多视图光谱聚类（MVSC）由于多样化的数据源而引起了越来越多的关注。但是，大多数现有作品在样本外预测中被禁止，并且忽略了模型的解释性和聚类结果的探索。在本文中，通过限制内核机框架通过共享潜在空间提出了一种新的MVSC方法。通过偶联特征双重性的镜头，我们为MVSC施加了加权内核主成分分析问题，并开发了修改的加权共轭特征二重性以制定二元变量。在我们的方法中，双重变量扮演着隐藏特征的角色，所有视图都共享了构造一个常见的潜在空间，并通过从特定的空间中学习预测来耦合视图。这种潜在空间可促进分离的簇，并提供直接的数据探索，促进可视化和解释。我们的方法只需要一个单一的特征分类，其维度独立于视图数量。为了提高高阶相关性，引入了基于张量的建模而不增加计算复杂性。我们的方法可以通过样本外扩展灵活地应用，从而极大地提高了具有固定尺寸内核方案的大规模数据的效率。数值实验验证了我们的方法在准确性，效率和可解释性方面有效，显示出明显的特征值衰减和不同的潜在变量分布。

我们介绍了螺旋（一种超线性收敛的增量近端算法），用于在相对平滑度假设下求解非凸的正则有限总和问题。本着Svrg和Sarah的精神，螺旋的每一个迭代都由一个内部和外循环组成。它将增量和完整（近端）梯度更新与LineSearch相结合。结果表明，在使用准牛顿方向时，在极限点的轻度假设下达到了超线性收敛。更重要的是，多亏了该线路搜索，确保全球融合得以确保最终将始终接受单位步骤。在不同的凸，非凸和非lipschitz可区分问题上的仿真结果表明，我们的算法以及其自适应变体都与最新的状态竞争。

Mapping the seafloor with underwater imaging cameras is of significant importance for various applications including marine engineering, geology, geomorphology, archaeology and biology. For shallow waters, among the underwater imaging challenges, caustics i.e., the complex physical phenomena resulting from the projection of light rays being refracted by the wavy surface, is likely the most crucial one. Caustics is the main factor during underwater imaging campaigns that massively degrade image quality and affect severely any 2D mosaicking or 3D reconstruction of the seabed. In this work, we propose a novel method for correcting the radiometric effects of caustics on shallow underwater imagery. Contrary to the state-of-the-art, the developed method can handle seabed and riverbed of any anaglyph, correcting the images using real pixel information, thus, improving image matching and 3D reconstruction processes. In particular, the developed method employs deep learning architectures in order to classify image pixels to "non-caustics" and "caustics". Then, exploits the 3D geometry of the scene to achieve a pixel-wise correction, by transferring appropriate color values between the overlapping underwater images. Moreover, to fill the current gap, we have collected, annotated and structured a real-world caustic dataset, namely R-CAUSTIC, which is openly available. Overall, based on the experimental results and validation the developed methodology is quite promising in both detecting caustics and reconstructing their intensity.

Automatic fake news detection is a challenging problem in misinformation spreading, and it has tremendous real-world political and social impacts. Past studies have proposed machine learning-based methods for detecting such fake news, focusing on different properties of the published news articles, such as linguistic characteristics of the actual content, which however have limitations due to the apparent language barriers. Departing from such efforts, we propose FNDaaS, the first automatic, content-agnostic fake news detection method, that considers new and unstudied features such as network and structural characteristics per news website. This method can be enforced as-a-Service, either at the ISP-side for easier scalability and maintenance, or user-side for better end-user privacy. We demonstrate the efficacy of our method using data crawled from existing lists of 637 fake and 1183 real news websites, and by building and testing a proof of concept system that materializes our proposal. Our analysis of data collected from these websites shows that the vast majority of fake news domains are very young and appear to have lower time periods of an IP associated with their domain than real news ones. By conducting various experiments with machine learning classifiers, we demonstrate that FNDaaS can achieve an AUC score of up to 0.967 on past sites, and up to 77-92% accuracy on newly-flagged ones.

Verifying the input-output relationships of a neural network so as to achieve some desired performance specification is a difficult, yet important, problem due to the growing ubiquity of neural nets in many engineering applications. We use ideas from probability theory in the frequency domain to provide probabilistic verification guarantees for ReLU neural networks. Specifically, we interpret a (deep) feedforward neural network as a discrete dynamical system over a finite horizon that shapes distributions of initial states, and use characteristic functions to propagate the distribution of the input data through the network. Using the inverse Fourier transform, we obtain the corresponding cumulative distribution function of the output set, which can be used to check if the network is performing as expected given any random point from the input set. The proposed approach does not require distributions to have well-defined moments or moment generating functions. We demonstrate our proposed approach on two examples, and compare its performance to related approaches.

We propose AstroSLAM, a standalone vision-based solution for autonomous online navigation around an unknown target small celestial body. AstroSLAM is predicated on the formulation of the SLAM problem as an incrementally growing factor graph, facilitated by the use of the GTSAM library and the iSAM2 engine. By combining sensor fusion with orbital motion priors, we achieve improved performance over a baseline SLAM solution. We incorporate orbital motion constraints into the factor graph by devising a novel relative dynamics factor, which links the relative pose of the spacecraft to the problem of predicting trajectories stemming from the motion of the spacecraft in the vicinity of the small body. We demonstrate the excellent performance of AstroSLAM using both real legacy mission imagery and trajectory data courtesy of NASA's Planetary Data System, as well as real in-lab imagery data generated on a 3 degree-of-freedom spacecraft simulator test-bed.

The de facto standard of dynamic histogram binning for radiomic feature extraction leads to an elevated sensitivity to fluctuations in annotated regions. This may impact the majority of radiomic studies published recently and contribute to issues regarding poor reproducibility of radiomic-based machine learning that has led to significant efforts for data harmonization; however, we believe the issues highlighted here are comparatively neglected, but often remedied by choosing static binning. The field of radiomics has improved through the development of community standards and open-source libraries such as PyRadiomics. But differences in image acquisition, systematic differences between observers' annotations, and preprocessing steps still pose challenges. These can change the distribution of voxels altering extracted features and can be exacerbated with dynamic binning.

在本文中，我们为自主机器人提供了一种新型的模型预测控制方法，受到任意形式的不确定性。拟议的风险感知模型预测路径积分（RA-MPPI）控制利用条件价值（CVAR）度量来为安全关键的机器人应用生成最佳控制动作。与大多数现有的随机MPC和CVAR优化方法不同，这些方法将原始动力学线性化并将控制任务制定为凸面程序，而拟议的方法直接使用原始动力学，而无需限制成本函数或噪声的形式。我们将新颖的RA-MPPI控制器应用于自动驾驶汽车，以在混乱的环境中进行积极的驾驶操作。我们的仿真和实验表明，与基线MPPI控制器相比，提出的RA-MPPI控制器可以达到大约相同的圈时间，而碰撞的碰撞明显少得多。所提出的控制器以高达80Hz的更新频率执行在线计算，利用现代图形处理单元（GPU）来进行多线程轨迹以及CVAR值的生成。

我们介绍了第一个机器学习引力波搜索模拟数据挑战（MLGWSC-1）的结果。在这一挑战中，参与的小组必须从二进制黑洞合并中识别出复杂性和持续时间逐渐嵌入在逐渐更现实的噪声中的引力波信号。 4个提供的数据集中的决赛包含O3A观察的真实噪声，并发出了20秒的持续时间，其中包含进动效应和高阶模式。我们介绍了在提交前从参与者未知的1个月的测试数据中得出的6个输入算法的平均灵敏度距离和运行时。其中4个是机器学习算法。我们发现，最好的基于机器学习的算法能够以每月1个的错误警报率（FAR）的速度（FAR）实现基于匹配过滤的生产分析的敏感距离的95％。相反，对于真实的噪音，领先的机器学习搜索获得了70％。为了更高的范围，敏感距离缩小的差异缩小到某些数据集上选择机器学习提交的范围$ \ geq 200 $以优于传统搜索算法的程度。我们的结果表明，当前的机器学习搜索算法可能已经在有限的参数区域中对某些生产设置有用。为了改善最新的技术，机器学习算法需要降低他们能够检测信号并将其有效性扩展到参数空间区域的虚假警报率，在这些区域中，建模的搜索在计算上很昂贵。根据我们的发现，我们汇编了我们认为，将机器学习搜索提升到重力波信号检测中的宝贵工具，我们认为这是最重要的研究领域。

在本文中，我们开发了一种方法，该方法使自主机器人能够从点云数据构建和压缩语义环境表示。我们的方法从传感器数据中构建了环境的三维语义树表示，然后通过一种新型的信息理论树木修复方法来压缩。所提出的方法是概率的，并将其纳入现实世界中固有的语义分类中。此外，我们的方法允许机器人在生成压缩树时优先考虑单个语义类，以设计保留相关语义信息的多分辨率表示，同时丢弃不需要的语义类别。我们通过压缩大型户外，语义丰富，真实世界环境的语义OCTREE模型来演示方法。此外，我们还展示了如何使用OCTREE抽象来创建语义信息图以进行运动计划，并使用未知的图形构造方法（例如Halton序列）进行比较。