Principles of appropriate antibiotic use for treatment of uncomplicated acute bronchitis: background.

The following principles of appropriate antibiotic use for adults with acute bronchitis apply to immunocompetent adults without complicating comorbid conditions, such as chronic lung or heart disease. The evaluation of adults with an acute cough illness or a presumptive diagnosis of uncomplicated acute bronchitis should focus on ruling out serious illness, particularly pneumonia. In healthy, nonelderly adults, pneumonia is uncommon in the absence of vital sign abnormalities or asymmetrical lung sounds, and chest radiography is usually not indicated. In patients with cough lasting 3 weeks or longer, chest radiography may be warranted in the absence of other known causes. Routine antibiotic treatment of uncomplicated acute bronchitis is not recommended, regardless of duration of cough. If pertussis infection is suspected (an unusual circumstance), a diagnostic test should be performed and antimicrobial therapy initiated. Patient satisfaction with care for acute bronchitis depends most on physician--patient communication rather than on antibiotic treatment

Predictive Entropy Search for Bayesian optimization with unknown constraints

Unknown constraints arise in many types of expensive black-box optimization problems. Several methods have been proposed recently for performing Bayesian optimization with constraints, based on the expected improvement (EI) heuristic. However, EI can lead to pathologies when used with constraints. For example, in the case of decoupled constraints---i.e., when one can independently evaluate the objective or the constraints---EI can encounter a pathology that prevents exploration. Additionally, computing EI requires a current best solution, which may not exist if none of the data collected so far satisfy the constraints. By contrast, information-based approaches do not suffer from these failure modes. In this paper, we present a new information-based method called Predictive Entropy Search with Constraints (PESC). We analyze the performance of PESC and show that it compares favorably to EI-based approaches on synthetic and benchmark problems, as well as several real-world examples. We demonstrate that PESC is an effective algorithm that provides a promising direction towards a unified solution for constrained Bayesian optimization.José Miguel Hernández-Lobato acknowledges support from the Rafael del Pino Foundation. Zoubin Ghahramani acknowledges support from Google Focused Research Award and EPSRC grant EP/I036575/1. Matthew W. Hoffman acknowledges support from EPSRC grant EP/J012300/1.This is the final published version. It first appeared at http://jmlr.org/proceedings/papers/v37/hernandez-lobatob15.html

A General Framework for Constrained Bayesian Optimization using Information-based Search

We present an information-theoretic framework for solving global black-box optimization problems that also have black-box constraints. Of particular interest to us is to efficiently solve problems with $\textit{decoupled}$ constraints, in which subsets of the objective and constraint functions may be evaluated independently. For example, when the objective is evaluated on a CPU and the constraints are evaluated independently on a GPU. These problems require an acquisition function that can be separated into the contributions of the individual function evaluations. We develop one such acquisition function and call it Predictive Entropy Search with Constraints (PESC). PESC is an approximation to the expected information gain criterion and it compares favorably to alternative approaches based on improvement in several synthetic and real-world problems. In addition to this, we consider problems with a mix of functions that are fast and slow to evaluate. These problems require balancing the amount of time spent in the meta-computation of PESC and in the actual evaluation of the target objective. We take a bounded rationality approach and develop a partial update for PESC which trades off accuracy against speed. We then propose a method for adaptively switching between the partial and full updates for PESC. This allows us to interpolate between versions of PESC that are efficient in terms of function evaluations and those that are efficient in terms of wall-clock time. Overall, we demonstrate that PESC is an effective algorithm that provides a promising direction towards a unified solution for constrained Bayesian optimization.Rafael del Pino Foundation, Google Focused Research Award, Engineering and Physical Sciences Research Council (Grant IDs: EP/I036575/1, EP/J012300/1

Blueshifted galaxies in the Virgo Cluster

We examine a sample of 65 galaxies in the Virgo cluster with negative radial velocities relative to the Local Group. Some features of this sample are pointed out. All of these objects are positioned compactly within a virial zone of radius 6{\deg} in the cluster, but their centroid is displaced relative to the dynamic center of the cluster, M87, by 1.1{\deg} to the northwest. The dwarf galaxies in this sample are clumped on a scale of ~10' (50 kpc). The observed asymmetry in the distribution of the blueshifted galaxies may be caused by infall of a group of galaxies around M86 onto the main body of the cluster. We offer another attempt to explain this phenomenon, assuming a mutual tangential velocity of ~300 km/s between the Local Group and the Virgo cluster owing to their being repelled from the local cosmological void.Comment: 10 pages, 4 figures, 1 table. Published in Astrophysics, Vol. 53, No. 1, pp. 32-41, 201

MM Algorithms for Geometric and Signomial Programming

This paper derives new algorithms for signomial programming, a generalization of geometric programming. The algorithms are based on a generic principle for optimization called the MM algorithm. In this setting, one can apply the geometric-arithmetic mean inequality and a supporting hyperplane inequality to create a surrogate function with parameters separated. Thus, unconstrained signomial programming reduces to a sequence of one-dimensional minimization problems. Simple examples demonstrate that the MM algorithm derived can converge to a boundary point or to one point of a continuum of minimum points. Conditions under which the minimum point is unique or occurs in the interior of parameter space are proved for geometric programming. Convergence to an interior point occurs at a linear rate. Finally, the MM framework easily accommodates equality and inequality constraints of signomial type. For the most important special case, constrained quadratic programming, the MM algorithm involves very simple updates.Comment: 16 pages, 1 figur

The age of anxiety? It depends where you look: changes in STAI trait anxiety, 1970–2010

Purpose Population-level surveys suggest that anxiety has been increasing in several nations, including the USA and UK. We sought to verify the apparent anxiety increases by looking for systematic changes in mean anxiety questionnaire scores from research publications. Methods We analyzed all available mean State–Trait Anxiety Inventory scores published between 1970 and 2010. We collected 1703 samples, representing more than 205,000 participants from 57 nations. Results Results showed a significant anxiety increase worldwide, but the pattern was less clear in many individual nations. Our analyses suggest that any increase in anxiety in the USA and Canada may be limited to students, anxiety has decreased in the UK, and has remained stable in Australia. Conclusions Although anxiety may have increased worldwide, it might not be increasing as dramatically as previously thought, except in specific populations, such as North American students. Our results seem to contradict survey results from the USA and UK in particular. We do not claim that our results are more reliable than those of large population surveys. However, we do suggest that mental health surveys and other governmental sources of disorder prevalence data may be partially biased by changing attitudes toward mental health: if respondents are more aware and less ashamed of their anxiety, they are more likely to report it to survey takers. Analyses such as ours provide a useful means of double-checking apparent trends in large population surveys

Spin and charge order in the vortex lattice of the cuprates: experiment and theory

I summarize recent results, obtained with E. Demler, K. Park, A. Polkovnikov, M. Vojta, and Y. Zhang, on spin and charge correlations near a magnetic quantum phase transition in the cuprates. STM experiments on slightly overdoped BSCCO (J.E. Hoffman et al., Science 295, 466 (2002)) are consistent with the nucleation of static charge order coexisting with dynamic spin correlations around vortices, and neutron scattering experiments have measured the magnetic field dependence of static spin order in the underdoped regime in LSCO (B. Lake et al., Nature 415, 299 (2002)) and LaCuO_4+y (B. Khaykovich et al., Phys. Rev. B 66, 014528 (2002)). Our predictions provide a semi-quantitative description of these observations, with only a single parameter measuring distance from the quantum critical point changing with doping level. These results suggest that a common theory of competing spin, charge and superconducting orders provides a unified description of all the cuprates.Comment: 18 pages, 7 figures; Proceedings of the Mexican Meeting on Mathematical and Experimental Physics, Mexico City, September 2001, to be published by Kluwer Academic/Plenum Press; (v2) added clarifications and updated reference