Zobrazeno 1 - 10
of 43 891
pro vyhledávání: '"A. Ilias"'
Enhancing 3D Object Detection in Autonomous Vehicles Based on Synthetic Virtual Environment Analysis
Autor:
Li, Vladislav, Siniosoglou, Ilias, Karamitsou, Thomai, Lytos, Anastasios, Moscholios, Ioannis D., Goudos, Sotirios K., Banerjee, Jyoti S., Sarigiannidi, Panagiotis, Argyriou, Vasileios
Autonomous Vehicles (AVs) use natural images and videos as input to understand the real world by overlaying and inferring digital elements, facilitating proactive detection in an effort to assure safety. A crucial aspect of this process is real-time,
Externí odkaz:
http://arxiv.org/abs/2412.07509
Autor:
Tripodi, Roberta, Martis, Nicholas, Markov, Vladan, Bradač, Maruša, Di Mascia, Fabio, Cammelli, Vieri, D'Eugenio, Francesco, Willott, Chris, Curti, Mirko, Bhatt, Maulik, Gallerani, Simona, Rihtaršič, Gregor, Singh, Jasbir, Gaspar, Gaia, Harshan, Anishya, Judež, Jon, Merida, Rosa M., Desprez, Guillaume, Sawicki, Marcin, Goovaerts, Ilias, Muzzin, Adam, Noirot, Gaël, Sarrouh, Ghassan T. E., Abraham, Roberto, Asada, Yoshihisa, Brammer, Gabriel, Carpenter, Vicente Estrada, Felicioni, Giordano, Fujimoto, Seiji, Iyer, Kartheik, Mowla, Lamiya, Strait, Victoria
The James Webb Space Telescope (JWST) has recently discovered a new population of objects at high redshift referred to as `Little Red Dots' (LRDs). Their nature currently remains elusive, despite their surprisingly high inferred number densities. Thi
Externí odkaz:
http://arxiv.org/abs/2412.04983
The advent of 6G/NextG networks comes along with a series of benefits, including extreme capacity, reliability, and efficiency. However, these networks may become vulnerable to new security threats. Therefore, 6G/NextG networks must be equipped with
Externí odkaz:
http://arxiv.org/abs/2412.03483
Motivation: With the rapid expansion of large-scale biological datasets, DNA and protein sequence alignments have become essential for comparative genomics and proteomics. These alignments facilitate the exploration of sequence similarity patterns, p
Externí odkaz:
http://arxiv.org/abs/2411.19427
Large enterprise databases can be complex and messy, obscuring the data semantics needed for analytical tasks. We propose a semantic layer in-between the database and the user as a set of small and easy-to-interpret database views, effectively acting
Externí odkaz:
http://arxiv.org/abs/2412.07786
Autor:
Tzoumanekas, Georgios, Chatzianastasis, Michail, Ilias, Loukas, Kiokes, George, Psarras, John, Askounis, Dimitris
Social media platforms, including X, Facebook, and Instagram, host millions of daily users, giving rise to bots-automated programs disseminating misinformation and ideologies with tangible real-world consequences. While bot detection in platform X ha
Externí odkaz:
http://arxiv.org/abs/2411.16285
Autor:
Diakonikolas, Ilias, Kane, Daniel M.
We study the task of learning latent-variable models. An obstacle towards designing efficient algorithms for such models is the necessity of approximating moment tensors of super-constant degree. Motivated by such applications, we develop a general e
Externí odkaz:
http://arxiv.org/abs/2411.15669
Autor:
Kokorev, Vasily, Atek, Hakim, Chisholm, John, Endsley, Ryan, Chemerynska, Iryna, Muñoz, Julian B., Furtak, Lukas J., Pan, Richard, Berg, Danielle, Fujimoto, Seiji, Oesch, Pascal A., Weibel, Andrea, Adamo, Angela, Blaizot, Jeremy, Bouwens, Rychard, Dessauges-Zavadsky, Miroslava, Khullar, Gourav, Korber, Damien, Goovaerts, Ilias, Jecmen, Michelle, Labbé, Ivo, Leclercq, Floriane, Marques-Chaves, Rui, Mason, Charlotte, McQuinn, Kristen B. W., Naidu, Rohan, Natarajan, Priyamvada, Nelson, Erica, Rosdahl, Joki, Saldana-Lopez, Alberto, Schaerer, Daniel, Trebitsch, Maxime, Volonteri, Marta, Zitrin, Adi
We report the discovery of five galaxy candidates at redshifts between $15.9
Externí odkaz:
http://arxiv.org/abs/2411.13640
We study the problem of PAC learning halfspaces in the reliable agnostic model of Kalai et al. (2012). The reliable PAC model captures learning scenarios where one type of error is costlier than the others. Our main positive result is a new algorithm
Externí odkaz:
http://arxiv.org/abs/2411.11238
We study the problem of learning a single neuron with respect to the $L_2^2$-loss in the presence of adversarial distribution shifts, where the labels can be arbitrary, and the goal is to find a ``best-fit'' function. More precisely, given training s
Externí odkaz:
http://arxiv.org/abs/2411.06697