dataesr/scientific-paragraphs-categorization huggingface dataset:
Multivariate decoding analyses To investigate whether TPJ/pSTS was selectively involved in bribery-related decision-making that harmed a third party, we performed an ROI-based multivariate decoding analysis via python-based nltools package (v 0. 3. 6 in Python 3. 7. 1; http://github. com/ljchang/nltools). Specifically, we trained a linear support vector machine (slack parameter C = 1 chosen as default) classifier, a widely used algorithm to deal with the binary classification, to discriminate decision-relevant activity pattern in TPJ/pSTS in Bribe vs. Control for the Solo (i. e. , individual contrast maps in GLM1: SB vs. SC; the first condition was coded as 1 and the second as À1, same below) and the Dyad scenario (DB vs. DC) separately. A LOSO cross-validation procedure was adopted such that it trained the classifier on N-1 participants and generated a weight map that best classified the sample, and then tested the classification on the left-out participant.
This procedure looped for all participants once to obtain their respective cross-validated signature response values. As a result, the classifier obtained a hyperplane that best discriminates the individual contrast maps in two conditions (e. g. , SB vs. SC). Receiver operating characteristic curve was created based on the performance of classification (i. e. , the two-choice forced-alternative accuracy) via Canlab Matlab toolbox (https://github. com/canlab). Statistical significance was obtained via the permutation test (i. e. , 5000 times of permutation).
sourceforge. net/; Gla ¨scher, 2009). Each line refers to the linear fit between the bin and the PSC. (C) Multivariate patterns of TPJ/pSTS distinguished bribe (vs. Control) only in the Dyad scenario. The regions of interest (ROI) of TPJ/pSTS was defined based on a whole-brain parcellation map from the Neurosynth database (left). The receiver operating characteristic curve showed that the multivoxel TPJ/pSTS activity pattern during decision-making can distinguish the Bribe condition from the Control condition only in the Dyad scenario but not in Figure 3 continued on next page.
e. , Bribe vs. Control). The seed region of vmPFC was defined based on a whole-brain parcellation given a meta-analytic functional coactivation map of the Neurosynth database (http://neurovault. org/images/39711/). To visualize the relationship, we extracted the contrast value (cv) of the psycho-physiological interaction regressors of the Bribe and Control conditions within the activated cluster and plotted the correlation between the cv and the individual parameter. Each gray dot represents the data of a single participant; the blue line represents the linear fit using robust correlation (r = À0. 54, 95% CI: À0. 88,-0. 19, p=0. 003); the gray density curve indicates the distribution of each variable, respectively. The online version of this article includes the following figure supplement(s) for figure 5: Figure supplement 1.
Acknowledgements This research has benefited from the financial support of  IDEXLYON from Universitede Lyon  (project  INDEPTH ) within the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 ) and of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Universitede Lyon , within the  program Investissements d'Avenir  ( ANR-11-IDEX-007 ) operated by the  French National Research Agency. This work was also supported by grants from the  Agence Nationale pour la Recherche and NSF  in the  CRCNS program  to  JCD  ( ANR n˚16-NEUC-0003-01 ),  National Science Foundation of China  to QC ( 31470995 ), and  China Postdoctoral Science Foundation  to YH (2019M660007). We thank  Sima Hakimi ,  Zixuan Tang ,  Siying Li , and  Yaner Su  for helpful assistance with data collection. We also thank  Dr. Xiaoxue Gao  for the assistance of implementing the multivariate fMRI analyses and  Zhewen (Vane) He  for proofreading the early draft of the manuscript.
The CMS detector, data sets, and event reconstruction The H → 4 decay candidates are produced in proton-proton (pp) collisions at the LHC and are collected and reconstructed in the CMS detector  [74]. The data sample used in this analysis corresponds to integrated luminosities of 35. 9 fb -1 collected in 2016, 41. 5 fb -1 collected in 2017, and 59. 7 fb -1 collected in 2018, for a total of 137 fb -1 collected during Run 2 at a pp center-ofmass energy of 13 TeV. The CMS detector comprises a silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter (ECAL), and a brass/scintillator hadron calorimeter, each composed of a barrel and two endcap sections, all within a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3. 8 T. Extensive forward calorimetry complements the coverage provided by the barrel and endcap detectors.
Outside the solenoid are the gas-ionization detectors for muon measurements, which are embedded in the steel flux-return yoke. A detailed description of the CMS detector can be found in Ref. [74]. Events of interest are selected using a two-tiered trigger system. The first level, composed of custom hardware processors, uses information from the calorimeters and muon detectors to select events at a rate of around 100 KHz within a fixed latency of about 4 µs  [75]. The second level, known as the high-level trigger, consists of a farm of processors running a version of the full event reconstruction software optimized for fast processing, and reduces the event rate to around 1 KHz before data storage  [76].
The JHUGEN 7. 3. 0 [29] [30] [31] [32] [33]  MC program is used to simulate all anomalous couplings in the H boson production and H → ZZ / Zγ * / γ * γ * → 4 decay as discussed in Section 2. The MELA  [29] [30] [31] [32] [33]  package contains a library of matrix elements from JHUGEN for the signal and MCFM 7. 0. 1  [88]  for the background; these matrix elements are used to apply weights to events in any MC sample to model any other set of anomalous or SM couplings. The SM production of the H boson through VBF, in association with a W or Z boson, or with a tt pair is simulated using both JHUGEN at LO in QCD and POWHEG 2  [89] [90] [91] [92] [93]  at next-to-leading order (NLO) in QCD. Production in association with a bb pair or single top quark is simulated at LO in QCD via JHUGEN. In the VBF, VH, and ttH production modes, the JHUGEN and POWHEG simulations are explicitly compared after parton showering in the SM case, and no significant differences are found in kinematic observables.
Gluon fusion production is simulated with the POWHEG 2 event generator at NLO in QCD. The kinematic features of events produced in gluon fusion with two associated jets are also modified by anomalous Hgg couplings. These effects are studied using MADGRAPH5 aMC@NLO 2. 6. 0  [63, 94]  and JHUGEN. Simulation with the MINLO  [95]  program at NLO in QCD is used for evaluation of systematic uncertainties related to the modeling of two associated jets. The relationship between the Hff and Hgg couplings follows JHUGEN with the relative sign of CP-odd and CP-even coefficients opposite to that assumed in MADGRAPH5 aMC@NLO 2. 6. 0, as discussed in Ref. [33]. The sign convention of the photon field in JHUGEN 7. 3. 0 is opposite to that in MADGRAPH5 aMC@NLO 2. 6. 0, which leads to the opposite sign of the HZγ couplings.
The probability densities P are calculated from the matrix elements provided by the MELA package and are normalized to give the same integrated cross section for both processes in the relevant phase space. This normalization leads to a balanced distribution of events in the range between 0 and 1 for the D alt discriminants, or between -1 and 1 for D int. In the special case where the D int is calculated between CP-even and CP-odd models, it is denoted as D CP. The D CP observable is CP-odd, and a forward-backward asymmetry in its distribution would indicate CP violation. This motivates the index "CP". When events are split into the VBF-1/2jet and VH-hadronic categories, a set of discriminants D 1/2jet is constructed, following Eq. (  20 ), where P sig corresponds to the signal probability density for the VBF (WH or ZH) production hypothesis in the VBF-tagged (VH-tagged) category, and P alt corresponds to that of H boson production in association with two jets via gluon fusion.
When both HVV (in decay) and Hgg or Htt (in production) couplings are measured at the same time in a given process, the number of terms is multiplied (15 × 3 = 45), since the two probabilities factorize. The P sig jk, lm(np) templates are extracted from simulation, discussed in Section 3. 3, typically using from three to twelve samples generated with various f j values chosen to map different points of phase-space well and reweighted with the MELA package to cover all possibilities with (C + L -1)!/(C!(L -1)!) combinations of couplings. In parameterizing the signal templates P sig jk, lm(np) , it is important to ensure that the expected number of events in every bin of the probability densities, defined in Eqs. (  23 ) and (  24 ), remains nonnegative at all possible values of f j , because a negative yield would cause the likelihood function used for the final fit to become ill-defined.
To detect a negative event yield, we first minimize Eq. (  23 ) or Eq. (  24 ), which are polynomials in | f l |, by finding where the gradient is zero. For Eq. (  24 ), which is quadratic, this is a simple problem in linear algebra. Equation (  23 ) is quartic, so its gradient is a system of cubic equations, which cannot be solved exactly for L > 1. We use the HOM4PS program  [105] [106] [107]  to numerically solve the system of equations. If we find the minimum to be negative, we adjust the estimated P sig jk, lmnp until the yield is always positive. This adjustment is made using the statistical uncertainty on P sig jk, lmnp through a cutting planes algorithm  [108]  implemented using the GUROBI program  [109]. In all cases, it is found that only small modifications to the initial estimates of P sig jk, lmnp are needed.
These results also surpass our earlier studies of on-shell production of the H boson in this decay channel with a partial Run 2 dataset  [16, 17]. The parameterization of the H boson production and decay processes is based on a scatter-ing amplitude or, equivalently, an effective field theory Lagrangian, with operators up to dimension six. Additional symmetries and prior measurements allow us to reduce the number of independent parameters and make a connection to the standard model effective field theory (SMEFT) formulation. Dedicated Monte Carlo programs and matrix-element re-weighting techniques provide modeling of all kinematic effects in the production and decay of the H boson, with any variation of parameters of the scattering amplitude and with full simulation of detector effects. Each production process of the H boson is identified using the kinematic features of its associated particles.
Acknowledgments We thank  Markus Schulze  for optimizing the  JHUGEN Monte Carlo simulation program  and matrix element library for this analysis. We thank  Amitabh Basu  for guidance on implementing the cutting planes algorithm and  Tianran Chen  for updating the  HOM4PS program  for this analysis and providing support. We are grateful to the members of the  LHC Higgs and EFT Working Groups  for stimulating the development of several phenomenological aspects of this work, among which is relating the EFT operator bases for the Higgs boson couplings. We congratulate our colleagues in the CERN accelerator departments for the excellent perfor-the  US Department of Energy , and the  US National Science Foundation.
Monitoring the evolution of the snowpack properties in mountainous areas is crucial for avalanche hazard forecasting and water resources management. In-situ and remotely sensed observations provide precious information on the snowpack but usually offer a limited spatio-temporal coverage of bulk or surface variables only. In particular, visible-near infrared (VIS-NIR) reflectance observations can inform on the snowpack surface properties but are limited by terrain shading and clouds. Snowpack modelling enables to estimate any physical variable, virtually anywhere, but is affected by large errors and uncertainties. Data assimilation offers a way to combine both sources of information, and to propagate information from observed areas to non observed areas. Here, we present CrocO, (Crocus-Observations) an ensemble data assimilation system able to ingest any snowpack observation (applied as a first step to the height of snow (HS) and VIS-NIR reflectances) in a spatialised geometry.
However, in the case of small simulation domains or modelled systems driven by large-scale coherent causalities, large scale correlations (relative to the domain size) may be physically sound, and defining a localisation radius may be a difficult task. In order to face this issue, we developed a new localisation approach called the k-localisation, where localisation domains are based on background correlation patterns. These developments were implemented into CrocO (Crocus-Observations) an ensemble data assimilation system able to sequentially assimilate snowpack observations with a PF in a spatialised context. CrocO can be implemented in any geometry, (e. g. within a distributed (gridded) framework or any irregular spatial discretisation). Here, we apply CrocO in a semidistributed framework, which is a conceptual spatialised geometry, operationally used by Météo-France for avalanche hazard forecasting  (Lafaysse et al.
Ensemble of meteorological forcings Meteorological forcings are taken from SAFRAN  (Durand et al. , 1993)  reanalysis, where forecasts from the ARPEGE Numerical Weather Prediction (NWP) model are downscaled and adjusted with surface observations within the massif area. They are combined with MOCAGE LAP fluxes  (Josse et al. , 2004)  interpolated at Col du Lautaret (2058 m. a. s. l, inside the Grandes-Rousses) to constitute the reference forcing dataset. Before the beginning of the simulation, spatially homogeneous stochastic perturbations are applied to this forcing to generate an ensemble of forcings (F i ) 0<i≤Ne as described in  Cluzet et al. (2020). Each forcing F i is associated with the corresponding M i ESCROC configuration and this relation is fixed during the whole simulation.
Evaluation Scores The performance of the assimilation and openloop run is evaluated against the synthetic truth using several scores. The Absolute Error of the ensemble mean (AE) and ensemble spread σ are two common metrics of ensemble modelling. Given an ensemble E m,c,t of N e members m in topographic class c at time t and the corresponding observations o c,t , the ensemble mean is described by Eq. 4: From which we can compute the absolute error AE (Eq. 5) and the spread (or dispersion) σ (Eq. 6): E c,t = 1 N e Ne m=1 E m,c,t (4) AE c,t = |E c,t -o c,t | ∀(c, t) ∈ [1, N pts ] × [1, N t ] (5) σ c,t = 1 N e Ne m=1 (E m,c,t -E c,t ) 2 ∀(c, t) ∈ [1, N pts ] × [1, N t ] (6) Where N t is the number of evaluation time steps. The Continuous Ranked Probability Score (CRPS, (Eq. 7)  Matheson and Winkler, 1976)  evaluates the reliability and resolution of an ensemble based on a verification dataset.
The results are shown in Fig. 9. The assimilation improves the performance in all aspects and slopes. Naturally, this suffers from the same limitation as discussed in Sec. 5. 3, not to mention the limited spatial representativeness of in situ observations but it shows some potential for this idea. In that way, a more rational use of the available observations could be implemented towards a new ensemble data assimilation system. In the present CrocO system, SAFRAN reanalysis are only assimilating weather station information (precipitation phase, temperature, wind), and makes no use of the numerous snow observations available. Here, snow observations are assimilated by the PF, but are not used to correct meteorological forcings (only snow variables, see Fig. 2 ). In the way of a new ensemble data assimilation system, within CrocO, the SAFRAN meteorological analysis could be bypassed, the PF operating directly both on the meteorological and snowpack variables in a more comprehensive and coupled strategy.
Acknowledgements. The authors are grateful to  François Tuzet ,  Jesus Revuelto ,  Rafife Nheili ,  César Deschamps-Berger ,  Stéphanie Faroux  (SODA) and  Matthieu Vernay  for their precious help in collecting input data and code implementation. They also would like to thank  Fanny Larue ,  Joseph Bellier ,  Pierre De Mey  and  Guilhem Candille  for helpful discussions on the PF inflation and CRPS decomposition.
Code availability. The Crocus snowpack model (including all physical options of the ESCROC system) and the Particle Filter algorithm are developed inside the opensource SURFEX project. The source files of SURFEX code are provided at https://doi. org/10. 5281/zenodo. 3774861 to guarantee the permanent reproductibility of results. However, we recommend potential future users and developers to access to the code from its git repository (git. umr-cnrm. fr/git/Surfex_Git2. git) to benefit from all tools of code management (history management, bug fixes, documentation, interface for technical support, etc. ). This needs a quick registration, the procedure is described at https://opensource. cnrm-game-meteo. fr/projects/snowtools/wiki/Procedure_for_new_users. The version used in this work is tagged as CrocO_v1.
0. A python software called CrocO_toolbox was specifically developed, in order to pre-post process and launch CrocO experiments. It is available on Github (https://github. com/bertrandcz/CrocO, release v1. 0) along with a documentation. The article version of CrocO_toolbox is archived at: https://doi. org/10. 5281/zenodo. 3784980. This software strongly relies on two external python projects ensuring the files management between the different steps of a simulation and the interface with Meteo-France HPC system (including parallelization and data storage): snowtools and vortex. Their sources are available at https://doi. org/10. 5281/zenodo. 3774861 (same archive as SURFEX) to guarantee the permanent reproducibility of results. However, as for the SURFEX project and for the same reasons, it is recommended to access snowtools code from its git repository (git.
umr-cnrm. fr/git/snowtools_git. git). The version used in this work is also tagged as CrocO_v1. 0. The vortex project gathers all environment-specific codes of Météo-France modelling systems relative to its HPC computing system. For this project, only the sources which are specific to this article simulations are provided. The common objects inheritance is based on Vortex version 1. 6. 1. The version used in this work is also tagged as CrocO_v1. 0 in the vortex git repository. Because these softwares could not be applied outside Météo-France HPC environment, CrocO python software offers the possibility to run CrocO simulations locally. This functionality was not used here due to the high numerical cost of our simulations, which required the use of Météo-France HPC environment. Data availability.
Input and output data necessary to reproduce the manuscript simulations and figures are provided at https://doi. org/10. 5281/zenodo. 3775007. This archive includes : SAFRAN reanalyses, (also available at https://doi. org/10. 25326/37), MOCAGE forcings, namelists, configuration files and spinup files enabling to reproduce the simulations. Raw model outputs can be provided on request but since they amount up to 500+ Gigabytes, only post-processed simulations outputs are provided in this archive, along with scores and scripts to reproduce the manuscript figures.
Operating System Selection To have a variety of systems, we consider a few versions of widely used operating systems, that we expect representative: Android, iOS/MacOS, Ubuntu and Windows. They are used to explain the first steps of data preparation and in the case study. We analyze 8 different operating systems, two from each family: Android5. 1, Android6. 0, iOS5, MacOS10. 10, Ubuntu12. 04, Ubuntu14. 10, Windows7. 0 and Windows8. 1. In these systems, we consider separately four mechanisms: brightness, contrast and color, text style and zoom. This division has been proposed by one co-author who is an expert in visual accessibility for persons with low vision.
Feature-to-Ontology Concept Mapping In parallel, a mapping between the specific features and the ontology concepts is established. Table  1  shows the 8 OS brightness features mapped onto the ontology. This is schematized for iOS5 and MacOS10. 10 in Figure  5. This mapping is very important because it traces the identified correspondences. It is also used to build standardized input data for the data-mining step. It shows, for example, that AutoBrightness of iOS5 and AutomaticallyAdjustBrightness of MacOS10. 10 are mapped to DisplayAutoBrightness. Level of MacOS10. 10 is mapped onto ManualDisplayBrightnessLevel concept, making it a specialization of BrightnessMechanism on which iOS5_Brightness of iOS5 is mapped. Accidental options, such as Display1 or Display2 are discarded from the ontology and the mapping.
However, we could imagine introducing attributes, like the system families (system for personal computer, system for mobile phone, Unix, Linux, MacOS, Windows, etc. ). Relational contexts, also called object-object contexts, express relations between objects of several formal contexts. Here they are introduced to connect the systems and their four mechanism implementations. Table  5  (Relational Context os2Contrast) shows the relational context which connects systems (rows) to their contrast mechanism implementations (columns). They are all built using the same scheme, with a diagonal of crosses. RCF building. We build a Relational Context Family composed of: • One system formal context: objects are the operating systems, attributes are empty (or intrinsic attributes), e. g. , Table  4. a 2 ∈ F O such that a 1 is a specialization of a 2 , if (o, a 1 ) ∈ J m , then we also have (o, a 2 ) ∈ J m.
It has many extensions from the original binary formalism, to deal with numbers, intervals, sequences, trees, graphs and multi-relational datasets  [21, [31] [32] [33]. FCA methods fit our problem area since operating system description is mainly symbolic, with a few numerical descriptions, for example for text size. There is an implicit underlying data model, revealed during feature model and ontology extraction. Separating the analysis of the different mechanisms is made possible by Relational Concept Analysis (RCA). In this section, we detail FCA and RCA for our purpose. 3  and Concept_Contrast_5 in Figure  7 ). A specialization relation is established between the extracted concepts. Let us consider two concepts C 1 = (E 1 , I 1 ), and C 2 = (E 2 , I 2 ), the specialization relation states that C 1 ≤ s C 2 if and only if E 1 ⊆ E 2 (and equivalently I 1 ⊇ I 2 ).
For example, if we consider the concept (E 3 , I 3 ) with E 3 = {SOcaMContrast SOiContrast} and I 3 = {ChangeContrast, HighContrast, NormalContrast, ReduceTransparency}, we have (E 3 , I 3 ) ≤ s (E 5 , I 5 ). The concept lattice is the set of all concepts C K of the formal context K, provided with the specialization relation ≤ s. We denote by AC K the set of concepts introducing attributes (attribute introducer). A concept is an introducer concept for attribute a, whenever it is the highest concept containing this attribute. For example Concept_Contrast_5 introduces NormalContrast, because it is the highest concept containing this attribute (see Figure  7 ). We used the tool RCAexplore (http://dataqual. engees. unistra. fr/logiciels/rcaExplore, accessed on 1 September 2021) to generate these lattices.