dataesr/scientific-paragraphs-categorization huggingface dataset:
2010) , we adopt the Vander-Plas &  Ivezić (2015)  shared-phase model when calculating the multi-band periodogram (i. e. , we set gatspy parameters N base = 1 and N band = 0; see Section 5 of VanderPlas & Ivezić 2015 for details). Furthermore, since RR Lyrae stars have periods between 0. 2 and 0. 9 days, we limit the period search to the same range. To test the accuracy of the VanderPlas & Ivezić (2015) period-finding algorithm on PS1 data, we use gatspy to calculate multi-band periodograms for 440 RR Lyrae stars previously studied by  Sesar et al. (2010) , but with the PS1 data at hand. From each periodogram, we select the period associated with the highest periodogram peak. We considered the periods measured by  Sesar et al. (2010)  from more densely sampled Sloan Digital Sky Survey (SDSS;  York et al.
Since the VanderPlas &  Ivezić (2015)  algorithm fails to accurately measure the period for almost half of the RR Lyrae stars with the PS1 data at hand, we cannot use it to phase the light curves. However, we still calculate and use the multi-   Ivezić (2015). Even though the best-fit multi-band model (sine curves) agrees with the phased PS1 light curves (symbols with errorbars) in the minimum χ 2 sense, the period inferred by this modeling approach is incorrect. This happens because the algorithm permits light curve models that are not physical: the model's colors do not change as a function of phase. band periodogram in this work in the subsequent classification, as it improves the selection of RR Lyrae stars (see Section 4. 5 below).
Resulting RR Lyrae Distance Precision Along with measuring an accurate period (87% of the time for RRab stars), an important aspect of multi-band light curve fitting is also the increased precision in estimating the star's average flux (or magnitude). As RR Lyrae stars follow a tight period-absolute magnitude-metallicity (PLZ) relation, we show that PS1 data constrain distances of RR Lyrae stars with a 3% precision, even if the metallicity of an RR Lyrae star is unknown. Theoretical and empirical studies (e. g. ,  Catelan et al. 2004; Marconi et al. 2015; Sollima et al. 2006; Braga et al. 2015)  have shown that the absolute magnitudes of RR Lyrae stars can be modeled as M = α log 10 (P/P ref ) + β([Fe/H] -[Fe/H] ref ) + M ref + , ( 4 ) where P is the period of pulsation, M ref is the absolute magnitude at some reference period P ref and metallicity [Fe/H] ref (here chosen to be P ref = 0.
6 days and [Fe/H] ref = -1. 5 dex), and α and β describe the dependence of the absolute magnitude on period and metallicity, respectively. The is a standard normal random variable with mean 0 and standard deviation σ M , that models the intrinsic scatter in the absolute magnitude convolved with unaccounted measurement uncertainties. To constrain the PLZ relations for RRab stars in PS1 bandpasses, we use a probabilistic approach described in detail in Appendix B, where the data include metallicities and distance moduli of PS1 RR Lyrae stars in five Galactic globular clusters. The end product of this approach is a joint posterior distribution of all model parameters. To describe the marginal posterior distributions of individual model parameters, we measure the median, the difference between the 84th percentile and the median, and the difference between the median and the 16th percentile of each marginal posterior distribution (for a Gaussian distribution, these differences are equal to ±1 standard deviation).
WISE Data Quasars (QSOs) are one of the biggest sources of contamination when selecting RR Lyrae stars, especially at faint magnitudes (i. e. , as the probed volume of the Universe increases). They overlap with RR Lyrae stars in gr and redder optical colors (e. g. , Figure  4  of  Sesar et al. 2007 ), and may look as variable as RR Lyrae stars when observed in sparse datasets (such as PS1, Figure  3  of  Hernitschek et al. 2016). Because most QSOs have a hot dust torus, they show an excess of radiation in the mid-infrared part of the spectrum, and have the WISE  (Wright et al. 2010 ) midinfrared color W 12 = W 1 -W 2 > 0. 5 mag (Figure  2  of  Nikutta et al. 2014). RR Lyrae stars, on the other hand, have W 12 ∼ 0 mag. To better separate QSOs and RR Lyrae stars, we supplement PS1 data with the W 12 color provided by the all-sky WISE mission by matching PS1 and WISE positions using a 1 radius.
RR LYRAE IDENTIFICATION We wish to build a model that returns the probability of an object to be an RR Lyrae star, given the data from Section 2 and the light curve fits from Section 3. We will address this problem with a supervised machine learning approach, where we use a training set (labeled or classified objects and their data) to infer a function that determines the class of unlabeled objects from their data. Since we have a reliable training set (see Section 4. 1), supervised learning techniques (Section 4. 2) represent a natural choice for building a classification model for a selection of RR Lyrae stars. The light curve fitting and its χ 2 value (Section 3. 2) play a crucial role in this process.
Supervised Learning To build this classification model we use XGBoost 6  (Chen & Guestrin 2016) , an open-source implementation of the gradient tree boosting supervised machine learning technique  (Friedman 2001). We use gradient tree boosting because the technique produces a prediction model in the form of an ensemble of decision trees, and because tree-based models are robust to unin-6 https://github. com/dmlc/xgboost formative features 7   (Hastie et al. 2009; Richards et al. 2011; Dubath et al. 2011). This fact supports the usage of a large number of features when building the classification model, even when some of them may not be useful. By permitting the classification algorithm to consider even seemingly uninformative features, we allow it to consider potential correlations between data that may improve the classification in the end.
This is attributable perhaps foremost to a substantially greater number of observations per object in our dataset  (67 vs. 35 in Hernitschek et al. 2016). The hyperparameter tuning, and use of a different machine learning algorithm (XGBoost vs. scikit-learn Random Forest) may also contribute to better performance. Using a cut on the classification score of score 1 > 0. 01 (score 1 ), we are able to reduce the number of objects under consideration by more than three orders of magnitude (from about 1. 9 million to ∼ 1500), while losing only 2% of RR Lyrae stars. However, the purity of the selected sample is still unacceptably low (only 34%). In order to improve the purity of the selected sample we need to train the classifier using additional features, such as the multi-band periodogram (Section 3.
Nov 1 2017 in the electronic edition of the Journal. A portion is shown here for guidance regarding its form and content. For collaborations on projects and earlier access to the PS1 catalog of RR Lyrae stars, please contact the first author. B. S. , N. H. and H. -W. R. acknowledge funding from the European Research Council under the European Unions Seventh Framework Programme (FP 7) ERC Grant Agreement n. [321035]. H. -W. R. acknowledges support of the Miller 13 Radial velocities of RR Lyrae stars are straightforward to measure (Sesar 2012).
Introduction The INFRARISK Decision Support Tool (IDST) version 2. 0  [1]  is an information system tool that allows urban planners, civil engineers, crisis managers, urban development agencies and enterprise consortia to assess potential multiple risks from natural hazards to which critical infrastructure may be exposed. These may include earthquakes, landslides, floods or a combination of all. The IDST hosts specialized databases with supporting scenario simulations derived from models of a number of natural hazards allowing their likelihood of occurrence and intensity levels for different statistical return periods to be estimated at the locations of critical infrastructure elements. Two exemplar case studies have been considered using the IDST. These consist of two large European transport networks (road and rail), located in Italy and Croatia respectively.
They demonstrate the use of the adopted generic and overarching INFRARISK methodology for the evaluation of risks which are engendered by natural hazards on critical infrastructure. The IDST has options for applying the risk methodology to other transport networks of interest, provided that the necessary input data is uploaded into the system. The development of the IDST required the deployment of phase driven software development tasks using an agile approach. The first version  [2]  of the IDST specification was initially developed through consultation with a large number of domain knowledge experts and end-users, both within the project partnership and externally. The IDST specification v1. 0 was focused on capturing the functionality requirements for the IDST decision-support system. This exercise has led to the development of the IDST System  [3]  with its basic functionalities.
Agile IDST System Design and Development The IDST has been specified to integrate tools, databases and user interfaces in consultation with end users and knowledge experts in crisis management under INFRARISK. It is primarily a web-based system (or portal) which is user-accessible via a web browser on multiple client platforms (laptop, tablet, etc. ) and operating systems (Windows, Linux, etc. ). For the IDST v2. 0, commonly used browsers are supported (e. g. Internet Explorer, Firefox), and run on Windows or Linux operation systems. The design of the graphical user interface (GUI) took into account multiple platforms, by exploiting the latest platform independent user interface (UI) toolkits. The IDST software system is deployed on a central server, which has secure remote access available for registered users and selected stakeholders.
Access is enabled via HTTPS and, for the main IDST pages, the user will be required to be logged in using their existing user account information. The initial welcome page of the IDST is available to all users, therefore providing some background information about INFRARISK and the IDST while providing links to the secure parts of the portal. The IDST system is modular, i. e. based on multiple autonomous and interacting components. These include: The IDST uses the Django framework which allows multiple components to be incorporated easily, while we integrated the GUI features of these modules concurrently. Certain components were included as modules (or executables) and launched by the main IDST component (i. e. IDST Process Workflow Engine). The components were executed with their required inputs and the results returned either by direct visual display or indeed as input to a subsequent module within the workflow.
Certain components were deployed as a web service, either on the same host as the IDST or another remote server. Modules which require significant processing time (e. g. greater than one minute) were made available as results in a pre-populated database since it would not be appropriate for the user to practically experience running these simulations dynamically. Some components have been included as look-up tables and provide fast access to pre-run simulation results. In any case, the GUI made use of Ajax calls to the IDST server for any browser requests (e. g. page updates) that require more than a few seconds to perform. The agile development of the IDST system scaled successfully by supporting a large number of concurrent users. Its databases are also scalable since they can handle a mix of structured and unstructured data.
IDST Data Storage The IDST stores various datasets in its database system. The database system is preloaded with datasets to support the basic case studies, e. g. geometry and characteristics of the Northern Italian road network. In addition, users can upload the necessary datasets in order to run their own stress tests. Generated results for stress tests can be also exported and used outside the IDST workflow for further analysis. The type of datasets the IDST system supports are: • Infrastructure element characteristics such as road, rail lines, bridges, tunnels, embankments, intersections, etc. • Hazard models/maps (e. g. seismic, landslide, flooding) • Simulation results from stress tests • Analysis results.
IDST Models The risk evaluation in the IDST is based on specialized models which are pluggable modules that compute transport network elements of various stages of the IDST workflow. These are classified into (a) hazard and (b) vulnerability models. Hazard Models provide information derived from numerical models of different hazards. This information specifies the relevant intensity levels, depending on the model inputs specified by the user for a particular scenario. Network Models estimate the probability of the various infrastructure elements experiencing different levels of damage, given the occurrence of a particular hazard scenario. During this step, fragility curves (Figure  4 ) are assigned to the network elements. Fragility curves, specific to a particular infrastructure element (e.
3. 6. 1 The IDST Hazard Scenario The IDST hazard scenario is used to define a set of cascading hazard events to be considered for a particular stress test. In particular, a hazard event is the combination of (a) a hazard source, e. g. an earthquake; (b) a hazard event, e. g. a ground shaking caused by the earthquake; and (c) a model that describes that hazard event. In most cases the hazard model will require a detailed configuration and a dataset to operate upon, depending on the complexity of the model used. The hazard source in the first hazard event is considered as the primary hazard source which can trigger secondary hazard events, e. g. a landslide caused by the occurrence of an earthquake.
3. 6. 2 The IDST Network Scenario The IDST network scenario allows the user to define multiple sets of network events. A network event is defined by a combination of a network type, e. g. a road network, a hazard event deemed to impact that element type, and a network model that assigns fragility functions to that element type. For each network event a dataset of network elements (bridges, road segments, tunnels, etc. ) has to be considered. That set of network elements can be selected from the pre-populated IDST datasets for the case study regions which are confined by stress the test boundary polygon, or uploaded directly from the user, e. g. as a shape file. Risk estimation, i. e. damage state calculation, for each network event is carried out by using the appropriate hazard model along with the vulnerability models for each of the network elements to estimate the potential damage to each of the associated infrastructure elements.
Conclusion and Future Work The IDST as a decision-support tool has concretely enabled civil engineers and critical infrastructure management stakeholders set up cascading risk scenarios with low probabilities of occurrence and high impact on and damages to Critical Infrastructure (CI). The IDST was successfully demonstrated to the referred communities of specialised CI engineering firms and stakeholders at a large scale INFRARISK project conference  [11]. The integration of the heterogeneous data sources, information and outputs under specialised databases and modelling tools in the IDST gives a unique common operational picture for the engineering communities who specialise in the management of CIs. Furthermore, the overarching risk assessment methodology which provides an automated Process Workflow Engine in the IDST clearly enables crisis management experts to usefully set "what if" scenarios on exposure of urban CIs to extreme natural hazards.
The next step of this research and development work is for testing the scalability the IDST system towards architectures for supporting very large spatial coverages, mining of big data sources and extreme analytics for the computation of highly complex cascading risks of extreme and rare natural events on CIs. This is clearly a big data problem and challenge which can be met with supporting cloud and high perfor-mance computing infrastructure in the future. This will be our research goal for advancing our understanding and quantification of the socio-economic impact of very complex cascading natural hazards on CIs in Europe and beyond.
Acknowledgements INTRARISK is funded by the  European Commission 's  FP7 programme , Grant Agreement No. 603960. Further information can be found at www. infrarisk-fp7. eu. The authors gratefully acknowledge the contributions of the other INFRARISK consortium partners: Draga-dos SA, Gavin and Doherty Geosolutions Ltd. , Probabilistic Solutions Consult and Training BV, the  Spanish National Research Council, PSJ ,  Stiftelsen SINTEF  and  Ritchey Consulting AB.
Finite element model simulation of the nano-beam heating effect. To simulate the heating effect of the nano-beam we developed a 3D finite element model (FEM), using the commercial software COMSOL Multiphysics. The heat propagation, including both thermal conduction and convection, has been described by the following equation: ρ ρ ∂ ∂ + ⋅∇ + ∇ ⋅ -∇ = C T t C u T k T Q ( ) p p where T represents the temperature field, ρ is the material density, C p is the heat capacity at constant pressure, u is the air velocity, k is the thermal conductivity, and Q represents the heat source. Boundary conditions have been assumed by imposing a fixed temperature T = 300 K on all the external surfaces of the model. Further details of the model can be found in our previous publications, which report simulations in the stationary state  14, 28.
Acknowledgements The authors thank the staff of the ID16B beamline for the support during experiment MA-3012. They also gratefully acknowledge  Compagnia di San Paolo  for financial support to  NanoFacility Piemonte  at  INRIM. This work has been carried out under project NANO-X jointly approved and funded by  University of Torino and Compagnia di San Paolo. C. L. acknowledges the mega-grant of the  Ministry of Education and Science of the Russian Federation  ( 14. Y26. 31. 0001 ).
Wave-Structure Interaction Model The wave-structure interaction solver used in this research is the open-source software package Nemoh, developed at Ecole Centrale de Nantes  [12]. The current Version v2. 03 is based on linear potential flow theory and thus makes the following assumptions: 1. The fluid is inviscid; 2. The fluid is incompressible ; 3. The flow is irrotational; 4. The wave amplitude is small w. r. t. the wavelength; 5. The amplitude of the body motion is small w. r. t. its dimension; 6. The sea bottom is flat; The flow is described by a velocity potential Φ, as a function of time and space. The flow field ν is expressed as the gradient of the velocity potential: ν = ∇Φ (6) Combining Equation (  6 ) with Assumption 2 leads to the well-known Laplace equation: ∆Φ = 0 ( 7 ) This equation is valid in the entire fluid domain, denoted Ω.
Wave Propagation Models The second part of the coupling methodology is the wave propagation model. This model propagates the incident waves and predicts their interaction with the diffracted and radiated waves as predicted by Nemoh. For this, two separate software packages are applied: MILDwave, a 2D model based on mild-slope equations, and OceanWave3D, a 3D model based on fully non-linear potential flow equations. We will denote the coupled models as the MILDwave-Nemoh model and the OceanWave3D-Nemoh model, respectively.
OceanWave3D The second applied wave propagation model is called OceanWave3D. The software is open-source and applies a fully-non-linear potential flow solver  [29, 47]. It is aimed at closing the performance gap between traditional Boussinesq-type models and volume-based solvers and enables fast (near) real-time hydrodynamics calculations. The fully-non-linear potential flow problem for waves on a fluid of variable depth is applied to find the free surface elevations on a 3D grid, with vertical sigma layers. This allows for the prediction of velocity profiles, in contrast to MILDwave, which only supports depth-averaged velocities. The evolution of the free surface is governed by the kinematic and dynamic boundary conditions: ∂ t η = -∇η • ∇ Φ + w(1 + ∇η • ∇η) ( 11 ) ∂ t Φ = -gη - 1 2 (∇ Φ • ∇ Φ -w2 (1 + ∇η • ∇η)) (12) These are expressed as a function of the free surface quantities Φ = Φ(x, y, η, t) and w = ∂ z Φ| z=η.
Experimental Dataset In 2013, as part of the WECwakes research project funded by the EU FP7 HYDRALAB IV program, experiments have been conducted in the Shallow Water Wave Basin of the Danish Hydraulic Institute (DHI) in Hørsholm, Denmark  [3]. A large number of tests was performed on large arrays of point absorber-type WECs (up to 25 units, see Figure  6 ). The research performed within the WECwakes project focuses on generic heaving WECs, intended for validation purposes of numerical models. A range of WEC array geometric configurations and wave conditions have been tested. Each WEC unit is composed of a buoy, designed to heave along a vertical shaft only, and can thus be modeled as a single degree of freedom system (see Figure  7 ). The WEC consists of a cylindrical body and a spherical bottom.
The diameter is 0. 3150 m, while the draft is 0. 3232 m. The water depth is fixed at 0. 7000 m. Energy absorption through the WECs' PTO system, is modeled by realizing energy dissipation through friction-based damping of the WECs' heave motion. Wave gauges are used to measure the wave field within and around the arrays. Displacement meters are mounted on each WEC unit for the measurement of the heave displacement. The wave induced surge force is measured on five WECs along the central line of the array. The experimental setup of 25 individual WEC units in an array layout, placed in the large wave tank, is at present the largest setup of its kind, studying the important impacts on power absorption and wave conditions of WEC array effects. Most importantly, the WECwakes database is comprehensive and is applicable not only to WEC arrays, but also to floating structures/platforms, stationary cylinders under wave action, etc.
The choice of which wave propagation model to use is easily made based on the following guideline. Since MILDwave calculates significantly faster, the only reasons to apply OceanWave3D are when: the bathymetry has slopes larger than 1/3 and there is a need for calculating velocity profiles or simulations with weakly non-linear waves. If these conditions or requirements are not applicable, then the use of MILDwave-Nemoh is strongly recommended, as this robust model provides quick and accurate results. RMSE values for whole domain between coupled wave propagation models MILDwave-Nemoh and OceanWave3D-Nemoh and the wave-structure interaction solver Nemoh. be due to the incident wave period, which is outside the natural period T m of the WEC. A more quantitative comparison between the data will be given in Section 4.
Background: Chimeric transcripts are commonly defined as transcripts linking two or more different genes in the genome, and can be explained by various biological mechanisms such as genomic rearrangement, read-through or trans-splicing, but also by technical or biological artefacts. Several studies have shown their importance in cancer, cell pluripotency and motility. Many programs have recently been developed to identify chimeras from Illumina RNA-seq data (mostly fusion genes in cancer). However outputs of different programs on the same dataset can be widely inconsistent, and tend to include many false positives. Other issues relate to simulated datasets restricted to fusion genes, real datasets with limited numbers of validated cases, result inconsistencies between simulated and real datasets, and gene rather than junction level assessment.