Abstract: A system for training a machine learning framework to estimate depths of objects captured in 2-D images includes a first trained machine learning network and a second untrained or minimally trained machine learning framework. The first trained machine learning network is configured to analyze 2-D images of target spaces including target objects and to provide output indicative of 3-D positions of the target objects in the target spaces. The second machine learning network can be configured to provide an output responsive to receiving a 2-D input image. A comparator receives the outputs from the first and second machine learning networks based on a particular 2-D image. The comparator compares the output of the first trained machine learning network with the output of the second machine learning network. A feedback mechanism is operative to alter the second machine learning network based at least in part on the output of the comparator.

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: A method and a system for managing a set of alerts related to an application workflow are disclosed. The method includes: detecting, by a processor via a build time architecture digitizer (BTAD), request for new infrastructure component provisioning; generating via BTAD, updated architecture model; monitoring, via runtime architecture digitizer (RAD), set of services related to new infrastructure component; generating, via RAD, final architecture model; receiving, at alert enricher module (AEM) from database, set of alerts; mapping, via AEM, alerts with corresponding components of final architecture model; assigning, via AEM, corresponding components from final architecture model to alerts; enriching, via AEM, alerts with relevant metrics and log details; generating, via AEM, set of enriched alerts; and providing, via AEM, enriched alerts to manage set of alerts.

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: A system is provided for automatically generating electronic artifacts using extended functionality. In particular, the system may use a template-based process to automatically generate artifacts based on a defined set of parameters and/or variables. The system may further use one or more plugins which may provide extended functionality with respect to the artifact generation process. Accordingly, the artifact generation process may include initializing a parameter list based on application parameters and/or plugin parameters, processing the parameters, generating variables based on the parameters, and replacing variables in scheme template files with appropriate values (e.g., user supplied or plugin generated values) to output an artifact file to a predetermined location. In this way, the system provides a robust and efficient way to automatically generate artifacts.

Abstract: A system is provided for automatically generating electronic artifacts using extended functionality. In particular, the system may use a template-based process to automatically generate artifacts based on a defined set of parameters and/or variables. The system may further use one or more plugins which may provide extended functionality with respect to the artifact generation process. Accordingly, the artifact generation process may include initializing a parameter list based on application parameters and/or plugin parameters, processing the parameters, generating variables based on the parameters, and replacing variables in scheme template files with appropriate values (e.g., user supplied or plugin generated values) to output an artifact file to a predetermined location. In this way, the system provides a robust and efficient way to automatically generate artifacts.

Abstract: A system is provided for automatically generating electronic artifacts using extended functionality. In particular, the system may use a template-based process to automatically generate artifacts based on a defined set of parameters and/or variables. The system may further use one or more plugins which may provide extended functionality with respect to the artifact generation process. Accordingly, the artifact generation process may include initializing a parameter list based on application parameters and/or plugin parameters, processing the parameters, generating variables based on the parameters, and replacing variables in scheme template files with appropriate values (e.g., user supplied or plugin generated values) to output an artifact file to a predetermined location. In this way, the system provides a robust and efficient way to automatically generate artifacts.

Abstract: Methods, devices, and systems for testing a number of combinations of memory in a computer system. A modular memory device is installed in a memory channel in communication with a processor. The modular memory device includes a number of memory storage devices. The number of memory storage devices include a number of pins. For each of a number of subsets of the number of memory storage devices, a subset of the number of memory storage devices is selected, each pin of a subset of the number of pins which do not correspond to the subset of the number of memory storage devices is configured with a termination impedance, and the subset of the number of memory storage devices is tested.

Abstract: Systems and methods for categorizing an object captured in an image are disclosed. An example method includes providing a neural network configured to receive the image and to provide a corresponding output. The method additionally includes defining a plurality of known object classes, each corresponding to a real-world object class and being defined by a class-specific subset of visual features identified by the neural network. The method includes acquiring a first two-dimensional (2-D) image including a first object and providing the first 2-D image to the neural network. The neural network identifies a particular subset of the visual features corresponding to the first object in the first 2-D image. The method also includes identifying a first known object class most likely to include the first object, and identifying a second known object class that is next likeliest to include the first object.

Abstract: A system for training a machine learning framework to estimate depths of objects captured in 2-D images includes a first trained machine learning network and a second untrained or minimally trained machine learning framework. The first trained machine learning network is configured to analyze 2-D images of target spaces including target objects and to provide output indicative of 3-D positions of the target objects in the target spaces. The second machine learning network can be configured to provide an output responsive to receiving a 2-D input image. A comparator receives the outputs from the first and second machine learning networks based on a particular 2-D image. The comparator compares the output of the first trained machine learning network with the output of the second machine learning network. A feedback mechanism is operative to alter the second machine learning network based at least in part on the output of the comparator.

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, devices, and systems for testing a number of combinations of memory in a computer system. A modular memory device is installed in a memory channel in communication with a processor. The modular memory device includes a number of memory storage devices. The number of memory storage devices include a number of pins. For each of a number of subsets of the number of memory storage devices, a subset of the number of memory storage devices is selected, each pin of a subset of the number of pins which do not correspond to the subset of the number of memory storage devices is configured with a termination impedance, and the subset of the number of memory storage devices is tested.

Abstract: A system is provided for automatically generating electronic artifacts using extended functionality. In particular, the system may use a template-based process to automatically generate artifacts based on a defined set of parameters and/or variables. The system may further use one or more plugins which may provide extended functionality with respect to the artifact generation process. Accordingly, the artifact generation process may include initializing a parameter list based on application parameters and/or plugin parameters, processing the parameters, generating variables based on the parameters, and replacing variables in scheme template files with appropriate values (e.g., user supplied or plugin generated values) to output an artifact file to a predetermined location. In this way, the system provides a robust and efficient way to automatically generate artifacts.

Abstract: Methods, devices, and systems for testing a number of combinations of memory in a computer system. A modular memory device is installed in a memory channel in communication with a processor. The modular memory device includes a number of memory storage devices. The number of memory storage devices include a number of pins. A subset of the number of memory storage devices is selected. A subset of the plurality of pins which do not correspond to the subset of the number of memory storage devices and are not part of a memory map of the computer system is selected. Each pin of the subset of the plurality of pins configured with a termination impedance. The subset of the number of memory storage devices is tested.

Abstract: Methods, devices, and systems for testing a number of combinations of memory in a computer system. A modular memory device is installed in a memory channel in communication with a processor. The modular memory device includes a number of memory storage devices. The number of memory storage devices include a number of pins. For each of a number of subsets of the number of memory storage devices, a subset of the number of memory storage devices is selected, each pin of a subset of the number of pins which do not correspond to the subset of the number of memory storage devices is configured with a termination impedance, and the subset of the number of memory storage devices is tested.

Abstract: Systems, methods, and computer-readable media are disclosed for digital photo frames with personalized content. In one embodiment, an example device may include at least one memory that stores computer-executable instructions, and at least one processor configured to access the at least one memory and execute the computer-executable instructions to determine a human face in an ambient environment, determine a user identifier associated with the human face, and determine a database index comprising relationships between media content and user identifiers. The at least one processor may be configured to determine a set of media content available to the device, the set of media content comprising pre-indexed images and videos, determine first media content of the set of media content associated with the user identifier in the database index, and initiate presentation of the first media content at a display in the ambient environment.

Abstract: Transaction data is written to one or more records in a datastore using key-value pairs. The record key for a record comprises a hash key and a range key, where the hash key corresponds to a particular transaction and the range key corresponds to a transaction type. The record key also comprises a counter to distinguish between different records storing data for the same transaction. A serialized data stream of transaction data may be apportioned into multiple data records and stored in a non-relational datastore. Each record for a transaction is individually readable, independently of the other records for the transaction. Accordingly, data records storing a large amount of transaction data for a transaction may be individual retrieved and presented at an access device, enabling a paginated view of the large amount of data with low latency in its retrieval.

Abstract: A method and system for providing decision-time brokerage in a hybrid cloud ecosystem is disclosed. Disclosed embodiments may include receiving a workload input by a brokerage engine executing in a computing device communicably connected to at least one cloud computing node in a cloud platform of a cloud service provider, determining resource optimization for the workload input by the brokerage engine, monitoring the workload input for compliance with one or more audit and regulatory metrics, monitoring the cost consumption of the workload input, capturing non-functional context data associated with the workload input into a context repository database, applying one or more rules to the workload, deploying the workload across the one or more cloud platforms. In some embodiments, a deployment recommendation may be provided prior to deployment of the workload. In some embodiments, a social collaboration workflow may be triggered whereby one or more users are provided the deployment recommendation.

Abstract: The invention discloses a process for the preparation of Zileuton of formula I by employing acetic acid-1-benzo[b]thiophen-2-yl-ethyl-ester of formula-III as an intermediate.

Abstract: The invention discloses a process for the preparation of Zileuton of formula I by employing acetic acid-1-benzo[b]thiophen-2-yl-ethyl-ester of formula-III as an intermediate.