Viral News - CybAI news

06 Jun

Azure Databricks at Databricks Data + AI Summit 2024 featuring Industry Leaders and Pioneers

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This is a collaborative post from Databricks and Microsoft. We thank Mohini Verma, Senior Product Marketing Manager, for her contributions.Data + AI Summit 2024: Register now to join this in-person and virtual event June 10-13 and learn from the global data community.Microsoft is a Legend Sponsor of the Databricks Data + AI Summit 2024, the premier event for the global data community. Join us to learn how data intelligence enables every organization to harness the power of generative AI on their own data. Hear from Microsoft leaders who will share how customers have successfully leveraged the Databricks Data Intelligence Platform…

06 Jun

Graph Neural Networks for Brain Graph Learning: A Survey

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arXiv:2406.02594v1 Announce Type: new Abstract: Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology, a novel approach has emerged that involves modeling the human brain as a graph-structured pattern, with different brain regions represented as nodes and the functional relationships among these regions as edges. Moreover, graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. Developing GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. However, there is a lack of systematic survey work summarizing…

06 Jun

ORACLE: Leveraging Mutual Information for Consistent Character Generation with LoRAs in Diffusion Models

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol

06 Jun

Exploring Robustness in Doctor-Patient Conversation Summarization: An Analysis of Out-of-Domain SOAP Notes

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arXiv:2406.02826v1 Announce Type: new Abstract: Summarizing medical conversations poses unique challenges due to the specialized domain and the difficulty of collecting in-domain training data. In this study, we investigate the performance of state-of-the-art doctor-patient conversation generative summarization models on the out-of-domain data. We divide the summarization model of doctor-patient conversation into two configurations: (1) a general model, without specifying subjective (S), objective (O), and assessment (A) and plan (P) notes; (2) a SOAP-oriented model that generates a summary with SOAP sections. We analyzed the limitations and strengths of the fine-tuning language model-based methods and GPTs on both configurations. We also…

06 Jun

Shutterstock’s Content Datasets Now on Databricks Marketplace

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In today's data-driven world, the fusion of visual assets and analytical capabilities unlocks a realm of untapped potential. Image datasets are crucial in developing and training Generative AI (GenAI) technologies. We are thrilled to announce a groundbreaking collaboration that brings the vast collection of Shutterstock imagery to the Databricks Marketplace — our first listing of Volume (aka non-tabular) datasets on our Marketplace. This free sample dataset, which consists of 1,000 images and accompanying metadata sourced from Shutterstock's 550+ million image library, is available for immediate access. This blog will explore Shutterstock's image library on Databricks Marketplace and the industry use…

06 Jun

LOLAMEME: Logic, Language, Memory, Mechanistic Framework

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol

06 Jun

LADI v2: Multi-label Dataset and Classifiers for Low-Altitude Disaster Imagery

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arXiv:2406.02780v1 Announce Type: new Abstract: ML-based computer vision models are promising tools for supporting emergency management operations following natural disasters. Arial photographs taken from small manned and unmanned aircraft can be available soon after a disaster and provide valuable information from multiple perspectives for situational awareness and damage assessment applications. However, emergency managers often face challenges finding the most relevant photos among the tens of thousands that may be taken after an incident. While ML-based solutions could enable more effective use of aerial photographs, there is still a lack of training data for imagery of this type from multiple perspectives…

06 Jun

Chain of Agents: Large Language Models Collaborating on Long-Context Tasks

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arXiv:2406.02818v1 Announce Type: new Abstract: Addressing the challenge of effectively processing long contexts has become a critical issue for Large Language Models (LLMs). Two common strategies have emerged: 1) reducing the input length, such as retrieving relevant chunks by Retrieval-Augmented Generation (RAG), and 2) expanding the context window limit of LLMs. However, both strategies have drawbacks: input reduction has no guarantee of covering the part with needed information, while window extension struggles with focusing on the pertinent information for solving the task. To mitigate these limitations, we propose Chain-of-Agents (CoA), a novel framework that harnesses multi-agent collaboration through natural language…

06 Jun

How PepsiCo established an enterprise-grade data intelligence platform powered by Databricks Unity Catalog

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This blog is authored by Bhaskar Palit, Senior Director, Data & Analytics, PepsiCo, and Sudipta Das, Data Architect Senior Manager, PepsiCo PepsiCo has woven itself into the fabric of our daily life. Our products are enjoyed by consumers more than one billion times a day in more than 200 countries and territories around the world. PepsiCo generated more than $91 billion in net revenue in 2023, driven by a complimentary beverage and convenient foods portfolio that includes Lay's, Doritos, Cheetos, Gatorade, Pepsi-Cola, Mountain Dew, Quaker and SodaStream. PepsiCo has more than 200,000 products. We operate across the globe and manage a great…

06 Jun

Unveiling the Potential of AI for Nanomaterial Morphology Prediction

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arXiv:2406.02591v1 Announce Type: new Abstract: Creation of nanomaterials with specific morphology remains a complex experimental process, even though there is a growing demand for these materials in various industry sectors. This study explores the potential of AI to predict the morphology of nanoparticles within the data availability constraints. For that, we first generated a new multi-modal dataset that is double the size of analogous studies. Then, we systematically evaluated performance of classical machine learning and large language models in prediction of nanomaterial shapes and sizes. Finally, we prototyped a text-to-image system, discussed the obtained empirical results, as well as the…