Abstract: A classification system includes: one or more processors; and memory including instructions that, when executed by the one or more processors, cause the one or more processors to: calculate reference Shapley values for features of a data sample based on a first classification model; and train a second classification model though multi-task distillation to: predict Shapley values for the features of the data sample based on the reference Shapley values and a distillation loss; and predict a class label for the data sample based on the predicted Shapley values and a ground truth class label for the data sample.

Abstract: According to some embodiments, a system includes: a memory, an encoder; a decoder, wherein the system is operable to: receive, at the encoder, an input video; divide, by the encoder, the input video into a plurality of video patches; select, by the encoder, codes corresponding to the plurality of video patches of the input video, from a codebook comprising the codes; determine, by the encoder, an assigned code matrix comprising the codes corresponding to the plurality of video patches of the input video; receive, by the decoder, the assigned code matrix from the encoder; and generate, by the decoder, a reconstructed video based on the assigned code matrix.

Abstract: A system and method for defect detection. In some embodiments, the method includes: identifying, by a first neural network, a suspicious area in a first image; selecting, from among a set of defect-free reference images, by a second neural network, a defect-free reference image corresponding to the first image; identifying, by a third neural network, in the defect-free reference image, a reference region corresponding to the suspicious area; and determining, by a fourth neural network, a measure of similarity between the suspicious area and the reference region.

Abstract: A system including: a memory, an encoder, a decoder, and a processor, the processor being connected to the memory, the encoder, and the decoder. The system is configured to: receive, at the encoder, an input image, divide, by the encoder, the input image into a plurality of image patches, select, by the encoder, codes corresponding to the plurality of image patches of the input image, from a codebook including the codes. The system is further configured to determine, by the encoder, an assigned code matrix including the codes corresponding to the plurality of image patches of the input image, receive, by the decoder, the assigned code matrix from the encoder. The system is further configured to generate, by the decoder, a reconstructed image based on the assigned code matrix.

Abstract: A system and method for defect detection. The method may include training, with a first set of images, a first neural network including a first student neural network, and a first teacher neural network. The training of the first neural network may include introducing defects into a first subset of the first set of images, and training the first student neural network with the first set of images. The training of the first student neural network may include using a first cost function, that: for an image of the first set and not of the first subset, rewards similarity between a feature map of the first student neural network and a feature map of the first teacher neural network, and for an image of the first subset, rewards dissimilarity between a feature map of the first student neural network and a feature map of the first teacher neural network.

Abstract: An ingestible electronic capsule for the collection of samples along a gastric intestinal tract and methods relating thereto are provided. The ingestible electronic capsule includes a housing and a cap that form an interior chamber. The cap includes a sampling port and one or more sample collection chambers are disposed within the interior chamber. A motor is also disposed within the interior chamber and is configured to rotate one of the cap and the one or more sample collection chambers so to align one or the one or more sample collection chambers and the sampling port of the cap so to allow for sample collection. A microcontroller is also disposed within the interior chamber and is in communication with at least the motor. The microcontroller is configured to control the selective alignment of the sampling port and one of the one or more sample collection chambers and induce gastric intestinal fluid sampling.

Abstract: An ingestible electronic capsule for the collection of samples along a gastric intestinal tract and methods relating thereto are provided. The ingestible electronic capsule includes a housing and a cap that form an interior chamber. The cap includes a sampling port and one or more sample collection chambers are disposed within the interior chamber. A motor is also disposed within the interior chamber and is configured to rotate one of the cap and the one or more sample collection chambers so to align one or the one or more sample collection chambers and the sampling port of the cap so to allow for sample collection. A microcontroller is also disposed within the interior chamber and is in communication with at least the motor. The microcontroller is configured to control the selective alignment of the sampling port and one of the one or more sample collection chambers and induce gastric intestinal fluid sampling.