可再生能源资源（RERS）已越来越纳入现代电力系统，尤其是在大规模分配网络（DNS）中。在本文中，我们提出了一种深度加强学习（DRL）基础的方法来动态搜索最佳操作点，即最佳功率流（OPF），在具有高摄取RER的DNS中。考虑到由RERS引起的不确定性和电压波动问题，我们将OPF分为多目标优化（MOO）问题。为了解决MOO问题，我们开发了一种利用分发网络图形信息的新型DRL算法。具体而言，我们采用最先进的DRL算法，即深度确定性政策梯度（DDPG），以学习OPF的最佳策略。由于DN中的电力流重新分配是连续的过程，其中节点是在时间和空间视图中自相关和相互关联的，以充分利用DNS的图形信息，我们开发了一种基于多粒的关注的空间 - 时间图卷积用于空间颞曲线图信息提取的网络（MG-ASTGCN），为其顺序DDPG准备。我们在修改IEEE 33,69和118总线径向分布系统（RDS）中验证了基于DRL的基于DRL的方法，并显示了基于DRL的方法优于其他基准算法。我们的实验结果还揭示了MG-ASTGCN可以显着加速DDPG训练过程，并提高DDPG在重新分配OPF电流中的能力。所提出的基于DRL的方法还促进了节点故障存在下的DNS的稳定性，特别是对于大型DNS。

磁共振成像（MRI）是重要的医学成像模型，而需要长时间的采集时间。为了减少采集​​时间，已经提出了各种方法。然而，这些方法未能以明确的结构重建图像，以两种主要原因。首先，在MR图像中广泛存在的类似补丁，而最先前的基于深度学习的方法忽略此属性，并且仅采用CNN学习本地信息。其次，现有方法仅使用清晰的图像来限制解决方案空间的上限，而下限不会受约束，从而无法获得网络的更好参数。为了解决这些问题，我们向本地和全球学习MRI重建网络（CLGNET）提出了对比的学习。具体地，根据傅立叶理论，傅里叶域中的每个值由空间域中的所有值计算。因此，我们提出了一种空间和傅里叶层（SFL），以同时学习空间和傅立叶域中的本地和全局信息。此外，与自我关注和变压器相比，SFL具有更强的学习能力，可以在更短的时间内实现更好的性能。基于SFL，我们设计了一个空间和傅里叶的剩余块作为模型的主要组成部分。同时，要限制解决方案空间的下限和上限，我们引入了对比度学习，这可以将结果拉到清晰图像上，并将结果推到远离下采样的图像。不同数据集和加速率的广泛实验结果表明，所提出的CLGNET实现了新的最先进的结果。

Global power systems are increasingly reliant on wind energy as a mitigation strategy for climate change. However, the variability of wind energy causes system reliability to erode, resulting in the wind being curtailed and, ultimately, leading to substantial economic losses for wind farm owners. Wind curtailment can be reduced using battery energy storage systems (BESS) that serve as onsite backup sources. Yet, this auxiliary role may significantly hamper the BESS's capacity to generate revenues from the electricity market, particularly in conducting energy arbitrage in the Spot market and providing frequency control ancillary services (FCAS) in the FCAS markets. Ideal BESS scheduling should effectively balance the BESS's role in absorbing onsite wind curtailment and trading in the electricity market, but it is difficult in practice because of the underlying coordination complexity and the stochastic nature of energy prices and wind generation. In this study, we investigate the bidding strategy of a wind-battery system co-located and participating simultaneously in both the Spot and Regulation FCAS markets. We propose a deep reinforcement learning (DRL)-based approach that decouples the market participation of the wind-battery system into two related Markov decision processes for each facility, enabling the BESS to absorb onsite wind curtailment while simultaneously bidding in the wholesale Spot and FCAS markets to maximize overall operational revenues. Using realistic wind farm data, we validated the coordinated bidding strategy for the wind-battery system and find that our strategy generates significantly higher revenue and responds better to wind curtailment compared to an optimization-based benchmark. Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to individual market participation.

The rapid adoption of residential solar photovoltaics (PV) has resulted in regular overvoltage events, due to correlated reverse power flows. Currently, PV inverters prevent damage to electronics by curtailing energy production in response to overvoltage. However, this disproportionately affects households at the far end of the feeder, leading to an unfair allocation of the potential value of energy produced. Globally optimizing for fair curtailment requires accurate feeder parameters, which are often unknown. This paper investigates reinforcement learning, which gradually optimizes a fair PV curtailment strategy by interacting with the system. We evaluate six fairness metrics on how well they can be learned compared to an optimal solution oracle. We show that all definitions permit efficient learning, suggesting that reinforcement learning is a promising approach to achieving both safe and fair PV coordination.

Multi-hop Question Answering over Knowledge Graph~(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question on a large-scale Knowledge Graph (KG). To cope with the vast search space, existing work usually adopts a two-stage approach: it firstly retrieves a relatively small subgraph related to the question and then performs the reasoning on the subgraph to accurately find the answer entities. Although these two stages are highly related, previous work employs very different technical solutions for developing the retrieval and reasoning models, neglecting their relatedness in task essence. In this paper, we propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning. For model architecture, UniKGQA consists of a semantic matching module based on a pre-trained language model~(PLM) for question-relation semantic matching, and a matching information propagation module to propagate the matching information along the edges on KGs. For parameter learning, we design a shared pre-training task based on question-relation matching for both retrieval and reasoning models, and then propose retrieval- and reasoning-oriented fine-tuning strategies. Compared with previous studies, our approach is more unified, tightly relating the retrieval and reasoning stages. Extensive experiments on three benchmark datasets have demonstrated the effectiveness of our method on the multi-hop KGQA task. Our codes and data are publicly available at https://github.com/RUCAIBox/UniKGQA.

量化，知识蒸馏和修剪是NLP中神经网络压缩的最流行方法之一。独立地，这些方法降低了模型的大小并可以加速推断，但是尚未严格研究它们的相对益处和组合相互作用。对于这些技术的八个可能子集中的每一个，我们比较了六个BERT体系结构和八个胶水任务的准确性与模型大小的权衡。我们发现量化和蒸馏始终比修剪更大的好处。出乎意料的是，除了将多种方法一起使用多种修剪和量化之外，很少会产生回报的减少。取而代之的是，我们观察到互补和超级义务减少了模型大小。我们的工作定量表明，结合压缩方法可以协同降低模型大小，并且从业者应优先考虑（1）量化，（2）知识蒸馏，（3）修剪以最大程度地提高准确性与模型大小的权衡。

知识基础问题回答（KBQA）旨在通过知识库（KB）回答问题。早期研究主要集中于回答有关KB的简单问题，并取得了巨大的成功。但是，他们在复杂问题上的表现远非令人满意。因此，近年来，研究人员提出了许多新颖的方法，研究了回答复杂问题的挑战。在这项调查中，我们回顾了KBQA的最新进展，重点是解决复杂问题，这些问题通常包含多个主题，表达复合关系或涉及数值操作。详细说明，我们从介绍复杂的KBQA任务和相关背景开始。然后，我们描述用于复杂KBQA任务的基准数据集，并介绍这些数据集的构建过程。接下来，我们提出两个复杂KBQA方法的主流类别，即基于语义解析的方法（基于SP）的方法和基于信息检索的方法（基于IR）。具体而言，我们通过流程设计说明了他们的程序，并讨论了它们的主要差异和相似性。之后，我们总结了这两类方法在回答复杂问题时会遇到的挑战，并解释了现有工作中使用的高级解决方案和技术。最后，我们结论并讨论了与复杂的KBQA有关的几个有希望的方向，以进行未来的研究。

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.

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.

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.