We present the first accurate damping profiles acquired in atomic-resolution hydration force spectroscopy, revealing a monotonic damping profile at the nanoscopic tip apex. Atomic resolution is confirmed by the observed inhomogeneity caused by random substitution of Si by Al on the mica surface, and two distinct damping regimes are identified. Damping only appears above our detection limit of 1...

We investigate the effects of patchy (clustered) connectivity in sparsely connected attractor neural networks (NNs). This study is motivated by the fact that the connectivity of pyramidal neurons in layer II/III of the mammalian visual cortex is patchy and sparse. The storage capacity of hypercolumnar attractor NNs that use the Hopfield and Willshaw learning rules with this kind of connectivity...

The accurate description of molecule-surface interactions requires a detailed knowledge of the underlying potential-energy surface (PES). Recently, neural networks (NNs) have been shown to be an efficient technique to accurately interpolate the PES information provided for a set of molecular configurations, e.g., by first-principles calculations. Here, we further develop this approach by buildi...

We demonstrate the use of Reconstructability Analysis to reduce the number of input variables for a neural network. Using the heart disease dataset we reduce the number of independent variables from 13 to two, with limited loss of accuracy compared with those of NNs using the full variable set. We also demonstrate that rule lookup tables obtained directly from the data for the RA models are alm...

Numerous articles comparing performances of statistical and Neural Networks (NNs) models are available in the literature, however, very few involved Classification and Regression Tree (CART) models in their comparative studies. We perform a three-way comparison of prediction accuracy involving nonlinear regression, NNs and CART models using a continuous dependent variable and a set of dichotomo...

There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed safety-critical domains. A new approach with uncertainty-aware regression-based neural networks (NNs), based on evidential distributions aleatoric and epistemic uncertainties, shows promise over traditional deterministic methods typical Bayesian NNs, notably the capabil...

Solving high electrical-energy input for pollutants degradation is one of the core requirements practical application photoelectrocatalytic (PEC) technology. Herein, we developed a self-driven dual-photoelectrode PEC system (TiO2 NNs-Co3O4) composed TiO2 nanoneedle arrays NNs) photoanode and Co3O4 photocathode first time. Under light-emitting-diode (LED) illumination, bias-free NNs-Co3O4 exhibi...

Objective: This study assessed the incidence, mortality, and pathogens associated with nosocomial neonatal sepsis (NNS) in Shaanxi Province to improve the prevention and control of NNS. Methods: Nine large neonatal departments in Shaanxi Province participated in this retrospective epidemiological investigation of NNS during the period of 2008-2010, using standardized protocol. The incidence, mo...

BACKGROUND Vertebral fracture, one of the most frequent osteoporotic fractures in both sexes, is a powerful indicator of future osteoporotic fractures. Vertebral fractures are associated with increased mortality and decreased quality of life. Osteoporosis is a major predictor of low-trauma fracture. The Male Osteoporosis Risk Estimation Score (MORES), a clinical prediction tool that uses age, w...