Retrieving the solar EUV spectrum from a reduced set of spectral lines

Abstract. The solar EUV irradiance is a key input for thermospheric and ionospheric models. Difficulties in continuously measuring the calibrated spectrum has prompted the use of various surrogate quantities. Although most proxies correlate quite well with the spectral variability, their use for modelling purposes becomes increasingly unsatisfactory. A different and data-driven approach is considered here, in which the EUV spectrum is reconstructed from a linear combination of a few, calibrated and carefully selected spectral lines. This approach is based on a statistical analysis of the temporal variability of EUV spectra, as recorded by the TIMED satellite. A basic set of lines is extracted, from which the salient features of the spectral variability can be reconstructed. The best results are achieved with a selection of 5 to 8 of these lines. This study focuses on the methodology for selecting these lines, which can also be used for instrument specification and provides new insight into the comparison of solar proxies against the EUV irradiance.

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A data-driven approach to the selection of academic vocabulary

Academic Vocabulary in Learner Writing : From Extraction to Analysis ◽

10.5040/9781474211697.ch-002 ◽

2014 ◽

Keyword(s):

Data Driven ◽

Academic Vocabulary ◽

Data Driven Approach ◽

Selection Of

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Genetic Contributions to Multivariate Data-Driven Brain Networks Constructed via Source-Based Morphometry

Cerebral Cortex ◽

10.1093/cercor/bhaa082 ◽

2020 ◽

Vol 30 (9) ◽

pp. 4899-4913

Author(s):

Amanda L Rodrigue ◽

Aaron F Alexander-Bloch ◽

Emma E M Knowles ◽

Samuel R Mathias ◽

Josephine Mollon ◽

...

Keyword(s):

Brain Function ◽

Complex Traits ◽

Brain Regions ◽

Data Driven ◽

Additional Insight ◽

Genetic Overlap ◽

Data Driven Approach ◽

The Uk ◽

Genetic Contributions ◽

Insight Into

Abstract Identifying genetic factors underlying neuroanatomical variation has been difficult. Traditional methods have used brain regions from predetermined parcellation schemes as phenotypes for genetic analyses, although these parcellations often do not reflect brain function and/or do not account for covariance between regions. We proposed that network-based phenotypes derived via source-based morphometry (SBM) may provide additional insight into the genetic architecture of neuroanatomy given its data-driven approach and consideration of covariance between voxels. We found that anatomical SBM networks constructed on ~ 20 000 individuals from the UK Biobank were heritable and shared functionally meaningful genetic overlap with each other. We additionally identified 27 unique genetic loci that contributed to one or more SBM networks. Both GWA and genetic correlation results indicated complex patterns of pleiotropy and polygenicity similar to other complex traits. Lastly, we found genetic overlap between a network related to the default mode and schizophrenia, a disorder commonly associated with neuroanatomic alterations.

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Automatic Detection and Analysis of the “Game Hack” Scam

Journal of Web Engineering ◽

10.13052/jwe1540-9589.1881 ◽

2020 ◽

Cited By ~ 1

Author(s):

Emad Badawi ◽

Guy-Vincent Jourdan ◽

Gregor Bochmann ◽

Iosif-Viorel Onut

Keyword(s):

Social Media ◽

Market Research ◽

Large Scale ◽

Personal Information ◽

Data Driven ◽

Search Queries ◽

Vast Array ◽

Low Profile ◽

Data Driven Approach ◽

Insight Into

The “Game Hack” Scam (GHS) is a mostly unreported cyberattack in which attackers attempt to convince victims that they will be provided with free, unlimited “resources” or other advantages for their favorite game. The endgame of the scammers ranges from monetizing for themselves the victims time and resources by having them click through endless “surveys”, filing out “market research” forms, etc., to collecting personal information, getting the victims to subscribe to questionable services, up to installing questionable executable files on their machines. Other scams such as the “Technical Support Scam”, the “Survey Scam”, and the “Romance Scam” have been analyzed before but to the best of our knowledge, GHS has not been well studied so far and is indeed mostly unknown. In this paper, our aim is to investigate and gain more knowledge on this type of scam by following a data-driven approach; we formulate GHS-related search queries, and used multiple search engines to collect data about the websites to which GHS victims are directed when they search online for various game hacks and tricks. We analyze the collected data to provide new insight into GHS and research the extent of this scam. We show that despite its low profile, the click traffic generated by the scam is in the hundreds of millions. We also show that GHS attackers use social media, streaming sites, blogs, and even unrelated sites such as change.org or jeuxvideo.com to carry out their attacks and reach a large number of victims. Our data collection spans a year; in that time, we uncovered 65,905 different GHS URLs, mapped onto over 5,900 unique domains.We were able to link attacks to attackers and found that they routinely target a vast array of games. Furthermore, we find that GHS instances are on the rise, and so is the number of victims. Our low-end estimation is that these attacks have been clicked at least 150 million times in the last five years. Finally, in keeping with similar large-scale scam studies, we find that the current public blacklists are inadequate and suggest that our method is more effective at detecting these attacks.

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Inferring simple but precise quantitative models of human oocyte and early embryo development

Journal of The Royal Society Interface ◽

10.1098/rsif.2021.0475 ◽

2021 ◽

Vol 18 (182) ◽

pp. 20210475

Author(s):

Brian D. Leahy ◽

Catherine Racowsky ◽

Daniel Needleman

Keyword(s):

Embryo Development ◽

Oocyte Maturation ◽

Statistical Physics ◽

Early Embryo ◽

Data Driven ◽

Human Oocyte ◽

Early Embryo Development ◽

Data Driven Approach ◽

Insight Into

Macroscopic, phenomenological models are useful as concise framings of our understandings in fields from statistical physics to finance to biology. Constructing a phenomenological model for development would provide a framework for understanding the complicated, regulatory nature of oogenesis and embryogenesis. Here, we use a data-driven approach to infer quantitative, precise models of human oocyte maturation and pre-implantation embryo development, by analysing clinical in-vitro fertilization (IVF) data on 7399 IVF cycles resulting in 57 827 embryos. Surprisingly, we find that both oocyte maturation and early embryo development are quantitatively described by simple models with minimal interactions. This simplicity suggests that oogenesis and embryogenesis are composed of modular processes that are relatively siloed from one another. In particular, our analysis provides strong evidence that (i) pre-antral follicles produce anti-Müllerian hormone independently of effects from other follicles, (ii) oocytes mature to metaphase-II independently of the woman’s age, her BMI and other factors, (iii) early embryo development is memoryless for the variables assessed here, in that the probability of an embryo transitioning from its current developmental stage to the next is independent of its previous stage. Our results both provide insight into the fundamentals of oogenesis and embryogenesis and have implications for the clinical IVF.

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Analysis of the ROA of an anaerobic digestion process via data-driven Koopman operator

Nonlinear Engineering ◽

10.1515/nleng-2021-0009 ◽

2021 ◽

Vol 10 (1) ◽

pp. 109-131

Author(s):

Camilo Garcia-Tenorio ◽

Eduardo Mojica-Nava ◽

Mihaela Sbarciog ◽

Alain Vande Wouwer

Keyword(s):

Anaerobic Digestion ◽

Initial Conditions ◽

Data Driven ◽

Koopman Operator ◽

Anaerobic Digestion Process ◽

Digestion Process ◽

Biochemical Systems ◽

Data Driven Approach ◽

Selection Of

Abstract Nonlinear biochemical systems such as the anaerobic digestion process experience the problem of the multi-stability phenomena, and thus, the dynamic spectrum of the system has several undesired equilibrium states. As a result, the selection of initial conditions and operating parameters to avoid such states is of importance. In this work, we present a data-driven approach, which relies on the generation of several system trajectories of the anaerobic digestion system and the construction of a data-driven Koopman operator to give a concise criterion for the classification of arbitrary initial conditions in the state space. Unlike other approximation methods, the criterion does not rely on difficult geometrical analysis of the identified boundaries to produce the classification.

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