Impacts of Climate Variables on Residential Water Consumption in the Czech Republic

The paper investigates whether there is a statistically significant impact of short-term climate variables (specifically air temperature and rainfall) on residential water consumption at two selected case sites in the Czech Republic. The analysis is based on a unique time series of daily data from 2004–2009. The statistical methods used are CART methodology and a decomposition of these time series based on a locally weighted regression method. Apart from the data analysis results, the investigation raises several methodological questions regarding the use of daily data and the scope of analysis based on such data sets.     

Fast automatic registration of range images from 3D imaging systems using sphere targets

The use of 3D imaging systems (e.g., laser scanners) in construction has grown significantly in the past decade. Range images acquired with such systems often require registration. This paper describes an automatic method to rapidly locate spheres and perform a registration based on three pairs of matching points (centers of fitted spheres) in two range images. The proposed method is directly applicable for regularly gridded datasets obtained with instruments that are typically used for construction applications and whose maximum ranges are greater than 50 m. A lab was scanned from two locations at three different scan densities. Four spheres were located in the lab, and the total number of points hitting the four spheres was a small fraction (< 0.01%) of all the points in the dataset. At the highest scan density, the registration of two datasets with 6.4 × 10⁶ and 3.4 × 10⁶ points is obtained in less than 30 s. At the medium scan density, two range images with 1.6 × 10⁶ and 0.8 × 10⁶ points can be registered in less than 2 s.     

Colony-blot assay with anti-p60 antibodies as a method for quick identification of Listeria in food

The present study evaluated the ability to isolate Listeria from foods, using shortened procedure of sample enrichment followed by immunomagnetic separation or filtration methods, and serological identification of isolated bacteria by colony-blot and Western blot methods with anti-p60 antibodies. By these rapid methods, identification of Listeria was achieved in much shorter time (40-48 h) than with standard cultivation and biochemical identification procedures. The rapid methods used are easy to perform and, what is most important, their specificity is very high and fulfills the expectations. The possibility to select Listeria colonies growing on non-selective media by blotting with anti-p60 antiserum seems to be particularly valuable in examination of food samples containing/not too many Listeria (1-10 CFU/25 g). However, the blot method using anti-PepD mAb specific to unique region of L. monocytogenes p60 is necessary to distinguish L. monocytogenes from other Listeria species.     

Learning parallel portfolios of algorithms

A wide range of combinatorial optimization algorithms have been developed for complex reasoning tasks. Frequently, no single algorithm outperforms all the others. This has raised interest in leveraging the performance of a collection of algorithms to improve performance. We show how to accomplish this using a Parallel Portfolio of Algorithms (PPA). A PPA is a collection of diverse algorithms for solving a single problem, all running concurrently on a single processor until a solution is produced. The performance of the portfolio may be controlled by assigning different shares of processor time to each algorithm. We present an effective method for finding a PPA in which the share of processor time allocated to each algorithm is fixed. Finding the optimal static schedule is shown to be an NP-complete problem for a general class of utility functions. We present bounds on the performance of the PPA over random instances and evaluate the performance empirically on a collection of 23 state-of-the-art SAT algorithms. The results show significant performance gains over the fastest individual algorithm in the collection.      

Improved Approximation Algorithms for the Quality of Service Multicast Tree Problem

The Quality of Service Multicast Tree Problem is a generalization of the Steiner tree problem which appears in the context of multimedia multicast and network design. In this generalization, each node possesses a rate and the cost of an edge with length l in a Steiner tree T connecting the source to non-zero rate nodes is l · r_e, where r_e is the maximum node rate in the component of T-{e} that does not contain the source. The best previously known approximation ratios for this problem (based on the best known approximation factor of 1.549 for the Steiner tree problem in networks) are 2.066 for the case of two non-zero rates and 4.212 for the case of an unbounded number of rates. In this paper we give improved approximation algorithms with ratios of 1.960 and 3.802, respectively. When the minimum spanning tree heuristic is used for finding approximate Steiner trees, then the previously best known approximation ratios of 2.667 for two non-zero rates and 5.542 for an unbounded number of rates are reduced to 2.414 and 4.311, respectively.     

Pitch Detection Enhancement Employing Music Prediction

Pitch detection methods are widely used for extracting musical data from digital signals. A review of those methods is presented in the paper. Since musical signals may contain noise and distortion, detection results can be erroneous. In this paper a new method employing music prediction to support pitch determination is introduced. This method was developed in order to override disadvantages of standard pitch detection algorithms. The new approach utilizes signal segmentation and pitch prediction based on musical knowledge extraction employing artificial neural networks. Signal segmentation allows for estimating the pitch for a single note as a whole, therefore suppressing errors in transient and decay phases. Pitch prediction helps correcting pitch estimation errors by tracking musical context of the analyzed signal. As it was shown in the experimental results, pitch estimation errors may be reduced by using both signal segmentation and music prediction techniques.     

Symmetries, Group Actions, and Entanglement

We address several problems concerning the geometry of the space of Hermitian operators on a finite-dimensional Hilbert space, in particular the geometry of the space of density states and canonical group actions on it. For quantum composite systems we discuss and give examples of entanglement measures.     

Optimal design of shells against buckling subjected to combined loadings

In this paper, the problem of optimal design of shells against instability is considered. A thin-walled shell is loaded, in general, by overall bending moment, constant or varying along an axis of a shell, by the appropriate shearing force and by an axial force and a constant torsional moment. We look for the shape of middle surface as well as the thickness of a shell, which ensures the maximum critical value of the loading parameter. The volume of material and the capacity of a shell are considered as equality constraints. The concept of a shell of uniform stability is applied.