dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work builds on the effort of the LHC collaborations and the ILC and CLIC simulation studies. We would like to acknowledge the work of our colleagues in this place. We owe special thanks to  Otto Eberhardt ,  Sunghoon Jung ,  Michael Peskin ,  Toni Pich ,  Junping Tian  and  Cen Zhang  for their feedback on the project and write-up. MP and MV are supported by the  Spanish national program  for particle physics, project  FPA2015-65652-C4-3-R (MINECO/FEDER ), and  PROMETEO grant   2018/060  of the  Generalitat Valenciana. MP is supported by the  "Severo Ochoa"  Grant  SEV-2014-0398-05 , reference  BES-2015-072974. The work of GD is supported in part at the  Technion  by a fellowship from the  Lady Davis Foundation. VM and AP are supported by the  Spanish Government and ERDF  funds from the  EU Commission  [Grant  FPA2017-84445-P] , the  Generalitat Valenciana  [Grant  Prometeo/2017/053 ], the  Spanish Centro de Excelencia Severo Ochoa Programme  [Grant  SEV-2014-0398 ] and the  DFG cluster of excellence "Origin and Structure of the Universe".
e. inverse Compton scattering). Some of the UV/optical photons might also be absorbed by dust. The dust is thus heated and reemits the energy as infrared radiation. Considering the tight link between AGN multiwavelength SEDs and these physical processes, it is feasible to infer source properties from modelling the observed photometric data. On the other hand, the observed SED is often complicated, involving factors such as host-galaxy contributions and dust extinction. Misinterpretation of the SED could lead to unrealistic physical properties. Therefore, it is critical to decipher the ob-served data appropriately with a powerful and reliable SED fitting code. The Code Investigating GALaxy Emission (CIGALE) is a state-of-the-art python code for SED fitting of extragalactic sources  (Boquien et al.
The sample and the models The SDSS sample is optically selected from the DR14 quasar catalog  (Pâris et al. 2018). All the sources are spectroscopically confirmed type 1 AGNs. In addition to the SDSS ugriz bands, the Pâris et al. (  2018 ) catalog provides X-ray data from XMM-Newton archival observations when avail-2. 0 2. 5 3. 0 3. 5 4. 0 4. 5 5. 0 5. 5 6. 0 log (nm, rest-frame) able (the 3XMM catalog;  Rosen et al. 2016). We require the sources to be detected in the 2-12 keV band at > 3σ significance levels. Here, the choice of the hard X-ray band (2-12 keV) is to minimize the effects of X-ray obscuration (see §2. 2. 1). 5 We do not include the IR photometry compiled in the DR14 catalog, because our main goal here is to test X-CIGALE on the simple cases, i. e. the quasar-dominated SED. In the X-ray to optical wavelengths, the AGN component is dominant; but in the IR wavelengths, the galaxy component may be non-negligible.
We discuss the cases of AGNgalaxy mixed SEDs in §3. 2 and §3. 3. We require the sources to have Galactic extinctions estimated in the DR14 quasar catalog, because we need to correct for Galactic extinction before providing the photometry to X-CIGALE. These criteria lead to a final sample of 1986 AGNs (Table  1 ). For the SDSS sample, we can neglect the galaxy SED component, because the sources are optically bright quasars which often dominate the observed UV/optical SEDs. The 5 Alternatively, one can perform X-ray spectral fitting to obtain the absorption-corrected X-ray fluxes. However, extracting and analyzing the X-ray spectra from the public XMM-Newton archival data are beyond the scope of this work. AGN-dominant (X-ray to IR) models in X-CIGALE can be achieved by setting frac AGN to a value close to unity (e.
The sample and the models The COSMOS sample is X-ray selected (> 3σ, 2-10 keV band) from the COSMOS-Legacy survey performed by Chandra  (Civano et al. 2016). The COSMOS-Legacy catalog assumes Γ = 1. 4. Similarly as in §3. 1. 1, we apply a correc- tion factor of 0. 87 (calculated with PIMMS) to the 2-10 keV fluxes to make them consistent with our adopted Γ = for AGN. Marchesi et al. (2016)  matched these X-ray sources with the optical/NIR counterparts in the COSMOS2015 catalog  (Laigle et al. 2016 ) and compiled their spectroscopic information when available. We select the sources with spectroscopic classifications of AGN types. We adopt the photometric data in the COSMOS2015 catalog, including 14 broad bands from u to IRAC 8. 0 µm. In addition, when available, we also include photometric data from Spitzer /MIPS (24 µm) and Herschel /PACS (100 µm and 160 µm), from the PEP survey  (Lutz et al.
The sample and the models The AKARI-NEP sample is also X-ray selected (> 3σ, 2-7 keV band) based on Chandra observations of the AKARI-NEP field  (Krumpe et al. 2015). The  Krumpe et al. (2015)  catalog assumes Γ = 1. 4. Similarly as in §3. 1. 1 and §3. 2. 1, we apply a correction factor of 0. 94 (calculated with PIMMS) to the 2-7 keV fluxes to make them consistent with our adopted Γ = 1. 8 (AGN). We match the X-ray with the multiwavelength catalog compiled by  Buat et al. (2015)  using a 1 matching radius. This multiwavelength catalog has 19 bands from u to Herschel /PACS (100µm and 160µm). Notably, these bands include an excellent set of 9band MIR data from the AKARI telescope, allowing us to test X-CIGALE on the MIR wavelengths. The final sample has 74 sources (Table  1 ). For AKARI-NEP, we adopt the same X-CIGALE fitting parameters as for COSMOS (see Table  3 ) except the viewing angle.
We also compare the fittings results with vs. without X-ray data. We find that the resulting AGN power is sometimes different in the two cases, when both AGN and galaxy components are present. Therefore, the AGN-galaxy SED decomposition may be unphysical without the constraints from X-ray data. We discuss constrainability and degeneracy of model parameters in the fitting of AKARI-NEP, for which excellent mid-IR photometric coverage is available. (iv) We also test X-CIGALE on a random sample of galaxies with only Chandra X-ray upper limits in the AKARI-NEP field, where excellent MIR data are available ( §4). We compare the fitting results with and without the X-ray upper limits. After using the X-ray upper limits, frac AGN sometimes becomes lower, indicating that the current Chandra upper limit can effectively constrain logical Development of the Republic of Serbia through the projects Astrophysical Spectroscopy of Extragalactic Objects (176001) and Gravitation and the Large Scale Structure of the Universe (176003).
Moreover, some issues affect the measurement of precipitation in altitude: number and sitting of gauges, lack of reliable and accurate snowfall measurements, and the need for analysis or spatial interpolation methods  [3, 14, 15]. Finally, many other phenomena impact the precipitation at short time scales, which can make it difficult to estimate altitudinal gradients of precipitation. In order to better investigate the relation between these altitudinal gradients and the bias observed between radar QPEs and rain gauge measurements, a more detailed analysis of the 2016 dataset has been carried out over a limited area around the Grenoble conurbation with a subset of 23 daily rain gauges. This paper focuses on the altitudinal gradients of precipitation observed at ground level by these rain gauges for different temporal accumulations (yearly, three months periods, monthly, daily) and for three neighboring massifs.
• Data for 22 rain gauges was provided by the Météo-France national databases, which include sensors from partner's networks: Météo-France, EDF (Electricité de France), DGPR, and local authorities. All the data benefit from a routinely non-real time validation procedure with human expertise. These gauges provide data at the daily time step, and half of them also provide hourly data. • One rain gauge location corresponds to two hourly rain gauges of the CEN observation site in Chartreuse, with a distance of few meters between the two stations. The hourly measurements of these two rain gauges (correlation coefficient = 0. 96), not yet validated, were critically analyzed in this study. The most reliable gauge was selected, but for some periods with missing values, measurements from the other gauge were used in order to provide a unique continuous series.
The all year 2016 represents more than 105,000 composites (288 a day). The number of missing values does not exceed 2 composites a day, and the number of days with such missing data does not exceed 12 days for the whole year 2016. The XPORT research radar operation was not continuous but alert-based, and IGE recorded a great number of precipitation events in Grenoble. In this study, data from 2016 and 2017 was used. The 0 °C isotherm altitudes have been estimated by the analysis of the AROME (Applications de la Recherche à l'Opérationnel à Méso-Echelle) numerical weather prediction (NWP) model. The AROME version used has a 0. 025° horizontal resolution (about 2. 5 km at Grenoble), and 60 nonequidistant vertical levels. The vertical resolution varies with the altitude above the ground, from 17 m near the ground to a few hundred meters.
Even when the precipitation was relatively spatially homogeneous, it has been very difficult to estimate simultaneously altitudinal gradients of precipitation for all the three massifs (only two massifs for the example on Figure  7 ). This difficulty disappears for longer temporal accumulations (monthly, seasonal, annual periods). This study did not consider the possibility of precipitation underestimation at high altitude due to difficulties of rain gauge measurement, and no specific correction was applied in this study on data provided by the Météo-France national databases (the data validation and correction being performed by the databases administrators). However, if the rain gauge data underestimated the true precipitation amounts at high altitude, then the gradient values were also underestimated, as well as the radar QPE bias, these QPE clearly underestimating the precipitation in altitude.
Acknowledgments: This work benefits from the collaboration and exchanges between the CMR team of  Météo-France  and the researchers of the HMCIS team of IGE. It also benefits from exchanges with researchers of the  French Snow Research Center , a research unit of the  CNRM (Centre National de Recherches Météorologiques ): special thanks to  Fatima Karbou ,  Camille Birman , and  Laurent Merindol. This study also greatly benefits from the  EDF rain gauges network.
data collecting All participants came from Jining Medical University. We included all participants who signed the ethical approval document. Students (freshmen) who were entranced in 2016 and 2017 were recruited in this study in their first year. And students (sophomores) who were entranced in 2016 also took online survey in 2017 in their second year. We recruited 10 518 survey data in total. To minimise environmental influences, students were gathered and asked to complete the online scale together. data processing Figure  1  shows the steps of our data processing. First, we dropped samples with one score feedback questions, such as 'this survey is meaningless', 'harry when answering questions', 'hard to understand this questionnaire' and 'answers do not reflect the truth'. In addition, only answering times within 99% CI were included.
Second, the data size was reduced to 10 272 in total. Then, dummy features (table  1 ) were encoded to one-hot codes and binary classifying features were encoded to 0/1 codes, which were the proper format for machine learning. Third, standardisation was adopted to eliminate different orders' problem. After principal component analysis of SWL and PANAS, the foremost two components were calculated as the 'pca' score. Whole data were divided into two datasets according to freshmen and sophomore. Data 1 contained all freshmen's information (N=6 886), and data 2 contained all sophomores' information (N=3 386). The top 30% samples with 'pca' were labelled as 1 (data 1: N=2062; data 2: N=1016), and the bottom 30% were labelled as 0 (data 1: N=2063; data 2: N=1016). The remaining 40% of data were excluded in this study.
Feature selection Each item of every scale was input as a feature, we included 298 features in total. To avoid overfitting and facilitate practical application, fewer features would be better. We assessed all 298 features simultaneously with elastic net regularisation, which can avoid correlated factors overfitting. This method would remove uninformative features and assign low weight to correlated ones. We selected the best 20 features from data 1 and data 2 separately. Further model construction would only consider these 20 features. This number was selected to balance prediction accuracy and usability in practical analysis. We also assessed models with 10 or 30 features.
Machine learning algorithm This research applied computational language python and Gradient Boosting Classifier (GBC) in scikit-learn (sklearn, http:// scikit-learn. org/ stable/ index. html), a machine learning module in python, to build our predicting model. It was the model parameters which constructed one specific model. As an ensemble method, GBC combined multiple weak classifiers to produce more accurate prediction significantly; it was much better than any base classifiers. Initially, one specific classifier fits the data. The next new classifier would re-weight parameters to the direction of gradient descend, which would minimise loss of function. Finally, a model with the minimum error on training data (70% dataset) was generated. Model performance was evaluated by confusion matrix, receiver operating characteristic curve (ROC) and area under curve (AUC).
Acknowledgements The authors appreciate the contribution of the members participating in this study. Contributors NZ helped in the analysis of data, proof-reading and result formation. CL, LA, ZC, DR, FY, RY, LJ, YB, ZG, GM, FX and FY helped in the data collection and compilation of analysis. GR, YX, LH, BB, GH and TY provided valuable guidance and input to complete the task on time.
storage of long term observations in database (Supplementary methods -Database description), 8. visual data inspection with the development of a specific data player (Supplementary methods -Database player) and 9. online data sharing with the community (databases, videos, analysis scripts and results) (Supplementary methods -Collaborative website sharing data). We used LMT with four animals to compare the behavioral profile of female mice lacking Shank2  8, 9  or Shank3  8  , two genes that were found mutated in a subgroup of patients with autism and coding for post-synaptic scaffolding proteins from excitatory synapses  [10] [11] [12] [13]. We report behavioral differences in the individual profiles between the two strains in the activity levels and involvement in complex social configuration.
Extraction of additional data such as the orientation (Supplementary methods -Head/tail detection post-processing) or the detection of ears, eyes and nose are then performed (Fig. 1f-g , Supplementary methods -Head sub-parts detection) thus enabling the detection of the tilt-orientation of the head. Detections are then processed for tracking (Supplementary methods -Tracking extender association process). Identity of tracks is retrieved in real-time by combining machine learning (Supplementary methods -Identity recovery with machine learning) with RFID (Supplementary methods -RFID calibration, Triggered-RFID closed-loop control, RFID hardware). Detection, tracking, and RFID readings information are stored in a database (Supplementary methods -Database description) and can be investigated live during the tracking (Supplementary methods -Querying database information with R and Python), or accessed via our online network (Supplementary methods -UDP live network information stream).
1m ), essential to understand inter-individual relationships. Experts used the integrated database player (Supplementary methods -Database player) to manually validate frame by frame the tracker performances. In a group of four, mice were correctly segmented (i. e. , the mask fit the real body shape exactly, no extra inclusion of objects or reflection on the walls were taken into account, no 4 merging of two animals) in more than 95. 75% of the frames in which the mouse was detected (Fig. 1k ). Detected orientation was accurate in more than 99. 36% of the detected frames (Fig. 1l ). Finally, the identity error rate did not exceed 2. 69% for a group of four mice (Fig. 1m ). Overall, the system keeps track of the identities of the animals, corrects false identifications and prevents any error from propagating thanks to the RFID used in the system.
The system then logs the number of either ID confirmation (match) or ID correction (mismatch) in the database. Therefore, users can monitor the tracking quality of their experiments using the metrics described in supplementary methods or one of their own choice (Supplementary methods -Automatic tracking control (ATC), ATC based on RFID, ATC based on detection). proportion of frames where the animal is not detected. (k) Segmentation error rate: proportion of frames where the animal is detected but not correctly segmented (i. e. its shape is not exact). (l) Orientation error rate: proportion of frames where the animal is detected and well segmented but head and tail are not correctly detected or reversed. (m) Identity error rate: proportion of frames where the animal is correctly detected and segmented but its identity is not correct.
For each timeframe and each detected mouse, LMT provides the mask of the animal, its depth mask relative to the background, the location of the head and of the tail, and finally the ears, eyes and nose detection. This data served as the basis for computing a number of events (list in Supplementary methods -Behavioral event extraction), inferred from 6 shape geometry (Supplementary methods -Event computation). Overall, we defined 35 behavioral events related to intrinsic and relative positions of the animals and we extracted them automatically. They can be split-up into five categories (Fig  2a , list in Supplementary methods -Behavioral event extraction). i) Events related to individual behavior reflect activity and individual postures. ii) Social dyadic events reflect the different types of contacts between two mice (see an example of the different types of contacts established between pairs of animals during 4 min in Supplementary methods -Social contact timelines; Supplementary Fig.
During 23 hours, we recorded nine mixed-genotype groups of four mice for the Shank2 strain and during three days six mixed-genotype groups of four mice for the Shank3 strain. When not specified otherwise in the text, we analyzed only the first 23 hours of the Shank3 recordings to remain comparable with Shank2 mice. In this analysis, LMT automatically extracted 33 behavioral traits for each individual mouse (see list in Supplementary data -Behavioral event extraction). These traits were the ones that could be compared between wild-type and mutant mice. The distribution of a subsample of behavioral traits (Supplementary Fig. S8 ) revealed that Shank2 -/-mice were more affected than Shank3 -/-mice compared to their respective wild-type littermates. Indeed, Shank2 -/-mice moved significantly more and spent significantly shorter time in side-by-side contact with individuals of the same genotype in comparison with wild-type mice (Supplementary Fig.
DISCUSSION LMT makes it possible to phenotype animals in groups and over long periods of time (days to weeks). This new methodology, based on the acquisition of rich sets of data, opens new avenues for examining how different genotypes, pharmacological tests or enriched environments influence decision-making or social interaction in a robust manner. This system also provides new information on social interactions and more specifically on interactions involving more than two freely-moving mice. Gathering data on several animals simultaneously and over long periods of time generates large datasets highly representative of individual behaviors. This in turn will stimulate massive analysis of large datasets to comprehensively study complex behaviors and should allow a statistical analysis of persistent individual traits.
The system should therefore be used to develop new behavioral tests to better answer the phenotyping needs  24. Finally, LMT provides a built-in repertoire of behavioral events that can be analyzed with scripts as illustrated in this paper. This repertoire includes individual events such as stop or rearing, but also social interaction events such as the different types of contacts or social configuration involving more than two mice. An exhaustive set of additional data, including the positions (x,y,z) of the head, tail and mass center for each animal at each time point, as well as the complete mask of each animal are available for custom extensive and elaborate analyses. This rich description of individual mice opens the possibility to conduct complex offline analyzes on large datasets, such as the analysis of event behavioral sequences  25  , individual movement tracking or social network analysis.
For example, among the Shank2 one-day group recordings that we conducted as pilot experiments, we observed a group of four animals that were not nesting in the provided house, while all other groups nested there. We investigated this case (Supplementary movie -Investigating an abnormal nest building behavior). One mouse appeared to avoid the house and refused to get in it. This animal spent most of its time at the opposite location of the house. The other mice first set the nest in the house, and then, after a few hours, moved its location and all the nesting material to the location of the other animal. Finally, the structure of the database complies also with the addition of extra measurements to monitor the environment for further developments. This should increase the reproducibility of experiments, and be used to optimize housing conditions by analyzing behavioral markers of welfare (social isolation, stress) and abnormal behavior.
Their count is stored for further analysis in the "frame. particle" field in the database as it reflects the sawdust spread by mice when moving, digging or fighting. If the user sets the option "Manage wired animal", extra filtering is applied, based on ellipse fitting of objects. The longest size of a mouse being around 60 pixels, objects greater than 70 pixels in their main fitted axis are rejected. Then we process the validated segmentation list. If a detection is bigger than MaxSizeDetection, it might be because several animals are in contact, therefore the detection is processed by the detection splitter (see below). Code: detection. MouseDetector. detectMice().
95. In that case, we apply the solution found and affect identities to all tracks. If the condition is not satisfied, the identity is not recovered and anonymous track are left anonymous. The Identity Manager will therefore start later on a new Identifier on those tracks. Meanwhile, the knowledge of the machine learning will be updated with new observations that may better fit the data that remain to be identified, meaning that future observations can solve past tracks. Note that for memory consideration, data are streamed to database after 5 minutes, meaning that if no identity is found during 5 minutes, track will be saved as anonymous, flushed from memory and will never be identified. Code: identity. TrackIdentityGlobalSolver.