Zobrazeno 1 - 10
of 23 181
pro vyhledávání: '"P. Scherer"'
Autor:
E. Forde, M. Josipovic, M. Kamphuis, J. Lopez, P. Remeijer, S. Rivera, P. Scherer, L. Wiersema, R. de Jong
Publikováno v:
Technical Innovations & Patient Support in Radiation Oncology, Vol 29, Iss , Pp 100227- (2024)
The roles and responsibilities of radiation therapists (RTTs) are many and varied. Professional expectations are influenced by the technology available, as well as the level of autonomy RTTs have in their daily practice. This professional range requi
Externí odkaz:
https://doaj.org/article/9f45bee0322348feb1ae528ac1a37d1e
Publikováno v:
Technical Innovations & Patient Support in Radiation Oncology, Vol 28, Iss , Pp 100219- (2023)
Externí odkaz:
https://doaj.org/article/3c20e68f606541c2ac3ed38a705d103d
Autor:
Scherer, Marcel
We prove that for every compact, convex subset $K\subset\mathbb{R}^2$ the operator system $A(K)$, consisting of all continuous affine functions on $K$, is hyperrigid in the C*-algebra $C(\mathrm{ex}(K))$. In particular, this result implies that the w
Externí odkaz:
http://arxiv.org/abs/2411.11709
Increasingly frequent publications in the literature report voice quality differences between depressed patients and controls. Here, we examine the possibility of using voice analysis as an early warning signal for the development of emotion disturba
Externí odkaz:
http://arxiv.org/abs/2411.11541
Autor:
Bonetti, Pietro M., Jiang, Yi, Hu, Haoyu, Călugăru, Dumitru, Scherer, Michael M., Bernevig, B. Andrei, Classen, Laura
The discovery of a charge density wave in FeGe extends the discussion of the nature of charge order in kagome metals to a magnetic compound. Motivated by this observation, we combine density functional theory (DFT) and functional-renormalization-grou
Externí odkaz:
http://arxiv.org/abs/2411.10931
Autor:
Mohseni, Masoud, Scherer, Artur, Johnson, K. Grace, Wertheim, Oded, Otten, Matthew, Aadit, Navid Anjum, Bresniker, Kirk M., Camsari, Kerem Y., Chapman, Barbara, Chatterjee, Soumitra, Dagnew, Gebremedhin A., Esposito, Aniello, Fahim, Farah, Fiorentino, Marco, Khalid, Abdullah, Kong, Xiangzhou, Kulchytskyy, Bohdan, Li, Ruoyu, Lott, P. Aaron, Markov, Igor L., McDermott, Robert F., Pedretti, Giacomo, Gajjar, Archit, Silva, Allyson, Sorebo, John, Spentzouris, Panagiotis, Steiner, Ziv, Torosov, Boyan, Venturelli, Davide, Visser, Robert J., Webb, Zak, Zhan, Xin, Cohen, Yonatan, Ronagh, Pooya, Ho, Alan, Beausoleil, Raymond G., Martinis, John M.
In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubi
Externí odkaz:
http://arxiv.org/abs/2411.10406
Autor:
Silva, Allyson, Scherer, Artur, Webb, Zak, Khalid, Abdullah, Kulchytskyy, Bohdan, Kramer, Mia, Nguyen, Kevin, Kong, Xiangzhou, Dagnew, Gebremedhin A., Wang, Yumeng, Nguyen, Huy Anh, Olfert, Katiemarie, Ronagh, Pooya
We propose a novel technique for optimizing a modular fault-tolerant quantum computing architecture, taking into account any desired space-time trade--offs between the number of physical qubits and the fault-tolerant execution time of a quantum algor
Externí odkaz:
http://arxiv.org/abs/2411.04270
VQC can be understood through the lens of Fourier analysis. It is already well-known that the function space represented by any circuit architecture can be described through a truncated Fourier sum. We show that the spectrum available to that truncat
Externí odkaz:
http://arxiv.org/abs/2411.03450
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