Abstract: Generally, the present disclosure is directed to user interface understanding. More particularly, the present disclosure relates to training and utilization of machine-learned models for user interface prediction and/or generation. A machine-learned interface Nprediction model can be pre-trained using a variety of pre-training tasks for eventual downstream task training and utilization (e.g., interface prediction, interface generation, etc.).

Abstract: Disclosed herein are microorganism and methods that can be used for the synthesis of cannabigerolic acid (CBGA) and cannabinoids. The methods disclosed can be used to produce CBGA, ?9-tetrahydrocannabinolic acid (THCA), cannabidiolic acid (CBDA), cannabichromenic acid (CBCA), ?9-tetrahydrocannabinol (THC), cannabidiol (CBD), cannabichromene (CBC). Enzymes useful for the synthesis of CBGA and cannabinoids, include but are not limited to acyl activating enzyme (AAE1), polyketide synthase (PKS), olivetolic acid cyclase (OAC), prenyltransferase (PT), THCA synthase (THCAS), CBDA synthase (CBDAS), CBCA synthase (CBCAS), HMG-Co reductase (HMG1), and/or farnesyl pyrophosphate synthetase (ERG20). The microorganisms can also have one or more genes disrupted, such as gene that that controls beta oxidation of long chain fatty acids.

Abstract: The system can include a set of joints, a controller, and a model engine; and can optionally include a support structure and an end effector. Joints can include: a motor, a transmission mechanism, an input sensor, and an output sensor. The system can enable articulation of the plurality of joints.

Abstract: Disclosed are systems, apparatuses, processes, and computer-readable media to provide haptic feedback in electronic devices based on contextual awareness. A method of processing image data includes receiving at least one image of a patient; providing the image of the patient to a trained model for segmenting the image; and processing the image based on a multi-stage trained model to segment the image into a plurality of segmented images that are separated based on plaque components.

Abstract: A method of and system for human interactions with a computer user interface using neurological signals from the frontal part of the human brain, also known as the prefrontal cortex. In one embodiment, the system comprised a brain-computer interface system and a method of filtering, processing and analyzing neurological signals over a specific period of time to trigger, activate or control on-demand a computer user interface without the need of any preliminary brain state recording nor traditionally-required external stimulus.

Abstract: A method for aborting a command for PCIe based NVMe SSD includes receiving an abort command in an Admin submission queue to abort a target command present in an I/O submission queue of host or in an I/O queue of the NVMe SSD and updating a tail doorbell of NVMe doorbell registers of the NVMe SSD after receiving the abort command. The abort command includes a slot of the target command to be aborted. Thereafter, the method includes placing the abort command into an Admin queue of the NVMe SSD and executing the abort command using the slot of the target command to be aborted by updating an I/O completion queue of the host with the target command to be aborted. The method further includes updating a head doorbell of the doorbell registers of the NVMe SSD and updating the abort command in an Admin completion queue of the host.

Abstract: Generally, the present disclosure is directed to user interface understanding. More particularly, the present disclosure relates to training and utilization of machine-learned models for user interface prediction and/or generation. A machine-learned interface prediction model can be pre-trained using a variety of pre-training tasks for eventual downstream task training and utilization (e.g., interface prediction, interface generation, etc.).

Abstract: The present disclosure relates to a method of producing metallurgical coke from a combination of non-coking and non-metallic carbon-based microwave susceptor. The method is energy efficient, economical, and environmentally friendly. The present disclosure also relates to metallurgical coke having improved coke quality, such as improved coke strength after reaction.

Abstract: A vehicle includes a primary axle powered by an actuator and a secondary axle powered by a motor and including a wheel and a clutch selectively coupling the wheel to the motor via mating components. A controller is electrically connected to the clutch and the motor. The controller is programmed to, in response to an engagement of the clutch being unsuccessful within a first duration of time, command a series of speeds to the motor based on wheel speed and an alternating offset that changes between positive and negative signs at predefined periods so that relative speeds between the mating components oscillate due to the alternating offset to jiggle the clutch into engagement.

Abstract: The present disclosure relates to synthetic biology and, in particular, the bioproduction of bakuchiol, and engineered enzymes for producing the same.

Abstract: Generally, the present disclosure is directed to user interface understanding. More particularly, the present disclosure relates to training and utilization of machine-learned models for user interface prediction and/or generation. A machine-learned interface prediction model can be pre-trained using a variety of pre-training tasks for eventual downstream task training and utilization (e.g., interface prediction, interface generation, etc.).

Abstract: A method includes receiving data indicating a plurality of dimensions of a manufactured device. The method further includes providing the data to a trained machine learning model. The method further includes receiving, from the trained machine learning model, a synthetic microscopy image associated with the manufactured device, wherein the synthetic microscopy image is generated in view of the first data. The method further includes performing at least one of (i) outputting the synthetic microscopy image to a display or (ii) performing one or more operations on the synthetic microscopy image.

Abstract: An analytics system identifies interventions for individual samples from a set of samples with a mixture of interventions. Given a causal graph, a set of baseline samples, and a set of samples with interventions, a set of intervention tuples is determined that represents the mixture of interventions for the set of samples with interventions. Each intervention tuple in the set of intervention tuples identifies an intervention and a mixing coefficient representing a percentage of samples with the intervention. An iterative process is used in which a set of intervention tuples is determined for N variables and then lifted to a set of intervention tuples for N+1 variables until all variables from the causal graph have been considered, providing a final set of intervention tuples. The final set of intervention tuples is used to match individual samples from the set of samples with interventions to interventions.

Abstract: A method for aborting a command in PCIe based NVMe SSD includes receiving an abort command in an Admin submission queue to abort a target command present in an I/O submission queue of host or in an I/O queue of the NVMe SSD and updating a tail doorbell of NVMe doorbell registers of the NVMe SSD after receiving the abort command. The abort command includes a slot of the target command to be aborted. Thereafter, the method includes placing the abort command into an Admin queue of the NVMe SSD and executing the abort command using the slot of the target command to be aborted by updating an I/O completion queue of the host with the target command to be aborted. The method further includes updating a head doorbell of the doorbell registers of the NVMe SSD and updating the abort command in an Admin completion queue of the host.

Abstract: The invention relates to methods of diagnosing, prognosing, or monitoring or staging the progression of non-alcoholic fatty liver disease (NAFLD) using biomarkers. The invention also relates to a method of scoring to determine the severity of NAFLD, and a method of treating NAFLD.

Abstract: A method includes determining a plurality of features of a first original image of a first product that are expected to be different for one or more products to be produced via manufacturing parameters of a manufacturing process compared to the first product. The method further includes adjusting one or more of the plurality of features of the first original image to generate a first synthetic image. The method further includes providing a plurality of images including the first original image and the first synthetic image to train a machine learning model to generate a trained machine learning model configured to generate output associated with updating the manufacturing parameters of the manufacturing process.

Abstract: A method includes: receiving, by a storage device from a core in a host, a request to provide exclusive resource to a command of a predefined submission queue of a non-volatile memory (NVM) set in the storage device, wherein the request pertains to operating the NVM set in a deterministic state; generating a virtual NVM set identifier for a virtual NVM set based on a predefined mapping of the predefined submission queue and the NVM set; determining a storage controller associated with the NVM set based on a predefined mapping of the predefined submission queue, the NVM set, and the virtual NVM set identifier; enabling at least one core to operate in a Predictable Latency Mode; and operating the storage controller and the NVM set in the deterministic state by allocating predetermined resources to execute the command and return data with a predictable latency.

Abstract: There is disclosed a system for transmitting digital data with error detection, the system comprising a sender, configured to receive source data and to send transfer data, and a receiver configured to receive the transfer data and to output result data, wherein the sender is further configured to receive the source data, to numerically multiply the source data by an integer number greater than 2, and to output the multiplied source data as the transfer data, and wherein the receiver is further configured to receive the transfer data, to check if dividing the transfer data by the integer number results in an integer result, and, if the checking fails, to output an error indication, and, if the checking succeeds, to output the transfer data divided by the integer number as the result data. Also, a corresponding method is disclosed.

Abstract: Generally, the present disclosure is directed to systems and methods for performing task-oriented response generation that can provide advantages for artificial intelligence systems or other computing systems that include natural language processing for interpreting user input. Example implementations can process natural language descriptions of various services that can be accessed by the system. In response to a natural language input, systems can identify relevant values for executing one of the service(s), based in part on comparing embedded representations of the natural language input and the natural language description using a machine learned model.

Abstract: A transcription service may receive a request from a developer to build a custom speech-to-text model for a specific domain of speech. The custom speech-to-text model for the specific domain may replace a general speech-to-text model or add to a set of one or more speech-to-text models available for transcribing speech. The transcription service may receive a training data and instructions representing tasks. The transcription service may determine respective schedules for executing the instructions based at least in part on dependencies between the tasks. The transcription service may execute the instructions according to the respective schedules to train a speech-to-text model for a specific domain using the training data set. The transcription service may deploy the trained speech-to-text model as part of a network-accessible service for an end user to convert audio in the specific domain into texts.