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We propose to run first-of-a-kind simulations of the embedded phase of star formation, covering the crucial time when protoplanetary disks are formed.
This project will develop a 980 million-parameter transformer trained on 214 million structure-sequence pairs, by integrating structural masking tokens and a supervised training regime to awaken high-precision inpainting abilities pushing Multiclass Accuracy for masked positions from 50–55 % to 92%.
This proposal aims to unveil still debated properties of cosmic ray transport in the Universe.
The aberrant expression of CD44 variants has been associated with malignancy of various cancer cells, including glioblastoma, for which effective treatments are unavailable. Moreover, CD44 is a promising therapeutic target for hyaluronic acid (HA)-based drug delivery carriers.
The resulting LLM will be able to generate reports, written in natural language, of the market trends forecasted by the Axyon AI platform, with insightful explanations of such predictions.
The project seeks to deliver significantly enhanced biomedical IE and retrieval capabilities, with direct contributions to the Horizon Europe ECHOLOT project.
Close interaction between theory and experiment has led to the success of the Standard Model of particle physics, which has now entered the precision frontier in the search for New Physics.
The success story of the Standard Model of particle physics (SM), enabled through close interaction between theory and experiment, has entered the precision frontier as small discrepancies between observations and predictions are tackled in the search for New Physics (NP).
The project aims to investigate the aerodynamic performance of acoustic liners interacting with non-zero pressure gradient turbulent boundary layers.
The project distils a dense decoder-only student from strong open foundation models that share a single tokenizer with the student: NVIDIA’s Nemotron-3 family and the European sovereign Soofi line.