Hemiballism is continuous, nonpatterned involuntary movement characterized by irregular, coarse, flinging movement involving the limbs on one side. Hemiballism is most commonly caused by stroke. However, very rarely a transient ischemic attack (TIA) presents as hemiballism. We describe 2 such patients with hemiballism presenting as TIA.

This paper proposes a verification method whether fake fingerprints generated by DCGAN are similar to actual fake fingerprints in order to augment fake fingerprint data. The first method to verify is to compare the distributions of the mean and standard deviation of fake fingerprints generated by deep convolutional generative adversarial network (DCGAN) with those of actual fake fingerprints. In the second method, the mean Hamming distance, which is a method of evaluating the similarity of images, is used for measuring the similarity between the generated fake fingerprints and the actual fake fingerprints. The third method is to obtain the histograms of the generated fake fingerprints and actual fake fingerprints and measure the similarity by calculating Pearson correlation of the histograms. The fourth method is to calculate intersection of union, which is a method of evaluating the shape similarity of images, between generated fake fingerprints and actual fake fingerprints. From extensive experiments it was confirmed that fake fingerprints generated by DCGAN could be used to augment fake fingerprint data because generated fake fingerprints are similar to actual fake fingerprints in terms of four similarity measures.

Acquisition of fine-grained segments in semantic segmentation is important in most sementic segmentation applications, especially for clothing images composed of fine-grained textures. However, most existing semantic segmentation methods based on fully convolutional network (FCN) were not enough to acquire fine-grained segments because they are based on a single resolution and can not well distinguish between objects in the images. To stabilize the acquisition of fine-grained segments, we propose a method that is composed of two additional components in the U-Net structure for processing multi-scale fine-grained segments. The first component is to use normalization at all layers. We found from experiments that normalization is a key process in stabilizing the acquisition of fine-grained segments, especially in the U-Net based methods because they operate on a multi-scale fine-grained segment. An additional component is to use model prediction correction using focal loss with L1 regularization. Focal loss can be used to control the model prediction term as regularization in the training process. From experiments, we found that our method was better than the existing methods.

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