The typical way for relation extraction is fine-tuning large pre-trained language models on task-specific datasets, then selecting the label with the highest probability of the output distribution as the final prediction. However, the usage of the Top-k prediction set for a given sample is commonly overlooked. In this paper, we first reveal that the Top-k prediction set of a given sample contains useful information for predicting the correct label. To effectively utilizes the Top-k prediction set, we propose Label Graph Network with Top-k Prediction Set, termed as KLG. Specifically, for a given sample, we build a label graph to review candidate labels in the Top-k prediction set and learn the connections between them. We also design a dynamic $k$-selection mechanism to learn more powerful and discriminative relation representation. Our experiments show that KLG achieves the best performances on three relation extraction datasets. Moreover, we observe that KLG is more effective in dealing with long-tailed classes.

Sequence generation demonstrates promising performance in recent information extraction efforts, by incorporating large-scale pre-trained Seq2Seq models. This paper investigates the merits of employing sequence generation in relation extraction, finding that with relation names or synonyms as generation targets, their textual semantics and the correlation (in terms of word sequence pattern) among them affect model performance. We then propose Relation Extraction with Label Augmentation (RELA), a Seq2Seq model with automatic label augmentation for RE. By saying label augmentation, we mean prod semantically synonyms for each relation name as the generation target. Besides, we present an in-depth analysis of the Seq2Seq model's behavior when dealing with RE. Experimental results show that RELA achieves competitive results compared with previous methods on four RE datasets.

为了开发有效和高效的脑电器界面（BCI）系统，非常需要精确地解码脑电图（EEG）测量的大脑活动。传统作品在不考虑电极之间的拓扑关系的情况下分类EEG信号。然而，神经科学研究越来越强调了脑动力学的网络模式。因此，电极的欧几里德结构可能无法充分反映信号之间的相互作用。为了填补差距，提出了一种基于图形卷积神经网络（GCNS）的新型深度学习框架，以增强在不同类型的电动机图像（MI）任务期间的原始EEG信号的解码性能，同时与电极的功能拓扑关系协作。基于绝对Pearson的总体信号矩阵，建立了EEG电极的图拉普拉斯。由图形卷积层构建的GCNS-NET学会了广义特征。遵循的汇集层减少了维度，并且完全连接的软墨幅层衍射最终预测。已介绍的方法已被证明可以为个性化和群体的预测汇聚。与现有研究相比，它分别在受试者和组级别实现了最高平均准确度，93.056％和88.57％（物理仪数据集），96.24％和80.89％（高伽玛数据集），这表明个人适应性和鲁棒性变化性。此外，在交叉验证的重复实验中，性能稳定地再现。为了得出结论，基于功能拓扑关系的GCNS-Net滤波器EEG信号，该关系管理用于解码脑电机图像的相关特征。

识别准确性和响应时间既批判性均在建筑实际脑电图（EEG）的脑电电脑界面（BCI）领先期。然而，最近的方法在分类准确度或响应时间内损害。本文提出了一种新颖的深度学习方法，旨在基于头皮EEG的显着准确和敏感的电动机图像（MI）识别。双向长期内存（BILSTM），带有注意机制管理，从原始EEG信号中导出相关特征。连接的图形卷积神经网络（GCN）通过与来自整体数据的拓扑结构协作来促进解码性能。 0.4-第二检测框架显着基于个体和群体培训的有效和有效的预测，分别具有98.81％和94.64％的准确性，这取得了卓越的所有最先进的研究。引入的深度特征挖掘方法可以精确地识别来自原始EEG信号的人类运动意图，该信号铺设了将基于EEG的MI识别转换为实用BCI系统。

变异量子算法（VQA）在NISQ时代表现出巨大的潜力。在VQA的工作流程中，Ansatz的参数迭代更新以近似所需的量子状态。我们已经看到了各种努力，以较少的大门起草更好的安萨兹。在量子计算机中，栅极Ansatz最终将转换为控制信号，例如TransMons上的微波脉冲。并且对照脉冲需要精心校准，以最大程度地减少误差（例如过度旋转和旋转）。在VQA的情况下，此过程将引入冗余，但是VQAS的变异性能自然可以通过更新幅度和频率参数来处理过度旋转和重组的问题。因此，我们提出了PAN，这是一种用于VQA的天然脉冲ANSATZ GENTARATOR框架。我们生成具有可训练参数用于振幅和频率的天然脉冲ansatz。在我们提出的锅中，我们正在调整参数脉冲，这些脉冲在NISQ计算机上得到了内在支持。考虑到本机 - 脉冲ANSATZ不符合参数迁移规则，我们需要部署非级别优化器。为了限制发送到优化器的参数数量，我们采用了一种生成本机 - 脉冲ANSATZ的渐进式方式。实验是在模拟器和量子设备上进行的，以验证我们的方法。当在NISQ机器上采用时，PAN获得的延迟平均提高了86％。 PAN在H2和HEH+上的VQE任务分别能够达到99.336％和96.482％的精度，即使NISQ机器中有很大的噪声。

在本文中，我们提出了一种基于量化的蒸馏式低级神经辐射场（QDLR-NERF）表示的新型光场压缩方法。当现有的压缩方法编码光场子孔径图像集时，我们提出的方法以神经辐射场（NERF）的形式学习了隐式场景表示，这也可以使视图合成。为了降低其大小，该模型首先是在低级（LR）约束下使用张量列（TT）分解以交替的乘数（ADMM）优化框架进行的。为了进一步降低模型尺寸，需要量化张量列车分解的组件。但是，通过同时考虑低等级约束并考虑到速率受限的权重量化来实现NERF模型的优化是具有挑战性的。为了解决这个困难，我们引入了一个网络蒸馏操作，该操作将低级近似值和网络训练中的权重量化分开。根据LR-NERF的TT分解，将初始LR约束NERF（LR-NERF）的信息提炼为较小尺寸（DLR-NERF）的模型。然后，学会了优化的全局代码簿来量化所有TT组件，从而产生最终的QDLRNERF。实验结果表明，与最先进的方法相比，我们所提出的方法具有更好的压缩效率，并且还具有允许允许具有高质量的任何光场视图的合成。

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.

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.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.