Zobrazeno 1 - 10
of 6 345
pro vyhledávání: '"Kostić, P."'
Autor:
Calcagno, Salvatore, Kavasidis, Isaak, Palazzo, Simone, Brondi, Marco, Sità, Luca, Turri, Giacomo, Giordano, Daniela, Kostic, Vladimir R., Fellin, Tommaso, Pontil, Massimiliano, Spampinato, Concetto
Understanding complex animal behaviors hinges on deciphering the neural activity patterns within brain circuits, making the ability to forecast neural activity crucial for developing predictive models of brain dynamics. This capability holds immense
Externí odkaz:
http://arxiv.org/abs/2412.07264
Autor:
Pargoo, Navid Salami, Ghasemi, Mahshid, Xia, Shuren, Turkcan, Mehmet Kerem, Ehsan, Taqiya, Zang, Chengbo, Sun, Yuan, Ghaderi, Javad, Zussman, Gil, Kostic, Zoran, Ortiz, Jorge
As urban populations grow, cities are becoming more complex, driving the deployment of interconnected sensing systems to realize the vision of smart cities. These systems aim to improve safety, mobility, and quality of life through applications that
Externí odkaz:
http://arxiv.org/abs/2411.19714
Autor:
Kostic, Vladimir R., Lounici, Karim, Halconruy, Hélène, Devergne, Timothée, Novelli, Pietro, Pontil, Massimiliano
Markov processes serve as a universal model for many real-world random processes. This paper presents a data-driven approach for learning these models through the spectral decomposition of the infinitesimal generator (IG) of the Markov semigroup. The
Externí odkaz:
http://arxiv.org/abs/2410.14477
We present an analytical model of $\Sigma-D$ relation for supernova remnants (SNRs) evolving in a clumpy medium. The model and its approximations were developed using the hydrodynamic simulations of SNRs in environments of low-density bubbles and clu
Externí odkaz:
http://arxiv.org/abs/2409.07905
Autor:
Turkcan, Mehmet Kerem, Li, Yuyang, Zang, Chengbo, Ghaderi, Javad, Zussman, Gil, Kostic, Zoran
We introduce Boundless, a photo-realistic synthetic data generation system for enabling highly accurate object detection in dense urban streetscapes. Boundless can replace massive real-world data collection and manual ground-truth object annotation (
Externí odkaz:
http://arxiv.org/abs/2409.03022
We present a novel data-driven simulation environment for modeling traffic in metropolitan street intersections. Using real-world tracking data collected over an extended period of time, we train trajectory forecasting models to learn agent interacti
Externí odkaz:
http://arxiv.org/abs/2408.00943
Autor:
Perez-Ramirez, Daniel F., Pérez-Penichet, Carlos, Tsiftes, Nicolas, Kostic, Dejan, Boman, Magnus, Voigt, Thiemo
Battery-free sensor tags are devices that leverage backscatter techniques to communicate with standard IoT devices, thereby augmenting a network's sensing capabilities in a scalable way. For communicating, a sensor tag relies on an unmodulated carrie
Externí odkaz:
http://arxiv.org/abs/2407.08479
Autor:
Wadhwa, Surjit S., Popowicz, Adam, Michel, Raul, Kostic, Petar, Vince, Oliver, Tothill, Nick F. H., De Horta, Ain Y., Filipovic, Miroslav D.
Low mass ratio contact binary systems are more likely to have unstable orbits and potentially merge. In addition, such systems exhibit characteristics such as starspots and high energy emissions (UV) suggestive of chromospheric and magnetic activity.
Externí odkaz:
http://arxiv.org/abs/2407.08365
Could information about future incoming packets be used to build more efficient CPU-based packet processors? Can such information be obtained accurately? This paper studies novel packet processing architectures that receive external hints about which
Externí odkaz:
http://arxiv.org/abs/2407.04344
Autor:
Kostic, Vladimir R., Lounici, Karim, Pacreau, Gregoire, Novelli, Pietro, Turri, Giacomo, Pontil, Massimiliano
We introduce NCP (Neural Conditional Probability), a novel operator-theoretic approach for learning conditional distributions with a particular focus on inference tasks. NCP can be used to build conditional confidence regions and extract important st
Externí odkaz:
http://arxiv.org/abs/2407.01171