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].

Charge-imbalance effects in intrinsic Josephson systems

We report on two types of experiments with intrinsic Josephson systems made from layered superconductors which show clear evidence of nonequilibrium effects: 1. In 2-point measurements of IV-curves in the presence of high- frequency radiation a shift of the voltage of Shapiro steps from the canonical value hf/(2e) has been observed. 2. In the IV-curves of double-mesa structures an influence of the current through one mesa on the voltage measured on the other mesa is detected. Both effects can be explained by charge-imbalance on the superconducting layers produced by the quasi-particle current, and can be described successfully by a recently developed theory of nonequilibrium effects in intrinsic Josephson systems.Comment: 8pages, 9figures, submitted to Phys. Rev.

Nonequilibrium effects due to charge fluctuations in intrinsic Josephson systems

Nonequilibrium effects in layered superconductors forming a stack of intrinsic Josephson junctions are investigated. We discuss two basic nonequilibrium effects caused by charge fluctuations on the superconducting layers: a) the shift of the chemical potential of the condensate and b) charge imbalance of quasi-particles, and study their influence on IV-curves and the position of Shapiro steps.Comment: 17 pages, 2 figures, revised version slightly shortene

Measuring Luttinger Liquid Correlations from Charge Fluctuations in a Nanoscale Structure

We suggest an experiment to study Luttinger liquid behavior in a one-dimensional nanostructure, avoiding the usual complications associated with transport measurements. The proposed setup consists of a quantum box, biased by a gate voltage, and side-coupled to a quantum wire by a point contact. Close to the degeneracy points of the Coulomb blockaded box, and in the presence of a magnetic field sufficiently strong to spin polarize the electrons, the setup can be described as a Luttinger liquid interacting with an effective Kondo impurity. Using exact nonperturbative techniques we predict that the differential capacitance of the box will exhibit distinctive Luttinger liquid scaling with temperature and gate voltage.Comment: REVTeX, 4 pages, 1 figure included. Final version, two references adde

Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution

Given a set of images containing objects from the same category, the task of image co-localization is to identify and localize each instance. This paper shows that this problem can be solved by a simple but intriguing idea, that is, a common object detector can be learnt by making its detection confidence scores distributed like those of a strongly supervised detector. More specifically, we observe that given a set of object proposals extracted from an image that contains the object of interest, an accurate strongly supervised object detector should give high scores to only a small minority of proposals, and low scores to most of them. Thus, we devise an entropy-based objective function to enforce the above property when learning the common object detector. Once the detector is learnt, we resort to a segmentation approach to refine the localization. We show that despite its simplicity, our approach outperforms state-of-the-art methods.Comment: Accepted to Proc. European Conf. Computer Vision 201

Studies of hepatic synthesis in vivo of plasma proteins, including orosomucoid, transferrin, α-antitrypsin, C8, and factor B

Serum protein types were determined in eight recipients and donors in cases of hepatic homotransplantation. A change from recipient type to donor type was observed for factor B, C8, orosomucoid, haptoglobin, transferrin, α1-antitrypsin, C3 and C6, but not for Gm and Inv immunoglobulin markers. The results indicate that all the proteins studied (except immunoglobulins) are produced primarily by the liver in vivo. © 1980