We propose in this work the gradient-enhanced deep neural networks (DNNs) approach for function approximations and uncertainty quantification. More precisely, the proposed approach adopts both the function evaluations and the associated gradient information to yield enhanced approximation accuracy. In particular, the gradient information is included as a regularization term in the gradient-enhanced DNNs approach, for which we present similar posterior estimates (by the two-layer neural networks) as those in the path-norm regularized DNNs approximations. We also discuss the application of this approach to gradient-enhanced uncertainty quantification, and present several numerical experiments to show that the proposed approach can outperform the traditional DNNs approach in many cases of interests.

由直觉的激励，即在相应的3D点云中定位2D图像的关键步骤正在建立它们之间的2d-3d对应关系，我们提出了第一个基于特征的密度通信框架，以解决图像到点云注册问题，称为Corri2p，由三个模块组成，即特征嵌入，对称重叠区域检测和通过已建立的对应关系构成估计。具体而言，给定一对2D图像和3D点云，我们首先将它们转换为高维特征空间，并将结果特征馈入对称重叠区域检测器，以确定图像和点云相互重叠的区域。然后，我们使用重叠区域的功能在RANSAC内运行EPNP之前以估算相机的姿势，以建立2D-3D对应关系。 Kitti和Nuscenes数据集的实验结果表明，我们的Corri2p优于最先进的图像到点云注册方法。我们将公开提供代码。

许多支付平台持有大规模的营销活动，为鼓励用户通过他们的申请进行奖励。为了最大限度地提高投资回报，在两阶段程序中通常会解决激励拨款。在训练响应估计模型以估计用户的移动支付概率（MPP）之后，应用线性编程过程来获得最佳激励分配。然而，由先前偏置分配策略生成的训练集中的大量偏置数据导致偏置估计。此偏差劣化响应模型的性能并误导线性编程过程，显着降低了所产生的分配策略的性能。为了克服这种障碍，我们提出了偏置校正对抗性网络。我们的方法利用了在全随机分配策略下获得的一小集非偏见数据来培训一个无偏的模型，然后使用它来减少对抗性学习的偏差。离线和在线实验结果表明，我们的方法优于最先进的方法，并显着提高了现实世界营销活动中所产生的分配政策的绩效。

在这项工作中，我们提出了一种基于时间归一化流的自适应学习方法，用于解决时间依赖于依赖的Fokker-Planck（TFP）方程。众所周知，这种等式的解决方案是概率密度函数，因此我们的方法依赖于使用时间标准化流程建模目标解决方案。然后基于TFP损耗函数训练时间归一化流量，而不需要任何标记的数据。作为一种机器学习方案，所提出的方法是无网线的，并且可以很容易地应用于高维度问题。我们提出了各种测试问题以表明学习方法的有效性。

实现通用语言情报是自然语言处理的长期目标，标准评估基准发挥基本和指导作用。我们认为，对于通用语言智能评估，基准本身需要全面和系统。为此，我们提出了Cuge，一种中文语言理解和生成评估基准，具有以下特征：（1）分层基准框架，其中数据集主要选择和组织语言能力 - 任务数据集层次结构。 （2）多级评分策略，其中基于分层框架提供了不同级别的模型性能。为了促进CUGE，我们提供了一个公共排行榜，可以自定义，以支持灵活的模型判断标准。代表性预先训练的语言模型的评估结果表明了对通用语言智能的完善的充足空间。 Cuge在Cuge.baai.ac.cn上公开提供。

今天的大部分AI系统都专注于使用自我关注机制和变压器架构在大量多样化的数据中实现令人印象深刻的性能收益。在本文中，我们建议使用外部注意机制增强变压器架构，以带来外部知识和背景。通过将外部信息集成到预测过程中，我们希望减少对更大的模型的需求，并增加AI系统的民主化。我们发现所提出的外部注意机制可以显着提高现有AI系统的性能，使从业者可以轻松地将基础AI模型自定义到许多不同的下游应用程序。特别是，我们专注于勤杂朗语推理的任务，展示所提出的外部注意机制可以增加现有的变压器模型，并显着提高模型的推理能力。拟议的系统，知识外部关注推理（Kear），达到了开放的铜商QA研究基准的人类奇偶校验，其准确性为89.4 \％，与人类准确性为88.9 \％。

尽管在一般强化学习（RL）中建立了良好的建立，但很少在受约束的RL（CRL）中探索基于价值的方法，因为它们无法找到可以在多个动作中随机进行随机的策略的能力。为了将基于价值的方法应用于CRL，最新的游戏理论方法采用了混合策略，该策略将一组精心生成的策略之间随机进行随机，以收敛到所需的约束可满足的策略。但是，这些方法需要存储大量的政策，这不是政策效率的，并且可能会在约束深度RL中产生过高的记忆成本。为了解决这个问题，我们提出了一种替代方法。我们的方法首先将CRL重新制定为等效距离优化问题。使用专门设计的线性优化Oracle，我们得出了一个元叠层，该元值使用任何现成的RL算法和任何条件梯度（CG）型算法作为子例程来求解它。然后，我们提出了CG型算法的新变体，该变体概括了最小范数（MNP）方法。所提出的方法与现有游戏理论方法的收敛速率相匹配，并实现了最差的最佳政策效率。导航任务上的实验表明，我们的方法将记忆成本降低了一个数量级，同时达到了更好的性能，并证明了其有效性和效率。

Increasing research interests focus on sequential recommender systems, aiming to model dynamic sequence representation precisely. However, the most commonly used loss function in state-of-the-art sequential recommendation models has essential limitations. To name a few, Bayesian Personalized Ranking (BPR) loss suffers the vanishing gradient problem from numerous negative sampling and predictionbiases; Binary Cross-Entropy (BCE) loss subjects to negative sampling numbers, thereby it is likely to ignore valuable negative examples and reduce the training efficiency; Cross-Entropy (CE) loss only focuses on the last timestamp of the training sequence, which causes low utilization of sequence information and results in inferior user sequence representation. To avoid these limitations, in this paper, we propose to calculate Cumulative Cross-Entropy (CCE) loss over the sequence. CCE is simple and direct, which enjoys the virtues of painless deployment, no negative sampling, and effective and efficient training. We conduct extensive experiments on five benchmark datasets to demonstrate the effectiveness and efficiency of CCE. The results show that employing CCE loss on three state-of-the-art models GRU4Rec, SASRec, and S3-Rec can reach 125.63%, 69.90%, and 33.24% average improvement of full ranking NDCG@5, respectively. Using CCE, the performance curve of the models on the test data increases rapidly with the wall clock time, and is superior to that of other loss functions in almost the whole process of model training.

The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this paper tackles this practical yet challenging issue by proposing a federated multi-agent deep reinforcement learning (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy-sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data is not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge national laboratory distributed energy control communication lab microgrid (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL.

This paper presents a safety-critical locomotion control framework for quadrupedal robots. Our goal is to enable quadrupedal robots to safely navigate in cluttered environments. To tackle this, we introduce exponential Discrete Control Barrier Functions (exponential DCBFs) with duality-based obstacle avoidance constraints into a Nonlinear Model Predictive Control (NMPC) with Whole-Body Control (WBC) framework for quadrupedal locomotion control. This enables us to use polytopes to describe the shapes of the robot and obstacles for collision avoidance while doing locomotion control of quadrupedal robots. Compared to most prior work, especially using CBFs, that utilize spherical and conservative approximation for obstacle avoidance, this work demonstrates a quadrupedal robot autonomously and safely navigating through very tight spaces in the real world. (Our open-source code is available at github.com/HybridRobotics/quadruped_nmpc_dcbf_duality, and the video is available at youtu.be/p1gSQjwXm1Q.)