智能杂草系统为了执行植物特定的运营，可以有助于农业和环境的可持续性。尽管近年来对精密杂草管理的自主机器人技术造成巨大进展，但尚未实现在领域的底盖内的工作。这种系统的先决条件是可靠的检测和杂草的分类，以避免错误地喷涂，从而损坏周围的植物。实时多级杂草鉴定使特异性的杂草治疗能够显着降低除草剂的使用量。在这里，我们的第一个贡献是第一个充分的大型现实图像数据集\ texit {aiweeds}（一个图像中的一个/多种杂草），一个约10,000个亚麻的注释图像，以及在田间和花园中最常见的14个杂草从北达科他州，加利福尼亚州和中国中部的20个不同的地方取自20个不同的地方。其次，我们提供了一个完整的管道，从模型培训，最大效率将规则解优化模型部署到单板计算机上。基于\ Texit {Aiweeds}和管道，我们使用五个基准CNN模型提出了一种分类性能的基线。其中，MobileNetv2具有最短的推理时间和最低记忆消耗，是实时应用程序的合格候选者。最后，我们将MobileNetv2部署到我们自己的紧凑型自主机器人\ Textit {Sambot}以进行实时杂草检测。在亚麻领域的先前看不见的场景中实现了90 \％测试精度（具有0.2-0.3米的行间距，杂草和杂草，失真，模糊和阴影，是真实世界中精确杂草控制的里程碑。我们公开发布了DataSet和代码以生成\ URL {https://github.com/structurescomp/multi-class-weed-classification}。

Speech-driven 3D facial animation has been widely explored, with applications in gaming, character animation, virtual reality, and telepresence systems. State-of-the-art methods deform the face topology of the target actor to sync the input audio without considering the identity-specific speaking style and facial idiosyncrasies of the target actor, thus, resulting in unrealistic and inaccurate lip movements. To address this, we present Imitator, a speech-driven facial expression synthesis method, which learns identity-specific details from a short input video and produces novel facial expressions matching the identity-specific speaking style and facial idiosyncrasies of the target actor. Specifically, we train a style-agnostic transformer on a large facial expression dataset which we use as a prior for audio-driven facial expressions. Based on this prior, we optimize for identity-specific speaking style based on a short reference video. To train the prior, we introduce a novel loss function based on detected bilabial consonants to ensure plausible lip closures and consequently improve the realism of the generated expressions. Through detailed experiments and a user study, we show that our approach produces temporally coherent facial expressions from input audio while preserving the speaking style of the target actors.

Quantum machine learning (QML) has received increasing attention due to its potential to outperform classical machine learning methods in various problems. A subclass of QML methods is quantum generative adversarial networks (QGANs) which have been studied as a quantum counterpart of classical GANs widely used in image manipulation and generation tasks. The existing work on QGANs is still limited to small-scale proof-of-concept examples based on images with significant down-scaling. Here we integrate classical and quantum techniques to propose a new hybrid quantum-classical GAN framework. We demonstrate its superior learning capabilities by generating $28 \times 28$ pixels grey-scale images without dimensionality reduction or classical pre/post-processing on multiple classes of the standard MNIST and Fashion MNIST datasets, which achieves comparable results to classical frameworks with 3 orders of magnitude less trainable generator parameters. To gain further insight into the working of our hybrid approach, we systematically explore the impact of its parameter space by varying the number of qubits, the size of image patches, the number of layers in the generator, the shape of the patches and the choice of prior distribution. Our results show that increasing the quantum generator size generally improves the learning capability of the network. The developed framework provides a foundation for future design of QGANs with optimal parameter set tailored for complex image generation tasks.

Inspired by the impressive success of contrastive learning (CL), a variety of graph augmentation strategies have been employed to learn node representations in a self-supervised manner. Existing methods construct the contrastive samples by adding perturbations to the graph structure or node attributes. Although impressive results are achieved, it is rather blind to the wealth of prior information assumed: with the increase of the perturbation degree applied on the original graph, 1) the similarity between the original graph and the generated augmented graph gradually decreases; 2) the discrimination between all nodes within each augmented view gradually increases. In this paper, we argue that both such prior information can be incorporated (differently) into the contrastive learning paradigm following our general ranking framework. In particular, we first interpret CL as a special case of learning to rank (L2R), which inspires us to leverage the ranking order among positive augmented views. Meanwhile, we introduce a self-ranking paradigm to ensure that the discriminative information among different nodes can be maintained and also be less altered to the perturbations of different degrees. Experiment results on various benchmark datasets verify the effectiveness of our algorithm compared with the supervised and unsupervised models.

Artificial intelligence is to teach machines to take actions like humans. To achieve intelligent teaching, the machine learning community becomes to think about a promising topic named machine teaching where the teacher is to design the optimal (usually minimal) teaching set given a target model and a specific learner. However, previous works usually require numerous teaching examples along with large iterations to guide learners to converge, which is costly. In this paper, we consider a more intelligent teaching paradigm named one-shot machine teaching which costs fewer examples to converge faster. Different from typical teaching, this advanced paradigm establishes a tractable mapping from the teaching set to the model parameter. Theoretically, we prove that this mapping is surjective, which serves to an existence guarantee of the optimal teaching set. Then, relying on the surjective mapping from the teaching set to the parameter, we develop a design strategy of the optimal teaching set under appropriate settings, of which two popular efficiency metrics, teaching dimension and iterative teaching dimension are one. Extensive experiments verify the efficiency of our strategy and further demonstrate the intelligence of this new teaching paradigm.

Federated learning allows multiple clients to collaboratively train a model without exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant performance degradation under heterogeneous data at clients. Common solutions in local training involve designing a specific auxiliary loss to regularize weight divergence or feature inconsistency. However, we discover that these approaches fall short of the expected performance because they ignore the existence of a vicious cycle between classifier divergence and feature mapping inconsistency across clients, such that client models are updated in inconsistent feature space with diverged classifiers. We then propose a simple yet effective framework named Federated learning with Feature Anchors (FedFA) to align the feature mappings and calibrate classifier across clients during local training, which allows client models updating in a shared feature space with consistent classifiers. We demonstrate that this modification brings similar classifiers and a virtuous cycle between feature consistency and classifier similarity across clients. Extensive experiments show that FedFA significantly outperforms the state-of-the-art federated learning algorithms on various image classification datasets under label and feature distribution skews.

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.

社交媒体的日益普及引起了人们对儿童在线安全的关注。未成年人与具有掠夺性意图的成年人之间的互动是一个特别严重的关注点。在线性修饰的研究通常依靠领域专家来手动注释对话，从而限制了规模和范围。在这项工作中，我们测试了良好的方法如何检测对话行为并取代专家的人类注释。在在线修饰的心理理论中，我们将$ 6772的$ 6772 $聊天消息标记为儿童性犯罪者以十一种掠夺性行为之一发送的聊天消息。我们训练字袋和自然语言推断模型来对每种行为进行分类，并表明，最佳性能模型以一致但不与人类注释的方式分类的方式对行为进行了分类。

每个自动驾驶数据集都有不同的传感器配置，源自不同的地理区域并涵盖各种情况。结果，3D检测器倾向于过度拟合他们的数据集。当在一个数据集上训练检测器并在另一个数据集上进行测试时，这会导致精度急剧下降。我们观察到激光扫描模式差异构成了这种降低性能的很大组成部分。我们通过设计一个新颖的以观看者为中心的表面完成网络（VCN）来完成我们的方法，以在无监督的域适应框架内完成感兴趣的对象表面，从而解决此问题。使用See-VCN，我们获得了跨数据集的对象的统一表示，从而使网络可以专注于学习几何形状，而不是过度拟合扫描模式。通过采用域不变表示，可以将SEE-VCN归类为一种多目标域适应方法，在该方法中无需注释或重新训练才能获得新的扫描模式的3D检测。通过广泛的实验，我们表明我们的方法在多个域适应设置中优于先前的域适应方法。我们的代码和数据可在https://github.com/darrenjkt/see-vcn上找到。