text:
---

@string{aps = {American Physical Society}}
@string{FRENCH = {French}}

#### Preprint

@preprint{arble2022bayes,
	author = {Arbel, Julyan and Dang, Hong-Phuong and Elvira, Clément and Herzet, Cédric and Naulet, Zacharie and Vladimirova, Mariia},
	title  = {Bayes in action in deep learning and dictionary learning},
	year   = {2022},
	bibtex_show={true},
}

#### International journal Journal

@article{elvira2021safeSLOPE,
  title  = {Safe Rules for the Identification of Zeros in the Solutions of the {SLOPE} Problem},
	author = {Elvira, Clément and Herzet, Cédric},
	year={2023},
  journal = {{SIAM} Journal on Mathematics of Data Science},
	month = {March},
  publisher = {Society for Industrial {\&} Applied Mathematics ({SIAM})},
  volume = {5},
  number = {1},
  pages = {147--173},
	hal   = {hal-03400322},
	arxiv = {2110.11784},
	code  = {https://gitlab-research.centralesupelec.fr/2020elvirac/slope-screening},
	supp  = {articles/elvira2021slope/elvira2021SLOPEsuppmat.pdf},
  doi   = {10.1137/21m1457631},
	bibtex_show={true},
}

#### International conferencesl

@inproceedings{herzet2022Eusipco,
  TITLE = {{Region-free Safe Screening Tests for {\ell} 1 -penalized Convex Problems}},
  AUTHOR = {Herzet, C{\'e}dric and Elvira, Cl{\'e}ment and Dang, Hong-Phuong},
  URL = {https://hal-centralesupelec.archives-ouvertes.fr/hal-03806099},
  booktitle = {2022 30th European Signal Processing Conference ({EUSIPCO})},
  doi = {10.23919/eusipco55093.2022.9909532},
  ADDRESS = {Belgrade, Serbia},
  YEAR = {2022},
  MONTH = Aug,
  KEYWORDS = {sparsity ; convex problem ; safe screening},
  hal = {hal-03806099},
  bibtex_show={true},
}

@inproceedings{GuyardPeeling23,
	arxiv  = {2302.14471},
	hal    = {hal-04118979},
  author = {Guyard, Théo and Monnoyer, Gilles and Elvira, Clément and Herzet, Cédric},
  title  = {Safe peeling for l0-regularized least-squares with supplementary material},
  booktitle = {Accepted at {EUSIPCO}},
  year   = {2023},
  code   = {https://github.com/TheoGuyard/BnbPeeling.jl},
	bibtex_show={true},
}

@inproceedings{Le2022HolderDome,
	author = {Tran, Thu-Le and Elvira, Cl{\'e}ment and Dang, Hong-Phuong and Herzet, C{\'e}dric},
	supp    = {tecreports/Le2022TR.pdf},
	title  = {Beyond {GAP} screening for {Lasso}  by exploiting new dual cutting half-spaces},
	year   = {2022},
	booktitle = {2022 30th European Signal Processing Conference ({EUSIPCO})},
	doi = {10.23919/eusipco55093.2022.9909943},
	code   = {https://gitlab.inria.fr/cherzet/holder-safe},
	month  = {August},
	arxiv = {2203.00987},
	hal   = {hal-03805966},
	doi   = {},
	bibtex_show={true},
}

@inproceedings{guyard2021screen,
	title  = {Screen \& Relax: Accelerating the resolution of Elastic-net by safe identification of the solution support},
	author = {Guyard, Th{\'e}o and Herzet, C{\'e}dric and Elvira, Cl{\'e}ment},
	booktitle = {ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	pages = {5443-5447},
	year  = {2021},
	doi   = {10.1109/ICASSP43922.2022.9747412},
	arxiv ={2110.07281},
	hal   = {hal-03462191},
	code  = {https://gitlab.insa-rennes.fr/Theo.Guyard/screen-and-relax},
	bibtex_show={true},
}

@inproceedings{Guyard2021techreport,
	author = {Guyard, Théo and Herzet, Cédric and Elvira, Clément},
	keywords = {Branch-and-bound ; Cardinality constraint ; Sparse approximation ; Continuous relaxation ; Homotopy continuation},
	title = {Node-screening tests for l0-penalized Least-Squares problem},
	booktitle={ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	month = {October},
	year = {2021},
	pages={5448-5452},
	doi={10.1109/ICASSP43922.2022.9747563},
	arxiv = {2110.07308},
	hal   = {hal-03462171},
	supp = {tecreports/Guyard2021_l0.pdf},
	code = {https://gitlab.insa-rennes.fr/Theo.Guyard/bnb-screening},
	bibtex_show={true},
}

@inproceedings{elvira2020_itwist,
	TITLE = {{Continuous dictionaries meet low-rank tensor approximations}},
	AUTHOR = {Elvira, Cl{\'e}ment and Cohen, J{\'e}r{\'e}my E and Herzet, C{\'e}dric and Gribonval, R{\'e}mi},
	URL = {https://hal.archives-ouvertes.fr/hal-02567115},
	booktitle = {{iTwist 2020 - International Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques}},
	ADDRESS = {Nantes, France},
	PAGES = {1-3},
	YEAR = {2020},
	MONTH = {june},
	hal = {hal-02567115},
	PDF = {workshop/elvira2020_itwist.pdf},
	bibtex_show={true},
}

@inproceedings{elvira2020icassp2,
	title = {{BLASTER: An Off-Grid Method for Blind and Regularized\\ Acoustic Echoes Retrieval}},
	author = {Di Carlo, Diego and Elvira, Cl{\'e}ment and Deleforge, Antoine and Bertin, Nancy and Gribonval, Rémi},
	booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	PUBLISHER = {{IEEE}},
	year = {2020},
	doi = {10.1109/ICASSP40776.2020.9054647},
	code= {https://gitlab.inria.fr/panama-team/blaster},
	hal={hal-02469901},
	bibtex_show={true},
	pages={156-160}
}

@inproceedings{elvira2020icassp,
	title = {{Short and squeezed: accelerating the computation of antisparse representations with safe squeezing}},
	author = {Cl{\'e}ment Elvira and Cédric Herzet},
	booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	PUBLISHER = {{IEEE}},
	year = {2020},
	code = {https://gitlab.inria.fr/celvira/safe-squeezing},
	doi={10.1109/ICASSP40776.2020.9053156},
	hal = {hal-03070344},
	pages={5615-5619},
	bibtex_show={true},
	code = {https://gitlab.inria.fr/celvira/safe-squeezing},
}

@inproceedings{elvira2019spars,
	TITLE = {{Uniform k-step recovery with CMF dictionaries}},
	AUTHOR = {Elvira, Cl{\'e}ment and Gribonval, R{\'e}mi and Herzet, C{\'e}dric and Soussen, Charles},
	BOOKTITLE = {{SPARS 2019 - Signal Processing with Adaptive Sparse Structured Representations}},
	ADDRESS = {Toulouse, France},
	PAGES = {1-2},
	YEAR = {2019},
	MONTH = Jul,
	hal = {hal-02157561},
	abstract = {We present new theoretical results on sparse recovery guarantees for a greedy algorithm, orthogonal matching pursuit (OMP), in the context of continuous parametric dictionaries, i.e., made up of an infinite uncountable number of atoms. We build up a family of dictionaries for which k-step recovery is possible.},
	bibtex_show={true},
}

@inproceedings{elvira2019icassp,
	title = {{OMP and continuous dictionaries: is k-step recovery possible?}},
	author = {Cl{\'e}ment Elvira and R{\'e}mi Gribonval and Charles Soussen and Cédric Herzet},
	url = {https://hal.archives-ouvertes.fr/hal-02049486},
	booktitle={ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	address = {Brighton, United Kingdom},
	ORGANIZATION = {{IEEE}},
	PUBLISHER = {{IEEE}},
	doi={10.1109/ICASSP.2019.8683617},
	issn={2379-190X},
	year = {2019},
	pages={5546-5550},
	month = {may},
	KEYWORDS = {Index Terms-Sparse representation ; exact recovery ; orthogonal matching pursuit ; continuous dictionaries ; sparse representation ; continuous dictio-naries},
	hal = {hal-02049486},
	pdf = {conferences/elvira2019icassp.pdf},
	abstract = {In this work, we present new theoretical results on sparse recovery guarantees for a greedy algorithm, orthogonal matching pursuit (OMP), in the context of continuous parametric dictionaries, i.e., made up of an infinite uncountable number of atoms.We build up a family of dictionaries for which k-step recovery is possible with OMP for 1-dimensional parameters. In higher dimension, algebraic conditions become necessary and will lead us to revisit some well-known k-step discrete analyses. Finally, a toy-example illustrates the level of tightness of our sufficient conditions.},
	bibtex_show={true},
}

@INPROCEEDINGS{dang2019eusipco,
  author={Dang, Hong-Phuong and Elvira, Clément},
  booktitle={2019 27th European Signal Processing Conference (EUSIPCO)},
  title={Parameter-free Small Variance Asymptotics for Dictionary Learning},
  year={2019},
  volume={},
  number={},
  pages={1-5},
  doi={10.23919/EUSIPCO.2019.8903025},
 	bibtex_show={true},
}

@inproceedings{elvira2018CS,
	TITLE = {{A case of exact recovery with OMP using continuous dictionaries}},
	AUTHOR = {Cl{\'e}ment Elvira and R{\'e}mi Gribonval and Cédric Herzet and Charles Soussen},
	BOOKTITLE = {{CS 2018 - 9th International Conference on Curves and Surfaces}},
	ADDRESS = {Arcachon, France},
	YEAR = {2018},
	MONTH = {June},
	hal = {hal-01937532},
	abstract = {We present new theoretical results on sparse recovery guarantees for a greedy algorithm, orthogonal matching pursuit (OMP), in the context of continuous dictionaries. Consider a sparse linear combination of atoms from a dictionary parameterized by some real parameters, for example, a combination of shifted versions of a basic waveform as in the context of sparse spike deconvolution. Currently, performance guarantees for greedy algorithms are typically carried out in the discrete setting associated to a grid of atom parameters, and based on, e.g., the coherence of the considered discretized dictionary \cite\{tropp2004\}. However, such analyses fail to be conclusive for grid-based approaches when the discretization step tends to zero, as the coherence goes to one. Instead, our analysis is directly conducted in the continuous setting. For atoms parametrized by a real parameter that are elements of the (infinite-dimensional) Hilbert space $L_2(\mathbb\{R\})$ of square integrable real functions, and such that the inner product between two atoms is the exponential of the negative absolute difference of the corresponding parameters, we show in the noise-free setting that OMP exactly recovers the atom parameters as well as their amplitudes, regardless of the number of distinct atoms. We exhibit a convolutive dictionary of exponentially decaying pulses for which the atoms have an analytic definition while their pairwise inner products have the prescribed form. The established guarantees rely on a proof technique which is the continuous equivalent of Tropp’s Exact Recovery Condition (ERC) \cite\{tropp2004\}. The proof exploits specific properties of the positive definite kernel between atom parameters defined by the inner product between the corresponding atoms. Future work will aim at characterizing the class of kernels for which such an analysis holds –in particular for higher dimensional parameters– and the compatibility of the guarantees with dimension reduction techniques such as sketching, which would pave the way to provably good greedy algorithms for compressive statistical learning \cite\{Gribonval2017\}. In light of the existing links between Tropp’s ERC and recovery guarantees for $\ell_1$ minimization \cite\{tropp2006\}, an interesting question is whether these guarantees extend to sparse spike recovery with total variation norm minimization~\cite\{Duval2015,candes2014\}.},
	bibtex_show={true},
}

@article{elvira2021,
	title={When does OMP achieve exact recovery with continuous dictionaries?},
	AUTHOR = {Cl{\'e}ment Elvira and R{\'e}mi Gribonval and Charles Soussen and Cédric Herzet},
	title = "When does OMP achieve exact recovery with continuous dictionaries?",
	journal = "Applied and Computational Harmonic Analysis",
	volume = "51",
	pages = "374 - 413",
	year = "2021",
	issn = "1063-5203",
	doi = "10.1016/j.acha.2020.12.002",
	url = "http://www.sciencedirect.com/science/article/pii/S1063520320300841",
	arxiv = {1904.06311},
	hal = {hal-02099464v4},
	abstract = {This paper presents new theoretical results on sparse recovery guarantees for a greedy algorithm, Orthogonal Matching Pursuit (OMP), in the context of continuous parametric dictionaries. Here, the continuous setting means that the dictionary is made up of an infinite (potentially uncountable) number of atoms. In this work, we rely on the Hilbert structure of the observation space to express our recovery results as a property of the kernel defined by the inner product between two atoms. Using a continuous extension of Tropp's Exact Recovery Condition, we identify two key notions of admissible kernel and admissible support that are sufficient to ensure exact recovery with OMP. We exhibit a family of admissible kernels relying on completely monotone functions for which admissibility holds for any support in the one-dimensional setting. For higher dimensional parameter spaces, an additional notion of axis admissibility is shown to be sufficient to ensure a form of delayed recovery. An additional algebraic condition involving a finite subset of (known) atoms further yields exact recovery guarantees. Finally, a coherence-based viewpoint on these results provides recovery guarantees in terms of a minimum separation assumption.},
	bibtex_show={true},
}

@article{elvira2020_TSP,
	TITLE = {{Safe Squeezing for Antisparse Coding}},
	AUTHOR = {Elvira, Cl{\'e}ment and Herzet, Cedric},
	hal = {hal-02368134},
	YEAR = {2020},
	arxiv = {1911.07508},
	doi = {10.1109/tsp.2020.2995192},
	url = {https://doi.org/10.1109/tsp.2020.2995192},
	publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
	pages = {1--1},
	journal = {{IEEE} Transactions on Signal Processing},
	KEYWORDS = {antisparse coding ; safe screening ; scaled projected-gradient algorithm ; Frank-Wolfe algorithm},
	PDF = {articles/elvira2020_TSP.pdf},
	code = {https://gitlab.inria.fr/celvira/safe-squeezing},
	abstract = {Spreading the information over all coefficients of a representation is a desirable property in many applications such as digital communication or machine learning. This so-called antisparse representation can be obtained by solving a convex program involving an l_∞-norm penalty combined with a quadratic discrepancy. In this paper, we propose a new methodology , dubbed safe squeezing, to accelerate the computation of antisparse representation. We describe a test that allows to detect saturated entries in the solution of the optimization problem. The contribution of these entries is compacted into a single vector, thus operating a form of dimensionality reduction. We propose two algorithms to solve the resulting lower dimensional problem. Numerical experiments show the effectiveness of the proposed method to detect the saturated components of the solution and illustrates the induced computational gains in the resolution of the antisparse problem.},
	bibtex_show={true},
}

@ARTICLE{elvira2017_TSP,
	author={Clément Elvira and Pierre Chainais and Nicolas Dobigeon},
	journal={IEEE Transactions on Signal Processing},
	title={Bayesian Antisparse Coding},
	year={2017},
	volume={65},
	number={7},
	pages={1660-1672},
	keywords={Bayes methods;Gaussian distribution;Markov processes;Monte Carlo methods;encoding;inverse problems;maximum likelihood estimation;mean square error methods;ℓ∞-norm penalty;Bayesian antisparse coding;Gaussian linear model;Markov chain Monte Carlo algorithm;antisparse regularization;image processing;inverse problem;log-posterior distribution;maximum a posteriori estimator;minimum mean square error estimator;posterior distribution;probability distribution;signal processing;sparse representation;standard Gibbs sampler;Approximation algorithms;Bayes methods;Encoding;Image coding;Monte Carlo methods;Signal processing algorithms;Standards;Democratic distribution;anti-sparse representation;proximal operator},
	abstract={Sparse representations have proven their efficiency in solving a wide class of inverse problems encountered in signal and image processing. Conversely, enforcing the information to be spread uniformly over representation coefficients exhibits relevant properties in various applications such as robust encoding in digital communications. Antisparse regularization can be naturally expressed through an ℓ∞-norm penalty. This paper derives a probabilistic formulation of such representations. Anew probability distribution, referred to as the democratic prior, is first introduced. Its main properties as well as three random variate generators for this distribution are derived. Then this probability distribution is used as a prior to promote antisparsity in a Gaussian linear model, yielding a fully Bayesian formulation of antisparse coding. Two Markov chain Monte Carlo algorithms are proposed to generate samples according to the posterior distribution. The first one is a standard Gibbs sampler. The second one uses Metropolis-Hastings moves that exploit the proximity mapping of the log-posterior distribution. These samples are used to approximate maximum aposteriori and minimum mean square error estimators of both parameters and hyperparameters. Simulations on synthetic data illustrate the performances of the two proposed samplers, for both complete and over-complete dictionaries. All results are compared to the recent deterministic variational FITRA algorithm.},
	doi={10.1109/TSP.2016.2645543},
	ISSN={1053-587X},
	month={April},
	pdf = {articles/elvira2017_TSP.pdf},
	arxiv = {1512.06086},
	hal = {hal-01433706},
	code = {https://github.com/c-elvira/bayesian_antisparse_algorithm},
	bibtex_show={true},
}

@inproceedings{elvira2018eusipco,
	doi = {10.23919/eusipco.2018.8553142},
	year = {2018},
	month = {september},
	publisher = {{IEEE}},
	author = {Clement Elvira and Hong-Phuong Dang and Pierre Chainais},
	title = {Small variance asymptotics and bayesian nonparametrics for dictionary learning},
	booktitle = {2018 26th European Signal Processing Conference ({EUSIPCO})},
	pdf = {conferences/elvira2018eusipco.pdf},
	code = {https://github.com/c-elvira/IBPDL-SVA},
	hal = {hal-01961852},
	abstract={Bayesian nonparametric (BNP) is an appealing framework to infer the complexity of a model along with the parameters. To this aim, sampling or variational methods are often used for inference. However, these methods come with numerical disadvantages for large-scale data. An alternative approach is to relax the probabilistic model into a non-probabilistic formulation which yields a scalable algorithm. One limitation of BNP approaches can be the cost of Monte-Carlo sampling for inference. Small-variance asymptotic (SVA) approaches paves the way to much cheaper though approximate methods for inference by taking benefit from a fruitful interaction between Bayesian models and optimization algorithms. In brief, SVA lets the variance of the noise (or residual error) distribution tend to zero in the optimization problem corresponding to a MAP estimator with finite noise variance for instance. We propose such an SVA analysis of a BNP dictionary learning (DL) approach that automatically adapts the size of the dictionary or the subspace dimension in an efficient way. Numerical experiments illustrate the efficiency of the proposed method.},
	bibtex_show={true},
}

@INPROCEEDINGS{Elvira_IEEE_ICASSP_2017,
	author={Clément Elvira and Pierre Chainais and Nicolas Dobigeon},
	Booktitle = {2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	Doi = {10.1109/ICASSP.2017.7952556},
	Keywords = {Bayes methods;Estimation;Manifolds;Monte Carlo methods;Numerical models;Principal component analysis;Probabilistic logic;Bayesian inference;Indian buffet process;dimension reduction;distribution on the Stiefel manifold},
	Month = {March},
	Pages = {2247-2251},
	Title = {Bayesian nonparametric subspace estimation},
	Year = {2017},
	pdf = {conferences/elvira2017iccasp.pdf},
	hal = {hal-01687163},
	abstract={Principal component analysis is a widely used technique to perform dimension reduction. However, selecting a finite number of significant components is essential and remains a crucial issue. Only few attempts have proposed a probabilistic approach to adaptively select this number. This paper introduces a Bayesian nonparametric model to jointly estimate the principal components and the corresponding intrinsic dimension. More precisely, the observations are projected onto a random orthogonal basis which is assigned a prior distribution defined on the Stiefel manifold. Then the factor scores take benefit of an Indian buffet process prior to model the uncertainty related to the number of components. The parameters of interest as well as the nuisance parameters are finally inferred within a fully Bayesian framework via Monte Carlo sampling. The performances of the proposed approach are assessed thanks to experiments conducted on various examples.},
	bibtex_show={true},
}

@INPROCEEDINGS{elvira2016ssp,
	author={Elvira, Clément and Chainais, Pierre and Dobigeon, Nicolas},
	booktitle={2016 IEEE Statistical Signal Processing Workshop (SSP)},
	title={Democratic prior for anti-sparse coding},
	year={2016},
	pages={1-4},
	keywords={Markov processes;Monte Carlo methods;inverse problems;probability;signal representation;ℓ∞-norm regularization;Gibbs sampler;anti-sparse coding;complete dictionary;democratic distribution;joint posterior distribution;linear Gaussian inverse problem;probabilistic formulation;probability distribution;proximal Markov chain Monte Carlo algorithm;representation coefficients;Bayes methods;Encoding;Inverse problems;Monte Carlo methods;Peak to average power ratio;Probability density function;Signal processing algorithms;Anti-sparse representation;democratic distribution;inverse problem},
	doi={10.1109/SSP.2016.7551813},
	month={June},
	pdf = {conferences/elvira2016ssp.pdf},
	code = {https://github.com/c-elvira/bayesian_antisparse_algorithm},
	hal = {hal-01433632},
	abstract={Bayesian nonparametric (BNP) is an appealing framework to infer the complexity of a model along with the parameters. To this aim, sampling or variational methods are often used for inference. However, these methods come with numerical disadvantages for large-scale data. An alternative approach is to relax the probabilistic model into a non-probabilistic formulation which yields a scalable algorithm. One limitation of BNP approaches can be the cost of Monte-Carlo sampling for inference. Small-variance asymptotic (SVA) approaches paves the way to much cheaper though approximate methods for inference by taking benefit from a fruitful interaction between Bayesian models and optimization algorithms. In brief, SVA lets the variance of the noise (or residual error) distribution tend to zero in the optimization problem corresponding to a MAP estimator with finite noise variance for instance. We propose such an SVA analysis of a BNP dictionary learning (DL) approach that automatically adapts the size of the dictionary or the subspace dimension in an efficient way. Numerical experiments illustrate the efficiency of the proposed method.},
	bibtex_show={true},
}

#### National conferences

@inproceedings{tran2022cap,
  TITLE = {{Une nouvelle m{\'e}thode d'acc{\'e}l{\'e}ration pour LASSO par {\'e}limination s{\^u}re de variables}},
  AUTHOR = {Tran, Thu-Le and Elvira, Cl{\'e}ment and Dang, Hong-Phuong and Herzet, C{\'e}dric},
  BOOKTITLE = {{Conf{\'e}rence sur l'Apprentissage automatique}},
  ADDRESS = {Vannes, France},
  YEAR = {2022},
  MONTH = Jul,
  KEYWORDS = {LASSO ; r{\'e}duction de dimensionalit{\'e} ; {\'e}limination de variables ; safe screening},
  hal = {hal-03806044},
  code   = {https://gitlab.inria.fr/cherzet/holder-safe},
  language = FRENCH,
  bibtex_show={true},
}

@inproceedings{guyard2022screen,
    title={Screen \& Relax: Acc{\'e}l{\'e}rer la r{\'e}solution du probl{\`e}me" Elastic-Net" par identification du support de la solution},
    author={Guyard, Theo and Herzet, C{\'e}dric and Elvira, Cl{\'e}ment},
    booktitle={GRETSI 2022-XXVIII{\`e}me Colloque Francophone de Traitement du Signal et des Images},
    year={2022},
    hal = {hal-03778139},
    language = FRENCH,
    code = {https://gitlab.insa-rennes.fr/Theo.Guyard/screen-and-relax},
	bibtex_show={true},
}

@inproceedings{guyard2022node,
    title={Node-screening pour le probl{\`e}me des moindres carr{\'e}s avec p{\'e}nalit{\'e} L0},
    author={Guyard, Theo and Herzet, C{\'e}dric and Elvira, Cl{\'e}ment and Arslan, Ay{\c{s}}e Nur},
    booktitle={GRETSI 2022-XXVIII{\`e}me Colloque Francophone de Traitement du Signal et des Images},
    year={2022},
    hal = {hal-03784682},
    pdf = {http://gretsi.fr/data/colloque/pdf/2022_guyard740.pdf},
    code = {https://gitlab.insa-rennes.fr/Theo.Guyard/bnb-screening},
	language = FRENCH,
	bibtex_show={true},
}

@inproceedings{elvira2019gretsi,
    TITLE = {{Identification de supports en k etapes avec OMP pour les dictionnaires continus}},
    AUTHOR = {Elvira, Cl{\'e}ment and Gribonval, R{\'e}mi and Soussen, Charles and Herzet, C{\'e}dric},
    URL = {https://hal.inria.fr/hal-02157571},
    BOOKTITLE = {{GRETSI 2019 - XXVII{\`e}me Colloque francophonede traitement du signal et des images}},
    ADDRESS = {Lille, France},
    PAGES = {1-4},
    YEAR = {2019},
    MONTH = Aug,
    language = FRENCH,
    hal = {https://hal.inria.fr/hal-02157571v1},
    abstract = {Nous présentons de nouveaux résultats concernant les garanties d'identification de support en k étapes pour un algorithme glouton, orthogonal matching pursuit (OMP), pour les dictionnaires continus. Un dictionnaire est dit continu s'il est constitué d'une infinité indénom-brable d'atomes. Nous étudions une famille de dictionnaires paramétrés, appelée CMF (pour completely monotone function), pour laquelle l'identification de support en k étapes est toujours possible lorsque le paramètre est de dimension 1 quels que soient le nombre et le choix des atomes du support. En dimension supérieure, des conditions algébriques deviennent nécessaires et nous amènent à revisiter les analyses classiques du cas discret. Finalement, nous discutons l'implémentation d'une version continue d'OMP.},
    bibtex_show={true},
}

@Inproceedings{dang2018cap,
    author = {Dang, Hong-Phuong and Elvira, Clément and Chainais, Pierre},
    title = { Vers une méthode d'optimisation non paramétrique pour l'apprentissage de dictionnaire en utilisant Small-Variance Asymptotics pour modèle probabiliste},
    booktitle = {CAP},
    year = {2018},
    address = {Rouen},
    language = FRENCH,
    pdf = {natConf/dang2018cap.pdf},
    code = {https://github.com/c-elvira/IBPDL-SVA},
    abstract = {Dans les modèles probabilistes, les méthodes d'échantillonnage   et les approximations variationnelles sont souvent utilisées pour l’inférence. Mais ces méthodes passent difficilement à l'échelle. Une  alternative consiste à relâcher le modèle probabiliste dans une formulation  non probabiliste et utiliser un algorithme évolutif pour résoudre le problème de minimisation associé. Nous proposons une analyse dite de Small-Variance  Asymptotics (SVA) du modèle bayésien non paramétrique de type Buffet Indien à deux paramètres qui apprend automatiquement un dictionnaire de taille adaptée. Cette  approche s'obtient en faisant tendre la variance de la  vraisemblance du modèle vers 0. L'analyse montre une interaction entre les méthodes bayésiennes et optimisation. Les résultats illustrent la pertinence de la méthode proposée.},
    bibtex_show={true},
}

@Inproceedings{elvira2017gretsi,
    author = {Clément Elvira and Pierre Chainais and Nicolas Dobigeon},
    title = {Une formulation bayésienne du codage antiparcimonieux},
    booktitle = {Actes du XXVIi`eme Colloque GRETSI},
    year = {2017},
    address = {Juan-les-Pins, France},
    language = FRENCH,
    pdf = {natConf/elvira2017gretsi.pdf},
    code = {https://github.com/c-elvira/bayesian_antisparse_algorithm},
    hal = {hal-01691387},
    abstract = {Dans un but de robustesse, un codage antiparcimonieux répartit uniformément l’information d’un signal sur toutes les composantes de sa représentation. La recherche d’un tel codage s’exprime naturellement sous la forme d’un problème variationnel impliquant une régularisation de type $\ell_\infty$. Dans cet article une formulation bayésienne du problème est proposée, impliquant une nouvelle loi de probabilité, la loi démocratique, qui pénalise les fortes amplitudes. Cette distribution est choisie comme loi a priori sur les coefficients de représentation, couplée avec une vraisemblance gaussienne. Les estimateurs bayésiens des coefficients de représentation peuvent être approchés à l’aide d’un échantillonneur de Gibbs. Cette méthode passe cependant difficilement à l’échelle et un algorithme de Monte Carlo proximal a été proposé. On discute une nouvelle façon de choisir et régler la loi a priori sur les paramètres de nuisance. Deux simulations numériques permettent de valider le réglage des hyperparamètres et la recherche du paramètre de régularisation.},
    bibtex_show={true},
}

#### Technical reports

@techreport{elvira2017preprint,
	title={Bayesian nonparametric principal component analysis},
	author={Cl{\'e}ment Elvira and Pierre Chainais and Nicolas Dobigeon},
	journal={arXiv preprint arXiv:1709.05667},
	year={2017},
	arxiv={1709.05667},
	hal = {hal-01687236/},
	abstract = {Principal component analysis (PCA) is very popular to perform dimension reduction. The selection of the number of significant components is essential but often based on some practical heuristics depending on the application. Only few works have proposed a probabilistic approach able to infer the number of significant components. To this purpose, this paper introduces a Bayesian nonparametric principal component analysis (BNP-PCA). The proposed model projects observations onto a random orthogonal basis which is assigned a prior distribution defined on the Stiefel manifold. The prior on factor scores involves an Indian buffet process to model the uncertainty related to the number of components. The parameters of interest as well as the nuisance parameters are finally inferred within a fully Bayesian framework via Monte Carlo sampling. A study of the (in-)consistence of the marginal maximum a posteriori estimator of the latent dimension is carried out. A new estimator of the subspace dimension is proposed. Moreover, for sake of statistical significance, a Kolmogorov-Smirnov test based on the posterior distribution of the principal components is used to refine this estimate. The behaviour of the algorithm is first studied on various synthetic examples. Finally, the proposed BNP dimension reduction approach is shown to be easily yet efficiently coupled with clustering or latent factor models within a unique framework.},
	bibtex_show={true},
}

@techreport{elvira2021safeSLOPEsuppmat,
	title  = {{Supplementary materials: Safe rules for the identification of zeros in the solutions of the SLOPE problem}},
	author = {Elvira, Clément and Herzet, Cédric},
	year   = {2021},
	month  = {October},
	pdf    = {articles/elvira2021slope/elvira2021SLOPEsuppmat.pdf},
	bibtex_show={true},
}

@techreport{Elvira2021techreport,
	author  = {Elvira, Clément and Herzet, Cédric},
	note    = {Technical report},
	title   = {A response to ``Fast OSCAR and OWL Regression via Safe Screening Rules'' by Bao et al.},
	institution = {},
	month   = {October},
	pdf     = {tecreports/Elvira2021_responseSLOPE.pdf},
	year    = {2021},
	bibtex_show={true},
}

#### Thesis National conferences

@phdthesis{elvira2017phdthesis,
	author = {Clément Elvira},
	title = {Modèles bayésiens pour l’identification de représentations antiparcimonieuses et l’analyse en composantes principales non paramétrique},
	language = FRENCH,
	year = {2017},
	month = {november},
	school = {Centrale Lille},
	address = {Lille, France},
	pdf = {articles/elvira2017manuscritthese.pdf},
	hal = {tel-01730077v2},
	bibtex_show={true},
}


text:
@online{noauthor_mediator_nodate,
	title = {Mediator vs Moderator variables},
	url = {http://psych.wisc.edu/henriques/mediator.html},
	urldate = {2013-07-16}
}

repository: pmean/build-website

license_type: permissive

repo_name: pmean/build-website

branch_name: refs/heads/master

extension: bib


text:
DESCRIPTION = "A computer version of the well-known electronic game named Simon"
HOMEPAGE = "http://code.google.com/p/neomis/"
LICENSE = "GPLv3"
AUTHOR = "Valéry Febvre <vfebvre@easter-eggs.com>"
SECTION = "x11/applications"
PRIORITY = "optional"
DEPENDS = "python-native edje-native"

SRCREV = "8"
# actually it's 1.0.3 :/ but don't want to downgrade now/bump PE
PV = "1.1.0+svnr${SRCPV}"
PR = "r2"

S = "${WORKDIR}/trunk"

PACKAGE_ARCH = "all"

SRC_URI = "svn://neomis.googlecode.com/svn;module=trunk;proto=http"

inherit distutils

do_configure_prepend_shr() {
	sed -e 's,^Exec=,Exec=/usr/bin/fsoraw -r Display ,g' -i ${S}/data/neomis.desktop
}

FILES_${PN} += "${datadir}/neomis ${datadir}/applications/neomis.desktop ${datadir}/pixmaps"

RDEPENDS_${PN} += "python-audio python-pyalsaaudio python-elementary"

do_compile_prepend() {
	${STAGING_BINDIR_NATIVE}/edje_cc -id ${S}/data ${S}/data/neomis.edc
}

license: MIT

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty

branch_name: HEAD

extension: bb


text:
# Generated by superflore -- DO NOT EDIT
#
# Copyright Open Source Robotics Foundation

inherit ros_distro_melodic
inherit ros_superflore_generated

DESCRIPTION = "Firmware binary releases and update script for Leo Rover"
AUTHOR = "Fictionlab <support@fictionlab.pl>"
ROS_AUTHOR = "Błażej Sowa <blazej@fictionlab.pl>"
HOMEPAGE = "http://wiki.ros.org/leo_fw"
SECTION = "devel"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://package.xml;beginline=12;endline=12;md5=58e54c03ca7f821dd3967e2a2cd1596e"

ROS_CN = "leo_robot"
ROS_BPN = "leo_fw"

ROS_BUILD_DEPENDS = ""

ROS_BUILDTOOL_DEPENDS = " \
    ${PYTHON_PN}-setuptools-native \
    catkin-native \
"

ROS_EXPORT_DEPENDS = ""

ROS_BUILDTOOL_EXPORT_DEPENDS = ""

ROS_EXEC_DEPENDS = " \
    ${PYTHON_PN}-rospkg \
    ${ROS_UNRESOLVED_DEP-python-whichcraft} \
    rosgraph \
    rosmon-msgs \
    rosnode \
    rospy \
    rosservice \
    std-srvs \
"

# Currently informational only -- see http://www.ros.org/reps/rep-0149.html#dependency-tags.
ROS_TEST_DEPENDS = ""

DEPENDS = "${ROS_BUILD_DEPENDS} ${ROS_BUILDTOOL_DEPENDS}"
# Bitbake doesn't support the "export" concept, so build them as if we needed them to build this package (even though we actually
# don't) so that they're guaranteed to have been staged should this package appear in another's DEPENDS.
DEPENDS += "${ROS_EXPORT_DEPENDS} ${ROS_BUILDTOOL_EXPORT_DEPENDS}"

RDEPENDS:${PN} += "${ROS_EXEC_DEPENDS}"

# matches with: https://github.com/fictionlab-gbp/leo_robot-release/archive/release/melodic/leo_fw/1.2.1-1.tar.gz
ROS_BRANCH ?= "branch=release/melodic/leo_fw"
SRC_URI = "git://github.com/fictionlab-gbp/leo_robot-release;${ROS_BRANCH};protocol=https"
SRCREV = "7d327f1ff5e5f7b0b911cac7f662e0753129e581"
S = "${WORKDIR}/git"

ROS_BUILD_TYPE = "catkin"

inherit ros_${ROS_BUILD_TYPE}

repository: DasRoteSkelett/meta-ros

repo_name: DasRoteSkelett/meta-ros

gha_license_id: MIT


text:
// *** WARNING: this file was generated by the Pulumi SDK Generator. ***
// *** Do not edit by hand unless you're certain you know what you are doing! ***

package v20210501

import (
	"context"
	"reflect"

	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

// The type of key used to encrypt the Account Key.
type AccountEncryptionKeyType pulumi.String

const (
	// The Account Key is encrypted with a System Key.
	AccountEncryptionKeyTypeSystemKey = AccountEncryptionKeyType("SystemKey")
	// The Account Key is encrypted with a Customer Key.
	AccountEncryptionKeyTypeCustomerKey = AccountEncryptionKeyType("CustomerKey")
)

func (AccountEncryptionKeyType) ElementType() reflect.Type {
	return reflect.TypeOf((*pulumi.String)(nil)).Elem()
}

func (e AccountEncryptionKeyType) ToStringOutput() pulumi.StringOutput {
	return pulumi.ToOutput(pulumi.String(e)).(pulumi.StringOutput)
}

func (e AccountEncryptionKeyType) ToStringOutputWithContext(ctx context.Context) pulumi.StringOutput {
	return pulumi.ToOutputWithContext(ctx, pulumi.String(e)).(pulumi.StringOutput)
}

func (e AccountEncryptionKeyType) ToStringPtrOutput() pulumi.StringPtrOutput {
	return pulumi.String(e).ToStringPtrOutputWithContext(context.Background())
}

func (e AccountEncryptionKeyType) ToStringPtrOutputWithContext(ctx context.Context) pulumi.StringPtrOutput {
	return pulumi.String(e).ToStringOutputWithContext(ctx).ToStringPtrOutputWithContext(ctx)
}

// The behavior for IP access control in Key Delivery.
type DefaultAction pulumi.String

const (
	// All public IP addresses are allowed.
	DefaultActionAllow = DefaultAction("Allow")
	// Public IP addresses are blocked.
	DefaultActionDeny = DefaultAction("Deny")
)

func (DefaultAction) ElementType() reflect.Type {
	return reflect.TypeOf((*pulumi.String)(nil)).Elem()
}

func (e DefaultAction) ToStringOutput() pulumi.StringOutput {
	return pulumi.ToOutput(pulumi.String(e)).(pulumi.StringOutput)
}

func (e DefaultAction) ToStringOutputWithContext(ctx context.Context) pulumi.StringOutput {
	return pulumi.ToOutputWithContext(ctx, pulumi.String(e)).(pulumi.StringOutput)
}

func (e DefaultAction) ToStringPtrOutput() pulumi.StringPtrOutput {
	return pulumi.String(e).ToStringPtrOutputWithContext(context.Background())
}

func (e DefaultAction) ToStringPtrOutputWithContext(ctx context.Context) pulumi.StringPtrOutput {
	return pulumi.String(e).ToStringOutputWithContext(ctx).ToStringPtrOutputWithContext(ctx)
}

// The identity type.
type ManagedIdentityType pulumi.String

const (
	// A system-assigned managed identity.
	ManagedIdentityTypeSystemAssigned = ManagedIdentityType("SystemAssigned")
	// No managed identity.
	ManagedIdentityTypeNone = ManagedIdentityType("None")
)

func (ManagedIdentityType) ElementType() reflect.Type {
	return reflect.TypeOf((*pulumi.String)(nil)).Elem()
}

func (e ManagedIdentityType) ToStringOutput() pulumi.StringOutput {
	return pulumi.ToOutput(pulumi.String(e)).(pulumi.StringOutput)
}

func (e ManagedIdentityType) ToStringOutputWithContext(ctx context.Context) pulumi.StringOutput {
	return pulumi.ToOutputWithContext(ctx, pulumi.String(e)).(pulumi.StringOutput)
}

func (e ManagedIdentityType) ToStringPtrOutput() pulumi.StringPtrOutput {
	return pulumi.String(e).ToStringPtrOutputWithContext(context.Background())
}

func (e ManagedIdentityType) ToStringPtrOutputWithContext(ctx context.Context) pulumi.StringPtrOutput {
	return pulumi.String(e).ToStringOutputWithContext(ctx).ToStringPtrOutputWithContext(ctx)
}

// Indicates whether the connection has been Approved/Rejected/Removed by the owner of the service.
type PrivateEndpointServiceConnectionStatus pulumi.String

const (
	PrivateEndpointServiceConnectionStatusPending  = PrivateEndpointServiceConnectionStatus("Pending")
	PrivateEndpointServiceConnectionStatusApproved = PrivateEndpointServiceConnectionStatus("Approved")
	PrivateEndpointServiceConnectionStatusRejected = PrivateEndpointServiceConnectionStatus("Rejected")
)

func (PrivateEndpointServiceConnectionStatus) ElementType() reflect.Type {
	return reflect.TypeOf((*pulumi.String)(nil)).Elem()
}

func (e PrivateEndpointServiceConnectionStatus) ToStringOutput() pulumi.StringOutput {
	return pulumi.ToOutput(pulumi.String(e)).(pulumi.StringOutput)
}

func (e PrivateEndpointServiceConnectionStatus) ToStringOutputWithContext(ctx context.Context) pulumi.StringOutput {
	return pulumi.ToOutputWithContext(ctx, pulumi.String(e)).(pulumi.StringOutput)
}

func (e PrivateEndpointServiceConnectionStatus) ToStringPtrOutput() pulumi.StringPtrOutput {
	return pulumi.String(e).ToStringPtrOutputWithContext(context.Background())
}

func (e PrivateEndpointServiceConnectionStatus) ToStringPtrOutputWithContext(ctx context.Context) pulumi.StringPtrOutput {
	return pulumi.String(e).ToStringOutputWithContext(ctx).ToStringPtrOutputWithContext(ctx)
}

// The type of the storage account.
type StorageAccountType pulumi.String

const (
	// The primary storage account for the Media Services account.
	StorageAccountTypePrimary = StorageAccountType("Primary")
	// A secondary storage account for the Media Services account.
	StorageAccountTypeSecondary = StorageAccountType("Secondary")
)

func (StorageAccountType) ElementType() reflect.Type {
	return reflect.TypeOf((*pulumi.String)(nil)).Elem()
}

func (e StorageAccountType) ToStringOutput() pulumi.StringOutput {
	return pulumi.ToOutput(pulumi.String(e)).(pulumi.StringOutput)
}

func (e StorageAccountType) ToStringOutputWithContext(ctx context.Context) pulumi.StringOutput {
	return pulumi.ToOutputWithContext(ctx, pulumi.String(e)).(pulumi.StringOutput)
}

func (e StorageAccountType) ToStringPtrOutput() pulumi.StringPtrOutput {
	return pulumi.String(e).ToStringPtrOutputWithContext(context.Background())
}

func (e StorageAccountType) ToStringPtrOutputWithContext(ctx context.Context) pulumi.StringPtrOutput {
	return pulumi.String(e).ToStringOutputWithContext(ctx).ToStringPtrOutputWithContext(ctx)
}

type StorageAuthentication pulumi.String

const (
	// System authentication.
	StorageAuthenticationSystem = StorageAuthentication("System")
	// Managed Identity authentication.
	StorageAuthenticationManagedIdentity = StorageAuthentication("ManagedIdentity")
)

func (StorageAuthentication) ElementType() reflect.Type {
	return reflect.TypeOf((*pulumi.String)(nil)).Elem()
}

func (e StorageAuthentication) ToStringOutput() pulumi.StringOutput {
	return pulumi.ToOutput(pulumi.String(e)).(pulumi.StringOutput)
}

func (e StorageAuthentication) ToStringOutputWithContext(ctx context.Context) pulumi.StringOutput {
	return pulumi.ToOutputWithContext(ctx, pulumi.String(e)).(pulumi.StringOutput)
}

func (e StorageAuthentication) ToStringPtrOutput() pulumi.StringPtrOutput {
	return pulumi.String(e).ToStringPtrOutputWithContext(context.Background())
}

func (e StorageAuthentication) ToStringPtrOutputWithContext(ctx context.Context) pulumi.StringPtrOutput {
	return pulumi.String(e).ToStringOutputWithContext(ctx).ToStringPtrOutputWithContext(ctx)
}

repository: johnbirdau/pulumi-azure-native

repo_name: johnbirdau/pulumi-azure-native

extension: go


text:
package cron

import (
	"context"
	"github.com/robfig/cron"
	"log"
	"strings"
	"url-shortner/logic"
)

var (
	crn         *cron.Cron
	crnShutdown = make(chan struct{})
	ctx         = context.Background()
)

func init() {
	crn = cron.New()
}

func Start() error {
	log.Print("Starting Cron.....")

	//TODO: Make this frequency configurable
	cronFrequency := "0 0/5 * * * ?"

	expressions := GetExpression(cronFrequency)

	for _, expression := range expressions {
		err := crn.AddFunc(expression, deleteExpiredURLs)
		if err != nil {
			log.Printf("Couldn't add deletion of expired URLs into cron with Expression %v", err.Error())
			return err
		}
	}

	crn.Start()
	return nil
}

func GetExpression(cronStateFrequency string) []string {
	return strings.Split(cronStateFrequency, "$")
}

func deleteExpiredURLs() {
	//TODO take a distributed lock on a common key before executing the logic
	logic.DeleteExpiredURLs(ctx)
}

func Stop() <-chan struct{} {
	go func() {
		log.Print("Stopping Cron...")
		crn.Stop()
		close(crnShutdown)
	}()
	return crnShutdown
}

gha_language: Go


text:
@inproceedings{ahamed2011towards,
  title={Towards TTP-free lightweight solution for location privacy using location-based anonymity prediction},
  author={Ahamed, Sheikh I and Hasan, Chowdhury S and Haque, Md M and Gani, Md O},
  booktitle={Proceedings of the 2011 ACM Symposium on Research in Applied Computation},
  pages={293--297},
  year={2011}
}

@inproceedings{gani2011recursive,
  title={Recursive Implementation of Markov Model, A New Approach},
  author={Gani, Md and Ashraf, Sarah Isnain Binte and Malik, Nafia and Hossain, Bushra and Hossain, M Afzal and Sarwar, Hasan and others},
  booktitle={Advanced Information Networking and Applications (WAINA), 2011 IEEE Workshops of International Conference on},
  pages={216--220},
  year={2011},
  organization={IEEE}
}

@article{gani2009prediction,
  title={Prediction of State of Wireless Network Using Markov and Hidden Markov Model.},
  author={Gani, MD Osman and Sarwar, Hasan and Rahman, Chowdhury Mofizur},
  journal={J. Networks},
  volume={4},
  number={10},
  pages={976--984},
  year={2009}
}

@inproceedings{gani2008prediction,
  title={Prediction of the density of active wireless device using markov model},
  author={Gani, MD Osman and Mehedi, Iftekhar Jahan and Seraj, Mehnaz and Sarwar, Hasan and Rahman, Chowdhury Mofizur},
  booktitle={2008 11th International Conference on Computer and Information Technology},
  pages={691--695},
  year={2008},
  organization={IEEE}
}

@inproceedings{gani2013rssi,
  title={RSSI based indoor localization for smartphone using fixed and mobile wireless node},
  author={Gani, Md Osman and OBrien, Casey and Ahamed, Sheikh I and Smith, Roger O},
  booktitle={2013 IEEE 37th Annual Computer Software and Applications Conference},
  pages={110--117},
  year={2013},
  organization={IEEE}
}

@article{johnson2014effect,
  title={Effect of a social script iPad application for children with autism going to imaging},
  author={Johnson, Norah and Bree, Octavia and Lalley, Erin E and Rettler, Kelly and Grande, Pam and Gani, Md O and Ahamed, Sheikh I},
  journal={Journal of pediatric nursing},
  volume={29},
  number={6},
  pages={651--659},
  year={2014},
  publisher={WB Saunders}
}

@inproceedings{rahman2014seeing,
  title={Seeing Beyond Visibility: A Four Way Fusion of User Authentication for Efficient Usable Security on Mobile Devices},
  author={Rahman, Farzana and Gani, Md Osman and Ahsan, Golam Mushih Tanimul and Ahamed, Sheikh Iqbal},
  booktitle={Software Security and Reliability-Companion (SERE-C), 2014 IEEE Eighth International Conference on},
  pages={121--129},
  year={2014},
  organization={IEEE}
}

@article{ganiresna,
  title={RESNA Annual Conference-2014},
  author={Gani, Md Osman and Ahamed, Sheikh Iqbal and Davis, Samantha Ann and Smith, Roger O}
}

@inproceedings{gani2015simple,
  title={Simple activity recognition using smartphone technologies for in-home rehabilitation},
  author={Gani, Md Osman and Fayezeen, Taskina and Ahamed, Sheikh Iqbal and Tomashek, Dennis B and Smith, Roger O},
  booktitle={RESNA Annual Conference. https://www. resna. org/sites/default/files/conference/2015/pdf\_versions/other/151. pdf},
  year={2015}
}

@incollection{williams2016challenges,
  title={Challenges in developing applications for aging populations},
  author={Williams, Drew Marie and Gani, Md Osman and Addo, Ivor D and Majumder, AKM Jahangir Alam and Tamma, Chandana P and Wang, Mong-Te and Chang, Chih-Hung and Ahamed, Sheikh Iqbal and Chu, Cheng-Chung},
  booktitle={Optimizing Assistive Technologies for Aging Populations},
  pages={1--21},
  year={2016},
  publisher={IGI Global}
}

@article{ganiapproach,
  title={AN APPROACH TO COMPLEX FUNCTIONAL ACTIVITY RECOGNITION USING SMARTPHONE TECHNOLOGIES},
  author={Gani, Md Osman and Ahamed, Sheikh Iqbal and Davis, Samantha Ann and Smith, Roger O}
}

@inproceedings{azam2011architecture,
  title={Architecture \& Implementation of Bangla Text to Braille Translation},
  author={Azam, Samiul and Khan, Imtiaz Ahamad and Gani, Md Osman and Islam, Mahbub Ul and Hossain, Syed Akhter},
  booktitle={International Conference on Computer Processing of Bangla},
  year={2011}
}

@inproceedings{mahbub2011computational,
  title={Computational model for bangla text to braille translation},
  author={Mahbub-ul-Islam, Fakhruddin Muhammad and Khan, Ahamad Imtiaz and Gani, Md Osman and Azam, Samiul and Hossain, Syed Akhter},
  booktitle={2nd International Conference on Computer Processing of Bangla},
  year={2011}
}

@inproceedings{gani2016approach,
  title={An approach to localization in crowded area},
  author={Gani, Md Osman and Ahsan, Golam Mushih Tanimul and Do, Duc and Drew, Williams and Balfas, Mohammed and Ahamed, Sheikha Iqbal and Arif, Muhammad and Kattan, Ahmed J},
  booktitle={2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)},
  pages={1--6},
  year={2016},
  organization={IEEE}
}

@article{yadloskyforward,
  title={Forward Thinking Poster Session/Colloquy Presentation Past Award Recipients},
  author={Yadlosky, Lauren and Fayezeen, Taskina and Gani, Md Osman and Bello-Almazan, Julissa and Berry, Mahala and Jain, Niharika and Saxena, Piyush and Carey, Daniel}
}

@phdthesis{gani2017novel,
  title={A novel approach to complex human activity recognition},
  author={Gani, Md Osman},
  year={2017},
  school={Marquette University}
}

@article{ganicomputationally,
  title={Computationally Efficient Human Activity Modeling and Its Application as a Service in Android Application Framework},
  author={Gani, Md Osman and Fayezeen, Taskina and Ahamed, Sheikh Iqbal and Povinelli, Richard J}
}

@inproceedings{gani2017novel,
  title={A novel framework to recognize complex human activity},
  author={Gani, Md Osman and Saha, Amit Kumar and Ahsan, Golam Mushih Tanimul and Ahamed, Sheikh Iqbal and Smith, Roger O},
  booktitle={2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)},
  volume={1},
  pages={948--956},
  year={2017},
  organization={IEEE}
}

@inproceedings{ahsan2017novel,
  title={A novel real-time non-invasive hemoglobin level detection using video images from smartphone camera},
  author={Ahsan, Golam MT and Gani, Md O and Hasan, Md K and Ahamed, Sheikh I and Chu, William and Adibuzzaman, Mohammad and Field, Joshua},
  booktitle={2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)},
  volume={1},
  pages={967--972},
  year={2017},
  organization={IEEE}
}

@article{dddwmbsiamaajk2016novel,
  title={A novel approach for localization in crowded area without gps},
  author={DDDWMBSIAMAAJK Md Osman Gani, Golam Mushih and Ahsan, Tanimul},
  journal={IEEE Healthcom},
  year={2016}
}

@article{gani2019light,
  title={A light weight smartphone based human activity recognition system with high accuracy},
  author={Gani, Md Osman and Fayezeen, Taskina and Povinelli, Richard J and Smith, Roger O and Arif, Muhammad and Kattan, Ahmed J and Ahamed, Sheikh Iqbal},
  journal={Journal of Network and Computer Applications},
  volume={141},
  pages={59--72},
  year={2019},
  publisher={Academic Press}
}

@inproceedings{silwal2019survey,
  title={A survey of taxi ride sharing system architectures},
  author={Silwal, Shrawani and Gani, Md Osman and Raychoudhury, Vaskar},
  booktitle={2019 IEEE International Conference on Smart Computing (SMARTCOMP)},
  pages={144--149},
  year={2019},
  organization={IEEE}
}

@inproceedings{nguyen2019yours,
  title={Yours truly? Survey on accessibility of our personal data in the connected world},
  author={Nguyen, Manh and Gani, Md Osman and Raychoudhury, Vaskar},
  booktitle={2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)},
  pages={292--297},
  year={2019},
  organization={IEEE}
}

@article{johnson2014effect,
  title={Effect of an iPad application for medical imaging procedure preparation for children with autism spectrum disorder},
  author={Johnson, N and Bree, O and Lalley, E and Rettler, K and Grande, P and Gani, Md and Ahamed, S},
  journal={Journal of Pediatric Nursing},
  volume={29},
  number={6},
  pages={651--659},
  year={2014}
}

@inproceedings{gani2019smart,
  title={Smart surface classification for accessible routing through built environment: A crowd-sourced approach},
  author={Gani, Md Osman and Raychoudhury, Vaskar and Edinger, Janick and Mokrenko, Valeria and Cao, Zheng and Zhang, Ce},
  booktitle={Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},
  pages={11--20},
  year={2019}
}

@inproceedings{jarvis2019miamimapper,
  title={Miamimapper: Crowd analysis using active and passive indoor localization through wi-fi probe monitoring},
  author={Jarvis, Nicholas and Hata, John and Wayne, Nicholas and Raychoudhury, Vaskar and Gani, Md Osman},
  booktitle={Proceedings of the 15th ACM International Symposium on QoS and Security for Wireless and Mobile Networks},
  pages={1--10},
  year={2019}
}

@inproceedings{saha2019personalized,
  title={Personalized Pain Study Platform using Evidence-Based Continuous Learning Tool},
  author={Saha, Amit Kumar and Ahsan, GM Tanimul and Gani, Md Osman and Ahamed, Sheikh Iqbal},
  booktitle={2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)},
  volume={2},
  pages={490--495},
  year={2019},
  organization={IEEE}
}

@inproceedings{farzanarahman2014golammushihtanimulahsan,
  title={GolamMushihTanimulAhsan and Sheikh IqbalAhamed" Seeing beyond visibility: A Four Way Fusion of User Authentication for Efficient Usable Security on Mobile Devices},
  author={FarzanaRahman, Md Osman Gani},
  booktitle={2014 Eighth International Conference on Software Security and Reliability}
}

@inproceedings{hata2019care,
  title={CARE: Campus-wide Accessible Route Estimation through Surface Analysis},
  author={Hata, John and Gani, Osman and Raychoudhury, Vaskar},
  booktitle={Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications},
  pages={173--173},
  year={2019}
}

@inproceedings{bikak2020structural,
  title={STRUCTURAL CAUSAL MODEL WITH EXPERT AUGMENTED KNOWLEDGE TO ESTIMATE THE EFFECT OF OXYGEN THERAPY},
  author={Bikak, Marvi and Kethireddy, Shravan and Gani, Md Osman and Adibuzzaman, Mohammad},
  booktitle={CHEST},
  volume={158},
  number={4},
  pages={A636},
  year={2020},
  organization={Elsevier}
}

@article{gani2023structural,
  title={Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU},
  author={Gani, Md Osman and Kethireddy, Shravan and Adib, Riddhiman and Hasan, Uzma and Griffin, Paul and Adibuzzaman, Mohammad},
  journal={Artificial Intelligence in Medicine},
  volume={137},
  pages={102493},
  year={2023},
  publisher={Elsevier}
}

@inproceedings{silwal2020dynamic,
  title={A Dynamic Taxi Ride Sharing System Using Particle Swarm Optimization},
  author={Silwal, Shrawani and Raychoudhury, Vaskar and Saha, Snehanshu and Gani, Md Osman},
  booktitle={IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)},
  pages={112--120},
  year={2020},
  organization={doi: 10.1109/MASS50613.2020.00024}
}

@inproceedings{mokrenko2021transfer,
  title={A Transfer Learning Approach to Surface Detection for Accessible Routing for Wheelchair Users},
  author={Mokrenko, Valeria and Yu, Haoxiang and Raychoudhury, Vaskar and Edinger, Janick and Smith, Roger O and Gani, Md Osman},
  booktitle={2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)},
  pages={794--803},
  year={2021},
  organization={IEEE}
}

@article{addaicdh,
  title={ICDH 2021},
  author={Adda, Medhi and Ahamed, Sheikh and Al-Yateem, Nabeel and Burns, Matthew and Chernich, Alison and Cjhang, Wan-Jung and Chang, Carl and Ching, Congo Tak-Shing and Choudhury, Salimur and Coppolino, Luigi and others}
}

@article{adib2022causal,
  title={Causal Discovery on the Effect of Antipsychotic Drugs on Delirium Patients in the ICU using Large EHR Dataset},
  author={Adib, Riddhiman and Gani, Md Osman and Ahamed, Sheikh Iqbal and Adibuzzaman, Mohammad},
  journal={arXiv preprint arXiv:2205.01057},
  year={2022}
}

@article{adib2022ckh,
  title={CKH: Causal Knowledge Hierarchy for Estimating Structural Causal Models from Data and Priors},
  author={Adib, Riddhiman and Naved, Md Mobasshir Arshed and Fang, Chih-Hao and Gani, Md Osman and Grama, Ananth and Griffin, Paul and Ahamed, Sheikh Iqbal and Adibuzzaman, Mohammad},
  journal={arXiv preprint arXiv:2204.13775},
  year={2022}
}

@article{yu2022automated,
  title={Automated Surface Classification System using Vibration Patterns-A Case Study with Wheelchairs},
  author={Yu, Haoxiang and Raychoudhury, Vaskar and Saha, Snehanshu and Edinger, Janick and Smith, Roger O and Gani, Md Osman},
  journal={IEEE Transactions on Artificial Intelligence},
  year={2022},
  publisher={IEEE}
}

@article{agarwal2020,
  title={2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)| 978-1-7281-9866-8/20/$31.00{\copyright} 2020 IEEE| D},
  author={Agarwal, Vidushi and Alsheakh, Hussein and Anjum, Khizar and Banerjee, Sourav and Barnes, Laura and Baset, Aniqua and Basnyat, Bipendra and Beck, Micah and Belgiovine, Mauro and Bemmoussat, ChemsEddine and others}
}

@inproceedings{rahaman2019perawarecity,
  title={PerAwareCity'19-4th IEEE International Workshop on Pervasive Context-Aware Smart Cities and Intelligent Transport Systems-Welcome and Committees},
  author={Rahaman, Mohammad Saiedur and Gani, Md Osman and Saxena, Divya},
  booktitle={2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019},
  pages={1--3},
  year={2019}
}

@article{hasan2022kcrl,
  title={KCRL: A Prior Knowledge Based Causal Discovery Framework With Reinforcement Learning},
  author={Hasan, Uzma and Gani, Md Osman},
  journal={Proceedings of Machine Learning Research},
  volume={182},
  number={2022},
  pages={1--24},
  year={2022}
}

@article{saha2023mypath,
  title={MyPath: Accessible Routing for Wheelchair Users},
  author={Saha, Shaswati and Selingo, Lauren and Olejniczak, Emily and Noyce, Hanna and Raychoudhury, Vaskar and Smith, Roger O and Gani, Md Osman},
  journal={UMBC Student Collection},
  year={2023}
}

@article{saharesna,
  title={RESNA Annual Conference-2022},
  author={Saha, Shaswati and Selingo, Lauren and Olejniczak, Emily and Noyce, Hanna and Raychoudhury, Vaskar and Smith, Roger O and Gani, Md Osman}
}

@article{ferdous2023cdans,
  title={CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data},
  author={Ferdous, Muhammad Hasan and Hasan, Uzma and Gani, Md Osman},
  journal={arXiv preprint arXiv:2302.03246},
  year={2023}
}

@article{saha2023rfc,
  title={RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget},
  author={Saha, Sourajit and Saha, Shaswati and Gani, Md Osman and Oates, Tim and Chapman, David},
  journal={arXiv preprint arXiv:2302.06134},
  year={2023}
}

@article{ferdous2023ecdans,
  title={eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract)},
  author={Ferdous, Muhammad Hasan and Hasan, Uzma and Gani, Md Osman},
  journal={arXiv preprint arXiv:2303.02833},
  year={2023}
}


text:
@article{2001ApJ...554..678B,
 abstract = {The gamma-ray burst of 1997 February 28 (GRB 970228) ushered in the
discovery of the afterglow phenomenon. Despite intense study of
the nearby galaxy, however, the nature of this galaxy and the
distance to the burst eluded the community. Here we present the
measurement of the redshift of the galaxy, the putative host
galaxy of GRB 970228, and, based on its spectroscopic and
photometric properties, identify the galaxy as a subluminous but
otherwise normal galaxy at redshift z=0.695 undergoing a modest
level of star formation. At this redshift, the GRB released an
isotropic-equivalent energy of
(1.4+/-0.3)×10$^52$ ergs (20-2000 keV rest
frame). We find no evidence that the host is significantly bluer
or is forming stars more vigorously than the general field
population. In fact, by all accounts in our analysis (color-
magnitude, magnitude-radius, star formation rate, and Balmer-
break amplitude), the host properties appear typical for faint
blue field galaxies at comparable redshifts. Partially based on
the observations obtained at the W. M. Keck Observatory, which
is operated by the California Association for Research in
Astronomy, a scientific partnership among California Institute
of Technology, the University of California, and the National
Aeronautics and Space Administration.},
 adsnote = {Provided by the SAO/NASA Astrophysics Data System},
 adsurl = {https://ui.adsabs.harvard.edu/abs/2001ApJ...554..678B},
 archiveprefix = {arXiv},
 author = {Bloom, J.~S. and Djorgovski, S.~G. and Kulkarni, S.~R.},
 doi = {10.1086/321398},
 eprint = {astro-ph/0007244},
 journal = {\apj},
 keywords = {Cosmology: Miscellaneous, Cosmology: Observations, Galaxies: Dwarf, Gamma Rays: Bursts, Astrophysics},
 month = {Jun},
 number = {2},
 pages = {678-683},
 primaryclass = {astro-ph},
 title = {The Redshift and the Ordinary Host Galaxy of GRB 970228},
 volume = {554},
 year = {2001}
}

repository: profjsb/my-content

repo_name: profjsb/my-content

gha_language: TeX


text:
@Manual{velloso,
  title = {Qualitative Activity Recognition of Weight Lifting Exercises},
  author = {{Velloso, E.; Bulling, A.; Gellersen, H.; Ugulino, W.; Fuks, H.}},
  organization = {Proceedings of 4th International Conference in Cooperation with SIGCHI (Augmented Human '13)},
  address = {Stuttgart, Germany},
  year = {2013},
  url = {http:/groupware.les.inf.puc-rio.br/har#ixzz4Tk2YB4Ft},
}


text:
@article{song_pet_2020,
	title = {{PET} image super-resolution using generative adversarial networks},
	volume = {125},
	issn = {0893-6080},
	url = {http://www.sciencedirect.com/science/article/pii/S0893608020300393},
	doi = {10.1016/j.neunet.2020.01.029},
	abstract = {The intrinsically low spatial resolution of positron emission tomography (PET) leads to image quality degradation and inaccurate image-based quantitation. Recently developed supervised super-resolution (SR) approaches are of great relevance to PET but require paired low- and high-resolution images for training, which are usually unavailable for clinical datasets. In this paper, we present a self-supervised SR (SSSR) technique for PET based on dual generative adversarial networks (GANs), which precludes the need for paired training data, ensuring wider applicability and adoptability. The SSSR network receives as inputs a low-resolution PET image, a high-resolution anatomical magnetic resonance (MR) image, spatial information (axial and radial coordinates), and a high-dimensional feature set extracted from an auxiliary CNN which is separately-trained in a supervised manner using paired simulation datasets. The network is trained using a loss function which includes two adversarial loss terms, a cycle consistency term, and a total variation penalty on the SR image. We validate the SSSR technique using a clinical neuroimaging dataset. We demonstrate that SSSR is promising in terms of image quality, peak signal-to-noise ratio, structural similarity index, contrast-to-noise ratio, and an additional no-reference metric developed specifically for SR image quality assessment. Comparisons with other SSSR variants suggest that its high performance is largely attributable to simulation guidance.},
	language = {en},
	urldate = {2020-07-02},
	journal = {Neural Networks},
	author = {Song, Tzu-An and Chowdhury, Samadrita Roy and Yang, Fan and Dutta, Joyita},
	month = may,
	year = {2020},
	keywords = {Super-resolution, CNN, GAN, Multimodality imaging, PET, Self-supervised},
	pages = {83--91},
	file = {ScienceDirect Full Text PDF:C\:\\Users\\Samadrita\\Zotero\\storage\\UDWVL3W4\\Song et al. - 2020 - PET image super-resolution using generative advers.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\Samadrita\\Zotero\\storage\\MSUV23CV\\S0893608020300393.html:text/html}
}


text:
BRANCH = "linux-sancloud-rt-5.4.y"
SRCREV = "cff4afad9506e00129fbfcd3abe1db5e10be743d"
PV = "v5.4.106-rt54-sc2"

require linux-bbe-5.4.inc

repository: bendoerr/meta-sancloud

repo_name: bendoerr/meta-sancloud


text:
#
# Install sip libraries from source
#

if (NOT sip_NAME)

CMAKE_MINIMUM_REQUIRED(VERSION 2.8)

include (ExternalProject)
include (ExternalSource)
include (BuildSupport)
#include (PatchSupport)

include (python)

external_source (sip
    4.15.4
    sip-4.15.4.tar.gz
    1d5c9e92bc7fca5ac11e088a4cf6c83d
    http://hivelocity.dl.sourceforge.net/project/pyqt/sip/sip-4.15.4)

message ("Installing ${sip_NAME} into FlyEM build area: ${BUILDEM_DIR} ...")

if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
    set (EXTRA_SIP_CONFIG_FLAGS
        --arch=x86_64 )
endif()

ExternalProject_Add(${sip_NAME}
    DEPENDS             ${python_NAME}
    PREFIX              ${BUILDEM_DIR}
    URL                 ${sip_URL}
    URL_MD5             ${sip_MD5}
    UPDATE_COMMAND      ""
    PATCH_COMMAND       ""
    LIST_SEPARATOR      ^^
    CONFIGURE_COMMAND   ${BUILDEM_ENV_STRING} ${PYTHON_EXE} ${sip_SRC_DIR}/configure.py
        ${EXTRA_SIP_CONFIG_FLAGS}
        #-b ${PYTHON_PREFIX}/bin
        #-d ${PYTHON_PREFIX}/lib/python2.7/site-packages
        #-e ${PYTHON_PREFIX}/include/python2.7
        #-v ${PYTHON_PREFIX}/share/sip

    BUILD_COMMAND       ${BUILDEM_ENV_STRING} $(MAKE)
    INSTALL_COMMAND     ${BUILDEM_ENV_STRING} $(MAKE) install
    BUILD_IN_SOURCE     1
)

set_target_properties(${sip_NAME} PROPERTIES EXCLUDE_FROM_ALL ON)

endif (NOT sip_NAME)

repository: janelia-flyem/buildem

repo_name: janelia-flyem/buildem

gha_language: CMake

extension: cmake


text:
.list-container {
  width: 90%;
  margin: 12rem auto;
}

.filters {
  display: flex;
  justify-content: center;
  align-items: center;
  margin-bottom: 5rem;
  background-color: #f7f7f7;
  padding: 1rem;
  border-radius: 5px;
}

.filters button {
  padding: 1rem 3rem;
  border-radius: 30px;
  border: 0;
  margin: 0 1rem;
  color: #222222;
  font-weight: 700;
  background-color: #fff;
}

.filters .active {
  background-color: #ff6839;
  color: #ffffff;
}

.content {
  display: grid;
  grid-gap: 5rem;
  grid-template-columns: repeat(auto-fit, minmax(22rem, 1fr));
  grid-template-rows: repeat(1, 1fr);
}

.content .diary-entry {
  border: 1px solid #e9ebee;
  transition: 0.25s ease;
  border-radius: 5px;
  border-top: 5px solid #200afc;
  border-bottom: 3px solid #dddddd;
}

.content .diary-entry:hover {
  box-shadow: 0 10px 50px -10px rgba(134, 134, 134, 0.64);
  transition: 0.25s ease;
  transform: translateY(-2%);
}

.content .diary-entry .header {
  margin-bottom: 0;
  padding: 1rem;
  padding-bottom: 0;
}

.content .header .edit {
  position: relative;
}

.content .drawer {
  position: absolute;
  background-color: #fff;
  box-shadow: 0 3px 8px rgba(0, 0, 0, 0.3);
  top: 80%;
  right: 0;
  border-radius: 3px;
  display: none;
}

/* .content .drawer {
  display: block;
} */

.content .edit .fa-ellipsis-h .drawer ul li {
  text-align: left;
  display: block;
  margin: 0.5rem 0;
  padding: 0;
}

.content .edit .fa-ellipsis-h .drawer ul li a:hover {
  background-color: #ff6839;
  color: #ffffff;
}

.content .edit .fa-ellipsis-h .drawer ul li a {
  font-size: 1.2rem;
  padding: 0.5rem 1rem;
  padding-right: 3rem;
  margin: 0;
  display: block;
  color: #222222;
}

.content .edit .fa-ellipsis-h .drawer ul li a .fas {
  margin-left: 0;
  display: inline-block;
}

.content .header .fa-ellipsis-h {
  padding: 0 0.5rem;
  border-radius: 5px;
  margin: 0;
  top: 0;
  right: 0;
}

.content .header .fa-ellipsis-h:hover>.drawer {
  display: block;
}

.content .diary-entry .category {
  padding-left: 1rem;
  /* border-left: 3px solid #0AD3FC; */
}

.content .diary-entry .description {
  padding: 1rem;
  margin: 0.5rem 0;
  font-size: 1.4rem;
  letter-spacing: 0.05rem;
  line-height: 1.8rem;
  border: 1px solid #e9ebee;
  border-right: 0;
  border-left: 0;
  color: #222222;
}

.content .diary-entry .notice {
  padding: 1rem;
}

.content .notice .fav {
  font-size: 1.2rem;
}

@media only screen and (max-width: 587px) {
  .list-container {
    margin: 8rem auto;
  }
  .content {
    /* grid-gap: 5rem; */
    grid-row-gap: 2rem
  }
}

repository: mindsworth/mydiary-project

repo_name: mindsworth/mydiary-project

branch_name: refs/heads/develop

gha_language: JavaScript

extension: css


text:
@book{preston_demography_2001,
	location = {Malden, Massachusetts},
	title = {Demography: Measuring and Modeling Population Processes},
	publisher = {Blackwell},
	author = {Preston, Samuel H. and Heuveline, Patrick and Guillot, Michel},
	date = {2001}
}

@article{leslie_use_1945,
	title = {On the Use of Matrices in Certain Population Mathematics},
	volume = {33},
	rights = {Copyright c 1945 Biometrika Trust},
	issn = {00063444},
	url = {http://www.jstor.org/stable/2332297},
	pages = {183--212},
	number = {3},
	journaltitle = {Biometrika},
	author = {Leslie, P. H.},
	date = {1945}
}

@article{leslie_further_1948,
	title = {Some Further Notes on the Use of Matrices in Population Mathematics},
	volume = {35},
	rights = {Copyright c 1948 Biometrika Trust},
	issn = {00063444},
	url = {http://www.jstor.org/stable/2332342},
	pages = {213--245},
	number = {3},
	journaltitle = {Biometrika},
	author = {Leslie, P. H.},
	date = {1948}
}

@article{lewis_generation_1942,
	title = {On the Generation and Growth of a Population},
	volume = {6},
	rights = {Copyright 1942 Indian Statistical Institute},
	issn = {00364452},
	url = {http://www.jstor.org/stable/25047743},
	pages = {93--96},
	number = {1},
	journaltitle = {Sankhya: The Indian Journal of Statistics (1933-1960)},
	author = {Lewis, E. G.},
	date = {1942}
}

@book{united_nations_world_2011,
	location = {New York, {NY}},
	title = {World Population Prospects: The 2010 Revision},
	publisher = {United Nations Department of Economic and Social Affairs, Population Division},
	author = {{United Nations}},
	date = {2011}
}

repository: markalava/CCMPP

repo_name: markalava/CCMPP


text:
@inproceedings{celik:iuadaptme:2018,
 author = {Ilknur Celik and Ilaria Torre and Frosina Koceva and Christine Bauer and Eva Zangerle and Bart Knijnenburg},
 booktitle = {Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization},
 doi = {10.1145/3213586.3226202},
 isbn = {978-1-4503-5784-5},
 pages = {137--139},
 publisher = {ACM},
 series = {UMAP '18},
 title = {UMAP 2018 Intelligent User-Adapted Interfaces: Design and Multi-Modal Evaluation (IUadaptMe) Workshop Chairs' Welcome and Organization},
 year = {2018}
}

branch_name: refs/heads/main


text:
DESCRIPTION = "A GNU collection of binary utilities"
HOMEPAGE = "http://www.gnu.org/software/binutils/"
SECTION = "devel"
LICENSE = "GPL"
MAINTAINER = "Gerald Britton <gbritton@doomcom.org>"
PR = "r1"

DEFAULT_PREFERENCE_linux-uclibc = "-1"

SRC_URI = \
    "http://ftp.gnu.org/gnu/binutils/binutils-${PV}.tar.bz2 \
     file://ld_makefile.patch;patch=1 \
     file://better_file_error.patch;patch=1 \
     file://signed_char_fix.patch;patch=1 \
     file://binutils-100_cflags_for_build.patch;patch=1"
# bit-rotten uclibc patches not yet updated to this version:
#    file://binutils-2.15.91.0.1-uclibc-100-conf.patch;patch=1
#    file://binutils-2.15.90.0.3-uclibc-200-build_modules.patch;patch=1

S = "${WORKDIR}/binutils-${PV}"
B = "${S}/build.${HOST_SYS}.${TARGET_SYS}"

include binutils.inc

do_configure_prepend() {
	for dir in bfd gas ld; do ( cd $dir; autoreconf ); done
}

repository: BackupTheBerlios/openslug-svn

repo_name: BackupTheBerlios/openslug-svn


text:
# xfce-mcs-plugins OE build file
# Copyright (C) 2004, Advanced Micro Devices, Inc.  All Rights Reserved
# Released under the MIT license (see COPYING.MIT)

DESCRIPTION = "Common XFCE4 configuration plugins."
DEPENDS = "libxfcegui4 xfce-mcs-manager gtk+"
SECTION = "x11"

inherit xfce

PACKAGES += "${PN}-mcs-plugins"
FILES_${PN}-mcs-plugins = "${libdir}/xfce4/mcs-plugins/*.so"

SRC_URI[md5sum] = "77fcc929058de530d4f91eb25de851a1"
SRC_URI[sha256sum] = "e16610264256248bfd8ca227c7861000abf0c323611ad34913b06c41af0c9be5"


text:
SUMMARY = "This is a very small helper app for ssh-agent"
LICENSE = "LGPL-2.1-only"
LIC_FILES_CHKSUM = "file://LICENSE;md5=435ed639f84d4585d93824e7da3d85da"

inherit lxqt qt5-translation

DEPENDS += "liblxqt"

SRCREV = "4c1c7339183ea26b99461ef64c98d732b6bb5abe"
PV = "1.2.0"