dataesr/scientific-paragraphs-categorization huggingface dataset:
e. conditional and marginal treatment effects will not coincide). Two more approximate strategies are typically used to deal with this issue: the use of a high number of simulations to determine the value of the conditional hazard ratio that induced the desired marginal hazard ratio  [16] ; or the post-hoc verification that conditional and marginal treatment effects are in the same range  [53]. Another solution is to use a collapsible estimate of exposure effect, like risk differences  [15] , but this type of estimator is less used to report the effect of an exposure in real studies. Nevertheless, even if we did not use a conditional model to generate simulated datasets, a rather similar issue remains in this article: our algorithm simulates a desired hazard ratio in the entire cohort (ATE), but not a desired hazard ratio in the treated population (ATT).
In mathematical general relativity, Yvonne Choquet-Bruhat made groundbreaking advances in solving the Einstein constraint equations and analyzing the evolution of initial data into space-time solutions of the Einstein field equations. Key aspects of her work include developing new mathematical tools and novel approaches for applying them to problems of interest. In addition, she introduced new formulations of Einstein's theory of gravitation that recently spurred great progress in numerical relativity, including the calculation of the gravitational waves emitted when two black holes collapse and merge together. These results are of great importance for giant interferometric gravitational wave detectors, such as Virgo or LIGO. The man who moves a mountain begins by carrying away small stones.
Types of outcome measures The primary outcomes will be OS and PFS. The secondary outcomes will be: ▸ Objective response defined as a complete response or a partial response, according to the Response Evaluation Criteria in Solid Tumors (RECIST). 18 ▸ Toxicity evaluated by serious adverse events (SAEs) as defined on ClinicalTrials. gov (https://clinicaltrials. gov/ct2/help/glossary/serious-adverse-event). ▸ And quality of life (QoL), whatever the scale reported in trials.
Search methods for identification of trials We have designed an exhaustive search strategy according to high standards to identify published and unpublished trials. 19 We will search a range of bibliographic databases (CENTRAL, MEDLINE, EMBASE) with no restriction on language, status or year of publication (for search equations see online supplementary appendix 1). We will screen the reference lists of all selected trials and the list of trials selected in previous systematic reviews. We will also search additional sources (figure  2 ).
Data extraction and management All data will be independently extracted by two reviewers using a standardised form. Disagreements will be discussed with a third reviewer. In case of several reports pertaining to the same trial, they will extract data from the different sources, compare them and in case of discrepancies, will give priority to the first available source among regulatory agency reports, results posted at ClinicalTrials. gov, full-text articles, pharmaceutical reports and conference abstracts. The authors of the trials will be contacted to provide missing data if needed. For each trial, they will extract the study phase; singlecentre or multicentre status; funding source ( private, public, both or unclear); number of randomised patients; drugs, dosage, frequency and modality of administration; patient age; stage (IIIB vs IV); performance status (0-1 vs 2); history of smoking (never-smoker vs former or current smoker); proportion of patients with second-line treatment; outcome data: HRs for PFS and OS and their 95% CIs; number of patients with an objective response, number of patients with SAEs and means and SDs for QoL.
We will also update this adaptive search strategy by querying new sources when they become available (eg, the OpenTrials database  50  ). We will also consider clinical trial data sharing repositories ( blinding was lacking. Blinding of outcome assessors will be considered at 'low risk' if blinding was insured or for an objective outcome; it will be considered at 'low risk' if an independent Clinical Endpoint Adjudication Committee assessed subjective outcomes and at 'high risk' otherwise.
Data synthesis We will assess clinical and methodological diversity by comparing summary characteristics of trials and study populations across the different pairwise comparisons between treatments. The assumption of transitivity will be evaluated by comparing the distribution of the potential effect modifiers across different pairwise comparisons. Two analyses will be performed: first, we will compare individual treatments with each other, and second we will compare the different categories of second-line treatments previously mentioned (monochemotherapy, targeted therapy, combination of a monochemotherapy and a targeted therapy, combination of two targeted therapies and placebo). For this second analysis, trials in which patients in the control arm receive chemotherapy (eg, docetaxel or pemetrexed) at the investigators' discretion will be included.
This community will consist of different groups with different skills. In the largest group, people interested in lung cancer will be able to voluntarily report new treatments and new ( planned, ongoing, completed) trials of second-line treatments for advanced NSCLC. Second, an open group of international experts in lung cancer (clinicians, triallists or members of cooperative oncology groups) will validate the methodological choices and will be involved in the screening and selection step. For instance, the group would validate selection criteria for population, treatments and outcomes. Finally, a smaller open group of researchers with expertise in systematic review methodology (risk-of-bias assessment and data extraction) and in statistical MA methods, will execute the remaining review steps.
com/). The community of experts in lung cancer will also monitor various sources to identify novel treatments and will validate treatments notified by the community of people interested in lung cancer. The search strategy will be then updated by adding relevant keywords so that specific requests of the various sources capture randomised trials of these new treatments (figure  2 ). As for the initial NMA, the different sources described in figure 2 will be searched with the last updated search strategy. The search will run from the previous iteration to the current one (covering a 4-month interval) for bibliographical databases, clinical trial registries and conference proceedings. With the search equation being known for each source, a script (html extraction by automated http requests) will be used to automatically and simultaneously query the multiple sources every 4 months.
Data extraction This step will be performed by two of the trained reviewers. The method for extraction will be as previously described except for results posted on ClinicalTrials. gov. In this case, an automatic data extraction process will be used to automatically abstract posted results from ClinicalTrials. gov. 32 sessment of risk of bias Two of the trained reviewers will assess the risk of bias and discuss disagreements with a third reviewer. Although automated methods such as text mining can assist with risk-of-bias assessments, we opted for a manual approach, as currently recommended. 27 28 33 34 ndeed, risk-of-bias assessments may rely on other sources than the published article such as protocols, whereas the automated tools rely on articles only.
DISCUSSION To the best of our knowledge, we present the first protocol for a live cumulative NMA assessing the relative efficacy and safety of all second-line treatments available for advanced NSCLC in patients with wild-type or unknown EGFR status. We chose to update the network every 4 months considering the pace of evidence. Once the initial NMA is performed, maintaining the evidence up-to-date over time seems manageable and a reasonable investment over time as compared with the substantial cost of producing the initial synthesis. Indeed, for our previous search up to March 2015, we screened about 8000 records and extracted data for 77 trials, which required a substantial amount of researcher working time; compared with this initial NMA, each iteration would represent about 5% in terms of records needed to screen and trials to extract.
Figure2Adaptive search strategy. These different sources will be searched for the initial network meta-analysis and for each iteration. A research community interested in lung cancer will identify new second-line treatments for advanced NSCLC. The search strategy (ie, specific requests for querying the different sources) will be updated over time to identify trials assessing these new treatments. We will also update this adaptive search strategy by querying new sources when they become available (eg, the OpenTrials database 50  ). We will also consider clinical trial data sharing repositories (eg, Clinical Study Data Request or Yale University Open Data Access Project) as potential sources to identify some unpublished trials. AHRQ, Agency for Healthcare Research and Quality; ASCO, American Society of Clinical Oncology; CENTRAL, Cochrane Central Register of Controlled Trials; EU CTR, European Union Clinical Trials Register; EPAR-EMA, European Public Assessment Reports-European Medicines Agency; ESMO, European Society of Medical Oncology; FDA, Food and Drug Administration; IQWIG, Institute for Quality and Efficiency in Health Care; NICE, National Institute for Health and Care Excellence; NSCLC, non-small-cell lung cancer; WCLC, World Conference on Lung Cancer; WHO ICTRP, WHO International Clinical Trials Registry Platform.
Acknowledgements The authors are grateful to  A. Alexeev ,  P. Clarkson ,  M. Kontsevich ,  O. Lisovyy ,  V. Pestun ,  P. Severa ,  Y. Soibelman  and  T. Sutherland  for helpful discussions. We are thankful to  B. V. Dang  for his help with SINGULAR package and to the two referees for their insightful questions which have resulted in a much enhanced paper. The work of L. O. Ch. was partially supported by the center of excellence grant " Centre for Quantum Geometry of Moduli Spaces " from the  Danish National Research Foundation  ( DNRF95 ) and by the  Russian Foundation for Basic Research  (Grant Nos. 14-01-00860-a  and  13-01-12405-ofi-m2 ). This research was supported by the  EPSRC   Research Grant   EP/J007234/1 , by the  Hausdorff Institute , by  ANR  " DIADEMS ", by  RFBR-12-01-00525 -a, by  RFBR-15-01-05990 ,  MPIM (Bonn)  and  SISSA (Trieste).
We propose a regression tree procedure to estimate the conditional distribution of a variable which is not directly observed due to censoring. The model that we consider is motivated by applications in insurance, including the analysis of guarantees that involve durations, and claim reserving. We derive consistency results for our procedure, and for the selection of an optimal subtree using a pruning strategy. These theoretical results are supported by a simulation study, and two applications involving insurance datasets. The first concerns income protection insurance, while the second deals with reserving in third-party liability insurance.
The procedure we develop is shown to be consistent, while its practical behavior is investigated through a simulation study and two real dataset analyses. The CART procedure  (Breiman et al. (1984) ) is a natural candidate for dealing with such problems, since it simultaneously provides a regression analy-sis (which allows us to consider nonlinearity in the way the response depends on covariates) and a clustering of the population under study. Moreover, its tree-based algorithmic simplicity makes it easy to implement. It consists of successively splitting the population into less heterogeneous groups. A model selection step then allows us to select from this recursive partition a final subdivision into groups of observations of reasonable size, with simple classification rules to affect an individual to one of these classes.
Nevertheless, the fact that the censored ones are not assigned to any leaf of the tree does not constitute an obstacle in view of performing the growing procedure. Indeed, if the i-th individual is censored, W i,n = 0. Therefore, at each step, a censored observation could be assigned to any subpopulation without modifying the value of L l (j, x (j) l ). This does not mean that the information contained in censored observations is not used, since they play an important role in computing Ĝ, and thus W i,n. To summarize, the aforementioned procedure thus produces clusters of individuals with rules to assign uncensored observations to one of them. The question about how to assign a censored observation should be considered separately; see the application in Section 5. 2. The details of our modified CART algorithm (with censoring weights) are as follows: Step 0: Compute the estimator Ĝ from the dataset with n individuals.
compute the estimator Ĝ from the entire generated sample (Y i , δ i ) 1≤i≤n+v. Descriptive statistics corresponding to various simulated datasets (of different sizes) are available in Table  3. To each simulated sample, we fit a regression tree with the algorithm in Section 2. 1, and prune it using the strategy in Section 2. 3. [ b, c[ [ c, d[ [ d, e]  10% 30% 50% 0. 08 0. 05 0. 16 0. 5  [0, 0. 3[ [0. 3, 0. 6[ [0. 6, 0. 8[ [0. 8, 1]  (λ, μ) Then, we compute the weighted squared errors given by (λ, μ) (λ, μ) 12. 5 20 6. 25 2 30% 30% 20% 20% (80,1. 03) (20,1. 2) (14,2) W SE i = δ i n -1 [1 - Ĝ(Y i -)] -1 (γ l(i) -π 0 (X i )) 2 , where the i th observation belongs to leaf l(i), where we know that π 0 (X i ) = 1/β. In order to gain some robustness in our results, we repeated 5000 times the simulation scheme above to compute empirical means of W SE i , leading to the MW SE.
Applications to real-life insurance datasets In this section, we consider two applications in insurance. The first, described in Section 5. 1, focuses on the prediction of a duration variable only (duration in a state of disability). The second, in Section 5. 2, is dedicated to claim reserving, and illustrates the need to introduce a supplementary variable M. In this situation, the key issue is to predict the claim amount, this being known only after some time T , subject to censoring.
Income protection insurance The real-life database we consider reports the claims of income protection guarantees over six years. It consists of 83 547 claims, with the following information for each claim: a policyholder ID, cause (sickness or accident), gender (male or female), socio-professional category (SPC: manager, employee or miscellaneous), age at the claim date, duration in the disability state (perhaps right-censored), commercial network (3 kinds of brokers). All insurance contracts considered have a common deductible of 30 days. Here, the censoring rate equals 7. 2%, the mean observed duration in the disability state is about 100 days (beyond the deductible of 30 days), with a median of 42 days. There is strong dispersion among the observed durations, the standard deviation being 162 days.
Reserving in third-party liability insurance This real-life database was extracted in the 2000s by an international insurance company, and reports about 650 claims related to medical malpractice insurance during seven successive years. The initial dataset contains information about various dates concerning the claims (date for reporting, opening or closing the case, etc. ), contract features, and some data on associated payments. These payments encompass indemnity payments and ALAE (Allocated Loss Adjustment Expenses), where ALAE are assignable to specific claims and represent fees paid to outside attorneys used to defend the claims. After some pre-processing, one can compute useful quantities for our purposes, especially (potentially censored) development times and total payments. Here T i is the "lifetime" of a claim, that is, the time between its issue date and the claim settlement date.
The consorship C i is the delay between the claim issue date and the extraction date of the database, and M i is the total amount of the i th claim. The latter is observed only if the claim has been fully settled (32% of the observations are censored). In this setting, it is reasonable to assume that C i does not depend on (M i , T i , X i ), but this would clearly be wrong in the case of covariates depending on the claim issue date. Table  7  summarizes some descriptive statistics about the covariates that are used when running the weighted CART algorithm to explain the response M i. As could be expected in this type of business, the data are highly skewed; for instance, many declared claims are assigned no payments because the company is still waiting for a court decision before paying. A parametric model would then be quite difficult to fit, which emphasizes the interest of using such techniques.
Each of these are estimated using the CART pro-cedure described in Section 2. Hence, for each censored claim, we use two regression trees to compute a prediction Mi , obtained as the ratio Mi = πNi,Yi 0,2 (X i )/ πNi,Yi 0,1 (X i ). Note that, for each censored claim, the trees we compute are different since the values of Y i and N i are. We now determine a reserve to be constituted by summing the Mi. To check that the proposed amount is reasonable, we can compare the values of Mi with the prediction of experts that are present in the database. The aggregated results are presented in Tables  8  and 9. The predictions are highly overdispersed for both "expert" and "tree" reserves (see Table  8 ) but, as mentioned earlier, this is not surprising from a business point of view. We observe that our regression tree approach produces reserve amounts which are significantly higher than the reserves made by the experts, except for lower amounts.
Conclusion In this paper, we defined a regression tree procedure well-adapted to the presence of incomplete observations due to censoring, and proved its consistency. The framework that we considered is motivated by the field of survival analysis, but also allows us to consider related applications, such as claim reserving in insurance. In such types of problem, a duration is present (and subject to censoring), but also an additional variable (the claim amount) that is observed only if the observation is uncensored. We presented two practical applications of this technique that demonstrate its feasibility and interest. The next step is to extend the procedure we have developed to random forests, in the same spirit as  Hothorn et al. (2006). Indeed, regression tree procedures, although they produce an easily understandable model at the end, are known for their sensitivity to changes in the dataset.
Andries Brouwer maintains a public database of existence results for strongly regular graphs on n ≤ 1300 vertices. We have implemented most of the infinite families of graphs listed there in the open-source software Sagemath [18], as well as provided constructions of the "sporadic" cases, to obtain a graph for each set of parameters with known examples. Besides providing a convenient way to verify these existence results from the actual graphs, it also extends the database to higher values of n.
Introduction Many researchers in algebraic combinatorics or an adjacent field at some point want to get their hands on a list of feasible parameters of strongly regular graphs, and on actual examples of graphs. These graphs are studied and/or used in hundreds of articles; recent highlights in using strongly regular graphs include A. Bondarenko's  [2]  and an improvement of the latter by T. Jenrich and A. E. Brouwer  [40]. While parameters are available from A. E. Brouwer's online database  [4] , actually constructing an example can easily take a lot of time and effort. The project described here aims at making these tasks almost trivial by providing the necessary graph constructions, and a way to obtain a strongly regular graph from a tuple of parameters, in the computer algebra system Sagemath  [18]  (also known as Sage).
It is worth mentioning that a large part of the project makes use of GAP  [22]  and its packages, in particular L. Soicher's package GRAPE  [52]. Exhaustive enumeration of the non-isomorphic strongly regular graphs has been performed for some tuples T of parameters (see T. Spence  [53] ). However, the sheer number of non-isomorphic examples (see e. g. D. Fon-Der-Flaass  [21]  or M. Muzychuk  [46] ) makes it hard to expect to be able to generate all of them, for a given T , in reasonable time. Thus we opted for a minimalist approach: for each T we generate an example, provided that one is known. We note, however, that some of constructions implemented can generate many examples with the same T ; e. g. we have implemented the construction to generate the point graph of the generalized quadrangle T * 2 (O) (see  [49] ) from any hyperoval O ⊂ P G(2, 2 k ).
In particular, one might come along simplifications of constructions, and this actually happened on couple of occasions, see Sect. 5. 3. • As time goes by, possible gaps in constructions are harder and harder to fill in. Reconstructing omitted proof details becomes a tricky and timeconsuming task. In particular, as far as the latter item is concerned, we seem to have uncovered at least one substantial gap in constructions (see Sect. 7). Furthermore, a number of constructions needed feedback from their authors or discussions with experts -sometimes quite substantial -to code them. A large part of the constructions use in a nontrivial way another combinatorial or algebraic object: block design, Hadamard matrix, two-graph, twodistance code, finite group, etc. In particular, at the start of the project some of these were lacking in Sagemath, we needed to implement constructions of certain block designs, regular symmetric Hadamard matrices with constant diagonal (where the gap just mentioned was uncovered), skew-Hadamard matrices, and two-graphs.
Structure and use of the implementation The strongly regular graphs are split into two categories: the fixed-size graphs (see Sect. 4) and the families of strongly regular graphs (see Sect. 5). The parameters (n, k, λ, µ) of fixed-size graphs are hardcoded, while each family of strongly regular graphs has a helper function which takes (n, k, λ, µ) as an INPUT and answers whether the graph family is able to produce a graph with the required parameters. Some families forward their queries to the databases of Balanced Incomplete Block Designs, of Orthogonal Arrays, of Hadamard matrices of various types, and of 2-weight codes. With this design, it takes ≤ 3 seconds on a modern laptop to know which graphs on < 1300 vertices can be produced by the implemented constructions (i. e. as far as the online database goes).
Two-weight codes database The rest of the fixed-size constructions of strongly regular graphs in the database originate from linear d-dimensional two-weight codes of length ℓ with weights w 1 and w 2 over F q. We use data shared by Eric Chen  [11] , data by Axel Kohnert  [43]  shared by Alfred Wassermann, data from I. Bouyukliev and J. Simonis [3, Theorem 4. 1], and from L. A. Disset  [19]. 96  [11]  Note that some of these codes are members of infinite families; this will be explored and extended in forthcoming work.