Zobrazeno 1 - 10
of 29 428
pro vyhledávání: '"Ria, A"'
Autor:
Karli, Yusuf, Schwarz, René, Kappe, Florian, Vajner, Daniel A., Krämer, Ria G., Bracht, Thomas K., da Silva, Saimon F. Covre, Richter, Daniel, Nolte, Stefan, Rastelli, Armando, Reiter, Doris E., Weihs, Gregor, Heindel, Tobias, Remesh, Vikas
The generation of single photons using solid-state quantum emitters is pivotal for advancing photonic quantum technologies, particularly in quantum communication. As the field continuously advances towards practical use cases and beyond shielded labo
Externí odkaz:
http://arxiv.org/abs/2409.13981
Magnetic microcalorimeters (MMCs) have become a key technology for applications requiring outstanding energy resolution, fast signal rise time and excellent linearity. MMCs measure the temperature rise upon absorption of a single particle within a pa
Externí odkaz:
http://arxiv.org/abs/2409.07971
Modern machine learning systems rely on large datasets to attain broad generalization, and this often poses a challenge in robot learning, where each robotic platform and task might have only a small dataset. By training a single policy across many d
Externí odkaz:
http://arxiv.org/abs/2408.11812
Autor:
Gualano, Ria J., Jiang, Lucy, Zhang, Kexin, Shende, Tanisha, Won, Andrea Stevenson, Azenkot, Shiri
With the increasing adoption of social virtual reality (VR), it is critical to design inclusive avatars. While researchers have investigated how and why blind and d/Deaf people wish to disclose their disabilities in VR, little is known about the pref
Externí odkaz:
http://arxiv.org/abs/2408.08193
Recent advancements in bio-inspired visual sensing and neuromorphic computing have led to the development of various highly efficient bio-inspired solutions with real-world applications. One notable application integrates event-based cameras with spi
Externí odkaz:
http://arxiv.org/abs/2408.00611
Bayesian estimation is a vital tool in robotics as it allows systems to update the belief of the robot state using incomplete information from noisy sensors. To render the state estimation problem tractable, many systems assume that the motion and me
Externí odkaz:
http://arxiv.org/abs/2408.00907
Simple function classes have emerged as toy problems to better understand in-context-learning in transformer-based architectures used for large language models. But previously proposed simple function classes like linear regression or multi-layer-per
Externí odkaz:
http://arxiv.org/abs/2407.19346
Autor:
Vardani, Juhi, Sain, Ria
Recently, the LHCb Collaboration provided updated measurements for the lepton flavour ratios $R_K$ and $R_{K^*}$. The currently observed values align with the predictions of the standard model. In light of these recent updates, our investigation delv
Externí odkaz:
http://arxiv.org/abs/2407.15577
We study open-world multi-label text classification under extremely weak supervision (XWS), where the user only provides a brief description for classification objectives without any labels or ground-truth label space. Similar single-label XWS settin
Externí odkaz:
http://arxiv.org/abs/2407.05609
Autor:
Abadi, Ehsan, Badano, Aldo, Bakic, Predrag, Bliznakova, Kristina, Bosmans, Hilde, Carton, Ann-Katherine, Frangi, Alejandro, Glick, Stephen, Kinahan, Paul, Lo, Joseph, Maidment, Andrew, Ria, Francesco, Samei, Ehsan, Sechopoulos, Ioannis, Segars, Paul, Tanaka, Rie, Vancoillie, Liesbeth
This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pio
Externí odkaz:
http://arxiv.org/abs/2405.05359