CybAI news

arXiv:2407.01702v1 Announce Type: new Abstract: Scene flow estimation predicts the 3D motion at each point in successive LiDAR scans. This detailed, point-level, information can help autonomous vehicles to accurately predict and understand dynamic changes in their surroundings. Current state-of-the-art methods require annotated data to train scene flow networks and the expense of labeling inherently limits their scalability. Self-supervised approaches can overcome the above limitations, yet face two principal challenges that hinder optimal performance: point distribution imbalance and disregard for object-level motion constraints. In this paper, we propose SeFlow, a self-supervised method that integrates efficient dynamic classification into a learning-based scene…

arXiv:2407.01687v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting has been shown to enhance the multi-step reasoning capabilities of Large Language Models (LLMs). However, debates persist about whether LLMs exhibit abstract generalization or rely on shallow heuristics when given CoT prompts. To understand the factors influencing CoT reasoning we provide a detailed case study of the symbolic reasoning task of decoding shift ciphers, where letters are shifted forward some number of steps in the alphabet. GPT-4 achieves zero accuracy on most shift ciphers with standard prompting, but with CoT its accuracy improves to an average of 32%. By focusing on a…

Data catalogs and metadata catalogs share some similarities, particularly in their nearly identical names. And while they have some common functions, there are also important differences between the two entities that big data practitioners should know about. Metadata catalogs, which are sometimes called metastores or technical data catalogs, have been in the news lately. If you’re a regular Datanami reader (and we certainly hope you are!), you would have read a lot metadata catalogs at the Snowflake and Databricks conferences last month, when the two competitors committed to open sourcing their respective metadata catalogs, Polaris and Unity Catalog. So what…

arXiv:2407.01596v1 Announce Type: new Abstract: This work presents a use case of federated learning (FL) applied to discovering a maze with LiDAR sensors-equipped robots. Goal here is to train classification models to accurately identify the shapes of grid areas within two different square mazes made up with irregular shaped walls. Due to the use of different shapes for the walls, a classification model trained in one maze that captures its structure does not generalize for the other. This issue is resolved by adopting FL framework between the robots that explore only one maze so that the collective knowledge allows them…