Zobrazeno 1 - 10
of 61 401
pro vyhledávání: '"A Balakrishnan"'
Autor:
Barwey, Shivam, Balin, Riccardo, Lusch, Bethany, Patel, Saumil, Balakrishnan, Ramesh, Pal, Pinaki, Maulik, Romit, Vishwanath, Venkatram
This work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, the focus is on enabling scalable operations that satisfy physical cons
Externí odkaz:
http://arxiv.org/abs/2410.01657
Autor:
Sahoo, Manaswini, Bonfà, Pietro, Hall, Amelia. E., Mayoh, Daniel. A., Corredor, Laura T., Wolter, Anja U. B., Büchner, Bernd, Balakrishnan, Geetha, De Renzi, Roberto, Allodi, Giuseppe
The discovery of chiral helical magnetism (CHM) in Cr$_{1/3}$NbS$_2$ and the stabilization of a chiral soliton lattice (CSL) has attracted considerable interest in view of their potential technological applications. However, there is an ongoing debat
Externí odkaz:
http://arxiv.org/abs/2410.01631
Understanding local risks from extreme rainfall, such as flooding, requires both long records (to sample rare events) and high-resolution products (to assess localized hazards). Unfortunately, there is a dearth of long-record and high-resolution prod
Externí odkaz:
http://arxiv.org/abs/2410.00381
We consider a novel multivariate nonparametric two-sample testing problem where, under the alternative, distributions $P$ and $Q$ are separated in an integral probability metric over functions of bounded total variation (TV IPM). We propose a new tes
Externí odkaz:
http://arxiv.org/abs/2409.15628
We introduce a novel representation named as the unified gripper coordinate space for grasp synthesis of multiple grippers. The space is a 2D surface of a sphere in 3D using longitude and latitude as its coordinates, and it is shared for all robotic
Externí odkaz:
http://arxiv.org/abs/2409.14519
Autor:
Jang, Jaeyeon, Klabjan, Diego, Liu, Han, Patel, Nital S., Li, Xiuqi, Ananthanarayanan, Balakrishnan, Dauod, Husam, Juang, Tzung-Han
Real-time dynamic scheduling is a crucial but notoriously challenging task in modern manufacturing processes due to its high decision complexity. Recently, reinforcement learning (RL) has been gaining attention as an impactful technique to handle thi
Externí odkaz:
http://arxiv.org/abs/2409.13571
Autor:
Paul, Soumyabrata, Arman, Lakshmibala, S., Panigrahi, Prasanta K., Ramanan, S., Balakrishnan, V.
We demonstrate that the Wasserstein distance $W_{1}$ corresponding to optical tomograms of nonclassical states faithfully captures changes that arise due to photon addition to, or subtraction from, these states. $W_{1}$ is a true measure of distance
Externí odkaz:
http://arxiv.org/abs/2409.12881
Autor:
Osuna-Vargas, Pamela, Wehrheim, Maren H., Zinz, Lucas, Rahm, Johanna, Balakrishnan, Ashwin, Kaminer, Alexandra, Heilemann, Mike, Kaschube, Matthias
Advances in microscopy imaging enable researchers to visualize structures at the nanoscale level thereby unraveling intricate details of biological organization. However, challenges such as image noise, photobleaching of fluorophores, and low tolerab
Externí odkaz:
http://arxiv.org/abs/2409.12078
Autor:
Vyas, Kushal, Humayun, Ahmed Imtiaz, Dashpute, Aniket, Baraniuk, Richard G., Veeraraghavan, Ashok, Balakrishnan, Guha
Implicit neural representations (INRs) have demonstrated success in a variety of applications, including inverse problems and neural rendering. An INR is typically trained to capture one signal of interest, resulting in learned neural features that a
Externí odkaz:
http://arxiv.org/abs/2409.09566
Autor:
Barwey, Shivam, Pal, Pinaki, Patel, Saumil, Balin, Riccardo, Lusch, Bethany, Vishwanath, Venkatram, Maulik, Romit, Balakrishnan, Ramesh
A graph neural network (GNN) approach is introduced in this work which enables mesh-based three-dimensional super-resolution of fluid flows. In this framework, the GNN is designed to operate not on the full mesh-based field at once, but on localized
Externí odkaz:
http://arxiv.org/abs/2409.07769