Zobrazeno 1 - 10
of 25 519
pro vyhledávání: '"Oz, A"'
Autor:
Sharma, Prateeksha, Oz, Dor, Lampadariou, Eleftheria, Doukas, Spyros, Lidorikis, Elefterios, Goykhman, Ilya
We investigate advanced CMOS-compatible Graphene/Silicon active metasurfaces based on guided-mode resonance filters. The simulated results show a high extinction ratio (>25 dB), narrow linewidth (~1.5 nm @1550 nm), quality factor of Q~1000, and polar
Externí odkaz:
http://arxiv.org/abs/2412.17177
Autor:
Piasetzky, Jonatan, Drori, Yehonatan, Warshavski, Yuval, Rotem, Amit, Cohen, Khen, Oz, Yaron, Suchowski, Haim
Directional couplers are essential components in integrated photonics. Given their widespread use, accurate characterization of directional couplers is crucial for ensuring optimal performance. However, it is challenging due to the coupling between f
Externí odkaz:
http://arxiv.org/abs/2412.11670
Autor:
Amram, Oz, Anzalone, Luca, Birk, Joschka, Faroughy, Darius A., Hallin, Anna, Kasieczka, Gregor, Krämer, Michael, Pang, Ian, Reyes-Gonzalez, Humberto, Shih, David
Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrates how data collected by the CMS experiment at the Large Hadron Collid
Externí odkaz:
http://arxiv.org/abs/2412.10504
Autor:
Rosenbaum, Andy, Kharazmi, Pegah, Banijamali, Ershad, Zeng, Lu, DiPersio, Christopher, Wei, Pan, Oz, Gokmen, Chung, Clement, Owczarzak, Karolina, Triefenbach, Fabian, Hamza, Wael
We present CALICO, a method to fine-tune Large Language Models (LLMs) to localize conversational agent training data from one language to another. For slots (named entities), CALICO supports three operations: verbatim copy, literal translation, and l
Externí odkaz:
http://arxiv.org/abs/2412.05388
Autor:
Krause, Claudius, Giannelli, Michele Faucci, Kasieczka, Gregor, Nachman, Benjamin, Salamani, Dalila, Shih, David, Zaborowska, Anna, Amram, Oz, Borras, Kerstin, Buckley, Matthew R., Buhmann, Erik, Buss, Thorsten, Cardoso, Renato Paulo Da Costa, Caterini, Anthony L., Chernyavskaya, Nadezda, Corchia, Federico A. G., Cresswell, Jesse C., Diefenbacher, Sascha, Dreyer, Etienne, Ekambaram, Vijay, Eren, Engin, Ernst, Florian, Favaro, Luigi, Franchini, Matteo, Gaede, Frank, Gross, Eilam, Hsu, Shih-Chieh, Jaruskova, Kristina, Käch, Benno, Kalagnanam, Jayant, Kansal, Raghav, Kim, Taewoo, Kobylianskii, Dmitrii, Korol, Anatolii, Korcari, William, Krücker, Dirk, Krüger, Katja, Letizia, Marco, Li, Shu, Liu, Qibin, Liu, Xiulong, Loaiza-Ganem, Gabriel, Madula, Thandikire, McKeown, Peter, Melzer-Pellmann, Isabell-A., Mikuni, Vinicius, Nguyen, Nam, Ore, Ayodele, Schweitzer, Sofia Palacios, Pang, Ian, Pedro, Kevin, Plehn, Tilman, Pokorski, Witold, Qu, Huilin, Raikwar, Piyush, Raine, John A., Reyes-Gonzalez, Humberto, Rinaldi, Lorenzo, Ross, Brendan Leigh, Scham, Moritz A. W., Schnake, Simon, Shimmin, Chase, Shlizerman, Eli, Soybelman, Nathalie, Srivatsa, Mudhakar, Tsolaki, Kalliopi, Vallecorsa, Sofia, Yeo, Kyongmin, Zhang, Rui
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few t
Externí odkaz:
http://arxiv.org/abs/2410.21611
Shell models provide a simplified mathematical framework that captures essential features of incompressible fluid turbulence, such as the energy cascade and scaling of the fluid observables. We perform a precision analysis of the direct and inverse c
Externí odkaz:
http://arxiv.org/abs/2409.11898
We extend the concept of loss landscape mode connectivity to the input space of deep neural networks. Mode connectivity was originally studied within parameter space, where it describes the existence of low-loss paths between different solutions (los
Externí odkaz:
http://arxiv.org/abs/2409.05800
We address a persistent challenge in text-to-image models: accurately generating a specified number of objects. Current models, which learn from image-text pairs, inherently struggle with counting, as training data cannot depict every possible number
Externí odkaz:
http://arxiv.org/abs/2408.11721
Federated Learning (FL) is commonly used in systems with distributed and heterogeneous devices with access to varying amounts of data and diverse computing and storage capacities. FL training process enables such devices to update the weights of a sh
Externí odkaz:
http://arxiv.org/abs/2405.20014
Current methods for reconstructing training data from trained classifiers are restricted to very small models, limited training set sizes, and low-resolution images. Such restrictions hinder their applicability to real-world scenarios. In this paper,
Externí odkaz:
http://arxiv.org/abs/2407.15845