Abstract: To configure a set of user devices, which comprises one or more user devices, to monitor a subset of beams, at least one past beam sequence indicating one or more beams, which served the set is determined, and inputted to a trained model which outputs a probability distribution. Then as many beams as is a number of beams determined for the set to monitor is selected from the probability distribution according to a first criteria, and the set of user devices is configured to monitor and report beams in the beam group. Past beam sequences are also used in training. From the past sequences, set-specifically, past beams that served a set within a first time interval are determined to be used as training data, and future beams that served the set within a second time interval following the first time interval are determined to be used as validation data.

Abstract: A process and a system for creating a visual guide for developing training data for a classification of image, where the training data includes images tagged with labels for the classification of the images. A processor may prompt a user to define a framework for the classification. For an initial set of images within the training data, qualified human classifiers are prompted to locate the images within the framework and to tag the images with labels. The processor determines whether the tagged images have consistent labels, and, if so, the processor adds images to the training data. The processor may add the images by providing a visual guide, the visual guide including tagged images arranged according to their locations within the framework their labels, and prompting human classifiers to tag the additional images with labels for the classification, according to the visual guide.

Abstract: A pluggable electric connector can communicate a communication data signal and a control signal with an optical communication device. An optical signal output unit is configured to be capable of selectively output a wavelength of an optical signal. An optical power adjustment unit-can adjust optical power of the optical signal. A pluggable optical receptor can output the optical signal to an optical fiber. A control unit controls a wavelength change operation according to the control signal. The control unit according to a wavelength change command, commands the optical power adjustment unit to block output of the optical signal, commands the light signal output unit to change the wavelength of the optical signal after the optical signal is blocked, and commands the light signal output unit and the optical power adjustment unit to output the optical signal after the wavelength change operation.

Abstract: A personal care system having a treatment device for applying a treatment to the skin or hair of a user is provided. The treatment device has a camera for taking an image of the skin. The system may include or be in communication with an application programming interface (API) that can process the image. The system or an associated API can recommend the use of treatment regimens or topical products according to information determined from the digital image of the skin.

Abstract: An image description information generation method includes obtaining a to-be-processed target image, and inputting the target image into a target-image description information generation network. The target-image description information generation network is a generation network that is obtained by performing adversarial training using a plurality of sample images and that is configured to generate image description information, the adversarial training is training an initialized image description information generation network and an initialized discriminative network alternately, and the discriminative network is configured to discriminate an output result of the image description information generation network. The method also includes according to the output result of the target-image description information generation network, generating target-image description information used for describing the target image.

Abstract: A method for ascertaining an explanation map of an image. All those pixels of the image are highlighted which are significant for a classification of the image ascertained with the aid of a deep neural network. The explanation map is being selected in such a way that it selects a smallest possible subset of the pixels of the image as relevant. The explanation map leads to the same classification result as the image when the explanation map is supplied to the deep neural network for classification. The explanation map is selected in such a way that an activation caused by the explanation map does not essentially exceed an activation caused by the image in feature maps of the deep neural network.

Abstract: A method is provided for generating test data for testing radio equipment. The method includes: determining, by a test apparatus, one or more beam identifiers; selecting, by the test apparatus, based on the one or more beam identifiers, one or more radio channel models; receiving, by the test apparatus, a baseband signal representing I/Q data of one or more beamforming antennas; processing, by the test apparatus, the baseband signal representing I/Q data according to the selected radio channel model; and transmitting, by the test apparatus, the processed baseband signal representing I/Q data to a radio equipment under test.

Abstract: A method for encoding a quasi-cyclic low-density parity-check (LDPC) code according to an embodiment of the present invention may comprise the steps of: generating a multi-edge LDPC code matrix including a high rate code matrix and a single parity check code matrix; and encoding a signal by using the multi-edge LDPC code matrix, wherein the single parity check code matrix includes a first matrix having a non-row-orthogonal structure matrix and a second matrix having a pure row-orthogonal structure, which are concatenated.

Abstract: Techniques for training a machine-learning (ML) model for captioning images are disclosed. A plurality of feature vectors and a plurality of visual attention maps are generated by a visual model of the ML model based on an input image. Each of the plurality of feature vectors correspond to different regions of the input image. A plurality of caption attention maps are generated by an attention model of the ML model based on the plurality of feature vectors. An attention penalty is calculated based on a comparison between the caption attention maps and the visual attention maps. A loss function is calculated based on the attention penalty. One or both of the visual model and the attention model are trained using the loss function.

Abstract: Provided are an image processing apparatus and a control method thereof. The image processing apparatus includes: a communication circuitry configured to communicate with an external device; a storage configured to store data; an image processor configured to perform image processing; and a controller configured to perform an operation, through a neural network, on an image frame contained in an image received by the communication circuitry, to determine a type of the image based on information according to the operation through the neural network, and to control the image processor based on the determined type of the image.

Abstract: The disclosure provides a target detection method and apparatus, a model training method and apparatus, a device, and a storage medium. The target detection method includes: obtaining a first image; obtaining a second image corresponding to the first image, the second image belonging to a second domain; and obtaining a detection result corresponding to the second image through a cross-domain image detection model, the detection result including target localization information and target class information of a target object, the cross-domain image detection model including a first network model configured to convert an image from a first domain into an image in the second domain, and the second network model configured to perform region localization on the image in the second domain.

Abstract: A face recognition method includes: extracting a first identity feature of a first face image by using a feature extraction module, and extracting a second identity feature of a second face image by using the feature extraction module, wherein the feature extraction module is implemented by using a neural network, and pre-trained in a manner such that a correlation coefficient of training batch data is obtained based on an identity feature and an age feature of a sample face image in the training batch data, and decorrelated training of the identity feature and the age feature is performed on the feature extraction module based on the correlation coefficient; and performing a face recognition based on determining a similarity between faces in the first face image and the second face image according to the first identity feature and the second identity feature.

Abstract: The present disclosure discloses a method and device for optimizing an object-class model based on a neural network. The method includes: establishing the object-class model based on the neural network, training the object-class model, and realizing classification of target images by using the object-class model that has been trained; and when a new target image is generated, and the new target image is an image corresponding to a new condition of a target and is capable of still being classified into an original classification system, judging a result of identification of the object-class model to the new target image, and if the object-class model is not capable of correctly classifying the new target image, according to the new target image, selecting some of parameters, adjusting the some of parameters, and training to obtain an object-class model that is capable of correctly classifying the new target image.

Abstract: A method and system for training an image classification model is disclosed. An aspect is to separate training processes of a feature value extraction model and an image classification model and train the feature value extraction model on a representative feature value suitable for image classification into a specific label value (e.g., “Peak”), thereby improving accuracy and performance of a classification model for a ground-penetrating radar (GPR) image that is captured by a GPR and is not easy for feature value extraction.

Abstract: An optical switch fabric comprises two or more optical switch elements. The optical switch elements are configured in a topology. A switch control has a plurality of bias control signals. The switch control can address one or more of the optical switch elements and can apply one of the bias control signals to bias of the addressed optical switch element to establish a switch setting. The topology and switch settings determine how each of one of the inputs is connected to each of one of the outputs of the optical switch fabric. The switch settings are determined by a machine learning process which includes a model creation. The model can be made to adapt dynamically during optical switch fabric operation.

Abstract: Systems and methods for testing visible chemical reactions of a reagent are provided. In one implementation, the method may include receiving from an image sensor associated with a mobile communications device an image of a reagent with a plurality of colored test reagent pads in proximity to a colorized surface having a plurality of colored reference elements of differing shades. The method further includes using the differing shades of the plurality of colored reference elements to determine local illumination conditions. Thereafter, the method includes using the determined local illumination conditions and an analysis of a depiction of the plurality of colored test reagent pads in the image to determine an extent of a chemical reaction on the reagent. Then the method includes causing the mobile communications device to provide to the user an indication that the testing of the reagent is complete.

Abstract: The technology relates to tuning a data translation block (DTB) including a generator model and a discriminator model. One or more processors may be configured to receive training data including an image in a second domain. The image in the second domain may be transformed into a first domain with a generator model. The transformed image may be processed to determine one or more outputs with one or more deep neural networks (DNNs) trained to process data in the first domain. An original objective function for the DTB may be updated based on the one or more outputs. The generator and discriminator models may be trained to satisfy the updated objective function.

Abstract: A system for disparity estimation includes one or more feature extractor modules configured to extract one or more feature maps from one or more input images; and one or more semantic information modules connected at one or more outputs of the one or more feature extractor modules, wherein the one or more semantic information modules are configured to generate one or more foreground semantic information to be provided to the one or more feature extractor modules for disparity estimation at a next training epoch.

Abstract: In some embodiments, a method can include receiving first images of produce. The method can further include executing a first machine learning model to generate second images of produce based on the first images of produce. The first images of produce can include (1) images of non-bagged produce or (2) images of bagged produce. The second images of produce can include the other of (1) images of non-bagged produce or (2) images of bagged produce. The method can further include training a second machine learning model based on the first images of produce and the second images of produce. The method can further include executing, after the training, the second machine learning model to classify as a bagged produce or a non-bagged produce an image not included in the first images and not included in the second images.

Abstract: A method for encoding a bitstream is described. The method includes receiving, by an error correction unit, the bitstream; performing, by the error correction unit, encoding on the bitstream to generate an encoded bit stream that includes the bitstream and parity bits; processing, by a constellation unit, the encoded bitstream to generate constellation points in one or more encoded streams; determining, by a low-density parity-check (LDPC) tone mapper, a distance between subcarriers in an Orthogonal Frequency Division Multiplexing (OFDM) symbol of the frame; and performing, by the LDPC tone mapper, LDPC tone mapping based on the one or more encoded streams and on the determined distance to generate a permuted stream of complex numbers, wherein the distance between subcarriers is determined to be either (1) 18 for a first resource unit size used in the frame and (2) 20 for a second resource unit size used in the frame.