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DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains
In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques— Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)— and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ~20%. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys.
Tau neutrino identification in atmospheric neutrino oscillations without particle identification or unitarity
Not Available
Identification of Low-Abundance Lipid Droplet Proteins in Seeds and Seedlings
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IncProf: Efficient Source-Oriented Phase Identification for Application Behavior Understanding.
Abstract not provided.
N-k Failure Analysis Algorithm for Identification of Extreme Events for Cascading Outage Pre-screening process
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Identification of Intraday False Data Injection Attack on DER Dispatch Signals
The urgent need for the decarbonization of power grids has accelerated the integration of renewable energy. Concurrently, increasing distributed energy resources (DER) and advanced metering infrastructure (AMI) have transformed the power grids into a more sophisticated cyber-physical system with numerous communication devices. While these transitions provide economic and environmental value, they also impose increased risk of cyber attacks and operation al challenges. This paper investigates the vulnerabilit y of the power grids with high renewable penetration against an intraday false data injection (FDI) attack on DER dispatch signals and proposes a kernel support vector regression (SVR) based detection model as a countermeasure. The intraday FDI attack scenario and the detection model are demonstrated in a numerical experiment using the HCE 187-bus test system.
Identification of the I38T PA Substitution as a Resistance Marker for Next-Generation Influenza Virus Endonuclease Inhibitors
ABSTRACT The clinical severity and annual occurrence of influenza virus epidemics, combined with the availability of just a single class of antivirals to treat infections, underscores the urgent need to develop new anti-influenza drugs. The endonuclease activity within the viral acidic polymerase (PA) protein is an attractive target for drug discovery due to the critical role it plays in viral gene transcription. RO-7 is a next-generation PA endonuclease inhibitor of influenza A and B viruses, but its drug resistance potential is unknown. Through serial passage of influenza A(H1N1) viruses in MDCK cells under selective pressure of RO-7, we identified an I38T substitution within the PA endonuclease domain that conferred in vitro resistance to RO-7 (up to a 287-fold change in 50% effective concentration [EC 50 ]). I38T emerged between 5 and 10 passages, and when introduced into recombinant influenza A(H1N1) viruses, alone conferred RO-7 resistance (up to an 81-fold change in EC 50 ). Cocrystal structures of mutant and wild-type endonuclease domains with RO-7 provided the structural basis of resistance, where a key hydrophobic interaction between RO-7 and the Ile38 side chain is compromised when mutated to the polar threonine. While Ile38 does not have a crucial role in coordinating the endonuclease active site, the switch to threonine does affect the polymerase activity of some viruses and influences RO-7 affinity for the PA N target (i.e., the ≈200-residue N-terminal domain of PA). However, the change does not lead to a complete loss of replication activity in vitro . Our results predict that RO-7-resistant influenza viruses carrying the I38T substitution may emerge under treatment. This should be taken into consideration for clinical surveillance and in refinement of these drugs. IMPORTANCE The effectiveness of antiviral drugs can be severely compromised by the emergence of resistant viruses. Therefore, determination of the mechanisms by which viruses become resistant is critical for drug development and clinical use. RO-7 is a compound that potently inhibits influenza virus replication and belongs to a new class of drugs in late-stage clinical trials for treatment of influenza virus infection. Here we demonstrate that a single amino acid change acquired under prolonged virus exposure to RO-7 renders influenza viruses significantly less susceptible to its inhibitory effects. We have discovered how the mutation can simultaneously interfere with drug activity and still maintain efficient virus replication. These findings have important implications for the development of more effective derivatives of RO-7-like drugs and provide guidance for how to monitor the emergence of resistance.
Identification of boosted Higgs bosons decaying into b -quark pairs with the ATLAS detector at 13 TeV
This study describes a study of techniques for identifying Higgs bosons at high transverse momenta decaying into bottom-quark pairs, $H → b\bar{b}$, for proton–proton collision data collected by the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy $\sqrt{\bar{s}}$ = 13 TeV. These decays are reconstructed from calorimeter jets found with the anti-$k_t R$ = 1.0 jet algorithm. To tag Higgs bosons, a combination of requirements is used: b -tagging of $R$ = 0.2 track-jets matched to the large- R calorimeter jet, and requirements on the jet mass and other jet substructure variables. The Higgs boson tagging efficiency and corresponding multijet and hadronic top-quark background rejections are evaluated using Monte Carlo simulation. Several benchmark tagging selections are defined for different signal efficiency targets. The modelling of the relevant input distributions used to tag Higgs bosons is studied in 36 fb -1 of data collected in 2015 and 2016 using $g → b\bar{b}$ and $Z(→ b\bar{b})γ$ event selections in data. Both processes are found to be well modelled within the statistical and systematic uncertainties.
Identification and Characterization of Persistent Cold Pool Events from Temperature and Wind Profilers in the Columbia River Basin
Cold pool events occur when deep layers of stable, cold air remain trapped in a valley or basin for multiple days, without mixing out from daytime heating. With large impacts on air quality, freezing events, and es-pecially on wind energy production, they are often poorly forecast by modern mesoscale numerical weather prediction (NWP) models. Understanding the characteristics of cold pools is, therefore, important to provide more accurate forecasts. This study analyzes cold pool characteristics with data collected during the Second Wind Forecast Improvement Project (WFIP2), which took place in the Columbia River basin and Gorge of Oregon and Washington from fall 2015 until spring 2017. A subset of the instrumentation included three microwave radiometer pro?lers, six radar wind pro?lers with radio acoustic sounding systems, and seven sodars, which together provided seven sites with collocated vertical pro?les of temperature, humidity, wind speed, and wind direction. Using these collocated observations, we developed a set of criteria to determine if a cold pool was present based on stability, wind speed, direction, and temporal continuity, and then developed an automated algorithm based on these criteria to identify all cold pool events over the 18 months of the ?eld project. Characteristics of these events are described, including statistics of the wind speed distributions and pro?les, stability conditions, cold pool depths, and descent rates of the cold pool top. The goal of this study is a better understanding of these characteristics and their processes to ultimately lead to improved physical parameterizations in NWP models, and consequently improve forecasts of cold pool events in the study region as well at other locations that experiences similar events.
Wind Turbine Wake Definition and Identification using Velocity Deficit and Turbulence Profile.
Abstract not provided.
Identification of specific requirements for a NASA aerospace law information system and identification of the acquisition requirements for an aerospace law collection for the NASA law library
The study to develop, implement, and maintain a space law library and information system is summarized. The survey plan; major interviews with individuals representative of potential sources, users and producers of information related to aerospace law; and system trade-off analyses are discussed along with the NASA/RECON system capability. The NASA publications of STAR and IAA are described, and the NASA legal micro-thesaurus is included.
A second-order structural identification procedure via state space-based system identification
A theory is presented for transforming the system theory-based realization models into the corresponding physical coordinate-based structural models. The theory has been implemented into a computational procedure and applied to several example problems. The results show that the present transformation theory yields an objective model basis possessing a unique set of structural parameters from an infinite set of equivalent system realization models. For proportionally damped systems, the transformation directly and systematically yields the normal modes and modal damping. When non-proportional damping is present, the relative magnitude and phase of the damped model shapes are separately characterized, and a corrective transformation is then employed to capture the undamped normal modes and non-diagonal modal damping matrix.
Identification of linear multivariable systems by identification of observers with assigned real eigenvalues
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Natural Language Processing for Topic Identification Supporting Document Search and Identification for Nuclear Proliferation Detection.
Abstract not provided.
Comparison of Five System Identification Algorithms for Rotorcraft Higher Harmonic Control
This report presents an analysis and performance comparison of five system identification algorithms. The methods are presented in the context of identifying a frequency-domain transfer matrix for the higher harmonic control (HHC) of helicopter vibration. The five system identification algorithms include three previously proposed methods: (1) the weighted-least- squares-error approach (in moving-block format), (2) the Kalman filter method, and (3) the least-mean-squares (LMS) filter method. In addition there are two new ones: (4) a generalized Kalman filter method and (5) a generalized LMS filter method. The generalized Kalman filter method and the generalized LMS filter method were derived as extensions of the classic methods to permit identification by using more than one measurement per identification cycle. Simulation results are presented for conditions ranging from the ideal case of a stationary transfer matrix and no measurement noise to the more complex cases involving both measurement noise and transfer-matrix variation. Both open-loop identification and closed- loop identification were simulated. Closed-loop mode identification was more challenging than open-loop identification because of the decreasing signal-to-noise ratio as the vibration became reduced. The closed-loop simulation considered both local-model identification, with measured vibration feedback and global-model identification with feedback of the identified uncontrolled vibration. The algorithms were evaluated in terms of their accuracy, stability, convergence properties, computation speeds, and relative ease of implementation.
Search-based model identification of smart-structure damage
This paper describes the use of a combined model and parameter identification approach, based on modal analysis and artificial intelligence (AI) techniques, for identifying damage or flaws in a rotating truss structure incorporating embedded piezoceramic sensors. This smart structure example is representative of a class of structures commonly found in aerospace systems and next generation space structures. Artificial intelligence techniques of classification, heuristic search, and an object-oriented knowledge base are used in an AI-based model identification approach. A finite model space is classified into a search tree, over which a variant of best-first search is used to identify the model whose stored response most closely matches that of the input. Newly-encountered models can be incorporated into the model space. This adaptativeness demonstrates the potential for learning control. Following this output-error model identification, numerical parameter identification is used to further refine the identified model. Given the rotating truss example in this paper, noisy data corresponding to various damage configurations are input to both this approach and a conventional parameter identification method. The combination of the AI-based model identification with parameter identification is shown to lead to smaller parameter corrections than required by the use of parameter identification alone.
A review of active probing-based system identification techniques with applications in power systems
System identification is becoming a relevant research area for numerous applications in power grids due to the increasing complexity of the system. A paradigm shift in power system infrastructure driven by renewable energy resources, controllable loads, and new power electronics technologies have given rise to new challenges in power systems operation and control. This increased complexity of power systems and unavailability of physics-based models of most inverter-based resources requires that traditional modeling of power systems approaches be complemented by system identification-based black or grey box modeling techniques. In light of the importance of system identification and little attention paid to the applications of these techniques in transforming power systems, this paper provides a comprehensive review of active probing-based system identification methods in the context of power system applications. It reviews applications of both linear and nonlinear system identification with discussion on their potential and key takeaways. To motivate a further practical use in power systems, the paper provides an example of system identification, to develop a state space model of an unknown plant with step-by-step details. The paper highlights the advantages of using modern power electronics-based sources in the identification process and discusses the emerging research directions for future.