低温电子显微镜（Cryo-EM），2D分类和比对的单个颗粒分析（SPA）的关键步骤，将嘈杂的粒子图像集合收集，以推导方向并将相似图像组合在一起。平均这些对齐和聚集的嘈杂图像会产生一组干净的图像，准备进一步分析，例如3D重建。傅立叶贝塞尔可进入的主成分分析（FBSPCA）可实现有效的，适应性的，低级别的旋转操作员。我们将FBSPCA扩展到额外处理翻译。在此扩展的FBSPCA表示中，我们使用概率的极性高斯混合模型，使用预期最大化（EM）算法以无监督的方式学习软簇。因此，获得的旋转簇还具有成对比对缺陷的存在。与标准的单粒子冷冻EM工具，EMAN2和Relion相比，模拟的冷冻EM数据集的多个基准表明概率Polargmm的性能改善了性能，就各种聚类指标和对齐错误而言。

Protein structure prediction is a fundamental problem in computational molecular biology. Classical algorithms such as ab-initio or threading as well as many learning methods have been proposed to solve this challenging problem. However, most reinforcement learning methods tend to model the state-action pairs as discrete objects. In this paper, we develop a reinforcement learning (RL) framework in a continuous setting and based on a stochastic parametrized Hamiltonian version of the Pontryagin maximum principle (PMP) to solve the side-chain packing and protein-folding problem. For special cases our formulation can be reduced to previous work where the optimal folding trajectories are trained using an explicit use of Langevin dynamics. Optimal continuous stochastic Hamiltonian dynamics folding pathways can be derived with use of different models of molecular energetics and force fields. In our RL implementation we adopt a soft actor-critic methodology however we can replace this other RL training based on A2C, A3C or PPO.

常规生成订单3及以上的数据张量。这些数据收集越来越大且增长。它们要么是张量字段（例如，图像，视频，地理数据），其中每个数据位置包含重要信息或排列不变的一般张量（例如，无监督的潜在空间学习，图形网络分析，建议系统等）。直接访问如此大的数据张量收集以获取信息已变得越来越令人难以置信。我们学习具有分解表示的近似全级和紧凑的张量草图，可提供紧凑的空间，时间和光谱嵌入量的张量场（P-SCT）和一般张量（P-SCT-Permute）。所有后续的信息查询都以高精度进行，在生成草图上进行。我们通过从张量切片的样品有效的子采样量构建张量图来产生任意阶数据张量的最佳级别-r tucker分解。我们的样本有效策略是通过使用与共轭先验的Dirichlet分布的适应性随机汤普森采样来学习的。

通过首先应用Pontryagin最大原理来解决最佳控制问题，然后计算相应的无约束Hamiltonian动态系统的解决方案。在本文中，在鲁棒性和效率之间实现平衡，我们学习减少无约束的汉密尔顿人的汉密尔顿人。通过在时间后向后，通过最大限度地降低汉密尔顿人，并通过在最大原理条件下最小化损失函数来学习。然后通过逐步学习减少的哈密顿人的后部分布，进一步改善了我们学习过程的鲁棒性。这导致了我们相位空间的广义坐标（位置，速度）的更有效的采样。我们的解决方案框架不仅适用于有限阶段（州）空间的最佳控制问题，还适用于无限尺寸尺寸外壳。

当前相机图像和信号处理管道（ISP）（ISP），包括深度训练的版本，倾向于应用一个均匀地应用于整个图像的单个过滤器。尽管大多数获取的相机图像具有空间异构的伪影。这种空间异质性在图像空间中表现为各种莫尔振铃，运动模糊，颜色漂白或透镜的投影失真。此外，这些图像伪像的组合可以存在于所获取的图像中的小或大像素邻域中。这里，我们介绍了一种在学习的潜在子空间中工作的深度加强学习模型，通过基于补丁的空间自适应伪影滤波和图像增强来递归地改善相机图像质量。我们的RSE-RL模型视图识别和纠正作为递归自学习和自我改善练习，并由两个主要子模块组成：（i）通过等级变分自动获得的潜在特征子空间聚类/分组-Encoder能够快速识别嘈杂和清洁图像补丁之间的对应和差异。 （ii）由信任区域软演员 - 批评代理控制的自适应学习转换，逐步过滤并使用其最接近的清洁补丁的特征距离邻居增强嘈杂的补丁。可以在基于贴剂的ISP中引入的人工伪影，也通过基于奖励的去阻断恢复和图像增强来消除。我们通过在图像上进行递归训练和测试来展示我们模型的自我改善特征，其中每个时代产生的增强图像为RSE-RL训练过滤管道提供自然数据增强和鲁棒性。

开发深度神经网络以生成3D场景是神经综合的基本问题，其立即应用于架构CAD，计算机图形，以及生成虚拟机器人训练环境。这项任务是具有挑战性的，因为3D场景呈现不同的模式，从连续的模式等等，例如对象尺寸和成对对之间的相对姿势，以离散模式，例如具有对称关系的对象的发生和共发生。本文介绍了一种新型神经场景综合方法，可以捕获3D场景的不同特征模式。我们的方法结合了神经网络和传统场景合成方法的强度。我们使用从训练数据中学到的参数上的分布，这提供了对象属性和相对属性的不确定性，以规范前馈神经模型的输出。此外，我们的方法不仅仅是预测场景布局，而不是预测场景布局。该方法允许我们利用预测属性之间的底层一致性约束来修剪不可行的预测。实验结果表明，我们的方法显着优于现有方法。生成的3D场景在保留连续和离散特征模式的同时忠实地插入训练数据。

相机图像信号处理（ISP）管道，包括深度学习训练的版本，可以在不同的图像信号处理任务中获得吸引力的结果。但是，大多数这些方法（如果不是全部）倾向于在整个图像上应用单个滤波器。当对任务训练编码器类型的深度体系结构时，也尤其如此。但是，自然要将摄像机图像视为异质，因为即使在单个图像的两个维度域上，颜色强度和人造噪声也大不相同。多样化的Moire响起，运动裂，颜色射或基于镜头的投影失真都可能导致异质图像伪影滤波问题。在本文中，我们提出了一个基于贴片的本地子空间深神经网络，该网络可改善相机ISP对异质伪像（尤其是图像denoising）具有稳健性。我们称我们的三倍训练的模型为补丁子空间学习自动编码器（PSL-AE）。 PSL-AE不一定假设图像失真级别，也不重复或相似的伪影类型。相反，PSL-AE首先诊断编码从嘈杂和干净的图像对提取的斑块，具有不同的人工类型和失真级别，相比之下。然后，使用先前的混合模型将每个图像的贴片编码为适当的潜在子空间的软群。最后，PSL-AE的解码器还以针对每个软群集中图像贴片的无监督方式进行训练。我们的实验结果表明，通过合成的伪影，又是现实的SIDD图像对，通过改进的异质过滤可以实现的灵活性和性能。

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

The ability to convert reciprocating, i.e., alternating, actuation into rotary motion using linkages is hindered fundamentally by their poor torque transmission capability around kinematic singularity configurations. Here, we harness the elastic potential energy of a linear spring attached to the coupler link of four-bar mechanisms to manipulate force transmission around the kinematic singularities. We developed a theoretical model to explore the parameter space for proper force transmission in slider-crank and rocker-crank four-bar kinematics. Finally, we verified the proposed model and methodology by building and testing a macro-scale prototype of a slider-crank mechanism. We expect this approach to enable the development of small-scale rotary engines and robotic devices with closed kinematic chains dealing with serial kinematic singularities, such as linkages and parallel manipulators.