Magnetic Resonance Fingerprinting (MRF) is an efficient quantitative MRI technique that can extract important tissue and system parameters such as T1, T2, B0, and B1 from a single scan. This property also makes it attractive for retrospectively synthesizing contrast-weighted images. In general, contrast-weighted images like T1-weighted, T2-weighted, etc., can be synthesized directly from parameter maps through spin-dynamics simulation (i.e., Bloch or Extended Phase Graph models). However, these approaches often exhibit artifacts due to imperfections in the mapping, the sequence modeling, and the data acquisition. Here we propose a supervised learning-based method that directly synthesizes contrast-weighted images from the MRF data without going through the quantitative mapping and spin-dynamics simulation. To implement our direct contrast synthesis (DCS) method, we deploy a conditional Generative Adversarial Network (GAN) framework and propose a multi-branch U-Net as the generator. The input MRF data are used to directly synthesize T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) images through supervised training on paired MRF and target spin echo-based contrast-weighted scans. In-vivo experiments demonstrate excellent image quality compared to simulation-based contrast synthesis and previous DCS methods, both visually as well as by quantitative metrics. We also demonstrate cases where our trained model is able to mitigate in-flow and spiral off-resonance artifacts that are typically seen in MRF reconstructions and thus more faithfully represent conventional spin echo-based contrast-weighted images.

对比性自我监督学习方法学会将图像（例如图像）映射到无需标签的情况下将图像映射到非参数表示空间中。尽管非常成功，但当前方法在训练阶段需要大量数据。在目标训练集规模限制的情况下，已知概括是差的。在大型源数据集和目标样本上进行微调进行预处理，容易在几杆方向上过度拟合，在几个弹药方面，只有少量的目标样本可用。在此激励的情况下，我们提出了一种用于自我监督的对比度学习的域适应方法，称为少数最大的学习方法，以解决对目标分布的适应问题，这些问题在几乎没有射击学习下。为了量化表示质量，我们在包括ImageNet，Visda和FastMRI在内的一系列源和目标数据集上评估了很少的最大最大速度，在这些数据集和FastMRI上，很少有最大最大的最大值始终优于其他方法。

Channel estimation is a critical task in multiple-input multiple-output (MIMO) digital communications that substantially effects end-to-end system performance. In this work, we introduce a novel approach for channel estimation using deep score-based generative models. A model is trained to estimate the gradient of the logarithm of a distribution and is used to iteratively refine estimates given measurements of a signal. We introduce a framework for training score-based generative models for wireless MIMO channels and performing channel estimation based on posterior sampling at test time. We derive theoretical robustness guarantees for channel estimation with posterior sampling in single-input single-output scenarios, and experimentally verify performance in the MIMO setting. Our results in simulated channels show competitive in-distribution performance, and robust out-of-distribution performance, with gains of up to $5$ dB in end-to-end coded communication performance compared to supervised deep learning methods. Simulations on the number of pilots show that high fidelity channel estimation with $25$% pilot density is possible for MIMO channel sizes of up to $64 \times 256$. Complexity analysis reveals that model size can efficiently trade performance for estimation latency, and that the proposed approach is competitive with compressed sensing in terms of floating-point operation (FLOP) count.

信道估计是数字通信中的一个关键任务，极大地影响了端到端系统性能。在这项工作中，我们使用深扩散模型介绍了一种用于多输入多输出（MIMO）信道估计的新方法。我们的方法使用深神经网络，该虚拟神经网络训练，以估计无线信道的任何点在高维空间中的无线信道的阶梯的梯度，并利用该模型通过后部采样解决信道估计。我们训练来自CDL-D模型的频道实现深度扩散模型，用于两个天线间距，表明，与生成的对抗网络（GaN）和压缩感测相比，​​该方法会导致竞争性和分发性能竞争和分发性能（CS ） 方法。当在训练期间从未见过的CDL-C信道测试或微调，我们的方法与CS方法和仅$ 0.5 $ DB的CS方法和损失相比，我们的方法导致最高$ 3 $ DB的最高元编码性能。理想的渠道知识。为了鼓励开放和可重复的研究，我们的源代码可以在https://github.com/utcsilab/diffusion-channels获得。

虽然开放数据库是深度学习（DL）时代的重要资源，但它们有时使用“Off-Label”：为一个任务发布的数据用于不同的数据。这项工作旨在强调在某些情况下，这种常见做法可能导致偏见，过于乐观的结果。我们展示了这种逆问题溶剂的这种现象，并展示了它们的偏置性能如何源于隐藏数据预处理管道。我们描述了两个典型的开放式访问数据库的预处理管道，并研究了对为磁共振成像（MRI）重建开发的三种熟发的算法的影响：压缩传感（CS），字典学习（DICTL）和DL。在这种大规模研究中，我们进行了广泛的计算。我们的结果表明，CS，DICTL和DL算法在看似适当的数据上天鹅训练时，CS，DICTL和DL算法产生了系统地偏见：归一化的根均方误差（NRMSE）随着预处理程度而一致地改善，显示人工增加25％-48％在某些情况下。由于这种现象通常是未知的，因此有时被公布为最先进的结果;我们将其称为细微的数据犯罪。因此，这项工作提出了关于大数据的天真的野外标签的红旗，并揭示了现代逆问题溶解于所产生的偏差的脆弱性。

CSGM框架（Bora-Jalal-Price-Dimakis'17）表明，深度生成前沿可能是解决逆问题的强大工具。但是，迄今为止，此框架仅在某些数据集（例如，人称和MNIST数字）上经验成功，并且已知在分布外样品上表现不佳。本文介绍了CSGM框架在临床MRI数据上的第一次成功应用。我们在FastMri DataSet上培训了大脑扫描之前的生成，并显示通过Langevin Dynamics的后验采样实现了高质量的重建。此外，我们的实验和理论表明，后部采样是对地面定语分布和测量过程的变化的强大。我们的代码和型号可用于：\ URL {https://github.com/utcsilab/csgm-mri-langevin}。

超越地球轨道的人类空间勘探将涉及大量距离和持续时间的任务。为了有效减轻无数空间健康危害，数据和空间健康系统的范式转移是实现地球独立性的，而不是Earth-Reliance所必需的。有希望在生物学和健康的人工智能和机器学习领域的发展可以解决这些需求。我们提出了一个适当的自主和智能精密空间健康系统，可以监控，汇总和评估生物医学状态;分析和预测个性化不良健康结果;适应并响应新累积的数据;并提供对其船员医务人员的个人深度空间机组人员和迭代决策支持的预防性，可操作和及时的见解。在这里，我们介绍了美国国家航空航天局组织的研讨会的建议摘要，以便在太空生物学和健康中未来的人工智能应用。在未来十年，生物监测技术，生物标志科学，航天器硬件，智能软件和简化的数据管理必须成熟，并编织成精确的空间健康系统，以使人类在深空中茁壮成长。

空间生物学研究旨在了解太空飞行对生物的根本影响，制定支持深度空间探索的基础知识，最终生物工程航天器和栖息地稳定植物，农作物，微生物，动物和人类的生态系统，为持续的多行星寿命稳定。要提高这些目标，该领域利用了来自星空和地下模拟研究的实验，平台，数据和模型生物。由于研究扩展到低地球轨道之外，实验和平台必须是最大自主，光，敏捷和智能化，以加快知识发现。在这里，我们介绍了由美国国家航空航天局的人工智能，机器学习和建模应用程序组织的研讨会的建议摘要，这些应用程序为这些空间生物学挑战提供了关键解决方案。在未来十年中，将人工智能融入太空生物学领域将深化天空效应的生物学理解，促进预测性建模和分析，支持最大自主和可重复的实验，并有效地管理星载数据和元数据，所有目标使生活能够在深空中茁壮成长。

Accurate determination of a small molecule candidate (ligand) binding pose in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ignore the pocket flexibility of protein, while the more accurate pose generation using molecular dynamics is hindered by slow protein dynamics. We develop a tiered tensor transform (3T) algorithm to rapidly generate diverse protein-ligand complex conformations for both pose and affinity estimation in drug screening, requiring neither machine learning training nor lengthy dynamics computation, while maintaining both coarse-grain-like coordinated protein dynamics and atomistic-level details of the complex pocket. The 3T conformation structures we generate are closer to experimental co-crystal structures than those generated by docking software, and more importantly achieve significantly higher accuracy in active ligand classification than traditional ensemble docking using hundreds of experimental protein conformations. 3T structure transformation is decoupled from the system physics, making future usage in other computational scientific domains possible.

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.