ClassCut for Unsupervised Class Segmentation

Abstract. We propose a novel method for unsupervised class segmentation on a set of images. It alternates between segmenting object instances and learning a class model. The method is based on a segmentation energy defined over all images at the same time, which can be optimized efficiently by techniques used before in interactive segmentation. Over iterations, our method progressively learns a class model by integrating observations over all images. In addition to appearance, this model captures the location and shape of the class with respect to an automatically determined coordinate frame common across images. This frame allows us to build stronger shape and location models, similar to those used in object class detection. Our method is inspired by interactive segmentation methods [1], but it is fully automatic and learns models characteristic for the object class rather than specific to one particular object/image. We experimentally demonstrate on the Caltech4, Caltech101, and Weizmann horses datasets that our method (a) transfers class knowledge across images and this improves results compared to segmenting every image independently; (b) outperforms Grabcut [1] for the task of unsupervised segmentation; (c) offers competitive performance compared to the state-of-the-art in unsupervised segmentation and in particular it outperforms the topic model [2].

Introducing EMMIE: An evidence rating scale to encourage mixed-method crime prevention synthesis reviews

Objectives This short report describes the need for, and the development of, a coding system to distil the quality and coverage of systematic reviews of the evidence relating to crime prevention interventions. The starting point for the coding system concerns the evidence needs of policymakers and practitioners. Methods The coding scheme (EMMIE) proposed builds on previous scales that have been developed to assess the probity, coverage and utility of evidence both in health and criminal justice. It also draws on the principles of realist synthesis and review. Results The proposed EMMIE scale identifies five dimensions to which systematic reviews intended to inform crime prevention should speak. These are the Effect of intervention, the identification of the causal Mechanism(s) through which interventions are intended to work, the factors that Moderate their impact, the articulation of practical Implementation issues, and the Economic costs of intervention

The impact of asking intention or self-prediction questions on subsequent behavior: a meta-analysis

The current meta-analysis estimated the magnitude of the impact of asking intention and self-prediction questions on rates of subsequent behavior, and examined mediators and moderators of this question–behavior effect (QBE). Random-effects meta-analysis on 116 published tests of the effect indicated that intention/prediction questions have a small positive effect on behavior (d+ = 0.24). Little support was observed for attitude accessibility, cognitive dissonance, behavioral simulation, or processing fluency explanations of the QBE. Multivariate analyses indicated significant effects of social desirability of behavior/behavior domain (larger effects for more desirable and less risky behaviors), difficulty of behavior (larger effects for easy-to-perform behaviors), and sample type (larger effects among student samples). Although this review controls for co-occurrence of moderators in multivariate analyses, future primary research should systematically vary moderators in fully factorial designs. Further primary research is also needed to unravel the mechanisms underlying different variants of the QBE

Estimation and prediction of the vehicle's motion basedon visual odometry and Kalman filter

Proceeding of: 14th International Conference, ACIVS 2012, Brno, Czech Republic, September 4-7, 2012The movement of the vehicle is an useful information for different applications, such as driver assistant systems or autonomous vehicles. This information can be known by different methods, for instance, by using a GPS or by means of the visual odometry. However, there are some situations where both methods do not work correctly. For example, there are areas in urban environments where the signal of the GPS is not available, as tunnels or streets with high buildings. On the other hand, the algorithms of computer vision are affected by outdoor environments, and the main source of difficulties is the variation in the ligthing conditions. A method to estimate and predict the movement of the vehicle based on visual odometry and Kalman filter is explained in this paper. The Kalman filter allows both filtering and prediction of vehicle motion, using the results from the visual odometry estimation.This work was also supported by Spanish Government through the CICYT projects FEDORA (Grant TRA2010-20255-C03-01), Driver Distraction Detector System (Grant TRA2011-29454-C03-02) and by CAM through the projects SEGVAUTO-II.Publicad

Dynamic Oligopoly Pricing: Evidence from the Airline Industry

We explore how pricing dynamics in the European airline industry vary with the competitive environment. Our results highlight substantial variations in pricing dynamics that are consistent with a theory of intertemporal price discrimination. First, the rate at which prices increase towards the scheduled travel date is decreasing in competition, supporting the idea that competition restrains the ability of airlines to price-discriminate. Second, the sensitivity to competition is substantially increasing in the heterogeneity of the customer base, reflecting further that restraints on price discrimination are only relevant if there is initial scope for price discrimination. These patterns are quantitatively important, explaining about 83 percent of the total within-flight price dispersion, and explaining 17 percent of the observed cross-market variation of pricing dynamics

Long-term Impact of sewage sludge application on rhizobium leguminosarum biovar trifolii: an evaluation using meta-analysis

The Long-Term Sludge Experiment (LTSE) began in 1994 at nine UK field sites as part of continuing research into the effects of sludge-borne heavy metals on soil fertility. The long-term effects of Zn, Cu, and Cd on the most probable numbers of cells (MPN) of Rhizobium leguminosarum biovar trifolii were monitored for 8 yr in sludge-amended soils. To assess the statutory limits set by the UK Sludge (Use in Agriculture) Regulations, the experimental data were reviewed using statistical methods of meta-analysis. Previous LTSE studies have focused predominantly on statistical significance rather than effect size, whereas meta-analysis focuses on the magnitude and direction of an effect, i.e., the practical significance rather than its statistical significance. Results showed Zn to be the most toxic element causing an overall significant decrease in Rhizobium MPN of −26.6% during the LTSE. The effect of Cu showed no significant effect on Rhizobium MPN at concentrations below the UK limits, although a −5% decrease in Rhizobium MPN was observed in soils where total Cu ranged from 100 to <135 mg kg−1. Overall, there was nothing to indicate that Cd had a significant effect on Rhizobium MPN below the current UK statutory limit. In summary, the UK statutory limit for Zn appears to be insufficient for protecting Rhizobium from Zn toxicity effects

Exposure to benzene at work and the risk of leukemia: a systematic review and meta-analysis

Background A substantial number of epidemiologic studies have provided estimates of the relation between exposure to benzene at work and the risk of leukemia, but the results have been heterogeneous. To bridge this gap in knowledge, we synthesized the existing epidemiologic evidence on the relation between occupational exposure to benzene and the risk of leukemia, including all types combined and the four main subgroups acute myeloid leukemia (AML), acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), and chronic myeloid leukemia (CML). Methods A systematic literature review was carried out using two databases 'Medline' and 'Embase' from 1950 through to July 2009. We selected articles which provided information that can be used to estimate the relation between benzene exposure and cancer risk (effect size). Results In total 15 studies were identified in the search, providing 16 effect estimates for the main analysis. The summary effect size for any leukemia from the fixed-effects model was 1.40 (95% CI, 1.23-1.57), but the study-specific estimates were strongly heterogeneous (I2 = 56.5%, Q stat = 34.47, p = 0.003). The random-effects model yielded a summary- effect size estimate of 1.72 (95% CI, 1.37-2.17). Effect estimates from 9 studies were based on cumulative exposures. In these studies the risk of leukemia increased with a dose-response pattern with a summary-effect estimate of 1.64 (95% CI, 1.13-2.39) for low (< 40 ppm-years), 1.90 (95% CI, 1.26-2.89) for medium (40-99.9 ppm-years), and 2.62 (95% CI, 1.57-4.39) for high exposure category (> 100 ppm-years). In a meta-regression, the trend was statistically significant (P = 0.015). Use of cumulative exposure eliminated heterogeneity. The risk of AML also increased from low (1.94, 95% CI, 0.95-3.95), medium (2.32, 95% CI, 0.91-5.94) to high exposure category (3.20, 95% CI, 1.09-9.45), but the trend was not statistically significant. Conclusions Our study provides consistent evidence that exposure to benzene at work increases the risk of leukemia with a dose-response pattern. There was some evidence of an increased risk of AML and CLL. The meta-analysis indicated a lack of association between benzene exposure and the risk of CML

A Time Purified Separated Antiproton Beam At The AGS

Physic