dataesr/scientific-paragraphs-categorization huggingface dataset:
The utility of model selection can be further extended to test for potential differences in the results from two or more datasets. In this case, the complete data are split into several subsets with each individual dataset being represented once and only once. The test question is: can the collection of datasets be modeled with a single set of parameters, or must each dataset be modeled with its own set? The two alternatives are: (a) are the different datasets similar or (b) not. In this situation, Bayesian Information Criterion (or BIC) should be used instead of AIC, because the true model is obviously among the tested alternatives (Equation  7 ). BIC = -2 ln L + p ln n (7) When the BIC statistic is used, all the priors of the models tested are assumed to be identical. It is also possible to estimate BIC weights by replacing AIC with BIC in the Akaike weight formula.
Storing Results BoneProfileR uses the new results management system (RM) introduced in HelpersMG R package version 4. 1: all the results are stored as part of the analyzed image. This result management software has been developed to help users store the results associated with the method used to obtain them (type of center used, background and foreground colors). It is part of the large movement in science of replicative research  (Amaral and Neves 2021 ). An analysis is stored with the image in a single file with all the necessary information to reproduce the analysis. Several analyses can be stored within a single object. They can be called using the parameter "analysis" available for most of the functions of BoneProfileR. The results of an analysis can be exported as a pdf file or as an XML spreadsheet compatible with Open Office or Microsoft Office.
Box (1979)  noted that "all models are wrong, but some are useful. " The large range of publications using the Bone Profiler model shows that this model is useful. In conclusion, the new methods implemented in BoneProfileR enhance the bone compactness model published in  Girondot and Laurin (2003). This update introduces new statistically sound methods (maximum likelihood, Bayesian analysis), a new function to model compactness, and a new radial analysis to estimate the parameters of bone compactness. The new software is an R package, and data can be directly used in statistical analyses considering phylogeny for example. A user-friendly Web-version is also available, but it gives access to only a subset of the options available through the command line interface (CLI) version within R.
Test case 4 is expected to model these conditions (Table  1 ). For numerical purposes, test cases where the temperature gradient is zero or positive (+400 K/m) are also considered (test cases 3 and 5, respectively). For these cases, the solution will be compared against a numerical reference solution provided by the code CLICET  [11] , which is a quite widely used solver for boundarylayer modeling  [9, 10, 24, 25]. Indeed, CLICET directly solves the boundary layer equations (Prandtl equations), and especially Equation (1), by using a 2D mesh and a finite volume method. It should be noted that the velocity fields in the boundary layer are not strictly the same for the two codes. BLIM2D is fed with the integral boundary layer solution exposed in the article of Bayeux et al. [12]  (see Appendix A for more information), whereas CLICET uses its own velocity field, which is solved jointly with the temperature field.
Discussion about the Computational Time It should be noted that the performance of the proposed method in terms of computational time has not been studied in this work. In particular, no effort has been made to optimize the code used. However, we can mention that the simulations performed with N = 4 to 5 modes, which are generally of sufficient accuracy, require only a few tens of seconds of computation on a simple single-core computer. On the other hand, the computational time increases very quickly with the number N of modes because of the very restrictive stability condition (in O(1/N 2 )) on the time step. The future use of a totally implicit time integration method, allowing us to get rid of the stability condition, should allow a gain of several orders of magnitude on the number of time steps necessary to reach the stationary solution and thus significantly increase the performance of the method.
Non-Functional Matching In the context of GWS discovery, in order to refine functional matching results, a number of geospatial research studies  [29] [30] [31] [32]  take into account the non-functional matching step, often referred to as constraints satisfaction, preferences satisfaction or, also, the service recommendation step. The non-functional matching step exploits non-functional properties (availability, cost, database quality of gazetteer, etc. ), which are most often QoS (Quality of Service) attributes. This step is used to assist the user in their selection. It follows the functional matching step if it proposes several services that are functionally similar. The non-functional matching methods are varied. They are closely related to the non-functional attributes considered and the evaluation model used (real numbers, interval numbers, linguistic expression, ontology concepts, etc.
SAWPS (Semantic Annotation for WPS) The method of describing GWS follows two steps: Section 3. 1. 1 designs a lightweight meta-model supporting the semantic description of functional properties and description of non-functional properties; Section 3. 1. 2 explains the protocol to instantiate our meta-model in WPS 2. 0. As shown in the diagram, each service description contains information about both functional and non-functional properties. Functional properties mainly concern service functionality and information about its I/O parameters. The service functionality will be semantically annotated with a service ontology concept. This allows us, on one hand, to link the different services to common knowledge and, on the other hand, to automate and lighten the discovery process. To enable the application of semantic I/O matching, I/O has to be semantically annotated with the I/O data category concept according to a data ontology.
An approach through annotation was chosen to instantiate the meta-model in the WPS description. Its implementation, based mainly on the use of the official metadata tag, ensures compliance with the standard. This tag has two attributes: role and href (role identifier indicates the role of the metadata and href references the metadata with HTTP (Hypertext Transfer Protocol)-URI (Uniform Resource Identifier) value type). Therefore, the annotations will be recorded in the metadata tag in the form of simple links with an appropriate role identifier. Table  2  (the presented HTTP-URI are not accessible on the web. They are mentioned as examples for illustration purposes) describes the defined new roles and the reused one (first line in the table). For functional properties, a link to the appropriate concept of ontology is instantiated, whereas, for non-functional properties, the link will point to an XML (eXtensible Markup Language) instance containing the information (last line in the table).
Architecture The contribution of this paper is twofold: (1) allowing for the creation of a semantic description for WPS according to the proposed annotation method (SAWPS); (2) supporting the discovery of a geospatial web service (GWSD) based on annotated descriptions. The functional architecture of the proposed SAWPS and GWSD approaches (illustrated in Figure  4 ) is composed of five modules : 1. WPS file annotation: the entry points of this module are the domain ontologies, a WPS file syntactically describing the functional properties of a geospatial service and information about non-functional properties (QoS). This module applies the annotation approach described in Section 3. 1. It subsequently allows for the creation of a directory of annotated WPS service descriptions; 2. SAWPS analyzer: handles the annotation concept extraction of functionality and I/O associated with the semantically annotated WPS descriptions; 3.
Semantic matching: performs semantic matching between the user request and the set of WPS services by comparing different concepts according to the proposed method (see Section 3). It uses the domain ontologies and the Jena API reasoner (Jena: https://jena. apache. org/, accessed on 12 January 2020) to infer relationships between concepts; 4. QoS file analyzer: handles the extraction of non-functional property (QoS) values from the description files; 5. QoS calculation: calculates a non-functional score (QoS score) for each service discovered by the semantic matching module.
To enrich our corpus, we retrieved 84 open source WPS process descriptions published by the 52 • North project  [36]  (52 • North WPS Processes: https://github. com/52North/WPS/tree/dev/52n-wps-webapp/src/main/ webapp/examples/localWPSFiles/xmlDescriptions, accessed on 12 June 2020). They propose several functionalities related to the geospatial domain, such as distance calculation, surface interpolation, etc. We updated these descriptions to the current version 2. 0 of WPS. Finally, the process of semantic annotation was carried out. In this context, the approach proposed in Section 3. 1 allows for flexibility in the choice of the ontologies used to annotate the functionalities and I/O of services. To perform our experimentation, we designed two domain ontologies, service ontology and data ontology, in order to semantically annotate the service functionality and the I/O of a service.
Abstract. In the Aerosol Robotic Network (AERONET) retrieval algorithm, smoothness constraints on the imaginary part of the refractive index provide control of retrieved spectral dependence of aerosol absorption by preventing the inversion code from fitting the noise in optical measurements and thus avoiding unrealistic oscillations of retrievals with wavelength. The history of implementation of the smoothness constraints in the AERONET retrieval algorithm is discussed. It is shown that the latest version of the smoothness constraints on the imaginary part of refractive index, termed standard and employed by Version 3 of the retrieval algorithm, should be modified to account for strong variability of light absorption by brown-carbon-containing aerosols in UV through mid-visible parts of the solar spectrum.
(2020). The data quality assurance procedures and cloud screening of AOD in V3 are presented in  Giles et al. (2019). The standard AERONET aerosol retrieval product is obtained by inverting measurements taken at four standard wavelengths: 440, 675, 875 and 1020 nm. However, the V3 aerosol retrieval algorithm can invert extended sets of wavelengths including the UV at 380 nm  (Sinyuk et al. , 2020). Absorption at 380 nm is particularly important as this is the wavelength range in which satellite observations and algorithms are able to retrieve atmospheric column absorption from existing  (Jethva et al. , 2014)  and future satellite sensors  (Werdell at al. , 2019). The unique utility of measurements in UV for satellite remote sensing is related to increased sensitivity to aerosol absorption due to absorption of molecular scattering by aerosols (e.
For each profile, 1 s sampled values of scattering coefficient measurements at 450, 550 and 700 nm from the nephelometer and absorption coefficient measurements at 470, 532 and 660 nm from the particle soot absorption photometer were provided, both from dried air samples. At 550 nm, an additional scattering measurement at ambient relative humidity allowed for the calculation of an ambient SSA (rather than dried aerosol) that is more suitable for comparison with the SSAs derived from AERONET radiance measurements. In order to produce a column SSA value to compare with AERONET, the 1 s SSA aircraft measurements were averaged for the duration of the profile sampling after weighting the values according to aerosol loading  (Schafer et al. , 2014). The analysis of  Schafer et al. (2014)  had shown that SSA retrieved by AERONET (interpolated to 550 nm) was on average 0.
into molecular subdomains contributions is presented and a software, DOCTRINE (atomic group Decomposition Of the Charge TRansfer INdExes) for the implementation of this novel model has been coded. Although our method applies to any fuzzy or to any disjoint exhaustive partitioning of the real space, it is here applied using a definition of chemically relevant molecular subdomains based on the Atoms in Molecules Bader basins. This choice has the relevant advantage of associating intra or inter subdomain contributions to rigorously defined quantum objects, yet bearing a clear chemical meaning. Our method allows for a quantitative evaluation of the subdomain contributions to the charge transfer, the charge transfer excitation length and the dipole moment change upon excitation. All these global indexes may be obtained either from the electron density increment or the electron density depletion upon excitation.
Clearly, in such a case, the atomic boundaries and the integral properties of the excited state need also to be computed to evaluate the integral properties changes upon electron transition. DOCTRINE evaluates ∥μ CT ∥(S n ← S 0 ) from eq 6 (or through eq 14 or eq 16). Comparison of the reconstructed value of ∥μ CT ∥(S n ← S 0 ) from its atomic basin Ω contributions with the value computed by the GAUSSIAN 16 code permits to evaluate the accuracy of the numerical integration. Since this is performed for all QTAIM atoms in spherical coordinates and on an atomic centered grid, the resulting accuracy is noteworthy, provided a suitable number of angular and radial points is used in the Gaussian quadrature integration procedure (see Computational Details). Further checks of integration accuracy are provided by comparing quantities that should be ideally equivalent, such as the ∥μ CT ∥(S n ← S 0 ) values obtained from eq 6 (or through eq 14 or eq 16), either using q CT + (Ω) or q CT -(Ω) data or the q CT values obtained from eq 7, either using ρ + or ρ -distributions.
Furthermore, the crystallized BM compound includes structural solvent molecules. 37, 38 This explicit solvent model for BM in water is hereafter referred to as BM + ES 2. For both solvent cases, the following four BM moieties were considered in our analysis: the carbonyl oxygen atom, the 6-Carbon Membered Ring plus its four linked H atoms, the two C atoms of the Central Bridge plus their two linked H atoms and the MethylPyridine ring along with its linked 4 H atoms. In the following, these four BM moieties (≡ BM Ω subdomains) will be called as O(CO), 6CMR, CB and MePy, respectively. The. wfn files for the two ground states and eight excited states investigated in the present study have been taken from our previous work on the BM molecule excitations. 32 Quantum chemical calculations were carried out through the Gaussian 16 software package and using DFT and TD-DFT procedures.
Geometry optimization [S 0 or singlet ground state (GS)] and TD-DFT treatment for the vertical excited states (S n , n = 1-4) were all performed using the longrange corrected (LRC) Coulomb attenuated B3LYP xc functional CAM-B3LYP  39  with the cc-pVDZ basis set and by including solvent effects through the implicit SMD solvation model. 40 n the case of BM + ES 2 for the water model, besides considering the implicit dielectric effect of solvent through SMD, two explicit water molecules were initially placed in the neighborhood of the C�O group and left totally free to geometrically relax during the geometry optimization step. The local energy minimum nature of the calculated structures was confirmed from their (harmonic) vibrational analysis implemented in the Gaussian 16 software package (no imaginary frequency).
The global CT indexes, along with their atomic group decomposition, have been obtained from the DOCTRINE code (see previous paragraph). For the BM + ES 2 in water system, the adopted. wfn files for both the GS and the excited states were those obtained by restraining excitations and charge transfer to the BM molecule only (i. e. , the wave functions were generated by setting zero charge on solvent molecules while using the basis set of the complete system). We had however to verify that no CT occurs to the explicit solvent molecules also when a QTAIM space partitioning is adopted. Indeed, the QTAIM net charge on the two explicit solvent water molecules has been always found to be below ±5 The Journal of Physical Chemistry A × 10 -3 electrons for all investigated cases. These results enable us to restrain the decomposition of global CT indexes to contributions from subdomains formed by atoms of the BM molecule only also for the BM + ES 2 in water system.
Atomic boundaries for the ground states were determined by PROMEGA code using typically 6144 angular points (96 and 64, for φ and θ, respectively). Evaluation by the DOCTRINE code of the atomic contributions to the global CT indexes used 200 radial points in the Gaussian quadrature integration procedure outside the so-called beta sphere and the same angular points of the previous determination of atomic boundaries by the PROMEGA code. The evaluation of atomic contributions to the global CT indexes is about 2 orders of magnitude faster than the atomic boundaries determination, and it is therefore very fast (typically 5-10 min for a system like BM, on a medium sized cluster of workstations). The CPU more demanding step, i. e. , the atomic boundaries determination, needs however to be performed only for the ground state of a system, regardless of the number of its investigated excited states.
■ RESULTS AND DISCUSSION Accuracy of Global and Atomic Group CT Indexes. Table  1  reports the values of Δ∥μ CT ∥ = ∥μ CT ∥ DOCTRINE code -∥μ CT ∥ Gaussian 16 code for all investigated excitations. Δ∥μ CT ∥ is the difference of the ∥μ CT ∥ value obtained from the separate atomic or atomic group contributions to ∥μ CT ∥ evaluated by the DOCTRINE code and that calculated directly by the Gaussian-16 code. The largest Δ∥μ CT ∥ difference reported in Table  1  is as small as -0. 173 D (D = Debye), relative to a dipole moment larger than 20 D, for the S 3 ← S 0 excitation of BM + ES 2 in water. Typically, Δ∥μ CT ∥ values are found to be one order or even 2 orders of magnitude smaller. Table 1 also lists the amounts of transferred charge q CT upon excitation calculated by integrating either ρ + (r) or ρ -(r) over the whole set of atomic or atomic groups subdomains of the BM molecule, for all studied excitations.
The q CT decomposition is presented both in terms of q CT + (Ω) and q CT -(Ω) contributions. Furthermore, the difference, Δq CT (Ω), between the q CT + (Ω) and the q CT -(Ω) contributions is also displayed. Table  2  gathers all these subdomain decomposition data, including, for each electronic transition, the value A of the sum of only positive Δq CT (Ω) values, [ ]> q ( ) q CT ( ) 0 CT (or, equivalently, minus the sum of the only negative Δq CT (Ω) values; see eqs 9a and 9b). The other global CT indexes reported in the left panels include (i) the dipole moment change between the ground and the excited state n, evaluated by the DOCTRINE code and reported in Debye (D); (ii) the CT excitation length, D CT , given in Å, and (iii) the locations of the positive and negative centroids of the transferred charge upon excitation.
This behavior complies with the carbon bridge being a conjugation entity covalently connected to both the D and A moieties of the BM molecule and supporting the Figure  1. BM + ES 2 in water: global CT indexes (left panels) and atomic groups decomposition (right panels) of the transferred charges q CT upon excitation for the first S n (n = 1-4) vertical excited states. The global CT indexes include the q CT value (reported in e -over the "+" sign in each left panel), the dipole moment change between the ground and the excited state n, evaluated by the DOCTRINE code and reported in Debye, D, the CT excitation length, D CT , given in Å, and the locations of the positive and negative centroids of the transferred charge upon excitation (the positive and negative centroids locations are indicated by purple balls in the left panels).
Employing reconfigurable devices that can adapt-even at runtime-the structure of the underlying hardware architecture to the specific requirements of a given application can lead to tremendous boosts in performance and energy efficiency compared to conventional architectures  [1]. The articles in this special issue feature different types of reconfigurable devices, including field-programmable gate arrays (FPGAs), coarse-grained reconfigurable arrays (CGRAs), and configurable heterogeneous platforms. The application areas are manifold, including image, video, and other digital signal processing, scientific computing, or near-data processing for databases, to name only a few, which are also considered in this special issue. This special issue of Springer's Journal of Signal Processing Systems covers various facets of the abovementioned topics.
The special issue is based on extended versions of selected top-level papers presented at the 17th International Symposium on Applied Reconfigurable Computing (ARC). The 2021 edition of ARC [2] was hosted by the Université de Rennes 1 and Inria, France, and took place as a virtual conference during June 29-30, 2021. From 36 submitted papers, 14 regular papers had been presented at ARC 2021, and after a careful peer-review process, four extended manuscripts were accepted for inclusion in this special issue. It is our pleasure to introduce these articles in the following briefly. The initial two articles of this special issue deal with configurable heterogeneous systems. Heinz and Koch's article "On-Chip and Distributed Dynamic Parallelism for Task-based Hardware Accelerators"  [3]  presents TaPaSCo, an open-source hardware/software framework for FPGA-based architectures.
Electric field simulation The design of the HeXe TPC has been optimized for a high electric-field homogeneity based on a detailed detector simulation. Using the commercial finite element software COM-SOL Multiphysics ®  [14] , a model containing all necessary electrical components was created. The number of field shaping rings or the design of the electrodes (section 3. 2) were optimized based on the outcome of this simulation. For a correct representation of the hexagonal pitched electrodes as well as their relative rotation, a full threedimensional geometry is chosen instead of the more common 2D approximation. Figure  2  shows the 3D geometry as implemented in COMSOL. The blue color scale displays the value of the potential at each metallic surface. The colored plane represents the intensity of the field in the target region.
The fourth level meter, having a length of 10 cm can be used to monitor the complete height of the drift volume. All sensors are read out by a LabVIEW [17] program, with the values stored into a periodically backed-up Post-greSQL [18] database for later access. They are running on the same machine to ensure database input in case of network outages. For each sensor, two separate sets of thresholds can be specified (warning and alarm). If the readings pass them, emails or SMS messages are being sent to HeXe operators to alert them. Both the program and the database are, in turn, monitored using Nagios [19] which is set up on a separate server, in order to also send alerts in case at least one of them is not responsive or accessible. The modules providing HV for the PMTs and grids are controlled by a tool written in Python.