Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc. This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting. We introduce research works with comparisons and summaries and provide systematic resources to help beginners. We also discuss the potential reasons for emerging such reasoning abilities and highlight future research directions.

Steiner树问题（STP）在图中旨在在连接给定的顶点集的图表中找到一个最小权重的树。它是一种经典的NP - 硬组合优化问题，具有许多现实世界应用（例如，VLSI芯片设计，运输网络规划和无线传感器网络）。为STP开发了许多精确和近似算法，但它们分别遭受高计算复杂性和弱案例解决方案保证。还开发了启发式算法。但是，它们中的每一个都需要应用域知识来设计，并且仅适用于特定方案。最近报道的观察结果，同一NP-COLLECLIAL问题的情况可能保持相同或相似的组合结构，但主要在其数据中不同，我们调查将机器学习技术应用于STP的可行性和益处。为此，我们基于新型图形神经网络和深增强学习设计了一种新型模型瓦坎。 Vulcan的核心是一种新颖的紧凑型图形嵌入，将高瞻度图形结构数据（即路径改变信息）转换为低维矢量表示。鉴于STP实例，Vulcan使用此嵌入来对其路径相关的信息进行编码，并基于双层Q网络（DDQN）将编码的图形发送到深度加强学习组件，以找到解决方案。除了STP之外，Vulcan还可以通过将解决方案（例如，SAT，MVC和X3C）来减少到STP来找到解决方案。我们使用现实世界和合成数据集进行广泛的实验，展示了vulcan的原型，并展示了它的功效和效率。

Though convolutional neural networks (CNNs) have demonstrated remarkable ability in learning discriminative features, they often generalize poorly to unseen domains. Domain generalization aims to address this problem by learning from a set of source domains a model that is generalizable to any unseen domain. In this paper, a novel approach is proposed based on probabilistically mixing instancelevel feature statistics of training samples across source domains. Our method, termed MixStyle, is motivated by the observation that visual domain is closely related to image style (e.g., photo vs. sketch images). Such style information is captured by the bottom layers of a CNN where our proposed style-mixing takes place. Mixing styles of training instances results in novel domains being synthesized implicitly, which increase the domain diversity of the source domains, and hence the generalizability of the trained model. MixStyle fits into mini-batch training perfectly and is extremely easy to implement. The effectiveness of MixStyle is demonstrated on a wide range of tasks including category classification, instance retrieval and reinforcement learning.

对分布（OOD）数据的概括是人类自然的能力，但对于机器而言挑战。这是因为大多数学习算法强烈依赖于i.i.d.〜对源/目标数据的假设，这在域转移导致的实践中通常会违反。域的概括（DG）旨在通过仅使用源数据进行模型学习来实现OOD的概括。在过去的十年中，DG的研究取得了长足的进步，导致了广泛的方法论，例如，基于域的一致性，元学习，数据增强或合奏学习的方法，仅举几例；还在各个应用领域进行了研究，包括计算机视觉，语音识别，自然语言处理，医学成像和强化学习。在本文中，首次提供了DG中的全面文献综述，以总结过去十年来的发展。具体而言，我们首先通过正式定义DG并将其与其他相关领域（如域适应和转移学习）联系起来来涵盖背景。然后，我们对现有方法和理论进行了彻底的审查。最后，我们通过有关未来研究方向的见解和讨论来总结这项调查。

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.

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.

With the development of technology and sharing economy, Airbnb as a famous short-term rental platform, has become the first choice for many young people to select. The issue of Airbnb's pricing has always been a problem worth studying. While the previous studies achieve promising results, there are exists deficiencies to solve. Such as, (1) the feature attributes of rental are not rich enough; (2) the research on rental text information is not deep enough; (3) there are few studies on predicting the rental price combined with the point of interest(POI) around the house. To address the above challenges, we proposes a multi-source information embedding(MSIE) model to predict the rental price of Airbnb. Specifically, we first selects the statistical feature to embed the original rental data. Secondly, we generates the word feature vector and emotional score combination of three different text information to form the text feature embedding. Thirdly, we uses the points of interest(POI) around the rental house information generates a variety of spatial network graphs, and learns the embedding of the network to obtain the spatial feature embedding. Finally, this paper combines the three modules into multi source rental representations, and uses the constructed fully connected neural network to predict the price. The analysis of the experimental results shows the effectiveness of our proposed model.

In this paper, we investigate the possibility of the backward-differential-flow-like algorithm which starts from the minimum of convexification version of the polynomial. We apply the heat evolution convexification approach through Gaussian filtering, which is actually an accumulation version of Steklov's regularization. We generalize the fingerprint theory which was proposed in the theory of computer vision by A.L. Yuille and T. Poggio in 1980s, in particular their fingerprint trajectory equation, to characterize the evolution of minimizers across the scale. On the other hand, we propose the "seesaw" polynomials $p(x|s)$ and we find a seesaw differential equation $\frac{\partial p(x|s)}{\,ds}=-\frac{1}{p''(x)}$ to characterize the evolution of global minimizer $x^*(s)$ of $p(x|s)$ while varying $s$. Essentially, both the fingerprints $\mathcal{FP}_2$ and $\mathcal{FP}_3$ of $p(x)$, consisting of the zeros of $\frac{\partial^2 p(x,t)}{\partial x^2}$ and $\frac{\partial^3 p(x,t)}{\partial x^3}$, respectively, are independent of seesaw coefficient $s$, upon which we define the Confinement Zone and Escape Zone. Meanwhile, varying $s$ will monotonically condition the location of global minimizer of $p(x|s)$, and all these location form the Attainable Zone. Based on these concepts, we prove that the global minimizer $x^*$ of $p(x)$ can be inversely evolved from the global minimizer of its convexification polynomial $p(x,t_0)$ if and only if $x^*$ is included in the Escape Zone. In particular, we give detailed analysis for quartic and six degree polynomials.

Inferring missing links or detecting spurious ones based on observed graphs, known as link prediction, is a long-standing challenge in graph data analysis. With the recent advances in deep learning, graph neural networks have been used for link prediction and have achieved state-of-the-art performance. Nevertheless, existing methods developed for this purpose are typically discriminative, computing features of local subgraphs around two neighboring nodes and predicting potential links between them from the perspective of subgraph classification. In this formalism, the selection of enclosing subgraphs and heuristic structural features for subgraph classification significantly affects the performance of the methods. To overcome this limitation, this paper proposes a novel and radically different link prediction algorithm based on the network reconstruction theory, called GraphLP. Instead of sampling positive and negative links and heuristically computing the features of their enclosing subgraphs, GraphLP utilizes the feature learning ability of deep-learning models to automatically extract the structural patterns of graphs for link prediction under the assumption that real-world graphs are not locally isolated. Moreover, GraphLP explores high-order connectivity patterns to utilize the hierarchical organizational structures of graphs for link prediction. Our experimental results on all common benchmark datasets from different applications demonstrate that the proposed method consistently outperforms other state-of-the-art methods. Unlike the discriminative neural network models used for link prediction, GraphLP is generative, which provides a new paradigm for neural-network-based link prediction.