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Characterization of left atrial hemodynamics with 4D flow magnetic resonance imaging, in-silico models and deep learning
Although the left ventricle has traditionally held the spotlight as the primary determinant of cardiovascular disease prognosis, the characterization of left atrial hemodynamics is increasingly being recognized as a ...
Large scale off-policy and offline learning
Reinforcement Learning (RL), a subfield of machine learning and artifical intelligence, is a learning paradigm where an artificial agent learns to reach a predefined goal by trying to maximize a reward signal while ...
Geopolitics of Violent Non-State Actors: Exploring manifestations of statehood by violent governors
In the twentieth century, violent non-state actors (VNSAs) have emerged as significant contenders to states. These entities thrive in areas of low (or non-existent) formal state control and engage in the delivery of public ...
Interactive language learning in deep network agents
Recent advances in language modeling led to remarkable performance in many NLP applications. However, the static and passive learning imposed by the nexttoken- prediction objective makes models suboptimal when deployed in ...
Exploring the boundaries of aromaticity through computational analysis of excited states and complex molecular topologies
Aromaticity is a widely used concept in the prediction, design, and understanding of key aspects related to reactivity, structure, and properties of molecules. Linked to cyclic or three-dimensional molecular systems, it ...
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