视网膜性状或表型，总结了单个数字的视网膜图像的特定方面。然后可以将其用于进一步的分析，例如使用统计方法。但是，将复杂图像的一个方面减少到一个有意义的数字是具有挑战性的。因此，计算视网膜性状的方法往往是复杂的多步管道，只能应用于高质量的图像。这意味着研究人员通常必须丢弃大量可用数据。我们假设可以通过一个更简单的步骤来近似此类管道，这可以使常见的质量问题变得强大。我们提出了视网膜特征（DART）的深近似，其中使用深神经网络预测了这些图像的合成降解版本的高质量图像的现有管道的输出。我们使用来自英国生物库的视网膜图像计算出的视网膜分形尺寸（FD）的飞镖，这些图像先前的工作被确定为高质量。我们的方法在看不见的测试图像上显示与FD吸血鬼非常一致（Pearson r = 0.9572）。即使这些图像严重退化，DART仍然可以恢复FD估计值，该估计值与从原始图像获得的FD吸血鬼表示良好（Pearson r = 0.8817）。这表明我们的方法可以使研究人员将来丢弃更少的图像。我们的方法可以使用单个GPU计算超过1,000IMG/s的FD。我们认为这些是非常令人鼓舞的初步结果，并希望将这种方法发展为视网膜分析的有用工具。

深度学习（DL）模型在许多计算机视觉问题上非常有效，并且越来越多地用于关键应用。他们也是黑人盒子。存在许多方法以生成图像明智的解释，其允许从业者理解和验证给定图像的模型预测。除此之外，希望验证DL Model \ Textit {一般}以明智的方式工作，即与域知识一致，而不是依赖于不期望的数据伪影。为此目的，需要在全球范围内解释模型。在这项工作中，我们专注于自然对齐的图像模态，使得每个像素位置表示成像对象上的相似位置，如在医学成像中常见。我们提出了图像明智的解释的像素明智的聚合作为获得标签和整体全局解释的简单方法。然后，这些可以用于模型验证，知识发现，以及传达从检查图像明智的解释的定性结论的有效方法。我们进一步提出了进步擦除加上渐进式恢复（PEPPR）作为定量验证这些全球解释忠于模型如何使其预测的方法。然后，我们将这些方法应用于超广域视网膜图像，是一种自然对齐的模态。我们发现全球解释与域知识一致，忠实地反映了模型的工作。

尽管学习已成为现代信息处理的核心组成部分，但现在有足够的证据表明它可以导致偏见，不安全和有偏见的系统。因此，对学习要求施加要求至关重要，尤其是在达到社会，工业和医疗领域的关键应用程序时。但是，大多数现代统计问题的非跨性别性只有通过限制引入而加剧。尽管通常可以使用经验风险最小化来学习良好的无约束解决方案，即使获得满足统计约束的模型也可能具有挑战性。更重要的是，一个好。在本文中，我们通过在经验双重领域中学习来克服这个问题，在经验的双重领域中，统计学上的统计学习问题变得不受限制和确定性。我们通过界定经验二元性差距来分析这种方法的概括特性 - 即，我们的近似，可拖动解决方案与原始（非凸）统计问题的解决方案之间的差异 - 并提供实用的约束学习算法。这些结果建立了与经典学习理论的约束，从而可以明确地在学习中使用约束。我们说明了这种理论和算法受到速率受限的学习应用，这是在公平和对抗性鲁棒性中产生的。

在本文中，我们研究了加强学习问题的安全政策的学习。这是，我们的目标是控制我们不知道过渡概率的马尔可夫决策过程（MDP），但我们通过经验访问样品轨迹。我们将安全性定义为在操作时间内具有高概率的期望安全集中的代理。因此，我们考虑受限制的MDP，其中限制是概率。由于没有直接的方式来优化关于加强学习框架中的概率约束的政策，因此我们提出了对问题的遍历松弛。拟议的放松的优点是三倍。 （i）安全保障在集界任务的情况下保持，并且它们保持在一个给定的时间范围内，以继续进行任务。 （ii）如果政策的参数化足够丰富，则约束优化问题尽管其非凸起具有任意小的二元间隙。 （iii）可以使用标准策略梯度结果和随机近似工具容易地计算与安全学习问题相关的拉格朗日的梯度。利用这些优势，我们建立了原始双算法能够找到安全和最佳的政策。我们在连续域中的导航任务中测试所提出的方法。数值结果表明，我们的算法能够将策略动态调整到环境和所需的安全水平。

We derive a set of causal deep neural networks whose architectures are a consequence of tensor (multilinear) factor analysis. Forward causal questions are addressed with a neural network architecture composed of causal capsules and a tensor transformer. The former estimate a set of latent variables that represent the causal factors, and the latter governs their interaction. Causal capsules and tensor transformers may be implemented using shallow autoencoders, but for a scalable architecture we employ block algebra and derive a deep neural network composed of a hierarchy of autoencoders. An interleaved kernel hierarchy preprocesses the data resulting in a hierarchy of kernel tensor factor models. Inverse causal questions are addressed with a neural network that implements multilinear projection and estimates the causes of effects. As an alternative to aggressive bottleneck dimension reduction or regularized regression that may camouflage an inherently underdetermined inverse problem, we prescribe modeling different aspects of the mechanism of data formation with piecewise tensor models whose multilinear projections are well-defined and produce multiple candidate solutions. Our forward and inverse neural network architectures are suitable for asynchronous parallel computation.

In the last years, the number of IoT devices deployed has suffered an undoubted explosion, reaching the scale of billions. However, some new cybersecurity issues have appeared together with this development. Some of these issues are the deployment of unauthorized devices, malicious code modification, malware deployment, or vulnerability exploitation. This fact has motivated the requirement for new device identification mechanisms based on behavior monitoring. Besides, these solutions have recently leveraged Machine and Deep Learning techniques due to the advances in this field and the increase in processing capabilities. In contrast, attackers do not stay stalled and have developed adversarial attacks focused on context modification and ML/DL evaluation evasion applied to IoT device identification solutions. This work explores the performance of hardware behavior-based individual device identification, how it is affected by possible context- and ML/DL-focused attacks, and how its resilience can be improved using defense techniques. In this sense, it proposes an LSTM-CNN architecture based on hardware performance behavior for individual device identification. Then, previous techniques have been compared with the proposed architecture using a hardware performance dataset collected from 45 Raspberry Pi devices running identical software. The LSTM-CNN improves previous solutions achieving a +0.96 average F1-Score and 0.8 minimum TPR for all devices. Afterward, context- and ML/DL-focused adversarial attacks were applied against the previous model to test its robustness. A temperature-based context attack was not able to disrupt the identification. However, some ML/DL state-of-the-art evasion attacks were successful. Finally, adversarial training and model distillation defense techniques are selected to improve the model resilience to evasion attacks, without degrading its performance.

Cybercriminals are moving towards zero-day attacks affecting resource-constrained devices such as single-board computers (SBC). Assuming that perfect security is unrealistic, Moving Target Defense (MTD) is a promising approach to mitigate attacks by dynamically altering target attack surfaces. Still, selecting suitable MTD techniques for zero-day attacks is an open challenge. Reinforcement Learning (RL) could be an effective approach to optimize the MTD selection through trial and error, but the literature fails when i) evaluating the performance of RL and MTD solutions in real-world scenarios, ii) studying whether behavioral fingerprinting is suitable for representing SBC's states, and iii) calculating the consumption of resources in SBC. To improve these limitations, the work at hand proposes an online RL-based framework to learn the correct MTD mechanisms mitigating heterogeneous zero-day attacks in SBC. The framework considers behavioral fingerprinting to represent SBCs' states and RL to learn MTD techniques that mitigate each malicious state. It has been deployed on a real IoT crowdsensing scenario with a Raspberry Pi acting as a spectrum sensor. More in detail, the Raspberry Pi has been infected with different samples of command and control malware, rootkits, and ransomware to later select between four existing MTD techniques. A set of experiments demonstrated the suitability of the framework to learn proper MTD techniques mitigating all attacks (except a harmfulness rootkit) while consuming <1 MB of storage and utilizing <55% CPU and <80% RAM.

Uncertainty quantification is crucial to inverse problems, as it could provide decision-makers with valuable information about the inversion results. For example, seismic inversion is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data. It is therefore of paramount importance to quantify the uncertainties associated to the inversion process to ease the subsequent interpretation and decision making processes. Within this framework of reference, sampling from a target posterior provides a fundamental approach to quantifying the uncertainty in seismic inversion. However, selecting appropriate prior information in a probabilistic inversion is crucial, yet non-trivial, as it influences the ability of a sampling-based inference in providing geological realism in the posterior samples. To overcome such limitations, we present a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence loss with a CNN-based denoiser by means of the Plug-and-Play methods. We call this new algorithm Plug-and-Play Stein Variational Gradient Descent (PnP-SVGD) and demonstrate its ability in producing high-resolution, trustworthy samples representative of the subsurface structures, which we argue could be used for post-inference tasks such as reservoir modelling and history matching. To validate the proposed method, numerical tests are performed on both synthetic and field post-stack seismic data.

We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs. Such ML case study includes data preparation, curation and labelling from 2D Ultrasound videos of 31 ICU patients in LMICs and model selection, validation and deployment of three thinner neural networks to classify apical four-chamber view. Results of the ML heuristics showed the promising implementation, validation and application of thinner networks to classify 4CV with limited datasets. We conclude this work mentioning the need for (a) datasets to improve diversity of demographics, diseases, and (b) the need of further investigations of thinner models to be run and implemented in low-cost hardware to be clinically translated in the ICU in LMICs. The code and other resources to reproduce this work are available at https://github.com/vital-ultrasound/ai-assisted-echocardiography-for-low-resource-countries.

User equipment is one of the main bottlenecks facing the gaming industry nowadays. The extremely realistic games which are currently available trigger high computational requirements of the user devices to run games. As a consequence, the game industry has proposed the concept of Cloud Gaming, a paradigm that improves gaming experience in reduced hardware devices. To this end, games are hosted on remote servers, relegating users' devices to play only the role of a peripheral for interacting with the game. However, this paradigm overloads the communication links connecting the users with the cloud. Therefore, service experience becomes highly dependent on network connectivity. To overcome this, Cloud Gaming will be boosted by the promised performance of 5G and future 6G networks, together with the flexibility provided by mobility in multi-RAT scenarios, such as WiFi. In this scope, the present work proposes a framework for measuring and estimating the main E2E metrics of the Cloud Gaming service, namely KQIs. In addition, different machine learning techniques are assessed for predicting KQIs related to Cloud Gaming user's experience. To this end, the main key quality indicators (KQIs) of the service such as input lag, freeze percent or perceived video frame rate are collected in a real environment. Based on these, results show that machine learning techniques provide a good estimation of these indicators solely from network-based metrics. This is considered a valuable asset to guide the delivery of Cloud Gaming services through cellular communications networks even without access to the user's device, as it is expected for telecom operators.