Federated learning (FL) on deep neural networks facilitates new applications at the edge, especially for wearable and Internet-of-Thing devices. Such devices capture a large and diverse amount of data, but they have memory, compute, power, and connectivity constraints which hinder their participation in FL. We propose Centaur, a multitier FL framework, enabling ultra-constrained devices to efficiently participate in FL on large neural nets. Centaur combines two major ideas: (i) a data selection scheme to choose a portion of samples that accelerates the learning, and (ii) a partition-based training algorithm that integrates both constrained and powerful devices owned by the same user. Evaluations, on four benchmark neural nets and three datasets, show that Centaur gains ~10% higher accuracy than local training on constrained devices with ~58% energy saving on average. Our experimental results also demonstrate the superior efficiency of Centaur when dealing with imbalanced data, client participation heterogeneity, and various network connection probabilities.

大多数图形神经网络（GNN）通过学习输入图和标签之间的相关性来预测看不见的图的标签。但是，通过对具有严重偏见的训练图进行图形分类调查，我们发现GNN始终倾向于探索伪造的相关性以做出决定，即使因果关系始终存在。这意味着在此类偏见的数据集中接受培训的现有GNN将遭受概括能力差。通过在因果观点中分析此问题，我们发现从偏见图中解开和去偏置因果和偏见的潜在变量对于偏见至关重要。在此鼓舞下，我们提出了一个普遍的分解GNN框架，分别学习因果子结构和偏见子结构。特别是，我们设计了一个参数化的边蒙版生成器，以将输入图明确分为因果和偏置子图。然后，分别由因果/偏见感知损失函数监督的两个GNN模块进行培训，以编码因果关系和偏置子图表中的相应表示。通过分离的表示，我们合成了反事实无偏的训练样本，以进一步脱离因果变量和偏见变量。此外，为了更好地基于严重的偏见问题，我们构建了三个新的图形数据集，这些数据集具有可控的偏置度，并且更容易可视化和解释。实验结果很好地表明，我们的方法比现有基线实现了优越的概括性能。此外，由于学习的边缘面膜，该拟议的模型具有吸引人的解释性和可转让性。代码和数据可在以下网址获得：https：//github.com/googlebaba/disc。

机器学习中的隐私和安全挑战（ML）已成为ML普遍的开发以及最近对大型攻击表面的展示，已成为一个关键的话题。作为一种成熟的以系统为导向的方法，在学术界和行业中越来越多地使用机密计算来改善各种ML场景的隐私和安全性。在本文中，我们将基于机密计算辅助的ML安全性和隐私技术的发现系统化，以提供i）保密保证和ii）完整性保证。我们进一步确定了关键挑战，并提供有关ML用例现有可信赖的执行环境（TEE）系统中限制的专门分析。我们讨论了潜在的工作，包括基础隐私定义，分区的ML执行，针对ML的专用发球台设计，TEE Awawe Aware ML和ML Full Pipeline保证。这些潜在的解决方案可以帮助实现强大的TEE ML，以保证无需引入计算和系统成本。

对于未来的家庭辅助机器人来说，在日常人类环境中了解和操纵不同的3D对象是必不可少的。旨在构建可以在各种3D形状上执行各种操纵任务的可扩展系统，最近的作品提倡并展示了有希望的结果学习视觉可行的负担能力，该结果标记了输入3D几何学上的每个点，并以完成下游任务的可能性（例如，推动下游任务）或接送）。但是，这些作品仅研究了单杆操纵任务，但是许多现实世界的任务需要两只手才能协作。在这项工作中，我们提出了一个新颖的学习框架Dualafford，以学习双手操纵任务的协作负担。该方法的核心设计是将两个抓手的二次问题减少到两个分离但相互联系的子任务中，以进行有效的学习。使用大规模的partnet-Mobility和Shapenet数据集，我们设置了四个基准任务，以进行双拖把操作。实验证明了我们方法比三个基线的有效性和优势。可以在https://hyperplane-lab.github.io/dualafford上找到其他结果和视频。

Many real-world problems are inherently multimodal, from the communicative modalities humans use to express social and emotional states to the force, proprioception, and visual sensors ubiquitous on robots. While there has been an explosion of interest in multimodal representation learning, these methods are still largely focused on a small set of modalities, primarily in the language, vision, and audio space. In order to accelerate generalization towards diverse and understudied modalities, this paper studies efficient representation learning for high-modality scenarios. Since adding new models for every new modality or task becomes prohibitively expensive, a critical technical challenge is heterogeneity quantification: how can we measure which modalities encode similar information and interactions in order to permit parameter sharing with previous modalities? We propose two new information-theoretic metrics for heterogeneity quantification: (1) modality heterogeneity studies how similar 2 modalities $\{X_1,X_2\}$ are by measuring how much information can be transferred from $X_1$ to $X_2$, while (2) interaction heterogeneity studies how similarly pairs of modalities $\{X_1,X_2\}, \{X_3,X_4\}$ interact by measuring how much interaction information can be transferred from $\{X_1,X_2\}$ to $\{X_3,X_4\}$. We show the importance of these proposed metrics in high-modality scenarios as a way to automatically prioritize the fusion of modalities that contain unique information or interactions. The result is a single model, HighMMT, that scales up to $10$ modalities and $15$ tasks from $5$ different research areas. Not only does HighMMT outperform prior methods on the tradeoff between performance and efficiency, it also demonstrates a crucial scaling behavior: performance continues to improve with each modality added, and transfers to entirely new modalities and tasks during fine-tuning.

部件组件是机器人中的典型但具有挑战性的任务，机器人将一组各个部件组装成完整的形状。在本文中，我们开发了用于家具组件的机器人组装仿真环境。我们将零件装配任务制定为混凝土加固学习问题，并提出了一种机器人的管道，以学习组装多种椅子。实验表明，当使用看不见的椅子进行测试时，我们的方法在以上对象的环境下实现了74.5％的成功率，并在完整环境下实现了50.0％。我们采用RRT-CONNECT算法作为基线，在计算时间明显更长的时间后，只能实现18.8％的成功率。我们的项目网页提供了补充材料和视频。

与3D铰接物体感知和互动，例如橱柜，门和龙头，对未来的家庭助手机器人进行人类环境中的日常任务构成特殊挑战。除了解析铰接部件和联合参数外，研究人员最近倡导学习操纵在输入形状几何形状上，这是更加任务感知和几何细粒度的。然而，只采用​​被动观测作为输入，这些方法忽略了许多隐藏但重要的运动限制（例如，联合位置和限制）和动态因素（例如，关节摩擦和恢复），因此对这种不确定性的测试用例失去了显着的准确性。在本文中，我们提出了一个名为Adaaveword的新颖框架，该框架是学习的，以便在更准确地将可怜的实例特定的后医中迅速调整可怜的地前沿来执行很少的测试时间相互作用。我们使用Partnet-Mobility DataSet进行大规模实验，并证明我们的系统比基线更好。

对协作学习的实证攻击表明，深度神经网络的梯度不仅可以披露训练数据的私有潜在属性，还可以用于重建原始数据。虽然先前的作品试图量化了梯度的隐私风险，但这些措施没有建立理论上对梯度泄漏的理解了解，而不是跨越攻击者的概括，并且不能完全解释通过实际攻击在实践中通过实证攻击观察到的内容。在本文中，我们介绍了理论上激励的措施，以量化攻击依赖和攻击无关方式的信息泄漏。具体而言，我们展示了$ \ mathcal {v} $ - 信息的适应，它概括了经验攻击成功率，并允许量化可以从任何所选择的攻击模型系列泄漏的信息量。然后，我们提出了独立的措施，只需要共享梯度，用于量化原始和潜在信息泄漏。我们的经验结果，六个数据集和四种流行型号，揭示了第一层的梯度包含最高量的原始信息，而（卷积）特征提取器层之后的（完全连接的）分类层包含最高的潜在信息。此外，我们展示了如何在训练期间诸如梯度聚集的技术如何减轻信息泄漏。我们的工作为更好的防御方式铺平了道路，例如基于层的保护或强聚合。

Deep learning-based methods have achieved significant performance for image defogging. However, existing methods are mainly developed for land scenes and perform poorly when dealing with overwater foggy images, since overwater scenes typically contain large expanses of sky and water. In this work, we propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes. To promote the recovery of the objects on water in the image, two loss functions are exploited for the network where a prior map is designed to invert the dark channel and the min-max normalization is used to suppress the sky and emphasize objects. However, due to the unpaired training set, the network may learn an under-constrained domain mapping from foggy to fog-free image, leading to artifacts and loss of details. Thus, we propose an intuitive Upscaling Inception Module (UIM) and a Long-range Residual Coarse-to-fine framework (LRC) to mitigate this issue. Extensive experiments on qualitative and quantitative comparisons demonstrate that the proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches.

We consider the inverse acoustic obstacle problem for sound-soft star-shaped obstacles in two dimensions wherein the boundary of the obstacle is determined from measurements of the scattered field at a collection of receivers outside the object. One of the standard approaches for solving this problem is to reformulate it as an optimization problem: finding the boundary of the domain that minimizes the $L^2$ distance between computed values of the scattered field and the given measurement data. The optimization problem is computationally challenging since the local set of convexity shrinks with increasing frequency and results in an increasing number of local minima in the vicinity of the true solution. In many practical experimental settings, low frequency measurements are unavailable due to limitations of the experimental setup or the sensors used for measurement. Thus, obtaining a good initial guess for the optimization problem plays a vital role in this environment. We present a neural network warm-start approach for solving the inverse scattering problem, where an initial guess for the optimization problem is obtained using a trained neural network. We demonstrate the effectiveness of our method with several numerical examples. For high frequency problems, this approach outperforms traditional iterative methods such as Gauss-Newton initialized without any prior (i.e., initialized using a unit circle), or initialized using the solution of a direct method such as the linear sampling method. The algorithm remains robust to noise in the scattered field measurements and also converges to the true solution for limited aperture data. However, the number of training samples required to train the neural network scales exponentially in frequency and the complexity of the obstacles considered. We conclude with a discussion of this phenomenon and potential directions for future research.