lassez, Catherine and Lassez, Jean-Louis and Maher, Michael J.},
title = {Separability of Polyhedra for Optimal Filtering of Spatial and Constraint Data},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212449},
doi = {10.1145/212433.212449},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {54–65},
numpages = {12},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212453,
author = {Grumbach, St\'{e}phane and Su, Jianwen},
title = {Dense-Order Constraint Databases (Extended Abstract)},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212453},
doi = {10.1145/212433.212453},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {66–77},
numpages = {12},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212455,
author = {Chomicki, Jan and Kuper, Gabriel},
title = {Measuring Infinite Relations},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212455},
doi = {10.1145/212433.212455},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {78–85},
numpages = {8},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212458,
author = {Pagel, Bernd-Uwe and Six, Hans-Werner and Winter, Mario},
title = {Window Query-Optimal Clustering of Spatial Objects},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212458},
doi = {10.1145/212433.212458},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {86–94},
numpages = {9},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.220198,
author = {Levy, Alon Y. and Mendelzon, Alberto O. and Sagiv, Yehoshua},
title = {Answering Queries Using Views (Extended Abstract)},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.220198},
doi = {10.1145/212433.220198},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {95–104},
numpages = {10},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.220199,
author = {Rajaraman, Anand and Sagiv, Yehoshua and Ullman, Jeffrey D.},
title = {Answering Queries Using Templates with Binding Patterns (Extended Abstract)},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.220199},
doi = {10.1145/212433.220199},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {105–112},
numpages = {8},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.220201,
author = {Jagadish, H. V. and Mumick, Inderpal Singh and Silberschatz, Abraham},
title = {View Maintenance Issues for the Chronicle Data Model (Extended Abstract)},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.220201},
doi = {10.1145/212433.220201},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {113–124},
numpages = {12},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.220202,
author = {Goodman, Nathan},
title = {Research Problems in Genome Databases},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.220202},
doi = {10.1145/212433.220202},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {125},
numpages = {1},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.220203,
author = {Picouet, Phillippe and Vianu, Victor},
title = {Semantics and Expressiveness Issues in Active Databases (Extended Abstract)},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.220203},
doi = {10.1145/212433.220203},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {126–138},
numpages = {13},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.220204,
author = {Dong, Guozhu and Su, Jianwen},
title = {Space-Bounded FOIES (Extended Abstract)},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.220204},
doi = {10.1145/212433.220204},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {139–150},
numpages = {12},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.220205,
author = {Cadoli, Marco and Donini, Francesco M. and Liberatore, Paolo and Schaerf, Marco},
title = {The Size of a Revised Knowledge Base},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.220205},
doi = {10.1145/212433.220205},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {151–162},
numpages = {12},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.220207,
author = {Levy, Alon Y. and Sagiv, Yehoshua},
title = {Semantic Query Optimization in Datalog Programs (Extended Abstract)},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.220207},
doi = {10.1145/212433.220207},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {163–173},
numpages = {11},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.220209,
author = {Wood, Peter T.},
title = {Magic Factoring of Closure Programs (Extended Abstract)},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.220209},
doi = {10.1145/212433.220209},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {174–183},
numpages = {10},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.220211,
author = {Ross, Kenneth A.},
title = {Structural Totality and Constraint Stratification},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.220211},
doi = {10.1145/212433.220211},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {184–195},
numpages = {12},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.220213,
author = {Chang, Ti-Pin and Hull, Richard},
title = {Using Witness Generators to Support Bi-Directional Update between Object-Based Databases (Extended Abstract)},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.220213},
doi = {10.1145/212433.220213},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {196–207},
numpages = {12},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.220216,
author = {Andries, Marc and Cabibbo, Luca and Paredaens, Jan and Van den Bussche, Jan},
title = {Applying an Update Method to a Set of Receivers (Extended Abstract)},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.220216},
doi = {10.1145/212433.220216},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {208–218},
numpages = {11},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.220217,
author = {Libkin, Leonid},
title = {Normalizing Incomplete Databases},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.220217},
doi = {10.1145/212433.220217},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {219–230},
numpages = {12},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212461,
author = {Kolaitis, Phokion G.},
title = {Combinatorial Games in Database Theory},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212461},
doi = {10.1145/212433.212461},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {231–232},
numpages = {2},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212464,
author = {Chaudhri, Vinay K. and Hadzilacos, Vassos},
title = {Safe Locking Policies for Dynamic Databases},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212464},
doi = {10.1145/212433.212464},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {233–244},
numpages = {12},
keywords = {correctness issues, concurrency control},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212468,
author = {Keidar, Idit and Dolev, Danny},
title = {Increasing the Resilience of Atomic Commit, at No Additional Cost},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212468},
doi = {10.1145/212433.212468},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {245–254},
numpages = {10},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212471,
author = {Chekuri, Chandra and Hasan, Waqar and Motwani, Rajeev},
title = {Scheduling Problems in Parallel Query Optimization},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212471},
doi = {10.1145/212433.212471},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {255–265},
numpages = {11},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212474,
author = {Vardi, Moshe Y.},
title = {On the Complexity of Bounded-Variable Queries (Extended Abstract)},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212474},
doi = {10.1145/212433.212474},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {266–276},
numpages = {11},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212477,
author = {Stolboushkin, Alexei P. and Taitslin, Michael A.},
title = {Finite Queries Do Not Have Effective Syntax},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212477},
doi = {10.1145/212433.212477},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {277–285},
numpages = {9},
location = {San Jose, California, USA},
series = {PODS '95}
}

@inproceedings{10.1145/212433.212479,
author = {de Rougemont, Michel},
title = {The Reliability of Queries (Extended Abstract)},
year = {1995},
isbn = {0897917308},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/212433.212479},
doi = {10.1145/212433.212479},
booktitle = {Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems},
pages = {286–291},
numpages = {6},
location = {San Jose, California, USA},
series = {PODS '95}
}

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr


text:
package GeneralHashFunctions

import "testing"

func TestGeneralHashFunctions(t *testing.T) {
	key := "abcdefghijklmnopqrstuvwxyz1234567890"
	t.Log("1. RS-Hash Function:", RSHash(key))
	t.Log("2. JS-Hash Function Value:", JSHash(key))
	t.Log("3. PJW-Hash Function Value:", PJWHash(key))
	t.Log("4. ELF-Hash Function Value:", ELFHash(key))
	t.Log("5. BKDR-Hash Function Value:", BKDRHash(key))
	t.Log("6. SDBM-Hash Function Value:", SDBHash(key))
	t.Log("7. DJB-Hash Function Value:", DJBHash(key))
	t.Log("8. DEK-Hash Function Value:", DEKHash(key))
	t.Log("9. BP-Hash Function Value:", BPHash(key))
	t.Log("10. FNV-Hash Function Value:", FNVHash(key))
	t.Log("11. AP-Hash Function Value:", APHash(key))
}

repository: hongweikkx/GeneralHashFunctions

repo_name: hongweikkx/GeneralHashFunctions


text:
package input

import (
	"encoding/json"
	"log"
	"net"
	"net/http"
	"strings"
)

type (
	/*Metric represents a single time series point

	Type is one of: histogram, counter, gauge or set

	SecondaryData represents values other than the primary value which should
	be treated as data and not metadata by the backend storage

	Tags are KV metadata
	*/
	Metric struct {
		Name          string
		Host          string
		Timestamp     float64
		Type          string
		Sampling      float64
		Value         float64
		SecondaryData map[string]interface{}
		Tags          map[string]string
		Aggregate     bool
	}

	//MetricBatch represent a batch of individual metrics that have been sent together
	MetricBatch struct {
		Batch []Metric
		Size  int32
	}

	//Event represents a single event instance
	Event struct {
		Name           string
		Text           string
		Host           string
		AggregationKey string
		Priority       string
		Timestamp      float64
		AlertType      string
		Tags           map[string]string
		SourceType     string
	}

	metricsHTTPHandler struct {
		metricsIn chan Metric
	}

	eventsHTTPHandler struct {
		eventsIn chan Event
	}

	metricsBatchHTTPHandler struct {
		metricsIn chan Metric
	}
)

//http handler function, unmarshalls json encoded metric into metric struct
func (handler *metricsHTTPHandler) ServeHTTP(w http.ResponseWriter, r *http.Request) {
	decoder := json.NewDecoder(r.Body)
	var receivedMetric Metric
	err := decoder.Decode(&receivedMetric)

	sourceAddress := r.RemoteAddr
	sourceIP, _, _ := net.SplitHostPort(r.RemoteAddr)
	log.Printf("Received metric from %s\n", sourceIP)
	receivedMetric.Aggregate = true
	if err == nil {
		parseMetric(receivedMetric, sourceIP, handler.metricsIn)
	} else {
		log.Println(err)
		log.Printf("Unable to decode metric from, %s", sourceAddress)
	}

	r.Body.Close()
}

func (handler *eventsHTTPHandler) ServeHTTP(w http.ResponseWriter, r *http.Request) {
	//unmarshall json encoded events into event struct
	decoder := json.NewDecoder(r.Body)
	var receivedEvent Event
	err := decoder.Decode(&receivedEvent)
	sourceAddress := r.RemoteAddr
	sourceIP := sourceAddress[0:strings.Index(r.RemoteAddr, ":")]

	if err == nil {
		if receivedEvent.Tags == nil {
			receivedEvent.Tags = make(map[string]string)
		}

		//append source address to metric
		receivedEvent.Tags["source"] = sourceIP
		handler.eventsIn <- receivedEvent
	} else {
		//if unable to parse the metric, drop it. This could be a problem for out of date clients.
		log.Println(err)
		log.Printf("Unable to decode event from, %s", sourceAddress)
	}

	r.Body.Close()
}

func (handler *metricsBatchHTTPHandler) ServeHTTP(w http.ResponseWriter, r *http.Request) {
	var receivedMetricBatch MetricBatch

	if r.Body == nil {
		http.Error(w, "Empty request body", 400)
		return
	}

	err := json.NewDecoder(r.Body).Decode(&receivedMetricBatch)
	if err != nil {
		http.Error(w, "Malformed batch", 400)
		log.Println("Malformed batch")
		return
	}

	sourceAddress := r.RemoteAddr
	sourceIP, _, _ := net.SplitHostPort(r.RemoteAddr)
	log.Printf("Received metric batch of %d metrics from %s\n", receivedMetricBatch.Size, sourceIP)

	if err == nil {
		if len(receivedMetricBatch.Batch) == 0 {
			log.Printf("metric batch from %s is empty\n", sourceIP)
		} else {
			for i := range receivedMetricBatch.Batch {
				parseMetric(receivedMetricBatch.Batch[i], sourceIP, handler.metricsIn)
			}
		}
	} else {
		log.Println(err)
		log.Printf("Unable to decode metric batch from, %s", sourceAddress)
	}

}

func parseMetric(receivedMetric Metric, sourceIP string, metricsIn chan Metric) {
	//add an aditional field specifing the host which forwarded aggregateD the metric
	//this might often be the same as the client specified host field but in situations
	//where the client is behind NAT, i.e many EVE clients this information is useful.

	if receivedMetric.SecondaryData == nil {
		receivedMetric.SecondaryData = make(map[string]interface{})
	}
	receivedMetric.SecondaryData["source"] = sourceIP

	//ensure that no secondary values are nil, clients should not
	//submit nil values but if they do they should not be sent to Influx
	for k := range receivedMetric.SecondaryData {
		if receivedMetric.SecondaryData[k] == nil {
			receivedMetric.SecondaryData[k] = 0.0
		}
	}

	receivedMetric.Aggregate = false
	metricsIn <- receivedMetric
}

//ServeHTTP exposes /events and /metrics and proceses JSON encoded events
func ServeHTTP(port string, metricsIn chan Metric, eventsIn chan Event) {
	server := http.NewServeMux()

	metricsHandler := new(metricsHTTPHandler)
	metricsHandler.metricsIn = metricsIn

	eventsHandler := new(eventsHTTPHandler)
	eventsHandler.eventsIn = eventsIn

	metricsBatchHandler := new(metricsBatchHTTPHandler)
	metricsBatchHandler.metricsIn = metricsIn

	server.Handle("/metrics", metricsHandler)
	server.Handle("/events", eventsHandler)
	server.Handle("/metrics_batch", metricsBatchHandler)

	log.Printf("Accepting json metrics on port %s", port)

	log.Fatal(http.ListenAndServe(":"+port, server))
}

repository: oneoaas-golib/aggregateD

repo_name: oneoaas-golib/aggregateD


text:
@InProceedings{wange14,
  supplementary = {Supplementary:wange14-supp.pdf},
  title = {The Falling Factorial Basis and Its Statistical Applications},
  author = {Yu-Xiang Wang and Alex Smola and Ryan Tibshirani},
  pages = {730-738},
  abstract = {We study a novel spline-like basis, which we name the   {\it falling factorial basis}, bearing many similarities to the  classic truncated power basis.  The advantage of the falling factorial  basis is that it enables rapid, linear-time computations in basis  matrix multiplication and basis matrix inversion.  The falling  factorial functions are not actually splines, but are close enough  to splines that they provably retain some of the favorable properties  of the latter functions.  We examine their application in two  problems: trend filtering over arbitrary input points, and a  higher-order variant of the two-sample Kolmogorov-Smirnov test.},
  section = {cycle-2},
}


text:
@article{sahu2018lightweight,
 author = {Sahu, Pranjal and Yu, Dantong and Dasari, Mallesham and Hou, Fei and Qin, Hong},
 journal = {IEEE journal of biomedical and health informatics},
 number = {3},
 pages = {960--968},
 publisher = {IEEE},
 title = {A lightweight multi-section CNN for lung nodule classification and malignancy estimation},
 volume = {23},
 year = {2018}
}

repository: PranjalSahu/starter-academic

repo_name: PranjalSahu/starter-academic


text:
require gst-plugins.inc

LICENSE = "GPL-2.0-or-later & LGPL-2.0-or-later"
LIC_FILES_CHKSUM = "file://COPYING;md5=0636e73ff0215e8d672dc4c32c317bb3 \
                    file://common/coverage/coverage-report.pl;beginline=2;endline=17;md5=622921ffad8cb18ab906c56052788a3f \
                    file://COPYING.LIB;md5=55ca817ccb7d5b5b66355690e9abc605 \
                    file://gst/ffmpegcolorspace/utils.c;beginline=1;endline=20;md5=9c83a200b8e597b26ca29df20fc6ecd0"

DEPENDS += "alsa-lib libogg libvorbis libtheora util-linux tremor glib-2.0-native"

SRC_URI += "file://gst-plugins-base-tremor.patch \
            file://configure.ac-fix-subparse-plugin.patch \
            file://audioresample-Fix-build-on-x86-if-emmintrin.h-is-ava.patch \
            file://0001-aclocal.m4-don-t-do-crazy-things-to-GLIB_CFLAGS.patch \
"

SRC_URI[md5sum] = "776c73883e567f67b9c4a2847d8d041a"
SRC_URI[sha256sum] = "2cd3b0fa8e9b595db8f514ef7c2bdbcd639a0d63d154c00f8c9b609321f49976"

PR = "r8"

inherit gettext gobject-introspection

GI_DATA_ENABLED = 'False'

EXTRA_OECONF += "--disable-freetypetest"

PACKAGECONFIG ??= "${@bb.utils.filter('DISTRO_FEATURES', 'x11', d)}"

PACKAGECONFIG[orc] = "--enable-orc,--disable-orc,orc"
PACKAGECONFIG[pango] = "--enable-pango,--disable-pango,pango"
PACKAGECONFIG[x11] = "--enable-x --enable-xvideo,--disable-x --disable-xvideo,virtual/libx11 libxv libsm libice"
PACKAGECONFIG[cdparanoia] = "--enable-cdparanoia,--disable-cdparanoia,cdparanoia"

do_configure:prepend() {
    # This m4 file contains nastiness which conflicts with libtool 2.2.2
    rm -f ${S}/m4/lib-link.m4
}

do_configure:append() {
    # remove stay \ introduced by make 4.3
    if make -v | grep '4\.3'; then
        sed -i 's:\\#include:#include:g' `find -name Makefile`
    fi
}

FILES:${PN} += "${datadir}/${BPN}"

CACHED_CONFIGUREVARS:append:x86 = " ac_cv_header_emmintrin_h=no ac_cv_header_xmmintrin_h=no"

# /usr/bin/gst-visualise-0.10 is a perl script.
RDEPENDS:${PN}-apps += "perl"

repository: schnitzeltony/meta-musicians

repo_name: schnitzeltony/meta-musicians

gha_language: BitBake


text:
@inproceedings{10.1145/3250031,
author = {Talukdar, Partha},
title = {Session Details: Oral Session},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250031},
doi = {10.1145/3250031},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
numpages = {1},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509559,
author = {Singh, Sameer and Riedel, Sebastian and Martin, Brian and Zheng, Jiaping and McCallum, Andrew},
title = {Joint Inference of Entities, Relations, and Coreference},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509559},
doi = {10.1145/2509558.2509559},
abstract = {Although joint inference is an effective approach to avoid cascading of errors when inferring multiple natural language tasks, its application to information extraction has been limited to modeling only two tasks at a time, leading to modest improvements. In this paper, we focus on the three crucial tasks of automated extraction pipelines: entity tagging, relation extraction, and coreference. We propose a single, joint graphical model that represents the various dependencies between the tasks, allowing flow of uncertainty across task boundaries. Since the resulting model has a high tree-width and contains a large number of variables, we present a novel extension to belief propagation that sparsifies the domains of variables during inference. Experimental results show that our joint model consistently improves results on all three tasks as we represent more dependencies. In particular, our joint model obtains 12% error reduction on tagging over the isolated models.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {1–6},
numpages = {6},
keywords = {joint inference, information extraction, relation extraction, named entity recognition, coreference resolution},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509560,
author = {Ling, Xiao and Clark, Peter and Weld, Daniel S.},
title = {Extracting Meronyms for a Biology Knowledge Base Using Distant Supervision},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509560},
doi = {10.1145/2509558.2509560},
abstract = {Knowledge of objects and their parts, meronym relations, are at the heart of many question-answering systems, but manually encoding these facts is impractical. Past researchers have tried hand-written patterns, supervised learning, and bootstrapped methods, but achieving both high precision and recall has proven elusive. This paper reports on a thorough exploration of distant supervision to learn a meronym extractor for the domain of college biology. We introduce a novel algorithm, generalizing the ``at least one'' assumption of multi-instance learning to handle the case where a fixed (but unknown) percentage of bag members are positive examples. Detailed experiments compare strategies for mention detection, negative example generation, leveraging out-of-domain meronyms, and evaluate the benefit of our multi-instance percentage model.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {7–12},
numpages = {6},
keywords = {relation extraction, distant supervision, information extraction, automated knowledge base construction},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509561,
author = {Wick, Michael and Singh, Sameer and Kobren, Ari and McCallum, Andrew},
title = {Assessing Confidence of Knowledge Base Content with an Experimental Study in Entity Resolution},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509561},
doi = {10.1145/2509558.2509561},
abstract = {The purpose of this paper is to begin a conversation about the importance and role of confidence estimation in knowledge bases (KBs). KBs are never perfectly accurate, yet without confidence reporting their users are likely to treat them as if they were, possibly with serious real-world consequences. We define a notion of confidence based on the probability of a KB fact being true. For automatically constructed KBs we propose several algorithms for estimating this confidence from pre-existing probabilistic models of data integration and KB construction. In particular, this paper focuses on confidence estimation in entity resolution. A goal of our exposition here is to encourage creators and curators of KBs to include confidence estimates for entities and relations in their KBs.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {13–18},
numpages = {6},
keywords = {entity resolution, information extraction, uncertain data},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509562,
author = {Pujara, Jay and Miao, Hui and Getoor, Lise and Cohen, William W.},
title = {Ontology-Aware Partitioning for Knowledge Graph Identification},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509562},
doi = {10.1145/2509558.2509562},
abstract = {Knowledge graphs provide a powerful representation of entities and the relationships between them, but automatically constructing such graphs from noisy extractions presents numerous challenges. Knowledge graph identification (KGI) is a technique for knowledge graph construction that jointly reasons about entities, attributes and relations in the presence of uncertain inputs and ontological constraints. Although knowledge graph identification shows promise scaling to knowledge graphs built from millions of extractions, increasingly powerful extraction engines may soon require knowledge graphs built from billions of extractions. One tool for scaling is partitioning extractions to allow reasoning to occur in parallel. We explore approaches which leverage ontological information and distributional information in partitioning. We compare these techniques with hash-based approaches, and show that using a richer partitioning model that incorporates the ontology graph and distribution of extractions provides superior results. Our results demonstrate that partitioning can result in order-of-magnitude speedups without reducing model performance.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {19–24},
numpages = {6},
keywords = {knowledge graph identification, partitioning probabilistic graphical models},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509563,
author = {Gordon, Jonathan and Van Durme, Benjamin},
title = {Reporting Bias and Knowledge Acquisition},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509563},
doi = {10.1145/2509558.2509563},
abstract = {Much work in knowledge extraction from text tacitly assumes that the frequency with which people write about actions, outcomes, or properties is a reflection of real-world frequencies or the degree to which a property is characteristic of a class of individuals. In this paper, we question this idea, examining the phenomenon of reporting bias and the challenge it poses for knowledge extraction. We conclude with discussion of approaches to learning commonsense knowledge from text despite this distortion.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {25–30},
numpages = {6},
keywords = {reporting bias, text frequency, knowledge extraction},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509564,
author = {Dalvi, Bhavana and Cohen, William W. and Callan, Jamie},
title = {Classifying Entities into an Incomplete Ontology},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509564},
doi = {10.1145/2509558.2509564},
abstract = {Exponential growth of unlabeled web-scale datasets, and class hierarchies to represent them, has given rise to new challenges for hierarchical classification. It is costly and time consuming to create a complete ontology of classes to represent entities on the Web. Hence, there is a need for techniques that can do hierarchical classification of entities into incomplete ontologies. In this paper we present Hierarchical Exploratory EM algorithm (an extension of the Exploratory EM algorithm [7]) that takes a seed class hierarchy and seed class instances as input. Our method classifies relevant entities into some of the classes from the seed hierarchy and on its way adds newly discovered classes into the hierarchy. Experiments with subsets of the NELL ontology and text datasets derived from the ClueWeb09 corpus show that our Hierarchical Exploratory EM approach improves seed class F1 by up to 21% when compared to its semi-supervised counterpart.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {31–36},
numpages = {6},
keywords = {expectation maximization (em), clustering, ontologies, knowledge bases, semi-supervised learning, hierarchical classification},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509565,
author = {Clark, Peter and Harrison, Philip and Balasubramanian, Niranjan},
title = {A Study of the Knowledge Base Requirements for Passing an Elementary Science Test},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509565},
doi = {10.1145/2509558.2509565},
abstract = {Our long-term interest is in machines that contain large amounts of general and scientific knowledge, stored in a "computable" form that supports reasoning and explanation. As a medium-term focus for this, our goal is to have the computer pass a fourth-grade science test, anticipating that much of the required knowledge will need to be acquired semi-automatically. This paper presents the first step towards this goal, namely a blueprint of the knowledge requirements for an early science exam, and a brief description of the resources, methods, and challenges involved in the semi-automatic acquisition of that knowledge. The result of our analysis suggests that as well as fact extraction from text and statistically driven rule extraction, three other styles of automatic knowledge base construction (AKBC) would be useful: acquiring definitional knowledge, direct 'reading' of rules from texts that state them, and, given a particular representational framework (e.g., qualitative reasoning), acquisition of specific instances of those models from text (e..g, specific qualitative models).},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {37–42},
numpages = {6},
keywords = {knowledge base construction, knowledge acquisition},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509566,
author = {Gal\'{a}rraga, Luis A. and Preda, Nicoleta and Suchanek, Fabian M.},
title = {Mining Rules to Align Knowledge Bases},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509566},
doi = {10.1145/2509558.2509566},
abstract = {The Semantic Web has made huge progress in the last decade, and now comprises hundreds of knowledge bases (KBs). The Linked Open Data cloud connects the KBs in this Web of data. However, the links between the KBs are mostly concerned with the instances, not with the schema. Aligning the schemas is not easy, because the KBs may differ not just in their names for relations and classes, but also in their inherent structure. Therefore, we argue in this paper that advanced schema alignment is needed to tie the Semantic Web together. We put forward a particularly simple approach to illustrate how that might look.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {43–48},
numpages = {6},
keywords = {linked data, ontology alignment, rule mining},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509567,
author = {Huet, Thomas and Biega, Joanna and Suchanek, Fabian M.},
title = {Mining History with Le Monde},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509567},
doi = {10.1145/2509558.2509567},
abstract = {The last decade has seen the rise of large knowledge bases, such as YAGO, DBpedia, Freebase, or NELL. In this paper, we show how this structured knowledge can help understand and mine trends in unstructured data. By combining YAGO with the archive of the French newspaper Le Monde, we can conduct analyses that would not be possible with word frequency statistics alone. We find indications about the increasing role that women play in politics, about the impact that the city of birth can have on a person's career, or about the average age of famous people in different professions.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {49–54},
numpages = {6},
keywords = {yago, knowledge base, le monde, culturomics},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/3250032,
author = {Suchanek, Fabian},
title = {Session Details: Poster Session},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250032},
doi = {10.1145/3250032},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
numpages = {1},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509568,
author = {Dalton, Jeffrey and Dietz, Laura},
title = {Constructing Query-Specific Knowledge Bases},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509568},
doi = {10.1145/2509558.2509568},
abstract = {Abstract Large general purpose knowledge bases (KB) support a variety of complex tasks because of their structured relationships. However, these KBs lack coverage for specialized topics or use cases. In these scenarios, users often use keyword search over large unstructured collections, such as the web. Instead, we propose constructing a 'knowledge sketch' that leverages existing KB data elements and relevant text documents to construct query-specific KB data. A knowledge sketch is a distribution over entities, documents, and relationships between entities, all for a specific information need. In our experiments we construct knowledge sketches for queries from the TREC 2004 Robust track, which emphasizes complex queries which perform poorly with existing text retrieval approaches.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {55–60},
numpages = {6},
keywords = {knowledge base construction, entity linking, relevance modeling},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509569,
author = {Downey, Doug and Bhagavatula, Chandra Sekhar and Yates, Alexander},
title = {Using Natural Language to Integrate, Evaluate, and Optimize Extracted Knowledge Bases},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509569},
doi = {10.1145/2509558.2509569},
abstract = {Web Information Extraction (WIE) systems extract billions of unique facts, but integrating the assertions into a coherent knowledge base and evaluating across different WIE techniques remains a challenge. We propose a framework that utilizes natural language to integrate and evaluate extracted knowledge bases (KBs). In the framework, KBs are integrated by exchanging probability distributions over natural language, and evaluated by how well the output distributions predict held-out text. We describe the advantages of the approach, and detail remaining research challenges.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {61–66},
numpages = {6},
keywords = {knowledge extraction, language modeling, knowledge integration},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509570,
author = {Wick, Michael and Singh, Sameer and Pandya, Harshal and McCallum, Andrew},
title = {A Joint Model for Discovering and Linking Entities},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509570},
doi = {10.1145/2509558.2509570},
abstract = {Entity resolution, the task of automatically determining which mentions refer to the same real-world entity, is a crucial aspect of knowledge base construction and management. However, performing entity resolution at large scales is challenging because (1) the inference algorithms must cope with unavoidable system scalability issues and (2) the search space grows exponentially in the number of mentions. Current conventional wisdom has been that performing coreference at these scales requires decomposing the problem by first solving the simpler task of entity-linking (matching a set of mentions to a known set of KB entities), and then performing entity discovery as a post-processing step (to identify new entities not present in the KB). However, we argue that this traditional approach is harmful to both entity-linking and overall coreference accuracy. Therefore, we embrace the challenge of jointly modeling entity-linking and entity-discovery as a single entity resolution problem. In order to make progress towards scalability we (1) present a model that reasons over compact hierarchical entity representations, and (2) propose a novel distributed inference architecture that does not suffer from the synchronicity bottleneck which is inherent in map-reduce architectures. We demonstrate that more test-time data actually improves the accuracy of coreference, and show that joint coreference is substantially more accurate than traditional entity-linking, reducing error by 75%.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {67–72},
numpages = {6},
keywords = {entity linking, entity resolution, coreference},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509571,
author = {Roth, Benjamin and Barth, Tassilo and Wiegand, Michael and Klakow, Dietrich},
title = {A Survey of Noise Reduction Methods for Distant Supervision},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509571},
doi = {10.1145/2509558.2509571},
abstract = {We survey recent approaches to noise reduction in distant supervision learning for relation extraction. We group them according to the principles they are based on: at-least-one constraints, topic-based models, or pattern correlations. Besides describing them, we illustrate the fundamental differences and attempt to give an outlook to potentially fruitful further research. In addition, we identify related work in sentiment analysis which could profit from approaches to noise reduction.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {73–78},
numpages = {6},
keywords = {relation extraction, machine learning, distant supervision},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509572,
author = {Yao, Limin and Riedel, Sebastian and McCallum, Andrew},
title = {Universal Schema for Entity Type Prediction},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509572},
doi = {10.1145/2509558.2509572},
abstract = {Categorizing entities by their types is useful in many applications, including knowledge base construction, relation extraction and query intent prediction. Fine-grained entity type ontologies are especially valuable, but typically difficult to design because of unavoidable quandaries about level of detail and boundary cases. Automatically classifying entities by type is challenging as well, usually involving hand-labeling data and training a supervised predictor.This paper presents a universal schema approach to fine-grained entity type prediction. The set of types is taken as the union of textual surface patterns (e.g. appositives) and pre-defined types from available databases (e.g. Freebase)---yielding not tens or hundreds of types, but more than ten thousands of entity types, such as financier, criminologist, and musical trio. We robustly learn mutual implication among this large union by learning latent vector embeddings from probabilistic matrix factorization, thus avoiding the need for hand-labeled data. Experimental results demonstrate more than 30% reduction in error versus a traditional classification approach on predicting fine-grained entities types.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {79–84},
numpages = {6},
keywords = {matrix factorization, entity},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509573,
author = {Siahbani, Maryam and Vadlapudi, Ravikiran and Whitney, Max and Sarkar, Anoop},
title = {Knowledge Base Population and Visualization Using an Ontology Based on Semantic Roles},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509573},
doi = {10.1145/2509558.2509573},
abstract = {This paper extracts facts using "micro-reading" of text in contrast to approaches that extract common-sense knowledge using "macro-reading" methods. Our goal is to extract detailed facts about events from natural language using a predicate-centered view of events (who did what to whom, when and how). We exploit semantic role labels in order to create a novel predicate-centric ontology for entities in our knowledge base. This allows users to find uncommon facts easily. To this end, we tightly couple our knowledge base and ontology to an information visualization system that can be used to explore and navigate events extracted from a large natural language text collection. We use our methodology to create a web-based visual browser of history events in Wikipedia.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {85–90},
numpages = {6},
keywords = {knowledge base, information extraction, ontology},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509574,
author = {Schuhmacher, Michael and Ponzetto, Simone Paolo},
title = {Exploiting DBpedia for Web Search Results Clustering},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509574},
doi = {10.1145/2509558.2509574},
abstract = {We present a knowledge-rich approach to Web search result clustering which exploits the output of an open-domain entity linker, as well as the types and topical concepts encoded within a wide-coverage ontology. Our results indicate that, thanks to an accurate and compact semantification of the search result snippets, we are able to achieve a competitive performance on a benchmarking dataset for this task.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {91–96},
numpages = {6},
keywords = {search result clustering, natural language processing, semantic networks, dbpedia},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509575,
author = {Li, Xinyu and Rastan, Roya and Shepherd, John and Paik, Hye Young},
title = {Automatic Affiliation Extraction from Calls-for-Papers},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509575},
doi = {10.1145/2509558.2509575},
abstract = {In this paper, we describe a system to collect information about academic affiliation (organisations where researchers work) from Calls-for-Papers for academic conferences. The system uses a range of heuristic approaches and open-source tools in order to extract and identify entities, and to incorporate the information into a pre-defined database schema. This forms part of a larger project to automatically populate and maintain a range of data related to academic research. The proposed system is currently being tested and some promising preliminary results are available.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {97–102},
numpages = {6},
keywords = {research data management, affiliation extraction, column-based structures},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509576,
author = {Yeh, Eric and Niekrasz, John and Freitag, Dayne},
title = {Unsupervised Discovery and Extraction of Semi-Structured Regions in Text via Self-Information},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509576},
doi = {10.1145/2509558.2509576},
abstract = {We describe a general method for identifying and extracting information from semi-structured regions of text embedded within a natural language document. These regions encode information according to ad hoc schemas and visual cues, instead of using the grammatical and presentational conventions of normal sentential language. Examples include tables, key-value listings, or repeated enumerations of properties. Because of their generally non-sentential nature, these regions can present problems for standard information extraction algorithms. Unlike previous work in table extraction, which relies on a relatively noiseless two-dimensional layout, our aim is to accommodate a wide variety of structure types. Our approach for identifying semi-structured regions is an unsupervised one, based on scoring unusual regularity inside the document. As content in semi-structured regions are governed by a schema, the occurrence of features encompassing textual content and visual appearance would be unusual compared to those seen in sentential language. Regularity refers to repetition of these unusual features, as semi-structured regions commonly encode more than a single row or group of information. To score this, we present a measure based on expected self-information, derived from statistics over patterns of textual categories and visual layout. We describe the results of an initial study to assess the ability of these measures to detect semi-structured text in a corpus culled from the web, and show that this measure outperform baseline methods on an average precision measure. We present initial work that uses these significant patterns to generate extraction rules, and conclude with a discussion of future directions.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {103–108},
numpages = {6},
keywords = {semi-structured information extraction},
location = {San Francisco, California, USA},
series = {AKBC '13}
}

@inproceedings{10.1145/2509558.2509577,
author = {Radhakrishnan, Priya and Varma, Vasudeva},
title = {Extracting Semantic Knowledge from Wikipedia Category Names},
year = {2013},
isbn = {9781450324113},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2509558.2509577},
doi = {10.1145/2509558.2509577},
abstract = {Wikipedia being a large, freely available, frequently updated and community maintained knowledge base, has been central to much recent research. However, quite often we find that the information extracted from it has extraneous content. This paper proposes a method to extract useful information from Wikipedia, using Semantic Features derived from Wikipedia categories. The proposed method provides good performance as a Wikipedia category based method. Experimental results on benchmark datasets show that the proposed method achieves a correlation coefficient of 0.66 with human judgments. The Semantic Features derived by this method gave good correlation with human rankings in a web search query completion application.},
booktitle = {Proceedings of the 2013 Workshop on Automated Knowledge Base Construction},
pages = {109–114},
numpages = {6},
keywords = {semantic relatedness, information extraction},
location = {San Francisco, California, USA},
series = {AKBC '13}
}


text:
% ab-fm.bib

%% TLA

@article{Lamport:TOPLAS1994,
 author = {Lamport, Leslie},
 title = {The Temporal Logic of Actions},
 journal = {ACM Trans. Program. Lang. Syst.},
 volume = {16},
 number = {3},
 month = may,
 year = {1994},
 pages = {872--923},
 doi = {10.1145/177492.177726},
}

@InProceedings{Yu:CHARME1999,
  author = {Yu, Yuan and Manolios, Panagiotis and Lamport, Leslie},
  title = {Model Checking {TLA$^+$} Specifications},
  booktitle = {Advanced Research Working Conference on Correct Hardware Design and Verification Methods},
  series = {CHARME '99},
  year = {1999},
  pages = {54--66},
  doi = {10.1007/3-540-48153-2\_6},
}

@article{Lamport:EATCS2018,
  author    = {Leslie Lamport},
  title     = {If You're Not Writing a Program, Don't Use a Programming Language},
  journal   = {Bulletin of the {EATCS}},
  volume    = {125},
  year      = {2018},
  url = {http://eatcs.org/beatcs/index.php/beatcs/article/view/539},
}

@book{Lamport:SS2002,
  author = {Lamport, Leslie},
  title = {Specifying Systems: The {TLA$^+$} Language and Tools for Hardware and Software Engineers},
  year = {2002},
  isbn = {0-3211-4306-X},
  publisher = {Addison-Wesley Longman Publishing Co., Inc.},
  address = {Boston, MA, USA},
}

@article{Lamport:arXiv2017,
  title = {Auxiliary Variables in {TLA$^+$}},
  author = {Leslie Lamport and Stephan Merz},
  note = {arXiv:1703.05121},
  year = {2017},
  url = {http://arxiv.org/abs/1703.05121},
}
  journal = {CoRR},
  volume = {abs/1703.05121},

@Misc{Lamport:Summary,
  title = {Summary of {TLA$^+$}},
  author= {Lamport, Leslie},
  howpublished = {\url{http://lamport.azurewebsites.net/tla/summary-standalone.pdf}},
  note = {[Accessed: Jan. 22, 2019]}
}

%% TLAPS
@Misc{TLAPS,
  title = {{Microsoft Research -- Inria Joint Centre: TLA$^+$ Proof System} ({TLAPS})},
  howpublished = {\url{https://tla.msr-inria.inria.fr/tlaps/content/Home.html}},
  note = "[Accessed: Jan. 21, 2019]"
}

%% refinement
@article{Hoare:ActaInf1972,
  author = {Hoare, C. A.},
  title = {Proof of Correctness of Data Representations},
  journal = {Acta Inf.},
  volume = {1},
  number = {4},
  month = dec,
  year = {1972},
  pages = {271--281},
  doi = {10.1007/BF00289507},
}

@InProceedings{Lamport:LICS1988,
  author = {Abadi, Martin and Lamport, Leslie},
  title = {The Existence of Refinement Mappings},
  booktitle = {Proceedings of the 3rd Annual Symposium on Logic in Computer Science},
  year = {1988},
  month = {July},
  pages = {165-175},
  edition = {Proceedings of the 3rd Annual Symposium on Logic in Computer Science},
  url = {https://www.microsoftcom/en-us/research/publication/the-existence-of-refinement-mappings/},
  note = {LICS 1988 Test of Time Award},
}

@article{Lamport:TCS1991,
  author    = {Mart{\'{\i}}n Abadi and
               Leslie Lamport},
  title     = {The Existence of Refinement Mappings},
  journal   = {Theor. Comput. Sci.},
  volume    = {82},
  number    = {2},
  pages     = {253--284},
  year      = {1991},
  doi       = {10.1016/0304-3975(91)90224-P},
  url       = {https://doi.org/10.1016/0304-3975(91)90224-P},
}

repository: hengxin/paper-bibs-and-notes

repo_name: hengxin/paper-bibs-and-notes