End-to-End Learning of Semantic Grid Estimation Deep Neural Network with Occupancy Grids

International audienceWe propose semantic grid, a spatial 2D map of the environment around an autonomous vehicle consisting of cells which represent the semantic information of the corresponding region such as car, road, vegetation, bikes, etc. It consists of an integration of an occupancy grid, which computes the grid states with a Bayesian filter approach, and semantic segmentation information from monocular RGB images, which is obtained with a deep neural network. The network fuses the information and can be trained in an end-to-end manner. The output of the neural network is refined with a conditional random field. The proposed method is tested in various datasets (KITTI dataset, Inria-Chroma dataset and SYNTHIA) and different deep neural network architectures are compared

Effects of Perampanel on Electroencephalography

Objective: Perampanel (PER), a noncompetitive α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor antagonist, has been approved as adjunctive therapy for focal and generalized epilepsy. Limited information is available regarding the measurable impact of anti-seizure medications (ASM). In this study, we aimed to investigate the effects of PER on electroencephalography (EEG) background activity and interictal epileptic discharge. Methods: This study included all patients with a clinical diagnosis of epilepsy who underwent routine EEG before and after PER treatment between 2018 and 2023. EEG findings were examined according to their background activity and clinical features such as risk factors of epilepsy, the occurrence of sleep-related seizures, sleep disorders, intellectual disability, abnormality of magnetic resonance imaging and EEG, multifocal features on EEG, the duration between EEG and initiation of PER treatment, frequency of seizures before and after PER treatment (seizure freedom or >50% reduction in seizures), previous epilepsy surgery, the number of current and previous ASM, and dosage of PER. Results: In a total of 11 patients, epilepsy type was focal in 8 (73%), all of the patients were on polytherapy, and 4 of them had undergone epilepsy surgery. PER treatment resulted in seizure freedom in 36% of patients and a >50% decrease in seizures in 55% of patients. There was no statistically significant relationship between background activity, phase reversal, and equipotential in EEG before and after PER treatment. In addition, pre- and posttreatment responses to activation procedures and disruption in sleep structure did not differ significantly. The relationship between seizure freedom and phase reversal decrease after PER treatment was statistically significant. The relationship between a >50% decrease in the frequency of seizures and epileptic discharges also reached statistical significance. Conclusion: To summarize, seizure freedom following PER treatment appears to be associated with reduced epileptic discharge, and EEG monitoring might help determine prognosis

WILL YOU CARRY THAT WATCH? INVESTIGATING FACTORS THAT AFFECT CONTINUANCE INTENTION OF SMARTWATCHES

The interest in wearable technologies, especially smartwatches rise day by day parallel with technological developments and an increasing need to monitor health. In line with those developments, this study aims to investigate the role of perceived ease of use, perceived usefulness, user satisfaction, healthology in explaining smartwatch continuance intention. In addition, this study investigates the relationships between perceived ease of use, perceived usefulness, healthology and user satisfaction. Questionnaire method was used to gather data from actual smartwatch consumers in Turkey and the data analyzed by utilizing structural equation modeling. Findings demonstrate that the most powerful variable to explain smartwatch continuance intention is perceived usefulness, whereas perceived ease of use contributes to user satisfaction the most. Also, healthology is positively related to both user satisfaction and continuance intention. The results also highlight the importance of continuance intention to increase intention to recommend smartwatches to other people

Semantic Segmentation with Unsupervised Domain Adaptation Under Varying Weather Conditions for Autonomous Vehicles

International audienceSemantic information provides a valuable source for scene understanding around autonomous vehicles in order to plan their actions and make decisions; however, varying weather conditions reduce the accuracy of the semantic segmentation. We propose a method to adapt to varying weather conditions without supervision, namely without labeled data. We update the parameters of a deep neural network (DNN) model that is pre-trained on the known weather condition (source domain) to adapt it to the new weather conditions (target domain) without forgetting the segmentation in the known weather condition. Furthermore, we don't require the labels from the source domain during adaptation training. The parameters of the DNN are optimized to reduce the distance between the distribution of the features from the images of old and new weather conditions. To measure this distance, we propose three alternatives: W-GAN, GAN and maximum-mean discrepancy (MMD). We evaluate our method on various datasets with varying weather conditions. The results show that the accuracy of the semantic segmentation is improved for varying conditions after adaptation with the proposed method

YOLO-based Panoptic Segmentation Network

International audienceAutonomous vehicles need information about their surroundings to safely navigate them. For this, the task of Panoptic Segmentation is proposed as a method of fully parsing the scene by assigning each pixel a label and instance id. Given the constraints of autonomous driving, this process needs to be done in a fast manner. In this paper, we propose the first panoptic segmentation network based on the YOLOv3 real-time object detection network by adding a semantic and instance segmentation branches. YOLO-panoptic is able to do real-time inference and achieves a performance similar to the state of the art methods in some metrics

Semantic Grid Estimation with Occupancy Grids and Semantic Segmentation Networks

International audienceWe propose a method to estimate the semantic grid for an autonomous vehicle. The semantic grid is a 2D bird's eye view map where the grid cells contain semantic characteristics such as road, car, pedestrian, signage, etc. We obtain the semantic grid by fusing the semantic segmentation information and an occupancy grid computed by using a Bayesian filter technique. To compute the semantic information from a monocular RGB image, we integrate segmentation deep neural networks into our model. We use a deep neural network to learn the relation between the semantic information and the occupancy grid which can be trained end-to-end extending our previous work on semantic grids. Furthermore, we investigate the effect of using a conditional random field to refine the results. Finally, we test our method on two datasets and compare different architecture types for semantic segmentation. We perform the experiments on KITTI dataset and Inria-Chroma dataset

A new approach to the management of acute appendicitis: Decision tree method

ABSTR A C T Background: It is important to distinguish between complicated acute appendicitis (CAA) and noncomplicated acute appendicitis (NCAA) because the treatment methods are different. We aimed to create an algorithm that determines the severity of acute appendicitis (AA) without the need for imaging methods, using the decision tree method. Methods: The patients were analyzed retrospectively and divided into two groups as CAA and NCAA. Age, gender, Alvarado scores, white blood cell values (WBC), neutrophil/lymphocyte ratios (NLR), C-reactive protein value (CRP), albumin value and CRP/Albumin ratios of the patients were recorded. Results: In the algorithm we created, the most important parameter in the distinction between CAA and NCAA is CRP. NLR is predictive in patients with a CRP value of 107.565 mg/L, albumin is the determinant and the critical value is 2.85 g/dL. Age, gen -der, alvarado score and CRP/albumin ratio have no significance in distinguishing between CAA and NCAA. In the statistical analysis, there were significant differences between NCAA and CAA groups in terms of age (39.56 years vs 13,675 years), gender (48.1% male vs 71.4% male), WBC (13,891.10/mL vs 11,614.76/mL), CRP (27 mg/L vs 127 mg/L), albumin (3 g/dL vs 3 g/dL) and CRP/albumin (9.50 vs. 41). Conclusion: Thanks to the algorithm we created, CAA and NCAA distinction can be made quickly. In addition, by avoiding unnecessary surgical procedures in NCAA cases, patients' quality of life can be increased and morbidity rates can be minimized.(c) 2022 Elsevier Inc. All rights reserved

Instance Segmentation with Unsupervised Adaptation to Different Domains for Autonomous Vehicles

International audienceDetection of the objects around a vehicle is important for a safe and successful navigation of an autonomous vehicle. Instance segmentation provides a fine and accurate classification of the objects such as cars, trucks, pedestrians, etc. In this study, we propose a fast and accurate approach which can detect and segment the object instances which can be adapted to new conditions without requiring the labels from the new condition. Furthermore, the performance of the instance segmentation does not degrade in detection of the objects in the original condition after it adapts to the new condition. To our knowledge, currently there are not other methods which perform unsupervised domain adaptation for the task of instance segmentation using non-synthetic datasets. We evaluate the adaptation capability of our method on two datasets. Firstly, we test its capacity of adapting to a new domain; secondly, we test its ability to adapt to new weather conditions. The results show that it can adapt to new conditions with an improved accuracy while preserving the accuracy of the original condition

Attentional PointNet for 3D-Object Detection in Point Clouds

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Recognize Moving Objects Around an Autonomous Vehicle Considering a Deep-learning Detector Model and Dynamic Bayesian Occupancy

International audiencePerception systems on autonomous vehicles have the challenge of understanding the traffic scene in different situations. The fusion of redundant information obtained from different sources has been shown considerable progress under different methodologies to achieve this objective. However, new opportunities are available to obtain better fusion results with the advance of deep-learning models and computing hardware. In this paper, we aim to recognize moving objects in traffic scenes through the fusion of semantic information with occupancy-grid estimations. Our approach considers a deep-learning model with inference times between 22 to 55 milliseconds. Moreover, we use a Bayesian occupancy framework with a Highly-parallelized design to obtain the occupancygrid estimations.We validate our approach using experimental results with real-world data on urban scenery