Zobrazeno 1 - 10
of 23 282
pro vyhledávání: '"Gianni, P"'
This article provides a tutorial on over-the-air electromagnetic signal processing (ESP) for next-generation wireless networks, addressing the limitations of digital processing to enhance the efficiency and sustainability of future 6th Generation (6G
Externí odkaz:
http://arxiv.org/abs/2412.14968
This study explores the application of Virtual Reality (VR) as a tool for safety training in high-risk industrial settings, specifically focusing on the IPLOM refinery, Busalla (Italy). As industries increasingly adopt digital tools to enhance safety
Externí odkaz:
http://arxiv.org/abs/2412.13725
This paper addresses a two-dimensional sharp interface variational model for solid-state dewetting of thin films with surface energies, introduced by Wang, Jiang, Bao, and Srolovitz in \cite{jiang2016solid}. Using the $H^{-1}$-gradient flow structure
Externí odkaz:
http://arxiv.org/abs/2412.10222
Uncertainty quantification in text-to-image (T2I) generative models is crucial for understanding model behavior and improving output reliability. In this paper, we are the first to quantify and evaluate the uncertainty of T2I models with respect to t
Externí odkaz:
http://arxiv.org/abs/2412.03178
Autor:
DeAndres-Tame, Ivan, Tolosana, Ruben, Melzi, Pietro, Vera-Rodriguez, Ruben, Kim, Minchul, Rathgeb, Christian, Liu, Xiaoming, Gomez, Luis F., Morales, Aythami, Fierrez, Julian, Ortega-Garcia, Javier, Zhong, Zhizhou, Huang, Yuge, Mi, Yuxi, Ding, Shouhong, Zhou, Shuigeng, He, Shuai, Fu, Lingzhi, Cong, Heng, Zhang, Rongyu, Xiao, Zhihong, Smirnov, Evgeny, Pimenov, Anton, Grigorev, Aleksei, Timoshenko, Denis, Asfaw, Kaleb Mesfin, Low, Cheng Yaw, Liu, Hao, Wang, Chuyi, Zuo, Qing, He, Zhixiang, Shahreza, Hatef Otroshi, George, Anjith, Unnervik, Alexander, Rahimi, Parsa, Marcel, Sébastien, Neto, Pedro C., Huber, Marco, Kolf, Jan Niklas, Damer, Naser, Boutros, Fadi, Cardoso, Jaime S., Sequeira, Ana F., Atzori, Andrea, Fenu, Gianni, Marras, Mirko, Štruc, Vitomir, Yu, Jiang, Li, Zhangjie, Li, Jichun, Zhao, Weisong, Lei, Zhen, Zhu, Xiangyu, Zhang, Xiao-Yu, Biesseck, Bernardo, Vidal, Pedro, Coelho, Luiz, Granada, Roger, Menotti, David
Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including diverse scenarios, quality, and demographic groups, among others. It a
Externí odkaz:
http://arxiv.org/abs/2412.01383
Autor:
Taioli, Francesco, Zorzi, Edoardo, Franchi, Gianni, Castellini, Alberto, Farinelli, Alessandro, Cristani, Marco, Wang, Yiming
Existing embodied instance goal navigation tasks, driven by natural language, assume human users to provide complete and nuanced instance descriptions prior to the navigation, which can be impractical in the real world as human instructions might be
Externí odkaz:
http://arxiv.org/abs/2412.01250
Unitary integrable models typically relax to a stationary Generalized Gibbs Ensemble (GGE), but in experimental realizations dissipation often breaks integrability. In this work, we use the recently introduced time-dependent GGE (t-GGE) approach to d
Externí odkaz:
http://arxiv.org/abs/2412.01896
We consider a dynamical system describing the motion of a test-particle surrounded by $N$ Brownian particles with different masses. Physical principles of conservation of momentum and energy are met. We prove that, in the limit $N\to\infty$, the test
Externí odkaz:
http://arxiv.org/abs/2411.18775
Autor:
Petrella, Gianni
We embed several copies of the derived category of a quiver and certain line bundles in the derived category of an associated moduli space of representations, giving the start of a semiorthogonal decomposition. This mirrors the semiorthogonal decompo
Externí odkaz:
http://arxiv.org/abs/2411.15125
Autor:
Song, Yuhang, Gianni, Mario, Yang, Chenguang, Lin, Kunyang, Chiu, Te-Chuan, Nguyen, Anh, Lee, Chun-Yi
This paper addresses the challenge of fine-grained alignment in Vision-and-Language Navigation (VLN) tasks, where robots navigate realistic 3D environments based on natural language instructions. Current approaches use contrastive learning to align l
Externí odkaz:
http://arxiv.org/abs/2411.14811