MDS-Rely

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  1. Jia, Xiaowei, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver, Steven Markstrom, Jared Willard, et al. “Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks.” ArXiv:2009.12575 [Physics], December 8, 2020. http://arxiv.org/abs/2009.12575.
  2. Rudy Bunel, Oliver Hinder, Srinadh Bhojanapalli, and Dj Dvijotham. An efficient nonconvex reformulation of stagewise convex optimization problems. Advances in Neural Information Processing Systems, 2020. https://arxiv.org/abs/2010.14322
  3. Willard, Jared, Xiaowei Jia, Shaoming Xu, Michael Steinbach, and Vipin Kumar. “Integrating Physics-Based Modeling with Machine Learning: A Survey.” ArXiv:2003.04919 [Physics, Stat], July 13, 2020. http://arxiv.org/abs/2003.04919.
  4. C. M. Andolina, P. Williamson, and W. A. Saidi, “Optimization and validation of a deep learning CuZr atomistic potential: Robust applications for crystalline and amorphous phases with near-DFT accuracy,” J. Chem. Phys., vol. 152, no. 15, p. 154701, Apr. 2020, doi: 10.1063/5.0005347.
  5. W. A. Saidi, W. Shadid, and I. E. Castelli, “Machine-learning structural and electronic properties of metal halide perovskites using a hierarchical convolutional neural network,” Npj Comput. Mater., vol. 6, no. 1, Art. no. 1, Apr. 2020, doi: 10.1038/s41524-020-0307-8.
  6. Lee, M. Abdulhafez, M. Bedewy, “Data Analytics Enables Significant Improvement of Robustness in Chemical Vapor Deposition of Carbon Nanotubes Based on Vacuum Baking.” Industrial & Engineering Chemistry Research, vol. 58, no. 27, pp. 11999–2009, Jul. 2019, doi:1021/acs.iecr.9b01725.
  7. Hsinjin Edwin Yang, Roger H. French, and Laura S. Bruckman, Eds., Durability and Reliability of Polymers and Other Materials in Photovoltaic Modules, 1st ed., ser. Plastics Design Library. Amsterdam: Elsevier, William Andrew Applied Science Publishers, Jun. 2019.
  8. Yu Wang, Wei-Heng Huang, Andrew Fairbrother, Lucas S. Fridman, Alan J. Curran, Nicholas R. Wheeler, Sophie Napoli, Adam W. Hauser, Scott Julien, Xiaohong Gu, Gregory S. O‘Brien, Kai-Tak Wan, Liang Ji, Kenneth P. Boyce, Michael D. Kempe, Roger H. French, and Laura S. Bruckman, “Generalized Spatio-Temporal Model of Backsheet Degradation from Field Surveys of Photovoltaic Modules,” Journal of Photovoltaics, 2019, (submitted).
  9. Yang Hu and Roger H. French, “Degradation and Failure Mechanisms of PV Module Interconnects,” in Durability and Reliability of Polymers and Other Materials in Photovoltaic Modules, 1st ed., ser. Plastics Design Library, Hsinjin Edwin Yang, Roger H. French, and Laura S. Bruckman, Eds. Amsterdam: Elsevier, William Andrew Applied Science Publishers, Jun. 2019, pp. 119–134.
  10. >Duchi, John C., Oliver Hinder, Andrew Naber, and Yinyu Ye. “Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices.” Advances in Neural Information Processing Systems, 2020. https://papers.nips.cc/paper/2020/file/5e5dd00d770ef3e9154a4257edcb80b8-Supplemental.pdf
  11. Xuan Ma, Wei-Heng Huang, Erdmut Schnabel, Michael Köhl, Jenný Brynjarsdóttir, Jennifer L. Braid, and Roger H. French, “Data-driven I -V Feature Extraction for Photo- voltaic Modules,” Journal of Photovoltaics, 2019, (submitted).
  12. A. Verma, W.-H. Huang, J. A. Hawk, L. S. Bruckman, R. H. French, V. Romanov, and J. L. W. Carter, “Screening of heritage data for improving toughness of creep-resistant martensitic steels,” Materials Science and Engineering: A, vol. 763, p. 138142, Aug. 2019, doi: 10.1016/j.msea.2019.138142.
  13. A. Verma, J. A. Hawk, L. S. Bruckman, R. H. French, V. Romanov, and J. L. W. Carter, “Mapping Multivariate Influence of Alloying Elements on Creep Behavior for Design of New Martensitic Steels,” Metallurgical and Materials Transactions A, vol. 50, no. 7, pp. 3106–3120, Jul. 2019, doi: 10.1007/s11661-019-05234-9.
  14. N. Romanov, N. Krishnamurthy, A. K. Verma, L. S. Bruckman, R. H. French, J. L. W. Carter, and J. A. Hawk, “Materials data analytics for 9% Cr family steel,” Statistical Analysis and Data Mining: The ASA Data Science Journal, vol. 0, no. 0, Feb. 2019, doi: 10.1002/sam.11406.
  15. Dee, M. Bedewy, A. Rao, J. Beroz, B. Lee, E. R. Meshot, C. A. C. Chazot, P. R. Kidambi, H. Zhao, T. Serbowiz, K. Teichert, P. K. Purohit, J. A. Hart, “In Situ Mechanochemical Modulation of Carbon Nanotube Forest Growth.” Chemistry of Materials, vol. 31, no. 2, Jan. 2019, pp. 407–18. ACS Publications, doi:10.1021/acs.chemmater.8b03627.
  16. Haghanifar, M. McCourt, B. Cheng, J. Wuenschell, P. Ohodnicki, and P. W. Leu, “Creating glasswing butterfly-inspired durable antifogging superomniphobic supertransmissive, superclear nanostructured glass through Bayesian learning and optimization,” Materials Horizons, Jun. 2019, doi: 10.1039/C9MH00589G.
  17. L. Braid and R. H. French, “Overview: Power Degradation and Failure of PV Systems,” in Durability and Reliability of Polymers and Other Materials in Photovoltaic Modules, 1st ed., ser. Plastics Design Library, H. E. Yang, R. H. French, and L. S. Bruckman, Eds. Amsterdam: Elsevier, William Andrew Applied Science Publishers, Jun. 2019, pp. 1–22, doi: 10.1016/B978-0-12-811545-9.00001-X.
  18. A. Karimi, J. S. Fada, M. A. Hossain, S. Yang, T. J. Peshek, J. L. Braid, and R. H. French, “Automated Pipeline for Photovoltaic Module Electroluminescence Image Processing and Degradation Feature Classification,” IEEE Journal of Photovoltaics, pp. 1–12, Jun. 2019, doi: 10.1109/JPHOTOV.2019.2920732.
  19. Abdulkerim Gok, Devin A. Gordon, Menghong Wang, Roger H. French, and Laura S. Bruckman, “Degradation Science of, and Pathways in, PV Systems,” in Durability and Reliability of Polymers and Other Materials in Photovoltaic Modules, 1st ed., ser. Plastics Design Library, Hsinjin Edwin Yang, Roger H. French, and Laura S. Bruckman, Eds. Amsterdam: Elsevier, William Andrew Applied Science Publishers, Jun. 2019, pp. 47–94, doi: 1016/C2016-0-01032-X.
  20. He, L. M. Maillart, and O. A. Prokopyev, “Optimal sequencing of heterogeneous, non-instantaneous interventions,” Annals of Operations Research, vol. 276, no. 1, pp. 109–135, May. 2019, doi: 10.1007/s10479-018-2813-3.
  21. Haghanifar, L. M Tomasovic, A. J Galante, D. Pekker, and P. W. Leu, “Stain-Resistant, Superomniphobic Flexible Optical Plastics Based on Nano-Enoki Mushrooms,” Journal of Materials Chemistry A, May. 2019, doi: 10.1039/C9TA01753D.
  22. A. Gordon, Z. Zhan, and L. S. Bruckman, “Characterizing the weathering induced degradation of Poly(ethylene-terephthalate) using PARAFAC modeling of fluorescence spectra,” Polymer Degradation and Stability, vol. 161, pp. 85–94, Mar. 2019, doi: 10.1016/j.polymdegradstab.2019.01.006.
  23. AGok, D. A. Gordon, D. M. Burns, S. P. Fowler, R. H. French, and L. S. Bruckman, “Reciprocity and spectral effects of the degradation of poly(ethylene-terephthalate) under accelerated weathering exposures,” Journal of Applied Polymer Science, vol. 136, no. 22, p. 47589, Mar. 2019, doi: 1002/app.47589.
  24. AGok, C. L. Fagerholm, R. H. French, and L. S. Bruckman, “Temporal evolution and pathway models of poly(ethylene-terephthalate) degradation under multi-factor accelerated weathering exposures,” PLOS ONE, vol. 14, no. 2, p. e0212258, Feb. 2019, doi: 10.1371/journal.pone.0212258.
  25. E. Gaunt, S. Iyengar, A. B. Olde Daalhuis, and B. Simsek, “An asymptotic expansion for the normalizing constant of the Conway–Maxwell–Poisson distribution,” Annals of the Institute of Statistical Mathematics, vol. 71, no. 1, pp. 163–180, Feb. 2019, doi: 10.1007/s10463-017-0629-6.
  26. S. Bruckman, “Transformative Opportunities from Data Science and Big Data Analytics: Applied to Photovoltaics,” The Electrochemical Society Interface, vol. 28, no. 1, pp. 57–61, Jan. 2019, doi: 10.1149/2.F07191if.
  27. A. Mostafaei, P. Rodriguez De Vecchis, I. Nettleship, and M. Chmielus, “Effect of powder size distribution on densification and microstructural evolution of binder-jet 3d-printed alloy 625,” Materials & Design, vol. 162, pp. 375–383, Jan. 2019, doi: 1016/j.matdes.2018.11.051.
  28. A. Verma, J. A. Hawk, L. S. Bruckman, R. H. French, V. Romanov, and J. L. W. Carter, “Mapping Multivariate Influence of Alloying Elements on Creep Behavior for Design of New Martensitic Steels,” Metallurgical and Materials Transactions A, Apr. 2019, doi: 10.1007/s11661-019-05234-9.
  29. X. Jia, J. Willard, A. Karpatne, J. Read, J. Zwart, M. Steinbach, and V. Kumar. “Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles.” ArXiv:1810.13075 [Physics], January 30, 2019. doi: http://arxiv.org/abs/1810.13075.
  30. Dee, N.T., Bedewy, M., Rao, A., Beroz, J., Lee, B., Meshot, E.R., Chazot, C.A.C., Kidambi, P.R., Zhao, H., Serbowicz, T., Teichert, K., Purohit, P.K., Hart, A.J., 2019. In Situ Mechanochemical Modulation of Carbon Nanotube Forest Growth. vol. 31, pp. 407–418, Jan. 2019, doi: 1021/acs.chemmater.8b03627.
  31. Zhao, and D. W. Rosen. “An Implementation of Real-Time Feedback Control of Cured Part Height in Exposure Controlled Projection Lithography with in-Situ Interferometric Measurement Feedback.” Additive Manufacturing, vol. 23, pp. 253-263, Oct. 2018, doi: https://doi.org/10.1016/j.addma.2018.07.016.
  32. L. Wallace, D. J. Buysse, A. Germain, M. H. Hall, and S. Iyengar, “Variable Selection for Skewed Model-Based Clustering: Application to the Identification of Novel Sleep Phenotypes,” Journal of the American Statistical Association, vol. 113, no. 521, pp. 95–110, Jan. 2018, doi: 10.1080/01621459.2017.1330202.
  33. Stevens, S. Schloder, E. Bono, D. Schmidt, and M. Chmielus, “Density variation in binder jetting 3d-printed and sintered Ti-6al-4v,” Additive Manufacturing, vol. 22, pp. 746–752, Aug. 2018, doi: 10.1016/j.addma.2018.06.017.
  34. Sajad Haghanifar, Ping Lu, Md Imrul Kayes, Susheng Tan, Ki-Joong Kim, Tongchuan Gao, Paul Ohodnicki, and Paul W. Leu, “Self-cleaning, high transmission, near unity haze OTS/silica nanostructured glass, Journal of Materials Chemistry C, 34, Aug. 2018, doi: 10.1039/C8TC02513D.
  35. Ren, Y. Kang, Y. Fan, and J. Lv, “Tuning-Free Heterogeneous Inference in Massive Networks,” Journal of the American Statistical Association, pp. 1–34, Dec. 2018, doi: 10.1080/01621459.2018.1537920.
  36. M. Pickering, M. A. Hossain, R. H. French, and A. R. Abramson, “Building electricity consumption: Data analytics of building operations with classical time series decomposition and case based subsetting,” Energy and Buildings, vol. 177, pp. 184–196, Oct. 2018, doi: 10.1016/j.enbuild.2018.07.056.
  37. Nishan Senanayake, Yoona Yang, Roger H. French, Anand Jagota, and Jennifer L.W. Carter, “An Automated Technique to Analyze Micro Indentation Load – Displacement Curve,” in Society of Experimental Mechanics, 2018.
  38. Cho, M. Abdulhafez, M. E. Lee, H. Jin, M. Bedewy, “Promoting Helix-Rich Structure in Silk Fibroin Films through Molecular Interactions with Carbon Nanotubes and Selective Heating for Transparent Biodegradable Devices.” ACS Applied Nano Materials, vol. 1, no. 10, pp. 5441–50, Oct. 2018, doi:10.1021/acsanm.8b00784.
  39. -B. Nguyen, D. Liu, M. I. Kayes, B. Wang, N. Rashin, P. W. Leu, and T. Tran, “Critical heat flux enhancement in pool boiling through increased rewetting on nanopillar array surfaces,” Scientific Reports, vol. 8, no. 1, p. 4815, Mar. 2018, doi: 10.1038/s41598-018-22693-z.
  40. A. Mostafaei, C. Hilla, E. L. Stevens, P. Nandwana, A. M. Elliott, and M. O. Chmielus, “Comparison of characterization methods for differently atomized nickel-based alloy 625 powders,” Powder Technology, vol. 333, Apr. 2018, doi: 10.1016/j.powtec.2018.04.014.
  41. AMostafaei, S. H. V. R. Neelapu, C. Kisailus, L. M. Nath, T. D. B. Jacobs, and M. Chmielus, “Characterizing surface finish and fatigue behavior in binder-jet 3d-printed nickel-based superalloy 625,” Additive Manufacturing, vol. 24, pp. 200–209, Dec. 2018, doi: 1016/j.addma.2018.09.012.
  42. Liu, A. J. Curran, J. S. Fada, X. Ma, W. Huang, C. B. Jones, E. Schnabel, M. Kohl, J. L. Braid, and R. H. French, “Cross-correlation Analysis of the Indoor Accelerated and Real World Exposed Photovoltaic Systems Across Multiple Climate Zones,” in 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC 34th EU PVSEC), Jun. 2018, pp. 3949–3954, doi: 10.1109/PVSC.2018.8547840.
  43. A. Klinke, A. Gok, S. I. Ifeanyi, and L. S. Bruckman, “A non-destructive method for crack quantification in photovoltaic backsheets under accelerated and real-world exposures,” Polymer Degradation and Stability, vol. 153, pp. 244–254, Jul. 2018, doi: 10.1016/j.polymdegradstab.2018.05.008.
  44. I. Kayes, A. J. Galante, N. A. Stella, S. Haghanifar, R. M. Q. Shanks, and P. W. Leu, “Stable lotus leaf-inspired hierarchical, fluorinated polypropylene surfaces for reduced bacterial adhesion,” Reactive and Functional Polymers, vol. 128, pp. 40–46, Jul. 2018, doi: 10.1016/j.reactfunctpolym.2018.04.013.
  45. Hooker and L. Mentch, “Bootstrap bias corrections for ensemble methods,” Statistics and Computing, vol. 28, no. 1, pp. 77–86, Jan. 2018, doi: 10.1007/s11222-016-9717-3.
  46. Haghanifar, R. T. R. D. Vecchis, K.-J. Kim, J. Wuenschell, S. P. Sharma, P. Lu, P. Ohodnicki, and P. W. Leu, “Flexible nanograss with highest combination of transparency and haze for optoelectronic plastic substrates,” Nanotechnology, vol. 29, no. 42, p. 42LT01, Aug. 2018, doi: 10.1088/1361-6528/aad671.
  47. A. Gordon, W.-H. Huang, D. M. Burns, R. H. French, and L. S. Bruckman, “Multivariate multiple regression models of poly(ethylene-terephthalate) film degradation under outdoor and multi-stressor accelerated weathering exposures,” PLOS ONE, vol. 13, no. 12, p. e0209016, Dec. 2018, doi: 10.1371/journal.pone.0209016.
  48. Haghanifar, R. T. R. D. Vecchis, K.-J. Kim, J. Wuenschell, S. P. Sharma, P. Lu, P. Ohodnicki, and P. W. Leu, “Flexible nanograss with highest combination of transparency and haze for optoelectronic plastic substrates,” Nanotechnology, vol. 29, no. 42, p. 42LT01, Aug. 2018, doi: 10.1088/1361-6528/aad671.
  49. P. Gillen, A. Veremyev, O. A. Prokopyev, and E. L. Pasiliao, “Critical arcs detection in influence networks,” Networks, vol. 71, no. 4, pp. 412–431, 2018, doi: 10.1002/net.21761.
  50. Gao, S. Haghanifar, M. G. Lindsay, P. Lu, M. I. Kayes, B. D. Pafchek, Z. Zhou, P. R. Ohodnicki, and P. W. Leu, “Fundamental Performance Limits and Haze Evaluation of Metal Nanomesh Transparent Conductors,” Advanced Optical Materials, vol. 6, no. 9, p. 1700829, 2018, doi: 10.1002/adom.201700829.
  51. AFairbrother, M. Boyd, Y. Lyu, J. Avenet, P. Illich, Y. Wang, M. Kempe, B. Dougherty, L. Bruckman, and X. Gu, “Differential degradation patterns of photovoltaic backsheets at the array level,” Solar Energy, vol. 163, pp. 62–69, Mar. 2018, doi: 1016/j.solener.2018.01.072.
  52. Chen, C. Gao, and Z. Ren, “Robust covariance and scatter matrix estimation under Huber’s contamination model,” The Annals of Statistics, vol. 46, no. 5, pp. 1932–1960, Oct. 2018, doi: 10.1214/17-AOS1607.
  53. L. Braid, A. J. Curran, J. Sun, E. J. Schneller, J. S. Fada, J. Liu, M. Wang, A. J. Longacre, J. Dai, B. D. Huey, K. O. Davis, J. Jaubert, L. S. Bruckman, and R. H. French, “EL and I-V Correlation for Degradation of PERC vs. Al-BSF Commercial Modules in Accelerated Exposures,” in 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC), Jun. 2018, pp. 1261–1266, doi: 10.1109/PVSC.2018.8547420.
  54. AAtamtürk and A. Gómez, “Strong formulations for quadratic optimization with M-matrices and indicator variables,” Mathematical Programming, vol. 170, no. 1, pp. 141–176, Jul. 2018, doi: 1007/s10107-018-1301-5.
  55. Yang Hu, Venkat Yashwanth Gunapati, Pei Zhao, Devin Gordon, Nicholas R. Wheeler, Mohammad A. Hossain, Timothy J. Peshek, Laura S. Bruckman, Guo-Qiang Zhang, and Roger H. French, “A Nonrelational Data Warehouse for the Analysis of Field and Laboratory Data From Multiple Heterogeneous Photovoltaic Test Sites,” IEEE Journal of Photovoltaics, vol. 7, no. 1, pp. 230–236, Jan. 2017, doi: 1109/JPHOTOV.2016.2626919.
  56. A. Verma, R. H. French, and J. L. W. Carter, “Physics-Informed Network Models: a Data Science Approach to Metal Design,” Integrating Materials and Manufacturing Innovation, vol. 6, no. 4, pp. 279–287, Dec. 2017, doi: 10.1007/s40192-017-0104-5.
  57. Park, M. Bedewy, K. K. Berggren, A. E. Keating, “Modular Assembly of a Protein Nanotriangle Using Orthogonally Interacting Coiled Coils.” Scientific Reports, vol. 7, no. 1, pp. 1–10, Sept. 2017, doi: 10.1038/s41598-017-10918-6.
  58. Rahimian, M. Amin, and Ali Jadbabaie. “Bayesian Learning Without Recall.” IEEE Transactions on Signal and Information Processing over Networks, vol. 3, no. 3, Sept. 2017, pp. 592–606. IEEE Xplore, doi:10.1109/TSIPN.2016.2631943.
  59.  Carmon, Y., Duchi, J.C., Hinder, O., & Sidford, A., “Convex Until Proven Guilty: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions.” In International Conference on Machine Learning, pp. 654-663.
  60. Simsek and S. Iyengar, “On the Distribution of Photon Counts with Censoring in Two-Photon Laser Scanning Microscopy,” Journal of Mathematical Imaging and Vision, vol. 58, no. 1, pp. 47–56, May 2017, doi: 10.1007/s10851-016-0690-4.
  61. Zhao, and D. W. Rosen. “A Data Mining Approach in Real-Time Measurement for Polymer Additive Manufacturing Process with Exposure Controlled Projection Lithography.” Journal of Manufacturing Systems, High Performance Computing and Data Analytics for Cyber Manufacturing,  vol. 43,  pp. 271-286, Apr. 2017, doi: https://doi.org/10.1016/j.jmsy.2017.01.005.
  62. Sajad Haghanifar, Tongchuan Gao, Rafael T. Rodriguez De Vecchis, Bradley Pafchek, Tevis D. B. Jacobs, and Paul W. Leu, “Ultrahigh-transparency, ultrahigh-haze nanograss glass with fluid-induced switchable haze,” Optica, vol. 4, no. 12, pp. 1522–1525, 2017, doi: 1364/OPTICA.4.001522.
  63. AMostafaei, J. Toman, E. L. Stevens, E. T. Hughes, Y. L. Krimer, and M. Chmielus, “Microstructural evolution and mechanical properties of differently heat-treated binder jet printed samples from gas- and water-atomized alloy 625 powders,” Acta Materialia, vol. 124, pp. 280–289, Feb. 2017, doi: 1016/j.actamat.2016.11.021.
  64. Mentch and G. Hooker, “Formal Hypothesis Tests for Additive Structure in Random Forests,” Journal of Computational and Graphical Statistics, vol. 26, no. 3, pp. 589–597, Jul. 2017, doi: 10.1080/10618600.2016.1256817.
  65. Ma, Z. Ren, and G. C. Tseng, “Variable screening with multiple studies,” arXiv preprint arXiv:1710.03892, 2017.
  66. He, L. M. Maillart, and O. A. Prokopyev, “Optimal planning of unpunctual preventive maintenance,” IISE Transactions, vol. 49, no. 2, pp. 127–143, Feb. 2017, doi: 10.1080/0740817X.2016.1224959.
  67. AGok, D. K. Ngendahimana, C. L. Fagerholm, R. H. French, J. Sun, and L. S. Bruckman, “Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures,” PLOS ONE, vol. 12, no. 5, p. e0177614, May 2017, doi: 1371/journal.pone.0177614.
  68. S. Fada, A. J. Loach, A. J. Curran, J. L. Braid, S. Yang, T. J. Peshek, and R. H. French, “Correlation of I-V Curve Parameters with Module-Level Electroluminescent Image Data Over 3000 Hours Damp-Heat Exposure,” in 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC), Jun. 2017, pp. 2697–2701, doi: 10.1109/PVSC.2017.8366659.
  69. AAtamtürk and A. Gómez, “Simplex QP-based methods for minimizing a conic quadratic objective over polyhedra,” arXiv:1706.05795 [math], Jun. 2017, arXiv: 1706.05795.
  70. Preciado, Victor M., and M. Amin Rahimian. “Moment-Based Spectral Analysis of Random Graphs with Given Expected Degrees.” ArXiv:1512.03489 [Physics, Stat], Mar. 2017. arXiv.org, http://arxiv.org/abs/1512.03489.
  71. Zhao, and D. W. Rosen, “Real-Time Interferometric Monitoring and Measuring of Photopolymerization Based Stereolithographic Additive Manufacturing Process: Sensor Model and Algorithim,” Measurement Science and Technology, vol. 28, no. 1, pp. 015001/1-015001/17, Nov. 2016, doi: 10.1088/0957-0233/28/1/015001.
  72. J. Peshek, J. S. Fada, Y. Hu, Y. Xu, M. A. Elsaeiti, E. Schnabel, M. Köhl, and R. H. French, “Insights into metastability of photovoltaic materials at the mesoscale through massive I–V analytics,” Journal of Vacuum Science & Technology B, vol. 34, no. 5, p. 050801, Sep. 2016, doi: 10.1116/1.4960628.
  73. Mentch and G. Hooker, “Quantifying uncertainty in random forests via confidence intervals and hypothesis tests,” The Journal of Machine Learning Research, vol. 17, no. 1, pp. 841–881, 2016, arXiv: 1404.6473.
  74. Mehrmohamadi, L. K. Mentch, A. G. Clark, and J. W. Locasale, “Integrative modelling of tumour DNA methylation quantifies the contribution of metabolism,” Nature Communications, vol. 7, p. 13666, Dec. 2016, doi: 10.1038/ncomms13666.
  75. Balakrishnan, M. Bedewy, E. R. Meshot, S. W. Pattinson, E. S. Polsen, F. Laye, D. N. Zakharov, E. A. Stach, J. A. Hart, “Real-Time Imaging of Self-Organization and Mechanical Competition in Carbon Nanotube Forest Growth.” ACS Nano, vol. 10, no. 12, Dec. 2016, pp. 11496–504. ACS Publications, doi:1021/acsnano.6b07251.
  76. “Distributed Estimation and Learning over Heterogeneous Networks.” ArXiv:1611.03328 [Cs, Math, Stat], Nov. 2016. arXiv.org, http://arxiv.org/abs/1611.03328.
  77. Laura S. Bruckman, Roger H. French, Yu Wang, Michael D. Kempe, Amy A. Lefebvre, Xiaohong Gu, Liang Ji, Kai-Tak Wan, and Christopher Flueckiger, “Cross-correlation of backsheet degradation between real-world exposed modules and accelerated exposures of backsheet materials (Conference Presentation), SPIE, 2016, doi: 10.1117/12.2238162.
  78. Karademir, O. A. Prokopyev, and R. J. Mailloux, “Irregular polyomino tiling via integer programming with application in phased array antenna design,” Journal of Global Optimization, vol. 65, no. 2, pp. 137–173, Jun. 2016, doi: 10.1007/s10898-015-0354-8.
  79. Justin S. Fada, Nicholas R. Wheeler, Davis Zabiyaka, Nikhil Goel, Timothy J. Peshek, and Roger H. French, “Democratizing an electroluminescence imaging apparatus and analytics project for widespread data acquisition in photovoltaic materials,” Review of Scientific Instruments, vol. 87, no. 8, p. 085109, Aug. 2016, doi: 1063/1.4960180.
  80. Hooker and L. Mentch, “Comments on: A random forest guided tour,” TEST, vol. 25, no. 2, pp. 254–260, Jun. 2016, doi: 10.1007/s11749-016-0485-3.
  81. Chen, C. Gao, and Z. Ren, “A general decision theory for Huber’s ϵ-contamination model,” Electronic Journal of Statistics, vol. 10, no. 2, pp. 3752–3774, 2016, doi: 10.1214/16-EJS1216.
  82. Bedewy, B. Vishwanath, E. R. Meshot, D. N. Zakharov, E. A. Stach, J. A. Hart, “Measurement of the Dewetting, Nucleation, and Deactivation Kinetics of Carbon Nanotube Population Growth by Environmental Transmission Electron Microscopy.” Chemistry of Materials, vol. 28, no. 11,  pp. 3804–13. ACS Publications, Jun. 2016, doi:10.1021/acs.chemmater.6b00798.
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