Zobrazeno 1 - 10
of 9 407
pro vyhledávání: '"Elango, A"'
Autor:
Mceowen, Skye, Calderone, Daniel J., Tiwary, Aman, Zhou, Jason S. K., Kim, Taewan, Elango, Purnanand, Acikmese, Behcet
This paper presents auto-tuned primal-dual successive convexification (Auto-SCvx), an algorithm designed to reliably achieve dynamically-feasible trajectory solutions for constrained hypersonic reentry optimal control problems across a large mission
Externí odkaz:
http://arxiv.org/abs/2411.08361
This paper presents an architecture for Ultrasound Beamforming using Synthetic Transmit Aperture with Low Complexity and High SNR for medical imaging. Synthetic Transmit Aperture is a novel approach in ultrasound imaging system by which frame rate an
Externí odkaz:
http://arxiv.org/abs/2407.10242
Autor:
Elango, Venmugil
Publikováno v:
2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Portland, OR, USA, 2021, pp. 1025-1034
Training a deep neural network (DNN) requires substantial computational and memory requirements. It is common to use multiple devices to train a DNN to reduce the overall training time. There are several choices to parallelize each layer in a DNN. Ex
Externí odkaz:
http://arxiv.org/abs/2407.04001
This paper introduces a generalized mean-based C^1-smooth robustness measure over discrete-time signals (D-GMSR) for signal temporal logic (STL) specifications. In conjunction with its C1-smoothness, D-GMSR is proven to be both sound and complete. Fu
Externí odkaz:
http://arxiv.org/abs/2405.10996
We introduce a GPU-accelerated Monte Carlo framework for nonconvex, free-final-time trajectory optimization problems. This framework makes use of the prox-linear method, which belongs to the larger family of sequential convex programming (SCP) algori
Externí odkaz:
http://arxiv.org/abs/2404.18034
We propose a nonlinear model predictive control (NMPC) framework based on a direct optimal control method that ensures continuous-time constraint satisfaction and accurate evaluation of the running cost, without compromising computational efficiency.
Externí odkaz:
http://arxiv.org/abs/2405.00061
Autor:
Elango, Purnanand, Luo, Dayou, Kamath, Abhinav G., Uzun, Samet, Kim, Taewan, Açıkmeşe, Behçet
We present successive convexification, a real-time-capable solution method for nonconvex trajectory optimization, with continuous-time constraint satisfaction and guaranteed convergence, that only requires first-order information. The proposed framew
Externí odkaz:
http://arxiv.org/abs/2404.16826
The purpose of this note is to highlight and address inaccuracies in the convergence guarantees of SCvx, a nonconvex trajectory optimization algorithm proposed by Mao et al. (arXiv:1804.06539), and make connections to relevant prior work. Specificall
Externí odkaz:
http://arxiv.org/abs/2403.00733
Autor:
Rouhani, Bita Darvish, Zhao, Ritchie, More, Ankit, Hall, Mathew, Khodamoradi, Alireza, Deng, Summer, Choudhary, Dhruv, Cornea, Marius, Dellinger, Eric, Denolf, Kristof, Dusan, Stosic, Elango, Venmugil, Golub, Maximilian, Heinecke, Alexander, James-Roxby, Phil, Jani, Dharmesh, Kolhe, Gaurav, Langhammer, Martin, Li, Ada, Melnick, Levi, Mesmakhosroshahi, Maral, Rodriguez, Andres, Schulte, Michael, Shafipour, Rasoul, Shao, Lei, Siu, Michael, Dubey, Pradeep, Micikevicius, Paulius, Naumov, Maxim, Verrilli, Colin, Wittig, Ralph, Burger, Doug, Chung, Eric
Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that combine a per-block scaling factor with narrow floating-point and int
Externí odkaz:
http://arxiv.org/abs/2310.10537
Autor:
Michelle A. Neller, George R. Ambalathingal, Nada Hamad, Joe Sasadeusz, Rebecca Pearson, Chien-Li Holmes-Liew, Deepak Singhal, Matthew Tunbridge, Wei Yang Ng, Kirsty Sharplin, Andrew Moore, David Deambrosis, Trisha Soosay-Raj, Peter McNaughton, Morag Whyte, Chris Fraser, Andrew Grigg, David Kliman, Ashish Bajel, Katherine Cummins, Mark Dowling, Zhi Han Yeoh, Simon J. Harrison, Amit Khot, Sarah Tan, Izanne Roos, Ray Mun Koo, Sara Dohrmann, David Ritchie, Brynn Wainstein, Karen McCleary, Adam Nelson, Bradley Gardiner, Shafqat Inam, Xavier Badoux, Kris Ma, Claudia Toro, Diane Hanna, David Hughes, Rachel Conyers, Theresa Cole, Shiqi Stacie Wang, Lynette Chee, Jacqueline Fleming, Ashley Irish, Duncan Purtill, Julian Cooney, Peter Shaw, Siok-Keen Tey, Stewart Hunt, Elango Subramonia Pillai, George John, Michelle Ng, Shanti Ramachandran, Peter Hopkins, Daniel Chambers, Scott Campbell, Ross Francis, Nicole Isbel, Paula Marlton, Hilary Reddiex, Katherine K. Matthews, Meggie Voogt, Archana Panikkar, Leone Beagley, Sweera Rehan, Shannon Best, Jyothy Raju, Laetitia Le Texier, Pauline Crooks, Matthew Solomon, Lea Lekieffre, Sriganesh Srihari, Corey Smith, Rajiv Khanna
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Adoptive T-cell immunotherapy holds great promise for the treatment of viral complications in immunocompromised patients resistant to standard anti-viral strategies. We present a retrospective analysis of 78 patients from 19 hospitals across
Externí odkaz:
https://doaj.org/article/8c099c749c664bdd91639b09344ded19
