深度神经网络需要特定的层来处理点云，因为点的分散和不规则位置使我们无法使用卷积过滤器。在这里，我们介绍了复合层，该复合层是点云的新卷积操作员。我们的复合层的特殊性是，它在将点与其特征向量结合之前从点位置提取和压缩空间信息。与众所周知的点横向跨层相比，我们的复合层提供了额外的正则化，并确保了参数和参数数量方面的灵活性更大。为了展示设计灵活性，我们还定义了一个集合复合层，该复合层以非线性方式组合空间信息和特征，并且我们使用这些层来实现卷积和聚集的综合材料。我们训练我们的复合烯类进行分类，最引人注目的是无监督的异常检测。我们对合成和现实世界数据集的实验表明，在这两个任务中，我们的CompositeNets都优于表现要点，尽管具有更简单的体系结构，但取得了与KPCONV相似的结果。此外，我们的复合烯类基本上优于现有的解决方案，用于点云上的异常检测。

任何电子设备中包含的芯片都是通过圆形硅晶片制造的，这些芯片是通过不同生产阶段的检查机对其进行监控的。检查机检测并找到晶圆中的任何缺陷，并返回晶圆缺陷图（WDM），即，缺陷为lie的坐标列表，可以将其视为巨大，稀疏和二进制图像。在正常情况下，晶片表现出少量随机分布的缺陷，而以特定模式分组的缺陷可能表明生产线中的已知或新颖类别。不用说，半导体行业的主要关注点是确定这些模式并尽快进行干预以恢复正常的生产条件。在这里，我们将WDM监视作为开放式识别问题，以准确地将WDM分类为已知类别并迅速检测到新颖的模式。特别是，我们提出了一条基于Submanifold稀疏卷积网络的晶圆监测的综合管道，这是一种深层体系结构，旨在以任意分辨率处理稀疏数据，并在已知类别上进行了培训。为了检测新颖性，我们根据拟合在分类器潜在表示上的高斯混合模型定义了一个离群检测器。我们在WDM的真实数据集上进行的实验表明，Submanifold稀疏卷积直接处​​理全分辨率WDMS在已知类别上比传统的卷积神经网络产生了卓越的分类性能，这需要初步的封装以减少代表WDM的二元图像的大小。此外，我们的解决方案优于最先进的开放式识别解决方案，以检测新颖性。

在图像中检测异常区域是工业监测中经常遇到的问题。一个相关的例子是对正常条件下符合特定纹理的组织和其他产品的分析，而缺陷会引入正常模式的变化。我们通过训练深层自动编码器来解决异常检测问题，我们表明，基于复杂的小波结构相似性（CW-SSIM）采用损失函数（CW-SSIM）与传统的自动编码器损失函数相比，这类图像上的检测性能出色。我们对众所周知的异常检测基准测试的实验表明，通过这种损失函数训练的简单模型可以实现可比性或优越的性能，从而利用更深入，更大，更大的计算要求的神经网络的最先进方法。

我们解决了多变量数据流中的在线变更检测问题，并介绍了Quanttree指数加权移动平均值（QT-EWMA），这是一种非参数变更检测算法，可以在误报之前控制预期的时间，从Arl $ _0 $）。在许多应用程序中，控制虚假警报至关重要，很少能通过在线变更检测算法来保证，这些算法可以监视多元数据串联而不知道数据分布。像许多变更检测算法一样，QT-EWMA从固定训练集中构建了数据分布的模型，在我们的情况下，量化量子三直方图。为了监视数据流，即使训练集非常小，我们提出了QT-Ewma-update，该QT-ewma-update在监视过程中会逐步更新Quanttree直方图，请始终保持ARL $ _0 $的控制。我们的实验在合成和真实的数据源上执行，证明了QT-Ewma和Qt-Ewma-update控制ARL $ _0 $和错误警报率比在类似条件下运行的最先进方法更好，从而实现了错误的警报率。较低或可比的检测延迟。

越来越多的工作表明，深层神经网络容易受到对抗例子的影响。这些采用适用于模型输入的小扰动的形式，这导致了错误的预测。不幸的是，大多数文献都集中在视觉上不可见量的扰动上，该扰动将应用于数字图像上，这些数字图像通常无法通过设计将其部署到物理目标上。我们提出了对抗性划痕：一种新颖的L0黑盒攻击，它采用图像中的划痕形式，并且比其他最先进的攻击具有更大的可部署性。对抗性划痕利用了b \'Ezier曲线，以减少搜索空间的维度，并可能将攻击限制为特定位置。我们在几种情况下测试了对抗划痕，包括公开可用的API和交通标志的图像。结果表明，我们的攻击通常比其他可部署的最先进方法更高的愚弄率更高，同时需要更少的查询并修改很少的像素。

Structural Health Monitoring (SHM) describes a process for inferring quantifiable metrics of structural condition, which can serve as input to support decisions on the operation and maintenance of infrastructure assets. Given the long lifespan of critical structures, this problem can be cast as a sequential decision making problem over prescribed horizons. Partially Observable Markov Decision Processes (POMDPs) offer a formal framework to solve the underlying optimal planning task. However, two issues can undermine the POMDP solutions. Firstly, the need for a model that can adequately describe the evolution of the structural condition under deterioration or corrective actions and, secondly, the non-trivial task of recovery of the observation process parameters from available monitoring data. Despite these potential challenges, the adopted POMDP models do not typically account for uncertainty on model parameters, leading to solutions which can be unrealistically confident. In this work, we address both key issues. We present a framework to estimate POMDP transition and observation model parameters directly from available data, via Markov Chain Monte Carlo (MCMC) sampling of a Hidden Markov Model (HMM) conditioned on actions. The MCMC inference estimates distributions of the involved model parameters. We then form and solve the POMDP problem by exploiting the inferred distributions, to derive solutions that are robust to model uncertainty. We successfully apply our approach on maintenance planning for railway track assets on the basis of a "fractal value" indicator, which is computed from actual railway monitoring data.

The polynomial kernels are widely used in machine learning and they are one of the default choices to develop kernel-based classification and regression models. However, they are rarely used and considered in numerical analysis due to their lack of strict positive definiteness. In particular they do not enjoy the usual property of unisolvency for arbitrary point sets, which is one of the key properties used to build kernel-based interpolation methods. This paper is devoted to establish some initial results for the study of these kernels, and their related interpolation algorithms, in the context of approximation theory. We will first prove necessary and sufficient conditions on point sets which guarantee the existence and uniqueness of an interpolant. We will then study the Reproducing Kernel Hilbert Spaces (or native spaces) of these kernels and their norms, and provide inclusion relations between spaces corresponding to different kernel parameters. With these spaces at hand, it will be further possible to derive generic error estimates which apply to sufficiently smooth functions, thus escaping the native space. Finally, we will show how to employ an efficient stable algorithm to these kernels to obtain accurate interpolants, and we will test them in some numerical experiment. After this analysis several computational and theoretical aspects remain open, and we will outline possible further research directions in a concluding section. This work builds some bridges between kernel and polynomial interpolation, two topics to which the authors, to different extents, have been introduced under the supervision or through the work of Stefano De Marchi. For this reason, they wish to dedicate this work to him in the occasion of his 60th birthday.

The proliferation of deep learning techniques led to a wide range of advanced analytics applications in important business areas such as predictive maintenance or product recommendation. However, as the effectiveness of advanced analytics naturally depends on the availability of sufficient data, an organization's ability to exploit the benefits might be restricted by limited data or likewise data access. These challenges could force organizations to spend substantial amounts of money on data, accept constrained analytics capacities, or even turn into a showstopper for analytics projects. Against this backdrop, recent advances in deep learning to generate synthetic data may help to overcome these barriers. Despite its great potential, however, synthetic data are rarely employed. Therefore, we present a taxonomy highlighting the various facets of deploying synthetic data for advanced analytics systems. Furthermore, we identify typical application scenarios for synthetic data to assess the current state of adoption and thereby unveil missed opportunities to pave the way for further research.

To make machine learning (ML) sustainable and apt to run on the diverse devices where relevant data is, it is essential to compress ML models as needed, while still meeting the required learning quality and time performance. However, how much and when an ML model should be compressed, and {\em where} its training should be executed, are hard decisions to make, as they depend on the model itself, the resources of the available nodes, and the data such nodes own. Existing studies focus on each of those aspects individually, however, they do not account for how such decisions can be made jointly and adapted to one another. In this work, we model the network system focusing on the training of DNNs, formalize the above multi-dimensional problem, and, given its NP-hardness, formulate an approximate dynamic programming problem that we solve through the PACT algorithmic framework. Importantly, PACT leverages a time-expanded graph representing the learning process, and a data-driven and theoretical approach for the prediction of the loss evolution to be expected as a consequence of training decisions. We prove that PACT's solutions can get as close to the optimum as desired, at the cost of an increased time complexity, and that, in any case, such complexity is polynomial. Numerical results also show that, even under the most disadvantageous settings, PACT outperforms state-of-the-art alternatives and closely matches the optimal energy cost.

Learned Bloom Filters, i.e., models induced from data via machine learning techniques and solving the approximate set membership problem, have recently been introduced with the aim of enhancing the performance of standard Bloom Filters, with special focus on space occupancy. Unlike in the classical case, the "complexity" of the data used to build the filter might heavily impact on its performance. Therefore, here we propose the first in-depth analysis, to the best of our knowledge, for the performance assessment of a given Learned Bloom Filter, in conjunction with a given classifier, on a dataset of a given classification complexity. Indeed, we propose a novel methodology, supported by software, for designing, analyzing and implementing Learned Bloom Filters in function of specific constraints on their multi-criteria nature (that is, constraints involving space efficiency, false positive rate, and reject time). Our experiments show that the proposed methodology and the supporting software are valid and useful: we find out that only two classifiers have desirable properties in relation to problems with different data complexity, and, interestingly, none of them has been considered so far in the literature. We also experimentally show that the Sandwiched variant of Learned Bloom filters is the most robust to data complexity and classifier performance variability, as well as those usually having smaller reject times. The software can be readily used to test new Learned Bloom Filter proposals, which can be compared with the best ones identified here.