By learning the relevant features of clinical images along with the relationships between them, the neural network can ...

Graph-theoretical (GT) representations, conceptually analogous to chemical formulas, offer a powerful and versatile framework for describing the structure of nanomaterials─including complex assemblies ...

International Medical Graduates Representation at International Oncology Conference Meetings: An Analysis of ASCO Annual Meetings The following represents disclosure information provided by authors of ...

Chicago Sun-Times prints summer reading list full of fake books Reading list in advertorial supplement contains 66% made up books with real author names.

A new technical paper titled “Scanning electron microscopy-based automatic defect inspection for semiconductor manufacturing: a systematic review” was published by researchers at KU Leuven and imec.

Sepehr Assadi showed how to color certain graphs much faster than the previous fastest algorithm.

The algorithm will serve as a foundation for the study and analysis of one of the modeled system’s behavioral properties—system reinitialization. Designing an iterative approach based on the adjacency ...

This paper mainly studies the algorithms of graph representation learning, including matrix analysis algorithm, random walk algorithm and deep learning algorithm, and constructs a large-scale corpus ...

This article reports the analysis of coevolutionary patterns and dynamical information for identifying functionally relevant sites. These findings are considered important due to the broad utility of ...

An AI model trained on dozens of hours of real-world conversation accurately predicts human brain activity and shows that features of language structure emerge without being coded in.

In our study, we propose a graph representation learning model with improved Transformer (GRL-ITransformer) to better integrate feature information, so that the model can capture the dynamic time ...

Changes to a key funding formula will reduce research grants at hospitals and universities by billions — and may discourage future research.