Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data to identify an optimal feature space that enhances the performances of a downstream ML model. Existing studies either combines preprocessing, feature selection, and generation skills to empirically transform data, or automate feature transformation by machine intelligence, such as reinforcement learning. However, existing studies suffer from: 1) high-dimensional non-discriminative feature space; 2) inability to represent complex situational states; 3) inefficiency in integrating local and global feature information. To fill the research gap, we formulate the feature transformation task as an iterative, nested process of feature generation and selection, where feature generation is to generate and add new features based on original features, and feature selection is to remove redundant features to control the size of feature space. Finally, we present extensive experiments and case studies to illustrate 24.7\% improvements in F1 scores compared with SOTAs and robustness in high-dimensional data.

Urban traffic speed prediction aims to estimate the future traffic speed for improving the urban transportation services. Enormous efforts have been made on exploiting spatial correlations and temporal dependencies of traffic speed evolving patterns by leveraging explicit spatial relations (geographical proximity) through pre-defined geographical structures ({\it e.g.}, region grids or road networks). While achieving promising results, current traffic speed prediction methods still suffer from ignoring implicit spatial correlations (interactions), which cannot be captured by grid/graph convolutions. To tackle the challenge, we propose a generic model for enabling the current traffic speed prediction methods to preserve implicit spatial correlations. Specifically, we first develop a Dual-Transformer architecture, including a Spatial Transformer and a Temporal Transformer. The Spatial Transformer automatically learns the implicit spatial correlations across the road segments beyond the boundary of geographical structures, while the Temporal Transformer aims to capture the dynamic changing patterns of the implicit spatial correlations. Then, to further integrate both explicit and implicit spatial correlations, we propose a distillation-style learning framework, in which the existing traffic speed prediction methods are considered as the teacher model, and the proposed Dual-Transformer architectures are considered as the student model. The extensive experiments over three real-world datasets indicate significant improvements of our proposed framework over the existing methods.

Recently, many deep learning based beamformers have been proposed for multi-channel speech separation. Nevertheless, most of them rely on extra cues known in advance, such as speaker feature, face image or directional information. In this paper, we propose an end-to-end beamforming network for direction guided speech separation given merely the mixture signal, namely MIMO-DBnet. Specifically, we design a multi-channel input and multiple outputs architecture to predict the direction-of-arrival based embeddings and beamforming weights for each source. The precisely estimated directional embedding provides quite effective spatial discrimination guidance for the neural beamformer to offset the effect of phase wrapping, thus allowing more accurate reconstruction of two sources' speech signals. Experiments show that our proposed MIMO-DBnet not only achieves a comprehensive decent improvement compared to baseline systems, but also maintain the performance on high frequency bands when phase wrapping occurs.

Graph generative models have broad applications in biology, chemistry and social science. However, modelling and understanding the generative process of graphs is challenging due to the discrete and high-dimensional nature of graphs, as well as permutation invariance to node orderings in underlying graph distributions. Current leading autoregressive models fail to capture the permutation invariance nature of graphs for the reliance on generation ordering and have high time complexity. Here, we propose a continuous-time generative diffusion process for permutation invariant graph generation to mitigate these issues. Specifically, we first construct a forward diffusion process defined by a stochastic differential equation (SDE), which smoothly converts graphs within the complex distribution to random graphs that follow a known edge probability. Solving the corresponding reverse-time SDE, graphs can be generated from newly sampled random graphs. To facilitate the reverse-time SDE, we newly design a position-enhanced graph score network, capturing the evolving structure and position information from perturbed graphs for permutation equivariant score estimation. Under the evaluation of comprehensive metrics, our proposed generative diffusion process achieves competitive performance in graph distribution learning. Experimental results also show that GraphGDP can generate high-quality graphs in only 24 function evaluations, much faster than previous autoregressive models.

The essential task of urban planning is to generate the optimal land-use configuration of a target area. However, traditional urban planning is time-consuming and labor-intensive. Deep generative learning gives us hope that we can automate this planning process and come up with the ideal urban plans. While remarkable achievements have been obtained, they have exhibited limitations in lacking awareness of: 1) the hierarchical dependencies between functional zones and spatial grids; 2) the peer dependencies among functional zones; and 3) human regulations to ensure the usability of generated configurations. To address these limitations, we develop a novel human-instructed deep hierarchical generative model. We rethink the urban planning generative task from a unique functionality perspective, where we summarize planning requirements into different functionality projections for better urban plan generation. To this end, we develop a three-stage generation process from a target area to zones to grids. The first stage is to label the grids of a target area with latent functionalities to discover functional zones. The second stage is to perceive the planning requirements to form urban functionality projections. We propose a novel module: functionalizer to project the embedding of human instructions and geospatial contexts to the zone-level plan to obtain such projections. Each projection includes the information of land-use portfolios and the structural dependencies across spatial grids in terms of a specific urban function. The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations. Finally, we present extensive experiments to demonstrate the effectiveness of our framework.

资金机构在很大程度上依赖于领域专家与研究建议之间的主题匹配来分配提案审查员。随着建议越来越跨学科，概述提案的跨学科性质是一项挑战，此后，找到具有适当专业知识的专家审阅者。解决这一挑战的重要步骤是准确对建议的跨学科标签进行分类。现有的方法论和申请相关文献，例如文本分类和提案分类，不足以共同解决跨学科建议数据引入的三个关键独特问题：1）提案的纪律标签的层次结构，谷物，例如，从信息科学到AI，再到AI的基础。 2）在提案中起着不同作用的各种主要文本部分的异质语义； 3）提案的数量在非学科和跨学科研究之间存在不平衡。我们可以同时解决该提案的跨学科性质时的三个问题吗？为了回答这个问题，我们提出了一个层次混音多标签分类框架，我们称之为H-Mixup。 H-Mixup利用基于变压器的语义信息提取器和基于GCN的跨学科知识提取器来解决第一期和第二个问题。 H-Mixup开发了Wold级混音，Word级cutmix，歧管混音和文档级混音的融合训练方法，以解决第三期。

传统的城市规划要求城市专家在许多建筑限制下花费大量时间和精力制定最佳的城市计划。深层生成学习的非凡富有想象力为翻新城市规划提供了希望。尽管已经检查了自动化的城市规划师，但由于以下情况，它们受到限制：1）忽略人类在城市规划中的要求； 2）省略城市规划中的空间层次结构，以及3）缺乏许多城市计划数据样本。为了克服这些局限性，我们提出了一个新颖的，深厚的人类建筑的城市规划师。在初步工作中，我们将其提出为编码器范式。编码器是学习周围环境，人类指示和土地使用配置的信息分布。解码器是重建土地使用配置和相关的城市功能区域。重建过程将捕获功能区和空间网格之间的空间层次结构。同时，我们引入了一种变异的高斯机制来减轻数据稀疏问题。即使早期的工作导致了良好的结果，但生成的性能仍然不稳定，因为捕获空间层次结构的方式可能会导致不清楚的优化方向。在此期刊版本中，我们提出了一个基于生成的对抗网络（GAN）的层叠的深层生成框架，以解决此问题，灵感来自城市专家的工作流程。特别是，第一个gan的目的是根据人类指示和周围环境的信息来建立城市功能区域。第二个GAN将基于已构造的功能区域产生土地使用构型。此外，我们为增强数据样本提供了调节增强模块。最后，我们进行了广泛的实验以验证工作的功效。

The peer merit review of research proposals has been the major mechanism for deciding grant awards. However, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge to assign interdisciplinary proposals to appropriate reviewers, so proposals are fairly evaluated. One of the critical steps in reviewer assignment is to generate accurate interdisciplinary topic labels for proposal-reviewer matching. Existing systems mainly collect topic labels manually generated by principal investigators. However, such human-reported labels can be non-accurate, incomplete, labor intensive, and time costly. What role can AI play in developing a fair and precise proposal reviewer assignment system? In this study, we collaborate with the National Science Foundation of China to address the task of automated interdisciplinary topic path detection. For this purpose, we develop a deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN). Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals. We then design an interdisciplinary graph and leverage GNNs for learning representations of each discipline in order to extract interdisciplinary knowledge. After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal. We conduct extensive experiments and expert evaluations on three real-world datasets to demonstrate the effectiveness of our proposed model.

功能转换旨在通过数学转换现有功能来提取良好的表示（功能）空间。应对维度的诅咒，增强模型概括，克服数据稀疏性并扩大经典模型的可用性至关重要。当前的研究重点是基于领域的知识特征工程或学习潜在表示；然而，这些方法并非完全自动化，不能产生可追溯和最佳的表示空间。在重建机器学习任务的功能空间时，可以同时解决这些限制吗？在这项扩展研究中，我们提出了一个用于特征转化的自优化框架。为了取得更好的性能，我们通过（1）获得高级状态表示来改善初步工作，以使加强代理能够更好地理解当前功能集； （2）解决Q值高估的Q值高估，以学习无偏见和有效的政策。最后，为了使实验比初步工作更具说服力，我们结论是通过五个数据集添加异常检测任务，评估各种状态表示方法，并比较不同的培训策略。广泛的实验和案例研究表明，我们的工作更有效和更高。

最近基于神经网络的到达方向（DOA）估计算法在未知数的声源场景上表现良好。这些算法通常是通过将多通道音频输入映射到单个输出（即所有来源的总空间伪谱（SP））来实现的，称为MISO。但是，这种误语算法在很大程度上取决于经验阈值设置和声音源之间的角度大于固定角度的角度假设。为了解决这些局限性，我们提出了一种新型的多通道输入和多个输出的DOA网络，称为MIMO-DOANET。与一般的误觉算法不同，Mimo-Doanet借助于信息的空间协方差矩阵预测了每个声源的SPS编码。通过这样做，检测声源数量的阈值任务成为检测每个输出中是否存在声音源的更容易的任务，并且在推理阶段，声源之间的严重交互消失。实验结果表明，与3，4个来源场景中的莫斯科基线相比，MIMO-DOANET的相对增长18.6％和绝对13.3％，相对34.4％和绝对20.2％的F1得分提高。结果还证明了Mimo-Doanet减轻了阈值设置问题，并有效地解决了角度假设问题。