生物医学实体的因果关系提取是生物医学文本挖掘中最复杂的任务之一，涉及两种信息：实体关系和实体功能。一种可行的方法是将关系提取和功能检测作为两个独立的子任务。但是，这种单独的学习方法忽略了它们之间的内在相关性，并导致性能不令人满意。在本文中，我们提出了一个联合学习模型，该模型结合了实体关系提取和实体功能检测以利用其共同点并捕获其相互关系，以提高生物医学因果关系提取的性能。同时，在模型训练阶段，损失函数中的不同功能类型分配了不同的权重。具体而言，负功能实例的惩罚系数增加以有效提高功能检测的精度。 Biocreative-V轨道4语料库的实验结果表明，我们的联合学习模型在BEL语句提取中的表现优于单独的模型，在第2阶段和第1阶段评估中的测试集中，F1得分分别达到58.4％和37.3％。这表明，与其他系统相比，我们的联合学习系统达到了第2阶段的最新性能。

本文为可以提取车辆间交互的自治车辆提供特定于自主车辆的驾驶员风险识别框架。在驾驶员认知方式下对城市驾驶场景进行了这种提取，以提高风险场景的识别准确性。首先，将群集分析应用于驱动程序的操作数据，以学习不同驱动程序风险场景的主观评估，并为每个场景生成相应的风险标签。其次，采用图形表示模型（GRM）统一和构建动态车辆，车间交互和静态交通标记的实际驾驶场景中的特征。驾驶员特定的风险标签提供了实践，以捕获不同司机的风险评估标准。此外，图形模型表示驾驶场景的多个功能。因此，所提出的框架可以了解不同驱动程序的驾驶场景的风险评估模式，并建立特定于驱动程序的风险标识符。最后，通过使用由多个驱动程序收集的现实世界城市驾驶数据集进行的实验评估所提出的框架的性能。结果表明，建议的框架可以准确地识别实际驾驶环境中的风险及其水平。

近年来，道路安全引起了智能运输系统领域的研究人员和从业者的重大关注。作为最常见的道路用户群体之一，行人由于其不可预测的行为和运动而导致令人震惊，因为车辆行人互动的微妙误解可以很容易地导致风险的情况或碰撞。现有方法使用预定义的基于碰撞的模型或人类标签方法来估计行人的风险。这些方法通常受到他们的概括能力差，缺乏对自我车辆和行人之间的相互作用的限制。这项工作通过提出行人风险级预测系统来解决所列问题。该系统由三个模块组成。首先，收集车辆角度的行人数据。由于数据包含关于自我车辆和行人的运动的信息，因此可以简化以交互感知方式预测时空特征的预测。使用长短短期存储器模型，行人轨迹预测模块预测后续五个框架中的时空特征。随着预测的轨迹遵循某些交互和风险模式，采用混合聚类和分类方法来探讨时空特征中的风险模式，并使用学习模式训练风险等级分类器。在预测行人的时空特征并识别相应的风险水平时，确定自我车辆和行人之间的风险模式。实验结果验证了PRLP系统的能力，以预测行人的风险程度，从而支持智能车辆的碰撞风险评估，并为车辆和行人提供安全警告。

在许多应用程序问题（例如计算机视觉和自然语言处理）中，转移学习已成为一种强大的技术。但是，该技术在应用于遗传数据分析中被忽略了。在本文中，我们将转移学习技术与基于神经网络的方法（预期神经网络）相结合。通过转移学习，我们没有从头开始学习过程，而是从解决不同任务时学习的一项任务开始。我们利用先前的学习，并避免从头开始，以通过在不同但相关的任务中获得的信息来提高模型性能。为了演示性能，我们运行两个真实的数据集。通过使用转移学习算法，与预期神经网络相比，预期神经网络的性能得到了改善，而无需使用转移学习技术。

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.

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.

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.

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.