Abstract: Networks devices interconnected via link groups may implement micro operations administration and maintenance (micro-OAM) sessions. The micro-OAM sessions may be used to monitor the status of individual links within the link groups at small time scales. The micro-OAM session may be implemented on a non-internet protocol (non-IP) layer. In some cases, independent micro-OAM sessions may be used to monitor different links within a link group.

Abstract: A toilet includes a bowl having an opening, an outlet, and two jet holes positioned above a water line defined by a weir of a trapway. The two jet holes are configured to evacuate waste from the bowl into a drain.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: A system for packet transmission over an MPLS tunnel may include a plurality of network devices. The network devices may include ingress and egress routers and intermediate routers. The system may be configured to use payload type indication labels and payload type headers to transmit packets from a plurality of user devices having different payload types and service instances over a single pseudo-wire service in a label switched path of the MPLS network.

Abstract: A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective deep neural network is trained for each of the marginal search spaces, resulting in a series of trained deep neural networks. Each of the trained deep neural networks can evaluate hypotheses in a current parameter space using discriminative classification or a regression function. An anatomical object is detected in a medical image by sequentially applying the series of trained deep neural networks to the medical image.

Abstract: A method of providing hybrid speech recognition between a local embedded speech recognition system and a remote speech recognition system relates to receiving speech from a user at a device communicating with a remote speech recognition system. The system recognizes a first part of speech by performing a first recognition of the first part of the speech with the embedded speech recognition system that accesses private user data, wherein the private user data is not available to the remote speech recognition system. The system recognizes the second part of the speech by performing a second recognition of the second part of the speech with the remote speech recognition system. The final recognition result is a combination of these two recognition processes. The private data can be such local information as a user location, a playlist, frequently dialed numbers or texted people, user contact list information, and so forth.

Abstract: A computer-implemented method for providing a see-through visualization of a patient includes receiving an image dataset representative of anatomical features of the patient acquired using a medical image scanner and acquiring a body surface model of the patient using an RGB-D sensor. The body surface model is aligned with the image dataset in a canonical/common coordinate system to yield an aligned body surface model. A relative pose of a mobile device is determined with respect to the RGB-D sensor and a pose dependent visualization of the patient is created by rendering the image dataset at a viewpoint corresponding to the relative pose of the mobile device. Then, the pose dependent visualization of the patient may be presented on the mobile device.

Abstract: Topics are determined for short text messages using an unsupervised topic model. In a training corpus created from a number of short text messages, a vocabulary of words is identified, and for each word a distributed vector representation is obtained by processing windows of the corpus having a fixed length. The corpus is modeled as a Gaussian mixture model in which Gaussian components represent topics. To determine a topic of a sample short text message, a posterior distribution over the corpus topics is obtained using the Gaussian mixture model.

Abstract: A computer-implemented method for automatically calibrating an RGB-D sensor and an imaging device using a transformation matrix includes using a medical image scanner to acquire a first dataset representative of an apparatus attached to a downward facing surface of a patient table, wherein corners of the apparatus are located at a plurality of corner locations. The plurality of corner locations are identified based on the first dataset and the RGB-D sensor is used to acquire a second dataset representative of a plurality of calibration markers displayed on an upward facing surface of the patient table at the corner locations. A plurality of calibration marker locations are identified based on the second dataset and the transformation matrix is generated by aligning the first dataset and the second dataset using the plurality of corner locations and the plurality of calibration marker locations.

Abstract: A method and system for approximating a deep neural network for anatomical object detection is discloses. A deep neural network is trained to detect an anatomical object in medical images. An approximation of the trained deep neural network is calculated that reduces the computational complexity of the trained deep neural network. The anatomical object is detected in an input medical image of a patient using the approximation of the trained deep neural network.

Abstract: A method and apparatus for X-ray tube scanner automation using a 3D camera is disclosed. An RGBD image of a patient on a patient table is received from a 3D camera mounted on an X-ray tube. A transformation between a coordinate system of the 3D camera and a coordinate system of the patient table is calculated. A patient model is estimated from the RGBD image of the patient. The X-ray tube is automatically controlled to acquire an X-ray image of a region of interest of the patient based on the patient model.

Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for bootstrapping a language translation system. A system configured to practice the method performs a bidirectional web crawl to identify a bilingual website. The system analyzes data on the bilingual website to make a classification decision about whether the root of the bilingual website is an entry point for the bilingual website. The bilingual site can contain pairs of parallel pages. Each pair can include a first website in a first language and a second website in a second language, and a first portion of the first web page corresponds to a second portion of the second web page. Then the system analyzes the first and second web pages to identify corresponding information pairs in the first and second languages, and extracts the corresponding information pairs from the first and second web pages for use in a language translation model.

Abstract: A computer-implemented method for automatically calibrating an RGB-D sensor and an imaging device using a transformation matrix includes using a medical image scanner to acquire a first dataset representative of an apparatus attached to a downward facing surface of a patient table, wherein corners of the apparatus are located at a plurality of corner locations. The plurality of corner locations are identified based on the first dataset and the RGB-D sensor is used to acquire a second dataset representative of a plurality of calibration markers displayed on an upward facing surface of the patient table at the corner locations. A plurality of calibration marker locations are identified based on the second dataset and the transformation matrix is generated by aligning the first dataset and the second dataset using the plurality of corner locations and the plurality of calibration marker locations.

Abstract: A computer-implemented method for providing a see-through visualization of a patient includes receiving an image dataset representative of anatomical features of the patient acquired using a medical image scanner and acquiring a body surface model of the patient using an RGB-D sensor. The body surface model is aligned with the image dataset in a canonical/common coordinate system to yield an aligned body surface model. A relative pose of a mobile device is determined with respect to the RGB-D sensor and a pose dependent visualization of the patient is created by rendering the image dataset at a viewpoint corresponding to the relative pose of the mobile device. Then, the pose dependent visualization of the patient may be presented on the mobile device.

Abstract: The present disclosure describes testing regression and compatibility of software and associated hardware. Embodiments herein disclosed receive an indication from a user to test the regression and the compatibility of proposed software; access software files related to installation of the proposed software; present a verification interface to the user, wherein the verification interface includes various validation functions for validating entries created or updated along with the proposed software being installed or uninstalled, such as a registry entry, a log file entry, an Add or Remove Programs entry, and the like, wherein when each of the validation functions is selected, the system automatically determines if the corresponding entry for installation or uninstallation of the proposed software is successful. Embodiments herein disclosed further receive an indication from the user for selection of the validation functions and present to the user an indication of success of the validation of the entries.

Abstract: Topics are determined for short text messages using an unsupervised topic model. In a training corpus created from a number of short text messages, a vocabulary of words is identified, and for each word a distributed vector representation is obtained by processing windows of the corpus having a fixed length. The corpus is modeled as a Gaussian mixture model in which Gaussian components represent topics. To determine a topic of a sample short text message, a posterior distribution over the corpus topics is obtained using the Gaussian mixture model.

Abstract: Embodiments of the invention are directed to a system, method, or computer program product for configuring multiple computing devices over a network using a single software application. Each of the multiple computing devices may be accessible over a network. The invention links each of the multiple computing devices to the single software application. The invention determines commands which are executable by each of the multiple computing devices to configure each of the multiple computing devices. The invention further communicates the commands based on receiving user input in the single software application. The invention then determines whether each of the multiple computing devices were correctly configured. The invention may further track the configuration process of the devices using tests and procedures. The invention tracks information related to the configuration of the devices and displays such information in report form as a result of receiving a report request in the single software application.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: Disclosed herein are cyclic nucleotide analogs, methods of synthesizing cyclic nucleotide analogs and methods of treating diseases and/or conditions such as viral infections, cancer, and/or parasitic diseases with cyclic nucleotide analogs.

Abstract: A method and apparatus for generating a 3D personalized mesh of a person from a depth camera image for medical imaging scan planning is disclosed. A depth camera image of a subject is converted to a 3D point cloud. A plurality of anatomical landmarks are detected in the 3D point cloud. A 3D avatar mesh is initialized by aligning a template mesh to the 3D point cloud based on the detected anatomical landmarks. A personalized 3D avatar mesh of the subject is generated by optimizing the 3D avatar mesh using a trained parametric deformable model (PDM). The optimization is subject to constraints that take into account clothing worn by the subject and the presence of a table on which the subject in lying.