dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and discussion The complete pipeline is applied to multiple simulated scenes by the MATLIS software to validate our approach. To cover most types of real objects that could be imaged, we select four representative categories: (i) objects spread over the 2-lateral dimensions (flat surface with large cross-section) (ii) object(s) producing multiple echoes in a single pixel, (iii) objects spread over the temporal dimension (slanted surface), (iv) sub-pixel objects.
By mentionning voxels, we want here to point out that the restriction to coupling via interfaces does not exclude advanced field descriptions, and even multiple physics at work at the same location in the field: only the coupling is not within the field. When randomizing the coupling at the interfaces, the features of the stochastic paths inside each subpart of the field are strictly preserved and the available schemes for accelerating their construction remain unchanged. This leads to Monte Carlo codes computing propagators inside systems involving coupled physics and displaying the same property of being insensitive to geometrical refinement. The implementation reported here is made within the Stardis code. Stardis is a code conceived as an evolving ensemble of dedicated thermal simulation routines for complex 2D and 3D environments.
In addition, the stardis-solver is implemented in SYRTHES  4  , the general thermal software developed by Electricté de France R&D. Stardis was recently used to explore ways toward the definition of new climate services for analysts and designers anticipating climate change in urban area. In this context, Stardis strength was its ability to deal simultaneously with all the spatial and temporal scales involved in the modeling of energy exchanges, from the milimeter scale of windows and heat seals to the kilometers extensions of cities, and from the minute scale of wind and solar fluctuations to typically fifty-year lifetimes of the ground installations to be planed. Radiative transfer inside and outside buildings could be modeled together with full three-dimension heat diffusion inside the solid structures, as well as convection inside each fluid cell, providing the first reported attempt to model energy transfers in a complete city, without any compromise on its geometrical description  [4].
A simulation is performed "as if" θ was the quantity to be estimated for a given set of sources, and, more importantly, when a source is visited, the pre-factor of the corresponding source contribution is stored for later use with different sets of sources. In short, the propagators expectation needs to be estimated only once. A main strength of the Stardis code used in the following practical illustration is its ability to deal with huge amounts of physical and geometrical data. In  [4] , the city is simulated with interacting buildings, each of them described in full details, room per room, and the main message is that the computation time is fully insensitive to the level of refinement of this description  6. The same observation can be made concerning the computation of propagators: the computation times required for the estimation of the propagators, and for their use in external codes with new sets of sources, are also both insensitive to the refinement level.
Data structures When building the Green function, the Monte-Carlo weights are not computed, but the data needed to compute them is stored, path by path, for later use. This storage requires two different types: one to store the data collected along individual Green paths, and another one to store the Green function itself, including the data of the sampled Green paths, as well as all the shared data referenced by the paths (materials, interfaces,. ): <green data structures> = <green path data structure> <green function data structure> Path storage. Green paths are constructed by stardis-solver, following the very same algorithm as when evaluating a temperature. The difference between temperature computation and the construction of the Green function is that when a path is sampled, some of the information is stored in the data structure corresponding to the Green path instead of being used on the fly to compute a temperature (see List.
<green function data structure> = struct sdis_green_function { <media collection> <interfaces collection> <paths collection> <miscellaneous variables> }; Collections of media and interfaces accumulate the media and interfaces that have been visited when constructing the Green function. Individual paths can then reference this shared information. These collections are hash tables, i. e. associative containers that favor fast and constant time lookup, to ensure unique storage of only the media and interfaces visited by the paths: <media collection> = struct htable_medium media; <interfaces collection> = struct htable_interf interfaces; The paths collection is the set of the paths sampled for the construction of the Green function. It is a dynamic array that is well suited for iterative storage and path iteration: <paths collection> = struct darray_green_path paths;.
Simulation examples This section illustrates a typical use of the storage and use of the propagation information described in the previous sections. The geometrical and physical descriptions of the configurations used for this illustration, as well as the Stardis input files, are available in the enclosed zip file. The objective is essentially to show that a computation performed using the stored propagation information recovers the result that would be obtained with a complete MC run (with a particular attention to the associated statistical errorbars) and to illustrate the benefits in terms of computation times. As far as validation is concerned, we concentrate on the parts of the code constructing and using the propagators, not on the main code itself: Stardis is already validated elsewhere  [18, 19].
If the Monte Carlo run is very accurate, there will be no need for any consideration of the Monte Carlo uncertainties when studying such source-uncertainty propagations. Of course, if the source uncertainty and the Monte Carlo uncertainty are of the same order of magnitude, then further attention will have to be devoted to the resulting statistical correlations, typically when several sources are uncertain, possibly correlated, their associated propagators being themselves correlated because they were computed using the same Monte Carlo run. But again, all the required information is in the data already stored at the Monte Carlo weight level when running Stardis and only a post treatment is required when such further statistics are required. h a n d l e ->p a t h -> e l a p s e d _ t i m e = e l a p s e d _ t i m e ; h a n d l e ->p a t h -> l i m i t.
Acknowledgements This project has received funding from the " Investissement d'avenir" program  of the  National Agency for Research of the French state  under award number " ANR-10-LBX-22-01-SOLSTICE " and was supported by the  ANR   HIGH-TUNE , grant  ANR-16-CE01-0010  and  ANR   MCG-RAD , grant  ANR-18-CE46-0012  and from the  Occitanie Region  ( Projet CLE-2016 ED-Star ).
The results remain not completely clear but show that the oxidation state of silver on the surface of the coating changes during the antimicrobial test. The single BE band of Ag 3d 5/2 of 368. 3 eV detected after the coating of the foam (Fig. S5 ), is attributed to Ag(0) (NIST X-ray Photoelectron Spectroscopy Database, 2000). After test (Fig. S7 ), two BE bands of 367. 2 eV and 368. 4 eV are detected, suggesting a change in the oxidation degree of silver, making it more oxidized (NIST X-ray Photoelectron Spectroscopy  Database, 2000; Kaushik, 1991). In other words, silver is in the form of chelated Ag(0) after coating of the foam and it is oxidized during the antimicrobial activity test. The synergy between the redox properties of the catechol/quinone groups of PDA and the Ag/Ag + couple can explain the good antimicrobial activity of the foam-filter.
This is interesting to put in perspective with the other proposed models as they often involve multiplicative interactions (i. e. multiplication between two state variables or a state variable and an input variable) like gating. Using our behavioral dataset, future studies will be able to evaluate and compare models to probe the algorithms underlying human learning. New data can also be collected easily and in large amounts using the experimental paradigm that we make available to the community (1 report per outcome every 1. 5 s). Relevance for artificial intelligence. Using a dynamic adaptive learning rate is relevant for machine learning and artificial intelligence. The two determinants we identified could guide the search for new efficient, adaptive learning methods. One advantage of change-point probability and prior uncertainty, compared to state of the art methods such as Adam, AdaGrad and RMSprop  (Goodfellow et al.
Note that we deliberately used a quantification of uncertainty that is commensurable-i. e. has the same unit-in the magnitude and the probability learning tasks, to be able to compare the uncertainty between the two tasks, by taking the standard deviation of the model's distribution whose support is the [0-1] interval of the estimation slider, rather than using the "relative" uncertainty as in other studies, which would break this commensurability  (McGuire et al. , 2014; Nassar et al. , 2012; Vaghi et al. , 2017). Probability learning task. We reused the Bayesian model described in  (Meyniel, 2020)  (code available online at https://github. com/florentmeyniel/TransitionProbModel). We made two modifications to the model to match the subject's actual task: • As the subjects were told that the hidden probabilities are drawn in [0.
Acknowledgements We gratefully acknowledge the support received for this work. C. F. was supported by a PhD fellowship from  ENS Paris-Saclay (France ). F. M. was supported by the  European Research Council  ( ERC  grant # 947105 ) and the  Inserm. We thank  Maëva L'Hôtellier  and  Alexander Paunov  for useful feedback on the project. We also thank them as well as  Steven Geysen  and  Tiffany Bounmy  for piloting the experiment.
ACKNOWLEDGEMENTS Funding for this work was provided by the  National Science Foundation (NSF)  ( CHE-2140762  and  CHE-2247426  to C. L. and Q. G. ;  CHE-2102266  to A. G. D. ;  CHE-2002158  (Center  for Synthetic Organic Electrochemistry)  to M. S. S. ), the  Joint Center for Energy Storage Research (JCESR) , an  Energy Innovation Hub  funded by the  U. S. Department of Energy, Office of Science, Basic Energy Sciences  (to J. R. L. ), and partially the  Agence Nationale de la Recherche  ( Labex ARCANE ,  CBH-EUR-GS ,  ANR-17-EURE-0003  to C. C. ). H. S. acknowledges  Dr. Shuangning Xu  and  Mr. Brandon Jolly  for helpful discussions about chemical purification techniques.
In Stage II, the workflow first determines a cutoff RX concentration, [RX]cutoff, below which avoids assessing unnecessarily high [RX] values in future inquiries (Supplementary Note 7). The workflow also creates an initial response surface of ipa/ipc as a function of [RX] and ν, from the existing CV data in Stage I, via a Gaussian process 40, 41. Based on the determined [RX]cutoff and the response surface of ipa/ipc, the workflow iteratively designs combinations of [RX] and ν for the next inquiry that may satisfy ipa/ipc ∈ [0. 65, 0. 75], and automatedly execute CV measurements in the glovebox. Each inquiry in Stage II includes a set of six voltammograms under an assigned [RX] value below [RX]cutoff and six proximate ν values (Supplementary Note 8). The workflow iteratively adds the resultant CV data to the cumulative database, determine whether the new data satisfy ipa/ipc ∈ [0.
We demonstrate that all the variables involved in simulating MRSs with FDTD codes of different dimensions can be expressed as functions of a reduced number of main variables. These main variables represent groups of independent input variables. This approach enables the definition of inputs using independent lists of variables and a list of dependencies that simulation scripts rely on, ultimately simplifying and generalizing all the simulation process. In Section 4, we discuss the automation of data processing for PPR systems. We introduce a new algorithm based on I/Q detection for obtaining the position error and detected signal amplitude. To validate these techniques, we test a system of 100 reflectometers, utilizing the 2017 DEMO baseline scenario available in the official EUROfusion database  [20].
The group delay can be decomposed into a smooth term (τ 0 ) and fluctuations ( τ ), given by the equation τ (F ) = τ 0 (F ) + τ (F ). ( 19 ) Various techniques have been developed to remove the fluctuations τ and improve the smoothness of the group delay. One commonly used method is the best path method or the first moment of the beating frequency, which is applied instead of the frequency with maximum amplitude  [25]. However, the correction process becomes more complex when the smooth term itself contains intrinsic errors. Artificial neural networks have been used in data processing to obtain smooth density profile reconstructions  [36]. In a recent study  [37] , a reconstruction scheme utilizing a database with the perturbation shapes was applied to density profiles with non-monotonic regions, resulting in an improved description of the density profiles.
The REFMUL* family of codes includes the (i) REFMUL, a 2D O-mode (TM z ) code  [44] , (ii) REFMULX/REFMULXp, a 2D X-mode (TE z ) simulation code  [45] , (iii) REFMULF, a 2D OpenMP [46] full polarization code and (iv) REFMUL3, a 3D hybrid OpenMP/MPI  [47]  full-wave code  [18, 19]. REFMUL* codes have been extensively used in studies involving ASDEX Upgrade, Tore Supra  [15] , ITER for the design of the PPR system  [16] , and for assessing microwave diagnostics for DEMO  [17]. Fig. 3  shows the scheme of a 2D REFMUL simulation setup with the snapshot of the positive part of the electric field at f = 28 GHz where is possible to observe the multiple plasma-wall reflections resulting from the interaction of the probing beam with the plasma. This simulation uses a region of interest of dimensions l x = 1.
2 m × l y = 1 m, corresponding to 3363 × 2803 grid points for a reference frequency of f 0 = 29 GHz. The slab electron density model corresponds to an exponential decay of 1. 5 cm from the LFS region separatrix point of the 2017 DEMO density model provided by the EUROfusion database  [48]. The simulation setup is common across different versions and dimensions, comprising four main elements: (i) the metallic structure, (ii) the plasma, (iii) the vacuum region, and (iv) the Perfectly Matched Layer (PML)  [39]. The input is defined in Cartesian coordinates following the Finite-Difference Time-Domain (FDTD) formulation. The metallic structure includes the antenna setup (the waveguides, the tapers, the antennas) and the surrounding structure, and is considered a perfect conductor. A perfect conductor is easily computationally implemented if the structure is described by a binary tensor.
4. Simulation with the plasma -run the simulation with the plasma and record the detected signal, S det. 5. Source signals -obtain the source signal to be used as reference in the data processing, S src. In case of a IQ detection, the 90 • shifted signal is also required. Suppose the reflectometer operates in N B bands (each with a different f 0 ). It is necessary to generate N B models for the metallic structure and N B plasma models, compute N B UTS impulse responses, perform N B simulations with the mirror, and run N B simulations with the plasma. Each band is associated with a different FDTD grid. The data processing for PPR systems requires finding N B group delays (in the case of a mono-static configuration) and combining them into a single full-band dataset to apply the inversion algorithm.
provide the necessary scripts to facilitate these tasks, such as running simulations, managing them on HPCs, data verification, and control of other operations. Apart from these functionalities, the framework should facilitate the integration of different users. Different elements of the research team can contribute to the same project. The structure of the framework should be designed to easily implement different functions for designing the input models and different system parameters. We propose a framework structure that is compatible with all these requirements. Simulating a set of independent simulations and processing and analyzing the results is simply a matter of defining the input configurations and running a few scripts. The procedure for running and processing a set of independent simulations is represented in Fig.
4. The workflow consists of five phases. In the first phase, the user defines the input data, including regions of interest, plasmas, reflectometer geometries, probing bands, and other relevant variables for the simulations. In the second phase, a code runs to create the simulation directory (SD), which contains all the necessary data (models, scripts, data lists) for running the simulations and analyzing the results based on the input. The third phase involves running the simulations using the respective scripts. This begins with running the simulations to find the UTS impulsive response, and then the independent simulations with the plasma/mirror are distributed across different processing units. Once the simulations are completed, the data processing scripts read the input data and process the simulation data, saving the final results in the output directory (fourth phase).
Finally, in the last phase, the user can plot the results and analyze the data based on the specific problem they are studying. This step can also be automated for different types of studies if the input follows a consistent format. An important condition to simplify the framework and the output scripts is to assume that the source excitation occurs in a plane, as opposed to using an oblique UTS source. This simplifies the definition of the source position and the calculation of many variables, allowing for the simplification and optimization of various parts of the code. It also enables the definition of a pure mode, and the UTS algorithm is faster in this case compared to the oblique case. Therefore, it is necessary to implement the required frame conversion operations on the input data, so that they are defined in the simulation frame (discussed in section 3.
4). A basic concept for simulating automation would involve having a list of simulations as input, where each variable is defined in a different column. The code would then generate the necessary scripts to run the simulations one by one, corresponding to the signals given by equation  (43). However, this approach does not optimize the time required to define the input as much as possible, as there are many variables to define for each simulation and there are simulations that share the same parameters. If we are simulating a set of N R reflectometers probing N P plasmas with N B bands, it would require the definition of N R × N B × N P independent input lines, which would be a time-consuming task. The solution is to identify key groups of independent input variables from which the other variables and operations of the problem are dependent.
The dependence has the same form when multiple signals are detected. The left side represents the simulated signals and the right side the simulation and its main input parameters. All the variables used in the simulations depend on the particular combination of the D -R -S -P -B indices. Each D -R -S -P -B has a D-R-S-P -B combination associated. The list of simulations is also given by all the possible D-R-S-P -B combinations, which in number are the same. Many variables are only dependent on a subset of the main variables, simplifying the code. For example, the density model is not dependent on the input data available in S, n D R S P B e (i, j, k, n) = f [D, R, P , B]. ( 52 ) All the different plasma models used in the simulations are associated with a D-R-P -B combination. Equation (  51 ) can be written in terms of the main variables values in the form S det (D, R, S, P , B) = f [S src (D, R, B), σ S (D, R, S, B), n e (D, R, P , B), B 0 (D, R, P , B)],  (53)  The automation of a large set of simulations is possible identifying the dependence of each variable on the main variables, D, R, S, P and B, and creating the necessary lists of combinations to the creation of the simulation scripts and the production of all the simulation models.
A predefined string can be used to indicate that all elements of a main variable should be included, simplifying the definition of dependencies for many studies. To handle the complexity arising from mixing variables of different dimensions, it is recommended to split the input into three separate datasets, one for each dimension. A list of the D-R-S-P -B combinations, denoted as L D , is then required to create the simulations list. All the necessary lists of combinations can be derived from it. Each line contains strings with the same format as used to define exceptions, but now they represent the possible D-R-S-P -B combinations for the simulations. For example, in the simulation of 100 reflectometers with the same antenna for all the defined plasmas and bands for two dimension simulations, the list of combinations can be written in one single line as:.
D[2D]R[ ALL]S[ ALL]P[ ALL]B[ ALL]. Here, the string ALL represents all the existing main variable values. If a 3D simulation is also required but only for two reflectometers and one plasma, a new string is added to the L D list with the respective combinations. A list of definitions related to REFMUL* (referred to as L R F ) and another list for computer parameters (denoted as L H ) are also necessary. These lists create different computer profiles, useful for local tests or running simulations on High-Performance Computing (HPC) systems. By changing the necessary parameters, scripts can be generated and executed on the respective computer profile. Since defining the geometry of the metallic structure or plasma involves the definition of many variables (e. g. , geometry, CAD model, geometry parameters, plasma displacement, turbulence level, etc.
), it is helpful to define some input variables in a separate file. The lists L S and L P include a variable that points to these configuration files. To summarize, the input definition process is simplified by five independent datasets: (i) The main lists: L R , L S , L P , L B , L 0 ; (ii) the list of combinations: L D ; (iii) the secondary lists: L R F , L H ; (iv) the structure configuration files; and the (v) the plasma configuration files.
Simulation scripts The main script reads the input data and creates a directory (SD) that contains all the necessary information to run the simulations and process the data. This process is divided into different phases. In the first phase, the input lists of variables are declared along with other important project parameters such as the project name, input data directory, output directory, number of simulations per script file, etc. In the second phase, the lists containing all possible combinations of main variables are generated and saved in the SD directory for easy access. The list of simulations, denoted as L SIM , encompasses all the combinations of D-R-S-P -B. Each combination represents a simulation of a setup, band and plasma, and a unique number from 1 to N SIM is assigned to each combination as the simulation number.