Publikationen

Due to the continuous digitalization of our society, distributed and web-based applications become omnipresent and making them more secure gains paramount relevance. Deep learning (DL) and its representation learning approach are increasingly been proposed for program code analysis potentially providing a powerful means in making software systems less vulnerable. This systematic literature review (SLR) is aiming for a thorough analysis and comparison of 32 primary studies on DL-based vulnerability analysis of program code. We found a rich variety of proposed analysis approaches, code embeddings and network topologies. We discuss these techniques and alternatives in detail. By compiling commonalities and differences in the approaches, we identify the current state of research in this area and discuss future directions. We also provide an overview of publicly available datasets in order to foster a stronger benchmarking of approaches. This SLR provides an overview and starting point for researchers interested in deep vulnerability analysis on program code.

Magnetic hybrid materials are a promising group of substances. Their interaction with matrices is challenging with regard to the underlying physical and chemical mechanisms. But thinking matrices as biological membranes or even structured cell layers they become interesting with regard to potential biomedical applications. Therefore, we established in vitro blood-organ barrier models to study the interaction and processing of superparamagnetic iron oxide nanoparticles (SPIONs) with these cellular structures in the presence of a magnetic field gradient. A one-cell-type-based blood-brain barrier model was used to investigate the attachment and uptake mechanisms of differentially charged magnetic hybrid materials. Inhibition of clathrin-dependent endocytosis and F-actin depolymerization led to a dramatic reduction of cellular uptake. Furthermore, the subsequent transportation of SPIONs through the barrier and the ability to detect these particles was of interest. Negatively charged SPIONs could be detected behind the barrier as well as in a reporter cell line. These observations could be confirmed with a two-cell-type-based blood-placenta barrier model. While positively charged SPIONs heavily interact with the apical cell layer, neutrally charged SPIONs showed a retarded interaction behavior. Behind the blood-placenta barrier, negatively charged SPIONs could be clearly detected. Finally, the transfer of the in vitro blood-placenta model in a microfluidic biochip allows the integration of shear stress into the system. Even without particle accumulation in a magnetic field gradient, the negatively charged SPIONs were detectable behind the barrier. In conclusion, in vitro blood-organ barrier models allow the broad investigation of magnetic hybrid materials with regard to biocompatibility, cell interaction, and transfer through cell layers on their way to biomedical application.

Defocus methods have become more and more popular for the estimation of the 3D position of particles in flows (Cierpka and Kähler, 2011; Rossi and Kähler, 2014). Typically the depth positions of particles are determined by the defocused particle images using image processing algorithms. As these methods allow the determination of all components of the velocity vector in a volume using only a single optical access and a single camera, they are often used in, but not limited to microfluidics. Since almost no additional equipment is necessary they are low-cost methods that are meanwhile widely applied in different fields. To overcome the ambiguity of perfect optical systems, often a cylindrical lens is introduced in the optical system which enhances the differences of the obtained particle images for different depth positions. However, various methods are emerging and it is difficult for non-experienced users to judge what method might be best suited for a given experimental setup. Therefore, the aim of the presentation is a thorough evaluation of the performance of general advanced methods, including also recently presented neural networks (Franchini and Krevor, 2020; König et al., 2020) based on typical images.

We investigate the separation problem for regular ω-languages with respect to the Wagner hierarchy where the input languages are given as deterministic Muller automata (DMA). We show that a minimal separating DMA can be computed in exponential time and that some languages require separators of exponential size. Further, we show that in this setting it can be decided in polynomial time whether a separator exists on a certain level of the Wagner hierarchy and that emptiness of the intersection of two languages given by DMAs can be decided in polynomial time. Finally, we show that separation can also be decided in polynomial time if the input languages are given as deterministic parity automata.