dataesr/scientific-paragraphs-categorization huggingface dataset:
Keyframe magnifier and video metadata Implicit knowledge from a scene depicted in a video keyframe, such as car plates, banners, signs, shop names, points of interest, etc. can be used by journalists to determine whether an image or a video keyframe is really related to the location where an event is allegedly taking place. To support the inspection of keyframes, the plug-in offers a "magnifier" feature (Figure  5 ) based on the Elevatezoom 14  JavaScript library. We have added two algorithms that the user may trigger: one to enhance the sharpness of the image and the other, a bicubic enhancement filter, to increase the image size without harming the image readability. This functionality allows journalists, if the quality of the image allows it, to detect meaningful details within the image to confirm a location or identity, or to spot pixel incoherences that may alert of possible tampering.
TARGETED SYSTEM DESCRIPTION The electronic devices market proposes different CPU and GPU architectures. Each family of architecture comes with its own advantages and-naturally-complexities. One of the main goals of this work is to construct a general dynamic model for embedded CPU-GPU chips. Thus, for testing and undertaking experimental validation, the choice of a System on Chip (SoC) to be studied is firstly based on the software it runs. For this study, we settled on an Android TM phone. Android TM phones are very popular, and with its programming framework, aoolications are effortlessly transferable to other devices. Moreover, Android is built on top of the popular open source operating system (OS) Linux. The availability of the code for this OS makes some of the needed parameters accessible for reading.
VARIABLES AND DATA ACQUISITION To analyse and model the dynamics of the CPU-GPU chip, one must initially recover the relevant readings characterising the operating state of the system. For our modelling purposes, the needed readings are the loads of both the CPU and the GPU, the working frequencies, temperatures of the cores and other components, the voltage of each core and the power delivered by the battery. All of these variables-except for the temperature-have very fast variations. Therefore, the quality of the results depends directly on the chosen sampling period. Hence, we choose the minimum period necessary to follow the changes of the fast changing variable: the frequency. Frequency is evaluated by by frequency Governor (see paragraph 4. 1). Hence, the minimum period for this program to reevaluate the load and change of the frequency accordingly was set as sampling period (in this case study, T s = 20 ms).
CONCLUSION A data acquisition and estimation systems have been developed for a CPU-GPU embedded chip. Measurements are acquired on the fly for operational state estimation. The estimation model developed is validated experimentally. The parameter and variable estimation is structured as an interconnected system with variable structure. The modularity of the estimation system is easily adaptable to changes in the system structure and its operation modes. In future works, the next step after developing the model is to use it to monitor the operating state and drifts in characteristics of the chip.
Sampling The dataset presented here consists of 29 samples and allows us to present a ~1300 km E-W LTT transect of the NWAC from the Atlantic passive margin to the Panafrican suture in the east. Our new data adds two AFT data to the 11 existing AFT data in the western Reguibat Shield  (Leprêtre et al. , 2015) , four new AFT and three new AHe datasets to the existing four AFT and one AHe datasets  (Leprêtre et al. , 2014)  and eight new AFT and 6 new AHe datasets for the eastern Reguibat Shield (Fig. 1b ). For the reader's information, the complete dataset encompassing samples from  Leprêtre et al. (2014 Leprêtre et al. ( , 2015) )  are presented in Supplementary data (Tables  S1, S2 ). The Reguibat Shield was geographically divided into three regions on the basis of the LTT results. With the exception of one AFT age (256 Ma from TEN1153 sample, in central Reguibat Shield), AFT age ranges largely overlap between western and central domains (Fig.
(2009)  and  Gautheron et al. (2009). These models consider that recoil damage annealing follows kinetics similar to fission-tracks, after the results of  Ketcham et al. (2007). Different closure temperatures can be monitored through the relationship of AHe age vs. efficient uranium (eU). The eU represents the amount of radioactive elements contributing to the 4 He production and its amount controls the closure temperature for a given thermal history  (Shuster et al. , 2006). Thus, scattered AHe ages emphasize, at first order, differences in AHe closure temperatures between replicates in the same sample. Moreover, the damage-annealing rates can be influenced by the apatite chemistry  (Gautheron et al. , 2013; Djimbi et al. , 2015). In particular,  Gautheron et al. (2013)  demonstrated that complex thermal histories in slow moving geological settings can greatly enhance the scattering in the data given the chemistry differences between grains.
Modeling procedure Inverse thermal modeling was performed using the software QTQt  (Gallagher, 2012)  while HeFTy  (Ketcham, 2005)  was used for forward modeling. We used the fission track annealing kinetics of  Ketcham et al. (2007)  and the He-trapping/diffusion model of  Flowers et al. (2009). The  Flowers et al. (2009)  radiation damage accumulation and annealing model is empirically calibrated and is not well-representative of the physical processes of diffusion compared to the  Gautheron et al. (2009)  model but it gives better predictions, mainly for low-eU apatite (< 20 ppm; majority of our samples, see Table  2 ). QTQt works with a probabilistic Bayesian approach for inverse modeling. It samples numerous thermal histories and from them builds a population of models selected according to the degree of agreement between data and model-this is the burn-in phase.
1 ). For eastern Reguibat Shield, two major stratigraphical constraints were used: (1) unconformable Cambrian deposits on the NWAC basement  (Trompette, 1973; Deynoux, 1980 for Taoudeni Basin; Boote et al. , 1998 for Tindouf Basin) ; (2) unconformable poorly-dated upper Cretaceous deposits on the eastern boundary of the Reguibat basement. We used a large temperature box (20-180°C) for the 470-230 Ma interval as no constrain exists for this period. Firstly, a general thermal path was modeled with inverse modeling for eastern Reguibat shield samples that have both AFT and AH data (Fig. 6a-c , 7a; Fig. S2-7 ). By combining AFT and AHe datasets, we can achieve a better resolution of the thermal history path. The important temperature/time (or (T,t)) nodes obtained in this first modeling were used as constraints for other eastern Reguibat Shield samples where only AFT data were available (CH2 and DEG6, Fig.
Firstly, we carried out a phase of forward modeling to explain the AHe age-eU positive correlation (Fig. 5b, 8 ). In this phase, we consider the AHe data of all samples as these data should behave coherently given that they are restricted to cratonic areas and the entire AHe dataset samples a larger eU range than any one sample. Various thermal paths were tested to check their AHe predictions and compare them to the measured data. Given that the AFT ages are all younger than 250 Ma, the tested thermal history paths are chosen in order to display cooling initiated by the Early Jurassic. Using the initial forward modeling step, we have identified the scenarios that best explain the observed AHe data. These scenarios yield (T,t) constraints, i. e. compulsory nodes in the (T,t) space through which the thermal path must go to fit the AHe age-eU relationship.
Results The new and published AFT and AHe ages presented for 29 samples give the first comprehensive dataset across the whole Reguibat Shield. Based on the data location and their thermal histories, the studied area has been divided into western, central and eastern domains. Published data from the western and central Reguibat Shield  (Leprêtre et al. , 2014; 2015)  are only briefly summarized, but the complete Reguibat dataset is presented together in Tables  S1  and S2.
4 ± 0. 3 to 12. 0 ± 0. 2 µm and are undistinguishable from the 11. 9 ± 0. 2 to 12. 4 ± 0. 2 µm range for published data (Fig. 3, 4 ). Dpar measurements range from 1. 6 ± 0. 2 to 2. 1 ± 0. 5 µm. AHe ages corrected from alpha ejection range from 38 ± 3 to 396 ± 32 Ma (Table  2 ) with eU content ranging from 7 to 83 ppm. Overall, they are younger than AFT ages with the exception of some replicates from samples AL10 and TGH3163. AHe age vs. eU plot shows a general positive correlation (Fig. 5b ). In the western Reguibat Shield, the two new samples yielded AFT ages of 137 ± 12 Ma and 118 ± 10 Ma with no measurable confined lengths (Table  1 ). Published AFT ages range from 107 ± 8 to 175 ± 16 Ma, with mean track length (MTL) ranging from 11. 8 ± 0. 2 µm to 12. 5 ± 0. 2 µm  (Leprêtre et al. , 2015).
This cooling is variable among samples. At the very end of the Jurassic, samples were generally at temperatures higher than 60°C (except for TL3) and were not undergoing cooling. Results are similar for AFT-dated samples CH2 and DEG6. Whereas the western and central domains suffered post-Early Cretaceous reheating, samples from the eastern domain cooled by 30 ± 10°C and stayed at relatively low temperatures up to the present-day. For the central Reguibat Shield, the forward modeling with AHe datasets clearly favored the scenario involving the occurrence of a Jurassic-Early Cretaceous cooling event followed by a lowamplitude reheating step (Fig. 8 ; Table  S3 ). Five forward scenarios were tested: a small Middle Jurassic-Lower Cretaceous cooling with stable thermal conditions during Late Cretaceous (HT1); a large Jurassic-Early Cretaceous cooling (> 40°C) bringing samples close to the surface (HT2 and HT3) before reheating episode; a continuous slow cooling (HT4); a major cooling between Early and Late Cretaceous (HT5).
AFT and AHe ages Data show a clear increase of AFT and AHe ages towards the east and both datasets also display more scattering eastwards (Fig. 2 ). From the LTT data and our thermal modelings one can clearly see that samples stayed in the Partial Retention Zone (PRZ)/Partial Annealing one (PAZ) for a long time. This more or less protracted stay within the PRZ strongly impacts the distribution of AHe ages with respect to the eU (Fig. 5 ). For example, the curved positive trend of the AHe age-eU plot in figure 5b can be explained by low-eU apatites experiencing greater diffusive loss of He (causing younger AHe ages) than high-eU apatites during a short time in the PRZ during Early Cretaceous (10-30 Myrs at T> 50-60°C; Fig. 7b ). This short stay within the PRZ was sufficient to partially reset the low-eU AHe ages in the case of the central Reguibat Shield.
, 1999; Verati et al. , 2005). Basal heat flow might have been higher during CAMP magmatism and/or rifting, but would not be anymore anomalous during Cretaceous (Fig. 7b-c ). LTT data from borehole samples in the cratonic Anti-Atlas domain, north of the Tindouf Basin  (Sehrt, 2014)  estimate an average 40°C. km -  palaeogeotherm during the Early-Middle Jurassic/Early Cretaceous without measurable changes afterward. We thus favor the hypothesis of burial of the Reguibat Shield by a Paleozoic sedimentary cover. This cover was removed during Lower-Middle Jurassic/Lower Cretaceous erosion in the western and central Reguibat Shield and fed the clastic deposits of the TLDB during the Early Cretaceous. Similarly, the reheating in the western and central domains between 150 and 100 Ma is interpreted as a re-burial of the samples as no large magmatic anomaly is recorded  (Matton & Jebrak, 2009).
The timing and, to a lesser extent, the amplitude of the reburial differs slightly to that proposed in  Leprêtre et al. (2014). This can be attributed to the additional AHe datasets used for thermal modeling in the present study. The models differ in the onset and duration of the heating after the Upper Jurassic/Lower Cretaceous transition for models of the central Reguibat (Fig. 7b ;  Leprêtre et al. , 2014, their figure   5 ). Our present study refines the  Leprêtre et al. (2014)  models and adds more consistency to them. The heating phase occurred from the end of Early Cretaceous up to the beginning of Late Cretaceous (Fig. 7b ) when the major Cenomanian-Turonian flooding is recorded. Plus, the beginning of cooling occurred at the beginning of the Late Cretaceous when the Africa-Europe convergence began.
We assert that AHe datasets have helped to better constrain the post-Jurassic evolution of central Reguibat Shield, in comparison with our previous investigations using only AFT analysis. Assuming a palaeogeotherm of 30-50°C. km -  during the cooling and reheating and using the duration and amplitude of the thermal changes predicted by our thermal models, we estimated the eroded/deposited rock thicknesses for each event. The ranges of Jurassic/Cretaceous erosion are between 0. 8-2. 1 km and 1-1. 6 km from western to eastern Reguibat Shield respectively. The amplitude of re-burial reaches 0. 4 to 1. 7 km for the western and central domains, with no re-burial in the eastern Reguibat Shield (Fig. 7a ). During the re-burial, the southern TLDB record Albian-Turonian thicknesses that can reach 1 km  (AUXINI, 1969) , which are compatible with our estimates.
The former N/S Panafrican faults separating the WAC from the Tuareg Shield were reactivated as the northern Tuareg Shield underwent anticlockwise rotation resulting from the opening of the South Atlantic Ocean. The cooling in the eastern Reguibat Shield might stem from the large-scale tectonic reorganization at that time. Few other studies have dealt with the evolution of cratonic domains on the NWAC. In the Anti-Atlas north of the Tindouf Basin,  Ruiz et al. (2011)  and  Oukassou et al. (2013)  demonstrated using LTT data that the northern tip of the craton also underwent uplift and erosion during Jurassic/Early Cretaceous times before a reheating step. Further east (Ahnet-Reggane-Timimoun basins), based on organic matter maturity, AFT and zircon fission-tracks from several wells,  Logan & Duddy (1998)  proposed a general cooling from Triassic to Early Cretaceous.
LTT record on the onshore eastern American passive margin Outcrops from the Appalachian orogen and surroundings basins have been used in numerous LTT studies but have never been considered as a whole dataset  (Crowley, 1991; Wang et al. , 1994; Boettcher & Milliken, 1994; Roden-Tice et al. , 2000; Roden-Tice & Wintsch, 2002; Grist and Zentilli, 2003; Lorencak et al. , 2004; Spotila et al. , 2004; Roden-Tice & Tice, 2005; Taylor & Fitzgerald, 2011; Roden-Tice et al. , 2012). We have collated all of the existing AFT data along eastern North America (Fig. 9a, c ). A clear division in AFT ages is observed with young AFT ages along a 600-km wide strip of land parallel to the hinge line (i. e. the place where the continental crust begin to thin toward the ocean; Fig. 1b, 9a ). The "young" AFT datings range between c.
a. 80-180 Ma, with the exception of slightly older samples along the Nova Scotia segment in the north. AFT ages become progressively older landward (up to 600 Ma, beyond 600-700 km off the hinge line). The spatial pattern of AFT age is strikingly similar to the NWAC (Fig. 9b-c ; after the data of  Ghorbal et al. , 2008; Saddiqi et al. , 2009; Ruiz et al. , 2011; Oukassou et al. , 2013;  this study), with ages being younger (c. a. 100-220 Ma) from the coast to 600 km. AFT-MTL plots are given for both margins (Fig. 9c-d ) and show that there is no relationship between the age of rifting and length datasets. This significant observation means that for samples within the 600/700-km proximal land strip, the effects of the Lower Jurassic rifting (c. a. 190 Ma) are barely distinguishable. Both margins seem coherently affected by a more regional event.
Leprêtre et al. , 2015). 1: conglomerates and coarse detrital sediments; 2: coarse to fine sandstones; 3: shales with variable sandy proportion; 4: Shales and clays, sometimes interbedded with limestones; 5: limestones. For the western domain, black thermal paths correspond to the thermal modelings realized by  Leprêtre et al. (2015). Due to the new AHe data, we have made new thermal modeling for the data from the central domain obtained by  Leprêtre et al. (2014). For all modelings, the way we have defined the constraint-boxes is explained within the text. AFT datings along perpendicular profiles, from the hinge line at x=0. For the African AFT profile, data are from:  Ghorbal et al. (2008) ;  Saddiqi et al. (2009) ;  Ruiz et al. (2011) ;  Oukassou et al. (2013) ,  Leprêtre et al. (2014 Leprêtre et al.
( , 2015) )  and this study. For the American profile, data come from the studies quoted in the text. Smaller panels on each profile show the MTL vs. AFT age plot. The grey bar indicates the age range of the rifting of the Central Atlantic Ocean  (Labails et al. , 2010). Cretaceous. This compilation is based on works by  Schettino & Turco (2009) ,  Labails et al. (2010)  and  Kneller et al. (2012). Fig. 12 : Representative thermal paths from the onshore eastern North America. Thermal paths are coming from: (a) Nova Scotia in the northern proximal passive margin  (Grist & Zentili, 2003) ; (b) the Michigan Basin, deep in the interior of the continent  (Wang et al. , 1994) ; (c) the southern Canadian Shield  (Lorencak et al. , 2004) ; (d) the Northern Appalachian  (Taylor & Fitzgerald, 2011).
Text S2 -influence of apatite chemistry on the single-grain AHe ages scattering To investigate the influence of the chemistry on the scattering of the single-grain AHe age, we used the mean thermal paths obtained through thermal modeling for all samples with AHe datasets. With forward modeling, we consider the whole range of Dpar and grain size of single grain within each sample. The grain size range is approximated through the equivalent sphere radius (Rs; see Table  2 ). The chemistry variations are taken into account through the rmr0 factor, as defined by  Ketcham et al. (2007). An empirical relationship links the Dpar and the rmr0 through the following equation: rmr0 = 0. 84 x [(4. 58-Dpar)/2. 98] 0. 21 , the rmr0 being used as the chemical determinant for the forward modeling with HeFTy for our tests.
Acknowledgements We thank  Eric Douville  for accessing the  ICP-MS (LSCE, Gif-sur-Yvette ) and  Louise Bordier  for the U-Th measurements. This work has been supported by the  Académie Hassan II des Sciences, Casablanca, Morocco. We acknowledged  P. F. Green  and an anonymous reviewer for the remarks and comments that greatly helped clarifying and precising numerous points in the present manuscript.
When the structure of the genotyped dataset is more complex, problems such as double counting of contributions from pedigree and phenotypes, and preselection bias  [4]  reduce accuracy. SNP best linear unbiased predictor (SNP BLUP) is equivalent to genomic BLUP (GBLUP) or BLUP with a genomic relationship matrix (GRM)  [2]. Single-step GBLUP (ssGBLUP), which is an extension of GBLUP, can incorporate pedigree, genomic, and phenotypic information jointly by using a relationship matrix that combines pedigree and genomic relationships  [5] ; an equivalent ssGBLUP based on SNP effects only has also been implemented  [6]. Due to its simplicity and accuracy, ssGBLUP is now a method of choice for genomic evaluation in many livestock species. When the number of genotyped animals is small, the use of Bayesian regression was found to increase accuracy of genomic prediction for many traits  [7, 8].
APY has a cubic computational cost for the size of the core subset but cost is only linear for the remaining animals. If the size of the core subset is not too large, APY can successfully invert GRM for millions of animals at a small cost. When tested in Holsteins, APY based on any core subset of more than 15,000 animals maximized the accuracy of genomic prediction  [11]. APY was successfully used with several datasets that included up to 500,000 genotyped animals  [12] [13] [14] , which indicates that the dimensionality of the genomic information is indeed limited. Misztal  [15]  suggested that the dimensionality of the genomic information is proportional to effective population size (Ne). In simulations that involved populations with different Ne, accuracy was maximized when the number of animals in the core subset was equal to 4NeL, where L is genome length in Morgan  [16].
In particular, a low value of this ratio for a trait-specific GRM that results in the highest accuracy of GEBV would indicate that relatively few genes control this trait. Recent advances in sequencing methodologies have renewed the interest in finding genes or QTN. If a trait is influenced by n QTN, the rank of the trait-specific genomic information (including GRM) is n, since only the QTN need to be used for the evaluation, and the accuracy of the genomic prediction reaches 100% if the dataset is large enough to estimate all QTN effects accurately. More realistically, if only a fraction of the causative QTN is identified, then both causative and non-causative SNPs must be used in the analyses. Some studies showed no improvement in accuracy of genetic evaluations when sequence data was included  [18, 19] , whereas other studies reported a small improvement  [20] [21] [22] [23] [24] [25].
Allele frequencies were calculated using all genotypes in G. Assume a priori unequal SNP variances: where σ 2 s,i is the variance of the i-th SNP effect and n is the number of SNPs. Then, it is possible to use a a = Zs, var(s) = Iσ 2 s , var(a) = Gσ 2 a = ZZ ′ σ 2 s , G = ZZ ′ σ 2 s σ 2 a. σ 2 s = σ 2 a m i 2p i q i , G = ZZ ′ σ 2 s σ 2 a = ZZ ′ m i 2p i q i , var(s) =     σ 2 s,1 0. 0 0 σ 2 s,2. 0. 0 0. σ 2 s,n     , SNP-BLUP with these variances  [27]  or, alternatively, GBLUP with a "weighted" genomic covariance matrix Var(a) = Zvar(s)Z ′. Specifically, GRM can include a diagonal matrix D of "weights", such that: where the factor m i=1 2p i q i is introduced for compatibil- ity with the current software so that for the unweighted GRM D = I and m is the number of SNPs. The contribu- tion of locus i to the covariance matrix G must be equal to its contribution in Zvar(s)Z ′ : Thus, In other words, d i is proportional to σ 2 s,i.
Simulation Using the software QMSim  [29] , we simulated a livestock population under selection for a single quantitative trait that has a heritability of 0. 3. A historical population was generated by mutation and drift over 1000 generations, expanding from 1000 to 10,000 individuals, in order to create initial linkage disequilibrium (LD). For each replicate, 180,000 animals were simulated across 11 overlapping generations. Phenotypes were available for all animals in generations 6 to 10. For the first generation, 15,000 males and 15,000 females were simulated. A litter size of one individual was set resulting in 15,000 progeny in each generation, with a male to female ratio of 1:1. Sire Var(a) = Zvar(s)Z ′ = ZDZ ′ m i=1 2p i q i σ 2 a = Gσ 2 a , z i z ′ i d i 1 m i=1 2p i q i σ 2 a = z i z ′ i σ 2 s,i.
We considered three cases: blending with either α = 0. 05 or 0. 01 of A 22 , or with α = 0. 01 of the identity matrix. The inverse of G b was obtained either by direct inversion or by APY  [15]. In the latter case, the number of core animals was either (a) the number of the largest eigenvalues explaining 98% of the variance of G b , or (b) twice the number of simulated QTN. The quality of predictions was assessed for the 5000 genotyped animals in the last generation. The accuracy was measured as the Pearson correlation between the genomic EBV (GEBV) and the simulated true breeding value (TBV). All calculations were done by using the BLUPF90 program suite  [33] , preGSf90  [34]  to calculate the genomic matrices and postGSf90 for the GWAS  [34]. All analyses were replicated 10 times. G 0 = aI + bG,.