On the Practicality of Deterministic Epistemic Uncertainty

A set of novel approaches for estimating epistemic uncertainty in deep neural networks with a single forward pass has recently emerged as a valid alternative to Bayesian Neural Networks. On the premise of informative representations, these deterministic uncertainty methods (DUMs) achieve strong performance on detecting out-of-distribution (OOD) data while adding negligible computational costs at inference time. However, it remains unclear whether DUMs are well calibrated and can seamlessly scale to real-world applications - both prerequisites for their practical deployment. To this end, we first provide a taxonomy of DUMs, and evaluate their calibration under continuous distributional shifts. Then, we extend them to semantic segmentation. We find that, while DUMs scale to realistic vision tasks and perform well on OOD detection, the practicality of current methods is undermined by poor calibration under distributional shifts.Comment: International Conference on Machine Learning 202

{SHIFT}: {A} Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation

Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous-driving systems. Existing image- and video-based driving datasets, however, fall short of capturing the mutable nature of the real world. In this paper, we introduce the largest multi-task synthetic dataset for autonomous driving, SHIFT. It presents discrete and continuous shifts in cloudiness, rain and fog intensity, time of day, and vehicle and pedestrian density. Featuring a comprehensive sensor suite and annotations for several mainstream perception tasks, SHIFT allows to investigate how a perception systems' performance degrades at increasing levels of domain shift, fostering the development of continuous adaptation strategies to mitigate this problem and assessing the robustness and generality of a model. Our dataset and benchmark toolkit are publicly available at www.vis.xyz/shift

Space Solar Cells – 3G30 and Next Generation Radiation Hard Products

The 3G30-Advanced, AZUR SPACE’s latest qualified solar cell product, provides highest end-of-life efficiencies in space. The cell reaches 27.8% at a fluence of 5 E14 cm−2 and 26.5% at a fluence of 1 E15 cm−2 1 MeV electrons. The cell mass can be reduced to a minimum by substrate thinning, the cell cost can be reduced by implementation of large area configurations and even higher radiation hardness can be achieved by using AZUR’s proprietary 3G30-1E16+ design. Various configurations are currently in production. The increasing demand for cells suited for LEO applications, made AZUR to develop a novel upright metamorphic triple junction solar cell with a BOL efficiency of 31% designed for a fluence of 1 E14 cm−2 1 MeV electrons. This cell design is already in production. AZUR’s next generation product 4G32 comprises an upright metamorphic 4-junction device with 28.5% EOL (1 E15 cm−2 1 MeV electrons) efficiency. Hence, the 4G32 even surpasses the EOL efficiency of the lattice-matched 3-junction cell 3G30-Advanced. It utilizes the excess current of the Ge subcell by a metamorphic cell concept and a fourth junction added to the stack. This cell will be qualified by mid-2017. This paper summarizes the results and achievements for various 3G and 4G solar cell products from AZUR SPACE, including radiation hardness and cell formats

Mechanisms of Transcranial Doppler Ultrasound phenotypes in paediatric cerebral malaria remain elusive.

BACKGROUND: Cerebral malaria (CM) results in significant paediatric death and neurodisability in sub-Saharan Africa. Several different alterations to typical Transcranial Doppler Ultrasound (TCD) flow velocities and waveforms in CM have been described, but mechanistic contributors to these abnormalities are unknown. If identified, targeted, TCD-guided adjunctive therapy in CM may improve outcomes. METHODS: This was a prospective, observational study of children 6 months to 12 years with CM in Blantyre, Malawi recruited between January 2018 and June 2021. Medical history, physical examination, laboratory analysis, electroencephalogram, and magnetic resonance imaging were undertaken on presentation. Admission TCD results determined phenotypic grouping following a priori definitions. Evaluation of the relationship between haemodynamic, metabolic, or intracranial perturbations that lead to these observed phenotypes in other diseases was undertaken. Neurological outcomes at hospital discharge were evaluated using the Paediatric Cerebral Performance Categorization (PCPC) score. RESULTS: One hundred seventy-four patients were enrolled. Seven (4%) had a normal TCD examination, 57 (33%) met criteria for hyperaemia, 50 (29%) for low flow, 14 (8%) for microvascular obstruction, 11 (6%) for vasospasm, and 35 (20%) for isolated posterior circulation high flow. A lower cardiac index (CI) and higher systemic vascular resistive index (SVRI) were present in those with low flow than other groups (p \u3c 0.003), though these values are normal for age (CI 4.4 [3.7,5] l/min/m2, SVRI 1552 [1197,1961] dscm-5m2). Other parameters were largely not significantly different between phenotypes. Overall, 118 children (68%) had a good neurological outcome. Twenty-three (13%) died, and 33 (19%) had neurological deficits. Outcomes were best for participants with hyperaemia and isolated posterior high flow (PCPC 1-2 in 77 and 89% respectively). Participants with low flow had the least likelihood of a good outcome (PCPC 1-2 in 42%) (p \u3c 0.001). Cerebral autoregulation was significantly better in children with good outcome (transient hyperemic response ratio (THRR) 1.12 [1.04,1.2]) compared to a poor outcome (THRR 1.05 [0.98,1.02], p = 0.05). CONCLUSIONS: Common pathophysiological mechanisms leading to TCD phenotypes in non-malarial illness are not causative in children with CM. Alternative mechanistic contributors, including mechanical factors of the cerebrovasculature and biologically active regulators of vascular tone should be explored