SlimML: Removing Non- critical Input Data in Large-scale Iterative Machine Learning

The core of many large-scale machine learning (ML) applications, such as neural networks (NN), support vector machine (SVM), and convolutional neural network (CNN), is the training algorithm that iteratively updates model parameters by processing massive datasets. From a plethora of studies aiming at accelerating ML, being data...

Current and future trends in topology optimization for additive manufacturing

Manufacturing-oriented topology optimization has been extensively studied the past two decades, in particular for the conventional manufacturing methods, for example, machining and injection molding or casting. Both design and manufacturing engineers have benefited from these efforts because of the close-to-optimal and friendly-to-manufacture...

A highly selective and stable ZnO-ZrO₂ solid solution catalyst for CO₂ hydrogenation to methanol

Although methanol synthesis via CO hydrogenation has been industrialized, CO₂ hydrogenation to methanol still confronts great obstacles of low methanol selectivity and poor stability, particularly for supported metal catalysts under industrial conditions. We report a binary metal oxide, ZnO-ZrO₂ solid solution catalyst,...