End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks

In this work we present a novel end-to-end framework for tracking and classifying a robot's surroundings in complex, dynamic and only partially observable real-world environments. The approach deploys a recurrent neural network to filter an input stream of raw laser measurements in order to directly infer object locations, along with their identity in both visible and occluded areas. To achieve this we first train the network using unsupervised Deep Tracking, a recently proposed theoretical framework for end-to-end space occupancy prediction. We show that by learning to track on a large amount of unsupervised data, the network creates a rich internal representation of its environment which we in turn exploit through the principle of inductive transfer of knowledge to perform the task of it's semantic classification. As a result, we show that only a small amount of labelled data suffices to steer the network towards mastering this additional task. Furthermore we propose a novel recurrent neural network architecture specifically tailored to tracking and semantic classification in real-world robotics applications. We demonstrate the tracking and classification performance of the method on real-world data collected at a busy road junction. Our evaluation shows that the proposed end-to-end framework compares favourably to a state-of-the-art, model-free tracking solution and that it outperforms a conventional one-shot training scheme for semantic classification

The South Residual CO2 Cap on Mars: Investigations with a Mars Global Climate Model

The CO2 cycle is one of the three controlling climate cycles on Mars. One aspect of the CO2 cycle that is not yet fully understood is the existence of a residual CO2 ice cap that is offset from the south pole. Previous investigations suggest that the atmosphere could control the placement of the south residual cap (e.g., Colaprete et al., 2005). These investigations show that topographically forced stationary eddies in the south during southern hemisphere winter produce colder atmospheric temperatures and increased CO2 snowfall over the hemisphere where the residual cap resides. Since precipitated CO2 ice produces higher surface albedos than directly deposited CO2 ice, it is plausible that CO2 snowfall resulting from the zonally asymmetric atmospheric circulation produces surface ice albedos high enough to maintain a residual cap only in one hemisphere. Our current work builds on these initial investigations with a version of the NASA Ames Mars Global Climate Model (GCM) that includes a sophisticated CO2 cloud microphysical scheme. Processes of cloud nucleation, growth, sedimentation, and radiative effects are accounted for. Simulated results thus far agree well with the Colaprete et al. studythe zonally asymmetric nature of the atmospheric circulation produces enhanced snowfall over the residual cap hemisphere throughout much of the winter season. However, the predicted snowfall patterns vary significantly with season throughout the cap growth and recession phases. We will present a detailed analysis of the seasonal evolution of the predicted atmospheric circulation and snowfall patterns to more fully evaluate the hypothesis that the atmosphere controls the placement of the south residual cap

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity.

Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism, and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design, and personalized medicine approaches for COVID-19

Predictive sensing for field robotics

The global autonomous robot market is expected to be worth more than eleven billion US dollars by 2024, with a need - across industries - for autonomous robots able to operate safely in complex and dynamic real-world environments. In this thesis, we argue that this will require developing robots able to develop situational awareness by constructing a higher level understanding of the world beyond the instantaneous and limited data provided by their sensors. We suggest this can be achieved by enabling robots to learn directly from their environment to predict the future evolution of the world and performance of their systems. Robust mobile autonomy is developed around the three pillars of navigation, perception, and planning. For an agent to effectively plan a route through its environment, it needs to know where it is in its environment and what is going on around it. To do so, it is equipped with localisation and perception systems which can interpret incoming data from onboard sensors. Localisation systems typically provide instantaneous information with regards to whether an agent is localised or lost. Perception systems typically tracks objects in the environment using multi-stage pipelines which operate independently, require much hand-engineering, and show little robustness to natural occlusions. These low-level interpretations of incoming data often contribute to poor situational awareness and make it difficult to plan ahead with far-sightedness. In this thesis we address both shortcomings and propose data-driven approaches to increase situational awareness for a localisation and perception system operating in the field. In the area of navigation, we propose a novel framework for predicting ahead of time how well a robot will be able to localise in a given environment given an appearance model of the scene. In the area of perception, we extend an end-to-end deep-learning framework for predicting near-future scene occupancy beyond natural occlusions, to operate in the real world, from a moving platform, and taking into consideration the scene context. In doing so, we contribute to developing greater situational awareness for robotic systems operating in real world environments. We argue that robotic systems can make greater sense of what their are perceiving by moving away from instant sensing to predictive sensing. </p

Predictive sensing for field robotics

The global autonomous robot market is expected to be worth more than eleven billion US dollars by 2024, with a need - across industries - for autonomous robots able to operate safely in complex and dynamic real-world environments. In this thesis, we argue that this will require developing robots able to develop situational awareness by constructing a higher level understanding of the world beyond the instantaneous and limited data provided by their sensors. We suggest this can be achieved by enabling robots to learn directly from their environment to predict the future evolution of the world and performance of their systems. Robust mobile autonomy is developed around the three pillars of navigation, perception, and planning. For an agent to effectively plan a route through its environment, it needs to know where it is in its environment and what is going on around it. To do so, it is equipped with localisation and perception systems which can interpret incoming data from onboard sensors. Localisation systems typically provide instantaneous information with regards to whether an agent is localised or lost. Perception systems typically tracks objects in the environment using multi-stage pipelines which operate independently, require much hand-engineering, and show little robustness to natural occlusions. These low-level interpretations of incoming data often contribute to poor situational awareness and make it difficult to plan ahead with far-sightedness. In this thesis we address both shortcomings and propose data-driven approaches to increase situational awareness for a localisation and perception system operating in the field. In the area of navigation, we propose a novel framework for predicting ahead of time how well a robot will be able to localise in a given environment given an appearance model of the scene. In the area of perception, we extend an end-to-end deep-learning framework for predicting near-future scene occupancy beyond natural occlusions, to operate in the real world, from a moving platform, and taking into consideration the scene context. In doing so, we contribute to developing greater situational awareness for robotic systems operating in real world environments. We argue that robotic systems can make greater sense of what their are perceiving by moving away from instant sensing to predictive sensing. </p

Parkinson's disease polygenic risk score is not associated with impulse control disorders: A longitudinal study

International audienceObjective: To examine the relationship between a Parkinson's disease (PD) polygenic risk score (PRS) and impulse control disorders (ICDs) in PD. Background: Genome wide association studies (GWAS) have brought forth a PRS associated with increased risk of PD and younger disease onset. ICDs are frequent adverse effects of dopaminergic drugs and are also more frequent in patients with younger disease onset. It is unknown whether ICDs and PD share genetic susceptibility. Methods: We used data from a multicenter longitudinal cohort of PD patients with annual visits up to 6 years (DIG-PD). At each visit ICDs, defined as compulsive gambling, buying, eating, or sexual behavior were evaluated by movement disorders specialists. We genotyped DNAs using the Megachip assay (Illumina) and calculated a weighted PRS based on 90 SNPs associated with PD. We estimated the association between PRS and prevalence of ICDs at each visit using Poisson generalized estimating equations, adjusted for dopaminergic treatment and other known risk factors for ICDs. Results: Of 403 patients, 185 developed ICDs. Patients with younger age at onset had a higher prevalence of ICDs (p < 0.001) as well as higher PRS values (p = 0.06). At baseline, there was no association between the PRS and ICDs (overall, p = 0.84). The prevalence of ICDs increased over time similarly across the quartiles of the PRS (overall, p = 0.88; DA users, p = 0.99). Conclusion: Despite younger disease onset being associated with both higher PRS and ICD prevalence, our findings are not in favor of common susceptibility genes for PD and ICDs

Performance characteristics of five immunoassays for SARS-CoV-2: a head-to-head benchmark comparison

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic in 2020. Testing is crucial for mitigating public health and economic effects. Serology is considered key to population-level surveillance and potentially individual-level risk assessment. However, immunoassay performance has not been compared on large, identical sample sets. We aimed to investigate the performance of four high-throughput commercial SARS-CoV-2 antibody immunoassays and a novel 384-well ELISA.We did a head-to-head assessment of SARS-CoV-2 IgG assay (Abbott, Chicago, IL, USA), LIAISON SARS-CoV-2 S1/S2 IgG assay (DiaSorin, Saluggia, Italy), Elecsys Anti-SARS-CoV-2 assay (Roche, Basel, Switzerland), SARS-CoV-2 Total assay (Siemens, Munich, Germany), and a novel 384-well ELISA (the Oxford immunoassay). We derived sensitivity and specificity from 976 pre-pandemic blood samples (collected between Sept 4, 2014, and Oct 4, 2016) and 536 blood samples from patients with laboratory-confirmed SARS-CoV-2 infection, collected at least 20 days post symptom onset (collected between Feb 1, 2020, and May 31, 2020). Receiver operating characteristic (ROC) curves were used to assess assay thresholds.At the manufacturers' thresholds, for the Abbott assay sensitivity was 92·7% (95% CI 90·2–94·8) and specificity was 99·9% (99·4–100%); for the DiaSorin assay sensitivity was 96·2% (94·2–97·7) and specificity was 98·9% (98·0–99·4); for the Oxford immunoassay sensitivity was 99·1% (97·8–99·7) and specificity was 99·0% (98·1–99·5); for the Roche assay sensitivity was 97·2% (95·4–98·4) and specificity was 99·8% (99·3–100); and for the Siemens assay sensitivity was 98·1% (96·6–99·1) and specificity was 99·9% (99·4–100%). All assays achieved a sensitivity of at least 98% with thresholds optimised to achieve a specificity of at least 98% on samples taken 30 days or more post symptom onset.Four commercial, widely available assays and a scalable 384-well ELISA can be used for SARS-CoV-2 serological testing to achieve sensitivity and specificity of at least 98%. The Siemens assay and Oxford immunoassay achieved these metrics without further optimisation. This benchmark study in immunoassay assessment should enable refinements of testing strategies and the best use of serological testing resource to benefit individuals and population health.Public Health England and UK National Institute for Health Research