dataesr/scientific-paragraphs-categorization huggingface dataset:
In our view, this dilemma calls for a careful examination of faunal and technological data side by side, beginning with individual sites with sufficiently rich and well-dated assemblages, and eventually combining these datasets into diachronic and regional comparisons. The Gravettian lithic record is characterised by the presence of distinct artefacts with morphologies suggestive of projectile function, including tanged points, Gravette and microgravette points, and fléchettes. Projectile use has been confirmed for some of them through usewear studies relying on tailored experimental reference material  [14, 57, 58] , but other functional analyses have demonstrated that not all pointed objects functioned as armatures  [59] [60] [61] [62]. Despite the fact that detailed use-wear data is often only marginally available and the direct archaeological evidence of spearthrowers and bows still postdates the Gravettian  [63, 64] , the new lithic point types that appear during this period have repeatedly been associated with the introduction of new kinds of weaponry and characterised as 'improvements' to hunting equipment  [65].
The material studied here belongs to the collections of Museum national d'Histoire naturelle (France) and is held at the laboratory of Abri Pataud (Les Eyzies-de-Tayac-Sireuil). The artefact identification numbers (format AP[Abri Pataud]/XX[excavation year]-X[level]-XXX[level-specific ID]) used here correspond to the existing find catalogues and databases. No permits were required for the described study, which complied with all relevant regulations. While Movius's excavation techniques were advanced for their time  [75] , the recent excavations of  Level 2 in 2004 Level 2 in -2009  have demonstrated that only fine sieving can recover the smallest size groups of backed pieces. The new assemblage is much more heavily dominated by backed bladelets than the older collection, with microlithic elements representing more than 70% of the retouched tools as opposed to the much more moderate c.
Screening and preliminary analysis The entire backed tool collection was first screened with the help of a stereomicroscope and selective use of a metallographic microscope, and basic data were recorded to aid the formulation of research questions and sampling of the material for further analysis. The artefacts were classified by morphological group and preliminary functional category. The recording differed somewhat between the analysed assemblages: for Level 3, a piece-to-piece recording was done for a large background sample (n = 329) to investigate potential causes of fracturing on a preliminary level, whereas for Level 2, the screened material (n = 693) was classified into preliminary functional categories without recording observations for individual artefacts. The Level 3 artefacts were initially classified according to two criteria, typo-technological group (on the most basic level, different size groups of Gravette and microgravette points versus truncated backed bladelets) and break characteristics.
The fragmentary nature of most pieces and the variability in overall design made straightforward grouping by techno-morphological criteria alone quite difficult. Measurement data on a sample (n = 113) were used to create three subcategories (size groups) to aid further comparisons. Each break in the background sample (n = 329) was briefly examined and classified as a potential production, impact, or taphonomic break, with non-characteristic breaks such as snaps marked as 'indeterminate', to gain a first view of the processes responsible for the assemblage consisting mostly of fragmentary artefacts. In contrast, the Level 2 collection that is largely made up of medial fragments, many of which could be expected to represent intentionally snapped pieces  [101, 103] , was classified according to the qualities of edge damage as well as breaks to distinguish between used and unused objects and to evaluate the proportion of armatures in the collection.
Data collection. The detailed low magnification analysis involved recording the attributes for each relevant macroscopic fracture individually following the approach developed at TraceoLab  [8, 14]. It builds on the attribute system created in the 1970s for the description of use-related fracturing on brittle solids  [106]. The system used in the present study was somewhat simplified from that used by Coppe  [8, 14]. Break and scar attributes (S2 Appendix) were recorded on Excel sheets so that each feature formed a single row and the wear pattern on each artefact typically became described by several rows of data. For artefacts with evidence suggestive of impact, all breaks were recorded despite the fact that particularly in the case of Level 2, many of them are probably production-related.
While this resulted in the recording of features irrelevant for use interpretations, it was considered a safer approach than attempting to filter out production breaks during analysis given that at the moment no simple criteria exist for doing this (see below). Simultaneously to the attribute recording, basic morphological and technological data (see S2 Appendix) were registered for each artefact on a separate Excel sheet to allow judging the effect of size, morphology, and location of retouch on fracture formation afterwards when relevant. The high magnification observations of functional wear were written down as verbal descriptions, and a cross-link was made to associated low magnification features. The severity of post-depositional alterations (scarring, rounding, patina, polish, bright spots, striations, polish erosion) was noted on a scale from 0 to 3 for each category individually, and presence of recent markings or additives such as metal traces, pencil marks, and refitting glue was also noted to aid the critical evaluation of high magnification use-wear observations.
4. 4. 3 Analytical equipment and protocols. The main part of the use-wear analysis was done in the laboratory of Abri Pataud. The analysed artefacts had been previously washed, and basic cleaning using cotton and ethanol and/or acetone was sufficient to remove adhering contaminants (e. g. handling grease). A Nikon SMZ-1 stereomicroscope (magnifications between 6. 6× and 40×) was used for low magnification analysis and a Nikon Optiphot metallographic microscope (magnifications between 50× and 400×) for high magnification observation. The photographic documentation of the traces was done with a Nikon D7200 digital camera mounted on the Optiphot with an adapter (NDPL-1(2X)). The images were captured with the software Nikon Camera Control Pro and z-stacked in Helicon Focus Lite. Most of the low magnification photos were taken directly with the Nikon D7200 and a macro lens (Nikon AF-S Micro NIKKOR 40mm) and stacked in Helicon Focus.
The experimental material is hosted at TraceoLab (University of Liège) and was analysed using the microscopes available there, including a Zeiss stereomicroscope Discovery V12 (magnifications between 8× and 100×, oblique lighting) and a Zeiss reflected light microscope AxioImager (magnifications between 50× and 1000×). The photographs were taken under high magnification using Zeiss AxioVision software for both image capture and z-stacking. Low magnification images were taken with a Zeiss Macro-Zoom Macroscope V16 using oblique lighting. The AxioVision software was employed for image capture and the images were stacked in Helicon Focus Pro.
When a piece only showed lateral scarring, at least two of the attributes mentioned above or MLITs in direct association with the scarring had to be present to warrant a secure projectile identification. While we consider our identification criteria conservative enough to ensure reliable functional inferences, the method has to be formalised and tested with additional experimental datasets. This work is currently in progress  [8, 14] , and the approach used here can be considered an application of the new method in its preliminary stage. At the core of this approach is the detailed recording of fracture frequencies and attributes (see above), which has the benefit of forcing the analyst to employ systematic and detailed recording of use-wear attributes instead of the use of somewhat opaque categories of 'diagnostic' features  [8].
Level 2. The low and high magnification samples from Level 2 overlap to some extent: of the 66 pieces for which damage was recorded in detail, 25 became analysed under high magnification. The remaining 16 pieces in the high magnification sample mostly represent artefacts with subtle forms of microwear, including pieces with minor lateral scarring (n = 11) or no scarring at all (n = 1). These were selected in an attempt to detect possible nonprojectile use in the assemblage. Substantial edge damage is represented by three pieces in addition to which a single piece with a suspected impact break was included. The sample shows a bias towards probable projectile armatures, and is therefore not used for drawing conclusions of the functional variability of the entire assemblage. The sample for a detailed recording of break and edge damage data (low magnification sample) was selected by including tools mainly from the three main screening categories that were judged to represent use-wear (categories 1-3 in S1 Appendix).
Tips or lateral inserts? The detailed recording of impact break and scar attributes allows comparing patterns between the samples and pushing the analysis one step beyond the identification of armatures. The  recorded data is used here to reconstruct the position of the armatures in their shafts (distal versus lateral) to address the question of overall weapon design in the Recent and Final Gravettian of Abri Pataud. Due to their suggestive morphology, Gravette and microgravette points have been commonly perceived as weapon tips, and experiments have confirmed their efficacy when hafted this way  [8, 57, 58, 121]  although lateral hafting of microgravettes has sometimes also been hypothesised  [68, 69, 112]  At Pataud, the data presented above allows confirming that laterally hafted armature elements are present in both Level 3 and Level 2 collections.
The Level 3 sample was divided into broad morphometric categories employed in the study to be able to detect variability between them. If the Level 3 truncated backed piece sample that is too small for the percentages to be reliable is ignored, the Level 2 sample shows the heaviest dominance of lateral removals, consistent with the impression gained early in the analysis. This sample is the one most likely to show a mix of production breaks (intentional snapping) and impact damage in the bending break category. The relative frequency of lateral scarring may therefore be even higher within these groups than the graph indicates. Since production-related breaks are not limited to snaps and can also show secondary scarring (see above), there was no straightforward way to filter out the suspected non-impact breaks from the dataset without risking errors.
Determining the dominant variables responsible for the patterns observed here requires further experimentation. Until this reference data becomes available, other lines of evidence are required to argue for particular projectile point designs in the present assemblage. 5. 4. 2 Bending break initiation and other characteristics. In addition to the often invasive lateral scarring, there are other features that seem to speak for the interpretation that laterally hafted armatures are well-represented in the Level 2 collection. Among them are bending-initiated breaks that start from a surface and have an oblique outline, removing the corner between the cutting edge and the extremity of the armature. It seems logical that this kind of a break would occur if the backed edge would be firmly glued to the shaft, allowing the bending forces to mostly act on the side of the cutting edge on impact.
These features have previously been overlooked at the expense of "diagnostic" breaks  [8]. Here, the reliance on combinations of features instead of individual breaks produced a high-quality dataset on securely identified projectiles that could subsequently be used for reconstructing armature hafting. Our study also raised questions regarding the usefulness of measurement data in projectile identification. Two points can be made here. First, the experiment where backed bladelets were intentionally snapped by bending produced secondary scars ("spin-offs") that are sometimes longer than the lower limit proposed for secondary scars diagnostic of projectile use https://doi. org/10. 1371/journal. pone. 0262185. g024 [107]. Second, when a broader range of features was examined, impact fracture length appeared to be determined more by armature morphology and the location of fracture termination than by the dimensions of the armature, which undermines the significance of establishing size limits for "diagnostic" impact fractures based on armature size as has been proposed in some earlier studies  [107, 110, 111, 127, 128].
Weapon design and hunting strategies in the Recent and Final Gravettian at Abri Pataud The functional data on lithic armatures from Level 3 and Level 2 at Pataud indicate that the Recent Gravettian assemblage represents both distally hafted weapon tips (Gravette and microgravette points) and lateral inserts (at least truncated backed pieces and nanogravettes) that once belonged to composite points. The Final Gravettian collection in turn consists dominantly, if not exclusively, of laterally hafted armatures, i. e. composite point inserts. The sixteen Level 3 artefacts with the strongest evidence of lateral hafting, namely truncated backed pieces with various signs of impact (n = 12) and nanogravettes with heavy lateral scarring often associated with minor breaks (n = 4) come from the upper and middle parts of the sequence, that is, from Lens 1 (n = 1), Eboulis b (n = 5), Lens 2 (n = 6), Lens 2 Main (n = 3), and Lens 2b (n = 1).
The effect of coat of hair has been mentioned as potentially relevant for projectile performance by some investigators  [135]  whereas others have seen it as of little importance unless the target is hit at a clearly oblique angle  [136]. Frison's comment concerned Clovis points, which can be considered sturdier than the Gravettes and particularly microgravettes in the Pataud Level 3 assemblage, and further data would be needed to evaluate the effect of this variable in the particular case of Pataud. All these aspects put together, the technological change from Level 3 to Level 2 can be explained by a shift in hunting tactics, linked to seasonality and social organisation, and/or by new demands posed on the weaponry by the altered environmental conditions and possible shifts in prey choice [for similar reasoning, see 112].
Conclusions Our detailed use-wear analysis of the backed tools from Movius's excavations at Abri Pataud allowed establishing the presence of composite points in both Level 3 (c. 24,000 BP) and Level 2 (c. 22,000 BP) assemblages. On the side of this design, weapon tips (Gravette and microgravette points) that were probably hafted axially rather than obliquely are abundantly present in the Level 3 material, but are either marginal or entirely lacking in Level 2. Whether the Level 3 collection represents adoption of composite point designs over time remains an open question until more data becomes available for the time frame represented by the oldest occupations. Even if Level 3 would show a chronological trend, composite points only become dominant in Level 2. Factors that may have encouraged the use of such points during this younger settlement phase include winter hunting conditions where individual reindeer were targeted, potentially by a limited number of hunters  [34] , which may have put more pressure on the performance of an individual hunter and a weapon as opposed to hunting by large groups of people during migration season.
Acknowledgments We express our gratitude to  Hugues Plisson  ( University of Bordeaux, CNRS ) who kindly provided us with the equipment that enabled the microscopic analysis of the archaeological material in Les Eyzies. We thank  Christian Lepers  and  Justin Coppe  ( TraceoLab, University of Liège ) for their involvement in the experiments that produced the reference material used in this study as well as for their insights that aided this research. We are also grateful to  Olivier Touze  ´and  Damien Flas  ( University of Liège ) for their comments on an earlier version of this manuscript and to  Chris Scott  ( University of Liverpool ) for his evaluation of our English.
This study was funded by the  European Research Council  under the European Union's  Seventh Framework Program  ( FP7/2007-2013 ,  ERC  Grant Agreement No. 312283 ,  EVO-HAFT, VR ) and by the  Kone Foundation  (Grant No. 088817 , NT) and published with the support of the  University Foundation of Belgium  and the  University of Liège. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgements Aaron D. Lightner  and  Edward H. Hagen  acknowledge financial support from the  Issachar Fund  and the  Templeton Religion Trust. Zachary H. Garfield acknowledges IAST funding from the  French National Research Agency (ANR)  under the Investments for the  Future (Investissements d'Avenir) program , grant  ANR-17-EURE-0010. Partial funding by  National Science Foundation Division of Behavioral and Cognitive Sciences  awards # 1628509  and # 1823324.
Introduction Nowadays, multi-criteria analysis and decision making become more and more complex due to conflicting criteria and query complexity. Skyline operator  [3] , known as Maxima in computational geometry or Pareto in the business management field, manages this complexity using Pareto dominance. It extracts interesting objects from a dataset by respecting user preferences. It is particularly very successful in the database field, it has undergone an exponential interest due to the benefit that can be derived from it, even in real contexts. Skyline queries return then the most interesting points based on Pareto dominance relationship defined as follows: let a and b be two points (or database tuples) with the same number of attributes (also called dimensions), a dominates b, noted: (a ≺ b), if a is as good as b in all dimensions and better than b in at least one dimension.
If neither a ≺ b and nor b ≺ a, then a and b are incomparable. Many propositions and research works were published to study the skyline semantics and optimize its computation. Efficient algorithms were proposed to retrieve objects that present the optimal combination of the dataset characteristics  [3, 5, 10, 16, 24, 26, 30, 31]. Recently, the skyline definition turns out to be poor to deal with new complex real-world decision applications to answer different queries that require choosing a group of objects rather than individual objects of a dataset. Let us consider an example where a user wants to get the best Volleyball teams from a set of players, the traditional skyline returns the best players but not the best combinations to create a team that can not be dominated by any other existing team generated from the set of players.
Another similar example consists of choosing a group of experts to review and evaluate papers based on the experts collective strength on multiple desired skills. This leads to the concept of Group Skyline 3  [14, 38] , noted G-SKY, which is very important and useful in many other domains like: groups recommendation, investments selection, detection of fire/crime (most dangerous places), etc. This novel concept has created new issues to the skyline community, for instance, generating groups and returning the appropriate skyline groups became the most challenging problems. Some solutions to these issues have been proposed in the literature  [32, 33]. However, querying a d-dimensional dataset using group skyline queries may lead to two particular scenarios: (i) a large number of skyline groups returned, which could be less informative for decision makers, (ii) a small number of skyline groups returned, which could be insufficient for decision makers.
To solve the first problem (i), various approaches [15,  29, 40, 43]  are proposed to refine the group skyline, therefore reducing its size, but none of the existing work has addressed the problem (ii) to relax the group skyline in order to increase the number of group skyline results and thus satisfy better the decision makers needs. Consider the following problem of finding the skyline l-groups (where l indicates the number of elements in each group from an n-tuple dataset): a decision maker (the trainer) wants to get the 5 best groups of 3 players by maximizing points and the number of blocks, when we run his/her query the system returns 2 groups of 3 Volleyball players each. Unfortunately, this answer does not satisfy the decision maker, he/she needs 5 teams of the best Beach-volleyball players but the traditional group skyline definition can return only 2 groups.
The nature of the relation MP makes it more demanding the dominance between groups of the target dataset. In this context, a group still belong to the group skyline unless it is much dominated, in the spirit of the MP relation, by another skyline group. By this way, many groups are considered as incomparable and then as elements of the new relaxed group skyline (noted RG-SKY). Note that using the traditional group skyline definition such groups are pruned from the skyline groups. Furthermore, two algorithms (naive and optimized versions) with different cases to compute RG-SKY efficiently are provided. We also develop a set of ex-periments on real, synthetic and generated data to study and analyze the relevance and effectiveness of our proposal. The remainder of the paper is organized as follows.
Group skyline returns groups that are not dominated by any other group in the Dataset. The dominance relationship between groups can be formulated in two different ways as follows: -The first formulation relies on Pareto dominance and an aggregate function conveniently chosen. The dominance between groups is named G-Skyline. -The second semantics introduces a particular generalized dominance relation applied on permutations of groups to be compared. This type of dominance is named G-Dominance. This leads thus to the two following formal definitions  [43]  (where g and g ′ are two groups with the same size): Definition 1. (G-Skyline). Let F be an aggregate function and g = {Q 1 , Q 2 ,. , Q l } (resp. g' = {Q ′ 1 , Q ′ 2 ,. , Q ′ l }) be a group represented by a point Q (resp. Q') with Q = F(Q 1 , Q 2 ,.
, Q l ) (resp. Q' = F(Q ′ 1 , Q ′ 2 ,. , Q ′ l ) ). For two distinct groups g and g', g dominates g' (denoted g ≺ gs g') if Q dominates Q' (Q ≺ Q') in Pareto sense. Definition 2. (G-Dominance). Let us consider the two previous groups g and g', g Gdominates g' (denoted g ≺ gd g'), if two permutations of l points can be found for g and g', g = {Q u1 , Q u2 ,. , Q ul } and g' = {Q ′ v1 , Q ′ v2 ,. , Q ′ vl }, such that Q ui ⪯ Q ′ vi 5 for all i (1 ≤ i ≤ l) and Q ui ≺ Q ′ vi for at least one i. The group skyline of a dataset D, denoted G-Sky(D), is a set of groups that are not dominated by any other group of D in the sense of definition 1 or 2. Formally, we write: G -SKY (D) = {g ∈ G | ∄g ′ ∈ G : g ′ ⋉ g} (3) where ⋉ stands for the relation ≺ gs or ≺ gd. Players points rebounds p1 3 3 p2 0 4 p3 4 1 p4 2 3 p5 2 2 p6 2 1 ✗ g3 p1(3,3) -p4(2,3) G3(3,3) ✗ g4 p1(3,3) -p5(2,2) G4(3,3) ✗ g5 p1(3,3) -p6(2,1) G5(3,3) ✗ g6 p2(0,4) -p3(4,1) G6(4,4) ✓ g7 p2(0,4) -p4(2,3) G7(2,4) ✗ g8 p2(0,4) -p5(2,2) G8(2,4) ✗ g9 p2(0,4) -p6(2,1) G9(2,4) ✗ g10 p3(4,1) -p4(2,3) G10(4,3) ✗ g11 p3(4,1) -p5(2,2) G11(4,2) ✗ g12 p3(4,1) -p6(2,1) G12(4,1) ✗ g13 p4(2,3) -p5(2,2) G13(2,3) ✗ g14 p4(2,3) -p6(2,1) G14(2,3) ✗ g15 p5(2,2) -p6(2,1) G15(2,2) ✗ ✓ Yes ✗ No Q j ⪯ Q ′ j means that Q j ≺ Q ′ j or Q j ∼ Q ′ j where ∼ stands for the indifference relation (i.