Abstract: A method and a system for multimodal emotion recognition in conversation (ERC) based on a graph neural network (GNN) are provided. The method includes: S1: acquiring a single-modal representation perceived by a speaker and a context of a conversation; S2: extracting cross-modal and cross-context multivariate and high-order information based on the single-modal representation perceived by the speaker and the context to acquire multivariate representation data; S3: extracting different importance of different cross-modal and cross-context frequency components to acquire multi-frequency representation data; S4: fusing the multivariate representation data and the multi-frequency representation data to acquire an emotional representation of each utterance in an input conversation; and S5: acquiring a prediction label for each utterance in the input conversation based on the emotional representation, and outputting the prediction label as a multimodal ERC result.

Abstract: A computer-implemented method for watermarking images includes providing a secret key network (SKN) that is adapted to output a standard multivariate normal (SMVN) distribution for a given input image distribution, applying an input image to the SKN, generating a secret key signature (SKS) as a real vector, and embedding a watermark in the input image by using an adversarial attack to modify the input image in a manner that aligns the SKN's output with the SKS. A computer-implemented method for detecting a watermark in an image is also provided.

Abstract: A method and a system for multimodal emotion recognition in conversation (ERC) based on a graph neural network (GNN) are provided. The method includes: S1: acquiring a single-modal representation perceived by a speaker and a context of a conversation; S2: extracting cross-modal and cross-context multivariate and high-order information based on the single-modal representation perceived by the speaker and the context to acquire multivariate representation data; S3: extracting different importance of different cross-modal and cross-context frequency components to acquire multi-frequency representation data; S4: fusing the multivariate representation data and the multi-frequency representation data to acquire an emotional representation of each utterance in an input conversation; and S5: acquiring a prediction label for each utterance in the input conversation based on the emotional representation, and outputting the prediction label as a multimodal ERC result.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using contrastive learning. One of the methods includes obtaining a network input representing an environment; processing the network input using a first subnetwork of the neural network to generate a respective embedding for each location in the environment; processing the embeddings for each location in the environment using a second subnetwork of the neural network to generate a respective object prediction for each location; determining, for each of a plurality of pairs of the plurality of locations in the environment, whether the respective object predictions of the pair of locations characterize the same possible object or different possible objects; computing a respective contrastive loss value for each of the plurality of pairs of locations; and updating values for a plurality of parameters of the first subnetwork using the computed contrastive loss values.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using contrastive learning. One of the methods includes obtaining a network input representing an environment; processing the network input using a first subnetwork of the neural network to generate a respective embedding for each location in the environment; processing the embeddings for each location in the environment using a second subnetwork of the neural network to generate a respective object prediction for each location; determining, for each of a plurality of pairs of the plurality of locations in the environment, whether the respective object predictions of the pair of locations characterize the same possible object or different possible objects; computing a respective contrastive loss value for each of the plurality of pairs of locations; and updating values for a plurality of parameters of the first subnetwork using the computed contrastive loss values.

Abstract: A method for magnetic resonance imaging (MRI) includes steps of acquiring by an MRI scanner undersampled magnetic-field-gradient-encoded k-space data; performing a self-calibration of a magnetic-field-gradient-encoding point-spread function using a first neural network to estimate systematic waveform errors from the k-space data, and computing the magnetic-field-gradient-encoding point-spread function from the systematic waveform errors; reconstructing an image using a second neural network from the magnetic-field-gradient-encoding point-spread function and the k-space data.

Abstract: A method for magnetic resonance imaging (MRI) includes steps of acquiring by an MRI scanner undersampled magnetic-field-gradient-encoded k-space data; performing a self-calibration of a magnetic-field-gradient-encoding point-spread function using a first neural network to estimate systematic waveform errors from the k-space data, and computing the magnetic-field-gradient-encoding point-spread function from the systematic waveform errors; reconstructing an image using a second neural network from the magnetic-field-gradient-encoding point-spread function and the k-space data.

Abstract: A method for magnetic resonance imaging performs unsupervised training of a deep neural network of an MRI apparatus using a training set of under-sampled MRI scans, where each scan comprises slices of under-sampled, unclassified k-space MRI measurements. The MRI apparatus performs an under-sampled scan to produce under-sampled k-space data, updates the deep neural network with the under-sampled scan, and processes the under-sampled k-space data by the updated deep neural network of the MRI apparatus to reconstruct a final MRI image.

Abstract: A method for magnetic resonance imaging performs unsupervised training of a deep neural network of an MRI apparatus using a training set of under-sampled MRI scans, where each scan comprises slices of under-sampled, unclassified k-space MRI measurements. The MRI apparatus performs an under-sampled scan to produce under-sampled k-space data, updates the deep neural network with the under-sampled scan, and processes the under-sampled k-space data by the updated deep neural network of the MRI apparatus to reconstruct a final MRI image.

Abstract: A method for performing wave-encoded magnetic resonance imaging of an object is provided. The method includes applying one or more wave-encoded magnetic gradients to the object, and acquiring MR signals from the object. The method further includes calibrating a wave point-spread function, and reconstructing an image from the MR signals based at least in part on the calibrated wave point-spread function. Calibration of the wave point-spread function is based at least in part on one or more intermediate images generated from the MR signals.

Abstract: A communication system includes a first electronic device, and a second electronic device that monitors a state of the first electronic device. The first electronic device includes a transmitter that transmits a first frame including a first verification value forming a Hash chain to a bus network. The second electronic device includes a storage unit that stores the first verification value included in the first frame received from the bus network. The transmitter transmits, after transmission of the first frame, a second frame including a second verification value forming the Hash chain to the bus network. The second electronic device further includes a determination unit that determines that the state of the first electronic device is normal when the second verification value included in the second frame received from the bus network and the first verification value stored in the storage unit construct the Hash chain.

Abstract: A method for performing wave-encoded magnetic resonance imaging of an object is provided. The method includes applying one or more wave-encoded magnetic gradients to the object, and acquiring MR signals from the object. The method further includes calibrating a wave point-spread function, and reconstructing an image from the MR signals based at least in part on the calibrated wave point-spread function. Calibration of the wave point-spread function is based at least in part on one or more intermediate images generated from the MR signals.