Zobrazeno 1 - 10
of 58 201
pro vyhledávání: '"NIR, A."'
We present X-sifter, a software package designed for near-optimal detection of sources in X-ray images and other forms of photon images in the Poisson-noise regime. The code is based on the Poisson-noise-matched filter (Ofek & Zackay), which provides
Externí odkaz:
http://arxiv.org/abs/2412.07858
Autor:
Gavish, Nir
Infectious diseases often involve multiple strains that interact through the immune response generated after an infection. This study investigates the conditions under which a two-strain epidemic model with partial cross-immunity can lead to self-sus
Externí odkaz:
http://arxiv.org/abs/2412.07536
Autor:
Goldfriend, Tomer, Reichental, Israel, Naveh, Amir, Gazit, Lior, Yoran, Nadav, Alon, Ravid, Ur, Shmuel, Lahav, Shahak, Cornfeld, Eyal, Elazari, Avi, Emanuel, Peleg, Harpaz, Dor, Michaeli, Tal, Erez, Nati, Preminger, Lior, Shapira, Roman, Garcell, Erik Michael, Samimi, Or, Kisch, Sara, Hallel, Gil, Kishony, Gilad, van Wingerden, Vincent, Rosenbloom, Nathaniel A., Opher, Ori, Vax, Matan, Smoler, Ariel, Danzig, Tamuz, Schirman, Eden, Sella, Guy, Cohen, Ron, Garfunkel, Roi, Cohn, Tali, Rosemarin, Hanan, Hass, Ron, Jankiewicz, Klem, Gharra, Karam, Roth, Ori, Azar, Barak, Asban, Shahaf, Linkov, Natalia, Segman, Dror, Sahar, Ohad, Davidson, Niv, Minerbi, Nir, Naveh, Yehuda
We present a scalable, robust approach to creating quantum programs of arbitrary size and complexity. The approach is based on the true abstraction of the problem. The quantum program is expressed in terms of a high-level model together with constrai
Externí odkaz:
http://arxiv.org/abs/2412.07372
Epistemic planning is the sub-field of AI planning that focuses on changing knowledge and belief. It is important in both multi-agent domains where agents need to have knowledge/belief regarding the environment, but also the beliefs of other agents,
Externí odkaz:
http://arxiv.org/abs/2412.07981
Active Learning (AL) is a user-interactive approach aimed at reducing annotation costs by selecting the most crucial examples to label. Although AL has been extensively studied for image classification tasks, the specific scenario of interactive imag
Externí odkaz:
http://arxiv.org/abs/2412.02310
Autor:
Wang, Tony T., Hughes, John, Sleight, Henry, Schaeffer, Rylan, Agrawal, Rajashree, Barez, Fazl, Sharma, Mrinank, Mu, Jesse, Shavit, Nir, Perez, Ethan
Defending large language models against jailbreaks so that they never engage in a broadly-defined set of forbidden behaviors is an open problem. In this paper, we investigate the difficulty of jailbreak-defense when we only want to forbid a narrowly-
Externí odkaz:
http://arxiv.org/abs/2412.02159
Quantum Error Correction (QEC) is widely regarded as the most promising path towards quantum advantage, with significant advances in QEC codes, decoding algorithms, and physical implementations. The success of QEC relies on achieving quantum gate fid
Externí odkaz:
http://arxiv.org/abs/2412.00289
In underwater images, most useful features are occluded by water. The extent of the occlusion depends on imaging geometry and can vary even across a sequence of burst images. As a result, 3D reconstruction methods robust on in-air scenes, like Neural
Externí odkaz:
http://arxiv.org/abs/2411.19588
Autor:
Bercovich, Akhiad, Ronen, Tomer, Abramovich, Talor, Ailon, Nir, Assaf, Nave, Dabbah, Mohammad, Galil, Ido, Geifman, Amnon, Geifman, Yonatan, Golan, Izhak, Haber, Netanel, Karpas, Ehud, Koren, Roi, Levy, Itay, Molchanov, Pavlo, Mor, Shahar, Moshe, Zach, Nabwani, Najeeb, Puny, Omri, Rubin, Ran, Schen, Itamar, Shahaf, Ido, Tropp, Oren, Argov, Omer Ullman, Zilberstein, Ran, El-Yaniv, Ran
Large language models (LLMs) have demonstrated remarkable capabilities, but their adoption is limited by high computational costs during inference. While increasing parameter counts enhances accuracy, it also widens the gap between state-of-the-art c
Externí odkaz:
http://arxiv.org/abs/2411.19146
Autor:
Nguyen, Nhan Thanh, Nguyen, Ly V., Shlezinger, Nir, Eldar, Yonina C., Swindlehurst, A. Lee, Juntti, Markku
In this paper, we propose a low-complexity and fast hybrid beamforming design for joint communications and sensing (JCAS) based on deep unfolding. We first derive closed-form expressions for the gradients of the communications sum rate and sensing be
Externí odkaz:
http://arxiv.org/abs/2411.17747