Abstract: A computing device receives an ingest preview request to preview events to be stored by at least one indexer. Responsive to the ingest preview request, the computing device sends a subscription request to the forwarders. The forwarders receive the subscription request and intercept the events that are being sent to at least one of the indexers. The forwarders then clone matching events to the subscription request and responds to the computing device with the matching events. When the computing device receives the matching events, the computing device adds the matching events to a dispatch directory. The user interface is then populated with events in the dispatch directory.

Abstract: An autonomous cleaning robot (e.g., an autonomous vacuum) may clean an environment using a cleaning head that is self-actuated. The cleaning head includes an actuator assembly comprising an actuator configured to control rotation and vertical movement of a cleaning roller, a controller, and a cleaning roller having an elongated cylindrical length connected to the actuator assembly. The cleaning head also includes a computer processor connected to the actuator assembly and a non-transitory computer-readable storage medium that causes the computer processor to map the environment based on sensor data captured by the autonomous vacuum. The computer processor may determine an optimal height for the cleaning head based on the map and instruct the actuator assembly to adjust the height of the cleaning head.

Abstract: A lidar method and system for training and using a machine learning model are disclosed. An example method includes: obtaining a map of a background scene, the map including three-dimensional (3D) point cloud data; obtaining at least one point cloud representation of at least one foreground object, the at least one point cloud representation including 3D point cloud data, wherein one or more lidar sensors were used to generate the 3D point cloud data for the map and the at least one point cloud representation; generating a lidar scene by placing the at least one point cloud representation within the map; and training the machine learning model using the generated lidar scene. The model can be used to identify objects and control a vehicle.

Abstract: Operating a low-latency database analysis system with phrase translation may include obtaining a locale-specific phrase localization rule and a canonical phrase localization rule for a phrase, generating a locale-specific index and a locale-specific finite state machine for the locale using the localization definition data and a canonical finite state machine, generating a resolved-request by obtaining a locale-specific token representing locale-specific input data by traversing the locale-specific index, obtaining a canonical token associated with locale-specific token, obtaining a locale-specific phrase by traversing the locale-specific finite state machine, obtaining a canonical phrase corresponding to the locale-specific phrase, the canonical phrase including the canonical token, generate a data-query based on the canonical phrase, obtaining results data responsive to the data expressing the usage intent by executing a query corresponding to the data-query by an in-memory database of the low-latency datab

Abstract: The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

Abstract: A storage device includes: a storage controller to receive one or more notifications corresponding to host data transferred from a host device to the storage device over a storage interface; and a response circuit connected to the storage controller, the response circuit to trigger a response to the host device, and including: a first counter to track the one or more notifications, the one or more notifications corresponding to an entirety of the host data such that each of the notifications corresponds to a portion of the host data; a second counter to track one or more acknowledgements received from the storage controller, the one or more acknowledgments corresponding to the one or more notifications such that each of the acknowledgments corresponds to a notification; and a response trigger to select one of the first counter and the second counter to trigger the response to the host device.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed to tag cloud resources. An example apparatus includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to obtain first accesses of a cloud resource, the first accesses defining a first usage pattern associated with the cloud resource, associate the cloud resource with a business environment based on the first accesses, compare the first usage pattern to a second usage pattern corresponding to historical accesses of reference cloud resources associated with the business environment, and when the first usage pattern is different from the second usage pattern, limit second accesses of the cloud resource based on the second usage pattern.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed to generate and monitor security infrastructure. An example apparatus comprises programmable circuitry to at least one of instantiate or execute the machine readable instructions to: provision the security infrastructure based on a security infrastructure generation request; detect a global infrastructure template based on the security infrastructure generation request; and generate the security infrastructure based on the provisioned security infrastructure, the global infrastructure template, and an identified security infrastructure generation authorization, the security infrastructure generation authorization to indicate whether the generation of the security infrastructure is permitted.

Abstract: Techniques for generating and presenting search filter recommendations are disclosed. A system determines a composition of search filter tiles, including a relative number of machine learning-generated search filter tiles and non-machine learning-generated search filter tiles, based on recommendation criteria. The recommendation criteria include an amount of user search history data that exists. The system applies search filter recommendation policies to generate a set of search filter recommendations, including hard-coded search filters, search filter recommendations generated by a user-specific machine learning model, search filter recommendations generated by a machine learning model trained on data from multiple different users, and algorithm-based recommendations. The system generates different sets of search filter recommendations made up of different ratios of recommendations from different sources based on determining if search filter criteria are met.

Abstract: Disclosed herein is a method for delivery stimulation to targeted deep brain structures by supplementing existing method using external electrodes with transnasal electrodes, disposed, for example, in the olfactory cleft and within the hyenoid sinus, or, more broadly, in the nasal cavity and/or in the sinuses, including, but not limited to, the frontal, ethmoidal, sphenoid sinus, etc. The method allows stimulation and/or sensing in deep brain regions which can be inaccessible from the scalp or for which existing methods are ineffective in targeting. The method can also be used for power delivery to implants that might be placed inside the brain or on its surface.

Abstract: An autonomous cleaning robot (e.g., an autonomous vacuum) may clean an environment using a cleaning head that is self-actuated. The cleaning head includes an actuator assembly comprising an actuator configured to control rotation and vertical movement of a cleaning roller, a controller, and a cleaning roller having an elongated cylindrical length connected to the actuator assembly. The cleaning head also includes a computer processor connected to the actuator assembly and a non-transitory computer-readable storage medium that causes the computer processor to map the environment based on sensor data captured by the autonomous vacuum. The computer processor may determine an optimal height for the cleaning head based on the map and instruct the actuator assembly to adjust the height of the cleaning head.

Abstract: A traceability system for a bulk commodity supply chain is provided. The system includes a tracking device, a location determination subsystem, and at least one computing device having at least one processor. The location determination subsystem is configured to determine positional information of the tracking device while placed in a bulk commodity traveling along the bulk commodity supply chain. The processor receives the positional information from the location subsystem, extracts positional values from the positional information, and processes the positional values to identify motion primitives. A modeling tool is applied to the identified motion primitives to produce a positional path of the tracking device, which is output, for example, via a user interface. The positional path represents travel of the bulk commodity along the supply chain.

Abstract: A process is disclosed to identify the minimal set of sequential and combinational signals needed to fully reconstruct the combinational layout after emulation is complete. A minimal subset of sequential and combinational elements is output from the emulator to maximize the emulator speed and limit the utilization of emulator resources, e.g., FPGA resources. An efficient reconstruction of combinational waveforms or SAIF data is performed using a parallel computing grid.

Abstract: A lidar method and system for training and using a machine learning model are disclosed. An example method includes: obtaining a map of a background scene, the map including three-dimensional (3D) point cloud data; obtaining at least one point cloud representation of at least one foreground object, the at least one point cloud representation including 3D point cloud data, wherein one or more lidar sensors were used to generate the 3D point cloud data for the map and the at least one point cloud representation; generating a lidar scene by placing the at least one point cloud representation within the map; and training the machine learning model using the generated lidar scene. The model can be used to identify objects and control a vehicle.

Abstract: Methods and systems related to intelligent space management are described. An example method of monitoring an environment includes receiving a first image of an environment captured by a camera, receiving a point cloud of the environment generated by a lidar device, registering at least a portion of the point cloud with at least a portion of the first image, receiving, from a user device, a user selection of a zone in the environment based on a user interaction with a second image presented on the user device, the second image including at least one of a portion of the first image or a portion of the point cloud, and monitoring point cloud data collected by the lidar device for the selected zone.

Abstract: A storage device includes: a storage controller to receive one or more notifications corresponding to host data transferred from a host device to the storage device over a storage interface; and a response circuit connected to the storage controller, the response circuit to trigger a response to the host device, and including: a first counter to track the one or more notifications, the one or more notifications corresponding to an entirety of the host data such that each of the notifications corresponds to a portion of the host data; a second counter to track one or more acknowledgements received from the storage controller, the one or more acknowledgments corresponding to the one or more notifications such that each of the acknowledgments corresponds to a notification; and a response trigger to select one of the first counter and the second counter to trigger the response to the host device.

Abstract: Disclosed is an autonomous vacuum system that detects and responds to obstructions in a cleaning channel of the vacuum. Pressure sensors repeatedly measure pressure at different points in the cleaning channel, and the autonomous vacuum system analyzes the pressure measurements to determine whether an obstruction is likely present. Such a determination may be made based on, e.g., computed pressure differentials across the pressure sensors, and/or pressures of individual pressures sensors. The autonomous vacuum system can respond to the detection of an obstruction by, for example, performing a change in vacuum speed, such as ramping up to or near a maximum speed, and then decreasing again to a more normal speed.

Abstract: An autonomous vacuum includes mopping and sweeping functionalities. A magnetic locking system secures a cleaning head of the autonomous vacuum into a mopping position when in a mopping state, allowing the vacuum to move a mop roller back and forth to scrub stubborn stains on a floor surface without the cleaning head shifting into an alternate sweeping position. The autonomous vacuum also includes snorkel ducts to direct fluid waste and water from the mop roller to a waste bag without mixing the fluid waste with dry waste collected by a sweeping system. The snorkel ducts are configured to kink shut when the cleaning head is in a sweeping state, to avoid unnecessary suction through the snorkel system. Additionally, the mopping system includes cleaning logic routines that can sense and control mop roller saturation and drying, and that can perform automatic mop cleaning.

Abstract: A cleaning system may have a brush roller that moves dust and debris towards a duct opening for collection. The cleaning system may be an autonomous vacuum robot or include an autonomous vacuum robot. The cleaning system may include guards that reduce the likelihood that fibrous debris wraps around rotating portions of the brush roller (e.g., around a drive pinion). Buffer zones in the brush roller may receive fibrous debris that would otherwise wrap around the brush roller and increase undesirable friction during operation. The brush roller is configured to be unlocked from a position contacting the inner surface of a cleaning head of the cleaning system. The fibrous debris stored in the buffer zones may be suctioned into a debris collection compartment of the cleaning system after the brush roller is unlocked. The cleaning system includes a wedge to seal a cavity for ingesting debris within the cleaning head from the environment.

Abstract: A storage device includes: a storage controller to receive one or more notifications corresponding to host data transferred from a host device to the storage device over a storage interface; and a response circuit connected to the storage controller, the response circuit to trigger a response to the host device, and including: a first counter to track the one or more notifications, the one or more notifications corresponding to an entirety of the host data such that each of the notifications corresponds to a portion of the host data; a second counter to track one or more acknowledgements received from the storage controller, the one or more acknowledgments corresponding to the one or more notifications such that each of the acknowledgments corresponds to a notification; and a response trigger to select one of the first counter and the second counter to trigger the response to the host device.