Abstract: A method comprises collecting message-oriented-middleware system parameters from a plurality of message-oriented-middleware clusters, analyzing the parameters using one or more machine learning algorithms, and predicting, based at least in part on the analyzing, at least one anomaly in a message-oriented-middleware cluster of the plurality of message-oriented-middleware clusters. In the method, message metadata is collected from the message-oriented-middleware cluster, and at least part of the message metadata is transmitted to one or more remaining ones of the plurality of message-oriented-middleware clusters. At least the part of the message metadata corresponds to messaging operations to be transferred from the message-oriented-middleware cluster to the one or more remaining ones of the plurality of message-oriented-middleware clusters.

Abstract: A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about at least one of respective protocols and respective interfaces associated with respective ones of the plurality of components. The data is analyzed to determine at least one of the respective protocols and the respective interfaces associated with the respective ones of the plurality of components. In the method, operations of one or more components of the plurality of components are tested based at least in part on the determination of the at least one of the respective protocols and the respective interfaces. The method further includes outputting respective statuses of the one or more components, wherein the respective statuses are derived at least in part from the testing.

Abstract: One example method includes detecting an operation of an input device, obtaining a clickstream associated with a user, using information from the clickstream, generating a prediction as to a next action by the user using the input device, and presenting the prediction to the user for possible selection by the user. Selection of the prediction by the user eliminates the need for the user to perform input device manipulations that would otherwise be required if the prediction were not selected.

Abstract: One example method includes pre-processing a dataset, the dataset including data and/or metadata indicating attributes of a user, and the dataset also includes data and/or metadata that was generated as a result of an interaction between the user and a computing system, after the dataset is pre-processed, providing the dataset as an input to a machine learning model, using the machine learning model to generate, based on the input, respective target variable value predictions for each target in a group of targets, and each of the targets corresponds to a respective attribute of the user, using the target value variable predictions to create, or modify, a digital human that has attributes corresponding to the attributes of the user, and deploying the digital human so that the digital human is available to interact with the user.

Abstract: Methods, systems, and devices for wireless communications are described. For instance, a first wireless device may receive, from a second wireless device, a first message, the first message including one or more first information blocks encoded based on a first key. The first wireless device may transmit, to the second wireless device, an indication that the one or more first information blocks have been successfully decoded. The first wireless device may communicate, with the second wireless device, a second message, where the second message includes information that is encoded based on a second key that is derived from at least a portion of the decoded one or more first information blocks.

Abstract: One example method includes pre-processing a dataset, wherein the dataset includes data and/or metadata that indicates a software configuration of an internet of things (IoT) device, and/or indicates a history of any performance issues and/or security issues experienced by the IoT device, after the dataset is pre-processed, providing the dataset as an input to a machine learning model, using the machine learning model to generate, based on the input, respective target variable value predictions for each target variable in a group of target variables, and a first one of the target variables corresponds to the software configuration, and a second one of the target variables corresponds to the history, and when the target variable value predictions indicate a potential security issue and/or a potential performance issue, with the IoT device, taking a remedial action to resolve the potential security issue and/or the potential performance issue.

Abstract: One example method includes instantiating an ambassador component in a cloud computing environment that comprises container instances and databases, accessing, by the ambassador component, a prediction as to a number of database connections expected to be used by a particular one of the container instances, and the prediction was generated by a prediction engine, receiving, by the ambassador component, a request from the particular container instance for access to one of the databases, and providing, by the ambassador component to the particular container instance from which the request for access was received, an access object usable by that particular container instance to use a database connection to access the database identified in the request.

Abstract: Methods, systems, and devices for wireless communications are described. The described techniques provide for beam training a reconfigurable intelligent surface (RIS) for sidelink communications between UEs. A base station may transmit an indication of a resource pool to a receiving UE and a transmitting UE for beam training a RIS. The transmitting UE may identify a resource pattern for training the RIS using the indicated resource pool where the resource pattern is associated with a pattern of beams at the RIS. The transmitting UE may transmit a sidelink reference signal using the identified resource. The receiving UE may receive the reference signal via the RIS according the pattern of beams and transmit a beam report to the transmitting UE. Beam training may be based on a capability of the RIS to use either a different beamformer or a different RIS configuration on different subchannels of the same resource pool.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit, to a network entity, multiple physical random access channel (PRACH) transmissions based at least in part on a PRACH slot frequency hopping. The UE may receive, from the network entity, a response based at least in part on the multiple PRACH transmissions associated with the PRACH slot frequency hopping. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network node may receive at least one communication of a plurality of communications associated with at least one physical layer channel, wherein a first set of the plurality of communications includes the at least one communication and is secured by a first security key, and a second set of the plurality of communications is secured by a second security key. The network node may decrypt the at least one communication based at least in part on the first security key, wherein the first security key is based at least in part on a first set of physical layer parameter values and the second security key is based at least in part on a second set of physical layer parameter values. Numerous other aspects are described.

Abstract: A method for wireless communication performed by a user equipment (UE) includes transmitting, to a network node, a group of physical random access channel (PRACH) preambles. Each PRACH preamble may be transmitted during a respective PRACH occasion of a group of PRACH occasions. The method also includes receiving, from the network node via a downlink control channel, a downlink grant indicating downlink resources for receiving a random access response (RAR) based on transmitting the group of PRACH preambles. The downlink control channel may be scrambled based on a random access-radio network temporary identifier (RA-RNTI) associated with a single PRACH occasion of the group of PRACH occasions. The method still further includes receiving, from the network node via the downlink resources, the RAR based on receiving the downlink grant. The method also includes transmitting, to the network node, a random access message based on receiving the RAR.

Abstract: One example method includes receiving, by a workspace size predicting engine, a workspace provisioning request regarding a customer machine learning (ML) model, predicting, by the workspace size predicting engine, a size of a workspace that corresponds to the workspace provisioning request, receiving, by a datacenter host prediction engine from the workspace size predicting engine, the workspace size, and predicting, by the datacenter host prediction engine, a datacenter and/or host that is able to support requirements of the workspace.

Abstract: One example method includes receiving, by a workspace size predicting engine, a workspace provisioning request including resource requirement information that specifies one or more features that are to be included when a workspace is provisioned. The one or more features include at least a machine learning (ML) model that is to be run in the workspace. The method also includes predicting, by the workspace size predicting engine, the one or more resources for provisioning the workspace that corresponds to the workspace provisioning request.

Abstract: A UE may receive a configuration of multiple TCI states including a TCI state for each of multiple TRPs. The UE may also receive an indication of dormancy for one or more TRP of the multiple TRPs. The UE may further transmit or receive communication based on a default TCI state, the default TCI based at least in part on the dormancy of the TRP. The UE may communicate with a network entity that configures multiple TCI states for the UE. The multiple TCI states may include a TCI state for each of multiple TRPs. The network entity may output for transmission an indication of dormancy for one or more TRP of the multiple TRPs. The network entity may further output or obtain communication based on a default TCI state, the default TCI based at least in part on the dormancy of the TRP.

Abstract: Methods, systems, and devices for wireless communications are described. The method includes a reconfigurable intelligent surface receiving, from a network entity, a synchronization signal block associated with a synchronization signal block index, reflecting the synchronization signal block as a set of multiple instances of the synchronization signal block via a corresponding set of multiple downlink transmit beams, receiving a random access message from a user equipment (UE) during a random access occasion that corresponds with the synchronization signal block index and on an uplink receive beam that corresponds with one of the set of multiple downlink transmit beams, identifying the phase-change pattern of multiple phase-change patterns that corresponds to the uplink receive beam, and reflecting the random access message to the network entity by applying the phase-change pattern to antenna elements of the reconfigurable intelligent surface that reflect the random access message to the network entity.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may be configured to transmit multiple repetitions of a random access message using multiple spatial filters. In some cases, the UE may transmit each repetition of the random access message using a same transmission power determined based on one of the spatial filters. In other cases, the UE may transmit each repetition of the random access message using a different transmission power determined in accordance with power control parameters corresponding to the respective spatial filters. The UE may utilize a spatial filter of a first-received responsive random access message to perform subsequent transmissions of a random access procedure. In the case of a failure of the random access procedure, the UE may increment (or refrain from incrementing) a power ramping counter based on spatial filters used to re-initiate the random access procedure.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive control signaling from a network entity scheduling a set of multiple periodic tracking reference signals (TRSs) and indicating a transmission power level for the periodic TRSs. For example, the control signaling may be received via radio resource control (RRC). The UE may receive a downlink control information (DCI) message from the network entity indicating an adjustment to the transmission power level and a resource for an aperiodic TRS. The UE may monitor the resource for the aperiodic TRS in accordance with the indicated adjustment to the transmission power level. The UE may transmit a message to the network entity indicating a measurement of the aperiodic TRS based on the adjustment to the power level. The adjustment to the power level may apply to one or more of the set of multiple periodic TRSs.

Abstract: Methods, systems, and devices for wireless communication are described to support phase continuity in uplink transmissions within a bundle interval. In a first example, a user equipment (UE) may determine bundle intervals for an uplink channel transmission based on a transmission time interval (TTI) format pattern and a bundle size. Bundle intervals may start on a next available uplink TTI after an end of a preceding bundle interval, such that each bundle interval may include at least one uplink TTI. In a second example, the UE may use frequency resources for repetitions of the uplink channel transmission within a bundle interval based on an index of the bundle interval. In a third example, the UE may not bundle some repetitions of the uplink channel transmission in a same bundle interval, and may use different frequency resources for the repetitions, based on one or more phase continuity rules.

Abstract: Aspects of the present disclosure provide mechanisms for reducing the payload size of the channel state information (CSI) multiplexed together with deferred acknowledgement information. In some examples, a user equipment (UE) may transmit combined acknowledgement information including the respective acknowledgement information for one or more of received packets, along with turbo acknowledgement information including both the respective acknowledgement information and the respective CSI feedback for one or more remaining ones of the packets within an uplink resource.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may transmit one or more messages indicating a first time period having a duration prior to expected traffic being available for transmission at the UE, and indicating a size associated with the expected traffic (e.g., a quantity of bits). In some examples, the one or more messages may include a UE assistance information (UAI) message or a buffer status report (BSR) message. The UE may receive a response message based on the one or more messages indicating that a network entity is to enter a sleep mode during a second time period based on the first time period and the size associated with the expected traffic. In some examples, the network entity may enter the sleep mode based on the first time period and the size associated with the expected traffic.