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].

Biodegradable microfluidic scaffolds for tissue engineering from amino alcohol-based poly(ester amide) elastomers

Biodegradable polymers with high mechanical strength, flexibility and optical transparency, optimal degradation properties and biocompatibility are critical to the success of tissue engineered devices and drug delivery systems. Most biodegradable polymers suffer from a short half life due to rapid degradation upon implantation, exceedingly high stiffness, and limited ability to functionalize the surface with chemical moieties. This work describes the fabrication of microfluidic networks from poly(ester amide), poly(1,3-diamino-2-hydroxypropane-co-polyol sebacate) (APS), a recently developed biodegradable elastomeric poly(ester amide). Microfluidic scaffolds constructed from APS exhibit a much lower Young’s Modulus and a significantly longer degradation half-life than those of previously reported systems. The device is fabricated using a modified replica-molding technique, which is rapid, inexpensive, reproducible, and scalable, making the approach ideal for both rapid prototyping and manufacturing of tissue engineering scaffolds.Charles Stark Draper Laborator

The Effect of Recombination on the Neutral Evolution of Genetic Robustness

Conventional population genetics considers the evolution of a limited number of genotypes corresponding to phenotypes with different fitness. As model phenotypes, in particular RNA secondary structure, have become computationally tractable, however, it has become apparent that the context dependent effect of mutations and the many-to-one nature inherent in these genotype-phenotype maps can have fundamental evolutionary consequences. It has previously been demonstrated that populations of genotypes evolving on the neutral networks corresponding to all genotypes with the same secondary structure only through neutral mutations can evolve mutational robustness [Nimwegen {\it et al.} Neutral evolution of mutational robustness, 1999 PNAS], by concentrating the population on regions of high neutrality. Introducing recombination we demonstrate, through numerically calculating the stationary distribution of an infinite population on ensembles of random neutral networks that mutational robustness is significantly enhanced and further that the magnitude of this enhancement is sensitive to details of the neutral network topology. Through the simulation of finite populations of genotypes evolving on random neutral networks and a scaled down microRNA neutral network, we show that even in finite populations recombination will still act to focus the population on regions of locally high neutrality.Comment: Accepted for publication in Math. Biosci. as part of the proceedings of BIOCOMP 200

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

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