Shah Rukh Qasim
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View article: THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENGINEERING PROJECT MANAGEMENT: A SYSTEMATIC REVIEW
THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENGINEERING PROJECT MANAGEMENT: A SYSTEMATIC REVIEW Open
This systematic study investigates the use of Artificial Intelligence (AI) in engineering project management, with an emphasis on its applications, advantages, problems, and future directions. A detailed examination of eight peer-reviewed …
View article: Leveraging Reinforcement Learning, Genetic Algorithms and Transformers for background determination in particle physics
Leveraging Reinforcement Learning, Genetic Algorithms and Transformers for background determination in particle physics Open
Experimental studies of beauty hadron decays face significant challenges due to a wide range of backgrounds arising from the numerous possible decay channels with similar final states. For a particular signal decay, the process for ascerta…
View article: Physics instrument design with Reinforcement Learning
Physics instrument design with Reinforcement Learning Open
We present a case for the use of Reinforcement Learning (RL) for the design of physics instruments as an alternative to gradient-based instrument-optimization methods. Its applicability is demonstrated using two empirical studies. One is l…
View article: Replacing detector simulation with heterogeneous GNNs in flavour physics analyses
Replacing detector simulation with heterogeneous GNNs in flavour physics analyses Open
Driven by the increasing volume of recorded data, the demand for simulation from experiments based at the Large Hadron Collider will rise sharply in the coming years. Addressing this demand solely with existing computationally intensive wo…
View article: Playing the devil's advocate to bound hidden systematic uncertainties
Playing the devil's advocate to bound hidden systematic uncertainties Open
View article: FastGraph: Optimized GPU-Enabled Algorithms for Fast Graph Building and Message Passing
FastGraph: Optimized GPU-Enabled Algorithms for Fast Graph Building and Message Passing Open
View article: Physics Instrument Design with Reinforcement Learning
Physics Instrument Design with Reinforcement Learning Open
We present a case for the use of Reinforcement Learning (RL) for the design\nof physics instrument as an alternative to gradient-based\ninstrument-optimization methods. It's applicability is demonstrated using two\nempirical studies. One i…
View article: GNN-based end-to-end reconstruction in the CMS Phase 2 High-Granularity Calorimeter
GNN-based end-to-end reconstruction in the CMS Phase 2 High-Granularity Calorimeter Open
We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Neural Networks (GNNs) and directly analyzes the hits in each…
View article: End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks
End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks Open
We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits…
View article: End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks
End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks Open
View article: Multi-particle reconstruction in the High Granularity Calorimeter using\n object condensation and graph neural networks
Multi-particle reconstruction in the High Granularity Calorimeter using\n object condensation and graph neural networks Open
The high-luminosity upgrade of the LHC will come with unprecedented physics\nand computing challenges. One of these challenges is the accurate\nreconstruction of particles in events with up to 200 simultaneous proton-proton\ninteractions. …
View article: Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networks
Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networks Open
The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events with up to 200 simultaneous protonproton interactions. The …
View article: Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networks
Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networks Open
The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events with up to 200 simultaneous protonproton interactions. The …
View article: Keras model and weights for GarNet-on-FPGA
Keras model and weights for GarNet-on-FPGA Open
Source code and input weights data for GarNet-on-FPGA This repository contains the Keras layer and model files for the simultaneous regression and classification task described in arXiv:2008.03601. Dataset in 10.5281/zenod…
View article: Particle Reconstruction with Graph Networks for irregular detector geometries
Particle Reconstruction with Graph Networks for irregular detector geometries Open
We use Graph Networks to learn representations of irregular detector geometries and perform on it typical tasks such as cluster segmentation or pattern recognition. Thanks to the flexibility and generality of the graph architecture, this k…
View article: Rethinking Table Recognition using Graph Neural Networks
Rethinking Table Recognition using Graph Neural Networks Open
Document structure analysis, such as zone segmentation and table recognition, is a complex problem in document processing and is an active area of research. The recent success of deep learning in solving various computer vision and machine…
View article: Learning representations of irregular particle-detector geometry with distance-weighted graph networks
Learning representations of irregular particle-detector geometry with distance-weighted graph networks Open
View article: Rethinking Table Parsing using Graph Neural Networks
Rethinking Table Parsing using Graph Neural Networks Open
Document structure analysis, such as zone segmentation and table parsing, is a complex problem in document processing and is an active area of research. The recent success of deep learning in solving various computer vision and machine lea…