Explanipedia

Hadron Identification Prospects with Granular Calorimeters Open

A. De Vita, Abhishek Abhishek, Max Aehle, Muhammad Awais, A. Breccia , et al. · 2025

In this work we consider the problem of determining the identity of hadrons at high energies based on the topology of their energy depositions in dense matter, along with the time of the interactions. Using GEANT4 simulations of a homogene…

Progress in end-to-end optimization of fundamental physics experimental apparata with differentiable programming Open

Max Aehle, Lorenzo Arsini, R. B. Barreiro, A. Belias, A. S. Boldyrev , et al. · 2025

Neuromorphic Readout for Hadron Calorimeters Open

Enrico Lupi, Abhishek, Max Aehle, Muhammad Awais, A. Breccia , et al. · 2025

We simulate hadrons impinging on a homogeneous lead tungstate (PbWO4) calorimeter using GEANT4 software to investigate how the resulting light yield and its temporal structure, as detected by an array of light-sensitive sensors, can be pro…

End-to-End Detector Optimization with Diffusion Models: A Case Study in Sampling Calorimeters Open

Kylian Schmidt, Krishna Nikhil Kota, J. Kieseler, A. De Vita, M. Klute , et al. · 2025

Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics processes has traditionally posed a significant challenge. …

Unsupervised Particle Tracking with Neuromorphic Computing Open

Emanuele Coradin, Fabio Cufino, Muhammad Awais, T. Dorigo, Enrico Lupi , et al. · 2025

We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model, the neu…

Efficient Ruddlesden-popper (RP) perovskites as electron selective layers yielding over 20 % efficiency in MAPb(I1-xClx)3 based organic-inorganic perovskite solar cells: A DFT and SCAPS-1D investigations Open

Muhammad Awais, T. Dorigo, Fredrik Sandin, Umair Mumtaz · 2025

The electron transport layer (ETL) is linchpin in perovskite solar cells (PSCs). It offers potent and discriminatory electron elicitation, minute resistivity, and lofty strength along with optimal device performance. In this study combined…

Identifying quantum phase transitions with minimal prior knowledge by unsupervised learning Open

Mohamad Ali Marashli, Hokyin Lai, Hamam Mokayed, Fredrik Sandin, Marcus Liwicki , et al. · 2025

In this work, we proposed a novel approach for identifying quantum phase transitions in one-dimensional quantum many-body systems using AutoEncoder (AE), an unsupervised machine learning technique, with minimal prior knowledge. The trainin…

Neuromorphic Readout for Hadron Calorimeters Open

Enrico Lupi, M Abhishek, Max Aehle, Muhammad Awais, A. Breccia , et al. · 2025

We simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4) calorimeter to investigate how the resulting light yield and its temporal structure, as detected by an array of light-sensitive sensors, can be processed by a neuromorph…

Hadron Identification Prospects With Granular Calorimeters Open

A. De Vita, Abhishek, Max Aehle, Muhammad Awais, A. Breccia , et al. · 2025

In this work we consider the problem of determining the identity of hadrons at high energies based on the topology of their energy depositions in dense matter, along with the time of the interactions. Using GEANT4 simulations of a homogene…

Unsupervised Particle Tracking with Neuromorphic Computing Open

Emanuele Coradin, Fabio Cufino, Muhammad Awais, T. Dorigo, Enrico Lupi , et al. · 2025

We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model, the neu…

End-to-End Detector Optimization with Diffusion models: A Case Study in Sampling Calorimeters Open

K. E. Schmidt, Nikhil Kota, Jan Kieseler, A. De Vita, M. Klute , et al. · 2025

Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics processes has traditionally posed a significant challenge. …

Artificial Intelligence in Science and Society: The Vision of USERN Open

T. Dorigo, Garrett Wallace Brown, Carlo Casonato, Artemi Cerdà, Joseph Ciarrochi , et al. · 2025

International audience

Learning the Approach During the Short-loading Cycle Using Reinforcement Learning Open

Carl Borngrund, Ulf Bodin, Henrik Andreasson, Fredrik Sandin · 2024

The short-loading cycle is a repetitive task performed in high quantities, making it a great alternative for automation. In the short-loading cycle, an expert operator navigates towards a pile, fills the bucket with material, navigates to …

Automating the Short-Loading Cycle: Survey and Integration Framework Open

Carl Borngrund, Ulf Bodin, Henrik Andreasson, Fredrik Sandin · 2024

The short-loading cycle is a construction task where a wheel loader scoops material from a nearby pile in order to move that material to the tipping body of a dump truck. The short-loading cycle is a vital task performed in high quantities…

AI Concepts for System of Systems Dynamic Interoperability Open

Jacob Nilsson, Saleha Javed, Kim Albertsson, Jerker Delsing, Marcus Liwicki , et al. · 2024

Interoperability is a central problem in digitization and System of Systems (SoS) engineering, which concerns the capacity of systems to exchange information and cooperate. The task to dynamically establish interoperability between heterog…

Unveiling the Optimal Optoelectronic Performance of Ruddlesden-Popper Perovskites A2sno4 (a= Sr, Ba) Using Density Functional Theory (Dft) Open

Muhammad Awais, Umair Mumtaz, T. Dorigo, Fredrik Sandin · 2024

Exploring Filter Banks and Spike Interval Statistics of Level-Crossing ADCs for Fault Diagnosis of Rolling Element Bearings Open

Ashwani Kumar, Daniel Strömbergsson, Pär Marklund, Fredrik Sandin · 2023

Nowadays, lots of data are generated in industries using vibration sensors to evaluate the equipment’s working condition and identify faults. A significant challenge is that only a small fraction of data can be transmitted for intelligent …

Co-design Model for Neuromorphic Technology Development in Rolling Element Bearing Condition Monitoring Open

Daniel Strömbergsson, Ashwani Kumar, Fredrik Sandin, Pär Marklund · 2023

This paper presents an end-to-end condition monitoring co-design model, from vibration measurement to anomaly detection, where conventional signal processing principles are combined with neuromorphic sensing and computing concepts to enabl…

Labelling of Annotated Condition Monitoring Data Through Technical Language Processing Open

Karl Löwenmark, Cees Taal, Joakim Nivre, Marcus Liwicki, Fredrik Sandin · 2023

We propose a novel approach to facilitate supervised fault diagnosis on unlabelled but annotated industry datasets using human-centric technical language processing and weak supervision. Fault diagnosis through Condition Monitoring (CM) is…

Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT Open

Saleha Javed, Muhammad Usman, Fredrik Sandin, Marcus Liwicki, Hamam Mokayed · 2023

The technical capabilities of modern Industry 4.0 and Industry 5.0 are vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnect…

Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming Open

Max Aehle, Lorenzo Arsini, R. B. Barreiro, A. Belias, F. Bury , et al. · 2023

In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and …

Deep Ontology Alignment using Natural Language Processing Approach for Automatic M2M Translation in IIoT Open

Saleha Javed, Muhammad Usman Joyia, Fredrik Sandin, Marcus Liwicki, Hamam Mokayed · 2023

The technical capabilities of modern Industry 4.0 and Industry 5.0 are rather vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time inter…

ReLU and Addition-based Gated RNN Open

Rickard Brännvall, Henrik Forsgren, Fredrik Sandin, Marcus Liwicki · 2023

We replace the multiplication and sigmoid function of the conventional recurrent gate with addition and ReLU activation. This mechanism is designed to maintain long-term memory for sequence processing but at a reduced computational cost, t…

A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons Open

Mattias Nilsson, Ton Juny Pina, Lyes Khacef, Foteini Liwicki, Elisabetta Chicca , et al. · 2023

With the expansion of AI-powered virtual assistants, there is a need for low-power keyword spotting systems providing a 'wake-up' mechanism for subsequent computationally expensive speech recognition. One promising approach is the use of n…

Deep Perceptual Similarity is Adaptable to Ambiguous Contexts Open

Gustav Grund Pihlgren, Fredrik Sandin, Marcus Liwicki · 2023

The concept of image similarity is ambiguous, and images can be similar in one context and not in another. This ambiguity motivates the creation of metrics for specific contexts. This work explores the ability of deep perceptual similarity…

Modular and Transferable Machine Learning for Heat Management and Reuse in Edge Data Centers Open

Rickard Brännvall, Jonas Gustafsson, Fredrik Sandin · 2023

This study investigates the use of transfer learning and modular design for adapting a pretrained model to optimize energy efficiency and heat reuse in edge data centers while meeting local conditions, such as alternative heat management a…

Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directions Open

Mattias Nilsson, Olov Schelén, Anders Lindgren, Ulf Bodin, Cristina Paniagua , et al. · 2023

Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the resource-constrained edges of the Internet. Meanwhile, the r…

A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions Open

Gustav Grund Pihlgren, Konstantina Nikolaidou, Prakash Chandra Chhipa, Nosheen Abid, Rajkumar Saini , et al. · 2023

In recent years, deep perceptual loss has been widely and successfully used to train machine learning models for many computer vision tasks, including image synthesis, segmentation, and autoencoding. Deep perceptual loss is a type of loss …

A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons Open

Mattias Nilsson, Ton Juny Pina, Lyes Khacef, Foteini Liwicki, Elisabetta Chicca , et al. · 2023

With the expansion of AI-powered virtual assistants, there is a need for low-power keyword spotting systems providing a "wake-up" mechanism for subsequent computationally expensive speech recognition. One promising approach is the use of n…

Identifying and Mitigating Flaws of Deep Perceptual Similarity Metrics Open

Oskar Sjögren, Gustav Grund Pihlgren, Fredrik Sandin, Marcus Liwicki · 2023

Measuring the similarity of images is a fundamental problem to computer vision for which no universal solution exists. While simple metrics such as the pixel-wise L2-norm have been shown to have significant flaws, they remain popular. One …

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