text:
Untuk setiap referensi, baris pertama (Newton1687) merupakan label dan baris-baris berikutnya
merupakan atribut dari referensi yang akan digunakan. Umumnya, format referensi berikut sudah
disediakan oleh penyedia sumber referensi secara online dan bisa langsung digunakan di sini.

Lihat https://www.bibtex.com/g/bibtex-format/ untuk informasi lebih lanjut mengenai format
BibTex yang digunakan pada file ini.

@article{Newton1687,
  author={I. {Newton}},
  journal={Philosophiæ Naturalis Principia Mathematica},
  title={Axioms or Laws of Motion},
  year={1687}}

@misc{DiscoverySpaceShuttle,
  title={Pesawat Ruang Angkasa Discovery},
  year=2021,
  url={https://airandspace.si.edu/explore-and-learn/topics/discovery/about.cfm},
  urldate={2021-02-03}}

license: MIT

license_type: permissive

branch_name: refs/heads/main

extension: bib


text:
@article{fraser2009pandemic,
  title={Pandemic potential of a strain of influenza A (H1N1): early findings},
  author={Fraser, Christophe and Donnelly, Christl A and Cauchemez, Simon and Hanage, William P and Van Kerkhove, Maria D and Hollingsworth, T D{\'e}irdre and Griffin, Jamie and Baggaley, Rebecca F and Jenkins, Helen E and Lyons, Emily J and others},
  journal={science},
  year={2009},
  publisher={American Association for the Advancement of Science}
}

@article{fisman2014early,
  title={Early epidemic dynamics of the West African 2014 Ebola outbreak: estimates derived with a simple two-parameter model},
  author={Fisman, David and Khoo, Edwin and Tuite, Ashleigh},
  journal={PLoS currents},
  volume={6},
  year={2014},
  publisher={Public Library of Science}
}

@misc{cdc2019fluview,
  title={National, Regional, and State Level Outpatient Illness and Viral Surveillance},
  author={CDC},
  howpublished={https://www.cdc.gov/flu/weekly/fluactivitysurv.htm},
  note={Accessed: January 2019}
}

@article{blossey2008compositionality,
  title={Compositionality, stochasticity, and cooperativity in dynamic models of gene regulation},
  author={Blossey, Ralf and Cardelli, Luca and Phillips, Andrew},
  journal={HFSP journal},
  volume={2},
  number={1},
  pages={17--28},
  year={2008},
  publisher={Taylor \& Francis}
}

@book{sangiorgi2003pi,
  title={The pi-calculus: a Theory of Mobile Processes},
  author={Sangiorgi, Davide and Walker, David},
  year={2003},
  publisher={Cambridge university press}
}

@article{nurse2008life,
  title={Life, logic and information},
  author={Nurse, Paul},
  journal={Nature},
  volume={454},
  number={7203},
  pages={424},
  year={2008},
  publisher={Nature Publishing Group}
}

@incollection{cardelli2005abstract,
  title={Abstract machines of systems biology},
  author={Cardelli, Luca},
  booktitle={Transactions on Computational Systems Biology III},
  pages={145--168},
  year={2005},
  publisher={Springer}
}

@article{harel1987statecharts,
  title={Statecharts: A visual formalism for complex systems},
  author={Harel, David},
  journal={Science of computer programming},
  volume={8},
  number={3},
  pages={231--274},
  year={1987},
  publisher={Elsevier}
}

@article{chaouiya2007petri,
  title={Petri net modelling of biological networks},
  author={Chaouiya, Claudine},
  journal={Briefings in bioinformatics},
  volume={8},
  number={4},
  pages={210--219},
  year={2007},
  publisher={Oxford University Press}
}

@article{chiola1993generalized,
  title={Generalized stochastic Petri nets: A definition at the net level and its implications},
  author={Chiola, Giovanni and Marsan, Marco Ajmone and Balbo, Gianfranco and Conte, Gianni},
  journal={IEEE Transactions on software engineering},
  volume={19},
  number={2},
  pages={89--107},
  year={1993},
  publisher={IEEE}
}

@article{movaghar1985performability,
  title={Performability modeling with stochastic activity networks},
  author={Movaghar, Ali},
  year={1985},
  publisher={University of Michigan}
}

@inproceedings{sanders2000stochastic,
  title={Stochastic activity networks: Formal definitions and concepts},
  author={Sanders, William H and Meyer, John F},
  booktitle={School organized by the European Educational Forum},
  pages={315--343},
  year={2000},
  organization={Springer}
}

@article{qureshi1996algorithms,
  title={Algorithms for the generation of state-level representations of stochastic activity networks with general reward structures},
  author={Qureshi, Muhammad A and Sanders, William H and Van Moorsel, Aad PA and German, Reinhard},
  journal={IEEE Transactions on Software Engineering},
  volume={22},
  number={9},
  pages={603--614},
  year={1996},
  publisher={IEEE}
}

@inproceedings{deavours1999efficient,
  title={An efficient well-specified check},
  author={Deavours, Daniel D and Sanders, William H},
  booktitle={Petri Nets and Performance Models, 1999. Proceedings. The 8th International Workshop on},
  pages={124--133},
  year={1999},
  organization={IEEE}
}

@inproceedings{ciardo1996well,
  title={Well-defined stochastic Petri nets},
  author={Ciardo, Gianfranco and Zijal, Robert},
  booktitle={Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, 1996. MASCOTS'96., Proceedings of the Fourth International Workshop on},
  pages={278--284},
  year={1996},
  organization={IEEE}
}

@article{sanders1991reduced,
  title={Reduced base model construction methods for stochastic activity networks},
  author={Sanders, William H and Meyer, John F},
  journal={IEEE Journal on Selected Areas in Communications},
  volume={9},
  number={1},
  pages={25--36},
  year={1991},
  publisher={IEEE}
}

@article{sanders1992dependability,
  title={Dependability evaluation using composed SAN-based reward models},
  author={Sanders, William H and Malhis, Luai M},
  journal={Journal of parallel and distributed computing},
  volume={15},
  number={3},
  pages={238--254},
  year={1992}
}

@article{freire1990technique,
  title={A technique for simulating composed SAN-based reward models},
  author={Freire, Roberto},
  year={1990},
  publisher={The University of Arizona.}
}

@article{sanders1988construction,
  title={Construction and solution of performability models based on stochastic activity networks},
  author={Sanders, William Harry},
  year={1988},
  publisher={University of Michigan}
}

@book{howard2012dynamic,
  title={Dynamic probabilistic systems: Markov models},
  author={Howard, Ronald A},
  volume={1},
  year={2012},
  publisher={Courier Corporation}
}

@inproceedings{lampka2002symbolic,
  title={Symbolic composition within the M{\"o}bius framework},
  author={Lampka, Kai and Siegle, Markus},
  booktitle={Proc. of the 2nd MMB Workshop},
  pages={63--74},
  year={2002}
}

@article{sanders1995ultrasan,
  title={The UltraSAN modeling environment},
  author={Sanders, William H. and Obal II, W Douglas and Qureshi, Muhammad A. and Widjanarko, FK},
  journal={Performance Evaluation},
  volume={24},
  number={1-2},
  pages={89--115},
  year={1995},
  publisher={Elsevier}
}

@book{bruce2002foundations,
  title={Foundations of object-oriented languages: types and semantics},
  author={Bruce, Kim B},
  year={2002},
  publisher={MIT press}
}

@book{montgomery2017design,
  title={Design and analysis of experiments},
  author={Montgomery, Douglas C},
  year={2017},
  publisher={John wiley \& sons}
}

@article{bailis2005mortality,
  title={Mortality and greenhouse gas impacts of biomass and petroleum energy futures in Africa},
  author={Bailis, Robert and Ezzati, Majid and Kammen, Daniel M},
  journal={Science},
  volume={308},
  number={5718},
  pages={98--103},
  year={2005},
  publisher={American Association for the Advancement of Science}
}

@article{murphy2004quantification,
  title={Quantification of modelling uncertainties in a large ensemble of climate change simulations},
  author={Murphy, James M and Sexton, David MH and Barnett, David N and Jones, Gareth S and Webb, Mark J and Collins, Matthew and Stainforth, David A},
  journal={Nature},
  volume={430},
  number={7001},
  pages={768},
  year={2004},
  publisher={Nature Publishing Group}
}

@article{morris1991factorial,
  title={Factorial sampling plans for preliminary computational experiments},
  author={Morris, Max D},
  journal={Technometrics},
  volume={33},
  number={2},
  pages={161--174},
  year={1991},
  publisher={Taylor \& Francis Group}
}

@article{srivastava2002stochastic,
  title={Stochastic vs. deterministic modeling of intracellular viral kinetics},
  author={Srivastava, Ranjan and You, L and Summers, J and Yin, J},
  journal={Journal of theoretical biology},
  volume={218},
  number={3},
  pages={309--321},
  year={2002},
  publisher={Academic Press}
}

@article{haseltine2002approximate,
  title={Approximate simulation of coupled fast and slow reactions for stochastic chemical kinetics},
  author={Haseltine, Eric L and Rawlings, James B},
  journal={The Journal of chemical physics},
  volume={117},
  number={15},
  pages={6959--6969},
  year={2002},
  publisher={AIP}
}

@article{weinberger2005stochastic,
  title={Stochastic gene expression in a lentiviral positive-feedback loop: HIV-1 Tat fluctuations drive phenotypic diversity},
  author={Weinberger, Leor S and Burnett, John C and Toettcher, Jared E and Arkin, Adam P and Schaffer, David V},
  journal={Cell},
  volume={122},
  number={2},
  pages={169--182},
  year={2005},
  publisher={Elsevier}
}

repository: uncharted-aske/AMIDOL

repo_name: uncharted-aske/AMIDOL

branch_name: refs/heads/master


text:
@article{labelle2019microbiome,
 author = {LaBelle, Edward V and Marshall, Christopher W and May, Harold D},
 journal = {Accounts of chemical research},
 number = {1},
 pages = {62--71},
 publisher = {American Chemical Society},
 title = {Microbiome for the electrosynthesis of chemicals from carbon dioxide},
 volume = {53},
 year = {2019}
}

repository: sirmicrobe/labofmarshall

repo_name: sirmicrobe/labofmarshall


text:
@preamble{ " \newcommand{\noop}[1]{} " }
@article{Mazor16,
	author={R. D. Mazor and A. C. Rehn and P. R. Ode and M. Engeln and K. C. Schiff and E. D. Stein and D. J. Gillett and D. B. Herbst and C. P. Hawkins},
	year={2016},
	title={Bioassessment in complex environments: designing an index for consistent meaning in different settings},
	journal={Freshwater Science},
	volume={35},
	number={1},
	pages={249-271}
}
@article{Ode16,
	author={P. R. Ode and A. C. Rehn and R. D. Mazor and K. C. Schiff and E. D. Stein and J. T. May and L. R. Brown and D. B. Herbst and D. Gillett and K. Lunde and C. P. Hawkins},
	year={2016},
	title={Evaluating the adequacy of a reference-site pool for ecological assessments in environmentally complex regions},
	journal={Freshwater Science},
	volume={35},
	number={1},
	pages={237-248}
}
@article{Therouxir,
	author={S. Theroux and R. D. Mazor and M. W. Beck and P. Ode and E. D. Stein and M. Sutula},
	pubstate={in review},
	title={A non-predictive algal index for complex environments},
	journal={Ecological Indicators},
	volume={},
	number={},
	pages={},
	doi={}
}

repository: SCCWRP/ASCIsop

repo_name: SCCWRP/ASCIsop


text:
@inproceedings{bontempi2017dsaa,
  title={A dynamic factor machine learning method for multi-variate and multi-step-ahead forecasting},
  author={Bontempi, Gianluca and Le Borgne, Yann-Aël and De Stefani, Jacopo},
  booktitle={IEEE DSAA17, International Conference on Data Science and Analytics},
  url = {https://www.researchgate.net/publication/320551970_A_dynamic_factor_machine_learning_method_for_multi-variate_and_multi-step-ahead_forecasting},
  volume={TBD},
  number={TBD},
  pages={TBD},
  year={2017},
  series = {TBD},
  address = {TBD},
  issn = {TBD},
  venue = {Tokyo, Japan},
  eventdate = {2017-10-18}
}


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

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

SRC_URI = "gitsm://github.com/Accelize/drm.git;protocol=https"
SRCREV = "${AUTOREV}"

DEPENDS += " \
    curl \
    jsoncpp \
"

S = "${WORKDIR}/git"
PR = "1.pl2021_1"

inherit pkgconfig cmake

FILES_${PN} += "${libdir}/*"

extension: bb


text:
DESCRIPTION = "A pure-Python implementation of the HTTP/2 priority tree"
HOMEPAGE = "https://github.com/python-hyper/priority"
LICENSE = "MIT"

LIC_FILES_CHKSUM = "file://LICENSE;md5=ae57d8a09fc8b6b164d7357339619045"

SRC_URI[sha256sum] = "c965d54f1b8d0d0b19479db3924c7c36cf672dbf2aec92d43fbdaf4492ba18c0"

inherit pypi setuptools3

repository: openembedded/meta-openembedded

repo_name: openembedded/meta-openembedded

gha_license_id: MIT

gha_language: BitBake


text:
@inproceedings{hegde2018ucnn,
  title={Ucnn: Exploiting computational reuse in deep neural networks via weight repetition},
  author={Hegde, Kartik and Yu, Jiyong and Agrawal, Rohit and Yan, Mengjia and Pellauer, Michael and Fletcher, Christopher},
  booktitle={2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA)},
  pages={674--687},
  year={2018},
  organization={IEEE}
}

repository: kartik-hegde/kartik-hegde-academic

repo_name: kartik-hegde/kartik-hegde-academic


text:
@article{Thiem_1989,
 author = {Thiem, S M and Miller, L K},
 title = {Identification, sequence, and transcriptional mapping of the major capsid protein gene of the baculovirus Autographa californica nuclear polyhedrosis virus.},
 journal = {Journal of Virology},
 publisher = {American Society for Microbiology},
 year = {1989},
 number = {5},
 volume = {63},
 pages = {2008--2018},
 url = {https://doi.org/10.1128%2Fjvi.63.5.2008-2018.1989},
 doi = {10.1128/jvi.63.5.2008-2018.1989}
}


text:
@InProceedings{hong11a,
  title = {Optimal Distributed Market-Based Planning for Multi-Agent Systems with Shared Resources},
  author = {Sue Ann Hong and Geoffrey Gordon},
  pages = {351--360},
  abstract = {Market-based algorithms have become popular in collaborative multi-agent planning due to their simplicity, distributedness, low communication requirements, and proven success in domains such as task allocation and robotic exploration.  Most existing market-based algorithms, however, suffer from two main drawbacks: resource prices must be carefully handcrafted for each problem domain, and there is no guarantee on final solution quality.  We present an optimal market-based algorithm, derived from a mixed integer program formulation of planning problems.  Our method is based on two well-known techniques for optimization: Dantzig-Wolfe decomposition and Gomory cuts. The former prices resources optimally for a relaxed version of the problem, while the latter introduces new derivative resources to correct pricing imbalances that arise from the relaxation. Our algorithm is applicable to a wide variety of multi-agent planning domains.  We provide optimality guarantees and demonstrate the effectiveness of our algorithm in both centralized and distributed settings on synthetic planning problems.

[pdf][supplementary]},
  pdf = {http://jmlr.org/proceedings/papers/v15/hong11a/hong11a.pdf},
  supplementary = {Supplementary:http://jmlr.org/proceedings/papers/v15/hong11a/hong11aSupple.pdf},
}

repository: mreid/papersite

repo_name: mreid/papersite

gha_language: TeX


text:
@inproceedings{grambow_is_2019,
	location = {Limassol, Cyprus},
	title = {Is it safe to dockerize my database benchmark?},
	isbn = {978-1-4503-5933-7},
	doi = {10.1145/3297280.3297545},
	series = {{SAC} '19},
	pages = {341--344},
	booktitle = {Proceedings of the 34th {ACM}/{SIGAPP} Symposium on Applied Computing},
	publisher = {{ACM}},
	author = {Grambow, Martin and Hasenburg, Jonathan and Pfandzelter, Tobias and Bermbach, David},
	year = {2019}
}


text:
#
# Follow the boost suggestions
#
execute_process(COMMAND ./bootstrap.sh --prefix=${BOOST_INSTALL_DIR}
  WORKING_DIRECTORY ${BUILD_DIR} RESULT_VARIABLE boostrap_result)

return(${bootstrap_result})

repository: NIRALUser/DTIAtlasBuilder

repo_name: NIRALUser/DTIAtlasBuilder

extension: cmake


text:
@article {hprc,
	author = {Liao, Wen-Wei and Asri, Mobin and Ebler, Jana and Doerr, Daniel and Haukness, Marina and Hickey, Glenn and Lu, Shuangjia and Lucas, Julian K. and Monlong, Jean and Abel, Haley J. and Buonaiuto, Silvia and Chang, Xian H. and Cheng, Haoyu and Chu, Justin and Colonna, Vincenza and Eizenga, Jordan M. and Feng, Xiaowen and Fischer, Christian and Fulton, Robert S. and Garg, Shilpa and Groza, Cristian and Guarracino, Andrea and Harvey, William T and Heumos, Simon and Howe, Kerstin and Jain, Miten and Lu, Tsung-Yu and Markello, Charles and Martin, Fergal J. and Mitchell, Matthew W. and Munson, Katherine M. and Mwaniki, Moses Njagi and Novak, Adam M. and Olsen, Hugh E. and Pesout, Trevor and Porubsky, David and Prins, Pjotr and Sibbesen, Jonas A. and Tomlinson, Chad and Villani, Flavia and Vollger, Mitchell R. and , and Bourque, Guillaume and Chaisson, Mark JP and Flicek, Paul and Phillippy, Adam M. and Zook, Justin M. and Eichler, Evan E. and Haussler, David and Jarvis, Erich D. and Miga, Karen H. and Wang, Ting and Garrison, Erik and Marschall, Tobias and Hall, Ira and Li, Heng and Paten, Benedict},
	title = {A Draft Human Pangenome Reference},
	elocation-id = {2022.07.09.499321},
	year = {2022},
	doi = {10.1101/2022.07.09.499321},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2022/07/09/2022.07.09.499321},
	eprint = {https://www.biorxiv.org/content/early/2022/07/09/2022.07.09.499321.full.pdf},
	journal = {bioRxiv}
}
@article {mc,
	author = {Hickey, Glenn and Monlong, Jean and Novak, Adam and Eizenga, Jordan M. and , and Li, Heng and Paten, Benedict},
	title = {Pangenome Graph Construction from Genome Alignment with Minigraph-Cactus},
	elocation-id = {2022.10.06.511217},
	year = {2022},
	doi = {10.1101/2022.10.06.511217},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2022/10/07/2022.10.06.511217},
	eprint = {https://www.biorxiv.org/content/early/2022/10/07/2022.10.06.511217.full.pdf},
	journal = {bioRxiv}
}
@article{odgi,
    author = {Guarracino, Andrea and Heumos, Simon and Nahnsen, Sven and Prins, Pjotr and Garrison, Erik},
    title = "{ODGI: understanding pangenome graphs}",
    journal = {Bioinformatics},
    volume = {38},
    number = {13},
    pages = {3319-3326},
    year = {2022},
    month = {05},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btac308},
    url = {https://doi.org/10.1093/bioinformatics/btac308},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/38/13/3319/44268770/btac308\_supplementary\_data.pdf},
}

repository: marschall-lab/GFAffix

repo_name: marschall-lab/GFAffix

gha_language: Rust


text:
/* base patch */
/* grid patch */
.flex-sp-eq, .flex-tab-eq, .flex-pc-eq {
	display: table;
	width: 100%;
	table-layout: fixed;
}
.flex-sp-eq > *, .flex-tab-eq > *, .flex-pc-eq > * {
	display: table-cell;
}

@media screen and (max-width: 480px) {
  .flex-tab-eq, .flex-tab-eq > * {
		display: block;
		height: auto;
  }
}
@media screen and (max-width: 768px) {
  .flex-pc-eq, .flex-pc-eq > * {
		display: block;
		height: auto;
  }
}

/*# sourceMappingURL=ie10.css.map */

repository: akinori-kawamitsu/lecss

repo_name: akinori-kawamitsu/lecss

extension: css


text:
file(REMOVE_RECURSE
  "CMakeFiles/interpolation.dir/interpolation.obj"
  "CMakeFiles/interpolation.dir/shapes.obj"
  "CMakeFiles/interpolation.dir/stdafx.obj"
  "CMakeFiles/interpolation.dir/storage.obj"
  "interpolation.exe"
  "interpolation.exe.manifest"
  "interpolation.pdb"
  "libinterpolation.dll.a"
)

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

repository: uon-projects/hexa-star-interpolation

repo_name: uon-projects/hexa-star-interpolation

gha_language: C


text:
/** @define utilities */

@import "@superflycss/variables-dimension";

@media (--md-viewport) {
  @for $factor from 0 to 20 {
    .u-md-border-width-$(factor)px {
      border-width: $(factor)px !important;
    }
    .u-md-bw$(factor)px {
      border-width: $(factor)px !important;
    }
    .u-md-border-radius-$(factor)px {
      border-radius: $(factor)px !important;
    }
    .u-md-br$(factor)px {
      border-radius: $(factor)px !important;
    }
    .u-md-btw$(factor)px {
      border-top-width: $(factor)px !important;
    }
    .u-md-bbw$(factor)px {
      border-bottom-width: $(factor)px !important;
    }
    .u-md-blw$(factor)px {
      border-left-width: $(factor)px !important;
    }
    .u-md-brw$(factor)px {
      border-right-width: $(factor)px !important;
    }
  }

  @each $dimension in top, bottom, left, right {
    @for $factor from 0 to 20 {
      .u-md-border-$(dimension)-width-$(factor)px {
        border-$(dimension)-width: $(factor)px !important;
      }
    }
  }

  @each $style in none, hidden, dotted, dashed, solid, double, groove, ridge, inset, outset, initial, inherit {
    .u-md-border-style-$(style) {
      border-style: $(style) !important;
    }
    .u-md-bs-$(style) {
      border-style: $(style) !important;
    }
    .u-md-bts-$(style) {
      border-top-style: $(style) !important;
    }
    .u-md-bbs-$(style) {
      border-bottom-style: $(style) !important;
    }
    .u-md-bls-$(style) {
      border-left-style: $(style) !important;
    }
    .u-md-brs-$(style) {
      border-right-style: $(style) !important;
    }
  }

  @each $dimension in top, bottom, left, right {
    @each $style in none, hidden, dotted, dashed, solid, double, groove, ridge, inset, outset, initial, inherit {
      .u-md-border-$(dimension)-style-$(style) {
        border-$(dimension)-style: $(style) !important;
      }
    }
  }
}

repository: superflycss/utilities-layout

repo_name: superflycss/utilities-layout

gha_language: CSS


text:
@article{lagomarsino_gateless_1999,
	title = {A gateless charge integrator for {Borexino} energy measurement},
	volume = {430},
	issn = {0168-9002},
	url = {http://www.sciencedirect.com/science/article/pii/S0168900299001709},
	doi = {10.1016/S0168-9002(99)00170-9},
	abstract = {A gateless charge integrator designed for the energy measurement in the Borexino experiment is described and the results of various tests performed on prototypes are shown. The circuit integrates always its input taking advantage of the AC coupling to the photomultipliers that equalizes to zero the charge associated with each event signal. A double sampling of the integrator output allows to perform a charge measurement in principle without dead time, avoiding the use of gate signals and baseline restoration networks and permitting a precise detection of the fast correlated events (delayed coincidences due to the decay of a nuclide producing a daughter having a lifetime in the tens of ns time range). The precision of the charge measurement is discussed together with the performances of the front end Borexino board where the integrator is mounted.},
	language = {en},
	number = {2},
	urldate = {2020-10-09},
	journal = {Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment},
	author = {Lagomarsino, V and Testera, G},
	month = jul,
	year = {1999},
	note = {Number: 2},
	pages = {435--446}
}

@book{dunger_event_2019,
	address = {Cham},
	series = {Springer {Theses}},
	title = {Event {Classification} in {Liquid} {Scintillator} {Using} {PMT} {Hit} {Patterns}: for {Neutrinoless} {Double} {Beta} {Decay} {Searches}},
	isbn = {978-3-030-31615-0 978-3-030-31616-7},
	shorttitle = {Event {Classification} in {Liquid} {Scintillator} {Using} {PMT} {Hit} {Patterns}},
	url = {http://link.springer.com/10.1007/978-3-030-31616-7},
	language = {en},
	urldate = {2020-10-15},
	publisher = {Springer International Publishing},
	author = {Dunger, Jack},
	year = {2019},
	doi = {10.1007/978-3-030-31616-7}
}

@book{villani_2009,
    place={Berlin},
    title={Optimal transport Old and New},
    publisher={Springer},
    author={Villani Cédric},
    year={2009}
}

@article{abe_xmass_2013,
	title = {{XMASS} detector},
	volume = {716},
	issn = {01689002},
	url = {http://arxiv.org/abs/1301.2815},
	doi = {10.1016/j.nima.2013.03.059},
	abstract = {The XMASS project aims to detect dark matter, pp and \${\textasciicircum}\{7\}\$Be solar neutrinos, and neutrinoless double beta decay using ultra pure liquid xenon. The first phase of the XMASS experiment searches for dark matter. In this paper, we describe the XMASS detector in detail, including its configuration, data acquisition equipment and calibration system.},
	urldate = {2020-10-12},
	journal = {Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment},
	author = {Abe, K. and others},
	month = jul,
	year = {2013},
	note = {arXiv: 1301.2815},
	keywords = {High Energy Physics - Experiment, Physics - Instrumentation and Detectors, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
	pages = {78--85}
}

@article{huang_flash_2018,
	title = {The {Flash} {ADC} system and {PMT} waveform reconstruction for the {Daya} {Bay} {Experiment}},
	volume = {895},
	issn = {01689002},
	url = {http://arxiv.org/abs/1707.03699},
	doi = {10.1016/j.nima.2018.03.061},
	abstract = {To better understand the energy response of the Antineutrino Detector (AD), the Daya Bay Reactor Neutrino Experiment installed a full Flash ADC readout system on one AD that allowed for simultaneous data taking with the current readout system. This paper presents the design, data acquisition, and simulation of the Flash ADC system, and focuses on the PMT waveform reconstruction algorithms. For liquid scintillator calorimetry, the most critical requirement to waveform reconstruction is linearity. Several common reconstruction methods were tested but the linearity performance was not satisfactory. A new method based on the deconvolution technique was developed with 1\% residual non-linearity, which fulfills the requirement. The performance was validated with both data and Monte Carlo (MC) simulations, and 1\% consistency between them has been achieved.},
	urldate = {2020-11-02},
	journal = {Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment},
	author = {Huang, Yongbo and others},
	month = jul,
	year = {2018},
	note = {arXiv: 1707.03699},
	keywords = {Physics - Instrumentation and Detectors},
	pages = {48--55}
}

@article{the_kamland_collaboration_production_2010,
	title = {Production of {Radioactive} {Isotopes} through {Cosmic} {Muon} {Spallation} in {KamLAND}},
	volume = {81},
	issn = {0556-2813, 1089-490X},
	url = {http://arxiv.org/abs/0907.0066},
	doi = {10.1103/PhysRevC.81.025807},
	abstract = {Radioactive isotopes produced through cosmic muon spallation are a background for rare-event detection in \${\textbackslash}nu\$ detectors, double-\${\textbackslash}beta\$-decay experiments, and dark-matter searches. Understanding the nature of cosmogenic backgrounds is particularly important for future experiments aiming to determine the pep and CNO solar neutrino fluxes, for which the background is dominated by the spallation production of \${\textasciicircum}\{11\}\$C. Data from the Kamioka liquid-scintillator antineutrino detector (KamLAND) provides valuable information for better understanding these backgrounds, especially in liquid scintillators, and for checking estimates from current simulations based upon MUSIC, FLUKA, and GEANT4. Using the time correlation between detected muons and neutron captures, the neutron production yield in the KamLAND liquid scintillator is measured to be \$(2.8 {\textbackslash}pm 0.3) {\textbackslash}times 10{\textasciicircum}\{-4\} {\textbackslash}mu{\textasciicircum}\{-1\} g{\textasciicircum}\{-1\} cm{\textasciicircum}\{2\}\$. For other isotopes, the production yield is determined from the observed time correlation related to known isotope lifetimes. We find some yields are inconsistent with extrapolations based on an accelerator muon beam experiment.},
	number = {2},
	urldate = {2020-10-11},
	journal = {Physical Review C},
	author = {{The KamLAND Collaboration}},
	month = feb,
	year = {2010},
	note = {Number: 2
arXiv: 0907.0066},
	keywords = {High Energy Physics - Experiment, Nuclear Experiment, Astrophysics - Solar and Stellar Astrophysics},
	pages = {025807}
}

@article{zhang_comparison_2019,
	title = {Comparison on {PMT} {Waveform} {Reconstructions} with {JUNO} {Prototype}},
	volume = {14},
	issn = {1748-0221},
	url = {http://arxiv.org/abs/1905.03648},
	doi = {10.1088/1748-0221/14/08/T08002},
	abstract = {JUNO is proposed to determine the neutrino mass hierarchy and rich in many other neutrino topics. A prototype is designed and set up for better understanding sub-systems of future detector. The preliminary results show that its threshold reaches {\textasciitilde}0.3MeV with trigger rate {\textasciitilde}290 Hz on the ground with cosmic muon rate {\textasciitilde}35 Hz. Aiming for a better detector understanding from PMT signal, three reconstruction algorithms are compared for PMT waveforms with different overshoot ratios, including charge integration, waveform fitting, and deconvolution. It is concluded that the three methods have similar performance on uncertainty and systematic bias while deconvolution algorithm is best to handle larger overshoot and the simplest charge integration could be considered with controlled overshoot for future fast preliminary reconstruction.},
	number = {08},
	urldate = {2020-10-10},
	journal = {Journal of Instrumentation},
	author = {Zhang, H. Q. and others},
	month = aug,
	year = {2019},
	note = {Number: 08
arXiv: 1905.03648},
	keywords = {High Energy Physics - Experiment, Physics - Instrumentation and Detectors},
	pages = {T08002--T08002}
}

@article{aprile_xenon1t_2019,
	title = {{XENON1T} {Dark} {Matter} {Data} {Analysis}: {Signal} {Reconstruction}, {Calibration} and {Event} {Selection}},
	volume = {100},
	issn = {2470-0010, 2470-0029},
	shorttitle = {{XENON1T} {Dark} {Matter} {Data} {Analysis}},
	url = {http://arxiv.org/abs/1906.04717},
	doi = {10.1103/PhysRevD.100.052014},
	abstract = {The XENON1T experiment at the Laboratori Nazionali del Gran Sasso is the most sensitive direct detection experiment for dark matter in the form of weakly interacting particles (WIMPs) with masses above \$6{\textbackslash},\$GeV/\$c{\textasciicircum}2\$ scattering off nuclei. The detector employs a dual-phase time projection chamber with 2.0 metric tons of liquid xenon in the target. A one metric \${\textbackslash}mathrm\{ton\}{\textbackslash}times{\textbackslash}mathrm\{year\}\$ exposure of science data was collected between October 2016 and February 2018. This article reports on the performance of the detector during this period and describes details of the data analysis that led to the most stringent exclusion limits on various WIMP-nucleon interaction models to date. In particular, signal reconstruction, event selection and calibration of the detector response to nuclear and electronic recoils in XENON1T are discussed.},
	number = {5},
	urldate = {2020-10-11},
	journal = {Physical Review D},
	author = {Aprile, E. and others},
    authortype = {XENON Collaboration},
	month = sep,
	year = {2019},
	note = {Number: 5
arXiv: 1906.04717},
	keywords = {High Energy Physics - Experiment, Physics - Instrumentation and Detectors},
	pages = {052014}
}

@article{li_separation_2016,
	title = {Separation of {Scintillation} and {Cherenkov} {Lights} in {Linear} {Alkyl} {Benzene}},
	volume = {830},
	issn = {01689002},
	url = {http://arxiv.org/abs/1511.09339},
	doi = {10.1016/j.nima.2016.05.132},
	abstract = {To separate scintillation and Cherenkov lights in water-based liquid scintillator detectors is a desired feature for future neutrino and proton decay researches. Linear alkyl benzene (LAB) is one important ingredient of a water-based liquid scintillator being developed. In this paper we observed a good separation of scintillation and Cherenkov lights in an LAB sample. The rising and decay times of the scintillation light of the LAB were measured to be \$(7.7{\textbackslash}pm3.0){\textbackslash} {\textbackslash}rm\{ns\}\$ and \$(36.6{\textbackslash}pm2.4){\textbackslash} {\textbackslash}rm\{ns\}\$, respectively, while the full width [-3\${\textbackslash}sigma\$, 3\${\textbackslash}sigma\$] of the Cherenkov light was 12 ns dominated by the time resolution of our photomultiplier tubes. The light yield of the scintillation was measured to be \$(1.01{\textbackslash}pm0.12){\textbackslash}times10{\textasciicircum}3{\textbackslash} {\textbackslash}rm\{photons\}/{\textbackslash}rm\{MeV\}\$.},
	urldate = {2020-07-03},
	journal = {Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment},
	author = {Li, Mohan and Guo, Ziyi and Yeh, Minfang and Wang, Zhe and Chen, Shaomin},
	month = sep,
	year = {2016},
	note = {arXiv: 1511.09339},
	keywords = {Physics - Instrumentation and Detectors},
	pages = {303--308}
}

@article{byrd_limited_1995,
	title = {A {Limited} {Memory} {Algorithm} for {Bound} {Constrained} {Optimization}},
	volume = {16},
	issn = {1064-8275},
	url = {https://epubs.siam.org/doi/abs/10.1137/0916069},
	doi = {10.1137/0916069},
	abstract = {An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based on the gradient projection method and uses a limited memory BFGS matrix to approximate the Hessian of the objective function. It is shown how to take advantage of the form of the limited memory approximation to implement the algorithm efficiently. The results of numerical tests on a set of large problems are reported.},
	number = {5},
	urldate = {2020-11-02},
	journal = {SIAM Journal on Scientific Computing},
	author = {Byrd, Richard H. and Lu, Peihuang and Nocedal, Jorge and Zhu, Ciyou},
	month = sep,
	year = {1995},
	note = {Publisher: Society for Industrial and Applied Mathematics},
	pages = {1190--1208}
}

@article{neal_mcmc_2012,
	title = {{MCMC} using {Hamiltonian} dynamics},
	url = {http://arxiv.org/abs/1206.1901},
	abstract = {Hamiltonian dynamics can be used to produce distant proposals for the Metropolis algorithm, thereby avoiding the slow exploration of the state space that results from the diffusive behaviour of simple random-walk proposals. Though originating in physics, Hamiltonian dynamics can be applied to most problems with continuous state spaces by simply introducing fictitious "momentum" variables. A key to its usefulness is that Hamiltonian dynamics preserves volume, and its trajectories can thus be used to define complex mappings without the need to account for a hard-to-compute Jacobian factor - a property that can be exactly maintained even when the dynamics is approximated by discretizing time. In this review, I discuss theoretical and practical aspects of Hamiltonian Monte Carlo, and present some of its variations, including using windows of states for deciding on acceptance or rejection, computing trajectories using fast approximations, tempering during the course of a trajectory to handle isolated modes, and short-cut methods that prevent useless trajectories from taking much computation time.},
	urldate = {2020-05-01},
	journal = {arXiv:1206.1901 [physics, stat]},
	author = {Neal, Radford M.},
	month = jun,
	year = {2012},
	note = {arXiv: 1206.1901},
	keywords = {Physics - Computational Physics, Statistics - Computation}
}

@article{beacom_physics_2017,
	title = {Physics prospects of the {Jinping} neutrino experiment},
	volume = {41},
	issn = {1674-1137},
	url = {https://doi.org/10.1088%2F1674-1137%2F41%2F2%2F023002},
	doi = {10.1088/1674-1137/41/2/023002},
	abstract = {The China Jinping Underground Laboratory (CJPL), which has the lowest cosmic-ray muon flux and the lowest reactor neutrino flux of any laboratory, is ideal to carry out low-energy neutrino experiments. With two detectors and a total fiducial mass of 2000 tons for solar neutrino physics (equivalently, 3000 tons for geo-neutrino and supernova neutrino physics), the Jinping neutrino experiment will have the potential to identify the neutrinos from the CNO fusion cycles of the Sun, to cover the transition phase for the solar neutrino oscillation from vacuum to matter mixing, and to measure the geo-neutrino flux, including the Th/U ratio. These goals can be fulfilled with mature existing techniques. Efforts on increasing the target mass with multi-modular neutrino detectors and on developing the slow liquid scintillator will increase the Jinping discovery potential in the study of solar neutrinos, geo-neutrinos, supernova neutrinos, and dark matter.},
	language = {en},
	number = {2},
	urldate = {2020-10-19},
	journal = {Chinese Physics C},
	author = {Beacom, John F. and others},
	month = feb,
	year = {2017},
	note = {Publisher: IOP Publishing},
	pages = {023002}
}

@article{an_neutrino_2016,
	title = {Neutrino {Physics} with {JUNO}},
	volume = {43},
	issn = {0954-3899, 1361-6471},
	url = {http://arxiv.org/abs/1507.05613},
	doi = {10.1088/0954-3899/43/3/030401},
	abstract = {The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purpose underground liquid scintillator detector, was proposed with the determination of the neutrino mass hierarchy as a primary physics goal. It is also capable of observing neutrinos from terrestrial and extra-terrestrial sources, including supernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos, atmospheric neutrinos, solar neutrinos, as well as exotic searches such as nucleon decays, dark matter, sterile neutrinos, etc. We present the physics motivations and the anticipated performance of the JUNO detector for various proposed measurements. By detecting reactor antineutrinos from two power plants at 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4 sigma significance with six years of running. The measurement of antineutrino spectrum will also lead to the precise determination of three out of the six oscillation parameters to an accuracy of better than 1{\textbackslash}\%. Neutrino burst from a typical core-collapse supernova at 10 kpc would lead to {\textasciitilde}5000 inverse-beta-decay events and {\textasciitilde}2000 all-flavor neutrino-proton elastic scattering events in JUNO. Detection of DSNB would provide valuable information on the cosmic star-formation rate and the average core-collapsed neutrino energy spectrum. Geo-neutrinos can be detected in JUNO with a rate of {\textasciitilde}400 events per year, significantly improving the statistics of existing geoneutrino samples. The JUNO detector is sensitive to several exotic searches, e.g. proton decay via the \$p{\textbackslash}to K{\textasciicircum}++{\textbackslash}bar{\textbackslash}nu\$ decay channel. The JUNO detector will provide a unique facility to address many outstanding crucial questions in particle and astrophysics. It holds the great potential for further advancing our quest to understanding the fundamental properties of neutrinos, one of the building blocks of our Universe.},
	number = {3},
	urldate = {2020-12-07},
	journal = {Journal of Physics G: Nuclear and Particle Physics},
	author = {An, Fengpeng and others},
	month = mar,
	year = {2016},
	note = {arXiv: 1507.05613},
	keywords = {Physics - Instrumentation and Detectors, High Energy Physics - Experiment},
	pages = {030401}
}

@article{noauthor_super-kamiokande_2003,
	title = {The {Super}-{Kamiokande} detector},
	volume = {501},
	issn = {0168-9002},
	url = {http://eproxy2.lib.tsinghua.edu.cn:80/rwt/33/https/P75YPLUUMNVXK5UDMWTGT6UFMN4C6Z5QNF/science/article/pii/S016890020300425X},
	doi = {10.1016/S0168-9002(03)00425-X},
	abstract = {Super-Kamiokande is the world's largest water Cherenkov detector, with net mass 50,000 tons. During the period April, 1996 to July, 2001, Super-Kamiok…},
	language = {en},
	number = {2-3},
	author = {The Super-Kamiokande Collaboration},
	urldate = {2020-05-06},
	journal = {Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment},
	month = apr,
	year = {2003},
	note = {Publisher: North-Holland},
	pages = {418--462}
}

@article{ludhova_particle_2020,
	title = {Particle {Identification} at {MeV} {Energies} in {JUNO}},
	url = {http://arxiv.org/abs/2007.02687},
	abstract = {JUNO is a multi-purpose neutrino experiment currently under construction in Jiangmen, China. It is primary aiming to determine the neutrino mass ordering. Moreover, its 20 kt target mass makes it an ideal detector to study neutrinos from various sources, including nuclear reactors, the Earth and its atmosphere, the Sun, and even supernovae. Due to the small cross section of neutrino interactions, the event rate of neutrino experiments is limited. In order to maximize the signal-to noise ratio, it is extremely important to control the background levels. In this paper we discuss the potential of particle identification in a large liquid scintillator detector like JUNO. We discuss the underlying principles of particle identification and its application in the experiment. In order to investigate the potential of event discrimination, several event pairings are analysed, i.e. alpha/beta?, e/p?, e+/e-, and e/gamma. We compare the discrimination performance of advanced analytical techniques based on neural networks and on the topological event reconstruction keeping the standard Gatti filter as a reference. We use the Monte Carlo samples generated in the physically motivated energy intervals. We study the dependence of our cuts on energy, radial position, PMT time resolution, and dark noise. The results show an excellent performance for alpha/beta? and e/p? with the Gatti method and the neural network. Furthermore, e+/e- and e/gamma can partly be distinguished by means of neural network and topological reconstruction on a statistical basis. Especially in the latter case, the topological method proved very successful.},
	urldate = {2020-07-23},
	journal = {arXiv:2007.02687 [hep-ex, physics:physics]},
	author = {Ludhova, Livia and Rebber, Henning and Wonsak, Björn Soenke and Xu, Yu},
	month = jul,
	year = {2020},
	note = {arXiv: 2007.02687},
	keywords = {Physics - Instrumentation and Detectors, High Energy Physics - Experiment}
}

@article{jetter_pmt_2012,
	title = {{PMT} waveform modeling at the {Daya} {Bay} experiment},
	volume = {36},
	issn = {1674-1137},
	url = {https://doi.org/10.1088%2F1674-1137%2F36%2F8%2F009},
	doi = {10.1088/1674-1137/36/8/009},
	abstract = {Detailed measurements of Hamamatsu R5912 photomultiplier signals are presented, including the single photoelectron charge response, waveform shape, nonlinearity, saturation, overshoot, oscillation, prepulsing, and afterpulsing. The results were used to build a detailed model of the PMT signal characteristics over a wide range of light intensities. Including the PMT model in simulated Daya Bay particle interactions shows no significant systematic effects that are detrimental to the experimental sensitivity.},
	language = {en},
	number = {8},
	urldate = {2020-10-10},
	journal = {Chinese Physics C},
	author = {Jetter, Sören and Dwyer, Dan and Jiang, Wen-Qi and Liu, Da-Wei and Wang, Yi-Fang and Wang, Zhi-Min and Wen, Liang-Jian},
	month = aug,
	year = {2012},
	note = {Publisher: IOP Publishing},
	pages = {733--741}
}

@article{li_richardson-lucy_2019,
	title = {The {Richardson}-{Lucy} {Deconvolution} method to {Extract} {LAMOST} {1D} {Spectra}},
	volume = {19},
	issn = {1674-4527, 2397-6209},
	url = {http://arxiv.org/abs/1905.07096},
	doi = {10.1088/1674-4527/19/10/145},
	abstract = {We use the Richardson-Lucy deconvolution algorithm to extract one dimensional (1D) spectra from LAMOST spectrum images. Compared with other deconvolution algorithms, this algorithm is much more fast. The practice on a real LAMOST image illustrates that the 1D resulting spectrum of this method has a higher SNR and resolution than those extracted by the LAMOST pipeline. Furthermore, our algorithm can effectively depress the ringings that are often shown in the 1D resulting spectra of other deconvolution methods.},
	number = {10},
	urldate = {2021-02-02},
	journal = {Research in Astronomy and Astrophysics},
	author = {Li, Min and Li, Guangwei and Lv, Ke and Duan, Fuqing and Haerken, Hasitieer and Zhao, Yongheng},
	month = oct,
	year = {2019},
	note = {arXiv: 1905.07096},
	keywords = {Astrophysics - Instrumentation and Methods for Astrophysics},
	pages = {145}
}

@article{phan2019composable,
    title={Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro},
    author={Phan, Du and Pradhan, Neeraj and Jankowiak, Martin},
    journal={arXiv preprint arXiv:1912.11554},
    year={2019}
}

@article{schniter_fast_nodate,
	title = {Fast {Bayesian} {Matching} {Pursuit}: {Model} {Uncertainty} and {Parameter} {Estimation} for {Sparse} {Linear} {Models}},
	abstract = {A low-complexity recursive procedure is presented for model selection and minimum mean squared error (MMSE) estimation in linear regression. Emphasis is given to the case of a sparse parameter vector and fewer observations than unknown parameters. A Gaussian mixture is chosen as the prior on the unknown parameter vector. The algorithm returns both a set of high posterior probability models and an approximate MMSE estimate of the parameter vector. Exact ratios of posterior probabilities serve to reveal potential ambiguity among multiple candidate solutions that are ambiguous due to observation noise or correlation among columns in the regressor matrix. Algorithm complexity is O(M N K), with M observations, N coefﬁcients, and K nonzero coefﬁcients. For the case that hyperparameters are unknown, an approximate maximum likelihood estimator is proposed based on the generalized expectation-maximization algorithm. Numerical simulations demonstrate estimation performance and illustrate the distinctions between MMSE estimation and maximum a posteriori probability model selection.},
	language = {en},
	journal = {IEEE TRANSACTIONS ON SIGNAL PROCESSING},
	author = {Schniter, Philip}
}
@article{savitzky_smoothing_1964,
	title = {Smoothing and {Differentiation} of {Data} by {Simplified} {Least} {Squares} {Procedures}.},
	volume = {36},
	issn = {0003-2700},
	url = {https://doi.org/10.1021/ac60214a047},
	doi = {10.1021/ac60214a047},
	number = {8},
	urldate = {2021-03-25},
	journal = {Analytical Chemistry},
	author = {Savitzky, Abraham. and Golay, M. J. E.},
	month = jul,
	year = {1964},
	note = {Publisher: American Chemical Society},
	pages = {1627--1639}
}

@article{kullback_information_1951,
	title = {On {Information} and {Sufficiency}},
	volume = {22},
	issn = {0003-4851, 2168-8990},
	url = {https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-22/issue-1/On-Information-and-Sufficiency/10.1214/aoms/1177729694.full},
	doi = {10.1214/aoms/1177729694},
	abstract = {The Annals of Mathematical Statistics},
	number = {1},
	urldate = {2021-04-05},
	journal = {The Annals of Mathematical Statistics},
	author = {Kullback, S. and Leibler, R. A.},
	month = mar,
	year = {1951},
	note = {Publisher: Institute of Mathematical Statistics},
	pages = {79--86}
}

@article{he_deep_2015,
	title = {Deep {Residual} {Learning} for {Image} {Recognition}},
	url = {http://arxiv.org/abs/1512.03385},
	abstract = {Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57\% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28\% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC \& COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.},
	urldate = {2021-04-05},
	journal = {arXiv:1512.03385 [cs]},
	author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
	month = dec,
	year = {2015},
	note = {arXiv: 1512.03385},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{vaswani_attention_2017,
	title = {Attention {Is} {All} {You} {Need}},
	url = {http://arxiv.org/abs/1706.03762},
	abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.},
	urldate = {2021-04-05},
	journal = {arXiv:1706.03762 [cs]},
	author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
	month = dec,
	year = {2017},
	note = {arXiv: 1706.03762},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning}
}

@article{mihoko_robust_2002,
	title = {Robust {Blind} {Source} {Separation} by {Beta} {Divergence}},
	volume = {14},
	doi = {10.1162/089976602760128045},
	abstract = {Blind source separation is aimed at recovering original independent signals when their linear mixtures are observed. Various methods for estimating a recovering matrix have been proposed and applied to data in many fields, such as biological signal processing, communication engineering, and financial market data analysis. One problem these methods have is that they are often too sensitive to outliers, and the existence of a few outliers might change the estimate drastically. In this article, we propose a robust method of blind source separation based on the beta divergence. Shift parameters are explicitly included in our model instead of the conventional way which assumes that original signals have zero mean. The estimator gives smaller weights to possible outliers so that their influence on the estimate is weakened. Simulation results show that the proposed estimator significantly improves the performance over the existing methods when outliers exist; it keeps equal performance otherwise.},
	journal = {Neural computation},
	author = {Mihoko, Minami and Eguchi, Shinto},
	month = sep,
	year = {2002},
	pages = {1859--86}
}

@inproceedings{levina_earth_2001,
	title = {The {Earth} {Mover}'s distance is the {Mallows} distance: some insights from statistics},
	volume = {2},
	shorttitle = {The {Earth} {Mover}'s distance is the {Mallows} distance},
	doi = {10.1109/ICCV.2001.937632},
	abstract = {The Earth Mover's distanc1e was first introduced as a purely empirical ways to measure texture and color similarities. We show that it has a rigorous probabilistic interpretation and is conceptually equivalent to the Mallows distance on probability distributions. The two distances are exactly the same when applied to probability distributions, but behave differently when applied to unnormalized distributions with different masses, called signatures. We discuss the advantages and disadvantages of both distances, and statistical issues involved in computing them from data. We also report some texture classification results for the Mallows distance applied to texture features and compare several ways of estimating feature distributions. In addition, we list some known probabilistic properties of this distance.},
	booktitle = {Proceedings {Eighth} {IEEE} {International} {Conference} on {Computer} {Vision}. {ICCV} 2001},
	author = {Levina, E. and Bickel, P.},
	month = jul,
	year = {2001},
	keywords = {Earth, Euclidean distance, Fluid flow measurement, Humans, Image retrieval, Image segmentation, Multidimensional systems, Physics, Probability distribution, Statistics},
	pages = {251--256 vol.2}
}

@article{lucy_iterative_1974,
	title = {An iterative technique for the rectification of observed distributions},
	volume = {79},
	issn = {0004-6256},
	url = {http://adsabs.harvard.edu/abs/1974AJ.....79..745L},
	doi = {10.1086/111605},
	abstract = {Abstract image available at:
http://adsabs.harvard.edu/abs/1974AJ.....79..745L},
	urldate = {2021-04-06},
	journal = {The Astronomical Journal},
	author = {Lucy, L. B.},
	month = jun,
	year = {1974},
	pages = {745}
}


text:
body.light {
	--primary: #0061a6;
	--on-primary: #ffffff;
	--primary-container: #d0e4ff;
	--on-primary-container: #001d36;
	--secondary: #535f70;
	--on-secondary: #ffffff;
	--secondary-container: #d6e3f7;
	--on-secondary-container: #101c2b;
	--tertiary: #6b5778;
	--on-tertiary: #ffffff;
	--tertiary-container: #f3daff;
	--on-tertiary-container: #251432;
	--error: #ba1b1b;
	--error-container: #ffdad4;
	--on-error: #ffffff;
	--on-error-container: #410001;
	--background: #fdfcff;
	--on-background: #1b1b1b;
	--surface: #fdfcff;
	--on-surface: #1b1b1b;
	--surface-variant: #dfe2eb;
	--on-surface-variant: #42474e;
	--outline: #73777f;
	--inverse-on-surface: #f1f0f4;
	--inverse-surface: #2f3033;
	--inverse-primary: #9ccaff;
	--shadow: #000000;
}

body.dark {
	--primary: #9ccaff;
	--on-primary: #00325a;
	--primary-container: #00497f;
	--on-primary-container: #d0e4ff;
	--secondary: #bbc8db;
	--on-secondary: #253140;
	--secondary-container: #3c4858;
	--on-secondary-container: #d6e3f7;
	--tertiary: #d6bee4;
	--on-tertiary: #3b2948;
	--tertiary-container: #523f5f;
	--on-tertiary-container: #f3daff;
	--error: #ffb4a9;
	--error-container: #930006;
	--on-error: #680003;
	--on-error-container: #ffdad4;
	--background: #1b1b1b;
	--on-background: #e2e2e6;
	--surface: #1b1b1b;
	--on-surface: #e2e2e6;
	--surface-variant: #42474e;
	--on-surface-variant: #c3c7d0;
	--outline: #8d9199;
	--inverse-on-surface: #1b1b1b;
	--inverse-surface: #e2e2e6;
	--inverse-primary: #0061a6;
	--shadow: #000000;
}

repository: Devolutions/IronRDP

repo_name: Devolutions/IronRDP


text:
@import "../components/header/header.css";
@import "../components/header/header-menu.css";
@import "../components/header/header-menu-item.css";
@import "../components/footer/footer.css";

@import "./css/main.css";

body {
  display: flex;
  min-height: 100vh;
  flex-direction: column;
}

.main-content {
  flex: 1;
  padding: 10px 0;
}

.transparent-btn {
  background-color: Transparent;
  background-repeat: no-repeat;
  border: none;
  cursor: pointer;
  overflow: hidden;
  outline: none;
}

.form-flex {
  display: flex;
  flex-wrap: wrap;
  align-items: center;
  justify-content: flex-start;
  margin-bottom: 20px;
}

.form-flex > * {
  flex: 1;
  margin: 2px !important;
}

.form-flex > button {
  flex: 0;
  flex-basis: 160px;
}

.card-flex {
  display: flex;
  justify-content: flex-start;
  flex-wrap: wrap;
}

.to-right {
  margin-left: auto !important;
}

.card-flex > * {
  margin: 0 4px;
}

.table-hover tbody tr:hover td {
  background-color: #E0E0E0;
}

.popover {
  max-width: 50%;
  white-space: pre-wrap;
}

.sticky-col {
  position: sticky;
  top: -1px;
  background-color: white !important;
  z-index: 10;
}

.scrollable-table-container {
  max-height: 70vh;
  overflow-y: scroll;
}

.input-timestamp {
  padding: 0rem 0.75em;
}

.input-group-text {
  padding: 0rem 0.75em;
  /*font-size: 1em;*/
}

label.centred {
  margin-top: 0.7em;
}
label {
  font-weight: bold;
  display: inline-flex;
}

a {
  text-decoration: none;
  font-weight: bold;
  color: #4f5050;
}

.topic-title {
  color: var(--bs-gray-dark);
  font-weight: bold;
  margin: 0 5px 0 5px;
  font-size: 16px;
}

repository: epam/eco-kafka-manager

repo_name: epam/eco-kafka-manager

gha_language: Java


text:
@article{bauer_simplifying_2020,
 author = {Bauer, Daniel J and Belzak, William CM and Cole, Veronica T},
 journal = {Structural Equation Modeling: A Multidisciplinary Journal},
 note = {Publisher: Routledge},
 number = {1},
 pages = {43--55},
 title = {Simplifying the assessment of measurement invariance over multiple background variables: Using regularized moderated nonlinear factor analysis to detect differential item functioning},
 volume = {27},
 year = {2020}
}


text:
@article{Kurkute-2020-Assessment,
    title = "Assessment and projection of the water budget over western Canada using convection-permitting weather research and forecasting simulations",
    author = "Kurkute, Sopan  and
      Li, Zhenhua  and
      Li, Yanping  and
      Huo, Fei",
    journal = "Hydrology and Earth System Sciences, Volume 24, Issue 7",
    volume = "24",
    number = "7",
    year = "2020",
    publisher = "Copernicus GmbH",
    url = "https://aclanthology.org/G20-94001",
    doi = "10.5194/hess-24-3677-2020",
    pages = "3677--3697",
    abstract = "Abstract. Water resources in cold regions in western Canada face severe risks posed by anthropogenic global warming as evapotranspiration increases and precipitation regimes shift. Although understanding the water cycle is key for addressing climate change issues, it is difficult to obtain high spatial- and temporal-resolution observations of hydroclimatic processes, especially in remote regions. Climate models are useful tools for dissecting and diagnosing these processes, especially the convection-permitting (CP) high-resolution regional climate simulation, which provides advantages over lower-resolution models by explicitly representing convection. In addition to better representing convective systems, higher spatial resolution also better represents topography, mountain meteorology, and highly heterogeneous geophysical features. However, there is little work with convection-permitting regional climate models conducted over western Canada. Focusing on the Mackenzie River and Saskatchewan River basins, this study investigated the surface water budget and atmospheric moisture balance in historical and representative concentration pathway (RCP8.5) projections using 4 km CP Weather Research and Forecasting (WRF). We compared the high-resolution 4 km CP WRF and three common reanalysis datasets, namely the North American Regional Reanalysis (NARR), the Japanese 55-year Reanalysis (JRA-55), and European Centre for Medium-Range Weather Forecasts reanalysis interim dataset (ERA-Interim). High-resolution WRF outperforms the reanalyses in balancing the surface water budget in both river basins with much lower residual terms. For the pseudo-global-warming scenario at the end of the 21st century with representative concentration pathway (RCP8.5) radiative forcing, both the Mackenzie River and Saskatchewan River basins show increases in the amplitude for precipitation and evapotranspiration and a decrease in runoff. The Saskatchewan River basin (SRB) shows a moderate increase in precipitation in the west and a small decrease in the east. Combined with a significant increase in evapotranspiration in a warmer climate, the Saskatchewan River basin would have a larger deficit of water resources than in the current climate based on the pseudo-global-warming (PGW) simulation. The high-resolution simulation also shows that the difference of atmospheric water vapour balance in the two river basins is due to flow orientation and topography differences at the western boundaries of the two basins. The sensitivity of water vapour balance to fine-scale topography and atmospheric processes shown in this study demonstrates that high-resolution dynamical downscaling is important for large-scale water balance and hydrological cycles.",
}

repository: gwf-uwaterloo/gwf-publications

repo_name: gwf-uwaterloo/gwf-publications

gha_language: Python


text:
set(BINGO_DEFAULT_VERSION "1.9.1")
set(BINGO_MAX_REVISION 1000)

find_package(Git)
if(GIT_EXECUTABLE)
    EXECUTE_PROCESS(COMMAND ${GIT_EXECUTABLE} describe --long --tags --match bingo-*
            OUTPUT_VARIABLE BINGO_FULL_VERSION
            OUTPUT_STRIP_TRAILING_WHITESPACE
            WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
    if (NOT ${BINGO_FULL_VERSION} STREQUAL "")
        string(REGEX REPLACE "bingo-(.+)-(.+)-(.+)" "\\2" BINGO_REVISION ${BINGO_FULL_VERSION})
        if (${BINGO_REVISION} GREATER ${BINGO_MAX_REVISION})
            message(SEND_ERROR "Bingo revision ${BINGO_REVISION} is greater than max revision ${BINGO_MAX_REVISION}. Please create appropriate version tag" )
        endif()
        string(REGEX REPLACE "bingo-(.+)-(.+)-(.+)" "\\1.\\2-\\3" BINGO_FULL_VERSION ${BINGO_FULL_VERSION})
        string(REGEX REPLACE "(.+)-(.+)" "\\1" BINGO_VERSION ${BINGO_FULL_VERSION})
    else()
        set(BINGO_VERSION "${BINGO_DEFAULT_VERSION}")
        set(BINGO_FULL_VERSION "${BINGO_DEFAULT_VERSION}-00000000")
    endif()
else()
    set(BINGO_VERSION "${BINGO_DEFAULT_VERSION}")
    set(BINGO_FULL_VERSION "${BINGO_DEFAULT_VERSION}-00000000")
endif()

message(STATUS "Bingo full version: " ${BINGO_FULL_VERSION})

# Do not forget to launch build_scripts/bingo-update-version.py after changing the version because it should be ${RV} changed in the Java and .NET files as well

if($ENV{BUILD_NUMBER})
    set(BINGO_BUILD_VERSION $ENV{BUILD_NUMBER})
else()
    set(BINGO_BUILD_VERSION 0)
endif()

set(BINGO_VERSION_EXT "${BINGO_FULL_VERSION}")

repository: epam/Indigo

repo_name: epam/Indigo


text:
SUMMARY = "Skins for Enigma2"
LICENSE = "CLOSED"
SRCREV = "${@opendreambox_srcrev('b3a46e566b5e6f6904d087d4a930494b094758cd', d)}"

inherit allarch autotools-brokensep opendreambox-github

PACKAGES_DYNAMIC = "^enigma2-skin-.*"
PACKAGES += "${PN}-meta"

FILES_${PN} = ""
FILES_${PN}-meta = "${datadir}/meta"

python enigma2_skins_split_packages() {
    output_pattern = 'enigma2-skin-%s'
    enigma2_skindir = os.path.join(d.getVar('datadir', True), 'enigma2')
    do_split_packages(d, enigma2_skindir, '(.*?)/.*', output_pattern, 'Enigma2 Skin: %s', recursive=True, extra_depends='enigma2', match_path=True)

    def parseControlFile(package, dir):
        filename = os.path.join(dir, 'CONTROL/control')
        if os.path.exists(filename):
            with open(filename) as f:
                for line in f.read().splitlines():
                    try:
                        name, value = line.strip().split(': ', 1)
                        if name == 'Description':
                            d.setVar('DESCRIPTION_%s' % package, value)
                        elif name in ('Breaks', 'Conflicts', 'Depends', 'Provides', 'Recommends', 'Replaces', 'Suggests'):
                            d.appendVar('R%s_%s' % (name.upper(), package), ' ' + ' '.join(value.split(', ')))
                    except:
                        bb.fatal("Error parsing control file for package %s" % package)

    srcdir = os.path.join(d.getVar('S', True), 'skins')
    packages = d.getVar('PACKAGES', True).split()
    for subdir in next(os.walk(srcdir))[1]:
        package = output_pattern % legitimize_package_name(subdir)
        if package in packages:
            parseControlFile(package, os.path.join(srcdir, subdir))
}

PACKAGESPLITFUNCS_prepend = "enigma2_skins_split_packages "

# No package will be generated for ${PN}, and enigma2-plugins doesn't exist as
# a package either, but it's enough to avoid a direct build dependency.
RDEPENDS_${PN} = "enigma2 enigma2-plugins"

repository: engycz/opendreambox

repo_name: engycz/opendreambox

branch_name: refs/heads/krogoth


text:
SUMMARY = "hierarchical pool based memory allocator"
DESCRIPTION = "A hierarchical pool based memory allocator with destructors. It uses \
reference counting to determine when memory should be freed."
HOMEPAGE = "http://talloc.samba.org/"

inherit debian-package

PV = "2.1.2"

LICENSE = "LGPLv3+ & ISC & BSD-3-Clause & PostgreSQL"
LIC_FILES_CHKSUM = "\
	file://lib/replace/inet_ntop.c;endline=16;md5=d202009b255fffe6953a0d61c2d6fab0 \
	file://lib/replace/inet_pton.c;endline=16;md5=d202009b255fffe6953a0d61c2d6fab0 \
	file://lib/replace/timegm.c;endline=32;md5=e1ed216ea15ed1643a625c6725e00e40 \
	file://lib/replace/getaddrinfo.c;endline=26;md5=2a9c0f540bb750338375017f470050c5 \
"

inherit waf-samba

EXTRA_OECONF += " \
	--with-libiconv=${STAGING_DIR_HOST}${prefix} \
	--disable-rpath \
	--disable-rpath-install \
"

PACKAGES =+ "python-talloc-dev python-talloc"

FILES_python-talloc-dev = "\
	${includedir}/pytalloc.h \
	${libdir}/pkgconfig/pytalloc-util.pc \
	${libdir}/libpytalloc-util.so"
FILES_python-talloc = "\
	${libdir}/${PYTHON_DIR}/dist-packages/talloc.so \
	${libdir}/libpytalloc-util${SOLIBS}"

DEBIAN_NOAUTONAME_python-talloc-dev = "1"
DEBIAN_NOAUTONAME_python-talloc = "1"
RPROVIDES_${PN} += "libtalloc2"
RPROVIDES_${PN}-dev += "libtalloc-dev"

repository: manut/meta-combo

repo_name: manut/meta-combo

branch_name: refs/heads/morty


text:
@article{BBLM2023,
  author  = {Christian Bayer and Benjamin Born and Ralph Luetticke and Gernot J. M\"uller},
  title   = {The Coronavirus Stimulus Package: How large is the transfer multiplier?},
  year    = {2023},
  journal = {Economic Journal},
  number  = {652},
  volume  = {133},
  pages   = {1318–-1347}
}

repository: BenjaminBorn/academic-kickstart

repo_name: BenjaminBorn/academic-kickstart


text:
@book{9788534603485,
  Author = {Aaron Tenenbaum},
  Title = {Estruturas de Dados Usando C (Em Portuguese do Brasil)},
  Publisher = {PEARSON - UNIVERSITARIOS/ KOTLER},
  Year = {1995},
  ISBN = {8534603480},
  URL = {http://www.amazon.com/Estruturas-Dados-Usando-Portuguese-Brasil/dp/8534603480%3FSubscriptionId%3D0JYN1NVW651KCA56C102%26tag%3Dtechkie-20%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D8534603480}
}

path: /analise artigos/bib.bib


text:
@article{balko2017propofol,
  title={Propofol as an Immersion Anesthetic and in a Minimum Anesthetic Concentration (MAC) Reduction Model In Goldfish (Carassius Auratus)},
  author={Balko, Julie A and Wilson, Sarah K and Lewbart, Gregory A and Gaines, Brian R and Posner, Lysa P},
  journal={Journal of Zoo and Wildlife Medicine},
  volume={48},
  number={1},
  pages={48--54},
  year={2017},
  publisher={BioOne}
}


text:
body{
    background-color: #335066;
    font-family: Helvetica, Verdana, sans-serif;
    color: white;
    letter-spacing: 2px;
    padding: 0;
    margin: 0;
}

#login {
    background-color: inherit;
    height: 100%;
    width: 100%;
    text-align: center;
    vertical-align: center;
    position: absolute;
    top: 0;
    left: 0;
}

label {
    font-weight: bold;
}

button {
    padding: 10px;
    background-color: #337777;
    color: white;
    display: block;
}

#login button {
    width: 90%;
    margin: 20px auto;
}

#login_form {
    position:absolute;
    width: 350px;
    height: 50px;
    top: 0;
    left: 0;
    right: 0;
    bottom: 0;
    margin: auto;
}

#chat_form {
    height: 35px;
    position: absolute;
    left: 0;
    right: 0;
    bottom: 10px;
    margin: 0;
    padding: 0;
    background-color: white;
}

#chat_form button{
    display: inline;
    position: absolute;
    right: 0px;
    width: 150px;
    height: 100%;
    padding: 0;

}

#chat_content {
    bottom: 45px;
    top: 0px;
    width: 100%;
    overflow-y: auto;
    position: absolute;
}

#chat_form input {
    width: 90%;
    height: 90%;
    border: none;
    left: 0px;
    position: absolute;
    padding: 0 10px;
    background-color: #fff;
}

#chat_messages {
    list-style-type: none;
    list-style-image: none;
    margin:5px;
    padding: 0;
}

#chat_messages .username {
    font-weight: bold;
    color: #559999;
}

#chat_messages .username.me{
    color: #aaccff;
}

#chat_messages .time {
    font-size: 12px;
    padding: 0 10px 0 0;
    color: #559999;
}

repository: node-bus/node-bus

repo_name: node-bus/node-bus


text:
@article{pannitto2018refining,
  author  = {Ludovica Pannitto, Lavinia Salicchi, Alessandro Lenci},
  title   = {Refining the Distributional Inclusion Hypothesis for Unsupervised Hypernym Identification},
  journal = {Italian Journal of Computational Linguistics},
  year    = 2018,
  number  = 4,
  pages   = {45-56},
  volume  = 2
}

repository: ellepannitto/my-website

repo_name: ellepannitto/my-website


text:
SUMMARY = "Virtual Midi Keyboard for Jack Audio Connection Kit"
HOMEPAGE = "https://github.com/brummer10/Mamba"
LICENSE = "0BSD"
LIC_FILES_CHKSUM = "file://LICENSE;md5=1a396d84ed72b4a017f02f5c5d1eaafe"

SRC_URI = "gitsm://github.com/brummer10/Mamba.git;branch=master;protocol=https"
SRCREV = "46bdd97df02b93c30eb6f7a1782e2ee454979e87"
PV = "2.3"
S = "${WORKDIR}/git"

inherit pkgconfig features_check mime-xdg gettext

REQUIRED_DISTRO_FEATURES = "x11"

DEPENDS += " \
    vim-native \
    cairo \
    virtual/libx11 \
    libsigc++-2.0 \
    lv2 \
    liblo \
    jack \
    fluidsynth \
    libsmf \
"

do_compile() {
    oe_runmake SSE_CFLAGS= STRIP=echo
}

do_install() {
    oe_runmake DESTDIR=${D} PREFIX=${prefix} install
}

repository: schnitzeltony/meta-musicians

repo_name: schnitzeltony/meta-musicians


text:
@Article{lee2010utility,
  title = {On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods},
  author = {Anthony Lee and Christopher Yau and Michael B Giles and Arnaud Doucet and Christopher C Holmes},
  journal = {Journal of Computational and Graphical Statistics},
  volume = {19},
  number = {4},
  pages = {769--789},
  year = {2010},
  publisher = {ASA},
  date = {2010-01-01},
}


text:
@article{Xing:2015:AHA,
 author = {Xing, Jun and Wei, Li-Yi and Shiratori, Takaaki and Yatani, Koji},
 title = {Autocomplete Hand-drawn Animations},
 journal = {ACM Trans. Graph.},
 issue_date = {November 2015},
 volume = {34},
 number = {6},
 month = oct,
 year = {2015},
 issn = {0730-0301},
 pages = {169:1--169:11},
 articleno = {169},
 numpages = {11},
 url = {http://doi.acm.org/10.1145/2816795.2818079},
 doi = {10.1145/2816795.2818079},
 acmid = {2818079},
 keywords = {analysis, animation, auto-complete, beautification, deformation, drawing, synthesis, user interface, workflow},
}

@article{Xing:2014:APR,
 author = {Xing, Jun and Chen, Hsiang-Ting and Wei, Li-Yi},
 title = {Autocomplete Painting Repetitions},
 journal = {ACM Trans. Graph.},
 issue_date = {November 2014},
 volume = {33},
 number = {6},
 month = nov,
 year = {2014},
 issn = {0730-0301},
 pages = {172:1--172:11},
 articleno = {172},
 numpages = {11},
 url = {http://doi.acm.org/10.1145/2661229.2661247},
 doi = {10.1145/2661229.2661247},
 acmid = {2661247},
 keywords = {analysis, autocomplete, context, digital painting, editing history, repetition, synthesis, texture, user interface, workflow},
}

@inproceedings{Dai:2014:SOT,
  title={The synthesizability of texture examples},
  author={Dai, Dengxin and Riemenschneider, Hayko and Van Gool, Luc},
  booktitle={CVPR},
  pages={3027--3034},
  year={2014},
}

repository: ljhnick/research-templates

repo_name: ljhnick/research-templates


text:
/*
Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with the License. A copy of the License is located at

    http://aws.amazon.com/apache2.0/

or in the "license" file accompanying this file. This file 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.
*/

/*

  Set CSS specific to the extension viewer view in this file.

*/

body,
button,
div,
html,
p,
span {
    margin: 0;
    padding: 0;
    line-height: 1em
}
html {
    padding: 0;
    margin: 0;
}

html * {
    text-decoration: none;
    box-sizing: border-box
}

html a:hover {
    color: #fff;
    text-decoration: underline
}

body {
	font: 12px 'Open Sans', sans-serif;
	text-align: center;
    padding: 0;
    margin: 0;
    font-weight: 300;
    font-size: 14px;
    color: #fff;
    width: 100%;
    height: 100%;
    overflow: hidden
    width
}
div {
	margin: 0px;
}
a.reload {
	color: #0f0;
}

.content {
	width: 318px;
	height: 300px;
	/*border: 1px solid red;*/
	background-color: #202020;
}

.carousel {
	width: 300px;
	height: 100px;
	background-color: #151515;
	padding: 5px;
}

/*================================================ Twitch Extension Styles ===========================================*/

.twitch{
    background-color: #202020;
    width: 100%;
    height: 100%;
    margin: 0 auto;
    padding-top: 15px;
    position: relative;
    overflow: hidden;
}

/*---------------- Title -----------------*/
.twitch .title{
    font-size: 18px;
    font-family: 'Open Sans', sans-serif;
    font-weight: bold;
    color: #FFFEFE;
    text-align: center;
    margin: 0;
    margin-bottom: 5px;
}

.twitch .subtitle{
    font-size: 16px;
    font-family: 'Open Sans', sans-serif;
    font-weight: bold;
    color: #FFFEFE;
    text-align: center;
    margin: 0;
    margin-bottom: 5px;
}

/*---------------- Overall Score and Comments ---------------*/
.twitch #total-score-wrapper {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-pack: center;
    -ms-flex-pack: center;
    justify-content: center;
    background-color:  #9B9A9B;
    width:  50px;
    height:  50px;
    margin: 15px auto;
    border-radius:  50%;
}

.twitch #total-score{
    width: 50px;
    height: 50px;
    background-color: #FFCC33;
    font-size: 18px;
    font-family: 'Open Sans', sans-serif;
    font-weight: bold;
    color: #FFFEFE;
    border-radius: 50%;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: center;
    -ms-flex-align: center;
    align-items: center;
    -webkit-box-pack: center;
    -ms-flex-pack: center;
    justify-content: center;
}

.twitch #total-score #total-score{
    position: relative;
    bottom: 2px;
}

.twitch .overall-comments-wrapper #overall-comments{
    font-size: 18px;
    font-family: 'Open Sans', sans-serif;
    color: #FFFEFE;
    text-align: center;
    margin: 0;
}

/*--------------------- Score Distribution --------------------------*/

.twitch .score-distribution-wrapper{
    margin: 15px;
    width: 60%;
    margin: 0 auto;
}

.twitch .score-distribution-wrapper .score-distribution{
    list-style-type: none;
    padding: -0px;
}

.twitch .score-distribution-wrapper .score-distribution .distribution-item{
    color: #FFFEFE;
    font-size: 12px;
    font-family: "Open Sans", sans-serif;
    margin-bottom: 10px;
}

.twitch .score-distribution-wrapper .score-distribution .distribution-item .score-area{
    text-transform: capitalize;
}

.twitch .score-distribution-wrapper .score-distribution .distribution-item .score{
    float: right;
}

/*---------------------- See More or Loading Button ----------------------*/

.twitch .twitch-msg-wrapper #twitch-message{
    color: #FFFEFE;
    font-size: 12px;
    font-family: "Open Sans", sans-serif;
    text-align: center;
    position: absolute;
    bottom: 0px;
    margin: 0;
    left: 50%;
    background-color: transparent;
    cursor: pointer;
    border: none;
    -webkit-transform: translateX(-50%);
    -ms-transform: translateX(-50%);
    transform: translateX(-50%);
}

.twitch .twitch-msg-wrapper #twitch-message:focus{
    outline: none;
}

repository: mikulthegreat/extensions-samples

repo_name: mikulthegreat/extensions-samples


text:
---
---
@article{Moreno:2019neq,
    author = "Moreno, Eric A. and Nguyen, Thong Q. and Vlimant, Jean-Roch and Cerri, Olmo and Newman, Harvey B. and Periwal, Avikar and Spiropulu, Maria and Duarte, Javier M. and Pierini, Maurizio",
    title = "{Interaction networks for the identification of boosted $H \rightarrow b\overline{b}$ decays}",
    eprint = "1909.12285",
    archivePrefix = "arXiv",
    primaryClass = "hep-ex",
    reportNumber = "FERMILAB-PUB-19-492-CMS-E",
    doi = "10.1103/PhysRevD.102.012010",
    journal = "Phys. Rev. D",
    volume = "102",
    pages = "012010",
    year = "2020"
}

@article{Sirunyan:2017ezt,
    author = "Sirunyan, A.M. and others",
    collaboration = "CMS",
    title = "{Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV}",
    eprint = "1712.07158",
    archivePrefix = "arXiv",
    primaryClass = "physics.ins-det",
    reportNumber = "CMS-BTV-16-002, CERN-EP-2017-326",
    doi = "10.1088/1748-0221/13/05/P05011",
    journal = "J. Instrum.",
    volume = "13",
    number = "05",
    pages = "P05011",
    year = "2018"
}

@book{Marzani:2019hun,
    author = "Marzani, Simone and Soyez, Gregory and Spannowsky, Michael",
    title = "{Looking inside jets: an introduction to jet substructure and boosted-object phenomenology}",
    eprint = "1901.10342",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    doi = "10.1007/978-3-030-15709-8",
    publisher = "Springer",
    volume = "958",
    year = "2019"
}

@article{Asquith:2018igt,
    author = "Kogler, Roman and others",
    title = "{Jet Substructure at the Large Hadron Collider: Experimental Review}",
    eprint = "1803.06991",
    archivePrefix = "arXiv",
    primaryClass = "hep-ex",
    reportNumber = "FERMILAB-PUB-18-123-PPD",
    doi = "10.1103/RevModPhys.91.045003",
    journal = "Rev. Mod. Phys.",
    volume = "91",
    number = "4",
    pages = "045003",
    year = "2019"
}

@article{Komiske:2018cqr,
    author = "Komiske, Patrick T. and Metodiev, Eric M. and Thaler, Jesse",
    title = "{Energy Flow Networks: Deep Sets for Particle Jets}",
    eprint = "1810.05165",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    reportNumber = "MIT-CTP 5064",
    doi = "10.1007/JHEP01(2019)121",
    journal = "J. High Energy Phys.",
    volume = "01",
    pages = "121",
    year = "2019"
}

@inproceedings{NIPS2017_6931,
    title = {Deep Sets},
    author = {Zaheer, Manzil and Kottur, Satwik and Ravanbakhsh, Siamak and Poczos, Barnabas and Salakhutdinov, Russ R and Smola, Alexander J},
    booktitle = {Advances in Neural Information Processing Systems 30},
    editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
    pages = {3391},
    year = {2017},
    publisher = {Curran Associates, Inc.},
    url = {http://papers.nips.cc/paper/6931-deep-sets.pdf},
    eprint = "1703.06114",
primaryClass = "cs.LG"
}

@inproceedings{Pivarski:2020qcb,
    author = "Pivarski, Jim and Elmer, Peter and Lange, David",
    title = "{Awkward Arrays in Python, C++, and Numba}",
    booktitle = "{24th International Conference on Computing in High Energy and Nuclear Physics}",
    eprint = "2001.06307",
    archivePrefix = "arXiv",
    primaryClass = "cs.MS",
    month = "1",
    year = "2020"
}

@misc{opendata,
    author = "Duarte, Javier",
    collaboration = "CMS",
    title = "{Sample with jet, track and secondary vertex properties for {Hbb} tagging {ML} studies {\texttt{HiggsToBBNTuple\_HiggsToBB\_QCD\_RunII\_13TeV\_MC}}}",
    doi = "10.7483/OPENDATA.CMS.JGJX.MS7Q",
    note = "{CERN Open Data Portal}",
    year = 2019,
    url = "http://opendata.cern.ch/record/12102"
}

@inproceedings{deepjet,
    author = "Markus Stoye and Jan Kieseler and Mauro Verzetti and Huilin Qu and Loukas Gouskos and Anna Stakia",
    title = "{DeepJet}: Generic physics object based jet multiclass classification for {LHC} experiments",
    collaboration = "CMS",
    booktitle = "Deep Learning for Physical Sciences Workshop at the 31st Conference on Neural Information Processing Systems (NeurIPS)",
    year = "2017",
    url = "https://dl4physicalsciences.github.io/files/nips_dlps_2017_10.pdf"
}

@article{Qu:2019gqs,
    author = "Qu, Huilin and Gouskos, Loukas",
    title = "{ParticleNet: Jet Tagging via Particle Clouds}",
    eprint = "1902.08570",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    doi = "10.1103/PhysRevD.101.056019",
    journal = "Phys. Rev. D",
    volume = "101",
    number = "5",
    pages = "056019",
    year = "2020"
}

@article{deOliveira:2015xxd,
    author = "de Oliveira, Luke and Kagan, Michael and Mackey, Lester and Nachman, Benjamin and Schwartzman, Ariel",
    title = "{Jet-images \textemdash{} deep learning edition}",
    eprint = "1511.05190",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    doi = "10.1007/JHEP07(2016)069",
    journal = "J. High Energy Phys.",
    volume = "07",
    pages = "069",
    year = "2016"
}

@article{Moreno:2019bmu,
    author = "Moreno, Eric A. and Cerri, Olmo and Duarte, Javier M. and Newman, Harvey B. and Nguyen, Thong Q. and Periwal, Avikar and Pierini, Maurizio and Serikova, Aidana and Spiropulu, Maria and Vlimant, Jean-Roch",
    title = "{JEDI-net: a jet identification algorithm based on interaction networks}",
    eprint = "1908.05318",
    archivePrefix = "arXiv",
    primaryClass = "hep-ex",
    reportNumber = "FERMILAB-PUB-19-360-PPD",
    doi = "10.1140/epjc/s10052-020-7608-4",
    journal = "Eur. Phys. J. C",
    volume = "80",
    number = "1",
    pages = "58",
    year = "2020"
}

@article{Dolen:2016kst,
    author = "Dolen, James and Harris, Philip and Marzani, Simone and Rappoccio, Salvatore and Tran, Nhan",
    title = "{Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure}",
    eprint = "1603.00027",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    reportNumber = "FERMILAB-PUB-16-046-PPD",
    doi = "10.1007/JHEP05(2016)156",
    journal = "J. High Energy Phys.",
    volume = "05",
    pages = "156",
    year = "2016"
}

@techreport{ATL-PHYS-PUB-2018-014,
      title         = "{Performance of mass-decorrelated jet substructure
                       observables for hadronic two-body decay tagging in ATLAS}",
      author = "{ATLAS Collaboration}",
      collaboration = "ATLAS",
      number        = "ATL-PHYS-PUB-2018-014",
      institution = "CERN",
      type = "ATLAS Public Note",
      year          = "2018",
      reportNumber  = "ATL-PHYS-PUB-2018-014",
      url           = "http://cds.cern.ch/record/2630973",
}

@article{Chatrchyan:2012jua,
    author = "Chatrchyan, Serguei and others",
    collaboration = "CMS",
    title = "{Identification of b-Quark Jets with the CMS Experiment}",
    eprint = "1211.4462",
    archivePrefix = "arXiv",
    primaryClass = "hep-ex",
    reportNumber = "CMS-BTV-12-001, CERN-PH-EP-2012-262",
    doi = "10.1088/1748-0221/8/04/P04013",
    journal = "J. Instrum.",
    volume = "8",
    pages = "P04013",
    year = "2013"
}

@techreport{CMS-PAS-JME-13-002,
      title         = "{Performance of quark/gluon discrimination in 8 TeV pp
                       data}",
      author = "{CMS Collaboration}",
      collaboration = "CMS",
      number        = "CMS-PAS-JME-13-002",
      year          = "2013",
      reportNumber  = "CMS-PAS-JME-13-002",
      institution = "CERN",
      type = "CMS Physics Analysis Summary",
      url           = "https://cds.cern.ch/record/1599732",
}

@inproceedings{Louppe:2016ylz,
    author = "Louppe, Gilles and Kagan, Michael and Cranmer, Kyle",
    title = "{Learning to Pivot with Adversarial Networks}",
    eprint = "1611.01046",
    archivePrefix = "arXiv",
    primaryClass = "stat.ML",
    year = "2017",
    booktitle = {Advances in Neural Information Processing Systems 30},
    editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
    pages = {981},
    publisher = {Curran Associates, Inc.},
    url = {http://papers.nips.cc/paper/6699-learning-to-pivot-with-adversarial-networks.pdf}
}

@InProceedings{ganin2014unsupervised,
    title = {Unsupervised Domain Adaptation by Backpropagation},
    author = {Yaroslav Ganin and Victor Lempitsky},
    pages = {1180},
    year = {2015},
    editor = {Francis Bach and David Blei},
    volume = {37},
    series = {Proceedings of Machine Learning Research},
    address = {Lille, France},
    publisher = {PMLR},
    url = {http://proceedings.mlr.press/v37/ganin15.html},
    booktitle = "Proceedings of the 32nd International Conference on Machine Learning",
    eprint={1409.7495},
    archivePrefix={arXiv},
    primaryClass={stat.ML}
    }

@article{Sirunyan:2019nfw,
    author = "Sirunyan, Albert M and others",
    collaboration = "CMS",
    title = "{A deep neural network to search for new long-lived particles decaying to jets}",
    eprint = "1912.12238",
    archivePrefix = "arXiv",
    primaryClass = "hep-ex",
    reportNumber = "CMS-EXO-19-011, CERN-EP-2019-281",
    year = "2020",
    volume = {1},
    pages = {035012},
    doi = {10.1088/2632-2153/ab9023},
    journal = {Mach. Learn.: Sci. Technol.},
}

@article{Shlomi:2020gdn,
    author = "Shlomi, Jonathan and Battaglia, Peter and Vlimant, Jean-Roch",
    title = "Graph Neural Networks in Particle Physics",
    eprint = "2007.13681",
    archivePrefix = "arXiv",
    primaryClass = "hep-ex",
    month = "7",
    year = "2020",
    volume = "2",
    pages = "021001",
    journal = "Mach. Learn.: Sci. Tech.",
    doi  = "10.1088/2632-2153/abbf9a"
}

@inproceedings{PyTorchGeometric,
  title={Fast Graph Representation Learning with {PyTorch Geometric}},
  author={Fey, Matthias and Lenssen, Jan E.},
  booktitle={ICLR Workshop on Representation Learning on Graphs and Manifolds},
  archivePrefix = {arXiv},
  eprint    = {1903.02428},
  primaryClass = "cs.LG",
  url = "https://pytorch-geometric.readthedocs.io/",
  year={2019},
}

@article{Jones:2014uza,
    author = "Jones, C.D. and Sexton-Kennedy, E.",
    editor = "Groep, D.L. and Bonacorsi, D.",
    title = "{Stitched Together: Transitioning CMS to a Hierarchical Threaded Framework}",
    doi = "10.1088/1742-6596/513/2/022034",
    journal = "J. Phys. Conf. Ser.",
    volume = "513",
    pages = "022034",
    year = "2014"
}

@article{Jones:2017iww,
    author = "Jones, C.D.",
    editor = "Mount, Richard and Tull, Craig",
    collaboration = "CMS",
    title = "{CMS event processing multi-core efficiency status}",
    doi = "10.1088/1742-6596/898/4/042008",
    journal = "J. Phys. Conf. Ser.",
    volume = "898",
    number = "4",
    pages = "042008",
    year = "2017"
}

@unpublished{battaglia2018relational,
      title={Relational inductive biases, deep learning, and graph networks},
      author={Peter W. Battaglia and Jessica B. Hamrick and Victor Bapst and Alvaro Sanchez-Gonzalez and Vinicius Zambaldi and Mateusz Malinowski and Andrea Tacchetti and David Raposo and Adam Santoro and Ryan Faulkner and Caglar Gulcehre and Francis Song and Andrew Ballard and Justin Gilmer and George Dahl and Ashish Vaswani and Kelsey Allen and Charles Nash and Victoria Langston and Chris Dyer and Nicolas Heess and Daan Wierstra and Pushmeet Kohli and Matt Botvinick and Oriol Vinyals and Yujia Li and Razvan Pascanu},
      year={2018},
      eprint={1806.01261},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      note={Preprint}
}

@inproceedings{bn,
  author    = {Sergey Ioffe and
               Christian Szegedy},
  title     = {Batch Normalization: Accelerating Deep Network Training by Reducing
               Internal Covariate Shift},
  archivePrefix = {arXiv},
  eprint    = {1502.03167},
  pages = {448},
  year = {2015},
  editor = {Francis Bach and David Blei},
  volume = {37},
  booktitle = "32nd International Conference on Machine Learning",
  address = {Lille, France},
  month = {07},
  publisher = {PMLR},
  url = {http://proceedings.mlr.press/v37/ioffe15.html},
  }

@incollection{Duarte:2020ngm,
    author = "Duarte, Javier and Vlimant, Jean-Roch",
    editor = "Paolo Calafiura and David Rousseau and Kazuhiro Terao",
    title = "Graph neural networks for particle tracking and reconstruction",
    eprint = "2012.01249",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    month = "12",
    year = "2020",
    booktitle = "{Artificial Intelligence for High Energy Physics}",
    publisher = "World Scientific Publishing",
    journal = "Int. J. Mod. Phys. A",
    note = "Submitted to \emph{Int. J. Mod. Phys. A}",
    keywords = {career,recent},
    contributioncodes = {13},
    doi={10.1142/12200},
}

@inproceedings{gnnexplainer,
    author = {Ying, Zhitao and Bourgeois, Dylan and You, Jiaxuan and Zitnik, Marinka and Leskovec, Jure},
    booktitle = {Advances in Neural Information Processing Systems},
    editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
    publisher = {Curran Associates, Inc.},
    title = {GNNExplainer: Generating Explanations for Graph Neural Networks},
    volume = {32},
    year = {2019},
    eprint={1903.03894},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
    }

gha_language: Jupyter Notebook


text:
@article{ricard-jgre-98-21895-1993,
 abstract = {Using Cenozoic and Mesozoic plate motion reconstructions,
we derive a model of present-day mantle density heterogeneity
under the assumption that subducted slabs sink vertically into the
mantle. The thermal buoyancy of these slabs is estimated from the
observed thermal subsidence (cooling) of oceanic lithosphere. Slab
velocities in the upper mantle are computed from the local
convergence rate. We assume that slabs cross the upper/lower
mantle interface and continue sinking into the lower mantle with a
reduced velocity. For a velocity reduction factor between 2 and 5,
our slab heterogeneity model is as correlated with current
tomographic models as these models are correlated with each other.
We have also computed a synthetic geoid from our density model.
For a viscosity increase of about a factor of 40 from the upper to
lower mantle, our model predicts the first 8 spherical harmonic
degrees of the geoid with statistical confidence larger than 95%
and explains 84% of the observed geoid assuming that the model C21
and S21 terms are absent due to a long relaxation time for Earth's
rotational bulge. Otherwise, 73% of the geoid variance is
explained. The viscosity increase is consistent with our velocity
reduction factor for slabs entering the lower mantle, since
downwelling velocities are expected to scale roughly as the
logarithm of viscosity (log(e) 40 = 3.7). These results show that
the history of plate tectonics can explain the main features of
the present-day structure of the mantle. The dynamic topography
induced by this heterogeneity structure consists mainly of about
1-km amplitude lows concentrated along the active continental
margins of the Pacific basin. Our model can also be used to
predict the time variation of mantle heterogeneity and the gravity
field. We find that the ''age'' of the geoid, defined as the time
in the past before which the geoid becomes uncorrelated with the
present geoid, is about 50 iii.y. Our model for the history of the
degree 2 geoid, which is equivalent to the history of the inertia
tensor, should give us a tool to study the variations in Earth's
rotation pole indicated in paleomagnetic studies.},
 author = {Yanick Ricard and Mark Richards and Carolina Lithgow-Bertelloni and Yves
Le Stunff},
 entrydate = {2020/07/13},
 journal = {J. Geophysical Research-solid Earth},
 number = {B12},
 pages = {21895--21909},
 title = {A geodynamic model of mantle density heterogeneity},
 volume = {98},
 year = {1993}
}

repository: xiyuanbao/kgb

repo_name: xiyuanbao/kgb


text:
@article{Athron:2017ard,
 archiveprefix = {arXiv},
 author = {Athron, Peter and others},
 collaboration = {GAMBIT},
 doi = {10.1140/epjc/s10052-017-5321-8},
 eprint = {1705.07908},
 journal = {Eur. Phys. J. C},
 note = {[Addendum: Eur.Phys.J.C 78, 98 (2018)]},
 number = {11},
 pages = {784},
 primaryclass = {hep-ph},
 reportnumber = {COEPP-MN-17-6, NORDITA-2017-074, DESY-17-236, CERN-TH-2017-166, CoEPP-MN-17-6, NORDITA 2017-074},
 title = {GAMBIT: The Global and Modular Beyond-the-Standard-Model Inference Tool},
 volume = {77},
 year = {2017}
}

repository: cweniger/personal-website

repo_name: cweniger/personal-website


text:
# Put your BibTeX entries in this file.
@inproceedings{beazley1996swig,
  title={{SWIG}: An Easy to Use Tool for Integrating Scripting Languages with {C} and {C++}.},
  author={Beazley, David M and others},
  booktitle={4th Conference on USENIX Tcl/Tk Workshop},
  year={1996}
}

repository: vetter/ECP-ST-CAR-PUBLIC

repo_name: vetter/ECP-ST-CAR-PUBLIC


text:
set(Eigen3_DIR ${CMAKE_INSTALL_PREFIX}/share/eigen3/cmake/)
set(OpenMesh_DIR ${CMAKE_INSTALL_PREFIX}/share/OpenMesh/cmake/)
set(cpplocate_DIR ${CMAKE_INSTALL_PREFIX})


text:
@book{dscd,
author = {Coulouris, George and Dollimore, Jean and Kindberg, Tim and Blair, Gordon},
title = {Distributed Systems: Concepts and Design},
year = {2011},
isbn = {0132143011},
publisher = {Addison-Wesley Publishing Company},
address = {USA},
edition = {5th}
}

repository: serial-coder/university-course

repo_name: serial-coder/university-course


text:
require optee-os-fio.inc

PV = "3.10.0+git"
SRCREV = "11a9d2e1364d0f6054c6f0d3bacabcb63bdc67c5"
SRCBRANCH = "3.10+fio"

repository: ricardosalveti/meta-lmp

repo_name: ricardosalveti/meta-lmp


text:
@article{li2022leveraging,
  title={Leveraging OpenStreetMap and Multimodal Remote Sensing Data with Joint Deep Learning for Wastewater Treatment Plants Detection},
  author={Li, Hao and Zech, Johannes and Hong, Danfeng and Ghamisi, Pedram and Schultz, Michael and Zipf, Alexander},
  journal={International Journal of Applied Earth Observation and Geoinformation},
  volume={110},
  pages={102804},
  year={2022},
  publisher={Elsevier}
}

@article{li2021automatic,
  title={Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning},
  author={Li, Hao and Zech, Johannes and Ludwig, Christina and Fendrich, Sascha and Shapiro, Aurelie and Schultz, Michael and Zipf, Alexander},
  journal={International Journal of Applied Earth Observation and Geoinformation},
  volume={104},
  pages={102571},
  year={2021},
  publisher={Elsevier}
}

@article{li2022understanding,
  title={Understanding spatiotemporal trip purposes of urban micro-mobility from the lens of dockless e-scooter sharing},
  author={Li, Hao and Yuan, Zhendong and Novack, Tessio and Huang, Wei and Zipf, Alexander},
  journal={Computers, Environment and Urban Systems},
  volume={96},
  pages={101848},
  year={2022},
  publisher={Elsevier}
}

@article{li2020multi,
  title={A multi-sensor fusion framework based on coupled residual convolutional neural networks},
  author={Li, Hao and Ghamisi, Pedram and Rasti, Behnood and Wu, Zhaoyan and Shapiro, Aurelie and Schultz, Michael and Zipf, Alexander},
  journal={Remote Sensing},
  volume={12},
  number={12},
  pages={2067},
  year={2020},
  publisher={MDPI}
}

@article{li2018hyperspectral,
  title={Hyperspectral and LiDAR fusion using deep three-stream convolutional neural networks},
  author={Li, Hao and Ghamisi, Pedram and Soergel, Uwe and Zhu, Xiao Xiang},
  journal={Remote Sensing},
  volume={10},
  number={10},
  pages={1649},
  year={2018},
  publisher={MDPI}
}

@article{li2020exploration,
  title={Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique},
  author={Li, Hao and Herfort, Benjamin and Huang, Wei and Zia, Mohammed and Zipf, Alexander},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  volume={166},
  pages={41--51},
  year={2020},
  publisher={Elsevier}
}

@article{li2022improving,
  title={Improving OpenStreetMap missing building detection using few-shot transfer learning in sub-Saharan Africa},
  author={Li, Hao and Herfort, Benjamin and Lautenbach, Sven and Chen, Jiaoyan and Zipf, Alexander},
  journal={Transactions in GIS},
  year={2022},
  publisher={Wiley Online Library}
}

gha_language: SCSS


text:
@article{melymcb-0095,
 art_number = {P01001},
 author = {Carlevaro, C.M. and Wilkinson, M.V. and Barrios, L.A.},
 document_type = {Article},
 doi = {10.1088/1748-0221/3/01/P01001},
 journal = {Journal of Instrumentation},
 note = {cited By 10},
 number = {1},
 source = {Scopus},
 title = {A genetic algorithm approach to routine gamma spectra analysis},
 url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-62649149246&doi=10.1088%2f1748-0221%2f3%2f01%2fP01001&partnerID=40&md5=d814f3b855cfb15a98a13746c65aec6f},
 volume = {3},
 year = {2008}
}


text:
@InProceedings{singh:sii:2020,
  author    = {Singh, Rohan, P. and Kumagai, Iori and Gabas, Antonio and Benallegue, Mehdi and Yoshiyasu, Yusuke and Kanehiro, Fumio},
  title     = {Instance-specific 6-DoF Object Pose Estimation from Minimal Annotations},
  booktitle = {IEEE/SICE International Symposium on System Integration},
  year      = {2020},
  pages     = {109--114},
  address   = {Honolulu (HI), USA},
  month     = {January 12-January 15},
  keywords  = {Robotics technology, Human-Robot cooperation/interaction, Software systems},
  doi       = {10.1109/SII46433.2020.9026239},
  abstract  = {In many robotic applications, the environment setting in which the 6-DoF pose estimation of a known, rigid object and its subsequent grasping is to be performed, remains nearly unchanging and might even be known to the robot in advance. In this paper, we refer to this problem as instance-specific pose estimation: the robot is expected to estimate the pose with a high degree of accuracy in only a limited set of familiar scenarios. Minor changes in the scene, including variations in lighting conditions and background appearance, are acceptable but drastic alterations are not anticipated. To this end, we present a method to rapidly train and deploy a pipeline for estimating the continuous 6-DoF pose of an object from a single RGB image. The key idea is to leverage known camera poses and rigid body geometry to partially automate the generation of a large labeled dataset. The dataset, along with sufficient domain randomization, is then used to supervise the training of deep neural networks for predicting semantic keypoints. Experimentally, we demonstrate the convenience and effectiveness of our proposed method to accurately estimate object pose requiring only a very small amount of manual annotation for training}
}