Abstract: A system and method for creating at least one new network, comprising: selecting at least one node and at least one set of reactions where reagents in each reaction of the at least one set of reactions are known in at least one reference network; and creating at least one new network by causing the at least one new network to behave in a similar way with respect to the at least one node and the at least one set of reactions as the at least one reference network reacts with the at least one node and the at least one set of reactions.

Abstract: The present invention introduces an innovative data mining technique to identify precursory signals associated with earthquakes. It involves a multistrategy approach that employs one-dimensional wavelet transformations to identify singularities in data, and analyzes the continuity of wavelet maxima in time and space to determine the singularities that could be precursory signals. Surface Latent Heat Flux (SLHF) data may be used. A single prominent SLHF anomaly may be found to be associated some days prior to a main earthquake event.

Abstract: Retinal images can be classified by selecting a classifier and a figure of merit for quantifying classification quality; selecting a transform to generate features from input data; using a recursive process of functional dissipation to generate dissipative features from the features generated according to the transform; computing the figure of merit for all of the dissipative features generated; searching for at least one of the dissipative features that maximize the figure of merit on a training set; and classifying a test set with the classifier by using the “at least one of the dissipative features.

Abstract: The present invention introduces an innovative data mining technique to identify precursory signals associated with earthquakes. It involves a multistrategy approach that employs one-dimensional wavelet transformations to identify singularities in data, and analyzes the continuity of wavelet maxima in time and space to determine the singularities that could be precursory signals. Surface Latent Heat Flux (SLHF) data may be used. A single prominent SLHF anomaly may be found to be associated some days prior to a main earthquake event.

Abstract: The invention provides for a technique of extracting information from signals by allowing a user to select a classifier and a figure of merit for quantifying classification quality; select a transform to generate features from input data; use a recursive process of functional dissipation to generate dissipative features from features that are generated according to the transform; search for one or more dissipative features that maximize the figure of merit on a training set; and classify a test set with the classifier by using one or more of the dissipative features. Functional dissipation uses the transforms recursively by generating random masking functions and extracting features with one or more generalized matching pursuit iterations. In each iteration, the recursive process may modify several features of the transformed signal with the largest absolute values according to a specific masking function.

Abstract: The present invention introduces an innovative data mining technique to identify precursory signals associated with earthquakes. It involves a multistrategy approach that employs one-dimensional wavelet transformations to identify singularities in data, and analyzes the continuity of wavelet maxima in time and space to determine the singularities that could be precursory signals. Surface Latent Heat Flux (SLHF) data may be used. A single prominent SLHF anomaly may be found to be associated some days prior to a main earthquake event.

Abstract: Retinal images can be classified by selecting a classifier and a figure of merit for quantifying classification quality; selecting a transform to generate features from input data; using a recursive process of functional dissipation to generate dissipative features from the features generated according to the transform; computing the figure of merit for all of the dissipative features generated; searching for at least one of the dissipative features that maximize the figure of merit on a training set; and classifying a test set with the classifier by using the “at least one of the dissipative features.

Abstract: A denoising mechanism uses chosen signal classes and selected analysis dictionaries. The chosen signal class includes a collection of signals. The analysis dictionaries describe signals. The embedding threshold value is initially determined for a training set of signals in the chosen signal class. The update signal is initialized with a signal corrupted by noise.

Abstract: The invention provides for a technique of extracting information from signals by allowing a user to select a classifier and a figure of merit for quantifying classification quality; select a transform to generate features from input data; use a recursive process of functional dissipation to generate dissipative features from features that are generated according to the transform; search for one or more dissipative features that maximize the figure of merit on a training set; and classify a test set with the classifier by using one or more of the dissipative features. Functional dissipation uses the transforms recursively by generating random masking functions and extracting features with one or more generalized matching pursuit iterations. In each iteration, the recursive process may modify several features of the transformed signal with the largest absolute values according to a specific masking function.

Abstract: The present invention introduces an innovative data mining technique to identify precursory signals associated with earthquakes. It involves a multistrategy approach that employs one-dimensional wavelet transformations to identify singularities in data, and analyzes the continuity of wavelet maxima in time and space to determine the singularities that could be precursory signals. Surface Latent Heat Flux (SLHF) data may be used. A single prominent SLHF anomaly may be found to be associated some days prior to a main earthquake event.