Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification

We present 'AiTLAS: Benchmark Arena' -- an open-source benchmark framework for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, we present a comprehensive comparative analysis of more than 400 models derived from nine different state-of-the-art architectures, and compare them to a variety of multi-class and multi-label classification tasks from 22 datasets with different sizes and properties. In addition to models trained entirely on these datasets, we also benchmark models trained in the context of transfer learning, leveraging pre-trained model variants, as it is typically performed in practice. All presented approaches are general and can be easily extended to many other remote sensing image classification tasks not considered in this study. To ensure reproducibility and facilitate better usability and further developments, all of the experimental resources including the trained models, model configurations and processing details of the datasets (with their corresponding splits used for training and evaluating the models) are publicly available on the repository: https://github.com/biasvariancelabs/aitlas-arena

In-Domain Self-Supervised Learning Can Lead to Improvements in Remote Sensing Image Classification

Self-supervised learning (SSL) has emerged as a promising approach for remote sensing image classification due to its ability to leverage large amounts of unlabeled data. In contrast to traditional supervised learning, SSL aims to learn representations of data without the need for explicit labels. This is achieved by formulating auxiliary tasks that can be used to create pseudo-labels for the unlabeled data and learn pre-trained models. The pre-trained models can then be fine-tuned on downstream tasks such as remote sensing image scene classification. The paper analyzes the effectiveness of SSL pre-training using Million AID - a large unlabeled remote sensing dataset on various remote sensing image scene classification datasets as downstream tasks. More specifically, we evaluate the effectiveness of SSL pre-training using the iBOT framework coupled with Vision transformers (ViT) in contrast to supervised pre-training of ViT using the ImageNet dataset. The comprehensive experimental work across 14 datasets with diverse properties reveals that in-domain SSL leads to improved predictive performance of models compared to the supervised counterparts

With food to health : proceedings of the 10th International scientific and professional conference

Proceedings contains 13 original scientific papers, 10 professional papers and 2 review papers which were presented at "10th International Scientific and Professional Conference WITH FOOD TO HEALTH", organised in following sections: Nutrition, Dietetics and diet therapy, Functional food and food supplemnents, Food safety, Food analysis, Production of safe food and food with added nutritional value

Significant Improvements in Glycaemic Control without Weight Gain with Insulin Detemir in Clinical Reality: Experience from Macedonian Clinical Practice

Unwanted weight gain is a recognized side-effect of insulin therapy, and can act as a barrier to insulin initiation and intensification. This prospective, multi-center, 24-week observational study explored efficacy, weight change and safety for insulin detemir (IDet) use in type 1 and 2 diabetes patients under normal clinical practice conditions in Macedonia. Results presented are for 1053 patients on various regimens from 30 diabetes centers (44.7% male; mean age 60.0±12.3 y; type 2 diabetes 93.4%; diabetes’ duration 8.7±6.4 y; BMI 28.1±4.9 kg/m2) inadequately controlled on prior treatment (50.8% with OAD; 38.7% with insulin). Most patients were initiated with (76.6%) and/or completed the study (80.3%) on once-daily IDet. Baseline and 24-week total daily insulin doses (U/day) were 25.2±10.9 and 29.4±9.7, respectively. For patients on basal–bolus therapy, total daily bolus insulin (insulin aspart) dose was reduced from 27.0±11.9 U at baseline to 22.1±13.0 U at week 24 (n=197). At 24 weeks, mean HbA1c and FPG were significantly improved compared to baseline. The proportion of patients achieving target HbA1c <7% increased from baseline (11.7% vs. 38.8% at week 24). Despite an improvement in HbA1c of 1.78%, there was a trend for modest weight reduction (Table). No major hypoglycemic events were reported and no safety issues were raised during the study. Our findings support data from randomized controlled trials that show once-daily IDet can be used to bring about clinically important improvements in glycemic control without weight gain