Abstract: A refining method of a midbody of latamoxef sodium includes: dissolving 7?-amino-7?-methoxy-3-(5-tetrazolyl)thiomethyl-1-oxa-3-cephem-4-carboxylic acid in dichloromethane, and then dividing into a dichloromethane layer and a water layer; extracting the dichloromethane layer, adding a NaHCO3 solution to stir, and remaining an organic layer after stratification; stirring, crystallizing and filtering the organic layer in turn, to obtain a filtrate, preparing a second powder by stirring the filtrate under conditions of a catalyst and a normal temperature; preparing a first powder by extracting the water layer, dropping the hydrochloric acid to adjust a pH value thereof, performing crystal cultivation and suction filtration after performing cooling, and then performing vacuum drying; combining the first powder and the second powder to prepare the midbody of latamoxef sodium: 7?3-amino-7?-methoxy-3-(5-tetrazolyl)thiomethyl-1-oxa-3-cephem-4-carboxylic acid, which has a yield of 95.6-96.8% and a purity of 99.2-99.5%.

Abstract: A refining method of a midbody of latamoxef sodium includes: dissolving 7?-amino-7?-methoxy-3-(5-tetrazolyl)thiomethyl-1-oxa-3-cephem-4-carboxylic acid in dichloromethane, and then dividing into a dichloromethane layer and a water layer; extracting the dichloromethane layer, adding a NaHCO3 solution to stir, and remaining an organic layer after stratification; stirring, crystallizing and filtering the organic layer in turn, to obtain a filtrate, preparing a second powder by stirring the filtrate under conditions of a catalyst and a normal temperature; preparing a first powder by extracting the water layer, dropping the hydrochloric acid to adjust a pH value thereof, performing crystal cultivation and suction filtration after performing cooling, and then performing vacuum drying; combining the first powder and the second powder to prepare the midbody of latamoxef sodium: 7?3-amino-7?-methoxy-3-(5-tetrazolyl)thiomethyl-1-oxa-3-cephem-4-carboxylic acid, which has a yield of 95.6-96.8% and a purity of 99.2-99.5%.

Abstract: A corpus of documents (and other data objects) stored for an entity can be analyzed to determine one or more topics for each document. Elements of the documents can be analyzed to also assign a risk score. The types of topics and security elements, and the associated risk scores, can be learned and adapted over time using, for example, a topic model and random forest regressor. Activity with respect to the documents is monitored, and expected behavior for a user determined using a trained recurrent neural network. Ongoing user activity is processed to determine whether the activity excessively deviates from the expected user activity. The activity can also be compared against the activity of user peers to determine whether the activity is also anomalous among the user peer group. For anomalous activity, risk scores of the accessed documents can be analyzed to determine whether to generate an alert.

Abstract: A corpus of documents (and other data objects) stored for an entity can be analyzed to determine one or more topics for each document. Elements of the documents can be analyzed to also assign a risk score. The types of topics and security elements, and the associated risk scores, can be learned and adapted over time using, for example, a topic model and random forest regressor. Activity with respect to the documents is monitored, and expected behavior for a user determined using a trained recurrent neural network. Ongoing user activity is processed to determine whether the activity excessively deviates from the expected user activity. The activity can also be compared against the activity of user peers to determine whether the activity is also anomalous among the user peer group. For anomalous activity, risk scores of the accessed documents can be analyzed to determine whether to generate an alert.

Abstract: A corpus of documents (and other data objects) stored for an entity can be analyzed to determine one or more topics for each document. Elements of the documents can be analyzed to also assign a risk score. The types of topics and security elements, and the associated risk scores, can be learned and adapted over time using, for example, a topic model and random forest regressor. Activity with respect to the documents is monitored, and expected behavior for a user determined using a trained recurrent neural network. Ongoing user activity is processed to determine whether the activity excessively deviates from the expected user activity. The activity can also be compared against the activity of user peers to determine whether the activity is also anomalous among the user peer group. For anomalous activity, risk scores of the accessed documents can be analyzed to determine whether to generate an alert.

Abstract: The subject disclosure is generally directed towards eye gaze detection based upon both eyes being fixated on the same gaze location. In one aspect, an image including a subject's left and right eyes is captured, from which left and right glint and pupil center information are extracted. The left and right glint data and the left and right pupil data are used with left and right bias correction matrices to jointly determine a gaze location.

Abstract: A corpus of documents (and other data objects) stored for an entity can be analyzed to determine one or more topics for each document. Elements of the documents can be analyzed to also assign a risk score. The types of topics and security elements, and the associated risk scores, can be learned and adapted over time using, for example, a topic model and random forest regressor. Activity with respect to the documents is monitored, and expected behavior for a user determined using a trained recurrent neural network. Ongoing user activity is processed to determine whether the activity excessively deviates from the expected user activity. The activity can also be compared against the activity of user peers to determine whether the activity is also anomalous among the user peer group. For anomalous activity, risk scores of the accessed documents can be analyzed to determine whether to generate an alert.

Abstract: Examples are disclosed herein that are related to gaze tracking via image data. One example provides, on a gaze tracking system comprising an image sensor, a method of determining a gaze direction, the method comprising acquiring image data via the image sensor, detecting in the image data facial features of a human subject, determining an eye rotation center based upon the facial features using a calibrated face model, determining an estimated position of a center of a lens of an eye from the image data, determining an optical axis based upon the eye rotation center and the estimated position of the center of the lens, determining a visual axis by applying an adjustment to the optical axis, determining the gaze direction based upon the visual axis, and providing an output based upon the gaze direction.

Abstract: The subject disclosure is directed towards eye gaze detection based upon multiple cameras and/or light sources along with an adaptive homography mapping model. Learning of the model includes compensating for spatially-varying gaze errors and head pose dependent errors simultaneously in a unified framework. Aspects including training the model of adaptive homography offline using simulated data at various head positions.

Abstract: The subject disclosure is generally directed towards eye gaze detection based upon both eyes being fixated on the same gaze location. In one aspect, an image including a subject's left and right eyes is captured, from which left and right glint and pupil center information are extracted. The left and right glint data and the left and right pupil data are used with left and right bias correction matrices to jointly determine a gaze location.

Abstract: The subject disclosure is generally directed towards eye gaze detection based upon both eyes being fixated on the same gaze location. In one aspect, an image including a subject's left and right eyes is captured, from which left and right glint and pupil center information are extracted. The left and right glint data and the left and right pupil data are used with left and right bias correction matrices to jointly determine a gaze location.

Abstract: The subject disclosure is directed towards eye gaze detection based upon multiple cameras and/or light sources. The cameras and/or light sources are configured to provide eye gaze detection for a device display at different orientations, at different tilt angles, at different user positions, at different user distances, and so on. Also described is a controller that selectively controls light source power and camera on/off state to provide images of the eye having sufficient quality for eye gaze detection and/or to conserve power.

Abstract: The subject disclosure is directed towards eye gaze detection based upon multiple cameras and/or light sources. The cameras and/or light sources are configured to provide eye gaze detection for a device display at different orientations, at different tilt angles, at different user positions, at different user distances, and so on. Also described is a controller that selectively controls light source power and camera on/off state to provide images of the eye having sufficient quality for eye gaze detection and/or to conserve power.

Abstract: Examples are disclosed herein that are related to gaze tracking via image data. One example provides, on a gaze tracking system comprising an image sensor, a method of determining a gaze direction, the method comprising acquiring image data via the image sensor, detecting in the image data facial features of a human subject, determining an eye rotation center based upon the facial features using a calibrated face model, determining an estimated position of a center of a lens of an eye from the image data, determining an optical axis based upon the eye rotation center and the estimated position of the center of the lens, determining a visual axis by applying an adjustment to the optical axis, determining the gaze direction based upon the visual axis, and providing an output based upon the gaze direction.

Abstract: The subject disclosure is directed towards eye gaze detection based upon multiple cameras and/or light sources along with an adaptive homography mapping model. Learning of the model includes compensating for spatially-varying gaze errors and head pose dependent errors simultaneously in a unified framework. Aspects including training the model of adaptive homography offline using simulated data at various head positions.

Abstract: The subject disclosure is generally directed towards eye gaze detection based upon both eyes being fixated on the same gaze location. In one aspect, an image including a subject's left and right eyes is captured, from which left and right glint and pupil center information are extracted. The left and right glint data and the left and right pupil data are used with left and right bias correction matrices to jointly determine a gaze location.

Abstract: The subject disclosure is directed towards eye gaze detection based upon multiple cameras and/or light sources. The cameras and/or light sources are configured to provide eye gaze detection for a device display at different orientations, at different tilt angles, at different user positions, at different user distances, and so on. Also described is a controller that selectively controls light source power and camera on/off state to provide images of the eye having sufficient quality for eye gaze detection and/or to conserve power.

Abstract: A method, system, and computer-readable storage media for model based video projection are provided herein. The method includes tracking an object within a video based on a three-dimensional parametric model via a computing device and projecting the video onto the three-dimensional parametric model. The method also includes updating a texture map corresponding to the object within the video and rendering a three-dimensional video of the object from any of a number of viewpoints by loosely coupling the three-dimensional parametric model and the updated texture map.

Abstract: Multiple images including a face presented by a user are accessed. One or more determinations are made based on the multiple images, such as a determination of whether the face included in the multiple images is a 3-dimensional structure or a flat surface and/or a determination of whether motion is present in one or more face components (e.g., eyes or mouth). If it is determined that the face included in the multiple images is a 3-dimensional structure or that that motion is present in the one or more face components, then an indication is provided that the user can be authenticated. However, if it is determined that the face included in the multiple images is a flat surface or that motion is not present in the one or more face components, then an indication is provided that the user cannot be authenticated.

Abstract: A multi-party spatial audio conferencing system is configured to receive far end signals from remote participants. The system comprises a speaker array that outputs spatialized sound signals and one or more microphones that capture and relay a sound signal comprising an echo of the spatialized sound signal to a multichannel acoustic echo cancellation (MC-AEC) unit having a plurality of echo cancellers. Respective echo cancellers perform cancellation of an echo associated with a far end signal from one of the multiple participants according to an algorithm based upon echo cancellation coefficients. The echo cancellation coefficients are determined from the input channel signals, the spatialization parameters associated with each input channel, and the audio signals captured by the microphones. This allows respective echo cancellation filters to be updated simultaneously even though the corresponding remote participant is not talking.