dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion We have studied a multifunctional solar system (ECS, heating and cooling) using the TRANSOL software in dynamic mode to see the thermal behavior of the studied block after the integration of solar systems. The results showed that the heating requirements are relatively important given the choice of our sizing and the scenario chosen; all associated with the low potential in winter for the region of Blida. Our solar installation covers only a fraction of 10% for the winter months, which is mainly because when the needs are at their maximum, solar gains are at their minimum. It can also be noted that having a roof slope favorable for the summer season leads to an under-sizing of the system and therefore a major constraint for such installations. For air conditioning needs, they vary according to the habits of the occupants and the period of year (climate) and the sizing choice (family of 4 people and an average drawing profile).
The α-β tubulin heterodimer undergoes subtle conformational changes during microtubule assembly. These can be modulated by external factors, whose effects on microtubule structure can be characterized on 2D views obtained by cryo-electron microscopy. Analysis of microtubule images is facilitated if they are straight enough to interpret and filter their image Fourier transform, which provide useful information concerning the arrangement of tubulin molecules inside the microtubule lattice. Here, we describe the use of the TubuleJ software to straighten microtubules and determine their lattice parameters. Basic 3D reconstructions can be performed to evaluate the relevance of these parameters. This approach can be used to analyze the effects of nucleotide analogues, drugs or MAPs on microtubule structure, or to select microtubule images prior to high-resolution 3D reconstructions.
, 2020). To facilitate analysis of microtubule images obtained by cryo-electron microscopy, we have developed TubuleJ as a plugin to the multi-platform ImageJ software  (Schneider et al. , 2012). The basic principles of TubuleJ were originally described in  (Blestel et al. , 2009). The current version has been updated with new routines, and incorporates TomoJ  (Messaoudi et al. , 2007)  to perform fast 3D reconstructions of microtubules. The workflow of TubuleJ is presented in Figure  1. 2. Select 'Plugins -> TubuleJ -> TubuleJ'. The interface of TubuleJ opens (Figure  1A ). 3. Click on 'Select fiber' and create a folder named 'MTa'. Select this folder as your working directory. Note: Several microtubules are frequently present in a single image, which requires creating a separate folder for each microtubule that will be analyzed.
The helical start number S can be estimated from the sign of the protofilament skew angle (Table  1 ). In the present case, N = 15 and θ is positive, hence S = 4. b. The 'approximate MT length' value determines the height of the volume that will be calculated at the following step so that it encompasses at least 4 monomer helical starts. inversion is thus necessary to examine these data in UCSF Chimera (J). Scanned images recorded on negatives display the protein density as white. In that case, uncheck the 'Invert intensities?' button. b. The weighting radius can be set to higher values (between 0 and 0. 5) in order to attenuate the contribution of low frequencies, which are amplified during back projection  (Radermacher, 1992 ). Yet, this is done at the expense of increasing the noise in the final 3D reconstruction.
Acknowledgments This work was funded by the  Agence Nationale de la Recherche  ( ANR-16-C11-0017-01 ). We thank  S. Blestel  who wrote the original version of TubuleJ. The determination of microtubule centers used in TubuleJ was published in Blestel et al. (2009). TubuleJ was used in  Estévez-Gallego  et al. (2020) to analyze the structure of microtubules assembled in the presence of GTP, GMPCPP and GDP-BeF3 -.
Analysis Techniques. ATR-FTIR spectra of the ACC samples were obtained using a Thermo Fisher Scientific Nicolet iS550 FTIR equipped with a single reflection ATR diamond crystal, in the 525-4000 cm -1 range with a 4 cm -1 resolution. A total of 64 scans were averaged for each sample and background. The mineralogical composition was determined by XRD using a Bruker D8 DISCOVER diffractometer with Cu Kα radiation (wavelength = 1. 54 Å). Data were recorded in a continuous-scan mode in the 20-60°2θ range with a step of 0. 026°. XRD patterns of fresh samples were measured within 30 min after drying, and in all cases, no crystalline peaks were observed. Thermogravimetric analysis (TGA) and differential scanning calorimetry analyses were performed using a Mettler Toledo TGA/DSC3+. Around 5 mg of dry powdered sample was placed in an alumina crucible, and the sample was heated from 25 to 750 °C under a 20 mL/min N 2 flow.
g. from the PDFs and the scale dependence, through to the physical observables. As the uncertainties on LO hard-scattering cross-sections are typically very large, owing in part to the missing higher-order terms, such theory error propagation to the cross-sections can only really be considered representative from NLO accuracy onwards. Even at NLO, we can see in Fig. 1  that the impact is considerable, and needs to be taken into account when fitting models. In this work we present xsec, an attempt at a general solution to the speed problem irrespective of the type of crosssection being evaluated, the size of the parameter space of the model, and the underlying precision of the calculation. We achieve this by performing machine-learning regression on a pre-generated training dataset consisting of cross-sections sampled from a model.
This is currently limited to a small set of the most phenomenologically relevant processes in the MSSM-19, and does not include uncertainties arising from PDFs, or the value of α s. The xsec 1. 0 code is currently designed to reproduce all the NLO results of Prospino 2. 1 for strong production in the MSSM in a small fraction of the time, including scale errors, and with the addition of PDF and α s errors based on a modern PDF set. It does not include NNLL or even NLL resummation as can be found in NNLL-fast and NLL-fast, however, unlike those codes it provides reliable results for non-degenerate squark masses, within the inherent limitations of the training set generated with Prospino. We intend to extend the code to also include NLL resummation in future releases. Unlike NNLL-fast, xsec performs a separate evaluation of the cross-section for all distinct flavour combinations of the first two generations of squarks.
The xsec code also treats sbottom and stop pair-production processes separately, but this has limited impact as the two cross-sections are the same at LO (for the same masses), 1 as Prospino -and therefore our training set -is limited to light quark initial states. A complete list of available processes can be found in Table  1. The code is written in Python with heavy use of the NumPy  [35]  package for numerical calculations, and is compatible with both Python 2 and 3. It can be installed using the pip package manager and run as a set of functions from a self-contained module. We also provide interfaces through SLHA files  [36]  and a command-line tool. The rest of this paper is structured as follows. We begin in Sect. 2 with an introduction to the GP regression framework that we employ.
( 22 ) Our complete kernel is then given by k(x, x ) = k AMM (x, x ; ν, σ 2 f , ) + k WN (x, x ; σ 2 ). ( 23 ) Fixing ν = 3 2 , as discussed above, we are left with the set θ = {σ 2 f , , σ 2 } of undetermined hyperparameters. To be fully Bayesian, one would introduce a prior pdf p(θ) for the hyperparameters and obtain the GP posterior p(y * |D, x * ) by marginalising over θ , p(y * |D, x * ) = p(y * , θ |D, x * ) dθ = p(y * |θ, D, x * ) p(θ |D) dθ ∝ p(y * |θ, D, x * ) p(D|θ) p(θ ) dθ. ( 24 ) In our high-dimensional case with large datasets, such integration would come at a steep computational expense, even with MCMC methods. We therefore follow the common approach of using a point estimate for the hyperparameters, found by maximising the log-likelihood function  [4]  log p(D|θ) = log p(y|X, θ ) = - 1 2 (y -m(X )) T Σ -1 (y -m(X )) - 1 2 log |Σ| - n 2 log 2π.
Tests with k-means clustering did not indicate further improvements in our case, nor did tests with sorting on less dominant features. The special role of the communication expert M c becomes evident at the prediction stage. For each of the experts {M i } d i=2 , we construct an improved expert M +i by replacing the corresponding dataset D i with the extended set D +i = {D i , D c }. That is, for prediction the communication dataset D c is shared by all the experts M +i. The communication expert M c serves as a common baseline to which the experts M +i can be compared. In the final combination, the prediction from expert M +i will be weighted according to the differential entropy difference between its predictive distribution and that of M c. The central approximation that allows for computational gains in DGPs and related approaches is an assumption that the individual experts can be treated as independent, which corresponds to approximating the kernel matrix of the combined problem, i.
D i-1 , y * , x * ) → p β i (D i |D c , y * , x * ) ( 35 ) in Eq. (  29 ) is exact for i = 2, β 2 = 1. For each remaining expert M +i≥3 , the weight is taken to be the difference in differential entropy between the baseline predictive distribution of the communication expert, p c (y * |D c , x * ), and that of the given expert, p +i (y * |D +i , x * ). Thus, if an expert M +i provides little additional predictive power over M c , its relative influence on the aggregated prediction is low. Requiring the experts to share a common set of hyperparameters effectively disfavours overfitting of individual experts. Moreover, the risk of overfitting is alleviated by the fact that after training, each expert is extended with the communication dataset D c that it did not see during training, and its weight in the prediction aggregation is regularised through the comparison to the communication expert.
This ensures that our training dataset is more densely sampled where the cross-sections of interest vary the most. To do this, we generate and then combine six different training sets: (i) All masses sampled from the uniform prior. (ii) All masses sampled from the hybrid prior. (iii) Employing the uniform prior for both the mass of the gluino and for a common squark mass scale, and taking a Gaussian prior with width 50 GeV for the difference between the common squark mass scale and the masses of the individual squarks. (iv) Employing the uniform prior for the mass of the gluino, the hybrid prior for a common squark mass scale, and the same Gaussian prior as in (iii) to draw values for the squark masses around the common scale. (v) Employing the uniform prior for a common mass scale, and using the same Gaussian prior as in (iii) to draw a gluino mass and the individual squark masses around the common scale.
Calculation of NLO training cross-sections We use Prospino 2. 1 to generate cross-sections for our training samples. This calculates, amongst other things, NLO cross-sections for strong production processes in the MSSM for proton-proton collisions at a given centre-ofmass (CoM) energy, and for a choice of renormalisation/factorisation scales. We have modified the code to set α s accordingly, and to accept generic PDF sets from LHAPDF 6. 2  [48]. For the current version of xsec we have used the PDF4LHC15_nlo_30_pdfas symmetric Hessian NLO PDF set with 30 eigenvector members and two members with varied strong coupling α s (m Z ) = 0. 1180 ± 0. 0015  [49]. Prospino performs the PDF integral of the partonic process using the VEGAS  [50]  importance sampling algorithm for Monte Carlo integration.
(31). As the aggregation step requires no matrix algebra, the first step dominates the computational cost of prediction. It scales The code relies on NumPy  [35]  functionality for these matrix operations. Significant speed gains are therefore possible if NumPy is properly linked to optimised numerical algebra libraries, like BLAS and LAPACK. Large performance differences exist between different BLAS implementations. Popular ones like Intel MKL, OpenBLAS and ATLAS support multi-threaded calculations, enabling NumPy to take advantage of multi-core machines and xsec to achieve the highest evaluation speeds. The NumPy package provides a function sho_config() to list the numerical libraries it detected in the system it was built on. The xsec code is compatible with Python 2 and 3. While the main program consists of the six interdependent modules shown in Fig.
Installation The xsec program is available from the Python Package Index (PyPI) and can be installed immediately with the pip package manager, by executing pip install xsec Instead, they are available as separate binaries in the GitHub release. We provide a script for their automatic download and extraction into a directory of choice, called by running xsec-download -gprocs -g gp_dir -t process_type on the command line. Here, gp_dir is the chosen target directory; a new directory gprocs is created in the current working directory if this argument is omitted. The other optional argument, process_type, specifies the final-state type for which data will be downloaded: gg (gluino pair production), sg (1st/2nd gen. squark-gluino pair production), ss (1st/2nd gen. squark pair production), sb (1st/2nd gen.
Python interface The primary envisaged use of xsec is as a component in a parameter scan code, where the user chooses the desired particle production processes and varies the input parameters. Appendix A contains a simple example script in Python where these parameters are set by hand. Examples of SLHA file input and usage within a loop over sparticle masses are available in the GitHub repository. To ensure that any time-consuming computations are only run when strictly necessary, the prediction process is split into four steps:.
Parameter input The next step is to set the values of all relevant parameters for the selected processes. The relevant parameters are shown in Table  1. There are three ways to enter the parameters: manually setting their values one by one, setting multiple values at once with a dictionary, or reading all parameters from an SLHA file describing the supersymmetric spectrum. xsec. set_parameter("name", value) xsec. set_parameters( {"name1": value1, "name2": value2,. } ) xsec. import_slha("filepath") The example script in Appendix A lists all available parameters. The SLHA interface is based on the PySLHA package  [54] , which is a dependency of xsec. The program currently works with the SLHA1 standard, because we give the decomposition of cross-sections with squarks in terms of their flavour eigenstates (with the exception of the third generation  8   This function has two optional keywords: verbose (default 2) and check_consistency (default True).
In order to return a symmetric α s error, we average the outputs from our two GPs trained separately on the upper and lower errors. This can be changed in the code by more advanced users if desired (for example, to allow α s to be treated more accurately as a nuisance parameter to be scanned over). The regression error is asymmetric because it comes from a log-normal distribution, while the lower (upper) scale error derives from the minimum (maximum) cross-section value obtained by doubling and halving the renormalisation/factorisation scale. The results from an evaluation for a given parameter point can be added to an existing SLHA file in the XSECTION block 9 : results = xsec. eval_xsection() xsec. write_slha("slha_path", results) The XSECTION structure does not allow for storing the regression error, so this is omitted.
Conclusions In this paper, we have presented a new phenomenology code xsec, which allows for the fast evaluation of higher-order cross-sections through regression on pre-generated training datasets. The regression in xsec is based on Generalised Robust Bayesian Committee Machines, a method that employs distributed Gaussian processes. In addition to the central value and the related regression variance provided by the Gaussian processes, we have trained separate Gaussian processes on the scale, PDF and α s errors, providing a com-plete ecosystem for the evaluation of cross-sections and their uncertainties. The current version of xsec 1. 0 includes all MSSM strong-production cross-sections at √ s = 13 TeV at NLO precision in QCD, separated into individual squark flavour final states, and allows for non-degenerate squark masses.
We plan to make future updates of xsec that will extend the code both in terms of the included energies and processes, as well as with higher-order corrections. The method used here can also be extended to other models, so long as appropriate training sets can be generated. The production of supersymmetric particles in the MSSM serves as a good starting point however, as the absence of s-channel resonances simplifies the training. We would like to emphasise that the xsec code described here is not a new calculation of the included cross-sections. It is a regression estimate based on a pre-generated sample of cross-sections taken from existing results. Thus, users of this code should also reference the original physics results on which the results of xsec are based when using them in publications.
Acknowledgements This work has been supported by the  Research Council of Norway  ( FRIPRO 230546/F20 ) and a NOTUR (Norway;  NN9284K ) Grant for computation time on the Abel cluster at the  University of Oslo. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Action Innovative Training Networks MCnetITN3 and SAGEX  (Grant agreement nos. 722104  and  764850 ), the  Royal Society  under Grant  UF160548 , and the  Australian Research Council  under grant  FT190100814. AB wishes to thank  Donatas Zaripovas  and  Laurynas Mince  for their contributions to earlier versions of this work, and AK and PS wish to thank  Ben Farmer ,  Nicholas Reed  and  Iza Velišček  for many useful discussions on Gaussian Processes.
set_energy(13000) Currently, only the 13 TeV dataset is available. We expect to add further energies in the near future. Then, one or more sparticle production processes can be specified. A single process is identified by a tuple process = (pid1, pid2) containing the PDG codes of its final-state particles. A list of such tuples is required as the argument to the loading function: xsec. load_processes([process1, process2,. ]).
Methods: We characterized users based on their writings, posting patterns, relations with other users, and images posted. We also evaluated statistical and deep learning approaches to handle multimodal data for the detection of users with signs of suicidal ideation (suicidal ideation risk group). Our methods were evaluated over a dataset of 252 users annotated by clinicians. To evaluate the performance of our models, we distinguished 2 control groups: users who make use of suicide-related vocabulary (focused control group) and generic random users (generic control group).
Considering this as a classification task, which manipulates sensitive data  [12] , a proper annotation process is required to obtain a reliable dataset. This has become one of the main challenges because diagnosing mental disorders involves the intervention of specialized and experienced clinicians who follow strict screening proceedings  [11]. The methods used for analyzing user-generated data related to suicide focus on tracking social networks at a post level, that is, a tweet  [13, 14] , or at a user level, that is, a sample of their tweets or posts  [15]. The latter is more related to risk assessment, as more data from a single user can be explored. In terms of the type of information extracted and explored, state-of-the-art approaches perform an exhaustive analysis of textual information contained in posts.
Regarding the use of visual information from posts shared in social networks to address mental disorders, the closest approaches to our goal are methods for personality prediction  [19] , and a few recent approaches address self-harm, depression, and anxiety  [20] [21] [22]. We believe that our study is the first image-based approach for suicide risk assessment on social media at the user level. When we refer to suicide-related collections, to the extent of our knowledge, no previous publications have been reported on the generation of a suicide-related dataset built over Twitter where the annotation process involves expert clinicians labeling tweets according to the presence of suicidal thoughts focused on two types of control groups. Furthermore, our annotation was performed at the user level after having processed and analyzed sequences of posts within a period of a year.
Objectives This study addressed the detection of mental health issues on social media, with suicide risk assessment as our use case. Our main contributions are listed as follows:  (1)  we defined a methodology to generate a reliable Twitter dataset for suicide risk assessment, which is also the first user-level dataset of this type dedicated to posts in Spanish;  (2)  we presented a method to obtain a subset of the user tweets related to a specific topic, in this case, suicidal ideation;  (3)  we generated models that explore the impact of not just relational and behavioral factors but also elements identified by specialists during consultations, which have been mapped to social networks; (4) we developed image-based predictive models to detect suicidal ideation;  (5)  we integrated the previous elements into a method that combines multimodal data to build predictive models that address the detection of mental health issues using cross-platform information (Reddit, Instagram, and Twitter); and  (6)  we refined the evaluation process of predictive models for mental health issues by considering 2 different types of control groups within the social media context: users with posts that might not use terms related to mental conditions (generic control cases) and users who make use of terms related to mental disorders (focused control group).
Data Collection In this section, we described the methodology designed to generate a reliable Twitter dataset for suicide risk assessment. We selected Twitter as our main data source, as it has been proven to be suitable for analyzing mental disorders on social media  [3, 13, 18, [25] [26] [27] , including suicidal ideation. We also highlighted the following aspects that this platform offers for our research: (1) the possibility of selecting posts in multiple languages; (2) the availability of relational and behavioral factors; and (3) the provision of a set of chronologically ordered posts from each user, which allowed us to do a cumulative analysis of the data referring to suicidal ideation.
Combining Reddit and Twitter for Gathering Suicide-Related Data Before starting the crawling process on Twitter, we elaborated a list of suicide-related sentences to build a first filtering approach to obtain tweets coming from users at risk. In doing so, we started by collecting a sample of 500 titles of posts published in Reddit's Suicide Watch forum  [28]. These posts were mostly written by users with suicidal ideations, so their titles can be considered to be suicide-related sentences. These phrases were then translated to Spanish and reviewed by clinic psychologists. Some sentences were added, discarded, and/or adapted by the clinicians. We kept 454 phrases after the analysis by experts. Then, based on their relevance and link to suicide risk factors  [29] , a subset of 110 phrases were selected for use as search terms on Twitter.