Abstract: An exemplary method for predicting one or more adipose depots for a patient includes receiving one or more Dual-energy X-ray Absorptiometry (DEXA) scans comprising at least a portion of a torso of the patient; providing at least one or more portions of the one or more DEXA scans to a trained machine-learning model, wherein the machine-learning model is trained using a training dataset comprising: a plurality of training DEXA scans of a plurality of subjects and a plurality of corresponding Magnetic Resonance Imaging (MRI)-image-based adiposity scores of the plurality of subjects; and predicting the one or more adipose depots for the patient utilizing the trained machine-learning model.

Abstract: The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.

Abstract: Methods, apparatuses, and/or articles of manufacture may be implemented to establish an audio and/or video stream from one or more control/management computing devices utilizing an electronic communications network. The method may further include subdividing, by the instructor computing platform, the plurality of participant computing platforms into one or more virtual groups, in which a first virtual group of the one or more virtual groups acquires the audio and/or video stream and exclusively shares the audio and/or video stream generated by the one or more participants of the first virtual group between or among members of the first virtual group.

Abstract: Methods, apparatuses, and/or articles of manufacture may be implemented to establish an audio and/or video stream from one or more control/management computing devices utilizing an electronic communications network. The method may further include subdividing, by the instructor computing platform, the plurality of participant computing platforms into one or more virtual groups, in which a first virtual group of the one or more virtual groups acquires the audio and/or video stream and exclusively shares the audio and/or video stream generated by the one or more participants of the first virtual group between or among members of the first virtual group.

Abstract: Methods, apparatuses, and/or articles of manufacture may be implemented to establish an audio and/or video stream from one or more control/management computing devices utilizing an electronic communications network. The method may further include subdividing, by the instructor computing platform, the plurality of participant computing platforms into one or more virtual groups, in which a first virtual group of the one or more virtual groups acquires the audio and/or video stream and exclusively shares the audio and/or video stream generated by the one or more participants of the first virtual group between or among members of the first virtual group.

Abstract: Methods, apparatuses, and/or articles of manufacture may be implemented to establish an audio and/or video stream from one or more control/management computing devices utilizing an electronic communications network. The method may further include subdividing, by the instructor computing platform, the plurality of participant computing platforms into one or more virtual groups, in which a first virtual group of the one or more virtual groups acquires the audio and/or video stream and exclusively shares the audio and/or video stream generated by the one or more participants of the first virtual group between or among members of the first virtual group.

Abstract: Embodiments of the disclosure include implementing a ML-enabled cellular disease model for validating an intervention, identifying patient populations that are likely responders to an intervention, and developing a therapeutic structure-activity relationship screen. To generate a cellular disease model, data is combined from human genetic cohorts, from the literature, and from general-purpose cellular or tissue-level genomic data to unravel the set of factors (e.g., genetic, environmental, cellular factors) that give rise to a particular disease. In vitro cells are engineered using the set of factors to generate training data for training machine learning models that are useful for implementing cellular disease models.

Abstract: Techniques for generating an assessment question about content of an educational course include obtaining, by processing circuitry of a computer, an electronic document of content of the course and generating a parsable text version of the electronic document. Importance scores for sentences of the parsable text version of the electronic document are generated and a sentence having an importance score that exceeds a first threshold value is identified. A key phrase in the identified sentence is identified, where the identified key phrase has a key phrase score that exceeds a second threshold value, and an assessment question about the content of the course related to the identified sentence is generated, where the assessment question is based on the identified sentence but where the identified key phrase is not present in the assessment question.

Abstract: Techniques for generating an assessment question about content of an educational course include obtaining, by processing circuitry of a computer, an electronic document of content of the course and generating a parsable text version of the electronic document. Importance scores for sentences of the parsable text version of the electronic document are generated and a sentence having an importance score that exceeds a first threshold value is identified. A key phrase in the identified sentence is identified, where the identified key phrase has a key phrase score that exceeds a second threshold value, and an assessment question about the content of the course related to the identified sentence is generated, where the assessment question is based on the identified sentence but where the identified key phrase is not present in the assessment question.

Abstract: Techniques of providing references to students of massive open online courses (MOOCs) involve automatically providing references based on semantic content of queries generated within a MOOC. Along these lines, a computer browser in which a user interacts with a MOOC may generate queries for additional reference material to supplement its content. For example, the browser may generate a query based on the results of an exam taken by a student in order to provide additional help in areas where the student did not do well. When the query is received by a reference generating server, the reference generating server computes similarity scores indicating a measure if similarity between keyword elements of the query and keyword elements of reference documents. The reference generating server then sends references to the student based on the similarity scores.

Abstract: Systems and methods can mine structured clinical event data in an electronic health record (EHR) system to determine patient outcomes. Mining the structured clinical event data instead of or in addition to mining discharge summaries can increase the accuracy of patient outcome identification. Sophisticated language models can be used to extract outcomes from discharge summaries while also inferring outcomes from cues or hints contained in the structured clinical event data. For example, the clinical event data can include information regarding treatments and medications prescribed by clinicians to specifically manage patient complications; thus, presence or absence of relevant treatments in the clinical event data can provide independent indicators to disambiguate cases where current language processing approaches fail.

Abstract: Motion capture animation, shape completion and markerless motion capture methods are provided. A pose deformation space model encoding variability in pose is learnt from a three-dimensional (3D) dataset. Body shape deformation space model encoding variability in pose and shape is learnt from another 3D dataset. The learnt pose model is combined with the learnt body shape model. For motion capture animation, given parameter set, the combined model generates a 3D shape surface of a body in a pose and shape. For shape completion, given partial surface of a body defined as 3D points, the combined model generates a 3D surface model in the combined spaces that fits the 3D points. For markerless motion capture, given 3D information of a body, the combined model traces the movement of the body using the combined spaces that fits the 3D information or reconstructing the body's shape or deformations that fits the 3D information.

Abstract: Methods, systems, devices and arrangements are implemented for motion tracking. One such system for tracking at least one object articulated in three-dimensional space is implemented using data obtained from a depth sensor. The system includes at least one processing circuit configured and arranged to determine location probabilities for a plurality of object parts by identifying, from image data obtained from the depth sensor, features of the object parts. The processing circuit selects a set of poses for the at least one object based upon the determined location probabilities and generates modeled depth sensor data by applying the selected set of poses to a model of the at least one object. The processing circuit selects a pose for the at least one object model-based based upon a probabilistic comparison between the data obtained from the depth sensor and the modeled depth sensor data.

Abstract: A method for finding similar search terms (word matching) is provided. The method receives a query of at least a first word and automatically applies the first word to a vocabulary amplifier. The vocabulary amplifier retrieves an associated word for the first word and accesses one or more database sources to retrieve an associated word. Each associated word is presented to an output interface. The amplifier then receives information from an input interface for the associated word and classifies the associated word based upon the received characterizing information.

Abstract: Motion capture animation, shape completion and markerless motion capture methods are provided. A pose deformation space model encoding variability in pose is learnt from a three-dimensional (3D) dataset. Body shape deformation space model encoding variability in pose and shape is learnt from another 3D dataset. The learnt pose model is combined with the learnt body shape model. For motion capture animation, given parameter set, the combined model generates a 3D shape surface of a body in a pose and shape. For shape completion, given partial surface of a body defined as 3D points, the combined model generates a 3D surface model in the combined spaces that fits the 3D points. For markerless motion capture, given 3D information of a body, the combined model traces the movement of the body using the combined spaces that fits the 3D information or reconstructing the body's shape or deformations that fits the 3D information.

Abstract: The invention comprises a method and apparatus for learning probabilistic models (PRM's) with attribute uncertainty. A PRM with attribute uncertainty defines a probability distribution over instantiations of a database. A learned PRM is useful for discovering interesting patterns and dependencies in the data. Unlike many existing techniques, the process is data-driven rather than hypothesis driven. This makes the technique particularly well-suited for exploratory data analysis. In addition, the invention comprises a method and apparatus for handling link uncertainty in PRM's. Link uncertainty is uncertainty over which entities are related in our domain. The invention comprises of two mechanisms for modeling link uncertainty: reference uncertainty and existence uncertainty. The invention includes learning algorithms for each form of link uncertainty. The third component of the invention is a technique for performing database selectivity estimation using probabilistic relational models.