The interplay between quantum physics and machine learning gives rise to the emergent frontier of quantum machine learning, where advanced quantum learning models may outperform their classical counterparts in solving certain challenging problems. However, quantum learning systems are vulnerable to adversarial attacks: adding tiny carefully-crafted perturbations on legitimate input samples can cause misclassifications. To address this issue, we propose a general scheme to protect quantum learning systems from adversarial attacks by randomly encoding the legitimate data samples through unitary or quantum error correction encoders. In particular, we rigorously prove that both global and local random unitary encoders lead to exponentially vanishing gradients (i.e. barren plateaus) for any variational quantum circuits that aim to add adversarial perturbations, independent of the input data and the inner structures of adversarial circuits and quantum classifiers. In addition, we prove a rigorous bound on the vulnerability of quantum classifiers under local unitary adversarial attacks. We show that random black-box quantum error correction encoders can protect quantum classifiers against local adversarial noises and their robustness increases as we concatenate error correction codes. To quantify the robustness enhancement, we adapt quantum differential privacy as a measure of the prediction stability for quantum classifiers. Our results establish versatile defense strategies for quantum classifiers against adversarial perturbations, which provide valuable guidance to enhance the reliability and security for both near-term and future quantum learning technologies.

我们解决了从一般标记（例如电影海报）估计对应关系到捕获这种标记的图像的问题。通常，通过拟合基于稀疏特征匹配的同型模型来解决此问题。但是，他们只能处理类似平面的标记，而稀疏功能不能充分利用外观信息。在本文中，我们提出了一个新颖的框架神经标记器，训练神经网络估计在各种具有挑战性的条件下（例如标记变形，严格的照明等）估算密集标记的对应关系。此外，我们还提出了一种新颖的标记通信评估方法，对真实标记的注释进行了注释。 - 图像对并创建一个新的基准测试。我们表明，神经标记的表现明显优于以前的方法，并实现了新的有趣应用程序，包括增强现实（AR）和视频编辑。

流量预测是智能交通系统中时空学习任务的规范示例。现有方法在图形卷积神经操作员中使用预定的矩阵捕获空间依赖性。但是，显式的图形结构损失了节点之间关系的一些隐藏表示形式。此外，传统的图形卷积神经操作员无法在图上汇总远程节点。为了克服这些限制，我们提出了一个新型的网络，空间 - 周期性自适应图卷积，并通过注意力网络（Staan）进行交通预测。首先，我们采用自适应依赖性矩阵，而不是在GCN处理过程中使用预定义的矩阵来推断节点之间的相互依存关系。其次，我们集成了基于图形注意力网络的PW注意，该图形是为全局依赖性设计的，而GCN作为空间块。更重要的是，在我们的时间块中采用了堆叠的散布的1D卷积，具有长期预测的效率，用于捕获不同的时间序列。我们在两个现实世界数据集上评估了我们的Staan，并且实验验证了我们的模型优于最先进的基线。

在过去的十年中，机器学习取得了巨大的成功，其应用程序从面部识别到自然语言处理不等。同时，在量子计算领域已经取得了快速的进步，包括开发强大的量子算法和高级量子设备。机器学习与量子物理学之间的相互作用具有将实际应用带给现代社会的有趣潜力。在这里，我们以参数化量子电路的形式关注量子神经网络。我们将主要讨论各种结构和编码量子神经网络的策略，以进行监督学习任务，并利用Yao.jl进行基准测试，这是用朱莉娅语言编写的量子模拟软件包。这些代码是有效的，旨在为科学工作中的初学者提供便利，例如开发强大的变分量子学习模型并协助相应的实验演示。

包含联系和象征主义范式的胶囊网络使新的见解成为人工智能。作为胶囊网络的构建块的胶囊是由向量表示的一组神经元以编码实体的不同特征。通过路由算法通过胶囊层分层地提取信息。在这里，我们将量子胶囊网络（被称为QCaPSnet）与量子动态路由算法一起介绍。我们的模型在动态路由过程中享有指数加速，并展示增强的表示电源。为了基准QCAPPAPSNET的性能，我们对手写数字和对称保护的拓扑阶段进行了广泛的数值模拟，并表明QCAPPSNET可以显然地实现最先进的准确度并优于传统量子分类器。我们进一步解开输出胶囊状态，并发现特定子空间可以对应于输入数据的人类可理解的特征，其指示这种网络的潜在可解释性。我们的工作揭示了量子机器学习中量子胶囊网络的有趣前景，这可能为可解释的量子人工智能提供有价值的指导。

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based ones, have been developed and applied widely to RS data analysis. The successful application of AI covers almost all aspects of Earth observation (EO) missions, from low-level vision tasks like super-resolution, denoising, and inpainting, to high-level vision tasks like scene classification, object detection, and semantic segmentation. While AI techniques enable researchers to observe and understand the Earth more accurately, the vulnerability and uncertainty of AI models deserve further attention, considering that many geoscience and RS tasks are highly safety-critical. This paper reviews the current development of AI security in the geoscience and RS field, covering the following five important aspects: adversarial attack, backdoor attack, federated learning, uncertainty, and explainability. Moreover, the potential opportunities and trends are discussed to provide insights for future research. To the best of the authors' knowledge, this paper is the first attempt to provide a systematic review of AI security-related research in the geoscience and RS community. Available code and datasets are also listed in the paper to move this vibrant field of research forward.

Searching for a path between two nodes in a graph is one of the most well-studied and fundamental problems in computer science. In numerous domains such as robotics, AI, or biology, practitioners develop search heuristics to accelerate their pathfinding algorithms. However, it is a laborious and complex process to hand-design heuristics based on the problem and the structure of a given use case. Here we present PHIL (Path Heuristic with Imitation Learning), a novel neural architecture and a training algorithm for discovering graph search and navigation heuristics from data by leveraging recent advances in imitation learning and graph representation learning. At training time, we aggregate datasets of search trajectories and ground-truth shortest path distances, which we use to train a specialized graph neural network-based heuristic function using backpropagation through steps of the pathfinding process. Our heuristic function learns graph embeddings useful for inferring node distances, runs in constant time independent of graph sizes, and can be easily incorporated in an algorithm such as A* at test time. Experiments show that PHIL reduces the number of explored nodes compared to state-of-the-art methods on benchmark datasets by 58.5\% on average, can be directly applied in diverse graphs ranging from biological networks to road networks, and allows for fast planning in time-critical robotics domains.

压缩高准确性卷积神经网络（CNN）的最新进展已经见证了实时对象检测的显着进步。为了加速检测速度，轻质检测器总是使用单路主链几乎没有卷积层。但是，单路径架构涉及连续的合并和下采样操作，始终导致粗糙和不准确的特征图，这些图形不利，无法找到对象。另一方面，由于网络容量有限，最近的轻质网络在表示大规模的视觉数据方面通常很弱。为了解决这些问题，本文提出了一个名为DPNET的双路径网络，并采用了实时对象检测的轻巧注意方案。双路径体系结构使我们能够与提取物相对于高级语义特征和低级对象详细信息。尽管DPNET相对于单路检测器几乎具有重复的形状，但计算成本和模型大小并未显着增加。为了增强表示能力，轻巧的自相关模块（LSCM）旨在捕获全局交互，只有很少的计算开销和网络参数。在颈部，LSCM扩展到轻质互相关模块（LCCM），从而捕获相邻尺度特征之间的相互依赖性。我们已经对Coco和Pascal VOC 2007数据集进行了详尽的实验。实验结果表明，DPNET在检测准确性和实施效率之间实现了最新的权衡。具体而言，DPNET在MS COCO Test-DEV上可实现30.5％的AP，Pascal VOC 2007测试集上的81.5％地图，MWITH近250万型号，1.04 GFLOPS，1.04 GFLOPS和164 fps和196 fps和196 fps，320 x 320输入图像的320 x 320输入图像。

基于文本描述的高分辨率遥感图像的合成在许多实际应用方案中具有巨大的潜力。尽管深度神经网络在许多重要的遥感任务中取得了巨大的成功，但是从文本描述中生成现实的遥感图像仍然非常困难。为了应对这一挑战，我们提出了一个新颖的文本形象现代霍普菲尔德网络（TXT2IMG-MHN）。 TXT2IMG-MHN的主要思想是在具有现代Hopfield层的文本和图像嵌入方式上进行层次原型学习。 TXT2IMG-MHN并没有直接学习具体但高度多样化的文本图像联合特征表示，而是旨在从文本图像嵌入中学习最具代表性的原型，从而实现一种粗略的学习策略。然后可以利用这些学到的原型来代表文本到图像生成任务中更复杂的语义。为了更好地评估生成图像的现实主义和语义一致性，我们使用对合成图像训练的分类模型对真实遥感数据进行零击分类。尽管它很简单，但我们发现，零弹性分类的总体准确性可以作为评估从文本生成图像的能力的良好指标。基准遥感文本图像数据集上的广泛实验表明，所提出的TXT2IMG-MHN比现有方法可以生成更现实的遥感图像。代码和预培训模型可在线获得（https://github.com/yonghaoxu/txt2img-mhn）。