text:
require netmap.inc

DEPENDS = "virtual/kernel"
do_configure[depends] += "virtual/kernel:do_shared_workdir"

inherit module

CLEANBROKEN = "1"

export INSTALL_MOD_DIR="kernel/netmap-modules"

EXTRA_OECONF = "--kernel-dir=${STAGING_KERNEL_BUILDDIR} \
                --kernel-sources=${STAGING_KERNEL_DIR} \
                --install-mod-path=${D} \
                --driver-suffix="-netmap" \
                "

EXTRA_OECONF += "--no-drivers=ixgbe --no-drivers=virtio_net.c"

LDFLAGS := "${@'${LDFLAGS}'.replace('-Wl,-O1', '')}"
LDFLAGS := "${@'${LDFLAGS}'.replace('-Wl,--as-needed', '')}"

do_configure () {
    cd ${S}/LINUX
    ./configure ${EXTRA_OECONF}
}

do_configure_append () {
    cat >>  ${S}/LINUX/netmap_linux_config.h <<EOF
#define NETMAP_LINUX_HAVE_HRTIMER_MODE_REL
#define NETMAP_LINUX_HAVE_HRTIMER_FORWARD_NOW
#define NETMAP_LINUX_HAVE_PHYS_ADDR_T
#define NETMAP_LINUX_HAVE_ACCESS_ONCE
#define NETMAP_LINUX_HAVE_NETDEV_OPS
#define NETMAP_LINUX_ALLOC_NETDEV_4ARGS
#define NETMAP_LINUX_HAVE_INIT_NET
#define NETMAP_LINUX_HAVE_LIVE_ADDR_CHANGE
#define NETMAP_LINUX_HAVE_TX_SKB_SHARING
#define NETMAP_LINUX_HAVE_UNLOCKED_IOCTL
#define NETMAP_LINUX_HAVE_PERNET_OPS_ID
#define NETMAP_LINUX_VIRTIO_FUNCTIONS
#define NETMAP_LINUX_VIRTIO_FREE_PAGES
#define NETMAP_LINUX_VIRTIO_GET_VRSIZE
#define NETMAP_LINUX_TIMER_RTYPE enum hrtimer_restart
#define NETMAP_LINUX_VIRTIO_MULTI_QUEUE
#define NETMAP_LINUX_HAVE_E1000E_EXT_RXDESC
#define NETMAP_LINUX_HAVE_E1000E_DOWN2
EOF
}

do_compile () {
    cd ${S}/LINUX
    oe_runmake
}

do_install () {
    cd ${S}/LINUX
    oe_runmake install
}

repository: nghiaht-tsdv/meta-openembedded

repo_name: nghiaht-tsdv/meta-openembedded


license: MIT

repository: Rayal/robotics-workshop

license_type: permissive

repo_name: Rayal/robotics-workshop

branch_name: refs/heads/master

extension: cmake


text:
# Distributed under the OSI-approved BSD 3-Clause License.  See accompanying
# file COPYING-CMAKE-SCRIPTS or https://cmake.org/licensing for details.

#[=======================================================================[.rst:
FindPDFium
-----------

Copyright (c) 2017, Hiroshi Miura <miurahr@linux.com>

Find the PDFium headers and libraries.

IMPORTED Targets
^^^^^^^^^^^^^^^^

This module defines :prop_tgt:`IMPORTED` target ``PDFIUM::PDFium``, if
PDFium has been found.

Result Variables
^^^^^^^^^^^^^^^^

This module defines the following variables:

``PDFIUM_FOUND``
  True if PDFium found.

``PDFIUM_INCLUDE_DIRS``
  where to find header files.

``PDFIUM_LIBRARIES``
  List of PDFium libraries to link.

``PDFIUM_VERSION_STRING``
  Version of PDFium library.

#]=======================================================================]

find_package(PkgConfig QUIET)
if(PKG_CONFIG_FOUND)
    pkg_check_modules(PC_PDFIUM QUIET pdfium)
endif()

find_path(PDFIUM_INCLUDE_DIR
          NAMES POFDoc.h
          HINTS ${PC_PDFIUM_INCLUDE_DIRS}
          PATH_SUFFIXES pdfium)

find_library(PDFIUM_LIBRARY
             NAMES pdfium libpdfium
             HINTS ${PC_PDFIUM_LIBRARY_DIRS})

include(FindPackageHandleStandardArgs)
find_package_handle_standard_args(PDFium
                                  FOUND_VAR PDFIUM_FOUND
                                  REQUIRED_VARS PDFIUM_LIBRARY PDFIUM_INCLUDE_DIR)

if(PDFIUM_FOUND)
  set(PDFIUM_INCLUDE_DIRS ${PDFIUM_INCLUDE_DIR})
  set(PDFIUM_LIBRARIES ${PDFIUM_LIBRARY})
  if(NOT TARGET PDFIUM:PDFium)
    add_library(PDFIUM::PDFium UNKNOWN IMPORTED)
    set_target_properties(PDFIUM::PDFium PROPERTIES
                          INTERFACE_INCLUDE_DIRECTORIES ${PDFIUM_INCLUDE_DIR}
                          IMPORTED_LINK_INTERFACE_LANGUAGES "C"
                          IMPORTED_LOCATION ${PDFIUM_LIBRARY})
  endif()
endif()

gha_license_id: MIT

gha_language: CMake


text:
% This file was created with JabRef 2.10.
% Encoding: UTF-8

@InProceedings{alahi2012freak,
  Title                    = {Freak: Fast retina keypoint},
  Author                   = {Alahi, Alexandre and Ortiz, Raphael and Vandergheynst, Pierre},
  Booktitle                = {Computer vision and pattern recognition (CVPR), 2012 IEEE conference on},
  Year                     = {2012},
  Organization             = {Ieee},
  Pages                    = {510--517}
}

@Article{barnich2011vibe,
  Title                    = {ViBe: A universal background subtraction algorithm for video sequences},
  Author                   = {Barnich, Olivier and Van Droogenbroeck, Marc},
  Journal                  = {IEEE Transactions on Image processing},
  Year                     = {2011},
  Number                   = {6},
  Pages                    = {1709--1724},
  Volume                   = {20},

  Publisher                = {IEEE}
}

@Article{Berclaz2011Multipleobjecttrackingusingk-shortestpathsoptimization,
  Title                    = {Multiple object tracking using k-shortest paths optimization},
  Author                   = {Berclaz, Jerome and Fleuret, Francois and Turetken, Engin and Fua, Pascal},
  Journal                  = {IEEE transactions on pattern analysis and machine intelligence},
  Year                     = {2011},
  Number                   = {9},
  Pages                    = {1806--1819},
  Volume                   = {33},

  Publisher                = {IEEE}
}

@Article{Bernardin2008Evaluatingmultipleobjecttrackingperformance:theCLEARMOTmetrics,
  Title                    = {Evaluating multiple object tracking performance: the CLEAR MOT metrics},
  Author                   = {Bernardin, Keni and Stiefelhagen, Rainer},
  Journal                  = {EURASIP Journal on Image and Video Processing},
  Year                     = {2008},
  Number                   = {1},
  Pages                    = {1--10},
  Volume                   = {2008},

  Publisher                = {Springer}
}

@InProceedings{bilinski2009multiple,
  Title                    = {Multiple object tracking with occlusions using HOG descriptors and multi resolution images},
  Author                   = {Bilinski, Piotr and Bremond, Fran{\c{c}}ois and Kaaniche, Mohamed Becha},
  Booktitle                = {Crime Detection and Prevention (ICDP 2009), 3rd International Conference on},
  Year                     = {2009},
  Organization             = {IET},
  Pages                    = {1--6}
}

@InProceedings{Butt2013Multi-targettrackingbylagrangianrelaxationtomin-costnetworkflow,
  Title                    = {Multi-target tracking by lagrangian relaxation to min-cost network flow},
  Author                   = {Butt, Asad A and Collins, Robert T},
  Booktitle                = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  Year                     = {2013},
  Pages                    = {1846--1853}
}

@InProceedings{danelljan2014adaptive,
  Title                    = {Adaptive color attributes for real-time visual tracking},
  Author                   = {Danelljan, Martin and Shahbaz Khan, Fahad and Felsberg, Michael and Van de Weijer, Joost},
  Booktitle                = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  Year                     = {2014},
  Pages                    = {1090--1097}
}

@InProceedings{goyette2012changedetection,
  Title                    = {Changedetection. net: A new change detection benchmark dataset},
  Author                   = {Goyette, Nil and Jodoin, Pierre-Marc and Porikli, Fatih and Konrad, Janusz and Ishwar, Prakash},
  Booktitle                = {2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
  Year                     = {2012},
  Organization             = {IEEE},
  Pages                    = {1--8}
}

@Article{ha2011multiple,
  Title                    = {Multiple object tracking using SIFT features and location matching},
  Author                   = {Ha, Seok-Wun and Moon, Yong-Ho},
  Journal                  = {International Journal of Smart Home},
  Year                     = {2011},
  Number                   = {4},
  Pages                    = {17--26},
  Volume                   = {5},

  Publisher                = {Citeseer}
}

@InProceedings{hofmann2012background,
  Title                    = {Background segmentation with feedback: The pixel-based adaptive segmenter},
  Author                   = {Hofmann, Martin and Tiefenbacher, Philipp and Rigoll, Gerhard},
  Booktitle                = {2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
  Year                     = {2012},
  Organization             = {IEEE},
  Pages                    = {38--43}
}

@Article{huang2014surveillance,
  Title                    = {Surveillance video: the biggest big data},
  Author                   = {Huang, Tiejun},
  Journal                  = {Computing Now},
  Year                     = {2014},
  Number                   = {2},
  Pages                    = {82--91},
  Volume                   = {7}
}

@Article{jalal2012state,
  Title                    = {The state-of-the-art in visual object tracking},
  Author                   = {Jalal, Anand Singh and Singh, Vrijendra},
  Journal                  = {Informatica},
  Year                     = {2012},
  Number                   = {3},
  Volume                   = {36}
}

@InProceedings{Jaward2006Multipleobjecttrackingusingparticlefilters,
  Title                    = {Multiple object tracking using particle filters},
  Author                   = {Jaward, M and Mihaylova, L and Canagarajah, N and Bull, D},
  Booktitle                = {2006 IEEE Aerospace Conference},
  Year                     = {2006},
  Organization             = {IEEE},
  Pages                    = {8--pp}
}

@InProceedings{jodoin2014urbanTracker,
  Title                    = {Urban tracker: Multiple object tracking in urban mixed traffic},
  Author                   = {Jodoin, Jean-Philippe and Bilodeau, Guillaume-Alexandre and Saunier, Nicolas},
  Booktitle                = {IEEE Winter Conference on Applications of Computer Vision},
  Year                     = {2014},
  Organization             = {IEEE},
  Pages                    = {885--892}
}

@InProceedings{Johnsen2009Real-timeobjecttrackingandclassificationusingastaticcamera,
  Title                    = {Real-time object tracking and classification using a static camera},
  Author                   = {Johnsen, Swantje and Tews, Ashley},
  Booktitle                = {Proceedings of IEEE International Conference on Robotics and Automation, workshop on People Detection and Tracking},
  Year                     = {2009},
  Organization             = {Citeseer}
}

@InProceedings{leutenegger2011brisk,
  Title                    = {BRISK: Binary robust invariant scalable keypoints},
  Author                   = {Leutenegger, Stefan and Chli, Margarita and Siegwart, Roland Y},
  Booktitle                = {2011 International conference on computer vision},
  Year                     = {2011},
  Organization             = {IEEE},
  Pages                    = {2548--2555}
}

@InProceedings{Ma2006Multiplehypothesistargettrackingusingmergeandsplitofgraphnodes,
  Title                    = {Multiple hypothesis target tracking using merge and split of graph nodes},
  Author                   = {Ma, Yunqian and Yu, Qian and Cohen, Isaac},
  Booktitle                = {International Symposium on Visual Computing},
  Year                     = {2006},
  Organization             = {Springer},
  Pages                    = {783--792}
}

@InProceedings{noh2012new,
  Title                    = {A new framework for background subtraction using multiple cues},
  Author                   = {Noh, SeungJong and Jeon, Moongu},
  Booktitle                = {Asian Conference on Computer Vision},
  Year                     = {2012},
  Organization             = {Springer},
  Pages                    = {493--506}
}

@InProceedings{Perera2006Multi-objecttrackingthroughsimultaneouslongocclusionsandsplit-mergeconditions,
  Title                    = {Multi-object tracking through simultaneous long occlusions and split-merge conditions},
  Author                   = {Perera, AG Amitha and Srinivas, Chukka and Hoogs, Anthony and Brooksby, Glen and Hu, Wensheng},
  Booktitle                = {2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
  Year                     = {2006},
  Organization             = {IEEE},
  Pages                    = {666--673},
  Volume                   = {1}
}

@InProceedings{saunier2006feature-basedTrackingAlgorithm,
  Title                    = {A feature-based tracking algorithm for vehicles in intersections},
  Author                   = {Saunier, Nicolas and Sayed, Tarek},
  Booktitle                = {The 3rd Canadian Conference on Computer and Robot Vision (CRV'06)},
  Year                     = {2006},
  Organization             = {IEEE},
  Pages                    = {59--59}
}

@InProceedings{sobral2013bgslibrary,
  Title                    = {BGSLibrary: An opencv c++ background subtraction library},
  Author                   = {Sobral, Andrews},
  Booktitle                = {IX Workshop de Visao Computacional (WVC 2013)},
  Year                     = {2013},
  Volume                   = {7}
}

@InProceedings{st2014improving,
  Title                    = {Improving background subtraction using local binary similarity patterns},
  Author                   = {St-Charles, Pierre-Luc and Bilodeau, Guillaume-Alexandre},
  Booktitle                = {IEEE Winter Conference on Applications of Computer Vision},
  Year                     = {2014},
  Organization             = {IEEE},
  Pages                    = {509--515}
}

@Article{tuytelaars2008local,
  Title                    = {Local invariant feature detectors: a survey},
  Author                   = {Tuytelaars, Tinne and Mikolajczyk, Krystian},
  Journal                  = {Foundations and trends{\textregistered} in computer graphics and vision},
  Year                     = {2008},
  Number                   = {3},
  Pages                    = {177--280},
  Volume                   = {3},

  Publisher                = {Now Publishers Inc.}
}

@InProceedings{wang2014cdnet,
  Title                    = {CDnet 2014: an expanded change detection benchmark dataset},
  Author                   = {Wang, Yi and Jodoin, Pierre-Marc and Porikli, Fatih and Konrad, Janusz and Benezeth, Yannick and Ishwar, Prakash},
  Booktitle                = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  Year                     = {2014},
  Pages                    = {387--394}
}

@InProceedings{wu2013online,
  Title                    = {Online object tracking: A benchmark},
  Author                   = {Wu, Yi and Lim, Jongwoo and Yang, Ming-Hsuan},
  Booktitle                = {Proceedings of the IEEE conference on computer vision and pattern recognition},
  Year                     = {2013},
  Pages                    = {2411--2418}
}

@Article{yilmaz2006object,
  Title                    = {Object tracking: A survey},
  Author                   = {Yilmaz, Alper and Javed, Omar and Shah, Mubarak},
  Journal                  = {Acm computing surveys (CSUR)},
  Year                     = {2006},
  Number                   = {4},
  Pages                    = {13},
  Volume                   = {38},

  Publisher                = {Acm}
}

@Article{zhou2009object,
  Title                    = {Object tracking using SIFT features and mean shift},
  Author                   = {Zhou, Huiyu and Yuan, Yuan and Shi, Chunmei},
  Journal                  = {Computer vision and image understanding},
  Year                     = {2009},
  Number                   = {3},
  Pages                    = {345--352},
  Volume                   = {113},

  Publisher                = {Elsevier}
}

@Article{Liu2016SSDSS,
  Title                    = {SSD: Single Shot MultiBox Detector},
  Author                   = {Wei Liu and Dragomir Anguelov and Dumitru Erhan and Christian Szegedy and Scott E. Reed and Cheng-Yang Fu and Alexander C. Berg},
  Journal                  = {CoRR},
  Year                     = {2016},
  Volume                   = {abs/1512.02325}
}

@InProceedings{Redmon2016YouOL,
  Title                    = {You Only Look Once: Unified, Real-Time Object Detection},
  Author                   = {Joseph Redmon and Santosh Kumar Divvala and Ross B. Girshick and Ali Farhadi},
  Booktitle                = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  Year                     = {2016}
}

@Article{Redmon2016YOLO9000BF,
  Title                    = {YOLO9000: Better, Faster, Stronger},
  Author                   = {Joseph Redmon and Ali Farhadi},
  Journal                  = {CoRR},
  Year                     = {2016},
  Volume                   = {abs/1612.08242}
}

@article{Girshick2015FastR,
  title={Fast R-CNN},
  author={Ross B. Girshick},
  journal={CoRR},
  year={2015},
  volume={abs/1504.08083}
}

@inproceedings{Wang2014StaticAM,
  title={Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models},
  author={Rui Wang and Filiz Bunyak and Guna Seetharaman and Kannappan Palaniappan},
  booktitle={2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  year={2014}
}

@inproceedings{Wang2014AFS,
  title={A Fast Self-Tuning Background Subtraction Algorithm},
  author={Bin Wang and Piotr Dudek},
  booktitle={2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  year={2014}
}

@inproceedings{StCharles2014FlexibleBS,
  title={Flexible Background Subtraction with Self-Balanced Local Sensitivity},
  author={Pierre-Luc St-Charles and Guillaume-Alexandre Bilodeau and Robert Bergevin},
  booktitle={2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  year={2014}
}

@inproceedings{Gregorio2014ChangeDW,
  title={Change Detection with Weightless Neural Networks},
  author={Massimo De Gregorio and Maurizio Giordano},
  booktitle={2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  year={2014}
}

@article{Chen2014SemanticIS,
  title={Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs},
  author={Liang-Chieh Chen and George Papandreou and Iasonas Kokkinos and Kevin Murphy and Alan L. Yuille},
  journal={CoRR},
  year={2014},
  volume={abs/1412.7062}
}

@article{Badrinarayanan2017SegNetAD,
  title={SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation.},
  author={Vijay Badrinarayanan and Alex Kendall and Roberto Cipolla},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2017}
}

@article{Liu2016DeepLM,
  title={Deep Learning Markov Random Field for Semantic Segmentation},
  author={Ziwei Liu and Xiaoxiao Li and Ping Luo and Chen Change Loy and Xiaoou Tang},
  journal={CoRR},
  year={2016},
  volume={abs/1606.07230}
}

@article{Wu2016HighperformanceSS,
  title={High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks},
  author={Zifeng Wu and Chunhua Shen and Anton van den Hengel},
  journal={CoRR},
  year={2016},
  volume={abs/1604.04339}
}

@article{Babaee2017ADC,
  title={A Deep Convolutional Neural Network for Background Subtraction},
  author={Mohammadreza Babaee and Duc Tung Dinh and Gerhard Rigoll},
  journal={CoRR},
  year={2017},
  volume={abs/1702.01731}
}

@inproceedings{Moussy2015ACV,
  title={A comparative view on exemplar 'tracking-by-detection' approaches},
  author={E. Moussy and Alhayat Ali Mekonnen and G. Marion and Fr{\'e}d{\'e}ric Lerasle},
  booktitle={AVSS},
  year={2015}
}

@article{Smeulders2014VisualTA,
  title={Visual Tracking: An Experimental Survey},
  author={Arnold W. M. Smeulders and Dung Manh Chu and Rita Cucchiara and Simone Calderara and Afshin Dehghan and Mubarak Shah},
  journal={IEEE Trans. Pattern Anal. Mach. Intell.},
  year={2014},
  volume={36},
  pages={1442-1468}
}

@article{Dollr2014FastFP,
  title={Fast Feature Pyramids for Object Detection},
  author={Piotr Doll{\'a}r and Ron Appel and Serge J. Belongie and Pietro Perona},
  journal={IEEE Trans. Pattern Anal. Mach. Intell.},
  year={2014},
  volume={36},
  pages={1532-1545}
}

@inproceedings{Dalal2005HistogramsOO,
  title={Histograms of oriented gradients for human detection},
  author={Navneet Dalal and Bill Triggs},
  booktitle={2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)},
  year={2005}
}

@article{Forsyth2010ObjectDW,
  title={Object Detection with Discriminatively Trained Part-Based Models},
  author={David A. Forsyth},
  journal={IEEE Computer},
  year={2010},
  volume={47},
  pages={6-7}
}

@article{Girshick2014RichFH,
  title={Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation},
  author={Ross B. Girshick and Jeff Donahue and Trevor Darrell and Jitendra Malik},
  journal={CoRR},
  year={2014},
  volume={abs/1311.2524}
}

@inproceedings{Breitenstein2009RobustTU,
  title={Robust tracking-by-detection using a detector confidence particle filter},
  author={Michael D. Breitenstein and Fabian Reichlin and Bastian Leibe and Esther Koller-Meier and Luc Van Gool},
  booktitle={2009 IEEE 12th International Conference on Computer Vision},
  year={2009}
}

@book{Kaewtrakulpong2002An,
  title={An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection},
  author={Kaewtrakulpong, P. and Bowden, R.},
  publisher={Springer US},
  pages={135-144},
  year={2002},
}

@inproceedings{Zivkovic2004Improved,
  title={Improved Adaptive Gaussian Mixture Model for Background Subtraction},
  author={Zivkovic, Zoran},
  booktitle={Pattern Recognition,  International Conference on},
  pages={28-31},
  year={2004},
}

@inproceedings{Elgammal2000NonparametricMF,
  title={Non-parametric Model for Background Subtraction},
  author={Ahmed M. Elgammal and David Harwood and Larry S. Davis},
  booktitle={ECCV},
  year={2000}
}

@inproceedings{Yoshinaga2013BackgroundMB,
  title={Background model based on intensity change similarity among pixels},
  author={S. Yoshinaga and A. Shimada and H. Nagahara and R. Taniguchi},
  booktitle={The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision},
  year={2013}
}

@article{Maddalena2008ASA,
  title={A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications},
  author={Lucia Maddalena and Alfredo Petrosino},
  journal={IEEE Trans. Image Processing},
  year={2008},
  volume={17},
  pages={1168-1177}
}

@inproceedings{Singh2010ImplementationOA,
  title={Implementation of a Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications},
  author={Yashpal Singh and Pritee Gupta and Vikram S Yadav},
  year={2010}
}

@article{Bouwmans2014TraditionalAR,
  title={Traditional and recent approaches in background modeling for foreground detection: An overview},
  author={Thierry Bouwmans},
  journal={Computer Science Review},
  year={2014},
  volume={11-12},
  pages={31-66}
}

@inproceedings{Huang2008RobustOT,
  title={Robust Object Tracking by Hierarchical Association of Detection Responses},
  author={Chang Huang and Bo Wu and Ramakant Nevatia},
  booktitle={ECCV},
  year={2008}
}

repository: yzbx/yzbxLib

repo_name: yzbx/yzbxLib

extension: bib


text:
@article{fortin2017harmonization,
  title={Harmonization of multi-site diffusion tensor imaging data},
  author={Fortin, Jean-Philippe and Parker, Drew and Tun{\c{c}}, Birkan and Watanabe, Takanori and Elliott, Mark A and Ruparel, Kosha and Roalf, David R and Satterthwaite, Theodore D and Gur, Ruben C and Gur, Raquel E and others},
  journal={Neuroimage},
  volume={161},
  pages={149--170},
  year={2017},
  publisher={Elsevier}
}

@article{johnson2007adjusting,
  title={Adjusting batch effects in microarray expression data using empirical Bayes methods},
  author={Johnson, W Evan and Li, Cheng and Rabinovic, Ariel},
  journal={Biostatistics},
  volume={8},
  number={1},
  pages={118--127},
  year={2007},
  publisher={Oxford University Press}
}

@article{casella1985introduction,
  title={An introduction to empirical Bayes data analysis},
  author={Casella, George},
  journal={The American Statistician},
  volume={39},
  number={2},
  pages={83--87},
  year={1985},
  publisher={Taylor \& Francis}
}

@article{harris2022quantifying,
  title={Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images},
  author={Harris, Coleman R and McKinley, Eliot T and Roland, Joseph T and Liu, Qi and Shrubsole, Martha J and Lau, Ken S and Coffey, Robert J and Wrobel, Julia and Vandekar, Simon N},
  journal={Bioinformatics (Oxford, England)},
  pages={btab877},
  year={2022}
}

  @Manual{sva_pack,
    title = {sva: Surrogate Variable Analysis},
    author = {Jeffrey T. Leek and W. Evan Johnson and Hilary S. Parker and Elana J. Fertig and Andrew E. Jaffe and Yuqing Zhang and John D. Storey and Leonardo Collado Torres},
    year = {2020},
    note = {R package version 3.38.0},
  }

@article{ramsay2004functional,
  title={Functional data analysis},
  author={Ramsay, James O},
  journal={Encyclopedia of Statistical Sciences},
  volume={4},
  year={2004},
  publisher={Wiley Online Library}
}

@Manual{rman,
    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{fda_pack,
    title = {fda: Functional Data Analysis},
    author = {J. O. Ramsay and Spencer Graves and Giles Hooker},
    year = {2021},
    note = {R package version 5.5.1},
    url = {https://CRAN.R-project.org/package=fda},
  }

@Manual{refund_pack,
    title = {refund: Regression with Functional Data},
    author = {Jeff Goldsmith and Fabian Scheipl and Lei Huang and Julia Wrobel and Chongzhi Di and Jonathan Gellar and Jaroslaw Harezlak and Mathew W. McLean and Bruce Swihart and Luo Xiao and Ciprian Crainiceanu and Philip T. Reiss},
    year = {2021},
    note = {R package version 0.1-24},
    url = {https://CRAN.R-project.org/package=refund},
  }

@Manual{registr_pack,
    title = {registr: Curve Registration for Exponential Family Functional Data},
    author = {Julia Wrobel and Alexander Bauer and Jeff Goldsmith and Erin McDonnell},
    year = {2021},
    note = {R package version 2.0.7},
  }

@Manual{mica_pack,
    title = {mica: Multi Image CDF Alignment (or multi site intensity harmonization
by CDF alignment)},
    author = {Julia Wrobel},
    year = {2021},
    note = {R package version 0.1.0},
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  @Manual{uwot_pack,
    title = {uwot: The Uniform Manifold Approximation and Projection (UMAP) Method
for Dimensionality Reduction},
    author = {James Melville},
    year = {2021},
    note = {R package version 0.1.11},
    url = {https://CRAN.R-project.org/package=uwot},
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  @article{mcinnes2018umap,
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  author={McInnes, Leland and Healy, John and Melville, James},
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  @Manual{psych_pack,
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    note = {R package version 2.1.9},
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    @Article{fossil_pack,
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    note = {R package version 0.4.0},
  }

repository: ColemanRHarris/mxnorm

repo_name: ColemanRHarris/mxnorm

branch_name: refs/heads/main

gha_license_id: NOASSERTION

gha_language: R


text:
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    NUMBER = {5},
     PAGES = {249--273},
      ISSN = {0271-2091},
   MRCLASS = {65M60 (76Nxx)},
  MRNUMBER = {3302403},
       DOI = {10.1002/fld.3965},
       URL = {https://doi-org.libproxy.berkeley.edu/10.1002/fld.3965},
}

@inproceedings{Weiss16_imr,
  title = {Spatially accelerated shape embedding in multimaterial simulations},
  author = {Weiss, K. and Zagaris, G. and Rieben, R. and Cook, A.},
  booktitle = {Proceedings 25$^{th}$ International Meshing Roundtable},
  year = {2016},
  address = {Washington, D.C.},
  editor = {Canann, S.},
  month = {September 27--30},
  series = {IMR '16},
  url = {http://imr.sandia.gov/papers/abstracts/We843.html}
}
@article{Anderson2018,
  doi = {10.1137/17M1116453},
  url = {https://doi.org/10.1137/17m1116453},
  year = {2018},
  month = {Jan},
  publisher = {Society for Industrial {\&} Applied Mathematics ({SIAM})},
  volume = {40},
  number = {1},
  pages = {B32--B58},
  author = {Robert W. Anderson and Veselin A. Dobrev and Tzanio V. Kolev and Robert N. Rieben and Vladimir Z. Tomov},
  title = {High-Order Multi-Material {ALE} Hydrodynamics},
  journal = {{SIAM} Journal on Scientific Computing}
}

repository: dhermes/solution-transfer-curved-meshes

repo_name: dhermes/solution-transfer-curved-meshes


text:
package main

import (
	"fmt"
	"io"
	"os"

	"github.com/spf13/cobra"
	"gopkg.in/yaml.v2"

	"github.com/cbroglie/mustache"
)

var rootCmd = &cobra.Command{
	Use: "mustache [--layout template] [data] template",
	Example: `  $ mustache data.yml template.mustache
  $ cat data.yml | mustache template.mustache
  $ mustache --layout wrapper.mustache data template.mustache
  $ mustache --override over.yml data.yml template.mustache`,
	Args: cobra.RangeArgs(0, 2),
	Run: func(cmd *cobra.Command, args []string) {
		err := run(cmd, args)
		if err != nil {
			fmt.Fprintf(os.Stderr, "Error: %s\n", err.Error())
			os.Exit(1)
		}
	},
}
var layoutFile string
var overrideFile string

func main() {
	rootCmd.Flags().StringVar(&layoutFile, "layout", "", "location of layout file")
	rootCmd.Flags().StringVar(&overrideFile, "override", "", "location of data.yml override yml")
	rootCmd.Flags().BoolVar(&mustache.AllowMissingVariables, "allow-missing-variables", true, "allow missing variables")
	if err := rootCmd.Execute(); err != nil {
		os.Exit(1)
	}
}

func run(cmd *cobra.Command, args []string) error {
	if len(args) == 0 {
		return cmd.Usage()
	}

	var data interface{}
	var templatePath string
	if len(args) == 1 {
		var err error
		data, err = parseDataFromStdIn()
		if err != nil {
			return err
		}
		templatePath = args[0]
	} else {
		var err error
		data, err = parseDataFromFile(args[0])
		if err != nil {
			return err
		}
		templatePath = args[1]
	}

	if overrideFile != "" {
		override, err := parseDataFromFile(overrideFile)
		if err != nil {
			return err
		}
		for k, v := range override.(map[interface{}]interface{}) {
			data.(map[interface{}]interface{})[k] = v
		}
	}
	var output string
	var err error
	if layoutFile != "" {
		output, err = mustache.RenderFileInLayout(templatePath, layoutFile, data)
	} else {
		output, err = mustache.RenderFile(templatePath, data)
	}
	if err != nil {
		return err
	}
	fmt.Print(output)
	return nil
}

func parseDataFromStdIn() (interface{}, error) {
	b, err := io.ReadAll(os.Stdin)
	if err != nil {
		return nil, err
	}
	var data interface{}
	if err := yaml.Unmarshal(b, &data); err != nil {
		return nil, err
	}
	return data, nil
}

func parseDataFromFile(filePath string) (interface{}, error) {
	b, err := os.ReadFile(filePath)
	if err != nil {
		return nil, err
	}
	var data interface{}
	if err := yaml.Unmarshal(b, &data); err != nil {
		return nil, err
	}
	return data, nil
}

repository: cbroglie/mustache

repo_name: cbroglie/mustache

gha_language: Go

extension: go


text:
package main

import (
	"fmt"
	"github.com/jfeng45/servicetmpl1/app"
	"github.com/jfeng45/servicetmpl1/app/container"
	"github.com/jfeng45/servicetmpl1/app/container/containerhelper"
	"github.com/jfeng45/servicetmpl1/app/logger"
	"github.com/jfeng45/servicetmpl1/domain/model"
	"github.com/jfeng45/servicetmpl1/tool/timea"
	"github.com/pkg/errors"
	"time"
)

const (
	DEV_CONFIG  string = "../app/config/appConfigDev.yaml"
	PROD_CONFIG string = "../app/config/appConfigProd.yaml"
)

func main() {
	testMySql()
	//testCouchDB()
}

func testMySql() {

	filename := DEV_CONFIG
	container, err := buildContainer(filename)
	if err != nil {
		fmt.Printf("%+v\n", err)
		return
	}
	testListUser(container)
	testFindById(container)
	testRegisterUser(container)
	testModifyUser(container)
	testUnregister(container)
	testModifyAndUnregister(container)
	testModifyAndUnregisterWithTx(container)

}
func testCouchDB() {
	filename := PROD_CONFIG
	container, err := buildContainer(filename)
	if err != nil {
		fmt.Printf("%+v\n", err)
		//logger.Log.Errorf("%+v\n", err)
		return
	}
	testFindById(container)
}

func testUnregister(container container.Container) {

	ruci, err := containerhelper.GetRegistrationUseCase(container)
	if err != nil {
		logger.Log.Fatal("registration interface build failed:%+v\n", err)
	}
	username := "Aditi"
	err = ruci.UnregisterUser(username)
	if err != nil {
		logger.Log.Fatalf("testUnregister failed:%+v\n", err)
	}
	logger.Log.Infof("testUnregister successully")
}

func testRegisterUser(container container.Container) {
	ruci, err := containerhelper.GetRegistrationUseCase(container)
	if err != nil {
		logger.Log.Fatal("registration interface build failed:%+v\n", err)
	}
	created, err := time.Parse(timea.FORMAT_ISO8601_DATE, "2018-12-09")
	if err != nil {
		logger.Log.Errorf("date format err:%+v\n", err)
	}

	user := model.User{Name: "Brian", Department: "Marketing", Created: created}

	resultUser, err := ruci.RegisterUser(&user)
	if err != nil {
		logger.Log.Errorf("user registration failed:%+v\n", err)
	} else {
		logger.Log.Info("new user registered:", resultUser)
	}
}

func testModifyUser(container container.Container) {
	ruci, err := containerhelper.GetRegistrationUseCase(container)
	if err != nil {
		logger.Log.Fatal("registration interface build failed:%+v\n", err)
	}
	created, err := time.Parse(timea.FORMAT_ISO8601_DATE, "2019-12-01")
	if err != nil {
		logger.Log.Errorf("date format err:%+v\n", err)
	}
	user := model.User{Id: 29, Name: "Aditi", Department: "HR", Created: created}
	err = ruci.ModifyUser(&user)
	if err != nil {
		logger.Log.Infof("Modify user failed:%+v\n", err)
	} else {
		logger.Log.Info("user modified succeed:", user)
	}
}

func testListUser(container container.Container) {
	rluf, err := containerhelper.GetListUserUseCase(container)
	if err != nil {
		logger.Log.Fatal("RetrieveListUser interface build failed:", err)
	}
	users, err := rluf.ListUser()
	if err != nil {
		logger.Log.Errorf("user list failed:%+v\n", err)
	}
	logger.Log.Info("user list:", users)
}

func testModifyAndUnregister(container container.Container) {
	ruci, err := containerhelper.GetRegistrationUseCase(container)
	if err != nil {
		logger.Log.Fatal("RegisterRegistration interface build failed:%+v\n", err)
	}
	created, err := time.Parse(timea.FORMAT_ISO8601_DATE, "2018-12-09")
	if err != nil {
		logger.Log.Errorf("date format err:%+v\n", err)
	}
	user := model.User{Id: 31, Name: "Richard", Department: "Sales", Created: created}
	err = ruci.ModifyAndUnregister(&user)
	if err != nil {
		logger.Log.Errorf("ModifyAndUnregister failed:%+v\n", err)
	} else {
		logger.Log.Infof("ModifyAndUnregister succeed")
	}
}

func testModifyAndUnregisterWithTx(container container.Container) {
	rtuci, err := containerhelper.GetRegistrationTxUseCase(container)
	if err != nil {
		logger.Log.Fatal("RegisterRegistration interface build failed:%+v\n", err)
	}
	created, err := time.Parse(timea.FORMAT_ISO8601_DATE, "2018-12-09")
	if err != nil {
		logger.Log.Errorf("date format err:%+v\n", err)
	}
	user := model.User{Id: 32, Name: "Richard", Department: "Finance", Created: created}
	err = rtuci.ModifyAndUnregisterWithTx(&user)
	if err != nil {
		logger.Log.Errorf("ModifyAndUnregisterWithTx failed:%+v\n", err)
	} else {
		logger.Log.Infof("ModifyAndUnregisterWithTx succeed")
	}
}

func testFindById(container container.Container) {
	//It is uid in database. Make sure you have it in database, otherwise it won't find it.
	id := 10
	rluf, err := containerhelper.GetListUserUseCase(container)
	if err != nil {
		logger.Log.Fatalf("RetrieveListUser interface build failed:%+v\n", err)
	}
	user, err := rluf.Find(id)
	if err != nil {
		logger.Log.Errorf("fin user failed failed:%+v\n", err)
	}
	logger.Log.Info("find user:", user)
}

func buildContainer(filename string) (container.Container, error) {
	container, err := app.InitApp(filename)
	if err != nil {
		return nil, errors.Wrap(err, "")
	}
	return container, nil
}


text:
#
# Base recipe: meta/recipes-graphics/xorg-app/mkfontscale_debian.bb
# Base branch: daisy
#

require xorg-app-common.inc

SUMMARY = "A program to create an index of scalable font files for X"

DESCRIPTION = "For each directory argument, mkfontscale reads all of the \
scalable font files in the directory. For every font file found, an X11 \
font name (XLFD) is generated, and is written together with the file \
name to a file fonts.scale in the directory.  The resulting fonts.scale \
is used by the mkfontdir program."

PR = "${INC_PR}.0"

DEPENDS = "util-macros-native zlib libfontenc freetype xproto"

BBCLASSEXTEND = "native"

LIC_FILES_CHKSUM = "file://COPYING;md5=2e0d129d05305176d1a790e0ac1acb7f"

DPN = "xfonts-utils"

# There is no debian patch
DEBIAN_PATCH_TYPE = "nopatch"

S = "${DEBIAN_UNPACK_DIR}/mkfontscale"

repository: nghiaht-tsdv/meta-debian

repo_name: nghiaht-tsdv/meta-debian

branch_name: refs/heads/jethro

gha_language: BitBake

extension: bb


text:
FILE(REMOVE_RECURSE
  "CMakeFiles/ppsFetch.dir/fetch_vme.cpp.o"
  "ppsFetch.pdb"
  "ppsFetch"
)

# Per-language clean rules from dependency scanning.
FOREACH(lang CXX)
  INCLUDE(CMakeFiles/ppsFetch.dir/cmake_clean_${lang}.cmake OPTIONAL)
ENDFOREACH(lang)


text:
package max

func MaximumDistance(xx, yy []int) (out int) {
	for ii := 0; ii < len(xx); ii++ {
		for jj := ii; jj < len(yy); jj++ {
			if yy[jj] >= xx[ii] && jj-ii > out {
				out = jj - ii
			}
		}
	}
	return
}

repository: ezralalonde/turnt-octo-hipster

repo_name: ezralalonde/turnt-octo-hipster


text:
@book{BoydVandenberghe:2004,
	author =    {S. Boyd and L. Vandenberghe},
	title =     {Convex Optimization},
	publisher = {Cambridge University Press},
	year =      {2004},
}

@book{ESL,
	title = {The Elements of Statistical Learning},
	author = {T. Hastie and R. Tibshirani and J. Friedman},
	publisher = {Springer},
	year = {2001}
}

% CVX like systems
@misc{cvx,
	author       = {M. Grant and S. Boyd},
	title        = {{CVX}: {MATLAB} Software for Disciplined Convex Programming, version 2.1},
	month        = {March},
	year         = {2014},
	howpublished = {\url{http://cvxr.com/cvx}}
}

@article{cvxpy,
	Author =    {S. Diamond and S. Boyd},
	Title =     {{CVXPY}: A {Python}-embedded modeling language for
	convex optimization},
	Journal =   {Journal of Machine Learning Research},
	Year =      {2016},
	Volume =    {17},
	Number =    {83},
	Pages =     {1--5},
}

@inproceedings{cvxjl,
	author = {M. Udell and K. Mohan and D. Zeng and J. Hong and S. Diamond and S. Boyd},
	title = {Convex optimization in {J}ulia},
	booktitle = {Proceedings of the Workshop for High Performance Technical Computing in Dynamic Languages},
	year = 2014,
	pages = {18--28},
	location = {New Orleans},
}

@inproceedings{YALMIP,
	author = {J. Lofberg},
	title = {{YALMIP}: A Toolbox for Modeling and Optimization in {MATLAB}},
	booktitle = {Proceedings of the {IEEE} International Symposium on Computed Aided Control Systems Design},
	year = {2004},
	month = {September},
	pages = {294--289},
	note = {\url{http://users.isy.liu.se/johanl/yalmip}}
}

% Related R libraries
@article{glmnet,
	Author = {J. Friedman and T. Hastie and R. Tibshirani},
	Title = {Regularization Paths for Generalized Linear Models via Coordinate Descent},
	Journal = {Journal of Statistical Software},
	Year = {2010},
	Month = {January},
	Volume = {33},
	Number = {1},
	Pages = {1--22},
}

@article{optimx,
	title = {Unifying Optimization Algorithms to Aid Software System Users: {optimx} for {R}},
	author = {J. C. Nash and R. Varadhan},
	journal = {Journal of Statistical Software},
	year = {2011},
	volume = {43},
	number = {9},
	pages = {1--14},
}

@manual{ROI,
	title = {ROI: R Optimization Infrastructure},
	author = {K. Hornik and D. Meyer and F. Schwendinger and S. Theussl},
	year = {2017},
	note = {\url{https://CRAN.R-project.org/package=ROI}}
}

@manual{nloptr,
	title = {The {NLopt} Nonlinear-Optimization Package},
	author = {S. G. Johnson},
	year = {2008},
	note = {{http://ab-initio.mit.edu/nlopt/}}
}

@article{Nash:2014,
	title = {On Best Practice Optimization Methods in {R}},
	author = {J. C. Nash},
	journal = {Journal of Statistical Software},
	year = {2014},
	volume = {60},
	number = {2},
	pages = {1--14},
}

% Convexity detection
@incollection{GrantBoydYe:2006,
	author = {M. Grant and S. Boyd and Y. Ye},
	title = {Disciplined Convex Programming},
	booktitle = {Global Optimization: From Theory to Implementation},
	series = {Nonconvex Optimization and its Applications},
	pages = {155--210},
	publisher = {Springer},
	year = {2006},
	editor = {L. Liberti and N. Maculan}
}

% ECOS
@inproceedings{ECOS,
	author = {A. Domahidi and E. Chu and S. Boyd},
	booktitle = {Proceedings of the European Control Conference},
	title = {{ECOS}: {A}n {SOCP} solver for embedded systems},
	year = {2013},
	pages = {3071--3076}
}

@inproceedings{QCML,
	title = {Code generation for embedded second-order cone programming},
	author = {E. Chu and N. Parikh and A. Domahidi and S. Boyd},
	booktitle = {Proceedings of the European Control Conference},
	pages = {1547--1552},
	year = {2013}
}

% SCS
@article{SCS,
	author = {B. O'Donoghue and E. Chu and N. Parikh and S. Boyd},
	title = {Conic Optimization via Operator Splitting
	and Homogeneous Self-Dual Embedding},
	journal = {Journal of Optimization Theory and Applications},
	pages = {1--27},
	year = {2016}
}

@inproceedings{DCSP,
	author = {A. Ali and Z. Kolter and S. Diamond and S. Boyd},
	title = {Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization},
	booktitle = {Proceedings of the Conference on Uncertainty in Artifial Intelligence},
	pages = {62--71},
	year = {2015}
}

@incollection{MOSEK,
	author = {E. D. Andersen and K. D. Andersen},
	title = {The {MOSEK} interior point optimizer for linear programming: an implementation of the homogeneous algorithm},
	booktitle = {High Performance Optimization},
	pages = {197--232},
	year = {2000},
	publisher = {Springer}
}

@manual{GUROBI,
	author = {{Gurobi Optimization, Inc}},
	title = {Gurobi Optimizer Reference Manual},
	year = {2016},
	note = {\url{http://www.gurobi.com}}
}

% Optimization algorithms
@book{Wright:1997,
	author = {S. J. Wright},
	title = {Primal-dual interior-point methods},
	year = {1997},
	publisher = {SIAM}
}

@article{AndersenDahl:2011,
	author = {M. Andersen and J. Dahl and Z. Liu and L. Vandenberghe},
	title = {Interior-point methods for large-scale cone programming},
	journal = {Optimization for machine learning},
	volume = {5583},
	year = {2011},
	publisher = {MIT}
}

@article{SkajaaYe:2015,
	author = {A. Skajaa and Y. Ye},
	title = {A homogeneous interior-point algorithm for nonsymmetric convex conic optimization},
	journal = {Mathematical Programming},
	volume = {150},
	number = {2},
	pages = {391--422},
	year = {2015},
	publisher = {Springer}
}

@article{ADMM,
	author = {S. Boyd and N. Parikh and E. Chu and B. Peleato and J. Eckstein},
	title = {Distributed optimization and statistical learning via the alternating direction method of multipliers},
	journal = {Foundations and Trends in Machine Learning},
	volume = {3},
	number = {1},
	pages = {1--122},
	year = {2011}
}

% Examples
@article{Huber:1964,
	title = {Robust Estimation of a Location Parameter},
	author = {P. J. Huber},
	journal = {Annals of Mathematical Statistics},
	volume = {35},
	number = {1},
	pages = {73--101},
	year = {1964}
}

@book{quantile,
	title = {Quantile Regression},
	author = {R. Koenker},
	publisher = {Cambridge University Press},
	year = {2005}
}

@article{Tibshirani:1996,
	title = {Regression Shrinkage and Selection via the {L}asso},
	author = {R. Tibshirani},
	journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
	volume = {58},
	number = {1},
	pages = {267--288},
	year = {1996}
}

@article{elasticnet,
	title = {Regularization and Variable Selection via the Elastic--Net},
	author = {H. Zou, T. Hastie},
	journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
	volume = {67},
	number = {2},
	pages = {301--320},
	year = {2005}
}

@article{Cox:1958,
	title = {The Regression Analysis of Binary Sequences},
	author = {D. R. Cox},
	journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
	volume = {20},
	number= {2},
	pages = {215--242},
	year = {1958}
}

@article{spinvcov,
	title = {Sparse Inverse Covariance Estimation with the Graphical Lasso},
	author = {J. Friedman and T. Hastie and R. Tibshirani},
	journal = {Biostatistics},
	volume = {9},
	number = {3},
	pages = {432--441},
	year = {2008}
}

@article{BoydHastie:2016,
	title = {Saturating Splines and Feature Selection},
	author = {N. Boyd and T. Hastie and S. Boyd and B. Recht and M. Jordan},
	journal = {arXiv preprint arXiv:1609.06764},
	year = {2016}
}

@article{mlelogcave,
	title = {Maximum Likelihood Estimation of a Log-Concave Density and its Distribution Function: Basic Properties and Uniform Consistency},
	author = {L. D{\"u}mbgen and K. Rufibach},
	journal = {Bernoulli},
	volume = {15},
	number = {1},
	pages = {40--68},
	year = {2009}
}

@book{dirstd,
	title = {Statistical Methods for Rates and Proportions},
	author = {J. L. Fleiss and B. Levin and M. C. Paik},
	publisher = {Wiley-Interscience},
	month = {September},
	year = {2003}
}

@article{TibshiraniHoefling:2011,
	title = {Nearly-Isotonic Regression},
	author = {R. J. Tibshirani and H. Hoefling and R. Tibshirani},
	journal = {Technometrics},
	volume = {53},
	number = {1},
	pages = {54--61},
	year = {2011}
}

@misc{worstrisk,
	title = {The Worst-Case Risk of a Portfolio},
	author = {M. Lobo and S. Boyd},
	month = {September},
	year = {2000},
	howpublished = {\url{http://stanford.edu/~boyd/papers/pdf/risk_bnd.pdf}}
}

@article{catenary,
	title = {Case studies in optimization: Catenary problem},
	author = {I. A. Griva and R. J. Vanderbei},
	journal = {Optimization and Engineering},
	volume = {6},
	number = {4},
	pages = {463--482},
	year = {2005},
	publisher = {Springer}
}

@article{Markowitz:1952,
	title = {Portfolio Selection},
	author = {H. M. Markowitz},
	journal = {Journal of Finance},
	volume = {7},
	number = {1},
	pages = {77--91},
	month = {March},
	year = {1952}
}

@article{Roy:1952,
	title = {Safety First and the Holding of Assets},
	author = {A. D. Roy},
	journal = {Econometrica},
	volume = {20},
	number = {3},
	pages = {431--449},
	month = {July},
	year = {1952}
}

@article{LoboFazelBoyd:2007,
	title = {Portfolio Optimization with Linear and Fixed Transaction Costs},
	author = {M. S. Lobo and M. Fazel and S. Boyd},
	journal = {Annals of Operations Research},
	volume = {152},
	number = {1},
	pages = {341--365},
	year = {2007}
}

@article{BoydBusseti:2017,
	title = {Multi-Period Trading via Convex Optimization},
	author = {S. Boyd and E. Busseti and S. Diamond and R. N. Kahn and K. Koh and P. Nystrup and J. Speth},
	journal = {Foundations and Trends in Optimization},
	year = {2017}
}

@article{kelly,
	title = {A New Interpretation of Information Rate},
	author = {J. L. Kelly},
	journal = {Bell System Technical Journal},
	volume = {35},
	number = {4},
	pages = {917--926},
	month = {July},
	year = {1956}
}

@article{RCK,
	title = {Risk--Constrained {K}elly Gambling},
	author = {E. Busseti and E. K. Ryu and S. Boyd},
	journal = {Journal of Investing},
	volume = {25},
	number = {3},
	pages = {118--134},
	year = {2016}
}

@article{FMMC,
	title = {Fastest Mixing {M}arkov Chain on a Graph},
	author = {S. Boyd and P. Diaconis and L. Xiao},
	journal = {SIAM Review},
	volume = {46},
	number = {4},
	pages = {667--689},
	year = {2004},
	publisher = {SIAM}
}

@book{ShannonWeaver:1949,
	title = {The Mathematical Theory of Communication},
	author = {C. E. Shannon and W. Weaver},
	publisher = {University of Illinois Press},
	year = {1949}
}

@book{GelfandFomin:1963,
	title = {Calculus of Variations},
	author = {I. M. Gelfand and S. V. Fomin},
	publisher = {Prentice-Hall},
	year = {1963}
}

@book{Freedman:2009,
	title = {Statistical Models: Theory and Practice},
	author = {D. A. Freedman},
	publisher = {Cambridge University Press},
	year = {2009}
}

@article{isotone,
	title = {Isotone Optimization in {R}: Pool-Adjacent-Violators Algorithm ({PAVA}) and Active Set Methods},
	author = {J. {de Leeuw} and K. Hornik and P. Mair},
    journal = {Journal of Statistical Software},
    year = {2009},
    volume = {32},
    number = {5},
    pages = {1--24},
}

@article{neariso,
	title = {Nearly-Isotonic Regression},
	author = {R. J. Tibshirani and H. Hoefling and R. Tibshirani},
	journal = {Technometrics},
	volume = {53},
	number = {1},
	pages = {54--61},
	year  = {2011},
	publisher = {Taylor & Francis},
}

@article{PotthoffRoy:1964,
	title = {A generalized multivariate analysis of variance model useful especially for growth curve problems},
	author = {R. F. Potthoff and S. N. Roy},
	journal = {Biometrika},
	volume = {51},
	number = {3--4},
	pages = {313--326},
	year = {1964},
}

@article{fu:naras:boyd:2017,
	title = {CVXR: An R package for Disciplined Convex Optimization},
	author = {Anqi Fu and Balasubramanian Narasimhan and Stephen Boyd},
	publisher = {Working Paper},
	year = {2017},
        url = {https://web.stanford.edu/~boyd/papers/cvxr_paper.html},
}

repository: aszekMosek/CVXR

repo_name: aszekMosek/CVXR


text:
/*
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at

     https://www.apache.org/licenses/LICENSE-2.0

 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
*/

package cluster

import (
	"github.com/spf13/cobra"

	"opendev.org/airship/airshipctl/pkg/clusterctl/client"
	clusterctlcmd "opendev.org/airship/airshipctl/pkg/clusterctl/cmd"
	"opendev.org/airship/airshipctl/pkg/config"
)

const (
	getKubeconfigLong = `
Retrieve cluster kubeconfig and print it to stdout
`
	getKubeconfigExample = `
# Retrieve target-cluster kubeconfig
airshipctl cluster get-kubeconfig target-cluster --kubeconfig /tmp/kubeconfig
`
)

// NewGetKubeconfigCommand creates a command which retrieves cluster kubeconfig
func NewGetKubeconfigCommand(cfgFactory config.Factory) *cobra.Command {
	o := &client.GetKubeconfigOptions{}
	cmd := &cobra.Command{
		Use:     "get-kubeconfig [cluster_name]",
		Short:   "Retrieve kubeconfig for a desired cluster",
		Long:    getKubeconfigLong[1:],
		Example: getKubeconfigExample[1:],
		Args:    cobra.ExactArgs(1),
		RunE:    getKubeconfigRunE(cfgFactory, o),
	}

	initFlags(o, cmd)

	return cmd
}

func initFlags(o *client.GetKubeconfigOptions, cmd *cobra.Command) {
	flags := cmd.Flags()

	flags.StringVar(
		&o.ParentKubeconfigPath,
		"kubeconfig",
		"",
		"path to kubeconfig associated with parental cluster")

	flags.StringVarP(
		&o.ManagedClusterNamespace,
		"namespace",
		"n",
		"default",
		"namespace where cluster is located, if not specified default one will be used")

	flags.StringVar(
		&o.ParentKubeconfigContext,
		"context",
		"",
		"specify context within the kubeconfig file")
}

// getKubeconfigRunE returns a function to cobra command to be executed in runtime
func getKubeconfigRunE(cfgFactory config.Factory, o *client.GetKubeconfigOptions) func(
	cmd *cobra.Command, args []string) error {
	return func(cmd *cobra.Command, args []string) error {
		o.ManagedClusterName = args[0]
		return clusterctlcmd.GetKubeconfig(cfgFactory, o, cmd.OutOrStdout())
	}
}

repository: vladiskuz/airshipctl

repo_name: vladiskuz/airshipctl


text:
@article{twyman_theyre_2019,
 author = {Twyman, L. and Cowles, C. and Walsberger, S.C. and Baker, A.L. and Bonevski, B.},
 doi = {10.3389/fpsyt.2019.00503},
 issn = {1664-0640},
 journal = {Frontiers in Psychiatry},
 month = {January},
 note = {Place: Switzerland
Publisher: Frontiers Research Foundation},
 number = {JUN},
 pages = {1--11},
 title = {“They're going to smoke anyway”: A qualitative study of community mental health staff and consumer perspectives on the role of social and living environments in tobacco use and cessation},
 volume = {10},
 year = {2019}
}

repository: ChrisOldmeadow/academic-kickstart

repo_name: ChrisOldmeadow/academic-kickstart


text:
@article{frolova2012,
  title={Overview of methods of reverse engineering of gene regulatory networks: Boolean and Bayesian networks},
  author={Frolova, AO},
  journal={Biopolymers and cell},
  number={28,№ 3},
  pages={163--170},
  year={2012},
  publisher={National academy of sciences of Ukraine, Institute of molecular biology and genetics, Національна академія наук України, Інститут молекулярної біології і генетики}
}


text:
@misc{HalketNesheim_OptPubHouse,
 abstract = {We study a system of optimal taxes and transfers under private information (Mirrlees 1971) in an urban setting (Mirrlees 1972). We show that rationed, subsidized housing (i.e. public housing) is generally part of the optimal policy whenever the optimal income tax policy leads to bunching. Under a plausible calibration, this public housing is situated in the high value, downtown area of the city.},
 author = {Jonathan Halket and Lars Nesheim},
 journal = {In Progress},
 note = {In Progress},
 title = {Optimal Public Housing},
 year = {Coming soon}
}

repository: jrhalket/Website-academic

repo_name: jrhalket/Website-academic


text:
# Generate compile_commands.json for clang based tools
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)

if (WIN32)
  set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS 1)
endif (WIN32)

repository: Semedi/ProtoRl

repo_name: Semedi/ProtoRl


text:
include(${CMAKE_TOOLCHAIN_FILE})
include(cpp_unit_test_helpers)
_cpp_setup_test_env("get_from_gh")

set(cpp_repo "github.com/CMakePackagingProject/CMakePackagingProject")

set(output ${test_prefix}/${test_number}/cpp.tar.gz)
_cpp_add_test(
TITLE "Basic usage"
CONTENTS
    "include(recipes/cpp_get_from_gh)"
    "_cpp_get_from_gh(${output} \"\" ${cpp_repo} False \"\")"
    "_cpp_assert_exists(${output})"
)

_cpp_add_test(
TITLE "Crashes if github.com is not in URL"
SHOULD_FAIL REASON  "github.com\" not contained in string \"organization/repo\""
CONTENTS
    "include(recipes/cpp_get_from_gh)"
    "_cpp_get_from_gh(output \"\" organization/repo False \"\")"
)

repository: keipertk/CMakePackagingProject

repo_name: keipertk/CMakePackagingProject


text:
// +build !notable

package termios

var (
	Color256Black                = Color{Spectrum8, 0, 0, 0}
	Color256Maroon               = Color{Spectrum8, 1, 0, 0}
	Color256Green                = Color{Spectrum8, 2, 0, 0}
	Color256Olive                = Color{Spectrum8, 3, 0, 0}
	Color256Navy                 = Color{Spectrum8, 4, 0, 0}
	Color256Purple               = Color{Spectrum8, 5, 0, 0}
	Color256Teal                 = Color{Spectrum8, 6, 0, 0}
	Color256Silver               = Color{Spectrum8, 7, 0, 0}
	Color256Grey                 = Color{Spectrum8, 8, 0, 0}
	Color256Red                  = Color{Spectrum8, 9, 0, 0}
	Color256Lime                 = Color{Spectrum8, 10, 0, 0}
	Color256Yellow               = Color{Spectrum8, 11, 0, 0}
	Color256Blue                 = Color{Spectrum8, 12, 0, 0}
	Color256Fuchsia              = Color{Spectrum8, 13, 0, 0}
	Color256Aqua                 = Color{Spectrum8, 14, 0, 0}
	Color256White                = Color{Spectrum8, 15, 0, 0}
	Color256Grey0                = Color{Spectrum8, 16, 0, 0}
	Color256NavyBlue             = Color{Spectrum8, 17, 0, 0}
	Color256DarkBlue             = Color{Spectrum8, 18, 0, 0}
	Color256Blue3Dark            = Color{Spectrum8, 19, 0, 0}
	Color256Blue3Light           = Color{Spectrum8, 20, 0, 0}
	Color256Blue1                = Color{Spectrum8, 21, 0, 0}
	Color256DarkGreen            = Color{Spectrum8, 22, 0, 0}
	Color256DeepSkyBlue4Dark     = Color{Spectrum8, 23, 0, 0}
	Color256DeepSkyBlue4Medium   = Color{Spectrum8, 24, 0, 0}
	Color256DeepSkyBlue4Light    = Color{Spectrum8, 25, 0, 0}
	Color256DodgerBlue3          = Color{Spectrum8, 26, 0, 0}
	Color256DodgerBlue2          = Color{Spectrum8, 27, 0, 0}
	Color256Green4               = Color{Spectrum8, 28, 0, 0}
	Color256SpringGreen4         = Color{Spectrum8, 29, 0, 0}
	Color256Turquoise4           = Color{Spectrum8, 30, 0, 0}
	Color256DeepSkyBlue3Dark     = Color{Spectrum8, 31, 0, 0}
	Color256DeepSkyBlue3Light    = Color{Spectrum8, 32, 0, 0}
	Color256DodgerBlue1          = Color{Spectrum8, 33, 0, 0}
	Color256Green3Dark           = Color{Spectrum8, 34, 0, 0}
	Color256SpringGreen3Dark     = Color{Spectrum8, 35, 0, 0}
	Color256DarkCyan             = Color{Spectrum8, 36, 0, 0}
	Color256LightSeaGreen        = Color{Spectrum8, 37, 0, 0}
	Color256DeepSkyBlue2         = Color{Spectrum8, 38, 0, 0}
	Color256DeepSkyBlue1         = Color{Spectrum8, 39, 0, 0}
	Color256Green3Light          = Color{Spectrum8, 40, 0, 0}
	Color256SpringGreen3Light    = Color{Spectrum8, 41, 0, 0}
	Color256SpringGreen2Dark     = Color{Spectrum8, 42, 0, 0}
	Color256Cyan3                = Color{Spectrum8, 43, 0, 0}
	Color256DarkTurquoise        = Color{Spectrum8, 44, 0, 0}
	Color256Turquoise2           = Color{Spectrum8, 45, 0, 0}
	Color256Green1               = Color{Spectrum8, 46, 0, 0}
	Color256SpringGreen2Light    = Color{Spectrum8, 47, 0, 0}
	Color256SpringGreen1         = Color{Spectrum8, 48, 0, 0}
	Color256MediumSpringGreen    = Color{Spectrum8, 49, 0, 0}
	Color256Cyan2                = Color{Spectrum8, 50, 0, 0}
	Color256Cyan1                = Color{Spectrum8, 51, 0, 0}
	Color256DarkRedDark          = Color{Spectrum8, 52, 0, 0}
	Color256DeepPink4Dark        = Color{Spectrum8, 53, 0, 0}
	Color256Purple4Dark          = Color{Spectrum8, 54, 0, 0}
	Color256Purple4Light         = Color{Spectrum8, 55, 0, 0}
	Color256Purple3              = Color{Spectrum8, 56, 0, 0}
	Color256BlueViolet           = Color{Spectrum8, 57, 0, 0}
	Color256Orange4Dark          = Color{Spectrum8, 58, 0, 0}
	Color256Grey37               = Color{Spectrum8, 59, 0, 0}
	Color256MediumPurple4        = Color{Spectrum8, 60, 0, 0}
	Color256SlateBlue3Dark       = Color{Spectrum8, 61, 0, 0}
	Color256SlateBlue3Light      = Color{Spectrum8, 62, 0, 0}
	Color256RoyalBlue1           = Color{Spectrum8, 63, 0, 0}
	Color256Chartreuse4          = Color{Spectrum8, 64, 0, 0}
	Color256DarkSeaGreen4Dark    = Color{Spectrum8, 65, 0, 0}
	Color256PaleTurquoise4       = Color{Spectrum8, 66, 0, 0}
	Color256SteelBlue            = Color{Spectrum8, 67, 0, 0}
	Color256SteelBlue3           = Color{Spectrum8, 68, 0, 0}
	Color256CornflowerBlue       = Color{Spectrum8, 69, 0, 0}
	Color256Chartreuse3Dark      = Color{Spectrum8, 70, 0, 0}
	Color256DarkSeaGreen4Light   = Color{Spectrum8, 71, 0, 0}
	Color256CadetBlueDark        = Color{Spectrum8, 72, 0, 0}
	Color256CadetBlueLight       = Color{Spectrum8, 73, 0, 0}
	Color256SkyBlue3             = Color{Spectrum8, 74, 0, 0}
	Color256SteelBlue1Dark       = Color{Spectrum8, 75, 0, 0}
	Color256Chartreuse3Light     = Color{Spectrum8, 76, 0, 0}
	Color256PaleGreen3Dark       = Color{Spectrum8, 77, 0, 0}
	Color256SeaGreen3            = Color{Spectrum8, 78, 0, 0}
	Color256Aquamarine3          = Color{Spectrum8, 79, 0, 0}
	Color256MediumTurquoise      = Color{Spectrum8, 80, 0, 0}
	Color256SteelBlue1Light      = Color{Spectrum8, 81, 0, 0}
	Color256Chartreuse2Dark      = Color{Spectrum8, 82, 0, 0}
	Color256SeaGreen2            = Color{Spectrum8, 83, 0, 0}
	Color256SeaGreen1Dark        = Color{Spectrum8, 84, 0, 0}
	Color256SeaGreen1Light       = Color{Spectrum8, 85, 0, 0}
	Color256Aquamarine1Dark      = Color{Spectrum8, 86, 0, 0}
	Color256DarkSlateGray2       = Color{Spectrum8, 87, 0, 0}
	Color256DarkRedLight         = Color{Spectrum8, 88, 0, 0}
	Color256DeepPink4Medium      = Color{Spectrum8, 89, 0, 0}
	Color256DarkMagentaDark      = Color{Spectrum8, 90, 0, 0}
	Color256DarkMagentaLight     = Color{Spectrum8, 91, 0, 0}
	Color256DarkVioletDark       = Color{Spectrum8, 92, 0, 0}
	Color256Purple2              = Color{Spectrum8, 93, 0, 0}
	Color256Orange4Light         = Color{Spectrum8, 94, 0, 0}
	Color256LightPink4           = Color{Spectrum8, 95, 0, 0}
	Color256Plum4                = Color{Spectrum8, 96, 0, 0}
	Color256MediumPurple3Dark    = Color{Spectrum8, 97, 0, 0}
	Color256MediumPurple3Light   = Color{Spectrum8, 98, 0, 0}
	Color256SlateBlue1           = Color{Spectrum8, 99, 0, 0}
	Color256Yellow4Dark          = Color{Spectrum8, 100, 0, 0}
	Color256Wheat4               = Color{Spectrum8, 101, 0, 0}
	Color256Grey53               = Color{Spectrum8, 102, 0, 0}
	Color256LightSlateGrey       = Color{Spectrum8, 103, 0, 0}
	Color256MediumPurpleDark     = Color{Spectrum8, 104, 0, 0}
	Color256LightSlateBlue       = Color{Spectrum8, 105, 0, 0}
	Color256Yellow4Light         = Color{Spectrum8, 106, 0, 0}
	Color256DarkOliveGreen3Dark  = Color{Spectrum8, 107, 0, 0}
	Color256DarkSeaGreen         = Color{Spectrum8, 108, 0, 0}
	Color256LightSkyBlue3Dark    = Color{Spectrum8, 109, 0, 0}
	Color256LightSkyBlue3Light   = Color{Spectrum8, 110, 0, 0}
	Color256SkyBlue2             = Color{Spectrum8, 111, 0, 0}
	Color256Chartreuse2Light     = Color{Spectrum8, 112, 0, 0}
	Color256DarkOliveGreen3Light = Color{Spectrum8, 113, 0, 0}
	Color256PaleGreen3Light      = Color{Spectrum8, 114, 0, 0}
	Color256DarkSeaGreen3Dark    = Color{Spectrum8, 115, 0, 0}
	Color256DarkSlateGray3       = Color{Spectrum8, 116, 0, 0}
	Color256SkyBlue1             = Color{Spectrum8, 117, 0, 0}
	Color256Chartreuse1          = Color{Spectrum8, 118, 0, 0}
	Color256LightGreenDark       = Color{Spectrum8, 119, 0, 0}
	Color256LightGreenLight      = Color{Spectrum8, 120, 0, 0}
	Color256PaleGreen1Dark       = Color{Spectrum8, 121, 0, 0}
	Color256Aquamarine1Light     = Color{Spectrum8, 122, 0, 0}
	Color256DarkSlateGray1       = Color{Spectrum8, 123, 0, 0}
	Color256Red3Dark             = Color{Spectrum8, 124, 0, 0}
	Color256DeepPink4Light       = Color{Spectrum8, 125, 0, 0}
	Color256MediumVioletRed      = Color{Spectrum8, 126, 0, 0}
	Color256Magenta3             = Color{Spectrum8, 127, 0, 0}
	Color256DarkVioletLight      = Color{Spectrum8, 128, 0, 0}
	Color256PurpleLight          = Color{Spectrum8, 129, 0, 0}
	Color256DarkOrange3Dark      = Color{Spectrum8, 130, 0, 0}
	Color256IndianRedDark        = Color{Spectrum8, 131, 0, 0}
	Color256HotPink3Dark         = Color{Spectrum8, 132, 0, 0}
	Color256MediumOrchid3        = Color{Spectrum8, 133, 0, 0}
	Color256MediumOrchid         = Color{Spectrum8, 134, 0, 0}
	Color256MediumPurple2Dark    = Color{Spectrum8, 135, 0, 0}
	Color256DarkGoldenrod        = Color{Spectrum8, 136, 0, 0}
	Color256LightSalmon3Dark     = Color{Spectrum8, 137, 0, 0}
	Color256RosyBrown            = Color{Spectrum8, 138, 0, 0}
	Color256Grey63               = Color{Spectrum8, 139, 0, 0}
	Color256MediumPurple2Light   = Color{Spectrum8, 140, 0, 0}
	Color256MediumPurple1        = Color{Spectrum8, 141, 0, 0}
	Color256Gold3Dark            = Color{Spectrum8, 142, 0, 0}
	Color256DarkKhaki            = Color{Spectrum8, 143, 0, 0}
	Color256NavajoWhite3         = Color{Spectrum8, 144, 0, 0}
	Color256Grey69               = Color{Spectrum8, 145, 0, 0}
	Color256LightSteelBlue3      = Color{Spectrum8, 146, 0, 0}
	Color256LightSteelBlue       = Color{Spectrum8, 147, 0, 0}
	Color256Yellow3Dark          = Color{Spectrum8, 148, 0, 0}
	Color256DarkOliveGreen3      = Color{Spectrum8, 149, 0, 0}
	Color256DarkSeaGreen3Light   = Color{Spectrum8, 150, 0, 0}
	Color256DarkSeaGreen2Dark    = Color{Spectrum8, 151, 0, 0}
	Color256LightCyan3           = Color{Spectrum8, 152, 0, 0}
	Color256LightSkyBlue1        = Color{Spectrum8, 153, 0, 0}
	Color256GreenYellow          = Color{Spectrum8, 154, 0, 0}
	Color256DarkOliveGreen2      = Color{Spectrum8, 155, 0, 0}
	Color256PaleGreen1Light      = Color{Spectrum8, 156, 0, 0}
	Color256DarkSeaGreen2Light   = Color{Spectrum8, 157, 0, 0}
	Color256DarkSeaGreen1Dark    = Color{Spectrum8, 158, 0, 0}
	Color256PaleTurquoise1       = Color{Spectrum8, 159, 0, 0}
	Color256Red3Light            = Color{Spectrum8, 160, 0, 0}
	Color256DeepPink3Dark        = Color{Spectrum8, 161, 0, 0}
	Color256DeepPink3Light       = Color{Spectrum8, 162, 0, 0}
	Color256Magenta3Dark         = Color{Spectrum8, 163, 0, 0}
	Color256Magenta3Light        = Color{Spectrum8, 164, 0, 0}
	Color256Magenta2Dark         = Color{Spectrum8, 165, 0, 0}
	Color256DarkOrange3Light     = Color{Spectrum8, 166, 0, 0}
	Color256IndianRedLight       = Color{Spectrum8, 167, 0, 0}
	Color256HotPink3Light        = Color{Spectrum8, 168, 0, 0}
	Color256HotPink2             = Color{Spectrum8, 169, 0, 0}
	Color256Orchid               = Color{Spectrum8, 170, 0, 0}
	Color256MediumOrchid1Dark    = Color{Spectrum8, 171, 0, 0}
	Color256Orange3              = Color{Spectrum8, 172, 0, 0}
	Color256LightSalmon3Light    = Color{Spectrum8, 173, 0, 0}
	Color256LightPink3           = Color{Spectrum8, 174, 0, 0}
	Color256Pink3                = Color{Spectrum8, 175, 0, 0}
	Color256Plum3                = Color{Spectrum8, 176, 0, 0}
	Color256Violet               = Color{Spectrum8, 177, 0, 0}
	Color256Gold3Light           = Color{Spectrum8, 178, 0, 0}
	Color256LightGoldenrod3      = Color{Spectrum8, 179, 0, 0}
	Color256Tan                  = Color{Spectrum8, 180, 0, 0}
	Color256MistyRose3           = Color{Spectrum8, 181, 0, 0}
	Color256Thistle3             = Color{Spectrum8, 182, 0, 0}
	Color256Plum2                = Color{Spectrum8, 183, 0, 0}
	Color256Yellow3Light         = Color{Spectrum8, 184, 0, 0}
	Color256Khaki3               = Color{Spectrum8, 185, 0, 0}
	Color256LightGoldenrod2      = Color{Spectrum8, 186, 0, 0}
	Color256LightYellow3         = Color{Spectrum8, 187, 0, 0}
	Color256Grey84               = Color{Spectrum8, 188, 0, 0}
	Color256LightSteelBlue1      = Color{Spectrum8, 189, 0, 0}
	Color256Yellow2              = Color{Spectrum8, 190, 0, 0}
	Color256DarkOliveGreen1Dark  = Color{Spectrum8, 191, 0, 0}
	Color256DarkOliveGreen1Light = Color{Spectrum8, 192, 0, 0}
	Color256DarkSeaGreen1Light   = Color{Spectrum8, 193, 0, 0}
	Color256Honeydew2            = Color{Spectrum8, 194, 0, 0}
	Color256LightCyan1           = Color{Spectrum8, 195, 0, 0}
	Color256Red1                 = Color{Spectrum8, 196, 0, 0}
	Color256DeepPink2            = Color{Spectrum8, 197, 0, 0}
	Color256DeepPink1Dark        = Color{Spectrum8, 198, 0, 0}
	Color256DeepPink1Light       = Color{Spectrum8, 199, 0, 0}
	Color256Magenta2Light        = Color{Spectrum8, 200, 0, 0}
	Color256Magenta1             = Color{Spectrum8, 201, 0, 0}
	Color256OrangeRed1           = Color{Spectrum8, 202, 0, 0}
	Color256IndianRed1Dark       = Color{Spectrum8, 203, 0, 0}
	Color256IndianRed1Light      = Color{Spectrum8, 204, 0, 0}
	Color256HotPinkDark          = Color{Spectrum8, 205, 0, 0}
	Color256HotPinkLight         = Color{Spectrum8, 206, 0, 0}
	Color256MediumOrchid1Light   = Color{Spectrum8, 207, 0, 0}
	Color256DarkOrange           = Color{Spectrum8, 208, 0, 0}
	Color256Salmon1              = Color{Spectrum8, 209, 0, 0}
	Color256LightCoral           = Color{Spectrum8, 210, 0, 0}
	Color256PaleVioletRed1       = Color{Spectrum8, 211, 0, 0}
	Color256Orchid2              = Color{Spectrum8, 212, 0, 0}
	Color256Orchid1              = Color{Spectrum8, 213, 0, 0}
	Color256Orange1              = Color{Spectrum8, 214, 0, 0}
	Color256SandyBrown           = Color{Spectrum8, 215, 0, 0}
	Color256LightSalmon1         = Color{Spectrum8, 216, 0, 0}
	Color256LightPink1           = Color{Spectrum8, 217, 0, 0}
	Color256Pink1                = Color{Spectrum8, 218, 0, 0}
	Color256Plum1                = Color{Spectrum8, 219, 0, 0}
	Color256Gold1                = Color{Spectrum8, 220, 0, 0}
	Color256LightGoldenrod2Dark  = Color{Spectrum8, 221, 0, 0}
	Color256LightGoldenrod2Light = Color{Spectrum8, 222, 0, 0}
	Color256NavajoWhite1         = Color{Spectrum8, 223, 0, 0}
	Color256MistyRose1           = Color{Spectrum8, 224, 0, 0}
	Color256Thistle1             = Color{Spectrum8, 225, 0, 0}
	Color256Yellow1              = Color{Spectrum8, 226, 0, 0}
	Color256LightGoldenrod1      = Color{Spectrum8, 227, 0, 0}
	Color256Khaki1               = Color{Spectrum8, 228, 0, 0}
	Color256Wheat1               = Color{Spectrum8, 229, 0, 0}
	Color256Cornsilk1            = Color{Spectrum8, 230, 0, 0}
	Color256Grey100              = Color{Spectrum8, 231, 0, 0}
	Color256Grey3                = Color{Spectrum8, 232, 0, 0}
	Color256Grey7                = Color{Spectrum8, 233, 0, 0}
	Color256Grey11               = Color{Spectrum8, 234, 0, 0}
	Color256Grey15               = Color{Spectrum8, 235, 0, 0}
	Color256Grey19               = Color{Spectrum8, 236, 0, 0}
	Color256Grey23               = Color{Spectrum8, 237, 0, 0}
	Color256Grey27               = Color{Spectrum8, 238, 0, 0}
	Color256Grey30               = Color{Spectrum8, 239, 0, 0}
	Color256Grey35               = Color{Spectrum8, 240, 0, 0}
	Color256Grey39               = Color{Spectrum8, 241, 0, 0}
	Color256Grey42               = Color{Spectrum8, 242, 0, 0}
	Color256Grey46               = Color{Spectrum8, 243, 0, 0}
	Color256Grey50               = Color{Spectrum8, 244, 0, 0}
	Color256Grey54               = Color{Spectrum8, 245, 0, 0}
	Color256Grey58               = Color{Spectrum8, 246, 0, 0}
	Color256Grey62               = Color{Spectrum8, 247, 0, 0}
	Color256Grey66               = Color{Spectrum8, 248, 0, 0}
	Color256Grey70               = Color{Spectrum8, 249, 0, 0}
	Color256Grey74               = Color{Spectrum8, 250, 0, 0}
	Color256Grey78               = Color{Spectrum8, 251, 0, 0}
	Color256Grey82               = Color{Spectrum8, 252, 0, 0}
	Color256Grey85               = Color{Spectrum8, 253, 0, 0}
	Color256Grey89               = Color{Spectrum8, 254, 0, 0}
	Color256Grey93               = Color{Spectrum8, 255, 0, 0}
)

repository: scrouthtv/termios

repo_name: scrouthtv/termios


text:
DESCRIPTION = "A portable, lightweight MessagePack serializer and deserializer written in pure Python."

LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://LICENSE;md5=17df11353545c53a2df0ce7655859306"

SRC_URI[sha256sum] = "b801a83d6ed75e6df41e44518b4f2a9c221dc2da4bcd5380e3a0feda520bc61a"

inherit pypi setuptools3 ptest

SRC_URI += " \
        file://run-ptest \
"

RDEPENDS:${PN}-ptest += " \
       ${PYTHON_PN}-pytest \
"

do_install_ptest() {
       cp -f ${S}/test_umsgpack.py ${D}${PTEST_PATH}/
}

RDEPENDS:${PN} += " \
    ${PYTHON_PN}-datetime \
"

BBCLASSEXTEND = "native nativesdk"

repository: openembedded/meta-openembedded

repo_name: openembedded/meta-openembedded


text:
@article{rancoita_bayesian_2009,
	title = {Bayesian {DNA} copy number analysis},
	volume = {10},
	issn = {1471-2105},
	url = {http://www.biomedcentral.com/1471-2105/10/10},
	doi = {10.1186/1471-2105-10-10},
	abstract = {{BACKGROUND}:Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of {DNA} copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents.

Bayesian Piecewise Constant Regression ({BPCR}) is a Bayesian regression method for data that are noisy observations of a piecewise constant function. The method estimates the unknown segment number, the endpoints of the segments and the value of the segment levels of the underlying piecewise constant function. The Bayesian Regression Curve ({BRC}) estimates the same data with a smoothing curve. However, in the original formulation, some estimators failed to properly determine the corresponding parameters. For example, the boundary estimator did not take into account the dependency among the boundaries and succeeded in estimating more than one breakpoint at the same position, losing segments.{RESULTS}:We derived an improved version of the {BPCR} (called {mBPCR}) and {BRC}, changing the segment number estimator and the boundary estimator to enhance the fitting procedure. We also proposed an alternative estimator of the variance of the segment levels, which is useful in case of data with high noise. Using artificial data, we compared the original and the modified version of {BPCR} and {BRC} with other regression methods, showing that our improved version of {BPCR} generally outperformed all the others. Similar results were also observed on real data.{CONCLUSIONS}:We propose an improved method for {DNA} copy number estimation, {mBPCR}, which performed very well compared to previously published algorithms. In particular, {mBPCR} was more powerful in the detection of the true position of the breakpoints and of small aberrations in very noisy data. Hence, from a biological point of view, our method can be very useful, for example, to find targets of genomic aberrations in clinical cancer samples.},
	pages = {10},
	number = {1},
	journaltitle = {{BMC} Bioinformatics},
	author = {Rancoita, Paola and Hutter, Marcus and Bertoni, Francesco and Kwee, Ivo},
	urldate = {2009-02-24},
	date = {2009},
	tags = {Data mining}
}

repository: pmean/build-website

repo_name: pmean/build-website


text:
package codegen

import (
	"fmt"
	"sort"
	"strings"

	"goa.design/goa/v3/expr"
)

// GoNativeTypeName returns the Go built-in type corresponding to the given
// primitive type. GoNativeType panics if t is not a primitive type.
func GoNativeTypeName(t expr.DataType) string {
	switch t.Kind() {
	case expr.BooleanKind:
		return "bool"
	case expr.IntKind:
		return "int"
	case expr.Int32Kind:
		return "int32"
	case expr.Int64Kind:
		return "int64"
	case expr.UIntKind:
		return "uint"
	case expr.UInt32Kind:
		return "uint32"
	case expr.UInt64Kind:
		return "uint64"
	case expr.Float32Kind:
		return "float32"
	case expr.Float64Kind:
		return "float64"
	case expr.StringKind:
		return "string"
	case expr.BytesKind:
		return "[]byte"
	case expr.AnyKind:
		return "any"
	default:
		panic(fmt.Sprintf("cannot compute native Go type for %T", t)) // bug
	}
}

// AttributeTags computes the struct field tags from its metadata if any.
func AttributeTags(_, att *expr.AttributeExpr) string {
	var elems []string
	keys := make([]string, len(att.Meta))
	i := 0
	for k := range att.Meta {
		keys[i] = k
		i++
	}
	sort.Strings(keys)
	for _, key := range keys {
		val := att.Meta[key]
		if strings.HasPrefix(key, "struct:tag:") {
			name := key[11:]
			value := strings.Join(val, ",")
			elems = append(elems, fmt.Sprintf("%s:\"%s\"", name, value))
		}
	}
	if len(elems) > 0 {
		return " `" + strings.Join(elems, " ") + "`"
	}
	return ""
}

repository: goadesign/goa

repo_name: goadesign/goa


text:
/*
Copyright 2019 The Kubernetes Authors.
Modifications copyright (C) 2020 Alex Neo

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/

package scheduler

import (
  "github.com/alexnjh/epsilon/general_purpose_scheduler/k8s.io/kubernetes/pkg/features"
  corev1 "k8s.io/api/core/v1"
  framework "github.com/alexnjh/epsilon/general_purpose_scheduler/framework/v1alpha1"
  utilfeature "k8s.io/apiserver/pkg/util/feature"
)

// Compute pod resource requirements similar to the one used by the resource plugin
func computePodResourceRequest(pod *corev1.Pod) *framework.Resource {
	result := &framework.Resource{}
	for _, container := range pod.Spec.Containers {
		result.Add(container.Resources.Requests)
	}

	// take max_resource(sum_pod, any_init_container)
	for _, container := range pod.Spec.InitContainers {
		result.SetMaxResource(container.Resources.Requests)
	}

	// If Overhead is being utilized, add to the total requests for the pod
	if pod.Spec.Overhead != nil && utilfeature.DefaultFeatureGate.Enabled(features.PodOverhead) {
		result.Add(pod.Spec.Overhead)
	}

	return result
}

repository: alexnjh/epsilon

repo_name: alexnjh/epsilon


text:
@article{mendez2019welfare,
  title={Lack of Global Convergence and the Formation of Multiple Welfare Clubs across Countries: An Unsupervised Machine Learning Approach},
  author={Mendez, Carlos},
  journal={Economies},
  volume={7},
  number={3},
  pages={1-–17},
  year={2019},
  publisher={MDPI}
}


text:
@article{Li-2019-Temporal,
    title = "Temporal variation in zooplankton and phytoplankton community species composition and the affecting factors in Lake Taihu{---}a large freshwater lake in China",
    author = "Li, Cuicui  and
      Feng, Weiying  and
      Chen, Haiyan  and
      Li, Xiaofeng  and
      Song, Fanhao  and
      Guo, Wenjing  and
      Giesy, John P.  and
      Sun, Fuhong",
    journal = "Environmental Pollution, Volume 245",
    volume = "245",
    year = "2019",
    publisher = "Elsevier BV",
    url = "https://aclanthology.org/G19-89001",
    doi = "10.1016/j.envpol.2018.11.007",
    pages = "1050--1057",
    abstract = "Monitoring diverse components of aquatic ecosystems is vital for elucidation of diversity dynamics and processes, which alter freshwater ecosystems, but such studies are seldom conducted. Phytoplankton and zooplankton are integral components which play indispensable parts in the structure and ecological service function of water bodies. However, few studies were made on how zooplankton and phytoplankton community may respond simultaneously to change of circumstance and their mutual relationship. Therefore, we researched synchronously the phytoplankton communities as well as zooplankton communities based on monthly monitoring data from September 2011 to August 2012 in heavily polluted areas and researched their responses to variation in environmental parameters and their mutual relationship. As indicated by Time-lag analysis (TLA), the long-term dynamics of phytoplankton and zooplankton were undergoing directional variations, what's more, there exists significant seasonal variations of phytoplankton and zooplankton communities as indicated by Non-Metric Multidimensional scaling (NMDS) methods. Also, Redundancy Analysis (RDA) demonstrated that environmental indicators together accounted for 25.6{\%} and 50.1{\%} variance of phytoplankton and zooplankton, respectively, indicating that environmental variations affected significantly on the temporal dynamics of phytoplankton as well as zooplankton communities. What's more, variance partioning suggested that the major environmental factors influencing variation structures of zooplankton communities were water temperature, concentration of nitrogen, revealing the dominating driving mechanism which shaped the communities of zooplankton. It was also found that there was significant synchronization between zooplankton biomass and phytoplankton biomass (expressed as Chl-a concentration), which suggested that zooplankton respond to changes in dynamic structure of phytoplankton community and can initiate a decrease in phytoplankton biomass through grazing in a few months.",
}

repository: gwf-uwaterloo/gwf-publications

repo_name: gwf-uwaterloo/gwf-publications

gha_language: Python