Abstract: Within a video frame, elements are identified. A graph is constructed for a portion of video content including the video frame. Using the graph and an excitement level corresponding to an element in the plurality of elements, the video frame is divided into an alterable region and an unalterable region. By solving an optimization problem, a compute resource and a rendering application are selected, the compute resource represented by a runtime feature vector encoding a plurality of features describing execution of the rendering application on the compute resource. Using the compute resource and the rendering application, a background image corresponding to the alterable region is rendered. The unalterable region and the background image are combined into a rendered video frame, the rendered video frame replacing the video frame within the portion of video content.

Abstract: The present inventive concept provides for a method of battery pack thermal and gaseous stress mitigation. The method includes obtaining data related to batteries within a battery pack. Features are extracted from the obtained data related to the batteries. The extracted features include effected batteries, battery positions, gas and temperature measurements, and gas and temperature thresholds. The extracted features are mapped. Effected battery patterns are identified. Space is created between the effected batteries and adjacent batteries based on the identified effected battery patterns.

Abstract: From data of a first interaction environment, user activity data while using the first interaction environment, and user biometric data while using the first interaction environment, a first cognitive load index is generated. A first directed acyclic graph corresponding to the first cognitive load index is generated. Using a graph convolutional network, the first directed acyclic graph, and a second directed acyclic graph generated from a second cognitive load index, a set of cognitive overload causation factors is generated. Using the set of cognitive overload causation factors, the first interaction environment is adjusted.

Abstract: A system can comprise a memory that stores computer executable components, and a processor, operably coupled to the memory, that executes the computer executable components comprising: an evaluating component that employs a quantum register to output a processed vector based on a first variable, of set of variables indexed over a selected dimension, and a valuation component that approximates a value function and that, based on the approximating, outputs a valuation amount representing provision of an aspect over the selected dimension, wherein the value function is a function of an inner product of a set of processing functions that are based on the set of variables, including the first variable, wherein a first processing function of the set of processing functions and of the inner product is based on the processed vector, and wherein the set of variables correspond to conditions defining the provision of the aspect.

Abstract: Using a model executing on a classical processor, a set of classical features is scored. The scored set of classical features is divided into a set of feature groups, a number of classical features in a group determined according to a qubit capability of a quantum processor. Using a model executing on the quantum processor and a group of the scored set of classical features, a set of quantum features is scored. The score of a quantum feature is adjusted according to an accuracy of the quantum data model. The scored set of classical features and the scored set of quantum features are combined according to a measure of differences between the scored set of classical features and the scored set of quantum features. Using the combined set of scored features and a first set of input data of a resource, a valuation of a resource is calculated.

Abstract: Using a model executing on a classical processor, a set of classical features is scored. The scored set of classical features is divided into a set of feature groups, a number of classical features in a group determined according to a qubit capability of a quantum processor. Using a model executing on the quantum processor and a group of the scored set of classical features, a set of quantum features is scored. The score of a quantum feature is adjusted according to an accuracy of the quantum data model. The scored set of classical features and the scored set of quantum features are combined according to a measure of differences between the scored set of classical features and the scored set of quantum features. Using the combined set of scored features and a first set of input data of a resource, a valuation of a resource is calculated.

Abstract: Provided are a computer-implemented method, a system, and a computer program product for futureproofing a machine learning model, in which historical data for updates and changes to a baseline machine learning model are received. A futureproofing metric is generated. An enhanced machine learning model comprising a futureproofed version of the baseline machine learning model is generated with the historical data and the baseline machine learning model as inputs.

Abstract: An embodiment includes selecting, using a first attribute of a first object, the first object in a first volumetric video. The embodiment also includes selecting, using a second attribute of a second object, the second object in a second volumetric video, where the first attribute and the second attribute satisfy an aggregation rule. The embodiment also includes generating an aggregated volumetric video from the first volumetric video and the second volumetric video, where the generating of the aggregated video comprises rendering the first object and the second object simultaneously in the aggregated volumetric video based on the aggregation rule.

Abstract: A computer-implemented method for orchestrating operation of multiple vehicles includes generating a single display image stream based on multiple image streams, each image stream corresponding to a respective vehicle, the vehicles being assigned to be controlled by a remote operating client. The method further includes receiving, from the remote operating client, a vehicle operation instruction in response to the single display image stream being displayed by the remote operating client. The method further includes transmitting, to at least one vehicle from the multiple vehicles, the operation instruction to manipulate operation of the at least one vehicle.

Abstract: A computer-implemented method for orchestrating operation of multiple vehicles includes detecting, based on a monitoring of a first remote operating client assigned to control the multiple vehicles, that the first remote operating client is to be replaced. Further, the method includes identifying, from multiple remote operating clients, a second remote operating client as a replacement of the first operating client, the identifying includes determining compatibility of the second remote operating client with the multiple vehicles. Further, the method includes reassigning control of the multiple the vehicles to the second remote operating client, which provides an operation instruction to manipulate operation of the multiple vehicles.

Abstract: Provided are a computer-implemented method, a system, and a computer program product for futureproofing a machine learning model, in which historical data for updates and changes to a baseline machine learning model are received. A futureproofing metric is generated. An enhanced machine learning model comprising a futureproofed version of the baseline machine learning model is generated with the historical data and the baseline machine learning model as inputs.

Abstract: Provided are techniques for enhancing silent features with adversarial networks for improved model versions. Input features are obtained. Hidden features are identified. Quantum feature importance scoring is performed to assign an importance score to each of the hidden features. Silent features are identified as the hidden features with the importance score below a first threshold. Important features are identified as the input features and as the hidden features with the importance score above a second threshold. A silent feature model is built using the silent features. An important feature model is built using the important features. An ensemble model is built with the silent feature model and the important feature model. The ensemble model is used to generate one or more predictions and one or more prescriptions.

Abstract: An embodiment includes building a hierarchical data structure using a hybrid hierarchical clustering process. The hybrid hierarchical clustering process comprises one or more iterations of a level-building process. An embodiment of the level-building process comprises building, by a classical processor, a first parent level of a current uppermost level of the hierarchical data structure by clustering classically-encoded clusters of the current uppermost level. The embodiment of the level-building process also comprises identifying, by a quantum processor, a set of candidate clustering options for clustering quantum-encoded clusters of the current uppermost level for a second parent level, including forming each of the set of candidate clustering options in parallel using respective different quantum encoding spaces. The embodiment of the level-building process also comprises building, by the classical processor, the second parent level based on a subset of the candidate clustering options.

Abstract: Aspects of the invention include systems and methods configured for hyper-personalized feature morphing of an avatar within a metaverse. A non-limiting example computer-implemented method includes predicting a next interaction for the avatar and comparing one or more current features of the avatar to one or more feature requirements for the predicted next interaction. The method further includes determining, based on the comparison, that one or more current features of the avatar do not match one or more feature requirements for the predicted next interaction. Responsive to the determination, features of the avatar are altered to satisfy the one or more feature requirements for the predicted next interaction.

Abstract: Providing a hybrid neural network architecture by training a plurality of models using a set of training data, the plurality comprising quantum models and classical models, evaluating each model using a common test data set, assigning one or more evaluation metrics to each model according to the evaluation, generating a plurality of networks, each network comprising a combination of the models, evaluating a flow of each network, selecting a network according to the flow, and providing the selected network to a user.

Abstract: A price for use of a digital asset in a set of digital assets is determined. The set of digital assets stored in a digital asset repository. A time slot during which the digital asset is available for use is determined. The digital asset is leased out at the price and during the time slot, the leasing allowing use of the digital asset during the time slot in return for payment of the price. the leased digital asset is integrated with a set of base characteristics of a virtualized user. The integrated leased digital asset is presented in a virtual environment during the time slot.

Abstract: Using a digital twin model of a user, a digital twin model of a geographical location, a digital twin model of an event venue located at the geographical location, and a plurality of digital twin models of clothing items, a product recommendation customized to the user and a planned event is generated, the planned event planned to occur at the event venue. A product recommendation depiction is generated, the product recommendation depiction comprising a depiction of the product recommendation being worn by the user at the planned event. An answer to a natural language query regarding the product recommendation depiction is generated.

Abstract: An embodiment includes building a hierarchical data structure using a hybrid hierarchical clustering process. The hybrid hierarchical clustering process comprises one or more iterations of a level-building process. An embodiment of the level-building process comprises building, by a classical processor, a first parent level of a current uppermost level of the hierarchical data structure by clustering classically-encoded clusters of the current uppermost level. The embodiment of the level-building process also comprises identifying, by a quantum processor, a set of candidate clustering options for clustering quantum-encoded clusters of the current uppermost level for a second parent level, including forming each of the set of candidate clustering options in parallel using respective different quantum encoding spaces. The embodiment of the level-building process also comprises building, by the classical processor, the second parent level based on a subset of the candidate clustering options.

Abstract: Using a model executing on a classical processor, a set of classical features is scored. The scored set of classical features is divided into a set of feature groups, a number of classical features in a group determined according to a qubit capability of a quantum processor. Using a model executing on the quantum processor and a group of the scored set of classical features, a set of quantum features is scored. The score of a quantum feature is adjusted according to an accuracy of the quantum data model. The scored set of classical features and the scored set of quantum features are combined according to a measure of differences between the scored set of classical features and the scored set of quantum features. Using the combined set of scored features and a first set of input data of a resource, a valuation of a resource is calculated.

Abstract: Using a classical data model executing on a classical processor, a set of classical features is scored. A classical feature comprises a first attribute of a resource, and a score of the classical feature comprises an evaluation of a utility of the classical feature in predicting a result involving the resource. Using a quantum data model executing on a quantum processor and the scored set of classical features, a set of quantum features is scored. The scored set of classical features and the scored set of quantum features are correlated, forming a combined set of scored features. Using the combined set of scored features and a first set of input data of a resource, a valuation of the resource is calculated.