dataesr/scientific-paragraphs-categorization huggingface dataset:
Basic Idea of the DOMINO Algorithm We now introduce a novel method for reasoning over data for automated decision making. We call it DOMINO as its basic functionality reminds of the well-known social game where bricks with two numbers on each side are connected to a chain where bricks with equal numbers lay side by side (see Fig. 1 ). In our DOMINO system, the bricks correspond to term logic premises, where each premise is composed of two terms (subject and predicate). Like with the domino game, bricks are linked according to syllogistic rules  [10] , which can be computed by software  [6]. In principle, DOMINO may use the full set of syllogism rules. However, just like with Horn clauses  [12] , for decision-making based on positive affirmation using a single rule is sufficient, and we therefore restrict ourselves to a well-known rule which is traditionally called "Barbara" and chains propositions which are of type "all" and have the term x in common: A(p,x),A(x,q)→A(p,q).
Summarizing, DOMINO is a computational syllogism system using a subset of term logic figures and conditions to reason over data represented as term logic premises. The basic exercise of concluding new information out of existing premises is performed by linking the term of one premise with a term of another premise. Two premises may be linked if they have a common term, which computationally is verified by checking if the terms in string format are equal. DOMINO extends this notion of identity also to checking number range and regular expressions, as basically any kind of rule or procedure could be used to declare two terms as being equal. Also multistep comparison is possible, where initially high accuracy strict checking is applied, and if this does not bring the desired result, in a second run comparison checks can be relaxed, to produce a match according to the "Barbara" rule.
While DOMINO is not capable of quantifying variables, it is possible to define rules the same way it is done for Horn clauses in propositional logic, and any Horn clause construct can be expressed as a DOMINO sequence, see Fig. 1. Basically, DOMINO expresses implications by splitting conditions when they are defined, and recombining them when they are evaluated with actual data. For example, a single condition for buying a product when the price matches 99. 90 ("price = 99. 9  buy") reads with DOMINO as "{(trigger, price), (99. 9,buy)}". When only these tuples are present, no conclusions can be drawn, but if another tuple is added (e. g. by browsing through a database or checking the annotated web page of an online shop) which says "(price, 99. 9)", applying the conjunction results in (price, 99.
Advantages over Conventional Semantic Reasoning The advantages of DOMINO over existing reasoning systems based on propositional or first order logic are manifold. As it does rely on term based calculation for its reasoning instead of logical operations, it allows for • human readable data and rule definitions; • easy integration with tag based systems and hence relating messages and media of different topics down the conclusion chain and defining their in-formational distance using cardinalities (for example, given T1 containing tags #x and #y, T2 containing tags #y and #z and T3 containing #z and #w, T1 on topic #x would be associated to T3 about topic #w because of T2); • easy integration of pattern matching or any other equality concept (e. g. range checks, probability or similarity statements) into the reasoning algorithm.
Furthermore, DOMINO allows for closing knowledge gaps by asking the user simple yes/no questions: tuples that might be worth being combined, as they have a certain cardinality value, can be presented to the user asking if the second value of one tuple can be related to the first value of another tuple. In contrast to reasoners based on predicate logic, DOMINO avoids running into loops, which is especially important when it comes to allowing user-defined rules and reasoning on mobile devices. Finally, DOMINO does not require any special set up or topology descriptions for distributed evaluation of rules. Instead, distributed reasoning becomes as simple as putting tuples together from any data sources. As DOMINO's knowledge base solely consists of tuples and does not distinct between rules and facts, rules may change dynamically based on new data arriving or additional data sources tied in the reasoning process.
The set of 16 p values testing for differences in baseline characteristics across randomized groups did not follow a Uniform distribution on [0,1] (p = 0. 045). The p values obtained by explicit computations were different from the results reported by the authors for the two following variables: urine output at 5 hours (calculated p value < 10 -6 , reported p ≥ 0. 05); packed red blood cells (PRBC) during surgery (calculated p value = 0. 08; reported p < 0. 05). Finally, parametric bootstrap found p value > 0. 05 in only 5 of the 10,000 simulated datasets concerning urine output 5 hours after surgery. Concerning PRBC transfused during surgery, parametric bootstrap showed that only the corresponding p value had less than a 50% chance to be inferior to 0. 05 (3,920/10,000, p value < 0. 05). Conclusions: Such simple evaluation methods might offer some warning signals.
Parametric bootstrap The parametric bootstrap  [16]  is a simulation procedure that consists in randomly generating many independent datasets on the basis of the reported characteristics of the population and some knowledge about the distribution law. Simulations allow one to recreate a large number of virtual datasets retrospectively. Simulating a dataset requires: 1) a location parameter (mean or median for example); 2) a dispersion parameter (standard deviation or 95% confidence intervals for example); and 3) some knowledge about the distribution law. The parametric bootstrap consists in randomly generating N independent datasets of size n on the basis of the reported sample size, mean and variance (Additional file 1). Let's consider that a variable, such as age, is reported with a mean of 60 years with a SD of 40 in a sample of size 1,000.
If one assumes that age is usually normally distributed in the population, one can use the reported mean and SD to draw N independent sample from a Normal distribution with mean 60 and SD 40. The simulated datasets may first be used to look at the distribution of the data with two specific questions: 1) Is this distribution possible? (e. g. , it is obviously impossible to observe negative values for a variable such as serum creatinine); 2) Is the distribution compatible with the variable itself? (e. g. , variable for which the measurement method has a large intrinsic variability should have a variance in agreement). Second, it is possible to rerun the statistical analyses in each simulated dataset using the same tests as those used by the authors and building the distribution of the p values.
It should be emphasized that such an approach does not aim to provide new inference but only to detect some potential inconsistencies. Moreover, variable simulation from reported means and standard deviations rely on an assumption concerning the underlying distribution. Because some variables are obviously not normally distributed, it could be of interest to compare simulations under normal distribution to simulations obtained under alternate distributions. Finally, simulating 10,000 datasets is not the same as "redoing 10,000 times the same clinical trial, with the same sample size. " In such simulations, we assume observed means and standard deviations are the true population parameters, while they are in fact the observed parameters of a random sample drawn from the underlying population.
Illustrative example To illustrate these methods, we selected the data from a randomized study published in 2009  [10]  that was based on two groups of 25 patients (Additional file 2). We chose this paper because it is known to be fraudulent and it was retracted by the Editor-in-Chief of the journal in 2010  [11]. To perform our retrospective simulations, we selected in this paper the continuous variables, the mean and the standard deviation of which were tabulated. Assuming that the reporting was appropriate, we considered that, when such variables were expressed as mean (SD), they should be normally distributed. We then used these reported means, standard deviations and the normal distribution assumption to build N = 10,000 datasets of size n = 50 (25 subjects in each group). Based on these simulated datasets, we reran the statistical analysis using Student t tests, and plotted the distribution of the observed p values across the 10,000 datasets.
Parametric bootstrap We limited the simulations to those variables where (  1 ) there seemed to be some clinical differences between the groups, but the authors reported no statistical difference, (2) the authors reported a statistically significant difference but such a difference did not seem to be of clinical relevance, or (3) the normal distribution assumption seemed to be violated. The results of the simulation are given in Table  1. Figure  1C  exemplified two situations where the probability of finding the results the authors actually reported was low: the authors reported a nonstatistically significant difference in terms of urine output 5 hours after surgery, but the simulation found p > 0. 05 in only 5 of the 10,000 simulated datasets; the volume of PRBC transfused during surgery was reported to be significantly higher in the Albumin group, but simulation showed that the probability to observe p < 0.
C Distribution of the simulated p values corresponding to the comparison of two variables in the two groups across the 10,000 simulated datasets. The left panel shows a very high probability for the comparison of the urine output at 5 hours to be statistically significant, whereas it was reported as nonsignificant by the authors. The left panel shows the distribution of the simulated p values concerning the comparison of the PRBC during surgery: the black vertical line represents the 0. 05 threshold of statistical significance, and the dashed line represents the p value that has been explicitly computed, given the observed mean and SD in the two groups.
The COST (European Cooperation in Science and Technology) Action ES0601: Advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies. The algorithms were validated against a realistic benchmark dataset. Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including i) the centered root mean square error relative to the true homogeneous values at various averaging scales, ii) the error in linear trend estimates and iii) traditional contingency skill scores.
INTRODUCTION To study climate variability the original surface observations are indispensable, but these have to be treated with care. Long observational records always contain changes due to non-climatic factors as well. Such inhomogeneities can be either sudden jumps (breaks) or gradual trends in one station. In a recent paper  [1] , the methods to remove these factors were blindly tested on a benchmark temperature and precipitation dataset with inserted inhomogeneities. This abstract will present the main ideas from this study. All the most common and the most developed algorithms were tested. Most surface stations are not operated for climatic purposes, but rather to meet the needs of weather forecasting, agriculture and hydrology  [2]. Consequently, the average period between detected breaks in Western instrumental records is 15 to 20 years.
BENCHMARKING The benchmark dataset mimics station networks and their data problems with unprecedented realism. Homogeneous data was generated using the so-called surrogate data approach, which reproduces the crossand auto-correlation functions, as well as the non-Gaussian distribution of climate observations  [15, 16]. To this data random break-type inhomogeneities, as well as breaks that occur simultaneously in multiple stations, were added. Furthermore, local trends, which either continue at the end (to model for instance the urban heat island effect) or go back to baseline (to model growing vegetation that is cut back at the end) were inserted. The sizes of the breaks and local trends follow a normal distribution with a width of 0. 8 °C for temperature  [17] , and 15 % for precipitation.
In contrast to the results for temperature the positive results for precipitation are more mixed, still all but one relative method did improve the station trends. Many inhomogeneities are documented. Had such documentation been provided for part of the breaks in the benchmark data, the errors would have been lower. The second main conclusion is that direct relative homogenization algorithms are clearly better than traditional ones. It needed a realistic benchmark dataset to see this difference with such clarity. With mathematical argumentation, climatological reasoning and the benchmark metrics all pointing in the same direction, we thus strongly recommend the use of direct homogenization algorithms. The performance ranking of the homogenization methods depends on the error metric considered, on whether the root mean square error is computed on the monthly, yearly or decadal data and on whether it is computed on the station data or on the network mean climate signal.
These rankings also do not correlate strongly with the error in the linear trend estimates (or break detection scores). In other words, it is difficult to compute one error metric that would signify the remaining error after homogenization for all climatic purposes. The computation and communication of the remaining uncertainties of homogenized data should be one of the research priorities for the coming years. We feel that benchmarking has helped the homogenization community to mature  [24]. The discussions on the properties of the benchmark, the nature of inhomogeneities in the various regions and on homogenization methods, as well as the joint work on the same dataset helped to bring scientists closer together in a way that writing individual papers cannot. The International Surface Temperature Initiative has started a follow-up benchmarking program for homogenization algorithms  [25].
This benchmark will be global and be even more realistic, especially due to the inclusion of metadata, biased inhomogeneities and random missing data. Everyone is invited to download and analyze the benchmark dataset. The homogeneous, inhomogeneous and homogenized datasets are published in the internet. Another offspring of the Action is HOMER, an open-source state-of-the-art homogenization package based on the best methods. The package is written in R and also performs basic quality control. Furthermore a mailing list for researchers working on homogenization has been started. All these resources can be accessed via the webpage of the Action, http://www. homogenisation. org, which will be kept running for the coming years and which also contains an extensive bibliography. With advanced and well-validated statistical methods, the homogenization of annual and monthly station data is a mature field.
Numerical results This section presents numerical simulation results aiming to evaluate the performance of the EKF and UKF methods regarding system identification based on data produced numerically by the reference model (30). The forcing term, u, is a square signal. In the reference problem, (30) and (31), the terms [F], [B] and [C] are assumed known and {x} is determined through a numerical integration method implemented in Matlab (Ode23; Runge-Kutta method). In the Figure  3  and 4 , we compare the response of the reference model (31) at points observation, Z, to the response of the identified system model (33), Z k , using Kalman filters, EKF and UKF, respectively,to evaluate its accuracy at five different times, 2 , 4 , 6 ,8 ,10 t s s s s s ≈. Both EKF and UKF produce a good accuracy with almost equivalent convergence speed in that case, e.
Source of TV programs We analyzed all French broadcasts from analog TV that dealt with ADHD and/or Ritalin from 1995 till December 2010. We searched the database of the Institut National de l'Audiovisuel (http://inatheque. ina. fr/Ina/ws/dltv/dlweb/general/MultiSearchNames9), which indexes all the French TV documents, with the keyword Boolean search: ((hyperacti* OR ritaline) AND attention). TV programs that only mentioned "hyperactivity" as a behavior of healthy people were discarded. We obtained a corpus of 60 programs that included news, talk shows and debates (listed in supplementary Table  S1 ). Although fictions are representative of the society and of its perception, fictions mentioning ADHD were not included. Instead, we were looking for TV programs in which opinions on ADHD were put forward by medical experts.
Current situation Part 1 of the study investigated the current situation. The first question showed that optimal design software tools in NLMEM are being used by nearly all companies (9 of 10), mostly during phase I and II for pharmacokinetics/pharmacodynamics, sometimes for biopharmaceutical studies later in development in special populations: pediatric patients, patients with renal or hepatic impairment, and the elderly. All currently available software tools were used among the respondents. Here, a list of the programs and their frequency of usage are given: PFIM (INSERM, University Paris Diderot, 6 of 9), POPED (University of Uppsala, 3 of 9), POPDES (University of Manchester, 3 of 9), and POPT (University of Otago, 3 of 9). Optimal design approaches are used for a variety of investigations and design complexity (Table  1 ).
Adaptive designs and further developments Adaptive design in NLMEM is of high priority for most companies with a median of 4, on a scale of 0-5, with 4 companies quoting a 5 (very useful). The answers to specific needs were: (i) start from prior information (8 of 9), (ii) design optimization after each new cohort (8 of 9), and (iii) use stopping rules (6 of 9). One company highlighted that adaptive design is not Methods and software tools for optimal design in nonlinear mixed effect models, based on the Fisher information matrix, have been developed for a decade. 1,2 Academic groups regularly proposed new versions. 3-5 Present tools do not incorporate adaptive designs for these models although prior information is needed and adaptive designs are increasingly used in drug development. 6  possible in therapeutic areas where end points are attained slowly while recruitment is fast.
Acknowledgments. The research leading to these results has received support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement no. 115156 , resources of which are composed of financial contributions from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and  European Federation of Pharmaceutical Industries and Associations ' companies in kind contribution. The  DDMoRe  project is also financially supported by contributions from  Academic  and small and medium enterprise partners.
2 using the dataset D m. Theorem 12 (Adaptivity in α and β). Let K be a compact subset of ]0; 1[×]0; ∞[. There exists a constant C > 0 such that for all n ≥ 1, for any (α, β) ∈ K, the maximum expected excess of ranking risk of the estimator radp n is bounded as follows: sup P ∈P α,β,µmax E(r adp n ) ≤ C • n -β(1+α) d+(2+α)β. (4. 3) The cardinality of Σ n is an exponential of n so the estimators r n ,β , for a given ( n , β), are not easily implementable. However the procedure is very interesting from a theoretical standpoint since it is adaptive to the parameters and it achieves fast rates when αβ > d. Finally, notice that this estimator can achieve fast rates when αβ > d i. e. when the regression function is very smooth.
If there are already a few or several reviews of the literature on your issue, my advice is not to give up, but to carry on with your own literature review, (i) discussing in your review the approaches, limitations, and conclusions of past reviews, (ii) trying to find a new angle that has not been covered adequately in the previous reviews, and (iii) incorporating new material that has inevitably accumulated since their appearance. When searching the literature for pertinent papers and reviews, the usual rules apply: (i) be thorough, (ii) use different keywords and database sources (e. g. , DBLP, Google Scholar, ISI Proceedings, JSTOR Search, Medline, Scopus, Web of Science), and (iii) look at who has cited past relevant papers and book chapters.
Rule 7: Find a Logical Structure Like a well-baked cake, a good review has a number of telling features: it is worth the reader's time, timely, systematic, well written, focused, and critical. It also needs a good structure. With reviews, the usual subdivision of research papers into introduction, methods, results, and discussion does not work or is rarely used. However, a general introduction of the context and, toward the end, a recapitulation of the main points covered and take-home messages make sense also in the case of reviews. For systematic reviews, there is a trend towards including information about how the literature was searched (database, keywords, time limits)  [20]. How can you organize the flow of the main body of the review so that the reader will be drawn into and guided through it? It is generally helpful to draw a conceptual scheme of the review, e.
Ideally, a literature review should not identify as a major research gap an issue that has just been addressed in a series of papers in press (the same applies, of course, to older, overlooked studies (''sleeping beauties''  [26] )). This implies that literature reviewers would do well to keep an eye on electronic lists of papers in press, given that it can take months before these appear in scientific databases. Some reviews declare that they have scanned the literature up to a certain point in time, but given that peer review can be a rather lengthy process, a full search for newly appeared literature at the revision stage may be worthwhile. Assessing the contribution of papers that have just appeared is particularly challenging, because there is little perspective with which to gauge their significance and impact on further research and society.
Acknowledgments Many thanks to  M. Barbosa ,  K. Dehnen-Schmutz ,  T. Do ¨ring ,  D. Fontaneto ,  M. Garbelotto ,  O. Holdenrieder ,  M. Jeger ,  D. Lonsdale ,  A. MacLeod ,  P. Mills ,  M. Moslonka-Lefebvre ,  G. Stancanelli ,  P. Weisberg , and  X. Xu  for insights and discussions, and to  P. Bourne ,  T. Matoni , and  D. Smith  for helpful comments on a previous draft.
Editorial There are times in research when speed really is of the essence in getting results published. For those working in the infectious disease field, there is a particularly pressing need when an outbreak occurs and it becomes paramount to get data communicated both efficiently and promptly. The 2009 influenza pandemic highlighted the limited options available for scholarly publications to rapidly communicate research findings in a time of crisis. In response to the needs of the community, PLOS launched PLOS Currents: Influenza as a publication channel that would meet the need for swift publication of research during that influenza pandemic. PLOS Currents: Influenza allowed the process of peer review and publication to be reduced from months to a matter of days. PLOS Currents: Influenza showed that agile and reactive publication avenues that facilitated the rapid exchange of scientific results and ideas during an emergency period were possible (since then we have launched another section, PLOS Currents: Disasters, where timely publication is also of the essence).