MIT researchers say they have found a more efficient way to train machine learning models that predict how complex metal alloys will behave.

As instruments grow faster, leaders say clinical impact will depend on trust, standards, data, and assays built for real ...

Background The widespread mobilisation to improve maternal health over recent decades has led to increased prenatal ...

Zhu, Weiqiang, and Gregory C. Beroza. "PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method." arXiv preprint arXiv:1803.03211 (2018). Liu, Min, et al. "Rapid characterization of ...

Abstract: Recent research has shown that the unrolling network is an effective and efficient method for suppressing interrupted sampling repeater jamming (ISRJ). However, in practice, variability in ...

Osi Momoh is an expert on corporate finance and accounting, bonds, trading, cryptocurrency, and much more. Osi has 10+ years of experience in the investment industry, having served as a client-facing ...

Abstract: With complicated structures and complex physicochemical reactions, most industrial processes are intrinsically characterized by high dimensionality, nonlinearity, non-Gaussianity, and ...

We establish a theoretical framework for network predictability by mapping the link prediction problem onto a spin glass model from statistical physics. This approach reveals that a network’s global ...

SINGAPORE - Fourteen-year-old Dean Joachim Aszrin’s rare genetic disorder (Cri du chat syndrome) resulted in his intellectual disability, developmental delays, and poor muscle tone. He needs ...

Advanced debug logging is the cornerstone of high-performance applications. Whether working in cloud-native, microservice or monolithic architecture, strong debug logging practices enable developers ...

When someone says that ‘everything happens for a reason’ they betray their deterministic wiring. Yet connecting cause and effect is a tricky business, and never more so than when probability is ...