Recent graph-based models for joint multiple intent detection and slot filling have obtained promising results through modeling the guidance from the prediction of intents to the decoding of slot filling. However, existing methods (1) only model the \textit{unidirectional guidance} from intent to slot; (2) adopt \textit{homogeneous graphs} to model the interactions between the slot semantics nodes and intent label nodes, which limit the performance. In this paper, we propose a novel model termed Co-guiding Net, which implements a two-stage framework achieving the \textit{mutual guidances} between the two tasks. In the first stage, the initial estimated labels of both tasks are produced, and then they are leveraged in the second stage to model the mutual guidances. Specifically, we propose two \textit{heterogeneous graph attention networks} working on the proposed two \textit{heterogeneous semantics-label graphs}, which effectively represent the relations among the semantics nodes and label nodes. Experiment results show that our model outperforms existing models by a large margin, obtaining a relative improvement of 19.3\% over the previous best model on MixATIS dataset in overall accuracy.

Recent joint multiple intent detection and slot filling models employ label embeddings to achieve the semantics-label interactions. However, they treat all labels and label embeddings as uncorrelated individuals, ignoring the dependencies among them. Besides, they conduct the decoding for the two tasks independently, without leveraging the correlations between them. Therefore, in this paper, we first construct a Heterogeneous Label Graph (HLG) containing two kinds of topologies: (1) statistical dependencies based on labels' co-occurrence patterns and hierarchies in slot labels; (2) rich relations among the label nodes. Then we propose a novel model termed ReLa-Net. It can capture beneficial correlations among the labels from HLG. The label correlations are leveraged to enhance semantic-label interactions. Moreover, we also propose the label-aware inter-dependent decoding mechanism to further exploit the label correlations for decoding. Experiment results show that our ReLa-Net significantly outperforms previous models. Remarkably, ReLa-Net surpasses the previous best model by over 20\% in terms of overall accuracy on MixATIS dataset.

基于注意力的模型已在许多领域（例如计算机视觉和自然语言处理）广泛使用。但是，尚未深入探索时间序列分类（TSC）中的相关应用，导致大量TSC算法仍然遭受注意机制的一般问题，例如二次复杂性。在本文中，我们通过提出灵活的多头线性注意力（FMLA）来促进注意机制的效率和性能，从而通过与可变形的卷积块和在线知识蒸馏来提高局部意识。更重要的是，我们提出了一种简单但有效的遮罩机制，有助于减少时间序列中的噪声影响，并通过按比例掩盖每个给定序列的某些位置来减少所提出的FMLA的冗余。为了稳定这种机制，将样品通过随机掩模层几次转发，并将其输出聚合以使用常规掩码层教相同的模型。我们在85 UCR2018数据集上进行了广泛的实验，以将我们的算法与11个知名算法进行比较，结果表明，我们的算法在TOP-1准确性方面具有可比性的性能。我们还将模型与三个基于变压器的模型相对于每秒的浮点操作和参数数量进行了比较，并发现我们的算法在较低的复杂性方面可显着提高效率。

符号知识图（kgs）是通过昂贵的人众包或特定于域特异性的复杂信息提取管道来构建的。诸如BERT之类的新兴大型语言模型（LMS）已显示出隐式编码的大量知识，可以使用正确设计的提示来查询。但是，与明确的公斤相比，黑盒LMS中的知识通常很难访问或编辑，并且缺乏解释性。在这项工作中，我们旨在从LMS收获符号KG，这是一个由神经LMS的灵活性和可扩展性增强的自动kg构造的新框架。与通常依赖大型人类注释的数据或现有大量KG的先前作品相比，我们的方法仅需要对关系的最小定义作为输入，因此适合于以前无法提取有关丰富新关系的知识。该方法会自动生成多样化的提示，并在给定的LM内执行有效的知识搜索，以进行一致和广泛的输出。与以前的方法相比，使用我们的方法收获的知识要准确得多，如自动和人类评估所示。结果，我们源于多元化的LMS，一个新的KG家族（例如Bertnet和Robertanet），其中包含一套更丰富的常识关系，包括复杂的关系（例如，A对B的能力，但不擅长B”）人类注销的kg（例如概念网）。此外，由此产生的kg也是解释各自的源LMS的工具，从而导致对不同LMS不同知识能力的新见解。

在许多重要领域（例如计算机视觉，自然语言处理和推荐系统）中，使用深度神经网络（DNNS）的深度学习取得了很多成功。 DNN缺乏凸度已被视为许多优化方法的主要缺点，例如随机梯度下降，大大降低了神经网络应用的基因化。我们意识到，凸度在神经网络中是有意义的，并提出了指数多层神经网络（EMLP），这是一类参数凸神经网络（PCNN），在某些情况下可以在某些条件下进行有关神经网络的参数的凸面。实现。此外，我们提出了两层EGCN的凸度度量，并在凸度度变化时测试准确性。对于晚期实验，我们使用相同的体系结构来制作指数图卷积网络（EGCN），并在图形分类数据集上进行实验，其中我们的模型EGCN的性能优于图形卷积网络（GCN）和图形注意力网络（GAT GAT网络（GAT）（GAT） ）。

在方面情绪分类（ASC）中，最先进的模型编码语法图形或关系图以捕获本地语法信息或全局关系信息。尽管语法和关系图的优点，但它们具有忽略的缺点，这限制了图形建模过程中的表示功率。为了解决他们的局限性，我们设计了一种新的本地 - 全局交互图，它通过互动边缘缝合两个图来结合它们的优势。为了模拟本地全局交互图形，我们提出了一个新的神经网络被称为Dignet，其核心模块是执行两个进程的堆叠本地 - 全局交互（LGI）层：图中媒体消息传递和跨图形消息传递。通过这种方式，可以在理解方面的情绪方面整体和解局部句法和全局关系信息。具体而言，我们设计了具有不同种类的交互边缘和LGI层的三种变体的局部全局交互图的两种变体。我们对几个公共基准数据集进行实验，结果表明，在LAP14，Res14和Res15数据集的宏F1方面，我们以前的3 \％，2.32 \％和6.33 \％以3 \％，2.32 \％和6.33 \％。拟议的本地 - 全球互动图和赤霞珠的效力与优越性。

本文提出了一种有效的联邦蒸馏学习系统（EFDLS），用于多任务时间序列分类（TSC）。 EFDL由中央服务器和多个移动用户组成，不同的用户可能运行不同的TSC任务。 EFDLS有两种新型组件，即基于特征的学生 - 教师（FBST）框架和基于距离的权重匹配（DBWM）方案。在每个用户中，FBST框架通过知识蒸馏将教师隐藏层的知识转移到学生的隐藏层，与具有相同网络结构的教师和学生。对于每个连接的用户，其学生模型的隐藏层的权重定期上传到EFDLS服务器。 DBWM方案部署在服务器上，具有最小的方距离，用于测量两个给定模型的权重之间的相似性。该方案为每个连接的用户找到合作伙伴，使得用户及其伴侣的权重是上载的所有权重中最接近的权重。服务器交换并将其伙伴的权重发送给这两个用户，然后将所接收的权重加载到其教师隐藏的层。实验结果表明，所提出的EFDLS在一组选择的UCR2018数据集上实现了卓越的性能，这是一个精度的精度。

Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation. Specifically, we construct a forward graph diffusion process on both graph structures and inherent features through stochastic differential equations (SDE) and derive discrete graph structures as the condition for reverse generative processes. We present a specialized hybrid graph noise prediction model that extracts the global context and the local node-edge dependency from intermediate graph states. We further utilize ordinary differential equation (ODE) solvers for efficient graph sampling, based on the semi-linear structure of the probability flow ODE. Experiments on diverse datasets validate the effectiveness of our framework. Particularly, the proposed method still generates high-quality molecular graphs in a limited number of steps.