dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 4. Notes, Files and Dataset Using these three sources, we first wrote a qualitative note about each Nuit Debout gathering with these basic details: -which one of us wrote the note, and when; -the date of the first post in the Facebook page or group (if it exists); -photos from the local press coverage and counts of the number of people; -other remarkable features, especially links with other movements (trade unions, political parties, other Nuit Debout gatherings, etc. ) and a rough sketch of who the initiators were. The dataset created from these notes provides the following information: -Id: unique numerical code for each assembly; -Id_NdB: name of the municipality where the Nuit Debout gathering took place -code in the Parisian case where 5 different Nuit Debout locations appeared; -Date: format Year-Month-Day; -Address: precise location of the Nuit Debout gathering; -INSEECode: official geographical code of the municipality; -Latitude and longitude wgs 84 reference; -Audience_photo: number of participants according to picture of the assembly; -Audience_Internal_recoded: number of participants according to internal source (wiki, Facebook, etc.
The  2016 French Nuit Debout | 10. 1163/24523666-00401005  research journal for the humanities and social sciences ( 2019) 1-10 individual militant trajectories, local support from organizations, and the regional and local political context. Our dataset gives opportunities for searching patterns and anomalies but is so far one more aid to understand what happened in France in spring 2016. Nuit debout was far from being limited to Paris.
6. Hadron-hadron correlations from pythia For the measurement of cc yields as a function of ∆φ or pair p T , correlated hadron pairs are a major background source. To estimate the uncertainty in the description of Tune A pythia, we compare distributions of like-sign pairs between data and simulation in the same mass region (1. 5-2. 5 GeV/c 2 ) where other contributions, including b b are negligible. We observe that the width of the back-to-back peak at ∆φ = π is slightly wider in data compared to pythia simulation. This is seen in the p T distributions as well, because p T is strongly correlated with ∆φ. The discrepancy is strictly less than 12% and varies with ∆φ or p T. One data driven approach would be to apply an additional weight to the unlike-sign hadronic pair background as a function of ∆φ or p T , where the weight is computed by taking the ratio between data and simulations using the like-sign pairs as a function of ∆φ The χ 2 for the pair pT spectrum of Drell-Yan pairs in the mass region 4.
8-8. 6 GeV/c 2 compared to pythia simulations with different intrinsic kT. The χ 2 is minimized at a kT of 1. 1 GeV/c. or p T in the same mass region. This is motivated by the fact that the like-sign pairs are dominated by hadronic contributions in the mass region of interest. Here we take the average between the Tune A setup and this data driven modification to be our central value, and assign a systematic uncertainty on the cc yields as the difference between these two approaches. The resultant systematic uncertainty is strongly ∆φ and p T dependent, ranging from 0% to 14%.
The product of individual corrections to the normalization is α double × α occ × α live × α lum = 1. 33  (1. 34)  for the south (north) arm. These values are consistent within uncertainties with 1. 30 ± 0. 16 (1. 38 ± 0. 17), the values based on the J/ψ measurement. The spectra for the two pseudorapidity regions are combined using the method documented in Appendix B and compared to model calculations based on pythia and powheg. The comparison is shown in Figs. 25  and 26. Pairs generated by the models are filtered with the same kinematic cuts that are applied in the data analysis. The model curves are normalized using the fitting procedure outlined in Sec. VI B. For cc the model calculations are normalized in the kinematic region 1. 4 < m < 2. 5 GeV/c 2 and p T < 2 GeV/c to the data. Consequently, as seen in Fig.
26 , the p T spectrum is adequately described by both pythia and powheg for p T < 2 GeV/c. However, for p T > 2 GeV/c, the yield predicted by powheg is systematically higher than the data, while the yield from pythia is more consistent with the data. The larger yield predicted by powheg also manifests itself in the ∆φ projection at ∆φ < 1. 5. For cc, the azimuthal correlation determined with powheg is significantly wider compared to the one from pythia. Again the data favor pythia in the probed kinematic region. This is particularly apparent at ∆φ < π/2. Because both pythia and powheg use the pythia fragmentation scheme and very similar parton distribution functions, the differences between the model calculations must result from the underlying correlation between the c and c quarks that originate from the pQCD differential-cross-section calculation.
Our data are more consistent with pythia than with powheg. We note that this preference is not limited to data taken in the kinematic region accessible in this analysis; it also holds true for the mid-forward kinematic region probed by the PHENIX electron-muon measurement  [35]  and mid-mid kinematic region probed by the PHENIX dielectron measurement  [7]. For b b, pythia shows a slightly wider peak in ∆φ than powheg. However, within uncertainties the data are well described by both generators in ∆φ and p T. The smaller model dependence can be traced back to the larger b quark mass, which is much larger than the muon mass  [7]. For the bulk of B meson decays, the momentum of the muon is nearly uncorrelated to the momentum of the decay muon. Therefore, the opening angle between two muons from b b is randomized.
For both cc and b b, the default ratios and shapes of the three different processes from pythia describe the data well. Although for powheg a similar separation is not possible, it seems as if contributions from higher order processes with characteristics similar to gluon splitting are more frequent in powheg than in pythia, leading to a broader azimuthal opening angle distribution and a harder p T spectrum for pairs from cc. More constraints on the cc correlations, which seem to drive the observed model differences, could be obtained from a quantitative and systematic study of heavy flavor correlations for p+p collisions at √ s = 200 GeV obtained from different kinematic regions. A simultaneous analysis of the ee  [7] , eµ  [35]  and µµ data can provide stronger discriminating power to different theoretical models.
IX. BOTTOM CROSS SECTION To determine heavy flavor production cross sections, the µµ pair data need to be extrapolated from the small kinematic region covered by the experiment to the full phase space. This extrapolation has to rely on model calculations. For the case of charm, there are significant discrepancies between the differential distributions calculated by different models, hence an extrapolation to full phase space is model dependent  [7]. However, this is less of an issue for bottom production. The distributions of µµ pairs from b b are dominated by decay kinematics and model dependent systematic uncertainties on the extrapolation are much less dominant. In the following we determine the average of the bottom cross sections obtained from pythia and powheg using the fitting procedure, and assign systematic uncertainties according to the difference between models.
This measurement gives input to a previously unexplored phase space and serves as a solid baseline for future measurements. framework takes into account the detector's geometrical acceptance and all inefficiencies from dead channels. To account for variations of detector performance during the data taking period, the data are split into run groups with similar performance. For each group a map of dead channels is created for the MuTr. The simulation randomly selects these maps according to the sampled luminosity for each run group. Muon pairs from physical sources are simulated with a z-vertex distribution taken from MB p+p data. Once the pairs are processed through the detector simulation, they are reconstructed using the same procedure and filtered with the same cuts as used for real data.
Thus, all detector effects including acceptance, dead areas, track reconstruction, and analysis cuts are taken properly into account. Because the analyzed data are triggered with the MuIDLL1-2D trigger, the effects of the trigger also need to be accounted for. To achieve this, we apply an offline software trigger to all simulated tracks, which is an exact replication of the online hardware MuIDLL1-1D trigger. We require that both tracks of a pair fulfill the MUIDLL1-1D trigger condition. Here we make use of the fact that after enforcing a spatial separation of 20 cm between two MuID tracks, the MuIDLL1-2D pair trigger is reduced to a logical AND of the MuIDLL1-1D single track triggers. The separation cut necessary to achieve this factorization was determined from experimental data. In Fig.
ACKNOWLEDGEMENTS We thank the staff of the  Collider-Accelerator and Physics Departments at Brookhaven National Laboratory  and the staff of the other PHENIX participating institutions for their vital contributions. We acknowledge support from the  Office of Nuclear Physics in the Office of Science of the Department of Energy , the  National Science Foundation ,  Abilene Christian University Research Council, Research Foundation of SUNY, and Dean of the College of Arts and Sciences, Vanderbilt University (U. S. A) ,  Ministry of Education, Culture, Sports, Science, and Technology  and the  Japan Society for the Promotion of Science (Japan) ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico and Fundação de Amparo à Pesquisa do Estado de São Paulo (Brazil) ,  Natural Science Foundation of China (People's Republic of China) ,  Croatian Science Foundation and Ministry of Science and Education (Croatia) ,  Ministry of Education, Youth and Sports (Czech Republic) ,  Centre National de la Recherche Scientifique ,  Commissariat à l' Énergie Atomique , and  Institut National de Physique Nucléaire et de Physique des Particules (France) ,  Bundesministerium für Bildung und Forschung ,  Deutscher Akademischer Austausch Dienst , and  Alexander von Humboldt Stiftung (Germany) ,  J.
Explosives molecules have been widely used since World War II, leading to considerable contamination of soil and groundwater. Recently, bioremediation has emerged as an environmentally friendly approach to solve such contamination issues. However, the 1,3,5,7-tetranitro -1,3,5,7-tetrazocane (HMX) explosive, which has very low solubility in water, does not provide satisfying results with this approach. In this study, we used a rational design strategy for improving the specificity of the nitroreductase from E. Cloacae (PDB ID 5J8G) toward HMX. We used the Coupled Moves algorithm from Rosetta to redesign the active site around HMX. Molecular Dynamics (MD) simulations and affinity calculations allowed us to study the newly designed protein. Five mutations were performed. The designed nitroreductase has a better fit with HMX.
Phe124 was also excluded, as the amino acid was identified as conferring NR activity  [29]. Moreover, the side chain of Phe124 interacts with the nicotinamide ring of NADP  [26]. These particularities are shared with several other NR homologs  [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30]. As a final verification, the remaining residues were cross-checked against a sequence alignment clustering the 50 most similar protein sequences in the UniProt Database filtered according to a UniRef90 BLAST Search. Highly conserved residues across the alignment were excluded. We finally allowed the modification of Tyr68, Phe 70, Glu120, Tyr123, and Ala125 into every natural amino acid (Figure  4 ). A restrained energy minimization was then performed to relax the structure in order to keep the HMX molecule in a relevant position.
From this ensemble of amino acids, we excluded Glu165 and Gly166, which support efficient hydride transfer from NADH to FMN. We also excluded Asn71, Thr41, and Phe124, which are involved in interactions between NADH and FMN  [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28]. Phe124 was also excluded, as the amino acid was identified as conferring NR activity  [29]. Moreover, the side chain of Phe124 interacts with the nicotinamide ring of NADP  [26]. These particularities are shared with several other NR homologs  [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30]. As a final verification, the remaining residues were cross-checked against a sequence alignment clustering the 50 most similar protein sequences in the UniProt Database filtered according to a UniRef90 BLAST Search.
Highly conserved residues across the alignment were excluded. We finally allowed the modification of Tyr68, Phe 70, Glu120, Tyr123, and Ala125 into every natural amino acid (Figure  4 ). A restrained energy minimization was then performed to relax the structure in order to keep the HMX molecule in a relevant position. Then, we performed several trials using the Coupled Moves algorithm, using 30 identical starting structures (see Supplementary Materials for script and param files). A total of 10,000 steps per structure was conducted. Residues Tyr68, Phe70, Glu120, Tyr123, and Ala125 were allowed to be mutated into any natural amino acid, including their associated side chain rotamers. The selection of amino acid identity and rotamer at each step was based on the calculated ROSETTA energy scores and chosen according to Boltzmann weighted probability.
The simulation produced 6093 low-energy sequences. The results of the trials are illustrated under a logo sequence (Figure  5 ). The logo depicts the amino acid conservation among the sequences previously generated. Then, we performed several trials using the Coupled Moves algorithm, using 30 identical starting structures (see Supplementary Materials for script and param files). A total of 10,000 steps per structure was conducted. Residues Tyr68, Phe70, Glu120, Tyr123, and Ala125 were allowed to be mutated into any natural amino acid, including their associated side chain rotamers. The selection of amino acid identity and rotamer at each step was based on the calculated ROSETTA energy scores and chosen according to Boltzmann weighted probability. The simulation produced 6093 low-energy sequences.
Global MD Analysis A first MD simulation of 130 ns was performed on the NR in a ligand-bound complex with p-NBA (PDB ID: 5J8G). Pitsawong et al. [26]  showed that the NR does not generate paminobenzoic acid and thus does not appear to reduce nitro groups into amines  [14] [15] [16] [17] [18] [19] [20]. p-NBA has been co-crystallized with the NR from Enterobacter Cloacae. We used this crystal as a starting structure to understand how p-NBA was stabilized in the active site. The dynamics show that the p-NBA is not stable in the active site during the whole dynamic but moves in and out of the pocket. When in the pocket, the aromatic ring of the p-NBA is stabilized against the flavin ring, probably involved in pi stacking interactions. The nitro group is oriented in a non-productive way, meaning that the nitro group is too far from the N5 atom of the flavin to trigger a hydride transfer.
Protein Preparation A 1. 9 Å protein crystal structure (PDB ID: 5J8G  [26] ) of the bacterial NAD(P)H NR from Enterobacter cloacae in a ligand-bound complex with acid para nitrobenzoic (p-NBA) was obtained from the PDB  [39]  for use as a starting structure for computational modeling. This structure was examined and prepared for manipulation using the Structure Preparation feature in the Molecular Operating Environment software (MOE)  [40]. Acid para nitro benzoic was removed and the structure was minimized using the Amber14 force field  [41]  to reach an energetically favorable conformation. Then all the unbound water and cofactor FMN molecules were removed from the structures.
Model Evaluation (Data Analysis) Low-energy sequences generated by the Coupled Moves protocol were discarded to remove redundant sequences across multiple protocol runs, with the lowest energy rotamer conformations saved for each unique sequence. Then, all the sequences were aligned, and the results were compiled to form a logo sequence. For each designed position, the overall height of the stack indicates the sequence conservation at that position, while the height of symbols within the stack indicates the relative frequency of each amino or nucleic acid at that position. The sequence with the most conserved amino acid at each designed position were retrieved and pushed through a molecular dynamics simulation.
Binding Free Energy Calculation An ensemble average molecular mechanics energies combined with the generalized born and surface area continuum solvation (MM/GBSA) binding free-energy calculation was performed on the snapshots from the MD simulation to compare the binding affinity of HMX for the mutant and the WT NR. A total number of 200 snapshots were taken from the last 4 ns of the MD trajectory with an interval of 20 ps (only for all the trials where the ligand was successfully kept within the active state). The calculations were rendered by the MMPBSA. py [47]  module of AMBER14. The MMGBSA method can be conceptually summarized as ∆G MM/GBSA = G complex -G receptor -G ligand = ∆E MM + ∆G GB + ∆G NP -T∆S where ∆E MM is the molecular mechanics interaction energy between the protein and the inhibitor, ∆G GB and ∆G NP are the electrostatic and nonpolar contributions to desolvation upon inhibitor binding, respectively, and -T∆S is the conformational entropy change.
Docking Studies Docking studies were performed with the MOE software. The last frame of the MD simulation involving the mutant NR complexed with HMX was extracted as. pdb. HMX, water molecules and ions were removed. The NADPH 3D conformer was downloaded from PubChem (PubChem CID:5884) and minimized in MOE using the AMBER14ff. A collection of poses was then generated from the pool of ligand conformations using one of the Triangle matcher placement methods. A total of 150 poses were generated. Each of the generated pose was attributed a London dG score. Poses generated by the placement methodology were then refined using the induced fit refinement scoring function GBVI/WSA dG with the generalized born solvation model (GBVI). Twenty-five top scoring poses were sorted out. The top five scores were then evaluated.
These theories are thereby reconstructed in a split formulation that decomposes all coordinates and tensor components as in Kaluza-Klein but without truncating the coordinate dependence. In particular, the components of the generalized metric M MN then encode the components of the internal Riemannian metric g mn and the internal components of various other tensor fields present in supergravity. In this way, ExFT provides a theory encompassing the complete, untruncated 10-or 11-dimensional supergravities, thereby rendering manifest the emergence of exceptional symmetries in lower dimensions. While one can argue, under certain assumptions, that there are only two different 'U-duality orbits' of solutions of the section constraint, leading to M-theory/type IIA or type IIB, respectively, we will use the opportunity to illustrate how ExFT provides a rich set of consistent truncations and 'alternative parametrizations' that give rise to novel applications.
It should be stated that due to the complexity and diversity of the steps constituting the product quality control tool, a number of commercially available software can be used to achieve its envisaged capabilities, e. g. GeoMagic Control X, AutoDesk PowerShape and PowerMill in this research. The description of how these software tools can be deployed is provided below for each step of the proposed quality control procedure. Step 1. After the data capture employing an optical metrology module, the generated datasets are first filtered to minimise or even eliminate common problems, such as the existences of spikes, holes and noise, that can significantly impair the subsequent geometrical analysis. The filtering of measurement points should be performed directly on the metrology module employing suitable inspection software, e.
g. GeoMagic Control X in this study that provides advanced algorithms for selecting the most relevant and discriminant metrology data. Step 2. The filtered metrology data is imported into a CAD environment, e. g. AutoDesk Powershape, and then WCS is correlated to OMCS to obtain the workpiece position in LMCS. This is possible because a modular workholding system is used across all the processing modules of the multi-stage production line (see Section 3. 1). Figure  6  exemplifies the extraction of the workpiece position in relation to OMCS. In particular, the horizontal displacement of the preform centre in regards to the OMCS is determined along the X and Y axes, respectively, together with the lateral preform rotation in the XY plane in regards to OMCS. Step 3. The geometrical analysis of the scanned workpiece against its nominal CAD model and thus to extract volumes for further processing.
In particular, the CAD model is imposed on top of the imported scan data as shown in Figure  6  and thus to identify volumetric geometrical deviations. As a result, "rest" volumes can be extracted for further processing and thus to meet the parts' GPS. Such geometrical analyses can be performed in suitable CAD environment, e. g. AutoDesk PowerShape in this research. Step 4. The CNC programmes for the follow up processing module are generated based on the rest volumes obtained in Step 3. In particular, the extracted rest volumes should be imported into a CAM software, e. g. AutoDesk PowerMill in this research, and thus to generate the toolpaths for further processing.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the grant agreement No  723826  (MAESTRO). The research was also funded by the  European Union 's  Horizon 2020 research and innovation programme  under the grant agreement No  766871  (HIMALAIA). The authors also acknowledge the support received from Systems  3R  for the modular workholding system.
The rest of the paper is organized as follows. In the Preliminaries and Problem formulation section, we review the compressive crowdsensing and the matrix factorization approach. Then we introduce the parallelized stochastic gradient descent and present the problem formulation. In the Frameworks and Algorithms section, we propose framework of EdgeSense and present the algorithms in details. In the Experiments section, we evaluate EdgeSense on real-world datasets and compare it with baseline algorithms. Finally, in the Conclusion section, we summarize the work in this paper.
e. , S t ⊆ S. Given the collected sensing data, the compressive crowdsensing system aggregates the data and assigns each covered subarea an unique sensor data value. For example, if multiple sensor data values are collected (from multiple participants) that cover the same subarea in a sensing cycle, the averaged value would be used as the value of such subarea in the sensing cycle. In this way, each subarea s ∈ S t has been covered with one sensor data value, through data aggregation, and the compressive crowdsensing system needs to infer the missing sensor data of the subareas in S\S t to obtain the sensor data of the whole target area.
IV. EXPERIMENTS In order to evaluate the EdgeSense algorithm, we use the Temperature (TEMP) and PM 2. 5 air quality (PM25) dataset, where the Experimental Setup section will cover all the settings and assumptions. Based on the above dataset, we first introduce the baseline algorithms which are commonly used in sensor data recovery. Specifically, the baseline algorithms adopt the matrix completion method and leverage the centralized computing patterns to recover the target sensing data. Then, we compare the performance of EdgeSense with baseline algorithms on two real-world datasets.