Abstract: Embodiments of this application provide a device interaction method and a core network device. The method includes: receiving, by a first core network device, identifier information of at least one UE sent by a first device, where the at least one UE includes at least one of the following: a UE that has established UE-to-network relay connection with a first UE and a UE that is to establish UE-to-network relay connection with the first UE; and interacting, by the first core network device based on identifier information of a second UE, with a second core network device, where the second UE is any one of the at least one UE; where the first core network device is a core network device serving the first UE, and the second core network device is a core network device serving the second UE.

Abstract: Embodiments of the present application provide a state indication method, a device, and a system. The method comprises: a relay core network device receives first state indication information sent by a first device, the first state indication information comprising at least one of first indication information and second indication information, the first indication information instructing a relay UE to perform UE-to-network relay communication, the second indication information instructing the relay UE to perform UE-to-network relay communication for an emergency service, and the first state indication information being used for determining not to initiate a NAS signaling connection release process of the relay UE.

Abstract: Embodiments of this disclosure provide a serving node update method and a device. The method includes: sending a first message to a target node; receiving, from the target node, a second message used for responding to the first message; and sending a third message to a terminal device when the second message indicates that the target node rejects a multi-connectivity serving node update, where the third message is used to indicate the terminal device to perform a serving node update in a legacy serving node update manner.

Abstract: A transmission mode conversion indication method includes: sending a first message to a core network or sending a second message to a terminal device, so as to indicate that a transmission mode conversion is required, where the first message carries first user information and first multicast group information.

Abstract: A system and method for configuring a device includes using a machine learning model to generate a user behavior model based on user behavior data. The user behavior data may include time series data collected from user interactions with a first device, and the machine learning model may include a classification model configured to classify the user behavior data into the one or more classifications. A mapping may be created by training a machine learning model, using user behavior models from a plurality of users and device settings from the plurality of users, to identify one or more relationships between device settings and classifications of the user behavior data. The system and method configures one or more settings of a second device based on the user behavior model and the mapping.

Abstract: Provided are a random access method, a terminal device and a network device. The random access method includes transmitting a two-step random access request message to a network device. A data part is carried in the two-step random access request message, and the data part is acquired through performing layer-two processing in accordance with layer-two configuration information.

Abstract: The embodiments of the disclosure provide a bandwidth switching method and a user equipment. The method includes: when a BWP switching command is received while successfully receiving the random access completion message of the non-contention-based random access process, determine the non-contention-based random access process has been completed successfully in accordance with random access completion message, and determine not to perform the BWP switching process, or perform the BWP switching process in accordance with the random access completion message and the BWP switching command.

Abstract: A computer-implemented method includes receiving first inputs associated with a first modality and second inputs associated with a second modality; processing the received first and second inputs with convolutional neural networks (CNN), wherein a set of first weights are used to handle the first inputs and a second set of weights are used to handle the second inputs; determining a loss for each of the first and the second inputs based on a loss function that applies the first set of weights, the second set of weights, and a presence of a co-occurrence; generating a shared feature space as an output of the CNNs, wherein a distance between cells associated with the first inputs and the second inputs in the shared feature space is determined based on the loss associated with each of the first inputs and the second inputs; and based on the shared feature space, providing an output.

Abstract: A method includes fitting a ML trained model to data features, the fitting generates complete data feature-set outputs that are associated with a first set of accuracy values, iteratively fitting, after an iterative removal of each data feature from the data feature-set, the ML trained model to subsets of the plurality of data features to determine respective reduced feature-set outputs, each subset lacks a different data feature of the plurality of data features, determining one or more of the reduced feature-set outputs as corresponding to a second set of accuracy values, designating the iteratively removed data features as accuracy-modifying data features, generating a first linear model, generating a second linear model based on one of the accuracy-modifying data features having a weight that is highest relative to respective different weights of the remaining ones of the accuracy-modifying data features, and identifying the second linear model as a generative model.

Abstract: The embodiments of the present disclosure provide a method for supporting quality of service of time-sensitive communication and a communication device. The method includes: obtaining first information, where the first information includes at least one of the following: transfer configuration information of a time-sensitive data stream, or bridge capability information; and performing a first operation according to the first information.

Abstract: A method implemented by a processor of a device, the method including determining one or more user values, determining a prompt requiring a decision, and receiving a user selection to one or more choice options related to the one or more user values and pertaining to the prompt requiring a decision. The method further includes comparing the user selection to a risk-neutral utility function and assessing a risk of a potential decision based on the comparison.

Abstract: Electronic devices and methods for displaying information on electronic displays of electronic devices based on learned user behavior are disclosed. In one embodiment, an electronic device includes an electronic display, a processor, and a non-transitory computer-readable medium storing instructions that, when executed by the processor, causes the processor to cause for display on the electronic display a selected representation of information among a plurality of representations based at least in part on learned behavior of a user.

Abstract: Systems and methods for selecting a channel of communication based on a type of thinking employed by a user for a task are disclosed. The systems and methods include determining that a user is employing System 1 type thinking or System 2 type thinking for the task based on one or more properties of the task, one or more properties of the user, and a state of the user based on physiological response data from one or more physiological sensors monitoring the user, and implementing a channel of communication to utilize for the task that corresponds to a determined type of thinking the user is employing for the task.

Abstract: Systems and methods for determining whether a user employs System 1 type thinking or System 2 type thinking when engaged in a task are disclosed. The systems and methods include determining one or more properties of the task based on information regarding the task received from a database storing information regarding the task, determining one or more properties of the user with respect to the task, determining a state of the user based on one or more physiological sensors configured to sense one or more characteristics of the user, and determining that the user employs System 1 type thinking or System 2 type thinking when engaged in the task based on the determined one or more properties of the task, the determined one or more properties of the user, and the determined state of the user.

Abstract: A method for generating and outputting a prompt for performing a task in a designated time segment is provided. The method includes obtaining, from a plurality of sensors, context data associated of the user related to time segments, categorizing each of the time segments into one of a plurality of thought states based on the context data, mapping a task from a task dataset associated with the user into one of the plurality of thought states, and generating a prompt for performing the task during a designated time segment of the time segments, the designated time segment corresponding to the one of the plurality of thought states to which the task is mapped.

Abstract: Systems and methods for content framing monitoring and intervention are disclosed. In one example, a method includes receiving, at a computing device, first text conveying first information, performing natural language processing of the first text, determining a first framing of the first information based on the natural language processing of the first text, determining second text conveying the first information with a second framing, different from the first framing, and outputting the second text on an electronic display.

Abstract: The present invention provides a multicast service processing method, a multicast service configuration method, and a communications device. The multicast service processing method includes: receiving a multicast area configuration sent by a network device, where the multicast area configuration includes area parameter information of a multicast service; obtaining wireless area information in a case that the multicast area configuration sent by the network device has been received; and determining, based on the area parameter information and the wireless area information, whether the terminal is located in a service area of the multicast service.

Abstract: A transmission method and a transmission processing method of multicast service and a related device are provided. The method includes: receiving a unicast-to-multicast request sent by a network device for a multicast service; and receiving the multicast service through a target mode, where the target mode includes a unicast mode and/or a multicast mode.

Abstract: A method of transmitting configuration information and a related device are provided. The method includes sending DC assistance information to a network-side device, wherein the network-side device includes at least one of an MN and an SN; receiving assistance configuration information of DC interference cancellation sent by the network-side device, wherein the assistance configuration information of DC interference cancellation corresponds to the IDC assistance information.

Abstract: A system for evaluating a treatment for a mental or physiological condition. The system includes a sensor configured to measure eye movements of a user, a processor, and a memory. The memory includes instructions, which, when executed by the processor, cause the system to: identify a task; prior to the treatment and while the user performs the task, measure the eye movements of the user with the sensor; while the user is under an effect of the treatment and while the user performs the task, measure eye movements of the user with the sensor; determine a difference between the eye movements of the user prior to and after the treatment based on a trained machine learning model; determine a measure of an efficacy of the treatment based on the determined difference in the measured eye movements of the user; and display a recommended a course of treatment based on the determined measure of efficacy.