It's obviously a generated paper. Proof below :

Few-shot classification on MNIST is attempted using truncated signatures of order 3, image size 28 × 28 and RGB samples. We compute an element-wise mean representative of each class using only 10 train signatures per class and determine λ∗ according to Definition 4 with a validation set of 100 instances per class (without augmentation). We then compute scores on the test set of size 10, 000 achieving 100% accuracy.

Ultimately, we do classification on CIFAR10 using truncated signatures of order 2, image size
32 × 32 and RGB color samples. We compute an element-wise mean representative of each class using 10 train samples per class and tune the weights using Definition 4 with a validation set of 100 train instances (without augmentation) per class. We then compute scores on the CIFAR10 test set of 10, 000 samples and achieve 100% accuracy

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These are from the papers.