0000000000205597
AUTHOR
Ying Sun
showing 5 related works from this author
Journal of High Energy Physics
2014
The Double Chooz experiment presents improved measurements of the neutrino mixing angle $\theta_{13}$ using the data collected in 467.90 live days from a detector positioned at an average distance of 1050 m from two reactor cores at the Chooz nuclear power plant. Several novel techniques have been developed to achieve significant reductions of the backgrounds and systematic uncertainties with respect to previous publications, whereas the efficiency of the $\bar\nu_{e}$ signal has increased. The value of $\theta_{13}$ is measured to be $\sin^{2}2\theta_{13} = 0.090 ^{+0.032}_{-0.029}$ from a fit to the observed energy spectrum. Deviations from the reactor $\bar\nu_{e}$ prediction observed ab…
Precision Muon Reconstruction in Double Chooz
2014
We describe a muon track reconstruction algorithm for the reactor anti-neutrino experiment Double Chooz. The Double Chooz detector consists of two optically isolated volumes of liquid scintillator viewed by PMTs, and an Outer Veto above these made of crossed scintillator strips. Muons are reconstructed by their Outer Veto hit positions along with timing information from the other two detector volumes. All muons are fit under the hypothesis that they are through-going and ultrarelativistic. If the energy depositions suggest that the muon may have stopped, the reconstruction fits also for this hypothesis and chooses between the two via the relative goodness-of-fit. In the ideal case of a thro…
Probability of major depression diagnostic classification based on the SCID, CIDI and MINI diagnostic interviews controlling for Hospital Anxiety and…
2020
Objective Two previous individual participant data meta-analyses (IPDMAs) found that different diagnostic interviews classify different proportions of people as having major depression overall or by symptom levels. We compared the odds of major depression classification across diagnostic interviews among studies that administered the Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D). Methods Data accrued for an IPDMA on HADS-D diagnostic accuracy were analysed. We fit binomial generalized linear mixed models to compare odds of major depression classification for the Structured Clinical Interview for DSM (SCID), Composite International Diagnostic Interview (CIDI), and…
Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis.
2020
Objectives Validated diagnostic interviews are required to classify depression status and estimate prevalence of disorder, but screening tools are often used instead. We used individual participant data meta-analysis to compare prevalence based on standard Hospital Anxiety and Depression Scale – depression subscale (HADS-D) cutoffs of ≥8 and ≥11 versus Structured Clinical Interview for DSM (SCID) major depression and determined if an alternative HADS-D cutoff could more accurately estimate prevalence. Methods We searched Medline, Medline In-Process & Other Non-Indexed Citations via Ovid, PsycINFO, and Web of Science (inception-July 11, 2016) for studies comparing HADS-D scores to SCID major…
Semi-supervised deep learning-driven anomaly detection schemes for cyber-attack detection in smart grids
2022
Modern power systems are continuously exposed to malicious cyber-attacks. Analyzing industrial control system (ICS) traffic data plays a central role in detecting and defending against cyber-attacks. Detection approaches based on system modeling require effectively modeling the complex behavior of the critical infrastructures, which remains a challenge, especially for large-scale systems. Alternatively, data-driven approaches which rely on data collected from the inspected system have become appealing due to the availability of big data that supports machine learning methods to achieve outstanding performance. This chapter presents an enhanced cyber-attack detection strategy using unlabeled…