Typesafety for Explicitly-Coded Probabilistic Inference Procedures

Researchers have recently proposed several systems that ease the process of developing Bayesian probabilistic inference algorithms. These include systems for automatic inference algorithm synthesis as well as stronger abstractions for manual algorithm development. However, existing systems whose performance relies on the developer manually constructing a part of the inference algorithm have limited support for reasoning about the correctness of the resulting algorithm. In this paper, we present Shuffle, a programming language for developing manual inference algorithms that enforces 1) the basic rules of probability theory and 2) statistical dependencies of the algorithm's corresponding probabilistic model. We have used Shuffle to develop inference algorithms for several standard probabilistic models. Our results demonstrate that Shuffle enables a developer to deliver performant implementations of these algorithms with the added benefit of Shuffle's correctness guarantees

Programming and Reasoning with Partial Observability

Computer programs are increasingly being deployed in partially-observable environments. A partially observable environment is an environment whose state is not completely visible to the program, but from which the program receives partial observations. Developers typically deal with partial observability by writing a state estimator that, given observations, attempts to deduce the hidden state of the environment. In safety-critical domains, to formally verify safety properties developers may write an environment model. The model captures the relationship between observations and hidden states and is used to prove the software correct. In this paper, we present a new methodology for writing and verifying programs in partially observable environments. We present belief programming, a programming methodology where developers write an environment model that the program runtime automatically uses to perform state estimation. A belief program dynamically updates and queries a belief state that captures the possible states the environment could be in. To enable verification, we present Epistemic Hoare Logic that reasons about the possible belief states of a belief program the same way that classical Hoare logic reasons about the possible states of a program. We develop these concepts by defining a semantics and a program logic for a simple core language called BLIMP. In a case study, we show how belief programming could be used to write and verify a controller for the Mars Polar Lander in BLIMP. We present an implementation of BLIMP called CBLIMP and evaluate it to determine the feasibility of belief programming

INDICATORS AND TARGETS FOR SOCIAL INCLUSION IN THE EUROPEAN UNION. ESRI WP151(?). 2003

Until recently, policy co-ordination at European Union (EU) level has mostly been applied to economic policy, with multilateral surveillance provided for in the Maastricht Treaty, and to employment, where the European Council agrees employment guidelines for the Member States and progress is monitored through regular reviews of National Action Plans. In December 2001, the European Council held at Laeken in Belgium adopted a set of commonly agreed and defined indicators, which should play a central role in monitoring the performance of the Member States in promoting social inclusion. These indicators are intended to allow the Member States and the European Commission to monitor national and EU progress towards the four key EU objectives in the area of social inclusion set by the Nice European Council in December 20002, and to support mutual learning and exchange of good practices in terms of policies. They can also prove useful for illustrating areas where more policy action is needed. The detailed content of those common objectives and the implementation arrangements endorsed at the European Council of Nice have been confirmed by the Council of Ministers for Employment and Social Affairs at their December 2002 meeting with a few amendments emphasising the importance of setting targets (following on the decision of the Barcelona European Council, as discussed below), of the need to strengthen the gender perspective in National Action Plans on social inclusion, and of the risks of poverty and social exclusion faced by immigrant

Simplifying Dependent Reductions in the Polyhedral Model

A Reduction -- an accumulation over a set of values, using an associative and commutative operator -- is a common computation in many numerical computations, including scientific computations, machine learning, computer vision, and financial analytics. Contemporary polyhedral-based compilation techniques make it possible to optimize reductions, such as prefix sums, in which each component of the reduction's output potentially shares computation with another component in the reduction. Therefore an optimizing compiler can identify the computation shared between multiple components and generate code that computes the shared computation only once. These techniques, however, do not support reductions that -- when phrased in the language of the polyhedral model -- span multiple dependent statements. In such cases, existing approaches can generate incorrect code that violates the data dependences of the original, unoptimized program. In this work, we identify and formalize the optimization of dependent reductions as an integer bilinear program. We present a heuristic optimization algorithm that uses an affine sequential schedule of the program to determine how to simplfy reductions yet still preserve the program's dependences. We demonstrate that the algorithm provides optimal complexity for a set of benchmark programs from the literature on probabilistic inference algorithms, whose performance critically relies on simplifying these reductions. The complexities for 10 of the 11 programs improve siginifcantly by factors at least of the sizes of the input data, which are in the range of $10^4$ to $10^6$ for typical real application inputs. We also confirm the significance of the improvement by showing speedups in wall-clock time that range from $1.1\text{x}$ to over $10^6\text{x}$

Monitoring the evolution of income poverty and real incomes over time

This paper brings together two approaches to the monitoring of household living standards: the macro-economic (national accounts) analysis of aggregates and the social indicators based on household microdata (European Union Statistics on Income and Living Conditions [EU-SILC]). Both are essential. The national accounts are necessary to provide an overall perspective; the distributional data in EU-SILC are necessary to measure income poverty. The progress, or lack of progress, in reducing income poverty has to be seen in relation to what is happening to the level of real incomes. We begin with the EU-SILC-based headline at-risk-of-poverty indicator, and then consider its relation to the level of household real income as presented in the national accounts. Moving step by step, we seek to identify the reasons for differences between EU-SILC and national accounts measures of real incomes. From this, we make a number of recommendations about possible improvements in the underlying data and in the construction of the social indicators. The substantive results help illuminate the differing experience of the pre-crisis period 2005 to 2008 and the subsequent three year period 2008 to 2011 (income reference years)

West Virginia Economic Outlook 2019-2023

West Virginia’s economy enjoyed its strongest year of growth in nearly a decade during 2017, emerging from several years of severe economic weakness. Most of the bounce back in the state’s economy is connected to the energy sector, not only from the increased production of coal and natural gas but also as a result of a massive build-out of new natural gas pipeline infrastructure throughout the state. Growth has broadened to include more of the state’s regions over the past year or so, but the overall magnitude of gains in jobs and output have remained concentrated in just a few areas as some portions of West Virginia continue to struggle with a range of weak economic fundamentals. Overall, this report provides a broad and detailed foundation to help you understand the long-run economic challenges and opportunities facing West Virginia

The gut microbiome of freshwater Unionidae mussels is determined by host species and is selectively retained from filtered seston

© 2019 Weingarten et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Freshwater mussels are a species-rich group of aquatic invertebrates that are among the most endangered groups of fauna worldwide. As filter-feeders that are constantly exposed to new microbial inoculants, mussels represent an ideal system to investigate the effects of species or the environment on gut microbiome composition. In this study, we examined if host species or site exerts a greater influence on microbiome composition. Individuals of four co-occurring freshwater mussel species, Cyclonaias asperata, Fusconaia cerina, Lampsilis ornata, and Obovaria unicolor were collected from six sites along a 50 km stretch of the Sipsey River in Alabama, USA. High throughput 16S rRNA gene sequencing revealed that mussel gut bacterial microbiota were distinct from bacteria on seston suspended in the water column, and that the composition of the gut microbiota was influenced by both host species and site. Despite species and environmental variation, the most frequently detected sequences within the mussel microbiota were identified as members of the Clostridiales. Sequences identified as the nitrogen-fixing taxon Methylocystis sp. were also abundant in all mussel species, and sequences of both bacterial taxa were more abundant in mussels than in water. Site physicochemical conditions explained almost 45% of variation in seston bacterial communities but less than 8% of variation in the mussel bacterial microbiome. Together, these findings suggest selective retention of bacterial taxa by the freshwater mussel host, and that both species and the environment are important in determining mussel gut microbiome composition

Understanding Why Some Whistleblowers are Venerated and Others Vilified

Whistleblowers are individuals who witness a moral infraction committed within their organization and report this infraction publicly to hold the group accountable. Whistleblowers often face ridicule, vilification, and exclusion both within their group and sometimes within broader society. Thus, whistleblowers put themselves at personal risk to adhere to their moral code and protect others; these criteria commonly classify someone as a hero. We argue diverse reactions to whistleblowers are influenced by numerous situational factors that influence perceptions of a whistleblower’s intentions as well as the expected consequences of their whistleblowing. Whether a whistleblower is viewed as a virtuous reformer (i.e., hero) or a harmful dissident may depend partly on the degree to which individuals believe that there is a discrepancy between an organization’s lived values and their stated values. While whistleblowers ostensibly provide evidence that this discrepancy exists, cognitive dissonance processes may forestall acceptance of this evidence in many cases. Believing that one is affiliated with a corrupt organization—while one also believes that they are a good, moral and adequate person—may lead to uncomfortable experiences of dissonance. It may be easier for many to reduce this dissonance by disparaging or discounting whistleblowers, rather than altering their own actions (which may involve becoming a whistleblower themselves) to reflect their personal values