Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression

Uncertainty estimation in machine learning is paramount for enhancing the reliability and interpretability of predictive models, especially in high-stakes real-world scenarios. Despite the availability of numerous methods, they often pose a trade-off between the quality of uncertainty estimation and computational efficiency. Addressing this challenge, we present an adaptation of the Multiple-Input Multiple-Output (MIMO) framework -- an approach exploiting the overparameterization of deep neural networks -- for pixel-wise regression tasks. Our MIMO variant expands the applicability of the approach from simple image classification to broader computer vision domains. For that purpose, we adapted the U-Net architecture to train multiple subnetworks within a single model, harnessing the overparameterization in deep neural networks. Additionally, we introduce a novel procedure for synchronizing subnetwork performance within the MIMO framework. Our comprehensive evaluations of the resulting MIMO U-Net on two orthogonal datasets demonstrate comparable accuracy to existing models, superior calibration on in-distribution data, robust out-of-distribution detection capabilities, and considerable improvements in parameter size and inference time. Code available at github.com/antonbaumann/MIMO-UnetComment: 8 pages (references do not count), Accepted at UnCV (Workshop on Uncertainty Quantification for Computer Vision at ICCV

Hydra: practical metadata security for contact discovery, messaging, and voice calls

Protecting communications’ metadata can be as important as protecting their content, i.e., recognizing someone contacting a medical service may already allow to infer sensitive information. There are numerous proposals to implement anonymous communications, yet none provides it in a strong (but feasible) threat model in an efficient way. We propose Hydra, an anonymity system that is able to efficiently provide metadata security for a wide variety of applications. Main idea is to use latency-aware, padded, and onion-encrypted circuits even for connectionless applications. This allows to implement strong metadata security for contact discovery and text-based messages with relatively low latency. Furthermore, circuits can be upgraded to support voice calls, real-time chat sessions, and file transfers - with slightly reduced anonymity in presence of global observers. We evaluate Hydra using an analytical model as well as call simulations. Compared to other systems for text-based messaging, Hydra is able to decrease end-to-end latencies by an order of magnitude without degrading anonymity. Using a dataset generated by performing latency measurements in the Tor network, we further show that Hydra is able to support anonymous voice calls with acceptable quality of service in real scenarios. A first prototype of Hydra is published as open source

CAR-NK Cells Targeting HER1 (EGFR) Show Efficient Anti-Tumor Activity against Head and Neck Squamous Cell Carcinoma (HNSCC)

(1) Background: HNSCC is a highly heterogeneous and relapse-prone form of cancer. We aimed to expand the immunological tool kit against HNSCC by conducting a functional screen to generate chimeric antigen receptor (CAR)-NK-92 cells that target HER1/epidermal growth factor receptor (EGFR). (2) Methods: Selected CAR-NK-92 cell candidates were tested for enhanced reduction of target cells, CD107a expression and IFNγ secretion in different co-culture models. For representative HNSCC models, patient-derived primary HNSCC (pHNSCC) cell lines were generated by employing an EpCAM-sorting approach to eliminate the high percentage of non-malignant cells found. (3) Results: 2D and 3D spheroid co-culture experiments showed that anti-HER1 CAR-NK-92 cells effectively eliminated SCC cell lines and primary HNSCC (pHNSCC) cells. Co-culture of tumor models with anti-HER1 CAR-NK-92 cells led to enhanced degranulation and IFNγ secretion of NK-92 cells and apoptosis of target cells. Furthermore, remaining pHNSCC cells showed upregulated expression of putative cancer stem cell marker CD44v6. (4) Conclusions: These results highlight the promising potential of CAR-NK cell therapy in HNSCC and the likely necessity to target multiple tumor-associated antigens to reduce currently high relapse rates

Chitinase activities, scab resistance, mycorrhization rates and biomass of own-rooted and grafted transgenic apple

This study investigated the impact of constitutively expressed Trichoderma atroviride genes encoding exochitinase nag70 or endochitinase ech42 in transgenic lines of the apple cultivar Pinova on the symbiosis with arbuscular mycorrhizal fungi (AMF). We compared the exo- and endochitinase activities of leaves and roots from non-transgenic Pinova and the transgenic lines T386 and T389. Local and systemic effects were examined using own-rooted trees and trees grafted onto rootstock M9. Scab susceptibility was also assessed in own-rooted and grafted trees. AMF root colonization was assessed microscopically in the roots of apple trees cultivated in pots with artificial substrate and inoculated with the AMF Glomus intraradices and Glomus mosseae. Own-rooted transgenic lines had significantly higher chitinase activities in their leaves and roots compared to non-transgenic Pinova. Both of the own-rooted transgenic lines showed significantly fewer symptoms of scab infection as well as significantly lower root colonization by AMF. Biomass production was significantly reduced in both own-rooted transgenic lines. Rootstock M9 influenced chitinase activities in the leaves of grafted scions. When grafted onto M9, the leaf chitinase activities of non-transgenic Pinova (M9/Pinova) and transgenic lines (M9/T386 and M9/T389) were not as different as when grown on their own roots. M9/T386 and M9/T389 were only temporarily less infected by scab than M9/Pinova. M9/T386 and M9/T389 did not differ significantly from M9/Pinova in their root chitinase activities, AMF root colonization and biomass