dataesr/scientific-paragraphs-categorization huggingface dataset:
Module overview GAMBIT is built around the ideas of modularity and re-usability. All calculations required to get from experimental data and model parameters to likelihood values and observables are performed in separate GAM-BIT module functions. Each module function is able to calculate exactly one quantity, its capability. The type of this capability can be just about anything, from a simple integer to any complex C++ structure required to carry the result of the calculation. Examples of capabilities are: model parameters, particle spectra, experimental data, and the values of likelihood functions. Most module functions will also have dependencies on capabilities that were calculated by other module functions. These dependencies will be automatically resolved at run time, based on choices of the user about what particle physics model to analyse, what observables to include etc.
This capability is required in particular by the gamLike backend for the computation of gamma-ray fluxes from dark matter annihilations within the Milky Way. The other capability describing the dark matter halo is (2) LocalHalo, which is of type LocalMaxwellianHalo. This object is simply a container for the parameters relevant to direct detection and capture in the Sun, i. e. the local density ρ 0 as well as the velocity parameters v 0 , v rot and v esc. Depending on the halo model in use, the capability GalacticHalo can be provided by the module functions GalacticHalo_gNFW or GalacticHalo_Einasto; for all halo models, the LocalHalo capability is obtained through the module function ExtractLocalMaxwellianHalo (see also Table  A3 ). The rest of the GAMBIT code is designed such that only the module functions providing the capabilities GalacticHalo or LocalHalo explicitly depend on a halo model, while all other module and backend functions requiring access to the astrophysical properties of dark matter instead depend on these capabilities.
The second alternative is to determine W eff directly from the Process Catalogue (Sec. 6. 3. 1). The corresponding function obviously has TH_ProcessCatalogue as a dependency, as well as DarkMatter_ID, and can be used for models where coannihilations are not important. Here, the identity of the dark matter particle, as it exists in the GAMBIT particle database (see Ref. [10] ), is provided by the capability DarkMatter_ID (Table  A2 ). The main capability of the relic density part of Dark-Bit is RD_oh2, which returns Ω χ h 2 for a given model point. If the user wishes, the result can be expressed as a fraction of the total, measured DM density. This capability is RD_fraction, and is provided by three different module functions: RD_fraction_one, RD_fraction_leq_one and RD_fraction_rescale. The choice of function dictates whether indirect and direct detection routines should use the observed relic density or the one calculated for the particular parameter point.
RD_oh2_DarkSUSY is only compatible with the MSSM, while RD_oh2_MicrOmegas can be used with any model implemented in micrOMEGAs. This latter function has two options: fast, which determines the numerical accuracy of the calculation (int; 0: accurate [default], 1: fast), and Beps (double; default 10 -5 ), which corresponds to a minimum value for the parameter B = exp - T m 1 (m i + m j -2m 1 ) m 1. ( 17 ) If B is less than the Beps value for a certain process, that process will not be included in the calculation of the effective annihilation rate (this serves the same purpose as CoannMaxMass for DarkSUSY). Lastly, the capability of the likelihood function constraining the relic density is lnL_oh2. This capability is provided by two module functions. First, lnL_oh2_Simple is a Gaussian likelihood that implements the observational limits from Ref.
We implement the IceCube search likelihood on a model-by-model basis, using the nulike package  [17, 133]  to compute the likelihood function for each model, given its predicted neutrino spectrum at Earth. Nulike computes a fully unbinned likelihood based on the event-level energy and angular information contained in the three independent event selections of the 79-string IceCube dataset  [134]. We do not implement a Super-K likelihood for now, as a) unlike IceCube, Super-K have not publicly released their event data, b) IceCube does have some sensitivity at low mass, and c) Super-K data only become more constraining for relatively light DM particles (at least in the context of SUSY and scalar singlet models, given other constraints).
By default, extrapolation is not performed, in which case the DM mass is limited to values below 5 TeV both for DarkSUSY and micrOMEGAs, while there is a lower limit of 10 GeV for DarkSUSY and 2 GeV for micrOMEGAs. Setting the flag to true allows for DM masses of up to 1 PeV and down to the mass of the final state particle both for DarkSUSY and micrOMEGAs. event generators, but also due to different methods (or the absence of a method) for including contributions from higher-order processes. With the above structure, DarkBit offers a flexible and convenient way to select the desired yields and switch between them for detailed comparisons. The gamma-ray yield is calculated by module functions with the capability GA_AnnYield, based on information from both the ProcessCatalog and the SimYieldTable.
4  compares the accuracy of some example limits obtained with the different speed settings. Following the original nulike paper  [17] , we assume a flat theoretical error on the predicted neutrino yield of 5% for DM masses below 100 GeV, rising logarithmically to 50% at masses of 10 TeV, and onward at even higher masses. This form of the error term is designed to account for neglected higher-order contributions and round off errors, present at all masses, and the increasing error introduced by DarkSUSY's interpolation in its WimpSim tables with increasing mass above ∼100 GeV. As side products of the various likelihood calculations, DarkBit also provides module function access to various related nulike outputs for the different analyses (Table  A10 ). For X ∈ {IC22, IC79WH, IC79WL, IC79SL}, these are: X_signal The predicted number of signal events (from DM annihilation in the Sun).
For all of these observables, we also calculate a corresponding experimental likelihood using an appropriate backend code included with DarkBit: the LUX 2013 likelihood from DDCalc (Sec. 5. 2), the IceCube 79-string likelihood for WIMP annihilation in the Sun from nulike (Sec. 6. 3. 3), and the the stacked dwarf spherodial likelihood based on six years of Fermi data from gamLike (Sec. 6. 2. 2). For the relic density, we calculate the simple Gaussian likelihood based on the best fit value from the Planck analysis  [1]  described in Sec. 4. 3. The results of these scans are shown in Fig. 6 , where the colour-coding of the points represents the likelihood value. For comparison, we plot the limits from corresponding analyses from the LUX  [75] , IceCube  [17] , and Fermi  [136]  collaborations.
Outlook As detailed in the preceding sections, DarkBit is equipped with sophisticated tools for calculating observables and likelihoods for the DM relic density, direct detection experiments and indirect searches with neutrinos and gamma rays. Each of these cases demonstrates the modularity of the code, and the ease with which external codes can interfaced with DarkBit. This modularity also implies that extensions of DarkBit in all possible directions are straight-forward to implement, and do not in general require the expertise of GAMBIT Collaboration members or highly experienced external users of the code. The focus of future developments will thus be steered largely by the needs (and indeed, contributions) of the community. Nevertheless, here we list a few obvious code extensions that we expect to include in future releases (aside from obvious additions of new experimental likelihoods to existing components like gamLike, DDCalc and nulike).
The modular implementation of the DM relic density calculations in DarkBit allows it to solve the Boltzmann equation independently of the actual particle model chosen for DM (Fig. 2 ). Alternatively, the user can directly call relic density routines provided by backend codes for specific models, allowing, for example, a systematic comparison between the results of DarkSUSY and micrOMEGAs. The new backend code DDCalc provides a general solution to the problem of testing DM models against direct detection data, including detailed likelihoods for many of the most important experiments. This allows both spin-dependent and spin-independent signals of the same model to be calculated and combined selfconsistently across the full range of relevant experiments. For liquid noble gas detectors, the sensitivities in DDCalc are based on the output of TPCMC  [84] , a dedicated detector Monte Carlo simulation.
A. 2: Running the example program To demonstrate how DarkBit can be used in a fullyfledged scan, we provide 2 annotated examples of of a DarkBit yaml file: yaml_files/DarkBit_SingletDM. yaml and yaml_files/DarkBit_MSSM7. yaml. These examples show how to specify a model, prior ranges over which to sample its parameters, a scanner and an output device ('printer'), then run the relic density, gamma-ray, neutrino and direct search likelihoods. The user can also select the parameters of the halo model and the nuclear parameters relevant for direct detection. The examples require the micrOMEGAs, gamLike, DDCalc, DarkSUSY, nulike, FeynHiggs and SUSY-HIT backends to be present, which can be accomplished by running the following commands in the GAMBIT build directory make micromegas_MSSM make micromegas_SingletDM make gamlike make ddcalc make darksusy make nulike make feynhiggs make susyhit The yaml file is complete, i.
Acknowledgements We wish to thank  Lauren Hsu  for contributing to the DDCalc treatment of SuperCDMS, and  Ankit Beniwal  and  Andre Scaffidi  for helpful conversations regarding DDCalc. We warmly thank the  Casa Matemáticas Oaxaca , affiliated with the  Banff International Research Station , for hospitality whilst part of this work was completed, and the staff at  Cyfronet , for their always helpful supercomputing support. GAMBIT has been supported by  STFC (UK ;  ST/K00414X/1 ,  ST/P000762/1 ), the  Royal Society (UK ;  UF110191 ), Glasgow University (UK; Leadership Fellowship), the  Research Council of Norway  ( FRIPRO 230546/F20 ), NO-TUR (Norway;  NN9284K ), the  Knut and Alice Wallenberg Foundation (Sweden; Wallenberg Academy Fellowship) , the  Swedish Research Council  ( 621-2014-5772 ), the  Australian Research Council  ( CE110001004 ,  FT130100018 ,  FT140100244 ,  FT160100274 ),  The University of Sydney (Australia ;  IRCA-G162448 ),  PLGrid Infrastructure (Poland) ,  Polish National Science Center (Sonata   UMO-2015/17/D/ST2/03532 ), the  Swiss National Science Foundation  ( PP00P2-144674 ),  European Commission Horizon 2020  ( Marie Skłodowska-Curie  actions  H2020-MSCA-RISE-2015-691164 ,  European Research Council Starting  Grant  ERC-2014-STG-638528 ), the  ERA-CAN+ Twinning Program  ( EU & Canada ), the  Netherlands Organisation for Scientific Research  ( NWO-Vidi 016.
Participants As declared in the pre-registration format, our study included 40 participants from a total of 47 participants initially recruited through an online announcement. All the participants were students from different faculties of the University of Padova. All participants were rewarded with 10€ for their participation in the study. They had no history of neurological, neuropsychiatric disorders or drug consumption and had normal or corrected-to-normal visual acuity. After being informed of the conditions of the study, all participants signed a voluntary consent form, and they were told that they could stop participating in the experiment at any time. The study was approved by the ethical committee of the School of Psychology of the University of Padova (id. protocol 1930). We used a 20% threshold of rejected epochs as an inclusion criterion to the final dataset.
Study design We used a simplified driving simulation as behavioral task. To this purpose, a graphic interface was specifically designed by one of the co-authors (MM), who programmed the driving-simulation software environment using the open source software 'Unreal1 Engine4' on a PC running Windows 10. The simulation was presented on a 21. 5" monitor with a screen resolution of 1920 ×1080 pixels and a driving-simulation resolution of 1280 × 720. The experiment was divided into a passive and an active task. A third, baseline task was further administered (see  Fig 1). All participants underwent all the experimental tasks.
However, in the present task a car crash could occur. Importantly, if and when the car crash will be presented is totally random. Your task is to try to mentally anticipate the car presentation'. In order to avoid any implicit or explicit prediction about the timing of the trial end, the duration of each run was randomly varied between 25 and 40s. Overall, the car accident was present in 50% of trials, with the remaining trials being 'no crash' trials. A total of 20 trials was delivered for each condition. Crucially, this information was not given to the participants in order to prevent them from using any conscious strategy based on the prediction of future events by counting the number of the previous ones. The trial type sequence was randomized within and between participants by using the randomization algorithm implemented in the Unreal1 Engine software, so that the occurrence of a 'crash' or 'no crash' trial was fully unpredictable.
EEG recording During the entire session, the electroencephalogram (EEG) was continuously acquired using a Micromed: SD MRI 64 system (Micromed/Treviso, Italy), amplified and digitized with sampling frequency of 512 Hz. A 32-channel Electro-Cup International montage was employed, in accordance with 10-20 international system  [22] , referenced to the bilateral linked mastoids, using Ag/AgCl electrodes. All electrode impedances were less than 10 KΩ and balanced. Eye movements were recorded using two electrical-oculogram (EOG), placed at the outer canthus of each eye, respectively. The raw data of all included and excluded participants are available here: https://figshare. com/articles/Driving_with_Intuition_raw_data/3573888.
Passive task: ERP analysis. All EEG recordings were processed offline using the MATLAB toolbox EEGLAB  [22]. The data were first band-pass filtered between 0. 1 and 30 Hz and segmented into epochs starting -3000 ms before stimulus onset and ending 500 ms after it. Epochs were then visually inspected to interpolate bad channels and remove rare artifacts. Artifact-reduced data were then subjected to Independent Component Analysis  [23]. All independent components were visually inspected, and only those related to eye blinks or eye movements according to morphology and scalp distribution were discarded. The remaining components were then projected back to the electrode space to obtain cleaner EEG epochs. The remaining epochs containing excessive noise or drift (± 100 μV at any electrode) were further rejected.
For the Mass Univariate Analysis we reported pvalue, critical t-test score and correspondent alpha-wise level, whereas Fieldtrip toolbox analyses  [26]  we reported p-value, where cluster metric corresponds to the sum of all the t-values of all the data points in the cluster and cluster size corresponds to the number of data points included in each cluster (the sum of the number of signals in the cluster over all the time points). We applied a lower-tailed t-test because we hypothesized that the anticipatory effect (the PPAA) could be included in the negative slow wave classification related to anticipatory neural activity, such as CNV or SPN. As an additional statistical analysis, the mean voltage amplitude for each subject and over all electrodes was measured and extracted (-1000 ms time window) performing Bayesian parameters analysis estimating the effect size with the corresponding High Density Intervals (HDIs) of the different comparisons using the BEST package  [27]  and BayesFactors (BF) with a Cauchy prior =.
Explorative analyses: time on task effect. To deepen the understanding of anticipatory brain activity, we decided to explore its dynamic evolution across the task, known as Time on Task Effect (TOT;  [8] ). Addressing the presence of TOT is important because it provides a dynamic picture of changes in anticipatory brain activity over time, allowing us to make further inferences about attentional resource allocation. More specifically, as previously reported  [8] , the dynamic evolution of anticipatory ERP activity may unveil the presence of implicit learning effects. As a matter of fact, the use of a randomized experimental design should abolish TOT implicit learning effects, allowing to control for any possible bias in the trials condition distribution over time. The epoched ERP dataset relative to each task of interest (i.
e. , -1000 to 0 ms). A window overlap ratio of 50% was used and the delta, theta, alpha and beta frequency bands were targeted. We did not extract either gamma 1 or gamma 2 because a band-pass filter between 0. 1 and 30Hz was originally applied to analyze ERP activity, this excluded high-frequency data from the dataset used to calculate the power spectrum. Subsequently, we performed statistical comparisons between tasks by using the same approach employed for conventional and TOT ERP analysis but, in this case, separately for each frequency band. 1 , there was no statistical evidence of above chance performance (equal to 10), either for the 'True accident prediction' or for the 'True no accident prediction'.
Passive task: ERP results. From the visual inspection of ERP morphology, we noticed that the tasks started to differentiate each other at around -1000 ms before stimulus onset, while no appreciable differences were present before this point. To be sure that eventual ERP modulations did not occur before the interval in which the difference was actually observed, we performed pair-wise statistical analyses by contrasting the mean ERP amplitude relative to the time window from -3000 ms to -1000 ms, measured in the Baseline, Crash End Passive and No-Crash End Passive conditions. Specifically, 1000-permutation cluster-based, two-tailed analyses, using both Mass univariate toolbox and Field trip brainbox toolbox implemented in the Brainstorm software, were performed. Since these analyses did not yield significant differences, we concluded that the ERP signal between -3000 ms and -1000 ms did not differ between tasks.
In the attempt to address any possible methodological shortcomings of the present study, we considered several possible technical or design biases. Concerning the first aspect, we can exclude differences due to the frame's screen refresh because no frame screen refreshed errors were detected, which could otherwise have been interpreted as a cue of the forthcoming stimulus. We can also exclude differences in the randomization procedure. The randomization procedure implemented by the Unreal1 Engine4 software uses a pseudo random generator algorithm, which can be found on the web site https://www. unrealengine. com/ what-is-unreal-engine-4. The random sequences of all 40 participants are made available here: https://figshare. com/s/8d43eac9f73bf7e29bf5. Concerning other possible biases, we asked whether the observed ERP difference may be simply due to a classical Gambler's fallacy phenomenon in the passive task, that is, the expectation that a 'Crash End' probability will increase linearly with the number of the previous 'No Crash End' trials.
); glacier outline (OL); digital elevation model of the glacier surface (DEM); surface mass balance (SMB); surface ice flow velocity (Vel. ); rate of ice thickness change (∂h/∂t); ice thickness measurements (H); unpublished data (Unpub. ). References to the data are given. oping a new generation of improved ice thickness estimation approaches. 2 Experimental set-up ITMIX was conducted as an open experiment, with a call for participation posted on the email distribution list "Cryolist" (http://cryolist. org/) on 13 October 2015. Individual researchers known to have developed a method for estimating glacier ice thickness were invited personally. Upon registration, participants were granted access to the input data necessary for the experiment and the corresponding set of instructions. The input data referred to the surface characteristics of a predefined set of 21 test cases (see next section, Table  1 , and Fig.
1 ) and participants were asked to use these data for generating an estimate of the corresponding ice thickness distribution. Results were collected and compared to direct ice thickness measurements. No prior information about ice thickness was provided, and the participants were asked not to make use of published ice thickness measurements referring to the considered test cases for model calibration. This was to mimic the general case in which the ice thickness distribution for unmeasured glaciers has to be estimated. The compliance to the above rule relied on honesty. www. the-cryosphere. net/11/949/2017/ The Cryosphere, 11, 949-970, 2017 Participants were asked to treat as many test cases as possible and to consider data availability (see next section and Table  1 ) as the only factor limiting the number of addressed cases.
For obtaining a possibly complete coverage, velocities from separate sources were therefore merged, which often led to discontinuities along the tile margins. Ice thickness measurements were only used for quantifying model performance but were not distributed to the ITMIX participants. Bedrock elevations were obtained by subtracting observed ice thicknesses from surface elevations, and the bedrock was assumed to remain unchanged over time. The time periods the individual data sets are referring to are given in Supplement Table  S1. Note that no specific information about the uncertainties associated to individual measurements were available. Reported uncertainties for ice thickness measurements, however, are typically below 5 %  (Plewes and Hubbard, 2001). The synthetic test cases were generated by "growing" ice masses over known bedrock topographies with the Elmer/Ice ice flow model  (Gagliardini et al.
No sliding at the glacier base was assumed, and all three resulting geometries were close to steady state. Note that, to avoid numerical instabilities, the DEM used for prescribing the bedrock topography had to be smoothed significantly. For anonymizing the individual locations, the original coordinates were removed, and the individual tiles arbitrarily rotated and shifted in elevation. All data provided as input to the ITMIX participants, as well as the results submitted by individual models, will be provided as an electronic Supplement to this article. The direct ice thickness measurements were additionally included in the Glacier Thickness Database (GlaThiDa) version 2 (WGMS, 2016).
In contrast to , which evaluates the cost function for steady-state surfaces, this approach allows for transient surface geometries to be taken into account. If available, the mismatch between calculated and observed surface velocities is used for both stopping the iteration procedure and for optimizing the model parameters affecting basal sliding and deformational flow. The implemented forward model "SIADYN" is part of the ICEDYN package (Sect. 3. 3 in  Reerink et al. , 2010) , relies on the vertically integrated shallow ice approximation (e. g. Hutter, 1983) , and includes Weertman-type sliding  (Huybrechts, 1991). -"Fuerst" (Fürst et al. , unpublished; Supplement Sect. S1. 4) differs from the two above approaches in that the cost function is not linked to surface elevations. Instead, the function penalizes (i) negative thickness values, (ii) the mismatch between modelled and observed surface velocities, (iii) the mismatch between modelled and observed SMB, and (iv) strong spatial variations in ice thickness or surface velocities.