Deep learning for graphs

Deep learning for graphs encompasses all those neural models endowed with multiple layers of computation operating on data represented as graphs. The most common building blocks of these models are graph encoding layers, which compute a vector embedding for each node in a graph using message-passing operators. In this paper, we provide an overview of the key concepts in the field, point towards open questions, and frame the contributions of the ESANN 2021 special session into the broader context of deep learning for graphs.acceptedVersio

A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems

ABSTRACT We present the TCS Alignment Toolbox, which offers a flexible framework to calculate and visualize (dis)similarities between sequences in the context of educational data mining and intelligent tutoring systems. The toolbox offers a variety of alignment algorithms, allows for complex input sequences comprised of multi-dimensional elements, and is adjustable via rich parameterization options, including mechanisms for an automatic adaptation based on given data. Our demo shows an example in which the alignment measure is adapted to distinguish students' Java programs w.r.t. different solution strategies, via a machine learning technique

T cells in ANCA-associated vasculitis: what can we learn from lesional versus circulating T cells?

Anti-neutrophil cytoplasmic antibody (ANCA) - associated vasculitis (AAV) is a life-threatening autoimmune disease characterized by an antibody-mediated glomerulonephritis and necrotizing vasculitis. Apart from antibodies, T cells are also involved in disease pathogenesis. This review stresses the hallmarks of T cell-mediated pathology in AAV and highlights the characteristics of lesional and circulating T cells in the immune response in AAV. Circulating effector T-cell populations are expanded and are in a persistent state of activation. Circulating regulatory T-cell subsets are less well characterized but seem to be impaired in function. Lesional effector T cells are present in granulomas, vasculitic lesions, and nephritis. Lesional T cells usually show pro-inflammatory properties and promote granuloma formation. Apart from T cells, dendritic cells are abundantly present at the sites of inflammation and locally orchestrate the immune response. Targeting the above-mentioned T cell-mediated disease mechanisms will potentially provide powerful therapeutic tools for AAV

Metric learning for sequences in relational LVQ

Mokbel B, Paaßen B, Schleif F-M, Hammer B. Metric learning for sequences in relational LVQ. Neurocomputing. 2015;169(SI):306-322.Metric learning constitutes a well-investigated field for vectorial data with successful applications, e.g. in computer vision, information retrieval, or bioinformatics. One particularly promising approach is offered by low-rank metric adaptation integrated into modern variants of learning vector quantization (LVQ). This technique is scalable with respect to both data dimensionality and the number of data points, and it can be accompanied by strong guarantees of learning theory. Recent extensions of LVQ to general (dis-)similarity data have paved the way towards LVQ classifiers for non-vectorial, possibly discrete, structured objects such as sequences, which are addressed by classical alignment in bioinformatics applications. In this context, the choice of metric parameters plays a crucial role for the result, just as it does in the vectorial setting. In this contribution, we propose a metric learning scheme which allows for an autonomous learning of parameters (such as the underlying scoring matrix in sequence alignments) according to a given discriminative task in relational LVQ. Besides facilitating the often crucial and problematic choice of the scoring parameters in applications, this extension offers an increased interpretability of the results by pointing out structural invariances for the given task

Bio-inspired geotechnical engineering: Principles, current work, opportunities and challenges

publishedVersio

Bio-inspired geotechnical engineering: principles, current work, opportunities and challenges

A broad diversity of biological organisms and systems interact with soil in ways that facilitate their growth and survival. These interactions are made possible by strategies that enable organisms to accomplish functions that can be analogous to those required in geotechnical engineering systems. Examples include anchorage in soft and weak ground, penetration into hard and stiff subsurface materials and movement in loose sand. Since the biological strategies have been ‘vetted’ by the process of natural selection, and the functions they accomplish are governed by the same physical laws in both the natural and engineered environments, they represent a unique source of principles and design ideas for addressing geotechnical challenges. Prior to implementation as engineering solutions, however, the differences in spatial and temporal scales and material properties between the biological environment and engineered system must be addressed. Current bio-inspired geotechnics research is addressing topics such as soil excavation and penetration, soil–structure interface shearing, load transfer between foundation and anchorage elements and soils, and mass and thermal transport, having gained inspiration from organisms such as worms, clams, ants, termites, fish, snakes and plant roots. This work highlights the potential benefits to both geotechnical engineering through new or improved solutions and biology through understanding of mechanisms as a result of cross-disciplinary interactions and collaborations

Adaptive distance measures for sequential data

Abstract. Recent extensions of learning vector quantization (LVQ) to general (dis-)similarity data have paved the way towards LVQ classifiers for possibly discrete, structured objects such as sequences addressed by classical alignment. In this contribution, we propose a metric learning scheme based on this framework which allows for autonomous learning of the underlying scoring matrix according to a given discriminative task. Besides facilitating the often crucial and problematic choice of the scoring matrix in applications, this extension offers an increased interpretability of the results by pointing out structural invariances for the given task.

Reservoir Memory Machines as Neural Computers

Paassen B, Schulz A, Stewart TC, Hammer B. Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems. 2021:1-11