Abstract: A method for determining regression of a database query associated with a first execution plan includes receiving a first performance measure for the database query and the first execution plan; determining a presence of at least one stored performance measure for the database query in a data repository; adding the first performance measure and identifiers for the database query and the first execution plan to a synchronization list; retrieving a second performance measure associated with a second execution plan from the data repository; determining a regression of the database query by comparing the first performance measure to the second performance measure; in response to a determination that the database query is regressed, adding the first performance measure, the second performance measure, and identifiers for the database query and the first execution plan to a regression report.

Abstract: Methods, systems, and devices for wireless communications are described. In some examples, a user equipment (UE) may support dual connectivity. That is, the UE may establish communications with a master node and a secondary node of a wireless communications network. The master node may correspond to a master cell group (MCG) and the secondary node may correspond to a secondary cell group (SCG). In some examples, as described herein, the UE operating in dual connectivity may collect data for optimization of the wireless communications network or upon detecting a failure associated with the master cell group or the SCG and transmit the collected data to a network entity (e.g., one of the master node or the secondary node), where the collected data is based on the SCG being in a deactivated state. In some examples, upon receiving the collected data, the network entity may attempt to recover from the failure.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may obtain an indication to perform a selective secondary cell group activation or a subsequent conditional handover associated with a subsequent conditional primary cell change, the subsequent conditional handover associated with the subsequent conditional primary cell change being performed after a previous conditional handover associated with a previous conditional primary cell change. The UE may initiate the selective secondary cell group activation or the subsequent conditional handover. The UE may detect a failure occurrence associated with the selective secondary cell group activation or the subsequent conditional handover.

Abstract: Synthetic endornaviral satellite RNA molecules and satellite particles containing the same are disclosed. The synthetic endornaviral satellite RNA molecules can include coding and/or non-coding cargo sequences, and are heritable through generations of plants. Also disclosed are methods of using the endornaviral satellite RNA molecules and satellite particles containing the same to change plant phenotypes, improve plant stress resistance, and improve plant pest and pathogen resistance.

Abstract: Certain aspects of the present disclosure provide techniques for data collection for non-terrestrial networks (NTN). One aspect provides a method for wireless communications by a user equipment (UE). The method generally includes transmitting an indication of a capability of the UE to connect to a network via both terrestrial network (TN) cells and non-terrestrial network (NTN) cells and transmitting one or more data collection reports in accordance with the indicated capability.

Abstract: A computer mediated communication system includes a plurality of communication devices, a wireless transceiver, a computer system configured to wirelessly communicate with the communication devices via the wireless transceiver and mediate communications among the communication devices. The computer system a memory coupled with a processor.

Abstract: This disclosure provides systems, methods, and devices for wireless communication that support AI model-based enhancements for RRC IDLE and INACTIVE state operations. In a first aspect, a method of wireless communication includes receiving, by a wireless communication device, artificial intelligence (AI) model configuration information for IDLE/INACTIVE state procedures; retrieving, by the wireless communication device, an AI model for IDLE/INACTIVE state procedures based on the AI model configuration information; and performing, by the wireless communication device, one or more IDLE/INACTIVE state procedures based on the AI model. Other aspects and features are also claimed and described.

Abstract: A source network node and a UE may obtain at least one mobility related prediction associated with the UE or at least one target network node, the at least one mobility related prediction being derived by at least one neural network, and the source network node may handover the UE from the source network node to the at least one target network node based on the at least one mobility related prediction. The target network node may receive the handover request, obtain at least one mobility related prediction associated with the UE or the target network node, and output for transmission a handover request ACK, the handover request ACK based at least in part on the at least one mobility related prediction.

Abstract: A system for processing image data and position data, the system comprising: a memory for storing the image data and the position data; and processing circuitry in communication with the memory. The processing circuitry is configured to: apply a first encoder to extract, from the image data, a first set of features; apply a first decoder to determine, based on the first set of features, a first uncertainty score. Additionally, the processing circuitry is configured to apply a second encoder to extract, from the position data, a second set of features; apply a second decoder to determine, based on the second set of features, a second uncertainty score; and fuse the first set of features and the second set of features based on the first uncertainty score and the second uncertainty score.

Abstract: Methods, apparatus, systems and articles of manufacture to compensate radar system calibration are disclosed. A radio-frequency (RF) subsystem having a transmit channel, a receive channel, and a loopback path comprising at least a portion of the transmit channel and at least a portion of the receive channel, a loopback measurer to measure a first loopback response of the RF subsystem for a first calibration configuration of the RF subsystem, and to measure a second loopback response of the RF subsystem for a second calibration configuration of the RF subsystem, and a compensator to adjust at least one of a transmit programmable shifter or a digital front end based on a difference between the first loopback response and the second loopback response to compensate for a loopback response change when the RF subsystem is changed from the first calibration configuration to the second calibration configuration.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit, to a network node, a request for network assisted information associated with one or more of a machine learning (ML) model or analytics, the ML model or the analytics being associated with an application layer communication that is associated with the UE. The UE may receive, from the network node, one or more of the ML model or the analytics. Numerous other aspects are described.

Abstract: Methods, systems, and devices for wireless communication are described. A network entity may configure a user equipment (UE) to report applicability information associated with artificial intelligence (AI) and/or machine learning (ML) functionalities and/or models. The applicability information indicates an applicability of a functionality and/or model per AI/ML-enabled feature or feature group. Based on the applicability information and a handover of the UE from a source network entity to a target network entity, the target network entity may configure a functionality and/or model. Additionally, or alternatively, the UE may be configured with a reference configuration and one or more additional configurations corresponding to respective AI/ML functionalities and/or models. The UE may apply an additional configuration based on a handover to the target network entity, the additional configuration satisfying an applicability condition, or both.

Abstract: In a method for managing and distributing content in a plurality of observation platforms, a message is generated at a cloud based component of a content distribution manager in response to user interaction via an interface of the content distribution manager. Responsive to the user interactions and to interactions of third party software via an application program interface with the cloud based component, delivery of the message is scheduled and a plurality of specified devices belonging to a specified class of employees are specified for delivery in a plurality of observation platforms at a designated time and location via the cloud based component. The plurality of observation platforms are located in a plurality of different enterprise locations. The message is delivered to the plurality of observation platforms which relay the message to the plurality of specified devices.

Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method includes receiving an image frame representing a scene; receiving point cloud data representing the scene; determining first sets of image frame features; determining second sets of point cloud data features based on a plurality of voxels representing the point cloud data; determining a third set of features of the image frame based on a first set of features of the plurality of first sets of features of the image frame and a second set of features of the plurality of second sets of features of the point cloud data; and outputting fused data that combines the third set of features of the image frame and a fourth set of features of the point cloud data. Other aspects and features are also claimed and described.

Abstract: Methods, systems, and devices for wireless communications are described, in which a base station may determine a load balancing adjustment for traffic communicated through the base station. The load balancing adjustment may be provided at a beam level for one or more beams of a plurality of beams, at a network slice level for one or more network slice IDs of a number of network slice IDs, or combinations thereof. The base station may use the load balancing adjustment to identify one or more updated parameters for triggering a handover of at least one user equipment (UE) between beams or slice IDs of a same cell or different cells. The base station may format a mobility change request that indicates the one or more parameters that are updated, and transmit the mobility change request to another base station to establish parameters for handovers between the base stations.

Abstract: Systems, apparatus, and methods are provided that identify a querying user's top peers, recommends an optimal product interaction journey, and tracks the value delivered. Peers of the querying user are identified based on the common profile attributes, product interaction, and similar user initiatives. A champion peer is recommended by sorting the peers based on the cumulative quality of product interactions which is modeled by a learnable parametric equation. A metric is formulated by measuring the value delivered to the user by computing the number of product interactions aligning to the user's initiative. Most engaging product interactions (e.g., documents read, events attended) by the champion peer aligning to querying user's initiatives are identified and are recommended to the querying user as a product interaction journey.

Abstract: A joint segmenting and ASR model includes an encoder to receive a sequence of acoustic frames and generate, at each of a plurality of output steps, a higher order feature representation for a corresponding acoustic frame. The model also includes a decoder to generate based on the higher order feature representation at each of the plurality of output steps a probability distribution over possible speech recognition hypotheses, and an indication of whether the corresponding output step corresponds to an end of segment (EOS).

Abstract: In a method of query derivation in an observation platform a signal from a first communication device is received at a second communication device that is associated with a computer system. The computer system is associated with an organization and a first characteristic of the signal corresponds to an audible source. The signal is parsed, by the computer system according to a policy, to determine metadata associated with the signal, wherein the first communication device is operated by a user and wherein the policy dictates rules for use of the metadata. The computer system determines a prior user history of the user. The computer system converts the audible source of the signal to text or machine understandable language. The computer system derives a query related to the organization based on at least one of the prior user history, the metadata, and the text or machine understandable language.

Abstract: Generally, the present disclosure is directed to enhanced federated learning (FL) that employs a set of clients with varying amounts of computational resources (e.g., system memory, storage, and processing bandwidth). To overcome limitations of conventional FL methods that employ a set of clients with varying amounts of computational resources, the embodiments run multi-directional knowledge distillation between the server models produced by each federated averaging (FedAvg) pool, using unlabeled server data as the distillation dataset. By co-distilling the two (or more) models frequently over the course of FedAvg rounds, information is shared between the pools without sharing model parameters. This leads to increased performance and faster convergence (in fewer federated rounds).

Abstract: A method includes receiving distillation data including a plurality of out-of-domain training utterances. For each particular out-of-domain training utterance of the distillation data, the method includes generating a corresponding augmented out-of-domain training utterance, and generating, using a teacher ASR model trained on training data corresponding to a target domain, a pseudo-label corresponding to the corresponding augmented out-of-domain training utterance. The method also includes distilling a student ASR model from the teacher ASR model by training the student ASR model using the corresponding augmented out-of-domain training utterances paired with the corresponding pseudo-labels generated by the teacher ASR model.