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19,536 results on '"KUMAR, Manoj"'

65. Minimal structure for neutrino mass matrix

Author

Kumar, Manoj, Awasthi, Nikhila, Randhawa, Monika, and Gupta, Manmohan

Subjects
High Energy Physics - Phenomenology
Abstract

Taking clue from the minimal structure of texture 4-zero hermitian mass matrices, which are very successful in accommodating quark mixing data, we propose a form of texture 2-zero complex symmetric neutrino mass matrix with only one phase parameter. This minimal mass matrix not only accommodates the available neutrino oscillation data, but also makes interesting predictions for the unknown parameters like the lightest neutrino mass $m_{\nu_1}$ (for normal ordering(NO)), Jarlskog's rephasing invariant $J_{CP}$, Dirac type CP violating phase $\delta_{CP}$ and effective neutrino mass $\left< m_{ee} \right>$. We also explore the correlations between the parameters of the model, that lead us to minimizing the parameters further.

Published
2024

66. XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration

Author

Rozumnyi, Denys, Bertsch, Nadine, Sbai, Othman, Arcadu, Filippo, Chen, Yuhua, Sanakoyeu, Artsiom, Kumar, Manoj, Herold, Catherine, and Kips, Robin

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Tracking the full body motions of users in XR (AR/VR) devices is a fundamental challenge to bring a sense of authentic social presence. Due to the absence of dedicated leg sensors, currently available body tracking methods adopt a synthesis approach to generate plausible motions given a 3-point signal from the head and controller tracking. In order to enable mixed reality features, modern XR devices are capable of estimating depth information of the headset surroundings using available sensors combined with dedicated machine learning models. Such egocentric depth sensing cannot drive the body directly, as it is not registered and is incomplete due to limited field-of-view and body self-occlusions. For the first time, we propose to leverage the available depth sensing signal combined with self-supervision to learn a multi-modal pose estimation model capable of tracking full body motions in real time on XR devices. We demonstrate how current 3-point motion synthesis models can be extended to point cloud modalities using a semantic point cloud encoder network combined with a residual network for multi-modal pose estimation. These modules are trained jointly in a self-supervised way, leveraging a combination of real unregistered point clouds and simulated data obtained from motion capture. We compare our approach against several state-of-the-art systems for XR body tracking and show that our method accurately tracks a diverse range of body motions. XR-MBT tracks legs in XR for the first time, whereas traditional synthesis approaches based on partial body tracking are blind., Comment: Accepted to WACV 2025

Published
2024

67. SM/11-120 (IC0637593; INGR21073), a Potato (Solanum tuberosum) Germplasm Highly Resistant to Potato Cyst Nematode (Globodera pallida and G. rostochiensis). Highly Resistant to Late Blight (Phytophthora infestans) and Non Preference for White Fly. Performing Well Under Long Day Conditions

Author

Bhardwaj, Vinay, Kaushik, SK, Sood, Salej, Dalamu, Sharma, Sanjeev, Bairwa, Aarti, Naga, Kailash, Sundaresha, S., Singh, Rajendra, Patil, Vanishree, Lal, Mehi, Chourasia, K.N., Tiwari, Jagesh, Kumar, Ashwani, Kumar, Vinod, Singh, BP, Buckseth, Tanuja, Singh, RK, and Kumar, Manoj

Published
2023

84. Analysis of a mathematical model for malaria using data-driven approach

Author

Rajnarayanan, Adithya, Kumar, Manoj, and Tridane, Abdessamad

Subjects
Quantitative Biology - Populations and Evolution, Computer Science - Machine Learning, Mathematics - Dynamical Systems
Abstract

Malaria is one of the deadliest diseases in the world, every year millions of people become victims of this disease and many even lose their lives. Medical professionals and the government could take accurate measures to protect the people only when the disease dynamics are understood clearly. In this work, we propose a compartmental model to study the dynamics of malaria. We consider the transmission rate dependent on temperature and altitude. We performed the steady state analysis on the proposed model and checked the stability of the disease-free and endemic steady state. An artificial neural network (ANN) is applied to the formulated model to predict the trajectory of all five compartments following the mathematical analysis. Three different neural network architectures namely Artificial neural network (ANN), convolution neural network (CNN), and Recurrent neural network (RNN) are used to estimate these parameters from the trajectory of the data. To understand the severity of a disease, it is essential to calculate the risk associated with the disease. In this work, the risk is calculated using dynamic mode decomposition(DMD) from the trajectory of the infected people.

Published
2024

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