dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Generalized Principal Component Analysis (PCA) is an important research tool in the Symbolic Data Analysis (SDA)  [1]. PCA is a statistical procedure that uses orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components (PCs)  [2] [3] [4]. PCA is commonly used for dimension reduction  [5, 6]  by specifying a few PCs that account for as much of the variability in the original dataset as possible. It is well known that PCA has been primarily developed for single-valued variables. However, the explosion of big data from a wide range of application domains presents new challenges for traditional PCA, as it is difficult to gain insight from mass observation even in a low-dimensional space.
Several studies contribute to the extension of PCA to histogram data. Reference  [11]  attempted to analyze a symbolic dataset for dimensionality reduction when the features are of histogram type. This approach assumes that the modal variables have the same number of ordered bins and uses the PCA of tables associated with each bin. References  [12, 13]  assumed that the same number of bins was ordered for each variable. References  [1, 14]  proposed approaches based on distributions of histogram transformation, which is not possible in the case of nonordinal bins. Nevertheless, the Ichino method solves this case by ranking bins by their frequency among the whole population of individuals. Reference  [15]  presented a strategy for extending standard PCA to histogram data. This method uses metabins that mix bins from different bar charts and enhance interpretability.
Because this method uses complex algorithms and large-scale data, it is quite time consuming. To speed up the method, we undertake a parallel implementation of the new method through a multicore MapReduce framework. MapReduce is a popular parallel programming model for cloud computing platforms and has been effective in processing large datasets using multicore or multiprocessor systems  [21] [22] [23] [24] [25]. We conducted a simulation experiment to confirm that the new method is effective and can be substantially accelerated. It should be emphasized that we only propose the MapReduce framework for sampling-based histogram PCA for big data. When there is sufficient data, we can use a big data platform like Hadoop and adopt parallel computing methods, such as GPU computing and MPI (Message Passing Interface).
MapReduce for Multicore, Sampling-Based Histogram PCA The sampling-based histogram PCA approach presented in Section 2 involves complex algorithms and large-scale data, which makes the method time-consuming and degrades its computation efficiency. To speed up the method, we undertake its parallel implementation using a multicore MapReduce framework in this section. MapReduce is a programming model for processing large datasets with a parallel, distributed algorithm on a cluster. A MapReduce program is composed of a map procedure that filters and sorts data and a reduce procedure that performs a summary operation  [21]. Reference  [22]  show that algorithms that can be written in a "summation form" can be easily parallelized on multicore computers. Coincidentally, Equations (  8 ), (  9 ) and (  13 ) are written in this form.
In addition, the random sampling procedure can also be parallelized. Consequently, we adapt the MapReduce paradigm to implement our histogram PCA algorithm. The multicore MapReduce frame for histogram PCA is presented in Figure  1 , which shows a highlevel view of our architecture and how it processes the data. In Step 1, the MapReduce engine calculates the mean of each histogram variable using Equation (6) using pseudocode shown in Algorithm 1. return (column_id, W j ) 26:.
The single-value data points can be viewed as the original data, which we aim to approximate using histogram-valued data. Consequently, experimental PCA results obtained from single-value data can work as an evaluation benchmark for the histogram PCA algorithm. The detailed process of Example 1 is as follows: (1) Generate a synthetic histogram dataset X with p variables and n observations and the corresponding single-value dataset, denoted as X NUM. (2) Adopt the new histogram PCA method on X and perform classical PCA on X NUM. The obtained PC coefficients are denoted as u j and u NUM j , and the corresponding PC variances are represented by λ j and λ NUM j , j = 1, 2,. , p. (3) Compute the following two indicators  [30] : (a) Absolute cosine value (ACV) of PC coefficients: ACV(u j ) = (u j ) T u NUM j u j u NUM j.
Example 2 In this example, we consider the precision of the sampling-based projection process. The simulated symbolic data tables were built in the same way as in Example 1. To obtain n observations associated with histogram-valued variable X k , we simulated 5000 real values x NUM ik corresponding to each unit. These values were then organized in histograms. The distribution of the microdata was also randomly selected from a mixture of distributions: {U(1, 3), N(1, 1), χ 2 (1), lnN(0, 0. 5), -lnN(0, 0. 5)}. As in Example 1, for all observations, the widths of the subintervals in each histogram are the same. Thus, we generate n histogram observations and the corresponding n × 5000 single-value data points. The histogram dataset was adopted for comparing our sampling-based histogram PCA with the PSPCA method proposed by  [16].
Single-value data points can also be viewed as the original data we aim to approximate using histogram-valued data. Consequently, experimental PCA results obtained from single-value data can work as the evaluation benchmark for the results of the histogram PCA algorithm. Per Section 2. 3, during the projection process, we select m i single-value samples from the ith observation. In this example, we assume that the sizes of single-value samples in each observation are all the same and are denoted as M. The detailed process of Example 2 follows: (1) Generate a synthetic histogram dataset X with p variables and n observations and the corresponding single-value data set, denoted as X NUM. ( ) between Y j and Y NUM j  [31]. d(Y j , Y NUM j ) = 1 n n ∑ i=1 d(Y ij , Y NUM ij ). ( 19 ) (4) Compute the Wasserstein distance d(Y j , Y PSPCA j ) between Y j and Y PSPCA j.
Empirical Study Nowadays, the rapid development of the Internet has created opportunities for the development of movie sites. The major functions of movie sites are to calculate movie ratings based on user rating data and to then recommend high-quality movies. Since the set of user rating data is massive, symbolic data can be utilized. In this section, we use real data to evaluate the performance of sampling-based histogram PCA. The data consists of 198,315 ratings from a Chinese movie site from October 2009 to May 2014. The ratings come from three different types of users: visitors, normal registered users, and expert users. In the first step, we summarize movie ratings from the different user types to obtain scoring histograms for each type of user for a movie. Then, the dataset becomes a histogram symbolic table of 500 observations and 3 variables.
Preliminary Results One important quantity in the analysis of the data is the correlation between E x and E gamma. E x represents the excitation energy of the target nucleus, deduced from the measurement of the inelastically scattered particle (alpha or proton) in the Grand Raiden spectrometer. E gamma is the energy of the gamma rays emitted following the de-excitation of the target nucleus, it is measured by the HPGe clover detectors of CAGRA or by the LaBr 3 :Ce scintillation detectors of the HECTOR+ array. This correlation can be seen in the 2D-histogram shown in Figure  3. This refers to the case of the 90 Zr(α,α'γ) run (E beam = 130 MeV) with E gamma representing the gamma energy measured in the HPGe clover detectors. The events lying on the diagonal are those for which holds the condition E x =E gamma , they are associated to the direct decay to the ground state (this is shown in Figure  3  with the red dotted line).
Acknowledgements The work of both laboratories is supported by the  Agence Nationale de la Recherche , Grant no. ANR-2013-BSV6-0002-01. The UMR 1136 is also supported by a Grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( ANR-11-LABX-0002-01 ,  Lab of Excellence ARBRE ). This publication is based upon work from  COST Action   CA15133 , supported by  COST (European Cooperation in Science and Technology). The authors are grateful to  Pr Jean-Pierre Jacquot  for its careful reading of the manuscript.
2 K and applied the same scaling factor to all other XMCD curves. In doing so, the XMCD-detected magnetization curves for all temperatures and all magnetic fields are automatically expressed in Bohr magnetons. We noted some small shifts between XMCD and SQUID data for low magnetic fields; they are caused by a temperature increase during the AE 3 T cycles because of eddy current heating. For example, for an initial T = 300 mK, the sample temperature oscillates upon field sweep around 325 AE 25 mK. For T > 400 mK, the cooling power of the device is sufficient to compensate the eddy current heating and this temperature drift no longer appears. Knowing that the TEY detection mode probes about the first 5 nm to 10 nm of the sample surface, we checked the validity of the cleaning procedure by comparing the integrated XMCD intensity as a function of magnetic field in the 2-4 K range with that of Er 2 O 3.
Conclusions In this paper we have described a new ULT-XMCD setup installed on the DEIMOS beamline and dedicated to soft X-ray XMCD experiments. Its improved performances compared with our previous setup have been illustrated by measuring two different physical phenomena with a marked temperature dependence. First, we measured the magnetization of paramagnetic Er impurities in a palladium ingot. In the second experiment, we measured the opening of magnetic hysteresis loops in a SMM monolayer. Both experiments demonstrate, unambiguously, that sub-Kelvin XMCD data can be recorded on two very different systems, thus indicating the versatility and the enormous potential of this spectroscopic tool for magnetic studies well beyond diluted paramagnetic systems or surface science. Despite many worldwide attempts on various synchrotron facilities, this device is certainly unique in providing such performances.
Funding information This project has been financially supported by the  DiLux-Consortium , composed of the following laboratories: Synchrotron SOLEIL, France;  Institut de Mineralogie, de Physique des Materiaux et de Cosmochimie, France; Institut de Chimie Moleculaire et des Materiaux d'Orsay, France; Institut de Physique et Chimie des Materiaux de Strasbourg, France; Laboratorio di Magnetismo Molecolare, Universita degli Studi Firenze, Italy ;  Dipartimento di Scienze Chimiche e Geologiche, Universitadegli Studi di Modena e Reggio Emilia, Italy; Karlsruher Institut fu ¨r Nanotechnologie, Germany; Fakulta ¨t fu ¨r Physik, Universita ¨t Duisburg-Essen, Germany ;  Max Planck Institut fu ¨r Chemische Physik Fester Stoffe, Germany; Physikalisches Institut, Universita ¨t zu Ko ¨ln, Germany. Part of the work described here was performed in the glove box provided by the  Institut de Mineralogie et de Physique des Milieux Condenses , and has been funded by the  Agence National de la Recherche  (grant No.
A. The isolated [Fe(PM-BiA) 2 (NCS) 2 ] complex The IR and the Raman spectra that we computed in the region of interest and in the LS and HS states are displayed in Figure  5. One should stress that large evolutions of the absorption are also recorded in the 20 cm -1 -200 cm -1 wavenumbers range. This clearly indicates that the LS to HS spin transition affects a large number of IR and Raman active modes. As displayed in Figure  6 , in this compound, the agreement between computed and measured Raman peaks in the high wavenumber region is quite good in both the HS and LS states. To compare more accurately these data we applied the wavenumber scaling factor 0. 967 that correct for the approximation made in the computation using the hybrid functional "B3LYP" as well as a modified 'tunable' one, B3LYP* provided a large and complete enough basis set such as all electrons "6-311g(d,p)" and/or effective core LANL2DZ (Los Alamos National Laboratory with Double Zeta polarization).
Acknowledgements The authors acknowledge support through  ANR project  " OptoMaTS ",  Conseil Régional d'Aquitaine for funding equipment of the platform "COLA"  and the project " MatTS ", A. O. H. is grateful to the  Conseil Régional d'Aquitaine  for financial support. Computation facilities were provided by  MCIA-University of Bordeaux center. The  Conseil Régional d'Aquitaine and Europe (FEDER program ) are also thanked for funding equipment of the Vibrational Spectroscopy and Imaging (SIV) platform at ISM.
Influence of pH As pH could influence the hydrolysis of DTPA in water  [33, 46] , and further adsorption onto the resin, it was essential to establish if pH has an overall effect on the interaction of the Tl(III) and the resin. The results indicate that solutions containing Tl(I) and Tl(III) at various pH's (from 1 to 7), showed near quantitative recoveries of Tl (95-103%; Table  3  and Additional file 1: Table  S2 ). There was no significant differences in recoveries under different pH conditions according to the Duncan's new multiple range test base on the SPSS software, therefore, no optimal pH can be established.
The resulting solution was then dilution to a total volume of 20 mL and assessed by the SPE method described above. The results showed that when preparing the standard in this way, Tl(I) was fully oxidised to Tl(III), the same results are obtained when replacing DTPA solution with solid DTPA. However, the Tl(III) oxidized by saturated bromine water was quickly reduced to Tl(I) without the addition of DTPA. It should be noted that, although NaOH is frequently used to prepare DTPA solutions  [25] , DTPA with NaOH is not recommended to treat aqueous samples, as we observed that the addition of NaOH interferes with the oxidation process of Tl(I) to Tl(III) (data not shown).
Acknowledgments: The LERMAB of the  University of Lorraine  is supported by a grant overseen by the  French National Research Agency (ANR)  as part of the  Laboratory of Excellence (Labex) ARBRE. The first author thanks the  Council of the University of Brasilia  to grant him staff leave and funding to develop postdoctoral research during the senior internship at the  LERMAB/ENSTIB/Université de Lorraine, Epinal, France. The fourth author thanks the  China Scholarship Council  for the study bursary granted to him.
Unfortunately, these two phases cannot be distinguished due to their isostructural character: both are cubic and have similar unit cell parameters. A few different peaks for these two iron oxides can appear but with a low intensity and easily blurred by background noise or crystallite size effects  [34]. TGA was used to determine the relative amounts of iron oxide and polymer template in the nanocomposite particles. Decomposition of the G1 polymer started at ca. 350 • C and ended at ca. 450 • C (green dotted curve, Figure  2f ). The thermogravimetric trace for a physically blended mixture of dry G1 powder and Fe 3 O 4 paste (blue, long-dashed) was similar to that of the pure G1 sample. In comparison, the thermal decomposition (in N 2 ) or combustion (in air) of the templated MNPs G1@Fe 3 O 4 was characterized by a three-stage weight loss profile, more clearly visible in the differential weight loss curve for G1@Fe 3 O 4 (Figure  2g ) as follows: • A small weight loss at 140 • C, attributed to the elimination of physically adsorbed water (bound to the surface of the MNPs) and surface hydroxyl groups  [35, 36] , as also observed for the mixture of dry G1 powder and Fe 3 O 4 paste (data not shown).
Iron oxide crystallites with high electron contrast appeared inside large intracellular endosomes or lysosomes, visible in a clearer area that could be differentiated from the grey background of the cytoplasm (Figure  4i  and Figure  S7 ). This location of the iron oxide crystallites is in good agreement with the results reported in the literature  [47, 48] , suggesting that MPIC micelles could be also internalized through the cell membrane by clathrin-dependent endocytosis (Figure  S8 ), although the formal proof of this internalization pathway would require complementary experiments with clathrin inhibitors as in Sanchez et al. [49]. Cell internalization was also quantified by the fluorescence-activated cell sorting (FACS) assay. The number of fluorescence-positive cells counted per 10,000 cells, expressed as % positive cells and the ratio of fluorescence intensity of 10,000 treated cells to that of 10,000 untreated cells, expressed as their mean fluorescence intensity (MFI), were extracted from the FACS data to evaluate the extent of cellular uptake.
Acknowledgments: We thank  Franck Couillaud  and  Coralie Genevois  for providing cell culture facilities for the cellular hyperthermia experiments and  Sandra Ormenese  (Imaging and flow cytometry platform-GIGA-R) for her help with confocal laser scanning microscopy. The TEM images were acquired at the  Bordeaux Imaging Center (BIC) , a service unit of the  CNRS-INSERM and Bordeaux University , member of the national infrastructure " France BioImaging ," with the acknowledged help of  Sabrina Lacomme  and  Etienne Gontier.
Q tells the degree of puckering (0 corresponds a planar ring), while the other two parameters tell the kind of distortion (chair -C, envelope -E, boat -B, twist-boat -T, screw boat -S, and half chair -H). Natural transition orbitals (NTOs)  [59]  have been computed using the program package TheoDORE  [60] [61] [62]. The ADC(2) calculations were performed with the TUBOMOLE suite of programs  [63]. Spectra and dynamics were computed with the NEWTON-X/TURBOMOLE interface  [64, 65]. Cremer-Pople parameters were determined using the PLATON program  [66].
Acknowledgment AJAA and HL acknowledge financial support from the  School of Pharmaceutical Science and Technology, Tianjin University, China. MB work was supported by  Excellence Initiative of Aix-Marseille University  (A*MIDEX) and the project  Equip@Meso  ( ANR-10-EQPX-29-01 ), both funded by the  French Government "Investissements d'Avenir" program. MB also acknowledges funding from and of the  WSPLIT  project ( ANR-17-CE05-0005-01 ).
Acknowledgements The authors gratefully acknowledge  J. Guigand ,  S. Lupone , and  N. Tournier  ( ISMO ) for their efficient technical support for maintenance of the COLTRIMS set-up. This research was supported by the  EU-H2020-MSCA-ITN-MEDEA-641789 ,  EU-H2020-LASERLAB-EUROPE-654148 , the  ANR-15-CE30-0001-CIMBAAD ,  ANR-11-EQPX0005-ATTO-LAB ,  ANR-10-LABX-0039-PALM , and  Région Ile-de-France   SESAME2012-ATTOLITE.
, Tadworth, UK). SAXS measurements were conducted after dilution of each as-synthesized 20% w/w copolymer dispersion to the desired concentration using deionized water. 2D scattering data obtained at synchrotron sources were reduced to 1D patterns using standard beamline data-handling protocols, whereas data collected by the Xenocs Xeuss 2. 0 instrument were reduced using the Foxtrot software package developed at SOLEIL synchrotron. All such data were further reduced (i. e. , normalized and background-subtracted) and then fitted by a structural model using Irena SAS macros for Igor Pro. 65 tructural data were determined by fitting 1D SAXS patterns to an appropriate model for spheres and/or worms (see the Supporting Information for the model description). In particular, this approach enabled calculation of the mean worm core radius, (R cw ), sphere core radius, (R cs ), the radius of gyration of the PGMA stabilizer chains (R g ), the mean water content within the PHPMA cores (x sol ), the worm contour length (L c ), and the worm Kuhn length (L k ).
The dispersion effective viscosity was measured simultaneously by the mechanical rheometer as a function of temperature at a heating/cooling rate of 1. 0 °C min -1 at a constant angular speed of 0. 8 rad s -1. For worm relaxation studies, dispersions were subjected to this angular speed for 60 s at various temperatures, prior to cessation of shear. Images were captured at 250 ms intervals until the birefringence was no longer visible (i. e. , complete disappearance of the characteristic Maltese cross pattern). The resulting polarized light images were stacked and then sliced by a plane forming an angle of 45°with polarizer and analyzer axes and perpendicular to the imaging plane 60 using ImageJ software. The obtained 2D image was subsequently converted to grayscale and exported as a text file containing the relative intensities of each pixel.
Single-crystal structure determination (SCXRD) The SCXRD data for compounds Bi-1, Bi-2, and Zn-1 were collected at room temperature (293 K) on a Rigaku XTALAB-MINI diffractometer using MoKα radiation (0. 71073 Å) monochromated by graphite. The In-1 compound was collected at room temperature (296 K) with a Bruker APEX-II CCD diffractometer using MoKα radiation (0. 71073 Å) monochromated by graphite. The cell determination and the final cell parameters of Bi-1, Bi-2, and Zn-1 were obtained on all reflections using the software CrystalClear. 23 Data integration and scaling were carried out using the software CrystalClear  23  and CrysAlisPro. 24 The structures were solved and refined with SHELXS-2013  25  software, included in WinGX  26  and Olex2. 27 ell determination and final cell parameters of In-1 crystals were obtained on all reflections using the software Bruker SAINT included in the APEX2 software suite.
28 Data integration and scaling were carried out using the software Bruker SAINT. 27 In all cases, non-hydrogen atoms of the molecules were clearly resolved, and full-matrix least-squares refinements of these atoms with anisotropic thermal parameters were performed. Additionally, hydrogen atoms were stereochemically positioned and refined by the riding model. 25 ORTEP diagrams for all structures were prepared with the Diamond  29  program. TOPOS  30  and Mercury 31 programs were used in the preparation of the artwork of the polyhedral and topological representations.
Acknowledgements This work was supported by the  Consejo Nacional de Investigaciones Científicas y Técnicas ,  ANPCyT   PICT 2012-2087  and  CNEA. G. E. G. acknowledges the postdoctoral  CONICET fellowship  and  HYMADE  project. R. D. acknowledges  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior  for the  CAPES/PNPD  scholarship from the  Brazilian Ministry of Education and Universidad Santiago de Cali. D. F. L. acknowledges doctoral  CONICET  fellowship. Ramón Castillo Guerra ,  Ricardo Montero ,  Guillermo Arnaldo ,  Rodrigo Taboada , and  Gonzalo Zbihlei , from  Departamento de Materiales -(CNEA) , are gratefully thanked for helping with sample preparation for NI testing. L. M. A. -D. , M. I. and M. A. M. acknowledge the  Spanish Ministry of Economy, Industry and Competitiveness (MINECO-Spain)  projects  MAT2013-45460-R ,  MAT2014-52085-C2-2-P ,  MAT2016-78465-R , and  CTQ2014-61748-EXP ,  Fondo Social Europeo  from the  European Union.
Comunidad Autónoma de Madrid   S2013/ MIT-2740 (PHAMA 2. 0 ). R. V. D. and A. M. K. acknowledge  Ghent University's Special Research Fund (BOF)  for a  Posdoctoral Mandate  ( project  BOF15/PDO/091 ). J. E. is grateful to  CNPq  for the research fellowships. G. J. A. A. S. I. and M. C. F. are members of  CIC-CONICET. Additionally , this project has received funding from the  European Union 's  Horizon 2020 research and innovation program   H2020-MSCA-RISE-2014 , GA No. 645686.