Abstract: Systems and methods for text processing are described. Embodiments of the present disclosure receive a query comprising a natural language expression; extract a plurality of mentions from the query; generate a relation vector between a pair of the plurality of mentions using a relation encoder network, wherein the relation encoder network is trained using a contrastive learning process where mention pairs from a same document are labeled as positive samples and mention pairs from different documents are labeled as negative samples; combine the plurality of mentions with the relation vector to obtain a virtual knowledge graph of the query; identify a document corresponding to the query by comparing the virtual knowledge graph of the query to a virtual knowledge graph of the document; and transmit a response to the query, wherein the response includes a reference to the document.

Abstract: A method is provided in accordance with an aspect of the present disclosure. The method includes processing data related to a group of contacts, where the data includes at least one attribute related to each of the contacts. The method also includes defining the at least one attribute based on a probability distribution of a context in relation to attribute values of the at least one attribute for all contacts. The method further includes determining clusters of contacts based on the probability distribution of the context in relation to the attribute values of the at least one attribute for all contacts, and constructing a plurality of segments of similar contacts by using the clusters.

Abstract: A method is provided in accordance with an aspect of the present disclosure. The method includes processing data related to a group of contacts, where the data includes at least one attribute related to each of the contacts. The method also includes defining the at least one attribute based on a probability distribution of a context in relation to attribute values of the at least one attribute for all contacts. The method further includes determining clusters of contacts based on the probability distribution of the context in relation to the attribute values of the at least one attribute for all contacts, and constructing a plurality of segments of similar contacts by using the clusters.

Abstract: A method for finding sets of data (SDDs), which are similar to a target SDD, is invented. The method leverages a new category of signatures, called equivalence signatures, to characterize the SDDs and is applicable to all types of data that may be presented in two-dimensions. These signatures have the salient feature that, at worst, they change in a bounded manner when small changes are made to the SDDs and when used to find SDDs that are similar to a target SDDs, they allow for a significant reduction in the number of SDDs to be compared with the target. This is an improvement over the state of the art wherein the computational expensive process of performing a complete search against the entire corpus must be applied.

Abstract: A method for finding sets of data (SDDs) for presentation in one-dimension, which are similar to a target SDD, is invented. The method leverages a new category of signatures, called equivalence signatures, to characterize the SDDs and is applicable to all types of data with special interpretation for data, such as text, binaries and audio, that may be presented in one-dimension. The equivalence signature is computed as the functional for the kinetic energy of a point particle whose path is specified by the values of the digital data. These signatures have the salient feature that, at worst, they change in a bounded manner when small changes are made to the SDDs and when used to find SDDs that are similar to a target SDDs, they allow for a significant reduction in the number of SDDs to be compared with the target. This is an improvement over the state of the art wherein the computational expensive process of performing a complete search against the entire corpus must be applied.

Abstract: A method for finding sets of two-dimensional data (S2DDs), which are similar to a target S2DD, is invented. The method leverages a new category of signatures, called equivalence signatures, to characterize the S2DDs. These signatures have the salient feature that, at worst, they change in a bounded manner when changes are made to the S2DD and when used to find S2DDs that are similar to a target S2DDs, they allow for a significant reduction in the number of SDDs to be compared with the target. This is an improvement over the state of the art wherein the computational expensive process of performing a complete search against the entire corpus must be applied.

Abstract: A method for finding sets of data (SDDs) for presentation in two-dimensions, which are similar to a target SDD, is invented. The method leverages a new category of signatures, called equivalence signatures, to characterize the SDDs. The equivalence signature is computed as the action for the motion of a string which sweeps out a sheet specified by the values of the digital data. These signatures have the salient feature that, at worst, they change in a bounded manner when small changes are made to the SDDs and when used to find SDDs that are similar to a target SDDs, they allow for a significant reduction in the number of SDDs to be compared with the target. This is an improvement over the state of the art wherein the computational expensive process of performing a complete search against the entire corpus must be applied.

Abstract: A method for finding sets of data (SDDs) which are similar to a target SDD, is invented. By virtue of the isometrics on the space of equivalence signatures the lengths of the equivalence signature vectors of similar SDDs must be equal. A filter is then applied to the database of SDDs to find those SDDs that have the same length of the equivalence signatures vectors as that of the target SDD. With this, a significant reduction in the number of SDDs to be compared with the target for a final determination of similarity is obtained. This is an improvement over the state of the art wherein the computational expensive process of performing a complete search against the entire corpus must be applied.

Abstract: A method for finding sequences of similar data (SDDs), which are similar to a target sequence of digital data, is invented. The method leverages a new category of signatures, called equivalence signatures, to characterize the SDDs. These signatures have the salient feature that, at worst, they change in a bounded manner when changes are made to the sequence of digital data and when used to find SDDs that are similar to a target SDD, they allow for a significant reduction in the number of SDDs to be compared with the target. This is an improvement over the state of the art wherein the cryptographic message digests used as signatures respond unpredictably to changes in the sequence of digital data and the comparison of a target SDD to a corpus of SDDs requires the computational expensive process of applying a complete search against the entire corpus.

Abstract: A method for finding sets of data (SDDs) for presentation in one-dimension, which are similar to a target SDD, is invented. The method leverages a new category of signatures, called equivalence signatures, to characterize the SDDs and is applicable to all types of data with special interpretation for data that may be presented in two-dimensions. These signatures have the salient feature that, at worst, they change in a bounded manner when small changes are made to the SDDs and when used to find SDDs that are similar to a target SDDs, they allow for a significant reduction in the number of SDDs to be compared with the target. This is an improvement over the state of the art wherein the computational expensive process of performing a complete search against the entire corpus must be applied.

Abstract: A method for finding sets of data (SDDs) for presentation in two-dimensions, which are similar to a target SDD, is invented. The method leverages a new category of signatures, called equivalence signatures, to characterize the SDDs. The equivalence signature is computed as the action for the motion of a string which sweeps out a sheet specified by the values of the digital data. These signatures have the salient feature that, at worst, they change in a bounded manner when small changes are made to the SDDs and when used to find SDDs that are similar to a target SDDs, they allow for a significant reduction in the number of SDDs to be compared with the target. This is an improvement over the state of the art wherein the computational expensive process of performing a complete search against the entire corpus must be applied.

Abstract: A method for finding sets of data (SDDs) for presentation in one-dimension, which are similar to a target SDD, is invented. The method leverages a new category of signatures, called equivalence signatures, to characterize the SDDs and is applicable to all types of data with special interpretation for data that may be presented in two-dimensions. These signatures have the salient feature that, at worst, they change in a bounded manner when small changes are made to the SDDs and when used to find SDDs that are similar to a target SDDs, they allow for a significant reduction in the number of SDDs to be compared with the target. This is an improvement over the state of the art wherein the computational expensive process of performing a complete search against the entire corpus must be applied.

Abstract: A method for finding sets of data (SDDS) for presentation in one-dimension, which are similar to a target SDD, is invented. The method leverages a new category of signatures, called equivalence signatures, to characterize the SDDs and is applicable to all types of data with special interpretation for data, such as text, binaries and audio, that may be presented in one-dimension. The equivalence signature is computed as the functional for the kinetic energy of a point particle whose path is specified by the values of the digital data. These signatures have the salient feature that, at worst, they change in a bounded manner when small changes are made to the SDDs and when used to find SDDs that are similar to a target SDDs, they allow for a significant reduction in the number of SDDs to be compared with the target. This is an improvement over the state of the art wherein the computational expensive process of performing a complete search against the entire corpus must be applied.

Abstract: A method for finding sets of data (SDDs) which are similar to a target SDD, is invented. By virtue of the isometrics on the space of equivalence signatures the lengths of the equivalence signature vectors of similar SDDs must be equal. A filter is then applied to the database of SDDs to find those SDDs that have the same length of the equivalence signatures vectors as that of the target SDD. With this, a significant reduction in the number of SDDs to be compared with the target for a final determination of similarity is obtained. This is an improvement over the state of the art wherein the computational expensive process of performing a complete search against the entire corpus must be applied.

Abstract: A method for finding sets of data (SDDs), which are similar to a target SDD, is invented. The method leverages a new category of signatures, called equivalence signatures, to characterize the SDDs and is applicable to all types of data that may be presented in two-dimensions. These signatures have the salient feature that, at worst, they change in a bounded manner when small changes are made to the SDDs and when used to find SDDs that are similar to a target SDDs, they allow for a significant reduction in the number of SDDs to be compared with the target. This is an improvement over the state of the art wherein the computational expensive process of performing a complete search against the entire corpus must be applied.

Abstract: A method for finding sets of two-dimensional data (S2DDs), which are similar to a target S2DD, is invented. The method leverages a new category of signatures, called equivalence signatures, to characterize the S2DDs. These signatures have the salient feature that, at worst, they change in a bounded manner when changes are made to the S2DD and when used to find S2DDs that are similar to a target S2DDs, they allow for a significant reduction in the number of SDDs to be compared with the target. This is an improvement over the state of the art wherein the computational expensive process of performing a complete search against the entire corpus must be applied.

Abstract: A method for finding sequences of similar data (SDDs), which are similar to a target sequence of digital data, is invented. The method leverages a new category of signatures, called equivalence signatures, to characterize the SDDs. These signatures have the salient feature that, at worst, they change in a bounded manner when changes are made to the sequence of digital data and when used to find SDDs that are similar to a target SDD, they allow for a significant reduction in the number of SDDs to be compared with the target. This is an improvement over the state of the art wherein the cryptographic message digests used as signatures respond unpredictably to changes in the sequence of digital data and the comparison of a target SDD to a corpus of SDDs requires the computational expensive process of applying a complete search against the entire corpus.

Abstract: A method for forming an output stream of data includes determining an output resolution for the output stream of data, determining an output frame rate for the output stream of data, determining an output color depth for the output stream of data, retrieving a first frame of data, a second frame of data, and a third frame of data from an input stream of data, the input stream of data having an input resolution, an input frame rate, and an input color depth, subsampling the first frame of data, the second frame of data, and the third frame of data to respectively form a first subsampled frame of data, a second subsampled frame of data, and a third subsampled frame of data, when the output resolution is lower than the input resolution, dropping the second subsampled frame of data, when the output frame rate is lower than the input frame rate, reducing color depth for the first subsampled frame of data and the second subsampled frame of data to respectively form a first reduced frame of data and a second reduced

Abstract: According to one embodiment, a circuit configured to form an output video stream includes a resolution modification circuit configured to receive a plurality of video frames from a frame buffer, and configured to modify resolution of the plurality of video frames, when the desired resolution for the output video stream is different than a resolution of the input video stream, the plurality of frames of data derived from an input video stream, a frame reducing circuit coupled to the resolution reducing circuit configured to reduce a number of video frames in the plurality of video frames from the resolution reducing circuit, when a desired frame rate for the output video stream is different than a frame rate of the input video stream, a depth reduction circuit coupled to the frame reducing circuit configured to reduce bit depth of the plurality of video frames from the frame reducing circuit, when a desired bit depth for the output video stream is different than a bit depth of the input video stream, and a rat

Abstract: According to one embodiment, a computer program product for a system including a processor includes a tangible memory including code that directs the processor to determine an output resolution for an output stream of data, code that directs the processor to determine an output frame rate for the output stream of data, code that directs the processor to determine an output color depth for the output stream of data, code that directs the processor to retrieve a first frame of data, a second frame of data, and a third frame of data from an input stream of data, the input stream of data having an input resolution, an input frame rate, and an input color depth, code that directs the processor to subsample the first frame of data, the second frame of data, and the third frame of data to respectively form a first subsampled frame of data, a second subsampled frame of data, and a third subsampled frame of data, when the output resolution is lower than the input resolution are also included, code that directs the pro