Most regularized tensor regression research focuses on tensors predictors with scalars responses or vectors predictors to tensors responses. We consider the sparse low rank tensor on tensor regression where predictors $\mathcal{X}$ and responses $\mathcal{Y}$ are both high-dimensional tensors. By demonstrating that the general inner product or the contracted product on a unit rank tensor can be decomposed into standard inner products and outer products, the problem can be simply transformed into a tensor to scalar regression followed by a tensor decomposition. So we propose a fast solution based on stagewise search composed by contraction part and generation part which are optimized alternatively. We successfully demonstrate our method can out perform current methods in terms of accuracy, predictors selection by effectively incorporating the structural information.

采样在机器学习方法中无处不在。由于大数据集和模型复杂性的增长，我们希望在训练A表示时学习和适应采样过程。为了实现这一宏伟的目标，已经提出了各种抽样技术。但是，他们中的大多数要么使用固定采样方案，要么基于简单的启发式方法调整采样方案。他们不能选择在不同阶段进行模型培训的最佳样本。受认知科学中的“思考，快速和系统2）的启发，我们提出了一种奖励指导的采样策略，称为自适应样本，并奖励（ASR）来应对这一挑战。据我们所知，这是利用强化学习（RL）解决代表学习中抽样问题的第一项工作。我们的方法最佳地调整了采样过程以实现最佳性能。我们通过基于距离的采样来探索样品之间的地理关系，以最大程度地提高整体累积奖励。我们将ASR应用于基于相似性的损失函数中的长期抽样问题。信息检索和聚类中的经验结果证明了ASR在不同数据集中的出色性能。我们还讨论了一种令人着迷的现象，我们将其称为实验中的“ ASR重力”。

元素是单细胞曲线的不相交和均匀的组，代表离散和高度颗粒细胞状态。现有的元算法倾向于仅使用一种模态来推断元素，即使单细胞多摩变数据集谱图在同一细胞内多个分子模态。在这里，我们提出\ textbf {c} ross-m \ textbf {o} dal \ textbf {e} mbedding for \ textbf {m} etacell标识（coem），它利用嵌入式空间，利用scatac-seq和scatac-seq和scatac-seq和SCRNA-SEQ执行聚合，平衡精细分辨率和足够的测序覆盖范围之间的权衡。COEM通过有效识别具有连续和离散细胞类型的数据集的准确且分离良好的元素来优于最先进的方法海科。此外，COEM显着改善了峰到基因的关联分析，并促进了复杂的基因调节推理任务。

文本消息传递是计算机介导的通信（CMC）最广泛使用的形式。先前的发现表明，语言因素可以可靠地表明信息为欺骗性。例如，用户要花更长的时间并使用更多的单词来制作欺骗性消息，而不是做真实的消息。现有的研究还研究了诸如学生身份和性别等因素如何影响欺骗性信息中的欺骗和单词选择率。但是，这项研究受到小样本量的限制，并返回了与发现相矛盾的结果。本文旨在使用使用Android消息传递应用程序从大型参与者集中收集的文本消息的数据集来解决这些问题。本文的结果表明，男女参与者以及学生和非学生之间的欺骗性信息的单词选择和欺骗性信息的频率有显着差异。

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

Surgical robot automation has attracted increasing research interest over the past decade, expecting its huge potential to benefit surgeons, nurses and patients. Recently, the learning paradigm of embodied AI has demonstrated promising ability to learn good control policies for various complex tasks, where embodied AI simulators play an essential role to facilitate relevant researchers. However, existing open-sourced simulators for surgical robot are still not sufficiently supporting human interactions through physical input devices, which further limits effective investigations on how human demonstrations would affect policy learning. In this paper, we study human-in-the-loop embodied intelligence with a new interactive simulation platform for surgical robot learning. Specifically, we establish our platform based on our previously released SurRoL simulator with several new features co-developed to allow high-quality human interaction via an input device. With these, we further propose to collect human demonstrations and imitate the action patterns to achieve more effective policy learning. We showcase the improvement of our simulation environment with the designed new features and tasks, and validate state-of-the-art reinforcement learning algorithms using the interactive environment. Promising results are obtained, with which we hope to pave the way for future research on surgical embodied intelligence. Our platform is released and will be continuously updated in the website: https://med-air.github.io/SurRoL/

Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage. Existing computational methods on MLS quantification not only require intensive labeling in millimeter-level measurement but also suffer from poor performance due to their dependence on specific landmarks or simplified anatomical assumptions. In this paper, we propose a novel semi-supervised framework to accurately measure the scale of MLS from head CT scans. We formulate the MLS measurement task as a deformation estimation problem and solve it using a few MLS slices with sparse labels. Meanwhile, with the help of diffusion models, we are able to use a great number of unlabeled MLS data and 2793 non-MLS cases for representation learning and regularization. The extracted representation reflects how the image is different from a non-MLS image and regularization serves an important role in the sparse-to-dense refinement of the deformation field. Our experiment on a real clinical brain hemorrhage dataset has achieved state-of-the-art performance and can generate interpretable deformation fields.

While inferring common actor states (such as position or velocity) is an important and well-explored task of the perception system aboard a self-driving vehicle (SDV), it may not always provide sufficient information to the SDV. This is especially true in the case of active emergency vehicles (EVs), where light-based signals also need to be captured to provide a full context. We consider this problem and propose a sequential methodology for the detection of active EVs, using an off-the-shelf CNN model operating at a frame level and a downstream smoother that accounts for the temporal aspect of flashing EV lights. We also explore model improvements through data augmentation and training with additional hard samples.

Text-to-SQL semantic parsing is an important NLP task, which greatly facilitates the interaction between users and the database and becomes the key component in many human-computer interaction systems. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English. In this work, we present MultiSpider, the largest multilingual text-to-SQL dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese). Upon MultiSpider, we further identify the lexical and structural challenges of text-to-SQL (caused by specific language properties and dialect sayings) and their intensity across different languages. Experimental results under three typical settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. Qualitative and quantitative analyses are conducted to understand the reason for the performance drop of each language. Besides the dataset, we also propose a simple schema augmentation framework SAVe (Schema-Augmentation-with-Verification), which significantly boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.

Table-and-text hybrid question answering (HybridQA) is a widely used and challenging NLP task commonly applied in the financial and scientific domain. The early research focuses on migrating other QA task methods to HybridQA, while with further research, more and more HybridQA-specific methods have been present. With the rapid development of HybridQA, the systematic survey is still under-explored to summarize the main techniques and advance further research. So we present this work to summarize the current HybridQA benchmarks and methods, then analyze the challenges and future directions of this task. The contributions of this paper can be summarized in three folds: (1) first survey, to our best knowledge, including benchmarks, methods and challenges for HybridQA; (2) systematic investigation with the reasonable comparison of the existing systems to articulate their advantages and shortcomings; (3) detailed analysis of challenges in four important dimensions to shed light on future directions.