Pneumothorax and Pneumomediastinum in a Sputum Positive Tuberculosis Patient: The Continuous Diaphragm Sign

Secondary pneumothorax is a very common medical emergency. At times it is associated with pneumomediastinum, which could be fatal at times if not identified. We present a case of a 11 years old sputum positive child who presented with both these conditions and was diagnosed on chest x ray

A contrario patch matching, with an application to keypoint matches validation

We describe a simple metric for image patches similarity, together with a robust criterion for unsupervised patch matching. The gradient orientations at corresponding positions in the two patches are compared and the normalized errors are accumulated. Based on the a contrario framework, the matching criterion validates a match between two patches when this cumulative error is too small to have occurred as the result of an accidental agreement. The method is illustrated in the validation of keypoint matches.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICIP.2015.735093

The Hardness of Embedding Grids and Walls

The dichotomy conjecture for the parameterized embedding problem states that the problem of deciding whether a given graph $G$ from some class $K$ of "pattern graphs" can be embedded into a given graph $H$ (that is, is isomorphic to a subgraph of $H$) is fixed-parameter tractable if $K$ is a class of graphs of bounded tree width and $W[1]$-complete otherwise. Towards this conjecture, we prove that the embedding problem is $W[1]$-complete if $K$ is the class of all grids or the class of all walls

Decadal Evolution of Ocean Thermal Anomalies in the North Atlantic: The Effects of Ekman, Overturning, and Horizontal Transport

Basin-scale thermal anomalies in the North Atlantic, extending to depths of 1–2 km, are more pronounced than the background warming over the last 60 years. A dynamical analysis based on reanalyses of historical data from 1965 to 2000 suggests that these thermal anomalies are formed by ocean heat convergences, augmented by the poorly known air–sea fluxes. The heat convergence is separated into contributions from the horizontal circulation and the meridional overturning circulation (MOC), the latter further separated into Ekman and MOC transport minus Ekman transport (MOC-Ekman) cells. The subtropical thermal anomalies are mainly controlled by wind-induced changes in the Ekman heat convergence, while the subpolar thermal anomalies are controlled by the MOC-Ekman heat convergence; the horizontal heat convergence is generally weaker, only becoming significant within the subpolar gyre. These thermal anomalies often have an opposing sign between the subtropical and subpolar gyres, associated with opposing changes in the meridional volume transport driving the Ekman and MOC-Ekman heat convergences. These changes in gyre-scale convergences in heat transport are probably induced by the winds, as they correlate with the zonal wind stress at gyre boundaries

Zero-Annotation Object Detection with Web Knowledge Transfer

Object detection is one of the major problems in computer vision, and has been extensively studied. Most of the existing detection works rely on labor-intensive supervision, such as ground truth bounding boxes of objects or at least image-level annotations. On the contrary, we propose an object detection method that does not require any form of human annotation on target tasks, by exploiting freely available web images. In order to facilitate effective knowledge transfer from web images, we introduce a multi-instance multi-label domain adaption learning framework with two key innovations. First of all, we propose an instance-level adversarial domain adaptation network with attention on foreground objects to transfer the object appearances from web domain to target domain. Second, to preserve the class-specific semantic structure of transferred object features, we propose a simultaneous transfer mechanism to transfer the supervision across domains through pseudo strong label generation. With our end-to-end framework that simultaneously learns a weakly supervised detector and transfers knowledge across domains, we achieved significant improvements over baseline methods on the benchmark datasets.Comment: Accepted in ECCV 201

Sensitivity of global warming to carbon emissions: effects of heat and carbon uptake in a suite of Earth system models

Climate projections reveal global-mean surface warming increasing nearly linearly with cumulative carbon emissions. The sensitivity of surface warming to carbon emissions is interpreted in terms of a product of three terms: the dependence of surface warming on radiative forcing, the fractional radiative forcing from CO2, and the dependence of radiative forcing from CO2 on carbon emissions. Mechanistically each term varies, respectively, with climate sensitivity and ocean heat uptake, radiative forcing contributions, and ocean and terrestrial carbon uptake. The sensitivity of surface warming to fossil-fuel carbon emissions is examined using an ensemble of Earth system models, forced either by an annual increase in atmospheric CO2 or by RCPs until year 2100. The sensitivity of surface warming to carbon emissions is controlled by a temporal decrease in the dependence of radiative forcing from CO2 on carbon emissions, which is partly offset by a temporal increase in the dependence of surface warming on radiative forcing. The decrease in the dependence of radiative forcing from CO2 is due to a decline in the ratio of the global ocean carbon undersaturation to carbon emissions, while the increase in the dependence of surface warming is due to a decline in the ratio of ocean heat uptake to radiative forcing. At the present time, there are large intermodel differences in the sensitivity in surface warming to carbon emissions, which are mainly due to uncertainties in the climate sensitivity and ocean heat uptake. These uncertainties undermine the ability to predict how much carbon may be emitted before reaching a warming target

Birnbaum-Saunders spatial modelling and diagnostics applied to agricultural engineering data

Applications of statistical models to describe spatial dependence in geo-referenced data are widespread across many disciplines including the environmental sciences. Most of these application assume that the data follow a Gaussian distributions. However, in many of them the normality assumption, and even a more general assumption of symmetry, are not appropriate. In non-spatial applications, where the data are uni-modal and positively skewed, the Birnbaum-Saunders distribution has excelled. This paper proposes a spatial log-linear model based in the Birnbaum-Saunders distribution. Model parameters are estimated using the maximum likelihood method. Local influence diagnostics are derived to assess the sensitivity of the estimators to perturbations in the response variable. As illustration, the proposed model and its diagnostics are used to analyse a real-world agricultural data-set, where the spatial variability of phosphorus concentration in the soil is considered- which is extremely important for agricultural management

Lovelock gravity from entropic force

In this paper, we first generalize the formulation of entropic gravity to (n+1)-dimensional spacetime. Then, we propose an entropic origin for Gauss-Bonnet gravity and more general Lovelock gravity in arbitrary dimensions. As a result, we are able to derive Newton's law of gravitation as well as the corresponding Friedmann equations in these gravity theories. This procedure naturally leads to a derivation of the higher dimensional gravitational coupling constant of Friedmann/Einstein equation which is in complete agreement with the results obtained by comparing the weak field limit of Einstein equation with Poisson equation in higher dimensions. Our study shows that the approach presented here is powerful enough to derive the gravitational field equations in any gravity theory. PACS: 04.20.Cv, 04.50.-h, 04.70.Dy.Comment: 10 pages, new versio

Birnbaum–Saunders autoregressive conditional duration models applied to high-frequency financial data

Modern financial markets now record the precise time of each stock trade, along with price and volume, with the aim of analysing the structure of the times between trading events—leading to a big data problem. In this paper, we propose and compare two Birnbaum–Saunders autoregressive conditional duration models specified in terms of time-varying conditional median and mean durations. These models provide a novel alternative to the existing autoregressive conditional duration models due to their flexibility and ease of estimation. Diagnostic tools are developed to allow goodness-of-fit assessment and to detect departures from assumptions, including the presence of outliers and influential cases. These diagnostic tools are based on the parameter estimates using residual analysis and the Cook distance for global influence, and different perturbation schemes for local influence. A thorough Monte Carlo study is presented to evaluate the performance of the maximum likelihood estimators, and the forecasting ability of the models is assessed using the traditional and density forecast evaluation techniques. The Monte Carlo study suggests that the parameter estimators are asymptotically unbiased, consistent and normally distributed. Finally, a full analysis of a real-world financial transaction data set, from the German DAX in 2016, is presented to illustrate the proposed approach and to compare the fitting and forecasting performances with existing models in the literature. One case related to the duration time is identified as potentially influential, but its removal does not change resulting inferences demonstrating the robustness of the proposed approach. Fitting and forecasting performances favor the proposed models and, in particular, the median-based approach gives additional protection against outliers, as expected

Dating the emergence of Influenza A (H5N1) Virus

Since the first detection of highly pathogenic avian influenza (H5N1) virus in geese in Guangdong, China, H5N1 viruses have transmitted to poultry throughout southern China. In late 2003 the first transmission wave spread the virus to multiple Southeast Asian countries. In May 2005, the second transmission wave of H5N1 virus westwards to Europe and Africa was initiated following a major outbreak in migratory birds at Qinghai Lake, China, while a third transmission wave has been initiated since mid-2005. Those viruses are now endemic in poultry populations in some affected regions and cause repeated outbreaks in poultry and increasing human infection cases, creating persistent pandemic concerns. Genetic data from systematic surveillance of H5N1 for the past seven years in marketing poultry, along with sequence data from outbreaks throughout the region, provide us with a unique opportunity to estimate the most recent common ancestor (MRCA) and postulate the dates of introduction of H5N1 variants into different affected countries. In this study, we estimated the time of emergence of those three transmission waves …postprin