Data-driven Science - 2015

Zig-Zag Numberlink is NP-Complete. (2015). Journal of Information Processing, 23(3), 239–245. https://doi.org/10.2197/ipsjjip.23.239

Ding, Y., Shen, X., Musuvathi, M., & Mytkowicz, T. (2015). Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup (Technical Report No. TR-2015-2). North Carolina State University.

Ding, Y., Shen, X., Musuvathi, M., & Mytkowicz, T. (2015). TOP: A Framework for Enabling Algorithmic Optimizations for Distance-Related Problems” (Technical Report No. TR-2015-3). North Carolina State University.

Ding, Y., Shen, X., Musuvathi, M., & Mytkowicz, T. (2015). TOP: A Framework for Enabling Algorithmic Optimizations for Distance-Related Problems. In C. Li & V. Markl (Eds.), 41st International Conference on Very Large Data Bases (VLDB 2015) : proceedings of the VLDB Endowment, volume 8, number 1-13, Kohala Coast, Hawaii, USA, 31 August-4 September 2015. Stanford, CA: VLDB Endowment.

Ding, Y., Zhao, Y., Shen, X., Musuvathi, M., & Mytkowicz, T. (2015). Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup. Proceedings of the 32nd International Conference on Machine Learning, 37, 579–587. Lille, France.

Wu, B., Chen, G., Li, D., Shen, X., & Vetter, J. (2015). Enabling and Exploiting Flexible Task Assignment on GPU through SM-Centric Program Transformations. Proceedings of the 29th ACM on International Conference on Supercomputing - ICS '15. Presented at the the 29th ACM. https://doi.org/10.1145/2751205.2751213

Chen, G., & Shen, X. (2015). Free launch. Proceedings of the 48th International Symposium on Microarchitecture - MICRO-48. Presented at the the 48th International Symposium. https://doi.org/10.1145/2830772.2830818

Zhu, Q., Wu, B., Shen, X., Shen, L., & Wang, Z. (2015). Understanding Co-run Degradations on Integrated Heterogeneous Processors. In Languages and Compilers for Parallel Computing (pp. 82–97). https://doi.org/10.1007/978-3-319-17473-0_6

Farrell, M., Goodrich, T. D., Lemons, N., Reidl, F., Villaamil, F. S., & Sullivan, B. D. (2015). Hyperbolicity, Degeneracy, and Expansion of Random Intersection Graphs. ALGORITHMS AND MODELS FOR THE WEB GRAPH, (WAW 2015), Vol. 9479, pp. 29–41. https://doi.org/10.1007/978-3-319-26784-5_3

Drange, P. G., Dregi, M. S., Lokshtanov, D., & Sullivan, B. D. (2015). On the Threshold of Intractability. ALGORITHMS - ESA 2015, Vol. 9294, pp. 411–423. https://doi.org/10.1007/978-3-662-48350-3_35

Zhao, Z., & Shen, X. (2015, April). On-the-Fly Principled Speculation for FSM Parallelization. ACM SIGPLAN NOTICES, Vol. 50, pp. 619–630. https://doi.org/10.1145/2775054.2694369

Bridges, R. A., Collins, J. P., Ferragut, E. M., Laska, J. A., & Sullivan, B. D. (2015). Multi-Level Anomaly Detection on Time-Varying Graph Data. PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2015), pp. 579–583. https://doi.org/10.1145/2808797.2809406

Dickinson, R. M., Freeman, L. J., Simpson, B. A., & Wilson, A. G. (2015). Statistical methods for combining information: Stryker family of vehicles reliability case study. Journal of Quality Technology, 47(4), 400–415. https://doi.org/10.1080/00224065.2015.11918142

Stracuzzi, D. J., Brost, R. C., Phillips, C. A., Robinson, D. G., Wilson, A. G., & Woodbridge, D. M.-K. (2015). Computing quality scores and uncertainty for approximate pattern matching in geospatial semantic graphs. STATISTICAL ANALYSIS AND DATA MINING, Vol. 8, pp. 340–352. https://doi.org/10.1002/sam.11294

Berry, J. W., Fostvedt, L. A., Nordman, D. J., Phillips, C. A., Seshadhri, C., & Wilson, A. G. (2015). WHY DO SIMPLE ALGORITHMS FOR TRIANGLE ENUMERATION WORK IN THE REAL WORLD? INTERNET MATHEMATICS, 11(6), 555–571. https://doi.org/10.1080/15427951.2015.1037030

Chen, G., Wu, B., Li, D., & Shen, X. (2015). Enabling Portable Optimizations of Data Placement on GPU. IEEE Micro, 35(4), 16–24. https://doi.org/10.1109/mm.2015.53

Ding, Y., Ansel, J., Veeramachaneni, K., Shen, X., O'Reilly, U.-M., & Amarasinghe, S. (2015, June). Autotuning Algorithmic Choice for Input Sensitivity. ACM SIGPLAN NOTICES, Vol. 50, pp. 379–390. https://doi.org/10.1145/2813885.2737969

Zhu, Q., Wu, B., Shen, X. P., Shen, L., & Wang, Z. Y. (2015). Understanding co-run degradations on integrated heterogeneous processors. Languages and compilers for parallel computing (lcpc 2014), 8967, 82–97.