Volume 9, Issue 4

A human observer is able to determine the color of objects independent of the light illuminating these objects. This ability is known as color constancy. In the first stages of visual information processing, data are analyzed with respect to wavelength composition, orientation, motion, and depth. With this contribution, we investigate whether depth information can help in estimating the color of the objects. We assume that local space average color is computed in V4 through resistively coupled neurons to estimate the color of the illuminant. We show how this computational model can be extended to incorporate depth information.

This article demonstrates a technique of generating digital endocasts of the cranial interior using the three-dimensional (3D) vision system. Obtained digital endocasts reflected the shape and size of the brain, pattern of dural vascularization, position, and interlocking of the cranial sutures. Hence, some minor anatomical structures (granulations faveole, digital impressions) were recorded by the 3D vision system and were revealed on the digital endocasts.

Causal inference in survival analysis has been centered on treatment effect assessment with adjustment of covariates. The direct adjustment method is usually employed to find the survival function of a treatment. A Cox model that stratifies the cumulative hazard by treatment is an ideal choice for performing direct adjustment because the treatment effects are allowed to vary over time. A SAS macro was developed to implement comparison of direct adjusted survivals between treatments at a selected time point. The restricted mean survival time can be derived from a direct adjusted survival function. This statistic summarizes the survival outcome of a treatment. Comparison of restricted means provides assessment of treatment effect over a time interval. The first aim of this article was to provide an overview of the restricted mean survival time. The second aim was to introduce a SAS macro that computes the restricted mean survival times from direct adjusted survivals based on a stratified Cox model. Data preparation and macro invocation are illustrated in an analysis of survival data involving three types of stem cell transplants.

This article examines the effectiveness of artificial neural networks (ANNs) as forensic voice comparison techniques. This study specifically considers feed-forward multilayer perceptron (MLP) and radial basic function (RBF) network models. Formant frequencies of Polish vowel e (stressed or unstressed) in selected contexts were used as predictors. This has already been confirmed in an earlier investigation that determined that dynamic formant frequencies of vowels are powerful elements in distinguishing the voice. It has been concluded that neural networks might assist in distinguishing speakers from the others with very good accuracy, reaching 100%. MLP models should be given preference. The results of the investigation have shown the influence of vowel e triads on the effectiveness of correct classification rates. In addition, the authors have determined that the accuracy of classification is greater when based on a single context than for similar input data aggregated over several different contexts.

The model (under consideration) to simulate the protein folding process assumes two steps: early stage (ES) and late stage (LS). The first is assumed to define the preliminary structure, which when applied to an optimization procedure, may produce the proper structure of the protein. However, the ES model produces the structures with clashes. This work demonstrates the possible solution to remove clashes before proceeding to the LS. Additionally, the presented solution describes mathematically the precession phenomenon, which might be useful in other fields of studies aside from protein folding such as medical imaging, quantum physics, and astronomy.

The nervous system is one of the most complex known dynamical systems; thus, its disorders are among the most severe. Scientists and clinicians look for the best possible methods allowing for comprehensive understanding and for reliable assessment and treatment of human nervous system disorders. Noise may be perceived as a useful control signal for particular nervous system functions, including further development of neurorehabilitation and clinical applications of brain-computer interfaces (BCIs), neuroprostheses (NPs), deep brain stimulation (DBS), etc. The awareness of associated chances and limitations allow for the wise planning and management of further clinical practice, especially in the area of long-term neurorehabilitation and care. This article aims at investigating the extent to which the available knowledge and experience may be identified and utilized, including potential future applications.