Search results for "Reduction"
showing 10 items of 2058 documents
Syntheses, X-ray structures, and physicochemical properties of phenoxo-bridged dinuclear nickel(II) complexes: kinetics of transesterification of 2-h…
2009
Four dinuclear nickel(II) complexes [Ni(II)(2)(L(1))(O(2)CMe)(2)(H(2)O)(2)][PF(6)].MeOH.3H(2)O (1), [Ni(II)(2)(L(1))(O(2)CMe)(2)(NCS)] (2), [Ni(II)(2)(L(2))(O(2)CMe)(2)(MeOH)(H(2)O)][ClO(4)] (3), and [Ni(II)(2)(L(2))(O(2)CMe)(2)(MeOH)(H(2)O)][BPh(4)].3MeOH.H(2)O (4) have been synthesized [HL(1): 2,6-bis[N-methyl-N-(2-pyridylethyl)amino]-4-methylphenol; HL(2): 2,6-bis[3-(pyridin-2-yl)pyrazol-1-ylmethyl]-4-methylphenol]. Complexes 1, 3, and 4 are new while complex 2 was reported previously by Fenton and co-workers (the structure of 2 was presented but no physicochemical properties of this complex were reported; in this work such studies have been completed). X-ray crystallographic analyses of…
Reduction of stored-particle background by a magnetic pulse method at the KATRIN experiment
2018
Arenz, M., et al. “Reduction of Stored-Particle Background by a Magnetic Pulse Method at the KATRIN Experiment.” The European Physical Journal C, vol. 78, no. 9, Sept. 2018. © 2018 The Authors
Fast spiking neural network architecture for low-cost FPGA devices
2012
Spiking Neural Networks (SNN) consist of fully interconnected computation units (neurons) based on spike processing. This type of networks resembles those found in biological systems studied by neuroscientists. This paper shows a hardware implementation for SNN. First, SNN require the inputs to be spikes, being necessary a conversion system (encoding) from digital values into spikes. For travelling spikes, each neuron interconnection is characterized by weights and delays, requiring an internal neuron processing by a Postsynaptic Potential (PSP) function and membrane potential threshold evaluation for a postsynaptic output spike generation. In order to model a real biological system by arti…
A branch-and-cut algorithm for the soft-clustered vehicle-routing problem
2021
Abstract The soft-clustered vehicle-routing problem is a variant of the classical capacitated vehicle-routing problem (CVRP) in which customers are partitioned into clusters and all customers of the same cluster must be served by the same vehicle. We introduce a novel symmetric formulation of the problem in which the clustering part is modeled with an asymmetric sub-model. We solve the new model with a branch-and-cut algorithm exploiting some known valid inequalities for the CVRP that can be adapted. In addition, we derive problem-specific cutting planes and new heuristic and exact separation procedures. For square grid instances in the Euclidean plane, we provide lower-bounding techniques …
Bacterial 2,3-butanediol dehydrogenases
1978
Enterobacter aerogenes, Aeromonas hydrophila, Serratia marcescens and Staphylococcus aureus possessing L(+)-butanediol dehydrogenase produced mainly meso-butanediol and small amounts of optically active butanediol; Acetobacter suboxydans, Bacillus polymyxa and Erwinia carotovora containing D(-)-butanediol dehydrogenase produced more optically active butanediol than meso-butanediol. Resting and growing cells of these organisms oxidezed only one enantiomer of racemic butanediol. The D(-)-butanediol dehydrogenase from Bacillus polymyxa was partially purified (30-fold) with a specific activity of 24.5. Except NAD and NADH no other cofactors were required. Optimum pH-values for oxidation and red…
Sample size planning for survival prediction with focus on high-dimensional data
2011
Sample size planning should reflect the primary objective of a trial. If the primary objective is prediction, the sample size determination should focus on prediction accuracy instead of power. We present formulas for the determination of training set sample size for survival prediction. Sample size is chosen to control the difference between optimal and expected prediction error. Prediction is carried out by Cox proportional hazards models. The general approach considers censoring as well as low-dimensional and high-dimensional explanatory variables. For dimension reduction in the high-dimensional setting, a variable selection step is inserted. If not all informative variables are included…
Online Principal Component Analysis in High Dimension: Which Algorithm to Choose?
2017
Summary Principal component analysis (PCA) is a method of choice for dimension reduction. In the current context of data explosion, online techniques that do not require storing all data in memory are indispensable to perform the PCA of streaming data and/or massive data. Despite the wide availability of recursive algorithms that can efficiently update the PCA when new data are observed, the literature offers little guidance on how to select a suitable algorithm for a given application. This paper reviews the main approaches to online PCA, namely, perturbation techniques, incremental methods and stochastic optimisation, and compares the most widely employed techniques in terms statistical a…
A review of second‐order blind identification methods
2021
Second-order source separation (SOS) is a data analysis tool which can be used for revealing hidden structures in multivariate time series data or as a tool for dimension reduction. Such methods are nowadays increasingly important as more and more high-dimensional multivariate time series data are measured in numerous fields of applied science. Dimension reduction is crucial, as modeling such high-dimensional data with multivariate time series models is often impractical as the number of parameters describing dependencies between the component time series is usually too high. SOS methods have their roots in the signal processing literature, where they were first used to separate source sign…
Variance Estimation and Asymptotic Confidence Bands for the Mean Estimator of Sampled Functional Data with High Entropy Unequal Probability Sampling …
2013
For fixed size sampling designs with high entropy it is well known that the variance of the Horvitz-Thompson estimator can be approximated by the Hajek formula. The interest of this asymptotic variance approximation is that it only involves the first order inclusion probabilities of the statistical units. We extend this variance formula when the variable under study is functional and we prove, under general conditions on the regularity of the individual trajectories and the sampling design, that it asymptotically provides a uniformly consistent estimator of the variance function of the Horvitz-Thompson estimator of the mean function. Rates of convergence to the true variance function are gi…
Intensity estimation for inhomogeneous Gibbs point process with covariates-dependent chemical activity
2014
Recent development of intensity estimation for inhomogeneous spatial point processes with covariates suggests that kerneling in the covariate space is a competitive intensity estimation method for inhomogeneous Poisson processes. It is not known whether this advantageous performance is still valid when the points interact. In the simplest common case, this happens, for example, when the objects presented as points have a spatial dimension. In this paper, kerneling in the covariate space is extended to Gibbs processes with covariates-dependent chemical activity and inhibitive interactions, and the performance of the approach is studied through extensive simulation experiments. It is demonstr…