Abstract Tumor cells are genetically unstable carrying continuously changing mutations which the basis for neoantigen vaccines. Pre-existing immune responses against tumor-associated self-antigens as well semi-nonself neoantigens have been reported in cancer patients. These data self-nonself (SNS) model of immunity, proposing that tumor expressing induce tolerance. The adaptation proposes expre...

For various reasons such as ideology of patronage, fear of censorial measures and specific cultural and literary plans, authors may present their original texts as translation, and sometimes translators manipulate the original text to the extent that it can not be considered as translation anymore. This research was aimed at investigating Bastin's adaptation as a kind of pseudo-translation. He ...

In computer vision, it has achieved great transfer learning performance via adapting large-scale pretrained vision models (e.g., transformers) to downstream tasks. Common approaches for model adaptation either update all parameters or leverage linear probes. this paper, we aim study parameter-efficient strategies transformers on the image classification task. We formulate efficient as a subspac...

Deep neural networks have been applied successfully to a wide variety of inverse problems arising in computational imaging. These are typically trained using forward model that describes the measurement process be inverted, which is often incorporated directly into network itself. However, these approaches sensitive changes model: if at test time varies (even slightly) from one was for, reconst...

Conventional model transfer techniques, requiring the labelled source data, are not applicable in privacy-protected medical fields. For challenging scenarios, recent data-free domain adaptation (SFDA) has become a mainstream solution but losing focus on inter-sample class information. This paper proposes new Credible Local Context Representation approach for SFDA. Our main idea is to exploit cr...