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We are excited to announce the General Availability of serverless compute for notebooks, jobs and Delta Live Tables (DLT) on AWS and Azure. Databricks customers already enjoy fast, simple and reliable serverless compute for Databricks SQL and Databricks Model Serving. The same capability is now available for all ETL workloads on the Data Intelligence Platform, including Apache Spark and Delta Live Tables. You write the code and Databricks provides rapid workload startup, automatic infrastructure scaling and seamless version upgrades of the Databricks Runtime. Importantly, with serverless compute you are only billed for work done instead of time spent acquiring and…

arXiv:2407.08910v1 Announce Type: new Abstract: Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep Reinforcement Learning (DRL) based methods offer promising enhancements for sequential optimization processes and can be used for reducing carbon emissions. However, existing DRL methods need a pre-defined reward function to assess the impact of each action on the final sustainable development goals (SDG). In many real applications, such a reward function cannot be given in advance. To address the problem, this study…

[Submitted on 11 Jul 2024] View a PDF of the paper titled Manipulating a Tetris-Inspired 3D Video Representation, by Mihir Godbole View PDF HTML (experimental) Abstract:Video Synopsis is a technique that performs video compression in a way that preserves the activity in the video. This technique is particularly useful in surveillance and monitoring applications. Although it is still a nascent field of research, there have been several approaches proposed over the last two decades varying with the application, optimization type, nature of data feed, etc. The primary data required for these algorithms arises from some sort of object tracking method.…

arXiv:2407.08940v1 Announce Type: new Abstract: The rapid growth of biomedical knowledge has outpaced our ability to efficiently extract insights and generate novel hypotheses. Large language models (LLMs) have emerged as a promising tool to revolutionize knowledge interaction and potentially accelerate biomedical discovery. In this paper, we present a comprehensive evaluation of LLMs as biomedical hypothesis generators. We construct a dataset of background-hypothesis pairs from biomedical literature, carefully partitioned into training, seen, and unseen test sets based on publication date to mitigate data contamination. Using this dataset, we assess the hypothesis generation capabilities of top-tier instructed models in zero-shot, few-shot, and…