text:
SUMMARY = "Suil is a lightweight C library for loading and wrapping LV2 plugin UIs"
HOMEPAGE = "https://drobilla.net/software/suil"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://COPYING;md5=83836dc85960985c534b2a881cca21c0"

inherit waf distro_features_check pkgconfig

REQUIRED_DISTRO_FEATURES = "x11"

DEPENDS = "gtk+ gtk+3 qtbase lv2"

SRC_URI = "http://download.drobilla.net/${BPN}-${PV}.tar.bz2"
SRC_URI[md5sum] = "ba2f6bf631d8f3686772c2b5c9467628"
SRC_URI[sha256sum] = "9895c531f80c7e89a2b4b47de589d73b70bf48db0b0cfe56e5d54237ea4b8848"

FILES_${PN} += "${libdir}/suil-0"

gha_license_id: MIT

extension: bb


text:
@article{McNeish2018,
abstract = {Empirical studies in psychology commonly report Cronbach's alpha as a measure of internal consistency reliability despite the fact that many methodological studies have shown that Cronbach's alpha is riddled with problems stemming from unrealistic assumptions. In many circumstances, violating these assumptions yields estimates of reliability that are too small, making measures look less reliable than they actually are. Although methodological critiques of Cronbach's alpha are being cited with increasing frequency in empirical studies, in this tutorial we discuss how the trend is not necessarily improving methodology used in the literature. That is, many studies continue to use Cronbach's alpha without regard for its assumptions or merely cite methodological articles advising against its use to rationalize unfavorable Cronbach's alpha estimates. This tutorial first provides evidence that recommendations against Cronbach's alpha have not appreciably changed how empirical studies report reliability. Then, we summarize the drawbacks of Cronbach's alpha conceptually without relying on mathematical or simulation-based arguments so that these arguments are accessible to a broad audience. We continue by discussing several alternative measures that make less rigid assumptions which provide justifiably higher estimates of reliability compared to Cronbach's alpha. We conclude with empirical examples to illustrate advantages of alternative measures of reliability including omega total, Revelle's omega total, the greatest lower bound, and Coefficient H. A detailed software appendix is also provided to help researchers implement alternative methods. (PsycINFO Database Record},
author = {McNeish, Daniel},
doi = {10.1037/met0000144},
issn = {1939-1463},
journal = {Psychological Methods},
month = {sep},
number = {3},
pages = {412--433},
pmid = {28557467},
title = {{Thanks coefficient alpha, we'll take it from here.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/28557467 http://doi.apa.org/getdoi.cfm?doi=10.1037/met0000144},
volume = {23},
year = {2018}
}
@article{Raykov2017,
abstract = {This note discusses the merits of coefficient alpha and their conditions in light of recent critical publications that miss out on significant research findings over the past several decades. That earlier research has demonstrated the empirical relevance and utility of coefficient alpha under certain empirical circumstances. The article highlights the fact that as an index aimed at informing about multiple-component measuring instrument reliability, coefficient alpha is dependable then as a reliability estimator. Therefore, alpha should remain in service when these conditions are fulfilled and not be abandoned.},
author = {Raykov, Tenko and Marcoulides, George A.},
doi = {10.1177/0013164417725127},
issn = {0013-1644},
journal = {Educational and Psychological Measurement},
keywords = {coefficient alpha,measuring instrument,population discrepancy,reliability,single-factor model},
month = {aug},
pages = {001316441772512},
publisher = {SAGE PublicationsSage CA: Los Angeles, CA},
title = {{Thanks Coefficient Alpha, We Still Need You!}},
url = {http://journals.sagepub.com/doi/10.1177/0013164417725127},
year = {2017}
}

@incollection{Nunnally1994,
author = {Nunnally, J.C. and Bernstein, I.H.},
booktitle = {Psychometric Theory},
edition = {3},
pages = {248--292},
title = {{The Assessment of Reliability.}},
year = {1994}
}

@article{Lance2006,
author = {Lance, C. E. and Butts, Marcus M. and Michels, Lawrence C.},
doi = {10.1177/1094428105284919},
file = {:C$\backslash$:/Users/Johannes.Karl/Dropbox/Papers/Lance, Butts, Michels/2006/The Sources of Four Commonly Reported Cutoff Criteria What Did They Really Say/Organizational Research Methods.pdf:pdf},
issn = {1094-4281},
journal = {Organizational Research Methods},
month = {apr},
number = {2},
pages = {202--220},
publisher = {SAGE Publications},
title = {{The Sources of Four Commonly Reported Cutoff Criteria: What Did They Really Say?}},
url = {http://orm.sagepub.com/cgi/doi/10.1177/1094428105284919},
volume = {9},
year = {2006}
}

@article{Norman2010,
abstract = {Reviewers of research reports frequently criticize the choice of statistical methods. While some of these criticisms are well-founded, frequently the use of various parametric methods such as analysis of variance, regression, correlation are faulted because: (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. In this paper, I dissect these arguments, and show that many studies, dating back to the 1930s consistently show that parametric statistics are robust with respect to violations of these assumptions. Hence, challenges like those above are unfounded, and parametric methods can be utilized without concern for "getting the wrong answer".},
author = {Norman, Geoff},
doi = {10.1007/s10459-010-9222-y},
issn = {1382-4996},
journal = {Advances in Health Sciences Education},
month = {dec},
number = {5},
pages = {625--632},
pmid = {20146096},
title = {{Likert scales, levels of measurement and the "laws" of statistics}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20146096 http://link.springer.com/10.1007/s10459-010-9222-y},
volume = {15},
year = {2010}
}

@article{Carifio2007,
abstract = {A recent article by Jamieson in Medical Education outlined some of the (alleged) abuses of "Likert scales" with suggestions about how researchers can overcome some of the (alleged) methodological pitfalls and limitations [1]. However, many of the ideas advanced in the Jamison article, as well as a great many of articles it cited, and similar recent articles in medical, health, psychology, and educational journals and books, are themselves common misunderstandings, misconceptions, conceptual errors, persistent myths and "urban legends" about "Likert scales" and their characteristics and qualities that have been propagated and perpetuated across six decades, for a variety of different reasons. This article identifies, analyses and traces many of these aforementioned problems and presents the arguments, counter arguments and empirical evidence that show these many persistent claims and myths about "Likert scales" to be factually incorrect and untrue. Many studies have shown that Likert Scales (as opposed to single Likert response format items) produce interval data and that the F-test is very robust to violations of the interval data assumption and moderate skewing and may be used to analyze "Likert data" (even if it is ordinal), but not on an item-by-item "shotgun" basis, which is simply a current research and analysis practice that must stop. After sixty years, it is more than time to dispel these particular research myths and urban legends as well as the various damage and problems they cause, and put them to bed and out of their misery once and for all.},
author = {Carifio, James and Perla, Rocco J},
file = {:C$\backslash$:/Users/Johannes.Karl/Dropbox/Papers/Carifio, Perla/2007/Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and their A./Journal of Social Sciences.pdf:pdf},
issn = {1549-3652},
journal = {Journal of Social Sciences},
keywords = {Likert,formats,measurement,psychological,scales},
number = {3},
pages = {106--116},
title = {{Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and their Antidotes}},
url = {https://thescipub.com/PDF/jssp.2007.106.116.pdf},
volume = {3},
year = {2007}
}

license: MIT

repository: Jo-Karl/JoKarl

license_type: permissive

repo_name: Jo-Karl/JoKarl

branch_name: refs/heads/master

extension: bib


text:
body {
  background-color: #1b1d1f;
  color: #b5b6b7; }

.menu ul li a {
  color: #4d5053; }
  .menu ul li a:hover {
    color: #b5b6b7; }
.menu ul li.selected a {
  color: #fcb514; }

.header {
  color: #444;
  background-color: #000;
  width: 100%;
  text-align: center;
}

.grid img {
  border-color: black; }

.main h2 a {
  color: white; }
  .main h2 a:hover {
    color: #b5b6b7; }

.meta {
  color: #4d5053; }
  .meta a {
    color: #4d5053; }
    .meta a:hover {
      color: #fcb514; }

a.typo {
  background: black url(social.png) no-repeat;
  border-color: black;
  background-position: 0px 0px;
  background-size: 140px 20px; }

.post p img {
  border-color: black; }
.post p code {
  color: black; }
.post blockquote {
  border-color: #4d5053; }
.post .info .name:after, .post .info .domain:after {
  color: #4d5053; }

.browse p:first-child {
  border-bottom: #404448 1px solid; }

.archive ol li a:hover {
  background-color: black; }
.archive .title {
  color: #b5b6b7 !important; }
.archive .date {
  color: #b5b6b7; }

extension: css


text:
@article{Singer:Distance2016,
 abstract = {The Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) discovered gravitational waves (GWs) from a binary black hole merger in 2015 September and may soon observe signals from neutron star mergers. There is considerable interest in searching for their faint and rapidly fading electromagnetic (EM) counterparts, though GW position uncertainties are as coarse as hundreds of square degrees. Because LIGO’s sensitivity to binary neutron stars is limited to the local universe, the area on the sky that must be searched could be reduced by weighting positions by mass, luminosity, or star formation in nearby galaxies. Since GW observations provide information about luminosity distance, combining the reconstructed volume with positions and redshifts of galaxies could reduce the area even more dramatically. A key missing ingredient has been a rapid GW parameter estimation algorithm that reconstructs the full distribution of sky location and distance. We demonstrate the first such algorithm, which takes under a minute, fast enough to enable immediate EM follow-up. By combining the three-dimensional posterior with a galaxy catalog, we can reduce the number of galaxies that could conceivably host the event by a factor of 1.4, the total exposure time for the Swift X-ray Telescope by a factor of 2, the total exposure time for a synoptic optical survey by a factor of 2, and the total exposure time for a narrow-field optical telescope by a factor of 3. This encourages us to suggest a new role for small field of view optical instruments in performing targeted searches of the most massive galaxies within the reconstructed volumes.},
 author = {Leo P. Singer and Hsin-Yu Chen and Daniel E. Holz and Will M. Farr and Larry R. Price and Vivien Raymond and S. Bradley
Cenko and Neil Gehrels and John Cannizzo and Mansi M. Kasliwal and Samaya Nissanke and Michael Coughlin and Ben Farr and Alex L.
Urban and Salvatore Vitale and John Veitch and Philip Graff and Christopher P. L. Berry and Satya Mohapatra and Ilya Mandel},
 journal = {The Astrophysical Journal Letters},
 number = {1},
 pages = {L15},
 title = {Going the Distance: Mapping Host Galaxies of LIGO and Virgo Sources in Three Dimensions Using Local Cosmography and Targeted Follow-up},
 url = {http://stacks.iop.org/2041-8205/829/i=1/a=L15},
 volume = {829},
 year = {2016}
}

repository: mcoughlin/personal-website

repo_name: mcoughlin/personal-website

gha_language: TeX


text:
@article{yusalzmann,
  author    = {Kaicheng Yu and
               Mathieu Salzmann},
  title     = {Second-order Convolutional Neural Networks},
  journal   = {CoRR},
  volume    = {abs/1703.06817},
  year      = {2017},
  url       = {http://arxiv.org/abs/1703.06817},
  archivePrefix = {arXiv},
  eprint    = {1703.06817},
  timestamp = {Wed, 07 Jun 2017 14:40:08 +0200},
  biburl    = {http://dblp.org/rec/bib/journals/corr/YuS17a},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}
@ARTICLE{chengzhouhan,
author={G. Cheng and P. Zhou and J. Han},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images},
year={2016},
volume={54},
number={12},
pages={7405-7415},
doi={10.1109/TGRS.2016.2601622},
ISSN={0196-2892},
month={Dec},}
@inproceedings{jaderberg,
  title={Spatial transformer networks},
  author={Jaderberg, Max and Simonyan, Karen and Zisserman, Andrew and others},
  booktitle={Advances in Neural Information Processing Systems},
  pages={2017--2025},
  year={2015}
}
@inproceedings{marcos,
  title={Learning rotation invariant convolutional filters for texture classification},
  author={Marcos, Diego and Volpi, Michele and Tuia, Devis},
  booktitle={Pattern Recognition (ICPR), 2016 23rd International Conference on},
  pages={2012--2017},
  year={2016},
  organization={IEEE}
}

repository: travers-rhodes/ricnn

repo_name: travers-rhodes/ricnn


text:
SUMMARY = "Development package for building Applications that use numa"
HOMEPAGE = "http://oss.sgi.com/projects/libnuma/"
DESCRIPTION = "Simple NUMA policy support. It consists of a numactl program \
to run other programs with a specific NUMA policy and a libnuma to do \
allocations with NUMA policy in applications."
LICENSE = "GPL-2.0 & LGPL-2.1"
SECTION = "apps"

inherit autotools-brokensep ptest

LIC_FILES_CHKSUM = "file://README.md;beginline=19;endline=32;md5=f8ff2391624f28e481299f3f677b21bb"

SRCREV = "dd6de072c92c892a86e18c0fd0dfa1ba57a9a05d"
PV = "2.0.14+git${SRCPV}"

SRC_URI = "git://github.com/numactl/numactl \
    file://Fix-the-test-output-format.patch \
    file://Makefile \
    file://run-ptest \
    file://0001-define-run-test-target.patch \
"

S = "${WORKDIR}/git"

# ARM does not currently support NUMA
COMPATIBLE_HOST = "^((?!arm).*)$"

LDFLAGS_append_riscv64 = " -latomic"
LDFLAGS_append_riscv32 = " -latomic"

do_install() {
    oe_runmake DESTDIR=${D} prefix=${D}/usr install
    #remove the empty man2 directory
    rm -r ${D}${mandir}/man2
}

do_compile_ptest() {
    oe_runmake test
}

do_install_ptest() {
    #install tests binaries
    local test_binaries="distance ftok mbind_mig_pages migrate_pages move_pages \
    mynode    nodemap node-parse pagesize prefered randmap realloc_test \
    tbitmap tshared"

    [ ! -d ${D}/${PTEST_PATH}/test ] && mkdir -p ${D}/${PTEST_PATH}/test
    for i in $test_binaries; do
        install -m 0755 ${B}/test/.libs/$i ${D}${PTEST_PATH}/test
    done

    local test_scripts="checktopology checkaffinity printcpu regress regress2 \
        shmtest  runltp bind_range"
    for i in $test_scripts; do
        install -m 0755 ${B}/test/$i ${D}${PTEST_PATH}/test
    done

    install -m 0755 ${WORKDIR}/Makefile ${D}${PTEST_PATH}/
    install -m 0755 ${B}/.libs/numactl ${D}${PTEST_PATH}/
}

RDEPENDS_${PN}-ptest = "bash"

repository: ampere-openbmc/openbmc

repo_name: ampere-openbmc/openbmc

gha_license_id: NOASSERTION


text:
@article{mccabe1976complexity,
  title={A complexity measure},
  author={McCabe, Thomas J},
  journal={IEEE Transactions on software Engineering},
  number={4},
  pages={308--320},
  year={1976},
  publisher={IEEE}
}

@book{lanza2007object,
  title={Object-oriented metrics in practice: using software metrics to characterize, evaluate, and improve the design of object-oriented systems},
  author={Lanza, Michele and Marinescu, Radu},
  year={2007},
  publisher={Springer Science \& Business Media}
}

@inproceedings{de2019assessing,
  title={Assessing diffusion and perception of test smells in scala projects},
  author={De Bleser, Jonas and Di Nucci, Dario and De Roover, Coen},
  booktitle={Proceedings of the 16th International Conference on Mining Software Repositories},
  pages={457--467},
  year={2019},
  organization={IEEE Press}
}

@article{shaik2012object,
  title={Object oriented software metrics and quality assessment: Current state of the art},
  author={Shaik, Amjan and Reddy, CRK and Damodaram, A},
  journal={International Journal of Computer Applications},
  volume={37},
  number={11},
  pages={6--15},
  year={2012},
  publisher={International Journal of Computer Applications, 244 5 th Avenue,\# 1526, New~…}
}

@inproceedings{barkmann2009quantitative,
  title={Quantitative evaluation of software quality metrics in open-source projects},
  author={Barkmann, Henrike and Lincke, R{\"u}diger and L{\"o}we, Welf},
  booktitle={2009 International Conference on Advanced Information Networking and Applications Workshops},
  pages={1067--1072},
  year={2009},
  organization={IEEE}
}

@inproceedings{marin2007integrated,
  title={An integrated crosscutting concern migration strategy and its application to JHotDraw},
  author={Marin, Marius and Moonen, Leon and van Deursen, Arie},
  booktitle={Seventh IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2007)},
  pages={101--110},
  year={2007},
  organization={IEEE}
}

@inproceedings{allen1970control,
  title={Control flow analysis},
  author={Allen, Frances E},
  booktitle={ACM Sigplan Notices},
  volume={5},
  number={7},
  pages={1--19},
  year={1970},
  organization={ACM}
}

@inproceedings{de2013relationships,
  title={Relationships Between Communication Structure and Software Architecture: An Empirical Investigation of the Conway's Law at the Federal University of Pernambuco},
  author={de Santana, Anderson M and da Silva, Fabio QB and de Miranda, Regina CG and Mascaro, Ang{\'e}lica A and Gouveia, Tatiana B and Monteiro, Cleviton VF and Santos, Andr{\'e} LM},
  booktitle={2013 3rd International Workshop on Replication in Empirical Software Engineering Research},
  pages={34--42},
  year={2013},
  organization={IEEE}
}

@article{marinescu2012assessing,
  title={Assessing technical debt by identifying design flaws in software systems},
  author={Marinescu, Radu},
  journal={IBM Journal of Research and Development},
  volume={56},
  number={5},
  pages={9--1},
  year={2012},
  publisher={IBM}
}

@inproceedings{du2006does,
  title={Does god class decomposition affect comprehensibility?},
  author={Du Bois, Bart and Demeyer, Serge and Verelst, Jan and Mens, Tom and Temmerman, Marijn},
  booktitle={IASTED Conf. on Software Engineering},
  pages={346--355},
  year={2006}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%                         Page 2                         %%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

@inproceedings{schroter2006if,
  title={If your bug database could talk},
  author={Schr{\"o}ter, Adrian and Zimmermann, Thomas and Premraj, Rahul and Zeller, Andreas},
  booktitle={Proceedings of the 5th international symposium on empirical software engineering},
  volume={2},
  pages={18--20},
  year={2006}
}
@inproceedings{mantyla2003taxonomy,
  title={A taxonomy and an initial empirical study of bad smells in code},
  author={Mantyla, Mika and Vanhanen, Jari and Lassenius, Casper},
  booktitle={International Conference on Software Maintenance, 2003. ICSM 2003. Proceedings.},
  pages={381--384},
  year={2003},
  organization={IEEE}
}
@article{basili1996validation,
  title={A validation of object-oriented design metrics as quality indicators},
  author={Basili, Victor R and Briand, Lionel C. and Melo, Walc{\'e}lio L},
  journal={IEEE Transactions on software engineering},
  volume={22},
  number={10},
  pages={751--761},
  year={1996},
  publisher={IEEE}
}
@book{odersky2008programming,
  title={Programming in scala},
  author={Odersky, Martin and Spoon, Lex and Venners, Bill},
  year={2008},
  publisher={Artima Inc}
}
@techreport{boehm1972software,
  title={Software and its impact: A quantitative assessment},
  author={Boehm, Barry W},
  year={1972},
  institution={RAND CORP SANTA MONICA CALIF},
  url={\url{https://www.rand.org/content/dam/rand/pubs/papers/2008/P4947.pdf}}
}
@article{kosaraju1974analysis,
  title={Analysis of structured programs},
  author={Kosaraju, S Rao},
  journal={Journal of Computer and System Sciences},
  volume={9},
  number={3},
  pages={232--255},
  year={1974},
  publisher={Elsevier}
}
@article{syeed2013evolution,
  title={Evolution of open source software projects: A systematic literature review.},
  author={Syeed, MM Mahbubul and Hammouda, Imed and Syst{\"a}, Tarja},
  journal={JSW},
  volume={8},
  number={11},
  pages={2815--2829},
  year={2013}
}
@article{jeanrenaud1970software,
  title={Software product evaluation metrics: a methodological approach},
  author={Jeanrenaud, A and Romanazzi, P},
  journal={WIT Transactions on Information and Communication Technologies},
  volume={9},
  year={1970},
  publisher={WIT Press}
}
@inproceedings{smith2000software,
  title={Software performance antipatterns.},
  author={Smith, Connie U and Williams, Lloyd G},
  booktitle={Workshop on Software and Performance},
  volume={17},
  pages={127--136},
  year={2000},
  organization={Ottawa, Canada}
}
@book{beck1997smalltalk,
  title={Smalltalk Best Practice Patterns. Volume 1: Coding},
  author={Beck, Kent},
  year={1997},
  publisher={Prentice Hall, Englewood Cliffs, NJ}
}
@inproceedings{van2002java,
  title={Java quality assurance by detecting code smells},
  author={Van Emden, Eva and Moonen, Leon},
  booktitle={Ninth Working Conference on Reverse Engineering, 2002. Proceedings.},
  pages={97--106},
  year={2002},
  organization={IEEE}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%% Above this line are the references from google Scholar %%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

@book{lanza2007objectOverviewPyramid,
  title={Object-oriented metrics in practice: using software metrics to characterize, evaluate, and improve the design of object-oriented systems},
  author={Lanza, Michele and Marinescu, Radu},
  year={2007},
  pages={24--33},
  publisher={Springer Science \& Business Media}
}

@misc{GithubScalametaContributers,
  author = {Eugene Burmako},
  title = {scalameta contributors},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/scalameta/scalameta/graphs/contributors}},
}

@INPROCEEDINGS{Marinescu05iplasma,
    author = {C. Marinescu and R. Marinescu and P. F. Mihancea and R. Wettel},
    title = {iplasma: An integrated platform for quality assessment of object-oriented design},
    booktitle = {In ICSM (Industrial and Tool Volume},
    year = {2005},
    pages = {77--80},
    publisher = {Society Press}
}

@inbook{Marek2010MetricsOOSoftware,
  author = {Borys, Magdalena and Miłosz, Marek},
  year = {2010},
  month = {01},
  pages = {81},
  title = {Metrics of Object Oriented Software},
  isbn = {978-83-931710-2-6}
}

@misc{Landkroon2017CodeQualityEvaluationScala,
  title = {Code Quality Evaluation for the Multi-Paradigm Programming Language Scala},
  author = {Erik Landkroon},
  publisher = {Universiteit van Amsterdam},
  howpublished = {\url{https://research.infosupport.com/wp-content/uploads/2019/05/08-17-Code-Quality-Evaluation-for-the-Multi-Paradigm-Programming-Language-Scala-Master-Thesis-Erik-Landkroon.pdf}},
}

gha_language: Java


text:
SUMMARY = "libsass command line driver "
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://LICENSE;md5=2f8a76980411a3f1f1480b141ce06744"

DEPENDS = "libsass"

inherit autotools pkgconfig

SRC_URI = "git://github.com/sass/sassc.git;branch=master;protocol=https"
SRCREV = "66f0ef37e7f0ad3a65d2f481eff09d09408f42d0"
S = "${WORKDIR}/git"
PV = "3.6.2"

BBCLASSEXTEND = "native"

repository: openembedded/meta-openembedded

repo_name: openembedded/meta-openembedded

gha_language: BitBake


text:
@InProceedings{lucic16,
  supplementary = {Supplementary:lucic16-supp.pdf},
  title = {Horizontally Scalable Submodular Maximization},
  author = {Mario Lucic and Olivier Bachem and Morteza Zadimoghaddam and Andreas Krause},
  pages = {2981-2989},
  abstract = {A variety of large-scale machine learning problems can be cast as instances of constrained submodular maximization. Existing approaches for distributed submodular maximization have a critical drawback: The capacity - number of instances that can fit in memory - must grow with the data set size. In practice, while one can provision many machines, the capacity of each machine is limited by physical constraints. We propose a truly scalable approach for distributed submodular maximization under fixed capacity. The proposed framework applies to a broad class of algorithms and constraints and provides theoretical guarantees on the approximation factor for any available capacity. We empirically evaluate the proposed algorithm on a variety of data sets and demonstrate that it achieves performance competitive with the centralized greedy solution.},
}

repository: mreid/papersite

repo_name: mreid/papersite


text:
# - Find Ragel executable and provides macros to generate custom build rules
# The module defines the following variables:
#
#  RAGEL_EXECUTABLE - path to the bison program
#  RAGEL_VERSION - version of bison
#  RAGEL_FOUND - true if the program was found
#
# If ragel is found, the module defines the macros:
#
#  RAGEL_TARGET(<Name> <RagelInp> <CodeOutput>
#              [COMPILE_FLAGS <string>])
#
# which will create  a custom rule to generate a state machine. <RagelInp> is
# the path to a Ragel file. <CodeOutput> is the name of the source file
# generated by ragel. If  COMPILE_FLAGS option is specified, the next
# parameter is  added in the bison  command line.
#
# The macro defines a set of variables:
#  RAGEL_${Name}_DEFINED       - true is the macro ran successfully
#  RAGEL_${Name}_INPUT         - The input source file, an alias for <RagelInp>
#  RAGEL_${Name}_OUTPUT_SOURCE - The source file generated by ragel
#  RAGEL_${Name}_OUTPUT_HEADER - The header file generated by ragel
#  RAGEL_${Name}_OUTPUTS       - The sources files generated by ragel
#  RAGEL_${Name}_COMPILE_FLAGS - Options used in the ragel command line
#
#  ====================================================================
#  Example:
#
#   find_package(RAGEL) # or e.g.: find_package(RAGEL 6.6 REQUIRED)
#   RAGEL_TARGET(MyMachine machine.rl ${CMAKE_CURRENT_BINARY_DIR}/machine.cc)
#   add_executable(Foo main.cc ${RAGEL_MyMachine_OUTPUTS})
#  ====================================================================

# 2014-02-09, Georg Sauthoff <mail@georg.so>
#
# I don't think that these few lines are even copyrightable material,
# but I am fine with using the BSD/MIT/GPL license on it ...
#
# I've used following references:
# http://www.cmake.org/cmake/help/v2.8.12/cmake.html
# /usr/share/cmake/Modules/FindFLEX.cmake
# /usr/share/cmake/Modules/FindBISON.cmake

# uses some features which are not available in 2.6
cmake_minimum_required(VERSION 2.8)

find_program(RAGEL_EXECUTABLE NAMES ragel DOC "path to the ragel executable")
mark_as_advanced(RAGEL_EXECUTABLE)

if(RAGEL_EXECUTABLE)

  execute_process(COMMAND ${RAGEL_EXECUTABLE} --version
    OUTPUT_VARIABLE RAGEL_version_output
    ERROR_VARIABLE  RAGEL_version_error
    RESULT_VARIABLE RAGEL_version_result
    OUTPUT_STRIP_TRAILING_WHITESPACE)

  if(${RAGEL_version_result} EQUAL 0)
    string(REGEX REPLACE "^Ragel State Machine Compiler version ([^ ]+) .*$"
                         "\\1"
                         RAGEL_VERSION "${RAGEL_version_output}")
  else()
    message(SEND_ERROR
            "Command \"${RAGEL_EXECUTABLE} --version\" failed with output:
${RAGEL_version_error}")
  endif()

  #============================================================
  # RAGEL_TARGET (public macro)
  #============================================================
  #
  macro(RAGEL_TARGET Name Input Output)
    set(RAGEL_TARGET_usage
              "RAGEL_TARGET(<Name> <Input> <Output> [COMPILE_FLAGS <string>]")
    if(${ARGC} GREATER 3)
      if(${ARGC} EQUAL 5)
        if("${ARGV3}" STREQUAL "COMPILE_FLAGS")
          set(RAGEL_EXECUTABLE_opts  "${ARGV4}")
          separate_arguments(RAGEL_EXECUTABLE_opts)
        else()
          message(SEND_ERROR ${RAGEL_TARGET_usage})
        endif()
      else()
        message(SEND_ERROR ${RAGEL_TARGET_usage})
      endif()
    endif()

    add_custom_command(OUTPUT ${Output}
      COMMAND ${RAGEL_EXECUTABLE}
      ARGS    ${RAGEL_EXECUTABLE_opts} -o${Output} ${Input}
      DEPENDS ${Input}
      COMMENT
          "[RAGEL][${Name}] Compiling state machine with Ragel ${RAGEL_VERSION}"
      WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})

    set(RAGEL_${Name}_DEFINED       TRUE)
    set(RAGEL_${Name}_OUTPUTS       ${Output})
    set(RAGEL_${Name}_INPUT         ${Input})
    set(RAGEL_${Name}_COMPILE_FLAGS ${RAGEL_EXECUTABLE_opts})
  endmacro()

endif()

# use this include when module file is located under /usr/share/cmake/Modules
#include(${CMAKE_CURRENT_LIST_DIR}/FindPackageHandleStandardArgs.cmake)
# use this include when module file is located in build tree
include(FindPackageHandleStandardArgs)
FIND_PACKAGE_HANDLE_STANDARD_ARGS(RAGEL REQUIRED_VARS  RAGEL_EXECUTABLE
                                        VERSION_VAR    RAGEL_VERSION)

repository: penberg/sphinx

repo_name: penberg/sphinx

extension: cmake


text:
DESCRIPTION = "TI Codecs and Server Combo for OMAP3530"
SECTION = "multimedia"
LICENSE = "TI"

require recipes-ti/includes/ti-paths.inc
require recipes-ti/includes/ti-staging.inc

PR="${MACHINE_KERNEL_PR}"
PR_append = "a"

PV="4_00_00_00"

LIC_FILES_CHKSUM = "file://packages/ti/sdo/server/cs/docs/cs1omap3530_software_manifest.pdf;md5=84444ba7c7abe117c30f9c960d1930ec"

CODEC_SUITE_NAME="${WORKDIR}/${PN}_${PV}"

SRCREV = "8393c892b09e0ac42b19ff1531e232478c3b1a6c"

require recipes-ti/includes/ti-eula-unpack.inc

SRC_URI="http://software-dl.ti.com/dsps/dsps_public_sw/codecs/OMAP35xx//OMAP35xx_latest/omap3530_h264enc_2_01_013_production.bin;name=h264enc \
    http://software-dl.ti.com/dsps/dsps_public_sw/codecs/OMAP35xx//OMAP35xx_latest/omap3530_h264dec_2_01_007_production.bin;name=h264dec \
    http://software-dl.ti.com/dsps/dsps_public_sw/codecs/OMAP35xx//OMAP35xx_latest/omap3530_jpegenc_02_01_01_00_production.bin;name=jpegenc \
    http://software-dl.ti.com/dsps/dsps_public_sw/codecs/C64XPlus_Video//C64XPlus_Video_latest/c64xplus_jpegdec_02_00_01_01_production.bin;name=jpegdec \
    http://software-dl.ti.com/dsps/dsps_public_sw/codecs/C64XPlus_Video//C64XPlus_Video_latest/c64xplus_mpeg2dec_02_00_02_00_production.bin;name=mpeg2dec \
    http://software-dl.ti.com/dsps/dsps_public_sw/codecs/OMAP35xx//OMAP35xx_latest/omap3530_mpeg4enc_02_04_00_00_production.bin;name=mpeg4enc \
    http://software-dl.ti.com/dsps/dsps_public_sw/codecs/C64XPlus_Video//C64XPlus_Video_latest/c64xplus_mpeg4dec_02_01_00_00_production.bin;name=mpeg4dec \
    http://software-dl.ti.com/dsps/dsps_public_sw/codecs/C64XPlus_Audio//C64XPlus_Audio_latest/c64xplus_aachedec_01_30_03_00_production.bin;name=aachedec \
    http://software-dl.ti.com/dsps/dsps_public_sw/codecs/C64XPlus_Speech//C64XPlus_Speech_latest/c64xplus_g711_1_12_00_000_production.bin;name=g711 \
    http://software-dl.ti.com/dsps/dsps_public_sw/sdo_tii/OMAP35xx_DM37xx_C64xPLUS_Algorithms/01_00_00_07//exports/c64xplus_deinterlacer_01_00_00_07_production.bin;name=i2p \
    git://arago-project.org/git/projects/codec-servers.git;protocol=git \
    \
    file://disable-c6accel.diff \
"

SRC_URI[h264enc.md5sum] = "4a7a4698b1db360fe103aae76127a4ec"
SRC_URI[h264enc.sha256sum] = "8fd970d83004bb099f51420b0eecd660b4ba9dccc87b2759d0b5a0be46f8f1a0"

SRC_URI[h264dec.md5sum] = "81980df2d9dbedc5b64789c4e5575819"
SRC_URI[h264dec.sha256sum] = "19d65e71ba0342670cb217e0fa6617263ff68a513e8444a8dfd5f34bd641b24f"

SRC_URI[jpegenc.md5sum] = "c2e8ad88e90c04d2de7b199517019ac8"
SRC_URI[jpegenc.sha256sum] = "6525e067cb5dd00cfc0b38045c44dcbed05866f8ad20188ceac630812502d473"

SRC_URI[jpegdec.md5sum] = "cf2886c3406ab41409a586e5d550918d"
SRC_URI[jpegdec.sha256sum] = "6b0c9f1b8f023070c6a59af690f015f84f2c7f3143235f788ddd1a4a7b229089"

SRC_URI[mpeg2dec.md5sum] = "da3d0561f3073352be43dce96ce1ea62"
SRC_URI[mpeg2dec.sha256sum] = "06cdb31242b8649bdd46bc07b9276de0ccd5f4e1c137d3cb79e0866c1ed04264"

SRC_URI[mpeg4enc.md5sum] = "07c36e5d03368e1326df75a1f0c4934d"
SRC_URI[mpeg4enc.sha256sum] = "a9566c8978f7230936053de9b1f3bfe8820ae555262ceba87243abdf60e5193f"

SRC_URI[mpeg4dec.md5sum] = "4a27cda2d5a859e6322680a4855b6b88"
SRC_URI[mpeg4dec.sha256sum] = "ca35db6841586fc2c22dd9c07a7f5b8557f480fa907e8a3471b660d4ada76e40"

SRC_URI[aachedec.md5sum] = "649f2e79b4950719295cfecbff2ea82b"
SRC_URI[aachedec.sha256sum] = "34d19e40d624ccdc1b371f9a5d6594b4793bdf3b7223ac65912d15d75320e020"

SRC_URI[g711.md5sum] = "fd8e9f939cc505dc5761705ed17a726c"
SRC_URI[g711.sha256sum] = "c87021e8df2a3f494f47e5bdce8a5fad04d667aa1b792fd9b3ecff634867b48d"

SRC_URI[i2p.md5sum] = "f67c04eec9ee49c7a686eecf5d54be33"
SRC_URI[i2p.sha256sum] = "3fbf8801f3ce2aabb6d31eb18e1e24e41ca861696b3140536f1d66adc76f0323"

TI_BIN_UNPK_CMDS = "Y:workdir"

S = "${CODEC_SUITE_NAME}"

DEPENDS = "ti-cgt6x ti-xdctools ti-dspbios ti-codec-engine ti-linuxutils"

#generic codec
DSPSUFFIX_omap3530 = "x64P"

python do_unpack () {
    bb.build.exec_func('base_do_unpack', d)

    bb.data.setVar("BINFILE", "omap3530_h264enc_2_01_013_production.bin", d)
    bb.data.setVar("TARFILE", "omap3530_h264enc_2_01_013_production.tar", d)
    bb.build.exec_func('ti_bin_do_unpack', d)

    bb.data.setVar("BINFILE", "omap3530_h264dec_2_01_007_production.bin", d)
    bb.data.setVar("TARFILE", "omap3530_h264dec_2_01_007_production.tar", d)
    bb.build.exec_func('ti_bin_do_unpack', d)

    bb.data.setVar("BINFILE", "omap3530_jpegenc_02_01_01_00_production.bin", d)
    bb.data.setVar("TARFILE", "omap3530_jpegenc_02_01_01_00_production.tar", d)
    bb.build.exec_func('ti_bin_do_unpack', d)

    bb.data.setVar("BINFILE", "c64xplus_jpegdec_02_00_01_01_production.bin", d)
    bb.data.setVar("TARFILE", "c64xplus_jpegdec_02_00_01_01_production.tar", d)
    bb.build.exec_func('ti_bin_do_unpack', d)

    bb.data.setVar("BINFILE", "c64xplus_mpeg2dec_02_00_02_00_production.bin", d)
    bb.data.setVar("TARFILE", "c64xplus_mpeg2dec_02_00_02_00_production.tar", d)
    bb.build.exec_func('ti_bin_do_unpack', d)

    bb.data.setVar("BINFILE", "omap3530_mpeg4enc_02_04_00_00_production.bin", d)
    bb.data.setVar("TARFILE", "omap3530_mpeg4enc_02_04_00_00_production.tar", d)
    bb.build.exec_func('ti_bin_do_unpack', d)

    bb.data.setVar("BINFILE", "c64xplus_mpeg4dec_02_01_00_00_production.bin", d)
    bb.data.setVar("TARFILE", "c64xplus_mpeg4dec_02_01_00_00_production.tar", d)
    bb.build.exec_func('ti_bin_do_unpack', d)

    bb.data.setVar("BINFILE", "c64xplus_aachedec_01_30_03_00_production.bin", d)
    bb.data.setVar("TARFILE", "dm6446_aachedec_01_30_03_00_production.tar", d)
    bb.build.exec_func('ti_bin_do_unpack', d)

    bb.data.setVar("BINFILE", "c64xplus_g711_1_12_00_000_production.bin", d)
    bb.data.setVar("TARFILE", "dm6446_g711enc_1_12_00_000_production.tar", d)
    bb.build.exec_func('ti_bin_do_unpack', d)

    bb.data.setVar("BINFILE", "c64xplus_g711_1_12_00_000_production.bin", d)
    bb.data.setVar("TARFILE", "dm6446_g711dec_1_12_00_000_production.tar", d)
    bb.build.exec_func('ti_bin_do_unpack', d)

    bb.data.setVar("BINFILE", "c64xplus_deinterlacer_01_00_00_07_production.bin", d)
    bb.data.setVar("TARFILE", "c64xplus_deinterlacer_01_00_00_07/c64xplus_deinterlacer_01_00_00_07_production.tar", d)
    bb.data.setVar("TI_BIN_UNPK_CMDS", "y: :q: ", d)
    bb.build.exec_func('ti_bin_do_unpack', d)

}

addtask prepsources after do_unpack before do_patch

do_prepsources () {

    mkdir -p ${CODEC_SUITE_NAME}/packages/ti/sdo/server/cs
    cp ${WORKDIR}/git/omap3530/cs1omap3530/rel-files/*  ${CODEC_SUITE_NAME}/
    cp ${WORKDIR}/git/omap3530/cs1omap3530/source/*  ${CODEC_SUITE_NAME}/packages/ti/sdo/server/cs
    cp -a "${WORKDIR}/git/omap3530/cs1omap3530/docs"  ${CODEC_SUITE_NAME}/packages/ti/sdo/server/cs

    mkdir -p ${CODEC_SUITE_NAME}/packages/ti/sdo/codecs
    cp -a "${WORKDIR}/omap3530_h264enc_2_01_013_production/packages/ti/sdo/codecs/h264enc" "${CODEC_SUITE_NAME}/packages/ti/sdo/codecs"
    cp -a "${WORKDIR}/omap3530_h264dec_2_01_007_production/packages/ti/sdo/codecs/h264dec" "${CODEC_SUITE_NAME}/packages/ti/sdo/codecs"
    cp -a "${WORKDIR}/omap3530_jpegenc_02_01_01_00_production/packages/ti/sdo/codecs/jpegenc" "${CODEC_SUITE_NAME}/packages/ti/sdo/codecs"
    cp -a "${WORKDIR}/c64xplus_jpegdec_02_00_01_01_production/packages/ti/sdo/codecs/jpegdec" "${CODEC_SUITE_NAME}/packages/ti/sdo/codecs"
    cp -a "${WORKDIR}/c64xplus_mpeg2dec_02_00_02_00_production/packages/ti/sdo/codecs/mpeg2dec" "${CODEC_SUITE_NAME}/packages/ti/sdo/codecs"
    cp -a "${WORKDIR}/omap3530_mpeg4enc_02_04_00_00_production/packages/ti/sdo/codecs/mpeg4enc" "${CODEC_SUITE_NAME}/packages/ti/sdo/codecs"
    cp -a "${WORKDIR}/c64xplus_mpeg4dec_02_01_00_00_production/packages/ti/sdo/codecs/mpeg4dec" "${CODEC_SUITE_NAME}/packages/ti/sdo/codecs"
    chmod -R +w "${WORKDIR}/dm6446_aachedec_01_30_03_00_production/packages/ti/sdo/codecs/aachedec/docs"
    cp -a "${WORKDIR}/dm6446_aachedec_01_30_03_00_production/packages/ti/sdo/codecs/aachedec" "${CODEC_SUITE_NAME}/packages/ti/sdo/codecs"
    cp -a "${WORKDIR}/dm6446_g711enc_1_12_00_000_production/packages/ti/sdo/codecs/g711enc" "${CODEC_SUITE_NAME}/packages/ti/sdo/codecs"
    cp -a "${WORKDIR}/dm6446_g711dec_1_12_00_000_production/packages/ti/sdo/codecs/g711dec" "${CODEC_SUITE_NAME}/packages/ti/sdo/codecs"
    cp -a "${WORKDIR}/c64xplus_deinterlacer_01_00_00_07_production/packages/ti/sdo/codecs/deinterlacer" "${CODEC_SUITE_NAME}/packages/ti/sdo/codecs"
    chmod 755 -R ${CODEC_SUITE_NAME}
}

do_compile() {

    cd "${S}"

    make \
             CE_INSTALL_DIR=${CE_INSTALL_DIR} \
             FC_INSTALL_DIR=${FC_INSTALL_DIR} \
             LINK_INSTALL_DIR=${LINK_INSTALL_DIR} \
             CMEM_INSTALL_DIR=${CMEM_INSTALL_DIR} \
             LPM_INSTALL_DIR=${LPM_INSTALL_DIR} \
             BIOS_INSTALL_DIR=${BIOS_INSTALL_DIR} \
             CODEGEN_INSTALL_DIR=${CODEGEN_INSTALL_DIR} \
             XDC_INSTALL_DIR=${XDC_INSTALL_DIR} \
             CODEC_INSTALL_DIR="${S}" \
             XDCARGS="prod" \
             C6ACCEL_INSTALL_DIR=${C6ACCEL_INSTALL_DIR} \
             clean

    make \
             CE_INSTALL_DIR=${CE_INSTALL_DIR} \
             FC_INSTALL_DIR=${FC_INSTALL_DIR} \
             LINK_INSTALL_DIR=${LINK_INSTALL_DIR} \
             CMEM_INSTALL_DIR=${CMEM_INSTALL_DIR} \
             LPM_INSTALL_DIR=${LPM_INSTALL_DIR} \
             BIOS_INSTALL_DIR=${BIOS_INSTALL_DIR} \
             CODEGEN_INSTALL_DIR=${CODEGEN_INSTALL_DIR} \
             XDC_INSTALL_DIR=${XDC_INSTALL_DIR} \
             CODEC_INSTALL_DIR="${S}" \
             XDCARGS="prod" \
             C6ACCEL_INSTALL_DIR=${C6ACCEL_INSTALL_DIR} \
             all
}

do_install() {

    install -d ${D}/${installdir}/ti-codecs-server
    cd ${S}

    # Install the DSP Server Binary
    for file in `find . -name *.${DSPSUFFIX}`; do
        cp ${file} ${D}/${installdir}/ti-codecs-server
    done

    # Install docs (codec qualiTI test reports, server config datasheet, etc)
    for file in `find . -name *.html`; do
        cp ${file} ${D}/${installdir}/ti-codecs-server
    done

    install -d ${D}${CODEC_INSTALL_DIR_RECIPE}
    cp -pPrf ${CODEC_SUITE_NAME}/* ${D}${CODEC_INSTALL_DIR_RECIPE}
}

PACKAGE_ARCH = "${MACHINE_ARCH}"

PACKAGES += "ti-codecs-omap3530-server"
FILES_ti-codecs-omap3530-server = "${installdir}/ti-codecs-server/*"

repository: Angstrom-distribution/meta-ti

repo_name: Angstrom-distribution/meta-ti

gha_language: BlitzBasic


text:
@article{TCS-054,
url = {http://dx.doi.org/10.1561/0400000054},
year = {2013},
volume = {8},
journal = {Foundations and Trends® in Theoretical Computer Science},
title = {Lx = b},
doi = {10.1561/0400000054},
issn = {1551-305X},
number = {1–2},
pages = {1-141},
author = {Nisheeth K. Vishnoi}
}


text:
DESCRIPTION = "Utilities for generating documentation from source code"
HOMEPAGE = "http://www.doxygen.org/"
SECTION = "console/utils"
LICENSE = "GPL-2.0"
LIC_FILES_CHKSUM = "file://${COREBASE}/meta/files/common-licenses/GPL-2.0;md5=801f80980d171dd6425610833a22dbe6"

DEPENDS = "flex-native bison-native virtual/libiconv"

SRC_URI = "git://github.com/doxygen/doxygen.git;protocol=https"

SRCREV = "${AUTOREV}"

S ="${WORKDIR}/git"

inherit cmake pythonnative

BBCLASSEXTEND = "native"

repository: ahmedmoselhi/meta-neutrino

repo_name: ahmedmoselhi/meta-neutrino


text:
DESCRIPTION = "Free GPS navigation for car and outdoor with OpenStreetMap maps"
HOMEPAGE = "http://www.gps-routes.info/index.php?name=Content&pa=showpage&pid=1"
LICENSE = "GPLv3"
LIC_FILES_CHKSUM = "file://COPYING;md5=8f0e2cd40e05189ec81232da84bd6e1a"
DEPENDS = "ecore evas imlib2 gpsd edje-native mysql5"

SRC_URI = "http://www.gps-routes.info/debian/pool/main/m/mcnavi/mcnavi_${PV}.tar.gz"
SRC_URI[md5sum] = "acc07debcdb3ef448b95e6a6cfcf53d8"
SRC_URI[sha256sum] = "d3ce1f5901f13985d877b2b6ad06b64094ba43a4e13cbfe06c7cb2f1abdda309"

S = "${WORKDIR}/${PN}"

do_configure_prepend() {
  # for some reason our mysql_config returns just sysroot/include_dir while mysql.h is in include_dir/mysql
  sed -i 's#`mysql_config --include`#`mysql_config --include`/mysql#g' ${S}/configure.ac
}

inherit autotools

repository: sgh/meta-openembedded

repo_name: sgh/meta-openembedded


text:
@proceedings{PJM19,
author={Pote,Yash and  Joshi, Saurabh and Meel, Kuldeep S. },
title={ Phase Transition Behavior of Cardinality and XOR Constraints },
year={2019},
booktitle={Proceedings of International Joint Conference on Artificial Intelligence (IJCAI)}
}

repository: meelgroup/meelgroup-website

repo_name: meelgroup/meelgroup-website


text:
@inproceedings{2023-CFRB,
 author = {Luca Cappelletti and Tommaso Fontana and Justin Reese and David A. Bader},
 booktitle = {The Workshop on Graphs, Architectures, Programming, and Learning (GrAPL), May 15, 2023},
 groups = {Edited},
 title = {Billion-scale Detection of Isomorphic Nodes},
 year = {2023},
 pages = {230--233},
 address = {St. Petersburg, FL},
 url  = {},
 doi = {10.1109/IPDPSW59300.2023.00046},
 abstract = {This paper presents an algorithm for detecting attributed
high-degree node isomorphism. High-degree isomorphic
nodes seldom happen by chance and often represent duplicated
entities or data processing errors. By definition, isomorphic nodes
are topologically indistinguishable and can be problematic in
graph ML tasks. The algorithm employs a parallel, ``degreebounded''
approach that fingerprints each node’s local properties
through a hash, which constrains the search to nodes within hashdefined
buckets, thus minimising the number of comparisons.
This method scales on graphs with billions of nodes and edges.
Finally, we provide isomorphic node oddities identified in realworld
data.}
}

gha_language: Shell


text:
@inproceedings{szegedy2015going,
  title={Going deeper with convolutions},
  author={Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={1--9},
  year={2015}
}

@article{stewart2015end,
  title={End-to-end people detection in crowded scenes},
  author={Stewart, Russell and Andriluka, Mykhaylo},
  journal={arXiv preprint arXiv:1506.04878},
  year={2015}
}

@article{setio2016validation,
  title={Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge},
  author={Setio, Arnaud Arindra Adiyoso and Traverso, Alberto and de Bel, Thomas and Berens, Moira SN and Bogaard, Cas van den and Cerello, Piergiorgio and Chen, Hao and Dou, Qi and Fantacci, Maria Evelina and Geurts, Bram and others},
  journal={arXiv preprint arXiv:1612.08012},
  year={2016}
}

@article{hochreiter1997long,
  title={Long short-term memory},
  author={Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
  journal={Neural computation},
  volume={9},
  number={8},
  pages={1735--1780},
  year={1997},
  publisher={MIT Press}
}

@article{sermanet2013overfeat,
  title={Overfeat: Integrated recognition, localization and detection using convolutional networks},
  author={Sermanet, Pierre and Eigen, David and Zhang, Xiang and Mathieu, Micha{\"e}l and Fergus, Rob and LeCun, Yann},
  journal={arXiv preprint arXiv:1312.6229},
  year={2013}
}

@article{abadi2016tensorflow,
  title={Tensorflow: Large-scale machine learning on heterogeneous distributed systems},
  author={Abadi, Mart{\i}n and Agarwal, Ashish and Barham, Paul and Brevdo, Eugene and Chen, Zhifeng and Citro, Craig and Corrado, Greg S and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and others},
  journal={arXiv preprint arXiv:1603.04467},
  year={2016}
}
@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}

repository: xhongz/cancer

repo_name: xhongz/cancer


text:
@article{bezsudnova2020molekulyarnyi301692610,
 address = {Москва},
 author = {Хренова, М. Г. and Завьялова, С. В. and Безсуднова, Е. Ю.},
 doi = {10.3103/S0027131420030050},
 issn = {0579-9384},
 journal = {Вестник Московского университета. Серия 2: Химия},
 language = {russian},
 number = {3},
 pages = {22--27},
 publisher = {Москва},
 title = {Молекулярный механизм стереоспецифичности трансаминазы из Desulfohalobium retbaense к d-лейцину по данным молекулярно-динамического моделирования},
 volume = {61},
 year = {2020}
}

repository: polyakoviv/LCC

repo_name: polyakoviv/LCC

branch_name: refs/heads/main


text:
@article{bates2019effect,
 author = {Bates, Lyndel and Hawkins, Alana and Rodwell, David and Anderson, Levi and Watson, Barry and Filtness, Ashleigh J and Larue, Grégoire S},
 journal = {Transportation research part F: traffic psychology and behaviour},
 pages = {151--161},
 publisher = {Elsevier},
 title = {The effect of psychosocial factors on perceptions of driver education using the goals for driver education framework},
 volume = {66},
 year = {2019}
}

license: Unlicense,MIT

detected_licenses: Unlicense MIT


text:
@article{amiranashvili_scaling_2020,
	title = {Scaling {Imitation} {Learning} in {Minecraft}},
	url = {http://arxiv.org/abs/2007.02701},
	abstract = {Imitation learning is a powerful family of techniques for learning sensorimotor coordination in immersive environments. We apply imitation learning to attain state-of-the-art performance on hard exploration problems in the Minecraft environment. We report experiments that highlight the influence of network architecture, loss function, and data augmentation. An early version of our approach reached second place in the MineRL competition at NeurIPS 2019. Here we report stronger results that can be used as a starting point for future competition entries and related research. Our code is available at https://github.com/amiranas/minerl\_imitation\_learning.},
	urldate = {2020-11-25},
	journal = {arXiv:2007.02701 [cs, stat]},
	author = {Amiranashvili, Artemij and Dorka, Nicolai and Burgard, Wolfram and Koltun, Vladlen and Brox, Thomas},
	month = jul,
	year = {2020},
	note = {arXiv: 2007.02701},
	keywords = {deep learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning, imitation learning, data augmentation},
	file = {arXiv Fulltext PDF:/Users/mcrowley/Zotero/storage/8EHXXMJQ/Amiranashvili et al. - 2020 - Scaling Imitation Learning in Minecraft.pdf:application/pdf},
}

@misc{noauthor_deep_2016,
	title = {Deep learning},
	copyright = {Creative Commons Attribution-ShareAlike License},
	url = {https://en.wikipedia.org/w/index.php?title=Deep_learning&oldid=701771907},
	abstract = {Deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations.
Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction.
Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.
Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.
Alternatively, deep learning has been characterized as a buzzword, or a rebranding of neural networks.},
	language = {en},
	urldate = {2016-01-28},
	journal = {Wikipedia, the free encyclopedia},
	month = jan,
	year = {2016},
	note = {Page Version ID: 701771907},
}

@inproceedings{ronneberger_u-net_2015,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {U-{Net}: {Convolutional} {Networks} for {Biomedical} {Image} {Segmentation}},
	isbn = {978-3-319-24574-4},
	shorttitle = {U-{Net}},
	doi = {10.1007/978-3-319-24574-4_28},
	abstract = {There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer}-{Assisted} {Intervention} – {MICCAI} 2015},
	publisher = {Springer International Publishing},
	author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
	editor = {Navab, Nassir and Hornegger, Joachim and Wells, William M. and Frangi, Alejandro F.},
	year = {2015},
	keywords = {deep learning, medical imaging, Convolutional Layer, Data Augmentation, Ground Truth Segmentation, Training Image, compact encoding},
	pages = {234--241},
	file = {Springer Full Text PDF:/Users/mcrowley/Zotero/storage/W8EHC6MP/Ronneberger et al. - 2015 - U-Net Convolutional Networks for Biomedical Image.pdf:application/pdf},
}

@article{bai_empirical_2018,
	title = {An {Empirical} {Evaluation} of {Generic} {Convolutional} and {Recurrent} {Networks} for {Sequence} {Modeling}},
	url = {http://arxiv.org/abs/1803.01271},
	abstract = {For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given a new sequence modeling task or dataset, which architecture should one use? We conduct a systematic evaluation of generic convolutional and recurrent architectures for sequence modeling. The models are evaluated across a broad range of standard tasks that are commonly used to benchmark recurrent networks. Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory. We conclude that the common association between sequence modeling and recurrent networks should be reconsidered, and convolutional networks should be regarded as a natural starting point for sequence modeling tasks. To assist related work, we have made code available at http://github.com/locuslab/TCN .},
	urldate = {2020-12-29},
	journal = {arXiv:1803.01271 [cs]},
	author = {Bai, Shaojie and Kolter, J. Zico and Koltun, Vladlen},
	month = apr,
	year = {2018},
	note = {arXiv: 1803.01271},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Computation and Language, recurrent networks, gradient optimizers},
	file = {arXiv Fulltext PDF:/Users/mcrowley/Zotero/storage/IQKUDEP5/Bai et al. - 2018 - An Empirical Evaluation of Generic Convolutional a.pdf:application/pdf;arXiv.org Snapshot:/Users/mcrowley/Zotero/storage/LSNPTAIX/1803.html:text/html},
}

@inproceedings{pennington_glove_2014,
	address = {Doha, Qatar},
	title = {Glove: {Global} {Vectors} for {Word} {Representation}},
	shorttitle = {Glove},
	url = {http://aclweb.org/anthology/D14-1162},
	doi = {10.3115/v1/D14-1162},
	abstract = {Recent methods for learning vector space representations of words have succeeded in capturing ﬁne-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efﬁciently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75\% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.},
	language = {en},
	urldate = {2020-12-27},
	booktitle = {Proceedings of the 2014 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing} ({EMNLP})},
	publisher = {Association for Computational Linguistics},
	author = {Pennington, Jeffrey and Socher, Richard and Manning, Christopher},
	year = {2014},
	pages = {1532--1543},
	file = {Pennington et al. - 2014 - Glove Global Vectors for Word Representation.pdf:/Users/mcrowley/Zotero/storage/FVYB3XPZ/Pennington et al. - 2014 - Glove Global Vectors for Word Representation.pdf:application/pdf},
}

@article{white_speech_1976,
	title = {Speech {Recognition}: {A} {Tutorial} {Overview}},
	volume = {9},
	issn = {0018-9162},
	shorttitle = {Speech {Recognition}},
	url = {https://www.computer.org/csdl/magazine/co/1976/05/01647361/13rRUB6SpUP},
	doi = {10.1109/C-M.1976.218586},
	abstract = {Research toward mechanical recognition of speech is laying the foundation for significant advances in pattern recognition and artificial intelligence. This paper explains the nature of some of these advances and provides an introduction to the state of the art of automatic speech recognition.},
	language = {English},
	number = {05},
	urldate = {2020-12-27},
	author = {White, G. M.},
	month = may,
	year = {1976},
	note = {Publisher: IEEE Computer Society},
	pages = {40--53},
	file = {Snapshot:/Users/mcrowley/Zotero/storage/HD5XI5D3/13rRUB6SpUP.html:text/html},
}

@article{uijlings_selective_2013,
	title = {Selective {Search} for {Object} {Recognition}},
	volume = {104},
	issn = {0920-5691, 1573-1405},
	url = {http://link.springer.com/10.1007/s11263-013-0620-5},
	doi = {10.1007/s11263-013-0620-5},
	abstract = {This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 \% recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi. unitn.it/{\textasciitilde}uijlings/SelectiveSearch.html).},
	language = {en},
	number = {2},
	urldate = {2020-12-26},
	journal = {International Journal of Computer Vision},
	author = {Uijlings, J. R. R. and van de Sande, K. E. A. and Gevers, T. and Smeulders, A. W. M.},
	month = sep,
	year = {2013},
	keywords = {ablation},
	pages = {154--171},
	file = {Uijlings et al. - 2013 - Selective Search for Object Recognition.pdf:/Users/mcrowley/Zotero/storage/PVYV9SFP/Uijlings et al. - 2013 - Selective Search for Object Recognition.pdf:application/pdf},
}

@article{girshick_rich_2014,
	title = {Rich feature hierarchies for accurate object detection and semantic segmentation},
	url = {http://arxiv.org/abs/1311.2524},
	abstract = {Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30\% relative to the previous best result on VOC 2012---achieving a mAP of 53.3\%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also compare R-CNN to OverFeat, a recently proposed sliding-window detector based on a similar CNN architecture. We find that R-CNN outperforms OverFeat by a large margin on the 200-class ILSVRC2013 detection dataset. Source code for the complete system is available at http://www.cs.berkeley.edu/{\textasciitilde}rbg/rcnn.},
	urldate = {2020-12-26},
	journal = {arXiv:1311.2524 [cs]},
	author = {Girshick, Ross and Donahue, Jeff and Darrell, Trevor and Malik, Jitendra},
	month = oct,
	year = {2014},
	note = {arXiv: 1311.2524},
	keywords = {ablation, Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: Extended version of our CVPR 2014 paper; latest update (v5) includes results using deeper networks (see Appendix G. Changelog)},
	file = {arXiv Fulltext PDF:/Users/mcrowley/Zotero/storage/QDWDUAGU/Girshick et al. - 2014 - Rich feature hierarchies for accurate object detec.pdf:application/pdf;arXiv.org Snapshot:/Users/mcrowley/Zotero/storage/PGHFW7UW/1311.html:text/html},
}

@article{coupe_different_2019,
	title = {Different languages, similar encoding efficiency: {Comparable} information rates across the human communicative niche},
	volume = {5},
	copyright = {Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.},
	issn = {2375-2548},
	shorttitle = {Different languages, similar encoding efficiency},
	url = {https://advances.sciencemag.org/content/5/9/eaaw2594},
	doi = {10.1126/sciadv.aaw2594},
	abstract = {{\textless}p{\textgreater}Language is universal, but it has few indisputably universal characteristics, with cross-linguistic variation being the norm. For example, languages differ greatly in the number of syllables they allow, resulting in large variation in the Shannon information per syllable. Nevertheless, all natural languages allow their speakers to efficiently encode and transmit information. We show here, using quantitative methods on a large cross-linguistic corpus of 17 languages, that the coupling between language-level (information per syllable) and speaker-level (speech rate) properties results in languages encoding similar information rates ({\textasciitilde}39 bits/s) despite wide differences in each property individually: Languages are more similar in information rates than in Shannon information or speech rate. These findings highlight the intimate feedback loops between languages’ structural properties and their speakers’ neurocognition and biology under communicative pressures. Thus, language is the product of a multiscale communicative niche construction process at the intersection of biology, environment, and culture.{\textless}/p{\textgreater}},
	language = {en},
	number = {9},
	urldate = {2020-12-16},
	journal = {Science Advances},
	author = {Coupé, Christophe and Oh, Yoon Mi and Dediu, Dan and Pellegrino, François},
	month = sep,
	year = {2019},
	keywords = {encodings, language, nlp},
	pages = {eaaw2594},
	file = {Full Text PDF:/Users/mcrowley/Zotero/storage/LDTTFNTN/Coupé et al. - 2019 - Different languages, similar encoding efficiency .pdf:application/pdf},
}

@article{fawcett_analysing_2016,
	title = {Analysing differences between algorithm configurations through ablation},
	volume = {22},
	issn = {1572-9397},
	url = {https://doi.org/10.1007/s10732-014-9275-9},
	doi = {10.1007/s10732-014-9275-9},
	abstract = {Developers of high-performance algorithms for hard computational problems increasingly take advantage of automated parameter tuning and algorithm configuration tools, and consequently often create solvers with many parameters and vast configuration spaces. However, there has been very little work to help these algorithm developers answer questions about the high-quality configurations produced by these tools, specifically about which parameter changes contribute most to improved performance. In this work, we present an automated technique for answering such questions by performing ablation analysis between two algorithm configurations. We perform an extensive empirical analysis of our technique on five scenarios from propositional satisfiability, mixed-integer programming and AI planning, and show that in all of these scenarios more than 95 \% of the performance gains between default configurations and optimised configurations obtained from automated configuration tools can be explained by modifying the values of a small number of parameters (1–4 in the scenarios we studied). We also investigate the use of our ablation analysis procedure for producing configurations that generalise well to previously-unseen problem domains, as well as for analysing the structure of the algorithm parameter response surface near and between high-performance configurations.},
	language = {en},
	number = {4},
	urldate = {2020-12-26},
	journal = {Journal of Heuristics},
	author = {Fawcett, Chris and Hoos, Holger H.},
	month = aug,
	year = {2016},
	keywords = {ablation},
	pages = {431--458},
}

@misc{noauthor_neurips_nodate,
	title = {{NeurIPS} 2020 : {Unsupervised} {Data} {Augmentation} for {Consistency} {Training}},
	url = {https://nips.cc/virtual/2020/public/poster_44feb0096faa8326192570788b38c1d1.html},
	urldate = {2020-12-17},
	keywords = {medical imaging, data augmentation, nlp},
}

@article{radford_improving_nodate,
	title = {Improving {Language} {Understanding} by {Generative} {Pre}-{Training}},
	abstract = {Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classiﬁcation. Although large unlabeled text corpora are abundant, labeled data for learning these speciﬁc tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative ﬁne-tuning on each speciﬁc task. In contrast to previous approaches, we make use of task-aware input transformations during ﬁne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speciﬁcally crafted for each task, signiﬁcantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9\% on commonsense reasoning (Stories Cloze Test), 5.7\% on question answering (RACE), and 1.5\% on textual entailment (MultiNLI).},
	language = {en},
	author = {Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya},
	keywords = {attention, nlp},
	pages = {12},
	file = {Radford et al. - Improving Language Understanding by Generative Pre.pdf:/Users/mcrowley/Zotero/storage/U8WM3WNS/Radford et al. - Improving Language Understanding by Generative Pre.pdf:application/pdf},
}

@article{tan_fast_nodate,
	title = {Fast {Anomaly} {Detection} for {Streaming} {Data}},
	abstract = {This paper introduces Streaming Half-Space-Trees (HS-Trees), a fast one-class anomaly detector for evolving data streams. It requires only normal data for training and works well when anomalous data are rare. The model features an ensemble of random HS-Trees, and the tree structure is constructed without any data. This makes the method highly efﬁcient because it requires no model restructuring when adapting to evolving data streams. Our analysis shows that Streaming HS-Trees has constant amortised time complexity and constant memory requirement. When compared with a state-of-theart method, our method performs favourably in terms of detection accuracy and runtime performance. Our experimental results also show that the detection performance of Streaming HS-Trees is not sensitive to its parameter settings.},
	language = {en},
	author = {Tan, Swee Chuan and Ting, Kai Ming and Liu, Tony Fei},
	keywords = {anomaly detection},
	pages = {6},
	file = {Tan et al. - Fast Anomaly Detection for Streaming Data.pdf:/Users/mcrowley/Zotero/storage/3UE7QVRF/Tan et al. - Fast Anomaly Detection for Streaming Data.pdf:application/pdf},
}

@article{chandola_anomaly_2009,
	title = {Anomaly detection: {A} survey},
	volume = {41},
	number = {3},
	journal = {ACM computing surveys (CSUR)},
	author = {Chandola, Varun and Banerjee, Arindam and Kumar, Vipin},
	year = {2009},
	note = {Publisher: ACM},
	keywords = {anomaly detection},
	pages = {15},
}

@inproceedings{liu_isolation_2008,
	title = {Isolation forest},
	booktitle = {2008 {Eighth} {IEEE} {International} {Conference} on {Data} {Mining}},
	publisher = {IEEE},
	author = {Liu, Fei Tony and Ting, Kai Ming and Zhou, Zhi-Hua},
	year = {2008},
	keywords = {anomaly detection},
	pages = {413--422},
}

@article{liu_isolation-based_2012,
	title = {Isolation-based anomaly detection},
	volume = {6},
	number = {1},
	journal = {ACM Transactions on Knowledge Discovery from Data (TKDD)},
	author = {Liu, Fei Tony and Ting, Kai Ming and Zhou, Zhi-Hua},
	year = {2012},
	note = {Publisher: Acm},
	keywords = {anomaly detection},
	pages = {3},
}

@inproceedings{lakshminarayanan_mondrian_2014,
	title = {Mondrian forests: {Efficient} online random forests},
	booktitle = {{NeurIPS} conference},
	author = {Lakshminarayanan, Balaji and Roy, Daniel M and Teh, Yee Whye},
	year = {2014},
	keywords = {ensemble methods, streaming ensembles},
	pages = {3140--3148},
}

@inproceedings{lakshminarayanan_mondrian_2016,
	title = {Mondrian forests for large-scale regression when uncertainty matters},
	booktitle = {Artificial {Intelligence} and {Statistics}},
	author = {Lakshminarayanan, Balaji and Roy, Daniel M and Teh, Yee Whye},
	year = {2016},
	keywords = {ensemble methods, streaming ensembles},
	pages = {1478--1487},
}

@inproceedings{roy_mondrian_2008,
	title = {The {Mondrian} {Process}},
	booktitle = {{NeurIPS} conference},
	author = {Roy, Daniel M and Teh, Yee Whye},
	year = {2008},
	keywords = {ensemble methods, streaming ensembles},
	pages = {1377--1384},
}

@inproceedings{breunig_lof_2000,
	title = {{LOF}: identifying density-based local outliers},
	volume = {29},
	booktitle = {{ACM} sigmod record},
	publisher = {ACM},
	author = {Breunig, Markus M and Kriegel, Hans-Peter and Ng, Raymond T and Sander, Jörg},
	year = {2000},
	note = {Issue: 2},
	keywords = {anomaly detection},
	pages = {93--104},
}

@inproceedings{pokrajac_incremental_2007,
	title = {Incremental local outlier detection for data streams},
	booktitle = {2007 {IEEE} symposium on {CIDM}},
	publisher = {IEEE},
	author = {Pokrajac, Dragoljub and Lazarevic, Aleksandar and Latecki, Longin Jan},
	year = {2007},
	pages = {504--515},
}

@article{pimentel_review_2014,
	title = {A review of novelty detection},
	volume = {99},
	journal = {Signal Processing},
	author = {Pimentel, Marco AF and Clifton, David A and Clifton, Lei and Tarassenko, Lionel},
	year = {2014},
	note = {Publisher: Elsevier},
	keywords = {anomaly detection},
	pages = {215--249},
}

@inproceedings{scholkopf_support_2000,
	title = {Support vector method for novelty detection},
	booktitle = {{NeurIPS} conference},
	author = {Schölkopf, Bernhard and Williamson, Robert C and Smola, Alex J and Shawe-Taylor, John and Platt, John C},
	year = {2000},
	keywords = {anomaly detection, support vector machines},
	pages = {582--588},
}

@article{rousseeuw_fast_1999,
	title = {A fast algorithm for the minimum covariance determinant estimator},
	volume = {41},
	number = {3},
	journal = {Technometrics},
	author = {Rousseeuw, Peter J and Driessen, Katrien Van},
	year = {1999},
	note = {Publisher: Taylor \& Francis Group},
	pages = {212--223},
}

@book{rayana_outlier_2019,
	title = {Outlier {Detection} {Data} {Sets}},
	url = {http://odds.cs.stonybrook.edu/},
	author = {Rayana, Shebuti},
	year = {2019},
	keywords = {anomaly detection},
}

@book{canadian_institute_for_cybersecurity_intrusion_2017,
	title = {Intrusion {Detection} {Evaluation} {Dataset} ({CICIDS2017})},
	url = {https://www.unb.ca/cic/datasets/ids-2017.html},
	author = {{Canadian Institute for Cybersecurity}},
	year = {2017},
	keywords = {anomaly detection},
}

@inproceedings{sharafaldin_toward_2018,
	title = {Toward {Generating} a {New} {Intrusion} {Detection} {Dataset} and {Intrusion} {Traffic} {Characterization}.},
	booktitle = {{ICISSP}},
	author = {Sharafaldin, Iman and Lashkari, Arash Habibi and Ghorbani, Ali A},
	year = {2018},
	keywords = {anomaly detection},
	pages = {108--116},
}

@incollection{yeh_anomaly_2009,
	title = {Anomaly detection via over-sampling principal component analysis},
	booktitle = {New {Advances} in {Intelligent} {Decision} {Technologies}},
	publisher = {Springer},
	author = {Yeh, Yi-Ren and Lee, Zheng-Yi and Lee, Yuh-Jye},
	year = {2009},
	keywords = {anomaly detection},
	pages = {449--458},
}

@article{lee_anomaly_2013,
	title = {Anomaly detection via online oversampling principal component analysis},
	volume = {25},
	number = {7},
	journal = {IEEE transactions on knowledge and data engineering},
	author = {Lee, Yuh-Jye and Yeh, Yi-Ren and Wang, Yu-Chiang Frank},
	year = {2013},
	note = {Publisher: IEEE},
	keywords = {anomaly detection},
	pages = {1460--1470},
}

@inproceedings{ahmed_online_2009,
	title = {Online anomaly detection using {KDE}},
	booktitle = {2009 {IEEE} conference on global telecommunications},
	publisher = {IEEE},
	author = {Ahmed, Tarem},
	year = {2009},
	keywords = {anomaly detection},
	pages = {1--8},
}

@inproceedings{saffari_-line_2009,
	title = {On-line random forests},
	booktitle = {2009 {IEEE} {ICCV} workshops},
	publisher = {IEEE},
	author = {Saffari, Amir and Leistner, Christian and Santner, Jakob and Godec, Martin and Bischof, Horst},
	year = {2009},
	keywords = {ensemble methods, streaming ensembles},
	pages = {1393--1400},
}

@inproceedings{domingos_mining_2000,
	title = {Mining high-speed data streams},
	volume = {2},
	booktitle = {{KDD}},
	author = {Domingos, Pedro and Hulten, Geoff},
	year = {2000},
	keywords = {streaming ensembles},
	pages = {71--80},
	annote = {The paper that introduced Hoeffding Trees.},
}

@inproceedings{abdulsalam_streaming_2007,
	title = {Streaming random forests},
	booktitle = {11th {IDEAS} 2007},
	publisher = {IEEE},
	author = {Abdulsalam, Hanady and Skillicorn, David B and Martin, Patrick},
	year = {2007},
	keywords = {ensemble methods, streaming ensembles},
	pages = {225--232},
}

@article{ghojogh_theory_2019,
	title = {The {Theory} {Behind} {Overfitting}, {Cross} {Validation}, {Regularization}, {Bagging}, and {Boosting}: {Tutorial}},
	journal = {arXiv preprint arXiv:1905.12787},
	author = {Ghojogh, Benyamin and Crowley, Mark},
	year = {2019},
	keywords = {tutorial},
}

@article{geurts_extremely_2006,
	title = {Extremely randomized trees},
	volume = {63},
	number = {1},
	journal = {Machine learning},
	author = {Geurts, Pierre and Ernst, Damien and Wehenkel, Louis},
	year = {2006},
	note = {Publisher: Springer},
	keywords = {ensemble methods},
	pages = {3--42},
}

@article{criminisi_decision_2012,
	title = {Decision forests: {A} unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning},
	volume = {7},
	number = {2–3},
	journal = {Foundations and Trends® in Computer Graphics and Vision},
	author = {Criminisi, Antonio and Shotton, Jamie and Konukoglu, Ender},
	year = {2012},
	note = {Publisher: Now Publishers, Inc.},
	keywords = {ensemble methods},
	pages = {81--227},
}

@article{breiman_random_2001,
	title = {Random forests},
	volume = {45},
	number = {1},
	journal = {Machine learning},
	author = {Breiman, Leo},
	year = {2001},
	note = {Publisher: Springer},
	keywords = {ensemble methods},
	pages = {5--32},
}

@book{barnett_elements_1974,
	title = {Elements of sampling theory},
	publisher = {English Universities Press, London},
	author = {Barnett, Vick},
	year = {1974},
}

@inproceedings{fan_binary_2019,
	title = {Binary {Space} {Partitioning} {Forests}},
	volume = {89},
	booktitle = {22nd {AISTATS} conference},
	author = {Fan, Xuhui and Li, Bin and Sisson, Scott Anthony},
	year = {2019},
	keywords = {ensemble methods},
	pages = {1--10},
}

@inproceedings{fan_binary_2018,
	title = {The binary space partitioning-tree process},
	volume = {84},
	booktitle = {21st {AISTATS} conference},
	author = {Fan, Xuhui and Li, Bin and Sisson, Scott Anthony},
	year = {2018},
	keywords = {ensemble methods},
	pages = {1--9},
}

@book{preiss_data_2000,
	title = {Data {Structures} and {Algorithms} with {Object}-{Oriented} {Design} {Patterns} in {Java}},
	publisher = {John Wiley \& Sons Incorporated},
	author = {Preiss, Bruno R.},
	year = {2000},
}

@phdthesis{louppe_understanding_2014,
	type = {{PhD} {Thesis}},
	title = {Understanding random forests: {From} theory to practice},
	school = {University of Liège, Faculty of Applied Sciences},
	author = {Louppe, Gilles},
	year = {2014},
	keywords = {ensemble methods},
}

@book{murphy_machine_2012,
	title = {Machine {Learning}: {A} {Probabilistic} {Perspective}},
	url = {http://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020},
	publisher = {MIT Press},
	author = {Murphy, Kevin},
	year = {2012},
	keywords = {text},
	annote = {If there is one book to help with everything in this course, this would be it.},
}

@book{duda_pattern_2000,
	title = {Pattern {Classification}},
	publisher = {Wiley Interscience},
	author = {Duda, R O and Hart, P E and Stork, D G},
	year = {2000},
	keywords = {text},
}

@book{goodfellow_deep_2016,
	title = {Deep {Learning}},
	url = {http://www.deeplearningbook.net/book},
	publisher = {MIT Press},
	author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
	year = {2016},
	keywords = {text},
	annote = {The notation is sometimes hard to follow, but this book has a similar structure to the course, the first part goes quickly through the basics of data analysis and preparation, the rest goes into the definitions, tradeoffs and theory of neural networks and deep learning.
Also, freely available online on the website.},
}

@article{lecun_deep_2015,
	title = {Deep learning},
	volume = {521},
	number = {7553},
	journal = {nature},
	author = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
	year = {2015},
	note = {Publisher: Nature Publishing Group},
	keywords = {seminal},
	pages = {436--444},
}

@article{ruder_overview_2017,
	title = {An overview of gradient descent optimization algorithms},
	url = {http://arxiv.org/abs/1609.04747},
	abstract = {Gradient descent optimization algorithms, while increasingly popular, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This article aims to provide the reader with intuitions with regard to the behaviour of different algorithms that will allow her to put them to use. In the course of this overview, we look at different variants of gradient descent, summarize challenges, introduce the most common optimization algorithms, review architectures in a parallel and distributed setting, and investigate additional strategies for optimizing gradient descent.},
	urldate = {2020-12-31},
	journal = {arXiv:1609.04747 [cs]},
	author = {Ruder, Sebastian},
	month = jun,
	year = {2017},
	note = {arXiv: 1609.04747},
	keywords = {Computer Science - Machine Learning},
	annote = {Comment: Added derivations of AdaMax and Nadam},
	file = {arXiv Fulltext PDF:/Users/mcrowley/Zotero/storage/T2Y2MY5T/Ruder - 2017 - An overview of gradient descent optimization algor.pdf:application/pdf;arXiv.org Snapshot:/Users/mcrowley/Zotero/storage/J76MX48L/1609.html:text/html},
}

@article{kingma_adam_2014,
	title = {Adam: {A} method for stochastic optimization},
	journal = {arXiv preprint arXiv:1412.6980},
	author = {Kingma, Diederik P and Ba, Jimmy},
	year = {2014},
	keywords = {gradient optimizers, adam optimizer},
}

@article{ruder_overview_2017-1,
	title = {An overview of gradient descent optimization algorithms},
	url = {http://arxiv.org/abs/1609.04747},
	abstract = {Gradient descent optimization algorithms, while increasingly popular, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This article aims to provide the reader with intuitions with regard to the behaviour of different algorithms that will allow her to put them to use. In the course of this overview, we look at different variants of gradient descent, summarize challenges, introduce the most common optimization algorithms, review architectures in a parallel and distributed setting, and investigate additional strategies for optimizing gradient descent.},
	urldate = {2021-01-03},
	journal = {arXiv:1609.04747 [cs]},
	author = {Ruder, Sebastian},
	month = jun,
	year = {2017},
	note = {arXiv: 1609.04747},
	keywords = {Computer Science - Machine Learning},
	annote = {Comment: Added derivations of AdaMax and Nadam},
	file = {arXiv Fulltext PDF:/Users/mcrowley/Zotero/storage/Q4FUTXPI/Ruder - 2017 - An overview of gradient descent optimization algor.pdf:application/pdf;arXiv.org Snapshot:/Users/mcrowley/Zotero/storage/F2SNEMC5/1609.html:text/html},
}

@misc{us_environmental_protection_agency_meteorological_2000,
	title = {Meteorological {Monitoring} {Guidance} for {Regulatory} {Modeling} {Applications}},
	shorttitle = {Section 6.8.1 {Substitution} {Procedures}},
	url = {https://www.epa.gov/sites/production/files/2020-10/documents/mmgrma_0.pdf},
	urldate = {2021-01-12},
	author = {U.S. ENVIRONMENTAL PROTECTION AGENCY},
	month = feb,
	year = {2000},
	annote = {Policy on Missing Data
See Section 6.8.1 Substitution Procedures for their policy and explanation of the ways they deal with missing data.
 },
	annote = {This report has been reviewed by the U.S. Environmental Protection Agency (EPA) and has been approved for publication as an EPA document. Any mention of trade names or commercial products does not constitute endorsement or recommendation for use.
 },
	file = {mmgrma_0.pdf:/Users/mcrowley/Zotero/storage/R2DWD9I9/mmgrma_0.pdf:application/pdf},
}

@article{vaswani_attention_2017,
	title = {Attention is {All} you {Need}},
	volume = {30},
	url = {https://papers.nips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html},
	language = {en},
	urldate = {2021-01-05},
	journal = {Advances in Neural Information Processing Systems},
	author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Łukasz and Polosukhin, Illia},
	year = {2017},
	pages = {5998--6008},
	annote = {The initial paper on the idea of using "attention" in deep learning to focus training updates.},
	file = {Full Text PDF:/Users/mcrowley/Zotero/storage/KBZ8XIUA/Vaswani et al. - 2017 - Attention is All you Need.pdf:application/pdf;Snapshot:/Users/mcrowley/Zotero/storage/IIRB358J/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html:text/html},
}

@misc{lamba_intuitive_2019,
	title = {Intuitive {Understanding} of {Attention} {Mechanism} in {Deep} {Learning}},
	url = {https://towardsdatascience.com/intuitive-understanding-of-attention-mechanism-in-deep-learning-6c9482aecf4f},
	abstract = {A TensorFlow Implementation of Neural Machine Translation with Attention},
	language = {en},
	urldate = {2021-01-05},
	journal = {Medium},
	author = {Lamba, Harshall},
	month = may,
	year = {2019},
	keywords = {attention, blog},
	file = {Snapshot:/Users/mcrowley/Zotero/storage/JMWE8YVP/intuitive-understanding-of-attention-mechanism-in-deep-learning-6c9482aecf4f.html:text/html},
}

@misc{brownlee_how_2017,
	title = {How {Does} {Attention} {Work} in {Encoder}-{Decoder} {Recurrent} {Neural} {Networks}},
	url = {https://machinelearningmastery.com/how-does-attention-work-in-encoder-decoder-recurrent-neural-networks/},
	abstract = {Attention is a mechanism that was developed to improve the performance of the Encoder-Decoder RNN on machine translation. In this tutorial, you will discover the attention mechanism for the Encoder-Decoder model. After completing this tutorial, you will know: About the Encoder-Decoder model and attention mechanism for machine translation. How to implement the attention mechanism step-by-step. […]},
	language = {en-US},
	urldate = {2021-01-05},
	journal = {Machine Learning Mastery},
	author = {Brownlee, Jason},
	month = oct,
	year = {2017},
	keywords = {attention, blog},
	file = {Snapshot:/Users/mcrowley/Zotero/storage/UUDL5SLJ/how-does-attention-work-in-encoder-decoder-recurrent-neural-networks.html:text/html},
}

@article{lu_data_2020,
	title = {Data {Efficient} and {Weakly} {Supervised} {Computational} {Pathology} on {Whole} {Slide} {Images}},
	url = {http://arxiv.org/abs/2004.09666},
	abstract = {The rapidly emerging field of computational pathology has the potential to enable objective diagnosis, therapeutic response prediction and identification of new morphological features of clinical relevance. However, deep learning-based computational pathology approaches either require manual annotation of gigapixel whole slide images (WSIs) in fully-supervised settings or thousands of WSIs with slide-level labels in a weakly-supervised setting. Moreover, whole slide level computational pathology methods also suffer from domain adaptation and interpretability issues. These challenges have prevented the broad adaptation of computational pathology for clinical and research purposes. Here we present CLAM - Clustering-constrained attention multiple instance learning, an easy-to-use, high-throughput, and interpretable WSI-level processing and learning method that only requires slide-level labels while being data efficient, adaptable and capable of handling multi-class subtyping problems. CLAM is a deep-learning-based weakly-supervised method that uses attention-based learning to automatically identify sub-regions of high diagnostic value in order to accurately classify the whole slide, while also utilizing instance-level clustering over the representative regions identified to constrain and refine the feature space. In three separate analyses, we demonstrate the data efficiency and adaptability of CLAM and its superior performance over standard weakly-supervised classification. We demonstrate that CLAM models are interpretable and can be used to identify well-known and new morphological features. We further show that models trained using CLAM are adaptable to independent test cohorts, cell phone microscopy images, and biopsies. CLAM is a general-purpose and adaptable method that can be used for a variety of different computational pathology tasks in both clinical and research settings.},
	urldate = {2021-01-13},
	journal = {arXiv:2004.09666 [cs, eess, q-bio]},
	author = {Lu, Ming Y. and Williamson, Drew F. K. and Chen, Tiffany Y. and Chen, Richard J. and Barbieri, Matteo and Mahmood, Faisal},
	month = may,
	year = {2020},
	note = {arXiv: 2004.09666},
	keywords = {toread, 657a, attention, classification, clustering, deep learning, medical imaging, weak learners, Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, digital pathology, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Tissues and Organs},
	annote = {This is a great example of attention mechanisms being used for the Digital Pathology domain (which has a lot of research being done in UWaterloo).
Once you've ready about attention mechanisms in general this is a good way to see it applied to something impactful.
 },
	file = {[2004.09666] Data Efficient and Weakly Supervised .pdf:/Users/mcrowley/Zotero/storage/YMHIEEH4/[2004.09666] Data Efficient and Weakly Supervised .pdf:application/pdf},
}

repository: subhambiswajit/DKMA

repo_name: subhambiswajit/DKMA


text:
require connman.inc

SRC_URI  = "${KERNELORG_MIRROR}/linux/network/${BPN}/${BP}.tar.xz \
            file://0001-plugin.h-Change-visibility-to-default-for-debug-symb.patch \
            file://wifi_default.patch \
            file://add_xuser_dbus_permission.patch \
            file://disable_p2p.patch \
            file://connman \
            "
SRC_URI[md5sum] = "4f4b3be54da000c65b153c1b9afcadf2"
SRC_URI[sha256sum] = "13997824c076af150c68d6d79e48277216e8192278a5c6615cfd4905d65600f5"
RRECOMMENDS_${PN} = "connman-conf"

repository: ii/edison-src

repo_name: ii/edison-src


text:
#
# This file is the initcamera recipe.
#

SUMMARY = "Simple initcamera application"
SECTION = "PETALINUX/apps"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://${COMMON_LICENSE_DIR}/MIT;md5=0835ade698e0bcf8506ecda2f7b4f302"

SRC_URI = "file://initcamera.c \
	file://cameraiic.h \
	file://Makefile \
	"

S = "${WORKDIR}"

DEPENDS += " uiotools"

do_compile() {
	oe_runmake
}

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

repository: Catabit/Self-Driving-Car-DevBranch

repo_name: Catabit/Self-Driving-Car-DevBranch


text:
@book{nachoMSc,
 author = {Echegoyen, Ignacio},
 publisher = {UAM Ediciones - Másteres de la UAM},
 title = {Brain Network’s Synchronization Measures: a statistical approach},
 url = {https://libros.uam.es/?press=tfm&page=catalog&op=view&path%5B%5D=920&path%5B%5D=1630&path%5B%5D=1606-1},
 year = {2018}
}

repository: nachoenblanco/academic-kickstart

repo_name: nachoenblanco/academic-kickstart


text:
require include/iccom-control.inc
require recipes-kernel/linux-libc-headers/linux-libc-headers.inc

RENESAS_BSP_URL = " \
    git://git.kernel.org/pub/scm/linux/kernel/git/horms/renesas-bsp.git"
BRANCH = "v4.9/rcar-3.5.9"
SRCREV = "2c20c9881d53f1b5c7a3ebf55bb83ec15902d0d0"

SRC_URI = "${RENESAS_BSP_URL};branch=${BRANCH}"

# Add some patches to support ICCOM module
SRC_URI_append = " \
    ${@base_conditional("USE_ICCOM", "1", " file://iccom_feature.scc", "", d)} \
"
S = "${WORKDIR}/git"

repository: klogg/meta-renesas

repo_name: klogg/meta-renesas


text:
@InProceedings{gogate07a,
  title = {SampleSearch: A Scheme that Searches for Consistent Samples},
  author = {Vibhav Gogate and Rina Dechter},
  pages = {147--154},
  abstract = {Sampling from belief networks which have a substantial number of zero probabilities is problematic. MCMC algorithms like Gibbs sampling do not converge and importance sampling schemes generate many zero weight samples that are rejected, yielding an inefficient sampling process (the rejection problem). In this paper, we propose to augment importance sampling with systematic constraint-satisfaction search in order to overcome the rejection problem. The resulting SampleSearch scheme can be made unbiased by using a computationally expensive weighting scheme. To overcome this an approximation is proposed such that the resulting estimator is asymptotically unbiased. Our empirical results demonstrate the potential of our new scheme.},
  pdf = {http://jmlr.org/proceedings/papers/v2/gogate07a/gogate07a.pdf},
}