text:
@article{barany1987,
	title={Solid-phase peptide synthesis: a silver anniversary report},
	author={Barany, George and Kneib-Cordonier, Nancy and Mullem, Daniel G},
	journal={Chemical Biology \& Drug Design},
	volume={30},
	number={6},
	pages={705--739},
	year={1987},
	publisher={Wiley Online Library}
}

@ARTICLE{synthetic-long-peptides,
	title       = "Dendritic cells process synthetic long peptides better than
	whole protein, improving antigen presentation and T-cell
	activation",
	author      = "Rosalia, Rodney A and Quakkelaar, Esther D and Redeker, Anke
	and Khan, Selina and Camps, Marcel and Drijfhout, Jan W and
	Silva, Ana Luisa and Jiskoot, Wim and van Hall, Thorbald and
	van Veelen, Peter A and Janssen, George and Franken, Kees and
	Cruz, Luis J and Tromp, Angelino and Oostendorp, Jaap and van
	der Burg, Sjoerd H and Ossendorp, Ferry and Melief, Cornelis J
	M",
	affiliation = "Department of Clinical Pharmacy and Toxicology, Leiden
	University Medical Center, Leiden, The Netherlands; Department
	of Immunohematology and Blood Transfusion, Leiden University
	Medical Center, Leiden, The Netherlands.",
	abstract    = "The efficiency of antigen (Ag) processing by dendritic cells
	(DCs) is vital for the strength of the ensuing T-cell
	responses. Previously, we and others have shown that in
	comparison to protein vaccines, vaccination with synthetic
	long peptides (SLPs) has shown more promising (pre-)clinical
	results. Here, we studied the unknown mechanisms underlying
	the observed vaccine efficacy of SLPs. We report an in vitro
	processing analysis of SLPs for MHC class I and class II
	presentation by murine DCs and human monocyte-derived DCs.
	Compared to protein, SLPs were rapidly and much more
	efficiently processed by DCs, resulting in an increased
	presentation to CD4⁺ and CD8⁺ T cells. The mechanism of access
	to MHC class I loading appeared to differ between the two
	forms of Ag. Whereas whole soluble protein Ag ended up largely
	in endolysosomes, SLPs were detected very rapidly outside the
	endolysosomes after internalization by DCs, followed by
	proteasome- and transporter associated with Ag
	processing-dependent MHC class I presentation. Compared to the
	slower processing route taken by whole protein Ags, our
	results indicate that the efficient internalization of SLPs,
	accomplished by DCs but not by B or T cells and characterized
	by a different and faster intracellular routing, leads to
	enhanced CD8⁺ T-cell activation.",
	journal     = "Eur. J. Immunol.",
	volume      =  43,
	number      =  10,
	pages       = "2554--2565",
	month       =  oct,
	year        =  2013,
	keywords    = "Antigen presentation/processing; CD8 T cells; Cellular
	immunology; Dendritic cells",
	language    = "en"
}

@ARTICLE{Sette1994,
	title       = "The relationship between class {I} binding affinity and
	immunogenicity of potential cytotoxic {T} cell epitopes",
	author      = "Sette, A and Vitiello, A and Reherman, B and Fowler, P and
	Nayersina, R and Kast, W M and Melief, C J and Oseroff, C and
	Yuan, L and Ruppert, J and Sidney, J and del Guercio, M F and
	Southwood, S and Kubo, R T and Chesnut, R W and Grey, H M and
	Chisari, F V",
	affiliation = "Cytel, San Diego, CA 92121, USA.",
	abstract    = "The relationship between binding affinity for HLA class I
	molecules and immunogenicity of discrete peptide epitopes has
	been analyzed in two different experimental approaches. In the
	first approach, the immunogenicity of potential epitopes
	ranging in MHC binding affinity over a 10,000-fold range was
	analyzed in HLA-A*0201 transgenic mice. In the second
	approach, the antigenicity of approximately 100 different
	hepatitis B virus (HBV)-derived potential epitopes, all
	carrying A*0201 binding motifs, was assessed by using PBL of
	acute hepatitis patients. In both cases, it was found that an
	affinity threshold of approximately 500 nM (preferably 50 nM
	or less) apparently determines the capacity of a peptide
	epitope to elicit a CTL response. These data correlate well
	with class I binding affinity measurements of either naturally
	processed peptides or previously described T cell epitopes.
	Taken together, these data have important implications for the
	selection of epitopes for peptide-based vaccines, and also
	formally demonstrate the crucial role of determinant selection
	in the shaping of T cell responses. Because in most (but not
	all) cases, high affinity peptides seem to be immunogenic, our
	data also suggest that holes in the functional T cell
	repertoire, if they exist, may be relatively rare.",
	journal     = "J. Immunol.",
	volume      =  153,
	number      =  12,
	pages       = "5586--5592",
	month       =  "15~" # dec,
	year        =  1994,
	language    = "en"
}

@ARTICLE{netmhcpan2007,
  title       = "{NetMHCpan}, a method for quantitative predictions of peptide
                 binding to any {HLA-A} and {-B} locus protein of known
                 sequence",
  author      = "Nielsen, Morten and Lundegaard, Claus and Blicher, Thomas and
                 Lamberth, Kasper and Harndahl, Mikkel and Justesen, Sune and
                 R{\o}der, Gustav and Peters, Bjoern and Sette, Alessandro and
                 Lund, Ole and Buus, S{\o}ren",
  affiliation = "Center for Biological Sequence Analysis, BioCentrum-DTU,
                 Technical University of Denmark, Lyngby, Denmark.
                 mniel@cbs.dtu.dk",
  abstract    = "BACKGROUND: Binding of peptides to Major Histocompatibility
                 Complex (MHC) molecules is the single most selective step in
                 the recognition of pathogens by the cellular immune system.
                 The human MHC class I system (HLA-I) is extremely polymorphic.
                 The number of registered HLA-I molecules has now surpassed
                 1500. Characterizing the specificity of each separately would
                 be a major undertaking. PRINCIPAL FINDINGS: Here, we have
                 drawn on a large database of known peptide-HLA-I interactions
                 to develop a bioinformatics method, which takes both peptide
                 and HLA sequence information into account, and generates
                 quantitative predictions of the affinity of any peptide-HLA-I
                 interaction. Prospective experimental validation of peptides
                 predicted to bind to previously untested HLA-I molecules,
                 cross-validation, and retrospective prediction of known HIV
                 immune epitopes and endogenous presented peptides, all
                 successfully validate this method. We further demonstrate that
                 the method can be applied to perform a clustering analysis of
                 MHC specificities and suggest using this clustering to select
                 particularly informative novel MHC molecules for future
                 biochemical and functional analysis. CONCLUSIONS: Encompassing
                 all HLA molecules, this high-throughput computational method
                 lends itself to epitope searches that are not only genome- and
                 pathogen-wide, but also HLA-wide. Thus, it offers a truly
                 global analysis of immune responses supporting rational
                 development of vaccines and immunotherapy. It also promises to
                 provide new basic insights into HLA structure-function
                 relationships. The method is available at
                 http://www.cbs.dtu.dk/services/NetMHCpan.",
  journal     = "PLoS One",
  volume      =  2,
  number      =  8,
  pages       = "e796",
  month       =  "29~" # aug,
  year        =  2007,
  language    = "en"
}

@article{mhcflurry,
	title={Predicting Peptide-MHC Binding Affinities With Imputed Training Data},
	author={Rubinsteyn, Alex and O'Donnell, Timothy and Damaraju, Nandita and Hammerbacher, Jeffrey},
	journal={bioRxiv},
	pages={054775},
	year={2016},
	publisher={Cold Spring Harbor Labs Journals}
}

@ARTICLE{iedb,
	title       = "The immune epitope database ({IEDB}) 3.0",
	author      = "Vita, Randi and Overton, James A and Greenbaum, Jason A and
	Ponomarenko, Julia and Clark, Jason D and Cantrell, Jason R
	and Wheeler, Daniel K and Gabbard, Joseph L and Hix, Deborah
	and Sette, Alessandro and Peters, Bjoern",
	affiliation = "Division of Vaccine Discovery, La Jolla Institute for Allergy
	and Immunology, La Jolla, 9420 Athena Circle, CA 92037, USA
	rvita@liai.org. Division of Vaccine Discovery, La Jolla
	Institute for Allergy and Immunology, La Jolla, 9420 Athena
	Circle, CA 92037, USA. Bioinformatics Core, La Jolla Institute
	for Allergy and Immunology, La Jolla, CA 92037, USA. San Diego
	Supercomputer Center, University of California, San Diego, CA
	92093, USA. Leidos Health, LLC, San Diego, CA 92121, USA.
	Leidos Health, LLC, San Diego, CA 92121, USA. Leidos Health,
	LLC, San Diego, CA 92121, USA. Grado Department of Industrial
	and Systems Engineering, Virginia Polytechnic Institute and
	State University, Blacksburg, VA 24061, USA. Grado Department
	of Industrial and Systems Engineering, Virginia Polytechnic
	Institute and State University, Blacksburg, VA 24061, USA.
	Division of Vaccine Discovery, La Jolla Institute for Allergy
	and Immunology, La Jolla, 9420 Athena Circle, CA 92037, USA.
	Division of Vaccine Discovery, La Jolla Institute for Allergy
	and Immunology, La Jolla, 9420 Athena Circle, CA 92037, USA.",
	abstract    = "The IEDB, www.iedb.org, contains information on immune
	epitopes--the molecular targets of adaptive immune
	responses--curated from the published literature and submitted
	by National Institutes of Health funded epitope discovery
	efforts. From 2004 to 2012 the IEDB curation of journal
	articles published since 1960 has caught up to the present
	day, with >95\% of relevant published literature manually
	curated amounting to more than 15,000 journal articles and
	more than 704,000 experiments to date. The revised curation
	target since 2012 has been to make recent research findings
	quickly available in the IEDB and thereby ensure that it
	continues to be an up-to-date resource. Having gathered a
	comprehensive dataset in the IEDB, a complete redesign of the
	query and reporting interface has been performed in the IEDB
	3.0 release to improve how end users can access this
	information in an intuitive and biologically accurate manner.
	We here present this most recent release of the IEDB and
	describe the user testing procedures as well as the use of
	external ontologies that have enabled it.",
	journal     = "Nucleic Acids Res.",
	publisher   = "Oxford Univ Press",
	volume      =  43,
	number      = "Database issue",
	pages       = "D405--12",
	month       =  jan,
	year        =  2015,
	language    = "en"
}

@ARTICLE{netmhciipan,
	title       = "Quantitative predictions of peptide binding to any {HLA-DR}
	molecule of known sequence: {NetMHCIIpan}",
	author      = "Nielsen, Morten and Lundegaard, Claus and Blicher, Thomas and
	Peters, Bjoern and Sette, Alessandro and Justesen, Sune and
	Buus, S{\o}ren and Lund, Ole",
	affiliation = "Center for Biological Sequence Analysis, Department of Systems
	Biology, Technical University of Denmark, Lyngby, Denmark.
	mniel@cbs.dtu.dk",
	abstract    = "CD4 positive T helper cells control many aspects of specific
	immunity. These cells are specific for peptides derived from
	protein antigens and presented by molecules of the extremely
	polymorphic major histocompatibility complex (MHC) class II
	system. The identification of peptides that bind to MHC class
	II molecules is therefore of pivotal importance for rational
	discovery of immune epitopes. HLA-DR is a prominent example of
	a human MHC class II. Here, we present a method, NetMHCIIpan,
	that allows for pan-specific predictions of peptide binding to
	any HLA-DR molecule of known sequence. The method is derived
	from a large compilation of quantitative HLA-DR binding events
	covering 14 of the more than 500 known HLA-DR alleles. Taking
	both peptide and HLA sequence information into account, the
	method can generalize and predict peptide binding also for
	HLA-DR molecules where experimental data is absent. Validation
	of the method includes identification of endogenously derived
	HLA class II ligands, cross-validation,
	leave-one-molecule-out, and binding motif identification for
	hitherto uncharacterized HLA-DR molecules. The validation
	shows that the method can successfully predict binding for
	HLA-DR molecules-even in the absence of specific data for the
	particular molecule in question. Moreover, when compared to
	TEPITOPE, currently the only other publicly available
	prediction method aiming at providing broad HLA-DR allelic
	coverage, NetMHCIIpan performs equivalently for alleles
	included in the training of TEPITOPE while outperforming
	TEPITOPE on novel alleles. We propose that the method can be
	used to identify those hitherto uncharacterized alleles, which
	should be addressed experimentally in future updates of the
	method to cover the polymorphism of HLA-DR most efficiently.
	We thus conclude that the presented method meets the challenge
	of keeping up with the MHC polymorphism discovery rate and
	that it can be used to sample the MHC ``space,'' enabling a
	highly efficient iterative process for improving MHC class II
	binding predictions.",
	journal     = "PLoS Comput. Biol.",
	publisher   = "journals.plos.org",
	volume      =  4,
	number      =  7,
	pages       = "e1000107",
	month       =  "4~" # jul,
	year        =  2008,
	language    = "en"
}

@ARTICLE{netmhccons,
	title       = "{NetMHCcons}: a consensus method for the major
	histocompatibility complex class {I} predictions",
	author      = "Karosiene, Edita and Lundegaard, Claus and Lund, Ole and
	Nielsen, Morten",
	affiliation = "Center for Biological Sequence Analysis, Department of Systems
	Biology, Technical University of Denmark, Building 208,
	Kemitorvet, Lyngby, 2800, Denmark. edita@cbs.dtu.dk",
	abstract    = "A key role in cell-mediated immunity is dedicated to the major
	histocompatibility complex (MHC) molecules that bind peptides
	for presentation on the cell surface. Several in silico
	methods capable of predicting peptide binding to MHC class I
	have been developed. The accuracy of these methods depends on
	the data available characterizing the binding specificity of
	the MHC molecules. It has, moreover, been demonstrated that
	consensus methods defined as combinations of two or more
	different methods led to improved prediction accuracy. This
	plethora of methods makes it very difficult for the non-expert
	user to choose the most suitable method for predicting binding
	to a given MHC molecule. In this study, we have therefore made
	an in-depth analysis of combinations of three state-of-the-art
	MHC-peptide binding prediction methods (NetMHC, NetMHCpan and
	PickPocket). We demonstrate that a simple combination of
	NetMHC and NetMHCpan gives the highest performance when the
	allele in question is included in the training and is
	characterized by at least 50 data points with at least ten
	binders. Otherwise, NetMHCpan is the best predictor. When an
	allele has not been characterized, the performance depends on
	the distance to the training data. NetMHCpan has the highest
	performance when close neighbours are present in the training
	set, while the combination of NetMHCpan and PickPocket
	outperforms either of the two methods for alleles with more
	remote neighbours. The final method, NetMHCcons, is publicly
	available at www.cbs.dtu.dk/services/NetMHCcons , and allows
	the user in an automatic manner to obtain the most accurate
	predictions for any given MHC molecule.",
	journal     = "Immunogenetics",
	volume      =  64,
	number      =  3,
	pages       = "177--186",
	month       =  mar,
	year        =  2012,
	language    = "en"
}

@ARTICLE{netmhc2016,
  title       = "Gapped sequence alignment using artificial neural networks:
                 application to the {MHC} class {I} system",
  author      = "Andreatta, Massimo and Nielsen, Morten",
  affiliation = "Instituto de Investigaciones Biotecnol{\'o}gicas, Universidad
                 Nacional de San Mart{\'\i}n, Buenos Aires, Argentina and.
                 Instituto de Investigaciones Biotecnol{\'o}gicas, Universidad
                 Nacional de San Mart{\'\i}n, Buenos Aires, Argentina and
                 Center for Biological Sequence Analysis, Technical University
                 of Denmark, Kgs. Lyngby, Denmark.",
  abstract    = "MOTIVATION: Many biological processes are guided by receptor
                 interactions with linear ligands of variable length. One such
                 receptor is the MHC class I molecule. The length preferences
                 vary depending on the MHC allele, but are generally limited to
                 peptides of length 8-11 amino acids. On this relatively simple
                 system, we developed a sequence alignment method based on
                 artificial neural networks that allows insertions and
                 deletions in the alignment. RESULTS: We show that prediction
                 methods based on alignments that include insertions and
                 deletions have significantly higher performance than methods
                 trained on peptides of single lengths. Also, we illustrate how
                 the location of deletions can aid the interpretation of the
                 modes of binding of the peptide-MHC, as in the case of long
                 peptides bulging out of the MHC groove or protruding at either
                 terminus. Finally, we demonstrate that the method can learn
                 the length profile of different MHC molecules, and quantified
                 the reduction of the experimental effort required to identify
                 potential epitopes using our prediction algorithm.
                 AVAILABILITY AND IMPLEMENTATION: The NetMHC-4.0 method for the
                 prediction of peptide-MHC class I binding affinity using
                 gapped sequence alignment is publicly available at:
                 http://www.cbs.dtu.dk/services/NetMHC-4.0.",
  journal     = "Bioinformatics",
  volume      =  32,
  number      =  4,
  pages       = "511--517",
  month       =  "15~" # feb,
  year        =  2016,
  language    = "en"
}

@ARTICLE{pgv-pipeline,
  title     = "Abstract A022: Computational pipeline for a personalized genomic
               vaccine trial",
  author    = "Rubinsteyn, Alexander and Hodes, Isaac and O'Donnell, Timothy
               and Mondet, Sebastien F and Finnigan, John P and Friedlander,
               Philip and Sabado, Rachel and Bhardwaj, Nina and Hammerbacher,
               Jeffrey",
  abstract  = "Abstracts: Second CRI-CIMT-EATI-AACR International Cancer
               Immunotherapy Conference: Translating Science into Survival;
               September 25-28, 2016; New York, NY PGV-001 (Personalized
               Genomic Vaccine) is a Phase I clinical trial at Mount Sinai
               testing the safety, feasibility, and immunogenicity of a
               neoantigen + Poly-ICLC vaccine derived from exome and RNA
               sequencing [1]. The personalized component of the vaccine
               consists of ten peptides (25 amino acids in length) predicted to
               contain highly expressed mutated cytotoxic T-cell epitopes.
               Selecting peptides for each patient requires a number of
               standard bioinformatics tasks such as somatic variant calling,
               sequence-based HLA typing, and class I peptide-MHC binding
               prediction, as well as new methods to determine the coding
               sequence for each identified mutation, quantify the RNA support
               for each variant allele, and scoring each vaccine peptide. We
               present the overall design of PGV, its implementation as an open
               source computational pipeline, and several independently useful
               libraries that implement aspects of mutant epitope prediction.
               One of the unique characteristics of the PGV pipeline is that
               the amino acid sequence of a candidate vaccine peptide is
               determined from RNA reads overlapping a variant [2].
               Consequently, variants which lack any evidence in RNA cannot be
               used in PGV. Determining a coding sequence from RNA allows us to
               capture atypical splicing around a mutation as well as phase
               somatic variants with nearby germline variants. Once a mutant
               coding sequence is established, candidate vaccine peptides are
               ranked by combining their predicted Class I MHC binding affinity
               with allele-specific expression [3]. We demonstrate the impact
               of different ranking criteria on vaccine efficacy using a tumor
               mouse model. Execution and dependency management of the PGV
               pipeline is orchestrated using a a novel workflow engine [4, 5]
               which allows us to run complex tools across many machines with
               fault tolerance and reproducibility. The pipeline can be run on
               single machines, local clusters, or on public infrastructure
               using the Google Cloud Platform. We hope that by making the PGV
               pipeline and its underlying components available as open source
               software we can facilitate faster experimentation in the design
               of personalized cancer vaccines. [1] [2] [3] [4] [5] Citation
               Format: Alexander Rubinsteyn, Isaac Hodes, Timothy O'Donnell,
               Sebastien F. Mondet, John P. Finnigan, Philip Friedlander,
               Rachel Sabado, Nina Bhardwaj, Jeffrey Hammerbacher.
               Computational pipeline for a personalized genomic vaccine trial
               [abstract]. In: Proceedings of the Second CRI-CIMT-EATI-AACR
               International Cancer Immunotherapy Conference: Translating
               Science into Survival; 2016 Sept 25-28; New York, NY.
               Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(11
               Suppl):Abstract nr A022.",
  journal   = "Cancer Immunol Res",
  publisher = "American Association for Cancer Research",
  volume    =  4,
  number    = "11 Supplement",
  pages     = "A022--A022",
  month     =  "1~" # nov,
  year      =  2016,
  language  = "en"
}

@ARTICLE{neoantigens-gubin,
  title    = "Tumor neoantigens: building a framework for personalized cancer
              immunotherapy",
  author   = "Gubin, Matthew M and Artyomov, Maxim N and Mardis, Elaine R and
              Schreiber, Robert D",
  abstract = "It is now well established that the immune system can recognize
              developing cancers and that therapeutic manipulation of immunity
              can induce tumor regression. The capacity to manifest remarkably
              durable responses in some patients has been ascribed in part to T
              cells that can (a) kill tumor cells directly, (b) orchestrate
              diverse antitumor immune responses, (c) manifest long-lasting
              memory, and (d) display remarkable specificity for tumor-derived
              proteins. This specificity stems from fundamental differences
              between cancer cells and their normal counterparts in that the
              former develop protein-altering mutations and undergo epigenetic
              and genetic alterations, resulting in aberrant protein
              expression. These events can result in formation of tumor
              antigens. The identification of mutated and aberrantly expressed
              self-tumor antigens has historically been time consuming and
              laborious. While mutant antigens are usually expressed in a
              tumor-specific manner, aberrantly expressed antigens are often
              shared between cancers and, therefore, in the past, have been the
              major focus of therapeutic cancer vaccines. However, advances in
              next-generation sequencing and epitope prediction now permit the
              rapid identification of mutant tumor neoantigens. This review
              focuses on a discussion of mutant tumor neoantigens and their use
              in personalizing cancer immunotherapies.",
  journal  = "J. Clin. Invest.",
  volume   =  125,
  number   =  9,
  pages    = "3413--3421",
  month    =  sep,
  year     =  2015,
  language = "en"
}

@BOOK{PeptideChemistry,
	title     = "Peptide Chemistry: A Practical Textbook",
	author    = "Bodanszky, Professor Dr Miklos",
	abstract  = "... 127 B. Stepwise Chain-lengthening Starting with the N -
	terminal Residue ..... 129 C. Stepwise Chain-lengthening
	Starting with the C - terminal Residue ..... ... base sensi-
	tive blocking in the form of the 9-fluorenylmethyloxycarbonyl (
	Fmoc ) group (Carpino ...",
	publisher = "Springer Berlin Heidelberg",
	year      =  1988
}

@ARTICLE{Kyte1982,
	title     = "A simple method for displaying the hydropathic character of a
	protein",
	author    = "Kyte, J and Doolittle, R F",
	abstract  = "... mole fraction, the volume of the solute in the aqueous phase
	by definition will be ... 8. Plot of mean hydropathies (GRAVY
	scores) of various proteins against their lengths ; ( x ) 84 ...
	that any description of the folding process that fails to
	consider either hydrophobicity or hydrophilicity ...",
	journal   = "J. Mol. Biol.",
	publisher = "Elsevier",
	volume    =  157,
	number    =  1,
	pages     = "105--132",
	month     =  "5~" # may,
	year      =  1982,
	language  = "en"
}

@ARTICLE{neoantigens-finnigan,
  title    = "{Mutation-Derived} Tumor Antigens: Novel Targets in Cancer
              Immunotherapy",
  author   = "Finnigan, Jr, John P and Rubinsteyn, Alex and Hammerbacher,
              Jeffrey and Bhardwaj, Nina",
  journal  = "Oncology",
  volume   =  29,
  number   =  12,
  month    =  dec,
  year     =  2015,
  language = "en"
}

@ARTICLE{neoantigens-schumacher,
  title       = "Neoantigens in cancer immunotherapy",
  author      = "Schumacher, Ton N and Schreiber, Robert D",
  affiliation = "Division of Immunology, Netherlands Cancer Institute,
                 Plesmanlaan 121, 1066 CX, Amsterdam, Netherlands.
                 t.schumacher@nki.nl schreiber@immunology.wustl.edu. Department
                 of Pathology and Immunology, Washington University School of
                 Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, USA.
                 t.schumacher@nki.nl schreiber@immunology.wustl.edu.",
  abstract    = "The clinical relevance of T cells in the control of a diverse
                 set of human cancers is now beyond doubt. However, the nature
                 of the antigens that allow the immune system to distinguish
                 cancer cells from noncancer cells has long remained obscure.
                 Recent technological innovations have made it possible to
                 dissect the immune response to patient-specific neoantigens
                 that arise as a consequence of tumor-specific mutations, and
                 emerging data suggest that recognition of such neoantigens is
                 a major factor in the activity of clinical immunotherapies.
                 These observations indicate that neoantigen load may form a
                 biomarker in cancer immunotherapy and provide an incentive for
                 the development of novel therapeutic approaches that
                 selectively enhance T cell reactivity against this class of
                 antigens.",
  journal     = "Science",
  volume      =  348,
  number      =  6230,
  pages       = "69--74",
  month       =  "3~" # apr,
  year        =  2015,
  language    = "en"
}

@ARTICLE{neovax-sharma,
  title       = "Personalized cancer vaccines: Targeting the cancer mutanome",
  author      = "Zhang, Xiuli and Sharma, Piyush K and Peter Goedegebuure, S
                 and Gillanders, William E",
  affiliation = "Department of Surgery, Washington University School of
                 Medicine, St. Louis, MO 63110, United States. Department of
                 Surgery, Washington University School of Medicine, St. Louis,
                 MO 63110, United States. Department of Surgery, Washington
                 University School of Medicine, St. Louis, MO 63110, United
                 States; Alvin J. Siteman Cancer Center, Washington University
                 School of Medicine, St. Louis, MO 63110, United States.
                 Department of Surgery, Washington University School of
                 Medicine, St. Louis, MO 63110, United States; Alvin J. Siteman
                 Cancer Center, Washington University School of Medicine, St.
                 Louis, MO 63110, United States. Electronic address:
                 gillandersw@wudosis.wustl.edu.",
  abstract    = "The development of next generation sequencing technologies has
                 revolutionized our understanding of how specific genetic
                 events contribute to cancer initiation and progression.
                 Dramatic improvements in instrument design and efficiency,
                 combined with significant cost reductions has permitted a
                 systematic analysis of the mutational landscape in a variety
                 of cancer types. At the same time, a detailed map of the
                 cancer mutanome in individual cancers offers a unique
                 opportunity to develop personalized cancer vaccine strategies
                 targeting neoantigens. Recent studies in both preclinical
                 models and human cancer patients demonstrate that neoantigens
                 (1) are important targets following checkpoint inhibition
                 therapy, (2) have been identified as the target of adoptive T
                 cell therapies, and (3) can be successfully targeted with
                 personalized vaccines. Taken together, these observations
                 provide strong rationale for the clinical translation of
                 personalized cancer vaccines.",
  journal     = "Vaccine",
  volume      =  35,
  number      =  7,
  pages       = "1094--1100",
  month       =  "15~" # feb,
  year        =  2017,
  keywords    = "Cancer vaccine; Genome sequencing; Neoantigen; Personalized",
  language    = "en"
}

@ARTICLE{neovax-sahin,
  title       = "Targeting the Heterogeneity of Cancer with Individualized
                 Neoepitope Vaccines",
  author      = "T{\"u}reci, {\"O}zlem and Vormehr, Mathias and Diken, Mustafa
                 and Kreiter, Sebastian and Huber, Christoph and Sahin, Ugur",
  affiliation = "TRON - Translational Oncology at the University Medical Center
                 of Johannes Gutenberg University, Mainz, Germany. Research
                 Center for Immunotherapy (FZI), Mainz, Germany. TRON -
                 Translational Oncology at the University Medical Center of
                 Johannes Gutenberg University, Mainz, Germany. TRON -
                 Translational Oncology at the University Medical Center of
                 Johannes Gutenberg University, Mainz, Germany. TRON -
                 Translational Oncology at the University Medical Center of
                 Johannes Gutenberg University, Mainz, Germany. TRON -
                 Translational Oncology at the University Medical Center of
                 Johannes Gutenberg University, Mainz, Germany. Research Center
                 for Immunotherapy (FZI), Mainz, Germany. Biopharmaceutical New
                 Technologies (BioNTech) Corporation, Mainz, Germany.
                 ugur.sahin@tron-mainz.de.",
  abstract    = "Somatic mutations binding to the patient's MHC and recognized
                 by autologous T cells (neoepitopes) are ideal cancer vaccine
                 targets. They combine a favorable safety profile due to a lack
                 of expression in healthy tissues with a high likelihood of
                 immunogenicity, as T cells recognizing neoepitopes are not
                 shaped by central immune tolerance. Proteins mutated in cancer
                 (neoantigens) shared by patients have been explored as vaccine
                 targets for many years. Shared (``public'') mutations,
                 however, are rare, as the vast majority of cancer mutations in
                 a given tumor are unique for the individual patient. Recently,
                 the novel concept of truly individualized cancer vaccination
                 emerged, which exploits the vast source of patient-specific
                 ``private'' mutations. Concurrence of scientific advances and
                 technological breakthroughs enables the rapid, cost-efficient,
                 and comprehensive mapping of the ``mutanome,'' which is the
                 entirety of somatic mutations in an individual tumor, and the
                 rational selection of neoepitopes. How to transform tumor
                 mutanome data to actionable knowledge for tailoring
                 individualized vaccines ``on demand'' has become a novel
                 research field with paradigm-shifting potential. This review
                 gives an overview with particular focus on the clinical
                 development of such vaccines.",
  journal     = "Clin. Cancer Res.",
  volume      =  22,
  number      =  8,
  pages       = "1885--1896",
  month       =  "15~" # apr,
  year        =  2016,
  language    = "en"
}


text:
@inproceedings{salas-moreno_slam_2013,
	address = {Portland, OR, USA},
	title = {{SLAM}++: {Simultaneous} {Localisation} and {Mapping} at the {Level} of {Objects}},
	isbn = {978-0-7695-4989-7},
	shorttitle = {{SLAM}++},
	url = {http://ieeexplore.ieee.org/document/6619022/},
	doi = {10.1109/CVPR.2013.178},
	abstract = {We present the major advantages of a new ‘object oriented’ 3D SLAM paradigm, which takes full advantage in the loop of prior knowledge that many scenes consist of repeated, domain-speciﬁc objects and structures. As a hand-held depth camera browses a cluttered scene, realtime 3D object recognition and tracking provides 6DoF camera-object constraints which feed into an explicit graph of objects, continually reﬁned by efﬁcient pose-graph optimisation. This offers the descriptive and predictive power of SLAM systems which perform dense surface reconstruction, but with a huge representation compression. The object graph enables predictions for accurate ICP-based camera to model tracking at each live frame, and efﬁcient active search for new objects in currently undescribed image regions. We demonstrate real-time incremental SLAM in large, cluttered environments, including loop closure, relocalisation and the detection of moved objects, and of course the generation of an object level scene description with the potential to enable interaction.},
	language = {en},
	urldate = {2020-05-08},
	booktitle = {2013 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
	publisher = {IEEE},
	author = {Salas-Moreno, Renato F. and Newcombe, Richard A. and Strasdat, Hauke and Kelly, Paul H.J. and Davison, Andrew J.},
	month = jun,
	year = {2013},
	pages = {1352--1359}
}

@inproceedings{rusu_fast_2009,
	address = {Kobe},
	title = {Fast {Point} {Feature} {Histograms} ({FPFH}) for {3D} registration},
	isbn = {978-1-4244-2788-8},
	url = {http://ieeexplore.ieee.org/document/5152473/},
	doi = {10.1109/ROBOT.2009.5152473},
	abstract = {In our recent work [1], [2], we proposed Point Feature Histograms (PFH) as robust multi-dimensional features which describe the local geometry around a point p for 3D point cloud datasets. In this paper, we modify their mathematical expressions and perform a rigorous analysis on their robustness and complexity for the problem of 3D registration for overlapping point cloud views. More concretely, we present several optimizations that reduce their computation times drastically by either caching previously computed values or by revising their theoretical formulations. The latter results in a new type of local features, called Fast Point Feature Histograms (FPFH), which retain most of the discriminative power of the PFH. Moreover, we propose an algorithm for the online computation of FPFH features for realtime applications. To validate our results we demonstrate their efﬁciency for 3D registration and propose a new sample consensus based method for bringing two datasets into the convergence basin of a local non-linear optimizer: SAC-IA (SAmple Consensus Initial Alignment).},
	language = {en},
	urldate = {2020-05-07},
	booktitle = {2009 {IEEE} {International} {Conference} on {Robotics} and {Automation}},
	publisher = {IEEE},
	author = {Rusu, Radu Bogdan and Blodow, Nico and Beetz, Michael},
	month = may,
	year = {2009},
	pages = {3212--3217}
}

@inproceedings{bylow_real-time_2013,
	title = {Real-{Time} {Camera} {Tracking} and {3D} {Reconstruction} {Using} {Signed} {Distance} {Functions}},
	isbn = {978-981-07-3937-9},
	url = {http://www.roboticsproceedings.org/rss09/p35.pdf},
	doi = {10.15607/RSS.2013.IX.035},
	abstract = {The ability to quickly acquire 3D models is an essential capability needed in many disciplines including robotics, computer vision, geodesy, and architecture. In this paper we present a novel method for real-time camera tracking and 3D reconstruction of static indoor environments using an RGB-D sensor. We show that by representing the geometry with a signed distance function (SDF), the camera pose can be efﬁciently estimated by directly minimizing the error of the depth images on the SDF. As the SDF contains the distances to the surface for each voxel, the pose optimization can be carried out extremely fast. By iteratively estimating the camera poses and integrating the RGB-D data in the voxel grid, a detailed reconstruction of an indoor environment can be achieved. We present reconstructions of several rooms using a hand-held sensor and from onboard an autonomous quadrocopter. Our extensive evaluation on publicly available benchmark data shows that our approach is more accurate and robust than the iterated closest point algorithm (ICP) used by KinectFusion, and yields often a comparable accuracy at much higher speed to feature-based bundle adjustment methods such as RGB-D SLAM for up to medium-sized scenes.},
	language = {en},
	urldate = {2020-05-06},
	booktitle = {Robotics: {Science} and {Systems} {IX}},
	publisher = {Robotics: Science and Systems Foundation},
	author = {Bylow, Erik and Sturm, Jürgen and Kerl, Christian and Kahl, Fredrik and Cremers, Daniel},
	month = jun,
	year = {2013}
}

@article{chaplot_learning_2020,
	title = {{LEARNING} {TO} {EXPLORE} {USING} {ACTIVE} {NEURAL} {SLAM}},
	abstract = {This work presents a modular and hierarchical approach to learn policies for exploring 3D environments, called ‘Active Neural SLAM’. Our approach leverages the strengths of both classical and learning-based methods, by using analytical path planners with learned SLAM module, and global and local policies. The use of learning provides ﬂexibility with respect to input modalities (in the SLAM module), leverages structural regularities of the world (in global policies), and provides robustness to errors in state estimation (in local policies). Such use of learning within each module retains its beneﬁts, while at the same time, hierarchical decomposition and modular training allow us to sidestep the high sample complexities associated with training end-to-end policies. Our experiments in visually and physically realistic simulated 3D environments demonstrate the effectiveness of our approach over past learning and geometry-based approaches. The proposed model can also be easily transferred to the PointGoal task and was the winning entry of the CVPR 2019 Habitat PointGoal Navigation Challenge.},
	language = {en},
	author = {Chaplot, Devendra Singh and Gandhi, Dhiraj and Gupta, Saurabh and Gupta, Abhinav and Salakhutdinov, Ruslan},
	year = {2020},
	pages = {18}
}

@article{rabiner_tutorial_1989,
	title = {A tutorial on hidden {Markov} models and selected applications in speech recognition},
	volume = {77},
	issn = {1558-2256},
	doi = {10.1109/5.18626},
	abstract = {This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and gives practical details on methods of implementation of the theory along with a description of selected applications of the theory to distinct problems in speech recognition. Results from a number of original sources are combined to provide a single source of acquiring the background required to pursue further this area of research. The author first reviews the theory of discrete Markov chains and shows how the concept of hidden states, where the observation is a probabilistic function of the state, can be used effectively. The theory is illustrated with two simple examples, namely coin-tossing, and the classic balls-in-urns system. Three fundamental problems of HMMs are noted and several practical techniques for solving these problems are given. The various types of HMMs that have been studied, including ergodic as well as left-right models, are described.{\textless}{\textgreater}},
	number = {2},
	journal = {Proceedings of the IEEE},
	author = {Rabiner, L.R.},
	month = feb,
	year = {1989},
	note = {Conference Name: Proceedings of the IEEE},
	keywords = {Hidden Markov models, Markov processes, Speech recognition, Tutorial, balls-in-urns system, coin-tossing, discrete Markov chains, ergodic models, hidden Markov models, hidden states, left-right models, probabilistic function, speech recognition},
	pages = {257--286}
}

@book{bishop_pattern_2006,
	address = {New York},
	series = {Information science and statistics},
	title = {Pattern recognition and machine learning},
	isbn = {978-0-387-31073-2},
	language = {en},
	publisher = {Springer},
	author = {Bishop, Christopher M.},
	year = {2006},
	keywords = {Machine learning, Pattern perception}
}

@inproceedings{rusinkiewicz_efficient_2001,
	title = {Efficient variants of the {ICP} algorithm},
	doi = {10.1109/IM.2001.924423},
	abstract = {The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is known. Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points to the minimization strategy. We enumerate and classify many of these variants, and evaluate their effect on the speed with which the correct alignment is reached. In order to improve convergence for nearly-flat meshes with small features, such as inscribed surfaces, we introduce a new variant based on uniform sampling of the space of normals. We conclude by proposing a combination of ICP variants optimized for high speed. We demonstrate an implementation that is able to align two range images in a few tens of milliseconds, assuming a good initial guess. This capability has potential application to real-time 3D model acquisition and model-based tracking.},
	booktitle = {Proceedings {Third} {International} {Conference} on 3-{D} {Digital} {Imaging} and {Modeling}},
	author = {Rusinkiewicz, S. and Levoy, M.},
	month = may,
	year = {2001},
	keywords = {Convergence, Geometry, Image sampling, Iterative algorithms, Iterative closest point algorithm, Iterative methods, Layout, Minimization methods, Rough surfaces, Solid modeling, distance measurement, geometric alignment, image processing, inscribed surfaces, iterative closest point algorithm, minimisation, minimization strategy, model-based tracking, nearly-flat meshes, range images, real-time 3D model acquisition, real-time systems, three-dimensional models, uniform sampling},
	pages = {145--152}
}

@inproceedings{rusinkiewicz_efficient_2001-1,
	address = {Quebec City, Que., Canada},
	title = {Efficient variants of the {ICP} algorithm},
	isbn = {978-0-7695-0984-6},
	url = {http://ieeexplore.ieee.org/document/924423/},
	doi = {10.1109/IM.2001.924423},
	abstract = {The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is known. Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points to the minimization strategy. We enumerate and classify many of these variants, and evaluate their effect on the speed with which the correct alignment is reached. In order to improve convergence for nearly-ﬂat meshes with small features, such as inscribed surfaces, we introduce a new variant based on uniform sampling of the space of normals. We conclude by proposing a combination of ICP variants optimized for high speed. We demonstrate an implementation that is able to align two range images in a few tens of milliseconds, assuming a good initial guess. This capability has potential application to real-time 3D model acquisition and model-based tracking.},
	language = {en},
	urldate = {2020-04-17},
	booktitle = {Proceedings {Third} {International} {Conference} on 3-{D} {Digital} {Imaging} and {Modeling}},
	publisher = {IEEE Comput. Soc},
	author = {Rusinkiewicz, S. and Levoy, M.},
	year = {2001},
	pages = {145--152}
}

@article{runz_maskfusion_2018,
	title = {{MaskFusion}: {Real}-{Time} {Recognition}, {Tracking} and {Reconstruction} of {Multiple} {Moving} {Objects}},
	shorttitle = {{MaskFusion}},
	url = {https://arxiv.org/abs/1804.09194v2},
	abstract = {We present MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D
SLAM system that goes beyond traditional systems which output a purely
geometric map of a static scene. MaskFusion recognizes, segments and assigns
semantic class labels to different objects in the scene, while tracking and
reconstructing them even when they move independently from the camera.
  As an RGB-D camera scans a cluttered scene, image-based instance-level
semantic segmentation creates semantic object masks that enable real-time
object recognition and the creation of an object-level representation for the
world map. Unlike previous recognition-based SLAM systems, MaskFusion does not
require known models of the objects it can recognize, and can deal with
multiple independent motions. MaskFusion takes full advantage of using
instance-level semantic segmentation to enable semantic labels to be fused into
an object-aware map, unlike recent semantics enabled SLAM systems that perform
voxel-level semantic segmentation. We show augmented-reality applications that
demonstrate the unique features of the map output by MaskFusion:
instance-aware, semantic and dynamic.},
	language = {en},
	urldate = {2020-04-13},
	author = {Rünz, Martin and Buffier, Maud and Agapito, Lourdes},
	month = apr,
	year = {2018}
}

@incollection{leibe_fast_2016,
	address = {Cham},
	title = {Fast {Global} {Registration}},
	volume = {9906},
	isbn = {978-3-319-46474-9 978-3-319-46475-6},
	url = {http://link.springer.com/10.1007/978-3-319-46475-6_47},
	abstract = {We present an algorithm for fast global registration of partially overlapping 3D surfaces. The algorithm operates on candidate matches that cover the surfaces. A single objective is optimized to align the surfaces and disable false matches. The objective is deﬁned densely over the surfaces and the optimization achieves tight alignment with no initialization. No correspondence updates or closest-point queries are performed in the inner loop. An extension of the algorithm can perform joint global registration of many partially overlapping surfaces. Extensive experiments demonstrate that the presented approach matches or exceeds the accuracy of state-of-the-art global registration pipelines, while being at least an order of magnitude faster. Remarkably, the presented approach is also faster than local reﬁnement algorithms such as ICP. It provides the accuracy achieved by well-initialized local reﬁnement algorithms, without requiring an initialization and at lower computational cost.},
	language = {en},
	urldate = {2020-04-06},
	booktitle = {Computer {Vision} – {ECCV} 2016},
	publisher = {Springer International Publishing},
	author = {Zhou, Qian-Yi and Park, Jaesik and Koltun, Vladlen},
	editor = {Leibe, Bastian and Matas, Jiri and Sebe, Nicu and Welling, Max},
	year = {2016},
	doi = {10.1007/978-3-319-46475-6_47},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {766--782}
}

@article{yang_monocular_2019,
	title = {Monocular {Object} and {Plane} {SLAM} in {Structured} {Environments}},
	volume = {4},
	issn = {2377-3766},
	doi = {10.1109/LRA.2019.2924848},
	abstract = {In this letter, we present a monocular simultaneous localization and mapping (SLAM) algorithm using high-level object and plane landmarks. The built map is denser, more compact and semantic meaningful compared to feature point based SLAM. We first propose a high-order graphical model to jointly infer the three-dimensional object and layout planes from single images considering occlusions and semantic constraints. The extracted objects and planes are further optimized with camera poses in a unified SLAM framework. Objects and planes can provide more semantic constraints such as Manhattan plane and object supporting relationships compared to points. Experiments on various public and collected datasets, including ICL NUIM and TUM Mono show that our algorithm can improve camera localization accuracy compared to state-of-the-art SLAM, especially when there is no loop closure, and also generate dense maps robustly in many structured environments.},
	number = {4},
	journal = {IEEE Robotics and Automation Letters},
	author = {Yang, Shichao and Scherer, Sebastian},
	month = oct,
	year = {2019},
	note = {Conference Name: IEEE Robotics and Automation Letters},
	keywords = {Cameras, ICL NUIM, Layout, Object and Plane SLAM, Optimization, Proposals, SLAM, SLAM (robots), Semantic Scene Understanding, Semantics, Simultaneous localization and mapping, TUM Mono, Three-dimensional displays, camera localization accuracy, cameras, dense maps, extracted objects, high-level object, high-order graphical model, layout planes, mobile robots, monocular object, monocular simultaneous localization, object supporting relationships, plane SLAM, plane landmarks, point based SLAM, robot vision, semantic constraints, single images, structured environments, three-dimensional object, unified SLAM framework},
	pages = {3145--3152}
}

@inproceedings{lai_unsupervised_2014,
	title = {Unsupervised feature learning for {3D} scene labeling},
	doi = {10.1109/ICRA.2014.6907298},
	abstract = {This paper presents an approach for labeling objects in 3D scenes. We introduce HMP3D, a hierarchical sparse coding technique for learning features from 3D point cloud data. HMP3D classifiers are trained using a synthetic dataset of virtual scenes generated using CAD models from an online database. Our scene labeling system combines features learned from raw RGB-D images and 3D point clouds directly, without any hand-designed features, to assign an object label to every 3D point in the scene. Experiments on the RGB-D Scenes Dataset v.2 demonstrate that the proposed approach can be used to label indoor scenes containing both small tabletop objects and large furniture pieces.},
	booktitle = {2014 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},
	author = {Lai, Kevin and Bo, Liefeng and Fox, Dieter},
	month = may,
	year = {2014},
	note = {ISSN: 1050-4729},
	keywords = {3D point cloud data, 3D scene labeling, CAD, CAD model, Dictionaries, Feature extraction, HMP3D classifiers, Labeling, Matching pursuit algorithms, RGB-D images, RGB-D scenes dataset v.2, Solid modeling, Three-dimensional displays, Videos, furniture pieces, hand-designed feature, hierarchical sparse coding technique, image colour analysis, indoor scenes, learning features, object label, online database, scene labeling system, solid modelling, synthetic dataset, tabletop objects, unsupervised feature learning, unsupervised learning, virtual reality, virtual scenes},
	pages = {3050--3057}
}

@inproceedings{park_colored_2017,
	address = {Venice},
	title = {Colored {Point} {Cloud} {Registration} {Revisited}},
	isbn = {978-1-5386-1032-9},
	url = {http://ieeexplore.ieee.org/document/8237287/},
	doi = {10.1109/ICCV.2017.25},
	abstract = {We present an algorithm for aligning two colored point clouds. The key idea is to optimize a joint photometric and geometric objective that locks the alignment along both the normal direction and the tangent plane. We extend a photometric objective for aligning RGB-D images to point clouds, by locally parameterizing the point cloud with a virtual camera. Experiments demonstrate that our algorithm is more accurate and more robust than prior point cloud registration algorithms, including those that utilize color information. We use the presented algorithms to enhance a state-of-the-art scene reconstruction system. The precision of the resulting system is demonstrated on real-world scenes with accurate ground-truth models.},
	language = {en},
	urldate = {2020-04-04},
	booktitle = {2017 {IEEE} {International} {Conference} on {Computer} {Vision} ({ICCV})},
	publisher = {IEEE},
	author = {Park, Jaesik and Zhou, Qian-Yi and Koltun, Vladlen},
	month = oct,
	year = {2017},
	pages = {143--152}
}

@article{kirillov_pointrend_2020,
	title = {{PointRend}: {Image} {Segmentation} as {Rendering}},
	shorttitle = {{PointRend}},
	url = {http://arxiv.org/abs/1912.08193},
	abstract = {We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches. Code has been made available at https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend.},
	urldate = {2020-04-04},
	journal = {arXiv:1912.08193 [cs]},
	author = {Kirillov, Alexander and Wu, Yuxin and He, Kaiming and Girshick, Ross},
	month = feb,
	year = {2020},
	note = {arXiv: 1912.08193},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{claraco_tutorial_nodate,
	title = {A tutorial on {SE}(3) transformation parameterizations and on-manifold optimization},
	language = {en},
	journal = {Technical report},
	author = {Claraco, Jose Luis Blanco},
	pages = {66}
}

@article{mur-artal_orb-slam2_2017,
	title = {{ORB}-{SLAM2}: an {Open}-{Source} {SLAM} {System} for {Monocular}, {Stereo} and {RGB}-{D} {Cameras}},
	volume = {33},
	issn = {1552-3098, 1941-0468},
	shorttitle = {{ORB}-{SLAM2}},
	url = {http://arxiv.org/abs/1610.06475},
	doi = {10.1109/TRO.2017.2705103},
	abstract = {We present ORB-SLAM2 a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities. The system works in real-time on standard CPUs in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end based on bundle adjustment with monocular and stereo observations allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches to map points that allow for zero-drift localization. The evaluation on 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.},
	number = {5},
	urldate = {2020-03-25},
	journal = {IEEE Transactions on Robotics},
	author = {Mur-Artal, Raul and Tardos, Juan D.},
	month = oct,
	year = {2017},
	note = {arXiv: 1610.06475},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics},
	pages = {1255--1262}
}

@article{millane_c-blox_2018,
	title = {C-blox: {A} {Scalable} and {Consistent} {TSDF}-based {Dense} {Mapping} {Approach}},
	shorttitle = {C-blox},
	url = {http://arxiv.org/abs/1710.07242},
	abstract = {In many applications, maintaining a consistent dense map of the environment is key to enabling robotic platforms to perform higher level decision making. Several works have addressed the challenge of creating precise dense 3D maps from visual sensors providing depth information. However, during operation over longer missions, reconstructions can easily become inconsistent due to accumulated camera tracking error and delayed loop closure. Without explicitly addressing the problem of map consistency, recovery from such distortions tends to be difficult. We present a novel system for dense 3D mapping which addresses the challenge of building consistent maps while dealing with scalability. Central to our approach is the representation of the environment as a collection of overlapping TSDF subvolumes. These subvolumes are localized through feature-based camera tracking and bundle adjustment. Our main contribution is a pipeline for identifying stable regions in the map, and to fuse the contributing subvolumes. This approach allows us to reduce map growth while still maintaining consistency. We demonstrate the proposed system on a publicly available dataset and simulation engine, and demonstrate the efficacy of the proposed approach for building consistent and scalable maps. Finally we demonstrate our approach running in real-time on-board a lightweight MAV.},
	urldate = {2020-03-25},
	journal = {arXiv:1710.07242 [cs]},
	author = {Millane, Alexander and Taylor, Zachary and Oleynikova, Helen and Nieto, Juan and Siegwart, Roland and Cadena, César},
	month = sep,
	year = {2018},
	note = {arXiv: 1710.07242},
	keywords = {Computer Science - Robotics}
}

@article{dellaert_factor_2017,
	title = {Factor {Graphs} for {Robot} {Perception}},
	volume = {6},
	issn = {1935-8253, 1935-8261},
	url = {http://www.nowpublishers.com/article/Details/ROB-043},
	doi = {10.1561/2300000043},
	language = {en},
	number = {1-2},
	urldate = {2020-03-23},
	journal = {Foundations and Trends in Robotics},
	author = {Dellaert, Frank and Kaess, Michael},
	year = {2017},
	pages = {1--139}
}

@article{zhou_open3d_2018,
	title = {{Open3D}: {A} {Modern} {Library} for {3D} {Data} {Processing}},
	shorttitle = {{Open3D}},
	url = {http://arxiv.org/abs/1801.09847},
	abstract = {Open3D is an open-source library that supports rapid development of software that deals with 3D data. The Open3D frontend exposes a set of carefully selected data structures and algorithms in both C++ and Python. The backend is highly optimized and is set up for parallelization. Open3D was developed from a clean slate with a small and carefully considered set of dependencies. It can be set up on different platforms and compiled from source with minimal effort. The code is clean, consistently styled, and maintained via a clear code review mechanism. Open3D has been used in a number of published research projects and is actively deployed in the cloud. We welcome contributions from the open-source community.},
	urldate = {2020-03-21},
	journal = {arXiv:1801.09847 [cs]},
	author = {Zhou, Qian-Yi and Park, Jaesik and Koltun, Vladlen},
	month = jan,
	year = {2018},
	note = {arXiv: 1801.09847},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics, Computer Science - Robotics}
}

@misc{noauthor_3dmatch_nodate,
	title = {{3DMatch}: {Learning} {Local} {Geometric} {Descriptors} from {RGB}-{D} {Reconstructions}},
	url = {http://3dmatch.cs.princeton.edu/},
	urldate = {2020-03-21}
}

@article{niesner_real-time_2013,
	title = {Real-time {3D} reconstruction at scale using voxel hashing},
	volume = {32},
	issn = {07300301},
	url = {http://dl.acm.org/citation.cfm?doid=2508363.2508374},
	doi = {10.1145/2508363.2508374},
	abstract = {Online 3D reconstruction is gaining newfound interest due to the availability of real-time consumer depth cameras. The basic problem takes live overlapping depth maps as input and incrementally fuses these into a single 3D model. This is challenging particularly when real-time performance is desired without trading quality or scale. We contribute an online system for large and ﬁne scale volumetric reconstruction based on a memory and speed efﬁcient data structure. Our system uses a simple spatial hashing scheme that compresses space, and allows for real-time access and updates of implicit surface data, without the need for a regular or hierarchical grid data structure. Surface data is only stored densely where measurements are observed. Additionally, data can be streamed efﬁciently in or out of the hash table, allowing for further scalability during sensor motion. We show interactive reconstructions of a variety of scenes, reconstructing both ﬁne-grained details and large scale environments. We illustrate how all parts of our pipeline from depth map pre-processing, camera pose estimation, depth map fusion, and surface rendering are performed at real-time rates on commodity graphics hardware. We conclude with a comparison to current state-of-the-art online systems, illustrating improved performance and reconstruction quality.},
	language = {en},
	number = {6},
	urldate = {2020-03-21},
	journal = {ACM Transactions on Graphics},
	author = {Nießner, Matthias and Zollhöfer, Michael and Izadi, Shahram and Stamminger, Marc},
	month = nov,
	year = {2013},
	pages = {1--11}
}

@article{newcombe_kinectfusion_nodate,
	title = {{KinectFusion}: {Real}-{Time} {Dense} {Surface} {Mapping} and {Tracking}},
	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.},
	language = {en},
	author = {Newcombe, Richard A and Davison, Andrew J and Izadi, Shahram and Kohli, Pushmeet and Hilliges, Otmar and Shotton, Jamie and Molyneaux, David and Hodges, Steve and Kim, David and Fitzgibbon, Andrew},
	pages = {10}
}

@article{whelan_kintinuous_nodate,
	title = {Kintinuous: {Spatially} {Extended} {KinectFusion}},
	abstract = {In this paper we present an extension to the KinectFusion algorithm that permits dense mesh-based mapping of extended scale environments in real-time. This is achieved through (i) altering the original algorithm such that the region of space being mapped by the KinectFusion algorithm can vary dynamically, (ii) extracting a dense point cloud from the regions that leave the KinectFusion volume due to this variation, and, (iii) incrementally adding the resulting points to a triangular mesh representation of the environment. The system is implemented as a set of hierarchical multi-threaded components which are capable of operating in real-time. The architecture facilitates the creation and integration of new modules with minimal impact on the performance on the dense volume tracking and surface reconstruction modules. We provide experimental results demonstrating the system’s ability to map areas considerably beyond the scale of the original KinectFusion algorithm including a two story apartment and an extended sequence taken from a car at night. In order to overcome failure of the iterative closest point (ICP) based odometry in areas of low geometric features we have evaluated the Fast Odometry from Vision (FOVIS) system as an alternative. We provide a comparison between the two approaches where we show a trade off between the reduced drift of the visual odometry approach and the higher local mesh quality of the ICP-based approach. Finally we present ongoing work on incorporating full simultaneous localisation and mapping (SLAM) pose-graph optimisation.},
	language = {en},
	author = {Whelan, Thomas and McDonald, John and Kaess, Michael and Fallon, Maurice and Johannsson, Hordur and Leonard, John J},
	pages = {8}
}

@article{jatavallabhula_gradslam_2019,
	title = {{gradSLAM}: {Dense} {SLAM} meets {Automatic} {Differentiation}},
	shorttitle = {{gradSLAM}},
	url = {http://arxiv.org/abs/1910.10672},
	abstract = {The question of "representation" is central in the context of dense simultaneous localization and mapping (SLAM). Newer learning-based approaches have the potential to leverage data or task performance to directly inform the choice of representation. However, learning representations for SLAM has been an open question, because traditional SLAM systems are not end-to-end differentiable.},
	language = {en},
	urldate = {2020-03-21},
	journal = {arXiv:1910.10672 [cs]},
	author = {Jatavallabhula, Krishna Murthy and Iyer, Ganesh and Paull, Liam},
	month = oct,
	year = {2019},
	note = {arXiv: 1910.10672},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Robotics}
}

@article{black_unification_1996,
	title = {On the unification of line processes, outlier rejection, and robust statistics with applications in early vision},
	volume = {19},
	issn = {0920-5691, 1573-1405},
	url = {http://link.springer.com/10.1007/BF00131148},
	doi = {10.1007/BF00131148},
	abstract = {The modeling of spatial discontinuities for problems such as surface recovery, segmentation, image reconstruction, and optical ﬂow has been intensely studied in computer vision. While “line-process” models of discontinuities have received a great deal of attention, there has been recent interest in the use of robust statistical techniques to account for discontinuities. This paper uniﬁes the two approaches. To achieve this we generalize the notion of a “line process” to that of an analog “outlier process” and show how a problem formulated in terms of outlier processes can be viewed in terms of robust statistics. We also characterize a class of robust statistical problems for which an equivalent outlier-process formulation exists and give a straightforward method for converting a robust estimation problem into an outlier-process formulation. We show how prior assumptions about the spatial structure of outliers can be expressed as constraints on the recovered analog outlier processes and how traditional continuation methods can be extended to the explicit outlier-process formulation. These results indicate that the outlierprocesses approach provides a general framework which subsumes the traditional line-process approaches as well as a wide class of robust estimation problems. Examples in surface reconstruction, image segmentation, and optical ﬂow are presented to illustrate the use of outlier processes and to show how the relationship between outlier processes and robust statistics can be exploited. An appendix provides a catalog of common robust error norms and their equivalent outlier-process formulations.},
	language = {en},
	number = {1},
	urldate = {2020-03-21},
	journal = {International Journal of Computer Vision},
	author = {Black, Michael J. and Rangarajan, Anand},
	month = jul,
	year = {1996},
	pages = {57--91}
}

@article{czarnowski_deepfactors_2020,
	title = {{DeepFactors}: {Real}-{Time} {Probabilistic} {Dense} {Monocular} {SLAM}},
	volume = {5},
	issn = {2377-3766, 2377-3774},
	shorttitle = {{DeepFactors}},
	url = {https://ieeexplore.ieee.org/document/8954779/},
	doi = {10.1109/LRA.2020.2965415},
	abstract = {The ability to estimate rich geometry and camera motion from monocular imagery is fundamental to future interactive robotics and augmented reality applications. Different approaches have been proposed that vary in scene geometry representation (sparse landmarks, dense maps), the consistency metric used for optimising the multi-view problem, and the use of learned priors. We present a SLAM system that uniﬁes these methods in a probabilistic framework while still maintaining real-time performance. This is achieved through the use of a learned compact depth map representation and reformulating three different types of errors: photometric, reprojection and geometric, which we make use of within standard factor graph software. We evaluate our system on trajectory estimation and depth reconstruction on real-world sequences and present various examples of estimated dense geometry.},
	language = {en},
	number = {2},
	urldate = {2020-03-21},
	journal = {IEEE Robotics and Automation Letters},
	author = {Czarnowski, Jan and Laidlow, Tristan and Clark, Ronald and Davison, Andrew J.},
	month = apr,
	year = {2020},
	pages = {721--728}
}

@inproceedings{sunderhauf_switchable_2012,
	title = {Switchable {Constraints} for {Robust} {Pose} {Graph} {SLAM}},
	abstract = {c○2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating},
	booktitle = {In {Proc}. of {IEEE} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS}},
	author = {Sünderhauf, Niko and Protzel, Peter},
	year = {2012}
}

@inproceedings{sungjoon_choi_robust_2015,
	address = {Boston, MA, USA},
	title = {Robust reconstruction of indoor scenes},
	isbn = {978-1-4673-6964-0},
	url = {http://ieeexplore.ieee.org/document/7299195/},
	doi = {10.1109/CVPR.2015.7299195},
	abstract = {We present an approach to indoor scene reconstruction from RGB-D video. The key idea is to combine geometric registration of scene fragments with robust global optimization based on line processes. Geometric registration is error-prone due to sensor noise, which leads to aliasing of geometric detail and inability to disambiguate different surfaces in the scene. The presented optimization approach disables erroneous geometric alignments even when they signiﬁcantly outnumber correct ones. Experimental results demonstrate that the presented approach substantially increases the accuracy of reconstructed scene models.},
	language = {en},
	urldate = {2020-03-21},
	booktitle = {2015 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	publisher = {IEEE},
	author = {{Sungjoon Choi} and Zhou, Qian-Yi and Koltun, Vladlen},
	month = jun,
	year = {2015},
	pages = {5556--5565}
}

license: MIT

license_type: permissive

branch_name: refs/heads/main

extension: bib


text:
@article{moreau_sofar_2000,
 author = {Moreau, Luc and Gibbins, Nick and DeRoure, David and El-Beltagy, Samhaa and Hall, Wendy and Hughes, Gareth and Joyce, Dan and Kim, Sanghee and Michaelides, Danius and Millard, Dave},
 issn = {190242607X},
 title = {SoFAR with DIM agents: An agent framework for distributed information management},
 year = {2000}
}

repository: danwjoyce/technical-blog

repo_name: danwjoyce/technical-blog

branch_name: refs/heads/master


text:
%
% This is file `coppe.bib'.
%
% Bibliographic references for the documentation.
%
% Copyright (C) 2011 CoppeTeX Project and any individual authors listed
% elsewhere in this file.
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License version 3 as
% published by the Free Software Foundation.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License version 3 for more details.
%
% You should have received a copy of the GNU General Public License
% version 3 along with this package (see COPYING file).
% If not, see <http://www.gnu.org/licenses/>.
%
% $URL: https://coppetex.svn.sourceforge.net/svnroot/coppetex/trunk/coppe.bib $
% $Id: coppe.bib 118 2008-10-18 14:17:06Z helano $
%
% Author(s): Vicente H. F. Batista
%            George O. Ainsworth Jr.
%

@BOOK{book-example,
   author = "R. Abraham and J. E. Marsden and T. Ratiu",
   title = "Manifolds, Tensor Analysis, and Applications",
   edition = "2",
   address = "New York",
   publisher = "Springer-Verlag",
   year = "1988",
}

@ARTICLE{article-example,
   author = "D. Iesan",
   title = {Existence Theorems in the Theory of Mixtures},
   journal = {Journal of Elasticity},
   volume = {42},
   number = {2},
   pages = {145--163},
   month = feb,
   year = 1996,
}

@TECHREPORT{techreport-exampleIn,
  author = "D. A. Garret",
  title = "The Microscopic Detection of Corrosion in Aluminum Aircraft Structures with Thermal Neutron Beams and Film Imaging Methods",
  type = "In: Report",
  number = "NBSIR 78-1434",
  institution = "National Bureau of Standards",
  address = "Washington, D.C.",
  year = "1977",
}

@TECHREPORT{techreport-example,
   author = "L. Maestrello",
   title = "Two-Point Correlations of Sound Pressure in the Far Field of a Jet: Experiment",
   type = "{NASA}",
   number = "{TM}~{X}-72835",
   year = 1976,
}

@INPROCEEDINGS{inproceedings-example,
   author = "M. E. Gurtin",
   title = "On the nonlinear theory of elasticity",
   booktitle = "Proceedings of the International Symposium on Continuum Mechanics and Partial Differential Equations: Contemporary Developments in Continuum Mechanics and Partial Differential Equations",
   pages = "237-253",
   address = "Rio de Janeiro",
   month = aug,
   year = 1977,
}

@INCOLLECTION{incollection-example,
   author = "S. C. Cowin",
   title = "Adaptive Anisotropy: An Example in Living Bone",
   booktitle = "Non-Classical Continuum Mechanics",
   series = "London Mathematical Society Lecture Note Series",
   volume = "122",
   publisher = "Cambridge University Press",
   pages = "174--186",
   year = 1987,
}

@INBOOK{inbook-example,
   author = "D. K. Edwards",
   title = "Thermal Radiation Measurements",
   address = "New York, USA",
   booktitle = "Measurements in Heat Transfer",
   publisher = "Hemisphere Publishing Corporation",
   editor = "E. R. G. Eckert and R. J. Goldstein",
   edition = 2,
   year = "1976",
   chapter = "10",
}

@MASTERSTHESIS{mastersthesis-example,
   author = "A. Tuntomo",
   title = "Transport Phenomena in a Small Particle with Internal Radiant Absorption",
   school = "University of California at Berkeley",
   type = "{Ph.D.} dissertation",
   address = "Berkeley, California, USA",
   year = 1990,
}

@PHDTHESIS{phdthesis-example,
   author = "Paes~Junior, H. R.",
   title = "Influ{\^e}ncia da Espessura da Camada Intr{\'i}nseca e Energia do Foton na Degrada{\c c}\~ao de C{\'e}lulas Solares de Sil{\'i}cio Amorfo Hidrogenado",
   school = "COPPE/UFRJ",
   address = "Rio de Janeiro, RJ, Brasil",
   type = "Tese de {D.Sc.}",
   year = 1994,
}

repository: COPPE-UFRJ/coppedown

repo_name: COPPE-UFRJ/coppedown

gha_license_id: MIT

gha_language: TeX


text:
# Merges two or more FMUs that contain the same model, but compiled for
# different platforms.
#
# The modelDescription.xml files of the different FMUs are compared to
# verify that the FMUs contain the same model. All other files are assumed
# to be the same if they have the same paths.
#
# Configuration variables:
#
#   INPUT_FMUS
#       A list of the FMUs that will be merged.
#
#   OUTPUT_FMU
#       The name of the output FMU.
#
cmake_minimum_required(VERSION 3.0)

function(unzip zipFile targetDir)
    execute_process(
        COMMAND "${CMAKE_COMMAND}" "-E" "tar" "xf" "${zipFile}" "--format=zip"
        WORKING_DIRECTORY "${targetDir}"
        RESULT_VARIABLE exitCode
    )
    if(NOT exitCode EQUAL 0)
        message(FATAL_ERROR "Unzip command failed")
    endif()
endfunction()

function(zip zipFile sourceDir)
    file(GLOB inputFiles RELATIVE "${sourceDir}" "${sourceDir}/*")
    execute_process(
        COMMAND "${CMAKE_COMMAND}" "-E" "tar" "cf" "${zipFile}" "--format=zip" ${inputFiles}
        WORKING_DIRECTORY "${sourceDir}"
        RESULT_VARIABLE exitCode
    )
    if(NOT exitCode EQUAL 0)
        message(FATAL_ERROR "Zip command failed")
    endif()
endfunction()

function(read_text_file filePath outputVar)
    file(READ "${filePath}" content)
    string(REGEX REPLACE "\r\n" "\n" unixContent "${content}")
    set("${outputVar}" "${unixContent}" PARENT_SCOPE)
endfunction()

function(make_absolute path outputVar)
    if(IS_ABSOLUTE "${path}")
        set(${outputVar} "${path}" PARENT_SCOPE)
    else()
        set(${outputVar} "${CMAKE_BINARY_DIR}/${path}" PARENT_SCOPE)
    endif()
endfunction()

# --- Script starts here ---

if(NOT INPUT_FMUS)
    message(FATAL_ERROR "No input FMUs specified")
endif()
if(NOT OUTPUT_FMU)
    message(FATAL_ERROR "No output FMU specified")
endif()

get_filename_component(fmuName "${OUTPUT_FMU}" NAME)
string(RANDOM random)
set(mergeDir "${CMAKE_BINARY_DIR}/${fmuName}.${random}")
file(MAKE_DIRECTORY "${mergeDir}")

set(oldFmu)
set(oldModelDescription)
foreach(fmu IN LISTS INPUT_FMUS)
    make_absolute("${fmu}" absPath)
    unzip("${absPath}" "${mergeDir}")
    read_text_file("${mergeDir}/modelDescription.xml" modelDescription)
    if(oldFmu AND NOT modelDescription STREQUAL oldModelDescription)
        message(FATAL_ERROR "Model descriptions of '${oldFmu}' and '${fmu}' do not match")
    endif()
    set(oldFmu "${fmu}")
    set(oldModelDescription "${modelDescription}")
endforeach()

make_absolute("${OUTPUT_FMU}" absOutputFmu)
zip("${absOutputFmu}" "${mergeDir}")
file(REMOVE_RECURSE "${mergeDir}")

repository: mfkiwl/cpp-fmus

repo_name: mfkiwl/cpp-fmus

extension: cmake


text:
@inproceedings{zilly2017recurrent,
  title={Recurrent highway networks},
  author={Zilly, Julian Georg and Srivastava, Rupesh Kumar and Koutn{\i}k, Jan and Schmidhuber, J{\"u}rgen},
  booktitle={International Conference on Machine Learning},
  pages={4189--4198},
  year={2017},
  organization={PMLR}
}

repository: rbt-c/DL-NLP-Readings

path: /Bibtex/Recurrent Highway Networks.bib

repo_name: rbt-c/DL-NLP-Readings

filename: Recurrent Highway Networks.bib


text:
.form {
}

.actions {
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}

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extension: css


text:
@proceedings{HEPML14,
  editor = {Cowan, Glen and Germain, Cécile and Guyon, Isabelle and Kégl, Balázs and Rousseau, David},
  booktitle = {NIPS 2014 Workshop on High-energy Physics and Machine Learning},
  shortname = {HEPML},
  volume = {42},
  year = {2014},
  sections={preface=Preface|default=Accepted Papers}
}

repository: mreid/papersite

repo_name: mreid/papersite


text:
@article{carlson2020,
 author = {Carlson, H. Jacob},
 doi = {10.1111/cico.12482},
 file = {/Users/jakecarlson/Zotero/storage/TGFHNW37/Carlson_2020_MeasuringDisplacement.pdf},
 issn = {1535-6841, 1540-6040},
 journal = {City & Community},
 language = {en},
 month = {September},
 number = {3},
 pages = {573--592},
 shorttitle = {Measuring Displacement},
 title = {Measuring Displacement: Assessing Proxies for Involuntary Residential Mobility},
 volume = {19},
 year = {2020}
}


text:
@Manual{R-base,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2019},
  url = {https://www.R-project.org/},
}
@Manual{R-bookdown,
  title = {bookdown: Authoring Books and Technical Documents with R Markdown},
  author = {Yihui Xie},
  year = {2018},
  note = {R package version 0.9},
  url = {https://CRAN.R-project.org/package=bookdown},
}
@Manual{R-car,
  title = {car: Companion to Applied Regression},
  author = {John Fox and Sanford Weisberg and Brad Price},
  year = {2018},
  note = {R package version 3.0-2},
  url = {https://CRAN.R-project.org/package=car},
}
@Manual{R-carData,
  title = {carData: Companion to Applied Regression Data Sets},
  author = {John Fox and Sanford Weisberg and Brad Price},
  year = {2018},
  note = {R package version 3.0-2},
  url = {https://CRAN.R-project.org/package=carData},
}
@Manual{R-corrplot,
  title = {corrplot: Visualization of a Correlation Matrix},
  author = {Taiyun Wei and Viliam Simko},
  year = {2017},
  note = {R package version 0.84},
  url = {https://CRAN.R-project.org/package=corrplot},
}
@Manual{R-gamlss,
  title = {gamlss: Generalised Additive Models for Location Scale and Shape},
  author = {Mikis Stasinopoulos and Bob Rigby},
  year = {2019},
  note = {R package version 5.1-3},
  url = {https://CRAN.R-project.org/package=gamlss},
}
@Manual{R-gamlss.data,
  title = {gamlss.data: GAMLSS Data},
  author = {Mikis Stasinopoulos and Bob Rigby and Fernanda {De Bastiani}},
  year = {2018},
  note = {R package version 5.1-0},
  url = {https://CRAN.R-project.org/package=gamlss.data},
}
@Manual{R-gamlss.dist,
  title = {gamlss.dist: Distributions for Generalized Additive Models for Location Scale
and Shape},
  author = {Mikis Stasinopoulos and Robert Rigby},
  year = {2018},
  note = {R package version 5.1-1},
  url = {https://CRAN.R-project.org/package=gamlss.dist},
}
@Manual{R-ggplot2,
  title = {ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics},
  author = {Hadley Wickham and Winston Chang and Lionel Henry and Thomas Lin Pedersen and Kohske Takahashi and Claus Wilke and Kara Woo},
  year = {2018},
  note = {R package version 3.1.0},
  url = {https://CRAN.R-project.org/package=ggplot2},
}
@Manual{R-knitr,
  title = {knitr: A General-Purpose Package for Dynamic Report Generation in R},
  author = {Yihui Xie},
  year = {2018},
  note = {R package version 1.21},
  url = {https://CRAN.R-project.org/package=knitr},
}
@Manual{R-MASS,
  title = {MASS: Support Functions and Datasets for Venables and Ripley's MASS},
  author = {Brian Ripley},
  year = {2018},
  note = {R package version 7.3-51.1},
  url = {https://CRAN.R-project.org/package=MASS},
}
@Manual{R-nlme,
  title = {nlme: Linear and Nonlinear Mixed Effects Models},
  author = {José Pinheiro and Douglas Bates and {R-core}},
  year = {2018},
  note = {R package version 3.1-137},
  url = {https://CRAN.R-project.org/package=nlme},
}
@Manual{R-nortest,
  title = {nortest: Tests for Normality},
  author = {Juergen Gross and Uwe Ligges},
  year = {2015},
  note = {R package version 1.0-4},
  url = {https://CRAN.R-project.org/package=nortest},
}
@Manual{R-numDeriv,
  title = {numDeriv: Accurate Numerical Derivatives},
  author = {Paul Gilbert and Ravi Varadhan},
  year = {2016},
  note = {R package version 2016.8-1},
  url = {https://CRAN.R-project.org/package=numDeriv},
}
@Manual{R-readxl,
  title = {readxl: Read Excel Files},
  author = {Hadley Wickham and Jennifer Bryan},
  year = {2019},
  note = {R package version 1.3.0},
  url = {https://CRAN.R-project.org/package=readxl},
}
@Manual{R-rmarkdown,
  title = {rmarkdown: Dynamic Documents for R},
  author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone},
  year = {2018},
  note = {R package version 1.11},
  url = {https://CRAN.R-project.org/package=rmarkdown},
}
@Manual{R-stests,
  title = {stests: Package with useful statistical tests},
  author = {Freddy Hernandez and Olga Usuga},
  year = {2019},
  note = {R package version 0.1.0},
  url = {https://fhernanb.github.io/stests},
}
@Manual{R-usefultools,
  title = {usefultools: Useful tools for data analysis},
  author = {Freddy Hernandez},
  year = {2019},
  note = {R package version 0.1.0},
  url = {https://github.com/fhernanb/usefultools},
}

repository: Mendez-Daniel/Manual-de-R

repo_name: Mendez-Daniel/Manual-de-R


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{AvalosContreras2011,
abstract = {“Node.JS”, an event-oriented framework for coding JavaScript programs on the server side, is$\backslash$r$\backslash$ncoming out as an emergent technology for creating efficient and scalable network applications$\backslash$r$\backslash$nof high performance and low memory consumption. Yet, its characteristic of handling several,$\backslash$r$\backslash$neven thousands, of connections by using one single process, opts to be a vulnerability when$\backslash$r$\backslash$ncreating highly available applications. Thus, a research has been conducted to confirm if this$\backslash$r$\backslash$nframework is capable of meeting such requirements despite the odds.$\backslash$r$\backslash$nDuring the course of this investigation, a study about failures in Internet Services has been$\backslash$r$\backslash$nconducted, showing that the technology chosen is not the most common reason for service$\backslash$r$\backslash$ndisruptions. In addition, a prototype, based on a Fault Model Enforcement and design patterns$\backslash$r$\backslash$nfor fault tolerant software, was developed to monitor an Instant Messaging service (also written$\backslash$r$\backslash$nin JavaScript) at system and application level and to provide redundancy by communicating$\backslash$r$\backslash$nwith other nodes within a cluster system whenever it crashes.$\backslash$r$\backslash$nThe results obtained through a series of fault-injection testings show the functionalities of the$\backslash$r$\backslash$nnewly created system, confirming that Node.JS seems to meet the requirements needed to$\backslash$r$\backslash$ndevelop a highly available program. Further testing in regards to stability and CPU usage,$\backslash$r$\backslash$ntogether with the implementation of better tools for monitoring, can improve the robustness of$\backslash$r$\backslash$nthe system.},
author = {{Avalos Contreras}, Sergio},
file = {:Users/sepmein/Qsync/Papers/Avalos Contreras{\_}2011{\_}An Application Framework for High Available Systems in Node.pdf:pdf},
journal = {Master of Science Thesis Stockholm, Sweden 2011},
title = {{An Application Framework for High Available Systems in Node}},
year = {2011}
}

license: Unlicense

repository: sepmein/references

repo_name: sepmein/references


text:
# Copyright 2022 Open Source Robotics Foundation, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and
# limitations under the License.

# Generated from generate_version_header.cmake.in
# This file is used by ament_generate_version_header()

set(GENERATED_HEADER_FILE "@GENERATED_HEADER_FILE@")
set(VERSION_TEMPLATE_FILE "@VERSION_TEMPLATE_FILE@")

set(VERSION_MAJOR "@VERSION_MAJOR@")
set(VERSION_MINOR "@VERSION_MINOR@")
set(VERSION_PATCH "@VERSION_PATCH@")
set(VERSION_STR "@VERSION_STR@")

set(PROJECT_NAME_UPPER "@PROJECT_NAME_UPPER@")

configure_file("${VERSION_TEMPLATE_FILE}" "${GENERATED_HEADER_FILE}")

branch_name: refs/heads/rolling

gha_language: CMake

extension: in


text:
@article{Parmentier-2019-Is,
    title = "Is the Northern Permafrost Zone a Source or a Sink for Carbon?",
    author = "Parmentier, Frans-Jan  and
      Sonnentag, Oliver  and
      Mauritz, Marguerite  and
      Virkkala, Anna-Maria  and
      Schuur, Edward",
    journal = "Eos, Volume 100",
    volume = "100",
    year = "2019",
    publisher = "American Geophysical Union (AGU)",
    url = "https://aclanthology.org/G19-137001",
    doi = "10.1029/2019eo130507",
    abstract = "Thawing permafrost could release large amounts of carbon into the atmosphere, but finding out how much requires better collection and curation of data.",
}

repository: gwf-uwaterloo/gwf-publications

repo_name: gwf-uwaterloo/gwf-publications

gha_language: Python


text:
DESCRIPTION = "Automatic management of removable drives and media for thunar"
SECTION = "x11"
LICENSE = "GPLv2"
LIC_FILES_CHKSUM = "file://COPYING;md5=94d55d512a9ba36caa9b7df079bae19f"
DEPENDS = "exo gtk+ libxfce4ui libxfce4util xfconf libnotify udev"

inherit xfce

SRC_URI[md5sum] = "2f166662dd100d5195da238af417f305"
SRC_URI[sha256sum] = "b1afd2b3b285cfcf07c11caa3d37fa5ff1fc06f7dc68fcca07f9a0d70daa694f"

extension: bb


text:
@article{matthey2013independence,
 abstract = {A central understanding in experimental economics is that subjects’ decisions in the lab are independent of history. We test whether this assumption of between-experiment independence is indeed justified. We analyze experiments with an allocation decision (like a dictator or ultimatum game) and find that participation in previous experiments tends to increase the amount subjects allocate to themselves. Hence, independence between experiments cannot be presumed if subjects participate repeatedly. The finding has implications for the interpretation of previous allocation decision results and deserves attention when running future experiments.},
 author = {Matthey, Astrid and Regner, Tobias},
 doi = {10.1007/s11238-012-9346-z},
 journal = {Theory and decision},
 number = {3},
 pages = {403--419},
 publisher = {Springer},
 title = {On the independence of history: experience spill-overs between experiments},
 volume = {75},
 year = {2013}
}

repository: TobReg/academic-kickstart

repo_name: TobReg/academic-kickstart


text:
body{ padding-top:70px;
	}
.jumbotron{color:#2c3e50;
	background:url("C:\\Users\\faizan\\Desktop\\ipl_logo.jpg");
	background-repeat: no-repeat;
	background-size: contain;
	background-position: right;}
.algn{text-align: center;
	}
.navbar{background:#2c3e50;
	}

repository: Delightkc/HTML-CSS

path: /IPL Website/ipl.css

repo_name: Delightkc/HTML-CSS


text:
.root {
    -fx-border-radius: 5px;
    -fx-background-radius: 5px;
    -fx-background-color: rgba(255, 255, 255, 0.85);
    -fx-effect: dropshadow(gaussian, whitesmoke, 50, 0, 0, 0);
    -fx-background-insets: 50;
    -fx-font-family: "Noto Sans CJK SC";
}

.label {
    -fx-font-family: "Noto Sans CJK SC";
}

.title {
    -fx-effect: dropshadow(one-pass-box, #898999, 8, 0.0, 2, 3);
    -fx-font-family: "Noto Sans CJK SC";
    -fx-font-weight: 900;
    -fx-font-size: 3.25em;
    -fx-text-fill: white;
}

.inputFiled {
    -fx-font-size: 18;
    -fx-font-family: "Noto Sans CJK SC";
    -fx-prompt-text-fill: #969DA2;
    -jfx-unfocus-color: #D4D7D9;
}

.loginBtn {
    -fx-border-width: 1px;
    -fx-border-radius: 5px;
    -fx-background-color: #0076FF;
    -fx-font-family: "Noto Sans CJK SC";
    -fx-font-size: 1.3em;
    -fx-text-fill: white;
    -fx-font-weight: bold;
}

.switchBtn {
    -fx-border-width: 2px;
    -fx-border-radius: 5px;
    -fx-border-color: #D4D7D9;
    -fx-font-family: "Noto Sans CJK SC";
    -fx-font-size: 1.3em;
    -fx-text-fill: #0076FF;
}

.slideImage {
    -fx-background-radius: 10px;
    -fx-background-color: linear-gradient(to right top,
    #D16BA5, #C777B9, #BA83CA,
    #AA8FD8, #9A9AE1, #8AA7EC,
    #79B3F4, #69BFF8, #52CFFE,
    #41DFFF, #46EEFA, #5FFBF1);
}

repository: Reki-code/employee-management-system

repo_name: Reki-code/employee-management-system

gha_language: Java


text:
@inproceedings{Heck_IWPSE_2013,
  title = {An Analysis of Requirements Evolution in Open Source Projects: Recommendations for Issue Trackers},
  shorttitle = {An Analysis of Requirements Evolution in Open Source Projects},
  booktitle = {Proceedings of the 2013 {{International Workshop}} on {{Principles}} of {{Software Evolution}}},
  author = {Heck, Petra and Zaidman, Andy},
  year = {2013},
  month = aug,
  pages = {43--52},
  publisher = {{Association for Computing Machinery}},
  address = {{New York, NY, USA}},
  doi = {10.1145/2501543.2501550},
  isbn = {978-1-4503-2311-6},
  series = {{{IWPSE}} 2013}
}

url: https://raw.githubusercontent.com/retorquere/bibtex-parser/05fd68b215b96634dea5a7e6ea5e1be187caf3ed/test/better-bibtex/export/Using the Extra field in the exported Citation Key #1571.bibtex

repository: retorquere/bibtex-parser

repo_name: retorquere/bibtex-parser

extension: bibtex


text:
@InProceedings{dimva11,
  author = {Greg Banks, Aristide Fattori, Richard Kemmerer, Christopher Kruegel and Giovanni Vigna},
  title = {{MISHIMA: Multilateration of Internet hosts hidden using malicious fast-flux agents}},
  year = 2011,
  month = "July",
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  booktitle = {Proceedings of the 8$^{th}$ Conference on Detection of
               Intrusions and Malware \& Vulnerability Assessment (DIMVA),
               Amsterdam, The Netherlands},
}


text:
@article{Morales-Marín-2018-Potential,
    title = "Potential Changes of Annual-Averaged Nutrient Export in the South Saskatchewan River Basin under Climate and Land-Use Change Scenarios",
    author = "Morales-Mar{\'\i}n, Luis  and
      Wheater, Howard  and
      Lindenschmidt, Karl-Erich",
    journal = "Water, Volume 10, Issue 10",
    volume = "10",
    number = "10",
    year = "2018",
    publisher = "MDPI AG",
    url = "https://aclanthology.org/G18-90001",
    doi = "10.3390/w10101438",
    pages = "1438",
    abstract = "Climate and land-use changes modify the physical functioning of river basins and, in particular, influence the transport of nutrients from land to water. In large-scale basins, where a variety of climates, topographies, soil types and land uses co-exist to form a highly heterogeneous environment, a more complex nutrient dynamic is imposed by climate and land-use changes. This is the case of the South Saskatchewan River (SSR) that, along with the North Saskatchewan River, forms one of the largest river systems in western Canada. The SPAtially Referenced Regression On Watershed (SPARROW) model is therefore implemented to assess water quality in the basin, in order to describe spatial and temporal patterns and identify those factors and processes that affect water quality. Forty-five climate and land-use change scenarios comprehended by five General Circulation Models (GCMs) and three Representative Concentration Pathways (RCPs) were incorporated into the model to explain how total nitrogen (TN) and total phosphorus (TP) export could vary across the basin in 30, 60 and 90 years from now. According to model results, annual averages of TN and TP export in the SSR are going to increase in the range 0.9{--}1.28 kg km − 2 year − 1 and 0.12{--}0.17 kg km − 2 year − 1 , respectively, by the end of the century, due to climate and land-use changes. Higher increases of TP compared to TN are expected since TP and TN are going to increase ∼36{\%} and ∼21{\%}, respectively, by the end of the century. This research will support management plans in order to mitigate nutrient export under future changes of climate and land use.",
}