Abstract: An example method to determine an object spin rate may include training a neural network with a set of initial data. The set of initial data may be generated based on a plurality of initial radar signals of a plurality of initial objects in motion. The method may include receiving a radar signal of a particular object in motion. The method may include converting the radar signal into an input vector. The input vector may include time and frequency information of the particular object in motion. The method may include providing the input vector as input to a trained neural network. The method may include determining a spin rate of the particular object in motion based on an analysis performed by the trained neural. The analysis may include analyzing the input vector including time and frequency information of the object in motion in view of the set of initial data.

Abstract: Some embodiments provide a novel method for deploying cloud gateways between a set of cloud machines in a first network and a set of on-premises machines in an external network. The method collects a set of statistics for a first cloud gateway used to connect the set of cloud machines and the set of on-premises machines. The method analyzes the set of statistics to determine that a second cloud gateway is needed to connect the set of cloud machines and the set of on-premises machines. The method identifies a subset of the set of cloud machines. The method distributes a set of one or more forwarding rules to the subset of cloud machines to forward a set of data message flows from the subset of cloud machines to the set of on-premises machines through the second cloud gateway.

Abstract: Some embodiments provide a novel method for dynamically deploying gateways for a first network connecting machines. The first network includes segments, routers, and a first gateway that connects to an external network. The method identifies a set of two or more segments that consumes more than a threshold amount of bandwidth of the first gateway. The identified set includes at least first and second segments. The method identifies one or more segment groups by aggregating two or more segments in the identified set. A first segment group includes the first and second segments and a third segment that is not in the identified set of two or more segments. The method configures a second gateway to process flows associated with each identified group including the first group. The method configures a set of routers to forward flows from machines of each segment of each identified group to the second gateway.

Abstract: Some embodiments provide a novel method for preemptively deploying gateways in a first network to one or more external networks. The first network of some embodiments includes a first gateway connecting to the one or more external networks. The method collects a set of statistics for the first gateway associated with bandwidth usage of the first gateway. The method determines that a second gateway needs to be deployed in the first network (1) by using the collected set of statistics to perform predictive modeling computations to predict a future load on the first gateway, and (2) by determining that the predicted future load exceeds a particular threshold. The method distributes a set of one or more forwarding rules to forward data message flows from a subset of machines in the first network to a particular external network through the second gateway.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a consolidated graphical user interface for visually presenting the state of a user profile with respect to a workflow journey. For instance, in one or more embodiments, the disclosed systems provide, for display within a graphical user interface of a client device, a visual representation of a workflow journey comprising a plurality of nodes and one or more edges connecting the plurality of nodes. Additionally, the disclosed systems receive, via the graphical user interface of the client device, an identifier associated with a user profile. The disclosed systems further modify, within the graphical user interface of the client device, the visual representation of the workflow journey to reflect a state of the user profile with respect to the workflow journey.

Abstract: A stump device may include a first image-capturing sensor configured to couple to at least one stump of a wicket positioned at a bowling end of a cricket field and capture image data of an initial motion of a cricket ball. The stump device may also include a second image-capturing sensor configured to couple to at least one stump of the wicket and capture image data of a trajectory and a flight path of the cricket ball. The stump device may additionally include a first radar sensor configured to couple to at least one stump of the wicket and capture radar data describing one or more initial launch parameters of the cricket ball. The stump device may include a second radar sensor configured to couple to at least one of the stumps of the wicket and capture radar data describing one or more movement parameters of a bowler.

Abstract: Aspects of the disclosure relate to using machine learning models to automatically generate spine-leaf network topologies. A computing system may receive one or more prompts to generate a spine-leaf network topology based on a non-spine-leaf network topology of a non-spine-leaf network. Based on inputting the prompts into a natural language processing model, network criteria for generating the spine-leaf network topology may be generated. Non-spine-leaf network topology data comprising network metadata, network dependency parameters, and network constraint parameters, may be retrieved. Based on inputting the network criteria and the non-spine-leaf network data into a generative adversarial network implemented on a quantum computing device, candidate spine-leaf topologies comprising a qualified candidate spine-leaf network topology that meets the network criteria may be generated.

Abstract: Aspects of the disclosure relate to reimaging an ATM using an extended reality interface and machine learning models that are configured to reimage the ATM. A computing system may send a request for authorization to access an ATM. Based on the request being granted the ATM may be accessed via an extended reality interface. Reimaging the ATM may be based on inputting ATM data associated with a state of the ATM into machine learning models that are configured to generate instructions to reimage the ATM and then execute threads based on processes that were determined based on the instructions. The threads may be executed in a RAM of the ATM in accordance with machine learning model determined priorities of the threads.

Abstract: A cricket sensor may include one or more first image-capturing sensors configured to capture image data of a pitching motion of a bowler and image data of an initial motion of a cricket ball at a bowling end of a cricket field. The cricket sensor may include one or more second image-capturing sensors configured to capture image data of a trajectory and a flight path of the cricket ball towards a batting end of the cricket field. The cricket sensor may also include one or more first radar sensors configured to capture radar data describing one or more initial launch parameters of the cricket ball related to the trajectory and the flight path of the cricket ball towards the batting end of the cricket field.

Abstract: An example method to determine an object spin rate may include training a neural network with a set of initial data. The set of initial data may be generated based on a plurality of initial radar signals of a plurality of initial objects in motion. The method may include receiving a radar signal of a particular object in motion. The method may include converting the radar signal into an input vector. The input vector may include time and frequency information of the particular object in motion. The method may include providing the input vector as input to a trained neural network. The method may include determining a spin rate of the particular object in motion based on an analysis performed by the trained neural. The analysis may include analyzing the input vector including time and frequency information of the object in motion in view of the set of initial data.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a consolidated graphical user interface for visually presenting the state of a user profile with respect to a workflow journey. For instance, in one or more embodiments, the disclosed systems provide, for display within a graphical user interface of a client device, a visual representation of a workflow journey comprising a plurality of nodes and one or more edges connecting the plurality of nodes. Additionally, the disclosed systems receive, via the graphical user interface of the client device, an identifier associated with a user profile. The disclosed systems further modify, within the graphical user interface of the client device, the visual representation of the workflow journey to reflect a state of the user profile with respect to the workflow journey.

Abstract: Systems and methods for customer journey optimization are described.

Abstract: Systems and methods for customer journey orchestration are described. One or more aspects of the systems and methods include identifying, by a customer journey orchestration application, a customer journey having a previously unidentified fault; initiating, by a mode selection component, a debug mode of the customer journey orchestration application for the customer journey; receiving, by a graphical user interface of the customer journey orchestration application, a user input corresponding to an event of a plurality of events of the customer journey; simulating, by an event simulation component, the event based on the user input and the debug mode; determining, by a status component, a status of the event based on the simulation; and identifying, by a fault identification component, the previously unidentified fault based on the status of the event.

Abstract: An example method to determine an object spin rate may include receiving a radar signal of a particular object in motion. The method may further include converting the radar signal into an input vector. The method may also include providing the input vector as input to a neural network. The neural network may include access to a set of initial data that has been generated based on multiple initial radar signals of multiple initial objects in motion. The method may further include determining a spin rate of the particular object in motion based on an analysis performed by the neural network of the input vector including time and frequency information of the particular object in motion in view of the set of initial data. The analysis may include comparing one or more elements of the input vector to one or more elements of the set of initial data.

Abstract: Arrangements for providing dual-functionality device control functions are provided. In some aspects, a selection of a mode of functionality may be received. For instance, a user may select an option of at least two options available for selection on the payment device. In response to receiving the selected mode, a device identifier that may be or include a non-fungible token (NFT) may be generated. The NFT may be transmitted to the payment device for display on the payment device. The payment device may then be used to initiate a transaction. A transaction request including an encrypted version of the NFT, as well as additional data, may be received. The NFT may be decrypted and evaluated to determine whether it matches a pre-stored NFT. If not, the transaction may be denied and if so, the transaction may be authorized.

Abstract: A control device configuration system may receive, store, process, and/or display control device configuration information. The control device configuration system may filter the control device configuration information based on user selections of configuration options for configuration parameters. The control device configuration system may identify compatible and incompatible configuration options for various configuration parameters. The control device configuration system may allow selections of the incompatible configuration options. The control device configuration system may adjust how it filters the control device configuration information based on the selections of the incompatible configuration options. The control device configuration system may implement a configuration model that includes configuration parameter groups for efficient evaluation of user selections.

Abstract: A control device configuration system may receive, store, process, and/or display control device configuration information. The control device configuration system may filter the control device configuration information based on user selections of configuration options for configuration parameters. The control device configuration system may identify compatible and incompatible configuration options for various configuration parameters. The control device configuration system may allow selections of the incompatible configuration options. The control device configuration system may adjust how it filters the control device configuration information based on the selections of the incompatible configuration options. The control device configuration system may implement a configuration model that includes configuration parameter groups for efficient evaluation of user selections.

Abstract: A cricket sensor may include one or more first image-capturing sensors configured to capture image data of a pitching motion of a bowler and image data of an initial motion of a cricket ball at a bowling end of a cricket field. The cricket sensor may include one or more second image-capturing sensors configured to capture image data of a trajectory and a flight path of the cricket ball towards a batting end of the cricket field. The cricket sensor may also include one or more first radar sensors configured to capture radar data describing one or more initial launch parameters of the cricket ball related to the trajectory and the flight path of the cricket ball towards the batting end of the cricket field.

Abstract: A stump device may include a first image-capturing sensor configured to couple to at least one stump of a wicket positioned at a bowling end of a cricket field and capture image data of an initial motion of a cricket ball. The stump device may also include a second image-capturing sensor configured to couple to at least one stump of the wicket and capture image data of a trajectory and a flight path of the cricket ball. The stump device may additionally include a first radar sensor configured to couple to at least one stump of the wicket and capture radar data describing one or more initial launch parameters of the cricket ball. The stump device may include a second radar sensor configured to couple to at least one of the stumps of the wicket and capture radar data describing one or more movement parameters of a bowler.

Abstract: Methods and apparatus for isolation of sub-system resources (such as clocks, power, and reset) within independent domains. In one embodiment, each sub-system of a system has one or more dedicated power and clock domains that operate independent of other sub-system operation. For example, in an exemplary mobile device with cellular, WLAN and PAN connectivity, each such sub-system is connected to a common memory mapped bus function, yet can operate independently. The disclosed architecture advantageously both satisfies the power consumption limitations of mobile devices, and concurrently provides the benefits of memory mapped connectivity for high bandwidth applications on such mobile devices.