The Multi-Objective Shortest Path Problem, typically posed on a graph, determines a set of paths from a start vertex to a destination vertex while optimizing multiple objectives. In general, there does not exist a single solution path that can simultaneously optimize all the objectives and the problem thus seeks to find a set of so-called Pareto-optimal solutions. To address this problem, several Multi-Objective A* (MOA*) algorithms were recently developed to quickly compute solutions with quality guarantees. However, these MOA* algorithms often suffer from high memory usage, especially when the branching factor (i.e., the number of neighbors of any vertex) of the graph is large. This work thus aims at reducing the high memory consumption of MOA* with little increase in the runtime. In this paper, we first extend the notion of "partial expansion" (PE) from single-objective to multi-objective and then fuse this new PE technique with EMOA*, a recent runtime efficient MOA* algorithm. Furthermore, the resulting algorithm PE-EMOA* can balance between runtime and memory efficiency by tuning a user-defined hyper-parameter.

对具有代理商初始位置未知的有限3D环境的多代理探索是一个具有挑战性的问题。它需要快速探索环境，并坚定合并代理商构建的子图。我们认为现有方法是侵略性或保守的：在检测到重叠时，积极的策略合并了两种由不同代理构建的子图，这可能导致由于对重叠的错误阳性检测而导致不正确的合并，因此是如此。不健全。保守策略指导一个代理人在合并之前重新审视另一个代理商的过量验证历史轨迹，这可以降低由于对同一空间的反复探索而引起的勘探效率。为了巧妙地平衡子图合并和勘探效率的鲁棒性，我们为基于激光雷达的多代理探索开发了一种新方法，该方法可以指导一个代理商以\ emph {自适应}方式重复另一个代理商的轨迹子图合并过程的指标。此外，我们的方法通过计划合并子图的代理人共同计划，以进一步提高勘探效率，以\ emph {Cooperative}方式将最近的单格分层勘探策略扩展到多个代理。我们的实验表明，我们的方法平均比基线高出50 \％，同时稳固地合并子映射。

在不同情况下，机器人有可能执行搜索各种应用程序。我们的工作是由人道主义助理和灾难救济（HADR）激发的，在存在冲突的标准，目标和信息的情况下，找到生命的迹象通常至关重要。我们认为，厄运搜索可以提供一个框架来利用可用信息，并为HADR等应用程序探索新信息，尤其是在时间本质上。千古搜索算法规划轨迹，使得在一个地区所花费的时间与该地区的信息量成正比，并且能够自然平衡剥削（近视搜索高信息搜索区域）和探索（访问搜索空间的所有位置以获取新的信息）。现有的Ergodic搜索算法以及其他基于信息的方法通常仅使用单个信息图考虑搜索。但是，在许多情况下，使用多个编码不同类型相关信息的多个信息图很常见。当前的厄运搜索方法没有同时的能力，也不具有平衡信息优先级的方法。这使我们提出了一个多目标的千古搜索（MOES）问题，旨在找到所谓的帕累托最佳解决方案，目的是为人类的决策者提供各种解决方案，这些解决方案在相互矛盾的标准之间进行贸易。为了有效地解决MOE，我们开发了一个称为顺序局部Ergodic搜索（SLE）的框架，该框架将MOES问题转换为“重量空间覆盖率”问题。它利用了厄隆搜索方法的最新进展以及局部优化的想法，以有效地近似帕累托最佳前沿。我们的数值结果表明，SLE的运行速度明显快于基线方法。

增量图诸如D * Lite重用之前的算法，并且可能部分搜索，以加快后续路径规划任务。在本文中，我们有兴趣开发增量图搜索算法，以便寻找问题，同时优化旅行风险，到达时间等的多个目标。这是具有挑战性的，因为在多目标设置中，“帕累托 - 最优” “解决方案可以对图表的大小呈指数级增长。本文提出了一种新的多目标增量搜索算法，称为基于多目标路径的D * Lite（MOPBD *），它利用基于路径的扩展策略来修剪主导的解决方案。此外，我们介绍了MOPBD *的两个变体，以进一步提高搜索效率，并近似帕累托最优的前沿。我们在数值上评估了MOPBD *及其在各种地图中的变体的性能，其中包括两个和三个目标。结果表明，我们的方法比从头开始搜索的方法更有效，并且比多目标路径规划的现有增量方法快速升至幅度速度快。

传统的多代理路径规划者通常在优化单个物镜的同时计算路径的集合，例如路径长度。然而，许多应用可能需要多个目标，例如在规划期间同时优化的燃料消耗和完井时间，并且这些标准可能无法容易地进行比较，有时彼此竞争。天真地应用现有的多目标搜索算法，例如多目标A *（MoA *），以多代理路径查找可能被证明是效率低，作为可能的解决方案的空间的大小，即帕累托最优集合，可以用代理的数量（搜索空间的维度）指数增长。本文介绍了一种名为基于多目标冲突的搜索（Mo-CBS）的方法，该方法通过利用基于冲突的搜索（CBS），是单目标多代理的公知算法来绕过这种所谓的维度诅咒路径发现，以及多目标优化文献的优势原则。我们还开发了MO-CBS的几种变体，以进一步提高其性能。我们证明了MO-CBS及其变体能够计算整个帕累托最优集合。数值结果表明，Mo-CBS优于MoA *以及妈妈*，最近开发的最先进的多目标多功能策划员。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

This paper presents a practical global optimization algorithm for the K-center clustering problem, which aims to select K samples as the cluster centers to minimize the maximum within-cluster distance. This algorithm is based on a reduced-space branch and bound scheme and guarantees convergence to the global optimum in a finite number of steps by only branching on the regions of centers. To improve efficiency, we have designed a two-stage decomposable lower bound, the solution of which can be derived in a closed form. In addition, we also propose several acceleration techniques to narrow down the region of centers, including bounds tightening, sample reduction, and parallelization. Extensive studies on synthetic and real-world datasets have demonstrated that our algorithm can solve the K-center problems to global optimal within 4 hours for ten million samples in the serial mode and one billion samples in the parallel mode. Moreover, compared with the state-of-the-art heuristic methods, the global optimum obtained by our algorithm can averagely reduce the objective function by 25.8% on all the synthetic and real-world datasets.

Score-based diffusion models have captured widespread attention and funded fast progress of recent vision generative tasks. In this paper, we focus on diffusion model backbone which has been much neglected before. We systematically explore vision Transformers as diffusion learners for various generative tasks. With our improvements the performance of vanilla ViT-based backbone (IU-ViT) is boosted to be on par with traditional U-Net-based methods. We further provide a hypothesis on the implication of disentangling the generative backbone as an encoder-decoder structure and show proof-of-concept experiments verifying the effectiveness of a stronger encoder for generative tasks with ASymmetriC ENcoder Decoder (ASCEND). Our improvements achieve competitive results on CIFAR-10, CelebA, LSUN, CUB Bird and large-resolution text-to-image tasks. To the best of our knowledge, we are the first to successfully train a single diffusion model on text-to-image task beyond 64x64 resolution. We hope this will motivate people to rethink the modeling choices and the training pipelines for diffusion-based generative models.

Deep learning-based methods have achieved significant performance for image defogging. However, existing methods are mainly developed for land scenes and perform poorly when dealing with overwater foggy images, since overwater scenes typically contain large expanses of sky and water. In this work, we propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes. To promote the recovery of the objects on water in the image, two loss functions are exploited for the network where a prior map is designed to invert the dark channel and the min-max normalization is used to suppress the sky and emphasize objects. However, due to the unpaired training set, the network may learn an under-constrained domain mapping from foggy to fog-free image, leading to artifacts and loss of details. Thus, we propose an intuitive Upscaling Inception Module (UIM) and a Long-range Residual Coarse-to-fine framework (LRC) to mitigate this issue. Extensive experiments on qualitative and quantitative comparisons demonstrate that the proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches.

Conversational recommender systems (CRSs) often utilize external knowledge graphs (KGs) to introduce rich semantic information and recommend relevant items through natural language dialogues. However, original KGs employed in existing CRSs are often incomplete and sparse, which limits the reasoning capability in recommendation. Moreover, only few of existing studies exploit the dialogue context to dynamically refine knowledge from KGs for better recommendation. To address the above issues, we propose the Variational Reasoning over Incomplete KGs Conversational Recommender (VRICR). Our key idea is to incorporate the large dialogue corpus naturally accompanied with CRSs to enhance the incomplete KGs; and perform dynamic knowledge reasoning conditioned on the dialogue context. Specifically, we denote the dialogue-specific subgraphs of KGs as latent variables with categorical priors for adaptive knowledge graphs refactor. We propose a variational Bayesian method to approximate posterior distributions over dialogue-specific subgraphs, which not only leverages the dialogue corpus for restructuring missing entity relations but also dynamically selects knowledge based on the dialogue context. Finally, we infuse the dialogue-specific subgraphs to decode the recommendation and responses. We conduct experiments on two benchmark CRSs datasets. Experimental results confirm the effectiveness of our proposed method.