Visual SLAM -- Simultaneous Localization and Mapping -- in dynamic environments typically relies on identifying and masking image features on moving objects to prevent them from negatively affecting performance. Current approaches are suboptimal: they either fail to mask objects when needed or, on the contrary, mask objects needlessly. Thus, we propose a novel SLAM that learns when masking objects improves its performance in dynamic scenarios. Given a method to segment objects and a SLAM, we give the latter the ability of Temporal Masking, i.e., to infer when certain classes of objects should be masked to maximize any given SLAM metric. We do not make any priors on motion: our method learns to mask moving objects by itself. To prevent high annotations costs, we created an automatic annotation method for self-supervised training. We constructed a new dataset, named ConsInv, which includes challenging real-world dynamic sequences respectively indoors and outdoors. Our method reaches the state of the art on the TUM RGB-D dataset and outperforms it on KITTI and ConsInv datasets.

我们提出了选择性多功能迭代（SMPI），一种新的算法来解决重要的张量PCA问题，该问题由高斯噪声Tensor $ \ BF {z损坏的峰值$ \ bf {v_0} ^ {\ otimesk} $损坏} \ in（\ mathbb {r} ^ n）^ {\ otimes k} $，使得$ \ bf {t} = \ sqrt {n} \ beta \ bf {v_0} ^ {\ otimesk} + \ bf { $ \ beta $的$ \β$是信噪比（SNR）。 SMPI在于生成多项式的随机初始化，在每次初始化上执行对称性的张力功率迭代的多项式数量，然后选择最大化$ \ langle \ bf {t}，\ bf {v} ^ {\ otimesk}的那个\ rangle $。通常考虑范围的$ K = 3美元的各种数值模拟$ N \ LEQ 1000 $表明SMPI的实验性能急剧改善存在于存在的算法上，并且与理论最佳恢复相当。我们表明这些意外的表现是由于强大的机制，其中噪声对信号恢复发挥着关键作用，并且在低$ \ beta $处发生。此外，这种机制是由SMPI的五个基本特征产生，它基于功率迭代将其与先前的算法区分开来。这些显着的结果可能对张量PCA的实际和理论应用产生强烈影响。 （i）我们提供了这种算法的变体来解决低秩CP张量分解。即使在实际数据上，这些所提出的算法也优于存在的方法，这对于实际应用具有巨大的潜在影响。 （ii）我们对各种机器学习问题中存在的高维非凸景观优化的SMPI和梯度下降方法的行为进行了新的理论见解。 （iii）我们预计这些结果可能有助于讨论猜测统计算法间隙的存在。

激光诱导的击穿光谱是用于在环境压力下快速和直接的样品的快速和直接多元素映射的优选技术，而没有对靶元素的任何限制。然而，Libs映射数据具有两个特殊性：由于单次测量而导致的内在低信噪比，并且由于获得用于成像的高频频谱而导致的高维度。这使得所有变速器都变高：在这种情况下，消融光斑直径减小，以及烧结质量和发射信号，而给定表面的光谱数量增加。因此，从嘈杂和大型数据集中有效地提取物理化学信息是一个主要问题。几位作者引入多变量方法作为应对这些数据的手段，特别是主要成分分析。然而，已知PCA为数据集的一致重建呈现了理论限制，因此有利于Libs映射数据的有效解释的限制。在本文中，我们使用离散小波变换和基于内核的稀疏PCA来引入HyperPCA，用于高光谱图像的新分析工具，用于使用基于内核的稀疏PCA来降低噪声对数据的影响，并始终重建光谱信号，特别强调libs数据。首先使用模拟Libs映射数据集来说明该方法，以强调其具有高噪声和/或高度干扰的光谱的性能。提供了标准PCA和传统的单变量数据分析的比较。最后，它用于在两种情况下处理实际数据，清楚地说明所提出的算法的潜力。我们表明该方法在回收的信息的数量和质量上具有优势，从而提高了分析表面的物理化学表征。

Specularity prediction is essential to many computer vision applications, giving important visual cues usable in Augmented Reality (AR), Simultaneous Localisation and Mapping (SLAM), 3D reconstruction and material modeling. However, it is a challenging task requiring numerous information from the scene including the camera pose, the geometry of the scene, the light sources and the material properties. Our previous work addressed this task by creating an explicit model using an ellipsoid whose projection fits the specularity image contours for a given camera pose. These ellipsoid-based approaches belong to a family of models called JOint-LIght MAterial Specularity (JOLIMAS), which we have gradually improved by removing assumptions on the scene geometry. However, our most recent approach is still limited to uniformly curved surfaces. This paper generalises JOLIMAS to any surface geometry while improving the quality of specularity prediction, without sacrificing computation performances. The proposed method establishes a link between surface curvature and specularity shape in order to lift the geometric assumptions made in previous work. Contrary to previous work, our new model is built from a physics-based local illumination model namely Torrance-Sparrow, providing an improved reconstruction. Specularity prediction using our new model is tested against the most recent JOLIMAS version on both synthetic and real sequences with objects of various general shapes. Our method outperforms previous approaches in specularity prediction, including the real-time setup, as shown in the supplementary videos.

我们越早努力计算Calabi-yau歧管的切线空间协调尺寸的努力使用深度学习。在本文中，我们考虑所有Calabi-yau四折的数据集，构建为投影空间的产品中的完整交叉点。采用最先进的计算机视觉架构启发的神经网络，我们改进了早期的基准，并证明所有四个非琐碎的霍奇格数可以使用多任务架构同时学习。30％（80％）培训率，我们达到$ H ^ {（1,1）} $的准确性为100％，以H ^ {（2,1）} $（两者为100％），81％（96％）为$ h ^ {（3,1）} $，49％（83％）为$ h ^ {（2,2）} $。假设欧拉数是已知的，因为它易于计算，并且考虑到从指数计算引起的线性约束，我们得到100％的总精度。

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed.

We introduce a machine-learning (ML)-based weather simulator--called "GraphCast"--which outperforms the most accurate deterministic operational medium-range weather forecasting system in the world, as well as all previous ML baselines. GraphCast is an autoregressive model, based on graph neural networks and a novel high-resolution multi-scale mesh representation, which we trained on historical weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF)'s ERA5 reanalysis archive. It can make 10-day forecasts, at 6-hour time intervals, of five surface variables and six atmospheric variables, each at 37 vertical pressure levels, on a 0.25-degree latitude-longitude grid, which corresponds to roughly 25 x 25 kilometer resolution at the equator. Our results show GraphCast is more accurate than ECMWF's deterministic operational forecasting system, HRES, on 90.0% of the 2760 variable and lead time combinations we evaluated. GraphCast also outperforms the most accurate previous ML-based weather forecasting model on 99.2% of the 252 targets it reported. GraphCast can generate a 10-day forecast (35 gigabytes of data) in under 60 seconds on Cloud TPU v4 hardware. Unlike traditional forecasting methods, ML-based forecasting scales well with data: by training on bigger, higher quality, and more recent data, the skill of the forecasts can improve. Together these results represent a key step forward in complementing and improving weather modeling with ML, open new opportunities for fast, accurate forecasting, and help realize the promise of ML-based simulation in the physical sciences.

Low Earth Orbit (LEO) constellations, each comprising a large number of satellites, have become a new source of big data "from the sky". Downloading such data to a ground station (GS) for big data analytics demands very high bandwidth and involves large propagation delays. Federated Learning (FL) offers a promising solution because it allows data to stay in-situ (never leaving satellites) and it only needs to transmit machine learning model parameters (trained on the satellites' data). However, the conventional, synchronous FL process can take several days to train a single FL model in the context of satellite communication (Satcom), due to a bottleneck caused by straggler satellites. In this paper, we propose an asynchronous FL framework for LEO constellations called AsyncFLEO to improve FL efficiency in Satcom. Not only does AsynFLEO address the bottleneck (idle waiting) in synchronous FL, but it also solves the issue of model staleness caused by straggler satellites. AsyncFLEO utilizes high-altitude platforms (HAPs) positioned "in the sky" as parameter servers, and consists of three technical components: (1) a ring-of-stars communication topology, (2) a model propagation algorithm, and (3) a model aggregation algorithm with satellite grouping and staleness discounting. Our extensive evaluation with both IID and non-IID data shows that AsyncFLEO outperforms the state of the art by a large margin, cutting down convergence delay by 22 times and increasing accuracy by 40%.

A Complete Computer vision system can be divided into two main categories: detection and classification. The Lane detection algorithm is a part of the computer vision detection category and has been applied in autonomous driving and smart vehicle systems. The lane detection system is responsible for lane marking in a complex road environment. At the same time, lane detection plays a crucial role in the warning system for a car when departs the lane. The implemented lane detection algorithm is mainly divided into two steps: edge detection and line detection. In this paper, we will compare the state-of-the-art implementation performance obtained with both FPGA and GPU to evaluate the trade-off for latency, power consumption, and utilization. Our comparison emphasises the advantages and disadvantages of the two systems.

Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Challenge has provided a large number of multi-institutional MRI scans as a benchmark for glioma segmentation algorithms. This paper describes our contribution to the BraTS 2022 Continuous Evaluation challenge. We propose a new ensemble of multiple deep learning frameworks namely, DeepSeg, nnU-Net, and DeepSCAN for automatic glioma boundaries detection in pre-operative MRI. It is worth noting that our ensemble models took first place in the final evaluation on the BraTS testing dataset with Dice scores of 0.9294, 0.8788, and 0.8803, and Hausdorf distance of 5.23, 13.54, and 12.05, for the whole tumor, tumor core, and enhancing tumor, respectively. Furthermore, the proposed ensemble method ranked first in the final ranking on another unseen test dataset, namely Sub-Saharan Africa dataset, achieving mean Dice scores of 0.9737, 0.9593, and 0.9022, and HD95 of 2.66, 1.72, 3.32 for the whole tumor, tumor core, and enhancing tumor, respectively. The docker image for the winning submission is publicly available at (https://hub.docker.com/r/razeineldin/camed22).