dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, selecting subgroups effect or treatment-subgroup interactions that are clinically important increase subgroups credibility as stated by  Dijkman et al. (2009). In addition, clinicians may rule out hypotheses that are clinically absurd as false positives from the discovery dataset, increasing thus the chances of selecting true ones. All choices made by clinical experts must be noted as an integral part of the hypothesis generation process. 6. 9. 7 Set and select Q-Finder parameters Q-Finder proposes to define the exploration strategy(ies) upstream of obtaining the results and thus to set the hyperparameters on the basis of what the user "wishes" to obtain first. Nevertheless, the user can always restart the Q-Finder on the basis of the results, by defining a more conservative exploration (e.
g. if too many good results were obtained) or more permissive (e. g. if too few results were obtained). Thus, depending on the level of the signal in the database, the user can vary his level of requirement and the level of credibility of the results. Special attention must be paid to the hyperparameter C max , whose value has a significant impact on the algorithm calculation times. For example, one may want to increase the level of complexity to obtain subgroups with larger effect sizes. However, this is at the expense of the size of the subgroups (smaller groups), as well as the simplicity of interpretation of the results by the physicians (more basic patterns). In addition, this is accompanied by a drastic increase in the number of subgroups to be tested and thus the risk of finding false positives: it then becomes more difficult to obtain low p-values adjusted for multiple testing.
For example, increasing the minimal coverage or effect size thresholds will reduce the number of subgroups in the remainder of the pipeline. Moreover, removing or performing the adjustment step on confounding factors after (and not before) the selection of top-k subgroups is a way to strongly reduce computation times. The confounding bias correction step is indeed the most time-consuming. Applying the algorithm on a random sample of the database or constraining the exploration so that it is not exhaustive are two other possible approaches.
General comprehensibility of the approach The confidence in the results is based on the trust one has of the algorithm that has generated them. We think that the comprehensibility of the approach proposed by Q-Finder makes it possible to bring this level of confidence. Indeed, Q-Finder mimics the human process of hypothesis generation, where the physician generates hypotheses that are then tested on a dataset by computing the presented credibility metrics. With Q-Finder, this generation is driven by the discovery dataset and controlled by the test dataset. In a recent paper,  Murdoch et al. (2019)  introduced the important concept of descriptive accuracy in Data Analysis as "the degree to which an interpretation method objectively captures the relationships learned by machine-learning models".
Acknowledgement We deeply thanks Sanofi medical,  Jean-Marc Chantelot  and the  IDMPS Steering Committee  for their medical expertise, financial support and proofreading. We also express thanks to  Martin Montmerle ,  Mélissa Rollot ,  Margot Blanchon  and  Alexandre Civet  for their remarks and invaluable feedbacks. Finally we thank the whole  Quinten team  for their dedication to the Q-Finder development during the past twelve years.
33 [1. 7; 3. 1] 1. 30E-8 8. 27E-4 2. 44 [1. 9; 3. 2] 1. 92E-11 3. 84E-11 Cumulated # of individual therapies taken by the patient ≤ 4 Follow healthy diet and exercise plan = Yes AND S8 Times seen by a diabetologist in the past 3 months = 0 AND 16% / 17% 2. 43 [1. 8; 3. 3] 1. 85E-8 1. 18E-3 2. 25 [1. 7; 3. 0] 5. 46E-9 6. 82E-9 Cumulated # of individual therapies taken by the patient ≤ 4 Follow healthy diet and exercise plan = Yes AND S9 Covered by a health insurance = Yes AND 22% / 19% 2. 33 [1. 7; 3. 2] 1. 94E-7 1. 24E-2 2. 64 [2. 0; 3. 5] 4. 87E-11 7. 48E-11 Treated for other form of dyslipidemia = Yes Follow healthy diet and exercise plan = Yes AND S10 Covered by a health insurance = Yes AND 12% / 8% 2. 40 [1. 7; 3. 3] 2. 66E-7 1. 70E-2 1. 86 [1. 3; 2. 6] 3. 07E-4 3. 07E-4 Received biguanides = No * Subgroup ranking is based on p-values on discovery dataset ** Odds-ratios are adjusted for confounding factors through multiple regression model *** Adjusted p-values for multiple testing are based on a Bonferroni correction (resp.
28E-2 2. 38E-2 Receives GLP-1 analogues = No Last postprandial glucose measurement (mg/dL) ≤ 144 AND S6 Receives amylin agonist = No AND 3. 27E-2 2. 77E-2 Receives GLP-1 analogues = No Last postprandial glucose measurement (mg/dL) ≤ 144 AND S7 Receives GLP-1 analogues = No AND 3. 27E-2 2. 68E-2 # of sorts of required hospitalization (macro/microvascular, hypo) = 0 S8 Last postprandial glucose measurement (mg/dL) ≤ 144 AND Receives GLP-1 analogues = Yes 3. 27E-2 2. 77E-2 Last postprandial glucose measurement (mg/dL) ≤ 144 AND S9 Self-monitoring testing performed at bed time = No AND 3. 27E-2 2. 46E-2 Receives amylin agonist = No Follow healthy diet and exercise plan = Yes AND S10 Receives more than 2 OGLD = No AND 3. 08E-2 3. 43E-2 Patient living in = Urban area * Subgroup ranking is based on WRAcc measure in discovery dataset.
73 [3. 5; 6. 5] 3. 95E-22 4. 17 [3. 1; 5. 6] 1. 41E-21 3. 52E-21 Cumulated # of individual therapies taken by the patient ≤ 4 Last postprandial glucose measurement (mg/dL) ≤ 144 AND S9 Self monitoring testing performed at bed time = No AND 15% / 12% 4. 7 [3. 4; 6. 4] 3. 86E-22 3. 72 [2. 8; 5. 0] 3. 74E-18 4. 68E-18 Receives amylin agonist = No Follow healthy diet and exercise plan = Yes AND S10 Receives more than 2 OGLD = No AND 42% / 25% 2. 6 [2. 0; 3. 4] 1. 01E-12 2. 41 [1. 9; 3. 0] 1. 10E-14 1. 10E-14 Patient living in = Urban area * Subgroup ranking is the same as inTable 2 ** Odds-ratios are adjusted for confounding factors through multiple regression model *** Adjusted p-values for multiple testing are based on Benjamini-Hochberg procedure on the test dataset.
7 AND 4% / 1% 0. 53 [0. 2; 1. 5] 2. 51E-1 0. 68 [0. 1; 4. 5] 7. 03E-1 7. 03E-1 # of different lipid-lowering agents for dyslipidemia = 0 * Subgroup ranking is the same as in Table 4 ** Odds-ratios are adjusted for confounding factors through multiple regression model *** Adjusted p-values for multiple testing are based on a Benjamini-Hochberg procedure on the test dataset.
Both models are tested with actual data collected from the Tuy Phong wind power plant, which is located in Binh Thuan Province, Vietnam; and • Increase prediction accuracy in comparison with other forecasting methods. The accuracy indicator of the proposed wind power forecasting models is then compared with that of several known approaches to verify efficiency and advancement. The rest of this paper is organized as follows. In Section 2, fundamental concepts and structures of the ANN, PSO, GA, and PSO-ANN models are introduced. In Section 3, the two new algorithms GA-PSO-ANN and PSO-PSO-ANN are proposed in detail. These algorithms are used to develop a wind power forecasting tool based on Python, a programming language. As the proposed models are implemented to forecast wind power hourly, Section 4 provides insights into the experimental results and evaluates the accuracy, given some forecasting error benchmarks.
• With the hth neuron in the hidden layer  [29, 30] : net = w x + b (1) z = f (net ) (2) where The input layer consists of three neurons that represent wind speed, wind direction, and temperature. The output layer has one neuron, which is wind power. The neural network also has ten neurons in the hidden layer. The number of neurons in the hidden layer has been carefully chosen. If this number is too small, it may cause the inability to fully identify the signals in a complex dataset. However, if there are too many, it will increase the time of network training and can lead to overfitting in the forecast. This number depends on factors such as the number of inputs, the output of the network, the number of cases in the sample set, the noise of the target data, the complexity of the error function, the network architecture, and the network training algorithm.
• With the hth neuron in the hidden layer  [29, 30] : • net h = n i=1 w ih x i + b h (1) z h = f 1 (net h ) (2) where • x i is the ith input variable; • w ih is the connection weights between ith input and hth neuron; • b h is the bias; • net h is the net input or argument of the activation function; • z h is the net output; and • f 1 (neth) is the activation function. The input layer consists of three neurons that represent wind speed, wind direction, and temperature. The output layer has one neuron, which is wind power. The neural network also has ten neurons in the hidden layer. The number of neurons in the hidden layer has been carefully chosen. If this number is too small, it may cause the inability to fully identify the signals in a complex dataset. However, if there are too many, it will increase the time of network training and can lead to Energies 2020, 13, 2873 6 of 22 overfitting in the forecast.
Particle Swarm Optimization-Artificial Neural Network (PSO-ANN) Hybrid Algorithm The PSO algorithm is used in training the ANN to determine the set of parameters (w, b), so that the neural network can be best built and fit with the data  [40]. The concept of the PSO-ANN algorithm is shown in Figure  6 , with the PSO being used to train the ANN. Energies 2020, 13, 2873 8 of 22 The number of parameters of ANN is determined by: N = n × h + m × h + h + m = 3 × h + 1 × h + h + 1 = 5h + 1 (5) where • N is the number of parameters of the neural network; • n is the number of neurons in the input layer; • h is the number of neurons in the hidden layer; and • m is the number of neurons in the output layer. Energies 2020, 13, x FOR PEER REVIEW 8 of 22 The number of parameters of ANN is determined by: N = n × h + m × h + h + m = 3 × h + 1 × h + h + 1 = 5h + 1 (5) where • N is the number of parameters of the neural network; • n is the number of neurons in the input layer; • h is the number of neurons in the hidden layer; and • m is the number of neurons in the output layer.
Data The historical data including wind turbine output power, wind speed, wind direction, and temperature were used to train the models, as shown in Figure  12. These data were collected periodically with a 30-min cycle, taken from Tuy Phong wind farm, located in Binh Thuan Province, Vietnam. The entire dataset consists of 3866 records  [8]. Energies 2020, 13, x FOR PEER REVIEW 14 of 22.
Data The historical data including wind turbine output power, wind speed, wind direction, and temperature were used to train the models, as shown in Figure  12. These data were collected periodically with a 30-min cycle, taken from Tuy Phong wind farm, located in Binh Thuan Province, Vietnam. The entire dataset consists of 3866 records  [8]. The 3866 data records were divided into two sets (i. e. , training set and test set). The former had 3479 records, which were used to train the neural network, while the latter had 387 records used to check the accuracy of the model.
Programing Language Based on the proposed GA-PSO-ANN and PSO-PSO-ANN algorithms, the wind power forecasting tool was built using Python, a high-level and object-oriented programming language. Python has a clear structure that is easy to understand and easy to use. Additionally, codes written in Python are often found to be shorter than other programming languages. In particular, Python supports a lot of math libraries, machine learning, data science, and optimization algorithm libraries  [41].
Discussion This paper demonstrated a tool that was built for the forecasting of wind power generation output with a high level of accuracy. By means of an open-source Python programming language, a wind power forecasting program was built with a hybrid application of an artificial neural network, particle swarm optimization algorithm, and genetic algorithm. Specifically, the particle swarm optimization algorithm was used to train the artificial neural networks (PSO-ANN model). In order to reduce errors and improve accuracy in forecasting, the authors used another particle swarm optimization algorithm and genetic algorithm (PSO-PSO-ANN and GA-PSO-ANN models) for the optimal adjustment of the first PSO parameters during neural network training. The two models were successfully applied for the forecasting power output of the Tuy Phong wind power plant in Binh Thuan Province, Vietnam.
One of the major quality control parameters in bundle adjustment are the posterior estimates of the covariance of the estimated parameters. Posterior covariance computations have been part of the open source Damped Bundle Adjustment Toolbox in Matlab (DBAT) since its first public release. However, for large projects, the computation of especially the posterior covariances of object points have been time consuming. The non-zero structure of the normal matrix depends on the ordering of the parameters to be estimated. For some algorithms, the ordering of the parameters highly affect the computational effort needed to compute the results. If the parameters are ordered to have the object points first, the non-zero structure of the normal matrix forms an arrowhead. In this paper, the legacy DBAT posterior computation algorithm was compared to three other algorithms: The Classic algorithm based on the reduced normal equation, the Sparse Inverse algorithm by Takahashi, and the novel Inverse Cholesky algorithm.
The Inverse Cholesky algorithm computes the explicit inverse of the Cholesky factor of the normal matrix in arrowhead ordering. The algorithms were applied to normal matrices of ten data sets of different types and sizes. The project sizes ranged from 21 images and 100 object points to over 900 images and 400,000 object points. Both self-calibration and non-self-calibration cases were investigated. The results suggest that the Inverse Cholesky algorithm is the fastest for projects up to about 300 images. For larger projects, the Classic algorithm is faster. Compared to the legacy DBAT implementation, the Inverse Cholesky algorithm provides a performance increase by one to two orders of magnitude. The largest data set was processed in about three minutes on a five year old workstation. The legacy and Inverse Cholesky algorithms were implemented in Matlab.
The Classic and Sparse Inverse algorithms included code written in C. For a general toolbox as DBAT, a pure Matlab implementation is advantageous, as it removes any dependencies on, e. g. , compilers. However, for a specific lab with mostly large projects, compiling and using the classic algorithm will most likely give the best performance. Nevertheless, the Inverse Cholesky algorithm is a significant addition to DBAT as it enables a relatively rapid computation of more statistical metrics, further reinforcing its application for reprocessing bundle adjustment results of black-box solutions.
The Damped Bundle Adjustment Toolbox (DBAT) for Matlab was conceived as an open source toolbox to perform bundle adjustment computations  (Börlin and Grussenmeyer, 2013). It has been used for several applications, most notably to reprocess the bundle adjustment results of commercial solutions  (Murtiyoso et al. , 2018). The main highlight of DBAT has always been its modularity  (Börlin et al. , 2019)  and openness, with the possibility to access bundle adjustment metrics contrary to the black-box nature of several commercial software.
Thus, the diagonal elements of Coo are the inner products of ki with themselves. Furthermore, the (2,1) element within each block is the inner product of ki and ki+1, and similar for the other off-diagonal elements. Due to symmetry, only the sub-diagonal elements need to be computed. Given that ki = pi qi , (18) the inner product can be computed as k T i kj = p T i pj + q T i qj, ( 19 ) where pi is sparse with at most three non-zeros, but qi may be dense. Thus, the computation may be split into a combination of sparse and dense linear algebra. The computation of the diagonal elements can be performed efficiently in Matlab using a combination of the Hadamard (element-wise) product and the colsum function. The call v=colsum(A) returns a vector such that vi holds the sum of the elements of the i:th column of A.
Data sets Ten data sets of varying sizes were prepared for the experiments: Camcal A 21-image camera calibration data set from  Börlin and Grussenmeyer (2014). Vexcel A 41-image vertical aerial dataset flown at an altitude of 950 m above sea level (ASL) above the city of Strasbourg with an UltraCam Osprey Mark 3 Premium camera. Roma A 60-image terrestrial close-range data set of the Arco di Constantino monument in Rome, Italy from  Börlin and Grussenmeyer (2013). StPierre A 239-image close-range data set of the St-Pierre-le-Jeune church in Strasbourg from  Murtiyoso et al. (2017). Sewu A 210-image data set acquired by the DJI Phantom 4 RTK drone flown at 60 m ASL over the Sewu temple in Central Java, Indonesia. UmU Five data sets from several flights with a DJI Phantom 4 V2. 0 drone over the Umeå University Campus, Umeå, Sweden.
UmU-3D A 272-image data set flown at 70 m ASL in "3D" cross-hatch mode. The data set includes both east-west and north-south strips. UmU-2D A 133-image subset of UmU-3D with the eastwest strips only. UmU-3D-R The UmU-3D data set, reduced to 210 images to match Sewu. The reduction was done by removing every other north-south strip. UmU-2-lyr UmU-3D extended with another "3D" flight at 120 m ASL over a larger area. UmU-3-lyr UmU-2-lyr extended with another "3D" flight at 40 m ASL over a smaller area. All data sets were self-calibration data sets. The Camcal and Roma images were processed by PhotoModeler Scanner 2012 1  and imported to DBAT. The remaining data sets were processed by AgiSoft Metashape v1. 6 2. During import of the Metashape projects into DBAT, all object points with fewer than 3 rays and/or an estimated intersection angle below 5 degrees were removed before processing by DBAT.
Thus, the time to compute the normal matrix was not included. Furthermore, the time to permute the normal matrix to the ordering preferred by each algorithm was excluded. Additionally, the density of the L22 and K21 blocks of the IC algorithm were recorded, as well as the density of the K22 block. The timings were performed on an HP Z440 workstation from 2015 with a 6-core Intel Xeon E5-1650 v3 @ 3. 50GHz and 64GB of RAM running Matlab 2019b Update 1 under Debian 10.
Results The density numbers are given in Table  2. The L22 density varied between 30% and 50%. Except for the camera calibration data set, the K21 density varied between 53% and 66%. All K22 densities were very close to 50%. The execution times for the CC, SI 1 , IC, and LD algorithms for the SC and NSC experiment are given in Table  3. The CC execution times were dominated (80-90%) by the computation of the 3-by-3 blocks in step 8. For the large problems (data sets 6-10), the IC execution times were dominated by (50-85%) by the K21 (Q) computation in step 5. The execution times for the SI 2 algorithm was within 15% of the SI 1 algorithm. The IC-S algorithm was several orders of magnitude slower than the IC algorithm. The results show that the IC algorithm is fastest on SC data sets 1-8 and on NSC data sets 1-7.
The CC algorithm is fastest on the remaining data sets, with the SI algorithm somewhere in between. In both cases, the cross-over point correspond to a B density of about 2%. Except for the camera calibration data set, the IC algorithm is 20-120 times faster than the legacy LD algorithm. For the TV experiment, also presented in Table  3 , the IC algorithm was fastest for SC data sets 1-7 and NSC data sets 1-6. In this case, the cross-over point corresponds to a B density of about 2. 5%. On average, the execution times for the SC data sets were 40-50% higher for the CC and SI algorithms compared to their NSC counterparts. In contrast, with the exception of the camera calibration data set, the SC and NSC times were within 1% of each other. In the diagonal-only TV experiment, the execution times decreased by about 40% for the CC algorithm and about 20% for the IC algorithm compare the full-block SC and NSC results.
The Sewu and UmU-3D-R data sets has the same number of images and object points. However, the number of image points are 38% higher in the Sewu data set. The same difference is seen in the number of rays per object point and the B-block density for the NSC data set, with a slightly lower difference for the SC data set. The increased point density translated to a 75-100% longer execution time for the CC algorithm. In contrast, the corresponding increase for the IC algorithm was about 6%. The result of the speedup Experiment 4 are shown in Figure  5. Only the IC algorithm benefited from an increased number of cores, but with a decreasing efficiency as the number of cores are increased. Using six cores the speedup was about 2. 5. On a single core, the IC algorithm was faster the the CC algorithm for data sets 1-4, and within a factor of 2 for data sets 1-8.
DISCUSSION In this paper, several algorithm for the computation of posterior covariance of object points were applied to self-calibration and non-self-calibration data sets of varying sizes, ranging from 21-944 images. On the smallest data sets, the fastest algorithm was the Inverse Cholesky (IC) algorithm. On the largest data sets, the classic (CC) algorithm was fastest. The performance of the Sparse Inverse (SI) algorithm was somewhere in between CC and IC. The relative results between SI and IC are consistent with the results by  Kallin (2019) , on similar problem sizes. Compared to the legacy algorithm in DBAT, the IC algorithm was 1-2 orders of magnitude faster. Several factors affect the computation time for a given algorithm, including the number of images, object points, and image points.
5%. The IC algorithm is written completely in the Matlab language. As such, it benefits from an increased number of available cores due to the efficiency of the underlying numeric libraries used by Matlab. The performance increase was sub-linear. In contrast, the SI and CC algorithms were partially implemented in the C language and called from Matlab. The C code was not optimised for multiple cores. Further performance improvements are likely if the C code is parallellised, although the level is uncertain. For a general toolbox as DBAT, a pure Matlab implementation is advantageous, as it removes any dependencies on, e. g. , compilers. However, for a specific lab with mostly large projects, compiling and using the classic algorithm will give improved performance. Compared to the previous version of DBAT, users can expect posterior covariance computations that are a few orders of magnitude faster.
CONCLUSIONS On medium-sized data sets of up to about 300 images, the Matlab-only IC algorithm is faster than the classic algorithm. On larger data sets, the classic algorithm is faster. However, on data sets up to about 900 images, and if a computation time of a few minutes at the end of a bundle adjustment processing is acceptable, the IC algorithm is still a useful high-level alternative. We have shown throughout this paper that the novel IC algorithm implemented in DBAT managed to accelerate its computation of covariance values significantly. Experiments by comparing it to other methods, including the classical approach, showed that the results are comparable, albeit with a few caveats. For DBAT this is a significant addition as it enables a relatively rapid computation of more statistical metrics, further reinforcing its application for reprocessing bundle adjustment results of black-box solutions.