Exploring the stigma of food stamps

This paper reports on theoretical research into the effect of stigma and social norms on policy outcomes of the Food Stamp program, in particular the effect on the caseload. As a general rule, it is impossible to predict whether norms will amplify or dampen the response of caseloads to any given policy intervention. Sometimes they have an effect, sometimes they do not. Much depends on whether the norms themselves change very much in response to policy changes. Social feedback (each norm violation encourages more violations) makes policy predictions uncertain. It can translate very small shocks into very large changes in the caseload. Norm systems can collapse abruptly. Norms can alleviate administrative problems involving targeting, since norms can define "true need" in a social sense and allow all of the truly needy to claim benefits. Eligible nonparticipants are viewed as "not needy" in the social sense, though they may be needy according to objective criteria. Norms may also lessen a program's incentive effects (against work, for example). Norms may exacerbate administrative problems involving resource availability. To the extent that program eligibility differs from socially defined need, the program will be unpopular. Norms also add considerable uncertainty to the environment of policy planning and execution. Policymakers who hope to reduce the influence of stigma on program resources and administration should consider localizing program eligibility rules, so that the rules correspond more closely to social definitions of need. Intense, broad-based local outreach efforts may also reduce stigma's power.

Detecting Bimodality in Astronomical Datasets

We discuss statistical techniques for detecting and quantifying bimodality in astronomical datasets. We concentrate on the KMM algorithm, which estimates the statistical significance of bimodality in such datasets and objectively partitions data into sub-populations. By simulating bimodal distributions with a range of properties we investigate the sensitivity of KMM to datasets with varying characteristics. Our results facilitate the planning of optimal observing strategies for systems where bimodality is suspected. Mixture-modeling algorithms similar to the KMM algorithm have been used in previous studies to partition the stellar population of the Milky Way into subsystems. We illustrate the broad applicability of KMM by analysing published data on globular cluster metallicity distributions, velocity distributions of galaxies in clusters, and burst durations of gamma-ray sources. PostScript versions of the tables and figures, as well as FORTRAN code for KMM and instructions for its use, are available by anonymous ftp from kula.phsx.ukans.edu.Comment: 32 page

Fibrational induction rules for initial algebras

This paper provides an induction rule that can be used to prove properties of data structures whose types are inductive, i.e., are carriers of initial algebras of functors. Our results are semantic in nature and are inspired by Hermida and Jacobs’ elegant algebraic formulation of induction for polynomial data types. Our contribution is to derive, under slightly different assumptions, an induction rule that is generic over all inductive types, polynomial or not. Our induction rule is generic over the kinds of properties to be proved as well: like Hermida and Jacobs, we work in a general fibrational setting and so can accommodate very general notions of properties on inductive types rather than just those of particular syntactic forms. We establish the correctness of our generic induction rule by reducing induction to iteration. We show how our rule can be instantiated to give induction rules for the data types of rose trees, finite hereditary sets, and hyperfunctions. The former lies outside the scope of Hermida and Jacobs’ work because it is not polynomial; as far as we are aware, no induction rules have been known to exist for the latter two in a general fibrational framework. Our instantiation for hyperfunctions underscores the value of working in the general fibrational setting since this data type cannot be interpreted as a set

Shell Model of Two-dimensional Turbulence in Polymer Solutions

We address the effect of polymer additives on two dimensional turbulence, an issue that was studied recently in experiments and direct numerical simulations. We show that the same simple shell model that reproduced drag reduction in three-dimensional turbulence reproduces all the reported effects in the two-dimensional case. The simplicity of the model offers a straightforward understanding of the all the major effects under consideration

Consistent particle-based algorithm with a non-ideal equation of state

A thermodynamically consistent particle-based model for fluid dynamics with continuous velocities and a non-ideal equation of state is presented. Excluded volume interactions are modeled by means of biased stochastic multiparticle collisions which depend on the local velocities and densities. Momentum and energy are exactly conserved locally. The equation of state is derived and compared to independent measurements of the pressure. Results for the kinematic shear viscosity and self-diffusion constants are presented. A caging and order/disorder transition is observed at high densities and large collision frequency.Comment: 7 pages including 4 figure

Polymeric filament thinning and breakup in microchannels

The effects of elasticity on filament thinning and breakup are investigated in microchannel cross flow. When a viscous solution is stretched by an external immiscible fluid, a low 100 ppm polymer concentration strongly affects the breakup process, compared to the Newtonian case. Qualitatively, polymeric filaments show much slower evolution, and their morphology features multiple connected drops. Measurements of filament thickness show two main temporal regimes: flow- and capillary-driven. At early times both polymeric and Newtonian fluids are flow-driven, and filament thinning is exponential. At later times, Newtonian filament thinning crosses over to a capillary-driven regime, in which the decay is algebraic. By contrast, the polymeric fluid first crosses over to a second type of flow-driven behavior, in which viscoelastic stresses inside the filament become important and the decay is again exponential. Finally, the polymeric filament becomes capillary-driven at late times with algebraic decay. We show that the exponential flow thinning behavior allows a novel measurement of the extensional viscosities of both Newtonian and polymeric fluids.Comment: 7 pages, 7 figure

XMM-Newton and INTEGRAL analysis of the Supergiant Fast X-ray Transient IGR J17354-3255

We present the results of combined INTEGRAL and XMM-Newton observations of the supergiant fast X-ray transient (SFXT) IGR J17354$-$3255. Three XMM-Newton observations of lengths 33.4 ks, 32.5 ks and 21.9 ks were undertaken, the first an initial pointing to identify the correct source in the field of view and the latter two performed around periastron. Simultaneous INTEGRAL observations across $\sim66\%$ of the orbital cycle were analysed but the source was neither detected by IBIS/ISGRI nor by JEM-X. The XMM-Newton light curves display a range of moderately bright X-ray activity but there are no particularly strong flares or outbursts in any of the three observations. We show that the spectral shape measured by XMM-Newton can be fitted by a consistent model throughout the observation, suggesting that the observed flux variations are driven by obscuration from a wind of varying density rather than changes in accretion mode. The simultaneous INTEGRAL data rule out simple extrapolation of the simple powerlaw model beyond the XMM-Newton energy range.Comment: 13 pages, 9 figures, This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society Published by Oxford University Pres

Cosmology with velocity dispersion counts: an alternative to measuring cluster halo masses

The evolution of galaxy cluster counts is a powerful probe of several fundamental cosmological parameters. A number of recent studies using this probe have claimed tension with the cosmology preferred by the analysis of the Planck primary CMB data, in the sense that there are fewer clusters observed than predicted based on the primary CMB cosmology. One possible resolution to this problem is systematic errors in the absolute halo mass calibration in cluster studies, which is required to convert the standard theoretical prediction (the halo mass function) into counts as a function of the observable (e.g., X-ray luminosity, Sunyaev-Zel'dovich flux, optical richness). Here we propose an alternative strategy, which is to directly compare predicted and observed cluster counts as a function of the one-dimensional velocity dispersion of the cluster galaxies. We argue that the velocity dispersion of groups/clusters can be theoretically predicted as robustly as mass but, unlike mass, it can also be directly observed, thus circumventing the main systematic bias in traditional cluster counts studies. With the aid of the BAHAMAS suite of cosmological hydrodynamical simulations, we demonstrate the potential of the velocity dispersion counts for discriminating even similar $\Lambda$CDM models. These predictions can be compared with the results from existing redshift surveys such as the highly-complete Galaxy And Mass Assembly (GAMA) survey, and upcoming wide-field spectroscopic surveys such as the Wide Area Vista Extragalactic Survey (WAVES) and the Dark Energy Survey Instrument (DESI).Comment: 15 pages, 13 figures. Accepted for publication in MNRAS. New section on cosmological forecasts adde