This technical report briefly describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard. SuperGLUE is more challenging than the widely used general language understanding evaluation (GLUE) benchmark, containing eight difficult language understanding tasks, including question answering, natural language inference, word sense disambiguation, coreference resolution, and reasoning. [Method] Instead of arbitrarily increasing the size of a pretrained language model (PLM), our aim is to 1) fully extract knowledge from the input pretraining data given a certain parameter budget, e.g., 6B, and 2) effectively transfer this knowledge to downstream tasks. To achieve goal 1), we propose self-evolution learning for PLMs to wisely predict the informative tokens that should be masked, and supervise the masked language modeling (MLM) process with rectified smooth labels. For goal 2), we leverage the prompt transfer technique to improve the low-resource tasks by transferring the knowledge from the foundation model and related downstream tasks to the target task. [Results] According to our submission record (Oct. 2022), with our optimized pretraining and fine-tuning strategies, our 6B Vega method achieved new state-of-the-art performance on 4/8 tasks, sitting atop the SuperGLUE leaderboard on Oct. 8, 2022, with an average score of 91.3.

机器的图像编码（ICM）旨在压缩图像进行AI任务分析，而不是满足人类的看法。学习一种既是一般（用于AI任务）的特征，也是紧凑的（用于压缩）的功能，这对于其成功而言至关重要。在本文中，我们试图通过学习通用功能，同时考虑压缩来开发ICM框架。我们将诸如无所不能功能和相应框架的功能命名为Omni-ICM。考虑到自我监督学习（SSL）提高了特征的概括，我们将其与压缩任务集成到OMNI-ICM框架中，以学习无所不能的功能。但是，在SSL中协调语义建模并在压缩中删除冗余是不平凡的，因此我们通过合作实例区分和熵最小化以自适应掉落的信息来设计新颖的信息过滤（如果）模块，以较弱相关的信息执行AI任务（例如，某些纹理冗余）。与以前的特定解决方案不同，Omni-ICM可以直接基于学习的无能功能的AI任务分析，而无需联合培训或额外的转换。尽管简单而直观，但Omni-ICM在多个基本愿景任务上大大优于现有的传统和基于学习的编解码器。

潜在空间基于能量的模型（EBM），也称为基于能量的先验，引起了对生成建模的日益兴趣。由于其在潜在空间的配方和强大的建模能力方面的灵活性所推动，最近构建的作品已经进行了有趣的尝试，目的是针对文本建模的解释性。但是，潜在空间EBM还继承了数据空间中EBM的一些缺陷。实践中退化的MCMC抽样质量会导致培训中的发电质量和不稳定差，尤其是在具有复杂潜在结构的数据上。受到最近的努力的启发，该努力利用扩散恢复的可能性学习是解决抽样问题的一种方法，我们在变异学习框架中引入了扩散模型和潜在空间EBM之间的新型共生，这是潜在扩散能量基于能量的模型。我们与信息瓶颈共同开发基于几何聚类的正则化，以进一步提高学到的潜在空间的质量。对几个具有挑战性的任务进行的实验证明了我们模型在可解释的文本建模上的优越性能而不是强大的同行。

人类除了符号语言之外的图形草图与图形草图沟通。虽然最近对紧急沟通的研究主要关注象征语言，但它们的设置忽略了人类交流中存在的图形草图;他们不考虑演进过程，符号标志系统在iconicity和象征性之间的权衡中出现。在这项工作中，我们采取了第一步，通过两个播放视觉通信游戏的神经代理商来模拟和模拟这种演变过程;发件人通过在画布上素描来与接收器通信。我们设计了一种新颖的加强学习方法，使得代理商共同发展，以成功的沟通和抽象图形惯例。要检查出现的惯例，我们仔细定义了三个关键属性 - iconicity，象征性和语义 - 以及设计评估方法。我们在不同控制下的实验结果与人类图形公约的研究观察一致。注意，我们发现进化的草图可以在适当的环境压力下保持语义的连续体。更有趣的是，共同进化的代理可以基于他们熟悉引用的常规和标志性的通信。我们希望本研究能够铺设与草图的未开发方式进行研究的道路。

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.