Abstract: A method of wireless communications by a user equipment (UE) includes communicating with a network based on a machine learning model for wireless communication. The method also includes monitoring a status of the machine learning model for wireless communication. The method further includes reporting the status of the machine learning model for wireless communication using a predetermined resource according to a predetermined format. The method also includes falling back to communicating with a fallback procedure, instead of the machine learning model for wireless communication, to maintain wireless communication with the network in response to the status of the machine learning model indicating a model failure.

Abstract: Methods, systems, and devices for wireless communications are described. In some systems, devices use machine learning (ML) models to support wireless communications. For example, a user equipment (UE) may download ML model information from a network to determine an ML model. The network may additionally configure a status reporting procedure, a fallback procedure, or both for the ML model. In some examples, based on a configuration, the UE may transmit a status report to a base station according to a reporting periodicity, a UE-based trigger, a network-based trigger, or some combination thereof. Additionally or alternatively, the UE may determine to fallback from operating using the ML model to operating in a second mode based on a fallback trigger. In some examples, to restore operating using a downloaded ML model, the UE may download an updated ML model or receive iterative updates to a previously downloaded ML model.

Abstract: A configuration for reporting OOD samples for neural network optimization. The apparatus receives, from a base station, a configuration to report an OOD dataset for a machine learning model. The apparatus detects an occurrence of one or more OOD events. The apparatus reports the OOD dataset comprising the one or more OOD events based on the configuration to report OOD dataset. The apparatus receives, from the base station, an update to the machine learning model. The OOD dataset may comprise raw data related to the one or more OOD events, or may comprise extracted latent data corresponding to features of raw data related to the one or more OOD events.

Abstract: A method for the synthesis of method for the manufacture of carbide cermet powders, comprises high energy ball milling a mixture of precursor powders and a carbon source, followed by annealing the milled powder mixture. The precursor powders are selected from materials suitable for the formation of cermets, for example silicon, titanium, thorium, hafnium, vanadium, chromium, tungsten, tantalum, niobium, and zirconium-containing materials. The precursors further include a source of carbon. Tungsten cobalt carbide powders produced by this method are submicron-sized (0.2 to 0.4 microns) with internal nanograins (10 to 40 nanometers in diameter).

Abstract: A method for the synthesis of micron- and submicron-sized, nanostructured metal carbide powders, comprising high energy milling of metal oxide and carbon precursors followed by annealing of the as-milled powders. The annealing is preferably carried out under a flow of inert gas or subatmospheric pressure to drive the reaction to completion in one to two hours. The powders thus synthesized comprise high purity particles having a narrow particle size range.