dataesr/scientific-paragraphs-categorization huggingface dataset:
Spinky: A Matlab GUI Interface for Spindles and K-Complex Detection The spindle and K-complex detection pipeline used in this article has been implemented in a freely available toolbox called Spinky. The MATLAB package contains three modules allowing for: (i) visual K-complex and spindle scoring (on a subset of data for training purposes); (ii) automatic detection; and (iii) manual correction of false detections. In principle, the output of the first module is used in the automatic detection, and the result of this module is the input to the third module (Figure  1 ). Once installed, the toolbox can be launched by typing ''spinky'' in the MATLAB command window. This will activate the main toolbox window (Figure  1 -Left), and allows to launch one of the three main modules. The basic principles of how to use each module is described below.
Case 2 To determine the best threshold value using a visually scored data sample, the user must select ''Compute thresholds and run detection''. The user will be asked to choose the training EEG data files and associated visual scoring files. In such a case, the visual scoring file should either be the output of the Visual Scoring module, or a text file in the exact same format (see Appendix A in the Spinky online manual for details). Once the detection is complete and the results are saved to disk, the user can compute statistics on the detected events for the whole data sets (all epochs), by clicking on the menu button ''Export results''. This function will compute statistics and save them to a. txt or. mat file. The statistics that are currently available for K-complex events are: total number, density, frequency and mean amplitude.
e. , 30 pages of 20 s) seems to be sufficient. In addition, we also tested the effect of using a ''minimal number of spindles'' approach vs. a ''minimal number of pages'' approach on the robustness of the identified threshold. With a model ''threshold ∼ log 2 (pages) + scorer'' we obtained R 2 = 0. 419 (i. e. , we explained 41. 9% of the variance in our data). Alternatively, using the number of spindles with a model ''threshold ∼ log 2 (spindles) + scorer'', we obtained R 2 = 0. 423. This high similarity between the two values of R 2 indicates that we can interchangeably use the number of spindles or number of epochs to characterize the duration of the training. The current study builds upon, and extends, our previous work in several significant ways. First, we provide critical validation and benchmarking of our TQWT-MCA approach by applying it to an open-access database, namely the MASS  (O'Reilly et al.
, 2014). The automatic detection results reported here confirm the high detection performances we had previously obtained with this method in a different set of subjects  (Lajnef et al. , 2015a). Furthermore, the scores obtained in the current study were compared to those previously reported for other methods tested on the same database. For spindle detection, our method provided higher, or at least similar, performance on all statistics, taking into account both sensitivity and precision (i. e. , MCC, F1, Cohen κ). As shown in Table  1 , the sensitivity of spindle detection with Spinky was substantially higher than that of all the other methods (for both experts), except for the A3, A7 and A8 detectors, which obtain good sensitivity only by accepting a much lowered precision. No such benchmarking was possible for K-complex detection, since-to the best of our knowledge-no open-access tools for k-complex tools are available.
A further important contribution of this study is the extensive evaluation of performance that was conducted using a wide range of metrics (sensitivity, PPV, MCC, Cohen's κ and the F1 measure). Importantly, with the publication of this report, we provide open-source Matlab code of our Spinky toolbox, along with Python-based interactive resources. The ''Materials and Methods'' Section of the present article, the step-by-step procedure, and the Matlab GUIs, that will hopefully allow other researchers, clinicians and students to use Spinky on their own data. The thorough testing that we conducted on the training part of our pipeline (i. e. , estimation of the best detection threshold based on annotated data samples) confirms the efficiency of the global framework. Indeed, the TQWT-MCA procedure implemented in Spinky would have been of limited value if copious amounts of visually scored pages were required for the training phase, as this would defeat the point of an automatic detector.
To the best of our knowledge, Spinky is the first open-source tool that has been thoroughly evaluated for the detection of both spindles and K-complexes. An alternative pragmatic approach to determining the best detection threshold, aside from systematically training Spinky on sample data, would be to run the automatic detector with a relatively low threshold, and then to correct the output manually using Spinky's visual correction interface. This semi-automatic detection framework would yield high sensitivity by ensuring that all or most events are detected in the automatic detection module, as well as low false positive rates by manually discarding false detections in the visual correction module. Although this comes at the expense of more time for the manual correction, it could be a powerful technique, in particular when working with data with poor signal-to-noise ratio or with data sets collected across multiple centers using different EEG acquisition systems and settings.
ACKNOWLEDGMENTS The authors would like to acknowledge that this work greatly benefited from the open access TQWT toolbox developed and made available online by  Dr Ivan Selesnick  ( New York University, NY, USA ). TL was supported in part by a fellowship based on funding from  Natural Sciences and Engineering Research Council (NSERC) of Canada , and by  travel funds from EDST doctoral program  and the  LETI Laboratory, Sfax, Tunisia. EC was supported in part by a PhD Scholarship by the  Ecole Doctorale Inter-Disciplinaire Sciences-Santé (EDISS) ,  Lyon, France , and by PhD funding from the  NSERC of Canada. J-BE was supported by the  Fyssen Foundation. KJ acknowledges funding from the  Canada Research Chairs program  and a  Discovery Grant  ( RGPIN-2015-04854 ) awarded by  NSERC. JC was supported by an  NSERC  Grant ( RGPIN-2016-05149 ).
In this study the climate evolution from 1961 to 2050, simulated by 12 RCM, is analysed. Details on RCM and their driving GCM are summarized in Table  2. In the ENSEMBLES datasets, wind data are available with daily resolution at 10 m height all along the simulations time spans. ENSEMBLES wind data are known to be biased when compared with observations  [7]  but methodologies for bias corrections for wind data usually require quite a large amount of work and are at the moment much less robust than for temperature and precipitation. Moreover, the scope of this study is not the precise assessment of the absolute values of point-by-point wind potential, but the investigation of the main features of its temporal and spatial patterns and how wind power patterns are expected to respond to climatic signals.
Countries power production: A Monte Carlo assessment Once theoretical loading factors are computed, the actual power production in a given country depends on the location of installed turbines and their capacity  [28]. Details of turbines installed in European are at least partially available from several commercial and open source databases (see again  [28]  for a summary) each offering different data quality as far as key parameters such as coverage, updating frequency and spatial resolution are concerned. The spatial allocation of future wind turbines on the other hand, is difficult to forecast, as the localization process is dependent on social as well as economic and practical aspects, and are thus generally difficult to investigate. Studies exist that have evaluated the possible spatial evolution of the European wind power park in next years  [28, 7]  based on power production optimization, cost minimization and practical geographical constraints leading to the identification of most suitable areas.
The modifications in WPC between future (2031-2050) and present (1991-2010) climate conditions are presented in Fig. 4. It results that most of the changes are observed offshore, while the differences inland are limited and sparse, and almost all the observed modifications are limited to one class. The spatial pattern of potential wind energy sites is thus largely unaffected by climate change as found by Tobin et al. [7]  and, as expected, the climate stability of actual wind patterns on the European continent leads to climatically stable wind power production in the EU countries. National wind power production was estimated for each day of the simulated period and for each model as described in Section 2. 2. 2, repeated N ¼10 times the random allocation of installed capacity and then analysed to assess statistically significant differences among the different datasets.
Open issues and outlook for future research Some points remain open after the present study, providing guidelines for further future analyses. 1. Results arising from other combinations of GCM/RCM should be included in order to further populate Tables 4, 5 and 8 and better addressing the issue of model scales. 2. A similar analysis could be produced based on retrospective data from e. g. , reanalysis in order to assess synchronicity, correlation and power production values closer to the actual past wind features. 3. More details could be provided on specific subsets of countries in order to guide regional markets. 4. As the ENSEMBLES data set provided daily values only, intraday correlation analysis has not been possible. Such an analysis could provide further insights on short term power exchanges among EU countries, allowing taking into consideration time shifts between daily cycles spread in a geographical area spanning about 40°of longitude and 35°of latitude.
Discussions The refinement of Al 2 Cu column may attribute to its fragmentation  21  caused by the TEMF because the present solidification conditions permit the interaction between thermoelectric currents and magnetic field. The thermal gradient along solid-liquid interface together with the dissimilar thermo-physical properties (e. g. thermoelectric power) between solid and the melt give rise to the occurring of Seebeck effect, which results in thermoelectric currents flowing through both solid and melt  25. To confirm this and understand how TEMF fragment the Al 2 Cu column, a 3D simulation of TEMF was performed based on the real Al 2 Cu structure that got from the tomography data and shown in Fig. 3(a). The detailed description of simulation method can be found in ref. 26    1. The temperature field, electric current density and fluid flow field was coupled to simulate TEMF using a finite element method based commercial code COMSOL Multiphysics.
Acknowledgements This study is partly supported by the  Shanghai Committee of Science and Technology  (No. 13521101102 ,  13DZ1108200 ,  13521101102 ,  16YF1404300 ), the program of  Youth Eastern Scholar  ( QD2015035 ) and  CHENGUAN  project from  Shanghai Municipal Education Commission , and the program of Youth Promotion project from  China Association for Science and Technology. The authors greatly appreciate  Prof. Thierry Duffar  ( SiMAP/EPM, Grenoble, France ),  Prof. Koulis Pericleous  and  Dr. Andrew Kao  ( University of Greenwich, UK ) for their kind and fruitful discussion.
None of the cord plasma titers and less than 1% (2/502) of the maternal plasma samples had titers that fell below 10. 3 EU. For the Kaplan Meier estimation, infants were censored either when they (1) dropped below 10. 3 EU (2) had any rise in IgG titer or (3) were lost to follow up. Finally, linear regression was used to evaluate the effect of covariates on anti-S. sonnei-O IgG cord titer as well as the relationship between log 10 cord titer and log 10 increase in titer between serial follow up visits. All analyses were performed in STATA v13 (TX, USA) with the exception of the mixed effects modeling which was performed in R (version 3. 0. 2) using the lme4 package  [35]. Plots were made in R using the ggplot package v1. 0. 1  [36].
sonnei transmission in HCMC is more common between April and June then infants born in during this time are likely better equipped against S. sonnei exposure at birth as the cord titers are highest during this season. However, annual trends are difficult to evaluate from our yearlong dataset. There were several limitations with this study. Firstly, the infrequent early blood samples from infants prevented high-resolution temporal analyses regarding maternal half-life duration and survival analysis of the waning of maternal IgG. Next, the lack of a similar cohort from a non-endemic area limits our ability to fully interpret the serology data in an epidemiological context. Furthermore, a lack of disease data prohibits an analysis of the protective effect of presence of antibody as well as a more detailed analysis of anti-S.
Acknowledgements We would like to thank the  Hung Vuong Hospital study nurses  whose tireless efforts have made this study possible ( Nguyen Thi En ,  Le Thi Hanh ,  Hoang Thi Sen ,  Nguyen Thi Hong Nhat and Nguyen Thi Tuyet Hanh ). Thanks also go to  Dr. Phung Khanh Lam  and  Dr. Marcel Wolbers  for statistical advice. Finally, we thank the families who participated in the study. The  Wellcome Trust of Great Britain  funded this work. SB is a Sir Henry Dale Fellow, jointly funded by the  Wellcome Trust  and the  Royal Society  ( 100087/Z/12/Z ). The funders had no role in the study design, data collection or interpretation or the decision to submit the work for publication.
e. combinations of number of grain legumes in the rotation and cover crop treatments) on soil aboveground C and N inputs. Moreover, for each cash crop, differences between cover crop treatments on grain yield and C and N harvest indexes were also analyzed. Grain yield and C and N harvest indexes of durum wheat were used as indicators of the performance of the different cropping systems studied, since durum wheat was used in all the rotations. When significant, differences between treatments were identified at 0. 05 probability level of significance using a LSD test. Finally, the impact of C and N inputs and their C:N ratio on the changes of SOC and SON stocks was tested using the standard least square personality of the JMP 11 Pro statistical package (SAS Institute Inc. , 2014).
Acknowledgements The constructive suggestions of an anonymous Reviewer greatly improved this manuscript. We acknowledge the field and laboratory assistance of  Didier Chesneau ,  André Gavaland  and  Eric Bazerthe. This research was supported by the  FP6 Grain Legumes Integrated Project  ( Food-CT-2004-506223 ) and  INRA. We also acknowledge the  French Ministry of Agriculture  for funding the  CASDAR Leg-N-GES  project coordinated by  Jean-Pierre Cohan (Arvalis Institut du Vegetal).
modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. Additional shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited realworld engineering data. As with the prior challenge, this work not only documents the 'state-of-the-art' in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods.
The slow-rate test imaging was controlled by Correlated Solutions VicSnap software with NI-DAQ synced data collection from the MTS FlexTest Controller. The imaging setup included two Point Grey Research 5MP Grasshopper monochromatic CCDs (2448 × 2048 pixels) with 50mm Schneider lenses, viewing the front and back surfaces of the samples, with a frame rate of 2 fps. The fast-rate test imaging included a Phantom V7 highspeed camera (800×600 pixels) with a 24-85 mm lens, viewing the front of the samples, and controlled by Phantom camera software based on a trigger sent by the MTS FlexTest controller at the start of the test, with a frame rate of 3200 fps and a 0. 3-ms exposure. Black and white fiducial markings applied to the sample close to the COD gage knife edges enabled a secondary COD measurement via a digital image correlation (DIC) algorithm  (Chu et al.
Approach An over-the-counter methodology was used to predict fracture initiation and propagation in the challenge specimen. Specifically, the finite element software Abaqus/Explicit was used to simulate deformation and damage in the challenge specimen geometry with nominal dimensions. The continuum (Ti-6Al-4V) was modeled as linear elastic isotropic with the von Mises yield criterion. Hardening was defined by a tabular function of plastic strain. Damage initiation, in turn, was modeled with a ductile damage initiation criterion wherein the equivalent plastic strain at failure was a function of stress triaxiality. Damage propagation, finally, was modeled with an energy-based law with exponential softening. To discriminate between the fast (dynamic) and slow (static) actuation rates, a different set of plasticity and damage parameters was calibrated for each individually.
2014; Münstermann et al. 2012). The distinction of the model is that it differentiates the damage initiation and fracture and it is developed in a hybrid way by combining the uncoupled model to act as the damage threshold and coupled model to represent the microstructure degradation till final fracture. The model is implemented into the FE code Abaqus/Explicit by means of a user material subroutine (VUMAT) and all the simulations presented here are conducted in this environment. The yield function of the model is given in Eq. 7. It is noted that the coupling effect of the damage into the yield function is only valid once the damage initiation criterion is fulfilled. Φ = σ -(1 -D) σ y ε p , η, θ, ε p , T ≤ 0 ( 7 ) Generally, the isotropic yielding and hardening are employed based on the negligible difference between the flow responses from tensile tests along rolling and transverse direction for the investigated material.
Material parameter calibration The model involves a large number of material parameters for accurate characterization of material behavior. For a complete material parameter calibration procedure, several types of tests and specimens are required as described by  Lian et al. (2013 Lian et al. ( , 2015) )  and  Buchkremer et al. (2014). In the SFC2, as only limited tests were provided, the calibration of the plasticity parameters, including the effect of stress state, strain rate and temperature, and damage initiation locus was assisted by material database of the Steel Institute. The reference flow curve was derived from the tensile tests along the rolling direction (specimen RD5) provided by Sandia National Laboratory. The Ludwik equation was used to extrapolate the flow curve to large strains.
Even though shear tests were carried out by Sandia, and the test data were provided, the fact that slip occurred during the tests and the axial displacement was measured with an LVDT attached to fixtures whose compliance was not negligible is attributed to not including shear tests for the present fracture calibration. As a consequence, the identification of five fracture parameters had to rely solely on two uniaxial tensile tests. The MIT team was in possession of the fracture parameters for a similar alloy, so a, c, and n were taken from our database. This basically assumes that the current alloy of interest has the same dependence on η and θ as the similar alloy in our database. The remaining parameters of b 0 and γ control the height of a fracture envelope and its strain-rate sensitivity, respectively.
Approach The 2D plane strain simulation was performed with Abaqus CAE 6. 14 software using an elasto-plastic model (continuum) and a damage model (damage initiation and damage evolution). The Abaqus Standard/Static solver and isotropic elastic plastic material behaviour including isotropic deformation hardening was employed. The elastic, plastic, damage initiation and evolution parameters were fit to experimental data. The parameter fitting utilized both tensile and shear data at both loading rates. The Extended Finite Element Method (XFEM) was used to simulate damage in the specimens. The finite elements mesh was refined in the regions where predicted crack propagation, as shown in Fig. 74. The damage initiation criterion was based on the quadratic traction-interaction law and the damage evolution criterion was energy dissipation with linear softening.
Acknowledgments BLB and HEF would like to thank  Dr. James Redmond  for managing Sandia's role in this work through the  DOE Advanced Scientific Computing program. SLBK and TRB would like to thank  Dr. Dennis Croessmann  and  Dr. David Epp  for their management role supporting the experimental efforts at  Sandia  for this work through the  NNSA Weapon System Engineering and Assessment Technology Engineering Campaign. JLB, SES, and CAJ would like to thank  DOE/NE  and  Ryan Bechtel  for partially supporting their participation in this challenge. The Sandia authors would like to thank the follow-.
The metarossite structure is characterized by chains of edge-sharing  [CaO 8  ] polyhedra parallel to [100] that are themselves connected by chains of alternating  [VO 5  ] trigonal bipyramids parallel to  [010]. The two H 2 O molecules are bonded to Ca. Analysis of the displacement parameters show that the [VO 5 ] chains librate around  [010]. In addition, we measured the Raman spectrum of metarossite and compared it with IR and Raman data previously reported. Moreover, heating of metarossite led to a loss of water, which results in a transformation to the brannerite-type structure, CaV 2 O 6 , implying a possible dehydration pathway for the compounds M 2+ V 2 O 6 ÁxH 2 O, with M = Cu, Cd, Mg or Mn, and x = 2 or 4.
Acknowledgements This project was the work of summer intern AK, and she thanks the  Downs lab at the University of Arizona , the  E ´cole Normale Superieure de Lyon  and the  UniversiteClaude Bernard Lyon 1  for the opportunity. Funding was provided by the  E ´cole Normale Superieure de Lyon and the region Auvergne-Rhone-Alpes (France) , through the grant Explo'ra Sup.
Thermal dose mapping First introduced by Sapareto et al. [11] , cumulative TD was formulated in each pixel as follows: TD ¼ Z t 0 2 T t ð Þ-43 ð Þ dt if T t ð Þ > 43 ○ C Z t 0 4 T t ð Þ-43 ð Þ dt if T t ð Þ < 43 ○ C 8 > > < > > : Where T(t) = T ref + ΔT is the absolute temperature at the time t. Rectal temperature served as a reference temperature value T ref. The dose corresponding to a constant heating at 43 °C for 240 min was considered as the lethal threshold. Computed TD maps were corrected to take in account the uncertainty of the temperature measurement σ T , using the following formula  [24] : TD corrected x; y ð Þ ¼ TD x; y ð Þ⋅e -0;5 ln 2 ð Þ⋅σ T x;y ð Þ ð Þ 2 Where σ T is the temperature standard deviation map, calculated on each pixel before RFA over 20 repetitions (~20 s). Computed temperature and TD maps were sent in real-time to a dedicated thermometry software (Thermoguide, Image Guided Therapy, Pessac, France) for display and online monitoring of the ablation.
Funding This work received the financial support from the  French National Investments for the Future Programs   ANR-10-IAHU-04  ( IHU Liryc ),  Laboratory of Excellence   ANR-10-LABX-57  (TRAIL) and the research programs  ANR-11-TecSan-003-01  ( TACIT)  and  Equipex   ANR-11-EQPX-0030  (MUSIC).