Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are more accurate than pure satellite precipitation products. Machine and statistical learning regression algorithms are regularly utilized in this endeavour. At the same time, tree-based ensemble algorithms for regression are adopted in various fields for solving algorithmic problems with high accuracy and low computational cost. The latter can constitute a crucial factor for selecting algorithms for satellite precipitation product correction at the daily and finer time scales, where the size of the datasets is particularly large. Still, information on which tree-based ensemble algorithm to select in such a case for the contiguous United States (US) is missing from the literature. In this work, we conduct an extensive comparison between three tree-based ensemble algorithms, specifically random forests, gradient boosting machines (gbm) and extreme gradient boosting (XGBoost), in the context of interest. We use daily data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and the IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets. We also use earth-observed precipitation data from the Global Historical Climatology Network daily (GHCNd) database. The experiments refer to the entire contiguous US and additionally include the application of the linear regression algorithm for benchmarking purposes. The results suggest that XGBoost is the best-performing tree-based ensemble algorithm among those compared. They also suggest that IMERG is more useful than PERSIANN in the context investigated.

糖尿病足溃疡（DFUS）对于糖尿病患者来说是严重的并发症。为了实现现有溃疡的早期诊断，预防溃疡和并发症管理，可以通过自我管理来大大改善DFU患者的护理。在本文中，我们研究了两类图像到图像翻译技术（ITITT），这些技术将支持糖尿病足溃疡的决策和监测：降噪和超分辨率。在前一种情况下，我们调查了消除噪声的功能，用于卷积神经网络堆叠的AutoCoders（CNN-SAE）。在用高斯噪声诱导的RGB图像上测试了CNN-SAE。后一种情况涉及部署四个深度学习超分辨率模型。对于两种情况，所有模型的性能均以执行时间和感知质量进行评估。结果表明，应用技术组成了可行且易于实现的替代方案，该替代方案应由设计用于DFU监视的任何系统使用。

在本文中，我们研究了使用深度学习模型时的可转移性限制，用于对CT图像中肺炎感染区域的语义分割。拟议的方法采用4通道输入；基于Hounsfield量表的3个通道，以及一个表示肺部区域的通道（二进制）。我们使用了3个不同的公开可用的CT数据集。如果没有肺部面罩，深度学习模型会生成代理图像。实验结果表明，在创建共同分割模型时，应仔细使用可转移性；在大量数据中重新训练该模型多次以上会导致分割精度的降低。

在文化遗产中，高光谱图像通常使用，因为它们提供了有关材料光学特性的扩展信息。因此，从要应用的机器学习技术的角度来看，这种高维数据的处理变得具有挑战性。在本文中，我们提出了一种基于排名的基于张量的学习模型，以识别和对文化遗产纪念碑的物质缺陷进行分类。与常规的深度学习方法相反，拟议的高阶基于张量的学习表明，具有更高的准确性和鲁棒性，以防止过度拟合。来自联合国教科文组织保护区的现实世界数据的实验结果表明，与常规深度学习模型相比，该计划的优越性。

非侵入性负载监控（NILM）是将总功率消耗分为单个子组件的任务。多年来，已经合并了信号处理和机器学习算法以实现这一目标。关于最先进的方法，进行了许多出版物和广泛的研究工作，以涉及最先进的方法。科学界最初使用机器学习工具的尼尔姆问题制定和描述的最初兴趣已经转变为更实用的尼尔姆。如今，我们正处于成熟的尼尔姆时期，在现实生活中的应用程序方案中尝试使用尼尔姆。因此，算法的复杂性，可转移性，可靠性，实用性和普遍的信任度是主要的关注问题。这篇评论缩小了早期未成熟的尼尔姆时代与成熟的差距。特别是，本文仅对住宅电器的尼尔姆方法提供了全面的文献综述。本文分析，总结并介绍了大量最近发表的学术文章的结果。此外，本文讨论了这些方法的亮点，并介绍了研究人员应考虑的研究困境，以应用尼尔姆方法。最后，我们表明需要将传统分类模型转移到一个实用且值得信赖的框架中。

Networks have become indispensable and ubiquitous structures in many fields to model the interactions among different entities, such as friendship in social networks or protein interactions in biological graphs. A major challenge is to understand the structure and dynamics of these systems. Although networks evolve through time, most existing graph representation learning methods target only static networks. Whereas approaches have been developed for the modeling of dynamic networks, there is a lack of efficient continuous time dynamic graph representation learning methods that can provide accurate network characterization and visualization in low dimensions while explicitly accounting for prominent network characteristics such as homophily and transitivity. In this paper, we propose the Piecewise-Velocity Model (PiVeM) for the representation of continuous-time dynamic networks. It learns dynamic embeddings in which the temporal evolution of nodes is approximated by piecewise linear interpolations based on a latent distance model with piecewise constant node-specific velocities. The model allows for analytically tractable expressions of the associated Poisson process likelihood with scalable inference invariant to the number of events. We further impose a scalable Kronecker structured Gaussian Process prior to the dynamics accounting for community structure, temporal smoothness, and disentangled (uncorrelated) latent embedding dimensions optimally learned to characterize the network dynamics. We show that PiVeM can successfully represent network structure and dynamics in ultra-low two-dimensional spaces. It outperforms relevant state-of-art methods in downstream tasks such as link prediction. In summary, PiVeM enables easily interpretable dynamic network visualizations and characterizations that can further improve our understanding of the intrinsic dynamics of time-evolving networks.

Functionality and dialogue experience are two important factors of task-oriented dialogue systems. Conventional approaches with closed schema (e.g., conversational semantic parsing) often fail as both the functionality and dialogue experience are strongly constrained by the underlying schema. We introduce a new paradigm for task-oriented dialogue - Dialog2API - to greatly expand the functionality and provide seamless dialogue experience. The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs. The model also manages the dialogue policy and interact with the user through generating appropriate natural language responses. By allowing generating free-form programs, Dialog2API supports composite goals by combining different APIs, whereas unrestricted program revision provides natural and robust dialogue experience. To facilitate Dialog2API, the core model is provided with API documents, an execution environment and optionally some example dialogues annotated with programs. We propose an approach tailored for the Dialog2API, where the dialogue states are represented by a stack of programs, with most recently mentioned program on the top of the stack. Dialog2API can work with many application scenarios such as software automation and customer service. In this paper, we construct a dataset for AWS S3 APIs and present evaluation results of in-context learning baselines.

Designing powerful adversarial attacks is of paramount importance for the evaluation of $\ell_p$-bounded adversarial defenses. Projected Gradient Descent (PGD) is one of the most effective and conceptually simple algorithms to generate such adversaries. The search space of PGD is dictated by the steepest ascent directions of an objective. Despite the plethora of objective function choices, there is no universally superior option and robustness overestimation may arise from ill-suited objective selection. Driven by this observation, we postulate that the combination of different objectives through a simple loss alternating scheme renders PGD more robust towards design choices. We experimentally verify this assertion on a synthetic-data example and by evaluating our proposed method across 25 different $\ell_{\infty}$-robust models and 3 datasets. The performance improvement is consistent, when compared to the single loss counterparts. In the CIFAR-10 dataset, our strongest adversarial attack outperforms all of the white-box components of AutoAttack (AA) ensemble, as well as the most powerful attacks existing on the literature, achieving state-of-the-art results in the computational budget of our study ($T=100$, no restarts).

In this work, we study the numerical solution of inverse eigenvalue problems from a machine learning perspective. Two different problems are considered: the inverse Strum-Liouville eigenvalue problem for symmetric potentials and the inverse transmission eigenvalue problem for spherically symmetric refractive indices. Firstly, we solve the corresponding direct problems to produce the required eigenvalues datasets in order to train the machine learning algorithms. Next, we consider several examples of inverse problems and compare the performance of each model to predict the unknown potentials and refractive indices respectively, from a given small set of the lowest eigenvalues. The supervised regression models we use are k-Nearest Neighbours, Random Forests and Multi-Layer Perceptron. Our experiments show that these machine learning methods, under appropriate tuning on their parameters, can numerically solve the examined inverse eigenvalue problems.

In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of smart farming tools. While AI-driven digital agriculture tools can offer high-performing predictive functionalities, they lack tangible quantitative evidence on their benefits to the farmers. Field experiments can derive such evidence, but are often costly, time consuming and hence limited in scope and scale of application. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators (e.g., yield in this case). This way, we can increase farmers' trust via enhancing the transparency of the digital agriculture market and accelerate the adoption of technologies that aim to secure farmer income resilience and global agricultural sustainability. As a case study, we designed and implemented a recommendation system for the optimal sowing time of cotton based on numerical weather predictions, which was used by a farmers' cooperative during the growing season of 2021. We then leverage agricultural knowledge, collected yield data, and environmental information to develop a causal graph of the farm system. Using the back-door criterion, we identify the impact of sowing recommendations on the yield and subsequently estimate it using linear regression, matching, inverse propensity score weighting and meta-learners. The results reveal that a field sown according to our recommendations exhibited a statistically significant yield increase that ranged from 12% to 17%, depending on the method. The effect estimates were robust, as indicated by the agreement among the estimation methods and four successful refutation tests. We argue that this approach can be implemented for decision support systems of other fields, extending their evaluation beyond a performance assessment of internal functionalities.