text:
% If you wish to cite FFTW in a general publication, we recommend this paper:

@Article{FFTW05,
  author = 	 {Frigo, Matteo and Johnson, Steven~G.},
  title = 	 {The Design and Implementation of {FFTW3}},
  journal = 	 {Proceedings of the IEEE},
  year = 	 2005,
  volume =	 93,
  number =	 2,
  pages =	 {216--231},
  note =	 {Special issue on ``Program Generation, Optimization, and Platform Adaptation''}
}

% The following are additional papers on FFTW and related topics that
% you may wish to cite in more specialized works.

% This paper describes the codelet generator introduced in FFTW 2
@InProceedings{FFTWgen99,
  author = 	 {Frigo, Matteo},
  title = 	 {A fast {Fourier} transform compiler},
  booktitle = 	 {Proc. 1999 ACM SIGPLAN Conf. on Programming Language Design and Implementation},
  pages =	 {169--180},
  year =	 1999,
  volume =	 34,
  number =	 5,
  month =	 {May},
  publisher =	 {ACM}
}

% This was the main paper describing FFTW 1
@InProceedings{FFTW98,
  author =       {Frigo, Matteo and Johnson, Steven~G.},
  title =        {{FFTW}: An adaptive software architecture for the {FFT}},
  booktitle =    {Proc. 1998 IEEE Intl. Conf. Acoustics Speech and Signal Processing},
  pages =        {1381--1384},
  year =         1998,
  volume =       3,
  publisher =    {IEEE}
}

% This is an early technical report on FFTW, but since it is not readily
% accessible we normally prefer the FFTW98 citation.
@TechReport{FFTW97,
  author = 	 {Frigo, Matteo and Johnson, Steven~G.},
  title = 	 {The Fastest {Fourier} Transform in the West},
  institution =  {Massachusetts Institute of Technology},
  year = 	 1997,
  number =	 {MIT-LCS-TR-728},
  month =	 {September}
}

% This paper describes the general theory of cache-oblivious algorithms,
% which can use the cache optimally without knowing the cache size by
% exploiting recursion, ideas that are heavily used in FFTW.
@InProceedings{CacheObliv99,
  author = 	 {Frigo, Matteo and Leiserson, Charles~E. and Prokop, Harald and Ramachandran, Sridhar},
  title = 	 {Cache-oblivious algorithms},
  booktitle = 	 {Proc. 40th Ann. Symp. on Foundations of Comp. Sci. (FOCS)},
  pages =	 {285--297},
  year =	 1999,
  publisher =	 {IEEE Comput. Soc.}
}

% This paper describes a new FFT algorithm that lowers the total exact count
% of real-arithmetic operations (additions and multiplications) over
% the previous record set by Yavne in 1968 (the split-radix algorithm).
@Article{JohnsonFrigo07:splitradix,
  author = 	 {Steven G. Johnson and Matteo Frigo},
  title = 	 {A modified split-radix {FFT} with fewer arithmetic operations},
  journal = 	 {IEEE Trans. Signal Processing},
  year = 	 2007,
  volume =	 55,
  number =	 1,
  pages =	 {111--119}
}

repository: mklca/attic-distfft

repo_name: mklca/attic-distfft


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text:
@article{vgg,
  title={Very deep convolutional networks for large-scale image recognition},
  author={Simonyan, Karen and Zisserman, Andrew},
  journal={arXiv preprint arXiv:1409.1556},
  year={2014}
}

@article{li2015automatic,
  title={Automatic instrument recognition in polyphonic music using convolutional neural networks},
  author={Li, Peter and Qian, Jiyuan and Wang, Tian},
  journal={arXiv preprint arXiv:1511.05520},
  year={2015}
}

@article{han2016deep,
  title={Deep convolutional neural networks for predominant instrument recognition in polyphonic music},
  author={Han, Yoonchang and Kim, Jaehun and Lee, Kyogu},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  volume={25},
  number={1},
  pages={208--221},
  year={2016},
  publisher={IEEE}
}

@article{choi2017tutorial,
  title={A tutorial on deep learning for music information retrieval},
  author={Choi, Keunwoo and Fazekas, Gy{\"o}rgy and Cho, Kyunghyun and Sandler, Mark},
  journal={arXiv preprint arXiv:1709.04396},
  year={2017}
}

@inproceedings{humphrey2018openmic,
  title={OpenMIC-2018: An Open Data-set for Multiple Instrument Recognition.},
  author={Humphrey, Eric and Durand, Simon and McFee, Brian},
  booktitle={ISMIR},
  pages={438--444},
  year={2018}
}

@inproceedings{humphrey2012,
  author       = {Eric J. Humphrey and
                  Juan Pablo Bello and
                  Yann LeCun},
  title        = {{Moving Beyond Feature Design: Deep Architectures
                   and Automatic Feature Learning in Music
                   Informatics.}},
  booktitle    = {{Proceedings of the 13th International Society for
                   Music Information Retrieval Conference}},
  year         = 2012,
  pages        = {403-408},
  publisher    = {ISMIR},
  address      = {Porto, Portugal},
  month        = oct,
  venue        = {Porto, Portugal},
  doi          = {10.5281/zenodo.1415726},
  url          = {https://doi.org/10.5281/zenodo.1415726}
}

@article{burred2010,
author = {Burred, Juan José and Roebel, Axel and Sikora, Thomas},
year = {2010},
month = {04},
pages = {663 - 674},
title = {Dynamic Spectral Envelope Modeling for Timbre Analysis of Musical Instrument Sounds},
volume = {18},
journal = {Audio, Speech, and Language Processing, IEEE Transactions on},
doi = {10.1109/TASL.2009.2036300}
}

@inproceedings{florian2010,
  author       = {Florian Eyben and
                  Sebastian Böck and
                  Björn W. Schuller and
                  Alex Graves},
  title        = {{Universal Onset Detection with Bidirectional Long
                   Short-Term Memory Neural Networks.}},
  booktitle    = {{Proceedings of the 11th International Society for
                   Music Information Retrieval Conference}},
  year         = 2010,
  pages        = {589-594},
  publisher    = {ISMIR},
  address      = {Utrecht, Netherlands},
  month        = aug,
  venue        = {Utrecht, Netherlands},
  doi          = {10.5281/zenodo.1417131},
  url          = {https://doi.org/10.5281/zenodo.1417131}
}

@inproceedings{egginkandbrown2003,
author = {Eggink, J. and Brown, Guy},
year = {2003},
month = {11},
pages = {49-},
title = {A missing feature approach to instrument identification in polyphonic music},
volume = {5},
isbn = {0-7803-7850-4},
doi = {10.1109/ICASSP.2003.1200029}
}

@article{Downie2003,
author = {Downie, J. Stephen},
title = {Music information retrieval},
journal = {Annual Review of Information Science and Technology},
volume = {37},
number = {1},
pages = {295-340},
doi = {10.1002/aris.1440370108},
url = {https://asistdl.onlinelibrary.wiley.com/doi/abs/10.1002/aris.1440370108},
eprint = {https://asistdl.onlinelibrary.wiley.com/doi/pdf/10.1002/aris.1440370108},
year = {2003}
}

@article{bhalke2015,
author = {Bhalke, Daulappa and Rao, C. and Bormane, D.},
year = {2015},
month = {05},
pages = {},
title = {Automatic musical instrument classification using fractional fourier transform based- MFCC features and counter propagation neural network},
volume = {46},
journal = {Journal of Intelligent Information Systems},
doi = {10.1007/s10844-015-0360-9}
}

@article{Jiang2013,
author = {Jiang, Wenxin and Ras, Zbigniew},
year = {2013},
month = {04},
pages = {149-170},
title = {Multi-label automatic indexing of music by cascade classifiers},
volume = {11},
journal = {Web Intelligence and Agent Systems},
doi = {10.3233/WIA-130268}
}

@article{giannoulisandklapuri2013,
author = {Giannoulis, Dimitrios and Klapuri, Anssi},
year = {2013},
month = {09},
pages = {1805-1817},
title = {Musical Instrument Recognition in Polyphonic Audio Using Missing Feature Approach},
volume = {21},
journal = {Audio, Speech, and Language Processing, IEEE Transactions on},
doi = {10.1109/TASL.2013.2248720}
}

@INPROCEEDINGS{Hamel2010,
     author = {Hamel, Philippe and Eck, Douglas},
      month = aug,
      title = {Learning features from music audio with deep belief networks},
  booktitle = {Proceedings of the 11th {I}nternational {S}ociety for {M}usic {I}nformation {R}etrieval {C}onference ({ISMIR})},
       year = {2010},
      pages = {339--344},
   location = {Utrecht, The Netherlands},
   abstract = {Feature extraction is a crucial part of many MIR tasks. In this work, we present a system that can automatically ex-
tract relevant features from audio for a given task. The feature extraction system consists of a Deep Belief Network  (DBN) on Discrete Fourier Transforms (DFTs) of the audio. We then use the activations of the trained network as inputs for a non-linear Support Vector Machine (SVM) classifier. In particular, we learned the features to solve the task of genre recognition. The learned features perform significantly better than MFCCs. Moreover, we obtain a classification accuracy of 84.3\% on the Tzanetakis dataset, which compares favorably against state-of-the-art genre classifiers using frame-based features. We also applied these same features to the task of auto-tagging. The autotaggers trained with our features performed better than those that were trained with timbral and temporal features.}
}

@inproceedings{Martin1998,
  title={Musical instrument identification: A pattern‐recognition approach},
  author={Keith Dana Martin and Youngmoo E. Kim},
  year={1998}
}

@article{brown1999,
author = {Brown, Judith},
year = {1999},
month = {04},
pages = {1933-41},
title = {Computer Identification of Musical Instruments Using Pattern Recognition With Cepstral Coefficients as Features},
volume = {105},
journal = {The Journal of the Acoustical Society of America},
doi = {10.1121/1.426728}
}

@INPROCEEDINGS{eronenklapuri2000,  author={A. {Eronen} and A. {Klapuri}},  booktitle={2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)},  title={Musical instrument recognition using cepstral coefficients and temporal features},   year={2000},  volume={2},  number={},  pages={II753-II756 vol.2},}

@article{deng2008,
author = {Deng, Jeremiah and Simmermacher, Christian and Cranefield, Stephen},
year = {2008},
month = {05},
pages = {429-38},
title = {A Study on Feature Analysis for Musical Instrument Classification},
volume = {38},
journal = {IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society},
doi = {10.1109/TSMCB.2007.913394}
}

@article{garcia2011,
author = {Barbedo, Jayme and Tzanetakis, George},
year = {2011},
month = {02},
pages = {111 - 122},
title = {Musical Instrument Classification Using Individual Partials},
volume = {19},
journal = {Audio, Speech, and Language Processing, IEEE Transactions on},
doi = {10.1109/TASL.2010.2045186}
}

@article{Choi2017,
  author    = {Keunwoo Choi and
               Gy{\"{o}}rgy Fazekas and
               Kyunghyun Cho and
               Mark B. Sandler},
  title     = {A Tutorial on Deep Learning for Music Information Retrieval},
  journal   = {CoRR},
  volume    = {abs/1709.04396},
  year      = {2017},
  url       = {http://arxiv.org/abs/1709.04396},
  archivePrefix = {arXiv},
  eprint    = {1709.04396},
  timestamp = {Thu, 21 Mar 2019 11:19:44 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1709-04396.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{spectogram2019,
  title={Music Instrument Recognition from Spectrogram Images Using Convolution Neural Network},
  author={G Jawaherlalnehru, S Jothilakshmi},
  journal={International Journal of Innovative Technology and Exploring Engineering (IJITEE)},
  year={2019}
}

@article{fma2016,
  author    = {Kirell Benzi and
               Micha{\"{e}}l Defferrard and
               Pierre Vandergheynst and
               Xavier Bresson},
  title     = {{FMA:} {A} Dataset For Music Analysis},
  journal   = {CoRR},
  volume    = {abs/1612.01840},
  year      = {2016},
  url       = {http://arxiv.org/abs/1612.01840},
  archivePrefix = {arXiv},
  eprint    = {1612.01840},
  timestamp = {Mon, 13 Aug 2018 16:48:00 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/BenziDVB16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@Inbook{LeCun2012,
author="LeCun, Yann A.
and Bottou, L{\'e}on
and Orr, Genevieve B.
and M{\"u}ller, Klaus-Robert",
editor="Montavon, Gr{\'e}goire
and Orr, Genevi{\`e}ve B.
and M{\"u}ller, Klaus-Robert",
title="Efficient BackProp",
bookTitle="Neural Networks: Tricks of the Trade: Second Edition",
year="2012",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="9--48",
abstract="The convergence of back-propagation learning is analyzed so as to explain common phenomenon observed by practitioners. Many undesirable behaviors of backprop can be avoided with tricks that are rarely exposed in serious technical publications. This paper gives some of those tricks, and offers explanations of why they work.",
isbn="978-3-642-35289-8",
doi="10.1007/978-3-642-35289-8_3",
url="https://doi.org/10.1007/978-3-642-35289-8_3"
}

@software{librosa,
  author       = {Brian McFee and
                  Matt McVicar and
                  Stefan Balke and
                  Vincent Lostanlen and
                  Carl Thomé and
                  Colin Raffel and
                  Dana Lee and
                  Kyungyun Lee and
                  Oriol Nieto and
                  Frank Zalkow and
                  Dan Ellis and
                  Eric Battenberg and
                  Ryuichi Yamamoto and
                  Josh Moore and
                  Ziyao Wei and
                  Rachel Bittner and
                  Keunwoo Choi and
                  nullmightybofo and
                  Pius Friesch and
                  Fabian-Robert Stöter and
                  Thassilo and
                  Matt Vollrath and
                  Siddhartha Kumar Golu and
                  nehz and
                  Simon Waloschek and
                  Seth and
                  Rimvydas Naktinis and
                  Douglas Repetto and
                  Curtis "Fjord" Hawthorne and
                  CJ Carr},
  title        = {librosa/librosa: 0.6.3},
  month        = feb,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {0.6.3},
  doi          = {10.5281/zenodo.2564164},
  url          = {https://doi.org/10.5281/zenodo.2564164}
}

@online{ai,
  author="Dr. Michael J. Garbade",
  title="{Clearing the Confusion: AI vs Machine Learning vs Deep Learning Differences}",
  url="https://towardsdatascience.com/clearing-the-confusion-ai-vs-machine-learning-vs-deep-learning-differences-fce69b21d5eb",
  addendum="Felkeresve: 2020. 05. 04"
}

@online{melspec,
  author="Dalya Gartzman",
  title="{Getting to Know the Mel Spectrogram}",
  url="https://towardsdatascience.com/getting-to-know-the-mel-spectrogram-31bca3e2d9d0",
  addendum="Felkeresve: 2020. 05. 09"
}

@online{fscore,
  author="Christopher Riggio",
  title="{What’s the deal with Accuracy, Precision, Recall and F1?}",
  url="https://towardsdatascience.com/whats-the-deal-with-accuracy-precision-recall-and-f1-f5d8b4db1021",
  addendum="Felkeresve: 2020. 05. 11"
}

@online{imbalanced,
  author="Tara Boyle",
  title="{Dealing with Imbalanced Data}",
  url="https://towardsdatascience.com/methods-for-dealing-with-imbalanced-data-5b761be45a18",
  addendum="Felkeresve: 2020. 05. 13"
}

@online{traintestvalid,
  author="Tarang Shah",
  title="{About Train, Validation and Test Sets in Machine Learning}",
  url="https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7",
  addendum="Felkeresve: 2020. 05. 13"
}

@online{vggish,
  title="{VGGish}",
  url="https://github.com/tensorflow/models/tree/master/research/audioset/vggish",
  addendum="Felkeresve: 2020. 05. 14"
}

@online{azure,
  title="{Gépi tanulási algoritmusok}",
  url="https://azure.microsoft.com/hu-hu/overview/machine-learning-algorithms/\#popular-algorithms",
  addendum="Felkeresve: 2020. 05. 16"
}

@online{azuredl,
  title="{Mélyreható tanulás és gépi tanulás}",
  url="https://docs.microsoft.com/hu-hu/azure/machine-learning/concept-deep-learning-vs-machine-learning",
  addendum="Felkeresve: 2020. 05. 26"
}

@book{deeplearningbook,
  added-at = {2017-11-10T09:58:27.000+0100},
  address = {Beijing},
  author = {Patterson, Josh and Gibson, Adam},
  biburl = {https://www.bibsonomy.org/bibtex/2861e7eedb0f4c75409500abe08c9ad2d/flint63},
  file = {eBook:2017/PattersonGibson17.pdf:PDF;O'Reilly Product page:http\://shop.oreilly.com/product/0636920035343/:URL;Amazon Search inside:http\://www.amazon.de/gp/reader/1491914254/:URL;Related Web site:https\://deeplearning4j.org/:URL},
  groups = {public},
  interhash = {aaa0845182fff81206ef3b2dbc0beaea},
  intrahash = {861e7eedb0f4c75409500abe08c9ad2d},
  isbn = {978-1-4919-1425-0},
  keywords = {01841 102 safari book numerical ai software development learn java tool},
  publisher = {O'Reilly},
  timestamp = {2018-04-16T11:39:52.000+0200},
  title = {Deep Learning: A Practitioner's Approach},
  url = {https://www.safaribooksonline.com/library/view/deep-learning/9781491924570/},
  username = {flint63},
  year = 2017
}

@online{ismirdataset,
  author="ISMIR community",
  title="{ISMIR list of datasets}",
  url="http://www.ismir.net/resources/datasets/",
  addendum="Felkeresve: 2020. 05. 10"
}

@book{PattersonGibson17,
  abstract = {Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning---especially deep neural networks---make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. The authors provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you will learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.},
  added-at = {2017-11-10T09:58:27.000+0100},
  address = {Beijing},
  author = {Patterson, Josh and Gibson, Adam},
  biburl = {https://www.bibsonomy.org/bibtex/2861e7eedb0f4c75409500abe08c9ad2d/flint63},
  file = {eBook:2017/PattersonGibson17.pdf:PDF;O'Reilly Product page:http\://shop.oreilly.com/product/0636920035343/:URL;Amazon Search inside:http\://www.amazon.de/gp/reader/1491914254/:URL;Related Web site:https\://deeplearning4j.org/:URL},
  groups = {public},
  interhash = {aaa0845182fff81206ef3b2dbc0beaea},
  intrahash = {861e7eedb0f4c75409500abe08c9ad2d},
  isbn = {978-1-4919-1425-0},
  keywords = {01841 102 safari book numerical ai software development learn java tool},
  publisher = {O'Reilly},
  timestamp = {2018-04-16T11:39:52.000+0200},
  title = {Deep Learning: A Practitioner's Approach},
  url = {https://www.safaribooksonline.com/library/view/deep-learning/9781491924570/},
  username = {flint63},
  year = 2017
}

@article{Schedl2013,
author = {Schedl, Markus and Flexer, Arthur and Urbano, Juli\'{a}n},
title = {The Neglected User in Music Information Retrieval Research},
year = {2013},
issue_date = {December  2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {41},
number = {3},
issn = {0925-9902},
url = {https://doi.org/10.1007/s10844-013-0247-6},
doi = {10.1007/s10844-013-0247-6},
journal = {J. Intell. Inf. Syst.},
month = dec,
pages = {523–539},
numpages = {17},
keywords = {User-centric music retrieval, Experimental design, Interpretation, Evaluation}
}

@article{Schedl2014,
author = {Schedl, Markus and Gómez, Emilia and Urbano, Julián},
year = {2014},
month = {01},
pages = {127-261},
title = {Music Information Retrieval: Recent Developments and Applications},
volume = {8},
journal = {Foundations and Trends in Information Retrieval},
doi = {10.1561/1500000042}
}

@book{Goodfellow-et-al-2016,
    title={Deep Learning},
    author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
    publisher={MIT Press},
    note={\url{http://www.deeplearningbook.org}},
    year={2016}
}

@article{hangszin1,
          volume = {130},
          number = {5},
           title = {The timbre toolbox: extracting audio descriptors from musical signals},
          author = {G. Peeters and B.L. Giordano and P. Susini and N. Misdariis and S. McAdams},
            year = {2011},
           pages = {2902--2916},
         journal = {Journal of the Acoustical Society of America},
             url = {http://eprints.gla.ac.uk/68491/},
             doi = {10.1121/1.3642604}
}

@article{hangszin2,
author = {Herrera, Perfecto and Peeters, Geoffroy and Dubnov, Shlomo},
year = {2010},
month = {08},
pages = {},
title = {Automatic Classification of Musical Instrument Sounds},
volume = {32},
journal = {Journal of New Music Research},
doi = {10.1076/jnmr.32.1.3.16798}
}

@book{kotta1,
author = {Klapuri, Anssi and Davy, Manuel},
year = {2006},
month = {01},
pages = {},
title = {Signal Processing Methods for Music Transcription},
journal = {Signal Processing Methods for Music Transcription},
doi = {10.1007/0-387-32845-9}
}

@article {kotta2,
	title = {Melody Extraction from Polyphonic Music Signals using Pitch Contour Characteristics},
	journal = {IEEE Transactions on Audio, Speech and Language Processing},
	volume = {20},
	year = {2012},
	month = {08/2012},
	pages = {1759-1770},
	url = {http://hdl.handle.net/10230/42183},
	author = {Justin Salamon and Emilia G{\'o}mez}
}

@book{kotta3,
author = {Walter B Hewlett and Eleanor Selfridge-Field},
year = {1998},
pages = {},
title = {Melodic similarity: Concepts, procedures, and applications, volume 11.},
journal = {The MIT Press}
}

@article{tempo1,
author = {Bello, Juan and Daudet, Laurent and Abdallah, Samer and Duxbury, Chris and Davies, Mike and Sandler, Mark},
year = {2005},
month = {10},
pages = {1035 - 1047},
title = {A Tutorial on Onset Detection in Music Signals},
volume = {13},
journal = {Speech and Audio Processing, IEEE Transactions on},
doi = {10.1109/TSA.2005.851998}
}

@BOOK{tempo2,
   author = {Gouyon, F.},
   title = {Computational Rhythm Description},
   publisher = {VDM Verlag},
   isbn={978-3836477697},
   year = 2008,
	url = {}
}

@inproceedings{tempo3,
  title={Extracting the perceptual tempo from music},
  author={Martin F. McKinney and Dirk Moelants},
  booktitle={ISMIR},
  year={2004}
}

@inproceedings{tonality1,
author = {Gregory H. Wakefield},
title = {{Mathematical representation of joint time-chroma distributions}},
volume = {3807},
booktitle = {Advanced Signal Processing Algorithms, Architectures, and Implementations IX},
editor = {Franklin T. Luk},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {637 -- 645},
year = {1999},
doi = {10.1117/12.367679},
URL = {https://doi.org/10.1117/12.367679}
}

@inproceedings{tonality2,
  title={Towards a mathematical model of tonality},
  author={Elaine Chew},
  year={2000}
}

@article{tonality3,
author = {G\'{o}mez, Emilia},
title = {Tonal Description of Polyphonic Audio for Music Content Processing},
year = {2006},
issue_date = {Summer 2006},
publisher = {INFORMS},
address = {Linthicum, MD, USA},
volume = {18},
number = {3},
issn = {1526-5528},
url = {https://doi.org/10.1287/ijoc.1040.0126},
doi = {10.1287/ijoc.1040.0126},
journal = {INFORMS J. on Computing},
month = jan,
pages = {294–304},
numpages = {11},
keywords = {sound and music computing, key estimation, content analysis and indexing, tonal description}
}

@article{tonality4,
  title={Large-Scale Study of Chord Estimation Algorithms Based on Chroma Representation and HMM},
  author={H{\'e}l{\`e}ne Papadopoulos and Geoffroy Peeters},
  journal={2007 International Workshop on Content-Based Multimedia Indexing},
  year={2007},
  pages={53-60}
}

@inproceedings{tonality5,
  author       = {Laurent Oudre and
                  Yves Grenier and
                  Cédric Févotte},
  title        = {{Template-based Chord Recognition : Influence of
                   the Chord Types.}},
  booktitle    = {{Proceedings of the 10th International Society for
                   Music Information Retrieval Conference}},
  year         = 2009,
  pages        = {153-158},
  publisher    = {ISMIR},
  address      = {Kobe, Japan},
  month        = oct,
  venue        = {Kobe, Japan},
  doi          = {10.5281/zenodo.1414884},
  url          = {https://doi.org/10.5281/zenodo.1414884}
}

@article{tonality6,
	doi = {10.2307/40285812},
	url = {https://doi.org/10.2307\%2F40285812},
	year = 1999,
	month = {oct},
	publisher = {University of California Press},
	volume = {17},
	number = {1},
	pages = {65-100},
	author = {David Temperley},
	title = {What's Key for Key? The Krumhansl-Schmuckler Key-Finding Algorithm Reconsidered},
	journal = {Music Perception: An Interdisciplinary Journal}
}

@inproceedings{structural1,
  title={Automatic Music Summarization via Similarity Analysis},
  author={Matthew L. Cooper and Jonathan Foote},
  booktitle={ISMIR},
  year={2002}
}

@INPROCEEDINGS{structural2,
    author = {Geoffroy Peeters and Amaury La Burthe and Xavier Rodet},
    title = {Toward Automatic Music Audio Summary Generation from Signal Analysis},
    booktitle = {In Proc. International Conference on Music Information Retrieval},
    year = {2002},
    pages = {94--100}
}

@article{structural3,
author = {Chai, Wei},
year = {2006},
month = {01},
pages = {},
title = {Semantic Segmentation and Summarization of Music},
journal = {IEEE Signal Processing Magazine}
}

@inproceedings{similarity1,
author = {Bogdanov, Dmitry and Serrà, Joan and Wack, Nicolas and Herrera, Perfecto},
year = {2009},
month = {01},
pages = {453-458},
title = {From Low-Level to High-Level: Comparative Study of Music Similarity Measures},
doi = {10.1109/ISM.2009.72}
}

@MISC{similarity2,
    author = {Michael A. Casey and Remco Veltkamp and Masataka Goto and Marc Leman and Christophe Rhodes and Malcolm Slaney},
    title = {Content-Based Music Information Retrieval: Current Directions and Future Challenges},
    year = {2008}
}

@article{similarity3,
author = {Schedl, Markus and Pohle, Tim and Knees, Peter and Widmer, Gerhard},
title = {Exploring the Music Similarity Space on the Web},
year = {2011},
issue_date = {July 2011},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {29},
number = {3},
issn = {1046-8188},
url = {https://doi.org/10.1145/1993036.1993038},
doi = {10.1145/1993036.1993038},
journal = {ACM Trans. Inf. Syst.},
month = jul,
articleno = {14},
numpages = {24},
keywords = {Music information retrieval, Web content mining, evaluation, term space}
}

@article{similarity4,
author = {Schedl, Markus},
year = {2012},
month = {06},
pages = {},
title = {#nowplaying Madonna: A large-scale evaluation on estimating similarities between music artists and between movies from microblogs},
volume = {15},
journal = {Information Retrieval},
doi = {10.1007/s10791-012-9187-y}
}

@Inbook{cover1,
author="Serr{\`a}, Joan
and G{\'o}mez, Emilia
and Herrera, Perfecto",
editor="Ra{\'{s}}, Zbigniew W.
and Wieczorkowska, Alicja A.",
title="Audio Cover Song Identification and Similarity: Background, Approaches, Evaluation, and Beyond",
bookTitle="Advances in Music Information Retrieval",
year="2010",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="307--332",
isbn="978-3-642-11674-2",
doi="10.1007/978-3-642-11674-2_14",
url="https://doi.org/10.1007/978-3-642-11674-2_14"
}

@article{cover2,
author = {Bertin-Mahieux, Thierry and Ellis, Daniel},
year = {2012},
month = {01},
pages = {},
title = {Large-scale cover song recognition using the 2D Fourier transform magnitude}
}

@inproceedings{humming1,
author = {Kosugi, Naoko and Nishihara, Yuichi and Sakata, Tetsuo and Yamamuro, Masashi and Kushima, Kazuhiko},
year = {2000},
month = {01},
pages = {333-342},
title = {A practical query-by-humming system for a large music database},
doi = {10.1145/354384.354520}
}

@article{humming2,
author = {Salamon, Justin and Serrà, Joan and Gómez, Emilia},
year = {2013},
month = {03},
pages = {45-58},
title = {Tonal representations for music retrieval: From version identification to query-by-humming},
volume = {2},
journal = {International Journal of Multimedia Information Retrieval, special issue on Hybrid Music Information Retrieval},
doi = {10.1007/s13735-012-0026-0}
}

@article{humming3,
author = {Dannenberg, Roger and Birmingham, William and Pardo, Bryan and Hu, Ning and Meek, Colin and Tzanetakis, George},
year = {2007},
month = {03},
pages = {687-701},
title = {A comparative evaluation of search techniques for query-by-humming using the MUSART testbed},
volume = {58},
journal = {JASIST},
doi = {10.1002/asi.20532}
}

@article{mood1,
author = {Yang, yi-hsuan and Chen, Homer},
year = {2012},
month = {05},
pages = {},
title = {Machine Recognition of Music Emotion: A Review},
volume = {3},
journal = {ACM Transactions on Intelligent Systems and Technology (TIST)},
doi = {10.1145/2168752.2168754}
}

@article{mood2,
author = {Laurier, Cyril and Meyers, Owen and Serrà, Joan and Blech, Martin and Herrera, Perfecto and Serra, Xavier},
year = {2010},
month = {05},
pages = {161-184},
title = {Indexing music by mood: Design and integration of an automatic content-based annotator},
volume = {48},
journal = {Multimedia Tools Appl.},
doi = {10.1007/s11042-009-0360-2}
}

@article{genre1,
author = {Tzanetakis, George and Cook, Perry},
year = {2002},
month = {01},
pages = {293-302},
title = {Musical Genre Classification of Audio Signals},
volume = {10},
journal = {IEEE Transactions on Speech and Audio Processing}
}

@inproceedings{genre2,
author = {Knees, Peter and Pampalk, Elias and Widmer, Gerhard},
year = {2004},
month = {01},
pages = {},
title = {Artist Classification with Web-Based Data.}
}

@inbook{instrument1,
author = {Herrera, Perfecto and Klapuri, Anssi and Davy, Manuel},
year = {2006},
month = {01},
pages = {163-200},
title = {Automatic Classification of Pitched Musical Instrument Sounds},
doi = {10.1007/0-387-32845-9_6}
}

@article{singer1,
author = {Kim, Youngmoo and Whitman, Brian},
year = {2002},
month = {09},
pages = {},
title = {Singer Identification in Popular Music Recordings Using Voice Coding Features}
}

@phdthesis{tagging1,
author = {Sordo, Mohamed},
year = {2012},
month = {01},
pages = {},
title = {Semantic annotation of music collections: A computational approach}
}

@article{tagging2,
author = {Coviello, Emanuele and Chan, Antoni and Lanckriet, Gert},
year = {2011},
month = {08},
pages = {1343 - 1359},
title = {Time Series Models for Semantic Music Annotation},
volume = {19},
journal = {Audio, Speech, and Language Processing, IEEE Transactions on},
doi = {10.1109/TASL.2010.2090148}
}

@article{tagging3,
author = {Miotto, Riccardo and Orio, Nicola},
year = {2012},
month = {05},
pages = {1-29},
title = {A Probabilistic Model to Combine Tags and Acoustic Similarity for Music Retrieval},
volume = {30},
journal = {ACM Transactions on Information Systems - TOIS},
doi = {10.1145/2180868.2180870}
}

@inproceedings{fingerprint1,
author = {Wang, Xinxi and Rosenblum, David and Wang, Ye},
year = {2012},
month = {10},
pages = {},
title = {Context-Aware Mobile Music Recommendation for Daily Activities},
journal = {MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia},
doi = {10.1145/2393347.2393368}
}

@inbook{fingerprint2,
author = {Cano, Pedro and Batlle, Eloi and Gómez, Emilia and Gomes, Leandro and Bonnet, Madeleine},
year = {2005},
month = {09},
pages = {233-245},
title = {Audio Fingerprinting: Concepts And Applications},
volume = {2},
doi = {10.1007/10966518_17}
}

@article{contentbased1,
author = {Casey, Michael and Veltkamp, Remco and Goto, Masataka and Leman, Marc and Rhodes, Christophe and Slaney, Malcolm},
year = {2008},
month = {05},
pages = {668 - 696},
title = {Content-Based Music Information Retrieval: Current Directions and Future Challenges},
volume = {96},
journal = {Proceedings of the IEEE},
doi = {10.1109/JPROC.2008.916370}
}

@book{recommend1,
author = {Celma, Oscar},
year = {2010},
month = {01},
pages = {},
title = {Music Recommendation and Discovery - The Long Tail, Long Fail, and Long Play in the Digital Music Space.},
isbn = {978-3-642-13286-5},
journal = {Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space},
doi = {10.1007/978-3-642-13287-2}
}

@inproceedings{recommend2,
author = {Zhang, Yuan and Séaghdha, Diarmuid and Quercia, Daniele and Jambor, Tamas},
year = {2012},
month = {02},
pages = {13-22},
title = {Auralist: Introducing serendipity into music recommendation},
journal = {WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining},
doi = {10.1145/2124295.2124300}
}

@inproceedings{recommend3,
author = {Kaminskas, Marius and Ricci, Francesco and Schedl, Markus},
year = {2013},
month = {10},
pages = {17-24},
title = {Location-aware music recommendation using auto-tagging and hybrid matching},
journal = {RecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems},
doi = {10.1145/2507157.2507180}
}

@article{playlist1,
author = {Pohle, Tim and Knees, Peter and Schedl, Markus and Pampalk, Elias and Widmer, Gerhard},
year = {2007},
month = {05},
pages = {567 - 575},
title = {“Reinventing the Wheel”: A Novel Approach to Music Player Interfaces},
volume = {9},
journal = {Multimedia, IEEE Transactions on},
doi = {10.1109/TMM.2006.887991}
}

@article{playlist2,
author = {Reynolds, Gordon and Barry, Dan and Burke, Ted and Coyle, Eugene},
year = {2007},
month = {01},
pages = {},
title = {Towards a Personal Automatic Music Playlist Generation Algorithm: The Need for Contextual Information}
}

@inproceedings{playlist3,
author = {Pampalk, Elias and Pohle, Tim and Widmer, Gerhard},
year = {2005},
month = {01},
pages = {634-637},
title = {Dynamic Playlist Generation Based on Skipping Behavior.}
}

@inproceedings{playlist4,
author = {Aucouturier, J.-J and Pachet, Francois},
year = {2002},
month = {02},
pages = {105 - 108 vol.1},
title = {Scaling up music playlist generation},
volume = {26},
isbn = {0-7803-7304-9},
journal = {New York},
doi = {10.1109/ICME.2002.1035729}
}
@inproceedings{score1,
author = {Dixon and Lui, Simon and Widmer, Gerhard},
year = {2005},
month = {09},
pages = {},
title = {MATCH: A Music Alignment Tool Chest}
}

@inproceedings{score2,
author = {Müller, Meinard and Mattes, Henning and Kurth, Frank},
year = {2006},
month = {10},
pages = {},
title = {An Efficient Multiscale Approach to Audio Synchronization}
}

@inproceedings{score3,
author = {Niedermayer, Bernhard and Widmer, Gerhard},
year = {2010},
month = {12},
pages = {417-422},
title = {A Multi-Pass Algorithm for Accurate Audio-to-Score Alignment}
}

@inproceedings{popularity1,
author = {Schedl, Markus and Pohle, Tim and Koenigstein, Noam and Knees, Peter},
year = {2010},
month = {01},
pages = {117-122},
title = {What's Hot? Estimating Country-specific Artist Popularity.}
}

@inproceedings{popularity2,
author = {Pachet, Francois and Roy, Pierre},
year = {2008},
month = {01},
pages = {355-360},
title = {Hit Song Science Is Not Yet a Science.}
}

@inproceedings{popularity3,
author = {Koenigstein, Noam and Shavitt, Yuval},
year = {2009},
month = {01},
pages = {633-638},
title = {Song Ranking based on Piracy in Peer-to-Peer Networks.},
journal = {Proceedings of the 10th International Society for Music Information Retrieval Conference, ISMIR 2009}
}

@article{visualization1,
author = {Müller, Meinard and Jiang, Nanzhu},
year = {2012},
month = {01},
pages = {},
title = {A scape plot representation for visualizing repetitive structures of music recordings},
journal = {Proceedings of the 13th International Society for Music Information Retrieval Conference, ISMIR 2012}
}

@inproceedings{visualization2,
author = {Mardirossian, Arpi and Chew, Elaine},
year = {2007},
month = {01},
pages = {189-194},
title = {Visualizing Music: Tonal Progressions and Distributions.}
}

@article{visualization3,
author = {Cooper, Matthew and Foote, Jonathan and Pampalk, Elias and Tzanetakis, George},
year = {2006},
month = {06},
pages = {42-62},
title = {Visualization in Audio-Based Music Information Retrieval},
volume = {30},
journal = {Computer Music Journal},
doi = {10.1162/comj.2006.30.2.42}
}

@inproceedings{visualization4,
author = {Foote, Jonathan},
year = {1999},
month = {01},
pages = {77-80},
title = {Visualizing Music and Audio using Self-Similarity},
doi = {10.1145/319463.319472}
}

@article{visualization5,
author = {Gómez, Emilia and Bonada, Jordi},
year = {2005},
month = {01},
pages = {},
title = {Tonality visualization of polyphonic audio}
}

@inproceedings{ui1,
author = {Stober, Sebastian and Nürnberger, Andreas},
year = {2010},
month = {06},
pages = {273-302},
title = {MusicGalaxy: A Multi-focus Zoomable Interface for Multi-facet Exploration of Music Collections},
volume = {6684},
doi = {10.1007/978-3-642-23126-1_18}
}

@inproceedings{ui2,
author = {Leitich, Stefan and Topf, Martin},
year = {2007},
month = {01},
pages = {167-170},
title = {Globe of Music - Music Library Visualization Using Geosom.}
}

@inproceedings{ui3,
author = {Lamere, Paul and Eck, Douglas},
year = {2007},
month = {01},
pages = {173-174},
title = {Using 3D Visualizations to Explore and Discover Music.}
}

@inproceedings{ui4,
author = {Pampalk, Elias and Goto, Masataka},
year = {2006},
month = {01},
pages = {367-370},
title = {MusicRainbow: A New User Interface to Discover Artists Using Audio-based Similarity and Web-based Labeling.}
}

@inproceedings{ui5,
author = {Stewart, Rebecca and Sandler, Mark},
year = {2011},
month = {07},
pages = {1-6},
title = {The amblr: A mobile spatial audio music browser},
journal = {Proceedings - IEEE International Conference on Multimedia and Expo},
doi = {10.1109/ICME.2011.6012203}
}

@inproceedings{adaptive1,
author = {Schedl, Markus and Schnitzer, Dominik},
year = {2013},
month = {07},
pages = {793-796},
title = {Hybrid retrieval approaches to geospatial music recommendation},
doi = {10.1145/2484028.2484146}
}

@phdthesis{adaptive2,
author = {Stober, Sebastian},
year = {2011},
month = {11},
pages = {},
title = {Adaptive Methods for User-Centered Organization of Music Collections}
}

@inproceedings{adaptive3,
author = {Kaminskas, Marius and Ricci, Francesco and Schedl, Markus},
year = {2013},
month = {10},
pages = {17-24},
title = {Location-aware music recommendation using auto-tagging and hybrid matching},
journal = {RecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems},
doi = {10.1145/2507157.2507180}
}

@inproceedings{adaptive4,
author = {Baltrunas, Linas and Kaminskas, Marius and Ludwig, Bernd and Moling, Omar and Ricci, Francesco and Aydin, Aykan and Lüke, Karl-Heinz and Schwaiger, Roland},
year = {2011},
month = {08},
pages = {89-100},
title = {InCarMusic: Context-Aware Music Recommendations in a Car},
volume = {85},
journal = {Lect. Note. Bus. Info. Proc.},
doi = {10.1007/978-3-642-23014-1_8}
}

@inproceedings{loukadakis2018,
author = {Loukadakis, Manolis and Cano, José and O'Boyle, Michael},
year = {2018},
month = {01},
pages = {},
title = {Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures}
}

repository: jhamrak/instrument-recognition-lab

repo_name: jhamrak/instrument-recognition-lab

branch_name: refs/heads/master

extension: bib


text:
@article{lee2020cococonet,
  title={CoCoCoNet: conserved and comparative co-expression across a diverse set of species},
  author={Lee, John and Shah, Manthan and Ballouz, Sara and Crow, Megan and Gillis, Jesse},
  journal={Nucleic Acids Research},
  year={2020}
}

repository: sarbal/academic-kickstart

repo_name: sarbal/academic-kickstart


text:
td.win {
    background-color: lightGreen;
}

td.loss {
    background-color: red;
}

td.tie {
    background-color: paleGoldenRod;
}

td.nomatchup {
  background-color: white
}

td.total {
    font-weight: bold;
}

td.matchup {
    font-weight: bold;
}

repository: KeyMenSam/fb-power-rankings

repo_name: KeyMenSam/fb-power-rankings


text:
@charset "UTF-8";

html, body {
	height: 100%; /* 높이를 전체 브라우저를 가득 채우도록 함*/
	margin: 0;
	padding: 0;
}

body {
	background-size: cover;
	/* 	font-family: 'Jeju Gothic', serif; */
}

#layout { /*layout 안의 자식 요소는 크게 header, container, footer*/
	display: flex; /*flex*/
	flex-direction: column;
	height: 100%; /*layout-container-in_con height를 100% 으로 주어 in_con의 크기를 조절함*/
}

#header {
	background: #515ce6; /*헤더의 색상*/
	flex: 1.5;
	display: flex; /* 내부 요소를 flex로 정렬함  */
}

#toast {
	flex: 7;
	padding: 50px 50px;
}

#toast>in_con { /* 컨테이너 안의 div 가운데 정렬 */
	text-align: center;
}

#in_con { /*index 페이지 외에 다른 페이지에서 사용될 안쪽 container css*/
	display: flex; /*내부 요소를 가운데 정렬 하기 위한 flex 설정*/
	flex-direction: column; /* 내부 요소 컬럼식으로 정렬*/
	align-items: center; /*flex > 세로 가운데 정렬 */
	justify-content: center; /* flex > 가로 가운데 정렬 */
	height: 100%; /*layout-container-in_con height를 100% 으로 주어 in_con의 크기를 조절함*/
	background-color: white;
	border: 1px solid black;
	border-radius: 5px;
	padding: 20px;
}

#in_con_index { /*index 페이지 외에 다른 페이지에서 사용될 안쪽 container css*/
	display: flex; /*내부 요소를 가운데 정렬 하기 위한 flex 설정*/
	flex-direction: column; /* 내부 요소 컬럼식으로 정렬*/
	align-items: center; /*flex > 내부 요소 세로 가운데 정렬 */
	justify-content: center; /* flex > 내부 요소 가로 가운데 정렬 */
	height: 100%; /*layout-container-in_con height를 100% 으로 주어 in_con의 크기를 조절함*/
}

#tab_layout { /**/
	background-color: pink;
	width: 100%;
	height: 100%;
	display: flex;
	align-items: center; /*flex > 세로 가운데 정렬 */
	justify-content: center;
	display: flex; /* flex > 가로 가운데 정렬 */
}

#x_layout {
	width: 100%;
	height: 100%;
	display: flex;
	background-color: black;
}

.menu {
	background-color: pink;
	flex: 1 1 0;
	display: flex;
	align-items: center; /*flex > 세로 가운데 정렬 */
	justify-content: center;
}

.menu>a {
	width: 100px; /*가로길이와 높이를 고정*/
	height: 100px;
	background-color: blue;
	border: 1px solid black;
	border-radius: 50%; /*동그라미 형태로 만들어주기 위함*/
	text-decoration: none;
	color: white;
}

#footer {
	flex: 1.5;
	display: flex; /*내부 요소(p태그)를 flex를 이용하여 정렬*/
	align-items: center; /*footer안의 p태그를 수직 중앙정렬*/
	justify-content: center; /*p 태그를 수평 가운데 정렬*/
	background: #515ce6;
}

#footer>p {
	font-size: 0.5em;
	color: white;
	line-height: 20px; /* 줄 간격 옵션 */
}

#logo {
	display: flex; /*내부 요소(logo)를 flex를 이용하여 정렬*/
	align-items: center; /*logo를 수직 중앙정렬*/
	padding: 20px;
}

#logo>a>img {
	margin-left: 20px;
	height: 50px;
}

#sign { /* 헤더 안에 있는 welcome, mypage, login, signup 묶어주는 div */
	display: flex; /*내부 요소(welcome, mypage, login, signup)를 flex를 이용하여 정렬*/
	align-items: center; /* 수직 가운데 정렬 */
	margin-left: auto; /* 로그인과 회원가입 버튼 가장자리로 */
}

#sign>a {
	text-decoration: none;
	margin-right: 30px;
	color: white;
}

#sign>a:hover:not(#welcome){ /* id가 welcome인 a 태그 외에 sign에 있는 a 태그에 hover를 줌*/
	color: #cccccc;
}

#signup, #logout {
	margin-right: 30px;
}


text:
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************

function(my_lib_import_SVLib _target)

   target_link_libraries(${_target} SVLib)

endfunction()

function(my_inc_import_SVLib _target)

   target_include_directories(${_target} PRIVATE $<TARGET_PROPERTY:SVLib,INTERFACE_INCLUDE_DIRECTORIES>)

endfunction()

#*******************************************************************************
#*******************************************************************************
#*******************************************************************************

function(my_lib_import_DisplayLib _target)

   target_link_libraries(${_target} DisplayLib)

endfunction()

function(my_inc_import_DisplayLib _target)

   target_include_directories(${_target} PRIVATE $<TARGET_PROPERTY:DisplayLib,INTERFACE_INCLUDE_DIRECTORIES>)

endfunction()

#*******************************************************************************
#*******************************************************************************
#*******************************************************************************

extension: cmake


text:
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extension: liquid


text:
@InProceedings{bhaskara15,
  supplementary = {Supplementary:bhaskara15-supp.pdf},
  title = {{Sparse Solutions to Nonnegative Linear Systems and Applications}},
  author = {Bhaskara, Aditya and Suresh, Ananda and Zadimoghaddam, Morteza},
  pages = {83-92},
  abstract = {We give an efficient algorithm for finding sparse approximate solutions to linear systems of  equations with nonnegative coefficients. Unlike most known results for sparse recovery, we do not require \emph{any} assumption on the matrix other than non-negativity. Our algorithm is combinatorial in nature, inspired by techniques for the ``set cover'' problem, as well as the multiplicative weight update method.   We then present a natural application to learning mixture models in the PAC framework. For learning a mixture of $k$ axis-aligned Gaussians in $d$ dimensions, we give an algorithm that outputs a mixture of $O(k/\epsilon^3)$ Gaussians that is $\epsilon$-close in statistical distance to the true distribution, without any separation assumptions. The time and sample complexity is roughly $O(kd/\epsilon^3)^d$. This is polynomial when $d$ is constant -- precisely the regime in which known methods fail to identify the components efficiently.  Given that non-negativity is a natural assumption, we believe that our result may find use in other settings in which we wish to approximately ``explain'' data using a small number of a (large) candidate set of components.},
}

repository: mreid/papersite

repo_name: mreid/papersite

gha_language: TeX


text:
@article{voss2017accuracy,
  title={Accuracy of an automated knowledge base for identifying drug adverse reactions},
  author={Voss, Erica A and Boyce, Richard D and Ryan, Patrick B and van der Lei, Johan and Rijnbeek, Peter R and Schuemie, Martijn J},
  journal={J Biomed Inform},
  volume={66},
  pages={72--81},
  year={2017},
  publisher={Elsevier}
}

@article{winnenburg2015leveraging,
  title={Leveraging {MEDLINE} indexing for pharmacovigilance--Inherent limitations and mitigation strategies},
  author={Winnenburg, Rainer and Sorbello, Alfred and Ripple, Anna and Harpaz, Rave and Tonning, Joseph and Szarfman, Ana and Francis, Henry and Bodenreider, Olivier},
  journal={Journal of Biomedical Informatics},
  volume={57},
  pages={425--435},
  year={2015},
  publisher={Elsevier}
}

@article{duke2013consistency,
  title={Consistency in the safety labeling of bioequivalent medications},
  author={Duke, Jon and Friedlin, Jeff and Li, Xiaochun},
  journal={Pharmacoepidemiology and Drug Safety},
  volume={22},
  number={3},
  pages={294--301},
  year={2013},
  publisher={Wiley Online Library}
}

@article{evans2001use,
  title={Use of proportional reporting ratios ({PRR}s) for signal generation from spontaneous adverse drug reaction reports},
  author={Evans, SJW and Waller, Patrick C and Davis, S},
  journal={Pharmacoepidemiology and Drug Safety},
  volume={10},
  number={6},
  pages={483--486},
  year={2001},
  publisher={Wiley Online Library}
}

@article{banda2016curated,
  title={A curated and standardized adverse drug event resource to accelerate drug safety research},
  author={Banda, Juan M and Evans, Lee and Vanguri, Rami S and Tatonetti, Nicholas P and Ryan, Patrick B and Shah, Nigam H},
  journal={Scientific data},
  volume={3},
  pages={160026},
  year={2016},
  publisher={Nature Publishing Group}
}

@article{suchard2019comprehensive,
  title={Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis},
  author={Suchard, Marc A and Schuemie, Martijn J and Krumholz, Harlan M and You, Seng Chan and Chen, RuiJun and Pratt, Nicole and Reich, Christian G and Duke, Jon and Madigan, David and Hripcsak, George and others},
  journal={The Lancet},
  volume={394},
  number={10211},
  pages={1816--1826},
  year={2019},
  publisher={Elsevier}
}


text:
% Cited sources go into this document. You can obtain the right format from google scholar.

@book{eiben2003introduction,
  title={Introduction to evolutionary computing},
  author={Eiben, Agoston E and Smith, James E and others},
  volume={53},
  year={2003},
  publisher={Springer}
}

repository: AnwarAsif/TinyML

repo_name: AnwarAsif/TinyML

branch_name: refs/heads/main


text:
@article{poledna_spitalsubventionen_2014,
	title = {Spitalsubventionen und neue {Spitalfinanzierung}},
	url = {https://jusletter.weblaw.ch/juslissues/2014/766/spitalsubventionen-u_426f36268a.html},
	abstract = {Die neue Spitalfinanzierung soll ein wettbewerbsneutrales und leistungsorientiertes System ermöglichen. Dies schliesst die Gewährung von kantonalen Spitalsubventionen grösstenteils aus. Der Beitrag untersucht, wie die Kantone Subventionen, die vor der Einführung der neuen Spitalfinanzierung an Spitäler ausgerichtet wurden, unter den neuen Vorgaben des KVG regeln. Dabei fällt nicht nur die Heterogenität der kantonalen Vorgehensweisen auf, sondern auch die Mühe, die es macht, Transparenz und Vergleichbarkeit zu erzielen. Lösungsvorschläge, wie Wettbewerbsverzerrungen verhindert und Transparenz geschaffen werden können, runden den Beitrag ab.},
	language = {de},
	number = {766},
	urldate = {2020-11-17},
	journal = {Jusletter},
	author = {Poledna, Tomas and Vokinger, Kerstin Noëlle},
	year = {2014},
	file = {Snapshot:C\:\\Users\\pdanio\\Zotero\\storage\\7IT35RGZ\\spitalsubventionen-u_426f36268a.pdf:application/pdf}
}

repository: pdanio/academic-kickstart

repo_name: pdanio/academic-kickstart


text:
@article{CUI20118186,
 abstract = {Anti-germ performance test is critical in the production of detergents. However, actual biochemical tests are often costly and time-consuming. In this paper, we present an Elman neural network-based model to predict detergents’ anti-germ performance and ingredient levels, respectively. The model made it much faster and cost effective than doing actual biochemical tests. We also present preprocessing methods that can reduce data conflicts while keeping the monotonicity on limited experimental data. The model can find out the relationship between ingredient levels and the corresponding anti-germ performance, which is not widely used in solving biochemical problems. The results of experiments are generated on the base of two detergent products for two types of bacteria, and appear reasonable according to natural rules. The prediction results show a high accuracy and fitting with the monotonicity rule mostly.},
 author = {Anqi Cui and Hua Xu and Peifa Jia},
 doi = {https://doi.org/10.1016/j.eswa.2010.12.164},
 issn = {0957-4174},
 journal = {Expert Systems with Applications},
 keywords = {Anti-germ performance prediction, Ingredient level prediction, Artificial neural networks, Monotonicity rule, Preprocessing methods},
 number = {7},
 pages = {8186 - 8192},
 title = {An Elman neural network-based model for predicting anti-germ performances and ingredient levels with limited experimental data},
 url = {http://www.sciencedirect.com/science/article/pii/S0957417410015277},
 volume = {38},
 year = {2011}
}

url: https://raw.githubusercontent.com/MFYDev/THUIARWeb/bdc2d227d26c552de594f6c64360e0c9c0c6f89f/content/zh/publication/An Elman neural network-based model for predicting anti-germ performances and ingredient levels with limited experimental data/cite.bib

repository: MFYDev/THUIARWeb

repo_name: MFYDev/THUIARWeb


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    margin-top: 0;
    margin-bottom: 0;
}
#pagin li a.current {
    color: #171717 !important;
    font-weight: 700;
    text-decoration: underline;
    background: #b3b9b5;
}
.dark #pagin li a.current {
    color: #d4d32b !important;
    font-weight: 700;
    text-decoration: underline;
    background: #4c5850;
}

#pagin li {
    display: inline-block;
}
#pagin li a {
    display: inline-block;
    height: 25px;
    width: 40px;
    padding: 5px;
    text-align: center;
    text-decoration: none;
}
#pagin li a {
    color: #fff;
    background: #424242;
}
.dark #pagin li a {
    color: #fff;
    background: #000;
}

#pagin li a:hover,
#pagin li.next:hover,
#pagin li.prev:hover{
    background: #747775;
    color: #fff !important;
}
.dark #pagin li a:hover,
.dark #pagin li.next:hover,
.dark #pagin li.prev:hover{
    background: #eee !important;
    color: #000 !important;
}
#pagin li.prev {
    cursor: pointer;
    width: 80px !important;
    height: 25px;
    text-align: center;
    padding: 5px;
}

#pagin li.prev,
#pagin li.next{
    color: #fff;
    background: #121312;
}

.dark #pagin li.prev,
.dark #pagin li.next{
    color: #fff;
    background: #000;
}

#pagin li.next {
    cursor: pointer;
    width: 80px !important;
    height: 25px;
    text-align: center;
    padding: 5px;
}

.last{
    cursor:pointer;
    margin-left:5px;
}

.first{
    cursor:pointer;
    margin-right:5px;
}
#pagin {
    display: flex;
    justify-content: center;
    margin-left: 15%;
    margin-right: 15%;
    padding: 0;
}
blockquote {
      font-size: .5em;
    background: #eee;
    margin: 5px;
    color: #000;
    box-shadow: 1px 1px 8px 2px;
    width: 70%;
}
.social-icons p{
  font-size: 12px;
}
.grid-container .main-content .xsmall {
  font-size: .5em;
}
.line-content a {
    color: #a78304;
}
.line-content a:hover {
    color: #e4cd5a;
}
.dark .line-content a {
    color: yellow;
}

.client-app-main div,
.client-app-main p{
    font-size: 1.4rem;
}

.tabs {
  position: relative;
  width: 100%;
  overflow: hidden;
  margin: 0 0 2em;
  font-weight: 300;
}

/* Nav */
.tabs nav {
  text-align: center;
}

.tabs nav ul {
  padding: 0;
  margin: 0;
  list-style: none;
  display: inline-block;
}

.tabs nav ul li {
  border: 1px solid #becbd2;
  border-bottom: none;
  margin: 0 0.25em;
  display: block;
  float: left;
  position: relative;
}

.dark .tabs nav li a.tab-current {
  border-left: 1px solid #47a3da;
  border-right: 1px solid #47a3da;
  border-top: 1px solid #47a3da;
  box-shadow: inset 0 2px #47a3da;
}

.tabs nav li a.tab-current {
  border: 1px solid #2e3437;
  border-bottom: none;
  box-shadow: inset 0 2px #2e3437;
  z-index: 100;
}

.dark .tabs nav li a.tab-current:before,
.dark .tabs nav li a.tab-current:after {
  background: #47a3da;
}
.tabs nav li a.tab-current:before,
.tabs nav li a.tab-current:after {
  content: '';
  position: absolute;
  height: 1px;
  right: 100%;
  bottom: 0;
  width: 1000px;
  background: #2e3437;
}

.tabs nav li a.tab-current:after {
  right: auto;
  left: 100%;
  width: 4000px;
}

.tabs nav a {
  color: #141515;
  display: block;
  font-size: 1.45rem;
  line-height: 2.5;
  padding: 0 1.25em;
  white-space: nowrap;
}
.dark .tabs nav a {
  color: #649edb;
}
.tabs nav a:hover {
  color: #299ed8;
}
.dark .tabs nav a:hover {
  color: #fff;
}

.dark .tabs nav li a.tab-current {
  color: #000;
  background:#d0dfe2;
}
.tabs nav li a.tab-current {
  color: #060606;
  background: #8a8a8a;
}

.tabs nav a:before {
  display: inline-block;
  vertical-align: middle;
  text-transform: none;
  font-weight: normal;
  font-variant: normal;
  font-family: 'FontAwesome';
  line-height: 1;
  speak: none;
  -webkit-font-smoothing: antialiased;
}

/* Content */
.content section {
  font-size: 1.25em;
  padding: 3em 1em;
  display: none;
  max-width: 1230px;
  margin: 0 auto;
}

.content section:before,
.content section:after {
  content: '';
  display: table;
}

.content section:after {
  clear: both;
}

/* Fallback example */
.no-js .content section {
  display: block;
  padding-bottom: 2em;
  border-bottom: 1px solid #47a3da;
}

.content section.content-current {
  display: block;
}

.mediabox {
  float: left;
  width: 100%;
  padding: 0;
}

.dash-mediabox {
  float: left;
  width: 31%;
  padding: 0 .6em;
}

.dash-mediabox img, .mediabox img {
  max-width: 100%;
  display: block;
  margin: 0 auto;
}

.dash-mediabox h3, .mediabox h3 {
  margin: 0.75em 0 0.5em;
}

.dash-mediabox p, .mediabox p {
  padding: 0 0 1em 0;
  margin: 0;
  line-height: 1.7;
}

.check-sec {
    display: inline-block;
    width: 15%;
    float: left;
    margin: 0 .5em 0 0;
    font-size: 1.3rem !important;
}

@media screen and (max-width: 52.375em) {
  .word span {
    font-size: 3em;
  }
  nav[class="select"] ul > li {
    font-size: .67em;
  }
  .margin-side {
    margin: 0 1.3em;
  }
  .tabs nav a span {
    display: none;
  }

  .tabs nav a:before {
    margin-right: 0;
  }

  .dash-mediabox, .mediabox {
    float: none;
    width: auto;
    padding: 0 0 35px 0;
    font-size: 90%;
  }

  .dash-mediabox img, .mediabox img {
    float: left;
    margin: 0 25px 10px 0;
    max-width: 40%;
  }

  .dash-mediabox h3, .mediabox h3 {
    margin-top: 0;
  }

  .dash-mediabox p, .mediabox p {
    margin-left: 40%;
    margin-left: calc(40% + 25px);
  }

  .mediabox:before,
  .mediabox:after,
  .dash-mediabox:before,
  .dash-mediabox:after {
    content: '';
    display: table;
  }

  .mediabox:after,
  .dash-mediabox:after {
    clear: both;
  }
  .check-sec {
    width: 100%;
    text-align: left;
  }
}

@media screen and (max-width: 32em) {
  .margin-side {
    margin: 0 5px;
}
  .tabs nav ul,
  .tabs nav ul li a {
    width: 100%;
    padding: 0;
  }

  .tabs nav ul li {
    width: 19%;
    width: calc(19% + 1px);
    margin: 0 0 0 -1px;
  }

  .tabs nav ul li:last-child {
    border-right: none;
  }

  .dash-mediabox,
  .mediabox,
  .line-content p {
    text-align: center;
  }

  .dash-mediabox img, .mediabox img {
    float: none;
    margin: 0 auto;
    max-width: 100%;
  }

  .dash-mediabox h3, .mediabox h3 {
    margin: 1.25em 0 1em;
  }

  .dash-mediabox p, .mediabox p {
    margin: 0;
  }
}
.info-container {
    margin-top: 1.5em;
}
ul.ulListItem {
    list-style-type: none;
  padding: 0;
  margin: 0;
}
li.liListItem {
    list-style-type: none;
    border-bottom: 2px solid #eee;
    display: inline-block;
}
.info-container a {
    color: #482aea;
}
.info-container a:hover {
  color: #534b82;
}
.dark .info-container a {
  color: #d8cc3c;
}
.dark .info-container a:hover {
  color: #dad493;
}

h3.title {
    margin: 0;
    padding: 0;
    font-size: 1.6rem;
    font-weight: 500;
}
h3.publish {
    padding: 0;
    margin: 0 0px 1.5em;
    font-size: 1.4rem;
    font-weight: 400;
    font-style: italic;
}
.quotes-container h3.publish {
    border-top: 1px solid #ddd;
    padding-top: 10px;
}
h3.title span,
h3.publish span {
    color: #7d7878;
}
.dark h3.title span,
.dark h3.publish span {
    color: #3f94bd;
}
h2.title {
	margin: 2em 0 .4em;
    font-size: 2.1rem;
    font-weight: 500;
    text-align: left;
    line-height: 1.7;
    letter-spacing: 5px;
}
p small {
    font-size: .7em;
    font-style: italic;
}
input[type="checkbox"] {
  position: absolute;
  opacity: 0;
}
input[type="checkbox"]:checked + label svg path {
  stroke-dashoffset: 0;
}
input[type="checkbox"]:focus + label {
  transform: scale(1.03);
}

input[type="checkbox"] + label {
  display: inline-block;
  border: 5px solid #333;
  width: 1.5em;
  height: 1.7em;
  border-radius: 14px;
  cursor: pointer;
  transition: all .2s ease;
  margin: .5em;
}
input[type="checkbox"] + label:active {
  transform: scale(1.05);
  border-radius: 30px;
}
input[type="checkbox"] + label svg {
  pointer-events: none;
}
input[type="checkbox"] + label svg path {
  fill: none;
  stroke: #333;
  stroke-width: 4px;
  stroke-linecap: round;
  stroke-linejoin: round;
  stroke-dasharray: 100;
  stroke-dashoffset: 101;
  transition: all 350ms cubic-bezier(1, 0, 0.37, 0.91);
}

.label-val {
    display: inline-block;
    font-size: 1.4rem !important;
}
.checkbox-info-container {
  display: inline-block;
    width: 100%;
}
.dark input[type="checkbox"] + label svg path {
    stroke: #5790e4;
}
.dark input[type="checkbox"] + label {
  border: 5px solid #649edb;
}
.double-content .double-qs {
  font-size: 1.9rem !important;
}
.single-content p,
.double-content p {
  font-size: 1.7rem !important;
}
.client-app-bottom {
    display: inline-block;
    width: 100%;
}
@media screen and (max-width: 52.375em) {
  .tabs {
    margin-bottom: .3em;
  }
  .tabs nav ul li {
    border-bottom: 1px solid #becbd2;
  }
  .logo-banner {
    min-height: 10em;
}
}

.info-container, .info-container div, .info-container p {
    font-size: 1.3rem;
}
.margin-all-0 {
  margin: 0;
  padding: 0;
}
.client-app-container {
    display: inline-block;
    width: 100%;
}
.client-app-main {
    display: inline-block;
}
#lab_social_icon_footer .social-icons {
    margin-top: 2em;
}
.btn {
    font-size: 1.5rem;
    border-radius: 2em;
    padding: 10px 30px;
    width: 8em;
    line-height: 1.8em;
}

.btn:hover,.btn:active,.btn:focus {
    outline: none;
    -webkit-box-shadow: none!important;
    box-shadow: none!important
}

.btn.btn-primary {
    border-width: 2px;
    background: #c1c1c1;
    border-color: #c1c1c1;
    color: #000;
    position: relative;
    top: 0;
    -webkit-transition: .3s all ease;
    -o-transition: .3s all ease;
    transition: .3s all ease;
    -webkit-box-shadow: 0 4px 20px -5px rgba(92, 96, 103, 0.4);
    box-shadow: 0 4px 20px -5px rgba(73, 76, 80, 0.4);
}

.btn.btn-primary:hover {
    top: -2px;
    background: #4f545d;
    border-color: #4f545d;
    color: #fff;
    -webkit-box-shadow: 0 5px 30px -5px rgba(66,133,244,.7)!important;
    box-shadow: 0 5px 30px -5px rgba(66,76,83,.7)!important;
}

.dark .btn.btn-primary {
    background: #77bad4;
    border-color: #066990;
    color: #fff;
    -webkit-box-shadow: 0 4px 20px -5px rgba(161, 217, 239, 0.4);
    box-shadow: 0 4px 20px -5px rgba(161, 217, 239, 0.4);
}

.dark .btn.btn-primary:hover {
    background: #3fc6fb;
    border-color: #0c8dbf;
    color: #fff;
    -webkit-box-shadow: 0 5px 30px -5px rgba(161, 217, 239,.7)!important;
    box-shadow: 0 5px 30px -5px rgba(161, 217, 239,.7)!important;
}

/* Style inputs, select elements and textareas */
input[type=text], select, textarea{
  width: 100%;
  padding: 12px;
  border: 1px solid #ccc;
  border-radius: 4px;
  box-sizing: border-box;
  resize: vertical;
}

/* Style the label to display next to the inputs */
label {
  padding: 12px 12px 12px 0;
  display: inline-block;
}

/* Style the submit button */
input[type=submit] {
  cursor: pointer;
  float: right;
}

/* Style the container */
.form-container {
  border-radius: 5px;
  padding: 20px;
}

/* Floating column for labels: 25% width */
.col-25 {
  float: left;
  width: 25%;
  margin-top: 6px;
}

/* Floating column for inputs: 75% width */
.col-75 {
  float: left;
  width: 75%;
  margin-top: 6px;
}

/* Clear floats after the columns */
.row:after {
  content: "";
  display: table;
  clear: both;
}

/* Responsive layout - when the screen is less than 600px wide, make the two columns stack on top of each other instead of next to each other */
@media screen and (max-width: 600px) {
  .col-25, .col-75, input[type=submit] {
    width: 100%;
    margin-top: 0;
  }
}

.contact-left h2.title {
  font-size: 2.3rem;
  border-bottom: 13px solid #6d6868;
  margin-right: 1em;
  margin-top: .3em;
  line-height: 1.7em;
  padding-left: 1px;
}
.contact-left p
{
  line-height: 2em;
  margin-right: 3em;
  font-size: 1.2em;
}

.dark .contact-left h2.title {
 border-bottom: 13px solid #77bad4;
}
.gen-row::after {
  content: "";
  clear: both;
  display: table;
}

[class*="gen-col-"] {
  float: left;
  padding: 15px;
  box-sizing: border-box;
}

/* For mobile phones: */
[class*="gen-col-"] {
  width: 100%;
}

@media only screen and (min-width: 768px) {
  /* For desktop: */
  .gen-col-1 {width: 8.33%;}
  .gen-col-2 {width: 16.66%;}
  .gen-col-3 {width: 25%;}
  .gen-col-4 {width: 33.33%;}
  .gen-col-5 {width: 41.66%;}
  .gen-col-6 {width: 50%;}
  .gen-col-7 {width: 58.33%;}
  .gen-col-8 {width: 66.66%;}
  .gen-col-9 {width: 75%;}
  .gen-col-10 {width: 83.33%;}
  .gen-col-11 {width: 91.66%;}
  .gen-col-12 {width: 100%;}
}
.dark .modal {
  background-color: #000;
}

.modal {
    display: none;
    position: fixed;
    left: 0;
    right: 0;
    background-color: #fafafa;
    padding: 0;
    max-height: 70%;
    width: 55%;
    margin: auto;
    overflow-y: auto;
    border-radius: 2px;
    will-change: top, opacity
}

.modal-overlay {
    position: fixed;
    z-index: 999;
    top: -25%;
    left: 0;
    bottom: 0;
    right: 0;
    height: 125%;
    width: 100%;
    background: #000;
    display: none;
    will-change: opacity
}
a.modal-close {
    float: right;
    margin: 10px;
    background: #ddd;
    padding: .5em;
    font-size: 1.4rem;
    color: #000;
    border-radius: 25px;
    width: 80px;
    text-align: center;
}
img.responsive-img {
    margin: 1.5em 0;
}
.search-box i {
  position: absolute;
}
.search-box label {
   width: 100%;
}
.search-box {
   position: relative;
}

.search-box i {
  padding: 8px;
    min-width: 50px;
    text-align: center;
    right: 0px;
    line-height: 1.6em;
    font-size: 1.6rem;
}

.search-box input {
  width: 100%;
  padding: 10px;
  line-height: 1.7em;
  border-radius: 1px;
}

.search-box input:focus {
  border-left: 1px solid #a2a2a2;;
  border-top: 1px solid #a2a2a2;;
  border-right: 1px solid #a2a2a2;;
  border-bottom: 3px solid #000;
}
.profile-details span {
  display: block;
  color: #4f82d5;
  font-size: 1.6rem;
}
.profile-details p {
  display: block;
  font-size: 1.3rem;
  line-height: 1.6em;
}
.profile-details a {
  color: #000;
}
.profile-details a:hover {
  color: #766c6c;
}
.opacity-blur {
  opacity: 0.3
}
.symbol {
    display: inline;
    background: #969696;
    color: #fff;
    padding: 5px 1em;
    line-height: 1.7em;
}
.dark .symbol {
  background: #5ba5e4;
}
.profile .exchange {
    margin-bottom: .5em;
    font-style: italic;
    font-size: 1.2rem;
    color: #d3d3d3;
}

.profile .price-amount {
    font-size: 3em;
    line-height: 1.7em;
    font-weight: 700;
}

.label-1 {
    color: #a09999;
    margin-bottom: .2em;
}

.changes, .percent, .range, .mkt-cap {
    margin-bottom: 1.2em;
}

.changes.red,
.percent.red,
.changes i.fa-arrow-down,
.percent i.fa-arrow-down {
    color: red;
}
.changes.green,
.percent.green,
.changes i.fa-arrow-up,
.percent i.fa-arrow-up {
    color: green;
}
.dark .mkt-cap,
.dark .range,
.dark .vol-avg {
    color: #4ea4fd;
}
.mkt-cap,
.range,
.vol-avg {
    color: #101010;
}

.finance-container {
    margin-bottom: 1em;
}
.dark .finance-container h2.title{
  border-bottom: 11px solid #4ea4fd;
}

.finance-container h2.title{
  padding-bottom: 0;
  margin-bottom: 0;
}
.finance-container .profile {
padding-top: 0;
    padding-left: 0;
}

//TODO
.accordion {
  width: 100%;
  displat: inline-block;
}
.panel {
  background-color: #f0ebe1;
}
.panel__label {
  position: relative;
  display: block;
  width: 100%;
  background: none;
  border: none;
  text-align: left;
  padding: 25px 60px 25px 25px;
  font-weight: 500;
  font-size: 17px;
  font-family: inherit;
  -webkit-transition: color 0.2s linear;
  transition: color 0.2s linear;
  cursor: pointer;
}
.panel__label:focus {
  outline: none;
}
.panel__label:after,
.panel__label:before {
  content: '';
  position: absolute;
  right: 25px;
  top: 50%;
  width: 22px;
  height: 2px;
  margin-top: -@height;
  background-color: #372717;
}
.panel__label:before {
  -webkit-transform: rotate(-90deg);
          transform: rotate(-90deg);
  -webkit-transition: -webkit-transform 0.35s cubic-bezier(0.65, 0.05, 0.36, 1);
  transition: -webkit-transform 0.35s cubic-bezier(0.65, 0.05, 0.36, 1);
  transition: transform 0.35s cubic-bezier(0.65, 0.05, 0.36, 1);
  transition: transform 0.35s cubic-bezier(0.65, 0.05, 0.36, 1), -webkit-transform 0.35s cubic-bezier(0.65, 0.05, 0.36, 1);
}
.panel[aria-expanded='true'] .panel__content {
  opacity: 1;
}
.panel[aria-expanded='true'] .panel__label {
  color: #957029;
}
.panel[aria-expanded='true'] .panel__label:before {
  -webkit-transform: rotate(0deg);
          transform: rotate(0deg);
}
.panel__inner {
  overflow: hidden;
  will-change: height;
  -webkit-transition: height 0.4s cubic-bezier(0.65, 0.05, 0.36, 1);
  transition: height 0.4s cubic-bezier(0.65, 0.05, 0.36, 1);
}
.panel__content {
  margin: 5px 25px 25px;
  font-size: 14px;
  color: #756658;
  opacity: 0;
  -webkit-transition: opacity 0.3s linear 0.18s;
  transition: opacity 0.3s linear 0.18s;
}
.panel:not(:last-child) {
  margin-bottom: 3px;
}
.suggest-results li {
    list-style-type: none;
    margin-bottom: 1em;
    line-height: 2em;
    border-bottom: 1px solid #ddd;
}

.suggest-results li:hover {
    background: #d0d0d0;
    cursor: pointer;
}

.suggest-results li p {
    margin: 0;
}
.edit .finance-container,
.edit .quotes-container {
    display: none
}

.edit .suggest-results {
    opacity: 1
}
.edit.opacity-blur{
    opacity: 1
}

.padding-left-0 {
    padding-left: 0;
}
.padding-all-0 {
    padding: 0;
}
.padding-horizantal-0 {
    padding-top: 0;
    padding-bottom: 0;
}
.margin-all-0 {
    margin: 0
}
ul.finance-report-select {
    list-style-type: none;
}
.quotes-key-data div > p {
    line-height: 1.4em;
    width: 100%;
    display: inline-block;
    border-bottom: 1px solid #dad2d2;
    margin-top: 10px;
    margin-bottom: 0;
}
.heading-1 {
    font-size: 2.5rem;
    font-weight: 300;
    color: #fff;
    margin-bottom: 0;
    background: #05b6de;
    border-bottom: 1px solid #eee;
}
button.btn-lg.load-btn {
    height: 2em;
    width: 6em;
    margin-left: 0.6em;
}
.chart-box{
  background: #343f57;
}

.profile-charts {
  max-width: 780px;
  width: 780px;
}
.table-scroll {
  position: relative;
  width:100%;
  display: inline-block;
  z-index: 1;
  margin: 0;
  overflow: auto;
}
.table-scroll table {
  width: 100%;
  min-width: 670px;
  margin: 0;
  display: inline-block;
  border-collapse: separate;
  border-spacing: 0;
  font-size: 10px;
  text-align: center;
  vertical-align: middle;
}
.table-wrap {
  position: relative;
}
.table-scroll th,
.table-scroll td {
  padding: 5px 10px;
  border: 1px solid #000;
  background: #fff;
  vertical-align: top;
}
.table-scroll thead th {
  background: #333;
  color: #fff;
  position: -webkit-sticky;
  position: sticky;
  top: 0;
  vertical-align: middle;
  text-align: center;
}
/* safari and ios need the tfoot itself to be position:sticky also */
.table-scroll tfoot,
.table-scroll tfoot th,
.table-scroll tfoot td {
  position: -webkit-sticky;
  position: sticky;
  bottom: 0;
  background: #666;
  color: #fff;
  z-index:4;
}

th:first-child, td.first-col {
  position: -webkit-sticky;
  position: sticky;
  left: 0;
  z-index: 2;
  background: #ccc;
}
thead th:first-child,
tfoot th:first-child {
  z-index: 5;
}

.profile-charts .graph-report,
.profile-charts .table-report {
  display: none;
}

.profile-charts .graph-report.display-active,
.profile-charts .table-report.display-active {
  display: block;
}
.chart-view a {
    color:  #d3d3d3;
    padding: 5px;
    font-weight: 700;
    font-size: 1.6rem;
    line-height: 1.7em;
}
.chart-view a.display-active {
    color: #fff;
    background: #000;
    border-radius: 6px;
}

.chart-view {
    float: right;
    clear: both;
    z-index: 1;
    position: relative;
}
.mixed-chart {
  display: inline-block;
  width: 100%;
}

.finance-report-statement {
    /* Size & position */
    position: relative;
    /* width: 200px; */
    margin: 0 auto;
    padding: 10px 10px;

    /* Styles */
    background: #fff;
    border-radius: 5px;
    box-shadow: 0px 0px 7px 3px rgba(0,0,0,0.2);
    cursor: pointer;
    outline: none;
    transition: all 0.3s ease-out;
}

.finance-report-statement:after { /* Little arrow */
    content: "";
    width: 0;
    height: 0;
    position: absolute;
    top: 50%;
    right: 15px;
    margin-top: -3px;
    border-width: 10px 10px 0 10px;
    border-style: solid;
    border-color: #3da2dc transparent;
}

.finance-report-statement .dropdown {
    /* Size & position */
    position: absolute;
    top: 100%;
    left: 0;
    right: 0;

    /* Styles */
    background: #5bb7df;
    border-radius: 0 0 5px 5px;
    border: 1px solid rgba(0,0,0,0.2);
    border-top: none;
    border-bottom: none;
    list-style: none;
    transition: all 0.3s ease-out;

    /* Hiding */
    max-height: 0;
    overflow: hidden;
    z-index: 1;
}

.finance-report-statement .dropdown li {
    padding: 0 10px ;
}

.finance-report-statement .dropdown li  {
    display: block;
    text-decoration: none;
    color: #333;
    padding: 10px 0;
    transition: all 0.3s ease-out;
    border-bottom: 1px solid #e6e8ea;
}

.finance-report-statement .dropdown li:last-of-type a {
    border: none;
}

.finance-report-statement .dropdown li  {
    margin-right: 5px;
    color: inherit;
    vertical-align: middle;
}

/* Hover state */

.finance-report-statement .dropdown li:hover  {
    color: #3d3f40;
}

.finance-report-statement.active {
    border-radius: 5px 5px 0 0;
    background: #4cbeff;
    box-shadow: none;
    border-bottom: none;
    color: white;
}

.finance-report-statement.active:after {
    border-color: #f9f9f9 transparent;
}

.finance-report-statement.active .dropdown {
    border-bottom: 1px solid rgba(0,0,0,0.2);
    max-height: 400px;
}

.user {
  display: inline-block;
  width: 60px;
  height: 60px;
  border-radius: 50%;

  object-fit: cover;
}


text:
@article{Weniger:2012tx,
 archiveprefix = {arXiv},
 author = {Weniger, Christoph},
 doi = {10.1088/1475-7516/2012/08/007},
 eprint = {1204.2797},
 journal = {JCAP},
 pages = {007},
 primaryclass = {hep-ph},
 reportnumber = {MPP-2012-73},
 title = {A Tentative Gamma-Ray Line from Dark Matter Annihilation at the Fermi Large Area Telescope},
 volume = {08},
 year = {2012}
}

repository: cweniger/personal-website

repo_name: cweniger/personal-website


text:
/* css */

.img_1
{
	width:88%;
	height:auto;
	float: left;
    margin:18px 6% 0;
}
.img_3
{
	width:94%;
	height:auto;
	float: left;
	margin:-10px auto 0 5.3%;
}
.img_4
{
	width:72%;
	height:auto;
	float: left;
	margin:-25px 14% 18px;
}

#fare
{
	width:100%;
	height: 100%;
	float:left;
	position: relative;
}
.login
{
    width:83%;
    float: left;

    margin-left:8.5%;
}
.login h1
{
	width:100%;
	float: left;
	background: url(../images/act04.png) no-repeat;
	background-size: 100%;
	font-size: 12px;
	color: #000;
	height: 58px;
	line-height: 58px;
}
.login .img
{
	width:10%;
	float: left;
	margin:14px 10px;
}
.login h1 span
{
	color:#f62626;
}
.login .h1
{
	width:100%;
	float: left;
	background: #fff8e9;
	position: relative;
}
.login .h1 input
{
	width:80%;
	float: left;
	margin:14px 10%;
	height: 35px;
	border:1px solid #e6e5e2;
	box-shadow: 0px -2px 2px #e6e5e2;
	border-radius: 4px;
	text-align: center;
	font-size: 12px;
	color: #b5b5b5;
	outline: none;
	background: #fff;
}
.login .h1 .a
{
	width:60%;
	float: left;
	margin:0 10%;
	height: 35px;
	border-radius: 4px;
	font-size: 14px;
	color: #fff;
	background: #f62626;
	line-height: 35px;
	padding-left: 20%;
}
.login .h1 .a span
{
	width:auto;
	float: left;
}
.up
{
    margin:8px 3px auto auto;
    float: left;
}
.down
{
	margin:20px 2px auto 2px;
	float: left;
}
.login .h1 .span
{
	width:100%;
	font-size: 10px;
	color: #323232;
	text-align: center;
	float: left;
	line-height: 14px;
	padding-top:12px;
}
.card
{
    width:83%;
    float: left;
    margin-left:8.5%;
    position: relative;
    background:  #fff8e9;
}
.bank
{
	position: absolute;
	float: left;
	z-index: 10;
	margin-left:-7.5%;

}
.bank1
{
	position: absolute;
	float: left;
	z-index: 999;
	margin:0 auto;
}
.detail
{
	width:100%;

	float: left;

	background-size: 100%;
}
.detail .p1
{
	width:100%;
	float: left;
	font-size: 14px;
	color: #010101;
	text-align: center;
	line-height: 100px;
}
.infor
{
	width:80%;
	float: left;
	border-top:2px solid #fff;
	margin:0 10%;
}
.infor h2
{
	width:40%;
	float: left;
	margin:-15px 30% 12px;
	background: #fff;
	height: 30px;
	font-size: 14px;
	color: #909090;
	line-height: 30px;
	text-align: center;
	border-radius: 6px;
}
.p2
{
	width:90%;
	float: left;
	margin:4px 5% 30px;
	font-size: 12px;
	line-height: 20px;
	color: #909090;
}
.rule
{
	width:100%;
	height: 20px;
	float: left;
	text-align: center;
	color: #b08105;
	font-size: 14px;
	margin-bottom: 60px;
}
.rule span
{
	width:15px;
	height: 15px;
	background: #b08105;
	display:inline-block;
	border-radius: 10px;
	margin-left:8px;
	color: #fff;
	line-height: 15px;
	text-align: center;
}
.home
{
	width:100%;
	float: left;
	background: #fff;
	padding:20px 0 40px;
	margin-top:-10px;
}
.home img
{
	margin:0 auto;
}
.phone
{
	width:60%;
	margin:0 20%;
	background: url(../images/bodybg.png) repeat;
	position: absolute;
	height: 60px;
	line-height: 60px;
	font-size: 16px;
	color: #fff;
	text-align: center;
	border-radius: 8px;
}
.bodybg
{
    width:100%;
    height:100%;
    float:left;
    position:fixed;
    left:0;
    background:url(../images/bodybg.png) repeat;
    z-index:2;
    display:none;
}
.term
{
	width:84%;
	margin:5%;
	padding:3%;
	float: left;
	background: #fff8e9;
	border-radius: 12px;
	position: absolute;
	overflow: scroll;
	height: 200px;
	display: none;
	z-index: 333;
}
.term h3
{
	width:90%;
	font-size: 16px;
	color: #000;
	height: 30px;
	line-height: 24px;
	text-align: center;
	margin:0 5% 14px;
	border-bottom: 1px solid #000;
}
.term p
{
	width:90%;
	margin:0 5%;
	font-size: 12px;
	color: #000;
	line-height: 18px;
	padding-bottom: 8px;
}
.close
{
	float: right;
	position: absolute;
	right:3%;
	top:3%;
}
.center
{
	width:80%;
	text-align: center;
	float: left;
	margin:0 10%;
	height: 35px;
	border-radius: 4px;
	font-size: 14px;
	color: #fff;
	background: #f62626;
	line-height: 35px;
}
.center1
{
	width:100%;
	float: left;
	background: url(../images/act04.png) no-repeat;
	background-size: 100%;
	height: 58px;
	line-height: 58px;
	text-align: center;
	font-size: 16PX;
	color: #000;
}
.image
{
	width:60%;
	height: 70px;
	float: left;
    background: url(../images/act013.png) no-repeat #fff;
	margin: 18px 20% 4px;
	background-size: 100%;
	font-size: 16px;
	color: #f62626;
	line-height: 70px;
	text-align: center;

}
.image > span{
	vertical-align: middle;
}
.image .span1
{

	font-size: 20PX;
	color: #ff3c15;
}
.image .span2
{
	font-size: 44PX;
	color: #f62626;
	padding-right: 8px;
}
.image .span3
{
	font-size: 12PX;
	color: #c1c1c1;
}
.login .h1 .spana
{
	width:80%;
	font-size: 10px;
	color: #323232;
	text-align: center;
	float: left;
	line-height: 14px;
	padding:8px 10% 22px;
}
.login .h1 .spana span
{
    color: #f62626;
    padding-right: 8px;
}
.login .h1 .spana a
{
    color: #f62626;
    text-decoration: underline;
}
.code
{
	width:70%;
	float: left;
	margin:14px 15% 10px;
}
.code dt
{
	width:40%;
	float: left;
}
.code .dd1
{
	width:55%;
	float: right;
	font-size: 16px;
	color: #141414;
	padding-top:8px;
	padding-bottom: 6px;
	text-align: left;
}
.code .dd2
{
	width:55%;
	float: right;
	font-size: 14px;
	color: #909090;
    line-height: 20px;
	text-align: left;
}
.huankuan
{
	width:100%;
	float: left;
	margin-top:230px;
	background: #fff;
	padding-bottom: 30px;
}
.word
{
	width:80%;
	float: left;
    padding: 14px 0;
    margin:0 10%;
    border-bottom: 1px dashed #e5e5e5;
}
.word dt
{
	width:12%;
	float: left;
}
.word .dd1
{
	width:74%;
	padding-left: 4%;
	float: left;
}
.word .dd1 .word1
{
	width:100%;
	float: left;
	font-size: 12px;
	line-height: 20px;
	color: #5e5e5e;
	overflow: hidden;
}
.word .dd2
{
	width:8%;
	float: left;
	padding-left:2%;
	font-size: 12px;
	color: #5e5e5e;
	line-height: 20px;
}
.high
{
	margin-top:230px;
	background: url(../images/act011.png) no-repeat left top;
	background-size: 100%;
}
.high1
{
	margin-top:0;
	background: url(../images/act017.png) no-repeat left top #fff       ;
	background-size: 100%;
}
.bk
{
	margin-top:-60px;
	width:100%;
	float: left;
}
input::-webkit-input-placeholder{color:red;}
input:focus::-webkit-input-placeholder{color:#EEEEEE;}

repository: SlienXu/hello-world

repo_name: SlienXu/hello-world