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      Vitt, Thorsten  and
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@phdthesis{dorigo-thesis,
	author = {Dorigo, M.},
	title = "{Optimization, Learning and Natural Algorithms}",
	school = "Politecnico di Milano, Italy",
	year = "1992"
}

@article{ant-algorithms-dorigo,
	author = {Dorigo, M. and Di Caro, G.},
	title = "{Ant Algorithms for Discrete Optimization}",
	journal  = "Artificial Life",
	volume = "5",
	issue = "2",
	pages = "137-172",
	year = "1999"
}

@article{socio-evolution-algorithm,
	author = {Kumar, M. and Kulkarni, A. and Satapathy, S.},
	title = "Socio evolution \& learning optimization algorithm: A socio-inspired optimization methodology",
	journal = "Future Generation Computer Systems",
	volume = "81",
	pages = "252-272",
	year = "2018",
	month = "April"
}

@article{poa-article,
	author = {Borji, A. and Hamidi, M.},
	year = {2008},
	month = {January},
	pages = {},
	title = "{A new approach to global optimization motivated by parliamentary political competitions}",
	volume = {5},
	journal = {International journal of innovative computing, information \& control: IJICIC}
}

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	author = {Sanchez, E. and Mange, D. and Sipper, M. and Tomassini, M. and Perez-Uribe, A. and Stauffer, A.},
	title = "{Phylogeny, ontogeny, and epigenesis: Three sources of biological inspiration for softening hardware}",
	booktitle = "1st International Conference on Evolvable Systems, ICES",
	year = "1996"
}

@conference{ica-conference,
	author = {Atashpaz-Gargari, E. and Lucas, C.},
	year = {2007},
	month = {October},
	pages = {4661-4667},
	title = "{Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition}",
	volume = {7},
	booktitle = {2007 IEEE Congress on Evolutionary Computation, CEC 2007}
}

@conference{aso-conference,
	author = {Ahmadi Javid, A.},
	year = {2011},
	month = {June},
	pages = {2586-2592},
	title = "{Anarchic Society Optimization: A human-inspired method}",
	booktitle = {2011 IEEE Congress of Evolutionary Computation, CEC 2011}
}

@article{bioinspired-systems-article,
	author = {Sanchez, E. and Mange, D. and Sipper, M. and Tomassini, M. and Perez-Uribe, A. and Stauffer, A.},
	title = "{A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems}",
	journal = "IEEE Xplore",
	year = "1997"
}

@article{aso-article,
	author = {Shayeghi, H. and Dadashpour, J.},
	year = {2012},
	month = {August},
	pages = {199-207},
	title = "{Anarchic Society Optimization Based PID Control of an Automatic Voltage Regulator (AVR) System}",
	volume = {2},
	journal = {Electrical and Electronic Engineering}
}

@article{ia-article,
	author = {Ting Huan, T. and Kulkarni, A. and Kanesan, J. and Chuah, J. and Abraham, A.},
	year = {2016},
	month = {June},
	pages = {},
	title = "{Ideology algorithm: a socio-inspired optimization methodology}",
	journal = {Neural Computing and Applications}
}

@article{slc-article,
	author = {Moosavian, N. and Kasaee Roodsari, B.},
	year = {2014},
	month = {August},
	pages = {},
	title = "{Soccer League Competition Algorithm: A Novel Meta-heuristic Algorithm For Optimal Design of Water Distribution Networks}",
	volume = {17},
	journal = {Swarm and Evolutionary Computation}
}

@article{slc-comparison,
	author = {Moosavian, N. and Moosavian, H.},
	year = {2017},
	month = {July},
	pages = {926-936},
	title = "{Testing Soccer League Competition Algorithm in Comparison with Ten Popular Meta-heuristic Algorithms for Sizing Optimization of Truss Structures}",
	volume = {30},
	journal = {IJE TRANSACTIONS A: Basics}
}

@article{slc-knapsack,
	author = {Moosavian, N.},
	year = {2014},
	month = {October},
	pages = {},
	title = "{Soccer league competition algorithm for solving knapsack problems}",
	volume = {20},
	journal = {Swarm and Evolutionary Computation}
}

@article{slc-nonlinear-eq,
	author = {Moosavian, N. and Kasaee Roodsari, B.},
	year = {2014},
	month = {January},
	pages = {7-16},
	title = "{Soccer League Competition Algorithm, a New Method for Solving Systems of Nonlinear Equations}",
	volume = {4},
	journal = {International Journal of Intelligence Science}
}

@article{pso-article,
	author = {Kennedy, J. and Eberhart, R.},
	year = {1995},
	pages = {1942-1948},
	title = "{Particle Swarm Optimization}",
	volume = {4},
	journal = {IEEE Intl. Conf. on Neural Networks}
}

@article{roulette-wheel-selection,
	author = {Kumar, R. and Jyotishree},
	year = {2012},
	month = {January},
	pages = {365-370},
	title = "{Blending Roulette Wheel Selection \& Rank Selection in Genetic Algorithms}",
	journal = {International Journal of Machine Learning and Computing}
}

@chapter{aso-chapter,
	author = {Bozorgi, A. and Bozorg-Haddad, O. and Chu, X.},
	year = {2018},
	month = {July},
	pages = {31-38},
	title = "{Anarchic Society Optimization (ASO) Algorithm}",
	isbn = {978-981-10-5220-0},
	booktitle = {Studies in Computational Intelligence}
}

@chapter{sea-chapter,
	author = {Xu, Y. and Cui, Z. and Zeng, J.},
	year = {2010},
	month = {January},
	pages = {583-590},
	title = "{Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems}",
	volume = {6466},
	booktitle = {Lecture Notes in Computer Science}
}

@book{sea-book,
	author = {Panigrahi, B. and Das, S. and Suganthan, P. and Dash, S.},
	year = {2010},
	month = {January},
	pages = {},
	title = {Swarm, evolutionary, and memetic computing. First international conference on swarm, evolutionary, and memetic computing, SEMCCO 2010, Chennai, India, December 16–18, 2010. Proceedings},
	volume = {6466},
	booktitle = {Lecture Notes in Computer Science}
}

@misc{scopus-website,
	title = "{Elsevier Scopus Database}",
	note = "\url{https://www.scopus.com}"
}

@misc{research-gate-website,
	title = "{ResearchGate Scientific Knowledge Database}",
	note = "\url{https://www.researchgate.net}"
}

@misc{vscode-github,
	title = "{Visual Studio Code - Open Source, repositorio de GitHub}",
	note = "\url{https://github.com/Microsoft/vscode}"
}

@misc{texstudio,
	title = "{TeXstudio, página oficial}",
	note = "\url{https://www.texstudio.org}"
}

@misc{cluster-hercules,
	title = "{CITIC-UGR Sala de Servidores y Clústeres de Computadores}",
	note = "\url{http://citic.ugr.es/pages/informacion_general/equipamiento/cluster}"
}

@misc{ec-bestiary-github,
	title = "{Evolutionary Computation Bestiary, repositorio de GitHub}",
	note = "\url{https://github.com/fcampelo/EC-Bestiary}"
}

@misc{python-3.6-doc,
	title = "{Documentación de Python 3.6}",
	note = "\url{https://docs.python.org/3.6}"
}

@misc{tacolab,
	title = "{TACO Website}",
	note = "\url{https://tacolab.org/}"
}

@misc{numpy-doc,
	title = "{Documentación de NumPy}",
	note = "\url{http://www.numpy.org}"
}

@misc{repositorio-tfg,
	title = "{Repositorio del proyecto en GitHub}",
	note = "\url{https://github.com/JJSrra/Research-SocioinspiredAlgorithms}"
}

@misc{ia-github,
	title = "{Repositorio del Ideology Algorithm en GitHub}",
	note = "\url{https://github.com/NestorRV/IdeologyAlgorithm}"
}

@misc{pso-github,
	title = "{Repositorio del Particle Swarm Optimization en GitHub}",
	note = "\url{https://github.com/nathanrooy/Particle-Swarm-Optimization-with-Python}"
}

@misc{ica-matlab,
	title = "{Imperialist Competitive Algorithm (ICA), File Exchange, MATLAB Central}",
	note = "\url{https://www.mathworks.com/matlabcentral/fileexchange/22046-imperialist-competitive-algorithm-ica}"
}

@misc{slc-matlab,
	title = "{Soccer League Competition (SLC) Algorithm For Discrete Problems, File Exchange, MATLAB Central}",
	note = "\url{https://www.mathworks.com/matlabcentral/fileexchange/56480-soccer-league-competition-slc-algorithm-for-discrete-problems}"
}

@misc{cec2005-website,
	title = "{Special Session on Real-Parameter Optimization at CEC-05, Edinburgh}",
	note = "\url{http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC-05/CEC05.htm}"
}

@article{cec2005-functions,
	author = {Suganthan, P. and Hansen, N. and Liang, J. and Deb, K. and Chen, Y. and Auger, A. and Tiwari, S.},
	year = {2005},
	month = {January},
	pages = {},
	title = "{Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization}",
	volume = {341-357},
	journal = {Natural Computing}
}

@article{memetic-conference,
	author = {Molina, D. and Lozano, M. and García-Martínez, C. and Herrera, F.},
	year = {2008},
	month = {October},
	pages = {58-71},
	title = {Memetic Algorithm for Intense Local Search Methods Using Local Search Chains},
	volume = {5296},
	journal = {Hybrid Metaheuristics: 5th International Workshop}
}

repository: JJSrra/Documentacion-TFG

repo_name: JJSrra/Documentacion-TFG


text:
include iptstate.inc

PR = "${INC_PR}.0"

SRC_URI += "file://iptstate-use-ldflags.patch"

SRC_URI[iptstate-2.2.2.md5sum] = "b3f2e89ef38d6e8a85c8ab88a9c514d8"
SRC_URI[iptstate-2.2.2.sha256sum] = "2d9654a30a1e22159d93b2988c140571d048d08370b4869b1cb91562c29e1c39"

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty

branch_name: HEAD

extension: bb


text:
@ARTICLE{li2019d3m,
  author={K. {Li} and K. {Tang} and T. {Wu} and Q. {Liao}},
  journal={IEEE Access},
  title={D3M: A Deep Domain Decomposition Method for Partial Differential Equations},
  year={2020},
  volume={8},
  number={},
  pages={5283-5294},}

repository: akatoshking/qf-lab

repo_name: akatoshking/qf-lab


text:
@inproceedings{DaiKhaYuyetal17,
 author = {Dai, Hanjun and Khalil, Elias B. and Zhang, Yuyu and Dilkina, Bistra and Song, Le},
 booktitle = {Advances in Neural Information Processing Systems (NIPS)},
 keywords = {machine learning,deep learning,embedding,optimization,search,reinforcement learning},
 title = {Learning Combinatorial Optimization Algorithms over Graphs},
 url = {https://papers.nips.cc/paper/7214-learning-combinatorial-optimization-algorithms-over-graphs},
 year = {2017}
}

repository: lyeskhalil/academic-kickstart

repo_name: lyeskhalil/academic-kickstart


text:
@INPROCEEDINGS{ReiherLampe2020Cam2BEV,
  author={L. {Reiher} and B. {Lampe} and L. {Eckstein}},
  booktitle={2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)},
  title={A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird’s Eye View},
  year={2020},
  doi={10.1109/ITSC45102.2020.9294462}
}

gha_language: Python


text:
DESCRIPTION = "A small network printer daemon for embedded situations that passes the job directly to the printer"
HOMEPAGE = "http://p910nd.sourceforge.net/"
SECTION = "console/utils"
LICENSE = "GPL-2.0-only"
LIC_FILES_CHKSUM = "file://LICENSE.md;md5=3d82780e8917b360cbee7b9ec3e40734"

PR = "r2"

# v0.97
SRCREV = "57ebc07ad8723ea4106090536c58c7f7160743e2"
SRC_URI = "git://github.com/kenyapcomau/p910nd;protocol=https;branch=master \
           file://fix-var-lock.patch"

S = "${WORKDIR}/git"

inherit update-rc.d

INITSCRIPT_NAME = "p910nd"
INITSCRIPT_PARAMS = "start 90 2 3 4 5 . stop 60 0 1 6 ."

do_compile () {
    ${CC} ${CFLAGS} ${LDFLAGS} -o p910nd p910nd.c
}

do_install () {
    install -D -m 0755 ${S}/p910nd ${D}${sbindir}/p910nd
    install -D -m 0644 ${S}/aux/p910nd.conf ${D}${sysconfdir}/sysconfig/p910nd.conf
    install -D -m 0644 ${S}/aux/p910nd.init ${D}${sysconfdir}/init.d/p910nd
}

repository: openembedded/meta-openembedded

repo_name: openembedded/meta-openembedded

gha_language: BitBake


text:
body {
    background: #B0D0E1;
    font-family: "Open Sans", sans-serif;
    text-align: center;
}

h1 {
    font-size: 3em;
}

table {
    margin: auto;
}

#roomList table, #roomList th, #roomList td {
    border: 1px solid black;
    border-collapse: collapse;
    padding: 0 1em;
}

#roomList input {
    display: inline;
    border: 0;
    margin-top: 1em;
    padding: 0;
    font-size: 1em;
    overflow: visible;
    cursor: pointer;
    background: transparent;
    color: red;
}

.error {
    color: red;
}

#roomCreateSubmit {
    color: blue;
    margin: auto;
    cursor: pointer;
}

repository: HotelMasters/superHotel

repo_name: HotelMasters/superHotel

branch_name: refs/heads/develop

gha_language: Java


text:
@Article{doblas-miranda_reassessing_2015,
  author = {E. Doblas-Miranda and J. Mart{\a'\i}nez-Vilalta and F. Lloret and A. {\a'A}lvarez and A. {\a'A}vila and F. J. Bonet and L. Brotons and J. Castro and J. Curiel Yuste and M. D{\a'\i}az and P. Ferrandis and E. Garc{\a'\i}a-Hurtado and J. M. Iriondo and T. F. Keenan and J. Latron and J. Llusi{\a`a} and L. Loepfe and M. Mayol and G. Mor{\a'e} and D. Moya and J. Pe{\~n}uelas and X. Pons and R. Poyatos and J. Sardans and O. Sus and V. R. Vallejo and J. Vayreda and J. Retana},
  title = {Reassessing global change research priorities in mediterranean terrestrial ecosystems: how far have we come and where do we go from here?},
  journal = {Global Ecology and Biogeography},
  volume = {24},
  number = {1},
  pages = {25--43},
  year = {2015},
  keywords = {Assessment, atmosphere–biosphere interaction, biodiversity, climate change, factors interaction, fire regime, forest management, land-use change, research gaps, water reserves},
  doi = {10.1111/geb.12224},
  url = {http://onlinelibrary.wiley.com/doi/10.1111/geb.12224/abstract},
  abstract = {Aim

Mediterranean terrestrial ecosystems serve as reference laboratories for the investigation of global change because of their transitional climate, the high spatiotemporal variability of their environmental conditions, a rich and unique biodiversity and a wide range of socio-economic conditions. As scientific development and environmental pressures increase, it is increasingly necessary to evaluate recent progress and to challenge research priorities in the face of global change.

Location

Mediterranean terrestrial ecosystems.

Methods

This article revisits the research priorities proposed in a 1998 assessment.

Results

A new set of research priorities is proposed: (1) to establish the role of the landscape mosaic on fire-spread; (2) to further research the combined effect of different drivers on pest expansion; (3) to address the interaction between drivers of global change and recent forest management practices; (4) to obtain more realistic information on the impacts of global change and ecosystem services; (5) to assess forest mortality events associated with climatic extremes; (6) to focus global change research on identifying and managing vulnerable areas; (7) to use the functional traits concept to study resilience after disturbance; (8) to study the relationship between genotypic and phenotypic diversity as a source of forest resilience; (9) to understand the balance between C storage and water resources; (10) to analyse the interplay between landscape-scale processes and biodiversity conservation; (11) to refine models by including interactions between drivers and socio-economic contexts; (12) to understand forest–atmosphere feedbacks; (13) to represent key mechanisms linking plant hydraulics with landscape hydrology.

Main conclusions

(1) The interactive nature of different global change drivers remains poorly understood. (2) There is a critical need for the rapid development of regional- and global-scale models that are more tightly connected with large-scale experiments, data networks and management practice. (3) More attention should be directed to drought-related forest decline and the current relevance of historical land use.},
  file = {Doblas-Miranda et al_2015_Reassessing global change research priorities in mediterranean terrestrial.pdf:/media/rafap/Data/Documents/Zotero/storage/IDFSJW35/Doblas-Miranda et al_2015_Reassessing global change research priorities in mediterranean terrestrial.pdf:application/pdf},
  shorttitle = {Reassessing global change research priorities in mediterranean terrestrial ecosystems},
  issn = {1466-8238},
  language = {en},
  urldate = {2014-12-05},
  copyright = {© 2014 John Wiley \& Sons Ltd},
  pubtype = {2},
  date = {2015-01-01},
}

repository: rafapoyatos/academic-kickstart

repo_name: rafapoyatos/academic-kickstart


text:
@incollection{menichinelli_il_2013,
	title = {Il societing come pratica collaborativa: tecnologie, spazi e processi di open design},
	copyright = {All rights reserved},
	isbn = {978-88-238-3340-1},
	booktitle = {Societing reloaded. {Pubblici} produttivi e innovazione sociale},
	publisher = {EGEA},
	author = {Menichinelli, Massimo},
	editor = {Arvidsson, Adam and Giordano, Alex},
	year = {2013},
	pages = {105--128}
}


text:
require u-boot-fw-utils_2019.07.bb

SRC_URI = "git://${FSL_ARM_GIT_SERVER}/imx/uboot-imx.git;branch=imx_v2019.04;protocol=ssh"
SRCREV = "f0238679d332f1af2148d467804a93de8f868bd5"

inherit fsl-u-boot-localversion

LOCALVERSION ?= "-${SRCBRANCH}"

repository: kevinior/meta-kobo-clara-hd

repo_name: kevinior/meta-kobo-clara-hd

branch_name: refs/heads/main


text:
@book{kozlov2008,
  title={Combinatorial Algebraic Topology},
  author={Kozlov, D.},
  isbn={9783540719618},
  lccn={2007933072},
  series={Algorithms and Computation in Mathematics},
  url={http://books.google.com.mx/books?id=f3OGV4BR-6QC},
  year={2008},
  publisher={Springer}
}
@book{munkres1984,
  title={Elements of Algebraic Topology},
  author={Munkres, J.R.},
  isbn={9780201627282},
  lccn={84006250},
  series={Advanced book classics},
  url={http://books.google.com.mx/books?id=Qw6m\_xGryPoC},
  year={1984},
  publisher={Perseus Books}
}
@book{herlihy2013,
  title={Distributed Computing Through Combinatorial Topology},
  author={Herlihy, M. and Kozlov, D. and Rajsbaum, S.},
  isbn={9780124047280},
  url={http://books.google.com.mx/books?id=QbKKAAAAQBAJ},
  year={2013},
  publisher={Elsevier Science}
}

repository: fromeroj/iimas

repo_name: fromeroj/iimas


text:
@InProceedings{hildebrandt2018get,
  title = {Get Well Soon! Human Factors’ Influence on Cybersickness After Redirected Walking Exposure in Virtual Reality},
  author = {Julian Hildebrandt and Patric Schmitz and Andr{\'e} {Calero Valdez} and Leif Kobbelt and Martina Ziefle},
  editor = {{TBC}},
  booktitle = {International Conference on Virtual, Augmented and Mixed Reality},
  pages = {82--101},
  year = {2018},
  organization = {Springer, Cham},
  date = {2018-01-01},
}

repository: Sumidu/academic-kickstart

repo_name: Sumidu/academic-kickstart


text:
@PROCEEDINGS{acml14,
  year = {2014},
  editor = {Phung, Dinh and Li, Hang},
  volume = {39},
  conference_number = 6,
  booktitle = {Proceedings of the Sixth Asian Conference on Machine Learning},
  name = {Asian Conference on Machine Learning},
  published = {2015-02-16},
  shortname = {ACML},
  address = {Nha Trang City, Vietnam},
  start = {2014-11-26},
  end = {2014-11-28},
  sections = {preface=Preface|default=Accepted Papers},
}

repository: mlresearch/papersite

repo_name: mlresearch/papersite


text:
@article{kalloch2018semi,
  title={Semi-automated generation of individual computational models of the human head and torso from MR images},
  author={Kalloch, Benjamin and Bode, Jens and Kozlov, Mikhail and Pampel, Andr{\'e} and Hlawitschka, Mario and Sehm, Bernhard and Villringer, Arno and M{\"o}ller, Harald E and Bazin, Pierre-Louis},
  journal={Magnetic resonance in medicine},
  year={2018},
  publisher={Wiley Online Library}
}

@article{lucas2010java,
  title={The Java Image Science Toolkit (JIST) for rapid prototyping and publishing of neuroimaging software},
  author={Lucas, Blake C and Bogovic, John A and Carass, Aaron and Bazin, Pierre-Louis and Prince, Jerry L and Pham, Dzung L and Landman, Bennett A},
  journal={Neuroinformatics},
  volume={8},
  number={1},
  pages={5--17},
  year={2010},
  publisher={Springer}
}

@inproceedings{mcauliffe2001medical,
  title={Medical image processing, analysis and visualization in clinical research},
  author={McAuliffe, Matthew J and Lalonde, Francois M and McGarry, Delia and Gandler, William and Csaky, Karl and Trus, Benes L},
  booktitle={Computer-Based Medical Systems, 2001. CBMS 2001. Proceedings. 14th IEEE Symposium on},
  pages={381--386},
  year={2001},
  organization={IEEE}
}

@article{wolters2006influence,
  title={Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: a simulation and visualization study using high-resolution finite element modeling},
  author={Wolters, Carsten Hermann and Anwander, Alfred and Tricoche, Xavier and Weinstein, D and Koch, Martin A and Macleod, Robert S},
  journal={NeuroImage},
  volume={30},
  number={3},
  pages={813--826},
  year={2006},
  publisher={Elsevier}
}

@article{attene2010lightweight,
  title={A lightweight approach to repairing digitized polygon meshes},
  author={Attene, Marco},
  journal={The visual computer},
  volume={26},
  number={11},
  pages={1393--1406},
  year={2010},
  publisher={Springer}
}

repository: benjamin-kalloch/tdcs-sim-utils

repo_name: benjamin-kalloch/tdcs-sim-utils


text:
@article{melymcb-0168,
 author = {Kuz, V.A. and Vericat, F.},
 document_type = {Article},
 doi = {10.1103/PhysRevE.50.1672},
 journal = {Physical Review E},
 note = {cited By 6},
 number = {2},
 pages = {1672-1673},
 source = {Scopus},
 title = {Water content in micelles and the Laplace formula},
 url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34447646624&doi=10.1103%2fPhysRevE.50.1672&partnerID=40&md5=52d12daf0c85446b4beb08780f95b449},
 volume = {50},
 year = {1994}
}


text:
@article{clowers2006hadamard,
 author = {Clowers, Brian H and Siems, William F and Hill, Herbert H and Massick, Steven M},
 doi = {10.1021/ac050615k},
 journal = {Analytical chemistry},
 number = {1},
 pages = {44--51},
 publisher = {American Chemical Society},
 title = {Hadamard transform ion mobility spectrometry},
 volume = {78},
 year = {2006}
}

repository: bhclowers/academic-clowers

repo_name: bhclowers/academic-clowers


text:
Automatically generated by Mendeley Desktop 1.19.4
Any changes to this file will be lost if it is regenerated by Mendeley.

BibTeX export options can be customized via Preferences -> BibTeX in Mendeley Desktop

@article{Zhang2018,
abstract = {Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.},
author = {Zhang, Xike and Zhang, Qiuwen and Zhang, Gui and Nie, Zhiping and Gui, Zifan and Que, Huafei},
doi = {10.3390/ijerph15051032},
file = {:Users/sepmein/Qsync/Papers/Zhang et al.{\_}2018{\_}A novel hybrid data-driven model for daily land surface temperature forecasting using long short-term memory neural ne.pdf:pdf},
issn = {16604601},
journal = {International Journal of Environmental Research and Public Health},
keywords = {Daily land surface temperature,Data-driven,Dongting lake basin,Ensemble empirical mode decomposition (EEMD),Forecasting,Hybrid model,Long short-term memory (LSTM),Neural network (NN)},
number = {5},
title = {{A novel hybrid data-driven model for daily land surface temperature forecasting using long short-term memory neural network based on ensemble empirical mode decomposition}},
volume = {15},
year = {2018}
}

license: Unlicense

repository: sepmein/references

repo_name: sepmein/references


text:
@InProceedings{kaufmann12,
  title = {On Bayesian Upper Confidence Bounds for Bandit Problems},
  author = {Emilie Kaufmann and Olivier Cappe and Aurelien Garivier},
  pages = {592--600},
  abstract = {Stochastic bandit problems have been analyzed from two different perspectives: a frequentist view, where the parameter is a deterministic unknown quantity, and a Bayesian approach, where the parameter is drawn from a prior distribution.  We show in this paper that methods derived from this second perspective prove optimal when evaluated using the frequentist cumulated regret as a measure of performance. We give a general formulation for a class of Bayesian index policies that rely on quantiles of the posterior distribution. For binary bandits, we prove that the corresponding algorithm, termed Bayes-UCB, satisfies finite-time regret bounds that imply its asymptotic optimality.  More generally, Bayes-UCB appears as an unifying framework for several variants of the UCB algorithm addressing different bandit problems (parametric multi-armed bandits, Gaussian bandits with unknown mean and variance, linear bandits). But the generality of the Bayesian approach makes it possible to address more challenging models. In particular, we show how to handle linear bandits with sparsity constraints by resorting to Gibbs sampling.},
  pdf = {http://jmlr.org/proceedings/papers/v22/kaufmann12/kaufmann12.pdf},
}

repository: mreid/papersite

repo_name: mreid/papersite


text:
@book{bibunsho,
    author="奥村晴彦，黒木裕介",
    title="\LaTeXe 美文書作成入門",
    publisher="技術評論社",
    year="2017"
}

@online{jlisting,
    author="29man",
    title="jlisting.styのインストール",
    year=2009,
    url="https://ie.u-ryukyu.ac.jp/e075729/2009/08/02/jlistingsty%e3%81%ae%e3%82%a4%e3%83%b3%e3%82%b9%e3%83%88%e3%83%bc%e3%83%ab/",
    urldate="2020-08-15",
}

@Misc{why_fragile,
    title="listings - Can't insert code in my beamer slide - TeX - LaTeX Stack Exchange",
    howpublished="https://tex.stackexchange.com/questions/130109/cant-insert-code-in-my-beamer-slide",
}

@Misc{beamer_manual,
    title="The BEAMER class User Guide for version 3.59",
    howpublished="http://tug.ctan.org/macros/latex/contrib/beamer/doc/
    beameruserguide.pdf",
}


text:
---
---

@article{bian2023learning,
  abbr={TCOM},
  selected={true},
  arxiv={2206.15065},
  selected={true},
  title={Learning-Based Near-Orthogonal Superposition Code for MIMO Short Message Transmission},
  author={Bian, Chenghong and Hsu, Chin-Wei and Lee, Changwoo and Kim, Hun-Seok},
  journal={IEEE Transactions on Communications},
  year={2023},
  publisher={IEEE}
}

@inproceedings{lee2023deep,
    abbr={AISTATS23},
    bibtex_show={true},
    selected={true},
    preview={ISEC.gif},
    arxiv={2302.09174},
    code={https://github.com/changwoolee/isec-deep-jscc},
    title={Deep Joint Source-Channel Coding with Iterative Source Error Correction},
    author={Lee, Changwoo and Hu, Xiao and Kim, Hun-Seok},
    booktitle={International Conference on Artificial Intelligence and Statistics},
    year={2023}
}

@inproceedings{fan2022audio,
  title={Audio and Image Cross-Modal Intelligence via a 10TOPS/W 22nm SoC with Back-Propagation and Dynamic Power Gating},
  author={Fan, Zichen and An, Hyochan and Zhang, Qirui and Xu, Boxun and Xu, Li and Tseng, Chien-Wei and Peng, Yimai and Cao, Ang and Liu, Bowen and Lee, Changwoo and others},
  booktitle={2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)},
  pages={18--19},
  year={2022},
  organization={IEEE}
}

@inproceedings{kim2020confidence,
  bibtex_show={true},
  selected={true},
  title={A confidence-calibrated moba game winner predictor},
  author={Kim, Dong-Hee and Lee, Changwoo and Chung, Ki-Seok},
  booktitle={2020 IEEE Conference on Games (CoG)},
  pages={622--625},
  year={2020},
  organization={IEEE}
}

@inproceedings{lee2019gram,
  bibtex_show={true},
  selected={true},
  title={Gram: Gradient rescaling attention model for data uncertainty estimation in single image super resolution},
  author={Lee, Changwoo and Chung, Ki-Seok},
  booktitle={2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)},
  pages={8--13},
  year={2019},
  organization={IEEE}
}


text:
DESCRIPTION = "Genivi Software Loading Manager"
SECTION = "base"
LICENSE = "MPL-2.0"
LIC_FILES_CHKSUM = "file://${S}/LICENSE;md5=b278a92d2c1509760384428817710378"

SRCREV = "987c2f77da7e8bcfa212b47b6723497c7acc8f46"
SRC_URI = "\
    git://github.com/GENIVI/genivi_swm;branch=master;protocol=https \
    file://package_manager.service \
    file://partition_manager.service \
    file://lifecycle_manager.service \
    file://software_loading_manager.service \
    file://module_loader_ecu1.service \
    file://org.genivi.SoftwareLoadingManager.conf \
    "

PR = "r0"

S = "${WORKDIR}/git"

FILES_${PN} = " \
    ${libdir}/${PN}/* \
    ${systemd_system_unitdir}/package_manager.service \
    ${systemd_system_unitdir}/partition_manager.service \
    ${systemd_system_unitdir}/module_loader_ecu1.service \
    ${systemd_system_unitdir}/software_loading_manager.service \
    ${systemd_system_unitdir}/lifecycle_manager.service \
    ${sysconfdir}/dbus-1/system.d/org.genivi.SoftwareLoadingManager.conf \
    ${bindir}/sota-demo-reset.sh \
    "

DEPENDS = "systemd"

RDEPENDS_${PN} = " \
    python-subprocess \
    python-pygobject \
    python-dbus \
    python-storm \
    python-pysqlite \
    squashfs-tools \
    rpm \
    "

inherit systemd
SYSTEMD_SERVICE_${PN} = "package_manager.service partition_manager.service module_loader_ecu1.service software_loading_manager.service lifecycle_manager.service"

do_install () {
    install -d ${D}${libdir}/${PN}

    # TODO: Probably want to just copy in the needed files
    cp -r ${S}/* ${D}${libdir}/${PN}

    install -d ${D}${systemd_system_unitdir}
    install -m 0644 -c "${WORKDIR}/package_manager.service" ${D}${systemd_system_unitdir}
    install -m 0644 -c "${WORKDIR}/partition_manager.service" ${D}${systemd_system_unitdir}
    install -m 0644 -c "${WORKDIR}/module_loader_ecu1.service" ${D}${systemd_system_unitdir}
    install -m 0644 -c "${WORKDIR}/software_loading_manager.service" ${D}${systemd_system_unitdir}
    install -m 0644 -c "${WORKDIR}/lifecycle_manager.service" ${D}${systemd_system_unitdir}

    install -d ${D}${sysconfdir}/dbus-1/system.d/
    install -m 0644 -c "${WORKDIR}/org.genivi.SoftwareLoadingManager.conf" ${D}${sysconfdir}/dbus-1/system.d

    install -d ${D}${bindir}
    install -m 0755 -c "${S}/nano_samples_rpi/sota-demo-reset.sh" ${D}${bindir}
}

repository: GENIVI/genivi-dev-platform

repo_name: GENIVI/genivi-dev-platform

gha_language: Shell


text:
@inproceedings{zhang-nivre-12,
author = {Zhang, Yue and Nivre, Joachim},
booktitle = {{COLING}},
title = {{Analyzing the Effect of Global Learning and Beam-Search on Transition-Based Dependency Parsing}},
year = {2012}
}

@inproceedings{mccallum09:factorie:,
  Author = {Andrew McCallum and Karl Schultz and Sameer Singh},
  Booktitle = {Neural Information Processing Systems (NIPS)},
  Title = {{FACTORIE}: Probabilistic Programming via Imperatively Defined Factor Graphs},
  Year = {2009}
}

@article{Hovy:2006wi,
    author = {Hovy, Eduard and Marcus, Mitchell and Palmer, Martha and Ramshaw, Lance and Weischedel, Ralph},
    title = {{OntoNotes: the 90\% solution}},
    journal = {{HLT-NAACL}},
    year = {2006},
    pages = {57--60}
}

@article{Sang:2003gh,
    author = {Sang, Erik F Tjong Kim and De Meulder, Fien},
    title = {{Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition}},
    journal = {{CoNLL}},
    year = {2003}
}

repository: strubell/nlp-class-proj

repo_name: strubell/nlp-class-proj


text:
Automatically generated by Mendeley Desktop 1.19.4
Any changes to this file will be lost if it is regenerated by Mendeley.

BibTeX export options can be customized via Preferences -> BibTeX in Mendeley Desktop

@article{2007a,
author = {张, 山鹰},
file = {:Users/sepmein/Qsync/Papers//张{\_}2007{\_}美国疾病预防控制中心与公共卫生应急体系介绍.pdf:pdf;:Users/sepmein/Qsync/Papers//张{\_}2007{\_}美国疾病预防控制中心与公共卫生应急体系介绍.pdf:pdf},
journal = {海峡预防医学杂志},
keywords = {公共卫生应急体系,疾病预防控制体系,美国},
pages = {96},
title = {美国疾病预防控制中心与公共卫生应急体系介绍},
volume = {13},
year = {2007}
}


text:
.example-icon {
  padding: 0 10px;
}

.example-spacer {
  flex: 1 1 auto;
}

.main_page{
  margin: auto auto;
  width: auto;
  height: 100%;
  background-color: #80e27e;
}

.center {
  margin: auto;
  width: 50%;
  border: 3px solid green;
  padding: 10px;

}


text:
DESCRIPTION = "ADSP Driver for Linux"
LICENSE = "GPLv2 & MIT"

LIC_FILES_CHKSUM = " \
    file://COPYING;md5=55979d94ccbb973fdea772250e0c54d6 \
    file://MIT-COPYING;md5=f932f6ad0feea4f97b6e8316e172070e \
"

require include/adsp-control.inc
inherit module

FILESEXTRAPATHS_prepend := "${THISDIR}/xtensa-hifi:"

SRC_URI = "${@base_conditional('USE_ADSP', '1', 'file://RCG3AHPDL4001ZDO.tar.gz', '', d)}"

S = "${WORKDIR}/RCG3AHPDL4001ZDO"

# Define the extra config for using module.bbclass to build ADSP driver
EXTRA_OEMAKE_append = " KDIR=${STAGING_KERNEL_DIR}"

do_install_append(){
    # Create install directories
    install -d ${D}/${includedir}
    install -d ${D}/${includedir}/sys/xt-shmem

    # Install shared header files
    install -m 644 ${S}/include/*.h ${D}/${includedir}/
    install -m 644 ${S}/include/sys/xt-shmem/*.h ${D}/${includedir}/sys/xt-shmem/
}

# The ADSP driver need adsp firmware for running
RDEPENDS_${PN}_append = " adsp-fw-module"


text:
@InProceedings{Chawla_2021_WACV,
    author    = {Chawla, Akshay and Yin, Hongxu and Molchanov, Pavlo and Alvarez, Jose},
    title     = {Data-Free Knowledge Distillation for Object Detection},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2021},
    pages     = {3289-3298}
}

url: https://raw.githubusercontent.com/rbt-c/DL-NLP-Readings/82ca76e9184a3b00ac87129d99391477c85cfcd2/Bibtex/Data-free Knowledge Distillation for Object Detection.bib

repository: rbt-c/DL-NLP-Readings

repo_name: rbt-c/DL-NLP-Readings


text:
@article{Yagi2018,
 abstract = {A light field, which is often understood as a set of dense multi-view images, has been utilized in various 2D/3D applications. Efficient light field acquisition using a coded aperture camera is the target problem considered in this paper. Specifically, the entire light field, which consists of many images, should be reconstructed from only a few images that are captured through different aperture patterns. In previous work, this problem has often been discussed from the context of compressed sensing (CS), where sparse representations on a pre-trained dictionary or basis are explored to reconstruct the light field. In contrast, we formulated this problem from the perspective of principal component analysis (PCA) and non-negative matrix factorization (NMF), where only a small number of basis vectors are selected in advance based on the analysis of the training dataset. From this formulation, we derived optimal non-negative aperture patterns and a straight-forward reconstruction algorithm. Even though our method is based on conventional techniques, it has proven to be more accurate and much faster than a state-of-the-art CS-based method.},
 author = {Yagi, Yusuke and Takahashi, Keita and Fujii, Toshiaki and Sonoda, Toshiki and Nagahara, Hajime},
 doi = {10.1587/transinf.2017PCP0007},
 issn = {17451361},
 journal = {IEICE Transactions on Information and Systems},
 keywords = {Coded aperture,Light field,Nonnegative matrix factorization,Principal component analysis},
 month = {sep},
 number = {9},
 pages = {2190--2200},
 publisher = {Institute of Electronics, Information and Communication, Engineers, IEICE},
 title = {Designing coded aperture camera based on PCA and NMF for light field acquisition},
 volume = {E101D},
 year = {2018}
}

repository: conscienceli/islab-website

repo_name: conscienceli/islab-website


text:
@article{10.1093/bib/bbad294,
    author = {Liu, Jiayi and Kreimer, Anat and Li, Wei Vivian},
    title = "{Differential variability analysis of single-cell gene expression data}",
    journal = {Briefings in Bioinformatics},
    pages = {bbad294},
    year = {2023},
    month = {08},
    abstract = "{The advent of single-cell RNA sequencing (scRNA-seq) technologies has enabled gene expression profiling at the single-cell resolution, thereby enabling the quantification and comparison of transcriptional variability among individual cells. Although alterations in transcriptional variability have been observed in various biological states, statistical methods for quantifying and testing differential variability between groups of cells are still lacking. To identify the best practices in differential variability analysis of single-cell gene expression data, we propose and compare 12 statistical pipelines using different combinations of methods for normalization, feature selection, dimensionality reduction and variability calculation. Using high-quality synthetic scRNA-seq datasets, we benchmarked the proposed pipelines and found that the most powerful and accurate pipeline performs simple library size normalization, retains all genes in analysis and uses denSNE-based distances to cluster medoids as the variability measure. By applying this pipeline to scRNA-seq datasets of COVID-19 and autism patients, we have identified cellular variability changes between patients with different severity status or between patients and healthy controls.}",
    issn = {1477-4054},
    doi = {10.1093/bib/bbad294},
    url = {https://doi.org/10.1093/bib/bbad294},
    eprint = {https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbad294/51175639/bbad294.pdf},
}


text:
SUMMARY = "Web-based Traffic and Security Network Traffic Monitoring"
DESCRIPTION = "ntopng is a web-based network traffic monitoring application \
released under GPLv3. It is the new incarnation of the original \
ntop written in 1998, and now revamped in terms of performance, \
usability, and features."

SECTION = "console/network"

DEPENDS = "curl hiredis libmaxminddb libpcap lua mariadb ndpi json-c rrdtool zeromq"
RDEPENDS:${PN} = "bash redis"
LICENSE = "GPL-3.0-only"
LIC_FILES_CHKSUM = "file://COPYING;md5=d32239bcb673463ab874e80d47fae504"

SRCREV = "d3ce78948e8d81dc1e2c5298ce556f9f2247aac1"
SRC_URI = "git://github.com/ntop/ntopng.git;protocol=https;branch=5.2-stable \
           file://0001-configure.ac.in-fix-configure-error.patch \
           file://0001-configure.ac.in-fix-host-contamination.patch \
           file://0001-Makefile.in-don-t-use-the-internal-lua.patch \
           file://0001-autogen.sh-generate-configure.ac-only.patch \
           file://0001-configure.ac.in-not-check-clang-on-host.patch \
           file://0001-configure.ac.in-Allow-dynamic-linking-against-ndpi-3.patch \
           file://ntopng.service \
           "

S = "${WORKDIR}/git"

# don't use the lua under thirdparty as it supports cross compiling badly
export LUA_LIB = "${STAGING_LIBDIR}/liblua.a"

LDFLAGS:append:mipsarch = " -latomic"
LDFLAGS:append:powerpc = " -latomic"
LDFLAGS:append:riscv32 = " -latomic"
inherit autotools-brokensep gettext pkgconfig systemd

do_install:append() {
    install -d ${D}${systemd_unitdir}/system/
    install -m 0644 ${WORKDIR}/ntopng.service ${D}${systemd_unitdir}/system
}

FILES:${PN} += "\
    ${systemd_unitdir}/system/ntopng.service"

FILES:${PN}-doc += "\
    /usr/man/man8/ntopng.8"

do_configure:prepend() {
    ${S}/autogen.sh
}

SYSTEMD_SERVICE:${PN} = "ntopng.service"


text:
DESCRIPTION="U-Boot port for NextThing Co C.H.I.P"

require recipes-bsp/u-boot/u-boot.inc

DEPENDS += "dtc-native"

LICENSE = "GPLv2"

LIC_FILES_CHKSUM = " \
    file://Licenses/Exceptions;md5=338a7cb1e52d0d1951f83e15319a3fe7 \
    file://Licenses/bsd-2-clause.txt;md5=6a31f076f5773aabd8ff86191ad6fdd5 \
    file://Licenses/bsd-3-clause.txt;md5=4a1190eac56a9db675d58ebe86eaf50c \
    file://Licenses/eCos-2.0.txt;md5=b338cb12196b5175acd3aa63b0a0805c \
    file://Licenses/gpl-2.0.txt;md5=b234ee4d69f5fce4486a80fdaf4a4263 \
    file://Licenses/ibm-pibs.txt;md5=c49502a55e35e0a8a1dc271d944d6dba \
    file://Licenses/isc.txt;md5=ec65f921308235311f34b79d844587eb \
    file://Licenses/lgpl-2.0.txt;md5=5f30f0716dfdd0d91eb439ebec522ec2 \
    file://Licenses/lgpl-2.1.txt;md5=4fbd65380cdd255951079008b364516c \
"

COMPATIBLE_MACHINE = "(chip)"

DEFAULT_PREFERENCE_chip="1"

SRCREV = "79e0a6e1bb9003e43c756c0cacfc99b2a9c76b88"

SRC_URI = " \
    git://github.com/NextThingCo/CHIP-u-boot.git;protocol=git;branch=nextthing/2015.10/chip \
    file://uboot-env-chip \
"

PV = "v2015.10${SRCPV}"

PE = "1"

# sunxi-spl.bin
SUNXI_SPL_IMAGE   = "sunxi-spl"
SUNXI_SPL_BINARY  = "${SUNXI_SPL_IMAGE}.bin"
SUNXI_SPL_SYMLINK = "${SUNXI_SPL_BINARY}-${MACHINE}"

# sunxi-spl-with-ecc.bin
SUNXI_SPL_PADDED_IMAGE   = "sunxi-spl-with-ecc"
SUNXI_SPL_PADDED_BINARY  = "${SUNXI_SPL_PADDED_IMAGE}.bin"
SUNXI_SPL_PADDED_SYMLINK = "${SUNXI_SPL_PADDED_BINARY}-${MACHINE}"

# u-boot-dtb.bin
UBOOT_DTB_IMAGE   = "u-boot-dtb"
UBOOT_DTB_BINARY  = "${UBOOT_DTB_IMAGE}.bin"
UBOOT_DTB_SYMLINK = "${UBOOT_DTB_IMAGE}-${MACHINE}"

# uboot-env.bin
UBOOT_ENV_SUFFIX  = "bin"
UBOOT_ENV         = "uboot-env"

do_compile_append() {
    ${S}/tools/mkenvimage -s "0x400000" -o ${WORKDIR}/${UBOOT_ENV_BINARY} ${WORKDIR}/uboot-env-chip
}

do_install_append() {
    # Install sunxi-spl
    install ${S}/spl/${SUNXI_SPL_BINARY} ${D}/boot/${SUNXI_SPL_IMAGE}-${type}-${PV}-${PR}
    ln -sf ${SUNXI_SPL_IMAGE}-${type}-${PV}-${PR} ${D}/boot/${SUNXI_SPL_BINARY}-${type}
    ln -sf ${SUNXI_SPL_IMAGE}-${type}-${PV}-${PR} ${D}/boot/${SUNXI_SPL_BINARY}

    # Install sunxi-spl-with-ecc
    install ${S}/spl/${SUNXI_SPL_PADDED_BINARY} ${D}/boot/${SUNXI_SPL_PADDED_IMAGE}-${type}-${PV}-${PR}
    ln -sf ${SUNXI_SPL_PADDED_IMAGE}-${type}-${PV}-${PR} ${D}/boot/${SUNXI_SPL_PADDED_BINARY}-${type}
    ln -sf ${SUNXI_SPL_PADDED_IMAGE}-${type}-${PV}-${PR} ${D}/boot/${SUNXI_SPL_PADDED_BINARY}

    # Install u-boot-dtb
    install ${S}/${UBOOT_DTB_BINARY} ${D}/boot/${UBOOT_DTB_IMAGE}-${type}-${PV}-${PR}
    ln -sf ${UBOOT_DTB_IMAGE}-${type}-${PV}-${PR} ${D}/boot/${UBOOT_DTB_BINARY}-${type}
    ln -sf ${UBOOT_DTB_IMAGE}-${type}-${PV}-${PR} ${D}/boot/${UBOOT_DTB_BINARY}
}

do_deploy_append() {
    # Deploy sunxi-spl
    install ${S}/spl/${SUNXI_SPL_BINARY} ${DEPLOYDIR}/${SUNXI_SPL_IMAGE}-${type}-${PV}-${PR}
    rm -f ${DEPLOYDIR}/${SUNXI_SPL_BINARY} ${DEPLOYDIR}/${SUNXI_SPL_SYMLINK}-${type}
    ln -sf ${SUNXI_SPL_IMAGE}-${type}-${PV}-${PR} ${DEPLOYDIR}/${SUNXI_SPL_BINARY}-${type}
    ln -sf ${SUNXI_SPL_IMAGE}-${type}-${PV}-${PR} ${DEPLOYDIR}/${SUNXI_SPL_BINARY}
    ln -sf ${SUNXI_SPL_IMAGE}-${type}-${PV}-${PR} ${DEPLOYDIR}/${SUNXI_SPL_SYMLINK}-${type}
    ln -sf ${SUNXI_SPL_IMAGE}-${type}-${PV}-${PR} ${DEPLOYDIR}/${SUNXI_SPL_SYMLINK}

    # Deploy sunxi-spl-with-ecc
    install ${S}/spl/${SUNXI_SPL_PADDED_BINARY} ${DEPLOYDIR}/${SUNXI_SPL_PADDED_IMAGE}-${type}-${PV}-${PR}
    rm -f ${DEPLOYDIR}/${SUNXI_SPL_PADDED_BINARY} ${DEPLOYDIR}/${SUNXI_SPL_PADDED_SYMLINK}-${type}
    ln -sf ${SUNXI_SPL_PADDED_IMAGE}-${type}-${PV}-${PR} ${DEPLOYDIR}/${SUNXI_SPL_PADDED_BINARY}-${type}
    ln -sf ${SUNXI_SPL_PADDED_IMAGE}-${type}-${PV}-${PR} ${DEPLOYDIR}/${SUNXI_SPL_PADDED_BINARY}
    ln -sf ${SUNXI_SPL_PADDED_IMAGE}-${type}-${PV}-${PR} ${DEPLOYDIR}/${SUNXI_SPL_PADDED_SYMLINK}-${type}
    ln -sf ${SUNXI_SPL_PADDED_IMAGE}-${type}-${PV}-${PR} ${DEPLOYDIR}/${SUNXI_SPL_PADDED_SYMLINK}

    # Deploy u-boot-dtb
    install ${S}/${UBOOT_DTB_BINARY} ${DEPLOYDIR}/${UBOOT_DTB_IMAGE}-${type}-${PV}-${PR}
    rm -f ${DEPLOYDIR}/${UBOOT_DTB_BINARY} ${DEPLOYDIR}/${UBOOT_DTB_SYMLINK}-${type}
    ln -sf ${UBOOT_DTB_IMAGE}-${type}-${PV}-${PR} ${DEPLOYDIR}/${UBOOT_DTB_BINARY}-${type}
    ln -sf ${UBOOT_DTB_IMAGE}-${type}-${PV}-${PR} ${DEPLOYDIR}/${UBOOT_DTB_BINARY}
    ln -sf ${UBOOT_DTB_IMAGE}-${type}-${PV}-${PR} ${DEPLOYDIR}/${UBOOT_DTB_SYMLINK}-${type}
    ln -sf ${UBOOT_DTB_IMAGE}-${type}-${PV}-${PR} ${DEPLOYDIR}/${UBOOT_DTB_SYMLINK}
}

repository: ArnaudNe/meta-chip

repo_name: ArnaudNe/meta-chip


text:
% Encoding: UTF-8

@misc{uci:mlr,
  author    = {M. Lichman},
  year      = {2013},
  title     = {{UCI} Machine Learning Repository},
  url       = {http://archive.ics.uci.edu/ml},
  institution = {University of California, Irvine, School of Information and Computer Sciences},
}

@Article{kennedy:pso,
  author    = {J Kennedy and R C Eberhart},
  title     = {Particle swarm optimization},
  journal   = {Micro Machine and Human Science},
  year      = {1995},
  volume    = {5},
  number    = {500},
  pages     = {39–43},
  abstract  = {In this paper, the utilization of different chaotic systems as pseudo-random number generators (PRNGs) for velocity calculation in the PSO algorithm are proposed. Two chaos-based PRNGs are used alternately within one run of the PSO algorithm and dynamically switched over when a certain criterion is met. By using this unique technique, it is possible to improve the performance of PSO algorithm as it is demonstrated on different benchmark functions.},
  publisher = {Proceedings of the Sixth International Symposium},
  url       = {https://link.springer.com/article/10.1007/s00500-014-1222-z},
}

@Conference{vanwyk:overfitting-psoffnn,
  author       = {Andrich Bosof van Wyk and Andries Petrus Engelbrecht},
  title        = {Overfitting by PSO trained feedforward neural networks},
  booktitle    = {IEEE Congress on Evolutionary Computation},
  year         = {2010},
  month        = jul,
  organization = {IEEE},
  publisher    = {IEEE},
  abstract     = {The purpose of this paper is to investigate the overfitting behavior of particle swarm optimization (PSO) trained neural networks. Neural networks trained with PSOs using the global best, local best and Von Neumann information sharing topologies are investigated. Experiments are conducted on five classification and five time series regression problems. It is shown that differences exist in the degree of overfitting between the different topologies. Additionally, non-convergence of the swarms is witnessed, which is hypothetically attributed to the use of a bounded activation function in the neural networks. The hypothesis is supported by experiments conducted using an unbounded activation function in the neural network hidden layer, which lead to convergent swarms. Additionally this also lead to drastically reduced overfitting by the neural networks.},
  file         = {Overfitting by PSO trained feedforward neural networks:/home/qweenink/Dropbox/uni/cos700/Research/PapersToRead/p2/06.overfitting_andrich_OverfittingBehaviourPSOonNNtraining.pdf:PDF},
  keywords     = {anna, PSO trained FFNN, PSO, NN, rank4},
  review       = {Measuring overfitting in PSO trained FFNN},
  timestamp    = {2017-03-21},
  url          = {http://ieeexplore.ieee.org/abstract/document/5586333/?section=abstract},
}

@Conference{anna:meas-sat-nn,
  author       = {Anna Rakitianskaia and Andries Engelbrecht},
  title        = {Measuring Saturation in Neural Networks},
  booktitle    = {Symposium Series on Computational Intelligence},
  year         = {2015},
  month        = dec,
  organization = {IEEE},
  publisher    = {IEEE},
  file         = {Measuring Saturation in Neural Networks:/home/qweenink/Dropbox/uni/cos700/Research/PapersToRead/p2/09.Anna_HowToMeasureSaturationInNN.pdf:PDF},
  keywords     = {anna, Artificial neural networks, Training, Optimization, Biological neural networks, Benchmark testing, Histograms, Computer science, rank4},
  timestamp    = {2017-03-21},
  url          = {http://ieeexplore.ieee.org/abstract/document/7376778/},
}

@Comment{jabref-meta: databaseType:bibtex;}


text:
#ifndef PATHS_H__
#define PATHS_H__

#include <string>

const std::string CONF_PATH = "@CONF_PATH@";

#endif // PATHS_H__

repository: pashinov/http-server

repo_name: pashinov/http-server

extension: cmake


text:
---
---

@inproceedings{conext20:aalwines,
author = {Jensen, Peter Gj\o{}l and Kristiansen, Dan and Schmid, Stefan and Schou, Morten Konggaard and Schrenk, Bernhard Clemens and Srba, Ji\v{r}\'{i}},
title = {AalWiNes: A Fast and Quantitative What-If Analysis Tool for MPLS Networks},
year = {2020},
isbn = {9781450379489},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3386367.3431308},
doi = {10.1145/3386367.3431308},
abstract = {We present an automated what-if analysis tool AalWiNes for MPLS networks which allows us to verify both logical properties (e.g., related to the policy compliance) as well as quantitative properties (e.g., concerning the latency) under multiple link failures. Our tool relies on weighted pushdown automata, a quantitative extension of classic automata theory, and takes into account the actual dataplane configuration, rendering it especially useful for debugging. In particular, our tool collects the different router forwarding tables and then builds a pushdown system, on which quantitative reachability is performed based on an expressive query language. Our experiments show that our tool outperforms state-of-the-art approaches (which until now have been restricted to logical properties) by several orders of magnitude; furthermore, our quantitative extension only entails a moderate overhead in terms of runtime. The tool comes with a platform-independent user interface and is publicly available as open-source, together with all other experimental artefacts.},
booktitle = {Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies},
pages = {474–481},
numpages = {8},
keywords = {tools, network performance, network verification},
location = {Barcelona, Spain},
series = {CoNEXT '20},
selected={true},
abbr={CoNEXT},
code={https://zenodo.org/record/4056504}
}

@inproceedings{8788747,
author={A. {Gitz-Johansen} and M. E. {Holm} and L. V. {Kirkeby} and D. {Kristiansen} and A. S. {Ostenfeld} and M. K. {Schou} and B. {Yang}},
booktitle={2019 20th IEEE International Conference on Mobile Data Management (MDM)},
title={A Practical Delivery Route Planning System},
year={2019},
volume={},
number={},
pages={349-350},
doi={10.1109/MDM.2019.00-37},
abstract={Thanks to recent e-commerce growth, the parcel delivery industry is booming. We demonstrate a system that provides a practical solution for scheduling and planning parcel delivery routes. Given a parcel delivery workload, e.g., the number of parcels to be delivered and the sizes of the parcels, the system tries to identify a set of delivery routes such that the workload is satisfied and the total delivery cost is minimized. The system is developed on top of aSTEP, a spatio-temporal data analytics platform developed at Aalborg University, and is tested with parcel delivery workloads provided by a large logistic company in Denmark.}}


text:
@comment{
  A document having an author and title, but not formally published

  required fields: author, title, note
  optional fields: month, year
}

@UnPublished{,
  author  = "",
  title   = "",
  note    = "",
  month   = "",
  year    = ""
}

@UnPublished{meltzer:c2070,
	author  = {Meltzer, R. and Zeng, C. and Cecka, C.},
	title   = {{Micro-benchmarking the C2070}},
	note = {{GPU Technology Conference}},
	year    = {2013}
}

repository: wangbiaouestc/clpeak

repo_name: wangbiaouestc/clpeak


text:
@InProceedings{benallegue:iros:2021,
  author    = {Benallegue, Mehdi and Cisneros, Rafael and Benallegue, Abdelaziz and Tanguy, Arnaud and Escande, Adrien and Morisawa, Mitsuharu and Kanehiro, Fumio},
  title     = {On compliance and safety with torque-control for robots with high reduction gears and no joint-torque feedback},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year      = {2021},
  address   = {Prague, Czech Republic},
  month     = {September 27-October 1},
  url       = {https://ieeexplore.ieee.org/document/9636081},
  keywords  = {Torque, Gears, Trajectory tracking, Friction, Torque control, Position control, Safety},
  doi       = {10.1109/IROS51168.2021.9636081},
  abstract  = {In this paper we report the safety-oriented framework for controlling the torque in the case of robots with high reduction gears and having no joint torque feedback. This kind of robots suffer from high joint friction and low backdrivability, requiring high gains and integral feedback, which can be dangerous. Our optimization-based framework includes feasibility and safety features borrowed from position control, and we introduce novel ones. We show how we limit the integral terms using a QP-based anti-windup which produces the optimal torque that maintains the best performances under safety limits. We show also a new controller for null-space compliance, providing strong guarantees of convergence in the task-space and ignoring the corresponding null-space where the robot can be moved freely. We validate these features with experiments on one 9 DoF arm of the robot HRP-5P performing a Cartesian task, and then a dual Cartesian / admittance task.}
}


text:
@inproceedings{2017-Bad,
 abstract = {Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.},
 address = {Los Alamitos, CA},
 author = {David A. Bader},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/bib/conf/ipps/Bader17},
 booktitle = {2017 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPS Workshops 2017, Orlando / Buena Vista, FL, USA, May 29 - June 2, 2017},
 doi = {10.1109/IPDPSW.2017.219},
 groups = {Edited},
 pages = {1557},
 publisher = {IEEE Computer Society},
 timestamp = {Wed, 24 Jul 2019 01:00:00 +0200},
 title = {Introduction to EMBRACE Workshop},
 url = {https://doi.org/10.1109/IPDPSW.2017.219},
 year = {2017}
}


text:
SUMMARY = "A collection of cryptographic algorithms and protocols"
SECTION = "devel/python"
LICENSE = "PD & Python-2.0"
LIC_FILES_CHKSUM = "file://COPYRIGHT;md5=35f354d199e8cb7667b059a23578e63d"
DEPENDS = "gmp"

SRC_URI = "http://ftp.dlitz.net/pub/dlitz/crypto/pycrypto/pycrypto-${PV}.tar.gz \
           file://no-usr-include.patch"
SRC_URI[md5sum] = "783e45d4a1a309e03ab378b00f97b291"
SRC_URI[sha256sum] = "e950a78184e2a7defccf5d45e0c29c1e9edeb29984433f0d110a21e9631e38de"

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

inherit autotools-brokensep distutils

BBCLASSEXTEND = "native"

repository: opendreambox/opendreambox

repo_name: opendreambox/opendreambox

branch_name: refs/heads/pyro