Abstract: Techniques for time-bound hyperparameter tuning are disclosed. The techniques enable the determination of optimized hyperparameters for a machine learning (ML) model given a specified time bound using a three-stage approach. A series of trials are executed, during each of which the ML model is trained using a distinct set of hyperparameters. In the first stage, a small number of trials are executed to initialize the algorithm. In the second and third stages, a certain number of trials are executed in each stage. The number of trials to run in each stage are determined using one or more computer-implemented techniques. The computer-implemented techniques can also be used to narrow the hyperparameter search space and the feature space. Following the third stage, a set of optimized hyperparameters is adopted based a predefined optimization criterion like minimization of an error function.

Abstract: Techniques are disclosed for automatically generating Subjective, Objective, Assessment and Plan (SOAP) notes. Particularly, techniques are disclosed for automatic SOAP note generation using task decomposition. A text transcript is accessed and segmented into portions. The text transcript can correspond to an interaction between a first entity and a second entity. Machine-learning model prompts are used to extract entities and facts for the respective portions and generate SOAP note sections based at least in-part on the facts. A SOAP note is generated by combining the SOAP note sections. The SOAP note can be stored in a database in association with at least one of the first entity and the second entity.

Abstract: The techniques described herein provide a novel medication order pipeline may be used to facilitate medication orders by identifying the medication ordering intent from a natural language utterance, and using the FHIR-compliance data structure to generate medication order information to fulfill medication orders through an EHR system. The medication order information may be a concise search phrase containing the medical entities extracted from the data structure, or converted EHR system-specific medical codes based on the standard medical codes in the data structure.

Abstract: Techniques are disclosed for automatically generating Subjective, Objective, Assessment and Plan (SOAP) notes. Particularly, techniques are disclosed for identifying entities for automatic SOAP note generation. A text transcript is accessed and segmented into portions. The text transcript can correspond to an interaction between a first entity and a second entity. Entities for the respective portions are identified using machine-learning models. A SOAP note is generated using the one or more machine-learning models and facts are derived from the text transcript based at least in-part on the entities. The SOAP note can be stored in a database in association with at least one of the first entity and the second entity.

Abstract: A summary generation and summary selection system is disclosed that is capable of automatically evaluating multiple summaries generated for content and selecting a single summary that is deemed to be the “best” among the multiple generated summaries. The system includes capabilities to use multiple different selection techniques to select the best summary from multiple generated summaries. A first selection technique involves identifying entities and entity relationships from the content to be summarized and selecting a summary from multiple summaries generated for the content based on the entities and entity relationships identified in the content. A second selection technique involves determining a set of questions that are answered by each summary. The technique then selects a summary based upon the set of questions answered by each summary. The system then outputs the selected summary as the summary for the content.

Abstract: Techniques are disclosed for providing an authenticated model customization for a machine-learning model. A cloud service provider platform accesses a message including, at least, timestamp data and user identification data. A training group of data entities is identified based on the data in the message. A training dataset is determined based on the training group of data entities. A machine-learning model is modified based on the training dataset. The modified machine-learning model is provided during an authenticated network session associated with the user identification data. In some embodiments, the modification of the machine-learning model is removed based on a determination that the authenticated network session had ended.

Abstract: A method for detecting a deserialization attack may include identifying, in a byte stream, a class name corresponding to a class, generating, for the class, a feature vector, generating, by applying a benign deserialization model to the feature vector, a benign probability window, generating, by applying a malicious deserialization model to the feature vector, a malicious probability window, comparing the benign probability window and the malicious probability window to obtain a comparison result, and determining, based on the comparison result, that the class is malicious.

Abstract: A system accesses transaction data associated with a plurality of transactions, and based on characteristics of the transaction data, determines a set of functions to be applied to the transaction data at different corresponding levels of granularity. Determining the set of functions includes determining parallel processing requirements corresponding to the set of functions and determining an execution order corresponding to the set of functions based on the parallel processing requirements. The system schedules parallel execution of (a) a first function on the transaction data at a first level of granularity to generate a first dataset having the first level of granularity, and (b) a second function on the transaction data at a second level of granularity to generate a second dataset having the second level of granularity.

Abstract: Techniques are disclosed for automatically generating prompts. A method comprises accessing first prompts, wherein each of the first prompts is a prompt for generating a portion of a SOAP note using a machine-learning model. For each respective first prompt of the first prompts: (i) using the respective first prompt to obtain a first result from a first machine-learning model, (ii) using the respective first prompt and the first result to obtain a second result from a second machine-learning model, the second result including an assessment of the first result, (iii) using the second result to obtain a third result from a third machine-learning model, the third result including a second prompt, (iv) setting the second prompt as the respective first prompt, (v) repeating steps (i)-(iv) a number of times to obtain a production prompt, (vi) adding the production prompt to a collection of prompts; and storing the collection of prompts.

Abstract: Techniques are disclosed herein for contextual query rewriting. The techniques include inputting a first user utterance and a conversation history to a first language model. The first language model identifies an ambiguity in the first user utterance and one or more terms in the conversation history to resolve the ambiguity, modifies the first user utterance to include the one or more terms identified to resolve the ambiguity to generate a modified utterance, and outputs the modified utterance. The computing system provides the modified utterance as input to a second language model. The second language model performs a natural language processing task based on the input modified utterance and outputs a result. The computing system outputs a response to the first user utterance based on the result.

Abstract: The present disclosure relates to resource allocation among a plurality of clients, for using a cloud-based service, e.g., a generative artificial intelligence (GenAI) service. A first target amount of resource and a second target amount of resource can be allocated to a first client and a second client (respectively). A first and a second client, a first target amount of resource can be allocated to a first client, and a second target amount of resource can be allocated to a second client for using the service. A request can be received from a third client for allocating resources; estimating that (i) the first client is using a first subset of the first target amount and not using a second subset of first target amount, and (ii) the second client is using a third subset of the second target amount and not using a fourth subset of second target amount. It can be determined that the second subset is greater than the fourth subset.

Abstract: Techniques are disclosed for returning references associated with an answer to a query. The techniques include accessing a text portion and identifying a plurality of sentences in the text portion. Each of the sentences is embedded to generate a respective plurality of text sentence embeddings. The text portion or a derivative thereof and a query are provided to a language model and a response to the query based on the text portion is received from the language model. A plurality of sentences are identified in the response. The plurality of sentences in the response is embedded to generate a plurality of response embeddings. The response embeddings are compared to the sentence embeddings to generate a similarity score for each sentence embedding-response embedding pair. Based on the similarity scores, an indication of a subset of the plurality of sentences is output with the response to the query.

Abstract: Techniques for managing events that record occurrences in a computing environment are disclosed. The system identifies events, and the system applies event processing mechanisms to the events. The event processing mechanisms generate incidents to represent the events. The system presents an interface that demonstrates how the events are mapped to the incidents. A user may interact with the interface to modify the event processing mechanisms and/or define new event processing mechanisms. Furthermore, the system may identify a group of uncompressed events, and the system may determine a candidate compression policy that would generate a single incident to represent the group of uncompressed events. The system may generate the candidate compression policy by applying a trained machine learning model to the group of uncompressed events. The system may simulate applying the candidate compression policy, and the system may present the results of the simulated application to the user on the interface.

Abstract: Techniques for generating terms to replace an initial set of search terms for a query are disclosed. A system generates a training data set for training a machine learning model. Generating the training data set includes generating search value vectors for each of a set of labels based on sets of search values associated respectively with the labels in the set of labels. The system trains a machine learning model to predict a target label for a target search vector based on the set of labels and the respectively associated search value vectors. The system generates a target search value vector based on an initial set of search values. The system then applies the trained machine learning model to the target search value vector to predict the target label. The target label is used as a search term, that replaces the initial set of search values, for executing the query.

Abstract: Techniques are disclosed for automatically generating Subjective, Objective, Assessment and Plan (SOAP) notes. Particularly, techniques are disclosed for identifying entities for automatic SOAP note generation. A text transcript is accessed and segmented into portions. The text transcript can correspond to an interaction between a first entity and a second entity. One or more entities for the respective portions are identified using one or more machine-learning models. Facts are from the respective portions using the one or more machine-learning models based at least in-part on the context of the respective portions. A SOAP note is generated using the one or more machine-learning models and based at least in-part on the facts. The SOAP note can be stored in a database in association with at least one of the first entity and the second entity.

Abstract: Operations of a certificate bundle distribution service may include: detecting a trigger condition to distribute a certificate bundle that includes a set of certificate authority certificates; determining, for each of a plurality of network entities associated with a computer network, a fault domain representing at least one single point of failure; partitioning the plurality of network entities into a plurality of certificate distribution groups, based on a set of partitioning criteria that includes a fault domain of each particular network entity, in which each particular certificate distribution group includes a particular subset of network entities, and the particular subset of network entities are associated with a particular fault domain; selecting a particular certificate distribution group, of the plurality of certificate distribution groups, for distribution of the certificate bundle; and transmitting the certificate bundle to the particular subset of network entities in the particular certificate di

Abstract: Techniques are disclosed for session management for variable-length message streams. In an example method, a computing system establishes a first session by receiving, from a first computer system, registration information including a first session identifier and a specification of a channel; determining a stream orchestration instance for the channel; and joining the first computer system to the first session for the stream orchestration instance based on the first session identifier. The computing system receives, from a second computer system, a message including context information, the context information including the first session identifier. The computing system identifies the first session based on the first session identifier, the first session having one or more member computer systems. The computing system outputs the message to at least one of the one or more member computer systems of the first session.

Abstract: A system and computer-implemented method include receiving a request for allocating graphical processing unit (GPU) resources for performing an operation. The request includes metadata identifying a client identifier (ID) associated with a client, throughput, and latency of the operation. A resource limit is determined for performing the operation based on the metadata. Attributes associated with each GPU resource of a plurality of GPU resources available for assignment are obtained. The attribute is analyzed that is associated with each GPU resource with respect to the resource limit. A set of GPU resources is identified from the plurality of GPU resources based on the analysis. A dedicated AI cluster is generated by patching the set of GPU resources within a single cluster. The dedicated AI cluster reserves a portion of a computation capacity of a computing system for a period of time and the dedicated AI cluster is allocated to the client associated with the client ID.

Abstract: Techniques for maintaining state and context of conversations between a user and digital assistant using threads. In one aspect, a method includes receiving a natural language utterance from a user during a session, obtaining a topic context instance for the natural language utterance, and generating, by a GenAI model, a list comprising an executable action based on candidate actions associated with the topic context instance. The executable action is then executed to produce an output. The executing includes determining there is no thread running within the session that is associated with the topic context instance, the executable action, or both, and responsive to determining there is no thread running, creating a thread associated with the topic context instance, the executable action, or both, and executing, using the thread, the executable action to obtain the output. The output or a communication derived from the output is then sent to the user.

Abstract: Techniques are disclosed for stream orchestration for variable-length message streams, including routes specified using an implementation-independent stream orchestration language (SOL). In an example method, a computing system receives a variable-length message, the variable-length message including context information and a payload. The computing system determines, from the context information, routing information that identifies at least one consumer of the variable-length message. The computing system outputs the variable-length message to the consumer.

Abstract: A method may include receiving a request for a big data query including characteristics and user parameters and accessing a set of rules based at least in part on respective properties of one or more big data query engines, the set of rules correlating the one or more characteristics, the one or more user parameters and the respective properties. The method may include determining a candidate list including a subset of the big data query engines, determined based on the set of rules. Using a machine learning model, the method may include generating respective probability scores for each big data query engine. The method may include selecting and executing the big data query using a particular big data query engine. The method may include identifying a trigger indicating a performance issue with the particular big data query engine and switching the execution to a second big data query engine.

Abstract: Described herein is a token exchange framework between two different cloud services providers. A multi-cloud infrastructure included in a first cloud environment that is provided by a first cloud services provider (CSP) receives a first request from a user associated with an account in a second cloud environment that is provided by a second CSP. The first request corresponds to using of a service provided by the first cloud environment and includes a first token issued by the second CSP. The multi-cloud infrastructure obtains a second token issued by the first CSP based on validating the first token with respect to a trust configuration corresponding to the second CSP. The trust configuration is previously generated and maintained by the first CSP in the first cloud environment. The multi-cloud infrastructure transmits the second token to the service to enable the user to utilize the service provided by the first cloud environment.

Abstract: Described herein is a token exchange framework between two different cloud services providers. A multi-cloud infrastructure included in a first cloud environment that is provided by a first cloud services provider (CSP) receives a first request from a user associated with an account in a second cloud environment that is provided by a second CSP. The first request corresponds to using of a service provided by the first cloud environment and includes a first token issued by the second CSP. The multi-cloud infrastructure obtains a second token issued by the first CSP based on validating the first token with respect to a trust configuration corresponding to the second CSP. The trust configuration is previously generated and maintained by the first CSP in the first cloud environment. The multi-cloud infrastructure transmits the second token to the service to enable the user to utilize the service provided by the first cloud environment.

Abstract: Techniques are described for using taints and assertions to protect data within one or more networks. Instead of being restricted to perimeter-based security and defining and creating rules that are difficult to maintain, techniques described herein allow users to protect data using assertions that are enforced at different enforcement points within one or more networks. According to some configurations, the assertions/policy statements defined by a user specify where data is allowed to travel throughout one or more networks. Assertions/policy statements can be as simple as “Red data never leaves my tenancy”, “Blue data never reaches the internet”, “Blue data is not stored with Red data”, “Green data never leaves Data Zone 2”, and the like. In some examples, a policy statement can protect the flow of data based on a number of hops the resource is from where the data is stored.

Abstract: Operations of a certificate authority (CA) service may include aggregating in a certificate repository, a plurality of sets of CA certificates, in which each set of CA certificates is issued by a particular CA that is associated with a particular trust zone and that is trusted by a particular set of network entities located in the particular trust zone. The operations may further include distributing for access by an additional set of network entities, an aggregate set of CA certificates that includes the plurality of sets of CA certificates. The additional set of network entities may utilize the plurality of sets of CA certificates to authenticate network entities located in different trust zones.

Abstract: Techniques for filtering queries to a large language model (LLM) based on their relevance to an enterprise domain associated with the LLM involve training a machine learning model using historical LLM query data and associated relevance scores. These scores indicate how closely a query relates to the enterprise's operations. The trained model is then applied to new input queries, generating relevance scores for the input queries. Queries meeting a predetermined relevance threshold are passed to the LLM for processing. For queries falling below this threshold, remedial actions are taken instead of processing by the LLM. The techniques optimize computational resource allocation by prioritizing queries relevant to the enterprise while filtering out less pertinent ones. The techniques create a relevance-based gatekeeping mechanism for LLM query processing, enhancing efficiency and focusing the LLM's capabilities on enterprise-specific tasks.

Abstract: Described herein is a token exchange framework between two different cloud services providers. A multi-cloud infrastructure included in a first cloud environment that is provided by a first cloud services provider (CSP) receives a first request from a user associated with an account in a second cloud environment that is provided by a second CSP. The first request corresponds to using of a service provided by the first cloud environment and includes a first token issued by the second CSP. The multi-cloud infrastructure obtains a second token issued by the first CSP based on validating the first token with respect to a trust configuration corresponding to the second CSP. The trust configuration is previously generated and maintained by the first CSP in the first cloud environment. The multi-cloud infrastructure transmits the second token to the service to enable the user to utilize the service provided by the first cloud environment.

Abstract: Operations of a certificate bundle validation service may include receiving a first certificate bundle that includes a first set of one or more digital certificates, and a digital signature, associated with the first certificate bundle; determining, using a public key of an asymmetric key pair associated with a second set of one or more digital certificates, that the digital signature is generated using a private key of the asymmetric key pair; and responsive to determining that the digital signature is generated using the private key, storing the first certificate bundle in a certificate repository as a trusted certificate bundle.

Abstract: Operations of a certificate bundle distribution service may include: detecting a trigger condition to distribute a certificate bundle that includes a set of one or more certificate authority certificates; partitioning each particular network entity of a plurality of network entities associated with a computer network into one of a plurality of certificate distribution groups based on a network address of the particular network entity, in which each particular certificate distribution group includes a particular subset of network entities from the plurality of network entities; selecting a particular certificate distribution group, of the plurality of certificate distribution groups, for distribution of the certificate bundle; and transmitting the certificate bundle to the particular subset of network entities in the particular certificate distribution group.

Abstract: Operations may include receiving, from a first network entity, a first request for a first certificate revocation list (CRL) that identifies a first CRL distribution point (CDP) corresponding to the first CRL; mapping the first CDP to a first CRL identifier of a set of available CRL identifiers; locating, in a CRL repository, a first CRL based on the first CRL identifier; and transmitting the first CRL to the first network entity.

Abstract: The present disclosure relates to network connectivity and reachability monitoring in cloud environments. In an example, a computer receives, from a device of a customer, input indicating a destination that belongs to a virtual cloud network and a set of vantage points for testing the destination. Based on the input, a first source is deployed to a first network location associated with a first vantage point and a second source is deployed to a second network location associated with a second vantage point. Each source is configured to send probes of different types to the destination. The computer system receives, probe data from each source based on probes sent by the source to the destination. Based on the received probe data, the computer system generates test data indicating at least one of connectivity or reachability of the destination. The test data is presented at a user interface.

Abstract: A method utilizes a policy engine for supply chain security analysis. The method includes processing programming code, written in an imperative programming language, using a fact collector to instantiate a programming object representing a set of facts of a build system that includes an artifact. The method further includes processing the programming object using a mapper to expose the set of facts as a fact database. The method further includes evaluating a policy, written in a declarative programming language, and the fact database using a policy engine to construct a result corresponding to the artifact of the build system. The method further includes presenting the result with a supply chain security analysis of the artifact.

Abstract: A method may include receiving a primary document including one or more selection boxes, one or more text lines, and one or more annotations. The method may include determining, a class based on the annotations. The method may include identifying the one or more selection boxes and one or more text lines of the primary document. The method may include generating a graph representing the one or more selection boxes and the one or more text lines. The method may include mapping each of the one or more selection boxes to a respective text line of the one or more text lines of the graph based at least in part on one or more characteristics associated with the selection boxes. The method may include generating a key-value pair associated with each of the one or more text lines and generating a document model of the primary document.

Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

Abstract: Techniques for increasing the precision of machine learning models that are trained to detect anomalous data points in a time series. The techniques including methods and systems for training machine learning models offline, using the trained machine learning models to predict anomalies in an online runtime environment, and updating anomaly detection models in the runtime and offline environments. The machine learning models may include a multitask model for predicting one or more anomalous events present in input time series data, and for each identified anomalous event type, predicting a type of machine learning model that is best suited for predicting that anomalous event type. The models may further include model instances selected using the predicted one or more anomalous events present in the input time series data and the predicted anomalous event type and used to predict an anomaly event in the input time series data.

Abstract: A pod hosted on a node is generated via the cloud orchestration platform. The pod is to be assigned to managing the one or more task processes corresponding to the data set. The pod is designated as a master pod. E ach of one or more worker pods replicates data from the master pod. Each of the one or more worker pods is hosted on another node. An input that triggers an upgrade for the corresponding virtual machines is detected. Execution of a custom script is initiated, which triggers: polling the microservice for status information of the pod and the set of replicas; and determining whether a condition for iteration advancement for the upgrade is satisfied based on the status upgrade. An incremental advancement of the upgrade to a next virtual machine is initiated upon determining that the condition for iteration advancement for the upgrade is satisfied.

Abstract: Described herein are various techniques for managing media related to a construction project, which may relate to one or more buildings to be built and/or renovated as part of the construction project. In accordance with techniques described herein, the media may be managed according to information regarding the construction project that is stored by one or more other systems separate from a media management system. The other systems may be construction information management systems that each store information regarding a construction project, including information relating to planning and/or execution of the construction project. For example, the media management system may generate tags based on the information regarding the construction project, may be trained to recognize features in the media that relate to the tags, and add the appropriate tags to the media based on the recognized features.

Abstract: Techniques for multi-output model forecasting are provided herein. An example method can include a computing system receiving a request to forecast a value for a variable at a future time point based upon a time series, the time series comprising a sequence of data points, each data point in the sequence of data points identifying a time point and at least one value associated with the time point. The computing system can predict, using a first trained machine learning model and based upon the times series, a plurality of forecast values for the future time point, the plurality of forecast values including: a first forecast value forecasted for the variable at the future time point; and a set of one or more forecast attribute values for one or more attributes of the time series, each of the set of one or more forecast attribute values predicted for the future time point.

Abstract: Aspects of the present application relate to systems, methods and non-transitory computer readable media for network virtualization in a rack-based switch. The method can include sending a communication from a first virtual machine (“VM”) instantiated on a first host machine to a first network virtualization Top of Rack (“ToR”) switch. The first network virtualization ToR can include a peripheral component interconnect express (“PCIe”) switch coupled to a plurality of host-side Ethernet ports, a virtualization device communicatingly coupled to the PCIe switch, which virtualization device can include a plurality of virtualization functions, and a switching ASIC coupled to the virtualization device and to a network-side Ethernet port. The method can include forming the communication into an Internet Protocol (“IP”) packet with a first virtualization function of the virtualization device, and sending the IP packet to a second VM with the switching ASIC.

Abstract: Techniques for improving system performance based on data characteristics are disclosed. A system may receive updates to a first data set at a first frequency. The system selects a first storage configuration, from a plurality of storage configurations, for storing the first data set based on the first frequency, and stores the first data set in accordance with the first storage configuration. The system may further receive updates to a second data set at a second frequency. The system selects a second storage configuration, from the plurality of storage configurations, for storing the second data set based on the second frequency, and stores the second data set in accordance with the second storage configuration. The second storage configuration is different than the first storage configuration.

Abstract: Techniques for anomaly detection are disclosed. An anomaly detection system transforms observation signals received from signal generators into multidimensional representations. The system extracts portions of the multidimensional representation indicative of anomalies. Based on the extracted portions of the multidimensional representation, the system determines metrics representing the observation signals and determines whether the metrics satisfy anomaly detection criteria.

Abstract: Techniques for ingesting data using an attribute definition file are disclosed. The system receives an attribute definition file that defines an attribute name, an entity type, and one or more validation criteria for an attribute. The system receives a dataset comprising a plurality of attribute values with a respective plurality of attribute names. The system determines whether an attribute name in the dataset maps to an attribute name in the definition file and maps the attribute value in the dataset to the attribute definition of the attribute. The system then determines whether the attribute value meets the validation criteria in the definition and stores the attribute value in a data repository when the value meets the validation criteria.

Abstract: Techniques for constraint programming-based periodic task scheduling are disclosed, including: determining a set of tasks to be scheduled across a set of shared resources, the set of tasks including multiple periodic tasks; filtering out one or more high-utilization tasks from the set of tasks to be scheduled; generating a constraint programming (CP) model based on the set of tasks, the CP model including a set of constrained variables, a set of constraints, and a search directive; applying a CP solver to the CP model, to obtain a CP solution for scheduling the set of tasks across the set of shared resources; where the CP solution assigns two or more of the periodic tasks to a same resource in the set of shared resources, based at least on the two or more periodic tasks having periods that are harmonically compatible.

Abstract: A method may include detecting one or more selection boxes and one or more text lines in a primary document. The method may include determining respective vectors associated with the selection box and adjacent text lines to the selection box in a plurality of directions. The method may include determining a set of respective vectors associated with a unique selection box. The method may include determining a variance between respective vectors in the set of respective vectors and identifying a particular direction corresponding to a minimal variance between the respective vectors in the set of respective vectors as compared to a variance of other sets of respective vectors. The method may include generating a key-value pair based on the set of respective vectors characterized by the minimal variance. The method may include generating a document model, including the key-value pair, and extracting data according to the document model.

Abstract: Techniques are disclosed for provisioning a cloud service of a first cloud service provider using a control plane of a second cloud service provider. The techniques include detecting that a request for a cloud service provided by the first cloud service provider has been received from the second cloud environment of a second cloud service provider different from the first cloud service provider. The techniques further include, after detecting that the request for the cloud service has been received, provisioning a first set of resources within the first cloud environment and linking the first set of resources to a second set of resources within the second cloud environment. Linking the first set of resources to the second set of resources enables data pertaining to the cloud service to be transferred from the second cloud environment to the first cloud environment.

Abstract: Techniques are disclosed for providing services based on infrastructure distributed between multiple cloud service providers. Low-latency high-bandwidth cross-cloud services can be provisioned and managed by and between private clouds of cloud service providers. The techniques include forming a cloud network between a first set of compute resources of a first infrastructure of a first cloud environment and a second set of compute resources of a second infrastructure of a second cloud environment. The first cloud environment is provided by a first cloud service provider and the second cloud environment is provided by a second cloud service provider different from the first cloud service provider.

Abstract: A flow graph of a first compilation unit of the program is expanded. The first corresponding parameter states of statements of the first compilation unit are marked as empty. The statements of the first compilation unit are processed to update the first corresponding parameter states with an exact type and a value set. Processing the statements include tracking a control flow predicate of a block in the first compilation unit, wherein the block comprises a first subset of the statements. Only after the control flow predicate indicates that the block is executable, the method includes processing an invocation statement in the first subset of the first statements, and expanding, responsive to the invocation statement, the flow graph for a second compilation unit of the program that is identified by the invocation statement.

Abstract: Techniques described herein relate to systems and methods of data storage, and more particularly to providing layering of file system functionality on an object interface. In certain embodiments, file system functionality may be layered on cloud object interfaces to provide cloud-based storage while allowing for functionality expected from a legacy applications. For instance, POSIX interfaces and semantics may be layered on cloud-based storage, while providing access to data in a manner consistent with file-based access with data organization in name hierarchies. Various embodiments also may provide for memory mapping of data so that memory map changes are reflected in persistent storage while ensuring consistency between memory map changes and writes. For example, by transforming a ZFS file system disk-based storage into ZFS cloud-based storage, the ZFS file system gains the elastic nature of cloud storage.

Abstract: Techniques are disclosed for provisioning and managing resources within a cloud infrastructure of a first cloud service provider for a cloud service offered by a second cloud service provider. Cross-cloud services can be provisioned and managed by and between private clouds of cloud service providers. The techniques include receiving a request for a cloud service by a component of a first private cloud within a first cloud environment and from a component of a second private cloud within a second cloud environment. The techniques further include the component of the first private cloud performing one or more operations to establish network connectivity prerequisites for network connectivity between the first private cloud and the second private cloud and causing one or more components of the first private cloud to provision the cloud service in the second private cloud.

Abstract: In some implementations, the techniques may include receiving an accuracy target for one or more machine learning models. In addition, the techniques may include training the models on a labeled training set of labeled data. The techniques may include, until the accuracy of the models satisfies the accuracy target: sampling, a set of unlabeled data to obtain a random training set of unlabeled data; labeling the random training set of unlabeled data using the models to produce a pseudo labeled training set; correcting the labels on a random subset of the pseudo labeled training set; training the models on the labeled training set, the corrected random subset, and the pseudo labeled training set; and evaluating the accuracy of the models using an evaluation set of labeled data. The one or more models can be deployed based at least in part on the models satisfying the accuracy target.