Do higher solvency ratios reduce the costs of bailing out insured banks?

The relationship between solvency constraints and bank behaviour in the presence of fixed rate deposit insurance is investigated. A rise in the minimum solvency ratio does not necessarily reduce the adverse consequences of moral hazard: bank efficiency may fall and expected bailout costs may rise. Such outcomes are possible even if credit risk is purely systemic. Similar results obtain in respect of level increases in bank capital, tangible or intangible, although in this case purely systemic risk excludes perverse outcomes

LAND AND ASSET SIZE, STRUCTURE AND DISTRIBUTION AND THE LINKS TO INCOME IN THREE DRYLANDS

At a late stage in preparing this work, we felt responding to the view of all partners that, before analyzing the consequences of asset situations for fertility, migration or environment, we should present, and to some extent explain, some facts about the size, composition, and distribution of assets for the three countries. This paper presents some general contextual evidence, and some main results for South Africa. Work is in progress in the India data, and will be undertaken later for Botswana.International Development,

CONSUMER PREFERENCES FOR LOCAL VERSUS OUT-OF-STATE GROWN SELECTED FRESH PRODUCE: THE CASE OF KNOXVILLE, TENNESSEE

Consumer behavior with respect to purchase regularity, satisfaction, origin, and willingness to pay for selected local versus non-Tennessee grown fresh produce is examined. Except for origin, consumer behavior with respect to the above is affected by income, of respondent, college education, and occupation. The pattern of significant variables changed by commodity. Tomatoes, followed by peaches, had the greatest local market potential. Local promotion of other products may be more difficult. Results suggested consumers have no strong preferences for or against locally grown fresh produce. The prices of locally grown commodities in Knoxville should be less than or equal to those of comparable quality non-Tennessee commodities.Consumer/Household Economics,

CONSUMERS' PERCEPTIONS OF LOCALLY GROWN PRODUCE AT RETAIL OUTLETS

Consumer/Household Economics,

Bowen-York Tensors

There is derived, for a conformally flat three-space, a family of linear second-order partial differential operators which send vectors into tracefree, symmetric two-tensors. These maps, which are parametrized by conformal Killing vectors on the three-space, are such that the divergence of the resulting tensor field depends only on the divergence of the original vector field. In particular these maps send source-free electric fields into TT-tensors. Moreover, if the original vector field is the Coulomb field on $\mathbb{R}^3\backslash \lbrace0\rbrace$, the resulting tensor fields on $\mathbb{R}^3\backslash \lbrace0\rbrace$ are nothing but the family of TT-tensors originally written down by Bowen and York.Comment: 12 pages, Contribution to CQG Special Issue "A Spacetime Safari: Essays in Honour of Vincent Moncrief

Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views

Learning object-centric representations of multi-object scenes is a promising approach towards machine intelligence, facilitating high-level reasoning and control from visual sensory data. However, current approaches for unsupervised object-centric scene representation are incapable of aggregating information from multiple observations of a scene. As a result, these "single-view" methods form their representations of a 3D scene based only on a single 2D observation (view). Naturally, this leads to several inaccuracies, with these methods falling victim to single-view spatial ambiguities. To address this, we propose The Multi-View and Multi-Object Network (MulMON) -- a method for learning accurate, object-centric representations of multi-object scenes by leveraging multiple views. In order to sidestep the main technical difficulty of the multi-object-multi-view scenario -- maintaining object correspondences across views -- MulMON iteratively updates the latent object representations for a scene over multiple views. To ensure that these iterative updates do indeed aggregate spatial information to form a complete 3D scene understanding, MulMON is asked to predict the appearance of the scene from novel viewpoints during training. Through experiments, we show that MulMON better-resolves spatial ambiguities than single-view methods -- learning more accurate and disentangled object representations -- and also achieves new functionality in predicting object segmentations for novel viewpoints.Comment: Accepted at NeurIPS 2020 (Spotlight

The Effect of a Break on Textbook Reading Comprehension

Abstract When, if ever, is it beneficial to take a break from intellectual work or study? Here begins an investigation into the broad, important, and scientifically untapped issue of the benefit of break taking by placing subjects into one of two experimental conditions: (a) those who took a 5-min break during the reading of a college level astronomy textbook chapter, or (b) those who did not take a break. The effect of the break was quantified by subjects’ score on two multiple-choice tests designed to measure comprehension of the material contained in the textbook chapter, and by the amount of time it took subjects in each condition to read the chapter. One multiple choice test was administered immediately after subjects read the chapter, and the other 48 hrs later. An equal number of questions on each test corresponded either to the section of the text read prior to the break, or to the section of the text read after the break. Reading time was also broken down by the same two sections of the text. There was no effect of the break on either reading comprehension or reading time. In other words, the break neither helped nor harmed subjects’ reading comprehension, and neither increased nor decreased subjects’ reading speed. Nevertheless, strong conclusions about the effectiveness or lack thereof of breaks from intellectual work cannot be drawn from this novel investigation. Instead, a future body of research should seek to build upon the groundwork that has been laid here

Safe Multi-objective Planning with a Posteriori Preferences

Autonomous planning in safety critical systems is a difficult task where decisions must carefully balance optimisation for performance goals of the system while also keeping the system away from safety hazards. These tasks often conflict, and hence present a challenging multi-objective planning problem where at least one of the objectives relates to safety risk. Recasting safety risk into an objective introduces additional requirements on planning algorithms: safety risk cannot be "averaged out" nor can it be combined with other objectives without loss of information and losing its intended purpose as a tool in risk reduction. Thus, existing algorithms for multi-objective planning cannot be used directly as they do not provide any facility to accurately track and update safety risk. A common work around is to restrict available decisions to those guaranteed safe a priori, but this can be overly conservative and hamper performance significantly. In this paper, we propose a planning algorithm based on multiobjective Monte-Carlo Tree Search to resolve these problems by recognising safety risk as a first class objective. Our algorithm explicitly models the safety of the system separately from the performance of the system, uses safety risk to both optimise and provide constraints for safety in the planning process, and uses an ALARP-based preference selection method to choose an appropriate safe plan from its output. The preference selection method chooses from the set of multiple safe plans to weigh risk against performance. We demonstrate the behaviour of the algorithm using an example representative of safety critical decision-making