Zobrazeno 1 - 10
of 7 373
pro vyhledávání: '"A. Ambrus"'
Exploiting the first measurements of the same ion species in OO collisons at RHIC and LHC, we propose an observable to distinguish whether collective behavior builds up through a hydrodynamic expansion of a strongly interacting QGP or few final state
Externí odkaz:
http://arxiv.org/abs/2411.19709
High-energy nuclear collisions exhibit collective flow, which emerges as a dynamical response of the Quark-Gluon Plasma (QGP) to the initial state geometry of the collision. Collective flow in heavy-ion collisions is usually described within multi-st
Externí odkaz:
http://arxiv.org/abs/2411.19708
Autor:
Xu, Yinshuang, Chen, Dian, Liu, Katherine, Zakharov, Sergey, Ambrus, Rares, Daniilidis, Kostas, Guizilini, Vitor
Incorporating inductive bias by embedding geometric entities (such as rays) as input has proven successful in multi-view learning. However, the methods adopting this technique typically lack equivariance, which is crucial for effective 3D learning. E
Externí odkaz:
http://arxiv.org/abs/2411.07326
Autor:
Soplanes, Emiliano Manuel Fortes, Sánchez, Eduardo Javier Pérez, Both, Ambrus, Grenga, Temistocle, Mira, Daniel
Preferential diffusion plays a critical role in the evolution of lean premixed hydrogen flames, influencing flame surface corrugation and overall flame behavior. Simulating such flames with tabulated chemistry (TC) methods remains challenging due to
Externí odkaz:
http://arxiv.org/abs/2411.03526
Autor:
Irshad, Muhammad Zubair, Comi, Mauro, Lin, Yen-Chen, Heppert, Nick, Valada, Abhinav, Ambrus, Rares, Kira, Zsolt, Tremblay, Jonathan
Neural Fields have emerged as a transformative approach for 3D scene representation in computer vision and robotics, enabling accurate inference of geometry, 3D semantics, and dynamics from posed 2D data. Leveraging differentiable rendering, Neural F
Externí odkaz:
http://arxiv.org/abs/2410.20220
3D reconstruction from a single image is a long-standing problem in computer vision. Learning-based methods address its inherent scale ambiguity by leveraging increasingly large labeled and unlabeled datasets, to produce geometric priors capable of g
Externí odkaz:
http://arxiv.org/abs/2409.09896
Autor:
Chaudhury, Arkadeep Narayan, Vasiljevic, Igor, Zakharov, Sergey, Guizilini, Vitor, Ambrus, Rares, Narasimhan, Srinivasa, Atkeson, Christopher G.
Synthesizing accurate geometry and photo-realistic appearance of small scenes is an active area of research with compelling use cases in gaming, virtual reality, robotic-manipulation, autonomous driving, convenient product capture, and consumer-level
Externí odkaz:
http://arxiv.org/abs/2409.03061
We discuss the effect of rigid rotation on critical temperatures of deconfinement and chiral transitions in the linear sigma model coupled to quarks and the Polyakov loop. We point out the essential role of the causality condition, which requires tha
Externí odkaz:
http://arxiv.org/abs/2407.07828
Stochastic multi-armed bandits (MABs) provide a fundamental reinforcement learning model to study sequential decision making in uncertain environments. The upper confidence bounds (UCB) algorithm gave birth to the renaissance of bandit algorithms, as
Externí odkaz:
http://arxiv.org/abs/2406.05710
The common trade-offs of state-of-the-art methods for multi-shape representation (a single model "packing" multiple objects) involve trading modeling accuracy against memory and storage. We show how to encode multiple shapes represented as continuous
Externí odkaz:
http://arxiv.org/abs/2406.04309