Results 111 to 120 of about 3,243,734 (347)
Predicting the Optimal Input Parameters for the Desired Print Quality Using Machine Learning. [PDF]
Micromachines (Basel), 2022 Ratnavel R +5 more
europepmc +1 more source
Operators for transforming kernels into quasi-local kernels that improve SVM accuracy [PDF]
, 2008 Motivated by the crucial role that locality plays in various learning approaches, we present, in the framework of kernel machines for classification, a novel family of operators on kernels able to integrate local information into any kernel obtaining ...
Blanzieri, Enrico, Segata, Nicola
core +1 more source
FEBS Open Bio, EarlyView.dUTPases are involved in balancing the appropriate nucleotide pools. We showed that dUTPase is essential for normal development in zebrafish. The different zebrafish genomes contain several single‐nucleotide variations (SNPs) of the dut gene. One of the dUTPase variants displayed drastically lower protein stability and catalytic efficiency as compared ...
Viktória Perey‐Simon +6 more
wiley +1 more source
Study of nuclear physics input-parameters via high-resolution γ-ray spectroscopy
EPJ Web of Conferences, 2017 For nucleosynthesis networks of isotopes heavier than those in the iron-peak region, reaction rates are often calculated within the Hauser-Feshbach statistical model.
Scholz Philipp +4 more
doaj +1 more source
Metals, 2018 Zinc electroplating is a coating process controlled by several input process parameters. However, the commonly used input parameters for setting the process of zinc deposition are current density, temperature of the coating solution, zinc concentration ...
Ruben Lostado Lorza +3 more
doaj +1 more source
DoE Approach to Setting Input Parameters for Digital 3D Printing of Concrete for Coarse Aggregates up to 8 mm. [PDF]
Materials (Basel), 2023 Vespalec A, Podroužek J, Koutný D.
europepmc +1 more source
Supervisory sampling and control: Sources of suboptimality in a prediction task [PDF]
A process supervisor is defined as a person who decides when to sample the process input and what values of a control variable to specify in order to maximize (minimize) a given value function of input sampling period, control setting, and process state.
Rouse, W. B., Sheridan, T. B.
core +1 more source
Slim Embedding Layers for Recurrent Neural Language Models
, 2017 Recurrent neural language models are the state-of-the-art models for language modeling. When the vocabulary size is large, the space taken to store the model parameters becomes the bottleneck for the use of recurrent neural language models. In this paper,
Kulhanek, Raymond +4 more
core +1 more source
FEBS Open Bio, EarlyView.Unique biological samples, such as site‐specific mutant proteins, are available only in limited quantities. Here, we present a polarization‐resolved transient infrared spectroscopy setup with referencing to improve signal‐to‐noise tailored towards tracing small signals. We provide an overview of characterizing the excitation conditions for polarization‐
Clark Zahn, Karsten Heyne
wiley +1 more source
EVALUATION OF STOCHASTIC LINEAR SYSTEM INPUT SIGNAL PARAMETERS BASED ON CORRELATIONAL FUNCTION [PDF]
Avtomobilʹnyj Transport (Harʹkov), 2009 This article considers the task of evaluating output signal parameters of a discrete linear dynamic system. The estimation of autocorrelation and spectral density functions are build to solve the task.
S. Gerasin, N. Matiichenko
doaj