FOLD-R ++是一种针对二进制分类任务的高效且基于规则的机器学习算法。它以（可解释的）训练有素的模型生成分层的正常逻辑程序。我们对称为fold-se的fold-r ++算法进行了改进，该算法在继承fold-r ++的所有优点时提供了可扩展的解释性（SE）。可扩展的解释性意味着，无论数据集的大小如何，学识渊博的规则和学识关的数量保持很小，因此人类可以理解，同时保持分类的良好表现。 Fold-SE具有最新的算法（例如XGBoost和Multi-Layer Perceptrons（MLP））的性能竞争力。但是，与XGBoost和MLP不同，Fold-SE算法生成具有可扩展性的模型。 FOLD-SE算法在效率，性能和解释性方面优于fold-r ++和开膛手算法，尤其是对于大型数据集。 fold-rm算法是用于多类分类任务的fold-r ++的扩展。还提出了一种改进的折叠式RM算法。

fold-r ++是一种用于二进制分类任务的新电感学习算法。它为混合类型（数值和分类）数据生成（可解释的）正常逻辑程序。我们提出了一种具有排名框架（称为fold-tr）的自定义的折叠式R ++算法，该算法旨在按照培训数据中的排名模式对新项目进行排名。与Fold-R ++一样，Fold-Tr算法能够直接处理混合型数据，并提供本机的理由来解释一对项目之间的比较。

本文提出了一种基于答案设置编程（ASP）的方法，用于代表自然语言文本生成的知识。文本中的知识是使用Neo Davidsonian的形式主义建模的，然后将其表示为答案集计划。相关的致辞知识另外导入Wordnet等资源，并在ASP中表示。然后可以使用所产生的知识库来在ASP系统的帮助下执行推理。这种方法可以促进许多自然语言任务，如自动问题应答，文本摘要和自动化问题。基于ASP的技术表示，例如默认推理，分层知识组织，默认值等的首选项，用于模拟完成这些任务所需的致辞推理方法。在本文中，我们描述了我们开发的CaspR系统，以自动解决在给出英语文本时回答自然语言问题的任务。 CASPR可以被视为一个系统，通过“了解”文本并已在队列数据集上进行了测试，具有有希望的结果。

多核和高度连接的架构已成为无处不在的，这为基于语言的剥削的方法带来了更新的兴趣。自成立以来，逻辑编程已被认为是一种编程范例，具有巨大的自动开发并行性。 2001年出版的“并行逻辑编程研究”对并行逻辑编程研究的综合调查曾作为对研究人员和开发人员的基本提及。目前内容非常有效，但同时该领域在遵循的岁月中继续快速发展。这些成就和持续的研究已经受到技术创新的快速速度驱动的，这导致了非常大的集群，多核处理器广泛扩散，普通目的图形处理单元的游戏变化作用以及云计算的无处不在的采用。这一直在逻辑编程中的显着进展并行于显着的静态分析和验证，答案集编程的快速增长，以及一般，更成熟的实现和系统。本次调查介绍了自2001年以来的并行逻辑编程研究的审查，从而提供了先前调查的自然延续。该调查的目标不仅可以作为逻辑编程系统的研究人员和开发人员的参考，而且还可以作为对逻辑感兴趣的任何人的阅读以及作为并行系统外的研究人员的有用来源的阅读。逻辑编程理论与实践的考虑（TPLP）。

Fold-R是一种自动感应学习算法，用于学习混合（数值和分类）数据的默认规则。它生成（可解释的）应答集编程（ASP）规则集，用于分类任务。我们提出了一种改进的折叠R算法，称为折叠-R ++，显着提高了折叠-R的效率和可扩展性。 FOLD-R ++在编码或特征选择阶段期间，在没有损害或丢失输入训练数据中的信息的情况下改善了FOL-R。折叠-R ++算法在具有广泛使用的XGBoost算法的性能中具有竞争力，但是，与XGBoost不同，折叠-R ++算法产生可说明的模型。折叠-R ++在具有RIPPER系统的性能中也具有竞争性，但是，在大型数据集上折叠-R ++优于Ripper。我们还通过将Fold-R ++与S（CASP）-A -A的ASP执行引擎组合来创建一个强大的工具集 - 使用Fold-R ++生成的答案集程序对新数据样本进行预测。 S（CASP）系统还为预测产生了理由。本文提出的实验表明，我们改进的折叠率-R ++算法是对原始设计的显着改进，并且S（CASP）系统也可以以有效的方式进行预测。

Existing federated classification algorithms typically assume the local annotations at every client cover the same set of classes. In this paper, we aim to lift such an assumption and focus on a more general yet practical non-IID setting where every client can work on non-identical and even disjoint sets of classes (i.e., client-exclusive classes), and the clients have a common goal which is to build a global classification model to identify the union of these classes. Such heterogeneity in client class sets poses a new challenge: how to ensure different clients are operating in the same latent space so as to avoid the drift after aggregation? We observe that the classes can be described in natural languages (i.e., class names) and these names are typically safe to share with all parties. Thus, we formulate the classification problem as a matching process between data representations and class representations and break the classification model into a data encoder and a label encoder. We leverage the natural-language class names as the common ground to anchor the class representations in the label encoder. In each iteration, the label encoder updates the class representations and regulates the data representations through matching. We further use the updated class representations at each round to annotate data samples for locally-unaware classes according to similarity and distill knowledge to local models. Extensive experiments on four real-world datasets show that the proposed method can outperform various classical and state-of-the-art federated learning methods designed for learning with non-IID data.

The rise in data has led to the need for dimension reduction techniques, especially in the area of non-scalar variables, including time series, natural language processing, and computer vision. In this paper, we specifically investigate dimension reduction for time series through functional data analysis. Current methods for dimension reduction in functional data are functional principal component analysis and functional autoencoders, which are limited to linear mappings or scalar representations for the time series, which is inefficient. In real data applications, the nature of the data is much more complex. We propose a non-linear function-on-function approach, which consists of a functional encoder and a functional decoder, that uses continuous hidden layers consisting of continuous neurons to learn the structure inherent in functional data, which addresses the aforementioned concerns in the existing approaches. Our approach gives a low dimension latent representation by reducing the number of functional features as well as the timepoints at which the functions are observed. The effectiveness of the proposed model is demonstrated through multiple simulations and real data examples.

Landing an unmanned aerial vehicle unmanned aerial vehicle (UAV) on top of an unmanned surface vehicle (USV) in harsh open waters is a challenging problem, owing to forces that can damage the UAV due to a severe roll and/or pitch angle of the USV during touchdown. To tackle this, we propose a novel model predictive control (MPC) approach enabling a UAV to land autonomously on a USV in these harsh conditions. The MPC employs a novel objective function and an online decomposition of the oscillatory motion of the vessel to predict, attempt, and accomplish the landing during near-zero tilt of the landing platform. The nonlinear prediction of the motion of the vessel is performed using visual data from an onboard camera. Therefore, the system does not require any communication with the USV or a control station. The proposed method was analyzed in numerous robotics simulations in harsh and extreme conditions and further validated in various real-world scenarios.

Multiple studies have focused on predicting the prospective popularity of an online document as a whole, without paying attention to the contributions of its individual parts. We introduce the task of proactively forecasting popularities of sentences within online news documents solely utilizing their natural language content. We model sentence-specific popularity forecasting as a sequence regression task. For training our models, we curate InfoPop, the first dataset containing popularity labels for over 1.7 million sentences from over 50,000 online news documents. To the best of our knowledge, this is the first dataset automatically created using streams of incoming search engine queries to generate sentence-level popularity annotations. We propose a novel transfer learning approach involving sentence salience prediction as an auxiliary task. Our proposed technique coupled with a BERT-based neural model exceeds nDCG values of 0.8 for proactive sentence-specific popularity forecasting. Notably, our study presents a non-trivial takeaway: though popularity and salience are different concepts, transfer learning from salience prediction enhances popularity forecasting. We release InfoPop and make our code publicly available: https://github.com/sayarghoshroy/InfoPopularity

The ability for an agent to continuously learn new skills without catastrophically forgetting existing knowledge is of critical importance for the development of generally intelligent agents. Most methods devised to address this problem depend heavily on well-defined task boundaries, and thus depend on human supervision. Our task-agnostic method, Self-Activating Neural Ensembles (SANE), uses a modular architecture designed to avoid catastrophic forgetting without making any such assumptions. At the beginning of each trajectory, a module in the SANE ensemble is activated to determine the agent's next policy. During training, new modules are created as needed and only activated modules are updated to ensure that unused modules remain unchanged. This system enables our method to retain and leverage old skills, while growing and learning new ones. We demonstrate our approach on visually rich procedurally generated environments.