Stereocomplexation and monolayer morphologies of a stereoregular poly(methylmethacrylate) mixture formed at the air/water surface

The stereocomplexation of stereoregular PMMA at the air/water interface was proved by structure determination using reflection-absorption IR and grazing incidence X-ray diffraction. Morphological studies on LB films of i- and s-PMMA blends with different ratios help to disclose the stereocomplexation process of stereoregular PMMA at the air/water interface. It was found that the stereocomplexes exist in particle aggregates randomly dispersed at the air/water interface. In the systems with the i:s ratio deviated from 1:2, the molecules, either i-PMMA or s-PMMA, that do not participate in the stereocomplexation build separate layer surrounding the stereocomplexes. This layer is much thinner than the particle aggregates of the stereocomplexes. If the i-PMMA molecules are rich in this thinner layer, crystallization of i-PMMA takes place, which generates lamellar structure besides the stereocomplexes.

Influence of N2O flow rate on reliability of SiOx films deposited by SiH4-N2O gas mixture plasma

SiO_x films have several advantages as an interlayer dielectric in electronic devices owing to the strong adhesion between SiO_x and the substrate. In this study, the coating performance as a function of the N₂O flow rate was evaluated by electrochemical impedance spectroscopy and potentiodynamic polarization tests in an undisturbed environment. In addition, the coatings were examined by atomic force microscopy and Fourier transform infrared reflection spectroscopy. The SiO_x films on a stainless-steel substrate showed the highest coating performance at a N₂O flow rate of 120 sccm. This was attributed to the films having the lowest porosity value among those examined as a result of the fragmentation of SiO and SiO₂ bonds and the improved surface roughness.

Incremental learning of temporally-coherent gaussian mixture models

In this paper we address the problem of learning Gaussian Mixture Models (GMMs) incrementally. Unlike previous approaches which universally assume that new data comes in blocks representable by GMMs which are then merged with the current model estimate, our method works for the case when novel data points arrive oneby- one, while requiring little additional memory. We keep only two GMMs in the memory and no historical data. The current fit is updated with the assumption that the number of components is fixed, which is increased (or reduced) when enough evidence for a new component is seen. This is deduced from the change from the oldest fit of the same complexity, termed the Historical GMM, the concept of which is central to our method. The performance of the proposed method is demonstrated qualitatively and quantitatively on several synthetic data sets and video sequences of faces acquired in realistic imaging conditions

Incremental learning of temporally-coherent Gaussian mixture models

In this paper we address the problem of learning Gaussian Mixture Models (GMMs) incrementally. Unlike previous approaches which universally assume that new data comes in blocks representable by GMMs which are then merged with the current model estimate, our method works for the case when novel data points arrive one- by-one, while requiring little additional memory. We keep only two GMMs in the memory and no historical data. The current fit is updated with the assumption that the number of components is fixed which is increased (or reduced) when enough evidence for a new component is seen. This is deducedfrom the change from the oldest fit of the same complexity, termed the Historical GMM, the concept of which is central to our method. The performance of the proposed method is demonstrated qualitatively and quantitatively on several synthetic data sets and video sequences of faces acquired in realistic imaging conditions.

On weak monotonicity of some mixture functions

Monotonicity with respect to all arguments is fundamental to the definition of aggregation functions. Here we study means that are not necessarily monotone. Weak monotonicity was recently proposed as a relaxation of the monotonicity condition for averaging functions. We provide results for the weak monotonicity of some importantclasses of mixture functions. With these results we are able to extend and improve the understanding of this very important class of functions.

On the weak monotonicity of Gini means and other mixture functions

Weak monotonicity was recently proposed as a relaxation of the monotonicity condition for averaging aggregation, and weakly monotone functions were shown to have desirable properties when averaging data corrupted with outliers or noise. We extended the study of weakly monotone averages by analyzing their ϕ-transforms, and we established weak monotonicity of several classes of averaging functions, in particular Gini means and mixture operators. Mixture operators with Gaussian weighting functions were shown to be weakly monotone for a broad range of their parameters. This study assists in identifying averaging functions suitable for data analysis and image processing tasks in the presence of outliers.

Microstructure and hardness characterisation of laser coatings produced with a mixture of AISI 420 stainless steel and Fe-C-Cr-Nb-B-Mo steel alloy powders

Fe-C-Cr-Nb-B-Mo alloy powder and AISI 420 SS powder are deposited using laser cladding to increase the hardness for wear resistant applications. Mixtures from 0 to 100 wt.% were evaluated to understand the effect on the elemental composition, microstructure, phases, and microhardness. The mixture of carbon, boron and niobium in the Fe-C-Cr-Nb-B-Mo alloy powder introduces complex carbides into a Fe-based matrix of AISI 420 SS which increases its hardness. Hardness increased linearly with increasing Fe-C-Cr-Nb-B-Mo alloy, but substantial micro-cracking was observed in the clad layer at additions of 60 wt.% and above; related to a transition from a hypoeutectic alloy containing α-Fe/α' dendrites with an (Fe,Cr)2B and γ-Fe eutectic to primary and continuous carbo-borides M2B (where M represents Fe and Cr) and M23(B,C)6 carbides (where M represents Fe, Cr, Mo) with MC particles (where M represents Nb and Mo). The highest average hardness, for an alloy without micro-cracking, of 952 HV was observed in a 40 wt.% alloy. High stress abrasive scratch testing was conducted on all alloys at various loads (500, 1500, 2500 N). Alloy content was found to have a strong effect on the wear mode and the abrasive wear rate, and the presence of micro-cracks was detrimental to abrasive wear resistance.

Dirichlet process mixture models with pairwise constraints for data clustering

The Dirichlet process mixture (DPM) model, a typical Bayesian nonparametric model, can infer the number of clusters automatically, and thus performing priority in data clustering. This paper investigates the influence of pairwise constraints in the DPM model. The pairwise constraint, known as two types: must-link (ML) and cannot-link (CL) constraints, indicates the relationship between two data points. We have proposed two relevant models which incorporate pairwise constraints: the constrained DPM (C-DPM) and the constrained DPM with selected constraints (SC-DPM). In C-DPM, the concept of chunklet is introduced. ML constraints are compiled into chunklets and CL constraints exist between chunklets. We derive the Gibbs sampling of the C-DPM based on chunklets. We further propose a principled approach to select the most useful constraints, which will be incorporated into the SC-DPM. We evaluate the proposed models based on three real datasets: 20 Newsgroups dataset, NUS-WIDE image dataset and Facebook comments datasets we collected by ourselves. Our SC-DPM performs priority in data clustering. In addition, our SC-DPM can be potentially used for short-text clustering.