Abstract: Solutions are disclosed that enable efficient federated graph queries across multiple isolated data stores. Examples leverage the connectedness of the expected data that spans the data stores by defining the entities and relationships and inferring the intent of the queries. These are used to optimize data searches in the individual data stores. Examples map each of two or more variables of the input query to elements of a public schema and use the mapping to determining a storage tag (identifying a data store) for each of the variables of the input query. Store-specific queries are scheduled and performed based on at least the storage tags.

Abstract: A method, computer program product, and computing system for receiving an input speech signal. A transcription of the input speech signal may be received. A speaker embedding may be extracted from the input speech signal. Acoustic properties from the input speech signal may be extracted. An obscured transcription may be generated from the transcription, where the obscured transcription includes obscured representations of sensitive content from the transcription. An obscured speech signal may be generated based upon, at least in part, the extracted speaker embedding and the obscured transcription, where the obscured speech signal includes obscured representations of sensitive content from the input speech signal. The obscured speech signal may be augmented based upon, at least in part, the extracted acoustic properties.

Abstract: The disclosure herein describes managing artificial intelligence (AI) workloads in a cloud infrastructure platform. A set of distributed infrastructure resources are integrated into the cloud infrastructure platform via native support interfaces. AI workloads are received from a plurality of tenants, wherein the AI workloads include training workloads and inferencing workloads and resource subsets of the set of distributed infrastructure resources are assigned to the received AI workloads. The received AI workloads are scheduled for execution on the assigned resource subsets and based on the scheduling of the AI workloads, they are executed on the assigned resource subsets. The described cloud infrastructure platform provides efficient, secure execution of AI workloads for many different tenants and enables the flexible use of a wide variety of both third-party and first-party infrastructure resources.

Abstract: A method comprising, by first computer equipment: obtaining an input data point comprising a set of values, each being a value of a different element of an input feature vector; inputting the input data point to a first machine learning model on the first computer equipment to generate at least one associated output label based on the input data point; sending a partial data point to second computer equipment, the partial data point comprising the values of only part of the feature vector; and sending the associated label to the second computer equipment in association with the partial data point, thereby causing the second computer equipment to train a second machine learning model on the second computer equipment based on the sent part and the associated label.

Abstract: Example solutions for clustering data include: encoding a plurality of source records into a plurality of vectors, each source record containing source terms being encoded as one vector of the plurality of vectors; computing a similarity score for each unique pair of vectors of the plurality of vectors; constructing a similarity graph by: adding a node to the similarity graph for each vector; and adding an edge between each pair of nodes in which the similarity score for the associated pair of vectors exceeds a first similarity threshold; identifying a cluster of nodes within the similarity graph, the cluster of nodes representing a disconnected subgraph within the similarity graph; and generating a graphical representation of the one or more clusters on a display device.

Abstract: Example solutions read compressed data from a file in persistent storage, directly into an in-memory store without decompression. Some examples transcode the compression scheme used by the file into the compression scheme used by the in-memory store whereas, in other examples, the in-memory store is compatible with the compression scheme used by the file. In some examples that use transcoding, radix clustering is used to speed up compression dictionary transcoding. The radix clustering minimizes cache misses, thereby increasing the efficiency of memory access. These approaches significantly improve cold start times when responding to queries.

Abstract: In various examples, input queries (e.g. open user queries) are used combination with predefined queries to perform security policy-related actions using a generative machine learning (GML) model or GML models. In one example, an input query relating to a security policy is matched with a predefined query stored in an instruction database. In some examples, the instruction database contains examples of structured configuration data, which in turn can be used by a GML model to configure a predetermined extractor code module to perform a specific policy-related action. In other examples, a security context relating to a security policy is used together with an input query and template query to generate a GML model query. In some examples, the two approaches are combined.

Abstract: A computer-implemented method, computer program product and computing system for: enabling a generative AI system to effectuate an analysis protocol; enabling a user to utilize the generative AI system during an interactive session concerning the analysis protocol; receiving content during the interactive session, thus defining received content; and determining whether the received content is relevant with respect to the analysis protocol.

Abstract: A method for utilizing contextual data in generating machine translations. The method includes receiving a translation request including an initial prompt received via a user interface. The initial prompt includes a first language passage and a translation instruction. The initial prompt also includes a context data signal received via a context data source. The method further includes generating a context instruction based on the context data signal and generating a modified prompt including the initial prompt and the context instruction. The method further includes sending the modified prompt to a neural machine translation (NMT) model to process the modified prompt and receiving a second language translation passage as a response to the modified prompt. The second translation language passage being a second language translation of the first language passage translated according to the translation instruction and the context instruction.

Abstract: A method comprising: sampling a first causal graph from a first graph distribution modelling causation between variables in a feature vector, and sampling a second causal graph from a second graph distribution modelling presence of possible confounders, a confounder being an unobserved cause of both of two variables. The method further comprises: identifying a parent variable which is a cause of a selected variable according to the first causal graph, and which together with the selected variable forms a confounded pair having a respective confounder being a cause of both according to the second causal graph. A machine learning model encodes the parent to give a first embedding, and encodes information on the confounded pair give a second embedding. The embeddings are combined and then decoded to give a reconstructed value. This mechanism may be used in training the model or in treatment effect estimation.

Abstract: Optimization of data synchronization latency when load balancing requests is provided. A request directed to a first server device in the first region is offloaded to a second server device in a second region that is different from the first region for load balancing. Data is generated based on processing the request by the second server device in the second region. A target region of a client device requesting the generated data is dynamically determined such that the target region is different from the second region. The generated data is stored in a datastore of the target region of the client device requesting the generated data. Thus, data is advantageously directly stored in the region(s) where clients are requesting that data without needing to wait for eventual synchronization of data to the region(s) where clients are requesting that data.

Abstract: An image processing system is described which has a memory holding at least one image depicting at least one person previously unseen by the image processing system. The system has a trained probabilistic model which describes a relationship between image features, context, identities and a plurality of names of people, wherein at least one of the identities identifies a person depicted in the image without an associated name in the plurality of names. The system has a feature extractor which extracts features from the image, and a processor which predicts an identity of the person depicted in the image using the extracted features and the probabilistic model.

Abstract: A method, computer program product, and computing system for increasing communication session downtime. A first computing device of a plurality of computing devices transmits a handshake signal to a second computing device of the plurality of computing devices at a predetermined time interval and the second device receives the handshake signals. A communication session between the first computing device and the second computing device is ended when the second computing device does not receive a number X of expected consecutive handshake signals from the first computing device. Prior to a reboot of the first computing device, the number X of expected consecutive handshake signals is increased to a number Y of expected consecutive handshake signals, such that the communication session between the first computing device and the second computing device will be caused to end when the second device does not receive the number Y of expected consecutive handshake signals from the first computing device.

Abstract: A system for establishing network reliability for a computer network includes a plurality of initiating nodes to transmit a plurality of packets across the network and a plurality of receiving nodes to receive the plurality of packets via the network. A portion of the plurality of packets transmitted from the initiating nodes are appended with identifiers that correspond to characteristics of entities using the network. The plurality of receiving nodes transmit acknowledgement receipts associated with packets appended with the identifiers to a network monitoring system that monitors quality of service associated with the characteristics.

Abstract: A computerized method determines erasure coding (EC) formats for data extents in storage systems. Traffic history data of a data extent to be stored in the storage system is obtained. Estimated future traffic data is generated using a traffic estimation model, wherein the traffic history data is provided to the model as input. A current storage system constraint is calculated using the obtained traffic history data, and a constraint adjustment value is determined using a proportional, integral, derivative (PID) controller based on the current storage system constraint. An EC format for the data extent is determined using the estimated future traffic data and the determined constraint adjustment value and the data extent is stored in the storage system using the determined EC format. Use of the model and the PID controller balances optimized storage use with efficient data traffic handling.

Abstract: Systems and methods for processing a distributed transaction are provided. A distributed execution statement including a plurality of tasks for executing a query and a table to be modified by the executed query is received. A first task is transmitted to a first backend node and a second task is transmitted to a second backend node. A first confirmation that the first task is executed and a second confirmation that the second task is executed are received. Executing the first task includes making a first modification to the table and a first identification of the first modification, and executing the second task includes making a second modification to the table and a second identification of the second modification. Based at least on each of the first task and the second task being completed, committing the first modification and the second modification to the table.

Abstract: Operations of a workload are assigned to physical resources of a physical device array. The workload includes a graph of operations to be performed on a physical device array. The graph of operations is partitioned into subgraphs. Partitioning includes at least minimizing the quantity of subgraphs and maximizing resource utilization per subgraph. A logical mapping of the subgraph to logical processing engine (PE) units is generated using features of the subgraph and tiling factors of the logical PE units. The logical mapping is assigned to physical PE units of the physical device array at least by minimizing network traffic across the physical PE units. The operations of the subgraph are performed using the physical PE units to which the logical mapping is assigned. This process enhances the computational efficiency of the array when executing the workload.

Abstract: Images captured by a camera moving in an environment are received, and for each of a plurality of points in one of the images, outputs are computed using a neural network. The outputs comprise: a trajectory depicting the point in each of the plurality of images, as well as, for each trajectory, a prediction of visibility of the trajectory in each of the images and a prediction of whether the trajectory depicts a static or moving surface in the environment. The neural network receives the images and points as input and computes the outputs, wherein the outputs comprise for each of the trajectories, confidence data. The outputs are sent to a downstream process selected from any of: visual odometry, structure from motion, human body tracking, video editing, vehicle tracking.

Abstract: Example solutions for augmenting text inputs for analyzing clinical documents include: identifying a clinical named entity within text content of a clinical input document; adding an anchor tag to the text content, the anchor tag including an entity ID and a clinical attribute associated with the clinical named entity, thereby generating an enhanced input document; and submitting a first query prompt and the enhanced input document to a generative artificial intelligence (GAI) model, the first query prompt including task text and anchoring markup text, the task text includes instructions of a task to be performed by the GAI model on the enhanced input document, the anchoring markup text includes a template of the anchor tag and instruction to add a reference anchor tag to output generated by the GAI model, where the reference anchor tag is to include the entity ID and the clinical attribute of the anchor tag.

Abstract: A stream tool is disclosed that allows a user to seamlessly connect with the different data streams, regardless of the streams' transmission platforms or communication protocols, in order to visually see a representation of the type of data that the data streams are transmitting. A user may specify a particular data stream and provide corresponding connection details. A collection of abstracted software functions enable interaction with the different stream platforms and protocols. Using these abstracted functions, a stream-processing service accesses a requested data stream and samples its data events for either sample timeframe or up to a threshold number of data events. The sampled data events are parsed and visually presented to the user in an easy-to-understand format. The user may then inspect the data stream's data for use in developing robust applications that may integrate and use such data.