Development and Function of Dendritic Cell Subsets

Classical dendritic cells (cDCs) form a critical interface between innate and adaptive immunity. As myeloid immune cell sentinels, cDCs are specialized in the sensing of pathogen challenges and cancer. They translate the latter for T cells into peptide form. Moreover, cDCs provide additional critical information on the original antigen context to trigger a diverse spectrum of appropriate protective responses. Here we review recent progress in our understanding of cDC subsets in mice. We will discuss cDC subset ontogeny and transcription factor dependencies, as well as emerging functional specializations within the cDC compartment in lymphoid and nonlymphoid tissues

Spectral Distribution Aware Image Generation

Recent advances in deep generative models for photo-realistic images have led to high quality visual results. Such models learn to generate data from a given training distribution such that generated images can not be easily distinguished from real images by the human eye. Yet, recent work on the detection of such fake images pointed out that they are actually easily distinguishable by artifacts in their frequency spectra. In this paper, we propose to generate images according to the frequency distribution of the real data by employing a spectral discriminator. The proposed discriminator is lightweight, modular and works stably with different commonly used GAN losses. We show that the resulting models can better generate images with realistic frequency spectra, which are thus harder to detect by this cue.Comment: Accepted at AAAI 2021 (conference version). Code: https://github.com/steffen-jung/SpectralGA

Non-Identical Twins – Microglia and Monocyte-Derived Macrophages in Acute Injury and Autoimmune Inflammation

The brain has been commonly regarded as a “tissue behind walls.” Appearance of immune cells in the brain has been taken as a sign of pathology. Moreover, since infiltrating monocyte-derived macrophages and activated resident microglia were indistinguishable by conventional means, both populations were considered together as inflammatory cells that should be mitigated. Yet, because the microglia permanently reside in the brain, attributing to them negative properties evoked an ongoing debate; why cells that are supposed to be the brain guardians acquire only destructive potential? Studies over the last two decades in the immune arena in general, and in the context of central nervous system pathology in particular, have resulted in a paradigm shift toward a more balanced appreciation of the contributions of immune cells in the context of brain maintenance and repair, and toward the recognition of distinct roles of resident microglia and infiltrating monocyte-derived macrophages

Learning Where To Look -- Generative NAS is Surprisingly Efficient

The efficient, automated search for well-performing neural architectures (NAS) has drawn increasing attention in the recent past. Thereby, the predominant research objective is to reduce the necessity of costly evaluations of neural architectures while efficiently exploring large search spaces. To this aim, surrogate models embed architectures in a latent space and predict their performance, while generative models for neural architectures enable optimization-based search within the latent space the generator draws from. Both, surrogate and generative models, have the aim of facilitating query-efficient search in a well-structured latent space. In this paper, we further improve the trade-off between query-efficiency and promising architecture generation by leveraging advantages from both, efficient surrogate models and generative design. To this end, we propose a generative model, paired with a surrogate predictor, that iteratively learns to generate samples from increasingly promising latent subspaces. This approach leads to very effective and efficient architecture search, while keeping the query amount low. In addition, our approach allows in a straightforward manner to jointly optimize for multiple objectives such as accuracy and hardware latency. We show the benefit of this approach not only w.r.t. the optimization of architectures for highest classification accuracy but also in the context of hardware constraints and outperform state-of-the-art methods on several NAS benchmarks for single and multiple objectives. We also achieve state-of-the-art performance on ImageNet. The code is available at http://github.com/jovitalukasik/AG-Net .Comment: Accepted to European Conference on Computer Vision 202

Severe B Cell Deficiency in Mice Lacking the Tec Kinase Family Members Tec and Btk

The cytoplasmic protein tyrosine kinase Tec has been proposed to have important functions in hematopoiesis and lymphocyte signal transduction. Here we show that Tec-deficient mice developed normally and had no major phenotypic alterations of the immune system. To reveal potential compensatory roles of other Tec kinases such as Bruton's tyrosine kinase (Btk), Tec/Btk double-deficient mice were generated. These mice exhibited a block at the B220+CD43+ stage of B cell development and displayed a severe reduction of peripheral B cell numbers, particularly immunoglobulin (Ig)MloIgDhi B cells. Although Tec/Btknull mice were able to form germinal centers, the response to T cell–dependent antigens was impaired. Thus, Tec and Btk together have an important role both during B cell development and in the generation and/or function of the peripheral B cell pool. The ability of Tec to compensate for Btk may also explain phenotypic differences in X-linked immunodeficiency (xid) mice compared with human X-linked agammaglobulinemia (XLA) patients

FrequencyLowCut Pooling -- Plug & Play against Catastrophic Overfitting

Over the last years, Convolutional Neural Networks (CNNs) have been the dominating neural architecture in a wide range of computer vision tasks. From an image and signal processing point of view, this success might be a bit surprising as the inherent spatial pyramid design of most CNNs is apparently violating basic signal processing laws, i.e. Sampling Theorem in their down-sampling operations. However, since poor sampling appeared not to affect model accuracy, this issue has been broadly neglected until model robustness started to receive more attention. Recent work [17] in the context of adversarial attacks and distribution shifts, showed after all, that there is a strong correlation between the vulnerability of CNNs and aliasing artifacts induced by poor down-sampling operations. This paper builds on these findings and introduces an aliasing free down-sampling operation which can easily be plugged into any CNN architecture: FrequencyLowCut pooling. Our experiments show, that in combination with simple and fast FGSM adversarial training, our hyper-parameter free operator significantly improves model robustness and avoids catastrophic overfitting

Local Spherical Harmonics Improve Skeleton-Based Hand Action Recognition

Hand action recognition is essential. Communication, human-robot interactions, and gesture control are dependent on it. Skeleton-based action recognition traditionally includes hands, which belong to the classes which remain challenging to correctly recognize to date. We propose a method specifically designed for hand action recognition which uses relative angular embeddings and local Spherical Harmonics to create novel hand representations. The use of Spherical Harmonics creates rotation-invariant representations which make hand action recognition even more robust against inter-subject differences and viewpoint changes. We conduct extensive experiments on the hand joints in the First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations, and on the NTU RGB+D 120 dataset, demonstrating the benefit of using Local Spherical Harmonics Representations. Our code is available at https://github.com/KathPra/LSHR_LSHT