text:
@inproceedings{nelson2015exodus,
	title={Exodus: toward automatic migration of enterprise network configurations to {SDN}s},
	author={Nelson, Tim and Ferguson, Andrew D and Yu, Da and Fonseca, Rodrigo and Krishnamurthi, Shriram},
	booktitle={Proceedings of the 1st ACM SIGCOMM Symposium on Software Defined Networking Research},
	pages={13},
	year={2015},
	organization={ACM}
}

@inproceedings{guha2013machine,
	author = {Guha, Arjun and Reitblatt, Mark and Foster, Nate},
	title = {{M}achine-verified {N}etwork {C}ontrollers},
	booktitle = {Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation},
	series = {PLDI '13},
	year = {2013},
	isbn = {978-1-4503-2014-6},
	location = {Seattle, Washington, USA},
	pages = {483--494},
	numpages = {12},
	url = {http://doi.acm.org/10.1145/2491956.2462178},
	doi = {10.1145/2491956.2462178},
	acmid = {2462178},
	publisher = {ACM},
	address = {New York, NY, USA},
	keywords = {coq, domain-specific languages, formal verification, frenetic, netcore, openflow, software-defined networking},
}
@inproceedings{dobrescu2014software,
	title={Software dataplane verification},
	author={Dobrescu, Mihai and Argyraki, Katerina},
	booktitle={Proceedings of the 11th Symposium on Networked Systems Design and Implementation (NSDI), Seattle, WA},
	year={2014}
}
@inproceedings{zhang2000intrusion,
	title={Intrusion detection in wireless ad-hoc networks},
	author={Zhang, Yongguang and Lee, Wenke},
	booktitle={Proceedings of the 6th annual international conference on Mobile computing and networking},
	pages={275--283},
	year={2000},
	organization={ACM}
}
@inproceedings{nelson2010margrave,
	title={{The Margrave Tool for Firewall Analysis}},
	author={Nelson, Timothy and Barratt, Christopher and Dougherty, Daniel J and Fisler, Kathi and Krishnamurthi, Shriram},
	booktitle={Proceedings of the Large Installation System Administration Conference},
	year={2010}
}
@inproceedings {kazemian12header,
	author = {Peyman Kazemian and George Varghese and Nick McKeown},
	title = {{Header Space Analysis: Static Checking for Networks}},
	booktitle = {Presented as part of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12)},
	year = {2012},
	isbn = {978-931971-92-8},
	address = {San Jose, CA},
	pages = {113--126},
	url = {https://www.usenix.org/conference/nsdi12/technical-sessions/presentation/kazemian},
	publisher = {USENIX},
}
@inproceedings{ball2014vericon,
	title={Veri{C}on: {T}owards verifying controller programs in software-defined networks},
	author={Ball, Thomas and Bj{\o}rner, Nikolaj and Gember, Aaron and Itzhaky, Shachar and Karbyshev, Aleksandr and Sagiv, Mooly and Schapira, Michael and Valadarsky, Asaf},
	booktitle={ACM SIGPLAN Notices},
	volume={49},
	pages={282--293},
	year={2014},
	organization={ACM}
}
@incollection{brucker2007test,
	title={Test-{S}equence {G}eneration with {HOL}-{T}est{G}en with an {A}pplication to {F}irewall {T}esting},
	author={Brucker, Achim D and Wolff, Burkhart},
	booktitle={Tests and Proofs},
	pages={149--168},
	year={2007},
	publisher={Springer}
}
@incollection{brucker2008model,
	title={Model-based firewall conformance testing},
	author={Brucker, Achim D and Br{\"u}gger, Lukas and Wolff, Burkhart},
	booktitle={Testing of Software and Communicating Systems},
	pages={103--118},
	year={2008},
	publisher={Springer}
}
@inproceedings{brucker2010verified,
	title={Verified firewall policy transformations for test case generation},
	author={Brucker, Achim D and Brügger, Lukas and Kearney, Paul and Wolff, Burkhart},
	booktitle={Software Testing, Verification and Validation (ICST), 2010 Third International Conference on},
	pages={345--354},
	year={2010},
	organization={IEEE}
}
@article{el2006survey,
	title={Survey on the use of formal languages/models for the specification, verification, and enforcement of network access-lists},
	author={El-Atawy, Adel},
	journal={School of Computer Science, Telecommunication, and Information Systems, DePaul University, Chicago, Illinois},
	volume={60604},
	year={2006}
}
@article{pozo2009model,
	title={Model-based development of firewall rule sets: Diagnosing model inconsistencies},
	author={Pozo, S and Ceballos, Rafael and Gasca, Rafael Martinez},
	journal={Information and Software Technology},
	volume={51},
	number={5},
	pages={894--915},
	year={2009},
	publisher={Elsevier}
}
@inproceedings{lopes2015checking,
	title={Checking beliefs in dynamic networks},
	author={Lopes, Nuno P and Bj{\o}rner, Nikolaj and Godefroid, Patrice and Jayaraman, Karthick and Varghese, George},
	booktitle={Proceedings of the 12th USENIX Symposium on Networked Systems Design and Implementation, NSDI},
	volume={15},
	year={2015}
}
@article{anderson2014netkat,
	title={{NetKAT}: Semantic foundations for networks},
	author={Anderson, Carolyn Jane and Foster, Nate and Guha, Arjun and Jeannin, Jean-Baptiste and Kozen, Dexter and Schlesinger, Cole and Walker, David},
	journal={ACM SIGPLAN Notices},
	volume={49},
	number={1},
	pages={113--126},
	year={2014},
	publisher={ACM}
}
@article{mai2011debugging,
	title={Debugging the data plane with anteater},
	author={Mai, Haohui and Khurshid, Ahmed and Agarwal, Rachit and Caesar, Matthew and Godfrey, P and King, Samuel Talmadge},
	journal={ACM SIGCOMM Computer Communication Review},
	volume={41},
	number={4},
	pages={290--301},
	year={2011},
	publisher={ACM}
}
@misc{specification15,
	title={{OpenFlow Switch Specification} V1.5.1},
	month={March},
	year={2015},
}
@misc{specification10,
	title={{OpenFlow Switch Specification} V1.0.0},
	month={December},
	year={2009},
}
@Book{nipkow2002isabelle,
	author	= {Tobias Nipkow and Lawrence C. Paulson and Markus Wenzel},
	title		= {{Isabelle/HOL} --- A Proof Assistant for Higher-Order Logic},
	publisher	= {Springer},
	series	= {LNCS},
	volume	= 2283,
	year	= 2015,
}
@inproceedings{canini2012nice,
	title={A {NICE} Way to Test {O}pen{F}low Applications},
	author={Canini, Marco and Venzano, Daniele and Peresini, Peter and Kostic, Dejan and Rexford, Jennifer and others},
	booktitle={NSDI},
	volume={12},
	pages={127--140},
	year={2012}
}
@article{mckeown2008openflow,
	author = {McKeown, Nick and Anderson, Tom and Balakrishnan, Hari and Parulkar, Guru and Peterson, Larry and Rexford, Jennifer and Shenker, Scott and Turner, Jonathan},
	title = {{OpenFlow: Enabling Innovation in Campus Networks}},
	journal = {SIGCOMM Comput. Commun. Rev.},
	issue_date = {April 2008},
	volume = {38},
	number = {2},
	month = {March},
	year = {2008},
	issn = {0146-4833},
	pages = {69--74},
	numpages = {6},
	url = {http://doi.acm.org/10.1145/1355734.1355746},
	doi = {10.1145/1355734.1355746},
	acmid = {1355746},
	publisher = {ACM},
	address = {New York, NY, USA},
	keywords = {ethernet switch, flow-based, virtualization},
}
@article{monsanto2012compiler,
	title={A compiler and run-time system for network programming languages},
	author={Monsanto, Christopher and Foster, Nate and Harrison, Rob and Walker, David},
	journal={ACM SIGPLAN Notices},
	volume={47},
	number={1},
	pages={217--230},
	year={2012},
	publisher={ACM}
}
@inproceedings{kang2013optimizing,
	author = {Kang, Nanxi and Liu, Zhenming and Rexford, Jennifer and Walker, David},
	title = {{Optimizing the "One Big Switch" Abstraction in Software-defined Networks}},
	booktitle = {Proceedings of the Ninth ACM Conference on Emerging Networking Experiments and Technologies},
	series = {CoNEXT '13},
	year = {2013},
	isbn = {978-1-4503-2101-3},
	location = {Santa Barbara, California, USA},
	pages = {13--24},
	numpages = {12},
	url = {http://doi.acm.org.eaccess.ub.tum.de/10.1145/2535372.2535373},
	doi = {10.1145/2535372.2535373},
	acmid = {2535373},
	publisher = {ACM},
	address = {New York, NY, USA},
	keywords = {network virtualization, software-defined networks},
}
@inproceedings{casado2010virtualizing,
	author = {Casado, Mart\'{\i}n and Koponen, Teemu and Ramanathan, Rajiv and Shenker, Scott},
	title = {{Virtualizing the Network Forwarding Plane}},
	booktitle = {Proceedings of the Workshop on Programmable Routers for Extensible Services of Tomorrow},
	series = {PRESTO '10},
	year = {2010},
	isbn = {978-1-4503-0467-2},
	location = {Philadelphia, Pennsylvania},
	pages = {8:1--8:6},
	articleno = {8},
	numpages = {6},
	url = {http://doi.acm.org/10.1145/1921151.1921162},
	doi = {10.1145/1921151.1921162},
	acmid = {1921162},
	publisher = {ACM},
	address = {New York, NY, USA},
}
@inproceedings{monsanto2013composing,
	title={{Composing Software Defined Networks}},
	author={Monsanto, Christopher and Reich, Joshua and Foster, Nate and Rexford, Jennifer and Walker, David and others},
	booktitle={{Networked Systems Design and Implementation}},
	pages={1--13},
	year={2013}
}
@incollection{diekmann2015semantics,
	year={2015},
	isbn={978-3-319-19248-2},
	booktitle={FM 2015: Formal Methods},
	volume={9109},
	series={Lecture Notes in Computer Science},
	editor={Bjørner, Nikolaj and de Boer, Frank},
	doi={10.1007/978-3-319-19249-9_13},
	title={{Semantics-Preserving Simplification of Real-World Firewall Rule Sets}},
	url={http://dx.doi.org/10.1007/978-3-319-19249-9_13},
	publisher={Springer International Publishing},
	keywords={Computer networks; Firewalls; Isabelle; Netfilter Iptables; Semantics},
	author={Diekmann, Cornelius and Hupel, Lars and Carle, Georg},
	pages={195-212},
	language={English}
}
@inproceedings{marmorstein2005tool,
	title={{A Tool for Automated iptables Firewall Analysis}},
	author={Marmorstein, Robert M and Kearns, Phil},
	booktitle={Usenix annual technical conference, Freenix Track},
	pages={71--81},
	year={2005}
}
@misc{openvswitch,
	title={{Open vSwitch}},
	howpublished="\url{http://openvswitch.org/}",
}
@inproceedings{chipounov2009selective,
	title={Selective symbolic execution},
	author={Chipounov, Vitaly and Georgescu, Vlad and Zamfir, Cristian and Candea, George},
	booktitle={Workshop on Hot Topics in Dependable Systems},
	year={2009},
	organization={Citeseer}
}
@unpublished{shenkerfuture,
	title  = {The Future of Networking, and the Past of Protocols},
	author = {Scott Shenker and Martín Casado and Teemu Koponen and Nick McKeown},
	year   = {2011},
	note   = {Talk at Open Networking Summit},
}
@misc{ponemon,
	title = {Study on Datacenter Outages},
	year = {2013},
	author = {{Ponemon Institute LLC}},
	month={September},
}
@book{bidgoli2004internet,
	title={The {I}nternet encyclopedia},
	author={Bidgoli, Hossein},
	volume={3},
	year={2004},
	publisher={John Wiley \& Sons}
}
@ARTICLE{bryant1986graph,
	author={Bryant, R.E.},
	journal={Computers, IEEE Transactions on},
	title={Graph-Based Algorithms for Boolean Function Manipulation},
	year={1986},
	volume={C-35},
	number={8},
	pages={677-691},
	keywords={Boolean functions;binary decision diagrams;logic design verification;symbolic manipulation;Algebra;Application software;Artificial intelligence;Boolean functions;Combinatorial mathematics;Computer science;Data structures;Digital systems;Logic design;Logic testing;Boolean functions;binary decision diagrams;logic design verification;symbolic manipulation},
	doi={10.1109/TC.1986.1676819},
	ISSN={0018-9340},
	month={August},
}
@INPROCEEDINGS{yuan2006fireman,
	author={Lihua Yuan and Hao Chen and Jianning Mai and Chuah, Chen-Nee and Zhendong Su and Mohapatra, P.},
	booktitle={Security and Privacy, 2006 IEEE Symposium on},
	title={FIREMAN: a toolkit for firewall modeling and analysis},
	year={2006},
	pages={15 pp.-213},
	keywords={program diagnostics;security of data;software tools;FIREMAN;binary decision diagrams;distributed firewalls;enterprise networks;firewall analysis;firewall modeling;misconfiguration checking;network security;networked systems;policy violations;static analysis toolkit;symbolic model checking;Boolean functions;Data privacy;Data security;Data structures;Filtering;Hardware;Production;Routing;Telecommunication traffic;Wool},
	doi={10.1109/SP.2006.16},
	ISSN={1081-6011},
	month={May},
}
@article{liaw1992obdd,
	title={On the {OBDD}-representation of general {B}oolean functions},
	author={Liaw, Heh-Tyan and Lin, Chen-Shang},
	journal={IEEE Transactions on computers},
	volume={6},
	pages={661--664},
	year={1992},
	publisher={IEEE}
}
@INPROCEEDINGS{xie2005static,
	author={Xie, G.G. and Jibin Zhan and Maltz, D.A. and Hui Zhang and Greenberg, Albert and Hjalmtysson, G. and Rexford, J.},
	booktitle={INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE},
	title={On static reachability analysis of {IP} networks},
	year={2005},
	volume={3},
	pages={2170-2183 vol. 3},
	keywords={IP networks;computer network reliability;failure analysis;reachability analysis;routing protocols;IP networks;failure analysis;packet filters;packet transformations;routers;routing protocols;static reachability analysis;Bones;Computer networks;Failure analysis;Filters;IP networks;Performance analysis;Probes;Reachability analysis;Routing protocols;Telecommunication traffic},
	doi={10.1109/INFCOM.2005.1498492},
	ISSN={0743-166X},
	month={March},
}
@inproceedings{nelson2014tierless,
	title={Tierless programming and reasoning for software-defined networks},
	author={Nelson, Tim and Ferguson, Andrew D and Scheer, Michael JG and Krishnamurthi, Shriram},
	booktitle={11th USENIX Symposium on Networked Systems Design and Implementation (NSDI 14)},
	pages={519--531},
	year={2014}
}
@incollection{z3,
	year={2008},
	isbn={978-3-540-78799-0},
	booktitle={Tools and Algorithms for the Construction and Analysis of Systems},
	volume={4963},
	series={Lecture Notes in Computer Science},
	editor={Ramakrishnan, C.R. and Rehof, Jakob},
	doi={10.1007/978-3-540-78800-3_24},
	title={{Z3: An Efficient SMT Solver}},
	url={http://dx.doi.org/10.1007/978-3-540-78800-3_24},
	publisher={Springer Berlin Heidelberg},
	author={de Moura, Leonardo and Bjørner, Nikolaj},
	pages={337-340},
	language={English}
}
@inproceedings{diekmann2016verified,
	author={Cornelius Diekmann and Julius Michaelis and Maximilian Haslbeck and Georg Carle},
	title={{V}erified iptables {F}irewall {A}nalysis},
	year={2016},
	month={May},
	booktitle={Proceedings of IFIP Networking 2016 (NETWORKING 16)},
	language={English}
}
@inproceedings{michaelis2016middlebox,
	author={Julius Michaelis and Cornelius Diekmann},
	title={Middlebox Models in Network Verification Research},
	booktitle={Proceedings of the Seminars Future Internet (FI) and Innovative Internet Technologies and Mobile Communications (IITM), Winter Semester 2015/2016},
	year={2016},
}
@article{barras1997coq,
	title={The Coq proof assistant reference manual: Version 6.1},
	author={Barras, Bruno and Boutin, Samuel and Cornes, Cristina and Courant, Judica{\"e}l and Filliatre, Jean-Christophe and Gimenez, Eduardo and Herbelin, Hugo and Huet, Gerard and Munoz, Cesar and Murthy, Chetan and others},
	year={1997}
}
@inproceedings{casado2007ethane,
	title={Ethane: taking control of the enterprise},
	author={Casado, Martin and Freedman, Michael J and Pettit, Justin and Luo, Jianying and McKeown, Nick and Shenker, Scott},
	booktitle={ACM SIGCOMM Computer Communication Review},
	volume={37},
	number={4},
	pages={1--12},
	year={2007},
	organization={ACM}
}
@inproceedings{casado2006sane,
	title={SANE: A Protection Architecture for Enterprise Networks.},
	author={Casado, Martin and Garfinkel, Tal and Akella, Aditya and Freedman, Michael J and Boneh, Dan and McKeown, Nick and Shenker, Scott},
	booktitle={Usenix Security},
	year={2006}
}
@article{greenberg2005clean,
	title={A clean slate 4D approach to network control and management},
	author={Greenberg, Albert and Hjalmtysson, Gisli and Maltz, David A and Myers, Andy and Rexford, Jennifer and Xie, Geoffrey and Yan, Hong and Zhan, Jibin and Zhang, Hui},
	journal={ACM SIGCOMM Computer Communication Review},
	volume={35},
	number={5},
	pages={41--54},
	year={2005},
	publisher={ACM}
}
@inproceedings{lantz2010network,
	title={A network in a laptop: rapid prototyping for software-defined networks},
	author={Lantz, Bob and Heller, Brandon and McKeown, Nick},
	booktitle={Proceedings of the 9th ACM SIGCOMM Workshop on Hot Topics in Networks},
	pages={19},
	year={2010},
	organization={ACM}
}


text:
@misc{BactDatingBayesianInference,
  title = {{{BactDating}}: {{Bayesian}} Inference of Ancestral Dates on Bacterial Phylogenetic Trees},
  shorttitle = {{{BactDating}}},
  abstract = {BactDating is a R package to perform Bayesian inference of ancestral dates on bacterial phylogenetic trees.},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/8Z5GZFGC/BactDating.html},
  howpublished = {https://xavierdidelot.github.io/BactDating/},
  language = {en}
}

@article{basuEstimatingInfectionFatality2020,
  title = {Estimating {{The Infection Fatality Rate Among Symptomatic COVID}}-19 {{Cases~In The United States}}},
  author = {Basu, Anirban},
  year = {2020},
  month = may,
  pages = {10.1377/hlthaff.2020.00455},
  publisher = {{Health Affairs}},
  issn = {0278-2715},
  doi = {10.1377/hlthaff.2020.00455},
  abstract = {Knowing the infection fatality rate (IFR) of SARS-CoV and SARS-CoV-2 infections is essential for the fight against the COVID-19 pandemic. Using data through April 20, 2020, we fit a statistical model to COVID-19 case fatality rates over time at the US county level to estimate the COVID-19 IFR among symptomatic cases (IFR-S) as time goes to infinity. The IFR-S in the US was estimated to be 1.3\% (95\% central credible interval: 0.6\% to 2.1\%). County-specific rates varied from 0.5\% to 3.6\%. The overall IFR for COVID-19 should be lower when we account for cases that remain and recover without symptoms. When used with other estimating approaches, our model and our estimates can help disease and policy modelers to obtain more accurate predictions for the epidemiology of the disease and the impact of alternative policy levers to contain this pandemic. The model could also be used with future epidemics to get an early sense of the magnitude of symptomatic infection at the population-level before more direct estimates are available. Substantial variation across patient demographics likely exists and should be the focus of future studies. [Editor's Note: This Fast Track Ahead Of Print article is the accepted version of the peer-reviewed manuscript. The final edited version will appear in an upcoming issue of Health Affairs.]},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/EES43SAA/Basu - 2020 - Estimating The Infection Fatality Rate Among Sympt.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/YF2NUV9P/hlthaff.2020.html},
  journal = {Health Affairs}
}

@article{dehningInferringChangePoints2020,
  title = {Inferring Change Points in the Spread of {{COVID}}-19 Reveals the Effectiveness of Interventions},
  author = {Dehning, Jonas and Zierenberg, Johannes and Spitzner, F. Paul and Wibral, Michael and Neto, Joao Pinheiro and Wilczek, Michael and Priesemann, Viola},
  year = {2020},
  month = may,
  publisher = {{American Association for the Advancement of Science}},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.abb9789},
  abstract = {{$<$}p{$>$}As COVID-19 is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A major challenge for short-term forecasts is the assessment of key epidemiological parameters and how they change when first interventions show an effect. By combining an established epidemiological model with Bayesian inference, we analyze the time dependence of the effective growth rate of new infections. Focusing on COVID-19 spread in Germany, we detect change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we can quantify the effect of interventions, and we can incorporate the corresponding change points into forecasts of future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region.{$<$}/p{$>$}},
  copyright = {Copyright \textcopyright{} 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/5F7A6GIB/Dehning et al. - 2020 - Inferring change points in the spread of COVID-19 .pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/JPRIIXTA/science.html},
  journal = {Science},
  language = {en}
}

@article{fauverCoasttocoastSpreadSARSCoV22020,
  title = {Coast-to-Coast Spread of {{SARS}}-{{CoV}}-2 in the {{United States}} Revealed by Genomic Epidemiology},
  author = {Fauver, Joseph R. and Petrone, Mary E. and Hodcroft, Emma B. and Shioda, Kayoko and Ehrlich, Hanna Y. and Watts, Alexander G. and Vogels, Chantal B. F. and Brito, Anderson F. and Alpert, Tara and Muyombwe, Anthony and Razeq, Jafar and Downing, Randy and Cheemarla, Nagarjuna R. and Wyllie, Anne L. and Kalinich, Chaney C. and Ott, Isabel and Quick, Josh and Loman, Nicholas J. and Neugebauer, Karla M. and Greninger, Alexander L. and Jerome, Keith R. and Roychoundhury, Pavitra and Xie, Hong and Shrestha, Lasata and Huang, Meei-Li and Pitzer, Virginia E. and Iwasaki, Akiko and Omer, Saad B. and Khan, Kamran and Bogoch, Isaac and Martinello, Richard A. and Foxman, Ellen F. and Landry, Marie-Louise and Neher, Richard A. and Ko, Albert I. and Grubaugh, Nathan D.},
  year = {2020},
  month = mar,
  pages = {2020.03.25.20043828},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.03.25.20043828},
  abstract = {{$<$}p{$>$}Since its emergence and detection in Wuhan, China in late 2019, the novel coronavirus SARS-CoV-2 has spread to nearly every country around the world, resulting in hundreds of thousands of infections to date. The virus was first detected in the Pacific Northwest region of the United States in January, 2020, with subsequent COVID-19 outbreaks detected in all 50 states by early March. To uncover the sources of SARS-CoV-2 introductions and patterns of spread within the U.S., we sequenced nine viral genomes from early reported COVID-19 patients in Connecticut. Our phylogenetic analysis places the majority of these genomes with viruses sequenced from Washington state. By coupling our genomic data with domestic and international travel patterns, we show that early SARS-CoV-2 transmission in Connecticut was likely driven by domestic introductions. Moreover, the risk of domestic importation to Connecticut exceeded that of international importation by mid-March regardless of our estimated impacts of federal travel restrictions. This study provides evidence for widespread, sustained transmission of SARS-CoV-2 within the U.S. and highlights the critical need for local surveillance.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/FYBA3SL4/Fauver et al. - 2020 - Coast-to-coast spread of SARS-CoV-2 in the United .pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/IL5P9IFY/2020.03.25.html},
  journal = {medRxiv},
  language = {en}
}

@article{gargHospitalizationRatesCharacteristics2020,
  title = {Hospitalization {{Rates}} and {{Characteristics}} of {{Patients Hospitalized}} with {{Laboratory}}-{{Confirmed Coronavirus Disease}} 2019 \textemdash{} {{COVID}}-{{NET}}, 14 {{States}}, {{March}} 1\textendash{}30, 2020},
  author = {Garg, Shikha},
  year = {2020},
  volume = {69},
  issn = {0149-21951545-861X},
  doi = {10.15585/mmwr.mm6915e3},
  abstract = {The Coronavirus Disease 2019\textendash{}Associated Hospitalization Surveillance Network (COVID-NET) was implemented to produce robust, weekly, age-stratified COVID-19\textendash{}associated hospitalization rates.},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/QEWK9JCU/Garg - 2020 - Hospitalization Rates and Characteristics of Patie.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/FY3S4M8Y/mm6915e3.html},
  journal = {MMWR. Morbidity and Mortality Weekly Report},
  language = {en-us}
}

@article{gonzalez-reicheIntroductionsEarlySpread2020,
  title = {Introductions and Early Spread of {{SARS}}-{{CoV}}-2 in the {{New York City}} Area},
  author = {{Gonzalez-Reiche}, Ana S. and Hernandez, Matthew M. and Sullivan, Mitchell and Ciferri, Brianne and Alshammary, Hala and Obla, Ajay and Fabre, Shelcie and Kleiner, Giulio and Polanco, Jose and Khan, Zenab and Alburquerque, Bremy and van de Guchte, Adriana and Dutta, Jayeeta and Francoeur, Nancy and Melo, Betsaida Salom and Oussenko, Irina and Deikus, Gintaras and Soto, Juan and Sridhar, Shwetha Hara and Wang, Ying-Chih and Twyman, Kathryn and Kasarskis, Andrew and Altman, Deena Rose and Smith, Melissa and Sebra, Robert and Aberg, Judith and Krammer, Florian and {Garcia-Sarstre}, Adolfo and Luksza, Marta and Patel, Gopi and {Paniz-Mondolfi}, Alberto and Gitman, Melissa and Sordillo, Emilia Mia and Simon, Viviana and van Bakel, Harm},
  year = {2020},
  month = apr,
  pages = {2020.04.08.20056929},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.04.08.20056929},
  abstract = {{$<$}p{$>$}New York City (NYC) has emerged as one of the epicenters of the current SARS-CoV2 pandemic. To identify the early events underlying the rapid spread of the virus in the NYC metropolitan area, we sequenced the virus causing COVID19 in patients seeking care at the Mount Sinai Health System. Phylogenetic analysis of 84 distinct SARS-CoV2 genomes indicates multiple, independent but isolated introductions mainly from Europe and other parts of the United States. Moreover, we find evidence for community transmission of SARS-CoV2 as suggested by clusters of related viruses found in patients living in different neighborhoods of the city.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/8XDK6RS7/Gonzalez-Reiche et al. - 2020 - Introductions and early spread of SARS-CoV-2 in th.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/LUMEC2Z2/2020.04.08.html},
  journal = {medRxiv},
  language = {en}
}

@article{gorbalenyaSpeciesSevereAcute2020,
  title = {The Species {{Severe}} Acute Respiratory Syndrome-Related Coronavirus : Classifying 2019-{{nCoV}} and Naming It {{SARS}}-{{CoV}}-2},
  shorttitle = {The Species {{Severe}} Acute Respiratory Syndrome-Related Coronavirus},
  author = {Gorbalenya, Alexander E. and Baker, Susan C. and Baric, Ralph S. and {de Groot}, Raoul J. and Drosten, Christian and Gulyaeva, Anastasia A. and Haagmans, Bart L. and Lauber, Chris and Leontovich, Andrey M. and Neuman, Benjamin W. and Penzar, Dmitry and Perlman, Stanley and Poon, Leo L. M. and Samborskiy, Dmitry V. and Sidorov, Igor A. and Sola, Isabel and Ziebuhr, John and {Coronaviridae Study Group of the International Committee on Taxonomy of Viruses}},
  year = {2020},
  month = apr,
  volume = {5},
  pages = {536--544},
  publisher = {{Nature Publishing Group}},
  issn = {2058-5276},
  doi = {10.1038/s41564-020-0695-z},
  abstract = {The present outbreak of a coronavirus-associated acute respiratory disease called coronavirus disease 19 (COVID-19) is the third documented spillover of an animal coronavirus to humans in only two decades that has resulted in a major epidemic. The Coronaviridae Study Group (CSG) of the International Committee on Taxonomy of Viruses, which is responsible for developing the classification of viruses and taxon nomenclature of the family Coronaviridae, has assessed the placement of the human pathogen, tentatively named 2019-nCoV, within the Coronaviridae. Based on phylogeny, taxonomy and established practice, the CSG recognizes this virus as forming a sister clade to the prototype human and bat severe acute respiratory syndrome coronaviruses (SARS-CoVs) of the species Severe acute respiratory syndrome-related coronavirus, and designates it as SARS-CoV-2. In order to facilitate communication, the CSG proposes to use the following naming convention for individual isolates: SARS-CoV-2/host/location/isolate/date. While the full spectrum of clinical manifestations associated with SARS-CoV-2 infections in humans remains to be determined, the independent zoonotic transmission of SARS-CoV and SARS-CoV-2 highlights the need for studying viruses at the species level to complement research focused on individual pathogenic viruses of immediate significance. This will improve our understanding of virus\textendash{}host interactions in an ever-changing environment and enhance our preparedness for future outbreaks.},
  copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/R3KJWXJ2/Gorbalenya et al. - 2020 - The species Severe acute respiratory syndrome-rela.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/SWH2QQ95/s41564-020-0695-z.html},
  journal = {Nature Microbiology},
  language = {en},
  number = {4}
}

@article{griffithColliderBiasUndermines2020,
  title = {Collider Bias Undermines Our Understanding of {{COVID}}-19 Disease Risk and Severity},
  author = {Griffith, Gareth and Morris, Tim T and Tudball, Matt and Herbert, Annie and Mancano, Giulia and Pike, Lindsey and Sharp, Gemma C and Palmer, Tom M and Davey Smith, George and Tilling, Kate and Zuccolo, Luisa and Davies, Neil M and Hemani, Gibran},
  year = {2020},
  month = jan,
  pages = {2020.05.04.20090506},
  doi = {10.1101/2020.05.04.20090506},
  abstract = {Observational data on COVID-19 including hypothesised risk factors for infection and progression are accruing rapidly. Here, we highlight the challenge of interpreting observational evidence from non-random samples of the population, which may be affected by collider bias. We illustrate these issues using data from the UK Biobank in which individuals tested for COVID-19 are highly selected for a wide range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the sampling mechanisms that leave aetiological studies of COVID-19 infection and progression particularly susceptible to collider bias. We also describe several tools and strategies that could help mitigate the effects of collider bias in extant studies of COVID-19 and make available a web app for performing sensitivity analyses. While bias due to non-random sampling should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis research has been conducted using the UK Biobank Resource under Application Number 16729. The Medical Research Council (MRC) and the University of Bristol support the MRC Integrative Epidemiology Unit [MC\_UU\_12013/1, MC\_UU\_12013/9, MC\_UU\_00011/1]. NMD is supported by a Norwegian Research Council Grant number 295989. GH is supported by the Wellcome Trust and Royal Society [208806/Z/17/Z].Author DeclarationsAll relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.YesAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll analysis was performed on UK Biobank datahttps://github.com/explodecomputer/covid\_ascertainment},
  journal = {medRxiv}
}

@misc{InfluenzaSurveillanceSystem2020,
  title = {U.{{S}}. {{Influenza Surveillance System}}: {{Purpose}} and {{Methods}} | {{CDC}}},
  shorttitle = {U.{{S}}. {{Influenza Surveillance System}}},
  year = {2020},
  month = feb,
  abstract = {Overview of Influenza Surveillance in the United States - CDC},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/H6DQQ6ZV/overview.html},
  howpublished = {https://www.cdc.gov/flu/weekly/overview.htm},
  language = {en-us}
}

@techreport{kaashoekCOVID19PositiveCases2020,
  title = {{{COVID}}-19 {{Positive Cases}}, {{Evidence}} on the {{Time Evolution}} of the {{Epidemic}} or {{An Indicator}} of {{Local Testing Capabilities}}? {{A Case Study}} in the {{United States}}},
  shorttitle = {{{COVID}}-19 {{Positive Cases}}, {{Evidence}} on the {{Time Evolution}} of the {{Epidemic}} or {{An Indicator}} of {{Local Testing Capabilities}}?},
  author = {Kaashoek, Justin and Santillana, Mauricio},
  year = {2020},
  month = apr,
  address = {{Rochester, NY}},
  institution = {{Social Science Research Network}},
  doi = {10.2139/ssrn.3574849},
  abstract = {The novel SARS-CoV-2 coronavirus, first identified in Wuhan (Hubei), China, in December 2019, has spread to more than 180 countries and caused over 1,700,000 cases of COVID-19 worldwide to date. In an effort to limit human-to-human contact and slow the transmission of COVID-19, the disease caused by this novel coronavirus, the United States have implemented a collection of shelter-in-place public health interventions. To monitor if these interventions are working and to determine when people may go back to (perhaps a new) business as usual requires reliable monitoring systems that provide an accurate real-time picture of the trajectory of the epidemic outbreak. Here, we present evidence that our current healthcare-based monitoring systems, aimed at detecting the new daily number of COVID-19-positive individuals across the US, may be better at tracking the local testing (detection) capabilities than at monitoring the time evolution of the outbreak. This suggests that other data sources are necessary to inform (real-time) critical decisions about when to stop (and perhaps when to restart) shelter-in-place mitigation strategies.},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/Y7HYHG8Z/papers.html},
  keywords = {COVID-19,COVID-19 testing,Disease surveillance},
  language = {en},
  number = {ID 3574849},
  type = {{{SSRN Scholarly Paper}}}
}

@article{lourencoFundamentalPrinciplesEpidemic2020,
  title = {Fundamental Principles of Epidemic Spread Highlight the Immediate Need for Large-Scale Serological Surveys to Assess the Stage of the {{SARS}}-{{CoV}}-2 Epidemic},
  author = {Lourenco, Jose and Paton, Robert and Ghafari, Mahan and Kraemer, Moritz and Thompson, Craig and Simmonds, Peter and Klenerman, Paul and Gupta, Sunetra},
  year = {2020},
  month = mar,
  pages = {2020.03.24.20042291},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.03.24.20042291},
  abstract = {{$<$}p{$>$}The spread of a novel pathogenic infectious agent eliciting protective immunity is typically characterised by three distinct phases: (I) an initial phase of slow accumulation of new infections (often undetectable), (II) a second phase of rapid growth in cases of infection, disease and death, and (III) an eventual slow down of transmission due to the depletion of susceptible individuals, typically leading to the termination of the (first) epidemic wave. Before the implementation of control measures (e.g. social distancing, travel bans, etc) and under the assumption that infection elicits protective immunity, epidemiological theory indicates that the ongoing epidemic of SARS-CoV-2 will conform to this pattern. Here, we calibrate a susceptible-infected-recovered (SIR) model to data on cumulative reported SARS-CoV-2 associated deaths from the United Kingdom (UK) and Italy under the assumption that such deaths are well reported events that occur only in a vulnerable fraction of the population. We focus on model solutions which take into consideration previous estimates of critical epidemiological parameters such as the basic reproduction number (R0), probability of death in the vulnerable fraction of the population, infectious period and time from infection to death, with the intention of exploring the sensitivity of the system to the actual fraction of the population vulnerable to severe disease and death. Our simulations are in agreement with other studies that the current epidemic wave in the UK and Italy in the absence of interventions should have an approximate duration of 2-3 months, with numbers of deaths lagging behind in time relative to overall infections. Importantly, the results we present here suggest the ongoing epidemics in the UK and Italy started at least a month before the first reported death and have already led to the accumulation of significant levels of herd immunity in both countries. There is an inverse relationship between the proportion currently immune and the fraction of the population vulnerable to severe disease. This relationship can be used to determine how many people will require hospitalisation (and possibly die) in the coming weeks if we are able to accurately determine current levels of herd immunity. There is thus an urgent need for investment in technologies such as virus (or viral pseudotype) neutralization assays and other robust assays which provide reliable read-outs of protective immunity, and for the provision of open access to valuable data sources such as blood banks and paired samples of acute and convalescent sera from confirmed cases of SARS-CoV-2 to validate these. Urgent development and assessment of such tests should be followed by rapid implementation at scale to provide real-time data. These data will be critical to the proper assessment of the effects of social distancing and other measures currently being adopted to slow down the case incidence and for informing future policy direction.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/9CETW3QE/Lourenco et al. - 2020 - Fundamental principles of epidemic spread highligh.pdf},
  journal = {medRxiv},
  language = {en}
}

@article{lourencoFundamentalPrinciplesEpidemic2020a,
  title = {Fundamental Principles of Epidemic Spread Highlight the Immediate Need for Large-Scale Serological Surveys to Assess the Stage of the {{SARS}}-{{CoV}}-2 Epidemic},
  author = {Lourenco, Jose and Paton, Robert and Ghafari, Mahan and Kraemer, Moritz and Thompson, Craig and Simmonds, Peter and Klenerman, Paul and Gupta, Sunetra},
  year = {2020},
  month = mar,
  pages = {2020.03.24.20042291},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.03.24.20042291},
  abstract = {{$<$}p{$>$}The spread of a novel pathogenic infectious agent eliciting protective immunity is typically characterised by three distinct phases: (I) an initial phase of slow accumulation of new infections (often undetectable), (II) a second phase of rapid growth in cases of infection, disease and death, and (III) an eventual slow down of transmission due to the depletion of susceptible individuals, typically leading to the termination of the (first) epidemic wave. Before the implementation of control measures (e.g. social distancing, travel bans, etc) and under the assumption that infection elicits protective immunity, epidemiological theory indicates that the ongoing epidemic of SARS-CoV-2 will conform to this pattern. Here, we calibrate a susceptible-infected-recovered (SIR) model to data on cumulative reported SARS-CoV-2 associated deaths from the United Kingdom (UK) and Italy under the assumption that such deaths are well reported events that occur only in a vulnerable fraction of the population. We focus on model solutions which take into consideration previous estimates of critical epidemiological parameters such as the basic reproduction number (R0), probability of death in the vulnerable fraction of the population, infectious period and time from infection to death, with the intention of exploring the sensitivity of the system to the actual fraction of the population vulnerable to severe disease and death. Our simulations are in agreement with other studies that the current epidemic wave in the UK and Italy in the absence of interventions should have an approximate duration of 2-3 months, with numbers of deaths lagging behind in time relative to overall infections. Importantly, the results we present here suggest the ongoing epidemics in the UK and Italy started at least a month before the first reported death and have already led to the accumulation of significant levels of herd immunity in both countries. There is an inverse relationship between the proportion currently immune and the fraction of the population vulnerable to severe disease. This relationship can be used to determine how many people will require hospitalisation (and possibly die) in the coming weeks if we are able to accurately determine current levels of herd immunity. There is thus an urgent need for investment in technologies such as virus (or viral pseudotype) neutralization assays and other robust assays which provide reliable read-outs of protective immunity, and for the provision of open access to valuable data sources such as blood banks and paired samples of acute and convalescent sera from confirmed cases of SARS-CoV-2 to validate these. Urgent development and assessment of such tests should be followed by rapid implementation at scale to provide real-time data. These data will be critical to the proper assessment of the effects of social distancing and other measures currently being adopted to slow down the case incidence and for informing future policy direction.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/A3IZ6B23/Lourenco et al. - 2020 - Fundamental principles of epidemic spread highligh.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/UIVL7YMP/2020.03.24.html},
  journal = {medRxiv},
  language = {en}
}

@article{luEstimatingPrevalenceCOVID192020,
  title = {Estimating the {{Prevalence}} of {{COVID}}-19 in the {{United States}}: {{Three Complementary Approaches}}},
  shorttitle = {Estimating the {{Prevalence}} of {{COVID}}-19 in the {{United States}}},
  author = {Lu, Fred S. and Nguyen, Andrew and Link, Nick and Santillana, Mauricio},
  year = {2020},
  month = apr,
  abstract = {Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the week to week burden of COVID-19. Unfortunately, a lack of systematic testing across the United States (US) due to equipment shortages and varying testing strategies has hindered the usefulness of the available positive COVID-19 case counts.
We introduce three complementary approaches aimed at estimating the prevalence of COVID-19 in each state in the US as well as in New York City. Instead of relying on an estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources.
Across our three approaches, there is a consistent conclusion that estimated state-level COVID-19 case counts usually vary from 10 to 100 times greater than the official positive test counts. Nationally, our lowest and highest estimates of COVID-19 cases in the US from March 1, 2020 to April 4, 2020 are 2.7 and 8.3 million (9 to 27 times greater). These estimates are to be compared to the cumulative confirmed cases of about 311,000 as of April 4th. Our approaches demonstrate the value of leveraging existing influenza-like-illness surveillance systems for measuring the burden of new diseases that share symptoms with influenza-like-illnesses. Our methods may prove useful in assessing the burden of COVID-19 in other countries with comparable influenza surveillance systems.},
  copyright = {open},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/U35MASVA/Lu et al. - 2020 - Estimating the Prevalence of COVID-19 in the Unite.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/PJ7CMPRJ/42660046.html},
  language = {en\_US},
  note = {Accepted: 2020-04-20T16:43:37Z}
}

@article{luForecastingFluActivity2020,
  title = {Forecasting {{Flu Activity}} in the {{United States}}: {{Benchmarking}} an {{Endemic}}-{{Epidemic Beta Model}}},
  shorttitle = {Forecasting {{Flu Activity}} in the {{United States}}},
  author = {Lu, Junyi and Meyer, Sebastian},
  year = {2020},
  month = feb,
  volume = {17},
  publisher = {{Multidisciplinary Digital Publishing Institute  (MDPI)}},
  doi = {10.3390/ijerph17041381},
  abstract = {Accurate prediction of flu activity enables health officials to plan disease prevention and allocate treatment resources. A promising forecasting approach is to adapt the well-established endemic-epidemic modeling framework to time series of infectious ...},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/2M3W6H5N/Lu and Meyer - 2020 - Forecasting Flu Activity in the United States Ben.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/YRZ5SP8B/PMC7068443.html},
  journal = {International Journal of Environmental Research and Public Health},
  language = {en},
  number = {4},
  pmid = {32098038}
}

@article{mirzaInfluenzaNegativeInfluenzaLikeIllness2020,
  title = {Influenza-{{Negative Influenza}}-{{Like Illness}} ({{fnILI}}) {{Z}}-{{Score}} as a {{Proxy}} for {{Incidence}} and {{Mortality}} of {{COVID}}-19},
  author = {Mirza, Fatima N. and Malik, Amyn A. and Omer, Saad B.},
  year = {2020},
  month = apr,
  pages = {2020.04.22.20075770},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.04.22.20075770},
  abstract = {{$<$}p{$>$}Though ideal for determining the burden of disease, SARS-CoV2 test shortages preclude its implementation as a robust surveillance system in the US. We correlated the use of the derivative influenza-negative influenza-like illness (fnILI) z-score from the CDC as a proxy for incident cases and disease-specific deaths. For every unit increase of fnILI z-score, the number of cases increased by 70.2 (95\%CI[5.1,135.3]) and number of deaths increased by 2.1 (95\%CI[1.0,3.2]). FnILI data may serve as an accurate outcome measurement to track the spread of the and allow for informed and timely decision-making on public health interventions.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/H89GL3PR/Mirza et al. - 2020 - Influenza-Negative Influenza-Like Illness (fnILI) .pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/RWKQPJ2Y/2020.04.22.html},
  journal = {medRxiv},
  language = {en}
}

@article{NeartermForecastsInfluenzalike2019,
  title = {Near-Term Forecasts of Influenza-like Illness: {{An}} Evaluation of Autoregressive Time Series Approaches},
  shorttitle = {Near-Term Forecasts of Influenza-like Illness},
  year = {2019},
  month = jun,
  volume = {27},
  pages = {41--51},
  publisher = {{Elsevier}},
  issn = {1755-4365},
  doi = {10.1016/j.epidem.2019.01.002},
  abstract = {Seasonal influenza in the United States is estimated to cause 9\textendash{}35 million illnesses annually, with resultant economic burden amounting to \$47-\$150 bi\ldots{}},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/ZZEZI5FR/2019 - Near-term forecasts of influenza-like illness An .pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/LMVLGNL2/S1755436518301336.html},
  journal = {Epidemics},
  language = {en}
}

@misc{NovelCoronavirusPatients,
  title = {A {{Novel Coronavirus}} from {{Patients}} with {{Pneumonia}} in {{China}}, 2019 | {{NEJM}}},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/87VX3R68/NEJMoa2001017.html},
  howpublished = {https://www.nejm.org/doi/10.1056/NEJMoa2001017}
}

@article{rothanEpidemiologyPathogenesisCoronavirus2020,
  title = {The Epidemiology and Pathogenesis of Coronavirus Disease ({{COVID}}-19) Outbreak},
  author = {Rothan, Hussin A. and Byrareddy, Siddappa N.},
  year = {2020},
  month = may,
  volume = {109},
  pages = {102433},
  issn = {0896-8411},
  doi = {10.1016/j.jaut.2020.102433},
  abstract = {Coronavirus disease (COVID-19) is caused by SARS-COV2 and represents the causative agent of a potentially fatal disease that is of great global public health concern. Based on the large number of infected people that were exposed to the wet animal market in Wuhan City, China, it is suggested that this is likely the zoonotic origin of COVID-19. Person-to-person transmission of COVID-19 infection led to the isolation of patients that were subsequently administered a variety of treatments. Extensive measures to reduce person-to-person transmission of COVID-19 have been implemented to control the current outbreak. Special attention and efforts to protect or reduce transmission should be applied in susceptible populations including children, health care providers, and elderly people. In this review, we highlights the symptoms, epidemiology, transmission, pathogenesis, phylogenetic analysis and future directions to control the spread of this fatal disease.},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/J4QVVBCN/Rothan and Byrareddy - 2020 - The epidemiology and pathogenesis of coronavirus d.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/ESYRC2YR/S0896841120300469.html},
  journal = {Journal of Autoimmunity},
  keywords = {Coronavirus,COVID-19,Pathogenesis,Pneumonia,Wuhan city},
  language = {en}
}

@article{SevereAcuteRespiratory2020,
  title = {Severe Acute Respiratory Syndrome Coronavirus 2 Isolate {{Wuhan}}-{{Hu}}-1, Complete Genome},
  year = {2020},
  month = mar,
  copyright = {Public domain},
  keywords = {insd,rna,Severe acute respiratory syndrome coronavirus 2},
  language = {en-US},
  lccn = {MN908947.3},
  note = {\{:itemType: dataset\}}
}

@article{spaederMultitieredTimeseriesModelling2012,
  title = {A Multi-Tiered Time-Series Modelling Approach to Forecasting Respiratory Syncytial Virus Incidence at the Local Level},
  author = {Spaeder, M. C. and Fackler, J. C.},
  year = {2012},
  month = apr,
  volume = {140},
  pages = {602--607},
  publisher = {{Cambridge University Press}},
  issn = {1469-4409, 0950-2688},
  doi = {10.1017/S0950268811001026},
  abstract = {Respiratory syncytial virus (RSV) is the most common cause of documented viral respiratory infections, and the leading cause of hospitalization, in young children. We performed a retrospective time-series analysis of all patients aged {$<$}18 years with laboratory-confirmed RSV within a network of multiple affiliated academic medical institutions. Forecasting models of weekly RSV incidence for the local community, inpatient paediatric hospital and paediatric intensive-care unit (PICU) were created. Ninety-five percent confidence intervals calculated around our models' 2-week forecasts were accurate to {$\pm$}9{$\cdot$}3, {$\pm$}7{$\cdot$}5 and {$\pm$}1{$\cdot$}5 cases/week for the local community, inpatient hospital and PICU, respectively. Our results suggest that time-series models may be useful tools in forecasting the burden of RSV infection at the local and institutional levels, helping communities and institutions to optimize distribution of resources based on the changing burden and severity of illness in their respective communities.},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/K9YAXILY/Spaeder and Fackler - 2012 - A multi-tiered time-series modelling approach to f.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/5VY4WGRW/core-reader.html},
  journal = {Epidemiology \& Infection},
  keywords = {Modelling,respiratory infections,respiratory syncytial virus},
  language = {en},
  number = {4}
}

@article{vandorpEmergenceGenomicDiversity2020,
  title = {Emergence of Genomic Diversity and Recurrent Mutations in {{SARS}}-{{CoV}}-2},
  author = {{van Dorp}, Lucy and Acman, Mislav and Richard, Damien and Shaw, Liam P. and Ford, Charlotte E. and Ormond, Louise and Owen, Christopher J. and Pang, Juanita and Tan, Cedric CS and Boshier, Florencia AT},
  year = {2020},
  pages = {104351},
  publisher = {{Elsevier}},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/GZ59X8ZX/S1567134820301829.html},
  journal = {Infection, Genetics and Evolution}
}

@misc{WHODirectorGeneralRemarks,
  title = {{{WHO Director}}-{{General}}'s Remarks at the Media Briefing on 2019-{{nCoV}} on 11 {{February}} 2020},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/9IKQSPFC/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020.html},
  howpublished = {https://www.who.int/dg/speeches/detail/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020},
  language = {en}
}

@misc{WHOWhatPandemic,
  title = {{{WHO}} | {{What}} Is a Pandemic?},
  publisher = {{World Health Organization}},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/DDI5UILJ/en.html},
  howpublished = {http://www.who.int/csr/disease/swineflu/frequently\_asked\_questions/pandemic/en/},
  journal = {WHO}
}

@article{wuNewCoronavirusAssociated2020,
  title = {A New Coronavirus Associated with Human Respiratory Disease in {{China}}},
  author = {Wu, Fan and Zhao, Su and Yu, Bin and Chen, Yan-Mei and Wang, Wen and Song, Zhi-Gang and Hu, Yi and Tao, Zhao-Wu and Tian, Jun-Hua and Pei, Yuan-Yuan and Yuan, Ming-Li and Zhang, Yu-Ling and Dai, Fa-Hui and Liu, Yi and Wang, Qi-Min and Zheng, Jiao-Jiao and Xu, Lin and Holmes, Edward C. and Zhang, Yong-Zhen},
  year = {2020},
  month = mar,
  volume = {579},
  pages = {265--269},
  publisher = {{Nature Publishing Group}},
  issn = {1476-4687},
  doi = {10.1038/s41586-020-2008-3},
  abstract = {Phylogenetic and metagenomic analyses of the complete viral genome of a new coronavirus from the family Coronaviridae reveal that the virus is closely related to a group of SARS-like coronaviruses found in bats in China.},
  copyright = {2020 The Author(s)},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/CZ9YH8WN/Wu et al. - 2020 - A new coronavirus associated with human respirator.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/6LMMGP37/s41586-020-2008-3.html},
  journal = {Nature},
  language = {en},
  number = {7798}
}

license_type: permissive

branch_name: refs/heads/master

extension: bib


text:
.items-container {
    background-color: white;
}

.list-page {
    background-color: #EEEEEE;
}

.list-page-name {
    color: #333333;
    font-family: 'Roboto';
    font-weight: 500;
    font-size: 18px;
}

@media screen and (min-width: 768px) {
    .items-container {
        padding: 48px 170px;
        margin: 80px 89px 0 89px !important;
    }
}

@media screen and (max-width: 768px) {
    .items-container {
        padding: 48px 35px;
        margin: 80px 0 0 0 !important;
    }
}

license: Unlicense

repository: olegmusin/MudahTest

repo_name: olegmusin/MudahTest

extension: css


text:
@Article{Boudreaux_2011,
  doi = {10.1111/j.1468-0270.2011.02096.x},
  url = {https://doi.org/10.1111/j.1468-0270.2011.02096.x},
  year = {2011},
  month = {jun},
  publisher = {Wiley-Blackwell},
  volume = {31},
  number = {2},
  pages = {17--24},
  author = {Karol Boudreaux and Fred Nelson},
  title = {{COMMUNITY} {CONSERVATION} {IN} {NAMIBIA}: {EMPOWERING} {THE} {POOR} {WITH} {PROPERTY} {RIGHTS}},
  journal = {Economic Affairs},
}

@Article{Sarker_2011,
  doi = {10.1017/s0030605310001067},
  url = {https://doi.org/10.1017/s0030605310001067},
  year = {2011},
  month = {jul},
  publisher = {Cambridge University Press ({CUP})},
  volume = {45},
  number = {03},
  pages = {391--400},
  author = {A. H. M. R. Sarker and E. R{\o}skaft},
  title = {Human attitudes towards the conservation of protected areas: a case study from four protected areas in Bangladesh},
  journal = {Oryx},
}

@Article{Bunce_2010,
  doi = {10.1016/j.envsci.2010.06.003},
  url = {https://doi.org/10.1016/j.envsci.2010.06.003},
  year = {2010},
  month = {oct},
  publisher = {Elsevier {BV}},
  volume = {13},
  number = {6},
  pages = {485--497},
  author = {Matthew Bunce and Katrina Brown and Sergio Rosendo},
  title = {Policy misfits, climate change and cross-scale vulnerability in coastal Africa: how development projects undermine resilience},
  journal = {Environmental Science {\&} Policy},
}

@Article{Brunnschweiler_2010,
  doi = {10.1080/09669580903071987},
  url = {https://doi.org/10.1080/09669580903071987},
  year = {2010},
  month = {jan},
  publisher = {Informa {UK} Limited},
  volume = {18},
  number = {1},
  pages = {29--42},
  author = {Juerg M. Brunnschweiler},
  title = {The Shark Reef Marine Reserve: a marine tourism project in Fiji involving local communities},
  journal = {Journal of Sustainable Tourism},
}

@Article{McCusker_2006,
  doi = {10.1007/s10708-006-9006-3},
  url = {https://doi.org/10.1007/s10708-006-9006-3},
  year = {2006},
  month = {aug},
  publisher = {Springer Science $\mathplus$ Business Media},
  volume = {66},
  number = {4},
  pages = {325--339},
  author = {Brent McCusker and Ann M. Oberhauser},
  title = {An assessment of women's access to natural resources through communal projects in South Africa},
  journal = {{GeoJournal}},
}

@Article{Derman_1995,
  doi = {10.1007/bf01191649},
  url = {https://doi.org/10.1007/bf01191649},
  year = {1995},
  month = {jun},
  publisher = {Springer Science $\mathplus$ Business Media},
  volume = {23},
  number = {2},
  pages = {199--215},
  author = {Bill Derman},
  title = {Environmental {NGOs}, dispossession, and the state: The ideology and praxis of African nature and development},
  journal = {Hum Ecol},
}

repository: marty-downs/evidence-based-heatmap

repo_name: marty-downs/evidence-based-heatmap


text:
% This file was created with JabRef 2.9.2.
% Encoding: Cp1252

@MASTERSTHESIS{Furiani2013,
  author = {Francesco Furiani},
  title = {HPC acceleration of topological operators in the linear algebric representation},
  school = {University of Rome, Roma Tre, Faculty of Engineering},
  year = {2013},
  owner = {furio},
  timestamp = {2013.08.07}
}

@ARTICLE{dicarlo2013linear,
  author = {DiCarlo, Antonio and Paoluzzi, Alberto and Shapiro, Vadim},
  title = {Linear algebraic representation for topological structures},
  journal = {Computer-Aided Design},
  year = {2013},
  note = {Article in press},
  abstract = {With increased complexity of geometric data, topological models play
	an increasingly important role beyond boundary representations, assemblies,
	finite elements, image processing, and other traditional modeling
	applications. While many graph- and index-based data structures have
	been proposed, no standard representation has emerged as of now.
	Furthermore, such representations typically do not deal with representations
	of mappings and functions and do not scale to support parallel processing,
	open source, and client-based architectures. We advocate that a proper
	mathematical model for all topological structures is a (co)chain
	complex: a sequence of (co)chain spaces and (co)boundary mappings.
	This in turn implies all topological structures may be represented
	by a collection of sparse matrices. We propose a Linear Algebraic
	Representation (LAR) scheme for mod 2 (co)chain complexes using CSR
	matrices and show that it supports a variety of topological computations
	using standard matrix algebra, without any overhead in space or running
	time. A full open source implementation of LAR is available and is
	being used for a variety of applications.},
  doi = {http://dx.doi.org/10.1016/j.cad.2013.08.044},
  keywords = {Solid modeling; Representation scheme; Sparse matrix; Chain complex},
  publisher = {Elsevier}
}

license: MIT

repository: mjirik/larVolumeToObj

repo_name: mjirik/larVolumeToObj


text:
# FindPandoc
# ----------
#
# Find Pandoc executable, this modules defines:
#
# Pandoc_EXECUTABLE, where to find pandoc's executable
# Pandoc_FOUND, if it is found
# Pandoc_VERSION, the version
#
# This module also defines the following macros:
#
# pandoc(
#	SOURCES file1 [file2 ...]
#	OUTPUT output
#	[FROM format]
#	[TO format]
#	[TARGET target]
#	[DEPENDS dependency ...]
#	[ALL]
#	[TOC]
#	[STANDALONE]
#	[MAKE_DIRECTORY]
#	[TEMPLATE file]
#	[FILTER filter]
#	[HEADER header ...]
#	[FOOTER footer ...]
#	[BODY body ...]
#	[VARIABLE var ...]
#	[METADATA meta ...]
#	[ARGS argument ...]
#	[WORKING_DIRECTORY directory]
# )
#
# The sources files are listed in the parameter SOURCES, all files are passed
# in the same order they are passed to that variable.
#
# The OUTPUT file is set with OUTPUT. It is generated only if one of the file
# has changed.
#
# The FROM (-f) and TO (-t) arguments specify respectively the source and
# destinations formats.
#
# If the parameter TARGET is set, then a target named `target` will be added
# with the OUTPUT file as the dependency but not listed as sources files.
# But the SOURCES files will be added as the target sources in the IDE.
#
# Optional dependencies can be added to the output command (not the target) with
# the DEPENDS parameter.
#
# If ALL is set and TARGET is also set, the target will be added to the ALL_BUILD.
#
# If TOC (--toc) is specified, a table of content will be automatically created.
#
# If STANDALONE (-s) is set, the compilation will assume that it is standalone
# and adds the necessary of the output format.
#
# Optional MAKE_DIRECTORY can be set to create the output directory before
# pandoc processes the file (recommended).
#
# The TEMPLATE parameter can be used to specify the formate template file.
#
# You can set a filter with the parameter FILTER. The filter will be added to
# the output dependencies so you can safely use CMake's targets.
#
# The HEADER (-H), FOOTER (-A) and BODY (-B) are copied verbatim before, just
# after and after the body respectively. They can be set more than once.
#
# You can pass variables (-V) and metadata (-M) to the parameters VARIABLE
# and METADATA, be sure to pass the same syntax as pandoc.  (e.g VARIABLE foo=1)
#
# ARGS is an optional list of additional arguments to pass to pandoc.
#
# The parameter WORKING_DIRECTORY can be set to change the directory when pandoc
# is invoked.
#

find_program(
	Pandoc_EXECUTABLE
	NAMES pandoc
	DOC "Pandoc executable"
)

include(FindPackageHandleStandardArgs)
include(CMakeParseArguments)

# Extract the version
if (Pandoc_EXECUTABLE)
	execute_process(
		COMMAND pandoc --version
		OUTPUT_VARIABLE _pandoc_version_tmp
	)

	string(REGEX REPLACE "^pandoc ([0-9]*\\.[0-9]*\\.[0-9]*).*$" "\\1" Pandoc_VERSION ${_pandoc_version_tmp})
endif ()

find_package_handle_standard_args(
	Pandoc
	FOUND_VAR Pandoc_FOUND
	VERSION_VAR Pandoc_VERSION
	REQUIRED_VARS Pandoc_EXECUTABLE
)

if (Pandoc_FOUND)
	function(pandoc)
		set(options MAKE_DIRECTORY STANDALONE TOC)
		set(oneValueArgs FILTER FROM TARGET TEMPLATE TO OUTPUT WORKING_DIRECTORY)
		set(multiValueArgs ARGS FOOTER HEADER METADATA SOURCES VARIABLE DEPENDS)

		#
		# The following variables will be set in that scope:
		#   _pandoc_arguments		- List of all arguments that will passed to pandoc invocation.
		#   _pandoc_depends		- List of all dependencies attached to the add_custom_command.
		#   _pandoc_mkdir		- The mkdir command if MAKE_DIRECTORY is set
		#   _pandoc_output_base		- The base output directory
		#
		cmake_parse_arguments(PANDOC "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})

		#
		# Output and sources are mandatory
		#
		if (NOT PANDOC_OUTPUT)
			message(FATAL_ERROR "Please define OUTPUT")
		elseif (NOT PANDOC_SOURCES)
			message(FATAL_ERROR "Please defines SOURCES")
		endif ()

		#
		# Handle the filter with care.
		#
		# 1. If it is a target, depend on it and use a generator
		#    expression to get its full path on the disk.
		# 2. If it is not a target, just use the user provided path.
		#
		if (PANDOC_FILTER)
			# If it is a target, add a dependency so that it is built
			if (TARGET ${PANDOC_FILTER})
				list(APPEND _pandoc_arguments --filter "$<TARGET_FILE:${PANDOC_FILTER}>")
				list(APPEND _pandoc_depends ${PANDOC_FILTER})
			else ()
				list(APPEND _pandoc_arguments --filter ${PANDOC_FILTER})
			endif ()
		endif ()

		if (PANDOC_TOC)
			list(APPEND _pandoc_arguments --toc)
		endif ()
		if (PANDOC_STANDALONE)
			list(APPEND _pandoc_arguments -s)
		endif ()
		if (PANDOC_FROM)
			list(APPEND _pandoc_arguments -f ${PANDOC_FROM})
		endif ()
		if (PANDOC_TO)
			list(APPEND _pandoc_arguments -t ${PANDOC_TO})
		endif ()
		if (PANDOC_TEMPLATE)
			list(APPEND _pandoc_arguments --template ${PANDOC_TEMPLATE})
			list(APPEND _pandoc_depends ${PANDOC_TEMPLATE})
		endif ()

		# Header, footers and body
		foreach (h ${PANDOC_HEADER})
			list(APPEND _pandoc_arguments -H ${h})
			list(APPEND _pandoc_depends ${h})
		endforeach ()
		foreach (b ${PANDOC_BODY})
			list(APPEND _pandoc_arguments -B ${b})
			list(APPEND _pandoc_depends ${b})
		endforeach ()
		foreach (f ${PANDOC_FOOTER})
			list(APPEND _pandoc_arguments -A ${f})
			list(APPEND _pandoc_depends ${f})
		endforeach ()

		# Variables and metadata
		foreach (var ${PANDOC_VARIABLE})
			list(APPEND _pandoc_arguments -V ${var})
		endforeach ()
		foreach (meta ${PANDOC_METADATA})
			list(APPEND _pandoc_arguments -M ${meta})
		endforeach ()

		# Optional list of arguments
		foreach (arg ${PANDOC_ARGS})
			list(APPEND _pandoc_arguments ${arg})
		endforeach ()

		# Output and sources
		list(APPEND _pandoc_arguments -o ${PANDOC_OUTPUT})

		#
		# The following variables are set within the loop:
		#
		#   _pandoc_input		- The absolute path to the input file.
		#   _pandoc_output_base		- The base output directory.
		#
		foreach (s ${PANDOC_SOURCES})
			get_filename_component(_pandoc_input ${s} ABSOLUTE)
			get_filename_component(_pandoc_output_base ${PANDOC_OUTPUT} DIRECTORY)
			list(APPEND _pandoc_depends ${_pandoc_input})
			list(APPEND _pandoc_arguments ${_pandoc_input})
		endforeach ()

		# Create the output directory if requested
		if (PANDOC_MAKE_DIRECTORY)
			set(_pandoc_mkdir ${CMAKE_COMMAND} -E make_directory ${_pandoc_output_base})
		endif ()

		add_custom_command(
			OUTPUT ${PANDOC_OUTPUT}
			COMMAND	${_pandoc_mkdir}
			COMMAND	${Pandoc_EXECUTABLE} ${_pandoc_arguments}
			DEPENDS ${_pandoc_depends} ${PANDOC_DEPENDS}
			WORKING_DIRECTORY ${PANDOC_WORKING_DIRECTORY}
			VERBATIM
		)

		if (PANDOC_TARGET)
			add_custom_target(
				${PANDOC_TARGET} ${PANDOC_ALL}
				SOURCES ${_pandoc_depends}
				DEPENDS ${PANDOC_OUTPUT}
				WORKING_DIRECTORY ${PANDOC_WORKING_DIRECTORY}
			)
		endif ()
	endfunction()
endif ()

mark_as_advanced(Pandoc_EXECUTABLE)

license: ISC

repository: bdrewery/irccd

repo_name: bdrewery/irccd

extension: cmake


text:
Automatically generated by Mendeley Desktop 1.19.4
Any changes to this file will be lost if it is regenerated by Mendeley.

BibTeX export options can be customized via Preferences -> BibTeX in Mendeley Desktop

@article{Goodfellow2014,
abstract = {We propose a new framework for estimating generative models via an adversar- ial process; in which we simultaneously train two models: a generative model G that captures the data distribution; and a discriminative model D that estimates the probability that a sample came from the training data rather thanG. The train- ing procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D; a unique solution exists; with G recovering the training data distribution andD equal to 1 2 everywhere. In the case where G andD are defined by multilayer perceptrons; the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference net- works during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples. 1},
archivePrefix = {arXiv},
arxivId = {arXiv:1406.2661v1},
author = {Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
doi = {10.1017/CBO9781139058452},
eprint = {arXiv:1406.2661v1},
file = {:Users/sepmein/Qsync/Papers/Goodfellow et al.{\_}2014{\_}Generative Adversarial Nets.pdf:pdf},
isbn = {1406.2661},
issn = {10495258},
journal = {Advances in Neural Information Processing Systems 27},
pages = {2672--2680},
pmid = {1000183096},
title = {{Generative Adversarial Nets}},
url = {http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf},
year = {2014}
}

repository: sepmein/references

repo_name: sepmein/references


text:
@inproceedings{mahendran2020nlp,
 abstract = {This paper describes our participation in the Social Media Mining for Health Application
(SMM4H 2020) Challenge Track 2 for identifying tweets containing Adverse Effects (AEs).
Our system uses Convolutional Neural Networks. We explore downsampling, oversampling,
and adjusting the class weights to account for the imbalanced nature of the dataset. Our results
showed downsampling outperformed oversampling and adjusting the class weights on the test set
however all three obtained similar results on the development set.},
 author = {Mahendran, Darshini and Lewis, Cora and McInnes, Bridget},
 booktitle = {Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task},
 pages = {158--160},
 title = {NLP@ VCU: Identifying adverse effects in English tweets for unbalanced data},
 year = {2020}
}


text:
@article{doi:10.3732/ajb.1700045,
author = {Ekwealor, Jenna T. B. and Payton, Adam C. and Paasch, Amber E. and Fisher, Kirsten M. and McDaniel, Stuart F.},
title = {Multiple factors influence population sex ratios in the Mojave Desert moss Syntrichia caninervis},
journal = {American Journal of Botany},
volume = {104},
number = {5},
pages = {733-742},
keywords = {bryophyte, Mojave Desert, moss, Pottiaceae, reproductive strategy, sex expression, sex ratio, Syntrichia caninervis},
doi = {10.3732/ajb.1700045},
url = {https://bsapubs.onlinelibrary.wiley.com/doi/abs/10.3732/ajb.1700045},
eprint = {https://bsapubs.onlinelibrary.wiley.com/doi/pdf/10.3732/ajb.1700045},
abstract = {PREMISE OF RESEARCH: Natural populations of many mosses appear highly female-biased based on the presence of reproductive structures. This bias could be caused by increased male mortality, lower male growth rate, or a higher threshold for achieving sexual maturity in males. Here we test these hypotheses using samples from two populations of the Mojave Desert moss Syntrichia caninervis. METHODS: We used double-digest restriction-site associated DNA (RAD) sequencing to identify candidate sex-associated loci in a panel of sex-expressing plants. Next, we used putative sex-associated markers to identify the sex of individuals without sex structures. KEY RESULTS: We found a 17:1 patch-level phenotypic female to male sex ratio in the higher elevation site (Wrightwood) and no sex expression at the low elevation site (Phelan). In contrast, on the basis of genetic data, we found a 2:1 female bias at the Wrightwood site and only females at the Phelan site. The relative area occupied by male and female genets was indistinguishable, but males were less genetically diverse. CONCLUSIONS: Our data suggest that both male-biased mortality and sexual dimorphism in thresholds for sex expression could explain genetic and phenotypic sex ratio biases and that phenotypic sex expression alone over-estimates the extent of actual sex ratio bias present in these two populations of S. caninervis.},
year = {2017}
}


text:
html {
margin: 0;
padding: 0;font-family: 'Simonetta', cursive;
}

body{
	background: url(background.jpg)
	no-repeat center center fixed;
	background-size:cover;

}
#main{ /*selectoy id*/
width: 90%;
height: 100%;
margin: 0 auto;
padding: 12px;
background: rgba(95,79,50,0.3);/*red, gree, blue, alpha*/

}
h1{
	color: #D9BFA9; /*hex color, adobe color*/
    text-transform: uppercase;
    font-size: 50px;
}

h2 {

}

p{
	color: #D9BFA9;
	font-size: 16px;

}

img{
	width: 100%;
	max-width: 100%;

}
.tagline{

}

.article{
	width: 33%;
	display: inline-block;

}

section{

}


text:
@import url("https://fonts.googleapis.com/css?family=Fira+Sans");
@font-face {
  font-family: "FiraCodeFixed";
  src: url("FiraCodeFixed-Regular.ttf"); }
.no-display {
  display: none; }

html, body, #elm-app, #main {
  width: 100%;
  height: auto; }

body {
  color: #465a60; }

#menu {
  display: inline-block;
  height: 4em;
  line-height: 4em;
  top: 0;
  position: fixed;
  width: 100%;
  background-color: #465a60;
  color: #fff;
  text-align: left;
  white-space: nowrap;
  /* prevent text selection */
  -webkit-touch-callout: none;
  /* iOS Safari */
  -webkit-user-select: none;
  /* Safari */
  -khtml-user-select: none;
  /* Konqueror HTML */
  -moz-user-select: none;
  /* Firefox */
  -ms-user-select: none;
  /* Internet Explorer/Edge */
  user-select: none;
  /* Non-prefixed version, currently
     supported by Chrome and Opera */ }
  #menu .menu-button {
    height: inherit;
    line-height: inherit;
    width: 15%;
    min-width: 5em;
    transition: background-color .5s;
    display: inline-block;
    text-align: center; }
    #menu .menu-button:hover {
      background-color: #42c5f4;
      cursor: pointer; }
  #menu #import-export-window-button {
    min-width: 7em; }
  #menu .disabled {
    opacity: .5; }
    #menu .disabled:hover {
      background-color: inherit;
      cursor: default; }

table {
  border-collapse: collapse;
  width: 100%; }

th {
  font-family: inherit !important;
  font-size: .8em;
  background-color: #f9f6e3;
  white-space: nowrap; }

tr {
  width: 100%;
  text-align: center;
  line-height: 2em;
  height: 2em; }

input {
  font-family: inherit;
  font-size: inherit;
  text-align: inherit; }

#proof-box {
  margin: 5em 5%;
  margin-bottom: 0;
  width: 90%;
  z-index: 1; }
  #proof-box tr {
    border-bottom: 1px solid rgba(70, 90, 96, 0.5);
    width: 100%;
    font-size: 1.2em; }
    #proof-box tr td {
      font-family: 'FiraCodeFixed', monospace; }
  #proof-box .assumptions, #proof-box .line-number {
    width: 10%;
    display: inline-block; }
    #proof-box .assumptions input, #proof-box .line-number input {
      color: inherit;
      width: 80%; }
  #proof-box .formula {
    width: 50%;
    display: inline-block; }
    #proof-box .formula input {
      color: inherit;
      width: 80%; }
  #proof-box .reason {
    width: 30%;
    display: inline-block; }
    #proof-box .reason input {
      color: inherit;
      width: 20%; }
  #proof-box #reason-dropdown {
    font-family: inherit;
    font-size: inherit;
    color: inherit;
    display: inline-block;
    width: 14%;
    margin: 3%; }
  #proof-box #input-dropdown {
    font-family: inherit;
    display: inline-block;
    width: 44%;
    height: auto;
    margin: 0 3%; }
    #proof-box #input-dropdown select {
      font-family: inherit;
      font-size: inherit;
      color: inherit;
      width: 100%; }
  #proof-box #reference-input {
    text-align: left; }
  #proof-box .short {
    display: none !important; }
  #proof-box .long {
    width: 70% !important; }

#add-line {
  color: #fff;
  background-color: #465a60;
  padding: 1em;
  margin-top: 1em;
  font-size: .8em;
  transition: background-color 1s;
  float: right;
  font-size: 1em; }
  #add-line:hover {
    cursor: pointer;
    background-color: #42c5f4; }

#error {
  display: block;
  position: fixed;
  line-height: 3em;
  bottom: 0;
  width: 100%;
  background-color: #cc516a;
  text-align: center;
  color: #fff; }

.floating {
  top: 4em;
  display: block;
  position: fixed;
  width: 70%;
  max-width: 40em;
  background-color: #465a60;
  border-top: 0.2em solid #f9f6e3;
  color: #fff; }
  .floating .button {
    width: 100%;
    background-color: #465a60;
    color: #fff;
    border: none;
    border-bottom: 0.2em solid #f9f6e3;
    height: 3em;
    line-height: 3em;
    font-size: 1em;
    text-align: center;
    transition: background-color .5s; }
    .floating .button:hover {
      background-color: #42c5f4;
      cursor: pointer; }
  .floating input {
    color: #465a60; }
  .floating select {
    font-size: inherit;
    color: #465a60; }
  .floating tr, .floating td {
    font-size: inherit;
    font-family: inherit; }

#symbol-box {
  left: 15%; }
  #symbol-box #symbol-list {
    width: 100%;
    font-family: 'FiraCodeFixed', monospace;
    font-size: 1.2em; }
    #symbol-box #symbol-list .symbol-name {
      text-align: right;
      width: 20%; }
    #symbol-box #symbol-list .equiv-symbol {
      width: 5%;
      text-align: center; }
    #symbol-box #symbol-list .symbol-def {
      text-align: left;
      width: 50%; }
  #symbol-box #new-symbol {
    border-bottom: 0.2em solid #f9f6e3;
    line-height: 3em;
    height: 3em; }
  #symbol-box #choose-name {
    margin: .5em;
    width: 2em; }
  #symbol-box #set-def {
    width: 80%; }
  #symbol-box #alias-select {
    font-family: 'FiraCodeFixed', monospace;
    font-size: 1.2em; }

#sequent-box {
  left: 20%; }
  #sequent-box #sequent-list {
    width: 100%;
    font-family: 'FiraCodeFixed', monospace;
    font-size: 1.2em; }
  #sequent-box #new-sequent {
    border-bottom: 0.2em solid #f9f6e3;
    line-height: 3em;
    height: 3em; }

#import-export-box {
  left: 25%; }
  #import-export-box #json-input {
    width: 100%;
    font-size: 1em;
    border-bottom: 0.2em solid #f9f6e3;
    padding: 1em; }
  #import-export-box #json-output {
    width: 100%;
    font-size: 1em;
    border-bottom: 0.2em solid #f9f6e3;
    padding: 1em; }
    #import-export-box #json-output a {
      color: inherit;
      text-decoration: underline; }

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

repository: olligobber/ProofCheck

repo_name: olligobber/ProofCheck

gha_language: PureScript


text:
PR = "r8"

require gcc-${PV}.inc
require gcc-cross4.inc
require gcc-configure-cross.inc
require gcc-package-cross.inc

extension: bb


text:
.delivery-options__to {
  margin: 13px 10px;
  font-weight: 300;
  font-size: 16px;
  line-height: 19px;
  color: #626262;
}

repository: nikitavlaev/layout-train

repo_name: nikitavlaev/layout-train


text:
@Article{gonzalez-cabrera_joint_2019,
  title = {Joint association of bullying and cyberbullying in health-related quality of life in a sample of adolescents},
  issn = {0962-9343, 1573-2649},
  url = {http://link.springer.com/10.1007/s11136-019-02353-z},
  doi = {10.1007/s11136-019-02353-z},
  abstract = {Purpose  Health-related quality of life (HRQoL) has been analyzed in relation to multiple psychosocial and health problems. However, only a few studies have analyzed the impact of bullying and cyberbullying on HRQoL. The main purpose of this study was to analyze the level of severity of bullying and cyberbullying on HRQoL. The effects of different roles, especially the conjunctions of of victim–cybervictim and bully–victim/cyberbully–cybervictim on HRQoL, were explored.Methods  An analytical and cross-sectional study was conducted in a region of northern Spain. Random and representative sampling was employed. The participants included 12, 285 adolescents between 11 and 18 years of age, with a mean age of 14.69 ± 1.73. The Spanish version of the KIDSCREEN-27, the Spanish version of the European bullying intervention project questionnaire (EBIPQ), and the cyberbullying triangulation questionnaire (CTQ) were employed.Results  The prevalence of bullying victimization, cybervictimization, bullying perpetration, and cyberbullying perpetration was 12%, 8.1%, 10.4%, and 7%, respectively. Significant and negative correlations between all the dimensions of the EBIPQ and the CTQ with the KIDSCREEN-27 were found. Victimization and cybervictimization had more impact than bullying perpetration and cyberbullying perpetration, especially on psychological well-being and school environment. The mixed roles of the victim–cybervictim and victim–cybervictim/bully–cyberbully obtained lower scores than the remaining roles in all the dimensions of KIDSCREEN-27.Conclusions  Those in mixed roles related to victimization and cybervictimization obtained the lowest scores in all HRQoL dimensions. The results enhance an understanding of the severity of the problem of bullying and cyberbullying and their impact on HRQoL.},
  language = {en},
  urldate = {2020-01-03},
  journal = {Quality of Life Research},
  author = {Joaquin Gonzalez-Cabrera and Juan Manuel Machimbarrena and Jessica Ortega-Baron and Aitor alvarez-Bardon},
  month = {nov},
  year = {2019},
  file = {Gonzalez-Cabrera et al. - 2019 - Joint association of bullying and cyberbullying in.pdf:C\:\\Users\\mmach\\Zotero\\storage\\6CNQKK85\\Gonzalez-Cabrera et al. - 2019 - Joint association of bullying and cyberbullying in.pdf:application/pdf},
  pubtype = {2},
}

repository: mmachimbarrena/Pruebaweb

repo_name: mmachimbarrena/Pruebaweb


text:
@inproceedings{Girrbach2017a,
 address = {Freiburg, Germany},
 author = {Girrbach, Fabian and Hol, Jeroen D and Bellusci, Giovanni and Diehl, Moritz},
 booktitle = {Book of Abstracts of the International Airborne Wind Energy Conference (AWEC 2017)},
 date = {2017-10-05/2017-10-06},
 editor = {Diehl, Moritz and Leuthold, Rachel and Schmehl, Roland},
 pages = {164},
 publisher = {University of Freiburg | Delft University of Technology},
 title = {On Robust Sensor Fusion of GNSS and IMU for Airborne Wind Energy Systems},
 url = {http://resolver.tudelft.nl/uuid:5149b4cc-0fb5-4996-ab07-7dd04662a9cf},
 year = {2017}
}


text:
@article{brown2008strong,
 author = {Brown, Joseph W and Rest, Joshua S and Garcı́a-Moreno, Jaime and Sorenson, Michael D and Mindell, David P},
 journal = {BMC biology},
 number = {1},
 pages = {1--18},
 publisher = {BioMed Central},
 title = {Strong mitochondrial DNA support for a Cretaceous origin of modern avian lineages},
 volume = {6},
 year = {2008}
}

repository: joshrest/academic-kickstart

repo_name: joshrest/academic-kickstart


text:
DESCRIPTION = "lcd driver"
SECTION = "userland"

FILESEXTRAPATHS_prepend := "${THISDIR}/files/:"

RDEPENDS_${PN} = "python"
RDEPENDS_${PN} = "python-evdev"
RDEPENDS_${PN} += "mraa"
RDEPENDS_${PN} += "bash"
RDEPENDS_${PN} += "buzzer"
RDEPENDS_${PN} += "tft-8340"
RDEPENDS_${PN} += "automount"
RDEPENDS_${PN} += "decode"
RDEPENDS_${PN} += "marlin"

SRC_URI = "file://mostfun.des3 \
           file://panel-install.service \
           file://panel-install.sh \
           file://mostfun_panel.service"

LICENSE = "CLOSED"

<<<<<<< HEAD
PV = "1.46.a8bf781"
=======
PV = "1.43.08f7834"
>>>>>>> 5a4b8b1f33e303fe8af48e271206a87db760c5a1
PR = "r40"

SYSTEMD_SERVICE_${PN} = "panel-install.service"
SYSTEMD_SERVICE_${PN} += "mostfun_panel.service"
SYSTEMD_AUTO_ENABLE = "enable"

inherit systemd update-alternatives

S = "${WORKDIR}"

FILESDIR = "${FILE_DIRNAME}/files/"

FILES_${PN} = "/etc/panel-install.sh \
        /mostfun/mostfun.des3 \
        "

do_install() {
    install -v -d  ${D}/mostfun/
    install -v -d  ${D}/etc/
    install -m 0755 panel-install.sh ${D}/etc/
    install -m 0755 mostfun.des3 ${D}/mostfun/

    if ${@base_contains('DISTRO_FEATURES','systemd','true','false',d)}; then
        install -d ${D}/${systemd_unitdir}/system
        install -m 644 ${WORKDIR}/panel-install.service ${D}${systemd_unitdir}/system/
        install -m 644 ${WORKDIR}/mostfun_panel.service ${D}${systemd_unitdir}/system/
    fi
}


text:
@article{e23091134,
 abstract = {The Schrödinger bridge problem (SBP) finds the most likely stochastic evolution between two probability distributions given a prior stochastic evolution. As well as applications in the natural sciences, problems of this kind have important applications in machine learning such as dataset alignment and hypothesis testing. Whilst the theory behind this problem is relatively mature, scalable numerical recipes to estimate the Schrödinger bridge remain an active area of research. Our main contribution is the proof of equivalence between solving the SBP and an autoregressive maximum likelihood estimation objective. This formulation circumvents many of the challenges of density estimation and enables direct application of successful machine learning techniques. We propose a numerical procedure to estimate SBPs using Gaussian process and demonstrate the practical usage of our approach in numerical simulations and experiments.},
 article-number = {1134},
 author = {Vargas, Francisco and Thodoroff, Pierre and Lamacraft, Austen and Lawrence, Neil},
 bdsk-url-1 = {https://www.mdpi.com/1099-4300/23/9/1134},
 bdsk-url-2 = {https://doi.org/10.3390/e23091134},
 doi = {10.3390/e23091134},
 issn = {1099-4300},
 journal = {Entropy},
 number = {9},
 title = {Solving Schrödinger Bridges via Maximum Likelihood},
 url = {https://www.mdpi.com/1099-4300/23/9/1134},
 volume = {23},
 year = {2021}
}

repository: AustenLamacraft/dooftown

repo_name: AustenLamacraft/dooftown


text:
@inproceedings{treiber2015comparison,
  title={Comparison of Numerical Models and Statistical Learning for Wind Speed Prediction},
  author={Treiber, Nils Andr{\'e} and Sp{\"a}th, Stephan and Heinermann, Justin and von Bremen, Lueder and Kramer, Oliver},
  year={2015},
  organization={ESANN}
}

repository: cigroup-ol/windml

repo_name: cigroup-ol/windml

gha_license_id: NOASSERTION

gha_language: Python


text:
@inproceedings{kuhn_introspective_bb,
  author    = {Kuhn, Christopher and  Hofbauer, Markus and  Petrovic, Goran and  Steinbach, Eckehard},
  booktitle = {2020 IEEE Intelligent Vehicles Symposium (IV)},
  title     = {Introspective Black Box Failure Prediction for Autonomous Driving},
  year      = {2020},
  volume    = {},
  number    = {},
  pages     = {1907-1913},
  address   = {Las Vegas, NV, USA},
  doi       = {10.1109/IV47402.2020.9304844}
}

gha_license_id: MIT

gha_language: Jupyter Notebook


text:
@inproceedings{Xu09,
    author = {Xu, Wei and Huang, Ling and Fox, Armando and Patterson, David and Jordan, Michael I.},
    title = {Detecting Large-scale System Problems by Mining Console Logs},
    booktitle = {Proceedings of the ACM SIGOPS 22Nd Symposium on Operating Systems Principles},
    series = {SOSP '09},
    year = {2009},
    isbn = {978-1-60558-752-3},
    location = {Big Sky, Montana, USA},
    pages = {117--132},
    numpages = {16},
    url = {http://doi.acm.org/10.1145/1629575.1629587},
    doi = {10.1145/1629575.1629587},
    acmid = {1629587},
    publisher = {ACM},
    address = {New York, NY, USA},
    keywords = {console log analysis, monitoring, pca, problem detection, source code analysis, statistical learning, tracing},
}

@ARTICLE{Lloyd82,
    author={Lloyd, S.},
    journal={Information Theory, IEEE Transactions on},
    title={Least squares quantization in PCM},
    year={1982},
    volume={28},
    number={2},
    pages={129-137},
    keywords={Least-squares approximation;PCM communication;Quantization (signal);Signal
        quantization;Amplitude modulation;Frequency;Helium;Laplace equations;Least squares
            methods;Phase change materials;Pulse modulation;Quantization;Sampling methods;Voltage},
    doi={10.1109/TIT.1982.1056489},
    ISSN={0018-9448},
}

@article {Fenwick94,
    author = {Fenwick, Peter M.},
    title = {A new data structure for cumulative frequency tables},
    journal = {Software: Practice and Experience},
    volume = {24},
    number = {3},
    publisher = {John Wiley & Sons, Ltd.},
    issn = {1097-024X},
    url = {http://dx.doi.org/10.1002/spe.4380240306},
    doi = {10.1002/spe.4380240306},
    pages = {327--336},
    keywords = {Binary indexed tree, Arithmetic coding, Cumulative frequencies},
    year = {1994},
}

@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
        and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
            and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
            Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}

@inproceedings{kmeans,
    added-at = {2007-08-23T16:58:27.000+0200},
    author = {MacQueen, J. B.},
    biburl = {http://www.bibsonomy.org/bibtex/25dcdb8cd9fba78e0e791af619d61d66d/gromgull},
    booktitle = {Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability},
    editor = {Cam, L. M. Le and Neyman, J.},
    interhash = {8d7d4dfe7d3a06b8c9c3c2bb7aa91e28},
    intrahash = {5dcdb8cd9fba78e0e791af619d61d66d},
    keywords = {kmeans},
    pages = {281-297},
    publisher = {University of California Press},
    timestamp = {2007-08-23T16:58:27.000+0200},
    title = {Some Methods for Classification and Analysis of MultiVariate Observations},
    volume = 1,
    year = 1967
}

@article{D311,
    title = {D3: Data-Driven Documents},
    author = {Michael Bostock AND Vadim Ogievetsky AND Jeffrey Heer},
    journal = {IEEE Trans. Visualization \& Comp. Graphics (Proc. InfoVis)},
    year = {2011},
    url = {http://vis.stanford.edu/papers/d3}
}

@article{Massie04,
    title = "The ganglia distributed monitoring system: design, implementation, and experience ",
    journal = "Parallel Computing ",
    volume = "30",
    number = "7",
    pages = "817 - 840",
    year = "2004",
    note = "",
    issn = "0167-8191",
    doi = "http://dx.doi.org/10.1016/j.parco.2004.04.001",
    url = "http://www.sciencedirect.com/science/article/pii/S0167819104000535",
    author = "Matthew L Massie and Brent N Chun and David E Culler",
    keywords = "Monitoring",
    keywords = "Clusters",
    keywords = "Distributed systems "
}

@MISC{Oetiker99,
    author = {Oetiker, Tobias},
    title = {RRDtool},
    year = {1999},
    howpublished={\url{http://oss.oetiker.ch/rrdtool/}}
}

@article{hangalseaview,
    title={Seaview: Using Fine-Grained Type Inference to Aid Log File Analysis},
    author={Hangal, Sudheendra},
    year = {2011},
}

@inproceedings{makanju2012spatio,
    title={Spatio-temporal decomposition, clustering and identification for alert detection in system logs},
    author={Makanju, Adetokunbo and Zincir-Heywood, A Nur and Milios, Evangelos E and Latzel, Markus},
    booktitle={Proceedings of the 27th Annual ACM Symposium on Applied Computing},
    pages={621--628},
    year={2012},
    organization={ACM}
}

@inproceedings{XuOSP,
    author = {Xu, Wei and Huang, Ling and Fox, Armando and Patterson, David and Jordan, Michael},
    title = {Online System Problem Detection by Mining Patterns of Console Logs},
    booktitle = {Proceedings of the 2009 Ninth IEEE International Conference on Data Mining},
    series = {ICDM '09},
    year = {2009},
    isbn = {978-0-7695-3895-2},
    pages = {588--597},
    numpages = {10},
    url = {http://dx.doi.org/10.1109/ICDM.2009.19},
    doi = {10.1109/ICDM.2009.19},
    acmid = {1677125},
    publisher = {IEEE Computer Society},
    address = {Washington, DC, USA},
    keywords = {console logs, system management, monitoring, problem detection, logs, pattern mining},
}

@article{jin2006fast,
    title={Fast and exact out-of-core and distributed k-means clustering},
    author={Jin, Ruoming and Goswami, Anjan and Agrawal, Gagan},
    journal={Knowledge and Information Systems},
    volume={10},
    number={1},
    pages={17--40},
    year={2006},
    publisher={Springer}
}
@inproceedings{kanungo2002local,
  title={A local search approximation algorithm for k-means clustering},
  author={Kanungo, Tapas and Mount, David M and Netanyahu, Nathan S and Piatko, Christine D and Silverman, Ruth and Wu, Angela Y},
  booktitle={Proceedings of the eighteenth annual symposium on Computational geometry},
  pages={10--18},
  year={2002},
  organization={ACM}
}
@article{pattermatch,
    title={Pattern Matching: The Gestalt Approach},
    author={Ratcliff, John W. and Metzener, David},
    journal={Dr. Dobb's Journal},
    pages={46},
    month={July},
    year={1988}
}
@incollection{preprocessor,
    year={2013},
    isbn={978-3-642-32383-6},
    booktitle={Image Processing and Communications Challenges 4},
    volume={184},
    series={Advances in Intelligent Systems and Computing},
    editor={Choraś, Ryszard S.},
    doi={10.1007/978-3-642-32384-3_28},
    title={Anomaly Detection Preprocessor for SNORT IDS System},
    url={http://dx.doi.org/10.1007/978-3-642-32384-3_28},
    publisher={Springer Berlin Heidelberg},
    author={Saganowski, Lukasz and Goncerzewicz, Marcin and Andrysiak, Tomasz},
    pages={225-232}
}


text:
.heroBanner {
	--ifm-heading-line-height: 1;
	--ifm-heading-font-weight: normal;
	padding: 1rem;
	text-align: center;
}

.heroBanner :global(.hero__title) {
	font-size: 2.8rem;
	font-family: 'Bree Serif', Georgia, serif;
}

.heroBanner :global(.hero__subtitle) {
	font-size: 1.4rem;
	font-family: 'Bree Serif', Georgia, serif;
	line-height: 1.3;
}

@media screen and (min-width: 768px) {
	.heroBanner :global(.hero__title) {
		font-size: 4.2rem;
	}

	.heroBanner :global(.hero__subtitle) {
		font-size: 1.8rem;
	}
}

@media screen and (min-width: 966px) {
	.heroBanner {
		padding: 2rem 0;
	}
}

.main {
	padding: 3rem 0;
}

.section + .section {
	margin-top: 2rem;
}

.feature {
	--ifm-h3-font-size: 1.8rem;
	--ifm-heading-font-weight: normal;
}

.featureFigureKawaii {
	position: relative;
}

.featureFigureKawaii::after {
	content: '';
	position: absolute;
	top: 0;
	right: 0;
	bottom: 0;
	left: 0;
	background-image: url(/img/playground2.png);
	background-size: 100%;
	opacity: 0;
	transition: opacity 0.2s ease-in-out;
}

.featureFigureKawaii:hover::after,
.featureFigureKawaii:active::after,
.featureFigureKawaii:focus::after {
	opacity: 1;
}

repository: styleguidist/react-styleguidist

repo_name: styleguidist/react-styleguidist

gha_language: TypeScript


text:
.equipment{
    padding:10px;
}
.container-equip{
    padding:10px;
}
.container-equip>p{
    font-size:2rem;
}
.container-equip li{
    width:100%;
    display:flex;
    justify-content: space-between;
    align-items: center;
    padding:1rem 0;
    border-bottom: 1px solid rgba(0,0,0,0.1);
}
.container-equip li:last-of-type{
    border:none;
}
.ul-equip{
    margin-top:2rem;
}
.ul-equip li{
    margin:0px;
}

repository: wpjabc/AiLv

repo_name: wpjabc/AiLv


text:
@article{peckus2019polarization,
  title={Polarization-dependent ultrafast plasmon relaxation dynamics in nanoporous gold thin films and nanowires},
  author={Peckus, Domantas and Chauvin, Adrien and Tamulevi{\v{c}}ius, Tomas and Juod{\.e}nas, Mindaugas and Ding, Junjun and Choi, Chang-Hwan and El Mel, Abdel-Aziz and Tessier, Pierre-Yves and Tamulevi{\v{c}}ius, Sigitas},
  journal={Journal of Physics D: Applied Physics},
  volume={52},
  number={22},
  pages={225103},
  year={2019},
  publisher={IOP Publishing},
  abstract={Metal materials structured at nanoscale are extensively used in a variety of applications, including molecular sensing, nanoscale heaters for photothermal therapy, etc. These applications depend on the strong absorption and electric field enhancements associated with localized surface plasmon resonance (LSPR). Despite multiple investigations of LSPR relaxation dynamics in plasmonic metallic nanoparticles (i.e. nanospheres, nanocubes, etc), no detailed studies of porous thin films or nanowires have been reported so far. In this paper, LSPR relaxation dynamics of nanoporous gold thin film, gold nanowires, and nanoporous gold nanowires (structures with the potential for a vast range of sensing and optical applications) were studied by means of transient absorption spectroscopy (TAS). Au and Au/Cu thin films produced by magnetron sputtering deposition over a nanograted silicon substrate enabled the creation of highly organized nanowire arrays of centimetres length. Nanoporous structures were created by the dealloying of Au/Cu alloy. We found that formation of such a subwavelength period grating structure in gold increases the efficiency of hot electron relaxation compared to continuous film, while the porosity of gold nanostructures has the opposite effect. The results also expressed a clear dependence of the TAS signal on the polarization (i.e. orientation of the electric field with respect to the nanoporous gold nanowires) of the pump and probe pulses, compared to non-porous gold nanowires.},
}

repository: JunjunDing/jding-academic-kickstart

repo_name: JunjunDing/jding-academic-kickstart


text:
@misc{ds-diabetes,
  title={Pima Indians Diabetes Database},
  author={UCI Machine Learning},
  publisher={kaggle},
  year={2016},
  url={https://www.kaggle.com/uciml/pima-indians-diabetes-database},
  urldate={2021-04-23}
}
@misc{ds-heart,
  title={Heart Disease UCI},
  author={ronit},
  publisher={kaggle},
  year={2018}
  url={https://www.kaggle.com/ronitf/heart-disease-uci},
  urldate={2021-04-23}
}

repository: markanewman/DataScienceRecipes

repo_name: markanewman/DataScienceRecipes

gha_language: R


text:
@article{DBLP:journals/corr/abs-1811-04551,
  author    = {Danijar Hafner and
               Timothy P. Lillicrap and
               Ian Fischer and
               Ruben Villegas and
               David Ha and
               Honglak Lee and
               James Davidson},
  title     = {Learning Latent Dynamics for Planning from Pixels},
  journal   = {CoRR},
  volume    = {abs/1811.04551},
  year      = {2018},
  url       = {http://arxiv.org/abs/1811.04551},
  archivePrefix = {arXiv},
  eprint    = {1811.04551},
  timestamp = {Fri, 23 Nov 2018 12:43:51 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1811-04551.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

repository: RohRav/Latent-Learning-Project

repo_name: RohRav/Latent-Learning-Project

branch_name: refs/heads/main