Microswimmers can acquire information on the surrounding fluid by sensing mechanical queues. They can then navigate in response to these signals. We analyse this navigation by combining deep reinforcement learning with direct numerical simulations to resolve the hydrodynamics. We study how local and non-local information can be used to train a swimmer to achieve particular swimming tasks in a non-uniform flow field, in particular a zig-zag shear flow. The swimming tasks are (1) learning how to swim in the vorticity direction, (2) the shear-gradient direction, and (3) the shear flow direction. We find that access to lab frame information on the swimmer's instantaneous orientation is all that is required in order to reach the optimal policy for (1,2). However, information on both the translational and rotational velocities seem to be required to achieve (3). Inspired by biological microorganisms we also consider the case where the swimmers sense local information, i.e. surface hydrodynamic forces, together with a signal direction. This might correspond to gravity or, for micro-organisms with light sensors, a light source. In this case, we show that the swimmer can reach a comparable level of performance as a swimmer with access to lab frame variables. We also analyse the role of different swimming modes, i.e. pusher, puller, and neutral swimmers.
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In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.
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Deep neural networks (DNNs) detect patterns in data and have shown versatility and strong performance in many computer vision applications. However, DNNs alone are susceptible to obvious mistakes that violate simple, common sense concepts and are limited in their ability to use explicit knowledge to guide their search and decision making. While overall DNN performance metrics may be good, these obvious errors, coupled with a lack of explainability, have prevented widespread adoption for crucial tasks such as medical image analysis. The purpose of this paper is to introduce SimpleMind, an open-source software framework for Cognitive AI focused on medical image understanding. It allows creation of a knowledge base that describes expected characteristics and relationships between image objects in an intuitive human-readable form. The SimpleMind framework brings thinking to DNNs by: (1) providing methods for reasoning with the knowledge base about image content, such as spatial inferencing and conditional reasoning to check DNN outputs; (2) applying process knowledge, in the form of general-purpose software agents, that are chained together to accomplish image preprocessing, DNN prediction, and result post-processing, and (3) performing automatic co-optimization of all knowledge base parameters to adapt agents to specific problems. SimpleMind enables reasoning on multiple detected objects to ensure consistency, providing cross checking between DNN outputs. This machine reasoning improves the reliability and trustworthiness of DNNs through an interpretable model and explainable decisions. Example applications are provided that demonstrate how SimpleMind supports and improves deep neural networks by embedding them within a Cognitive AI framework.
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The NASA Astrophysics Data System (ADS) is an essential tool for researchers that allows them to explore the astronomy and astrophysics scientific literature, but it has yet to exploit recent advances in natural language processing. At ADASS 2021, we introduced astroBERT, a machine learning language model tailored to the text used in astronomy papers in ADS. In this work we: - announce the first public release of the astroBERT language model; - show how astroBERT improves over existing public language models on astrophysics specific tasks; - and detail how ADS plans to harness the unique structure of scientific papers, the citation graph and citation context, to further improve astroBERT.
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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.
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从分布式敏感数据中学习隐私的模型是一个越来越重要的问题,通常在联邦学习环境中提出。最近通过分区的变异推理算法扩展到了非私有联盟学习设置。为了保护隐私,当前的黄金标准称为差异隐私。差异隐私在强大的数学上明确定义的意义上保证了隐私。在本文中,我们介绍了差异化的分区变异推断,这是学习与联合学习环境中贝叶斯后分布的差异近似的第一个通用框架,同时最大程度地减少了通信弹的数量并为数据主体提供差异隐私保证。我们在通用框架中提出了三个替代实现,一个基于单个方面的本地优化,而两个基于扰动全局更新(一种使用联合平均版本,一个将虚拟方添加到协议中),并比较其属性,并比较其属性理论上和经验。我们表明,只要各方都有足够的本地数据,扰动本地优化与简单且复杂的模型效果很好。但是,每个方始终独立保证隐私。相比之下,扰动全局更新与相对简单的模型最有效。鉴于可以访问合适的安全原始词,例如安全聚合或安全的改组,所有各方都可以共同保证隐私。
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2型糖尿病(T2DM)的早期诊断对于及时的治疗干预措施和生活方式改变至关重要。随着医学成像数据在许多患者群体中变得更广泛可用,我们试图研究是否可以在表格学习分类器模型中利用图像衍生的表型数据来预测T2DM的发病率,而无需使用侵入性血液实验室测量。我们表明,使用图像衍生表型的神经网络和决策树模型都可以预测患者T2DM状态的召回评分高达87.6%。我们还提出了与“ Syntha1c编码器”相同的结构的新颖使用,这些结构能够输出模仿血液血红蛋白A1C经验实验室测量值的可解释值。最后,我们证明了T2DM风险预测模型对输入矢量成分中小扰动的敏感性可用于预测从以前看不见的患者人群中取样的协变量的性能。
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当从人类行为中推断出奖励功能(无论是演示,比较,物理校正或电子停靠点)时,它已证明对人类进行建模作为做出嘈杂的理性选择,并具有“合理性系数”,以捕获多少噪声或熵我们希望看到人类的行为。无论人类反馈的类型或质量如何,许多现有作品都选择修复此系数。但是,在某些情况下,进行演示可能要比回答比较查询要困难得多。在这种情况下,我们应该期望在示范中看到比比较中更多的噪音或次级临时性,并且应该相应地解释反馈。在这项工作中,我们提倡,将每种反馈类型的实际数据中的理性系数扎根,而不是假设默认值,对奖励学习具有重大的积极影响。我们在模拟反馈以及用户研究的实验中测试了这一点。我们发现,从单一反馈类型中学习时,高估人类理性可能会对奖励准确性和遗憾产生可怕的影响。此外,我们发现合理性层面会影响每种反馈类型的信息性:令人惊讶的是,示威并不总是最有用的信息 - 当人类的行为非常卑鄙时,即使在合理性水平相同的情况下,比较实际上就变得更加有用。 。此外,当机器人确定要要求的反馈类型时,它可以通过准确建模每种类型的理性水平来获得很大的优势。最终,我们的结果强调了关注假定理性级别的重要性,不仅是在从单个反馈类型中学习时,尤其是当代理商从多种反馈类型中学习时,尤其是在学习时。
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给定尺寸$ d $中的独立标准高斯点$ v_1,\ ldots,v_n $,对于$(n,d)$的值(n,d)$的值很高,概率很高,同时通过所有要点?将椭圆形拟合到随机点的基本问题与低级别矩阵分解,独立的组件分析和主成分分析有连接。基于有力的数值证据,桑德森,帕里洛和威尔斯基[Proc。关于决策和控制会议,第6031-6036页,2013年]猜想,椭圆形拟合问题的问题从可行的到不可行的$ n $增加,并在$ n \ sim d^2/4处急剧阈值$。我们通过为某些$ n = \ omega(\,d^2/\ log^5(d)\,)$构建合适的椭圆形来解决这个猜想,从而改善了Ghosh等人的先前工作。 [Proc。关于计算机科学基础的研讨会,第954-965、2020页],需要$ n = o(d^{3/2})$。我们的证明证明了Saunderson等人的最小二乘结构的可行性。使用对特定非标准随机矩阵的特征向量和特征值进行仔细的分析。
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从演示中学习(LFD)是一种从人提供的演示中复制和概括机器人技能的流行方法。在本文中,我们提出了一种基于优化的新型LFD方法,该方法将演示描述为弹性图。弹性图是通过弹簧网格连接的节点的图。我们通过将弹性地图拟合到一组演示中来构建技能模型。我们方法中的公式优化问题包括三个具有自然和物理解释的目标。主术语奖励笛卡尔坐标中的平方误差。第二项惩罚了导致最佳轨迹总长度的点的非等应存在分布。第三学期奖励平滑度,同时惩罚非线性。这些二次目标形成了凸问题,可以通过局部优化器有效地解决。我们研究了九种用于构建和加权弹性图并研究其在机器人任务中的性能的方法。我们还使用UR5E操纵器组在几个模拟和现实世界中评估了所提出的方法,并将其与其他LFD方法进行比较,以证明其在各种指标中的好处和灵活性。
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