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

Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection

We present a novel approach for vanishing point detection from uncalibrated monocular images. In contrast to state-of-the-art, we make no a priori assumptions about the observed scene. Our method is based on a convolutional neural network (CNN) which does not use natural images, but a Gaussian sphere representation arising from an inverse gnomonic projection of lines detected in an image. This allows us to rely on synthetic data for training, eliminating the need for labelled images. Our method achieves competitive performance on three horizon estimation benchmark datasets. We further highlight some additional use cases for which our vanishing point detection algorithm can be used.Comment: Accepted for publication at German Conference on Pattern Recognition (GCPR) 2017. This research was supported by German Research Foundation DFG within Priority Research Programme 1894 "Volunteered Geographic Information: Interpretation, Visualisation and Social Computing

Palm pairs and the general mass-transport principle

We consider a lcsc group G acting properly on a Borel space S and measurably on an underlying sigma-finite measure space. Our first main result is a transport formula connecting the Palm pairs of jointly stationary random measures on S. A key (and new) technical result is a measurable disintegration of the Haar measure on G along the orbits. The second main result is an intrinsic characterization of the Palm pairs of a G-invariant random measure. We then proceed with deriving a general version of the mass-transport principle for possibly non-transitive and non-unimodular group operations first in a deterministic and then in its full probabilistic form.Comment: 26 page

Tomonaga-Luttinger model with an impurity for a weak two-body interaction

The Tomonaga-Luttinger model with impurity is studied by means of flow equations for Hamiltonians. The system is formulated within collective density fluctuations but no use of the bosonization formula is made. The truncation scheme includes operators consisting of up to four fermion operators and is valid for small electron-electron interactions. In this regime, the exact expression for the anomalous dimension is recovered. Furthermore, we verify the phase diagram of Kane and Fisher also for intermediate impurity strength. The approach can be extended to more general one-body potentials.Comment: 10 pages, 1 figur

Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image

We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and show that 2D joints alone carry a surprising amount of information about body shape. The problem is challenging because of the complexity of the human body, articulation, occlusion, clothing, lighting, and the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D body joint locations. We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints. We do so by minimizing an objective function that penalizes the error between the projected 3D model joints and detected 2D joints. Because SMPL captures correlations in human shape across the population, we are able to robustly fit it to very little data. We further leverage the 3D model to prevent solutions that cause interpenetration. We evaluate our method, SMPLify, on the Leeds Sports, HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect to the state of the art.Comment: To appear in ECCV 201

Observation of isotonic symmetry for enhanced quadrupole collectivity in neutron-rich 62,64,66Fe isotopes at N=40

The transition rates for the 2_{1}^{+} states in 62,64,66Fe were studied using the Recoil Distance Doppler-Shift technique applied to projectile Coulomb excitation reactions. The deduced E2 strengths illustrate the enhanced collectivity of the neutron-rich Fe isotopes up to N=40. The results are interpreted by the generalized concept of valence proton symmetry which describes the evolution of nuclear structure around N=40 as governed by the number of valence protons with respect to Z~30. The deformation suggested by the experimental data is reproduced by state-of-the-art shell calculations with a new effective interaction developed for the fpgd valence space.Comment: 4 pages, 2 figure

Possible Airborne Person-to-Person Transmission of \u3ci\u3eMycobacterium bovis\u3c/i\u3e — Nebraska 2014–2015

Mycobacterium bovis, one of several mycobacteria of the M. tuberculosis complex, is a global zoonotic pathogen that primarily infects cattle. Humans become infected by consuming unpasteurized dairy products from infected cows (1,2); possible person-to-person airborne transmission has also been reported (3). In April 2014, a man in Nebraska who was born in Mexico was determined to have extensive pulmonary tuberculosis (TB) caused by M. bovis after experiencing approximately 3 months of cough and fever. Four months later, a U.S.-born Hispanic girl from a nearby town who had been ill for 4–5 months was also determined to have pulmonary TB caused by M. bovis. The only social connection between the two patients was attendance at the same church, and no common dietary exposure was identified. Both patients had pulmonary cavities on radiography and acid-fast bacilli (AFB) on sputum-smear microscopy, indicators of being contagious (4). Whole-genome sequencing results of the isolates were nearly indistinguishable. Initial examination of 181 contacts determined that 39 (22%) had latent infection: 10 (42%) of 24 who had close exposure to either patient, 28 (28%) of 100 who were exposed to one or both patients in church, and one (2%) of 57 exposed to the second patient at a school. Latent infection was diagnosed in six contacts on follow-up examination, 2 months after an initial negative test result (4), for an overall latent infection rate of 25%. No infected contacts recalled consuming unpasteurized dairy products, and none had active TB disease at the initial or secondary examination. Persons who have M. bovis TB should be asked about consumption of unpasteurized dairy products (2), and contact investigations should follow the same guidance as for M. tuberculosis TB (4)

Joint Inference in Weakly-Annotated Image Datasets via Dense Correspondence

We present a principled framework for inferring pixel labels in weakly-annotated image datasets. Most previous, example-based approaches to computer vision rely on a large corpus of densely labeled images. However, for large, modern image datasets, such labels are expensive to obtain and are often unavailable. We establish a large-scale graphical model spanning all labeled and unlabeled images, then solve it to infer pixel labels jointly for all images in the dataset while enforcing consistent annotations over similar visual patterns. This model requires significantly less labeled data and assists in resolving ambiguities by propagating inferred annotations from images with stronger local visual evidences to images with weaker local evidences. We apply our proposed framework to two computer vision problems, namely image annotation with semantic segmentation, and object discovery and co-segmentation (segmenting multiple images containing a common object). Extensive numerical evaluations and comparisons show that our method consistently outperforms the state-of-the-art in automatic annotation and semantic labeling, while requiring significantly less labeled data. In contrast to previous co-segmentation techniques, our method manages to discover and segment objects well even in the presence of substantial amounts of noise images (images not containing the common object), as typical for datasets collected from Internet search

Kpna6 deficiency causes infertility in male mice by disrupting spermatogenesis

Spermatogenesis is driven by an ordered series of events, which rely on trafficking of specific proteins between nucleus and cytoplasm. The importin α family of proteins mediates movement of specific cargo proteins when bound to importin β. Importin α genes have distinct expression patterns in mouse testis, implying they may have unique roles during mammalian spermatogenesis. Here we use a loss-of-function approach to specifically determine the role of importin α7 in spermatogenesis and male fertility. We show that ablation of importin α7 in male mice leads to infertility and has multiple cumulative effects on both germ cells and Sertoli cells. Importin α7-deficient mice exhibit an impaired Sertoli cell function, including loss of Sertoli cells and a compromised nuclear localization of the androgen receptor. Furthermore, our data demonstrate devastating defects in spermiogenesis including incomplete sperm maturation and massive loss of sperms that are accompanied by disturbed histone-protamine-exchange, differential localization of the transcriptional regulator Brwd1 and altered expression of Rfx2 target genes. Our work uncovers the essential role of importin α7 in spermatogenesis and hence in male fertility

Improving object segmentation by using EEG signals and rapid serial visual presentation

This paper extends our previous work on the potential of EEG-based brain computer interfaces to segment salient objects in images. The proposed system analyzes the Event Related Potentials (ERP) generated by the rapid serial visual presentation of windows on the image. The detection of the P300 signal allows estimating a saliency map of the image, which is used to seed a semi-supervised object segmentation algorithm. Thanks to the new contributions presented in this work, the average Jaccard index was improved from $0.47$ to $0.66$ when processed in our publicly available dataset of images, object masks and captured EEG signals. This work also studies alternative architectures to the original one, the impact of object occupation in each image window, and a more robust evaluation based on statistical analysis and a weighted F-score