Dysregulation von GABA A-Rezeptoruntereinheiten nach Induktion fokaler kortikaler Dysplasien

Fokale kortikale Dysplasien umfassen eine heterogene Gruppe meist angeborener kortikaler Entwicklungsstörungen, die häufig mit therapierefraktären Epilepsien und/ oder neuropsychologischen Defiziten assoziiert sind. In dieser Studie wurden fokale kortikale Dysplasien durch Aufbringen einer Gefrierläsion auf die rechte Hemisphäre an neugeborenen Ratten induziert. Hierbei konnte eine weitreichende bilaterale modulierte Expression der GABAA-Rezeptoruntereinheiten alpha1, alpha3 und alpha5 nach 7, 14 und 21 Lebenstagen immunhistochemisch dargestellt werden. Eine Störung des Verteilungsmusters dieser Untereinheiten als morphologisches Korrelat der erst ab dem 12. Lebenstag elektrophysiologisch nachweisbaren Hyperexzitabilität, ist schon ab dem 7. Lebenstag zu belegen. Zusätzlich zeigte sich, dass die typische Struktur des somatosensorischen Kortex - dargestellt durch die Rezeptoruntereinheit alpha1 - insbesondere des posteriomedialen Barrel-Subfields in unmittelbarer Umgebung der Malformation weitgehend erhalten blieb. Lediglich die Flächen des posteriomedialen Barrel-Subfields und der Septen waren bilateral im Areal der Repräsentation der Vorder- bzw. Hinterpfote nach 1 und 2 Wochen aufgrund reduzierter Projektionen thalamokortikaler Neurone und kallosaler Faserverbindungen vermindert

DeepDyve: Dynamic Verification for Deep Neural Networks

Deep neural networks (DNNs) have become one of the enabling technologies in many safety-critical applications, e.g., autonomous driving and medical image analysis. DNN systems, however, suffer from various kinds of threats, such as adversarial example attacks and fault injection attacks. While there are many defense methods proposed against maliciously crafted inputs, solutions against faults presented in the DNN system itself (e.g., parameters and calculations) are far less explored. In this paper, we develop a novel lightweight fault-tolerant solution for DNN-based systems, namely DeepDyve, which employs pre-trained neural networks that are far simpler and smaller than the original DNN for dynamic verification. The key to enabling such lightweight checking is that the smaller neural network only needs to produce approximate results for the initial task without sacrificing fault coverage much. We develop efficient and effective architecture and task exploration techniques to achieve optimized risk/overhead trade-off in DeepDyve. Experimental results show that DeepDyve can reduce 90% of the risks at around 10% overhead

Building Tomograph – From Remote Sensing Data of Existing Buildings to Building Energy Simulation Input

Existing buildings often have low energy efficiency standards. For the preparation of retrofits, reliable high-quality data about the status quo is required. However, state-of-the-art analysis methods mainly rely on on-site inspections by experts and hence tend to be cost-intensive. In addition, some of the necessary devices need to be installed inside the buildings. As a consequence, owners hesitate to obtain sufficient information about potential refurbishment measures for their houses and underestimate possible savings. Remote sensing measurement technologies have the potential to provide an easy-to-use and automatable way to energetically analyze existing buildings objectively. To prepare an energetic simulation of the status quo and of possible retrofit scenarios, remote sensing data from different data sources have to be merged and combined with additional knowledge about the building. This contribution presents the current state of a project on the development of new and the optimization of conventional data acquisition methods for the energetic analysis of existing buildings solely based on contactless measurements, general information about the building, and data that residents can obtain with little effort. For the example of a single-family house in Morschenich, Germany, geometrical, semantical, and physical information are derived from photogrammetry and quantitative infrared measurements. Both are performed with the help of unmanned aerial vehicles (UAVs) and are compared to conventional methods for energy efficiency analysis regarding accuracy of and necessary effort for input data for building energy simulation. The concept of an object-oriented building model for measurement data processing is presented. Furthermore, an outlook is given on the project involving advanced remote sensing techniques such as ultrasound and microwave radar application for the measurement of additional energetic building parameters

Unique Features of a Global Human Ectoparasite Identified Through Sequencing of the Bed Bug Genome

The bed bug, Cimex lectularius, has re-established itself as a ubiquitous human ectoparasite throughout much of the world during the past two decades. This global resurgence is likely linked to increased international travel and commerce in addition to widespread insecticide resistance. Analyses of the C. lectularius sequenced genome (650 Mb) and 14,220 predicted protein-coding genes provide a comprehensive representation of genes that are linked to traumatic insemination, a reduced chemosensory repertoire of genes related to obligate hematophagy, host-symbiont interactions, and several mechanisms of insecticide resistance. In addition, we document the presence of multiple putative lateral gene transfer events. Genome sequencing and annotation establish a solid foundation for future research on mechanisms of insecticide resistance, human-bed bug and symbiont-bed bug associations, and unique features of bed bug biology that contribute to the unprecedented success of C. lectularius as a human ectoparasite

Unique features of a global human ectoparasite identified through sequencing of the bed bug genome

The bed bug, Cimex lectularius, has re-established itself as a ubiquitous human ectoparasite throughout much of the world during the past two decades. This global resurgence is likely linked to increased international travel and commerce in addition to widespread insecticide resistance. Analyses of the C. lectularius sequenced genome (650 Mb) and 14,220 predicted protein-coding genes provide a comprehensive representation of genes that are linked to traumatic insemination, a reduced chemosensory repertoire of genes related to obligate hematophagy, host–symbiont interactions, and several mechanisms of insecticide resistance. In addition, we document the presence of multiple putative lateral gene transfer events. Genome sequencing and annotation establish a solid foundation for future research on mechanisms of insecticide resistance, human–bed bug and symbiont–bed bug associations, and unique features of bed bug biology that contribute to the unprecedented success of C. lectularius as a human ectoparasite

Low-overhead fault tolerance for safety-critical neural network applications

The recent success of deep neural networks (DNNs) in challenging perception tasks makes them a powerful tool for robotic applications in open context environments, such as autonomous cars, trucks, or drones. These and other potential DNN applications require reliable and fault tolerant implementations of their safety-critical functionalities. Nevertheless, growing complexity and extreme miniaturization of modern integrated circuit technology have made it increasingly difficult to ensure reliable operation at the hardware level. Cross-layer resilience has been proposed as an approach towards solving the reliability issues of future hardware platforms more efficiently. It follows the principal idea of not handling every error at the lower hardware levels but including error resilience mechanisms at the algorithmic and system levels. The inherent redundancy generally attributed to DNNs makes them a promising candidate for algorithmic fault tolerance. However, a clear understanding of DNN error resilience, its determinants and efficient ways to exploit it are missing, which sets the research objectives of this thesis. Firstly, a new analytical method for the characterization of error resilience at the neuron level of DNNs is developed. A benchmark against previously proposed gradient-based resilience prediction approaches shows a significant improvement in resilience attribution to neurons. Furthermore, a resilience-based mapping to hardware parts with heterogeneous reliability is proposed and evaluated exemplarily with approximate arithmetic units. It is shown that a large fraction of the resilient neurons can be approximated without significant loss of classification accuracy of the DNN and power consumption can be reduced. In a second step, two new methods for the optimization of neural network error resilience are proposed and evaluated. One method focuses on the multi-objective architectural optimization of DNNs for an improved robustness against errors and better efficiency on hardware. Pareto-optimal network architectures are generated using an evolutionary algorithm and the trade-off between resilience, performance, and efficiency is evaluated. The second method is a weight adjustment procedure for pre-trained neural networks, which is able to reduce the worst-case failure rate of a DNN classifier in case of random bit-flip errors significantly. Finally, a novel error detection and mitigation technique for DNNs is proposed. The main idea of this technique is to use a trainable error detector that can detect critical bit-flip errors inside the intermediate output values of a DNN with high accuracy. Furthermore, it offers an error correction proposal that allows for a partial recovery from faults. It is experimentally shown that the overhead for computing the error detection and mitigation network is very small in comparison to the main classification network, so that it can be efficiently implemented and safeguarded