OCTraN: 3D Occupancy Convolutional Transformer Network in Unstructured Traffic Scenarios

Modern approaches for vision-centric environment perception for autonomous navigation make extensive use of self-supervised monocular depth estimation algorithms that output disparity maps. However, when this disparity map is projected onto 3D space, the errors in disparity are magnified, resulting in a depth estimation error that increases quadratically as the distance from the camera increases. Though Light Detection and Ranging (LiDAR) can solve this issue, it is expensive and not feasible for many applications. To address the challenge of accurate ranging with low-cost sensors, we propose, OCTraN, a transformer architecture that uses iterative-attention to convert 2D image features into 3D occupancy features and makes use of convolution and transpose convolution to efficiently operate on spatial information. We also develop a self-supervised training pipeline to generalize the model to any scene by eliminating the need for LiDAR ground truth by substituting it with pseudo-ground truth labels obtained from boosted monocular depth estimation.Comment: This work was accepted as a spotlight presentation at the Transformers for Vision Workshop @CVPR 202

Application of IoT Framework for Prediction of Heart Disease using Machine Learning

Prognosis of illnesses is a difficult problem these days throughout the globe. Elder people of twenty years and over are taken into consideration to be laid low with this sickness now a days. For example, human beings having  HbA1c level more than 6.5% are diagnosed as infected with diabetic diseases. This paper uses IoT to evaluate threat factors which have been similar to heart diseases which are not treated properly. Diagnosis, prevention of heart disease may be done by use of machine learning (ML). There has been an extensive disconnect among Machine Learning architects, health care researchers, patients and physicians in their technology. This paper intends to perform an in-intensity evaluation on Machine Learning to make us of new advance technologies. Latest advances within the development of IoT implanted devices and other medicine delivery gadgets, disease diagnostic methods and other medical research have considerably helped human beings diagnosed heart diseases. New soft computing models can be helpful for remedy of various heart diseases. The Food and Drug Administration (FDA) employs several particularly creative thoughts to get their capsules to the client. Artificial Neural Community offers a first-rate chance to deal with heart diseases with advance IoT and cloud applications

Case Reports1. A Late Presentation of Loeys-Dietz Syndrome: Beware of TGFβ Receptor Mutations in Benign Joint Hypermobility

Background: Thoracic aortic aneurysms (TAA) and dissections are not uncommon causes of sudden death in young adults. Loeys-Dietz syndrome (LDS) is a rare, recently described, autosomal dominant, connective tissue disease characterized by aggressive arterial aneurysms, resulting from mutations in the transforming growth factor beta (TGFβ) receptor genes TGFBR1 and TGFBR2. Mean age at death is 26.1 years, most often due to aortic dissection. We report an unusually late presentation of LDS, diagnosed following elective surgery in a female with a long history of joint hypermobility. Methods: A 51-year-old Caucasian lady complained of chest pain and headache following a dural leak from spinal anaesthesia for an elective ankle arthroscopy. CT scan and echocardiography demonstrated a dilated aortic root and significant aortic regurgitation. MRA demonstrated aortic tortuosity, an infrarenal aortic aneurysm and aneurysms in the left renal and right internal mammary arteries. She underwent aortic root repair and aortic valve replacement. She had a background of long-standing joint pains secondary to hypermobility, easy bruising, unusual fracture susceptibility and mild bronchiectasis. She had one healthy child age 32, after which she suffered a uterine prolapse. Examination revealed mild Marfanoid features. Uvula, skin and ophthalmological examination was normal. Results: Fibrillin-1 testing for Marfan syndrome (MFS) was negative. Detection of a c.1270G > C (p.Gly424Arg) TGFBR2 mutation confirmed the diagnosis of LDS. Losartan was started for vascular protection. Conclusions: LDS is a severe inherited vasculopathy that usually presents in childhood. It is characterized by aortic root dilatation and ascending aneurysms. There is a higher risk of aortic dissection compared with MFS. Clinical features overlap with MFS and Ehlers Danlos syndrome Type IV, but differentiating dysmorphogenic features include ocular hypertelorism, bifid uvula and cleft palate. Echocardiography and MRA or CT scanning from head to pelvis is recommended to establish the extent of vascular involvement. Management involves early surgical intervention, including early valve-sparing aortic root replacement, genetic counselling and close monitoring in pregnancy. Despite being caused by loss of function mutations in either TGFβ receptor, paradoxical activation of TGFβ signalling is seen, suggesting that TGFβ antagonism may confer disease modifying effects similar to those observed in MFS. TGFβ antagonism can be achieved with angiotensin antagonists, such as Losartan, which is able to delay aortic aneurysm development in preclinical models and in patients with MFS. Our case emphasizes the importance of timely recognition of vasculopathy syndromes in patients with hypermobility and the need for early surgical intervention. It also highlights their heterogeneity and the potential for late presentation. Disclosures: The authors have declared no conflicts of interes

DOCUMENT IMAGE REGISTRATION FOR IMPOSED LAYER EXTRACTION

Extraction of filled-in information from document images in the presence of template poses challenges due to geometrical distortion. Filled-in document image consists of null background, general information foreground and vital information imposed layer. Template document image consists of null background and general information foreground layer. In this paper a novel document image registration technique has been proposed to extract imposed layer from input document image. A convex polygon is constructed around the content of the input and the template image using convex hull. The vertices of the convex polygons of input and template are paired based on minimum Euclidean distance. Each vertex of the input convex polygon is subjected to transformation for the permutable combinations of rotation and scaling. Translation is handled by tight crop. For every transformation of the input vertices, Minimum Hausdorff distance (MHD) is computed. Minimum Hausdorff distance identifies the rotation and scaling values by which the input image should be transformed to align it to the template. Since transformation is an estimation process, the components in the input image do not overlay exactly on the components in the template, therefore connected component technique is applied to extract contour boxes at word level to identify partially overlapping components. Geometrical features such as density, area and degree of overlapping are extracted and compared between partially overlapping components to identify and eliminate components common to input image and template image. The residue constitutes imposed layer. Experimental results indicate the efficacy of the proposed model with computational complexity. Experiment has been conducted on variety of filled-in forms, applications and bank cheques. Data sets have been generated as test sets for comparative analysis

Farmers coping strategies for climate shock: Is it differentiated by gender?

Several studies have recognized that the agriculture sector is one of the major contributor to climate change, as well as largely affected adversely by climate change. Agricultural productivity is known to be sensitive to climate change induced effects and it has impact on livelihood of families linked with farming. Thus it is important to understand what are the existing coping strategies that farmer deploy in case of climate shocks like flood and drought and who is involved in making decision relating to these coping strategies. This paper uses the household level data of 641 households from 12 randomly selected villages in Vaishali district of Bihar to understand the household coping mechanisms with emphasis on role of gender. This study has moved away from the conventional division of households by male and female-headed households and thus capturing the intra-household gender dynamics by understanding the role of men and women within the household as decision makers of the coping strategy to manage climate shock. The study uses a multivariate probit model and the results suggest that there is a higher probability that the male farmers will make the decision on choice of the coping strategy. The most prominent coping mechanism is to find alternative employment in urban locations; however, when consumption levels have to be reduced because of climate shock, all family members then contribute to the decision-making process collectively. The results show that exposure to agriculture extension and training programs have a positive influence on choosing appropriate coping mechanisms, but female farmers have poor access to these resources. These policies should look into providing outreach to both male and female farmers in any given locality

Multicenter Case–Control Study of COVID-19–Associated Mucormycosis Outbreak, India

We performed a case–control study across 25 hospitals in India for the period of January–June 2021 to evaluate the reasons for an COVID-19–associated mucormycosis (CAM) outbreak. We investigated whether COVID-19 treatment practices (glucocorticoids, zinc, tocilizumab, and others) were associated with CAM. We included 1,733 cases of CAM and 3,911 age-matched COVID-19 controls. We found cumulative glucocorticoid dose (odds ratio [OR] 1.006, 95% CI 1.004–1.007) and zinc supplementation (OR 2.76, 95% CI 2.24–3.40), along with elevated C-reactive protein (OR 1.004, 95% CI 1.002–1.006), host factors (renal transplantation [OR 7.58, 95% CI 3.31–17.40], diabetes mellitus [OR 6.72, 95% CI 5.45–8.28], diabetic ketoacidosis during COVID-19 [OR 4.41, 95% CI 2.03–9.60]), and rural residence (OR 2.88, 95% CI 2.12–3.79), significantly associated with CAM. Mortality rate at 12 weeks was 32.2% (473/1,471). We emphasize the judicious use of COVID-19 therapies and optimal glycemic control to prevent CAM