
YFCC100M-HNfc6
YFCC100M-HNfc6: A Large-Scale Deep Features Benchmark for Similarity Search
Physical AI refers to using AI techniques to solve problems that involve direct interaction with the physical world, e.g., by observing the world through sensors or by modifying the world through actuators. The data is generated from various sources, including physical sensors and ”human sources,” such as social networks or smartphones. Actuation may range from support to human decisions to managing automated devices(e.g., traffic lights, gates) and actively directing autonomous cars, drones, etc.
One intrinsic feature of Physical AI is the uncertainty associated with the acquired information, its incompleteness, and the uncertainty about the effects of actions over (physical) systems that share the environment with humans. In other words, Physical AI deals with unreliable, heterogeneous, and high-dimensional sources of data/information and a significant set of actuation variables/actions to learn models, detect events, or classify situations, to name just a few cases. In some cases, a decision-making loop is closed over physical systems with their dynamics, often complicated and challenging to model (e.g., weather dynamics, human crowd behavior).
To tackle such large physical problems, existing techniques for data processing and decision-making are not tractable. Thus, one should develop and improve methods that exploit redundancy, combine/infer partial/missing data, transfer knowledge (e.g., through learning) and exploit low-rank characteristics of data to reduce the several relevant dimensions of the problems (in terms of observation, state and action spaces).
YFCC100M-HNfc6: A Large-Scale Deep Features Benchmark for Similarity Search
A collection of Python methods intended as a practical tool for fetching and preprocessing data related to climate and weather conditions, useful in climate science studies. Developed by AMIGO s.r.l. for the ARIA project, part of the I-NERGY 2nd Open Cal...
A data set of 380 million measurements from a hydraulic pump that can be mounted on an industrial robot, for example, to pump liquid paint for spray painting. The data set spans two months in 1-second intervals.
The problem of finding faults in oscillating data in the form of 3-phase current and voltage values is considered. While these kind of data have high resolutions, we give a solution to multivariate changing point detection, framed as an anomaly detection...
This dataset contains hyperspectral images of denim fabric over 224 reflectance bands. It was used to test automated composition analysis in the DIH4AI Open Call 2 FABCOD project (Fabric Composition Detector).
A data set of 1.8 billion measurements from a mechanical wrist with three axes that can hold tools, for example, for spray painting in combination with a pump. The data set spans six months in 1-second intervals.
Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
3 Clinical Use Cases with supporting databases are offered to the community
A dataset containing real data related to energy consumption from 3 industrial sites in EU
A decision-making framework for active perception with POMDPs.
Set of libraries and ROS nodes to detect and track people using 2D lidar