System and method for processing a graph that defines a set of nodes and a set of edges, the nodes each having an associated set of node attributes, the edges each representing a relationship that connects two respective nodes, comprising: generating a first node embedding for each node by: generating, for the node and each of a plurality of neighbour nodes, a respective first edge attribute defining a respective relationship type between the node and the neighbour node based on the node attributes of the node and the node attributes of the neighbour node; generating a first neighborhood vector that aggregates information from the generated first edge attributes and the node attributes of the neighbour nodes; generating the first node embedding based on the node attributes of the node and the generated first neighborhood vector.

Embodiments provide a scheme for non-parametric PV-level modeling and an optimal read threshold voltage estimation in a memory system. A controller is configured to: generate multiple optimal read threshold voltages corresponding to multiple sets of two cumulative distribution function (CDF) values, respectively; perform read operations on the cells using a plurality of read threshold voltages; generate cumulative mass function (CMF) samples based on the results of the read operations; receive first and second CDF values, selected from among a plurality of CDF values, each CDF value corresponding to each CMF sample; and estimate an optimal read threshold voltage corresponding to the first and second CDF values, among the multiple optimal read threshold voltages.

Persistent storage contains configuration items representing computing hardware and software, wherein each configuration item is respectively associated with a set of attributes, and wherein pairwise relationships are defined between some of the configuration items. One or more processors are configured to: select a subset of the configuration items that are connected by way of a subset of the pairwise relationships; form a graph representation in which the subset of the configuration items is represented as nodes and the subset of the pairwise relationships is represented as edges between pairs of the nodes; train a graph neural network with k layers on the graph representation, wherein training the graph neural network involves sequentially generating k embeddings for the sets of attributes associated with the nodes, wherein the embeddings are in an f-dimensional feature space; and based a kth of the embeddings, determine that a particular node of the nodes is anomalous.

Disclosed is a method of analyzing a programmable logic controller (PLC) logic to detect whether an anomaly that deviates from a standard pattern occurs in a repeated cycle. After modeling and patterning an operation pattern of automation equipment and processes with a graph, an anomaly detecting model capable of detecting whether a pattern is abnormal may be constructed as a graph AutoEncoder model. By detecting the change in the process pattern, it is possible to early detect the anomaly of the equipment and processes.

Techniques are disclosed for generating a database schema using trained machine learning models that, in some embodiments, may include graph neural networks (GNN). A GNN may identify source to target database schema mappings using, among other features of the graph, context data associated with each node in a graph. Context data describes relationships between a particular node and some (or all) of the other nodes in the graph. The system may use this context data (and other graph data) in combination with a trained GNN model to identify a mapping between one or more source database entities to corresponding target database entities.

Mechanisms are provided for performing artificial intelligence-based video question answering. A video parser parses an input video data sequence to generate situation data structure(s), each situation data structure comprising data elements corresponding to entities, and first relationships between entities, identified by the video parser as present in images of the input video data sequence. First machine learning computer model(s) operate on the situation data structure(s) to predict second relationship(s) between the situation data structure(s). Second machine learning computer model(s) execute on a received input question to predict an executable program to execute to answer the received question. The program is executed on the situation data structure(s) and predicted second relationship(s). An answer to the question is output based on results of executing the program.

Disclosed are methods, systems, and computer-readable medium for providing report results. Viscous attributes and non-viscous may be identified. A smart cube may be received and may include viscous values for the viscous attributes. The smart cube may be stored at a local cache. A report associated with an organization may be initiated. A runtime generation of the report may be generated based on initiating the report. The report may call a viscous attribute from the viscous attributes and call a non-viscous attribute from the non-viscous attributes. The runtime generation may be modified to remove the viscous attribute from the runtime generation. A viscous value for the viscous attribute may be retrieved from the smart cube at the local cache. The modified runtime generation may be executed to retrieve a non-viscous value for the non-viscous attribute from a remote database and a report result may be provided.

Custom-tailored warranties are provided with improved service level agreements (SLA) based upon an estimated turnaround time for service and/or parts. The turnaround time is calculated using an artificial intelligence or machine learning engine considering parameters such as the transit time from the nearest service centers and warehouses, the availability of service engineers at the service centers, and the availability of replacement parts in the warehouse. A custom-tailored warranty also may be offered for a specific customer-selected SLA if supported by the estimated turnaround time for the location. A warranty recommendation may be based on device location for data centers in multiple locations. A Location-Based Warranty Monitor (LBWM) provides fine-grained warranty suggestions and Un-bound Warranty Tokens (UWTs) can be bound to a system to assign a warranty with a desired. SLA.

A system and a method for improved generation of 3D avatars for virtual try-on of garments is provided. Inputs from a first user type are received, via a first input unit, for generating one or more garment types in a graphical format. Further, a 3D avatar of a second user type is generated in a semi-automatic manner or an automatic manner based on capturing a first input type or a second input type respectively received via a second input unit. The first input type comprises measurements of body specifications of the second user type and the second input type comprises body images of the second user type. Further, the generated garments are rendered on the generated 3D avatar of the second user type for carrying out a virtual try-on operation.

Some disclosed systems are configured to obtain a knowledge module configured to receive one or more knowledge inputs corresponding to one or more different modalities and generate a set of knowledge embeddings to be integrated with a set of multi-modal embeddings generated by a multi-modal main model. The systems receive a knowledge input at the knowledge module, identify a knowledge type associated with the knowledge input, and extract a knowledge unit from the knowledge input. The systems select a representation model that corresponds to the knowledge type and select a grounding type configured to ground the at least one knowledge unit into the representation model. The systems then ground the knowledge unit into the representation model according to the grounding type.