Zobrazeno 1 - 10
of 7 054
pro vyhledávání: '"P Asgari"'
Autor:
Leahy, Kevin, Asgari, Hamid, Dennis, Louise A., Feather, Martin S., Fisher, Michael, Ibanez-Guzman, Javier, Logan, Brian, Olszewska, Joanna I., Redfield, Signe
Autonomous systems use independent decision-making with only limited human intervention to accomplish goals in complex and unpredictable environments. As the autonomy technologies that underpin them continue to advance, these systems will find their
Externí odkaz:
http://arxiv.org/abs/2411.14155
Autor:
Katsaros, Konstantinos, Mavromatis, Ioannis, Antonakoglou, Kostantinos, Ghosh, Saptarshi, Kaleshi, Dritan, Mahmoodi, Toktam, Asgari, Hamid, Karousos, Anastasios, Tavakkolnia, Iman, Safi, Hossein, Hass, Harald, Vrontos, Constantinos, Emami, Amin, Ullauri, Juan Parra, Moazzeni, Shadi, Simeonidou, Dimitra
The development of the sixth generation of communication networks (6G) has been gaining momentum over the past years, with a target of being introduced by 2030. Several initiatives worldwide are developing innovative solutions and setting the directi
Externí odkaz:
http://arxiv.org/abs/2411.06870
Autor:
Alizadeh, Meysam, Asgari, Yasaman, Samei, Zeynab, Yari, Sara, Dehghani, Shirin, Kubli, Mael, Zare, Darya, Bermeo, Juan Diego, Batzdorfer, Veronika, Gilardi, Fabrizio
Academics increasingly acknowledge the predictive power of social media for a wide variety of events and, more specifically, for financial markets. Anecdotal and empirical findings show that cryptocurrencies are among the financial assets that have b
Externí odkaz:
http://arxiv.org/abs/2411.05577
Autor:
Gatsak, Tanya, Abhishek, Kumar, Yedder, Hanene Ben, Taghanaki, Saeid Asgari, Hamarneh, Ghassan
PET imaging is an invaluable tool in clinical settings as it captures the functional activity of both healthy anatomy and cancerous lesions. Developing automatic lesion segmentation methods for PET images is crucial since manual lesion segmentation i
Externí odkaz:
http://arxiv.org/abs/2411.01758
Autor:
Yan, Ziang, Wright, Angus H., Chisari, Nora Elisa, Georgiou, Christos, Joudaki, Shahab, Loureiro, Arthur, Reischke, Robert, Asgari, Marika, Bilicki, Maciej, Dvornik, Andrej, Heymans, Catherine, Hildebrandt, Hendrik, Jalan, Priyanka, Joachimi, Benjamin, Lesci, Giorgio Francesco, Li, Shun-Sheng, Linke, Laila, Mahony, Constance, Moscardini, Lauro, Napolitano, Nicola R., Stoelzner, Benjamin, Von Wietersheim-Kramsta, Maximilian, Yoon, Mijin
Photometric galaxy surveys, despite their limited resolution along the line of sight, encode rich information about the large-scale structure (LSS) of the Universe thanks to the large number density and extensive depth of the data. However, the compl
Externí odkaz:
http://arxiv.org/abs/2410.23141
Autor:
Alimohammadi, Amirhossein, Nag, Sauradip, Taghanaki, Saeid Asgari, Tagliasacchi, Andrea, Hamarneh, Ghassan, Amiri, Ali Mahdavi
Segmenting an object in a video presents significant challenges. Each pixel must be accurately labelled, and these labels must remain consistent across frames. The difficulty increases when the segmentation is with arbitrary granularity, meaning the
Externí odkaz:
http://arxiv.org/abs/2410.18538
In this paper, a new heterostructure based on the hybridization of graphene-LiF layers with a nonlinear material is introduced and studied. The numerical results are depicted and discussed in detail. A high value of FOM (FOM=24.5) at the frequency of
Externí odkaz:
http://arxiv.org/abs/2410.12739
This paper studies the problem of federated training of a deep neural network (DNN) over a random access channel (RACH) such as in computer networks, wireless networks, and cellular systems. More precisely, a set of remote users participate in traini
Externí odkaz:
http://arxiv.org/abs/2410.11986
Autor:
Gerami, Armin, Asgari, Bahar
Sparse matrix-vector multiplication (SpMV) plays a vital role in various scientific and engineering fields, from scientific computing to machine learning. Traditional general-purpose processors often fall short of their peak performance with sparse d
Externí odkaz:
http://arxiv.org/abs/2410.09106
We present TuringQ, the first benchmark designed to evaluate the reasoning capabilities of large language models (LLMs) in the theory of computation. TuringQ consists of 4,006 undergraduate and graduate-level question-answer pairs, categorized into f
Externí odkaz:
http://arxiv.org/abs/2410.06547