text:
@article{tu2019joint,
  title={Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning},
  author={Tu, Xiaoguang and Zhao, Jian and Jiang, Zihang and Luo, Yao and Xie, Mei and Zhao, Yang and He, Linxiao and Ma, Zheng and Feng, Jiashi},
  journal={arXiv preprint arXiv:1903.09359},
  year={2019}
}

@article{Guo_2019,
   title={CNN-Based Real-Time Dense Face Reconstruction with Inverse-Rendered Photo-Realistic Face Images},
   volume={41},
   ISSN={1939-3539},
   url={http://dx.doi.org/10.1109/tpami.2018.2837742},
   DOI={10.1109/tpami.2018.2837742},
   number={6},
   journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Guo, Yudong and Zhang, Juyong and Cai, Jianfei and Jiang, Boyi and Zheng, Jianmin},
   year={2019},
   month={Jun},
   pages={1294–1307}
}

@Article{betschard2016,
	author = {Christoph Betschard},
	title = {Fitting a 3D Morphable Face Model to Captures from Consumer Depth Cameras},
	journal = {University of Basel, Department of Mathematics and Computer Science, Master's Thesis},
	institution = {University of Basel, Department of Mathematics and Computer Science},
	year = {2016}
}

@Article{Schoenborn2017,
	author="Sch{\"o}nborn, Sandro
	and Egger, Bernhard
	and Morel-Forster, Andreas
	and Vetter, Thomas",
	title="Markov Chain Monte Carlo for Automated Face Image Analysis",
	journal="International Journal of Computer Vision",
	year="2017",
	month="Jun",
	day="01",
	volume="123",
	number="2",
	pages="160--183",
	abstract="We present a novel fully probabilistic method to interpret a single face image with the 3D Morphable Model. The new method is based on Bayesian inference and makes use of unreliable image-based information. Rather than searching a single optimal solution, we infer the posterior distribution of the model parameters given the target image. The method is a stochastic sampling algorithm with a propose-and-verify architecture based on the Metropolis--Hastings algorithm. The stochastic method can robustly integrate unreliable information and therefore does not rely on feed-forward initialization. The integrative concept is based on two ideas, a separation of proposal moves and their verification with the model (Data-Driven Markov Chain Monte Carlo), and filtering with the Metropolis acceptance rule. It does not need gradients and is less prone to local optima than standard fitters. We also introduce a new collective likelihood which models the average difference between the model and the target image rather than individual pixel differences. The average value shows a natural tendency towards a normal distribution, even when the individual pixel-wise difference is not Gaussian. We employ the new fitting method to calculate posterior models of 3D face reconstructions from single real-world images. A direct application of the algorithm with the 3D Morphable Model leads us to a fully automatic face recognition system with competitive performance on the Multi-PIE database without any database adaptation.",
	issn="1573-1405",
	doi="10.1007/s11263-016-0967-5",
	url="https://doi.org/10.1007/s11263-016-0967-5"
}

@Article{Schoenborn2014,
	author = {Sch{\"o}nborn, Sandro},
	title = {Markov Chain Monte Carlo for
Integrated Face Image Analysis},
	journal = {University of Basel, Department of Mathematics and Computer Science, PhD Thesis},
	institution = {University of Basel, Department of Mathematics and Computer Science},
	year = {2014},
}

@InProceedings{10.1007/978-3-642-40602-7_11,
	author="Sch{\"o}nborn, Sandro
	and Forster, Andreas
	and Egger, Bernhard
	and Vetter, Thomas",
	editor="Weickert, Joachim
	and Hein, Matthias
	and Schiele, Bernt",
	title="A Monte Carlo Strategy to Integrate Detection and Model-Based Face Analysis",
	booktitle="Pattern Recognition",
	year="2013",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="101--110",
	abstract="We present a novel probabilistic approach for fitting a statistical model to an image. A 3D Morphable Model (3DMM) of faces is interpreted as a generative (Top-Down) Bayesian model. Random Forests are used as noisy detectors (Bottom-Up) for the face and facial landmark positions. The Top-Down and Bottom-Up parts are then combined using a Data-Driven Markov Chain Monte Carlo Method (DDMCMC). As core of the integration, we use the Metropolis-Hastings algorithm which has two main advantages. First, the algorithm can handle unreliable detections and therefore does not need the detectors to take an early and possible wrong hard decision before fitting. Second, it is open for integration of various cues to guide the fitting process. Based on the proposed approach, we implemented a completely automatic, pose and illumination invariant face recognition application. We are able to train and test the building blocks of our application on different databases. The system is evaluated on the Multi-PIE database and reaches state of the art performance.",
	isbn="978-3-642-40602-7"
}

@book{vision2013,
	author = {Andrea Fossati and Juergen Gall and Helmut Grabner and Xiaofeng Ren and Kurt Konolige},
	title = {Consumer Depth Cameras for Computer Vision},
	year = {2013},
	publisher = {Springer-Verlag London},
	isbn = {978-1-4471-4640-7},
	doi = {10.1007/978-1-4471-4640-7},
	url = {https://doi.org/10.1007/978-1-4471-4640-7},
}

@Inbook{Henry2014,
	author="Henry, Peter
	and Krainin, Michael
	and Herbst, Evan
	and Ren, Xiaofeng
	and Fox, Dieter",
	editor="Khatib, Oussama
	and Kumar, Vijay
	and Sukhatme, Gaurav",
	title="RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments",
	bookTitle="Experimental Robotics: The 12th International Symposium on Experimental Robotics",
	year="2014",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="477--491",
	abstract="RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used in the context of robotics, specifically for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. We present RGB-D Mapping, a full 3D mapping system that utilizes a novel joint optimization algorithm combining visual features and shape-based alignment. Visual and depth information are also combined for view-based loop closure detection, followed by pose optimization to achieve globally consistent maps.We evaluate RGB-D Mapping on two large indoor environments, and show that it effectively combines the visual and shape information available from RGB-D cameras.",
	isbn="978-3-642-28572-1",
	doi="10.1007/978-3-642-28572-1_33",
	url="https://doi.org/10.1007/978-3-642-28572-1_33"
}

@Inbook{Smisek2013,
	author="Smisek, Jan
	and Jancosek, Michal
	and Pajdla, Tomas",
	editor="Fossati, Andrea
	and Gall, Juergen
	and Grabner, Helmut
	and Ren, Xiaofeng
	and Konolige, Kurt",
	title="3D with Kinect",
	bookTitle="Consumer Depth Cameras for Computer Vision: Research Topics and Applications",
	year="2013",
	publisher="Springer London",
	address="London",
	pages="3--25",
	abstract="We analyze Kinect as a 3D measuring device, experimentally investigate depth measurement resolution and error properties, and make a quantitative comparison of Kinect accuracy with stereo reconstruction from SLR cameras and a 3D-TOF camera. We propose a Kinect geometrical model and its calibration procedure providing an accurate calibration of Kinect 3D measurement and Kinect cameras. We compare our Kinect calibration procedure with its alternatives available on Internet, and integrate it into an SfM pipeline where 3D measurements from a moving Kinect are transformed into a common coordinate system, by computing relative poses from matches in its color camera.",
	isbn="978-1-4471-4640-7",
	doi="10.1007/978-1-4471-4640-7_1",
	url="https://doi.org/10.1007/978-1-4471-4640-7_1"
}

@Inbook{Weiss2013,
	author="Weiss, Alexander
	and Hirshberg, David
	and Black, Michael J.",
	editor="Fossati, Andrea
	and Gall, Juergen
	and Grabner, Helmut
	and Ren, Xiaofeng
	and Konolige, Kurt",
	title="Home 3D Body Scans from a Single Kinect",
	bookTitle="Consumer Depth Cameras for Computer Vision: Research Topics and Applications",
	year="2013",
	publisher="Springer London",
	address="London",
	pages="99--117",
	abstract="The 3D shape of the human body is useful for applications in fitness, games, and apparel. Accurate body scanners, however, are expensive, limiting the availability of 3D body models. Although there has been a great deal of interest recently in the use of active depth sensing cameras, such as the Microsoft Kinect, for human pose tracking, little has been said about the related problem of human shape estimation. We present a method for human shape reconstruction from noisy monocular image and range data using a single inexpensive commodity sensor. The approach combines low-resolution image silhouettes with coarse range data to estimate a parametric model of the body. Accurate 3D shape estimates are obtained by combining multiple monocular views of a person moving in front of the sensor. To cope with varying body pose, we use a SCAPE body model which factors 3D body shape and pose variations. This enables the estimation of a single consistent shape, while allowing pose to vary. Additionally, we describe a novel method to minimize the distance between the projected 3D body contour and the image silhouette that uses analytic derivatives of the objective function. We use a simple method to estimate standard body measurements from the recovered SCAPE model and show that the accuracy of our method is competitive with commercial body scanning systems costing orders of magnitude more.",
	isbn="978-1-4471-4640-7",
	doi="10.1007/978-1-4471-4640-7_6",
	url="https://doi.org/10.1007/978-1-4471-4640-7_6"
}

@inproceedings{Blanz:1999:MMS:311535.311556,
 author = {Blanz, Volker and Vetter, Thomas},
 title = {A Morphable Model for the Synthesis of 3D Faces},
 booktitle = {Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques},
 series = {SIGGRAPH '99},
 year = {1999},
 isbn = {0-201-48560-5},
 pages = {187--194},
 numpages = {8},
 url = {http://dx.doi.org/10.1145/311535.311556},
 doi = {10.1145/311535.311556},
 acmid = {311556},
 publisher = {ACM Press/Addison-Wesley Publishing Co.},
 address = {New York, NY, USA},
 keywords = {computer vision, facial animation, facial modeling, morphing, photogrammetry, registration},
}

@incollection{Romdhani3DM,
title = "CHAPTER 4 - 3D MORPHABLE FACE MODEL, A UNIFIED APPROACH FOR ANALYSIS AND SYNTHESIS OF IMAGES",
editor = "Wenyi Zhao and Rama Chellappa",
booktitle = "Face Processing",
publisher = "Academic Press",
address = "Burlington",
pages = "127 - 158",
year = "2006",
isbn = "978-0-12-088452-0",
doi = "https://doi.org/10.1016/B978-012088452-0/50005-4",
url = "http://www.sciencedirect.com/science/article/pii/B9780120884520500054"
}

@misc{D415,
  title = {Intel® RealSense™ D415 Description},
  howpublished = {\url{https://www.intelrealsense.com}},
  note = {Accessed: 2019-04-12}
}

@misc{D415_SDK,
  title = {Intel® RealSense™ SDK 2.0},
  howpublished = {\url{https://github.com/IntelRealSense/librealsense
}},
  note = {Accessed: 2019-04-12}
}

@article{oliverwisler18,
	title = {3D-Morphable Model Fitting incorporating contour information},
	author = {Oliver Wisler},
	year = {2018},
	journal = {University of Basel, Department of Mathematics and Computer Science, Master's Thesis},
	institution = {University of Basel, Department of Mathematics and Computer Science}
}

@misc{lmdetect,
  title = {Facial mapping (landmarks) with Dlib + Python},
  howpublished = {\url{https://towardsdatascience.com/facial-mapping-landmarks-with-dlib-python-160abcf7d672}},
  note = {Accessed: 2019-04-16}

}
@misc{scalismo,
  title = {Scalismo - Scalable Image Analysis and Shape Modelling},
  howpublished = {\url{https://github.com/unibas-gravis/scalismo
}},
  note = {Accessed: 2019-04-16}

}

@misc{dlib,
	title = {dlib - A toolkit for making real world machine learning and data analysis applications in C++},
	howpublished = {\url{http://dlib.net}},
	note = {Accessed: 2019-04-19}
}

@misc{scalismofaces,
  title = {Scalable Image Analysis and Shape Modelling: Module to work with 2d images, with a focus on face images},
  howpublished = {\url{https://github.com/unibas-gravis/scalismo-faces
}},
  note = {Accessed: 2019-04-16}

}

@misc{opencv,
  title = {Open Source Computer Vision Library(OpenCV)},
  howpublished = {\url{https://opencv.org/
}},
  note = {Accessed: 2019-04-16}

}
@misc{thrift,
  title = {The Apache Thrift software framework},
  howpublished = {\url{https://thrift.apache.org/},\url{https://en.wikipedia.org/wiki/Apache_Thrift

}},
  note = {Accessed: 2019-04-20}

}

@misc{rs-projection,
	title={Projection in RealSense SDK 2.0},
	howpublished = {\url{https://dev.intelrealsense.com/docs/projection-in-intel-realsense-sdk-20#section-point-coordinates}},
	note={Access Date: 2019-10-01}
}

@ARTICLE{121791,
author={P. J. {Besl} and N. D. {McKay}},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={A method for registration of 3-D shapes},
year={1992},
volume={14},
number={2},
pages={239-256},
keywords={computational geometry;convergence of numerical methods;iterative methods;optimisation;pattern recognition;picture processing;3D shape registration;pattern recognition;point set registration;iterative closest point;geometric entity;mean-square distance metric;convergence;geometric model;Solid modeling;Motion estimation;Iterative closest point algorithm;Iterative algorithms;Testing;Inspection;Shape measurement;Iterative methods;Convergence;Quaternions},
doi={10.1109/34.121791},
ISSN={0162-8828},
month={Feb},
}

@INPROCEEDINGS{6618317,
author={G. P. {Meyer} and M. N. {Do}},
booktitle={2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)},
title={Real-time 3D face modeling with a commodity depth camera},
year={2013},
volume={},
number={},
pages={1-4},
keywords={cameras;face recognition;image registration;image segmentation;interactive devices;solid modelling;real-time 3D face modeling;commodity depth camera;Kinect sensor;user head depth image registration;high-quality 3D face models;user head depth image integration;fixed depth camera;KinectFusion system;depth image segmentation;Three-dimensional displays;Solid modeling;Face;Cameras;Image segmentation;Computational modeling;3D Face Modeling;Kinect;Real-time},
doi={10.1109/ICMEW.2013.6618317},
ISSN={},
month={July},}

@INPROCEEDINGS{6334272,
author={M. {Hernandez} and J. {Choi} and G. {Medioni}},
booktitle={2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)},
title={Laser scan quality 3-D face modeling using a low-cost depth camera},
year={2012},
volume={},
number={},
pages={1995-1999},
keywords={cameras;face recognition;graphics processing units;image registration;image representation;iterative methods;object detection;optical scanners;smoothing methods;spatiotemporal phenomena;laser scan quality;3D face model;depth camera;geometric model;star shaped object;object representation;cylindrical coordinate;filtering operation;depth image;3D points cloud;image registration;GPU;graphics processing unit;ICP;iterative closest point;ICP algorithm;facial expression;occlusion;temporal smoothing;spatial smoothing;Face;Computational modeling;Solid modeling;Estimation;Image reconstruction;Cameras;Laser modes;Kinect;face modeling;graphics},
doi={},
ISSN={2076-1465},
month={Aug},}

@Inproceedings{kinectfusion-real-time-dense-surface-mapping-and-tracking,
	author = {Newcombe, Richard A. and  and  and  and Davison, Andrew J. and  and Shotton, Jamie and Hodges, Steve and Fitzgibbon, Andrew},
	title = {KinectFusion: Real-Time Dense Surface Mapping and Tracking},
	booktitle = {IEEE ISMAR},
	year = {2011},
	month = {October},
	abstract = {
	We present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware. We fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real-time. The current sensor pose is simultaneously obtained by tracking the live depth frame relative to the global model using a coarse-to-fine iterative closest point (ICP) algorithm, which uses all of the observed depth data available. We demonstrate the advantages of tracking against the growing full surface model compared with frame-to-frame tracking, obtaining tracking and mapping results in constant time within room sized scenes with limited drift and high accuracy. We also show both qualitative and quantitative results relating to various aspects of our tracking and mapping system. Modelling of natural scenes, in real-time with only commodity sensor and GPU hardware, promises an exciting step forward in augmented reality (AR), in particular, it allows dense surfaces to be reconstructed in real-time, with a level of detail and robustness beyond any solution yet presented using passive computer vision.
	},
	publisher = {IEEE},
	url = {https://www.microsoft.com/en-us/research/publication/kinectfusion-real-time-dense-surface-mapping-and-tracking/},
	edition = {IEEE ISMAR},
}

@INPROCEEDINGS{924423,
author={S. {Rusinkiewicz} and M. {Levoy}},
booktitle={Proceedings Third International Conference on 3-D Digital Imaging and Modeling},
title={Efficient variants of the ICP algorithm},
year={2001},
volume={},
number={},
pages={145-152},
keywords={distance measurement;image processing;minimisation;real-time systems;iterative closest point algorithm;geometric alignment;three-dimensional models;minimization strategy;nearly-flat meshes;inscribed surfaces;uniform sampling;range images;real-time 3D model acquisition;model-based tracking;Iterative closest point algorithm;Iterative algorithms;Convergence;Solid modeling;Iterative methods;Geometry;Layout;Minimization methods;Image sampling;Rough surfaces},
doi={10.1109/IM.2001.924423},
ISSN={},
month={May},}

@ARTICLE{1227983,
author={V. {Blanz} and T. {Vetter}},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Face recognition based on fitting a 3D morphable model},
year={2003},
volume={25},
number={9},
pages={1063-1074},
keywords={face recognition;solid modelling;image morphing;lighting;image texture;image representation;visual databases;face recognition;3D morphable model fitting;pose variations;profile view;frontal views;illuminations;3D shape;CMU-PIE database;FERET database;image database;shadows;specular reflections;image texture;statistical morphable model;face identification;image formation;3D space;computer graphics;Face recognition;Lighting;Shape;Head;Deformable models;Computational modeling;Computer simulation;Computer graphics;Image databases;Image recognition},
doi={10.1109/TPAMI.2003.1227983},
ISSN={0162-8828},
month={Sep.},}

@article{Schonborn:2015:BMG:2798342.2798359,
 author = {Sch\"{o}nborn, Sandro and Egger, Bernhard and Forster, Andreas and Vetter, Thomas},
 title = {Background Modeling for Generative Image Models},
 journal = {Comput. Vis. Image Underst.},
 issue_date = {July 2015},
 volume = {136},
 number = {C},
 month = jul,
 year = {2015},
 issn = {1077-3142},
 pages = {117--127},
 numpages = {11},
 url = {http://dx.doi.org/10.1016/j.cviu.2015.01.008},
 doi = {10.1016/j.cviu.2015.01.008},
 acmid = {2798359},
 publisher = {Elsevier Science Inc.},
 address = {New York, NY, USA},
 keywords = {Bayesian model, Face analysis, Face model, Generative models, Implicit background models, Morphable Model},
}
@ARTICLE{1000239,
author={ Zhuowen Tu and Song-Chun Zhu },
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Image segmentation by data-driven Markov chain Monte Carlo},
year={2002},
volume={24},
number={5},
pages={657-673},
keywords={image segmentation;Markov processes;Monte Carlo methods;Data-Driven Markov Chain Monte Carlo;image segmentation;Bayesian statistical framework;computational paradigm;split-merge;snake/balloon;region competition;data clustering;Markov random field;Markov chain sequence;clustering;edge detection;Image segmentation;Monte Carlo methods},
doi={10.1109/34.1000239},
ISSN={0162-8828},
month={May},}

@misc{multicam,
	title={Using the Intel® RealSense™ Depth cameras D4xx in Multi-Camera Configurations.},
	author={Anders Grunnet-Jepsen and Paul Winer and Aki Takagi and John Sweetser and Kevin Zhao and Tri Khuong and Dan Nie and John Woodfill},
	journal={Intel},
	url ={https://dev.intelrealsense.com/docs/multiple-depth-cameras-configuration},
	note={Access Date: 2019-10-01}
}

@misc{cal,
	title={Calibration Tools User Guide for Intel® RealSense™ D400 Series},
	journal={Intel},
	url = {https://dev.intelrealsense.com/docs/intel-realsensetm-d400-series-calibration-tools-user-guide},
	note={Access Date: 2019-10-01}
}

@misc{bestcal,
	title={Best-Known-Methods for Tuning Intel® RealSense™ D400 Depth Cameras for
Best Performance},
	author={Anders Grunnet-Jepsen and John N. Sweetser and John Woodfill},
	journal={Intel},
	revision={Revision 1.9},
	url ={https://dev.intelrealsense.com/docs/tuning-depth-cameras-for-best-performance},
	note={Access Date: 2019-10-01}
}

@incollection{EGGER2017115,
title = "Chapter 5 - Probabilistic Morphable Models",
editor = "Guoyan Zheng and Shuo Li and Gabor Székely",
booktitle = "Statistical Shape and Deformation Analysis",
publisher = "Academic Press",
pages = "115 - 135",
year = "2017",
isbn = "978-0-12-810493-4",
doi = "https://doi.org/10.1016/B978-0-12-810493-4.00006-7",
url = "http://www.sciencedirect.com/science/article/pii/B9780128104934000067",
author = "Bernhard Egger and Sandro Schönborn and Clemens Blumer and Thomas Vetter",
keywords = "Face image analysis, Markov chain Monte Carlo, Morphable model, Generative models, Top-down and bottom-up integration, Occlusion-aware model fitting, Facial expressions",
abstract = "3D Morphable Face Models have been introduced for the analysis of 2D face photographs. The analysis is performed by actively reconstructing the three-dimensional face from the image in an Analysis-by-Synthesis loop, exploring statistical models for shape and appearance. Here we follow a probabilistic approach to acquire a robust and automatic model adaptation. The probabilistic formulation helps to overcome two main limitations of the classical approach. First, Morphable Model adaptation is highly depending on a good initialization. The initial position of landmark points and face pose was given by manual annotation in previous approaches. Our fully probabilistic formulation allows us to integrate unreliable Bottom-Up cues from face and feature point detectors. This integration is superior to the classical feed-forward approach, which is prone to early and possibly wrong decisions. The integration of uncertain Bottom-Up detectors leads to a fully automatic model adaptation process. Second, the probabilistic framework gives us a natural way to handle outliers and occlusions. Face images are recorded in highly unconstrained settings. Often parts of the face are occluded by various objects. Unhandled occlusions can mislead the model adaptation process. The probabilistic interpretation of our model makes possible to detect and segment occluded parts of the image and leads to robust model adaptation. Throughout this chapter we develop a fully probabilistic framework for image interpretation. We start by reformulating the Morphable Model as a probabilistic model in a fully Bayesian framework. Given an image, we search for a posterior distribution of possible image explanations. The integration of Bottom-Up information and the model parameters adaptation is performed using a Data Driven Markov Chain Monte Carlo approach. The face model is extended to be occlusion-aware and explicitly segments the image into face and non-face regions during the model adaptation process. The segmentation and model adaptation is performed in an Expectation-Maximization-style algorithm utilizing a robust illumination estimation method. The presented fully automatic face model adaptation can be used in a wide range of applications like face analysis, face recognition or face image manipulation. Our framework is able to handle images containing strong outliers, occlusions and facial expressions under arbitrary poses and illuminations. Furthermore, the fully probabilistic embedding has the additional advantage that it also delivers the uncertainty of the resulting image interpretation."
}

@INPROCEEDINGS{8373814,
author={T. {Gerig} and A. {Morel-Forster} and C. {Blumer} and B. {Egger} and M. {Luthi} and S. {Schoenborn} and T. {Vetter}},
booktitle={2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018)},
title={Morphable Face Models - An Open Framework},
year={2018},
volume={},
number={},
pages={75-82},
keywords={computer vision;face recognition;Gaussian processes;image registration;principal component analysis;public domain software;splines (mathematics);face registration;neutral faces;open-source software framework;registration model-building;BU3D-FE database;2D face images;open framework;Gaussian processes;nonrigid registration;Morphable face models;nonrigid deformation models;PCA models;GPMM separate problem specific requirements;registration algorithm;prior model;modeling technique;facial expression model;open-source pipeline;Gaussian process morphable models;analysis-by-synthesis model adaption;Basel face model;morphable face models;Face;Adaptation models;Strain;Kernel;Shape;Deformable models;Gaussian processes;Morphable Model;Gaussian processes;Gaussian Process Morphable Model;Registration;Basel Face Model;Face Reconstruction},
doi={10.1109/FG.2018.00021},
ISSN={},
month={May},}

@ARTICLE{8010438,
author={M. {Lüthi} and T. {Gerig} and C. {Jud} and T. {Vetter}},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Gaussian Process Morphable Models},
year={2018},
volume={40},
number={8},
pages={1860-1873},
keywords={Gaussian processes;image registration;image segmentation;Karhunen-Loeve transforms;principal component analysis;splines (mathematics);Gaussian Process Morphable Models;shape variation;PDMs;shape models;classical spline models;Gaussian processes;spline model;shape characteristics;model-based segmentation;statistical model;point distribution models;point variations;principal component analysis;Karhunen-Loeve expansion;registration schemes;3D forearm images;Shape;Computational modeling;Mathematical model;Gaussian processes;Analytical models;Deformable models;Kernel;Statistical shape modeling;Gaussian processes;image analysis;non-rigid registration},
doi={10.1109/TPAMI.2017.2739743},
ISSN={},
month={Aug},}

@article{DBLP:journals/corr/KeselmanWGB17,
  author    = {Leonid Keselman and
               John Iselin Woodfill and
               Anders Grunnet{-}Jepsen and
               Achintya Bhowmik},
  title     = {Intel RealSense Stereoscopic Depth Cameras},
  journal   = {CoRR},
  volume    = {abs/1705.05548},
  year      = {2017},
  url       = {http://arxiv.org/abs/1705.05548},
  archivePrefix = {arXiv},
  eprint    = {1705.05548},
  timestamp = {Mon, 13 Aug 2018 16:47:02 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/KeselmanWGB17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{serg,
title={The basics of stereo depth vision},
author={Sergey Dorodnicov},
url={https://www.intelrealsense.com/stereo-depth-vision-basics/},
note={Access Date: 2019-10-01}
}

@misc{pinhole,
title={Turning any surface into a touch surface},
author={Natalie Polansky and Yaniv Hasbani},
}
@inproceedings{Lorusso:1995:CFA:236190.236213,
 author = {Lorusso, A. and Eggert, D. W. and Fisher, R. B.},
 title = {A Comparison of Four Algorithms for Estimating 3-D Rigid Transformations},
 booktitle = {Proceedings of the 1995 British Conference on Machine Vision (Vol. 1)},
 series = {BMVC '95},
 year = {1995},
 isbn = {0-9521898-2-8},
 location = {Birmingham, United Kingdom},
 pages = {237--246},
 numpages = {10},
 url = {http://dl.acm.org/citation.cfm?id=236190.236213},
 acmid = {236213},
 publisher = {BMVA Press},
 address = {Surrey, UK, UK},
}

@misc{scalismoTut,
title={Scalismo Tutorials},
url={https://scalismo.org/tutorials},
note={Access Date: 2019-10-01}
}

@book{Hartley:2003:MVG:861369,
 author = {Hartley, Richard and Zisserman, Andrew},
 title = {Multiple View Geometry in Computer Vision},
 year = {2003},
 isbn = {0521540518},
 edition = {2},
 publisher = {Cambridge University Press},
 address = {New York, NY, USA},
}

@article{ALBRECHT2013959,
title = "Posterior shape models",
journal = "Medical Image Analysis",
volume = "17",
number = "8",
pages = "959 - 973",
year = "2013",
issn = "1361-8415",
doi = "https://doi.org/10.1016/j.media.2013.05.010",
url = "http://www.sciencedirect.com/science/article/pii/S1361841513000844",
author = "Thomas Albrecht and Marcel Lüthi and Thomas Gerig and Thomas Vetter",
keywords = "Statistical shape model, Conditional shape model, Posterior shape model, Image segmentation, Trochlear dysplasia",
abstract = "We present a method to compute the conditional distribution of a statistical shape model given partial data. The result is a “posterior shape model”, which is again a statistical shape model of the same form as the original model. This allows its direct use in the variety of algorithms that include prior knowledge about the variability of a class of shapes with a statistical shape model. Posterior shape models then provide a statistically sound yet easy method to integrate partial data into these algorithms. Usually, shape models represent a complete organ, for instance in our experiments the femur bone, modeled by a multivariate normal distribution. But because in many application certain parts of the shape are known a priori, it is of great interest to model the posterior distribution of the whole shape given the known parts. These could be isolated landmark points or larger portions of the shape, like the healthy part of a pathological or damaged organ. However, because for most shape models the dimensionality of the data is much higher than the number of examples, the normal distribution is singular, and the conditional distribution not readily available. In this paper, we present two main contributions: First, we show how the posterior model can be efficiently computed as a statistical shape model in standard form and used in any shape model algorithm. We complement this paper with a freely available implementation of our algorithms. Second, we show that most common approaches put forth in the literature to overcome this are equivalent to probabilistic principal component analysis (PPCA), and Gaussian Process regression. To illustrate the use of posterior shape models, we apply them on two problems from medical image analysis: model-based image segmentation incorporating prior knowledge from landmarks, and the prediction of anatomically correct knee shapes for trochlear dysplasia patients, which constitutes a novel medical application. Our experiments confirm that the use of conditional shape models for image segmentation improves the overall segmentation accuracy and robustness."
}

@conference{icpram19,
author={Claudio Ferrari. and Stefano Berretti. and Pietro Pala. and Alberto Del Bimbo.},
title={3D Face Reconstruction from RGB-D Data by Morphable Model to Point Cloud Dense Fitting},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={728-735},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007521007280735},
isbn={978-989-758-351-3},
}

@conference{visapp16,
author={Patrik Huber. and Guosheng Hu. and Rafael Tena. and Pouria Mortazavian. and Willem P. Koppen. and William J. Christmas. and Matthias Rätsch. and Josef Kittler.},
title={A Multiresolution 3D Morphable Face Model and Fitting Framework},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={79-86},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005669500790086},
isbn={978-989-758-175-5},
}

@InProceedings{10.1007/3-540-47967-8_46,
author="Liu, Ce
and Shum, Heung-Yeung
and Zhang, Changshui",
editor="Heyden, Anders
and Sparr, Gunnar
and Nielsen, Mads
and Johansen, Peter",
title="Hierarchical Shape Modeling for Automatic Face Localization",
booktitle="Computer Vision --- ECCV 2002",
year="2002",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="687--703",
abstract="Many approaches have been proposed to locate faces in an image. There are, however, two problems in previous facial shape models using feature points. First, the dimension of the solution space is too big since a large number of key points are needed to model a face. Second, the local features associated with the key points are assumed to be independent. Therefore, previous approaches require good initialization (which is often done manually), and may generate inaccurate localization. To automatically locate faces, we propose a novel hierarchical shape model (HSM) or multi-resolution shape models corresponding to a Gaussian pyramid of the face image. The coarsest shape model can be quickly located in the lowest resolution image. The located coarse model is then used to guide the search for a finer face model in the higher resolution image. Moreover, we devise a Global and Local (GL) distribution to learn the likelihood of the joint distribution of facial features. A novel hierarchical data-driven Markov chain Monte Carlo (HDDMCMC) approach is proposed to achieve the global optimum of face localization. Experimental results demonstrate that our algorithm produces accurate localization results quickly, bypassing the need for good initialization.",
isbn="978-3-540-47967-3"
}

@inproceedings{surrey809478,
           month = {February},
          author = {P Huber and G Hu and R Tena and P Mortazavian and P Koppen and WJ Christmas and M Ratsch and J Kittler},
            note = {Paper accepted for presentation at 11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 27-29 February 2016.  Full text may be available at a later date.},
       booktitle = {11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications},
           title = {A Multiresolution 3D Morphable Face Model and Fitting Framework},
         journal = {Proceedings of the 11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications},
            year = {2016},
             doi = {10.5220/0005669500790086},
             url = {http://epubs.surrey.ac.uk/809478/},
        abstract = {3D Morphable Face Models are a powerful tool in computer vision. They consist of a PCA model of face shape and colour information and allow to reconstruct a 3D face from a single 2D image. 3D Morphable Face Models are used for 3D head pose estimation, face analysis, face recognition, and, more recently, facial landmark detection and tracking. However, they are not as widely used as 2D methods - the process of building and using a 3D model is much more involved. In this paper, we present the Surrey Face Model, a multi-resolution 3D Morphable Model that we make available to the public for non-commercial purposes. The model contains different mesh resolution levels and landmark point annotations as well as metadata for texture remapping. Accompanying the model is a lightweight open-source C++ library designed with simplicity and ease of integration as its foremost goals. In addition to basic functionality, it contains pose estimation and face frontalisation algorithms. With the tools presented in this paper, we aim to close two gaps. First, by offering different model resolution levels and fast fitting functionality, we enable the use of a 3D Morphable Model in time-critical applications like tracking. Second, the software library makes it easy for the community to adopt the 3D Morphable Face Model in their research, and it offers a public place for collaboration.}
}

repository: grigala/MScThesis

repo_name: grigala/MScThesis


text:
DESCRIPTION = "create Dreambox NAND boot images"
SECTION = "console/utils"
MAINTAINER = "Felix Domke <tmbinc@elitedvb.net>"
PV = "1.1"
PR = "r2"

SRC_URI = "file://buildimage.c"

inherit native

do_compile() {
	cp ${WORKDIR}/buildimage.c .
	${CXX} -I. -o buildimage buildimage.c
}

do_stage() {
	install -m 0755 buildimage ${STAGING_BINDIR}/
}

license: MIT

repository: demsey/openembedded

license_type: permissive

repo_name: demsey/openembedded

branch_name: HEAD

extension: bb


text:
DESCRIPTION = "The Enlightenment freedesktop.org library"
DEPENDS = "ecore"
LICENSE = "MIT BSD"
PV = "0.5.0.060+svnr${SRCPV}"
PR = "r4"
SRCREV = "${EFL_SRCREV}"

inherit efl

PACKAGES =+ "${PN}-mime ${PN}-trash"
FILES_${PN}-mime = "${libdir}/libefreet_mime.so.*"
FILES_${PN}-trash = "${libdir}/libefreet_trash.so.*"

# efreet_desktop_cache_create is needed for e-wm start, don't include it in -tests
FILES_${PN} += "${libdir}/efreet/efreet_desktop_cache_create"

repository: trini/openembedded

repo_name: trini/openembedded

branch_name: refs/heads/master


text:
DESCRIPTION = "Compact server for private LoRa networks"
HOMEPAGE = "https://gotthardp.github.io/lorawan-server/"
SECTION = "console/utils"
# https://github.com/joaohf/meta-erlang
DEPENDS = "erlang"

LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://${COMMON_LICENSE_DIR}/MIT;md5=0835ade698e0bcf8506ecda2f7b4f302"

SRC_URI = "https://github.com/gotthardp/lorawan-server/archive/v${PV}.tar.gz"
SRC_URI[md5sum] = "b22f6e3510180ca960b6346c9483477d"
SRC_URI[sha256sum] = "fd46d092cc7fc9c98d615435e95e83757f1ce9f2b6277fb076fdac21902e06d8"

S = "${WORKDIR}/${PN}-${PV}"

RDEPENDS_${PN} += "erlang-compiler erlang-inets erlang-mnesia erlang-public-key erlang-runtime-tools erlang-ssl"

do_compile() {
    oe_runmake release
}

do_install() {
    mkdir -p ${D}${libdir}
    cp -r ${S}/_build/default/rel/lorawan-server ${D}${libdir}
}


text:
@ARTICLE{Post2006-gh,
  title     = "Foraging behavior and predation risk in male and female Eurasian
               Blackbirds (Turdus merula) during the breeding season",
  author    = "Post, P and Gotmark, F",
  abstract  = "In many birds, breeding males display bright colors, sing, and
               engage in active territory defense; whereas females are less
               conspicuous. Therefore, it is sometimes assumed that in the
               breeding season males suffer higher predation than females.
               Several Studies have reported, however, higher female predation
               rates, which suggests that traits other than coloration and
               mate-acquisition behaviors are important in determining
               predation rates for the sexes. Theoretical and empirical work
               suggests that foraging behavior and foraging rate are major
               determinants of predation risk. Here, we examine this
               possibility in a study of breeding Eurasian Blackbirds (Turdus
               merula) and predation on them by Eurasian Sparrowhawks
               (Accipiter nisus). Female Eurasian Blackbirds foraged more than
               males and foraged closer to the ground, both early and late in
               the breeding season. On the basis of this observation, we
               predicted that females should suffer higher predation than
               males. Of 98 Eurasian Blackbirds taken in 33 Eurasian
               Sparrowhawk territories during four years, 56 were females and
               42 males. On the basis of the male-biased sex ratio in the
               Population, female Eurasian Blackbirds suffered higher predation
               risk than males. For breeding birds, these results indicate a
               trade-off between foraging effort and predation rate, which is
               of importance for sexual dichromatism (selection for female
               crypsis), population sex ratio, and behaviors of the sexes.",
  journal   = "Auk",
  publisher = "Oxford Univ Press Inc",
  volume    =  123,
  number    =  1,
  pages     = "162--170",
  year      =  2006,
  address   = "Journals Dept, 2001 Evans Rd, Cary, Nc 27513 USA",
  keywords  = "Accipiter nisus; crypsis; Eurasian Blackbirds; Eurasian
               Sparrowhawks; feeding; predator; sex; songbirds; Turdus merula",
  language  = "en"
}

@ARTICLE{Tyller2018-ag,
  title     = "Postfledging behavior of the Common Cuckoo (Cuculus canorus)
               attended by the Chaffinch (Fringilla coelebs): a comprehensive
               approach to study the least-known stage of brood parasite-host
               coevolution",
  author    = "Tyller, Zdenek and Kysucan, Michal and Grim, Tomas",
  abstract  = "In contrast to the thoroughly studied incubation and nestling
               periods, the postfledging period of the Common Cuckoo (Cuculus
               canorus) remains virtually unknown. Here, we report detailed
               observations of a cuckoo fledgling attended by a male Chaffinch
               (Fringilla coelebs). Molecular data (nuclear DNA) showed the
               fledgling was a male belonging to the Cuculus c. canorus/C.
               saturatus clade while mitochondrial DNA data confirmed that it
               did not belong to blue egg gens that parasitizes Redstarts
               (Phoenicurus phoenicurus), which is the most common local host
               and the only regular Common Cuckoo host. During one week of
               observations, feeding rates did not change, body mass decreased
               (by 10\%), and wing length increased (by 16\%). Video recordings
               showed that the provided diet consisted mostly of larvae and
               that the fledgling also self-fed on lichens. A radio transmitter
               fitted on the fledgling revealed that daily movement distances
               ranged from 0 to 650 m and significantly increased with age. We
               suggest that future studies should focus on the postfledging
               period in brood parasite young because this stage currently
               represents a major gap in our understanding of parasite-host
               arms races. Received 22 December 2016. Accepted 15 April 2017.",
  journal   = "Wilson J. Ornithol.",
  publisher = "Wilson Ornithological Soc",
  volume    =  130,
  number    =  2,
  pages     = "536--542",
  year      =  2018,
  address   = "5400 Bosque Blvd, Ste 680, Waco, Tx 76710 USA",
  keywords  = "arms race; brood parasitism; coevolution; Cuculus canorus;
               fledgling; Fringilla coelebs; telemetry",
  language  = "en"
}

branch_name: refs/heads/main

extension: bib


text:
@article{lidar14,
   author = {Martin Kellner and Ulrich Hofmann and Mohamed Essayed Bouzouraa, Hannah Kasper and Stephan Neumaier},
   title = {Laserscanner based Road Curb Feature Detection and efficientMapping using Local Curb Descriptions},
   journal = {IEEE},
   year = {2014}
}

@article{lidar13,
   author = {Alexandre Hervieu and Bahman Soheilian},
   title = {Road Side Detection and Reconstruction Using LIDAR Sensor},
   journal = {IEEE},
   year = {2013}
}

@article{lidar15,
   author = {Haiyan Guan and Jonathan Li and Senior Member Yongtao Yu and Michael Chapman and Cheng Wang},
   title = {Automated Road Information ExtractionFrom Mobile Laser Scanning Data},
   journal = {IEEE},
   year = {2015}
}

@article{evaluation,
   author = {Martin Kellner and Mohamed Essayed Bouzouraa and Ulrich Hofmann},
   title = {Road Curb Detection Based on Different Elevation Mapping Techniques},
   journal = {IEEE},
   year = {2014}
}

@article{elvis,
   author = {Ravi Kumar Satzoda and Mohan M. Trivedi},
   title = {Efficient Lane and Vehicle Detection With Integrated Synergies (ELVIS)},
   journal = {IEEE},
   year = {2014}
}

@article{gradient,
   author = {Tao Sun and Shuming Tang and Jinqiao Wang and and Weibin Zhang },
   title = {A Robust Lane Detection Method for Autonomous Car-like Robot},
   journal = {IEEE},
   year = {2013}
}

@article{robustcurb,
   author = {Hata, Alberto Y. and Osorio, Fernando S. and Wolf, Denis F.},
   title = {Robust Curb Detection and Vehicle Localization in Urban Environments},
   journal = {IEEE},
   year = {2014}
}

@article{stereo,
   author = {Long, Qian and Xie, Qiwei and Mita,Seiichi and Ishimaru, Kazuhisa  and Shirai, Noriaki},
   title = {A Real-time Dense Stereo Matching Method for Critical Environment Sensing in Autonomous Driving},
   journal = {IEEE},
   year = {2014}
}

@article{virtuallane,
title = {Predictive Virtual Lane Using Relative Motions Between a Vehicle and Lanes},
author = {Son, Young Seop and Lee, Seung-Hi and Chung, Chung Choo},
  journal = {IEEE},
   year = {2013}
}

@article {bewegung,
title={Belegungskartenbasierte Umfeldwahrnehmung in Kombination mit objektbasierten Ansätzen für Fahrerassistenzsysteme},
author = {M. E. Bouzouraa},
year = {2012}
}

@article {ransac,
title={Lane Detection Method Based on Improved RANSAC Algorithm},
author = {Jie Guo and Zhihua Wei and Duoqian Miao1},
year = {2015}
}

@article {ransac1,
author={A. Guiducci},
title= {Parametric model of the perspective projection of aroad
with applications to lane keeping and 3d road reconstruction},
journal = {Computer Vision and Image Understanding 73},
year = {1999}
}

@article {ransac2,
author = {M. Fischler and R. Bones},
title = {Random sample consensus: a paradigm
for model fitting with applications to image analysis and automated
cartography},
year = {1999}
}

@article {hoffmann,
author={U. Hoffmann},
title={Zur visuellen Umfeldwahrnehmung autonomer Fahrzeuge},
year={2004}
}

@article{lanesystem,
author={Daniel Töpfer and Jens Spehr and Jan Effertz and Christoph Stiller},
title={Efficient Road Scene Understanding for Intelligent Vehicles Using Compositional Hierarchical Models},
year = {2015},
journal = {IEEE Transactions On Intelligent Transportation Systems, fol. 16, no. 1}
}

repository: Sophiealex/kognitive-automobile-seminar

repo_name: Sophiealex/kognitive-automobile-seminar


text:
@article{wu2019emerging,
  title={Emerging Cross-lingual Structure in Pretrained Language Models},
  author={Wu, Shijie and Conneau, Alexis and Li, Haoran and Zettlemoyer, Luke and Stoyanov, Veselin},
  journal={arXiv preprint arXiv:1911.01464},
  year={2019}
}

url: https://raw.githubusercontent.com/Hugh-Shi/DL-NLP-Readings/924e4d189e5660c5b5d4854278f3df755ae2231d/Bibtex/Emerging Cross-lingual Structure in Pretrained Language Models.bib

repository: Hugh-Shi/DL-NLP-Readings

repo_name: Hugh-Shi/DL-NLP-Readings

gha_license_id: MIT


text:
% Information general to a particular conference year is in names-long.bib
% and names-short.bib

@inproceedings{tucek:triage:sosp2007,
author = "Joseph Tucek and Shan Lu and Chengdu Huang and Spiros Xanthos and Yuanyuan Zhou",
title = "Triage: {D}iagnosing Production Run Failures at the User's Site",
crossref = {sosp2007},
}
@inproceedings{terry:bayou:sosp1995,
  author    = {Douglas B. Terry and
               Marvin Theimer and
               Karin Petersen and
               Alan J. Demers and
               Mike Spreitzer and
               Carl Hauser},
  title     = {Managing Update Conflicts in {Bayou}, a Weakly Connected Replicated Storage System},
  crossref = {sosp1995},
  pages     = {172--183},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{ghemawat:google-fs:sosp2003,
  author = {Ghemawat, Sanjay and Gobioff, Howard and Leung, Shun-Tak},
  title = {The {G}oogle File System},
  crossref = {sosp2003},
  doi = {10.1145/945445.945450},
  keywords = {architecture, system},
  pages = {29--43},
  url = {http://dx.doi.org/10.1145/945445.945450},
}

%  note = "Available at \url{http://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf}",
@inproceedings{decandia:dynamo:sosp2007,
  title = "Dynamo: {A}mazon's highly available key-value store",
  author = "Guiseppe DeCandia and Deniz Hastorun and Madan Jampani and Gunavardhan Kakulapati and Avinash Lakshman and Alex Pilchin and Swami Sivasubramanian and Peter Vosshall and Werner Vogels",
  crossref = {sosp2007},
  doi = {10.1145/1294261.1294281},
  pages = "205--220",
}

@InProceedings{rowstron:PAST:sosp2001,
  title     = {Storage management and caching in {PAST}, a
                large-scale, persistent peer-to-peer storage utility},
  author   = "Antony Rowstron and Peter Druschel",
  crossref = {sosp2001},
  pages    = "188--201",
  abstract = {This paper presents and evaluates the storage management
and caching in PAST, a large-scale peer-to-peer persistent
storage utility. PAST is based on a self-organizing, Internetbased
overlay network of storage nodes that cooperatively
route file queries, store multiple replicas of files, and cache
additional copies of popular files.
In the PAST system, storage nodes and files are each assigned
uniformly distributed identifiers, and replicas of a file
are stored at nodes whose identifier matches most closely the
files identifier. This statistical assignment of files to storage
nodes approximately balances the number of files stored on
each node. However, non-uniform storage node capacities
and file sizes require more explicit storage load balancing
to permit graceful behavior under high global storage utilization;
likewise, non-uniform popularity of files requires
caching to minimize fetch distance and to balance the query
load.
We present and evaluate PAST, with an emphasis on its
storage management and caching system. Extensive tracedriven
experiments show that the system minimizes fetch
distance, that it balances the query load for popular files,
and that it displays graceful degradation of performance as
the global storage utilization increases beyond 95%.}
}

@inproceedings{vanhorn:criteria:sosp1967,
author = "Earl C. van Horn",
title = "Three criteria for designing computing systems to facilitate debugging",
pages = "7.1--7.5",
month = "October",
crossref = {sosp1967},
}

@inproceedings{draves+brd:continuations:sosp1991,
author = "Richard P. Draves and Brian N. Bershad and Richard F. Rashid and Randall W. Dean",
title = "Using continuations to implement thread management and communication in operating systems",
crossref = {sosp1991},
pages = "122--136",
}

@inproceedings{welsh+cb:seda:sosp2001,
author = "Matt Welsh and David Culler and Eric Brewer",
title = "{SEDA}: {A}n architecture for well-conditioned, scalable {I}nternet services",
crossref = {sosp2001},
pages = "230--243",
}

@inproceedings{vonbehren:capriccio:sosp2003,
author = "Rob von Behrenm and Jeremy Condit and Feng Zhou and George C. Necula and Eric Brewer",
title = "Capriccio: {S}calable threads for {I}nternet services",
crossref = {sosp2003},
pages = "268--281",
}

repository: dataseries/Lintel

repo_name: dataseries/Lintel

gha_license_id: NOASSERTION

gha_language: Perl


text:
@inproceedings{Oommen1990,
 acmid = {99108},
 address = {Charleston, South Carolina},
 author = {B. J. Oommen and R. S. Valiveti and J. Zgierski},
 booktitle = {Proc. of the Third International Conf. on Industrial Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE-90)},
 doi = {10.1145/98894.99108},
 isbn = {0-89791-372-8},
 location = {Charleston, South Carolina, USA},
 month = {July},
 note = {July 15-18},
 numpages = {10},
 owner = {rvaliveti},
 pages = {981-990},
 publisher = {ACM},
 series = {IEA/AIE '90},
 timestamp = {2014.03.11},
 title = {A Fast Learning Automaton Solution to the Keyboard Optimization Problem},
 url = {http://doi.acm.org/10.1145/98894.99108},
 year = {1990}
}

gha_language: TeX


text:
require linux-omap3.inc

COMPATIBLE_MACHINE = "ti811x"

KVER = "2.6.37"
PSPREL = "04.07.00.02"

BRANCH = "ti81xx-master"
# Use literal tags in SRCREV, when available, instead of commit IDs
SRCREV = "v2.6.37_TI811XPSP_04.07.00.02.patch1"

SRC_URI += "file://0001-VideoDecoder-enabling-TVP5158-for-video-capture.patch \
"


text:
require gdb-cross.inc
LICENSE = "GPLv2"

PR = "r1"

SRC_URI[md5sum] = "7dc98022ee96bba5331f195dc8a5491a"
SRC_URI[sha256sum] = "719f2bbd79d55cdd6f5e31b49cec49df71ddb32a4439dba678de0e09330096da"


text:
DESCRIPTION = "The "Specio" distribution provides classes for representing type \
constraints and coercion, along with syntax sugar for declaring them."

SECTION = "libs"
LICENSE = "Artistic-2.0"
PR = "r0"

MAINTAINER=	"Poky <poky@yoctoproject.org>"
HOMEPAGE=	"https://metacpan.org/release/Specio"

LIC_FILES_CHKSUM = "file://${COMMON_LICENSE_DIR}/Artistic-2.0;md5=8bbc66f0ba93cec26ef526117e280266"

SRC_URI = "https://cpan.metacpan.org/authors/id/D/DR/DROLSKY/Specio-0.47.tar.gz"

SRC_URI[md5sum] = "42a6734e5c02836f6d99646167694b96"
SRC_URI[sha256sum] = "f41307f14444f8777e572f27eeb6a964084399e7e382c47c577827ad8a286a1c"
RDEPENDS:${PN} += "devel-stacktrace-perl"
RDEPENDS:${PN} += "eval-closure-perl"
RDEPENDS:${PN} += "module-runtime-perl"
RDEPENDS:${PN} += "mro-compat-perl"
RDEPENDS:${PN} += "role-tiny-perl"
RDEPENDS:${PN} += "sub-quote-perl"
RDEPENDS:${PN} += "test-fatal-perl"
RDEPENDS:${PN} += "try-tiny-perl"
RRECOMMENDS:${PN} += "ref-util-perl"
DEPENDS += "test-needs-perl-native"

S = "${WORKDIR}/Specio-${PV}"

inherit cpan allarch

BBCLASSEXTEND = "native"

repository: rehsack/meta-cpan

repo_name: rehsack/meta-cpan

gha_language: BitBake


text:
% This file was created with JabRef 2.3.1.
% Encoding: Cp1252

@BOOK{Agresti2007,
  title = {An Introduction to Categorical Data Analysis},
  publisher = {Wiley},
  year = {2007},
  author = {Agresti, A.},
  series = {Wiley Series in Probability and Statistics},
  isbn = {9780470114742},
  url = {http://books.google.com.mx/books?id=YRtEiDevAi0C}
}
@Manual{R14,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2014},
    url = {http://www.R-project.org/},
  }
  @ARTICLE{Per10,
  author = {P\'erez Rodr\'iguez, Paulino and Villase\~nor Alva, Jos\'e A.},
  title = {On testing the skew normal hypothesis},
  journal = {Journal of Statistical Planning and Inference},
  year = {2010},
  volume = {140},
  pages = {3148-3159},
  number = {11},
  month = {November}
}

license: Unlicense

repository: FranciscoAriel/LaTeX

repo_name: FranciscoAriel/LaTeX


text:
var args = arguments[0];

if(args.type == 'image'){

}else{
	$.field = Alloy.createWidget('ti.ux.forms.text', args);

	$.field.getView().top = '40dp'; 		//TODO: ugly! set using classes styles

	$.view.add($.field.getView());
}

$.getComponent = function(){
	return $.field;
};

function blurField(e){
	if(e.source === $.view){
		$.field.blur();
	}
}

extension: js


text:
file(REMOVE_RECURSE
  "CMakeFiles/zed_interfaces_generate_messages_nodejs"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/msg/objects.js"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/msg/object_stamped.js"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/srv/start_3d_mapping.js"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/srv/stop_object_detection.js"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/srv/stop_3d_mapping.js"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/srv/stop_svo_recording.js"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/srv/set_pose.js"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/srv/set_led_status.js"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/srv/stop_remote_stream.js"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/srv/toggle_led.js"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/srv/start_object_detection.js"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/srv/reset_tracking.js"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/srv/reset_odometry.js"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/srv/start_remote_stream.js"
  "/home/qi/catkin_qi/devel/share/gennodejs/ros/zed_interfaces/srv/start_svo_recording.js"
)

# Per-language clean rules from dependency scanning.
foreach(lang )
  include(CMakeFiles/zed_interfaces_generate_messages_nodejs.dir/cmake_clean_${lang}.cmake OPTIONAL)
endforeach()

extension: cmake


text:
inherit raw-build

PV = "1.0"

SRC_URI = "file://hoge.c"

do_raw_build() {
	${SCH} gcc hoge.c -o hoge
}

repository: toshiba/meta-eid

repo_name: toshiba/meta-eid


text:
SUMMARY = "SysV nilrt init scripts"
DESCRIPTION = "nilrt distro-specific initscripts to provide basic system functionality."
LIC_FILES_CHKSUM = "file://${COMMON_LICENSE_DIR}/MIT;md5=0835ade698e0bcf8506ecda2f7b4f302"
LICENSE = "MIT"
SECTION = "base"

inherit ptest

DEPENDS += "shadow-native pseudo-native update-rc.d-native niacctbase"

SRC_URI = "\
	file://cleanvarcache \
	file://firewall \
	file://iso3166-translation.txt \
	file://mountconfig \
	file://mountdebugfs \
	file://nicheckbiosconfig \
	file://nicleanefivars \
	file://nicleanstalelinks \
	file://nicreatecpuacctgroups \
	file://nicreatecpusets \
	file://nidisablecstates \
	file://nipopulateconfigdir \
	file://nisetcommitratio \
	file://nisetreboottype \
	file://nisetupkernelconfig \
	file://populateconfig \
	file://run-ptest \
	file://test-nisetcommitratio-common.sh \
	file://test-nisetcommitratio-system \
	file://test-nisetcommitratio-unit \
	file://test-safemode-runlevel-init \
	file://wirelesssetdomain \
"

S = "${WORKDIR}"

do_install () {
	install -d ${D}${sysconfdir}/init.d/
	install -m 0755 ${WORKDIR}/cleanvarcache         ${D}${sysconfdir}/init.d
	install -m 0755 ${WORKDIR}/mountconfig           ${D}${sysconfdir}/init.d
	install -m 0755 ${WORKDIR}/mountdebugfs          ${D}${sysconfdir}/init.d
	install -m 0755 ${WORKDIR}/nicheckbiosconfig     ${D}${sysconfdir}/init.d
	install -m 0755 ${WORKDIR}/nicleanefivars        ${D}${sysconfdir}/init.d
	install -m 0755 ${WORKDIR}/nicleanstalelinks     ${D}${sysconfdir}/init.d
	install -m 0755 ${WORKDIR}/nicreatecpuacctgroups ${D}${sysconfdir}/init.d
	install -m 0755 ${WORKDIR}/nicreatecpusets       ${D}${sysconfdir}/init.d
	install -m 0755 ${WORKDIR}/nidisablecstates      ${D}${sysconfdir}/init.d
	install -m 0755 ${WORKDIR}/nipopulateconfigdir   ${D}${sysconfdir}/init.d
	install -m 0755 ${WORKDIR}/nisetcommitratio      ${D}${sysconfdir}/init.d
	install -m 0755 ${WORKDIR}/nisetreboottype       ${D}${sysconfdir}/init.d
	install -m 0755 ${WORKDIR}/nisetupkernelconfig   ${D}${sysconfdir}/init.d
	install -m 0755 ${WORKDIR}/populateconfig        ${D}${sysconfdir}/init.d
	install -m 0755 ${WORKDIR}/wirelesssetdomain     ${D}${sysconfdir}/init.d

	install -m 0755 ${WORKDIR}/firewall              ${D}${sysconfdir}/init.d
	# Substitute configfs paths
	sed -i 's|^IPTABLES_CONF=.*$|IPTABLES_CONF=/etc/natinst/share/iptables.conf|g' ${D}${sysconfdir}/init.d/firewall
	sed -i 's|^IP6TABLES_CONF=.*$|IP6TABLES_CONF=/etc/natinst/share/ip6tables.conf|g' ${D}${sysconfdir}/init.d/firewall
	# sanity check: break build if new _CONF vars exist which aren't substituted above
	! egrep '^[a-zA-Z0-9]*_CONF=.*$' ${D}${sysconfdir}/init.d/firewall | egrep -v '^(IPTABLES_CONF)|(IP6TABLES_CONF)=.*$'

	update-rc.d -r ${D} cleanvarcache         start 38 0 6 S .
	update-rc.d -r ${D} firewall              start 39 S .
	update-rc.d -r ${D} mountconfig           start 35 S .
	update-rc.d -r ${D} mountdebugfs          start 82 S .
	update-rc.d -r ${D} nicheckbiosconfig     start 99 4 5 .
	update-rc.d -r ${D} nicleanefivars        start 10 S .
	update-rc.d -r ${D} nicleanstalelinks     start 5  S .
	update-rc.d -r ${D} nicreatecpuacctgroups start 2  4 5 .
	update-rc.d -r ${D} nicreatecpusets       start 1  4 5 .
	update-rc.d -r ${D} nidisablecstates      start 2 3 4 5 S .
	update-rc.d -r ${D} nipopulateconfigdir   start 35 S .
	update-rc.d -r ${D} nisetcommitratio      start 99 S .
	update-rc.d -r ${D} nisetreboottype       stop  55 6 .
	update-rc.d -r ${D} nisetupkernelconfig   start 3  5 .
	update-rc.d -r ${D} populateconfig        start 35 S . start 30 0 6 .
	update-rc.d -r ${D} wirelesssetdomain     start 36 S .

	install -d ${D}${sysconfdir}/natinst
	install -m 0644 ${WORKDIR}/iso3166-translation.txt ${D}${sysconfdir}/natinst

}

pkg_postinst_ontarget:${PN} () {
	# Make sure /boot is mounted so that fw_printenv is usable
	if /sbin/fw_printenv TargetClass > /dev/null 2>&1; then
		mountstate=1
	else
		mountstate=0
		mount /boot || mountstate=1
	fi

	# Get target class, may be empty-string on VMs
	class="`/sbin/fw_printenv -n TargetClass || true`"

	# Use persistent names on PXI, not on any other targets
	if [ "$class" != "PXI" -a "$class" != "USRP Stand-Alone Devices" ]; then
		touch /etc/udev/rules.d/80-net-name-slot.rules
	fi

	# Enable core dumps on PXI, not on any other targets
	[ "$class" = "PXI" ] && echo "* soft core unlimited" > /etc/security/limits.d/allow-core-dumps.conf

	# Restore the original state of /boot
	[ $mountstate == 0 ] && umount /boot || true
}

do_install_ptest () {
	cp ${WORKDIR}/test-nisetcommitratio-* ${D}${PTEST_PATH}/
	cp ${WORKDIR}/test-safemode-runlevel-init ${D}${PTEST_PATH}/
}

# /etc/init.d/populateconfig invokes wpa-supplicant.ipk scripts.
RDEPENDS:${PN} += "\
	bash \
	niacctbase \
	update-rc.d \
	wpa-supplicant \
"
RDEPENDS:${PN}-ptest += "bash"

repository: ni/meta-nilrt

repo_name: ni/meta-nilrt

branch_name: refs/heads/nilrt/master/kirkstone

gha_language: Shell


text:
package argotunnel

const (
	endpointKind = "endpoint"
	ingressKind  = "ingress"
	secretKind   = "secret"
	serviceKind  = "service"
)

type resource struct {
	name      string
	namespace string
}

extension: go


text:
body {
  background-color: #D7DBDD;
  text-align: center;
}

.center_header {
  font-size: 1.8em;
}

.about_center_header {
  font-size: 1em;
}

.seperator_horizontal {
  border-bottom-style: solid;
  border-color: #45B39D;
  margin: .5em;
}

.div_green {
  color: green;
}

.div_red {
  color: red;
}

.div_blue {
  color: blue;
}

.header_section {
  font-size: 1.1em;
  margin: .5em;
  height: 20px;
  border-bottom-style: solid;
  border-color: #45B39D;
}

.resume_contact {
  font-size: 1.3em;
}

.resume_paragraph {
  margin: .5em;
  font-size: 1.1em;
}

.span_red {
  color: red;
}

.resume_list {
  margin: .5em;
  font-size: 0.9em;
}

.section_center {
  text-align: center;
}
.resume_horizontal_list ul li {
  display: inline;
}

repository: Xamdes/resume-site

repo_name: Xamdes/resume-site

extension: css


text:
require gtkmm.inc

PR = "r0"

# Hack! Remove once gtkmm likes libtool 2x
do_configure() {
	gnu-configize
	oe_runconf
}


text:
include(@CMAKE_HELP_DIR@/functions.cmake)

dynamic_lib_file_ending(DYNAMIC_LIB_FILE_ENDING)

# Remove all libs
file(GLOB CURRENT_FILES @RUNTIME_CONTEXT@/*.${DYNAMIC_LIB_FILE_ENDING})
list(REMOVE_ITEM CURRENT_FILES "app_common_video.${DYNAMIC_LIB_FILE_ENDING}")

if (CURRENT_FILES)
    file(REMOVE ${CURRENT_FILES})
endif ()

# Copy fltk libs
file(GLOB LIBRARY_FILES @FLTK_BIN_DIR@/*.${DYNAMIC_LIB_FILE_ENDING})
file(COPY ${LIBRARY_FILES} DESTINATION @RUNTIME_CONTEXT@)

# Copy tello libs
file(GLOB LIBRARY_FILES @TELLO_BIN_DIR@/*.${DYNAMIC_LIB_FILE_ENDING})
file(COPY ${LIBRARY_FILES} DESTINATION @RUNTIME_CONTEXT@)

# Copy ffmpeg libs
file(GLOB LIBRARY_FILES @FFMPEG_BIN_DIR@/*.${DYNAMIC_LIB_FILE_ENDING})
file(COPY ${LIBRARY_FILES} DESTINATION @RUNTIME_CONTEXT@)

# Copy opencv libs
file(GLOB LIBRARY_FILES @OPENCV_BIN_DIR@/*.${DYNAMIC_LIB_FILE_ENDING})
file(COPY ${LIBRARY_FILES} DESTINATION @RUNTIME_CONTEXT@)

gha_language: C


text:
@inproceedings{koenig2021-roomcanvas,
 author = {K\"{o}nig, Bastian and Limberger, Daniel and Klimke, Jan and Hagedorn, Benjamin and D\"{o}llner, J\"{u}rgen},
 title = {RoomCanvas: A Visualization System for Spatiotemporal Temperature Data in Smart Homes},
 booktitle = {Proceedings of the 23rd EG Conference on Visualization},
 series = {EuroVis '20},
 year = {2021},
 publisher = {EG}
}

repository: jchrisli/portfolio

repo_name: jchrisli/portfolio


text:
DESCRIPTION = "musicxml2midi provides an element that converts MusicXML in to MIDI format"
LICENSE = "LGPLv2"

DEPENDS = "gstreamer libxml2"

inherit autotools

PV = "0.10.0.1"
PR_append = "+gitr${SRCPV}"
SRCREV = "7d7453fdbe81653c203db9c29cb0bfc9e06c6caa"
SRC_URI = "git://github.com/Elleo/${PN}.git;protocol=git"

S = "${WORKDIR}/git"

FILES_${PN}-dbg += "${libdir}/gstreamer-*/.debug"
FILES_${PN}-dev += "${libdir}/gstreamer-*/*.la"
FILES_${PN}-static += "${libdir}/gstreamer-*/*.a"
FILES_${PN} += "${libdir}/gstreamer-*/*.so"

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty


text:
@inproceedings{Anand2017a,
 abstract = {New extensions to Segment Routing are introduced here that allow for an end-to-end path to include optical transport network segments that steer packets across optical networks for maximal performance with minimal operational changes.},
 author = {Madhukar Anand and Ramesh Subrahmaniam and Soumya Roy and Radhakrishna Valiveti},
 booktitle = {Optical Fiber Communication Conference},
 doi = {10.1364/OFC.2017.Th1I.3},
 journal = {Optical Fiber Communication Conference},
 pages = {Th1I.3},
 publisher = {Optical Society of America},
 title = {Extending Segment Routing into Optical Networks},
 url = {http://www.osapublishing.org/abstract.cfm?URI=OFC-2017-Th1I.3},
 year = {2017}
}


text:
require mingw-w32api.inc

PR = "r1"

SRC_URI = "${SOURCEFORGE_MIRROR}/mingw/w32api-${PV}-mingw32-src.tar.gz"

S = "${WORKDIR}/w32api-${PV}-mingw32"

SRC_URI[md5sum] = "8eae788a09a589414b83adc91bb0c069"
SRC_URI[sha256sum] = "02cec615f8cfc94786ea9c1f41645cf43cc2414ca36b92fdd041304723bf2fc4"


text:
@inproceedings{2017-GFK+,
 abstract = {The k-truss of a graph is a subgraph such that each edge is tightly connected to the remaining elements in the k-truss. The k-truss of a graph can also represent an important community in the graph. Finding the k-truss of a graph can be done in a polynomial amount of time, in contrast finding other subgraphs such as cliques. While there are numerous formulations and algorithms for finding the maximal k-truss of a graph, many of these tend to be computationally expensive and do not scale well. Many algorithms are iterative and use static graph triangle counting in each iteration of the graph. In this work we present a novel algorithm for finding both the k-truss of the graph (for a given k), as well as the maximal k-truss using a dynamic graph formulation. Our algorithm has two main benefits. 1) Unlike many algorithms that rerun the static graph triangle counting after the removal of non-conforming edges, we use a new dynamic graph formulation that only requires updating the edges affected by the removal. As our updates are local, we only do a fraction of the work compared to the other algorithms. 2) Our algorithm is extremely scalable and is able to concurrently detect deleted triangles in contrast to past sequential approaches. While our algorithm is architecture independent, we show a CUDA based implementation for NVIDIA GPUs. In numerous instances, our new algorithm is anywhere from 100X-10000X faster than the Graph Challenge benchmark. Furthermore, our algorithm shows significant speedups, in some cases over 70X, over a recently developed sequential and highly optimized algorithm.},
 address = {Los Alamitos, CA},
 author = {Oded Green and James Fox and Euna Kim and Federico Busato and Nicola Bombieri and Kartik Lakhotia and Shijie Zhou and Shreyas G. Singapura and Hanqing Zeng and Rajgopal Kannan and Viktor K. Prasanna and David A. Bader},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/bib/conf/hpec/GreenFKBBLZSZKP17},
 booktitle = {The 21st Annual IEEE High Performance Extreme Computing Conference, HPEC 2017, Waltham, MA, USA, September 12-14, 2017},
 doi = {10.1109/HPEC.2017.8091038},
 groups = {Edited},
 note = {Graph Challenge Innovation Award},
 pages = {1--7},
 publisher = {IEEE Computer Society},
 timestamp = {Fri, 02 Nov 2018 00:00:00 +0100},
 title = {Quickly finding a truss in a haystack},
 url = {https://doi.org/10.1109/HPEC.2017.8091038},
 year = {2017}
}


text: set(CIBCMD $<TARGET_FILE:cib> -i pub -o exp -b cib -m Example --value-class CPoint --value-class Dummy)


text:
import React, {Component, PropTypes} from 'react';
import {connect} from 'react-redux';
import Helmet from 'react-helmet';
import {editModule} from 'redux/modules/modules';
import Input from 'react-bootstrap/lib/Input';
import Alert from 'react-bootstrap/lib/Alert';
import { routeActions } from 'react-router-redux';

@connect(
  (state, props) => props,
  {editModule, pushState: routeActions.push}
)
export default class EditModule extends Component {
  static propTypes = {
    editModule: PropTypes.func.isRequired,
    pushState: PropTypes.func.isRequired
  };

  state = {
    moduleName: '',
    moduleText: '',
    imageUploaded: false
  };

  send() {
    let {moduleName, moduleText, imageUploaded} = this.state;
    let {addModule, pushState} = this.props;
    if (moduleName.length > 0) {
      addModule(moduleName, moduleText).then(() => pushState('/modulePreview'));
    } else {
      this.setState({ validationEnabled: true });
    }
  }

  validateModuleName() {
    if (!this.state.validationEnabled) {
      return null;
    }
    let length = this.state.moduleName.length;
    if (length === 0) return 'danger';
  }

  render() {
    let {validationEnabled} = this.state;

    return (

        <Helmet title="Редактировать модуль"/>

Добавить модуль

        <Input type="textarea"
               style={{resize: 'none'}}
               placeholder="Module Name"
               label="Module Name"
               onChange={({target}) => this.setState({ moduleName: target.value })}
        />
        {
          validationEnabled && this.state.moduleName.length === 0 &&
          <Alert bsStyle={this.validateModuleName()}>
            Введите имя
          </Alert>
        }
        <Input type="textarea"
               label="Module Text"
               placeholder="Module Text"
               onChange={({target}) => this.setState({ moduleText: target.value })}
        />
        <button type="button" className="btn btn-primary" onClick={::this.send}>Send!</button>

    );
  }
}


text:
@article{gouillart_belief-propagation_2013,
 abstract = {We consider the reconstruction of a two-dimensional discrete image from a set of tomographic measurements corresponding to the Radon projection. Assuming that the image has a structure where neighbouring pixels have a larger probability of taking the same value, we follow a Bayesian approach and introduce a fast message-passing reconstruction algorithm based on belief propagation. For numerical results, we specialize to the case of binary tomography. We test the algorithm on binary synthetic images with different length scales and compare our results against a more usual convex optimization approach. We investigate the reconstruction error as a function of the number of tomographic measurements, corresponding to the number of projection angles. The belief-propagation algorithm turns out to be more efficient than the convex-optimization algorithm, both in terms of recovery bounds for noise-free projections and reconstruction quality when moderate Gaussian noise is added to the projections.},
 author = {Gouillart, E. and Krzakala, F. and Mézard, M. and Zdeborová, L.},
 doi = {10.1088/0266-5611/29/3/035003},
 file = {IOP Full Text PDF:/Users/florentkrzakala/Zotero/storage/XQZXIY97/Gouillart et al. - 2013 - Belief-propagation reconstruction for discrete tom.pdf:application/pdf},
 issn = {0266-5611},
 journal = {Inverse Problems},
 language = {en},
 month = {February},
 number = {3},
 pages = {035003},
 title = {Belief-propagation reconstruction for discrete tomography},
 url = {https://doi.org/10.1088%2F0266-5611%2F29%2F3%2F035003},
 urldate = {2019-07-01},
 volume = {29},
 year = {2013}
}

repository: krzakala/academic-kickstart

repo_name: krzakala/academic-kickstart


text:
var chai = require('chai')
  , sinon = require('sinon')
  , sinonChai = require('sinon-chai')
  , chaiAsPromised = require('chai-as-promised')

chai.use(chaiAsPromised)
chai.use(sinonChai)

global.sinon = sinon
global.chai = chai
global.expect = require('chai').expect

process.env.NODE_TLS_REJECT_UNAUTHORIZED = 0

repository: devpaul/grunt-devserver

repo_name: devpaul/grunt-devserver

gha_language: JavaScript


text:
@Misc{migot-dussault-stopping-2021,
  author       = {Migot, T. and
                  Dussault, J.-P.},
  title        = {{Stopping.jl} -- A framework to implement iterative algorithms},
  month        = {June},
  year         = {2022},
  doi          = {10.5281/zenodo.6705082},
  howpublished = {\url{https://doi.org/10.5281/zenodo.6705082}}
}

gha_language: Julia


text:
require qt4-embedded.inc

PR = "${INC_PR}.2"

SRC_URI[md5sum] = "9a639aec44a1e4c70040117183d247a3"
SRC_URI[sha256sum] = "05d06b93f95092f1318634fca24f0c2d0a1252c9f1dc2fbb427b07e8ecbb4f39"


text:
@article{wang_neurocognitive_2020,
	title = {Neurocognitive patterns dissociating semantic processing from executive control are linked to more detailed off-task mental time travel},
	volume = {10},
	copyright = {2020 The Author(s)},
	issn = {2045-2322},
	doi = {10.1038/s41598-020-67605-2},
    language = {en},
	number = {1},
	journal = {Scientific Reports},
	author = {Wang, Hao-Ting and Ho, Nerissa Siu Ping and Bzdok, Danilo and Bernhardt, Boris C. and Margulies, Daniel S. and Jefferies, Elizabeth and Smallwood, Jonathan},
	month = jul,
	year = {2020},
	pages = {11904}
}

@article{wang_finding_2020,
	title = {Finding the needle in a high-dimensional haystack: {Canonical} correlation analysis for neuroscientists},
	volume = {216},
	issn = {10538119},
	shorttitle = {Finding the needle in a high-dimensional haystack},
	doi = {10.1016/j.neuroimage.2020.116745},
	language = {en},
	journal = {NeuroImage},
	author = {Wang, Hao-Ting and Smallwood, Jonathan and Mourao-Miranda, Janaina and Xia, Cedric Huchuan and Satterthwaite, Theodore D. and Bassett, Danielle S. and Bzdok, Danilo},
	month = aug,
	year = {2020},
	pages = {116745}
}

@article{wang_patterns_2018,
	title = {Patterns of thought: {Population} variation in the associations between large-scale network organisation and self-reported experiences at rest},
	volume = {176},
	copyright = {All rights reserved},
	issn = {10538119},
	doi = {10.1016/j.neuroimage.2018.04.064},
	number = {1},
	journal = {NeuroImage},
	author = {Wang, Hao-Ting and Bzdok, Danilo and Margulies, Daniel S. and Craddock, R. Cameron and Milham, Michael P. and Jefferies, Elizabeth and Smallwood, Jonathan},
	month = aug,
	year = {2018},
	pages = {518--527}
}

@article{wang_dimensions_2018,
	title = {Dimensions of {Experience}: {Exploring} the {Ontology} of the {Wandering} {Mind}},
	volume = {29},
	issn = {0956-7976},
	doi = {10.1177/0956797617728727},
	number = {1},
	journal = {Psychological Science},
	author = {Wang, Hao-Ting and Poerio, Giulia L. and Murphy, Charlotte E. and Bzdok, Danilo and Jefferies, Elizabeth and Smallwood, Jonathan},
	month = nov,
	year = {2018},
	pages = {56--71}
}
@article{turnbull_left_2019,
	title = {Left dorsolateral prefrontal cortex supports context-dependent prioritisation of off-task thought},
	volume = {10},
	issn = {2041-1723},
	doi = {10.1038/s41467-019-11764-y},
	language = {en},
	number = {1},
	journal = {Nature Communications},
	author = {Turnbull, A. and Wang, H. T. and Murphy, C. and Ho, N. S. P. and Wang, X. and Sormaz, M. and Karapanagiotidis, T. and Leech, R. M. and Bernhardt, B. and Margulies, D. S. and Vatansever, D. and Jefferies, E. and Smallwood, J.},
	month = dec,
	year = {2019}
}

@article{sormaz_default_2018,
	title = {Default mode network can support the level of detail in experience during active task states},
	volume = {115},
	issn = {0027-8424, 1091-6490},
	doi = {10.1073/pnas.1721259115},
	language = {en},
	number = {37},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Sormaz, Mladen and Murphy, Charlotte and Wang, Hao-ting and Hymers, Mark and Karapanagiotidis, Theodoros and Poerio, Giulia and Margulies, Daniel S. and Jefferies, Elizabeth and Smallwood, Jonathan},
	month = sep,
	year = {2018},
	pmid = {30150393},
	pages = {9318--9323}
}

repository: htwangtw/CV

repo_name: htwangtw/CV


text:
SUMMARY = "SIP is a C++/Python Wrapper Generator"
AUTHOR = "Phil Thompson"
HOMEPAGE = "http://www.riverbankcomputing.co.uk/sip"
SECTION = "devel"
LICENSE = "GPLv2"
LIC_FILES_CHKSUM = "file://../LICENSE-GPL2;md5=e91355d8a6f8bd8f7c699d62863c7303"

# riverbankcomputing is upstream, but keeps only latest version, sf usually have few older
#SRC_URI = "http://www.riverbankcomputing.com/static/Downloads/sip4/sip-${PV}.tar.gz"
SRC_URI = "${SOURCEFORGE_MIRROR}/project/pyqt/sip/sip-${PV}/sip-${PV}.tar.gz"
SRC_URI[md5sum] = "1ca96f90a7496a3c9664fdaa76205719"
SRC_URI[sha256sum] = "a55a2324a46ab42e42ae57c52ef06583b17d25c987973fe2e7ff2e8a649294ce"
S = "${WORKDIR}/sip-${PV}/sipgen"

BBCLASSEXTEND = "native"

inherit qmake5 native python-dir

EXTRA_QMAKEVARS_POST += "DESTDIR=${S} CONFIG=console"

export BUILD_SYS
export HOST_SYS
export STAGING_LIBDIR
export STAGING_INCDIR

PYTHON_SITEPACKAGES_DIR = "${libdir_native}/${PYTHON_DIR}/site-packages"

do_configure_prepend() {
    pwd
    cat ${S}/sipgen.sbf | sed s,target,TARGET, | sed s,sources,SOURCES, | sed s,headers,HEADERS, > ${S}/sipgen.pro
}

do_install_append() {
    install -d ${D}${bindir_native}
    install -m 0755 ${S}/sip ${D}${bindir_native}/sip
    # python-pyqt expects sip4
    ln -sf sip ${D}${bindir_native}/sip4
    cd ${WORKDIR}/sip-${PV} && python configure.py
    install -d ${D}${PYTHON_SITEPACKAGES_DIR}
    install -m 0755 sip*.py ${D}${PYTHON_SITEPACKAGES_DIR}
}

sysroot_stage_all_append() {
    sysroot_stage_dir ${D}${bindir_native} ${SYSROOT_DESTDIR}${bindir}
    sysroot_stage_dir ${D}${PYTHON_SITEPACKAGES_DIR} ${SYSROOT_DESTDIR}/${libdir}/${PYTHON_DIR}/site-packages
}

repository: GENIVI/genivi-dev-platform

repo_name: GENIVI/genivi-dev-platform


text:
const path = require('path');
const HtmlWebpackPlugin = require('html-webpack-plugin');
const CleanWebpackPlugin = require('clean-webpack-plugin');

module.exports = {
    entry: './src/index.js',

    output: {
        filename: 'bundle.js',
        path: path.resolve(__dirname, 'target'),
    },

    module: {
        rules: [
            {test: /.js$/, exclude: /node_modules/, use: "babel-loader"},
            {test: /\.(eot|svg|ttf|woff|woff2)$/, use: "url-loader"},
            {test: /\.jade/, use: ["raw-loader", "pug-html-loader"]}
        ]
    },

    plugins: [
        new CleanWebpackPlugin(['target']),
        new HtmlWebpackPlugin({
            template: 'src/templates/index.jade'
        })
    ]
};

repository: hlipinski/webpack-example

repo_name: hlipinski/webpack-example


text:
package main

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	client_key = `<REDACTED_PRIVATE_KEY>`
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	ca_key = `<REDACTED_PRIVATE_KEY>`
	ca_srl      = `9F107575B81EB250`
	server_name = `16699.offer365.io`

	clusterToken = "e7eb40f8124fadf8b7c215b1c73f5dad764af8aef2cbbab054740db0e533a486"
	embedAuthPwd = "2bcd64c640eb72d2bf101b5af2812ec9d0031aaca5392476665ab86acad0d7da"

	storeLicenseKey            = "/dc626f79986c9c8cfd1c50b22863c9225c80f77d6ce26f3b1fc9bbbfd4b38ba1/ae20dff7a522c8d869fee021712f2eac6f33c34fa853c699e82b65c98a150858"
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	storeHashSalt1             = "86b309d437a73627d1ffb64d8bec1417601c28b2d64abb6929680bfe080f3063"
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	grpcUser = "92966d2fe713b5479d554ae7a7d2f206675278324aa8c22bf443de8b5302d1de"
	grpcPwd  = "ce9ab7e845f10ea94d078ac37751c015ba6e78b56b8b8a9f227fdce7f19ae0c1"
)

repository: hbinr/odin

repo_name: hbinr/odin


text:
@incollection{Losson2010c,
abstract = {Mono-CCD color cameras acquire only one color component at each pixel by means of their color filter array (CFA) covering the CCD sensor. To obtain a color image, a procedure - called demosaicing - is then necessary to estimate the other two missing color components at each pixel. This chapter deals with the quality of color images generated in such a way. We attempt to determine which demosaicing method provides the best results according to several comparison criteria, particularly for subsequent needs of color image analyses like edge detection.},
author = {Losson, O. and Macaire, L. and Yang, Y.},
booktitle = {Advances in Imaging and Electron Physics},
doi = {10.1016/S1076-5670(10)62005-8},
file = {:Users/kelsolaar/Google Drive/Documents/Mendeley Desktop/Losson, Macaire, Yang - 2010 - Comparison of Color Demosaicing Methods.pdf:pdf},
isbn = {9780123813169},
issn = {10765670},
number = {C},
pages = {173--265},
title = {{Comparison of Color Demosaicing Methods}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1076567010620058},
volume = {162},
year = {2010}
}
@inproceedings{Malvar2004a,
abstract = {
This paper introduces a new interpolation technique for demosaicing of color
images produced by single-CCD digital cameras. We show that the proposed simple
linear filter can lead to an improvement in PSNR of over 5.5 dB when compared to
bilinear demosaicing, and about 0.7 dB improvement in R and B interpolation when
compared to a recently introduced linear interpolator. The proposed filter also
outperforms most nonlinear demosaicing algorithms, without the artifacts due to
nonlinear processing, and a much reduced computational complexity.
},
author = {Malvar, Henrique S and He, Li-Wei and Cutler, Ross and Way, One Microsoft},
booktitle = {International Conference of Acoustic, Speech and Signal Processing},
file = {:Users/kelsolaar/Google Drive/Documents/Mendeley Desktop/Malvar et al. - 2004 - High-Quality Linear Interpolation for Demosaicing of Bayer-Patterned Color Images.pdf:pdf},
month = {may},
pages = {5--8},
publisher = {Institute of Electrical and Electronics Engineers, Inc.},
title = {{High-Quality Linear Interpolation for Demosaicing of Bayer-Patterned Color Images}},
url = {http://research.microsoft.com/apps/pubs/default.aspx?id=102068},
year = {2004}
}
@article{Menon2007c,
abstract = {Most digital cameras use a color filter array to capture the colors of the scene. Downsampled versions of the red, green, and blue components are acquired, and an interpolation of the three colors is necessary to reconstruct a full representation of the image. This color interpolation is known as demosaicing. The most effective demosaicing techniques proposed in the literature are based on directional filtering and a posteriori decision. In this paper, we present a novel approach to this reconstruction method. A refining step is included to further improve the resulting reconstructed image. The proposed approach requires a limited computational cost and gives good performance even when compared to more demanding techniques.},
author = {Menon, Daniele and Andriani, Stefano and Calvagno, Giancarlo},
doi = {10.1109/TIP.2006.884928},
file = {:Users/kelsolaar/Google Drive/Documents/Mendeley Desktop/Menon, Andriani, Calvagno - 2007 - Demosaicing With Directional Filtering and a posteriori Decision.pdf:pdf},
isbn = {2003506969},
issn = {1057-7149},
journal = {IEEE Transactions on Image Processing},
keywords = {Bayer pattern,Color filter array (CFA) interpolation,Demosaicing,Digital cameras,Directional filtering},
month = {jan},
number = {1},
pages = {132--141},
pmid = {17283772},
title = {{Demosaicing With Directional Filtering and a posteriori Decision}},
url = {http://ieeexplore.ieee.org/document/4032820/},
volume = {16},
year = {2007}
}

repository: kimkuang/colour-demosaicing

repo_name: kimkuang/colour-demosaicing


text:
@article{Mnih13,
	author    = {Volodymyr Mnih and
	Koray Kavukcuoglu and
	David Silver and
	Alex Graves and
	Ioannis Antonoglou and
	Daan Wierstra and
	Martin A. Riedmiller},
	title     = {Playing Atari with Deep Reinforcement Learning},
	journal   = {CoRR},
	volume    = {abs/1312.5602},
	year      = {2013},
	url       = {http://arxiv.org/abs/1312.5602},
	timestamp = {Wed, 01 Apr 2015 20:06:15 +0200},
	biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/MnihKSGAWR13},
	bibsource = {dblp computer science bibliography, http://dblp.org}
}

@article{yudeep,
	title={Deep Reinforcement Learning for Simulated Autonomous Vehicle Control},
	author={Yu, April and Palefsky-Smith, Raphael and Bedi, Rishi}
}

@inproceedings{chen2015deepdriving,
	title={Deepdriving: Learning affordance for direct perception in autonomous driving},
	author={Chen, Chenyi and Seff, Ari and Kornhauser, Alain and Xiao, Jianxiong},
	booktitle={Proceedings of the IEEE International Conference on Computer Vision},
	pages={2722--2730},
	year={2015}
}

@article{jia2014caffe,
	Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
	Journal = {arXiv preprint arXiv:1408.5093},
	Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
	Year = {2014}
}