text:
url = "https://www.aclweb.org/anthology/P19-1645",
    doi = "10.18653/v1/P19-1645",
    pages = "6429--6441",
}
@inproceedings{shukla-etal-2019-ask,
    title = "What Should {I} Ask? Using Conversationally Informative Rewards for Goal-oriented Visual Dialog.",
    author = "Shukla, Pushkar  and
      Elmadjian, Carlos  and
      Sharan, Richika  and
      Kulkarni, Vivek  and
      Turk, Matthew  and
      Wang, William Yang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1646",
    doi = "10.18653/v1/P19-1646",
    pages = "6442--6451",
}
@inproceedings{chrupala-2019-symbolic,
    title = "Symbolic Inductive Bias for Visually Grounded Learning of Spoken Language",
    author = "Chrupa{\l}a, Grzegorz",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1647",
    doi = "10.18653/v1/P19-1647",
    pages = "6452--6462",
}
@inproceedings{gan-etal-2019-multi,
    title = "Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog",
    author = "Gan, Zhe  and
      Cheng, Yu  and
      Kholy, Ahmed  and
      Li, Linjie  and
      Liu, Jingjing  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1648",
    doi = "10.18653/v1/P19-1648",
    pages = "6463--6474",
}
@inproceedings{zhang-etal-2019-lattice,
    title = "Lattice Transformer for Speech Translation",
    author = "Zhang, Pei  and
      Ge, Niyu  and
      Chen, Boxing  and
      Fan, Kai",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1649",
    doi = "10.18653/v1/P19-1649",
    pages = "6475--6484",
}
@inproceedings{zhao-etal-2019-informative,
    title = "Informative Image Captioning with External Sources of Information",
    author = "Zhao, Sanqiang  and
      Sharma, Piyush  and
      Levinboim, Tomer  and
      Soricut, Radu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1650",
    doi = "10.18653/v1/P19-1650",
    pages = "6485--6494",
}
@inproceedings{kim-etal-2019-codraw,
    title = "{C}o{D}raw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication",
    author = "Kim, Jin-Hwa  and
      Kitaev, Nikita  and
      Chen, Xinlei  and
      Rohrbach, Marcus  and
      Zhang, Byoung-Tak  and
      Tian, Yuandong  and
      Batra, Dhruv  and
      Parikh, Devi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1651",
    doi = "10.18653/v1/P19-1651",
    pages = "6495--6513",
}
@inproceedings{fan-etal-2019-bridging,
    title = "Bridging by Word: Image Grounded Vocabulary Construction for Visual Captioning",
    author = "Fan, Zhihao  and
      Wei, Zhongyu  and
      Wang, Siyuan  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1652",
    doi = "10.18653/v1/P19-1652",
    pages = "6514--6524",
}
@inproceedings{ive-etal-2019-distilling,
    title = "Distilling Translations with Visual Awareness",
    author = "Ive, Julia  and
      Madhyastha, Pranava  and
      Specia, Lucia",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1653",
    doi = "10.18653/v1/P19-1653",
    pages = "6525--6538",
}
@inproceedings{madhyastha-etal-2019-vifidel,
    title = "{VIFIDEL}: Evaluating the Visual Fidelity of Image Descriptions",
    author = "Madhyastha, Pranava  and
      Wang, Josiah  and
      Specia, Lucia",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1654",
    doi = "10.18653/v1/P19-1654",
    pages = "6539--6550",
}
@inproceedings{hu-etal-2019-looking,
    title = "Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation",
    author = "Hu, Ronghang  and
      Fried, Daniel  and
      Rohrbach, Anna  and
      Klein, Dan  and
      Darrell, Trevor  and
      Saenko, Kate",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1655",
    doi = "10.18653/v1/P19-1655",
    pages = "6551--6557",
}
@inproceedings{tsai-etal-2019-multimodal,
    title = "Multimodal Transformer for Unaligned Multimodal Language Sequences",
    author = "Tsai, Yao-Hung Hubert  and
      Bai, Shaojie  and
      Liang, Paul Pu  and
      Kolter, J. Zico  and
      Morency, Louis-Philippe  and
      Salakhutdinov, Ruslan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1656",
    doi = "10.18653/v1/P19-1656",
    pages = "6558--6569",
}
@inproceedings{jing-etal-2019-show,
    title = "Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-ray Reports",
    author = "Jing, Baoyu  and
      Wang, Zeya  and
      Xing, Eric",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1657",
    doi = "10.18653/v1/P19-1657",
    pages = "6570--6580",
}
@inproceedings{hsu-etal-2019-visual,
    title = "Visual Story Post-Editing",
    author = "Hsu, Ting-Yao  and
      Huang, Chieh-Yang  and
      Hsu, Yen-Chia  and
      Huang, Ting-Hao",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1658",
    doi = "10.18653/v1/P19-1658",
    pages = "6581--6586",
}
@inproceedings{palaskar-etal-2019-multimodal,
    title = "Multimodal Abstractive Summarization for How2 Videos",
    author = "Palaskar, Shruti  and
      Libovick{\'y}, Jind{\v{r}}ich  and
      Gella, Spandana  and
      Metze, Florian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1659",
    doi = "10.18653/v1/P19-1659",
    pages = "6587--6596",
}
@inproceedings{garg-etal-2019-learning,
    title = "Learning to Relate from Captions and Bounding Boxes",
    author = "Garg, Sarthak  and
      Moniz, Joel Ruben Antony  and
      Aviral, Anshu  and
      Bollimpalli, Priyatham",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1660",
    doi = "10.18653/v1/P19-1660",
    pages = "6597--6603",
}

repository: emorynlp/nlprankings

repo_name: emorynlp/nlprankings


text:
#afui .button {
	position:relative;
	display:inline-block;
	padding:3% 4%;
	margin:8px 0;
	font-weight:bold;
	color:#333333;
	text-align:center;
	vertical-align:top;
	cursor:pointer;
	background-color:#eee;
	border:1px solid white;
	text-decoration: none;
	z-index:2;
}

/* Active */
#afui .button.pressed { background:#fff; }

#afui .button.previous {

	margin-left:16px;
	padding-left:6px;
	border-color:white transparent transparent !important;
}
#afui .button.next {
	border-color:white transparent #666 #666 !important;
	margin-right:16px;
	padding-right:6px;
}

#afui .button.previous::after {

    z-index:-1;
	content:'';
	position:absolute;
	width:25px; height:25px;
	background-color:inherit;
  	top:3px; left:-11px;
    border-radius:5px;
    border:1px solid;
	border-color:transparent transparent inherit transparent;
     -webkit-transform:rotate(45deg); transform:rotate(45deg);
}
#afui .button.next::after {
	z-index:-1;
	content:'';
	position:absolute;
	width:25px; height:25px;
	background-color:inherit;
  	top:3px; right:-11px;
    border-radius:5px;
    border:1px solid;
    border-color:transparent transparent transparent transparent;
   -webkit-transform:rotate(45deg); transform:rotate(45deg);
}

#afui .button.block {	display:block; }

#afui .button.flat {
	border-radius: 0;
	box-shadow:none;
}

#afui .header .button-grouped>.button {
	margin:0;
    border-color:#fff;
}

#afui .button-grouped {
    display:inline-block;
    margin: 5px;
}
#afui .button-grouped * {
	border-radius:0px;
	float:left;
	border-left:0px solid #E6E6E6;
	border-right: 1px solid #E6E6E6;
	border-bottom: 1px solid #E6E6E6;
	border-top: 1px solid #E6E6E6;
    margin:0;
}
#afui .button-grouped > .button:first-child {
	border-left: 1px solid #E6E6E6;
	border-top-left-radius: 6px;
	border-bottom-left-radius: 6px;
}
#afui .button-grouped > .button:last-child {
	border-top-right-radius: 6px;
	border-bottom-right-radius: 6px;
}

#afui .button-grouped.flex {
	display: -webkit-box;
  	display: -moz-box;
  	display: -ms-flexbox;
  	display: -webkit-flex;
  	display: flex;
}

#afui .button-grouped.flex > .button {
	-webkit-box-flex: 1;
  	-moz-box-flex: 1 auto;
  	-webkit-flex: 1 auto;
  	-ms-flex: 1 auto;
  	flex: 1 auto;

	white-space: nowrap;
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#afui .button-grouped.flex.vertical {
	display: inline-block;
}

#afui .button-grouped.vertical * {
	border-radius:0px;
	display:block;
	float:none;
	width:100%;
	border-left: 1px solid #666;
	border-right: 1px solid #666;
	border-top: 1px solid #666;
    border-bottom: 0px solid #666;
}

#afui .button-grouped.vertical > .button:first-child {
	border-top-left-radius: 6px;
	border-top-right-radius: 6px;
	border-bottom-left-radius:0px;
    border-bottom-right-radius:0px;
}
#afui .button-grouped.vertical > .button:last-child {
	border-top-right-radius:0px;
    border-top-left-radius:0px;
	border-bottom-right-radius: 6px;
	border-bottom-left-radius: 6px;
	border-bottom:1px solid #666;
}

#afui .button.gray {
	background:#D8D8D8;
	border-color:white;
}

#afui .button.yellow {
	background-color:#F1C222;
	border-color:#999;
}

#afui .button.red {
	color:#fff;
	text-shadow:0 -1px 0 #666;
	background:#FF0000;
	border-color:#E6E6E6;
	opacity: .7;
}

#afui .button.green {
	color:#fff;
	text-shadow:0 -1px 0 #666;
	background:#086A87;
	opacity: .75;
	border-color:#E6E6E6;
}

#afui .button.orange {
	color:#fff;
	text-shadow:0 -1px 0 #666;
	background-color:#FF8000;
	border-color:#E6E6E6;
	opacity: .8;
}

#afui .button.black {
	color:#fff;
	text-shadow:none;
	background:#000;
	border-color:#666;
}

#afui .button.slate {
	color:#fff;
	text-shadow:0 -1px 0 #000;
	background:#171F28;
	border-color:#666;
}

#afui .header .button {
    color: #fff;
    background:none;
    border-color: transparent;
 	font-size:20px;
	padding:7px;
 	height:32px;
	margin:5px;
    text-overflow: ellipsis;
    white-space: nowrap;
}

.header .button.icon:before{
    padding-left:6px;
}

#afui #backButton {
    text-overflow: ellipsis;
    white-space: nowrap;
}

#afui .button.recipe_btn.recipe_in_deck_btn{
	height: 7%;
	color: gray;
	width: 80%;
	text-align: center;
	padding: 8px 0px;
	margin: 5% 10%;
	border: none;
	background-color: white;
}

#afui .button.recipe_btn.recipe_add_btn{
	color: #fff;
	background: hsl(204.1,35%,53.1%);
	height: 7%;
	width: 80%;
	text-align: center;
	text-shadow: -1px -1px gray;
	padding: 8px 0px;
	margin: 5% 10%;
	border: none;
}

#afui .button.recipe_btn.recipe_remove_btn{
	float: right;
	width: 55%;
	text-align: center;
	height: 7%;
	margin-top: 5%;
	margin-bottom: 0;
	margin-right: 3%;
	border: none;
	border-radius: 0;
	color: #fff;
	background: hsl(5.6,39%,65%);
}
/*for tablets*/
@media all and (device-width: 600px) and (orientation: portrait) {
	#afui .button.recipe_btn.recipe_add_btn{
		font-size: 0.7cm;
		padding: 12px 0px;
	}
	#afui .button.recipe_btn.recipe_in_deck_btn{
		font-size: 0.7cm;
		padding: 12px 0px;
	}
	#afui .button.recipe_btn.recipe_remove_btn{
		font-size: 0.7cm;
		width: 55%;
		margin-right: 1%;
	}
	#RecipeContentBtn {
		font-size: 0.7cm;
	}
	#DoneBtn {
		font-size: 30pt !important;
	}
}
/*for iPad*/
@media all and (min-device-width:768px) and (max-device-width: 768px) and (orientation: portrait) and (-webkit-device-pixel-ratio: 2) {
	#afui .button.recipe_btn.recipe_add_btn{
		font-size: 0.9cm;
		padding: 15px 0px;
	}
	#afui .button.recipe_btn.recipe_in_deck_btn{
		font-size: 0.9cm;
		padding: 15px 0px;
	}
	#afui .button.recipe_btn.recipe_remove_btn{
		font-size: 0.9cm;
		width: 50%;
		margin-right: 3%;
	}
	#RecipeContentBtn {
		font-size: 0.7cm;
	}
	#DoneBtn {
		font-size: 30pt !important;
	}
}
/*for big screen like note3*/
@media all and (min-device-width: 1080px) and (max-device-width: 1080px) and (orientation: portrait) and (-webkit-device-pixel-ratio: 2) {
	#afui .button.recipe_btn.recipe_add_btn{
		font-size: 0.6cm;
		padding: 10px 0px;
	}
	#afui .button.recipe_btn.recipe_in_deck_btn{
		font-size: 0.6cm;
		padding: 10px 0px;
	}
	#afui .button.recipe_btn.recipe_remove_btn{
		font-size: 0.5cm;
		width: 50%;
		margin-right: 3%;
	}
	#RecipeContentBtn {
		font-size: 0.7cm;
	}
	#DoneBtn {
		font-size: 30pt !important;
	}
}

license: MIT

license_type: permissive

branch_name: refs/heads/master

extension: css


text:
#canvas {
  width: 700px;
  height: 10000px;
  margin: 0 auto;
  position: absolute;
  margin-left: 40rem;
  margin-right: 25rem;
  background-image: url(images/background.jpg);
}

.bird {
  padding-left: 330px;
  padding-top: 300px;
  position: fixed;
  z-index: 4;
  animation: bird 3s infinite;
}

@keyframes bird {
  0%
  {
    transform: rotate(0deg);
      border-radius: 0%;
  }
  50%
  {
    transform: rotate(50deg);
      border-radius: 0%;
  }
  100%
  {
    transform: rotate(0deg);
      border-radius: 0%;
  }
}

#cloud1 {
  position: relative;
  background-image: url(images/cloud.png);
  z-index: 5;
}
#cloud2 {
  position: relative;
  background-image: url(images/cloud.png);
  z-index: 5;
}
#cloud3 {
  position: relative;
  background-image: url(images/cloud.png);
  z-index: 5;
}
#cloud4 {
  position: relative;
  background-image: url(images/cloud.png);
  z-index: 5;
}
#cloud5 {
  position: relative;
  background-image: url(images/cloud.png);
  z-index: 5;
}
#cloud6 {
  position: relative;
  background-image: url(images/cloud.png);
  z-index: 5;
}
#cloud7 {
  position: relative;
  background-image: url(images/cloud.png);
  z-index: 5;
}
#cloud8 {
  position: relative;
  background-image: url(images/cloud.png);
  z-index: 5;
}
#cloud9 {
  position: relative;
  background-image: url(images/cloud.png);
  z-index: 5;
}
#cloud10 {
  position: relative;
  background-image: url(images/cloud.png);
  z-index: 5;
}
#cloud11 {
  position: relative;
  background-image: url(images/cloud.png);
  z-index: 5;
}


text:
@Book{ISO:CSpecialFunctions,
  author =       "{ISO}",
  title =        "{ISO\slash IEC 29124:2010}: Information technology ---
                 Programming languages, their environments and system
                 software interfaces --- Extensions to the {C++ Library}
                 to support mathematical special functions",
  year =         "2010",
  bibdate =      "Thu Nov 25 08:56:44 2010",
  bibsource =    "http://www.iso.org/iso/search.htm;
                 http://www.math.utah.edu/pub/tex/bib/elefunt.bib;
                 http://www.math.utah.edu/pub/tex/bib/isostd.bib",
  series =       "Technical report",
  URL =          "http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=50511",
  subject =      "programming languages (electronic computers)",
  urldate = {2021-09-23}
}

@Book{ISO:MathML,
  author =       "{ISO}",
  title =        "{ISO\slash IEC 40314:2016}: Mathematical Markup Language (MathML) Version 3.0 2nd Edition",
  year =         "2016",
  series =       "Technical report",
  URL =          "https://www.iso.org/standard/58439.html",
  pages =      	 "403",
  urldate = {2021-09-23}
}

@Book{ISO:FloatingPoint,
  author =       "{ISO}",
  title =        "{ISO\slash IEC 60559:2020}: Information technology --- Microprocessor Systems --- Floating-Point arithmetic",
  year =         "2020",
  series =       "Technical report",
  URL =          "https://www.iso.org/standard/80985.html",
  pages =        "74",
  urldate = {2021-09-23}
}

branch_name: refs/heads/main

extension: bib


text:
ul.sidebar-menu {
  font-size: 1.5em;
}

.box-header {
  padding: 0 10px 0 0;
}
.box-header h3 {
  width: 100%;
  padding: 10px;
}

#say_hi {
  background-color: rgb(221, 75, 57);
  color: white;
  font-weight: bold;
}

.img-btn {
  background-color: transparent;
  border: 5px solid transparent;
  padding: 0;
}

.img-btn.selected[id$="_r"] { border-color: red; }
.img-btn.selected[id$="_o"] { border-color: orange; }
.img-btn.selected[id$="_y"] { border-color: yellow; }
.img-btn.selected[id$="_g"] { border-color: green; }
.img-btn.selected[id$="_b"] { border-color: blue; }
.img-btn.selected[id$="_p"] { border-color: purple; }

.shiny-flow-layout > div { width: 310px; }

repository: PsyTeachR/shiny-tutorials

repo_name: PsyTeachR/shiny-tutorials

gha_license_id: MIT

gha_language: R


text:
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

@inproceedings{xie2017aggregated,
  title={Aggregated residual transformations for deep neural networks},
  author={Xie, Saining and Girshick, Ross and Doll{\'a}r, Piotr and Tu, Zhuowen and He, Kaiming},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1492--1500},
  year={2017}
}

@inproceedings{smith2017cyclical,
  title={Cyclical learning rates for training neural networks},
  author={Smith, Leslie N},
  booktitle={2017 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  pages={464--472},
  year={2017},
  organization={IEEE}
}


text:
john tower|defense secretary-designate|mod
contradicted|john tower|subj
sworn|earlier|mod
statement|sworn|mod
contradicted|statement|obj
contradicted|when|wha
told|he|subj
told|senate|obj
work|his|gen
firm|british|mod
work|firm|for
told|involved|fc
involved|work|subj
matters|military|mod
involved|matters|obj
involved|reports|according to
reports|published|vrel
published|reports|obj
published|02/11/1989|on
put|senate armed services committee|subj
put|nomination|obj
nomination|tower|of
senator|former|mod
senator|texas|nn
tower|senator|conj
senator|and|punc
chairman|onetime|mod
senator|chairman|conj
chairman|committee|of
put|hold|on
put|while|mod
while|takes|comp1
takes|it|subj
look|second|post
takes|look|obj
habits|tower|gen
habits|personal|mod
look|habits|at
takes|including|mod
including|it|subj
use|his|gen
including|use|obj
use|alcohol|of
alcohol|and|punc
links|his|gen
alcohol|links|conj
contractors|defense|nn
links|contractors|to
vote|senate|gen
vote|53-47|nn
came|vote|subj
came|after|mod
bitter|and|punc
debate|divisive|mod
bitter|debate|conj
after|focused on|comp1
focused on|bitter|subj
habits|tower|gen
habits|drinking|nn
focused on|habits|obj
habits|behavior|conj
behavior|women|toward
women|and|punc
dealings|his|gen
dealings|business|mod
women|dealings|conj
contractors|defense|nn
dealings|contractors|with

gha_language: Jupyter Notebook

extension: be


text:
body {

      background-image: url(../images/background-min.jpg);
      background-repeat: repeat;
      background-size: 100%;
      background-attachment: fixed;
      background-blend-mode: color-dodge;

  }

  @keyframes slideInFromTop {
    0% {
        transform: translateY(-100%);
    }

    100% {
    transform: translateY(0);
    }
  }

  .heading-container {
      text-align: center;
      margin: 100px;
      font-family: -apple-system, BlinkMacSystemFont, Segoe UI, 'Roboto', Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
      font-weight: bolder;
      font-size: 300%;

      animation: 1.5s ease-out 0s 1 slideInFromTop;
  }

  .main-block {
      color: white;
      font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif
  }

  a {
    color: white;
    display: block;
    width: 100%;
    height: 100%;
    vertical-align: middle;
    text-align: center;
  }

  a:link {
    text-decoration: none;
  }

  .squares-container-row1, .squares-container-row2, .squares-container-row3 {

    display: flex;
    align-items: center;
    justify-content: center;
    margin-top: 25px;
    text-align: center;
  }

  .purple-square, .blue-square, .green-square{
    width: 300px;
    height: 300px;
    margin-left: 25px;
    background-size: 180%;
    background-position: center;
    background-repeat: no-repeat;
    text-align: center;
    vertical-align: middle;
    line-height: 290px;
    font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
    font-size: 200%;
    font-weight: bold;
  }

  .yellow-square, .red-square, .black-square{
      width: 300px;
      height: 300px;
      margin-left: 25px;
      margin-right: 25px;
      background-size: 180%;
      background-position: center;
      background-repeat: no-repeat;
      text-align: center;
      vertical-align: middle;
      line-height: 290px;
      font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
      font-size: 200%;
      font-weight: bold;
  }

  .red-square:hover, .blue-square:hover, .green-square:hover, .purple-square:hover, .yellow-square:hover{
    transition: .25s ease-in;
    transform: scale(1.1);
    font-size: 220%;
    text-shadow: 2px 2px 8px rgba(0, 0, 0, 0.42);
  }

  .black-square:hover{
    transition: .25s ease-in;
    transform: scale(1.1);
    font-size: 220%;
    text-shadow: 2px 2px 8px rgba(255, 255, 255, 0.42);
  }

    .purple-square {
    background-color: #B10DC9;
    background-image: url(../images/french_press-min.jpg);
    animation: 1.5s ease-out 0s 1 slideInFromTop;
    }

    .yellow-square {
      background-color: yellow;
      background-image: url(../images/chemex-min.jpg);
      animation: 1.4s ease-out 0s 1 slideInFromTop;
    }

    .green-square {
    background-color: green;
    background-image: url(../images/moka-min.jpg);
    animation: 1.3s ease-out 0s 1 slideInFromTop;
    }

    .red-square {
    background-color: red;
    background-image: url(../images/v60-min.jpg);
    animation: 1.2s ease-out 0s 1 slideInFromTop;
    }

    .blue-square {
     background-color: blue;
     background-image: url(../images/aeropress-min.jpg);
     animation: 1.1s ease-out 0s 1 slideInFromTop;
    }

    .black-square {
      background-color: black;
      background-image: url(../images/syphon-min.jpg);
      animation: 1s ease-out 0s 1 slideInFromTop;
    }

    .credits {
      margin-top: 50px;
      color: white;
      padding: 20px 0;
      text-align: center;
      background-color: rgb(66, 66, 66);
      font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
      display: flex;
      justify-content: center;
    }

    .onas{
      text-decoration: underline;
    }

    @media screen and (max-width: 700px) {

      body{
      justify-content: center;
      position: static;
      margin-left: auto;
      margin-right: auto;
      }

      .heading-container{
        font-size: 200%;
        margin: 1px;
        padding-top: 30px;
        padding-bottom: 30px;
      }

      .purple-square {
        margin: 50px;
        width: auto;
        height: auto;
        background-size: cover;
      }

      .black-square{
        margin: 50px;
        width: auto;
        height: auto;
        background-size: cover;
      }

      .blue-square{
        margin: 50px;
        width: auto;
        height: auto;
        background-size: cover;
      }

      .green-square{
        margin: 50px;
        width: auto;
        height: auto;
        background-size: cover;
      }

      .red-square{
        margin: 50px;
        width: auto;
        height: auto;
        background-size: cover;
      }
      .yellow-square{
        margin: 50px;
        width: auto;
        height: auto;
        background-size: cover;
      }

      .squares-container-row1, .squares-container-row2, .squares-container-row3 {

        display: block;
        margin-top: 0px;
        padding: 0px;
      }

  }


text:
@InProceedings{gregor14,
  supplementary = {Supplementary:gregor14-supp.pdf},
  title = {Deep AutoRegressive Networks},
  author = {Karol Gregor and Ivo Danihelka and Andriy Mnih and Charles Blundell and Daan Wierstra},
  pages = {1242-1250},
  abstract = {We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data.  Successive deep stochastic hidden layers are equipped with autoregressive connections, which enable the model to be sampled from quickly and exactly via ancestral sampling.  We derive an efficient approximate parameter estimation method based on the minimum  description length (MDL) principle,  which can be seen as maximising a variational lower bound on the log-likelihood, with a feedforward neural network implementing approximate inference.   We demonstrate state-of-the-art generative performance on a number of classic data sets: several UCI data sets, MNIST and Atari 2600 games.},
  section = {cycle-2},
}

repository: mreid/papersite

repo_name: mreid/papersite

gha_language: TeX


text:
include summit-supplicant.inc

OPENSSLVER = "_openssl_1_0_2"

DEPENDS += "openssl"

RDEPENDS_${PN} += "libssl (< 1.1.0) libcrypto (< 1.1.0)"

extension: bb


text:
# FindVLE_DEPS.cmake
# =============

# Try to find VLE dependencies
#
# Copyright 2012 INRA
# Gauthier Quesnel <quesnel@users.sourceforge.net>
#
# Distributed under the OS-approved BSD License (the "License");
# This software is distributed WITHOUT ANY WARRANTY; without even the
# implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the License for more information.
#
# Once done this will define
#
#  VLE_DEPS_FOUND - system has VLE
#  VLE_DEPS_INCLUDE_DIRS - the VLE include directory
#  VLE_DEPS_LIBRARY_DIRS - Directories containing libraries to link
#  VLE_DEPS_LIBRARIES - Link these to use shared libraries of VLE
#
# Options:
#
#  FIND_VLE_USING_CMAKE - If true, on windows, use cmake for finding GVLE,
#                         else use pkgconfig
#

#=============================================================================
#
# Changelog
# ---------
#
# 1.0 Initial version.
#
# Usage
# -----
#
# find_package(VLE_DEPS REQUIRED)
#
#=============================================================================

#
# Set default behavior of find vle
#

if (WIN32)
  if (DEFINED FIND_VLE_USING_CMAKE)
     set (_find_vle_using_cmake ${FIND_VLE_USING_CMAKE})
  else (DEFINED FIND_VLE_USING_CMAKE)
     set (_find_vle_using_cmake 1)
  endif (DEFINED FIND_VLE_USING_CMAKE)
else (WIN32)
  set (_find_vle_using_cmake 0)
endif (WIN32)

#
# Find VLE_DEPS
#

if (${_find_vle_using_cmake})

  find_path(_base1 zlib.h PATHS
    $ENV{VLE_BASEPATH}/include
    "[HKEY_LOCAL_MACHINE\\SOFTWARE\\VLE Development Team\\VLE 1.1.0;Path]/include")
  find_path(_base2 libz.a PATHS
    $ENV{VLE_BASEPATH}/lib
    "[HKEY_LOCAL_MACHINE\\SOFTWARE\\VLE Development Team\\VLE 1.1.0;Path]/lib")

  if(NOT _base1 OR NOT _base2)
     message (" debug _base1 ${_base1}")
     message (" debug _base2 ${_base2}")
     message (FATAL_ERROR "Missing vle dependencies")
  endif ()

  set (VLE_DEPS_INCLUDE_DIRS ${_base1};${_base1}/libxml2;
       ${_base1}/glibmm-2.4;${_base2}/glibmm-2.4/include;
       ${_base1}/sigc++-2.0;${_base2}/sigc++-2.0/include;
       ${_base1}/glib-2.0;${_base2}/glib-2.0/include)
  set (VLE_DEPS_LIBRARY_DIRS "")
  set (VLE_DEPS_LIBRARIES ${_base2}/libxml2.a;${_base2}/libglibmm-2.4.a;
       ${_base2}/libgobject-2.0.a;${_base2}/libsigc-2.0.a;
       ${_base2}/libgthread-2.0.a;${_base2}/libglib-2.0.a;
       ${_base2}/libintl.a;${_base2}/libiconv.a;${_base2}/libpcre.a;
       ${_base2}/libws2_32.a;${_base2}/liblzma.a)

else (${_find_vle_using_cmake})

  set (VLE_DEPS_INCLUDE_DIRS "")
  set (VLE_DEPS_LIBRARY_DIRS "")
  set (VLE_DEPS_LIBRARIES "")
  set (VLE_DEPS_FOUND TRUE)
  mark_as_advanced(VLE_DEPS_INCLUDE_DIRS VLE_DEPS_LIBRARIES
    VLE_DEPS_LIBRARY_DIRS)

endif (${_find_vle_using_cmake})

mark_as_advanced(VLE_DEPS_INCLUDE_DIRS VLE_DEPS_LIBRARY_DIRS
               VLE_DEPS_LIBRARIES)

extension: cmake


text:
require ntp.inc
DEPENDS = "openssl"

S = "${WORKDIR}/ntp-${PV}"

EXTRA_OECONF += "--with-openssl-libdir=${STAGING_LIBDIR} \
		 --with-openssl-incdir=${STAGING_INCDIR}/openssl \
		 --with-crypto"

FILES_${PN} += "${bindir}/sntp ${bindir}/ntp-keygen"

repository: rossburton/meta-oe

repo_name: rossburton/meta-oe


text:
FILE(REMOVE_RECURSE
  "CMakeFiles/msv-dataTestSuite.dir/msv-dataTestSuite.cpp.o"
  "msv-dataTestSuite.cpp"
  "msv-dataTestSuite.pdb"
  "msv-dataTestSuite"
)

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


text:
@article{Eeke21,
 abstract = {Cellularity estimation forms an important aspect of the visual examination of bone marrow biopsies. In clinical practice, cellularity is estimated by eye under a microscope, which is rapid, but subjective and subject to inter- and intraobserver variability. In addition, there is little consensus in the literature on the normal variation of cellularity with age. Digital image analysis may be used for more objective quantification of cellularity. As such, we developed a deep neural network for the segmentation of six major cell and tissue types in digitized bone marrow trephine biopsies. Using this segmentation, we calculated the overall bone marrow cellularity in a series of biopsies from 130 patients across a wide age range. Using intraclass correlation coefficients (ICC), we measured the agreement between the quantification by the neural network and visual estimation by two pathologists and compared it to baseline human performance. We also examined the age-related changes of cellularity and cell lineages in bone marrow and compared our results to those found in the literature. The network was capable of accurate segmentation (average accuracy and dice score of 0.95 and 0.76, respectively). There was good neural network-pathologist agreement on cellularity measurements (ICC=0.78, 95% CI 0.58-0.85). We found a statistically significant downward trend for cellularity, myelopoiesis and megakaryocytes with age in our cohort. The mean cellularity began at approximately 50% in the third decade of life and then decreased ±2% per decade to 40% in the seventh and eighth decade, but the normal range was very wide (30-70%).},
 author = {van Eekelen, Leander and Pinckaers, Hans and van den Brand, Michiel and Hebeda, Konnie M. and Litjens, Geert},
 citation-subset = {IM},
 country = {England},
 doi = {10.1016/j.pathol.2021.07.011},
 file = {:pdf/Eeke21.pdf:PDF},
 issn = {1465-3931},
 issn-linking = {0031-3025},
 journal = {Pathology},
 keywords = {Bone marrow; cellularity; deep learning; digital pathology; segmentation},
 month = {November},
 nlm-id = {0175411},
 optnote = {DIAG, PATHOLOGY},
 owner = {NLM},
 pii = {S0031-3025(21)00476-1},
 pmid = {34772487},
 pubmodel = {Print-Electronic},
 pubstate = {aheadofprint},
 revised = {2021-11-13},
 title = {Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation.},
 year = {2021}
}


text:
@article{meng2016mllib,
  title={Mllib: Machine learning in apache spark},
  author={Meng, Xiangrui and Bradley, Joseph and Yavuz, Burak and Sparks, Evan and Venkataraman, Shivaram and Liu, Davies and Freeman, Jeremy and Tsai, DB and Amde, Manish and Owen, Sean and others},
  journal={Journal of Machine Learning Research},
  volume={17},
  number={34},
  pages={1--7},
  year={2016}
}

@misc{www-keystoneml1,
  title = {{KeystoneML}},
  url = {http://keystone-ml.org/programming_guide.html},
  urldate = {2017-03-29},
  file = {KeystoneML:files/47/programming_guide.html:text/html}
}

@misc{www-keystoneml,
  title = {{KeystoneML}},
  url = {http://keystone-ml.org/},
  urldate = {2017-03-29},
  file = {KeystoneML:files/49/keystone-ml.org.html:text/html}
}


text:
/*背景*/
body
{
	background-color:#ffffff;	/*背景色*/
	color:#000000;				/*文字の色*/
}

/*未訪問のリンク*/
a:link
{
	color:#0000ff;
}

/*訪問済みのリンク*/
a:visited
{
	color:#808080;
}

/*ポイント時のリンク*/
a:hover
{
	color:#ff0000;
}

/*何個目？のラジオボタン*/
label.radio
{
	font-size:24px;
}

/*選択リストのボックス*/
select.list
{
	font-size:20px;
	width:180px;
}

/*選択リスト(連動時)のボックス*/
select.list_linkage
{
	font-size:20px;
	width:50px;
}

/*選択リストの送信ボタン*/
input.list_button
{
	font-size:20px;
}


text:
@inproceedings{Bredin2020,
 address = {Barcelona, Spain},
 author = {Bredin, Hervé and Yin, Ruiqing and Coria, Juan Manuel and Gelly, Gregory and Korshunov, Pavel and Lavechin, Marvin and Fustes, Diego and Titeux, Hadrien and Bouaziz, Wassim and Gill, Marie-Philippe},
 booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
 month = {May},
 title = {pyannote.audio: neural building blocks for speaker diarization},
 year = {2020}
}


text:
SET(qlgen_shared_srcdir ${CMAKE_CURRENT_LIST_DIR}/src)
SET(shared_srcfiles
  qlgen-client.mm
  qlgen.mm
  system-qlgen.mm
  gen-thumbnail.m
  gen-preview.m
  utils.mm
  NSDictionary+SFJSON.m
)
FOREACH(srcfile ${shared_srcfiles})
    SET(qlgen_shared_sources ${qlgen_shared_sources} ${qlgen_shared_srcdir}/${srcfile})
ENDFOREACH()

## Build with warnings
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fobjc-arc -Wall -Wextra -Wsign-compare -Wno-long-long -Wno-unused-parameter")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fobjc-arc -Wall -Wextra -Wsign-compare -Wno-long-long -Wno-unused-parameter -Woverloaded-virtual")

## Find the dependencies
FIND_LIBRARY(AppKit_LIBRARY AppKit)
FIND_LIBRARY(WebKit_LIBRARY WebKit)
FIND_LIBRARY(QuickLook_LIBRARY QuickLook)
FIND_LIBRARY(ApplicationServices_LIBRARY ApplicationServices)

INCLUDE_DIRECTORIES(
  ${CMAKE_CURRENT_SOURCE_DIR}
  ${qlgen_shared_srcdir}
)

repository: shoeper/seadrive-gui

repo_name: shoeper/seadrive-gui


text:
require hostap-daemon.inc

SRC_URI_append_nylon = "\
	file://hostap-no-bsd.patch"

PR = "r2"

SRC_URI[md5sum] = "79810ff32f6f12a37dc7827ce18af533"
SRC_URI[sha256sum] = "684636fd31ad2cbe989a19f2ce844b7d1c46c1e009cda37c92f5d9ead9ba18b8"


text:
@techreport{___2012,
 abstract = {画像や映像には人物の顔などのプライバシーセンシティブな情報(Privacy Sensitive Information: PSI)が含まれる可能性があるため，画像や映像を公開する際には，PSIを含む領域に対して，ぼかしや塗りつぶしなどの画像処理により視覚的抽象化を施すことでプライバシー侵害のリスクを低減する．しかし，これらの画像処理がどの程度プライバシー保護に有効であるかは明らかにされていない．そこで本報告では，プライバシー保護の有効性を定量的に評価する指標としてID可到達性を提案し，アンケート調査によりさまざまな画像処理のプライバシー保護に対する有効性を明らかにする．さらに，ぼかしなどについては，一定のID可到達性を達成するために必要なパラメータの導出を可能にする．},
 author = {中島 悠太 and 池野 知顕 and 馬場口 登},
 file = {顔画像に対するプライバシー保護処理の有効性の定量的評価 Snapshot:/Users/n-yuta/Zotero/storage/8JSWXZFS/110009626162.html:text/html},
 keywords = {プライバシー保護, ID可到達性, 画像処理, 視覚的抽象化, 定量的評価},
 month = {July},
 number = {電子情報通信学会 技術研究報告, EMM2012-9},
 pages = {59--66},
 title = {顔画像に対するプライバシー保護処理の有効性の定量的評価},
 url = {https://ci.nii.ac.jp/naid/110009626162/},
 urldate = {2019-04-29},
 year = {2012}
}


text:
html, body {
	height: 100%;
	background: url(../images/megafind_bg.png);
	background-size: cover;
	background-repeat: no-repeat;
	background-position: center center;
	background-attachment: fixed;
}

#title {
	height: 33%;
	vertical-align: center;

}


text:
set(CPACK_PACKAGE_CONTACT "cppinclude@yandex.com")
set(CPACK_PACKAGE_DESCRIPTION "")
set(CPACK_PACKAGE_VENDOR "cppinclude")

set(CPACK_DEBIAN_PACKAGE_NAME "${PROJECT_NAME}-${CMAKE_HOST_SYSTEM_PROCESSOR}-${CMAKE_PROJECT_VERSION}")
set(CPACK_DEBIAN_PACKAGE_MAINTAINER ${CPACK_PACKAGE_CONTACT} )
set(CPACK_DEBIAN_PACKAGE_SHLIBDEPS ON)

if(NOT DEFINED CMAKE_INSTALL_PREFIX AND NOT WIN32)
	set(CMAKE_INSTALL_PREFIX "/usr/bin/")
endif()

install(TARGETS ${PROJECT_NAME} RUNTIME DESTINATION bin)


text:
@import 'part:@sanity/base/theme/variables-style';

.root {
  display: flex;
  align-items: center;
  justify-content: center;
  text-align: center;
  height: 100%;
}

.iconContainer {
  font-size: 2rem;
}

.strong {
  font-weight: 600;
  font-size: var(--font-size-large);
  margin: 0;
  padding: 0;
}

.light {
  opacity: 0.7;
  margin: 0;
  padding: 0;
}

repository: ws-tools/sanity

repo_name: ws-tools/sanity


text:
%% This BibTeX bibliography file was created using BibDesk.
%% https://bibdesk.sourceforge.io/

%% Created for Peter Szolovits at 2022-05-15 15:24:41 -0400

%% Saved with string encoding Unicode (UTF-8)

@article{Long2001,
	author = {Long, William J},
	date-added = {2022-05-15 15:22:02 -0400},
	date-modified = {2022-05-15 15:23:17 -0400},
	journal = {Artificial Intelligence in Medicine},
	pages = {71-87},
	title = {Medical informatics: reasoning methods},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/aim01/AIM2001.pdf},
	volume = {23},
	year = {2001}}

@inproceedings{Fraser2000,
	author = {Fraser, Hamish S F and Naimi, Shapur and Long, William J},
	booktitle = {Proc AMIA Symp},
	date-added = {2022-05-15 15:19:04 -0400},
	date-modified = {2022-05-15 15:20:42 -0400},
	pages = {255-259},
	title = {New Approaches to Measuring the Performance of Programs that Generate Differential Diagnoses using {ROC} Curves and Other Metrics},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/amia00/D200696.PDF},
	year = {2000}}

@inproceedings{Long2020,
	author = {Long, William J and Fraser, Hamish and Naimi, Shapur and Perry, K and Cotter, L and Konstam, M A},
	booktitle = {Proc AMIA Symp},
	date-added = {2022-05-15 15:17:07 -0400},
	date-modified = {2022-05-15 15:21:30 -0400},
	pages = {1070},
	title = {Developing a Program for Tracking Heart Failure},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/amia00/D200260.PDF},
	year = {2000}}

@inproceedings{Fraser1999a,
	author = {Fraser, Hamish S F and Long, William J and Naimi, Shapur},
	booktitle = {Proc AMIA Symp},
	date-added = {2022-05-15 15:16:00 -0400},
	date-modified = {2022-05-15 15:16:46 -0400},
	title = {Evaluating A Decision Support System Using the Web},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/amia99/D005709.PDF},
	year = {1999}}

@inproceedings{Fraser1999,
	author = {Fraser, Hamish S F and Long, William J},
	booktitle = {Proc AMIA Symp},
	date-added = {2022-05-15 15:14:43 -0400},
	date-modified = {2022-05-15 15:21:07 -0400},
	title = {{CompareDx}: a Software Toolkit for Measuring the Performance of Programs that Generate Multiple Diagnoses},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/amia99/D005615.PDF},
	year = {1999}}

@inproceedings{Long1999,
	author = {Long, William J and Fraser, Hamish and Naimi, Shapur},
	booktitle = {Proc AMIA Symp},
	date-added = {2022-05-15 15:13:03 -0400},
	date-modified = {2022-05-15 15:14:12 -0400},
	title = {The Heart Disease Program: Assisting Diagnosis},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/amia99/D005302.PDF},
	year = {1999}}

@inproceedings{Fraser1998,
	author = {Fraser, Hamish S F and Long, William J and Naimi, Shapur},
	booktitle = {Proc AMIA Symp},
	date-added = {2022-05-15 15:10:45 -0400},
	date-modified = {2022-05-15 15:11:59 -0400},
	title = {Differential Diagnoses of the Heart Disease Program have better Sensitivity than Resident Physicians},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/amia98/D004840.PDF},
	year = {1998}}

@inproceedings{Long1998,
	author = {Long, William J and Fraser, Hamish and Naimi, Shapur},
	booktitle = {Proc AMIA Symp},
	date-added = {2022-05-15 15:08:24 -0400},
	date-modified = {2022-05-15 15:09:27 -0400},
	title = {Interfacing the Heart Disease Program to Coded Data},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/amia98/D004971.PDF},
	year = {1998}}

@inproceedings{Long1997a,
	author = {Long, William J and Fraser, Hamish and Naimi, Shapur and Stahl, James},
	booktitle = {Proc AMIA Symp},
	date-added = {2022-05-15 15:06:30 -0400},
	date-modified = {2022-05-15 15:07:48 -0400},
	title = {Collecting and Interpreting Temporal Data in an Expert System},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/amia97/D004205.PDF},
	year = {1997}}

@inproceedings{Fraser1997,
	author = {Fraser, Hamish S F and Long, William J and Naimi, Shapur},
	booktitle = {Proc AMIA Symp},
	date-added = {2022-05-15 15:04:43 -0400},
	date-modified = {2022-05-15 15:05:57 -0400},
	title = {Comparing Complex Diagnoses: A Formative Evaluation of the Heart Disease Program},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/amia97/D004203.PDF},
	year = {1997}}

@article{Long1997,
	author = {Long, William J and Fraser, Hamish and Naimi, Shapur},
	date-added = {2022-05-15 15:01:29 -0400},
	date-modified = {2022-05-15 15:03:28 -0400},
	journal = {Artificial Intelligence in Medicine},
	pages = {5-24},
	title = {Reasoning Requirements for Diagnosis of Heart Disease},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/aim97/aimj-96.pdf},
	volume = {10},
	year = {1997}}

@inproceedings{Long1996a,
	author = {Long, William J},
	booktitle = {Proc AMIA Annu Fall Symposium},
	date-added = {2022-05-15 14:58:13 -0400},
	date-modified = {2022-05-15 15:00:14 -0400},
	title = {Web Interface for the Heart Disease Program},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2232996/},
	year = {1996}}

@article{Long1996,
	author = {Long, William J},
	date-added = {2022-05-15 14:56:34 -0400},
	date-modified = {2022-05-15 14:57:46 -0400},
	journal = {Artificial Intelligence in Medicine},
	pages = {193-215},
	title = {Temporal Reasoning for Diagnosis in a Causal Probabilistic Knowledge Base},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/aim96/aimj-95.pdf},
	volume = {8},
	year = {1996}}

@article{Long1994,
	author = {Long, William J and Naimi, Shapur and Criscitiello, Modestino G},
	date-added = {2022-05-15 14:54:38 -0400},
	date-modified = {2022-05-15 14:55:52 -0400},
	journal = {Journal of the American Medical Informatics Association},
	pages = {127-141},
	title = {Evaluation of a New Method for Cardiovascular Reasoning},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/jamia94/eval-paper.pdf},
	volume = {1},
	year = {1994}}

@article{Long1993,
	author = {Long, William J and Griffith, John L and Selker, Harry P and D'Agostino, Ralph B},
	date-added = {2022-05-15 14:51:53 -0400},
	date-modified = {2022-05-15 14:54:05 -0400},
	journal = {Computers in Biomedical Research},
	pages = {74-97},
	title = {A Comparison of Logistic Regression to Decision Tree Induction in a Medical Domain},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/cbr93/ml-paper.pdf},
	volume = {26},
	year = {1993}}

@incollection{Long1992a,
	author = {Long, William J and Naimi, Shapur and Criscitiello, Modestino G and Adusumilli, Ravi K},
	booktitle = {Deep Models for Medical Knowledge Engineering},
	date-added = {2022-05-15 14:48:05 -0400},
	date-modified = {2022-05-15 14:51:17 -0400},
	editor = {Keravnou, E},
	pages = {147-160},
	publisher = {Elsevier Sciences Publishers B.V.},
	title = {Validation of a causal probabilistic medical knowledge base for diagnostic reasoning},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/dmmke/validation.pdf},
	year = {1992}}

@article{Long1992,
	author = {Long, William J and Naimi, Shapur and Criscitiello, Modestino G},
	date-added = {2022-05-15 14:46:09 -0400},
	date-modified = {2022-05-15 14:47:32 -0400},
	journal = {Computers in Biomedical Research},
	pages = {292-311},
	title = {Development of a Knowledge Base for Diagnostic Reasoning in Cardiology},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/cbr92/kb-develop-paper.pdf},
	volume = {25},
	year = {1992}}

@inproceedings{Long1991a,
	author = {Long, William J},
	booktitle = {Proceedings of the Fifteenth Annual Symposium on Computer Applications in Medical Care},
	date-added = {2022-05-15 14:44:20 -0400},
	date-modified = {2022-05-15 14:45:31 -0400},
	pages = {619-623},
	title = {The Probability of Disease},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/scamc91/prob-disease-2c.pdf},
	year = {1991}}

@article{Long1991,
	author = {Long, William J},
	date-added = {2022-05-15 14:42:31 -0400},
	date-modified = {2022-05-15 14:43:58 -0400},
	journal = {Artificial Intelligence in Medicine},
	pages = {3-20},
	title = {Flexible Reasoning about Patient Management using Multiple Models},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/aim91/aim-jour-90.pdf},
	volume = {3},
	year = {1991}}

@article{Long1989,
	author = {Long, William},
	date-added = {2022-05-15 14:23:28 -0400},
	date-modified = {2022-05-15 14:26:47 -0400},
	doi = {https://doi.org/10.1080/08839518908949932},
	journal = {Applied Artificial Intelligence},
	pages = {367-383},
	title = {Medical Diagnosis Using a Probabilistic Causal Network},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/aai89/aaij88.pdf},
	volume = {3},
	year = {1989},
	bdsk-url-1 = {https://groups.csail.mit.edu/medg/ftp/wjl/aai89/aaij88.pdf},
	bdsk-url-2 = {https://doi.org/10.1080/08839518908949932}}

@article{Long1988a,
	author = {W J. Long, S. Naimi, M. G. Criscitiello S. Kurzrok},
	date-added = {2022-05-15 14:17:40 -0400},
	date-modified = {2022-05-15 14:23:06 -0400},
	journal = {Biomedical Measurement Informatics and Control},
	pages = {40-45},
	title = {Reasoning About Therapy from a Physiological Model},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/bmic88/LNCK86.pdf},
	volume = {2},
	year = {1988},
	bdsk-url-1 = {https://groups.csail.mit.edu/medg/ftp/wjl/bmic88/LNCK86.pdf}}

@inproceedings{Long1988,
	author = {Long, William J and Naimi, Shapur and Criscitiello, Modestino G and Larsen, G},
	booktitle = {Computers in Cardiology Conference},
	date-added = {2022-05-15 14:10:46 -0400},
	date-modified = {2022-05-15 14:15:43 -0400},
	doi = {10.1109/CIC.1988.72595},
	pages = {185-188},
	title = {Differential Diagnosis Generation from a Causal Network with Probabilities},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/cinc88/cinc88.html},
	year = {1988},
	bdsk-url-1 = {https://groups.csail.mit.edu/medg/ftp/wjl/cinc88/cinc88.html},
	bdsk-url-2 = {https://doi.org/10.1109/CIC.1988.72595}}

@inproceedings{Long1986,
	author = {Long, William J and Naimi, Shapur and Criscitiello, Modestino G and Jayes, Robert},
	booktitle = {1986 Computers in Cardiology Conference},
	date-added = {2022-05-15 13:56:08 -0400},
	date-modified = {2022-05-15 13:58:14 -0400},
	month = {oct},
	pages = {15-20},
	title = {Using a Physiological Model for Prediction of Therapy Effects in Heart Disease},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/cinc86/cinc86.html},
	year = {1986},
	bdsk-url-1 = {https://groups.csail.mit.edu/medg/ftp/wjl/cinc86/cinc86.html}}

@inproceedings{Long1983,
	author = {Long, William J},
	booktitle = {Proceedings of National Joint Conference on Artificial Intelligence (AAAI-83).},
	date-added = {2022-05-15 13:53:05 -0400},
	date-modified = {2022-05-15 13:55:35 -0400},
	title = {Reasoning About State from Causation and Time in a Medical Domain},
	url = {https://groups.csail.mit.edu/medg/ftp/wjl/aaai83.PDF},
	year = {1983},
	bdsk-url-1 = {https://groups.csail.mit.edu/medg/ftp/wjl/aaai83.PDF}}

@article{Dutta2013-wx,
	author = {Dutta, Sayon and Long, William J and Brown, David F M and Reisner, Andrew T},
	date-modified = {2022-05-15 13:37:01 -0400},
	journal = {Ann. Emerg. Med.},
	language = {en},
	month = aug,
	number = 2,
	pages = {162--169},
	pmid = {23548405},
	publisher = {Elsevier BV},
	title = {Automated detection using natural language processing of radiologists recommendations for additional imaging of incidental findings},
	url = {https://pubmed.ncbi.nlm.nih.gov/23548405/},
	volume = 62,
	year = 2013,
	abstract = {STUDY OBJECTIVE: As use of radiology studies increases, there is
               a concurrent increase in incidental findings (eg, lung nodules)
               for which the radiologist issues recommendations for additional
               imaging for follow-up. Busy emergency physicians may be
               challenged to carefully communicate recommendations for
               additional imaging not relevant to the patient's primary
               evaluation. The emergence of electronic health records and
               natural language processing algorithms may help address this
               quality gap. We seek to describe recommendations for additional
               imaging from our institution and develop and validate an
               automated natural language processing algorithm to reliably
               identify recommendations for additional imaging. METHODS: We
               developed a natural language processing algorithm to detect
               recommendations for additional imaging, using 3 iterative cycles
               of training and validation. The third cycle used 3,235 radiology
               reports (1,600 for algorithm training and 1,635 for validation)
               of discharged emergency department (ED) patients from which we
               determined the incidence of discharge-relevant recommendations
               for additional imaging and the frequency of appropriate
               discharge documentation. The test characteristics of the 3
               natural language processing algorithm iterations were compared,
               using blinded chart review as the criterion standard. RESULTS:
               Discharge-relevant recommendations for additional imaging were
               found in 4.5\% (95\% confidence interval [CI] 3.5\% to 5.5\%) of
               ED radiology reports, but 51\% (95\% CI 43\% to 59\%) of
               discharge instructions failed to note those findings. The final
               natural language processing algorithm had 89\% (95\% CI 82\% to
               94\%) sensitivity and 98\% (95\% CI 97\% to 98\%) specificity
               for detecting recommendations for additional imaging. For
               discharge-relevant recommendations for additional imaging,
               sensitivity improved to 97\% (95\% CI 89\% to 100\%).
               CONCLUSION: Recommendations for additional imaging are common,
               and failure to document relevant recommendations for additional
               imaging in ED discharge instructions occurs frequently. The
               natural language processing algorithm's performance improved
               with each iteration and offers a promising error-prevention
               tool.},
	bdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23548405/}}

@article{Lojun2010-gv,
	author = {Lojun, Sharon L and Sauper, Christina J and Medow, Mitchell and Long, William J and Mark, Roger G and Barzilay, Regina},
	date-modified = {2022-05-15 13:34:36 -0400},
	journal = {AMIA Annu. Symp. Proc.},
	language = {en},
	month = nov,
	pages = {467--471},
	pmid = {21347022},
	title = {Investigating resuscitation code assignment in the intensive care unit using structured and unstructured data},
	url = {https://pubmed.ncbi.nlm.nih.gov/21347022/},
	volume = 2010,
	year = 2010,
	abstract = {This study investigates the feasibility of using structured data
              (age, gender, and medical condition), and unstructured medical
              notes on classification accuracy for resuscitation code status.
              Data was extracted from the MIMICII database. Natural language
              processing (NLP) was used to evaluate the social section of the
              nurses' progress notes. BoosTexter was used to predict the
              code-status using notes, age, gender, and Simplified Acute
              Physiology Score (SAPS). The relative impact of features was
              analyzed by feature ablation. Unstructured notes were the
              greatest single indicator of code status. The addition of text to
              medical condition features increased classification accuracy
              significantly (phusband.) Logistic regression on structured
              features was used determine gender bias or ageism. Evidence of
              bias was found; both females (OR=1.45) and patients over age 70
              (OR=3.72) were more likely to be Do-Not-Resuscitate (DNR).},
	bdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/21347022/}}

@article{Lacson2006-fd,
	author = {Lacson, Ronilda C and Barzilay, Regina and Long, William J},
	copyright = {https://www.elsevier.com/open-access/userlicense/1.0/},
	date-modified = {2022-05-15 13:36:28 -0400},
	journal = {J. Biomed. Inform.},
	language = {en},
	month = oct,
	number = 5,
	pages = {541--555},
	pmid = {16488194},
	publisher = {Elsevier BV},
	title = {Automatic analysis of medical dialogue in the home hemodialysis domain: structure induction and summarization},
	url = {https://pubmed.ncbi.nlm.nih.gov/16488194/},
	volume = 39,
	year = 2006,
	abstract = {Spoken medical dialogue is a valuable source of information for
               patients and caregivers. This work presents a first step towards
               automatic analysis and summarization of spoken medical dialogue.
               We first abstract a dialogue into a sequence of semantic
               categories using linguistic and contextual features integrated
               in a supervised machine-learning framework. Our model has a
               classification accuracy of 73\%, compared to 33\% achieved by a
               majority baseline (p<0.01). We then describe and implement a
               summarizer that utilizes this automatically induced structure.
               Our evaluation results indicate that automatically generated
               summaries exhibit high resemblance to summaries written by
               humans. In addition, task-based evaluation shows that physicians
               can reasonably answer questions related to patient care by
               looking at the automatically generated summaries alone, in
               contrast to the physicians' performance when they were given
               summaries from a naïve summarizer (p<0.05). This work
               demonstrates the feasibility of automatically structuring and
               summarizing spoken medical dialogue.},
	bdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/16488194/}}

@article{Long2003-di,
	author = {Long, William J},
	date-modified = {2022-05-15 13:33:34 -0400},
	journal = {AMIA Annu. Symp. Proc.},
	language = {en},
	pages = {917},
	pmid = {14728423},
	title = {Parsing free text nursing notes},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1480019/},
	year = 2003,
	abstract = {Parsing nursing notes requires tokenization, recognition of
              special forms, abbreviation expansion, and classification in the
              context of identified sections},
	bdsk-url-1 = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1480019/}}

@article{Fraser2003-xa,
	author = {Fraser, Hamish S F and Long, William J and Naimi, Shapur},
	date-modified = {2022-05-15 13:35:33 -0400},
	journal = {J. Am. Med. Inform. Assoc.},
	language = {en},
	month = jul,
	number = 4,
	pages = {373--381},
	pmid = {12668689},
	title = {Evaluation of a cardiac diagnostic program in a typical clinical setting},
	url = {https://pubmed.ncbi.nlm.nih.gov/12668689/},
	volume = 10,
	year = 2003,
	abstract = {CONTEXT: The Heart Disease Program (HDP) is a novel computerized
              diagnosis program incorporating a computer model of
              cardiovascular physiology. Physicians can enter standard clinical
              data and receive a differential diagnosis with explanations.
              OBJECTIVE: To evaluate the diagnostic performance of the HDP and
              its usability by physicians in a typical clinical setting.
              DESIGN: A prospective observational study of the HDP in use by
              physicians in departments of medicine and cardiology of a
              teaching hospital. Data came from 114 patients with a broad range
              of cardiac disorders, entered by six physicians. MEASUREMENTS:
              Sensitivity, specificity, and positive predictive value (PPV).
              Comprehensiveness: the proportion of final diagnoses suggested by
              the HDP or physicians for each case. RELEVANCE: the proportion of
              HDP or physicians' diagnoses that are correct. Area under the
              receiver operating characteristic (ROC) curve (AUC) for the HDP
              and the physicians. Performance was compared with a final
              diagnosis based on follow-up and further investigations. RESULTS:
              Compared with the final diagnoses, the HDP had a higher
              sensitivity (53.0\% vs. 34.8\%) and significantly higher
              comprehensiveness (57.2\% vs. 39.5\%, p < 0.0001) than the
              physicians. Physicians' PPV and relevance (56.2\%, 56.0\%) were
              higher than the HDP (25.4\%, 28.1\%). Combining the diagnoses of
              the physicians and the HDPs, sensitivity was 61.3\% and
              comprehensiveness was 65.7\%. These findings were significant in
              the two collection cohorts and for subanalysis of the most
              serious diagnoses. The AUCs were similar for the HDP and the
              physicians. CONCLUSIONS: The heart disease program has the
              potential to improve the differential diagnoses of physicians in
              a typical clinical setting.},
	bdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/12668689/}}


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#nav ul li {
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#nav > ul > li:nth-child(2) {
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#nav ul li a {
	display: inline-block;
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#nav ul li ul {
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repository: comeCU/some-js

repo_name: comeCU/some-js

gha_language: JavaScript


text:
@article{Logan2013,
 author = {Logan, Liz and Catania, Ginny and Lavier, Luc and Choi, Eunseo},
 doi = {10.3189/2013JoG12J210},
 file = {Logan et al_2013_A novel method for predicting fracture in floating ice.pdf:C\:\\Users\\eddy_\\Google Drive\\Mendeley Desktop\Łogan et al_2013_A novel method for predicting fracture in floating ice.pdf:application/pdf},
 issn = {00221430},
 journal = {Journal of Glaciology},
 number = {216},
 pages = {750--758},
 title = {A novel method for predicting fracture in floating ice},
 url = {http://www.igsoc.org/journal/59/216/j12J210.html},
 urldate = {2013-07-31},
 volume = {59},
 year = {2013}
}


text:
require xorg-lib-common.inc

PR = "r1"

DESCRIPTION = "Base X libs."

DEPENDS += " bigreqsproto xproto xextproto xtrans libxau xcmiscproto \
	libxdmcp xf86bigfontproto kbproto inputproto"
PROVIDES = "virtual/libx11"
RPROVIDES = "virtual/libx11"

XORG_PN = "libX11"

FILES_${PN} += "${datadir}/X11/XKeysymDB ${datadir}/X11/XErrorDB ${libdir}/X11/Xcms.txt"
FILES_${PN}-locale += "${datadir}/X11/locale ${libdir}/X11/locale"

do_compile() {
	(
		unset CC LD CXX CCLD
		oe_runmake -C src/util 'X11_CFLAGS=' 'CC=${BUILD_CC}' 'LD=${BUILD_LD}' 'CXX=${BUILD_CXX}' 'CCLD=${BUILD_CCLD}' 'CFLAGS=-D_GNU_SOURCE ${BUILD_CFLAGS}' 'LDFLAGS=${BUILD_LDFLAGS}' 'CXXFLAGS=${BUILD_CXXFLAGS}' 'CPPFLAGS=${BUILD_CPPFLAGS}' makekeys
	) || exit 1
	rm -f ${STAGING_INCDIR}/X11/Xlib.h
	oe_runmake
}


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text:
@article{diaz2021empirical,
 author = {Diaz, Renata M and Ye, Hao and Ernest, SK Morgan},
 journal = {Ecology Letters},
 number = {9},
 pages = {2025--2039},
 title = {Empirical abundance distributions are more uneven than expected given their statistical baseline},
 volume = {24},
 year = {2021}
}

repository: weecology/website

repo_name: weecology/website


text:
@Article{leon-mejia_factor_2020,
  title = {Factor structure and sex and age cut-off points for the Spanish version of the Nomophobia Questionnaire},
  volume = {In press},
  doi = {https://doi.org/10.20882/adicciones.1316},
  abstract = {Nomophobia is a situational phobia leading to a deep, irrational, and disproportionate fear of not being able to use the smartphone. An instrumental study on the Spanish version of the Nomophobia Questionnaire (NMP-Q) was carried out. The objectives were: 1) To analyse its factor structure and reliability; 2) to test for the invariance of sex and age groups, and 3) to obtain specific cut-off points by sex and age non-existent to date. Sampling was incidental and non-probabilistic with 5012 participants (57.9%, females) aged 12-24 years (M = 18.04, SD = 3.3). The confirmatory factor analysis revealed a hierarchical model with four correlated factors explained by a general second-order factor. The internal validity and reliability values of the NMP-Q dimensions are satisfactory, ranging between .78, .85, .86, and .92 (Omega w). A multigroup analysis confirmed the invariance across sex and age groups. Building on the NMP-Q scores, we calculated 3 cut-off points using percentiles 15th, 80th and 95th (unnomophobic, at risk of nomophobia, and nomophobic). Females aged 12-15 years had the highest nomophobic scores. We can conclude that the proposed sex and age cut-off points will allow us to better identify nomophobic problems from a clinical point of view.},
  journal = {Adicciones},
  author = {Ana Leon-Mejia and Esther Calvete and Carmen Patino-Alonso and Juan Manuel Machimbarrena and Joaquin Gonzalez-Cabrera},
  year = {2020},
  pubtype = {2},
}

repository: mmachimbarrena/Pruebaweb

repo_name: mmachimbarrena/Pruebaweb


text:
@article{2016_eurovis_clue,
    title = {From Visual Exploration to Storytelling and Back Again},
    author = {Samuel Gratzl and Alexander Lex and Nils Gehlenborg and Nicola Cosgrove and Marc Streit},
    journal = {Computer Graphics Forum (EuroVis '16)},
    doi = {10.1111/cgf.12925},
    url = {https://onlinelibrary.wiley.com/doi/10.1111/cgf.12925/abstract},
    volume = {35},
    number = {3},
    pages = {491--500},
    month = {jun},
    year = {2016}
}


text:
@article{li2020DVG,
    title={Differentiable Vector Graphics Rasterization for Editing and Learning},
    author={Li, Tzu-Mao and Lukac, Michal and Gharbi Michael and Jonathan Ragan-Kelley},
    journal={ACM Trans. Graph. (Proc. SIGGRAPH Asia)},
    year={2020},
    url={https://people.csail.mit.edu/tzumao/diffvg/}
}

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