dataesr/scientific-paragraphs-categorization huggingface dataset:
D. from the  European Research Council  under the  Seventh Framework Program  ( FP7/2007 2013/ ERC grant  agreement  281287 ) and to R. K. from the  European Research Council  under the  Horizon 2020 Program  ( ERC grant  agreement  260822 ). TG was supported by a PhD fellowship from  CEA ,  ANR  and a  Fondation ARC  young researcher grant.
Data and software availability Raw data are available at http://dx. doi. org/10. 17632/3hcgk2n27j. 1 HiC whole genome and high-resolution maps are available at http://dx. doi. org/10. 17632/2hdp26c722. 1 and HiC sequencing data are available at https://www. ncbi. nlm. nih. gov/bioproject/PRJNA542278 Table S1. Yeast Strains Used in This Study. Related to STAR Methods. Strains are from the W303-1a background (ade2-1 trp1-1 ura3-1 leu2-3,112 his3-11,15 can1-100 RAD5) except strain 234-1a from the RM11 background (leu2-∆ ura3-∆ ho::loxP-KANr-loxP).
We present the framework of the modeling package IonoSeis. This software models Global Navigation Satellite System (GNSS) derived slant total electron content (sTEC) perturbations in the ionosphere due to the interaction of the neutral atmosphere and charged particles in the ionosphere. We use a simplified model to couple the neutral particle momentum into the ionosphere and reconstruct time series of sTEC perturbations that match observed data in both arrival time and perturbation shape. We propagate neutral atmosphere disturbances to ionospheric heights using a three-dimensional ray-tracing code in spherical coordinates called Windy Atmospheric Sonic Propagation (WASP3D), which works for a stationary or non-stationary atmospheric models. The source of the atmosphere perturbation can be an earthquake or volcanic eruption; both couple significant amounts of energy into the atmosphere in the frequency range of a few Millihertz.
These acoustic waves travel through the atmosphere and couple into the ionosphere to produce local perturbations in the electron density. We are currently working to improve our ability to model the GNSS-derived TEC response to acoustic and infra-gravity wave disturbances sourced from Earth's surface and the atmosphere. The most successful attempts to model near-source earthquakes or volcano-induced ionospheric disturbances have been based on acoustic ray tracing combined with an atmosphere-ionosphere coupling model. Thus, over the past few years, we have been developing the software package IonoSeis to model CIDs systematically registered with GNSS receivers following the occurrence of shallow earthquakes. IonoSeis combines existing and original pieces of code into a single package to rapidly model the electron density from 100 to 500 km altitude and computes TEC signals.
g. ,  [52] ). This package solves the linearized hydrodynamic equations for a compressible fluid in the high-frequency approximation. We do not model acoustic resonances due to multiple bounces in the low atmosphere waveguide. We also do not currently model lower frequency gravity waves. We only model the acoustic wave with IonoSeis. From the WASP3D ray tracing, we determine the arrival time (t w ), wave vector ( k w ), and an amplification factor (A w ) related to geometrical spreading and kinetic energy conservation. Note that WASP3D-derived variables contain the subscript w. After computing these parameters along rays propagated throughout the 3D model, the parameters (t w , k w , and A w ) are linearly interpolated to the 3D ionosphere grid such that we can write the neutral particle velocity as a function of space and time: v n ( r, t) = v( r, t)A w ( r) k w ( r, t), (7) where v defines the time-history of acoustic wave amplitude (i.
5. Perform LOS integration for a given receiver and satellite pair-create SAC formatted data files that contain the LOS integration for both the background TEC and the CIP TEC (i. e. , Equation (  3 )). The user can select the time step used in time-integration (Step 3); this is usually 1 or 5 s. It is possible to interpolate or decimate the numerical data to match observed data (e. g. , 30 s GNSS data are common), but nothing for this is yet implemented in IonoSeis. The user can set parameters at each step in a corresponding BASH script that is used to run each step (Figure  1 ). Below, we demonstrate the code capabilities with a dataset from the recent Sulawesi earthquake in Indonesia.
Example: 2018 Sulawesi Earthquakes To demonstrate the current capabilities of the IonoSeis package, we present a comparison of observed and modeled sTEC data for the shallow (20 km depth) M w 7. 5 event that occurred on 28 September 2018, Sulawesi, Indonesia. This sequence consisted of two cascading events (M w 6. 1 and M w 7. 5 earthquakes) that struck the city of Palu on the Indonesian island of Sulawesi  [55]. A remarkable and rare feature is the fact that the rupture propagated at super-shear speed  [56]. There was liquefaction, landslides, and a near-field tsunami as a result. We show the location of the earthquake epicenter in Figure  2  with the beach ball diagram that indicates this was a dip-slip earthquake. We downloaded the Rinex file for GNSS receiver BTNG from UNAVCO. This is the only dual-band GNSS receiver within 600 km of the epicenter available at UNAVCO.
Discussion The IonoSeis software package can be used to rapidly model sTEC due to a point source on Earth's surface. The code has now been used in a few studies (e. g. ,  [35, 36] ), and we continue to make improvements, including reducing the approximations made in the physics. As is evidenced by the modeled data in Figure  3 , the synthetic model predicts the electron density perturbations 1. 3 min after the observed perturbation (Figure  3 ). This has been noted previously (i. e. ,  [35] ), and research is underway to determine the cause of this arrival time discrepancy. This delay increases with distance between the event epicenter and the ionospheric sounding point along the LOS. It is possible that this delay is due to an inaccurate velocity model or other physics at high altitudes, which rays must pass through to make it to epicentral distances of 500 km or more.
Conclusions We present the framework of a modeling code called IonoSeis. This software enables the computation of GNSS-derived sTEC perturbations due to earthquakes and tsunamis (i. e. , CIDs). We demonstrate that the code reproduces the sTEC signal recorded at GNSS station BTNG to within 1. 3 min for the M w 7. 5 2018 Sulawesi, Indonesia event. The method is based on ray-tracing and a simplified coupling of the neutral atmosphere to the ionosphere. In an ad-hoc way, the modeling accounts for viscous dissipation and dispersion due to propagation in the rarefied atmosphere at ionospheric heights. The package accurately honors the geometry of the moving satellite-receiver line-of-sight integration. This new software package can be used in the future to study processes related acoustic coupling from the lithosphere into the atmosphere, which can eventually perturb the ionosphere.
Funding: L. M. R. and F. Z. acknowledge support for this research from the  French Space Agency CNES  ( Centre National d'Etudes Spatiales ) and  Université Côte d'Azur Investissement  d' Avenir project  funded by  ANR  ( Agence Nationale de la Recherche ), reference number  ANR-15-IDEX-01. The authors acknowledge two anonymous reviewers who helped improve the quality of this article. The authors thank all contributors to the reference models (IRI2106, IGRF, and  HWM14 ) for providing these open source products to the community. The authors thank  David Themens  and the  ECHAIM  project of the  Canadian High Arctic Ionospheric Network  for providing the F10. 7 data and the  GFZ German Research Centre for Geosciences  for providing Kp index data. This material is based on services provided by the GAGE Facility, operated by  UNAVCO, Inc.
Introduction: Assess the cost-effectiveness (US healthcare payer perspective) of sarilumab subcutaneous (SC) 200 mg ? methotrexate versus conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) or targeted DMARD ? methotrexate for moderate-to-severe rheumatoid arthritis (RA) in adults with inadequate response to methotrexate. Methods: Microsimulation based on patient profiles from MOBILITY (NCT01061736) was conducted via a 6-month decision tree and lifetime Markov model with 6-monthly cycles. Treatment response at 6 months was informed by a network meta-analysis and based on American College of Rheumatology (ACR) response. Responders: patients with ACR20 response who continued with therapy; non-responders: ACR20 non-responders who transitioned to the subsequent treatment. Utilities and quality-adjusted life-years (QALYs) were estimated via mapping 6-month ACR20/50/70 response to relative change in Health Assessment Questionnaire Disability Index score (short term) and based on published algorithms (long term).
Treatment Comparators The treatment comparators in the model included bDMARDs and the tsDMARD, tofacitinib. In addition, while the population was patients with inadequate response or intolerance to csDMARDs, csDMARDs were also included to compare results with previously published US cost-effectiveness analyses. Efficacy and costs of the tocilizumab SC treatment regimen assumed in the model were based on a 65% q1w and 35% q2w weighted average of the two available dosing regimens. This was consistent with clinical guidance on usage and on results of claims databases analyses.
Treatment Duration After the initial 6 months, transitions between health states instead of the ACR response were based on long-term treatment discontinuation. Inputs for durations of long-term treatment were based on a de novo analysis of the Canadian RHUMADATA registry (https://rhumadata. wordpress. com/) (Supplementary Fig. 1 ). Parametric models were fitted to the time to treatment discontinuation data by drug class (TNFi, IL-6 inhibitors and other modes of action) and for the combined drug classes. Gompertz, generalized gamma and log-normal models had the best fit after consideration of the Akaike information criterion and Bayesian information criterion. Based on visual comparison of the observed and predicted curves and probability plots, Gompertz distributions were selected as the best fits.
Utilities and Mortality QALYs were based on patient life expectancy and utility weights applied to the life-years accrued. Utilities were calculated via an equation fitted on data from several trials for adalimumab  [25, 26] : utility = 0. 76 -0. 28 9 HAQ-DI ? 0. 05 9 female and according to HAQ-DI score and gender at each model cycle. Additional utility equations were tested in the sensitivity analyses (Supplementary Table  1 ). The estimation of mortality was based on US life tables adjusted to RA according to changes in HAQ-DI  [27] , with general population mortality rates for males and females in the US obtained from the National Vital Statistics Reports from 2015  [28]. Adverse events were not separately evaluated in the model as their effects were included in the utility equations.
As for all cost-effectiveness models, the use of multiple sources and assumptions leads to important limitations. Similar to other longterm CEAs in RA  [40] , the limitations of the present study stem from the uncertainty inherent to the limited, and often conflicting, evidence regarding short-term treatment response rates and long-term benefits of bDMARDs. For example, the 6-month response rates are based on an NMA, which can be affected by a heterogeneous trial population and placebo arm response rates that have proven to be heterogeneous across trials. In the long-term, due to lack of long-term disease activity data, after the initial 6 months, long-term treatment discontinuation from real-world evidence (RHUMA-DATA registry) was used as a proxy for determining the efficacy of treatment in reducing disease activity.
Data from the Canadian RHUMADATA registry were used in lieu of US data. In addition, the present model assumed a linear progression for csDMARDs, while separate studies have assumed non-linear progression  [21]. Because of linear progression, worse HAQ-DI scores were modeled in the long-term, therefore resulting in relatively worse QALYs and higher costs for csDMARDs compared with a non-linear approach. This is an important limitation compared with csDMARDs; however, since the population in this study is csDMARD-IR, a comparison is only conducted to compare results with prior publications  [41] , most of which use linear progression. If non-linear progression was used here, the results for this comparison would most likely change. Consistent with previously published costeffectiveness models in RA  [21] , the initial 6-month treatment response was based on ACR 20/50/70 response criteria and assumed to be independent from the line of treatment.
Peter Gal is an employee of a company which has received consulting fees from Sanofi/Regeneron. Andreas Kuznik is a current employee of and stockholder in Regeneron Pharmaceuticals, Inc. C-I Chen is a current employee of and stockholder in Regeneron Pharmaceuticals, Inc. Marie Fournier is an employee of and stockholder in Sanofi. Clare Proudfoot is a former employee of and current stockholder in Sanofi and current employee and stockholder in Novartis. Kaleb Michaud has received grant funding from Pfizer and the Rheumatology Research Foundation. Compliance with Ethics Guidelines. This article does not contain any studies with human participants or animals performed by any of the authors. Data Availability. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
To this end, efficient numerical tools to evaluate theta functions are needed. The algcurves package distributed with Maple originally due to Deconinck and van Hoeij, see  [4, 6] , has integrated theta functions as outlined in  [5]  starting from Maple 7. Note that the algcurves package is currently being transferred to a new platform  [39]. A purely numerical approach to compact Riemann surfaces via algebraic curves was given in  [10] [11] [12] [13] [14] [15] , see  [22, 23]  for applications to integrable PDEs. For a review of the current state of the art of computational approaches to Riemann surfaces, the reader is referred to  [3]. The basic idea of the algorithms for theta functions in  [3]  is to approximate the expression (1) via a truncated series 1. Obviously the convergence of the series (1) depends on the bilinear term, more precisely on the shortest vector N min of the lattice Z g equipped with the inner product 1 For alternative approaches based on arithmetic-geometric means and Newton iterations, see  [8, 28] , which were developed for studying modular functions, i.
Since we work in double precision, we typically choose ǫ = 10 -16 , i. e. , of the order of the smallest difference between floating point numbers that can be handled in Matlab. Note that in contrast to  [5] , we do not give a specific bound for each N i , i = 1,. , g, i. e. , we sum over a g-dimensional sphere instead of an ellipsoid. The reason for this is that it does not add much to the computational cost, but that it simplifies a parallelization of the computation of the theta function in which we are interested. Taking into account that we can choose z in the fundamental domain of the Jacobian because of (  12 ), we get with (2) for the Riemann theta function the estimate (  13 ) N ǫ > - ln ǫ πy min + 1 2. Thus the greater the norm of the shortest lattice vector, the more rapid will be the convergence of the theta series.
Matlab timings have to be taken with a grain of salt since they depend crucially on the coding, in particular on how many precompiled commands could be used. Still in applications it is important to know how long a certain task takes on a given computer. For the above example, the LLL code is not very efficient, but converges in roughly 1 ms. The SVP code takes in this case 4-5 times longer, which is still completely negligible compared to what can be gained by applying the above algorithm in the computation of a theta function associated to this surface. The above example is in fact typical. If we consider an example of even higher genus, the curve (26) f (x, y) := y 9 + 2x 2 y 6 + 2x 4 y 3 + x 6 + y 2 = 0 of genus 16, we find a similar behavior. Using Siegel's algorithm on the Riemann matrix for this curve computed with the code  [15] , we find that the variant with the LLL algorithm converges within three iterations.
Simulation Monitoring and Control The core of the developed Digital Twin is a Virtual Machine Tool module based on SAMCEF Mecano  [4]  finite element (FE) software that is able to integrate the toolpath simulation and process effects (Fig. 2. 3). This integration leads to a complete understanding of machine tool dynamic performance during real machining processes and will allow the prediction of the most important features like surface roughness and form errors. The integrated process models include features like cutting force estimation, stability analysis and even surface roughness estimation. The usage of ModuleWorks libraries provides advanced capabilities for tool-workpiece engagement calculation and also the possibility to visualize process results over workpiece geometry, improving user experience.
Virtual Machine Tool The energy efficiency module provides the energy consumption of machine tools. To gain a most wide transparency, the energy consumption is observed on a component level. This leads to the possibility to design energy efficient machine tools and processes. The Simscape model library to be developed will be the basis for the configurator. The physical input parameters are obtained from data sheets: if detailed characteristic curves are available, they are directly used for simulation; if less information is available, the behaviour will be modelled. The link with the real world is obtained, in one direction, by the usage of the fleet-level knowledge stored in the cloud for validation (inputs and outputs for the simulations) and, in the other direction, by uploading simplified models or simulated results and parameters to the cloud ("reference" machine values).
Local Monitoring and Control System The most important variables of the machine tool and machining process performance will be monitored and managed at machine and fleet level. For monitoring purposes, ARTIS Genior Modular is installed in the machines. This device allows direct exchange of CNC data at high real-time rate. Depending on the CNC model installed in the machine, CANOpen or Profibus communication protocol can be applied. Additionally, an ARTIS OPR unit-Offline Process Recorder-will be used and connected via Ethernet to the Genior Modular to store the real-time data capturing and also to receive OPC data in non-real-time as a second data source. The OPR will act as a gateway by pushing real-time and OPC data to the remote or local service hosting the fleet-based database, in this case, KASEM ® (Fig.
2. 4). The real-time local data management does not only monitor data, it allows implementing some intelligence that can be used to detect anomalous performance or even directly act on the machine controller to optimize its behaviour. For example, ARTIS process monitoring capabilities are used to safeguard production  [6]. Spindle torque monitoring on all machines will be used to detect abnormal behaviour during processing. This could be a sudden action in case of tool breakage, delayed stop in  the case of tool breakage during tapping operation or tool wear events. Currently, process monitoring is done based on a learning stage and, hence, can be only applied to medium-to-large batch sizes. Within Twin-Control, the application of parts of the developed Digital Twin in the real world is proposed.
The proposed monitoring hardware is the best environment to do this integration. For example, the integration of process models with real monitored data could be used for process monitoring  [3] , replacing the learning stage when it is not feasible, for example, in aerospace applications where batches are normally small. A new energy monitoring system that avoids the extensive use of hardware (hall) sensors has been also implemented, leading to a drastic reduction in investment costs for an energy monitoring solution on component level. Using CNC and PLC data, the module will be able to determine energy consumption per component. Other similar features like the integration of CNC simulation capabilities have been developed in the project and will be covered in specific chapters of this book.
e. system health monitoring level). Starting from the definition of the system functioning in relation to its environment, the formalization of the system malfunctioning analysis is then considered. The corresponding knowledge (i. e. functioning and malfunctioning models) is incrementally built within a knowledge-based system that supports a structured and hierarchical description of the fleet. This formalization enables to reduce the effort for data consolidation within system health management as well as for knowledge aggregation within fleet health management, since it enhances understanding of the impacts between and within levels. The fleet-level data management platform proposed for the Twin-Control project is based on KASEM ® (Knowledge and Advanced Service for E-Maintenance)  [8]. It is a collaborative e-maintenance platform, integrating engineering, proactive maintenance, decision-making and expertise tools [9] and used in former Power-OM project [10].
Data Exchanges with Machine Tools Twin-Control's fleet platform is in the "cloud", i. e. on the Internet, and hosted to a predict secure server. Machine tools push data to the server to feed the knowledge base. Details of the proposed monitoring and data management architecture are presented in Sect. 3. 1 of this book. The concept of fleet data management is presented in Fig. 2. 7. The different machines are monitored using the correspondent. A detailed architecture for each of the use cases of the project will be presented in Sect. 3. 1. Figure  2. 8 summarizes presents the architecture proposed for Twin-Control fleet data management. All collected, computed and extracted information by the platform will be stored in the knowledge base. For each machine tool, expected inputs of the fleet platform can be classified in two groups: -Automatic inputs that are regularly pushed to the fleet platform.
Event's Sequence Analysis Events include both alarms from local monitoring and alerts from fleet drift detection. This module will oversee the interpretation of event sequences, i. e. the interpretation of frequency and order of the events. Information about event types and the time stamps of their occurrence will be used to analyse the time series data and thus finding dependencies between different alarms. Generic causal models, i. e. models shared by all machines of the fleet, will be used during the analysis. A causal model represents the relationship between the different event types. These causal relationships can be used to recognize an event sequence on a given period. Once the sequence is identified, causal models allow to identify the sequence root cause (diagnosis) but also to know what could be the next event (prognostic) of the sequence.