dataesr/scientific-paragraphs-categorization huggingface dataset:
In these Zscores the -1. 0 to +1. 0 interval comprises the 95% of the variation in the reference sample. Additional statistical tests were performed with SPSS software (v. 20, IBM Corp. ). The normal distribution was assessed using the Shapiro-Wilk Test. Since normality was not assumed in any of our samples, we used the non-parametric Mann-Whitney U-test for comparisons between groups represented by four or more specimens (as in Smith et al. ,  [29] ). Means were determined to be significantly different at the 0. 05 level. In addition, standard box and whisker plots were computed to represent three set of variables of linear and volume measurements (including 2D and 3D (complete cap and lateral) AET, RET and percentage of dentine) of the complete sample. Upper molars: TD6: H. antecessor from Gran Dolina  [41].
HER: H. erectus  [45]. SH: Sima de los Huesos (original data). NEA: Neanderthals from various sites  [25, 28, 50]. MH: modern humans  [25, 41 and original data]. Lower molars: TD6: H. antecessor from Gran Dolina  [41]. NAH: North African Homo (Tf: Tighenif  [46] ). EAH: East African Homo (MA93: Buia  [52] ). SH: Sima de los Huesos (original data). HER: H. erectus  [45]. NEA: Neanderthals  [25, 28]  Additionally, and in order to visualize the enamel thickness topographic distribution in SH molar crowns we generated the chromatic maps using the surface distance module (SDM) in Amira (6. 3. 0, FEI Inc. ). We specifically generated the chromatic maps in molars with a wear score of 2 or lower following Molnar's classification  [43]  The SDM module computes several different distance measures between two triangulated surfaces.
For each vertex of one surface it computes the closest point on the other surface (Amira 6. 3. 0, FEI Inc. ). We generated the OES and EDJ surfaces independently and connected the OES and EDJ surfaces through the SDM module. The computed distances between the OES and the ESJ are defined by a chromatic scale from thinnest (blue) to thickest (red)  [28, 66]. For comparative purposes, we also generated the chromatic maps of a selected sample of specimens of European and African origin, including: Neanderthal from Abri Suard (S14-7 M 1 and S43 M 3 ) and Krapina (D10-M 2 , D96-M 2 and M 3 ); La Quina-H18 (M 1 ) modern humans (all molar classes); Eritrea (M 1 ) and Tighenif (lower M 2 and M 3 ).
Acknowledgments The authors acknowledge all members of the  Atapuerca Research Team  for their dedication and effort. To the members of the  Sima de los Huesos team and Centro Mixto de Evolucion y Comportamiento Humanos (UCM-ISCIII)  research team for their dedication, effort and conservation of the specimens. To the  Conservation and Restoration Laboratory team from CEN-IEH  for their work with the conservation and manipulation of the specimens. The authors are grateful to  Ana Pantoja-Pe  ´rez for her valuable comments and scientific discussion that improved the study. The scan of the specimens was performed by L M-F in the  ICTS-CENIEH Microscopy Laboratory , and with the collaboration of the CENIEH staff.
This study received financial support from  Direccion General de Universidades e Investigacion  in the form of a grant awarded to JMBC and  MM-T (PGC2018-093925-B-C31 ) and  Consejerı ´a de Cultura y Turismo de la Junta de Castilla y Leon. This study also received financial plesiomorphic condition in this group. Still, more data is needed on other Early and Middle Pleistocene populations to fully understand the evolutionary meaning of this trait. support from  Initiative d'Excellence  in the form of a grant awarded to LM-F ( ANR-10-IDEX-03-02 ), from the  British Academy  in the form of a grant awarded to MM-T ( International Partnership and Mobility Scheme   PM160019 ), and from the  Leakey Foundation  through the personal support of  Gordon Getty  (2013) and  Dub Crook  ( 2014-2016 ,  2018 ,  2019 ) to MM-T.
The STAC corpus is not only a rich source of data on strategic conversation, but also the first corpus that we are aware of that provides discourse structures for multiparty dialogues situated within a virtual environment. The corpus was annotated in two stages: we initially annotated the chat moves only, but later decided to annotate interactions between the chat moves and non-linguistic events from the virtual environment. This two-step procedure allows us quantify various ways in which adding information from the nonlinguistic context affects dialogue structure. 2.
In total we annotated the complete chat transcripts of 46 games using the Glozz annotation tool  (Widlöcher and Mathet, 2009)  to build what we refer to as the chat-only annotations. To make the annotation process more manageable, we originally divided the chat history for each game into a number of (what we refer to as) dialogues, each of which involved one or more bargaining sessions. The criteria for dividing the dialogues were not precise, though there was the obvious goal of keeping each trading session intact. Typically dialogues contained just one negotiation session with one player leading the bargaining. However, occasionally annotators linked elements in one negotiation session with elements in another, and in that case, we considered the linked sessions as contributing to a single dialogue.
ERC grant n. 269427. 2. We gratefully acknowledge support from  ERC  grant  269427  ( Strategic Conversation ),  BPI France  grant  P169201  (LinTO-Assistant vocal open-source respectueux des données personnelles pour l'entreprise), and  3IA  grant  ANITI ANR-19-PI3A-0004  ( ANITI -Artificial and Natural Intelligence Toulouse Institute ). We would also like to thank four anonymous reviewers and the editor for their extensive and very helpful comments.
More specifically, we use a classical bilateral-based metric for local analysis, combined with a proposed global approach, that we refer to as the Area Mismatch metric, to give a vivid overview of geometrical facial asymmetry. Finally, we construct a metric for textural facial asymmetry assessment by employing the Structural Similarity Index (SSIM) for dual regions in a given face. We apply the aforementioned metrics on two databases, a recently collected meth-addicted database and a regular aging database (FERET). Statistical analysis indicated a significant increment of facial asymmetry for meth addicts.
In this work, face asymmetry for illicit drug addicts (mainly meth) is assessed by employing the most credible state-the-of-art tools for local and global 2D face symmetry assessment. More specifically, the use of a classical bilateral-based metric for local analysis is combined with a proposed global approach that we name as the Area Mismatch metric to give a vivid overview of face geometrical symmetry. Moreover, a metric for textural face symmetry is constructed by employing the Structural Similarity Index (SSIM) for dual regions in a given face. Lastly, the aforementioned metrics are applied to two databases, a recently collected illicit drug-addicted database and an ordinary aging database (FERET). The statistical results were analyzed to answer the question does meth affect face symmetry?.
In fact, state-of-the-art 2D face symmetry analysis tools can be effectively employed to answer this question. Some of the methodology developed in this work is inspired by the work done in  [8] , where the increase of face asymmetry against aging was analyzed using publicly available databases  [1]. The key point in this research is to provide a good statistical analysis using widely accepted assessment tools for the data on hand. It is well known that face symmetry relates to beauty  [13]. Hence, most of the developed tools to quantify face symmetry were proposed to threshold the necessity for surgical intervention on several medical cases such as unbalanced mandibular  [14] , cancer patients after therapy  [10]  and cleft-lip  [11, 16, 17]. While from a technical point of view, face symmetry has been used as a critical factor for several applications.
In  [18] , face symmetry was used to pose correction in 3D face models and improving face recognition. In  [19] , an opticalflow-based facial symmetry is introduced for face recognition and reconstruction using certain dominant bilateral features that are least affected by pose variations. Others have used face symmetry to improve 3D reconstructed models  [20]. However, we find that the most credible 2D face symmetry assessment tool in the medical field is associated with a software known as SymNose  [17]  which requires manual annotation for the regions of interest (ROIs), and performs an area mismatch measure on the annotated dual face regions. Up to this date, SymNose is still accepted as the most reliable tool to measure 2D face symmetry  [16]  in the medical field. In contrast with technical papers, where bilateral features are the most used.
Employed databases Assessing the asymmetry of faces across temporal variations from 2D images requires clear frontal face shots for each individual under consideration, captured in separated time slots. This arises a major difficulty in this research due to the very limited publicly available databases offering such a setting. Nowadays, several aging databases that were collected to model aging are surveyed in  [1]. However, only the FERET database in its dup I and dup II directories offers clear frontal images that match with this setting for ordinary people aging. Fortunately, the database that was collected for the illicit drug abusers (mostly meth) is specially designed to model the distortion of faces versus temporal variations and meets the requirement for this research. In the following subsections, the two used databases are briefed for the reader.
We collected a database from internet photo collections on drug abuse, mainly using the faces of meth project in  [15]. Very similar databases were collected and used in previous works  [4, 5, 6] ; however, none of these databases was made publicly available up to this date. The collected database has 120 members with two color frontal face shots at least for each member with an average resolution of (572 x 838), which shows the progress of drug abuse and its effect on the addict's face as shown in Figure  1(b). The collected database has a variety of members which guarantees generality for a range of adults, covering both genders with close percentages. Besides, since its greatest part was collected in the USA, it combines multiple races due to the country's demography. Moreover, if we consider the similarity of temporal variations in the images for members of the aforementioned databases, we can justify the assumption that usage of this database for the purpose of comparison is valid.
Face normalization (align and resize). 3. Face cropping. In this work, we employ the 68-landmarks-extractor of Dlib's implementation in  [21]  with a shape detector trained on the iBUG 300-W face landmark database  [22]. Therefore, affine transformations are used for rotating, scaling and translating in aligning each face using the centers of eyes. This is achieved through finding the rotation angle that makes eye centers lay in the same Y coordinates, then applying a scaling factor to make all output faces in the desired size (300x300). Consequently, an affine transformation matrix M is employed (rotation according to the midpoint between eyes centers and scaling). face images are cropped from the normalized face using the coordinates of its landmarks indicated in Table  1  and a demonstration of face cropping in Figure  5.
Statistical analysis on the FERET database To simulate random temporal variations (similar to those exist in the illicit drug addicts database), the results presented in Table  2  were generated with a random selection of before and after shots for the 80 subjects in the Dup I directory of FERET. The results show an increase in facial asymmetry with aging for all metrics. Moreover, textural analysis demonstrates a textural-wise increase in face asymmetry with normal aging. Precisely, a maximum increase in textural asymmetry was found around the cheeks area. A visual illustration of the findings can be found in the bar graph presented in Figure  13(a). Finally, the cloud representation of the samples is found in Figure  13(b) , where it presents the mean values of geometrical versus textural asymmetry, and it shows a slight positive shift on both axes, corresponding to a slight geometrical/textural increment face asymmetry against normal aging.
Discussions We collected a database of meth addicts for investigating long-term effects of meth abuse and addiction on face asymmetry, compared with a control database of ordinary people aging. Generally, the task of assessing facial asymmetry consists of two parts: de-275 termining a symmetry plane which fits best to the present partial bilateral symmetry and quantifying the amount of violation of this symmetry. Several machine-learning-based methods perform the first part with high-accuracy, e. g. , the Dlib face landmarks extractor  [21]  used in this work. In contrast, the second part of the task is not straight forward; actually, existent methods for quantify-ing face asymmetry were found to have a low scoring correlation in  [11]. Hence, in our comparison of facial asymmetry, we combine the two most credible methods in the literature for assessing geometrical asymmetry, namely, the bilateral features and the area mismatch.
Additionally, for supporting the hypothesis, we employ a new metric for assessing textural face asymmetry using the structural similarity index (SSIM). All three metrics mentioned above showed consistency in their indications; with the major indication being a significant increment of facial asymmetry for meth addicts while aging, three to five times more than ordinary people (Table  4 ). These findings align with a medical evidence on how meth abuse and addiction cause several face distortions  [2, 3]  as summarized in Section 1. 1. At this point, we would like to bring to the reader's attention that most of the facial images analyzed for meth-addicts were collected by the US police authority in the faces of meth project  [15]. Undoubtedly, this database is one of a kind, acknowledging that it gathers a significant population of meth-addicts at two different stages of meth abuse for each of its members.
Consequently, this database was welcomed by the research community and employed in different face analysis researches on drug abuse as in  [4, 5, 6, 7 ]. However, it remains possible that the police authority selected particular cases to achieve a deterrent effect on meth abuse. Therefore, the increase in facial asymmetry among meth addicts could be lower on average than what we estimated in this research. Conclusively, each of the faces we analyzed in this work shows some degree of asymmetry. This finding is consistent with the results in  [19] ; where they used an optical-flow approach based on four bilateral features and analyzed 4000 face images. On the one hand, the results we obtained on the aging of ordinary people using bilateral features on the FERET database are semi-identical to the ones obtained in  [8] ; more specifically, we both concluded that face asymmetry increases in aging by employing this metric.
The difference between their work and ours is the new step we performed of calculating the percentage of asymmetry increase. Also, both results showed that the geometrical asymmetry in the lower part of the face is more affected in aging. On the other hand, in this work, we extended these findings to show that textural-wise asymmetry around the cheeks area increases the most with aging. Moreover, in relation to the existing literature, both the purpose of the study and employed tools align with previous works on face symmetry distortion. In  [10] , they show an increment of facial asymmetry for rhabdomyosarcoma survivors caused by medical therapy by combining bilateral features with surveys (human-opinion based). Where in  [16]  and  [17] , they analyzed face asymmetry for cleft lip and palate patients and defined an aesthetic range for lip and nose features respectively, both works used the area mismatch-based metric of nose and lip areas using software known by Symnose.
Similarly, in this work, we combine bilateral features and an area-mismatch based metric to give a score on geometrical face asymmetry. It is worth mentioning that regarding the area mismatch metric, the existing software (Symnose) requires a manual annotation for the region where symmetry analysis is concerned, wherein this work, we automated the usage of this metric using face feature extraction. However, automating an area mismatch-based and a textural-based face asymmetry assessment metrics are original contributions of this research. Although they were tailored to serve for this research, their concepts can be generally adapted for face asymmetry assessment. Interestingly enough, few researchers built specific-to-their-need tools to analyze facial asymmetry. Although in most of the cases such tools are not applicable on a general scope, however, they opened the door for new methods to be accepted for this purpose.
Notably, in  [14] , a method based on Optimal Symmetry Planes (OSP) of CT scans was introduced for studying the correlation between face asymmetry and jawbone asymmetries. Also, in  [18] , a wavelet-based method for 2D images was introduced to give a signature of faces based on an acceptable range of face symmetry. Noteworthy, two limitations are associated with this study. Firstly, A new database named as PCSO LS has been recently collected and employed in  [7]  for aging studies, and is considered as the most populated aging database so far  [1]. This database could serve in this research as a control database besides the FERET database that we employed. However, PCSO LS was not made public up to today, and our attempts for obtaining this database were fruitless. Secondly, the number of detected landmarks of all available face-landmarks extractor is somehow limited, e.
The FERET database FERET is a large publicly available database that combines 3540 2D color face images of resolution (512 x 768) for 1196 subjects in the age of 10-70 years. Although it has multiple pose and expression variations, only frontal images were employed in this work. It has two subdirectories titled with dup I and dup II representing temporal variations and are usually used to study the effect of aging on faces. Where dup II is a subset of dup I and contains larger temporal variations. However, 80 subjects are present in dup I with clear frontal face images that were collected at different time instants with a similar range to the data collected for drug addicts. Besides, FERET contains a similar gender percentage. And since it was collected in the USA (similarly to the Illicit Drug Addicts database), we assume that the variety of races is already covered.
ages (early vs late stages of drug abuse) of 120 subjects in the Illicit Drug Addicts database. The results show a significant increase in face asymmetry both geometrical and textural wise with aging, in comparison with the increase 255 of asymmetry in people with normal aging in 3. 1. A visual illustration of the findings can be found in the bar graph presented in Figure14(a). Finally, the cloud representation of the samples used to generate the results in Figure14(b)presented in geometrical vs textural asymmetry shows a relatively considerable positive shift on both axes when compared with the results obtained on the 260 FERET, indicating the increment of these asymmetry measures.
1. 3. 2. 32 The FERET database FERET is a large publicly available database that combines 3540 2D color face images of resolution (512 x 768) for 1196 subjects in the age of 10-70 years. Although it has multiple pose and expression variations, only frontal images were employed in this work. It has two subdirectories titled with dup I and dup II representing temporal variations and are usually used to study the effect of aging on faces. Where dup II is a subset of dup I and contains larger temporal variations. However, 80 subjects are present in dup I with clear frontal face images that were collected at different time instants with a similar range to the data collected for drug addicts. Besides, FERET contains a similar gender percentage. And since it was collected in the USA (similarly to the Illicit Drug Addicts database), we assume that the variety of races is already covered.
Data collection The tali (N = 98) collected in this study belong to three modern human groups of urban societies from the late 19th and early 20th century, for which the sex is known from cemetery and municipal records. The sample consists of 39 individuals from Bologna (18 females and 21 males), 36 individuals from Sassari (17 females and 19 males) and 23 individuals from New York (9 females and 14 males). The identified skeletal remains (by age, sex, cause of death, occupation) of Sassari and Bologna are part of the Frassetto collection housed at the Museum of Anthropology of the University of Bologna (Italy). The Bologna sample consists of individuals from the Certosa Cemetery (Bologna, Italy) who died between 1898 and 1944  [64]. The Sassari sample (Sardinia, Italy) consists of individuals who died in the first half of the twentieth century and were exhumed from municipal cemeteries  [65].
In detail, the Italian sample from the Frassetto collection was CT scanned with a 64-slice Brilliance, Philips Medical System, Eindhoven-the Netherlands at the Department of Diagnostic Imaging of Santa Maria delle Croci Hospital in Ravenna (Italy), with the following relevant parameter setting: 140kVp, a tube exposure time of 1645 ms, a slice thickness of 1. 00mm, filter type YD, Reconstruction Field of View (FOV) of 500 mm for Sassari sample and 343 mm for Bologna sample. Subsequently, the raw data were reconstructed as 16-bit unsigned DICOM images using the following voxel sizes: 1) Bologna sample: 0. 960 x 0. 960 x 0. 7 mm; 2) Sassari sample: 0. 976 x 0. 976 x 0. 5 mm. The CT image data were segmented in Avizo 7. 1 (Visualization Science Group Inc. ) to obtain 3D digital models of each talus.
The tali from New York were digitally acquired using a Konica Minolta Vivid 910 surface laser scanner (X: ± 0. 22 mm, Y: ± 0. 16 mm, Z: ± 0. 10 mm). Surface scan data were processed (i. e. , mesh alignment, cleaning) using the scanner's associated software (Polygon Editing Tool, Konica Minolta, 2006) and Geomagic Studio 8 (3D Systems). In each case the 3D models were saved in STL format and loaded into Viewbox 4 (dHAL Software) for landmarking. 1 ) and subsequently applied to the targets. The semilandmarks were allowed to slide on the curves (curves semilandmarks) and on the surface (surface semilandmarks) to minimize the thinplate spline (TPS) bending energy between the target and the template  [46, 59]. Semilandmarks are not anatomical landmarks but when a TPS interpolation function is applied they became geometrically homologous between individuals, thus allowing for analysis in conjunction with traditional landmarks  [58, 59].
Reconstruction of fragmentary talus In order to assess the utility of GM in cases where virtual reconstruction is necessary, we selected a talus of a female individual from the Bologna sample (BO-F-45) that was damaged at the talar head (Fig  2A ). In this case the damage would make it impossible for linear measurements to be taken that are frequently used for discriminant function equations  [35, 36, 77] , resulting in the exclusion of the specimen from sex assessment  [35]. Here the damage was exaggerated, whereby a more extensive "digital fracture" was created by resecting a portion of the talus (i. e. , from the most lateral anterior margin of the trochlea to the mid of the flexor hallucis longus groove) using a cutting plane in Geomagic Design X (3D System) (Fig  2B ). The missing portions were then estimated using morphological information from reference specimens by means of the TPS interpolation function in Viewbox 4 software  [59, 62].