text:
/* Set widths on the form inputs since otherwise they're 100% wide */
input,
select,
textarea {
    max-width: 280px;
}

.input-validation-error {
    border-color: red;
}

.inline {
    display: inline;
}

.top-bar {
    background-color: #212121;
    color: #e7e7e7;
}

    .top-bar > div > ul > li > a, .top-bar .top-bar > div > ul > li > a:visited {
        color: #e7e7e7;
    }

    .top-bar .btn-link {
        padding: 0;
        color: #e7e7e7;
    }

.main-header {
    padding: 15px 0;
    background-color: #f9f9f9;
}

@media (max-width : 992px) {
    .logo {
        text-align: center
    }
}

.main-header .form-inline .form-group {
    margin: 0px -3px;
}

.main-header .search-box {
    margin-top: 7px;
}

.main-header .search-input {
    width: calc(100% - 215px);
}

    .main-header .search-input input {
        height: 35px;
        width: 100%;
        max-width: 100%;
        border-radius: 0;
    }

.main-header .search-category select {
    height: 35px;
    border-radius: 0;
    width: 180px;
}

.main-header .search-btn {
    background-color: #2196f3;
    border: 0;
    border-radius: 0;
    color: #fff;
    width: 35px;
    height: 35px;
    position: absolute;
    top: 0;
}

@media (max-width : 768px) {
    .main-header .search-input {
        width: calc(100% - 35px)
    }

    .main-header .search-btn {
        right: 15px
    }

    .main-nav {
        min-height: 40px;
    }
}

.main-nav {
    background-color: #f3f3f3;
}

@media (min-width : 768px) {
    .main-menu {
        display: table;
        float: none;
        margin: 0 auto;
    }
}

.navbar-default .navbar-nav > li > a {
    font-size: 15px;
    text-transform: uppercase;
}

li.nav-item-group, li.nav-item-group a {
    font-weight: bold;
}

.cart-badge {
    position: relative;
    top: 2px;
    right: 0px;
    margin-left: 20px;
    padding-top: 8px;
    padding-right: 18px;
}

    .cart-badge i {
        font-size: 34px;
    }

    .cart-badge span.badge {
        top: 0;
        right: 5px;
        position: absolute;
        background: red;
    }

.navbar-toggle {
    float: left;
    margin-right: 0;
    margin-left: 15px;
    padding: 5px 7px
}

.breadcrumb {
    background-color: #FFFFFF;
    padding: 0;
}

.footer-content {
    margin-top: 30px;
    background-color: #eee;
}

    .footer-content hr {
        border-color: #ddd;
    }

/* Carousel */
.carousel-caption {
    z-index: 10 !important;
}

    .carousel-caption p {
        font-size: 20px;
        line-height: 1.4;
    }

@media (min-width: 768px) {
    .carousel-caption {
        z-index: 10 !important;
    }
}

/* product list*/
.product-list a:hover {
    text-decoration: none;
}

.product-list .product-price {
    color: red;
}

.product-list .product-ratings > li {
    margin-left: -2px;
    margin-right: 0;
    color: #777;
    font-size: 90%;
}

.product-list .product-price-saving {
    background-color: #ff9601;
    color: #fff;
    display: inline-block;
    font-size: 12px;
    font-weight: 400;
    margin-left: 15px;
    margin-top: -2px;
    padding: 4px;
    vertical-align: middle;
}

.product-list .product-price-regular {
    text-decoration: line-through;
    font-weight: 100;
    color: #4c4849;
    font-size: 11px;
}

.product-list .thumbnail:hover {
    border-color: #337ab7;
}

.product-list .buttons {
    padding: 9px;
}

.product-list-filters h3 {
    margin-top: 27px;
}

.product-list .btn {
    background: #ddd;
    font-size: 18px;
    padding: 5px 10px;
    border: 0;
    color: #18ba9b;
    border-radius: 0;
    margin: -1px;
}

    .product-list .btn:hover {
        background: #eee;
    }

.product-list .btn-add-cart-style {
    background: #f0f0f0;
    padding: 8px 0px;
    font-size: 14px;
    color: #777;
    text-align: center;
    width: 100%
}

.product-list .thumbnail:hover .btn-add-cart-style {
    background: #2cd5b6;
    color: #fff;
}

#accordion-brand ul, #accordion-category ul {
    padding-left: 30px;
}

#priceSlider {
    margin: 17px
}

#minPrice {
    width: 110px;
    float: left;
}

#maxPrice {
    width: 110px;
    float: right;
    text-align: right;
}

.price-actions {
    float: right;
    margin-top: 15px;
}

.category-result h2 {
    float: left;
    font-size: 30px;
    margin-right: 20px;
}

.category-result .badge-results {
    top: 26px;
    color: #fff;
    font-size: 12px;
    padding: 3px 8px;
    position: relative;
    background: #c9253c;
}

.product-list-display-options {
    margin-top: 14px;
}

    .product-list-display-options label {
        font-weight: normal;
    }

    .product-list-display-options .show-option {
        float: right;
        padding-right: 0;
    }

        .product-list-display-options .show-option i {
            color: #fff;
            width: 30px;
            height: 30px;
            padding: 6px;
            font-size: 18px;
            line-height: 20px;
            text-align: center;
            background: #18ba9b;
            display: inline-block;
        }

    .product-list-display-options .sort-by {
        float: right;
    }

    .product-list-display-options .pagination-option {
        float: right;
    }

/* car list*/
.cart-list .product-image {
    width: 120px;
}

    .cart-list .product-image img {
        width: 120px;
    }

.cart-list .quantity-button {
    color: #555;
    width: 35px;
    height: 30px;
    padding: 3px;
    border: none;
    outline: none;
    cursor: pointer;
    font-size: 14px;
    background: #eee;
    text-align: center;
    font-weight: normal;
    white-space: nowrap;
    display: inline-block;
    background-image: none;
}

.cart-list .quantity-field {
    width: 35px;
    height: 30px;
    outline: none;
    margin: 0 -4px;
    font-size: 14px;
    text-align: center;
    border: 1px solid #eee;
}

.cart-list .table > thead > tr > th {
    border-bottom: none;
    text-transform: uppercase;
    color: #687074;
}

.cart-list .table > tbody > tr > td {
    border-top: none;
}

.cart-list .order-summary h4 {
    text-transform: uppercase;
    color: #687074;
    padding-bottom: 20px;
}

.cart-list .order-summary dt {
    text-align: left;
    width: 100px;
}

.cart-list .order-summary dd {
    text-align: right;
    margin-left: 100px;
}

.cart-list .order-summary .btn {
    background: #18ba9b;
    padding: 8px 43px;
    font-size: 14px;
    color: #fff;
    width: 100%;
    border: 0;
    border-radius: 0;
    text-transform: uppercase;
}

.coupon-form {
    margin-top: 20px;
}

.coupon-form .form-group input {
    width: 197px;
}

/* Checkout*/
.btn-order {
    background: #18ba9b;
    padding: 8px 43px;
    font-size: 14px;
    color: #fff;
    width: 100%;
    border: 0;
    border-radius: 0;
    text-transform: uppercase;
    width: 200px;
}

    .btn-order:hover {
        color: #fff;
        background: #2cd5b6;
    }

.order-history img {
    width: 60px;
}

.list-inline {
    margin-bottom: 6px;
    margin-top: 6px;
}

    .list-inline li {
        height: 22px;
        line-height: 22px;
    }

    .list-inline a {
        text-decoration: none;
    }

#logoutForm {
    display: inline-flex;
    line-height: 22px;
}

    #logoutForm button {
        text-decoration: none;
    }

/*Responsive Navbar*/
@media (max-width: 991px) {
    .main-nav .navbar-header {
        float: none;
    }

    .main-nav .navbar-toggle {
        display: block;
    }

    .main-nav .navbar-collapse.collapse {
        display: none !important;
    }

        .main-nav .navbar-collapse.collapse.in {
            display: block !important;
            overflow-y: auto !important;
        }

    .main-nav .navbar-nav {
        margin: 0;
        float: none !important;
    }

        .main-nav .navbar-nav > li {
            float: none;
        }

        /*Dropdown Menu Slide Down Effect*/
        .main-nav .navbar-nav .open .dropdown-menu {
            border: 0;
            float: none;
            width: auto;
            margin-top: 0;
            position: static;
            box-shadow: none;
            background-color: transparent;
        }

            .main-nav .navbar-nav .open .dropdown-menu > li > a,
            .main-nav .navbar-nav .open .dropdown-menu .dropdown-header {
                padding: 5px 15px 5px 25px;
            }

            .main-nav .navbar-nav .open .dropdown-menu > li > a {
                line-height: 20px;
            }

                .main-nav .navbar-nav .open .dropdown-menu > li > a:hover,
                .main-nav .navbar-nav .open .dropdown-menu > li > a:focus {
                    background-image: none;
                }

    .main-nav .navbar-default .navbar-nav .open .dropdown-menu > li > a {
        color: #777;
    }

        .main-nav .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
        .main-nav .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
            color: #333;
            background-color: transparent;
        }

    .main-nav .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
    .main-nav .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
    .main-nav .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
        color: #555;
        background-color: #e7e7e7;
    }

    .main-nav .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
    .main-nav .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
    .main-nav .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
        color: #ccc;
        background-color: transparent;
    }

    .main-nav .navbar-default .dropdown-menu.no-bottom-space {
        padding-bottom: 0;
    }

    .main-nav .navbar-collapse,
    .main-nav .navbar-collapse .container {
        padding-left: 0 !important;
        padding-right: 0 !important;
    }

    .main-nav .navbar-default .navbar-nav > li > a {
        font-size: 14px;
        padding: 9px 10px;
    }

    .main-nav .navbar-default .navbar-nav > li a {
        border-bottom: solid 1px #eee;
    }

    .main-nav .navbar-default .navbar-nav > li > a:focus {
        background: none;
    }

    .main-nav .navbar-default .navbar-nav > li > a:hover {
        color: #72c02c;
    }

    .main-nav .navbar-default .navbar-nav > .active > a,
    .main-nav .navbar-default .navbar-nav > .active > a:hover,
    .main-nav .navbar-default .navbar-nav > .active > a:focus {
        color: #fff !important;
        background: #72c02c !important;
    }

    .main-nav .dropdown .dropdown-submenu > a {
        color: #333 !important;
        text-transform: uppercase;
    }
}

@media (min-width: 992px) {
    /*Navbar Collapse*/
    .main-nav .navbar-collapse {
        padding: 0;
    }

    .main-nav .navbar-default .navbar-nav > li > a:last-child {
        padding-right: 15px;
    }

    .main-nav .navbar-default .navbar-nav > li > a,
    .main-nav .navbar-default .navbar-nav > li > a:focus {
        border-top: solid 2px transparent;
    }

        .main-nav .navbar-default .navbar-nav > li > a:hover,
        .main-nav .navbar-default .navbar-nav > .active > a {
            border-top: solid 2px #72c02c;
        }

            .main-nav .navbar-default .navbar-nav > li > a,
            .main-nav .navbar-default .navbar-nav > li > a:hover,
            .main-nav .navbar-default .navbar-nav > li > a:focus,
            .main-nav .navbar-default .navbar-nav > .active > a,
            .main-nav .navbar-default .navbar-nav > .active > a:hover,
            .main-nav .navbar-default .navbar-nav > .active > a:focus {
                background: none;
            }

    .main-nav .navbar-default .navbar-nav > .open > a,
    .main-nav .navbar-default .navbar-nav > .open > a:hover,
    .main-nav .navbar-default .navbar-nav > .open > a:focus {
        color: #72c02c;
    }

    .main-nav .navbar-default .navbar-nav > li:hover > a {
        color: #72c02c;
    }

    /*Dropdown Menu*/
    .main-nav .dropdown-menu {
        padding: 0;
        border: none;
        min-width: 200px;
        border-radius: 0;
        z-index: 9999 !important;
        border-top: solid 2px #72c02c;
        box-shadow: 0 5px 5px 0px rgba(90,90,90,0.075);
    }

        .main-nav .dropdown-menu li a {
            color: #687074;
            font-weight: 400;
            padding: 8px 15px;
            border-bottom: solid 1px #dedede;
        }

        .main-nav .dropdown-menu .active > a,
        .main-nav .dropdown-menu li > a:hover {
            color: #fff;
            background: #72c02c;
            filter: none !important;
        }

        .main-nav .dropdown-menu li > a:focus {
            color: inherit;
            background: none;
            filter: none !important;
        }

    .main-nav .navbar-nav > li.dropdown:hover > .dropdown-menu {
        display: block;
    }

    .main-nav .open > .dropdown-menu {
        display: none;
    }

    .main-nav .dropdown-menu li.dropdown-submenu:hover > a {
        color: #fff;
        background: #72c02c;
    }
}

/*Dropdown Submenu*/
.main-nav .dropdown-submenu {
    position: relative;
}

    .main-nav .dropdown > a:after,
    .main-nav .dropdown-submenu > a:after {
        top: 8px;
        right: 9px;
        font-size: 11px;
        content: "\f105";
        position: absolute;
        font-weight: normal;
        display: inline-block;
        font-family: FontAwesome;
    }

@media (max-width: 991px) {
    .main-nav .dropdown-submenu > a:after {
        content: " ";
    }

    .main-nav .dropdown > a:after {
        top: 9px;
        right: 15px;
        content: "\f107";
    }
}

@media (min-width: 992px) {
    .main-nav .dropdown > a:after {
        content: " ";
    }
}

.main-nav .dropdown-submenu > .dropdown-menu {
    top: 3px;
    left: 100%;
    margin-top: -5px;
    margin-left: 0px;
}

.main-nav .dropdown-submenu:hover > .dropdown-menu {
    display: block;
}

@media (max-width: 991px) {
    .main-nav .dropdown-submenu > .dropdown-menu {
        display: block;
        margin-left: 15px;
    }
}

.rating-container .caption {
    font-size: 70%;
    margin-top: -2px;
}

.rating-container .filled-stars {
    -webkit-text-stroke: 0;
    text-shadow: none;
    color: #18ba9b;
}

.rating-xs {
    font-size: 1.2em;
}

.product-comparison-result-remove {
    padding-top: 10px;
}

/*Contact form*/
.input-contact {
    max-width: 100%;
}

#googleMap {
    height: 334px;
    margin-top: 75px;
}

repository: turkmvc/SimplCommerce

repo_name: turkmvc/SimplCommerce


text:
@InProceedings{bhojanapalli16,
  author = {Bhojanapalli, Srinadh and Kyrillidis, Anastasios and Sanghavi, Sujay},
  title = {Dropping Convexity for Faster Semi-definite Optimization},
  pages = {530-582},
  abstract = {We study the minimization of a convex function $f(X)$ over the set of $n \times n$ positive semi-definite matrices,
but when the problem is recast as $\min_U g(U) :=  f(UU^\top)$, with $U \in \mathbb{R}^{n \times r}$ and $r \leq n$.
We study the performance of gradient descent on $g$---which we refer to as Factored Gradient Descent (\textsc{Fgd})---under standard assumptions on the {\em original} function $f$.

We provide a rule for selecting the step size and, with this choice, show that the \emph{local} convergence rate of \textsc{Fgd} mirrors that of standard gradient descent on the original $f$: \emph{i.e.}, after $k$ steps, the error is $O(1/k)$ for smooth $f$, and exponentially small in $k$ when $f$ is (restricted) strongly convex.
In addition, we provide a procedure to initialize \textsc{Fgd} for (restricted) strongly convex objectives and when one only has access to $f$ via a first-order oracle; for several problem instances, such proper initialization leads to \emph{global} convergence guarantees.

\textsc{Fgd} and similar procedures are widely used in practice for problems that can be posed as matrix factorization.
To the best of our knowledge, this is the first paper to provide precise convergence rate guarantees for general convex functions under standard convex assumptions.},
}

license: MIT

repository: mreid/papersite

license_type: permissive

repo_name: mreid/papersite

branch_name: refs/heads/master

gha_license_id: MIT

gha_language: TeX

extension: bib


text:
@article{Kullback1951,
  author  = {Kullback, S. and Leibler, R. A.},
  doi     = {10.1214/aoms/1177729694},
  journal = {The Annals of Mathematical Statistics},
  number  = {1},
  pages   = {79--86},
  title   = {On information and sufficiency},
  url     = {https://doi.org/10.1214/aoms/1177729694},
  volume  = {22},
  year    = {1951}
}

@book{Cover1991,
  title     = {Elements of information theory},
  author    = {T. M. Cover and J. A. Thomas},
  year      = {1991},
  isbn      = {0-471-06259-6},
  address   = {New York},
  publisher = {John Wiley \& Sons}
}

@article{Shannon1948,
  author  = {C. E. Shannon},
  journal = {The Bell System Technical Journal},
  title   = {A mathematical theory of communication},
  year    = {1948},
  volume  = {27},
  number  = {3},
  pages   = {379--423},
  doi     = {10.1002/j.1538-7305.1948.tb01338.x},
  issn    = {0005-8580}
}

@article{Lizier2012,
  title   = {Local measures of information storage in complex distributed computation},
  journal = {Information Sciences},
  volume  = {208},
  number  = {Supplement C},
  pages   = {39--54},
  year    = {2012},
  issn    = {0020-0255},
  doi     = {10.1016/j.ins.2012.04.016},
  url     = {http://www.sciencedirect.com/science/article/pii/S0020025512002800},
  author  = {J. T. Lizier and M. Prokopenko and A. Y. Zomaya},
}

@article{Schreiber2000,
  title   = {Measuring information transfer},
  journal = {Physical Review Letters},
  volume  = {85},
  number  = {2},
  pages   = {461--464},
  year    = {2000},
  doi     = {10.1103/PhysRevLett.85.461},
  author  = {T. Schreiber},
}

@article{Bialek2001a,
  title   = {Predictability, complexity, and learning},
  journal = {Neural Computation},
  volume  = {13},
  number  = {11},
  pages   = {2409--2463},
  year    = {2001},
  doi     = {10.1162/089976601753195969},
  url     = {https://doi.org/10.1162/089976601753195969},
  author  = {W. Bialek and I. Nemenman and N. Tishby}
}

@article{Bialek2001b,
  title   = {Complexity through nonextensivity},
  journal = {Physica A: Statistical Mechanics and its Applications},
  volume  = {302},
  number  = {1},
  pages   = {89--99},
  year    = {2001},
  issn    = {0378-4371},
  doi     = {10.1016/S0378-4371(01)00444-7},
  url     = {http://www.sciencedirect.com/science/article/pii/S0378437101004447},
  author  = {W. Bialek and I. Nemenman and N. Tishby}
}

@article{Crutchfield2003,
  author  = {J. P. Crutchfield and D. P. Feldman},
  title   = {Regularities unseen, randomness observed: Levels of entropy convergence},
  journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  volume  = {13},
  number  = {1},
  pages   = {25--54},
  year    = {2003},
  doi     = {10.1063/1.1530990}
}

@article{Feldman2003,
  title   = {Structural information in two-dimensional patterns: Entropy convergence and excess entropy},
  author  = {D. P. Feldman and J. P. Crutchfield},
  journal = {Physical Review E},
  volume  = {67},
  issue   = {5},
  pages   = {051104},
  year    = {2003},
  doi     = {10.1103/PhysRevE.67.051104},
  url     = {https://link.aps.org/doi/10.1103/PhysRevE.67.051104}
}

@article{Tononi2003,
  author  = {G. Tononi and O. Sporns},
  title   = {Measuring information integration},
  journal = {BMC Neuroscience},
  year    = {2003},
  volume  = {4},
  number  = {1},
  pages   = {31},
  issn    = {1471-2202},
  doi     = {10.1186/1471-2202-4-31},
  url     = {https://doi.org/10.1186/1471-2202-4-31}
}

@article {Hoel2013,
  author  = {E. P. Hoel and L. Albantakis and G. Tononi},
  title   = {Quantifying causal emergence shows that macro can beat micro},
  volume  = {110},
  number  = {49},
  pages   = {19790--19795},
  year    = {2013},
  doi     = {10.1073/pnas.1314922110},
  issn    = {0027-8424},
  url     = {http://www.pnas.org/content/110/49/19790},
  journal = {Proceedings of the National Academy of Sciences}
}

@article{Hoel2017,
  author  = {E. P. Hoel},
  title   = {When the map is better than the territory},
  journal = {Entropy},
  volume  = {19},
  year    = {2017},
  number  = {5},
  pages   = {188},
  url     = {http://www.mdpi.com/1099-4300/19/5/188},
  issn    = {1099-4300},
  doi     = {10.3390/e19050188}
}

@article{Dobrushin1959,
  title   = {A general formulation of the fundamental theorem of Shannon in the theory of information},
  author  = {R. L. Dobrushin},
  journal = {Uspekhi Matematicheskikh Nauk},
  volume  = {14},
  number  = {6},
  pages   = {3--104},
  year    = {1959}
}

@article{Wyner1978,
  title   = {A definition of conditional mutual information for arbitrary ensembles},
  journal = {Information and Control},
  volume  = {38},
  number  = {1},
  pages   = {51--59},
  year    = {1978},
  issn    = {0019-9958},
  doi     = {10.1016/S0019-9958(78)90026-8},
  url     = {http://www.sciencedirect.com/science/article/pii/S0019995878900268},
  author  = {A.D. Wyner}
}

@inproceedings{Biehl2016,
  title     = {Towards information based spatiotemporal patterns as a foundation for agent representation in dynamical systems},
  author    = {M. Biehl and T. Ikegami and D. Polani},
  booktitle = {Proceedings of the Artificial Life Conference 2016},
  pages     = {722--729},
  year      = {2016},
  doi       = {10.7551/978-0-262-33936-0-ch115}
}

@article{Ay2008,
  author  = {N. Ay and D. Polani},
  title   = {Information flows in causal networks},
  journal = {Advances in Complex Systems},
  volume  = {11},
  number  = {01},
  pages   = {17-41},
  year    = {2008},
  doi     = {10.1142/S0219525908001465}
}

@article{Lizier2010,
  author  = {J. T. Lizier and M. Prokopenko and A. Y. Zomaya},
  title   = {Information modification and particle collisions in distributed computation},
  journal = {Chaos},
  volume  = {20},
  number  = {3},
  pages   = {037109},
  year    = {2010},
  doi     = {10.1063/1.3486801}
}

repository: ELIFE-ASU/rinform

repo_name: ELIFE-ASU/rinform

gha_language: R

extension: bibtex


text:
@article{BonillaCarreno2017,
abstract = {La discriminaci{\'{o}}n de las emociones expresadas a nivel facial es importante para las relaciones sociales, la empat{\'{i}}a y la interacci{\'{o}}n social. El objetivo de este estudio fue observar si exist{\'{i}}an diferencias en el procesamiento cortical ante dos emociones b{\'{a}}sicas, la ira y el miedo y definir si la percepci{\'{o}}n de la ira intensa genera una mayor modulaci{\'{o}}n del componente N170 en amplitud y latencia en comparaci{\'{o}}n con las im{\'{a}}genes de rostros con expresi{\'{o}}n de miedo intenso. Para este estudio se utiliz{\'{o}} la t{\'{e}}cnica de potenciales evocados con un montaje de 32 canales. Se encontraron diferencias significativas en latencia para las im{\'{a}}genes de rostros que expresan ira intensa, comparados con la condici{\'{o}}n de im{\'{a}}genes de rostros de miedo intenso. Se encontraron diferencias tanto de la amplitud como de latencia ante im{\'{a}}genes de rostros de ira y miedo intensos en comparaci{\'{o}}n con im{\'{a}}genes de rostros neutros.},
author = {{Bonilla Carre{\~{n}}o}, Carlos Fidel Mauricio and Leong{\'{o}}mez, Juan David},
doi = {10.21500/19002386.3100},
file = {:D$\backslash$:/GDrive/Papers/Psychologia/Bonilla Carre{\~{n}}o, Leong{\'{o}}mez, 2017, Efectos en la amplitud y la latencia del componente N170 ante la presentaci{\'{o}}n de rostros emocionales d.pdf:pdf},
issn = {1900-2386},
journal = {Psychologia},
keywords = {N170,P1,emoci{\'{o}}n,rostros},
month = {jan},
number = {1},
pages = {39--48},
title = {{Efectos en la amplitud y la latencia del componente N170 ante la presentaci{\'{o}}n de rostros emocionales de ira y miedo}},
url = {http://revistas.usb.edu.co/index.php/Psychologia/article/view/3100},
volume = {11},
year = {2017}
}


text:
@book{best_damned_2001,
	edition = {1},
	title = {Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists},
	isbn = {0520219783},
	url = {http://www.ucpress.edu/book.php?isbn=9780520219786},
	shorttitle = {Damned Lies and Statistics},
	abstract = {Description from the publisher's website: "Does the number of children gunned down double each year? Does anorexia kill 150,000 young women annually? Do white males account for only a sixth of new workers? Startling statistics shape our thinking about social issues. But all too often, these numbers are wrong. This book is a lively guide to spotting bad statistics and learning to think critically about these influential numbers. Damned Lies and Statistics is essential reading for everyone who reads or listens to the news, for students, and for anyone who relies on statistical information to understand social problems."},
	publisher = {University of California Press},
	author = {Best, Joel},
	date = {2001-05-08}
}

repository: pmean/build-website

repo_name: pmean/build-website


text:
@article{kickstarting,
    author        = {Andreas Olofsson, Tomas Nordström and Zain Ul-Abdin},
    title         = {Kickstarting High-performance Energy-efficient Manycore Architectures with Epiphany},
    journaltitle  = {Cornell University Librairy},
    year          = {2014},
    volume        = {},
    number        = {},
    pages         = {1},
    doi           = {}
}

@online{levenshtein,
    author        = {rosettacode},
    title         = {Java levenshtein},
    year          = {2016},
    url           = {https://rosettacode.org/wiki/Levenshtein_distance#Java},
    note = "[Online; accessed 4-April-2016]"
}

@online{levenshteinwiki,
    author        = {Wikipedia},
    title         = {Levenshtein},
    year          = {2016},
    url           = {https://en.wikipedia.org/wiki/Levenshtein_distance},
    note = "[Online; accessed 4-April-2016]"
}

@online{aparapiquestions,
    author        = {Aparapi},
    title         = {Aparapi repos},
    year          = {2016},
    url           = {https://github.com/aparapi/aparapi/blob/master/doc/FrequentlyAskedQuestions.md},
    note = "[Online; accessed 4-mai-2016]"
}

@online{cudahome,
    author        = {NVIDIA},
    title         = {CUDA website},
    year          = {2016},
    url           = {http://www.nvidia.com/object/cuda_home_new.html},
    note = "[Online; accessed 4-mai-2016]"
}

@online{aparapiblog,
    author        = {AMD},
    title         = {AMD dev blog},
    year          = {2011},
    url           = {http://developer.amd.com/community/blog/2011/09/14/i},
    note = "[Online; accessed 11-mai-2016]"
}

@online{aparapibug,
    author        = {AMD},
    title         = {Aparapi repo issues},
    year          = {2016},
    url           = {https://github.com/steelted/aparapi/issues/117},
    note = "[Online; accessed 11-mai-2016]"
}

@article{aparapiarticle,
    author        = {Shrinivas Joshi},
    title         = {Leveraging Aparapi to Help Improve Financial Java Application Performance},
    year          = {2012},
    journaltitle  = {AMD-dev}
}

@article{cudagpgpu,
    author        = {Jayshree Ghorpade, Jitendra Parande},
    title         = {GPGPU PROCESSING IN CUDA ARCHITECTURE},
    year          = {2012},
    journaltitle  = {arxiv}
}

repository: archenroot/JavaGPU

repo_name: archenroot/JavaGPU


text:
@article{bhattacharya2018co,
  title={Co-occurrence of medical conditions: Exposing patterns through probabilistic topic modeling of snomed codes},
  author={Bhattacharya, Moumita and Jurkovitz, Claudine and Shatkay, Hagit},
  journal={Journal of biomedical informatics},
  volume={82},
  pages={31--40},
  year={2018},
  publisher={Elsevier}
}

url: https://raw.githubusercontent.com/Ryan-Laird/PhenoClustR/c294c3b04449503e726a89c205d8800805c029af/bibliography/Bhattacharya M - J Biomed Inform (2018) Co-occurrence of medical conditions Exposing patterns through probabilistic topic modeling of snomed codes.bib

repository: Ryan-Laird/PhenoClustR

repo_name: Ryan-Laird/PhenoClustR


text:
@inbook{wakeman2011dpafm,
 abstract = {Farmers' markets are the source of a rich and pleasurable consumption experience. In this chapter, we report on our attempts to support and augment these experiences through the deployment of pervasive advertising. We describe the ethnographic approach we used to delineate the areas of enjoyment and pleasure, how narratives are weaved around and through the stalls and their products, and how trust is formed and maintained between the stallholders and the customers. We show how this understanding can be applied in the design of supporting advertising applications. We then evaluate the applications using the same ethnographic approach, uncovering problems which would not have been visible with other evaluation techniques.},
 author = {Ian Wakeman and Ann Light and Jon Robinson and Dan Chalmers and Basu, Anirban},
 chapter = {Pervasive Advertising and Shopping},
 publisher = {Springer HCI Series},
 title = {Deploying Pervasive Advertising in a Farmers' Market},
 url = {https://link.springer.com/chapter/10.1007/978-0-85729-352-7_12},
 year = {2011}
}


text:
/* ==UserStyle==
@name           ACS PE Course - Outline report
@namespace      USO Archive
@author         adiserio
@description    `Outline pages`
@version        2017102.6.45
@license        NO-REDISTRIBUTION
@preprocessor   uso
==/UserStyle== */
@namespace url(http://www.w3.org/1999/xhtml);

@-moz-document url-prefix("https://learn2.acseducation.edu.au/report/outline") {
  .col-md-9{
    width:100%!important;
  }
  #page-report-outline-user .section{
    margin:0px;
    border:none;
    border-top:solid rgba(0,256,0,.5) 4px;
    border-radius: 5px;
  }
}

branch_name: refs/heads/flomaster

gha_license_id: NOASSERTION

extension: css


text:
* { margin:0; padding:0; word-break:break-all; }
body { background:#FFF; color:#333; font:14px/1.5em "微软雅黑", "Microsoft Yahei",Helvetica, Arial, sans-serif; }
h1, h2, h3, h4, h5, h6 { font-size:1em; }
a { color:#2b475e; text-decoration:none; }
a:hover { color:#1cadf0; text-decoration:underline; }
ul, li { list-style:none; }
fieldset, img { border:none; }
#container{
	position:relative;
	width: 100%;
	height: 100vh;
}
.wrap{
	text-align: center;

}
.logo{
	text-align: center;
}
.logo img{
	margin-top: 10%;
	width: 270px;
}
.s_btn{
	width: 100px;
    height: 34px;
    border:none;
    color: white;
    letter-spacing: 1px;
    background: #3385ff;
    border-bottom: 1px solid #2d78f4;
    outline: medium;
    -webkit-appearance: none;
    -webkit-border-radius: 0;
    font-size: 14px;
    cursor: pointer;
}
.kw{
	width: 521px;
    height: 14px;
    padding: 9px 7px;
    margin-right: -8px;
    font: 16px arial;
    border: 1px solid #b8b8b8;
    border-bottom: 1px solid #ccc;
    border-right: 0;
    vertical-align: top;
    outline: none;
    box-shadow: none;
}
.kw:focus{
	border: 1px solid #38f !important;
	border-right: none !important;
}
#bdsug{
	width: 638px;
    z-index: 1;
    /*border: 1px solid #ccc;
    box-shadow: 1px 1px 3px #ededed;
    -webkit-box-shadow: 1px 1px 3px #ededed;
    -moz-box-shadow: 1px 1px 3px #ededed;
    -o-box-shadow: 1px 1px 3px #ededed;*/
    margin: 0 auto;
    text-align: left;
    display: none;
}
#bdsug ul{
	width: 531px;
	border: 1px solid #ccc;
    box-shadow: 1px 1px 3px #ededed;
    -webkit-box-shadow: 1px 1px 3px #ededed;
    -moz-box-shadow: 1px 1px 3px #ededed;
    -o-box-shadow: 1px 1px 3px #ededed;
}
#bdsug li{
	padding: 3px 0 2px 10px;
	cursor: pointer;

}


text:
@article{кушназаров2015сравнение,
 author = {Кушназаров, Фаррух Исакулович and Яковлев, Валентин Васильевич and Турдиев, Одилжан Акрамович},
 journal = {Известия Петербургского университета путей сообщения},
 number = {4 (45)},
 publisher = {Федеральное государственное бюджетное образовательное учреждение высшего~…},
 title = {Сравнение производительности протоколов доступа к облачным ресурсам},
 year = {2015}
}

repository: k-farruh/academic-kickstart

repo_name: k-farruh/academic-kickstart

gha_language: JavaScript


text:
SUMMARY = "Dreamcrypt/Firecrypt Conditional Access Daemon"
DEPENDS = "openssl systemd"

SRC_URI[aarch64.md5sum] = "5b29d583f3b5a77ff4a5e575611219ff"
SRC_URI[aarch64.sha256sum] = "69543743c7d03b97fc4abe30b509fed8855d5b8f04f7231de7b7658c61efc756"
SRC_URI[cortexa15hf-neon-vfpv4.md5sum] = "f0eb29ddf8527c3347917ecb9a75745d"
SRC_URI[cortexa15hf-neon-vfpv4.sha256sum] = "213b6b748aea8094ca4d87f505582d50d956c367cc4c279478dc739d0b008aa0"
SRC_URI[mips32el.md5sum] = "2c59dede28f7b669a48a5cb772e412d8"
SRC_URI[mips32el.sha256sum] = "042e7caf0baf75fc9555da6d0a1e7d79193bc35e4c0839eba4623bb825905551"

SYSTEMD_SERVICE_${PN} = "dccamd.service"

inherit opendreambox-precompiled-binary systemd

repository: opendreambox/opendreambox

repo_name: opendreambox/opendreambox

branch_name: refs/heads/pyro

gha_language: BitBake

extension: bb


text:
.root {
    --window-buttons-height: var(--url-bar-height); /* defined in App.module.css under .root{} */
    --pagination-height: var(--url-bar-height); /* defined in App.module.css under .root{} */
    --padding: 10px;
    --resize-handle-width: 5px;
}

.expandable {
    position: absolute;
    right: 0;
    height: 100%;
    border: 1px solid #ebebeb;
    background-color: rgba(255, 255, 255);
    overflow: auto;
    display: flex;
    justify-content: center;
}

.expandable-transition {
    transition: width var(--transition-duration); /* defined in App.module.css under .root{} */
}

.window-buttons {
    width: 100%;
    height: var(--window-buttons-height);

    /* flex parent */
    display: flex;
    justify-content: flex-end;
    flex-wrap: wrap;
    position: absolute;
    right: 0;
}

.window-buttons-container {
    width: 100%;
    height: var(--window-buttons-height);
}

.default {
    width: var(--file-details-width); /* defined in App.module.css under .root{} */
    min-width: 175px;
    max-width: 95%;
}

.minimized {
    overflow: hidden;
}

.maximized {
    /* 100% of the nearest relatively sized container, which _needs_ to be the .everything-except-header-ribbon div
    rendered in App.tsx */
    width: 100%;
}

.hidden {
    visibility: hidden;
}

.pagination {
    width: 100%;
    height: var(--pagination-height);

    /* flex parent */
    display: flex;
    flex-direction: row;
    justify-content: space-between;
}

.content-container {
    --teaser-height: 3rem;

    position: relative;
    width: 100%;
    height: calc(100% - var(--window-buttons-height) - var(--pagination-height));
}

.overflow-container {
    --padding-bottom: 1.5em;
    overflow: auto;
    width: 100%;
    height: 100%;
}

.file-actions {
    padding: 0 var(--padding) 0 var(--padding);

    /* flex parent */
    display: flex;
}

.file-actions button {
    background-color: darkgrey;
    font-size: 12px;
}

.file-actions > * {
    margin: 0 5px 0 5px;
}

.file-actions > *:first-child {
    border-radius: 10px 0 0 10px;
    margin-left: 0;
}

.file-actions > *:last-child {
    margin-right: 0;
    width: 100%;
}

.font-size-button-container {
    display: flex;
    height: min-content;
}

.font-size-button {
    background-color: darkgrey;
    border-radius: 0;
    font-size: 10px;
    font-weight: normal;
    height: min-content;
    padding: 0.25em;
    width: min-content;
}

.font-size-button:hover:not(.disabled) {
    background-color: #878787;
    color: white;
}

.disabled {
    cursor: default;
}

.font-size-button:first-child {
    border-right: 1px solid black;
    border-top-left-radius: 5px;
    border-bottom-left-radius: 5px;
    margin-left: auto;
}

.font-size-button:last-child {
    border-top-right-radius: 5px;
    border-bottom-right-radius: 5px;
}

.annotation-list {
    padding: var(--padding);
}

.gradient-teaser {
    position: absolute;
    height: var(--teaser-height);
    width: 100%;
    bottom: 0;
    background: linear-gradient(to top, #ffffffff, #ffffff00);
}

.spacer {
    height: var(--teaser-height);
}

.file-thumbnail-container {
    margin: var(--margin) auto 10px; /* --margin defined in App.module.css under .root{} */
    height: 200px;
    transition: width var(--transition-duration);
}

.no-thumbnail {
    fill: var(--grey);
}

.file-thumbnail-container.thumbnail-default {
    width: 90%;
}

.file-thumbnail-container.thumbnail-maximized {
    width: 20%;
}

.thumbnail-skeleton {
    width: 100%;
    height: 200px;
    background-color: #ebebeb;
}

.file-details-content {
    width: 96%;
}

.resize-handle {
    width: var(--resize-handle-width);
    height: 100%;
    cursor: col-resize;
    position: absolute;
    left: 0px;
}

.resize-handle:hover {
    background-color: var(--primary-brand-dark-blue);
}

gha_language: TypeScript


text:
@article{graeser_synthesis_2014,
 author = {Graeser, Brian K. and Hages, Charles J. and Yang, Wei Chang and Carter, Nathaniel J. and Miskin, Caleb K. and Stach, Eric A. and Agrawal, Rakesh},
 copyright = {All rights reserved},
 doi = {10.1021/cm501017z},
 file = {Graeser et al. - 2014 - Synthesis of (CuInS sub2sub ) sub0.5sub .pdf:C\:\\Users\\wdy\\Zotero\\storage\\YDACG6CR\\Graeser et al. - 2014 - Synthesis of (CuInS sub2sub ) sub0.5sub .pdf:application/pdf},
 issn = {0897-4756, 1520-5002},
 journal = {Chemistry of Materials},
 language = {en},
 month = {July},
 number = {14},
 pages = {4060--4063},
 title = {Synthesis of (CuInS $_\textrm2$ ) $_\textrm0.5$ (ZnS) $_\textrm0.5$ Alloy Nanocrystals and Their Use for the Fabrication of Solar Cells via Selenization},
 url = {https://pubs.acs.org/doi/10.1021/cm501017z},
 urldate = {2020-01-02},
 volume = {26},
 year = {2014}
}

repository: wcdyang/academic-kickstart

repo_name: wcdyang/academic-kickstart


text:
@misc{stańczak2021quantifying,
      title={Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models},
      author={Karolina Stańczak and Sagnik Ray Choudhury and Tiago Pimentel and Ryan Cotterell and Isabelle Augenstein},
      year={2021},
      eprint={2104.07505},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

repository: copenlu/academic-kickstart

repo_name: copenlu/academic-kickstart


text:
@Article{mukerjee2022political,
  title = {The Political Landscape of the US Twitterverse},
  author = {Subhayan Mukerjee and Kokil Jaidka and Yphtach Lelkes},
  journal = {Political Communication},
  pages = {1--31},
  year = {2022},
  publisher = {Routledge},
  journaltitle = {Political Communication},
  pubtype = {2},
}

repository: ylelkes/academic-kickstart

repo_name: ylelkes/academic-kickstart


text:
/*
Created on : Mar 21, 2016, 3:20:16 PM
Author     : Kiril Kirkov
Gitgub     : https://github.com/kirilkirkov
*/
html, body { height: 100%;}
body { color:#6b6b6b; font-size: 13px; }
#wrapper { min-height: 100%; position: relative;}
#content {padding-bottom:100px;}
h1 {font-size:30px; }
nav.navbar, div.left-side, h1 {text-transform: uppercase; }
input.form-control, textarea.form-control {border:1px solid #dadada; border-radius: 0; box-shadow:none;}
.login-container{    position: relative;    width: 300px;    margin: 80px auto;    padding: 20px 40px 40px;    text-align: center;    background: #fff;    border: 1px solid #ccc;}
#output{    position: absolute;    width: 300px;    top: -75px;    left: 0;    color: #fff;}
#output.alert-success{    background: rgb(25, 204, 25);}
#output.alert-danger{    background: rgb(228, 105, 105);}
.login-container::before,.login-container::after{    content: "";    position: absolute;    width: 100%;height: 100%;    top: 3.5px;left: 0;    background: #fff;    z-index: -1;    -webkit-transform: rotateZ(4deg);    -moz-transform: rotateZ(4deg);    -ms-transform: rotateZ(4deg);    border: 1px solid #ccc;}
.login-container::after{    top: 5px;    z-index: -2;    -webkit-transform: rotateZ(-2deg);     -moz-transform: rotateZ(-2deg);      -ms-transform: rotateZ(-2deg);}
.avatar{    width: 100px;height: 100px;    margin: 10px auto 30px;    border-radius: 100%;    border: 2px solid #aaa;    background-size: cover;}
.form-box input{    width: 100%;    padding: 10px;    text-align: center;    height:40px;    border: 1px solid #ccc;    background: #fafafa;    transition:0.2s ease-in-out;}
.form-box input:focus{    outline: 0;    background: #eee;}
.form-box input[type="text"]{    border-radius: 5px 5px 0 0;    text-transform: lowercase;}
.form-box input[type="password"]{    border-radius: 0 0 5px 5px;    border-top: 0;}
.form-box button.login{    margin-top:15px;    padding: 10px 20px;}
.animated {  -webkit-animation-duration: 1s;  animation-duration: 1s;  -webkit-animation-fill-mode: both;  animation-fill-mode: both;}
#theSliderDescrption {display:none;}
.more-info-purchase > tbody > tr > td, .more-info-purchase > tbody > tr > th, .more-info-purchase > tfoot > tr > td, .more-info-purchase > tfoot > tr > th, .more-info-purchase > thead > tr > td, .more-info-purchase > thead > tr > th {border-top: none; border-bottom: 1px solid #ddd;}
.cookie-law-themes { padding-top: 20px; padding-bottom: 20px; margin-left: 10px; margin-right: 10px;}
.cookie-law-themes a .theme {margin-bottom:10px;}
.cookie-law-themes a .ok {display:none; position: absolute; width:40px; top:0; left:0;}
.show-menu {display: none;}
#show-xs-nav { border: 1px solid #666;  padding: 5px; text-decoration: none; color: #666;  font-size: 13px; text-align: center; margin-top: 10px; border-radius: 5px;}
#nav-categories, #show-xs-nav .hidde-sp {display: none;}
.confirm-result {position:absolute; bottom:0; right:0;}
#calc-board {width:200px; margin:0 auto;}
#calc-board a {margin-bottom:5px; box-shadow: 0 1px 0 rgba(255, 255, 255, 0.2) inset, 0 1px 2px rgba(0, 0, 0, 0.05); border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) #b3b3b3; background: #ffffff;  background: -moz-linear-gradient(top,  #ffffff 0%, #e6e6e6 100%);  background: -webkit-linear-gradient(top,  #ffffff 0%,#e6e6e6 100%); background: linear-gradient(to bottom,  #ffffff 0%,#e6e6e6 100%);filter: progid:DXImageTransform.Microsoft.gradient( startColorstr='#ffffff', endColorstr='#e6e6e6',GradientType=0 ); color:#333;}
.other-img {display: inline-block; position:relative;}
.other-img a {position:absolute; right:0; top:0; background-color: #fff; color:red;}
.selected-template {height: 50px; position: absolute; left:25px; top:10px;}
.header-img {float: left;height: 40px;margin-right: 10px;margin-top: -10px;}
.divLangs { padding:10px; margin-bottom: 10px;}
#new-style {border-radius: 0;}
#categorieEditor {position: absolute; display: none; width:230px;}
#categorieEditor input, #categorieEditor button {float: left;}
#categorieEditor input {width: 150px;}
#categorieEditor .yesSaveEdit {display: none;}
#categorieEditor button {width:39px;}
#categorieSubEdit {display: none; position: absolute;}
#categorieSubEdit button {width:200px;}

@-webkit-keyframes fadeInUp {
    0% {
        opacity: 0;
        -webkit-transform: translateY(20px);
        transform: translateY(20px);
    }

    100% {
        opacity: 1;
        -webkit-transform: translateY(0);
        transform: translateY(0);
    }
}

@keyframes fadeInUp {
    0% {
        opacity: 0;
        -webkit-transform: translateY(20px);
        -ms-transform: translateY(20px);
        transform: translateY(20px);
    }

    100% {
        opacity: 1;
        -webkit-transform: translateY(0);
        -ms-transform: translateY(0);
        transform: translateY(0);
    }
}

.fadeInUp {  -webkit-animation-name: fadeInUp;  animation-name: fadeInUp;}
nav.navbar {z-index: 55;}
nav.navbar a { color:#fff;}
nav.navbar li a:hover, nav.navbar li a:focus, nav.navbar li a:active {background-color: transparent;}
.navbar-nav li a i {margin-right: 7px;}
nav.navbar ul.dropdown-menu li {margin-bottom: 3px;}
nav.navbar ul.dropdown-menu li a { color:#333;}
.nav .open > a, .nav .open > a:focus, .nav .open > a:hover {background-color: transparent;}
div.left-side { border-right:1px solid #e7e7e7; padding:0; bottom: 0;  display: block;left: 0; overflow: auto;position: absolute;    top: 51px;}
div.left-side ul.sidebar-menu {list-style: none; margin:0 0 80px;padding:0;}
div.left-side ul.sidebar-menu li {margin: 0; padding: 0; position: relative; border-bottom: 1px solid #e7e7e7;}
div.left-side ul.sidebar-menu li a {display: block;   border-left: 3px solid transparent;  color: #337ab7;    padding: 12px 5px 12px 15px; }
div.left-side ul.sidebar-menu li a i {margin-right: 7px;}
div.left-side ul.sidebar-menu li a:hover, div.left-side ul.sidebar-menu li a.active { background-color: #eeeeee; text-decoration: none; }
div.left-side ul.sidebar-menu li.header {  color: #4b646f; padding: 12px 5px 12px 15px; }
.sidebar-menu .sidebar-search {padding: 15px !important;}
div.left-side span.alert-admin {padding-left:10px; padding-right: 10px; padding-bottom: 10px; display: inline-block;}
.custab{ border: 1px solid #ccc;  padding: 5px; box-shadow: 3px 3px 2px #ccc; transition: 0.5s; }
.custab:hover{ box-shadow: 3px 3px 0px transparent; transition: 0.5s;}
.relative {position:relative;}
.settings {position: absolute; width:320px;  z-index: 1000; display: none; color:#222d32 !important;}
.settings .panel-body a:hover, .settings .panel-body a:focus {background-color: #2989d8;}
.settings #pass_result {display:none;}
ul.list-none {list-style:none;}
.red {color:red}
.green {color:green}
div.well {margin-top:15px;}
h3.title {word-break: break-all;overflow: hidden;}
.find {margin-bottom:36px;}
a.article-image img {height:250px;}
div.article.invisible-status {opacity:0.4;}
span.dropdown-toggle {cursor: pointer;}
.url {overflow: hidden;}
.url a {word-break: break-all;}
.description-article {max-height: 140px; overflow:hidden; margin-bottom:10px;}
#categories a.btn-xs {margin-bottom: 15px;}
footer {padding: 20px 0; color:#333; text-align: center; background-color: #f8f8f8; border-top:1px solid #e7e7e7; width:100%;height:60px;position:absolute;bottom:0;left:0;}
footer a {color:#333; text-decoration:underline;}
.orders-settings { margin-top: -60px; font-size:20px; color:#6b6b6b;}
.orders-settings span {display: block;}
.orders-settings i {margin-left: 24px;}
.home-page .fast-view-panel {height: 130px;}
.home-page .panel-green {border-color: #5cb85c;}
.home-page .panel-green .panel-heading { background-color: #5cb85c; border-color: #5cb85c; color: #fff;}
.home-page .panel-yellow {  border-color: #f0ad4e;}
.home-page .panel-yellow .panel-heading {  background-color: #f0ad4e;    border-color: #f0ad4e;    color: #fff;}
.home-page .panel-red {  border-color: #d9534f;}
.home-page .panel-red .panel-heading {   background-color: #d9534f;  border-color: #d9534f;  color: #fff;}
.home-page .huge {font-size: 40px;}
.home-page .panel {font-size:14px;}
a.orders-referral {display:block; word-break:break-all;}
.bordered-group {border:1px solid #e7e7e7; padding: 5px;}
.loadUploadOthers {display: none;}
.others-images-container {margin-bottom: 5px;}
#positionEditor {position: absolute; display: none; width:230px;}
#positionEditor input, #positionEditor button {float: left;}
#positionEditor input {width: 150px;}
#positionEditor .yesSavePosition {display: none;}
#positionEditor button {width:39px;}

@media (min-width: 767px) {
    .sidebar-menu {display: block !important;}
}

@media screen and (max-width: 767px) {
    .left-side {position:relative !important; top:0 !important; margin-bottom: 20px;}
    nav.navbar {margin-bottom:0;}
    nav.navbar .navbar-brand {height:55px;}
    .settings {position: relative;}
    .show-menu {display: block; padding:0 10px !important;}
    .sidebar-menu {display: none;}
}


text:
.nav {
  background-color: crimson;
  color: white;
  box-shadow: 1px 1px 15px #666;
  max-height: 66px;
  min-height: 50px;
}
.nav-content {
  display: flex;
  justify-content: space-between;
  align-items: center;
}
.nav-content h4 {
  cursor: pointer;
}
.nav-content h4:focus {
  outline: none;
}
.nav ul {
  display: flex;
  list-style: none;
  margin-top: 13px;
  margin-bottom: 15px;
}
.nav ul li{
  margin-right: 3px;
}
.nav ul li a {
  color: white;
  transition: .3s ease-out;
}
.nav ul li a:hover,
.nav ul li a.active,
.nav ul li a:focus {
  text-decoration: none;
  background-color: rgb(241, 71, 105);
  outline: none;
  box-shadow: none;
}
.cart-length-par {
  position: relative;
}
.cart-length {
  position: absolute;
  top: 0;
  right: 0;
  background-color: #ff8d00;
  width: 20px;
  height: 20px;
  line-height: 20px;
  text-align: center;
  border-radius: 50%;
}
.user-img {
  width: 40px;
  height: 40px;
  border-radius: 50%;
  position: relative;
  cursor: pointer;
  padding: 2px;
  background-color: transparent;
  transition: .2s;
}
.user-img.active,
.user-img:hover {
  background-color: white;
}
.user-img img {
  width: 100%;
  height: 100%;
  border-radius: 50%;
  object-fit: cover;
  object-position: center;
}
.user-img-open {
  position: absolute;
  right: 0px;
  top: 37px;
  display: flex;
  flex-flow: column;
  width: 150px;
  color: #121212;
  line-height: 2;
  background-color: white;
  border-radius: 5px;
  box-shadow: -2px 2px 6px #666;
  padding: 7px 0;
  z-index: 2001;
}
.user-img-open::before {
  content: '';
  position: absolute;
  top: -17px;
  right: 10px;
  border-color: transparent transparent white transparent;
  border-style: solid;
  border-width: 9px;
}
.user-img-open li {
  width: 100%;
  margin-bottom: 5px;
}
.user-img-open li a {
  color: #121212 !important;
  width: 100%;
  padding: 7px;
  display: block;
  cursor: pointer;
}
.user-img-open li a:hover {
  background-color: crimson;
  color: white !important;
}
.min-cart-contain {
  position: absolute;
  right: -6px;
  top: 39px;
  display: flex;
  flex-flow: column;
  width: 375px;
  max-height: 360px;
  overflow-y: auto;
  color: #121212;
  line-height: 2;
  background-color: white;
  border-radius: 5px;
  box-shadow: -2px 2px 6px #666;
  padding: 7px 0;
  z-index: 2000;
}
.min-cart-contain::-webkit-scrollbar {
  width: 10px;
  background: white;
}
.min-cart-contain::-webkit-scrollbar-thumb {
  width: 10px;
  background-color: crimson;
  border-radius: 5px;
}
.cart {
  content: '';
  position: absolute;
  right: 22px;
  bottom: -2px;
  border-color: transparent transparent white transparent;
  border-style: solid;
  border-width: 9px;
  z-index: 2001;
}

repository: amrwheab/AngularFirbaseE-Commerce

repo_name: amrwheab/AngularFirbaseE-Commerce

branch_name: refs/heads/main


text:
@InProceedings{Li_2019_ICCV,
author = {Li, Da and Zhang, Jianshu and Yang, Yongxin and Liu, Cong and Song, Yi-Zhe and Hospedales, Timothy M.},
title = {Episodic Training for Domain Generalization},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}

url: https://raw.githubusercontent.com/Hugh-Shi/DL-NLP-Readings/924e4d189e5660c5b5d4854278f3df755ae2231d/Bibtex/Episodic Training for Domain Generalization.bib

repository: Hugh-Shi/DL-NLP-Readings

repo_name: Hugh-Shi/DL-NLP-Readings


text:
body{
background: #000000 url(../images/header.jpg) no-repeat center top;
margin:0 auto;
text-align:center;

}

repository: fotobank/add

repo_name: fotobank/add


text:
.container {
  /*padding-top: 4%*/
}

.mother_div {
  margin-left: 15%;
}

.main-wrapper {
  text-align: center;
}

.mainPage {
  position: absolute;
  display: inline-block;
  margin-top: 3%
}

.mainPage-tabs {
  background-color: #f3f4f5;
  padding-top: 1.75%;
  position: fixed;
  width: 100%;
  /*background-color: #ecf0f1;*/
  z-index: 101;
}

.col-md-3 {
  padding-left: 0px !important;
}

.searchBar{
  padding-top: 10px;
  padding-left: 3.75%;
}

.search-submit-btn {
  padding-top: 10px;
  padding-left: 0px;
  width: 100px;
}

.navContainer {
  background-color: #ecf0f1;
}

.container-fluid {
  margin-bottom: 10px;
}

.list-detail-container {
  margin-bottom: 10px;
  margin-top: 3%;
  padding-left: 5%;
}

.navbar {
  display: block;
  background-color: #fff;
  height: 7.75%;
}

.img-responsive2 {
  max-height: 15%;
  max-width: 15%
}

.btn {
  margin-right: 10px;
  border-radius: 0px
}

.btn-social {
  height: 42px;
  margin: 0 auto 10px;
  width: 380px;
}

button.close {
  color: #ff3f00;
  opacity: 0.75;
}

.login-form_inner-container {
  display: block;
}

.login-signup__sep {
  margin: 35px auto 35px;
  position: relative;
  width: 380px;
  border-top: 1px solid #d0d0d0;
  height: 0;
}

.login-signup__sep-text {
  background: #fff;
  border: 1px solid #d0d0d0;
  border-radius: 13px;
  color: #d0d0d0;
  font-size: 15px;
  height: 26px;
  left: 50%;
  line-height: 22px;
  margin: 0 0 0 -13px;
  position: absolute;
  text-align: center;
  top: -13px;
  width: 26px;
}

.modal_title {
  text-align: center;
  margin-top: 5px;
  margin-bottom: 5px;
}

.modal-body {
  padding: 25px 60px 50px;
}

.login-form__container {
}

.login_tagline {
  color: #b3b1b3;
  text-align: center;
  margin-bottom: 5%;
}

.login_labels {
  font-size: 16px;
  line-height: 1;
  margin: 0 auto;
  width: 380px;
  display: block;
  text-align: center;
}

.login_btn {
  background-color: #ff3f00;
  border: 0;
  display: block;
  width: 380px;
  height: 42px;
  line-height: 50%;
  margin: 18px auto 5%;
  font-size: 16px;
  color: #fff;
  cursor: pointer;
}

.login_inputs {
  width: 380px;
  margin: 8px auto 0px;
  border-radius: 0px;
}

.login_signup_redirect {
  font-size: 12px;
  line-height: 15px;
  margin: 10px 14% 0px;
  display: inline-block;
}

.mainBtns {
  margin-top: 0.75%;
  margin-right: 17.25%;
}

.navBtns {
  margin-left: 4%;
  margin-top: 0.2%;
}

.navSearch {
  margin-top: 2%;
}

.search_by_cat_container {
  margin-bottom: 10%
}

.search-not-found {
  margin-left: 1%;
  margin-top: 1%;
}

.label-default:before {
  content:'';

  width:1.30em;
  height:1.358em;

  background-image: -webkit-linear-gradient(left top, rgb(254, 218, 113), rgb(254, 186, 71));
  background-image: -moz-linear-gradient(left top, rgb(254, 218, 113), rgb(254, 186, 71));
  background-image: -o-linear-gradient(left top, rgb(254, 218, 113), rgb(254, 186, 71));
  background-image: -ms-linear-gradient(left top, rgb(254, 218, 113), rgb(254, 186, 71));
  background-image: linear-gradient(left top, rgb(254, 218, 113), rgb(254, 186, 71));
  filter: progid:DXImageTransform.Microsoft.gradient(GradientType=1,StartColorStr='#feda71', EndColorStr='#feba47');

  position:absolute;
  left:-0.69em;
  top:.2em;

  -webkit-transform:rotate(45deg);
  -moz-transform:rotate(45deg);
  -o-transform:rotate(45deg);
  transform:rotate(45deg);

  border-left:1px solid #d99d38;
  border-bottom:1px solid #d99d38;

  -webkit-border-radius:0 0 0 0.25em;
  -moz-border-radius:0 0 0 0.25em;
  border-radius:0 0 0 0.25em;

  z-index:1;
}

.label-default {
  /*margin-right: 0.5%;*/
  float:left;
  margin: 1% 1% 7px 20px;
  position:relative;

  font-family:'Signika', Helvetica, Arial, sans-serif;
  font-size:0.75em;
  font-weight:bold;
  text-decoration:none;

  color:#996633;
  text-shadow:0px 1px 0px rgba(255,255,255,.4);

  padding:0.417em 0.417em 0.417em 0.917em;

  border-top:1px solid #d99d38;
  border-right:1px solid #d99d38;
  border-bottom:1px solid #d99d38;

  -webkit-border-radius:0 0.25em 0.25em 0;
  -moz-border-radius:0 0.25em 0.25em 0;
  border-radius:0 0.25em 0.25em 0;

  background-image: -webkit-linear-gradient(top, rgb(254, 218, 113), rgb(254, 186, 71));
  background-image: -moz-linear-gradient(top, rgb(254, 218, 113), rgb(254, 186, 71));
  background-image: -o-linear-gradient(top, rgb(254, 218, 113), rgb(254, 186, 71));
  background-image: -ms-linear-gradient(top, rgb(254, 218, 113), rgb(254, 186, 71));
  background-image: linear-gradient(top, rgb(254, 218, 113), rgb(254, 186, 71));
  filter: progid:DXImageTransform.Microsoft.gradient(GradientType=0,StartColorStr='#feda71', EndColorStr='#feba47');

  -webkit-box-shadow:
      inset 0 1px 0 #faeaba,
      0 1px 1px rgba(0,0,0,.1);
  -moz-box-shadow:
      inset 0 1px 0 #faeaba,
      0 1px 1px rgba(0,0,0,.1);
  box-shadow:
      inset 0 1px 0 #faeaba,
      0 1px 1px rgba(0,0,0,.1);

  z-index:100;
}

.label-default:after {
  content:'';

  width:0.5em;
  height:0.5em;

  background:#fff;

  -webkit-border-radius:4.167em;
  -moz-border-radius:4.167em;
  border-radius:4.167em;

  border:1px solid #d99d38;

  -webkit-box-shadow:0 1px 0 #faeaba;
  -moz-box-shadow:0 1px 0 #faeaba;
  box-shadow:0 1px 0 #faeaba;

  position:absolute;
  top:0.667em;
  left:-0.083em;
  z-index:9999;
}

.label-default:hover {
  background-image: -webkit-linear-gradient(top, rgb(254, 225, 141), rgb(254, 200, 108));
  background-image: -moz-linear-gradient(top, rgb(254, 225, 141), rgb(254, 200, 108));
  background-image: -o-linear-gradient(top, rgb(254, 225, 141), rgb(254, 200, 108));
  background-image: -ms-linear-gradient(top, rgb(254, 225, 141), rgb(254, 200, 108));
  background-image: linear-gradient(top, rgb(254, 225, 141), rgb(254, 200, 108));
  filter: progid:DXImageTransform.Microsoft.gradient(GradientType=0,StartColorStr='#fee18d', EndColorStr='#fec86c');

  border-color:#e1b160;
}

.label-default:hover:before {
  background-image: -webkit-linear-gradient(left top, rgb(254, 225, 141), rgb(254, 200, 108));
  background-image: -moz-linear-gradient(left top, rgb(254, 225, 141), rgb(254, 200, 108));
  background-image: -o-linear-gradient(left top, rgb(254, 225, 141), rgb(254, 200, 108));
  background-image: -ms-linear-gradient(left top, rgb(254, 225, 141), rgb(254, 200, 108));
  background-image: linear-gradient(left top, rgb(254, 225, 141), rgb(254, 200, 108));
  filter: progid:DXImageTransform.Microsoft.gradient(GradientType=1,StartColorStr='#fee18d', EndColorStr='#fec86c');

  border-color:#e1b160;
}

.list-legend {
  margin-top: 20px
}

.list-group {
  border-radius: 0px;
  margin-top: 5.7%;
  padding-left: 3%;
}

.profile-list-group {
  margin-left: -1%;
}

.list_main_container {
  display: inline-block;
}

.list-group-item {
  margin-bottom: 1% !important;
  padding: 0px;
  border: 0.5px solid #eaebec;
  border-radius: 0px !important;
}

.list_image_container {
  float: left;
  position: relative;
  width: 10em;
  height: 10em;
  max-width: 10em;
  max-height: 10em;
  margin: 10px;
}

.img-thumbnail {
  padding: 0px;
  border: none;
  border-radius: 0px;
  position: absolute;
  top: 50%;
  transform: translate(0, -50%);
  width: 10em;
}

.list_overview_container {
  display: inline-block;
  margin-top: 3%;
  padding-left: 4%;
  width: 60rem;
}

.list_overview_fav {
  float: left;
  margin-left: 4.85%;
  margin-top: 0.85%;
}

.list_overview_title {
  display: block;
  white-space: nowrap;
  overflow: hidden;
  text-overflow: ellipsis;
  padding-left: 2%;
  font-family: 'Signika', sans-serif;
}

.list_detail_fav {
  float: left;
  margin-top: 2%;
}

.list_detail_title {
  display: inline-block;
  font-size: 3.75rem;
  padding-left: 2%;
  font-family: 'Signika', sans-serif;
  width: 600px;
}

.list_overview_inner_container {
  display: block;
  margin-left: 3.75%;
}

.list_overview_inner {
  display: inline-block;
  margin-left: 8.5%;
  font-size: 1.5rem;
}

.list_detail_overview_inner_container {
  /*margin-left: 5.5%;*/
  font-size: 1.5rem;
}

.list_detail_overview_inner {
  margin-left: 6.5%;
}

.list_overview_words {
  font-family: 'Signika', sans-serif;
}

.list_overview_inner > a {
  text-decoration: none;
  font-family: 'Signika', sans-serif;
}

.list_overview_votes {
  float: right;
  margin-top: 3.3%;
  margin-right: 3.3%;
}

.list_overview_votes_count {
  font-size: 1.75rem;
}

.list_overview_votes_inner {
  /*display: block*/
}

.list_overview_cats {
  display: block;
  margin-left: 9%;
}

.list_detail_overview_cats {
  display: block;
  margin-left: 4.25%;
}

.overview-list-item-separation {
  margin-top: 10%;
}

.login_favorite {
  display: inline-block;
}

.navbar-brand {
  padding: 0 !important;
  margin: 0 !important;
}

.navbar-brand>img {
  margin: 5% !important;
}

.nav-tabs {
  background-color: #e6e6e6;
}

.ReactTabs__TabList {
  margin-bottom: 0px !important;
}

.detail-img {
  max-width: 70%;
}

.detail-lists {
  font-size: 22px;
  font-family: 'Signika', sans-serif;
  margin-top: 7.5%;
}

.detail-lists-caption {
  padding: 2px;
  font-size: 16px;
  font-family: 'Nunito', sans-serif;
}

.comments {
  position: fixed;
  padding-right: 0px;
  padding-left: 0px;
}

.submit-comment {
  color: white;
  font-weight: bold;
  border-radius: 5px;
  padding: 5px !important;
}

.textarea-comment {
  width: 100%;
  height: 100px;
  padding: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border: 2px solid #ccc;
  border-radius: 5px;
  background-color: #f8f8f8;
  resize: none;
  transition: height 0.5s;
  -moz-transition: height 0.5s;
  -webkit-transition: height 0.5s;
  -o-transition: height 0.5s;
}

.textarea-comment:focus {
  border: 2px solid #555;
}

.comments-lists {
  position: fixed;
  max-height: 70%;
  overflow-y:scroll;
  padding-right: 0px;
  padding-left: 0px;
}

.remove-comment {
  background: none;
  border: 0;
  line-height: 1;
  opacity: 0;
}

.remove-comment:hover {
  color: red;
}

.comment {
  border-bottom: 1px solid lightgrey;
  padding: 0.5rem 0
}

.comment p {
  font-size: 1.2rem;
  margin: 0
}

.comment strong {
  color: darkblue;
  margin-right: 5px
}

.comment:hover .remove-comment {
  opacity: 1
}

.comment-form input, textarea {
  width: 100%;
  border: 0;
  font-size: 1.3rem;
  padding: 1rem 0;
  border-bottom: 1px solid lightgrey;
  outline: none;
  resize: vertical
}

.search-tabs {
  margin-top: 5%;
}

.profile-tabs {

}

.profile {
  margin-top: 3%;
}

.button:hover {
  cursor: pointer
}

.media-heading {
  font-size: 2.75rem;
  text-decoration: none !important
}

.col-md-5 {
  padding: 0px;
}

.form-control {
  border-radius: 0px !important;
}

body {
  background-color: #f3f4f5;
}

.pagination {
  padding-left: 15px;
}

.pagination>.active>a, .pagination>.active>a:focus, .pagination>.active>span, .pagination>.active>span:focus, .pagination>.active>span:hover{
  z-index: 3;
  color: #fff;
  cursor: default;
  background-color: #ff3f00;
  border-color: #ff3f00;
}

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@article{Watson2019,
 author = {Simon Watson and Alberto Moro and Vera Reis and Charalampos Baniotopoulos and Stephan Barth and Gianni Bartoli and Florian Bauer and Elisa Boelman and Dennis Bosse and Antonello Cherubini and Alessandro Croce and Lorenzo Fagiano and Marco Fontana and Adrian Gambier and Konstantinos Gkoumas and Christopher Golightly and Mikel Iribas Latour and Peter Jamieson and John Kaldellis and Andrew Macdonald and Jimmy Murphy and Michael Muskulus and Francesco Petrini and Luca Pigolotti and Flemming Rasmussen and Philippe Schild and Roland Schmehl and Nafsika Stavridou and John Tande and Nigel Taylor and Thomas Telsnig and Ryan Wiser},
 date = {2019-07-23},
 doi = {10.1016/j.rser.2019.109270},
 journal = {Renewable and Sustainable Energy Reviews},
 pages = {109270},
 title = {Future emerging technologies in the wind power sector: A European perspective},
 volume = {113},
 year = {2019}
}


text:
@article{ardywibowo2018switching,
  title={Switching-state dynamical modeling of daily behavioral data},
  author={Ardywibowo, Randy and Huang, Shuai and Gui, Shupeng and Xiao, Cao and Cheng, Yu and Liu, Ji and Qian, Xiaoning},
  journal={Journal of Healthcare Informatics Research},
  volume={2},
  number={3},
  pages={228--247},
  year={2018},
  publisher={Springer}
}


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repository: TermitCG/dtyt

repo_name: TermitCG/dtyt


text:
@Patent{邓一刚2006--,
  author = {邓一刚},
  title  = {全智能节电器},
  number = {200610171314.3},
  year   = {2006},
  date   = {2006-12-13},
}

@Patent{西安电子科技大学2002--,
  author  = {西安电子科技大学},
  title   = {光折变自适应光外差探测方法},
  number  = {01128777.2},
  year    = {2002},
  date    = {2002-03-06},
  url     = {http://211.152.9.47/sipoasp/zljs/hyjs-yx-new.asp?recid=01128777.2&leixin=0},
  urldate = {2002-05-28},
}

@Patent{Tachibana2005--,
  author  = {Tachibana, Ryuki and Shimizu, Shuhichi and Kobayshi, Seiji and Nakamura, Taiga},
  title   = {Electronic watermarking method and system},
  number  = {US6915001},
  year    = {2005},
  date    = {2005-07-05},
  url     = {http://www.google.co.in/patents/US6915001},
  urldate = {2013-11-11},
}

@Patent{姜锡洲1989--,
  Title                    = {一种温热外敷药制备方案},
  Author                   = {姜锡洲},
  Date                     = {1989-07-26},
  Number                   = {88105607.3},
  Titleaddon               = {中国}
}

@Patent{刘加林1993--,
  Title                    = {多功能一次性压舌板},
  Author                   = {刘加林},
  Date                     = {1993-04-14},
  Number                   = {92214985.2},
  Location                 = {中国}
}

@Patent{河北绿洲生态环境科技有限公司2001--,
  Title                    = {一种荒漠化地区生态植被综合培育种植方法},
  Author                   = {河北绿洲生态环境科技有限公司},
  Date                     = {2001-10-24},
  Number                   = {01129210.5},
  Url                      = {http://211.152.9.47/sipoasp/zlijs/hyjs-yx-new.asp?recid=01129210.5&leixin},
  Urldate                  = {2002-05-28}
}

@Patent{西安电子科技大学2002--a,
  author  = {西安电子科技大学},
  title   = {光折变自适应光外差探测方法},
  number  = {01128777.2},
  date    = {2002-03-06},
  url     = {http://211.152.9.47/sipoasp/zljs/hyjs-yx-new.asp?recid=01128777.2&leixin=0},
  urldate = {2002-05-28},
}

@Patent{TACHIBANA2002--,
  Title                    = {Electronic watermarking method and system},
  Author                   = {R TACHIBANA and S SHIMIZU and S KOBAYSHI and others},
  Date                     = {2002-04-25},
  Number                   = {US6915001},
  Url                      = {http://patftuspto.gov/netacgi/nph-Parser?Seetl=PTO2&Sect2=HITOFF&=1&u=/netahhml/search-bool.html&r=1&f=G&1=50&col=AND&d=ptxt&sl=`Electronic+watermarking+method+system'.TTL.&OS=TTL/},
  Urldate                  = {2002-05-28}
}

@Patent{KOSEKI2002--,
  Title                    = {Compiler},
  Author                   = {A KOSEKI and H MOMOSE and M KAWAHITO and others},
  Date                     = {2002-05-25},
  Number                   = {US828402},
  Url                      = {http://FF&p=1&u=netahtml/PTO/search-bool.html&r=5&f=G&l=50&col=AND&d=PG01&sl=IBM.AS.&0S=AN/IBM&RS=AN/IBM},
  Urldate                  = {2002-05-28}
}

@Patent{张凯军2012-04-05--,
  Title                    = {轨道火车及高速轨道火车紧急安全制动辅助装置},
  Author                   = {张凯军},
  Number                   = {201220158825.2},
  Year                     = {2012-04-05}
}

repository: hushidong/biblatex-map

repo_name: hushidong/biblatex-map


text:
---
---

@string{aps = {American Physical Society,}}

@article{jin2022XAIguideline,
    url = {https://arxiv.org/abs/2202.10553},
    arxiv = {2202.10553},
    author = {Jin, Weina and Li, Xiaoxiao and Fatehi, Mostafa and Hamarneh, Ghassan},
    keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
    title = {Guidelines and Evaluation for Clinical Explainable {AI} in Medical Image Analysis},
    publisher = {arXiv},
    journal = {Under revision at Medical Image Analysis},
    year = {2022},
    abstract = {Explainable artificial intelligence (XAI) is essential for enabling clinical users to get informed decision support from AI and comply with evidence-based medical practice. Applying XAI in clinical settings requires proper evaluation criteria to ensure the explanation technique is both technically sound and clinically useful, but specific support is lacking to achieve this goal.
    To bridge the research gap, we propose the Clinical XAI Guidelines that consist of five criteria a clinical XAI needs to be optimized for. The guidelines recommend choosing an explanation form based on Guideline 1 (G1) Understandability and G2 Clinical relevance. For the chosen explanation form, its specific XAI technique should be optimized for G3 Truthfulness, G4 Informative plausibility, and G5 Computational efficiency.
    Following the guidelines, we conducted a systematic evaluation on a novel problem of multi-modal medical image explanation with two clinical tasks, and proposed new evaluation metrics accordingly.
    16 commonly-used heatmap XAI techniques were evaluated and found to be insufficient for clinical use due to their failure in G3 and G4. Our evaluation demonstrated the use of Clinical XAI Guidelines to support the design and evaluation for clinically viable XAI. },
    bibtex_show={true},
    preview = {graphicabstract_XAI_guideline.jpg},
    selected={true},
    abbr = {arXiv},
    project = {xai_eval},
    blog= {https://weina.me/clinical_xai_guideline.html},
    pdf = {manuscript_Clinical_XAI_Guidelines_cleaned.pdf},
    code = {https://github.com/weinajin/multimodal_explanation},
    tldr = {The Clinical XAI Guidelines provides criteria that explanation should fulfill in critical decision support.},
}

@article{Jin_Li_Hamarneh_2022,
    title={Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?},
    volume={36},
    url={https://ojs.aaai.org/index.php/AAAI/article/view/21452},
    DOI={10.1609/aaai.v36i11.21452},
    abstract={Being able to explain the prediction to clinical end-users is a necessity to leverage the power of artificial intelligence (AI) models for clinical decision support. For medical images, a feature attribution map, or heatmap, is the most common form of explanation that highlights important features for AI models’ prediction. However, it is unknown how well heatmaps perform on explaining decisions on multi-modal medical images, where each image modality or channel visualizes distinct clinical information of the same underlying biomedical phenomenon. Understanding such modality-dependent features is essential for clinical users’ interpretation of AI decisions. To tackle this clinically important but technically ignored problem, we propose the modality-specific feature importance (MSFI) metric. It encodes clinical image and explanation interpretation patterns of modality prioritization and modality-specific feature localization. We conduct a clinical requirement-grounded, systematic evaluation using computational methods and a clinician user study. Results show that the examined 16 heatmap algorithms failed to fulfill clinical requirements to correctly indicate AI model decision process or decision quality. The evaluation and MSFI metric can guide the design and selection of explainable AI algorithms to meet clinical requirements on multi-modal explanation.},
    number={11},
    journal={Proceedings of the AAAI Conference on Artificial Intelligence},
    author={Jin, Weina and Li, Xiaoxiao and Hamarneh, Ghassan},
    year={2022},
    month={Jun.},
    pages={11945-11953},
    arxiv = {2203.06487},
    html = {https://ojs.aaai.org/index.php/AAAI/article/view/21452},
    bibtex_show={true},
    abbr = {AAAI},
    preview = {Poster_AAAI22_Evaluating_ExplainableAI_Jin_Li_Hamarneh.jpg},
    selected={true},
    tldr = {Our systematic evaluation showed the examined 16 heatmap algorithms failed to fulfill clinical requirements to correctly indicate AI model decision process or decision quality.},
    project = {xai_eval_aaai},
    poster = {Poster_AAAI22_Evaluating_ExplainableAI_Jin_Li_Hamarneh.pdf},
    slides = {https://docs.google.com/presentation/d/1CfhnonnQl7ZD6yCCPqW37SEuC2dFrdPV_06R4jDyGsA/edit?usp=sharing},
    blog = {https://weina.me/clinical_xai_guideline.html#AAAI22},
    pdf = {https://ojs.aaai.org/index.php/AAAI/article/view/21452/21201},
    code = {https://github.com/weinajin/multimodal_explanation},
    significance = {Acceptance rate: 15%}
}

@article{jin2022doctorUserStudy,
    author = {Jin, Weina and Hamarneh, Ghassan},
    journal = {Manuscript in preparation},
    title = {What Explanations Do Doctors Require From Artificial Intelligence?},
    year = {2022},
    bibtex_show={true},
    tldr = {An interview study with 25 physicians using low-fidelity explainable AI prototypes reveals the clinical requirements for explanation objectives and forms.},
    project = {jin2022doctorUserStudy},
    preview = {card_sorting.jpg},
    blog = {https://weina.me/doctor-ai.html},
}

@article{brain_ai_clinical_study,
    author = {Jin, Weina and
    Fatehi, Mostafa and
    Hamarneh, Ghassan},
    journal = {In submission to Neuro-Oncology},
    title = {Assessing the Clinical Utility of Artificial Intelligence Assistance and its Explanation on Glioma Grading Task},
    year = {2022},
    bibtex_show={true},
    preview = {brain_ai.png},
    significance = {Weina Jin and Mostafa Fatehi are co-first authors},
    project = {brain_ai_clinical_study},
    blog = {https://weina.me/brain-ai.html},
}

@article{jin2021one_map_not_fit_all,
    author    = {Weina Jin and
               Xiaoxiao Li and
               Ghassan Hamarneh},
    title     = {One Map Does Not Fit All: Evaluating Saliency Map Explanation on Multi-Modal
               Medical Images},
    abstract = {Being able to explain the prediction to clinical end-users is a necessity to leverage the power of AI models for clinical decision support. For medical images, saliency maps are the most common form of explanation. The maps highlight important features for AI model's prediction. Although many saliency map methods have been proposed, it is unknown how well they perform on explaining decisions on multi-modal medical images, where each modality/channel carries distinct clinical meanings of the same underlying biomedical phenomenon. Understanding such modality-dependent features is essential for clinical users' interpretation of AI decisions. To tackle this clinically important but technically ignored problem, we propose the MSFI (Modality-Specific Feature Importance) metric to examine whether saliency maps can highlight modality-specific important features. MSFI encodes the clinical requirements on modality prioritization and modality-specific feature localization. Our evaluations on 16 commonly used saliency map methods, including a clinician user study, show that although most saliency map methods captured modality importance information in general, most of them failed to highlight modality-specific important features consistently and precisely. The evaluation results guide the choices of saliency map methods and provide insights to propose new ones targeting clinical applications.},
    journal   = {ICML 2021 Workshop on Interpretable Machine Learning in Healthcare},
    year      = {2021},
    url       = {https://arxiv.org/abs/2107.05047},
    arxiv = {2107.05047},
    eprinttype = {arXiv},
    eprint    = {2107.05047},
    timestamp = {Tue, 20 Jul 2021 15:08:33 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/abs-2107-05047.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org},
    bibtex_show={true},
    abbr = {ICML-w},
    preview = {thumbnail_One_Map.png},
    project = {one_map},
    significance = {Spotlight paper (top 10%), oral presentation},
    blog = {https://weina.me/one-map-not-fit-all.html},
    code = {https://github.com/weinajin/multimodal_explanation},
    poster = {poster_OneMap.png},
    slides = {https://youtu.be/J-ceZ20cBJk},
    pdf = {https://arxiv.org/pdf/2107.05047.pdf},
    tldr = {The precursor of the AAAI22' paper - Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?},
}

@article{Jin_2020_review_brain_ai,
    author = {Jin, Weina and Fatehi, Mostafa and Abhishek, Kumar and Mallya, Mayur and Toyota, Brian and Hamarneh, Ghassan},
    doi = {10.1088/1741-2552/ab8131},
    journal = {Journal of Neural Engineering},
    month = {apr},
    number = {2},
    pages = {21002},
    publisher = {{\{}IOP{\}} Publishing},
    title = {{Artificial Intelligence in Glioma Imaging: Challenges and Advances}},
    volume = {17},
    year = {2020},
    abstract = {Primary brain tumors including gliomas continue to pose significant management challenges to clinicians. While the presentation, the pathology, and the clinical course of these lesions are variable, the initial investigations are usually similar. Patients who are suspected to have a brain tumor will be assessed with computed tomography (CT) and magnetic resonance imaging (MRI). The imaging findings are used by neurosurgeons to determine the feasibility of surgical resection and plan such an undertaking. Imaging studies are also an indispensable tool in tracking tumor progression or its response to treatment. As these imaging studies are non-invasive, relatively cheap and accessible to patients, there have been many efforts over the past two decades to increase the amount of clinically-relevant information that can be extracted from brain imaging. Most recently, artificial intelligence (AI) techniques have been employed to segment and characterize brain tumors, as well as to detect progression or treatment-response. However, the clinical utility of such endeavours remains limited due to challenges in data collection and annotation, model training, and the reliability of AI-generated information.

    We provide a review of recent advances in addressing the above challenges. First, to overcome the challenge of data paucity, different image imputation and synthesis techniques along with annotation collection efforts are summarized. Next, various training strategies are presented to meet multiple desiderata, such as model performance, generalization ability, data privacy protection, and learning with sparse annotations. Finally, standardized performance evaluation and model interpretability methods have been reviewed. We believe that these technical approaches will facilitate the development of a fully-functional AI tool in the clinical care of patients with gliomas.},
    arxiv = {1911.12886},
    bibtex_show={true},
    preview = {brain_review_paper_outline.jpg},
    abbr = {JNE},
    pdf = {https://arxiv.org/pdf/1911.12886.pdf},
    slides = {https://youtu.be/i2rX6NSH27k},
    html = {https://pubmed.ncbi.nlm.nih.gov/32191935/},
    tldr = {A review if the challenges and advances of implementing AI in clinical settings (including the interpretable AI challenge) in the neuro-oncology domain.},
}

@article{jin2021euca,
    title={{EUCA}: the End-User-Centered Explainable {AI} Framework},
    author={Weina Jin and Jianyu Fan and Diane Gromala and Philippe Pasquier and Ghassan Hamarneh},
    url       = {https://arxiv.org/abs/2102.02437},
    abstract = {The ability to explain decisions to end-users is a necessity to deploy AI as critical decision support. Yet making AI explainable to non-technical end-users is a relatively ignored and challenging problem. To bridge the gap, we first identify twelve end-user- friendly explanatory forms that do not require technical knowledge to comprehend, including feature-, example-, and rule-based explanations. We then instantiate the explanatory forms as prototyping cards in four AI-assisted critical decision-making tasks, and conduct a user study to co-design low-fidelity prototypes with 32 layperson participants. The results confirm the relevance of using explanatory forms as building blocks of explanations, and identify their proprieties — pros, cons, applicable explanation goals, and design implications. The explanatory forms, their proprieties, and prototyping supports (including a suggested prototyping process, design templates and exemplars, and associated algorithms to actualize explanatory forms) constitute the End-User-Centered explainable AI framework EUCA, and is available at http://weinajin.github.io/end-user-xai. It serves as a practical prototyping toolkit for HCI/AI practitioners and researchers to understand user requirements and build end-user-centered explainable AI.},
    year={2021},
    arxiv={2102.02437},
    archivePrefix={arXiv},
    primaryClass={cs.HC},
    bibtex_show={true},
    website = {http://weina.me/end-user-xai},
    pdf = {https://weina.me/end-user-xai/paper/EndUserXAI_manuscript.pdf},
    preview = {EUCA.png},
    selected={true},
    abbr = {arXiv},
    project = {euca},
    poster = {end_user_xai_cartoon.jpg},
    blog = {https://weina.me/end-user-xai.html},
    code = {https://github.com/weinajin/end-user-xai},
    tldr = {EUCA provides design suggestions from end-users' perspective on explanation forms and goals.}
}

@inproceedings{Jin2019vis,
    abstract = {Researchers in the re-emerging field of explainable/interpretable artificial intelligence (XAI) have not paid enough attention to the end users of AI, who may be lay persons or domain experts such as doctors, drivers, and judges. We took an end-user-centric lens and conducted a literature review of 59 technique papers on XAI algorithms and/or visualizations. We grouped the existing explanatory forms in the literature into the end-user-friendly XAI taxonomy. It consists of three forms that explain AI's decisions: feature attribute , instance, and decision rules/trees. We also analyzed the visual representations for each explanatory form, and summarized them as the XAI visual vocabularies. Our work is a synergy of XAI algorithm, visualization, and user-centred design. It provides a practical toolkit for AI developers to define the explanation problem from a user-centred perspective, and expand the visualization space of explanations to develop more end-user-friendly XAI systems.},
    author = {Jin, Weina and Carpendale, Sheelagh and Hamarneh, Ghassan and Gromala, Diane},
    keywords = {Explainable AI,Visualization},
    booktitle = {IEEE VIS 2019 Conference Poster Abstract},
    title = {{Bridging AI Developers and End Users: an End-User-Centred Explainable AI Taxonomy and Visual Vocabularies}},
    year = {2019},
    preview = {end_user_xai_image.jpg},
    abbr = {IEEE VIS poster},
    significance = {VIS Best Poster Presentation Award},
    tldr = {We conducted a literature review and summarize the end-user-friendly explanation forms as visual vocabularies. This is The precursor of the EUCA framework.},
    project = {euca},
    poster = {201910_IEEE_VIS_poster.pdf},
    video = {https://youtu.be/b5JnaSG1AYM},
    slides = {https://vimeo.com/361161864},
    html = {http://ieeevis.org/year/2019/info/awards/best-poster-awards},
    supp = {Explainable_AI_lit_review_list.pdf},
    pdf = {End-User-CentredExplainable_AI_Taxonomy_VisualVocabularies.pdf},
    poster = {201910_IEEE_VIS_poster.pdf},
    bibtex_show={true},

}

@inproceedings{jin2016asif,
    author = {Jin, Weina and Ulas, Servet and Tong, Xin},
    title = {AS IF: A Game as an Empathy Tool for Experiencing the Activity Limitations of Chronic Pain Patients},
    year = {2016},
    isbn = {9781450340823},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi-org.proxy.lib.sfu.ca/10.1145/2851581.2890369},
    doi = {10.1145/2851581.2890369},
    abstract = {Pain is both a universal and unique experience for its sufferers. Nonetheless, pain is also invisible and incommunicable that it becomes difficult for the public to understand or even believe the suffering, especially for the persistent form of pain: Chronic Pain. Therefore, we designed and developed the game -- AS IF -- to foster non-patients' empathy for Chronic Pain sufferers. In this game, players engage with the connecting dots tasks through whole body interaction. After they generate the connection with their virtual body, they will experience a certain degree of activity limitation that mimics one of the sufferings of Chronic Pain. In this paper, we introduce the game design that facilitates the enhancement of empathy for Chronic Pain experience, and illustrate how this game acts as a form of communication media that may help to enhance understanding.},
    booktitle = {Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems},
    pages = {172–175},
    numpages = {4},
    keywords = {chronic pain experience, gaming for a purpose, mobility, stimulate empathy, body interaction},
    location = {San Jose, California, USA},
    series = {CHI EA '16},
    preview = {ASIF_empathy_game.jpg},
    bibtex_show={true},
    abbr = {CHI poster},
    significance = {CHI Student Game Competition Finalist},
    slides = {https://youtu.be/_HfoHFr8F84},
    pdf = {ASIF_JIN.pdf},
                tldr = {The design of an empathy game for people with chronic pain.},

}

@inproceedings{jin2017cscw,
    author = {Jin, Weina and Gromala, Diane and Neustaedter, Carman and Tong, Xin},
    title = {A Collaborative Visualization Tool to Support Doctors' Shared Decision-Making on Antibiotic Prescription},
    year = {2017},
    isbn = {9781450346887},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi-org.proxy.lib.sfu.ca/10.1145/3022198.3026311},
    doi = {10.1145/3022198.3026311},
    abstract = {The inappropriate prescription of antibiotics may cause severe medical outcomes such as antibiotic resistance. To prevent such situations and facilitate appropriate antibiotic prescribing, we designed and developed an asynchronous collaborative visual analytics tool. It visualizes the antibiotics' coverage spectrum that allows users choose the most appropriate antibiotics. The asynchronous collaboration around visualization mimics the actual collaboration scenarios in clinical settings, and provides supportive information during physician's decision-making process. Our work contributes to the CSCW community by providing a design prototype to support asynchronous collaboration among healthcare professionals, which is crucial but lacks in many of the present clinical decision support systems.},
    booktitle = {Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing},
    pages = {211–214},
    numpages = {4},
    keywords = {clinical decision support, asynchronous collaboration, information visualization},
    location = {Portland, Oregon, USA},
    series = {CSCW '17 Companion},
        bibtex_show={true},
            preview = {antibiotic_vis.png},
    abbr = {CSCW poster},
    poster = {Antibiotic_vis_Jin_poster.pdf},
    pdf = {vis_for_antibiotic_JIN.pdf},
                tldr = {A visualization prototype to support asynchronous collaboration among healthcare professionals.},
}

@INPROCEEDINGS{jin2015likemind,
    author={Jin, Weina and Gromala, Diane and Tong, Xin},
    booktitle={2015 IEEE Games Entertainment Media Conference (GEM)},
    title={Serious game for serious disease: Diminishing stigma of depression via game experience},
    year={2015},  volume={},  number={},  pages={1-2},  doi={10.1109/GEM.2015.7377256},
    abstract = {Stigma is a common and serious problem for patients who suffer from depression and other mental illnesses. We designed a serious game to address this problem. The game enables player to experience and strive to overcome the disempowering aspects of depression during the journey to recovery. Through the game's interaction, player may gain a better understanding of the relationship between patient and the disease, which in turn may help to change the players' moral model with the disease model, and thereby diminish the stigma of depression.},
    bibtex_show={true},
    preview = {LikeMind.jpg},
    abbr = {IEEE GEM poster},
    slides = {https://youtu.be/RsPeC6pelE0},
    poster = {LikeMInd_poster.pdf},
    pdf = {likemind_game_JIN.pdf},
    tldr = {The design of an empathy game for people with depression.},
    project = {likemind},
    code = {https://github.com/weinajin/LikeMind_empathy_game_for_depression},
}

@INPROCEEDINGS{jin2018cybersickness,
author={Jin, Weina and Fan, Jianyu and Gromala, Diane and Pasquier, Philippe},
booktitle={2018 IEEE Games, Entertainment, Media Conference (GEM)},
title={Automatic Prediction of Cybersickness for Virtual Reality Games},
year={2018},  volume={},  number={},  pages={1-9},
doi={10.1109/GEM.2018.8516469},
    bibtex_show={true},
abstract = {Cybersickness, which is also called Virtual Reality (VR) sickness, poses a significant challenge to the VR user experience. Previous work demonstrated the viability of predicting cybersickness for VR 360°videos. Is it possible to automatically predict the level of cybersickness for interactive VR games? In this paper, we present a machine learning approach to automatically predict the level of cybersickness for VR games. First, we proposed a novel ranking-rating (RR) score to measure the ground-truth annotations for cybersickness. We then verified the RR scores by comparing them with the Simulator Sickness Questionnaire (SSQ) scores. Next, we extracted features from heterogeneous data sources including the VR visual input, the head movement, and the individual characteristics. Finally, we built three machine learning models and evaluated their performances: the Convolutional Neural Network (CNN) trained from scratch, the Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) trained from scratch, and the Support Vector Regression (SVR). The results indicated that the best performance of predicting cybersickness was obtained by the LSTM-RNN, providing a viable solution for automatically cybersickness prediction for interactive VR games.},
abbr = {IEEE GEM},
pdf = {cybersickness_ml_JIN.pdf},
            tldr = {Applying machine learning to predict cybersickness.},
}


text:
@article{Leppert2022,
  title = {GasChromatographySimulator.jl},
  author = {Jan Leppert},
  journal = {Journal of Open Source Software},
  year = {2022},
  volume = {7},
  number = {76},
  pages = {4565},
  publisher = {The Open Journal},
  doi = {10.21105/joss.04565},
  url = {https://doi.org/10.21105/joss.04565},
}

gha_language: Julia