Search for squarks and gluinos in final states with hadronically decaying τ -leptons, jets, and missing transverse momentum using p p collisions at s = 13 TeV with the ATLAS detector
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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Results for “MISSE”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources
Abstract Motivation Precision medicine leverages patient-specific multimodal data to improve prevention, diagnosis, prognosis, and treatment of diseases. Advancing precision medicine requires the non-trivial integration of complex, heterogeneous, and potentially high-dimensional data sources, such as multi-omics and clinical data. In the literature, several approaches have been proposed to manage missing data, but are usually limited to the recovery of subsets of features for a subset of patients. A largely overlooked problem is the integration of multiple sources of data when one or more of them are completely missing for a subset of patients, a relatively common condition in clinical practice. Results We propose miss-Similarity Network Fusion (miss-SNF), a novel general-purpose data integration approach designed to manage completely missing data in the context of patient similarity networks. miss-SNF integrates incomplete unimodal patient similarity networks by leveraging a non-linear message-passing strategy borrowed from the SNF algorithm. miss-SNF is able to recover missing patient similarities and is “task agnostic”, in the sense that can integrate partial data for both unsupervised and supervised prediction tasks. Experimental analyses on nine cancer datasets from The Cancer Genome Atlas (TCGA) demonstrate that miss-SNF achieves state-of-the-art results in recovering similarities and in identifying patients subgroups enriched in clinically relevant variables and having differential survival. Moreover, amputation experiments show that miss-SNF supervised prediction of cancer clinical outcomes and Alzheimer’s disease diagnosis with completely missing data achieves results comparable to those obtained when all the data are available. Availability and implementation miss-SNF code, implemented in R, is available at https://github.com/AnacletoLAB/missSNF.
Biochemistry & Molecular Biology↗
MBIR: A cryo-ET 3D reconstruction method that effectively minimizes missing wedge artifacts and restores missing information
Cryo-Electron Tomography (cryo-ET) has become an essential technique in revealing cellular and macromolecular assembly structures in their native states. However, due to radiation damage and the limited tilt range, cryo-ET suffers from low contrast and missing wedge artifacts, which limits the tomograms to low resolution and hinders further biological interpretation. We applied the Model-Based Iterative Reconstruction (MBIR) method to obtain tomographic 3D reconstructions of experimental cryo-ET datasets and demonstrated the advantages of MBIR in contrast improvement, missing wedge artifacts reduction, missing information restoration, and subtomogram averaging compared with other reconstruction approaches. Considering the outstanding reconstruction quality, MBIR has a great potential in the determination of high resolution biological structures with cryo-ET.
59 BASIC BIOLOGICAL SCIENCES↗
Search for large missing transverse momentum in association with one top-quark in proton-proton collisions at s $ \sqrt{s} $ = 13 TeV with the ATLAS detector
This paper describes a search for events with one top-quark and large missing transverse momentum in the final state. Data collected during 2015 and 2016 by the ATLAS experiment from 13 TeV proton–proton collisions at the LHC corresponding to an integrated luminosity of 36.1 fb –1 are used. Two channels are considered, depending on the leptonic or the hadronic decays of the W boson from the top quark. The obtained results are interpreted in the context of simplified models for dark-matter production and for the single production of a vector-like T quark. In the absence of significant deviations from the Standard Model background expectation, 95% confidence-level upper limits on the corresponding production cross-sections are obtained and these limits are translated into constraints on the parameter space of the models considered.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Search for chargino and neutralino production in final states with a Higgs boson and missing transverse momentum at s√=13 TeV with the ATLAS detector
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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Search for new phenomena using the invariant mass distribution of same-flavour opposite-sign dilepton pairs in events with missing transverse momentum in $\sqrt{s}=13$ TeV pp collisions with the ATLAS detector
A search for new phenomena in final states containing an e + e - or μ + μ - pair, jets, and large missing transverse momentum is presented. This analysis makes use of proton–proton collision data with an integrated luminosity of 36.1 fb -1 , collected during 2015 and 2016 at a centre-of-mass energy s√=13 TeV with the ATLAS detector at the Large Hadron Collider. The search targets the pair production of supersymmetric coloured particles (squarks or gluinos) and their decays into final states containing an e + e - or μ + μ - pair and the lightest neutralino ($\sim\atop{χ}$ $0\atop{1}$) via one of two next-to-lightest neutralino ($\sim\atop{χ}$ $0\atop{2}$) decay mechanisms: $\sim\atop{χ}$ $0\atop{2}$→Z$\sim\atop{χ}$ $0\atop{1}$, where the Z boson decays leptonically leading to a peak in the dilepton invariant mass distribution around the Z boson mass; and $\sim\atop{χ}$ $0\atop{2}$→ℓ + ℓ - $\sim\atop{χ}$ $0\atop{1}$ with no intermediate ℓ + ℓ - resonance, yielding a kinematic endpoint in the dilepton invariant mass spectrum. The data are found to be consistent with the Standard Model expectation. Finally, results are interpreted using simplified models, and exclude gluinos and squarks with masses as large as 1.85 and 1.3 TeV at 95% confidence level, respectively.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Subsurface biogeochemistry is a missing link between ecology and hydrology in dam-impacted river corridors
Not Available
Graham, Emily B. (ORCID:0000000246237076)↗
Locating the Missing Superconducting Electrons in the Overdoped Cuprates La 2 – x Sr x CuO 4 [Locating the Missing Superconducting Electrons in Overdoped Cuprates]
Overdoped high-temperature cuprate superconductors have often been understood within the standard BCS framework of superconductivity. However, measurements in a variety of overdoped cuprates indicate that the superfluid density is much smaller than expected from BCS theory and decreases smoothly to zero as the doping is increased. Here, we combine time-domain THz spectroscopy with kHz range mutual inductance measurements on the same overdoped La 2–x Sr x CuO 4 films to determine the total, superfluid, and uncondensed spectral weight as a function of doping. A significant fraction of the carriers remains uncondensed in a wide Drude-like peak as T → 0, while the superfluid density remains linear in temperature. These observations are seemingly inconsistent with existing, realistic theories of impurity scattering suppressing the superfluid density in a BCS-like d-wave superconductor. Furthermore, our large measurement frequency range gives us a unique look at the low frequency spectral weight distribution, which may suggest the presence of quantum phase fluctuations as the critical doping is approached.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Search for a new heavy gauge-boson resonance decaying into a lepton and missing transverse momentum in 36 fb$^{-1}$ of pp collisions at $\sqrt{s} = 13$ TeV with the ATLAS experiment
The results of a search for new heavy W ' bosons decaying to an electron or muon and a neutrino using proton–proton collision data at a centre-of-mass energy of √s = 13 TeV are presented. The dataset was collected in 2015 and 2016 by the ATLAS experiment at the Large Hadron Collider and corresponds to an integrated luminosity of 36.1 fb –1 . As no excess of events above the Standard Model prediction is observed, the results are used to set upper limits on the W ' boson cross-section times branching ratio to an electron or muon and a neutrino as a function of the W ' mass. Here, assuming a W ' boson with the same couplings as the Standard Model W boson, W ' masses below 5.1 TeV are excluded at the 95% confidence level.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Observation of large missing-momentum ( e , e ' p ) cross-section scaling and the onset of correlated-pair dominance in nuclei
Herein we report the first measurement of $x_B$-scaling in $(e,e'p)$ cross-section ratios off nuclei relative to deuterium at large missing-momentum of $350 \leq p_{miss} \leq 600$ MeV/c. The observed scaling extends over a kinematic range of $0.7 \leq x_B \leq 1.8$, which is significantly wider than $1.4 \leq x_B \leq 1.8$ previously observed for inclusive $(e,e')$ cross-section ratios. The $x_B$-integrated cross-section ratios become constant (i.e., scale) beginning at $p_{miss}\approx k_F$, the nuclear Fermi momentum. Comparing with theoretical calculations we find good agreement with Generalized Contact Formalism calculations for high missing-momentum ($> 375$ MeV/c), suggesting the observed scaling results from interacting with nucleons in short-range correlated (SRC) pairs. For low missing-momenta, mean-field calculations show good agreement with the data for $p_{miss}\le k_F$, and suggest that contributions to the measured cross-section ratios from scattering off single, un-correlated, nucleons are non-negligible up to $p_{miss}\approx 350$ MeV/c. Therefore, SRCs become dominant in nuclei at $p_{miss}\approx 350$ MeV/c, well above the nuclear Fermi Surface of $k_F \approx 250$ MeV/c.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
First Observation of Large Missing-Momentum (e,e'p) Cross-Section Scaling and the onset of Correlated-Pair Dominance in Nuclei
We report the first measurement of $x_B$-scaling in $(e,e'p)$ cross-section ratios off nuclei relative to deuterium at large missing-momentum of $350 \leq p_{miss} \leq 600$ MeV/c. The observed scaling extends over a kinematic range of $0.7 \leq x_B \leq 1.8$, which is significantly wider than $1.4 \leq x_B \leq 1.8$ previously observed for inclusive $(e,e')$ cross-section ratios. The $x_B$-integrated cross-section ratios become constant (i.e., scale) beginning at $p_{miss}\approx k_F$, the nuclear Fermi momentum. Comparing with theoretical calculations we find good agreement with Generalized Contact Formalism calculations for high missing-momentum ($> 375$ MeV/c), suggesting the observed scaling results from interacting with nucleons in short-range correlated (SRC) pairs. For low missing-momenta, mean-field calculations show good agreement with the data for $p_{miss}\le k_F$, and suggest that contributions to the measured cross-section ratios from scattering off single, un-correlated, nucleons are non-negligible up to $p_{miss}\approx 350$ MeV/c. Therefore, SRCs become dominant in nuclei at $p_{miss}\approx 350$ MeV/c, well above the nuclear Fermi Surface of $k_F \approx 250$ MeV/c.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Physics-Informed Deep Learning for Reconstruction of Spatial Missing Climate Information in the Antarctic
Understanding the influence of the Antarctic on the global climate is crucial for the prediction of global warming. However, due to very few observation sites, it is difficult to reconstruct the rational spatial pattern by filling in the missing values from the limited site observations. To tackle this challenge, regional spatial gap-filling methods, such as Kriging and inverse distance weighted (IDW), are regularly used in geoscience. Nevertheless, the reconstructing credibility of these methods is undesirable when the spatial structure has massive missing pieces. Inspired by image inpainting, we propose a novel deep learning method that demonstrates a good effect by embedding the physics-aware initialization of deep learning methods for rapid learning and capturing the spatial dependence for the high-fidelity imputation of missing areas. We create the benchmark dataset that artificially masks the Antarctic region with ratios of 30%, 50% and 70%. The reconstructing monthly mean surface temperature using the deep learning image inpainting method RFR (Recurrent Feature Reasoning) exhibits an average of 63% and 71% improvement of accuracy over Kriging and IDW under different missing rates. With regard to wind speed, there are still 36% and 50% improvements. In particular, the achieved improvement is even better for the larger missing ratio, such as under the 70% missing rate, where the accuracy of RFR is 68% and 74% higher than Kriging and IDW for temperature and also 38% and 46% higher for wind speed. In addition, the PI-RFR (Physics-Informed Recurrent Feature Reasoning) method we proposed is initialized using the spatial pattern data simulated by the numerical climate model instead of the unified average. Compared with RFR, PI-RFR has an average accuracy improvement of 10% for temperature and 9% for wind speed. When applied to reconstruct the spatial pattern based on the Antarctic site observations, where the missing rate is over 90%, the proposed method exhibits more spatial characteristics than Kriging and IDW.
54 ENVIRONMENTAL SCIENCES↗
Characteristics of missed prostate cancer lesions on 3T multiparametric-MRI in 518 patients: based on PI-RADSv2 and using whole-mount histopathology reference
Purpose: To determine the characteristics of missed prostate cancer (PCa) lesions on 3T multiparametric-MRI (mpMRI) based on PI-RADSv2 with whole-mount histopathology (WMHP) correlation. Materials and methods: This IRB-approved, HIPAA-compliant study, included 614 consecutive men with 3T mpMRI prior to prostatectomy at a single tertiary center between 12/2009 and 4/2017. Clinical, mpMRI, and pathologic features were obtained. PI-RADSv2-based MRI detected lesions were matched with previously finalized WMHP by a genitourinary (GU) radiologist and a GU pathologist. Patients with no mpMRI detected PCa lesion, but with at least one lesion ≥ 1 cm on WMHP, were reviewed retrospectively and assigned a PI-RADSv2 score. Tumor characteristics were compared between missed and detected lesions. Result: The final cohort included 518 patients with 1085 WMHP lesions. 51.9% (563/1085) of lesions were missed on 3T mpMRI. 71.4% (402/563), 21.7% (122/563), 4.4% (25/563), and 2.5% (14/563) of the missed lesions were Gleason scores (GS) 3 + 3, 3 + 4, 4 + 3, and 8 − 10, respectively. Missed PCa lesions had significantly lower proportion of GS ≥ 7 (p < 0.001) and smaller size for overall (p < 0.001) and index subcohorts (p < 0.001), as compared to detected lesions. 34.5% (194) of overall and 71.2% (79) index missed lesions were larger than 1 cm. In 13.7% (71/518) of patients without MR detected PCa, 149 lesions were detected on WMHP, with 70 (47%) lesions ≥ 1 cm. In retrospective review of these lesions, 42.9% (30), 18.6% (13), 21.5% (15), 10% (7), and 7% (5) were PI-RADSv2 1, 2, 3, 4, and 5, respectively. Conclusion: 3T mpMRI has an excellent per patients diagnostic performance for PCa and majority of missed lesions are clinically nonsignificant.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
A General Spatiotemporal Imputation Framework for Missing Sensor Data
Many applications from precision agriculture, environmental monitoring and transportation networks rely on data collected across space and time over a large geographic area. Missing data poses a significant challenge for any data-driven inference and control tasks. Data imputation or the estimation of missing data can help fill these gaps by utilizing inherent spatial relationships and temporal patterns. A variety of spatiotemporal imputation models have been developed to address missing data in spatiotemporal datasets. However, these classical methods rely on the assumption that the underlying data follows a smooth trend and fail to provide accurate estimates when there is a large number of missing points in the data. Even though there are machine learning driven tensor completion approaches such as convolutional neural network based tensor completion (CoSTCo) that capture the non-linear relationships in the dataset, the transductive nature makes the algorithm less scalable. Thus, existing approaches for estimating the missing information do not effectively capture all dimensions of the spatiotemporal data structure, resulting in erroneous predictions and poor performance. The main contributions of this paper are: (1) We propose a novel inductive framework (G-LSTM) for missing data imputation that integrates a graph neural network with LSTMs to effectively capture both spatial and temporal dependencies. (2) Experimental results on a traffic dataset demonstrate that the proposed GNN integrated with an LSTM framework achieves improved imputation and maintains steady performance even when there are extreme missing conditions in comparison with the state-of-the-art imputation framework (i.e, CoSTCo). (3) The simulation results on a traffic network show up to 69% reduction in mean absolute error and 61% reduction in root mean square error when compared to CoSTCo.
Tharzeen, Aabila↗
GRIP: Constraint-based Explanation of Missing Answers for Graph Queries
Abstract: A useful feature in graph query engines is to clarify “Why certain entities (nodes, attribute values or edges) are missing” in query answers. This task is even more challenging when the relevant data is already missing in the underlying data source. Missing data, on the other hand, can be inferred by enforcing data constraints for graphs. We demonstrate GRIP, a system that exploits data constraints to clarify missing answers for graph queries. (1) Constraint-based ex- planation. Given a desired yet missing entity in the query answer, GRIP ensures to generate finite and minimal sequences of data con- strains (an “explanation”) that should be consecutively enforced to ?? to ensure its occurrence for the same query. (2) Answering “why” and“how” questions. Users can query GRIP with both“Why”(“Why” the element is missing) and “How” questions (“How” to refine the graph to include the missing answer). GRIP engine supports run- time generation of explanations by incrementally maintaining a set of bi-directional search trees. (3) Interactive exploration. GRIP provides a user-friendly GUI to support interactive ad visual exploration of explanations, including both automated generation and step-by-step inspection of graph manipulations.
graphs↗
Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Missing Photovoltaic Data Imputation
The integration of the global Photovoltaic (PV) market with real time data-loggers has enabled large scale PV data analytical pipelines for power forecasting and long-term reliability assessment of PV fleets. Nevertheless, the performance of PV data analysis heavily depends on the quality of PV timeseries data. This paper proposes a novel Spatio-Temporal Denoising Graph Autoencoder (STD-GAE) framework to impute missing PV Power Data. STDGAE exploits temporal correlation, spatial coherence, and value dependencies from domain knowledge to recover missing data. It is empowered by two modules. (1) To cope with sparse yet various scenarios of missing data, STD-GAE incorporates a domain-knowledge aware data augmentation module that creates plausible variations of missing data patterns. This generalizes STD-GAE to robust imputation over different seasons and environment. (2) STD-GAE nontrivially integrates spatiotemporal graph convolution layers (to recover local missing data by observed “neighboring” PV plants) and denoising autoencoder (to recover corrupted data from augmented counterpart) to improve the accuracy of imputation accuracy at PV fleet level. We have evaluated our proposed model on two realworld PV datasets. Experimental results show that STD-GAE can achieve a gain of 43.14% in imputation accuracy and remains less sensitive to missing rate, different seasons, and missing scenarios, compared with state-of-the-art data imputation methods such as MIDA and LRTC-TNN.
Fan, Yangxin↗
Performance of missing transverse momentum reconstruction in proton-proton collisions at √ s = 13 TeV using the CMS detector
The performance of missing transverse momentum (p → T miss ) reconstruction algorithms for the CMS experiment is presented, using proton-proton collisions at a center-of-mass energy of 13 TeV, collected at the CERN LHC in 2016. The data sample corresponds to an integrated luminosity of 35.9 fb -1 . The results include measurements of the scale and resolution of p → T miss , and detailed studies of events identified with anomalous p → T miss . The performance is presented of a p → T miss reconstruction algorithm that mitigates the effects of multiple proton-proton interactions, using the "pileup per particle identification" method. The performance is shown of an algorithm used to estimate the compatibility of the reconstructed p → T miss with the hypothesis that it originates from resolution effects.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
The performance of missing transverse momentum reconstruction and its significance with the ATLAS detector using 140 $\hbox {fb}^{-1}$ of $\sqrt{s}=13$ TeV pp collisions
This paper presents the reconstruction of missing transverse momentum ($p_{\text {T}}^{\text {miss}}$ ) in proton–proton collisions, at a center-of-mass energy of 13 TeV. This is a challenging task involving many detector inputs, combining fully calibrated electrons, muons, photons, hadronically decaying $\tau$ -leptons, hadronic jets, and soft activity from remaining tracks. Possible double counting of momentum is avoided by applying a signal ambiguity resolution procedure which rejects detector inputs that have already been used. Several $p_{\text {T}}^{\text {miss}}$ ‘working points’ are defined with varying stringency of selections, the tightest improving the resolution at high pile-up by up to 39% compared to the loosest. The $p_{\text {T}}^{\text {miss}}$ performance is evaluated using data and Monte Carlo simulation, with an emphasis on understanding the impact of pile-up, primarily using events consistent with leptonic Z decays. The studies use $140~\text {fb}^{-1}$ of data, collected by the ATLAS experiment at the Large Hadron Collider between 2015 and 2018. The results demonstrate that $p_{\text {T}}^{\text {miss}}$ reconstruction, and its associated significance, are well understood and reliably modelled by simulation. Finally, the systematic uncertainties on the soft $p_{\text {T}}^{\text {miss}}$ component are calculated. After various improvements the scale and resolution uncertainties are reduced by up to 76% and 51%, respectively, compared to the previous calculation at a lower luminosity.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗