Search results for " basis"
showing 10 items of 224 documents
Optimized Kernel Entropy Components
2016
This work addresses two main issues of the standard Kernel Entropy Component Analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of by variance as in Kernel Principal Components Analysis. In this work, we propose an extension of the KECA method, named Optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular…
Estimation of formamide harmonic and anharmonic modes in the Kohn-Sham limit using the polarization consistent basis sets.
2010
Formamide harmonic and anharmonic frequencies of fundamental vibrations in the gas phase and in several solvents were successfully estimated in the B3LYP Kohn-Sham complete basis set limit (KS CBS). CBS results were obtained by extrapolating a power function (two-parameter formula) to the results calculated with polarization-consistent basis sets. Anharmonic corrections using the second order perturbation treatment (PT2) and hybrid B3LYP functional combined with polarization consistent pc-n (n = 0, 1, 2, 3, 4) and several Pople’s basis sets were analyzed for all fundamental formamide vibrational modes in the gas phase and solution. Solvent effects were modeled within a PCM method. The anhar…
Selective Activation of Trophoblast-specific PLAC1 in Breast Cancer by CCAAT/Enhancer-binding Protein β (C/EBPβ) Isoform 2
2009
The trophoblast-specific gene PLAC1 (placenta-specific 1) is ectopically expressed in a wide range of human malignancies, most frequently in breast cancer, and is essentially involved in cancer cell proliferation, migration, and invasion. Here we show that basal activity of the PLAC1 promoter is selectively controlled by ubiquitous transcription factor SP1 and isoform 2 of CCAAT/enhancer-binding protein beta that we found to be selectively expressed in placental tissue and cancer cells. Binding of both factors to their respective elements within the PLAC1 promoter was essential to attain full promoter activity. Estrogen receptor alpha (ERalpha) signaling further augmented transcription and …
Generality of Henstock-Kurzweil type integral on a compact zero-dimensional metric space
2011
ABSTRACT A Henstock-Kurzweil type integral on a compact zero-dimensional metric space is investigated. It is compared with two Perron type integrals. It is also proved that it covers the Lebesgue integral.
The inhibition of glycerol permeation through aquaglyceroporin-3 induced by mercury(II)
2016
Mercurial compounds are known to inhibit water permeation through aquaporins (AQPs). Although in the last years some hypotheses were proposed, the exact mechanism of inhibition is still an open question and even less is known about the inhibition of the glycerol permeation through aquaglyceroporins. Molecular dynamics (MD) simulations of human aquaporin-3 (AQP3) have been performed up to 200 ns in the presence of Hg2+ ions. For the first time, we have observed the unbiased passage of a glycerol molecule from the extracellular to cytosolic side. Moreover, the presence of Hg2+ ions covalently bound to Cys40 leads to a collapse of the aromatic/arginine selectivity filter (ar/R SF), blocking th…
Semisupervised nonlinear feature extraction for image classification
2012
Feature extraction is of paramount importance for an accurate classification of remote sensing images. Techniques based on data transformations are widely used in this context. However, linear feature extraction algorithms, such as the principal component analysis and partial least squares, can address this problem in a suboptimal way because the data relations are often nonlinear. Kernel methods may alleviate this problem only when the structure of the data manifold is properly captured. However, this is difficult to achieve when small-size training sets are available. In these cases, exploiting the information contained in unlabeled samples together with the available training data can si…
Kernel-Based Inference of Functions Over Graphs
2018
Abstract The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced. A particularly interesting—and prevalent in several fields of study—problem is that of inferring a function defined over the nodes of a network. This work presents a versatile kernel-based framework for tackling this inference problem that naturally subsumes and generalizes the reconstruction approaches put forth recently for the signal processing by the community studying graphs. Both the static and the dynamic settings are considered along with effective modeling approaches for addressing real-world problems. The analytical discussion herein is complement…
Model selection based product kernel learning for regression on graphs
2013
The choice of a suitable graph kernel is intrinsically hard and often cannot be made in an informed manner for a given dataset. Methods for multiple kernel learning offer a possible remedy, as they combine and weight kernels on the basis of a labeled training set of molecules to define a new kernel. Whereas most methods for multiple kernel learning focus on learning convex linear combinations of kernels, we propose to combine kernels in products, which theoretically enables higher expressiveness. In experiments on ten publicly available chemical QSAR datasets we show that product kernel learning is on no dataset significantly worse than any of the competing kernel methods and on average the…
A structural cluster kernel for learning on graphs
2012
In recent years, graph kernels have received considerable interest within the machine learning and data mining community. Here, we introduce a novel approach enabling kernel methods to utilize additional information hidden in the structural neighborhood of the graphs under consideration. Our novel structural cluster kernel (SCK) incorporates similarities induced by a structural clustering algorithm to improve state-of-the-art graph kernels. The approach taken is based on the idea that graph similarity can not only be described by the similarity between the graphs themselves, but also by the similarity they possess with respect to their structural neighborhood. We applied our novel kernel in…
Evolution of Carotenoid Content, Antioxidant Activity and Volatiles Compounds in Dried Mango Fruits (Mangifera Indica L.)
2020
The aim of this research was to study the evolution of carotenoid compounds, antioxidant &beta