Impetigo herpetiformis in the second trimester: a case report and review of the literature.

Impetigo herpetiformis is a rare skin disease that most often occurs in the third trimester of pregnancy. It is currently considered as a form of generalized pustular psoriasis and the typical skin lesions comprise small sterile pustules. Here, a case of impetigo herpetiformis in the second trimester of pregnancy after 7 weeks of hydroxychloroquine administration for suspected Sjogren's syndrome is reported. Treatment with anti-infective, anti-inflammatory and immunosuppressive medication did not improve the patient's condition. Following delivery of a live male by emergency caesarean section at 29 weeks' gestation, the rash was reported to be completely resolved by 3 months postpartum. Previously published cases of impetigo herpetiformis in the second trimester of pregnancy that were retrieved from a search of the PubMed database are also reviewed and discussed.

Viewpoint planning with transition management for active object recognition.

Active object recognition (AOR) provides a paradigm where an agent can capture additional evidence by purposefully changing its viewpoint to improve the quality of recognition. One of the most concerned problems in AOR is viewpoint planning (VP) which refers to developing a policy to determine the next viewpoints of the agent. A research trend is to solve the VP problem with reinforcement learning, namely to use the viewpoint transitions explored by the agent to train the VP policy. However, most research discards the trained transitions, which may lead to an inefficient use of the explored transitions. To solve this challenge, we present a novel VP method with transition management based on reinforcement learning, which can reuse the explored viewpoint transitions. To be specific, a learning framework of the VP policy is first established via the deterministic policy gradient theory, which provides an opportunity to reuse the explored transitions. Then, we design a scheme of viewpoint transition management that can store the explored transitions and decide which transitions are used for the policy learning. Finally, within the framework, we develop an algorithm based on twin delayed deep deterministic policy gradient and the designed scheme to train the VP policy. Experiments on the public and challenging dataset GERMS show the effectiveness of our method in comparison with several competing approaches.