Abstract: The present invention relates to a method for generating hypotheses automatically from graphical models built directly from data. The method of the present invention links three key scientific concepts to enable hypothesis generation from data driven hypothesis-models: including the use of information theory based measures to identify informative feature subsets within the data; the automatic generation of graphical models from the informative data subsets identified from step one; and the application of optimization methods to graphical models to enable hypothesis generation. The integration of these three concepts can enable scalable approaches to hypothesis generation from large, complex data environments. The use of graphical models as the model representation can allow prior knowledge to be effectively integrated into the modeling environment.

Abstract: The present invention relates to a method for the automatic identification of at least one informative data filter from a data set that can be used to identify at least one relevant data subset against a target feature for subsequent hypothesis generation, model building and model testing. The present invention describes methods, and an initial implementation, for efficiently linking relevant data both within and across multiple domains and identifying informative statistical relationships across this data that can be integrated into agent-based models. The relationships, encoded by the agents, can then drive emergent behavior across the global system that is described in the integrated data environment.

Abstract: The present invention relates to a method for generating hypotheses automatically from graphical models built directly from data. The method of the present invention links three key scientific concepts to enable hypothesis generation from data driven hypothesis-models: including the use of information theory based measures to identify informative feature subsets within the data; the automatic generation of graphical models from the informative data subsets identified from step one; and the application of optimization methods to graphical models to enable hypothesis generation. The integration of these three concepts can enable scalable approaches to hypothesis generation from large, complex data environments. The use of graphical models as the model representation can allow prior knowledge to be effectively integrated into the modeling environment.

Abstract: The present invention provides a means for performing scalable, computationally efficient and rapid simulations of complex or complex adaptive systems realized through the dynamic interaction of multiple modeling components to generate outputs suited to decision support, analysis and planning. In the context of disease modeling, these outputs can be used for analyzing the impact of disease and the potential value of the use of pharmaceutical and non-pharmaceutical interventions.

Abstract: A method of discovering one or more patterns in two sequences of symbols S1 and S2 includes the formation, for each sequence, of a master offset table that groups for each symbol the position in the sequence occupied by each occurrence of that symbol. The difference in position between each occurrence of a symbol in one of the sequences and each occurrence of that same symbol in the other sequence is determined and a Pattern Map is formed. For each given value of a difference in position the Pattern Map lists the position in the first sequence of each symbol therein that appears in the second sequence at that difference in position. The collection of the symbols tabulated for each value of difference in position thereby defines a parent pattern in the first sequence that is repeated in the second sequence.

Abstract: A distributed hierarchical evolutionary modeling and visualization of empirical data method and machine readable storage medium for creating an empirical modeling system based upon previously acquired data. The data represents inputs to the systems and corresponding outputs from the system. The method and machine readable storage medium utilize an entropy function based upon information theory and the principles of thermodynamics to accurately predict system outputs from subsequently acquired inputs. The method and machine readable storage medium identify the most information-rich (i.e., optimum) representation of a data set in order to reveal the underlying order, or structure, of what appears to be a disordered system. Evolutionary programming is one method utilized for identifying the optimum representation of data.

Abstract: A method of discovering one or more patterns in two sequences of symbols S1 and S2 includes the formation, for each sequence, of a master offset table that groups for each symbol the position in the sequence occupied by each occurrence of that symbol. The difference in position between each occurrence of a symbol in one of the sequences and each occurrence of that same symbol in the other sequence is determined and a Pattern Map is formed. For each given value of a difference in position the Pattern Map lists the position in the first sequence of each symbol therein that appears in the second sequence at that difference in position. The collection of the symbols tabulated for each value of difference in position thereby defines a parent pattern in the first sequence that is repeated in the second sequence.

Abstract: A method of texturing fluoropolymer film and the textured product produced, which product retains the texture imparted after further processing, such as in thermoforming or molding processes.

Abstract: When identifying an object in an image, very often redundant identifications of the object may occur. The present invention relates to methods of image analysis for resolving such redundant identifications of an object. More specifically, the present invention relates to object identification schemes which obtain multiple representations of the same object. In addition, such identification schemes may identify void, or donut-shaped objects when a single search, or multiples searches, of the image are performed. Further, the present invention is useful in resolving several distinct objects where other identification schemes employing either single or multiple searching have identified these distinct objects as a single clumped object. The present invention may be used, for instance, to determine whether a smaller object is contained within another, larger object.

Abstract: Image analysis methods and systems are used for identifying objects in a background by defining a data space, such as a histogram or a color space. The data space comprises a plurality of sub-spaces, which could be selected based, for example, on a histogram, or on the way pixel values or color parameters cluster in their respective spaces. The threshold values in the data space are selected, a list of all ordered pairs of thresholds is generated to define multiple data sub-spaces, and the image is multiply searched, once in each sub-space for at least one representation of a candidate object, where the candidate object has at least one predetermined attribute value. Valid objects are identified by comparing the candidate object attribute values to a defined set of valid object attribute values contained in a driver.

Abstract: The present invention relates to image analysis methods and systems for identifying objects in a background by generating a description, which may be either a histogram or co-occurrence matrix, of the gray level space of the image by using an entropic kernel to recursively analyze the gray level space for candidate objects and validating the presence of valid objects by comparing the candidate object attribute values to a defined set of valid object attribute values contained in a driver. The present invention includes recursive, iterative and parallel processing methods. The methods may be used in a wide variety of industrial inspection techniques, including colony counting and the identification of discrete features in carpets and of pigment elements embedded in a polymer.

Abstract: The present invention relates to an image analysis method and system for identifying objects in a background by generating a description, which in this case is a co-occurrence matrix, of the gray level space of the image by using an entropic kernel to recursively analyze the gray level space for candidate objects and validating the presence of valid objects by comparing the candidate object attribute values to a defined set of valid object attribute values contained in a driver. The present invention includes recursive, iterative and parallel processing methods. The methods may be used in a wide variety of industrial inspection techniques, including colony counting and the identification of discrete features in carpets and of pigment elements embedded in a polymer.

Abstract: The present invention relates to an image analysis method and system for separately identifying each clumped homogeneous object in an image. The image includes at least one clump of homogeneous objects and at least one isolated homogeneous object. This image analysis method may be used in image analysis methods and systems for identifying objects in a background by generating a description, which may be either a histogram or co-occurrence matrix, of the gray level space of the image by using an entropic kernel to recursively analyze the gray level space for candidate objects and validating the presence of valid objects by comparing the candidate object attribute values to a defined set of valid object attribute values contained in a driver. The present invention includes recursive, iterative and parallel processing methods.