dataesr/scientific-paragraphs-categorization huggingface dataset:
01; Figure  2 ). All other category comparisons for relatedness coefficients were significantly different for both markers, We analyzed the relationship between individual heterozygosity H (Figure  3a ) and F estimates of inbreeding (Figure  3b ) at SNPs and STRs, and we found slight but positive correlations (R = 0. 327, p = 0. 004 and R = 0. 441, p = 0. 002 for H and F, respectively). For both markers, mean inbreeding in F1 juveniles (F1-JUV; mean F = 0. 093 SNPs and F = 0. 088 STRs) was significantly higher than that in BREEDERS (mean F = 0. 022 for SNPs and STRs; Wilcoxon test; p-value <0. 001). The analysis from INBREEDR package showed that the variation around g2 estimates is higher for STRs than that for SNPs, and decreases when a higher number of markers are used (Figure  4a ). The precision on the g2 estimates is higher for the set of 79 SNPs than that for STRs, for which variance is high.
| D ISCUSS I ON The use of high-throughput sequencing techniques in nonmodel organisms has opened the way for obtaining numerous SNP markers that may compensate the limited power of other markers, especially in scenarios of small and/or decreasing populations composed of related individuals. Our study showed that double-digest RAD sequencing  (ddRADseq-ion; Recknagel, Jacobs, Herzyk, & Elmer, 2015)  worked well for the rapid and cost-effective generation of a significant number of polymorphic and reliable SNPs in a species with very low diversity and with no a priori genomic information, the critically endangered European sturgeon. As well as developing new markers, we also get massive sequence datasets for the whole breeding stock of the species (the origin of all released individuals and future breeders), information that we considered potentially useful in the future.
Different sets of STRs and SNPs were simulated and drawn from distributions based on inbreeding level f and heterozygosity level from Acipenser sturio empirical datasets (i. e. , BREEDERS) reduced number of SNPs (n = 24) had sufficient power to discriminate even between closely related individuals (Kleinman-  Ruiz et al. , 2017). To take into account a more realistic scenario for the future management of the species, in which we will have to resolve paternity of next generations (F2), we tested the sensitivity of SNPs considering the allelic composition of the future captive breeders  (i. e. , F1 juveniles born between 2007 and 2009). We showed that the power of resolution with this last setting was still very high and almost similar to that based on the original broodstock's allelic frequencies, corroborating the similarity between the patterns of genetic variability of captive breeders and F1 captive stock, as reported earlier  (Roques et al.
We observed that the two likelihood-based methods were the most reliable and had the lowest variation in individual pairwise r values, while estimates differed for SNPs and STRs (DyadML and TrioML estimators, respectively). Interestingly, the mean relatedness over all pairs of individuals in each kin group corresponds well to the expected pedigree relatedness, but these distributions are overlapping for different kinship categories, especially for the half-sib category (see Figure  2 ). Kleinman-Ruiz et al. (  2017 ) also observed that the discrimination of the half-sib from unrelated, for example, needed a higher number of SNPs, because it was one of the most demanding comparisons. The distributions observed here, however, were very similar and even less overlapping than those observed in others studies using simulated or empirical datasets  (Blouin, 2003; Blouin, Parsons, Lacaille, & Lotz, 1996; Russello & Amato, 2004 ).
estimations. Specific aims were to (a) detect novel SNP markers for the species using double-digest sequencing protocol for Ion Torrent sequencing (Life technologies; Ion Proton, Ion PGM) and select an optimal set for parentage assignment through validation by genotyping using the MassARRAY system, (b) apply the novel set of markers for parentage assignment of 192 juveniles of the French captive stock, for which putative parents are recorded in the breeding database, (c) compare the resolution of SNPs and STRs for parentage testing and for their effectiveness in measuring genetic diversity, relatedness, and inbreeding, for empirical and simulated datasets, and (d) define relevant and precise genetic indicators for the long-term monitoring of the breeding and restocking program of A. sturio (Studbook implementation) that can be transposed to other sturgeon species.
Grantn°20009612), and Conseil général Gironde. For the sampling of the broodstock, we wish to thank Migado association and the technicians from the Irstea experimentation station of St Seurin sur l'Isle. Part of the experiments (library construction, sequencing and genotyping) were performed at the Genome Transcriptome Facility of Bordeaux (grants from the Conseil Régional d'Aquitaine n°20030304002FA and 20040305003FA, from the European Union FEDER n°2003227, and from Investissements d'Avenir ANR-10-EQPX-16-01). We would like to sincerely thank Lisa O' Connor for proofreading this paper and anonymous reviewers for constructive comments. CO N FLI C T O F I NTE R E S T None declared. AUTH O R CO NTR I B UTI O N S. R. designed the study, performed SNP development and analysis, analyzed and interpreted data for parentage, relatedness and inbreeding assessment, and wrote the manuscript with help from all coauthors.
( Grantn°20009612), and Conseil général Gironde. For the sampling of the broodstock, we wish to thank Migado association and the technicians from the Irstea experimentation station of St Seurin sur l'Isle. Part of the experiments (library construction, sequencing and genotyping) were performed at the Genome Transcriptome Facility of Bordeaux (grants from the Conseil Régional d'Aquitaine n°20030304002FA and 20040305003FA, from the European Union FEDER n°2003227, and from Investissements d'Avenir ANR-10-EQPX-16-01). We would like to sincerely thank Lisa O' Connor for proofreading this paper and anonymous reviewers for constructive comments. CO N FLI C T O F I NTE R E S T None declared. AUTH O R CO NTR I B UTI O N S. R. designed the study, performed SNP development and analysis, analyzed and interpreted data for parentage, relatedness and inbreeding assessment, and wrote the manuscript with help from all coauthors.
| Data collection and study species Data were collected in Wytham Woods, Oxfordshire, UK (51°77′N, 1°33′W), a 385 ha broadleaf deciduous forest surrounded by farmland. In these woods, bats roost in woodcrete nest-boxes provided and maintained by the Edward Grey Institute for monitoring of great and blue tit (Parus major, Cyanistes caeruleus) populations  (Perrins & Gosler, 2010). Bats mainly take up residence in boxes in June (after the bird breeding season), but sometimes roost in boxes not used by birds prior to June. Between 2007 and 2015, boxes were checked for bat occupancy between March and October. However, 98% of captures occurred between May and September (see Supporting Information Table  S1 ). Over 26,000 box checks resulted in 1,719 recorded bat roosts containing between 1 and 45 individuals in 751 boxes.
When captured for the first time, females were marked with a unique ID tag (2. 9 mm aluminium ring, under licence from SNCO) and aged as juveniles or adults, based on unfused or fused epiphyseal joints  (Racey, 1974)  and/or reproductive traits (pregnant or parous; adults). At each encounter, females were assigned to one of four categories: no signs of pregnancy, pregnant, lactating or post-lactating (see below). We analysed data on 583 M. daubentonii and 474 M. nattereri, of which 305 and 313 females were captured more than once, with the average number of captures per female being 2. 2 and 2. 8, respectively. The numbers of females aged as juveniles when captured for the first time (and thus of known age) were 280 M. daubentonii and 203 M. nattereri. At temperate latitudes, the bat mating season occurs from autumn until spring, with both sexes storing sperm through winter hibernation  (Altringham, 2011).
If a female is only seen in late autumn, then it is no longer possible to reliably distinguish between post-lactating breeders and parous non-breeders. Thus, based on breeding phenology, we divided our reproductive assessment data into early (pregnancy) and late (post-parturition) maternity periods and defined eight events (coded 0-7, Table  1 ) that relate field observations to the true reproductive state of females in a given breeding season. The capture history of each female consists of a series of event codes, one code per year  (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015). To model survival and transition of females between states, and to relate the observations of females to the true underlying state, we used five probabilistic matrices: the first three matrices describe state transition of individuals between two subsequent years (t-1 and t), while the second three matrices describe the observation process (captures and the assessment of the individ-  The observation process is represented in three event matrices.
| Model selection procedures To test for age-related patterns of survival and reproduction, and for trade-offs, we imposed different restrictions on the parameters of interest in the transition matrices. We used the Akaike information criterion AICc values  (Burnham & Anderson, 2002)  as a measure of the support for a model. Datasets used in the analyses, for females of both species separately, can be found in Zenodo repository  (Culina, Linton, Pradel, Bouwhuis, & Macdonald, 2018). We fitted all models in the program E-surge  (Choquet, Rouan, & Pradel, 2009). We conducted the analyses in three steps. In the first step, we tested for the presence of age effects on probabilities of survival, pregnancy and lactation. We also tested whether onset of first reproduction and its success varied with age.
In the second step, we tested whether there were any costs of current breeding to survival and/or future breeding, whether lactation was costlier than pregnancy, and whether there were costs specific to first breeding. In these analyses, we used all females (of known and unknown age), and we kept any cohort or age effect as detected in the previous step, but restricted to known age females only. For the three parameters of interest (survival, pregnancy and lactation), we first sequentially tested whether there was any trade-off with the previous reproductive state. When reproductive state was found to influence a parameter of interest, we additionally tested whether this effect depended on age (again, on the full dataset, but with the age dependence restricted to the females of known age).
| Actuarial and reproductive senescence We found indication for some senescence in survival to occur after the age of 4 or 5 in M. daubentonii females, but we did not detect senescence in reproductive parameters. Such decoupling of senescence patterns between traits has been observed more often and shown to be especially likely in long-lived species of birds and mammals  (Bouwhuis et al. , 2012). No senescence was detected in M. nattereri. Given the longevity of our species, even the 8 years of our dataset might not have been long enough to detect senescence  (Nussey et al. , 2006; Warner, Miller, Bronikowski, & Janzen, 2016). Two much longer studies  (Greiner et al. , 2014 for 18 years, and Fleicher et al. 2017  for 19 years) also detected no decline in reproductive performance or survival in female Saccopteryx bilineata and Myotis bechsteinii bats, respectively.
INTRODUCTION Multimedia collections have become a cornerstone data resource in a variety of scientific and industrial fields. One of the most difficult Figure  1 : The Exquisitor demonstration interface. Exquisitor interactively learns new analytic categories over the full YFCC100M image collection, with average latency of less than 30 ms per interaction round, using hardware comparable with standard desktops and modern mobile devices. challenges for interactive exploration of such collections-not only for data scientists working with these collections, but also for the multimedia community as a whole-is their scale. How can we facilitate efficient access to multimedia collections comprising tens or hundreds of millions of images, let alone billions? Interactive learning, which was embraced by the multimedia community in the early days of content-based image and video retrieval  [4, 12] , has recently experienced revival as an umbrella method capable of satisfying a variety of multimedia information needs, ranging from exploratory browsing to seeking a particular known item  [17].
For the text modality, 100 LDA topics were extracted from the image title, tags and description using the gensim framework  [11]. Uncompressed features for the 99. 2M images require nearly 880GB of memory. Exquisitor uses the recently proposed Ratio-64 representation, which preserves only the top visual concepts and text topics for each image  [18]. The compressed feature collections require less than 6GB of storage, thus fitting into the memory of a standard consumer PC, as well as some high-end mobile devices. This effective compression method has been shown to preserve the semantic descriptiveness of the visual and text features  [18]. The interactive learning process is facilitated using a linear SVM model, proven to provide a good balance between efficiency and accuracy when classifying large datasets based on few training examples  [5, 9].
DEMONSTRATION The main emphasis of the demonstration will be to allow conference participants to explore the 99. 2 million images of the YFCC100M collection using Exquisitor. The authors will also prepare some interesting exploration scenarios that highlight aspects of the YFCC100M collection. During the demonstration we hope to engage conference participants in a discussion that can inspire the multimedia community to work on scalable multimedia techniques and applications. Consider, as an example, the YFCC100M collection  [16] , comprising 99. 2M images and 0. 8M videos. It has existed for some time now, but very few have a good idea about what its actual contents are. This is no surprise, as it is difficult to tackle this collection with existing techniques: the simple metadata-based filtering approach is impeded by the sparse and noisy nature of the metadata and, at this scale, similarity search is akin to shooting in the dark.
Semantic concept detectors can be used to generate additional content-based metadata for both approaches, but that does not alleviate their problems. And thus, the YFCC100M collection remains a mystery. We have recently developed Exquisitor, a highly scalable interactive multimodal learning approach  [6]. A key feature that sets Exquisitor apart from related approaches is its scalability: Exquisitor can retrieve suggestions from 100 million images with sub-second latency, using extremely modest computing resources, thus breaking the interaction barrier for large-scale interactive learning. In this demonstration, we propose to allow ACM Multimedia attendees to interactively explore the YFCC100M collection with Exquisitor.
Pedigree, Genotyped, and Phenotyped Animals The data sets included phenotypes, pedigree, genotypes (Illumina goat SNP50 BeadChip), and environmental effects of the 2 main French dairy goat breeds (Alpine and Saanen) obtained from the French National Milk Recording System (http: / / fr. france -genetique -elevage. org). Data from the official genetic evaluation in January 2016 were used in this study. All analyses were within breed. The standard traits selected in French dairy goats include 4 milk production traits, milk yield (MY in kg), fat and protein yields (FY and PY, respectively, in kg), and fat content (FC in g/kg), 5 udder type traits, teat angle (TA: scored from 1 to 9), udder floor position (UFP: scored from 1 to 9), rear udder attachment (RUA: scored from 1 to 9), fore udder (FU: scored from 1 to 9), and udder shape (US: scored from 1 to 9), and SCS (log-transformed SCC) were analyzed.
The WssGBLUP was applied to each trait studied with model 1 and 2, respectively, using blupf90 family software (blup90iod2,  Misztal et al. , 2002). The SNP effects and SNP weights were estimated using postGSf90 software. In this study, 3 WssGBLUP approaches were investigated, each one using a specific G*: WssGBLUP, WssGBLUP Sum , and WssGBLUP Max. The WssGBLUP is the method presented by  Wang et al. (2012) , which consists in attributing one weight to each SNP. With WssGBLUP Sum and WssGBLUP Max  (Zhang et al. , 2016) , SNP on the whole genome are split into differentsized nonoverlapping windows, and the same weight is given to each SNP of the window. Windows of 2, 5, 10, 15, 20, 25, 30, 35, 40, 45 , and 50 consecutive SNP were tested. To compute these weights, the sum of all SNP weights present in the window was given to those SNP (WssGBLUP Sum ), or the SNP with the highest weight in the window was given to all SNP in the same window (WssGBLUP Max ).
Accuracy of the Genomic Evaluation The ssGBLUP used female phenotypes, pedigrees, and genotypes. In the French Alpine and Saanen dairy goat breeding scheme, genetic selection is performed on progeny-tested bucks and all these bucks born after 1993 were genotyped. The reference population used to assess the accuracy of genomic evaluation comprised only genotyped males even if genotypes of females were also used in ssGBLUP and WssGBLUP evaluations. This reference population was split into 2 subsets: a training set and a validation set. The training population included 307 Alpine bucks and 247 Saanen bucks born between 1993 and 2007, all the information on these animals (genotype, the pedigree of their ancestry and their progeny, and the phenotypes of their progeny) was kept in the data sets to estimate GEBV.
thank the  French Genovicap and PhenofinNational Research Agency (ANR) ,  Apis-Gene , specialized fund for agricultural and rural development (CASDAR), FranceAgriMer,  France Genetique Elevage , and the  French Ministry of Agriculture Agrifood, and Forestry ] and the European  3SR  project, which funded part of this work. The first author also received financial support from the  Occitanie region  and the  French National Institute for Agricultural Research (INRA, France) Sélection Génomique (SELGEN) program (INCoMINGS).
MOCK CATALOGUE CONSTRUCTION For this work we use a pair of mock catalogues built using the methodology of  Smith et al. (2017) , who populate the dark matter 10 -3 10 -2 10 - 1  10 0 10 1 10 2 10 3. Non-linear matter power spectra (top panel) and dark matter twopoint correlation function (bottom panel) for an MXXL cosmology for redshifts in range 0. 8 z 2, as indicated by the inset colour bar. All power spectra and correlation functions were computed using the CLASS and HALOFIT functionality in the NBodyKit python package  (Hand et al. 2018 ). In the upper panel a vertical dotted line indicates the wavenumber corresponding to a co-moving distance of 150 h -1 Mpc. In the lower panel the BAO peak is clearly visible at approximately 110 h -1 Mpc. a total matter density Ωm = Ω b + ΩCDM = 0. 25, where ΩCDM is the density in cold dark matter; a dark energy density ΩΛ = 0.
MOCK CATALOGUE CALIBRATION Before we can compute the galaxy bias it is necessary to calibrate the lightcone catalogues so that they reproduce, as closely as possible, existing observations of Hα-emitting galaxies. To calibrate the mock catalogue we apply dust attenuation to the galaxy luminosities, using χ 2 minimisation to determine the dust attenuation, AHα, that leads to the best agreement with a particular observational dataset. The dust attenuation AHα relates the un-attenuated luminosity, L 0 Hα , and attenuated luminosity, L att Hα , according to, log 10 L att Hα = log 10 L 0 Hα -0. 4AHα. (4) Here we choose to calibrate the catalogues to match the luminosity functions and cumulative number counts of Hα-emitters. Being able to reproduce the luminosity function helps ensure that we have the correct number density, whilst reproducing the cumulative number counts confirms that we have the correct total number of galaxies (e.
Observational datasets For calibration of the MXXL-15K and MXXL-2K lightcones we adopt (i) the cumulative number counts of Hα-emitters from the Wide Field Camera 3 (WFC3) Infrared Spectroscopic Parallels Survey (WISP,  Atek et al. 2010 Atek et al. , 2011) )  as measured by  Mehta et al. (2015) , and (ii) the luminosity function of Hα-emitters from HiZELS as measured by  Sobral et al. (2013). The WISP survey is a pure-parallel near-infrared grism spectroscopic survey that was carried out using the G141 (1. 2 -1. 7µm, R ∼ 130) and G102 (0. 8 -1. 2µm, R ∼ 210) grisms on the Hubble Space Telescope WFC3. Given the wavelength ranges of these grisms, WISP is able to directly detect the Hα emission line in galaxies out to z 1. 5. Note that the Euclid and WFIRST observing strategies will be comparable to the WISP observing strategy.
-16 10 -15 f lim [erg s -1 cm -2 ] 10 2 10 3 10 4 N(f Hα + [NII] > f lim ) [deg -2 ] MXXL-2K (dust free) MXXL-2K (WISP calib. ) MXXL-2K (HiZELS calib. ) 0. 7 < z < 1. 5 Mehta et al. (2015) 10 -16 10 -15 f lim [erg s -1 cm -2 ] 0. 8 < z < 1. 2 Mehta et al. ( 2015 ) Figure  4. Cumulative number counts measured from the MXXL-2K lightcone over the redshift range 0. 7 < z < 1. 5 (left-hand panel) and the redshift range 0. 8 < z < 1. 2 (right-hand panel). Black data points show the WISP number counts measured by  Mehta et al. (2015). In each panel the blue dotted line shows the MXXL-2K number counts assuming no dust attenuation. The blue solid lines show the number counts for the MXXL galaxies when assuming a dust attenuation, A Hα , that leads to the best match to the WISP counts in that redshift range.
2015). As such, for a given Hα flux limit the galaxies detected by WISP will typically have a lower stellar mass compared to those detected by HiZELS (J. Colbert, private communication). Therefore, given the relations observed between stellar mass and dust attenuation AHα  (Garn & Best 2010; Domínguez et al. 2013) , we expect the WISP dataset to typically have a weaker dust attenuation, as we can see in Fig. 5. Additionally, the thin-shell volumes probed by narrow band surveys mean that volume densities estimated by such surveys are sensitive to cosmic variance introduced by the presence of large-scale structure  (Mehta et al. 2015). As such, cosmic variance may also be contributing to the discrepancy in dust attenuation that our calibration procedure has returned. Using the HiZELS-calibrated attenuation results in Fig.
To assign redshifts to the randoms we do not sample a uniform distribution as this would not reproduce the correct shape of the galaxy redshift distribution across the bin. Instead we assign redshifts by first replicating the redshifts of the galaxies, until we match the required number of randoms, and then randomly shuffling the galaxy redshifts. We compute the angle-averaged galaxy two-point correlation function, ξ(r), using the open-source TREECORR package  (Jarvis et al. 2004 ), and the  Landy & Szalay (1993)  estimator, ξLS(r) = DD(r) -2DR(r) + RR(r) RR(r) , (5) where DD(r) are the weighted galaxy-galaxy pair counts, DR(r) are the weighted galaxy-random pair counts and RR(r) are the weighted random-random pair counts as a function of separation r in units of h -1 Mpc. For each redshift bin the correlation function is calculated 5 times, each time using a different random seed when applying the survey incompleteness (see § 4.