Abstract: A method for managing range of an electric vehicle includes acquiring temperature forecast data for an upcoming time period and determining a range of the electric vehicle over the upcoming time period in view of the temperature forecast data. The method also includes determining a driving distance from the electric vehicle to a stationary electric vehicle charging station. Additionally, the method includes informing a user of the electric vehicle if, at one or more times during the upcoming time period, the range of the electric vehicle will be less than a threshold relative to the driving distance from the electric vehicle to the stationary electric vehicle charging station. Further, another method for managing range of an electric vehicle may take into account distance to one or more intended destinations.

Abstract: A method is described which includes receiving a number of object images of an object. Each object image corresponds to a different view direction. First and second object images correspond to first and second directions. A mesh is determined corresponding to the target region of the object surface based on a first subset of the number of object images. Diffuse and specular maps are determined corresponding to the target region of the object surface based on processing a second subset of the object images using a deep learning neural network model trained to estimate diffuse and specular albedo components based on an input image. A tangent normal map is determined corresponding to the target region of the object surface based on high-pass filtering each object image of the second subset. The method also includes storing and/or outputting the mesh, the diffuse map, the specular map and the tangent normal map.

Abstract: Embodiments of the disclosure disclose a method for applying an effect in a video by an electronic device. The method includes: detecting a first object and a second object in an image frame of the video; determining a type of motion of the first object and the second object in video; determining a speed of the motion of the first object and the second object in the video; determining a first effect to be applied to the first object and a second effect to be applied to the second object based on the type of motion of the first object and the second object and the speed of the motion of the first object and the second object; and applying the first effect to the first object and the second effect to the second object.

Abstract: Provided are systems, methods, and computer-readable medium for identifying security risks in applications executing in a cloud environment. In various implementations, a security monitoring and management system can obtain application data from a service provider system. The application data can include a record of actions performed by an application during use of the application by users associated with a tenant. The application executes in a service platform provided for the tenant by the service provider system. In various implementations, the application data is analyzed to identify an event associated with a security risk, where the event is identified from one or more actions performed by the application. The system can determine an action to perform in response to identifying the event. In various examples, an agent executing on the service platform can add instrumentation codes used by the application, where the instrumentation provides the application data.

Abstract: A system control processor manager uses composed information handling systems that utilize resource sets of information handling systems and an infrastructure manager. The infrastructure manager obtains a composition request for a composed information handling system; allocates a portion of resource sets to the composed information handling system using a telemetry data map; makes a determination that at least one of the portion of the allocated resource sets is hosted by an information handling system that does not include a physical system control processor; and in response to the determination: provides the information handling system with access to a system control processor without adding any physical system control processors to the information handling system; and directs access requests, by entities hosted by the information handling system and directed to the portion of the allocated resource sets, through the system control processor.

Abstract: An elastic request handling technique limits a number of threads used to service input/output (I/O) requests of a low-latency I/O workload received by a file system server executing on a cluster having a plurality of nodes deployed in a virtualization environment. The limited number of threads (server threads) is constantly maintained as “active” and running on virtual central processing units (vCPUs) of a node. The file system server spawns and organizes the active server threads as one or more pools of threads. The server prioritizes the low-latency I/O requests by loading them onto the active threads and allowing the requests to run on those active threads to completion, thereby obviating overhead associated with lock contention and vCPU migration after a context switch (i.e., to avoid rescheduling a thread on a different vCPU after execution of the thread was suspended).

Abstract: A system for a multi-chambered electrochemical cell for carbon dioxide removal includes an electrochemical cell. The electrochemical cell includes an anodic chamber at a first end of the electrochemical cell, which includes an anode. The electrochemical cell includes an acid swing chamber adjacent to the anodic chamber and separated by a cation exchange membrane. The electrochemical cell includes a desalination chamber separated from the acid swing chamber by a anion exchange membrane. The electrochemical cell includes a base swing chamber in fluid communication with the acid swing chamber and the desalination chamber. The base swing chamber is adjacent to the desalination chamber and separated by a cation exchange membrane. The electrochemical cell includes a cathodic chamber at a second end of the electrochemical cell, the cathodic chamber comprising a cathode, wherein the cathodic chamber is adjacent to the base swing chamber and separated by a second anion exchange membrane.

Abstract: Multi-object tracking in autonomous vehicles uses both camera data and LiDAR data for training, but not LiDAR data at query time. Thus, no LiDAR sensor is on a piloted autonomous vehicle. Example systems and methods rely on camera 2D object detections alone, rather than 3D annotations. Example systems/methods utilize a single network that is given a camera image as input and can learn both object detection and dense depth in a multimodal regression setting, where the ground truth LiDAR data is used only at training time to compute depth regression loss. The network uses the camera image alone as input at test time (i.e., when deployed for piloting an autonomous vehicle) and can predict both object detections and dense depth of the scene. LiDAR is only used for data acquisition and is not required for drawing 3D annotations or for piloting the vehicle.

Abstract: Methods and systems are provided for predicting reward liability data of reward programs. A method includes accessing, by a server system, historical reward related data associated with one or more reward programs administered by a reward program provider of reward program providers. The historical reward related data includes past redeemed reward points for each reward program aggregated on a particular time basis. Method includes identifying first seasonality patterns and second seasonality patterns associated with the historical reward related data. Method includes training a reward liability prediction model based on first and second seasonality patterns, wherein the trained time-series prediction model is configured to predict future reward liability data associated with the one or more reward programs.

Abstract: Methods and systems provide access to acquired waveforms from a test and measurement instrument for multiple users. A method includes storing acquired waveforms from the instrument in a cloud-based platform and rendering, on a display of a first remote user device, a timeline illustrating in chronological order each acquired waveform from the instrument in response to the acquired waveform being stored in the cloud-based platform. An acquired waveform on the timeline is selected for viewing and at least one user-selectable feature view configured to display corresponding characteristics of the selected acquired waveform. A file from the cloud-based platform is received including data for the at least one user-selectable feature view and the configurable viewing window including the at least one user-selectable feature view using the file received is rendered on the first remote user device. The configurable viewing window may be shared with at least one other remote user device.

Abstract: An autonomous off-road vehicle (AOV) includes a vehicle body and a vehicle arm coupled to the vehicle body and including a set of prongs and a clamp extending outward from an end of the vehicle arm. A controller causes the vehicle arm to pick up a pile by: aligning the set of prongs with an end of the pile such that a first prong aligns with an opening on a first side of a web of the pile and a second prong aligns with a space on a second side of the web, inserting the set of prongs into the opening on the first side and the space on the second side, and compressing the clamp into an exterior surface of a flange of the pile such that the set of prongs are reciprocally compressed into an interior surface of the flange on either side of the web.

Abstract: A computer-implemented method comprises obtaining a source image and a target image, determining a first point cluster by processing the source image using a machine learning mode, determining a second point cluster by processing the target image using the machine learning model, uplifting the first point cluster to obtain an uplifted first point cluster, uplifting the second point cluster to obtain an uplifted second point cluster, estimating a transformation that maps the uplifted first point cluster to the uplifted second point cluster, applying the estimated transformation to the uplifted first point cluster to obtain a candidate reconstruction of the uplifted second point cluster, and updating the machine learning model to reduce a loss function including a term penalizing a difference between the uplifted second point cluster and the candidate reconstruction of the uplifted second point cluster.

Abstract: Processes comprising: heat treating a heavy hydrocarbon feedstock in a heat treatment unit to produce a first effluent comprising a heat treated product; at least partially removing a mixture of gas and distillate from the first effluent in a first separation unit to produce a second effluent comprising a separation bottom product; deasphalting the second effluent in a second separation unit in the presence of a first solvent to produce: a soluble product fraction comprising a first portion of the first solvent, a deasphalted oil (DAO) product, and a first pitch product; an insoluble product fraction comprising a second portion of the first solvent and a portion of the first pitch product; and at least partially removing the second portion of the first solvent from the first pitch product in a third separation unit to produce a purified pitch product.

Abstract: A computer-implemented method includes selecting an image from a set of images, processing the image using a detector portion of a machine learning model to determine a point cluster comprising points representing candidate keypoints and a set of uncertainties, each uncertainty corresponding to a respective point in the point cluster. Includes using the determined point cluster and the determined set of uncertainties to generate a differentiable map representing the point cluster and a spatial variation of uncertainty associated with the point cluster, and processing the differentiable map and a portion of the image using a decoder portion of the model to generate a candidate reconstructed image. Includes updating the model to reduce a loss function comprising a term penalizing a difference between at least one representation of the image and a corresponding at least one representation of the candidate reconstructed image.

Abstract: A data platform for providing large scale data discovery with near real time data cataloguing and detailed lineage. Data is received from one or more sources, and the received data is written to a data storage systems. The received data is processed at the data storage systems using sensors at the data storage systems. Metadata and data definitions associated with the data are sent to a data service in near real-time. A data catalog is generated at the data service based on the metadata and data definitions associated with the data. Data lineage for the data is created at the data service. The data catalog and data lineage are presented at a data discovery interface.

Abstract: A modelling platform comprising at least one processor in communication with at least one memory device and a payment processor is disclosed. The at least one processor is programmed to apply one or more data fields of an authorization request message received from a payment processor as one or more inputs to at least one trained machine learning model to generate a first output and a second output. The at least one processor is further programmed to transmit, to an issuer computing device, in real-time as part of an enhanced authorization request message, the first output and the second output. The enhanced authorization request message instructs the issuer computing device to cause a value to be displayed that is viewable by the account holder based upon the first output for a period of time that is based upon the second output.

Abstract: An event trigger to flush key-value data from a volatile memory device is detected. In response to detection of the event trigger, a determination is made whether the key-value data comprises uncommitted data for one or more active database transactions. If the key-value data comprises uncommitted data for one or more active database transactions, the uncommitted data is moved to a hold aside buffer in the volatile memory device, and committed data in the key-value data is stored on a non-volatile memory device.

Abstract: Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and/or request bodies, structuring user-generated content, and/or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and/or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Abstract: Systems and methods are disclosed for determining portions of media assets for cooperative reception by multiple devices. The disclosed techniques herein include identifying cooperating devices that are able to communicate with a selected device for relaying portions of a media asset. For example, a group of people each travelling together in a vehicle may wish to view a media asset by cooperatively using their network resources of their smartphones to relay portions of the media asset for viewing. Network service data for each of the plurality of cooperating devices is then received. Based on the network service data, a determination is made for each cooperating device regarding reception of a respective portion of the media asset to each cooperating device. Instructions to receive and relay the respective portions of the media asset are transmitted to each cooperating device.

Abstract: A first plurality of center points of first two-dimensional bounding boxes corresponding to a vehicle occurring in a first plurality of images acquired by a first camera can be determined. A second plurality of center points of second two-dimensional bounding boxes corresponding to the vehicle occurring in a second plurality of images acquired by a second camera can also be determined. A plurality of non-linear equations based on the locations of the first and second pluralities of center points and first and second camera parameters corresponding to the first and second cameras can be determined. The plurality of non-linear equations can be solved simultaneously for the locations of the vehicle with respect to the first and second cameras and the six degree of freedom pose of the second camera with respect to the first camera.