dataesr/scientific-paragraphs-categorization huggingface dataset:
It is difficult to find a natural threshold for the radial distance to separate reliable and imprecise data, so we chose to set the radial distance threshold as the median value of the radial distance on our first dataset. This choice has the advantage of testing the discriminating power of the algorithm and avoids balance issues in the training dataset, but it does not enable to capture how the algorithm fares in a setting where objects of dubious quality are rare and can be considered as anomalies. The binary algorithm then labels objects whose inferred radial distance is higher than the threshold as positives, and the other as negatives. For the input indicators, we use a set of fourteen intrinsic indicators, described in  (Maidaneh Abdi et al. , 2020). We chose to test a wide range of indicators , because we had little preconceived notion of which indicators would be the most useful, and because random forests methods are robust to the addition of indicators that carry little information.
Settings for the initial classification and the study of area transferability For our study, we use two datasets in two distinct study areas, to study the transferability of our algorithm. To do that, we first train and evaluate a random-forest classifier on the first dataset, and then study transferability using both datasets. For the initial classification on the first area, the random-forest algorithm is trained and validated through a three-fold cross validation approach, where buildings of the dataset are separated in three subsets of roughly equal sizes with a uniformly random procedure. When studying the transferability of the algorithm on a second distinct study area, we conduct three new experiments: For the construction of the random forest classifiers, our implementation uses 500 trees, with a maximum depth of 10, and for each split, 4 ≈ √ 13 indicators are considered.
There, buildings do not follow the strict structures of more densely populated areas, and one observes other kinds of dispositions, with more space between buildings and generally looser alignments. In this area, we found 29, 152 buildings in the OSM database, among which 22, 989 were successfully matched with an homologous object in the BDTOPO® (the French reference database). We constitute a dataset with 10, 530 buildings, picked randomly among the successfully matched OSM buildings. The second study area is the Gers department, which is situated in the South-West of France. It is a rectangle of length 72. 4 km and width 42. 8 km, with an area of 3, 138 km 2. Figure  4  shows the second study area and the distribution of buildings in this area. There, one observes large areas with very sparse density, and concentrations of buildings in small or middle-sized towns isolated from each other, the largest of them being Auch (with a population of 22, 000).
Initial classification Figure  5  shows the ROC curve for the initial classification experiment. The value of the AUC is 75. 88%. In Figure  5 , confidence intervals are obtained by applying ten three-fold splits of the dataset and training and evaluating ten random forests with three-fold cross-validation. Confidence intervals correspond to plus or minus one standard deviation over these ten realizations. According to  (Hosmer Jr et al. , 2013) , a classifier with an AUC in the range [0. 7, 0. 8] can be considered as providing acceptable discrimination. The value of the AUC could be considered as reasonably good for a classifier that only has access to intrinsic indicators. This result indicate that intrinsic indicators of OSM features truly contain relevant information about data quality (a classifier labelling completely at random would have an AUC of 50 %), and that these indicators can help reconstruct a significant proportion of the precision measure we used.
Area transfer results Figure  6  shows the four ROC curves corresponding to the four experiments decribed in Section 3. If we consider the pairs of algorithms that used the same validation set, we observe that their performances are similar between the algorithm trained on the same dataset and the algorithm trained on the other dataset. This results shows that the information captured by the algorithm for one dataset is still mostly valid for the other dataset, and thus that the results found in subsection 4. 2 can be transferred to new areas. This good transferability could be explained by the fact that the two study areas are not completely different from each other ; both are in the same country, and, more importantly, both span several cities, providing relatively diverse building distributions, which gives algorithms trained on these areas tools to classify buildings in other areas with good performance.
The drop in performance could be more important if the algorithms were tested on a more radically area, that would be situated in another country, or in with a distinct spatial context, like a seashore or a mountainous area. Yet, there are still meaningful differences between our study areas, the initial area does not feature sparsely populated country areas, and the second one does not feature densely populated and regular suburbs like the initial area. This tends to indicate that area transfer is possible even when the spatial context changes. We also observe that he AUC is slightly higher for algorithms validated on the second dataset, which could be explained by the fact that this dataset could be slightly easier than the first, in the sense that the link between intrinsic indicators and shape precision is stronger for this dataset.
CONCLUSION The aim of this paper is to assess the quality of polygons features representing building from OSM with respect to their shape. To reach this goal, we considered two matched datasets linking OSM and authoritative homologous buildings and we compute radial distances between homologous buildings to measure the similarity of their shapes. Based on these considerations, we have proposed a random-forest based classifier that distinguish OSM buildings with low shape quality (i. e. with a high radial distance) and OSM buildings with a low radial distance. Although the results, are promising, there are still not sufficient to be used without human intervention in operational context such as deriving a building reference dataset from OSM for countries where the reference data does not exist (e.
g. Djibouti). Further research can be considered. First, in this study, the intrinsic indicators we considered were all related to individual buildings. The performance of the classification model could be improved by adding context indicators that take into account the neighbourhoods of the buildings, either by computing aggregate indicators or by computing indicators that capture how much a building differs from its neighbours. Information on the editing history of buildings and the contributors who edited them could also be added to improve the assessment of the quality of OSM building data. Other measures than shape accuracy should also be investigated to get a more complete picture of data quality Second, area transfer could also be tested in areas with more distinct spatial context : either mountainous, or seashore areas, or areas in other countries, or anther continent.
If the first expansion proposed could be conducted with only OSM and BDTOPO® data, working on other countries could pose more significant problems : many countries do not have freely accessible reference building footprint data created by a national mapping agency, and even when such data exist, the specifications on how buildings are integrated and represented in the database can be very different from those of OSM. When it is the case, it is difficult to assess how much a computed discrepancy is due to data quality and how much is caused by specification differences. Finally, in the context of deriving a building reference dataset from OSM for countries where authoritative data does not exist, it is necessary to combine both positional accuracy and shape accuracy, knowing that positional accuracy is a relevant trigger, to measure the geometric accuracy of the building.
(1) the classifier is trained on the initial area and validated on the second area; (2) the classifier is trained on the second area and validated on the initial area; (3) the classifier is both trained and tested on the second area. We want all versions of the classifier to be trained with the same number of examples, and as we do cross-validation for the classifiers trained and validated on the same area, we set the number of examples in every training sets a two thirds of the smallest dataset. In our study, the initial dataset has less examples than the second, so we fix T, the size of the training sets, as two thirds of the size of the initial dataset. Then, for experiments where the classifier is trained and validated on the same area, we perform three-fold cross validation, with folds constituted randomly.
The survey, called the Calán/Tololo survey, aimed to rigorously test SN Ia as standard candles, using redshift as an accurate proxy for relative distance The Calán/Tololo survey used the CTIO Curtis Schmidt telescope to take plates, each covering a field of 25 sq-deg on the sky. The plates were developed and dispatched the next morning to the University of Chile's Department of Astronomy, where they were examined. Objects that could be supernovae were observed the next day using a 0. 9 m telescope equipped with a CCD camera, which had been exclusively set aside for that task. Between 1990 and 1993, 50 supernovae were identified, of which 32 were Type Ia  [159]. In addition, the Calán/Tololo survey provided a uniform photometric and spectroscopic dataset of supernovae which led to the discovery of a method which used Type Ia supernovae as reliable standard candles: the "Phillips relationship"  [160].
There are different Boltzmann solvers, starting from the pioneering code of Uros Seljak and Matias Zaldarriaga, called CMB-Fast, in the mid 1990s, as well as many others that came later. Today, the most used ones are CAMB and CLASS, which are coupled to MCMC solvers. There are six cosmological parameters to solve (the present amount of baryons and dark matter; the angle of the CMB acoustic scale; the reionization parameter; and two inflation signatures: the amplitude of primordial perturbations and the spectral index). This may depend on additional parameters for extended models, depending on, e. g. , the universe's curvature, the number of relativistic species, the mass of neutrinos, etc. The Hubble constant is tightly constrained in combination with density parameters, so this technique permits us to indirectly determine it.
Astronomers could not reach a consensus on how they could adjust their data to account for various effects that might lead to bias in their observations. Differences in the choice of secondary distance indicator methods were at the root of almost all the current debate at that time concerning the value of the Hubble constant. Later on, with the advent of the HST, uncertainties diminished. Using different techniques, Wendy Freedman et al. reported the final results from the measured a value of H 0 of around 72 to ca. 10% accuracy  [237]. This value was in the middle of the short and long timescales. Alternatives arose to end this stagnation. They were proposed by independent groups, and were not based on climbing the rugs of the cosmic ladder. They included, namely, gravitational lensing, the S-Z method, and CMB and BAO analysis.
3 +1. 7  -1. 8 km/s/Mpc at 2. 4% precision using the light from six multiply-imaged quasar systems  [239] ; although different results have been obtained since the modeling of the lens, mass distribution is an important systematic. These methods use late-time physics. On the contrary, early-universe physics at last scattering are anchored to the sound horizon, yielding CMB and BAO results that indicate smaller values of H 0 , around 67 km/s/Mpc. The uncertainties reported in these papers show a discrepancy of around four sigma, or larger in some cases. In Figure  12 , we show the curves of the ΛCDM model that predict SH0ES data together with clustering data from the BOSS collaboration, which measured the Hubble expansion rate as a function of redshift. The differences are clear. On the other hand, based on TRGB the CCHP collaboration recent results by Freedman et al.
In the meantime, this controversy has inspired many theoretical papers to attempt to resolve this discrepancy. Recent papers have appeared to explain the discrepancy of the Hubble constant determination using late-and early-universe physics. They are based on different types of physics, anchored in different scales, but both are apparently correct. A compilation of recent H 0 measurements is shown in Figure  14. It is clear that early physics determinations were smaller than those of late physics, except for those measurements from the TRGB obtained by CCHP. See further details in reference  [242] , and this reference  [243]  added during galley proofing corrections that accounts for the different modern techniques to measure H 0. In the near future, one hope lies in the emerging data that the JWST is certainly collecting.
In fact, there are already plenty of possible explanations to the Hubble tension; see, for instance, a review of solutions [245,Error! Reference source not found. Also, several hints have been put forward  [247]  suggesting that In the near future, one hope lies in the emerging data that the JWST is certainly collecting. The JWST is making new infrared and more precise optical observations available. As stated by  Freedman and Madore [143] , the JWST has a resolution three times that of the HST, with nearly ten times the sensitivity. Thus, it will reduce the Cepheids, TRGB, and Miras systematics, among others, in order to better determine the Hubble constant. At the same time, new CMB experiments are nearly ready, such as The Simons Observatory and plans such as CMB-S4. In addition, new BAO probes such as DESI, EUCLID, and LSST are underway; these will improve our precision in calculating the Hubble constant.
Introduction The Meteor Data Center of the International Astronomical Union (IAU) currently lists 921 meteor showers 1 , most of which are in the working list and are awaiting confirmation or more data. According to this number, on average, 2. 6 near-Earth objects per day were active enough in the past 10 3 -10 4 yr to produce such showers. If this is confirmed, it would greatly impact our current understanding of the Solar System. This prompted us to examine how IAU meteor showers are determined. Most methods used to find new meteor showers involve computing the radiant and an orbit dissimilarity criterion (D-criterion). D-criteria quantify the proximity of the orbits of two objects. Many D-criteria have been developed (e. g. Valsecchi et al. 1999; Jenniskens 2008; Rudawska et al. 2015)  but the D S H from  Southworth & Hawkins (1963)  is still largely used today.
The encounters were detected when the distance between the particle and the planet was smaller than its Hill radius. First, we worked with large particles (radius chosen randomly between 10 and 100 mm), on which non-gravitational forces (NGFs) have a negligible effect. We named these data sets IC1 BIN10100 and IC2 BIN10100, depending on the initial conditions used. Then, we also investigated the effect of the NGFs. For this purpose, we replicated IC1 and IC2, changing only the radius. It was picked randomly between 1 and 10 mm (BIN110) and then between 0. 1 and 1 mm (BIN011). We obtained six sets of particles: IC1 BIN10100, IC2 BIN10100, IC1 BIN110, IC2 BIN110, IC1 BIN011, and IC2 BIN011, which are summarised in Table  4.
Resonances The first maps we drew (Fig. 2 ) are from IC1 BIN10100 and IC2 BIN10100. We note the difference in colour scale between the two data sets: the chaos keeps rising after 500 yr. The smallest values are similar, showing the long-term stability of some particles. The maps present several dark vertical lines, where the chaoticity is much lower. Those lines are a perfect match to the mean-motion resonances (MMRs) listed in Table  5. Three of them are mostly present in the BIN10100 IC1 map, and are   slightly visible in BIN10100 IC2. Two more (the 2:3 and 3:4 with the Earth) only appear in the BIN10100 IC2 map. Two different MMRs fit the dark line at around 1. 27 AU: the resonance 1:6 with Mercury and the resonance 9:13 with the Earth. As the Earth plays a determinant role in the evolution of the Geminids, the resonance with the Earth seems more likely.
We interpret this as finding the lower bound in eccentricity for the Geminid meteor shower: meteoroids with too low an initial eccentricity will never meet with the Earth and are therefore not part of the shower. This allows us to check the data we already have on Geminids: particles whose eccentricity is too low are probably contaminants from another dynamical origin. A161, page 5 of 8 A&A 673, A161 (2023) Fig. 5. Maps from BIN011 (small particles). One map is drawn from the IC1 data set and one from the IC2 data set (see titles).
Impact of non-gravitational forces To investigate the impact of NGFs, we chose the data sets with smaller radius (BIN110 and BIN011). Maps from BIN110 produce very similar results to the previous section. However, maps from BIN011 (see Fig. 5 ) lack the dark vertical lines related to the MMR we analysed; they only present a uniform background for IC1 and the gradient in eccentricity for IC2 (see previous section for explanation). Analyses of the interaction between MMRs and NGFs have been conducted before  (Liou & Zook 1997). Here, to better understand this effect, we studied the evolution of a few particles. We chose five particles from BIN10100 IC2 characterised by their initial position with respect to the largest MMRs (2:3 and 3:4 with Earth). Particles n • 1 and 2 are well outside these MMRs, particle n • 3 is close to them but not inside, and particles n • 4 and 5 are initially inside the MMRs.
We then selected the clones of all of these particles in the BIN110 IC2 and BIN011 IC2 data sets. This allowed us to compare the evolution of particles that differ only from their radius, and thus to measure the influence of MMRs and NGFs for each size. We plotted the evolution of orbital elements and added grey lines that mark the MMRs with the Earth (their respective size corresponds to the size visible in the BIN10100 IC2 map). The resulting Fig. 6  shows the strong diffusion of the small particles from the NGFs, which prevents them from being captured by the MMR. This explains why they do not appear on the BIN011 maps. Particles of intermediate radius are influenced by MMRs but their evolution becomes slightly blurred. This blurry aspect is even more pronounced in the evolution of small particles, with some exotic points that do not seem to be aligned with the general evolution of the orbital elements.
Acknowledgements. We would like to thank  Giovanni Valsecchi ,  Massimiliano Guzzo  and  Elena Lega  for their interesting insights and advice. A161, page 7 of 8 A&A 673, A161 (2023)  Ariane Courtot  acknowledges support from the  École Doctorale d'Astronomie et d'Astrophysique d'Île-de-France  ( ED 127 ).
The length of all clones was restored to 314 nt, corresponding to the average size of the captured fragments. Each DHS region was annotated with a unique ID, then each clone was annotated by the ID of the overlapping region. We extended the original DHS coordinates by considering the start of the first clone and the end of the last clone overlapping the DHS region using BedTools and python homemade scripts. These regions are considered as the extended DHS. The extended DHS regions were split using bins of 50 bp. Using a R homemade script, we computed the activity of the extended DHS regions computing the fold change of the sum of the clones overlapping the same region in the transfected condition over the non-transfected condition. The count of clones was normalized using FPKM and activities were centered.
Epigenomic analyses ChIP-seq datasets from P5424 cells and DNaseI-seq and ChIP-seq datasets from mouse DP thymocytes were downloaded from Gene Expression Omnibus (GEO) database as detailed in Supplementary Table  S1. ChIP-seq data were processed as described in  (31). For average profiles and heatmaps, Wiggle files were converted to BigWiggle files using wigTo BigWig  (43). Average profiles were generated with deepTools (44) using MNase-seq, DNaseI-seq and ChIP-seq signals from bigwig files around the DHS center (±5 kb for histone modifications, ±1 kb for TFs and chromatin features). For matrix clustering, H3K27ac, H3K4me3, H3K9me3, H3K27me3 histone marks were used as well as CTCF and DNaseI. Then clusters were generated using plotHeatmap with 8 k-means option in order to isolate as possible the different regions.
Association of silencers with variable genes RefSeq genes in a window of ±100 kb around all silencer candidates were retrieved using BedTools window (v2. 28. 0). The different conditions were annotated using the mm9 file from UCSC. To retrieve the top 5% most variable genes, the variance through T cell differentiation was computed for each gene using merged replicates of the RNA-seq dataset  (45). Those 5% most variable genes were considered as T cell regulated genes.
Motif research analysis and clustering The HOMER  (47)  software was used to perform research motif analysis with the option 'findMotifsGenome. pl input. file. bed mm9 output. file. bed -len 6,8,10,12,15 -size given -bg dhs'. We choose as background all the DHS sites in order to isolate the core silencer-specific binding sites. This method was also used within STR contained in the core silencers subregions. From the known motif output files, a list of transcription factors (TFs) was obtained. The relative matrix files were downloaded from JASPAR 2018 database  (48). In order to reduce the redundancy, the RSAT matrix clustering tool  (49)  was used with default parameters to cluster motifs based on their sequences. The best transcription factor P-values provided by HOMER were kept by cluster and the expression of the individual genes in P5424 was retrieved (Supplementary Table  S4 ).
Functional analysis Functional enrichment analyses of putative silencers identified in each condition and putative enhancers coming from the SCP1 strategy were produced using a custom pipeline to automate multi-sample queries to the GREAT web-service. In summary, putative silencers regions and putative enhancers regions were queried with rGREAT R package (https://github. com/jokergoo/rGREAT,http: //great. stanford. edu/public/html/) against GO Biological Nucleic Acids Research, 2023 5 Process. Genomic association rules with genes were done using default GREAT 'Basal plus extension'. Significant terms were filtered as those having Binomial Fold Enrichment higher than 2 and both Binomial and Hypergeometric tests Benjamini-Hochberg adjusted P-values lower than 0. 05. GOSemSim R package  (55)  was used to compute the Wang similarity distance between all terms.
Gene expression analyses Total RNA from P5424 from WT and deleted clones in nonstimulated or PMA/Ionomycin treated conditions was extracted using the RNAeasy mini kit (Qiagen). Three micrograms of RNA were treated with DNase I (Thermo Fisher Scientific) and were quickly reverse transcribed into cDNA using Superscript VILO Master Mix (Thermo Fisher Scientific). The Real-time PCR was performed using Power SYBR Master Mix (Thermo Fisher Scientific) on a Quant Studio 6 Flex instrument (Thermo Fisher Scientific). Primer sequences are listed in Supplementary Table  S6. The expression of the gene was normalized to that of Rlp32. Relative expression was calculated by the C T method, and all the data shown are reported as fold change over the control. For each of the cell clones, from the three independent RNA/cDNA preparations, the Student's t-test was performed (unpaired, two-tailed, 95% confidence interval).