Manual prescription of the field of view (FOV) by MRI technologists is variable and prolongs the scanning process. Often, the FOV is too large or crops critical anatomy. We propose a deep-learning framework, trained by radiologists' supervision, for automating FOV prescription. An intra-stack shared feature extraction network and an attention network are used to process a stack of 2D image inputs to generate output scalars defining the location of a rectangular region of interest (ROI). The attention mechanism is used to make the model focus on the small number of informative slices in a stack. Then the smallest FOV that makes the neural network predicted ROI free of aliasing is calculated by an algebraic operation derived from MR sampling theory. We retrospectively collected 595 cases between February 2018 and February 2022. The framework's performance is examined quantitatively with intersection over union (IoU) and pixel error on position, and qualitatively with a reader study. We use the t-test for comparing quantitative results from all models and a radiologist. The proposed model achieves an average IoU of 0.867 and average ROI position error of 9.06 out of 512 pixels on 80 test cases, significantly better (P<0.05) than two baseline models and not significantly different from a radiologist (P>0.12). Finally, the FOV given by the proposed framework achieves an acceptance rate of 92% from an experienced radiologist.

展开的神经网络最近实现了最先进的MRI重建。这些网络通过在基于物理的一致性和基于神经网络的正则化之间交替来展开迭代优化算法。但是，它们需要大型神经网络的几次迭代来处理高维成像任务，例如3D MRI。这限制了基于反向传播的传统训练算法，这是由于较大的记忆力和计算梯度和存储中间激活的计算要求。为了应对这一挑战，我们提出了加速MRI（GLEAM）重建的贪婪学习，这是一种高维成像设置的有效培训策略。 GLEAM将端到端网络拆分为脱钩的网络模块。每个模块都以贪婪的方式优化，并通过脱钩的梯度更新，从而减少了训练过程中的内存足迹。我们表明，可以在多个图形处理单元（GPU）上并行执行解耦梯度更新，以进一步减少训练时间。我们介绍了2D和3D数据集的实验，包括多线圈膝，大脑和动态心脏Cine MRI。我们观察到：i）闪闪发光的概括以及最先进的记忆效率基线，例如具有相同内存足迹的梯度检查点和可逆网络，但训练速度更快1.3倍； ii）对于相同的内存足迹，闪光在2D中产生1.1dB PSNR的增益，而3D在端到端基线中产生1.8 dB。

在临床实践中，MR图像通常首先在扫描后长期看到辐射药剂。如果图像质量不充分，则患者必须返回额外的扫描，或者呈现次优解释。自动图像质量评估（IQA）将实现实时修复。对于MRI的现有IQA工作仅提供一般的质量得分，不可知论是对低质量扫描的原因和解决方案。此外，放射科医师的图像质量要求随扫描类型和诊断任务而异。因此，相同的分数可能对不同的扫描具有不同的影响。我们提出了一个训练训练的多任务CNN模型的框架，并用校准标签推断出来。由人类投入校准的标签遵循明确明确和高效的标签任务。图像统治者解决了不同的质量标准，并提供了一种从CNN中解释原始分数的具体方法。该模型支持对MRI中两个最常见的工件的评估：噪音和运动。它达到了约90％的准确度，比以前的最佳方法更好地达到6％，比噪声评估的人类专家更好3％。我们的实验表明，标签校准，图像统治者和多任务培训提高了模型的性能和概括性。

Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. We present our results comparing performance in terms of both accuracy and time-to-detection for multimodal data vs. a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.

Nostradamus, inspired by the French astrologer and reputed seer, is a detailed study exploring relations between environmental factors and changes in the stock market. In this paper, we analyze associative correlation and causation between environmental elements and stock prices based on the US financial market, global climate trends, and daily weather records to demonstrate significant relationships between climate and stock price fluctuation. Our analysis covers short and long-term rises and dips in company stock performances. Lastly, we take four natural disasters as a case study to observe their effect on the emotional state of people and their influence on the stock market.

We propose a method for in-hand 3D scanning of an unknown object from a sequence of color images. We cast the problem as reconstructing the object surface from un-posed multi-view images and rely on a neural implicit surface representation that captures both the geometry and the appearance of the object. By contrast with most NeRF-based methods, we do not assume that the camera-object relative poses are known and instead simultaneously optimize both the object shape and the pose trajectory. As global optimization over all the shape and pose parameters is prone to fail without coarse-level initialization of the poses, we propose an incremental approach which starts by splitting the sequence into carefully selected overlapping segments within which the optimization is likely to succeed. We incrementally reconstruct the object shape and track the object poses independently within each segment, and later merge all the segments by aligning poses estimated at the overlapping frames. Finally, we perform a global optimization over all the aligned segments to achieve full reconstruction. We experimentally show that the proposed method is able to reconstruct the shape and color of both textured and challenging texture-less objects, outperforms classical methods that rely only on appearance features, and its performance is close to recent methods that assume known camera poses.

我们提出了一种基于神经辐射场（NERF）的单个$ 360^\ PANORAMA图像合成新视图的方法。在类似环境中的先前研究依赖于多层感知的邻居插值能力来完成由遮挡引起的丢失区域，这导致其预测中的伪像。我们提出了360Fusionnerf，这是一个半监督的学习框架，我们介绍几何监督和语义一致性，以指导渐进式培训过程。首先，将输入图像重新投影至$ 360^\ Circ $图像，并在其他相机位置提取辅助深度图。除NERF颜色指导外，深度监督还改善了合成视图的几何形状。此外，我们引入了语义一致性损失，鼓励新观点的现实渲染。我们使用预先训练的视觉编码器（例如剪辑）提取这些语义功能，这是一个视觉变压器，经过数以千计的不同2D照片，并通过自然语言监督从网络中挖掘出来。实验表明，我们提出的方法可以在保留场景的特征的同时产生未观察到的区域的合理完成。 360fusionnerf在各种场景中接受培训时，转移到合成结构3D数据集（PSNR〜5％，SSIM〜3％lpips〜13％）时，始终达到最先进的性能，SSIM〜3％LPIPS〜9％）和replica360数据集（PSNR〜8％，SSIM〜2％LPIPS〜18％）。

与许多返回点值估计值的城市本地化方法不同，设定值表示可以通过确保可能的位置的连续体遵守安全限制来实现鲁棒性。具有设置值估计的一种策略是基于GNSS的阴影匹配〜（SM），其中使用三维（3-D）地图来计算GNSS阴影（在视线范围内被阻止）。但是，SM需要一个值值的网格才能计算障碍，并且精确限制了网格分辨率。我们建议针对Set值3-D MAPAID ADED GNSS本地化的Zonotope Shadow匹配（ZSM）。 ZSM代表建筑物和GNSS阴影，使用约束的ZONOTOPE，这是一种凸多属表示，该表示可以使用快速矢量串联操作实现传播设置值估计。 ZSM从粗糙的设定值开始，根据接收到的载体到噪声密度所判断的接收器在每个阴影内部或外部的接收器。我们使用模拟实验在简单的3-D示例图和旧金山密集的3-D地图上展示了算法的性能。

在存在对抗数据攻击的情况下，我们研究在线和分布式方案中的强大平均估计。在每个时间步骤中，网络中的每个代理都会收到一个潜在损坏的数据点，其中数据点最初是独立的，并且是随机变量的相同分布的样本。我们建议所有代理商在线和分发算法，以渐近地估计平均值。我们将估计值的错误结合和收敛属性提供给我们算法下的真实均值。基于网络拓扑，我们进一步评估了每个代理商在合并邻居的数据和仅在本地观察中学习之间的融合率的权衡。

我们研究以分布式和在线方式估算未知参数的问题。现有在分布式在线学习的工作通常专注于渐近分析，或者为后悔提供界限。但是，这些结果可能不会直接转化为有限的时间段数后学习模型的误差的界限。在本文中，我们提出了一种分布式的在线估计算法，该算法使网络中的每个代理都可以通过与邻居进行通信来提高其估计精度。我们在估计误差上提供了非反应界限，利用了基础模型的统计特性。我们的分析表明，估计错误和通信成本之间的权衡。此外，我们的分析使我们能够确定可以停止通信的时间（由于与通信相关的成本），同时达到所需的估计准确性。我们还提供了一个数值示例来验证我们的结果。