Abstract: A system and method that include receiving seismic survey data, the seismic survey data including a seismic geological property. The system and method also include receiving resistivity sensor data from a downhole resistivity sensor, the resistivity sensor data being for a reference length uphole of a reference depth. The system and method additionally include determining a sensed geological property of a geological feature in the reference length, the sensed geological property being determined based on the resistivity sensor data. The system and method also include determining a covariance of the sensed geological property and the seismic geological property and applying an uncertainty model to the sensed geological property and the covariance of the sensed geological property, the uncertainty model generating an output including an uncertainty distribution of a projected geological property downhole of the reference depth.

Abstract: Systems and methods for mitigating certain spoofing of vehicle features are disclosed herein. An example method can include determining input torque values obtained from a steering torque sensor associated with a steering wheel of a vehicle, wherein the input torque values are obtained over a period of time, determining road disturbances using a road disturbance model, applying a driver model that is indicative of human driver hands-on-wheel behaviors, determining when input torque values are indicative of a spoof or human interaction with the steering wheel using the input torque values, the road disturbance model, and the driver model, and executing a remediating measure when the input torque values are indicative of the spoof.

Abstract: A geological projection system may receive seismic survey data, the seismic survey data including a seismic surface of a geological feature. A geological projection system may receive resistivity sensor data from a downhole resistivity sensor, the resistivity sensor data being for a reference length uphole of a reference depth. A geological projection system may generate a sensed surface over the reference length using the resistivity sensor data. A geological projection system may generate a displacement field of a difference between the sensed surface and the seismic surface for the reference length. A geological projection system may apply an uncertainty model to at least one of the resistivity sensor data, the seismic survey data, or the displacement field, the uncertainty model generating an output including an uncertainty distribution of a projected surface of the geological feature downhole of the reference depth.

Abstract: The disclosure is generally directed to systems and methods associated with communicating privacy rights pertaining to data captured from a vehicle. An example method executed by a processor of a data capture apparatus in a vehicle can include capturing an image. In an example scenario, the image may include an individual who is located outside the vehicle. Furthermore, in some cases, the image can be a part of a video clip containing multiple images. The method further includes generating an identifier for identifying the image(s). The identifier can be a machine-readable symbol such as, for example, a QR-code symbol or a barcode. A notification that includes the identifier may be generated and conveyed to the individual for use by the individual to exercise his/her rights to privacy with respect to the images in which he/she is present.

Abstract: Removal of PII is provided. Sensor data is captured using sensors of a vehicle. Object detection is performed on the sensor data to create a sematic labeling of objects in the sensor data. A model is utilized to classify regions of the sensor data with a public or private labeling according to the sematic labeling and a PII filter corresponding to a jurisdiction of a current location of the vehicle. The sensor data is utilized in accordance with the public or private labeling.

Abstract: The present disclosure describes the engineering of microbial cells for fermentative production of histamine and provides novel engineered microbial cells and cultures, as well as related histamine production methods.

Abstract: A system is disclosed that includes a computer that includes a processor and a memory, the memory including instructions executable by the processor to acquire real world sensor data from a mobile platform for a first time period. The real world sensor data from the mobile platform can be input to a first neural network to predict sensor data of the mobile platform for a second time period that is prior to the first time period. The predicted sensor data and the real world sensor data can be input to a simulation of the mobile platform; wherein the simulation outputs predicted real world operation of the mobile platform based on the predicted sensor data for the second time period and the real world sensor data for the first time period.

Abstract: Rider identification systems and methods using motion sensing are disclosed herein. An example method includes obtaining a location of a mobile device associated with an individual, obtaining sensor data from the mobile device that includes a first motion profile, the sensor data also including environment information around the location, generating a motion model for the individual using the environment information, generating a second motion profile using the motion model, comparing the second motion profile to the first motion profile, and confirming when the second motion profile matches the first motion profile to confirm that the individual is at the location.

Abstract: A method for training a machine learning model. The method includes: determining a plurality of training sequences of training-input data elements, wherein for each training sequence each training-input data element contains sensor data for a time point from a time period assigned to the training sequence in which a prespecified event takes place at least once at one or more respective event time points; determining, for each training-input data element, the temporal distance between the time point for which the training-input data element contains sensor data and one of the one or more respective event time points; and training the machine learning model depending on the determined temporal distances.

Abstract: Methods for managing non-player characters and power centers in a computer game are based on character hierarchies and individualized correspondences between each character's traits or rank and events that involve other non-player characters or objects. Players may share power centers, character hierarchies, non-player characters, and related quests involving the shared objects with other players playing separate and unrelated game instances over a computer network, with the outcome of the quests reflected in different the games. Various configurations of game machines are used to implement the methods.

Abstract: The disclosure is generally directed to systems and methods for detecting that the vehicle is in a reverse mode, receiving a road surface topology, determining a feature of interest is within a collected image and outside a displayed field of view (FOV) for a rear-facing camera due to the road surface topology, adjusting the displayed FOV for the rear-facing camera to place the feature of interest within the displayed FOV. Receiving a reverse mode indication may be over a controller area network (CAN) bus in the vehicle. Receiving a road surface topology includes receiving an estimate of the road surface topology via at least one of monocular depth estimation, photogrammetric range imaging using structure from motion (SFM), multi-view stereo, imaging radar, lidar and a sensor system on the vehicle.

Abstract: A method for generating additional training data for training a machine learning algorithm is disclosed. The method includes (i) providing training data for training the machine learning algorithm, wherein the training data includes labeled sensor data from at least one sensor, (ii) transforming the training data for training the machine learning algorithm in a graph structure, wherein nodes in the graph structure represent objects represented in the corresponding sensor data, and wherein a starting node of the graph structure represents the position of the at least one sensor with respect to the objects represented in the corresponding sensor data, and (iii) generating additional training data for training the machine learning model by modifying the graph structure.

Abstract: The present disclosure relates to a system and a method for addressing an error in a localization system that includes monitoring a plurality of sensors of a driver assistance system in real-time, with each sensor generating a data stream. The method further includes identifying a sensor having an anomalous data stream and calculating a primary localization and a backup localization. The primary localization calculation includes the anomalous data stream and the backup localization calculation does not include the anomalous data stream. Further, the method includes executing an action when the backup localization error estimate exceeds a threshold.

Abstract: An object can be detected in contact with a steering wheel of a vehicle. A first image of the steering wheel at a first angle of rotation and a second image of the steering wheel at a second angle of rotation are then obtained. A type of the object is identified based on at least one of the first image or the second image, as one of a human hand or a foreign object. A vehicle component is controlled based on the type of object.

Abstract: An electrochemical additive manufacturing method includes positioning a build plate into an electrolyte solution. The conductive layer comprises at least one conductive-layer segment forming a pattern corresponding with a component. The method further comprises connecting the at least one conductive-layer segment and one or more deposition anodes to a power source. The one or more deposition anodes correspond with at least a portion of the pattern formed by the at least one conductive-layer segment. The method additionally comprises transmitting electrical energy from the power source through the one or more deposition anodes of the plurality of deposition anodes corresponding with the at least the portion of the pattern formed by the at least one conductive-layer segment, through the electrolyte solution, and to the at least one conductive-layer segment, such that material is deposited onto the at least one conductive-layer segment and forms at least a portion of the component.

Abstract: Handling personally identifiable information (PII) in data streams is provided. Processed sensor data is received, from a plurality of vehicles including sensors capturing raw sensor data, the raw sensor data including captured PII and non-PII. The processed sensor data includes simulated PII created based on the captured PII and one or more layers of the captured PII corresponding to the simulated PII. A request is received from a client device for a portion of the processed sensor data. Access keys corresponding to the request are identified. A result is constructed according to the access keys using the processed sensor data. The constructed result is sent to the client device responsive to the request.