机器学习（ML）是人工智能（AI）的子场，其放射学中的应用正在以不断加速的速度增长。研究最多的ML应用程序是图像的自动解释。但是，可以将自然语言处理（NLP）与文本解释任务组合的ML结合使用，在放射学中也具有许多潜在的应用。一种这样的应用是放射学原始胶体的自动化，涉及解释临床放射学转介并选择适当的成像技术。这是一项必不可少的任务，可确保执行正确的成像。但是，放射科医生必须将专门用于原始胶片的时间进行报告，与推荐人或教学进行报告，交流。迄今为止，很少有使用临床文本自动选择协议选择的ML模型的出版物。本文回顾了该领域的现有文献。参考机器学习公约建议的最佳实践对已发布模型进行系统评估。讨论了在临床环境中实施自动质胶的进展。

来自光学相干断层造影血管造影（OctA）的en面部图像的变形缺陷区（FAZ）是基于该技术的最常见的测量之一。然而，它在诊所的使用受到正常对象的FAZ区域的高变化的限制，而FAZ的体积测量的计算受到Octa扫描表征的高噪音的限制。我们设计了一种算法，该算法利用EN面图像的较高信噪比，以便在单独的丛中的毛细管不重叠的情况下有效地识别3维度（3D）中的内视网膜的毛细管网络。然后通过形态学操作处理网络以识别内视网膜的边界分割内的3D FAZ。为430只眼的数据集计算了不同丛的FAZ音量和区域。然后，使用线性混合效果模型进行测量以识别三组眼睛之间的差异：健康，糖尿病，没有糖尿病视网膜病变（DR）和糖尿病患者。结果表明，不同组之间的FAZ体积差异显着差异，但不在面积测量中。这些结果表明，比平面FAZ，体积FAZ可能是一个更好的诊断探测器。我们介绍的有效方法可以允许在诊所的FAZ音量快速计算，以及提供内视网膜毛细管网络的3D分段。

基于自我监督的基于学习的预科可以使用小标签的数据集开发可靠和广义的深度学习模型，从而减轻了标签生成的负担。本文旨在评估基于CL的预处理对可转介的性能与非转介糖尿病性视网膜病（DR）分类的影响。我们已经开发了一个基于CL的框架，具有神经风格转移（NST）增强，以生成具有更好表示和初始化的模型，以检测颜色底面图像中的DR。我们将CL预估计的模型性能与用成像网权重预测的两个最先进的基线模型进行了比较。我们通过减少标记的训练数据（降至10％）进一步研究模型性能，以测试使用小标签数据集训练模型的鲁棒性。该模型在EYEPACS数据集上进行了培训和验证，并根据芝加哥伊利诺伊大学（UIC）的临床数据进行了独立测试。与基线模型相比，我们的CL预处理的基础网模型具有更高的AUC（CI）值（0.91（0.898至0.930），在UIC数据上为0.80（0.783至0.820）和0.83（0.783至0.820）（0.801至0.853）。在10％标记的培训数据时，在UIC数据集上测试时，基线模型中的FoldusNet AUC为0.81（0.78至0.84），比0.58（0.56至0.64）和0.63（0.56至0.64）和0.63（0.60至0.66）。基于CL的NST预处理可显着提高DL分类性能，帮助模型良好（可从Eyepacs转移到UIC数据），并允许使用小的带注释的数据集进行培训，从而减少临床医生的地面真相注释负担。

Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning. In the application of high-throughput thickness as a downstream process of the high-throughput optimization of optoelectronic thin films with autonomous workflows, data scarcity occurs especially for new materials. To achieve high-throughput thickness characterization, we propose a machine learning model called thicknessML that predicts thickness from UV-Vis spectrophotometry input and an overarching transfer learning workflow. We demonstrate the transfer learning workflow from generic source domain of generic band-gapped materials to specific target domain of perovskite materials, where the target domain data only come from limited number (18) of refractive indices from literature. The target domain can be easily extended to other material classes with a few literature data. Defining thickness prediction accuracy to be within-10% deviation, thicknessML achieves 92.2% (with a deviation of 3.6%) accuracy with transfer learning compared to 81.8% (with a deviation of 3.6%) 11.7% without (lower mean and larger standard deviation). Experimental validation on six deposited perovskite films also corroborates the efficacy of the proposed workflow by yielding a 10.5% mean absolute percentage error (MAPE).

通过在未标记的目标域中应用良好的模型，通过对标记的源域的监督应用了良好的模型，已经通过对未标记的目标域应用了良好的模型，对无监督的域适应（UDA）进行了大量探索，以减轻源和目标域之间的域变化。然而，最近的文献表明，在存在重大领域变化的情况下，性能仍然远非令人满意。但是，由于绩效的实质性增长，划定一些目标样本通常是易于管理的，尤其是值得的。受此启发的启发，我们旨在开发半监督域的适应性（SSDA）进行医学图像分割，这在很大程度上没有被置于脑海中。因此，除了以统一的方式使用未标记的目标数据外，我们建议利用标记的源和目标域数据。具体而言，我们提出了一种新型的不对称共同训练（ACT）框架，以整合这些子集并避免源域数据的统治。遵循分歧和纠纷策略，我们将SSDA的标签监督分为两个不对称的子任务，包括半监督学习（SSL）和UDA，并利用两个细分市场的不同知识来考虑在两个部分之间的区别，以考虑到不同的知识。来源和目标标签监督。然后，在两个模块中学习的知识与ACT自适应地整合，通过基于置信度的伪标签进行迭代教学。此外，伪标签噪声与指数混合衰减方案可以很好地控制，以进行平滑传播。使用BRATS18数据库进行跨模式脑肿瘤MRI分割任务的实验表明，即使标记有限的目标样本，ACT也对UDA和最先进的SSDA方法产生了明显的改进，并接近了受监督的联合训练的“上限” 。

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.

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.

Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based Neural Architecture Search (NAS) method. Since the introduction of DARTS, there has been little work done on adapting the action space based on state-of-art architecture design principles for CNNs. In this work, we aim to address this gap by incrementally augmenting the DARTS search space with micro-design changes inspired by ConvNeXt and studying the trade-off between accuracy, evaluation layer count, and computational cost. To this end, we introduce the Pseudo-Inverted Bottleneck conv block intending to reduce the computational footprint of the inverted bottleneck block proposed in ConvNeXt. Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2. Furthermore, with less layers, not only does it achieve higher accuracy with lower GMACs and parameter count, GradCAM comparisons show that our network is able to better detect distinctive features of target objects compared to DARTS.

Deep learning techniques with neural networks have been used effectively in computational fluid dynamics (CFD) to obtain solutions to nonlinear differential equations. This paper presents a physics-informed neural network (PINN) approach to solve the Blasius function. This method eliminates the process of changing the non-linear differential equation to an initial value problem. Also, it tackles the convergence issue arising in the conventional series solution. It is seen that this method produces results that are at par with the numerical and conventional methods. The solution is extended to the negative axis to show that PINNs capture the singularity of the function at $\eta=-5.69$

The Government of Kerala had increased the frequency of supply of free food kits owing to the pandemic, however, these items were static and not indicative of the personal preferences of the consumers. This paper conducts a comparative analysis of various clustering techniques on a scaled-down version of a real-world dataset obtained through a conjoint analysis-based survey. Clustering carried out by centroid-based methods such as k means is analyzed and the results are plotted along with SVD, and finally, a conclusion is reached as to which among the two is better. Once the clusters have been formulated, commodities are also decided upon for each cluster. Also, clustering is further enhanced by reassignment, based on a specific cluster loss threshold. Thus, the most efficacious clustering technique for designing a food kit tailored to the needs of individuals is finally obtained.