The abundance of dark matter (DM) subhalos orbiting a host galaxy is a generic prediction of the cosmological framework, and is a promising way to constrain the nature of DM. In this paper, we investigate the use of machine learning-based tools to quantify the magnitude of phase-space perturbations caused by the passage of DM subhalos. A simple binary classifier and an anomaly detection model are proposed to estimate if stars or star particles close to DM subhalos are statistically detectable in simulations. The simulated datasets are three Milky Way-like galaxies and nine synthetic Gaia DR2 surveys derived from these. Firstly, we find that the anomaly detection algorithm, trained on a simulated galaxy with full 6D kinematic observables and applied on another galaxy, is nontrivially sensitive to the DM subhalo population. On the other hand, the classification-based approach is not sufficiently sensitive due to the extremely low statistics of signal stars for supervised training. Finally, the sensitivity of both algorithms in the Gaia-like surveys is negligible. The enormous size of the Gaia dataset motivates the further development of scalable and accurate data analysis methods that could be used to select potential regions of interest for DM searches to ultimately constrain the Milky Way's subhalo mass function, as well as simulations where to study the sensitivity of such methods under different signal hypotheses.

This paper presents a solution to the Weather4cast 2022 Challenge Stage 2. The goal of the challenge is to forecast future high-resolution rainfall events obtained from ground radar using low-resolution multiband satellite images. We suggest a solution that performs data preprocessing appropriate to the challenge and then predicts rainfall movies using a novel RainUNet. RainUNet is a hierarchical U-shaped network with temporal-wise separable block (TS block) using a decoupled large kernel 3D convolution to improve the prediction performance. Various evaluation metrics show that our solution is effective compared to the baseline method. The source codes are available at https://github.com/jinyxp/Weather4cast-2022

Accurate and timely rain prediction is crucial for decision making and is also a challenging task. This paper presents a solution which won the 2 nd prize in the Weather4cast 2022 NeurIPS competition using 3D U-Nets and EarthFormers for 8-hour probabilistic rain prediction based on multi-band satellite images. The spatial context effect of the input satellite image has been deeply explored and optimal context range has been found. Based on the imbalanced rain distribution, we trained multiple models with different loss functions. To further improve the model performance, multi-model ensemble and threshold optimization were used to produce the final probabilistic rain prediction. Experiment results and leaderboard scores demonstrate that optimal spatial context, combined loss function, multi-model ensemble, and threshold optimization all provide modest model gain. A permutation test was used to analyze the effect of each satellite band on rain prediction, and results show that satellite bands signifying cloudtop phase (8.7 um) and cloud-top height (10.8 and 13.4 um) are the best predictors for rain prediction. The source code is available at https://github.com/bugsuse/weather4cast-2022-stage2.

Stereo images, containing left and right view images with disparity, are utilized in solving low-vision tasks recently, e.g., rain removal and super-resolution. Stereo image restoration methods usually obtain better performance than monocular methods by learning the disparity between dual views either implicitly or explicitly. However, existing stereo rain removal methods still cannot make full use of the complementary information between two views, and we find it is because: 1) the rain streaks have more complex distributions in directions and densities, which severely damage the complementary information and pose greater challenges; 2) the disparity estimation is not accurate enough due to the imperfect fusion mechanism for the features between two views. To overcome such limitations, we propose a new \underline{Stereo} \underline{I}mage \underline{R}ain \underline{R}emoval method (StereoIRR) via sufficient interaction between two views, which incorporates: 1) a new Dual-view Mutual Attention (DMA) mechanism which generates mutual attention maps by taking left and right views as key information for each other to facilitate cross-view feature fusion; 2) a long-range and cross-view interaction, which is constructed with basic blocks and dual-view mutual attention, can alleviate the adverse effect of rain on complementary information to help the features of stereo images to get long-range and cross-view interaction and fusion. Notably, StereoIRR outperforms other related monocular and stereo image rain removal methods on several datasets. Our codes and datasets will be released.

由于难以收集配对的现实世界训练数据，因此图像deraining目前由监督学习主导，并通过Photoshop渲染生成的合成数据。但是，由于合成数据和现实世界数据之间的差距，通常限制了对真实下雨场景的概括。在本文中，我们首先从统计学上探讨了为什么监督模型不能很好地推广到真实的雨天，并找到合成和真实雨水数据的实质差异。受我们的研究的启发，我们建议通过从其他连接的任务中学习有利的代表来消除雨水。在连接的任务中，可以轻松获得真实数据的标签。因此，我们的核心思想是通过任务传输从真实数据中学习表示形式，以改善概括。因此，我们将学习策略称为\ textit {任务传输学习}。如果有多个连接的任务，我们建议通过知识蒸馏降低模型大小。连接任务的预处理模型被视为教师，他们的所有知识都被蒸馏到学生网络，以便我们减少模型规模，同时保留所有连接的任务中有效的先前表示。最后，学生网络对少数配对的合成雨数据进行了微调，以指导预定的先前表示以去除雨水。广泛的实验表明，提出的任务转移学习策略令人惊讶地成功，并与最先进的监督学习方法相比，并显然超过了其他半监督者在合成数据上的方法。特别是，它显示出对现实世界的概括性的概括。

现有的DERANE方法主要集中于单个输入图像。只有单个输入图像，很难准确检测到雨条，去除雨条并恢复无雨图像。与单个2D图像相比，光场图像（LFI）通过通过元素摄像机记录每个事件射线的方向和位置，嵌入了广泛的3D结构和纹理信息，该镜头已成为计算机中的流行设备视觉和图形研究社区。在本文中，我们提出了一个新颖的网络4D-MGP-SRRNET，以从LFI中删除雨条。我们的方法将大雨LFI的所有子视图作为输入。为了充分利用LFI，我们采用4D卷积层来构建拟议的雨牛排清除网络，以同时处理LFI的所有子视图。在拟议的网络中，提出了带有新颖的多尺度自引导高斯工艺（MSGP）模块的雨水检测模型MGPDNET，以检测输入LFI的所有子视图中的雨条。引入了半监督的学习，以通过对虚拟世界LFI和现实世界中的LFI进行多个尺度上的虚拟世界LFI和现实世界中的LFI来准确检测雨季，这是通过计算现实世界中雨水条纹的伪地面真相。然后，所有减去预测的雨条的子视图都将馈送到4D残差模型中，以估计深度图。最后，所有子视图与相应的雨条和从估计的深度图转换的相应雨条和雾图都馈送到基于对抗性复发性神经网络的雨天LFI恢复模型，以逐步消除雨水条纹并恢复无雨的LFI LFI LFI。 。对合成LFI和现实世界LFI进行的广泛的定量和定性评估证明了我们提出的方法的有效性。

在遥感，图像或视频检索中捕获的图像在现实世界中捕获，室外监视受到天气不良的质量降低质量。雨水和薄雾等条件引入文物，使视觉分析具有挑战性并限制高级计算机视觉方法的性能。对于需要快速响应的时间临界应用，开发自动去除降​​雨的算法至关重要，而不会降低图像内容的质量。本文旨在开发一种新型的四个季节多阶段多尺度神经网络，该神经网络具有一个称为QSAM-NET的自我发场模块，以消除雨条。该算法的新颖性在于，在先前的方法上，它需要更少的参数为3.98，同时提高视觉质量。这是通过对合成和现实世界图像的广泛评估和基准测试来证明的。 QSAM-NET的此功能使网络适合在边缘设备和需要接近实时性能的应用程序上实现。实验表明，通过提高图像的视觉质量来表明。此外，对象检测准确性和训练速度也得到提高。

降雨事件的遥感对于运营和科学需求至关重要，包括天气预报，极端洪水，水循环监测等。降水量的降水量。然而，这种雷达的观察范围仅限于几百公里，促使对其他遥感方法的探索，在开阔的海洋上，这代表了不被陆基雷达覆盖的大面积。几十年来，众所周知，诸如Sentinel-1图像之类的C波段SAR图像在海面上表现出降雨签名。但是，SAR来源的降雨产品的开发仍然是一个挑战。在这里，我们提出了一种深度学习方法，以从SAR图像中提取降雨信息。我们证明，在接触和预处理的Sentinel-1/Nexrad数据集中训练的卷积神经网络，例如U-NET，显然优于最先进的过滤方案。我们的结果表明，在分割降水状态下的性能高，由1、3和10 mm/h的阈值描绘。与当前依靠Koch过滤器绘制二进制降雨图的方法相比，这些基于多阈值的模型可以为更高的风速提供降雨估计，因此对于数据同化天气预测或提高SAR的资格可能引起了极大的兴趣 - 衍生的风场数据。

在防御中开发和部署可信赖的自主系统时，负责和响应迅速的意义是什么？在这篇简短的反思性文章中，我们描述了一个案例研究，该案例研究在一个由行业领导的，政府资助的项目中与不同的合作者和利益相关者建立信任的自主系统-Athena AI-。利用此案例研究，我们在整个技术开发过程中以高转化准备水平的整个技术开发整个技术开发整个技术的发展和原理的嵌入和创新一致的，道德的方法和原则的经验教训。

这项工作研究了关节降雨和雾霾清除问题。在现实情况下，雨水和阴霾通常是两个经常共同发生的共同天气现象，可以极大地降低场景图像的清晰度和质量，从而导致视觉应用的性能下降，例如自动驾驶。但是，在场景图像中共同消除雨水和雾霾是艰难而挑战，在那里，阴霾和雨水的存在以及大气光的变化都可以降低现场信息。当前的方法集中在污染部分上，因此忽略了受大气光的变化影响的场景信息的恢复。我们提出了一个新颖的深神经网络，称为不对称双重编码器U-NET（ADU-NET），以应对上述挑战。 ADU-NET既产生污染物残留物，又产生残留的现场，以有效地去除雨水和雾霾，同时保留场景信息的保真度。广泛的实验表明，我们的工作在合成数据和现实世界数据基准（包括RainCityScapes，Bid Rain和Spa-data）的相当大的差距上优于现有的最新方法。例如，我们在RainCityScapes/spa-data上分别将最新的PSNR值提高了2.26/4.57。代码将免费提供给研究社区。