Brain networks subserving functional core processes of emotions identified with componential modeling

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Functional magnetic resonance imaging (fMRI) relies upon the blood-oxygenation-level-dependent (BOLD) signal as a proxy for neuronal activity. Whole-brain coverage and millimetric resolution have propelled this modality as a workhorse for human neuroscienc ...

Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the r ...

Aims. As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these imag ...

For Graph Neural Networks, oversmoothing denotes the homogenization of vertex embeddings as the number of layers increases. To better understand this phenomenon, we study community detection with a linearized Graph Convolutional Network on the Contextual S ...

For almost five decades of its existence, imaging neuroscience—the research branch that uses noninvasive brain imaging to provide inferences about brain structure and function, has focused almost exclusively on studies pertaining to well-controlled laborat ...

It is widely believed that the success of deep networks lies in their ability to learn a meaningful representation of the features of the data. Yet, understanding when and how this feature learning improves performance remains a challenge. For example, it ...

The majority of uncertainty quantification methods for deep object detectors are based on the network output, such as sampling strategies like Monte-Carlo dropout or deep ensembles with straight-forward transfers to object detection. Here, we study gradien ...

Brain networks subserving functional core processes of emotions identified with componential modeling

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