Narrow Results

We comment on the brane solutions for the boundary model that have been proposed so far and point out that they should be distinguished according to the patterns regular/irregular and discrete/continuous. In the literature, mostly irregular branes have been studied, while results on the regular ones are rare. For all types of branes, there are questions about how a second factorization constraint in the form of a b^-2/2-shift equation can be derived. Here, we assume analyticity of the boundary two-point function, which means that the Cardy–Lewellen constraints remain unweakened. This enables us to derive unambiguously the desired b^-2/2-shift equations. They serve as important additional consistency conditions. For some regular branes, we also derive 1/2-shift equations that were not known previously. Case by case, we discuss possible solutions to the enlarged system of constraints. We find that the well-known irregular continuous AdS₂ branes are consistent with our new factorization constraint. Furthermore, we establish the existence of a new type of brane: the shift equations in a certain regular discrete case possess a nontrivial solution that we write down explicitly. All other types are found to be inconsistent when using our second constraint. We discuss these results in view of the Hosomichi–Ribault proposal and some of our earlier results on the derivation of b^-2/2-shift equations.

Having significantly interacted over 17 years with Abdus Salam, as an undergraduate, postgraduate, postdoc, and eventually as an academic colleague, I will try to paint a personal picture of Salam which may convey something about the man, his greatness and his humanity.

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.

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.