Underwater image reconstruction methods require the knowledge of wideband attenuation coefficients per color channel. Current estimation methods for these coefficients require specialized hardware or multiple images, and none of them leverage the multitude of existing ocean optical measurements as priors. Here, we aim to constrain the set of physically-feasible wideband attenuation coefficients in the ocean by utilizing water attenuation measured worldwide by oceanographers. We calculate the space of valid wideband effective attenuation coefficients in the 3D RGB domain and find that a bound manifold in 3-space sufficiently represents the variation from the clearest to murkiest waters. We validate our model using in situ experiments in two different optical water bodies, the Red Sea and the Mediterranean. Moreover, we show that contradictory to the common image formation model, the coefficients depend on the imaging range and object reflectance, and quantify the errors resulting from ignoring these dependencies.

Letting one's thoughts wander is not simply an arbitrary or rambling process. It can better be described as "associative thinking", where a complex chain of associative thoughts and ideas are linked. It is our contention that this seemingly chaotic process can be modeled by a random walk in a weighted directed graph. Furthermore, is it possible to predict mathematically the "steady state" of such a process, to determine where such wandering is leading. The random walk process uses rules of association, defined by the Local Confidence Gain (LCG) interestingness measure. Extracted concepts are used as nodes of a directed graph. The associative "forces" between any two concepts (measured by LCG) are used to weigh the edges connecting the nodes that create a graph of associations. It is common, yet not trivial, for people to look for data about a subject without knowing its exact nomenclature (for example, finding the name of a disease just by knowing its symptoms). Random walk in association graphs can discover highly informative phrases that can be used for query expansion in a way that better expresses the user's initial search goals. A different usage is to create a user profile representing his current interests. We used a modified version of the Turing Test to show that the random walk process discovers association rules that conform to a human associations generating process. By constructing the user associations we were able to build a profile representing the user's "line of thoughts". The suggested algorithm can be used in any database and can implement the ranking measures of other association rules.

In information retrieval and text mining, information on word senses is usually taken from dictionaries or lexical databases that have been prepared by lexicographers. We propose an automatic method for word sense induction, i.e. for the discovery of a set of sense descriptors to a given ambiguous word. The approach is based on the statistics of word co-occurrence as derived from Web pages. The underlying assumption is that the senses of an ambiguous word are best described by terms that, although bearing a strong association to this word, are mutually exclusive, i.e. whose association strength within the retrieved Web pages is as weak as possible. Measuring association strength is based upon a novel confidence gain approach that relates the observed co-occurrence frequency for two sense descriptor candidates to an average co-occurrence frequency for pairs of arbitrary words. The proposed approach is fully unsupervised and takes into account the contemporary meanings of words, as reflected in texts from the Internet. Our results are evaluated using a list of ambiguous words commonly referred to in the literature.