学习来自观察数据的行为模式一直是运动预测的遗传方法。然而，目前的范式遭受了两种缺点：协会变化下的脆性和知识转移的低效。在这项工作中，我们建议从因果表现形式解决这些挑战。我们首先介绍了运动预测的因果形式主义，这将问题作为一种动态过程，其中三组潜在变量，即不变的机制，风格混乱和虚假功能。然后我们介绍一个学习框架，分别对待每个组：（i）与从不同地点收集的数据集的共同做法不同，我们通过不变性的损失来利用它们的微妙区分，鼓励模型抑制虚假相关; （ii）我们设计了一种模块化的架构，可以修理不变机制和风格混淆的表示，以近似因果图; （iii）我们介绍了一种风格的一致性损失，不仅强制实施了风格表示的结构，而且还用作自我监控信号，以便在飞行中进行测试时间改进。合成和实时数据集的实验结果表明，我们的三个提出的组件显着提高了学习运动表示的鲁棒性和可重用性，优于出现的先前最先进的运动预测模型，用于分发外概括和低次转移。

In the era of digital healthcare, the huge volumes of textual information generated every day in hospitals constitute an essential but underused asset that could be exploited with task-specific, fine-tuned biomedical language representation models, improving patient care and management. For such specialized domains, previous research has shown that fine-tuning models stemming from broad-coverage checkpoints can largely benefit additional training rounds over large-scale in-domain resources. However, these resources are often unreachable for less-resourced languages like Italian, preventing local medical institutions to employ in-domain adaptation. In order to reduce this gap, our work investigates two accessible approaches to derive biomedical language models in languages other than English, taking Italian as a concrete use-case: one based on neural machine translation of English resources, favoring quantity over quality; the other based on a high-grade, narrow-scoped corpus natively written in Italian, thus preferring quality over quantity. Our study shows that data quantity is a harder constraint than data quality for biomedical adaptation, but the concatenation of high-quality data can improve model performance even when dealing with relatively size-limited corpora. The models published from our investigations have the potential to unlock important research opportunities for Italian hospitals and academia. Finally, the set of lessons learned from the study constitutes valuable insights towards a solution to build biomedical language models that are generalizable to other less-resourced languages and different domain settings.

Quantum computing is a promising paradigm based on quantum theory for performing fast computations. Quantum algorithms are expected to surpass their classical counterparts in terms of computational complexity for certain tasks, including machine learning. In this paper, we design, implement, and evaluate three hybrid quantum k-Means algorithms, exploiting different degree of parallelism. Indeed, each algorithm incrementally leverages quantum parallelism to reduce the complexity of the cluster assignment step up to a constant cost. In particular, we exploit quantum phenomena to speed up the computation of distances. The core idea is that the computation of distances between records and centroids can be executed simultaneously, thus saving time, especially for big datasets. We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version, still obtaining comparable clustering results.

Warning: this paper contains content that may be offensive or upsetting. In the current context where online platforms have been effectively weaponized in a variety of geo-political events and social issues, Internet memes make fair content moderation at scale even more difficult. Existing work on meme classification and tracking has focused on black-box methods that do not explicitly consider the semantics of the memes or the context of their creation. In this paper, we pursue a modular and explainable architecture for Internet meme understanding. We design and implement multimodal classification methods that perform example- and prototype-based reasoning over training cases, while leveraging both textual and visual SOTA models to represent the individual cases. We study the relevance of our modular and explainable models in detecting harmful memes on two existing tasks: Hate Speech Detection and Misogyny Classification. We compare the performance between example- and prototype-based methods, and between text, vision, and multimodal models, across different categories of harmfulness (e.g., stereotype and objectification). We devise a user-friendly interface that facilitates the comparative analysis of examples retrieved by all of our models for any given meme, informing the community about the strengths and limitations of these explainable methods.

A significant drawback of eXplainable Artificial Intelligence (XAI) approaches is the assumption of feature independence. This paper focuses on integrating causal knowledge in XAI methods to increase trust and help users assess explanations' quality. We propose a novel extension to a widely used local and model-agnostic explainer that explicitly encodes causal relationships in the data generated around the input instance to explain. Extensive experiments show that our method achieves superior performance comparing the initial one for both the fidelity in mimicking the black-box and the stability of the explanations.

Localization of autonomous unmanned aerial vehicles (UAVs) relies heavily on Global Navigation Satellite Systems (GNSS), which are susceptible to interference. Especially in security applications, robust localization algorithms independent of GNSS are needed to provide dependable operations of autonomous UAVs also in interfered conditions. Typical non-GNSS visual localization approaches rely on known starting pose, work only on a small-sized map, or require known flight paths before a mission starts. We consider the problem of localization with no information on initial pose or planned flight path. We propose a solution for global visual localization on a map at scale up to 100 km2, based on matching orthoprojected UAV images to satellite imagery using learned season-invariant descriptors. We show that the method is able to determine heading, latitude and longitude of the UAV at 12.6-18.7 m lateral translation error in as few as 23.2-44.4 updates from an uninformed initialization, also in situations of significant seasonal appearance difference (winter-summer) between the UAV image and the map. We evaluate the characteristics of multiple neural network architectures for generating the descriptors, and likelihood estimation methods that are able to provide fast convergence and low localization error. We also evaluate the operation of the algorithm using real UAV data and evaluate running time on a real-time embedded platform. We believe this is the first work that is able to recover the pose of an UAV at this scale and rate of convergence, while allowing significant seasonal difference between camera observations and map.

In this new computing paradigm, named quantum computing, researchers from all over the world are taking their first steps in designing quantum circuits for image processing, through a difficult process of knowledge transfer. This effort is named Quantum Image Processing, an emerging research field pushed by powerful parallel computing capabilities of quantum computers. This work goes in this direction and proposes the challenging development of a powerful method of image denoising, such as the Total Variation (TV) model, in a quantum environment. The proposed Quantum TV is described and its sub-components are analysed. Despite the natural limitations of the current capabilities of quantum devices, the experimental results show a competitive denoising performance compared to the classical variational TV counterpart.

目标。借助（子）毫米观测值的大量分子发射数据和詹姆斯·韦伯（James Webb）空间望远镜红外光谱，访问原磁盘的化学成分的快进模型至关重要。方法。我们使用了热化学建模代码来生成各种多样的原行星磁盘模型。我们训练了一个最初的邻居（KNN）回归剂，以立即预测其他磁盘模型的化学反应。结果。我们表明，由于所采用的原行业磁盘模型中局部物理条件之间的相关性，可以仅使用一小部分物理条件来准确地重现化学反应。我们讨论此方法的不确定性和局限性。结论。所提出的方法可用于对线排放数据的贝叶斯拟合，以从观测值中检索磁盘属性。我们提出了在其他磁盘化学模型集上再现相同方法的管道。

最近提出的神经网络的规模不断增加，因此很难在嵌入式设备上实现它们，在嵌入式设备上，内存，电池和计算功率是一种非平凡的瓶颈。因此，在过去几年中，网络压缩文献一直在蓬勃发展，并且已经发布了大量解决方案，以减少模型的操作数量和参数。不幸的是，大多数这些还原技术实际上是启发式方法，通常需要至少一个重新训练的步骤才能恢复准确性。在验证和性能评估领域中，对模型降低的程序的需求也众所周知，在这些领域中，大量努力致力于保留可观察到的潜在行为的商的定义。在本文中，我们试图弥合最流行和非常有效的网络减少策略与正式概念（例如块状性）之间的差距，以验证和评估马尔可夫链。详细阐述肿块，我们提出了一种修剪方法，该方法可以减少网络中的神经元数，而无需使用任何数据或微调，同时完全保留了确切的行为。放松对商方法的确切定义的限制，我们可以对一些最常见的还原技术进行形式解释。

由于管理部分微分方程的半差异，例如通过有限元方法。这些系统的复杂性提出了直接应用自动控制的计算挑战。虽然模型还原已在控制中看到无处不在的应用，但在这种情况下使用非线性模型还原方法仍然很困难。问题在于在降低的订单模型中保留非线性动力学的结构，以进行高保真控制。在这项工作中，我们利用光谱亚曼佛（SSM）理论的最新进展来使模型在明确的假设下降低，以有效地合成反馈控制器。