Faculty & Staff Research
http://hdl.handle.net/1911/75172
2017-11-19T16:24:00ZAtmospheric species concentrations and reaction rates relevant to N2 chemistry derived from WACCM-X model used in Yeung et al., Extreme enrichment in atmospheric 15N15N. Sci. Adv. 3, eaao6741 (2017)
http://hdl.handle.net/1911/98842
Atmospheric species concentrations and reaction rates relevant to N2 chemistry derived from WACCM-X model used in Yeung et al., Extreme enrichment in atmospheric 15N15N. Sci. Adv. 3, eaao6741 (2017)
Data description: This dataset contains species concentrations and reaction rates relevant to N2 chemistry derived from the Whole Atmosphere Community Climate Model with thermospere and ionosphere extension (WACCM-X), data set f.e10.FWX.f19_f19.control.001, for model year 2001. Grid spacing and pressure levels are standard quantities used for the National Center for Atmospheric Research Community Earth System Models. Assumptions used when calculating reaction rates are detailed in the related paper (Yeung et al. 2017). Dataset format is netCDF.
Experimental and modeling data used in Yeung et al., Extreme enrichment in atmospheric 15N15N. Sci. Adv. 3, eaao6741 (2017)
http://hdl.handle.net/1911/98841
Experimental and modeling data used in Yeung et al., Extreme enrichment in atmospheric 15N15N. Sci. Adv. 3, eaao6741 (2017)
Data description: This dataset contains two types of data. The first is a set of isotopic data (d15N, d29, d30, and D30 values) collected on an ultrahigh resolution mass spectrometer. The second is a set of model parameters and results. Both were used to support the findings reported in L. Y. Yeung, S. Li, I. E. Kohl, J. A. Haslun, N. E. Ostrom, H. Hu, T. P. Fischer, E. A. Schauble, E. D. Young, Extreme enrichment in atmospheric 15N15N. Sci. Adv. 3, eaao6741 (2017). doi: 10.1126/sciadv.aao6741.
An Anatomy of U.S. Personal Bankruptcy under Chapter 13
http://hdl.handle.net/1911/98840
An Anatomy of U.S. Personal Bankruptcy under Chapter 13
Eraslan, Hülya; Koşar, Gizem; Li, Wenli; Sarte, Pierre-Daniel
We build a structural model that captures salient features of personal bankruptcy under Chapter 13. We estimate our model using a novel data set that we construct from bankruptcies filed in Delaware between 2001 and 2002. Our estimation results highlight the importance of a debtor's choice of repayment plan length on other Chapter 13 outcomes. We use the estimated model to conduct policy experiments to evaluate the impact of more stringent laws that impose restrictions on the length of repayment plans. We find that these provisions would not materially affect creditor recovery rates and would not necessarily make discharge more likely.
2017-01-01T00:00:00ZA Distributed-Memory Randomized Structured Multifrontal Method for Sparse Direct Solutions
http://hdl.handle.net/1911/98834
A Distributed-Memory Randomized Structured Multifrontal Method for Sparse Direct Solutions
Xin, Zixing; Xia, Jianlin; de Hoop, Maarten V.; Cauley, Stephen; Balakrishnan, Venkataramanan
We design a distributed-memory randomized structured multifrontal solver for large sparse matrices. Two layers of hierarchical tree parallelism are used. A sequence of innovative parallel methods are developed for randomized structured frontal matrix operations, structured update matrix computation, skinny extend-add operation, selected entry extraction from structured matrices, etc. Several strategies are proposed to reuse computations and reduce communications. Unlike an earlier parallel structured multifrontal method that still involves large dense intermediate matrices, our parallel solver performs the major operations in terms of skinny matrices and fully structured forms. It thus significantly enhances the efficiency and scalability. Systematic communication cost analysis shows that the numbers of words are reduced by factors of about $O(\sqrt{n}/r)$ in two dimensions and about $O(n^{2/3}/r)$ in three dimensions, where $n$ is the matrix size and $r$ is an off-diagonal numerical rank bound of the intermediate frontal matrices. The efficiency and parallel performance are demonstrated with the solution of some large discretized PDEs in two and three dimensions. Nice scalability and significant savings in the cost and memory can be observed from the weak and strong scaling tests, especially for some 3D problems discretized on unstructured meshes.
2017-01-01T00:00:00Z