To enable more accurate estimation of connectivity, we propose a data-driven and theoretically grounded framework for optimally designing perturbation inputs, based on formulating the neural model as ...
Estimating things that exist is generally easy, but when it comes to estimating things that do not exist, it's more difficult. This is something physicists from Poland and the UK are well aware of. To ...
A study from the Research Center for Materials Nanoarchitectonics (MANA) has uncovered a theoretical mechanism showing how the electronic band structures of strongly correlated insulators can be ...
A study from the Research Center for Materials Nanoarchitectonics (MANA) has uncovered a theoretical mechanism showing how the electronic band structures of strongly correlated insulators can be ...
ABSTRACT: In recent times, the World has experienced the impact of artificial intelligence (AI) and machine learning (ML) on the digital ecosystem, organizations now face both unprecedented ...
COVID-19 interventions slowed the transmission of many respiratory pathogens in different ways, raising questions about the mechanisms driving the variation in responses to interventions. To address ...
In this work we explore some aspects of the spectral instability of back hole quasi-normal modes, using a specific model as an example. The model is that of a small bump perturbation to the effective ...
Impact Statement: Adversarial attacks pose a significant threat to deep learning models, undermining their reliability and performance. Our proposed APR-Net presents a robust defense mechanism against ...
Earthquake ruptures typically propagate unilaterally or bilaterally from their hypocenters. However, in some cases rupture takes a backward turn after propagating certain distances along the forward ...
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