A Compositional Approach to Creating Architecture Frameworks with an Application to Distributed AI Systems

Artificial intelligence (AI) in its various forms finds more and more its way into complex distributed systems. For instance, it is used locally, as part of a sensor system, on the edge for low-latency high-performance inference, or in the cloud, e.g. for data mining. Modern complex systems, such as connected vehicles, are often part of an Internet of Things (IoT). To manage complexity, architectures are described with architecture frameworks, which are composed of a number of architectural views connected through correspondence rules. Despite some attempts, the definition of a mathematical foundation for architecture frameworks that are suitable for the development of distributed AI systems still requires investigation and study. In this paper, we propose to extend the state of the art on architecture framework by providing a mathematical model for system architectures, which is scalable and supports co-evolution of different aspects for example of an AI system. Based on Design Science Research, this study starts by identifying the challenges with architectural frameworks. Then, we derive from the identified challenges four rules and we formulate them by exploiting concepts from category theory. We show how compositional thinking can provide rules for the creation and management of architectural frameworks for complex systems, for example distributed systems with AI. The aim of the paper is not to provide viewpoints or architecture models specific to AI systems, but instead to provide guidelines based on a mathematical formulation on how a consistent framework can be built up with existing, or newly created, viewpoints. To put in practice and test the approach, the identified and formulated rules are applied to derive an architectural framework for the EU Horizon 2020 project ``Very efficient deep learning in the IoT" (VEDLIoT) in the form of a case study

Mesenchymal cell migration on one-dimensional micropatterns

Quantitative studies of mesenchymal cell motion are important to elucidate cytoskeleton function and mechanisms of cell migration. To this end, confinement of cell motion to one dimension (1D) significantly simplifies the problem of cell shape in experimental and theoretical investigations. Here we review 1D migration assays employing micro-fabricated lanes and reflect on the advantages of such platforms. Data are analyzed using biophysical models of cell migration that reproduce the rich scenario of morphodynamic behavior found in 1D. We describe basic model assumptions and model behavior. It appears that mechanical models explain the occurrence of universal relations conserved across different cell lines such as the adhesion-velocity relation and the universal correlation between speed and persistence (UCSP). We highlight the unique opportunity of reproducible and standardized 1D assays to validate theory based on statistical measures from large data of trajectories and discuss the potential of experimental settings embedding controlled perturbations to probe response in migratory behavior.Peer Reviewe

Imaging correlated wave functions of few-electron quantum dots: Theory and scanning tunneling spectroscopy experiments

We show both theoretically and experimentally that scanning tunneling spectroscopy (STS) images of semiconductor quantum dots may display clear signatures of electron-electron correlation. We apply many-body tunneling theory to a realistic model which fully takes into account correlation effects and dot anisotropy. Comparing measured STS images of freestanding InAs quantum dots with those calculated by the full configuration interaction method, we explain the wave function sequence in terms of images of one- and two-electron states. The STS map corresponding to double charging is significantly distorted by electron correlation with respect to the non-interacting case.Comment: RevTeX 4.0, 5 pages, 3 B/W figures, 1 table. This paper is based on an invited talk presented by the authors at the 28th International Conference on the Physics of Semiconductors, which was held 24-28 July 2006, in Vienna, Austri

The Secret to Better AI and Better Software (Is Requirements Engineering)

Much has been written about the algorithmic role that AI plays for automation in SE. But what about the role of AI, augmented by human knowledge? Can we make a profound advance by combining human and artificial intelligence? Researchers in requirements engineering think so, arguing that requirement engineering is the secret weapon for better AI and better software

Modulation of mammalian cell behavior by nanoporous glass

The introduction of novel bioactive materials to manipulate living cell behavior is a crucial topic for biomedical research and tissue engineering. Biomaterials or surface patterns that boost specific cell functions can enable innovative new products in cell culture and diagnostics. This study investigates the influence of the intrinsically nano-patterned surface of nanoporous glass membranes on the behavior of mammalian cells. Three different cell lines and primary human mesenchymal stem cells (hMSCs) proliferate readily on nanoporous glass membranes with mean pore sizes between 10 and 124 nm. In both proliferation and mRNA expression experiments, L929 fibroblasts show a distinct trend toward mean pore sizes >80 nm. For primary hMSCs, excellent proliferation is observed on all nanoporous surfaces. hMSCs on samples with 17 nm pore size display increased expression of COL10, COL2A1, and SOX9, especially during the first two weeks of culture. In the upside down culture, SK-MEL-28 cells on nanoporous glass resist the gravitational force and proliferate well in contrast to cells on flat references. The effect of paclitaxel treatment of MDA-MB-321 breast cancer cells is already visible after 48 h on nanoporous membranes and strongly pronounced in comparison to reference samples, underlining the material's potential for functional drug screening

Mesenchymal cell migration on one-dimensional micropatterns

Quantitative studies of mesenchymal cell motion are important to elucidate cytoskeleton function and mechanisms of cell migration. To this end, confinement of cell motion to one dimension (1D) significantly simplifies the problem of cell shape in experimental and theoretical investigations. Here we review 1D migration assays employing micro-fabricated lanes and reflect on the advantages of such platforms. Data are analyzed using biophysical models of cell migration that reproduce the rich scenario of morphodynamic behavior found in 1D. We describe basic model assumptions and model behavior. It appears that mechanical models explain the occurrence of universal relations conserved across different cell lines such as the adhesion-velocity relation and the universal correlation between speed and persistence (UCSP). We highlight the unique opportunity of reproducible and standardized 1D assays to validate theory based on statistical measures from large data of trajectories and discuss the potential of experimental settings embedding controlled perturbations to probe response in migratory behavior

Discovery of the Cobalt Isotopes

Twenty-six cobalt isotopes have so far been observed; the discovery of these isotopes is discussed. For each isotope a brief summary of the first refereed publication, including the production and identification method, is presented.Comment: to be published in Atomic Data and Nuclear Data Table