dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets Social graphs We evaluate the performance of DynaSoRe by comparing it against our baselines on three different social networks (summarized in Table  1 ): a sample from the Twitter social graph from August 2009  [3]  a sample from the Facebook social graph from 2008  [17]  a sample from the LiveJournal social graph  [2]  # users # links Twitter 1. 7M 5M Facebook 3M 47M LiveJournal 4. 8M 69M  Request log In this section, we rely on two different kinds of request logs for our experiments, a synthetic one and a real one. The real one is obtained from Yahoo! News. We discuss the logs we used in more details below. Synthetic logs. Huberman et al. [9]  argue that the read and write activity of users in social networks is proportional to the logarithm of their in and out degrees in the social graphs.
We focus in this experiment on users who performed at least one read and one write during the two weeks. This selection results in a dataset of 2. 5M users with 17M writes and 9. 8M reads. Figure  2  depicts the distribution of read and write activities per day. Users can consult the activity of their friends both on the Yahoo! website, or on Facebook. In the latter case, the reads are not processed by the Yahoo! website and do not appear in the log, which explains the prevalence of writes in our dataset. Because we do not have access to the Facebook social graph, we map the users of Yahoo! News Activity to the users in the Facebook social graph presented in Section 4. 2. We rank both lists of users according to their number of writes and their number of friends, respectively, and connect users with the same rank.
Initial data placement and performance The random placement and graph partitioning approaches produce static assignments of views to servers, which persists during the whole experiment. SPAR places views as the structure of the social network evolves. We first create one replica for each user, and we simulate the addition of all the edges of the social graph to obtain the its view placement. Once the memory of all servers has been used, the view layout remains constant. For DynaSoRe, the system is deployed on an existing social platform and uses this configuration as an initial setup. It then modifies this initial view placement by reacting to the request traffic. We consider three different view placement strategies when initializing DynaSoRe: Random, METIS and hierarchical METIS (hMETIS).
Using the synthetic request log, we evaluate the performance of each system after convergence, i. e. , once the content of the servers stabilizes. Figures  3a, 3b  and 3c depict the traffic of the top switch for the 3 different social graphs. The traffic is normalized with respect to the traffic of Random. On the x-axis, we vary the extra memory capacity of the cluster. x = 0% means the capacity matches exactly the space required to store all the views without replication. With x = 100% memory capacity doubles, so the algorithms can replicate views up to 2 times on average. Considering the initial data assignment (x = 0%), we can clearly see that graph partitioning approaches (METIS and hMETIS) outperform Random data placement. Furthermore, hierarchical partitioning leads to a two-fold improvement over standard clustering.
These results are expected: partitioning increases the probability that views of social friends will be stored on the same rack, which reduces the traffic of the top switch upon accessing them. hMETIS further improves this result by taking into account the hierarchy of the cluster. Thus, when the views of 2 friends are not located on the same rack, they are likely to be communicated through an intermediate switch rather than the top switch. As we increase the memory capacity, both DynaSoRe and SPAR are able to replicate and move views. Yet, the results indicate that DynaSoRe is much more efficient than SPAR for using the available memory space. For example, in the case of Twitter, with 30% memory in addition to the amount of data stored, SPAR reduces the traffic by 42% compared to a Random, while DynaSoRe reduces it by 80%.
These figures also demonstrate the importance of the initial data placement in the case of DynaSoRe. As DynaSoRe relies on heuristics to place views in the cluster, a good initial placement allows it to converge to better overall configurations, while a random placement converges to slightly worse performance. As the amount of available memory further increases, the performance of DynaSoRe converges and part of the memory remains unused. Indeed, DynaSoRe detects that replicating some views does not provide an overall benefit, since the cost of writing to the extra replicas outweighs the benefits of reading them locality, which induces higher network traffic. Table  2  and Table  3  present the average switch traffic at the top, intermediate, and rack levels for two memory configurations. We normalize the traffic value by the equivalent switch traffic using Random.
Flash Events Online social networks are often subject to flash events, in which the activity of a subset of users suddenly spikes. To evaluate the reactivity of DynaSoRe, we simulate a sudden increase of popularity of some users, and measure the evolution of their number of replicas and the number of requests each of them processes. More precisely, at time t = 2 days in the simulation run, we randomly select a user and make this user popular by adding 100 random followers to read her view. Five days later (t = 7 days) all these additional followers are removed. We repeat this experiment 100 times on the Facebook dataset with a 30% extra memory capacity. We present the average results in Figure  5. At the beginning of the experiment, the user is not particularly active, and has 1. 15 replicas on average.
RAMCloud recovers from failures using a distributed log accessed in parallel on multiple disks. This is similar to write-ahead logging approach described in Section 3. 3 and could be also used in DynaSoRe. Offline data placement Curino et al. describe Schism  [4] , a partitioning and replication approach for distributed databases to minimize the amount of transactions executed across multiple servers. Schism uses an offline standard graph partitioning algorithms on the request log graph to assign database tuples to servers. DynaSoRe is an online strategy, creating and placing views dynamically. As a consequence, it is much easier to react to changes in access patterns that frequently occur in social applications. DynaSoRe benefits from graph clustering techniques similar to those used in Schism to generate more effective initial placement of views for faster convergence to ideal data placement.
However, DynaSoRe addresses different problem and focuses on determining where to maintain the views, which will lead to performance gain in addition to this approach. DynPart  [11]  is a data partitioning algorithm triggered upon inserting tuples in a database. DynPart analyzes requests matching a tuple and places the tuple on the servers that are accessed when executing these requests. While DynPart handles insertion of data, it never reverts previous decisions and therefore cannot deal with new requests or changes in request frequency. Social networks are frequently modified, leading to different requests, and are subject to unpredictable flash events. For these reasons, DynaSoRe is a better fit for social applications.
ABC is a simulation based method that allows a Bayesian estimation of parameters and the comparison of models  [4]. In this method, data are simulated using a predefined evolutionary model for which parameters are randomly chosen in an a priori distribution. Summary statistics are calculated on the simulated data and compared to the summary statistics calculated on the real dataset. Using the distance between the summary statistics calculated on simulated data and on real data, an a posteriori distribution of the parameters can be built. ABC is currently widely used to test scenarios and to estimate parameters including divergence time and population size in plants, animals and pathogens e. g. [5] [6] [7] [8] [9]. Thanks to these new tools, it is now possible to go beyond the description of the genetic diversity of asexual organisms and to advance our knowledge of their evolutionary history.
Simulated data -test of the method Estimations of the age of artificial varieties are shown in Figure  2. Detailed results can be found in the supplementary data file (Additional file 1: Figure  S1 , Additional file 1: Table  S1  and Additional file 1: Table  S2 ). The method was tested using simulated datasets (Figure  2 ). We first confirmed significant correlations between the statistics used (K k , Nb k , Fst k_k' and CLst k_k' ) and parameter T, ranging from 0. 071 (p < 0. 05) to 0. 479 (p < 0. 0001). This suggests that the statistics used are sufficiently informative to be used for the estimation of T. Detailed results for each simulation model can be found in the supplementary data file. Overall, the method was able to correctly estimate the age of the simulated varieties (Figure  2 ).
Here, we chose to present the result of the simulation for the mutation rate (0. 00001) that best fitted the number of observed genotypes in our real dataset. Without knowing the exact mutation rate, it is still possible to estimate the relative ages of varieties, which was the purpose of this study. Finally, our method was able to estimate when a variety split into several populations in the village in which we collected our samples. To interpret our data, we thus need to consider that the date we obtained corresponds to the divergence time of several populations from the same variety and that the original clone could be older.
Plant material and genotyping A total of 820 samples were collected in one village in northern Benin in 2010. Fifteen farmers' fields and seven varieties were sampled. Each variety was sampled in two to four different fields. Data concerning the population size of each variety was recorded (Table  4 ). Leaf samples were collected randomly in each field and dried with silica gel. DNA extraction and genotyping were performed as described in  [30]. We used 12 microsatellite loci: Dab2D06, Dpr3F10, Dab2D08, Dpr3B12, Da3G04, Dpr3D06, Dab2C05, Da1D08, Dpr3F12, Dpr3F04, Da1F08 and YM13  [31, 32]. Migration was performed using an ABI Prism™ 3100 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA). Gene-Mapper software was used to score alleles. Genotypes observed in fewer than five samples were genotyped twice (new PCR, new migration, new scoring) to ensure they were not the result of a mistake.
Approximate Bayensian Computation (ABC) The home-made program was used to generate X simulated data and to estimate simulated summary statistics. Observed data were used to estimate the observed summary The surface area of land used for each variety in the village was calculated based on the estimated number of plants of each variety by interviewing 15 farmers (a total of 56 varieties corresponding to 80,000 individual yam plants). statistics. We used the module ABCESTIMATOR from the ABCTOOLBOX package  [34]  to compute the ABC estimation of the parameters N and T. The number of simulations selected to estimate the general linear model (GLM) was set to 1% of the total number of simulations (X). Other parameters were set to their default values. Correlations between the summary statistics and the parameters were tested using a Student's t-test.
DISCUSSION In this paper, we have demonstrated methods for combining measures of predictive ability across multiple studies. Relevant Stata code (StataCorp LP, College Station, Texas) is available from the University of Cambridge (http://www. phpc. cam. ac. uk/ceu/research/erfc/stata). We have described assessment of the predictive ability of a risk prediction model, the change in predictive ability upon moving from one model to another, and the comparison of predictive abilities across subgroups of the population. Various approaches to weighting estimates of predictive ability when pooling across studies have been discussed, and we recommend using the number of events in each study. Study designs other than the prospective cohort study and risk prediction models other than the Cox proportional hazards model with duration of time in the study as the time scale have also been considered.
ACKNOWLEDGMENTS Author affiliations:  Strangeways Research Laboratory, Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom  ( Lisa Pennells ,  Stephen Kaptoge ,  Simon G. Thompson ,  Angela M. Wood ); and  MRC Biostatistics Unit, University of Cambridge, Cambridge ,  United Kingdom  ( Ian R. White ). This work was supported the  United Kingdom Medical Research Council  (grant  G0701619  and  Unit Programme   U105260558 ). The  Emerging Risk Factors Collaboration Coordinating Centre  was supported by the  British Heart Foundation  (grant  RG/08/014 ), the  Medical Research Council , the  United Kingdom National Institute of Health Research Cambridge Biomedical Research Centre , a specific grant from the  Bupa Foundation , and an unrestricted educational grant from  GlaxoSmithKline.
Introduction The French syndromic surveillance system SurSaUD® was set up by the French institute for public health surveillance (InVS) in 2004. The system is based on three main data sources: 1/the attendances in the Emergency departments (ED), 2/the consultations to emergency General Practitioners' associations SOS Médecins 3/the mortality data from civil status offices and e-certificates. In 2012, 400 of the 710 ED and 59 of the 62 GP's associations are involved in the system. 80% of the national mortality is also collected. Given this large database and the need to analyze data in a short delay to reach the early warning objective of the system, a specific software has been developed.
Methods The application has been designed in order to support the users with automatised process of the three main following steps: 1. Data integration in a database, data management and the control of data quality transmitted by the providers 2. The statistical analysis of the data, according to different demographic, geographic and syndromic criteria. Individual and aggregated data are consultated using reporting tools (graphs and tables). 3. The output and reporting for decision makers and feedback for data providers through epidemiological bulletins and dashboards. Each night, XML files containing data recorded during the previous day are sent from the ED and GP's association SOS Médecins to InVS. All files are automatically integrated in the national database. A data quality process is done to check the completeness and the validity of the transmitted data.
Results Sursaud software is a login and password-protected and secured internet web site. Since its deployment in 2010, about 300 users have had an access to the software and 25% are data providers. In August 2012, almost 35,000 new patients are daily caught by the system and nearly 50 millions of patients are recorded in the database since 2004. To analyse data, the diagnosis coded either in ICD10 for ED visits or with specific thesaurus for GP's calls are pre-aggregated in about 200 syndromes covering a large part of the medical diagnosis and call reasons collected in the system. 22 age groups are also available with a focus on the most frailty population, like the youngest and the elderly. Finally, various geographic levels can be chosen, from the local ED or association to the national level.
EXPERIMENTAL RESULTS The experiments in this section test the learning capabilities of the control network both with the iCub robot and in simulation. The control network is simulated with the Matlab scripts provided as support material. The experiments were also video recorded. Both Matlab scripts and the illustrative video can be downloaded at the author's associate website http://andrea. soltoggio. net/icub. The robotic experiments require a real robot, or a robot simulator. The Matlab code can be also used as a stand-alone script with simulated input/output flow. The simulation without a real robot is used to test precisely controlled input-output regimes and timing which are difficult to achieve in a real-life human-robot interaction.
Simulated input/output flow The previous experiment can be run as a stand-alone script in Matlab without the interface with the robot. In the simulated version, the signals representing the people are generated by means of a Poisson process that ensures random patterns in the sequence of stimuli. Thus, the experiments in this section eliminate possible bias in the pattern of appearance of people and tests rigorously the neural learning. The stand-alone experiments offer the possibility of reproducing the results with the provided Matlab scripts without a robot. Each stimulus (representing one person) has a probability 0. 15%/s of appearing, i. e. , all stimuli are independent and may be present at any time. Once present, one stimulus lasts for a variable interval in the range [3, 30] s. As before, one particular stimulus S * ∈ (S1.
For GFP-RecA, samples were taken at T1, T2, T3, T5 and T6. Cells were rinsed in MMS and mounted on 1. 2% agarose pads. When required, cells were stained with FM4-64 (Molecular Probes) to visualize the cell membrane or DAPI to visualize the nucleoid. Fluorescence microscopy was performed on a Leica DMR2A (100 UplanAPO objective with an aperture of 1. 35 and a CoolSnap HQ camera (Roper Scientific) and on a Nikon Eclipse Ti (100 FluoPlan objective with an aperture of 1. 30 and a ORCA R2 camera (Hamamatsu)). System control and image processing were performed using MetaMorph software. Three independent experiments were performed for each strain, observing a minimum of 500 cells.
Peptide ions were analysed using Xcalibur 2. 1 with the following data-dependent acquisition steps: (i) full MS scan [mass-to-charge ratio (m/z) 400 to 1400, resolution 70 000] and (ii) MS/MS (normalized collision energy = 25%, resolution 17 500). Step 2 was repeated for the 8 major ions detected in step 1. Dynamic exclusion was set to 40 s. A database search was performed with XTandem (version 2011. 12. 01. 1) (http://www. thegpm. org/TANDEM/) and confirmed with MaxQuant (http://www. maxquant. org/). Cys carboxyamidomethylation and Met oxidation were set to static and possible modifications respectively. Precursor mass was 10 ppm and fragment mass tolerance was 0. 02 Th. Identified phosphopeptides were filtered and grouped using XTandem Pipeline (http://pappso. inra. fr/bioinfo/xtandempipeline/) and manually validated.
Clinical assessment and management Standard assessment was made according to patients' clinical presentation and according to current recommendations. [1] atients' management was not modified by participating in this protocol. Functional status was determined using the World Health Organization (WHO) classification. A 6-Minute Walk Test was performed according to the American Thoracic Society recommendations. [12] Resting hemodynamics were assessed according to our routine protocol. [13] asoreactivity testing was performed using inhaled nitric oxide 20-40 ppm during or more than five minutes. [1] For VR-IPAH patients, pulmonary hemodynamics data after the positive acute vasoreactivity test were used for analyses. NT-proBNP (sandwich immunoassay, Modular Analytics E170 instrument, Roche Diagnostics, Laval, Canada) and plasma creatinine (Jaffé alkaline picrate method, Cobas, Modular P instrument, Roche Diagnostics) levels were measured.
introduction Over the last decade, ant taxonomy has experienced a renewal in Europe due to the description of new species  (Seifert 2005) , the revision of genera or species groups (e. g. Radchenko and Elmes 2003, Rigato 2011 ) and the publication of checklists of the ant fauna of several European countries (e. g. Austria:  Steiner et al. 2003 , Benelux: Boer 2010 , Bulgaria: Lapeva-Gjonova et al. 2010 , Croatia: Bračko 2006; Romania: Markó et al. 2006 , Montenegro: Karaman 2009 , Poland: Czechowski et al. 2012 , Slovenia: Bračko 2007). This renewal is also due to the publication of excellent taxonomic keys for ant identification (e. g. Seifert 2007 , Boer 2010 , Radchenko and Elmes 2010) , the development of Internet databases accessible on-line (e. g. antbase. org: antbase. org; Ant Genera of the World: www.
Methods A preliminary checklist was assembled from a thorough and comprehensive review of the literature and from the information given by several databases available on the Internet (e. g. FORMIS 2011: www. ars. usda. gov/saa/cmave/ifahi/formis, ScienceDirect: www. sciencedirect. com, Google Scholar: www. scholar. google. com, etc. ) using searching requests such as "Andorra + Formicidae". This first checklist was compared with the material we collected during several sampling campaigns conducted in the last 8 years in different parts of Andorra: low and high altitude grasslands, low and high altitude forests, anthropized areas, etc Ants were searched on the ground and on vegetation; potential nesting sites were also inspected: dead wood, underneath of stones / bark, grass stems, acorns. Ants were collected by hand and were placed in plastic vials filled with 90° ethanol.
Once in the laboratory, they were sorted and identified to the species level. All the material collected was identified by Dr. Xavier Espadaler. As no identification keys exist for the ants of Andorra, keys created for other Europeans countries (e. g. Czechowski et al. 2012 , Seifert 2007 ) and for the taxonomic revisions of some ant genera  (Seifert 1988 (Seifert , 1992 (Seifert , 2000 (Seifert , 2007) )  were used. Most of the material we collected is stored in the personal collection of Dr. Xavier Espadaler at the Department of Animal Biology, Plant Biology and Ecology, Autonomous University of Barcelona, Spain. The final checklist was assembled based on every taxonomical/biodiversity papers related to Andorra as well as on other papers referring to data collected in Andorra (e. g. phylogeographical studies) and on the material collected by ourselves.
Acknowledgements A. B. was financed by a doctoral grant from the  Fundació Crèdit Andorrà. Part of this work was supported by the program " Entomological Inventory of the Madriu-Perafita-Claror Valley " funded by the  Department of Agriculture of the Principality of Andorra. X. E. is currently supported by  MICINN-FEDER   CGL2010-18182.
The strain 4834-R is a highly aggressive strain that was isolated from a seed-borne epidemic in France in 1998  [13]. Twelve complete genome sequences of Xanthomonas are currently available and more than 90 draft Xanthomonas genomes (www. xanthomonas. org/genomes. html) are deposited in public databases. Altogether, genomes are available for strains representing 13 pathovars spanning over 11 Xanthomonas species. Most of the sequenced strains are pathogenic to five plant taxa (cabbage, cassava, citrus, rice, and sugarcane). No complete fully assembled genome sequence is yet available for any xanthomonads pathogenic to legumes. However the draft sequence of X. axonopodis pv. glycines strain 12-2, a pathogen of soybean, was recently made available (accession number: AJJO00000000). Common characteristics of previously released Xanthomonas genomes are to hold a great number of genes encoding proteins devoted to plant environment recognition such as methyl-accepting chemotaxis protein (MCP) and other sensors, to plant substrates exploitation such as TonB-dependent transporters (TBDT) and cell wall-degrading enzymes (CWDE), and to manipulation of plant defense machinery such as T3Es  [30].