·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Meta removes language that raised scraping and AI training concerns15h◆Anthropic says its workforce has become less junior and is spending more time in meetings16h◆Replit makes OpenAI its default over Anthropic, CEO says18h◆Meta removes language that may have allowed it to scrape news sites21h◆Websites are closing their doors to AI agents23h◆How Itaú is building generative AI for its entire customer base1d◆Roundtables: A Conversation With the Creator of AI-Designed Viruses1d◆India becomes the latest country to report rogue AI agent activity1d◆Physical AI is more than robots, says the Nvidia veteran now investing in it1d◆An Nvidia insider-turned-VC on why robotics hasn’t hit its ChatGPT moment, yet1d◆Elorian’s first AI model wants to reason with images, not just about them1d◆Jev is Vercel’s fastest-adopted AI model ever1d◆A week with RTX Spark: everything we’ve learned using Nvidia’s new laptops1d◆Mark Wahlberg on using AI in film ‘the right way’ and being open to an AI clone carrying on his legacy1d◆Meta’s new terms may allow it to train AI on news sites for free1d◆Veritas expands its anti-counterfeit chip to jewelry, trophies, and fragrance bottles1d◆Replit’s CEO says the future of work is the ‘self-driving company’2d◆Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets2d◆Judging the Judges: Confidence and Disagreement in Rubrics as Rewards2d◆Content associations with answer selection and engagement in supply chain community question answering2d◆Meta removes language that raised scraping and AI training concerns15h◆Anthropic says its workforce has become less junior and is spending more time in meetings16h◆Replit makes OpenAI its default over Anthropic, CEO says18h◆Meta removes language that may have allowed it to scrape news sites21h◆Websites are closing their doors to AI agents23h◆How Itaú is building generative AI for its entire customer base1d◆Roundtables: A Conversation With the Creator of AI-Designed Viruses1d◆India becomes the latest country to report rogue AI agent activity1d◆Physical AI is more than robots, says the Nvidia veteran now investing in it1d◆An Nvidia insider-turned-VC on why robotics hasn’t hit its ChatGPT moment, yet1d◆Elorian’s first AI model wants to reason with images, not just about them1d◆Jev is Vercel’s fastest-adopted AI model ever1d◆A week with RTX Spark: everything we’ve learned using Nvidia’s new laptops1d◆Mark Wahlberg on using AI in film ‘the right way’ and being open to an AI clone carrying on his legacy1d◆Meta’s new terms may allow it to train AI on news sites for free1d◆Veritas expands its anti-counterfeit chip to jewelry, trophies, and fragrance bottles1d◆Replit’s CEO says the future of work is the ‘self-driving company’2d◆Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets2d◆Judging the Judges: Confidence and Disagreement in Rubrics as Rewards2d◆Content associations with answer selection and engagement in supply chain community question answering2d◆
News/Multi-Step Forecasting of Grape Berry Temperature based on LSTM Model with Feed-Forward Attention
arxiv
PublishedSeptember 1, 2026 at 4:00 AM

Multi-Step Forecasting of Grape Berry Temperature based on LSTM Model with Feed-Forward Attention

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2608.29008v1 Announce Type: new Abstract: Accurate forecasting of grape berry temperature (Tb) is essential for enabling timely heat stress management in vineyards. In this study, a feed-forward attention mechanism integrated with a Long Short-Term Memory network (FAM-LSTM) was developed and e

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivOptimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets2darxivJudging the Judges: Confidence and Disagreement in Rubrics as Rewards2darxivContent associations with answer selection and engagement in supply chain community question answering2d
The Bubble Brief
MON · WED · FRI

Read AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews