dataesr/scientific-paragraphs-categorization huggingface dataset:
Machine Learning (ML) Using our oracle, we assigned a faulty or non faulty label for each video of the sample. We use a Machine Learning (ML) algorithm to understand the relationship between faultiness of videos and features/attributes' values. Different ML methods exist. Some of them are sophisticated and perform only a few classification errors while others are less advanced but are much more understandable when vizualizing the output model. In our case, we wanted to obtain a high level of understandability when extracting constraints. Specifically, we employed Binary Decision Trees. Figure  5  presents an excerpt of the decision tree obtained from the Weka 2  software. In this tree: • ovals represent features (written inside) on which decisions will have to be taken; • labels over edges represent threshold value to decide which path to take; • rectangles represent leaves of the tree and groups of configurations that are mainly of the same class.
RELATED WORK Mining variability and constraints. Numerous works address the problem of synthesizing a variability model  [4-6, 9, 28]. Given a Boolean formula or a set of configurations, a procedure automatically produces a variability model, including cross-tree constraints. The resulting models tend to represent the exact set of configurations, in line with the formula. However, there are cases in which the set of constraints and valid configurations is incomplete or uncertain. For instance, Nadi et al. showed that a large portion of configuration constraints in the Linux kernel originates from domain knowledge or necessitates testing  [24]. In our case study, we report similar observations and testing needs for extracting constraints (see Section 3. 1). Perrouin et al. [17]  proposed an automated search-based process to test and fix feature models.
Preliminary results on the Linux kernel are reported. In our case, we are mostly interested in discovering new constraints rather than validating or refuting existing ones. A significant difference is that our strategy of adding constraints significantly differs. Instead of simply negating faulty configurations, we rely on machine learning to discover and capture more general faulty configurations. Some works address the synthesis of constraints with the use of data mining techniques. In  [9] , Davril et al. present an automated approach to extract feature models from informal, textual product descriptions. In  [36] , Yi et al. applied support vector machine and genetic techniques to mine binary constraints (requires and excludes) from Wikipedia. These works are not applicable in our context since i) we have to learn from a set of faulty configurations (not from textual content); ii) we have to deal with constraints among numerical values.
Thanks to constraints, we can now consider the synthesis of very large datasets of truly relevant videos. We believe the method is general enough to be applicable to product lines in other contexts and domains. For instance, a learning-based approach has the potential to infer constraints of the Linux kernel; but is it an effective solution? Further empirical studies are needed to understand the effects of sampling strategies, oracle definition, and learning process on the ability to infer constraints. More generally we invite researchers to investigate the conditions for which our method is effective or not.
Studies of the processes B + → K + π -π + γ and B 0 → K 0 S π -π + γ including measurements of the branching fractions have been performed by both BABAR  [12]  and Belle  [13]  using samples of 232 and 152 million BB pairs, respectively. The latter analysis also determined the branching fraction of the resonant decay B + → K 1 (1270) + γ. The article is organized as follows. In Sec. II we briefly describe the BABAR detector and the data set. In Sec. III we describe the analysis strategy. The amplitude analysis of B + → K + π -π + γ decays and the time-dependent analysis of B 0 → K 0 S π -π + γ decays are described in Sec. IV and Sec. V, respectively. Finally, we summarize the results in Sec. VI.
II. THE BABAR DETECTOR AND DATASET The data used in this analysis were collected with the BABAR detector at the PEP-II asymmetric-energy e + e -storage ring at SLAC. The sample consists of an integrated luminosity of 426. 0 fb -1  [14] , corresponding to (470. 9 ± 2. 8) × 10 6 BB pairs collected at the Υ (4S) resonance ("on-resonance"), and 44. 5 fb -1 collected about 40 MeV below the Υ (4S) ("off-resonance"). A detailed description of the BABAR detector is presented in Refs. [15, 16]. The tracking system used for track and vertex reconstruction has two components: a silicon vertex tracker (SVT) and a drift chamber (DCH), both operating within a 1. 5 T magnetic field generated by a superconducting solenoidal magnet. A detector of internally reflected Čerenkov light (DIRC) is used for charged particle identification.
III. ANALYSIS STRATEGY The main goal of the present study is to perform a time-dependent analysis of B 0 → K 0 S π -π + γ decays to extract the decay and mixing-induced CP asymmetry parameters, C K 0 S ργ and S K 0 S ργ , in the B 0 → K 0 S ρ 0 γ mode. However, due to the large natural width of the ρ(770) 0 , a non negligible number of B 0 → K * ± (K 0 S π ± )π ∓ γ events, which do not contribute to S K 0 S ργ , are expected to lie under the ρ(770) 0 resonance and modify S K 0 S ργ. Using the formalism developed in Ref. [17] , which assumes the SM, the "so called" dilution factor D K 0 S ργ can be expressed as D K 0 S ργ ≡ S K 0 S π + π -γ S K 0 S ργ = A ρK 0 S 2 -A K * + π - 2 -A (Kπ) * + 0 π - 2 + 2 A * ρK 0 S A K * + π -+ 2 A * ρK 0 S A (Kπ) * + 0 π - dm 2 A ρK 0 S 2 + A K * + π - 2 + A (Kπ) * + 0 π - 2 + 2 A * ρK 0 S A K * + π -+ 2 A * ρK 0 S A (Kπ) * + 0 π - dm 2 (1) where S K 0 S π + π -γ is the effective value of the mixinginduced CP asymmetry measured for the whole B 0 → K 0 S π -π + γ dataset and A RP is the (complex) amplitude of the mode RP , where R represents a hadronic resonance and P a pseudoscalar particle.
The momentum-weighted Legendre polynomials are defined by L 0 = i |p i | and L 2 = i |p i | 1 2 3 cos 2 θ i -1 , where θ i is the angle with respect to the B thrust axis of track or neutral cluster i and p i is its momentum. The sums exclude the B candidate and all quantities are calculated in the Υ (4S) frame. The Fisher discriminant is trained using off-resonance data for the continuum and a mixture of simulated exclusive decays for the signal. The final sample of candidates is selected with a requirement on the Fisher discriminant output value (F) that retains 90% of the signal and rejects 73% of the continuum background. We use simulated events to study the background from B decays other than our signal (B background). In preliminary studies, a large number of channels were considered, of which only those with at least one event expected after selection are considered here.
Class PDFs Varied Number of events ing the s Plot technique. Note that this technique may produce bins with negative entries. The fit is performed using the Laura++ package  [25]. The fit uses the variables m ES , ∆E, and the Fisher-discriminant output F, to discriminate CR signal events from other event categories. mES ∆E F B 0 → X sd ( Kπ)γ G + Exp. Gaussian no 2872 ± 242 B + → Xsu( Kπ)γ ARGUS B 0 → K * 0 (→ Kπ)γ Two-dimensional G yes 1529 ± 116 B 0 → X sd (→ Kπ)γ nonparametric B + → K * + (→ Kπ)γ Linear + Exp. G no 442 ± 50 B + → Xsu(→ Kπ)γ ARGUS B 0 → K * 0 η G + Gaussian + G no 56 ± 21 ARGUS Constant B + → a + 1 (→ ρ 0 π + )π 0 γ CB Asymmetric Asymmetric no 17 ± 9 B + → K * 0 (→ Kπ)π + π 0 γ Gaussian Gaussian B → The likelihood function L i for the event i is the sum L i = j N j P i j (m ES , ∆E, F), (3) where j stands for the event species (signal, continuum and the various B backgrounds) and N j is the corresponding yield.
( 12 ) The sum of fit fractions is defined as the algebraic sum of all fit fractions. This quantity is not necessarily unity due to the possible presence of net constructive or destructive interference. The branching fraction to the K + π + π -γ final state is determined from the fitted yield of the CR signal event category, N CR sig , the weighted CR signal efficiency, + , and the number of charged B events, N B ± B(B + → K + π + π -γ) = N CR sig + × N B ± , (13) with + = k + k FF(k) l FF(l). (14) Here, k and l run over the kaonic resonances, + k represents the efficiency without requirement on m Kππ for resonance k listed in Table  III  and FF are the fit fractions extracted from a binned maximum-likelihood fit to the CR signal s Plot of m Kππ plotted in 80 bins. The term N B ± is obtained from the total number of BB pairs in the full BABAR dataset, N BB , and the corresponding Υ (4S) branching fraction taken from Ref.
For each new fit performed this way, we derive the corresponding m Kππ s Plot distribution that we then fit using the nominal m Kππ model. Assuming no correlations among the fixed parameters, we combine each of the negative (positive) difference between the new fit value and nominal fit value of each free parameter, and take the resulting values as negatively (positively) signed uncertainties. To assign systematic uncertainties due to the choice of bin size in the fitted dataset, we perform new fits using either 60 or 100 bins, instead of 80 in the nominal fit model. To assign systematic uncertainties due to the fixed parameters of the line-shape resonances in the m Kππ fit model, we vary each of the eight fixed parameters according to its uncertainties, taken from Ref. [18] , and redo the fit to the nominal CR signal m Kππ s Plot distribution.
Branching fractions To assign systematic uncertainties on the yield for the CR signal category due to the fixed parameters in the fit to m ES , ∆E and F, we use the same procedure as the one described in Sec. IV D 1. For each new fit, we obtain a new value of the CR signal event category yield. Using the method described in Ref. [36]  and assuming no correlations among the fixed parameters, we combine each of the negative (positive) difference between the new fit value and nominal fit value of each free parameter, and take the resulting values as negatively (positively) signed uncertainties. We use 0. 6% as the systematic uncertainty on N BB , corresponding to the uncertainty on the official BB count for the full BABAR dataset  [37]. Similarly to Ref. [12] , to account for possible differences between data and simulation in the tracking and particle identification efficiencies, we assign for each charged particle in the final state a systematic uncertainty of 0.
Branching fraction determination The branching fraction to the K 0 S π + π -γ final state is determined from the fitted yield of the correctlyreconstructed signal event category, N CR sig = N sig × f CR , the weighted signal efficiency, 0 , and the number of neutral B events, N B 0 B(B 0 → K 0 S π + π -γ) = N CR sig 0 × N B 0. ( 43 ) where 0 = 0. 0553 +0. 0010 -0. 0009 is obtained from Eq. (  14 ) replacing the efficiencies + k by those of the neutral kaonic resonances listed in Table XV and, assuming isospin symmetry, using the FFs listed in Table  V. The small value of 0 compared to that of + is due to the additional requirements on m ππ and m Kπ (see Sec. V B). The term f CR = 0. 728 ± 0. 004 is the fraction of correctlyreconstructed signal events. The term N B 0 is obtained from the total number of BB pairs in the full BABAR dataset, N BB , and the corresponding Υ (4S) branching fraction taken from Ref.
ACKNOWLEDGMENTS We thank  E. Kou  for help throughout the analysis, especially with the computation of the dilution factor, and  A. Le Yaouanc  for valuable discussions. We are grateful for the extraordinary contributions of our PEP-II colleagues in achieving the excellent luminosity and machine conditions that have made this work possible. The success of this project also relies critically on the expertise and dedication of the computing organizations that support BABAR. The collaborating institutions wish to thank  SLAC  for its support and the kind hospitality extended to them. This work is supported by the  US Department of Energy and National Science Foundation , the  Natural Sciences and Engineering Research Council (Canada) , the  Commissariat à l'Energie Atomique  and  Institut National de Physique Nucléaire et de Physique des Particules (France) , the  Bundesministerium für Bildung und Forschung and Deutsche Forschungsgemeinschaft (Germany) , the  Istituto Nazionale di Fisica Nucleare (Italy) , the  Foundation for Fundamental Research.
In this paper we present a large database of weak lensing light cones constructed using different snapshots from the Big MultiDark simulation (BigMDPL). The raytracing through different multiple plane has been performed with the GLAMER code accounting both for single source redshifts and for sources distributed along the cosmic time. This first paper presents weak lensing forecasts and results according to the geometry of the VIPERS-W1 and VIPERS-W4 field of view. Additional fields will be available on our database and new ones can be run upon request. Our database also contains some tools for lensing analysis. In this paper we present results for convergence power spectra, one point and high order weak lensing statistics useful for forecasts and for cosmological studies. Covariance matrices have also been computed for the different realisations of the W1 and W4 fields.
The underlying cosmological simulation is the BigMDPL simulation  (Prada et al. 2014). The light-cone construction and ray-tracing through this simulation is done with the GLAMER code  (Metcalf & Petkova 2014; Petkova et al. 2014). Shear maps, shear catalogs and some analysis tools are provided. The BigMDPL is big enough to provide many independent light-cones and its resolution is high enough to resolve the relevant nonlinear structures. GLAMER calculates the light paths, shear and convergence without resorting to the weak lensing approximation or the Born (unperturbed light paths) approximation so that the impact of such approximations can be evaluated. The first fields reported on here are in the shape of the W1 and W4 fields observed in the VIPERS survey  (Guzzo et al. 2014). The paper is organised as follows: in section 2 we describe the cosmological simulations used; in section 3 we present the methodology adopted in constructing the lens density maps as well as our multi-plane ray-tracing code.
THE BIG MULTIDARK SIMULATION In this work we perform full ray-tracing simulations with the matter density distributions extracted from the BigMDPL 1 simulation  (Prada et al. 2014). This simulation has been performed to meet the science requirements of the BOSS galaxy survey, i. e. the numerical requirements for mass and force resolution that allow one to well resolve those haloes 1 http://www. multidark. org, https://www. cosmosim. org/cms/ simulations/multidark-project/bigmdpl and subhaloes that can host typical BOSS massive galaxies at z ∼ 0. 5. This allows for the creation of mock catalogs with appropriate galaxy bias and clustering. The simulation is in a ΛCDM universe comprised of 3840 3 particles in a box of 2. 5 comoving Gpc/h on a side. Initial conditions have been generated at redshift zinit = 100 using GINNUNGAGAP 2  publicly available full MPI-OpenMP initial conditions generator code that uses Zeldovich approximation with an number of particles only limited by the computer resources.
Light-cone geometry For the analyses conducted in this work, we build two different light-cones geometries -many other will be available in our database -one covering a region of 8. 7 × 1. 8 deg 2 and another a region of 5. 5 × 1. 6 deg 2 , that mimic the W1 and W4 fields observed by the VIPERS extragalactic survey  (Guzzo et al. 2014) , respectively. In order to maximise the number of independent light-cone realisations, we remap the 2. 5 3  (Gpc/h) 3 cubical volume of the simulation using BoxRemap 3 (Carlson & White 2010). BoxRemap takes advantage of the periodicity of the simulated box to break  it into cells, which are then translated by integer offsets to form cuboids. The remapping procedure keeps local structures intact. We adjust the parameters in order to maximise the number of light-cones that can be embedded in the comoving simulation volume.
Ray-Tracing through multiple planes We run our ray-tracing code GLAMER on each of the M realisations of N lens planes. The ray-tracing code is run for N source redshifts in the light-cone, i. e. those corresponding to the boundaries between 2 consecutive snapshots zsi = Di+1. In Fig. 4  we show the number of planes through which we perform multi-plane ray-tracing as a function of the source redshift. The vertical dashed lines show zs = 0. 5, 1 and 2. 3, that we will be considered as reference in presenting some lensing measurements in the following sections. The particle distributions from the simulation snapshots are projected onto different matter density planes; these are given as input to GLAMER  (Metcalf & Petkova 2014; Petkova et al. 2014)  to trace the light-rays from different source redshifts to the observer -although technically the rays are shot the other way around to be sure that all rays intersect the the observer.
CFHTLS source redshift distribution Another important ingredient that needs to be taken into account when producing lensing simulations is the redshift distribution of the sources. Here we consider the CFHTLens source redshift distribution  (Hildebrandt et al. 2012 ) as computed within the W1 and W4 fields. In Fig. 6  we show with the dashed curve the redshift distribution of the sources observed in the two fields by CHFTLens, for comparison the solid histogram displays the distribution extracted from one realisation of the W1 field. Since the impact of the source galaxy clustering on lensing statistics is beyond the purpose of this first work, the location of the sources within the field of view is performed randomly. The tool for extracting the lensing catalogue with a desired source redshift distribution and a generated light-cone is also available in our database.
ACKNOWLEDGEMENTS The BigMDPL simulation has been performed on the Super-MUC supercomputer at the  Leibniz-Rechenzentrum (LRZ) in Munich , using the computing resources awarded to the  PRACE  project number 2012060963. CG thanks  CNES  for financial support. CG and RBM's research is part of the project  GLENCO , funded under the  European Seventh Framework Programme ,  Ideas , Grant Agreement n. 259349. GY acknowledges support from  MINECO (Spain)  under research grants  AYA2012-31101  and  FPA2012-34694  and  Consolider Ingenio SyeC   CSD2007-0050  CG, EJ and Sdlt acknowledge the support of the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French government program  managed by the  ANR. We thank the  Red Española de Supercomputación  for granting us computing time in the Marenostrum Supercomputer at the  BSC-CNS  where part of the analyses presented in this paper have been performed.
Robustness of phenotypes We have carefully checked that our K-means clustering results are robust. Lacking the 'ground truth' behind our data in terms of different types of individual behaviors, the significance and robustness of our clustering analysis must be tested by checking its dependence on the dataset itself. We studied this issue in several complementary manners. First, we applied the same algorithm to a randomized version of our dataset (preserving the total number of cooperative actions in the population, but destroying any correlation among the actions of any given subject), showing no significant clustering structure at all (see SI Appendix, Sec. S. 4. 7 for details). Second, we ran the K-means clustering algorithm on portions of the original data with the so-called 'leave-p-out' procedure  (36).
Materials and Methods The experiment was conducted as a lab-in-the-field one, i. e. , to avoid restricting ourselves to the typical samples of university undergraduate students, we took our lab to a festival in Barcelona and recruited subjects from the general audience there  (28). This setup allows, at the very least, to obtain results from a very wide age range, as was the case in a previous study where it was found that teenagers behave differently  (55). All participants in the experiment signed an in-formed consent to participate. In agreement with the Spanish Law for Personal Data Protection, no association was ever made between their real names and the results. This procedure was checked and approved by the Viceprovost of Research of Universidad Carlos III de Madrid, the institution funding the experiment.
In every session, all individuals played a different, randomly picked number of rounds between 13 and 18. In each round of a session each participant was randomly assigned a different opponent and a payoff matrix corresponding to a different (T, S) point among our 11 × 11 different games. Couples and payoff matrices were randomized in each new round, and players did not know the identity of their opponents. In case there was an odd number of players, or a given player was non-responsive, the experimental software took over and made the game decision for her, labeling its corresponding data accordingly to discard it in the analysis (143 actions). When the action was actually carried out by the software, the stipulation was that it repeated the previous choice of C or D with an 80% probability.
Nonetheless, we did not instruct them to play in any particular way nor with any one particular goal in mind. In Fig. 6  we show the tutorial screens. After a player had read the tutorial, she pressed a button to indicate the system that she was ready to start playing. Once everyone was ready, the game administrator started the game. Each game session was carried out for a random number of rounds, between 13 and 18. The players did not know the total number of rounds they were going to play. For each round, subjects were randomly assigned different opponents, and nobody knew who they were playing against. In each round of the game, the players had 40 seconds to make their action choice. If they did not choose anything, a random choice was generated by the system (and saved in our database, properly labeled to be discarded in the analysis).
S. 4. 6. Response times We have also examined the response times of the individuals in our experiment, separating the data by cooperation/defection actions, and as a function of the round number. Fig. 17  shows that the average response time is around 15 seconds. We did not find any dependence with the round number nor with the type of action. Finally, Fig. 18  displays the distributions of response times for all individuals, for each of the two possible actions.
S. 4. 7. Clustering Analysis We hypothesized that there are distinct, well-defined types of individuals (or phenotypes) in our dataset, that can be told apart by using an unsupervised clustering algorithm. Hence, we run a K-means clustering algorithm on our data (using the Scikit-learn Python package) to analyze its clustering structure. We represent each participant in the dataset by a four-dimensional vector, corresponding to her average fraction of cooperative actions in each one of the four dyadic games (Prisoner's Dilemma, Stag Hunt, Snowdrift and Harmony). The K-means unsupervised clustering algorithm groups the data into a user-defined number of clusters, by both minimizing the dispersion within each cluster and maximizing the distance between the centroids of each pair of clusters.