The QUILT-1M dataset is the first openly available dataset containing images
harvested from various online sources. While it provides a huge data variety,
the image quality and composition is highly heterogeneous, impacting its
utility for text-conditional image synthesis. We propose an automatic pipeline
that provides predictions of the most common impurities within the images,
e.g., visibility of narrators, desktop environment and pathology software, or
text within the image. Additionally, we propose to use semantic alignment
filtering of the image-text pairs. Our findings demonstrate that by rigorously
filtering the dataset, there is a substantial enhancement of image fidelity in
text-to-image tasks.

通过使用自动化流程和语义对齐筛选图像与文本对，我们的研究发现从 QUILT-1M 数据集中滤除常见杂质可显著提高文本到图像任务中的图像保真度。

基于模型的 QUILT-1M 病理学数据集清洗用于文本条件图像合成

Model-based Cleaning of the QUILT-1M Pathology Dataset for  Text-Conditional Image Synthesis

Mechanistic understanding of organic reactions can facilitate reaction
development, impurity prediction, and in principle, reaction discovery. While
several machine learning models have sought to address the task of predicting
reaction products, their extension to predicting reaction mechanisms has been
impeded by the lack of a corresponding mechanistic dataset. In this study, we
construct such a dataset by imputing intermediates between experimentally
reported reactants and products using expert reaction templates and train
several machine learning models on the resulting dataset of 5,184,184
elementary steps. We explore the performance and capabilities of these models,
focusing on their ability to predict reaction pathways and recapitulate the
roles of catalysts and reagents. Additionally, we demonstrate the potential of
mechanistic models in predicting impurities, often overlooked by conventional
models. We conclude by evaluating the generalizability of mechanistic models to
new reaction types, revealing challenges related to dataset diversity,
consecutive predictions, and violations of atom conservation.

通过机理模型，我们可以预测反应产物、发现新反应，并预测杂质。本研究构建了一个数据集，通过专家反应模板在实验报告的反应物和产物之间填补中间体，然后使用机器学习模型对该数据集进行训练。我们探索了模型的性能和能力，特别关注其预测反应途径以及催化剂和试剂的作用。此外，机理模型在预测杂质方面具有潜力，这通常被传统模型所忽视。最后，我们评估了机理模型在新的反应类型上的推广能力，并揭示了数据集多样性、连续预测和原子守恒违规方面的挑战。

超越主要产品预测：基于大规模机制数据集训练的机器学习模型再现反应机理

Beyond Major Product Prediction: Reproducing Reaction Mechanisms with  Machine Learning Models Trained on a Large-Scale Mechanistic Dataset

Detection and characterization of hidden defects, impurities, and damages in
layered composites like Fibre laminates, e.g., Fibre Metal Laminates (FML), as
well as in monolithic materials, e.g., aluminum die casting materials, is still
a challenge. This work discusses methods and challenges in data-driven modeling
of automated damage and defect detectors using X-ray single- and
multi-projection (CT) images. Three main issues are identified: Data and
feature variance, data feature labeling (for supervised machine learning), and
the missing ground truth. It will be shown that only simulation of data can
deliver a ground truth data set and accurate labeling. Noise has significant
impact on the feature detection and will be discussed. Data-driven feature
detectors are implemented with semantic pixel- or z-profile Convolutional
Neural Networks and LSTM Auto-encoders. Data is measured with three different
devices: A low-quality and low-cost (Low-Q), a mid- and a high-quality
(micro-CT, Mid-/High-Q) device. The goals of this work are the training of
robust and generalized feature detectors with synthetic data and the transition
from High- and Mid-Q laboratory measuring technologies towards in-field usable
technologies and methods.

使用 X 射线单投影和多投影（CT）图像进行自动损伤和缺陷检测的基于数据驱动建模的方法和挑战，主要包括数据和特征的变异性，数据特征标注（用于监督机器学习），以及缺乏地面实况数据等。

利用低质量 X 射线摄影术、通过模拟进行的合成 X 射线数据增强，以及机器学习自动检测铝合金压铸材料和纤维金属层压板中的隐藏损伤和杂质

Automated Detection of hidden Damages and Impurities in Aluminum Die  Casting Materials and Fibre-Metal Laminates using Low-quality X-ray  Radiography, Synthetic X-ray Data Augmentation by Simulation, and Machine  Learning