Abstract: Systems, methods, and other embodiments associated with a bot communication system for communicating between channels and bots are described. In one embodiment, a system includes a bot manager configured to provide communication between an individual bot from a plurality of bots and a target channel of a plurality of channels. The bot manager is configured to process a bot response from the individual bot to the target channel by at least: identifying channel configuration metadata from a channel metadata repository that defines a channel dependent format for communicating data with the target channel; converting the bot response to the channel dependent format associated with the target channel including formatting data components from the bot response according to the channel configuration metadata of the target channel; and transmitting, via a network connection, the converted bot response to the target channel in the channel dependent format of the target channel.

Abstract: Systems and methods for continuous monitoring of the behavior of animals, such as small rodents, are provided. Monitoring can include video, audio, and other sensor modalities. In one embodiment, the system can include cameras, arena design, environmental sensors, and ultrasonic sensors. The system uniquely provides a continuous long-term monitoring system suitable for mouse behavioral study. Further provided is a neural network based tracker configured for use with video data acquired by the monitoring system. Three different neural network architectures have been tested to determine their performance on genetically diverse mice under varying environmental conditions. It has been observed that that an encoder-decoder segmentation neural network achieves high accuracy and speed with minimal training data.

Abstract: A device may receive training data that includes datasets associated with natural language processing, and may mask the training data to generate masked training data. The device may train a masked event C-BERT model, with the masked training data, to generate pretrained weights and a trained masked event C-BERT model, and may train an event aware C-BERT model, with the training data and the pretrained weights, to generate a trained event aware C-BERT model. The device may receive natural language text data identifying natural language events, and may process the natural language text data, with the trained masked event C-BERT model, to determine weights. The device may process the natural language text data and the weights, with the trained event aware C-BERT model, to predict causality relationships between the natural language events, and may perform actions, based on the causality relationships.

Abstract: A method, a set-top box, and a non-transitory computer readable medium for content switching and content control based on multi-user profiles. The method includes: capturing, by the set-top box, the facial information of two or more users of the plurality of users viewing content on a media play device, the media play device configured to display content from the set-top box; sending, by the set-top box, the captured facial information of the two or more users of the plurality of user viewing content on the media play device to the server; and receiving, on the set-top box, recommended content for the two or more users viewing the media play device based on a multi-user profile for the two or more users from the plurality of user profiles.

Abstract: Disclosed herein are short interfering nucleic acid (siNA) molecules comprising modified nucleotides and uses thereof. The siNA molecules may be double stranded and comprise modified nucleotides selected from 2?-O-methyl nucleotides and 2?-fluoro nucleotides. Further disclosed herein are siNA molecules comprising (a) a phosphorylation blocker, conjugated moiety, or 5?-stabilized end cap; and (b) a short interfering nucleic acid (siNA).

Abstract: The present disclosure provides for the division of environmental sounds from speech sounds to extract and analyze the behaviors and activities occurring in the environment; thus expanding and improving the functionality of AI assistant devices. These environmental sounds can be securely uploaded to an electronic chart, and may be used to aid in the treatment of existing conditions or the prophylaxis/mitigation of conditions not yet experienced by a patient under observation. Accordingly, the present disclosure provides for improved functionality in assistant devices and devices linked to the AI assistant devices.

Abstract: The disclosure relates to system and method for generating recommendations for capturing images of a real-life object with essential features. The method includes detecting an Augmented Reality (AR) plane for a target object. The method further includes capturing a set of poses corresponding to the target object and a set of coordinate points in the AR plane. The set of poses includes a tracking marker, and the set of coordinate points indicates a location of the target object. The method further includes determining an instant distance between the AR imaging device and the target object, and an instant angle of the AR imaging device with respect to the target object. The method further includes dynamically generating the recommendations for adjusting a position and an orientation of the AR imaging device with respect to the target object.

Abstract: Techniques for predicting caregiver retention using machine learning (ML) are discussed. These techniques include predicting an impact of one or more caregiver tasks on continued employment of the caregiver with a care provider, including determining a plurality of intermediate prediction scores relating to characteristics for the caregiver using one or more first ML models trained to determine intermediate prediction scores. The techniques further include determining a retention prediction for the caregiver using the plurality of intermediate prediction scores, including: generating the retention prediction by providing the plurality of intermediate prediction scores to a second ML model trained to determine the retention prediction based on intermediate prediction scores.

Abstract: Certain aspects of the present disclosure provide techniques for predicting electronic health record data using ML. This includes determining a plurality of attributes of a textual description of a patient medical examination, including: detecting the plurality of attributes based on analyzing the textual description using a first ML model trained to parse patient textual descriptions. This further includes predicting a change to an electronic health record for the patient, including: providing to a second ML model the plurality of attributes of the textual description and patient medical data for the patient, where the second ML model is trained to predict changes to patient electronic health records based on attributes of textual data relating to the patient and patient medical data. The predicted change is provided to an electronic system to change the electronic health record and affect medical treatment for the patient.

Abstract: Example multi-channel display systems are disclosed. An example apparatus includes at least one processor to execute instructions to at least in response to determining that a first display controller is to provide at least a first portion of video data on a first channel, provide a first control signal to the first display controller instructing the first display controller to provide the at least the first portion of the video data on the first channel, and in response to determining that a second display controller is to provide at least a second portion of the video data on a second channel, provide a second control signal to the second display controller instructing the second display controller to provide the at least the second portion of the video data on the second channel, the second channel different from the first channel.

Abstract: Techniques for improved machine learning are provided. Resident data describing a first condition of a first resident of a residential care facility is received. An optimized treatment plan is selected for the first resident by extracting a first plurality of resident attributes, from the resident data, generating a first approach to remediate the first condition, and generating a first predicted recovery score by inputting the first approach and the first plurality of resident attributes into a trained machine learning model. The optimized treatment plan is implemented for the first resident.

Abstract: Techniques for improved computer modeling are provided. Resident data describing a resident of a healthcare facility is received, and a set of resident attributes corresponding to a defined set of features is extracted from the resident data. An acuity score is generated for the resident by processing the set of resident attributes using an acuity model, the acuity model specifying a respective weight for each respective feature in the defined set of features. One or more interventions are initiated for the resident based on the acuity score.

Abstract: Techniques for improved machine learning are provided. User data describing a user is received, and a set of user attributes corresponding to a defined set of features is extracted from the user data. A risk score is generated by processing the set of user attributes using a trained machine learning model, where the risk score indicates a probability that the user has or will develop depression. One or more interventions are initiated for the user based on the risk score.

Abstract: Techniques for improved machine learning are provided. Patient data describing a patient is received, and a medication to be evaluated with respect to the patient is identified. An efficacy score is generated by processing at least a subset of the patient data using a hybrid machine learning model comprising a static portion and a dynamic portion, where the efficacy score indicates predicted efficacy of the medication for the patient. The medication is provided for the patient based at least in part on the efficacy score.

Abstract: Embodiments of the present disclosure relate to parallel mask rule checking on evolving mask shapes in optical proximity correction (OPC) flows for integrated circuit designs. Systems and methods are disclosed that perform mask (manufacturing) rule checks (MRC) in parallel, sharing information to maintain symmetry when violations are corrected. In an embodiment the shared information is also used to minimize changes to the geometric area of proposed mask shapes resulting from the OPC. In contrast to conventional systems, MRC is performed for multiple edges in parallel, sharing information between the different edges to encourage symmetry. In an embodiment, all edges may be adjusted in parallel to reduce mask-edge traversal bias.

Abstract: The disclosure relates to system and method for providing assistance to a user using augmented reality. The method includes acquiring a video stream and a set of data associated with a task being performed by a user, in real-time, using a camera and/or a sensor device. The video stream includes sequential frames. The method further includes determining a present state associated with the task based on the sequential frames using an Artificial Neural Network (ANN) based action prediction model; determining scenarios and events corresponding to the scenarios based on the video stream and the set of data using an ANN based augmented intelligence model; and determining sequential instructions required for assisting the user to accomplish the task, dynamically, based on the present state and the events associated with the task, using at least one of a rule-based engine and an ANN based instruction prediction model.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: Systems, methods, and other embodiments associated with automatically expanding channels for bots are described. In one embodiment, a system includes a bot repository configured to identify each bot of a plurality of bots available in the computing system. A channel metadata repository is configured with channel configuration metadata for each channel of a plurality of channels available to the plurality of bots. The channel configuration metadata includes metadata that defines a specified format for communicating data with a target channel. A bot manager is configured to provide communication between each bot of the plurality of bots and each channel of the plurality of channels by at least converting output responses from each bot based on the channel configuration metadata associated with a target channel.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: Systems and methods for evaluating reliability of a model are disclosed, including a processor that may include a data augmentor and a model evaluator. The data augmentor may receive a task data pertaining to information related to a pre-defined task to be performed by the model. The data augmentor may augment the task data to obtain an augmented aspect data. The model evaluator may evaluate a trained model based on the augmented aspect data to obtain aspect evaluation metrics. The model may be an artificial intelligence (AI) model that may be trained using the task data. The evaluation may enable to assess performance of the trained model by computing a performance score based on the aspect evaluation metrics. The performance score may help evaluate the reliability of the model in a pre-defined domain.