dataesr/scientific-paragraphs-categorization huggingface dataset:
Results A set of check points is measured for each photograph. They are homologous points between topographic database and image, and are selected at cross-roads for one-half and at building corners for the other half. The quality of georeferencing is given in Table  2  and Table  3. Table  2  shows image residuals, which are distances between image coordinates and coordinates of ground points projected on the image. Table  3  shows terrain residuals, which are distances between ground coordinates and vector of back projected image points. It has to be noted that the check points are themselves soiled by a pointing error which is more important than errors met with current images. It is due to the important noise in images, to the difficulty to retrieve the same objects in different time data between a completely changed surrounding, and to the fact that invisible modifications may happen, such as road enlargement or translation, or building raising.
The projection function of Equation 1 is estimated from check-points, to check both projection model relevance and check-points consistency. Doubtful check-points are then removed or replaced when possible. Check-points precision is set to about 3 pixels. Besides topographic database precision, reliability and completeness are not checked or discussed here, but its precision is said to be about one meter. In addition, when manually processing historical photographs, GCP are usually removed when their residuals are higher than one meter to match final orthoimage quality requirement. Hence this value is taken as goal precision. Although this method involves several iterative computations at different scales, it takes about 2 minutes per image to complete all the steps, including line detection, topographic data extraction and multi-scale registration.
CONCLUSIONS A method to automatically georeference old analog photographs was presented. Digitized photographs are registered one by one with a topographic database as reference, which contains road network and building limits in the form of 3D polylines. A multi-scale approach is used to deal with very coarse initial georeferencing. At each scale, iterative processing matches detected lines in images with topographic lines and estimates registration parameters, based on three criteria: distance, orientation difference and overlap between matched lines. Data selection, line extraction, registration model and parameter computation are adapted to current scale. The high number of buildings is taken into account by a local geometric filtering. The algorithm is tested on images, which represent the different kind of landscapes (rural, urban and suburban areas) that are subject to very important changes.
Results are highly promising as georeferencing precision measured on check-points is much improved, and because the registration converges in all scenarios even when ground control point retrieval is an extremely difficult task for operators. Moreover topographic building limits are useful data in georeferencing as they lower check-point residuals and make registration more reliable on off-ground objects. This method can also be used for recent photograph georeferencing when automatic algorithms for GCP extraction fail. However, results do not reach expected quality requirements yet. Further improvements include registration precision enhancement by filtering building line matches and by using statistical discrepancy lowering provided by bundle adjustment on whole data set when available.
Introduction Today, security has become a fundamental point in the information era. Just as all eyes are on this critical point and it affects all levels from the individual human being to international corporations. The cyber-crime has grown exponentially since the massive use of Internet and mobile devices, always protected by the anonymity of the criminals. The new technologies and their ability to analyse large amounts of information (Big Data) have made a breakthrough, the anonymity of aggressors is no longer such. Every step we take is reflected on the Internet in different ways, through different databases scattered around the globe, is what has been called digital fingerprint. Every adverse comment we left on Facebook, every tweet that may contain traces of a past or future crime, every comment in a blog that may damage the self-image, all of them can be now analysed and linked with our personal data.
Extracting Information from the Web The semantic web has tried to simplify the process of extracting information, but the fact is that today the semantic capacity has not yet been implemented in most of the websites. Scraping tools (download web content most used) today are Wget 1 , cURL 2 or Pillow 3 , although there are numerous libraries for most languages as Scrapy 4 and Beautiful Shop 5 (both for Phyton), Html Agility Pack (. NET) 6 , JSoup (Java) 7 , htmlSQL (PHP) 8. Beautiful Shop is the most complete of all bookstores because it supports even malformed HTML. The crude extract of the contents of the web is the most developed stage of the analysis and involves minor problem. The difficulty increases when we talk about information processing to get useful information obtained. There is no doubt that the nature of the information obtained depends on the type of paper used, but also we must consider that in each document can be found associated information does not correspond to the nature of this: for example, a photograph is mainly graphical information, but the metadata of the image file may also contain information about where it was made (many GPS mobile devices leave a mark) or the date and time it was made.
://www. gnu. org/software/wget/ 2 cURL. Command line tool for transferring data with URL syntax. http://curl. haxx. se 3 Pillow. Tool for Phyton for transferring data with URL syntax https://pypi. python. org/pypi/Pillow/ 4 Scrapy. Screen scraping and web crawling framework http://scrapy. org/ 5 Beautiful Soup. Scraping framework for Python. http://www. crummy. com/software/BeautifulSoup/ 6 Html Agility Pack L. Scraping framework for PHP. https://github. com/hxseven/htmlSQL 7 JSopup. Scraping framework for Java. https://github. com/hxseven/htmlSQL 8 htmlSQL. Scraping framework for PHP. https://github. com/hxseven/htmlSQL.
Dataset description The dataset we use is from the System T1 data of the Rome Air Development Center (RADC)  [22]. Although this is quite an old dataset, it is widely used and it contains both fault detection data and fault correction data. Additionally, it contains information of testing effort, which is characterized by computer time (CPU hours) consumed in each week. Hence this familiar dataset is used for illustration. The cumulative numbers of detected faults and corrected faults during the first 21 weeks are shown in Table  1. During the time span, totally 300. 1 CPU hours were consumed. Till the end of the testing, 136 faults were detected and all of them were corrected.
Select the most suitable TEF for this dataset Parameters in the different types of TEF are estimated by Least Square Error (LSE). In order to select a TEF that best fits this dataset, some criteria are used to compare the performances of different TEFs. (1) RMSE The Root Mean Square Error (RMSE) is defined as RMSE ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 1 n ∑ n i ¼ 1 ðwðt i ÞÀw i Þ 2 s ð26Þ A smaller RMSE indicates a smaller fitting error and better performance.
Performance analysis The paired model (  9 ) and (  10 ) is used for illustration. After substituting the cumulative logistic testing effort function  (24)  with the estimated parameters N ¼321. 482, η¼0. 3826, and A¼423. 788 into (  9 ) and (  10 ), the paired model is applied to fit against the real dataset. The LSE estimation of the parameters are obtained as a ¼100. 97, b ¼0. 0094, α¼0. 0021 and c¼ 0. 0418. According to the estimated parameters, there are about 100. 97 faults at the beginning of testing. The total number of faults when t approaches infinity is expected to be lim t-1 aðtÞ ¼ 198:01. Fig. 2  is plotted for graphical illustration. 6. Software release policies Determination of the optimal release time is a critical decision for software projects and has been studied in many papers  [1, 11, 20].
APD counts were acquired using a FPGA-based data collection board (PCIe-7852R, National Instruments) and custom acquisition software programmed in LabView (National Instruments). Recorded pixel values were linearized (on the DAQ card) to account for the sinusoidal velocity profile of the resonant mirror and normalized according to the pixel dwell times such that the center pixel was divided by unity. Dual-color imaging of ATTO647N and STAR470SX were performed using sequential frame acquistions similar to previously published reports using a long Stoke's shift fluorophore as the second color channel  (Schmidt et al. , 2008). Laser powers (measured at the objective back aperture) were ∼16 μW of 510 nm excitation light and ∼130 mW of 760 nm STED light for STAR470SX and ∼17 μW of 640 nm excitation light and ∼130 mW of 770 nm STED light for ATTO647N.
Comparison of colocalization between the experiment and a random distribution To assess whether the overlapping signals of βCOP, clathrin, KDELR (in NRK cells) and αCOP, garz (for S2 cell) with BODIPY was erratic a Matlab script was written. The population of the immunostained foci was denoted A and BODIPY stained LDs were denoted LD. For each colocalization experiment, a minimum of 15 snapshots was taken. Each image was first analyzed to assess the frequency of colocalization between A and LD from the experiment (1), from a random situation where A-type particles were randomly distributed (2); both situations were then compared (3). 1. The brightness contrast was adjusted for each channel of the picture using ImageJ. After applying a threshold binary images were generated for each channel.
Acknowledgements We thank  Drs Christopher Burd ,  Nora Kory ,  Natalie Krahmer ,  Gregory Lavieu ,  Lena Schroeder  and  Yuan Xue  for critical discussions and  Gary Howard  for editorial assistance. We also thank  Nora Kory ,  Huajin Wang  and  Yuan Xue  for help with experiments. We thank  Dr Vivek Malhotra  for the HRP construct,  Dr James Rothman  for the βCOP, β'COP, and KDELR antibodies and  Dr Achim Paululat  for the garz antibody.
Microsatellite amplification and genotyping The 264 samples used in this study were genotyped at 14 variable autosomal microsatellite loci (TGLA227, TGLA263, ETH225, ABS010, BM1824, ETH010, SPS115, INRA006, BM4028, INRA128, CSSM19, AGLA293, ILSTS026, DIK020-described by  [39, 40] ). In addition, within this 264 samples set, all 86 males were also genotyped at three Y-chromosomal microsatellites (UMN1113, INRA189, UMN0304-described by  [41] ) (Additional file 1: Table  S1 ). The three Y-chromosomal microsatellites were only used to reconstruct a minimum spanning network. Thirteen male samples failed to amplify at least at one Y-chromosomal microsatellite and were discarded for the minimum spanning network reconstruction (N Males = 73). All those microsatellites were selected because they displayed good quality and high polymorphic amplification.
The forward primer of each locus was 5'-end labeled with a fluorescent dye. Four multiplex sets were elaborated based on size limitations and amplification specificity: set 1 (TGLA227, TGLA263, ETH225, ABS010, UMN0304), set 2 (BM1824, ETH010, SPS115, INRA189), set 3 (INRA006, BM4028, INRA128, CSSM19), set 4 (AGLA293, ILSTS026, UMN113, DIK020). PCRs were carried out in 10 μl volumes containing between 0. 1 and 0. 2 μl of each 10 μM   1 ), and the data fit to the Hardy-Weinberg equilibrium (HWE) proportions for each locus separately and over all loci for each SL, were performed with GENEPOP (  [43]  accessible online at: http://genepop. curtin. edu. au/). For both analyses, the Markov chain method was used with 1000 dememorizations, 1000 batches and 1000 iterations per batch. Fisher's method for combining independent test results across SL and loci was used to determine the statistical significance of the test results.
Haplogroups were defined as a combination of the haplotypes defined for each of the three Y-chromosomal microsatellites, as described in the study of van Hooft et al. [41] , while taking all three loci {n 1 , n 2 , n 3 } into account. Haplotypes for each microsatellite had to be defined because they could appear as multicopies on the Y-chromosome  [41]. A minimum spanning network reconstruction was manually drawn by minimization of the number of mutations between haplotypes. A factorial correspondence analysis (FCA), representing the proximity between each individual genotype in a 2D plot was performed based on the microsatellite allele frequencies using R software version 2. 15. 2 (R Development Core Team 2008) and the ade-4 package  [57]  using the 'fuzzygenet' function. Recent demographic bottlenecks were further investigated with BOTTLENECK 1.
Demographic history For all subsequent analyses, software were run on two distinct microsatellite matrices: (i) a first one that included all individuals pooled together in each cluster determined by STRUCTURE 2. 3, and (ii) a second matrix that only included individuals displaying a probability of belonging to each of the clusters over 0. 9 (STRUCTURE 2. 3). This measure was necessary to avoid bias associated with the high number of translocations that have taken place over the last decades in southern Africa (see Discussion). The evolutionary history of S. c. caffer in southern Africa was inferred using coalescent-based DIYABC 1. 0. 4. 45 beta software  [60]. This program infers the population history by looking backwards in time to examine the genealogy of alleles until reaching the most recent common ancestor.
The coalescent-based approximate Bayesian computation algorithm of DIYABC estimated the splitting time (in generation) as well as the effective population size of each tested cluster. Three clusters (or populations) were defined with STRUCTURE 2. 3, with one almost exclusively composed of individuals from the Hluhluwe-iMfolozi PA. However, this last SL originated from an estimated population size of 75 individuals in 1929, after a population crash due to a rinderpest epidemic that eradicated around 95% of the South African buffalo population  [20]. Only the two other clusters were considered in this analysis in order to avoid bias associated with a recent founder event and subsequent genetic drift. Alternative biogeographic divergence scenarios were inferred and compared using the DIYABC software package.
In-depth information about scenario building procedure and alternative competitive scenario representations are available as additional file (Additional file 2: Figure  S1A  and S1B ). Two runs were performed, a first one consisting of all scenarios (Additional file 2: Figure  S1A  and S1B), and a second one whereby only scenarios having the highest posterior probabilities were taken into consideration. The range and distribution of prior for parameters used to describe these scenarios (effective population size, time of splitting or merging events, and admixture rates) are given as additional file (Additional file 3: Table  S2 ). For the second run, 3,000,000 datasets were simulated for each scenario (Figure  2 ) by building a reference table from a specified set of prior parameter distributions.
A principal component analysis (PCA-DIYABC, Additional file 4: Figure  S2 ) was performed on the first 30,000 simulated datasets to check if the set of scenarios and the prior distributions of their parameters were able to generate datasets similar to the observed dataset. A normalized Euclidean distance between each simulated dataset of the reference table and the observed dataset was calculated to identify the most likely scenario. To estimate the relative posterior probability of each scenario, 1% of the closest simulated datasets was used in a logistic regression. The most likely scenario was the one with the highest posterior probability. Similarly, an isolation by distance (IBD) analysis among and within the three clusters defined with STRUCTURE 2. 3  [52]  was performed with GENEPOP 4.
e. (im)migration rate (M)), and the confidence intervals. This analysis was developed by measuring similarities among our three clusters based on optimized F ST -based measures. This software employs a Metropolis-Hastings Markov chain Monte Carlo algorithm (MH) to search through genealogies, and a likelihood ratio test to obtain estimates of theta (Θ) and M. It assumes a constant Θ for each population but a variable Θ between them (pairwise migration rate estimates). MIGRATE 3. 4. 4 was used with default parameters, with the first five runs including F ST -based statistics of Θ and M involving 10 short chains with 10,000 sampled genealogies and three long chains with 100,000 sampled genealogies. A second analysis with five additional runs was performed with the parameter estimates of Θ and M from the previous run as starting values.
Population structure The STRUCTURE 2. 3 software output was interpreted using the ΔK method, as described by Evanno  [55]. The highest ΔK was for K = 3 (Figure  3  and Additional file 5: Figure  S3 ), suggesting the existence of three clusters in our dataset (ΔK = 262. 2). These clusters were considered as different "populations" in the subsequent analyses. The proportion of each cluster within every sampled Figure  2  Representation of three final competing scenarios tested with approximate Bayesian computation (ABC). This analysis was based on a matrix including individuals displaying a probability of belonging to one of the two clusters over 0. 9 (STRUCTURE software). N i corresponds to effective population size of each cluster, and T i corresponds to the time expressed in numbers of generations since divergence.
One pair of loci (SPS115 and DIK020) was found to show significant linkage disequilibrium in two populations (Northern and Central clusters). No single microsatellite marker exhibited an especially high number of private alleles (Additional file 1: Table  S1 ). Overall, the genetic variation level was high in all clusters. Inbreeding coefficients (F IS ) were low to moderate but significant for all three clusters (Table  3 ). This index showed more variation when calculated for each SL separately, with a high F IS value (0. 12) for Mana Pools (Table  1 ). The microsatellite genetic diversity analysis showed that pairwise differences and F ST were significant between all three clusters (Table  4 ), which was not always the case between animals from different SLs within each of the clusters (Table  5 ).
Demographic history The DIYABC 1. 0. 4. 45 beta software package was used to determine which historical demographic scenario could best explain the observed microsatellite polymorphism. The approximate Bayesian computation approach was used on 16 distinct demographic scenarios (Additional file 2: Figure  S1A  and S1B ), followed by a second analysis with only three demographic scenarios that presented the highest posterior probabilities (PP) in the first run  [67, 68]. The last three competing scenarios included in the second run revealed a similar general evolutive demographic pattern (Figure  2 ), namely a binary split without admixture events. After polychotomous logistic regression on the 1% closest simulated datasets to the observed one, the most likely scenario according to DIYABC 1.
Assuming a generation time of 5-7 years, divergence time corresponds to the Holocene epoch (T 1 : 6000 to 8400 years ago). The effective population size estimates for the C and N clusters reached a mean of 4700 and 6400 individuals, respectively (Figure2). The effective population sizes assessed with MIGRATE 3. 4. 4, assuming a mean mutation rate of 4. 5*10 -5 to 15*10 -5 per generation, was estimated between 7000 to 25,000 for the Northern cluster, 600 to 2000 for the Southern cluster and 3000 to 10,000 for the Central cluster. In agreement with the summary statistics of DIYABC 1. 0. 4. 45 beta and the previously estimated summary statistics described hereafter, there were no obvious differences between estimates of heterozygosity, genetic diversity and F ST. Migration rate could not be assessed with the IM software (for more information-see Additional file 7: IM), probably linked to the very high variation percentage within clusters (87.
Leptospiral load in urine The leptospiral burden in urine was determined by quantitative real-time DNA PCR (qPCR). Total DNA was extracted from a drop of urine (5 to 100 ml) using the Maxwell 16 instrument and Cell LEV DNA purification kit (Promega). The qPCR reaction was calibrated using a known number of heat-killed L. interrogans. The DNA concentration was adjusted to around 100 ng in the qPCR reaction. Primers were designed in the peculiar lpxA gene of L. interrogans Fiocruz strain  [17]  to specifically detect pathogenic Leptospira spp but not other spirochetes or bacteria, using Primer Express 3 software (Forward (Fw): 59-TTTTGCGTTTATTT-CGGGACTT-39; Reverse primer (Rv): 59-CAACCATTGAG-TAATCTCCGACAA-39; Probe: 59-TGCTGTACATCAGTTT-TG -39). qPCR reactions were run on a Step one Plus real-time PCR apparatus using the absolute quantification program (Applied Biosystems), with the following conditions: 50uC for 2 min, 95uC for 10 min, followed by 40 cycles with denaturation at 95uC for 15 s and annealing temperature 60uC for 1 min, according to the manufacturer's instructions.
qPCR reactions were run on a Step one Plus real-time PCR apparatus (Applied Biosystems), with the following conditions : 50uC for 2 min, 95uC for 10 min, followed by 40 cycles with denaturation at 95uC for 15 s, and annealing temperature 60uC for 1 min. Data were analyzed according to the method of relative gene expression using the comparative C T method also referred to as the 2 2DDCT method. PCR data were reported as the relative increase in mRNA transcripts versus that found in kidneys from naive mice, corrected by the respective levels of hypoxanthine-guanine phosphoribosyltransferase (HPRT) mRNA used as an internal standard. The sequences of primers and probes for iNOS, IL6, TNF, and RANTES have already been described  [10]. Primers for TGFß (NM_011577) were Fw:59-TGACGTCACTGGAGTTGTACGG-39 (nt1461-1482), Rv: 59-GGTTCATGTCATGGATGGTGC-39 (nt 1610-1630), probe 59-TTCAGCGCTCACTGCTCTTGTGACAG-39 (nt 1522-1547).
Thereafter mice were sacrificed at 24-day p. i. , and renal fibrosis and inflammation quantified by Red Sirius morphometry and scoring (data not shown). In parallel, the carriage of Leptospira was detected by qPCR of leptospiral DNA in the urine. High amounts of Leptospira were detected in the urine of non penicillin-treated infected mice, and to a much lesser extent in the urine of D3-D7 antibiotic-treated mice, whereas no Leptospira were detected in the urine of D1-D5 antibiotic-treated mice (Fig. 2A , left panel). In parallel, the presence in the kidneys of leptospiral antigens was monitored by immunohistochemistry using an antibody directed against LipL32, the major lipoprotein of Leptospira (Fig. 2A , right panel). Interestingly, LipL32 was detected in all infected mice, with a more pronounced labeling in the infected, non-antibiotic treated mice, confirming that the timing of penicillin treatment was adequate to allow Leptospira to reach the kidneys.