Partial MaxSAT (PMS) and Weighted PMS (WPMS) are two practical generalizations of the MaxSAT problem. In this paper, we propose a local search algorithm for these problems, called BandHS, which applies two multi-armed bandits to guide the search directions when escaping local optima. One bandit is combined with all the soft clauses to help the algorithm select to satisfy appropriate soft clauses, and the other bandit with all the literals in hard clauses to help the algorithm select appropriate literals to satisfy the hard clauses. These two bandits can improve the algorithm's search ability in both feasible and infeasible solution spaces. We further propose an initialization method for (W)PMS that prioritizes both unit and binary clauses when producing the initial solutions. Extensive experiments demonstrate the excellent performance and generalization capability of our proposed methods, that greatly boost the state-of-the-art local search algorithm, SATLike3.0, and the state-of-the-art SAT-based incomplete solver, NuWLS-c.

旅行推销员问题（TSP）是许多实用变体的经典NP-HARD组合优化问题。 Lin-Kernighan-Helsgaun（LKH）算法是TSP的最先进的本地搜索算法之一，LKH-3是LKH的强大扩展，可以解决许多TSP变体。 LKH和LKH-3都将一个候选人与每个城市相关联，以提高算法效率，并具有两种不同的方法，称为$ \ alpha $ - 计算和Popmusic，以决定候选人集。在这项工作中，我们首先提出了一种可变策略加强LKH（VSR-LKH）算法，该算法将三种强化学习方法（Q-Learning，SARSA和Monte Carlo）与LKH算法结合在一起，以解决TSP。我们进一步提出了一种称为VSR-LKH-3的新算法，该算法将可变策略强化学习方法与LKH-3结合在一起，用于典型的TSP变体，包括带有时间窗口（TSPTW）和彩色TSP（CTSP）的TSP。所提出的算法取代了LKH和LKH-3中的不灵活的遍历操作，并让算法学会通过增强学习在每个搜索步骤中做出选择。 LKH和LKH-3都具有$ \ alpha $量或Popmusic方法，我们的方法都可以显着改善。具体而言，对236个公共和广泛使用的TSP基准的经验结果具有多达85,900个城市，证明了VSR-LKH的出色表现，扩展的VSR-LKH-3也显着超过了TSPTW和TSPTW和TSPTW和TSPTW的最新启发式方法CTSP。

我们解决了部分MaxSat（PMS）和加权PMS（WPM），这是MaxSat问题的两个实际概括，并为这些问题（称为BandMaxSat）提出了一种局部搜索算法，该算法应用了多臂Bantit模型来指导搜索方向。我们方法中的匪徒与输入（w）pms实例中的所有软子句相关联。每个手臂对应于软子句。 Bandit模型可以通过选择要在当前步骤中满足的软子句，即选择要拉的臂来帮助BandmaxSat选择一个良好的方向以逃脱本地Optima。我们进一步提出了一种初始化方法（w）PMS，在生产初始解决方案时优先考虑单元和二进制条款。广泛的实验表明，BandMaxSat显着优于最先进的（W）PMS本地搜索算法SATLIKE3.0。具体而言，BandMaxSat获得更好结果的实例数量大约是Satlike3.0获得的两倍。此外，我们将bandmaxsat与完整的求解器tt-open-wbo-inc相结合。最终的求解器bandmaxsat-c还胜过一些最好的最新完整（W）PMS求解器，包括satlike-c，loandra和tt-open-wbo-inc。

部分MaxSAT（PMS）和加权部分MaxSAT（WPMS）都是MaxSAT典型组合问题的实用概括。在这项工作中，我们提出了一种有效的远视概率采样的基于本地搜索算法，称为FPS，用于解决这两个问题，表示为（W）PMS。 FPS算法替换了每个迭代步骤翻转单个变量的机制，该步骤广泛用于拟议的远视本地搜索策略，并提供更高质量的本地最佳解决方案。远视策略采用概率采样技术，允许该算法广泛有效地寻找。以这种方式，FPS可以提供​​更多更好的搜索方向并提高性能而不降低效率。关于最近四年的MaxSAT评估的不完整轨迹的所有基准的广泛实验表明，我们的方法显着优于Satlike3.0，最先进的本地搜索算法，用于解决PMS和WPMS问题。我们进一步与Satlike-C的扩展求解器进行比较，这是最近MaxSAT评估中不完全轨道的四个（PMS和WPMS类别相关的三类类别中的三个类别的冠军（MSE2021 ）。我们用拟议的远视采样本地搜索方法替换Satlike-C中的本地搜索组件，并且所产生的求解器FPS-C也优于Satlike-C来解决PMS和WPMS问题。

我们提出了一种称为钢筋混合遗传算法（RHGA）的新型方法，用于解决着名的NP-Hard Travel推销员问题（TSP）。具体地，我们将加强学习技术与众所周知的边缘组装交叉遗传算法（EAX-GA）和Lin-Kernighan-Helsgaun（LKH）本地搜索启发式组合。借助拟议的混合机制，EAX-GA的遗传演进和LKH的本地搜索可以促进彼此的性能。基于Q学习的加强学习技术进一步促进了混合遗传算法。在138众名知名度和广泛使用的TSP基准测试中的实验结果与1,000至85,900的城市数量呈现出rhGA的优异性能，显着优于EAX-GA和LKH。

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.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.