Abstract: A data processing method and device are provided. The method includes: extracting a plurality of data sets from unlabeled data; and for each data set, creating a plurality of sample sets by assigning labels to data samples in the data set, respectively training, for each sample set created from the data set, a classifier by using the sample set and labeled data, obtaining a sample set that corresponds to a trained classifier with the highest performance, and adding the obtained sample set to a candidate training set. Each sample set includes the first preset number of data samples with respective labels, the labels of the data samples in each sample set constitutes a label combination, and label combinations corresponding to different sample sets are different from each other. The method also includes adding a second preset number of sample sets in the candidate training set to the labeled data.

Abstract: A tracking device and method of determining a location of a tag connected to an object. The method comprises transmitting a first message from a server over a long range communication protocol to the tag so that the tag emits short and long range distress messages; receiving at a server the long range distress messages including location data using the long range communication protocol; using the location data to determine a geographical area in which the tag is located; transmitting a message from the server to one or more mobile devices in the geographic area to enable the mobile devices to receive the short range distress messages; receiving at the mobile devices the short range distress messages from the tag and using the received short range distress message to locate the tag; and transmitting a second message from the mobile devices to the server including the location of the tag.

Abstract: The present application provides a comparison method and a modeling method for a chip product, a device and a storage medium. According to the method, the chip product is modeled by using a neural network based on a slice sequence of the chip product in advance to obtain a three-dimensional stereoscopic model. When the chip products are compared, a comparison feature is acquired responsive to an operation of a user. For each chip product, a comparison result corresponding to the comparison feature is acquired from the three-dimensional stereoscopic model corresponding to each chip product. Then, the comparison result corresponding to each chip product is displayed.

Abstract: Described are techniques for training an image classifier using an augmentation loss function. The techniques including inputting corresponding pairs of a plurality of training images to an image classifier, where respective pairs of the corresponding pairs comprise at least two images having a same classification and different augmentations. The techniques further including training an artificial neural network of the image classifier to classify the plurality of training images using an augmentation loss function, wherein the augmentation loss function reduces differences in model outputs between the corresponding pairs of the plurality of training images.

Abstract: A method of fusing frame based image sensors (FBIS) images with dynamic vision sensor (DVS) event data includes concatenating a plurality of image tensors into a single image input tensor; concatenating a plurality of event tensors into a single event input tensor; concatenating the event input tensor and the image input tensor wherein a single input tensor containing data from both the image input tensor and the event input tensor is generated; processing the single input tensor with a fully convolutional neural network (FCNN), wherein in a contracting path of the FCNN, a number of channels is increased, and in an expansion path of the FCNN, the number of channels is decreased; and combining channels wherein an output image tensor is generated with a reduced number of channels.

Abstract: A dual autoencoder includes a first domain encoder for mapping a first domain image into a first latent space, a first domain decoder for reconstructing the first domain image, a second domain encoder for mapping a second domain image into a second latent space, a second domain decoder for reconstructing the second domain image; and a latent space association network for defining a cross-domain relationship between the first domain and the second domain. An image translation method using a dual encoder, may include: taking a first domain image as an input; determining an output condition; if the output condition is the same domain, reconstructing the first domain image by a first domain encoder and a first domain decoder; and if the output condition is the cross domain, reconstructing a second domain image by the first domain encoder, a latent space association network, and a second domain decoder.

Abstract: A machine learning model is described that is trained without labels to predict a motion field between a pair of images. The trained model can be applied to a distinguished pair of images to predict a motion field between the distinguished pair of images.

Abstract: A deep learning model and dimensionality reduction are applied to each of a plurality of records of physiological information to derive a plurality of feature vectors. A similarities algorithm is applied to the plurality of feature vectors to form a plurality of clusters, each including a set of feature vectors. An output comprising information that enables a display of one or more of the plurality of clusters is provided, and a mechanism for selecting at least one feature vector within a selected cluster of the plurality of clusters is enabled. Upon selection of a feature vector, an output comprising information that enables a display of the record of physiological information corresponding to the selected feature vector is provided, and a mechanism for assigning a label to the displayed record is enabled. The assigned label is then automatically assigned to the records corresponding to the remaining feature vectors in the selected cluster.

Abstract: A method comprising receiving a first set of aerial images of a geographic area, receiving locations of assets, determining a likely location of at least one asset within each image, creating one or more bounding boxes, encompassing the likely location of one or more assets within each of the images, providing any number of the images to a convolutional neural network to classify pixels, the classification of each of the pixels indicating if the pixels are part of one or more obstructions or are part of a different classification, determining at least one zone, the zone encompassing the at least one asset, determining a distance between at least one pixel part of an obstruction and the zone, generating a criticality score based on the distance, comparing the criticality score to a threshold, and providing an alert of a future hazardous condition based on the criticality score.

Abstract: Systems and methods for automatically detecting and isolating objects in images. An image containing at least one object of interest is segmented by a segmentation module, based on the class of object each pixel of the image depicts. A bounding module then determines coordinates of a predetermined shape that covers at least a portion of the at least one object of interest. An application module then applies a bounding box having those coordinates and having the predetermined shape to the original image. In some embodiments, the coordinates are determined based on a mask layer that is based on the object classes in the image. In other embodiments, the coordinates are determined based on the mask layer and on an edge mask layer. Some embodiments comprise at least one neural network. In some embodiments, the objects of interest are text objects.

Abstract: Disclosed are a method and an apparatus for identifying animal species by using audiovisual information. A method for identifying animal species, according to one embodiment of the present invention, may include: a step of receiving an input signal for an object to be identified; a step of processing image information and acoustic information based on the input signal, wherein a processing result of the image information and a processing result of the acoustic information are represented by class-specific scores; a step of determining whether the image information processing result and the acoustic information processing result corresponding to the input signal exist; and a final result derivation step of fusing the image information processing result and the acoustic information processing result according to the determination result and classifying the object to be identified as a certain animal species by using the fused processing result.

Abstract: In some implementations, a near field communication (NFC) device may determine whether the NFC device is to perform a wireless transaction in a powered mode or an unpowered mode, and may selectively perform the wireless transaction in the powered mode or the unpowered mode based on determining whether the NFC device is to perform the wireless transaction in the powered mode or the unpowered mode. The wireless transaction may be performed using power from a power source internal to the NFC device when the wireless transaction is performed in the powered mode. The wireless transaction may be performed without using power from the power source internal to the NFC device when the wireless transaction is performed in the unpowered mode.

Abstract: A display apparatus and a controlling method thereof are disclosed. The display apparatus includes: a memory storing one or more instructions; and a processor configured to execute the stored one or more instructions to: obtain encoded data of a first digital image and artificial intelligence (AI) meta-information indicating a specification of a deep neural network (DNN), obtain a second digital image corresponding to the first digital image by decoding the encoded data, obtain a light signal converted from the second digital image according to a previously determined electro-optical transfer function (EOTF), and obtain a display signal by processing the light signal by using an opto-optical transfer function (OOTF) and a high dynamic range (HDR) DNN set according to the AI meta-information.

Abstract: An automated communication design analysis and construction system that includes one or more intelligent communication design servers, comprising: a normalization module that converts communication content files for different recipients to normalized intermediate format files; an objects identification and quantification module that identifies text objects and image objects in the normalized intermediate format files; a cross-recipient group analysis module configured to identify static global objects that are invariant between recipients, data variables, and variable global objects that vary between recipients in the normalized intermediate format files; and an intelligent communication content learning and constructing engine that can construct standard communication design files based on the static global objects, the data variables, and the variable global objects. A data storage stores the communication content files and the standard communication design files.

Abstract: A method for communicating data by an electronic device is provided. The method includes transmitting a signal including audio/video (A/V) data to a first external electronic device through a first communication module, while connected with the first external electronic device, generating data or a signal unrelated to the A/V data or receiving the data or signal unrelated to the A/V data from an external electronic device other than the first external electronic device, and providing an audio or a video through a display or an embedded sound device based on at least a portion of the data or the signal unrelated to the A/V data or transmitting the at least a portion of the data or the signal unrelated to the A/V data to a second external electronic device through a second communication module.

Abstract: A monocular image often does not contain enough information to determine, with certainty, the depth of an object in a scene reflected in the image. Combining image data and LIDAR data may enable determining a depth estimate of the object relative to the camera. Specifically, LIDAR points corresponding to a region of interest (“ROI”) in the image that corresponds to the object may be combined with the image data. These LIDAR points may be scored according to a monocular image model and/or a factor based on a distance between projections of the LIDAR points into the ROI and a center of the region of interest may improve the accuracy of the depth estimate. Using these scores as weights in a weighted median of the LIDAR points may improve the accuracy of the depth estimate, for example, by discerning between a detected object and an occluding object and/or background.

Abstract: Embodiments of the present disclosure relate to generating explanation maps for explaining convolutional neural networks through attribution-based input sampling and block-wise feature aggregation. An example of a disclosed method for generating an explanation map for a convolutional neural network (CNN) includes obtaining an input image resulting in an output determination of the CNN, selecting a plurality of feature maps extracted from a plurality of pooling layers of the CNN, generating a plurality of attribution masks based on the plurality of feature maps, applying the generated attribution masks to the input image to obtain a plurality of visualization maps, and generating an explanation map of the output determination of the CNN based on the plurality of visualization maps.

Abstract: This application relates to a point cloud segmentation method, a computer-readable storage medium, and a computer device. The method includes encoding a to-be-processed point cloud to obtain a shared feature, the shared feature referring to a feature shared at a semantic level and at an instance level; decoding the shared feature to obtain a semantic feature and an instance feature respectively; adapting the semantic feature to an instance feature space and fusing the semantic feature with the instance feature, to obtain a semantic-fused instance feature of the point cloud, the semantic-fused instance feature representing an instance feature fused with the semantic feature; dividing the semantic-fused instance feature of the point cloud, to obtain a semantic-fused instance feature of each point in the point cloud; and determining an instance category to which each point belongs according to the semantic-fused instance feature of each point.

Abstract: Systems and methods for forecasting cashflows across one or more accounts of a user disclosed. One example method may include retrieving a data set for each of a plurality of accounts from a database, constructing a graph including a plurality of nodes linked together by a multitude of edges, wherein each node identifies a time series value corresponding to one of the accounts, and each edge indicates a time series value of a corresponding set of transactions occurring between a corresponding pair of accounts, determining a plurality of constraints, determining a specified loss function based on the plurality of constraints, back-propagating a derivative of the specified loss function into a deep neural network (DNN) to determine a set of neural network parameters, forecasting, using the DNN, a time sequence for one or more of the nodes and one or more of the edges, and providing the forecasted time sequences to the user.

Abstract: A system and method for detection of gas in the atmosphere front a multispectral image including a plurality of pixels, each pixel having a spectral signature including a set of intensities of electromagnetic (EM) energy reflected at various bands, the method including: clustering the plurality of pixels based on channels of the spectral signatures that are indicative of presence of the gas, to produce a first set of clusters; clustering the plurality of pixels based on channels of the spectral signatures that are non-indicative of presence of the gas, to produce a second set of clusters; matching clusters from the first set of clusters and the second set of clusters; and labeling clusters that are present in the first set of clusters and not present in the second set of clusters as suspected as including the gas.