text:
KBRANCH ?= "standard/base"

require recipes-kernel/linux/linux-yocto.inc

SRCREV_machine ?= "a2f2be49cd60b8d022fa47daae0a8293c3066b78"
SRCREV_meta ?= "d5456dd830cad14bd844753b751b83744ced3793"

SRC_URI = "git://git.yoctoproject.org/linux-yocto-3.10.git;bareclone=1;branch=${KBRANCH},${KMETA};name=machine,meta"

LINUX_VERSION ?= "3.10.65"

PV = "${LINUX_VERSION}+git${SRCPV}"

KMETA = "meta"

# Functionality flags
KERNEL_EXTRA_FEATURES ?= "features/netfilter/netfilter.scc"
KERNEL_FEATURES_append = " ${KERNEL_EXTRA_FEATURES}"

repository: koenkooi/meta-minnow

repo_name: koenkooi/meta-minnow


text:
SUMMARY = "C library for the MaxMind DB file format"
DESCRIPTION = "The libmaxminddb library provides a C library for reading MaxMind DB files, \
ncluding the GeoIP2 databases from MaxMind. This is a custom binary \
format designed to facilitate fast lookups of IP addresses while allowing \
for great flexibility in the type of data associated with an address."

HOMEPAGE = "https://github.com/maxmind/libmaxminddb"
SECTION = "libs"
LICENSE = " Apache-2.0"

LIC_FILES_CHKSUM = "file://LICENSE;md5=3b83ef96387f14655fc854ddc3c6bd57 \
"
SRC_URI = "https://github.com/maxmind/libmaxminddb/releases/download/${PV}/${BPN}-${PV}.tar.gz \
"
SRC_URI[sha256sum] = "a5fdf6c7b4880fdc7620f8ace5bd5cbe9f65650c9493034b5b9fc7d83551a439"

inherit autotools-brokensep

license: MIT

repository: openembedded/meta-openembedded

license_type: permissive

repo_name: openembedded/meta-openembedded

branch_name: refs/heads/master

gha_license_id: MIT

gha_language: BitBake

extension: bb


text:
##################################################################################################
#
# CMake script for finding leveldb.
#
# Cache variables (not intended to be used in CMakeLists.txt files)
#
# - leveldb_INCLUDE_DIR: Absolute path to package headers.
# - leveldb_LIBRARY: Absolute path to library.
#
#
# Output variables:
#
# - leveldb_FOUND: Boolean that indicates if the package was found
# - leveldb_INCLUDE_DIRS: Paths to the necessary header files
# - leveldb_LIBRARIES: Package libraries
#
# Example usage:
#
#  find_package(leveldb)
#  if(NOT leveldb_FOUND)
#    # Error handling
#  endif()
#  ...
#  include_directories(${leveldb_INCLUDE_DIRS} ...)
#  ...
#  target_link_libraries(my_target ${leveldb_LIBRARIES})
#
##################################################################################################

find_package(Threads REQUIRED)
find_package(leveldb CONFIG QUIET)

if (NOT leveldb_FOUND)

  # Get package location hint from environment variable (if any)
  if(NOT leveldb_ROOT_DIR AND DEFINED ENV{leveldb_ROOT_DIR})
      set(leveldb_ROOT_DIR "$ENV{leveldb_ROOT_DIR}" CACHE PATH
          "leveldb base directory location (optional, used for nonstandard installation paths)")
  endif()

   # Search path for nonstandard package locations
   if(leveldb_ROOT_DIR)
       set(leveldb_INCLUDE_PATH PATHS "${leveldb_ROOT_DIR}/include" NO_DEFAULT_PATH)
       set(leveldb_LIBRARY_PATH PATHS "${leveldb_ROOT_DIR}/lib"     NO_DEFAULT_PATH)
   else()
       set(leveldb_INCLUDE_PATH "")
       set(leveldb_LIBRARY_PATH "")
   endif()

    # Find headers and libraries
    find_path(leveldb_INCLUDE_DIR NAMES db.h PATH_SUFFIXES "leveldb" ${leveldb_INCLUDE_PATH})
    find_library(leveldb_LIBRARY  NAMES leveldb PATH_SUFFIXES "leveldb" ${leveldb_LIBRARY_PATH})

    mark_as_advanced(leveldb_INCLUDE_DIR leveldb_LIBRARY)

    # Output variables generation
    include(FindPackageHandleStandardArgs)
    find_package_handle_standard_args(leveldb DEFAULT_MSG leveldb_LIBRARY leveldb_INCLUDE_DIR)

    set(leveldb_FOUND ${LEVELDB_FOUND}) # Enforce case-correctness: Set appropriately cased variable...
    unset(LEVELDB_FOUND) # ...and unset uppercase variable generated by find_package_handle_standard_args

    if(leveldb_FOUND)
        set(leveldb_INCLUDE_DIR ${leveldb_INCLUDE_DIR})
        set(leveldb_LIBRARY ${leveldb_LIBRARY})

        if(NOT TARGET leveldb::leveldb)
            add_library(leveldb::leveldb UNKNOWN IMPORTED)
            set_property(TARGET leveldb::leveldb PROPERTY IMPORTED_LOCATION ${leveldb_LIBRARY})
            set_property(TARGET leveldb::leveldb PROPERTY INTERFACE_INCLUDE_DIRECTORIES ${leveldb_INCLUDE_DIR})
        endif()
        list(APPEND leveldb_TARGETS leveldb::leveldb)
    elseif(leveldb_FIND_REQUIRED)
        message(FATAL_ERROR "Unable to find leveldb")
    endif()

else()
    message(STATUS "Found leveldb from installed package in ${leveldb_DIR}")
    list(APPEND leveldb_TARGETS leveldb::leveldb)
endif()

branch_name: refs/heads/main

extension: cmake


text:
/*
Copyright The clickpaas-controller Authors.
*/

// Code generated by informer-gen. DO NOT EDIT.

package v1alpha1

import (
	internalinterfaces "l0calh0st.cn/clickpaas-operator/pkg/client/informers/externalversions/internalinterfaces"
)

// Interface provides access to all the informers in this group version.
type Interface interface {
	// Diamonds returns a DiamondInformer.
	Diamonds() DiamondInformer
	// IdGenerates returns a IdGenerateInformer.
	IdGenerates() IdGenerateInformer
	// LtsJobTrackers returns a LtsJobTrackerInformer.
	LtsJobTrackers() LtsJobTrackerInformer
	// MongoClusters returns a MongoClusterInformer.
	MongoClusters() MongoClusterInformer
	// MysqlClusters returns a MysqlClusterInformer.
	MysqlClusters() MysqlClusterInformer
	// RedisGCaches returns a RedisGCacheInformer.
	RedisGCaches() RedisGCacheInformer
	// Rocketmqs returns a RocketmqInformer.
	Rocketmqs() RocketmqInformer
	// ZookeeperClusters returns a ZookeeperClusterInformer.
	ZookeeperClusters() ZookeeperClusterInformer
}

type version struct {
	factory          internalinterfaces.SharedInformerFactory
	namespace        string
	tweakListOptions internalinterfaces.TweakListOptionsFunc
}

// New returns a new Interface.
func New(f internalinterfaces.SharedInformerFactory, namespace string, tweakListOptions internalinterfaces.TweakListOptionsFunc) Interface {
	return &version{factory: f, namespace: namespace, tweakListOptions: tweakListOptions}
}

// Diamonds returns a DiamondInformer.
func (v *version) Diamonds() DiamondInformer {
	return &diamondInformer{factory: v.factory, namespace: v.namespace, tweakListOptions: v.tweakListOptions}
}

// IdGenerates returns a IdGenerateInformer.
func (v *version) IdGenerates() IdGenerateInformer {
	return &idGenerateInformer{factory: v.factory, namespace: v.namespace, tweakListOptions: v.tweakListOptions}
}

// LtsJobTrackers returns a LtsJobTrackerInformer.
func (v *version) LtsJobTrackers() LtsJobTrackerInformer {
	return &ltsJobTrackerInformer{factory: v.factory, namespace: v.namespace, tweakListOptions: v.tweakListOptions}
}

// MongoClusters returns a MongoClusterInformer.
func (v *version) MongoClusters() MongoClusterInformer {
	return &mongoClusterInformer{factory: v.factory, namespace: v.namespace, tweakListOptions: v.tweakListOptions}
}

// MysqlClusters returns a MysqlClusterInformer.
func (v *version) MysqlClusters() MysqlClusterInformer {
	return &mysqlClusterInformer{factory: v.factory, namespace: v.namespace, tweakListOptions: v.tweakListOptions}
}

// RedisGCaches returns a RedisGCacheInformer.
func (v *version) RedisGCaches() RedisGCacheInformer {
	return &redisGCacheInformer{factory: v.factory, namespace: v.namespace, tweakListOptions: v.tweakListOptions}
}

// Rocketmqs returns a RocketmqInformer.
func (v *version) Rocketmqs() RocketmqInformer {
	return &rocketmqInformer{factory: v.factory, namespace: v.namespace, tweakListOptions: v.tweakListOptions}
}

// ZookeeperClusters returns a ZookeeperClusterInformer.
func (v *version) ZookeeperClusters() ZookeeperClusterInformer {
	return &zookeeperClusterInformer{factory: v.factory, namespace: v.namespace, tweakListOptions: v.tweakListOptions}
}

repository: yirentech/clickpaas-operator

repo_name: yirentech/clickpaas-operator

extension: go


text:
@article{ewing2013direct,
 author = {Ewing, Robert G and Atkinson, David A and Clowers, Brian H},
 journal = {Analytical chemistry},
 number = {1},
 pages = {389--397},
 publisher = {American Chemical Society},
 title = {Direct real-time detection of RDX vapors under ambient conditions},
 volume = {85},
 year = {2013}
}

repository: bhclowers/academic-clowers

repo_name: bhclowers/academic-clowers

extension: bib


text:
@inproceedings{goodfellow2014generative,
  title={Generative adversarial nets},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  booktitle={Advances in neural information processing systems},
  pages={2672--2680},
  year={2014}
}

@article{goodfellow2016nips,
  title={NIPS 2016 tutorial: Generative adversarial networks},
  author={Goodfellow, Ian},
  journal={arXiv preprint arXiv:1701.00160},
  year={2016}
}

@article{radford2015unsupervised,
  title={Unsupervised representation learning with deep convolutional generative adversarial networks},
  author={Radford, Alec and Metz, Luke and Chintala, Soumith},
  journal={arXiv preprint arXiv:1511.06434},
  year={2015}
}

@book{Goodfellow-et-al-2016,
  title={Deep Learning},
  author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
  publisher={MIT Press},
  note={\url{http://www.deeplearningbook.org}},
  year={2016}
}

@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}

@article{bengio2013representation,
  title={Representation learning: A review and new perspectives},
  author={Bengio, Yoshua and Courville, Aaron and Vincent, Pascal},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={35},
  number={8},
  pages={1798--1828},
  year={2013},
  publisher={IEEE}
}

@article{fukushima1980neo,
  title={Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position},
  author={Fukushima, Kunihiko},
  journal={Biological Cybernetics},
  number={36},
  pages={193--302},
  year={1980},
  publisher={Springer-Verlag}
}

@article{ioffe2015batch,
  title={Batch normalization: Accelerating deep network training by reducing internal covariate shift},
  author={Ioffe, Sergey and Szegedy, Christian},
  journal={arXiv preprint arXiv:1502.03167},
  year={2015}
}

@article{lecun1998gradient,
  title={Gradient-based learning applied to document recognition},
  author={LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick and others},
  journal={Proceedings of the IEEE},
  volume={86},
  number={11},
  pages={2278--2324},
  year={1998},
  publisher={Taipei, Taiwan}
}

@inproceedings{zhu2017unpaired,
  title={Unpaired image-to-image translation using cycle-consistent adversarial networks},
  author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={2223--2232},
  year={2017}
}

@article{karras2017progressive,
  title={Progressive growing of gans for improved quality, stability, and variation},
  author={Karras, Tero and Aila, Timo and Laine, Samuli and Lehtinen, Jaakko},
  journal={arXiv preprint arXiv:1710.10196},
  year={2017}
}

@article{arjovsky2017wasserstein,
  title={Wasserstein gan},
  author={Arjovsky, Martin and Chintala, Soumith and Bottou, L{\'e}on},
  journal={arXiv preprint arXiv:1701.07875},
  year={2017}
}

@inproceedings{yu2017seqgan,
  title={Seqgan: Sequence generative adversarial nets with policy gradient},
  author={Yu, Lantao and Zhang, Weinan and Wang, Jun and Yu, Yong},
  booktitle={Thirty-First AAAI Conference on Artificial Intelligence},
  year={2017}
}

@inproceedings{zhang2018mitigating,
  title={Mitigating unwanted biases with adversarial learning},
  author={Zhang, Brian Hu and Lemoine, Blake and Mitchell, Margaret},
  booktitle={Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society},
  pages={335--340},
  year={2018},
  organization={ACM}
}

@inproceedings{karras2019style,
  title={A style-based generator architecture for generative adversarial networks},
  author={Karras, Tero and Laine, Samuli and Aila, Timo},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={4401--4410},
  year={2019}
}

@article{mirza2014conditional,
  title={Conditional generative adversarial nets},
  author={Mirza, Mehdi and Osindero, Simon},
  journal={arXiv preprint arXiv:1411.1784},
  year={2014}
}

@inproceedings{bergstra2011algorithms,
  title={Algorithms for hyper-parameter optimization},
  author={Bergstra, James S and Bardenet, R{\'e}mi and Bengio, Yoshua and K{\'e}gl, Bal{\'a}zs},
  booktitle={Advances in neural information processing systems},
  pages={2546--2554},
  year={2011}
}

@article{goodfellow2014explaining,
  title={Explaining and harnessing adversarial examples},
  author={Goodfellow, Ian J and Shlens, Jonathon and Szegedy, Christian},
  journal={arXiv preprint arXiv:1412.6572},
  year={2014}
}

@article{bau2018gan,
  title={Gan dissection: Visualizing and understanding generative adversarial networks},
  author={Bau, David and Zhu, Jun-Yan and Strobelt, Hendrik and Zhou, Bolei and Tenenbaum, Joshua B and Freeman, William T and Torralba, Antonio},
  journal={arXiv preprint arXiv:1811.10597},
  year={2018}
}

@incollection{NIPS2018_7350,
  title = {Are GANs Created Equal? A Large-Scale Study},
  author = {Lucic, Mario and Kurach, Karol and Michalski, Marcin and Gelly, Sylvain and Bousquet, Olivier},
  booktitle = {Advances in Neural Information Processing Systems 31},
  editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
  pages = {700--709},
  year = {2018},
  publisher = {Curran Associates, Inc.},
  url = {http://papers.nips.cc/paper/7350-are-gans-created-equal-a-large-scale-study.pdf}
}

@inproceedings{salimans2016improved,
  title={Improved techniques for training gans},
  author={Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi},
  booktitle={Advances in neural information processing systems},
  pages={2234--2242},
  year={2016}
}

@article{reed2016generative,
  title={Generative adversarial text to image synthesis},
  author={Reed, Scott and Akata, Zeynep and Yan, Xinchen and Logeswaran, Lajanugen and Schiele, Bernt and Lee, Honglak},
  journal={arXiv preprint arXiv:1605.05396},
  year={2016}
}

@article{donahue2018adversarial,
  title={Adversarial audio synthesis},
  author={Donahue, Chris and McAuley, Julian and Puckette, Miller},
  journal={arXiv preprint arXiv:1802.04208},
  year={2018}
}

@inproceedings{isola2017image,
  title={Image-to-image translation with conditional adversarial networks},
  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1125--1134},
  year={2017}
}

@inproceedings{liu2018generative,
  title={Generative adversarial network for abstractive text summarization},
  author={Liu, Linqing and Lu, Yao and Yang, Min and Qu, Qiang and Zhu, Jia and Li, Hongyan},
  booktitle={Thirty-second AAAI conference on artificial intelligence},
  year={2018}
}

@article{creswell2018generative,
  title={Generative adversarial networks: An overview},
  author={Creswell, Antonia and White, Tom and Dumoulin, Vincent and Arulkumaran, Kai and Sengupta, Biswa and Bharath, Anil A},
  journal={IEEE Signal Processing Magazine},
  volume={35},
  number={1},
  pages={53--65},
  year={2018},
  publisher={IEEE}
}

@article{bang2018mggan,
  title={Mggan: Solving mode collapse using manifold guided training},
  author={Bang, Duhyeon and Shim, Hyunjung},
  journal={arXiv preprint arXiv:1804.04391},
  year={2018}
}

repository: ant-mak/tfm

repo_name: ant-mak/tfm

gha_language: Jupyter Notebook


text:
PACKAGES = "${PN}"
DESCRIPTION = "Meta-package for MythTV diskless frontend"
MAINTAINER = "Phil Blundell <pb@handhelds.org>"
ALLOW_EMPTY = 1
PR = "r6"

RDEPENDS = "xserver-xorg mythtv lirc lirc-modules drm-module-via ttf-bitstream-vera libfontconfig-utils setserial snes9x ntp mythfront-config gpe-dm mythfront-session bootlogd drm-module-drm"
DEPENDS = "xserver-xorg mythtv lirc lirc-modules drm-kernel ttf-bitstream-vera setserial snes9x ntp mythfront-config gpe-dm mythfront-session"
LICENSE = MIT

repository: Terentyev/familiar-linux

repo_name: Terentyev/familiar-linux


text:
SECTION = "devel/chicken"
DESCRIPTION = "Represents unicode with the limited ASCII subset supported by DNS (RFC3492)"
LICENSE = "BSD"
LIC_FILES_CHKSUM = "file://punycode.meta;md5=eba5c06488c9fed239b49ca505ed6e38"
DEPENDS_append_class-cross = " chicken-egg-miscmacros-cross chicken-egg-utf8-cross"
DEPENDS_append_class-crosssdk = " chicken-egg-miscmacros-crosssdk chicken-egg-utf8-crosssdk"
DEPENDS_append_class-nativesdk = " nativesdk-chicken-egg-miscmacros nativesdk-chicken-egg-utf8 chicken-egg-miscmacros-crosssdk chicken-egg-utf8-crosssdk"
DEPENDS = " chicken-egg-miscmacros chicken-egg-utf8 chicken-egg-miscmacros-cross chicken-egg-utf8-cross"
RDEPENDS_${PN} = " chicken-egg-miscmacros chicken-egg-utf8"

SRC_URI = "http://code.call-cc.org/egg-tarballs/4/punycode/punycode-${PV}.tar.gz"

SRC_URI[md5sum] = "88937da07274600c3310fa9bd77890a3"
SRC_URI[sha256sum] = "70078a806ebb6ba9c1317c5e7dba0fee8ab4eb903939e4d40a1608932ba23f6e"

S = "${WORKDIR}/punycode-${PV}"

inherit chicken_install

BBCLASSEXTEND = "cross crosssdk nativesdk"

repository: OSSystems/meta-chicken

repo_name: OSSystems/meta-chicken


text:
@inproceedings{hashimoto2017joint,
  author = {Hashimoto, Kazuma and xiong, caiming and Tsuruoka, Yoshimasa and Socher, Richard},
  title = {A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  year = {2017},
  publisher = {Association for Computational Linguistics},
  pages = {1923--1933},
  location = {Copenhagen, Denmark},
  url = {http://aclweb.org/anthology/D17-1206}
}

url: https://raw.githubusercontent.com/Hugh-Shi/DL-NLP-Readings/924e4d189e5660c5b5d4854278f3df755ae2231d/Bibtex/A Joint Many-Task Model - Growing a Neural Network for Multiple NLP Tasks.bib

repository: Hugh-Shi/DL-NLP-Readings

repo_name: Hugh-Shi/DL-NLP-Readings


text:
@ARTICLE{Hertel2014-nx,
title       = "Global market integration increases likelihood that a future
African Green Revolution could increase crop land use and
{CO2} emissions",
author      = "Hertel, Thomas W and Ramankutty, Navin and Baldos, Uris Lantz
C",
affiliation = "Department of Agricultural Economics, Purdue University, West
Lafayette, IN 47907; and hertel@purdue.edu. Department of
Geography, McGill University, Montreal, QC, Canada H3A 0B9.
Department of Agricultural Economics, Purdue University, West
Lafayette, IN 47907; and.",
journal     = "Proceedings of the National Academy of Sciences of the United
States of America",
volume      =  111,
number      =  38,
pages       = "13799--13804",
month       =  "23~" # sep,
year        =  2014,
url         = "http://dx.doi.org/10.1073/pnas.1403543111",
keywords    = "Carbon Dioxide; Crop Competition for Land; Emissions; Food
Security; Food Trade",
language    = "en",
issn        = "0027-8424, 1091-6490",
pmid        = "25201962",
doi         = "10.1073/pnas.1403543111",
pmc         = "PMC4183291"
}


text:
@article{Benjamini1995,
    author = {Benjamini, Yoav and Hochberg, Yosef},
    doi = {10.2307/2346101},
    issn = {00359246},
    journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
    keywords = {multiple-testing, significance},
    number = {1},
    pages = {289--300},
    posted-at = {2007-02-12 20:49:34},
    priority = {2},
    publisher = {Blackwell Publishing for the Royal Statistical Society},
    title = {{Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing}},
    url = {http://dx.doi.org/10.2307/2346101},
    volume = {57},
    year = {1995}
}
@article{Flores2002,
    author = {Elsa R. Flores and Kenneth Y. Tsai and Denise Crowley and Shomit Sengupta and Annie Yang and Frank McKeon and Tyler Jacks},
    journal = {Nature},
    pages = {560--564},
    title = {p63 and p73 are required for p53-dependent apoptosis in response to DNA damage},
    volume = {416},
    year = {2002}
}

@article{Liu2009,
    author = {Liu, Tao and Liu, Pei Y. and Marshall, Glenn M.},
    citeulike-article-id = {4999886},
    citeulike-linkout-0 = {http://dx.doi.org/10.1158/0008-5472.CAN-08-3365},
    citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19244112},
    citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19244112},
    day = {1},
    doi = {10.1158/0008-5472.CAN-08-3365},
    issn = {1538-7445},
    journal = {Cancer research},
    keywords = {cancer, epigenetics, methylation, sirt1},
    month = mar,
    number = {5},
    pages = {1702--1705},
    pmid = {19244112},
    posted-at = {2009-06-29 14:32:08},
    priority = {2},
    title = {{The critical role of the class III histone deacetylase SIRT1 in cancer.}},
    url = {http://dx.doi.org/10.1158/0008-5472.CAN-08-3365},
    volume = {69},
    year = {2009}
}
@article{Subramanian2005,
    author = {Subramanian, Aravind and Tamayo, Pablo and Mootha, Vamsi K. and Mukherjee, Sayan and Ebert, Benjamin L. and Gillette, Michael A. and Paulovich, Amanda and Pomeroy, Scott L. and Golub, Todd R. and Lander, Eric S. and Mesirov, Jill P.},
    booktitle = {Proceedings of the National Academy of Sciences of the United States of America},
    day = {25},
    doi = {10.1073/pnas.0506580102},
    issn = {0027-8424},
    journal = {Proceedings of the National Academy of Sciences of the United States of America},
    keywords = {bio, enrichment, expression, gene, gsea, set},
    month = oct,
    number = {43},
    pages = {15545--15550},
    pmcid = {PMC1239896},
    pmid = {16199517},
    posted-at = {2011-04-08 10:57:32},
    priority = {2},
    publisher = {National Academy of Sciences},
    title = {{Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.}},
    url = {http://dx.doi.org/10.1073/pnas.0506580102},
    volume = {102},
    year = {2005}
}

gha_language: R


text:
export const getError = state => state.mosaic.get('error');

export const getIsFetching = state => state.mosaic.get('isFetching');

export const getIsHydrated = state => state.mosaic.get('isHydrated');

export const getTracks = state => state.mosaic.get('tracks');

export const getTrack = (state, trackId) => state.mosaic.getIn(['tracks', trackId]);

gha_language: JavaScript

extension: js


text:
# The set of languages for which implicit dependencies are needed:
set(CMAKE_DEPENDS_LANGUAGES
  "C"
  )
# The set of files for implicit dependencies of each language:
set(CMAKE_DEPENDS_CHECK_C
  "/home/charlotte/Git/crispy-enigma/projet_algo/algo_principal/fonctions.c" "/home/charlotte/Git/crispy-enigma/projet_algo/algo_principal/cmake-build-debug/CMakeFiles/algo_principal.dir/fonctions.c.o"
  "/home/charlotte/Git/crispy-enigma/projet_algo/algo_principal/fonctions_PSSM.c" "/home/charlotte/Git/crispy-enigma/projet_algo/algo_principal/cmake-build-debug/CMakeFiles/algo_principal.dir/fonctions_PSSM.c.o"
  "/home/charlotte/Git/crispy-enigma/projet_algo/algo_principal/fonctions_masques_kmers.c" "/home/charlotte/Git/crispy-enigma/projet_algo/algo_principal/cmake-build-debug/CMakeFiles/algo_principal.dir/fonctions_masques_kmers.c.o"
  "/home/charlotte/Git/crispy-enigma/projet_algo/algo_principal/fonctions_sequences.c" "/home/charlotte/Git/crispy-enigma/projet_algo/algo_principal/cmake-build-debug/CMakeFiles/algo_principal.dir/fonctions_sequences.c.o"
  "/home/charlotte/Git/crispy-enigma/projet_algo/algo_principal/main.c" "/home/charlotte/Git/crispy-enigma/projet_algo/algo_principal/cmake-build-debug/CMakeFiles/algo_principal.dir/main.c.o"
  )
set(CMAKE_C_COMPILER_ID "GNU")

# The include file search paths:
set(CMAKE_C_TARGET_INCLUDE_PATH
  )

# Targets to which this target links.
set(CMAKE_TARGET_LINKED_INFO_FILES
  )

# Fortran module output directory.
set(CMAKE_Fortran_TARGET_MODULE_DIR "")

license: Unlicense

repository: charlotte-brunel/crispy-enigma

repo_name: charlotte-brunel/crispy-enigma

gha_license_id: Unlicense

gha_language: C


text:
SUMMARY = "Intel OEM IPMI commands"
DESCRIPTION = "Intel OEM IPMI commands"

LICENSE = "Apache-2.0"
LIC_FILES_CHKSUM = "file://LICENSE;md5=a6a4edad4aed50f39a66d098d74b265b"

SRC_URI = "git://github.com/openbmc/intel-ipmi-oem;branch=master;protocol=https"
SRCREV = "af65268e15a3a491f4548243254a686f42518e2e"

S = "${WORKDIR}/git"
PV = "0.1+git${SRCPV}"

DEPENDS = "boost phosphor-ipmi-host phosphor-logging systemd phosphor-dbus-interfaces libgpiod libtinyxml2"

inherit cmake obmc-phosphor-ipmiprovider-symlink pkgconfig

EXTRA_OECMAKE="-DENABLE_TEST=0 -DYOCTO=1"

LIBRARY_NAMES = "libzinteloemcmds.so"

HOSTIPMI_PROVIDER_LIBRARY += "${LIBRARY_NAMES}"
NETIPMI_PROVIDER_LIBRARY += "${LIBRARY_NAMES}"

FILES:${PN}:append = " ${libdir}/ipmid-providers/lib*${SOLIBS}"
FILES:${PN}:append = " ${libdir}/host-ipmid/lib*${SOLIBS}"
FILES:${PN}:append = " ${libdir}/net-ipmid/lib*${SOLIBS}"
FILES:${PN}-dev:append = " ${libdir}/ipmid-providers/lib*${SOLIBSDEV}"

do_install:append(){
   install -d ${D}${includedir}/intel-ipmi-oem
   install -m 0644 -D ${S}/include/*.hpp ${D}${includedir}/intel-ipmi-oem
}

repository: openbmc/openbmc

repo_name: openbmc/openbmc

gha_license_id: NOASSERTION

gha_language: Python


text:
package cmd

import (
	"fmt"
	"net/url"

	"github.com/QuickBase/quickbase-cli/qbcli"
	"github.com/QuickBase/quickbase-cli/qbclient"
	"github.com/cpliakas/cliutil"
	"github.com/pkg/browser"
	"github.com/spf13/cobra"
	"github.com/spf13/viper"
)

var appOpenCfg *viper.Viper

var appOpenCmd = &cobra.Command{
	Use:   "open",
	Short: "Open an app's homepage in a browser",

	Args: func(cmd *cobra.Command, args []string) (err error) {
		err = globalCfg.Validate()
		if err == nil {
			globalCfg.SetDefaultAppID(appOpenCfg)
			qbcli.SetOptionFromArg(appOpenCfg, args, 0, qbclient.OptionAppID)
		}
		return
	},

	Run: func(cmd *cobra.Command, args []string) {
		ctx, logger, _ := qbcli.NewLogger(cmd, globalCfg)

		opts := &AppOpenOpts{}
		qbcli.GetOptions(ctx, logger, opts, appOpenCfg)

		u := fmt.Sprintf("https://%s/db/%s%s", globalCfg.RealmHostname(), url.PathEscape(opts.AppID), opts.Vars())
		ctx = cliutil.ContextWithLogTag(ctx, "url", u)

		err := browser.OpenURL(u)
		qbcli.HandleError(ctx, logger, "error opening app in browser", err)
	},
}

func init() {
	var flags *cliutil.Flagger
	appOpenCfg, flags = cliutil.AddCommand(appCmd, appOpenCmd, qbclient.EnvPrefix)
	flags.SetOptions(&AppOpenOpts{})
}

// AppOpenOpts contains the options for the app open command.
type AppOpenOpts struct {
	AppID    string `cliutil:"option=app-id"`
	Settings string `cliutil:"option=settings short=s"`
}

// Vars returns the query string variables based on AppOpenOpts.Settings.
func (o AppOpenOpts) Vars() string {
	if !appOpenCfg.IsSet("settings") {
		return ""
	}

	a := "?a="
	switch o.Settings {
	case "branding":
		a += "AppPropertiesBrandGuide"
	case "management", "mngmt", "mgmt", "mgt":
		a += "AppMisc"
	case "pages":
		a += "AppDBPages"
	case "properties", "props":
		a += "AppProperties"
	case "roles":
		a += "AppRolesList"
	case "tables":
		a += "AppTablesList"
	case "variables", "vars":
		a += "AppVariables"
	default:
		a += "AppSettingsHome"
	}

	return a
}

repository: proc/quickbase-cli

repo_name: proc/quickbase-cli


text:
@article{heidlmayrFerragneIsel2021,
title = {Neuroplasticity in the phonological system: The PMN and the N400 as markers for the perception of non-native phonemic contrasts by late second language learners},
journal = {Neuropsychologia},
pages = {107831},
year = {2021},
issn = {0028-3932},
doi = {https://doi.org/10.1016/j.neuropsychologia.2021.107831},
url = {https://www.sciencedirect.com/science/article/pii/S0028393221000828},
author = {Karin Heidlmayr and Emmanuel Ferragne and Frédéric Isel},
keywords = {second language acquisition, neuroplasticity, phonology, semantic unification, PMN, N400}
}

repository: emmanuelferragne/academic-kickstart

repo_name: emmanuelferragne/academic-kickstart


text:
#
# Copyright (c) Siemens AG, 2021
#
# Authors:
#  Jan Kiszka <jan.kiszka@siemens.com>
#
# This file is subject to the terms and conditions of the MIT License.  See
# COPYING.MIT file in the top-level directory.
#

inherit dpkg

SRC_URI = "git://github.com/siemens/k3-rti-wdt.git;protocol=https"
SRCREV = "33a6680184996074ca9731710161520edf334725"

S = "${WORKDIR}/git"

DEBIAN_BUILD_DEPENDS += "gcc-arm-linux-gnueabihf"

do_prepare_build[cleandirs] += "${S}/debian"

do_prepare_build() {
    deb_debianize

    echo "k3-rti-wdt.fw /lib/firmware/" > ${S}/debian/install
}

dpkg_runbuild_prepend() {
    export CC="gcc"
}


text:
@ARTICLE{Slingsby2020-to,
  title    = "Near-real time forecasting and change detection for an open
              ecosystem with complex natural dynamics",
  author   = "Slingsby, Jasper A and Moncrieff, Glenn R and Wilson, Adam M",
  abstract = "Managing fire, water, biodiversity and carbon stocks can greatly
              benefit from early warning of changes in the state of vegetation.
              While near-real time tools to detect forest change based on
              satellite remote sensing exist, these ecosystems have relatively
              stable natural vegetation dynamics. Open (i.e. non-forest)
              ecosystems like grasslands, savannas and shrublands are more
              challenging as they show complex natural dynamics due to factors
              such as fire, postfire recovery, greater contribution of bare
              soil to observed vegetation indices, as well as high sensitivity
              to rainfall and strong seasonality. Tools to aid the management
              of open ecosystems are desperately required as they dominate much
              of the globe and harbour substantial biodiversity and carbon. We
              present an innovative approach that overcomes the difficulties
              posed by open ecosystems by using a spatio-temporal hierarchical
              Bayesian model that uses data on climate, topography, soils and
              fire history to generate ecological forecasts of the expected
              land surface signal under natural conditions. This allows us to
              monitor and detect abrupt or gradual changes in the state of an
              ecosystem in near-real time by identifying areas where the
              observed vegetation signal has deviated from the expected natural
              variation. We apply our approach to a case study from the
              hyperdiverse fire-dependent African shrubland, the fynbos of the
              Cape Floristic Region, a Global Biodiversity Hotspot and UNESCO
              World Heritage Site that faces a number of threats to vegetation
              health and ecosystem function. The case study demonstrates that
              our approach is useful for identifying a range of change agents
              such as fire, alien plant species invasions, drought, pathogen
              outbreaks and clearing of vegetation. We describe and provide our
              full workflow, including an interactive web application. Our
              approach is highly versatile, allowing us to collect data on the
              impacts of change agents for research in ecology and earth system
              science, and to predict aspects of ecosystem structure and
              function such as biomass, fire return interval and the influence
              of vegetation on hydrology.",
  journal  = "ISPRS J. Photogramm. Remote Sens.",
  volume   =  166,
  pages    = "15--25",
  month    =  aug,
  year     =  2020,
  keywords = "Fynbos; Fire; Time series; Global change; Hierarchical Bayes;
              Ecological forecasting"
}

repository: SAEON/academic-kickstart

repo_name: SAEON/academic-kickstart


text:
DESCRIPTION = "Set of tools for managing notebook power consumption."
SECTION = "base"
PRIORITY = "required"
DEPENDS = "libtool-cross"
LICENSE = "GPL"
PR = "r13"

SRC_URI = "${DEBIAN_MIRROR}/main/a/apmd/apmd_${PV}.orig.tar.gz \
           file://debian.patch \
           file://workaround.patch \
           file://zaurus24.patch \
           file://unlinux.patch \
           file://libtool.patch \
           file://init \
           file://default \
           file://apmd_proxy \
           file://apmd_proxy.conf"

S = "${WORKDIR}/apmd-${PV}.orig"

inherit update-rc.d

INITSCRIPT_NAME = "apmd"
INITSCRIPT_PARAMS = "defaults"

LIBTOOL = "${HOST_SYS}-libtool"
EXTRA_OEMAKE = "'LIBTOOL=${LIBTOOL}' 'CC=${CC}' 'CFLAGS=${CFLAGS}' \
                'LDFLAGS=${LDFLAGS}'"

do_compile() {
	oe_runmake apm apmd
}

do_install() {
	install -d ${D}${sysconfdir}
	install -d ${D}${sysconfdir}/apm
	install -d ${D}${sysconfdir}/apm/event.d
	install -d ${D}${sysconfdir}/apm/other.d
	install -d ${D}${sysconfdir}/apm/suspend.d
	install -d ${D}${sysconfdir}/apm/resume.d
	install -d ${D}${sysconfdir}/apm/scripts.d
	install -d ${D}${sysconfdir}/default
	install -d ${D}${sysconfdir}/init.d
	install -d ${D}${sbindir}
	install -d ${D}${bindir}
	install -d ${D}${libdir}
	install -d ${D}${datadir}/apmd
	install -d ${D}${includedir}

	install -m 4755 ${S}/.libs/apm ${D}${bindir}/apm
	install -m 0755 ${S}/.libs/apmd ${D}${sbindir}/apmd
	install -m 0755 ${WORKDIR}/apmd_proxy ${D}${sysconfdir}/apm/
	install -m 0644 ${WORKDIR}/apmd_proxy.conf ${D}${datadir}/apmd/
	install -m 0644 ${WORKDIR}/default ${D}${sysconfdir}/default/apmd
	oe_libinstall -so libapm ${D}${libdir}
	install -m 0644 apm.h ${D}${includedir}
        for i in `find ${D} -name "*.la"` ; do \
                sed -i -e s:${STAGING_LIBDIR}:${libdir}:g $i
                sed -i -e s:${STAGING_DIR_HOST}::g $i
        done

	cat ${WORKDIR}/init | sed -e 's,/usr/sbin,${sbindir},g; s,/etc,${sysconfdir},g;' > ${D}${sysconfdir}/init.d/apmd
	chmod 755 ${D}${sysconfdir}/init.d/apmd
}

PACKAGES =+ "libapm libapm-dev apm"

FILES_libapm = "${libdir}/libapm.so.*"
FILES_libapm-dev = "${libdir}/libapm.* ${includedir}"
FILES_apm = "${bindir}/apm*"

SRC_URI[md5sum] = "b1e6309e8331e0f4e6efd311c2d97fa8"
SRC_URI[sha256sum] = "7f7d9f60b7766b852881d40b8ff91d8e39fccb0d1d913102a5c75a2dbb52332d"

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty

branch_name: HEAD


text:
@InProceedings{Dai15,
  title = {Statistical Unfolded Logic Learning},
  author = {Dai, Wang-Zhou and Zhou, Zhi-Hua},
  pages = {349-361},
  abstract = {During the past decade, Statistical Relational Learning (SRL) and
Probabilistic Inductive Logic Programming (PILP), owing to their
strength in capturing structure information, have attracted much
attention for learning relational models such as weighted logic
rules. Typically, a generative model is assumed for the structured
joint distribution, and the learning process is accomplished in an
enormous relational space. In this paper, we propose a new framework,
i.e., Statistical Unfolded Logic (SUL) learning. In contrast to
learning rules in the relational space directly, SUL propositionalizes
the structure information into an attribute-value data set, and thus,
statistical discriminative learning which is much more efficient than
generative relational learning can be executed. In addition to
achieving better generalization performance, SUL is able to conduct
predicate invention that is hard to be realized by traditional SRL and
PILP approaches. Experiments on real tasks show that our proposed
approach is superior to state-of-the-art weighted rules learning
approaches.},
}

repository: mreid/papersite

repo_name: mreid/papersite

gha_language: TeX


text:
# Defines a macro to make it easy to build a python library

MACRO(PY_LIBRARY)
  SET(argList "${ARGN}")
  CMAKE_PARSE_ARGUMENTS("" # Arg prefix is just "_"
                      "" # Option type arguments
                      "NAME;" # Single value arguments
                      "SOURCES;DATA;PACKAGES;" # List valued arguments
                      ${argList} )
  REQUIRE_NOT_EMPTY(_NAME _SOURCES)

  IF(_UNPARSED_ARGUMENTS)
    MESSAGE(FATAL_ERROR "unexpected arguments: ${_UNPARSED_ARGUMENTS}")
  ENDIF()

  INCLUDE(${cmakesnap_DIR}/internal/py_common.cmake)

  DISPLAY_PACKAGE_STATUS(
    TYPE "PY LIB"
    URI ${target_uri}
    MISSING_URIS ${missing_package_uris}
  )
ENDMACRO(PY_LIBRARY)


text:
@InProceedings{grubb12,
  title = {SpeedBoost: Anytime Prediction with Uniform Near-Optimality},
  author = {Alex Grubb and Drew Bagnell},
  pages = {458--466},
  abstract = {We present SpeedBoost, a natural extension of functional gradient descent, for learning anytime predictors, which automatically trade computation time for predictive accuracy by selecting from a set of simpler candidate predictors. These anytime predictors not only generate approximate predictions rapidly, but are capable of using extra resources at prediction time, when available, to improve performance. We also demonstrate how our framework can be used to select weak predictors which target certain subsets of the data, allowing for efficient use of computational resources on difficult examples.  We also show that variants of the SpeedBoost algorithm produce predictors which are provably competitive with any possible sequence of weak predictors with the same total complexity.},
  pdf = {http://jmlr.org/proceedings/papers/v22/grubb12/grubb12.pdf},
}


text:
@article{AGANJ2015238,
title = "Avoiding symmetry-breaking spatial non-uniformity in deformable image registration via a quasi-volume-preserving constraint",
journal = "NeuroImage",
volume = "106",
pages = "238 - 251",
year = "2015",
issn = "1053-8119",
doi = "https://doi.org/10.1016/j.neuroimage.2014.10.059",
url = "http://www.sciencedirect.com/science/article/pii/S1053811914008982",
author = "Iman Aganj and Martin Reuter and Mert R. Sabuncu and Bruce Fischl"
}


text:
require task-sdk-native.inc

RPROVIDES_${PN} = "task-native-sdk"

PR = "${INC_PR}.1"

ALLOW_EMPTY = "1"
PACKAGES = "${PN}"

repository: demsey/openembedded

repo_name: demsey/openembedded


text:
@article{Aksamentov2021,
  doi = {10.21105/joss.03773},
  url = {https://doi.org/10.21105/joss.03773},
  year = {2021},
  publisher = {The Open Journal},
  volume = {6},
  number = {67},
  pages = {3773},
  author = {Ivan Aksamentov and Cornelius Roemer and Emma B. Hodcroft and Richard A. Neher},
  title = {Nextclade: clade assignment, mutation calling and quality control for viral genomes},
  journal = {Journal of Open Source Software}
}

repository: nextstrain/nextclade

repo_name: nextstrain/nextclade

gha_language: Rust


text:
discrete.bib

http://www.math.cmu.edu/~mradclif/teaching/228F16/Kuratowski.pdf

https://www.ocr.org.uk/Images/308752-specification-accredited-a-level-gce-further-mathematics-a-h245.pdf

repository: st-pauls-school/discrete-mathematics

repo_name: st-pauls-school/discrete-mathematics


text:
@article{marmot2010fair,
  title={Fair society, healthy lives},
  author={Marmot, Michael and Allen, Jessica and Goldblatt, Peter and Boyce, Tammy and McNeish, Di and Grady, Mike and others},
  journal={The Marmot Review},
  volume={14},
  year={2010},
  publisher={University College London London, England}
}
@article{lancee2012income,
  title={Income inequality and participation: A comparison of 24 European countries},
  author={Lancee, Bram and Van de Werfhorst, Herman G},
  journal={Social science research},
  volume={41},
  number={5},
  pages={1166--1178},
  year={2012},
  publisher={Elsevier}
}

@article{kranzinger2018decomposition,
  title={The Decomposition of Income Inequality in the EU-28},
  author={Kranzinger, Stefan},
  year={2018},
  publisher={WU Vienna University of Economics and Business}
}

@article{beblo2001measuring,
  title={Measuring income inequality in Euroland},
  author={Beblo, Miriam and Knaus, Thomas},
  journal={Review of Income and Wealth},
  volume={47},
  number={3},
  pages={301--333},
  year={2001},
  publisher={Wiley Online Library}
}

@article{hoffmeister2009spatial,
  title={The spatial structure of income inequality in the enlarged EU},
  author={Hoffmeister, Onno},
  journal={Review of Income and Wealth},
  volume={55},
  number={1},
  pages={101--127},
  year={2009},
  publisher={Wiley Online Library}
}

@article{papatheodorou2014income,
  title={Income inequality in the EU: how do member states contribute?},
  author={Papatheodorou, Christos and Pavlopoulos, Dimitris},
  journal={International Journal of Social Economics},
  volume={41},
  number={6},
  pages={450--466},
  year={2014},
  publisher={Emerald Group Publishing Limited}
}

@techreport{paas2006regional,
  title={Regional income inequality and convergence processes in the EU-25},
  author={Paas, Tiiu and Schlitte, Friso},
  year={2006},
  institution={HWWA Discussion Paper}
}

@article{fredriksen2012income,
  title={Income inequality in the European Union},
  author={Fredriksen, Kaja Bonesmo},
  year={2012},
  publisher={OECD Publishing}
}


text:
@article{Peskett-2021-Tracers,
    title = "Tracers reveal limited influence of plantation forests on surface runoff in a UK natural flood management catchment",
    author = "Peskett, Leo M.  and
      Heal, Kate V.  and
      MacDonald, Alan M.  and
      Black, Andrew R.  and
      McDonnell, Jeffrey J.",
    journal = "Journal of Hydrology: Regional Studies, Volume 36",
    volume = "36",
    year = "2021",
    publisher = "Elsevier BV",
    url = "https://aclanthology.org/G21-34001",
    doi = "10.1016/j.ejrh.2021.100834",
    pages = "100834",
    abstract = "{\mbox{$\bullet$}} Natural tracers reveal runoff sources in UK natural flood management catchment. {\mbox{$\bullet$}} Water already stored in the catchments dominated runoff in high flow events. {\mbox{$\bullet$}} Plantation forest cover reduced the fraction of rapid rainfall runoff. {\mbox{$\bullet$}} Soils and geology dominated forest cover as control on rapid rainfall runoff fraction. {\mbox{$\bullet$}} Differences in sources were greater between events than between catchments. United Kingdom (UK). Natural flood management (NFM) schemes are increasingly prominent in the UK. Studies of NFM have not yet used natural tracers at catchment scale to investigate how interventions influence partitioning during storms between surface rainfall runoff and water already stored in catchments. Here we investigate how catchment properties, particularly plantation forestry, influence surface storm rainfall runoff. We used hydrograph separation based on hydrogen and oxygen isotopes ( 2 H, 18 O) and acid neutralising capacity from high flow events to compare three headwater catchments (2.4-3.1 km 2 ) with differences in plantation forest cover ( Picea sitchensis: 94{\%}, 41{\%}, 1{\%}) within a major UK NFM pilot, typical of the UK uplands. Plantation forest cover reduced the total storm rainfall runoff fraction during all events (by up to 11{\%}) when comparing two paired catchments with similar soils, geology and topography but ∼50{\%} difference in forest cover. However, comparison with the third catchment, with negligible forest cover but different characteristics, suggests that soils and geology were dominant controls on storm rainfall runoff fraction. Furthermore, differences between events were greater than differences between catchments. These findings suggest that while plantation forest cover may influence storm rainfall runoff fractions, it is not a dominant control in temperate upland UK catchments, especially for larger events. Soils and geology may exert greater influence, with implications for planning NFM.",
}

repository: gwf-uwaterloo/gwf-publications

repo_name: gwf-uwaterloo/gwf-publications


text:
@article{2012-SAA+,
 abstract = {Background
Accurate and efficient RNA secondary structure prediction remains an important open problem in computational molecular biology. Historically, advances in computing technology have enabled faster and more accurate RNA secondary structure predictions. Previous parallelized prediction programs achieved significant improvements in runtime, but their implementations were not portable from niche high-performance computers or easily accessible to most RNA researchers. With the increasing prevalence of multi-core desktop machines, a new parallel prediction program is needed to take full advantage of today’s computing technology.

Findings:
We present here the first implementation of RNA secondary structure prediction by thermodynamic optimization for modern multi-core computers. We show that GTfold predicts secondary structure in less time than UNAfold and RNAfold, without sacrificing accuracy, on machines with four or more cores.

Conclusions:
GTfold supports advances in RNA structural biology by reducing the timescales for secondary structure prediction. The difference will be particularly valuable to researchers working with lengthy RNA sequences, such as RNA viral genomes.},
 author = {M. Shel Swenson and Joshua Anderson and Andrew Ash and Prashant Gaurav and Zsuzsanna Sükösd and David A. Bader and Stephen C. Harvey and Christine E. Heitsch},
 doi = {10.1186/1756-0500-5-341},
 groups = {Edited},
 journal = {BMC Research Notes},
 pages = {341--347},
 title = {GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops},
 url = {https://doi.org/10.1186/1756-0500-5-341},
 volume = {5},
 year = {2012}
}

gha_language: Shell


text:
@Book{Vygotsky:1978,
	author	= "Vygotsky, Lev S.",
	title	= "Mind in Society",
	publisher = "Cambridge, MA: Harvard University Press",
	year	= "1978",
}

@Book{Vygotsky:1986,
	author	= "Vygotsky, Lev S.",
	title	= "Thought and Language",
	publisher = "Cambridge, MA: The MIT Press",
	year	= "1986",
}

@Book{Wertsch:1985,
	author	= "Wertsch, James V.",
	title	= "Vygotsky and the Social Formation of Mind",
	publisher = "Cambridge, MA and London: Harvard University Press",
	year	= "1985",
}

@Book{Wertsch:1991,
	author	= "Wertsch, James V.",
	title	= "Voices of the Mind",
	publisher = "Cambridge, MA: Harvard University Press",
	year	= "1991",
}

@Book{Osborne:2001,
	author	= "Wellington, Jerry and Osborne, Jonathan",
	title	= "Language and literacy in science education",
	publisher = "Maidenhead and New York: Open University Press",
	year	= "2001",
}

@Book{Mortimer:2003,
	author	= "Mortimer, Eduardo F. and Scott, Philip H.",
	title	= "Meaning Making in Secondary Science Classrooms",
	publisher = "Maidenhead and Philadelphia: Open University Press",
	year	= "2003",
}

@Book{Ogborn:1996,
	author	= "Ogborn, J. and Kress, G. and Martins, I. and McGillicuddy, K.",
	title	= "Explaining Science in the Classroom",
	publisher = "Buckingham: Open University Press",
	year	= "1996",
}

@Book{Mazur:1997,
	author	= "Mazur, Eric",
	title	= "Peer Instruction",
	publisher = "Upper Saddle River, NJ: Prentice Hall",
	year	= "1997",
}

@Article{Angell:2008,
	author 	= "Angell, Carl and Kind, Per Morten and Henriksen, Ellen K. and Guttersrud, {\O}ystein",
	title 	= "An empirical-mathematical modelling approach to upper secondary physics",
	journal = "Physics Education",
	year 	= "2008",
	edition	= "43",
	month	= "May",
	pages 	= "256-264",
}

@Article{Angell:2004,
	author 	= "Angell, Carl",
	title 	= "Exploring students' intuitive ideas based on physics items in {TIMSS} -- 1995",
	journal = "Proceedings of the IEA International Research Conference IRC-2004, Cyprus",
	year 	= "2004",
}

@Article{Henriksen:2010,
	author 	= "Henriksen, Ellen K. and Angell, Carl",
	title 	= "The role of `talking physics' in an undergraduate physics class using an electronic audience response system",
	journal = "Physics Education",
	year 	= "2010",
	edition = "45",
	pages	= "278-284",
}

@Article{Muller:2008,
	author 	= "Muller, D.A. and Bewes, J. and Sharma, M.D. and Reimann, P.",
	title 	= "Saying the wrong thing: improving learning with multimedia by including misconceptions",
	journal = "Journal of Computer Assisted Learning",
	year 	= "2008",
	pages 	= "144-155",
	edition = "24",
}

@Article{Gautreau:1997,
	author 	= "Gautreau, Ronald and Novemsky, Lisa",
	title 	= "Concepts first -- A small group approach to physics learning",
	journal = "Am. J. Phys.",
	year 	= "1997",
	month 	= "May",
	edition	= "65",
	pages 	= "418-428",
}

@Article{Driver:1994,
	author 	= "Driver, Rosalind and Asoko, Hilary and Leach, John and Mortimer, Eduardo and Scott, Philip",
	title 	= "Constructing Scientific Knowledge in the Classroom",
	journal = "Educational Researcher",
	year 	= "1994",
	volume	= "23",
	pages 	= "5-12",
}

@Article{Kubli:2005,
	author 	= "Kubli, Fritz",
	title 	= "Science Teaching as a Dialogue -- {B}akhtin, {V}ygotsky and some Applications in the Classroom",
	journal = "Science \& Education",
	year 	= "2005",
	edition = "14",
	pages 	= "501-534",
}

@Article{Wells:2007,
	author 	= "Wells, Gordon",
	title 	= "Semiotic Mediation, Dialogue and the Construction of Knowledge",
	journal = "Human Development",
	year 	= "2007",
	pages 	= "244-274",
	edition = "50",
}

@Article{Gilbert:2004,
	author 	= "Gilbert, John K.",
	title 	= "Models and modelling: Routes to a more authentic science education",
	journal = "International Journal of Science and Mathematics Education",
	year 	= "2004",
	pages 	= "115-130",
}

@Article{Sins:2005,
	author 	= "Sins, Patrick H. M. and Savelsbergh, Elwin R and van Joolingen, Wouter R.",
	title 	= "The Difficult Process of Scientific Modelling: An analysis of novices' reasoning during computer-based modelling",
	journal = "International Journal of Science Education",
	year 	= "2005",
	month 	= "November",
	edition  = "27",
	pages 	= "1695-1721",
}

@Article{Oliphant:2007,
	author 	= "Oliphant, Travis E.",
	title 	= "Python for Scientific Computing",
	journal = "Computing in Science \& Engineering",
	year 	= "2007",
	month 	= "Mai/June",
	pages 	= "10-20",
}

@Article{Futschek:2006,
	author 	= "Futschek, Gerald",
	title 	= "Algorithmic Thinking: The Key for Understanding Computer Science",
	journal = "Lecture Notes in Computer Science",
	year 	= "2006",
	edition = "4226",
	pages 	= "159-168",
}

@Article{Landau:2006,
	author 	= "Landau, Rubin",
	title 	= "Computational Physics -- A Better Model for Physics Education?",
	journal = "Computing in Science \& Engineering",
	year 	= "2006",
	month 	= "September/October",
	pages 	= "22-30",
}

@Article{Yasar:2003,
	author 	= "Yasar, Osman and Landau, Rubin H.",
	title 	= "Elements of Computational Science and Engineering Education",
	journal = "Society for Industrial and Applied Mathematics",
	year 	= "2003",
	volume	= "45",
	pages 	= "787-805",
}

@Article{Hestenes:1985,
	author 	= "Halloun, Ibrahim Abou and Hestenes, David",
	title 	= "Common sense concepts about motion",
	journal = "Am. J. Phys.",
	year 	= "1985",
	month 	= "November",
	edition	= "53",
	pages 	= "141-158",
}

@Article{Hestenes:1992,
	author 	= "Hestenes, David and Wells, Malcolm and Swackhamer, Gregg",
	title 	= "Force Concept Inventory",
	journal = "The Physics Teacher",
	year 	= "1992",
	month 	= "March",
	edition	= "30",
	pages 	= "141-158"
}

@Misc{CMA_CSE,
	author 	= "{University of Oslo}",
	title	= "The {C}omputers in {S}cience {E}ducation project",
	note 	= "{Website}: \texttt{http://www.cma.uio.no/\\projects/collaborative/cse.html}",
	month	= "May",
	year	= "2009",
}

repository: CompPhysics/ThesisProjects

repo_name: CompPhysics/ThesisProjects


text:
SUMMARY = "C library providing simple use of LV2 plugins"
HOMEPAGE = "http://drobilla.net/software/lilv"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://COPYING;md5=7aceb3a3edc99517b08f5cdd557e11fb"

inherit waf bash-completion pkgconfig

DEPENDS += "lv2 serd sord sratom"

SRC_URI = "http://download.drobilla.net/${BPN}-${PV}.tar.bz2"
SRC_URI[md5sum] = "cae7e1700f83b0f1272f437919a68458"
SRC_URI[sha256sum] = "f7ec65b1c1f1734ded3a6c051bbaf50f996a0b8b77e814a33a34e42bce50a522"

EXTRA_OECONF = "--configdir=${sysconfdir}"


text:
DESCRIPTION = "Key store for key installation"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "\
    file://${COMMON_LICENSE_DIR}/MIT;md5=0835ade698e0bcf8506ecda2f7b4f302 \
"

S = "${WORKDIR}"

inherit user-key-store

ALLOW_EMPTY:${PN} = "1"

KEY_DIR = "${sysconfdir}/keys"
# For RPM verification
RPM_KEY_DIR = "${sysconfdir}/pki/rpm-gpg"

# For ${PN}-system-trusted-cert
SYSTEM_CERT = "${KEY_DIR}/system_trusted_key.crt"

# For ${PN}-secondary-trusted-cert
SECONDARY_TRUSTED_CERT = "${KEY_DIR}/secondary_trusted_key.crt"
SECONDARY_TRUSTED_DER_ENC_CERT = "${KEY_DIR}/x509_secondary_system_trusted_key.der"

# For ${PN}-modsign-cert
MODSIGN_CERT = "${KEY_DIR}/modsign_key.crt"

# For ${PN}-ima-cert
IMA_CERT = "${KEY_DIR}/x509_ima.der"

python () {
    if not (uks_signing_model(d) in "sample", "user"):
        return

    pn = d.getVar('PN', True) + '-rpm-pubkey'
    d.setVar('PACKAGES:prepend', pn + ' ')
    d.setVar('FILES:' + pn, d.getVar('RPM_KEY_DIR', True) + '/RPM-GPG-KEY-' + d.getVar('RPM_GPG_NAME', True))
    d.setVar('CONFFILES:' + pn, d.getVar('RPM_KEY_DIR', True) + '/RPM-GPG-KEY-' + d.getVar('RPM_GPG_NAME', True))
    mlprefix = d.getVar('MLPREFIX')
    d.appendVar('RDEPENDS:' + pn, ' %srpm' % mlprefix)
}

do_install() {
    install -d "${D}${RPM_KEY_DIR}"

    for f in `ls ${WORKDIR}/RPM-GPG-KEY-* 2>/dev/null`; do
        [ ! -f "$f" ] && continue

        install -m 0644 "$f" "${D}${RPM_KEY_DIR}"
    done

    key_dir="${@uks_rpm_keys_dir(d)}"
    if [ -n "$key_dir" ]; then
        for f in `ls $key_dir/RPM-GPG-KEY-* 2>/dev/null`; do
            [ ! -s "$f" ] && continue

            install -m 0644 "$f" "${D}${RPM_KEY_DIR}"
        done
    fi

    install -d "${D}${KEY_DIR}"

    key_dir="${@uks_system_trusted_keys_dir(d)}"
    install -m 0644 "$key_dir/system_trusted_key.crt" "${D}${SYSTEM_CERT}"

    key_dir="${@uks_secondary_trusted_keys_dir(d)}"
    install -m 0644 "$key_dir/secondary_trusted_key.crt" \
        "${D}${SECONDARY_TRUSTED_CERT}"
    openssl x509 -inform PEM -outform DER -in "${D}${SECONDARY_TRUSTED_CERT}" \
        -out "${D}${SECONDARY_TRUSTED_DER_ENC_CERT}"

    key_dir="${@uks_modsign_keys_dir(d)}"
    install -m 0644 "$key_dir/modsign_key.crt" \
        "${D}${MODSIGN_CERT}"

    key_dir="${@uks_ima_keys_dir(d)}"
    install -m 0644 "$key_dir/x509_ima.der" "${D}${IMA_CERT}"
}

do_install[prefuncs] += "check_deploy_keys"

SYSROOT_PREPROCESS_FUNCS += "key_store_sysroot_preprocess"

key_store_sysroot_preprocess() {
    sysroot_stage_dir "${D}${sysconfdir}" "${SYSROOT_DESTDIR}${sysconfdir}"
}

pkg_postinst_ontarget:${PN}-rpm-pubkey() {
    keydir="${RPM_KEY_DIR}"

    [ ! -d "$keydir" ] && mkdir -p "$keydir"

    # XXX: only import the new key
    for keyfile in `ls $keydir/RPM-GPG-KEY-*`; do
        [ -s "$keyfile" ] || continue

        rpm --import "$keyfile" || {
            echo "Unable to import the public key $keyfile"
            exit 1
        }
    done
}

PACKAGES = "\
    ${PN}-system-trusted-cert \
    ${PN}-secondary-trusted-cert \
    ${PN}-modsign-cert \
    ${PN}-ima-cert \
"

# Note any private key is not available if user key signing model used.
PACKAGES_DYNAMIC = "\
    ${PN}-rpm-pubkey \
"

FILES:${PN}-system-trusted-cert = "${SYSTEM_CERT}"
CONFFILES:${PN}-system-trusted-cert = "${SYSTEM_CERT}"

FILES:${PN}-secondary-trusted-cert = "\
    ${SECONDARY_TRUSTED_CERT} \
    ${SECONDARY_TRUSTED_DER_ENC_CERT} \
    "
CONFFILES:${PN}-secondary-trusted-cert = "\
    ${SECONDARY_TRUSTED_CERT} \
    ${SECONDARY_TRUSTED_DER_ENC_CERT} \
    "

FILES:${PN}-modsign-cert = "${MODSIGN_CERT}"
CONFFILES:${PN}-modsign-cert = "${MODSIGN_CERT}"

FILES:${PN}-ima-cert = "${IMA_CERT}"
CONFFILES:${PN}-ima-cert = "${IMA_CERT}"


text:
# Download and set up Tensorflow

include(${PROJECT_SOURCE_DIR}/cmake/Externals.cmake)

if(WIN32)
    set(GIT_EXECUTABLE "git.exe")
    # Use CMake to build tensorflow on windows
    ExternalProject_Add(tensorflow
            PREFIX ${FAST_EXTERNAL_BUILD_DIR}/tensorflow
            BINARY_DIR ${FAST_EXTERNAL_BUILD_DIR}/tensorflow
            GIT_REPOSITORY "https://github.com/smistad/tensorflow.git"
            GIT_TAG "fast"
            # Need to override this because tensorflow creates a file in the source dir
            # and cmake files to stash these files
            UPDATE_COMMAND
                ${GIT_EXECUTABLE} pull origin fast
            # Must use CONFIGURE_COMMAND because CMakeLists.txt is not in the src root of tensorflow
            CONFIGURE_COMMAND
                ${CMAKE_COMMAND}
                ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/tensorflow/contrib/cmake/
                -G${CMAKE_GENERATOR}
		-Thost=x64
                -DCMAKE_BUILD_TYPE:STRING=Release
                -Dtensorflow_BUILD_PYTHON_BINDINGS=OFF
                -Dtensorflow_ENABLE_SNAPPY_SUPPORT=ON
                -Dtensorflow_BUILD_CC_EXAMPLE=OFF
                -Dtensorflow_BUILD_SHARED_LIB=ON
                -Dtensorflow_BUILD_CONTRIB_KERNELS=OFF
                -Dtensorflow_ENABLE_GRPC_SUPPORT=OFF
                -Dtensorflow_WIN_CPU_SIMD_OPTIONS=/arch:AVX
                #-Dtensorflow_ENABLE_GPU=ON
                -DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF
                -DCMAKE_INSTALL_MESSAGE:BOOL=LAZY
                -DCMAKE_INSTALL_PREFIX:STRING=${FAST_EXTERNAL_INSTALL_DIR}
        )
else(WIN32)
    # Use bazel to build tensorflow on linux
    set(GIT_EXECUTABLE "git")
    if(FAST_BUILD_TensorFlow_CPU OR FAST_BUILD_TensorFlow_CUDA)
        ExternalProject_Add(tensorflow_download
            PREFIX ${FAST_EXTERNAL_BUILD_DIR}/tensorflow
            BINARY_DIR ${FAST_EXTERNAL_BUILD_DIR}/tensorflow
            GIT_REPOSITORY "https://github.com/smistad/tensorflow.git"
            GIT_TAG "fast-updated"
            UPDATE_COMMAND ""
            CONFIGURE_COMMAND ""
            BUILD_COMMAND ""
            INSTALL_COMMAND ""
        )
    endif()
    if(FAST_BUILD_TensorFlow_ROCm)
        # Need a seperate repo for rocm atm
        ExternalProject_Add(tensorflow_download_rocm
            PREFIX ${FAST_EXTERNAL_BUILD_DIR}/tensorflow_rocm
            BINARY_DIR ${FAST_EXTERNAL_BUILD_DIR}/tensorflow_rocm
            GIT_REPOSITORY "https://github.com/ROCmSoftwarePlatform/tensorflow-upstream"
            GIT_TAG "r1.14-rocm"
            UPDATE_COMMAND ""
            CONFIGURE_COMMAND ""
            BUILD_COMMAND ""
            INSTALL_COMMAND ""
        )
        ExternalProject_Add(tensorflow_ROCm
            DEPENDS tensorflow_download_rocm
            PREFIX ${FAST_EXTERNAL_BUILD_DIR}/tensorflow_rocm
            BINARY_DIR ${FAST_EXTERNAL_BUILD_DIR}/tensorflow_rocm
            DOWNLOAD_COMMAND ""
            UPDATE_COMMAND ""
            # Run TF configure in the form of a shell script.
            CONFIGURE_COMMAND
                cd ${FAST_EXTERNAL_BUILD_DIR}/tensorflow_rocm/src/tensorflow_download_rocm/ && sh ${PROJECT_SOURCE_DIR}/cmake/TensorflowConfigureROCm.sh
            # Build using bazel
            BUILD_COMMAND
                echo "Building tensorflow with bazel and ROCm (AMD) GPU support" &&
                cd ${FAST_EXTERNAL_BUILD_DIR}/tensorflow_rocm/src/tensorflow_download_rocm/ && bazel build -c opt --config=rocm //tensorflow:libtensorflow_cc.so
            INSTALL_COMMAND
                echo "Installing tensorflow binary" &&
                cp -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow_rocm/src/tensorflow_download_rocm/bazel-bin/tensorflow/libtensorflow_cc.so.1.14.0 ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_cc_ROCm.so &&
                cp -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow_rocm/src/tensorflow_download_rocm/bazel-bin/tensorflow/libtensorflow_framework.so.1.14.0 ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_framework_ROCm.so &&
                chmod a+w ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_cc_ROCm.so &&
                chmod a+w ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_framework_ROCm.so &&
                patchelf --set-soname libtensorflow_cc_ROCm.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_cc_ROCm.so &&
                patchelf --set-soname libtensorflow_framework_ROCm.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_framework_ROCm.so &&
                patchelf --replace-needed libtensorflow_framework.so.1 libtensorflow_framework_ROCm.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_cc_ROCm.so &&
                echo "Installing tensorflow headers" &&
                cp -rf ${FAST_EXTERNAL_BUILD_DIR}/tensorflow_rocm/src/tensorflow_download_rocm/tensorflow/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ &&
                echo "Installing tensorflow generated headers" &&
                cp -rf ${FAST_EXTERNAL_BUILD_DIR}/tensorflow_rocm/src/tensorflow_download_rocm/bazel-genfiles/tensorflow/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ &&
                echo "Installing tensorflow third_party headers" &&
                cp -rf ${FAST_EXTERNAL_BUILD_DIR}/tensorflow_rocm/src/tensorflow_download_rocm/third_party/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ &&
                echo "Installing protobuf headers" &&
                bash -c "cp $(readlink -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow_rocm/src/tensorflow_download_rocm/bazel-out/)/../../../external/protobuf_archive/src/google/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ -Rf" &&
                echo "Installing nsync headers" &&
                bash -c "cp $(readlink -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow_rocm/src/tensorflow_download_rocm/bazel-out/)/../../../external/nsync/public/*.h ${FAST_EXTERNAL_INSTALL_DIR}/include/ -Rf" &&
                echo "Installing absl headers" &&
                bash -c "cp $(readlink -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow_rocm/src/tensorflow_download_rocm/bazel-out/)/../../../external/com_google_absl/absl/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ -Rf"
    )
    endif()
    if(FAST_BUILD_TensorFlow_CUDA)
    ExternalProject_Add(tensorflow_CUDA
            DEPENDS tensorflow_download
            PREFIX ${FAST_EXTERNAL_BUILD_DIR}/tensorflow
            BINARY_DIR ${FAST_EXTERNAL_BUILD_DIR}/tensorflow
            DOWNLOAD_COMMAND ""
            UPDATE_COMMAND ""
            # Run TF configure in the form of a shell script. CUDA should be installed in /usr/local/cuda
            CONFIGURE_COMMAND
                cd ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/ && sh ${PROJECT_SOURCE_DIR}/cmake/TensorflowConfigureCUDA.sh
            # Build using bazel
            BUILD_COMMAND
                echo "Building tensorflow with bazel and CUDA GPU support" &&
                cd ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/ && bazel build -c opt --config=cuda --copt=-mfpmath=both --copt=-march=core-avx2 //tensorflow:libtensorflow_cc.so
            INSTALL_COMMAND
                echo "Installing tensorflow binary" &&
                cp -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/bazel-bin/tensorflow/libtensorflow_cc.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_cc_CUDA.so &&
                cp -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/bazel-bin/tensorflow/libtensorflow_framework.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_framework_CUDA.so &&
                chmod a+w ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_cc_CUDA.so &&
                chmod a+w ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_framework_CUDA.so &&
                patchelf --set-soname libtensorflow_cc_CUDA.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_cc_CUDA.so &&
                patchelf --set-soname libtensorflow_framework_CUDA.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_framework_CUDA.so &&
                patchelf --replace-needed libtensorflow_framework.so libtensorflow_framework_CUDA.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_cc_CUDA.so &&
                #echo "Installing mkl binaries" &&
                #bash -c "cp $(readlink -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/bazel-out/)/../../../external/mkl/lib/libmklml_intel.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/ -Rf" &&
                #bash -c "cp $(readlink -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/bazel-out/)/../../../external/mkl/lib/libiomp5.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/ -Rf" &&
                echo "Installing tensorflow headers" &&
                cp -rf ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/tensorflow/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ &&
                echo "Installing tensorflow generated headers" &&
                cp -rf ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/bazel-genfiles/tensorflow/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ &&
                echo "Installing tensorflow third_party headers" &&
                cp -rf ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/third_party/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ &&
                echo "Installing protobuf headers" &&
                bash -c "cp $(readlink -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/bazel-out/)/../../../external/protobuf_archive/src/google/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ -Rf" &&
                echo "Installing nsync headers" &&
                bash -c "cp $(readlink -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/bazel-out/)/../../../external/nsync/public/*.h ${FAST_EXTERNAL_INSTALL_DIR}/include/ -Rf" &&
                echo "Installing absl headers" &&
                bash -c "cp $(readlink -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/bazel-out/)/../../../external/com_google_absl/absl/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ -Rf"
    )
    endif()
    if(FAST_BUILD_TensorFlow_CPU)
    ExternalProject_Add(tensorflow_CPU
            DEPENDS tensorflow_download
            PREFIX ${FAST_EXTERNAL_BUILD_DIR}/tensorflow
            BINARY_DIR ${FAST_EXTERNAL_BUILD_DIR}/tensorflow
            DOWNLOAD_COMMAND ""
            UPDATE_COMMAND ""
            # Run TF configure in the form of a shell script.
            CONFIGURE_COMMAND
                cd ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/ && sh ${PROJECT_SOURCE_DIR}/cmake/TensorflowConfigureCPU.sh
            # Build using bazel
            BUILD_COMMAND
                echo "Building tensorflow with bazel for CPU" &&
                cd ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/ && bazel build --config=opt --copt=-mfpmath=both --copt=-march=core-avx2 //tensorflow:libtensorflow_cc.so
            INSTALL_COMMAND
                echo "Installing tensorflow binary" &&
                cp -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/bazel-bin/tensorflow/libtensorflow_cc.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_cc_CPU.so &&
                cp -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/bazel-bin/tensorflow/libtensorflow_framework.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_framework_CPU.so &&
                chmod a+w ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_cc_CPU.so &&
                chmod a+w ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_framework_CPU.so &&
                patchelf --set-soname libtensorflow_cc_CPU.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_cc_CPU.so &&
                patchelf --set-soname libtensorflow_framework_CPU.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_framework_CPU.so &&
                patchelf --replace-needed libtensorflow_framework.so libtensorflow_framework_CPU.so ${FAST_EXTERNAL_INSTALL_DIR}/lib/libtensorflow_cc_CPU.so &&
                echo "Installing tensorflow headers" &&
                cp -rf ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/tensorflow/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ &&
                echo "Installing tensorflow generated headers" &&
                cp -rf ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/bazel-genfiles/tensorflow/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ &&
                echo "Installing tensorflow third_party headers" &&
                cp -rf ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/third_party/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ &&
                echo "Installing protobuf headers" &&
                bash -c "cp $(readlink -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/bazel-out/)/../../../external/protobuf_archive/src/google/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ -Rf" &&
                echo "Installing nsync headers" &&
                bash -c "cp $(readlink -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/bazel-out/)/../../../external/nsync/public/*.h ${FAST_EXTERNAL_INSTALL_DIR}/include/ -Rf" &&
                echo "Installing absl headers" &&
                bash -c "cp $(readlink -f ${FAST_EXTERNAL_BUILD_DIR}/tensorflow/src/tensorflow_download/bazel-out/)/../../../external/com_google_absl/absl/ ${FAST_EXTERNAL_INSTALL_DIR}/include/ -Rf"
    )
    endif()
endif(WIN32)

repository: IIAT-MR-XWW/FAST

repo_name: IIAT-MR-XWW/FAST


text:
@inproceedings{Walzer.2007,
 author = {Walzer, Karen and Anke, Jürgen and Loser, Alexander and Wu, Huaigu},
 booktitle = {1st International Workshop on Distributed Event Processing, Systems and Applications},
 doi = {10.1109/ICDCSW.2007.6},
 file = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4279068},
 title = {A Concept for Flexible Event-Driven Invocation of Distributed Service Compositions},
 year = {2007}
}


text:
.Modal {
  display: flex;
  flex-flow: column;
  height: 100vh;
  padding: 90px;
  position: absolute;
  top: 0;
  left: 0;
  right: 0;
  bottom: 0;
  box-sizing: border-box;
  justify-content: space-between;
  background: rgba(36,41,46,0.94);
  cursor: pointer;
}

.Modal-container {
  height: 100%;
  display: flex;
  flex-direction: column;
}

.Modal-image-container {
  flex-grow: 1;
  position: relative;
}

.Modal-image {
  max-width: 100%;
  max-height: 100%;
  position: absolute;
  top: 0;
  bottom: 0;
  left: 0;
  right: 0;
  margin: auto;
  box-shadow: 0 2px 12px 0 rgba(12,16,20,0.2)
}

.Modal-footer {
  width: 100%;
  height: 40px;
  text-align: center;
  font-size: 20px;
  margin-top: 30px;
  color: #FFFFFF;
  white-space: nowrap;
  overflow: hidden;
  text-overflow: ellipsis;
}

repository: anterioforks/Dashboard

repo_name: anterioforks/Dashboard

extension: css


text:
@article{mosbach2018logit,
 author = {Mosbach, Marius and Andriushchenko, Maksym and Trost, Thomas and Hein, Matthias and Klakow, Dietrich},
 journal = {arXiv preprint arXiv:1810.12042},
 title = {Logit Pairing Methods Can Fool Gradient-Based Attacks},
 year = {2018}
}

repository: mmarius/academic-kickstart

repo_name: mmarius/academic-kickstart


text:
KBRANCH ?= "standard/preempt-rt/base"

require recipes-kernel/linux/linux-yocto.inc

# Skip processing of this recipe if it is not explicitly specified as the
# PREFERRED_PROVIDER for virtual/kernel. This avoids errors when trying
# to build multiple virtual/kernel providers, e.g. as dependency of
# core-image-rt-sdk, core-image-rt.
python () {
    if d.getVar("PREFERRED_PROVIDER_virtual/kernel") != "linux-yocto-rt":
        raise bb.parse.SkipPackage("Set PREFERRED_PROVIDER_virtual/kernel to linux-yocto-rt to enable it")
}

SRCREV_machine ?= "b6f5c933ce66e4a00412bb5c5691a2ab4187c979"
SRCREV_meta ?= "803b8d600e45afa0375459bf599fe365571a3866"

SRC_URI = "git://git.yoctoproject.org/linux-yocto-4.9.git;branch=${KBRANCH};name=machine \
           git://git.yoctoproject.org/yocto-kernel-cache;type=kmeta;name=meta;branch=yocto-4.9;destsuffix=${KMETA}"

LINUX_VERSION ?= "4.9.21"

PV = "${LINUX_VERSION}+git${SRCPV}"

KMETA = "kernel-meta"
KCONF_BSP_AUDIT_LEVEL = "2"

LINUX_KERNEL_TYPE = "preempt-rt"

COMPATIBLE_MACHINE = "(qemux86|qemux86-64|qemuarm|qemuppc|qemumips)"

KERNEL_DEVICETREE_qemuarm = "versatile-pb.dtb"

# Functionality flags
KERNEL_EXTRA_FEATURES ?= "features/netfilter/netfilter.scc features/taskstats/taskstats.scc"
KERNEL_FEATURES_append = " ${KERNEL_EXTRA_FEATURES}"
KERNEL_FEATURES_append_qemuall=" cfg/virtio.scc"
KERNEL_FEATURES_append_qemux86=" cfg/sound.scc cfg/paravirt_kvm.scc"
KERNEL_FEATURES_append_qemux86-64=" cfg/sound.scc"


text:
require libgee.inc
PE = "1"

SRC_URI = "http://ftp.gnome.org/pub/GNOME/sources/libgee/0.5/${BPN}-${PV}.tar.bz2"
S = "${WORKDIR}/${BPN}-${PV}"

SRC_URI[md5sum] = "fc5a36eb5f61154a1456cbb8b1798e41"
SRC_URI[sha256sum] = "8625ebfb479600046b9e36e1a8fd1142d624645dce66a6cd8c9067d8f7f5ce51"

inherit autotools vala

do_configure() {
	gnu-configize
	oe_runconf
}

repository: libo/openembedded

repo_name: libo/openembedded


text:
DESCRIPTION = "evlog logging utility"
SECTION = "devel"
LICENSE = "LGPL"
LIC_FILES_CHKSUM = "file://COPYING;md5=9b06af1a3b4b964300b585224ea2158e"

SRCREV = "e3166d42eb59d72b876f380d27f39fe20e27d8b8"
PV = "1.0+gitr${SRCPV}"
PR = "r0"

FILES_${PN}-dbg += "/var/evlog/test/.debug"
FILES_${PN}-dbg += "/usr/share/evlog/.debug"

SRC_URI = " \
  git://github.com/shivdasgujare/evlog.git;protocol=git;branch=master \
  "
S = "${WORKDIR}/git"

inherit autotools

DEPENDS = "zlib binutils"
EXTRA_OECONF = "CFLAGS=-D_GNU_SOURCE"

repository: openagriculture/meta-anar

repo_name: openagriculture/meta-anar


text:
@article{Moncrieff2016-kr,
 abstract = {Biomes are globally distributed, structurally and functionally
similar vegetation units defined without reference to plant
species composition. The boundaries between biomes are presumed
to correspond with species turnover and changes in
biogeochemical cycling …},
 author = {Moncrieff, G R and Bond, W J and Higgins, S I},
 journal = {J. Biogeogr.},
 publisher = {Wiley Online Library},
 title = {Revising the biome concept for understanding and predicting
global change impacts},
 year = {2016}
}


text:
var siteConfig = {

    content: [
        {
            id: 'intro',
            template: 'layout/side_menu_item',
            data: {
                title: 'Intro',
                icon: 'info_outline',
                link: '#/intro',
                file: 'content/intro.html'
            }
        },
        {
            id: 'read',
            template: 'layout/side_menu_item',
            data: {
                title: 'Read',
                icon: 'import_contacts',
                link: '#/read/strings_theory'
            },
            list: [
                {
                    id: 'strings_theory',
                    template: 'layout/side_menu_subitem',
                    data: {
                        title: 'Strings Theory',
                        link: '#/read/strings_theory',
                        file: 'content/docs/strings_theory.html'
                    }
                },
                {
                    id: 'mind_god',
                    template: 'layout/side_menu_subitem',
                    data: {
                        title: 'Mind of God',
                        link: '#/read/mind_god',
                        file: 'content/docs/mind_god.html'
                    }
                }
            ]
        },
        {
            id: 'kit',
            template: 'layout/side_menu_item',
            data: {
                title: 'Author',
                icon: 'extension',
                link: 'https://danielkapexhiu.com/'
            }
        }
    ],

    strings: {
        startPage: '#/intro',
        baseUrl: '/',
        title: 'Intro'
    }

};
if (typeof module !== 'undefined') {
    module.exports = siteConfig;
}

repository: dkapexhiu/math-universe

repo_name: dkapexhiu/math-universe


text:
@inproceedings{E17-4001,
 abstract = {This research proposal discusses pragmatic factors in image description, arguing that current automatic image description systems do not take these
factors into account. I present a general model of the human image description
process, and propose to study this process using corpus analysis, experiments, and computational modeling. This will lead to a better characterization of
human image description behavior, providing a road map for future research in
automatic image description, and the automatic description of perceptual
stimuli in general.},
 address = {Valencia, Spain},
 author = {van Miltenburg, Emiel},
 booktitle = {Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics},
 month = {April},
 pages = {1--10},
 publisher = {Association for Computational Linguistics},
 title = {Pragmatic descriptions of perceptual stimuli},
 year = {2017}
}

@inproceedings{E17-4002,
 abstract = {Spelling variation in non-standard language, e.g. computer-mediated
communication and historical texts, is usually treated as a deviation from a
standard spelling, e.g. 2mr as an non-standard spelling for tomorrow.
Consequently, in normalization -- the standard approach of dealing with
spelling variation -- so-called non-standard words are mapped to their
corresponding standard words. However, there is not always a corresponding
standard word. This can be the case for single types (like emoticons in
computer-mediated communication) or a complete language, e.g. texts from
historical languages that did not develop to a standard variety. The approach
presented in this
thesis proposal deals with spelling variation in absence of reference to a
standard. The task is to detect pairs of types that are variants of the same
morphological word. An approach for spelling-variant detection is presented, where pairs of potential spelling variants are generated with Levenshtein
distance and subsequently filtered by supervised machine learning. The
approach is evaluated on historical Low German texts. Finally, further
perspectives are discussed.},
 address = {Valencia, Spain},
 author = {Barteld, Fabian},
 booktitle = {Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics},
 month = {April},
 pages = {11--22},
 publisher = {Association for Computational Linguistics},
 title = {Detecting spelling variants in non-standard texts},
 year = {2017}
}

@inproceedings{E17-4003,
 abstract = {Reproducing experiments is an important instrument to validate previous work
and build upon existing approaches. It has been tackled numerous times in
different areas of science. In this paper, we introduce an empirical
replicability study of three well-known algorithms for syntactic centric
aspect-based opinion mining. We show that reproducing results continues to be a
difficult endeavor, mainly due to the lack of details regarding preprocessing
and parameter setting, as well as due to the absence of available
implementations that clarify these details. We consider these are important
threats to validity of the research on the field, specifically when compared to
other problems in NLP where public datasets and code availability are critical
validity components. We conclude by encouraging code-based research, which we
think has a key role in helping researchers to understand the meaning of the
state-of-the-art better and to generate continuous advances.},
 address = {Valencia, Spain},
 author = {Marrese-Taylor, Edison  and  Matsuo, Yutaka},
 booktitle = {Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics},
 month = {April},
 pages = {23--32},
 publisher = {Association for Computational Linguistics},
 title = {Replication issues in syntax-based aspect extraction for opinion mining},
 year = {2017}
}

@inproceedings{E17-4004,
 abstract = {Sense classification of discourse relations is a sub-task of shallow discourse
parsing. Discourse relations can occur both across sentences
(\textit{inter-sentential}) and within sentences (\textit{intra-sentential}), and more than one discourse relation can hold between the same units. Using a
newly available corpus of discourse-annotated intra-sentential conjoined verb
phrases, we demonstrate a sequential classification pipeline for their multi-label sense
classification.
We assess the importance of each feature used in the classification, the
feature scope, and what is lost in moving
from gold standard manual parses to the output of an off-the-shelf parser.},
 address = {Valencia, Spain},
 author = {Pyatkin, Valentina  and  Webber, Bonnie},
 booktitle = {Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics},
 month = {April},
 pages = {33--42},
 publisher = {Association for Computational Linguistics},
 title = {Discourse Relations and Conjoined VPs: Automated Sense Recognition},
 year = {2017}
}

@inproceedings{E17-4005,
 abstract = {Humans are known to detect deception in speech randomly and it is therefore
important to develop tools to enable them to detect deception. The problem of
deception detection has been studied for a significant amount of time, however
the last 10-15 years have seen methods of computational linguistics being
employed. Texts are processed using different NLP tools and then classified as
deceptive/truthful using machine learning methods. While most research has been
performed for English, Slavic languages have never been a focus of detection
deception studies. The paper deals with deception detection in Russian
narratives. It employs a specially designed corpus of truthful and deceptive
texts on the same topic from each respondent, N = 113. The texts were processed
using Linguistic Inquiry and Word Count software that is used in most studies
of text-based deception detection. The list of parameters computed using the
software was expanded due to the designed users' dictionaries. A variety of
text classification methods was employed. The accuracy of the model was found
to depend on the author's gender and text type (deceptive/truthful).},
 address = {Valencia, Spain},
 author = {Litvinova, Olga  and  Seredin, Pavel  and  Litvinova, Tatiana  and  Lyell, John},
 booktitle = {Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics},
 month = {April},
 pages = {43--52},
 publisher = {Association for Computational Linguistics},
 title = {Deception detection in Russian texts},
 year = {2017}
}

@inproceedings{E17-4006,
 abstract = {We present a cognitive computational model of pronoun resolution that
reproduces the human interpretation preferences of the Subject Assignment
Strategy and the Parallel Function Strategy. Our model relies on a
probabilistic pronoun resolution system trained on corpus data. Factors
influencing pronoun resolution are represented as features weighted by their
relative importance. The importance the model gives to the preferences is in
line with psycholinguistic studies. We demonstrate the cognitive plausibility
of the model by running it on experimental items and simulating antecedent
choice and reading times of human participants. Our model can be used as a new
means to study pronoun resolution, because it captures the interaction of
preferences.},
 address = {Valencia, Spain},
 author = {Seminck, Olga  and  Amsili, Pascal},
 booktitle = {Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics},
 month = {April},
 pages = {53--63},
 publisher = {Association for Computational Linguistics},
 title = {A Computational Model of Human Preferences for Pronoun Resolution},
 year = {2017}
}

@inproceedings{E17-4007,
 abstract = {Headlines play a crucial role in attracting audiences' attention to online
artefacts (e.g. news articles, videos, blogs). The ability to carry out an
automatic, large-scale analysis of headlines is critical to facilitate the
selection and prioritisation of a large volume of digital content. In
journalism studies news content has been extensively studied using manually
annotated news values - factors used implicitly and explicitly when making
decisions on the selection and prioritisation of news items. This paper
presents the first attempt at a fully automatic extraction of news values from
headline text. The news values extraction methods are applied on a large
headlines corpus collected from The Guardian, and evaluated by comparing it
with a manually annotated gold standard. A crowdsourcing survey indicates that
news values affect people's decisions to click on a headline, supporting the
need
for an automatic news values detection.},
 address = {Valencia, Spain},
 author = {Piotrkowicz, Alicja  and  Dimitrova, Vania  and  Markert, Katja},
 booktitle = {Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics},
 month = {April},
 pages = {64--74},
 publisher = {Association for Computational Linguistics},
 title = {Automatic Extraction of News Values from Headline Text},
 year = {2017}
}

@inproceedings{E17-4008,
 abstract = {We propose a new method in the field of argument analysis in social media to
determining convincingness of arguments in online debates, following previous
research by Habernal and Gurevych (2016). Rather than using argument specific
feature values, we measure feature values relative to the average value in the
debate, allowing us to determine argument convincingness with fewer features
(between 5 and 35) than normally used for natural language processing tasks. We
use a simple forward-feeding neural network for this task and achieve an
accuracy of 0.77 which is comparable to the accuracy obtained using 64k
features and a support vector machine by Habernal and Gurevych.},
 address = {Valencia, Spain},
 author = {Chalaguine, Lisa Andreevna  and  Schulz, Claudia},
 booktitle = {Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics},
 month = {April},
 pages = {75--83},
 publisher = {Association for Computational Linguistics},
 title = {Assessing Convincingness of Arguments in Online Debates with Limited Number of Features},
 year = {2017}
}

@inproceedings{E17-4009,
 abstract = {Social networks have transformed communication dramatically in recent years
through the rise of new platforms and the development of a new language of
communication. This landscape requires new forms to describe and predict the
behaviour of users in networks.
This paper presents an analysis of the frequency distribution of hashtag
popularity in Twitter conversations. Our objective is to determine if these
frequency distribution follow some well-known frequency distribution that many
real-life sets of numerical data satisfy.
In particular, we study the similarity of frequency distribution of hashtag
popularity with respect to Zipf’s law, an empirical law referring to the
phenomenon that many types of data in social sciences can be approximated with
a Zipfian distribution.
Additionally, we also analyse  Benford’s law, is a special case of Zipf's
law, a common pattern about the frequency distribution of leading digits. In
order to compute correctly the frequency distribution of hashtag popularity, we
need to correct many spelling errors that Twitter's users introduce. For this
purpose we introduce a new filter to correct hashtag mistake based on string
distances. The experiments obtained employing datasets of Twitter streams
generated under controlled conditions  show that  Benford’s law and Zipf's
law can be used to model hashtag  frequency distribution.},
 address = {Valencia, Spain},
 author = {P\'{e}rez-Meli\'{a}n, Jos\'{e} Alberto  and  Conejero, J. Alberto  and  Ferri Ram\'{i}rez, Cesar},
 booktitle = {Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics},
 month = {April},
 pages = {84--93},
 publisher = {Association for Computational Linguistics},
 title = {Zipf's and Benford's laws in Twitter hashtags},
 year = {2017}
}

@inproceedings{E17-4010,
 abstract = {Several methods for automatic essay scoring (AES) for English language have
been
proposed. However, multi-aspect AES systems for other languages are unusual.
Therefore, we propose a multi-aspect AES system to apply on a dataset of
Brazilian
Portuguese essays, which human experts evaluated according to five
aspects defined by Brazilian Government to the National
Exam to High School Student (ENEM). These aspects are skills that
student must master and every skill is assessed apart from each other.
Besides the
prediction of each aspect, the feature analysis
also was performed for each aspect. The AES system proposed
employs several features already employed by AES systems for
English language. Our results show that predictions for some aspects performed
well with
the features we employed, while predictions for other aspects performed poorly.
Also, it is possible to note the difference between the five aspects
in the detailed feature analysis we performed. Besides these contributions, the eight millions of enrollments every year for ENEM
raise some challenge issues for future directions
in our research.},
 address = {Valencia, Spain},
 author = {Amorim, Evelin  and  Veloso, Adriano},
 booktitle = {Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics},
 month = {April},
 pages = {94--102},
 publisher = {Association for Computational Linguistics},
 title = {A Multi-aspect Analysis of Automatic Essay Scoring for Brazilian Portuguese},
 year = {2017}
}

@inproceedings{E17-4011,
 abstract = {Non-compositional multiword expressions (MWEs) still pose serious issues for a
variety of natural language processing tasks and their ubiquity makes it
impossible to get around methods which automatically identify these kind of
MWEs. The method presented in this paper was inspired by Sporleder and Li
\shortcite{sporleder2009unsupervised} and is able to discriminate between the
literal and non-literal use of an MWE in an unsupervised way. It is based on
the assumption that words in a text form cohesive units. If the cohesion of
these units is weakened by an expression, it is classified as literal, and
otherwise as idiomatic. While Sporleder an Li used \textit{Normalized Google
Distance} to modell semantic similarity, the present work examines the use of a
variety of different word embeddings.},
 address = {Valencia, Spain},
 author = {Ehren, Rafael},
 booktitle = {Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics},
 month = {April},
 pages = {103--112},
 publisher = {Association for Computational Linguistics},
 title = {Literal or idiomatic? Identifying the reading of single occurrences of German multiword expressions using word embeddings},
 year = {2017}
}

@inproceedings{E17-4012,
 abstract = {Feature design and selection is a crucial
aspect when treating terminology extraction
as a machine learning classification
problem. We designed feature classes
which characterize different properties of
terms based on distributions, and propose
a new feature class for components of term
candidates. By using random forests, we
infer optimal features which are later used
to build decision tree classifiers. We evaluate
our method using the ACL RD-TEC
dataset. We demonstrate the importance
of the novel feature class for downgrading
termhood which exploits properties of
term components. Furthermore, our classification
suggests that the identification
of reliable term candidates should be performed
successively, rather than just once.},
 address = {Valencia, Spain},
 author = {H\"{a}tty, Anna  and  Dorna, Michael  and  Schulte im Walde, Sabine},
 booktitle = {Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics},
 month = {April},
 pages = {113--121},
 publisher = {Association for Computational Linguistics},
 title = {Evaluating the Reliability and Interaction of Recursively Used Feature Classes for Terminology Extraction},
 year = {2017}
}


text:
package paizaio

import "errors"
import "fmt"
import "net/url"

func (a *API) RunnerCreate(v url.Values) (*Runner, error) {
	lang := v.Get("language")
	included := false
	for _, v := range Languages {
		if v == lang {
			included = true
			break
		}
	}
	if !included {
		return nil, errors.New(fmt.Sprint("%s is not allowed language.", lang))
	}

	resp := &Runner{}
	err := a.apiPost(BaseURL+"/runners/create", v, resp)
	if err != nil {
		return nil, err
	}

	return resp, nil
}

func (a *API) RunnerStatus(id string) (*Runner, error) {
	v := url.Values{}
	v.Add("id", id)

	resp := &Runner{}
	err := a.apiGet(BaseURL+"/runners/get_status", v, resp)
	if err != nil {
		return nil, err
	}

	return resp, nil
}

func (a *API) RunnerDetails(id string) (*Details, error) {
	v := url.Values{}
	v.Add("id", id)

	resp := &Details{}
	err := a.apiGet(BaseURL+"/runners/get_details", v, resp)
	if err != nil {
		return nil, err
	}

	return resp, nil
}

repository: pocke/paizaio

repo_name: pocke/paizaio


text:
package movies

import (
	"context"
	"errors"
	"fmt"

	"github.com/ceiba-meli-demo/movies/domain/model"
	"github.com/ceiba-meli-demo/movies/infrastructure/adapters/repository/models"
	"github.com/ceiba-meli-demo/movies/infrastructure/mappers/movie_mapper"
	"github.com/ceiba-meli-demo/movies/infrastructure/utils/logger"
	"go.mongodb.org/mongo-driver/bson"
	"go.mongodb.org/mongo-driver/bson/primitive"
	"go.mongodb.org/mongo-driver/mongo"
)

const (
	Schema = "movie_db"
	Table  = "movie"
)

type MovieNoSqlRepository struct {
	Connection *mongo.Client
}

func (MovieNoSqlRepository *MovieNoSqlRepository) GetAll() ([]model.Movie, error) {
	var moviesDb []models.MovieDb
	collection := MovieNoSqlRepository.Connection.Database(Schema).Collection(Table)
	result, err := collection.Find(context.TODO(), bson.D{})
	if err != nil {
		logger.Error("collection is empty or dont exists", err)
		return []model.Movie{}, errors.New("collection is empty or dont exists")
	}
	for result.Next(context.TODO()) {
		var movie models.MovieDb
		err := result.Decode(&movie)
		if err != nil {
			logger.Error("error when trying to decode movie record", err)
			return []model.Movie{}, errors.New("error when trying to decode movie record")
		}
		moviesDb = append(moviesDb, movie)
	}
	if err := result.Err(); err != nil {
		logger.Error("error in result generated", err)
		return []model.Movie{}, errors.New("error in result generated")
	}
	movies := movie_mapper.MoviesDbToMovies(moviesDb)
	return movies, nil
}

func (MovieNoSqlRepository *MovieNoSqlRepository) GetById(movieId string) (model.Movie, error) {
	var movieDb models.MovieDb
	IDMovie, _ := primitive.ObjectIDFromHex(movieId)
	filter := bson.M{"_id": IDMovie}
	collection := MovieNoSqlRepository.Connection.Database(Schema).Collection(Table)
	if err := collection.FindOne(context.TODO(), filter).Decode(&movieDb); err != nil {
		logger.Error(fmt.Sprintf("Can't find this id %s", movieId), err)
		return model.Movie{}, fmt.Errorf("Can't find this id %s", movieId)
	}
	movie := movie_mapper.MovieDbToMovie(movieDb)
	return movie, nil
}

func (MovieNoSqlRepository *MovieNoSqlRepository) Save(movie *model.Movie) error {
	var movieDb models.MovieDb
	movieDb = movie_mapper.MovieToMovieDb(*movie)
	collection := MovieNoSqlRepository.Connection.Database(Schema).Collection(Table)
	result, err := collection.InsertOne(context.TODO(), movieDb)
	if err != nil {
		logger.Error(fmt.Sprintf("Can't work with %s", movieDb.Title), err)
		return fmt.Errorf("Can't work with %s", movieDb.Title)
	}
	movie.ID = result.InsertedID.(primitive.ObjectID).Hex()
	return nil
}

repository: SergioPortilla/movies

repo_name: SergioPortilla/movies

gha_language: Java


text:
SUMMARY = "node.js init"
DESCRIPTION = "Node.js based init system"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://${COREBASE}/meta/COPYING.MIT;md5=3da9cfbcb788c80a0384361b4de20420"

PR = "r2"
DEPENDS = "nodejs"
SRC_URI = "file://init"

do_configure() {
        :
}

do_compile() {
        :
}

do_install() {
  install -d  ${D}/sbin
  install -m 0755 ${WORKDIR}/init ${D}/sbin/init
}

FILES_${PN} = "/sbin/init"

repository: js-os/jsos-oe-core

repo_name: js-os/jsos-oe-core


text:
PR = "r3"
require u-boot.inc

DEFAULT_PREFERENCE = "-1"
DEFAULT_PREFERENCE_hipox = "1"

DEPENDS_append_hipox = " oxnas-boot-tools oxnas-boot-tools-native "

SRC_URI = "ftp://ftp.denx.de/pub/u-boot/u-boot-${PV}.tar.bz2 \
	file://cfi-amic-fixup.patch \
"

SRC_URI_append_hipox = " \
	file://00-hipox.patch \
	file://01-hipox-fix-gmac-reset.patch \
	file://02-hipox-enable-mmu.patch \
	file://03-hipox-direct-switch.patch \
	file://04-hipox-env.patch \
	file://10-hipox-boot-abort-key.patch \
"

TARGET_LDFLAGS = ""

UBOOT_MACHINE_hipox  = "hipox_config"

inherit base

do_compile () {
	oe_runmake ${UBOOT_MACHINE}
	oe_runmake all
}

do_deploy_append_hipox () {
	cat ${STAGING_DATADIR}/oxnas-boot-tools/arm/oxnasbt_header.bin ${DEPLOY_DIR_IMAGE}/${UBOOT_IMAGE} > ${DEPLOY_DIR_IMAGE}/${UBOOT_IMAGE}.wrapped
	oxnasbt_update_header ${DEPLOY_DIR_IMAGE}/${UBOOT_IMAGE}.wrapped
}

SRC_URI[md5sum] = "285e2184d2efef50e0fe43b71d2a7b11"
SRC_URI[sha256sum] = "a2889bee9c45575b284564dbc27cf3dc6cb9833d4ff643976a1b3dba2d763f86"


text:
@inproceedings{valdez2009effects,
  title={Effects of aging and domain knowledge on usability in small screen devices for diabetes patients},
  author={Calero Valdez, Andr{\'e} and Ziefle, Martina and Horstmann, Andreas and Herding, Daniel and Schroeder, Ulrik},
  booktitle={Symposium of the Austrian HCI and Usability Engineering Group},
  pages={366--386},
  year={2009},
  organization={Springer}
}


text:
file(REMOVE_RECURSE
  "CMakeFiles/RTInterception.i386.dir/interception_linux.cc.o"
  "CMakeFiles/RTInterception.i386.dir/interception_mac.cc.o"
  "CMakeFiles/RTInterception.i386.dir/interception_win.cc.o"
  "CMakeFiles/RTInterception.i386.dir/interception_type_test.cc.o"
)

# Per-language clean rules from dependency scanning.
foreach(lang CXX)
  include(CMakeFiles/RTInterception.i386.dir/cmake_clean_${lang}.cmake OPTIONAL)
endforeach()

license: NCSA

repository: canyuchen/Hacking-on-LLVM

repo_name: canyuchen/Hacking-on-LLVM