@inproceedings{cift-gomac:2021,
 author = {Haroldsen, Travis and French, Matthew and T. Sung and D. Glick and J. Danner and L. Lerner},
 title = {Independent Testing of Untrusted FPGAs for Faulty Interconnnect},
 booktitle = {Government Microcircuit Applications and Critical Technology Conference},
 month = {March},
 year = {2021},
 note = {to appear}
}

@conference {Glick2021,
    title = {Canary: An FPGA Assurance Plugin for Vendor EDA Tools},
    booktitle = {Government Microcircuit Applications and Critical Technology Conference (GOMACTech)},
    year = {2021},
    month = {March},
    author = {D. Glick and A. Schmidt and J. Nifong and T. Haroldsen and J. Monson E. M. Ruiz and M. French}
}

@conference {Menon2021,
    title = {Design and Performance Evaluation of Multispectral Sensing Algorithms on CPU, GPU, and FPGA},
    booktitle = {IEEE Aerospace Conference},
    year = {2021},
    author = {V. Menon and S. Siddiqui and S. Rao and A. Schmidt and V. Chirayath and A. Li and M. French}
}

@article{reing_entropy21,
	Abstract = {If regularity in data takes the form of higher-order functions among groups of variables, models which are biased towards lower-order functions may easily mistake the data for noise. To distinguish whether this is the case, one must be able to quantify the contribution of different orders of dependence to the total information. Recent work in information theory attempts to do this through measures of multivariate mutual information (MMI) and information decomposition (ID). Despite substantial theoretical progress, practical issues related to tractability and learnability of higher-order functions are still largely unaddressed. In this work, we introduce a new approach to information decomposition—termed Neural Information Decomposition (NID)—which is both theoretically grounded, and can be efficiently estimated in practice using neural networks. We show on synthetic data that NID can learn to distinguish higher-order functions from noise, while many unsupervised probability models cannot. Additionally, we demonstrate the usefulness of this framework as a tool for exploring biological and artificial neural networks.},
	Article-Number = {79},
	Author = {Reing, Kyle and Ver Steeg, Greg and Galstyan, Aram},
	Date-Added = {2021-01-29 10:34:27 -0800},
	Date-Modified = {2021-01-29 10:34:45 -0800},
	Doi = {10.3390/e23010079},
	Issn = {1099-4300},
	Journal = {Entropy},
	Number = {1},
	Pubmedid = {33430463},
	Title = {Discovering Higher-Order Interactions Through Neural Information Decomposition},
	Url = {https://www.mdpi.com/1099-4300/23/1/79},
	Volume = {23},
	Year = {2021},
	Bdsk-Url-1 = {https://www.mdpi.com/1099-4300/23/1/79},
	Bdsk-Url-2 = {https://doi.org/10.3390/e23010079}
}

If regularity in data takes the form of higher-order functions among groups of variables, models which are biased towards lower-order functions may easily mistake the data for noise. To distinguish whether this is the case, one must be able to quantify the contribution of different orders of dependence to the total information. Recent work in information theory attempts to do this through measures of multivariate mutual information (MMI) and information decomposition (ID). Despite substantial theoretical progress, practical issues related to tractability and learnability of higher-order functions are still largely unaddressed. In this work, we introduce a new approach to information decomposition—termed Neural Information Decomposition (NID)—which is both theoretically grounded, and can be efficiently estimated in practice using neural networks. We show on synthetic data that NID can learn to distinguish higher-order functions from noise, while many unsupervised probability models cannot. Additionally, we demonstrate the usefulness of this framework as a tool for exploring biological and artificial neural networks.

@inproceedings{myrl_aistats21,
	Author = {Marmerelis, Myrl G. and {Ver Steeg}, Greg and Galstyan, Aram},
	Booktitle = {The 24th International Conference on Artificial Intelligence and Statistics (AISTATS)},
	Date-Added = {2021-01-29 12:35:11 -0800},
	Date-Modified = {2021-01-29 12:35:47 -0800},
	Title = {Latent Embeddings of Point Process Excitations},
	Year = {2021}
}

@inproceedings{reing_aistats21,
	Author = {Reing, Kyle and {Ver Steeg}, Greg and Galstyan, Aram},
	Booktitle = {The 24th International Conference on Artificial Intelligence and Statistics (AISTATS)},
	Date-Added = {2021-01-29 12:33:15 -0800},
	Date-Modified = {2021-01-29 12:34:44 -0800},
	Title = {Influence Decompositions For Neural Network Attribution},
	Year = {2021}
}

@inproceedings{umang_isbi,
	Author = {Gupta, Umang and Lam, Pradeep and {Ver Steeg}, Greg and Thompson, Paul},
	Booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)},
	Date-Added = {2021-01-29 12:43:21 -0800},
	Date-Modified = {2021-01-29 12:44:04 -0800},
	Title = {Improved Brain Age estimation with slice-based Set Networks},
	Year = {2021}}

@inproceedings{elan_iclr,
	Author = {Markowitz, Elan Sopher and Balasubramanian, Keshav and Mirtaheri, Mehrnoosh and Abu-El-Haija, Sami and Perozzi, Bryan and {Ver Steeg}, Greg and Galstyan, Aram},
	Booktitle = {International Conference on Learning Representations (ICLR)},
	Date-Added = {2021-01-29 12:38:40 -0800},
	Date-Modified = {2021-01-29 12:39:47 -0800},
	Title = {Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning},
	Year = {2021}}

@misc{gupta_aaai,
	Author = {Gupta, Umang and Ferber, Aaron and Dilkina, Bistra and {Ver Steeg}, Greg},
	Booktitle = {AAAI Conference on Artificial Intelligence (AAAI-21)},
	Date-Added = {2021-01-29 12:31:15 -0800},
	Date-Modified = {2021-01-29 12:32:42 -0800},
	Title = {Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation},
	Url = {arXiv:2101.04108},
	Year = {2021},
	Eprint = {arXiv:2101.04108},
	howpublished = {https://arxiv.org/abs/2101.04108}
}

@inproceedings{canary-gomac:2021,
 author = {D. Glick and Schmidt, Andrew and J. Nifong and Haroldsen, Travis and J. Monson and E. M. Ruiz and French, Matthew},
 title = {Canary: An FPGA Assurance Plugin for Vendor EDA Tools},
 booktitle = {GOMACTech},
 month = {March},
 year = {2021},
 note = {to appear}
}

@misc{RFC8976,
	series =	{Request for Comments},
	number =	8976,
	howpublished =	{RFC 8976},
	publisher =	{RFC Editor},
	doi =		{10.17487/RFC8976},
	url =		{https://rfc-editor.org/rfc/rfc8976.txt},
    author =	{Wessels, Duane and Barber, Piet and Weinberg, Matt and Kumari, Warren Ace and Hardaker, Wes},
	title =		{Message Digest for DNS Zones},
	pagetotal =	31,
	year =		2021,
	month =		feb,
	abstract =	{This document describes a protocol and new DNS Resource Record that provides a cryptographic message digest over DNS zone data at rest. The ZONEMD Resource Record conveys the digest data in the zone itself. When used in combination with DNSSEC, ZONEMD allows recipients to verify the zone contents for data integrity and origin authenticity. This provides assurance that received zone data matches published data, regardless of how the zone data has been transmitted and received. When used without DNSSEC, ZONEMD functions as a checksum, guarding only against unintentional changes. ZONEMD does not replace DNSSEC: DNSSEC protects individual RRsets (DNS data with fine granularity), whereas ZONEMD protects a zone's data as a whole, whether consumed by authoritative name servers, recursive name servers, or any other applications. As specified herein, ZONEMD is impractical for large, dynamic zones due to the time and resources required for digest calculation. However, the ZONEMD record is extensible so that new digest schemes may be added in the future to support large, dynamic zones.},
}

This document describes a protocol and new DNS Resource Record that provides a cryptographic message digest over DNS zone data at rest. The ZONEMD Resource Record conveys the digest data in the zone itself. When used in combination with DNSSEC, ZONEMD allows recipients to verify the zone contents for data integrity and origin authenticity. This provides assurance that received zone data matches published data, regardless of how the zone data has been transmitted and received. When used without DNSSEC, ZONEMD functions as a checksum, guarding only against unintentional changes. ZONEMD does not replace DNSSEC: DNSSEC protects individual RRsets (DNS data with fine granularity), whereas ZONEMD protects a zone's data as a whole, whether consumed by authoritative name servers, recursive name servers, or any other applications. As specified herein, ZONEMD is impractical for large, dynamic zones due to the time and resources required for digest calculation. However, the ZONEMD record is extensible so that new digest schemes may be added in the future to support large, dynamic zones.

@article{DBLP:journals/pieee/SomandepalliGMK21,
  author    = {Krishna Somandepalli and
               Tanaya Guha and
               Victor R. Martinez and
               Naveen Kumar and
               Hartwig Adam and
               Shrikanth Narayanan},
  title     = {Computational Media Intelligence: Human-Centered Machine Analysis
               of Media},
  journal   = {Proc. {IEEE}},
  volume    = {109},
  number    = {5},
  pages     = {891--910},
  year      = {2021},
  url       = {https://doi.org/10.1109/JPROC.2020.3047978},
  doi       = {10.1109/JPROC.2020.3047978},
  timestamp = {Tue, 01 Jun 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/pieee/SomandepalliGMK21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Jati2021TemporalDynamicsofWorkplace,
 author = {Jati, Arindam and Nadarajan, Amrutha and Peri, Raghuveer and Mundnich, Karel and Feng, Tiantian and Girault, Benjamin and Narayanan, Shrikanth},
 journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing},
 title = {Temporal Dynamics of Workplace Acoustic Scenes: Egocentric Analysis and Prediction},
 doi = {https://doi.org/10.1109/TASLP.2021.3050265},
 link = {http://sail.usc.edu/publications/files/Jati-TASLP2021.pdf},
 year = {2021},
 month = {Jan}
}

@article{Paz2021IntrapersonalandInterpersonalVocal,
 author = {Paz, Adar and Rafaeli, Eshkol and Bar-Kalifa, Eran and Gilboa-Schectman, Eva and Gannot, Sharon and Laufer-Goldshtein, Bracha and Narayanan, Shrikanth and Keshet, Joseph and Atzil-Slonim, Dana },
 journal = {Journal of Consulting and Clinical Psychology},
 title = {Intrapersonal and Interpersonal Vocal Affect Dynamics during Psychotherapy},
 year = {2021},
 month = {Jan} 
 }

@article{NALLANCHAKRAVARTHULA2021101162,
 abstract = {The task of quantifying human behavior by observing interaction cues is an important and useful one across a range of domains in psychological research and practice. Machine learning-based approaches typically perform this task by first estimating behavior based on cues within an observation window, such as a fixed number of words, and then aggregating the behavior over all the windows in that interaction. The length of this window directly impacts the accuracy of estimation by controlling the amount of information being used. The exact link between window length and accuracy, however, has not been well studied, especially in spoken language. In this paper, we investigate this link and present an analysis framework that determines appropriate window lengths for the task of behavior estimation. Our proposed framework utilizes a two-pronged evaluation approach: (a) extrinsic similarity between machine predictions and human expert annotations, and (b) intrinsic consistency between intra-machine and intra-human behavior relations. We apply our analysis to real-life conversations that are annotated for a large and diverse set of behavior codes and examine the relation between the nature of a behavior and how long it should be observed. We find that behaviors describing negative and positive affect can be accurately estimated from short to medium-length expressions whereas behaviors related to problem-solving and dysphoria require much longer observations and are difficult to quantify from language alone. These findings are found to be generally consistent across different behavior modeling approaches.},
 author = {Sandeep {Nallan Chakravarthula} and Brian R.W. Baucom and Shrikanth Narayanan and Panayiotis Georgiou},
 doi = {https://doi.org/10.1016/j.csl.2020.101162},
 issn = {0885-2308},
 journal = {Computer Speech and Language},
 keywords = {Human behavior, Spoken language, Observation window length, Machine learning},
 link = {http://sail.usc.edu/publications/files/Sandeep-CSL-2021.pdf},
 pages = {101162},
 title = {An analysis of observation length requirements for machine understanding of human behaviors from spoken language},
 url = {http://www.sciencedirect.com/science/article/pii/S0885230820300954},
 volume = {66},
 year = {2021},
 month = {Jan}
}

The task of quantifying human behavior by observing interaction cues is an important and useful one across a range of domains in psychological research and practice. Machine learning-based approaches typically perform this task by first estimating behavior based on cues within an observation window, such as a fixed number of words, and then aggregating the behavior over all the windows in that interaction. The length of this window directly impacts the accuracy of estimation by controlling the amount of information being used. The exact link between window length and accuracy, however, has not been well studied, especially in spoken language. In this paper, we investigate this link and present an analysis framework that determines appropriate window lengths for the task of behavior estimation. Our proposed framework utilizes a two-pronged evaluation approach: (a) extrinsic similarity between machine predictions and human expert annotations, and (b) intrinsic consistency between intra-machine and intra-human behavior relations. We apply our analysis to real-life conversations that are annotated for a large and diverse set of behavior codes and examine the relation between the nature of a behavior and how long it should be observed. We find that behaviors describing negative and positive affect can be accurately estimated from short to medium-length expressions whereas behaviors related to problem-solving and dysphoria require much longer observations and are difficult to quantify from language alone. These findings are found to be generally consistent across different behavior modeling approaches.

@inproceedings{Krishnamachari2021DEVELOPINGNEURALREPRESENTATIONSFOR,
 author = {Krishnamachari, Suchitra and Kumar, Manoj and Kim, So Hyun and Lord, Catherine and Narayanan, Shrikanth },
 booktitle = {In proceedings of IEEE Spoken Language Technology Workshop},
 title = {DEVELOPING NEURAL REPRESENTATIONS FOR ROBUST CHILD-ADULT DIARIZATION},
 year = {2021},
 month = {Jan}
}

@article{Zhao2020Improved3DReal-TimeMRI,
author = {Zhao, Ziwei and Lim, Yongwan and Byrd, Dani and Narayanan, Shrikanth and Nayak, Krishna S.},
title = {Improved 3D real-time MRI of speech production},
journal = {Magnetic Resonance in Medicine},
pages = {1-14},
year = {2021},
month = {Jan},
keywords = {3D real-time MRI, golden angle spiral, speech production, stack-of-spiral sampling, variable-density sampling},
doi = {https://doi.org/10.1002/mrm.28651},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28651},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.28651},
abstract = {Purpose To provide 3D real-time MRI of speech production with improved spatio-temporal sharpness using randomized, variable-density, stack-of-spiral sampling combined with a 3D spatio-temporally constrained reconstruction. Methods We evaluated five candidate (k, t) sampling strategies using a previously proposed gradient-echo stack-of-spiral sequence and a 3D constrained reconstruction with spatial and temporal penalties. Regularization parameters were chosen by expert readers based on qualitative assessment. We experimentally determined the effect of spiral angle increment and kz temporal order. The strategy yielding highest image quality was chosen as the proposed method. We evaluated the proposed and original 3D real-time MRI methods in 2 healthy subjects performing speech production tasks that invoke rapid movements of articulators seen in multiple planes, using interleaved 2D real-time MRI as the reference. We quantitatively evaluated tongue boundary sharpness in three locations at two speech rates. Results The proposed data-sampling scheme uses a golden-angle spiral increment in the kx−ky plane and variable-density, randomized encoding along kz. It provided a statistically significant improvement in tongue boundary sharpness score (P < .001) in the blade, body, and root of the tongue during normal and 1.5-times speeded speech. Qualitative improvements were substantial during natural speech tasks of alternating high, low tongue postures during vowels. The proposed method was also able to capture complex tongue shapes during fast alveolar consonant segments. Furthermore, the proposed scheme allows flexible retrospective selection of temporal resolution. Conclusion We have demonstrated improved 3D real-time MRI of speech production using randomized, variable-density, stack-of-spiral sampling with a 3D spatio-temporally constrained reconstruction.}
}

Purpose To provide 3D real-time MRI of speech production with improved spatio-temporal sharpness using randomized, variable-density, stack-of-spiral sampling combined with a 3D spatio-temporally constrained reconstruction. Methods We evaluated five candidate (k, t) sampling strategies using a previously proposed gradient-echo stack-of-spiral sequence and a 3D constrained reconstruction with spatial and temporal penalties. Regularization parameters were chosen by expert readers based on qualitative assessment. We experimentally determined the effect of spiral angle increment and kz temporal order. The strategy yielding highest image quality was chosen as the proposed method. We evaluated the proposed and original 3D real-time MRI methods in 2 healthy subjects performing speech production tasks that invoke rapid movements of articulators seen in multiple planes, using interleaved 2D real-time MRI as the reference. We quantitatively evaluated tongue boundary sharpness in three locations at two speech rates. Results The proposed data-sampling scheme uses a golden-angle spiral increment in the kx−ky plane and variable-density, randomized encoding along kz. It provided a statistically significant improvement in tongue boundary sharpness score (P < .001) in the blade, body, and root of the tongue during normal and 1.5-times speeded speech. Qualitative improvements were substantial during natural speech tasks of alternating high, low tongue postures during vowels. The proposed method was also able to capture complex tongue shapes during fast alveolar consonant segments. Furthermore, the proposed scheme allows flexible retrospective selection of temporal resolution. Conclusion We have demonstrated improved 3D real-time MRI of speech production using randomized, variable-density, stack-of-spiral sampling with a 3D spatio-temporally constrained reconstruction.

@inproceedings{Sivakumar2021RNNBASEDINCREMENTALONLINE,
 author = {Sivakumar, Gurunath Prashanth and Kumar, Naveen and Georgiou, Panayiotis and Narayanan, Shrikanth},
 booktitle = {In proceedings of IEEE Spoken Language Technology Workshop},
 title = {RNN BASED INCREMENTAL ONLINE SPOKEN LANGUAGE UNDERSTANDING},
 year = {2021},
 month = {Jan}
}

@article{9145747,
  author={Jaiswal, Akhilesh and Agrawal, Amogh and Panda, Priyadarshini and Roy, Kaushik},
  journal={IEEE Transactions on Magnetics}, 
  title={Neural Computing With Magnetoelectric Domain-Wall-Based Neurosynaptic Devices}, 
  month=feb,
  year={2021},
  volume={57},
  number={2},
  pages={1-9},
  doi={10.1109/TMAG.2020.3010712},
  url={https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9145747}
}

@misc{hambardzumyan2021warp,
      title={WARP: Word-level Adversarial ReProgramming}, 
      author={Karen Hambardzumyan and Hrant Khachatrian and Jonathan May},
      year={2021},
      eprint={2101.00121},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{spangher2021multitask,
      title={Multitask Learning for Class-Imbalanced Discourse Classification}, 
      author={Alexander Spangher and Jonathan May and Sz-rung Shiang and Lingjia Deng},
      year={2021},
      eprint={2101.00389},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@incollection{Heimann2021,
author = {Heimann, Mark and Muri{\'{c}}, Goran and Ferrara, Emilio},
booktitle = {Complex Networks {\&} Their Applications IX},
doi = {10.1007/978-3-030-65351-4_1},
month = jan,
pages = {3--14},
publisher = {Springer International Publishing},
title = {Structural Node Embedding in Signed Social Networks: Finding Online Misbehavior at Multiple Scales},
url = {http://link.springer.com/10.1007/978-3-030-65351-4{\_}1},
year = {2021}
}

@inproceedings{crespi2021ISBI,
author ={Eleonara Ficiar\`a and Valentino Crespi and Shruti Prashant Gadewar and Sophia I. Thomopoulos and Joshua Boyd and Paul M. Thompson and Neda Jahanshad and Fabrizio Pizzagalli and the Alzheimer's Disease Neuroimaging Initiative},
title = {Predicting {P}rogression from {M}ild {C}ognitive {I}mpairment to {A}lzheimer’s {D}isease using {MRI}-based {C}ortical {F}eatures and a {T}wo-{S}tate {M}arkov {M}odel},
booktitle = {Proceedings of the International {S}ymposium on {B}iomedical {I}maging (ISBI)},
year = {2021},
month = {April},
address = {Nice, France}
}

@article{gupta2021aaai,
  title={Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation},
  author={Gupta, Umang and Ferber, Aaron and Dilkina, Bistra and Steeg, Greg Ver},
  journal={Proceedings of the 35th AAAI Conference on Artificial Intelligence },
  year={2021}
}

@inproceedings{markowitz2021iclr,
title={Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning},
author={Sopher Markowitz, Elan and Balasubramanian, Keshav and Mirtaheri, Mehrnoosh and Abu-El-Haija, Sami and Perozzi, Bryan and Ver Steeg, Greg and Galstyan, Aram},
booktitle={International Conference on Learning Representations},
year={2021},
}

@inproceedings{ge2021iclr,
title={Zero-shot Synthesis with Group-Supervised Learning},
author={Ge, Yunhao and Abu-El-Haija, Sami and Xin, Gan and Itti, Laurent},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=8wqCDnBmnrT}
}

@inproceedings{hassan_scan_2021,
	title = {SCAN: Sequence-Character Aware Network for Text Recognition},
	booktitle = {International Conference on Computer Vision Theory and Applications (VISAPP)},
	year = {2021},
	author = {Heba Hassan and Marwan Torki and Mohamed E.  Hussein}
}

@article{ilievski2021dimensions,
  title={Dimensions of commonsense knowledge},
  author={Ilievski, Filip and Oltramari, Alessandro and Ma, Kaixin and Zhang, Bin and McGuinness, Deborah L and Szekely, Pedro},
  journal={arXiv preprint arXiv:2101.04640},
  year={2021}
}

@article{gil-etal-tiis2021,
  title = {Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making},
  author = {Yolanda Gil and Daniel Garijo and Deborah Khider and Craig A. Knoblock and Varun Ratnakar and Maximiliano Osorio and Hern�n Vargas and Minh Pham and Jay Pujara and Basel Shbita and  Binh Vu and Yao-Yi Chiang and Dan Feldman and Yijun Lin and Hayley Song and Vipin Kumar and Ankush Khandelwal and Michael Steinbach and Kshitij Tayal and Shaoming Xu and Suzanne A. Pierce and Lissa Pearson and Daniel Hardesty-Lewis and Ewa Deelman and Rafael Ferreira da Silva and Rajiv Mayani and Armen R. Kemanian and Yuning Shi and Lorne Leonard and Scott Peckham and Maria Stoica and Kelly Cobourn and Zeya Zhang and Christopher Duffy and Lele Shu},
  journal = {ACM Transactions on Interactive Intelligent Systems (TiiS)},
  url = {http://www.isi.edu/~gil/papers/gil-etal-tiis2021.pdf},
  volume=11,
  number=2,
  year = 2021
}

@conference{Menon2021Fight-Club,
	author = {Vivek V. Menon and Uddipt Sharma and Shervin Roshanisefat and Sanket S. Shukla and Andrew Schmidt and Matthew French and Peter A. Beerel and Pierluigi Nuzzo},
	booktitle = {Government Microcircuit Applications \& Critical Techology Conference (GOMACTech)},
	date-added = {2020-09-28 12:51:58 -0400},
	date-modified = {2020-09-28 12:54:09 -0400},
	keywords = {intellectual property , cryptography , reverse engineering , semiconductors , signal processing , algorithms , dynamic range , standards , manufacturing , integrated circuits , fabrication , databases , linear programming , integer programming , security},
	month = mar,
	title = {{Fight Club: Maturing Defense in Depth Obfuscation Techniques}},
	year = {2021}}

@inproceedings{Menon2021Design-and,
	address = {Big Sky, MT, USA},
	author = {Vivek V. Menon and S. Siddiqui and S. Rao and Andrew Schmidt and Matt French and V. Chirayath and A. Li},
	booktitle = {2021 IEEE Aerospace Conference},
	date-added = {2021-01-22 10:47:25 -0500},
	date-modified = {2021-01-22 10:52:18 -0500},
	month = mar,
	title = {{Design and Performance Evaluation of Multispectral Sensing Algorithms on CPU, GPU, and FPGA}},
	year = {2021}}

@inproceedings{sun:aaai21,
    Author = {Sun, Kexuan and Rayudu, Harsha and Pujara, Jay},
    title = {A Hybrid Probabilistic Approach for Table Understanding},
    booktitle = {Conference on Artificial Intelligence (AAAI)},
    year = {2021},
    sec = {conf},
    pdf_url = {/pubs/2021/sun-aaai21/sun-aaai21.pdf},
    bib_url = {/pubs/bib/sun-aaai21.bib}
}

@INPROCEEDINGS{9489850,
  author={Alishahi, F. and Minoofar, A. and Fallahpour, A. and Zou, K. and Zhou, H. and Habif, J. and Tur, M. and Willner, A. E.},
  booktitle={2021 Optical Fiber Communications Conference and Exhibition (OFC)}, 
  title={Experimental Demonstration of Remotely Controlled and Powered Tunable Optical 2-4 Taps Correlator of a 20-100 Gbit/s QPSK Channel Based on Laser-Delivered Bias and Control Signals}, 
  year={2021},
  volume={},
  number={},
  pages={1-3},
  doi={}}

@inproceedings{10.1117/12.2596824,
author = {Andrew Rittenbach and Connor Finnerty and Jonathan L. Habif},
title = {{Detection and localization of near infrared lasers from atmospheric laser scattering}},
volume = {11868},
booktitle = {Emerging Imaging and Sensing Technologies for Security and Defence VI},
editor = {Gerald S. Buller and Richard C. Hollins and Robert A. Lamb and Martin Laurenzis},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {22 -- 32},
keywords = {near infrared laser detection, atmospheric scattering, near infrared laser localization, mie scattering},
year = {2021},
doi = {10.1117/12.2596824},
URL = {https://doi.org/10.1117/12.2596824}
}

@article{Fallahpour:21,
author = {Ahmad Fallahpour and Amir Minoofar and Fatemeh Alishahi and Kaiheng Zou and Samer Idres and Hossein Hashemi and Jonathan Habif and Moshe Tur and Alan E. Willner},
journal = {Opt. Lett.},
keywords = {Dense wavelength division multiplexing; Erbium doped fiber amplifiers; Optical circulators; Optical signals; Phase modulation; Quadrature phase shift keying},
number = {18},
pages = {4589--4592},
publisher = {OSA},
title = {Experimental demonstration of remotely powered, controlled, and monitored optical switching based on laser-delivered signals},
volume = {46},
month = {Sep},
year = {2021},
url = {http://www.osapublishing.org/ol/abstract.cfm?URI=ol-46-18-4589},
doi = {10.1364/OL.434608},
abstract = {We experimentally demonstrate remotely powered, controlled, and monitored optical switching. The control signal of the switch is modulated on an optical wave and sent from a transmitter. At the switch location, the control signal is converted from an optical to an electrical signal to drive the switch. In addition, to provide electrical power at the switch location, optical power is sent from a distance and converted to electrical power using a series of photodiodes. We experimentally demonstrate (a) 1 Gb/s on-off keying data channel transmission and switching with a 1 MHz optically delivered control signal, and (b) 40 Gb/s quadrature phase-shift keying data channel transmission and remotely monitoring switch state and bias drift. The switching function is demonstrated without using any local electrical power supply. Moreover, the monitoring tones are transmitted to the remote switch and fed back to the transmitter to realize a switch state and detect the bias drift.},
}

We experimentally demonstrate remotely powered, controlled, and monitored optical switching. The control signal of the switch is modulated on an optical wave and sent from a transmitter. At the switch location, the control signal is converted from an optical to an electrical signal to drive the switch. In addition, to provide electrical power at the switch location, optical power is sent from a distance and converted to electrical power using a series of photodiodes. We experimentally demonstrate (a) 1 Gb/s on-off keying data channel transmission and switching with a 1 MHz optically delivered control signal, and (b) 40 Gb/s quadrature phase-shift keying data channel transmission and remotely monitoring switch state and bias drift. The switching function is demonstrated without using any local electrical power supply. Moreover, the monitoring tones are transmitted to the remote switch and fed back to the transmitter to realize a switch state and detect the bias drift.

@inproceedings{Alishahi:21,
author = {F. Alishahi and A. Fallahpour and K. Zou and A. Minoofar and H. Zhou and C. Liu and M. Tur and J. Habif and A. E. Willner},
booktitle = {Conference on Lasers and Electro-Optics},
journal = {Conference on Lasers and Electro-Optics},
keywords = {Extinction ratios; Mach Zehnder interferometers; Optical directional couplers; Phase shift; Refractive index; Signal processing},
pages = {JTu3A.52},
publisher = {Optical Society of America},
title = {Optical Signal Processing Performance Dependence on Non- Ideal MZI Operation in a Tapped-Delay-Line},
year = {2021},
url = {http://www.osapublishing.org/abstract.cfm?URI=CLEO_QELS-2021-JTu3A.52},
abstract = {An optical tapped-delay-line based on a cascade of MZI under non-ideal operation is simulated. The effect of different non-ideal conditions on FIR filtering metrics and analog or digital signal qualities is investigated.},
}

An optical tapped-delay-line based on a cascade of MZI under non-ideal operation is simulated. The effect of different non-ideal conditions on FIR filtering metrics and analog or digital signal qualities is investigated.

@article{Habif:21,
author = {Jonathan L. Habif and Arunkumar Jagannathan and Samuel Gartenstein and Phoebe Amory and Saikat Guha},
journal = {Opt. Express},
keywords = {Coherent states; Heterodyne detection; Laser communications; Laser light; Optical sensing; Optical systems},
number = {5},
pages = {7418--7427},
publisher = {OSA},
title = {Quantum-limited discrimination of laser light and thermal light},
volume = {29},
month = {Mar},
year = {2021},
url = {http://www.osapublishing.org/oe/abstract.cfm?URI=oe-29-5-7418},
doi = {10.1364/OE.417989},
abstract = {Understanding the fundamental sensitivity limit of an optical sensor requires a full quantum mechanical description of the sensing task. In this work, we calculate the fundamental (quantum) limit for discriminating between pure laser light and thermal noise in a photon-starved regime. The Helstrom bound for discrimination error probability for single mode measurement is computed along with error probability bounds for direct detection, coherent homodyne detection and the Kennedy receiver. A generalized Kennedy (GK) receiver is shown to closely approach the Helstrom limit. We present an experimental demonstration of this sensing task and demonstrate a 15.4 dB improvement in discrimination sensitivity over direct detection using a GK receiver and an improvement of 19.4\&\#x0025; in error probability over coherent detection.},
}

Understanding the fundamental sensitivity limit of an optical sensor requires a full quantum mechanical description of the sensing task. In this work, we calculate the fundamental (quantum) limit for discriminating between pure laser light and thermal noise in a photon-starved regime. The Helstrom bound for discrimination error probability for single mode measurement is computed along with error probability bounds for direct detection, coherent homodyne detection and the Kennedy receiver. A generalized Kennedy (GK) receiver is shown to closely approach the Helstrom limit. We present an experimental demonstration of this sensing task and demonstrate a 15.4 dB improvement in discrimination sensitivity over direct detection using a GK receiver and an improvement of 19.4% in error probability over coherent detection.

@article{chua2021quantum,
  title={Quantum-limited estimation of coherence under thermal noise in photon-starved states},
  author={Chua, Zi and Habif, Jonathan and Spedalieri, Federico},
  journal={Bulletin of the American Physical Society},
  year={2021},
  publisher={APS}
}

@article{Shemirani:2021wk,
	abstract = {The ability to identify segments of genomes identical-by-descent (IBD) is a part of standard workflows in both statistical and population genetics. However, traditional methods for finding local IBD across all pairs of individuals scale poorly leading to a lack of adoption in very large-scale datasets. Here, we present iLASH, an algorithm based on similarity detection techniques that shows equal or improved accuracy in simulations compared to current leading methods and speeds up analysis by several orders of magnitude on genomic datasets, making IBD estimation tractable for millions of individuals. We apply iLASH to the PAGE dataset of ~52,000 multi-ethnic participants, including several founder populations with elevated IBD sharing, identifying IBD segments in ~3 minutes per chromosome compared to over 6 days for a state-of-the-art algorithm. iLASH enables efficient analysis of very large-scale datasets, as we demonstrate by computing IBD across the UK Biobank (~500,000 individuals), detecting 12.9 billion pairwise connections.},
	author = {Shemirani, Ruhollah and Belbin, Gillian M and Avery, Christy L and Kenny, Eimear E and Gignoux, Christopher R and Ambite, Jos{\'e} Luis},
	date-added = {2021-06-15 23:00:14 -0400},
	date-modified = {2021-06-15 23:00:14 -0400},
	doi = {10.1038/s41467-021-22910-w},
	journal = {Nat Commun},
	journal-full = {Nature communications},
	month = {06},
	number = {1},
	pages = {3546},
	pmid = {34112768},
	url = {https://pubmed.ncbi.nlm.nih.gov/34112768/},
	pst = {epublish},
	title = {Rapid detection of identity-by-descent tracts for mega-scale datasets},
	volume = {12},
	year = {2021},
	Bdsk-Url-1 = {https://doi.org/10.1038/s41467-021-22910-w}}

The ability to identify segments of genomes identical-by-descent (IBD) is a part of standard workflows in both statistical and population genetics. However, traditional methods for finding local IBD across all pairs of individuals scale poorly leading to a lack of adoption in very large-scale datasets. Here, we present iLASH, an algorithm based on similarity detection techniques that shows equal or improved accuracy in simulations compared to current leading methods and speeds up analysis by several orders of magnitude on genomic datasets, making IBD estimation tractable for millions of individuals. We apply iLASH to the PAGE dataset of ~52,000 multi-ethnic participants, including several founder populations with elevated IBD sharing, identifying IBD segments in ~3 minutes per chromosome compared to over 6 days for a state-of-the-art algorithm. iLASH enables efficient analysis of very large-scale datasets, as we demonstrate by computing IBD across the UK Biobank (~500,000 individuals), detecting 12.9 billion pairwise connections.

@misc{stripelis2021:isbi,
  Author = {Dimitris Stripelis and Jose Luis Ambite and Pradeep Lam and Paul Thompson},
  Title = {Scaling Neuroscience Research using Federated Learning},
  Year = {2021},
  Eprint = {arXiv:2102.08440},
  Howpublished = {http://arxiv.org/abs/2102.08440} 
}

@Article{belbin2021,
  author = 	 {Gillian M Belbin and Sinead Cullina and Stephane Wenric and Emily R Soper and Benjamin S Glicksberg and Denis Torre and Arden Moscati and Genevieve L Wojcik and Ruhollah Shemirani and Noam D Beckmann and Ariella Cohain and Elena P Sorokin and Danny S Park and Jose Luis Ambite and Steve Ellis Adam Auton and CBIPM Genomics Team and Regeneron Genetics Center and Erwin P. Bottinger and Judy H Cho and Ruth JF Loos and Noura S Abul-Husn and Noah A Zaitlen and Christopher R. Gignoux and Eimear E Kenny},
  title = 	 {Towards a fine-scale population health monitoring system},
  doi = {https://doi.org/10.1101/780668},
  journal = 	 {Cell},
  year = 	 {2021},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTpages = 	 {},
  OPTmonth = 	 {},
  note = 	 {In press},
  OPTannote = 	 {}
}

@conference{Menon2020Logic-Obfu,
	author = {Menon, Vivek V. and Kolhe, Gaurav and Fifty, Joseph and Schmidt, Andrew and Monson, Joshua and French, Matthew and Hu, Yinghua and Beerel, Peter A and Nuzzo, Pierluigi},
	booktitle = {Government Microcircuit Applications \& Critical Techology Conference (GOMACTech)},
	date-added = {2020-07-19 17:06:17 -0400},
	date-modified = {2020-07-19 17:08:13 -0400},
	keywords = {intellectual property , cryptography , reverse engineering , semiconductors , signal processing , algorithms , dynamic range , standards , manufacturing , integrated circuits , fabrication , databases , linear programming , integer programming , security},
	month = mar,
	title = {{Logic Obfuscation: Modeling Attack Resiliency}},
	year = {2020}}

@conference {French2020,
    title = {Emulating and Verifying Sensing, Computation, and Communication in Distributed Remote Sensing Systems},
    booktitle = {IEEE International Geosensing and Remote Sensing Systems},
    year = {2020},
    author = {M. French and M. Paolieri and V. Menon and A. Schmidt}
}

@conference {Paolieri2020,
    title = {Evaluation of Remote-Sensing Architectures using the Virtual Constellation Engine},
    booktitle = {American Geophysical Union Fall Meeting},
    year = {2020},
    author = {M. Paolieri and V. Menon and A. Schmidt and M. French}
}

@conference {Car2020,
    title = {StereoBit: StereoBit: An innovative SpaceCube Application for Atmospheric Science},
    booktitle = {IEEE International Geosensing and Remote Sensing Systems},
    year = {2020},
    author = {J. Carr and C. Wilson and D. Wu and M. French and M. Kelly}
}

@inproceedings{Quinkert2020Phish,
  title = {Be the Phisher - Understanding Users' Perception of Malicious Domains},
  author = {Quinkert, Florian and Degeling, Martin and Blythe, Jim and Holz, Thorsten},
  booktitle = {15th ACM ASIA Conference on Computer and Communications Security},
  series = {ACM ASIACCS 2020},
  year = {2020}
}

@incollection{ananth2020mismorph,
  title = {Mismorphism: the Heart of the Weird Machine},
  author={Anantharaman, Prashant and Kothari, Vijay and Jenkins, Ira and Millian, Michael and Bratus, Sergey and Blythe, Jim and Koppel, Ross and Smith, Sean},
  booktitle = {Security Protocols XXVII, 27th International Workshop, Revised Selected Papers},
  year = {2020}}

@inproceedings{Das2020Risk,
  title = {User-Centered Risk Communication for Safer Browsing},
  author = {Das, Sanchari and Abbott, Jacob and Gopavaram, Shakthidhar and Blythe, Jim and Camp, Jean},
  booktitle = {Proceedings of the First Asia USEC - Workshop on Usable Security, In Conjunction with the Twenty-Fourth International Conference International Conference on Financial Cryptography and Data Security 2020},
  year = {2020},
}

@inproceedings{DASHWS20,
  title = {Optimization of Large-Scale Agent-Based Simulations Through Automated Abstraction and Simplification},
  author = {Tregubov, Alexey and Blythe, Jim},
  booktitle = {AAMAS Workshop on Multi-Agent Systems and Agent-Based Simulation},
  year = {2020},
  }

@inproceedings{muric2020massive,
  title={Massive Cross-Platform Simulations of Online Social Networks},
  author={Muri{\'c}, Goran and Tregubov, Alexey and Blythe, Jim and Abeliuk, Andr{\'e}s and Choudhary, Divya and Lerman, Kristina and Ferrara, Emilio},
  booktitle={Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems},
  pages={895--903},
  year={2020}
}

@incollection{kothari2020boundaries,
  title={Human-Computability Boundaries},
  author={Kothari, Vijay and Anantharaman, Prashant and Jenkins, Ira and Millian, Michael and Bratus, Sergey and Blythe, Jim and Koppel, Ross and Smith, Sean},
  booktitle={Security Protocols XXVII, 27th International Workshop, Revised Selected Papers},
  year={2020}
}

@inproceedings{etra20urls,
  title = {Eyes on URLs: Relating Visual Behavior to Safety Decisions},
  author = {Kothari, Vijay and Mills, Caitlin and Ramkumar, Niveta and Blythe, Jim and Koppel, Ross and Smith, Sean and Kun, Andrew},
  booktitle = {ACM Symposium on Eye Tracking Research and Applications},
  year = {2020},
  }

@article{doi:10.1111/risa.13402,
author = {Kusumastuti, Sarah A. and Blythe, Jim and Rosoff, Heather and John, Richard S.},
title = {Behavioral Determinants of Target Shifting and Deterrence in an Analog Cyber-Attack Game},
journal = {Risk Analysis},
year = {2020},
volume={40},
number={3},
pages={476-493},
keywords = {Ambiguity bias, anchoring, cybersecurity, deterrence, near miss}
}

@article{barnhart2020initial,
  title={Initial safety posture investigations for earth regime rendezvous and proximity operations},
  author={Barnhart, David A and Rughani, Rahul and Allam, Jeremy J and Clarke, Kyle W},
  journal={Journal of Space Safety Engineering},
  volume={7},
  number={4},
  pages={519-524},
  year={2020},
  publisher={Elsevier}
}

@inproceedings{rughani2020using,
  title={Using Genetic Algorithms for Safe Swarm Trajectory Optimization},
  author={Rughani, Rahul and Barnhart, David},
  booktitle={AIAA Scitech 2020 Forum},
  pages={1916},
  year={2020}
}

@article{barnhart2020orbit,
  title={On-orbit servicing ontology applied to recommended standards for satellites in earth orbit},
  author={Barnhart, David A and Rughani, Rahul},
  journal={Journal of Space Safety Engineering},
  year={2020},
  publisher={Elsevier}
}

@inproceedings{narayanan2020reacch,
  title={REACCH-Reactive electro-adhesive capture cloth mechanism to enable safe grapple of cooperative/non-cooperative space debris},
  author={Narayanan, Sriram and Barnhart, David and Rogers, Rebecca and Ruffatto, Donald and Schaler, Ethan and Van Crey, Nikko and Dean, Gabriella and Bhanji, Alisha and Bernstein, Sofia and Singh, Amrita and others},
  booktitle={AIAA Scitech 2020 Forum},
  pages={2134},
  year={2020}
}

@article{villafana2020magneto,
  title={MAGNETO: Mapping the Earth?s Magnetic Field at 300 km using COTS Sensors},
  author={Villafana, Lizette and Yuan, Jessica and Broadus, Liam and Su, Warren and Carlton, Claire and Norrell, Ava and Rughani, Rahul and Park, Eugene and Barnhart, David},
  booktitle={34th Annual Conference on Small Satellites, Logan, Utah, USA (Virtual)},
  year={2020}
}

@article{rughani2020safe,
  title={Safe Construction in Space: Using Swarms of Small Satellites for In-Space Manufacturing},
  author={Rughani, Rahul and Barnhart, David A},
  booktitle={34th Annual Conference on Small Satellites, Logan, Utah, USA (Virtual)},
  year={2020}
}

@inproceedings{papadimitriou-indis-2020,
	author = {Papadimitriou, George and Lyons, Eric and Wang, Cong and Thareja, Komal and Tanaka, Ryan and Ruth, Paul and Villalobos, J.J. and Rodero, Ivan and Deelman, Ewa and Zink, Michael and Mandal, Anirban},
	booktitle = {2020 IEEE/ACM Innovating the Network for Data-Intensive Science (INDIS)},
	date-added = {2021-01-26 12:46:59 -0800},
	date-modified = {2021-01-26 12:46:59 -0800},
	month = nov,
	note = {Funding Acknowledgments: NSF 1826997},
	title = {Application Aware Software Defined Flows of Workflow Ensembles},
	year = {2020}}

@article{vahi-adass-2019,
	author = {Vahi, Karan and Goldstein, Danny and Papadimitriou, George and Nugent, Peter and Deelman, Ewa},
	date-added = {2021-01-26 12:46:59 -0800},
	date-modified = {2021-01-26 12:46:59 -0800},
	journal = {Astronomical Data Analysis Software and Systems (ADASS) XXIX},
	note = {Funding Acknowledgments: NSF 1664162, DOE DESC0012636},
	title = {Gearing the DECam Analysis Pipeline for Multi-Messenger Astronomy using Pegasus Workflows},
	year = {2020}}

@inproceedings{papadimitriou-pearc-2020,
	address = {New York, NY, USA},
	author = {Papadimitriou, George and Vahi, Karan and Kincl, Jason and Anantharaj, Valentine and Deelman, Ewa and Wells, Jack},
	booktitle = {Proceedings of the Practice and Experience in Advanced Research Computing},
	date-added = {2021-01-26 12:46:59 -0800},
	date-modified = {2021-01-26 12:46:59 -0800},
	doi = {10.1145/3311790.3396671},
	isbn = {978-1-4503-6689-2/20/07},
	location = {Portland, OR, USA},
	note = {Funding Acknowledgments: DOE DESC0012636},
	publisher = {Association for Computing Machinery},
	series = {PEARC 20},
	title = {Workflow Submit Nodes as a Service on Leadership Class Systems},
	url = {http://doi.acm.org/10.1145/3311790.3396671},
	year = {2020},
	Bdsk-Url-1 = {http://doi.acm.org/10.1145/3311790.3396671},
	Bdsk-Url-2 = {https://doi.org/10.1145/3311790.3396671}}

@InProceedings{lyons-dddas-2020,
    author={Lyons, Eric and Westbrook, David and Grote, Andrew and Papadimitriou, George and Thareja, Komal and Wang, Cong and Zink, Michael and Deelman, Ewa and Mandal, Anirban and Ruth, Paul},
    editor={Darema, Frederica and Blasch, Erik and Ravela, Sai and Aved, Alex},
    title={An On-Demand Weather Avoidance System for Small Aircraft Flight Path Routing},
    booktitle={Dynamic Data Driven Application Systems},
    year={2020},
    publisher={Springer International Publishing},
    address={Cham},
    pages={311--319},
    abstract={Convective weather events pose a challenge to the burgeoning low altitude aviation industry. Small aircraft are sensitive to winds and precipitation, but the uncertainty associated with forecasting and the frequency with which impactful weather occurs require an active detect and response system. In this paper, we propose a dynamic, data-driven decision support system, with components of forecasting, realtime sensor observations, and route planning. We demonstrate our technology in the Dallas/Fort Worth metroplex, a large urban area with frequent thunderstorms which hosts the CASA Doppler radar network.},
    isbn={978-3-030-61725-7},
    doi={10.1007/978-3-030-61725-7_36},
    note = {Funding Acknowledgments: NSF 1826997, 2018074}
}

Convective weather events pose a challenge to the burgeoning low altitude aviation industry. Small aircraft are sensitive to winds and precipitation, but the uncertainty associated with forecasting and the frequency with which impactful weather occurs require an active detect and response system. In this paper, we propose a dynamic, data-driven decision support system, with components of forecasting, realtime sensor observations, and route planning. We demonstrate our technology in the Dallas/Fort Worth metroplex, a large urban area with frequent thunderstorms which hosts the CASA Doppler radar network.

@inproceedings{wang-hpec-2020,
    title={Identifying Execution Anomalies for Data Intensive Workflows Using Lightweight ML Techniques},
    author={Wang, Cong and Papadimitriou, George and Kiran, Mariam and Mandal, Anirban and Deelman, Ewa},
    booktitle={2020 IEEE High Performance extreme Computing Conference (HPEC)},
    year={2020},
    volume={},
    number={},
    pages={},
    doi={},
    note={Funding Acknowledgments: DOE DESC0012636}
}

@article{lyons-ams100-2020,
  title = {DyNamo: Scalable Weather Workflow Processing in the Academic MultiCloud},
  author = {Lyons, Eric and Zink, Michael and Mandal, Anirban and Wang, Cong and Ruth, Paul and Radhakrishnan, Chandrasekar and Papadimitriou, George and Deelman, Ewa and Thareja, Komal and Rodero, Ivan},
  journal = {100th American Meteorological Society Annual Meeting},
  volume = {},
  number = {},
  pages = {},
  year = {2020},
  doi = {},
  note = {Funding Acknowledgments: NSF 1826997}
}

@article{ferreiradasilva2020jocs,
	author = {Rafael Ferreira da Silva and Henri Casanova and Anne-C\'{e}cile Orgerie and Ryan Tanaka and Ewa Deelman and Fr\'{e}d\'{e}ric Suter},
	date-added = {2021-01-26 12:46:59 -0800},
	date-modified = {2021-01-26 12:46:59 -0800},
	doi = {10.1016/j.jocs.2020.101157},
	journal = {Journal of Computational Science},
	note = {Funding Acknowledgments: NSF 1642335, NSF 1923539, NSF 1664162, DOE DE-SC0012636},
	pages = {101157},
	title = {Characterizing, Modeling, and Accurately Simulating Power and Energy Consumption of I/O-intensive Scientific Workflows},
	volume = {44},
	year = {2020},
	Bdsk-Url-1 = {https://doi.org/10.1016/j.jocs.2020.101157}}

@inproceedings{do2020iccs,
	author = {Tu Mai AnhDo and Loic Pottier and Stephen Thomas and Rafael Ferreira da Silva and Michel A. Cuendet and Harel Weinstein and Trilce Estrada and Michela Taufer and Ewa Deelman},
	booktitle = {International Conference on Computational Science (ICCS)},
	date-added = {2021-01-26 12:46:59 -0800},
	date-modified = {2021-01-26 12:46:59 -0800},
	doi = {10.1007/978-3-030-50371-0_40},
	note = {Funding Acknowledgments: NSF 1741040},
	pages = {538--553},
	title = {A Novel Metric to Evaluate In Situ Workflows},
	year = {2020},
	Bdsk-Url-1 = {https://doi.org/10.1007/978-3-030-50371-0_40}}

@inproceedings{ferreiradasilva2020works,
	author = {Rafael Ferreira da Silva and Lo\"ic Pottier and Tain\~a Coleman and Ewa Deelman and Henri Casanova},
	booktitle = {2020 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS)},
	date-added = {2021-01-26 12:46:59 -0800},
	date-modified = {2021-01-26 12:46:59 -0800},
	doi = {10.1109/WORKS51914.2020.00012},
	note = {Funding Acknowledgments: NSF 2016619, DOE DE-SC0012636, NSF 1664162, NSF 1923539},
	pages = {49--56},
	title = {WorkflowHub: Community Framework for Enabling Scientific Workflow Research and Development},
	year = {2020},
	Bdsk-Url-1 = {https://doi.org/10.1109/WORKS51914.2020.00012}}

@article{deelman2020jocs,
	author = {Ewa Deelman and Rafael Ferreira da Silva and Karan Vahi and Mats Rynge and Rajiv Mayani and Ryan Tanaka and Wendy Whitcup and Miron Livny},
	date-added = {2021-01-26 12:46:59 -0800},
	date-modified = {2021-01-26 12:46:59 -0800},
	doi = {10.1016/j.jocs.2020.101200},
	journal = {Journal of Computational Science},
	note = {Funding Acknowledgments: NSF 1664162},
	title = {The Pegasus Workflow Management System: Translational Computer Science in Practice},
	year = {2020},
	Bdsk-Url-1 = {https://doi.org/10.1016/j.jocs.2020.101200}}

@article{papadimitriou-fgcs-2020,
	author = {George Papadimitriou and Cong Wang and Karan Vahi and Rafael Ferreira da Silva and Anirban Mandal and Liu Zhengchun and Rajiv Mayani and Mats Rynge and Mariam Kiran and Vickie E. Lynch and Rajkumar Kettimuthu and Ewa Deelman and Jeffrey S. Vetter and Ian Foster},
	date-added = {2021-01-26 12:46:59 -0800},
	date-modified = {2021-01-26 12:46:59 -0800},
	doi = {10.1016/j.future.2020.11.024},
	issn = {0167-739X},
	journal = {Future Generation Computer Systems},
	note = {Funding Acknowledgments: DOE DE-SC0012636},
	pages = {387-400},
	title = {End-to-End Online Performance Data Capture and Analysis for Scientific Workflows},
	volume = {117},
	year = {2020},
	Bdsk-Url-1 = {https://doi.org/10.1016/j.future.2020.11.024}}

@article{do2020jocs,
	author = {Tu Mai Anh Do and Lo{\"i}c Pottier and Silvina Ca{\'i}no-Lores and Rafael Ferreira da Silva and Michel A. Cuendet and Harel Weinstein and Trilce Estrada and Michela Taufer and Ewa Deelman},
	date-added = {2021-01-26 12:46:59 -0800},
	date-modified = {2021-01-26 12:46:59 -0800},
	doi = {10.1016/j.jocs.2020.101259},
	journal = {Journal of Computational Science},
	note = {Funding Acknowledgments: NSF 1741040, DOE DE-SC0012636},
	pages = {101259},
	title = {A Lightweight Method for Evaluating In Situ Workflow Efficiency},
	volume = {48},
	year = {2020},
	Bdsk-Url-1 = {https://doi.org/10.1016/j.jocs.2020.101259}}

@article{versluis-tpds-2020,
	author = {Laurens Versluis and  Roland Math{\'a} and Sacheendra Talluri and Tim Hegeman and Radu Prodan and Ewa Deelman and Alexandru Iosup},
	date-added = {2021-01-26 12:46:59 -0800},
	date-modified = {2021-01-26 12:46:59 -0800},
	doi = {10.1109/TPDS.2020.2984821},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	note = {Funding Acknowledgments: NSF 1664162},
	number = {9},
	pages = {2170-2184},
	title = {The Workflow Trace Archive: Open-Access Data from Public and Private Computing Infrastructures},
	volume = {31},
	year = {2020},
	Bdsk-Url-1 = {https://doi.org/10.1109/TPDS.2020.2984821}}

@article{kiran-ml-2020,
	author = {Mariam Kiran andCong  Wang and George Papadimitriou and Anirban Mandal and Ewa Deelman},
	date-added = {2021-01-26 12:46:59 -0800},
	date-modified = {2021-01-26 12:46:59 -0800},
	doi = {10.1007/s10994-020-05870-y},
	issn = {1573-0565},
	journal = {Machine Learning},
	note = {Funding Acknowledgments: DOE DESC0012636},
	title = {Detecting Anomalous Packets in Network Transfers: Investigations using PCA, Autoencoder and Isolation Forest in TCP},
	url = {https://doi.org/10.1007/s10994-020-05870-y},
	year = {2020},
	Bdsk-Url-1 = {https://doi.org/10.1007/s10994-020-05870-y}}

@inproceedings{pottier2020cluster,
	author = {Loic Pottier and Rafael Ferreira da Silva and Henri Casanova and Ewa Deelman},
	booktitle = {2020 IEEE International Conference on Cluster Computing (CLUSTER)},
	date-added = {2021-01-26 12:46:59 -0800},
	date-modified = {2021-01-26 12:46:59 -0800},
	doi = {10.1109/CLUSTER49012.2020.00019},
	note = {Funding Acknowledgments: DOE DE-SC0012636, NSF 1664162, NSF 1741040, NSF 1923539, NSF 1923621},
	pages = {92--103},
	title = {Modeling the Performance of Scientific Workflow Executions on HPC Platforms with Burst Buffers},
	year = {2020},
	Bdsk-Url-1 = {https://doi.org/10.1109/CLUSTER49012.2020.00019}}

@inproceedings{DBLP:conf/www/HuangHHSAPLMG20,
  author    = {Di Huang and
               Zihao He and
               Yuzhong Huang and
               Kexuan Sun and
               Sami Abu{-}El{-}Haija and
               Bryan Perozzi and
               Kristina Lerman and
               Fred Morstatter and
               Aram Galstyan},
  title     = {Graph Embedding with Personalized Context Distribution},
  booktitle = {Companion of The 2020 Web Conference 2020, Taipei, Taiwan, April 20-24,
               2020},
  pages     = {655--661},
  year      = {2020},
  crossref  = {DBLP:conf/www/2020c},
  url       = {https://doi.org/10.1145/3366424.3391263},
  doi       = {10.1145/3366424.3391263},
  timestamp = {Mon, 11 May 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/www/HuangHHSAPLMG20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{chen2020you,
  title={What Are You Trying to Do? Semantic Typing of Event Processes},
  author={Chen, Muhao and Zhang, Hongming and Wang, Haoyu and Roth, Dan},
  booktitle={Proceedings of the 24th Conference on Computational Natural Language Learning},
  pages={531-542},
  year={2020}
}

@inproceedings{chen2020multilingual,
  title={Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer},
  author={Chen, Xuelu and Chen, Muhao and Fan, Changjun and Uppunda, Ankith and Sun, Yizhou and Zaniolo, Carlo},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings},
  pages={3227-3238},
  year={2020}
}

@inproceedings{zhang2020analogous,
  title={Analogous Process Structure Induction for Sub-event Sequence Prediction},
  author={Zhang, Hongming and Chen, Muhao and Wang, Haoyu and Song, Yangqiu and Roth, Dan},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  pages={1541-1550},
  year={2020}
}

@inproceedings{wang2020joint,
  title={Joint Constrained Learning for Event-Event Relation Extraction},
  author={Wang, Haoyu and Chen, Muhao and Zhang, Hongming and Roth, Dan},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  pages={696-706},
  year={2020}
}