activity
20242026
most citedOn the Codesign of Scientific Experiments and Industrial Systems

1 citations · 1 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CL2026

BatteryPass-12K: The First Dataset for the Novel Digital Battery Passport Conformance Task

Tosin Adewumi, Martin Karlsson, Lama Alkhaled +1

We introduce a novel task of digital battery passport (DBP) conformance classification and introduce the first public benchmark for the task: BatteryPass-12K, created synthetically…

physics.ins-det20261 cited

On the Codesign of Scientific Experiments and Industrial Systems

Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas +84

The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for in…

cs.CL2025

From the Rock Floor to the Cloud: A Systematic Survey of State-of-the-Art NLP in Battery Life Cycle

Tosin Adewumi, Martin Karlsson, Marcus Liwicki +5

We present a comprehensive systematic survey of the application of natural language processing (NLP) along the entire battery life cycle, instead of one stage or method, and introd…

cs.CL2025

Findings of MEGA: Maths Explanation with LLMs using the Socratic Method for Active Learning

Tosin Adewumi, Foteini Simistira Liwicki, Marcus Liwicki +3

This paper presents an intervention study on the effects of the combined methods of (1) the Socratic method, (2) Chain of Thought (CoT) reasoning, (3) simplified gamification and (…

cs.LG2025

Agent-based Condition Monitoring Assistance with Multimodal Industrial Database Retrieval Augmented Generation

Karl Löwenmark, Daniel Strömbergsson, Chang Liu +2

Condition monitoring (CM) plays a crucial role in ensuring reliability and efficiency in the process industry. Although computerised maintenance systems effectively detect and clas…

cs.LG2025

Meta-Sparsity: Learning Optimal Sparse Structures in Multi-task Networks through Meta-learning

Richa Upadhyay, Ronald Phlypo, Rajkumar Saini +1

This paper presents meta-sparsity, a framework for learning model sparsity, basically learning the parameter that controls the degree of sparsity, that allows deep neural networks…