Protecting Rural Amenities Through Farmland Preservation Programs

We investigate what farmland preservation programs reveal about the importance of protecting different rural amenities. An extensive content analysis of the enabling legislation of various farmland protection programs suggests wide variation exists in the protection of amenities. An analysis of 27 individual Purchase of Development Rights (PDR) programs' selection criteria suggests these programs favor preserving amenities that are jointly provided by cropland and livestock operations. These PDR selection criteria also reveal unique preferences regarding the spatial patterns of preserved agricultural lands. Variation in relative weights given to protecting most parcel characteristics in PDR programs is not easily explained by factors that characterize areas experiencing farmland losses.Land Economics/Use,

Do labor market networks have an important spatial dimension?

We test for evidence of spatial, residence-based labor market networks. Turnover is lower for workers more connected to their neighbors generally and more connected to neighbors of the same race or ethnic group. Both results are consistent with networks producing better job matches, while the latter could also reflect preferences for working with neighbors of the same race or ethnicity. For earnings, we find a robust positive effect of the overall residence-based network measure, whereas we usually find a negative effect of the same-group measure, suggesting that the overall network measure reflects productivity-enhancing positive network effects, while the same-group measure may capture a non-wage amenity

Social Capital Determinants and Labor Market Networks

We explore the links between determinants of social capital and labor market networks at the neighborhood level. We harness rich data taken from multiple sources, including matched employer-employee data with which we measure the strength of labor market networks, data on neighborhood homogeneity that has previously been tied to social capital, and new data – not previously used in the study of social capital – on the number and location of non-profit sector establishments at the neighborhood level. We use a machine learning algorithm to identify the potential determinants of social capital that best predict neighborhood-level variation in labor market networks. We find evidence suggesting that smaller and less centralized schools, and schools with fewer poor students, foster social capital that builds local labor market networks, as does a larger Republican vote share. The presence of establishments in a number of non-profit-oriented industries are identified as predictive of strong labor market networks, likely because they either provide public goods or facilitate social contacts. These industries include, for example, churches and other religious institutions, fire and rescue services including volunteer fire departments, country clubs and golf courses, labor unions, chamber music groups, hobby clubs, and schools

Proof of a conjecture of Polya on the zeros of successive derivatives of real entire functions

We prove Polya's conjecture of 1943: For a real entire function of order greater than 2, with finitely many non-real zeros, the number of non-real zeros of the n-th derivative tends to infinity with n. We use the saddle point method and potential theory, combined with the theory of analytic functions with positive imaginary part in the upper half-plane.Comment: 26 page

Quantifying Eventual Consistency with PBS

Data replication results in a fundamental trade-off between operation latency and consistency. At the weak end of the spectrum of possible consistency models is eventual consistency, which provides no limit to the staleness of data returned. However, anecdotally, eventual consistency is often “good enough ” for practitioners given its latency and availability benefits. In this work, we explain this phenomenon and demonstrate that, despite their weak guarantees, eventually consistent systems regularly return consistent data while providing lower latency than their strongly consistent counterparts. To quantify the behavior of eventually consistent stores, we introduce Probabilistically Bounded Staleness (PBS), a consistency model that provides expected bounds on data staleness with respect to both versions and wall clock time. We derive a closed-form solution for version-based staleness and model real-time staleness for a large class of quorum replicated, Dynamo-style stores. Using PBS, we measure the trade-off between latency and consistency for partial, non-overlapping quorum systems under Internet production workloads. We quantitatively demonstrate how and why eventually consistent systems frequently return consistent data within tens of milliseconds while offering large latency benefits. 1

Explicit kinetic heterogeneity: mechanistic models for interpretation of labeling data of heterogeneous cell populations

Estimation of division and death rates of lymphocytes in different conditions is vital for quantitative understanding of the immune system. Deuterium, in the form of deuterated glucose or heavy water, can be used to measure rates of proliferation and death of lymphocytes in vivo. Inferring these rates from labeling and delabeling curves has been subject to considerable debate with different groups suggesting different mathematical models for that purpose. We show that the three models that are most commonly used are in fact mathematically identical and differ only in their interpretation of the estimated parameters. By extending these previous models, we here propose a more mechanistic approach for the analysis of data from deuterium labeling experiments. We construct a model of "kinetic heterogeneity" in which the total cell population consists of many sub-populations with different rates of cell turnover. In this model, for a given distribution of the rates of turnover, the predicted fraction of labeled DNA accumulated and lost can be calculated. Our model reproduces several previously made experimental observations, such as a negative correlation between the length of the labeling period and the rate at which labeled DNA is lost after label cessation. We demonstrate the reliability of the new explicit kinetic heterogeneity model by applying it to artificially generated datasets, and illustrate its usefulness by fitting experimental data. In contrast to previous models, the explicit kinetic heterogeneity model 1) provides a mechanistic way of interpreting labeling data; 2) allows for a non-exponential loss of labeled cells during delabeling, and 3) can be used to describe data with variable labeling length