现有的基于学习的框架插值算法从高速自然视频中提取连续帧以训练模型。与自然视频相比，卡通视频通常处于较低的框架速度。此外，连续卡通框架之间的运动通常是非线性，它破坏了插值算法的线性运动假设。因此，它不适合直接从卡通视频中生成训练集。为了更好地适应从自然视频到动画视频的框架插值算法，我们提出了Autofi，这是一种简单有效的方法，可以自动渲染训练数据，以进行深层动画视频插值。 Autofi采用分层体系结构来渲染合成数据，从而确保线性运动的假设。实验结果表明，Autofi在训练Dain和Anin方面表现出色。但是，大多数框架插值算法仍将在容易出错的区域（例如快速运动或大闭塞）中失败。除了Autofi外，我们还提出了一个名为SKTFI的基于插件的后处理后处理模块，以手动使用用户提供的草图来完善最终结果。借助Autofi和SKTFI，插值动画框架显示出很高的感知质量。

我们考虑在大型混合搜索空间上有效的黑箱优化问题，由高尺寸连续空间和复杂的组合空间的混合物组成。这样的例子通常在进化计算中产生，也是最近，神经发展和架构寻求强化学习（RL）政策。然而，不幸的是，以前的基于突变的方法在理论上和实际上均在高尺寸连续空间中遭受。因此，我们通过以高效的神经结构搜索（ENAS）引入的高度可扩展和直观的方式，通过组合进化策略和组合优化技术来提出ES-ZHAS，这是一个简单的联合优化过程，通过高效的神经结构搜索（ENAS）引入的一拍或超空地范式。 。通过这种相对简单的婚姻之间的两种不同的研究，我们能够通过优化混合空间以及通过边缘修剪和量化在流行的RL上优化BBOB功能以及组合神经网络架构来验证我们最佳的方法。基准。由于算法的模块化，我们还能够包含各种流行的技术，从不同的连续和组合优化器以及约束优化。

我们考虑在培训深度学习模型的通信约束下分布式优化。我们提出了一种新的算法，其参数更新依赖于两个力量：常规渐变步骤，以及当前最佳性能的工人（领导者）决定的纠正方向。我们的方法以多种方式与参数平均方案EASGD不同：（i）我们的客观制定与原始优化问题相比，我们的客观制定不会改变静止点的位置; （ii）我们避免通过将彼此不同局部最小值下降的本地工人拉动的融合减速（即其参数的平均值）; （iii）我们的设计更新破坏了对称性的诅咒（被困在对称非凸景观中的透过透过透过次优溶液中的现象）; （iv）我们的方法更加沟通高效，因为它仅广播领导者而不是所有工人的参数。我们提供了对所提出的算法的批量版本的理论分析，我们称之为领导者梯度下降（LGD）及其随机变体（LSGD）。最后，我们实现了算法的异步版本，并将其扩展到多领导者设置，我们组成的工人组，每个人都由自己的本地领导者（组中最佳表现者）表示，并使用纠正措施更新每个工作人员方向由两个有吸引力的力量组成：一个到当地，一个到全球领导者（所有工人中最好的表演者）。多引导设置与当前的硬件架构良好对齐，其中形成组的本地工人位于单个计算节点内，不同的组对应于不同的节点。对于培训卷积神经网络，我们经验证明了我们的方法对最先进的基线比较。

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.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.

Hybrid unmanned aerial vehicles (UAVs) integrate the efficient forward flight of fixed-wing and vertical takeoff and landing (VTOL) capabilities of multicopter UAVs. This paper presents the modeling, control and simulation of a new type of hybrid micro-small UAVs, coined as lifting-wing quadcopters. The airframe orientation of the lifting wing needs to tilt a specific angle often within $ 45$ degrees, neither nearly $ 90$ nor approximately $ 0$ degrees. Compared with some convertiplane and tail-sitter UAVs, the lifting-wing quadcopter has a highly reliable structure, robust wind resistance, low cruise speed and reliable transition flight, making it potential to work fully-autonomous outdoor or some confined airspace indoor. In the modeling part, forces and moments generated by both lifting wing and rotors are considered. Based on the established model, a unified controller for the full flight phase is designed. The controller has the capability of uniformly treating the hovering and forward flight, and enables a continuous transition between two modes, depending on the velocity command. What is more, by taking rotor thrust and aerodynamic force under consideration simultaneously, a control allocation based on optimization is utilized to realize cooperative control for energy saving. Finally, comprehensive Hardware-In-the-Loop (HIL) simulations are performed to verify the advantages of the designed aircraft and the proposed controller.

Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN) based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexible in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness. The code and datasets of this article are available at the following address: https://github.com/gaopiaoliang/Evidential.

In contrast to the control-theoretic methods, the lack of stability guarantee remains a significant problem for model-free reinforcement learning (RL) methods. Jointly learning a policy and a Lyapunov function has recently become a promising approach to ensuring the whole system with a stability guarantee. However, the classical Lyapunov constraints researchers introduced cannot stabilize the system during the sampling-based optimization. Therefore, we propose the Adaptive Stability Certification (ASC), making the system reach sampling-based stability. Because the ASC condition can search for the optimal policy heuristically, we design the Adaptive Lyapunov-based Actor-Critic (ALAC) algorithm based on the ASC condition. Meanwhile, our algorithm avoids the optimization problem that a variety of constraints are coupled into the objective in current approaches. When evaluated on ten robotic tasks, our method achieves lower accumulated cost and fewer stability constraint violations than previous studies.