@article{DBLP:journals/corr/abs-1908-02624,
  author       = {Yolanda Gil and
                  Bart Selman},
  title        = {A 20-Year Community Roadmap for Artificial Intelligence Research in
                  the {US}},
  journal      = {CoRR},
  volume       = {abs/1908.02624},
  year         = {2019},
  url          = {http://arxiv.org/abs/1908.02624},
  eprinttype    = {arXiv},
  eprint       = {1908.02624},
  timestamp    = {Sat, 23 Jan 2021 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1908-02624.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{pujara2019common,
  title={A Common Framework for Developing Table Understanding Models.},
  author={Pujara, Jay and Rajendran, Arunkumar and Ghasemi-Gol, Majid and Szekely, Pedro A},
  booktitle={ISWC Satellites},
  pages={133--136},
  year={2019}
}

@inproceedings{Tiwari2019AComparativeStudyof,
 author = {Tiwari, Abhishek  and Cassani, Raymundo and   Narayanan, Shrikanth and  Falk, Tiago},
 booktitle = {In proceedings of 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC19)},
 location = {Berlin, Germany},
 title = {A Comparative Study of Stress and Anxiety Prediction in Ecological Settings Using a Smart-shirt and a Smart-bracelet},
 year = {2019},
 month = {Jul}
}

@misc{huang_2019, 
title={A Gentle Introduction to Graph Neural Network (Basics, DeepWalk, and GraphSage)}, url={https://towardsdatascience.com/a-gentle-introduction-to-graph-neural-network-basics-deepwalk-and-graphsage-db5d540d50b3}, 
publisher={Towards Data Science}, 
author={Huang, Kung-Hsiang}, 
year={2019}, 
month={Feb}}

@InProceedings{cardenas-EtAl:2019:N19-1,
  author    = {Cardenas, Ronald  and  Lin, Ying  and  Ji, Heng  and  May, Jonathan},
  title     = {A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages},
  booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota},
  publisher = {Association for Computational Linguistics},
  pages     = {2428--2439},
  abstract  = {Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but practical taggers need to ground their clusters as well. Grounding generally requires reference labeled data, a luxury a low-resource language might not have. In this work, we describe an approach for low-resource unsupervised POS tagging that yields fully grounded output and requires no labeled training data. We find the classic method of Brown et al. (1992) clusters well in our use case and employ a decipherment-based approach to grounding. This approach presumes a sequence of cluster IDs is a `ciphertext' and seeks a POS tag-to-cluster ID mapping that will reveal the POS sequence. We show intrinsically that, despite the difficulty of the task, we obtain reasonable performance across a variety of languages. We also show extrinsically that incorporating our POS tagger into a name tagger leads to state-of-the-art tagging performance in Sinhalese and Kinyarwanda, two languages with nearly no labeled POS data available. We further demonstrate our tagger's utility by incorporating it into a true `zero-resource' variant of the MALOPA (Ammar et al., 2016) dependency parser model that removes the current reliance on multilingual resources and gold POS tags for new languages. Experiments show that including our tagger makes up much of the accuracy lost when gold POS tags are unavailable.},
  url       = {http://www.aclweb.org/anthology/N19-1252}
}

Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but practical taggers need to ground their clusters as well. Grounding generally requires reference labeled data, a luxury a low-resource language might not have. In this work, we describe an approach for low-resource unsupervised POS tagging that yields fully grounded output and requires no labeled training data. We find the classic method of Brown et al. (1992) clusters well in our use case and employ a decipherment-based approach to grounding. This approach presumes a sequence of cluster IDs is a `ciphertext' and seeks a POS tag-to-cluster ID mapping that will reveal the POS sequence. We show intrinsically that, despite the difficulty of the task, we obtain reasonable performance across a variety of languages. We also show extrinsically that incorporating our POS tagger into a name tagger leads to state-of-the-art tagging performance in Sinhalese and Kinyarwanda, two languages with nearly no labeled POS data available. We further demonstrate our tagger's utility by incorporating it into a true `zero-resource' variant of the MALOPA (Ammar et al., 2016) dependency parser model that removes the current reliance on multilingual resources and gold POS tags for new languages. Experiments show that including our tagger makes up much of the accuracy lost when gold POS tags are unavailable.

@inproceedings{Schuler2019,
	abstract = {Databases are well suited to the task of describing and organizing research datasets, however, the difficulties of using database management systems effectively have resulted in their limited usage among domain scientists. Scientists operate in an environment that is changing steadily with new experimental protocols, instruments, and discoveries that impact what datasets they generate and how they describe and organize them. In order to manage datasets for a scientific application, scientists need to routinely revise their database schemas to reflect these changes. Unfortunately, evolving a database is one of the well-known and most difficult aspects of database usage. The conventional data definition and manipulation languages offer relatively low-level programming abstractions to perform complex database evolution tasks, and therefore require specialized technical skills not possessed by most domain scientists. A simplified means of expressing database evolution operations can reduce the effort of keeping the scientific database in sync with changing requirements. This paper presents a high-level, user-oriented, schema evolution framework with an algebra of specialized schema modification operators. The approach allows introduction of novel operators as motivated by new requirements and is amenable to well established optimization techniques for efficient planning and execution. We present the framework and its implementation, and we demonstrate its utility in an exemplar use case and performance evaluation.},
	address = {{New York, NY, USA}},
	author = {Schuler, Robert E. and Kessleman, Carl},
	booktitle = {Proceedings of the 31st {{International Conference}} on {{Scientific}} and {{Statistical Database Management}}},
	doi = {10.1145/3335783.3335787},
	file = {/Users/schuler/Zotero/storage/T8CSAR3I/Schuler and Kesselman - 2019 - A High-level User-oriented Framework for Database .pdf},
	isbn = {978-1-4503-6216-0},
	keywords = {Database evolution,Schema evolution,Scientific databases},
	pages = {157--168},
	publisher = {{Association for Computing Machinery}},
	series = {{{SSDBM}} '19},
	title = {A {{High-Level User-Oriented Framework}} for {{Database Evolution}}},
	year = {2019},
	bdsk-url-1 = {https://doi.org/10.1145/3335783.3335787}}

Databases are well suited to the task of describing and organizing research datasets, however, the difficulties of using database management systems effectively have resulted in their limited usage among domain scientists. Scientists operate in an environment that is changing steadily with new experimental protocols, instruments, and discoveries that impact what datasets they generate and how they describe and organize them. In order to manage datasets for a scientific application, scientists need to routinely revise their database schemas to reflect these changes. Unfortunately, evolving a database is one of the well-known and most difficult aspects of database usage. The conventional data definition and manipulation languages offer relatively low-level programming abstractions to perform complex database evolution tasks, and therefore require specialized technical skills not possessed by most domain scientists. A simplified means of expressing database evolution operations can reduce the effort of keeping the scientific database in sync with changing requirements. This paper presents a high-level, user-oriented, schema evolution framework with an algebra of specialized schema modification operators. The approach allows introduction of novel operators as motivated by new requirements and is amenable to well established optimization techniques for efficient planning and execution. We present the framework and its implementation, and we demonstrate its utility in an exemplar use case and performance evaluation.

@inproceedings{Cen2019APersonalVisualComfort,
 author = {Cen, Lingkai and Choi, Joon-Ho and Yao, Xiaomeng and Gil, Yolanda and Narayanan, Shrikanth and Pentz,  Maryann},
 booktitle = {In proceedings of 10th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings (IAQVEC2019)},
 location = {Bari, Italy},
 month = {Sep},
 title = {A Personal Visual Comfort Model: Predict Individuals Visual Comfort Using Occupant Eye Pupil Size and Machine Learning},
 year = {2019}
}

@inproceedings{cen-etal-iaqvec19,
author = {Lingkai Cen and Joon-Ho Choi and Xiaomeng Yao and Yolanda Gil and Shrikanth Narayanan and Maryann Pentz},
title = {A Personal Visual Comfort Model: Predict an Individual\textquotesingle s Visual Comfort Using Occupant Eye Pupil Sizes and Machine Learning},
booktitle = {Tenth International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings, IOP Conference Series in Materials Science and Engineering (609)4},
address = {Bari, Italy},
year = {2019}
}

@InProceedings{	  bogol-iwsg-2019,
  Title		= {A Secure Gateway for Enabling ASIC Design Collaborations},
  Author	= {Bogol, Steve and Brenner, Paul and Brinckman, Adam and
		  Deelman, Ewa and Ferreira da Silva, Rafael and Gupta,
		  Sandeep and Nabrzyski, Jarek and Park, Soowang and Perez,
		  Damian and Rynge, Mats and Taylor, Ian and Vahi, Karan and
		  Werf, Matt Vander and Rucker Sarah and Wyngaard,
		  Sebastian},
  BookTitle	= {11th International Workshop on Science Gateways (IWSG
		  2019)},
  Year		= {2019},
  Pages		= {},
  DOI		= {},
  Note		= {Funding Acknowledgments: DARPA HR0011-16-C-0043}
}

@article{DBLP:journals/corr/abs-1909-06516,
  author    = {Mozhdeh Gheini and
               Jonathan May},
  title     = {A Universal Parent Model for Low-Resource Neural Machine Translation
               Transfer},
  journal   = {CoRR},
  volume    = {abs/1909.06516},
  year      = {2019},
  url       = {http://arxiv.org/abs/1909.06516},
  archivePrefix = {arXiv},
  eprint    = {1909.06516},
  timestamp = {Mon, 23 Sep 2019 18:07:15 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1909-06516.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{jalal2019cooperative,
  title={A cooperative machine learning approach for pedestrian navigation in indoor IoT},
  author={Jalal Abadi, Marzieh and Luceri, Luca and Hassan, Mahbub and Chou, Chun Tung and Nicoli, Monica},
  journal={Sensors},
  volume={19},
  number={21},
  pages={4609},
  year={2019},
  publisher={MDPI}
}

@article{Alex2019Amodulararchitecturefor,
 author = {Alexander, Rachel  and  Sorensen, Tanner and  Toutios, Asterios and  Narayanan, Shrikanth},
 bib2html_rescat = {span},
 doi = {https://doi.org/10.1121/1.5139413},
 journal = {J. Acoust. Soc. Am.},
 link = {http://sail.usc.edu/publications/files/Alexander-JASA2020.pdf},
 month = {dec},
 number = {6},
 pages = {4458-4471},
 title = {A modular architecture for articulatory synthesis from gestural specification},
 volume = {146},
 year = {2019}
}

@article{PendergrassBuyskeJeffEtAl2019,
	abstract = {We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one {SNP} is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the {Population Architecture using Genomics and Epidemiology} (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each {SNP} on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.},
	author = {Pendergrass, Sarah A. and Buyske, Steven and Jeff, Janina M. and Frase, Alex and Dudek, Scott and Bradford, Yuki and Ambite, Jose-Luis and Avery, Christy L. and Buzkova, Petra and Deelman, Ewa and Fesinmeyer, Megan D. and Haiman, Christopher and Heiss, Gerardo and Hindorff, Lucia A. and Hsu, Chun-Nan and Jackson, Rebecca D. and Lin, Yi and Le Marchand, Loic and Matise, Tara C. and Monroe, Kristine R. and Moreland, Larry and North, Kari E. and Park, Sungshim L. and Reiner, Alex and Wallace, Robert and Wilkens, Lynne R. and Kooperberg, Charles and Ritchie, Marylyn D. and Crawford, Dana C.},
	citation-subset = {IM},
	completed = {2020-04-06},
	country = {United States},
	doi = {10.1371/journal.pone.0226771},
	issn = {1932-6203},
	issn-linking = {1932-6203},
	issue = {12},
	journal = {PloS one},
	keywords = {African Americans, genetics; Aged; Atherosclerosis, genetics; Epidemiologic Studies; Female; Genetic Pleiotropy; Genome-Wide Association Study; Humans; Male; Metagenomics; Middle Aged; Phenomics; Polymorphism, Single Nucleotide},
	nlm-id = {101285081},
	owner = {NLM},
	pages = {e0226771},
	pii = {PONE-D-19-33442},
	pmc = {PMC6938343},
	pmid = {31891604},
	url = {https://pubmed.ncbi.nlm.nih.gov/31891604/},

	pubmodel = {Electronic-eCollection},
	pubstate = {epublish},
	revised = {2020-04-30},
	title = {A phenome-wide association study ({PheWAS}) in the {Population Architecture using Genomics and Epidemiology} ({PAGE}) study reveals potential pleiotropy in {African Americans}.},
	volume = {14},
	year = {2019},
	bdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/31891604/},
	bdsk-url-2 = {https://doi.org/10.1371/journal.pone.0226771}}

We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.

@InProceedings{sticca2019:ASHG,
  author = 	 {E.L. Sticca and G.M. Belbin and J. Soto and R. Shemirani and D. Nelson and  S. Gravel and J.L. Ambite and G.L. Wojcik and E.E. Kenny and C.R. Gignoux},
  title = 	 {A pipeline for large-scale simulation of population-based linkage.},
  booktitle = {Annual Meeting of the American Society of Human Genetics},
  year = 	 {2019},
  address = 	 {Houston, TX},
  note = 	 {Abstract + Poster},
}

@inproceedings{Martinez2019Asystemforthe,
 author = {Martinez, Victor and Ramakrishna, Anil and Chiu, Ming-Chang and Singla, Karan and Narayanan, Shrikanth},
 booktitle = {In proceedings of Proceedings of the 8th International Conference on Affective  Computing Intelligent Interaction},
 location = {Cambridge, UK},
 month = {September},
 title = {A system for the 2019 Sentiment, Emotion and  Cognitive State Task of DARPAs LORELEI project},
 year = {2019}
}

@article{lu2019universal,
  title={A universal measure for network traceability},
  author={Lu, Xin and Horn, Abigail L and Su, Jiahao and Jiang, Jiang},
  journal={Omega},
  volume={87},
  pages={191--204},
  year={2019},
  publisher={Elsevier}
}

@article{DBLP:journals/corr/abs-1903-00788,
  author       = {Ayush Jaiswal and
                  Yue Wu and
                  Wael AbdAlmageed and
                  Iacopo Masi and
                  Premkumar Natarajan},
  title        = {{AIRD:} Adversarial Learning Framework for Image Repurposing Detection},
  journal      = {CoRR},
  volume       = {abs/1903.00788},
  year         = {2019},
  url          = {http://arxiv.org/abs/1903.00788},
  eprinttype    = {arXiv},
  eprint       = {1903.00788},
  timestamp    = {Sat, 30 Mar 2019 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1903-00788.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{	  ferreiradasilva-iccs-2019,
  Author	= {Ferreira da Silva, Rafael and Orgerie, Anne-C\'{e}cile and
		  Casanova, Henri and Tanaka, Ryan and Deelman, Ewa and
		  Suter, Fr\'{e}d\'{e}ric},
  Title		= {Accurately Simulating Energy Consumption of I/O-intensive
		  Scientific Workflows},
  BookTitle	= {Computational Science -- ICCS 2019},
  Year		= {2019},
  Pages		= {138--152},
  Publisher	= {Springer International Publishing},
  DOI		= {10.1007/978-3-030-22734-0_11},
  Note		= {Funding Acknowledgments: NSF 1642335, NSF 1664162, DOE
		  DE-SC0012636}
}

@inbook{Toutios2018AdvancesInVocalTractImaging,
 address = {New York, NY},
 author = {Toutios, Asterios and Byrd, Dani and Goldstein, Louis and Narayanan, Shrikanth S.},
 bib2html_rescat = {span},
 link = {},
 publisher = {Routledge London and New York},
 title = {Advances in vocal tract imaging and analysis},
 year = {2019},
 month = {Apr}
}

@article{DBLP:journals/f1000research/HornAAABBBBBFGGKLLMMOST19,
  author       = {John D. Van Horn and
                  Sumiko Abe and
                  Jos{\'{e}} Luis Ambite and
                  Teresa K. Attwood and
                  Niall Beard and
                  Louisa Bellis and
                  Avnish Bhattrai and
                  Alex A. T. Bui and
                  Gully Burns and
                  Lily Fierro and
                  Jonathan Gordon and
                  Jeffrey S. Grethe and
                  Jeana Kamdar and
                  Xiaoyu Lei and
                  Kristina Lerman and
                  Annette McGrath and
                  Nicola J. Mulder and
                  Caroline O'Driscoll and
                  Crystal Stewart and
                  Sonika Tyagi},
  title        = {Advancing the international data science workforce through shared
                  training and education},
  journal      = {F1000Research},
  volume       = {8},
  pages        = {251},
  year         = {2019},
  url          = {https://doi.org/10.12688/f1000research.18357.1},
  doi          = {10.12688/F1000RESEARCH.18357.1},
  timestamp    = {Mon, 28 Aug 2023 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/f1000research/HornAAABBBBBFGGKLLMMOST19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/mta/RiazAPCMN19,
  author       = {Sidra Riaz and
                  Zahid Ali and
                  Unsang Park and
                  Jongmoo Choi and
                  Iacopo Masi and
                  Prem Natarajan},
  title        = {Age-invariant face recognition using gender specific 3D aging modeling},
  journal      = {Multim. Tools Appl.},
  volume       = {78},
  number       = {17},
  pages        = {25163--25183},
  year         = {2019},
  url          = {https://doi.org/10.1007/s11042-019-7694-1},
  doi          = {10.1007/S11042-019-7694-1},
  timestamp    = {Mon, 11 May 2020 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/mta/RiazAPCMN19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Sharon2019AnEmpiricalStudyof,
 author = {Sharon, Rini and Narayanan, Shrikanth and Sur, Mriganka and Murthy, Hema},
 booktitle = {Proceedings of ICASSP},
 location = {Brighton, UK},
 month = {May},
 title = {An Empirical Study of Speech Processing in the Brain by Analyzing the Temporal Syllable Structure in Speech-input Induced EEG},
 year = {2019}
}

@inproceedings{garijo_intelligent_2019,
	address = {New York, NY, USA},
	series = {{IUI} '19},
	title = {An {Intelligent} {Interface} for {Integrating} {Climate}, {Hydrology}, {Agriculture}, and {Socioeconomic} {Models}},
	isbn = {978-1-4503-6673-1},
	url = {https://github.com/khider/khider.github.io/blob/master/papers/iuiDemo2019.pdf},
	doi = {10.1145/3308557.3308711},
	booktitle = {Proceedings of the 24th {International} {Conference} on {Intelligent} {User} {Interfaces}: {Companion}},
	publisher = {ACM},
	author = {Garijo, Daniel and Khider, Deborah and Ratnakar, Varun and Gil, Yolanda and Deelman, Ewa and da Silva, Rafael Ferreira and Knoblock, Craig and Chiang, Yao-Yi and Pham, Minh and Pujara, Jay and Vu, Binh and Feldman, Dan and Mayani, Rajiv and Cobourn, Kelly and Duffy, Chris and Kemanian, Armen and Shu, Lele and Kumar, Vipin and Khandelwal, Ankush and Tayal, Kshitij and Peckham, Scott and Stoica, Maria and Dabrowski, Anna and Hardesty-Lewis, Daniel and Pierce, Suzanne},
	year = {2019},
	note = {event-place: Marina del Ray, California},
	keywords = {environmental modeling, intelligent workflow systems, model integration, scientific discovery},
	pages = {111--112},
}

@InProceedings{	  garijo-iui-2019,
  Author	= {Garijo, Daniel and Khider, Deborah and Ratnakar, Varun and
		  Gil, Yolanda and Deelman, Ewa and Ferreira da Silva, Rafael
		  and Knoblock, Craig and Chiang, Yao-Yi and Pham, Minh and
		  Pujara, Jay and Vu, Binh and Feldman, Dan and Mayani, Rajiv
		  and Cobourn, Kelly and Duffy, Chris and Kemanian, Armen and
		  Shu, Lele and Kumar, Vipin and Khandelwal, Ankush and
		  Tayal, Kshitij and Peckham, Scott and Stoica, Maria and
		  Dabrowski, Anna and Hardesty-Lewis, Daniel and Pierce,
		  Suzanne},
  Title		= {An Intelligent Interface for Integrating Climate,
		  Hydrology, Agriculture, and Socioeconomic Models},
  BookTitle	= {ACM 24th International Conference on Intelligent User
		  Interfaces (IUI'19)},
  Year		= {2019},
  Pages		= {111--112},
  DOI		= {10.1145/3308557.3308711},
  Note		= {Funding Acknowledgments: DARPA W911NF-18-1-0027}
}

@article{Albash_2019,
	doi = {10.1088/2058-9565/ab13ea},
	url = {https://doi.org/10.1088%2F2058-9565%2Fab13ea},
	year = 2019,
	month = {apr},
	publisher = {{IOP} Publishing},
	volume = {4},
	number = {2},
	pages = {02LT03},
	author = {Tameem Albash and Victor Martin-Mayor and Itay Hen},
	title = {Analog errors in Ising machines},
	journal = {Quantum Science and Technology},
	abstract = {Recent technological breakthroughs have precipitated the availability of specialized devices that promise to solve NP-Hard problems faster than standard computers. These ‘Ising Machines’ are however analog in nature and as such inevitably have implementation errors. We find that their success probability decays exponentially with problem size for a fixed error level, and we derive a sufficient scaling law for the error in order to maintain a fixed success probability. We corroborate our results with experiment and numerical simulations and discuss the practical implications of our findings.}
}

Recent technological breakthroughs have precipitated the availability of specialized devices that promise to solve NP-Hard problems faster than standard computers. These ‘Ising Machines’ are however analog in nature and as such inevitably have implementation errors. We find that their success probability decays exponentially with problem size for a fixed error level, and we derive a sufficient scaling law for the error in order to maintain a fixed success probability. We corroborate our results with experiment and numerical simulations and discuss the practical implications of our findings.

@article{npjQ,
	Abstract = {Quantum annealing has the potential to provide a speedup over classical algorithms in solving optimization problems. Just as for any other quantum device, suppressing Hamiltonian control errors will be necessary before quantum annealers can achieve speedups. Such analog control errors are known to lead to {\$}{\$}J{\$}{\$}J-chaos, wherein the probability of obtaining the optimal solution, encoded as the ground state of the intended Hamiltonian, varies widely depending on the control error. Here, we show that {\$}{\$}J{\$}{\$}J-chaos causes a catastrophic failure of quantum annealing, in that the scaling of the time-to-solution metric becomes worse than that of a deterministic (exhaustive) classical solver. We demonstrate this empirically using random Ising spin glass problems run on the two latest generations of the D-Wave quantum annealers. We then proceed to show that this doomsday scenario can be mitigated using a simple error suppression and correction scheme known as quantum annealing correction (QAC). By using QAC, the time-to-solution scaling of the same D-Wave devices is improved to below that of the classical upper bound, thus restoring hope in the speedup prospects of quantum annealing.},
	Author = {Pearson, Adam and Mishra, Anurag and Hen, Itay and Lidar, Daniel A.},
	Da = {2019/11/28},
	Date-Added = {2020-05-11 16:55:01 -0700},
	Date-Modified = {2020-05-11 16:55:01 -0700},
	Doi = {10.1038/s41534-019-0210-7},
	Id = {Pearson2019},
	Isbn = {2056-6387},
	Journal = {npj Quantum Information},
	Number = {1},
	Pages = {107},
	Title = {Analog errors in quantum annealing: doom and hope},
	Ty = {JOUR},
	Url = {https://doi.org/10.1038/s41534-019-0210-7},
	Volume = {5},
	Year = {2019},
	Bdsk-Url-1 = {https://doi.org/10.1038/s41534-019-0210-7}}

Quantum annealing has the potential to provide a speedup over classical algorithms in solving optimization problems. Just as for any other quantum device, suppressing Hamiltonian control errors will be necessary before quantum annealers can achieve speedups. Such analog control errors are known to lead to \$}{\$J\$}{\$J-chaos, wherein the probability of obtaining the optimal solution, encoded as the ground state of the intended Hamiltonian, varies widely depending on the control error. Here, we show that \$}{\$J\$}{\$J-chaos causes a catastrophic failure of quantum annealing, in that the scaling of the time-to-solution metric becomes worse than that of a deterministic (exhaustive) classical solver. We demonstrate this empirically using random Ising spin glass problems run on the two latest generations of the D-Wave quantum annealers. We then proceed to show that this doomsday scenario can be mitigated using a simple error suppression and correction scheme known as quantum annealing correction (QAC). By using QAC, the time-to-solution scaling of the same D-Wave devices is improved to below that of the classical upper bound, thus restoring hope in the speedup prospects of quantum annealing.

@article{Slutskii_2019,
	doi = {10.1088/1367-2630/ab51f9},
	url = {https://doi.org/10.1088%2F1367-2630%2Fab51f9},
	year = 2019,
	month = {nov},
	publisher = {{IOP} Publishing},
	volume = {21},
	number = {11},
	pages = {113025},
	author = {Mikhail Slutskii and Tameem Albash and Lev Barash and Itay Hen},
	title = {Analog nature of quantum adiabatic unstructured search},
	journal = {New Journal of Physics},
	abstract = {The quantum adiabatic unstructured search algorithm is one of only a handful of quantum adiabatic optimization algorithms to exhibit provable speedups over their classical counterparts. With no fault tolerance theorems to guarantee the resilience of such algorithms against errors, understanding the impact of imperfections on their performance is of both scientific and practical significance. We study the robustness of the algorithm against various types of imperfections: limited control over the interpolating schedule, Hamiltonian misspecification, and interactions with a thermal environment. We find that the unstructured search algorithm’s quadratic speedup is generally not robust to the presence of any one of the above non-idealities, and in some cases we find that it imposes unrealistic conditions on how the strength of these noise sources must scale to maintain the quadratic speedup.}
}

The quantum adiabatic unstructured search algorithm is one of only a handful of quantum adiabatic optimization algorithms to exhibit provable speedups over their classical counterparts. With no fault tolerance theorems to guarantee the resilience of such algorithms against errors, understanding the impact of imperfections on their performance is of both scientific and practical significance. We study the robustness of the algorithm against various types of imperfections: limited control over the interpolating schedule, Hamiltonian misspecification, and interactions with a thermal environment. We find that the unstructured search algorithm’s quadratic speedup is generally not robust to the presence of any one of the above non-idealities, and in some cases we find that it imposes unrealistic conditions on how the strength of these noise sources must scale to maintain the quadratic speedup.

@article{pepke2019,
	Author = {Pepke, Shirley and Nelson, William M and {Ver Steeg}, Greg},
	Date-Added = {2020-01-20 20:40:51 +0000},
	Date-Modified = {2020-01-20 20:40:51 +0000},
	Journal = {JoVE (Journal of Visualized Experiments)},
	Number = {152},
	Pages = {e60431},
	Title = {Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal},
	Year = {2019}}

@article{luceri2019analyzing,
  title={Analyzing and inferring human real-life behavior through online social networks with social influence deep learning},
  author={Luceri, Luca and Braun, Torsten and Giordano, Silvia},
  journal={Applied network science},
  volume={4},
  number={1},
  pages={1--25},
  year={2019},
  publisher={Springer}
}

@InProceedings{	  chap2019precs,
  Author	= {Chapp, Dylan and Rorabaugh, Danny and Brown, Duncan A. and
		  Deelman, Ewa and Vahi, Karan and Welch, Von and Taufer,
		  Michela},
  Title		= {Applicability Study of the PRIMAD Model to LIGO
		  Gravitational Wave Search Workflows},
  Year		= {2019},
  DOI		= {10.1145/3322790.3330591},
  BookTitle	= {Proceedings of the 2nd International Workshop on Practical
		  Reproducible Evaluation of Computer Systems (P-RECS'19)},
  Pages		= {1–6}
}

@article{li_applying_2019,
	title = {Applying {Multivariate} {Segmentation} {Methods} to {Human} {Activity} {Recognition} {From} {Wearable} {Sensors}' {Data}.},
	volume = {7},
	copyright = {(c)Kenan Li, Rima Habre, Huiyu Deng, Robert Urman, John Morrison, Frank D Gilliland, Jose Luis Ambite, Dimitris Stripelis, Yao-Yi Chiang, Yijun Lin, Alex AT Bui, Christine King, Anahita Hosseini, Eleanne Van Vliet, Majid Sarrafzadeh, Sandrah P Eckel. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 07.02.2019.},
	issn = {2291-5222 2291-5222},
	url = {https://mhealth.jmir.org/2019/2/e11201/},
	doi = {10.2196/11201},
	abstract = {BACKGROUND: Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. OBJECTIVE: We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. METHODS: We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict  physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and  randomly concatenating the 60 activity bouts. Each dataset was divided into {\textasciitilde}90\%  training and {\textasciitilde}10\% holdout testing. RESULTS: In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06\% (the highest accuracy rate was 91.79\% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4\% of predictions for 6 physical activities. CONCLUSIONS: In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected,  it was slightly lower in the more real-world study using wrist-worn smartwatches  in children (BREATHE) than in the more tightly controlled study using waist-worn  smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data.},
	language = {eng},
	number = {2},
	journal = {JMIR mHealth and uHealth},
	author = {Li, Kenan and Habre, Rima and Deng, Huiyu and Urman, Robert and Morrison, John and Gilliland, Frank D. and Ambite, Jose Luis and Stripelis, Dimitris and Chiang, Yao-Yi and Lin, Yijun and Bui, Alex At and King, Christine and Hosseini, Anahita and Vliet, Eleanne Van and Sarrafzadeh, Majid and Eckel, Sandrah P.},
	month = feb,
	year = {2019},
	pmid = {30730297},
	pmcid = {PMC6386646},
	keywords = {machine learning, physical activity, smartphone, statistical data analysis wearable devices},
	pages = {e11201},
}

BACKGROUND: Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. OBJECTIVE: We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. METHODS: We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing. RESULTS: In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities. CONCLUSIONS: In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data.

@article{Proctor2019ArticulatoryCharacterizationofEnglish,
 abstract = {Articulation of liquid consonants in onsets and codas by four speakers of General American English was examined using real-time MRI. Midsagittal tongue posture was compared for laterals and rhotics produced in each syllable margin, adjacent to 13 different vowels and diphthongs. Vowel articulation was examined in words without liquids, before each liquid, and after each liquid, to assess the coarticulatory influence of each segment on the others. Overall, nuclear vocalic postures were more influenced by coda rhotics than onset rhotics or laterals in either syllable margin. Laterals exhibited greater temporal and spatial independence between coronal and dorsal gestures. Rhotics were produced with a variety of speaker-specific postures, but were united by a greater degree of coarticulatory resistance to vowel context, patterns consistent with greater coarticulatory influence on adjacent vowels, and less allophonic variation across syllable positions than laterals.},
 author = {Proctor, Michael and Walker, Rachel and Smith, Caitlin and Szalay, Tunde and Goldstein, Louis and Narayanan, Shrikanth},
 doi = {https://doi.org/10.1016/j.wocn.2019.100921},
 issn = {0095-4470},
 journal = {J. Phonetics},
 keywords = {Liquid consonant, Rhotic, Lateral, Coarticulation, Syllable structure},
 link = {http://sail.usc.edu/publications/files/proctor_jphon-2019.pdf},
 pages = {100921},
 title = {Articulatory Characterization of English Liquid-Final Rimes},
 url = {http://www.sciencedirect.com/science/article/pii/S0095447018300457},
 volume = {77},
 year = {2019},
 month = {Aug}
}

Articulation of liquid consonants in onsets and codas by four speakers of General American English was examined using real-time MRI. Midsagittal tongue posture was compared for laterals and rhotics produced in each syllable margin, adjacent to 13 different vowels and diphthongs. Vowel articulation was examined in words without liquids, before each liquid, and after each liquid, to assess the coarticulatory influence of each segment on the others. Overall, nuclear vocalic postures were more influenced by coda rhotics than onset rhotics or laterals in either syllable margin. Laterals exhibited greater temporal and spatial independence between coronal and dorsal gestures. Rhotics were produced with a variety of speaker-specific postures, but were united by a greater degree of coarticulatory resistance to vowel context, patterns consistent with greater coarticulatory influence on adjacent vowels, and less allophonic variation across syllable positions than laterals.

@article{Park2019Auto-TuningSpectralClusteringfor,
 author = {Park, Tae Jin and Han, Kyu and Kumar, Manoj and Narayanan, Shrikanth},
 doi = {10.1109/LSP.2019.2961071},
 journal = {IEEE Signal Processing Letters},
 title = {Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap},
 year = {2019},
 month = {Mar}
}

@inproceedings{aistats2019,
	Author = {Shuyang Gao and Robert Brekelmans and Greg {Ver Steeg} and Aram Galstyan},
	Booktitle = {Proceedings of the 22nd International Conference on AI and Statistics (AISTATS)},
	Date-Added = {2019-01-24 17:43:17 +0000},
	Date-Modified = {2019-01-24 17:44:47 +0000},
	Title = {Auto-encoding Correlation Explanation},
	Year = {2019}}

@inproceedings{gao2019auto,
  title={Auto-encoding total correlation explanation},
  author={Gao, Shuyang and Brekelmans, Rob and Ver Steeg, Greg and Galstyan, Aram},
  booktitle={The 22nd International Conference on Artificial Intelligence and Statistics},
  pages={1157--1166},
  year={2019},
  organization={PMLR}
}

@article{uhl2019automated,
  title={Automated extraction of human settlement patterns from historical topographic map series using weakly supervised convolutional neural networks},
  author={Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan, Weiwei and Knoblock, Craig A},
  journal={IEEE Access},
  year={2019},
  publisher={IEEE},
  urlLink={https://ieeexplore.ieee.org/document/8946322},
  urlPaper={http://usc-isi-i2.github.io/papers/uhl19-ieee.pdf}
}

@InProceedings{mayani2019a:ASHG,
  author = 	 {R. Mayani and K. Vahi and J.L. Ambite and S. Sharma and M. Azaro and S. Wilson and B. Ruocco and G. Davis and M. Romanella and L. Brzustowicz and E. Deelman and Y. Arens},
  title = 	 {Automated processing of phenotypic data submissions},
  booktitle = {Annual Meeting of the American Society of Human Genetics},
  year = 	 {2019},
  address = 	 {Houston, TX},
  note = 	 {Abstract + Poster},
}

@inproceedings{shi2019automatic,
  title={Automatic Adaptation to Sensor Replacements},
  author={Shi, Yuan and Kumar, TK Satish and Knoblock, Craig A},
  booktitle={The Thirty-Second International Flairs Conference},
  year={2019}
}

@article{duan2019automatic,
  title={Automatic alignment of contemporary vector data and georeferenced historical maps using reinforcement learning},
  author={Duan, Weiwei and Chiang, Yao-Yi and Leyk, Stefan and Uhl, Johannes H and Knoblock, Craig A},
  journal={International Journal of Geographical Information Science},
  pages={1--26},
  year={2019},
  publisher={Taylor \& Francis}
}

@article{ALOBAID2019100472,
	title        = {Automating ontology engineering support activities with OnToology},
	author       = {Ahmad Alobaid and Daniel Garijo and María Poveda-Villalón and Idafen Santana-Perez and Alba Fernández-Izquierdo and Oscar Corcho},
	year         = 2019,
	journal      = {Journal of Web Semantics},
	volume       = 57,
	pages        = 100472,
	doi          = {10.1016/j.websem.2018.09.003},
	issn         = {1570-8268},
	url          = {https://dgarijo.com/papers/JWSsupporting.pdf},
	keywords     = {Ontology engineering, Ontology evaluation, Ontology documentation, Ontology publication},
	abstract     = {Due to the increasing uptake of semantic technologies, ontologies are now part of a good number of information systems. As a result, software development teams that have to combine ontology engineering activities with software development practices are facing several challenges, since these two areas have evolved, in general, separately. In this paper we present OnToology, an approach to manage ontology engineering support activities (i.e., documentation, evaluation, releasing and versioning). OnToology is a web-based application that builds on top of Git-based environments and integrates existing semantic web technologies. We have validated OnToology against a set of representative requirements for ontology development support activities in distributed environments, and report on a survey of the system to assess its usefulness and usability.}
}

Due to the increasing uptake of semantic technologies, ontologies are now part of a good number of information systems. As a result, software development teams that have to combine ontology engineering activities with software development practices are facing several challenges, since these two areas have evolved, in general, separately. In this paper we present OnToology, an approach to manage ontology engineering support activities (i.e., documentation, evaluation, releasing and versioning). OnToology is a web-based application that builds on top of Git-based environments and integrates existing semantic web technologies. We have validated OnToology against a set of representative requirements for ontology development support activities in distributed environments, and report on a survey of the system to assess its usefulness and usability.

@article{ambite2019bd2k,
  title={BD2K Training Coordinating Center's ERuDIte: the Educational Resource Discovery Index for Data Science},
  author={Ambite, Jose Luis and Fierro, Lily and Gordon, Jonathan and Burns, Gully and Geigl, Florian and Lerman, Kristina and Van Horn, John D},
  journal={IEEE Transactions on Emerging Topics in Computing},
  year={2019},
  publisher={IEEE}
}

@Article{ambite2019:tetc-DONTCITEUPDATE2021,
  author = 	 {Jos\'{e} Luis Ambite and Lily Fierro and Jonathan Gordon and Gully A. Burns and Florian Geigl and Kristina Lerman and John D. Van Horn},
  title = 	 {{BD2K Training Coordinating Center’s ERuDIte: the Educational Resource Discovery Index for Data Science}},
  journal = 	 {IEEE Transactions on Emerging Topics in Computing},
  year = 	 {2019},
  DOI = {10.1109/TETC.2019.2903466},
  note = {10.1109/TETC.2019.2903466},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  month = {Jan}
}

@InBook{mythodologies-2019,
  author =    {Joseph A. Dane and Jonathan May},
  title =        {Begging The Question: Critical Reasoning in Chaucer Studies, Book History, and Humanistic Inquiry},
  chapter =      {III.3: Evidence and Artificial Intelligence},
  publisher =    {Marymount Institute Press},
  year =         2019,
  number =    {II},
  series =    {Mythodologies},
  pages =     {295--208}}

@article{ghazarian2019better,
  title={Better automatic evaluation of open-domain dialogue systems with contextualized embeddings},
  author={Ghazarian, Sarik and Wei, Johnny Tian-Zheng and Galstyan, Aram and Peng, Nanyun},
  journal={arXiv preprint arXiv:1904.10635},
  year={2019}
}

@misc{bd2k-erudite,
  author = {{BD2K-TCC}},
  title = {{Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data}},
  howpublished = {https://doi.org/10.5281/zenodo.2553415},
  year = {2019}, 
  note = {[Online; accessed 16-August-2021]}
}

@article{Mathew2019BiomedicalNE,
  title={Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping},
  author={Joel Mathew and Shobeir Fakhraei and J. Ambite},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.00282}
}

@inproceedings{khachatrian2019biorelex,
  title={Biorelex 1.0: Biological relation extraction benchmark},
  author={Khachatrian, Hrant and Nersisyan, Lilit and Hambardzumyan, Karen and Galstyan, Tigran and Hakobyan, Anna and Arakelyan, Arsen and Rzhetsky, Andrey and Galstyan, Aram},
  booktitle={Proceedings of the 18th BioNLP Workshop and Shared Task},
  pages={176--190},
  year={2019}
}

@inproceedings{Mundnich2019BluetoothbasedIndoorLocalization,
 author = {Mundnich, Karel and Girault, Benjamin and Narayanan, Shrikanth},
 booktitle = {Proceedings of ICASSP},
 link = {http://sail.usc.edu/publications/files/Mundnich-ICASSP2019.pdf},
 location = {Brighton, UK},
 month = {May},
 title = {Bluetooth based Indoor Localization using Triplet Embeddings},
 year = {2019}
}

@inproceedings{chevalier2019bootkeeper,
  title={BootKeeper: Validating Software Integrity Properties on Boot Firmware Images},
  author={Chevalier, Ronny and Cristalli, Stefano and Hauser, Christophe and Shoshitaishvili, Yan and Wang, Ruoyu and Kruegel, Christopher and Vigna, Giovanni and Bruschi, Danilo and Lanzi, Andrea},
  booktitle={Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy},
  pages={315--325},
  year={2019}
}

@InProceedings{8902700,
  author = {A. Tiwari and S. Narayanan and T. H. Falk},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Breathing Rate Complexity Features for “In-the-Wild” Stress and Anxiety Measurement},
  year = {2019},
  pages = {1-5},
  abstract = {Features extracted from respiratory activity signals have been shown to carry information about mental states such as anxiety and mental stress. Such findings, however, are based on studies conducted mostly in controlled laboratory environments with artificially-induced psychological responses. While this assures that high quality data are collected, the amount of data is limited and the transferability of the findings to more ecologically-appropriate natural settings (i.e., “in-the-wild”) remains unknown. In this paper, we propose new non-linear complexity measures computed from four different respiration activity time series (i.e., inter-breath interval, inhale-to-exhale ratio, inhale/exhale amplitude envelope, and interbreath difference) and show their discriminatory power for anxiety and stress monitoring in the workplace. The new features are tested on a dataset collected from 200 hospital workers (nurses and staff) during their normal work shifts. The proposed features are shown to be complementary to conventional measures of breathing rate and depth.},
  keywords = {diseases;feature extraction;medical signal processing;patient care;pneumodynamics;psychology;time series;artificially-induced psychological responses;ecologically-appropriate natural settings;nonlinear complexity measures;inter-breath interval;stress monitoring;respiratory activity signals;mental states;breathing rate complexity features;anxiety measurement;respiration activity time series;in-the-wild stress;feature extraction;Feature extraction;Stress;Benchmark testing;Biomedical measurement;Stress measurement;Time series analysis;Entropy},
  doi = {10.23919/EUSIPCO.2019.8902700},
  issn = {2076-1465},
  month = {Sep.},
}

Features extracted from respiratory activity signals have been shown to carry information about mental states such as anxiety and mental stress. Such findings, however, are based on studies conducted mostly in controlled laboratory environments with artificially-induced psychological responses. While this assures that high quality data are collected, the amount of data is limited and the transferability of the findings to more ecologically-appropriate natural settings (i.e., “in-the-wild”) remains unknown. In this paper, we propose new non-linear complexity measures computed from four different respiration activity time series (i.e., inter-breath interval, inhale-to-exhale ratio, inhale/exhale amplitude envelope, and interbreath difference) and show their discriminatory power for anxiety and stress monitoring in the workplace. The new features are tested on a dataset collected from 200 hospital workers (nurses and staff) during their normal work shifts. The proposed features are shown to be complementary to conventional measures of breathing rate and depth.

@inproceedings{ferreiradasilva2019escience,
    title = {Bridging Concepts and Practice in eScience via Simulation-driven Engineering},
    author = {Ferreira da Silva, Rafael and Casanova, Henri and Tanaka, Ryan and Suter, Frederic},
    booktitle = {Workshop on Bridging from Concepts to Data and Computation for eScience (BC2DC'19), 15th International Conference on eScience (eScience)},
    year = {2019},
    pages = {609--614},
    doi = {10.1109/eScience.2019.00084},
    note = {Funding Acknowledgements: NSF 1642335, NSF 1923539}
}

@INPROCEEDINGS{Chiang2019-yx,
  title     = "{Building Explainable Predictive Analytics for Location-Dependent
               Time-Series Data}",
  booktitle = "{Proceedings of the 2019 IEEE First International Conference on
               Cognitive Machine Intelligence (CogMI)}",
  author    = "Chiang, Y Y and Lin, Y and Franklin, M and Eckel, S P and
               Ambite, J L and Ku, W",
  pages     = "202--209",
  month     =  dec,
  year      =  2019,
  url       = "http://dx.doi.org/10.1109/CogMI48466.2019.00037",
  url-file      = "papers/Chiang-et-al.-2019-Building-Explainable-Predictive-Analytics-for-Location-Dependent-Time-Series-Data.pdf",
  doi       = "10.1109/CogMI48466.2019.00037"
}

@article{burns2019building,
  title={Building deep learning models for evidence classification from the open access biomedical literature},
  author={Burns, Gully A and Li, Xiangci and Peng, Nanyun},
  journal={Database},
  volume={2019},
  year={2019},
  publisher={Narnia}
}

@InProceedings{Moura19b,
	author = 	"Giovane C. M. Moura and John Heidemann and
 Ricardo de O. Schmidt and Wes Hardaker",
	title = 	"Cache Me If You Can: Effects of {DNS} {Time-to-Live}",
        booktitle =     "Proceedings of the " # "ACM Internet Measurement Conference",
        year =          2019,
	sortdate = "2019-10-31",
	project = "ant, lacanic, divoice, paaddos, nipet, ddidd",
	jsubject = "network_security",
        pages =      "to appear",
        month =      oct,
        address =    "Amsterdam, the Netherlands",
        publisher =  "ACM",
        jlocation =   "johnh: pafile",
	keywords = 	"anycast, dns, ttl, ddos, root ddos",
	doi = "https://doi.org/10.1145/3355369.3355568",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Moura19b.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Moura19b.pdf",
	blogurl = "https://ant.isi.edu/blog/?p=1355",
	dataurl =	"https://ant.isi.edu/datasets/dns/#Moura19b_data",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
	abstract = "DNS depends on extensive caching for good performance, and every DNS
zone owner must set \emph{Time-to-Live} (TTL) values to control their DNS
caching.  Today there is relatively little guidance backed by research
about how to set TTLs, and operators must balance conflicting demands
of caching against agility of configuration.  
Exactly how TTL value choices affect operational
networks is quite challenging to understand due to interactions across the
distributed DNS service, where resolvers receive TTLs in different ways
(answers and hints), TTLs are specified in multiple places (zones and
their parent's glue), and while DNS resolution must be security-aware.
This paper provides the first careful evaluation of how these multiple,
interacting factors affect the effective cache lifetimes of DNS records,
and provides recommendations for how to configure DNS TTLs based on
our findings.  We provide recommendations in TTL choice for different
situations, and for where they must be configured.  We show that longer
TTLs have significant promise in reducing latency, reducing it from
183ms to 28.7ms for one country-code TLD.",
}

DNS depends on extensive caching for good performance, and every DNS zone owner must set \emphTime-to-Live (TTL) values to control their DNS caching. Today there is relatively little guidance backed by research about how to set TTLs, and operators must balance conflicting demands of caching against agility of configuration. Exactly how TTL value choices affect operational networks is quite challenging to understand due to interactions across the distributed DNS service, where resolvers receive TTLs in different ways (answers and hints), TTLs are specified in multiple places (zones and their parent's glue), and while DNS resolution must be security-aware. This paper provides the first careful evaluation of how these multiple, interacting factors affect the effective cache lifetimes of DNS records, and provides recommendations for how to configure DNS TTLs based on our findings. We provide recommendations in TTL choice for different situations, and for where they must be configured. We show that longer TTLs have significant promise in reducing latency, reducing it from 183ms to 28.7ms for one country-code TLD.

@inproceedings{Moura:2019:CMY:3355369.3355568,
 author = {Moura, Giovane C. M. and Heidemann, John and Schmidt, Ricardo de O. and Hardaker, Wes},
 title = {Cache Me If You Can: Effects of DNS Time-to-Live},
 booktitle = {Proceedings of the Internet Measurement Conference},
 series = {IMC '19},
 year = {2019},
 isbn = {978-1-4503-6948-0},
 location = {Amsterdam, Netherlands},
 pages = {101--115},
 numpages = {15},
 url = {http://doi.acm.org/10.1145/3355369.3355568},
 pdfurl = {https://www.isi.edu/~hardaker/papers/2019-10-cache-me-ttls.pdf},
 doi = {10.1145/3355369.3355568},
 acmid = {3355568},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {DNS, caching, recursive DNS servers},
 ISIArea="NET",
}

@TechReport{Moura19a,
	author = 	"Giovane C. M. Moura and John Heidemann and
        		Ricardo de O. Schmidt and Wes Hardaker",
	title = 	"Cache Me If You Can: Effects of {DNS} {Time-to-Live} (extended)",
	institution = 	"USC/Information Sciences Institute",
	year = 		2019,
	sortdate = "2018-07-16",
	number = 	"ISI-TR-734b",
	note = "Released May 2018, updated Sept.~2019",
	project = "ant, lacanic, divoice, paaddos, nipet, ddidd",
	jsubject = "network_security",
	month = 	jul,
	jlocation = 	"johnh: pafile",
	keywords = 	"anycast, dns, ttl, ddos, root ddos",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Moura19a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Moura19a.pdf",
	xxxblogurl = "https://ant.isi.edu/blog/?p=1192",
	otherurl = "ftp://ftp.isi.edu/isi-pubs/tr-734.pdf",
	dataurl =	"https://ant.isi.edu/datasets/dns/#Moura19a_data",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
	abstract = "
DNS depends on extensive caching for good performance, and every DNS
zone owner must set Time-to-Live (TTL) values to control their
DNS caching.  Today there is relatively little guidance backed by
research about how to set TTLs, and operators must balance conflicting
demands of caching against agility of configuration.  Exactly how TTL
value choices affect operational networks is quite challenging to
understand for several reasons:  DNS is a distributed service, DNS
resolution is security-sensitive, and resolvers require multiple types
of information as they traverse the DNS hierarchy.  These complications
mean there are multiple frequently interacting, places TTLs can be
specified.  This paper provides the first careful evaluation of how
these factors affect the effective cache lifetimes of DNS records, and
provides recommendations for how to configure DNS TTLs based on our
findings.  We provide recommendations in TTL choice for different
situations, and for where they must be configured.  We show that longer
TTLs have significant promise, reducing median latency from 183ms to
28.7ms for one country-code TLD.
",
}

DNS depends on extensive caching for good performance, and every DNS zone owner must set Time-to-Live (TTL) values to control their DNS caching. Today there is relatively little guidance backed by research about how to set TTLs, and operators must balance conflicting demands of caching against agility of configuration. Exactly how TTL value choices affect operational networks is quite challenging to understand for several reasons: DNS is a distributed service, DNS resolution is security-sensitive, and resolvers require multiple types of information as they traverse the DNS hierarchy. These complications mean there are multiple frequently interacting, places TTLs can be specified. This paper provides the first careful evaluation of how these factors affect the effective cache lifetimes of DNS records, and provides recommendations for how to configure DNS TTLs based on our findings. We provide recommendations in TTL choice for different situations, and for where they must be configured. We show that longer TTLs have significant promise, reducing median latency from 183ms to 28.7ms for one country-code TLD.

@article{Turalska2019,
  title = {Cascading failures in scale-free interdependent networks},
  author = {Turalska, Malgorzata and Burghardt, Keith and Rohden, Martin and Swami, Ananthram and D'Souza, Raissa M.},
  journal = {Phys. Rev. E},
  volume = {99},
  issue = {3},
  pages = {032308},
  numpages = {9},
  year = {2019},
  month = {Mar},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.99.032308},
  url = {https://link.aps.org/doi/10.1103/PhysRevE.99.032308}
}

@InProceedings{	  thomas-escience-2019,
  Title		= {Characterization of In Situ and In Transit Analytics of
		  Molecular Dynamics Simulations for Next-generation
		  Supercomputers},
  Author	= {Thomas, Stephen and Wyatt, Michael and Do, Tu Mai Anh and
		  Pottier, Lo\"ic and Ferreira da Silva, Rafael and
		  Weinstein, Harel and Cuendet, Michel A. and Estrada, Trilce
		  and Deelman, Ewa and Taufer, Michela},
  BookTitle	= {15th International Conference on eScience (eScience)},
  Year		= {2019},
  Pages		= {188--198},
  DOI		= {10.1109/eScience.2019.00027},
  Note		= {Funding Acknowledgments: NSF 1741040}
}

@INPROCEEDINGS{Tavabi2019characterizing,
  author =       {Nazgol Tavabi and Nathan Bartley and Andres Abeliuk and Sandeep Soni and Emilio Ferrara and Kristina Lerman},
  title =        {Characterizing Activity on the Deep and DarkWeb},
   booktitle =    {Proceedings of the Companion to The Web Conference: CyberSafety Workshop},
  year =         {2019},
 }

@article{Badawy2019characterizing,
  title={Characterizing the 2016 Russian IRA influence campaign},
  author={Badawy, Adam and Addawood, Aseel and Lerman, Kristina and Ferrara, Emilio},
  journal={Social Network Analysis and Mining},
  volume={9},
  number={1},
  pages={31},
  year={2019},
  publisher={Springer}
}

@article{zhu_climate_2019,
	title = {Climate models can correctly simulate the continuum of global-average temperature variability},
	volume = {116},
	issn = {0027-8424},
	url = {https://github.com/khider/khider.github.io/blob/master/papers/zhu_pnas.pdf},
	doi = {10.1073/pnas.1809959116},
	abstract = {Climate models are foundational to formulations of climate policy and must successfully reproduce key features of the climate system. The temporal spectrum of observed global surface temperature is one such critical benchmark. This spectrum is known to obey scaling laws connecting astronomical forcings, from orbital to annual scales. We provide evidence that the current hierarchy of climate models is capable of reproducing the increase in variance in global-mean temperature at low frequencies. We suggest that successful climate predictions at decadal-to-centennial horizons hinge critically on the accuracy of initial and boundary conditions, particularly for the deep ocean state.Climate records exhibit scaling behavior with large exponents, resulting in larger fluctuations at longer timescales. It is unclear whether climate models are capable of simulating these fluctuations, which draws into question their ability to simulate such variability in the coming decades and centuries. Using the latest simulations and data syntheses, we find agreement for spectra derived from observations and models on timescales ranging from interannual to multimillennial. Our results confirm the existence of a scaling break between orbital and annual peaks, occurring around millennial periodicities. That both simple and comprehensive ocean–atmosphere models can reproduce these features suggests that long-range persistence is a consequence of the oceanic integration of both gradual and abrupt climate forcings. This result implies that Holocene low-frequency variability is partly a consequence of the climate system’s integrated memory of orbital forcing. We conclude that climate models appear to contain the essential physics to correctly simulate the spectral continuum of global-mean temperature; however, regional discrepancies remain unresolved. A critical element of successfully simulating suborbital climate variability involves, we hypothesize, initial conditions of the deep ocean state that are consistent with observations of the recent past.},
	number = {18},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Zhu, Feng and Emile-Geay, Julien and McKay, Nicholas P. and Hakim, Gregory J. and Khider, Deborah and Ault, Toby R. and Steig, Eric J. and Dee, Sylvia and Kirchner, James W.},
	year = {2019},
	pages = {8728--8733},
}

Climate models are foundational to formulations of climate policy and must successfully reproduce key features of the climate system. The temporal spectrum of observed global surface temperature is one such critical benchmark. This spectrum is known to obey scaling laws connecting astronomical forcings, from orbital to annual scales. We provide evidence that the current hierarchy of climate models is capable of reproducing the increase in variance in global-mean temperature at low frequencies. We suggest that successful climate predictions at decadal-to-centennial horizons hinge critically on the accuracy of initial and boundary conditions, particularly for the deep ocean state.Climate records exhibit scaling behavior with large exponents, resulting in larger fluctuations at longer timescales. It is unclear whether climate models are capable of simulating these fluctuations, which draws into question their ability to simulate such variability in the coming decades and centuries. Using the latest simulations and data syntheses, we find agreement for spectra derived from observations and models on timescales ranging from interannual to multimillennial. Our results confirm the existence of a scaling break between orbital and annual peaks, occurring around millennial periodicities. That both simple and comprehensive ocean–atmosphere models can reproduce these features suggests that long-range persistence is a consequence of the oceanic integration of both gradual and abrupt climate forcings. This result implies that Holocene low-frequency variability is partly a consequence of the climate system’s integrated memory of orbital forcing. We conclude that climate models appear to contain the essential physics to correctly simulate the spectral continuum of global-mean temperature; however, regional discrepancies remain unresolved. A critical element of successfully simulating suborbital climate variability involves, we hypothesize, initial conditions of the deep ocean state that are consistent with observations of the recent past.

@inproceedings{DBLP:conf/semweb/KejriwalS19,
  author    = {Mayank Kejriwal and
               Pedro A. Szekely},
  editor    = {Mari Carmen Su{\'{a}}rez{-}Figueroa and
               Gong Cheng and
               Anna Lisa Gentile and
               Christophe Gu{\'{e}}ret and
               C. Maria Keet and
               Abraham Bernstein},
  title     = {Co-LOD: Continuous Space Linked Open Data},
  booktitle = {Proceedings of the {ISWC} 2019 Satellite Tracks (Posters {\&} Demonstrations,
               Industry, and Outrageous Ideas) co-located with 18th International
               Semantic Web Conference {(ISWC} 2019), Auckland, New Zealand, October
               26-30, 2019},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2456},
  pages     = {333--337},
  publisher = {CEUR-WS.org},
  year      = {2019},
  url       = {http://ceur-ws.org/Vol-2456/paper94.pdf},
  timestamp = {Tue, 07 Sep 2021 14:10:34 +0200},
  biburl    = {https://dblp.org/rec/conf/semweb/KejriwalS19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@INPROCEEDINGS{8831193,
  author={Imes, Connor and Zhang, Huazhe and Zhao, Kevin and Hoffmann, Henry},
  booktitle={2019 IEEE International Conference on Autonomic Computing (ICAC)},
  title={CoPPer: Soft Real-Time Application Performance Using Hardware Power Capping},
  year={2019},
  volume={},
  number={},
  pages={31-41},
  abstract={Dynamic voltage and frequency scaling (DVFS) has been the cornerstone of innumerable software approaches to meeting application timing requirements with minimal energy. However, recent trends in technology-e.g., moving voltage converters on chip-favor hardware control of DVFS, as hardware can both react faster to external events and perform fine-grained power management across a device. We respond to these trends with CoPPer, which instead uses hardware power capping to meet application performance requirements with high energy efficiency. We find that meeting performance requirements with power capping is more challenging than using DVFS because the relationship between power and performance is non-linear and has diminishing returns at high power values. CoPPer overcomes these difficulties by using adaptive control to approximate non-linearities and a novel gain limit to avoid over-allocating power when it is no longer beneficial. We evaluate CoPPer with 20 parallel applications and compare it to both a classic linear DVFS controller and to a sophisticated control-theoretic, model-driven software DVFS manager. CoPPer provides all the functionality of the sophisticated DVFS-based approach, without requiring a user-specified model or time-consuming, exhaustive application/system pre-characterization. Compared to DVFS, CoPPer's gain limit reduces energy by 6% on average and by 12% for memory-bound applications. For high performance requirements, the energy savings are even greater: 8% on average and 18% for memory-bound applications.},
  keywords={energy conservation;multiprocessing systems;power aware computing;soft real-time application performance;hardware power capping;application timing requirements;minimal energy;voltage converters;chip-favor hardware control;fine-grained power management;adaptive control;model-driven software DVFS manager;sophisticated DVFS-based approach;memory-bound applications;dynamic voltage and frequency scaling;energy efficiency;parallel applications;linear DVFS controller;CoPPer;Copper;Software;Hardware;Timing;Power demand;Computational modeling;Market research;performance;power cap;rapl;control theory;dvfs;self aware systems;adaptive control},
  doi={10.1109/ICAC.2019.00015},
  ISSN={2474-0756},
  month={June},
  ISIArea = {CAS}
}

Dynamic voltage and frequency scaling (DVFS) has been the cornerstone of innumerable software approaches to meeting application timing requirements with minimal energy. However, recent trends in technology-e.g., moving voltage converters on chip-favor hardware control of DVFS, as hardware can both react faster to external events and perform fine-grained power management across a device. We respond to these trends with CoPPer, which instead uses hardware power capping to meet application performance requirements with high energy efficiency. We find that meeting performance requirements with power capping is more challenging than using DVFS because the relationship between power and performance is non-linear and has diminishing returns at high power values. CoPPer overcomes these difficulties by using adaptive control to approximate non-linearities and a novel gain limit to avoid over-allocating power when it is no longer beneficial. We evaluate CoPPer with 20 parallel applications and compare it to both a classic linear DVFS controller and to a sophisticated control-theoretic, model-driven software DVFS manager. CoPPer provides all the functionality of the sophisticated DVFS-based approach, without requiring a user-specified model or time-consuming, exhaustive application/system pre-characterization. Compared to DVFS, CoPPer's gain limit reduces energy by 6% on average and by 12% for memory-bound applications. For high performance requirements, the energy savings are even greater: 8% on average and 18% for memory-bound applications.

@conference {RN849,
	title = {Collaborating to leverage R\&E network infrastructures between Africa, Brazil, and the U.S.},
	booktitle = {UbuntuNet-Connect 2019},
	year = {2019},
	month = {2019},
	type = {Conference Paper},
	address = {Antananarivo, Madagascar},
	url = {https://urldefense.com/v3/__https://repository.ubuntunet.net/handle/10.20374/271__;!!FjuHKAHQs5udqho!OrfqNEjI8IYnpYEY3Wc7S4oSY6Kf82MHM8jGAJar7qa8MoXzNwtTTC2HiJsVSREP5YicoFyGz7HN7A$ },
	author = {Heidi Morgan and Julio Ibarra and Jeronimo Bezerra and Lopez, Luis Fernandez and Vasilka Chergarova and Donald A. Cox and Michael Stanton and Hazin, Aluizio and Lotz, Len and Mammen, Siju}
}

@article{muric2019collaboration,
  title={Collaboration Drives Individual Productivity},
  author={Muri{\'c}, Goran and Abeliuk, Andres and Lerman, Kristina and Ferrara, Emilio},
  journal={Proceedings of the ACM on Human-Computer Interaction},
  volume={3},
  number={CSCW},
  pages={1--24},
  year={2019},
  publisher={ACM New York, NY, USA}
}

@article{DBLP:journals/corr/abs-1911-11787,
  author       = {Goran Muric and
                  Andr{\'{e}}s Abeliuk and
                  Kristina Lerman and
                  Emilio Ferrara},
  title        = {Collaboration Drives Individual Productivity},
  journal      = {CoRR},
  volume       = {abs/1911.11787},
  year         = {2019},
  url          = {http://arxiv.org/abs/1911.11787},
  eprinttype    = {arXiv},
  eprint       = {1911.11787},
  timestamp    = {Sun, 02 Oct 2022 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1911-11787.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@Article{	  brinckman-fgcs-2018,
  Title		= {Collaborative Circuit Designs using the CRAFT Repository},
  Author	= {Brinckman, Adam and Deelman, Ewa and Gupta, Sandeep and
		  Nabrzyski, Jarek and Park, Soowang and Ferreira da Silva,
		  Rafael and Taylor, Ian J. and Vahi, Karan},
  Journal	= {Future Generation Computer Systems},
  Volume	= {94},
  Number	= {},
  Pages		= {841--853},
  Year		= {2019},
  DOI		= {10.1016/j.future.2018.01.018},
  Note		= {Funding Acknowledgments: DARPA HR0011-16-C-0043}
}

@inproceedings{embar:akbc19ws,
    author = "Embar, Varun and Pujara, Jay and Getoor, Lise",
    bib_url = "/pubs/bib/embar-akbc19ws.bib",
    booktitle = "AKBC Workshop on Federated KBs and the Open Knowledge Network",
    pdf_url = "/pubs/2019/embar-akbc19ws/embar-akbc19ws.pdf",
    sec = "ws",
    title = "Collective Alignment of Large-scale Ontologies",
    year = "2019"
}

@inprcoeedings{Yin2019ComprehensibleCT,
  title={Comprehensible Context-driven Text Game Playing},
  author={Xusen Yin and Jonathan May},
  booktitle={Proc. 2019 IEEE Conference on Games (CoG)},
  year={2019},
  month=August,
  pages={1-8}
}

@article{datta2019computational,
  title={Computational requirements for real-time ptychographic image reconstruction},
  author={Datta, Kaushik and Rittenbach, Andrew and Kang, Dong-In and Walters, John Paul and Crago, Stephen P and Damoulakis, John},
  journal={Applied Optics},
  volume={58},
  number={7},
  pages={B19--B27},
  year={2019},
  publisher={Optical Society of America}
}

@inproceedings{DBLP:conf/asunam/ZhangK19,
  author    = {Shuo Zhang and
               Mayank Kejriwal},
  editor    = {Francesca Spezzano and
               Wei Chen and
               Xiaokui Xiao},
  title     = {Concept drift in bias and sensationalism detection: an experimental
               study},
  booktitle = {{ASONAM} '19: International Conference on Advances in Social Networks
               Analysis and Mining, Vancouver, British Columbia, Canada, 27-30 August,
               2019},
  pages     = {601--604},
  publisher = {{ACM}},
  year      = {2019},
  url       = {https://doi.org/10.1145/3341161.3343690},
  doi       = {10.1145/3341161.3343690},
  timestamp = {Sun, 19 Jan 2020 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/conf/asunam/ZhangK19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{richey_considerations_2019,
	title = {Considerations for {Globigerinoides} ruber ({White} and {Pink}) {Paleoceanography}: {Comprehensive} {Insights} {From} a {Long}-{Running} {Sediment} {Trap}},
	volume = {34},
	url = {https://github.com/khider/khider.github.io/blob/master/papers/Richey_et_al-2019-Paleoceanography_and_Paleoclimatology.pdf},
	doi = {10.1029/2018PA003417},
	abstract = {Abstract We present a detailed analysis of the seasonal distribution, size, morphological variability, and geochemistry of co-occurring pink and white chromotypes of Globigerinoides ruber from a high-resolution (1–2 weeks) and long-running sediment trap time series in the northern Gulf of Mexico. We find no difference in the seasonal flux of the two chromotypes. Although flux of G. ruber is consistently lowest in winter, the flux-weighted signal exported to marine sediments represents mean annual conditions in the surface mixed layer. We observe the same morphological diversity among pink specimens of G. ruber as white. Comparison of the oxygen and carbon isotopic composition (δ18O and δ13C) of two morphotypes (sensu stricto and sensu lato) of pink G. ruber reveals the isotopes to be indistinguishable. The test size distribution within the population varies seasonally, with the abundance of large individuals increasing (decreasing) with increasing (decreasing) sea surface temperature. We find no systematic offsets in the Mg/Ca and δ18O of co-occurring pink and white G. ruber. The sediment trap data set shows that the Mg/Ca-temperature sensitivity for both chromotypes is much lower than the canonical 9\%/°C, which can likely be attributed to the secondary influence of both salinity and pH on foraminiferal Mg/Ca. Using paired Mg/Ca and δ18O, we evaluate the performance of a suite of published equations for calculating sea surface temperature, sea surface salinity, and isotopic composition of seawater (δ18Osw), including a new salinity-δ18Osw relationship for the northern Gulf of Mexico from water column observations.},
	number = {3},
	journal = {Paleoceanography and Paleoclimatology},
	author = {Richey, Julie N. and Thirumalai, Kaustubh and Khider, Deborah and Reynolds, Caitlin E. and Partin, Judson W. and Quinn, Terrence M.},
	year = {2019},
	keywords = {Globigerinoides ruber, Gulf of Mexico, Mg/Ca, Sediment Trap, morphotypes, planktic foraminifera},
	pages = {353--373},
}

Abstract We present a detailed analysis of the seasonal distribution, size, morphological variability, and geochemistry of co-occurring pink and white chromotypes of Globigerinoides ruber from a high-resolution (1–2 weeks) and long-running sediment trap time series in the northern Gulf of Mexico. We find no difference in the seasonal flux of the two chromotypes. Although flux of G. ruber is consistently lowest in winter, the flux-weighted signal exported to marine sediments represents mean annual conditions in the surface mixed layer. We observe the same morphological diversity among pink specimens of G. ruber as white. Comparison of the oxygen and carbon isotopic composition (δ18O and δ13C) of two morphotypes (sensu stricto and sensu lato) of pink G. ruber reveals the isotopes to be indistinguishable. The test size distribution within the population varies seasonally, with the abundance of large individuals increasing (decreasing) with increasing (decreasing) sea surface temperature. We find no systematic offsets in the Mg/Ca and δ18O of co-occurring pink and white G. ruber. The sediment trap data set shows that the Mg/Ca-temperature sensitivity for both chromotypes is much lower than the canonical 9%/°C, which can likely be attributed to the secondary influence of both salinity and pH on foraminiferal Mg/Ca. Using paired Mg/Ca and δ18O, we evaluate the performance of a suite of published equations for calculating sea surface temperature, sea surface salinity, and isotopic composition of seawater (δ18Osw), including a new salinity-δ18Osw relationship for the northern Gulf of Mexico from water column observations.

@conference {Schmidt2019,
    title = {Constellations in the Cloud: Virtualizing Remote Sensing Systems},
    booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
    year = {2019},
    author = {A. G. Schmidt, V. Venugopalan, M. Paolieri, and M. French}
}
  
    

@inproceedings{schmidt2019b,
	author = {Andrew G. Schmidt and Venugopalan, Vivek and Marco Paolieri and French, M.},
	booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
	title = {Constellations in the cloud: Virtualizing remote sensing systems},
	year = {2019}}

@inproceedings{mhamdi-etal-2019-contextualized,
    title = "Contextualized Cross-Lingual Event Trigger Extraction with Minimal Resources",
    author = "M{'}hamdi, Meryem  and
      Freedman, Marjorie  and
      May, Jonathan",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1061",
    doi = "10.18653/v1/K19-1061",
    pages = "656--665",
    abstract = "Event trigger extraction is an information extraction task of practical utility, yet it is challenging due to the difficulty of disambiguating word sense meaning. Previous approaches rely extensively on hand-crafted language-specific features and are applied mainly to English for which annotated datasets and Natural Language Processing (NLP) tools are available. However, the availability of such resources varies from one language to another. Recently, contextualized Bidirectional Encoder Representations from Transformers (BERT) models have established state-of-the-art performance for a variety of NLP tasks. However, there has not been much effort in exploring language transfer using BERT for event extraction. In this work, we treat event trigger extraction as a sequence tagging problem and propose a cross-lingual framework for training it without any hand-crafted features. We experiment with different flavors of transfer learning from high-resourced to low-resourced languages and compare the performance of different multilingual embeddings for event trigger extraction. Our results show that training in a multilingual setting outperforms language-specific models for both English and Chinese. Our work is the first to experiment with two event architecture variants in a cross-lingual setting, to show the effectiveness of contextualized embeddings obtained using BERT, and to explore and analyze its performance on Arabic.",
}

Event trigger extraction is an information extraction task of practical utility, yet it is challenging due to the difficulty of disambiguating word sense meaning. Previous approaches rely extensively on hand-crafted language-specific features and are applied mainly to English for which annotated datasets and Natural Language Processing (NLP) tools are available. However, the availability of such resources varies from one language to another. Recently, contextualized Bidirectional Encoder Representations from Transformers (BERT) models have established state-of-the-art performance for a variety of NLP tasks. However, there has not been much effort in exploring language transfer using BERT for event extraction. In this work, we treat event trigger extraction as a sequence tagging problem and propose a cross-lingual framework for training it without any hand-crafted features. We experiment with different flavors of transfer learning from high-resourced to low-resourced languages and compare the performance of different multilingual embeddings for event trigger extraction. Our results show that training in a multilingual setting outperforms language-specific models for both English and Chinese. Our work is the first to experiment with two event architecture variants in a cross-lingual setting, to show the effectiveness of contextualized embeddings obtained using BERT, and to explore and analyze its performance on Arabic.

@inproceedings{han2019contextualized,
  title={Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding},
  author={Han, Rujun and Liang, Mengyue and Alhafni, Bashar and Peng, Nanyun},
  booktitle={2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2019), Workshop on Narrative Understanding},
  year={2019}
}

@article{Soma2019Coregulationoftherapistand,
 author = {Soma, Christina S. and Baucom, Brian R. W. and Xiao, Bo and Butner, Jonathan E. and Hilpert, Peter and Narayanan,  Shrikanth and Atkins, David C. and Imel, Zac E.},
 journal = {Psychotherapy Research},
 title = {Coregulation of therapist and client emotion during psychotherapy},
 year = {2019},
 month = {Nov}
}

@inproceedings{liu2019coupled,
  title={Coupled clustering of time-series and networks},
  author={Liu, Yike and Zhu, Linhong and Szekely, Pedro and Galstyan, Aram and Koutra, Danai},
  booktitle={Proceedings of the 2019 SIAM International Conference on Data Mining},
  pages={531--539},
  year={2019},
  organization={Society for Industrial and Applied Mathematics}
}

@inproceedings{shbita2019creating,
  title={Creating a FAIR Data Catalog to Support Scientific Modeling},
  author={Shbita, Basel and Vu, Binh and Feldman, Dan and Pham, Minh and Rajendran, Arunkumar and Knoblock, Craig A and Pujara, Jay and Chiang, Yao-Yi},
  booktitle={Proceedings of the Workshop on Advanced Knowledge Technologies for Science in a FAIR World},
  location={San Diego, CA},
  year={2019},
  urlPaper={http://usc-isi-i2.github.io/papers/shbita19-escience.pdf},
  urlSlides={http://usc-isi-i2.github.io/slides/shbita19-escience-slides.pdf}
}

@article{Sorensen2019Cross-ModalCoordinationofFace-Directed,
 author = {Sorensen, Tanner and Zane, Emily and Feng, Tiantian and Narayanan, Shrikanth and Grossman, Ruth},
 doi = {10.1038/s41598-019-54587-z},
 journal = {Scientific Reports},
 link = {http://sail.usc.edu/publications/files/sorensen2019ASD.pdf},
 month = {Dec},
 number = {18301},
 title = {Cross-Modal Coordination of Face-Directed Gaze and Emotional Speech Production in School-Aged Children and Adolescents with ASD},
 volume = {9},
 year = {2019}
}

@article{uddin2019cross,
  title={Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages},
  author={Uddin Ahmad, Wasi and Zhang, Zhisong and Ma, Xuezhe and Chang, Kai-Wei and Peng, Nanyun},
  journal={arXiv preprint arXiv:1909.09265},
  year={2019}
}

@inproceedings{ahmad2019cross,
  title={Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages},
  author={Ahmad, Wasi Uddin and Zhang, Zhisong and Ma, Xuezhe and Chang, Kai-Wei and Peng, Nanyun},
  booktitle={The 2019 SIGNLL Conference on Computational Natural Language Learning (CoNLL)},
  year={2019}
}

@inproceedings{pan-etal-2019-cross,
    title = "Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining",
    author = "Pan, Xiaoman  and
      Gowda, Thamme  and
      Ji, Heng  and
      May, Jonathan  and
      Miller, Scott",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6107",
    doi = "10.18653/v1/D19-6107",
    pages = "56--66",
    abstract = "Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages. We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. We replace each anchor link in the source language with its corresponding entity title in the target language if it exists, or in the source language otherwise. A cross-lingual joint entity and word embedding learned from this kind of data not only can disambiguate linkable entities but can also effectively represent unlinkable entities. Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking. Experimental results show that CLEW significantly advances the state-of-the-art: up to 3.1{\%} absolute F-score gain for unsupervised cross-lingual entity linking. Moreover, it provides reliable alignment on both the word/entity level and the sentence level, and thus we use it to mine parallel sentences for all (302, 2) language pairs in Wikipedia.",
}

Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages. We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. We replace each anchor link in the source language with its corresponding entity title in the target language if it exists, or in the source language otherwise. A cross-lingual joint entity and word embedding learned from this kind of data not only can disambiguate linkable entities but can also effectively represent unlinkable entities. Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking. Experimental results show that CLEW significantly advances the state-of-the-art: up to 3.1% absolute F-score gain for unsupervised cross-lingual entity linking. Moreover, it provides reliable alignment on both the word/entity level and the sentence level, and thus we use it to mine parallel sentences for all (302, 2) language pairs in Wikipedia.

@inproceedings{pan-etal-2019-cross,
    title = "Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining",
    author = "Pan, Xiaoman  and
      \textbf{Gowda, Thamme}  and
      Ji, Heng  and
      May, Jonathan  and
      Miller, Scott",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6107",
    doi = "10.18653/v1/D19-6107",
    pages = "56--66",
    abstract = "Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages. We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. We replace each anchor link in the source language with its corresponding entity title in the target language if it exists, or in the source language otherwise. A cross-lingual joint entity and word embedding learned from this kind of data not only can disambiguate linkable entities but can also effectively represent unlinkable entities. Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking. Experimental results show that CLEW significantly advances the state-of-the-art: up to 3.1{\%} absolute F-score gain for unsupervised cross-lingual entity linking. Moreover, it provides reliable alignment on both the word/entity level and the sentence level, and thus we use it to mine parallel sentences for all (302, 2) language pairs in Wikipedia.",
}

Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages. We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. We replace each anchor link in the source language with its corresponding entity title in the target language if it exists, or in the source language otherwise. A cross-lingual joint entity and word embedding learned from this kind of data not only can disambiguate linkable entities but can also effectively represent unlinkable entities. Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking. Experimental results show that CLEW significantly advances the state-of-the-art: up to 3.1% absolute F-score gain for unsupervised cross-lingual entity linking. Moreover, it provides reliable alignment on both the word/entity level and the sentence level, and thus we use it to mine parallel sentences for all (302, 2) language pairs in Wikipedia.

@InProceedings{huang-ji-may:2019:N19-1,
  author    = {Huang, Lifu  and  Ji, Heng  and  May, Jonathan},
  title     = {Cross-lingual Multi-Level Adversarial Transfer to Enhance Low-Resource Name Tagging},
  booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota},
  publisher = {Association for Computational Linguistics},
  pages     = {3823--3833},
  abstract  = {We focus on improving name tagging for low-resource languages using annotations from related languages. Previous studies either directly project annotations from a source language to a target language using cross-lingual representations or use a shared encoder in a multitask network to transfer knowledge. These approaches inevitably introduce noise to the target language annotation due to mismatched source-target sentence structures. To effectively transfer the resources, we develop a new neural architecture that leverages multi-level adversarial transfer: (1) word-level adversarial training, which projects source language words into the same semantic space as those of the target language without using any parallel corpora or bilingual gazetteers, and (2) sentence-level adversarial training, which yields language-agnostic sequential features. Our neural architecture outperforms previous approaches on CoNLL data sets. Moreover, on 10 low-resource languages, our approach achieves up to 16\% absolute F-score gain over all high-performing baselines on cross-lingual transfer without using any target-language resources.},
  url       = {http://www.aclweb.org/anthology/N19-1383}
}

We focus on improving name tagging for low-resource languages using annotations from related languages. Previous studies either directly project annotations from a source language to a target language using cross-lingual representations or use a shared encoder in a multitask network to transfer knowledge. These approaches inevitably introduce noise to the target language annotation due to mismatched source-target sentence structures. To effectively transfer the resources, we develop a new neural architecture that leverages multi-level adversarial transfer: (1) word-level adversarial training, which projects source language words into the same semantic space as those of the target language without using any parallel corpora or bilingual gazetteers, and (2) sentence-level adversarial training, which yields language-agnostic sequential features. Our neural architecture outperforms previous approaches on CoNLL data sets. Moreover, on 10 low-resource languages, our approach achieves up to 16% absolute F-score gain over all high-performing baselines on cross-lingual transfer without using any target-language resources.

@inproceedings{subburathinam-etal-2019-cross,
    title = "Cross-lingual Structure Transfer for Relation and Event Extraction",
    author = "Subburathinam, Ananya  and
      Lu, Di  and
      Ji, Heng  and
      May, Jonathan  and
      Chang, Shih-Fu  and
      Sil, Avirup  and
      Voss, Clare",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1030",
    doi = "10.18653/v1/D19-1030",
    pages = "313--325",
    abstract = "The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages. We investigate the suitability of cross-lingual structure transfer techniques for these tasks. We exploit relation- and event-relevant language-universal features, leveraging both symbolic (including part-of-speech and dependency path) and distributional (including type representation and contextualized representation) information. By representing all entity mentions, event triggers, and contexts into this complex and structured multilingual common space, using graph convolutional networks, we can train a relation or event extractor from source language annotations and apply it to the target language. Extensive experiments on cross-lingual relation and event transfer among English, Chinese, and Arabic demonstrate that our approach achieves performance comparable to state-of-the-art supervised models trained on up to 3,000 manually annotated mentions: up to 62.6{\%} F-score for Relation Extraction, and 63.1{\%} F-score for Event Argument Role Labeling. The event argument role labeling model transferred from English to Chinese achieves similar performance as the model trained from Chinese. We thus find that language-universal symbolic and distributional representations are complementary for cross-lingual structure transfer.",
}

The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages. We investigate the suitability of cross-lingual structure transfer techniques for these tasks. We exploit relation- and event-relevant language-universal features, leveraging both symbolic (including part-of-speech and dependency path) and distributional (including type representation and contextualized representation) information. By representing all entity mentions, event triggers, and contexts into this complex and structured multilingual common space, using graph convolutional networks, we can train a relation or event extractor from source language annotations and apply it to the target language. Extensive experiments on cross-lingual relation and event transfer among English, Chinese, and Arabic demonstrate that our approach achieves performance comparable to state-of-the-art supervised models trained on up to 3,000 manually annotated mentions: up to 62.6% F-score for Relation Extraction, and 63.1% F-score for Event Argument Role Labeling. The event argument role labeling model transferred from English to Chinese achieves similar performance as the model trained from Chinese. We thus find that language-universal symbolic and distributional representations are complementary for cross-lingual structure transfer.

@InProceedings{	  vahi-escience-2019,
  Title		= {Custom Execution Environments with Containers in
		  Pegasus-enabled Scientific Workflows},
  Author	= {Vahi, Karan and Rynge, Mats and Papadimitriou, George and
		  Brown, Duncan and Mayani, Rajiv and Ferreira da Silva,
		  Rafael and Deelman, Ewa and Mandal, Anirban and Lyons, Eric
		  and Zink, Michael},
  BookTitle	= {15th International Conference on eScience (eScience)},
  Year		= {2019},
  Location	= {San Diego, CA, USA},
  Pages		= {281--290},
  DOI		= {10.1109/eScience.2019.00039},
  Note		= {Funding Acknowledgments: NSF 1664162, NSF 1826997, NSF
		  1443047}
}

@InProceedings{	  deelman-escience-2019,
  Title		= {Cyberinfrastructure Center of Excellence Pilot: Connecting
		  Large Facilities Cyberinfrastructure},
  Author	= {Deelman, Ewa and Mandal, Anirban and Pascucci, Valerio and
		  Sons, Susan and Wyngaard, Jane and Vardeman II, Charles F
		  and Petruzza, Steve and Baldin, Ilya and Christopherson,
		  Laura and Mitchell, Ryan and Pottier, Lo\"ic and Rynge,
		  Mats and Scott, Erik and Vahi, Karan and Kogank, Marina and
		  Mann, Jasmine A and Gulbransen, Tom and Allen, Daniel and
		  Barlow, David and Bonarrigo, Santiago and Clark, Chris and
		  Goldman, Leslie and Goulden, Tristan and Harvey, Phil and
		  Hulsander, David and Jacob, Steve and Laney, Christine and
		  Lobo-Padilla, Ivan and Sampson, Jeremey and Staarmann, John
		  and Stone, Steve},
  BookTitle	= {15th International Conference on eScience (eScience)},
  Year		= {2019},
  Location	= {San Diego, CA, USA},
  Pages		= {},
  DOI		= {},
  Note		= {Funding Acknowledgments: NSF 1842042}
}

@article{
 title = {Cyberinfrastructure Requirements to Enhance Multi-messenger Astrophysics},
 type = {article},
 year = {2019},
 websites = {http://arxiv.org/abs/1903.04590},
 month = {3},
 day = {11},
 id = {2bcb05ba-9331-34ad-b009-f2f4cba04f58},
 created = {2020-04-22T21:44:57.173Z},
 accessed = {2020-04-22},
 file_attached = {true},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2020-05-11T14:43:33.833Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 citation_key = {Chang2019},
 private_publication = {false},
 abstract = {The identification of the electromagnetic counterpart of the gravitational wave event, GW170817, and discovery of neutrinos and gamma-rays from TXS 0506+056 heralded the new era of multi-messenger astrophysics. As the number of multi-messenger events rapidly grow over the next decade, the cyberinfrastructure requirements to handle the increase in data rates, data volume, need for event follow up, and analysis across the different messengers will also explosively grow. The cyberinfrastructure requirements to enhance multi-messenger astrophysics will both be a major challenge and opportunity for astronomers, physicists, computer scientists and cyberinfrastructure specialists. Here we outline some of these requirements and argue for a distributed cyberinfrastructure institute for multi-messenger astrophysics to meet these challenges.},
 bibtype = {article},
 author = {Chang, Philip and Allen, Gabrielle and Anderson, Warren and Bianco, Federica B. and Bloom, Joshua S. and Brady, Patrick R. and Brazier, Adam and Cenko, S. Bradley and Couch, Sean M. and DeYoung, Tyce and Deelman, Ewa and Etienne, Zachariah B and Foley, Ryan J. and Fox, Derek B and Golkhou, V. Zach and Grant, Darren R and Hanna, Chad and Holley-Bockelmann, Kelly and Howell, D. Andrew and Huerta, E. A. and Johnson, Margaret W. G. and Juric, Mario and Kaplan, David L. and Katz, Daniel S. and Keivani, Azadeh and Kerzendorf, Wolfgang and Kopper, Claudio and Lam, Michael T. and Lehner, Luis and Marka, Zsuzsa and Marka, Szabolcs and Nabrzyski, Jarek and Narayan, Gautham and O'Shea, Brian W. and Petravick, Donald and Quick, Rob and Street, Rachel A. and Taboada, Ignacio and Timmes, Frank and Turk, Matthew J. and Weltman, Amanda and Zhang, Zhao}
}

The identification of the electromagnetic counterpart of the gravitational wave event, GW170817, and discovery of neutrinos and gamma-rays from TXS 0506+056 heralded the new era of multi-messenger astrophysics. As the number of multi-messenger events rapidly grow over the next decade, the cyberinfrastructure requirements to handle the increase in data rates, data volume, need for event follow up, and analysis across the different messengers will also explosively grow. The cyberinfrastructure requirements to enhance multi-messenger astrophysics will both be a major challenge and opportunity for astronomers, physicists, computer scientists and cyberinfrastructure specialists. Here we outline some of these requirements and argue for a distributed cyberinfrastructure institute for multi-messenger astrophysics to meet these challenges.

@inproceedings{kline19cacoe,
 title={{Cybersecurity Experimentation at Program Scale: Guidelines and Principles for Future Testbeds}},
 author={Kline, Erik and Schwab, Stephen},
 booktitle={Cyber Range Applications and Technologies (CACOE)},
 year={2019}
 }

@inproceedings{vu:kcap19,
    author = "Vu, Binh and Knoblock, Craig and Pujara, Jay",
    acceptrate = "18\%",
    bib_url = "/pubs/bib/vu-kcap19.bib",
    booktitle = "ACM International Conference on Knowledge Capture (K-CAP)",
    doi_url = "https://doi.org/10.1145/3308558.3313711",
    pdf_url = "/pubs/2019/vu-kcap19/vu-kcap19.pdf",
    sec = "conf",
    title = "D-REPR: A Language for Describing and Mapping Diversely-Structured Data Sources to RDF",
    year = "2019"
}

@inproceedings{vu2019d,
  title={D-REPR: A Language for Describing and Mapping Diversely-Structured Data Sources to RDF},
  author={Vu, Binh and Pujara, Jay and Knoblock, Craig A},
  booktitle={Proceedings of the 10th International Conference on Knowledge Capture},
  pages={189--196},
  year={2019},
  Urlpaper = {http://usc-isi-i2.github.io/papers/vu19-kcap.pdf},
  UrlSlides = {http://usc-isi-i2.github.io/slides/vu2019-slides.pdf}
}

@Misc{Hardaker19b,
title="DANE update metrics",
author="Viktor Dukhovni and  Wes Hardaker",
month=06,
year=2019,
sortdate = "2019-06-22",
URL="https://65.schedule.icann.org/meetings/1058208",
project = "ant, broot",
keywords="dns, dnssec, dane, security",
howpublished="Talk at ICANN DNSSEC Workshop",
pdfurl="https://ant.isi.edu/~hardaker/presentations/2019-06-DANE-hardaker-dukhovni.pdf"
}

% wjh:isi:gawseedresearch,

@Misc{Heidemann19a,
	author = 	"John Heidemann and Wes Hardaker and Jelena
                  Mirkovic and ASM Rizvi and Robert Story",
	title = 	"{DDoS} Defense in Depth for {DNS} (DDIDD)",
	howpublished = "Invited talk at the Trusted CI Webinar",
	month = 	dec,
	year = 	2019,
	sortdate = 	"2019-12-09", 
	project = "ant, ddidd, paaddos, diiner",
	jsubject = "topology_modeling",
	jlocation = 	"johnh: pafile",
	keywords = 	"anti-DDoS, network security, B-Root, invited talks",
	url =		"https://ant.isi.edu/%7ejohnh/PAPERS/Heidemann19a.html",
	pdfurl =	"https://ant.isi.edu/%7ejohnh/PAPERS/Heidemann19a.pdf",
	videourl = "https://youtu.be/g_IivqPLdQM",
	myorganization =	"USC/Information Sciences Institute",
	copyrightholder = "authors",
	abstract = "
he DDIDD Project (DDoS Defense in Depth for DNS) is
applying existing and developing new defenses against
Distributed-Denial-of-Service attacks for operational DNS
infrastructure.  We are building a defense-in-depth approach to
mitigate Distributed Denial-of-Service attacks for DNS servers, with
approaches to filter spoofed traffic, identify known-good traffic when
possible, and employ cloud-based scaling to handle the largest
attacks.  We are working with USC's B-Root team to test our approaches
as a case study, and are making approaches open source as they become
available.  This talk will summarize the project and our overall
approach, provide details about some of our early filters and filter
selection, and describe where we plan to go in the remaining year.
",
}

he DDIDD Project (DDoS Defense in Depth for DNS) is applying existing and developing new defenses against Distributed-Denial-of-Service attacks for operational DNS infrastructure. We are building a defense-in-depth approach to mitigate Distributed Denial-of-Service attacks for DNS servers, with approaches to filter spoofed traffic, identify known-good traffic when possible, and employ cloud-based scaling to handle the largest attacks. We are working with USC's B-Root team to test our approaches as a case study, and are making approaches open source as they become available. This talk will summarize the project and our overall approach, provide details about some of our early filters and filter selection, and describe where we plan to go in the remaining year.

@inproceedings{chatziagapi2019data,
	title = {Data augmentation using {GANs} for speech emotion recognition.},
	booktitle = {Interspeech},
	author = {Chatziagapi, Aggelina and Paraskevopoulos, Georgios and Sgouropoulos, Dimitris and Pantazopoulos, Georgios and Nikandrou, Malvina and Giannakopoulos, Theodoros and Katsamanis, Athanasios and Potamianos, Alexandros and Narayanan, Shrikanth},
	year = {2019},
	keywords = {⛔ No DOI found},
	pages = {171--175},
}

@inproceedings{mehrabi2019debiasing,
  title={Debiasing community detection: The importance of lowly connected nodes},
  author={Mehrabi, Ninareh and Morstatter, Fred and Peng, Nanyun and Galstyan, Aram},
  booktitle={2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},
  pages={509--512},
  year={2019},
  organization={IEEE}
}

@inproceedings{inproceedings,
author = {Han, Rujun and Hsu, I-Hung and Yang, Mu and Galstyan, Aram and Weischedel, Ralph and Peng, Nanyun},
year = {2019},
month = {01},
pages = {666-106},
title = {Deep Structured Neural Network for Event Temporal Relation Extraction},
doi = {10.18653/v1/K19-1062}
}

@inproceedings{han2019deep,
  title={Deep structured neural network for event temporal relation extraction},
  author={Han, Rujun and Hsu, I and Yang, Mu and Galstyan, Aram and Weischedel, Ralph and Peng, Nanyun and others},
booktitle={Proceedings  of the 2019 SIGNLL Conference on Computational Natural Language Learning (CoNLL)},
 year={2019}
}

@article{DBLP:journals/ijcv/ChangTHMNM19,
  author       = {Feng{-}Ju Chang and
                  Anh Tuan Tran and
                  Tal Hassner and
                  Iacopo Masi and
                  Ram Nevatia and
                  G{\'{e}}rard G. Medioni},
  title        = {Deep, Landmark-Free {FAME:} Face Alignment, Modeling, and Expression
                  Estimation},
  journal      = {Int. J. Comput. Vis.},
  volume       = {127},
  number       = {6-7},
  pages        = {930--956},
  year         = {2019},
  url          = {https://doi.org/10.1007/s11263-019-01151-x},
  doi          = {10.1007/S11263-019-01151-X},
  timestamp    = {Fri, 13 Mar 2020 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/ijcv/ChangTHMNM19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{tandon2019defending,
  title={Defending Web Servers Against Flash Crowd Attacks},
  author={Tandon, Rajat and Palia, Abhinav and Ramani, Jaydeep and Paulsen, Brandon and Bartlett, Genevieve and Mirkovic, Jelena},
  booktitle={Midscale Education and Research Infrastructure and Tools (MERIT) Workshop},
  pages={1--2},
  year={2019},
  organization={IEEE}
}