dataesr/scientific-paragraphs-categorization huggingface dataset:
The vertices arrive in a certain order: Random, BFS or DFS  [18] ,  [7] , and once the vertex is assigned to a shard, it is never replaced afterwards. The streaming graph model consists in a cluster of k machines each one of capacity C such that the total capacity of the k machines can hold the whole graph. When the vertex arrives in a stream, a partitioner must decide in which one of the k machines the vertex must be placed. 2) One Pass Streaming Heuristics: 1) Linear Deterministic Greedy. In  [6] , Stanton and Kliot proposed 10 different online heuristics for partitioning graphs. The most effective heuristic which performs well over all datasets they have used is Linear Deterministic Greedy LDG. The goal of LDG is to assign a vertex in a shard which contains the most of its neighbors while avoiding overloading shards.
LDG yields the best results in terms of edges cut in FEM datasets, this in due to the structure of FEMs, their edges are highly local which make it possible to have very good partitions. 2) FENNEL. Recent work by Tsourakakis et al. [7] , introduce another greedy heuristic approach for graph partitioning FENNEL. This heuristic is very performant, it yields very good quality partitions of low fraction of edge cut and balanced parts. 3) Multi-passes streaming Heuristics: In [10], Nishimura et al. introduced the problem of restreaming partitioning which consists in several passes of dataset stream. In the restreaming framework considered, subsequent streams of LDG and FEN-NEL have access to the result of previous streams  [10]. In particular, Joel Nishimura et al. chose to make a restreaming version of the two single shot streaming partitioning presented earlier LDG and FENNEL denoted by reLDG and reFENNEL.
Partial Restreaming Model: We consider a simple streaming model as described in Section 2, where vertices arrive in a random order with their adjacency lists, the heuristic used for partitioning must make a decision about the cluster to place the current vertex. In the partial restreaming model, two major phases exist: the restreaming phase and the one pass streaming phase. Namely, a first loaded portion of the graph dataset of size β * C is going to be restreamed and the rest is going to be processed in the simple streaming phase. In other words, in this model, multi-passes of the stream is allowed for only a part of the dataset. Let P t be the partition obtained at time t, P t-1 represents the partition obtained at the precedent iteration of the restream. When we attain the number of restreaming iterations allowed for the portion concerned, we continue streaming the rest of the graph dataset normally, such that we dont use information about the last partitioning to build a new one.
3) Fractional Greedy 1 The adaptation of FG to the partial restreaming model is as follows: In the restreaming phase, PartFG corresponds to: P artF G =argmax i (P t-1 i ∩ N (v)) - 1 1 - |P t i | C (9) And in the second phase, PartFG is defined as: P artF G =argmax i (P t i ∩ N (v)) - 1 1 - |P t i | C ( 10 ) 1 Refer to Section 3. 1 for FG description. Selective Partial Restreaming Partitioning: Instead of restreaming the first loaded portion of the graph dataset, we try to select portions of size C that would lead to a good quality partitioning. We set degree and density parameters to be the criteria for selecting portions to be restreamed in the first phase of the model. In other words, when a portion is loaded, we check its average degree and its average density, if it is higher than the average degree (respectively average density) of the whole graph, we select this portion for restreaming, otherwise it will be processed in the second phase of one pass streaming.
IV. EVALUATION SET UP 1) Evaluation datasets: Two types of graph datasets were used: web and social. We test our methods on ten graph datasets listed in Table  I , all obtained from the SNAP repository [20]. Vertices with 0 degree and self-loops were removed. All the graphs were made undirected by reciprocating the edges. Graph datasets were chosen in order to be small enough so that we can find offline solutions with METIS and still big enough to capture the behavior of the online heuristics. 2) Methodology: We first run FG, LDG and Fennel in one pass stream setting on our graph datatsets for k = 40 in order to compare the fraction of edge cut represented by λ and the balance represented by ρ. For F ennel, the parameter γ is set to 5 in order to give as balanced parts as those given by F G and LDG.
Then we examine results of edge cut for different values of β. We begin by running P artLDG and P artF ennel and P artF G (partially restreaming LDG and partially restreaming F ennel and partially restreaming F G respectively) on WebGoogle and LiveJournal for different values of β and see how the fraction of edge cut reacts to the change in β. After that, we run our methods on the ten graph datasets, for k = 40, s = 10 and β = k/2. Notice that the ordering of vertices is done at random. β = k/2 means that we are restreaming the half of the graph. We compare our results to the whole graph restreaming methods  [10]  and to METIS, which represents the offline methods. Afterwards, we test the partial restreaming methods (P artLDG, P artF ennel) and the partial selective restreaming methods (P artSLDG, P artSF ennel) on seven graphs.
Summary of our results • Fractional Greedy outperforms LDG and Fennel in most cases with exact balanced clusters and lower edge cut. • Results that were obtained show that by augmenting beta, or by augmenting the size of the portion to be restreamed, we obtain better quality partition. • By restreaming the first loaded half of a graph, we obtain partitions of similar quality than those yielded by restreaming the whole graph. • The partial selective restreaming method preserve the quality of a partition unlike the partial methods which depend on the stream order. The reason is that, no matter the order, the selective methods always pick the good portions to be restreamed. • Restreaming only a portion of a graph dataset incurs lower running time than restreaming the whole graph, for example, restreaming only the half of the graph takes the half running time taken by restreaming the whole graph while the results are almost the same.
Performance discussion Our proposed heuristic Fractional Greedy outperforms LDG and Fennel in terms of balance and also of edge cut. In Table  II  we show our results, we see that FG yields partitions of exact balance ρ = 1 and lower edge cut. However, LDG outperforms FG in web-google (0. 310 vs 0. 308) and web-berkstan (0. 368 vs 0. 342). Our proposed heuristic is the most adapted for the setting of constrained graph partitioning, it yields exact balanced partitions and also minimize the edge cut. In figure  1  we show average results of comparison between F G, LDG, F EN N EL and M ET IS in terms of fraction of edge cut over 10 graph datasets. METIS  is the best performing heuristic, FG is the second bestt heruistic outeerforming LDG and Fennel. In Figure  2 , we see that the bigger β is the lower the fraction of edge cut.
This shows that the information about the precedent stream iteration allows vertices to be more oriented toward the best cluster leading to lower edge cut. In other words, the more information we have about the precedent streaming iteration the better is the edge cut. Table  III  shows results in terms of fraction of edge cut and balance. The difference between edge cut of fully restreaming methods and partial restreaming methods are minimal in the most cases. Table  IV  shows the diffrence between the performances of partial restreaming methods and selective partial methods on seven different datasets. The results shows that in most cases, selective partial restreaming methods yields better quality partitions by leading low edge cut. However, in some datasets, it may be that portions which are conform to the criteria doesn't exist, then the selective partial method would not give the best results, that's why the simple partial restreaming method could be a good alternative for both speeding up computations and imporving partitions quality by restreaming several portions.
VI. CONCLUSION AND FUTURE WORK In this work, we provide new methods for the problem of balanced graph partitioning. Specifically, we introduce a new online heuristic for streaming partitioning and we show that we improve the partitions quality by partially restreaming several portions of the graph. We showed that our proposed heuristic produces partitions of significantly enhanced quality in terms of balance and edge cut: partitions that were produced are exactly balanced and have a lower edge cut. We also introduced the partial restreaming graph partitioning that aims to take several portions of the graph and restream it several times in order to improve the partitions quality. We evaluate our proposed methods on ten different graph datasets and compare it with the state-of-the-art partitioning heuristics (Fennel and LDG).
Regarding SWOT's nadir, this dataset is optional as per the Phase A description of the SWOT mission: we use a worst-case scenario without this dataset and we performed a few sensitivities studies to verify that our conclusions remain valid with the nadir instrument. CNES recently confirmed that SWOT would include a Jason-class altimeter and this change is discussed in Section 5. 3. Then we add an instrumental white noise ε(x,t). In the case of nadir measurements, ε has a constant standard deviation (STD) of 1 cm for Jason-CS and Sentinel-3 (i. e. , we assume they are in Cryosat-2's synthetic aperture radar mode), whereas the STD for SWOT's nadir is 3 cm (i. e. , assumed to be in Jason-2's low resolution mode). The noise STD for KaRIN's image is 1. 7 cm RMS for 1 km postings and spatially varying with the cross-track distance: the STD-law is U-shaped and ranging from 1.
9-day sub-cycle. Conversely, if we use SWOT's proposed "contingency" orbit, the one-day sub-cycle provides a much more roll-friendly input dataset as shown by Figure  8c. The one-day discontinuity is exceedingly smaller as per Figure  6  and the leakage on roll is also smaller. However, Figure  8d  shows that the images that were not perfectly seamless in Figure  8c  are now skewed into a perfect seamless composite. The leakage of oceanic variability is less pronounced in Figure  8d  than in Figure  8b  because we use one-day differences instead of 10-day differences but the basic phenomenon is the same.
Lastly Figure  17e ,f show the number of concurrent crossover zones (i. e. , size of the stacks from Figure  17a-d ). (d-f ) show the along-track length of the SWOT segment that is in the calibration zone (unit: km). The blue ellipses (plain) highlight crossovers between ascending/descending arcs and the red ellipses (dashed) highlight crossovers between ascending/ascending or descending/descending arcs. If crossover calibration is tackled with SWOT data only, each roll angle R(t) is seen (on average) by two independent cross-calibration zones. This number increases slowly to 3 at mid latitudes and 5 or more above 70°. For almost all latitudes, there is at least one segment with a time difference smaller than five days (as per the "butterfly shape" of Figure  16a ). In contrast, if Jason-CS and Sentinel-3 crossovers are used as well, each roll angle R(t) is seen by 4 zones on average and 5 to 20 for mid-latitudes to high-latitudes respectively.
This is illustrated by the sensitivity tests of Figure  21d : the residual error after calibration decreases by a factor of 2 in amplitude (or 4 in energy like in Table  2 ) as we increase the number of images used in each inversion. There is however an asymptotical limit of the order of 0. 012 arcsec RMS. As far as spectra are concerned (not shown), the gain is observed on high wavenumbers where leakages on roll (from noise or submesoscale) are not spatially correlated and therefore averaged out when multiple swaths are used concurrently. In contrast, the gain is limited on wavelengths longer than 50-100 km because the leakage from swaths N -1 and N (Figure  20a , blue and grey), and the leakage from N and N + 1 (Figure  20a , grey and green) can be correlated. Extending the calibration zone by using 3+ datasets from KaRIN can be generalized: multiple nadir and KaRIN crossovers could be solved concurrently instead of independently.
Materials and Methods 667 women aged 18 to 69 years old were enrolled during their regular visits to three local outpatient departments of gynecology in two hospitals and one family planning clinic, in urban Tunis. At the enrolment, informed consent was obtained from all participants. The women were screened for cervical cancer by conventional Pap smears and HPV test. Pregnant women, menstruating women, those with hysterectomy, and those who refused the HPV test were excluded. The ethic committee of the Pasteur Institute of Tunis approved the study. A questionnaire administered by clinicians at the three facilities was used to collect data from the women on socio-economic status and sexual partners. During gynaecological examination, cervical scrapes for conventional Pap smears were obtained using Ayre's spatula and then fixed with hair spray.
Statistical analysis Sample size calculation was performed using Epitable command (EpiInfo6 software): using the following estimations: Precision=2. 5%, point prevalence=10% and α=5%. The sample size estimation was about 553 subjects. HPV prevalence standardized by age was estimated on the basis of the female population of Tunisia and of the world standard population. Statistical significance was reported using a χ 2 test for categorical variables and the t test for continuous variables. Odds ratios (OR) and 95% confidence intervals (CI) were computed using unconditional binary logistic regression  (SPSS 20. 0). Association between HPV test and cytology results was assessed by χ 2 test (p≤0. 05).
Acknowledgements This work was supported by grants from the  Ministry of Health  "research unit on Human papillomavirus infection". It was carried out in the  Who HPV LabNet regional reference Laboratory Who Eastern Mediterranean region, department of Pathology  at the  Institut Pasteur de Tunis. We thank all the investigators, the technician, the staff of the gynecological departments and the family planning clinic for their help in the achievement of this work. The study sponsor did not influence the study design, the data collection, the analysis or the interpretation and writing of the manuscript.
Background: We herein evaluate the Spanish population? s trends in health burden by comparing results of two Global Burden of Diseases, Injuries, and Risk Factors Studies (the GBD studies) performed 20 years apart. Methods: Data is part of the GBD study for 1990 and 2010. We present results for mortality, years of life lost (YLLs), years lived with disability, and disability-adjusted life years (DALYs) for the Spanish population. Uncertainty intervals for all measures have been estimated. Results: Non-communicable diseases accounted for 3,703,400 (95% CI 3,648,270? 3,766,720) (91. 3%) of 4,057,400 total deaths, in the Spanish population. Cardiovascular and circulatory diseases were the main cause of mortality among non-communicable diseases (34. 7% of total deaths), followed by neoplasms (27.
Background The impact of diseases and injures on population health is usually assessed with measures of mortality and non-fatal health outcomes  [1]. These estimates are used to signal the most relevant public health problems, allow comparison between different populations and different health conditions, and assess changes over time. The only comprehensive effort to date to estimate summary measures of the global population health, by cause and by world region, is the ongoing Global Burden of Diseases, Injuries, and Risk Factors (GBD) initiative  [2, 3]. The first GBD study analyzed data from 1990  [4]  and was published in 1993. Since then, a number of updates have been published  [5]. The Global Burden of Diseases, Injuries, and Risk Factors Study 2010 (GBD 2010) has updated and expanded previous efforts to include 1,160 diseases and injury sequelae from the previous analysis, which included 483 diseases.
The most important limitation of previous GBD studies is that results were not estimated with uncertainty  [6]. Specifically, uncertainty can come from many sources, including heterogeneity in the empirical data that are available and uncertainty in the indirect estimation models used to make predictions for populations with little or no data. However, this limitation has been solved in the recent analysis of the GBD 2010 study. The GBD initiative uses disability-adjusted life years (DALYs) and mortality as the summary measurements of the impact of health conditions on population health. The DALY combines the years of life lost (YLLs) due to premature mortality and the years lived with disability (YLDs). The use of DALYs as measure of impact, the inclusion of a large number of communicable and non-communicable conditions, the analysis of the impact of health conditions stratified by gender and age, and the effort to use comparable methodologies across countries and regions make the GBD project an excellent tool to understand the determinants of health and their variability across time and regions.
1 million in Spain. Almost 90% of these were attributed to chronic diseases. Specifically, the leading causes of DALYs were neurological and mental disorders, followed by malignant neoplasms and cardiovascular diseases  [8]. However, the aforementioned results were partially limited, since the mortality data came from Spanish registers, while the incidence and severity rates were obtained from estimations of the WHO for a variety of European countries (i. e. , WHO European Region Eur-A). A more recent project, but only including data from Valencia, revealed similar results  [9] , reporting that the number of DALYs gradually increased with age and almost 27% of all DALYs occurred among people over the age of 70  [9]. Given the scarcity of analysis of the data from Spain and the relevance of learning from the experience and comparison with similar and not so similar countries, the aim of the present work was to evaluate the change in burden of disease in Spain, comparing the available data of the GBD over time, between 1990 and 2010.
Overview The GBD 2010 study estimates the impact of 291 diseases and injuries and 67 risk factors for 187 countries distributed into 21 world regions between 1990 and 2010. For each cause, 1 to 24 sequelae were defined. Sequelae are the clinical outcomes that can be related to specific diseases and injuries such as neuropathy due to diabetes. In total, the study includes 1,160 sequelae. More detailed information about data and analysis for the GBD 2010 have been previously reported  [2, 5, 6, 10, 11]. For the present analysis, only information regarding Spain will be reported.
Measurements We report data on mortality, YLLs due to premature mortality, YLDs, and DALYs. Age-specific mortality rates for Spain were estimated for each sex. As in other developed countries, information about deaths was predominantly driven by data from official vital registration systems  [12]. The denominators were based on Spanish census returns and intercensal estimates. Similarly to the other 187 countries of the GBD study, the estimated number of deaths and YLLs was based on 235 defined underlying causes of mortality from the list of 291 diseases and injuries, for 20 age groups and both sexes  [2, 13]. The YLLs were computed by multiplying the number of deaths in each age group by a reference life expectancy  [2]. The YLDs were computed by multiplying the prevalence of a sequelae by its disability weight (DW), used to quantify population? s health losses.
This procedure varies slightly compared to previous GBD studies, in which incidence and average duration of the case until remission was used instead of prevalence  [14]. A total of 1,160 possible sequelae of diseases and injuries were analyzed. Murray et al. [5]  have described the systematic analysis of available data conducted for each sequelae with regard to the prevalence, incidence, remission, and excess mortality. For each age-sex-year group, estimates were made for most sequelae using Bayesian meta-regression methods. DWs were obtained for 220 health states covering the 1,160 sequelae  [6]. For each sequelae, DWs were derived based on the scoring of short lay descriptions of the relevant health domains in large population-based studies in several countries (i. e. , Peru, USA) and through an open internet survey  [15].
e. , cognition reserve, smoking, unhealthy diet, physical activity, hypertension, etc. ), making the prevention of chronic diseases in older adults quite difficult. Efforts to improve and protect health, prevent disease and injury, and deliver high-quality health care to the population must be tailored to address the causes associated with the greatest burden mainly of chronic diseases  [32]. It is expected that preventive strategies can influence many of the aforementioned chronic conditions and non-communicable causes and increase the quality of life, while averting or minimizing the need for expensive medical care  [33, 34]. Furthermore, the engagement with the GBD collaborative group will provide more and better analysis data of the global burden of disease and, specifically, in Spain ? a fact that will contribute to continuous improvements of the health estimates in future iterations of the GBD study.
Limitations The previous results should be considered taking into account the following limitations. Although Spain follows the European Statistics Code of Practice of 2006? 2008 and data is collected in a consistent way across the country, there are a number of issues that should be considered when analyzing the results. Regarding mortality, the GBD project defined a number of garbage codes, which are causes of death that should not be identified as underlying causes of death but have been entered as the underlying causes of death on death certificates. Garbage codes were substituted by underlying causes based on pathophysiology. The fraction of garbage codes in Spain was within the world average. The substitution of garbage codes has impact on mortality causes. For example, in 2010, transport injury deaths based on raw data from Spain were 2,257.
Acknowledgements Dr. Stefano Tyrovolas  received a scholarship from the  Foundation for Education and European Culture (IPEP)  for his postdoctoral research, of which this work is part. Dr. Lidia Sanchez-Riera  received a research grant from Sociedad Espa? ola de Reumatolog? a. Noe Garin is grateful to the  FPU doctoral program  ( reference FPU12/05661 ) supported by the  Spanish Ministry of Education, Culture and Sport.
Introduction In the framework of CogALex-IV (co-located with COLING 2014 in Dublin) we invited colleagues to participate in a shared task devoted to the lexical access problem in language production. Our aim was to make a quantitative comparison between different systems based on a shared set of data and using the same evaluation metric. The lexical access problem is very relevant for this workshop series as the quality of a dictionary depends not only on its coverage, but also on the accessibility of the information. Put differently, a crucial point of dictionary development is word access by the language producer, an often neglected aspect. Access strategies vary with the task (text understanding versus text production) and the knowledge available at the very moment of consultation (words, concepts, speech sounds).