dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank  Kenichi Matsuoka ,  Massimo Frezzotti , and the anonymous reviewer for their constructive and insightful comments, which led to a much improved paper. Cécile Agosta performed MAR simulations during her  Belgian Fund for Scientific Research (F. R. S. -FNRS ) research fellowship. Computational resources have been provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  F. R. S. -FNRS  under grant no. 2. 5020. 11. We acknowledge  Jacopo Grazioli  for sharing the low-level sublimation product and his expertise on this dataset. We acknowledge  Yetang Wang  for sharing his updated version of the GLACIOCLIM-SAMBA dataset. We acknowledge  Christophe Genthon  for fruitful discussions and suggestions. The authors acknowledge the support from  Agence Nationale de la Recherche Scientifique  for the scientific traverses in Antarctica and the associated research on climate and surface mass balance modelling, projects  ANR-14-CE01-0001  ( ASUMA ) and  ANR-16-CE01-0011  (EAIIST).
2018) and should be regularly updated in climate models. Code and data availability. Python scripts developed for this study as well as all required data are available at https://doi. org/10. 5281/zenodo. 2548847 (Agosta,  2019). The last version of MAR is freely distributed at http://mar. cnrs. fr/ (last access: 24 January 2019). Monthly MARv3. 6. 4 outputs from this study are freely available at https://doi. org/10. 5281/zenodo. 2547637 (Agosta and Fettweis, 2019) , together with the associated MAR source code. The ECMWF reanalysis ERA-Interim 6-hourly outputs were downloaded from https://doi. org/10. 5065/D6CR5RD9 (ECMWF,  2009). The MERRA-2 reanalysis 6-hourly outputs were downloaded from https://disc. gsfc. nasa. gov/datasets/ (GMAO, 2015). The JRA-55 reanalysis 6-hourly outputs were downloaded from https://doi.
g. the encoding of MPI primitives. Already defining a correct independence relation from this formal model is difficult, due to the variety and complex semantics of actions. In addition, making UDPOR and in particular the computation of unfoldings and extensions efficient cannot directly rely on solutions of  [13] , which are tuned for concurrent programs with only mutexes, thus clever algorithms need to be designed. For now we prototyped our solutions in a simplified context, but we target the Sim-Grid tool which allows to run HPC code (in particular MPI) in a simulation environment  [5]. The paper is organized as follows. Section 2 recalls notions of interleaving and concurrency semantics, and how a transition system is unfolded into an event structure with respect to an independence relation.
Additional Property of the Model The model presented in the previous section may appear unusual, because the lock action on mutexes is split into an AsyncMutexLock and a MutexWait while most works in the literature consider atomic locks. Our model does not induce any loss of generality, since synchronous locks can trivially be simulated with asynchronous locks. One reason to introduce this specificity is that this entails the following lemma, that is the key to the efficiency of UDPOR in our model. Lemma 1 (Persistence). Let u be a prefix of an execution v of a program in our model. If an action a is enabled after u, it is either executed in v or still enabled after v. Intuitively, persistence says that once enabled, actions are never disabled by any subsequent action, thus remain enabled until executed.
Background and objective-The continual reassessment method (CRM) is a model-based dose-finding design for single-agent phase I oncology trials. With the advance of targeted therapies in oncology, more and more phase I trials investigate drug combinations rather than a single agent in order to find one or more maximum tolerated dose combinations. Several designs have been proposed for such dose-finding trials but only a few software packages are available to implement them. One of the designs is the two-dimensional Bayesian CRM proposed by Wang and Ivanova. Our goal was to provide an easy-to-use program to implement this design. Methods-We developed a new SAS macro, CRM2DIM, for implementing this design. This macro can be used to run a phase I dose-finding trial for two-drug combination and to perform simulations.
Introduction The continual reassessment method (CRM) developed by O'Quigley et al. [1]  is a modelbased dose-finding design for single-agent phase I oncology trials. The outcome of interest is the dose limiting toxicity (DLT) observed during a fixed time interval from the start of therapy. Dose escalation usually starts at the lowest dose. The next dose assignment is determined by fitting a dose-toxicity model to all available toxicity data. Some practical modifications of the CRM and a review of dose-finding methods can be found in Goodman et al. [2] , Iasonos and O'Quigley  [3],. The Bayesian version of the CRM is more used than the likelihood-based version. The CRM and its extensions have been implemented in R  [5] , SAS  [6]  and executable programs  [7]. Several SAS macros implementing the CRM, Bayesian model averaging CRM, fractional CRM and time-to-event CRM are available in  Menon and Zink [8].
Flexible Bayesian analysis was made possible with the introduction of the SAS MCMC procedure in SAS/STAT 9. 2. An illustration of this procedure for a Bayesian estimation of parameters in the CRM for a single agent is available at http://www. pharmasug. org/ proceedings/2016/SP/PharmaSUG-2016-SP06. pdf. The authors reported that the SAS MCMC procedure yields similar results as the commonly used dfcrm R package (https:// cran. r-project. org/web/packages/dfcrm/index. html). With the recent development of targeted therapies in oncology, many phase I oncology trials evaluate several agents simultaneously (for example, an existing chemotherapy with a new targeted therapy). The goal is usually to find at least one maximum tolerated dosecombination (MTDC). Several designs for such trials have been developed  [9, 10].
Riviere et al. [9]  compared different model-based methods and did not show any major differences, but Hirakawa et al. [10]  showed that models performance differ. In Table  1 , we show some available non-commercial software and open-source programs developed in R, C/C++. The two-dimensional Bayesian CRM design proposed by  Wang and Ivanova [11]  was one of the first published dose-finding designs for two-drug combination. It is an extension of the CRM to trials with two agents. It differs from the other model-based dose-finding methods for two-agent combination phase I trials in terms of the dose-toxicity model, the estimation method, and the dose-finding algorithm. To the best of our knowledge, to date, this method has been only implemented in C which is not widespread among the statistical community (http://www.
bios. unc. edu/aivanova). In this paper, we present a new SAS macro, CRM2DIM, for implementing the twodimensional Bayesian CRM design proposed by  Wang and Ivanova [11]. In Section 2, we describe the two-dimensional Bayesian CRM and show how to estimate the model parameters. Section 3 describes the SAS macro and its related sub-macros. Section 4 illustrates how to conduct simulations and run a dual-agent phase I dose-finding trial using the CRM2DIM macro. In Section 6, we discuss the main features of this new macro and compare to existing programs.
Incorporation of historical data Historical data available from completed single-agent dose-finding trials can be included. For instance, if toxicity data on k patients treated with the first agent is available, these data can be incorporated using the power prior approach  [18, 19]. The power prior is proportional to the likelihood of the historical data raised to the power of the weight w ϵ [0, 1] multiplied by the initial prior. These data are incorporated into the two-dimensional CRM by multiplying the likelihood of the current data by ∏ l = 1 k ψ a l 0 , b l 0 = 0, θ y l 0 1 -ψ a l 0 , b l 0 = 0, θ 1 -y l 0 w (3) with y l 0 , the toxicity response of the l-th patient of the historical data. Several versions of the power prior have been developed according to whether w is considered as a fixed value (conditional), as an estimated value (empirical Bayes), or as an unknown value (full Bayesian approach).
The SAS macro CRM2DIM and its sub-macros We describe the three SAS sub-macros (ESTIMATE, DE-FINE_DOSE and REPORT), and the main SAS macro CRM2DIM which calls these three sub-macros. Note that the value of the macro variables used in these sub-macros are transparent for the users. Figure  1  shows the functional block diagram of the SAS macro CRM2DIM with three main blocks: initialization, iterative process of the two-dimensional CRM and reporting.
SAS sub-macro DEFINE_DOSE The SAS sub-macro DEFINE_DOSE identifies the next recommended dose combination from the posterior mean of toxicity probabilities estimated by the SAS sub-macro ESTIMATE. Dose recommendation depends on whether we are in start-up (mode = startup) (see section 2. 3), or after the start-up in the two-dimensional CRM (mode = 2dim_design) (See section 2. 2) or in the final step of the trial (mode = recommend_dose). Generated output is a SAS data set containing the data prior to running this macro, and the difference between the posterior mean of toxicity probabilities and the target toxicity probability Γ. The input parameters used by this macro are: • mode=startup | 2dim_design | recommend_dose: specifies the algorithm to identify the maximum tolerated dose combination for the next cohort of patients.
mode=startup indicates that the next dose is the lowest level of the second agent at a dose combination with the posterior mean of toxicity probability closest to the target toxicity probability Γ (which is defined by the macro variable gamma of the main SAS macro CRM2DIM). mode=2dim_design indicates that the recommended dose combination for the next assignment is the combination adjacent to the current one with the posterior mean of toxicity probability closest to the target toxicity probability Γ. mode=recommend_dose identifies, at the end of the trial, the MTDCs i. e drug-combination with the posterior mean of toxicity probability closest to the target toxicity probability for each level of second agent.
SAS sub-macro REPORT The SAS sub-macro REPORT produces summary of a two-dimensional CRM for a given scenario and design parameters when simulations are run. Summary includes the percentage of experimentations assigned at each combination, the mean number of toxicities at each combination and the percentage of simulation runs that a dose combination is identified as the recommended MTDC. The input parameters used by this macro are: • in=: specifies the SAS data set containing the number of patients at each dose combination, the number of toxicities at each dose combination and the recommended dose for each dose level of the second agent for each simulation run. • var=n_pat | n_tox | recommended_dose: specifies the variable to be analyzed. var=n_pat computes the percentage of experimentations assigned to each combination.
SAS macro CRM2DIM The main SAS macro CRM2DIM can be used either for performing a simulation study or for conducting an actual dual-agent phase I dose-finding trial. In both cases, the program starts with the initialization of the dose level of the two agents (Figure  1  -yellow bloc) then the escalation/de-escalation starts (Figure  1  -blue bloc) and finally the identification of the final MTDCs (Figure  1  -red bloc). When performing a simulation study (simul = 1), the true probabilities of toxicity at each dose combination are inputted using the macro variable scenario. The first cohort of patients is treated either at the lowest dose-combination (si = 1, tj = 1) if start_up = 0 or at the the dose-combination resulting to the start-up rule (start_up = 1). In the latter case, the user has to specify the size of a cohort of simulated patients (g_size1) and only the dose level of the first agent (si) is identified since the two-dimensional CRM starts at the lowest dose level of the second agent (tj = 1) (See Section 2.
3). The binary toxicity outcome y is randomly drawn from a Bernoulli distribution according to the true probability of toxicity corresponding to the initial dose-combination. For this first cohort of patients, the posterior means of toxicity probabilities at each dose combination are estimated using the SAS sub-macro ESTIMATE (see Section 3. 1). The posterior means are then plugged into the SAS submacro DEFINE_DOSE (see Section 3. 2) to define the next recommended dose-combination for a target toxicity probability level defined by Γ (macro variable gamma). The toxicity outcome of a new cohort of patients of size group_size assigned at this new recommended dosecombination is simulated and the process is repeated until the total number of patients nb_patient is achieved. At the end of the simulation study, a table is produced by the SAS sub-macro REPORT (See Section 3.
3) and summarizes the percentage of subjects assigned at each combination, the mean number of toxicities at each combination and the proportion of successful dose-combination identification. When running an actual dual-agent phase I dose-finding trial (simul = 0), the main difference with the simulation study is that the user has to call the SAS macro CRM2DIM each time the user needs to record the outcomes of the most recent cohort of patients. The macro CRM2DIM invocation utilizes the following SAS macro variables: • simul=0 | 1: specifies if a simulation study is performed (simul=1) or not (simul=0). In the latter case, the data collected during a trial is continuously pooled by the user in a SAS data set called current_data • scenario=: specifies the true toxicity probabilities at each dose combination for performing a simulation study of a two-dimensional phase I dose-finding trial.
The values are entered in rows and separated by a "/" to facilitate their extraction. Each row corresponds to the true toxicity probabilities for the I levels of first agent at a given dose level of the second agent. This macro variable is only specified for a simulation study • start_up=0 | 1: specified if a start-up is used (start_up=1) or not (start_up=0) (See Section 2. 3). For a simulation study with a start-up, the size of each cohort is defined by the macro g_size1. At the end of the start-up, a recommended dose combination is identified and is assigned to the first cohort of the twodimensional CRM. In the case of a simulation study, the size of this cohort is defined by the macro variable group_size • histo=: specifies the SAS data set containing historical data that will be incorporated by the power prior approach (See equations 3 and 4) in the current analysis.
Simulation We evaluate the performance of the SAS macro CRM2DIM and its different features in a simulation study of a two drug combination phase I dose-finding trial. We used toxicity scenarios from Table  1  in Wang and Ivanova  [11]. The first scenario of this table is shown in Table  2. The rows of Table  2  represent the 3 dose levels of the second agent (t j , j = 1, …, 3) and the columns represent the 6 dose levels of first agent (s i , i = 1, …, 6). The target toxicity probability is 0. 20 and the MTDCs are in bold. We use the same set-up as in Wang and Ivanova  [11]  with a total of 54 patients (n_patient = 54) and assign patients in cohorts of 3 (group_size = 3). The constants defining the working model are the same as used in O'Quigley et al. [1]  for the first agent (_a_ = 0. 05/0.
1/0. 2/0. 3/0. 5/0. 7) and the second agent (_b_ = 0. 05/0. 1/0. 2). In this illustration, we use the two-parameter model (See equation 2, dist = 0 and nb_param = 2) with exponential prior distribution (prior_dist = E/E) with variance equal to 1 (val_param = 1/1), no start-up (start_up = 0 and g_size1 is then left unspecified) and no inclusion of historical data (histo and a0 are left unspecified). We ran 4000 replications (nb_simul = 4000) in the simulation study. The tuning of MCMC samples is nbi = 1000 and nmc = 5000 with a arbitrary seed = 1. The statement options nonotes = 0 was specified to avoid the SAS 'log window full' message. These input parameters are captured by the following code. The output generated by the SAS macro CRM2DIM with a two-parameter model, no startup and without including historical data is: This output contains the percentages of experimentation at each combination, the average number of toxicities observed at each combination and the percentages of the MTDC recommendation.
Agent A represents the first agent and agent B the second agent. The maximum tolerated dose combinations for the target toxicity probability of 0. 20 for dose levels 1, 2 and 3 of second agent, the combinations (AgentA = 5, AgentB = 1), (AgentA = 4, AgentB = 2) and (AgentA = 3, AgentB = 3), are identified in 35%, 53% and 34% of simulated trials. The percentage of experimentations assigned (average number of toxicities) at these recommended combinations are 5. 4 (0. 6), 13. 2 (1. 4) and 9. 9 (1. 0). This output is produced after approximately 7 hours using a PC Intel(R) Xeon (R) CPU E31225@ 3. 10GHz, RAM: 16. 0 Go, OS; Windows 7 Professional 64 bits. To repeat this simulation study with a start-up, we set at start_up = 1 and specify two subjects per cohort in the start-up with g_size1 = 2. The code becomes: The output generated by the SAS macro CRM2DIM with a two-parameter model, a start-up and no inclusion of historical data is: With a start-up, the percentages of the identification of the correct maximum tolerated dose combinations, 33%, 54% and 36%, are similar compared to the design with no start-up.
We also ran simulations with a three-parameter model (See equation 2) where the third parameter is an interaction term between the two agents. This is specified by nb_param = 3 using the following code: The output generated by the SAS macro CRM2DIM with a three-parameter model, a startup and no inclusion of historical data is: The combinations (5,1),(4,2) and (3,3) were identified in 40%, 56% and 41% of the trials. These results indicate that the three-parameter model provided a better precision of the MTDC selection compared to the two-parameter model. We also ran simulations with historical data for first agent, k = 10 patients were treated at the combination (5,0) with 2 toxicities observed. We add these k pseudo-observations made at a dose combination with level 0 of the second agent. This process is guided by the initialization of the following variables patid, si, tj and y when constructing the SAS data set, histo_data.
The following statements create this SAS data set and print the 10 historical observations. data histo_data (drop = i); For a partial incorporation of the historical data, we plug-in a value of 0. 5 for a0. The output generated by the SAS macro CRM2DIM with a three-parameter model, a startup and inclusion of historical data for first agent is: Correct combinations (5,1),(4,2) and (3,3) are selected in 43%, 64% and 40% after an execution time under 16 hours, approximately. We can evaluate the impact of historical data in other conditions by varying both the number of pseudo-observations k when additional historical information is available and the value of a0. For the former, we need to add pseudo-observations to the SAS histo_data data set. For the latter, we need to change the value of a0 according to the clinicians' prior belief about the commensurability between historical and current data.
In our simulations, we obtained almost identical results for all 5 scenarios to those reported in Wang and Ivanova  [11]  (See Appendix for the 4 other scenarios with a two-parameter model, a start-up and no inclusion of historical data) which validated the SAS macro, CRM2DIM. As a sensitivity analysis, we performed a simulation study to evaluate the impact of prior specification on design performance. We simulated the two-dimensional CRM with the exponential prior with variances 0. 25 and 2. 25 for scenarios 1-5. The column 'Patient' in Table  3  represents the percentages of experimentation assigned to the true MTDC at each dose level of second agent, the column 'Toxicity' shows the average number of toxicities observed at the true MTDC at each dose level of second agent and the column 'MTDC %' shows the proportion of times the correct MTDC was selected.
This logistic model with 3 or 4 parameters was used in the design proposed by Riviere et al. [13] (See appendix F). The prior distributions on the model parameters were normal and exponential. The macro includes also the possibility to incorporate historical data for this model. Whatever the type of regression model (dist = 0 or 1 for log-linear or logistic model, respectively), val_param allows to set the value for the variance for a number of prior distributions that are specified by prior_dist. We illustrate these options in model specifications in a simulation study with a start-up and no historical data for scenario 1 of Table  1 in   and Ivanova [11]. The aim here is not to compare the two designs but only to illustrate the different model specifications implemented by the SAS macro CRM2DIM.
As an example, we give the SAS code to evaluate the operating characteristics of the Wang and Ivanova design  [11]  with a 3-parameter logistic regression model and a normal prior N(0,50) on intercept and exponential priors with variance 10 on the other two parameters. Simulation results for the two models are reported in Table  4. The column 'Nb' shows the number of parameters in the model as we considered models with or without an interaction term. The column 'Prior dist. ' shows the prior distribution (N stands for normal with mean 0 and E is for exponential with the variances given in 'Variance' column). For exponential distribution, the mean can be derived directly from the variance. For example, a variance of 1 and 10 corresponds to a mean of 1 and square root(10), respectively. In this example, the MTDC for the target toxicity probability of 0.
We thus defined a block of parameters within the SAS MCMC procedure for a logistic regression model and increase the number of burn-in and MCMC samples to reduce autocorrelation. Using nbi=50000 and nmc=50000 allowed improved performance for a 3parameter logistic model and a 4-parameter logistic model with small variances. The number of replications with auto-correlations was less than 1%. With a 3-parameter logistic model, the proportion of selecting the correct MTDCs were similar to those with a 2-parameter loglinear model but lower than those with a 3-parameter log-linear model. The 4-parameter logistic model has higher recommendations rates compared to previous models. However, the 4-parameter logistic model with the variances (50/10/10/50) lead to a high number of inconclusive replications due to auto-correlations (>20%) (data not shown).
Running a trial with the two-dimensional Bayesian CRM As in other packages that implement dose-finding methods when running a phase I trial, the user needs to alternate between running the program, updating the data set after collecting outcomes of the recent patients treated at a certain dose combination (si, t j), and running the program again. To run a dual agent phase I dose-finding trial using the two-dimensional Bayesian CRM, we need to specify simul = 0 in the SAS macro, CRM2DIM. We note that g_sizel and group_size, for the number of patients per cohort in the start-up and in the twodimensional CRM, are not required since it is the user who enters the observed toxicity data for each patient. In practice, these numbers are 2 or 3 patients by cohort. The rest of the input parameters are identical to those used when running simulations.