Exa4mind – Extreme Analytics for Mining Data spaces

Exa4Mind

Publications

Exa4Mind

Publications

EXA4MIND relies on a co-design approach, where technology partners from computing centres and universities and application partners from industry, academia and SMEs design an Extreme Data infrastructure in close collaboration.

POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images

Scientific Publication

5

This research describes an approach to predict open-vocabulary 3D semantic voxel occupancy map from input 2D images with the objective of enabling 3D grounding, segmentation and retrieval of free-form language queries. This is a challenging problem because of the 2D-3D ambiguity and the open-vocabulary nature of the target tasks, where obtaining annotated training data in 3D is difficult. The contributions of this work are three-fold: a new model architecture for open-vocabulary 3D semantic occupancy prediction; a tri-modal self-supervised learning algorithm that leverages three modalities: (i) images, (ii) language and (iii) LiDAR point clouds, and enables training the proposed architecture using a strong pre-trained vision-language model without the need for any 3D manual language annotations; and a quantitative demonstration of the strengths of the proposed model on several open-vocabulary tasks.
Simple Adjustment of Intranucleotide Base-Phosphate Interaction in the OL3 AMBER Force Field Improves RNA Simulations

Scientific Publication

4

Molecular dynamics (MD) simulations represent an established tool to study RNA molecules. The outcome of MD studies depends, however, on the quality of the force field (ff). Here researchers suggest a correction for the widely used AMBER OL3 ff by adding a simple adjustment of the nonbonded parameters. The research suggests that the combination of OL3 RNA ff and NBfix0BPh modification is a viable option to improve RNA MD simulations.
MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments

Scientific Publication

3

Self-supervised learning can be used for mitigating the greedy needs of Vision Transformer networks for very large fully-annotated datasets. Different classes of self-supervised learning offer representations with either good contextual reasoning properties, e.g., using masked image modeling strategies, or invariance to image perturbations, e.g., with contrastive methods. In this work, we propose a single-stage and standalone method, MOCA, which unifies both desired properties using novel mask-and-predict objectives defined with high-level features (instead of pixel-level details).
Wine in the Cloud, or: Smart Vineyards with a Distributed "Extreme Data Database" and Supercomputing

Scientific Publication

2

In this contribution, researchers sketch an application of Earth System Sciences and Cloud-/Big-Data-based IT, which shall soon leverage European supercomputing facilities: smart viticulture, as put into practice by Terraview. TerraviewOS is a smart vineyard ‘operating system’, allowing wine cultivators to optimise irrigation, harvesting dates and measures against plant diseases. The system relies on satellite and drone imagery as well as in-situ sensors where available. The substantial need for computing power in TerraviewOS, in particular for training AI-based models to generate derived data products, makes the further development of some of its modules a prime application case for the EXA4MIND project.
Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?

Scientific Publication

1

Motion forecasting is crucial in enabling autonomous vehicles to anticipate the future trajectories of surrounding agents. To do so, it requires solving mapping, detection, tracking, and then forecasting problems, in a multi-step pipeline. In this complex system, advances in conventional forecasting methods have been made using curated data, i.e., with the assumption of perfect maps, detection, and tracking. In this work, researchers aim to bring forecasting models closer to real-world deployment and based on extensive experiments, they provide recommendations for critical areas that require improvement and guidance towards more robust motion forecasting in the real world.
Data Management Plan

Public Deliverable

5

The Data Management Plan lays out our planning for handling main aspects of the life cycle of the project data (data organisation and long-term storage, access, preserva- tion, and sharing). This document also includes a preliminary specification of outputs (what data will be generated during the project). It is a living document and will be continuously updated during the project.
Impact Master Plan

Public Deliverable

4

This deliverable outlines the planning of the dissemination, communication, exploitation and standardisation strategies for the EXA4MIND Horizon Europe project. This planning will be of relevance throughout the duration of the project and will be revisited periodically as it progresses.
Data and Workflow Management Toolbox Alpha Status Report

Public Deliverable

3

The EXA4MIND project connects pre-eminent databases and data management systems to supercomputing systems and European Data Spaces as well as the world of FAIR research data. The core purpose of this endeavour is running next-generation Extreme Data workfows, with emphasis on data analytics, Machine Learning / Artifcial Intelligence, or classical simulations. This deliverable reports on the Data and Workfow Management Toolbox provided for this purpose, building upon the successful LEXIS Platform (delivered by the H2020 project, GA 825532). Furthermore, it illustrates the first workfows run by our application cases at supercomputing centres.
Extreme Data Flow Patterns

Public Deliverable

2

This deliverable of the EXA4MIND project collects and analyses data flow patterns from all the project application cases. The collected data flow descriptions are used to identify a set of common occurring patterns that will be taken into account when designing the Extreme Data Database.
Application Cases And Architecture Requirements

Public Deliverable

1

This deliverable contains requirements provided by the project’s application-case work packages WP4-WP6 and their mapping to the EXA4MIND Platform features. The document is roughly divided into two parts. The first part is containing a unified description of each application case and its requirements. The second half of the document contains the mapping of the requirements to the technical features of the EXA4MIND Platform and the project objectives provided by the technical work packages WP1-WP3.

Newsletter

3

On to the second year of the project!

Welcome to the third newsletter of the EXA4MIND Project – On to the second year of the project!! In this edition, you will find the consortium partners review the first year of the project and the objectives for the second year, presentation of the EXA4MIND External Advisory Board,  highlights of international events attended by the project in the last months, and our last campaign ‘Faces of EXA4MIND’.

Newsletter

2

The journey continues!

Welcome to the second newsletter of the EXA4MIND Project – The journey continues! In this edition, you will find details about the last plenary meeting and co-design meeting with Application Cases partners, highlights from all national and international events attended by the EXA4MIND project in the last months, and a preview of upcoming events.

Newsletter

1

We are glad to have you on board!

Welcome to the first newsletter of the EXA4MIND project. We are glad to have you on board! In this edition, you will find a warm welcome from the EXA4MIND project coordinator, information about the organisations driving the project and their expectations of EXA4MIND, the presentation of our application cases, a recap of the events in which EXA4MIND has been actively involved, and interesting news about TerraviewOS, a consortium partner, which has emerged as the winner of the Gravity05 global sustainability challenge.