Abstract: Systems and methods are disclosed for functionality-based life cycle management (LCM) of artificial intelligence/machine learning (AI/ML) models in wireless communications. A user equipment (UE) transmits capability information indicating support for AI/ML sub-use cases and receives radio resource control (RRC) configuration comprising applicable conditions for model identification. These conditions include network-side configurations and associated identifiers that abstract additional proprietary deployment characteristics without explicit disclosure. The UE determines model availability for inferencing based on received conditions and reports this to the network. Model identification occurs through alignment of applicable conditions, associated identifiers, or datasets between network and UE.

Abstract: Systems and methods are disclosed for supporting artificial intelligence/machine learning (AI/ML)-based positioning. A Network Data Analytics Function (NWDAF) containing a Model Training Logical Function (MTLF) for model training and containing Analytics Logic Function (AnLF) for inference and analytics derivation are provided. A Location Management Function (LMF) provides input data through positioning protocols, collecting measurements from a user equipment (UE) and next generation radio access network (NG-RAN) nodes. The LMF may provide inference capabilities rather than the AnLF, which performs analytics derivation using trained models provided by NWDAF MTLF.

Abstract: The present invention discloses a gene mining method and system based on transcriptome and DNA methylome. The gene mining method comprises: acquiring samples; performing transcriptome sequencing on the samples and analyzing data obtained by the sequencing to obtain differentially expressed genes; performing DNA methylation sequencing on the samples and analyzing data obtained by the sequencing to obtain differentially methylated genes; performing gene function enrichment on the obtained differentially expressed genes and differentially methylated genes to obtain a molecular protein-protein interaction (PPI) network; and performing gene clustering with the molecular PPI network to identify target genes. The present invention solves the problems that it takes time and effort to mine core genes of existing transcriptome and methylome and the result is not accurate enough. The present invention mines target genes by organically integrating originally isolated transcriptome data and DNA methylome data.

Abstract: Embodiments are related to a fifth generation (5G) or sixth generation (6G) wireless communications system. A method for an access node of a wireless system comprises encoding a session start request message to start a data collection session (DCS) to collect data for a machine learning (ML) model from a user equipment (UE) by a base station of a wireless system, the session start request message including UE context information, decoding a session start response message to indicate the start of the DCS by the base station, the session start response message including DCS configuration information for the ML model, and encoding a data collection request message for the UE to collect measurements for the ML model based on the DCS configuration information for the ML model by the base station. Other embodiments are described and claimed.

Abstract: A computer-readable storage medium stores instructions for execution by one or more processors of a base station. The instructions configure the base station for 5G-NR QoS monitoring and reporting and cause the base station to encode a Downlink User Data frame for transmission from a CU-UP to a DU of the base station. The Downlink User Data frame includes an indicator requesting a delivery status for a downlink data transmission. A Downlink Data Delivery Status frame is encoded for transmission from the DU to the CU-UP using an F1-U interface. The Downlink Data Delivery Status frame includes a feedback delay measured at the DU based on the indicator. The feedback delay indicates a time between reception of the Downlink User Data frame and transmission of the Downlink Data Delivery Status frame. A downlink or uplink delay associated with the F1-U interface is determined based on the feedback delay.

Abstract: This disclosure describes systems, methods, and devices related to collaboration between user equipment (UE) and network for machine learning. A radio access network (RAN) node B device may transmit, to the CE device, an indication that the node B device supports machine learning; identify a service registration, received from the UE device, indicating that the UE device requests machine learning support from the node B device; transmit, to the UE device, a request for information associated with the UE device, the information associated with at least one of hardware capabilities or machine learning capabilities of the UE device; identify the information received from the UE based on the request for information; and transmit, to the UE device, a machine learning configuration for use by the UE device, wherein the machine learning configuration is based on the information.

Abstract: An apparatus for an access node, includes a memory interface to send or receive, to or from a data storage device, measurement information for measurement signaling between a first next generation radio access network (NG-RAN) node and a second NG-RAN node. The apparatus also includes processor circuitry communicatively coupled to the memory interface, the processor circuitry to initiate execution of a machine learning (ML) model by the first NG-RAN node to select an energy saving state for the first NG-RAN node, the second NG-RAN node or a user equipment (UE), and generate one or more messages with an information element (IE) with one or more parameters to request measurement information according to the one or more parameters, the measurement information to comprise feedback information to train the ML model. Other embodiments are described and claimed.

Abstract: An apparatus and system to provide a federated learning scheme between a RAN and connected UEs are described. A gNB-DU, gNB-CU, or LMF acts as a central server that selects an AI/ML model, trains the AI/ML model, and transmits the AI/ML model to UEs. The UEs act as local nodes that each send a model request to the central server, receive the AI/ML model in response to the request, trains the AI/ML model locally with data, and report updated parameters to the central server. The central server aggregates parameters from the local nodes and updates the AI/ML model.

Abstract: Systems, apparatuses, methods, and computer-readable media are provided for user equipment (UE) trajectory-assisted handovers. In particular, some embodiments may include artificial intelligence (AI) or machine learning (ML) to predict UE location information. Other embodiments may be described and/or claimed.

Abstract: This disclosure relates to apparatuses, systems, and methods for scheduling user equipment (UE) transmissions, and in particular for scheduling UE transmissions in a 5G New Radio system with a split architecture. The scheduler selects a beamforming algorithm for a UE group that includes a first UE and a second UE, where the beamforming algorithm is based on characteristics of the beamforming algorithm and/or the UE group. The scheduler determines an effective SINR for the UE group based on the beamforming algorithm and determines a summed proportion fair metric for the UE group based on the effective SINR for the UE group. The scheduler schedules a transmission for either the first UE or the UE group, based on a proportional fair metric for the first UE and the summed proportional fair metric for the UE group.

Abstract: The invention relates to a 2-isopropyl malate synthase, a genetically engineered bacterium for producing L-leucine and application thereof and belongs to the field of metabolic engineering. The genetically engineered bacterium is obtained by overexpressing an isopropyl malate synthase coding gene leuAM for relieving feedback inhibition by L-leucine, an acetohydroxy acid synthase coding gene ilvBNM for relieving feedback inhibition by L-isoleucine, a 3-isopropyl malate dehydrogenase coding gene leuB and a 3-isopropyl malate dehydratase coding gene leuCD in host cells. The genetically engineered bacterium for producing the L-leucine is free from nutritional deficiency, rapid in growth, short in fermentation period, high in yield and high in conversion rate.

Abstract: The present disclosure belongs to the technical field of artificial intelligence, and discloses an automatic classification method and system of teaching videos based on different presentation forms, which with three convolutional neural network models, may accurately locate the information required for teaching video classification by two self-trained YOLOV4 target detection neural network models and human body key point detection technology, solves the problem that the background and character features of teaching videos do not change significantly, and improves the accuracy of feature extraction. The structure of the self-built convolutional neural network models is suitable for classification of Interview type and Head type teaching videos. The depth of the network is just appropriate compared with several classical video classification algorithms, which reduces the energy consumption of computer hardware.

Abstract: The present disclosure generally relates to the field of wireless communications, cellular networks, network topologies, and communication system implementations, and in particular, to network controlled repeater (NCR) networking and associated implementations. Various solutions to support identification and initial access of an NCR, NCR protocol stacks, reduced capabilities/functionalities, and other aspects are described.

Abstract: An apparatus and system of providing energy saving in a network are described. A target node receives, from a source node, a handover request to handover a UE due to at least one of entry of the source node into a power saving state or mobility optimization. In response, the target node transmits a relative energy efficiency value of Bits per Joule and periodic feedback related to a status of the UE for training an AI/ML energy saving model and stops the feedback in response to a stop parameter being met. The stop parameter is a first of: a predicted period of the source node to stay in the power saving state or time for a UE to connect to the target node, a percentage of UEs handed over that enter an idle or inactive state, and a percentage of UEs handed over that are further handed to another node.

Abstract: An apparatus and system to provide a federated learning scheme between a RAN and connected UEs are described. A gNB-DU, gNB-CU, or LMF acts as a central server that selects an AI/ML model, trains the AI/ML model, and transmits the AI/ML model to UEs. The UEs act as local nodes that each send a model request to the central server, receive the AI/ML model in response to the request, trains the AI/ML model locally with data, and report updated parameters to the central server. The central server aggregates parameters from the local nodes and updates the AI/ML model.

Abstract: The invention relates to a 2-isopropyl malate synthase, a genetically engineered bacterium for producing L-leucine and application thereof and belongs to the field of metabolic engineering. The genetically engineered bacterium is obtained by overexpressing an isopropyl malate synthase coding gene leuAM for relieving feedback inhibition by L-leucine, an acetohydroxy acid synthase coding gene ilvBNM for relieving feedback inhibition by L-isoleucine, a 3-isopropyl malate dehydrogenase coding gene leuB and a 3-isopropyl malate dehydratase coding gene leuCD in host cells. The genetically engineered bacterium for producing the L-leucine is free from nutritional deficiency, rapid in growth, short in fermentation period, high in yield and high in conversion rate.

Abstract: An optical element for a lighting device of an automotive vehicle. The optical element includes a first portion configured to transmit electromagnetic (EM) radiation therethrough, the EM radiation including visible wavelengths and an ablation process wavelength. Also included is a second portion configured to absorb at least the ablation process wavelength, the second portion being in contact with the first portion to define an ablation process boundary which separates a surface of the first portion from an adjacent surface of the second portion. A patterned optical coating is provided on the optical element such that the optical coating material is provided on at least a part of the surface of the second portion, but not provided on the surface of the first portion.

Abstract: An optical element for a lighting device of an automotive vehicle. The optical element includes a first portion configured to transmit electromagnetic (EM) radiation therethrough, the EM radiation including visible wavelengths and an ablation process wavelength. Also included is a second portion configured to absorb at least the ablation process wavelength, the second portion being in contact with the first portion to define an ablation process boundary which separates a surface of the first portion from an adjacent surface of the second portion. A patterned optical coating is provided on the optical element such that the optical coating material is provided on at least a part of the surface of the second portion, but not provided on the surface of the first portion.

Abstract: The present disclosure relates to a light pipe arrangement for complex automotive lighting features. The light pipe arrangement includes proximately aligned light pipes comprising at least one grouping of reflecting facets for creating a striped aesthetic.

Abstract: The present disclosure relates to a light pipe arrangement for complex automotive lighting features. The light pipe arrangement includes proximately aligned light pipes comprising at least one grouping of reflecting facets for creating a striped aesthetic.