Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a first control message indicating an uplink power control configuration for the UE. The uplink power control configuration may be associated with a power control identifier and a resource set identifier. The UE may receive a second control message that schedules an uplink message for the UE. The second control message may indicate the resource set identifier and the power control identifier for transmission of the uplink message. The UE may determine a transmit power for the uplink message based on a set of power control parameters, the set of power control parameters corresponding to the resource set identifier and the power control identifier. The UE may transmit the uplink message using a resource set associated with the resource set identifier and in accordance with the determined transmit power.

Abstract: The present disclosure relates to an apparatus and a method for analyzing medical data based on unsupervised learning. By using a machine learning model based on an adversarial generative neural network to detect and notify anomalies in medical data, the present disclosure allows accurate and rapid reading, in addition to saving time and cost incurred by reading.

Abstract: The present application is applicable to the field of medical imaging technologies, and provides an image-driven brain atlas construction method and apparatus, a device and a storage medium. The method includes: acquiring multi-modal data of a brain to be predicted, where the multi-modal data is acquired according to image data collected when the brain is under at least three different modalities; inputting the multi-modal data into a preset fusion network for processing to output and acquire feature parameters of the brain; where the processing of the multi-modal data by the fusion network includes: extracting a non-Euclidean spacial feature and an Euclidean spacial feature of the multi-modal data, and performing hypergraph fusion on the non-Euclidean spacial feature and the Euclidean spacial feature to acquire the feature parameters, where the feature parameters are used to characterize a brain connection matrix and/or a disease category of the brain.

Abstract: A medical auxiliary information generation method and a medical auxiliary information generation system are provided. The method includes: obtaining a physiological image; identifying a target image region from the physiological image through at least one artificial intelligence model, wherein each pixel position in the target image region corresponds to an estimation value, and the estimation value meets a default condition; determining at least one target pixel position in the target image region according to a numerical distribution of the estimation value; and generating medical auxiliary information according to the target pixel position, wherein the medical auxiliary information carries prompt information related to the target pixel position.

Abstract: An embodiment of the invention provides a processing method of a medical image and a computing apparatus for processing a medical image. In the method, one or more image samples are obtained, a tumor region and an appearance feature thereof in the image sample are marked, and an image recognition model is trained according to the image sample, the tumor region thereof, and an appearance feature of a first tumor. The image sample is an image obtained by photographing an animal body. The appearance feature represents an appearance of the first tumor corresponding to the tumor region. The image recognition model identifies a second tumor in an image to be evaluated is a first type. The first type is related to the tumor region and the appearance feature of the first tumor. Accordingly, a prediction accuracy may be improved.

Abstract: A method and an apparatus for are provided direct communication with a user equipment. A method performed by an MCPTT client device includes transmitting, to a client device, a floor request message for an off-network group call; starting a timer; restarting the timer in response to the timer expiring before receiving a floor granted message; stopping the timer in response to receiving the floor granted message before the timer expires; retransmitting the floor request message each time the timer expires before receiving the floor granted message until a total number of transmissions reaches a predetermined number; transmitting, to the client device, a floor taken message indicating that the permission to send is granted to the MCPTT client device after transmitting the floor request message the predetermined number of times and the timer expires before receiving the floor granted message; and transmitting first media data to the client device.

Abstract: An apparatus may be configured to: receive, from a first network node, a first indication to activate a first set of transmission configuration indicator states associated with the first network node; receive, from the first network node, a second indication of a second network node, wherein the apparatus is configured to perform simultaneous transmission, with the first network node and the second network node; and transmit, to the first network node, a first report, wherein the first report comprises at least one indication of a presence of a power backoff value associated with a pair of transmission configuration indicator states.

Abstract: An indicator system for a surgical robotic system for indicating a state of at least a portion of patient anatomy, the surgical robotic system comprising a robot having abase and an arm extending from the base, the arm holding an endoscope at an end of the arm distal from the base, the endoscope being configured for insertion into a body cavity of the patient for observing a surgical site internal to a body of the patient, the indicator system comprising: a receiver configured to receive video data of at least a portion of patient anatomy at the surgical site from the endoscope; and a processor configured to:detect a spatio-temporal change in a pixel region of the received video data; identify, in response to the detected spatio-temporal change, a parameter of the patient anatomy; generate a health indicator indicative of the identified parameter or indicative of a profile of the identified parameter; and output the generated health indicator.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.

Abstract: The technology disclosed relates to training a convolutional neural network (CNN) to identify and classify images of sections of an image generating chip resulting in process cycle failures. The technology disclosed includes creating a training data set of images of dimensions M×N using labeled images of sections of image generating chip of dimensions J×K. The technology disclosed can fill the M×N frames using horizontal and vertical reflections along edges of J×K labeled images positioned in M×N frames. A pretrained CNN is further trained using the training data set. Trained CNN can classify a section image as normal or depicting failure. The technology disclosed can train a root cause CNN to classify process cycle images of sections causing process cycle failure. The trained CNN can classify a section image by root cause of process failure among a plurality of failure categories.

Abstract: A method of estimating hemoglobin concentration, may comprise: receiving a skin image of a subject; determining a formula into which the skin image is introduced; and estimating a hemoglobin concentration of the subject by introducing the skin image into the determined formula.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may adjust an amplifier of the UE to a highest gain state after a downlink period and before an uplink period. Additionally, the UE may adjust the amplifier of the UE to a lower gain state after the uplink period and before a subsequent downlink period. Numerous other aspects are described.

Abstract: A computer implemented method for uplink power control in a mobile network. The method is performed by obtaining (301) signal to interference and noise ratio, SINR, of a first cell of the mobile network; comparing (302) the SINR to current uplink power parameter of the first cell; determining (303) a new value for the uplink power parameter based on the comparison so that, if the SINR is higher than the current uplink power parameter, the current uplink power parameter is increased to obtain the new value for the uplink power parameter, and if the SINR is lower than the current uplink power parameter, the current uplink power parameter is decreased to obtain the new value for the uplink power parameter; and providing (304) the determined new value of the uplink power parameter for use in uplink power control of the first cell.

Abstract: A technique for power control including receiving, by a user device, a resource block allocation for uplink transmission that includes one or more resource blocks within a frequency channel; determining at least one resource allocation region or which an uplink resource block allocation within the resource allocation region will require an additional maximum power reduction (e.g., to reduce interference to a protected frequency band); and applying, by the user device, the additional maximum power reduction for a transmission power of the user device based on the resource block allocation being within the at least one resource allocation region.

Abstract: Provided are an electronic device and method for wireless communication, and a computer-readable storage medium. The electronic device comprises a processing circuit configured to: determine the first power limiting region of the first primary user of a main system, wherein the first power limiting region is a three-dimensional space defined by the directional beams of the first primary user to the third primary user of the main system; and determine one or more secondary users in the first power limiting regions.

Abstract: An electronic device and a method of training a classification model for age-related macular degeneration (AMD) are provided. The method includes the following steps. Training data is obtained. A loss function vector corresponding to the training data is calculated based on a machine learning algorithm, in which the loss function vector includes a first loss function value corresponding to a first stage of AMD and a second loss function value corresponding to a second stage of AMD. A first penalty weight is generated according to a stage difference between the first stage and the second stage. The first loss function value is updated according to the second loss function value and the first penalty weight, so as to generate an updated loss function vector. The classification model is trained according to the updated loss function vector.

Abstract: Systems and methods for training a model for identifying an imaging modality. The systems and methods can be performed by a computer system having one or more processors and memory. A plurality of image vectors can be generated from first image data using a convolutional neural network. A loss function can be applied to each of the plurality of image vectors to produce an intermediate dataset. The intermediate dataset can be projected in a space having lower dimensional space that the intermediate dataset. A plurality of clusters can be identified from the intermediate dataset in the space using a clustering technique. Each of the plurality of clusters can be classified into one of a plurality of imaging modalities.

Abstract: An image processing device, an image processing method, and an image processing program make it possible to accurately recognize property changes between medical images having different imaging times. A property classification unit classifies each pixel included in a target region of each of a first medical image and a second medical image of which an imaging time is later than an imaging time of the first medical image for the same subject into any one of a plurality of types of properties. A quantitative value derivation unit specifies property pairs each representing a property change between corresponding pixels in at least partial regions within the target regions, and derives a quantitative value in the at least partial regions for each of the property pairs.

Abstract: The first objective comparison of automated and human segmentation of magnetic resonance images, or MRI, using a blinded controlled assessment study. Computers connected over a network divide duties including computerized segmenting of the images, manual segmenting of the images, comparison of the computer segmented images and the manually segmented images, and scoring of the images for accuracy. The scores are evaluated to update configuration parameters of a neural network.

Abstract: A medical information processing apparatus comprises processing circuitry. The processing circuitry calculates a functional rate quantitively indicating a degree as to whether or not a body site related to a symptom of a target patient is properly functioning. The processing circuitry calculates a current state of the target patient as a first state. The processing circuitry estimates, with reference to time-series second medical information having a tendency similar to the first medical information and being related to a similar patient, a likelihood that the state of the target patient may be exacerbated at a future time as a second state, on a basis of a functional rate of the similar patient calculated from a selected part of the second medical information corresponding to time since the similarity to the first medical information was recognized. The processing circuitry causes the first state and the second state to be displayed.