0000000000230776
AUTHOR
Fan Li
showing 18 related works from this author
Search for high-mass dilepton resonances using 139 fb−1 of pp collision data collected at s=13 TeV with the ATLAS detector
2019
A search for high-mass dielectron and dimuon resonances in the mass range of 250 GeV to 6 TeV is presented. The data were recorded by the ATLAS experiment in proton–proton collisions at a centre-of-mass energy of s=13 TeV during Run 2 of the Large Hadron Collider and correspond to an integrated luminosity of 139 fb −1 . A functional form is fitted to the dilepton invariant-mass distribution to model the contribution from background processes, and a generic signal shape is used to determine the significance of observed deviations from this background estimate. No significant deviation is observed and upper limits are placed at the 95% confidence level on the fiducial cross-section times bran…
Properties of jet fragmentation using charged particles measured with the ATLAS detector in pp collisions at s=13 TeV
2019
This paper presents a measurement of quantities related to the formation of jets from high-energy quarks and gluons (fragmentation). Jets with transverse momentum 100 GeV 500 MeV and vertical bar ...
Classification of Heart Sounds Using Convolutional Neural Network
2020
Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global averag…
Resolution of the ATLAS muon spectrometer monitored drift tubes in LHC Run 2
2019
The momentum measurement capability of the ATLAS muon spectrometer relies fundamentally on the intrinsic single-hit spatial resolution of the monitored drift tube precision tracking chambers. Optimal resolution is achieved with a dedicated calibration program that addresses the specific operating conditions of the 354 000 high-pressure drift tubes in the spectrometer. The calibrations consist of a set of timing offsets and drift time to drift distance transfer relations, and result in chamber resolution functions. This paper describes novel algorithms to obtain precision calibrations from data collected by ATLAS in LHC Run 2 and from a gas monitoring chamber, deployed in a dedicated gas fac…
Electron and photon performance measurements with the ATLAS detector using the 2015-2017 LHC proton-proton collision data
2019
This paper describes the reconstruction of electrons and photons with the ATLAS detector, employed for measurements and searches exploiting the complete LHC Run 2 dataset. An improved energy clustering algorithm is introduced, and its implications for the measurement and identification of prompt electrons and photons are discussed in detail. Corrections and calibrations that affect performance, including energy calibration, identification and isolation efficiencies, and the measurement of the charge of reconstructed electron candidates are determined using up to 81 fb−1 of proton-proton collision data collected at √s=13 TeV between 2015 and 2017.
A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series
2021
Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. Automatic sleep scoring is crucial and urgent to help address the increasing unmet need for sleep research. Therefore, this paper aims to develop an end-to-end deep learning architecture using raw polysomnographic recordings to automate sleep scoring. The proposed model adopts two-dimensional convolutional neural networks (2D-CNN) to automatically learn features from multi-modality signals, together with a "squeeze and excitation" block for recalibrating channel-wise feature responses. The learnt representations are finally fed to a softmax classifier to generate predictions for each sleep stage. The model pe…
Search for direct stau production in events with two hadronic τ -leptons in s=13 TeV pp collisions with the ATLAS detector
2020
A search for the direct production of the supersymmetric partners of τ -leptons (staus) in final states with two hadronically decaying τ -leptons is presented. The analysis uses a dataset of p p collisions corresponding to an integrated luminosity of 139 fb − 1 , recorded with the ATLAS detector at the Large Hadron Collider at a center-of-mass energy of 13 TeV. No significant deviation from the expected Standard Model background is observed. Limits are derived in scenarios of direct production of stau pairs with each stau decaying into the stable lightest neutralino and one τ -lepton in simplified models where the two stau mass eigenstates are degenerate. Stau masses from 120 GeV to 390…
Search for a right-handed gauge boson decaying into a high-momentum heavy neutrino and a charged lepton in pp collisions with the ATLAS detector at s…
2019
A search for a right-handed gauge boson WR, decaying into a boosted right-handed heavy neutrino NR, in the framework of Left-Right Symmetric Models is presented. It is based on data from proton–proton collisions with a centre-of-mass energy of 13 TeV collected by the ATLAS detector at the Large Hadron Collider during the years 2015, 2016 and 2017, corresponding to an integrated luminosity of 80 fb$^{−1}$. The search is performed separately for electrons and muons in the final state. A distinguishing feature of the search is the use of large-radius jets containing electrons. Selections based on the signal topology result in smaller background compared to the expected signal. No significant d…
Search for a heavy charged boson in events with a charged lepton and missing transverse momentum from pp collisions at s=13 TeV with the ATLAS detec…
2019
A search for a heavy charged-boson resonance decaying into a charged lepton (electron or muon) and a neutrino is reported. A data sample of 139 fb−1 of proton-proton collisions at √s=13 TeV collected with the ATLAS detector at the LHC during 2015–2018 is used in the search. The observed transverse mass distribution computed from the lepton and missing transverse momenta is consistent with the distribution expected from the Standard Model, and upper limits on the cross section for pp→W′→lν are extracted (l=e or μ). These vary between 1.3 pb and 0.05 fb depending on the resonance mass in the range between 0.15 and 7.0 TeV at 95% confidence level for the electron and muon channels combined. …
An Automatic Sleep Scoring Toolbox : Multi-modality of Polysomnography Signals’ Processing
2019
Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. To speed up the process of sleep scoring without compromising accuracy, this paper develops an automatic sleep scoring toolbox with the capability of multi-signal processing. It allows the user to choose signal types and the number of target classes. Then, an automatic process containing signal pre-processing, feature extraction, classifier training (or prediction) and result correction will be performed. Finally, the application interface displays predicted sleep structure, related sleep parameters and the sleep quality index for reference. To improve the identification accuracy of minority stages, a layer-w…
Virtual Planning and 3D printing modeling for mandibular reconstruction with fibula free flap
2017
Background This study was to evaluate the use of virtual planning and 3D printing modeling in mandibular reconstruction and compare the operation time and surgical outcome of this technique with conventional method. Material and Methods Between 2014 and 2017, 15 patients underwent vascularized fibula flap mandibular reconstruction using virtual planning and 3D printing modeling. Titanium plates were pre-bent using the models and cutting guides were used for osteotomies. 15 patients who underwent mandibular reconstruction using fibula flap without aid of virtual planning and 3D printing models were selected as control group. The operation time was recorded and compared in two groups. Accurac…
SingleChannelNet : A model for automatic sleep stage classification with raw single-channel EEG
2022
In diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Various existing state-of-the-art approaches rely on hand-crafted features and multi-modality polysomnography (PSG) data, where prior knowledge is compulsory and high computation cost can be expected. Besides, it is a big challenge to handle the task with raw single-channel electroencephalogram (EEG). To overcome these shortcomings, this paper proposes an end-to-end framework with a deep neural network, namely SingleChannelNet, for automatic sleep stage classification based on raw single-channel EEG. The proposed model utilizes a 90s epoch as the textual input and employs two multi-conv…
Ensemble deep clustering analysis for time window determination of event-related potentials
2023
Objective Cluster analysis of spatio-temporal event-related potential (ERP) data is a promising tool for exploring the measurement time window of ERPs. However, even after preprocessing, the remaining noise can result in uncertain cluster maps followed by unreliable time windows while clustering via conventional clustering methods. Methods We designed an ensemble deep clustering pipeline to determine a reliable time window for the ERP of interest from temporal concatenated grand average ERP data. The proposed pipeline includes semi-supervised deep clustering methods initialized by consensus clustering and unsupervised deep clustering methods with end-to-end architectures. Ensemble clusterin…
Automatic sleep scoring: A deep learning architecture for multi-modality time series
2020
Background: Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This paper aims to develop a versatile deep-learning architecture to automate sleep scoring using raw polysomnography recordings. Method: The model adopts a linear function to address different numbers of inputs, thereby extending model applications. Two-dimensional convolution neural networks are used to learn features from multi-modality polysomnographic signals, a “squeeze and excitation” block to recalibrate channel-wise features, together with a long short-term memory module to exploit long-range co…
Search for low-mass resonances decaying into two jets and produced in association with a photon using pp collisions at s=13 TeV with the ATLAS detect…
2019
A search is performed for localised excesses in dijet mass distributions of low-dijet-mass events produced in association with a high transverse energy photon. The search uses up to 79.8 fb−1 of LHC proton–proton collisions collected by the ATLAS experiment at a centre-of-mass energy of 13 TeV during 2015–2017. Two variants are presented: one which makes no jet flavour requirements and one which requires both jets to be tagged as b-jets. The observed mass distributions are consistent with multi-jet processes in the Standard Model. The data are used to set upper limits on the production cross-section for a benchmark Z′ model and, separately, on generic Gaussian-shape contributions to the mas…
ATLAS data quality operations and performance for 2015-2018 data-taking
2020
The ATLAS detector at the Large Hadron Collider reads out particle collision data from over 100 million electronic channels at a rate of approximately 100 kHz, with a recording rate for physics events of approximately 1 kHz. Before being certified for physics analysis at computer centres worldwide, the data must be scrutinised to ensure they are clean from any hardware or software related issues that may compromise their integrity. Prompt identification of these issues permits fast action to investigate, correct and potentially prevent future such problems that could render the data unusable. This is achieved through the monitoring of detector-level quantities and reconstructed collision ev…
Two-particle azimuthal correlations in photonuclear ultraperipheral Pb+Pb collisions at 5.02 TeV with ATLAS
2021
We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina, YerPhI, Armenia, ARC, Australia, BMWFW and FWF, Austria, ANAS, Azerbaijan, SSTC, Belarus, CNPq and FAPESP, Brazil, NSERC, NRC, and CFI, Canada, CERN and ANID, Chile, CAS, MOST, and NSFC, China, COLCIENCIAS, Colombia, MSMT CR, MPO CR, and VSC CR, Czech Republic, DNRF and DNSRC, Denmark, IN2P3-CNRS and CEA-DRF/IRFU, France, SRNSFG, Georgia, BMBF, HGF, and MPG, Germany, GSRT, Greece, RGC and Hong Kong SAR, China, ISF and Benoziyo Center, Israel, INFN, Italy, MEXT and JSPS, Japan, CNR…
Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospecti…
2022
IntroductionPreeclampsia, one of the leading causes of maternal and fetal morbidity and mortality, demands accurate predictive models for the lack of effective treatment. Predictive models based on machine learning algorithms demonstrate promising potential, while there is a controversial discussion about whether machine learning methods should be recommended preferably, compared to traditional statistical models.MethodsWe employed both logistic regression and six machine learning methods as binary predictive models for a dataset containing 733 women diagnosed with preeclampsia. Participants were grouped by four different pregnancy outcomes. After the imputation of missing values, statistic…