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We demonstrate a proof-of-concept establishment of quantum-secure data transfer links in field trials at two locations in Denmark: on the campus of Technical University of Denmark in Lyngby and between power grid nodes owned and operated by Energinet in Odense. Several different links, implemented physically using optical ground wires, underground fibers as well as their combinations, were investigated. Coherent `quantum' states at 1550 nm, prepared and measured using a semi-autonomous continuous-variable quantum key distribution (CVQKD) prototype, were multiplexed in wavelength with `classical' 100Gbps encrypted data traffic from a pair of commercial layer-2 network encryption devices operating at around 1300 nm. Under the assumptions of real-time data processing, we estimate average secret key rates of $434.8, 148.6$, and $78.3$ kbps in the asymptotic limit for diverse channels with losses (at 1550 nm) of 4.1, 5.5, and 6.7 dB, respectively. The demonstrations permit an evaluation of the prototype's tolerance to harsh field conditions and showcase that CVQKD can serve as an additional layer to protect sensitive network traffic propagating on insecure channels., Comment: Major revision: Added new figure and text to elaborate the influence of the high-noise environment in which the CVQKD devices were deployed; Updated secret key rates after fixing errors in parameter estimation; Moved details of the SOP optimization to a supplemental document

The advancement of machine learning algorithms in medical image analysis requires the expansion of training datasets. A popular and cost-effective approach is automated annotation extraction from free-text medical reports, primarily due to the high costs associated with expert clinicians annotating chest X-ray images. However, it has been shown that the resulting datasets are susceptible to biases and shortcuts. Another strategy to increase the size of a dataset is crowdsourcing, a widely adopted practice in general computer vision with some success in medical image analysis. In a similar vein to crowdsourcing, we enhance two publicly available chest X-ray datasets by incorporating non-expert annotations. However, instead of using diagnostic labels, we annotate shortcuts in the form of tubes. We collect 3.5k chest drain annotations for CXR14, and 1k annotations for 4 different tube types in PadChest. We train a chest drain detector with the non-expert annotations that generalizes well to expert labels. Moreover, we compare our annotations to those provided by experts and show "moderate" to "almost perfect" agreement. Finally, we present a pathology agreement study to raise awareness about ground truth annotations. We make our annotations and code available.

Successive ionic layer adsorption and reaction (SILAR) method is based on the adsorption and reaction of the ions in the cationic solution and the ionic solution, respectively. This method is simple, inexpensive, large-scale deposition, effective way for deposition on 3D substrates, low-temperature process and represents an easy way for the preparation of doped, composite and heterojunction materials. To take advantage of this method and the ZnO nanostructures, various parameters have been optimized. Undoped and Aluminum (Al) doped ZnO nanostructures were prepared by the SILAR technique. The characterization of the nanostructures prepared was carried out using X-ray diffraction (XRD), scanning elektron microscopy (SEM), energy dispersive spectroscopy (EDS), X ray photoemission spectroscopy (XPS), UV Vis spectrometry and nonlinear optical analysis (NLO). The structural, compositional and optical properties confirm the introduction of Al3+ ions into the ZnO matrix. As a result, an enhancement of the crystallinity, enhancement of the light absorption and a change in the morphology of the nanostructures were observed. The laser stimulated nonlinear optical effects of the second and third harmonic generation were done using a fundamental laser beam. The laser stimulated NLO values obtained are at least 10% higher than the doped ZnO nanomaterials synthesized by other methods using the same set up.

Chemical vapor deposition (CVD) allows lateral edge epitaxy of transition metal dichalcogenide heterostructures with potential applications in optoelectronics. Critical for carrier and exciton transport is the quality of the two materials that constitute the monolayer and the nature of the lateral heterojunction. Important details of the optical properties were inaccessible in as-grown heterostructure samples due to large inhomogeneous broadening of the optical transitions. Here we perform optical spectroscopy at T = 4 K and also at 300 K to access the optical transitions in CVD grown MoSe2-WSe2 lateral heterostructures that are transferred from the growth-substrate and are encapsulated in hBN. Photoluminescence (PL), reflectance contrast and Raman spectroscopy reveal considerably narrowed optical transition linewidth similar to high quality exfoliated monolayers. In high-resolution transmission electron microscopy (HRTEM) we find near-atomically sharp junctions with a typical extent of 3nm for the covalently bonded MoSe2-WSe2. In PL imaging experiments we find effective excitonic diffusion length that are longer for WSe2 than for MoSe2 at low T=4 K, whereas at 300 K this trend is reversed., Comment: Main and supplement

Iterative decoding was not originally introduced as the solution to an optimization problem rendering the analysis of its convergence very difficult. In this paper, we investigate the link between iterative decoding and classical optimization techniques. We first show that iterative decoding can be rephrased as two embedded minimization processes involving the Fermi-Dirac distance. Based on this new formulation, an hybrid proximal point algorithm is first derived with the additional advantage of decreasing a desired criterion. In a second part, an hybrid minimum entropy algorithm is proposed with improved performance compared to the classical iterative decoding. Even if this paper focus on iterative decoding for BICM, the results can be applied to the large class of turbo-like decoders.

The paper first recalls the Blahut Arimoto algorithm for computing the capacity of arbitrary discrete memoryless channels, as an example of an iterative algorithm working with probability density estimates. Then, a geometrical interpretation of this algorithm based on projections onto linear and exponential families of probabilities is provided. Finally, this understanding allows also to propose to write the Blahut-Arimoto algorithm, as a true proximal point algorithm. it is shown that the corresponding version has an improved convergence rate, compared to the initial algorithm, as well as in comparison with other improved versions.

This paper recalls the proximal point method. We study two iterative algorithms: the Blahut-Arimoto algorithm for computing the capacity of arbitrary discrete memoryless channels, as an example of an iterative algorithm working with probability density estimates and the iterative decoding of the Bit Interleaved Coded Modulation (BICM-ID). For these iterative algorithms, we apply the proximal point method which allows new interpretations with improved convergence rate.