与原子分辨率上可实现的分子量相比，粗晶片（CG）能够研究较大系统和更长的时间尺度的分子特性。最近已经提出了机器学习技术来学习CG粒子相互作用，即开发CG力场。分子的图表和图形卷积神经网络结构的监督训练用于通过力匹配方案来学习平均力的潜力。在这项工作中，作用在每个CG粒子上的力与以Schnet的名义相关的其本地环境的表示，该代表通过连续过滤器卷积构建。我们探讨了Schnet模型在获得液体苯的CG潜力的应用，研究模型结构和超参数对模拟CG系统的热力学，动力学和结构特性的影响，并报告和讨论所设想的挑战以及未来的指导。

由于大分子系统中存在的各种时间尺度，其计算研究是必要的。粗粒（CG）允许在不同的系统分辨率之间建立联系，并为开发强大的多尺度模拟和分析提供骨干。 CG映射过程通常是系统和特定于应用程序的，它依赖于化学直觉。在这项工作中，我们探讨了基于变异自动编码器的机器学习策略的应用，以开发合适的映射方案，从原子体到分子的粗粒空间，并随着化学复杂性的增加而开发。对模型超级法对训练过程和最终输出的影响进行了广泛的评估，并通过定义不同的损失函数的定义进行了现有方法，并实施了确保输出物理一致性的选择标准。分析了输入特征选择与重建精度之间的关系，从而支持将旋转不变性引入系统的需求。在映射和背景步骤中，该方法的优势和局限性都得到了强调和严格的讨论。

The Internet of Things (IoT) is a system that connects physical computing devices, sensors, software, and other technologies. Data can be collected, transferred, and exchanged with other devices over the network without requiring human interactions. One challenge the development of IoT faces is the existence of anomaly data in the network. Therefore, research on anomaly detection in the IoT environment has become popular and necessary in recent years. This survey provides an overview to understand the current progress of the different anomaly detection algorithms and how they can be applied in the context of the Internet of Things. In this survey, we categorize the widely used anomaly detection machine learning and deep learning techniques in IoT into three types: clustering-based, classification-based, and deep learning based. For each category, we introduce some state-of-the-art anomaly detection methods and evaluate the advantages and limitations of each technique.

In 2021 300 mm of rain, nearly half the average annual rainfall, fell near Catania (Sicily island, Italy). Such events took place in just a few hours, with dramatic consequences on the environmental, social, economic, and health systems of the region. This is the reason why, detecting extreme rainfall events is a crucial prerequisite for planning actions able to reverse possibly intensified dramatic future scenarios. In this paper, the Affinity Propagation algorithm, a clustering algorithm grounded on machine learning, was applied, to the best of our knowledge, for the first time, to identify excess rain events in Sicily. This was possible by using a high-frequency, large dataset we collected, ranging from 2009 to 2021 which we named RSE (the Rainfall Sicily Extreme dataset). Weather indicators were then been employed to validate the results, thus confirming the presence of recent anomalous rainfall events in eastern Sicily. We believe that easy-to-use and multi-modal data science techniques, such as the one proposed in this study, could give rise to significant improvements in policy-making for successfully contrasting climate changes.

Despite significant advances, the performance of state-of-the-art continual learning approaches hinges on the unrealistic scenario of fully labeled data. In this paper, we tackle this challenge and propose an approach for continual semi-supervised learning -- a setting where not all the data samples are labeled. An underlying issue in this scenario is the model forgetting representations of unlabeled data and overfitting the labeled ones. We leverage the power of nearest-neighbor classifiers to non-linearly partition the feature space and learn a strong representation for the current task, as well as distill relevant information from previous tasks. We perform a thorough experimental evaluation and show that our method outperforms all the existing approaches by large margins, setting a strong state of the art on the continual semi-supervised learning paradigm. For example, on CIFAR100 we surpass several others even when using at least 30 times less supervision (0.8% vs. 25% of annotations).

Dynamical systems are found in innumerable forms across the physical and biological sciences, yet all these systems fall naturally into universal equivalence classes: conservative or dissipative, stable or unstable, compressible or incompressible. Predicting these classes from data remains an essential open challenge in computational physics at which existing time-series classification methods struggle. Here, we propose, \texttt{phase2vec}, an embedding method that learns high-quality, physically-meaningful representations of 2D dynamical systems without supervision. Our embeddings are produced by a convolutional backbone that extracts geometric features from flow data and minimizes a physically-informed vector field reconstruction loss. In an auxiliary training period, embeddings are optimized so that they robustly encode the equations of unseen data over and above the performance of a per-equation fitting method. The trained architecture can not only predict the equations of unseen data, but also, crucially, learns embeddings that respect the underlying semantics of the embedded physical systems. We validate the quality of learned embeddings investigating the extent to which physical categories of input data can be decoded from embeddings compared to standard blackbox classifiers and state-of-the-art time series classification techniques. We find that our embeddings encode important physical properties of the underlying data, including the stability of fixed points, conservation of energy, and the incompressibility of flows, with greater fidelity than competing methods. We finally apply our embeddings to the analysis of meteorological data, showing we can detect climatically meaningful features. Collectively, our results demonstrate the viability of embedding approaches for the discovery of dynamical features in physical systems.

Graph Neural Networks (GNNs) had been demonstrated to be inherently susceptible to the problems of over-smoothing and over-squashing. These issues prohibit the ability of GNNs to model complex graph interactions by limiting their effectiveness at taking into account distant information. Our study reveals the key connection between the local graph geometry and the occurrence of both of these issues, thereby providing a unified framework for studying them at a local scale using the Ollivier's Ricci curvature. Based on our theory, a number of principled methods are proposed to alleviate the over-smoothing and over-squashing issues.

Prescriptive Process Monitoring systems recommend, during the execution of a business process, interventions that, if followed, prevent a negative outcome of the process. Such interventions have to be reliable, that is, they have to guarantee the achievement of the desired outcome or performance, and they have to be flexible, that is, they have to avoid overturning the normal process execution or forcing the execution of a given activity. Most of the existing Prescriptive Process Monitoring solutions, however, while performing well in terms of recommendation reliability, provide the users with very specific (sequences of) activities that have to be executed without caring about the feasibility of these recommendations. In order to face this issue, we propose a new Outcome-Oriented Prescriptive Process Monitoring system recommending temporal relations between activities that have to be guaranteed during the process execution in order to achieve a desired outcome. This softens the mandatory execution of an activity at a given point in time, thus leaving more freedom to the user in deciding the interventions to put in place. Our approach defines these temporal relations with Linear Temporal Logic over finite traces patterns that are used as features to describe the historical process data recorded in an event log by the information systems supporting the execution of the process. Such encoded log is used to train a Machine Learning classifier to learn a mapping between the temporal patterns and the outcome of a process execution. The classifier is then queried at runtime to return as recommendations the most salient temporal patterns to be satisfied to maximize the likelihood of a certain outcome for an input ongoing process execution. The proposed system is assessed using a pool of 22 real-life event logs that have already been used as a benchmark in the Process Mining community.

In this paper, we introduced the novel concept of advisor network to address the problem of noisy labels in image classification. Deep neural networks (DNN) are prone to performance reduction and overfitting problems on training data with noisy annotations. Weighting loss methods aim to mitigate the influence of noisy labels during the training, completely removing their contribution. This discarding process prevents DNNs from learning wrong associations between images and their correct labels but reduces the amount of data used, especially when most of the samples have noisy labels. Differently, our method weighs the feature extracted directly from the classifier without altering the loss value of each data. The advisor helps to focus only on some part of the information present in mislabeled examples, allowing the classifier to leverage that data as well. We trained it with a meta-learning strategy so that it can adapt throughout the training of the main model. We tested our method on CIFAR10 and CIFAR100 with synthetic noise, and on Clothing1M which contains real-world noise, reporting state-of-the-art results.

本文解决了从第三人称角度捕获的单个图像中的目光目标检测问题。我们提出了一个多模式的深度建筑，以推断一个人在场景中所处的位置。该空间模型经过了代表丰富上下文信息的感兴趣人，场景和深度图的头部图像训练。我们的模型与几种先前的艺术不同，不需要对目光角度的监督，不依赖头部方向信息和/或利益人眼睛的位置。广泛的实验证明了我们方法在多个基准数据集上的性能更强。我们还通过改变多模式数据的联合学习来研究我们方法的几种变体。一些变化的表现也胜过一些先前的艺术。首次在本文中，我们检查了域名的凝视目标检测，并授权多模式网络有效地处理跨数据集的域间隙。该方法的代码可在https://github.com/francescotonini/multimodal-across-domains-domains-domains-domains-domains-warget-detection上获得。