Systems and methods are provided for enhancing text-based electronic communications. In one embodiment, a sending agent automatically, or upon request by a user, disambiguates identifiers and terms included in a communication internally or in collaboration with a remote device or server based on the identity and history of the sending or intended receiving device or user, the content or context of the communication and/or the sending or intended receiving device or user and automatically or at the request of the sender, appends, enhances or provides certain additional information related to the identifier or terms to the message in real time. This additional information is then included or referenced, but not necessarily initially displayed or communicated, in the communication. The integrated system also may include a receiving agent on the receiving device that can disambiguate the identifier or terms and provide additional information and services related to the identifier or terms in the communication.

Technology is described herein for facilitating a user's interaction with a digital ink document. The technology internally represents the ink document using a data structure having a hierarchy of nodes. The nodes describe respective elements in the ink document. The technology leverages the data structure to identify a set of nodes that grows upon the user's repeated selection of a particular part of the ink document. At each stage of the selection, the technology highlights a set of elements in the ink document that correspond to the current set of identified nodes. According to another illustrative aspect, the technology produces the data structure by modifying an original data structure provided by a text analysis engine. The technology performs this task with the objective of accommodating structured interaction by the user with the ink document.

Technology is described herein for facilitating a user's interaction with a digital ink document. The technology internally represents the ink document using a data structure having a hierarchy of nodes. The nodes describe respective elements in the ink document. The technology leverages the data structure to identify a set of nodes that grows upon the user's repeated selection of a particular part of the ink document. At each stage of the selection, the technology highlights a set of elements in the ink document that correspond to the current set of identified nodes. According to another illustrative aspect, the technology produces the data structure by modifying an original data structure provided by a text analysis engine. The technology performs this task with the objective of accommodating structured interaction by the user with the ink document.

In various embodiments, a codec comparison application independently encodes each source image included in a set of source images using a first encoding configuration to generate a first set of encoded images. The codec comparison application also independently encodes each source image included in the set of source images using a second encoding configuration to generate a second set of encoded images. For each encoded image in the first set of encoded images and each encoded image in the second set of encoded images, a visual quality score for a reconstructed source image derived from the encoded image falls within a tolerance of a target visual quality score. Subsequently, the codec comparison application computes a bitrate change based on a first total file size for the first set of encoded image and a second total file size for the second set of encoded images

In various embodiments, a codec comparison application independently encodes each source image included in a set of source images using a first encoding configuration to generate a first set of encoded images. The codec comparison application also independently encodes each source image included in the set of source images using a second encoding configuration to generate a second set of encoded images. For each encoded image in the first set of encoded images and each encoded image in the second set of encoded images, a visual quality score for a reconstructed source image derived from the encoded image falls within a tolerance of a target visual quality score. Subsequently, the codec comparison application computes a bitrate change based on a first total file size for the first set of encoded image and a second total file size for the second set of encoded images

Disclosed herein are techniques related to automated generation of regular expressions. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence.

Disclosed herein are techniques related to automated generation of regular expressions. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence.

Methods and apparatus for compressing data streams. In an embodiment, a method includes calculating a decomposition of matrix data to generate eigenvectors and associated eigenvalues, determining clusters of the eigenvectors based on weighting the eigenvalues, calculating an eigenvector centroid for each cluster so that a dictionary of centroids is generated, and tagging the eigenvectors with tags, respectively, that identify an associated eigenvector centroid for each eigenvector. The method also includes counting a number of eigenvectors associated with each eigenvector centroid to construct a probability distribution function (PDF) of centroids, matching the PDF of centroids to PDF templates to determine a closest matching PDF template, determining an encoder corresponding to the closest matching PDF template wherein a corresponding encoder identifier is identified, encoding the tags with the encoder to generate an encoded data stream, and transmitting the encoded data stream, the encoder identifier, the dictionary of centroids, and the eigenvalues.

Methods and apparatus for compressing data streams. In an embodiment, a method includes calculating a decomposition of matrix data to generate eigenvectors and associated eigenvalues, determining clusters of the eigenvectors based on weighting the eigenvalues, calculating an eigenvector centroid for each cluster so that a dictionary of centroids is generated, and tagging the eigenvectors with tags, respectively, that identify an associated eigenvector centroid for each eigenvector. The method also includes counting a number of eigenvectors associated with each eigenvector centroid to construct a probability distribution function (PDF) of centroids, matching the PDF of centroids to PDF templates to determine a closest matching PDF template, determining an encoder corresponding to the closest matching PDF template wherein a corresponding encoder identifier is identified, encoding the tags with the encoder to generate an encoded data stream, and transmitting the encoded data stream, the encoder identifier, the dictionary of centroids, and the eigenvalues.

Methods and apparatus for compressing data streams. In an embodiment, a method includes calculating a decomposition of matrix data to generate eigenvectors and associated eigenvalues, determining clusters of the eigenvectors based on weighting the eigenvalues, calculating an eigenvector centroid for each cluster so that a dictionary of centroids is generated, and tagging the eigenvectors with tags, respectively, that identify an associated eigenvector centroid for each eigenvector. The method also includes counting a number of eigenvectors associated with each eigenvector centroid to construct a probability distribution function (PDF) of centroids, matching the PDF of centroids to PDF templates to determine a closest matching PDF template, determining an encoder corresponding to the closest matching PDF template wherein a corresponding encoder identifier is identified, encoding the tags with the encoder to generate an encoded data stream, and transmitting the encoded data stream, the encoder identifier, the dictionary of centroids, and the eigenvalues.