DukeSpacehttps://localhost:443/dspace2019-11-23T01:07:32Z2019-11-23T01:07:32ZLibKey and Bento Discovery Usability TestPearson, KarlyCrichlow, Thomashttps://hdl.handle.net/10161/195192019-11-22T15:15:25Z2019-10-25T00:00:00ZLibKey and Bento Discovery Usability Test
Pearson, Karly; Crichlow, Thomas
Karly Pearson and Thomas Crichlow conducted a usability test using two mockups of the Bento-Style All Search results page on October 1, 2019. This test was conducted to gain insight into how patrons would respond to the addition of the LibKey Discovery tool into the Articles section of the Bento-Style All Search. The test was conducted in an A/B format and consisted of three general questions, three tasks, and a set of brief post task questions; each test took approximately 15 minutes to complete.
2019-10-25T00:00:00ZAn Alternative Theorization of Payments for Ecosystem Services from Mexico: Origins and InfluenceShapiro - Garza, ElizabethShapiro‐Garza, Elizabethhttps://hdl.handle.net/10161/195182019-11-22T14:37:15ZAn Alternative Theorization of Payments for Ecosystem Services from Mexico: Origins and Influence
Shapiro - Garza, Elizabeth; Shapiro‐Garza, Elizabeth
A Merge-Split Proposal for Reversible Monte Carlo Markov Chain Sampling
of Redistricting PlansMattingly, JonathanCarter, DanielHerschlag, GregoryHunter, Zachhttps://hdl.handle.net/10161/195172019-11-22T13:30:25ZA Merge-Split Proposal for Reversible Monte Carlo Markov Chain Sampling
of Redistricting Plans
Mattingly, Jonathan; Carter, Daniel; Herschlag, Gregory; Hunter, Zach
We describe a Markov chain on redistricting plans that makes relatively
global moves. The chain is designed to be usable as the proposal in a Markov
Chain Monte Carlo (MCMC) algorithm. Sampling the space of plans amounts to
dividing a graph into a partition with a specified number elements which each
correspond to a different district. The partitions satisfy a collection of hard
constraints and the measure may be weighted with regard to a number of other
criteria. When these constraints and criteria are chosen to align well with
classical legal redistricting criteria, the algorithm can be used to generate a
collection of non-partisan, neutral plans. This collection of plans can serve
as a baseline against which a particular plan of interest is compared. If a
given plan has different racial or partisan qualities than what is typical of
the collection plans, the given plan may have been gerrymandered and is labeled
as an outlier.
Singular vector distribution of sample covariance matricesDing, Xiucaihttps://hdl.handle.net/10161/195162019-11-21T19:41:53Z2019-03-01T00:00:00ZSingular vector distribution of sample covariance matrices
Ding, Xiucai
<jats:title>Abstract</jats:title><jats:p>We consider a class of sample covariance matrices of the form <jats:italic>Q</jats:italic> = <jats:italic>TXX</jats:italic>*<jats:italic>T</jats:italic>*, where <jats:italic>X</jats:italic> = (<jats:italic>x</jats:italic><jats:sub><jats:italic>ij</jats:italic></jats:sub>) is an <jats:italic>M</jats:italic>×<jats:italic>N</jats:italic> rectangular matrix consisting of independent and identically distributed entries, and <jats:italic>T</jats:italic> is a deterministic matrix such that <jats:italic>T</jats:italic>*<jats:italic>T</jats:italic> is diagonal. Assuming that <jats:italic>M</jats:italic> is comparable to <jats:italic>N</jats:italic>, we prove that the distribution of the components of the right singular vectors close to the edge singular values agrees with that of Gaussian ensembles provided the first two moments of <jats:italic>x</jats:italic><jats:sub><jats:italic>ij</jats:italic></jats:sub> coincide with the Gaussian random variables. For the right singular vectors associated with the bulk singular values, the same conclusion holds if the first four moments of <jats:italic>x</jats:italic><jats:sub><jats:italic>ij</jats:italic></jats:sub> match those of the Gaussian random variables. Similar results hold for the left singular vectors if we further assume that <jats:italic>T</jats:italic> is diagonal.</jats:p>
2019-03-01T00:00:00Z