dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Indaco Biazzo ,  Matteo Bisardi ,  Elodie Laine ,  Anna-Paola Muntoni ,  Edoardo Sarti  and  Kai Shimagaki  for helpful discussions and assistance with the data. We especially thank  Francisco McGee  and  Vincenzo Carnevale  for providing generated samples from DeepSequence as in Ref. [34]. Our work was partially funded by the  EU   H2020 Research and Innovation Programme MSCA-RISE-2016  under Grant Agreement No. 734439  InferNet. J. T. is supported by a  PhD Fellowship of the i-Bio Initiative  from the  Idex Sorbonne University Alliance.
1. 1 Sample the first residue from P (a 1 ) 2. Sample the second residue from P (a 2 |a 1 ) where a 1 is sampled in the previous step. L. Sample the last residue from P (a L |a L-1 , a L-2 ,. , a 2 , a 1 ) Each step is very fast because there are only 21 possible values for each probability. Both training and sampling are therefore extremely simple and computationally efficient in arDCA. Supplementary Figure 1. Comparison of the Pearson correlation of two-point connected correlations and the log-likelihood between different orders. The value of the random order is the mean of 100 random orders with one standard deviation given as a vertical bar. Results are for Pfam families PF00014 (a), PF00072 (b), PF00076 (c), PF0595 (d), and PF13354 (e).
• Providing a penalty-backwarding approach to fines the related weight to the faulty or invisible nodes for pruning them after identifying as the weak weights and neurons We describe different Internet of Things applications and map their nodes on the neural networks to analyze QAC efficiency in decision-making with high accuracy. After defining an application, we collect a dataset based on the described sensors and their valid values to import them into the simulated nodes and estimate energy consumption and communication delay. This work utilizes the different rates of fault injection and the nodes' availability assessment to determine a penalty for the identified failure and invisible nodes. By estimating decision-making accuracy and comparing delay and energy before and after performing QAC, the study proves the proposed algorithm and method efficiency to dedicate a service provider to a requester.
Defining IoT-Based Applications and Collecting a Dataset To analyze the efficiency of the proposed algorithm, the study first defines different real applications for mapping their nodes onto a neural network's neurons to dedicate an SP to a requester, as shown in Figure  2a-c. We describe three different applications that are inspired by the real relationship between a set of components in connecting the Internet of Things. Two applications follow medical purposes and the other aims to support road amenities, which are defined to analyze the idea efficiency for the various conditions. As an example, we describe an intensive care unit (ICU) facilities of the hospitals as an IoT-based application that their nodes are included the oxygen condensation, humidity, temperature, and ventilation performance sensors, and some fog and cloud services such as wireless access point (WAP), as shown in Figure  2a.
The sensors, and fog and cloud layers nodes are mapped onto the neurons of a NN's input, hidden, output layers, respectively. We investigate the ICUs and recovery rooms in the different hospitals with various facilities to analyze the sensors' output information in standard and critical situations. This work collects a dataset based on monitoring the described sensors' valid information and generates random values, according to the received information from monitoring the hospitals' ICUs and recovery rooms  [50, 51]. We simulate the applications and inject the fault with different rates to estimate the service availability and penalty for faulty nodes by the CupCarbon simulator tool  [52]. As shown in Figure  3 , the collected dataset consists of the sensors' output information, fault injection rate, and availability, which is shared on Github (https://github.
com/yasamanhosseini/Quasi-mapping-project (accessed on 20 October 2021)) [53]. We investigate the ICUs and recovery rooms in the different hospitals with various facilities to analyze the sensors' output information in standard and critical situations. This work collects a dataset based on monitoring the described sensors' valid information and generates random values, according to the received information from monitoring the hospitals' ICUs and recovery rooms  [50, 51]. We simulate the applications and inject the fault with different rates to estimate the service availability and penalty for faulty nodes by the CupCarbon simulator tool  [52]. As shown in Figure  3 , the collected dataset consists of the sensors' output information, fault injection rate, and availability, which is shared on Github (https://github.
Simulation Results According to the mentioned purposes and contributions of the idea, we evaluate the dedicated SPs' availability and decision-making accuracy to validate QAC efficiency in improving the Internet of Things performance. This section addresses the issue and proves the highlighted role of decision-making based on the availability-aware algorithm, which reports communication delay and energy consumption caused by transferring data between IoT nodes. To analyze the quasi-mapping and decision-making performance, we first map an application's nodes onto a neural network's neurons and train it based gradient-distance method. The study describes the neural networks based on python co-design and utilizes the TensorFlow libraries to define the functions by the Jupyter notebook  [54, 55].
We estimate the accuracy of decision-making and standard deviation by repeating QACs operations when facing a new request and condition at the available time. Also, this work estimates the total energy and delay by comparing the mentioned parameters before and after pruning the weak weights and neurons, in which the output neurons are available as the service providers at the cloud level. To evaluate the mentioned parameters, we simulate the application nodes and describe their operation based on Java and Python co-designs as the scripts by the CupCarbon tool. The connections between the application's nodes are defined due to the relationship between them after mapping onto the NN's neurons. We utilize the router nodes, in which several nodes have to connect to one device (A located node on the fog or cloud level such as a wireless access point node).
The CupCarbon determines a specific area for connecting a set of nodes without using the router. Additionally, the simulator reports the energy consumption, total energy consumption, battery level, and total battery level for each simulated node (In different levels), which we assess the total energy consumption, including all of the reported mentioned parameters. As the setting of the simulator to report delay, we determine the unit of estimating total runtime using CupCarbon, which estimates the parameter based on the communication between nodes after describing their operations and reading input files as a dataset. By assessing the energy consumption and communication delay, we prove increasing the Internet of Things performance with minimal cost before facing invisible an SP. Figure  12a-e    Also, this work estimates the total energy and delay by comparing the mentioned parameters before and after pruning the weak weights and neurons, in which the output neurons are available as the service providers at the cloud level.
To evaluate the mentioned parameters, we simulate the application nodes and describe their operation based on Java and Python co-designs as the scripts by the CupCarbon tool. The connections between the application's nodes are defined due to the relationship between them after mapping onto the NN's neurons. We utilize the router nodes, in which several nodes have to connect to one device (A located node on the fog or cloud level such as a wireless access point node). The CupCarbon determines a specific area for connecting a set of nodes without using the router. Additionally, the simulator reports the energy consumption, total energy consumption, battery level, and total battery level for each simulated node (In different levels), which we assess the total energy consumption, including all of the reported mentioned parameters.
As the setting of the simulator to report delay, we determine the unit of estimating total runtime using CupCarbon, which estimates the parameter based on the communication between nodes after describing their operations and reading input files as a dataset. By assessing the energy consumption and communication delay, we prove increasing the Internet of Things performance with minimal cost before facing invisible an SP. Figure  12a -e illustrates improving total delay and energy by approximately 47. 4% and 64. 71% after pruning the weak weights and neurons with losing minimal availability in approximately 1-3%. As shown in Figure  12c ,d, we estimate the total delay and energy for the other related works (Without CAS) and compare them with the proposed idea when running the algorithm before pruning them (Before pruning with CAS).
Additionally, we reported the total delay and energy caused by communications between the IoT-application nodes, which illustrated highlighted role of pruning the related weak weights and neurons to the detected faulty nodes. To verify the reported results, we shared all logs of simulating the applications on Github (https://github. com/yasamanhosseini/Quasimapping-project (accessed on 20 October 2021))  [53]. By reviewing the proposed idea and reported experimental and simulation results, the work can support the different IoT-based applications to decision-making with high accuracy and availability.
For images with four channels and for the verification of SODA analyses, Fiji (ImageJ) software was used. Images were thresholded using MaxEntropy Auto-Threshold command. Within a ROI, all clusters from each channel were counted and annotated, and clusters were deemed colocalized when distance separating them was below 210 nm which is the optical resolution of the confocal with the 40x objective. The analysis was done independently by two experimenters.
Purpose In this paper, we present new tools to ease the analysis of the effect of variability and uncertainty on life cycle assessment (LCA) results. Methods The tools consist of a standard protocol and an open-source library: lca_algebraic. This library, written in Python and based on the framework Brightway2 (Mutel in J Open Source Softw 2(12):236, 2017) provides functions to support sensitivity analysis by bringing symbolic calculus to LCA. The use of symbolic calculus eases the definition of parametric inventories and enables a very fast evaluation of impacts by factorizing the background activities. Thanks to this processing speed, a large number of Monte Carlo simulations can be generated to evaluate the variation of the impacts and apply advanced statistic tools such as Sobol indices to quantify the contribution of each parameter to the final variance (Sobol in Math Comput Simul 55 (1-3): [271][272][273][274][275][276][277][278][279][280] 2001).
It allows them to focus on a reduced number of parameters to (1) reduce the uncertainty of the environmental impacts by improving the parameters' characterization and (2) reduce the environmental impacts themselves by modifying these parameters at the design stage. The key parameters also allow to build simplified parametric models: These are short arithmetic formulas based on a few parameters, suitable for rapid environmental impact assessments by non-experts. Classic LCA software does not provide, to our knowledge, advanced statistical tools for the application of GSA methods. To overcome these limitations, a growing community of LCA experts is now using programming languages, such as Python or R together with libraries dedicated to LCA, such as Brightway2  (Mutel 2017). While they provide higher flexibility for custom analysis, these tools require learning a programming language and specific syntax of custom libraries.
In that context, the INCER-ACV project, funded by the French Agency ADEME, provided practical methodology and tools for the LCA community to build reference LCA models, apply sensitivity analyses, and generate simplified models. INCER-ACV was built upon previous work by  Padey et al. (2013) , who first provided guidelines to develop parametrized inventories and explored how GSA could be used to identify key parameters. In practice, we wrote a protocol and developed a new library, lca_algebraic, to generalize and ease the analysis of uncertainties and variability in LCA by bringing symbolic calculus to Brightway2. Traditional LCA software uses a procedural approach with direct numerical processing to calculate impacts. Symbolic calculation uses a declarative definition and manipulates full arithmetic formulas for deferred evaluation.
lca_algebraic library We developed the Python library lca_algebraic to support each step of the protocol. This library is published under the open-source BSD license. It is freely available on the internet, together with instructions for its installation and an example script (https:// github. com/ oie-mines-paris tech/ lca_ algeb raic/). It is based on two other open source libraries: Brightway2, a common tool for LCA, and Sympy, a library for symbolic calculus. The lca_algebraic library uses algebraic computation instead of numerical evaluation. This choice provides several benefits for the implementation of the protocol. The functions provided by lca_algebraic serve to build the models needed to carry out the inventory analysis and impact assessment phases of LCA application, as well as to facilitate sensitivity analyses required in the interpretation phase.
Construction of the reference parametric model The construction of the reference parametric model (step 2 in Fig. 1 ) of the life cycle inventory is eased by algebraic calculus. The parameters are defined as Sympy symbols. Unlike standard Python variables, expressions involving Sympy symbols will result in the definition of a symbolic formula, instead of a direct numerical evaluation. The complete formulas are stored in the inventory database. This enables a seamless and compact definition of parametric inventories for users, by writing standard Python expressions and without the need for additional technical syntax.
This leads to the definition of one formula f k , per impact category k, as a function of the parameters P i only, as shown in Eq. 2. Third, the formulas f k are compiled by Sympy into lowlevel functions, capable of processing large vectors of data at once. Typically, the library can compute environmental impact results for a million scenarios in a few seconds, for a large model with 30 parameters. This is several thousands of times faster than the standard parametrization mechanism of Brightway2, capable of processing hundreds of samples per second. Brightway2 itself is already an improvement over (2) Impact k = f k (P i ) Fig. 1  Overview of the protocol for sensitivity analysis in LCA other software, which can usually perform only a few calculations per second  (Heijungs 2019). This processing speed allows to perform quickly a large number of Monte Carlo simulations and to apply global sensitivity analysis on these results, thus providing insights on the variance of the environmental impact results and the role of each parameter.
They are modeled internally as a linear combination of a set of exclusive boolean parameters. Boolean and enum parameters are used to model conditional choices. By using a declarative definition instead of an imperative construction (if … then … else), we build a single static inventory for all cases, controlled by the values of the parameters. This allows the factorization of background activities and the gain of performance described in the Sec. 2. 2. lca_algebraic also provides a set of helper functions, to: (1) Select background activities from the background database; (2) Duplicate background activities in the foreground and modify them; (3) Create new activities referring to background activities. Overall, the declarative syntax and help functions make it possible to write compact and understandable code that is easier to maintain.
Distribution of input parameters Once a reference LCA model has been built, the distribution of all input parameters should be characterized. We recommend several sources of information for this purpose: (1) academic literature, (2) manufacturer's catalogs, (3) market studies, (4) atlas of resources for energy systems (sun, wind), or (5) open-data and crowd-sourcing. lca_algebraic supports the modeling of seven types of probabilistic distributions: • Fixed: for excluding parameters from the statistical study • Uniform: uniform distribution within the range of definition a = a � × s � a � + b � + c � , b = b � × s � a � + b � + c � , c = c � × s � a � + b � + c � • Triangle: null probability at the boundaries and highest probability for a default value, to be defined within this range. This type of distribution is useful for parameters for which literature only provides a range of extreme values and a usual one.
Construction of the reference model For this case study, we implemented the parametric model of mono-crystalline photovoltaic systems developed by  Besseau (2019). This model includes 30 parameters. These parameters were identified by focusing on activities with significant relative contributions to the total impacts or activities that depend on highly variable parameters. This step of the protocol is strongly dependent on the evaluated system and has to be, thus, carried out by an LCA expert with good knowledge on the sector of application. Figure  4  shows a sample code of the definition of the reference model. New exchanges are defined as a standard Python dictionary. The keys of the dictionary refer to background activities selected from the database or to other foreground activities defined previously.
We used an enum parameter as the choice of the electricity mix used for the manufacturing phase, with probability weights corresponding to the current reality of the market. All other parameters are modeled as float values, with the following distributions: For most parameters of the model, we used triangle distributions, with bounds corresponding to the minimum and maximum values found in the literature and manufacturer's catalogs, and the mode (higher probability) set as the most common value found in current installations. We used a crowd-sourced database of domestic PV production (BDPV) to assess the capacity of production (kWh/ kWp/year) and modeled it with a normal distribution. The complete list of parameters is provided as supporting information.
Conclusions In this article, an operational protocol and the complementary library lca_algebraic were presented to help spread the application of sensitivity analysis approaches in LCA studies. The suitability of these tools to generalize sensitivity analysis was validated by both academic and industrial partners on distinct case studies exploring the uncertainties of three different energy systems: photovoltaic (presented here), floating wind turbines  (Pérez-López et al. 2020) , and (5) I Climate = 3. 79 ⋅ 10 -6 ModuleEfficiency 1. 58 ⋅ 10 3 PvLifetime + 9. 88 ⋅ 10 3 +4. 48 ⋅ 10 -3 SiliconElecIntensity + 0. 932 ModuleEfficiency ⋅ Producible 1 -0. 994 PvLifetime geothermal energy  (Douziech et al. 2021 ). For each system, complex parametric models were built and accurate simplified models were generated for a given applicability range.
This prominence corresponds to one anchorage-footpoint of a long east-west filament visible in Hα on 16 April, with dark equidistant bushes along its axis (data in BASS2000. com). This prominence is very active with a tornado-like structure at its top. This joint observation provides a good opportunity to address several questions. Is the rotating motion real or is it an apparent motion? How is the global magnetic configuration of a tornado? What is the nature of the plasma in tornado-like prominences? In this paper, we present the data used in this study (Sect. 2); the large-scale evolution of the prominence in multiple temperatures (Sect. 3); with a focus on the dynamics of the tornadolike prominence and its global magnetic configuration (Sect. 4). Then, we present the analysis of its plasma parameters using Hα and Mg ii lines and comparison with 1D radiative transfer models (Sect.
We did not use the C ii and Si iv data because of the artifact of the aperture of the telescope which masks partly the field of view  (Wülser et al. 2018). Eighteen very large coarse 32-step rasters were recorded during the five hours of observations. It took 16 minutes to perform one raster scan. During this time, eight SJIs were obtained (see an example in Fig. 1a ). The SJIs allowed us to study the fast transverse dynamics of the prominence material. The details of the IRIS observations are summarised in Table 1. The data were downloaded from the IRIS database 1. We used IRIS level-2 data corrected for the dark current, flat field and geometric distortion  (De Pontieu et al. 2014). IRIS had a clockwise satellite rotation angle of 51 degrees, such that the solar limb was parallel to the y-axis of the instrument.
MSDP To study the Hα line, we use the observations obtained with the MSDP spectrograph operating in the Meudon solar tower at the Paris Observatory. The observations were obtained between 12:05 UT and 16:35 UT with sequences lasting 15 minutes each and an exposure time of 160 ms. The observations consisted of individual bands, each of them covers a field of view of about 370 arcsec × 60 arcsec with a pixel size approximately equal to 0. 5 arcsec. Five adjacent bands recorded in 30s allowed to recover an image of the full field of view (370 arcsec × 270 arcsec) after processing the data with the MSDP software  (Mein 1977 (Mein , 1991;; Mein et al. 2001). Our prominence was only covered by two individual bands, one covering the main part of the prominence and the other band the top. Each band is reconstructed from elementary spectral images of the entrance window (open slit) of the spectrograph obtained along a wavelength range ±0.
SDO/AIA To study the spatio-temporal dynamics of the prominence with high-resolution, we use data obtained by the space-based mission Solar Dynamics Observatory (SDO;  Pesnell et al. 2012). The Atmospheric Imaging Assembly (SDO/AIA;  Lemen et al. 2012 ) instrument on-board SDO provides full-disc images that cover the solar atmosphere from the photosphere to the corona. The seven EUV channels (e. g. AIA 304 Å, AIA 171 Å, AIA 193 Å) provide observations with a nominal spatial resolution of 1. 2 arcsec. (pixel size 0. 6 arcsec) and a temporal scale of 12 sec. In our analysis, we use the AIA data obtained in the same time interval as the IRIS data. We use AIA 304 Å filter where the main emission comes from the He ii line at 303. 78 Å formed by scattering of photons on ionised helium at a temperature of log T = 4.
Evolution of the large-scale prominence in different temperatures The MSDP, IRIS, and AIA instruments provide a view of the prominence in multiple temperatures. Due to opacity and different ionisation temperature effects the prominence shape observed in each wavelength is different, revealing different structures. A&A proofs: manuscript no. aa   The global shape of the prominence observed in Hα and in IRIS SJI 2796 Å is very different (Fig. 1a  and Fig. 2a ). In Hα, the prominence consists mainly of two narrow and low columns with weak emission loops joining the column to the solar surface (Fig. 1c , Fig. 2a ) while the IRIS prominence has a wide base of 100 arcsec. parallel to the y-axis and a height of about 50 arcsec. (37500 km). The top of IRIS prominence is narrower with one or two horns depending on the time.