Abstract: Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of “virtual revascularization” of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.

Abstract: An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

Abstract: Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of “virtual revascularization” of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.

Abstract: An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

Abstract: In an example embodiment, a system is provided whereby a machine learning model is trained to predict a standardization for a given raw title. A neural network may be trained whose input is a raw title (such as a query string) and a list of candidate titles (either title identifications in a taxonomy, or English strings), which produces a probability that the raw title and each candidate belong to the same title. The model is able to standardize titles in any language included in the training data without first having to perform language identification or normalization of the title. Additionally, the model is able to benefit from the existence of “loan words” (words adopted from a foreign language with little or no modification) and relations between languages.

Abstract: An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

Abstract: An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

Abstract: Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of “virtual revascularization” of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.

Abstract: In an example embodiment, a first DCNN is trained to output a value for a first metric by inputting a plurality of sample documents to the first DCNN, with each of the sample documents having been labeled with a value for the first metric. Then a plurality of possible transformations of a first input document are fed to the first DCNN, obtaining a value for the first metric for each of the plurality of possible transformations. A first transformation is selected from the plurality of possible transformations based on the values for the first metric for each of the plurality of possible transformations. Then a second DCNN is trained to output a transformation for a document by inputting the selected first transformation to the second DCNN. The second input document is fed to the second DCNN, obtaining a second transformation of the second input document.

Abstract: Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of “virtual revascularization” of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.

Abstract: Apparatuses, computer readable medium, and methods are disclosed for generating a database of clustered companies. The apparatus, computer readable medium, and methods may include comparing companies offering jobs with one another to determine a plurality of pairs of companies, determining a parent company and a child company for each of the plurality of pairs of companies to generate a plurality of pairs of parent-child companies, combining pairs of the plurality of pairs of parent-child companies to generate a plurality of clusters of companies, and storing the plurality of clusters of companies in a company database.

Abstract: In an example embodiment, a machine learning algorithm is used to train an objective prediction model to output a prediction value for an input member of a social networking service and a potential objective, based on member attribute information and action information. At prediction time, member attribute information and action information for a first user may be fed to the objective prediction model to obtain prediction values for a plurality of different potential objectives, one of which can be selected based on the prediction values. The selected objective can then be used to optimize coordinates, in a latent representation space, mapped to a plurality of different entities in a social network structure.

Abstract: An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

Abstract: In an example embodiment, a solution that automatically predicts an industry for a candidate company is provided. An existing industry classifier is trained using a first machine learning algorithm, the first machine learning algorithm taking as input first training data and existing industries listed in an industry taxonomy. A new industry classifier is trained using a second machine learning algorithm, the second machine learning algorithm taking as input second training data and new industries listed in an industry taxonomy. Then the candidate company is fed into the existing industry classifier, producing one or more predicted existing industries corresponding to the candidate company. The candidate company is also fed into the new industry classifier, producing one or more predicted new industries corresponding to the candidate company. One or more final predicted industries are selected from among the one or more predicted existing industries and the one or more predicted new industries.

Abstract: An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a stenosis determiner configured to receive a computerized model of a plurality of vascular segments of a patient, and analyze the model to determine locations of potential lesions. The example device further includes a vascular state score calculator configured to, for each potential lesion, determine a vascular state scoring tool (“VSST”) score based on at least one of a size of the potential lesion, a distance of the potential lesion from a branch point in the plurality of vascular segments, and a distance of the potential lesion to an adjacent potential lesion. The example device also includes a user interface configured to display the VSST scores for the potential lesions.

Abstract: Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of “virtual revascularization” of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.

Abstract: In an example embodiment, a system is provided whereby a machine learning model is trained to predict a standardization for a given raw title. A neural network may be trained whose input is a raw title (such as a query string) and a list of candidate titles (either title identifications in a taxonomy, or English strings), which produces a probability that the raw title and each candidate belong to the same title. The model is able to standardize titles in any language included in the training data without first having to perform language identification or normalization of the title. Additionally, the model is able to benefit from the existence of “loan words” (words adopted from a foreign language with little or no modification) and relations between languages.

Abstract: In an example embodiment, for each of a plurality of different titles in a social network structure, the title is mapped into a first vector having n coordinates, while kills are mapped into a second vector having n coordinates. The first and second vectors are stored in a deep representation data structure. One or more objective functions are applied to at least one combination of two or more of the vectors in the deep representation data structure. Then, an optimization test on each of the at least one combination is performed using a corresponding objective function output for each of the at least one combination of two or more of the vectors, and, for any combination that did not pass the optimization test, one or more coordinates for the vectors in the combination are altered so that the vectors in the combination become closer together within an n-dimensional space.

Abstract: In an example embodiment, for each of a plurality of different entities in a social network structure, the entity is mapped into a vector having n coordinates. The vector for each of the plurality of different entities is stored in a deep representation data structure. One or more objective functions are applied to at least one combination of two or more of the vectors in the deep representation data structure. Then, an optimization test on each of the at least one combination of two or more of the vectors is performed using a corresponding objective function output for each of the at least one combination of two or more of the vectors, and, for any combination that did not pass the optimization test, one or more coordinates for the vectors in the combination are altered so that the vectors in the combination become closer together within an n-dimensional space.

Abstract: Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of “virtual revascularization” of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.