dataesr/scientific-paragraphs-categorization huggingface dataset:
The agents can generate demand curves, assess technical feasibility of meeting demand, production cost, CO2 emissions, long-term impact of possible decisions, implement strategy for efficient or optimal operation. For the end-users, the two major advantages of using a multi-agent approach are its flexibility and its privacy capabilities. Data for simulations comes from real sources -historical data on fuel and electricity pricing from the European Energy Agency, prognoses of carbon emission certificates, performance tables of real boilers and High Temperature Gas-cooled Reactor (HTGR) designs. Real demand curves of chemical plants provided by our industrial partners will be used as a basis for simulating testing scenarios. We implemented several ontologies that are referred in our protocols.
Conclusion and Perspectives This paper proposes a decentralized control system of energy in chemical complexes with multi-stage auction mechanisms for short-term steam trading to optimize fuel consumption, increase profits and lower CO 2 emissions. The proposed auction mechanism sells all produced steam every timestep while meeting the demand and including all parties in the negotiations fairly. It combines classic and reverse dutch auctions with custom rules. This proposal is a first step towards an industrially deployable steam distribution system with multiple producers and consumers in the context of a HTR used as steam producer in chemical complexes and Hybrid Energy Systems (HES). To evaluate our work, we developed a simulator standing on real physical models and datasets. It will be tested with simple scenario parameters -steady-state or variable load and demand, based on historical data.
Introduction The dynamic of floating bodies is usually analyzed in the frequency domain using the linear diffraction-radiation method based on the boundary element methods (BEM). The problem is linearized supposing small displacements of the free surface and of the floating bodies. Several software programs have been developed to compute the solution of this problem such as HydroStar  [1] , Nemoh  [2] , Aqwa  [3] , and Wamit  [4]. This first order theory enables for evaluating added mass and wave damping coefficients which are both parts of the radiation forces. The time-domain formulation of the dynamic equation, including those parameters, was introduced by Cummins  [5]. In this formulation, a convolution term appears which is computationally demanding when its evaluation is made by simple quadrature.
Preliminaries Before studying state-space formulations, a few prerequisites have to be reviewed. Some conditions must be respected to pass to the time-domain from the frequencydomain, and the point of this section to introduce those conditions. To be able to evaluate the accuracy of the different models from HSVD's method, proposed in this paper, a basic case and calculation method is used. It has been chosen, for simplicity, to use a single cylinder for this test case. The diffraction-radiation calculation has been made using the HydroStar software  [1]  with respect to the mesh constraint (the smaller facet's diameter must be at least six times smaller than the smaller wavelength). The cylinder's characteristics are given in Table  1.
A Python version of this algorithm has been developed by the NREL and can be found in the BEMIO package  [19]. A MATLAB version can also be found in WECSIM package  [19]. Perez  [20]  details how to calculate the discrete state-space arrays from the Hankel matrix and then how to calculate the continuous state-space arrays using the discrete space state arrays by considering  [21]. In his method, Kung  [14]  considers that there is no feedthrough matrix D c. As a consequence, the kernel function from the state-space model is reduced to the expression of Equation (  14 ): K SSM (t) = C c e A c t B c (14) A more general formulation considering the feedthrough term, in the state-space formulation, is detailed by Perez  [20].
Alternatively, a supervised learning approach can be taken by using snapshots of the homogenized micromodel response to directly define a data-driven regression model for the macroscopic constitutive behavior  [16, 17, 18, 19, 20, 21]. Although resulting in models of distinct natures, both approaches rely on the existence of an observation database on the behavior of the original micromodel that is usually obtained offline (before deployment on a multiscale setting) and should cover every possible scenario the surrogate is expected to approximate online. However, building such a database of model snapshots can be a challenging task (see  [22, 20]  for interesting approaches based on Design of Experiments and  [14]  for a data-driven training framework based on Bayesian Optimization). For micromodels employing path-dependent materials, this offline training process entails sampling a highly-complex constitutive manifold that depends on an arbitrarily long strain history and can therefore be excessively sensitive to small changes in boundary conditions (e.
g. strain localization and crack propagation problems). Furthermore, training a surrogate with such a complex dataset often requires additional partitioning techniques in order to avoid computationally inefficient reduced models  [23, 24]. An alternative to the conventional offline-online approach that has been gaining popularity is the use of adaptive reduction frameworks that either preclude the need for offline training altogether  [25, 26]  or combine different reduction techniques into a single framework with only limited offline effort while employing online error indicators to continuously assess the quality of the approximation and trigger a refinement of the surrogates when necessary. Nevertheless, obtaining consistent adaptivity criteria for either hyper-reduced models  [25]  or machine learning models based on least-squares solutions  [27, 19]  is not straightforward.
This issue can be avoided by exploiting the fact that ε Ω (and therefore ε Ω h ) are often highly constrained by the geometry and boundary conditions of the macroscopic structure being modeled  [24]. We therefore opt for constructing a highly-tailored surrogate model S online based on a dataset D of observations coming from a small number of fully-solved micromodels: σ Ω = S ε Ω , D (9) which can then be used to compute the constitutive response for a fraction of the cost. When trying to keep D as small as possible for the case at hand, it is crucial to have a means for quantifying the uncertainty in probing S for any given ε Ω. In this work, a Bayesian approach is adopted to assess online whether D is large enough to provide the desired level of confidence in S at a given ε Ω.
Least-squares regression We start by building a parametric model y (w, x) that approximates a scalar target response t by fitting w with a dataset D of N observations t o at X o = [x o1 • • • x oN ]. We therefore assume that the target t can be written as: t = y (w, x) + with y (w, x) = M j w j φ j (x) and p ( ) = N |0, σ 2 n ( 10 ) where the noise is given by a zero-mean Gaussian distribution with variance σ 2 n and y is linear with respect to its M weights, gathered in the vector w. Note that the assumption of a linear model does not limit its fitting capabilities since the basis functions φ define a feature space that can be nonlinear in x. In the context of this work, t can be a single stress or traction component, but the discussion is equally applicable to the problem of modeling individual model components (e.
Here we do not opt for a least-squares approach for three reasons. Firstly, it can suffer from severe overfitting when the dataset D is small -which is the case in the present work since we have no offline training and only a small number of fully-solved micromodels to sample from. Secondly, the uncertainty associated with σ 2 n is constant throughout the input space x and does not provide an indication that the model is being used at a location far from data points (which could then be used to trigger a refinement of D). Finally, performing model selection in the least-squares framework, using cross-validation for instance, is rather tedious compared to performing model selection in the context of Bayesian regression methods  [36].
Hyperparameter optimization The process variance σ 2 f and length scale that compose the kernel and the target noise σ 2 n are hyperparameters that should be learned from the dataset D. Since a full Bayesian treatment for these parameters -introducing a prior, deriving a posterior and marginalizing -usually demands the use of expensive numerical techniques such as Markov Chain Monte Carlo (MCMC), here we opt for a maximum likelihood solution in order to minimize the computational overhead associated with the online calibration of the GP models. Furthermore, due to the limited amount of data available for estimation, here we refrain from optimizing for the derivative noise σ 2 d and instead assume derivative observations are noiseless. The aim here is to maximize the marginal likelihood, obtained by averaging the probability that the model reproduces the training targets over all possible values of y o associated with t o : p t o |σ 2 f , σ 2 n , = p t o |y o , σ 2 n p y o |σ 2 f , dy o (35) which is a function that depends only on the hyperparameters.
Numerical examples In this section, we put the proposed framework to the test on a number of numerical examples. The algorithms of Section 4 have been implemented in an in-house Finite Element code using the open-source Jem/Jive C++ numerical analysis library  [42]. We begin by demonstrating the applicability of the framework for FE 2 analysis and move on to performing an in-depth investigation of its performance with fast single-scale homogeneous examples that allow for extensive parametric studies to be performed. It is emphasized that, although it is our vision to use the surrogate model in a multiscale context, it does not matter for the purpose of testing the active learning framework whether the material update of the full model involves solving a micromechanical BVP or just evaluating a nonlinear constitutive relation.
Evolution of the GP regression We run the reduced model with k = 1, γ tol = 0. 4 MPa, γ cancel = 20 MPa and L retrain = 10, with hyperparameter values obtained by loading a single material point in the k-means centroid direction. We can visualize the gradual improvement of the surrogate model as data is added by plotting the GP predictions together with the exact constitutive response being approximated at different moments throughout the analysis. This can be seen in Fig. 8 , which shows snapshots made at four different time steps. As we start with an empty dataset, at first the GP model predicts linear-elastic behavior for the complete strain range, but with an uncertainty that quickly increases away from the observations (marked as crosses in Fig. 8 ). This increase is the mechanism that triggers the sampling of extra information from models in A.
Influence of γ tol The uncertainty tolerance γ tol is the main parameter controlling the active learning procedure: lower values of γ tol should lead to a higher sampling frequency while higher values should lead to smaller cardinalities for D and A at the cost of solution accuracy. In this section we put these claims to the test and investigate how much control can actually be exerted over the solution algorithm by changing γ tol. Going back to the one-dimensional example of the previous section, we solve the problem for multiple values of γ tol between 0. 3 MPa and 10. 0 MPa. In Fig. 10  we plot the evolution of the cardinality of the dataset D for six different γ tol values. We see that γ tol indeed influences the number of observations added    to D, but only to a limited extent. This is due to the presence of the remaining adaptive components of the framework, namely the time step cancelling mechanism related to γ cancel , the sampling of data with zero threshold after a canceled step and the hyperparameter retraining procedure linked to L retrain.
This combination of safeguards leads to datasets of similar sizes for most of the γ tol values adopted here. It is interesting to note how the curve for γ tol = 0. 3 MPa sharply increases in slope after time step 40, a moment when the hyperparameters are reoptimized and generate a modified model in which γ tol is crossed more often. This indicates that the γ tol level necessary to achieve a given sampling frequency is also influenced by the hyperparameter values. Similar observations can be made by looking at the degree of control that can be exerted over the acceleration level provided by the framework, which we quantify through the reduction ratio R of Eq. (  42 ). For each model we compute the evolution of the reduction ratio R with the time steps and plot them in Fig. 11a. During the first time steps, γ tol has the expected influence on the acceleration level, with ratios as high as 150 being obtained for γ tol = 10.
The acceleration brought by the greedy learning strategy was found to drastically increase as the macroscopic mesh is refined, with reduction ratios as high as 2800 times being obtained. An additional example was used to demonstrate the ability of the reduction approach to handle models with complex stress distributions. Although the acceleration was lower in this case due to the complexity of the constitutive manifold being approximated, the greedy sampling strategy successfully concentrated the learning effort on the most informative mesh regions. One final model involving mixed-mode crack propagation was presented. In contrast with the previous examples, the nature of the crack propagation problem allowed the GP to reuse previously obtained information rather than continuously growing the dataset.
The materialUpdate routine Input: strain ε at the integration point p Output: stress σ and stiffness D at the integration point if initialization step : if D e is not initialized (i. e. p is the first point ever computed) : use original constitutive model:(σ, D) ← fullModel :: materialUpdate (ε); initialize elastic stiffness: D e ← D; else assume p has the same stiffness as the first computed point: σ ← D e ε; D ← D e ; else compute elastic stresses: σ e ← D e ε; use GP to predict a constitutive correction: E σ, D , V [ σ] ← GP (ε); approximate the response: σ ← σ e + E [ σ] , D ← D e + E D ; compute the uncertainty indicator γ as in Eq. (38); if γ > γ cancel : cancel time step (Algorithm 5); if softening is detected (D ii < 0) : penalize point uncertainty: γ ← γ -D ii ; if last step has been cancelled : switch to a modified Newton-Raphson strategy: D ← D e ; store values for this point: ε n p ← ε; γ n p ← γ; return σ, D The checkSolution routine if first time step : initialize dataset and anchor point set : D ← ∅, A ← ∅; divide the integration points into k clusters in strain space: (C, ε n ) ← KMC (ε n , k); for every cluster C i ∈ C : find representative point p = arg min j∈Ci ε n jε n i ; anchor a fully-solved model at point p: A ← A ∪ p; solve the newly-created full model once: (σ, D) ← fullModel :: materialUpdate ε n p ; compute corrections and add data: σ ← σ -D e ε n p ; D ← D -D e ; D ← D ∪ ε n p , σ D ; update GP and store marginal likelihood: updateGP (D) ; L ← ln p (y|x); reject solution (revisit the first time step); else update D based on the tolerance γ tol using Algorithm 3: D ← D ∪ addData (γ tol ); if T has not changed : accept solution as is (Algorithm 4); else refactor covariance matrix: updateGP (D); if L/ ln p t > L retrain : recompute GP hyperparameters: retrainGP (D) ; L ← ln p t ; reject solution (continue on the same time step);.
Algorithm 1 :Algorithm 2 : 12 The materialUpdate routine Input: strain ε at the integration point p Output: stress σ and stiffness D at the integration point if initialization step : if D e is not initialized (i. e. p is the first point ever computed) : use original constitutive model:(σ, D) ← fullModel :: materialUpdate (ε); initialize elastic stiffness: D e ← D; else assume p has the same stiffness as the first computed point: σ ← D e ε; D ← D e ; else compute elastic stresses: σ e ← D e ε; use GP to predict a constitutive correction: E σ, D , V [ σ] ← GP (ε); approximate the response: σ ← σ e + E [ σ] , D ← D e + E D ; compute the uncertainty indicator γ as in Eq. (38); if γ > γ cancel : cancel time step (Algorithm 5); if softening is detected (D ii < 0) : penalize point uncertainty: γ ← γ -D ii ; if last step has been cancelled : switch to a modified Newton-Raphson strategy: D ← D e ; store values for this point: ε n p ← ε; γ n p ← γ; return σ, D The checkSolution routine if first time step : initialize dataset and anchor point set : D ← ∅, A ← ∅; divide the integration points into k clusters in strain space: (C, ε n ) ← KMC (ε n , k); for every cluster C i ∈ C : find representative point p = arg min j∈Ci ε n jε n i ; anchor a fully-solved model at point p: A ← A ∪ p; solve the newly-created full model once: (σ, D) ← fullModel :: materialUpdate ε n p ; compute corrections and add data: σ ← σ -D e ε n p ; D ← D -D e ; D ← D ∪ ε n p , σ D ; update GP and store marginal likelihood: updateGP (D) ; L ← ln p (y|x); reject solution (revisit the first time step); else update D based on the tolerance γ tol using Algorithm 3: D ← D ∪ addData (γ tol ); if T has not changed : accept solution as is (Algorithm 4); else refactor covariance matrix: updateGP (D); if L/ ln p t > L retrain : recompute GP hyperparameters: retrainGP (D) ; L ← ln p t ; reject solution (continue on the same time step);.
(t o |w) is the likelihood function of Eq. (11) and p (t o ) is the marginal likelihood of the model (i. e. the probability of producing the dataset D). Because both w and t|w are Gaussian variables, an analytical solution exists for p (w|t o ) [36]: t o |w) p (w) p (t o ) with p (t o ) = p (t o |w) p (w) dw (14) where p.
Prior to hematologic analysis, blood specimens were kept at room temperature (24℃), placed on an agitator (Specie mix, Drew Scientific Inc) for 20 minutes, and then gently inverted to ensure homogenization. Specimens with visible or microscopic clots were excluded. Measurements were performed within 2 hours after sampling with the Sysmex XN-V and Sysmex XT-2000iV (Sysmex Corporation), according to the manufacturer's instructions, using settings for "quality control material" or "dog" (Sysmex XN-V software v. 00-05; Sysmex XT-2000iV software v. 00-13).
This paper revisits the problem of computing empirical cumulative distribution functions (ECDF) efficiently on large, multivariate datasets. Computing an ECDF at one evaluation point requires O(N ) operations on a dataset composed of N data points. Therefore, a direct evaluation of ECDFs at N evaluation points requires a quadratic O(N 2 ) operations, which is prohibitive for large-scale problems. Two fast and exact methods are proposed and compared. The first one is based on fast summation in lexicographical order, with a O(N logN ) complexity and requires the evaluation points to lie on a regular grid. The second one is based on the divide-andconquer principle, with a O(N log(N ) (d-1)∨1 ) complexity and requires the evaluation points to coincide with the input points. The two fast algorithms are described and detailed in the general d-dimensional case, and numerical experiments validate their speed and accuracy.
g. , experimental databases) available to industry and regulators  [1] [2] [3] [4] [5]. With reference to the passive natural circulation of fluid for emergency cooling, the complex set of physical conditions that occurs in the passive safety systems, where no external sources of mechanical energy for the fluid motion are involved, has led the designers of the present-generation reactors to position the main heat sink (i. e. , the steam generators for pressurized water reactors and feed-water inlet for boiling water reactors) at a higher elevation with respect to the heat source location (i. e. , the core). By so doing, should the forced circulation driven by centrifugal pumps become unavailable, the removal of the decay heat produced by the core is still allowed  [6]. For their reliability assessment, mathematical models are typically built  [7]  to describe the mathematical relationship between the passive system physical parameters influencing the NPP behavior, then translated into detailed mechanistic Thermal-Hydraulic (T-H) computer codes for simulating the effects of various operational transients and accident scenarios on the system  [7] [8] [9] [10] [11] [12] [13] [14] [15].
• If we resort to empirical metamodels for estimating passive systems failure probabilities and carrying out uncertainty and SA, the following problems should be considered: i. the regression error should be carefully quantified (and possibly controlled) throughout the process, in order to reduce its impact on the entire reliability assessment  [81] ; ii. the higher the input dimensionality (e. g. , in the presence of time series data), the higher the size of the training dataset should be to obtain metamodel accuracy. Rigorous (linear or nonlinear) approaches to reduce the input dimensionality (e. g. , Principal Component Analysis, PCA, or Stacked Sparse Autoencoders) should be sought, with increased metamodel performances  [137] ; iii. the quality of metamodel training can be negatively affected by noisy data.
Background Record linkage is a process that allows to identify records appearing in different databases and referring to the same entity (e. g. an individual)  [1] , but which do not share a common unique identifier. In record linkage, the status of a pair of records is either matching (same individual) or non-matching (distinct individuals). This process consists in three successive steps: data preprocessing (curation of the data), record pair comparison and linkage. Data preprocessing includes harmonizing data formats and dealing with missing values. The record pair comparison can be computationally expensive, as the number of all possible record pairs is the product of the numbers of records in each dataset. To reduce the number of comparisons to run, it is common to perform blocking. Blocking consists in splitting the datasets into smaller sets that agree on one or more variables, called blocking variables.
Only records within the same blocks are then compared. When no unique person identifier is shared between the two datasets, linkage has to be performed by comparison of shared matching variables. The linkage performance is assessed by comparison with the gold standard (or ground truth) based on a confusion matrix  [2]. The record linkage matches may have two types of errors: False Positives (FP), i. e. true nonmatches classified as matches, and False Negatives (FN), i. e. true matches classified as non-matches. Linkage methods are usually classified as either deterministic or probabilistic  [1, 3, 4]. Deterministic record linkage methods assess matching status based on the exact agreement or disagreement of either all or a fraction of the matching variables. If data are of very good quality (i.
GEMO and GENEPSO are two independent ongoing nation-wide studies involving BRCA1 and BRCA2 carriers, with unconnected databases and whose individuals were not registered by a shared identifier. BRCA1 and BRCA2 genes testing has become part of routine clinical practice in European countries and North America since the identification of the two genes in the 90's, which greatly improved recommendations about breast and ovarian cancer risk management treatments. Nonetheless, both retrospective and prospective studies on large datasets of BRCA1 and BRCA2 (BRCA1/2) mutation carrier families are very much needed to refine individual cancer risk estimates by using different cancer risk factors such as genetic factors, lifestyle/environmental factors, family history and breast pathology. GEMO and GENEPSO provide an overview of a wellcharacterized sample of counseled Hereditary Breast and Ovarian Cancer (HBOC) families in France.