Data-driven Science - 2018

Kloster, K., Kuinke, P., O?Brien, M. P., Reidl, F., Villaamil, F. S., Sullivan, B. D., & Van Der Poel, A. (2018). A practical fpt algorithm for flow decomposition and transcript assembly. Proceedings of the Workshop on Algorithm Engineering and Experiments, 2018-January, 75–86. https://doi.org/10.1137/1.9781611975055.7

Demaine, E. D., Goodrich, T. D., Kloster, K., Lavallee, B., Liu, Q. C., Sullivan, B. D., … Poel, A. (2018). Structural rounding: Approximation algorithms for graphs near an algorithmically tractable class. ArXiv. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85094277709&partnerID=MN8TOARS

Kloster, K., Sullivan, B. D., & Van Der Poel, A. (2018). Mining maximal induced bicliques using odd cycle transversals. ArXiv. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85093320378&partnerID=MN8TOARS

Dumitrescu, E. F., Fisher, A. L., Goodrich, T. D., Humble, T. S., Sullivan, B. D., & Wrigh, A. L. (2018). Benchmarking treewidth as a practical component of tensor-network–based quantum simulation. ArXiv. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85095245427&partnerID=MN8TOARS

Goodrich, T. D., Horton, E., & Sullivan, B. D. (2018). An updated experimental evaluation of graph bipartization methods. ArXiv. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85104339918&partnerID=MN8TOARS

Horton, E., Kloster, K., & Sullivan, B. D. (2018). Subgraph centrality and walk-regularity. ArXiv. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85093754399&partnerID=MN8TOARS

Kun, J., O’Brien, M. P., Pilipczuk, M., & Sullivan, B. D. (2018). Polynomial treedepth bounds in linear colorings. ArXiv. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85092833264&partnerID=MN8TOARS

Cohen, A., Shen, X., Torrellas, J., Tuck, J., & Zhou, Y. (2018). Inter-Disciplinary Research Challenges in Computer Systems for the 2020s. National Science Foundation.

Goodrich, T. D., Sullivan, B. D., & Humble, T. S. (2018). Optimizing adiabatic quantum program compilation using a graph-theoretic framework. Quantum Information Processing, 17(5). https://doi.org/10.1007/S11128-018-1863-4

Kun, J., O’Brien, M. P., & Sullivan, B. D. (2018). Treedepth Bounds in Linear Colorings. In Graph-Theoretic Concepts in Computer Science: Vol. 11159 LNCS (pp. 331–343). https://doi.org/10.1007/978-3-030-00256-5_27

Zhang, F., Zhai, J., Shen, X., Mutlu, O., & Chen, W. (2018). Zwift. Proceedings of the 2018 International Conference on Supercomputing - ICS '18. Presented at the the 2018 International Conference. https://doi.org/10.1145/3205289.3205325

Pittman, R., Guan, H., Shen, X., Lim, S.-H., & Patton, R. M. (2018). Exploring Flexible Communications for Streamlining DNN Ensemble Training Pipelines. SC18: International Conference for High Performance Computing, Networking, Storage and Analysis. Presented at the SC18: International Conference for High Performance Computing, Networking, Storage and Analysis. https://doi.org/10.1109/sc.2018.00067

Zhu, Q., Wu, B., Shen, X., Shen, K., Shen, L., & Wang, Z. (2018). Resolving the GPU responsiveness dilemma through program transformations. Frontiers of Computer Science, 12(3), 545–559. https://doi.org/10.1007/s11704-016-6206-y

Guan, H., Ding, Y., Shen, X., & Krim, H. (2018). Reuse-Centric K-Means Configuration. 2018 IEEE 34TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), pp. 1224–1227. https://doi.org/10.1109/ICDE.2018.00116

Luo, H., Chen, G., Liu, F., Li, P., Ding, C., & Shen, X. (2018). Footprint Modeling of Cache Associativity and Granularity. PROCEEDINGS OF THE INTERNATIONAL SYMPOSIUM ON MEMORY SYSTEMS (MEMSYS 2018), pp. 232–242. https://doi.org/10.1145/3240302.3240419

Yang, S., & Shen, X. (2018). LEEM: Lean Elastic EM for Gaussian Mixture Model via Bounds-Based Filtering. 2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), pp. 677–686. https://doi.org/10.1109/ICDM.2018.00083

Shen, X. (2018). Rethinking Compilers in the Rise of Machine Learning and AI. CC'18: PROCEEDINGS OF THE 27TH INTERNATIONAL CONFERENCE ON COMPILER CONSTRUCTION, pp. 1–1. https://doi.org/10.1145/3178372.3183634

Kloster, K., Král', D., & Sullivan, B. D. (2018). Walk entropy and walk-regularity. Linear Algebra and Its Applications, 546, 115–121. https://doi.org/10.1016/j.laa.2018.02.009

Brown, C. T., Moritz, D., O’Brien, M. P., Reidl, F., Reiter, T., & Sullivan, B. D. (2018, November 5). Exploring neighborhoods in large metagenome assembly graphs reveals hidden sequence diversity. BioRxiv, Vol. 11. https://doi.org/10.1101/462788

Yang, S., & Shen, X. (2018). FALCON: A Fast Drop-In Replacement of Citation KNN for Multiple Instance Learning. CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, pp. 67–76. https://doi.org/10.1145/3269206.3271787

Dumitrescu, E. F., Fisher, A. L., Goodrich, T. D., Humble, T. S., Sullivan, B. D., & Wright, A. L. (2018). Benchmarking treewidth as a practical component of tensor network simulations. PLOS ONE, 13(12). https://doi.org/10.1371/journal.pone.0207827

Zhang, F., Zhai, J., Shen, X., Mutlu, O., & Chen, W. (2018). Efficient Document Analytics on Compressed Data: Method, Challenges, Algorithms, Insights. PROCEEDINGS OF THE VLDB ENDOWMENT, 11(11), 1522–1535. https://doi.org/10.14778/3236187.3236203

Ning, L., Pittman, R., & Shen, X. (2018). LCD: A Fast Contrastive Divergence Based Algorithm for Restricted Boltzmann Machine. NEURAL NETWORKS, 108, 399–410. https://doi.org/10.1016/j.neunet.2018.08.018

Rendon, H., Wilson, A., & Stegall, J. (2018). Is it "Fake News'? Intelligence Community expertise and news dissemination as measurements for media reliability. INTELLIGENCE AND NATIONAL SECURITY, 33(7), 1040–1052. https://doi.org/10.1080/02684527.2018.1507381

Shen, X., Lovas, R., & Liao, X. (2018, October). Editorial for the Special Issue on In-Memory Computing. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, Vol. 120, pp. 322–322. https://doi.org/10.1016/j.jpdc.2018.05.009

Zhao, Y., Li, J., Liao, C., & Shen, X. (2018). Bridging the Gap between Deep Learning and Sparse Matrix Format Selection. ACM SIGPLAN NOTICES, 53(1), 94–108. https://doi.org/10.1145/3178487.3178495

Xu, S., Xu, Y., Xue, W., Shen, X., Zheng, F., Huang, X., & Yang, G. (2018). Taming the "Monster": Overcoming Program Optimization Challenges on SW26010 Through Precise Performance Modeling. 2018 32ND IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS), pp. 763–773. https://doi.org/10.1109/IPDPS.2018.00086

Zhao, Y., Zhou, W., Shen, X., & Yiu, G. (2018). Overhead-Conscious Format Selection for SpMV-Based Applications. 2018 32ND IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS), pp. 950–959. https://doi.org/10.1109/IPDPS.2018.00104

Bakerman, J., Pazdernik, K., Wilson, A., Fairchild, G., & Bahran, R. (2018). Twitter geolocation: A hybrid approach. ACM Transactions on Knowledge Discovery from Data, 12(3).

Zoh, R., Wilson, A., Vander Wiel, S., & Lawrence, E. (2018). The negative log-gamma prior distribution for Bayesian assessment of system reliability. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY, 232(3), 308–319. https://doi.org/10.1177/1748006x17692154