Adaptive multi-source domain collaborative fine-tuning for transfer learning.

Fine-tuning is an important technique in transfer learning that has achieved significant success in tasks that lack training data. However, as it is difficult to extract effective features for single-source domain fine-tuning when the data distribution difference between the source and the target domain is large, we propose a transfer learning framework based on multi-source domain called adaptive multi-source domain collaborative fine-tuning (AMCF) to address this issue. AMCF utilizes multiple source domain models for collaborative fine-tuning, thereby improving the feature extraction capability of model in the target task.

Multi-Criterion Sampling Matting Algorithm via Gaussian Process.

Natural image matting is an essential technique for image processing that enables various applications, such as image synthesis, video editing, and target tracking. However, the existing image matting methods may fail to produce satisfactory results when computing resources are limited. Sampling-based methods can reduce the dimensionality of the decision space and, therefore, reduce computational resources by employing different sampling strategies.

Humanitarian Assistance and Disaster Relief mission by a tripartite medical team led by the Singapore Armed Forces after the 2015 Nepal earthquake.

Introduction: This study aimed to report the injury or disease patterns, challenges, key observations, and recommendations by the Singapore Armed Forces (SAF) team that embarked on an Humanitarian Assistance and Disaster Relief (HADR) mission in the aftermath of the April 2015 Nepal earthquake.

Methods: The SAF medical team that provided HADR assistance to Nepal consisted of personnel from the SAF, Singapore¢s Ministry of Health and the Royal Brunei Armed Forces. Upon arrival in Kathmandu, Nepal, the SAF medical team was assigned to the Gokarna district by the local health authorities.