LipGene: Lipschitz Continuity Guided Adaptive Learning Rates for Fast Convergence on Microarray Expression Data Sets.

Hyperparameter tuning, specifically tuning of learning rate, can often be a time-consuming process, especially when dealing with large data sets. A mathematical foundation in the choice of learning rate can minimize tuning efforts. We propose the application of a novel adaptive learning rate paradigm, guided by Lipschitz continuity of the loss functions (LipGene), to the task of Gene Expression Inference using shallow neural networks.

A methodology to design heuristics for model selection based on the characteristics of data: Application to investigate when the Negative Binomial Lindley (NB-L) is preferred over the Negative Binomial (NB).

Safety analysts usually use post-modeling methods, such as the Goodness-of-Fit statistics or the Likelihood Ratio Test, to decide between two or more competitive distributions or models. Such metrics require all competitive distributions to be fitted to the data before any comparisons can be accomplished. Given the continuous growth in introducing new statistical distributions, choosing the best one using such post-modeling methods is not a trivial task, in addition to all theoretical or numerical issues the analyst may face during the analysis.

CoQ selective miscibility and penetration into lipid monolayers with lower lateral packing density.

CoQ is ubiquitously present in eukaryotic cells. It acts as electron carrier in the electron transport chain of the inner membrane of the mitochondria to facilitate aerobic cellular respiration. A highly stable lipid nanodispersion formulation containing CoQ (BPM31510) is currently in clinical investigation for treatment of cancer.

A semiparametric negative binomial generalized linear model for modeling over-dispersed count data with a heavy tail: Characteristics and applications to crash data.

Crash data can often be characterized by over-dispersion, heavy (long) tail and many observations with the value zero. Over the last few years, a small number of researchers have started developing and applying novel and innovative multi-parameter models to analyze such data. These multi-parameter models have been proposed for overcoming the limitations of the traditional negative binomial (NB) model, which cannot handle this kind of data efficiently.

Evaluation of corn hybrids expressing Cry1F, cry1A.105, Cry2Ab2, Cry34Ab1/Cry35Ab1, and Cry3Bb1 against southern United States insect pests.

Studies were conducted across the southern United States to characterize the efficacy of multiple Bacillus thuringiensis (Bt) events in a field corn, Zea mays L., hybrid for control of common lepidopteran and coleopteran pests. Cry1F protein in event TC1507 and Cry1A.