Neural networks for slope movement prediction

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Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

We experimentally study the spectral lasing response of on-chip InP network random lasers under illumination of different input image shapes. Deep-learning models have become increasingly omipresent throughout society. However, they are blighted by exponen ...

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome densities, ...

Traffic forecasting problems of freeway networks are heavily tackled by deep learning methods because it requires learning highly complex correlations between variables both in time and space. Adopting a graph convolutional network (GCN) becomes a standard ...

We propose a machine learning-based method to build a system of differential equations that approximates the dynamics of 3D electromechanical models for the human heart, accounting for the dependence on a set of parameters. Specifically, our method permits ...

The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 x 10(3) variants) expressed in mammalian cells, which typically results in few o ...

Occupant detection and recognition support functional goals such as security, healthcare, and energy management in buildings. Typical sensing approaches, such as smartphones and cameras, undermine the privacy of building occupants and inherently affect the ...

Characterizing left ventricle (LV) systolic function in the presence of an LV assist device (LVAD) is extremely challenging. We developed a framework comprising a deep neural network (DNN) and a 0D model of the cardiovascular system to predict parameters o ...

Artificial neural networks are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and ...

Many complex networks are only a part of larger systems, where a number of coexisting topologies interact and depend on each other. We introduce a layered model to facilitate the description and analysis of such systems. As an example of its application, w ...

Neural networks for slope movement prediction

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Interpreting and comparing neural activity across systems by geometric deep learning

Machine learning-aided generative molecular design

Image sensitive spectral response of semiconductor random network lasers

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

TwoResNet: Two-level resolution neural network for traffic forecasting on freeway networks

A machine learning method for real-time numerical simulations of cardiac electromechanics

Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning

Using footstep-induced vibrations for occupant detection and recognition in buildings

Deep Neural Network to Accurately Predict Left Ventricular Systolic Function Under Mechanical Assistance

Neuroevolution: from architectures to learning

Layered Complex Networks