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Fluorescence takes place throughout the natural world. Conventional chemical wisdom proposes that in organic entities, fluorescence occurs in conjugated systems, such as the aromatics.
We plan to study point-defects in low-dimensional systems for the design and control of solid-state spin-defects for quantum technologies.
The goal of this project is to design a unified video–language grounding system towards robust understanding and tracking of object state changes over time.
Antibodies can rapidly evolve in specific response to antigens. During the affinity maturation process the immune system produces antibodies with higher specificity and affinity in response to an antigen.
This project aims to develop a domain-specific Vision Language Model (VLM) that can automatically classify operator actions in video data according to MTM-UAS standards.
The project focuses on creating Video-Based Language Models (VBLMs) that can precisely identify, measure, and categorize operator movements within manufacturing environments.
By employing transformer neural networks, the model aims to learn fundamental properties of protein sequences directly from mass spectra data.
This project will perform cross-datacenter training of a stage-of-the-art multilingual LLM at an unprecedented scale (tens of billions of parameters trained on tens of trillions of tokens) using coordinated allocations across multiple supercomputers.
The primary aim of this project is to develop large-scale brain-like machine learning algorithms.
Vision language models (VLM) represent a multimodal extension of text-based language models that allow to incorporate visual information into the generation process of the output text.