Establishing and evaluating trustworthy AI: overview and research challenges.

Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions about aspects that AI systems must fulfill to be considered trustworthy.

Machine learning based stellar classification with highly sparse photometry data.

Background: Identifying stars belonging to different classes is vital in order to build up statistical samples of different phases and pathways of stellar evolution. In the era of surveys covering billions of stars, an automated method of identifying these classes becomes necessary.

Methods: Many classes of stars are identified based on their emitted spectra.

Recent advances in understanding the neurobiology of pediatric functional neurological disorder.

Introduction: Functional neurological disorder (FND) is a neuropsychiatric disorder that manifests in a broad array of functional motor, sensory, or cognitive symptoms, which arise from complex interactions between brain, mind, body, and context. Children with FND make up 10%-20% of presentations to neurology services in children's hospitals and up to 20% of adolescents admitted to hospital for the management of intractable seizures.

Areas Covered: The current review focuses on the neurobiology of pediatric FND.

Illness-Promoting Psychological Processes in Children and Adolescents with Functional Neurological Disorder.

Previous studies suggest that subjective distress in children with functional neurological disorder (FND) is associated with stress-system dysregulation and modulates aberrant changes in neural networks. The current study documents illness-promoting psychological processes in 76 children with FND (60 girls and 16 boys, aged 10.00-17.