Freeport Union Free School District and Freeport Non-Teaching Unit (Custodial Unit), CSEA Local 1000, AFSCME, AFL-CIO

In the matter of the fact-finding between the Freeport Union Free School District, employer, and the Freeport Non-Teaching Unit (Custodial Unit), CSEA Local 1000, AFSCME, AFL-CIO, union. PERB case no. M2010-313. Before: Eugene S. Ginsberg, fact finder

Odious Debt, Odious Credit, Economic Development, and Democratization

When a country signs an international treaty, it is not the government but the state that is bound, and the obligation will stand until a subsequent government formally exits the treaty. Exit is presumed to be costly: a government that repudiates earlier treaty obligations will suffer reputational harm in its international relations. Moreover, this general background norm of international law applies as well to debt: a government can announce that it is renouncing debt, but it will suffer severe reputational harm in the debt marketplace, much as a government that repudiates public international law obligations suffers a reputational harm. Here, Ginsburg and Ulen talks about the odious debt and odious credit in relation to economic development and democratization

Capacity Bounded Grammars and Petri Nets

A capacity bounded grammar is a grammar whose derivations are restricted by assigning a bound to the number of every nonterminal symbol in the sentential forms. In the paper the generative power and closure properties of capacity bounded grammars and their Petri net controlled counterparts are investigated

Making Medical Homes Work: Moving From Concept to Practice

Explores practical considerations for implementing a medical home program of physician practices committed to coordinating and integrating care based on patient needs and priorities, such as how to qualify medical homes and how to match patients to them

Ultimate periodicity of b-recognisable sets : a quasilinear procedure

It is decidable if a set of numbers, whose representation in a base b is a regular language, is ultimately periodic. This was established by Honkala in 1986. We give here a structural description of minimal automata that accept an ultimately periodic set of numbers. We then show that it can verified in linear time if a given minimal automaton meets this description. This thus yields a O(n log(n)) procedure for deciding whether a general deterministic automaton accepts an ultimately periodic set of numbers.Comment: presented at DLT 201

Latent protein trees

Unbiased, label-free proteomics is becoming a powerful technique for measuring protein expression in almost any biological sample. The output of these measurements after preprocessing is a collection of features and their associated intensities for each sample. Subsets of features within the data are from the same peptide, subsets of peptides are from the same protein, and subsets of proteins are in the same biological pathways, therefore, there is the potential for very complex and informative correlational structure inherent in these data. Recent attempts to utilize this data often focus on the identification of single features that are associated with a particular phenotype that is relevant to the experiment. However, to date, there have been no published approaches that directly model what we know to be multiple different levels of correlation structure. Here we present a hierarchical Bayesian model which is specifically designed to model such correlation structure in unbiased, label-free proteomics. This model utilizes partial identification information from peptide sequencing and database lookup as well as the observed correlation in the data to appropriately compress features into latent proteins and to estimate their correlation structure. We demonstrate the effectiveness of the model using artificial/benchmark data and in the context of a series of proteomics measurements of blood plasma from a collection of volunteers who were infected with two different strains of viral influenza.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS639 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org