Publications

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2021 (35)
Independent Testing of Untrusted FPGAs for Faulty Interconnnect. Haroldsen, T.; French, M.; Sung, T.; Glick, D.; Danner, J.; and Lerner, L. In Government Microcircuit Applications and Critical Technology Conference, March 2021. to appear
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Canary: An FPGA Assurance Plugin for Vendor EDA Tools. Glick, D.; Schmidt, A.; Nifong, J.; Haroldsen, T.; Ruiz, J. M. E. M.; and French, M. March 2021.
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Design and Performance Evaluation of Multispectral Sensing Algorithms on CPU, GPU, and FPGA. Menon, V.; Siddiqui, S.; Rao, S.; Schmidt, A.; Chirayath, V.; Li, A.; and French, M. 2021.
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Discovering Higher-Order Interactions Through Neural Information Decomposition. Reing, K.; Ver Steeg, G.; and Galstyan, A. Entropy, 23(1). 2021.
Discovering Higher-Order Interactions Through Neural Information Decomposition [link]Paper doi link bibtex abstract
Latent Embeddings of Point Process Excitations. Marmerelis, M. G.; Ver Steeg, G.; and Galstyan, A. In The 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
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Influence Decompositions For Neural Network Attribution. Reing, K.; Ver Steeg, G.; and Galstyan, A. In The 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
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Improved Brain Age estimation with slice-based Set Networks. Gupta, U.; Lam, P.; Ver Steeg, G.; and Thompson, P. In IEEE International Symposium on Biomedical Imaging (ISBI), 2021.
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Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning. Markowitz, E. S.; Balasubramanian, K.; Mirtaheri, M.; Abu-El-Haija, S.; Perozzi, B.; Ver Steeg, G.; and Galstyan, A. In International Conference on Learning Representations (ICLR), 2021.
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Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation. Gupta, U.; Ferber, A.; Dilkina, B.; and Ver Steeg, G. https://arxiv.org/abs/2101.04108, 2021.
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation [link]Paper link bibtex
Canary: An FPGA Assurance Plugin for Vendor EDA Tools. Glick, D.; Schmidt, A.; Nifong, J.; Haroldsen, T.; Monson, J.; Ruiz, E. M.; and French, M. In GOMACTech, March 2021. to appear
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Message Digest for DNS Zones. Wessels, D.; Barber, P.; Weinberg, M.; Kumari, W. A.; and Hardaker, W. RFC 8976, February 2021.
Message Digest for DNS Zones [txt]Paper doi link bibtex abstract
Computational Media Intelligence: Human-Centered Machine Analysis of Media. Somandepalli, K.; Guha, T.; Martinez, V. R.; Kumar, N.; Adam, H.; and Narayanan, S. Proc. IEEE, 109(5): 891–910. 2021.
Computational Media Intelligence: Human-Centered Machine Analysis of Media [link]Paper doi link bibtex
Temporal Dynamics of Workplace Acoustic Scenes: Egocentric Analysis and Prediction. Jati, A.; Nadarajan, A.; Peri, R.; Mundnich, K.; Feng, T.; Girault, B.; and Narayanan, S. IEEE/ACM Transactions on Audio, Speech and Language Processing. Jan 2021.
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Intrapersonal and Interpersonal Vocal Affect Dynamics during Psychotherapy. Paz, A.; Rafaeli, E.; Bar-Kalifa, E.; Gilboa-Schectman, E.; Gannot, S.; Laufer-Goldshtein, B.; Narayanan, S.; Keshet, J.; and Atzil-Slonim, D. Journal of Consulting and Clinical Psychology. Jan 2021.
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An analysis of observation length requirements for machine understanding of human behaviors from spoken language. Nallan Chakravarthula, S.; Baucom, B. R.; Narayanan, S.; and Georgiou, P. Computer Speech and Language, 66: 101162. Jan 2021.
An analysis of observation length requirements for machine understanding of human behaviors from spoken language [link]Paper doi link bibtex abstract
DEVELOPING NEURAL REPRESENTATIONS FOR ROBUST CHILD-ADULT DIARIZATION. Krishnamachari, S.; Kumar, M.; Kim, S. H.; Lord, C.; and Narayanan, S. In In proceedings of IEEE Spoken Language Technology Workshop, Jan 2021.
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Improved 3D real-time MRI of speech production. Zhao, Z.; Lim, Y.; Byrd, D.; Narayanan, S.; and Nayak, K. S. Magnetic Resonance in Medicine,1-14. Jan 2021.
Improved 3D real-time MRI of speech production [link]Paper doi link bibtex abstract
RNN BASED INCREMENTAL ONLINE SPOKEN LANGUAGE UNDERSTANDING. Sivakumar, G. P.; Kumar, N.; Georgiou, P.; and Narayanan, S. In In proceedings of IEEE Spoken Language Technology Workshop, Jan 2021.
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Neural Computing With Magnetoelectric Domain-Wall-Based Neurosynaptic Devices. Jaiswal, A.; Agrawal, A.; Panda, P.; and Roy, K. IEEE Transactions on Magnetics, 57(2): 1-9. February 2021.
Neural Computing With Magnetoelectric Domain-Wall-Based Neurosynaptic Devices [link]Paper doi link bibtex
WARP: Word-level Adversarial ReProgramming. Hambardzumyan, K.; Khachatrian, H.; and May, J. 2021.
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Multitask Learning for Class-Imbalanced Discourse Classification. Spangher, A.; May, J.; Shiang, S.; and Deng, L. 2021.
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Structural Node Embedding in Signed Social Networks: Finding Online Misbehavior at Multiple Scales. Heimann, M.; Murić, G.; and Ferrara, E. In Complex Networks & Their Applications IX, pages 3–14. Springer International Publishing, January 2021.
Structural Node Embedding in Signed Social Networks: Finding Online Misbehavior at Multiple Scales [link]Paper doi link bibtex
Predicting Progression from Mild Cognitive Impairment to Alzheimer’s Disease using MRI-based Cortical Features and a Two-State Markov Model. Ficiarà, E.; Crespi, V.; Gadewar, S. P.; Thomopoulos, S. I.; Boyd, J.; Thompson, P. M.; Jahanshad, N.; Pizzagalli, F.; and Initiative, t. A. D. N. In Proceedings of the International Symposium on Biomedical Imaging (ISBI), Nice, France, April 2021.
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Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation. Gupta, U.; Ferber, A.; Dilkina, B.; and Steeg, G. V. Proceedings of the 35th AAAI Conference on Artificial Intelligence . 2021.
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Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning. Sopher Markowitz, E.; Balasubramanian, K.; Mirtaheri, M.; Abu-El-Haija, S.; Perozzi, B.; Ver Steeg, G.; and Galstyan, A. In International Conference on Learning Representations, 2021.
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Zero-shot Synthesis with Group-Supervised Learning. Ge, Y.; Abu-El-Haija, S.; Xin, G.; and Itti, L. In International Conference on Learning Representations, 2021.
Zero-shot Synthesis with Group-Supervised Learning [link]Paper link bibtex
SCAN: Sequence-Character Aware Network for Text Recognition. Hassan, H.; Torki, M.; and Hussein, M. E. In International Conference on Computer Vision Theory and Applications (VISAPP), 2021.
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Dimensions of commonsense knowledge. Ilievski, F.; Oltramari, A.; Ma, K.; Zhang, B.; McGuinness, D. L; and Szekely, P. arXiv preprint arXiv:2101.04640. 2021.
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Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making. Gil, Y.; Garijo, D.; Khider, D.; Knoblock, C. A; Ratnakar, V.; Osorio, M.; Vargas, H.; Pham, M.; Pujara, J.; Shbita, B.; Vu, B.; Chiang, Y.; Feldman, D.; Lin, Y.; Song, H.; Kumar, V.; Khandelwal, A.; Steinbach, M.; Tayal, K.; Xu, S.; Pierce, S. A; Pearson, L.; Hardesty-Lewis, D.; Deelman, E.; Silva, R. F. D.; Mayani, R.; Kemanian, A. R; Shi, Y.; Leonard, L.; Peckham, S.; Stoica, M.; Cobourn, K.; Zhang, Z.; Duffy, C.; and Shu, L. ACM Trans. Interact. Intell. Syst., 11(2): 1–49. July 2021.
Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making [link]Paper doi link bibtex
Fight Club: Maturing Defense in Depth Obfuscation Techniques. Menon, V. V.; Sharma, U.; Roshanisefat, S.; Shukla, S. S.; Schmidt, A.; French, M.; Beerel, P. A.; and Nuzzo, P. March 2021.
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Design and Performance Evaluation of Multispectral Sensing Algorithms on CPU, GPU, and FPGA. Menon, V. V.; Siddiqui, S.; Rao, S.; Schmidt, A.; French, M.; Chirayath, V.; and Li, A. In 2021 IEEE Aerospace Conference, Big Sky, MT, USA, March 2021.
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A Hybrid Probabilistic Approach for Table Understanding. Sun, K.; Rayudu, H.; and Pujara, J. In Conference on Artificial Intelligence (AAAI), 2021.
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Rapid detection of identity-by-descent tracts for mega-scale datasets. Shemirani, R.; Belbin, G. M; Avery, C. L; Kenny, E. E; Gignoux, C. R; and Ambite, J. L. Nat Commun, 12(1): 3546. 06 2021.
Rapid detection of identity-by-descent tracts for mega-scale datasets [link]Paper doi link bibtex abstract
Scaling Neuroscience Research using Federated Learning. Stripelis, D.; Ambite, J. L.; Lam, P.; and Thompson, P. http://arxiv.org/abs/2102.08440, 2021.
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Towards a fine-scale population health monitoring system. Belbin, G. M; Cullina, S.; Wenric, S.; Soper, E. R; Glicksberg, B. S; Torre, D.; Moscati, A.; Wojcik, G. L; Shemirani, R.; Beckmann, N. D; Cohain, A.; Sorokin, E. P; Park, D. S; Ambite, J. L.; Auton, S. E. A.; Team, C. G.; Center, R. G.; Bottinger, E. P.; Cho, J. H; Loos, R. J.; Abul-Husn, N. S; Zaitlen, N. A; Gignoux, C. R.; and Kenny, E. E Cell. 2021. In press
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2020 (308)
Logic Obfuscation: Modeling Attack Resiliency. Menon, V. V.; Kolhe, G.; Fifty, J.; Schmidt, A.; Monson, J.; French, M.; Hu, Y.; Beerel, P. A; and Nuzzo, P. March 2020.
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Emulating and Verifying Sensing, Computation, and Communication in Distributed Remote Sensing Systems. French, M.; Paolieri, M.; Menon, V.; and Schmidt, A. 2020.
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Evaluation of Remote-Sensing Architectures using the Virtual Constellation Engine. Paolieri, M.; Menon, V.; Schmidt, A.; and French, M. 2020.
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StereoBit: StereoBit: An innovative SpaceCube Application for Atmospheric Science. Carr, J.; Wilson, C.; Wu, D.; French, M.; and Kelly, M. 2020.
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Be the Phisher - Understanding Users' Perception of Malicious Domains. Quinkert, F.; Degeling, M.; Blythe, J.; and Holz, T. In 15th ACM ASIA Conference on Computer and Communications Security, of ACM ASIACCS 2020, 2020.
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Mismorphism: the Heart of the Weird Machine. Anantharaman, P.; Kothari, V.; Jenkins, I.; Millian, M.; Bratus, S.; Blythe, J.; Koppel, R.; and Smith, S. In Security Protocols XXVII, 27th International Workshop, Revised Selected Papers. 2020.
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User-Centered Risk Communication for Safer Browsing. Das, S.; Abbott, J.; Gopavaram, S.; Blythe, J.; and Camp, J. In 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, 2020.
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Optimization of Large-Scale Agent-Based Simulations Through Automated Abstraction and Simplification. Tregubov, A.; and Blythe, J. In AAMAS Workshop on Multi-Agent Systems and Agent-Based Simulation, 2020.
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Massive Cross-Platform Simulations of Online Social Networks. Murić, G.; Tregubov, A.; Blythe, J.; Abeliuk, A.; Choudhary, D.; Lerman, K.; and Ferrara, E. In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, pages 895–903, 2020.
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Human-Computability Boundaries. Kothari, V.; Anantharaman, P.; Jenkins, I.; Millian, M.; Bratus, S.; Blythe, J.; Koppel, R.; and Smith, S. In Security Protocols XXVII, 27th International Workshop, Revised Selected Papers. 2020.
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Eyes on URLs: Relating Visual Behavior to Safety Decisions. Kothari, V.; Mills, C.; Ramkumar, N.; Blythe, J.; Koppel, R.; Smith, S.; and Kun, A. In ACM Symposium on Eye Tracking Research and Applications, 2020.
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Behavioral Determinants of Target Shifting and Deterrence in an Analog Cyber-Attack Game. Kusumastuti, S. A.; Blythe, J.; Rosoff, H.; and John, R. S. Risk Analysis, 40(3): 476-493. 2020.
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Initial safety posture investigations for earth regime rendezvous and proximity operations. Barnhart, D. A; Rughani, R.; Allam, J. J; and Clarke, K. W Journal of Space Safety Engineering, 7(4): 519-524. 2020.
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Using Genetic Algorithms for Safe Swarm Trajectory Optimization. Rughani, R.; and Barnhart, D. In AIAA Scitech 2020 Forum, pages 1916, 2020.
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On-orbit servicing ontology applied to recommended standards for satellites in earth orbit. Barnhart, D. A; and Rughani, R. Journal of Space Safety Engineering. 2020.
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REACCH-Reactive electro-adhesive capture cloth mechanism to enable safe grapple of cooperative/non-cooperative space debris. Narayanan, S.; Barnhart, D.; Rogers, R.; Ruffatto, D.; Schaler, E.; Van Crey, N.; Dean, G.; Bhanji, A.; Bernstein, S.; Singh, A.; and others In AIAA Scitech 2020 Forum, pages 2134, 2020.
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MAGNETO: Mapping the Earth?s Magnetic Field at 300 km using COTS Sensors. Villafana, L.; Yuan, J.; Broadus, L.; Su, W.; Carlton, C.; Norrell, A.; Rughani, R.; Park, E.; and Barnhart, D. . 2020.
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Safe Construction in Space: Using Swarms of Small Satellites for In-Space Manufacturing. Rughani, R.; and Barnhart, D. A . 2020.
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Application Aware Software Defined Flows of Workflow Ensembles. Papadimitriou, G.; Lyons, E.; Wang, C.; Thareja, K.; Tanaka, R.; Ruth, P.; Villalobos, J.; Rodero, I.; Deelman, E.; Zink, M.; and Mandal, A. In 2020 IEEE/ACM Innovating the Network for Data-Intensive Science (INDIS), November 2020. Funding Acknowledgments: NSF 1826997
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Gearing the DECam Analysis Pipeline for Multi-Messenger Astronomy using Pegasus Workflows. Vahi, K.; Goldstein, D.; Papadimitriou, G.; Nugent, P.; and Deelman, E. Astronomical Data Analysis Software and Systems (ADASS) XXIX. 2020. Funding Acknowledgments: NSF 1664162, DOE DESC0012636
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Workflow Submit Nodes as a Service on Leadership Class Systems. Papadimitriou, G.; Vahi, K.; Kincl, J.; Anantharaj, V.; Deelman, E.; and Wells, J. In Proceedings of the Practice and Experience in Advanced Research Computing, of PEARC 20, New York, NY, USA, 2020. Association for Computing Machinery Funding Acknowledgments: DOE DESC0012636
Workflow Submit Nodes as a Service on Leadership Class Systems [link]Paper doi link bibtex
An On-Demand Weather Avoidance System for Small Aircraft Flight Path Routing. Lyons, E.; Westbrook, D.; Grote, A.; Papadimitriou, G.; Thareja, K.; Wang, C.; Zink, M.; Deelman, E.; Mandal, A.; and Ruth, P. In Darema, F.; Blasch, E.; Ravela, S.; and Aved, A., editor(s), Dynamic Data Driven Application Systems, pages 311–319, Cham, 2020. Springer International Publishing Funding Acknowledgments: NSF 1826997, 2018074
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Identifying Execution Anomalies for Data Intensive Workflows Using Lightweight ML Techniques. Wang, C.; Papadimitriou, G.; Kiran, M.; Mandal, A.; and Deelman, E. In 2020 IEEE High Performance extreme Computing Conference (HPEC), 2020. Funding Acknowledgments: DOE DESC0012636
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DyNamo: Scalable Weather Workflow Processing in the Academic MultiCloud. Lyons, E.; Zink, M.; Mandal, A.; Wang, C.; Ruth, P.; Radhakrishnan, C.; Papadimitriou, G.; Deelman, E.; Thareja, K.; and Rodero, I. 100th American Meteorological Society Annual Meeting. 2020. Funding Acknowledgments: NSF 1826997
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Characterizing, Modeling, and Accurately Simulating Power and Energy Consumption of I/O-intensive Scientific Workflows. da Silva, R. F.; Casanova, H.; Orgerie, A.; Tanaka, R.; Deelman, E.; and Suter, F. Journal of Computational Science, 44: 101157. 2020. Funding Acknowledgments: NSF 1642335, NSF 1923539, NSF 1664162, DOE DE-SC0012636
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A Novel Metric to Evaluate In Situ Workflows. AnhDo, T. M.; Pottier, L.; Thomas, S.; da Silva, R. F.; Cuendet, M. A.; Weinstein, H.; Estrada, T.; Taufer, M.; and Deelman, E. In International Conference on Computational Science (ICCS), pages 538–553, 2020. Funding Acknowledgments: NSF 1741040
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WorkflowHub: Community Framework for Enabling Scientific Workflow Research and Development. da Silva, R. F.; Pottier, L.; Coleman, T.; Deelman, E.; and Casanova, H. In 2020 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS), pages 49–56, 2020. Funding Acknowledgments: NSF 2016619, DOE DE-SC0012636, NSF 1664162, NSF 1923539
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The Pegasus Workflow Management System: Translational Computer Science in Practice. Deelman, E.; da Silva, R. F.; Vahi, K.; Rynge, M.; Mayani, R.; Tanaka, R.; Whitcup, W.; and Livny, M. Journal of Computational Science. 2020. Funding Acknowledgments: NSF 1664162
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End-to-End Online Performance Data Capture and Analysis for Scientific Workflows. Papadimitriou, G.; Wang, C.; Vahi, K.; da Silva, R. F.; Mandal, A.; Zhengchun, L.; Mayani, R.; Rynge, M.; Kiran, M.; Lynch, V. E.; Kettimuthu, R.; Deelman, E.; Vetter, J. S.; and Foster, I. Future Generation Computer Systems, 117: 387-400. 2020. Funding Acknowledgments: DOE DE-SC0012636
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A Lightweight Method for Evaluating In Situ Workflow Efficiency. Do, T. M. A.; Loïc Pottier; Caíno-Lores, S.; da Silva, R. F.; Cuendet, M. A.; Weinstein, H.; Estrada, T.; Taufer, M.; and Deelman, E. Journal of Computational Science, 48: 101259. 2020. Funding Acknowledgments: NSF 1741040, DOE DE-SC0012636
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The Workflow Trace Archive: Open-Access Data from Public and Private Computing Infrastructures. Versluis, L.; Mathá, R.; Talluri, S.; Hegeman, T.; Prodan, R.; Deelman, E.; and Iosup, A. IEEE Transactions on Parallel and Distributed Systems, 31(9): 2170-2184. 2020. Funding Acknowledgments: NSF 1664162
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Detecting Anomalous Packets in Network Transfers: Investigations using PCA, Autoencoder and Isolation Forest in TCP. andCong Wang, M. K.; Papadimitriou, G.; Mandal, A.; and Deelman, E. Machine Learning. 2020. Funding Acknowledgments: DOE DESC0012636
Detecting Anomalous Packets in Network Transfers: Investigations using PCA, Autoencoder and Isolation Forest in TCP [link]Paper doi link bibtex
Modeling the Performance of Scientific Workflow Executions on HPC Platforms with Burst Buffers. Pottier, L.; da Silva, R. F.; Casanova, H.; and Deelman, E. In 2020 IEEE International Conference on Cluster Computing (CLUSTER), pages 92–103, 2020. Funding Acknowledgments: DOE DE-SC0012636, NSF 1664162, NSF 1741040, NSF 1923539, NSF 1923621
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Graph Embedding with Personalized Context Distribution. Huang, D.; He, Z.; Huang, Y.; Sun, K.; Abu-El-Haija, S.; Perozzi, B.; Lerman, K.; Morstatter, F.; and Galstyan, A. In Companion of The 2020 Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, pages 655–661, 2020.
Graph Embedding with Personalized Context Distribution [link]Paper doi link bibtex
What Are You Trying to Do? Semantic Typing of Event Processes. Chen, M.; Zhang, H.; Wang, H.; and Roth, D. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 531-542, 2020.
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Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer. Chen, X.; Chen, M.; Fan, C.; Uppunda, A.; Sun, Y.; and Zaniolo, C. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 3227-3238, 2020.
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Analogous Process Structure Induction for Sub-event Sequence Prediction. Zhang, H.; Chen, M.; Wang, H.; Song, Y.; and Roth, D. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1541-1550, 2020.
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Joint Constrained Learning for Event-Event Relation Extraction. Wang, H.; Chen, M.; Zhang, H.; and Roth, D. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 696-706, 2020.
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Knowledge Graph Alignment Network with Gated Multi-Hop Neighborhood Aggregation. Sun, Z.; Wang, C.; Hu, W.; Chen, M.; Dai, J.; Zhang, W.; and Qu, Y. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 222-229, 2020.
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ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis. Meng, C.; Chen, M.; Mao, J.; and Neville, J. In European Conference on Information Retrieval, pages 33-49, 2020. Springer
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Bio-joie: Joint representation learning of biological knowledge bases. Hao, J.; Ju, C. J.; Chen, M.; Sun, Y.; Zaniolo, C.; and Wang, W. In Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pages 1-10, 2020.
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Diagnostic Prediction with Sequence-of-sets Representation Learning for Clinical Events. Zhang, T. In Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Minneapolis, MN, USA, August 25-28, 2020, Proceedings, pages 348, 2020. Springer Nature
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A benchmarking study of embedding-based entity alignment for knowledge graphs. Sun, Z.; Zhang, Q.; Hu, W.; Wang, C.; Chen, M.; Akrami, F.; and Li, C. Proceedings of the VLDB Endowment, 13(12): 2326-2340. 2020.
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Mutation effect estimation on protein–protein interactions using deep contextualized representation learning. Zhou, G.; Chen, M.; Ju, C. J.; Wang, Z.; Jiang, J.; and Wang, W. NAR genomics and bioinformatics, 2(2): 15. 2020.
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Knowledge Association with Hyperbolic Knowledge Graph Embeddings. Sun, Z.; Chen, M.; Hu, W.; Wang, C.; Dai, J.; and Zhang, W. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5704-5716, 2020.
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A Lightweight Method for Evaluating In Situ Workflow Efficiency. Do, T. M. A.; Pottier, L.; Caino-Lores, S.; Ferreira da Silva, R.; Cuendet, M. A.; Weinstein, H.; Estrada, T.; Taufer, M.; and Deelman, E. Journal of Computational Science, 48: 101259. 2020. Funding Acknowledgments: NSF 1741040, DOE DE-SC0012636
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A Novel Metric to Evaluate In Situ Workflows. Do, T. M. A.; Pottier, L.; Thomas, S.; Ferreira da Silva, R.; Cuendet, M. A.; Weinstein, H.; Estrada, T.; Taufer, M.; and Deelman, E. In International Conference on Computational Science (ICCS), pages 538–553, 2020. Funding Acknowledgments: NSF 1741040
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Modeling the Performance of Scientific Workflow Executions on HPC Platforms with Burst Buffers. Pottier, L.; Ferreira da Silva, R.; Casanova, H.; and Deelman, E. In 2020 IEEE International Conference on Cluster Computing (CLUSTER), pages 92–103, 2020. Funding Acknowledgments: DOE DE-SC0012636, NSF 1664162, NSF 1741040, NSF 1923539, NSF 1923621
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WorkflowHub: Community Framework for Enabling Scientific Workflow Research and Development. Ferreira da Silva, R.; Pottier, L.; Coleman, T.; Deelman, E.; and Casanova, H. In 2020 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS), pages 49–56, 2020. Funding Acknowledgments: NSF 2016619, DOE DE-SC0012636, NSF 1664162, NSF 1923539
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Can Badges Foster a More Welcoming Culture on Q&A Boards?. Santos, T.; Burghardt, K.; Lerman, K.; and Helic, D. In Proceedings of the Fourteenth International AAAI Conference on Web and Social Media, ICWSM 2020, Held Virtually, Original Venue: Atlanta, Georgia, USA, June 8-11, 2020, pages 969–973, 2020.
Can Badges Foster a More Welcoming Culture on Q&A Boards? [link]Paper link bibtex
Origins of Algorithmic Instabilities in Crowdsourced Ranking. Burghardt, K.; Hogg, T.; DSouza, R.; Lerman, K.; and Posfai, M. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2): 1-20. 2020.
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The transsortative structure of networks. Ngo, S.; Percus, A. G; Burghardt, K.; and Lerman, K. Proceedings of the Royal Society A, 476(2237): 20190772. 2020.
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Idle Time Optimization for Target Assignment and Path Finding in Sortation Centers. Kou, N. M.; Peng, C.; Ma, H.; Thittamaranahalli, S.; and Koenig, S. Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020). apr 2020.
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A Combinatorial Perspective on Ising Model Hysteresis. Guan, Y.; Li, A.; Koenig, S.; Haas, S.; and Thittamaranahalli, S. American Physical Society, March Meeting (APS-2020). 2020.
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. Li, J.; Tinka, A.; Kiesel, S.; Durham, J.; Thittamaranahalli, S.; and Koenig, S. Proceedings of the Nineteenth International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-2020). 2020.
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Moving Agents in Formation in Congested Environments. Li, J.; Sun, K.; Ma, H.; Felner, A.; Thittamaranahalli, S.; and Koenig, S. Proceedings of the Thirteenth International Symposium on Combinatorial Search (SOCS-2020). 2020.
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Hybrid Quantum-Classical Algorithms for Solving the Weighted CSP. Xu, H.; Sun, K.; Koenig, S.; Hen, I.; and Thittamaranahalli, S. Proceedings of the Sixteenth International Symposium on Artificial Intelligence and Mathematics (ISAIM-2020). 2020.
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Exact Approaches to the Multi-Agent Collective Construction Problem. Lam, E.; Stuckey, P.; Koenig, S.; and Thittamaranahalli, S. Proceedings of the Fourth International Workshop on Multi-Agent Path Finding (WoMAPF-2020). 2020.
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Embedding Directed Graphs in Potential Fields Using FastMap-D. Gopalakrishnan, S.; Cohen, L.; Koenig, S.; and Thittamaranahalli, S. Proceedings of the Thirteenth International Symposium on Combinatorial Search (SOCS-2020). 2020.
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Multi-Agent Path Finding with Mutex Propagation. Zhang, H.; Li, J.; Surynek, P.; Koenig, S.; and Thittamaranahalli, S. Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling (ICAPS-2020). 2020.
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Domain-Specific Compilers for Dynamic Simulations of Quantum Materials on Quantum Computers. Bassman, L.; Gulania, S.; Powers, C.; Li, R.; Linker, T.; Liu, K.; Thittamaranahalli, S.; Kalia, R.; Nakano, A.; and Vashishta, P. Special Issue Article in Journal of Quantum Science and Technology (QST-2020). 2020.
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Decision Tree Learning-Inspired Dynamic Variable Ordering for the Weighted CSP. Xu, H.; Sun, K.; Koenig, S.; and Thittamaranahalli, S. Proceedings of the Thirteenth International Symposium on Combinatorial Search (SOCS-2020). 2020.
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Generating the Top K Solutions to Weighted CSPs: A Comparison of Different Approaches. Li, A.; Guan, Y.; Koenig, S.; Haas, S.; and Thittamaranahalli, S. Proceedings of the Thirty-Second International Conference on Tools with Artificial Intelligence (ICTAI-2020). 2020.
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Denoising Autoencoders for High-Qubit Quantum Dynamics Simulations on Quantum Computers. Powers, C.; Bassman, L.; Geng, Y.; Kalia, R.; Thittamaranahalli, S.; Linker, T.; Liu, K.; Nakano, A.; Rajak, P.; and Vashishta, P. Proceedings of the NeurIPS-2020 Workshop on Machine Learning and the Physical Sciences. 2020.
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Mutex Propagation for SAT-Based Multi-Agent Path Finding. Surynek, P.; Li, J.; Zhang, H.; Thittamaranahalli, S.; and Koenig, S. Proceedings of the Twenty-Third International Conference on Principles and Practice of Multi-Agent Systems (PRIMA-2020). 2020.
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The genetic architecture of the human cerebral cortex. Grasby, K. L; Jahanshad, N.; Painter, J. N; Colodro-Conde, L.; Bralten, J.; Hibar, D. P; Lind, P. A; Pizzagalli, F.; Ching, C. R.; McMahon, M. A. B; and others Science, 367(6484). 2020.
The genetic architecture of the human cerebral cortex [pdf]Paper link bibtex
ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries. Thompson, P. M.; Jahanshad, N.; Ching, C. R. K.; Salminen, L. E.; Thomopoulos, S. I.; Bright, J.; Baune, B. T.; Bertol'in, S.; Bralten, J.; Bruin, W. B.; Bülow, R.; Chen, J.; Chye, Y.; Dannlowski, U.; de Kovel, C. G. F.; Donohoe, G.; Eyler, L. T.; Faraone, S. V.; Favre, P.; Filippi, C. A.; Frodl, T.; Garijo, D.; Gil, Y.; Grabe, H. J.; Grasby, K. L.; Hajek, T.; Han, L. K. M.; Hatton, S. N.; Hilbert, K.; Ho, T. C.; Holleran, L.; Homuth, G.; Hosten, N.; Houenou, J.; Ivanov, I.; Jia, T.; Kelly, S.; Klein, M.; Kwon, J. S.; Laansma, M. A.; Leerssen, J.; Lueken, U.; Nunes, A.; Neill, J. O.; Opel, N.; Piras, F.; Piras, F.; Postema, M. C.; Pozzi, E.; Shatokhina, N.; Soriano-Mas, C.; Spalletta, G.; Sun, D.; Teumer, A.; Tilot, A. K.; Tozzi, L.; van der Merwe, C.; Van Someren, E. J. W.; van Wingen, G. A.; Völzke, H.; Walton, E.; Wang, L.; Winkler, A. M.; Wittfeld, K.; Wright, M. J.; Yun, J.; Zhang, G.; Zhang-James, Y.; Adhikari, B. M.; Agartz, I.; Aghajani, M.; Aleman, A.; Althoff, R. R.; Altmann, A.; Andreassen, O. A.; Baron, D. A.; Bartnik-Olson, B. L.; Marie Bas-Hoogendam, J.; Baskin-Sommers, A. R.; Bearden, C. E.; Berner, L. A.; Boedhoe, P. S. W.; Brouwer, R. M.; Buitelaar, J. K.; Caeyenberghs, K.; Cecil, C. A. M.; Cohen, R. A.; Cole, J. H.; Conrod, P. J.; De Brito, S. A.; de Zwarte, S. M. C.; Dennis, E. L.; Desrivieres, S.; Dima, D.; Ehrlich, S.; Esopenko, C.; Fairchild, G.; Fisher, S. E.; Fouche, J.; Francks, C.; Frangou, S.; Franke, B.; Garavan, H. P.; Glahn, D. C.; Groenewold, N. A.; Gurholt, T. P.; Gutman, B. A.; Hahn, T.; Harding, I. H.; Hernaus, D.; Hibar, D. P.; Hillary, F. G.; Hoogman, M.; Hulshoff Pol, H. E.; Jalbrzikowski, M.; Karkashadze, G. A.; Klapwijk, E. T.; Knickmeyer, R. C.; Kochunov, P.; Koerte, I. K.; Kong, X.; Liew, S.; Lin, A. P.; Logue, M. W.; Luders, E.; Macciardi, F.; Mackey, S.; Mayer, A. R.; McDonald, C. R.; McMahon, A. B.; Medland, S. E.; Modinos, G.; Morey, R. A.; Mueller, S. C.; Mukherjee, P.; Namazova-Baranova, L.; Nir, T. M.; Olsen, A.; Paschou, P.; Pine, D. S.; Pizzagalli, F.; Rentería, Miguel E.; Rohrer, J. D.; S"amann, P. G.; Schmaal, L.; Schumann, G.; Shiroishi, M. S.; Sisodiya, S. M.; Smit, D. J. A.; S\onderby, I. E.; Stein, D. J.; Stein, J. L.; Tahmasian, M.; Tate, D. F.; Turner, J. A.; van den Heuvel, O. A.; van der Wee, N. J. A.; van der Werf, Y. D.; van Erp, T. G. M.; van Haren, N. E. M.; van Rooij, D.; van Velzen, L. S.; Veer, I. M.; Veltman, D. J.; Villalon-Reina, J. E.; Walter, H.; Whelan, C. D.; Wilde, E. A.; Zarei, M.; Zelman, V.; and Consortium, f. t. E. Nature Translational Psychiatry, 10(1): 100. 2020.
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FAIR Computational Workflows. Goble, C.; Cohen-Boulakia, a.; Soiland-Reyes, S.; Garijo, D.; Gil, Y.; Crusoe, M. R.; Peters, K.; and Schoberhidden, D. Data Intelligence, Special Issue on FAIR (Findable, Accessible, Interoperable and Reusable) Principles, 2(1-2). 2020.
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All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference. Brekelmans, R.; Masrani, V.; Wood, F.; Ver Steeg, G.; and Galstyan, A. In Thirty-seventh International Conference on Machine Learning (ICML 2020), July 2020.
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference [link] link All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference [pdf] paper All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference [link] arxiv link bibtex abstract
Improving Generalization by Controlling Label-Noise Information in Neural Network Weights. Harutyunyan, H.; Reing, K.; Ver Steeg, G.; and Galstyan, A. In International Conference on Machine Learning (ICML), July 2020.
Improving Generalization by Controlling Label-Noise Information in Neural Network Weights [pdf]Paper link bibtex