text:
@Manual{R-base,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2021},
  url = {https://www.R-project.org/},
}

@Manual{R-dplyr,
  title = {dplyr: A Grammar of Data Manipulation},
  author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller},
  year = {2021},
  note = {R package version 1.0.6},
  url = {https://CRAN.R-project.org/package=dplyr},
}

@Manual{R-magrittr,
  title = {magrittr: A Forward-Pipe Operator for R},
  author = {Stefan Milton Bache and Hadley Wickham},
  year = {2020},
  note = {R package version 2.0.1},
  url = {https://CRAN.R-project.org/package=magrittr},
}

@Manual{R-reportfactory,
  title = {reportfactory: Lightweight Infrastructure for Handling Multiple R Markdown Documents},
  author = {Thibaut Jombart and Tim Taylor},
  note = {R package version 0.3.1.9000},
  url = {https://github.com/reconverse/reportfactory},
  year = {2021},
}

@Manual{R-trendbreaker,
  title = {trendbreaker: Detect Changes in Temporal Trends},
  author = {Dirk Schumacher and Thibaut Jombart},
  year = {2021},
  note = {R package version 0.0.4},
}

@Manual{R-trending,
  title = {trending: Model Temporal Trends},
  author = {Dirk Schumacher and Thibaut Jombart},
  year = {2021},
  note = {R package version 0.0.3},
  url = {https://github.com/reconhub/trending},
}

gha_license_id: NOASSERTION


text:
FILE(REMOVE_RECURSE
  "CMakeFiles/cbr.dir/cbr.c.o"
  "../../lib/libcbr.pdb"
  "../../lib/libcbr.so"
)

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

repository: melbshark/DynamoRIO-for-ARM

license_type: permissive

repo_name: melbshark/DynamoRIO-for-ARM

branch_name: refs/heads/master

extension: cmake


text:
@article{ferguson2020impact,
  title={Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand},
  author={Ferguson, Neil M and Laydon, Daniel and Nedjati-Gilani, Gemma and Imai, Natsuko and Ainslie, Kylie and Baguelin, Marc and Bhatia, Sangeeta and Boonyasiri, Adhiratha and Cucunub{\'a}, Zulma and Cuomo-Dannenburg, Gina and others},
  journal={London: Imperial College COVID-19 Response Team, March},
  volume={16},
  year={2020}
}

@article{hellewellfeasibility,
  title={Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts.},
  journal={Lancet Glob Health; published online Feb 28},
  year={2020},
  author={Hellewell, J and Abbott, S and Gimma, A and Bosse, NI and Jarvis, CI and Russell, TW and others}
}

@article{smithHouseholds,
  title={Households and Household Composition in
England and Wales: 2001-11},
  journal={Office of National Statistics},
  year={2014},
  author={Smith, C.W.},
  url={https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/families/articles/householdsandhouseholdcompositioninenglandandwales/2014-05-29},
  note={Accessed 14th April, 2020}
}

@article{zhuang2020preliminary,
  title={Preliminary estimating the reproduction number of the coronavirus disease (COVID-19) outbreak in Republic of Korea from 31 January to 1 March 2020},
  author={Zhuang, Zian and Zhao, Shi and Lin, Qianying and Cao, Peihua and Lou, Yijun and Yang, Lin and Yang, Shu and He, Daihai},
  journal={medRxiv},
  year={2020},
  publisher={Cold Spring Harbor Laboratory Press}
}

@article{riou2020pattern,
  title={Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020},
  author={Riou, Julien and Althaus, Christian L},
  journal={Eurosurveillance},
  volume={25},
  number={4},
  year={2020},
  publisher={European Centre for Disease Prevention and Control}
}

@article{:/content/10.2807/1560-7917.ES.2020.25.10.2000180,
   author = "Mizumoto, Kenji and Kagaya, Katsushi and Zarebski, Alexander and Chowell, Gerardo",
   title = "Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020",
   journal = "Eurosurveillance",
   year = "2020",
   volume = "25",
   number = "10",
   eid = 2000180,
   url = "https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.10.2000180",
   doi = "https://doi.org/10.2807/1560-7917.ES.2020.25.10.2000180"
}

@article{backer2020incubation,
  title={The incubation period of 2019-nCoV infections among travellers from Wuhan, China},
  author={Backer, Jantien A and Klinkenberg, Don and Wallinga, Jacco},
  journal={medRxiv},
  year={2020},
  publisher={Cold Spring Harbor Laboratory Press}
}

@article{tang2020Contact,
  title={Contact-tracing strategies for SARS-CoV-2 eradication},
  author={Tang, D},
  year={2020},
  url={https://osf.io/p67v9}
}

@article{tang2020Mobile,
  title={A contact tracing mobile phone app for SARS-CoV-2 eradication that maintains user privacy and data protection},
  author={Tang, D},
  year={2020},
  url={https://osf.io/vzwgr}
}

@misc{abeler2020Support,
  title={Support for app-based contact tracing of COVID-19},
  author={Abeler,J. et.al.},
  year={2020},
  url={https://osf.io/huqtr/}
}

@article{zhou2006position,
  title={Position measurement using Bluetooth},
  author={Zhou, Sheng and Pollard, John K},
  journal={IEEE Transactions on Consumer Electronics},
  volume={52},
  number={2},
  pages={555--558},
  year={2006},
  publisher={IEEE}
}

@article{Ferrettieabb6936,
	author = {Ferretti, Luca and Wymant, Chris and Kendall, Michelle and Zhao, Lele and Nurtay, Anel and Abeler-D{\"o}rner, Lucie and Parker, Michael and Bonsall, David and Fraser, Christophe},
	title = {Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing},
	elocation-id = {eabb6936},
	year = {2020},
	doi = {10.1126/science.abb6936},
	publisher = {American Association for the Advancement of Science},
	issn = {0036-8075},
	URL = {https://science.sciencemag.org/content/early/2020/03/30/science.abb6936},
	eprint = {https://science.sciencemag.org/content/early/2020/03/30/science.abb6936.full.pdf},
	journal = {Science}
}

@misc{applegoogle,
  author={Apple Inc., Google Inc.},
  year={2020},
  title={Apple and Google partner on COVID-19 contact tracing technology},
  URL={https://www.apple.com/newsroom/2020/04/apple-and-google-partner-on-covid-19-contact-tracing-technology/},
  note={Accessed 14th April, 2020}
}

@misc{deloitte,
  author={Deloitte},
  year={2020},
  title={Global Mobile Consumer Survey 2019: UK cut},
  URL={https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/technology-media-telecommunications/deloitte-uk-plateauing-at-the-peak-the-state-of-the-smartphone.pdf},
  note={Accessed 14th April, 2020}
}

@article{OsongReport,
author = {Osong Public Health},
title = {Coronavirus Disease-19: The First 7,755 Cases in the Republic of Korea},
journal = {Osong Public Health Res Perspect},
volume = {11},
number = {2},
pages = {85-90},
year = {2020},
doi = {10.24171/j.phrp.2020.11.2.05},
URL = {http://ophrp.org/journal/view.php?number=550},
eprint = {http://ophrp.org/journal/view.php?number=550}
}

@article{burke2020active,
  title={Active monitoring of persons exposed to patients with confirmed COVID-19—United States, January--February 2020},
  author={Burke, Rachel M},
  journal={MMWR. Morbidity and Mortality Weekly Report},
  volume={69},
  year={2020}
}

@misc{statistica,
  author={Statistica},
  year={2020},
  title={Share of children owning tablets and smartphones in the United Kingdom (UK) from 2018, by age},
  URL = {https://www.statista.com/statistics/805397/children-ownership-of-tablets-smartphones-by-age-uk/},
  note={Accessed 14th April, 2020}
}

license: MIT

extension: bib


text:
@inproceedings{HongHaiDo.2006,
 abstract = {Auto-ID technologies allow capturing the time and location of products in the supply chain for tracking and tracing. This paves the way for a variety of business applications, such as anti-counterfeiting, pedigree, and genealogy, which analyze the trace history of products to detect patterns or anomalies in the supply chain. While these applications have gained considerable interest recently, further work is needed towards integration of event data from heterogeneous auto-ID nodes in order to obtain the complete trace history for products of interest. As a first step, we perform an architectural study on interoperable auto-ID systems and present the results in this paper. We first review established techniques for data integration and data sharing as well as relevant industrial efforts. We then clarify the requirements that need to be addressed by an auto-ID network. Finally, we discuss four possible architecture alternatives for implementing interoperability in such a network and comparatively evaluate the approaches according to the identified requirements},
 author = {Hong-Hai Do and Anke, Jürgen and Hackenbroich, Gregor},
 booktitle = {17th International Workshop on Database and Expert Systems Applications (DEXA'06)},
 doi = {10.1109/DEXA.2006.30},
 file = {https://doi.org/10.1109/DEXA.2006.30},
 pages = {30--34},
 title = {Architecture Evaluation for Distributed Auto-ID Systems},
 year = {2006}
}


text:
.radioWrapper {
  color: #a4b0bf;
  display: block;
  margin: 0 auto;
  width: 500px;
  border-radius: 2rem;
}

.radioHeader {
  display: flex;
  justify-content: space-between;
  background-color: rgba(242, 167, 102, 0.9);
  padding: 2rem 1.5rem;
  border-top-left-radius: 2rem;
  border-top-right-radius: 2rem;
}

.radioFooter {
  background-color: #1d1d26;
  padding-top: 2rem;
  text-align: center;
  border-bottom-left-radius: 2rem;
  border-bottom-right-radius: 2rem;
  border-top: 1px solid #616161;
  min-height: 100px;
}

.radioBody {
  background-color: rgba(29, 29, 38, 0.9);
  padding: 2rem 1rem;
  max-height: 500px;
  overflow-y: auto;
}

.radioData {
  display: flex;
  font-size: 30px;
  justify-content: space-between;
}

.radioName {
  font-size: 40px;
}

.radioItem:not(:last-child) {
  border-bottom: 1px solid #616161;
}

.radioThumb {
  display: flex;
  justify-content: space-between;
  align-items: center;
}

.radioThumbnail {
  border-radius: 100%;
  object-fit: cover;
}

.radioBody::-webkit-scrollbar {
  width: 0.5rem;
}

.radioBody::-webkit-scrollbar-track {
  box-shadow: inset 0 0 6px rgba(0, 0, 0, 0.3);
  -webkit-box-shadow: inset 0 0 6px rgba(0, 0, 0, 0.3);
}

.radioBody::-webkit-scrollbar-thumb {
  background-color: #a4b0bf;
  outline: 1px solid #a4b0bf;
}

.accordionButton {
  appearance: none;
  background: 0;
  border: 0;
  box-shadow: none;
  color: inherit;
  cursor: pointer;
  display: block;
  text-align: inherit;
  flex-grow: 1;
  flex-shrink: 0;
  font: inherit;
  font-weight: bold;
  margin: 0;
  padding: 10px 0;
}

.accordionPanel {
  padding: 10px;
}

.accordionWrapper {
  align-items: center;
  margin: 9px 0;
  display: flex;
  justify-content: space-between;
  padding: 4px 10px;
}

repository: ashfaqrafi/Corner-Case

repo_name: ashfaqrafi/Corner-Case

branch_name: refs/heads/main

extension: css


text:
.container{
    font-size: 22px;
    font-family: 'source_sans_proregular';
    max-width: 100%;
}

header{
    background:url(../img/hero-image.png) no-repeat center;
    background-size: cover;
    height: 628px;
}

header nav{
    display: flex;
    justify-content: space-between;
    align-items: center;
    flex-wrap: wrap;
    margin: 0 130px;
}

header nav h1{
    padding: 0;margin: 0;
    background: url(../img/logo.png) no-repeat;
    height: 41px;
    width: 38px;
}

header nav h1 span{
    visibility: hidden;
}

header nav ul{
    display: flex;
    align-items: center;
    flex-wrap: wrap;
}

header nav ul li{
    list-style: none;
    display: inline;
    margin-left: 90px;
}
header nav ul li a{
    text-decoration: none;
    color: #fff;
    opacity: .9;
}

header .menu-text{
    display: flex;
    flex-direction: column;
    align-items: center;
    text-align: center;
    margin-top: 70px;
    color:#fff;
    margin-top:15px;
    padding: 0 20px;
}

header .menu-text h2{
    font-size: 48px;
    margin-bottom: 0;
}
header .menu-text p{
    font-size: 22px;
    opacity: .9;
    margin-top: 10px;
    max-width: 630px;
    text-align: center;
}
header .menu-text a {
    text-decoration: none;
    color: #fff;
    opacity: .9;
    padding: 12px 50px;
    background-color: #fd5c63;
    border-radius: 50px;
}

/**/
.fotos {
    height: 527px;
    margin-top: -120px;
    background: linear-gradient(to top, #ffffff 0%, rgba(255, 255, 255, 0) 100%), url(../img/fotos.png) no-repeat center;
    background-size:contain;
    margin-right: 20px;
    margin-left: 20px;
}

/**/
.video{
    margin-top: -190px;
    display: flex;
    justify-content: space-around;
    align-items: center;
    margin-bottom: 140px;
        flex-wrap: wrap;
}

.video .texto-video{
        padding: 0 20px;
}

.video .texto-video h3{
    color: #4c4c51;
    font-size: 36px;
}
.video .texto-video p{
    color: #88939e;
    font-size: 22px;
    opacity: .9;
    max-width: 670px;
}

.video .imagem-video{
    background: linear-gradient(to top, rgba(255, 177, 180, 0.73) 0%, rgba(255, 75, 98, 0.73) 100%), url(../img/video.png) no-repeat center;
    width: 463px;
    height: 257px;
}

.video .imagem-video img{
    display: block;
    margin: 0 auto;
    margin-top: 70px;
}

@media screen and (max-width: 768px) {

    header nav{
        flex-direction: column;
        align-items: center;
        margin: 0 20px;
        padding-top: 40px;
    }
    header nav ul {
        flex-direction: column;
        padding: 0;
        margin-bottom: 0;
    }
    header nav ul li{
        margin-left: 0px;
    }

    header{
        height: 820px;
    }

    .fotos{
        margin: 0;
        margin-top: -180px;
        background: linear-gradient(to top, #ffffff 0%, rgba(255, 255, 255, 0) 75%), url(../img/fotos.png) no-repeat center;
        background-size:contain;
    }

    .video{
        margin-bottom: 50px;
    }
    .video .texto-video h3{
        margin-bottom: 0;
    }

}


text:
@article{PhysRevLett.108.218703,
  title = {Controlling Complex Networks: How Much Energy Is Needed?},
  author = {Yan, Gang and Ren, Jie and Lai, Ying-Cheng and Lai, Choy-Heng and Li, Baowen},
  journal = {Phys. Rev. Lett.},
  volume = {108},
  issue = {21},
  pages = {218703},
  numpages = {5},
  year = {2012},
  month = {May},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.108.218703},
  url = {https://link.aps.org/doi/10.1103/PhysRevLett.108.218703}
}

repository: jruths/netcontrolz

repo_name: jruths/netcontrolz


text:
@article{yaman_automated_2012,
 abstract = {Raising the level of abstraction for synthetic biology design requires solving several challenging problems, including mapping abstract designs to DNA sequences. In this paper we present the first formalism and algorithms to address this problem. The key steps of this transformation are feature matching, signal matching, and part matching. Feature matching ensures that the mapping satisfies the regulatory relationships in the abstract design. Signal matching ensures that the expression levels of functional units are compatible. Finally, part matching finds a DNA part sequence that can implement the design. Our software tool MatchMaker implements these three steps.},
 author = {Yaman, Fusun and Bhatia, Swapnil and Adler, Aaron and Densmore, Douglas and Beal, Jacob},
 date = {2012-08-17},
 doi = {10.1021/sb300032y},
 journaltitle = {ACS Synthetic Biology},
 number = {8},
 pages = {332--344},
 shortjournal = {ACS Synth. Biol.},
 title = {Automated Selection of Synthetic Biology Parts for Genetic Regulatory Networks},
 url = {https://doi.org/10.1021/sb300032y},
 urldate = {2020-09-19},
 volume = {1}
}


text:
@article{RN10,
 author = {Frantz, David and Stellmes, Marion and Röder, Achim and Udelhoven, Thomas and Mader, Sebastian and Hill, Joachim},
 geo = {Trier, Germany (1, 2, 3, 4, 5, 6)},
 doi = {10.1109/TGRS.2016.2537929},
 journal = {IEEE Transactions on Geoscience and Remote Sensing},
 number = {7},
 pages = {4153-4164},
 title = {Improving the Spatial Resolution of Land Surface Phenology by Fusing Medium- and Coarse-Resolution Inputs},
 type = {Journal Article},
 url = {http://ieeexplore.ieee.org/document/7452606/?arnumber=7452606},
 volume = {54},
 year = {2016}
}

repository: davidfrantz/academic-kickstart

repo_name: davidfrantz/academic-kickstart

gha_license_id: MIT


text:
@Article{p1,
author="Dehghani, Amin
and Moghaddam, Hamid Abrishami
and Moin, Mohammad-Shahram",
title="Optic disc localization in retinal images using histogram matching",
journal="EURASIP Journal on Image and Video Processing",
year="2012",
month="Oct",
day="08",
volume="2012",
number="1",
pages="19",
abstract="In this article, we propose a new method for localizing optic disc in retinal images. Localizing the optic disc and its center is the first step of most vessel segmentation, disease diagnostic, and retinal recognition algorithms. We use optic disc of the first four retinal images in DRIVE dataset to extract the histograms of each color component. Then, we calculate the average of histograms for each color as template for localizing the center of optic disc. The DRIVE, STARE, and a local dataset including 273 retinal images are used to evaluate the proposed algorithm. The success rate was 100, 91.36, and 98.9{\%}, respectively.",
issn="1687-5281",
doi="10.1186/1687-5281-2012-19",
url="https://doi.org/10.1186/1687-5281-2012-19"
}

@INPROCEEDINGS{p2,
author={J. {Almotiri} and K. {Elleithy} and A. {Elleithy}},
booktitle={2018 IEEE Long Island Systems, Applications and Technology Conference (LISAT)},
title={An automated region-of-interest segmentation for optic disc extraction},
year={2018},
volume={},
number={},
pages={1-6},
keywords={biomedical optical imaging;image segmentation;medical image processing;retinal image;optic disc segmentation techniques;optic disc extraction;retinal fundus images;automated region-of-interest segmentation;eye retina;DRISHTI-GS dataset;DRIVE dataset;DiaRetDB1 dataset;pre-processing technique;Optical imaging;Retina;Optical filters;Image segmentation;Pathology;Transforms;Optical sensors;Region-Of-Intreset;optic disc;optic disc segmentation;retina;Hough transform;fuzzy c-means},
doi={10.1109/LISAT.2018.8378019},
ISSN={null},
month={May},}

@ARTICLE{p3,
author={A. {Aquino} and M. E. {Gegúndez-Arias} and D. {Marín}},
journal={IEEE Transactions on Medical Imaging},
title={Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques},
year={2010},
volume={29},
number={11},
pages={1860-1869},
keywords={biomedical optical imaging;diseases;edge detection;eye;feature extraction;image segmentation;medical image processing;telemedicine;optic disc boundary;digital fundus images;feature extraction techniques;edge detection;diabetic retinopathy;image segmentation;segmentation algorithm;standard deviation;telemedicine;blindness;MESSIDOR database;circular hough transform;Image edge detection;Optical detectors;Feature extraction;Image segmentation;Diseases;Shape;Pathology;Retina;Retinopathy;Blindness;Diabetic retinopathy;glaucoma;optic disc (OD) segmentation;retinal imaging;telemedicine;Algorithms;Artificial Intelligence;Fundus Oculi;Humans;Image Enhancement;Image Interpretation, Computer-Assisted;Optic Disk;Pattern Recognition, Automated;Reproducibility of Results;Retinoscopy;Sensitivity and Specificity},
doi={10.1109/TMI.2010.2053042},
ISSN={1558-254X},
month={Nov},}

@Article{p4,
author="Reza, Ahmed Wasif
and Eswaran, C.
and Dimyati, Kaharudin",
title="Diagnosis of Diabetic Retinopathy: Automatic Extraction of Optic Disc and Exudates from Retinal Images using Marker-controlled Watershed Transformation",
journal="Journal of Medical Systems",
year="2011",
month="Dec",
day="01",
volume="35",
number="6",
pages="1491--1501",
abstract="Due to increasing number of diabetic retinopathy cases, ophthalmologists are experiencing serious problem to automatically extract the features from the retinal images. Optic disc (OD), exudates, and cotton wool spots are the main features of fundus images which are used for diagnosing eye diseases, such as diabetic retinopathy and glaucoma. In this paper, a new algorithm for the extraction of these bright objects from fundus images based on marker-controlled watershed segmentation is presented. The proposed algorithm makes use of average filtering and contrast adjustment as preprocessing steps. The concept of the markers is used to modify the gradient before the watershed transformation is applied. The performance of the proposed algorithm is evaluated using the test images of STARE and DRIVE databases. It is shown that the proposed method can yield an average sensitivity value of about 95{\%}, which is comparable to those obtained by the known methods.",
issn="1573-689X",
doi="10.1007/s10916-009-9426-y",
url="https://doi.org/10.1007/s10916-009-9426-y"
}

@article{idrid,
  title={IDRiD: Diabetic Retinopathy--Segmentation and Grading Challenge},
  author={Porwal, Prasanna and Pachade, Samiksha and Kokare, Manesh and Deshmukh, Girish and Son, Jaemin and Bae, Woong and Liu, Lihong and Wang, Jianzong and Liu, Xinhui and Gao, Liangxin and others},
  journal={Medical image analysis},
  volume={59},
  pages={101561},
  year={2020},
  publisher={Elsevier}
}

@article{canny,
  title={A computational approach to edge detection},
  author={Canny, John},
  journal={IEEE Transactions on pattern analysis and machine intelligence},
  number={6},
  pages={679--698},
  year={1986},
  publisher={Ieee}
}

@misc{drive,
  title = {Drive Dataset},
  howpublished = {\url{http://www.isi.uu.nl/Research/Databases/DRIVE/}},
  note = {Accessed: 2019-11-23}
}

@InProceedings{p5,
author="Usman Akram, M.
and Khan, Aftab
and Iqbal, Khalid
and Butt, Wasi Haider",
editor="Campilho, Aur{\'e}lio
and Kamel, Mohamed",
title="Retinal Images: Optic Disk Localization and Detection",
booktitle="Image Analysis and Recognition",
year="2010",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="40--49",
abstract="Automated localization and detection of the optic disc (OD) is an essential step in the analysis of digital diabetic retinopathy systems. Accurate localization and detection of optic disc boundary is very useful in proliferative diabetic retinopathy where fragile vessels develop in the retina. In this paper, we propose an automated system for optic disk localization and detection. Our method localizes optic disk using average filter and thresholding, extracts the region of interest (ROI) containing optic disk to save time and detects the optic disk boundary using Hough transform. This method can be used in computerized analysis of retinal images, e.g., in automated screening for diabetic retinopathy. The technique is tested on publicly available DRIVE, STARE, diaretdb0 and diaretdb1 databases of manually labeled images which have been established to facilitate comparative studies on localization and detection of optic disk in retinal images. The proposed method achieves an average accuracy of 96.7{\%} for localization and an average area under the receiver operating characteristic curve of 0.958 for optic detection.",
isbn="978-3-642-13775-4"
}

repository: shashankboosi/Machine-Vision

repo_name: shashankboosi/Machine-Vision


text:
/* Styles in src/style directory are applied to the whole page */
body {
  margin: 20px;
}

.pagination {
  margin: 0px
}

.modified-text {
  font-size: small;
  font-style: italic;
}

.modal-footer {
  padding-top: 0px;
  border-top: 0px !important;
}

/* Make first two columns minimum*/
td:nth-child(-n+2) {
  width: 1px;
}

repository: bartlettpsj/notes

repo_name: bartlettpsj/notes

gha_language: JavaScript


text:
@article{article1,
  author={John Doe and Jane Doe},
  journal={{Stencila Reproducible Journal}},
  number={1},
  pages={1-2},
  title={{We shall make science reproducible}},
  volume={1},
  year={2100}
}

@article{article2,
  author={Paul Smith},
  journal={{Stencila Reproducible Journal}},
  number={2},
  pages={1-2},
  title={{We shall make science reproducible faster}},
  volume={1},
  year={2100}
}

@article{article3,
  author={Mary Johson},
  journal={{Stencila Reproducible Journal}},
  number={1},
  pages={1-2},
  title={{We shall make science reproducible even faster}},
  volume={1},
  year={2100}
}

repository: stencila/encoda

repo_name: stencila/encoda

gha_language: Jupyter Notebook


text:
@InProceedings{gornitz14,
  supplementary = {Supplementary:gornitz14-supp.pdf},
  title = {{Learning and Evaluation in Presence of Non-i.i.d. Label Noise}},
  author = {Görnitz, Nico and Porbadnigk, Anne and Binder, Alexander and Sannelli, Claudia and Braun, Mikio and Mueller, Klaus-Robert and Kloft, Marius},
  pages = {293-302},
  abstract = {In many real-world applications, the simplified assumption of independent and identically distributed noise breaks down, and labels can have structured, systematic noise. For example, in brain-computer interface applications, training data is often the result of lengthy experimental sessions, where the attention levels of participants can change over the course of the experiment. In such application cases, structured label noise will cause problems because most machine learning methods assume independent and identically distributed label noise. In this paper, we present a novel methodology for learning and evaluation in presence of systematic label noise. The core of which is a novel extension of support vector data description / one-class SVM that can incorporate latent variables. Controlled simulations on synthetic data and a real-world EEG  experiment with 20 subjects from the domain of brain-computer-interfacing show that our method achieves accuracies that go beyond the state of the art.},
}

repository: mreid/papersite

repo_name: mreid/papersite

gha_language: TeX


text:
@inproceedings{patra_efficient_2019,
 abstract = {Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledge into deep learning architecture design. This is the approach considered in the paper where we propose to combine ultrasound video with point-of-gaze tracked for expert sonographers as they scan to train memory-efficient ultrasound image analysis models. Specifically we develop teacher-student knowledge transfer models for the exemplar task of frame classification for the fetal abdomen, head, and femur. The best performing memory-efficient models attain performance within 5% of conventional models that are 1000×1000×1000\\times \ larger in size.},
 address = {Cham},
 author = {Patra, Arijit and Cai, Yifan and Chatelain, Pierre and Sharma, Harshita and Drukker, Lior and Papageorghiou, Aris T. and Noble, J. Alison},
 booktitle = {Medical Image Computing and Computer Assisted Intervention – MICCAI 2019},
 doi = {10.1007/978-3-030-32251-9_43},
 editor = {Shen, Dinggang and Liu, Tianming and Peters, Terry M. and Staib, Lawrence H. and Essert, Caroline and Zhou, Sean and Yap, Pew-Thian and Khan, Ali},
 file = {Springer Full Text PDF:C\:\\Users\\engs1750\\Zotero\\storage\\34TTSJHI\\Patra et al. - 2019 - Efficient Ultrasound Image Analysis Models with So.pdf:application/pdf},
 isbn = {978-3-030-32251-9},
 keywords = {Expert knowledge, Gaze tracking, Model compression},
 language = {en},
 pages = {394--402},
 publisher = {Springer International Publishing},
 series = {Lecture Notes in Computer Science},
 title = {Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation},
 year = {2019}
}

repository: harshita-s/academic-kickstart

repo_name: harshita-s/academic-kickstart


text:
@InProceedings{jensen14,
  supplementary = {Supplementary:jensen14-supp.pdf},
  title = {{A Level-set Hit-and-run Sampler for Quasi-Concave Distributions}},
  author = {Jensen, Shane and Foster, Dean},
  pages = {439-447},
  abstract = {We develop a new sampling strategy that uses the hit-and-run algorithm within level sets of a target density. Our method can be applied to any quasi-concave density, which covers a broad class of models. Standard sampling methods often perform poorly on densities that are high-dimensional or multi-modal. Our level set sampler performs well in high-dimensional settings, which we illustrate on a spike-and-slab mixture model. We also extend our method to exponentially-tilted quasi-concave densities, which arise in Bayesian models consisting of a log-concave likelihood and quasi-concave prior density. We illustrate our exponentially-tilted level-set sampler on a Cauchy-normal model where our sampler is better able to handle a high-dimensional and multi-modal posterior distribution compared to Gibbs sampling and Hamiltonian Monte Carlo.},
}


text:
@inproceedings{coupled17icra,
  author={Mastalli, Carlos and Focchi, Michele and Havoutis, Ioannis and Radulescu, Andreea and Calinon, Sylvain and Buchli, Jonas and Caldwell, Darwin G. and Semini, Claudio},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  title={{Trajectory and Foothold Optimization using Low-Dimensional Models for Rough Terrain Locomotion}},
  year={2017},
  pages={1096-1103},
  doi={10.1109/ICRA.2017.7989131},
  issn={978-1-5090-4633-1}
}

license: Unlicense,MIT

detected_licenses: Unlicense MIT

gha_license_id: Unlicense


text:
@InProceedings{titsias09a,
  title = {Variational Learning of Inducing Variables in Sparse Gaussian Processes},
  author = {Michalis Titsias},
  pages = {567--574},
  abstract = {Sparse Gaussian process methods that use inducing variables require the selection of the inducing inputs and the kernel hyperparameters. We introduce a variational formulation for sparse approximations that jointly infers the inducing inputs and the kernel hyperparameters by maximizing a lower bound of the true log marginal likelihood. The key property of this formulation is that the inducing inputs  are defined to be variational parameters  which are selected by minimizing  the Kullback-Leibler divergence between  the variational distribution and the exact posterior distribution over the latent function values. We apply this technique to regression and we compare it with other approaches in the literature.},
  pdf = {http://jmlr.org/proceedings/papers/v5/titsias09a/titsias09a.pdf},
}


text:
@article{norman2019hotspot,
 author = {Norman, Kari EA and White, Ethan P},
 journal = {BioRxiv},
 pages = {685735},
 publisher = {Cold Spring Harbor Laboratory},
 title = {Hotspot prioritizations show sensitivity to data type},
 year = {2019}
}

repository: weecology/website

repo_name: weecology/website


text:
@article{dou2020TrainingNeural,
	Author = {Dou, Xialiang and Liang, Tengyuan},
	Journal = {Journal of the American Statistical Association},
	Number = {0},
	Pages = {1--14},
	Title = {Training Neural Networks as Learning Data-Adaptive Kernels: {{Provable}} Representation and Approximation Benefits},
	Volume = {0},
	Year = {2020},
	Doi = {10.1080/01621459.2020.1745812}}


text:
@article{vecchi_does_2009,
	title = {Does direction of results of abstracts submitted to scientific conferences on drug addiction predict full publication?},
	volume = {9},
	issn = {1471-2288},
	url = {http://www.biomedcentral.com/1471-2288/9/23},
	doi = {10.1186/1471-2288-9-23},
	abstract = {Abstract: "{BACKGROUND}: Data from scientific literature show that about 63% of abstracts presented at biomedical conferences will be published in full. Some studies have indicated that full publication is associated with the direction of results (publication bias). No study has looked into the occurrence of publication bias in the field of addiction. Objectives: To investigate whether the significance or direction of results of abstracts presented at the major international scientific conference on addiction is associated with full  publication. {METHODS}: The conference proceedings of the {US} Annual Meeting of the College on Problems of Drug Dependence ({CPDD}), were handsearched for abstracts of randomized controlled trials and controlled clinical trials that evaluated interventions for prevention, rehabilitation and treatment of drug addiction in humans (years searched 1993-2002). Data regarding the study designs and outcomes reported were extracted. Subsequent publication in peer reviewed journals was searched in {MEDLINE} and {EMBASE} databases, as of March 2006. {RESULTS}: Out of 5919 abstracts presented, 581 met the inclusion criteria; 359 (62%) conference abstracts had been published in a broad variety of peer reviewed journals (average time of publication 2.6 years, {SD} +/- 1.78). The proportion of published studies was almost the same for randomized controlled trials (62.4 %) and controlled clinical trials (59.5 %) while studies that reported positive results were significantly more likely to be published (74.5%) than those that did not report statistical results (60.9%), negative or null results (47.1%) and no results (38.6%). Abstracts reporting positive results had a significantly higher probability of being published in full, while abstracts reporting null or negative results were half as likely to be published compared with positive ones ({HR}=0.48; 95%{CI} 0.30-0.74). {CONCLUSIONS}: Clinical trials were the minority of abstracts presented at the {CPDD}; we found evidence of possible publication bias in the field of addiction, with negative or null results having half the likelihood of being published than positive ones."},
	pages = {23},
	number = {1},
	journaltitle = {{BMC} Medical Research Methodology},
	author = {Vecchi, Simona and Belleudi, Valeria and Amato, Laura and Davoli, Marina and Perucci, Carlo},
	urldate = {2009-04-23},
	date = {2009},
	tags = {Systematic overviews, a02}
}

repository: pmean/build-website

repo_name: pmean/build-website


text:
@article{LIGO:AdvancedLIGO2015,
 author = {The LIGO Scientific Collaboration},
 journal = {Classical and Quantum Gravity},
 number = {7},
 pages = {074001},
 title = {Advanced LIGO},
 url = {http://stacks.iop.org/0264-9381/32/i=7/a=074001},
 volume = {32},
 year = {2015}
}

repository: mcoughlin/personal-website

repo_name: mcoughlin/personal-website


text:
@article{Baltzer-2021-Increasing,
    title = "Increasing fire and the decline of fire adapted black spruce in the boreal forest",
    author = "Baltzer, Jennifer L.  and
      Day, Nicola J.  and
      Walker, Xanthe J.  and
      Greene, David  and
      Mack, Michelle C.  and
      Alexander, Heather D.  and
      Arseneault, Dominique  and
      Barnes, Jennifer  and
      Bergeron, Yves  and
      Boucher, Yan  and
      Bourgeau-Chavez, Laura  and
      Brown, Carissa D.  and
      Carri{\`e}re, Suzanne  and
      Howard, Brian K.  and
      Gauthier, Sylvie  and
      Parisien, Marc-Andr{\'e}  and
      Reid, Kirsten A.  and
      Rogers, Brendan M.  and
      Roland, Carl  and
      Sirois, Luc  and
      Stehn, Sarah  and
      Thompson, Dan K.  and
      Turetsky, Merritt R.  and
      Veraverbeke, Sander  and
      Whitman, Ellen  and
      Yang, Jian  and
      Johnstone, Jill F.",
    journal = "Proceedings of the National Academy of Sciences, Volume 118, Issue 45",
    volume = "118",
    number = "45",
    year = "2021",
    publisher = "Proceedings of the National Academy of Sciences",
    url = "https://aclanthology.org/G21-78001",
    doi = "10.1073/pnas.2024872118",
    abstract = "Intensifying wildfire activity and climate change can drive rapid forest compositional shifts. In boreal North America, black spruce shapes forest flammability and depends on fire for regeneration. This relationship has helped black spruce maintain its dominance through much of the Holocene. However, with climate change and more frequent and severe fires, shifts away from black spruce dominance to broadleaf or pine species are emerging, with implications for ecosystem functions including carbon sequestration, water and energy fluxes, and wildlife habitat. Here, we predict that such reductions in black spruce after fire may already be widespread given current trends in climate and fire. To test this, we synthesize data from 1,538 field sites across boreal North America to evaluate compositional changes in tree species following 58 recent fires (1989 to 2014). While black spruce was resilient following most fires (62{\%}), loss of resilience was common, and spruce regeneration failed completely in 18{\%} of 1,140 black spruce sites. In contrast, postfire regeneration never failed in forests dominated by jack pine, which also possesses an aerial seed bank, or broad-leaved trees. More complete combustion of the soil organic layer, which often occurs in better-drained landscape positions and in dryer duff, promoted compositional changes throughout boreal North America. Forests in western North America, however, were more vulnerable to change due to greater long-term climate moisture deficits. While we find considerable remaining resilience in black spruce forests, predicted increases in climate moisture deficits and fire activity will erode this resilience, pushing the system toward a tipping point that has not been crossed in several thousand years.",
}

repository: gwf-uwaterloo/gwf-publications

repo_name: gwf-uwaterloo/gwf-publications

gha_language: Python


text:
@article{IEEEexample:article_typical,
  author        = "S. Zhang and C. Zhu and J. K. O. Sin and P. K. T. Mok",
  title         = "A Novel Ultrathin Elevated Channel Low-temperature
                   Poly-{Si} {TFT}",
  journal       = IEEE_J_EDL,
  volume        = "20",
  month         = nov,
  year          = "1999",
  pages         = "569-571"
}

@inproceedings{IEEEexample:conf_typical,
  author        = "R. K. Gupta and S. D. Senturia",
  title         = "Pull-in Time Dynamics as a Measure of Absolute Pressure",
  booktitle     = "Proc. {IEEE} International Workshop on
                   Microelectromechanical Systems ({MEMS}'97)",
  address       = "Nagoya, Japan",
  month         = jan,
  year          = "1997",
  pages         = "290-294"
}

@book{IEEEexample:book_typical,
  author        = "B. D. Cullity",
  title         = "Introduction to Magnetic Materials",
  publisher     = "Addison-Wesley",
  address       = "Reading, MA",
  year          = "1972"
}


text:
@article{Morrow2008,
 abstract = {Background: Using a transcriptional profiling approach, we recently identified myeloid-related protein 8/14 (MRP-8/14) to be expressed by platelets during acute myocardial infarction (MI). Elevated concentrations of MRP-8/14 are associated with a higher risk for future cardiovascular events in apparently healthy individuals but have not been assessed with respect to prognosis in patients with acute coronary syndrome. Methods: We performed a nested case-control study (n = 237 case-control pairs) among patients enrolled in the Pravastatin or Atorvastatin Evaluation and Infection Theraphy: Thrombolysis in Myocardial Infarction 22 (PROVE IT-TIMI 22) trial (mean follow-up 24 months) to investigate the risk of cardiovascular death or MI associated with MRP-8/14 measured at 30 days after an acute coronary syndrome. Results: Patients with cardiovascular death or MI after 30 days (cases) had higher median [25th, 75th percentile] MRP-8/14 levels than patients who remained free of recurrent events (5.6 [2.8, 13.5] mg/L vs 4.0 [1.9, 10.1] mg/L, P = .020). The risk of a recurrent cardiovascular event increased with each increasing quartile of MRP-8/14 (P-trend = 0.007) such that patients with the highest levels had a 2.0-fold increased odds (95% CI 1.1-3.6, P = .029) of a recurrent event after adjusting for standard risk indicators, randomized treatment, and C-reactive protein. Patients with elevated levels of MRP-8/14 and high-sensitivity C-reactive protein showed significantly increased risk of cardiovascular death or MI compared with patients with the lowest levels of both markers (adjusted odds ratio 2.1, 95% CI 1.2-3.8). Conclusions: Myeloid-related protein 8/14 may be a useful biomarker of platelet and inflammatory disease activity in atherothrombosis and may serve as a novel target for therapeutic intervention. \textcopyright 2008.},
 author = {Morrow, D.A. and Wang, Y. and Croce, K. and Sakuma, M. and Sabatine, M.S. and Gao, H. and Pradhan, A.D. and Healy, A.M. and Buros, J. and McCabe, C.H. and Libby, P. and Cannon, C.P. and Braunwald, E. and Simon, D.I.},
 doi = {10.1016/j.ahj.2007.08.018},
 issn = {00028703 10976744},
 journal = {American Heart Journal},
 number = {1},
 title = {Myeloid-related protein 8/14 and the risk of cardiovascular death or myocardial infarction after an acute coronary syndrome in the Pravastatin or Atorvastatin Evaluation and Infection Theraphy: Thrombolysis in Myocardial Infarction (PROVE IT-TIMI 22) tria},
 volume = {155},
 year = {2008}
}

repository: jburos/another-biostats-blog

repo_name: jburos/another-biostats-blog


text:
@inproceedings{ECCV2018,
 abstract = {Demosaicking and denoising are among the most crucial steps of modern digital camera pipelines and their joint treatment is a highly ill-posed inverse problem where at-least two-thirds of the information are missing and the rest are corrupted by noise. This poses a great challenge in obtaining meaningful reconstructions and a special care for the efficient treatment of the problem is required. While there are several machine learning approaches that have been recently introduced to deal with joint image demosaicking-denoising, in this work we propose a novel deep learning architecture which is inspired by powerful classical image regularization methods and large-scale convex optimization techniques. Consequently, our derived network is more transparent and has a clear interpretation compared to alternative competitive deep learning approaches. Our extensive experiments demonstrate that our network outperforms any previous approaches on both noisy and noise-free data. This improvement in reconstruction quality is attributed to the principled way we design our network architecture, which also requires fewer trainable parameters than the current state-of-the-art deep network solution. Finally, we show that our network has the ability to generalize well even when it is trained on small datasets, while keeping the overall number of trainable parameters low.},
 author = {F. Kokkinos
and S. Lefkimmiatis},
 booktitle = {Computer Vision -- ECCV 2018},
 editor = {Ferrari, Vittorio
and Hebert, Martial
and Sminchisescu, Cristian
and Weiss, Yair},
 isbn = {978-3-030-01264-9},
 pages = {317--333},
 publisher = {Springer International Publishing},
 title = {Deep Image Demosaicking Using a Cascade of Convolutional Residual Denoising Networks},
 url = {https://arxiv.org/abs/1803.05215},
 year = {2018}
}

repository: slefkimmiatis/academic-kickstart

repo_name: slefkimmiatis/academic-kickstart


text:
.title {
    color: pink;
}

body {
    background-color: black;

}

p {
  color: pink;
}

.content {
    padding-left: 10%;
    padding-right: 10%;
    overflow-x:hidden;
}

.profilePic {
    border-radius: 50%;
    width: 220px;
    height: 250px;
    display: block;
  margin-left: auto;
  margin-right: auto;
}

.typing-container {
    display: flex;
    justify-content: center;
    align-items: center;
}
.input-cursor {
    display: inline-block;
    width: 2px;
    height: 42px;
    background-color: white;
    margin-left: 8px;
}

@keyframes blink {
    0% {opacity: 1;}
    40% {opacity: 1;}
    60% {opacity: 0;}
    100% {opacity: 0;}
  }

.input-cursor {
  animation: blink .6s linear infinite alternate;
}

.article {
    width: 100%;
    height: 100%;
}

.articlePhoto {
    width: 400px;
    height: 300px;
}
h3 {
  color: pink;
}

.glow {
    font-size: 60px;
    color: #fff;
    text-align: center;
    -webkit-animation: glow 1s ease-in-out infinite alternate;
    -moz-animation: glow 1s ease-in-out infinite alternate;
    animation: glow 1s ease-in-out infinite alternate;
  }
  @-webkit-keyframes glow {
    from {
      text-shadow: 0 0 10px #fff, 0 0 20px #fff, 0 0 30px #e60073, 0 0 40px #e60073, 0 0 50px #e60073, 0 0 60px #e60073, 0 0 70px #e60073;
    }
    to {
      text-shadow: 0 0 20px #fff, 0 0 30px #ff4da6, 0 0 40px #ff4da6, 0 0 50px #ff4da6, 0 0 60px #ff4da6, 0 0 70px #ff4da6, 0 0 80px #ff4da6;
    }
  }

  .fade-in {
    animation: fadeIn 5s;
    -webkit-animation: fadeIn 5s;
    -moz-animation: fadeIn 5s;
    -o-animation: fadeIn 5s;
    -ms-animation: fadeIn 5s;
  }
  @keyframes fadeIn {
    0% {opacity:0;}
    100% {opacity:1;}
  }

  @-moz-keyframes fadeIn {
    0% {opacity:0;}
    100% {opacity:1;}
  }

  @-webkit-keyframes fadeIn {
    0% {opacity:0;}
    100% {opacity:1;}
  }

  @-o-keyframes fadeIn {
    0% {opacity:0;}
    100% {opacity:1;}
  }

  @-ms-keyframes fadeIn {
    0% {opacity:0;}
    100% {opacity:1;}
  }

repository: ksalaheen/Website

repo_name: ksalaheen/Website