dataesr/scientific-paragraphs-categorization huggingface dataset:
Numerical Experiments The first task clustering is crucial for reducing the dimension of the dataset. We give some timings in order to illustrate how our approach can deal with tens of thousands of time series. Of course, the total computation time depends on the technical specification of the structure used to perform the computation. In our case, we restrict ourselves to a standard scientific workstation with 8 physical cores and 70 Gigabits of live memory. We use all the available cores to cluster chunks of 5000 observations following the algorithm described in Section 6 for both the first and second clustering task. A very simple pretreatment is done in order to eliminate load curves with eventual errors. For this, we measure the standard deviation of the contributions of each curve to keep only the 99% central observations eliminating the extremest ones.
With this, the leftmost point of the curve represents the largest cluster, while the following ones are sorted in decreasing order of size. To compare between sample sizes four curves are plotted, one for each sample size. A common decreasing trend of the curves appears producing several relatively small clusters. This is not a desired behaviour for a final clustering task. However, we are in an intermediate step which aims at reducing the number of curves n to a certain number K of super customers, here K = 200. The isolated super customers may merge together in the following step, producing meaningful aggregates. In what follows we focus on the results for the largest dataset, which is the one with over 25 millions of load curves. The resulting 200 super consumers are used to construct the WER dissimilarity matrix, which contains rich information about the clustering structure.
For each bivariate scatter plot, the points are drawn with a discrete scale of 15 colours, each one representing a different cluster. This low dimensional representation succeed to represent the clustering structure since points with the same colour are closer forming compact groups. Bottom-up forecast is the leading argument of using the individual load curve clustering. We test the appropriateness of our proposition by getting for a final number of clusters ranging from 2 to 20, 50, 100 and 200 the respective aggregates in terms of load demand. Then, we use KWF as an automatic prediction model for both strategies: prediction of the global demand using the global demand, and the one based on the bottom-up approach. We use the second year on the dataset to measure the quality of the daily prediction using a rolling basis.
Monolayer electrode fabrication The monolayers were formed in a KIBRON Langmuir-Blodgett (LB) trough (MicroTroughX). An adequate solution volume was spread over an ultrapure water surface (18. 2 M cm Millipore Simplicity, Billerica, MA) using a micro syringe. After solvent evaporation, the monolayer was compressed at a barrier speed of 10 mm/min and the surface pressure recorded. In the following, the isotherms were presented as a function of the trough area instead of the area per monomer. The monolayer homogeneity at the airwater interface was verified in-situ by Brewster Angle Microscopy; more detail on this technique is given in the Supplementary data (Section 2)  [31]. For the LS transfer, the monolayer was kept at a surface pressure of 15 mN/m and the PDMS substrate was horizontally moved towards the monolayer at 2 mm/min using a stepper motor from KIBRON.
Atomic force microscopy (AFM) 100 m-thick PDMS membranes were fabricated using the method reported by Rosset et al. ,  [32]  and used as substrates for the LS transfer of the monolayer electrode (Supplementary data, Section 3. 1). The AFM images were performed using the Peak Force Tapping mode with a Dimension ICON microscope from Brüker. AFM imaging was carried out in air using ScanAsyst-air cantilevers (Brüker) with a spring constant of 0. 4 Nm -1. 10 m × 10 m images were obtained with the height mode and a 256 × 256 resolution, at a 0. 5 Hz scan rate. Data processing was performed with NanoScope software version 1. 40 (Brüker).
A LabVIEW code was written to control the motor and the force sensor for the whole experiment process. After measuring the Young's modulus of bare PDMS membranes, the monolayer electrodes were LS transferred onto those PDMS membranes. The Young's modulus of the PDMS + electrode bilayer was determined using the same parameters as for the bare PDMS membrane. The PDMS + electrode bilayer was formed by two adherent layers with equal length and width, so the electrode Young's modulus was calculated using  [33] : Y bilayer t bilayer = Y PDMS t PDMS + Y e t e (3) where Y was the Young's modulus and t was the thickness.
Notably, the MagB:MagD and MagF:MagD sedimentation peaks were sharp, indicating rather homogeneous complexes, but the MagA:MagD peak displayed a clear shoulder indicating the presence of a mixture of higher order species (Fig. 3C ). Nevertheless, these data clearly indicate that MagD can form stable 1:1 complexes with these three specific Mag partners. We then sought to further characterize the binary MagD complexes by using small angle X-ray scattering (SAXS). Experiments were performed with MagD, MagA:MagD, MagF:MagD, and MagB:MagD that had been purified by size exclusion chromatography prior to experimentation. Data were collected at the D01B-SAXS1 beamline at the LNLS light source in Brazil (Fig. 4A  and Table  3 ). Scattering patterns, recorded at different concentrations for all samples, did not suggest any oligomerization or aggregation events.
Primary data reduction FIT2d Data processing PRIMUS/GNOM Table  3. Data collection and structural parameters of the Mag complexes obtained using SAXS. Rh and Rg values obtained from SEC and DLS were also included for comparative purposes. 1 Reported for the 6 mg/ mL measurements, except for MagA:MagD, at 2 mg/mL. Dmax (nm) (+/-0. 5 nm). Rh from SEC (nm) (+/-0. 5 nm). Rh from DLS (nm) (+/-1 nm). to the periplasm. The higher forms of MagD could thus correspond to its unprocessed, full-length form still harboring the 21 residue signal peptide, as well as degradation products. The inner membrane localization of MagD and its cleavage are determined by MagB. When full-length MagD was expressed in trans in a strain where the entire mag operon was knocked out (P. aeruginosa Δop), it lost its inner membrane localization  15.
Consecutive scans were automatically recorded at regular intervals and analyzed with Sedfit 23 using the continuous size distribution c(s) analysis method to determine s-values. Sedfit was also employed to fit a frictional ratio that was combined to the s-value in order to generate experimental molar masses, and sedimentation coefficients were corrected to s 20w. Small angle X-ray scattering. SAXS data were recorded at the Brazilian Synchrotron Light Laboratory (LNLS) on the SAXS1 beamline equipped with a Pilatus 300 k detector using a wavelength of 1. 54 Å and a 903 mm sample-to-detector distance. The s-range extended from 0. 1 nm -1 to 2. 8 nm -1. Protein samples were prepared in concentrations of 2 to 6 mg/mL in 25 mM HEPES pH 7. 5, 100 mM NaCl, 5% glycerol and 1 mM DTT. Measurements were carried out using a 1.
5 mm capillary cell, with a sample holder temperature kept constant at 10 °C. Scattering patterns for protein samples and buffers were collected alternatively with exposure times of 100 s. Data reduction included averaging of individual curves with FIT2D  24. The radius of gyration, Rg, was estimated using the Guinier approximation I(s) = I(0) exp(-(s 2 Rg 2 )/3), valid for small angles (sRg < 1. 3), PRIMUS  25. GNOM  26  was used to obtain the distance distribution function, p(r), and maximum intramolecular distance. The experimental curve of the MagD data was compared with the calculated scattering curves of the structures of the S. enterica and E. coli A2M (PDB codes 4U48 and 5A42, respectively) using CRYSOL  22. In real space, the structures were superimposed with the ab initio models of MagD with SUPCOMB  27.
Acknowledgements This work was supported by the  Laboratoire International Associé BACWALL  and by grants  11/52067-6  and  2013/01962-0  from  FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo ). This work used the platforms of the Grenoble Instruct-ERIC Center (ISBG:  UMS 3518 CNRS-CEA-UGA-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB). We thank the  Biosciences National Laboratory (LNBio)  and the  Synchrotron Light Laboratory (LNLS) at the Brazilian Center for Research in Energy and Materials (CNPEM) , for access to all facilities (LPP, LEC and the D01B-SAXS1 beamline). We especially thank  Juliana Fattori  for technical assistance at LEC and  Florian Meneau  for access to and assistance with SAXS data collection.
(2017)  compared autometrics and LASSO according to prediction and selection accuracy. The contribution of this paper is threefold. First, many procedures are considered, which are listed and clearly classified. We establish a topology of procedures under different model structures. We consider major ones: linear models, grouped variables models, additive models, partial linear models and non-parametric models. Secondly, we describe and compare state-of-the-art papers in the literature. We give contexts where each procedure should be used, to which specific problem they answer and compare them on this ground. Thirdly, we present appropriate software resources implementing the described methods, whenever they are available, on three different software packages: R, Matlab and SAS. In this sense, any practitioner with enough knowledge in statistics can refer to our paper as a methodological guide for doing variable selection.
One improvement is also to consider both. Usually, the selection within each step is made according to some criterion. One considers all one-variable increments from the actual model and chooses the best move according to this criterion, which might be the lowest p-value; highest adjusted R 2 ; lowest Mallow's C p  (Mallows 1973) ; lowest AIC  (Akaike 1973) , AICc  (Hurvich and Tsai 1989) , BIC  (Schwarz 1978)  or HQIC  (Hannan and Quinn 1979) ; lowest prediction error; leave-one-out cross validation; etc. Stepwise regression is available in almost any statistical software. In R, the package leaps  Lumley (2017)  provides this method with a wide range of criteria such as the ones cited above. In Matlab, the function stepwiselm is available from the "Statistical and Machine Learning Toolbox".
Usually, it reduces to LASSO and then algorithms such as the shooting algorithm (Fu 1998), the local quadratic approximation  (Fan and Li 2001) , the Least Angle Regression (LARS,  Efron et al. (2004) ) or the coordinate descent  (Wu and Lange 2008)  are employed. These methods are rather well-implemented in most software packages. In R, the package 5 One can get insights of how to connect LASSO to stepwise regression  (Efron et al. 2004 ) via the forward stagewise method of  Weisberg (2005). glmnet  (Friedman et al. 2010 ) has options for LASSO, AdaLASSO and ridge. Sparsestep can be found in the package SparseStep (van den  Burg et al. 2017 ). However, LASSO can be solved in many different ways; for each possible algorithm, there exists a corresponding package. The package lars  (Hastie and Efron 2013)  implements the LARS algorithm as well as the forward Stagewise and the stepwise approach but the shooting algorithm is only available in the lassoshooting package  Abenius (2012).
In Matlab, the "Statistics and Machine Learning Toolbox" implements only LASSO and ridge through functions of the same name. Matlab uses the ADMM (Alternating Direction Method of Multipliers of  Boyd et al. (2011) ). If one wants to use a specific algorithm to solve for LASSO, then there exists different resources available on Matlab Central. In addition, there exists a more complete toolbox named penalized  (McIlhagga 2016)  which has almost the same features as the R package glmnet. It implements the AdaLASSO which is missing in the "Statistics and Machine Learning Toolbox". In SAS, LASSO and AdaLASSO are available via PROC GLMSELECT using the LARS algorithm, and ridge via PROC REG. The SHIM model is only available in R via a github repository (https://github. com/sahirbhatnagar/shim).
(2014). In R, the package ncvreg  (Breheny and Huang 2011)  implements SCAD as well as MCP criteria while non-negative garotte is implemented in the package lqa  (Ulbricht 2012). In Matlab, SCAD and MCP can be found in the toolbox SparseReg available on github (http://hua-zhou. github. io/SparseReg/) while some attempts to implement non-negative garotte can be found on Matlab Central. Only SCAD is available in SAS via the command PROC SCADLS.
Since there is a great literature of reviews on these method  (Breheny and Huang (2009) ; Huang et al. (  2012 )), we do not spend time giving more details and advise readers interested in group models to have a look at them. All group-related methods can be found in the R package grpreg (Breheny and Huang 2015) except the ElasticNet, which is available in glmnet. In SAS, PROC GLMSELECT has all the previously cited features available (i. e. , LASSO, AdaLASSO, etc. ) for group selection and also includes the ElasticNet. In Matlab, however, only the ElasticNet is provided in the "Statistics and Machine Learning Toolbox" using the function lasso. Implementation for grouped structure can be found on Matlab Central.
Penalty The Sparse Additive Model (SpAM) of  Ravikumar et al. (2007)  applies to this kind of models. The idea is simply to apply LASSO to functions non-parametrically fitted with parametric coefficients coming on top of them. This is obviously the most natural extension of LASSO to the additive structure. The main program to solve is: min β y - p ∑ j=1 β j f j (x j ) 2 2 + λ p ∑ j=1 |β j |. ( 16 ) Even though the term ∑ p j=1 β j f j (x j ) might remind us of the very well-known Splines where the f j would be the basis functions, the authors claimed that any non-parametric method can be used for fitting them. The solution is given in the form of a backfitting algorithm  (Breiman and Friedman 1985). Another approach was investigated by  Meier et al. (2009) : the penalized General Additive Model (penGAM).
However, its selection consistency can be directly linked to the way variables are selected in trees, which is discussed in the next subsections. Using directly MARS is more similar to a non-linear version of stepwise regression using piecewise functions. This procedure is implemented in most statistical softwares. In R, the package earth  (Milborrow 2018)  has implemented the MARS procedure. In SAS, it is available in the ADAPTIVEREG procedure. In Matlab, the toolbox ARESlab provides an implementation. This toolbox can be downloaded from http://www. cs. rtu. lv/jekabsons/regression. html.
The key idea is to use a penalty term written in terms of a sum of Reproducible Kernel Hilbert Space (RHKS) norms. In a model with only two-way interactions, it would be: J( f ) = p(p-1)/2 ∑ α=1 P α f 2. ( 43 ) This time, the penalty is not designed to take into account the structure of the resulting model. There is no desire to limit interactions. Since the heredity constraint is not present as before the model authors of VANISH claimed it has trouble with high dimensional settings. Nevertheless, the heredity constraint can obviously be inadequate in some applications where only interactions matter; in this type of settings, COSSO is more advisable than VANISH. COSSO has been implemented in R through the package cosso  (Zhang and Lin 2013).
2). This coefficient is used as the measure of non-linear relationship. It can be defined as: ωj = sup h∈H ∑ n i=1 {h(x i,j ) -hj }(y i -ȳ) n ∑ n i=1 {h(x i,j ) 2 -h2 j }. ( 53 ) Then, these estimates are tested using bootstrap confidence interval instead of threshold as the others usually do. Finally, significant ones are ranked. Even though their method seems very general, empirically, h(•) are chosen to be polynomial functions. This can be restrictive in some situations and less non-parametric in some sense. Only DC-SIS and Gcorr have been implemented in R. The former can be found in the packages VariableScreening  (Li et al. 2018)  and cdcsis  (Wen et al. 2014 ). The latter is available from the author as a supplementary file to their paper. This file contains R code for the Generalized Correlation Coefficient and it can be found at https://www.
This is more computationally efficient. The threshold can be selected appropriately so that the expected number of false inclusion V is bounded. E[V] ≤ 1 2δ -1 q 2 λ p. ( 60 ) Thus, one will ensure P(V > 0) ≤ α by setting for example: δ = 0. 9 q λ = 0. 8αp. (61) The results are then presented as stability paths: Π λ j as a function of λ. This is in contrast to regularization paths of LASSO: β j as a function of λ. Extensions to Stabsel were proposed by  Bach (2008)  and  Shah and Samworth (2013). The first uses bootstrap with replacement instead of resampling without, while the latter uses subsampling of complementary pairs. StabSel has been implemented in R through the package stabs  (Hofner and Hothorn 2017).
The limits in main methods (LASSO and Stepwise regression) are now well understood and various answers have come to light. The area of model selection is still very investigated, much more now that many data have become available. Nevertheless, methods for handling large number of variables are restricted in terms of model complexity. This is mainly due to the curse of dimensionality and it prevents seeking very complex models in high dimensions. Sure independence screening is a powerful tool in linear models but has lower dataset capacities when it comes to non-linearities. In addition, the literature is lacking very complete algorithmic solutions. To the best of our knowledge, no statistical procedures have been developed to reach the level of completeness of autometrics. Other methods are only parts of the statistical work and do not cover as many problems as autometrics do.
The PEB consisted of a complex mixture of naturally-occurring soluble carbohydrates (including mono-, di-, oligo-saccharides and polyols, mainly pinitol, myoinositol plus D-chiro-inositol-soluble fibre) and minor compounds (organic acids, minerals, aminoacids) derived from carob pods. The sucrose-based beverage contained very similar amounts of non-polyol soluble carbohydrates, macronutrient composition and total number of calories to the PEB, but excluded inositols (Table  1 ). The sweetness and energy supply of inositols compared to other common sweeteners is showed in Supplementary Table  S1. This trial was registered on clinicaltrials. gov under the study number NCT01754792. The anthropometric and biochemical profile of the study population has been previously reported  [16, 17, 21, 22]  for healthy subjects, IGT subjects and in T2DM patients.
Two-Dimensional Gel Electrophoresis A protein load of 100 µg (analytical gels) and 300 µg (preparative gels) of the urea/chaps serum extracts was applied to 17-cm dry strips (pH 4-7 linear range; BioRad). The second dimension was resolved in 10% SDS-PAGE (sodium dodecyl sulfate-polyacrylamide gel electrophoresis). Gels were developed by fluorescent staining (Flamingo; BioRad, Hercules, CA, USA). For each independent condition, 2-DE for protein extracts from the baseline and the post-intake of placebo or Fruit-Up ® were processed in parallel to guarantee maximum comparability. Analysis for differences in the protein profiles was performed with the PD-Quest 8. 0 software (BioRad, Hercules, CA, USA). Each spot was assigned a relative value that corresponds to the single spot volume compared to the volume of all the spots in the gel, following background extraction and normalization between gels  [26].
0, Bruker Daltonics, Billerica, MA, USA) considering a signal-to-noise ratio over three, applying statistical calibration and eliminating background peaks. For identification, peaks between 850 m/z and 1000 m/z were not considered. After processing, spectra were sent to the BioTools interface (version 3. 2, Bruker Daltonics, Billerica, MA, USA) and a MASCOT server search on Swiss-Prot 57. 15 database was carried out (taxonomy: homo sapiens, mass tolerance 50 to 100, up to two trypsin miss cleavages, global modification: carbamidomethyl (C), variable modification: oxidation (M)). Identified proteins were accepted when a mascot score higher than 52 was obtained by peptide mass fingerprint and confirmed by peptide fragmentation was working in the reflector mode.
Western Blot Analysis Protein extracts were resolved by 1-DE under reducing conditions and electrotransferred to nitrocellulose membranes in semi-dry conditions (Trans-Blot Turbo system; BioRad, Hercules, CA, USA). C4A complement detection was performed using a rabbit policlonal antibody against a recombinant fragment corresponding to a region within amino acids 23-302 of the protein (NBP2-14,893, 1:1000 dilution, Novus bio, Littleton, CO, USA). Band detection was performed using a chemiluminiscent substrate dye (SuperSignal ® West Dura Extended Duration Substrate, Thermo Scientific, Waltham, MA, USA) and a molecular imager ChemiDoc XRS System, Universal Hood II (BioRad, Hercules, CA, USA). Band quantification was performed with Image Lab 4. 0 software (BioRad laboratories, Hercules, CA, USA).
Statistical Analysis Data are expressed as mean and standard error (SEM) unless stated. N indicates the number of subjects tested. Statistical analysis was performed with Stat View 5. 0. 1 software (SAS Institute, Cary, NC, USA). Differences between the basal condition and after six weeks of intake of the pinitol-enriched beverage were tested using a repeated measurement Willcoxon analysis. Differences between groups were tested with a nonparametric Mann-Whitney test. A p-value ≤ 0. 05 was considered significant.
Acknowledgments: The technical assistance of  María Dolores Fernández  and  Ona Catot  is gratefully acknowledged. Authors are indebted to  Josep Moreno  for the animal support. Finally, the authors thank the study participants for their valuable contribution and  ADM&WILD-Valencia SAU  especially  R Salom  and  R García-Bou , for providing the products used in the study. This work was supported by grants from  CDTI Spanish Ministry of Competitiveness and Economy (MINECO)  ( CEN-20101016 (HEFUFOOD)  to L. B. ); the  Spanish Ministry of Economy and Competitiveness of Science  [ SAF2016-76819-R  to L. B. ];  Institute of Health Carlos III, ISCIII  [ TERCEL RD16/00110018  to L. B. ; and  CB16/11/0041  to L. B. ; and  FIS PI16/01915  to T. P. ;  PI16/00301  and  PI15/01424  to M. R. and A. H. ];  FEDER "Una Manera de Hacer Europa "; the  Secretary of University and Research, Department of Economy and Knowledge of the Government of Catalonia  [ 2014SGR1303  to L.
Following the diagnostic coronary angiography, the ImageWire (Lightlab Imaging) was carefully placed distal to the stenosis. After administration of 200 mg of intra-coronary nitroglycerin, the target vessel was flushed via the guiding catheter with isomolar, nonionic contrast liquid. All OCT imaging analyses have been performed offline by the same independent core laboratory [Krakow Cardiovascular Research Institute (KCRI), Krakow, Poland] using the proprietary software from LightLab Imaging by two analysts blinded to the angiographic data and patients' clinical characteristics. Stent analyses for strut coverage, apposition and protrusion at frames were conducted at 1 mm longitudinal intervals, based on the methodology applied in previous studies using OCT evaluation of DES  [9, 14, 15, 19, 21, 22].