Zobrazeno 1 - 10
of 23 315
pro vyhledávání: '"Scherer, P."'
Autor:
Li, Bowen, Li, Zhaoyu, Du, Qiwei, Luo, Jinqi, Wang, Wenshan, Xie, Yaqi, Stepputtis, Simon, Wang, Chen, Sycara, Katia P., Ravikumar, Pradeep Kumar, Gray, Alexander G., Si, Xujie, Scherer, Sebastian
Recent years have witnessed the rapid development of Neuro-Symbolic (NeSy) AI systems, which integrate symbolic reasoning into deep neural networks. However, most of the existing benchmarks for NeSy AI fail to provide long-horizon reasoning tasks wit
Externí odkaz:
http://arxiv.org/abs/2411.00773
Quantum machine learning leverages quantum computing to enhance accuracy and reduce model complexity compared to classical approaches, promising significant advancements in various fields. Within this domain, quantum reinforcement learning has garner
Externí odkaz:
http://arxiv.org/abs/2410.21117
Autor:
Isopi, G., Capalbo, V., Hincks, A. D., Di Mascolo, L., Barbavara, E., Battistelli, E. S., Bond, J. R., Cui, W., Coulton, W. R., De Petris, M., Devlin, M., Dolag, K., Dunkley, J., Fabjan, D., Ferragamo, A., Gill, A. S., Guan, Y., Halpern, M., Hilton, M., Hughes, J. P., Lokken, M., van Marrewijk, J., Moodley, K., Mroczkowski, T., Orlowski-Scherer, J., Rasia, E., Santoni, S., Sifón, C., Wollack, E. J., Yepes, G.
According to CMB measurements, baryonic matter constitutes about $5\%$ of the mass-energy density of the universe. A significant population of these baryons, for a long time referred to as `missing', resides in a low density, warm-hot intergalactic m
Externí odkaz:
http://arxiv.org/abs/2410.14404
Autor:
Patel, D. K., Fijalkowski, K. M., Kruskopf, M., Liu, N., Götz, M., Pesel, E., Jaime, M., Klement, M., Schreyeck, S., Brunner, K., Gould, C., Molenkamp, L. W., Scherer, H.
The quantum anomalous Hall effect holds promise as a disruptive innovation in condensed matter physics and metrology, as it gives access to Hall resistance quantization in terms of the von-Klitzing constant RK = h/e2 at zero external magnetic field.
Externí odkaz:
http://arxiv.org/abs/2410.13365
Autor:
Chaitanya, Krishna, Damasceno, Pablo F., Fadnavis, Shreyas, Mobadersany, Pooya, Parmar, Chaitanya, Scherer, Emily, Zemlianskaia, Natalia, Surace, Lindsey, Ghanem, Louis R., Cula, Oana Gabriela, Mansi, Tommaso, Standish, Kristopher
Accurate assessment of disease severity from endoscopy videos in ulcerative colitis (UC) is crucial for evaluating drug efficacy in clinical trials. Severity is often measured by the Mayo Endoscopic Subscore (MES) and Ulcerative Colitis Endoscopic In
Externí odkaz:
http://arxiv.org/abs/2410.00536
Autor:
Scherer, Andrés, Cuadra, Jorge
High-energy (HE) and very high-energy (VHE) gamma-ray observations from the Galactic center (GC) detected extended emission correlated with the morphology of the central molecular zone (CMZ). Emission in both bands is expected to be produced by hadro
Externí odkaz:
http://arxiv.org/abs/2409.20436
Autor:
Potocnik, Viviane, Di Mauro, Alfio, Lamberti, Lorenzo, Kartsch, Victor, Scherer, Moritz, Conti, Francesco, Benini, Luca
Embodied artificial intelligence (AI) requires pushing complex multi-modal models to the extreme edge for time-constrained tasks such as autonomous navigation of robots and vehicles. On small form-factor devices, e.g., nano-sized unmanned aerial vehi
Externí odkaz:
http://arxiv.org/abs/2410.09054
Autor:
Ho, Cherie, Kim, Seungchan, Moon, Brady, Parandekar, Aditya, Harutyunyan, Narek, Wang, Chen, Sycara, Katia, Best, Graeme, Scherer, Sebastian
Exploration is a critical challenge in robotics, centered on understanding unknown environments. In this work, we focus on robots exploring structured indoor environments which are often predictable and composed of repeating patterns. Most existing a
Externí odkaz:
http://arxiv.org/abs/2409.15590
Spontaneous symmetry breaking can persist at all temperatures in certain biconical $\mathrm{O}(N)\times \mathbb{Z}_2$ vector models when the underlying field theories are ultraviolet complete. So far, the existence of such theories has been establish
Externí odkaz:
http://arxiv.org/abs/2409.10606
We propose the MAC-VO, a novel learning-based stereo VO that leverages the learned metrics-aware matching uncertainty for dual purposes: selecting keypoint and weighing the residual in pose graph optimization. Compared to traditional geometric method
Externí odkaz:
http://arxiv.org/abs/2409.09479