dataesr/scientific-paragraphs-categorization huggingface dataset:
Our new approaches, however, produce significantly better results than those established methods. Our test results suggest that spaced words performs slightly better than kmacs on genomic sequences, whereas on protein sequences, kmacs is superior. In our program evaluation, we used DNA sequence sets with large evolutionary distances. On these sequences, our new alignment-free methods performed better than established methods that rely on exact word matches. Algorithms using exact matches, on the other hand, seem to work better on smaller evolutionary distances. K r , for example, performs best on evolutionary distances of up to 0. 6 substitutions per site  (Haubold et al. , 2009). Similarly, we observed that on closely related DNA sequences, kmacs produces sometimes best results with k = 0, i.
Clustering, classification and remotehomology detection are other fundamental challenges in DNA and protein sequence analysis. With the rapidly growing size of sequence databases, alignment-free methods have become indispensable for these tasks  (Comin and Verzotto, 2012; Hauser et al. , 2013; Lingner and Meinicke, 2006). Given the speed of Fig. 10. Average common k-mismatch substring lengths depending on the substitution frequency in simulated DNA sequences, estimated with our greedy heuristic (lower curve) and calculated with an exact algorithm (upper curve) for various values of k Fig. 9. Average number z of maximal exact matches starting at a position i in one sequence to a substring in a second sequence. We used simulated DNA sequences with different lengths and substitution frequencies 2007 kmacs and the quality of the phylogenetic trees that we could produce with it, our approach should be useful not only for fast phylogeny reconstruction, but also for other tasks in comparative sequence analysis.
Co-association network among the top TF The parameters describing the network topology were calculated with CentiScaPe software, obtaining an average degree (Deg) of 62. 44 and an average distance (AvD G ) of 3. 21; hence, showing a high degree of connection. A total of 54 genes out of the 513 nodes (Fig. 1B ; blue colour in the network) belonged to growthrelated gene ontologies (S1 Table ). Noteworthy, a total of 20 genes were related to lipid metabolism (yellow colour in Fig. 1B ). Among the 513 nodes setting up network connections, seven genes (COPS7B, EFEMP1, ETV6, FRS2, HSPG2, SH3PXD2A and TGS1) had been associated with human height, which is driven by growth and developmental processes  [34, 35]. Interestingly, the indian hedgehog (IHH) gene product, identified in a human height GWAS study, binds to the patched domain containing 3 (PTCHD3) receptor, here identified as coassociated with ELF1  [36].
Biological pathways and functional analysis Functional analysis using IPA program allowed us to identify the biological functions overrepresented considering the 513 co-associated genes related to the top trio of TF. Among the networks identified with IPA there were: ''cell signalling, nucleic acid metabolism, cell-to-cell signalling and interaction'', ''organism development, DNA replication, recombination and repair, and lipid metabolism'' and ''hereditary disorder, neurological disease and developmental disorder'', all of them having a score 538, ''cell-to-cell signalling and interaction, nervous system development and function, cellular assembly and organization'' (score 533) and ''cellular development, nervous system development and function, behaviour'' (score 529) (S3 Table ). Remarkably, the top molecular and cellular functions identified were: ''post-translational modification'' (p-value 59.
Genome-wide association analysis Genome-wide association analysis (GWAS) for the twelve phenotypic growth traits were performed using a mixed model accounting for additive effects with Qxpak 5. 0 software  [78] : y ijlkm ~Sex i zBatch j zbc 1 zl 1 a k zu 1 ze ijlkm , in which y ijlkm was the i-th individual record, sex (two levels) and batch (nine levels) were fixed effects, ß was a covariate coefficient with c being the covariate used in each case (described below), l l was a 21, 0, +1 indicator variable depending on the l-th individual genotype for the k-th SNP, a k represented the additive effect associated with the k-th SNP, u l represented the infinitesimal genetic effect with random distribution N(0, As u ) where A was a numerator of the the pedigree-based relationship matrix and e ijlkm the residual.
Association weight matrix The association weight matrix (AWM) was built from the GWAS results. First, the SNP additive effects were normalized with a z-score method using a R script and a matrix was constructed with these values, being SNPs in rows and traits in columns. Another matrix with the same format was generated for the p-values obtained in the GWAS. For the analysis, the ham weight was selected as the key phenotype. Subsequently, the AWM script  [9]  available from authors was used in R (http://www. r-project. org/). Those SNPs associated (nominal p-value ,0. 05) with the ham weight or with 3 or more traits were selected for further analysis. We included in the analysis the SNPs with a distance of minor than 2. 5 kb (SNPs close) and major than 1,000 kb (SNPs far) from a gene. We also included SNPs located at less than 10 kb of miRNA.
Finally, to facilitate the analysis, for SNPs clustering at less than 1 Mb of distance from each other, the SNP associated with the major number of characters was selected. The hierarchical clustering option of PermutMatrix software  [79]  was used to visualize the results of both traits and genes. The trio of putative regulators spanning most of the network topology with a minimum redundancy  [10]  was selected. In this study we took into account all the transcription factors (TF) from the list reported by Vaquerizas et al. [80] ; additionally, those 22 genes belonging to the GO: 0050789 which accounts for the DNA binding TF activity were added. All miRNA annotated on Sscrofa10. 2 assembly were also included in the analysis as potential regulators. PCIT algorithm  [81]  was used to construct a file containing the reported gene-gene interactions among the 3 TFs.
Gene ontologies, pathways and network analysis The Ingenuity Pathways Analysis software (IPA; Ingenuity Systems, Redwood city, CA, USA; www. ingenuity. com) was used to identify the most relevant biological functions and pathways in which the genes associated with the phenotypic traits were involved. IPA, which uses its own databases, allowed the identification of overrepresented pathways using the BH multiple testing correction  [84]  of p-value (FDR ,0. 05) and generating biological networks. The Mouse Genome Database (MGD; http://www. informatics. jax. org) was used in order to identify how mutant alleles driven in mice for the identified growth-related genes present in the network affected the phenotype.
The catalogs are complete down to a magnitude I∼24. 5AB and cover an effective area of 2. 79 deg 2. Photometric zeropoints were calibrated using stellar transformation equations and refined internally, using a new technique based on the highly robust photometric redshifts measured for emission line galaxies. We calculate Bayesian photometric redshifts with the BPZ2. 0 code, obtaining a precision of δ z /(1+z s )=1% for I<22. 5 and δ z /(1+z s )=1. 4% for 22. 5<I<24. 5. The global n(z) distribution shows a mean redshift <z>=0. 56 for I<22. 5 AB and <z>=0. 86 for I<24. 5 AB. Given its depth and small cosmic variance, ALHAMBRA is a unique dataset for galaxy evolution studies.
2013 (in prep. ). The ALHAMBRA survey has used the 2MASS catalogue  (Cutri et al. 2003)  to calibrate its NIR images. As explained in  Cristóbal-Hornillos et al. (2009)  several dozens of stars common to both datasets with high S/N were selected in each field, yielding photometric zeropoint offsets with uncertainties of ∼0. 03 mag. We will discuss the procedure to refine the photometric zeropoints calibrations via SED-fitting techniques in Section 4. 2. 1.
7" to 1. 6", with the optical images having <FWHM>∼1. 1", the NIR images <FWHM>∼0. 9" and the synthetic F814W detection images <FWHM>∼1. 0". sary to sample the same physical region of the galaxy taking into account the smearing produced by different PSFs as seen in Fig. 4. We show the distribution of optical, NIR & synthetic F814W PSFs in the ALHAMBRA survey in Fig. 5 , for values between 0. 7" and 1. 6". One of the methods to correct for PSF effects is to smooth the whole dataset to the worst seeing condition, making all images look as they had been taken under the same seeing conditions  (Loh & Spillar 1986 , Labbé et al. 2003 , Capak et al. 2007 ). This methodology produces consistent apertures across the filters, but strongly degrades the best observations to the level of the worst. Here we have used ColorPro  (Coe et al.
Geometry and Magnitude. We used the COSMOS HST/ACS images to explore the star/galaxy selection algorithms, since they are considerably deeper and with an obviously much narrower PSF than the ALHAMBRA dataset. We ran SExtractor twice, first on the ACS/F814W images and then on the ALHAMBRA/F814W images in single-image mode, plotting the detected sources in a FWHM vs m ACS/F 814W diagram as shown in Fig. 14. We selected detections classified as PLS (point-like sources) in the ACS/F814W images and used them to match up the ALHAMBRA/F814W detections. As seen in Fig. 14 , sources brighter than m ACS/F 814W = 22. 5 classified as PLS on the ACS/F814W images were equally classified as PLS on the ALHAMBRA/F814W (open red circles). However, PLS fainter than m ACS/F 814W = 22. 5 showed progressively larger FWHM values on the ALHAMBRA/F814W.
As seen in the main panel, ∼60% of the selected spectroscopic sample is well accommodated within a 1-pixel distances. Inset panel illustrates the astrometric dispersion between ALHAMBRA and the overlapping surveys in terms of δRA (RA ALH -RA surv ) and δDec (Dec ALH -Dec surv ) in units of pixel. η2 = |δz| 1 + zs > 5 × σNMAD (13) As explained in Section 2, ALHAMBRA was designed to partially overlap with fields observed by other surveys with extensive spectroscopic coverage. We compiled a sample of ∼7200 galaxies with spectroscopic redshifts from the publicly available data by selecting high quality (secure) objects with a good astrometric match to our data. The first condition is essential to accurately estimate of our outlier rate, even if it slightly biases our sample to brightest magnitudes, since as Fernández-Soto et al.
We first looked at the P (z) derived using the ALHAMBRA-4/COSMOS data (red line) with the P (z) derived using the COSMOS data (blue line), as seen in the left panel of Fig. 32 , where both distributions consistently reproduce a double peak at redshifts z∼0. 3 and z∼0. 9, respectively. However, whereas the ALHAMBRA-4 field shows a mean redshift z = 0. 60 for mF 814W < 22. 5 and z = 0. 87 for mF 814W < 25. 5, the COS-MOS field shows a mean redshift z = 0. 66 for mF 814W < 22. 5 and z = 0. 96 for mF 814W < 25. 5, as seen in the right panel of Fig. 32. Meanwhile the global photometric redshift distribution derived for all the seven ALHAMBRA fields (excluding stars) shows a mean redshift z = 0. 56 for mF 814W < 22. 5 and z = 0. 85 for mF 814W < 25. 5, as seen in the right panel of Fig. 34. This result indicates that as it is known, the COSMOS field shows a clear over-density with respect to the mean value derived averaging the seven ALHAMBRA fields.
unresolved stellar pairs with clearly asymmetric morphologies (spite of its photometric colors) and 2. very strong broad emissionline objects, AGNs or variable sources. Precisely the templates not included in the BPZ library. The PSF-corrected multicolor photometry and photometric redshifts for ∼438,000 galaxies presented in this work, covers an effective area of 2. 79 deg 2 , split into 14 strips of 58. 5'x15. 5' and represents a ∼32 hrs of on-target exposure time. Given its depth, multiband coverage and a much smaller cosmic variance than other similar projects, ALHAMBRA is a unique dataset for galaxy evolution studies. Several of the techniques presented here will have a wide applicability to future large scale narrow-band photometric redshift surveys like JPAS (Javalambre Physics of the Accelerated Universe,  Benítez 2009a , Benítez 2013, in prep.
ACKNOWLEDGMENTS We acknowledge support from the  Spanish Ministerio de Educación y Ciencia  through grant  AYA2006-14056 BES-2007-16280. We acknowledge the financial support from the  Spanish MICINN  under the  Consolider-Ingenio 2010 Program  grant  CSD2006-00070 : First Science with the GTC. Part of this work was supported by  Junta de Andalucía , through grant  TIC-114  and the  Excellence  Project  P08-TIC-3531 , and by the  Spanish Ministry for Science and Innovation  through grants  AYA2006-1456 ,  AYA2010-15169 ,  AYA2010-22111-C03-02 ,  AYA2010-22111-C03-01  and  Generalitat Valenciana  project  Prometeo 2009/064. We acknowledge the excellent work carried out by the CAHA observatory. This project was based on observations collected at the  Centro Astronómico Hispano Alemán (CAHA) at Calar Alto , operated jointly by the  Max-Planck Institut für Astronomie  and the  Instituto de Astrofísica de Andalucía (IAA-CSIC ).
Survival The primary outcome measure was survival between November 2011 and December 2012. Data were censored on December 31 st , 2012. Vital status was recorded in this family-based cohort, with a new questionnaire that was sent to each participant and a followup interview carried out for all families. Information about the cause of death was collected retrospectively from a family questionnaire and medical reports. Survival from inclusion was determined for all participants and survival from birth and from RA diagnosis was also analyzed.
Statistical analysis The characteristics of the patients (sex ratio, age at inclusion, age at diagnosis, median follow-up) were compared between the groups in chi-squared tests, Fisher's exact tests or Kruskal-Wallis tests, as appropriate. Survival analysis was performed with the R package ''survival''  [24]. Kaplan-Meier survival curves were plotted with the ''plot. survfit'' function and the log-rank test, implemented in the ''survdiff'' function, was used to compare survival between groups. We adjusted for the effect of covariates and estimated hazard ratios (HR) while accounting for family correlations, by fitting Cox models with Gaussian random effects models to the data with the R package ''coxme''  [25]. Each individual was assumed to have a random genetic risk, but risks were correlated between individuals according to the strength of their pedigree relationship, as assessed by their kinship coefficient.
These high-risk patients should be eligible in priority in protocols evaluating new treatment interventions such as longterm macrolide therapy which may be beneficial in patients with non-CF bronchiectasis  [28] [29] [30] [31] [32] [33]  and significantly improve the signs and symptoms of RA  [34, 35]. It remains unclear which patients should receive antibiotic treatment  [36] , but we suggest that RA patients with early-onset DB carrying CF/CFTR-RD mutations should be among those treated and evaluated for long-term azithromycin therapy. Our study has several strengths. It is a family-based study, which made it possible to gather long-term data for all the individuals included. Such a family-based design limits research bias, by providing appropriate controls and follow-up. Furthermore, phenotypic characterization was carried out and mutational CFTR status was determined for all individuals.
Schema matching is the process of establishing correspondences between the attributes of database schemas for data integration purposes. Although several automatic schema matching tools have been developed, their results are often incomplete or erroneous. To obtain a correct set of correspondences, usually human effort is required to validate the generated correspondences. This validation process is often costly, as it is performed by highly skilled experts. Our paper analyzes how to leverage crowdsourcing techniques to validate the generated correspondences by a large group of non-experts. In our work we assume that one needs to establish attribute correspondences not only between two schemas but in a network. We also assume that the matching is realized in a pairwise fashion, in the presence of consistency expectations about the network of attribute correspondences.
Introduction More and more online services enable users to upload and share structured data, including Google Fusion Tables  [1] , Freebase  [2] , and Factual  [3]. These services primarily offer easy visualization of uploaded data as well as tools to embed the visualization to blogs or Web pages. As fragmentation of data in different sources is a common phenomenon, it is essential to create the interlinks between them  [4]. An example is the often quoted coffee consumption data found in Google Fusion Tables, which is distributed among different tables that represent a specific region  [1]. Extraction of information over all regions requires means to query and aggregate across multiple tables, thereby raising the need of interconnecting schemas to achieve an integrated view of the data. The number of publicly available datasets grows rapidly, making the integration more and more challenging.
In all of the above contexts one needs to integrate data that is stored using different schemas and the interactions between the datasets (or schemas) form a network. Designing a mediated schema for the entire network of schemas might be impractical, especially if the number of schemas that are involved in a network changes, as this might require modifications also in the mediated schema. For this reason we study schema matching techniques that construct the attribute correspondences in a pairwise fashion between the schemas of the network. In this case, we need to ensure that the created correspondences are globally (and not only pairwise) consistent in the network, since these natural consistency conditions are important for the applications. Since automatic schema matching tools rely on heuristic techniques  [5, 6] , their result is inherently uncertain.
Motivating Example Let us consider a scenario with online services, where three video content providers EoverI, BBC, and DVDizzy have their own websites to publicize their offers. Consumers can find the products they want by searching information on the sites (e. g. title, release date). Now the three providers would like to incorporate their websites to broaden the marketplace. Similar product information is stored in their different databases, whose simplified schemas are illustrated in Figure  1. A matching network is created by establishing pairwise matchings between the three schemas in order to facilitate integration scenarios (e. g. support search queries) between the three databases. The figure shows five correspondences c 1 , c 2 , c 3 , c 4 , and c 5 which were generated by an automatic matching tool for pairs of schemas.
Experiments The main goal of the following comprehensive experimental evaluation is to demonstrate the usage of crowdsourcing and integrity constraints in reconciling schema matching results in a matching network. To verify the effectiveness of our proposed methods, four experiments are performed: (i) evaluations on detecting spammers, (ii) relationship between the error rate and the matching accuracy, (iii) effects of spammer ratio on termination of crowdsourcing process, and (iv) evaluations on answer aggregation. We proceed to report the results on the real datasets using both real workers and simulated workers. The results highlight that the presented approach supports reconciling schema matching by effectively using crowdsourcing harnessing integrity constraints.
Experimental Settings Datasets. We have used many real-world datasets spanning various application domains, from Web forms to business schemas observed in data marketplaces. • Business Partner (BP): The set comprises database schemas that model business partners in enterprise systems. • PurchaseOrder (PO): We extracted purchase order e-business schemas from various resources. • University Application Form (UAF): We extracted schemas from Web interfaces of American university application forms. • WebForm: The schemas for this dataset have been automatically extracted from Web forms using OntoBuilder  [23]. These datasets are publicly available [24] and descriptive statistics for the schemas are given in Table  1. In the experiments, the topology of schema matching network is a complete graph (i.
Relationship between Error Rate and Matching Accuracy In order to assess the matching accuracy, we borrow the precision metric from information retrieval, which is the ratio of (true) correspondences existing in ground truth among all correspondences whose aggregated value is true. However, the ground truth is not known in general. Therefore, we use an indirect metric-error rate-to estimate the matching quality. We expect that the lower error rate, the higher quality of matching results. The following empirical results aim to validate this hypothesis. We conduct the experiment on all real datasets with the population of 100 simulated workers as above (20% experts, 35% normal workers, 45% spammers). Since the purpose of this experiment is to study the relationship between error rate and matching accuracy only, we do not consider spammers and imitating groups in the crowd.
Evaluations on Answer Aggregation In Section 5. 2, we already saw the benefit of using constraints in reducing error rate. In other words, with given requirement of low error, the constraints help to reduce the expected cost of crowd validation (i. e. the number of questions that need to be asked the workers). In this set of experiments, we will study the effects of harnessing such constraints on real datasets (BP, PO, WebForm, etc. ). We will analyze the effectiveness of constraints in three different settings: (i) effects of worker population, (ii) effects of spammers, and (iii) effects of imitating groups. All the settings have a common process as follows. Given an error threshold ( = 0. 1), we iteratively post questions to workers and aggregate the worker answers (with and without constraints) until the error rate is less than.
After the process ends, we instantiate an aggregated matching that consists of correspondences aggregated as correct (i. e. exists in ground truth). The reported numbers are averaged over all datasets. Effects of Worker Reliability. We create a population of 100 simulated workers with 55% normal workers and 45% spammers. We use the detection method in Section 4. 1 to detect the spammers and remove their answers from the answer set (we choose the filtering threshold = 0. 6 as it balances the accuracy trade-off as in Section 6. 2). The results are presented in Figure  11. The Y-axes is the expected cost, the matching precision and the matching recall of the aggregated matching. The X-axis is the reliability (denoted as r) of normal workers in the population, varying from 0. 6 to 0. 8. The reliability of a worker is r = 2sensitivity×specif icity sensitivity+specif icity , where sensitivity and specificity of a worker are already described in the experimental setting.
Schema matching Database schema matching is an active research field. The developments of this area have been summarized in two surveys  [6, 29]. Existing works on schema matching focused mainly on improving quality parameters of matchers, such as precision or recall of the generated matchings. Recently, however, ones started to realize that the extent to what precision and recall can be improved may be limited for generalpurpose matching algorithms. Instead of designing new algorithms, there has been a shift towards matching combination and tuning methods. These works include YAM  [30] , systematic matching ensemble selection  [31]  or automatic tuning of the matcher parameters  [32]. While there is a large body of works on schema matching, the post-matching reconciliation process (that is central to our work) has received little attention in the literature.