大量的时间序列数据通常被组织成具有不同聚集水平的横截面结构。示例包括产品和地理组。与此类数据集相干决策和计划的必要条件是针对分散的系列的预测，可以准确地添加到汇总的系列预测中，这激发了新型层次结构预测算法的创建。机器学习社区对横截面层次预测系统的兴趣日益增长，我们正处于一个有利的时刻，以确保科学的努力基于声音基线。因此，我们提出了层次Forecast库，该库包含预处理的公开可用数据集，评估指标和一组编译的统计基线模型。我们基于Python的框架旨在弥合统计，计量经济学建模和机器学习预测研究之间的差距。代码和文档可在https://github.com/nixtla/hierarchicalforecast中找到。

神经预测的最新进展加速了大规模预测系统的性能。然而，长途预测仍然是一项非常艰巨的任务。困扰任务的两个常见挑战是预测的波动及其计算复杂性。我们介绍了N-HITS，该模型通过结合新的分层插值和多率数据采样技术来解决挑战。这些技术使提出的方法能够顺序组装其预测，并在分解输入信号并合成预测的同时强调不同频率和尺度的组件。我们证明，在平稳性的情况下，层次结构插值技术可以有效地近似于任意长的视野。此外，我们从长远的预测文献中进行了广泛的大规模数据集实验，证明了我们方法比最新方法的优势，在该方法中，N-HITS可提供比最新的16％的平均准确性提高。变压器体系结构在减少计算时间的同时（50次）。我们的代码可在https://bit.ly/3jlibp8上找到。

在这项工作中，我们提出了使用量子缩放（MQ-DRN-S）的分位数回归和扩张的经常性神经网络，并将其应用于库存管理任务。该模型在统计基准（具有外源性变量，QAR-X）的统计基准（分位式自回归模型，QAR-X）而言，该模型更好地表现出更好的性能，而不是在没有时间缩放的MQ-DRNN的情况下更好。以上一系列10,000次销售的elllobo销售超过53周的地平线，每周使用滚动窗口为7天。

It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these periodic motions together with the inertial properties of the system. The classification of these periodic behaviours and their geometric characterisation are in an on-going secular debate, which recently led to the so-called eigenmanifold theory. The eigenmanifold characterises nonlinear oscillations as a generalisation of linear eigenspaces. With the motivation of performing periodic tasks efficiently, we use tools coming from this theory to construct an optimization problem aimed at inducing desired closed-loop oscillations through a state feedback law. We solve the constructed optimization problem via gradient-descent methods involving neural networks. Extensive simulations show the validity of the approach.

Our aim is to build autonomous agents that can solve tasks in environments like Minecraft. To do so, we used an imitation learning-based approach. We formulate our control problem as a search problem over a dataset of experts' demonstrations, where the agent copies actions from a similar demonstration trajectory of image-action pairs. We perform a proximity search over the BASALT MineRL-dataset in the latent representation of a Video PreTraining model. The agent copies the actions from the expert trajectory as long as the distance between the state representations of the agent and the selected expert trajectory from the dataset do not diverge. Then the proximity search is repeated. Our approach can effectively recover meaningful demonstration trajectories and show human-like behavior of an agent in the Minecraft environment.

Object instance segmentation is a key challenge for indoor robots navigating cluttered environments with many small objects. Limitations in 3D sensing capabilities often make it difficult to detect every possible object. While deep learning approaches may be effective for this problem, manually annotating 3D data for supervised learning is time-consuming. In this work, we explore zero-shot instance segmentation (ZSIS) from RGB-D data to identify unseen objects in a semantic category-agnostic manner. We introduce a zero-shot split for Tabletop Objects Dataset (TOD-Z) to enable this study and present a method that uses annotated objects to learn the ``objectness'' of pixels and generalize to unseen object categories in cluttered indoor environments. Our method, SupeRGB-D, groups pixels into small patches based on geometric cues and learns to merge the patches in a deep agglomerative clustering fashion. SupeRGB-D outperforms existing baselines on unseen objects while achieving similar performance on seen objects. Additionally, it is extremely lightweight (0.4 MB memory requirement) and suitable for mobile and robotic applications. The dataset split and code will be made publicly available upon acceptance.

Increasingly taking place in online spaces, modern political conversations are typically perceived to be unproductively affirming -- siloed in so called ``echo chambers'' of exclusively like-minded discussants. Yet, to date we lack sufficient means to measure viewpoint diversity in conversations. To this end, in this paper, we operationalize two viewpoint metrics proposed for recommender systems and adapt them to the context of social media conversations. This is the first study to apply these two metrics (Representation and Fragmentation) to real world data and to consider the implications for online conversations specifically. We apply these measures to two topics -- daylight savings time (DST), which serves as a control, and the more politically polarized topic of immigration. We find that the diversity scores for both Fragmentation and Representation are lower for immigration than for DST. Further, we find that while pro-immigrant views receive consistent pushback on the platform, anti-immigrant views largely operate within echo chambers. We observe less severe yet similar patterns for DST. Taken together, Representation and Fragmentation paint a meaningful and important new picture of viewpoint diversity.

This volume contains revised versions of the papers selected for the third volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.

Evaluating and comparing text-to-image models is a challenging problem. Significant advances in the field have recently been made, piquing interest of various industrial sectors. As a consequence, a gold standard in the field should cover a variety of tasks and application contexts. In this paper a novel evaluation approach is experimented, on the basis of: (i) a curated data set, made by high-quality royalty-free image-text pairs, divided into ten categories; (ii) a quantitative metric, the CLIP-score, (iii) a human evaluation task to distinguish, for a given text, the real and the generated images. The proposed method has been applied to the most recent models, i.e., DALLE2, Latent Diffusion, Stable Diffusion, GLIDE and Craiyon. Early experimental results show that the accuracy of the human judgement is fully coherent with the CLIP-score. The dataset has been made available to the public.

Real-time monocular 3D reconstruction is a challenging problem that remains unsolved. Although recent end-to-end methods have demonstrated promising results, tiny structures and geometric boundaries are hardly captured due to their insufficient supervision neglecting spatial details and oversimplified feature fusion ignoring temporal cues. To address the problems, we propose an end-to-end 3D reconstruction network SST, which utilizes Sparse estimated points from visual SLAM system as additional Spatial guidance and fuses Temporal features via a novel cross-modal attention mechanism, achieving more detailed reconstruction results. We propose a Local Spatial-Temporal Fusion module to exploit more informative spatial-temporal cues from multi-view color information and sparse priors, as well a Global Spatial-Temporal Fusion module to refine the local TSDF volumes with the world-frame model from coarse to fine. Extensive experiments on ScanNet and 7-Scenes demonstrate that SST outperforms all state-of-the-art competitors, whilst keeping a high inference speed at 59 FPS, enabling real-world applications with real-time requirements.