dataesr/scientific-paragraphs-categorization huggingface dataset:
The system is depicted in Figure  1. In order to follow the process of time stamping and its relationship to the process of time synchronization, Table  1  specifies the component, the time reference to which that component is synched, and a time stamp id associated with the component. As shown in Table  1 , at regular intervals, the application and database servers set their clock to the reference time tS1 of the NTP Time Server. When a user/agent logs into a workstation session, the time stamp is reset to the Application Server time, thus at the workstation t = tS3. When opening a new call on the system, a key database record is created with a time stamp of tR1 which is equal to the database clock time at that instance, tS2. In this case, if the server clock is skewed when the record is created, but gets reset subsequently, then a timing error could be introduced.
Within the six month study period a total of 35,527 calls were recorded. In February, a data extraction error led to the rounding of the time data to exclude the seconds. As such, the data from February were excluded yielding a total of 29,606 records. Of these calls, any record with any of the first four time stamps missing was excluded. Of note is the fact that no records were missing an entry for the created on time stamp; 102 records were missing the mission scheduled time stamp; 1039 were missing the ambulance departure time stamp; and 4450 records were missing the arrival to case time stamp. Each of the three time stamp types with missing entries requires that the ambulance crew report the time back to the dispatch center; when the crew fails to report the time, then the entry is missing.
Once the records with these missing entries were culled, the data set had 24,391 records that were considered useable for this study. Within the 24,391 valid records, time synchro- nization errors were identified on the basis of negative time progressions when moving logically through the data. For example, if the time stamp associated with the mission being scheduled was before the time stamp associated with the creation of the record, then the difference between these two time stamps would yield negative time. Negative time intervals were considered to be a function of time synchronization issues. Had these errors been ignored then the average response time would be incorrectly reduced. Alternatively, if the negative intervals were accommodated in absolute terms, the impact on the average response time would likely show an inconsistent bias.
This includes criteria for identifying where group-level cultural adaptations may exist, and if a cultural evolutionary approach can be implemented in studying a given system. We then outline a step-by-step process for designing a study of group-level cultural adaptation, including the major methodological considerations that researchers should address in study design, such as tradeoffs between validity and control, issues of time scale, and the value of both qualitative and quantitative data and analysis. We discuss how to evaluate multiple types of evidence synthetically, including historical accounts, new and existing data sets, case studies, and simulations. The electronic supplement provides a tutorial and simple computer code in the R environment to lead users from theory to data to an illustration of an empirical test for group-level adaptations in sustainability research.
The inequality (Expression 1) states that an altruistic trait will be favored by selection when the right side of the expression (the ratio of between-group variance, F ST , to within-group variance, 1 -F st ) exceeds the left side (the ratio of within group costs, c w , to global net benefits, b p ). The more the right side exceeds the left side, the more the trait will spread by group selection: c w b p \ F st 1 À F st ð Þ : ð1Þ This method requires four types of quantitative data: individual traits, and group membership, and the costs and benefits of the trait for both individuals and groups. Most importantly, the dataset needs to include multiple groups and independent estimates of these values for each group. Provided appropriate data are available, employing Roger's inequality is a simple process, but it only provides a true or false result.
In a social dilemma, these two components hold opposite signs, so that their sum will reveal whether, overall, selection will favor the trait. This method for computing and comparing the components of selection is demonstrated in the cultural group selection simulations of  Waring et al. (2017). Further methods can build upon these both approaches. To help researchers use both techniques, and develop their intuitions for the mechanisms of group-structured cultural selection, we have provided an open-source tutorial written in R (R Development Core Team 2008), as an appendix (see ESM). The tutorial steps through the application of both expressions to a simulated group-structured data set. Scientists can alter the dataset, use their own data, and modify and build on the analytical code.
Acknowledgements This work was conducted as part of the '' Evolution of Sustainability '' Venture at the  National Socio- Environmental Synthesis Center (SESYNC ), funded by  National Science Foundation   DBI-1052875. In addition, this research was supported by  National Science Foundation CAREER  award  SES-1352361 ,  USDA National Institute of Food and Agriculture ,  Hatch  project #  ME021515 , and was made possible through the support of a Grant from the  John Templeton Foundation  to the  Institute of Human Origins at Arizona State University. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the  John Templeton Foundation. We thank  Pete Richerson ,  Monique Borgerhoff-Mulder , the human and cultural evolution group at  UC Davis , and our research group at  SESYNC  including  Bill Burnside  for their feedback on the early concepts for this manuscript.
Moreover molecular subtype was found to be significantly associated with survival in both univariate and multivariate analysis. The ccA/ccB classification was subsequently validated in a meta-analysis of 480 ccRCC tumors, suggesting this profile may have value for risk stratification  [56]. The gene set used to classify RCC tumors in ccA/ccB was then optimized and simplified by Brooks and colleagues using a centroid-based classification algorithm (called prediction analysis of microarray (PAM)) to end with a 34-gene expression classifier named ClearCode34  [57]. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed (FFPE) clinical samples collected at the University of North Carolina. Finally, the authors developed a recurrence risk model with the addition of stage and Fuhrman grade to the ClearCode34 sub-classification.
Finally the gene set was tested in the TCGA samples. Somatic PBRM1 mutations were most frequently identified in ccrcc1/ccrcc2 tumors but rarely found in ccrcc3/ccrcc4 tumors. In both series, somatic VHL mutations were more frequently distributed in ccrcc1/ccrcc2 tumors. The BAP1 and SETD2 mutations also showed association with the molecular subtypes: BAP1 was most mutated in the ccrcc4 tumors (p = 0. 0098) and SETD2 was most mutated in the ccrcc1 tumors (p = 0. 06). As in the "in-house" dataset ccrcc2/ccrcc3 tumors display the best survival, ccrcc1 tumors an intermediate survival, and ccrcc4 tumors the poorest survival (p < 0. 0001). A 16-gene assay to predict recurrence after surgery in localized RCC was reported by Rini et al. in 2015  [64]. In this study, the expression of 732 genes was measured in 942 patients presented with Stage I-III diseases, and correlated with the risk of recurrence cancer-specific survival.
Conversely, ccrcc1 tumors, which were also associated with poor prognosis, had the poorest T cell infiltration and a global low expression of T cell inhibition markers. ccrcc1 could be considered as immune-desert or immune-cold tumors. Based on these results, a French academic and multicentric molecular-driven randomized phase 2 trial named Bionikk (phase 2 BIOmarker driven trial with Nivolumab and Ipilimumab or VEGFR TKI in naïve metastatic Kidney cancer, NCT02960906) was recently launched. Main objective is to evaluate the ability of the CIT classification to select patients to have either a TKI, or a checkpoint inhibitor alone or in combination. Primary endpoint is overall response rate within each arm. 150 patients with a metastatic ccRCC naïve of any systemic therapy with available frozen tumor are planned to be included.
ACKNOWLEDGMENTS J. J. H. is supported by the  J. Randall & Kathleen L. MacDonald Family Research Fund , the  Tom and Mila Tuttle Family Research Fund , the  Jill and Rafic Dahan Family Research Fund , and the  Jill and Jeffrey Weiss Family Research Fund  for the cure of metastatic kidney cancer. RELEVANT DISCLOSURES J. J. H. is a consultant for  Novartis ,  Eisai  and  Chugai  and received research funding from  Pfizer ,  Novartis ,  Eisai  and  Cancer Genomics Inc. Y. A. V is a consultant for  Bristol-Myers Squibb ,  Pfizer ,  Novartis ,  Ipsen ,  Sanofi ,  Astellas ,  Janssen  and  Roche.
The CVA is similar to PCA (Principal Component Analysis), a geometric and statistics method to analyze data. Two concepts are then tested, a Kalman filter and a reverse model, which show how models of the CVA state space can be used for the diagnosis of nonmeasurable inputs. These models are used to represent the non-linear behavior of a PEM in operating range similar to load cycles of vehicle driving cycles. The PCA to estimate a 50 cell PEMFC health status offline is realized in Ref. [20]. This analysis is based on data provided from temperature, voltage, current, flow and humidity sensors. In this work, the PCA makes it possible to exploit the correlations between variables and the similarities between the measurements at time t. To complete this process, an empirical model of PEMFC is proposed connecting the various input parameters to the cell voltage using a multiple linear regression.
Fuzzy logic was used in Ref. [21]. A diagnostic methodology based on fuzzy classification (FcM for Fuzzy c-Means) is developed in combination with data from the EIS. This method allows to develop a diagnostic methodology online with the ability to distinguish different degrees of flooding and drying inside the PEMFC. Bayesian methods to detect a drying up and flooding the PEMFC offline using data from the EIS are proposed in Ref. [22]. A naive Bayesian classifier was chosen as the approach. Twelve input variables allow differentiation between six operating modes: minor drying, moderate drying, light flooding, minor flooding, moderate flooding, and a normal mode. After a study of the parameters that can influence the correct classification rate, optimized classifier is proposed, able to detect 91% of the samples of the validation database.
Finally, in Ref. [25] , the authors demonstrated the possibility of using an ANN (Artificial Neural Network) based procedure for diagnosing water management issues, including drying out and flooding. Based on the recurrent ANN (RNN) based model, the threshold functions were defined and applied on the residuals between the actual and the model outputs (i. e. stack voltage and pressure drop) to discriminate both faults. Hamming neural networks are used in Ref. [26]  to estimate offline the health of a 20 cell PEMFC, each cell being monitored, with the voltage sensors and current information only. A diagnostic approach based on Hamming neural network for the identification of appropriate parameters of fuel cell model is proposed to diagnose the state of health (SOH) of a PEMFC. The output voltages of a 20 PEM fuel cell were measured, in addition to the parameters of the model, in order to be used as the training and validation dataset.
Stack current, stack temperature, air humidity, hydrogen and oxygen flow are inputs and the stack voltage is the output. A good consistency between the proposed ANN model and the experimental result were observed, i. e. the error rate is less than 1% for a single cell compared with 5%e10% given by mathematical or hybrid models. Obviously, the ANN are also used for the control of a PEMFC  [30]. In this article, a recurrent neural network is used to control the water management in PEMFC. The water concentration in cathode is controlled and the humidity fluctuation are reduced at the cathode. Simulation results showed that a controller based on RNN has shorter response time than PID controller. These results are obtained only with simulation in Matlab-Simulink environment. In Ref. [31] , a neural networks (NN)-based active and reactive power controller of a standalone PEMFC power plant (FCPP) is proposed.
The proposed controller is used to modify the inverter modulation index and the phase angle of the ac output voltage to control the active and reactive power output from the FCPP to match the terminal load. The proposed controller and the fuel cell power plant model have been tested with simulations and the data used corresponds to an active and reactive load demands of a single-family residence. Numerous studies have proved the ANN strong capability in the modelling or pattern recognition of nonlinear complex systems [28e32]. It can indeed approach any nonlinear mapping with arbitrary accuracy when properly trained  [33]. Compared with the physical models, the ANN method provides an attractive alternative since it doesn't need huge knowledge about the underlying physical processes. Moreover, an accurate identification of inner physical parameters is not required.
Fault diagnosis principle The objective of this section is to utilize RC directly as a fault detector and classifier to discriminate different health states of the PEMFC stack, without the necessity of modelling the whole system. In the first part of this work, stack voltage is applied as the only input signal of the RC for fault diagnosis. The stack voltage signal is considered as an informative signal which can reflect the FC health state. In Ref. [48] , the voltage signals were applied as database to discriminate influence of air flow rate, hydrogen flow rate and gas pressure. In Ref. [29] , the stack voltage drop is considered as a common consequence of both drying out and flooding. In Refs. [47e49], fault classification was successfully performed in a cell-voltage generated space. The principle idea of the diagnosis method, illustrated in Fig.
Offline training For offline training, a leave-one-out cross validation procedure is applied to calculate the output weight matrix and to evaluate initially the algorithm performance. Meanwhile the RC parameters above-mentioned are also optimized during this process. The objective of the training procedure is to calculate the output weight matrix W R , in order to satisfy the following condition: A Â W R ¼ B ( 4 ) where B is the target matrix formed by all the targets in the training subset. To solve this problem, multiple methods exist. In order to avoid overfitting to the training data, some Gaussian noises can be added into the training data or the ridge regression method can be applied. Herein, the second way is adopted, which introduces an additional penalty term. The penalty term serves the purpose of balancing the error on the training dataset and the weight norm  [35].
The calculation of W R by ridge regression is expressed as: W R opt ¼ argmin W R AW R À B 2 þ lkWk 2 (5) where jj$jj is the Euclidean norm, and l is a regularization parameter that controls the strength of the penalty term. When l ¼ 0, the expression becomes actually the linear regression. The term ljjWjj 2 (l > 0) is dedicated to keep the output weights as small as possible while minimizing the training error. It should be underlined that when using ridge regression for a specific task, l should be optimized firstly. The calculation of the output weight matrix can be further converted as follows  [50] : W R ¼ À AA T þ lI Á À1 A T B (6) During the leave-one-out cross validation procedure, an excellent classification rate of 99. 88% has been obtained based on the offline training dataset (under the parameter set b ¼ 0.
New dataset test Apart from one dataset for offline training, another new dataset obtained under the same configuration of operating conditions is utilized for test. The diagnostic results are listed in Table  3. As we can see, the worst classification rate obtained is 92. 435%. It can be observed that all the wrongly classified segments belong to F3, i. e. the defective cooling. They are diagnosed as "Normal". This could be reasonably explained by a detailed observation of the shapes of the two voltage signals under the F3 condition (low FRW, in yellow) and the F1 condition (2 Nl/min, in purple). It can be found that they evolve with time in a quite similar way (see Table  4 ).
Influence of the learning database During the training procedure as indicated in "Four degrading health states" Section, 846 segments are obtained based on the 6 operating signals. Each segment goes further through a STFT preprocess to be transferred into a time-frequency 2D representation and corresponds to 8 time samples, as illustrated in Fig. 9. The final RC matrix A used for offline training has a dimension of 400 (number of nodes) Â 6768 (number of time samples), where the 6768 samples are arranged one by one in a certain order. In this part, the aim is to analyze the influence of the construction of the learning database on the classification results. The Reservoir Computing is a tool in the field of artificial intelligence, and requires a learning phase to calibrate the synaptic weights.
This learning phase involves a dedicated database for this task, and it is interesting to define how the construction of the training base will influence the results. Therefore, to determine these effects, classification rate given at the end of each study corresponds to the worst case among the results, meaning that the simulation leading to the lowest value the good classification rate is reported. In light of this, the study realized in this section try to answer the following question: Is there a difference of the classification result if only a part of the learning database is used instead of the complete one (both databases remain in order)? A statistical tool called "P-value", which is used most often for hypothesis test in scientific research, is utilized herein. The general idea of the hypothesis test involves three steps: First, making an initial hypothesis H 0 , which is also called the null hypothesis; second, collecting the database; third, based on the available data, deciding whether to accept or reject H 0.
Study 1 of the learning database The objective herein is to compare the simulation results obtained based on a reduced learning database with the results presented in "Four degrading health states" Section. Remind than the previous worst classification rate is 92. 435%, so the m value of H 0 is set as 92. 43. To compare both situations, 100 simulations are performed by taking a smaller database, consisting of 50% of the previous database. These data are selected randomly throughout the learning database, but remain in order. Since we are interested to see whether the worst classification performance based on the reduced database is less than 92. 43%, and not better, a left-tailed t-test is conducted. More specifically, the Fig. 13  e Student table (study 1). null hypothesis is set as: H 0 : m ¼ 92.
43 versus the alternative hypothesis H a : m < 92. 43. The distribution of the 100 simulation results, corresponding to the initial population, are shown in Fig. 12. This initial population corresponds to the hypothesis H 0 : m ¼ 92. 43. Basically, the aim here is to compare another set of simulation (the tested base) to the initial population in order to analyze the influence between both treatments. Firstly, the t* value has to be calculated. This value is obtained using the formula t * ¼ xÀm s= ffiffi n p , with x the average of the samples to analyze (in our case, the tested database contains 10 random samples), m the average at which the samples are compared, s the standard deviation of the sample and n is the number of samples processed, i. e. n ¼ 10. The principle herein is to look at the value t* obtained for n À 1 degrees of freedom.
In this case, our value t* ¼À 3. 82. In order to calculate the P-value, the student table or (t-table) is introduced as below. It gives the probability value (P) either for one-tailed or two-two tailed test given the number of the degrees of freedom and the t* value (in absolute form) The aim is then to compare the obtained Pvalue with a reference value of a, 0. 05 here, a value which is often used by default in the statistic tests. In our case, the absolute value of t* is 3. 82 and it is between 3. 25 and 4. 297, the column value of the Student table, as shown in Fig. 13. These two columns correspond to a P-value between 0. 0005 and 0. 001, which is less than the significant level a ¼ 0. 05. As indicated in the descriptive part of the P-value test, the hypothesis H 0 is thus rejected, which indicated that the results are sensitive to the size of the learning database.
Nevertheless, these results are perfectly logical. It seems clear that the tools from the field of AI requiring learning are sensitive to the size of the database. However, even if this point is known to all, it remains difficult to quantify. This study let to highlight the fact that from a statistical point of view, the changes in the learning base are similar to apply a totally different treatment to the RC. This don't invalidate the study results, but it means that the way to build the learning base has importance. Upon completion of this statistical hypothesis tests, it appears that the results obtained with the RC data on fault diagnosis are dependent on the number of samples dedicated to learning. However, the study has shown that the worst simulation case give a classification rate of 88%.
43% for new unknown dataset test are obtained. Furthermore, the influence of a set of key parameters (the non-linearity gain b, the offset phase f 0 , the relative input scaling factor r and the regression parameter l) is emphasized on. It is summarized that RC is not extremely critical with respect to the parameters as long as they are kept within a certain range (In our case, a suggested range is: b > 0. 3, 4 < r < 10 and 3 Â 10 À5 l 5 Â 10 À5 ). Furthermore, the influence of the size and arranging order of the learning database is investigated. The conclusion of the study is that the classification results are dependent on these factors. However, even in the worst case, the good classification rate reach 88%. The current work is a first-stage application of RC method for PEM fuel cell fault diagnosis.
Indeed, remote sensing technology provides data for AGB estimates that cover large areas with a high spatial resolution and high revisit time. Three main remote sensing data types are used for AGB estimates: optical, SAR (Synthetic Aperture Radar), and LiDAR. Optical images at low or medium resolutions and radar backscattering coefficient data are robust enough to estimate low to medium level AGB due to saturation of remote sensing data. Zhao et al. [6]  and Lu et al. [7]  have shown that optical data allow AGB estimates until AGB levels between 55 and 159 t/ha, depending on the forest species composition. In addition, SAR amplitude data, mainly in the L-band, were used to estimate the AGB. Luckman et al. [8]  observed a saturation point of 60 t/ha when plotting the JERS L-band backscattering coefficients as a function of the forest biomass located in the Central Amazon Basin.