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.

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.

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。

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。

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。

通过研究视网膜生物结构的进展，可以识别眼病的存在和严重性是可行的。眼底检查是检查眼睛的生物结构和异常的诊断程序。诸如青光眼，糖尿病性视网膜病和白内障等眼科疾病是世界各地视觉障碍的主要原因。眼疾病智能识别（ODIR-5K）是研究人员用于多标签的多份多疾病分类的基准结构底面图像数据集。这项工作提出了一个歧视性内核卷积网络（DKCNET），该网络探讨了歧视区域的特征，而无需增加额外的计算成本。 DKCNET由注意力块组成，然后是挤压和激发（SE）块。注意块从主干网络中获取功能，并生成歧视性特征注意图。 SE块采用区分特征图并改善了通道相互依赖性。使用InceptionResnet骨干网络观察到DKCNET的更好性能，用于具有96.08 AUC，94.28 F1-SCORE和0.81 KAPPA得分的ODIR-5K底面图像的多标签分类。所提出的方法根据诊断关键字将通用目标标签拆分为眼对。基于这些标签，进行了过采样和不足采样以解决阶级失衡。为了检查拟议模型对培训数据的偏见，对ODIR数据集进行了训练的模型将在三个公开可用的基准数据集上进行测试。发现它在完全看不见的底面图像上也具有良好的性能。