单视点云完成旨在仅基于有限的观察结果来恢复对象的完整几何形状，这由于数据稀疏性和遮挡而非常困难。核心挑战是生成合理的几何形状，以基于部分扫描的局部扫描填充对象的未观察到的部分，该部分受限制不足，并且具有巨大的解决方案空间。受计算机图形中经典的影子音量技术的启发，我们提出了一种有效减少解决方案空间的新方法。我们的方法认为摄像机是向物体投射射线的光源。这样的光线建立了一个合理的约束但表达式的基础，以完成。然后将完成过程作为点位移优化问题进行配制。点在部分扫描处初始化，然后将每个点的两种运动类型移至目标位置：沿光线射线的方向运动和限制局部运动以进行形状细化。我们设计神经网络以预测理想点运动以获得完成结果。我们证明，通过详尽的评估和比较，我们的方法是准确，健壮和可推广的。此外，在MVP数据集上，它在定性和定量上优于最先进的方法。

从随机实验获得的数据培训模型是做出良好决策的理想选择。但是，随机实验通常是耗时的，昂贵的，冒险的，不可行的或不道德的，决策者别无选择，只能依靠培训模型时在历史策略下收集的观察数据。这不仅为实践中的决策政策发挥了最佳作用，还为不同的数据收集协议对数据培训的各种政策的绩效的影响，或者在问题上的稳健性方面的稳健性，对问题的绩效提出了疑问诸如观察结果中的动作或奖励 - 特定延迟之类的特征。我们的目的是为了在LinkedIn优化销售渠道分配的问题回答此类问题，其中销售帐户（线索）需要分配给三个渠道之一，目的是在一段时间内最大程度地提高成功转换的数量。关键问题特征构成了观察分配结果的随机延迟，其分布既是通道和结果依赖性的。我们构建了一个离散的时间模拟，可以处理我们的问题功能并将其用于评估：a）基于历史规则的策略； b）有监督的机器学习政策（XGBOOST）； c）多臂强盗（MAB）策略，在涉及的不同情况下：i）用于培训的数据收集（观察性与随机分组）； ii）铅转换方案； iii）延迟分布。我们的仿真结果表明，Linucb是一种简单的mAB策略，始终优于其他策略，相对于基于规则的策略，实现了18-47％的提升

去除阴影的关键是通过非阴影区域的指导恢复阴影区域的内容。由于远程建模不足，基于CNN的方法无法彻底研究非阴影地区的信息。为了解决这个问题，我们提出了一个新颖的清洁效果图网络（CNSNET），并具有面向阴影的自适应归一化（SOAN）模块，并根据阴影蒙版带有带有变压器（SAAT）模块的阴影吸引的聚合。在影子面罩的指导下，Soan模块制定了非阴影区域的统计数据，并将它们适应到阴影区域以进行区域修复。 SAAT模块利用阴影面膜来精确指导每个阴影像素的修复，通过考虑来自无阴影区域的高度相关像素以进行全球像素恢复。在三个基准数据集（ISTD，ISTD+和SRD）上进行了广泛的实验表明，我们的方法可实现出色的脱落性能。

视频中的自动烟熏车辆检测是用于传统昂贵的遥感遥控器，其中具有紫外线的紫外线设备，用于环境保护机构。但是，将车辆烟雾与后车辆或混乱道路的阴影和湿区域区分开来是一项挑战，并且由于注释数据有限，可能会更糟。在本文中，我们首先引入了一个现实世界中的大型烟熏车数据集，其中有75,000个带注释的烟熏车像图像，从而有助于对先进的深度学习模型进行有效的培训。为了启用公平算法比较，我们还构建了一个烟熏车视频数据集，其中包括163个带有细分级注释的长视频。此外，我们提出了一个新的粗到烟熏车辆检测（代码）框架，以进行有效的烟熏车辆检测。这些代码首先利用轻质的Yolo检测器以高召回率进行快速烟雾检测，然后采用烟极车匹配策略来消除非车辆烟雾，并最终使用精心设计的3D模型进一步完善结果，以进一步完善结果。空间时间空间。四个指标的广泛实验表明，我们的框架比基于手工的特征方法和最新的高级方法要优越。代码和数据集将在https://github.com/pengxj/smokyvehicle上发布。

Extracting building heights from satellite images is an active research area used in many fields such as telecommunications, city planning, etc. Many studies utilize DSM (Digital Surface Models) generated with lidars or stereo images for this purpose. Predicting the height of the buildings using only RGB images is challenging due to the insufficient amount of data, low data quality, variations of building types, different angles of light and shadow, etc. In this study, we present an instance segmentation-based building height extraction method to predict building masks with their respective heights from a single RGB satellite image. We used satellite images with building height annotations of certain cities along with an open-source satellite dataset with the transfer learning approach. We reached, the bounding box mAP 59, the mask mAP 52.6, and the average accuracy value of 70% for buildings belonging to each height class in our test set.

Sunquakes are seismic emissions visible on the solar surface, associated with some solar flares. Although discovered in 1998, they have only recently become a more commonly detected phenomenon. Despite the availability of several manual detection guidelines, to our knowledge, the astrophysical data produced for sunquakes is new to the field of Machine Learning. Detecting sunquakes is a daunting task for human operators and this work aims to ease and, if possible, to improve their detection. Thus, we introduce a dataset constructed from acoustic egression-power maps of solar active regions obtained for Solar Cycles 23 and 24 using the holography method. We then present a pedagogical approach to the application of machine learning representation methods for sunquake detection using AutoEncoders, Contrastive Learning, Object Detection and recurrent techniques, which we enhance by introducing several custom domain-specific data augmentation transformations. We address the main challenges of the automated sunquake detection task, namely the very high noise patterns in and outside the active region shadow and the extreme class imbalance given by the limited number of frames that present sunquake signatures. With our trained models, we find temporal and spatial locations of peculiar acoustic emission and qualitatively associate them to eruptive and high energy emission. While noting that these models are still in a prototype stage and there is much room for improvement in metrics and bias levels, we hypothesize that their agreement on example use cases has the potential to enable detection of weak solar acoustic manifestations.

Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior embedding and the deficiency in modeling capacity. Our work addresses these issues by proposing a unified diffusion framework that integrates both the image and degradation priors for highly effective shadow removal. In detail, we first propose a shadow degradation model, which inspires us to build a novel unrolling diffusion model, dubbed ShandowDiffusion. It remarkably improves the model's capacity in shadow removal via progressively refining the desired output with both degradation prior and diffusive generative prior, which by nature can serve as a new strong baseline for image restoration. Furthermore, ShadowDiffusion progressively refines the estimated shadow mask as an auxiliary task of the diffusion generator, which leads to more accurate and robust shadow-free image generation. We conduct extensive experiments on three popular public datasets, including ISTD, ISTD+, and SRD, to validate our method's effectiveness. Compared to the state-of-the-art methods, our model achieves a significant improvement in terms of PSNR, increasing from 31.69dB to 34.73dB over SRD dataset.

Learning about physical systems from quantum-enhanced experiments, relying on a quantum memory and quantum processing, can outperform learning from experiments in which only classical memory and processing are available. Whereas quantum advantages have been established for a variety of state learning tasks, quantum process learning allows for comparable advantages only with a careful problem formulation and is less understood. We establish an exponential quantum advantage for learning an unknown $n$-qubit quantum process $\mathcal{N}$. We show that a quantum memory allows to efficiently solve the following tasks: (a) learning the Pauli transfer matrix of an arbitrary $\mathcal{N}$, (b) predicting expectation values of bounded Pauli-sparse observables measured on the output of an arbitrary $\mathcal{N}$ upon input of a Pauli-sparse state, and (c) predicting expectation values of arbitrary bounded observables measured on the output of an unknown $\mathcal{N}$ with sparse Pauli transfer matrix upon input of an arbitrary state. With quantum memory, these tasks can be solved using linearly-in-$n$ many copies of the Choi state of $\mathcal{N}$, and even time-efficiently in the case of (b). In contrast, any learner without quantum memory requires exponentially-in-$n$ many queries, even when querying $\mathcal{N}$ on subsystems of adaptively chosen states and performing adaptively chosen measurements. In proving this separation, we extend existing shadow tomography upper and lower bounds from states to channels via the Choi-Jamiolkowski isomorphism. Moreover, we combine Pauli transfer matrix learning with polynomial interpolation techniques to develop a procedure for learning arbitrary Hamiltonians, which may have non-local all-to-all interactions, from short-time dynamics. Our results highlight the power of quantum-enhanced experiments for learning highly complex quantum dynamics.

Most shadow removal methods rely on the invasion of training images associated with laborious and lavish shadow region annotations, leading to the increasing popularity of shadow image synthesis. However, the poor performance also stems from these synthesized images since they are often shadow-inauthentic and details-impaired. In this paper, we present a novel generation framework, referred to as HQSS, for high-quality pseudo shadow image synthesis. The given image is first decoupled into a shadow region identity and a non-shadow region identity. HQSS employs a shadow feature encoder and a generator to synthesize pseudo images. Specifically, the encoder extracts the shadow feature of a region identity which is then paired with another region identity to serve as the generator input to synthesize a pseudo image. The pseudo image is expected to have the shadow feature as its input shadow feature and as well as a real-like image detail as its input region identity. To fulfill this goal, we design three learning objectives. When the shadow feature and input region identity are from the same region identity, we propose a self-reconstruction loss that guides the generator to reconstruct an identical pseudo image as its input. When the shadow feature and input region identity are from different identities, we introduce an inter-reconstruction loss and a cycle-reconstruction loss to make sure that shadow characteristics and detail information can be well retained in the synthesized images. Our HQSS is observed to outperform the state-of-the-art methods on ISTD dataset, Video Shadow Removal dataset, and SRD dataset. The code is available at https://github.com/zysxmu/HQSS.

Object compositing based on 2D images is a challenging problem since it typically involves multiple processing stages such as color harmonization, geometry correction and shadow generation to generate realistic results. Furthermore, annotating training data pairs for compositing requires substantial manual effort from professionals, and is hardly scalable. Thus, with the recent advances in generative models, in this work, we propose a self-supervised framework for object compositing by leveraging the power of conditional diffusion models. Our framework can hollistically address the object compositing task in a unified model, transforming the viewpoint, geometry, color and shadow of the generated object while requiring no manual labeling. To preserve the input object's characteristics, we introduce a content adaptor that helps to maintain categorical semantics and object appearance. A data augmentation method is further adopted to improve the fidelity of the generator. Our method outperforms relevant baselines in both realism and faithfulness of the synthesized result images in a user study on various real-world images.