Deep learning-based physical-layer secret key generation (PKG) has been used to overcome the imperfect uplink/downlink channel reciprocity in frequency division duplexing (FDD) orthogonal frequency division multiplexing (OFDM) systems. However, existing efforts have focused on key generation for users in a specific environment where the training samples and test samples obey the same distribution, which is unrealistic for real world applications. This paper formulates the PKG problem in multiple environments as a learning-based problem by learning the knowledge such as data and models from known environments to generate keys quickly and efficiently in multiple new environments. Specifically, we propose deep transfer learning (DTL) and meta-learning-based channel feature mapping algorithms for key generation. The two algorithms use different training methods to pre-train the model in the known environments, and then quickly adapt and deploy the model to new environments. Simulation results show that compared with the methods without adaptation, the DTL and meta-learning algorithms both can improve the performance of generated keys. In addition, the complexity analysis shows that the meta-learning algorithm can achieve better performance than the DTL algorithm with less time, lower CPU and GPU resources.

我们调查了无线网络中多个联合学习（FL）服务的数据质量感知动态客户选择问题，每个客户都有动态数据集，用于同时培训多个FL服务，每种FL服务都必须为客户付费。限制货币预算。在训练回合中，这个问题被正式化为不合作的马尔可夫游戏。提出了一种基于多代理的混合增强算法，以优化共同的客户选择和付款操作，同时避免采取行动冲突。仿真结果表明，我们提出的算法可以显着改善训练性能。

This paper describes the PASH participation in TREC 2021 Deep Learning Track. In the recall stage, we adopt a scheme combining sparse and dense retrieval method. In the multi-stage ranking phase, point-wise and pair-wise ranking strategies are used one after another based on model continual pre-trained on general knowledge and document-level data. Compared to TREC 2020 Deep Learning Track, we have additionally introduced the generative model T5 to further enhance the performance.

抗癌药物的发现是偶然的，我们试图介绍开放的分子图学习基准，称为Cantidrug4cancer，这是一个具有挑战性且逼真的基准数据集，可促进可扩展，健壮和可重复的图形机器学习用于抗癌药物发现的机器学习研究。候选物4CANCER数据集涵盖了多个最多的癌症靶标，涵盖了54869个与癌症相关的药物分子，其范围从临床前，临床和FDA批准的范围内。除了构建数据集外，我们还使用描述符和表达性图神经网络进行了有效的药物靶点相互作用（DTI）预测基准的基准实验。实验结果表明，候选物4Cancer在实际应用中对学习分子图和目标提出了重大挑战，这表明将来有机会开发用于治疗癌症的候选药物的研究。

机器学习在虚拟筛选中显示出巨大的潜力，用于药物发现。目前正在加速基于对接的虚拟筛选的努力不考虑使用其他先前开发的目标的现有数据。为了利用其他目标的知识并利用现有数据，在这项工作中，我们将多任务学习应用于基于对接的虚拟筛选问题。通过两个大型对接数据集，广泛实验结果表明，多任务学习可以实现对接分数预测的更好性能。通过在多个目标上学习知识，由多任务学习训练的模型显示了适应新目标的更好能力。额外的实证研究表明，药物发现中的其他问题，例如实验药物 - 目标亲和预测，也可能受益于多任务学习。我们的结果表明，多任务学习是基于对接的虚拟筛选和加速药物发现过程的有前途的机器学习方法。

无人驾驶航空公司（I-U-U-U-U-U-U-U-U-UV）的互联网承诺通过无人机之间的有效合作，快速，强大，经济高效地完成传感和传输任务。为实现有前途的好处，应解决至关重要的I-UAV网络问题。本文认为，I-UAV网络可以分为三类，服务质量（QoS）驱动网络，体验质量（QoE）驱动的网络，以及情况感知网络。每类网络都会带来了对我国无人机任务的安全有效地实现的严重影响的新兴挑战。本文精心详细分析了这些挑战，并阐述了相应的智能方法来解决I-UAV网络问题。此外，考虑到通过与高海拔平台（HAPS）合作扩展I-UAV网络可扩展性的升高效果，本文概述了集成的HAP和I-UAV网络，并提出了相应的网络挑战和智能方法。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.