Zobrazeno 1 - 10
of 87 816
pro vyhledávání: '"Ngo The. An"'
Autor:
Tran, Nguyen-Bao-Long, Ngo, Tuan V., Ngo, Mao V., Chen, Binbin, Park, Jihong, Quek, Tony Q. S.
As telecommunications systems progress to support diverse use cases with heterogeneous and dynamic Quality of Service (QoS) requirements, it becomes an increasingly complex task to automatically manage various resources involved -- from radio, comput
Externí odkaz:
http://arxiv.org/abs/2410.09765
Autor:
Ngo, Tuan V., Ngo, Mao V., Chen, Binbin, Gemmi, Gabriele, Baena, Eduardo, Polese, Michele, Melodia, Tommaso, Chien, William, Quek, Tony
Open Radio Access Networks (O-RAN) are expected to revolutionize the telecommunications industry with benefits like cost reduction, vendor diversity, and improved network performance through AI optimization. Supporting the O-RAN ALLIANCE's mission to
Externí odkaz:
http://arxiv.org/abs/2410.04416
Autor:
Galappaththige, Diluka, Mohammadi, Mohammadali, Ngo, Hien Quoc, Matthaiou, Michail, Tellambura, Chintha
Cell-free (CF) architecture and full-duplex (FD) communication are leading candidates for next-generation wireless networks. The CF framework removes cell boundaries in traditional cell-based systems, thereby mitigating inter-cell interference and im
Externí odkaz:
http://arxiv.org/abs/2412.04711
Autor:
Wang, Chaoyang, Zhuang, Peiye, Ngo, Tuan Duc, Menapace, Willi, Siarohin, Aliaksandr, Vasilkovsky, Michael, Skorokhodov, Ivan, Tulyakov, Sergey, Wonka, Peter, Lee, Hsin-Ying
We propose 4Real-Video, a novel framework for generating 4D videos, organized as a grid of video frames with both time and viewpoint axes. In this grid, each row contains frames sharing the same timestep, while each column contains frames from the sa
Externí odkaz:
http://arxiv.org/abs/2412.04462
Autor:
Barnes, Rory, Amaral, Laura N. R. do, Birky, Jessica, Carone, Ludmila, Driscoll, Peter, Livesey, Joseph R., Graham, David, Becker, Juliette, Cui, Kaiming, Schlecker, Martin, Garcia, Rodolfo, Gialluca, Megan, Adams, Arthur, Ahmed, MD Redyan, Bonney, Paul, Broussard, Wynter, Chawla, Chetan, Damasso, Mario, Danchi, William C., Deitrick, Russell, Ducrot, Elsa, Fromont, Emeline F., Gaches, Brandt A. L., Gupta, Sakshi, Hill, Michelle L., Jackman, James A. G., Janin, Estelle M., Karawacki, Mikolaj, Koren, Matheus Daniel, La Greca, Roberto, Leung, Michaela, Miranda-Rosete, Arturo, Olohoy, Michael Kent A., Ngo, Cecelia, Paul, Daria, Sahu, Chandan Kumar, Sarkar, Debajyoti Basu, Shadab, Mohammad Afzal, Schwieterman, Edward W., Sedler, Melissa, Texeira, Katie, Vazan, Allona, Vega, Karen N. Delgado, Vijayakumar, Rohit, Wojack, Jonathan T.
We present numerous aspects of the evolution of the LP 890-9 (SPECULOOS-2/TOI-4306) planetary system, focusing on the likelihood that planet c can support life. We find that the host star reaches the main sequence in 1 Gyr and that planet c lies clos
Externí odkaz:
http://arxiv.org/abs/2412.02743
Autor:
Hsieh, Jane, Zhang, Angie, Rasetarinera, Mialy, Chou, Erik, Ngo, Daniel, Lightman, Karen, Lee, Min Kyung, Zhu, Haiyi
The proliferating adoption of platform-based gig work increasingly raises concerns for worker conditions. Past studies documented how platforms leveraged design to exploit labor, withheld information to generate power asymmetries, and left workers al
Externí odkaz:
http://arxiv.org/abs/2412.02973
Autor:
Nguyen, Quang Duc, Nguyen, Tung, Nguyen, Duc Anh, Van, Linh Ngo, Dinh, Sang, Nguyen, Thien Huu
Uncovering hidden topics from short texts is challenging for traditional and neural models due to data sparsity, which limits word co-occurrence patterns, and label sparsity, stemming from incomplete reconstruction targets. Although data aggregation
Externí odkaz:
http://arxiv.org/abs/2412.00525
Learning diverse policies for non-prehensile manipulation is essential for improving skill transfer and generalization to out-of-distribution scenarios. In this work, we enhance exploration through a two-fold approach within a hybrid framework that t
Externí odkaz:
http://arxiv.org/abs/2411.14913
Large language models have gained widespread popularity for their ability to process natural language inputs and generate insights derived from their training data, nearing the qualities of true artificial intelligence. This advancement has prompted
Externí odkaz:
http://arxiv.org/abs/2411.14513
Parameter-efficient fine-tuning multimodal large language models (MLLMs) presents significant challenges, including reliance on high-level visual features that limit fine-grained detail comprehension, and data conflicts that arise from task complexit
Externí odkaz:
http://arxiv.org/abs/2411.12787