Abstract: Various embodiments described herein relate to provisioning edge layer to cloud layer connectivity for asset devices. In an embodiment, an application programming interface (API) payload to facilitate network provisioning related to a set of industrial assets within an industrial network system is received. Additionally, a set of subscription domain features included in the API payload is mapped to a set of application content features associated with a cloud platform and a gateway profile for configuring a connection between the industrial network system and the cloud platform is generated based at least in part on the set of application content features. Execution of the gateway profile via a gateway device implemented as a network node between the industrial network system and the cloud platform can also be caused.

Abstract: A system using large language models (LLMs) with multimodal inputs that has a low latency (within 500 ms) response time is described. This low latency produces a response that leads to a more engaging and empathetic user experience with all calculations done on consumer hardware. In so doing, the LLM derives the emotional state of the user using message sentiment analysis and/or face behavior and/or voice parameters that leads to a more engaging and empathetic user experience. This occurs by reviewing: (a) the user's mood state while providing an input prompt to the LLM in the current turn; (b) the user's mood and mental states while reacting to the LLM's response in the previous turn; (c) the sentiment of the LLM's response in the previous turn; and (d) the desired user mood and mental state as determined by the system's empathetic goal.

Abstract: An assembly includes a frame configured to couple to a valve, an actuating arm movably coupled to the frame and configured to move a movable component of the valve to adjust a position of the movable component, and a biasing member coupled to the actuating arm and the frame. The biasing member is configured to bias the actuating arm in a first direction relative to the frame. The assembly further includes one or more shape memory alloy (SMA) elements coupled to the actuating arm and the frame. Responsive to an activation that causes the one or more SMA elements undergo a dimensional transformation, the one or more SMA elements are configured to operate against the biasing member to bias the actuating arm in a second direction opposite the first direction relative to the frame.

Abstract: A system using large language models (LLMs) with multimodal inputs that has a low latency (within 500 ms) response time is described. This low latency produces a response that leads to a more engaging and empathetic user experience with all calculations done on consumer hardware. In so doing, the LLM derives the emotional state of the user using message sentiment analysis and/or face behavior and/or voice parameters that leads to a more engaging and empathetic user experience. This occurs by reviewing: (a) the user's mood state while providing an input prompt to the LLM in the current turn; (b) the user's mood and mental states while reacting to the LLM's response in the previous turn; (c) the sentiment of the LLM's response in the previous turn; and (d) the desired user mood and mental state as determined by the system's empathetic goal.

Abstract: A system using large language models (LLMs) with multimodal inputs that has a low latency (within 500 ms) response time is described. This low latency produces a response that leads to a more engaging and empathetic user experience with all calculations done on consumer hardware. In so doing, the LLM derives the emotional state of the user using message sentiment analysis and/or face behavior and/or voice parameters that leads to a more engaging and empathetic user experience. This occurs by reviewing: (a) the user's mood state while providing an input prompt to the LLM in the current turn; (b) the user's mood and mental states while reacting to the LLM's response in the previous turn; (c) the sentiment of the LLM's response in the previous turn; and (d) the desired user mood and mental state as determined by the system's empathetic goal.

Abstract: Various embodiments described herein relate to provisioning edge layer to cloud layer connectivity for asset devices. In an embodiment, an application programming interface (API) payload to facilitate network provisioning related to a set of industrial assets within an industrial network system is received. Additionally, a set of subscription domain features included in the API payload is mapped to a set of application content features associated with a cloud platform and a gateway profile for configuring a connection between the industrial network system and the cloud platform is generated based at least in part on the set of application content features. Execution of the gateway profile via a gateway device implemented as a network node between the industrial network system and the cloud platform can also be caused.

Abstract: Described is a task for an automobile interior having at least one subject that creates a video input, an audio input, and a context descriptor input. The video input relates to the at least one subject and is processed by a face detection module and a facial point registration module to produce a first output. The first output is further processed by at least one of: a facial point tracking module, a head orientation tracking module, a body tracking module, a social gaze tracking module, and an action unit intensity tracking module. The audio input relating to the at least one subject is processed by a valence and arousal affect states tracking module to produce a second output and to produce a valence and arousal scores output. A temporal behavior primitives buffer produce a temporal behavior output. Based on the foregoing, a mental state prediction module predicts the mental state of at least one subject in the automobile interior.

Abstract: A communications processor is operative in a plurality of modes including at least a high performance mode, a power savings mode with lower computational capability, and a shutdown mode with a wakeup capability. A memory for the communications processor has a high speed segment and a low speed segment, the high speed segment and low speed segment respectively on a high speed data bus and a low speed data bus, the high speed data bus and low speed data bus coupled by a bidirectional bridge.

Abstract: Systems, methods, and computer-readable media analyzing memory usage in a network node. A network assurance appliance may be configured to determine a hit count for a concrete level rule implemented on a node and identify one or more components of a logical model, wherein each of the one or more components are associated with the concrete level rule. The network assurance appliance may attribute the hit count for the concrete level rule to each of the components of the logical model, determine a number of hardware level entries associated with the each of the one or more components, and generate a report comprising the one or more components of the logical model, the hit count attributed to each of the one or more components of the logical model, and the number of hardware level entries associated with the one or more components of the logical model.

Abstract: Systems, methods, and computer-readable media analyzing memory usage in a network node. A network assurance appliance may be configured to query a node in the network fabric for a number of hardware level entries, stored in memory for the node, that are associated with a concrete level network rule. The network assurance appliance may identify a logical level network intent associated with the concrete level network rule, identify a logical level component of the logical level network intent, and attribute the number of hardware level entries to the logical level component.

Abstract: Systems, methods, and computer-readable media analyzing memory usage in a network node. A network assurance appliance may be configured to obtain reference concrete level rules for a node in the network, obtain implemented concrete level rules for the node from the node in the network, compare the reference concrete level rules with the implemented concrete level rules, and determining that the implemented concrete level rules are not appropriately configured based on the comparison.

Abstract: Systems, methods, and computer-readable media analyzing memory usage in a network node. A network assurance appliance may be configured to determine a hit count for a concrete level rule implemented on a node and identify one or more components of a logical model, wherein each of the one or more components are associated with the concrete level rule. The network assurance appliance may attribute the hit count for the concrete level rule to each of the components of the logical model, determine a number of hardware level entries associated with the each of the one or more components, and generate a report comprising the one or more components of the logical model, the hit count attributed to each of the one or more components of the logical model, and the number of hardware level entries associated with the one or more components of the logical model.

Abstract: Systems, methods, and computer-readable media analyzing memory usage in a network node. A network assurance appliance may be configured to determine a hit count for a concrete level rule implemented on a node and identify one or more components of a logical model, wherein each of the one or more components are associated with the concrete level rule. The network assurance appliance may attribute the hit count for the concrete level rule to each of the components of the logical model, determine a number of hardware level entries associated with the each of the one or more components, and generate a report comprising the one or more components of the logical model, the hit count attributed to each of the one or more components of the logical model, and the number of hardware level entries associated with the one or more components of the logical model.

Abstract: A communications processor is operative in a plurality of modes including at least a high performance mode, a power savings mode with lower computational capability, and a shutdown mode with a wakeup capability. A memory for the communications processor has a high speed segment and a low speed segment, the high speed segment and low speed segment respectively on a high speed data bus and a low speed data bus, the high speed data bus and low speed data bus coupled by a bidirectional bridge.

Abstract: A communications processor is operative in a plurality of modes including at least a high performance mode, a power savings mode with lower computational capability, and a shutdown mode with a wakeup capability. A memory for the communications processor has a high speed segment and a low speed segment, the high speed segment and low speed segment respectively on a high speed data bus and a low speed data bus, the high speed data bus and low speed data bus coupled by a bidirectional bridge.

Abstract: The present disclosure provides a system and method for predicting drowsiness of a subject. During facial landmark tracking of a received stream of images, feature extraction is split into two components, viz., landmark independent and landmark dependant features. Precomputing the landmark independent features during a first stage of a first cascade reduces the computational requirements of the approach without affecting accuracy of detection of landmark features. A second multi-stage cascade is used to distinguish and extract eye images from the facial landmarks. At the end of the second multi-stage cascade, the eye landmarks are again distinguished between open eyes and closed eyes using a third multi-stage cascade. Progressively, the subsequent stages of the third multi-stage cascade discards open eyes, and eventually detects closed eyes accurately. The occurrence of closed eye images and eye height variations in the received stream of images can enable determination of drowsiness of the subject.

Abstract: Systems, methods, and computer-readable media analyzing memory usage in a network node. A network assurance appliance may be configured to query a node in the network fabric for a number of hardware level entries, stored in memory for the node, that are associated with a concrete level network rule. The network assurance appliance may identify a logical level network intent associated with the concrete level network rule, identify a logical level component of the logical level network intent, and attribute the number of hardware level entries to the logical level component.

Abstract: Systems, methods, and computer-readable media analyzing memory usage in a network node. A network assurance appliance may be configured to obtain reference concrete level rules for a node in the network, obtain implemented concrete level rules for the node from the node in the network, compare the reference concrete level rules with the implemented concrete level rules, and determining that the implemented concrete level rules are not appropriately configured based on the comparison.

Abstract: Systems, methods, and computer-readable media analyzing memory usage in a network node. A network assurance appliance may be configured to query a node in the network fabric for a number of hardware level entries, stored in memory for the node, that are associated with a concrete level network rule. The network assurance appliance may identify a logical level network intent associated with the concrete level network rule, identify a logical level component of the logical level network intent, and attribute the number of hardware level entries to the logical level component.

Abstract: A system is provided that visualizes task progress update history. The system displays a project plan timeline within a user interface, where the project plan timeline includes tasks indicators that visually represent tasks and a timeline including time unit indicators that visually represent time units. The system further displaying task progress update history within the project plan timeline, where the task progress update history includes task progress update indicators that visually represent task progress updates, and where a task progress update indicator visually indicates a date-time of a task progress update and a task progress reported by the task progress update.