Integration of prior knowledge of measurement noise in kernel density classification

Samples can be measured with different precisions and reliabilities in different experiments, or even within the same experiment. These varying levels of measurement noise may deteriorate the performance of a pattern recognition system, if not treated with care. Here we seek to investigate the benefit of incorporating prior knowledge about measurement noise into system construction. We propose a kernel density classifier which integrates such prior knowledge. Instead of using an identical kernel for each sample, we transform the prior knowledge into a distinct kernel for each sample. The integration procedure is straightforward and easy to interpret. In addition, we show how to estimate the diverse measurement noise levels in a real world dataset. Compared to the basic methods, the new kernel density classifier can give a significantly better classification performance. As expected, this improvement is more obvious for small sample size datasets and large number of features.     

Synthesis and characterization of functional gradient copolymers of glycidyl methacrylate and butyl acrylate

Epoxy-functional spontaneous gradient copolymers of glycidyl methacrylate (G) and n-butyl acrylate (B) were synthesized via atom transfer radical polymerization (ATRP). The copolymerization reactions were carried out in toluene solution at 70 °C, using methyl 2-bromopropionate (MBrP) as initiator and copper chloride with N,N,N′,N′′,N′′-pentamethyldiethylenetriamine (PMDETA) as the catalyst system. The kinetic behaviour of the statistical copolymerizations was studied in a wide composition interval with molar fractions of G ranging from 0.10 to 0.75. The synthesized copolymers were characterized by size exclusion chromatography (SEC) and nuclear magnetic resonance (NMR) spectroscopy. ¹H NMR was employed to determine the copolymer composition, demonstrating the gradient character of the copolymers along the main chain in the whole monomer conversion interval. Apart from this, the sequence distribution and stereoregularity were analyzed. These microstructural experimental data agreed well with those calculated from Mayo-Lewis terminal model (MLTM) and a Bernoullian statistic with an isotacticity parameter of σ_G = 0.28 and a coisotacticity parameter of σ = 0.30.     

Grouped Approach for the Design of H.264/AVC Motion Estimation Architectures

This letter presents a novel approach for organizing computational resources into groups within H.264/AVC motion estimation architectures, leading to reductions of up to 75% in the equivalent gate count with respect to state‐of‐the‐art designs.     

Use of recycled copper slag for blended cements

Copper slag is a by‐product generated during smelting to extract copper metal from the ore. The copper slag obtained may exhibit pozzolanic activity and may therefore be used in the manufacture of addition‐containing cements. In this paper the effect of the incorporation of the copper slag in cement is measured. Blends of copper slag with Portland cement generally possess properties equivalent to Portland cement containing fly ash, but very different to the silica fume incorporation. Copper slag and fly ash reduce the heat of hydration more effectively than silica fume in mortars. The replacement of 30% cement by copper slag reduces the flexural and compressive strength in a similar way to fly ash; however, after 28 days, the reduction is less than the percentage of substitution. Hydrated calcium aluminate phases were analysed using scanning electron microscopy (SEM) and X‐ray diffraction (XRD) techniques. The pozzolanic activity of copper slag is similar to that of fly ash and higher than silica fume. In the presence of low water/cement ratios, certain pozzolanic materials produce a very compact cement paste that limits the space available for hydration products, a determining factor in the formation of hydrated calcium aluminates. SEM was found to be a useful analytical technique when aluminates are formed and can be clearly detected by XRD. Copyright © 2008 Society of Chemical Industry     

Application of autoregressive spectral analysis to missing data problems

Time series solutions for spectral analysis in missing data problems use reconstruction of the missing data, or a maximum likelihood approach that analyzes only the available measured data. Maximum likelihood estimation yields the most accurate spectra. An approximate maximum likelihood algorithm is presented that uses only previous observations falling in a finite interval to compute the likelihood, instead of all previous observations. The resulting nonlinear estimation algorithm requires no user-provided initial solution, is suited for order selection, and can give very accurate spectra even if less than 10% of the data remains.     

Finite sample properties of ARMA order selection

The cost of order selection is defined as the loss in model quality due to selection. It is the difference between the quality of the best of all available candidate models that have been estimated from a finite sample of N observations and the quality of the model that is actually selected. The order selection criterion itself has an influence on the cost because of the penalty factor for each additionally selected parameter. Also, the number of competitive candidate models for the selection is important. The number of candidates is, of itself, small for the nested and hierarchical autoregressive/moving average (ARMA) models. However, intentionally reducing the number of selection candidates can be beneficial in combined ARMA(p,q) models, where two separate model orders are involved: the AR order p and the MA order q. The selection cost can be diminished by creating a nested sequence of ARMA(r,r-1) models. Moreover, not evaluating every combination (p,q) of the orders considerably reduces the required computation time. The disadvantage may be that the true ARMA(p,q) model is no longer among the nested candidate models. However, in finite samples, this disadvantage is largely compensated for by the reduction in the cost of order selection by considering fewer candidates. Thus, the quality of the selected model remains acceptable with only hierarchically nested ARMA(r,r-1) models as candidates.     

It takes two to mimic: Behavioral consequences of self-construals.

The present studies demonstrated the moderation of self-construal orientation on mimicry. Recent research has indicated that an interdependent self-construal is associated with assimilation of the other to the self whereas an independent self-construal is associated with minimizing the influence of others on the self (H. R. Markus & S. Kitayama, 1991; D. Stapel & W. Koomen, 2001). Therefore, the authors hypothesized that an interdependent self-construal would be associated with more mimicry than an independent self-construal. When self-construal orientations were experimentally primed. as in Studies 1 and 2, independent self-construals produced less nonconscious mimicry than interdependent self-construals. When self-construals were examined as cultural differences with either a chronically dominant independent (Americans) or interdependent (Japanese) construal of the self, these results were replicated. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Critical Shear Stress of Bimodal Sediment in Sand-Gravel Rivers

A new model for the critical shear stress and the transport of graded sediment is presented. The model is based on the size distribution of the bed surface and can be used to compute sediment transport rates in numerical simulations with an active layer model. This model makes a distinction between unimodal and bimodal sediments. It is assumed that all size fractions of unimodal sediments have the same critical shear stress while there is selective transport for the gravel fractions of bimodal sediments. A recently published laboratory transport data set is used to calibrate our model.     

Treatment of essential arterial hypertension with hypercholesterolemia. Effects of an alpha-adrenergic blocker and an inhibitor of the angiotensin converting enzyme

BACKGROUND: Hypertension and hypercholesterolemia are frequently associated with this leading to considerable cardiovascular risk. METHODS: An open parallel randomized study was performed in which the effects of doxazosin, an alpha-adrenergic blocker and enalapril, an inhibitor of the angiotensin converting enzyme were compared in 70 patients with essential high blood pressure and plasma cholesterol levels greater than 240 mg/dl. Following 2-4 weeks of placebo administration the patients were randomly treated with one of the two drugs. When required doses were increased and hydrochlorothiazide added until blood pressure lower than 160/95 mmHg was achieved. After this period the patients were observed for a minimum of 8 weeks. The mean length of the study was of 22 weeks. RESULTS: Both drugs significantly reduced blood pressure without modifying cardiac frequency. Doxazosin tended to favorably modify the lipid profile of the plasma while enalapril significantly reduced the levels of cholesterol, lipids and high density lipoproteins (HDL). Upon termination of the study the total HDL/cholesterol index increased 8.6% in those treated with doxazosin and decreased 5.5% in those receiving enalapril (p < 0.05). CONCLUSIONS: Although doxazosin and enalapril are potent antihypertensive drugs, the effects on plasma lipid obtained with doxazosin indicate that a reduction in cardiovascular risk was achieved with this drug in the patients included in this study.