许多数据分析任务在很大程度上依赖对表的深入了解（多维数据）。在整个任务中，都存在表字段 /列的共同使用的元数据属性。在本文中，我们确定了四个这样的分析元数据：测量/维度二分法，公共场作用，语义场类型和默认聚集函数。尽管这些元数据面临不足的监督信号的挑战，利用现有的知识和理解分布。为了将这些元数据推理为原始表，我们提出了多任务元数据模型，该模型将现场分布和知识图信息融合到预训练的表格模型中。对于模型培训和评估，我们通过使用下游任务的各种智能监督来收集分析元数据的大型语料库（来自私人电子表格和公共表格数据集的〜582K表）。我们的最佳模型的精度= 98％，命中率在TOP-1> 67％，精度> 80％和四个分析元数据推理任务的精度= 88％。它的表现优于基于规则，传统机器学习方法和预训练的表格模型的一系列基线。分析元数据模型被部署在流行的数据分析产品中，帮助下游智能功能，例如Insights挖掘，图表 /枢轴表建议和自然语言QA ...

表的智能分析和可视化表使用技术自动从数据中推荐有用的知识，从而使用户免于乏味的多维数据挖掘。尽管许多研究成功地通过规则或机器学习来自动化建议，但很难概括专家知识并提供可解释的建议。在本文中，我们首次提出条件格式的建议，以及图表建议，以示例智能表分析。我们建议对表上的分析语义，以发现用户创建的分析背后的共同分析模式。在这里，我们通过将数据重点与用户意图分开，从而分别从数据和人类的角度提取了用户的动机来设计分析语义。此外，我们设计的ASTA框架是为了将分析语义应用于多个自动化建议。 ASTA框架通过根据专家知识设计签名来提取数据功能，并在现场（图）或细胞级（条件格式）（条件格式化）中启用数据引用。实验表明，我们的框架在公共图表中的62.86％中的前1位获得了召回率，在公共图表中，最佳基准优于14％的最佳基准，并在收集的语料库中获得了72.31％的召回，证明ASTA框架有效地提供了准确且可解释的建议。

为了更好地利用搜索日志和建模用户的行为模式，提出了许多点击模型来提取用户的隐式交互反馈。大多数传统点击模型都是基于概率图形模型（PGM）框架，该框架需要手动设计的依赖项，并且可能会过度简化用户行为。最近，提出了基于神经网络的方法来通过增强表达能力并允许灵活的依赖性来提高用户行为的预测准确性。但是，他们仍然遭受数据稀疏性和冷启动问题的困扰。在本文中，我们提出了一个新颖的图形增强点击模型（GraphCM），用于Web搜索。首先，我们将每个查询或文档视为顶点，并分别针对查询和文档提出新颖的均匀图构造方法，以完全利用会议内和会议间信息，以解决稀疏性和冷启动问题。其次，在考试假设之后，我们分别对吸引力估计量和检查预测值进行了建模，以输出吸引力得分和检查概率，在该分数中，应用图形神经网络和邻居相互作用技术用于提取在预构建的同质图中编码的辅助信息。最后，我们将组合功能应用于将考试概率和吸引力得分整合到点击预测中。在三个现实世界会话数据集上进行的广泛实验表明，GraphCM不仅胜过了最先进的模型，而且还可以在解决数据稀疏性和冷启动问题方面取得卓越的性能。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

准确的轨道位置是铁路支持驱动系统的重要组成部分，用于安全监控。激光雷达可以获得携带铁路环境的3D信息的点云，特别是在黑暗和可怕的天气条件下。在本文中，提出了一种基于3D点云的实时轨识别方法来解决挑战，如无序，不均匀的密度和大量点云的挑战。首先呈现Voxel Down-采样方法，用于铁路点云的密度平衡，并且金字塔分区旨在将3D扫描区域划分为具有不同卷的体素。然后，开发了一个特征编码模块以找到最近的邻点并聚合它们的局部几何特征。最后，提出了一种多尺度神经网络以产生每个体素和轨道位置的预测结果。该实验是在铁路的3D点云数据的9个序列下进行的。结果表明，该方法在检测直，弯曲和其他复杂的拓扑轨道方面具有良好的性能。

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clustering, we can first obtain clusters with a clustering algorithm and then extract cluster-specific topics. However, this naive strategy ignores the fact that text clustering and topic extraction are strongly correlated and follow a chicken-and-egg relationship. Performing them separately fails to make them mutually benefit each other to achieve the best overall performance. In this paper, we propose an unsupervised text clustering and topic extraction framework (ClusTop) which integrates text clustering and topic extraction into a unified framework and can achieve high-quality clustering result and extract topics from each cluster simultaneously. Our framework includes four components: enhanced language model training, dimensionality reduction, clustering and topic extraction, where the enhanced language model can be viewed as a bridge between clustering and topic extraction. On one hand, it provides text embeddings with a strong cluster structure which facilitates effective text clustering; on the other hand, it pays high attention on the topic related words for topic extraction because of its self-attention architecture. Moreover, the training of enhanced language model is unsupervised. Experiments on two datasets demonstrate the effectiveness of our framework and provide benchmarks for different model combinations in this framework.

This paper illustrates the technologies of user next intent prediction with a concept knowledge graph. The system has been deployed on the Web at Alipay, serving more than 100 million daily active users. Specifically, we propose AlipayKG to explicitly characterize user intent, which is an offline concept knowledge graph in the Life-Service domain modeling the historical behaviors of users, the rich content interacted by users and the relations between them. We further introduce a Transformer-based model which integrates expert rules from the knowledge graph to infer the online user's next intent. Experimental results demonstrate that the proposed system can effectively enhance the performance of the downstream tasks while retaining explainability.

Capturing feature information effectively is of great importance in vision tasks. With the development of convolutional neural networks (CNNs), concepts like residual connection and multiple scales promote continual performance gains on diverse deep learning vision tasks. However, the existing methods do not organically combined advantages of these valid ideas. In this paper, we propose a novel CNN architecture called GoogLe2Net, it consists of residual feature-reutilization inceptions (ResFRI) or split residual feature-reutilization inceptions (Split-ResFRI) which create transverse passages between adjacent groups of convolutional layers to enable features flow to latter processing branches and possess residual connections to better process information. Our GoogLe2Net is able to reutilize information captured by foregoing groups of convolutional layers and express multi-scale features at a fine-grained level, which improves performances in image classification. And the inception we proposed could be embedded into inception-like networks directly without any migration costs. Moreover, in experiments based on popular vision datasets, such as CIFAR10 (97.94%), CIFAR100 (85.91%) and Tiny Imagenet (70.54%), we obtain better results on image classification task compared with other modern models.