Guixin Dong

TL;DR: In this paper , a self-supervised network that relies on mutual information was proposed to improve the massive amount of untagged data that exists in the data set, which is employed to address the issue of sample imbalance in data sets.

TL;DR: Inspiration is drawn from an African elephant, which can employ both fingertip extensions on the trunk tip to efficiently grasp dispersed granular food all at once by mediating state transition of granules, and this bio‐inspired intelligent strategy is integrated into a soft pneumatic gripper for transporting dispersed granules.

TL;DR: Wang et al. as mentioned in this paper presented a captive adult male giant panda (Guangzhou Chimelong Safari Park, CHINA) presented with azoospermia and enlarged left testicle, and confirmed as testicular seminoma cases by testicular ultrasound, computed tomography (CT), testicular biopsy, and tumor marker examination findings.

Abstract: In recent years, with the rapid development of digital currency, digital currency brings us convenience and wealth, but also breeds some illegal and criminal behaviors. Different from traditional currencies, digital currency provides concealment to criminals while also exposing their behavior. The analysis of their behavior can be used to detect whether the current digital currency transaction is legal. There is a problem that most digital currency transactions are in compliance with laws and regulations, and only a small part of them uses digital currency to conduct illegal activities. It belongs to the problem of sample imbalance. It is quite challenging to accurately distinguish which transactions are legal and which are illegal in the massive digital currency transactions. For this reason, this study combines the mutual information and the traditional cross-entropy loss function and obtains the loss function based on the mutual information prior. The loss function based on the mutual information prior is that the bias of the category prior distribution is added after the output of the model (before the softmax), which makes the model consider category prior information to a certain extent when predicting. The experimental results show that the use of the loss function based on mutual information prior to the detection of digital currency illegal behavior has a good effect in SVM, DNN, GCN, and GAT methods.