A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a first action based on inputting sensor data to a deep reinforcement learning neural network and transform the first action to one or more first commands. One or more second commands can be determined by inputting the one or more first commands to control barrier functions and transforming the one or more second commands to a second action. A reward function can be determined by comparing the second action to the first action. The one or more second commands can be output.

An electronic device includes at least one processor configured to obtain user data associated with a plurality of devices from multiple data sources. The at least one processor is also configured to determine a static weight for each of the plurality of devices based on at least one source of the multiple data sources. The at least one processor is further configured to identify a portion of the plurality of devices that represents the plurality of devices based on the static weight and a dynamic weight. In addition, the at least one processor is configured to determine the dynamic weight for each of the portion of the plurality of devices while the portion of the plurality of devices is identified, where the dynamic weight is based on one or more sources of the multiple data sources.

Techniques described herein relate to a method for resource allocation using fingerprint representations of telemetry data. The method may include receiving, at a resource allocation device, a request to execute a workload; obtaining, by the resource allocation device, telemetry data associated with the workload; identifying, by the resource allocation device, a breakpoint based on the telemetry data; identifying, by the resource allocation device, a workload segment using the breakpoint; generating, by the resource allocation device, a fingerprint representation using the workload segment; performing, by the resource allocation device, a search in a fingerprint catalog using the fingerprint representation to identify a similar fingerprint; obtaining, by the resource allocation device, a resource allocation policy associated with the similar fingerprint; and performing, by the resource allocation device, a resource policy application action based on the resource allocation policy.

An apparatus for processing a narrowband speech input signal by conducting bandwidth extension of the narrowband speech input signal to obtain a wideband speech output signal according to an embodiment is provided. The apparatus includes a signal envelope extrapolator including a first neural network, wherein the first neural network is configured to receive as input values of the first neural network a plurality of samples of a signal envelope of the narrowband speech input signal, and configured to determine as output values of the first neural network a plurality of extrapolated signal envelope samples. Moreover, the apparatus includes an excitation signal extrapolator configured to receive a plurality of samples of an excitation signal of the narrowband speech input signal, and configured to determine a plurality of extrapolated excitation signal samples. Furthermore, the apparatus includes a combiner configured to generate the wideband speech output signal such that the wideband speech output signal is bandwidth extended with respect to the narrowband speech input signal depending on the plurality of extrapolated signal envelope samples and depending on the plurality of extrapolated excitation signal samples.

A text summarization system auto-generates text summarization models using a combination of neural architecture search and knowledge distillation. Given an input dataset for generating/training a text summarization model, neural architecture search is used to sample a search space to select a network architecture for the text summarization model. Knowledge distillation includes fine-tuning a language model for a given text summarization task using the input dataset, and using the fine-tuned language model as a teacher model to inform the selection of the network architecture and the training of the text summarization model. Once a text summarization model has been generated, the text summarization model can be used to generate summaries for given text.

A method for repairing a memory device with faulty memory cells. The method includes defining a RA environment comprising a location of each of the faulty memory cells and a plurality of SR and a plurality of SC. The method further includes repairing the faulty memory cells based on an RA training process using the defined RA environment and mapping of the location of each faulty memory cell with the plurality of SC or SR. The method further includes training, based on a determination that indicates the at least one faulty memory cell among the faulty memory cells is left unrepaired and the at least one SC or SR is remaining, a first NN to perform an action for repairing of the faulty memory cells such that a maximum number of faulty memory cells are reparable and a minimum number of SC and SR are utilized during the repairing.

According to some embodiments, systems and methods are provided, comprising receiving a code fragment exhibiting a static code issue; determining, via a trained exemption neural network, whether the received code fragment is exempt or not exempt from resolution; in a case it is not exempt, inputting the code fragment to a trained classification neural network; determining whether the static code issue is a syntactical static code issue or a non-syntactical static code issue; in a case it is a syntactical static code issue, inputting the code fragment to a first trained network to generate a first resolution; and in a case the static code issue is a non-syntactical static code issue, inputting the code fragment to a second trained network to generate a second resolution of the non-syntactical static code issue. Numerous other aspects are provided.

The invention is integrated heterogenous smart contracts using artificial intelligence and associated methodologies for transactions on blockchains. Embodiments of the invention are comprised of three elements. First, a user provides data through an online interface. Second, an artificial intelligence computer program processes the data to automatically preprocess and store the data in a centralized database. Third, a second artificial intelligence program interacts between the database and a blockchain to control smart contract processing.

A nervous system emulator engine includes working computational models of the vertebrate nervous system to generate lifelike animal behavior in a robot. These models include functions representing several anatomical features of the vertebrate nervous system, such as spinal cord, brainstem, basal ganglia, thalamus and cortex. The emulator engine includes a hierarchy of controllers in which controllers at higher levels accomplish goals by continuously specifying desired goals for lower-level controllers. The lowest levels of the hierarchy reflect spinal cord circuits that control muscle tension and length. Moving up the hierarchy into the brainstem and midbrain/cortex, progressively more abstract perceptual variables are controlled. The nervous system emulator engine may be used to build a robot that generates the majority of animal behavior, including human behavior. The nervous system emulator engine may also be used to build working models of nervous system functions for clinical experimentation.

A perception system is adapted to receive visual data from a camera and includes a controller having a processor and tangible, non-transitory memory on which instructions are recorded. A subsampling module, an object detection module and an attention module are each selectively executable by the controller. The controller is configured to sample an input image from the visual data to generate a rescaled whole image frame, via the subsampling module. The controller is configured to extract feature data from the rescaled whole image frame, via the object detection module. A region of interest in the rescaled whole image frame is identified, based on an output of the attention module. The controller is configured to generate a first image based on the rescaled whole image frame and a second image based on the region of interest, the second image having a higher resolution than the first image.