Many state-of-the-art natural language understanding (NLU) models are based on pretrained neural language models. These models often make inferences using information from multiple sources. An important class of such inferences are those that require both background knowledge, presumably contained in a model's pretrained parameters, and instance-specific information that is supplied at inference time. However, the integration and reasoning abilities of NLU models in the presence of multiple knowledge sources have been largely understudied. In this work, we propose a test suite of coreference resolution tasks that require reasoning over multiple facts. Our dataset is organized into subtasks that differ in terms of which knowledge sources contain relevant facts. We evaluate state-of-the-art coreference resolution models on our dataset. Our results indicate that several models struggle to reason on-the-fly over knowledge observed both at pretrain time and at inference time. However, with task-specific training, a subset of models demonstrates the ability to integrate certain knowledge types from multiple sources.

具有屏蔽语言建模目标（例如，BERT）编码的变压器模型，其行为探测器证明了致商语言;但是，通过系统推断通过对预训练前的语料库的语义来获得这种知识的程度是一个开放的问题。为了回答这个问题，我们在预训练期间选择性地将言语化的知识注入了BERT模型的小匹匹匹匹配，并评估模型推广的推论。我们发现普遍性不会改善培训的过程，表明从表面级，共同发生模式而不是诱导的系统推理获得致辞知识。

最先进的抽象摘要系统经常生成\ emph {幻觉};即，不直接从源文本中推断的内容。尽管被认为是不正确的，我们发现非常令人难潮的内容是事实，即与世界知识一致。这些事实幻觉通过提供有用的背景信息，可以在摘要中受益。在这项工作中，我们提出了一种新的检测方法，将事实与实体的非事实幻觉分开。我们的方法分别使用实体的先前和后验概率，分别是预训练和芬特的屏蔽语言模型。经验结果表明，我们的方法在精度和F1分数方面大大优于两种基线％，与人类判断强烈相关。百分比对事实分类任务。此外，我们显示我们的探测器，当用作离线增强学习（RL）算法中的奖励信号时，显着提高了摘要的事实性，同时保持抽象水平。

成语与大多数短语不同。首先，成语中的单词具有非规范含义。其次，习语中单词的非传统含义取决于习惯中其他单词的存在。语言理论在这些特性是否相互依赖，以及是否需要特殊的理论机制来容纳成语方面有所不同。我们定义了与上述属性相对应的两个度量，并使用BERT（Devlin等，2019）和XLNet实施它们（Yang等，2019）。我们表明，成语落在两个维度的预期交集处，但是尺寸本身并不相关。我们的结果表明，处理习语的特殊机械可能不保证。

在少数射击域适应（FDA）中，针对目标域的分类器在源域（SD）（SD）中使用可访问的标记数据进行训练，而目标域（TD）中的标记数据很少。但是，数据通常包含当前时代的私人信息，例如分布在个人电话上的数据。因此，如果我们直接访问SD中的数据以训练目标域分类器（FDA方法要求），则将泄漏私人信息。在本文中，为了彻底防止SD中的隐私泄漏，我们考虑了一个非常具有挑战性的问题设置，必须使用很少的标签目标数据和训练有素的SD分类器对TD的分类器进行培训，并将其命名为几个示例的假设适应（FHA）。在FHA中，我们无法访问SD中的数据，因此，SD中的私人信息将得到很好的保护。为此，我们提出了一个目标定向的假设适应网络（TOHAN）来解决FHA问题，在该问题中，我们生成了高度兼容的未标记数据（即中间域），以帮助培训目标域分类器。 Tohan同时保持了两个深网，其中一个专注于学习中间域，而另一个则要照顾中间靶向分布的适应性和目标风险最小化。实验结果表明，Tohan的表现要优于竞争基线。

The Government of Kerala had increased the frequency of supply of free food kits owing to the pandemic, however, these items were static and not indicative of the personal preferences of the consumers. This paper conducts a comparative analysis of various clustering techniques on a scaled-down version of a real-world dataset obtained through a conjoint analysis-based survey. Clustering carried out by centroid-based methods such as k means is analyzed and the results are plotted along with SVD, and finally, a conclusion is reached as to which among the two is better. Once the clusters have been formulated, commodities are also decided upon for each cluster. Also, clustering is further enhanced by reassignment, based on a specific cluster loss threshold. Thus, the most efficacious clustering technique for designing a food kit tailored to the needs of individuals is finally obtained.

Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space. Yet, achieving highly precise alignment is still challenging, especially when nodes with long-range connectivity to the labeled anchors are encountered. To alleviate this limitation, we purposefully designed WL-Align which adopts a regularized representation learning framework to learn distinctive node representations. It extends the Weisfeiler-Lehman Isormorphism Test and learns the alignment in alternating phases of "across-network Weisfeiler-Lehman relabeling" and "proximity-preserving representation learning". The across-network Weisfeiler-Lehman relabeling is achieved through iterating the anchor-based label propagation and a similarity-based hashing to exploit the known anchors' connectivity to different nodes in an efficient and robust manner. The representation learning module preserves the second-order proximity within individual networks and is regularized by the across-network Weisfeiler-Lehman hash labels. Extensive experiments on real-world and synthetic datasets have demonstrated that our proposed WL-Align outperforms the state-of-the-art methods, achieving significant performance improvements in the "exact matching" scenario. Data and code of WL-Align are available at https://github.com/ChenPengGang/WLAlignCode.

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.

Tensor robust principal component analysis (RPCA), which seeks to separate a low-rank tensor from its sparse corruptions, has been crucial in data science and machine learning where tensor structures are becoming more prevalent. While powerful, existing tensor RPCA algorithms can be difficult to use in practice, as their performance can be sensitive to the choice of additional hyperparameters, which are not straightforward to tune. In this paper, we describe a fast and simple self-supervised model for tensor RPCA using deep unfolding by only learning four hyperparameters. Despite its simplicity, our model expunges the need for ground truth labels while maintaining competitive or even greater performance compared to supervised deep unfolding. Furthermore, our model is capable of operating in extreme data-starved scenarios. We demonstrate these claims on a mix of synthetic data and real-world tasks, comparing performance against previously studied supervised deep unfolding methods and Bayesian optimization baselines.

We propose an extrinsic Bayesian optimization (eBO) framework for general optimization problems on manifolds. Bayesian optimization algorithms build a surrogate of the objective function by employing Gaussian processes and quantify the uncertainty in that surrogate by deriving an acquisition function. This acquisition function represents the probability of improvement based on the kernel of the Gaussian process, which guides the search in the optimization process. The critical challenge for designing Bayesian optimization algorithms on manifolds lies in the difficulty of constructing valid covariance kernels for Gaussian processes on general manifolds. Our approach is to employ extrinsic Gaussian processes by first embedding the manifold onto some higher dimensional Euclidean space via equivariant embeddings and then constructing a valid covariance kernel on the image manifold after the embedding. This leads to efficient and scalable algorithms for optimization over complex manifolds. Simulation study and real data analysis are carried out to demonstrate the utilities of our eBO framework by applying the eBO to various optimization problems over manifolds such as the sphere, the Grassmannian, and the manifold of positive definite matrices.