dataesr/scientific-paragraphs-categorization huggingface dataset:
Anthropogenic emissions VOC emissions (based on the European Monitoring and Evaluation Programme (EMEP) inventory in this study) are used as in  Menut et al. (2013) ; volatile organic compounds are split into CHIMERE model species according to a speciation database depending on the emission sector. For primary SVOC emissions, a SVOC/POA factor is applied to convert POA emissions into SVOC emissions. In  Couvidat et al. (2012) , a SVOC/POA factor of 5 was used on the basis that SVOC primary emissions are underestimated. With this factor, the model was able to simulate the strong concentrations of organic aerosols in winter and to give satisfactory results over most of Europe. Denier van der  Gon et al. (2015)  has shown that POA emissions are greatly underestimated due to a strong underestimation of residential wood burning emissions by a factor of 3 over Europe (between 1 and 10 depending on the countries) if SVOC emissions are included.
Thermodynamic of secondary organic and inorganic aerosols Two thermodynamic modules are implemented inside CHIMERE to take into account the formation of secondary aerosols: ISORROPIA v2. 1  (Fountoukis and Nenes, 2007)  for inorganic aerosols and SOAP  (Couvidat and Sartelet, 2015)  for organic aerosols. Due to the lack of information on the dust composition, crustal elements are not taken into account for the partitioning, as they can strongly impact the formation of ammonium nitrate  (Ansari and Pandis, 1999; Moya et al. , 2002). However, a simple reaction (described in Sect. 2. 1. 8) is added to CHIMERE to take into account the formation of calcium nitrate as done by  Hodzic et al. (2006). SOAP computes the partitioning of organic compounds between the gas and particle phases according to the complexity required by the user.
Observations Results of the model are compared to various measurements (NO - 3 , NH + 4 , SO 2- 4 , Na + , Cl -, OC, PM 1 , PM 2. 5 and PM 10 ) available in the EBAS database  (Tørseth et al. , 2012)  from various instruments (i. e. , filters, tapered element oscillating microbalances, beta ray absorption) for regional background stations. The stations cover most of Europe with the first measurements available beginning in the 1970s. EBAS (http://ebas. nilu. no/) is a database hosting observation data of atmospheric chemical composition and physical properties in support of a number of national and international programs ranging from monitoring activities to research projects. EBAS is developed and operated by the Norwegian Institute for Air Research (NILU). This database is mostly populated by EMEP measurements.
Organic aerosol Organic aerosol concentration measurements are not available in the database. However, measurements for organic carbon (OC) concentrations are available. OC is the mass of carbon inside the organic aerosols. For the comparison, OM / OC ratios (that depend on the composition of organic aerosols, especially the degree of oxidation of compounds) have to be assumed to estimate OC concentrations from modeled OM concentrations. Turpin and Lim (2001)  measured the OM / OC ratios at different locations and found ratios between 1. 2 and 2. 5, and recommended to use a ratio of 2. 1 for rural areas. Following  Couvidat et al. (2012) , modeled OC concentrations were calculated directly from the modeled concentrations of each organic surrogate compound using their molecular structure to estimate the OM / OC ratio of the surrogate compounds.
A quantile-quantile (Q-Q) plot of modeling results against measurements for PM 10 and PM 2. 5 is shown in Fig. 12. Q-Q plots can be used to assess the similarity of the distribution of two compared datasets. Figure  12  shows the quantile of modeled concentrations against the corresponding quantile of measured concentrations. For southern Europe, concentrations higher than 40 µg m -3 for PM 10 and higher than 20 µg m -3 for PM 2. 5 are significantly underestimated, especially for high concentrations of PM 2. 5. Western and central Europe have a similar distribution of concentrations with a strong overestimation of high concentrations of PM 2. 5 (higher than 35 µg m -3 ) and of PM 10 (higher than 55 µg m -3 ). This overestimation is probably due to the high overestimation of ammonium nitrate during the late autumn for these regions.
Conclusions Concentrations were compared to available information on PM concentrations and composition thanks to the EBAS database. Whereas the model gives satisfactory results with regard to the criteria defined by  Boylan and Russell (2006) , results could be improved in terms of seasonality and PM composition. Strongest errors were found to be probably due to ammonium nitrate which is often overestimated especially in late autumn (probably due to an overestimation of NH 3 emissions during this period). Only in summer, concentrations of ammonium nitrate could be underestimated. Strong errors were also found on OC concentrations in summer (especially over the northern half of Europe) indicating that strong concentrations of anthropogenic SOA could be missing from the models. The underestimation could also be due to a lack of biogenic emissions over the northern half of Europe.
Acknowledgements. This work was funded by the  French Ministry of the Environment, Energy  and  Marine Affairs. Simulations were performed using the TGCC-CCRT supercomputers under the  GENCI  time allocation gen7485. Measurements were extracted from the EBAS database. The EBAS database has largely been funded by the  UN-ECE CLRTAP (EMEP) ,  AMAP  and through  Norwegian Institute for Air Research (NILU)  internal resources. Edited by: Samuel Remy Reviewed by: three anonymous referees.
Neutron Scattering Neutron scattering data were collected on time-of-flight spectrometer IN6 at the Institut Laue Langevin (ILL) with an incident neutron wavelength of 5. 1 Å. The Bi2La8Ge6O27 sample was placed in a cylindrical Nb sample holder and data were collected at 800 °C with a collection time of 6. 5 hours. Data were also collected on a vanadium sample at 20 °C for 6 hours for use in normalisation. The data were analysed using the LAMP software. 45.
Computational All calculations were performed on 1 × 1 × 2 supercells of La10Ge6O27, Bi2La8Ge6O27, and Bi4La6Ge6O27 based on the triclinic Bi2La8Ge6O27 model described by Tate et al. 30  Interstitial oxygen atoms positions were chosen at random from the available O5 sites to ensure the correct stoichiometry and the full site occupancy. The locations of the Bi atoms were also chosen randomly from the available La2 sites to match the required stoichiometry. Before any other calculations occurred all structures underwent geometry optimisation in order to reduce the forces on the atoms. Ab-initio molecular dynamics (AIMD) calculations were carried out using DFT methods implemented in the VASP code  46. PAW pseudopotentials  47  were used with GGA-PBE functionals. 48 The electronic structure was sampled only at the gamma point.
POTENTIAL CONFLICTS OF INTEREST None of the authors have conflicts for this manuscript. Research reported in this publication was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number UM1 AI068634, UM1 AI068636 and UM1 AI106701. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This study is registered with ClinicalTrials. gov number NCT 00108862.
Quaternary structure of Lb-Tec2 The crystal structure of Lb-Tec2 revealed a distinctive quaternary structure with a tetramer created from three-fold symmetry in contrast to the two-or four-fold symmetry usually observed in other tetrameric structures deposited in the Protein Data Bank  (Rose et al. , 2017). Three protein chains, related by the three-fold axis, form a symmetric base (Figure  1C ), which is capped by the fourth protein chain with the symmetry axis in its central channel. This leads to the formation of a pseudo-sphere with dimensions of 64 x 71 x 73 Å (Figure  1D ). Using on-line size exclusion chromatography combined with small angle X-ray scattering (SAXS) measurements, we determined a ball shaped envelope in agreement with the crystal structure (Figures  1E  and S1B-D , Table  S1 ).
6 M MgSO4 and 100 mM sodium 2-(Nmorpholino)ethanesulfonic acid (MES) pH 6. 5. The crystals were soaked with 10 mM 2MeSeFuc  (Sommer et al. , 2016a)  for 2 min and then placed in 80% saturated Li2SO4 (~2. 5 M) for cryoprotection prior mounting in a cryoloop and flash freezing in liquid nitrogen. For the All4MeMan  (Beshr et al. , 2016)  complex, the crystals were directly transferred for 20 seconds in a solution containing 2. 25 M Li2SO4 and 10 mM ligand prior mounting and freezing. Data were collected at 100 K at ESRF, Grenoble, France on beamline BM30A-FIP for 2MeSeFuc/Lb-Tec2 complex and BM14 for All4MeMan/Lb-Tec2 complex using an ADSC Q315 CCD and a MARCCD detector respectively. Native crystals were tested on ID30-A using a Pilatus detector but the data were not of sufficient quality for further use.
The data were processed using the XDS package  (Kabsch, 2010)  and the scaling was performed with AIMLESS  (Evans, 2011)  in the CCP4 Suite  (Winn et al. , 2011). Structure solution and refinement. Using ShelXC/D/E  (Schneider and Sheldrick, 2002; Sheldrick, 2010)  in R32:h (also named H32) space group, initial phases for Lb-Tec2 were obtained to 1. 65 Å from the anomalous scattering of selenium present in 2MeSeFuc in a three-wavelength MAD diffraction data set comprising peak, inflection and high remote data over 180 degrees. A search for 6 selenium atoms led to an estimated mean FOM of 0. 7 and a Pseudo-Free CC of 72. 9%. 93% of the expected number of amino acids were observed in the polyalanine model constructed by ShelXE but they corresponded to 1. 33 protein chains indicating a possible incorrect spacegroup assignment.
SAXS measurements. Small-angle X-ray scattering (SAXS) data were collected at the BioSAXS beamline (BM29) of the ESRF using the HPLC mode  (Pernot et al. , 2013; Round et al. , 2013). 50 µL of Lb-Tec2 at 8. 5 mg mL -1 was loaded on a size-exclusion column Enrich TM SEC 70 (Bio-Rad Ltd) equilibrated in 20 mM Tris pH 7. 5. The data collected from the SEC run were combined into an idealized scattering curve using Primus from the ATSAS package  (Petoukhov et al. , 2012). Rg was estimated at low q values by the Guinier approximation. 40 ab-initio models were calculated using DAMMIF which were then averaged, aligned and compared using DAMAVER  (Franke and Svergun, 2009; Volkov and Svergun, 2003). The most representative ab-initio model selected by DAMAVER was aligned to the crystal structure of Lb-Tec2 using SUPCOMB and the PyMOL Molecular Graphics System, Schrödinger, LLC.
Molecular Docking. Coordinates of the crystal structure (PDB ID: 5FSB) were taken for calculations after deletion of the water molecules. Glide (Schrödinger program package) was previously shown to provide accurate saccharide molecule poses and thus chosen for calculations  (Friesner et al. , 2004; Nurisso et al. , 2008; Repasky et al. , 2002). Preparation of protein was fulfilled using program modules implemented in the Schrödinger program suite. Hydrogen atoms were added assessing neutral pH. Protein structure containing ligand molecules was minimized to provide an optimal hydrogen bond pattern. All calculations were performed in OPLS-2005 force field with corresponding partial atomic charges for both protein and sugar molecules. The docking procedure was performed on a rigid receptor while the ligand molecule was allowed to be flexible.
ACKNOWLEDGMENTS The authors thank  Emilie Gillon  for performing affinity measurements and  Dr. Anne Imberty  for insightful comments and discussions. A. V. is grateful to  Dr. J. Turkenburg  and  Prof. E. Dodson  for helpful advices for space group assignment. The authors are grateful to  European Synchrotron Radiation Facility, Grenoble, France  for technical support and access to beamlines BM30A-FIP,  BM14 ,  ID30-A  and  BM29  and to  Martha Brennich  for helpful discussions. Dr. Michael Hoffmann  and  Dr. Kirsten Harmrolfs  are acknowledged for HRMS and NMR measurement of compound 3. We further thank the  Helmholtz Association  (to AT, grant no  VH-NG-934 ), the  European Commission   Marie Curie Action ''Networks for Initial Training '' (to T. W. , grant  FP7/2007-2013-EuroGlycoArrays 215536 ), and the  Swiss National Science Foundation  (to MK, grant no  31003A-130671 ) for financial support.
Software and Algorithms XDS package  (Kabsch, 2010)  http://xds. mpimf-heidelberg. mpg. de/ CCP4i (Winn et al. , 2011) http://www. ccp4. ac. uk/ ShelXC/D/E (Sheldrick, 2010) http://shelx. uni-goettingen. de/ COOT (Emsley et al. , 2010) https://www2. mrclmb. cam. ac. uk/personal/pemsley/coot/ ATSAS software package (Petoukhov et al. , 2012) https://www. emblhamburg. de/biosaxs/software. html Molprobity (Chen et al. , 2010) http://molprobity. biochem. duke. edu/ wwPDB Validation server http://wwpdb-validation. wwpdb. org Glide and Prime Schrödinger https://www. schrodinger. com/ Dalilite (Holm and Rosenstrom, 2010) http://ekhidna. biocenter. helsinki. fi/dali_ser ver/ Blast (Altschul et al. , 1990) https://blast. ncbi. nlm. nih. gov/Blast. cgi Clustal omega (Sievers et al. , 2011) https://www.
, 2012) , a Matlab-like language, which is not suited to large-scale tests. In this paper, we present the first results obtained with the implementation of our method into the reference solver SUNDIALS IDA  (Hindmarsh et al. , 2005). In order to set our simulations, we used Dynamo  (Guironnet et al. , 2018) , which is a simulation engine based on the OpenModelica  (Fritzson et al. , 2006)  environment and developed by RTE for time-domain simulations. This paper is structured as follows. In the second section, the mathematical problem to solve when considering EMT simulations is introduced. In the third section, our numerical method is presented. In particular, we detail its implementation into the reference solver IDA and its interface with Dynamo. In the fourth section, some numerical results on two power systems are presented.
, 2005). From an implementation point of view, one of the greatest features of IDA is its wide variety of linear solvers for solving the linear systems that arise in Newton-Raphson iterations. In particular, it contains an implementation of the KLU solver for sparse matrices, which is particularly suited for power systems applications  (CRSA et al. , 2011). Then, the architecture of IDA roughly consists in: • a generic differential algebraic equations solver, which corresponds to the implementation of the BDF with variable step size and order. So, this general module performs the prediction, the correction and the step size and order adjustment. In our case, the maximum order is fixed to 2. More precisely, in the correction step, it executes the Newton-Raphson iterations by calling virtual functions for building and solving the linear system: initialization, setup and solve.
Practical Considerations of IDASPM Then, in order to implement the SPM into IDA, modifications have been made at the two architecture levels of IDA: at the general algorithm level and at the matrix-specific level. To begin, we added a member to the main data structure IDAMem, which contains the solver data. This member is a generic pointer to a SPM-specific data structure containing all the data needed for the SPM. More precisely, it contains the boolean vector distinguishing the oscillating and non-oscillating components of the solution, the vector of Fourier coefficients and the reference pulsation of the system. Furthermore, it contains integration and estimation work-space data. To initialize this data-structure, the user only has to properly set the above-mentioned boolean vector and pulsation.
In a SPM specific module, the estimator is implemented. As the estimator uses the Jacobian matrix of the DAE system, its implementation depends on the matrix storage format. Since the KLU is mainly used for power systems applications, our current implementation is done for CSR matrix storage. In a first implementation, the Jacobian matrix was updated within the estimator function but it resulted in a very important computational time by iteration. So, in our current implementation, the Jacobian matrix used in the estimator is updated only when IDA updates its Jacobian matrix. By this way, the number of Jacobian matrix evaluations is dramatically reduced while preserving a good level of approximation. Indeed IDA performs several robust numerical tests to infer on the validity of the Jacobian matrix.
Interface with Our Customized Framework based on Modelica OpenModelica  (Fritzson et al. , 2006 ) is an opensource suite based on Modelica language  (Fritzson and Engelson, 1998) , which is used for the modeling and simulation of complex systems. It is an objectoriented, formal and non-causal language for writing models in an equation-based style. Furthermore, OpenModelica contains a compiler which enables to generate simulation files, that are compatible with IDA, from a Modelica model file. In particular, Modelica enables to clearly separate the modeling and solving aspects. The European projects PEGASE  (Chieh et al. , 2011)  and then iTesla  (Vanfretti et al. , 2016)  have introduced and proved the industrial potential of Modelica for time-domain simulations of transmission grids. Furthermore, the latter resulted in the development of the iPSL library, which is opensource and still maintained.
Then, the dynamicalstudy tools team of RTE implemented Dynamo, a new simulation engine  (Guironnet et al. , 2018)  which is based on this framework. It especially enables to easily connect models with solvers to perform simulations in a very flexible way. Hence, in our framework, Modelica is used for the modeling part of the simulation. Then, our objective is to implement the model of the simulated system with the Modelica language and, then, to perform the simulation with IDASPM from Dynamo. To summarize, in OpenModelica-type frameworks the simulation process consists in three main steps: 1. The user implements a model from existing or user-defined libraries. A model generally consists in assembling unit models within a global model from their physical connections. 2. The model compiler, e.
g. OpenModelica Compiler, interprets this model to construct the associated DAE system to be solved. So, it constructs a "flat model" by injecting all the mathematical equations associated to the different unit components and connections. In output of this process, OpenModelica Compiler generates several source files corresponding to the simulation: residual function of the DAE system, parameters, initial values, etc. 3. To finish, as we have the files containing the DAE system at our disposal, it can be integrated using the chosen solver, e. g. SUNDIALS IDA. To do this, our simulation engine contains generic modules which automatically initialize the solver data structures and data, and perform the simulation from the chosen solver library. Therefore, in order to interface our solver with Dynamo, we have to generate the solver library corresponding to IDASPM and then to implement a customized solver module which properly initializes the data structures of IDASPM.
In video surveillance, pedestrian attributes such as gender, clothing or hair types are useful cues to identify people. The main challenge in pedestrian attribute recognition is the large variation of visual appearance and location of attributes due to different poses and camera views. In this paper, we propose a neural network combining high-level learnt Convolutional Neural Network (CNN) features and low-level handcrafted features to address the problem of highly varying viewpoints. We first extract low-level robust Local Maximal Occurrence (LOMO) features and learn a body part-specific CNN to model attribute patterns related to different body parts. For small datasets which have few data, we propose a new learning strategy, where the CNN is pre-trained in a triplet structure on a person re-identification task and then fine-tuned on attribute recognition.
INTRODUCTION Pedestrian attributes are defined as semantic midlevel descriptions of people, such as gender, accessories, clothing etc. (see Fig. 1 ). Since biometric features like faces are often not visible or of too low resolution to be helpful in surveillance, pedestrian attributes could be considered as soft-biometrics and used in many surveillance applications like person detection  (Tian et al. , 2015) , person retrieval  (Vaquero et al. , 2009) , person identification  (Layne et al. , 2012)  etc. A clear advantage of using attributes in this context is the possibility of querying a database of pedestrian images only by providing a semantic textual description (i. e. zero-short identification). Attributes have also been successfully used in object recognition  (Duan et al. , 2012) , action recognition  (Liu et al.
We will show that the two types of features are complementary and that combining them better models the divers appearances and locations of attributes. • We propose to use a specific Convolutional Neural Network (CNN) architecture with 1D convolution layers operating on different parts of feature maps to model attribute patterns related to different body parts. In order to deal with large spatial shifts, we extract LOMO features  (Liao et al. , 2015)  which have been specifically designed for viewpoint-invariant pedestrian re-identification. • For small datasets, we propose to pre-train the deep neural network with re-identification data. This allows for a more effective attribute learning. We show that the knowledge learnt from the re-identification task can be transferred and help the attribute learning.
RELATED WORK Numerous approaches for pedestrian attribute recognition have been proposed in the past. Midlevel attributes were first used for human recognition by  (Vaquero et al. , 2009). The person image is parsed into regions, and each region is associated with a classifier based on Haar-like features and dominant colours. Then, the attribute information is used to index surveillance video streams. The approach proposed by  (Layne et al. , 2012)  extracts a 2 784dimensional low-level colour and texture feature vector for each image and trains an SVM for each attribute. The attributes are further used as a mid-level representation to aid person re-identification. (Zhu et al. , 2013) , in their work, introduced the pedestrian attribute database APiS. Their method determines the upper and lower body regions according to the average image and extracts colour and gradient histogram features (HSV, MB-LBP, HOG) in these two regions.
Then, an Adaboost classifier is trained to recognise attributes. The drawback of these approaches is that all attributes are recognised independently, that is, the relation between attributes is not taken into account. To overcome this limitation,  (Zhu et al. , 2014 ) proposed an interaction model, based on their Adaboost approach, learning an attribute interaction regressor. The final prediction is a weighted combination of the independent score and the interaction score. (Deng et al. , 2014)  constructed the pedestrian attribute dataset "PETA". Their approach uses a Markov Random Field (MRF) to model the relation between attributes. The attributes are recognised by exploiting the context of neighbouring images on the MRF-based graph. (Chen et al. , 2017)  uses a multilabel Multi-layer perceptron to classify the attributes in the same time.
Recently, some deep features and "hand-crafted" features combination approaches have been also used in saliency detection  (Li et al. , 2017) , face recognition  (Lumini et al. , 2016)  and person reidentification  (Wu et al. , 2016). We further address the large intra-class variation issue by a specific CNN architecture operating on different image regions related to the pedestrian body parts and using 1D horizontal convolutions on these part-based feature maps. We experimentally show that our system works well for both larger and smaller datasets thanks to a pre-training stage on the related task of pedestrian re-identification.