Continuous-wave spatial quantum correlations of light induced by multiple scattering

We present theoretical and experimental results on spatial quantum correlations induced by multiple scattering of nonclassical light. A continuous mode quantum theory is derived that enables determining the spatial quantum correlation function from the fluctuations of the total transmittance and reflectance. Utilizing frequency-resolved quantum noise measurements, we observe that the strength of the spatial quantum correlation function can be controlled by changing the quantum state of an incident bright squeezed-light source. Our results are found to be in excellent agreement with the developed theory and form a basis for future research on, e.g., quantum interference of multiple quantum states in a multiple scattering medium.Comment: 8 pages, 6 figure

Structural measures for games and process control in the branch learning model

Process control problems can be modeled as closed recursive games. Learning strategies for such games is equivalent to the concept of learning infinite recursive branches for recursive trees. We use this branch learning model to measure the difficulty of learning and synthesizing process controllers. We also measure the difference between several process learning criteria, and their difference to controller synthesis. As measure we use the information content (i.e. the Turing degree) of the oracle which a machine need to get the desired power. The investigated learning criteria are finite, EX-, BC-, Weak BC- and online learning. Finite, EX- and BC-style learning are well known from inductive inference, while weak BC- and online learning came up with the new notion of branch (i.e. process) learning. For all considered criteria - including synthesis - we also solve the questions of their trivial degrees, their omniscient degrees and with some restrictions their inference degrees. While most of the results about finite, EX- and BC-style branch learning can be derived from inductive inference, new techniques had to be developed for online learning, weak BC-style learning and synthesis, and for the comparisons of all process learning criteria with the power of controller synthesis

Observations of Moisture Damages in Historic and Modern Wooden Constructions

With the introduction of new building products made of planar glued board lamellas, the CLT, the restriction of load-bearing structures to linear and thus additively used load-bearing members was abolished. As a result, new, technically determined boundary conditions for moisture management in the interior of buildings have arisen. Due to the emergence of massive, planar wall and floor components as in concrete construction, the integration of building services technology in timber construction must now take place differently than was traditionally the case. In addition, it can be observed that the damage to building components is increasing, the detection of moisture damage is becoming more difficult and, ultimately, the consequences and risks are not yet foreseeable. The study focuses on the cause-effect relationship of increased water input and uses selected examples to reveal the problem of moisture exposure in the interior of buildings with planar load-bearing structures, the damage mechanisms and direct consequences set in motion. This paper shows the necessity for moisture protection measures in modern timber structures in comparison to traditional ways to construct with timber. It shows where moisture intake with modern structures must be considered and avoided from the engineering perspective in order to minimize the risk of moisture damage

CAIPI in Practice: Towards Explainable Interactive Medical Image Classification

Would you trust physicians if they cannot explain their decisions to you? Medical diagnostics using machine learning gained enormously in importance within the last decade. However, without further enhancements many state-of-the-art machine learning methods are not suitable for medical application. The most important reasons are insufficient data set quality and the black-box behavior of machine learning algorithms such as Deep Learning models. Consequently, end-users cannot correct the model's decisions and the corresponding explanations. The latter is crucial for the trustworthiness of machine learning in the medical domain. The research field explainable interactive machine learning searches for methods that address both shortcomings. This paper extends the explainable and interactive CAIPI algorithm and provides an interface to simplify human-in-the-loop approaches for image classification. The interface enables the end-user (1) to investigate and (2) to correct the model's prediction and explanation, and (3) to influence the data set quality. After CAIPI optimization with only a single counterexample per iteration, the model achieves an accuracy of $97.48\%$ on the Medical MNIST and $95.02\%$ on the Fashion MNIST. This accuracy is approximately equal to state-of-the-art Deep Learning optimization procedures. Besides, CAIPI reduces the labeling effort by approximately $80\%$.Comment: Manuscript accepted at IFIP AIAI 202

Effects of probiotics and antibiotics on the intestinal homeostasis in a computer controlled model of the large intestine

Background: Antibiotic associated diarrhea and Clostridium difficile infection are frequent complications of broad spectrum antibiotic therapy. Probiotic bacteria are used as therapeutic and preventive agents in these disorders, but the exact functional mechanisms and the mode of action are poorly understood. The effects of clindamycin and the probiotic mixture VSL#3 (containing the 8 bacterial strains Streptococcus thermophilus, Bifidobacterium breve, Bifidobacterium longum, Bifidobacterium infantis, Lactobacillus acidophilus, Lactobacillus plantarum, Lactobacillus paracasei and Lactobacillus delbrueckii subsp. Bulgaricus) consecutively or in combination were investigated and compared to controls without therapy using a standardized human fecal microbiota in a computer-controlled in vitro model of large intestine. Microbial metabolites (short chain fatty acids, lactate, branched chain fatty acids, and ammonia) and the intestinal microbiota were analyzed. Results: Compared to controls and combination therapy, short chain fatty acids and lactate, but also ammonia and branched chain fatty acids, were increased under probiotic therapy. The metabolic pattern under combined therapy with antibiotics and probiotics had the most beneficial and consistent effect on intestinal metabolic profiles. The intestinal microbiota showed a decrease in several indigenous bacterial groups under antibiotic therapy, there was no significant recovery of these groups when the antibiotic therapy was followed by administration of probiotics. Simultaneous application of anti- and probiotics had a stabilizing effect on the intestinal microbiota with increased bifidobacteria and lactobacilli. Conclusions: Administration of VSL#3 parallel with the clindamycin therapy had a beneficial and stabilizing effect on the intestinal metabolic homeostasis by decreasing toxic metabolites and protecting the endogenic microbiota from destruction. Probiotics could be a reasonable strategy in prevention of antibiotic associated disturbances of the intestinal homeostasis and disorders. © 2012 Rehman et al; licensee BioMed Central Lt

Worker personality: Another skill bias beyond education in the digital age

We present empirical evidence suggesting that technological progress in the digital age will be biased not only with respect to skills acquired through education but also with respect to noncognitive skills (personality). We measure the direction of technological change by estimated future digitalization probabilities of occupations, and noncognitive skills by the Big Five personality traits from several German worker surveys. Even though we control extensively for education and experience, we find that workers characterized by strong openness and emotional stability tend to be less susceptible to digitalization. Traditional indicators of human capital thus measure workers’ skill endowments only imperfectly

Genetic diversity of the obligate intracellular bacterium Chlamydophila pneumoniae by genome-wide analysis of single nucleotide polymorphisms: evidence for highly clonal population structure

<p>Abstract</p> <p>Background</p> <p><it>Chlamydophila pneumoniae </it>is an obligate intracellular bacterium that replicates in a biphasic life cycle within eukaryotic host cells. Four published genomes revealed an identity of > 99 %. This remarkable finding raised questions about the existence of distinguishable genotypes in correlation with geographical and anatomical origin.</p> <p>Results</p> <p>We studied the genetic diversity of <it>C. pneumoniae </it>by analysing synonymous single nucleotide polymorphisms (sSNPs) that are under reduced selection pressure. We conducted an in silico analysis of the four sequenced genomes, chose 232 representative sSNPs and analysed the loci in 38 <it>C. pneumoniae </it>isolates. We identified 15 different genotypes that were separated in four major clusters. Clusters were not associated with anatomical or geographical origin. However, animal lineages are basal on the <it>C. pneumomiae </it>phylogeny, suggesting a recent transmission to humans through successive bottlenecks some 150,000 years ago. A lack of detectable variation in 17 isolates emphasizes the extraordinary genetic conservation of this species and the high clonality of the population. Moreover, the largest cluster, which encompasses 80% of all analysed strains, is an extremely young clade, that went through an important population expansion some 3,300 years ago.</p> <p>Conclusion</p> <p>sSNPs have proven useful as a sensitive marker to gain new insights into genetic diversity, population structure and evolutionary history of <it>C. pneumoniae</it>.</p