Different processes related to the phenomena of interaction across communities are of particular interest, especially since the beginning of the current century. It concerns such processes as disease spreading, opinio...
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We introduce an eigenvalue-preserving transformation algorithm from the generalized eigenvalue problem by matrix pencil of the upper and the lower bidiagonal matrices into a standard eigenvalue problem while preservin...
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One of the critical issues in the lifetime of metallic interconnects is related to their high oxidation rate and Cr diffusion, which negatively affect their performance. In the present study, a novel Fe modified Mn–C...
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Understanding the environmental factors influencing the allergenicity of Ambrosia artemisiifolia pollen is crucial for effective allergy prevention. This study, conducted from 2019 to 2022 in Bratislava, Slovakia, uti...
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A new Quantum Field Theory formalism for neutrino oscillations is introduced. It identifies charged-current vertices with neutrino emission and detection, including neutrino propagation. A master formula for the charg...
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The concept of neutrino oscillations is revisited. Using the Quantum Field Theory approach and applying plane waves for the initial and final states, the processes of neutrino production, propagation, and detection ar...
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In this paper, we introduce a previously not studied type of Euclidean tree called LED (Leaves of Equal Depth) tree. LED trees can be used, for example, in computational phylogeny, since they are a natural representat...
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Measurements of two-neutrino double beta decay (2νββ) have played a key role in advancing the understanding of neutrino properties. Further exploration of 2νββ and its possible exotic decay modes (decay with rig...
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Modern artificial intelligence based on deep neural networks has demonstrated in the past decade great achievements in concrete tasks, sometimes even surpassing human performance. On the other hand, there exist fundam...
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ISBN:
(数字)9798350379365
ISBN:
(纸本)9798350379372
Modern artificial intelligence based on deep neural networks has demonstrated in the past decade great achievements in concrete tasks, sometimes even surpassing human performance. On the other hand, there exist fundamental problems in these models, be it image classification or natural language tasks. Since deep networks are inherently black boxes, it is important to design and apply techniques that help shed light on the functioning of the trained models. In the talk, we will discuss two domains. First, in the context of image classification we will illustrate the effect of adversarial examples that can easily fool trained models, hence revealing their lack of robustness. In the second part, we will deal with sequential tasks, such as computer games, with an extremely sparse reward. Introducing the concept of intrinsic motivation, we will describe the neural networks based model that can successfully use reinforcement learning and self-supervised knowledge distillation to solve these tasks thanks to optimized organization of its internal representations. Finally, we briefly mention our current work related to cognitive robotics.
The neutrinoless double-beta decay (0νββ) nuclear matrix elements (NMEs) cannot be directly deduced from any experimental data, and their reliable calculation remains a significant challenge for the nuclear physics...
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