Zobrazeno 1 - 10
of 2 627
pro vyhledávání: '"Civitarese A"'
Autor:
Yang, Qidong, Giezendanner, Jonathan, Civitarese, Daniel Salles, Jakubik, Johannes, Schmitt, Eric, Chandra, Anirban, Vila, Jeremy, Hohl, Detlef, Hill, Chris, Watson, Campbell, Wang, Sherrie
Urgent applications like wildfire management and renewable energy generation require precise, localized weather forecasts near the Earth's surface. However, weather forecast products from machine learning or numerical weather models are currently gen
Externí odkaz:
http://arxiv.org/abs/2410.12938
Autor:
Schmude, Johannes, Roy, Sujit, Trojak, Will, Jakubik, Johannes, Civitarese, Daniel Salles, Singh, Shraddha, Kuehnert, Julian, Ankur, Kumar, Gupta, Aman, Phillips, Christopher E, Kienzler, Romeo, Szwarcman, Daniela, Gaur, Vishal, Shinde, Rajat, Lal, Rohit, Da Silva, Arlindo, Diaz, Jorge Luis Guevara, Jones, Anne, Pfreundschuh, Simon, Lin, Amy, Sheshadri, Aditi, Nair, Udaysankar, Anantharaj, Valentine, Hamann, Hendrik, Watson, Campbell, Maskey, Manil, Lee, Tsengdar J, Moreno, Juan Bernabe, Ramachandran, Rahul
Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downs
Externí odkaz:
http://arxiv.org/abs/2409.13598
Recognizing daily activities with unobtrusive sensors in smart environments enables various healthcare applications. Monitoring how subjects perform activities at home and their changes over time can reveal early symptoms of health issues, such as co
Externí odkaz:
http://arxiv.org/abs/2408.06352
The sensor-based recognition of Activities of Daily Living (ADLs) in smart home environments enables several applications in the areas of energy management, safety, well-being, and healthcare. ADLs recognition is typically based on deep learning meth
Externí odkaz:
http://arxiv.org/abs/2407.01238
The phenomenology of hadronic states at high energy is well described in the framework of Quantum Chromodynamics. The theory, well established by now, cannot be applied to the description of quark-antiquark states at low energy unless their degrees o
Externí odkaz:
http://arxiv.org/abs/2406.14454
Autor:
Ek, Sannara, Presotto, Riccardo, Civitarese, Gabriele, Portet, François, Lalanda, Philippe, Bettini, Claudio
Human Activity Recognition (HAR) based on the sensors of mobile/wearable devices aims to detect the physical activities performed by humans in their daily lives. Although supervised learning methods are the most effective in this task, their effectiv
Externí odkaz:
http://arxiv.org/abs/2404.15331
Context-aware Human Activity Recognition (HAR) is a hot research area in mobile computing, and the most effective solutions in the literature are based on supervised deep learning models. However, the actual deployment of these systems is limited by
Externí odkaz:
http://arxiv.org/abs/2403.06586
Autor:
Mukkavilli, S. Karthik, Civitarese, Daniel Salles, Schmude, Johannes, Jakubik, Johannes, Jones, Anne, Nguyen, Nam, Phillips, Christopher, Roy, Sujit, Singh, Shraddha, Watson, Campbell, Ganti, Raghu, Hamann, Hendrik, Nair, Udaysankar, Ramachandran, Rahul, Weldemariam, Kommy
Machine learning and deep learning methods have been widely explored in understanding the chaotic behavior of the atmosphere and furthering weather forecasting. There has been increasing interest from technology companies, government institutions, an
Externí odkaz:
http://arxiv.org/abs/2309.10808
Autor:
Kienzler, Romeo, Tizzei, Leonardo Pondian, Blumenstiel, Benedikt, Nagy, Zoltan Arnold, Mukkavilli, S. Karthik, Schmude, Johannes, Freitag, Marcus, Behrendt, Michael, Civitarese, Daniel Salles, Simumba, Naomi, Kimura, Daiki, Hamann, Hendrik
Storing and streaming high dimensional data for foundation model training became a critical requirement with the rise of foundation models beyond natural language. In this paper we introduce TensorBank, a petabyte scale tensor lakehouse capable of st
Externí odkaz:
http://arxiv.org/abs/2309.02094
Autor:
Presotto, Riccardo, Ek, Sannara, Civitarese, Gabriele, Portet, François, Lalanda, Philippe, Bettini, Claudio
The use of supervised learning for Human Activity Recognition (HAR) on mobile devices leads to strong classification performances. Such an approach, however, requires large amounts of labeled data, both for the initial training of the models and for
Externí odkaz:
http://arxiv.org/abs/2306.13735