Human civilization has an increasingly powerful influence on the earth system. Affected by climate change and land-use change, natural disasters such as flooding have been increasing in recent years. Earth observations are an invaluable source for assessing and mitigating negative impacts. Detecting changes from Earth observation data is one way to monitor the possible impact. Effective and reliable Change Detection (CD) methods can help in identifying the risk of disaster events at an early stage. In this work, we propose a novel unsupervised CD method on time series Synthetic Aperture Radar~(SAR) data. Our proposed method is a probabilistic model trained with unsupervised learning techniques, reconstruction, and contrastive learning. The change map is generated with the help of the distribution difference between pre-incident and post-incident data. Our proposed CD model is evaluated on flood detection data. We verified the efficacy of our model on 8 different flood sites, including three recent flood events from Copernicus Emergency Management Services and six from the Sen1Floods11 dataset. Our proposed model achieved an average of 64.53\% Intersection Over Union(IoU) value and 75.43\% F1 score. Our achieved IoU score is approximately 6-27\% and F1 score is approximately 7-22\% better than the compared unsupervised and supervised existing CD methods. The results and extensive discussion presented in the study show the effectiveness of the proposed unsupervised CD method.

众所周知，过度参数化的深网能够完全拟合训练数据，同时显示出良好的概括性能。从线性回归上的直觉中得出的常见范式表明，大型网络甚至可以插入嘈杂的数据，而不会显着偏离地面真相信号。目前，缺少这种现象的精确表征。在这项工作中，我们介绍了深网的损失景观清晰度的实证研究，因为我们系统地控制了模型参数和训练时期的数量。我们将研究扩展到培训数据的街区以及清洁和嘈杂标记的样本。我们的发现表明，输入空间中的损失清晰度均遵循模型和时期的双重下降，在嘈杂的标签周围观察到了较差的峰值。与现有直觉相比，小型插值模型尤其适合干净和嘈杂的数据，但大型模型表达了平稳而平坦的损失景观。

视觉变压器已经证明了在各种视觉任务中胜过CNN的潜力。但是这些模型的计算和内存要求禁止在许多应用中使用它们，尤其是依赖高分辨率图像的应用程序，例如医学图像分类。更有效地训练VIT的努力过于复杂，需要进行建筑变化或复杂的培训方案。在这项工作中，我们表明可以通过随机删除输入图像贴片来有效地以高分辨率进行标准VIT模型。这种简单的方法（PatchDropout）在标准的自然图像数据集（例如ImageNet）中将拖鞋和内存减少至少50％，而这些节省仅随图像尺寸而增加。在高分辨率医疗数据集CSAW上，我们使用PatchDropout可节省5倍的计算和内存，并提高性能。对于具有固定计算或内存预算的从业人员，PatchDropout可以选择图像分辨率，超参数或模型大小以使其从模型中获得最大的性能。

在构建培训迷你批次时，最半监督的学习方法在样本标记的数据上。本文研究了这种常见做法是否改善了学习和方法。我们将其与替代设置进行比较，其中每个迷你批次从所有训练数据均匀地采样，标有或不统计，这大大减少了典型的低标签制度中真正标签的直接监督。然而，这种更简单的设置也可以看作更通用，甚至是必要的，在多任务问题中，标记数据的过采样将变得棘手。我们对半监控的CiFar-10图像分类的实验，使用FixMatch显示使用均匀采样方法时的性能下降，当标记数据的量或训练时间增加时，在均匀采样方法增加时。此外，我们分析培训动态，了解标记数据的过采样如何比较均匀采样。我们的主要发现是，在训练中特别有益，但在更多伪标签变得正确时，在后期的阶段中不太重要。尽管如此，我们还发现，保持一些真正的标签仍然很重要，以避免从错误的伪标签中积累确认错误。

由于筛选乳房X线照片的假阴性评估，通常在晚期检测到与其他癌症更差的间隔和大型侵入性乳腺癌。错过的筛选时间检测通常由其周围乳腺组织模糊的肿瘤引起的，这是一种称为掩蔽的现象。为了研究和基准爆发癌症的乳房Xmmpare掩蔽，在这项工作中，我们引入CSAW-M，最大的公共乳房数据集，从10,000多个人收集并用潜在的掩蔽注释。与以前的方法对比测量乳房图像密度作为代理的方法，我们的数据集直接提供了五个专家屏蔽潜在评估的注释。我们还培训了CSAW-M的深入学习模型来估计掩蔽水平，并显示估计的掩蔽更加预测筛查患有间隔和大型侵入性癌症的参与者 - 而不是明确培训这些任务 - 而不是其乳房密度同行。

Recent results indicate that the generic descriptors extracted from the convolutional neural networks are very powerful. This paper adds to the mounting evidence that this is indeed the case. We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the OverFeat network which was trained to perform object classification on ILSVRC13. We use features extracted from the OverFeat network as a generic image representation to tackle the diverse range of recognition tasks of object image classification, scene recognition, fine grained recognition, attribute detection and image retrieval applied to a diverse set of datasets. We selected these tasks and datasets as they gradually move further away from the original task and data the OverFeat network was trained to solve. Astonishingly, we report consistent superior results compared to the highly tuned state-of-the-art systems in all the visual classification tasks on various datasets. For instance retrieval it consistently outperforms low memory footprint methods except for sculptures dataset. The results are achieved using a linear SVM classifier (or L2 distance in case of retrieval) applied to a feature representation of size 4096 extracted from a layer in the net. The representations are further modified using simple augmentation techniques e.g. jittering. The results strongly suggest that features obtained from deep learning with convolutional nets should be the primary candidate in most visual recognition tasks.

Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

Based on WHO statistics, many individuals are suffering from visual problems, and their number is increasing yearly. One of the most critical needs they have is the ability to navigate safely, which is why researchers are trying to create and improve various navigation systems. This paper provides a navigation concept based on the visual slam and Yolo concepts using monocular cameras. Using the ORB-SLAM algorithm, our concept creates a map from a predefined route that a blind person most uses. Since visually impaired people are curious about their environment and, of course, to guide them properly, obstacle detection has been added to the system. As mentioned earlier, safe navigation is vital for visually impaired people, so our concept has a path-following part. This part consists of three steps: obstacle distance estimation, path deviation detection, and next-step prediction, done by monocular cameras.

National Association of Securities Dealers Automated Quotations(NASDAQ) is an American stock exchange based. It is one of the most valuable stock economic indices in the world and is located in New York City \cite{pagano2008quality}. The volatility of the stock market and the influence of economic indicators such as crude oil, gold, and the dollar in the stock market, and NASDAQ shares are also affected and have a volatile and chaotic nature \cite{firouzjaee2022lstm}.In this article, we have examined the effect of oil, dollar, gold, and the volatility of the stock market in the economic market, and then we have also examined the effect of these indicators on NASDAQ stocks. Then we started to analyze the impact of the feedback on the past prices of NASDAQ stocks and its impact on the current price. Using PCA and Linear Regression algorithm, we have designed an optimal dynamic learning experience for modeling these stocks. The results obtained from the quantitative analysis are consistent with the results of the qualitative analysis of economic studies, and the modeling done with the optimal dynamic experience of machine learning justifies the current price of NASDAQ shares.

Recent advances in distributed artificial intelligence (AI) have led to tremendous breakthroughs in various communication services, from fault-tolerant factory automation to smart cities. When distributed learning is run over a set of wirelessly connected devices, random channel fluctuations and the incumbent services running on the same network impact the performance of both distributed learning and the coexisting service. In this paper, we investigate a mixed service scenario where distributed AI workflow and ultra-reliable low latency communication (URLLC) services run concurrently over a network. Consequently, we propose a risk sensitivity-based formulation for device selection to minimize the AI training delays during its convergence period while ensuring that the operational requirements of the URLLC service are met. To address this challenging coexistence problem, we transform it into a deep reinforcement learning problem and address it via a framework based on soft actor-critic algorithm. We evaluate our solution with a realistic and 3GPP-compliant simulator for factory automation use cases. Our simulation results confirm that our solution can significantly decrease the training delay of the distributed AI service while keeping the URLLC availability above its required threshold and close to the scenario where URLLC solely consumes all network resources.