Automated detection has become the essential safeguard against realistic synthetic media, but deploying it at scale requires training on sensitive data from multiple organizations, putting privacy and the transparency demanded by forensic review in direct conflict. Federated learning resolves the p…

ウクライナ軍は、前線にあるロシア製のBM-21グラード多連装ロケット砲システムを破壊するため、自爆ドローン（UAV）を配備した。

Join our one-day online workshop, AI-Powered Image Analysis, designed for PhD students, professors, and professional researchers working with biological, microscopy, high-throughput, or clinical imaging data. Led by Nikolay Oskolkov, Group Leader/PI at LIOS, this intensive hands-on seminar bridges deep learning theory and practical implementation, covering CNNs, transfer learning, UNet, Mask R-CNN, object detection, Bayesian uncertainty, GRAD-CAM interpretability, OpenCV, OTSU thresholding, and reproducible Python workflows in Jupyter on Linux. Participants will learn how to build publication-ready image analysis pipelines for classification, segmentation, detection, cell counting, morphometrics, and biologically meaningful model interpretation, while also using ChatGPT-assisted coding to accelerate prototyping, debugging, and documentation. The seminar includes live Zoom instruction, practical coding examples, access to recordings and materials for 30 days, a monitored Q&A forum, and an official Instats certificate of completion. Whether you are developing methods for a thesis, paper, grant, or research project, this workshop will help you turn complex image datasets into rigorous, reproducible, and publishable results. #ComputationalBiology #DataAnalytics #DataScience #MachineLearning #BiomedicalEngineering #Bioinformatics #ComputerScience #Engineering #Statistics #ChatGPT #Jupyter #Linux #Python #R #Research #ResearchTraining #Instats

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Turn image data into publication-ready insights with our one-day online workshop on AI-powered image analysis for PhD students, professors, and professional researchers, led by Nikolay Oskolkov, Group Leader/PI at LIOS. This intensive, hands-on seminar bridges rigorous theory and real-world implementation, covering CNNs, transfer learning, UNet, Mask R-CNN, object detection, Bayesian uncertainty quantification, GRAD-CAM interpretability, OpenCV, OTSU thresholding, reproducible Jupyter workflows on Linux, and effective ChatGPT-assisted coding in Python. Participants will learn how to build, train, evaluate, and interpret image classification, segmentation, and detection pipelines for biological, microscopy, high-throughput, and clinical imaging data, with a strong focus on reproducibility, statistical reporting, experiment design, and publication-quality outputs. All livestream seminars are delivered via Zoom, with recordings, materials, and a monitored Q&A forum available for 30 days after the seminar, plus an official Instats certificate of completion. #ComputationalBiology #DataAnalytics #DataScience #MachineLearning #BiomedicalEngineering #Bioinformatics #ComputerScience #ArtificialIntelligence #Statistics #SoftwareEngineering #Biology #ChatGPT #Jupyter #Linux #Python #R #Research #ResearchTraining #Instats

Combining YOLO object detection with Grad-CAM, SHAP, and LiDAR to make autonomous vehicle decisions explainable in real time — and what broke along the way.