Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Szwarcman, Daniela"'
Autor:
Muszynski, Michal, Klein, Levente, da Silva, Ademir Ferreira, Atluri, Anjani Prasad, Gomes, Carlos, Szwarcman, Daniela, Singh, Gurkanwar, Gu, Kewen, Zortea, Maciel, Simumba, Naomi, Fraccaro, Paolo, Singh, Shraddha, Meliksetian, Steve, Watson, Campbell, Kimura, Daiki, Srinivasan, Harini
Global vegetation structure mapping is critical for understanding the global carbon cycle and maximizing the efficacy of nature-based carbon sequestration initiatives. Moreover, vegetation structure mapping can help reduce the impacts of climate chan
Externí odkaz:
http://arxiv.org/abs/2406.19888
Autor:
Kurihana, Takuya, Yeo, Kyongmin, Szwarcman, Daniela, Elmegreen, Bruce, Mukkavilli, Karthik, Schmude, Johannes, Klein, Levente
To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source. However, the complexity of computation in resolving
Externí odkaz:
http://arxiv.org/abs/2312.13212
Autor:
Jakubik, Johannes, Roy, Sujit, Phillips, C. E., Fraccaro, Paolo, Godwin, Denys, Zadrozny, Bianca, Szwarcman, Daniela, Gomes, Carlos, Nyirjesy, Gabby, Edwards, Blair, Kimura, Daiki, Simumba, Naomi, Chu, Linsong, Mukkavilli, S. Karthik, Lambhate, Devyani, Das, Kamal, Bangalore, Ranjini, Oliveira, Dario, Muszynski, Michal, Ankur, Kumar, Ramasubramanian, Muthukumaran, Gurung, Iksha, Khallaghi, Sam, Hanxi, Li, Cecil, Michael, Ahmadi, Maryam, Kordi, Fatemeh, Alemohammad, Hamed, Maskey, Manil, Ganti, Raghu, Weldemariam, Kommy, Ramachandran, Rahul
Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth science and remote sensing. Foundation models are pre-trained on large unlabeled datasets t
Externí odkaz:
http://arxiv.org/abs/2310.18660
Autor:
Yang, Qidong, Hernandez-Garcia, Alex, Harder, Paula, Ramesh, Venkatesh, Sattegeri, Prasanna, Szwarcman, Daniela, Watson, Campbell D., Rolnick, David
Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computationally expensive to resolve complex climate processes at high spatial resolution. As one way to speed up climate s
Externí odkaz:
http://arxiv.org/abs/2305.14452
Autor:
Harder, Paula, Hernandez-Garcia, Alex, Ramesh, Venkatesh, Yang, Qidong, Sattigeri, Prasanna, Szwarcman, Daniela, Watson, Campbell, Rolnick, David
The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational cos
Externí odkaz:
http://arxiv.org/abs/2208.05424
Autor:
Szwarcman, Daniela1 (AUTHOR), Guevara, Jorge2 (AUTHOR), Macedo, Maysa M. G.2 (AUTHOR), Zadrozny, Bianca1 (AUTHOR), Watson, Campbell3 (AUTHOR), Rosa, Laura4 (AUTHOR), Oliveira, Dario A. B.1,5 (AUTHOR) dario.oliveira@ibm.com
Publikováno v:
Scientific Reports. 2/9/2024, Vol. 14 Issue 1, p1-13. 13p.
An impact of climate change is the increase in frequency and intensity of extreme precipitation events. However, confidently predicting the likelihood of extreme precipitation at seasonal scales remains an outstanding challenge. Here, we present an a
Externí odkaz:
http://arxiv.org/abs/2107.06846
Autor:
Zadrozny, Bianca, Watson, Campbell D., Szwarcman, Daniela, Civitarese, Daniel, Oliveira, Dario, Rodrigues, Eduardo, Guevara, Jorge
Extreme weather events have an enormous impact on society and are expected to become more frequent and severe with climate change. In this context, resilience planning becomes crucial for risk mitigation and coping with these extreme events. Machine
Externí odkaz:
http://arxiv.org/abs/2102.04534
Almost all work to understand Earth's subsurface on a large scale relies on the interpretation of seismic surveys by experts who segment the survey (usually a cube) into layers; a process that is very time demanding. In this paper, we present a new d
Externí odkaz:
http://arxiv.org/abs/1905.04307
Autor:
Silva, Reinaldo Mozart, Baroni, Lais, Ferreira, Rodrigo S., Civitarese, Daniel, Szwarcman, Daniela, Brazil, Emilio Vital
Machine learning and, more specifically, deep learning algorithms have seen remarkable growth in their popularity and usefulness in the last years. This is arguably due to three main factors: powerful computers, new techniques to train deeper network
Externí odkaz:
http://arxiv.org/abs/1904.00770