Analyse de cartes de profondeur pour la navigation active dans un volume image

Nous proposons une méthode de navigation automatique d'un endoscope virtuel à l'intérieur d'un volume image. Cette navigation est basée sur la vision, la décision de déplacement repose sur une perception locale des configurations spatiales sans traitement préalable (segmentation, modélisation) du volume. En effet nous privilégions ici une analyse de l'image -obtenue par lancer de rayons- à partir de sa carte de profondeur. De ce traitement nous obtenons la détection automatique des branchements et une information structurelle sur la scène observée

Farmers markets and Food-Borne illness

We study the relationship between farmers markets and food-borne illness in the United States. Using a state-level panel data set for the period 2004-2011, we find a positive relationship between the number of farmers markets per capita on the one hand and, on the other hand, the number of reported (i) outbreaks of food-borne illness, (ii) cases of food-borne illness, (iii) outbreaks of Campylobacter jejuni, and (iv) cases of Campylobacter jejuni. Our estimates indicate that a 1% increase in the number of farmers markets is associated with a 0.7% (3.9%) increase in the total number of reported outbreaks of food-borne illness (Campylobacter jejuni), and a 3.9% (2.1%) increase in the total number of reported cases of food-borne illness (Campylobacter jejuni) in the average state-year. Our estimates also suggest that a doubling of the number of farmers markets in the average state-year would be associated with an economic cost of over $900,000 in additional cases of food-borne illness. When controlling simultaneously for both the number of farmers markets and the number of farmers markets that accept SNAP benefits (i.e., food stamps), we find that they are respectively associated positively and negatively with reported food-borne illness outbreaks and cases. Our results are robust to different specifications and estimators, and falsification and placebo tests indicate that they are unlikely to be spurious

Volumetric Segmentation of Pelvic Organs from MRI Acquisitions

In this work we present part of a Pelvis Dynamics Modeling System for pre-surgical assistance in the pelvic organ prolapse disease. In this condition, the most common affected organs are the uterus, the bladder and the rectum. The Magnetic Resonance Imaging (MRI) is the gold standard non-invasive imaging technique to evaluate this condition. The MRI’s acquisitions provide spatial information that is essential to build tri-dimensional (3D) models and run physical simulations that recreate the prolapse. In these acquisitions, the above mentioned organs, present blurred borders and different textures. Therefore, its extraction in not trivial at all. We pose an hybrid semi-automatic segmentation strategy which combines Region Growing (RG) and Active Surfaces in MRI scans to retrieve surface meshes of the organs of interest. We show some real cases, one applying the complete process in detail and the others, providing final results attained by the method which shows high quality segmentations achieved with a low computational cost.Sociedad Argentina de Informática e Investigación Operativ

Oral rehydration versus intravenous therapy for treating dehydration due to gastroenteritis in children: a meta-analysis of randomised controlled trials

BACKGROUND: Despite treatment recommendations from various organizations, oral rehydration therapy (ORT) continues to be underused, particularly by physicians in high-income countries. We conducted a systematic review of randomised controlled trials (RCTs) to compare ORT and intravenous therapy (IVT) for the treatment of dehydration secondary to acute gastroenteritis in children. METHODS: RCTs were identified through MEDLINE, EMBASE, CENTRAL, authors and references of included trials, pharmaceutical companies, and relevant organizations. Screening and inclusion were performed independently by two reviewers in order to identify randomised or quasi-randomised controlled trials comparing ORT and IVT in children with acute diarrhea and dehydration. Two reviewers independently assessed study quality using the Jadad scale and allocation concealment. Data were extracted by one reviewer and checked by a second. The primary outcome measure was failure of rehydration. We analyzed data using standard meta-analytic techniques. RESULTS: The quality of the 14 included trials ranged from 0 to 3 (Jadad score); allocation concealment was unclear in all but one study. Using a random effects model, there was no significant difference in treatment failures (risk difference [RD] 3%; 95% confidence intervals [CI]: 0, 6). The Mantel-Haenzsel fixed effects model gave a significant difference between treatment groups (RD 4%; 95% CI: 2, 5) favoring IVT. Based on the four studies that reported deaths, there were six in the IVT groups and two in ORT. There were no significant differences in total fluid intake at six and 24 hours, weight gain, duration of diarrhea, or hypo/hypernatremia. Length of stay was significantly shorter for the ORT group (weighted mean difference [WMD] -1.2 days; 95% CI: -2.4,-0.02). Phlebitis occurred significantly more often with IVT (number needed to treat [NNT] 33; 95% CI: 25,100); paralytic ileus occurred more often with ORT (NNT 33; 95% CI: 20,100). These results may not be generalizable to children with persistent vomiting. CONCLUSION: There were no clinically important differences between ORT and IVT in terms of efficacy and safety. For every 25 children (95% CI: 20, 50) treated with ORT, one would fail and require IVT. The results support existing practice guidelines recommending ORT as the first course of treatment in appropriate children with dehydration secondary to gastroenteritis

Monitoring and Pay: An Experiment on Employee Performance under Endogenous Supervision

We present an experimental test of a shirking model where monitoring intensity is endogenous and effort a continuous variable. Wage level, monitoring intensity and consequently the desired enforceable effort level are jointly determined by the maximization problem of the firm. As a result, monitoring and pay should be complements. In our experiment, between and within treatment variation is qualitatively in line with the normative predictions of the model under standard assumptions. Yet, we also find evidence for reciprocal behavior. Our data analysis shows, however, that it does not pay for the employer to solely rely on the reciprocity of employees

Deep Reinforcement Learning: An Overview

In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern recognition, speech recognition, computer vision, and natural language processing. Recent research has also been shown that deep learning techniques can be combined with reinforcement learning methods to learn useful representations for the problems with high dimensional raw data input. This chapter reviews the recent advances in deep reinforcement learning with a focus on the most used deep architectures such as autoencoders, convolutional neural networks and recurrent neural networks which have successfully been come together with the reinforcement learning framework.Comment: Proceedings of SAI Intelligent Systems Conference (IntelliSys) 201

A Guide for the Design of Benchmark Environments for Building Energy Optimization

The need for algorithms that optimize building energy consumption is usually motivated with the high energy consumption of buildings on a global scale. However, the current practice for evaluating the performance of such algorithms does not reflect this goal, as in most cases the performance is reported for one specific simulated building only, which provides no indication about the generalization of the score on other buildings. One approach to overcome this severe issue is to establish a shared collection of environments, each representing one simulated building setup, that would enable researchers to systematically compare and contrast the efficacy of their building optimization algorithms at scale. However, this requires that the individual environments are well designed for this goal. This paper is thus targeting the design of suitable environments for such a collection based on a detailed analysis of related publications that allows the identification of relevant characteristics for suitable environments. Based on this analysis a guide is developed that distills these characteristics into questions, intended to support a discussion of relevant topics during the design of such environments. Additional explanations and examples are provided for each question to make the guide more comprehensible. Finally, it is demonstrated how the guide can be applied, by utilizing it for the design of a novel environment, which represents an office building in tropical climate. This environment is released open source alongside this publication. We also indicate how test scenarios from existing publications could be enhanced to comply with the required characteristics according to our guide, underlining its importance for the future development and evaluation of building energy optimization algorithms, and thus for the sustainability of buildings in general