A multimodal recommendation identification system analyzes data describing a sequence of past content item interactions to generate a recommendation for a content item for a user. An indication of the recommended content item is provided to a website hosting system or recommendation system so that the recommended content item is displayed or otherwise presented to the user. The multimodal recommendation identification system identifies a content item to recommend to the user by generating an encoding that encodes identifiers of the sequence of content items the user has interacted with and generating encodings that encode multimodal information for content items in the sequence of content items the user has interacted with. An aggregated information encoding for a user based on these encodings and a system analyzes the content item sequence encoding and interaction between the content item sequence encoding and the multiple modality encodings to generate the aggregated information encoding.

A system that incorporates the subject disclosure may include, for example, a gaming system that cooperates with a graphical user interface to enable user modification and enhancement of one or more audio streams associated with the gaming system. In embodiments, the audio streams may include a game audio stream, a chat audio stream of conversation among players of a video game, and a microphone audio stream of a player of the video game. Additional embodiments are disclosed.

Described are a system, method, and computer program product for user network activity anomaly detection. The method includes receiving network resource data associated with network resource activity of a plurality of users and generating a plurality of layers of a multilayer graph from the network resource data. Each layer of the plurality of layers may include a plurality of nodes, which are associated with users, connected by a plurality of edges, which are representative of node interdependency. The method also includes generating a plurality of adjacency matrices from the plurality of layers and generating a merged single layer graph based on a weighted sum of the plurality of adjacency matrices. The method further includes generating anomaly scores for each node in the merged single layer graph and determining a set of anomalous users based on the anomaly scores.

A method, computer system, and a computer program product for object modeling is provided. The present invention may include generating a temporary modeling structure based on at least a digital model and one or more printing preferences. The present invention may include sending printing instructions to a 3D printer based on the temporary modeling structure. The present invention may include receiving feedback from a sensory based system, the sensory based system monitoring a printing chamber of the 3D printer. The present invention may include updating the printing instructions based on an analysis of the feedback of the feedback received from the sensory based system.

A first six degree-of-freedom (DoF) pose of an object from a perspective of a first image sensor is determined with a neural network. A second six DoF pose of the object from a perspective of a second image sensor is determined with the neural network. A pose offset between the first and second six DoF poses is determined. A first projection offset is determined for a first two-dimensional (2D) bounding box generated from the first six DoF pose. A second projection offset is determined for a second 2D bounding box generated from the second six DoF pose. A total offset is determined by combining the pose offset, the first projection offset, and the second projection offset. Parameters of a loss function are updated based on the total offset. The updated parameters are provided to the neural network to obtain an updated total offset.

This application provides an artificial intelligence-based pathological image processing method performed by an electronic device. The method includes: determining a seed pixel of an immune cell region from a pathological image; obtaining a seed pixel mask image corresponding to the seed pixel of the immune cell region from the pathological image based on the seed pixel of the immune cell region; segmenting an epithelial cell region in the pathological image, to obtain an epithelial cell mask image of the pathological image; fusing the seed pixel mask image and the epithelial cell mask image of the pathological image, to obtain an effective seed pixel mask image corresponding to the immune cell region in the pathological image; and determining a ratio value of the immune cell region in the pathological image based on the effective seed pixel mask image.

A device for machine learning is provided, including a first neural network layer, a second neural network layer with a normalization layer arranged in between. The normalization layer is configured to, when the device is undergoing training on a batch of training samples, receive multiple outputs of the first neural network layer for a plurality of training samples of the batch, each output comprising multiple data values for different indices on a first dimension and a second dimension; group the outputs into multiple groups based on the indices on the first and second dimensions; form a normalization output for each group which are provided as input to the second neural network layer. According to the application, the training of a deep convolutional neural network with good performance that performs stably at different batch sizes and is generalizable to multiple vision tasks is achieved, thereby improving the performance of the training.

A medical-image processing apparatus according to the present invention includes an obtaining unit configured to obtain a medical image obtained by capturing an image of an examinee and a generation unit configured to input the medical image to a learning model selected based on an operation mode of a sensor at the image capturing to generate a medical image of a higher resolution than a resolution of the medical image.

A medical-image processing apparatus according to the present invention includes an obtaining unit configured to obtain a medical image obtained by capturing an image of an examinee and a generation unit configured to input the medical image to a learning model selected based on an operation mode of a sensor at the image capturing to generate a medical image of a higher resolution than a resolution of the medical image.

A method of modulating a depth of a hologram, the method includes: obtaining hologram data; determining a scale factor based on a hardware specification of a holographic display to display a three-dimensional (3D) hologram image in a space by using the hologram data; and modulating depth information of the hologram data based on the scale factor.