Leibniz University of Hanover

About: Leibniz University of Hanover is a education organization based out in Hanover, Niedersachsen, Germany. It is known for research contribution in the topics: Finite element method & Computer science. The organization has 14283 authors who have published 29845 publications receiving 682152 citations.

Detecting multiparticle entanglement of Dicke states.

Abstract: Recent experiments demonstrate the production of many thousands of neutral atoms entangled in their spin degrees of freedom. We present a criterion for estimating the amount of entanglement based on a measurement of the global spin. It outperforms previous criteria and applies to a wider class of entangled states, including Dicke states. Experimentally, we produce a Dicke-like state using spin dynamics in a Bose-Einstein condensate. Our criterion proves that it contains at least genuine 28-particle entanglement. We infer a generalized squeezing parameter of $\ensuremath{-}11.4(5)\text{ }\text{ }\mathrm{dB}$.

Close association of azospirillum and diazotrophic rods with different root zones of kallar grass.

Abstract: The populations of diazotrophic and nondiazotrophic bacteria were estimated in the endorhizosphere and on the rhizoplane of Kallar grass (Leptochloa fusca) and in nonrhizosphere soil. Microaerophilic diazotrophs were counted by the most-probable-number method, using two semisolid malate media, one of them adapted to the saline-sodic Kallar grass soil. Plate counts of aerobic heterotrophic bacteria were done on nutrient agar. The dominating N2-fixing bacteria were differentiated by morphological, serological, and physiological criteria. Isolates, which could not be assigned to a known species, were shown to fix nitrogen unequivocally by 15N2 incorporation. On the rhizoplane we found 2.0 × 107 diazotrophs per g (dry weight) of root, which consisted in equal numbers of Azospirillum lipoferum and Azospirillum-like bacteria showing characteristics different from those of known Azospirillum species. Surface sterilization by NaOCI treatment effectively reduced the rhizoplane population, so that bacteria released by homogenization of roots could be regarded as endorhizosphere bacteria. Azospirillum spp. were not detected in the endorhizosphere, but diazotrophic, motile, straight rods producing a yellow pigment occurred with 7.3 × 107 cells per g (dry weight) of root in the root interior. In nonrhizosphere soil we found 3.1 × 104 nitrogen-fixing bacteria per g. Diazotrophs were preferentially enriched in the Kallar grass rhizosphere. In nonrhizosphere soil they made up 0.2% of the total aerobic heterotrophic microflora, on the rhizoplane they made up 7.1%, and in the endorhizosphere they made up 85%. Owing to high numbers in and on roots and their preferential enrichment, we concluded that diazotrophs are in close association with Kallar grass. They formed entirely different populations on the rhizoplane and in the endorhizosphere.

Object Scene Flow

Abstract: This work investigates the estimation of dense three-dimensional motion fields, commonly referred to as scene flow. While great progress has been made in recent years, large displacements and adverse imaging conditions as observed in natural outdoor environments are still very challenging for current approaches to reconstruction and motion estimation. In this paper, we propose a unified random field model which reasons jointly about 3D scene flow as well as the location, shape and motion of vehicles in the observed scene. We formulate the problem as the task of decomposing the scene into a small number of rigidly moving objects sharing the same motion parameters. Thus, our formulation effectively introduces long-range spatial dependencies which commonly employed local rigidity priors are lacking. Our inference algorithm then estimates the association of image segments and object hypotheses together with their three-dimensional shape and motion. We demonstrate the potential of the proposed approach by introducing a novel challenging scene flow benchmark which allows for a thorough comparison of the proposed scene flow approach with respect to various baseline models. In contrast to previous benchmarks, our evaluation is the first to provide stereo and optical flow ground truth for dynamic real-world urban scenes at large scale. Our experiments reveal that rigid motion segmentation can be utilized as an effective regularizer for the scene flow problem, improving upon existing two-frame scene flow methods. At the same time, our method yields plausible object segmentations without requiring an explicitly trained recognition model for a specific object class.