在尚未解决反事实解释的挑战中（CE），存在稳定性，各种CE的综合以及缺乏合理性/稀疏性保证。从更实用的角度来看，最近的研究表明，规定的反事实回复通常并非完全由个人实现，并证明大多数最先进的CE算法在这种嘈杂的环境中很可能会失败。为了解决这些问题，我们提出了一个概率框架，为每个观察结果提供了稀疏的本地反事实规则：我们提供的规则可以提供一系列可以用给定的高概率改变决策的价值观，而不是给出不同的CE。此外，通过构造从这些规则中得出的回报是可靠的。这些本地规则被汇总为区域反事实规则，以确保跨观察结果的反事实解释的稳定性。我们的本地和区域规则保证了recourse忠实于数据分布，因为我们的规则使用一致的估计器对基于随机森林的决定的概率进行了始终如一的估计。此外，当我们选择具有更改决策概率的最小变量时，这些概率给出了可解释和稀疏的规则。可以使用计算反事实规则的代码，我们将其相关性与标准CE和最近的类似尝试进行比较。

我们提出了一个健壮的主成分分析（RPCA）框架，以从时间观察中恢复低级别和稀疏矩阵。我们开发了批处理时间算法的在线版本，以处理较大的数据集或流数据。我们从经验上将提出的方法与不同的RPCA框架进行比较，并在实际情况下显示出其有效性。

估计与机器学习预测（ML）模型相关的不确定性对于评估其稳健性和预测能力至关重要。在此提交中，我们介绍了Mapie（模型不可知的预测间隔估计器），这是一个开源Python库，可量化单输出回归和多类分类任务的ML模型的不确定性。Mapie实施了保形预测方法，使用户可以轻松地计算出在边际覆盖范围上具有强大理论保证的不确定性，并在模型或基础数据分布上进行了轻微的假设。Mapie托管在Scikit-Learn-Contrib上，完全“ Scikit-Learn兼容”。因此，它接受带有Scikit-Learn API的任何类型的回归器或分类器。该库可在以下网址获得：https：//github.com/scikit-learn-contrib/mapie/。

为了解释任何模型的决定，我们延长了概率充分解释（P-SE）的概念。对于每个实例，该方法选择足以产生具有高概率的相同预测的最小特征子集，同时删除其他特征。 P-SE的关键是计算保持相同预测的条件概率。因此，我们通过随机林为任何数据$（\ boldsymbol {x}，y）$，并通过理论分析来介绍这种概率的准确和快速估计器，并通过理论分析来展示其一致性的理论分析。结果，我们将P-SE扩展到回归问题。此外，我们处理非二进制特征，而无需学习$ x $的分发，也不会使模型进行预测。最后，我们基于P-SE介绍基于数分的回归/分类的解释，并比较我们的方法W.R.T其他可解释的AI方法。这些方法是公开可用作\ url {www.github.com/salimamoukou/acv00}的python包。

大型神经回路的全面突触接线图的出现已经创造了连接组学领域，并引起了许多开放研究问题。一个问题是，鉴于其突触连接矩阵，是否可以重建存储在神经元网络中的信息。在这里，我们通过确定在特定的吸引力网络模型中可以解决这种推理问题何时解决这个问题，并提供一种实用算法来解决这个问题。该算法基于从统计物理学到进行近似贝叶斯推论的思想，并且可以进行精确的分析。我们在三种不同模型上研究了它的性能，将算法与PCA等标准算法进行比较，并探讨了从突触连通性中重建存储模式的局限性。

This article formulates a generic representation of a path-following controller operating under contained motion, which was developed in the context of surgical robotics. It reports two types of constrained motion: i) Bilateral Constrained Motion, also called Remote Center Motion (RCM), and ii) Unilaterally Constrained Motion (UCM). In the first case, the incision hole has almost the same diameter as the robotic tool. In contrast, in the second state, the diameter of the incision orifice is larger than the tool diameter. The second case offers more space where the surgical instrument moves freely without constraints before touching the incision wall. The proposed method combines two tasks that must operate hierarchically: i) respect the RCM or UCM constraints formulated by equality or inequality, respectively, and ii) perform a surgical assignment, e.g., scanning or ablation expressed as a 3D path-following task. The proposed methods and materials were tested first on our simulator that mimics realistic conditions of middle ear surgery, and then on an experimental platform. Different validation scenarios were carried out experimentally to assess quantitatively and qualitatively each developed approach. Although ultimate precision was not the goal of this work, our concept is validated with enough accuracy (inferior to 100 micrometres) for ear surgery.

Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations. For real-world applications of RL, recovering underlying latent states is crucial, particularly when sensory inputs contain irrelevant and exogenous information. In this work, we study how information bottlenecks can be used to construct latent states efficiently in the presence of task-irrelevant information. We propose architectures that utilize variational and discrete information bottlenecks, coined as RepDIB, to learn structured factorized representations. Exploiting the expressiveness bought by factorized representations, we introduce a simple, yet effective, bottleneck that can be integrated with any existing self-supervised objective for RL. We demonstrate this across several online and offline RL benchmarks, along with a real robot arm task, where we find that compressed representations with RepDIB can lead to strong performance improvements, as the learned bottlenecks help predict only the relevant state while ignoring irrelevant information.

To face the dependency on fossil fuels and limit carbon emissions, fuel cells are a very promising technology and appear to be a key candidate to tackle the increase of the energy demand and promote the energy transition. To meet future needs for both transport and stationary applications, the time to market of fuel cell stacks must be drastically reduced. Here, a new concept to shorten their development time by introducing a disruptive and highefficiency data augmentation approach based on artificial intelligence is presented. Our results allow reducing the testing time before introducing a product on the market from a thousand to a few hours. The innovative concept proposed here can support engineering and research tasks during the fuel cell development process to achieve decreased development costs alongside a reduced time to market.

A learned system uses machine learning (ML) internally to improve performance. We can expect such systems to be vulnerable to some adversarial-ML attacks. Often, the learned component is shared between mutually-distrusting users or processes, much like microarchitectural resources such as caches, potentially giving rise to highly-realistic attacker models. However, compared to attacks on other ML-based systems, attackers face a level of indirection as they cannot interact directly with the learned model. Additionally, the difference between the attack surface of learned and non-learned versions of the same system is often subtle. These factors obfuscate the de-facto risks that the incorporation of ML carries. We analyze the root causes of potentially-increased attack surface in learned systems and develop a framework for identifying vulnerabilities that stem from the use of ML. We apply our framework to a broad set of learned systems under active development. To empirically validate the many vulnerabilities surfaced by our framework, we choose 3 of them and implement and evaluate exploits against prominent learned-system instances. We show that the use of ML caused leakage of past queries in a database, enabled a poisoning attack that causes exponential memory blowup in an index structure and crashes it in seconds, and enabled index users to snoop on each others' key distributions by timing queries over their own keys. We find that adversarial ML is a universal threat against learned systems, point to open research gaps in our understanding of learned-systems security, and conclude by discussing mitigations, while noting that data leakage is inherent in systems whose learned component is shared between multiple parties.

In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map over the "Landes de Gascogne" forest in France, a large maritime pine plantation of 13,000 km$^2$ with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-net models based on a combination of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each instrument in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole "Landes de Gascogne" forest area for 2020 with a mean absolute error of 2.02 m on the Test dataset. The best predictions were obtained using all available satellite layers from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region.