Abstract: A method for generating a synthesis voice is provided, which is performed by one or more processors, and includes acquiring a text-to-speech synthesis model trained to generate a synthesis voice for a training text, based on reference voice data and a training style tag represented by natural language, receiving a target text, acquiring a style tag represented by natural language, and inputting the style tag and the target text into the text-to-speech synthesis model and acquiring a synthesis voice for the target text reflecting voice style features related to the style tag.

Abstract: A negative active material composite, a method of preparing the same, and a negative electrode and a rechargeable lithium battery including the same, the negative active material composite including compound particles represented by SiOx, in which 0<x?2.0; silicon nanoparticles having an average particle diameter (D50) of greater than 0 nm and less than or equal to about 200 nm; and amorphous carbon, wherein an internal pore volume of the negative active material composite is greater than 0 cm3/g and less than or equal to about 5.0×10?2 cm3/g.

Abstract: A method of providing an object image includes collecting a global image and an additional image of a predetermined space using a fixed camera and a tracking camera, displaying broadcast contents including the collected global image and additional image on a display, receiving an input of an object of interest selected by a user from the broadcast contents displayed on the display, and analyzing the object of interest selected from the broadcast contents and displaying the additional image mapped to the global image configuring the broadcast contents on the display.

Abstract: The performance of traditional speech recognition systems (as applied to information extraction or translation) decreases significantly with, larger domain size, scarce training data as well as under noisy environmental conditions. This invention mitigates these problems through the introduction of a novel predictive feature extraction method which combines linguistic and statistical information for representation of information embedded in a noisy source language. The predictive features are combined with text classifiers to map the noisy text to one of the semantically or functionally similar groups. The features used by the classifier can be syntactic, semantic, and statistical.

Abstract: Method, system, and programs for mapping data. Information related to users and their interests is obtained by a first application from a second application. An interest space is determined based on one or more sources of information, each of which provides a plurality of concepts. A data structure is created with respect to the interest space, where the data structure has a plurality of attributes each of the attributes corresponds to a concept in the interest space. One or more interests for each of the users based on information obtained from the second application. Each user interest corresponds to an attribute in the structure. A user profile is generated for each user by mapping the interests of the user to the corresponding attributes in the structure.

Abstract: An online article is enhanced by displaying, in association with the article, supplemental content that includes entities that are extracted from the article and/or entities that are related to entities that are extracted from the article. The supplemental content further includes information about each of the entities. The information about an entity may be obtained by searching for the entity in one or more searchable repositories of data. For example, the supplemental content may include, for each entity, video, image, web, and/or news search results. The supplemental content may further include information such as stock quotes, abstracts, maps, scores, and so on. The entities are selected using a variety of analysis and ranking techniques based on contextual factors such as user-specific information, time-sensitive popularity trends, grammatical features, search result quality, and so on. The entities may further be selected for purposes such as generating ad-based revenue.

Abstract: An image correcting method includes capturing projected images, generating first captured images based on the capturing, analyzing a number of pixels depending on color intensities of colors of the first captured images, and correcting the first captured images based on the analyzing.

Abstract: The performance of traditional speech recognition systems (as applied to information extraction or translation) decreases significantly with, larger domain size, scarce training data as well as under noisy environmental conditions. This invention mitigates these problems through the introduction of a novel predictive feature extraction method which combines linguistic and statistical information for representation of information embedded in a noisy source language. The predictive features are combined with text classifiers to map the noisy text to one of the semantically or functionally similar groups. The features used by the classifier can be syntactic, semantic, and statistical.

Abstract: Method, system, and programs for mapping data. Information related to users and their interests is obtained by a first application from a second application. An interest space is determined based on one or more sources of information, each of which provides a plurality of concepts. A data structure is created with respect to the interest space, where the data structure has a plurality of attributes each of the attributes corresponds to a concept in the interest space. One or more interests for each of the users based on information obtained from the second application. Each user interest corresponds to an attribute in the structure. A user profile is generated for each user by mapping the interests of the user to the corresponding attributes in the structure.

Abstract: A method for constructing an image includes acquiring image data in a first domain. The acquired image data is transformed from the first domain into a second domain in which the acquired image data exhibits a high degree of sparsity. An initial set of transform coefficients is approximated for transforming the image data from the second domain into a third domain in which the image may be displayed. The approximated initial set of transform coefficients is updated based on a weighing of where substantial transform coefficients are likely to be located relative to the initial set of transform coefficients. An image is constructed in the third domain based on the updated set of transform coefficients. The constructed image is displayed.

Abstract: An online article is enhanced by displaying, in association with the article, supplemental content that includes entities that are extracted from the article and/or entities that are related to entities that are extracted from the article. The supplemental content further includes information about each of the entities. The information about an entity may be obtained by searching for the entity in one or more searchable repositories of data. For example, the supplemental content may include, for each entity, video, image, web, and/or news search results. The supplemental content may further include information such as stock quotes, abstracts, maps, scores, and so on. The entities are selected using a variety of analyses and ranking techniques based on contextual factors such as user-specific information, time-sensitive popularity trends, grammatical features, search result quality, and so on. The entities may further be selected for purposes such as generating ad-based revenue.

Abstract: An online article is enhanced by displaying, in association with the article, supplemental content that includes entities that are extracted from the article and/or entities that are related to entities that are extracted from the article. The supplemental content further includes information about each of the entities. The information about an entity may be obtained by searching for the entity in one or more searchable repositories of data. For example, the supplemental content may include, for each entity, video, image, web, and/or news search results. The supplemental content may further include information such as stock quotes, abstracts, maps, scores, and so on. The entities are selected using a variety of analyses and ranking techniques based on contextual factors such as user-specific information, time-sensitive popularity trends, grammatical features, search result quality, and so on. The entities may further be selected for purposes such as generating ad-based revenue.

Abstract: The performance of traditional speech recognition systems (as applied to information extraction or translation) decreases significantly with, larger domain size, scarce training data as well as under noisy environmental conditions. This invention mitigates these problems through the introduction of a novel predictive feature extraction method which combines linguistic and statistical information for representation of information embedded in a noisy source language. The predictive features are combined with text classifiers to map the noisy text to one of the semantically or functionally similar groups. The features used by the classifier can be syntactic, semantic, and statistical.

Abstract: A method for constructing an image includes acquiring image data in a sensing domain, transforming the acquired image data into a sparse domain, approximating sparse coefficients based on the transformed acquired image data, performing a Bayes Least Squares estimation on the sparse coefficients based on Gaussian Scale Mixtures Model to generate weights, approximating updated sparse coefficients by using the weights and acquired image, constructing an image based on the updated sparse coefficients, and displaying the constructed image.

Abstract: A content-providing entity receives a relatively short text from a user and attempts to determine, automatically, based on that short text (and on other available clues), a language that the user can read and understand. The content-providing entity may then provide, to the user, documents that are written in the determined language. The content-providing entity may determine a language of the input text based on several factors in combination: (a) the service provider's “market,” which is determined based on at least a portion of the URL of the Internet site to which the user directed his browser; (b) the user's “region,” which is determined based on the source Internet Protocol (IP) address of the IP packets that the user sends to the Internet site; (c) the “script” in which the short user-entered text is written; and (d) a statistical analysis of the frequency of the characters present in the short user-entered text.

Abstract: A method for reconstructing a digital image from a set of measurements includes providing a previous image frame in a time series of measurements of an image signal and a current image frame in the time series, calculating an estimated motion vector for a spatial point and current time point between the previous and current image frames, calculating a motion compensated current image frame from the previous image frame, estimating a known support set of a sparse signal estimate of the motion compensated current image frame where the support set comprises indices of non-zero elements of the sparse signal estimate, calculating a sparse signal corresponding to the current image frame whose support contains a smallest number of new additions to the known support set while satisfying a data consistency constraint, and correcting the motion compensated current image frame image frame from the sparse signal.

Abstract: The present invention adopts the fundamental architecture of a statistical machine translation system which utilizes statistical models learned from the training data and does not require expert knowledge for rule-based machine translation systems. Out of the training parallel data, a certain amount of sentence pairs are selected for manual alignment. These sentences are aligned at the phrase level instead of at the word level. Depending on the size of the training data, the optimal amount for manual alignment may vary. The alignment is done using an alignment tool with a graphical user interface which is convenient and intuitive to the users. Manually aligned data are then utilized to improve the automatic word alignment component. Model combination methods are also introduced to improve the accuracy and the coverage of statistical models for the task of statistical machine translation.

Abstract: A method for reconstructing a digital image from a set of measurements includes providing a previous image frame in a time series of measurements of an image signal and a current image frame in the time series, calculating an estimated motion vector for a spatial point and current time point between the previous and current image frames, calculating a motion compensated current image frame from the previous image frame, estimating a known support set of a sparse signal estimate of the motion compensated current image frame where the support set comprises indices of non-zero elements of the sparse signal estimate, calculating a sparse signal corresponding to the current image frame whose support contains a smallest number of new additions to the known support set while satisfying a data consistency constraint, and correcting the motion compensated current image frame image frame from the sparse signal.

Abstract: A method for constructing an image includes acquiring image data in a sensing domain, transforming the acquired image data into a sparse domain, approximating sparse coefficients based on the transformed acquired image data, performing a Bayes Least Squares estimation on the sparse coefficients based on Gaussian Scale Mixtures Model to generate weights, approximating updated sparse coefficients by using the weights and acquired image, constructing an image based on the updated sparse coefficients, and displaying the constructed image.

Abstract: An online article is enhanced by displaying, in association with the article, supplemental content that includes entities that are extracted from the article and/or entities that are related to entities that are extracted from the article. The supplemental content further includes information about each of the entities. The information about an entity may be obtained by searching for the entity in one or more searchable repositories of data. For example, the supplemental content may include, for each entity, video, image, web, and/or news search results. The supplemental content may further include information such as stock quotes, abstracts, maps, scores, and so on. The entities are selected using a variety of analyses and ranking techniques based on contextual factors such as user-specific information, time-sensitive popularity trends, grammatical features, search result quality, and so on. The entities may further be selected for purposes such as generating ad-based revenue.