Neural Estimator of Information for Time-Series Data with Dependency

Novel approaches to estimate information measures using neural networks are well-celebrated in recent years both in the information theory and machine learning communities. These neural-based estimators are shown to converge to the true values when estimating mutual information and conditional mutual information using independent samples. However, if the samples in the dataset are not independent, the consistency of these estimators requires further investigation. This is of particular interest for a more complex measure such as the directed information, which is pivotal in characterizing causality and is meaningful over time-dependent variables. The extension of the convergence proof for such cases is not trivial and demands further assumptions on the data. In this paper, we show that our neural estimator for conditional mutual information is consistent when the dataset is generated with samples of a stationary and ergodic source. In other words, we show that our information estimator using neural networks converges asymptotically to the true value with probability one. Besides universal functional approximation of neural networks, a core lemma to show the convergence is Birkhoff’s ergodic theorem. Additionally, we use the technique to estimate directed information and demonstrate the effectiveness of our approach in simulations.     

Temperature-sensitive swelling of poly(N-isopropylacrylamide) brushes with low molecular weight and grafting density

Temperature-sensitive poly(N-isopropylacrylamide) (PNIPAAm) brushes with different molecular weights M(n) and grafting densities σ were prepared by the "grafting-to" method. Changes in their physicochemical properties according to temperature were investigated with the help of in situ spectroscopic ellipsometry and in situ attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. Brush criteria indicate a transition between a brush conformation below the lower critical solution temperature (LCST) and an intermediate to mushroom conformation above the LCST. By in situ ellipsometry distinct changes in the brush layer parameters (wet thickness, refractive index, buffer content) were observed. A broadening of the temperature region with maximum deswelling occurred with decreasing grafting density. The brush layer properties were independent of the grafting density below the LCST, but showed a virtually monotonic behavior above the LCST. The midtemperature ?(half) of the deswelling process increased with increasing grafting density. Thus grafting density-dependent design parameters for such functional films were presented. For the first time, ATR-FTIR spectroscopy was used to monitor segment density and hydrogen bonding changes of these very thin PNIPAAm brushes as a function of temperature based on significant variations of the methyl stretching, Amide I, as well as Amide II bands with respect to intensity and wavenumber position. No dependence on M(n) and σ in the wavenumber shift of these bands above the LCST was found. The temperature profile of these band intensities and thus segment density was found to be rather step-like, exceeding temperatures around the LCST, while the respective profile of their wavenumber positions suggested continuous structural and hydration processes. Remaining buffer amounts and residual intermolecular segment/water interaction in the collapsed brushes above the LCST could be confirmed by both in situ methods.     

N‐phosphinyl ureas: Synthesis,characterization, X‐ray structure,and in vitro evaluation of antitumor activity

A new series of N‐phosphinylureas 5b, 6a–7c was synthesized and characterized by ¹H, ¹³C, ³¹P NMR, IR, and elemental analysis. The three‐dimensional structure of 5b has been determined by X‐ray crystallography. The crystal structure revealed the existence of four independent molecules. All structures form two chains with different arrangements and connect to each other via hydrogen bonds to produce two‐dimensional polymeric chains. The cytotoxicity of cyclophosphamide (a standard antitumor compound) and its nine analogues with formula R¹C₆H₄ NHC(O)NHP(O)XCH₂C(R²)₂ CH₂Y(X = Y = NH, R² = CH₃, R¹ = H ( 5a ), CH₃ ( 5b ), NO₂ ( 5c ), X = O, Y = NH, R² = H, R¹ = H ( 6a , CH₃ ( 6b ), NO₂ ( 6c ), and X = Y = O, R² = CH₃, R¹ = H ( 7a ), CH₃ ( 7b ), NO₂ ( 7c )) as well as phenyl urea were evaluated in vitro against three human tumor cell lines K562, MDA‐MB‐231, and HepG2. The results showed that most of the compounds have significant activity against the selected cell lines. Also, HepG2 cells were more sensitive to all the tested compounds than other cell lines. © 2011 Wiley Periodicals, Inc. Heteroatom Chem 23:74–83, 2012; View this article online at wileyonlinelibrary.com . DOI 10.1002/hc.20754     

Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.     

Sparse fast DCT for vectors with one-block support

Numerical Algorithms - In this paper, we present a new fast and deterministic algorithm for the inverse discrete cosine transform of type II that reconstructs the vector $\mathbf {x}\in \mathbb...     

Preparation and reductive lithiation of 2-deoxy-D-glucopyranosyl phenylsulfones: A highly stereoselective route to C-glycosides

2-Deoxy-D-glucopyranosyl phenylsulfones 3a–c have been synthesized in 75–83% yield from commercial tri-O-acetyl-D-glucal 1. Their reductive desulfonylation by lithium naphthalenide and reaction of the intermediate glycosyl-lithium 5 with aldehydes leads to α-D-C-glucopyranosides with high stereoselectivity.     

Prediction of rheological behavior of MWCNTs–SiO2/EG–water non-Newtonian hybrid nanofluid by designing new correlations and optimal artificial neural networks

In this paper, at the first, new correlations were proposed to predict the rheological behavior of MWCNTs–SiO₂/EG–water non-Newtonian hybrid nanofluid using different sets of experimental data for the viscosity, consistency and power law indices. Then, based on minimum prediction errors, two optimal artificial neural network models (ANNs) were considered to forecast the rheological behavior of the non-Newtonian hybrid nanofluid. One hundred and ninety-eight experimental data were employed for predicting viscosity (Model I). Two sets of forty-two experimental data also were considered to predict the consistency and power law indices (Model II). The data sets were divided to training and test sets which contained respectively 80 and 20% of data points. Comparisons between the correlations and ANN models showed that ANN models were much more accurate than proposed correlations. Moreover, it was found that the neural network is a powerful instrument in establishing the relationship between a large numbers of experimental data. Thus, this paper confirmed that the neural network is a reliable method for predicting the rheological behavior of non-Newtonian nanofluids in different models.