The Hunger Games: Stable Isotopes Indicate Winter Inter-Guild Competition for Resources by Marine Meso-Predators in the Sub-Arctic North Pacific.

Interspecific competition can significantly impact marine ecosystems by affecting species distributions and abundances. Understanding how sympatric species utilize available food helps identify potential competition and its effects when resources are limited. Here, we applied a suite of analytical methods (diet analysis, stable isotopes, and biomass estimates) to identify potential competitive interactions among North Pacific pelagic predators.

Data-centric approach for instance segmentation in optical waste sorting.

Computer vision systems have been integrated into facilities dealing with the sorting of household waste. This solution allows for the sorting efficiency improvement and cost reduction. However, challenges associated with the poor annotation quality of existing waste segmentation datasets, unsuitable environment for recognition on a conveyor belt, or limited data for creating an effective and cost-efficient sorting system using visible range cameras significantly limit the application efficiency of computer vision systems.

Deep Learning in Precision Agriculture: Artificially Generated VNIR Images Segmentation for Early Postharvest Decay Prediction in Apples.

Food quality control is an important task in the agricultural domain at the postharvest stage for avoiding food losses. The latest achievements in image processing with deep learning (DL) and computer vision (CV) approaches provide a number of effective tools based on the image colorization and image-to-image translation for plant quality control at the postharvest stage. In this article, we propose the approach based on Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) techniques to use synthesized and segmented VNIR imaging data for early postharvest decay and fungal zone predictions as well as the quality assessment of stored apples.

AI-enabled prediction of video game player performance using the data from heterogeneous sensors.

The emerging progress of video gaming and eSports lacks the tools for ensuring high-quality analytics and training in professional and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. For this reason, we collected the physiological, environmental, and the smart chair data from professional and amateur players.