This article proposes a new model that combines neural network with generalized linear (GLM) to estimate and predict health transition intensities. We introduce networks modeling incorporate socioeconomic lifestyle factors allow for nonlinear links between these variables. use transfer learning link the models different transitions improve estimation limited data. apply individual-level data fr...

• Neural acoustic models can be used to automatically model pronunciation variation. Pronunciation variation is best captured by intermediate layers of transformer models. Transformer-based embeddings capture details not expressed phonetic transcriptions. Variation in speech often quantified comparing transcriptions the same utterance. However, manually transcribing time-consuming and error pro...

Graph Convolutional Network (GCN) has shown remarkable potential of exploring graph representation. However, the GCN aggregating mechanism fails to generalize networks with heterophily where most nodes have neighbors from different classes, which commonly exists in real-world networks. In order make propagation and aggregation suitable for both homophily (or even their mixture), we introduce bl...

Embryogenesis, regeneration and cell differentiation in microbiological entities are influenced by mechanical forces. Therefore, development of mechanical properties of these materials is important. Neural network technique is a useful method which can be used to obtain cell deformation by the means of force-geometric deformation data or vice versa. Prior to insertion in the needle injection pr...

We study how statistical tools which are commonly used independently can advantageously be exploited together in order to improve neural network estimation and selection in nonlinear static modeling. The tools we consider are the analysis of the numerical conditioning of the neural network candidates, statistical hypothesis tests, and cross validation. We present and analyze each of these tools...