dataesr/scientific-paragraphs-categorization huggingface dataset:
Scanned 3D humans datasets With 4DHumanOutfit we explore the three axis of motion, identity, and clothing. We briefly review existing datasets that consider similar axes. Motion. Human pose plays a crucial role in many application fields, such as medicine, sports or graphics, thus it has attracted many research efforts. Marker based motion capture. A classical approach to capture human motion is to use motion capture (MoCap) systems with e. g. reflective markers. Following the pioneering dataset HumanEva  [52] , many other datasets have been acquired: Hu-man3. 6M  [26] , Total Capture  [56] , AMASS  [38]  or HUMAN4D  [14]. The reflective markers allow to extract a good approximation of the pose, which is considered ground truth. Other modalities, such as video, depth sensors or inertial sensors, are simultaneously acquired.
From this paired data, researchers have studied how to infer a pose from these other modalities. In addition, massive datasets, such as AMASS  [38] , have allowed to learn human pose priors which are widely used in the literature. While yielding precise information on poses with the marker locations, MoCap systems provide only sparse information on motion and imply complex setups with markers to be placed on subjects. Markerless approaches. Another strategy to capture the human pose and motion is to use markerless systems, relying for that purpose on monocular settings  [41, 5, 62, 42, 61] ; on passive multiview video systems, like for instance HUMBI  [66]  and Hi4D  [65] , the PanopticStudio  [53, 30] ; or on active systems such as 3DMD, used to acquire for example DynamicFaust  [11] , Flame  [33]  or Mano  [50].
Depth camera setups have also been used to capture 4D motion sequences of multiple subjects  [12]. For our work we use the Kinovis acquisition platform  [2] , a passive multi-camera system with a wide acquisition volume that enables dynamic displacements of the subjects and rich clothing dynamics. Identity. In the identity axis, the seminal CAE-SAR dataset  [49] , created to study body morphology and clothing sizing purposes, contains scans of over 4500 individuals in 3 static poses each. Further datasets have scanned different persons in static  [6, 23, 10]  or dynamic  [45]  situations. Hasler et al. [23]  provide a total of 500 static poses of 114 individuals, while the FAUST dataset  [10]  contains 10 individu-als in 10 static poses each. DYNA  [45]  contains 10 individuals in a total of 129 sequences, which have been accurately registered for benchmarking in the DynamicFAUST dataset  [11].
All these datasets have allowed the study of identity and static or dynamic pose, but have not considered the clothing axis. Outfit. Early efforts have focused on capturing and analyzing sequences of few individuals captured in a single outfit e. g. [54, 17, 59 ]. More recently, different tasks related to clothing have motivated the creation of additional 3D datasets of real clothed humans. For example, to explore how different sizes of cloths drape on the same human, the dataset SIZER  [55]  captured around 2000 static scans, from 100 subjects wearing clothes of different sizes. As all subjects strike the same A-pose, this dataset does not allow to study cloth dynamics. To tackle the problem of estimating the shape under clothing, Yang et al. [64]  and Zhang et al. [67]  acquired scans of different subjects, with and without clothing, performing several motions.
These datasets consider 6 subjects, 3 motions, and 3 outfits for Yang et al. [64]  and 5 subjects, 3, motions and 2 outfits where 4DHumanOutfit considers 20 subjects in 7 outfits and 11 motions. Other datasets have been acquired with consumer RGBD sensors  [68] , providing lower quality than 4DHumanOutfit. To learn a generative model of clothing, the dataset CAPE  [36]  was released. It also includes scans and SMPL mesh registrations from the ClothCap work  [44]  and contains 15 subjects in 4 different outfits performing different motions. The 4DHu-manOutfit dataset is larger with 20 subjects and 7 clothing styles with richer dynamics. In addition, the systematic acquisition of all subjects performing very similar motions in all outfits provides an unprecedented opportunity to study how dynamic cloth deformations behave depending on identity, motion, and clothing.
Data acquisition Motion sequences were captured by 68 calibrated RGB cameras (4 megapixels, 50 frames per second, focal lengths between 8 and 16mm) positioned roughly on a half-ellipsoid with radii 4m and 5m and height 5m looking towards the stage centre, for an average image resolution of 2. 5mm per pixel at the scene centre. The total capture area covers a length of 5. 5m and a width of 3. 5m. Fig. 2  shows the multicamera platform  [2]. The capture and reconstruction pipelines, shown in Fig. 3  and 4 , consist of several steps. First, synchronized video streams are acquired and silhouettes are segmented with the software  [1]  of the multi-camera platform  [2]. Second, the resulting images and silhouettes are undistorted using the calibration information and masked with projections of inflated visual hulls.
This significantly reduces the size of the data. 3D reconstructions are computed independently per frame, which results in a densely sampled 3D mesh per time instant. These meshes are obtained by performing multi-view reconstruction  [32]  on the undistorted and masked images. We decimate the resulting reconstruction into lower resolutions of 250k, 65k, 30k, and 15k vertices. The 3 lower resolution meshes are texture mapped using the capture platform software  [1] , which is not designed to handle higher resolutions.
Motions Actors were asked to perform 11 motions involving significant displacements within the scene. We focus on motions with large displacements as these are yet rare in existing captured 4D datasets. For instance DYNA  [45]  captures soft-tissue dynamics during motions but without large displacements. We further choose the 11 motions to contain variations in upper and lower body motions, while covering common motions including different variants of walking. The motions, illustrated in Fig. 11 , are: walk a simple walk across the studio; avoid a walk with last-second obstacle avoidance; back a walk with a U-turn; torso a walk with a torso rotation to look backwards; run a jog / run across the studio; jump jump on the spot; dance a dance with both legs and arms wide motion; hop hopscotch; 2 free motions to be chosen by the actor to increase the variability of the dataset, this included mostly martial art, dance and other sport motions; hidden we recorded one additional motion which will not be released to allow for future evaluations on unobserved data.
Summary A total of 1617 sequences were recorded, involving the processing of 459080 frames. The computations were handled on 2 clusters (17 16-core servers equipped with Nvidia Quadro 4000 cards and 20 16-core Intel Xeon CPU servers) resulting in the generation of 540TB of total data during the project. Fig. 12  provides an overview of the storage space required by From left to right: walk, avoid, back, torso, run, jump, dance, hopscotch, free 1, free 2. Here free 1 is boxing, free 2 is kick. Men (top), women (bottom). the data during the generation of 4DHumanOutfit. The final 4D dataset consists of meshes in different resolutions (250k, 65k, 30k, 15k vertices), and undistorted and masked images and silhouettes. The total volume is 22TB, 20. 5TB of which are occupied by the undistorted and masked images and silhouettes.
The resulting body shape parameters are used to reconstruct a model in T-pose, which is used as reference body shape. For each identity, its reference body shape is released along with the dataset. To quantitatively evaluate the quality of a body shape estimate computed from a dressed 4D motion sequence of identity i, we compute the Chamfer distance between the result and the reference body shape of i, in a standard T-pose. Baseline As a baseline method for estimating body-shape parameters, we use the same optimization method described above, based on keypoints and silhouettes, but applied directly to dressed sequences. The setting is more complex, as estimated keypoints are generally less precise on frames with loose clothing, and silhouettes are farther from the silhouette of the body shape.
Motion The third evaluation aims to examine the motion axis. Our evaluation considers the task of motion  retargeting where the objective is to generate a 4D motion sequence of a given identity that performs the same motion as another given 4D motion sequence. V retr → V M V M → V In particular, given as input the 4D motion sequence showing identity i 1 in outfit o performing motion m along with a 3D model of identity i 2 wearing minimal clothing, the goal is to compute the 4D sequence showing identity i 2 while performing motion m. A challenge when evaluating motion retargeting is the lack of realistic ground truth data. On the one hand, realistic 4D ground truth is lacking due to the sparse nature of existing large 4D human datasets e. g. [38, 12]  where all actors are not seen performing all motions.
This lack of data encourages state of the art to evaluate on synthetically generated 3D motions. These synthetic motions are often generated with skinning methods and lack realistic local dynamic details. On the other hand, smaller 4D datasets  [11, 45]  with dynamic details do not account for clothing. In the following, we show that 4DHumanOutfit can be leveraged to evaluate motion retargeting methods by providing captured reference solutions with accurate geometric details.
Some of these works require correspondences between the target and source bodies or temporal correspondences in the source motion. Our source motion is unregistered and we can leverage the template fitting from Sec. 4. 1 to have access to a target identity in minimal clothing in T-pose. From the applicable methods  [39, 60, 29] , we choose  [39]  as our baseline because it generalizes well under various motion and shape preservation metrics and was already tested on the raw data of a multi-view acquisition setup. The baseline operates in three steps. First, the source skeleton is extracted using a PointFormer network. Second, this skeletal motion is retargeted to the target at the skeletal level using a recurrent network. Third, the dense geometry of the target shape is recovered using a learnt skinning prior.
Conclusion We presented 4DHumanOutfit, a large-scale dataset of 20 actors wearing 7 outfits each and performing 11 motions per outfit. This data captures detailed spatio-temporal dynamics of varying outfits, and their interaction with different morphologies and motions. We demonstrated that each axis of the resulting data-cube contains unique information using simple evaluations. This data has the potential of serving in many different applications involving digital humans including augmented or virtual reality applications (e. g. virtual change rooms), and in entertainment (e. g. animation content generation).
This means their dataset cannot be used to understand biodiversity and yield outcomes of different farmland diversification pathways. A review of experiments reporting the effects of farmland diversification on both biodiversity and yield across various systems and regions is needed to better understand potential synergies and trade-offs between these outcomes. This analysis could help uncover opportunities and pitfalls for designing interventions that contribute to both agricultural production and biodiversity conservation goals  (Ortiz et al. , 2021). In this paper, we analysed local non-domesticated biodiversity and agricultural yield responses to farmland diversification, using a published database of field experiments that measured biodiversity and yield responses simultaneously in diversified treatments and simplified controls  (Jones et al.
Database on the effects of farming system diversification The data used here represent a subset of a published dataset of the effect of diversified farming on terrestrial biodiversity and/or agricultural yields  (Jones et al. , 2021). In this subset, diversified farming systems are those that aim to increase diversity on-farm at multiple temporal and/or spatial scales, such as through the association of different plant species, or different varieties/cultivars of crop, or the integration of livestock or fish production with crop production (Table  1 ). The studies included in the subset were identified through a systematic literature search in Scopus and Web of Science last updated on 5 January 2021 (Appendix A: Table  A. 1), following a pre-established protocol  (S anchez et al. , 2021). All primary articles directly retrieved through the search were screened, together with primary articles listed in all meta-analyses retrieved through the search, and 20 articles identified through other sources  (Jones et al.
, 2021). To be included in the present paper, articles had to meet the inclusion criteria described in Appendix A (Table  A. 2), notably to report quantitative responses for both (nondomesticated) biodiversity and yield, including means, sample sizes and variance measures. Articles needed to report responses collected using comparable approaches in a diversified farming system and a simplified control, with the latter representing either a monoculture or a farming system with an absence of embedded natural vegetation. In the case of intercropped treatments, the article must have provided sufficient data to enable calculation of the land equivalent ratio as a measure of yield (see Appendix A: Supplementary Methods). The resultant dataset used in this paper includes information from 43 articles reporting biodiversity and yield responses collected through field experiments in the same diversified and simplified farming systems and paired for analysis (see Appendix A: Fig.
A. 1 for an overview of the literature search and screening steps, and Table  A. 3 for a list of included articles). For each comparison of diversified farming systems relative to a simplified control, information on biodiversity and yield were extracted pertaining to the population, intervention, comparator, outcome, and context (PICOC) (Appendix A: Table  A. 4). We classified information in the database to group cases (i. e. , pairs of biodiversity and yield responses collected in the same control and treatment sites) by farming practice (control: simplified farming; treatment: agroforestry, associated plant species, crop rotation, cultivar mixtures, embedded natural features, intercropping, and combined practices À see Table  1 ), crop type (woodiness and life cycle), crop commodity (according to FAO commodity groups), agrochemical inputs (pesticide and fertiliser use), biodiversity organism assessed (taxonomic, functional, and ground-relation group, where the latter distinguished whether the organism was surveyed above or below ground), continent, latitude, and bioclimatic location (Appendix A: Table  A.
Second, we used randomForest  (Breiman, 2001)  to account for non-linear interactions amongst variables. RandomForest bootstraps the data iteratively and randomly selects variables on which to split each node, used here to grow 10,000 predictive and testing trees. Variable importance was measured as the average mean decrease in outcome classification accuracy for data classified after versus before splitting on a variable. We compared results to the likelihood ratio tests to identify which variables were consistently identified as important predictors of synergies and trade-offs across both methods. Multiple biodiversity-yield effect size pairs in our database were non-independent because they were sourced from the same studies, and/or from experiments where treatments shared a control plot, and/or from multiple experiments in the same control and treatment plots (e.
Code and data availability Statistical analyses were performed in R (R Core Team, 2021) using the metafor  (Viechtbauer, 2010) , QRLMM  (Morales & Lachos, 2015) , nnet  (Venables & Ripley, 2002)  and randomForest  (Breiman, 2001)  packages. The dataset was sourced from  Jones et al. (2021). The R code is available at: https://github. com/skatejones/Sustaina bleFoods.
Increases in richness and evenness of biodiversity are likely to coincide with increases in yield Sub-setting the dataset by biodiversity metric altered the likelihood of finding a synergy or trade-off for biodiversity and yield. Results show that diversification was significantly more likely to lead to synergistic gains for biodiversity and yield, compared to loses for both, when biodiversity was measured as species richness (RRR = 1. 61, p= 0. 010) or richness-evenness (RRR = 2. 14, p= 0. 002) (Fig. 3, Appendix A   A. 7). In contrast, synergistic outcomes and trade-offs were equally likely when biodiversity was measured in terms of abundance.
For this reason and given the small sample sizes in the richness and richness-evenness datasets (i. e. , <10 cases in the Lose B-Win Y and Other outcome categories), subsequent analyses were conducted on the dataset containing all cases. In temperate forest and Mediterranean biomes, win-win was the most likely outcome of farming system diversification and was, respectively, 3. 55 (p < 0. 001) and 4. 61 (p < 0. 001) times more likely than lose-lose (Fig. 4 , Appendix A: Table  A. 9). While win-win was the most likely outcome in temperate forests, diversification was also 2. 47 (p < 0. 001) times more likely to lead to gains in biodiversity and losses in yield, compared to losses in both. In tropical and sub-tropical forests, diversification was most likely to benefit only yield, and this outcome was 4.
, 2020). Different responses across biomes, continents, and latitudes, for both biodiversity and yield, may also reflect that the experiments in our database do not capture all diversification practices in every region and the included practices may not be the most effective at driving positive responses for the crops and taxon represented. For example, there were no experiments in our database on the effects of embedding natural vegetation on-farm in the tropical forest biome, and an absence of farms cultivating tree or perennial shrubs crops in the tropical forest and grassland biomes (Appendix A: Figure  9 ).
, 2007). However, abundance can be positively correlated with species richness for some taxa, such as plants, grasshoppers, butterflies, birds and rodents in pasture and semi-natural grasslands  (Bock et al. , 2007). Abundance was the dominant biodiversity measure in our database and, while positive effects on abundance were much more common than a neutral or negative effect, trade-offs with yield were more likely than positive synergies. This is in part related to the fact that agronomic pests (representing 32% cases) were predominantly measured in terms of abundance, and where diversification increased pests (positive biodiversity response), this was often associated with yield losses, and vice-versa (Appendix A: Fig. A. 10). While some pests may be invasive or over-abundant species and reducing numbers may be compatible with biodiversity conservation goals, often this is not the case.
Limitations and future research needs New field experiments and expansion of the  Jones et al. (2021b)  dataset as new publications emerge would help close evidence gaps. While the dataset used in this analysis includes hundreds of comparisons, increasing the number of cases and studies for under-represented taxa, production systems, and regions, would enable more in-depth analyses and strengthen the findings. For example, temperate and tropical regions, certain taxa, including mammals, reptiles, amphibians, and soil organisms (fungi, annelids), were poorly (or not at all) represented. As these are the regions where agriculture covers the most land and expansion is happening fastest  (Winkler et al. , 2021) , evidence gaps urgently need closing to help halt biodiversity loss. In terms of crops, our dataset included many experiments in cereal, fibre (cotton), fruit and vegetable cropping systems, which are important cash crops, while roots, tubers and pulses were under-represented despite their nutritional importance and reliance by many smallholder farmers.
Cereal crop species in the dataset were dominated by maize, rice, sorghum, and wheat, while millet, barley, rye, and oats were missing. Our analysis was constrained by the availability of studies reporting outcomes for both biodiversity and yield, in simplified and diversified farming systems. Inter-disciplinary experiments that collect data on agronomic, ecological, economic and human wellbeing outcomes are in short supply and experimental approaches are highly variable and often incomparable  (Hufnagel et al. , 2020). In intercropped and agroforestry treatments in our study, the lack of yield data needed to calculate land equivalent ratios may reflect the resource and technical challenges involved in conducting experiments that collect data on multiple outcomes and applying best practices to every measurement category  (Brooker et al.
Acknowledgments We are grateful to  David Makowski  and  Fabrice DeClerck  for excellent suggestions that improved the data analysis. We thank two anonymous reviewers for their constructive inputs. This work was supported by the  CGIAR Research Program on Water,   Land and Ecosystems as part of the Sustainable Foods through diversity-based practices project , and the  OneCGIAR Nexus Gains initiative  as part of the  Agrobiodiversity Index project.
The repair oligonucleotides are designed to replace the CTCF motif with an EcoRI site to help screening knock-outs. Two days after transfection, the cells were cultured for 24 h with 3 µg/mL, then 48 Imaging was performed on an inverted Nikon Eclipse Ti microscope equipped with a PFS (perfect focus system), a Yokogawa CSU-X1 confocal spinning disk unit, two sCMOS Photometrics Prime 95B cameras for simultaneous dual acquisition to provide 95% quantum efficiency at 11 µm x 11 µm pixels and a Leica Objective HC PL APO 100x/1. 4 oil. A Tokai Hit Stage incubator allowed for maintenance at 37ºC and 5% CO 2 throughout the experiment. The system was controlled using Metamorph 7. 10 software. h For double-label experiments, EGFP and IRFP were excited by 491-nm and 635-nm lasers. Green and far-red fluorescence were detected with an emission filter using a 525/50 nm and 708/75 nm detection window, respectively.
Cell cycle analysis 1 million cells were fixed in 66% ethanol on ice for 2 h, washed with PBS, then resuspended in 750 µL 50 µg/mL propidium iodide and 2 µg/mL RNase A in PBS, before incubation at 37ºC for 30 min. Analysis of propidium iodide staining with a FACS Fortesa (BD Biosciences) allowed cells to be gated to G1 (2n DNA content), S (2n < DNA content < 4n) and G2/M (4n DNA content) phases. Statistical comparisons were made by two-tailed t-tests. S6 ). PCR reactions were pooled, primers removed by washing with 1. 8× AMPure XP beads, then quantified on a Bioanalyzer (Agilent) before sequencing with a HiSeq 4000 (Illumina). Sequencing read fastq files were demultiplexed with sabre (https://github. com/najoshi/sabre) and aligned to the mm10 genome with Bowtie 72 , and intrachromosomal reads were assigned to DpnII fragments by utility tools coming with the 4See package  54.
4See was also used to visualize the 4C profiles. Interactions were called for each replicate with peakC  73  with window size set to 21 fragments, and were then filtered to only include the regions called as interacting across all wild-type replicates. For statistical comparison of specific interactions, the 4C read counts within 1 Mb of the bait for all replicates and conditions (from the same bait) were quantile normalized using the limma package  74. The means of summed normalized 4C scores over tested interacting regions were taken as "interaction scores", and were compared across conditions by two-tailed t-tests.