Zobrazeno 1 - 10
of 10 411
pro vyhledávání: '"A, Marras"'
Autor:
Carta, Salvatore Mario, Chessa, Stefano, Contu, Giulia, Corriga, Andrea, Deidda, Andrea, Fenu, Gianni, Frigau, Luca, Giuliani, Alessandro, Grassi, Luca, Manca, Marco Manolo, Marras, Mirko, Mola, Francesco, Mossa, Bastianino, Mura, Piergiorgio, Ortu, Marco, Piano, Leonardo, Pisano, Simone, Pisu, Alessia, Podda, Alessandro Sebastian, Pompianu, Livio, Seu, Simone, Tiddia, Sandro Gabriele
Minority languages are vital to preserving cultural heritage, yet they face growing risks of extinction due to limited digital resources and the dominance of artificial intelligence models trained on high-resource languages. This white paper proposes
Externí odkaz:
http://arxiv.org/abs/2411.13453
Tropical cyclones (TCs) are powerful, natural phenomena that can severely impact populations and infrastructure. Enhancing our understanding of the mechanisms driving their intensification is crucial for mitigating these impacts. To this end, researc
Externí odkaz:
http://arxiv.org/abs/2410.21607
Autor:
Malitesta, Daniele, Medda, Giacomo, Purificato, Erasmo, Boratto, Ludovico, Malliaros, Fragkiskos D., Marras, Mirko, De Luca, Ernesto William
Diffusion-based recommender systems have recently proven to outperform traditional generative recommendation approaches, such as variational autoencoders and generative adversarial networks. Nevertheless, the machine learning literature has raised se
Externí odkaz:
http://arxiv.org/abs/2409.04339
Over the recent years, the advancements in deep face recognition have fueled an increasing demand for large and diverse datasets. Nevertheless, the authentic data acquired to create those datasets is typically sourced from the web, which, in many cas
Externí odkaz:
http://arxiv.org/abs/2409.02867
Autor:
Mohamed, Sondos, Zimmer, Walter, Greer, Ross, Ghita, Ahmed Alaaeldin, Castrillón-Santana, Modesto, Trivedi, Mohan, Knoll, Alois, Carta, Salvatore Mario, Marras, Mirko
Accurately detecting 3D objects from monocular images in dynamic roadside scenarios remains a challenging problem due to varying camera perspectives and unpredictable scene conditions. This paper introduces a two-stage training strategy to address th
Externí odkaz:
http://arxiv.org/abs/2408.15637
Recent developments in recommendation have harnessed the collaborative power of graph neural networks (GNNs) in learning users' preferences from user-item networks. Despite emerging regulations addressing fairness of automated systems, unfairness iss
Externí odkaz:
http://arxiv.org/abs/2408.12208
Autor:
Rinella, Gianluca Aglieri, Aglietta, Luca, Antonelli, Matias, Barile, Francesco, Benotto, Franco, Beolè, Stefania Maria, Botta, Elena, Bruno, Giuseppe Eugenio, Carnesecchi, Francesca, Colella, Domenico, Colelli, Angelo, Contin, Giacomo, De Robertis, Giuseppe, Dumitrache, Florina, Elia, Domenico, Ferrero, Chiara, Fransen, Martin, Kluge, Alex, Kumar, Shyam, Lemoine, Corentin, Licciulli, Francesco, Lim, Bong-Hwi, Loddo, Flavio, Mager, Magnus, Marras, Davide, Martinengo, Paolo, Pastore, Cosimo, Patra, Rajendra Nath, Perciballi, Stefania, Piro, Francesco, Prino, Francesco, Ramello, Luciano, Ramos, Arianna Grisel Torres, Reidt, Felix, Russo, Roberto, Sarritzu, Valerio, Savino, Umberto, Schledewitz, David, Selina, Mariia, Senyukov, Serhiy, Sitta, Mario, Snoeys, Walter, Sonneveld, Jory, Suljic, Miljenko, Triloki, Triloki, Turcato, Andrea
In the context of the CERN EP R&D on monolithic sensors and the ALICE ITS3 upgrade, the Tower Partners Semiconductor Co (TPSCo) 65 nm process has been qualified for use in high energy physics, and adopted for the ALICE ITS3 upgrade. An Analog Pixel T
Externí odkaz:
http://arxiv.org/abs/2407.18528
Autor:
Asensio, Yaiza, Marras, Sergio, Spirito, Davide, Gobbi, Marco, Ipatov, Mihail, Casanova, Fèlix, Mateo-Alonso, Aurelio, Hueso, Luis E., Martín-García, Beatriz
Publikováno v:
Adv. Funct. Mater. 2022, 32, 2207988
Understanding the structural and magnetic properties in layered hybrid organic-inorganic metal halide perovskites (HOIPs) is key for their design and integration in spin-electronic devices. Here, we have conducted a systematic study on ten compounds
Externí odkaz:
http://arxiv.org/abs/2404.13403
Autor:
DeAndres-Tame, Ivan, Tolosana, Ruben, Melzi, Pietro, Vera-Rodriguez, Ruben, Kim, Minchul, Rathgeb, Christian, Liu, Xiaoming, 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
Publikováno v:
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRw 2024)
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some c
Externí odkaz:
http://arxiv.org/abs/2404.10378
Recent advances in deep face recognition have spurred a growing demand for large, diverse, and manually annotated face datasets. Acquiring authentic, high-quality data for face recognition has proven to be a challenge, primarily due to privacy concer
Externí odkaz:
http://arxiv.org/abs/2404.03537