Abstract: Systems and methods for automated control of human inhabited characters. In an example, control of human inhabited character may be achieved via a plurality of input devices, including, but not limited to, a microphone, a camera, or a hand-held controller, that can modify and trigger changes in the appearance and/or the behavioral response of a character during the live interactions with humans. In an example, a computing device may include a neural network that receives the input from the microphone and/or the camera and changes the appearance and/or the behavioral response of the character according to the input. Further, input from the hand-held controller may be used to adjust a mood of the character or, in other words, emphasize or deemphasize the changes to the appearance and/or the behavioral response of the character.

Abstract: An apparatus and method for generating a three-dimensional (3D) model of cardiac anatomy including an overlay. The apparatus includes at least a processor configured receive a set of images of a cardiac anatomy pertaining to a subject, generate a set of shape parameters based on the set of images, wherein generating the set of shape parameters includes receiving cardiac geometry training data including a plurality of image sets as input correlated to a plurality of shape parameter sets as output, training a shape identification model using the cardiac geometry training data, and generating the set of shape parameters using the shape identification model, generate a 3D model of the cardiac anatomy based on the set of shape parameters, generate a map by determine a level of uncertainty at each location of a plurality of locations on the generated 3D model, and overlay the map onto the 3D model.

Abstract: The technology is generally directed to using a machine learning model to generate a visualization of an intended wearer wearing one or more source garments by preserving the appearance and shape of the intended wearer and garments, even when the pose and/or environmental details are adjusted based on natural language inputs. The model may include a plurality of layers, each conditioned to warp a different type of garment, adjust the pose of the intended wearer, adjust the environmental details in the output image, etc. The plurality of layers may allow for the garments to be warped simultaneously, as compared to sequentially. When executing the model, the model may receive input images and/or natural language inputs corresponding to the intended wearer, garments, pose, environmental details, or the like. The output of the model may be a realistic visualization of the intended wearer wearing the input garments in an intended pose.

Abstract: Polyether polyols are prepared by polymerizing one or more alkylene oxides in the presence of a Lewis acid polymerization catalyst. The reaction is performed by forming a starting reaction mixture containing a starter and catalyst. The alkylene oxide and more catalyst are then added simultaneously. continuously and separately under reaction conditions that include a temperature of at least 75° C.

Abstract: Systems, computer program products, and methods are described herein for implementing natural language processing to determine natural language from computer programming language in an electronic environment.

Abstract: Techniques are disclosed for improving the intelligence, efficiency, distribution, routing, prioritization, and/or otherwise optimization of web and/or telecommunication channels regarding website and/or contact center traffic, visitors, load, and/or resources. In one aspect, contact connection data and initial activity data of a contact is obtained that comprises information regarding the contact's activity on an unassisted channel of a service provider. Agent data of a plurality of agents at a contact center is obtained and, based on the initial activity data and the agent data, a determination is made whether to (a) direct the contact to an assisted channel of the service provider, or (b) maintain the contact on the unassisted channel of the service provider. The contact may be paired to an agent of the contact center upon receipt of a request from the contact to establish an agent connection with the contact center system.

Abstract: A method for generating a dense light detection and ranging (LiDAR) representation by a vision system of a vehicle includes generating, at a depth estimation network, a depth estimate of an environment depicted in an image captured by an image capturing sensor integrated with the vehicle. The method also includes generating, via a sparse depth network, one or more sparse depth estimates of the environment, each sparse depth estimate associated with a respective sparse representation of one or more sparse representations. The method further includes generating the dense LiDAR representation based on a dense depth estimate that is generated based on the depth estimate and the one or more sparse depth estimates. The method still further includes controlling an action of the vehicle based on the dense LiDAR representation.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may determine, for a device-to-device communication with another UE, a modulation and coding scheme (MCS) configuration, of a set of MCS configurations, based at least in part on at least one of a transmission mode, a packet characteristic, a mobility state of the UE, a capability of the UE, a transmission mode of the device-to-device communication, a received indication from the other UE, or a combination thereof. The UE may transmit, to the other UE, data using an MCS selected based at least in part on the MCS configuration. Numerous other aspects are provided.

Abstract: A method of improving usability and transparency of machine-learning aspects of applications providing various types of services is disclosed. Based on a request submitted through an administrative user interface, a data readiness check is performed on underlying data associated with the application. Based on a successful completion of the data readiness check, a configuration file is retrieved from an application server. The configuration file specifies a plurality of keys for generating a machine-learned model for the application. The machine-learned model is trained based on the plurality of keys specified in the configuration file. The machine-learned model is selected from a plurality of machine-learned models based on dry runs of the each of the plurality of models. The machine-learned model is activated with respect to the application. Scores are identified from the underlying data items based on the selected machine-learned model.

Abstract: This application discloses technology generally relating to a method for generating production-ready images using entity-specific signature information. The methods may include receiving signature inputs relating to signature elements such as logos, attributes, and target audiences. Further, the signature elements may be stored and incorporated into the image generation model to create images relevant to the specific entity. To generate the images using the entity-specific signature, the image generation model determines which signature elements to incorporate into the image based on what is most related to a generative request.

Abstract: Aspects of the present disclosure relate to waveform calculation using a hybrid evaluation and simulation approach. Using one or more processors, one or more first portions of a design that are capable of being evaluated using waveform propagation are identified. The identified one or more first portions of the design are evaluated using waveform propagation. One or more second portions of the design that are not capable of being evaluated using waveform propagation are identified. Operation of the one or more second portions of the design is simulated.

Abstract: A framework for interpreting machine learning models is proposed that utilizes interpretability methods to determine the contribution of groups of input variables to the output of the model. Input variables are grouped based on correlation with other input variables. The groups are identified by processing a training data set with a clustering algorithm. Once the groups of input variables are defined, partial dependent plot (PDP) tables for each group are calculated and stored in a memory, which are used for calculating scores related to each group of input variables for a given instance of the input vector processed by the model. Furthermore, Shapley Additive Explanations (SHAP) values for each group can be calculated by summing the SHAP values of the input variables for a given instance of an input vector per group. These scores can then be sorted, ranked for each interpretability method, and then combined into one hybrid ranking.

Abstract: A method performed by a user equipment that is in communication with a network, includes receiving, a CAG information list from the network, the CAG information list including one or more CAG IDs and an association between each of the one or more CAG IDs and one or more NPN PLMN IDs. The method includes receiving, from the network, a network name list including one or more network names and the one or more CAG IDs and an association between each of the one or more network names and the one or more CAG IDs in the CAG information list, selecting a CAG cell as a serving cell based at least on the CAG information list or the network name list, and sending to a SIM of the UE, CAG data based on the CAG information list and the network name list.

Abstract: Systems and methods for inviting an external user to join a shared collaborative channel are provided. In particular, a computing device may generate a common channel invitation for one or more users and cause the common channel invitation to be transmitted to the one or more users. The common channel invitation may include an invitation link to a shared collaborative channel and a selectable option to redeem the common channel invitation to the shared collaboration channel. The computing device is further monitor whether the one or more users access the shared collaborative channel via the invitation link, and in response to detecting that at least one of the one or more users is accessing the shared collaborative channel via the invitation link, allow the at least one user to view at least some content of the shared collaborative channel.

Abstract: Disclosed are various examples for securing enterprise resources using a virtual private network. At least one computing device that can authenticate a client device for a virtual private network (VPN) connection based on a first device identifier received from the client device and a second device identifier received from a remote management service. The at least one computing device can determine that a network event associated with the client device has been observed and execute a machine learning routine to identify a pattern of access for the client device. A network access anomaly is determined in response to a network interaction of the client device deviating from the pattern of access for the client device. A remedial action is performed based on an anomaly type associated with the network access anomaly.

Abstract: Hydroxyl-containing copolymers of butylene oxide and ethylene oxide having a hydroxyl equivalent weight of at least 150, an average of 1.8 to 6 hydroxyl groups per molecule of which hydroxyl groups at least 70% are primary hydroxyl groups and an oxyethylene content of no greater than 10% by weight based on the weight of the copolymer, are useful for making polyurethanes. These polyols are characterized by high reactivity and fast curing times. Polyurethanes made using these polyols have excellent mechanical properties and are highly hydrophobic.

Abstract: Integrating buyer and/or merchant functionality with discoverable applications is described. In an example, applications associated with a service provider can be stored in a repository associated with the service provider. At least a part of an application of the applications is associated with a particular, discrete functionality. The service provider can determine context data associated with a user computing device and can select, based at least in part on the context data, the application from the applications, wherein at least the part of the application is to be surfaced on the user computing device. The service provider can cause a user interface associated with at least the part of the application to be presented via the user computing device, wherein the user interface enables a user of the user computing device to input data for enabling the particular, discrete functionality, and wherein a portion of the context data is used to customize the user interface for the user.

Abstract: In various examples, sensor data (e.g., image and/or RADAR data) may be used to detect occupants and classify them (e.g., as children or adults) using one or more predictions that represent estimated age (e.g., based on detected limb length, a detected face) and/or detected child presence (e.g., based on detecting an occupied child seat). In some embodiments, multiple predictions generated using multiple machine learning models (and optionally one or more corresponding confidence values) may be combined using a state machine and/or one or more machine learning models to generate a combined assessment of occupant presence and/or age for each occupant and/or supported occupant slot. As such, the techniques described herein may be utilized to detect child presence, detect unattended child presence, determine age or size of a particular occupant, and/or take some responsive action (e.g., trigger an alarm, control temperature, unlock door(s), permit or disable airbag deployment, etc.).

Abstract: In various examples, image-based three-dimensional occupant assessment for in-cabin monitoring systems and applications are provided. An evaluation function may determine a 3D representation of an occupant of a machine by evaluating sensor data comprising an image frame from an optical image sensor. The 3D representation may comprise at least one characteristic representative of a size of the occupant, (e.g., a 3D pose and/or 3D shape), which may be used to derive other characteristics such as, but not limited to weight, height, and/or age. A first processing path may generate a representation of one or more features corresponding to at least a portion of the occupant based on optical image data, and a second processing path may determine a depth corresponding to the one or more features based on depth data derived from the optical image data and ground truth depth data corresponding to the interior of the machine.

Abstract: In another example, a device includes a processor and a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations. The operations include acquiring a plurality of data items from a plurality of data sources, wherein the at least two data sources data sources of the plurality of data sources are maintained by different entities, normalizing attributes of the plurality of data items, using a first machine learning technique, matching at least two data items of the plurality of data items to form a grouping, wherein the matching is based on similarities observed in the attributes of the at least two data items subsequent to the normalizing, and creating a single profile for an individual associated with the at least two data items, based on the grouping, wherein the single profile consolidates the attributes of the at least two data items.