dataesr/scientific-paragraphs-categorization huggingface dataset:
Protocol implementation details Registration with authorities. In order to simplify the development of our prototype, the two authorities (opening and transport) have not been deployed on a real server. The registration phase described in Table  1  is simulated by a unique Java program that builds the exchanged messages in order to represent the communications. The four exchanges of the registration protocol are represented by four messages in Listing 1. These messages involve the three entities of Table  1 : the m-pass cardlet, the opening authority and the transport authority. First, the m-pass carldet generates G 1 and G 2 , represented by memCand, and builds C 1 and C 2 , stored in the message mess1. The call to joinStep1 executes the multiplication C1 = params. bigG. multiply(sku);. Such calls are directly accelerated by the smartcard using native libraries, as described later in Section 8.
Statistical analysis For comparison of the intracellular miR-122 level between models, the mean and the standard error of the mean of each model were determined. For comparisons between each model with human primary hepatocytes, the Mann-Whitney non-parametric test was used, whereas the Dunn's multiple comparison test was used for comparison of more than 2 models. For the cytotoxicity assays, the unpaired t-test was used for pairwise comparisons, while correlation analyses were performed using the Pearson correlation test. For all tests, P < 0. 05 was considered significant. Statistical analyses were performed using GraphPad Prism 6 (GraphPad Software, La Jolla, California).
FUNDING Funded by the  Stem Cells for Safer Medicines  ( SC4SM ) and supported by the  Medical Research Council (MRC) Centre for Drug Safety Science  (grant number  G0700654 ). R. K. is a  Medical Research Council  ( MRC) Clinical Training Fellow  supported by the  North West England MRC Fellowship Scheme in Clinical Pharmacology and Therapeutics , which is funded by the  Medical Research Council  (grant number  G1000417/94909 ),  ICON ,  GlaxoSmithKline ,  AstraZeneca , and the  Medical Evaluation Unit. R. L. C. S-Y. is funded by the  Innovative Medicines Initiative MIP-DILI programme  (grant agreement number  115336 ). J. A. H. is jointly funded by the  Biotechnology and Biological Sciences Research Council of UK  and  AstraZeneca. N. A. H. is funded by the  Wellcome Trust.
Finally, this IF signal is detected by an envelope detector (ED), which rectifies the 11. 7 GHz modulated signal to recover the data signal in the baseband. This scheme suppresses the residual frequency jitter and the phase noise present at the mixer output, originated from the ECL frequency jittering converted into the THz domain inside the UTC-PD. This enables us to take benefit from the heterodyne receiver sensitivity without being limited by the residual THz carrier phase noise. Lastly, a limiting amplifier is used to recover a clean eye shape, which is required to drive the serial data receiver. Baseband bandwidth detection analysis: The ED was tested in terms of video bandwidth capability, for an 11. 7 GHz carrier frequency. To this end, an analysis was conducted on the modulating system using a microwave mixer to produce an amplitude shift keying (ASK) signal to feed the ED, as shown in Fig.
7 GHz, we have estimated that <-50 dBm (10 nW) were feeding the mixer. To take into account the THz path losses, we have measured the THz path using a quasioptic setup based on a vector network analyser in the range of 325-500 GHz. The THz losses found were <5 dB. We assume that at 600 GHz the THz losses should be of the same order of magnitude (5 dB). Thus, the THz emitted power from the photomixer used with this very low photo current was ∼-45 dBm (30 nW) at 600 GHz. From the baseband bandwidth analysis, the ED was shown to be compatible with up to 3 Gbit/s data signals, thus compatible with the standard high-definition television (HD-TV) required data rates (1. 7 Gbit/s). As no bit error rate tester was available at the time of the measurements, the transmission was validated using a real HD-TV serial data flow (Fig.
Acknowledgments: This work has been partially supported by the  'Agence Nationale de la Recherche ' and the ' Japanese Science and Technology Agency' within the framework of the 'WITH' ANR/JST Franco-Japanese  project, and by the equipex 'FLUX' (Optical fibres for high Fluxes) of the IRCICA/IEMN laboratory and was partly supported by the  Ministry of Higher Education and Research , the  Nord-Pas de Calais Regional Council and FEDER  through the ' Contrat de Projets Etat Région' '  Campus Intelligente Ambiante ' (CPER-CIA) 2007-2013'.
Data analysis To analyze genetic distance between samples, both between and within countries, pairwise estimates of F ST were calculated in Arlequin 3. 11  [44]. The significance of the genetic distances at the 0. 05 level was tested by permuting the individuals or genotypes between the samples 110 times and adjusting P values with strict Bonferroni correction. Since all pairwise F ST between samples from Finland and Greece showed statistically significant differences, and due to the low probability of gene flow between the two distant countries, all further analyses were done for each country separately. The samples taken in two consecutive years from the same greenhouse in Finland showed no genetic differentiation, except for one sampling location (TJ-2 a and TJ-2 b). Therefore, in most analyses we used a combined dataset, which pooled samples collected from the same greenhouse in 2010 and 2011, (except for TJ-2 a and TJ-2 b, which were considered as separate samples).
For other analyses (specified below) we used a separated dataset, in which each sampling effort in Finland was considered a separate sample. Each sampling effort in Greece was considered a separate sample in all analyses, because none of the locations were sampled in consecutive years. For each sample, mean observed (H O ) and expected heterozygosity (H E ), and mean number of alleles (N A ) per locus were calculated using GenAlEx v. 6. 5  [45]. Observed and expected heterozygosities were also calculated for each locus over the total data from each country. Departure from Hardy-Weinberg expectations (HWE) was tested with 1000 permutations using a global test across loci or samples as implemented in GENE-POP v. 4. 2  [46]. The test was performed using Fisher's method, testing hypotheses of heterozygote deficiency and heterozygote excess  [47] , and producing global p value estimates for each sample over all loci and for each locus over samples from Finland and Greece.
6. 5. Isolation by distance was assessed with smaller subsets of the data as well as using the full datasets, to evaluate the influence of scale on the relationships. Analyses of molecular variance (AMOVA) were performed using Arlequin 3. 11  [44]  to estimate and compare the percentage of genetic variation explained by different hierarchical groups (i. e. individual, sample, group of samples). Four analyses were constructed to test the following groups: 1) country (Finland vs. Greece), 2) host plant species (cucumber vs. tomato) in samples from Finland only, 3) host plant botanical family (Cucurbitaceae vs. Solanaceae vs. Fabaceae vs. Rosaceae) in samples from Greece only, and 4) habitat (greenhouse vs. field) in samples from Greece only. For analysis 2, samples HR, ML and NL were excluded since the whiteflies in these greenhouses might have been exposed to both cucumber and tomato grown in the same compartment or greenhouse.
FSTAT v. 2. 9. 3  [50]  was used to calculate the average (over samples and loci; weighted by sample size) of the chosen statistics for each group and for their comparison. Statistical significance was assessed after 1000 permutations. As in the AMOVA groups, some samples were excluded because multiple hosts were grown in the same compartment or greenhouse (see above). The relationship between environmental variables and genetic structure of the studied populations was estimated using default settings of the software GESTE v. 2. 0  [51]. The software gives the highest posterior probability (Pr) to the model explaining genetic structure the best, evaluating environmental variables separately and in combination through a generalized linear model. In this analysis, the separated data set for the samples from Finland was used.
Analysis parameters included a burn-in period of 250,000 followed by 500,000 MCMC iterations. For each dataset, Finland and Greece, we tested K from 2 to 10, with ten replicate analyses per value of K. Subsets of each dataset were analyzed with the same settings. The most likely number of clusters in our samples was determined using the ΔK approach  [54]  as implemented in Structure Harvester v. 0. 56. 3  [55]. Results were visualized as bar plots by finding the optimal alignment of the ten replicate analyses of the "best" K in CLUMPP v. 1. 1. 2 [56]  using the Greedy algorithm and 1000 random input orders, and then by creating graphics in Distruct v. 1. 1  [57]. For Finland, the combined dataset was used first, and then two subsets of the data were created. In these subsets, the samples were grouped by host plant species and samples HR a, b and ML a, b were separated since they had been collected from different hosts (see Table  1 ).
Samples HR, ML and NL were included in both data subsets since these samples might have been exposed to several hosts grown in the same compartment or greenhouse. For Greece, all samples were first analyzed together, then data subsets were created grouping samples by habitat (field or greenhouse). The definition of the data subsets (by host plant species or habitat) was chosen after considering the results of initial analyses with the full data sets and our analysis with GESTE v. 2. 0  [51].
119 to 0. 250. No cases of large allele drop out were found. Even though null alleles are present, deviations from HWE could be also due to significant homozygosity in populations inhabiting the human-mediated environment (i. e. due to population bottlenecks and inbreeding), rather than due to significant genotyping errors. Genotypic linkage disequilibrium tested for each pair of loci for each sample revealed a potential association between loci Tvap-1-1 and Tvap 3-1 in sample PJ 4. Since locus Tvap-3-1 was characterized by homozygote excess and had a high frequency of null alleles only in sample PJ 4 (f = 0. 401), we suspect that the linkage disequilibrium indicated for this sample does not reflect a true association between the loci. Therefore, data from all nine loci were used in the analyses.
There was no evidence for isolation by distance when all Finnish samples were analyzed together (R XY = 0. 259, R 2 = 0. 067, P = 0. 190), nor when samples outside Ostrobothnia were excluded from the dataset (R XY = 0. 138, R 2 = 0. 019, P = 0. 211). However, it was moderately strong and significant (R XY = 0. 480, R 2 = 0. 231, P = 0. 001), when only the core samples in Ostrobothnia were analyzed (outlying samples HR, ML and KR). The data was explained best when only NR, PJ and TJ samples were analyzed (R XY = 0. 618, R 2 = 0. 382, P = 0. 001) and at the smallest geographic scale, when including only NR and PJ samples (R XY = 0. 876, R 2 = 0. 767, P = 0. 007). In AMOVA, the percentage of variation explained by host plant species (cucumber vs. tomato) was low (1. 69%) but statistically significant (P = 0.
Some samples were included in both subsequent runs (see Methods). The subset of samples from tomato (K 1 in the initial analysis) was characterized best by four sub-clusters (K = 4) (Figure  2B ). Whiteflies collected from PJ formed sub-cluster 1 (K 1. 1 ). NR 1 and NR 3 were not clearly resolved and partially formed subcluster 2 (K 1. 2 ) with NL (NR 1a, b: K 1. 2 = 0. 584 and K 1. 1 = 0. 287; NR 3a, b: K 1. 2 = 0. 542 and K 1. 3 = 0. 372). PR and OV formed sub-cluster 3 (K 1. 3 ), whereas HR and ML formed sub-cluster 4 (K 1. 4 ). Assignment was not resolved well for ML b: K 1. 4 = 0. 543 and K 1. 3 = 0. 297. Additional analysis of an even smaller subset of samples (PJ 1-PJ 5) collected from greenhouses that were all growing tomato and located within the natural dispersal range expected for the whitefly, revealed complete genetic homogeneity (data not shown) and indicated high gene flow at this location.
There was evidence for isolation by distance in the Greek dataset (R XY = 0. 436, R 2 = 0. 190, P = 0. 011), and this relationship was stronger when the three samples from Crete were removed from the analysis (R XY = 0. 579, R 2 = 0. 335, P = 0. 001). There was also a strong relationship when only samples from Peloponnese were analyzed (R XY = 678, R 2 = 0. 460, P = 0. 001). In Crete, however, isolation by distance was not detected (R XY = 0. 963, R 2 = 0. 927, P = 0. 186). Isolation by distance in the field samples was much greater than that between greenhouses (R XY = 0. 791/-0. 219, R 2 = 0. 625/ 0. 048, P = 0. 005/0. 113; fields/greenhouses). AMOVA indicated that genetic variation was not explained by host plant families, but there was a significant percentage of variation explained by habitat (fields vs.
Figure2Genetic clusters of T. vaporariorum sampled in Finland delineated by Bayesian analysis implemented in STRUCTURE [52]. In each graph individuals are represented by vertical bars broken into K differently colored genetic clusters, with length proportional to assignment in each cluster. Probability of assignment of each individual is indicated from 0 to 1. Individuals are grouped by sample with the sample code listed at the bottom of the bar graph. A: Analysis of all 18 samples, 800 individuals, K = 3. B: Analysis of a subset of 14 samples collected from tomato, 570 individuals, K = 4. C: Analysis of a subset of 11 samples collected from cucumber, 380 individuals, K = 3. The combined dataset was used in the initial run (A). Samples HR, ML and NL were included in analysis of both host plant groups (B and C) since these samples might have been exposed to several hosts grown in the same compartment or greenhouse.
Genetic diversity estimated over the nine microsatellite loci for samples of T. vaporariorumFor Finland the combined dataset, which pooled samples from consecutive years at the same location (except TJ 2) is described, since it was used in the majority of analyses. Lower case letters adjacent to population codes indicate the same location sampled in 2010 and 2011. N number of analyzed females, H O observed and H E expected heterozygosity and N A mean number of alleles per population averaged over 9 loci. * indicate H O /H E values in samples with null allele frequency > 0. 2. H O /H E in bold indicate loci with significant deviations from Hardy-Weinberg equilibrium in terms of heterozygote deficiency after Bonferroni correction (no significant heterozygote excess was detected).
Acknowledgements The  Finnish Ministry of Education and Culture ,  Finnish Ministry of Agriculture and Forestry, BIOINT Doctoral Programme at the University of Jyväskylä  and the  Oiva Kuusisto Foundation  provided financial support to  I. Ovcarenko , and  MTT  and the  Finnish Ministry of Agriculture and Forestry  to  I. Vänninen. The  Academy of Finland  provided financial support to  L. Lindström  (project  250248 ) and to  K. E. Knott  (project  258365 ). The  Centre of Excellence in Biological Interactions Research provided laboratory materials  for the research. The  EU European Social Fund (ESF)  and the  Greek State , as well as the  General Secretariat General for Research and Technology  ( SYNERGASIA 09SYN-22-617 ) provided financial support to  D. E. Kapantaidaki  and  A. Tsagkarakou.
000 WP 5 0. 078 0. 099 0. 042 0. 077 0. 028 0. 079 0. 078 0. 043 0. 003 0. 004 -0. 004 -0. 005 0. 000 WP 6 0. 087 0. 066 0. 011 0. 056 0. 017 0. 047 0. 051 0. 026 0. 004 0. 010 -0. 001 -0. 002 -0. 004 0. 000 WP 7 0. 097 0. 079 0. 021 0. 054 0. 023 0. 048 0. 067 0. 025 0. 002 0. 023 0. 004 0. 002 -0. 006 -0. 007 0. 000 EP 0. 093 0. 085 0. 064 0. 077 0. 018 0. 083 0. 057 0. 070 0. 045 0. 038 0. 051 0. 032 0. 030 0. 029 0. 034 0. 000 Most comparisons indicate significant difference after strict Bonferroni correction, those F ST that are not significantly different are indicated in bold. For Finland, the combined dataset is shown.
But the Einstein@Home production run on the second year of S5 LIGO data required about 9. 5 months, used a total of approximately 25000 CPU (central processing unit) years  [18] , and required five weeks for the post-processing on a cluster with 6720 CPU cores. The search presented in this paper used only 500 CPU months to process each of the two years of data, representing a computational cost more than two orders of magnitude smaller. This is also an order of magnitude smaller than the computational cost of the semi-coherent 'PowerFlux' search reported in a previous paper  [17]. The significance of our analysis is through offering an independent analysis to cross-check these results, and a method that allows the attainment of sensitivity close to that of the Einstein@Home search at substantially reduced computational burden.
This technique will be particularly important in the advanced LIGO and Virgo detector when applied to "quick-look" searches for nearby sources that may have detectable electromagnetic counterparts. Moreover, the Hough transform is more robust than other computationally efficient semi-coherent methods with respect to noise spectral disturbances  [10]  and phase modeling of the signal. In particular, it is also more robust than the Einstein@Home search to the non inclusion of second order frequency derivatives. An important feature to note is that the sensitivity of the Hough search is proportional to 1/(N 1/4 √ T coh ) or N 1/4 / √ T obs , assuming T obs = N T coh , being N the number of data segments coherently integrated over a time baseline T coh and combined using the Hough transform over the whole observation time T obs , while for a coherent search over the whole observation time, the sensitivity is proportional to 1/ √ T obs.
The search presented here is based on 30 minute long coherent integration times, being this the reason for the significant reduction of the computational time compared to the Einstein@Home search  [18]  in which the span of each segment was set equal to 25 hours. For an in-depth discussion on how to estimate and optimize the sensitivity of wide area searches for spinning neutron stars at a given computational cost, we refer the reader to  [22, 23, 30]. This paper is organized as follows: Section 2 briefly describes the LIGO interferometers and the data from LIGO's fifth science run. Section 3 defines the waveforms we seek and the associated assumptions we have made. In section 4 we briefly review the Hough-transform method. Section 5 describes the χ 2 test implemented for the analysis of the full S5 data.
Section 6 gives a detailed description of the search pipeline and results. Upper limit computations are provided in section 7. The study of some features related to the χ 2 -veto is presented in section 8. Section 9 discusses variations, further improvements and capabilities of alternative searches. Section 10 concludes with a summary of the results. During this run, all three LIGO detectors had displacement spectral amplitudes very near their design goals of 1. 1 × 10 -19 m•Hz -1/2 in their most sensitive frequency band near 150 Hz for the 4-km detectors and, in terms of gravitational-wave strain, the H2 interferometer was roughly a factor of two less sensitive than the other two over most of the relevant band. The data were acquired and digitized at a rate of 16384 Hz. Data acquisition was periodically interrupted by disturbances such as seismic transients (natural or anthropogenic), reducing the net running time of the interferometers.
In addition, there were 1-2 week commissioning breaks to repair equipment and address newly identified noise sources. The resulting duty factors for the interferometers, defined as the fraction of the total run time when the interferometer was locked (i. e. , all the interferometer control servos operating in their linear regime) and in its low configuration, were approximately 69% for H1, 77% for H2, and 57% for L1 during the first eight months. A nearby construction project degraded the L1 duty factor significantly during this early period of the S5 run. By the end of the S5 run, the cumulative duty factors had improved to 78% for H1, 79% for H2, and 66% for L1. In the paper the data from each of the three LIGO detectors is used to search for continuous gravitational wave signals. In table  1  we provide the reference GPS initial and final times for the data collected for each detector, together with the number of hours of data used for the analysis, where each data segment used was required to contain at least 30 minutes of continuous interferometer operation.
In this paper, a chi-square test is implemented as a veto, in order to reduce the number of candidates in the analysis of the full S5 data. The idea for this χ 2 discriminator is to split the data into p non-overlapping chunks, each of them containing a certain number of SFTs {N 1 , N 2 ,. , N p }, such that p j=1 N j = N , (14) and analyze them separately, obtaining the Hough number-count n j which, for the same pattern across the different chunks, would then satisfy p j=1 n j = n , ( 15 ) where n is the total number-count for a given point in parameter space. The χ 2 statistic will look along the different chunks to see if the number count accumulates in a way that is consistent with the properties of the signal and the detector noise. Small values of χ 2 are consistent with the hypothesis that the observed significance arose from a detector output which was a linear combination of Gaussian noise and the continuous wave signal.
For this reason, the discriminator we proposed is constructed by replacing η i → η * , where η * = n/N. In this way, from equation (  24 ) we get χ 2 ≈ p j=1 n j -n( i∈Ij w i )/N 2 η * (1 -η * ) i∈Ij w 2 i. (25) In principle, one is free to choose the different p chunks of data as one prefers, but it is reasonable to split the data into segments in such a way that they would contribute a similar relative contribution to the total number count. Therefore we split the SFT data in such a way that the sum of the weights in each block satisfies i∈Ij ω i ≈ N p. (26) Further details and applications of this χ 2 on LIGO S4 data can be found in  [27].
3123 s 1. 777 + 5( 2p -2 + 0. 1713 s 1. 621 ). a = 0. ( This curve vetoes 25 of the 177834 injections considered with significance lower than 70, that could translate into a false dismissal rate of 0. 014. In figure  1  we show the fitted curves and the χ2 veto curve compared to the result of the injections. 6. Description of the all-sky search. In this paper, we use a new pipeline to analyze the data from the fifth science run of the LIGO detectors to search for evidence of continuous gravitational waves, that might be radiated by nearby unknown rapidly spinning isolated neutron stars. Data from each of the three LIGO interferometers is used to perform the all-sky search. The key difference from previous searches is that, starting from 30 min SFTs, we perform a multi-interferometer search analyzing separately the two years of the S5 run, and we study coincidences among the source candidates produced by the first and second years of data.