The gettering effects of ion‐beam defect engineering (IBDE) in BF2‐implanted silicon have been studied. It has been shown that the IBDE technique may be useful in the improvement of the properties of BF2‐implanted silicon. The gettering layer introduced by MeV Si ion irradiation and formed during the process of thermal annealing collects not only impurities but also simple defects. Thus, it effected (1) reductions of secondary defects and F impurity accumulation in the BF2‐doped region; (2) reduction of the anomalous diffusion of B atoms; and (3) enhancement of the electrical activation of B atoms.
In-context learning (ICL) has become a classic approach for enabling LLMs to handle various tasks based on a few input-output examples. The effectiveness of ICL heavily relies on the quality of these examples, and previous works which focused on enhancing example retrieval capabilities have achieved impressive performances. However, two challenges remain in retrieving high-quality examples: (1) Difficulty in distinguishing cross-task data distributions, (2) Difficulty in making the fine-grained connection between retriever output and feedback from LLMs. In this paper, we propose a novel framework called TDR. TDR decouples the ICL examples from different tasks, which enables the retrieval module to retrieve examples specific to the target task within a multi-task dataset. Furthermore, TDR models fine-grained feedback from LLMs to supervise and guide the training of the retrieval module, which helps to retrieve high-quality examples. We conducted extensive experiments on a suite of 30 NLP tasks, the results demonstrate that TDR consistently improved results across all datasets and achieves state-of-the-art performance. Meanwhile, our approach is a plug-and-play method, which can be easily combined with various LLMs to improve example retrieval abilities for ICL. The code is available at https://github.com/Nnn-s/TDR.
A new approach for quantifying the elastic deformation behavior of one-dimensional nanostructures is presented by fitting the image profile measured using atomic force microscopy in contact mode along the entire length of a bridged/suspended nanobelt/nanowire/nanotube under different load forces. Consistently fitting the measured deformation profiles can uniquely determine if the measured data are best explained by either the clamped-clamped beam model or the free-free beam model without preassumption, and it eliminates the uncertainty in defining the central point of the suspended beam, thus, greatly increasing the precision and reliability of the measurements.
