Zobrazeno 1 - 10
of 20 566
pro vyhledávání: '"Stiller, A"'
Autonomous vehicles require road information for their operation, usually in form of HD maps. Since offline maps eventually become outdated or may only be partially available, online HD map construction methods have been proposed to infer map informa
Externí odkaz:
http://arxiv.org/abs/2411.10316
Autor:
Li, Xuesong, Hayder, Zeeshan, Zia, Ali, Cassidy, Connor, Liu, Shiming, Stiller, Warwick, Stone, Eric, Conaty, Warren, Petersson, Lars, Rolland, Vivien
Crop biomass offers crucial insights into plant health and yield, making it essential for crop science, farming systems, and agricultural research. However, current measurement methods, which are labor-intensive, destructive, and imprecise, hinder la
Externí odkaz:
http://arxiv.org/abs/2410.23901
Autor:
Feng, Chao, Guan, Hongjie, Celdrán, Alberto Huertas, von der Assen, Jan, Bovet, Gérôme, Stiller, Burkhard
Federated Learning (FL) performance is highly influenced by data distribution across clients, and non-Independent and Identically Distributed (non-IID) leads to a slower convergence of the global model and a decrease in model effectiveness. The exist
Externí odkaz:
http://arxiv.org/abs/2410.07678
Autor:
Celdrán, Alberto Huertas, Feng, Chao, Banik, Sabyasachi, Bovet, Gerome, Perez, Gregorio Martinez, Stiller, Burkhard
Federated Learning (FL), introduced in 2016, was designed to enhance data privacy in collaborative model training environments. Among the FL paradigm, horizontal FL, where clients share the same set of features but different data samples, has been ex
Externí odkaz:
http://arxiv.org/abs/2410.06127
Autor:
Saffer, Olivia, Cabello, Jesús Humberto Marines, Becker, Steven, Geilen, Andreas, Stiller, Birgit
Photonic memory is an important building block to delay, route and buffer optical information, for instance in optical interconnects or for recurrent optical signal processing. Photonic-phononic memory based on stimulated Brillouin-Mandelstam scatter
Externí odkaz:
http://arxiv.org/abs/2410.05156
Autor:
Feng, Chao, Celdrán, Alberto Huertas, Zeng, Zien, Ye, Zi, von der Assen, Jan, Bovet, Gerome, Stiller, Burkhard
Decentralized Federated Learning (DFL), a paradigm for managing big data in a privacy-preserved manner, is still vulnerable to poisoning attacks where malicious clients tamper with data or models. Current defense methods often assume Independently an
Externí odkaz:
http://arxiv.org/abs/2409.19302
Autor:
Polley, Nikolai, Pavlitska, Svetlana, Boualili, Yacin, Rohrbeck, Patrick, Stiller, Paul, Bangaru, Ashok Kumar, Zöllner, J. Marius
Effective traffic light detection is a critical component of the perception stack in autonomous vehicles. This work introduces a novel deep-learning detection system while addressing the challenges of previous work. Utilizing a comprehensive dataset
Externí odkaz:
http://arxiv.org/abs/2409.07284
The Internet of Things (IoT) involves complex, interconnected systems and devices that depend on context-sharing platforms for interoperability and information exchange. These platforms are, therefore, critical components of real-world IoT deployment
Externí odkaz:
http://arxiv.org/abs/2408.12081
Autor:
Schneider, Adam C., Cushing, Michael C., Stiller, Robert A., Munn, Jeffrey A., Vrba, Frederick J., Bruursema, Justice, Williams, Stephen J., Liu, Michael C., Bravo, Alexia, Faherty, Jacqueline K., Rothermich, Austin, Calamari, Emily, Caselden, Dan, Kabatnik, Martin, Sainio, Arttu, Bickle, Thomas P., Pendrill, William, Andersen, Nikolaj Stevnbak, Thevenot, Melina
We have used the UKIRT Hemisphere Survey (UHS) combined with the UKIDSS Galactic Cluster Survey (GCS), the UKIDSS Galactic Plane Survey (GPS), and the CatWISE2020 catalog to search for new substellar members of the nearest open cluster to the Sun, th
Externí odkaz:
http://arxiv.org/abs/2408.10112
Autor:
Wagner, Royden, Tas, Ömer Sahin, Steiner, Marlon, Konstantinidis, Fabian, Königshof, Hendrik, Klemp, Marvin, Fernandez, Carlos, Stiller, Christoph
Self-driving vehicles rely on multimodal motion forecasts to effectively interact with their environment and plan safe maneuvers. We introduce SceneMotion, an attention-based model for forecasting scene-wide motion modes of multiple traffic agents. O
Externí odkaz:
http://arxiv.org/abs/2408.01537