text:
SET(PYTHON_KNOWN_VERSIONS "2.5" "2.4" "2.3" "2.2" "2.1" "2.0" "1.6" "1.5")

MACRO(python_find_version _py_ver _py_inc _py_ld _py_lib)
	STRING(REPLACE "." "" _py_ver_nd "${_py_ver}")

	SET(PY_INSTALL_PATH
		[HKEY_LOCAL_MACHINE\\SOFTWARE\\Python\\PythonCore\\${_py_ver}\\InstallPath])

	SET(_py_libnames "python${_py_ver}" "python${_py_ver_nd}")

	SET(_py_found FALSE)
	FOREACH(_py_libname ${_py_libnames})
		IF (NOT _py_found)
			SET(_py_lib_full _py_lib_full-NOTFOUND)

			FIND_LIBRARY(_py_lib_full
				NAMES "${_py_libname}"
				PATHS "${PY_INSTALL_PATH}/libs"
				NO_SYSTEM_ENVIRONMENT_PATH
			)
			FIND_LIBRARY(_py_lib_full
				NAMES "${_py_libname}"
				PATHS "${PY_INSTALL_PATH}/libs"
				PATH_SUFFIXES "python${_py_ver}/config"
				NO_SYSTEM_ENVIRONMENT_PATH
			)
			IF(_py_lib_full)
				SET(_py_lib "${_py_libname}")
				GET_FILENAME_COMPONENT(_py_ld "${_py_lib_full}" PATH)
				SET(_py_found TRUE)
			ENDIF(_py_lib_full)
		ENDIF(NOT _py_found)
	ENDFOREACH(_py_libname)

	IF(_py_found)
		FIND_PATH(_py_inc
			NAMES Python.h
			PATHS
				${PY_INSTALL_PATH}/include
			PATH_SUFFIXES
				"python${_py_ver}"
		)
	ENDIF(_py_found)

	SET(PYTHON_REQ_VERSION_FOUND ${_py_found})
ENDMACRO(python_find_version)

repository: jimpick/nixos-upstream-svn-nixpkgs

repo_name: jimpick/nixos-upstream-svn-nixpkgs


text:
#
# Find the LibXml2 includes and library
#
include(${CMAKE_CURRENT_LIST_DIR}/../commands/commands.cmake)

arccon_return_if_package_found(LibXml2)

# Supprime temporairement CMAKE_MODULE_PATH pour éviter une récursion
# infinie.

set(_SAVED_CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH})
unset(CMAKE_MODULE_PATH)
find_package(LibXml2)
set(CMAKE_MODULE_PATH ${_SAVED_CMAKE_MODULE_PATH})

# Normalement cette cible est toujours définie depuis la version 3.12
# de CMake si LibXml2 est trouvé
if (NOT ARCCON_USE_LEGACY_FIND)
  if (TARGET LibXml2::LibXml2)
    arccon_register_cmake_config_target(LibXml2 CONFIG_TARGET_NAME LibXml2::LibXml2)
    return()
  endif()
endif()

# Ancienne version si on ne trouve pas la cible LibXml2::LibXml2
set(LibXml2_FOUND FALSE)
if (LIBXML2_FOUND)
  set(LibXml2_FOUND TRUE)
  set(LIBXML2_INCLUDE_DIRS ${LIBXML2_INCLUDE_DIR})
endif()

arccon_register_package_library(LibXml2 LIBXML2)

# ----------------------------------------------------------------------------
# Local Variables:
# tab-width: 2
# indent-tabs-mode: nil
# coding: utf-8-with-signature
# End:

repository: arcaneframework/framework

license_type: permissive

repo_name: arcaneframework/framework

branch_name: refs/heads/main

extension: cmake


text:
DESCRIPTION = "GPS navigation/map display software"
LICENSE = "GPL"
DEPENDS = "gdbm libconic sqlite3 gtk+ libhildonfm libosso osso-ic-oss libosso-help gnome-vfs dbus bluez-libs"
RDEPENDS_${PN} = "bluez-utils"
RRECOMMENDS_${PN} = "gpsd flite"
SRCREV = "118"
PV = "2.0.3+svnr${SRCPV}"
PR = "r3"

SRC_URI = "svn://garage.maemo.org/svn/maemo-mapper;proto=https;module=trunk \
           file://mapper-hildon1.diff;maxrev=108"

S = "${WORKDIR}/trunk"

inherit autotools pkgconfig

do_install_append () {
	install -d ${D}${datadir}/applications/
	mv ${D}/maemo-mapper.desktop ${D}${datadir}/applications/
}

#FILES_${PN} += "${datadir}/icons"

license: MIT

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty

branch_name: HEAD

extension: bb


text:
@article{Karimi2015,
abstract = {OpenCL, along with CUDA, is one of the main tools used to program GPGPUs. However, it allows running the same code on multi-core CPUs too, making it a rival for the long-established OpenMP. In this paper we compare OpenCL and OpenMP when developing and running compute-heavy code on a CPU. Both ease of programming and performance aspects are considered. Since, unlike a GPU, no memory copy operation is involved, our comparisons measure the code generation quality, as well as thread management efficiency of OpenCL and OpenMP. We evaluate the performance of these development tools under two conditions: a large number of short-running compute-heavy parallel code executions, when more thread management is performed, and a small number of long-running parallel code executions, when less thread management is required. The results show that OpenCL and OpenMP each win in one of the two conditions. We argue that while using OpenMP requires less setup, OpenCL can be a viable substitute for OpenMP from a performance point of view, especially when a high number of thread invocations is required. We also provide a number of potential pitfalls to watch for when moving from OpenMP to OpenCL.},
author = {Karimi, Kamran},
file = {:home/logan/Documents/Mendeley/arXiv1503.06532 cs/Karimi/Karimi - 2015 - The Feasibility of Using OpenCL Instead of OpenMP for Parallel CPU Programming.pdf:pdf},
journal = {arXiv:1503.06532 [cs]},
title = {{The Feasibility of Using OpenCL Instead of OpenMP for Parallel CPU Programming}},
url = {http://arxiv.org/abs/1503.06532{\%}5Cnhttp://www.arxiv.org/pdf/1503.06532.pdf},
year = {2015}
}
@misc{NVIDIA2010,
author = {NVIDIA},
title = {{OpenCL Best Practices Guide}},
url = {https://www.cs.cmu.edu/afs/cs/academic/class/15668-s11/www/cuda-doc/OpenCL{\_}Best{\_}Practices{\_}Guide.pdf},
year = {2010}
}
@misc{KhronosGroup2012,
author = {{Khronos Group}},
title = {{The OpenCL Specification}},
url = {https://www.khronos.org/registry/OpenCL/specs/opencl-1.2.pdf},
year = {2012}
}
@article{Choi2010,
abstract = {Wireless networks of smart sensors with computations distributed over multiple sensor packages have shown considerable promise in providing low-cost structural health monitoring. In these networks, microprocessors are typically embedded in individual smart sensor packages. The efficiency of embedded computational algorithms is of critical importance because the size, cost, and power requirements of the sensor arrays are central concerns. Here, very efficient methodologies are presented to compute statistical moments of a measured response time-history. These moments: the mean, standard deviation, skewness, and kurtosis are often used to characterize a measured irregular response. Two alternative approaches are presented, each of which can save substantial computer memory requirements and CPU time in certain applications. The first approach reconsiders the computational benefits of computing statistical moments by separating the data into bins and then computing the moments from the geometry of the resulting histogram, which effectively becomes a one-pass algorithm for higher moments. One benefit is that the statistical moment calculations can be carried out to arbitrary accuracy such that the computations can be tuned to the precision of the sensor hardware. The second approach is a new analytical methodology to combine statistical moments from individual segments of a time-history such that the resulting overall moments are those of the complete time-history. This methodology could be used to allow for parallel computation of statistical moments with subsequent combination of those moments, or for combination of statistical moments computed at sequential times. A worked example is presented comparing two implementations of the new methodologies with conventional calculations in monitoring the global performance of an offshore tension leg platform. Accuracy, efficiency, and storage requirements of the calculation methods are compared with those of conventional methods. The results show that substantial CPU and memory savings can be attained with no loss in accuracy and that more dramatic savings can be attained if a slight reduction in accuracy is acceptable.},
author = {Choi, Myoungkeun and Sweetman, Bert},
doi = {10.1177/1475921709341014},
file = {:home/logan/Documents/Mendeley/Structural Health Monitoring/Choi, Sweetman/Choi, Sweetman - 2010 - Efficient calculation of statistical moments for structural health monitoring.pdf:pdf},
issn = {14759217},
journal = {Structural Health Monitoring},
keywords = {Continuous health monitoring,Non-Gaussian,Statistical moments,Structural response,Time series analysis},
number = {1},
pages = {13--24},
title = {{Efficient calculation of statistical moments for structural health monitoring}},
url = {http://journals.sagepub.com/doi/10.1177/1475921709341014},
volume = {9},
year = {2010}
}
@article{Chan1979,
abstract = {A general formula is presented for computing the sample v;iiiancc for a sample of size m+ n given the means and variances for two subsnn+lcs of sizes m and n. This formula is used in the construction of a pa.irwisc nl{\~{}}:orithm for computing the variance. Other applications are discussed as v{\~{}}ll, including the use of updating formulae in a parallel computin g cnviornmcn t. Wc present numerical results and rounding error analyses for several numerical schcr{\~{}}{\~{}}s.},
author = {Chan, T.F. and Golub, G.H. and LeVeque, R.J. and Science., Stanford Univ Calif Dept of Computer},
doi = {10.1007/978-3-642-51461-6_3},
file = {:home/logan/Documents/Mendeley/Annals of Physics/Chan et al/Chan et al. - 1979 - Updating formulae and a pairwise algorithm for computing sample variances.pdf:pdf},
isbn = {3705100025},
journal = {Annals of Physics},
pages = {258},
title = {{Updating formulae and a pairwise algorithm for computing sample variances}},
url = {http://scholar.google.com/scholar?hl=en{\&}btnG=Search{\&}q=intitle:Updating+Formulae+and+pairwise+algorithm+for+computing+sample+variances{\#}0},
volume = {54},
year = {1979}
}
@article{Stone2010,
abstract = {The OpenCL standard offers a common API for program execution on systems composed of different types of computational devices such as multicore CPUs, GPUs, or other accelerators.},
author = {Stone, John E. and Gohara, David and Shi, Guochun},
doi = {10.1109/MCSE.2010.69},
file = {:home/logan/Documents/Mendeley/Computing in Science and Engineering/Stone, Gohara, Shi/Stone, Gohara, Shi - 2010 - OpenCL A parallel programming standard for heterogeneous computing systems.pdf:pdf},
isbn = {1521-9615 VO - 12},
issn = {15219615},
journal = {Computing in Science and Engineering},
keywords = {barrier synchronization,matrix multiply,opencl},
number = {3},
pages = {66--72},
pmid = {21037981},
title = {{OpenCL: A parallel programming standard for heterogeneous computing systems}},
url = {http://www.cs.nyu.edu/{~}lerner/spring12/Preso07-OpenCL.pdf},
volume = {12},
year = {2010}
}
@article{Pebay2008,
abstract = {We present a formula for the pairwise update of arbitrary-order centered statistical moments. This formula is of particular interest to compute such moments in parallel for large-scale, distributed data sets. As a corollary, we indicate a specialization of this formula for incremental updates, of particular interest to streaming implementations. Finally, we provide pairwise and incremental update formulas for the covariance.},
author = {P{\'{e}}bay, Philippe},
file = {:home/logan/Documents/Mendeley/Sandia Report/P{\'{e}}bay/P{\'{e}}bay - 2008 - Formulas for Robust, One-Pass Parallel Computation of Covariances and Arbitrary-Order Statistical Moments.pdf:pdf},
journal = {Sandia Report},
number = {September},
pages = {1--18},
title = {{Formulas for Robust, One-Pass Parallel Computation of Covariances and Arbitrary-Order Statistical Moments}},
url = {http://www.ntis.gov/search/product.aspx?ABBR=DE20111028931{\%}5Cninfoserve.sandia.gov/sand{\_}doc/2008/086212.pdf},
volume = {SAND2008-6},
year = {2008}
}
@misc{nasa_2016, title={How Many Decimals of Pi Do We Really Need? - Edu News | NASA/JPL Edu}, url={https://www.jpl.nasa.gov/edu/news/2016/3/16/how-many-decimals-of-pi-do-we-really-need/}, journal={NASA}, publisher={NASA}, year={2016}, month={Mar}}

branch_name: refs/heads/master

extension: bib


text:
@unpublished{thompsongithub,
  title={AllCraigslistLocations},
  author={Jeff Thompson},
  url={https://gist.github.com/jeffThompson/62fcd3540affdf194b9a},
  year={2017}
}

@INPROCEEDINGS{Garg2013,
  author={Garg, Vaibhav and Nilizadeh, Shirin},
  booktitle={2013 IEEE Security and Privacy Workshops},
  title={Craigslist Scams and Community Composition: Investigating Online Fraud Victimization},
  year={2013},
  volume={},
  number={},
  pages={123-126},
  doi={10.1109/SPW.2013.21}}

@article{Kuk2021,
title={The COVID-19 Pandemic and the Rental Market: Evidence From Craigslist},
ISSN={0002-7642},
DOI={10.1177/00027642211003149},
journal={American Behavioral Scientist},
publisher={SAGE Publications Inc},
author={Kuk, John and Schachter, Ariela and Faber, Jacob William and Besbris, Max},
year={2021},
month={Mar},
pages={00027642211003149} }

 @article{Boeing2017,
 title={New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings}, volume={37}, ISSN={0739-456X}, DOI={10.1177/0739456X16664789}, number={4}, journal={Journal of Planning Education and Research}, publisher={SAGE Publications Inc}, author={Boeing, Geoff and Waddell, Paul}, year={2017}, month={Dec}, pages={457–476} }

  @article{Medhat2014,
  title={Sentiment analysis algorithms and applications: A survey}, volume={5}, ISSN={2090-4479}, DOI={10.1016/j.asej.2014.04.011}, number={4}, journal={Ain Shams Engineering Journal}, author={Medhat, Walaa and Hassan, Ahmed and Korashy, Hoda}, year={2014}, month={Dec}, pages={1093–1113} }

   @article{Tseng2018, place={New York, Netherlands},
   title={Price prediction of e-commerce products through Internet sentiment analysis}, volume={18}, ISSN={13895753}, DOI={http://dx.doi.org.ezproxy.lib.ou.edu/10.1007/s10660-017-9272-9}, publisher={Springer Nature B.V.}, author={Tseng, Kuo-Kun and Lin, Regina Fang-Ying and Zhou, Hongfu and Kurniajaya, Kevin Jati and Li, Qianyu}, year={2018}, month={Mar}, pages={65–88} }

   @misc{Johnson20,
   title={11 mind-blowing facts about Craigslist, which makes more than \$1 billion a year and employs just 50 people}, url={https://www.businessinsider.com/craigslist-facts-2019-5}, journal={Business Insider}, author={Johnson, Dave} }

 @article{Fang2017,
 title={Online classified advertising: a review and bibliometric analysis}, volume={113}, ISSN={1588-2861}, DOI={10.1007/s11192-017-2524-6}, number={3}, journal={Scientometrics}, author={Fang, Chencheng and Zhang, Jiantong and Qiu, Wei}, year={2017}, month={Dec}, pages={1481–1511} }

@article{Hogan2011,
author = {Hogan, Bernie and Berry, Brent},
title = {Racial and Ethnic Biases in Rental Housing: An Audit Study of Online Apartment Listings},
journal = {City \& Community},
volume = {10},
number = {4},
pages = {351-372},
doi = {https://doi.org/10.1111/j.1540-6040.2011.01376.x},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-6040.2011.01376.x},
year = {2011}
}

gha_license_id: MIT

gha_language: Jupyter Notebook


text:
DESCRIPTION = "Openpandora packages for switchgui openbox"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://${COREBASE}/LICENSE;md5=3f40d7994397109285ec7b81fdeb3b58"

PR = "r1"

inherit packagegroup

RDEPENDS_${PN} = " \
		xprop \
		openbox \
		gtk-chtheme \
		lxterminal \
		menu-cache \
		obconf \
		openbox-menu \
		switchgui-openbox \
		tint2 \
		wbar \
		mousepad \
		xdotool \
		gtk-theme-clearlooks \
		gtk-theme-crux \
		gtk-theme-industrial \
		gtk-theme-mist \
		gtk-theme-redmond \
		gtk-theme-thinice \
		openbox-theme-artwiz-boxed \
		openbox-theme-bear2 \
		openbox-theme-clearlooks-3.4 \
		openbox-theme-clearlooks \
		openbox-theme-clearlooks-olive \
		openbox-theme-mikachu \
		openbox-theme-natura \
		openbox-theme-onyx \
		openbox-theme-onyx-citrus \
		openbox-theme-orang \
		openbox-theme-syscrash \
        libxinerama \
"

repository: DougBarry/meta-openpandora-vendor

repo_name: DougBarry/meta-openpandora-vendor


text:
DESCRIPTION = "U-boot bootloader"
PROVIDES = "virtual/bootloader"
SECTION = "bootloader"
MAINTAINER = "Holger Schurig"
PRIORITY = "optional"
LICENSE = "GPL"
PR = "r1"

DEPENDS = "fw-utils"

SRC_URI = "${SOURCEFORGE_MIRROR}/${PN}/${PN}-${PV}.tar.bz2"

SRC_URI_append_acs6k_ppc440 = "\
                file://add-88e1121-support.patch;patch=1 \
                file://bug-nand-ecc-smc.patch;patch=1 \
                file://bug-ppc4xx-etherr-lock.patch;patch=1 \
                file://add-ppc4xx-eth-debug.patch;patch=1 \
                file://gsp-oe-compile.patch;patch=1 \
                file://gsp-remove-cmds.patch;patch=1 \
                file://gsp-config.patch;patch=1 \
                file://gsp-ppc4xx-wdt.patch;patch=1 \
                file://gsp-ppc4xx-fecmode.patch;patch=1 \
                file://gsp-fallback.patch;patch=1 \
                file://gsp-avocent-header.patch;patch=1 \
                file://goldengate.patch;patch=1 \
"

SRC_URI_append_acs6k-oem1 = "\
                file://acs6000-oem1.patch;patch=1 \
"

# Intentionally left empty because PMHD HW should use uboot 1.3.1
#SRC_URI_append_pmhd_mpc8313 = ""

EXTRA_OEMAKE = "CROSS_COMPILE=${TARGET_PREFIX} STAGING_DIR=${STAGING_DIR}/${TARGET_SYS}"
TARGET_LDFLAGS = ""

UBOOT_MACHINE ?= "${MACHINE}_config"
UBOOT_MACHINE_acs6000 ?= "golden_gate_config"
UBOOT_MACHINE_pmhd_mpc8313 ?= "${MACHINE}_config"
UBOOT_IMAGE = "u-boot-${MACHINE}-${PV}-${PR}.bin"
AVOCENT_BOOTCODE_VERSION = ". AVCT-Boot 2.0.1.0"
AVOCENT_BOOTCODE_VERSION_acs6k-oem1 = ". FCL-Boot 2.0.1.0"

inherit base

do_compile () {
	oe_runmake ${UBOOT_MACHINE}
	oe_runmake EXTRAVERSION="${AVOCENT_BOOTCODE_VERSION}" all
}

#temporary to use acs6000 HW
do_compile_pmhd () {
	oe_runmake golden_gate_config
	oe_runmake EXTRAVERSION="${AVOCENT_BOOTCODE_VERSION}" all
}

do_stage() {
	install -d ${STAGING_DIR}/${HOST_SYS}/include
	install -d ${STAGING_DIR}/${HOST_SYS}/include/uboot
	install -d ${STAGING_DIR}/${HOST_SYS}/include/uboot/configs
	install include/environment.h ${STAGING_DIR}/${HOST_SYS}/include/uboot
	install include/config.h ${STAGING_DIR}/${HOST_SYS}/include/uboot
	install include/cmd_confdefs.h ${STAGING_DIR}/${HOST_SYS}/include/uboot
	install include/command.h ${STAGING_DIR}/${HOST_SYS}/include/uboot
	install include/configs/*.h ${STAGING_DIR}/${HOST_SYS}/include/uboot/configs
	install include/gsp-serialnb.h ${STAGING_DIR}/${HOST_SYS}/include/uboot
	install -m755 tools/mkimage ${STAGING_BINDIR}
	install -d ${STAGING_LOADER_DIR}
	install ${S}/u-boot.bin ${STAGING_LOADER_DIR}
	rm -f ${STAGING_LOADER_DIR}/u-boot.bin.gz
	gzip ${STAGING_LOADER_DIR}/u-boot.bin
}

do_deploy[dirs] = "${S} ${DEPLOY_DIR_IMAGE}"
do_deploy () {
    install -d ${DEPLOY_DIR_IMAGE}
    install ${S}/u-boot.bin ${DEPLOY_DIR_IMAGE}/${UBOOT_IMAGE}
}

addtask deploy before do_build after do_compile


text:
# Recipe for building DICOM Toolkit - dcmtk
#
# Test for compilation to qemuarm64 and qemux86-64
#
# Based on BSD-3-Clause
#
# Author(s)
#   Claus Stovgaard - clst@ambu.com
#

DESCRIPTION = "DCMTK - DICOM Toolkit"
SUMMARY = "DCMTK - DICOM Toolkit"
LICENSE = "BSD-3-Clause"

# Add dcmtk subfolder to search path
FILESEXTRAPATHS_prepend := "${THISDIR}/${PN}-${PV}:"

# General source setup
BRANCH = "cross-compile-options"
SRC_URI = "git://github.com/frosteyes/dcmtk.git;protocol=https;branch=${BRANCH}"
# not tagged - so just sha1 hash
SRCREV = "319368d6a3463f1e5149d2ca7e989d4535bb6cf4"

LIC_FILES_CHKSUM = "file://COPYRIGHT;md5=7461e0fb30398858fdde489b5dd44e80"

COMPATIBLE_MACHINE = "(qemux86-64|qemuarm64)"

# Below is setup for cross compilation for x86_64
# The setup is chosen together with the arith.h from running cmake on x86_64
SRC_URI += "file://arith_qemux86-64.h \
            file://arith_qemuarm64.h \
            "

# Setup for cross compilation x86_64
EXTRA_OECMAKE_qemux86-64 = "-DDCMTK_NO_TRY_RUN:BOOL=1 \
                            -DC_CHAR_UNSIGNED:BOOL=0 \
                            -DDCMTK_ICONV_FLAGS_ANALYZED:BOOL=TRUE \
                            -DDCMTK_FIXED_ICONV_CONVERSION_FLAGS:STRING=AbortTranscodingOnIllegalSequence \
                            -DDCMTK_STDLIBC_ICONV_HAS_DEFAULT_ENCODING:BOOL=TRUE \
                           "

# Setup for cross compilation arm64
EXTRA_OECMAKE_qemuarm64 += "-DDCMTK_NO_TRY_RUN:BOOL=1 \
                            -DC_CHAR_UNSIGNED:BOOL=1 \
                            -DDCMTK_ICONV_FLAGS_ANALYZED:BOOL=TRUE \
                            -DDCMTK_FIXED_ICONV_CONVERSION_FLAGS:STRING=AbortTranscodingOnIllegalSequence \
                            -DDCMTK_STDLIBC_ICONV_HAS_DEFAULT_ENCODING:BOOL=TRUE \
                           "

# We need to copy arith.h into the correct folder inside the build folder
# Depending on target arch - use the correct one
# For now do it based on machine.
# Later it could be based on TARGET_ARCH or similar if we have a more architecture generic arith.h
cmake_do_configure_append() {
    if [ -e ${WORKDIR}/arith_${MACHINE}.h ] ; then
        install ${WORKDIR}/arith_${MACHINE}.h ${B}/config/include/dcmtk/config/arith.h
    fi
}

# General dependency compilation setup

DEPENDS = "openssl ninja-native zlib virtual/libiconv openssl"
RDEPENDS_${PN} += " glibc-gconvs glibc-utils"

inherit cmake

S = "${WORKDIR}/git"

# Setup build to be a shared libs.
EXTRA_OECMAKE += "-DBUILD_SHARED_LIBS:BOOL=ON \
                  -DUSE_COMPILER_HIDDEN_VISIBILITY:BOOL=ON \
                  "

# Example setup of compile flags
EXTRA_OECMAKE += "-DDCMTK_WITH_TIFF:BOOL=ON \
                  -DDCMTK_WITH_XML:BOOL=ON \
                  -DDCMTK_WITH_ZLIB:BOOL=OFF \
                  -DDCMTK_WITH_OPENSSL:BOOL=ON \
                  -DDCMTK_WITH_ICONV:BOOL=ON \
                  -DDCMTK_ENABLE_PRIVATE_TAGS:BOOL=ON \
                  -DDCMTK_WITH_DOXYGEN:BOOL=OFF \
                  "

# Setup for STL / CXX and cross compilation
EXTRA_OECMAKE += "-DDCMTK_ENABLE_STL:BOOL=ON \
                  -DHAVE_STL_VECTOR_TEST_RESULT:BOOL=OFF \
                  -DHAVE_STL_ALGORITHM_TEST_RESULT:BOOL=OFF \
                  -DHAVE_STL_LIMITS_TEST_RESULT:BOOL=OFF \
                  -DHAVE_STL_LIST_TEST_RESULT:BOOL=OFF \
                  -DHAVE_STL_MAP_TEST_RESULT:BOOL=OFF \
                  -DHAVE_STL_MEMORY_TEST_RESULT:BOOL=OFF \
                  -DHAVE_STL_STACK_TEST_RESULT:BOOL=OFF \
                  -DHAVE_STL_STRING_TEST_RESULT:BOOL=OFF \
                  -DHAVE_STL_TYPE_TRAITS_TEST_RESULT:BOOL=OFF \
                  -DHAVE_STL_TUPLE_TEST_RESULT:BOOL=OFF \
                  -DHAVE_STL_SYSTEM_ERROR_TEST_RESULT:BOOL=OFF \
                  -DDCMTK_ENABLE_CXX11:BOOL=ON \
                 "

repository: frosteyes/yocto-dcmtk

repo_name: frosteyes/yocto-dcmtk


text:
@Manual{base,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2022},
  url = {https://www.R-project.org/},
}
@Manual{bookdown2023,
  title = {bookdown: Authoring Books and Technical Documents with R Markdown},
  author = {Yihui Xie},
  year = {2023},
  note = {R package version 0.33},
  url = {https://github.com/rstudio/bookdown},
}

@Book{bookdown2016,
  title = {bookdown: Authoring Books and Technical Documents with {R} Markdown},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2016},
  note = {ISBN 978-1138700109},
  url = {https://bookdown.org/yihui/bookdown},
}
@Manual{grateful,
  title = {grateful: Facilitate citation of R packages},
  author = {Francisco Rodríguez-Sánchez and Connor P. Jackson and Shaurita D. Hutchins},
  note = {R package version 0.1.5},
  year = {2022},
  url = {https://github.com/Pakillo/grateful},
}
@Manual{here,
  title = {here: A Simpler Way to Find Your Files},
  author = {Kirill Müller},
  year = {2020},
  note = {R package version 1.0.1},
  url = {https://CRAN.R-project.org/package=here},
}
@Manual{knitr2023,
  title = {knitr: A General-Purpose Package for Dynamic Report Generation in R},
  author = {Yihui Xie},
  year = {2023},
  note = {R package version 1.42},
  url = {https://yihui.org/knitr/},
}

@Book{knitr2015,
  title = {Dynamic Documents with {R} and knitr},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2015},
  edition = {2nd},
  note = {ISBN 978-1498716963},
  url = {https://yihui.org/knitr/},
}

@InCollection{knitr2014,
  booktitle = {Implementing Reproducible Computational Research},
  editor = {Victoria Stodden and Friedrich Leisch and Roger D. Peng},
  title = {knitr: A Comprehensive Tool for Reproducible Research in {R}},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  year = {2014},
  note = {ISBN 978-1466561595},
}
@Manual{measurements,
  title = {measurements: Tools for Units of Measurement},
  author = {Matthew A. Birk},
  year = {2023},
  note = {R package version 1.5.0},
  url = {https://CRAN.R-project.org/package=measurements},
}
@Article{RgoogleMaps,
  title = {{RgoogleMaps} and {loa}: Unleashing {R} Graphics Power on Map Tiles},
  author = {Markus Loecher and Karl Ropkins},
  journal = {Journal of Statistical Software},
  year = {2015},
  volume = {63},
  number = {4},
  pages = {1--18},
  url = {http://www.jstatsoft.org/v63/i04/},
}
@Manual{rmarkdown2023,
  title = {rmarkdown: Dynamic Documents for R},
  author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone},
  year = {2023},
  note = {R package version 2.20},
  url = {https://github.com/rstudio/rmarkdown},
}

@Book{rmarkdown2018,
  title = {R Markdown: The Definitive Guide},
  author = {Yihui Xie and J.J. Allaire and Garrett Grolemund},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2018},
  note = {ISBN 9781138359338},
  url = {https://bookdown.org/yihui/rmarkdown},
}

@Book{rmarkdown2020,
  title = {R Markdown Cookbook},
  author = {Yihui Xie and Christophe Dervieux and Emily Riederer},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2020},
  note = {ISBN 9780367563837},
  url = {https://bookdown.org/yihui/rmarkdown-cookbook},
}
@Manual{rstatix,
  title = {rstatix: Pipe-Friendly Framework for Basic Statistical Tests},
  author = {Alboukadel Kassambara},
  year = {2023},
  note = {R package version 0.7.2},
  url = {https://CRAN.R-project.org/package=rstatix},
}
@Manual{rticles,
  title = {rticles: Article Formats for R Markdown},
  author = {JJ Allaire and Yihui Xie and Christophe Dervieux and {R Foundation} and Hadley Wickham and {Journal of Statistical Software} and Ramnath  Vaidyanathan and {Association for Computing Machinery} and Carl Boettiger and {Elsevier} and Karl Broman and Kirill Mueller and Bastiaan Quast and Randall  Pruim and Ben Marwick and Charlotte Wickham and Oliver Keyes and Miao Yu and Daniel Emaasit and Thierry Onkelinx and Alessandro Gasparini and Marc-Andre Desautels and Dominik Leutnant and {MDPI} and {Taylor and Francis} and Oğuzhan Öğreden and Dalton Hance and Daniel Nüst and Petter Uvesten and Elio Campitelli and John Muschelli and Alex Hayes and Zhian N. Kamvar and Noam Ross and Robrecht Cannoodt and Duncan Luguern and David M. Kaplan and Sebastian Kreutzer and Shixiang Wang and Jay Hesselberth and Rob Hyndman},
  year = {2022},
  note = {R package version 0.24},
  url = {https://CRAN.R-project.org/package=rticles},
}
@Article{taxize2013,
  title = {taxize - taxonomic search and retrieval in R},
  journal = {F1000Research},
  author = {{Scott Chamberlain} and {Eduard Szocs}},
  year = {2013},
  url = {https://f1000research.com/articles/2-191/v2},
}

@Manual{taxize2020,
  title = {taxize: Taxonomic information from around the web},
  author = {Scott Chamberlain and Eduard Szoecs and Zachary Foster and Zebulun Arendsee and Carl Boettiger and Karthik Ram and Ignasi Bartomeus and John Baumgartner and James O'Donnell and Jari Oksanen and Bastian Greshake Tzovaras and Philippe Marchand and Vinh Tran and Maëlle Salmon and Gaopeng Li and Matthias Grenié},
  year = {2020},
  note = {R package version 0.9.98},
  url = {https://github.com/ropensci/taxize},
}
@Article{tidyverse,
  title = {Welcome to the {tidyverse}},
  author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani},
  year = {2019},
  journal = {Journal of Open Source Software},
  volume = {4},
  number = {43},
  pages = {1686},
  doi = {10.21105/joss.01686},
}


text:
@inproceedings{lu2017multi,
  title={Multi-model Data Management: What's New and What's Next?},
  author={Lu, Jiaheng and Holubov{\'a}, Irena},
  booktitle={EDBT},
  pages={602--605},
  year={2017}
}

@Article{brown:96,
  author = 	 {A.W. Brown and K. C. Wallnau},
  title = 	 {A Framework for Evaluating Software Technology},
  journal = 	 {IEEE Software},
  year = 	 1996,
  volume = 	 13,
  number = 	 5,
  pages = 	 {39-49},
  month = 	 {September}}


text:
@article{Ratcliffe2015,
 abstract = {European beech forest with a variable admixture is one of the most important forest types in Central Europe. Growing evidence has demonstrated the positive effect of increased biodiversity on vital forest ecosystem functions and services such as productivity and nutrient cycling. Both complementarity in resource use and species identity are known to influence tree productivity but they have received relatively little attention in observational studies. Using a large dataset of repeat inventory trees in a near-natural deciduous forest in Central Germany we test whether tree diversity enhances tree productivity at the tree and the stand level, whilst accounting for tree size, tree vitality, local topography and the potentially confounding effects of spatial autocorrelation and negative growth estimates. Beech and hornbeam individual tree growth was sensitive to their neighbourhood diversity and composition whilst ash trees were only sensitive to the neighbourhood tree density. Neighbourhood complementarity effects were driven by differences in species' competitive strengths, whilst at the stand level productivity gains were primarily attributable to the density of ash and diversity effects were less prominent. We conclude that small-scale admixture with patches of different species promotes tree growth in European beech forest; congruent with current management plans for beech and hardwood forests.},
 author = {Ratcliffe, Sophia and Holzwarth, Frédéric and Nadrowski, Karin and Levick, Shaun and Wirth, Christian},
 doi = {10.1016/j.foreco.2014.09.032},
 issn = {03781127},
 journal = {Forest Ecology and Management},
 keywords = {Basal area increment,Competition index,Diversity-productivity,Neighbourhood diversity},
 pages = {225--234},
 title = {Tree neighbourhood matters - tree species composition drives diversity-productivity patterns in a near-natural beech forest},
 volume = {335},
 year = {2015}
}

repository: geospatialeco/SpatialDataScience

repo_name: geospatialeco/SpatialDataScience


text:
@misc{wusampling,
  title={Sampling Theory and Practice},
  author={Wu, Changbao and Thompson, Mary E},
  publisher={Springer}
}

@Manual{citer,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2020},
    url = {https://www.R-project.org/},
}

@Manual{citedplyr,
    title = {dplyr: A Grammar of Data Manipulation},
    author = {Hadley Wickham and Romain Francois and Lionel {
             Henry} and Kirill Muller},
    year = {2020},
    note = {R package version 1.0.2},
    url = {https://CRAN.R-project.org/package=dplyr},
}

@Book{citeggplot2,
    author = {Hadley Wickham},
    title = {ggplot2: Elegant Graphics for Data Analysis},
    publisher = {Springer-Verlag New York},
    year = {2016},
    isbn = {978-3-319-24277-4},
    url = {https://ggplot2.tidyverse.org},
}

@Manual{citescales,
    title = {scales: Scale Functions for Visualization},
    author = {Hadley Wickham and Dana Seidel},
    year = {2020},
    note = {R package version 1.1.1},
    url = {https://CRAN.R-project.org/package=scales},
}

@article{xie2014knitr,
  title={knitr: a comprehensive tool for reproducible research in R},
  author={Xie, Yihui},
  journal={Implement Reprod Res},
  volume={1},
  pages={20},
  year={2014}
}

@Manual{citedon,
    title = {Don Valley North},
    author = {Wikipedia},
    year = {2020},
    url = {https://en.wikipedia.org/wiki/Don_Valley_North},
}

@Manual{citecen,
    title = {Census Profile, 2016 Census},
    author = {Statistics Canada},
    year = {2016},
    url = {https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/prof/details/page.cfm?Lang=E&Geo1=FED&Code1=35020&Geo2=PR&Code2=10&Data=Count&SearchText=Don%20Valley%20North&SearchType=Begins&SearchPR=01&B1=All},
}

@Manual{citemonkey,
    title = {SurveyMonkey.com},
    author = {SurveyMonkey},
    year = {2020},
    url = {https://www.surveymonkey.com/h},
}


text:
@article{InterfacingSynchronousAsynchronous_sjogren_2000,
 abstract = {This paper describes a new technique for integrating asynchronous modules within a high-speed synchronous pipeline. Our design eliminates potential metastability problems by using a clock generated by a stoppable ring oscillator, which is capable of driving the large clock load found in present day microprocessors. Using the ATACS design tool, we designed highly optimized transistor-level circuits to control the ring oscillator and generate the clock and handshake signals with minimal overhead. Our interface architecture requires no redesign of the synchronous circuitry. Incorporating asynchronous modules in a high-speed pipeline improves performance by exploiting data-dependent delay variations. Since the speed of the synchronous circuitry tracks the speed of the ring oscillator under different processes, temperatures, and voltages, the entire chip operates at the speed dictated by the current operating conditions, rather than being governed by the worst case conditions. These two factors together can lead to a significant improvement in average-case performance. The interface design is simulated using the 0.6-/spl mu/m HP CMOS14B process in HSPICE.},
 annotation = {00000},
 author = {Sjogren, A.E. and Myers, C.J.},
 doi = {10.1109/92.894162},
 issn = {1557-9999},
 journal = {IEEE Transactions on Very Large Scale Integration (VLSI) Systems},
 keywords = {0.6 micron,asynchronous circuits,asynchronous modules,ATACS design tool,average-case performance,circuit stability,Circuits,clock load,clocks,Clocks,current operating conditions,data-dependent delay variations,Delay,delays,Design optimization,handshake signals,high-speed integrated circuits,high-speed pipeline,HP CMOS14B process,HSPICE,integrated circuit design,logic CAD,Metastasis,microprocessor chips,Microprocessors,pipeline processing,Pipelines,Ring oscillators,Signal design,Signal generators,SPICE,stoppable ring oscillator,synchronous modules,transistor-level circuits},
 month = {October},
 number = {5},
 pages = {573--583},
 title = {Interfacing Synchronous and Asynchronous Modules within a High-Speed Pipeline},
 volume = {8},
 year = {2000}
}


text:
@InProceedings{thomason2017opportunistic,
  title = {Opportunistic Active Learning for Grounding Natural Language Descriptions},
  author = {Jesse Thomason and Aishwarya Padmakumar and Jivko Sinapov and Justin Hart and Peter Stone and Raymond J. Mooney},
  booktitle = {Proceedings of the 1st Annual Conference on Robot Learning},
  pages = {67--76},
  year = 	 {2017},
  editor = {Sergey Levine and Vincent Vanhoucke and Ken Goldberg},
  volume = {78},
  series = {Proceedings of Machine Learning Research},
  month = {13--15 Nov},
  publisher = {PMLR},
  pdf = {http://proceedings.mlr.press/v78/thomason17a/thomason17a.pdf},
  url = {http://proceedings.mlr.press/v78/thomason17a.html}
}

url: https://raw.githubusercontent.com/Hugh-Shi/DL-NLP-Readings/924e4d189e5660c5b5d4854278f3df755ae2231d/Bibtex/Opportunistic Active Learning for Grounding Natural Language Descriptions.bib

repository: Hugh-Shi/DL-NLP-Readings

repo_name: Hugh-Shi/DL-NLP-Readings


text:
require centerim.inc

PR = "r0"

SRC_URI[md5sum] = "9bbbdb34ab678d29387171139ae835e1"
SRC_URI[sha256sum] = "c68abb59043602a2b28fb55d45361d0b22fc1566848cc0c255580914df7b3eb3"


text:
.rect_device_provider_initialise {
    fill: #db76ed;
}

.rect_device_provider_initialise:hover {
    fill: #8f4d9b;
}

.rect_device_initialise {
    fill: #ca22e8;
}

.rect_device_initialise:hover {
    fill: #6a0f7a;
}

.rect_passed_test {
    fill: #5cb85c;
}

.rect_passed_test:hover {
    fill: #449d44
}

.rect_failed_test {
    fill: #00534d;
}

.rect_failed_test:hover {
    fill: #00302c;
}

.rect_ignored_test {
    fill: #cccccc;
}

.rect_ignored_test:hover {
    fill: #848484;
}

.rect_incomplete_test {
    fill: #e8e821;
}

.rect_incomplete_test:hover {
    fill: #a8a817;
}

.rect_assumption_failure {
    fill: #ca812d;
}

.rect_assumption_failure:hover {
    fill: #8c591e;
}

.tooltip_passed_test {
    color: #449d44;
}

.tooltip_failed_test {
    color: #c9302c;
}

.tooltip_ignored_test {
    color: #cccccc;
}

div.tooltip {
    position: absolute;
    text-align: left;
    font-size: 10px;
    padding-left: 0;
    width: auto;
    border: 0;
    border-left: thin solid #000000;
    pointer-events: none;
    line-height: 12px;
    padding-top: 0;
    display: block;
}

.ytitle {
    dominant-baseline: central;
    font-size: 12px;
}

.ytitle.link {
    cursor: pointer;
    fill: #07C;
}

.axis path,
.axis line {
    display: none;
}

.axis text {
    font-size: 12px;
    fill: #777;
}

.vert_grid {
    fill: none;
    stroke: #dddddd;
    stroke-width: 1px;
}

.horz_grid {
    fill: none;
    stroke: #dddddd;
    stroke-width: 1px;
}

.heading {
    font-size: 16px;
    font-weight: bold;
}

.subheading {
    font-size: 12px;
    fill: #777;
}

.legend {
    dominant-baseline: central;
    font-size: 12px;
    fill: #777;
}

repository: cdsap/Talaiot

repo_name: cdsap/Talaiot

gha_language: Kotlin

extension: css


text:
FIND_PATH(FASTINF_INCLUDE_DIR lbDefinitions.h)
FIND_LIBRARY(FASTINF_LOOPY_INF_LIBRARY  NAMES libLoopyInf )

# handle the QUIETLY and REQUIRED arguments and set FASTINF_FOUND to TRUE if
# all listed variables are TRUE
INCLUDE(FindPackageHandleStandardArgs)

FIND_PACKAGE_HANDLE_STANDARD_ARGS(FASTINF DEFAULT_MSG
FASTINF_INCLUDE_DIR
FASTINF_LOOPY_INF_LIBRARY
)

IF(FASTINF_FOUND)
   SET(FASTINF_LIBRARIES ${FASTINF_LOOPY_INF_LIBRARY} )
ELSE()
SET(FASTINF_INCLUDE_DIR                   FASTINF_INCLUDE_DIR-NOTFOUND)
SET(FASTINF_LOOPY_INF_LIBRARY             FASTINF_LOOPY_INF_LIBRARY-NOTFOUND)
ENDIF()


text:
@inproceedings{yang2018towards,
  title={Towards perceptual image dehazing by physics-based disentanglement and adversarial training},
  author={Yang, Xitong and Xu, Zheng and Luo, Jiebo},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={32},
  number={1},
  year={2018}
}

url: https://raw.githubusercontent.com/rbt-c/DL-NLP-Readings/82ca76e9184a3b00ac87129d99391477c85cfcd2/Bibtex/Towards Perceptual Image Dehazing by Physics-based Disentanglement and Adversarial Training.bib

repository: rbt-c/DL-NLP-Readings

repo_name: rbt-c/DL-NLP-Readings


text:
SUMMARY = "language bindings for libdbus, for Python 3.5 or later"
HOMEPAGE = "https://github.com/ldo/dbussy"
LICENSE = "LGPL-2.1-only"
LIC_FILES_CHKSUM = "file://COPYING;md5=a916467b91076e631dd8edb7424769c7"

SRC_URI = "git://github.com/ldo/dbussy.git;branch=master;protocol=https \
           file://setuptools.patch"

SRCREV = "37ede4242b48def73ada46c2747a4c5cae6abf45"

S = "${WORKDIR}/git"

inherit setuptools3

PIP_INSTALL_PACKAGE = "DBussy"

RDEPENDS:${PN} += "\
    python3-asyncio \
    python3-core \
    python3-ctypes \
    python3-xml \
"

BBCLASSEXTEND = "native nativesdk"

repository: openembedded/meta-openembedded

repo_name: openembedded/meta-openembedded

gha_language: BitBake


text:
SUMMARY = "Sintel movie trailer - 1080P"
LICENSE = "CC-BY-3.0"
LIC_FILES_CHKSUM = "file://${COREBASE}/meta/files/common-licenses/CC-BY-3.0;md5=dfa02b5755629022e267f10b9c0a2ab7"

SRC_URI = "https://download.blender.org/durian/trailer/sintel_trailer-1080p.mp4"
SRC_URI[md5sum] = "bfd19c4e7ad04c72bfb327cf7e6b9576"
SRC_URI[sha256sum] = "34bbd52a4b89fdf63c8ace50b268da26653a59508288100cd3c23de276db7931"

inherit allarch

do_install() {
    install -d ${D}${datadir}/movies
    install -m 0644 ${WORKDIR}/sintel_trailer-1080p.mp4 ${D}${datadir}/movies/
}

FILES_${PN} += "${datadir}/movies"

repository: FrankBau/meta-raspberrypi-avg

repo_name: FrankBau/meta-raspberrypi-avg


text:
@inproceedings{SpeckEtAl2016,
  	author="Speck, Robert and Ruprecht, Daniel and Minion, Michael and Emmett, Matthew and Krause, Rolf",
  	editor="Dickopf, Thomas and Gander, J. Martin and Halpern, Laurence and Krause, Rolf and Pavarino, F. Luca",
  	title="Inexact Spectral Deferred Corrections",
  	booktitle="Domain Decomposition Methods in Science and Engineering {XXII}",
  	year="2016",
  	pages="389--396",
  	doi="10.1007/978-3-319-18827-0_39",
  	url="http://dx.doi.org/10.1007/978-3-319-18827-0_39",
    publisher = {Springer International Publishing},
}

repository: comp-maths/website

repo_name: comp-maths/website


text:
@inproceedings{charles2021might,
 author = {Charles, K and Chen, C and Johnson, J and Lee, A and Lifset, E and Hogarth, M and Weibel, N and Farcas, E and Moore, A},
 booktitle = {JOURNAL OF THE AMERICAN GERIATRICS SOCIETY},
 organization = {WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
 pages = {S243--S244},
 title = {How might an intelligent voice assistant address older adults' health-related needs?},
 volume = {69},
 year = {2021}
}

repository: robinzhx/hxi-website

repo_name: robinzhx/hxi-website


text:
DESCRIPTION = "QCOM Firmware for Asus Google Nexus 7 (2013)"

LICENSE = "Proprietary"
LIC_FILES_CHKSUM = "file://license.txt;md5=0d238870f50c84250a14191d17aaf1d5"

SRC_URI = "https://dl.google.com/dl/android/aosp/qcom-flo-mob30x-43963492.tgz;name=google \
    git://android.googlesource.com/device/asus/flo;protocol=https;branch=master;name=aosp"
SRC_URI[google.md5sum] = "5c21950c751544cc92b5fe95c6f3be37"
SRC_URI[google.sha256sum] = "1ccc740a461be8ea84369b1c13fc89cb3f26f8bc1400fedec8b3dd1f630a7994"
SRCREV_aosp = "9d9fee956a9c4c7be4f69f7a472d3fc0e759c2dd"

FW_QCOM_NAME = "flo"

require recipes-bsp/firmware/firmware-qcom.inc

DEPENDS += "pil-squasher-native"

# extract the license file
do_extract() {
    head -n 280 ${WORKDIR}/extract-qcom-flo.sh | tail -n +16 > ${S}/license.txt
    tail -n +315 ${WORKDIR}/extract-qcom-flo.sh | tar xzfv - -C ${S}
}
addtask extract after do_unpack before do_patch

do_compile() {
    for fw in ${S}/vendor/qcom/flo/proprietary/*.mdt ; do
        pil-squasher ${B}/`basename $fw mdt`mbn $fw
    done
}

do_install() {
    install -d  ${D}${FW_QCOM_PATH}
    install -m 0644 ${B}/*.mbn ${D}${FW_QCOM_PATH}
    install -m 0644 vendor/qcom/flo/proprietary/vidcfw.elf ${D}${FW_QCOM_PATH}
    install -m 0644 vendor/qcom/flo/proprietary/vidc_1080p.fw ${D}${FW_QCOM_PATH}

    install -m 0644 license.txt ${D}${FW_QCOM_PATH}

    install -m 0644 ${WORKDIR}/git/WCNSS_cfg.dat ${D}${FW_QCOM_PATH}
    install -m 0644 ${WORKDIR}/git/WCNSS_qcom_wlan_nv_deb.bin ${D}${FW_QCOM_PATH}
    install -m 0644 ${WORKDIR}/git/WCNSS_qcom_wlan_nv_flo.bin ${D}${FW_QCOM_PATH}/WCNSS_qcom_wlan_nv.bin
}

repository: loicpoulain/meta-qcom

repo_name: loicpoulain/meta-qcom


text:
@INPROCEEDINGS{5539868,
author={A. Joulin and F. Bach and J. Ponce},
booktitle={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
title={Discriminative clustering for image co-segmentation},
year={2010},
pages={1943-1950},
keywords={combinatorial mathematics;computer vision;image segmentation;object recognition;optimisation;pattern classification;pattern clustering;background labels;bottom-up image segmentation;combinatorial optimization problem;computer vision;continuous convex optimization problem;discriminative clustering framework;foreground labels;image cosegmentation;kernel methods;object recognition;supervised classifier;unsupervised segmentation;Computer vision;Image segmentation;Kernel;Object recognition},
doi={10.1109/CVPR.2010.5539868},
ISSN={1063-6919},
month={June},}

@INPROCEEDINGS{7025663,
author={K. R. Jerripothula and J. Cai and F. Meng and J. Yuan},
booktitle={2014 IEEE International Conference on Image Processing (ICIP)},
title={Automatic image co-segmentation using geometric mean saliency},
year={2014},
pages={3277-3281},
keywords={computational geometry;image segmentation;GMS method;automatic image cosegmentation;colabelling multiple image complex process;geometric mean saliency;global saliency maps;single-image saliency maps;warping technique;Computer vision;Computers;Conferences;Educational institutions;Histograms;Image segmentation;Pattern recognition;co-segmentation;image segmentation;saliency;warping},
doi={10.1109/ICIP.2014.7025663},
ISSN={1522-4880},
month={Oct},}

@article{SHU201639,
title = "Unsupervised 3D shape segmentation and co-segmentation via deep learning",
journal = "Computer Aided Geometric Design",
volume = "43",
number = "",
pages = "39 - 52",
year = "2016",
note = "Geometric Modeling and Processing 2016",
issn = "0167-8396",
doi = "http://dx.doi.org/10.1016/j.cagd.2016.02.015",
url = "http://www.sciencedirect.com/science/article/pii/S0167839616300164",
author = "Zhenyu Shu and Chengwu Qi and Shiqing Xin and Chao Hu and Li Wang and Yu Zhang and Ligang Liu",
keywords = "3D shapes",
keywords = "Segmentation",
keywords = "Co-segmentation",
keywords = "Deep learning",
keywords = "High-level features"
}

@ARTICLE{7120111,
author={H. Fu and D. Xu and B. Zhang and S. Lin and R. K. Ward},
journal={IEEE Transactions on Image Processing},
title={Object-Based Multiple Foreground Video Co-Segmentation via Multi-State Selection Graph},
year={2015},
volume={24},
number={11},
pages={3415-3424},
keywords={graph theory;image representation;image segmentation;iterative methods;matrix algebra;video signal processing;category-independent object proposal;indicator matrix;iterative procedure;multistate selection graph;object-based multiple foreground video cosegmentation;video frame represention;Electronic mail;Feature extraction;Image segmentation;Learning systems;Proposals;TV;Training;Video co-segmentation;multiple foregrounds;object-based segmentation;video co-segmentation},
doi={10.1109/TIP.2015.2442915},
ISSN={1057-7149},
month={Nov},}

@ARTICLE{7401081,
author={K. Li and J. Zhang and W. Tao},
journal={IEEE Transactions on Image Processing},
title={Unsupervised Co-Segmentation for Indefinite Number of Common Foreground Objects},
year={2016},
volume={25},
number={4},
pages={1898-1909},
keywords={Big Data;image segmentation;message passing;search problems;unsupervised learning;Coseg-INCT;MSRC;adaptive decision criterion;big data;common foreground objects;common target extraction;iCoseg;loopy belief propagation;multiobject discovery;multisearch strategy;unsupervised object cosegmentation method;Algorithm design and analysis;Heating;Histograms;Image color analysis;Image segmentation;Optimization;Proposals;Co-segmentation;adaptive feature;loopy belief propagation;multi-object discovery},
doi={10.1109/TIP.2016.2526900},
ISSN={1057-7149},
month={April},}

@Inbook{Zhang2014,
author="Zhang, Dong
and Javed, Omar
and Shah, Mubarak",
editor="Fleet, David
and Pajdla, Tomas
and Schiele, Bernt
and Tuytelaars, Tinne",
title="Video Object Co-segmentation by Regulated Maximum Weight Cliques",
bookTitle="Computer Vision -- ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VII",
year="2014",
publisher="Springer International Publishing",
address="Cham",
pages="551--566",
isbn="978-3-319-10584-0",
doi="10.1007/978-3-319-10584-0_36",
url="https://doi.org/10.1007/978-3-319-10584-0_36"
}

@article{DBLP:journals/corr/SiamVJR16,
  author    = {Mennatullah Siam and
			   Sepehr Valipour and
			   Martin J{\"{a}}gersand and
			   Nilanjan Ray},
  title     = {Convolutional Gated Recurrent Networks for Video Segmentation},
  journal   = {CoRR},
  volume    = {abs/1611.05435},
  year      = {2016},
  url       = {http://arxiv.org/abs/1611.05435},
  timestamp = {Wed, 07 Jun 2017 14:41:08 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/SiamVJR16},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@article{DBLP:journals/corr/JainZSS15,
  author    = {Ashesh Jain and
			   Amir Roshan Zamir and
			   Silvio Savarese and
			   Ashutosh Saxena},
  title     = {Structural-RNN: Deep Learning on Spatio-Temporal Graphs},
  journal   = {CoRR},
  volume    = {abs/1511.05298},
  year      = {2015},
  url       = {http://arxiv.org/abs/1511.05298},
  timestamp = {Wed, 07 Jun 2017 14:42:51 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/JainZSS15},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@article{PAVEL2017105,
title = "Object class segmentation of RGB-D video using recurrent convolutional neural networks",
journal = "Neural Networks",
volume = "88",
number = "",
pages = "105 - 113",
year = "2017",
note = "",
issn = "0893-6080",
doi = "http://dx.doi.org/10.1016/j.neunet.2017.01.003",
url = "http://www.sciencedirect.com/science/article/pii/S0893608017300035",
author = "Mircea Serban Pavel and Hannes Schulz and Sven Behnke",
keywords = "Recurrent neural networks",
keywords = "Computer vision",
keywords = "Object class-segmentation"
}

@article{DBLP:journals/corr/NohHH15,
  author    = {Hyeonwoo Noh and
			   Seunghoon Hong and
			   Bohyung Han},
  title     = {Learning Deconvolution Network for Semantic Segmentation},
  journal   = {CoRR},
  volume    = {abs/1505.04366},
  year      = {2015},
  url       = {http://arxiv.org/abs/1505.04366},
  timestamp = {Wed, 07 Jun 2017 14:42:13 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/NohHH15},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@article{DBLP:journals/corr/GulcehreCB17,
  author    = {{\c{C}}aglar G{\"{u}}l{\c{c}}ehre and
			   Sarath Chandar and
			   Yoshua Bengio},
  title     = {Memory Augmented Neural Networks with Wormhole Connections},
  journal   = {CoRR},
  volume    = {abs/1701.08718},
  year      = {2017},
  url       = {http://arxiv.org/abs/1701.08718},
  timestamp = {Wed, 07 Jun 2017 14:42:41 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/GulcehreCB17},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}


text:
DESCRIPTION = "gptp - an AVnu sponsored repository for Time Sensitive Network (TSN and AVB) technology"
HOMEPAGE = "https://github.com/AVnu/gptp"
LICENSE = "BSD & MIT"
LIC_FILES_CHKSUM = " \
    file://LICENSE;md5=b3e2a6e620763288bcbc3190f6cb1704 \
"

COMPATIBLE_MACHINE = "(salvator-x|ulcb|ebisu|draak)"

PV = "1.1+git${SRCPV}"

SRCREV = "0baef8a36a13105112862919aac0f1eed21a44ea"
SRC_URI = "git://github.com/AVnu/gptp.git;branch=master;protocol=https"

# Fix QA issue of ldflags
SRC_URI_append = " file://0001-Fix-QA-issue-when-build-with-Yocto.patch"

S = "${WORKDIR}/git"

EXTRA_OEMAKE = "'CC=${CC}' 'CXX=${CXX}'"

do_configure() {
    cd ${S}/linux/build
    oe_runmake clean
}

do_compile() {
    cd ${S}/linux/build
    oe_runmake all
}

do_install() {
    install -d ${D}/${bindir}
    install -m 755 linux/build/obj/daemon_cl ${D}/${bindir}
}

repository: renesas-rcar/meta-renesas

repo_name: renesas-rcar/meta-renesas

branch_name: refs/heads/dunfell


text:
@article{Gweon2019,
    year = {2019},
    keywords = {amr,read-depth,metagenomics,classification,speciation},
    title = {{The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples}},
    author = {Gweon, H. Soon and Shaw, Liam P. and Swann, Jeremy and Maio, Nicola De and AbuOun, Manal and Niehus, Rene and Hubbard, Alasdair T. M. and Bowes, Mike J. and Bailey, Mark J. and Peto, Tim E. A. and Hoosdally, Sarah J. and Walker, A. Sarah and Sebra, Robert P. and Crook, Derrick W. and Anjum, Muna F. and Read, Daniel S. and Stoesser, Nicole and Abuoun, M. and Anjum, M. and Bailey, M. J. and Barker, L. and Brett, H. and Bowes, M. J. and Chau, K. and Crook, D. W. and Maio, N. De and Gilson, D. and Gweon, H. S. and Hubbard, A. T. M. and Hoosdally, S. and Kavanagh, J. and Jones, H. and Peto, T. E. A. and Read, D. S. and Sebra, R. and Shaw, L. P. and Sheppard, A. E. and Smith, R. and Stubberfield, E. and Swann, J. and Walker, A. S. and Woodford, N.},
    journal = {Environmental Microbiome},
    doi = {10.1186/s40793-019-0347-1},
    pmid = {33902704},
    abstract = {{Shotgun metagenomics is increasingly used to characterise microbial communities, particularly for the investigation of antimicrobial resistance (AMR) in different animal and environmental contexts. There are many different approaches for inferring the taxonomic composition and AMR gene content of complex community samples from shotgun metagenomic data, but there has been little work establishing the optimum sequencing depth, data processing and analysis methods for these samples. In this study we used shotgun metagenomics and sequencing of cultured isolates from the same samples to address these issues. We sampled three potential environmental AMR gene reservoirs (pig caeca, river sediment, effluent) and sequenced samples with shotgun metagenomics at high depth (\textbackslashtextasciitilde 200 million reads per sample). Alongside this, we cultured single-colony isolates of Enterobacteriaceae from the same samples and used hybrid sequencing (short- and long-reads) to create high-quality assemblies for comparison to the metagenomic data. To automate data processing, we developed an open-source software pipeline, ‘ResPipe’. Taxonomic profiling was much more stable to sequencing depth than AMR gene content. 1 million reads per sample was sufficient to achieve < 1\% dissimilarity to the full taxonomic composition. However, at least 80 million reads per sample were required to recover the full richness of different AMR gene families present in the sample, and additional allelic diversity of AMR genes was still being discovered in effluent at 200 million reads per sample. Normalising the number of reads mapping to AMR genes using gene length and an exogenous spike of Thermus thermophilus DNA substantially changed the estimated gene abundance distributions. While the majority of genomic content from cultured isolates from effluent was recoverable using shotgun metagenomics, this was not the case for pig caeca or river sediment. Sequencing depth and profiling method can critically affect the profiling of polymicrobial animal and environmental samples with shotgun metagenomics. Both sequencing of cultured isolates and shotgun metagenomics can recover substantial diversity that is not identified using the other methods. Particular consideration is required when inferring AMR gene content or presence by mapping metagenomic reads to a database. ResPipe, the open-source software pipeline we have developed, is freely available (https://gitlab.com/hsgweon/ResPipe).}},
    pages = {7},
    number = {1},
    volume = {14}
}

@article{Baccarella2018,
    year = {2018},
    keywords = {rna,rna-sq,read-depth},
    title = {{Empirical assessment of the impact of sample number and read depth on RNA-Seq analysis workflow performance}},
    author = {Baccarella, Alyssa and Williams, Claire R. and Parrish, Jay Z. and Kim, Charles C.},
    journal = {BMC Bioinformatics},
    doi = {10.1186/s12859-018-2445-2},
    pmid = {30428853},
    abstract = {{RNA-Sequencing analysis methods are rapidly evolving, and the tool choice for each step of one common workflow, differential expression analysis, which includes read alignment, expression modeling, and differentially expressed gene identification, has a dramatic impact on performance characteristics. Although a number of workflows are emerging as high performers that are robust to diverse input types, the relative performance characteristics of these workflows when either read depth or sample number is limited–a common occurrence in real-world practice–remain unexplored. Here, we evaluate the impact of varying read depth and sample number on the performance of differential gene expression identification workflows, as measured by precision, or the fraction of genes correctly identified as differentially expressed, and by recall, or the fraction of differentially expressed genes identified. We focus our analysis on 30 high-performing workflows, systematically varying the read depth and number of biological replicates of patient monocyte samples provided as input. We find that, in general for most workflows, read depth has little effect on workflow performance when held above two million reads per sample, with reduced workflow performance below this threshold. The greatest impact of decreased sample number is seen below seven samples per group, when more heterogeneity in workflow performance is observed. The choice of differential expression identification tool, in particular, has a large impact on the response to limited inputs. Among the tested workflows, the recall/precision balance remains relatively stable at a range of read depths and sample numbers, although some workflows are more sensitive to input restriction. At ranges typically recommended for biological studies, performance is more greatly impacted by the number of biological replicates than by read depth. Caution should be used when selecting analysis workflows and interpreting results from low sample number experiments, as all workflows exhibit poorer performance at lower sample numbers near typically reported values, with variable impact on recall versus precision. These analyses highlight the performance characteristics of common differential gene expression workflows at varying read depths and sample numbers, and provide empirical guidance in experimental and analytical design.}},
    pages = {423},
    number = {1},
    volume = {19}
}

@article{Maio2019,
    year = {2019},
    keywords = {nanopore,assembly,bacterial-genomics,hybrid-assembly,benchmark},
    title = {{Comparison of long-read sequencing technologies in the hybrid assembly of complex bacterial genomes}},
    author = {Maio, Nicola De and Shaw, Liam P and Hubbard, Alasdair and George, Sophie and Sanderson, Nicholas D and Swann, Jeremy and Wick, Ryan and AbuOun, Manal and Stubberfield, Emma and Hoosdally, Sarah J and Crook, Derrick W and Peto, Timothy E A and Sheppard, Anna E and Bailey, Mark J and Read, Daniel S and Anjum, Muna F and Walker, A Sarah and Stoesser, Nicole and Consortium, On Behalf Of The Rehab},
    journal = {Microbial Genomics},
    doi = {10.1099/mgen.0.000294},
    pmid = {31483244},
    pmcid = {PMC6807382},
    abstract = {{Illumina sequencing allows rapid, cheap and accurate whole genome bacterial analyses, but short reads (<300 bp) do not usually enable complete genome assembly. Long-read sequencing greatly assists with resolving complex bacterial genomes, particularly when combined with short-read Illumina data (hybrid assembly). However, it is not clear how different long-read sequencing methods affect hybrid assembly accuracy. Relative automation of the assembly process is also crucial to facilitating high-throughput complete bacterial genome reconstruction, avoiding multiple bespoke filtering and data manipulation steps. In this study, we compared hybrid assemblies for 20 bacterial isolates, including two reference strains, using Illumina sequencing and long reads from either Oxford Nanopore Technologies (ONT) or SMRT Pacific Biosciences (PacBio) sequencing platforms. We chose isolates from the family Enterobacteriaceae, as these frequently have highly plastic, repetitive genetic structures, and complete genome reconstruction for these species is relevant for a precise understanding of the epidemiology of antimicrobial resistance. We de novo assembled genomes using the hybrid assembler Unicycler and compared different read processing strategies, as well as comparing to long-read-only assembly with Flye followed by short-read polishing with Pilon. Hybrid assembly with either PacBio or ONT reads facilitated high-quality genome reconstruction, and was superior to the long-read assembly and polishing approach evaluated with respect to accuracy and completeness. Combining ONT and Illumina reads fully resolved most genomes without additional manual steps, and at a lower consumables cost per isolate in our setting. Automated hybrid assembly is a powerful tool for complete and accurate bacterial genome assembly.}},
    number = {9},
    volume = {5}
}

@misc{filtlong,
    author = {Wick, Ryan},
    title = {Filtlong: Quality filtering tool for long reads},
    year = {2021},
    publisher = {GitHub},
    journal = {GitHub repository},
    url = {https://github.com/rrwick/Filtlong}
}

@misc{seqtk,
    author = {Li, Heng},
    title = {Seqtk: Toolkit for processing sequences in FASTA/Q formats },
    year = {2018},
    publisher = {GitHub},
    journal = {GitHub repository},
    url = {https://github.com/lh3/seqtk}
}

gha_language: Rust


text:
@inproceedings{Amdahl1967,
author = {Amdahl, Gene M},
booktitle = {AFIPS Spring Joint Computer Conference},
title = {{Validity of the single processor approach to achieving large scale computing capabilities}},
year = {1967}
}
@article{Baca2017,
abstract = {The beetle suborder Adephaga has been the subject of many phylogenetic reconstructions utilizing a variety of data sources and inference methods. However, no strong consensus has yet emerged on the relationships among major adephagan lineages. Ultraconserved elements (UCEs) have proved useful for inferring difficult or unresolved phylogenies at varying timescales in vertebrates, arachnids and Hymenoptera. Recently, a UCE bait set was developed for Coleoptera using polyphagan genomes and a member of the order Strepsiptera as an outgroup. Here, we examine the utility of UCEs for reconstructing the phylogeny of adephagan families, in the first in vitro application a UCE bait set in Coleoptera. Our final dataset included 305 UCE loci for 18 representatives of all adephagan families except Aspidytidae, and two polyphagan outgroups, with a total concatenated length of 83 547 bp. We inferred trees using maximum likelihood analyses of the concatenated UCE alignment and coalescent species tree methods (astral ii, ASTRID, svdquartets). Although the coalescent species tree methods had poor resolution and weak support, concatenated analyses produced well-resolved, highly supported trees. Hydradephaga was recovered as paraphyletic, with Gyrinidae sister to Geadephaga and all other adephagans. Haliplidae was recovered as sister to Dytiscoidea, with Hygrobiidae and Amphizoidae successive sisters to Dytiscidae. Finally, Noteridae was recovered as monophyletic and sister to Meruidae. Given the success of UCE data for resolving phylogenetic relationships within Adephaga, we suggest the potential for further resolution of relationships within Adephaga using UCEs with improved taxon sampling, and by developing Adephaga-specific probes.},
author = {Baca, Stephen M. and Alexander, Alana and Gustafson, Grey T. and Short, Andrew E.Z.},
doi = {10.1111/syen.12244},
issn = {13653113},
journal = {Systematic Entomology},
number = {4},
pages = {786--795},
title = {{Ultraconserved elements show utility in phylogenetic inference of Adephaga (Coleoptera) and suggest paraphyly of ‘Hydradephaga'}},
volume = {42},
year = {2017}
}
@article{Blaimer2016,
abstract = {Obtaining sequence data from historical museum specimens has been a growing research interest, invigorated by next-generation sequencing methods that allow inputs of highly degraded DNA. We applied a target enrichment and next-generation sequencing protocol to generate ultraconserved elements (UCEs) from 51 large carpenter bee specimens (genus Xylocopa), representing 25 species with specimen ages ranging from 2–121 years. We measured the correlation between specimen age and DNA yield (pre- and post-library preparation DNA concentration) and several UCE sequence capture statistics (raw read count, UCE reads on target, UCE mean contig length and UCE locus count) with linear regression models. We performed piecewise regression to test for specific breakpoints in the relationship of specimen age and DNA yield and sequence capture variables. Additionally, we compared UCE data from newer and older specimens of the same species and reconstructed their phylogeny in order to confirm the validity of our data. We recovered 6–972 UCE loci from samples with pre-library DNA concentrations ranging from 0.06–9.8 ng/$\mu$L. All investigated DNA yield and sequence capture variables were significantly but only moderately negatively correlated with specimen age. Specimens of age 20 years or less had significantly higher pre- and post-library concentrations, UCE contig lengths, and locus counts compared to specimens older than 20 years. We found breakpoints in our data indicating a decrease of the initial detrimental effect of specimen age on pre- and post-library DNA concentration and UCE contig length starting around 21–39 years after preservation. Our phylogenetic results confirmed the integrity of our data, giving preliminary insights into relationships within Xylocopa. We consider the effect of additional factors not measured in this study on our age-related sequence capture results, such as DNA fragmentation and preservation method, and discuss the promise of the UCE approach for large-scale projects in insect phylogenomics using museum specimens.},
author = {Blaimer, Bonnie B. and Lloyd, Michael W. and Guillory, Wilson X. and Brady, Se{\'{a}}n G.},
doi = {10.1371/journal.pone.0161531},
issn = {19326203},
journal = {PLoS ONE},
number = {8},
pages = {1--20},
title = {{Sequence capture and phylogenetic utility of genomic ultraconserved elements obtained from pinned insect specimens}},
volume = {11},
year = {2016}
}
@article{Crawford2012,
abstract = {We present the first genomic-scale analysis addressing the phylogenetic position of turtles, using over 1000 loci from representatives of all major reptile lineages including tuatara. Previously, studies of morphological traits positioned turtles either at the base of the reptile tree or with lizards, snakes and tuatara (lepidosaurs), whereas molecular analyses typically allied turtles with crocodiles and birds (archosaurs). A recent analysis of shared microRNA families found that turtles are more closely related to lepidosaurs. To test this hypothesis with data from many single-copy nuclear loci dispersed throughout the genome, we used sequence capture, high-throughput sequencing and published genomes to obtain sequences from 1145 ultraconserved elements (UCEs) and their variable flanking DNA. The resulting phylogeny provides overwhelming support for the hypothesis that turtles evolved from a common ancestor of birds and crocodilians, rejecting the hypothesized relationship between turtles and lepidosaurs.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Crawford, Nicholas G. and Faircloth, Brant C. and McCormack, John E. and Brumfield, Robb T. and Winker, Kevin and Glenn, Travis C.},
doi = {10.1098/rsbl.2012.0331},
eprint = {NIHMS150003},
isbn = {1744-9561},
issn = {1744957X},
journal = {Biology Letters},
keywords = {Archosaurs,Evolution,Phylogenomics,Turtles,Ultraconserved elements},
number = {5},
pages = {783--786},
pmid = {22593086},
title = {{More than 1000 ultraconserved elements provide evidence that turtles are the sister group of archosaurs}},
volume = {8},
year = {2012}
}
@article{Faircloth2012,
abstract = {Although massively parallel sequencing has facilitated large-scale DNA sequencing, comparisons among distantly related species rely upon small portions of the genome that are easily aligned. Methods are needed to efficiently obtain comparable DNA fragments prior to massively parallel sequencing, particularly for biologists working with non-model organisms. We introduce a new class of molecular marker, anchored by ultraconserved genomic elements (UCEs), that universally enable target enrichment and sequencing of thousands of orthologous loci across species separated by hundreds of millions of years of evolution. Our analyses here focus on use of UCE markers in Amniota because UCEs and phylogenetic relationships are well-known in some amniotes. We perform an in silico experiment to demonstrate that sequence flanking 2030 UCEs contains information sufficient to enable unambiguous recovery of the established primate phylogeny. We extend this experiment by performing an in vitro enrichment of 2386 UCE-anchored loci from nine, non-model avian species. We then use alignments of 854 of these loci to unambiguously recover the established evolutionary relationships within and among three ancient bird lineages. Because many organismal lineages have UCEs, this type of genetic marker and the analytical framework we outline can be applied across the tree of life, potentially reshaping our understanding of phylogeny at many taxonomic levels.},
author = {Faircloth, Brant C. and McCormack, John E. and Crawford, Nicholas G. and Harvey, Michael G. and Brumfield, Robb T. and Glenn, Travis C.},
doi = {10.1093/sysbio/sys004},
isbn = {1076-836X (Electronic)$\backslash$n1063-5157 (Linking)},
issn = {10635157},
journal = {Systematic Biology},
keywords = {Flanking sequence,genetic markers,phylogenomics,sequence capture,target enrichment,ultraconserved elements},
number = {5},
pages = {717--726},
pmid = {22232343},
title = {{Ultraconserved elements anchor thousands of genetic markers spanning multiple evolutionary timescales}},
volume = {61},
year = {2012}
}
@article{Faircloth2013,
abstract = {Ray-finned fishes constitute the dominant radiation of vertebrates with over 30,000 species. Although molecular phylogenetics has begun to disentangle major evolutionary relationships within this vast section of the Tree of Life, there is no widely available approach for efficiently collecting phylogenomic data within fishes, leaving much of the enormous potential of massively parallel sequencing technologies for resolving major radiations in ray-finned fishes unrealized. Here, we provide a genomic perspective on longstanding questions regarding the diversification of major groups of ray-finned fishes through targeted enrichment of ultraconserved nuclear DNA elements (UCEs) and their flanking sequence. Our workflow efficiently and economically generates data sets that are orders of magnitude larger than those produced by traditional approaches and is well-suited to working with museum specimens. Analysis of the UCE data set recovers a well-supported phylogeny at both shallow and deep time-scales that supports a monophyletic relationship between Amia and Lepisosteus (Holostei) and reveals elopomorphs and then osteoglossomorphs to be the earliest diverging teleost lineages. Divergence time estimation based upon 14 fossil calibrations reveals that crown teleosts appeared {\~{}}270 Ma at the end of the Permian and that elopomorphs, osteoglossomorphs, ostarioclupeomorphs, and euteleosts diverged from one another by 205 Ma during the Triassic. Our approach additionally reveals that sequence capture of UCE regions and their flanking sequence offers enormous potential for resolving phylogenetic relationships within ray-finned fishes.},
archivePrefix = {arXiv},
arxivId = {1210.0120},
author = {Faircloth, Brant C. and Sorenson, Laurie and Santini, Francesco and Alfaro, Michael E.},
doi = {10.1371/journal.pone.0065923},
eprint = {1210.0120},
isbn = {10.1371/journal.pone.0065923},
issn = {19326203},
journal = {PLoS ONE},
number = {6},
pmid = {23824177},
title = {{A Phylogenomic Perspective on the Radiation of Ray-Finned Fishes Based upon Targeted Sequencing of Ultraconserved Elements (UCEs)}},
volume = {8},
year = {2013}
}
@article{Harrington2016,
abstract = {Flatfish cranial asymmetry represents one of the most remarkable morphological innovations among vertebrates, and has fueled vigorous debate on the manner and rate at which strikingly divergent phenotypes evolve. A surprising result of many recent molecular phylogenetic studies is the lack of support for flatfish monophyly, where increasingly larger DNA datasets of up to 23 loci have either yielded a weakly supported flatfish clade or indicated the group is polyphyletic. Lack of resolution for flatfish relationships has been attributed to analytical limitations for dealing with processes such as nucleotide non-stationarity and incomplete lineage sorting (ILS). We tackle this phylogenetic problem using a sequence dataset comprising more than 1,000 ultraconserved DNA element (UCE) loci covering 45 carangimorphs, the broader clade containing flatfishes and several other specialized lineages such as remoras, billfishes, and archerfishes. We present a phylogeny based on UCE loci that unequivocally supports flatfish monophyly and a single origin of asymmetry. We document similar levels of discordance among UCE loci as in previous, smaller molecular datasets. However, relationships among flatfishes and carangimorphs recovered from multilocus concatenated and species tree analyses of our data are robust to the analytical framework applied and size of data matrix used. By integrating the UCE data with a rich fossil record, we find that the most distinctive carangimorph bodyplans arose rapidly during the Paleogene (66.0–23.03 Ma). Flatfish asymmetry, for example, likely evolved over an interval of no more than 2.97 million years. The longstanding uncertainty in phylogenetic hypotheses for flatfishes and their carangimorph relatives highlights the limitations of smaller molecular datasets when applied to successive, rapid divergences. Here, we recovered significant support for flatfish monophyly and relationships among carangimorphs through analysis of over 1,000 UCE loci. The resulting time-calibrated phylogeny points to phenotypic divergence early within carangimorph history that broadly matches with the predictions of adaptive models of lineage diversification.},
author = {Harrington, Richard C. and Faircloth, Brant C. and Eytan, Ron I. and Smith, W. Leo and Near, Thomas J. and Alfaro, Michael E. and Friedman, Matt},
doi = {10.1186/s12862-016-0786-x},
isbn = {1471-2148 (Electronic)$\backslash$r1471-2148 (Linking)},
issn = {14712148},
journal = {BMC Evolutionary Biology},
keywords = {Adaptive radiation,Carangimorpha,Evolutionary innovation,Pleuronectiformes,UCE,Ultraconserved elements},
number = {1},
pages = {1--14},
pmid = {27769164},
publisher = {BMC Evolutionary Biology},
title = {{Phylogenomic analysis of carangimorph fishes reveals flatfish asymmetry arose in a blink of the evolutionary eye}},
url = {http://dx.doi.org/10.1186/s12862-016-0786-x},
volume = {16},
year = {2016}
}
@article{McCormack2012,
abstract = {Phylogenomics offers the potential to fully resolve the Tree of Life, but increasing genomic coverage also reveals conflicting evolutionary histories among genes, demanding new analytical strategies for elucidating a single history of life. Here, we outline a phylogenomic approach using a novel class of phylogenetic markers derived from ultraconserved elements and flanking DNA. Using species-tree analysis that accounts for discord among hundreds of independent loci, we show that this class of marker is useful for recovering deep-level phylogeny in placental mammals. In broad outline, our phylogeny agrees with recent phylogenomic studies of mammals, including several formerly controversial relationships. Our results also inform two outstanding questions in placental mammal phylogeny involving rapid speciation, where species-tree methods are particularly needed. Contrary to most phylogenomic studies, our study supports a first-diverging placental mammal lineage that includes elephants and tenrecs (Afrotheria). The level of conflict among gene histories is consistent with this basal divergence occurring in or near a phylogenetic "anomaly zone" where a failure to account for coalescent stochasticity will mislead phylogenetic inference. Addressing a long-standing phylogenetic mystery, we find some support from a high genomic coverage data set for a traditional placement of bats (Chiroptera) sister to a clade containing Perissodactyla, Cetartiodactyla, and Carnivora, and not nested within the latter clade, as has been suggested recently, although other results were conflicting. One of the most remarkable findings of our study is that ultraconserved elements and their flanking DNA are a rich source of phylogenetic information with strong potential for application across Amniotes.},
author = {McCormack, John E. and Faircloth, Brant C. and Crawford, Nicholas G. and Gowaty, Patricia Adair and Brumfield, Robb T. and Glenn, Travis C.},
doi = {10.1101/gr.125864.111},
isbn = {1088-9051},
issn = {10889051},
journal = {Genome Research},
number = {4},
pages = {746--754},
pmid = {22207614},
title = {{Ultraconserved elements are novel phylogenomic markers that resolve placental mammal phylogeny when combined with species-tree analysis}},
url = {http://genome.cshlp.org/cgi/doi/10.1101/gr.125864.111},
volume = {22},
year = {2012}
}
@article{Moyle2016,
abstract = {Undoped and antimony-doped tin oxide films were prepared by d.c. reactive ion sputtering in an argon atmosphere with oxygen partial pressures ranging from 0 to 50{\%}. The films were annealed in air, nitrogen and a vacuum in the temperature range 570-750 K. The influence of thermal annealing on the structural properties, electrical resistance and optical transmittance is described. {\textcopyright} 1984.},
author = {Moyle, Robert G. and Oliveros, Carl H. and Andersen, Michael J. and Hosner, Peter A. and Benz, Brett W. and Manthey, Joseph D. and Travers, Scott L. and Brown, Rafe M. and Faircloth, Brant C.},
doi = {10.1016/0040-6090(84)90224-4},
isbn = {2041-1723 (Electronic)$\backslash$r2041-1723 (Linking)},
issn = {00406090},
journal = {Nature Communications},
pages = {1--7},
pmid = {27575437},
publisher = {Nature Publishing Group},
title = {{Tectonic collision and uplift of Wallacea triggered the global songbird radiation}},
url = {http://dx.doi.org/10.1038/ncomms12709},
volume = {7},
year = {2016}
}
@article{Smith2014,
abstract = {Comparative genetic studies of non-model organisms are transforming rapidly due to major advances in sequencing technology. A limiting factor in these studies has been the identification and screening of orthologous loci across an evolutionarily distant set of taxa. Here, we evaluate the efficacy of genomic markers targeting ultraconserved DNA elements (UCEs) for analyses at shallow evolutionary timescales. Using sequence capture and massively parallel sequencing to generate UCE data for five co-distributed Neotropical rainforest bird species, we recovered 776-1516 UCE loci across the five species. Across species, 53-77{\%} of the loci were polymorphic, containing between 2.0 and 3.2 variable sites per polymorphic locus, on average. We performed species tree construction, coalescent modeling, and species delimitation, and we found that the five co-distributed species exhibited discordant phylogeographic histories. We also found that species trees and divergence times estimated from UCEs were similar to the parameters obtained from mtDNA. The species that inhabit the understory had older divergence times across barriers, contained a higher number of cryptic species, and exhibited larger effective population sizes relative to the species inhabiting the canopy. Because orthologous UCEs can be obtained from a wide array of taxa, are polymorphic at shallow evolutionary timescales, and can be generated rapidly at low cost, they are an effective genetic marker for studies investigating evolutionary patterns and processes at shallow timescales.},
archivePrefix = {arXiv},
arxivId = {1308.5342},
author = {Smith, Brian Tilston and Harvey, Michael G. and Faircloth, Brant C. and Glenn, Travis C. and Brumfield, Robb T.},
doi = {10.1093/sysbio/syt061},
eprint = {1308.5342},
isbn = {1063-5157},
issn = {1076836X},
journal = {Systematic Biology},
keywords = {Birds,Neotropics,SNPs,coalescent theory,isolation-with-migration,massively parallel sequencing,next-generation sequencing,phylogeography},
number = {1},
pages = {83--95},
pmid = {24021724},
title = {{Target capture and massively parallel sequencing of ultraconserved elements for comparative studies at shallow evolutionary time scales}},
volume = {63},
year = {2014}
}
@article{Tagliacollo2018,
abstract = {UltraConserved Elements (UCEs) are popular markers for phylogenomic studies. They are relatively simple to collect from distantly-related organisms, and contain sufficient information to infer relationships at almost all taxonomic levels. Most studies of UCEs use partitioning to account for variation in rates and patterns of molecular evolution among sites, for example by estimating an independent model of molecular evolution for each UCE. However, rates and patterns of molecular evolution vary substantially within as well as between UCEs, suggesting that there may be opportunities to improve how UCEs are partitioned for phylogenetic inference. We propose and evaluate new partitioning methods for phylogenomic studies of UCEs: Sliding-Window Site Characteristics (SWSC), and UCE Site Position (UCESP). The first method uses site characteristics such as entropy, multinomial likelihood, and GC content to generate partitions that account for heterogeneity in rates and patterns of molecular evolution within each UCE. The second method groups together nucleotides that are found in similar physical locations within the UCEs. We examined the new methods with seven published datasets from a variety of taxa. We demonstrate the UCESP method generates partitions that are worse than other strategies used to partition UCE datasets (e.g. one partition per UCE). The SWSC method, particularly when based on site entropies, generates partitions that account for within-UCE heterogeneity and leads to large increases in the model fit. All of the methods, code, and data used in this study are available from https://github.com/Tagliacollo/PartitionUCE. Simplified code for implementing the best method, the SWSC-EN, is available from https://github.com/Tagliacollo/PFinderUCE-SWSC-EN},
annote = {Missy Hawkins, wants me to implement tools for the two methods described herein: SWSC and UCESP.},
author = {Tagliacollo, Victor A. and Lanfear, Robert},
doi = {10.1093/molbev/msy069},
isbn = {0000000000},
issn = {15371719},
journal = {Molecular Biology and Evolution},
keywords = {Partition finder,Partitioning,Partitioning methods,Phylogenomics,Sliding-window site characteristics,UCEs},
number = {7},
pages = {1798--1811},
pmid = {29659989},
title = {{Estimating improved partitioning schemes for ultraconserved elements}},
volume = {35},
year = {2018}
}

repository: rhagenson/swsc

repo_name: rhagenson/swsc


text:
SUMMARY = "OpenTelemetry Python API"
HOMEPAGE = "https://github.com/open-telemetry/opentelemetry-python/tree/main/opentelemetry-api"
AUTHOR = "OpenTelemetry Authors <cncf-opentelemetry-contributors@lists.cncf.io>"
LICENSE = "Apache-2.0"
LIC_FILES_CHKSUM = "file://LICENSE;md5=5527ceb746f9fa9d6a341c4a813b4ce3"

SRC_URI = "https://files.pythonhosted.org/packages/75/87/10798069493919e264e392246f8dca3abe2475c50c637e382e62b8af5e03/opentelemetry-api-1.8.0.tar.gz"
SRC_URI[md5sum] = "eea598967000eb455eccb7e8ca4b8a1a"
SRC_URI[sha256sum] = "758f73610c08a03e9c8f29cfb364bc75ce35e9a62136ba98b82423d3d27498f5"

S = "${WORKDIR}/opentelemetry-api-1.8.0"

RDEPENDS:${PN} = "python3-deprecated"

inherit setuptools3


text:
SUMMARY = "Example applications for DE-Nano FPGA GPIO manipulation"
SECTION = "DE10-Nano"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://LICENSE;md5=d252b8b27e3e6ea89c9c6912b3c4373d"

PACKAGE_ARCH = "${MACHINE_ARCH}"

PR = "r0"
PV = "1.0"
PV_append = "+${PR}"

SRC_URI = "\
	file://build_poll_KEY0_pb_state.sh \
	file://build_show_KEY0_pb_state.sh \
	file://build_toggle_fpga_leds.sh \
	file://build_watch_switch_events_ioctl.sh \
	file://build_watch_switch_events.sh \
	file://LICENSE \
	file://poll_KEY0_pb_state.c \
	file://README_first.txt \
	file://README_gpio-keys.txt \
	file://README_gpio-leds.txt \
	file://README_gpio.txt \
	file://show_KEY0_pb_state.c \
	file://show_KEY0_pb_state.sh \
	file://toggle_fpga_leds.c \
	file://toggle_fpga_leds.sh \
	file://watch_switch_events.c \
	file://watch_switch_events_ioctl.c \
	file://watch_switch_events.sh \
	file://find_gpio_controllers_dt.src \
	file://find_gpio_leds_dt.src \
	file://find_gpio_keys_dt.src \
"
TOP_FILES  = "LICENSE"
TOP_FILES += "README_first.txt"

RAW_GPIO_FILES  = "build_poll_KEY0_pb_state.sh"
RAW_GPIO_FILES += "build_show_KEY0_pb_state.sh"
RAW_GPIO_FILES += "poll_KEY0_pb_state.c"
RAW_GPIO_FILES += "README_gpio.txt"
RAW_GPIO_FILES += "show_KEY0_pb_state.c"
RAW_GPIO_FILES += "show_KEY0_pb_state.sh"
RAW_GPIO_FILES += "find_gpio_controllers_dt.src"

GPIO_LEDS_FILES  = "build_toggle_fpga_leds.sh"
GPIO_LEDS_FILES += "README_gpio-leds.txt"
GPIO_LEDS_FILES += "toggle_fpga_leds.c"
GPIO_LEDS_FILES += "toggle_fpga_leds.sh"
GPIO_LEDS_FILES += "find_gpio_leds_dt.src"

GPIO_KEYS_FILES  = "build_watch_switch_events_ioctl.sh"
GPIO_KEYS_FILES += "build_watch_switch_events.sh"
GPIO_KEYS_FILES += "README_gpio-keys.txt"
GPIO_KEYS_FILES += "watch_switch_events.c"
GPIO_KEYS_FILES += "watch_switch_events_ioctl.c"
GPIO_KEYS_FILES += "watch_switch_events.sh"
GPIO_KEYS_FILES += "find_gpio_keys_dt.src"

S = "${WORKDIR}"

do_install () {

	cd ${S}

	install -d ${D}/examples/gpio/sandbox
	cp -a ${TOP_FILES} ${D}/examples/gpio/sandbox

	install -d ${D}/examples/gpio/sandbox/raw-gpio
	cp -a ${RAW_GPIO_FILES} ${D}/examples/gpio/sandbox/raw-gpio

	install -d ${D}/examples/gpio/sandbox/gpio-leds
	cp -a ${GPIO_LEDS_FILES} ${D}/examples/gpio/sandbox/gpio-leds

	install -d ${D}/examples/gpio/sandbox/gpio-keys
	cp -a ${GPIO_KEYS_FILES} ${D}/examples/gpio/sandbox/gpio-keys

	rm -f gpio_sandbox.tgz
	tar czf gpio_sandbox.tgz -C ${D}/examples/gpio sandbox
	cp -a gpio_sandbox.tgz ${D}/examples/gpio
}

FILES_${PN} += "examples/gpio/"

gha_language: C