Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a grant indicating a modulation and coding scheme (MCS) that indicates that the grant is for a retransmission of an initial communication. The UE may decode, based at least in part on the grant indicating the MCS that indicates that the grant is for the retransmission and using a transport block size (TBS) of a prior communication, a communication scheduled by the grant, wherein the prior communication is prior to the initial communication. Numerous other aspects are described.

Abstract: A method of wireless communication performed by a user equipment (UE) includes receiving one or more configuration messages indicating configuration of the UE with a first component carrier (CC) and at least a second CC for a first subscription corresponding to a first subscriber identity module (SIM) of the UE. The method further includes performing one or more operations associated with a second subscription corresponding to a second SIM of the UE during a time interval. During the time interval, communication by the first subscription using the second CC is avoided based on a first throughput associated with the first CC exceeding a second throughput associated with the second CC.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a grant indicating a modulation and coding scheme (MCS) that indicates that the grant is for a retransmission of an initial communication. The UE may decode, based at least in part on the grant indicating the MCS that indicates that the grant is for the retransmission and using a transport block size (TBS) of a prior communication, a communication scheduled by the grant, wherein the prior communication is prior to the initial communication. Numerous other aspects are described.

Abstract: A system can monitor applications and analyze the metrics to determine if one or more of the applications are regressing or performing as expected. The metric analysis includes performing a first short term data analysis and, if data is not as expected, a second short term analysis based on machine learning-based pattern recognition machines. If the short-term analysis finds the metrics aren't as expected, a long-term analysis is performed. The long-term analysis can compare chunks of streaming metric data to cached metric blocks and historical data, and can include a concept drift analysis.

Abstract: The present system provides continuous delivery and service regression detection in real time based on log data. The log data is clustered based on textual and contextual similarity and can serve as an indicator for the behavior of a service or application. The clusters can be augmented with the frequency distribution of its occurrences bucketed at a temporal level. Collectively, the textual and contextual similarity clusters serve as a strong signature (e.g., learned representation) of the current service date and a strong indicator for predicting future behavior. Machine learning techniques are used to generate a signature from log data to represent the current state and predict the future behavior of the service at any instant in time.

Abstract: A system can monitor applications and analyze the metrics to determine if one or more of the applications are regressing or performing as expected. The metric analysis includes performing a first short term data analysis and, if data is not as expected, a second short term analysis based on machine learning-based pattern recognition machines. If the short-term analysis finds the metrics aren't as expected, a long-term analysis is performed. The long-term analysis can compare chunks of streaming metric data to cached metric blocks and historical data, and can include a concept drift analysis.

Abstract: This disclosure provides methods, systems, and devices for wireless communications. In some aspects, an access point (AP) may receive an overlapping basic service set (OBSS) packet from another AP and determine timing information associated with the OBSS packet. The AP may align data symbols of the spatial reuse packet with data symbols associated with the OBSS packet, and in some examples, transmit a spatial reuse packet to a station (STA) based on the alignment. In some examples, a STA in communication with an AP may receive a spatial reuse packet from the AP and an OBSS packet from an additional AP. The STA may determine an alignment between timing information associated with the spatial reuse packet and timing information associated with the OBSS packet, and in some examples, apply one or more signal interference mitigation schemes based on the alignment.

Abstract: A system monitors applications, analyzes metrics, and provides a dashboard that communicates whether an application is performing as expected. The metric analysis includes performing one or more of a first short term data analysis, a second short term analysis based on machine learning-based pattern recognition machines, and a long-term analysis is performed. Transaction performance metrices are determined based on the monitored of the application. The transaction performance metrices are scored, scaled, and aggregated into a single scaled representation for the application. The scaled application value is then reported to a user through a dynamically updated dashboard. The dashboard displays graphical information representing the health of monitored transactions over time. The reported information can be expanded to additional layers of detail.

Abstract: The present system uses delegates installed in remote environments to called and transmit, to a remote manager, time series metric data (or data from which metrics can be determined) in real-time. The numerical time series data is persisted, and a learned representation is generated from the data, for example by discretization. The learned representation is then clustered, the clusters are compared to new data, anomalies are determined, and deviation scores are calculated for the anomalies. The derivation scores are compared to thresholds, and results are reported through, for example, a user interface, dashboard, and/or other mechanism.

Abstract: The present system provides continuous delivery and service regression detection in real time based on log data. The log data is clustered based on textual and contextual similarity and can serve as an indicator for the behavior of a service or application. The clusters can be augmented with the frequency distribution of its occurrences bucketed at a temporal level. Collectively, the textual and contextual similarity clusters serve as a strong signature (e.g., learned representation) of the current service date and a strong indicator for predicting future behavior. Machine learning techniques are used to generate a signature from log data to represent the current state and predict the future behavior of the service at any instant in time.

Abstract: The present system uses delegates installed in remote environments to called and transmit, to a remote manager, time series metric data (or data from which metrics can be determined) in real-time. The numerical time series data is persisted, and a learned representation is generated from the data, for example by discretization. The learned representation is then clustered, the clusters are compared to new data, anomalies are determined, and deviation scores are calculated for the anomalies. The derivation scores are compared to thresholds, and results are reported through, for example, a user interface, dashboard, and/or other mechanism.