Nowadays, Multi-purpose Messaging Mobile App (MMMA) has become increasingly prevalent. MMMAs attract fraudsters and some cybercriminals provide support for frauds via black market accounts (BMAs). Compared to fraudsters, BMAs are not directly involved in frauds and are more difficult to detect. This paper illustrates our BMA detection system SGRL (Self-supervised Graph Representation Learning) used in WeChat, a representative MMMA with over a billion users. We tailor Graph Neural Network and Graph Self-supervised Learning in SGRL for BMA detection. The workflow of SGRL contains a pretraining phase that utilizes structural information, node attribute information and available human knowledge, and a lightweight detection phase. In offline experiments, SGRL outperforms state-of-the-art methods by 16.06%-58.17% on offline evaluation measures. We deploy SGRL in the online environment to detect BMAs on the billion-scale WeChat graph, and it exceeds the alternative by 7.27% on the online evaluation measure. In conclusion, SGRL can alleviate label reliance, generalize well to unseen data, and effectively detect BMAs in WeChat.

In robust Markov decision processes (MDPs), the uncertainty in the transition kernel is addressed by finding a policy that optimizes the worst-case performance over an uncertainty set of MDPs. While much of the literature has focused on discounted MDPs, robust average-reward MDPs remain largely unexplored. In this paper, we focus on robust average-reward MDPs, where the goal is to find a policy that optimizes the worst-case average reward over an uncertainty set. We first take an approach that approximates average-reward MDPs using discounted MDPs. We prove that the robust discounted value function converges to the robust average-reward as the discount factor $\gamma$ goes to $1$, and moreover, when $\gamma$ is large, any optimal policy of the robust discounted MDP is also an optimal policy of the robust average-reward. We further design a robust dynamic programming approach, and theoretically characterize its convergence to the optimum. Then, we investigate robust average-reward MDPs directly without using discounted MDPs as an intermediate step. We derive the robust Bellman equation for robust average-reward MDPs, prove that the optimal policy can be derived from its solution, and further design a robust relative value iteration algorithm that provably finds its solution, or equivalently, the optimal robust policy.

Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data. However, its reliance on data augmentation and its quadratic computational complexity might lead to inconsistency and inefficiency problems. To mitigate these limitations, in this paper, we introduce a simple yet effective contrastive model named Localized Graph Contrastive Learning (Local-GCL in short). Local-GCL consists of two key designs: 1) We fabricate the positive examples for each node directly using its first-order neighbors, which frees our method from the reliance on carefully-designed graph augmentations; 2) To improve the efficiency of contrastive learning on graphs, we devise a kernelized contrastive loss, which could be approximately computed in linear time and space complexity with respect to the graph size. We provide theoretical analysis to justify the effectiveness and rationality of the proposed methods. Experiments on various datasets with different scales and properties demonstrate that in spite of its simplicity, Local-GCL achieves quite competitive performance in self-supervised node representation learning tasks on graphs with various scales and properties.

电动汽车（EV）在自动启动的按需（AMOD）系统中起关键作用，但是它们的独特充电模式增加了AMOD系统中的模型不确定性（例如，状态过渡概率）。由于通常存在训练和测试（真）环境之间的不匹配，因此将模型不确定性纳入系统设计至关重要。但是，在现有文献重新平衡的EV AMOD系统中，尚未明确考虑模型不确定性，并且仍然是一项紧急和挑战的任务。在这项工作中，我们为EV重新平衡和充电问题设计了一个强大而有限的多机构增强学习（MARL）框架。然后，我们提出了一种强大且受限的MARL算法（Rocoma），该算法训练了强大的EV重新平衡政策，以平衡供需比率和整个城市的充电利用率在国家过渡不确定性下。实验表明，Rocoma可以学习有效且强大的重新平衡政策。当存在模型不确定性时，它的表现优于非稳定MAL方法。它使系统公平性增加了19.6％，并使重新平衡成本降低了75.8％。

受限的强化学习是最大程度地提高预期奖励受到公用事业/成本的限制。但是，由于建模错误，对抗性攻击，非平稳性，训练环境可能与测试环境不一样，导致严重的性能降级和更重要的违反约束。我们提出了一个在模型不确定性下的强大约束强化学习框架，其中MDP不是固定的，而是在某些不确定性集中，目的是确保在不确定性集中满足所有MDP的限制，并最大程度地满足对公用事业/成本的限制不确定性集中最差的奖励性能。我们设计了一种强大的原始双重方法，并在理论上进一步发展了其收敛性，复杂性和可行性的保证。然后，我们研究了$ \ delta $ - 污染不确定性集的具体示例，设计一种在线且无模型的算法，并理论上表征了其样本复杂性。

具有线性函数近似的贪婪GQ，最初在\ cite {maei2010toward}中提出，是一种基于价值的基础外算法，用于增强增强学习中的最佳控制，并且具有非线性的两个时间尺度结构，具有非convex目标函数。本文开发其有限的时间误差范围。我们表明，贪婪的GQ算法在I.I.D. \ serat和$ \ Mathcal {O}下（{\ log t}（{\ log t}）下，贪婪的算法的收敛如$ \ Mathcal {O}（{1}/{{1}/{\ sqrt {t}}）$ /{\ sqrt {t}}）$在马尔可夫设置下。我们进一步设计了使用嵌套环方法的香草贪婪-GQ算法的变体，并证明其样品复杂性为$ \ Mathcal {o}（{\ log（1/\ epsilon）\ Epsilon^epsilon^{ - 2}}}}}} ）$，与香草贪婪的GQ之一相匹配。我们的有限时间误差界限与用于一般平滑非凸优化问题的随机梯度下降算法之一匹配。我们的有限样本分析提供了理论指南，以选择在实践中选择更快的融合的步骤尺寸，并建议在收敛速度和获得的政策质量之间进行权衡。本文我们的技术提供了一种通用方法，用于对非凸的两个基于时值的强化学习算法进行有限样本分析。

在医学图像中，准确，自动和完整的肺气道中的肺气道在分析胸部CT体积（例如肺癌检测，慢性阻塞性肺疾病（COPD）和支气管镜辅助手术导航）中起着重要作用。但是，由于气道的复杂树状结构，此任务仍然是挑战。在这份技术报告中，我们使用两阶段的完全卷积网络（FCN）自动从多站点进行胸腔CT扫描中的肺气道。具体而言，我们首先采用带有U形网络架构的3D FCN以粗分辨率分割肺气道，以加速医学图像分析管道。然后，另一个3D FCN进行了训练，可以以精细的分辨率分段肺气道。在2022 MICCAI多站点多域气道树建模（ATM）挑战中，对报告的方法进行了300例公共培训集和50个案例的独立私人验证集评估。最终的骰子相似性系数（DSC）为0.914 $ \ pm $ 0.040，假负错误（FNE）为0.079 $ \ pm $ 0.042，误差（FPE）为0.090 $ \ pm $ \ pm $ 0.066独立私人验证集。

可提供许多开源和商业恶意软件探测器。然而，这些工具的功效受到新的对抗性攻击的威胁，由此恶意软件试图使用例如机器学习技术来逃避检测。在这项工作中，我们设计了依赖于特征空间和问题空间操纵的对抗逃避攻击。它使用可扩展性导向特征选择来最大限度地通过识别影响检测的最关键的特征来最大限度地逃避。然后，我们将此攻击用作评估若干最先进的恶意软件探测器的基准。我们发现（i）最先进的恶意软件探测器容易受到简单的逃避策略，并且可以使用现成的技术轻松欺骗; （ii）特征空间操纵和问题空间混淆可以组合起来，以便在不需要对探测器的白色盒子理解的情况下实现逃避; （iii）我们可以使用解释性方法（例如，Shap）来指导特征操纵并解释攻击如何跨多个检测器传输。我们的调查结果阐明了当前恶意软件探测器的弱点，以及如何改善它们。

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.

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.