Elektrobit (Austria)

Abstract The new Austrian primary standard for the amount of substance fraction of carbon dioxide in nitrogen is presented. The primary standard is working according to the dynamic volumetric principle and is a fundamental representation of the measurement quantity. The direct traceability to the basic SI units is thereby thermodynamically realized without an artefact. A complete measurement uncertainty budget is calculated and verified with a bilateral inter-laboratory comparison.

Accurate classification of remote sensing (RS) images is perennial topic of interest in the RS community. Recently, transfer learning, especially for fine-tuning pre-trained convolutional neural networks (CNNs), has been proposed as a feasible strategy for RS scene classification. However, because the target domain (i.e., the RS images) and the source domain (e.g., ImageNet) are quite different, simply using the model pre-trained on an ImageNet dataset presents some difficulties. The RS images and the pre-trained models need to be properly adjusted to build a better classification system. In this study, an adaptive learning strategy for transferring a CNN-based model is proposed. First, an adaptive transform is used to adjust the original size of the RS image to a certain size, which is tailored to the input of the subsequent pre-trained model. Then, an adaptive transferring model is proposed to automatically learn what knowledge from the pre-trained model should be transferred to the RS scene classification model. Finally, in combination with a label smoothing approach, adaptive label is presented to generate soft labels based on the statistics of the classification model predictions for each category, which is beneficial for learning the relationships between the target and non-target categories of scenes. In general, the proposed methods adaptively manage the input, model, and label simultaneously, which leads to better classification performance for RS scene classification. The proposed methods are tested on three widely-used data sets and the obtained results show that the proposed methods provide competitive classification accuracy compared to the state-of-the-art methods.

Until 2005 the software development in the area of mainstream computing enjoyed a free lunch profiting from the exponential growth of transistor densities and resulting clock speeds of Moore's Law. Existing software benefitted from hardware changes without requiring modification. In 2005, however, mainstream computing hit a wall due to the limit in increasing the clock speed. As a result, chip manufacturers started to turn towards hyperthreading and homogeneous and later on heterogeneous multi-core architectures in order to achieve further performance gains. To benefit from these hardware changes, existing software had to be parallelized and modified to deal with the heterogeneity. The period of the unicore free lunch was over.