Verification of Size Invariance in DNN Activations using Concept Embeddings

The benefits of deep neural networks (DNNs) have become of interest for safety critical applications like medical ones or automated driving. Here, however, quantitative insights into the DNN inner representations are mandatory. One approach to this is concept analysis, which aims to establish a mapping between the internal representation of a DNN and intuitive semantic concepts. Such can be sub-objects like human body parts that are valuable for validation of pedestrian detection. To our knowledge, concept analysis has not yet been applied to large object detectors, specifically not for sub-parts. Therefore, this work first suggests a substantially improved version of the Net2Vec approach (arXiv:1801.03454) for post-hoc segmentation of sub-objects. Its practical applicability is then demonstrated on a new concept dataset by two exemplary assessments of three standard networks, including the larger Mask R-CNN model (arXiv:1703.06870): (1) the consistency of body part similarity, and (2) the invariance of internal representations of body parts with respect to the size in pixels of the depicted person. The findings show that the representation of body parts is mostly size invariant, which may suggest an early intelligent fusion of information in different size categories.Comment: 12 pages, 7 figures; Camera-ready version for AIAI202

A Critical Look at the Digital Scholarship Corpus: How Access Influences the Questions We (Can) Ask

Access to research materials is an issue that cuts across disciplines and impacts most researchers as they gather information. For a digital scholar in need of a textual corpus, however, these challenges may be particularly acute. Those studying mid-to-late 20th century works may find themselves in uncertain territory with regard to copyright and licensing. Those studying historically marginalized populations may have trouble finding a pre-compiled corpus, or finding texts at all. Researchers at smaller institutions or in underfunded departments may find that existing datasets are not available to them due to cost, or that they run into copyright and licensing barriers when attempting to compile a large corpus of texts. Even an existing or easily harvested corpus may present structural challenges for our tools. How do we diversify and democratize digital scholarship while also navigating the difficulties of equitable access to information

Molecular mechanisms of autophagy and the effect of autophagy dysfunction on mitochondrial function

PhD ThesisLong lifespan of evolutionary higher organisms including humans is associated with the challenge to maintain viability of post mitotic cells, such as neurons, for decades. Autophagy is increasingly recognized as an important prosurvival pathway in oxidative and proteotoxic stress conditions. Autophagy degrades cytosolic macromolecules in response to starvation and is involved in the selective degradation of damaged/toxic organelles, such as mitochondria. With age autophagy function declines, and is also compromised in several neurodegenerative diseases. We identified a novel role for autophagy in the maintenance of mitochondrial health, specifically respiratory complex I. Intriguingly, galactose-induced cell death of autophagy deficient cells was rescued by preventing ROS production at complex I or bypassing complex I-linked respiration. We propose that aberrant ROS production via complex I in response to autophagy deficiency could be pathogenic and result in neurodegeneration and preventing this could be an interesting therapeutic target. Furthermore, we found that vertebrates have evolved mechanisms to induce autophagy in response to oxidative stress. This involves the oxidation of the autophagy receptor p62, which promotes autophagy flux and the clearance of autophagy cargo, resulting in increased stress resistance in mammalian cells and survival under stress in flies. In addition, we obtained data revealing an important role for redox-regulated cysteines in NDP52 for the degradation of mitochondria via mitophagy and tools were created to study the role of other autophagy receptors in autophagy initiation and selective autophagy.Biotechnology and Biological Sciences Research Counci

Strategies for Safety Goal Decomposition for Neural Networks

Neural networks (NNs) have become a key technology for solving highly complex tasks, and require integration into future safety argumentations. New safety relevant aspects introduced by NN based algorithms are: representativity of test cases, robustness, inner representation and logic, and failure detection for NNs. In this paper, a general argumentation structure for safety cases respecting these four aspects is proposed together with possible sources of evidence

Navigating Copyright in Electronic Theses and Dissertations

Graduate students preparing electronic theses and dissertations (ETDs) encounter a broad range of questions related to copyright—both in terms of their use of copyrighted materials and their own status as copyrightholders. This project seeks to ascertain the extent to which these specific needs are addressed in published library literature, and to make recommendations to fill the gaps

Concept Enforcement and Modularization for the ISO 26262 Safety Case of Neural Networks

The ability to formulate formally verifiable requirements is crucial for the safety verification of software units in the automotive industries. However, it is very restricted for complex perception tasks involving deep neural networks (DNNs) due to their black-box character. For a solution we propose to identify or enforce human interpretable concepts as intermediate output of the DNN. Two effects are expected: Requirements can be formulated using these concepts. And the DNN is modularized, thus reduces complexity and therefore easing a safety case. A research project proposal for a PhD thesis is sketched in the following