我们建议一个基于深入强化学习的经理工作框架，以解决旅行推销员问题（TSP）的艰难而又非平凡的变体，\ ie〜有时间窗口和拒绝（MTSPTWR）的多车辆TSP（MTSPTWR），在此之前无法服务的客户截止日期将受到拒绝。特别是，在拟议的框架中，经理代理人通过基于图形同构网络（GIN）的策略网络将客户分配给每辆车，从而将MTSPTWR分为子路由任务。工人代理人通过根据每辆车的旅行长度和拒绝率来最大程度地降低成本来解决子路由任务，然后将其最多的最大值送回经理代理以学习更好的任务。实验结果表明，所提出的框架在更高的解决方案质量和较短的计算时间方面优于强基础。更重要的是，训练有素的代理商还取得了竞争性能，以解决看不见的较大实例。

我们提出了一种有效的神经邻域搜索（N2S），以解决取货和交付问题（PDPS）。具体而言，我们设计了强大的综合注意力，可以使香草自我注意力综合有关路线解决方案的各种特征。我们还利用了两个自定义的解码器，它们会自动学习执行拾取节点对的删除和重新插入以应对优先限制。此外，利用多样性增强方案以进一步改善性能。我们的N2是通用的，并且对两个规范PDP变体进行了广泛的实验表明，它可以在现有神经方法之间产生最新的结果。此外，它甚至超过了众所周知的LKH3求解器在更受限的PDP变体上。我们针对N2S的实施可在线获得。

最近，变压器已成为解决车辆路由问题（VRP）的盛行深度建筑。但是，它在学习VRP的学习改进模型方面的有效性较小，因为其位置编码（PE）方法不适合表示VRP解决方案。本文介绍了一种新颖的双重协作变压器（DACT），以分别学习节点和位置特征的嵌入，而不是像现有的那样将它们融合在一起，以避免潜在的噪音和不相容的相关性。此外，位置特征通过新型的循环位置编码（CPE）方法嵌入，以使变压器有效捕获VRP溶液（即环状序列）的圆形性和对称性。我们使用近端政策优化训练DACT，并设计一种课程学习策略，以提高样本效率。我们应用DACT来解决旅行推销员问题（TSP）和电容的车辆路由问题（CVRP）。结果表明，我们的DACT优于现有的基于变压器的改进模型，并且在合成和基准实例上分别在不同问题大小上表现出更好的概括性能。

回溯搜索算法通常用于解决约束满足问题（CSP）。回溯搜索的效率在很大程度上取决于可变排序启发式。目前，最常用的启发式是根据专家知识进行手工制作的。在本文中，我们提出了一种基于深度的加强学习方法，可以自动发现新的变量订购启发式，更好地适用于给定类CSP实例。我们显示，直接优化搜索成本很难用于自动启动，并建议优化在搜索树中到达叶节点的预期成本。为了捕获变量和约束之间的复杂关系，我们设计基于图形神经网络的表示方案，可以处理具有不同大小和约束的CSP实例。随机CSP实例上的实验结果表明，学习的政策在最小化搜索树大小的方面优于古典手工制作的启发式，并且可以有效地推广到比训练中使用的实例。

Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful application of neural machine translation (NMT) models in natural language processing (NLP), researchers have tried to build neural decompilers by borrowing the idea of NMT. They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability. However, state-of-the-art learning-based decompilers do not cope well with compiler-optimized binaries. Since real-world binaries are mostly compiler-optimized, decompilers that do not consider optimized binaries have limited practical significance. In this paper, we propose a novel learning-based approach named NeurDP, that targets compiler-optimized binaries. NeurDP uses a graph neural network (GNN) model to convert LPL to an intermediate representation (IR), which bridges the gap between source code and optimized binary. We also design an Optimized Translation Unit (OTU) to split functions into smaller code fragments for better translation performance. Evaluation results on datasets containing various types of statements show that NeurDP can decompile optimized binaries with 45.21% higher accuracy than state-of-the-art neural decompilation frameworks.

Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.

In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT for image style transfer and an edge loss which can enhance the content details apparently to avoid generating blurred results for excessive rendering on style features. Qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer methods while alleviating the content leak problem.

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.

The surrogate loss of variational autoencoders (VAEs) poses various challenges to their training, inducing the imbalance between task fitting and representation inference. To avert this, the existing strategies for VAEs focus on adjusting the tradeoff by introducing hyperparameters, deriving a tighter bound under some mild assumptions, or decomposing the loss components per certain neural settings. VAEs still suffer from uncertain tradeoff learning.We propose a novel evolutionary variational autoencoder (eVAE) building on the variational information bottleneck (VIB) theory and integrative evolutionary neural learning. eVAE integrates a variational genetic algorithm into VAE with variational evolutionary operators including variational mutation, crossover, and evolution. Its inner-outer-joint training mechanism synergistically and dynamically generates and updates the uncertain tradeoff learning in the evidence lower bound (ELBO) without additional constraints. Apart from learning a lossy compression and representation of data under the VIB assumption, eVAE presents an evolutionary paradigm to tune critical factors of VAEs and deep neural networks and addresses the premature convergence and random search problem by integrating evolutionary optimization into deep learning. Experiments show that eVAE addresses the KL-vanishing problem for text generation with low reconstruction loss, generates all disentangled factors with sharp images, and improves the image generation quality,respectively. eVAE achieves better reconstruction loss, disentanglement, and generation-inference balance than its competitors.

A storyboard is a roadmap for video creation which consists of shot-by-shot images to visualize key plots in a text synopsis. Creating video storyboards however remains challenging which not only requires association between high-level texts and images, but also demands for long-term reasoning to make transitions smooth across shots. In this paper, we propose a new task called Text synopsis to Video Storyboard (TeViS) which aims to retrieve an ordered sequence of images to visualize the text synopsis. We construct a MovieNet-TeViS benchmark based on the public MovieNet dataset. It contains 10K text synopses each paired with keyframes that are manually selected from corresponding movies by considering both relevance and cinematic coherence. We also present an encoder-decoder baseline for the task. The model uses a pretrained vision-and-language model to improve high-level text-image matching. To improve coherence in long-term shots, we further propose to pre-train the decoder on large-scale movie frames without text. Experimental results demonstrate that our proposed model significantly outperforms other models to create text-relevant and coherent storyboards. Nevertheless, there is still a large gap compared to human performance suggesting room for promising future work.