dataesr/scientific-paragraphs-categorization huggingface dataset:
Namely, if M P (γi1,γi2) Gi (g(i), g ′ (i)) > 0 then M P (γi1,γi2) Gi (g ′ (i), g(i)) = 0. Proposition 3. (Min-Transitivity) Let g, g' and g" be three groups of G, If (g, g ′ ) ∈ M P γ G and (g ′ , g") ∈ M P γ ′ G then (g, g") ∈ M P γ+γ ′ G. Proof. Proposition 3 can writes also in the form: If M P γ G (g, g ′ ) > 0 and M P γ ′ G (g ′ , g") > 0 then M P γ+γ ′ G (g, g") > 0. Now, due to the transitivity property of the fuzzy preferences  [28]  and to the fuzzy addition formula  [9] , one can write: Proof. To show that this Proposition is not always true, it suffices to exhibit a counterexample. Consider a 2-dimensional (points, rebounds) dataset of 4 players: p 1 = (0,4), p 2 = (1,2), p 3 = (2,1), and p 4 = (4, 0). It is easy to see that SKY = {p 1 , p 2 , p 3 , p 4 }. Let g = {p 1 , p 4 } (⊂ SKY).
One can check that g / ∈ RG-SKY for γ = ((0, 1), (0, 1)). Proof. Let us also find a counterexample. Consider a 2-dimensional (points, rebounds) dataset of 3 players: p 1 = (0, 2), p 2 = (1, 0), and p 3 = (2,1), we have SKY={p 3 } and G-SKY={{p 1 , p 3 }} (i. e. contains 1 group of 2 points). For γ = ((0, 1), (0, 1)), one can check that {p 2 , p 3 } ∈ RG -SKY while {p 2 , p 3 } ̸ ⊂ SKY. min(M P (γi1,γi2) Gi (g(i), g ′ (i)), M P (γ ′ i1 ,γ ′ i2 ) Gi (g ′ (i), g"(i))) ≤ M P (γi1+γ ′ i1 ,γi2+γ ′ i2 ) Gi (g(i), g"(i)), ∀i ∈ {1,. , d}.
In this comparative study, we have considered only the well-known algorithms in the skyline field. For a complete overview on skyline algorithms, see for instance  [24, 26]. SaLSa algorithm used in the traditional skyline query extraction, is an improvement of SFS and LESS (see Table  6 ). It strives to avoid scanning the entire sorted dataset as opposed to the previous propositions, it is the first algorithm that exploits the values of a monotonic notation (limitation) function to sort the data set to read and compare. SaLSa differs from the other generic algorithms because it consistently limits the number of points read and the dominance tests. The design of SaLSa is based on two key concepts: First, a sorting step of the input data and, second, suitably choose a sorting function that does not privilege any attribute over the others (the function does not influence the correctness of SaLSa but only its performance).
For these reasons, we adapt the Salsa algorithm to the group skyline problem and to optimize also the relaxation process of the RG-SKY approach. During the filter phase, the algorithm reads and examines the rest of the groups. Each time a new group is read, it is compared to the current skyline group list. If a group dominates the current group, it is ignored, otherwise it is inserted to the relaxed skyline groups list (as a final relaxed group skyline) and the algorithm checks its termination trigger (Pstop). If the current threshold Pstop is less than or equal to the fmin value of the point, the algorithm ends and returns the entire skyline RG-SKY group list. This termination condition ensures that no data groups examined later should be part of the RG-SKY list, thereby the algorithm avoids analyzing the entire rest of the dataset.
Experimental environment The algorithms are tested on a Dell Inc Machine, System Model: Precision T1650 and run in a Windows 10 Education 64-bit (10. 0, Build 16299) environment, using a 3. 4GHz Intel(R) Core(TM) i7-3770 CPU @ 3. 40GHz(8 CPUs) with a main memory of 8GB RAM, in sequential mode (1 thread) and a 500 GB of disk. Dataset benchmark is generated using the method described in  [3]  following three conventional distribution schema (correlated, anti-correlated and independent) and also "randataset". The approach was developed in Eclipse Modeling Tools Version: Oxygen. 3 Release (4. 7. 3), using Java. v9 language.
Experimental tests The tests can be classified into two main parts: Real Data (NBA, HOUSE, WEATHER) and Synthetic data where we change the data type and size, the groups and tuples dimensions and finally generate data for the purpose of the relaxation parameter vector tests (The choice a very low values can motivate our approach and prove the utility of getting results even if the user-defined values are small). Existing skyline researchers  [14, 38]  use the data generator proposed in  [3]  with standard parameters so that results can be directly compared. As our work targets the same kind of data, we propose to follow the same methodology to evaluate our proposal. The data generator in Borzsonyi et al. (2001)  generates database tuples (or points) with varying numbers of dimensions (or attributes).
Tuples are generated using one of the following three value distributions: -correlated (corr): tuples, which are good in one dimension, tend to be good in other dimensions, too; -anti-correlated (anti): tuples, which are good in one dimension, are bad in at least one other dimension; -independent (indep): tuples are generated using a uniform distribution. We also include three real datasets (see Table  8  for the specification of those datasets) that are commonly used to evaluate skyline algorithms  [4] : NBA (statistics of basketball players during regular seasons), HOUSE (money spent in one year by an American family for six different types of expenditures) and WEATHER (average monthly precipitation totals and elevation at over half a million sensor locations). Finally, a last test is done on generated real data using Skyline generator (randdataset).
We summarize our input values Name Indexed Limits and issues Index  [30]  ✓ -Does not support user-defined preferences (the order of the returned points is fixed and depends on the distribution of the data values) Bitmap  [30]  ✓ -The memory consumption limit due to the conversion of points to Bitmap structure -Bitmap also handles inefficiently updates because it implies the recalculation of all the bit vectors t -Does not work on several dimensions (expensive) -Does not allow the user to express preferences -Mandatory to code all the point values NN  [16, 31]  ✓ -Performance problem, in case, we have a single element, the algorithm continue the division to 4 regions whereas the program can check in advance the number of the existing points -It is not efficient if the data is not mass BBS  [25, 27, 31]  ✓ -Does not work when dimensions exceed 5 BNL  [3, 31]  ✗ -Requires a lot of iterations before the final skyline is calculated (it analyzes all the data) -It has a limit on the size of the window, the complexity of the algorithm depends on this size -A non-negligible comparison time is necessary -The skyline points are not defined progressively (they change) D&C  [3]  ✗ -The skyline points are not defined progressively -Problem if the data is so small (the process becomes useless), it is more efficient when dealing with a large amount of data -The algorithm scans the entire database SFS  [5]  ✗ -Scans all data -The necessity to define a good monotonous function LESS  [10]  ✗ -Elimination-filter (EF) mechanism can become full -All the data must be scanned at least once    5.
Experimental results Case 1: Real Data Figure  6  shows the number of relaxed skyline groups in a different data type and the execution time of the RG-SKY approach using the adapted aggregation function RG-SKY provides more groups comparing to the set G-SKY. One can observe that for the three datasets (NBA, WEATHER, HOUSE) G-SKY contains only one group. The execution time depicted in Figure  6  and resulting from Algorithm 1 is not similar for the three datasets due to their different correlations and sizes, while this time is similar for the (correlated) NBA dataset. On the other hand, one can observe that independent and anti-correlated data are time consuming compared to the correlated data. We note also that our RG-SKY Salsa algorithm (Algorithm 2) is equal or less time consuming compared to the existing G-SKY naive algorithm (Except for anti-correlated data).
Background Traditional Skyline Queries Let D = (D 1 , D 2 ,. , D d ) be a set of d-dimensional data points (that corresponds to a set of database tuples). We define a relation R(A 1 , A 2 ,. , A d ) in D and we assume the existence of a total order relation on each domain D i. The traditional skyline query is based on Pareto dominance relationship defined as follows. Let a and b be two different points in D, a dominates (in Pareto sense) b, denoted by a ≺ b, if for all i, a[i] ≤ b[i], and for at least one i, a[i] < b[i], where a[i] (Resp. b[i]) is the value of the point a (Resp. b) for the attribute A i and 1 ≤ i ≤ d. Formally, a ≺ b ⇔ ∀i ∈ {1,. , d} : a[i] ≤ b[i] and ∃j ∈ {1,. , d} : a[j] < b[j].
2. 1. 1 Background Traditional Skyline Queries Let D = (D 1 , D 2 ,. , D d ) be a set of d-dimensional data points (that corresponds to a set of database tuples). We define a relation R(A 1 , A 2 ,. , A d ) in D and we assume the existence of a total order relation on each domain D i. The traditional skyline query is based on Pareto dominance relationship defined as follows. Let a and b be two different points in D, a dominates (in Pareto sense) b, denoted by a ≺ b, if for all i, a[i] ≤ b[i], and for at least one i, a[i] < b[i], where a[i] (Resp. b[i]) is the value of the point a (Resp. b) for the attribute A i and 1 ≤ i ≤ d. Formally, a ≺ b ⇔ ∀i ∈ {1,. , d} : a[i] ≤ b[i] and ∃j ∈ {1,. , d} : a[j] < b[j].
Analysis. Our dataset was not originally designed for a multifactorial approach to testing the hypotheses presented. For example, some questions in the present dataset were only presented to subset of respondents who answered a particular way on a previous question. We could not therefore form indices by combining related variables because not all respondents replied to all questions, which would distort the indices due to a high number of "no data" entries. The questions are also not on a Likert scale, which forces us to adopt a categorical rather than a parametric or linear modelling statistical approach. Nevertheless, the interpretation of the tacit models of human behaviour is not intended to be dependent on a speci c validated survey instrument, but should be interpretable from a variety of forms of data.
We thus identi ed variables in the dataset that correspond to each of the key elements as shown in Table  1  (see Table  2 ). The variable expected to indicate belonging in the value-led actors cluster (or to have more extreme responses consistent with this tacit model), landowning and livestock owning, consists of rankings of importance in terms of income for each respondent. As all respondents answered the question about sources of income, we were able to form an index of landowning and livestock owning or "LLO" from these two variables. These two variables were combined to form a class for which land and livestock were rst and second (in either order) (LLO3), an intermediate class in which only one was rst or second (LLO2), and a nal class in which neither was the rst or second source of income (LLO1).
We then set up χ 2 comparison tables across these three classes, for each of the variables representing acceptability, success, and participation. We also separately set up χ 2 comparison tables looking at the inside-outside WCNP difference, considering only its relation to the acceptability variables, in accordance with our predictions (Table  1 ). The survey dataset also included open-ended explanations for the rating given for several of the variables, which, where available, we also consider in terms of the most common types of answers. Interviews. To supplement understanding of policy formation as well as values, attitudes and strategies associated with conservation in Bhutan, interviews were conducted with key stakeholders. We explained the purpose of the project beforehand and obtained the interviewee's consent.
Acknowledgements We are very grateful to the following people and institutions for permissions and support in carrying out this project: The  Royal Government of Bhutan ;  Dr Nawang Norbu ,  Dr Tshering Tempa ,  Wangchuk Dorji  and the staff at  Ugyen Wangchuck Institute of Conservation and Environment Research, Bhutan ;  Dr D. S. Rai  and staff at  Wangchuck Centennial Park ;  Dr Sonam Wangyel Wang  for sharing the initial questionnaire and for guidance on research design;  Phurba Lhendup ,  WWF Bhutan ;  Dr Paul Jepson  and  Dr Shonil Bhagwat ,  School of Geography and Environment, University of Oxford ;  Dr Uri Roll  from  Oxford University Centre  for the Environment for advice on data analysis for an earlier version of the manuscript;  Menlha Wangmo  for cultural.
Though child shared physical custody arrangements after divorce are much more frequent and parents who use it more diverse in many European countries, little is known about their economic consequences for parents. By relaxing family time constraints, does shared custody help divorced mothers return to or stay on work more easily? Since lone mothers are one of the least-employed groups, and they face high unemployment rates, the type of child custody arrangement adopted after divorce is of particular interest for their employability. This article analyses to what extent the type of child custody arrangement affects mothers' labour market patterns after divorce. Using a large sample of divorcees from an exhaustive French administrative income tax database, and taking advantage of the huge territorial discrepancies observed in the proportion of shared custody, we correct for the possible endogeneity of shared custody.
Indeed, as pointed by  Meyer & Carlson, (2014) , public benefits are not always or are only partially adapted to shared custody arrangements. For instance, in France, while family allowances and tax discounts associated with children ("quotient familial") may be shared in case of shared custody since 2007, for housing allowances the law was only voted in 2017 but has been only partially implemented so far. There is no possibility to share childcare subsidies. As recently pointed by  Bernardi and Mortelmans (2021) , the lack of good and representative data on shared custody arrangements is an issue. Though crucial, the individual information on child custody arrangements is often missing or concerns too few cases in usual surveys to study its consequences precisely. That is why we use an administrative and exhaustive income-tax returns database that has been recently made available in France.
After excluding missing data and residents of overseas territories (for whom some contextual data were missing), we obtain a representative sample of 60,700 mothers aged 20 to 55 who were married or "PACSed" (in 2008) and separated in 2009. The huge size of our dataset allows observing a substantial number of parents with shared custody arrangements (9,646), which contrasts with most previous studies using survey samples. The custody arrangement should be reported on the income tax return because it provides some tax refunds for having children. According to the tax administration, shared/joint custody means that the time children spend in each parent's home should be "roughly" equal, with no mention of any periodicity, such as every-other-week, for instance. 5 urthermore, compared to the usual survey data in which incomes are selfreported and subject to imprecise responses, incomes in tax-income datasets are already filled in by the fiscal administration and are thus definitively more reliable.
In case of repartnering, the type of child custody arrangement does not play a role anymore. Although we cannot disentangle time from income mechanisms, we find that mothers of several children and having a young child tend to work more in case of shared custody than in the case of sole custody. This suggests that shared custody may also ease the constraints of child care schedules for divorced mothers. Our study considers activity status one year after divorce. This is a short period for recovery and constitutes a limitation of our data being available only for this short time frame. Nevertheless, we should expect even stronger effects over time. Finding a job and organizing for child care may take some time, especially for mothers who interrupted their careers before divorce. This work calls for further analysis on both long-term consequences and on fathers' economic situation since shared custody may have also labour market consequences for them.
Acknowledgements We thank all the participants of the  LSE CASE Social Exclusion Seminar, Journées du labex OSE, European conference for the Sociological and demographic study of divorce, Al-Pop Conference, European Population Conference and Population Association of America conference  for their comments and suggestions. Financial support from the  French National Research Agency  through the  COMPRES  project. We are also grateful to  Jérôme Accardo ,  Cédric Houdré  and  Stefan Lollivier  for having facilitated the data access and use. This paper presents the authors' views and should not be interpreted as reflecting those of their institutions.
Quantitative and Qualitative Data We chose a hybrid method that utilizes in-depth interviews, case studies and quantitative analysis of company agreements. The comparative quantitative-descriptive analysis of German and French company deals on WTA uses two independent data sets. The data basis for Germany is 587 company accords on WTA from the years 1994 to 2015, which are available from the archives of the Hans Böckler Foundation. The collection of company and service agreements at the Hans Böckler Foundation is not a representative database, but it is unique in Germany. The contracts are made available on a voluntary basis by codetermination bodies and systematically stored in the non-public archive of company agreements. The WTA company accords archived were entirely included in the evaluation in order to obtain a comparable basis to French data.
For France, the data analysis is based on the services of the Ministry of Labour, which centrally archives systematically concluded agreements. In order to gain access to the company agreements, the services of the regional office for work (DIRECCTE) were involved. Their task is to centralize and validate the agreements. For the present research project, long-term access was granted to the digital database containing all accords signed in the national territory since 2006 to the end of 2013. Company agreements signed before 2006 are only sporadically accessible for research purposes. The database contained 390,982 company deals in the survey period. In order to obtain a selection of 1,000 company arrangements since 2006, a method was developed that identified WTA as a sub-item to the keyword 'working time' combined with geographical distribution.
Acknowledgments: We are grateful to  Atiqa Hachimi  and  Jacopo Falchetta  for reading an earlier draft of this paper and making valuable comments that improved its quality. We also want to express our gratitude to the two anonymous reviewers for their insightful comments and corrections. Any remaining errors are, of course, our own. Research funding: This work was supported by  AEI/FEDER ,  UE  ( FFI2017-87533-P ).
We examine the association between preferences and crime while also controlling for other differences across people that can explain criminal behavior. The link between experimental data and administrative records enables us to include an extraordinary rich set of relevant control variables. This includes school performance, area of residence, immigrant status, family size, birth order, parental socioeconomic status, criminal records of parents, and family stress as measured by parental divorce or unemployment. Moreover, when we collected the experimental data on preferences, we also asked about self-control which is known to be a strong predictor of crime  (12) (13) (14). We use this information to control for behavioral factors other than risk, time, and social preferences. We also investigate the relation between preferences and different types of criminal offenses.
The overall As with the other preference measures, we compute the mean giving rate across the 20 choice situations and then use this measure to rank the individual relative to other participants. SI Appendix, Fig. S1  shows a screenshot for one of the choice situations. We also asked individuals about age and gender, which can be verified in the registers, and about their school grade point average (GPA), which is used in a few special cases where grades are not included in the register data. Most importantly, we also included a question about the self-assessed capacity to exercise self-control, which is known to be a strong predictor of crime. On average, participants spent 45 min from start to end. We link the experimental data to administrative records that contain all criminal offenses as well as detailed background information about the individuals.
Data Availability. Our empirical analysis combines experimental data and administrative register data linked together using social security numbers. The project was approved by the Danish Data Protection Agency under Agreement 2015-57-0125-0008 and was also approved by Statistics Denmark and the Internal Review Board at the Department of Economics, University of Copenhagen. Data and programs are stored in a separate directory at Statistics Denmark with project number 704856. The empirical analyses were carried out with the software Stata/MP 16. 1 using the secure internet interface of Statistics Denmark. Individual-level data are subject to the European Union's General Data Protection Regulation. Due to privacy rules, the data may not be transferred to computers outside Statistics Denmark.
Till 2019, volunteers' insects and plants identifications were validated by experts from the OPIE and the MNHN. After this date, a participative validation was made possible: participants can now modify their identification according to other participants' comments. By 2021, more than 470 000 insect pictures were collected by 2484 participants, most of them without entomological or biological knowledge as them have declared during their inscription to the program. Spipoll's database is available at no charge to anyone, amateur or professional for any noncommercial use. It is used by researchers to study various aspects of the pollinating fauna in France  (Deguines et al. 2012; Desaegher et al. 2018; Le Féon et al. 2018; Levé, Baudry, and Bessa-Gomes 2019). These scientific studies are popularized via participants' meetings (three meetings have been organizedtwo in face-toface format and one in virtual format because of the COVID sanitary contextsince 2013), traditional media such as newspapers, magazines, and radio as well as in a Facebook page and in a newsletter presenting the program's news such as meetings, insects' information, etc.
To do so, we performed a Chi-square test on the number of comments per content category among the participant included in the dataset. We then looked for the participants whose comments' contents were significantly deviating from expectation by selecting participants having at least one standardized residual superior to 2 or inferior to -2. Such values indicate that content categories are over or under-represented in its comments, respectively. It is important to note that participants' agreed researchers to use information entered to Spipoll platform, and that all data exported from the platform was anonymized in order to respect the French law on data privacy.