HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence

The automation of railroad operations is a rapidly growing industry. In 2023, a new European standard for the automated Grade of Automation (GoA) 2 over European Train Control System (ETCS) driving is anticipated. Meanwhile, railway stakeholders are already planning their research initiatives for driverless and unattended autonomous driving systems. As a result, the industry is particularly active in research regarding perception technologies based on Computer Vision (CV) and Artificial Intelligence (AI), with outstanding results at the application level. However, executing high-performance and safety-critical applications on embedded systems and in real-time is a challenge. There are not many commercially available solutions, since High-Performance Computing (HPC) platforms are typically seen as being beyond the business of safety-critical systems. This work proposes a novel safety-critical and high-performance computing platform for CV- and AI-enhanced technology execution used for automatic accurate stopping and safe passenger transfer railway functionalities. The resulting computing platform is compatible with the majority of widely-used AI inference methodologies, AI model architectures, and AI model formats thanks to its design, which enables process separation, redundant execution, and HW acceleration in a transparent manner. The proposed technology increases the portability of railway applications into embedded systems, isolates crucial operations, and effectively and securely maintains system resources.The novel approach presented in this work is being developed as a specific railway use case for autonomous train operation into SELENE European research project. This project has received funding from RIA—Research and Innovation action under grant agreement No. 871467

Brain Mapping of Behavioral Domains Using Multi-Scale Networks and Canonical Correlation Analysis

Simultaneous mapping of multiple behavioral domains into brain networks remains a major challenge. Here, we shed some light on this problem by employing a combination of machine learning, structural and functional brain networks at different spatial resolutions (also known as scales), together with performance scores across multiple neurobehavioral domains, including sensation, motor skills, and cognition. Provided by the Human Connectome Project, we make use of three cohorts: 640 participants for model training, 160 subjects for validation, and 200 subjects for model performance testing thus enhancing prediction generalization. Our modeling consists of two main stages, namely dimensionality reduction in brain network features at multiple scales, followed by canonical correlation analysis, which determines an optimal linear combination of connectivity features to predict multiple behavioral performance scores. To assess the differences in the predictive power of each modality, we separately applied three different strategies: structural unimodal, functional unimodal, and multimodal, that is, structural in combination with functional features of the brain network. Our results show that the multimodal association outperforms any of the unimodal analyses. Then, to answer which human brain structures were most involved in predicting multiple behavioral scores, we simulated different synthetic scenarios in which in each case we completely deleted a brain structure or a complete resting state network, and recalculated performance in its absence. In deletions, we found critical structures to affect performance when predicting single behavioral domains, but this occurred in a lesser manner for prediction of multi-domain behavior. Overall, our results confirm that although there are synergistic contributions between brain structure and function that enhance behavioral prediction, brain networks may also be mutually redundant in predicting multidomain behavior, such that even after deletion of a structure, the connectivity of the others can compensate for its lack in predicting behavior.JC was funded by Ikerbasque: The Basque Foundation for Science and by the Department of Economic Development and Infrastructure of the Basque Country (Elkartek Program Grant KK-2021-00009). AJ-M was funded by a predoctoral contract from the Department of Education of the Basque Country Predoctoral Program PRE-2019-1-0070. IF-I was funded by a research assistant contract from the University of the Basque Country (Elkartek Program Grant KK-2021/00033). PB acknowledge financial support from Ikerbasque (The Basque Foundation for Science) and FEDER (AI-2021-039)

Multimedia edukien ulerpen semantikorako ekarpen metodologikoak: irudien behe-mailako analisitik bideoen ekintzen sailkapenera

203 p.Konputagailu bidezko ikusmenaren zientziaren helburua irudi eta bideoen ulerpen automatikoa lortzea da, hau da, sistemak adimen artifizialarekin hornitzeko bidea. Azken urteotan multimedia edukiek izan duten hazkundearen ondorioz, beharrezkoak dira datu kantitate handi horiek maneiatu, aztertu eta erabiltzeko aukera ematen dituzten erremintak.Ikerketa honen helburu nagusia irudi eta bideoen ezagutzarako metodo eta metodologia berriak aztertu, garatu eta aurkeztea da. Egindako lana, domeinu finko eta itxi bateko edukiaren azterketarako metodoen garapenean oinarritzen da. Hiru lerro nagusitan banatu da aurrera eraman den ikerketa. Lehenengoa, irudien ulerpen semantikorako behe-mailako deskriptoreen erabilera eta etiketatu semantikora bideratutako metodoen garapenera bideratutako lerroa izan da. Bigarren lerroa, lehenengoaren helburu berdina izanik, ikasketa automatikoko metodoen erabilera aztertzera bideratu da. Azkenik eta aurretik erabilitako ezagupena erabiliz, ikerketa bideoen analitikara bideratu da.Konputagailu bidezko ikusmenaren erronkari irtenbidea aurkitzeko bidean oso emaitza adierazgarriak lortu dira