Pedestrian motion prediction is a key part of the modular-based autonomous driving pipeline, ensuring safe, accurate, and timely awareness of human agents' possible future trajectories. The autonomous vehicle can use this information to prevent any possible accidents and create a comfortable and pleasant driving experience for the passengers and pedestrians. A wealth of research was done on the topic from the authors of robotics, computer vision, intelligent transportation systems, and other fields. However, a relatively unexplored angle is the integration of the state-of-art solutions into existing autonomous driving stacks and evaluating them in real-life conditions rather than sanitized datasets. We analyze selected publications with provided open-source solutions and provide a perspective obtained by integrating them into existing Autonomous Driving framework - Autoware Mini and performing experiments in natural urban conditions in Tartu, Estonia to determine valuability of traditional motion prediction metrics. This perspective should be valuable to any potential autonomous driving or robotics engineer looking for the real-world performance of the existing state-of-art pedestrian motion prediction problem. The code with instructions on accessing the dataset is available at https://github.com/dmytrozabolotnii/autoware_mini.

Deep learning is dramatically transforming the field of medical imaging and radiology, enabling the identification of pathologies in medical images, including computed tomography (CT) and X-ray scans. However, the performance of deep learning models, particularly in segmentation tasks, is often limited by the need for extensive annotated datasets. To address this challenge, the capabilities of weakly supervised semantic segmentation are explored through the lens of Explainable AI and the generation of counterfactual explanations. The scope of this research is development of a novel counterfactual inpainting approach (COIN) that flips the predicted classification label from abnormal to normal by using a generative model. For instance, if the classifier deems an input medical image X as abnormal, indicating the presence of a pathology, the generative model aims to inpaint the abnormal region, thus reversing the classifier's original prediction label. The approach enables us to produce precise segmentations for pathologies without depending on pre-existing segmentation masks. Crucially, image-level labels are utilized, which are substantially easier to acquire than creating detailed segmentation masks. The effectiveness of the method is demonstrated by segmenting synthetic targets and actual kidney tumors from CT images acquired from Tartu University Hospital in Estonia. The findings indicate that COIN greatly surpasses established attribution methods, such as RISE, ScoreCAM, and LayerCAM, as well as an alternative counterfactual explanation method introduced by Singla et al. This evidence suggests that COIN is a promising approach for semantic segmentation of tumors in CT images, and presents a step forward in making deep learning applications more accessible and effective in healthcare, where annotated data is scarce.

Mozilla has created a translation plugin for Firefox that works offline. Firefox Translations will need to download some files the first time you convert text in a specific language. However, it will be able to use your system's resources to handle the translation, rather than sending the information to a data center for cloud processing. The plugin emerged as a result of Mozilla's work with the European Union-funded Project Bergamot. Others involved include the University of Edinburgh, Charles University, University of Sheffield and University of Tartu.

The Tin Woodman first appeared in Frank Baum's The Wonderful Wizard of Oz 120 years ago. Now real robot foresters are making their debut, planting trees rather than cutting them down. The robotic foresters are the work of robot makers Milrem in partnership with the University of Tartu, both based in Estonia. Two versions are under development based on the company's range of driverless ground vehicles. One type is a planter, the other a brush cutter, and both are autonomous.

The Tin Woodman first appeared in Frank Baum's The Wonderful Wizard of Oz 120 years ago. Now real robot foresters are making their debut, planting trees rather than cutting them down. The robotic foresters are the work of robot makers Milrem in partnership with the University of Tartu, both based in Estonia. Two versions are under development based on the company's range of driverless ground vehicles. One type is a planter, the other a brush cutter, and both are autonomous.

An artificial intelligence system has identified a previously unknown human ancestor that roamed the planet tens of thousands of years ago and left a genomic footprint in Asian individuals, scientists say. By combining deep learning algorithms and statistical methods, researchers from the University of Tartu in Estonia, Institute of Evolutionary Biology (IBE), and the Centre for Genomic Regulation (CRG) in Spain found that the extinct species was a hybrid of Neanderthals and Denisovans and cross bred with modern humans in Asia. The finding, published in Nature Communications, would explain that the hybrid found last year in the caves of Denisova -- the offspring of a Neanderthal mother and a Denisovan father -- was not an isolated case, but rather was part of a more general introgression process. Researchers used deep learning for the first time ever to account for human evolution, paving the way for the application of this technology in other questions in biology, genomics and evolution. One of the ways of distinguishing between two species is that while both of them may cross breed, they do not generally produce fertile descendants.

The new research comes from Institute of Evolutionary Biology (IBE), the Centro Nacional de Análisis Genómico (CNAG-CRG) of the Centre for Genomic Regulation (CRG) and the Institute of Genomics at the University of Tartu. In studies researchers have applied deep learning algorithms and statistical methods to establish the footprint of a new hominid. The application of human DNA computational analysis indicates that the extinct species was a hybrid of Neanderthals and Denisovans. At some stage this hominid cross bred with'Out of Africa' modern humans within the region of the world that is now Asia. The scientific theory of recent African origin of modern humans is the most widely accepted model of the geographic origin and early migration of anatomically modern humans (Homo sapiens).

Researchers have identified what may be a previously unknown human ancestor, thanks to the help of artificial intelligence. A new investigation into the genome of Asian populations has spotted the footprint of a long-ago hominid that appears to have been bred from two different species of human ancestor – Neanderthal and Denisovan. This ancient hominid, who lived tens of thousands of years ago, then bred with modern humans who arrived to Asia after the'Out of Africa' migration. It comes just months after a different team revealed the discovery of a hybrid'love child' born from a Neanderthal mother and a Denisovan father. And, the new research from the Institute of Evolutionary Biology (IBE), Centro Nacional de Análisis Genómico (CNAG-CRG) of the Centre for Genomic Regulation (CRG), and the Institute of Genomics at the University of Tartu suggests such hominid hybrids may not have been all that uncommon after all.