Zobrazeno 1 - 10
of 20 046
pro vyhledávání: '"Aarts A"'
The probability distribution effectively sampled by a complex Langevin process for theories with a sign problem is not known a priori and notoriously hard to understand. Diffusion models, a class of generative AI, can learn distributions from data. I
Externí odkaz:
http://arxiv.org/abs/2412.01919
Autor:
Allton, Chris, Aarts, Gert, Anwar, M. Naeem, Bignell, Ryan, Burns, Timothy J., García-Mascaraque, Sergio Chaves, Hands, Simon, Jäger, Benjamin, Kim, Seyong, Lombardo, Maria Paola, Page, Benjamin, Ryan, Sínead, Skullerud, Jon-Ivar, Smecca, Antonio, Spriggs, Thomas
The FASTSUM Collaboration has developed a comprehensive research programme in thermal lattice QCD using 2+1 flavour ensembles. We review our recent hadron spectrum analyses of open charm mesons and charm baryons at non-zero temperature. We also detai
Externí odkaz:
http://arxiv.org/abs/2411.15937
During training, weight matrices in machine learning architectures are updated using stochastic gradient descent or variations thereof. In this contribution we employ concepts of random matrix theory to analyse the resulting stochastic matrix dynamic
Externí odkaz:
http://arxiv.org/abs/2411.13512
We show that recent experiments in hybrid qubit-oscillator devices that measure the phase-space characteristic function of the oscillator via the qubit can be seen through the lens of functional calculus and path integrals, drawing a clear analogy wi
Externí odkaz:
http://arxiv.org/abs/2411.05092
Autor:
Youssef, Ali, Vodorezova, Kristina, Aarts, Yannick, Agbeti, Wisdom E. K., Palstra, Arjan P., Foekema, Edwin, Aguilar, Leonel, Torres, Ricardo da Silva, Grübel, Jascha
IUMENTA (Latin for livestock) is an innovative software framework designed to construct and simulate digital twins of animals. By leveraging the powerful capability of the Open Digital Twin Platform (ODTP) alongside advanced software sensors, IUMENTA
Externí odkaz:
http://arxiv.org/abs/2411.10466
To analyse how diffusion models learn correlations beyond Gaussian ones, we study the behaviour of higher-order cumulants, or connected n-point functions, under both the forward and backward process. We derive explicit expressions for the moment- and
Externí odkaz:
http://arxiv.org/abs/2410.21212
We develop diffusion models for lattice gauge theories which build on the concept of stochastic quantization. This framework is applied to $U(1)$ gauge theory in $1+1$ dimensions. We show that a model trained at one small inverse coupling can be effe
Externí odkaz:
http://arxiv.org/abs/2410.19602
Autor:
de Rooij, Steven A. H., Fermin, Remko, Kouwenhoven, Kevin, Coppens, Tonny, Murugesan, Vignesh, Thoen, David J., Aarts, Jan, Baselmans, Jochem J. A., de Visser, Pieter J.
Disordered superconductors offer new impedance regimes for quantum circuits, enable a pathway to protected qubits and improve superconducting single photon detectors due to their high kinetic inductance and sheet resistance. However, the relaxation o
Externí odkaz:
http://arxiv.org/abs/2410.18802
Autor:
Fiorio, Luan Vinícius, Karanov, Boris, Defraene, Bruno, David, Johan, van Houtum, Wim, Widdershoven, Frans, Aarts, Ronald M.
We propose and analyze the use of an explicit time-context window for neural network-based spectral masking speech enhancement to leverage signal context dependencies between neighboring frames. In particular, we concentrate on soft masking and loss
Externí odkaz:
http://arxiv.org/abs/2408.15582
We demonstrate that the update of weight matrices in learning algorithms can be described in the framework of Dyson Brownian motion, thereby inheriting many features of random matrix theory. We relate the level of stochasticity to the ratio of the le
Externí odkaz:
http://arxiv.org/abs/2407.16427