dataesr/scientific-paragraphs-categorization huggingface dataset:
For the sake of operational stability, Iprod works on a limited number of operating systems (OS) as the basis for its production environment (SLES 11 and Ubuntu 12, 14 LTS at that time). Using different tool chains not available on the shelf in the different supported Linux distributions was a problem for an operation team which needed to constantly limit its technical landscape in order to maintain the same level of service quality. Docker (https:/www. docker. com/) was still in its early phase in 2015, but was the technical solution Iprod used to stick to its principles and to experiment in an agile way. Docker (which is a Linux technology) is a containerization solution. It allows the user to embed its application (e. g. , Unitex, CasEN) and its running dependencies (e. g. , the required part of an OS in a precise version) into an image, which can be used everywhere Docker is installed.
g. , software running only on Cent OS 7) from the location they have to run (e. g. , Debian 9), provided that the location is running under a Docker compatible OS (Linux but also Mac OS and Windows using user transparent Linux Virtual Machines), and that the embedded software is not compiled for a specific CPU instruction set which is not universally available. Cloud and Docker are often associated but some scientific communities quickly realized that Docker could be used as a convenient way to package and deploy "not so easy to build" software on different locations ranging from another user workstation up to grid environments (https:/biocontainers. pro/). The fact that other containerization solutions are available apart from Docker and Singularity is worth mentioning. Singularity is more recent than Docker (2015 vs.
2013), fits better in the High-Performance Computing (HPC) world as it does not require special privileges for the user needing to run a container (this is a no-go in highly shared environments), and integrates better in the HPC ecosystem  [22]. The main advantages of using Docker in the Unitex/CasEN project were the following: Iprod was able to use technical environments normally unsupported (ahead of time) in its production environment and Docker offered a convenient way to automate the build of the software stack made up of the assembly of Unitex, Ergonotics add-ons and CasEN. We can add the possibility of building easily different assembly combinations (e. g. , different versions of Unitex associated with different versions of CasEN, built using different toolchains), the opportunity of using these different (hard to build) combinations in different locations (development workstation, server) and the ability to maintain the Unitex/CasEN workflow over the years (as Docker helps the user to describe fully its building process from scratch).
• Step This two-step construction process had the advantage of saving the build time when assembling a different version of CasEN with the same Unitex base image, allowing us to effectively compare their outcomes or to highlight regressions cases. Without going into details, this build process was designed at the beginning of 2015 and would benefit from being improved. The reason is that since then, Docker has added a lot of new functionalities (https://docs. docker. com/develop/develop-images/multistage-build/), particularly the multistage build, as well as a smart solution to take corporate proxies into account without giving the proxy knowledge to the docker image (https://docs. docker. com/network/proxy/). Also it would be possible to use Singularity instead of Docker (The interested reader can refer to https://github.
com/ecirtap/unibat/ to have a look at the software stack that was used to setup this build process). The unitex/runable image uses four parameters: The number of threads to use to apply CasEN to different documents in parallel, the directory containing the documents to process, the directory which will contain the results (standoffs) and the name of the CasEN script to use (there is a script for the English and French cascades in each of the CasEN artefacts). The only shared resources between the running container and the host are the two directories mentioned above. A report is produced in real time during computation, including the version of the different components used, the time required to compute the standoff for each document and the output of Unitex cascades. As the computation is multithreaded, if one of the processed documents generates a segmentation fault, all the documents processed at the same time are aborted.
Introduction The abstract interpretation framework  [5]  enables the development of sound static analyzers by inferring and proving invariants on reachable states of programs. Invariants in the scope of abstract interpretation are elements of a lattice called an abstract domain. Most domains focus on numeric or pointer variables. By contrast, we propose here an abstract domain for variables whose values are tree data-structures. Tree values appear natively in some languages (such as OCaml) and applications (such as the DOM in web programming) or can be encoded through pointer manipulations (as in C). Trees can abstract terms in logic programming. A tree domain can also be useful to collect symbolic expressions appearing in a program. Used memory zones. Prog. 1 describes an append function defined in the C language, this function adds an integer at the end of a linked list.
2 can be expressed by r = 1 + 1/(1 + 1/. 1. ) with depth n. Please note that to infer such results we need to express numerical relations between the size of trees and the numeric variables from the program. Numerical environment. Consider now the OCaml Prog. 3, we want to prove that the assert false expression is never reached. This program builds a list of size 2 * n with alternating values x + 1 and x -1. The assertion states that the head of the list is x + 1. After the definition of t there are two types of reachable states. (1)  Those that have not gone through the loop (t → [], x → Z, n → 0), and (2) those that have gone through at least one iteration of the loop: (t → [a 1 ;a 2 ; a 3 ;. ], x → α, n > 0, a 1 → α + 1, a 2 → α -1, a 3 → α + 1), where α ∈ Z. Therefore we need to be able to keep numerical relations between the parametric and unbounded number of numeric values appearing in t and numeric variables from the program.
Conclusion In this article we presented a relational abstract environment for sets of trees over a finite algebra, with numerically labeled leaves. We emphasized the potential applications of being able to describe such trees: description of reachable memory zones, tracking symbolic equalities between program variables, description of tree like structures. In order to improve the precision of the analysis while not blowing up its cost we defined a novel abstraction for sets of maps with heterogeneous supports. This numeric abstraction is able to represent optional dimensions in numerical domains without losing relations with optional variables. All domains presented in the article were implemented as a library in the Mopsa framework. Reduce operator The following reduce operator (Algo. 6) is parameterized by a score function.
This result gives insight about the spatial variability of air ion Last but not least, the CSD plots exhibit two local maxima for discrete frequencies of 10 and 20Hz. These spikes are seen for the NAI data, for both 30 inches and 220 inches separation and for the PAI data using the 220 inches separation, but not for the PAI data while considering a separation of 30 inches. These spikes are most likely due to an unknown anthropic noise beginning after February 15, 2018. This confirms that Gerdien tubes are prone to anthropic perturbations. One may wonder how the CSD between two datasets evolves as the separation between stations increases. While additional separations between parallel sensors (e. g. 10m, 100m, 1 km) yet remain to be tested, we have taken advantage of the data collected by the QF array to compute the CSD between time series acquired with two PAI sensors separated by about 28 miles, i.
The modified air ions counters used by QF are automatically turned off every day for about 10 min, from 19:55 to 20:05 UTC. We used the non-zero voltage measured during the off-time to correct air ion data. In Figs. 8  and 9  we present our results for the PAI data collected at stations 870 and 833, respectively located in Benicia and in Bitterwater, California, US. In these Figures, the top plots depict the raw PAI data acquired over 92 days. In order to produce these plots, the voltage returned by the digitizer was averaged over 2 h non-overlapping windows. The vertical line indicates the date of the earthquake associated with each dataset: the August 24, 2014, M6 South Napa Earthquake in the case of Benicia (Fig. 8 ) and the October 21, 2012, M5. 3 Fresno earthquake in the case of Bitterwater (Fig.
9 ). The broken horizontal line corresponds to an amplitude of 0V. The middle plots show the offtime voltage measured while the Gerdien capacitor was being turned off: after averaging the voltages measured by the digitizer during the off-time interval, we end up with a single value per day. Finally, the bottom plots show the corrected PAI data, i. e. the raw PAI data from which the daily off-time voltage was subtracted. In both examples, subtracting the off-time voltage from the raw PAI allowed to recover positive voltages. However, it should be noted that for other datasets, applying this step yielded negative voltages, which made no physical sense. This could mean that either the fan or the polarizing voltage was not turned off properly during the off-time interval; it could also mean that zeroing the data every 24 h is not enough and that more frequent corrections should be applied.
The corrected data (bottom plots in Figs. 8  and 9 ) exhibit strong diurnal variations. For each of the two datasets discussed here, we plotted the daily PAI curves for the first two weeks of data in the same graph; the results are given in Fig. 10a  and b. The daily minimum is observed between 20:00 UTC and 22:00 UTC (13:00 LT and 15:00 LT) for the Benicia dataset, and between 22:00 UTC and 00:00 UTC (15:00 LT and 17:00 LT) for the Bitterwater datatset. The daily maximum is observed between 8:00 UTC and 12 UTC (1:00 LT and 5:00 LT) for both sites. These results should be put in perspective with those of  Hõrrak (2003) , who conducted air ions measurement at a continental station in rural Estonia; the authors observed a minimum of cluster ions  (0. 5-3. 2 cm 2 s -1. V -1 ) in the afternoon (around 18:00 LT) and a maximum cluster ions concentrations during early morning (06:00-07:00 LT).
Unipolarity coefficient analysis In order to control the quality of the air ion data acquired using the QF array, we computed the unipolarity coefficient for several datasets. The unipolarity coefficient is defined as the dimensionless ratio between PAI and NAI concentrations: = + - x n n / (6) Where + n is the PAI concentration and - n the NAI concentration [cm -3 ]. Normal unipolarity coefficient values are often reported to be slightly greater than 1, because the surface of the earth is negatively charged; indeed, according to the Global Electric Circuit (GEC) concept, there is a net transfer of negative charges from the world's thunderstorms and shower clouds to the surface. For instance,  Hõrrak (2003)  reports values of 1. 11 and 1. 15 during the warm and cold season, respectively, while  Černecky et al.
Data filtering based on dew point temperature analysis In order to test a more physics-based approach than simple RH thresholding, we attempted to process air ion data based on dew point temperature. Dew point is the temperature at which a vapor (such as water) begins or would begin to condense; in other words, it is the atmospheric temperature below which water droplets begin to condense and dew can form. Dew point can be derived from RH and air temperature. Several formulas allow computation of this parameter; for instance, the following relationship is valid when RH exceeds 50%: = - - T T RH 100 5 dp (7) where T dp is the dew point temperature [ o C], T is the atmospheric temperature [ o C] and RH is the relative humidity  [%]. According to this formula, the dew point is never greater than the temperature of air as the relative humidity cannot exceed 100%.
Both air temperature and RH data were acquired by the QF array at the same sampling rate as the air ion concentration, which allows to compute the dew point for each individual sample. In the following, we use the so-called Magnus formula  (Lawrence, 2004)   water should begin to condense on the sensor plate when the air temperature decreases to reach the dew point temperature. However, in the example given here, this never happens, yet the PAI sensor saturates. : = - T cγ b γ dp (8) Where: = + + γ R H bT c T ln( /100) (9 In order to explain this phenomenon, we assessed how the uncertainties on individual temperature and RH measurements affect the derived dew point temperature. We resorted to the approach suggested by  Lin and Hubbard (2004) : following the method of uncertainty analysis introduced by the National Institute of Standards and Technology (NIST, 1994), the authors express the expanded uncertainty of the derived dew point temperature U Tdp as a function of the standard un- certainties associated with the manufacturer's specifications for atmospheric temperature and RH sensors, labeled u(T a ) and u(RH): ⎜ ⎟ ⎜ ⎟ = ⎡ ⎣ ⎢ ⎛ ⎝ ∂ ∂ ⎞ ⎠ + ⎛ ⎝ ∂ ∂ ⎞ ⎠ ⎤ ⎦ ⎥ U k T T u T T RH u RH ( ) ( ) T dp a a dp 2 2 2 2 1 2 dp ( 10 ) In equation (  10 ), k is a coverage factor, set to k = 2, such that the uncertainty level for U Tdp has a 95% confidence interval.
Pollution could also impact our measurements at this specific station, as station 806 is located just a few meters away from a main road (North Imperial Avenue). In any case, this brief analysis highlights the difficulty of applying a dew point-based data filtering strategy to the air ion concentration time series acquired by the QF array. Considering the challenges posed by air ion data processing, we will introduce instrumental developments that could be implemented to prevent condensation from forming on the Gerdien capacitors in the discussion below (Section 6).
(2016 Kappler et al. ( , 2017) )  for magnetic data: the low Z-score that was obtained suggests that data quality should be improved and/or that more stringent data processing steps should be applied to the raw time series. While several strategies were tested to filter out untrustworthy data from the historic dataset acquired by QF over the past 12 years, a convenient solution for the future would be to modifiy the air ion counters so that high RH does not create leakage currents through condensation. A simple and cost-effective solution would be to install heaters (resistors) near the opening of the Gerdien capacitors to prevent dew formation. Preliminary tests conducted in the lab and in the field suggest that this modification may prevent the sensors from saturating, even when exposed to high RH levels.
Proteins are described using PB from which we have previously developed a sequence alignment procedure based on dynamic programming with a dedicated PB Substitution Matrix. We improved the procedure with a specific two-step search: (i) very similar regions are selected using very high weights and aligned, and (ii) the alignment is completed (if possible) with less stringent parameters. Our approach, iPBA, has shown to perform better than other available tools in benchmark tests. To facilitate the usage of iPBA, we designed and implemented iPBAvizu, a plugin for PyMOL that allows users to run iPBA in an easy way and analyse protein superimpositions. Conclusions: iPBAvizu is an implementation of iPBA within the well-known and widely used PyMOL software. iPBAvizu enables to generate iPBA alignments, create and interactively explore structural superimposition, and assess the quality of the protein alignments.
A SA consisting of 16 pentapeptide conformations, called Protein Blocks (PBs), was developed in our group  [5]. Based on this library, a protein superimposition approach was developed. A substitution matrix for PBs  [6]  was generated based on all PB substitutions observed in pairwise structure alignments in PALI dataset  [7]. The superimposition was carried out with simple dynamic programming approaches  [8]. We recently improved the efficiency of our structural alignment algorithm by (i) refining the substitution matrix and (ii) designing an improved dynamic programming algorithm based on preference for well-aligned regions as anchors. This improvement (improved Protein Block Alignment, iPBA) resulted in a better performance over other established methods like MUSTANG  [9]  for 89% of the alignments and DALI for 79%  [10].
Once the alignment process is over, results are displayed as two new protein objects in PyMOL. The two new objects correspond to the two aligned structures. A new window containing different alignment scores (e. g. , GDT-TS, RMSD, see Methods) and an interactive sequence alignment manager is also displayed. Both residue and Protein Block sequences of aligned structures are given. Users can highlight any residue or PB of one or both sequences. Highlighting selects the residues directly in the 2 new aligned protein objects created in PyMOL 3D window. This interactive functionality provides an efficient way to explore sequence and structural alignment. Figure  1  shows an example of structural superposition of two proteins of the monooxygenase protein family using iPBAvizu plugin: Cyclohexanone Monooxygenase (CHMO, PDB code 3GWD) and Phenylacetone Monooxygenase (PAMO, PDB code 1W4X)  [18].
The webserver (http://3d-blast. life. nctu. edu. tw/) needs Chime applet, and users do not have a direct access to simple alignment results. SA-FAST was developed for the same purpose  [23]  but was based on FASTA algorithm. Structural alphabet was generated using a Self-Organizing Map, taking into account the most frequent clusters. The final benchmark was done using 50 proteins. The webserver (http:// bioinfo. cis. nctu. edu. tw/safast/) is very fast. However, it is not possible to do simple pairwise alignments and the output needs Chime applet which is not very easy to install. The major drawback is that users do not have access to the alignment by itself for further analysis. CLePAPS  [24]  is based on the use of a dedicated structural alphabet built only to perform database search. In the first step, aligned fragment pairs (AFP) are found, which correspond to fragments that involve exact matches of similar letters.
CLePAPS then joins consistent AFPs guided by their similarity scores to extend the alignment by several "zoom-in" iteration steps; it does not use dynamic programming. CLePAPS was tested on a limited number of protein structure pairs. A standalone program is reported to be available, but not found. Hence, iPBAvizu is quite interesting approach. Indeed, it is an easy-to-use plugin for PyMOL that allows users to superimpose protein structures using iPBA methodology, an efficient way to superimpose protein 3D structures  [11]  and explore the structural alignment results. Its total integration as a plugin into PyMOL molecular viewer offers an easy but powerful way to process and study structural alignment with quantitative measurements. ; the structural superimposition is made using iPBAvizu.
Materials and methods iPBA program is fully written in Python (2. 7+). It depends on ProFit program stand-alone version (Martin, A. C. R. , http://www. bioinf. org. uk/software/profit) for generating the final structural alignment. iPBA provides an efficient way to align two protein structures using anchor-based alignment methodology  [11, 12]. iPBAvizu package has an installer to configure iPBA and manage its dependencies on the local machine before integrating it into PyMOL. Due to ProFit requirements, iPBAvizu is only available on Unix-based operating systems. iPBAvizu is embedded into PyMOL as a wizard plugin, and all iPBA functionalities are totally integrated into the graphic interface of PyMOL. iPBAvizu can be launched with the current PyMOL internal GUI. Users can easily align structures with a few clicks and access both scores and the alignment results that are displayed in PyMOL itself, as a Tkinter GUI.
PB is the most widely used structural alphabet and is composed of 16 local prototypes  [4]  of five residue length, it is dedicated to analyse local conformations of protein structures from the Protein DataBank (PDB)  [26]. Each PB is characterized by the φ and ψ dihedral angles of five consecutive residues. PBs give a reasonable approximation of all local protein 3D structures  [14, 27, 28]. PBs are labelled from a to p. PBs m and d can be roughly described as prototypes for α-helix and central β-strand, respectively. PBs a to c primarily represent β-strand N-caps and PBs e and f representing β-strand C-caps; PBs g to j are specific to coils; PBs k and l to α-helix N-caps while PBs n to p to α-helix C-caps. For each PB is associated 5 residues, its assignment is done on the central residue.
Funding This work was supported by grants from the  French Ministry of Research, University of Paris Diderot -Sorbonne Paris Cité, University de la Réunion, University des Antilles ,  French National Institute for Blood Transfusion (INTS) ,  French Institute for Health and Medical Research (INSERM). AGdB, APJ, NS and TJN acknowledge the  Indo-French Centre for the Promotion of Advanced Research / CEFIPRA  for collaborative grants (number  3903-E  and  5203-2 ). AdB, and  JR acknowledge ANR NaturaDyRe (France ,  ANR-2010-CD2I-014-04 ). This study was supported by grants from the  Laboratory of Excellence GR-Ex  (reference  ANR-11-LABX-0051 ). The labex GR-Ex is funded by the  programme "Investissements d'avenir " of the  French National Research Agency , reference  ANR-11-IDEX-0005-02. Calculations were performed on an SGI cluster granted by  Conseil Régional Ile de France  and  INTS  ( SESAME Grant ).
The authors were granted access to high performance computing (HPC) resources at the  French National Computing Centre CINES  under grants no. c2013037147 ,  c2016077621  and  A0010707621  funded by the  GENCI (Grand Equipement National de Calcul Intensif). Research in NS group is supported by  Mathematical Biology program  and  FIST program  sponsored by the  Department of Science and Technology  and also by the  Department of Biotechnology, Government of India  in the form of IISc-DBT partnership programme. Support from  UGC, India -Centre for Advanced Studies and Ministry of Human Resource Development, India  is gratefully acknowledged. NS is a J. C. Bose National Fellow. The funding bodies have no roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Availability of data and materials iPBAvizu is a PyMOL plugin freely available to the academic scientific community, i. e. the data is only informatics codes. It is composed of the PyMOL script code and the iPBA code. This last used python and some C codes. The downloadable archive can be freely accessed at our academic website: http://www. dsimb. inserm. fr/dsimb_tools/iPBAVizu/. As it is a PyMOL plugin, user needs to install independently PyMOL software: https://pymol. org. There is no restriction for use or modifications of iPBAvizu by any academic scientists. For commercial usage, please contact the authors.