FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier Convolutions

In this work we present FreDSNet, a deep learning solution which obtains semantic 3D understanding of indoor environments from single panoramas. Omnidirectional images reveal task-specific advantages when addressing scene understanding problems due to the 360-degree contextual information about the entire environment they provide. However, the inherent characteristics of the omnidirectional images add additional problems to obtain an accurate detection and segmentation of objects or a good depth estimation. To overcome these problems, we exploit convolutions in the frequential domain obtaining a wider receptive field in each convolutional layer. These convolutions allow to leverage the whole context information from omnidirectional images. FreDSNet is the first network that jointly provides monocular depth estimation and semantic segmentation from a single panoramic image exploiting fast Fourier convolutions. Our experiments show that FreDSNet has similar performance as specific state of the art methods for semantic segmentation and depth estimation. FreDSNet code is publicly available in https://github.com/Sbrunoberenguel/FreDSNetComment: 7 pages, 5 figures, 3 table

Multi-label affordance mapping from egocentric vision

Accurate affordance detection and segmentation with pixel precision is an important piece in many complex systems based on interactions, such as robots and assitive devices. We present a new approach to affordance perception which enables accurate multi-label segmentation. Our approach can be used to automatically extract grounded affordances from first person videos of interactions using a 3D map of the environment providing pixel level precision for the affordance location. We use this method to build the largest and most complete dataset on affordances based on the EPIC-Kitchen dataset, EPIC-Aff, which provides interaction-grounded, multi-label, metric and spatial affordance annotations. Then, we propose a new approach to affordance segmentation based on multi-label detection which enables multiple affordances to co-exists in the same space, for example if they are associated with the same object. We present several strategies of multi-label detection using several segmentation architectures. The experimental results highlight the importance of the multi-label detection. Finally, we show how our metric representation can be exploited for build a map of interaction hotspots in spatial action-centric zones and use that representation to perform a task-oriented navigation.Comment: International Conference on Computer Vision (ICCV) 202

Randomized, double-blind, three-arm, parallel-group study to compare the efficacy and safety of a single dose of 100 and 200 mg of mifepristone for cervical ripening in term pregnancies

Background: Mifepristone is an antiprogestin developed to antagonize the action of progesterone by inhibiting its receptors. It has had a recognized role in the medical termination of early pregnancy, reduction in the volume of uterine fibroids and endometriosis symptoms. A new indication for labor induction and cervical ripening in has been proposed. The objective was to compare the efficacy and safety of mifepristone 100 and 200 mg with placebo for cervical ripening in term pregnancies.Methods: Double-blind, placebo-controlled trial of 90 term pregnancy women randomly assigned to receive orally tablet of 100 mg and 200 mg mifepristone or placebo. Efficacy was assessed by measuring changes in cervical ripening according to Bishop 72 hours after treatment. Statistical analysis was using the t-student test and the chi-square test. The relative risk (RR) was determined with a 95% confidence interval.Results: The bishop score and the number of contractions at 48 hours in the group of 200mg of mifepristone presented a significantly higher mean value in relation to the placebo (p=0.04). At 72 hours, cervical length showed a significant difference (p<0.01) in both mifepristone groups compared to the placebo group. Also, at 72 hours a significant increase in the mean duration of contractions was demonstrated in the 100 mg mifepristone group.Conclusions: There was a significant increase in Bishop's score for the 200 mg mifepristone group probably due to a significant increase in contractions at 24 hours. No differences were observed between groups in adverse events

Portable Multi-Hypothesis Monte Carlo Localization for Mobile Robots

Self-localization is a fundamental capability that mobile robot navigation systems integrate to move from one point to another using a map. Thus, any enhancement in localization accuracy is crucial to perform delicate dexterity tasks. This paper describes a new location that maintains several populations of particles using the Monte Carlo Localization (MCL) algorithm, always choosing the best one as the sytems's output. As novelties, our work includes a multi-scale match matching algorithm to create new MCL populations and a metric to determine the most reliable. It also contributes the state-of-the-art implementations, enhancing recovery times from erroneous estimates or unknown initial positions. The proposed method is evaluated in ROS2 in a module fully integrated with Nav2 and compared with the current state-of-the-art Adaptive ACML solution, obtaining good accuracy and recovery times.Comment: Submission for ICRA 202

Commencement of flash glucose monitoring is associated with a decreased rate of depressive disorders among persons with diabetes (FLARE-NL7)

INTRODUCTION: Depressive disorders are more common among persons with diabetes, as compared with persons without diabetes. The burden of glucose management is known to associate with depressive symptoms. This study aims to assess the effects of commencement of FreeStyle Libre flash glucose monitoring (FSL-FGM) on the mental health status of persons with diabetes. RESEARCH DESIGN AND METHODS: Post-hoc analysis of data from a 1-year prospective nationwide FSL-FGM registry. Participants who used FSL-FGM for 12 months and completed the 12-Item Short Form Health Survey version 2 (SF-12v2) questionnaires at baseline, 6 and 12 months were included. An SF-12v2 Mental Component Score (MCS) of ≤45 was used as a cut-off to discriminate between persons with and without a depressive disorder. RESULTS: A total of 674 patients were included with a mean age of 48.2 (±15.8) years, 51.2% men, 78.2% type 1 diabetes and baseline HbA1c 62.8 (±13.4) mmol/mol (7.9±1.2%). At baseline, 235 (34.9%) persons had an SF-12 MCS ≤45 while after 6 and 12 months these numbers decreased: 202 (30.0%, p<0.01) and 173 (25.7%, p<0.01). Overall, MCS improved from 48.5 at baseline to 50.7 after 6 months and 51.3 after 12 months. In multivariable regression analysis, age and MCS at baseline were associated with improvement of MCS after 12 months of FSL-FGM use. CONCLUSIONS: This analysis suggests that use of FSL-FGM is associated with a decreased rate of depressive disorders among persons with diabetes. Future studies are needed to corroborate these findings