Complexity of the consistency problem for certain Post classes

The complexity of the consistency problem for several important classes of Boolean functions is analyzed. The classes of functions under investigation are those which are closed under function composition or superposition. Several of these so-called Post classes are considered within the context of machine learning with an application to breast cancer diagnosis. The considered Post classes furnish a user-selectable measure of reliability. It is shown that for realistic situations which may arise in practice, the consistency problem for these classes of functions is polynomial-time solvable.     

Kernel density estimation with adaptive varying window size

A new method of kernel density estimation with a varying adaptive window size is proposed. It is based on the so-called intersection of confidence intervals (ICI) rule. Several examples of the proposed method are given for different types of densities and the quality of the adaptive density estimate is assessed by means of numerical simulations.     

Combinatorial Optimization and Performance Analysis of a Multi-arm Cartesian Robotic Fruit Harvester—Extensions of Graph Coloring

A mobile melon robotic harvester consisting of multiple Cartesian manipulators, each with three degrees of freedom, is being developed. In order to design an optimal robot in terms of number of arms, manipulator capabilities, and robot speed, a method of allocating the fruits to be picked by each manipulator in a way that yields the maximum harvest has been developed. Such a method has already been devised for a multi-arm robot with 2DOF each. The maximum robotic harvesting problem was shown there to be an example of the maximum k-colorable subgraph problem (MKCSP) on an interval graph. However, for manipulators with 3DOF, the additional longitudinal motion results in variable intervals. To overcome this issue, we devise a new model based on the color-dependent interval graph (CDIG). This enables the harvest by multiple robotic arms to be modeled as a modified version of the MKCSP. Based on previous research, we develop a greedy algorithm that solves the problem in polynomial time, and prove its optimality using induction. As with the multi-arm 2DOF robot, when simulated numerous times on a field of randomly distributed fruits, the algorithm yields a nearly identical percentage of fruit harvested for given robot parameters. The results of the probabilistic analysis developed for the 2DOF robot was modified to yield a formula for the expected harvest ratio of the 3DOF robot. The significance of this method is that it enables selecting the most efficient actuators, number of manipulators, and robot forward velocity for maximal robotic fruit harvest.     

Perceptual Issues in Music Pattern Recognition: Complexity of Rhythm and Key Finding

We consider several perceptual issues in the context of machine recognition ofmusic patterns. It is argued that a successful implementation of a musicrecognition system must incorporate perceptual information and error criteria.We discuss several measures of rhythm complexity which are used fordetermining relative weights of pitch and rhythm errors. Then, a new methodfor determining a localized tonal context is proposed. This method is based onempirically derived key distances. The generated key assignments are then usedto construct the perceptual pitch error criterion which is based on noterelatedness ratings obtained from experiments with human listeners.     

Fourier transform infrared-attenuated total reflection nitrate determination of soil pastes using principal component regression, partial least squares, and cross-correlation

This paper investigates the use of Fourier transform infrared (FTIR) attenuated total reflectance (ATR) spectroscopy as a fast and simple way for direct determination of nitrate concentration in soil pastes, which would assist precision fertilizer placement and reduce nitrate pollution. Eight types of soils are investigated, with nitrate concentrations ranging from 0 to 1000 ppm-N. The spectral region around the nitrate band (1300-1550 cm(-1)) is analyzed by (1) principal component regression (PCR), (2) partial least squares (PLS), and (3) cross-correlation with reference libraries that include spectra of pure ions and/or soils. The main obstacle to accurate nitrate measurement appears to be an interfering band present in calcareous soils. This band, which may be due to carbonate, is located around 1450 cm(-1) and overlaps with the nitrate band centered around 1370 cm(-1). For non-calcareous soils, and in particular for light sandy agricultural soils, PLS and cross-correlation with a reference library containing only spectra of ions in water give similar results (about 8 ppm-N on dry soil basis), while PCR leads to slightly poorer results. When calcareous soils are included in the analysis, the prediction errors are about twice as large. In this case, the best results are obtained using PLS, followed by PCR, while cross-correlation with reference libraries leads to poorer results.     

Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes.