Discovery proteomics usually refers to the search for potential biomarkers of disease from clinical samples such as blood serum, plasma, urine or cerebrospinal fluid (CSF). These samples are typically analyzed using high-throughput Mass Spectral (MS) technologies yielding highdimensional data that needs interpretation in order to identify a small number of protein/peptide biomarkers predictive ...

A method is proposed to reconstruct signals from incomplete data. The method, which can be interpreted both as a discrete implementation of the so-called prior discrete Fourier transform (PDFT) spectral estimation technique and as a variant of the algebraic reconstruction technique, allows one to incorporate prior information about the reconstructed signal to improve the resolution of the signa...

In fuzzy modeling (FM), the quantity and quality of the training set is crucial to properly grasp the behavior of the system being modeled. However, the available data is often not large enough or is deficiently distributed along the input space, not revealing the system behavior completely. In such cases, the consideration of any prior knowledge about the system can be decisive for the accurac...

Canonical correlation analysis (CCA) is a method for reducing the dimension of data represented using two views. It has been previously used to derive word embeddings, where one view indicates a word, and the other view indicates its context. We describe a way to incorporate prior knowledge into CCA, give a theoretical justification for it, and test it by deriving word embeddings and evaluating...

aims: endotracheal suctioning is a vital process to keep the airways open. the method of performing endotracheal suctioning has a great effect on its complications and effectiveness. this study was carried out with the aim of assessing gap between the knowledge and performance of nurses' working in intensive care units (icu) in tracheal suctioning.   methods: in this cross sectional study,...

Orienting responses to audiovisual events in the environment can benefit markedly by the integration of visual and auditory spatial information. However, logically, audiovisual integration would only be considered successful for stimuli that are spatially and temporally aligned, as these would be emitted by a single object in space-time. As humans do not have prior knowledge about whether novel...

One of the ultimate goals of open ended learning systems is to take advantage of experience to get a future benefit. We can identify two levels in learning. One builds directly over the data : it captures the pattern and regularities which allow for reliable predictions on new samples. The other starts from such an obtained source knowledge and focuses on how to generalize it to new target conc...

Probabilistic models such as Bayesian Networks are now in widespread use in spoken dialogue systems, but their scalability to complex interaction domains remains a challenge. One central limitation is that the state space of such models grows exponentially with the problem size, which makes parameter estimation increasingly difficult, especially for domains where only limited training data is a...

Subtraction of aligned images is a means to assess changes in a wide variety of clinical applications. In this paper we explore the information theoretical origin of Mutual Information (MI), which is based on Shannon's entropy. However, the interpretation of standard MI registration as a communication channel suggests that MI is too restrictive a criterion. In this paper the concept of Mutual I...

Visual perception is fundamentally ambiguous because an infinite number of three-dimensional scenes are consistent with our retinal images. To circumvent these ambiguities, the visual system uses prior knowledge such as the assumption that light is coming from above our head. The use of such assumptions is rational when these assumptions are related to statistical regularities of our environmen...