An adaptive array for interference rejection

Adaptive array based on feedback system for rejection of interfering signal

The Digital Avatar on a Blockchain: E-Identity, Anonymity and Human Dignity

Finanzdienstleister sammeln immer größere Mengen an Daten von ihren Kunden, um konform mit speziellen Rechtsakten (eIDAS Verordnung, Zahlungsdiensterichtlinie, Geldwäscherichtlinie) zu handeln und Risiken zu minimieren. Die durch die fortschreitende Digitalisierung zunehmenden technischen Möglichkeiten der Datensammlung werfen Bedenken auf im Hinblick auf die Grundsätze der Verhältnismäßigkeit, Notwendigkeit und Datenminimierung. Über die Vereinbarkeit mit der Datenschutz-Grundverordnung hinaus ergeben sich jedoch weiterreichende Probleme, da bestimmte Identitätsarchitekturen und deren technische Umsetzungen potentiell die Rechte und Freiheiten einzelner beinträchtigen sowie ethische Fragestellungen aufwerfen. Der vorliegende Beitrag analysiert Aspekte digitaler Identität am Beispiel einer Distributed Ledger- beziehungsweise Blockchain-Architektur für die Registrierung neuer Kunden durch Finanzdienstleister, wo mithilfe von Hashing-Algorithmen individuelle Identifikatoren aus spezifischen Datenpunkten der Kunden gewonnen werden, die schließlich für Zwecke der Nachvollziehbarkeit und Überprüfbarkeit unveränderlich in der Datenstruktur gespeichert werden. Nach einer kurzen Einleitung in das Verständnis von Identität im digitalen Raum und der Anwendbarkeit der Datenschutz-Grundverordnung auf eine distribuierten Datenstruktur wird eine kritische Betrachtung der Entwicklung aus rechtlicher und soziologischer Perspektive vorgenommen, dass zunehmend die Mobiltelefone der Kunden von Finanzdienstleistern als Schnittstellen zu Blockchain-Netzwerken dienen. Die Diskussion reicht über die Frage digitaler Identität im Finanzsektor hinaus und zeigt die Notwendigkeit auf, angemessene und verhältnismäßige rechtliche Bestimmungen zu schaffen, die das Individuum effektiv vor Grundrechtsverletzungen vor dem Hintergrund der fortschreitenden Digitalisierung schützen.In order to comply with specific regulations (eIDAS, Payment Services Directive, Anti-Money Laundering Directive) and reduce risk profiles, financial service providers increasingly collect large amounts of information from their customers. The increasing opportunities and technical means for data collection afforded from digitalisation raise legal concerns related to proportionality, necessity, and data minimization. However, the concerns go beyond just GDPR compliance and legislative balance, as distinct architectures and technological deployments potentially impact rights, freedoms, and ethics. This paper will address the issue by examining aspects of digital identity, especially those that have proposed the use of a permissioned distributed ledger or blockchain as architecture for know your customer and onboarding evidential frameworks, using specific hashing schemes that derive unique identifiers from the combination of specific personal data points. Evidence is appended to a data structure, for the purpose of auditing and/or record keeping, potentially ensuring an immutable record of events is maintained. After elaborating on the notion of identity in the digital sphere and the applicability of the GDPR to such a data structure, the discussion will be developed to critically assess the current trend towards using the financial institutions’ customers’ mobile devices as interfaces to the distributed data structure and the legal and sociological implications of this technological development. The potential impact of the analysis goes beyond digital identity within the finance sector, positioning the discussion towards approaches for e-governance and the regulation of digital identity in a way that human dignity is preserved and the risks of creating a ubiquitous “digital avatar” are adequately addressed by the law

Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network

Depth estimation from a single image is a fundamental problem in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for depth prediction. Specifically, we adopt an efficient linear propagation model, where the propagation is performed with a manner of recurrent convolutional operation, and the affinity among neighboring pixels is learned through a deep convolutional neural network (CNN). We apply the designed CSPN to two depth estimation tasks given a single image: (1) To refine the depth output from state-of-the-art (SOTA) existing methods; and (2) to convert sparse depth samples to a dense depth map by embedding the depth samples within the propagation procedure. The second task is inspired by the availability of LIDARs that provides sparse but accurate depth measurements. We experimented the proposed CSPN over two popular benchmarks for depth estimation, i.e. NYU v2 and KITTI, where we show that our proposed approach improves in not only quality (e.g., 30% more reduction in depth error), but also speed (e.g., 2 to 5 times faster) than prior SOTA methods.Comment: 14 pages, 8 figures, ECCV 201

Static electric fields in an infinite plane condensor with one or three homogeneous layers

Various expressions are derived for the Green's functions for a point charge in an infinite plane condensor comprising one or three homogeneous isolating parallel dielectric layers. In view of numerical evaluations needed for calculating space charge effects in detectors (e.g. RPC's) the merits of these (series and integral) representations are discussed. It turns out that in most cases the integral representations are more favourable after their convergence has been improved. This is done by subtracting simple terms having the same asymptotic behaviour as certain too slowly converging terms and adding closed expressions resulting from the integration of the simple terms. The method is demonstrated in some detail. In addition analytic expressions for the weighting field of a strip electrode are derived which allow calculation of induced signals and crosstalk

Parallelization of chip-based fluorescence immuno-assays with quantum-dot labelled beads

This paper presents an optical concept for the read-out of a parallel, bead-based fluorescence immunoassay conducted on a lab-on-a-disk platform. The reusable part of the modular setup comprises a detection unit featuring a single LED as light source, two emission-filters, and a color CCD-camera as standard components together with a spinning drive as actuation unit. The miniaturized lab-on-a-disk is devised as a disposable. In the read-out process of the parallel assay, beads are first identified by the color of incorporated quantum dots (QDs). Next, the reaction-specific fluorescence signal is quantified with FluoSpheres-labeled detection anti-bodies. To enable a fast and automated read-out, suitable algorithms have been implemented in this work. Based on this concept, we successfully demonstrated a Hepatitis-A assay on our disk-based lab-on-a-chip

Analytic expressions for static electric fields in an infinite plane condenser with one or three homogeneous layers

Expressions for the electrostatic field of a point charge in an infinite plane condenser comprising one or three homogeneous isolating parallel dielectric layers are presented. These solutions are essential for detector physics simulations of Parallel Plate Chambers (PPCs) and Resistive Plate Chambers (RPCs). In addition, expressions for the weighting field of a strip electrode are presented which allow calculation of induced signals and crosstalk in these detectors. A detailed discussion of the derivation of these solutions can be found in \cite{schnizer}

On-Line AdaTron Learning of Unlearnable Rules

We study the on-line AdaTron learning of linearly non-separable rules by a simple perceptron. Training examples are provided by a perceptron with a non-monotonic transfer function which reduces to the usual monotonic relation in a certain limit. We find that, although the on-line AdaTron learning is a powerful algorithm for the learnable rule, it does not give the best possible generalization error for unlearnable problems. Optimization of the learning rate is shown to greatly improve the performance of the AdaTron algorithm, leading to the best possible generalization error for a wide range of the parameter which controls the shape of the transfer function.)Comment: RevTeX 17 pages, 8 figures, to appear in Phys.Rev.

Tephritid-microbial interactions to enhance fruit fly performance in sterile insect technique programs

Background: The Sterile Insect Technique (SIT) is being applied for the management of economically important pest fruit flies (Diptera: Tephritidae) in a number of countries worldwide. The success and cost effectiveness of SIT depends upon the ability of mass-reared sterilized male insects to successfully copulate with conspecific wild fertile females when released in the field. Methods: We conducted a critical analysis of the literature about the tephritid gut microbiome including the advancement of methods for the identification and characterization of microbiota, particularly next generation sequencing, the impacts of irradiation (to induce sterility of flies) and fruit fly rearing, and the use of probiotics to manipulate the fruit fly gut microbiota. Results: Domestication, mass-rearing, irradiation and handling, as required in SIT, may change the structure of the fruit flies’ gut microbial community compared to that of wild flies under field conditions. Gut microbiota of tephritids are important in their hosts’ development, performance and physiology. Knowledge of how mass-rearing and associated changes of the microbial community impact the functional role of the bacteria and host biology is limited. Probiotics offer potential to encourage a gut microbial community that limits pathogens, and improves the quality of fruit flies. Conclusions: Advances in technologies used to identify and characterize the gut microbiota will continue to expand our understanding of tephritid gut microbial diversity and community composition. Knowledge about the functions of gut microbes will increase through the use of gnotobiotic models, genome sequencing, metagenomics, metatranscriptomics, metabolomics and metaproteomics. The use of probiotics, or manipulation of the gut microbiota, offers significant opportunities to enhance the production of high quality, performing fruit flies in operational SIT programs