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View article: Fair Universe Higgs Uncertainty Challenge
Fair Universe Higgs Uncertainty Challenge Open
This competition in high-energy physics (HEP) and machine learning was the first to strongly emphasise uncertainties in $(H \rightarrow τ^+ τ^-)$ cross-section measurement. Participants were tasked with developing advanced analysis techniq…
View article: SuperSONIC: Cloud-Native Infrastructure for ML Inferencing
SuperSONIC: Cloud-Native Infrastructure for ML Inferencing Open
The increasing computational demand from growing data rates and complex machine learning (ML) algorithms in large-scale scientific experiments has driven the adoption of the Services for Optimized Network Inference on Coprocessors (SONIC) …
View article: Advanced ECG feature extraction and SVM classification for predicting defibrillation success in OHCA
Advanced ECG feature extraction and SVM classification for predicting defibrillation success in OHCA Open
Out-of-hospital cardiac arrest (OHCA) represents a critical challenge for emergency medical services, with the necessity for rapid and accurate prediction of defibrillation outcomes to enhance patient survival. This study leverages a datas…
View article: Track reconstruction as a service for collider physics
Track reconstruction as a service for collider physics Open
Optimizing charged-particle track reconstruction algorithms is crucial for efficient event reconstruction in Large Hadron Collider (LHC) experiments due to their significant computational demands. Existing track reconstruction algorithms h…
View article: Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature
Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature Open
As deep neural networks (DNNs) are widely applied in the physical world, many researches are focusing on physical-world adversarial examples (PAEs), which introduce perturbations to inputs and cause the model's incorrect outputs. However, …
View article: Building Machine Learning Challenges for Anomaly Detection in Science
Building Machine Learning Challenges for Anomaly Detection in Science Open
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of s…
View article: Research trends on stereotactic radiosurgery in brain metastases: a bibliometric analysis from 2013 to 2023
Research trends on stereotactic radiosurgery in brain metastases: a bibliometric analysis from 2013 to 2023 Open
As medical imaging informatics technology continues to advance, research into SRS for BM treatment has become increasingly in-depth. The immunoadjuvant therapy, biological effective dose, and radionecrosis have emerged as hot topics in rec…
View article: FAIR Universe 2024: Higgs ML Uncertainty Challenge
FAIR Universe 2024: Higgs ML Uncertainty Challenge Open
The HiggsML Uncertainty Challenge is a machine learning competition aimed at improving uncertainty-aware AI techniques in high-energy physics. Part of the FAIR Universe initiative, focuses on estimating the Higgs boson signal strength whil…
View article: Leakage current of high-fluence neutron-irradiated 8' silicon sensors for the CMS Endcap Calorimeter Upgrade
Leakage current of high-fluence neutron-irradiated 8' silicon sensors for the CMS Endcap Calorimeter Upgrade Open
The HL-LHC will challenge the detectors with a nearly 10-fold increase in integrated luminosity compared to the previous LHC runs combined, thus the CMS detector will be upgraded to face the higher levels of radiation and the larger amount…
View article: AthenaTriton: A tool for running machine learning inference as a service in Athena
AthenaTriton: A tool for running machine learning inference as a service in Athena Open
Machine learning (ML)-based algorithms play increasingly important roles in almost all aspects of the data analyses in ATLAS, including detector simulations, event reconstructions, and data analyses. These diverse ML models are being deplo…
View article: Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature
Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature Open
As deep neural networks (DNNs) are widely applied in the physical world, many researches are focusing on physical-world adversarial examples (PAEs), which introduce perturbations to inputs and cause the model's incorrect outputs. However, …
View article: FAIR Universe HiggsML Uncertainty Dataset and Competition
FAIR Universe HiggsML Uncertainty Dataset and Competition Open
The FAIR Universe HiggsML Uncertainty Challenge focused on measuring the physical properties of elementary particles with imperfect simulators. Participants were required to compute and report confidence intervals for a parameter of intere…
View article: Search for Dark Matter Produced in Association with a Higgs Boson Decaying to $b\overline{b}$ at $\sqrt{s}$ = 13 TeV with the ATLAS Detector
Search for Dark Matter Produced in Association with a Higgs Boson Decaying to $b\overline{b}$ at $\sqrt{s}$ = 13 TeV with the ATLAS Detector Open
Several extensions of the Standard Model predict associated production of Dark Matter particles with a Higgs boson. Such processes are searched for in final states with missing transverse momentum and a Higgs boson decaying to a $b\\bar b$…