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IntroductionOrganizations aiming to become AI and data-driven often need to provide their internal teams with high-quality and trusted data products. Building such data products ensures that organizations establish standards and a trustworthy foundation of business truth for their data and AI objectives. One approach for putting quality and usability at the forefront is through the use of the data mesh paradigm to democratize the ownership and management of data assets. Our blog posts (Part 1, Part 2) offer guidance on how customers can leverage Databricks in their enterprise to address data mesh's foundational pillars, one of which is "data as…

arXiv:2405.14918v1 Announce Type: new Abstract: Analog circuit design is a significant task in modern chip technology, focusing on the selection of component types, connectivity, and parameters to ensure proper circuit functionality. Despite advances made by Large Language Models (LLMs) in digital circuit design, the complexity and scarcity of data in analog circuitry pose significant challenges. To mitigate these issues, we introduce AnalogCoder, the first training-free LLM agent for designing analog circuits through Python code generation. Firstly, AnalogCoder incorporates a feedback-enhanced flow with tailored domain-specific prompts, enabling the automated and self-correcting design of analog circuits with a high success rate. Secondly,…

arXiv:2405.14880v1 Announce Type: new Abstract: Self-attention in vision transformers has been thought to perform perceptual grouping where tokens attend to other tokens with similar embeddings, which could correspond to semantically similar features in an image. However, contextualization is also an important and necessary computation for processing signals. Contextualization potentially requires tokens to attend to dissimilar tokens such as those corresponding to backgrounds or different objects, but this effect has not been reported in previous studies. In this study, we investigate whether self-attention in vision transformers exhibits a preference for attending to similar tokens or dissimilar tokens, providing evidence of perceptual…