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	url = {https://github.com/to-mi/gp-demo-js},
	year = {2014}
}
@inproceedings{Rasmussen2001,
	title = {Occam's Razor},
	author = {Rasmussen, Carl E. and Ghahramani, Zoubin},
	booktitle = {Advances in Neural Information Processing Systems},
	year = {2001},
	pages = {294—300},
	publisher = {The MIT Press},
	url = {http://www.gatsby.ucl.ac.uk/~edward/pub/occam.pdf}
}
@article{Matthews2017,
	title = {GPflow: A Gaussian Process Library using TensorFlow},
	author = {Matthews, Alexander G. de G. and Wilk, Mark van der and Nickson, Tom and Fujii, Keisuke and Boukouvalas, Alexis and Leon-Villagra, Pablo and Ghahramani, Zoubin and Hensman, James},
	journal = {Journal of Machine Learning Research},
	year = {2017},
	volume = {18},
	number = {40},
	pages = {1—6},
	url = {http://jmlr.org/papers/v18/16-537.html}
}
@article{Vanhatalo2013,
	title = {GPstuff: Bayesian Modeling with Gaussian Processes},
	author = {Jarno Vanhatalo and Jaakko Riihimäki and Jouni Hartikainen and Pasi Jylänki and Ville Tolvanen and Aki Vehtari},
	journal = {Journal of Machine Learning Research},
	year = {2013},
	volume = {14},
	number = {40},
	pages = {1175—1179},
	url = {http://jmlr.org/papers/v14/vanhatalo13a.html}
}
@article{Rasmussen2010,
	title = {Gaussian Processes for Machine Learning (GPML) Toolbox},
	author = {Carl E. Rasmussen and Hannes Nickisch},
	journal = {Journal of Machine Learning Research},
	year = {2010},
	volume = {11},
	pages = {3011—3015},
	url = {http://jmlr.org/papers/v11/rasmussen10a.html}
}
@misc{gpy2014,
	author = {GPy},
	title = {GPy: A Gaussian Process Framework in Python},
	url = {https://github.com/SheffieldML/GPy},
	year = {since 2012}
}
@article{Neumann2015,
	author = {Marion Neumann and Shan Huang and Daniel E. Marthaler and Kristian Kersting},
	title = {pyGPs—A Python Library for Gaussian Process Regression and Classification},
	journal = {Journal of Machine Learning Research},
	year = {2015},
	volume = {16},
	pages = {2611--2616},
	url = {http://jmlr.org/papers/v16/neumann15a.html}
}
@article{Shahriari2016,
	title = {Taking the Human out of the Loop: A Review of Bayesian Optimization},
	author = {Shahriari, Bobak and Swersky, Kevin and Wang, Ziyu and Adams, Ryan P. and de Freitas, Nando},
	journal = {Proceedings of the IEEE},
	year = {2016},
	month = {January},
	number = {1},
	pages = {148—175},
	volume = {104}
}
@inproceedings{Osborne2009,
	title = {Gaussian Processes for Global Optimization},
	author = {Osborne, Michael A. and Garnett, Roman and Roberts, Stephen J.},
	booktitle = {Proceedings of the International Conference on Learning and Intelligent Optimization},
	year = {2009},
	url = {http://www.robots.ox.ac.uk/~mosb/papers/OsborneGarnettRobertsGPGO.pdf}
}
@inproceedings{Wagberg2017,
	author = {Johan Wågberg and Dave Zachariah and Thomas Schön and Petre Stoica},
	title = {Prediction Performance After Learning in Gaussian Process Regression},
	booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics},
	year = {2017},
	editor = {Aarti Singh and Jerry Zhu},
	volume = {54},
	series = {Proceedings of Machine Learning Research},
	publisher = {PMLR},
	month = {April},
	pages = {1264—1272},
	url = {http://proceedings.mlr.press/v54/wagberg17a.html},
	abstract = {This paper considers the quantification of the prediction performance in Gaussian process regression. The standard approach is to base the prediction error bars on the theoretical predictive variance, which is a lower bound on the mean square-error (MSE). This approach, however, does not take into account that the statistical model is learned from the data. We show that this omission leads to a systematic underestimation of the prediction errors. Starting from a generalization of the Cramér-Rao bound, we derive a more accurate MSE bound which provides a measure of uncertainty for prediction of Gaussian processes. The improved bound is easily computed and we illustrate it using synthetic and real data examples.},
	pdf = {http://proceedings.mlr.press/v54/wagberg17a/wagberg17a.pdf}
}
@online{DuvenaudKernelCookbook,
	author = {David Duvenaud},
	title = {http://www.cs.toronto.edu/~duvenaud/cookbook/index.html},
	year = 2013,
	url = {http://www.cs.toronto.edu/~duvenaud/cookbook/index.html},
	urldate = {2017-28-11}
}
@article{engstrom2019learning,
	title = {Learning Perceptually-Aligned Representations via Adversarial Robustness},
	author = {Engstrom, Logan and Ilyas, Andrew and Santurkar, Shibani and Tsipras, Dimitris and Tran, Brandon and Madry, Aleksander},
	journal = {arXiv preprint arXiv:1906.00945},
	year = {2019}
}
@inproceedings{Santurkar2019ComputerVW,
	title = {Computer Vision with a Single (Robust) Classifier},
	author = {Shibani Santurkar and Dimitris Tsipras and Brandon Tran and Andrew Ilyas and Logan Engstrom and Aleksander Madry},
	year = {2019},
	journal = {arXiv preprint arXiv:1906.09453}
}
@article{mordvintsev2018differentiable,
	author = {Mordvintsev, Alexander and Pezzotti, Nicola and Schubert, Ludwig and Olah, Chris},
	title = {Differentiable Image Parameterizations},
	journal = {Distill},
	year = {2018},
	note = {https://distill.pub/2018/differentiable-parameterizations},
	doi = {10.23915/distill.00012}
}
@article{Hnaff2016GeodesicsOL,
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	year = {2016},
	volume = {abs/1511.06394},
	url = {https://arxiv.org/pdf/1511.06394.pdf}
}
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	title = {Batch Normalization is a Cause of Adversarial Vulnerability},
	author = {Angus Galloway and Anna Golubeva and Thomas Tanay and Medhat Moussa and Graham W. Taylor},
	journal = {ArXiv},
	year = {2019},
	volume = {abs/1905.02161}
}
@article{Hendrycks2019BenchmarkingNN,
	title = {Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
	author = {Dan Hendrycks and Thomas G. Dietterich},
	journal = {ArXiv},
	year = {2019},
	volume = {abs/1903.12261}
}
@article{Su2018IsRT,
	title = {Is Robustness the Cost of Accuracy? - A Comprehensive Study on the Robustness of 18 Deep Image Classification Models},
	author = {Dong Su and Huan Zhang and Hongge Chen and Jinfeng Yi and Pin-Yu Chen and Yupeng Gao},
	journal = {ArXiv},
	year = {2018},
	volume = {abs/1808.01688}
}
@article{liu1989limited,
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@misc{komorowicz2016neural,
	title = {Neural Style transfer with Deep Learning},
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	journal = {https://dawars.me},
	author = {Dávid Komorowicz},
	year = {2016},
	month = {Nov}
}
@article{donahue2016adversarial,
	title = {Adversarial Feature Learning},
	author = {Donahue, Jeff and Kr{\"a}henb{\"u}hl, Philipp and Darrell, Trevor},
	journal = {arXiv preprint arXiv:1605.09782},
	year = {2016},
	url = {https://arxiv.org/pdf/1605.09782.pdf}
}
@inproceedings{shi2016real,
	title = {Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network},
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	booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
	pages = {1874--1883},
	year = {2016},
	url = {https://arxiv.org/pdf/1609.05158.pdf},
	doi = {10.1109/cvpr.2016.207}
}
@article{nisbett1977,
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}
@incollection{pronin2009,
	title = "Chapter 1 The Introspection Illusion",
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	url = "http://www.sciencedirect.com/science/article/pii/S0065260108004012",
	author = "Emily Pronin"
}
% Encoding: UTF-8
@incollection{Rasmussen2004,
	author = {Carl Edward Rasmussen},
	title = {Gaussian Processes in Machine Learning},
	booktitle = {Advanced Lectures on Machine Learning},
	publisher = {Springer Berlin Heidelberg},
	year = {2004},
	pages = {63--71},
	doi = {10.1007/978-3-540-28650-9_4},
	url = {http://www.gaussianprocess.org/gpml/chapters/RW.pdf}
}
@article{Duvenaud2014,
	title = {Automatic model construction with Gaussian processes},
	author = {Duvenaud, David},
	year = {2014},
	journal = {University of Cambridge. PhD Thesis},
	url = {https://www.cs.toronto.edu/~duvenaud/cookbook/}
}
@inproceedings{Lee2018,
	author = {Jaehoon Lee and Jascha Sohl-Dickstein and Jeffrey Pennington and Roman Novak and Sam Schoenholz and Yasaman Bahri},
	title = {Deep Neural Networks as Gaussian Processes},
	booktitle = {International Conference on Learning Representations},
	year = {2018},
	url = {https://arxiv.org/pdf/1711.00165v3.pdf}
}
@article{Kapoor2010,
	author = {Kapoor, Ashish and Grauman, Kristen and Urtasun, Raquel and Darrell, Trevor},
	title = {Gaussian Processes for Object Categorization},
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	year = {2010},
	month = {Jun},
	volume = {88},
	number = {2},
	pages = {169--188},
	isbn = {1573-1405},
	doi = {10.1007/s11263-009-0268-3},
	url = {https://doi.org/10.1007/s11263-009-0268-3},
	issn = {1573-1405}
}
@book{Vapnik2000,
	title = {The Nature of Statistical Learning Theory},
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}
@book{Hazewinkel1994,
	title = {Covariance matrix (Encyclopedia of Mathematics)},
	publisher = {Springer Science+Business Media B.V. / Kluwer Academic Publishers},
	year = {1994},
	editor = {Michiel Hazewinkel},
	address = {Encyclopedia of Mathematics},
	isbn = {978-1-55608-010-4}
}
@incollection{Hinton2008,
	author = {Hinton, Geoffrey E and Salakhutdinov, Ruslan R},
	title = {Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes},
	booktitle = {Advances in Neural Information Processing Systems 20},
	publisher = {Curran Associates, Inc.},
	year = {2008},
	editor = {J. C. Platt and D. Koller and Y. Singer and S. T. Roweis},
	pages = {1249--1256},
	url = {http://papers.nips.cc/paper/3211-using-deep-belief-nets-to-learn-covariance-kernels-for-gaussian-processes.pdf}
}
@inproceedings{Wilson2016,
	author = {Andrew Gordon Wilson and Zhiting Hu and Ruslan Salakhutdinov and Eric P. Xing},
	title = {Deep Kernel Learning},
	booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
	year = {2016},
	editor = {Arthur Gretton and Christian C. Robert},
	volume = {51},
	pages = {370--378},
	address = {Cadiz, Spain},
	month = {09--11 May},
	url = {http://proceedings.mlr.press/v51/wilson16.pdf}
}
@inproceedings{Damianou2013,
	title = {Deep Gaussian Processes},
	author = {Andreas Damianou and Neil Lawrence},
	booktitle = {Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics},
	pages = {207--215},
	year = {2013},
	editor = {Carlos M. Carvalho and Pradeep Ravikumar},
	volume = {31},
	series = {Proceedings of Machine Learning Research},
	address = {Scottsdale, Arizona, USA},
	month = {29 Apr--01 May},
	publisher = {PMLR},
	url = {http://proceedings.mlr.press/v31/damianou13a.pdf}
}
@article{Kim2007,
	author = {Hyun-Chul Kim and Jaewook Lee},
	title = {Clustering Based on Gaussian Processes},
	journal = {Neural Computation},
	year = {2007},
	volume = {19},
	number = {11},
	pages = {3088--3107},
	month = {November},
	doi = {10.1162/neco.2007.19.11.3088},
	publisher = {MIT Press - Journals},
	url = {http://staging.csml.ucl.ac.uk/archive/talks/41e4e55330421a6e230d0ea2b89440ea/Paper_16.pdf}
}
@inproceedings{McHutchon2011,
	author = {McHutchon, Andrew and Rasmussen, Carl Edward},
	title = {Gaussian Process Training with Input Noise},
	booktitle = {Proceedings of the 24th International Conference on Neural Information Processing Systems},
	year = {2011},
	series = {NIPS'11},
	pages = {1341--1349},
	address = {USA},
	publisher = {Curran Associates Inc.},
	acmid = {2986609},
	isbn = {978-1-61839-599-3},
	location = {Granada, Spain},
	numpages = {9},
	url = {http://mlg.eng.cam.ac.uk/pub/pdf/MchRas11.pdf}
}
% Encoding: UTF-8
@article{irving2018debate,
	title = {AI safety via debate},
	author = {Irving, Geoffrey and Christiano, Paul and Amodei, Dario},
	journal = {arXiv preprint arXiv:1805.00899},
	year = {2018},
	url = {https://arxiv.org/abs/1805.00899},
	type = {Blog},
	number = {May 3}
}
@article{silver2017alphazero,
	title = {Mastering chess and shogi by self-play with a general reinforcement learning algorithm},
	author = {Silver, David and Hubert, Thomas and Schrittwieser, Julian and Antonoglou, Ioannis and Lai, Matthew and Guez, Arthur and Lanctot, Marc and Sifre, Laurent and Kumaran, Dharshan and Graepel, Thore and others},
	journal = {arXiv preprint arXiv:1712.01815},
	year = {2017},
	url = {https://arxiv.org/abs/1712.01815}
}
@article{campbell2002deepblue,
	title = "Deep Blue",
	journal = "Artificial Intelligence",
	volume = "134",
	number = "1",
	pages = "57--83",
	year = "2002",
	issn = "0004-3702",
	url = "https://doi.org/10.1016/S0004-3702(01)00129-1",
	author = "Murray Campbell and A.Joseph Hoane and Feng-hsiung Hsu",
	keywords = "Computer chess, Game tree search, Parallel search, Selective search, Search extensions, Evaluation function",
	abstract = "Deep Blue is the chess machine that defeated then-reigning World Chess Champion Garry Kasparov in a six-game match in 1997. There were a number of factors that contributed to this success, including: •a single-chip chess search engine,•a massively parallel system with multiple levels of parallelism,•a strong emphasis on search extensions,•a complex evaluation function, and•effective use of a Grandmaster game database. This paper describes the Deep Blue system, and gives some of the rationale that went into the design decisions behind Deep Blue."
}
@inproceedings{mishra2017simple,
	title = {A simple neural attentive meta-learner},
	author = {Mishra, Nikhil and Rohaninejad, Mostafa and Chen, Xi and Abbeel, Pieter},
	booktitle = {NIPS 2017 Workshop on Meta-Learning},
	year = {2017},
	url = {https://arxiv.org/abs/1707.03141}
}
@article{chi2006two,
	title = {Two approaches to the study of experts’ characteristics},
	author = {Chi, Michelene T. H.},
	journal = {The Cambridge Handbook of Expertise and Expert Performance},
	pages = {21--30},
	year = {2006},
	url = {https://learnlab.org/uploads/mypslc/publications/chi%20two%20approaches%20chapter%202006.pdf}
}
@article{dwyer2012evaluation,
	title = {An evaluation of argument mapping as a method of enhancing critical thinking performance in e-learning environments},
	author = {Dwyer, Christopher P and Hogan, Michael J and Stewart, Ian},
	journal = {Metacognition and Learning},
	volume = {7},
	number = {3},
	pages = {219--244},
	year = {2012},
	publisher = {Springer},
	url = {https://link.springer.com/article/10.1007/s11409-012-9092-1}
}
@article{falk2018persuasion,
	title = {Persuasion, influence, and value: Perspectives from communication and social neuroscience},
	author = {Falk, Emily and Scholz, Christin},
	journal = {Annual review of psychology},
	volume = {69},
	year = {2018},
	url = {https://doi.org/10.1146/annurev-psych-122216-011821}
}
@article{flynn2017nature,
	title = {The nature and origins of misperceptions: Understanding false and unsupported beliefs about politics},
	author = {Flynn, DJ and Nyhan, Brendan and Reifler, Jason},
	journal = {Political Psychology},
	volume = {38},
	pages = {127--150},
	year = {2017},
	publisher = {Wiley Online Library},
	url = {https://doi.org/10.1111/pops.12394}
}
@article{hahn2007rationality,
	title = {The rationality of informal argumentation: A Bayesian approach to reasoning fallacies},
	author = {Hahn, Ulrike and Oaksford, Mike},
	journal = {Psychological review},
	volume = {114},
	number = {3},
	pages = {704},
	year = {2007},
	publisher = {American Psychological Association},
	url = {https://doi.org/10.1037/0033-295X.114.3.704}
}
@article{haidt2000moral,
	title = {Moral dumbfounding: When intuition finds no reason},
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	year = {2000}
}
@article{hewstone2002intergroup,
	title = {Intergroup bias},
	author = {Hewstone, Miles and Rubin, Mark and Willis, Hazel},
	journal = {Annual Review of Psychology},
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	number = {1},
	pages = {575--604},
	year = {2002},
	publisher = {Annual Reviews 4139 El Camino Way, PO Box 10139, Palo Alto, CA 94303-0139, USA},
	url = {https://doi.org/10.1146/annurev.psych.53.100901.135109}
}
@article{gigerenzer1991make,
	title = {How to make cognitive illusions disappear: Beyond "heuristics and biases"},
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}
@article{graham2009liberals,
	title = {Liberals and conservatives rely on different sets of moral foundations},
	author = {Graham, Jesse and Haidt, Jonathan and Nosek, Brian A},
	journal = {Journal of personality and social psychology},
	volume = {96},
	number = {5},
	pages = {1029},
	year = {2009},
	publisher = {American Psychological Association},
	url = {https://www-bcf.usc.edu/~jessegra/papers/GrahamHaidtNosek.2009.Moral%20foundations%20of%20liberals%20and%20conservatives.JPSP.pdf}
}
@article{goel2011negative,
	title = {Negative emotions can attenuate the influence of beliefs on logical reasoning},
	author = {Goel, Vinod and Vartanian, Oshin},
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	number = {1},
	pages = {121--131},
	year = {2011},
	publisher = {Taylor & Francis},
	url = {https://doi.org/10.1080/02699931003593942}
}
@article{greene2002and,
	title = {How (and where) does moral judgment work?},
	author = {Greene, Joshua and Haidt, Jonathan},
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	pages = {517--523},
	year = {2002},
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}
@book{goodman1983fact,
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	author = {Goodman, Nelson},
	year = {1983},
	publisher = {Harvard University Press}
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@article{larrick2004debiasing,
	title = {Debiasing},
	author = {Larrick, Richard P},
	journal = {Blackwell Handbook of Judgment and Decision Making},
	pages = {316--338},
	year = {2004},
	publisher = {Wiley Online Library},
	url = {https://doi.org/10.1002/9780470752937.ch16}
}
@article{list2001epistemic,
	title = {Epistemic democracy: Generalizing the Condorcet jury theorem},
	author = {List, Christian and Goodin, Robert E},
	journal = {Journal of political philosophy},
	volume = {9},
	number = {3},
	pages = {277--306},
	year = {2001},
	publisher = {Wiley Online Library},
	url = {https://doi.org/10.1111/1467-9760.00128}
}
@article{list2002aggregating,
	title = {Aggregating sets of judgments: An impossibility result},
	author = {List, Christian and Pettit, Philip},
	journal = {Economics \& Philosophy},
	volume = {18},
	number = {1},
	pages = {89--110},
	year = {2002},
	publisher = {Cambridge University Press},
	url = {https://www.princeton.edu/~ppettit/papers/Aggregating_EconomicsandPhilosophy_2002.pdf}
}
@article{paluck2016overcome,
	title = {How to overcome prejudice},
	author = {Paluck, Elizabeth Levy},
	journal = {Science},
	volume = {352},
	number = {6282},
	pages = {147--147},
	year = {2016},
	publisher = {American Association for the Advancement of Science},
	url = {http://www.betsylevypaluck.com/s/Paluck2016.pdf}
}
@article{petty2010attitude,
	title = {Attitude change},
	author = {Petty, Richard E and Brinol, Pablo},
	journal = {Advanced social psychology: The state of the science},
	pages = {217--259},
	year = {2010},
	url = {https://doi.org/10.1146/annurev.psych.48.1.609}
}
@book{rawls2009theory,
	title = {A theory of justice},
	author = {Rawls, John},
	year = {2009},
	publisher = {Harvard university press},
	url = {https://en.wikipedia.org/wiki/A_Theory_of_Justice}
}
@article{rowe1999delphi,
	title = "The Delphi technique as a forecasting tool: issues and analysis",
	journal = "International Journal of Forecasting",
	volume = "15",
	number = "4",
	pages = "353--375",
	year = "1999",
	issn = "0169-2070",
	doi = "https://doi.org/10.1016/S0169-2070(99)00018-7",
	url = "http://www.sciencedirect.com/science/article/pii/S0169207099000187",
	author = "Gene Rowe and George Wright",
	keywords = "Delphi, Interacting groups, Statistical group, Structured groups",
	abstract = "This paper systematically reviews empirical studies looking at the effectiveness of the Delphi technique, and provides a critique of this research. Findings suggest that Delphi groups outperform statistical groups (by 12 studies to two with two ‘ties’) and standard interacting groups (by five studies to one with two ‘ties’), although there is no consistent evidence that the technique outperforms other structured group procedures. However, important differences exist between the typical laboratory version of the technique and the original concept of Delphi, which make generalisations about ‘Delphi’ per se difficult. These differences derive from a lack of control of important group, task, and technique characteristics (such as the relative level of panellist expertise and the nature of feedback used). Indeed, there are theoretical and empirical reasons to believe that a Delphi conducted according to ‘ideal’ specifications might perform better than the standard laboratory interpretations. It is concluded that a different focus of research is required to answer questions on Delphi effectiveness, focusing on an analysis of the process of judgment change within nominal groups.",
	publisher = {Elsevier}
}
@book{tetlock2017expert,
	title = {Expert political judgment: How good is it? How can we know?},
	author = {Tetlock, Philip E},
	year = {2017},
	publisher = {Princeton University Press},
	url = {https://press.princeton.edu/titles/11152.html}
}
@article{tetlock2014forecasting,
	title = {Forecasting tournaments: Tools for increasing transparency and improving the quality of debate},
	author = {Tetlock, Philip E and Mellers, Barbara A and Rohrbaugh, Nick and Chen, Eva},
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}
@inproceedings{ibarz2018,
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}
@inproceedings{buolamwini2018gender,
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@misc{ought2018factored,
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}
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@misc{victor2012learnable,
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@misc{hartl2013tau,
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@inproceedings{shafahi2018are,
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}

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.tooltip[_ngcontent-c39],
.channel-page[_ngcontent-c28] > aside[_ngcontent-c28] {
	background: none;
}
.tooltip[_ngcontent-c39]::before {
	display: inline-block;
	color: inherit;
	font-size: 14px;
	font-weight: 500;
	content: "Followers: ";
	margin-right: -6px;
}
.count[_ngcontent-c39] {
	margin-left: 65px;
	font-size: 14px;
	font-weight: 500;
}
.bui-btn-bordered.bui-btn-disabled[_ngcontent-c2] {
	pointer-events: none;
	border-color: transparent !important;
}
body > b-app > div.content.visible > b-channel-page-wrapper > router-outlet > b-channel-web-page > div > div.channel-page > section > div.profile-blocks.layout-row.layout-wrap.bui-padding-top-2x > div.flex-100.flex-gt-md-70.flex-gt-lg-80.layout-column.layout-column > bui-card > div:nth-child(2) > b-description-editor-panel > div > div > p:nth-child(1) > img {
	width: 1200px;
	height: 300px;
}

.interactive-controls.minimized[_ngcontent-c31] {
	margin-top: 10px;
}
}

branch_name: refs/heads/flomaster

gha_license_id: NOASSERTION

extension: css


text:
/* General */
body {
	background-color: #EEEEEE;
}
body, td, p {
	font-family: Arial,Helvetica,sans-serif;
	font-size: 11px;
	color: Black;
}
table {
	border-collapse: collapse;
}
table#gvSelected {
	border-collapse: separate;
}

/* Sections */
div#mainMenu {
	position: absolute;
	top: 0px;
	left: 0px;
	width: 100%;

	border-bottom: solid 1px White;
	padding: 0px 2px 0px 2px;
}
div#tabbedGraphs {
	position: absolute;
	top: 32px;
	left: 8px;
	width: 515px;
}
div#tabbedOptions {
	position: absolute;
	top: 32px;
	left: 531px;
	width: 240px;
}
div#controlPanel
{
	position: absolute;
	top: 465px;
	left: 8px;
	width: 760px;
	display: block;
	float: none;
	background-color: transparent;
}

/* Main Menu */
td.menu {
	font-family: Arial,Helvetica,sans-serif;
	font-size: 11px;
	color: Black;
	text-decoration: none;
	padding: 4px 8px 4px 8px;
	height: 22px;
	cursor: hand;
}
td.activeMenu {
	font-family: Arial,Helvetica,sans-serif;
	font-size: 11px;
	color: White;
	text-decoration: none;
	background-color: #316AC5;
	padding: 4px 8px 4px 8px;
	height: 22px;
	cursor: hand;
}
div.menuItem {
	font-family: Arial,Helvetica,sans-serif;
	font-size: 11px;
	color: Black;
	padding: 2px 20px 2px 8px;
	cursor: hand;
}
div.menuItemOver {
	font-family: Arial,Helvetica,sans-serif;
	font-size: 11px;
	color: White;
	background-color: #316AC5;
	padding: 2px 20px 2px 8px;
	cursor: hand;
}
div.menuItemDisabled {
	font-family: Arial,Helvetica,sans-serif;
	font-size: 11px;
	color: #ACA899;
	padding: 2px 20px 2px 8px;
}
table.separator {
	width: 90px;
	height: 1px;
	background-color: #ACA899;
	margin: 2px 0px 2px 0px;
}
div#fileMenu {
	position: absolute;
	top: 22px;
	left: 2px;
	background-color: White;
	border: solid 1px #ACA899;
	padding: 4px 4px 4px 4px;
	visibility: hidden;
}
div#graphMenu {
	position: absolute;
	top: 22px;
	left: 34px;
	background-color: White;
	border: solid 1px #ACA899;
	padding: 4px 4px 4px 4px;
	visibility: hidden;
}
div#dataMenu {
	position: absolute;
	top: 22px;
	left: 80px;
	background-color: White;
	border: solid 1px #ACA899;
	padding: 4px 4px 4px 4px;
	visibility: hidden;
}

/* Tabs */
div.activeTab {
	font-family: Arial,Helvetica,sans-serif;
	font-size: 11px;
	color: Black;
	text-decoration: none;
	width: 60px;
	padding: 5px 6px 5px 6px;
	border-top: solid 2px White;
	border-right: solid 2px #ACA899;
	border-bottom: 0px;
	border-left: solid 2px White;
	cursor: hand;
	text-align:center;
}
div.inactiveTab {
	font-family: Arial,Helvetica,sans-serif;
	font-size: 11px;
	color: Black;
	text-decoration: none;
	background-color: #E6E6E6;
	width: 60px;
	padding: 2px 6px 2px 6px;
	border-top: solid 1px White;
	border-right: outset 1px;
	border-bottom: solid 2px White;
	border-left: solid 1px White;
	cursor: hand;
	text-align:center;
}
table#graphTable {
	border-top: 0px;
	border-right: solid 2px #ACA899;
	border-bottom: solid 2px #ACA899;
	border-left: solid 2px White;
	width: 515px;
	height: 400px;
}
td#graphCell {
	text-align: center;
	vertical-align: baseline;
	background-image: url(images/loading.gif);
	background-repeat: no-repeat;
	background-position: center;
}
table#optionsTable {
	border-top: 0px;
	border-right: solid 2px #ACA899;
	border-bottom: solid 2px #ACA899;
	border-left: solid 2px White;
	width: 240px;
	height: 400px;
}
td#optionsCell {
	padding: 0px 10px 10px 10px;
	vertical-align:top
}
div.tabLine {
	border-bottom: solid 2px White;
}

/* Groups */
table#outerTable {
	border: groove 2px;
	border-collapse: separate;
	color: White;
	width: 100%;
	height: 350px;
}
table.groupTable {
	border: groove 2px;
	border-collapse: separate;
	color: White;
}
td.groupCell {
	padding: 6px 6px 6px 6px;
}
div.groupLabel {
	font-family: Arial,Helvetica,sans-serif;
	font-size: 11px;
	color: Black;

	background-color: #EEEEEE;
	padding: 0px 5px 0px 5px;
	position: relative;
	top: 6px;
	left: 8px;
}

/* Forms */
input.numericalInput {
	font-family: Arial,Helvetica,sans-serif;
	font-size: 13px;
	width: 70px;
	text-align: right;
}
td.inputLabel {
	text-align: right;
	padding: 5px 10px 5px 0px;
}
select#cboSite {
	font-family: Arial,Helvetica,sans-serif;
	font-size: 11px;
	width: 385px;
}
select#cboVariable {
	font-family: Arial,Helvetica,sans-serif;
	font-size: 11px;
	width: 385px;
}
.formElement {
	font-family: Arial,Helvetica,sans-serif;
	font-size: 13px;
}

/* Links */
a {
	text-decoration: none;
}

/* Error */
.error {
	font-family: Arial, Helvetica, Sans-Serif;
	/*font-size: 15px;*/
	font-weight: bold;
	text-transform: uppercase;
	color: Red;
	/*letter-spacing: 2px;*/
	padding-top: 10px;
	position: absolute;
	display: block;
	width: 200px;
}

/* CenterLine */
.CenterLine
{
	position: absolute;
}

repository: koson/HydroServer

repo_name: koson/HydroServer


text:
@misc{BiomedicalSignalImageProcessingMIT,
  Author = {Clifford, Gari and Fisher, John and Greenberg, Julie and Wells, William},
  Institution = {MIT},
  HowPublished = {OpenCourseWare},
  Year = {2007},
  Title = {Biomedical Signal and Image Processing},
  url = {https://ocw.mit.edu/courses/hst-582j-biomedical-signal-and-image-processing-spring-2007/},
}

gha_language: TeX


text:
@article{kolmogorov2009blossom,
  title={Blossom V: a new implementation of a minimum cost perfect matching algorithm},
  author={Kolmogorov, Vladimir},
  journal={Mathematical Programming Computation},
  volume={1},
  number={1},
  pages={43--67},
  year={2009},
  publisher={Springer}
}

repository: yuewuo/fusion-blossom

repo_name: yuewuo/fusion-blossom

branch_name: refs/heads/main

gha_license_id: MIT

gha_language: Rust