Abstract: Aspects of the subject disclosure may include, for example, a process that performs a statistical, natural-language processing analysis on a group of text documents to determine a group of topics. The topics are determined according to parameters obtained by training on a sample of documents. One or more topics in a subset of topics are associated to each document, resulting in topic-document pairs. A bias is identified for each topic-document pair, and clusters of topics are created from the subset of topics. Each cluster of topics is determined from a value for each bias of each topic-document pair and from a frequency of occurrence of each topic. Each cluster is presentable according to a corresponding image configuration based on all or a subset of the bias dimensions and the frequency of occurrence of topics in a cluster that distinguishes the cluster from other clusters. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a process that performs a statistical, natural-language processing analysis on a group of text documents to determine a group of topics. The topics are determined according to parameters obtained by training on a sample of documents. One or more topics in a subset of topics are associated to each document, resulting in topic-document pairs. A bias is identified for each topic-document pair, and clusters of topics are created from the subset of topics. Each cluster of topics is determined from a value for each bias of each topic-document pair and from a frequency of occurrence of each topic. Each cluster is presentable according to a corresponding image configuration based on all or a subset of the bias dimensions and the frequency of occurrence of topics in a cluster that distinguishes the cluster from other clusters. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a process that performs a statistical, natural-language processing analysis on a group of text documents to determine a group of topics. The topics are determined according to parameters obtained by training on a sample of documents. One or more topics in a subset of topics are associated to each document, resulting in topic-document pairs. A bias is identified for each topic-document pair, and clusters of topics are created from the subset of topics. Each cluster of topics is determined from a value for each bias of each topic-document pair and from a frequency of occurrence of each topic. Each cluster is presentable according to a corresponding image configuration based on all or a subset of the bias dimensions and the frequency of occurrence of topics in a cluster that distinguishes the cluster from other clusters. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a process that performs a statistical, natural-language processing analysis on a group of text documents to determine a group of topics. The topics are determined according to parameters obtained by training on a sample of documents. One or more topics in a subset of topics are associated to each document, resulting in topic-document pairs. A bias is identified for each topic-document pair, and clusters of topics are created from the subset of topics. Each cluster of topics is determined from a value for each bias of each topic-document pair and from a frequency of occurrence of each topic. Each cluster is presentable according to a corresponding image configuration based on all or a subset of the bias dimensions and the frequency of occurrence of topics in a cluster that distinguishes the cluster from other clusters. Other embodiments are disclosed.

Abstract: A system that incorporates teachings of the present disclosure may include, for example, sampling a variable effect distribution of viewing preference data to determine a first set of effects comprising a plurality of first distortion type effects associated with a first distortion type of a first image and to determine a second set of effects comprising a plurality of second distortion type effects associated with the second distortion type of a second image, calculating a preference estimate from a logistic regression model of the viewing preference data according to the first set of effects and the second set of effects, wherein the preference estimate comprises a probability that the first image is preferred over the second image, and selecting one of the first distortion type or the second distortion type according to the preference estimate. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a process that performs a statistical, natural-language processing analysis on a group of text documents to determine a group of topics. The topics are determined according to parameters obtained by training on a sample of documents. One or more topics in a subset of topics are associated to each document, resulting in topic-document pairs. A bias is identified for each topic-document pair, and clusters of topics are created from the subset of topics. Each cluster of topics is determined from a value for each bias of each topic-document pair and from a frequency of occurrence of each topic. Each cluster is presentable according to a corresponding image configuration based on all or a subset of the bias dimensions and the frequency of occurrence of topics in a cluster that distinguishes the cluster from other clusters. Other embodiments are disclosed.

Abstract: A system that incorporates teachings of the present disclosure may include, for example, sampling a variable effect distribution of viewing preference data to determine a first set of effects comprising a plurality of first distortion type effects associated with a first distortion type of a first image and to determine a second set of effects comprising a plurality of second distortion type effects associated with the second distortion type of a second image, calculating a preference estimate from a logistic regression model of the viewing preference data according to the first set of effects and the second set of effects, wherein the preference estimate comprises a probability that the first image is preferred over the second image, and selecting one of the first distortion type or the second distortion type according to the preference estimate. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a computer that performs a statistical natural language processing analysis on a plurality of text documents to determine a plurality of topics, creates a proper subset of topics from the plurality of topics, based on user input, maps one or more topics in the proper subset of topics to each document in the plurality of text documents, thereby creating a plurality of topic-document pairs, identifies n-dimensions of bias for each topic-document pair from the text, creates clusters of topics from the proper subset of topics, and generates presentable content depicting each cluster of the clusters of topics according to a corresponding image configuration. The topics and n-dimensions of bias data can be further analyzed with co-collected structured data for statistical relationships.

Abstract: A system that incorporates teachings of the present disclosure may include, for example, sampling a variable effect distribution of viewing preference data to determine a first set of effects comprising a plurality of first distortion type effects associated with a first distortion type of a first image and to determine a second set of effects comprising a plurality of second distortion type effects associated with the second distortion type of a second image, calculating a preference estimate from a logistic regression model of the viewing preference data according to the first set of effects and the second set of effects, wherein the preference estimate comprises a probability that the first image is preferred over the second image, and selecting one of the first distortion type or the second distortion type according to the preference estimate. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a computer that performs a statistical natural language processing analysis on a plurality of text documents to determine a plurality of topics, creates a proper subset of topics from the plurality of topics, based on user input, maps one or more topics in the proper subset of topics to each document in the plurality of text documents, thereby creating a plurality of topic-document pairs, identifies n-dimensions of bias for each topic-document pair from the text, creates clusters of topics from the proper subset of topics, and generates presentable content depicting each cluster of the clusters of topics according to a corresponding image configuration. The topics and n-dimensions of bias data can be further analyzed with co-collected structured data for statistical relationships.

Abstract: A system that incorporates teachings of the present disclosure may include, for example, sampling a variable effect distribution of viewing preference data to determine a first set of effects comprising a plurality of first distortion type effects associated with a first distortion type of a first image and to determine a second set of effects comprising a plurality of second distortion type effects associated with the second distortion type of a second image, calculating a preference estimate from a logistic regression model of the viewing preference data according to the first set of effects and the second set of effects, wherein the preference estimate comprises a probability that the first image is preferred over the second image, and selecting one of the first distortion type or the second distortion type according to the preference estimate. Other embodiments are disclosed.

Abstract: A system that incorporates teachings of the present disclosure may include, for example, sampling a variable effect distribution of viewing preference data to determine a first set of effects comprising a plurality of first distortion type effects associated with a first distortion type of a first image and to determine a second set of effects comprising a plurality of second distortion type effects associated with the second distortion type of a second image, calculating a preference estimate from a logistic regression model of the viewing preference data according to the first set of effects and the second set of effects, wherein the preference estimate comprises a probability that the first image is preferred over the second image, and selecting one of the first distortion type or the second distortion type according to the preference estimate. Other embodiments are disclosed.

Abstract: A system that incorporates teachings of the present disclosure may include, for example, sampling a variable effect distribution of viewing preference data to determine a first set of effects comprising a plurality of first distortion type effects associated with a first distortion type of a first image and to determine a second set of effects comprising a plurality of second distortion type effects associated with the second distortion type of a second image, calculating a preference estimate from a logistic regression model of the viewing preference data according to the first set of effects and the second set of effects, wherein the preference estimate comprises a probability that the first image is preferred over the second image, and selecting one of the first distortion type or the second distortion type according to the preference estimate. Other embodiments are disclosed.

Abstract: A system that incorporates teachings of the present disclosure may include, for example, sampling a variable effect distribution of viewing preference data to determine a first set of effects comprising a plurality of first distortion type effects associated with a first distortion type of a first image and to determine a second set of effects comprising a plurality of second distortion type effects associated with the second distortion type of a second image, calculating a preference estimate from a logistic regression model of the viewing preference data according to the first set of effects and the second set of effects, wherein the preference estimate comprises a probability that the first image is preferred over the second image, and selecting one of the first distortion type or the second distortion type according to the preference estimate. Other embodiments are disclosed.

Abstract: A system that incorporates teachings of the present disclosure may include, for example, sampling a variable effect distribution of viewing preference data to determine a first set of effects comprising a plurality of first distortion type effects associated with a first distortion type of a first image and to determine a second set of effects comprising a plurality of second distortion type effects associated with the second distortion type of a second image, calculating a preference estimate from a logistic regression model of the viewing preference data according to the first set of effects and the second set of effects, wherein the preference estimate comprises a probability that the first image is preferred over the second image, and selecting one of the first distortion type or the second distortion type according to the preference estimate. Other embodiments are disclosed.

Abstract: A water in oil emulsion system and a process for preparing such a emulsion has been provided for topically applying aminophylline for reducing cellulite conditions.

Abstract: A water in oil emulsion system and a process for preparing such a emulsion has been provided for topically applying aminophylline for reducing cellulite conditions.

Abstract: A water in oil emulsion system and a process for preparing such a emulsion has been provided for topically applying aminophylline for reducing cellulite conditions.