$B_1^+$ and $B_0$ field-inhomogeneities can significantly reduce accuracy and
robustness of MRF's quantitative parameter estimates. Additional $B_1^+$ and
$B_0$ calibration scans can mitigate this but add scan time and cannot be
applied retrospectively to previously collected data. Here, we proposed a
calibration-free sequence-adaptive deep-learning framework, to estimate and
correct for $B_1^+$ and $B_0$ effects of any MRF sequence. We demonstrate its
capability on arbitrary MRF sequences at 3T, where no training data were
previously obtained. Such approach can be applied to any previously-acquired
and future MRF-scans. The flexibility in directly applying this framework to
other quantitative sequences is also highlighted.

提出了一种无需校准的自适应深度学习框架，用于估计和纠正任何 MRF 序列的 $B_1^+$ 和 $B_0$ 效应，展示了其在 3T 任意 MRF 序列上的能力，可应用于以前获取的和未来的 MRF 扫描。强调了将该框架直接应用于其他定量序列的灵活性。

序列自适应场扭曲估计（SAFE）：增强 MRF 定量中 $B_1^+$ 和 $B_0$ 不均匀性的回顾性估计和校正

Sequence adaptive field-imperfection estimation (SAFE): retrospective  estimation and correction of $B_1^+$ and $B_0$ inhomogeneities for enhanced  MRF quantification

In-Context Learning (ICL), which formulates target tasks as prompt completion
conditioned on in-context demonstrations, has become the prevailing utilization
of LLMs. In this paper, we first disclose an actual predicament for this
typical usage that it can not scale up with training data due to context length
restriction. Besides, existing works have shown that ICL also suffers from
various biases and requires delicate calibration treatment. To address both
challenges, we advocate a simple and effective solution, $k$NN Prompting, which
first queries LLM with training data for distributed representations, then
predicts test instances by simply referring to nearest neighbors. We conduct
comprehensive experiments to demonstrate its two-fold superiority: 1)
Calibration-Free: $k$NN Prompting does not directly align LLM output
distribution with task-specific label space, instead leverages such
distribution to align test and training instances. It significantly outperforms
state-of-the-art calibration-based methods under comparable few-shot scenario.
2) Beyond-Context: $k$NN Prompting can further scale up effectively with as
many training data as are available, continually bringing substantial
improvements. The scaling trend holds across 10 orders of magnitude ranging
from 2 shots to 1024 shots as well as different LLMs scales ranging from 0.8B
to 30B. It successfully bridges data scaling into model scaling, and brings new
potentials for the gradient-free paradigm of LLM deployment. Code is publicly
available.

本文提出 $k$NN Prompting 作为在上下文学习中的一种简单有效的解决方案，该方法可以解决由于上下文长度限制导致的训练数据规模不可扩展的问题，并且不需要校准参数，可以有效地缩放数据。