scholarly journals Muon reconstruction and identification efficiency in ATLAS using the full Run 2 pp collision data set at $$\sqrt{s}=13$$ TeV

2021 ◽  
Vol 81 (7) ◽  
Author(s):  
◽  
G. Aad ◽  
B. Abbott ◽  
D. C. Abbott ◽  
A. Abed Abud ◽  
...  
Keyword(s):  
Phase Space ◽  
Percent Level ◽  
Muon Detector ◽  
Excellent Performance ◽  
Data Set ◽  
Atlas Experiment ◽  
Large Samples ◽  
Systematic Uncertainties ◽  
Detector Acceptance ◽  
Momentum Resolution

AbstractThis article documents the muon reconstruction and identification efficiency obtained by the ATLAS experiment for 139 $$\hbox {fb}^{-1}$$ fb - 1 of pp collision data at $$\sqrt{s}=13$$ s = 13 TeV collected between 2015 and 2018 during Run 2 of the LHC. The increased instantaneous luminosity delivered by the LHC over this period required a reoptimisation of the criteria for the identification of prompt muons. Improved and newly developed algorithms were deployed to preserve high muon identification efficiency with a low misidentification rate and good momentum resolution. The availability of large samples of $$Z\rightarrow \mu \mu $$ Z → μ μ and $$J/\psi \rightarrow \mu \mu $$ J / ψ → μ μ decays, and the minimisation of systematic uncertainties, allows the efficiencies of criteria for muon identification, primary vertex association, and isolation to be measured with an accuracy at the per-mille level in the bulk of the phase space, and up to the percent level in complex kinematic configurations. Excellent performance is achieved over a range of transverse momenta from 3 GeV to several hundred GeV, and across the full muon detector acceptance of $$|\eta |<2.7$$ | η | < 2.7 .

scholarly journals Current cancer driver variant predictors learn to recognize driver genes instead of functional variants

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Daniele Raimondi ◽  
Antoine Passemiers ◽  
Piero Fariselli ◽  
Yves Moreau
Keyword(s):  
Data Sets ◽  
Precision Oncology ◽  
Complex Task ◽  
Excellent Performance ◽  
Driver Genes ◽  
Significant Drop ◽  
Data Set ◽  
Gene Effects ◽  
Cancer Driver ◽  
Functional Variants

Abstract Background Identifying variants that drive tumor progression (driver variants) and distinguishing these from variants that are a byproduct of the uncontrolled cell growth in cancer (passenger variants) is a crucial step for understanding tumorigenesis and precision oncology. Various bioinformatics methods have attempted to solve this complex task. Results In this study, we investigate the assumptions on which these methods are based, showing that the different definitions of driver and passenger variants influence the difficulty of the prediction task. More importantly, we prove that the data sets have a construction bias which prevents the machine learning (ML) methods to actually learn variant-level functional effects, despite their excellent performance. This effect results from the fact that in these data sets, the driver variants map to a few driver genes, while the passenger variants spread across thousands of genes, and thus just learning to recognize driver genes provides almost perfect predictions. Conclusions To mitigate this issue, we propose a novel data set that minimizes this bias by ensuring that all genes covered by the data contain both driver and passenger variants. As a result, we show that the tested predictors experience a significant drop in performance, which should not be considered as poorer modeling, but rather as correcting unwarranted optimism. Finally, we propose a weighting procedure to completely eliminate the gene effects on such predictions, thus precisely evaluating the ability of predictors to model the functional effects of single variants, and we show that indeed this task is still open.

A New Uncertainty Analysis-Based Framework for Data-Driven Computational Mechanics

2021 ◽  
pp. 1-22
Author(s):  
Xu Guo ◽  
Zongliang Du ◽  
Chang Liu ◽  
Shan Tang
Keyword(s):  
Phase Space ◽  
Uncertainty Analysis ◽  
Distinctive Feature ◽  
Computational Mechanics ◽  
Problem Formulation ◽  
Solution Set ◽  
Data Driven ◽  
Data Set ◽  
Classical Counterpart ◽  
Single Solution

Abstract In the present paper, a new uncertainty analysis-based framework for data-driven computational mechanics (DDCM) is established. Compared with its practical classical counterpart, the distinctive feature of this framework is that uncertainty analysis is introduced into the corresponding problem formulation explicitly. Instated of only focusing on a single solution in phase space, a solution set is sought for in order to account for the influence of the multi-source uncertainties associated with the data set on the data-driven solutions. An illustrative example provided shows that the proposed framework is not only conceptually new, but also has the potential of circumventing the intrinsic numerical difficulties pertaining to the classical DDCM framework.

scholarly journals Edge Prior Multilayer Segmentation Network Based on Bayesian Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Chu He ◽  
Zishan Shi ◽  
Peizhang Fang ◽  
Dehui Xiong ◽  
Bokun He ◽  
...  
Keyword(s):  
Prior Information ◽  
Semantic Segmentation ◽  
Bayesian Framework ◽  
Excellent Performance ◽  
Transform Method ◽  
Data Set ◽  
Distance Map ◽  
Constraint Network ◽  
Average Accuracy ◽  
Complex Boundaries

In recent years, methods based on neural network have achieved excellent performance for image segmentation. However, segmentation around the edge area is still unsatisfactory when dealing with complex boundaries. This paper proposes an edge prior semantic segmentation architecture based on Bayesian framework. The entire framework is composed of three network structures, a likelihood network and an edge prior network at the front, followed by a constraint network. The likelihood network produces a rough segmentation result, which is later optimized by edge prior information, including the edge map and the edge distance. For the constraint network, the modified domain transform method is proposed, in which the diffusion direction is revised through the newly defined distance map and some added constraint conditions. Experiments about the proposed approach and several contrastive methods show that our proposed method had good performance and outperformed FCN in terms of average accuracy for 0.0209 on ESAR data set.

Invariant Manifold Detection From Phase Space Trajectories

2008 ◽  
Author(s):  
Joseph Kuehl ◽  
David Chelidze
Keyword(s):  
Phase Space ◽  
Invariant Manifold ◽  
Invariant Manifolds ◽  
Basins Of Attraction ◽  
Detection Methods ◽  
Trajectory Data ◽  
Space Trajectories ◽  
Data Set ◽  
Phase Space Warping ◽  
Phase Space Trajectories

Invariant manifolds provide important information about the structure of flows. When basins of attraction are present, the stable invariant manifold serves as the boundary between these basins. Thus, in experimental applications such as vibrations problems, knowledge of these manifolds is essential to understanding the evolution of phase space trajectories. Most existing methods for identifying invariant manifolds of a flow rely on knowledge of the flow field. However, in experimental applications only knowledge of phase space trajectories is available. We provide modifications to several existing invariant manifold detection methods which enables them to deal with trajectory only data, as well as introduce a new method based on the concept of phase space warping. The method of Stochastic Interrogation applied to the damped, driven Duffing equation is used to generate our data set. The result is a set of trajectory data which randomly populates a phase space. Manifolds are detected from this data set using several different methods. First is a variation on manifold “growing,” and is based on distance of closest approach to a hyperbolic trajectory with “saddle like behavior.” Second, three stretching based schemes are considered. One considers the divergence of trajectory pairs, another quantifies the deformation of a nearest neighbor cloud, and the last uses flow fields calculated from the trajectory data. Finally, the new phase space warping method is introduced. This method takes advantage of the shifting (warping) experienced by a phase space as the parameters of the system are slightly varied. This results in a shift of the invariant manifolds. The region spanned by this shift, provides a means to identify the invariant manifolds. Results show that this method gives superior detection and is robust with respect to the amount of data.

scholarly journals Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Juan-Antonio Martinez-Leon ◽  
Jose-Manuel Cano-Izquierdo ◽  
Julio Ibarrola
Keyword(s):  
Feature Selection ◽  
Motor Imagery ◽  
Group Method ◽  
Order Selection ◽  
Excellent Performance ◽  
Data Set ◽  
Selection Step ◽  
Group Method Data Handling ◽  
Selection Stage ◽  
Selection Phase

This paper presents an investigation aimed at drastically reducing the processing burden required by motor imagery brain-computer interface (BCI) systems based on electroencephalography (EEG). In this research, the focus has moved from the channel to the feature paradigm, and a 96% reduction of the number of features required in the process has been achieved maintaining and even improving the classification success rate. This way, it is possible to build cheaper, quicker, and more portable BCI systems. The data set used was provided within the framework of BCI Competition III, which allows it to compare the presented results with the classification accuracy achieved in the contest. Furthermore, a new three-step methodology has been developed which includes a feature discriminant character calculation stage; a score, order, and selection phase; and a final feature selection step. For the first stage, both statistics method and fuzzy criteria are used. The fuzzy criteria are based on the S-dFasArt classification algorithm which has shown excellent performance in previous papers undertaking the BCI multiclass motor imagery problem. The score, order, and selection stage is used to sort the features according to their discriminant nature. Finally, both order selection and Group Method Data Handling (GMDH) approaches are used to choose the most discriminant ones.

DETECTOR EFFICIENCY EVALUATION BY FIDUCIAL CUTS FOR AU+AU → J/ψ MID RAPIDITY DATA FROM PHENIX

2007 ◽  
Vol 16 (07n08) ◽  
pp. 2470-2475
Author(s):  
◽  
ERMIAS T. ATOMSSA
Keyword(s):  
Theoretical Calculation ◽  
Efficiency Evaluation ◽  
Detector Efficiency ◽  
Data Set ◽  
Comparison Of Results ◽  
Detector Acceptance ◽  
Spectrometer Data ◽  
Yield Calculation

This poster presents an alternate method for the estimation of detector acceptance and efficiency based on PHENIX Run 4 AuAu central spectrometer data sample and its application to J/ψ invariant yield calculation. After some descriptions of the data set used and comparison of results from this method with the published results from PHENIX, we show a very brief comparison of the latter to theoretical calculation.

scholarly journals A factorisation-aware Matrix element emulator

2021 ◽  
Vol 2021 (11) ◽  
Author(s):  
D. Maître ◽  
H. Truong
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Matrix Element ◽  
Percent Level ◽  
Matrix Elements ◽  
Leading Order ◽  
Jet Production ◽  
The Neural Network ◽  
The Matrix ◽  
The One

Abstract In this article we present a neural network based model to emulate matrix elements. This model improves on existing methods by taking advantage of the known factorisation properties of matrix elements. In doing so we can control the behaviour of simulated matrix elements when extrapolating into more singular regions than the ones used for training the neural network. We apply our model to the case of leading-order jet production in e+e− collisions with up to five jets. Our results show that this model can reproduce the matrix elements with errors below the one-percent level on the phase-space covered during fitting and testing, and a robust extrapolation to the parts of the phase-space where the matrix elements are more singular than seen at the fitting stage.

scholarly journals Systematic relationships of five newly sequenced cervid species

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2307 ◽  
Author(s):  
Nicola S. Heckeberg ◽  
Dirk Erpenbeck ◽  
Gert Wörheide ◽  
Gertrud E. Rössner
Keyword(s):  
Comparative Anatomy ◽  
Phylogenetic Analyses ◽  
Molecular Data ◽  
Data Set ◽  
Systematic Uncertainties ◽  
Mitochondrial Cytochrome B ◽  
In The Wild ◽  
History Of ◽  
Systematic Relationships ◽  
Partial Consensus

Cervid phylogenetics has been puzzling researchers for over 150 years. In recent decades, molecular systematics has provided new input for both the support and revision of the previous results from comparative anatomy but has led to only partial consensus. Despite all of the efforts to reach taxon-wide species sampling over the last two decades, a number of cervid species still lack molecular data because they are difficult to access in the wild. By extracting ancient DNA from museum specimens, in this study, we obtained partial mitochondrial cytochrome b gene sequences forMazama bricenii,Mazama chunyi,Muntiacus atherodes,Pudu mephistophiles, andRusa marianna, including three holotypes. These new sequences were used to enrich the existing mitochondrial DNA alignments and yielded the most taxonomically complete data set for cervids to date. Phylogenetic analyses provide new insights into the evolutionary history of these five species. However, systematic uncertainties withinMuntiacuspersist and resolving phylogenetic relationships withinPuduandMazamaremain challenging.

scholarly journals A Principled Method to Identify Individual Differences and Behavioral Shifts in Signaled Active Avoidance

2018 ◽  
Author(s):  
Angelos-Miltiadis Krypotos
Keyword(s):  
Individual Differences ◽  
Active Avoidance ◽  
Latent Growth Curve ◽  
Latent Growth ◽  
Data Set ◽  
Experimental Procedure ◽  
Large Samples ◽  
Curve Modeling ◽  
Threat Cues ◽  
Online Software

This is a preprint to a previous version of the manuscript. There may be deviations from the final paper. Please refer to the main article for the list of authors and their affiliations. ## MAIN ABSTRACT ## Signaled active avoidance (SigAA) is the key experimental procedure for studying the acquisition of instrumental responses towards conditioned threat cues. Traditional analytic approaches (e.g. general linear model) often obfuscate important individual differences. However, individual differences models (e.g. latent growth curve modeling) typically require large samples and onerous computational methods. Here, we present an analytic methodology that enables the detection of individual differences in SigAA performance at a high accuracy based at the n=1 level. We further show an online software that enables the easy application of our method to any SigAA data set.

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