dataesr/scientific-paragraphs-categorization huggingface dataset:
When the notification is received by a particular subscriber (in our testbed the laptop), its frontend module checks its contract that contains all the publishers' addresses to verify whether this publisher is among its associated publishers list. If the notification source address does exist in the list then the notification is accepted otherwise it is filtered. The implementation of the system behavior was challenging since SolidityC was not completely mature, many features were not yet implemented at the implementation time. To verify that the GUI was working properly, we have performed several tests using the Embark dashboard. The different steps of the system implementation and deployment are the following: (i) Setting up the private blockchain using Geth. (ii) Configuring the laptop and the Raspberry Pi and connecting them to the blockchain.
(iii) Programming the proposed smart contracts model using SolidityC. (iv) Deploying the contracts on the blockchain. (v) Programming the subscriber's frontend (the HTML webpage). (vi) Programming the backend (Javascript to link the smart contracts and the frontend) (vii) Configuring IPFS off-chain database. (viii) Connecting all the elements using Embark framework. The implementation details of the different steps are described as follows: the first step to implement the system was to create a private blockchain to ease testing and experimentation. For that, we have used Geth (the command line interface for running a full Ethereum node implemented in Go) to create the Genesis file (the Genesis block is the first block in the blockchain), which is a Json file. After creating the Genesis file, we have specified some properties of the blockchain such as identity, rpcport, port, data directory and network Id.
The next step was to test the blockchain by adding peers, connecting them to the blockchain, and sending Ethereum messages to make sure that the blockchain was working properly. Thanks are due to the Geth Javascript console and the Geth functions which allowed us to test the blockchain. Once the blockchain created, the following step was to create and deploy smart contracts. As previously stated, we have specified the contracts described in Section 5 and then used the SolidityC compiler to generate them. After successfully setting up the private blockchain and deploying the smart contracts, the backend was eventually complete. The next step was the deployment of GUI, i. e. , the frontend of the DApp. The implemented frontend is composed of different files: html, Javascript and css files for the portal webpage and its interactions, along with several Embark configuration files.
The visible part of the GUI is the portal webpage running on a webserver executed in the laptop. A webserver configuration file was also available in Embark. This environment was then used to deploy a blockchain storage GUI, the frontend and backend. Finally, we deployed the IPFS database and connected it to the private blockchain. All peers could then connect to the blockchain and store data in the IPFS off-chain database. This has completed the implementation and the deployment of the testbed. 10 Wireless Communications and Mobile Computing.
Conclusion and Future Works The objective of this work was to consider the possibility of using blockchain technology in the area of IoT data access protection with a possible application in the eHealth area with the protection of personal medical information collected from medical sensors and environmental sensors in smart homes. We have proposed an architecture of a solution designed for that purpose that is based on contracts between providers and consumers of data. To cope with the size of the data to store, we have proposed to associate the blockchain with an off-chain database. The block contains the main contract information as well as reference to where the complete data is stored. We have also discussed and presented the performance of such a system and the parameters that may have an impact on the time to process the transactions.
Some of the key issues to address realistic simulations of the Tokamak are: efficient and robust numerical schemes, accurate geometric description, good parallelization algorithms. The gyrokinetic framework considers a computational domain in five dimensions (3D in space describing a torus geometry, 2D in velocity). Time evolution of the system consists in solving Vlasov equation non-linearly coupled to a Poisson equation (electrostatic approximation, quasineutrality is assumed). The code has the originality to be based on a semi-Lagrangian scheme  [20]  and it is parallelized using an hybrid OpenMP/MPI paradigm  [3, 16]. Let z = (r, θ, ϕ, v , µ) be a variable describing the 5D phase space. The time evolution of the ionic distribution function of the guiding-center f ( z) (main unknown) is governed by the gyrokinetic Vlasov equation (simplified version without right-hand side terms): ∂ t f + 1 B * ∇ z • d z dt B * f = 0 (1) The guiding-center motion described by the previous Vlasov/transport equation is coupled to a field solver (3D quasi neutral solver which is a Poisson-like solver) that computes the electric potential φ(r, θ, ϕ) (adiabatic electron limit): e T e (φ -φ ) = 1 n 0 J 0 ( f -finit ) dv + ρ 2 i ∇ 2 ⊥ eφ T i (2) We will not describe this last equation (details can be found in  [8, 17] ).
2D finite differences for Poisson solver in polar coordinates As said in introduction, in a gyrokinetic code the 5D Vlasov equation is coupled to a 3D quasineutrality equation. In Gysela code this last equation is projected in Fourier space in the θ dimension and solved by 1D finite differences in the radial direction. This numerical treatment is well adapted to concentric circular magnetic configuration but will be no longer applicable to more realistic magnetic configuration. Radial and poloidal directions can indeed no more be split and a 2D treatment of the poloidal (r, θ) cross-section is required. A 2D finite element method is often used in the gyrokinetic codes including D-shape magnetic configurations. For the Poisson solver, we will examine two specific meshes: (i) a non-uniform circular mesh (see Figure  1 ) and (ii) a uniform mesh based on a large aspect ratio equilibrium and mapping (see Section 1.
The maximum relative errors obtained for case 1 and case 2 are of the order of the one obtained for a uniform mesh for the smallest number of N θ points. Therefore, as a summary, we have shown in this section that Poisson equation (  10 ) can be solved successfully on a non-uniform circular mesh with the coupling of 2D finite differences in polar coordinates and Lagrange interpolation of third order. The associated numerical scheme proposed in section 2. 4 is of second order. Higher order for Lagrange polynomials has not been tested in this paper but the matrix system (20)-(  23 ) could be easily generalized. In both cases, CPU time for the solve step remains quite reasonable even for the biggest meshes. This is a key point for a future implementation in Gysela code where the precomputation step will be only performed one times at the beginning.
Conclusion In the context of gyrokinetic simulation of turbulence inside a Tokamak plasma, we have developed a strategy that incorporates an adapted non-uniform meshing. Instead of having a circular geometry with a uniform grid along (r, θ) dimensions to describe the poloidal cross-section, we propose a non-uniform spacing along theta direction aiming at computational and memory savings. Additionally, a peculiar mapping coupled with the non-uniform mesh permits to match more complex realistic geometry such as D-shaped plasma, thus exceeding the former limited configuration of circular Tokamak cross-sections. We expect this mapping to rely on analytical formulas in order to keep a relatively low price in term of computations, which is quite crucial for a full-f global code as Gysela is. Several features that are typically used in the gyrokinetic code Gysela have been recast to handle such a new approach.
Participants Participants from 14 to 30 years of age were recruited from the database of the Center of Excellence for Pervasive Developmental Disorders at the Rivière-des-Prairies Hospital (University of Montreal, Canada), within the framework of three different functional MRI experiments  (Barbeau et al. , 2015; Samson et al. , 2011; Soulières et al. , 2009). AS individuals (N = 55) met the DSM-IV (American Psychiatric Association, 2000) criteria for autism or Asperger syndrome according to a multidisciplinary evaluation by experienced clinicians (all 55 participants), confirmed by the Autism Diagnostic Interview-Revised (ADI-R,  (Lord et al. , 1994) , eight participants), the Autistic Diagnostic Observation Schedule (ADOS,  (Lord et al. , 1989) , one participant), or both instruments (44 participants).
Cognitive assessment Participants' IQ was assessed at the time of their enrolment in the database with the Wechsler scales (either Wechsler Adult Intelligence Scale -WAIS-III  (Wechsler, 1997)  in 43 participants, WAIS-IV  (Wechsler, 2008)  in 1 participant, or Wechsler Intelligence Scale for Children -WISC-III  (Wechsler, 1991)  in 42 participants, WISC-IV  (Wechsler, 2003)  in 6 participants). The difference scores of Verbal IQ subtests (Information, Similarities, Vocabulary, Arithmetic and Comprehension) and Performance IQ subtests (Block Design, Picture Completion, Picture Arrangement, and Digit Symbol-Coding) were included in subsequent analyses. For each subtest, a difference score was computed as the difference score of the subtest minus the mean difference score of other subtests. Each difference score was compared to zero to assess cognitive strengths and weaknesses within the TYP, AS-SOD, and AS-NoSOD groups.
Surface reconstruction All data analyses were performed using version 5. 3 of FreeSurfer image analysis software (http://surfer. nmr. mgh. harvard. edu/). Anatomical images underwent the first steps of the FreeSurfer surfacebased procedure, including removal of non-brain tissue, transformation to standard space, segmentation of gray and white matter structures based on intensity information, and cortical surface reconstruction. Registration to a template enabled anatomical alignment of cortical folds for between-subject comparisons. The pipeline generated two 3-D surfaces per hemisphere in the form of meshes of small triangles: a pial surface, i. e. the entire brain surface, and an outer surface obtained through pial surface warping that excludes sulcal folds. FreeSurfer registration is very conservative compared to other methods  (Dierker et al.
Statistical analysis First, we verified that the groups exhibited no differences in terms of total intra-cranial volume (ICV, measured with FreeSurfer), which could have been a confounding factor in the following analysis: mean ICV was respectively 1637, 1671 and 1641 cm 3 for the TYP, AS-SOD and AS-NoSOD groups (F(101,2) = 0. 45, p = 0. 64). Structural brain differences between AS-SOD, AS-NoSOD and TYP groups were expected in regions supporting perceptual and/or language processing. Therefore, subsequent analyses were restricted to a large bilateral Region of Interest (ROI) encompassing the entire temporal (for auditory, secondary visual and language processing) and occipital (for primary visual processing) lobes (Fig. 1A ) defined using the PALS-B12 brain atlas  (Van Essen, 2005). Group differences in lGI were assessed using a General Linear Model (GLM) with the "Different Offset Different Slope" design matrix available in FreeSurfer.
01, cluster-wise significance level p = 0. 05, cluster-forming threshold = 15 vertices) was used to correct all results for multiple comparisons. The mean lGI of clusters showing differences in the age-by-group interaction was extracted for each subject and plotted against age to allow better visualization of gyrification changes with age for each group. Linear regression was calculated using the least squares method and its significance was assessed with an F-test at a 0. 05 significance level. A preliminary analysis was performed to ensure that parameters that differed between the three fMRI experiments did not have a significant effect on lGI measures. The factor 'dataset of origin' (stating which of the three fMRI experiments each subject participated in) was entered.
Conclusion We found unique gyrification features in cerebral regions associated with the specific domains of cognitive strengths of AS-SOD and AS-NoSOD groups. Differentiating AS individuals according to their speech acquisition history, which was the former basis of the DSM-IV diagnostic categories of prototypical autism and Asperger's syndrome  (American Psychiatric Association, 2000) , remains relevant. Indeed, symptomatic and structural heterogeneity in autism may partially arise from increased experience-dependent neural plasticity targeting either perceptual or speech cortical regions  (Mottron et al. , 2014). Structural brain metrics, such as gyrification and their evolution throughout life may support this hypothesis. Supplementary data to this article can be found online at https:// doi.
Acknowledgements The authors wish to thank all the participants and families who were involved in the study,  Julien Doyon  and  Habib Benali  for their methodological support, together with the staff of the  Functional Neuroimaging Unit at the Centre de Recherche, Institut Universitaire de Gériatrie de Montréal. This work was supported by a grant from the  Canadian Institutes for Health Research  awarded to.
Automatic process modeling has become an effective tool in reducing the lead-time and the cost for designing forming processes. The numerical modeling process is performed on a fully coupled damage constitutive equations and the advanced 3D adaptive remeshing procedure. Based on continuum damage mechanics, an isotropic damage model coupled with the Johnson-Cook flow law is proposed to satisfy the thermodynamic and damage requirements in metals. The Lemaitre damage potential was chosen to control the damage evolution process and the effective configuration. These fully coupled constitutive equations have been implemented into a Dynamic Explicit finite element code Abaqus using user subroutine. On the other hand, an adaptive remeshing scheme in three dimensions is established to constantly update the deformed mesh to enable tracking of the large plastic deformations.
Some phenomena (like strain localization) are hard to obtain and it will also lead the instability into the simulation system. Therefore, it is necessary to integrate a constitutive damage evolution into the JC model. Based on this aspect, a constitutive equation which couple fully the ductile damage into JC isotropic hardening model, is developed. These fully coupled constitutive equations have been implemented into a Dynamic Explicit finite element code (Abaqus/Explicit) using user subroutine. The local integration of the plastic-damage constitutive equations is performed using an asymptotic implicit scheme applied to solve the nonlinear local equations. Numerical errors are intrinsic in Finite Element Analysis (FEA) of sheet metal forming processes and possess additional difficulties related to the large inelastic deformations with damage imply a severe distortion of the computational domain  [28, 29].
Note that conditions in Equation (  21 ) are similar to the Kuhn-Tucker loading-unloading conditions in classical plasticity. The inequality u n ≤ 0 expresses the non-penetration condition, F n ≤ 0 expresses the fact that, at each contact point, the normal force is negative in the local triad. Finally u n • F n = 0 is valid for two cases: • Case 1: There is contact u n ≤ 0 but F n < 0 • Case 2: There no more contact u n < 0 but F n = 0 In the present work, the Dynamic Explicit resolution procedure is used within the general-purpose FE code ABAQUS/Explicit. The fully coupled constitutive equations presented above together with an iterative implicit procedure for the time integration were implemented using the user subroutine VUMAT.
• Until recently, the remeshing process was performed manually and potentially took several days for each remeshing (several are typically needed to model the entire process) of 3D domain. In addition, manual remeshing can potentially smooth the geometry thus preventing boundary defects from being detected, or, introduce constraints that result in false prediction of surface defects from process modeling. • Hence, a 3D modeling system, that would automatically generate a new mesh on the deformed domain and continue the analysis, can dramatically reduce the overall modeling time and result in this technology being widely used in the design of industrial forming processes. This section presents 3D adaptive remeshing scheme based on the linear tetrahedral element. The application environment for this scheme was established by python script, which integrates the 3D adaptive mesher, the Abaqus/Explicit solver and the point-to-point field transfer algorithm to new mesh.
Definition of a physical size map h ϕ,j (Ω) necessary to govern the remeshing of domain Ω. (d) Calculation of size map h j (Ω j ) = minimum (h g,j (Γ) and h ϕ,j (Ω)). (e) Definition of a unique size map with size gradation control parameter (fixed by the users between 1. 2 and 2. 15) resulting in a modified size map h j (Ω j ). (f) Adaptive rediscretization T j (Γ) of the domain boundary with respect to the size map h j (Ω j |Γ). (g) Adaptive remeshing T j (Ω) of the domain with respect to the size map h j (Ω). (h) Interpolation of mechanical fields F j-1 (Ω) of the old mesh on the new mesh T j (Ω). (i) Loop if necessary. The overall adaptive methodology is implemented in the Optiform mesher package (see Figure  1 ). It includes the remeshing strategy, the interpolation error and the field transfer from the old mesh to the new one.
For the simulations of sheet metal forming processes, a special procedure has been developed in order to execute Abaqus software  [47]  step by step. At each load increment, and after the convergence has been reached, the overall elements are tested in order to detect the fully damaged elements (elements where the damage variable has reach its critical value in all Gauss points). If so, the fully damaged element is removed from the structure and a new adaptive meshing of the part is worked out. The physical fields are interpolated from the old to the new mesh and the next loading step is worked out. worked out. The physical fields are interpolated from the old to the new mesh and the next loading step is worked out.
Results and Discussion This section is dedicated to the validation of the proposed numerical methodology to simulate tensile, shearing, compression, and sheet metal forming processes using Abaqus/Explicit coupled with adaptive remeshing procedure. The characterization of the behavior of a given structure (titanium, copper, steel, and aluminum alloys), needs the knowledge of the material parameters. The difficult and not yet satisfactorily solved problem consists to compute automatically the material parameters under concern, by comparison with the available experimental database. From a theoretical point of view, this defines a mathematical optimization problem using appropriated inverse analysis termed here as an identification procedure. Two different approaches (deterministic and statistical) exist to relate the problem of parameter identification to a least squares problem.
In the deterministic approach, the inverse problem is expressed in a relaxed form and one just trying to minimize a distance between the data from a model and the experimental measurements. In the statistical approach, the inverse problem is seen as the search for the set of parameters which maximizes the probability of carrying out the experimental measurement. In this study, the inverse constitutive parameter identification using Nelder-Mead Simplex algorithm is used  [53, 54]. The importance of this identification procedure is proportional to the increasing of the so-called advanced constitutive equations describing many coupled physical phenomena. The identification of the isotropic, isothermal elastoplastic constitutive equations accounting for isotropic hardening and ductile damage is based on the following steps  [22, 55] : • The used database contains only uniaxial tensile tests conducted on a given specimen until the final fracture; • The plastic parameters (A, B, C, n, 0 ε 1 =  ) are determined on the hardening stage when damage effect is very small and can be neglected.
• The damage parameters (Y 0 , α, β, γ, D c ) are determined using the softening stage of the stress-strain curve; The inverse identification procedure is performed by integrating the above constitutive equations on a single material point submitted to the tensile loading path using Matlab Software (Nelder-Mead Simplex algorithm) and the user's subroutine. Using material parameters determined above, the real specimen in tensile, shear or compression test is simulated by FEA and the global force-displacement curve is compared to the experimental one. If needed, the material parameters are adjusted and new FEA simulations are performed until the experimental and numerical force-displacement curves compares well. Some obvious phenomena, like strain localization and damage evolutions, were presented in order to test the capability of the proposed fully coupled model and adaptive remeshing scheme to simulate the sheet metal forming process like blanking, multi-point drawing, single incremental forming and deep-drawing.
According to the fact that the MPF operation is axisymmetric, only half of the billet section is modelled. Figure  20a  shows the upper and lower matrices of square punches with curved ends in the MPF press, 100 punches for an effective forming surface of 1700 mm × 1700 mm. The initial thick sheet of steel material is 1500 × 1500 mm 2 and 40 mm of thickness is discretized with linear tetrahedral finite element. Tools (punch and die) are supposed as rigid surfaces. The output of the simulation is given in terms of the sheet profile, the strain history as well as the punch forming forces. Comparison between the simulation results and the experimental data is made. The materials parameters of the used steel should be determined using the experimental results obtained from classical tensile tests conducted until the final fracture of the specimens.
Incremental Sheet Forming of Titanium Conical Shape The study will be focused on Numisheet'2014 benchmark for the manufacturing of a truncated cone shape made by single point incremental forming process (SPIF). The incremental step-down size (∆z) is the amount of material deformed for each revolution of the forming tool (similar to depth of cut in machining) which does not require dies  [62]. The advantages of this process are the flexibility of forming process for the manufacturing of complex shapes. Several applications of this process exist in the medical field for the manufacturing of personalized titanium prosthesis (cranial plate, knee prosthesis, etc. ) due to the need of product customization to each patient. The CAD package CATIA is used to create solid models of parts and the tool path is generated according to the obtained profile.
INTRODUCTION Gradual typing is typically viewed as a means to combine the agility of dynamic languages, like Python and Ruby, with the reliability of static languages, like OCaml and Scala  [Siek and Taha 2006]. But static and dynamic are merely relative notions, and several researchers have explored a more relativistic view. For example,  Disney and Flanagan [2011]  and  Fennell and Thiemann [2013]  develop languages where only information-flow security properties are enforced using both dynamic and static checking; Bañados  Schwerter et al. [2014 Schwerter et al. [ , 2016] ]  develop a language where only computational effect capabilities are gradualized;  Lehmann and Tanter [2017]  gradualize only the logical assertions of refinement types; and Jafery and Dunfield  [2017]  gradualize only refinements of sum types.