text:
@InProceedings{scott15,
  title = {{A Rate of Convergence for Mixture Proportion Estimation, with  Application to Learning from Noisy Labels}},
  author = {Scott, Clayton},
  pages = {838-846},
  abstract = {Mixture proportion estimation (MPE) is a fundamental tool for solving a number of weakly supervised learning problems -- supervised learning problems where label information is noisy or missing. Previous work on MPE has established a universally consistent estimator. In this work we establish a rate of convergence for mixture proportion estimation under an appropriate distributional assumption, and argue that this rate of convergence is useful for analyzing weakly supervised learning algorithms that build on MPE. To illustrate this idea, we examine an algorithm for classification in the presence of noisy labels based on surrogate risk minimization, and show that the rate of convergence for MPE enables proof of the algorithm's consistency. Finally, we provide a practical implementation of mixture proportion estimation and demonstrate its efficacy in classification with noisy labels.},
}

repository: mreid/papersite

repo_name: mreid/papersite

gha_language: TeX


text:
@article{nathan_cluster_2019,
 abstract = {© 2019 The Author(s). Background: In an attempt to improve children's physical activity levels governments have introduced policies specifying the minimum time schools are to schedule physical activity each week. Despite this, the majority of schools in many jurisdictions fail to implement these policies. This study will assess the effectiveness of a multi-component implementation strategy on increasing the minutes of planned physical activity scheduled by primary school teachers each week. Methods: A cluster randomised controlled trial will be conducted in 62 primary schools in the Hunter New England region of New South Wales, Australia. Schools will be randomly allocated to receive either a multi-component implementation strategy that includes; obtaining executive support, training in-school champions, provision of tools and resources, implementation prompts, reminders and feedback; or usual practice. The study will employ an effectiveness-implementation hybrid design, assessing both policy implementation and individual (student) behavioural outcomes. The primary trial outcome of mean minutes of physical activity scheduled by classroom teachers across the school week will be measured via teacher log-book at baseline and approximately 12 and 18 months post baseline. A nested evaluation of the impact of policy implementation on child physical activity will be undertaken of students in Grades 2 and 3. Analyses will be performed using an intention to treat framework. Linear mixed effects regression models will be used to assess intervention effects on the primary outcome at both follow-up periods. Discussion: This study will be the one of the first well powered randomised trials internationally to examine the impact of an implementation strategy for a physical activity policy in primary schools and will address a fundamental research translation gap. Given the dearth of research, the findings will be important in informing future implementation efforts in this setting. Trial registration: ANZCTR ACTRN12617001265369 version 1 registered 1st September 2017.},
 author = {Nathan, N. and Wiggers, J. and Bauman, A.E. and Rissel, C. and Searles, A. and Reeves, P. and Oldmeadow, C. and Naylor, P.J. and Cradock, A.L. and Sutherland, R. and Gillham, K. and Duggan, B. and Chad, S. and McCarthy, N. and Pettett, M. and Jackson, R. and Reilly, K. and Herrmann, V. and Hope, K. and Shoesmith, A. and Wolfenden, L.},
 doi = {10.1186/s12889-019-6492-z},
 issn = {1471-2458},
 journal = {BMC Public Health},
 keywords = {cluster randomised control trial},
 month = {February},
 number = {1},
 title = {A cluster randomised controlled trial of an intervention to increase the implementation of school physical activity policies and guidelines: Study protocol for the physically active children in education (PACE) study},
 volume = {19},
 year = {2019}
}

license: MIT

repository: ChrisOldmeadow/academic-kickstart

license_type: permissive

repo_name: ChrisOldmeadow/academic-kickstart

branch_name: refs/heads/master

extension: bib


text:
@article{Wiebe-2021-Impacts,
    title = "Impacts of Event-Based Recharge on the Vulnerability of Public Supply Wells",
    author = "Wiebe, Andrew J.  and
      Rudolph, David L.  and
      Pasha, Ehsan  and
      Brook, Jacqueline M.  and
      Christie, Mike  and
      Menkveld, Paul G.",
    journal = "Sustainability, Volume 13, Issue 14",
    volume = "13",
    number = "14",
    year = "2021",
    publisher = "MDPI AG",
    url = "https://aclanthology.org/G21-134001",
    doi = "10.3390/su13147695",
    pages = "7695",
}

repository: gwf-uwaterloo/gwf-publications

repo_name: gwf-uwaterloo/gwf-publications

branch_name: refs/heads/main

gha_language: Python


text:
@article{2008ApJ...684..635B,
 abstract = {We present observations of a new low-mass, double-lined eclipsing binary
system discovered using repeat observations of the celestial
equator from the Sloan Digital Sky Survey II. Using near-
infrared photometry and optical spectroscopy we have measured
the properties of this short-period [P = 0.407037(14) days]
system and its two components. We find the following parameters
for the two components: M$_1$ = 0.272 +/- 0.020 M$_☉$,
R$_1$ = 0.268 +/- 0.010 R$_☉$, M$_2$ = 0.240 +/- 0.022
M$_☉$, R$_2$ = 0.248 +/- 0.0090 R$_☉$, T$_1$ = 3320 +/-
130 K, and T$_2$ = 3300 +/- 130 K. The masses and radii of the
two components of this system agree well with theoretical
expectations based on models of low-mass stars, within the
admittedly large errors. Future synoptic surveys like Pan-STARRS
and LSST will produce a wealth of information about low-mass
eclipsing systems and should make it possible, with an increased
reliance on follow-up observations, to detect many systems with
low-mass and substellar companions. With the large numbers of
objects for which these surveys will produce high-quality
photometry, we suggest that it becomes possible to identify such
systems even with sparse time sampling and a relatively small
number of individual observations.},
 adsnote = {Provided by the SAO/NASA Astrophysics Data System},
 adsurl = {https://ui.adsabs.harvard.edu/abs/2008ApJ...684..635B},
 archiveprefix = {arXiv},
 author = {Blake, Cullen H. and Torres, Guillermo and Bloom, Joshua S. and
Gaudi, B. Scott},
 doi = {10.1086/589630},
 eprint = {0707.3604},
 journal = {\apj},
 keywords = {binaries: eclipsing, stars: low-mass, brown dwarfs, Astrophysics},
 month = {Sep},
 number = {1},
 pages = {635-643},
 primaryclass = {astro-ph},
 title = {A New Low-Mass Eclipsing Binary from SDSS-II},
 volume = {684},
 year = {2008}
}

repository: profjsb/my-content

repo_name: profjsb/my-content

gha_license_id: MIT


text:
@Online{python_hash_table,
  type = {article},
  key = {Python - Dictionaries},
  title = {{Python - Dictionaries (Hash table)}\eatdot},
  publisher = {Python Software Foundation},
  note =
  {\url{https://docs.python.org/3/tutorial/datastructures.html#dictionaries}\eatdot}
}
@Online{java_hash_map,
  type = {article},
  key = {Java - HashMap},
  title = {{Java - HashMap}\eatdot},
  publisher = {Oracle},
  note =
  {\url{https://docs.oracle.com/javase/7/docs/api/java/util/HashMap.html}\eatdot}
}
@Online{java_tree_map,
  type = {article},
  key = {Java - TreeMap},
  title = {{Java - TreeMap}\eatdot},
  publisher = {Oracle},
  note =
  {\url{https://docs.oracle.com/javase/7/docs/api/java/util/TreeMap.html}\eatdot}
}


text:
.tab-bar {
	background: #1095f3;
}

.tab-bar h1 {
	color: #102593;
}

.left-off-canvas-menu {
	background: #1095f3;

}

.right-off-canvas-menu {
	background: #1095f3;

}

ul.off-canvas-list li label {
	color: #ffffff;
	background: #1095f3;
}
ul.off-canvas-list li a {
	color: #102593;
}
ul.off-canvas-list li a:hover {
          background: #e9b96e;
}

extension: css


text:
@book{atalholivro,
  author        = {Autor do Livro},
  title         = {Nome do livro},
  subtitle      = {Subtítulo},
  edition       = {edição},
  address       = {Endereço},
  publisher     = {Editora},
  year          = {ano},
  volume        = {volume},
  series        = {serie},
  month         = {mês},
  note          = {Anotação},
  isbn          = {ISBN}
}

@online{atalhoonline,
  author        = {Autor Online},
  title         = {Título Online},
  year          = {ANO},
  month         = {MÊS},
  url           = {https://www.exemplo.com/algumacoisa},
  urlaccessdate = {03/03/2017}
}

repository: paulequilibrio/ufpatcc

repo_name: paulequilibrio/ufpatcc


text:
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2019},
    url = {https://www.R-project.org/},
  }

  @book{Python,
  author = {Van Rossum, Guido and Drake, Fred L.},
  title = {Python 3 Reference Manual},
  year = {2009},
  isbn = {1441412697},
  publisher = {CreateSpace},
  address = {Scotts Valley, CA}
}

@InProceedings{ mckinney-proc-scipy-2010,
  author    = { Wes McKinney },
  title     = { Data Structures for Statistical Computing in Python },
  booktitle = { Proceedings of the 9th Python in Science Conference },
  pages     = { 51 - 56 },
  year      = { 2010 },
  editor    = { St\'efan van der Walt and Jarrod Millman }
}

@article{scikit-learn,
 title={{Scikit-learn: Machine Learning in Python }},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}

@Manual{tidyverse,
    title = {tidyverse: Easily Install and Load the 'Tidyverse'},
    author = {Hadley Wickham},
    year = {2017},
    note = {R package version 1.2.1},
    url = {https://CRAN.R-project.org/package=tidyverse},
}

@InCollection{knitr,
    booktitle = {Implementing Reproducible Computational Research},
    editor = {Victoria Stodden and Friedrich Leisch and Roger D. Peng},
    title = {knitr: A Comprehensive Tool for Reproducible Research in {R}},
    author = {Yihui Xie},
    publisher = {Chapman and Hall/CRC},
    year = {2014},
    note = {ISBN 978-1466561595},
    url = {http://www.crcpress.com/product/isbn/9781466561595},
}

@Manual{docopt,
    title = {docopt: Command-Line Interface Specification Language},
    author = {Edwin {de Jonge}},
    year = {2018},
    note = {R package version 0.6.1},
    url = {https://CRAN.R-project.org/package=docopt},
}

@Manual{docoptpython,
    title = {docopt: Command-line interface description language},
    author = {Vladimir Keleshev},
    year = {2014},
    note = {Python package version 0.6.2},
    url = {https://github.com/docopt/docopt}
}

@misc{fivethirtyeight,
  author = "FiveThirtyEight",
  year = "2019",
  title = "fivethirtyeight data repository",
  url = "https://data.fivethirtyeight.com/",
  institution = "fivethirtyeight"
}

@misc{wiki-elo-rating,
  author = "Wikipedia",
  title = "Wikipedia Elo rating system",
  url = "https://en.wikipedia.org/wiki/Elo_rating_system#Theory",
  institution = "Wikipedia"
}

gha_language: HTML


text:
@ARTICLE{2017AGU,
    author = {{Camporeale}, E. and {Chandorkar}, M.},
    title = "{Bayesian inference of radiation belt loss timescales.}",
    journal = {AGU Fall Meeting Abstracts},
    keywords = {1942 Machine learning, INFORMATICS, 1986 Statistical methods: Inferential, INFORMATICS, 2799 General or miscellaneous, MAGNETOSPHERIC PHYSICS, 7924 Forecasting, SPACE WEATHER},
    year = 2017,
    month = dec,
    adsurl = {http://adsabs.harvard.edu/abs/2017AGUFMSM23A2583C},
    adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{GPDst,
    author={M. Chandorkar and E. Camporeale and S. Wing},
    journal={Space Weather},
    title={Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach},
    year={2017},
    volume={15},
    number={8},
    pages={1004-1019},
    abstract={We present a methodology for generating probabilistic predictions for the Disturbance Storm Time (Dst) geomagnetic activity index. We focus on the One Step Ahead prediction task and use the OMNI hourly resolution data to build our models. Our proposed methodology is based on the technique of Gaussian Process Regression. Within this framework we develop two models; Gaussian Process Autoregressive (GP-AR) and Gaussian Process Autoregressive with eXogenous inputs (GP-ARX). We also propose a criterion to aid model selection with respect to the order of autoregressive inputs. Finally, we test the performance of the GP-AR and GP-ARX models on a set of 63 geomagnetic storms between 1998 and 2006 and illustrate sample predictions with error bars for some of these events.},
    keywords={Autoregressive processes;Forecasting;Gaussian processes;Indexes;Mathematical model;Meteorology;Predictive models},
    doi={10.1002/2017SW001627},
    month={Aug}
}

@incollection{CHANDORKAR2018237,
    title = "Chapter 9 - Probabilistic Forecasting of Geomagnetic Indices Using Gaussian Process Models",
    editor = "Enrico Camporeale and Simon Wing and Jay R. Johnson",
    booktitle = "Machine Learning Techniques for Space Weather",
    publisher = "Elsevier",
    pages = "237 - 258",
    year = "2018",
    isbn = "978-0-12-811788-0",
    doi = "https://doi.org/10.1016/B978-0-12-811788-0.00009-3",
    url = "http://www.sciencedirect.com/science/article/pii/B9780128117880000093",
    author = "Mandar Chandorkar and Enrico Camporeale",
    keywords = "Gaussian process, Space weather, Probabilistic forecasting, Geomagnetic indices",
    abstract = "In this chapter, we give the reader an in-depth view into building of probabilistic forecasting models for geomagnetic time series using the Gaussian process methodology outlined in the previous chapters. We highlight design decisions and practical issues that must be addressed in order to use Gaussian process models for probabilistic prediction of a quantity of interest. As a pedagogical example, we formulate, train, and test a family of Gaussian process auto-regressive models for 1-h ahead prediction of the Dst geomagnetic index."
}

@misc{tensorflow2015-whitepaper,
    title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
    url={https://www.tensorflow.org/},
    note={Software available from tensorflow.org},
    author={
    Mart\'{\i}n~Abadi and
    Ashish~Agarwal and
    Paul~Barham and
    Eugene~Brevdo and
    Zhifeng~Chen and
    Craig~Citro and
    Greg~S.~Corrado and
    Andy~Davis and
    Jeffrey~Dean and
    Matthieu~Devin and
    Sanjay~Ghemawat and
    Ian~Goodfellow and
    Andrew~Harp and
    Geoffrey~Irving and
    Michael~Isard and
    Yangqing Jia and
    Rafal~Jozefowicz and
    Lukasz~Kaiser and
    Manjunath~Kudlur and
    Josh~Levenberg and
    Dandelion~Man\'{e} and
    Rajat~Monga and
    Sherry~Moore and
    Derek~Murray and
    Chris~Olah and
    Mike~Schuster and
    Jonathon~Shlens and
    Benoit~Steiner and
    Ilya~Sutskever and
    Kunal~Talwar and
    Paul~Tucker and
    Vincent~Vanhoucke and
    Vijay~Vasudevan and
    Fernanda~Vi\'{e}gas and
    Oriol~Vinyals and
    Pete~Warden and
    Martin~Wattenberg and
    Martin~Wicke and
    Yuan~Yu and
    Xiaoqiang~Zheng},
    year={2015},
}

@Misc{tfscala,
    author =   {Emmanuel Antonios Platanios},
    title =    {{Tensorflow Scala}: Scala API for Tensorflow},
    howpublished = {\url{http://platanios.org/tensorflow_scala/}},
    year = {2017}
}

@Misc{renjin,
    author =   {BeDataDriven B.V.},
    title =    {{Renjin}: JVM-based interpreter for the R language for the statistical analysis.},
    howpublished = {\url{http://www.renjin.org}}
}

@Misc{scala,
    author =   {Scala Center, École Polytechnique Fédérale Lausanne (EPFL).},
    title =    {{Scala}: The Scala Programming Language.},
    howpublished = {\url{https://www.scala-lang.org}},
    year = {2002}
}

repository: polymorpher/DynaML

repo_name: polymorpher/DynaML


text:
@misc{tensorflow2015-whitepaper,
title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
url={https://www.tensorflow.org/},
note={Software available from tensorflow.org},
author={
    Mart\'{\i}n~Abadi and
    Ashish~Agarwal and
    Paul~Barham and
    Eugene~Brevdo and
    Zhifeng~Chen and
    Craig~Citro and
    Greg~S.~Corrado and
    Andy~Davis and
    Jeffrey~Dean and
    Matthieu~Devin and
    Sanjay~Ghemawat and
    Ian~Goodfellow and
    Andrew~Harp and
    Geoffrey~Irving and
    Michael~Isard and
    Yangqing Jia and
    Rafal~Jozefowicz and
    Lukasz~Kaiser and
    Manjunath~Kudlur and
    Josh~Levenberg and
    Dandelion~Man\'{e} and
    Rajat~Monga and
    Sherry~Moore and
    Derek~Murray and
    Chris~Olah and
    Mike~Schuster and
    Jonathon~Shlens and
    Benoit~Steiner and
    Ilya~Sutskever and
    Kunal~Talwar and
    Paul~Tucker and
    Vincent~Vanhoucke and
    Vijay~Vasudevan and
    Fernanda~Vi\'{e}gas and
    Oriol~Vinyals and
    Pete~Warden and
    Martin~Wattenberg and
    Martin~Wicke and
    Yuan~Yu and
    Xiaoqiang~Zheng},
  year={2015},
}
@article{Cherry2007,
abstract = {BACKGROUND Pulmonary vein (PV) ablation plays an important role in atrial fibrillation (AF) therapy but suffers from a limited mechanistic understanding of PV arrhythmogenicity. Rapid focal activation has been suggested, but some evidence points to underlying reentry. OBJECTIVE This study was performed to evaluate how the electrophysiological properties of PVs may make them a site for reentry and to analyze specifically the roles of PV dimensions and coupling properties. METHODS A computer model designed to efficiently reproduce electrophysiological behaviors was fit to action potentials from canine left atria (LA) and PVs. To assess structural and functional arrhythmogenic determinants, an idealized PV of varying length and circumference was attached to LA tissue, and 5 seconds of activity after extrastimulation were simulated. RESULTS PV reentrant activity depended critically on vein size and coupling properties. With cumulative removal of transverse and longitudinal connections, sustained (n=23) or nonsustained (n=93) reentries could be observed (687 simulations) for veins 1-3 cm long and 1-2 cm in circumference. The prevalence of sustained reentry increased with PV length (8{\%} for 1 cm vs. 22{\%} and 31{\%} for 2 and 3 cm, respectively; P{\textless}.05 for each). The PV circumference did not affect the incidence of sustained reentry (25{\%}, 17{\%}, and 21{\%} for 1-, 1.5-, and 2-cm circumferences; P=NS), but the number of reentrant events increased from 12/201 simulations for PVs with a 1-cm circumference to 48/232 and 56/254 events for PVs with 1.5- and 2-cm circumferences, respectively (P{\textless}.05). Sustained reentry cycle lengths were approximately 200-250 ms (16/23), except for the longest PVs. CONCLUSION Reentry occurs readily in PVs with realistic properties in the context of specific connection heterogeneities. Reentry properties and incidence depend on PV anisotropy and dimensions but could certainly contribute significantly to PV arrhythmogenesis.},
author = {Cherry, Elizabeth M. and Ehrlich, Joachim R. and Nattel, Stanley and Fenton, Flavio H.},
doi = {10.1016/j.hrthm.2007.08.017},
issn = {15475271},
journal = {Heart Rhythm},
mendeley-groups = {af{\_}modeling},
month = {dec},
number = {12},
pages = {1553--1562},
pmid = {18068635},
title = {{Pulmonary vein reentry—Properties and size matter: Insights from a computational analysis}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18068635 http://linkinghub.elsevier.com/retrieve/pii/S1547527107008740},
volume = {4},
year = {2007}
}
@article{Beeler1977,
abstract = {1. A mathematical model of membrane action potentials of mammalian ventricular myocardial fibres is described. The reconstruction model is based as closely as possible on ionic currents which have been measured by the voltage-clamp method.2. Four individual components of ionic current were formulated mathematically in terms of Hodgkin-Huxley type equations. The model incorporates two voltage- and time-dependent inward currents, the excitatory inward sodium current, i(Na), and a secondary or slow inward current, i(s), primarily carried by calcium ions. A time-independent outward potassium current, i(K1), exhibiting inward-going rectification, and a voltage- and time-dependent outward current, i(x1), primarily carried by potassium ions, are further elements of the model.3. The i(Na) is primarily responsible for the rapid upstroke of the action potential, while the other current components determine the configuration of the plateau of the action potential and the re-polarization phase. The relative importance of inactivation of i(s) and of activation of i(x1) for termination of the plateau is evaluated by the model.4. Experimental phenomena like slow recovery of the sodium system from inactivation, frequency dependence of the action potential duration, all-or-nothing re-polarization, membrane oscillations are adequately described by the model.5. Possible inadequacies and shortcomings of the model are discussed.},
author = {Beeler, G W and Reuter, H},
doi = {10.1113/jphysiol.1977.sp011853},
issn = {0022-3751},
journal = {The Journal of physiology},
mendeley-groups = {af{\_}modeling},
month = {jun},
number = {1},
pages = {177--210},
pmid = {874889},
title = {{Reconstruction of the action potential of ventricular myocardial fibres.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/874889 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC1283659},
volume = {268},
year = {1977}
}
@article{Rush1978,
author = {Rush, Stanley and Larsen, Hugh},
doi = {10.1109/TBME.1978.326270},
issn = {0018-9294},
journal = {IEEE Transactions on Biomedical Engineering},
mendeley-groups = {af{\_}modeling},
month = {jul},
number = {4},
pages = {389--392},
title = {{A Practical Algorithm for Solving Dynamic Membrane Equations}},
url = {http://ieeexplore.ieee.org/document/4122859/},
volume = {BME-25},
year = {1978}
}


text:
BROKEN = "1"

DESCRIPTION = "Alsa Tools"
SECTION = "console/utils"
LICENSE = "GPL"
DEPENDS = "alsa-lib"
PR = "r0"

SRC_URI = "ftp://ftp.alsa-project.org/pub/tools/alsa-tools-${PV}.tar.bz2"

inherit autotools

# this bb file requires quite some work.
# There is no top level makefile in tools
# that is why install fails, but actually nothing is compiled
# in order to fix this, the bb file needs heavy work
# (but the tools are not very useful for oe, so I'll leave it for now)

repository: BackupTheBerlios/openslug-svn

repo_name: BackupTheBerlios/openslug-svn

extension: bb