Abstract: In one embodiment, a method comprises receiving, via a mobile station, contextual information or geographic location data relating to a plurality of members of the population within the geographic region, identifying a common element in the received contextual information relating to at least two members of the population as a basis for defining a geofence to include the at least two members, wherein the common element is identified upon a comparison of the first and second contextual information, and defining the boundary of the geofence.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for training and then applying computer-implemented models designed for segmentation of an object in the frames of video. By training and then applying the segmentation model in a cyclical manner, the errors encountered when performing segmentation can be eliminated rather than propagated. In particular, the approach to segmentation described herein allows the relationship between a reference mask and each target frame for which a mask is to be produced to be explicitly bridged or established. Such an approach ensures that masks are accurate, which in turn means that the segmentation model is less prone to distractions.

Abstract: The present disclosure relates to a method and device for obtaining a second image from a first image when the dynamic range of the luminance of the first image is greater than the dynamic range of the luminance of the second image. The disclosure describes deriving at least one component representative of the colors of the second image from the first image, and maximizing at least one derived component according to a maximum value depending on a linear-light luminance component of the first image.

Abstract: A core network device of a telecommunications network can receive a first request from a consumer network function (NF) and forward the first request to a first provider NF. The device can subsequently detect a violation by the first provider NF of a response criterion. The device can receive a second request and forward the second request to a second, different provider NF in response to the violation. In some examples, the device can later detect that the first provider NF has satisfied the response criterion with respect to a third request. The device can modify load data to increase the proportion of subsequent requests for which the first provider NF is selected. The device can select provider NFs based on the load data. In some examples, a system can include a network registry device that selects NFs based on violation indications received from the core network device.

Abstract: The position detection of user equipment within a network, and the corresponding methods performed at the network node, user equipment and location server are disclosed, along with these entities and a computer program. The method performed at the network node comprises within a position reference signal time period, broadcasting: a first position reference signal within a first frequency band and during a first time period; and at least one further position reference signal within at least one further frequency band and during at least one subsequent time period, said at least one further frequency band being different to said first frequency band.

Abstract: In a communications method of a wearable device, if a second BLUETOOTH connection between user equipment and the wearable device is disconnected, a first BLUETOOTH connection between a network connection device and the wearable device is established, so that the network connection device may send, to the wearable device through the first BLUETOOTH connection, first information sent by the user equipment through a third communication connection to the network connection device. In this way, indirect communication between the user equipment and the wearable device is implemented by using the network connection device, so that the user equipment and the wearable device are not limited by a communication range.

Abstract: Techniques are described for domain adaptation of image processing models using post-processing model correction According to an embodiment, a method comprises training, by a system operatively coupled to a processor, a post-processing model to correct an image-based inference output of a source image processing model that results from application of the source image processing model to a target image from a target domain that differs from a source domain, wherein the source image processing model was trained on source images from the source domain. In one or more implementations, the source imaging processing model comprises an organ segmentation model and the post-processing model can comprise a shape-autoencoder. The method further comprises applying, by the system, the source image processing model and the post-processing model to target images from the target domain to generate optimized image-based inference outputs for the target images.

Abstract: A computing system is disclosed including a convolutional neural configured to receive an input that describes a facial image and generate a facial object recognition output that describes one or more facial feature locations with respect to the facial image. The convolutional neural network can include a plurality of convolutional blocks. At least one of the convolutional blocks can include one or more separable convolutional layers configured to apply a depthwise convolution and a pointwise convolution during processing of an input to generate an output. The depthwise convolution can be applied with a kernel size that is greater than 3×3. At least one of the convolutional blocks can include a residual shortcut connection from its input to its output.

Abstract: A method for local approximation of a predictive model may include receiving unclassified data associated with a plurality of unclassified data items. The unclassified data may be classified based on a first predictive model to generate classified data. A first data item may be selected from the classified data. A plurality of generated data items associated with the first data item may be generated using a generative model. The plurality of generated data items may be classified based on the first predictive model to generate classified generated data. A second predictive model may be trained with the classified generated data. A system and computer program product are also disclosed.

Abstract: A method, computer program, and computer system for separating a target voice from among a plurality of speakers is provided. Video data associated with the plurality of speakers and audio data associated with each of the one or more speakers are received. Video feature data is extracted from the received video data. The target voice is identified from among the plurality of speakers based on the received audio data and the extracted video feature data.

Abstract: Provided is a method for processing images. The method can include: acquiring a target face image, and performing face key point detection on the target face image to acquire a face key point detection result; determining a face visible area which is not subject to an occluder and a face invisible area which is subject to the occluder in the target face image; and acquiring a target fusion image by fusing a virtual special effect and a face part matched in the target face image based on the face key point detection result, the face visible area and the face invisible area.

Abstract: A system for characterizing a specimen is disclosed. In one embodiment, the system includes a characterization sub-system configured to acquire one or more images a specimen, and a controller communicatively coupled to the characterization sub-system. The controller may be configured to: receive from the characterization sub-system one or more training images of one or more defects of a training specimen; generate one or more augmented images of the one or more defects of the training specimen; generate a machine learning classifier based on the one or more augmented images of the one or more defects of the training specimen; receive from the characterization sub-system one or more target images of one or more target features of a target specimen; and determine one or more defects of the one or more target features with the machine learning classifier.

Abstract: The present disclosure provides a method for imaging of moving subjects. The method may include determining a motion range of a region of interest (ROI) of a subject in an axial direction. The method may also include causing a radiation source to emit, at each of a plurality of axial positions relative to the subject, radiation beams to the ROI to generate an image frame of the ROI. The radiation beams corresponding to the plurality of axial positions may jointly cover the motion range of the ROI in the axial direction. The method may further include determining a position of the ROI in the axial direction based on the image frames of the ROI, and determining, based on the positions of the ROI in the axial directions, at least one time bin in which therapeutic beams are to be emitted to the ROI.

Abstract: A method includes receiving sensor data indicative of a paved surface, and identifying a defect associated with the paved surface based at least in part on the sensor data. The method also includes determining that the defect is of a defect type based on determining that a value associated with the defect is within a value range associated with the defect type. The method further includes generating a command associated with the defect that, when executed by a machine, at least partially remedies the defect. The method also includes providing the command to an electronic device via a network.

Abstract: A method for providing data to a user comprising communicating, from a radio access network to a Minimization of Drive Tests (“MDT”)-channel-communicative sensor device, at least one MDT signal selected from the group consisting of an MDT configuration signal and an MDT control signal. The method also comprises generating, at least in part in response to the MDT signal and using an onboard sensor of the MDT-channel-communicative sensor device, sensor data comprising non-MDT data. The method also comprises receiving at least a portion of the sensor data into the radio access network over an MDT channel and communicating at least the portion of the sensor data from the radio access network to the user.

Abstract: An information processing device acquires a material formation image representing a formation of a material, the material formation image being obtained by imaging the material. The information processing device generates a Fourier transform result representing a power spectrum by applying a Fourier transform to the acquired material formation image. The information processing device, on the basis of the Fourier transform result of the material formation image, employs an expectation-maximization algorithm to generate a size distribution of structures forming the material.

Abstract: The present disclosure discloses an industrial Internet of Things (IoT), a control method, and a storage medium based on a machine vision detection, and provides a technical solution for a timely adjustment of production line parameters according to image information generated during a machine vision data collection on the production line. Through a difference of the image information corresponding to different process operations, a processing situation of different process operations may be obtained, so that more accurate adjustment of the production line parameter may be achieved without increasing the system complexity, thereby effectively reducing a development cost of the industrial IoT, and increasing accuracy of the intelligent manufacturing control.

Abstract: Machine learning systems and methods that determine gaze direction by using face orientation information, such as facial landmarks, to modify eye direction information determined from images of the subject's eyes. System inputs include eye crops of the eyes of the subject, as well as face orientation information such as facial landmarks of the subject's face in the input image. Facial orientation information, or facial landmark information, is used to determine a coarse prediction of gaze direction as well as to learn a context vector of features describing subject face pose. The context vector is then used to adaptively re-weight the eye direction features determined from the eye crops. The re-weighted features are then combined with the coarse gaze prediction to determine gaze direction.

Abstract: Disclosed herein are a neural network generation method for neuromorphic computing and an apparatus for the same. The neural network generation method for neuromorphic computing includes selecting at least one part of a brain corresponding to a neural network function requested to be generated by an application, determining whether an existing neural network corresponding to the at least one part of the brain is present in a neural network database, when it is determined that no existing neural network is present in the neural network database, generating new neural network configuration information corresponding to the part of the brain based on a brain information database, generating a new neural network by mapping the new neural network configuration information to neuromorphic hardware based on the new neural network configuration information, and storing the new neural network in the neural network database.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a policy neural network having a plurality of policy parameters and used to select actions to be performed by an agent to control the agent to perform a particular task while interacting with one or more other agents in an environment. In one aspect, the method includes: maintaining data specifying a pool of candidate action selection policies; maintaining data specifying respective matchmaking policy; and training the policy neural network using a reinforcement learning technique to update the policy parameters. The policy parameters define policies to be used in controlling the agent to perform the particular task.