提出了一种新的算法来加速RANSAC模型质量计算。该方法基于将联合对应空间（例如，2D-2D点对应）分区为一对常规网格。网格电池被最小的样本模型映射，估计在Ransac内，以拒绝早期与模型参数不一致的对应关系。所提出的技术是一般的。即使将点映射到点集，例如，它也适用于任意变换，例如，作为对末极线的基本矩阵映射。该方法在来自公共可用数据集的数千个图像对上测试了来自基本和基本矩阵，同字和径向扭曲的相同估计。平均而言，它将RANSAC运行时间降低了41％，无需准确性劣化。它可以直接插入最先进的Ransac框架中，例如，vsac。

We introduce organism networks, which function like a single neural network but are composed of several neural particle networks; while each particle network fulfils the role of a single weight application within the organism network, it is also trained to self-replicate its own weights. As organism networks feature vastly more parameters than simpler architectures, we perform our initial experiments on an arithmetic task as well as on simplified MNIST-dataset classification as a collective. We observe that individual particle networks tend to specialise in either of the tasks and that the ones fully specialised in the secondary task may be dropped from the network without hindering the computational accuracy of the primary task. This leads to the discovery of a novel pruning-strategy for sparse neural networks

Common to all different kinds of recurrent neural networks (RNNs) is the intention to model relations between data points through time. When there is no immediate relationship between subsequent data points (like when the data points are generated at random, e.g.), we show that RNNs are still able to remember a few data points back into the sequence by memorizing them by heart using standard backpropagation. However, we also show that for classical RNNs, LSTM and GRU networks the distance of data points between recurrent calls that can be reproduced this way is highly limited (compared to even a loose connection between data points) and subject to various constraints imposed by the type and size of the RNN in question. This implies the existence of a hard limit (way below the information-theoretic one) for the distance between related data points within which RNNs are still able to recognize said relation.

Accomplishing safe and efficient driving is one of the predominant challenges in the controller design of connected automated vehicles (CAVs). It is often more convenient to address these goals separately and integrate the resulting controllers. In this study, we propose a controller integration scheme to fuse performance-based controllers and safety-oriented controllers safely for the longitudinal motion of a CAV. The resulting structure is compatible with a large class of controllers, and offers flexibility to design each controller individually without affecting the performance of the others. We implement the proposed safe integration scheme on a connected automated truck using an optimal-in-energy controller and a safety-oriented connected cruise controller. We validate the premise of the safe integration through experiments with a full-scale truck in two scenarios: a controlled experiment on a test track and a real-world experiment on a public highway. In both scenarios, we achieve energy efficient driving without violating safety.

This paper considers mixed traffic consisting of connected automated vehicles equipped with vehicle-to-everything (V2X) connectivity and human-driven vehicles. A control strategy is proposed for communicating pairs of connected automated vehicles, where the two vehicles regulate their longitudinal motion by responding to each other, and, at the same time, stabilize the human-driven traffic between them. Stability analysis is conducted to find stabilizing controllers, and simulations are used to show the efficacy of the proposed approach. The impact of the penetration of connectivity and automation on the string stability of traffic is quantified. It is shown that, even with moderate penetration, connected automated vehicle pairs executing the proposed controllers achieve significant benefits compared to when these vehicles are disconnected and controlled independently.

Periocular recognition has gained attention recently due to demands of increased robustness of face or iris in less controlled scenarios. We present a new system for eye detection based on complex symmetry filters, which has the advantage of not needing training. Also, separability of the filters allows faster detection via one-dimensional convolutions. This system is used as input to a periocular algorithm based on retinotopic sampling grids and Gabor spectrum decomposition. The evaluation framework is composed of six databases acquired both with near-infrared and visible sensors. The experimental setup is complemented with four iris matchers, used for fusion experiments. The eye detection system presented shows very high accuracy with near-infrared data, and a reasonable good accuracy with one visible database. Regarding the periocular system, it exhibits great robustness to small errors in locating the eye centre, as well as to scale changes of the input image. The density of the sampling grid can also be reduced without sacrificing accuracy. Lastly, despite the poorer performance of the iris matchers with visible data, fusion with the periocular system can provide an improvement of more than 20%. The six databases used have been manually annotated, with the annotation made publicly available.

Federated learning (FL) has emerged as an instance of distributed machine learning paradigm that avoids the transmission of data generated on the users' side. Although data are not transmitted, edge devices have to deal with limited communication bandwidths, data heterogeneity, and straggler effects due to the limited computational resources of users' devices. A prominent approach to overcome such difficulties is FedADMM, which is based on the classical two-operator consensus alternating direction method of multipliers (ADMM). The common assumption of FL algorithms, including FedADMM, is that they learn a global model using data only on the users' side and not on the edge server. However, in edge learning, the server is expected to be near the base station and have direct access to rich datasets. In this paper, we argue that leveraging the rich data on the edge server is much more beneficial than utilizing only user datasets. Specifically, we show that the mere application of FL with an additional virtual user node representing the data on the edge server is inefficient. We propose FedTOP-ADMM, which generalizes FedADMM and is based on a three-operator ADMM-type technique that exploits a smooth cost function on the edge server to learn a global model parallel to the edge devices. Our numerical experiments indicate that FedTOP-ADMM has substantial gain up to 33\% in communication efficiency to reach a desired test accuracy with respect to FedADMM, including a virtual user on the edge server.

在本文中，提出了一种颜色边缘检测方法，其中使用多尺度Gabor滤波器从输入颜色图像获得边缘。该方法的主要优点是在保持良好的噪声稳健性的同时，达到了高边缘检测精度。提出的方法包括三个方面：首先，RGB颜色图像由于其宽阔的着色区域和均匀的颜色分布而转换为CIE L*A*B*空间。其次，使用一组Gabor过滤器来平滑输入图像，并提取了色边缘强度图，并将其融合到具有噪声稳健性和准确边缘提取的新ESM中。第三，将熔融ESM嵌入精美探测器的途径中会产生噪声颜色边缘检测器。结果表明，所提出的检测器在检测准确性和噪声过程中具有更好的经验。

机器学习（ML）模型的开发不仅仅是软件开发的特殊情况（SD）：ML模型即使没有以看似无法控制的方式直接人类互动，也可以获取属性并满足要求。但是，可以形式上描述基础过程。我们为ML定义了一个全面的SD流程模型，该模型涵盖了文献中描述的大多数任务和文物。除了生产必要的工件外，我们还专注于以规格的形式生成和验证拟合描述。我们强调即使在初步训练和测试后，即使在生命周期中进一步发展ML模型的重要性。因此，我们提供了各种交互点，具有标准SD过程，其中ML通常是封装的任务。此外，我们的SD过程模型允许将ML作为（元）优化问题提出。如果严格自动化，则可以用来实现自适应自主系统。最后，我们的SD流程模型具有时间的描述，可以推理ML开发过程中的进度。这可能会导致ML领域内形式方法的进一步应用。

我们得出了存在圆锥映射的固定点的条件，而无需假设函数的可伸缩性。在搜索固定点的干涉映射点的背景下，文献中通常是不可分割的。在应用中，这种映射通过非阴性神经网络近似。但是，事实证明，训练非负网络的过程需要对模型的权重施加人为的限制。但是，在特定的非负数据的情况下，不能说如果映射是非负的，则仅具有非负权重。因此，我们考虑了一般神经网络存在固定点的问题，假设相对于特定锥体有相似条件的条件。这不会放松物理假设，因为即使假设输入和输出是非负的，权重也可以（小）小于零值。这种特性（通常在有关神经网络权重的解释性的论文中发现）导致对与神经网络相关的映射的单调性或可扩展性的假设的削弱。据我们所知，本文是第一个研究这种现象的文章。