统计物理学的最新进展显示了机器学习在识别阶段过渡时的显着性能。在本文中，我们基于转移学习施加域对抗性神经网络（DANN），以研究非平衡和平衡相变模型，分别是渗透模型和定向渗透（DP）模型。通过DANN，只需要标记一小部分输入配置（2D图像），以便自动选择，以便捕获临界点。要了解DP模型，该方法通过确定临界点的迭代过程来改进，这是计算临界指数$ \ nu _ {\ perp} $的数据崩溃的先决条件。然后，我们将DANN应用于二维站点的遗传筛选，该配置过滤以仅包括可能包含与订单参数相关的信息的最大群集。两种模型的DANN学习都会产生可靠的结果，它与来自蒙特卡罗模拟的结果相当。我们的研究还表明，与监督学习相比，Dann可以以更低的成本实现相当高的准确性。

Holron光谱函数携带HADRONS的所有信息，并在欧几里德两点相关函数中编码。来自相关器的Holron谱函数的提取是典型的不良反问题，并且存在无限数量的解决问题。我们提出了一种基于变异自动编码器（VAE）和贝叶斯定理的新型神经网络（SVAE）。灵感来自最大熵方法（MEM），我们构建神经工作的损失函数，使其包括Shannon-Jaynes熵项和似然术语。然后训练SVAE以提供最可能的光谱功能。对于光谱函数的训练样本，我们使用了由高斯混合模型产生的一般光谱函数。在完成训练之后，我们通过输入光谱功能进行了模拟数据测试，其中包括1）仅包括一个自由连续体，2）仅具有共振峰，3）共振峰加上自由连续体和4）NRQCD激励的光谱功能。从模拟数据测试中，我们发现大多数情况下的SVAE与重建光谱函数的质量中的最大熵方法相当，并且在频谱函数具有尖锐峰的情况下甚至优于MEM，其中数据点数不足相关器。通过在0.75 $ T_C $ 128 ^ 3 \ times96 $格和$ 128 ^ 3 \ times48 $格子的0.75 $ t_c $ 0.75 $ t_c $ 0.75 $ t_c $ 128 ^ 3 \ times48 $格子的伪影片QCD中的催化力柱中的催化态频道中的催化力频道临时相关函数。从SVAE和MEM中提取的$ \ eta_c $的共振峰值对晶格模拟中采用的时间方向（$ N_ \ TAU $）的点数大幅依赖，并且需要大于48美元的$ N_ \ TAU $大于48美元解决$ \ eta_c $的命运为1.5 $ t_c $。

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.

即使有效，模型的使用也必须伴随着转换数据的各个级别的理解（上游和下游）。因此，需求增加以定义单个数据与算法可以根据其分析可以做出的选择（例如，一种产品或一种促销报价的建议，或代表风险的保险费率）。模型用户必须确保模型不会区分，并且也可以解释其结果。本文介绍了模型解释的重要性，并解决了模型透明度的概念。在保险环境中，它专门说明了如何使用某些工具来强制执行当今可以利用机器学习的精算模型的控制。在一个简单的汽车保险中损失频率估计的示例中，我们展示了一些解释性方法的兴趣，以适应目标受众的解释。

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。