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Akademický článek
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Autor:
Narendra, Aditya, Dainotti, Maria, Sarkar, Milind, Lenart, Aleksander, Bogdan, Malgorzata, Pollo, Agnieszka, Zhang, Bing, Rabeda, Aleksandra, Petrosian, Vahe, Kazunari, Iwasaki
Context. Gamma-ray bursts (GRBs), observed at redshifts as high as 9.4, could serve as valuable probes for investigating the distant Universe. However, this necessitates an increase in the number of GRBs with determined redshifts, as currently, only
Externí odkaz:
http://arxiv.org/abs/2410.13985
Autor:
As, Yarden, Sukhija, Bhavya, Treven, Lenart, Sferrazza, Carmelo, Coros, Stelian, Krause, Andreas
Reinforcement learning (RL) is ubiquitous in the development of modern AI systems. However, state-of-the-art RL agents require extensive, and potentially unsafe, interactions with their environments to learn effectively. These limitations confine RL
Externí odkaz:
http://arxiv.org/abs/2410.09486
Integrability is an exceptional property believed to hold only for systems with fine-tuned parameters. Contrary, we explicitly show that in homogeneous nearest-neighbor qubit circuits with a U(1) symmetry, i.e., circuits that repeatedly apply the sam
Externí odkaz:
http://arxiv.org/abs/2410.06760
Autor:
Marko Motnik
Publikováno v:
Muzikološki Zbornik, Vol 51, Iss 2, Pp 27-40 (2015)
Lenart Mravlja, also named Leonhard Formica, is one of the numerous personalities from Carniola who worked abroad in the sixteenth century. After being educated in Ljubljana and in several protestant German towns, Formica moved to Vienna at the end o
Externí odkaz:
https://doaj.org/article/71cb1ddd7a474a89967b6a89599a7fda
We design a novel, nonlinear single-source-of-error model for analysis of multiple business cycles. The model's specification is intended to capture key empirical characteristics of business cycle data by allowing for simultaneous cycles of different
Externí odkaz:
http://arxiv.org/abs/2406.02321
We study the problem of nonepisodic reinforcement learning (RL) for nonlinear dynamical systems, where the system dynamics are unknown and the RL agent has to learn from a single trajectory, i.e., without resets. We propose Nonepisodic Optimistic RL
Externí odkaz:
http://arxiv.org/abs/2406.01175
Reinforcement learning (RL) excels in optimizing policies for discrete-time Markov decision processes (MDP). However, various systems are inherently continuous in time, making discrete-time MDPs an inexact modeling choice. In many applications, such
Externí odkaz:
http://arxiv.org/abs/2406.01163
Introducing a class of SU(2) invariant quantum unitary circuits generating chiral transport, we examine the role of broken space-reflection and time-reversal symmetries on spin transport properties. Upon adjusting parameters of local unitary gates, t
Externí odkaz:
http://arxiv.org/abs/2406.01571