dataesr/scientific-paragraphs-categorization huggingface dataset:
Here we describe the tools written in R. Python tools are described in the corresponding tutorial. The R tools are designed to be used in an integrated development environment such as RStudio, which provides a user-friendly environment to assist the user in writing and executing command lines. These command line tools are organized in the spirit of a grammar of data manipulation and they can be combined with the existing R tools like dplyr  (Francois and Wickham, 2015)  or ggplot2  (Wickham, 2009)  for manipulating and visualizing data (https://mpicbg-scicomp. github. io/tissue_miner/user_manual/Learning_the_R_basics_for_ TissueMiner. html). We developed generic 'multi-query functions' (mqf) to collect specific information for individual movies. These mqf tools are organized into fine-grained and coarse-grained categories according to the type of analysis to be carried out.
In the tracked-cell mask, all pixels inside the cell circumference have the same unique color. In consecutive frames, the same cell has the same color. In the cell-division mask, each cell is colored either in black or in blue. If a cell is blue, it is a daughter cell that emerged from a division between two consecutive frames. Otherwise, a cell is black in the cell-division mask. We wrote a custom C++ parser that converts information contained in the tracked-cell and cell-division masks into tables that can be easily transformed into the TissueMiner database. This parser first extracts topological and geometrical information about cells, bonds and vertices for each individual frame. Afterwards, it analyzes the continuity of cell existences across consecutive frames. In particular, it tries to infer reasons for appearance or disappearance of cells.
Implementing the TissueMiner relational database The TissueMiner parser generates tables from which we build the TissueMiner relational database (TM-DB). To do so, we used the formalism developed in the Merise method  (Tardieu et al. , 2000) , which includes the entity-relationship model (Peter Pin-Shan, 1976), the relational database theory  (Codd, 1970 (Codd, , 1972 ) and Codd's normal forms  (Codd, 1971 (Codd, , 1974)) ; thus, it allows one to translate the conceptual data model into a relational database scheme. We first establish the 'entity-relationship' scheme of the database to represent the information extracted with the parser in entities, and to establish relationships between and within entities. This conceptual approach defines the basic elements of the entity-relationship model (Peter Pin-Shan, 1976): the entity, the association, the cardinality and the identifier.
We therefore show them in Table  1. The rules to translate a conceptual scheme to a logical one can be found here  (Tardieu et al. , 2000). Below, we explain our conceptual scheme along with its translation into the logical scheme, which can be directly implemented using a chosen SQL language. Applying these rules to our TM-DB, these entities become physical tables in the logical scheme, and associations become table columns ('foreign keys' in blue) in related tables (Figure  6 -figure supplement 2A). The foreign keys constitute a referential integrity constraint between tables. The TM-DB consists of six entities, frames, cells, vertices, bonds, directed_bonds and cell_histories that are linked by logical associations (Figure  6A ). Their respective identifier is underlined in the conceptual scheme (Figure  6A ), and becomes the 'primary key' placed in the table header in the logical scheme (Figure  6 -figure supplement 2A).
The cell ancestry is represented by the 'be_daughter_of' association that relates each dividing cell to its two daughters (left_daughter_cell_id and right_daughter_cell_id columns, Figure  6 -figure supplement 2A). To relate a cell to the time evolution of its properties (center of mass, area, shape anisotropy, polarized protein distribution), we create an association between the cells and cell_histories entities, in which each entry is uniquely determined by the combination of cell_id and frame. As movies may be acquired at different frame rates, we also represent the real time evolution (in seconds) in the frames entity that we connect to the cells entity. To represent the cell topology in the database, we create a directed_bond entity along with a self-association 'be next left' that links each directed bond in each frame (dbond_id) to its next counter-clockwise follower (left_dbond_id column, Figure  6 -figure  supplement 2A ).
This helps clarifying the notion of entities and their content when creating the data model. It also helps reducing redundancy in the database. The logical scheme of the TM-DB is implemented using the SQLite management system  (Jay, 2010). We chose SQLite for its ease of use: there is no need to install a dedicated server and the DB is stored in a single file that is easily shared with collaborators. The source code is accessible on GitHub repository (see Box 1).
Each directed bond is complemented by a conjugated bond (magenta) and is linked to it next counter-clockwise follower (dashed). DOI: 10. 7554/eLife. 14334. 027 The following figure supplements are available for figure 6: Figure supplement 1. Tracked cells identified by unique colors in TissueAnalyzer. DOI: 10. 7554/eLife. 14334. 028 Figure supplement 2. Logical scheme of the relational database. DOI: 10. 7554/eLife. 14334. 029.
Figure7. Automated workflow using snakemake. (A) The snakemake engine can generate a directed acyclic graph (DAG) where we show an example here. This graph represents both the execution dependency (grey arrows) and the execution state of the workflow (solid or dashed line). Each box corresponds to an execution rule, namely a program to be run along with required input(s) and expected output(s). This DAG can be generated at any time when running the workflow (see documentation). Solid lines indicate the rules that have not been executed yet, whereas dashed lines depict completed jobs. The first rule to be executed is called 'prepare_movie': it prepares the tracked images from TissueAnalyzer to be converted by the parser into tables of values containing all necessary entities along with their properties ('parse_tables' rule).
Then the 'make_db' rule is executed for building the database. Following the grey arrows can one navigate into the next steps of the workflow. The 'roi_tracking' rule filters out cells in contact to margin cells including user-defined regions of interest, and the 'roi_movie' rule allows us to visualize regions of interest over time. The 'deformation_movies' and 'db_elongation_movies' rules generate annotated movies to visualize the deformation of the tissue and the cell state properties (area, elongation). The 'four_way' rule detects four-way vertices and performs basic statistics on vertices. The 'tri_create' rule performs the triangulation of the network for further shear calculation and visualization('shear_calculate' and 'shear_movies'). It also enables triangle tracking and mapping to each type of cell event ('tri_categorize').
Acknowledgements This work was a truly collaborative effort and the authors jointly wrote the manuscript. We are grateful to  Christian Dahmann ,  Marcus Michel  and  Jacques Boutet de Monvel  for critical reading of the manuscript,  Benoit Lombardo  for his help in Fiji macroing, and  Peter Steinbach  for useful discussion about workflow engines. We thank  Franz Gruber ,  Vincent Michel  and  Nathalie Gourreau  for testing the quickstart tutorials. RE acknowledges a  Marie Curie fellowship  from the  EU   7th Framework Programme  ( FP7 ). This work was supported by the  Max Planck Gesellschaft , and by the  BMBF. SE  acknowledges funding from the  ERC.
Each SOP is solved by metamodel assisted evolution strategies used in interaction with FEM simulation software for metal forming applications. The proposed method is first tested and demonstrated with known mathematical multi-objective problems, showing its ability to find a solution lying on the Pareto Frontier, even with a largely incomplete knowledge of it. The algorithm is then applied to the FEM optimization problem of wire drawing process with one and two passes, in order to simultaneously minimize the pulling force and the material damage. The K-S solutions are compared to results previously suggested in literature using more conventional methodologies and engineering expertise. The paper shows that K-S solutions are very promising for finding quite satisfactory engineering compromises, in a very efficient manner, in metal forming applications.
In order to formulate this problem in a symmetric way, the two functions (  11 ) and (  12 ) are combined and constrained using a penalty coefficient "ρ", as follows: g ρ (x) = g 1 (x) + g 2 (x) + ρ|g 1 (x) -g 2 (x)| (12) If "ρ" is large enough, the minimization of g ρ (x)  (13)  provides the localization of the Kalai and Smorodinsky solution. To summarise, a general bi-objective optimization problem can be divided into the solution of three SOPs steps as follows: { x 1 * = min x f 1 (x) x 2 * = min x f 2 (x) x * = min x g ρ (x) z j (x) ≥ 0 j = 1; … ; J h k (x) = 0 k = 1; … ; K a i inf ≤ x i ≤ a i sup i = 1; … ; m (13) The algorithm used for the mathematical validation of the proposed method is schematically described in Fig. 2. It is based on the minimizer Optim-Engine integrated inside the FEM software Forge ®.
Conclusions In this paper, a general methodology has been presented for computing the Kalai and Smorodinsky multi-objective solution by solving a series of mono-objective optimization problems. This constrained formulation, based on a penalty method, shows quite robust and efficient, and was validated on several analytical multi-objective problems. This approach can be easily implemented into any existing mono-objective algorithm, as was done in this paper with the Optimization Engine of Forge ® software. A by-product of this approach is the knowledge of the minimum solution with respect each objective function, independently from the other, which can be useful in the engineering practice, for instance if there is a strong hierarchy between the functions and if one function plays a greater role than the others.
Acknowledgments The research has been partly funded by  CEMEF  and partly by the  Politecnico di Milano , under the  PRIN I-foams research program. The authors gratefully thank  Abderrahmane Habbal  for fruitful discussions on this paper and for initiating the idea of using the Kalai and Smorodisky paradigm in engineering applications. The authors also want to thank  Fadi El Haddad  for the help given during the initial development of the algorithm code.
5 MHz wide (five of the native channels), corresponding to 70 km s -1 at 84 GHz, and 51 km s -1 at 115 GHz. The original frequency settings (A-H) are labeled in the bottom panel, together with the frequency blocks (a-k) used in the data reduction. At 1mm, the channels are 31. 3 ṀHz wide (four of the native channels), corresponding to 44 km s -1 at 212 GHz, and to 34 km s -1 at 272 GHz. To first order, we reach uniform sensitivity as a function of frequency in both bands. The increase in noise towards high frequencies (>113 GHz) in band 3 is due to the atmosphere (O2). The bandwidth of band 7 (∼ 100 GHz) requires more than 13 frequency tunings (each with a bandwidth of 8 GHz). For all of these reasons, band 6 is preferred over band 7 to complement the band 3 observations. We obtained full frequency scans in both ALMA band 3 and band 6.
Observations were carried out in a relatively compact (C34-2) array configuration with 29-41 antennas, with baselines ranging between 12 and 612 m. The quasar J0348-2749 was observed in the majority of the execution blocks as phase and amplitude calibrator, while Uranus and the quasars J0334-4010 and J0334-4008 were used as flux and bandpass calibrators. Data were calibrated and imaged with the Common Astronomy Software Applications package (CASA) version 4. 2. 2 of the ALMA pipeline. To combine the different setups we adopted the following approach: 1) For each execution block, we split out cubes in frequency ranges as shown in Fig. 2 (a-k ). 2) The frequency ranges marked with the letters e-g, i. e. , where upper and lower side band observations from different frequency settings overlap, were re-sampled using the CASA task ms.
5 × 2. 0 (FWHM) with PA=84 •. We thus adopted a pixel scale of 0. 5 pixel -1 , and an image size of 90 × 90. A primary beam correction has been applied for all quantitative analysis. The final data set covers the frequency range 84. 176-114. 928 GHz, and reaches an rms of 0. 1 -0. 25 mJy beam -1 per 19. 5 MHz channel (see Fig. 2 ). For comparison, the PdBI spectral scan at 3mm in the Hubble Deep Field North  (Decarli et al. 2014 , Walter et al. 2012 , 2014)  reached a sensitivity of ∼ 0. 3 mJy beam -1 per 90 km s -1 channel, or ∼ 0. 4 mJy beam -1 at the sampling adopted here. Therefore these ALMA observations are a factor 3-4 deeper at ν < 113 GHz than the previous 100 hour (on-source) effort with PdBI  (Decarli et al. 2014). The 1mm observations (ALMA Project ID: 2013. 1. 00718. S) were carried out between December 12 th , 2014 and April 21 st , 2015.
The blind line search For our blind search of line candidates, we developed an IRAF-based routine, findclumps, which operates directly on the imaged data cubes. The script performs floating averages of a number of channels, computes the rms of the averaged maps, and searches for peaks exceeding a certain S/N threshold using the IRAF task daofind. The position, frequency, and S/N of the recovered candidates is saved. As input, we used the 7. 8 MHz and 31. 2 MHz sampling for the 3 mm and 1 mm cubes respectively. Since the significance of a line detection is maximized when averaging over a frequency range comparable with the actual width of the line, we ran our search over 3, 5, 7, and 9-channel windows, i. e. , kernel line widths of ∼50-300 km s -1 (an inspection using larger line-widths did not result in additional detections -this is also supported by our completeness test, see below).
2. The total CO flux of all line candidates is 2. 55 Jy km s -1 , whereas the total flux of the candidates that have no optical/NIR counterpart is 0. 83 Jy km s -1 (from Tab. 2), i. e. ∼33% of the total. As some of the line Filled symbols highlight the candidates that we recover in our analysis. The histograms show the marginalization along the y-and x-axis respectively, showing the level of completeness (i. e. , the fraction of input line candidates that our script successfully identifies) as a function of the line width (∆v) and peak flux density (F line ν ), respectively. The 3mm case is shown on the left, the 1mm one is on the right. candidates that do not show an optical/IR counterpart are likely spurious, and considering that the brightest CO detections with optical/NIR counterparts dominate the total emission, the flux fraction of real objects without optical/NIR counterpart is likely lower.
COMPARISON WITH EXPECTATIONS We present a detailed comparison of the evolution of the CO luminosity functions, and the resulting cosmic density of molecular hydrogen in Paper III. As a sanity check, we here briefly compare the number of COdetected galaxies with previous expectations based on a multi-wavelength analysis of the galaxies in the UDF. For each galaxy in the UDF, da Cunha et al. (  2013 ) estimated stellar masses, SFRs, IR luminosities, and expected CO and [C ii] fluxes and luminosities by fitting the optical/NIR photometry provided by  Coe et al. (2006) , using the MAGPHYS spectral energy fitting code  (da Cunha et al. 2008). We show the resulting star formation rates, and stellar masses, in four redshift bins in the right hand panel of Fig. 3. Note that typical selections of main sequence galaxies for CO follow-up usually target stellar masses M star > 10 10 M and star formation rates SFR> 50 M yr -1 (e.
SUMMARY We present the rationale for and the observational description of ASPECS, our complete band 3 and band 6 spectral line scan with ALMA of the Hubble Ultra-Deep Field (UDF). This field was chosen because it has the deepest multi-wavelength data available, it will remain a key cosmological deep field in the future (in particular in the era of JWST) and is easily observable by ALMA. We discuss our survey design of the full frequency scans in band 3 (84-115 GHz) and band 6 (212-272 GHz) and report the relevant parameters of our final dataset. Critically, ALMA allows us to reach approximately uniform depth (line sensitivity: ∼ L CO ∼ 2 × 10 9 K km s -1 pc 2 ) across a broad range of redshifts. The spectral line scans cover the different rotational transitions of the CO molecule at different redshifts, leading to essentially full redshift coverage.
Tourism's Millennium Science Initiative through grant IC120009, awarded to The Millennium Institute of Astrophysics, MAS. IRS also acknowledges support from STFC (ST/L00075X/1) and a Royal Society / Wolfson Merit award. Support for RD and BM was provided by the DFG priority program 1573 'The physics of the interstellar medium'. AK and FB acknowledge support by the Collaborative Research Council 956, sub-project A1, funded by the Deutsche Forschungsgemeinschaft (DFG). PI acknowledges Conict grants Basal-CATA PFB-06/2007 and Anilo ACT1417. RJA was supported by FONDECYT grant number 1151408. This paper makes use of the following ALMA data: 2013. 1. 00146. S and 2013. 1. 00718. S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile.
ACKNOWLEDGMENTS We thank the referee for a constructive report that helped improve the presentation of the data. FW, IRS, and RJI acknowledge support through  ERC grants COSMIC-DAWN , DUSTYGAL, and COSMICISM, respectively. MA acknowledges partial support from  FONDECYT  through grant  1140099. DR acknowledges support from the  National Science Foundation  under grant number  AST-#1614213  to  Cornell University. FEB and LI acknowledge Conicyt grants  Basal-CATA PFB-06/2007  and Anilo  ACT1417. FEB also acknowledge support from  FONDECYT Regular 1141218 (FEB) , and the  Ministry of Economy , Development, and.
In addition, we performed more accurate measurements of a number of previously published lines which posed some problems for previous analysis using the same model. Conclusions: The remeasurements have shown that the problems existed with spectrum description were caused by underestimated experimental error of the previously published data. The final fit uses 99 parameters to give an overall weighted root-meansquare deviation of 0. 78 for the dataset consisting of 6233, 4868, and 4364 transitions with J up to 60 and a K up to 35, belonging, respectively, to the ground, first, and second excited torsional states of the acetone molecule.
Despite of more than fifty years of laboratory investigations of acetone spectrum (started in 1959 by  Swalen and Costain [4] ), the theoretical interpretation of the observed microwave spectrum of acetone still poses a problem. The fifty-year history of acetone spectrum studies [4-7] was documented in a series of papers by. In these works  [8] [9] [10] , the analysis of rotational transitions carried out with the ERHAM program  [11, 12] , which fits the rotational levels of individual torsional states separately, without explicitly introducing a potential function. The resulting theoretical models  [8] [9] [10]  showed significant fitting problems especially in the excited torsional states  [9, 10]. A new program PAM_C2v_2tops (Principal-Axis Method for 2v C molecules with 2 tops) developed in Kharkiv for fitting the high-resolution torsion-rotation spectra of molecules with two equivalent methyl rotors and 2v C symmetry at equilibrium has been recently applied to the analysis of acetone spectrum  [13].
Spectral Analysis and Fit We started our analysis from the results of  [13]  where the weighted standard deviation of 0. 94 was achieved for the dataset consisting of 13 FIR (Far Infrared) line frequencies and 1720 microwave transitions with 38 J  and 21 a K  that was fitted using 40 parameters of the Hamiltonian model which assumes two equivalent methyl rotors and 2v C symmetry at equilibrium (PAM_C2v_2tops program). Assigning and fitting the new data using the PAM_C2v_2tops program proceeded in a fairly conventional iterative way by going up in frequency, with assignments for all three torsional states done simultaneously since our previous study  [13]  provided rather good starting predictions. As a result, about 10000 new line frequencies within 34 150  GHz range have been added to the initial dataset  [13].
Furthermore, we remeasured a number of lines in the 480 620  GHz range which were previously obtained with 200 kHz uncertainty  [8]. These lines have posed some problem for our recent analysis of literature data using the PAM_C2v_2tops program  [13]. The comparison of the remeasured transition frequencies with the old data [8] will be discussed in more detail in Section 5. Here, it should be mentioned that all data with 200 kHz uncertainty in the 480 620  GHz range from [8] were replaced in the final dataset with the new more accurate measurements obtained in our current study. (1 4) { ,s6 } { ,s6 } B A A B z y y z p p J J J J       2 (1 2) s6 s3 s6 s3 A B B A J        2 2 2 2 (1 4)(с3 с3 ) A B z x x z J J J J       2 (1 4) { , ,с3 ,с3 } { , ,с3 ,с3 } A B A B B A B A x p p p p J      3 2 (1 2)( ) A B x p + p J J   3 3 (1 4) { ,s3 ,s3 } { ,s3 s3 } A A B B B A x p p J      (1 4)(с6 s3 с6 s3 )( ) B A A B x y y x J J J J         3 3 3 3 (1 2) A B B A p p p p    3 (1 4) { ,с3 } { ,с3 } A A B B z p p J      3 2 3 2 (1 2) A B B A z p p p p J    2 2 (1 4)(s3 s3 ) A B z x y y x z J J J J J J       2 2 2 (1 4)( ) A B z x x z p + p J J + J J J   2 2 2 (1 2) A B x p + p J    2 2 2 (1 4) { ,с3 } { ,с3 } B A A B z x x z p p J J J J J       3 3 2 (1 4) ( ) A B B A z x x z p p p p J J J J J      2 2 (1 4) { ,s3 ,s3 } { ,s3 ,s3 } A A B B B A z x x z p p J J J J         3 3 (1 4)(s3 s3 ) A B x y y x J J J J       5 (1 4) { ,с3 } { ,с3 } B A A B z p p J      2 2 (1 4) ( ) A B z x x z p p J J J J     3 (1 4) { ,s3 ,s3 } { ,s3 ,s3 } A A B B B A x p p J      4 (1 2)( ) A B B A z p p p p J    2 (1 4) { , ,с3 ,с3 } { , ,с3 ,с3 } The final dataset consists of 12128 microwave line frequencies and 7 FIR band origins.
Due to clustering in the acetone spectrum and occasional blending these 12128 line frequencies correspond to 15465 rotational transitions in the fit. In comparison with our previous study  [13] , the dataset was significantly enlarged, and more than 10000 new line frequencies were added to the analysis. Table  2  gives an overview of the ( , ) (0,0), (0,1), (1,1) A B A B B A B A z p p p p J      2 2 s3 s3 A B x J J     (1 4) { , ,с3 ,с3 } { , ,с3 ,с3 } A B A B B A B A p p p p        2 (1 4) { ,с3 } { ,с3 } B A A B z p p J J    Parameter b 6myK V  63J V  3 2 Kx V 33 2 m x V  2 xx J  33 2 mxm V  63xy VV  m m F    3[ ] m zK V zm m    3xyK V  xJK  2 x F 3mzJK V m zxJ F  33[ ] 2 m zx V  3 2 xyx V  3mzKK V zx F 33[ ] 2 m xx V  KK F 33m K V  33 2 Jx V  33m V  3mzJ V Value c , A B    and (1, 2) symmetry species coverage in 9 , G as well as the J, , a K and  quantum number coverage of the dataset used in the final fits of this paper.