Theory of the n=2 levels in muonic deuterium

The present knowledge of Lamb shift, fine- and hyperfine structure of the $\mathrm{2S}$ and $\mathrm{2P}$ states in muonic deuterium is reviewed in anticipation of the results of a first measurement of several $\mathrm{2S-2P}$ transition frequencies in muonic deuterium ($\mu\mathrm{d}$). A term-by-term comparison of all available sources reveals reliable values and uncertainties of the QED and nuclear structure-dependent contributions to the Lamb shift, which are essential for a determination of the deuteron rms charge radius from $\mu\mathrm{d}$. Apparent discrepancies between different sources are resolved, in particular for the difficult two-photon exchange contributions. Problematic single-sourced terms are identified which require independent recalculation.Comment: 26 pages, add missing feynman diagrams (Fig. 3), renumber items (Tab. IV), correct a sum (column 5, Tab. IV

Aging without disorder on long time scales

We study the Metropolis dynamics of a simple spin system without disorder, which exhibits glassy dynamics at low temperatures. We use an implementation of the algorithm of Bortz, Kalos and Lebowitz \cite{bortz}. This method turns out to be very efficient for the study of glassy systems, which get trapped in local minima on many different time scales. We find strong evidence of aging effects at low temperatures. We relate these effects to the distribution function of the trapping times of single configurations.Comment: 8 pages Revtex, 7 figures uuencoded (Revised version: the figures are now present

Fast calibration of heliostats

We present the HelioPoint method - a fast airborne method for calibrating entire heliostat fields

Single-Atom Resolved Fluorescence Imaging of an Atomic Mott Insulator

The reliable detection of single quantum particles has revolutionized the field of quantum optics and quantum information processing. For several years, researchers have aspired to extend such detection possibilities to larger scale strongly correlated quantum systems, in order to record in-situ images of a quantum fluid in which each underlying quantum particle is detected. Here we report on fluorescence imaging of strongly interacting bosonic Mott insulators in an optical lattice with single-atom and single-site resolution. From our images, we fully reconstruct the atom distribution on the lattice and identify individual excitations with high fidelity. A comparison of the radial density and variance distributions with theory provides a precise in-situ temperature and entropy measurement from single images. We observe Mott-insulating plateaus with near zero entropy and clearly resolve the high entropy rings separating them although their width is of the order of only a single lattice site. Furthermore, we show how a Mott insulator melts for increasing temperatures due to a proliferation of local defects. Our experiments open a new avenue for the manipulation and analysis of strongly interacting quantum gases on a lattice, as well as for quantum information processing with ultracold atoms. Using the high spatial resolution, it is now possible to directly address individual lattice sites. One could, e.g., introduce local perturbations or access regions of high entropy, a crucial requirement for the implementation of novel cooling schemes for atoms on a lattice

Improved X-ray detection and particle identification with avalanche photodiodes

Avalanche photodiodes are commonly used as detectors for low energy x-rays. In this work we report on a fitting technique used to account for different detector responses resulting from photo absorption in the various APD layers. The use of this technique results in an improvement of the energy resolution at 8.2 keV by up to a factor of 2, and corrects the timing information by up to 25 ns to account for space dependent electron drift time. In addition, this waveform analysis is used for particle identification, e.g. to distinguish between x-rays and MeV electrons in our experiment.Comment: 6 pages, 6 figure

Genome sequencing of the lizard parasite Leishmania tarentolae reveals loss of genes associated to the intracellular stage of human pathogenic species

The Leishmania tarentolae Parrot-TarII strain genome sequence was resolved to an average 16-fold mean coverage by next-generation DNA sequencing technologies. This is the first non-pathogenic to humans kinetoplastid protozoan genome to be described thus providing an opportunity for comparison with the completed genomes of pathogenic Leishmania species. A high synteny was observed between all sequenced Leishmania species. A limited number of chromosomal regions diverged between L. tarentolae and L. infantum, while remaining syntenic to L. major. Globally, >90% of the L. tarentolae gene content was shared with the other Leishmania species. We identified 95 predicted coding sequences unique to L. tarentolae and 250 genes that were absent from L. tarentolae. Interestingly, many of the latter genes were expressed in the intracellular amastigote stage of pathogenic species. In addition, genes coding for products involved in antioxidant defence or participating in vesicular-mediated protein transport were underrepresented in L. tarentolae. In contrast to other Leishmania genomes, two gene families were expanded in L. tarentolae, namely the zinc metallo-peptidase surface glycoprotein GP63 and the promastigote surface antigen PSA31C. Overall, L. tarentolae's gene content appears better adapted to the promastigote insect stage rather than the amastigote mammalian stage

Storage capacity of memory networks with binary couplings

We study the number p of unbiased random patterns which can be stored in a neural network of N neurons used as an associative memory, in the case where the synaptic efficacies are constrained to take the values ± 1. We find a solution with one step of replica symmetry breaking à la Parisi. This solution gives a critical capacity αc = p/N∼ 0.83 which seems to agree with known numerical results.Nous étudions le nombre p de prototypes aléatoires non biaisés qui peuvent être mémorisés dans un réseau de N neurones utilisé comme mémoire associative, dans le cas où les efficacités synaptiques ne peuvent prendre que les valeurs ± 1. Nous trouvons une solution avec une étape de brisure de symétrie des répliques à la Parisi. Cette solution prédit une capacité optimale αc = p/N ~ 0,83 qui semble en bon accord avec les résultats numériques connus

Towards deep learning based airborne monitoring methods for heliostats in solar tower power plants

While deep learning methods have proven their superiority over conventional image processing techniques in many domains, their use in airborne heliostat monitoring remains limited. Our aim is to bridge this gap by developing models to improve and extend existing image-based measurement methods in this field. We use Blender and BlenderProc to generate synthetic image data, which grants us access to vast amounts of training data essential for developing effective deep learning models. The exemplary model we train can potentially solve the following tasks related to airborne heliostat field monitoring: detection of heliostats and detection of mirror facet corners. Our promising preliminary results demonstrate the applicability of our approach to use synthetic training data for the development of the intended deep learning models