Random quantum correlations and density operator distributions

Consider the question: what statistical ensemble corresponds to minimal prior knowledge about a quantum system ? For the case where the system is in fact known to be in a pure state there is an obvious answer, corresponding to the unique unitarily-invariant measure on the Hilbert sphere. However, the problem is open for the general case where states are described by density operators. Here two approaches to the problem are investigated. The first approach assumes that the system is randomly correlated with a second system, where the ensemble of composite systems is described by a random pure state. Results for qubits randomly correlated with other systems are presented, including average entanglement entropies. It is shown that maximum correlation is guaranteed in the limit as one system becomes infinite-dimensional. The second approach relies on choosing a metric on the space of density operators, and generating a corresponding ensemble from the induced volume element. Comparisons between the approaches are made for qubits, for which the second approach (based on the Bures metric) yields the most symmetric, and hence the least informative, ensemble of density operators.Comment: 13 pages, no figures; a new page of additional notes at end draws attention to 3 new references and their relevanc

ConsInstancy: learning instance representations for semi-supervised panoptic segmentation of concrete aggregate particles

We present a semi-supervised method for panoptic segmentation based on ConsInstancy regularisation, a novel strategy for semi-supervised learning. It leverages completely unlabelled data by enforcing consistency between predicted instance representations and semantic segmentations during training in order to improve the segmentation performance. To this end, we also propose new types of instance representations that can be predicted by one simple forward path through a fully convolutional network (FCN), delivering a convenient and simple-to-train framework for panoptic segmentation. More specifically, we propose the prediction of a three-dimensional instance orientation map as intermediate representation and two complementary distance transform maps as final representation, providing unique instance representations for a panoptic segmentation. We test our method on two challenging data sets of both, hardened and fresh concrete, the latter being proposed by the authors in this paper demonstrating the effectiveness of our approach, outperforming the results achieved by state-of-the-art methods for semi-supervised segmentation. In particular, we are able to show that by leveraging completely unlabelled data in our semi-supervised approach the achieved overall accuracy (OA) is increased by up to 5% compared to an entirely supervised training using only labelled data. Furthermore, we exceed the OA achieved by state-of-the-art semi-supervised methods by up to 1.5%

Von der Defizitanalyse zur Potenzial förderung

Bundesumweltministerium und Umweltbundesamt geben seit 1996 im Zweijahresrhythmus repräsentative Bevölkerungsumfragen zum Umweltbewusstsein in Deutschland in Auftrag. Seitdem wird kontinuierlich an einer Betrachtung gearbeitet, die statt der Defizite die vorhandenen Potenziale stärker in den Blick nimmt

Farbmetrische Analyse zur quantitativen Bewertung der Farbe von glatten Sichtbetonflächen

Farb- und Helligkeitsunterschiede an glatten Sichtbetonflächen werden von einer Vielzahl von Einflussfaktoren beeinflusst. In der Planungsphase von Sichtbetonbauteilen wird die Farbe zumeist indirekt z. B. durch Verweis auf vergleichbare Bauwerke oder über Erprobungsflächen aus Beton eingegrenzt. Eine objektive und quantitative Festlegung findet jedoch nicht statt. Im Gegensatz dazu wird in vielen anderen Industriezweigen, wie z. B. der Automobil- oder Lebensmittelindustrie aber auch bei der Zementherstellung, die Lichtspektroskopie bzw. Farbmessung bereits erfolgreich zur quantitativen Farbmessung für Qualitätssicherungszwecke eingesetzt. Im vorliegenden Beitrag werden Einflüsse der Ausgansstoffe sowie einzelner betontechnologischer Eigenschaften auf die resultierende Farbe von Sichtbetonflächen systematisch und quantitativ mittels Lichtspektroskopie ermittelt und bewertet. Ferner wird die prinzipielle Eignung des Messverfahrens zur Anwendung in der Planungsphase glatter Sichtbetonflächen diskutiert.Colour and luminance differences on exposed concrete are influenced by a large number of factors. In the planing phase the colour is usually specified by referencing to other – similar – structures or by providing hand samples. However, an objective and quantitative assessment does not take place. In contrast, in many other industries such as automotive or food industry but also in cement production, the light spectroscopy or colour measurement is already successfully used for quality control purposes with both colour and luminance being quality criteria. In the present paper the influence of the raw materials and concrete properties on the resulting colour of exposed concrete surfaces is systematically und quantitatively determined and evaluated by light spectroscopy. Furthermore, the application of the measuring method for use in the planning phase of smooth exposed concrete surfaces is evaluated

Separability criteria for genuine multiparticle entanglement

We present a method to derive separability criteria for the different classes of multiparticle entanglement, especially genuine multiparticle entanglement. The resulting criteria are necessary and sufficient for certain families of states. Further, the criteria are superior to all known entanglement criteria for many other families; also they allow the detection of bound entanglement. We next demonstrate that they are easily implementable in experiments and discuss applications to the decoherence of multiparticle entangled states.Comment: five pages, one figure, v4: final version plus a remark on arXiv:0912.187

Bioinformatics for RNA‐Seq Data Analysis

While RNA sequencing (RNA‐seq) has become increasingly popular for transcriptome profiling, the analysis of the massive amount of data generated by large‐scale RNA‐seq still remains a challenge. RNA‐seq data analyses typically consist of (1) accurate mapping of millions of short sequencing reads to a reference genome, including the identification of splicing events; (2) quantifying expression levels of genes, transcripts, and exons; (3) differential analysis of gene expression among different biological conditions; and (4) biological interpretation of differentially expressed genes. Despite the fact that multiple algorithms pertinent to basic analyses have been developed, there are still a variety of unresolved questions. In this chapter, we review the main tools and algorithms currently available for RNA‐seq data analyses, and our goal is to help RNA‐seq data analysts to make an informed choice of tools in practical RNA‐seq data analysis. In the meantime, RNA‐seq is evolving rapidly, and newer sequencing technologies are briefly introduced, including stranded RNA‐seq, targeted RNA‐seq, and single‐cell RNA‐seq

A Connected Chair as Part of a Smart Home Environment

Hesse M, Krause AF, Vogel L, et al. A Connected Chair as Part of a Smart Home Environment. Proceedings of IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks. 2017:47-50