我们提出了一种基于最佳传输的分类数据集中分布变化的方法。它允许用户确定每个班级受轮班影响的程度，并检索相应的样本对以提供有关其性质的见解。我们说明了它在合成和自然转移示例中的使用。尽管我们提出的结果是初步的，但我们希望这激发了未来的可解释方法的工作，以分析分配变化。

了解机器学习模型如何推广到新环境是其安全部署的关键部分。最近的工作提出了各种复杂性度量，这些度量直接预测或理论上结合了模型的概括能力。但是，这些方法依赖于在实践中并不总是满足的一系列强有力的假设。受到有限的设置，可以采用现有措施的有限设置，我们提出了一种基于分类器的局部歧管平滑度的新颖复杂度度量。我们将局部歧管平滑度定义为分类器对给定测试点周围歧管社区中扰动的输出敏感性。直觉上，对这些扰动不太敏感的分类器应更好地概括。为了估计平滑度，我们使用数据扩展进行采样点，并测量分类为多数类的这些点的分数。我们的方法仅需要选择数据增强方法，并且对模型或数据分布没有其他假设，这意味着即使在现有方法无法使用的情况下，也可以在室外（OOD）设置中应用。在图像分类，情感分析和自然语言推断中的鲁棒性基准的实验中，我们证明了我们在100多个火车/测试域对上评估的超过3,000个模型上的流形光滑度量与实际的OOD概括之间存在很强而牢固的相关性。

Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key regions, such as corners, are not directly graspable, making manipulation difficult. We propose a system that leverages visual and tactile perception to unfold the cloth via grasping and sliding on edges. By doing so, the robot is able to grasp two adjacent corners, enabling subsequent manipulation tasks like folding or hanging. As components of this system, we develop tactile perception networks that classify whether an edge is grasped and estimate the pose of the edge. We use the edge classification network to supervise a visuotactile edge grasp affordance network that can grasp edges with a 90% success rate. Once an edge is grasped, we demonstrate that the robot can slide along the cloth to the adjacent corner using tactile pose estimation/control in real time. See http://nehasunil.com/visuotactile/visuotactile.html for videos.

Federated Deep Learning frameworks can be used strategically to monitor Land Use locally and infer environmental impacts globally. Distributed data from across the world would be needed to build a global model for Land Use classification. The need for a Federated approach in this application domain would be to avoid transfer of data from distributed locations and save network bandwidth to reduce communication cost. We use a Federated UNet model for Semantic Segmentation of satellite and street view images. The novelty of the proposed architecture is the integration of Knowledge Distillation to reduce communication cost and response time. The accuracy obtained was above 95% and we also brought in a significant model compression to over 17 times and 62 times for street View and satellite images respectively. Our proposed framework has the potential to be a game-changer in real-time tracking of climate change across the planet.

Ensuring safety is of paramount importance in physical human-robot interaction applications. This requires both an adherence to safety constraints defined on the system state, as well as guaranteeing compliant behaviour of the robot. If the underlying dynamical system is known exactly, the former can be addressed with the help of control barrier functions. Incorporation of elastic actuators in the robot's mechanical design can address the latter requirement. However, this elasticity can increase the complexity of the resulting system, leading to unmodeled dynamics, such that control barrier functions cannot directly ensure safety. In this paper, we mitigate this issue by learning the unknown dynamics using Gaussian process regression. By employing the model in a feedback linearizing control law, the safety conditions resulting from control barrier functions can be robustified to take into account model errors, while remaining feasible. In order enforce them on-line, we formulate the derived safety conditions in the form of a second-order cone program. We demonstrate our proposed approach with simulations on a two-degree of freedom planar robot with elastic joints.

Low-field (LF) MRI scanners have the power to revolutionize medical imaging by providing a portable and cheaper alternative to high-field MRI scanners. However, such scanners are usually significantly noisier and lower quality than their high-field counterparts. The aim of this paper is to improve the SNR and overall image quality of low-field MRI scans to improve diagnostic capability. To address this issue, we propose a Nested U-Net neural network architecture super-resolution algorithm that outperforms previously suggested deep learning methods with an average PSNR of 78.83 and SSIM of 0.9551. We tested our network on artificial noisy downsampled synthetic data from a major T1 weighted MRI image dataset called the T1-mix dataset. One board-certified radiologist scored 25 images on the Likert scale (1-5) assessing overall image quality, anatomical structure, and diagnostic confidence across our architecture and other published works (SR DenseNet, Generator Block, SRCNN, etc.). We also introduce a new type of loss function called natural log mean squared error (NLMSE). In conclusion, we present a more accurate deep learning method for single image super-resolution applied to synthetic low-field MRI via a Nested U-Net architecture.

早期发现阿尔茨海默氏病对于部署干预措施和减慢疾病进展至关重要。在过去的十年中，已经探索了许多机器学习和深度学习算法，目的是为阿尔茨海默氏症建立自动检测。数据增强技术和先进的深度学习体系结构的进步已经在该领域开辟了新的边界，研究正在快速发展。因此，这项调查的目的是概述有关阿尔茨海默氏病诊断深度学习模型的最新研究。除了对众多数据源，神经网络架构以及常用的评估措施进行分类外，我们还对实施和可重复性进行了分类。我们的目标是协助感兴趣的研究人员跟上最新的发展，并将早期的调查作为基准。此外，我们还指出了该主题的未来研究方向。

自从各种任务的自动化开始以来，自动驾驶车辆一直引起人们的兴趣。人类容易疲惫，在道路上的响应时间缓慢，最重要的是，每年约有135万道路交通事故死亡，这已经是一项危险的任务。预计自动驾驶可以减少世界上驾驶事故的数量，这就是为什么这个问题对研究人员感兴趣的原因。目前，自动驾驶汽车在使车辆自动驾驶时使用不同的算法来实现各种子问题。我们将重点关注增强学习算法，更具体地说是Q学习算法和增强拓扑的神经进化（NEAT），即进化算法和人工神经网络的组合，以训练模型代理，以学习如何在给定路径上驱动。本文将重点介绍上述两种算法之间的比较。

本文的目的是描述一个用于在合成数据库中生成合成顺序数据的系统。为了实现这一目标，我们在SDV中介绍了当前的顺序模型，SDV是一个端到端框架，该框架为多序列，现实世界数据构建生成模型。这包括一个新型的基于神经网络的机器学习模型，条件概率自动回归（CPAR）模型。总体系统和模型可在开源合成数据保险库（SDV）库中获得{https://github.com/sdv-dev/sdv}，以及用于不同合成数据需求的其他各种模型。构建顺序SDV后，我们使用它来生成合成数据，并将其质量与现有的非序列生成对抗网络的模型进行了比较。为了将顺序合成数据与其实际对应物进行比较，我们发明了一个称为多序列汇总相似性（MSA）的新指标。我们用它来得出结论，我们的顺序SDV模型比非综合数据质量的任何权衡取舍都学到了更高的级别模式。

联合学习（FL）是一种使用跨设备分布的数据训练模型的技术。差异隐私（DP）为敏感数据提供了正式的隐私保证。我们的目标是在使用FL和DP保护隐私的同时，在计算受限设备上训练大型神经网络语言模型（NNLM）。但是，随着模型大小的增长，引入模型的DP噪声增加，这通常会阻止收敛。我们提出了部分嵌入更新（PEU），这是一种新颖的技术，可以通过降低有效载荷大小来降低噪声。此外，我们采用低级适应（LORA）和噪声对比估计（NCE）来减少计算受限设备上大型模型的记忆需求。这种技术的组合使得可以在保留准确性和隐私的同时训练大型唱机语言模型。