Abstract: If a first condition for a handover of an analytics calculation for a user equipment by an analytics function is met, the analytics function requests, of at least one other analytics function of the communication network, preparation of the handover of the analytics calculation. If a second condition for the handover of the analytics calculation is met, the analytics function confirms the handover to one of the at least one other analytics function, the analytics calculation for the user equipment at the analytics function being deemed complete.

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: The present disclosure describes a system and method of developing a search database for automated content curation. The processing arrangement is configured to store a plurality of slides related to one or more fields in the database arrangement, generate a plurality of thumbnails from the stored plurality of slides, extract a plurality of metadata from the plurality of thumbnails, wherein the plurality of metadata are extracted by processing the generated plurality of thumbnails through an optical character reader, store the extracted plurality of metadata into the search database, create an index comprising of associations between the plurality of slides, the plurality of associated metadata and the generated plurality of thumbnails, store the created index in the search database and curate content from the index of the search database based on the one or more search strings.

Abstract: An apparatus for using a validated card in a virtual environment comprises a processor associated with a server. The processor is configured to receive a request to conduct an interaction between a first avatar and an entity in a virtual environment. The processor is further configured to receive card information and device information from an unknown user device. The processor is further configured to verify that the unknown user device is a first user device. The processor is further configured to receive virtual session information that is associated with a virtual environment session. The processor is further configured to determine if the request to conduct the interaction occurred in the same virtual environment session as the reception of the card information. The processor is further configured to conduct the interaction between the first avatar and the entity with the received card information.

Abstract: Disclosed herein are methods for analyzing one or more analytes of a cell by performing single-cell analysis. In one scenario, the one or more analytes are located on a surface of the cell. In one scenario, the one or more analytes are located internally within the cell. In one scenario, the one or more analytes include proteins located on a surface of the cell and located internally within the cell.

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: Methods, apparatus, systems, and articles of manufacture are disclosed to improve in-memory multiply and accumulate operations. An example apparatus includes a first multiplexer in a subarray of memory, the first multiplexer to receive first values representative of a column of a lookup table (LUT) including entries to represent products of four-bit numbers and return second values from an intersection of a row and the column of the LUT based on a first element of a first operand; shift and adder logic in the subarray, the shift and adder logic to shift the second values based on at least one of the first element of the first operand or a first element of a second operand; and accumulation storage in the subarray, the accumulation storage to store at least the shifted second values.

Abstract: Aspects of the disclosed technology provide solutions for identifying autonomous vehicle (AV) tests that provide a desired level of test coverage for testing or validating the AV software stack. A process of the disclosed technology can include steps for extracting a first set of features associated with a first set of test programs, tagging each respective test program with metadata tags, and identifying a second set of features associated with an updated set of AV program code. In some aspects, the process may further include steps for determining if the one or more tags match one or more features of the second set of features associated with the updated AV program code, and executing the respective test programs based on the one or more tags. Systems and machine-readable media are also provided.

Abstract: A method performed by a node in a communications network for determining a target network configuration for use in providing services to a first operator on the communications network. The method includes obtaining characteristics of the first operator; obtaining, for a plurality of previous operators in the communications network, characteristics of the previous operators and corresponding target network configurations used for the respective previous operators; matching the first operator to a second operator selected from the previous operators based on similarity between the characteristics of the first operator and the characteristics of the previous operators; and determining the target network configuration for the first operator, based on a target network configuration used for the second operator.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing and decompressing data. In one aspect, a method comprises: processing data using an encoder neural network to generate a latent representation of the data; processing the latent representation of the data using a hyper-encoder neural network to generate a latent representation of an entropy model; generating an entropy encoded representation of the latent representation of the entropy model; generating an entropy encoded representation of the latent representation of the data using the latent representation of the entropy model; and determining a compressed representation of the data from the entropy encoded representations of: (i) the latent representation of the data and (ii) the latent representation of the entropy model used to entropy encode the latent representation of the data.

Abstract: Example aspects of the present disclosure are directed to systems and methods that learn a compressed representation of a machine-learned model (e.g., neural network) via representation of the model parameters within a reparameterization space during training of the model. In particular, the present disclosure describes an end-to-end model weight compression approach that employs a latent-variable data compression method. The model parameters (e.g., weights and biases) are represented in a “latent” or “reparameterization” space, amounting to a reparameterization. In some implementations, this space can be equipped with a learned probability model, which is used first to impose an entropy penalty on the parameter representation during training, and second to compress the representation using arithmetic coding after training. The proposed approach can thus maximize accuracy and model compressibility jointly, in an end-to-end fashion, with the rate-error trade-off specified by a hyperparameter.

Abstract: An intermittent network connection between a source system and a destination system is established by establishing a first connection from a management resource to a first port of the destination system, causing a second port of the destination system to be enabled including by providing an instruction via the first connection to the first port of the destination system, establishing a second connection from the management resource to a first port of a source system, causing a second port of the source system to be enabled including by providing an instruction via the second connection to the first port of the source system, registering the destination system with the source system, and causing a third connection to be established between the second port of the source system and the second port of the destination system for transferring data from the source system to the destination system.

Abstract: A computer implemented method performed by a node in a communications network for matching parameters in a target network configuration with corresponding key performance indicators, KPIs, in the communications network. The method comprises obtaining (202) a first set of connections between a first subset of the parameters and a first subset of the KPIs. The method further comprises representing (204) the parameters as a first knowledge graph, representing (206) the KPIs as a second knowledge graph, and using (208) a graph-based machine learning process on the first knowledge graph and the second knowledge graph to match a second parameter to a second KPI.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for channel-wise autoregressive entropy models. In one aspect, a method includes processing data using a first encoder neural network to generate a latent representation of the data. The latent representation of data is processed by a quantizer and a second encoder neural network to generate a quantized latent representation of data and a latent representation of an entropy model. The latent representation of data is further processed into a plurality of slices of quantized latent representations of data wherein the slices are arranged in an ordinal sequence. A hyperprior processing network generates a hyperprior parameters and a compressed representation of the hyperprior parameters. For each slice, a corresponding compressed representation is generated using a corresponding slice processing network wherein a combination of the compressed representations form a compressed representation of the data.

Abstract: Methods and apparatuses are described for automatically provisioning and managing secure database resources in a cloud computing environment. A server provisions virtual computing resources in the cloud computing environment using resource templates and creates a database instance in each of the virtual computing resources. The server integrates the database instances and the virtual computing resources in the cloud computing environment with an identity authentication service. The server configures the database instances in two or more of the virtual computing resources to synchronize data. The server monitors each of the plurality of virtual computing resources using a monitoring service.

Abstract: The present disclosure relates to current control circuitry for controlling a current through a load, the current control circuitry comprising: amplifier circuitry; reference voltage generator circuitry configured to supply a fixed reference voltage to a first input of the amplifier circuitry; an output stage comprising: a control terminal coupled to an output of the amplifier circuitry; a current input terminal configured to be coupled to the load; a current output terminal; a clock-controlled variable resistance coupled to the current output terminal of the output stage, wherein a resistance of the variable resistance is based on a digital code input to the variable resistance; and a feedback path between the current output terminal of the output stage and a second terminal of the amplifier circuitry for providing a feedback voltage to a second input of the amplifier circuitry.

Abstract: An intermittent network connection between a source system and a destination system is established by establishing a first connection from a management resource to a first port of the destination system, causing a second port of the destination system to be enabled including by providing an instruction via the first connection to the first port of the destination system, establishing a second connection from the management resource to a first port of a source system, causing a second port of the source system to be enabled including by providing an instruction via the second connection to the first port of the source system, registering the destination system with the source system, and causing a third connection to be established between the second port of the source system and the second port of the destination system for transferring data from the source system to the destination system.

Abstract: A method may include receiving, by a calibration circuit, an output of a subsystem comprising the sensor and the analog front end. The method may further include separating the output individually into the sensor offset and the amplifier offset by using inherent properties of separate frequency ranges for the sensor offset and the amplifier offset. The method may also include calibrating, by the calibration circuit, the sensor offset by determining a first calibration value for the sensor offset such that the output approximates zero during an idle-channel condition. The method may additionally include calibrating, by the calibration circuit, the amplifier offset by determining a second calibration value for the amplifier offset such that the output approximates zero during the idle-channel condition.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for channel-wise autoregressive entropy models. In one aspect, a method includes processing data using a first encoder neural network to generate a latent representation of the data. The latent representation of data is processed by a quantizer and a second encoder neural network to generate a quantized latent representation of data and a latent representation of an entropy model. The latent representation of data is further processed into a plurality of slices of quantized latent representations of data wherein the slices are arranged in an ordinal sequence. A hyperprior processing network generates a hyperprior parameters and a compressed representation of the hyperprior parameters. For each slice, a corresponding compressed representation is generated using a corresponding slice processing network wherein a combination of the compressed representations form a compressed representation of the data.

Abstract: Methods and apparatuses are described for provisioning and managing data orchestration platforms in a cloud computing environment. A server provisions in a first region a first data orchestration platform comprising (i) a first data transformation instance, (ii) first endpoints, and (iii) a first data integration instance. The server provisions in a second region a second data orchestration platform comprising (i) a second data transformation instance, (ii) second endpoints, and (iii) a second data integration instance. The server integrates the first data integration instance and the second data integration instance with an identity authentication service. The server monitors operational status of the first orchestration platform and the second orchestration platform using a monitoring service. The server refreshes virtual computing resources in each of the first orchestration platform and the second orchestration platform using a rehydration service.