Color plays a crucial role in the visualization, interpretation, and analysis
of multi-wavelength astronomical images. However, generating color images that
accurately represent the full dynamic range of astronomical sources is
challenging. In response, Gnuastro v0.22 introduces the program
'astscript-color-faint-gray', which is extensively documented in the Gnuastro
manual. It employs a non-linear transformation to assign an 8-bit RGB
(Red-Green-Blue) value to brighter pixels, while the fainter ones are shown in
an inverse grayscale. This approach enables the simultaneous visualization of
low surface brightness features within the same image. This research note is
reproducible with Maneage, on the Git commit 48f5408.

多波长天文图像的颜色在可视化、解释和分析中起着关键作用，然而生成准确表达天文源完整动态范围的彩色图像具有一定挑战性，本研究通过非线性转换方法成功将亮度较高的像素赋予 8 位 RGB（红 - 绿 - 蓝）值，而较暗的像素通过反向灰度显示，从而实现了在同一图像中同时显示低表面亮度特征的效果。

Gnuastro: 色彩图像的完整动态范围可视化

Gnuastro: visualizing the full dynamic range in color images

This paper deals with the unsupervised domain adaptation problem, where one
wants to estimate a prediction function $f$ in a given target domain without
any labeled sample by exploiting the knowledge available from a source domain
where labels are known. Our work makes the following assumption: there exists a
non-linear transformation between the joint feature/label space distributions
of the two domain $\mathcal{P}_s$ and $\mathcal{P}_t$. We propose a solution of
this problem with optimal transport, that allows to recover an estimated target
$\mathcal{P}^f_t=(X,f(X))$ by optimizing simultaneously the optimal coupling
and $f$. We show that our method corresponds to the minimization of a bound on
the target error, and provide an efficient algorithmic solution, for which
convergence is proved. The versatility of our approach, both in terms of class
of hypothesis or loss functions is demonstrated with real world classification
and regression problems, for which we reach or surpass state-of-the-art
results.

本文针对无监督域自适应问题，假设两个域的联合特征 / 标签空间分布之间存在非线性变换，提出了使用最优输运解决此问题的方法，并在实际分类和回归问题中证明了方法的高效性及超越现有技术的性能。