Narrow Results

A wavelet-based forecasting method for time series is introduced. It is based on a multiple resolution decomposition of the signal, using the redundant "à trous" wavelet transform which has the advantage of being shift-invariant.

The result is a decomposition of the signal into a range of frequency scales. The prediction is based on a small number of coefficients on each of these scales. In its simplest form it is a linear prediction based on a wavelet transform of the signal. This method uses sparse modelling, but can be based on coefficients that are summaries or characteristics of large parts of the signal. The lower level of the decomposition can capture the long-range dependencies with only a few coefficients, while the higher levels capture the usual short-term dependencies.

We show the convergence of the method towards the optimal prediction in the autoregressive case. The method works well, as shown in simulation studies, and studies involving financial data.

In this paper, we propose a sparse tensor regression model for multi-view feature selection. Apart from the most of existing methods, our model adopts a tensor structure to represent multi-view data, which aims to explore their underlying high-order correlations. Based on this tensor structure, our model can effectively select the meaningful feature set for each view. We also develop an iterative optimization algorithm to solve our model, together with analysis about the convergence and computational complexity. Experimental results on several popular multi-view data sets confirm the effectiveness of our model.

Experimental and theoretical work has related rate modulation and gamma synchronization modulation to visual attention. Here, we review briefly some of the influential experiments and our modeling work on the subject. We show that attentional modulation generally gets stronger along the visual pathway and that rate and gamma synchronization can vary independently of each other. Moreover, we show that in a model system, reaction times are faster in the presence of gamma synchronization. This suggests behavioral relevance for gamma synchronization.

A two compartment mathematical model for the individual plant growth under the stress of toxic metal is studied. In the model it is assumed that the uptake of toxic metal by the plant is through root compartment. The toxic metal present in the soil interfere with the uptake and distribution of essential nutrients in plant causing decrease in the nutrient uptake eventually damaging the root structure. In the model it is further assumed that the resistance to nutrient transport from root to shoot compartment increases and nutrient use efficiency decreases due to the presence of toxic metal. In order to visualize the effect of toxic metal on plant growth, we have studied two models, that is, model for plant growth with no toxic effect and model for plant growth with toxic effect. From the analysis of the models the criteria for plant growth with and without toxic effects are derived. The numerical simulation is done using Matlab to support the analytical results.

In this paper, an SIRS epidemic model with high-risk immunization was investigated, where a susceptible neighbor of an infected node is immunized with rate h. Through analyzing the discrete-time model, we found that the epidemic threshold above which an epidemic can prevail and persist in a population is inversely proportional to 1 - h value. We also studied the continuous-time epidemic model and obtained a different result: the epidemic threshold does not depend on the immunization parameter h. Our results suggest that the difference between the discrete-time epidemic model and the continuous-time epidemic model exists in the high-risk immunization.

The fundamental metrological concept "traceability" is considered as a key notion for the measurement quality description and ensuring. The essential restrictions of the modern concept "traceability" are revealed, and some possible directions for its extending are outlined. A detailed analysis of measurement quality is presented, which is based on thorough decomposition of measurement errors. It is also proposed to introduce a basic concept of "measurement quality". The basic notions are demonstrated on the practical example of the measurement control of the geometrical parameters for work pieces.