dataesr/scientific-paragraphs-categorization huggingface dataset:
The forthcoming use of ANTs to perform the normalization should remove the dependency to Matlab. However, group analysis without electrode contacts visualization can be performed using MarsAtlas and Freesurfer parcels as MNI normalization is not required to match parcels between patients. ANTs brain extraction and cortical thickness process should also be callable soon from the IntrAnat interface. 2. The gray/white matter labeling is binary for now: a voxel is in gray matter or in white matter. In order to have a more nuanced labeling, which would better describe if electrode contacts are closer to the gray/white frontier or to the gray/pial frontier, we are implementing a calculation of the distance between the electrode contact and the gray/white matter frontier, normalized by the cortex thickness generated by Freesurfer or ANTs.
This procedure is inspired by the gray matter proximity index proposed by  Arnulfo et al. (2015a). 3. Nowadays, only rigid electrode models can be used, even if in a few cases the electrodes can be slightly curved due to the implantation process. It will soon be possible to adjust the positions of contacts to the actual curvature of electrodes, in a similar way as in  Arnulfo et al. (2015b). 4. The contact labeling according to a series of functional localizers based on induced beta and gamma activity  (Vidal et al. , 2011)  is still under development. The main goal will be here to have a summary in the IntrAnat Electrodes database of the main functions recorded on each electrode contact and cortical parcel. 5. The parcellation quality control will be improved based on geometrical metrics of the parcels.
FUNDING This work was funded by  European Research Council  under the  European Unions Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant Agreement no. 616268  F-TRACT, by the  Agence Nationale de la Recherche  ( Force  project,  ANR-13-TECS-0013-01 ) and by the  Fondation pour la Recherche Médicale  (Aide aux projets innovants, 2010).
Aims. The Herschel Extragalactic Legacy Project (HELP) focuses on the data from ESA's Herschel mission, which covered over 1300 deg 2 and is preparing to publish a multi-wavelength catalogue of millions of objects. Our main goal is to find the best approach to simultaneously fitting spectral energy distributions (SEDs) of millions of galaxies across a wide redshift range to obtain homogeneous estimates of the main physical parameters of detected infrared (IR) galaxies. Methods. We perform SED fitting on the ultraviolet(UV)/near-infrared(NIR) to far-infrared(FIR) emission of 42 047 galaxies from the pilot HELP field: ELAIS N1. To do this we use the latest release of CIGALE, a galaxy SED fitting code relying on energy balance, to deliver the main physical parameters such as stellar mass, star formation rate, and dust luminosity.
2016). Also, the Virtual Observatory tools allow for SED fitting. Bayesian analysis is included in many different software packages; for example GOSSIP  (Franzetti et al. 2008) , CIGALE  (Noll et al. 2009) , and BayeSED  (Han & Han 2014). The wealth of public and private SED fitting tools implies that different surveys tend to be analysed with different tools, with no common set of models or parameters. As a consequence, it is difficult to combine, compare or interpret large datasets for statistical analysis, as different approaches, models, and assumptions result in disparate accuracy, scaling factors, and non-uniform physical parameters across a wide redshift range. A lack of homogeneous multi-wavelength catalogues (covering over 10% of the entire sky), together with non-uniform physical parameters obtained based on different models and software, makes the analysis of the main physical properties of galaxies statistically limited and biased.
An FP7 project called the Herschel Extragalactic Legacy Project (HELP,  Vaccari 2016, Oliver et al. , in preparation)  funded by European Union will remove the barriers to multi-λ statistical survey science. The main aim of HELP is to provide homogeneously calibrated multi-wavelength catalogues covering roughly 1300 deg 2 of the Herschel Space Observatory  (Pil-bratt et al. 2010 ) survey fields. These catalogues are going to match individual galaxies across broad wavelengths, allowing for multi-wavelength SED fitting to be performed and for statistical studies of the local-to-intermediate redshift galaxy population. The selection criteria, depth maps, and master list creation details will be published together with the catalogues  (Shirley et al. in prep. ). The presence of IR data together with UV-NIR counterparts makes the HELP multi-wavelength catalogue a perfect data set for studying galaxy formation and evolution over cosmic time.
In this paper, we present the general HELP strategy for SED fitting for the millions of galaxies observed in multi-wavelength pass-bands. We discuss the software, models, and parameters which are going to be used uniformly for each data set. This approach ensures homogeneity of obtained physical parameters for the final HELP deliverable of 1300 deg 2 field. The paper is organised as follows. In Sect. 2 we describe the ELAIS N1 field as a pilot field of the HELP project. In Sect. 3 we present the method applied in this work to fit SEDs and the main physical models and parameters used for the fitting. Section 4 presents all reliability tests as well as two implemented quality checks. In Sect. 5 we discuss the general properties of the sample, while in Sects. 6 and 7 we explore how all physical parameters obtained by fitting SEDs with different dust attenuation models can be biased and whether or not it is possible to predict total dust luminosity from stellar emission only.
Data In our analysis, we focus on the pilot HELP field: European Large Area ISO Survey North 1 (hereafter ELAIS N1), 9 deg 2 area centred at 16 h 10 m 01 s +54 o 30 36  (Oliver et al. 2000). ELAIS N1 is one of 20 fields making up the European Large Area ISO Survey (ELAIS,  Oliver et al. 2000; Rowan-Robinson et al. 2004 ). The Herschel data in ELAIS N1 was obtained as part of the HerMES project  Oliver et al. (2012). It is representative of moderately deep fields for future HELP catalogues. In this paper we briefly summarise the data used for ELAIS N1. A detailed description of the data used for the HELP project (both FIR and ancillary data), the open source pipeline, which was developed for HELP, and the cross-matching procedure, astrometry corrections, and full data diagnostics will be presented in  Shirley et al.
A detailed description can be found in  Hurley et al. (2017). The original XID+, used for ELAIS N1 field, uses information IRAC bands as a prior. We note that XID+ can be run with more sophisticated priors, using both dust luminosity and redshift information (e. g. Pearson et al. 2018 , developed a method of incorporating flux predictions from SED fitting procedure as informed priors, finding improvements in the detection of faint sources). The XID+ was run on the ELAIS N1 Spitzer MIPS, and Herschel PACS and SPIRE map. The flux level, at which the average posterior probability distribution of the source flux becomes Gaussian (?, indicating that the information from data dominates over the prior, see)or more details]Hurley2017, is 20 mJy for MIPS, 12. 5 and 17. 5 mJy for PACS and for SPIRE is 4 µJy for all three bands.
Ancillary data The HELP master catalogue is built on a positional cross match of all the public survey data available in the optical to MIR range. This comprises observations from the Isaac Newton Telescope/Wide Field Camera (INT/WFC) survey  (González-Solares et al. 2011) , the Subaru Telescope/Hyper Suprime-Cam Strategic Program Catalogues (HSC-SSP)  (Aihara et al. 2018) , the Spitzer Adaptation of the Red-sequence Cluster Survey (SpARCS)  (Tudorica et al. 2017) , the UK Infrared Telescope Deep Sky Survey -Deep Extragalactic Survey (UKIDSS-DXS)  (Swinbank 2013; Lawrence et al. 2007) , the Spitzer Extragalactic Representative Volume Survey (SERVS,  Mauduit et al. 2012) , and the Spitzer Wide InfraRed Extragalatic survey (SWIRE,  Lonsdale et al. 2003; Surace et al. 2005). We use the Spitzer Data Fusion products 2 for the final two Spitzer 'MIR surveys presented in  Vaccari et al.
Final sample The catalogues produced by XID+ contain a flag for those sources that are either below a flux level in which there is a clear detection or the related XID+ Bayesian P-value maps indicate a poor fit in a region local to the source. The final sample includes 50 129 galaxies from the ELAIS N1 survey with flux measurements for PACS or SPIRE data (or both). The full sample and data files can be downloaded at http://hedam. lam. fr/HELP/ dataproducts/dmu28/dmu28_ELAIS-N1/data/zphot/ and also accessed with Virtual Observatory standard protocols at https://herschel-vos. phys. sussex. ac. uk/. Our procedure gives us a set of 19 bands: u, g, r, i, z, N921, y, J, K, Spitzer IRAC 3. 6, 4. 5, 5. 8 and 8. 0 µm, Spitzer MIPS 24 µm, and five passbands from Herschel PACS (100 and 160 µm) and SPIRE (250, 350 and 500 µm).
Infrared emission from dust The current public version of CIGALE (cigale version 0. 12. 1) includes four different modules to calculate dust properties: Casey (2012),  Dale et al. (2007) ,  Dale et al. (2014) , and an updated version of the  Draine & Li (2007)  model, which we decide to use (this model was also used by e. g. Lo  Faro et al. 2017 and Pearson et al. 2018  in the framework of the HELP project). In our analysis we apply the multi-parameter dust emission model proposed by  Draine et al. (2014) , which is described as a mixture of carbonaceous and amorphous silicate grains. Infrared emission SEDs have been calculated for dust grains heated by starlight for various distributions of starlight intensities. The majority of the dust is heated by a radiation field with constant intensity (marked as U min in Table  2 ) from the diffuse ISM.
Dust attenuation We perform three SED-fitting runs with three different dustattenuation laws to check which law gives the best fits: we use the  Charlot & Fall (2000)  model as the main dust attenuation recipe, and then we redo the whole analysis with the popular attenuation law of  Calzetti et al. (2000). Subsequently we also test the attenuation law for z∼2 ULIRGs derived by Lo  Faro et al. (2017). The standard formalism of Charlot & Fall 2000 (hereafter: CF00) assumed that stars are formed in interstellar birth clouds (BCs), and after 10 7 yr, young stars disrupt their BCs and migrate into the ambient ISM. The functional form for  Charlot & Fall (2000)  attenuation law (CIGALE's dustatt_2powerlaws module) refers to: A(λ) = A λ (BC) + A λ (ISM) , for young stars, age < 10 7 yr, A λ (ISM), for old stars (age> 10 7 yr).
Summary of SED templates Each created SED template consists of five components (SFH, single stellar population, dust attenuation and emission, and the AGN component) which are spread of all possible grid of the input parameters. All these parameters (with the exception of parameters related to the attenuation curve recipes published by  Calzetti et al. 2000 and Lo Faro et al. 2017 , which were used for comparing the main physical parameters presented in Sect. 6) are default parameters used for all HELP fields. The full list of the input parameters of the code is presented in Table  2. Based on the number of parameters presented in Table  2  and the redshift range for our sample of galaxies we built ∼850 million SED templates which were fitted with CIGALE to 50 129 ELAIS N1 galaxies detected by Herschel.
5 are very conservative in order to avoid excluding too many sources; the full sample with all calculated χ 2 s will be released to allow the user to make their own quality cuts. Values used for Eq. 5 are valid for ELAIS N1 field, and the thresholds for other HELP fields should be revised. Our two χ 2 s criteria (Eq. 5) remove an additional 2 634 galaxies (5. 25% of the sample) lying below magenta stars in Fig. 6. We check what kind of objects we reject. We suspect that some of the sources have problems with matching between optical and IR data and that the rejected sample includes possible incorrect photometric redshift, as in, for example, lensed galaxies, which are a good example of objects with energy budget issues. To check whether or not the mismatch between optical and FIR data is at the basis of the rejection, we calculate z phot,IRphotometric redshifts based on the PACS and SPIRE data only.
We make a standard check for photometric redshift outliers  (Ilbert et al. 2006 ): δ(redshiftz phot,IR )/(1 + redshift) > N, where redshift corresponds to the photometric redshift calculated based on UV-OPT data. In the literature, comparisons of photometric and spectroscopic data use a value of N equal to 0. 15, but this could be too small for comparison between two photometric values. We find that using double this value (N=0. 30, more suitable for less robust estimation) criteria select 78% of rejected objects. Requiring at least three IR measurements with S/N 3 for ELAIS N1 catalogues to obtain secure z phot,IR estimates reduces the ELAIS N1 3S/N sub-sample to 1 059 objects only. We also find that for this secure sample, only 10% of objects not rejected by the χ 2 s, and 99% of galaxies selected by Eqs.
Nonetheless, we have no solid basis to remove those objects from our analysis. To provide general properties of the ELAIS N1 sample we removed 7 533 galaxies in total based on the two χ 2 s cuts (Eqs. 5). We define the remaining 42 453 galaxies (84% of the original ELAIS N1 sample) as a clean sample for the physical analysis. The redshift distribution of the final sample used for SED fitting is presented in Fig. 2  (the red hatched histogram). We stress that χ 2 r , χ 2 r,stellar , and χ 2 r,IR are a part of deliverables and each user is free to choose different criteria (or to not use them at all). For each field we provide the main physical parameters without any subjective flags describing the quality of the fit. Both, the data and the SED fitting results can be downloaded at http://hedam.
Dust luminosity distribution for ELAIS N1 The majority of our sample consists of (LIRGs) and ultra luminous infrared galaxies (ULIRGs), characterised by 11 ≤ log(L dust /L ) < 12, and 12 ≤ log(L dust /L ) < 13, respectively. We have found 23 214 LIRGs (54. 68% of the sample), 11 947 ULIRGs (28. 14%), and 318 (0. 75%) hyper LIRGs (HLIRGs) (log(L dust /L ) ≥ 13). The main statistical properties of LIRGs, ULIRGs, and HLIRGs, as well as the galaxies characterised by log(L dust /L ) < 11 from the ELAIS N1 field, can be found in Table  3. All delivered parameters (dust luminosity, stellar mass, SFR, χ 2 r , χ 2 r,stellar , χ 2 r,IR , and calculated thresholds) will be published while the HELP database is being completed (end of 2018).
Dust luminosity prediction from stellar emission The FIR emission is a key component to accurately determine the SFR of galaxies. The strong correlation between L dust and SFR implies that an accurate estimate of total dust luminosity corresponds to a better SFR estimation. However, IR surveys are extremely expensive, and also usually suffer from low spatial resolution, with respect to optical, and therefore result in source confusion. Moreover, the high-redshift galaxies are mostly explored in the visible and NIR wavelengths only. We investigated the relation between L dust estimation based on different wavelength ranges. Based on the sample of ELAIS N1 galaxies we verified whether or not it was possible to predict the total dust luminosity based on the optical and NIR data only. Two additional runs were performed.
We defined a threshold between χ 2 r,stellar and χ 2 r,IR as 8 µm in the restframe. We demonstrate that the combination of two different χ 2 s can efficiently select outliers and peculiar objects based on the energy balance assumption. We find that only 4. 15% of galaxies of the ELAIS N1 field show an AGN contribution. We check, using four different widely known criteria for AGN selection based on the nNIR data  (Stern et al. 2005; Donley et al. 2012; Lacy et al. 2007 Lacy et al. , 2013)) , that the majority of AGNs found based on the SED fitting procedure (67. 7%, 58. 4%, 75. 0%, and 83. 6% respectively) fulfill the NIR criteria. We test the influence that different attenuation laws have on the main physical parameters of galaxies. We compare results using  Charlot & Fall (2000) ,  Calzetti et al.
Introduction An increase of road accident rates has become a source of increased casualties and deaths leading to a global road safety crisis  [1]. A total of 52,160 Road Traffic accidents (RTAs) were recorded during the year 2000 in the state of Qatar. Among them, there were 1130 injuries and 85 fatalities. The data consisted on RTAs was collected from the traffic department, Qatar. Almost 53% of the death victims were in the age 10-40 years old, and the remaining 53% who died due to RTAs were in the age of 10-19 years old  [2]. Furthermore, traffic accidents are amongst the major sources of public death in the US  [3]  for individuals of age 11 and of every age from 16 through 24 in 2014. The number of motor-vehicle deaths reported in 2016 was 40,200, a 6% increase from 2015, and the first time the total annual fatality has exceeded 40,000 since 2007  [4].
We have initially evaluated the resolution and classification performances of different QTFDs, allowing us to select the best amongst them, i. e. , the Extended modified beta distribution (EMBD). The performance of the proposed approach is evaluated on the publicly available MIVIA dataset  [13] , which contains two types of hazardous events: car crashes and tire skidding. The main aim of this work is to correctly classify events of these two types in the presence of background noise on the highways. For the performance evaluation, the resulting classification results are compared with some of state-of-the-art approaches  [13, 19].
The Proposed Approach The audio signals of the MIVIA dataset (to be discussed in Section 2. 4) were processed to extract 200 CC segments, 200 TS segments, and the same number of segments containing background sounds. Each segment has a duration of 0. 75 s, which is the minimum duration of an audio segment in the available database  [34]. The proposed architecture for audio analysis and classification includes four steps: (1) extraction of temporal and spectral features, (2) extraction of joint (t, f )-domain features, (3) feature selection and (4) classification. Figure  1  illustrates the proposed methodology used in this study in detecting events of interest. A detailed explanation of each layer is given below. Sensors 2018, 18, x FOR PEER REVIEW 4 of 18.
The Proposed Approach The audio signals of the MIVIA dataset (to be discussed in Section 2. 4) were processed to extract 200 CC segments, 200 TS segments, and the same number of segments containing background sounds. Each segment has a duration of 0. 75 s, which is the minimum duration of an audio segment in the available database  [34]. The proposed architecture for audio analysis and classification includes four steps: (1) extraction of temporal and spectral features, (2) extraction of joint (t, f) -domain features, (3) feature selection and (4) classification. Figure  1  illustrates the proposed methodology used in this study in detecting events of interest. A detailed explanation of each layer is given below. The highest ranked features among the computed features are selected.
Data Set and Experiment Setup In this paper, we have used the publicly available dataset for road surveillance applications described in  [13]. The dataset is composed of two hazardous road events: CCs and TSs where the sounds of interest are not isolated. To enable the consideration of such abnormal events occurring under real-world conditions, the signals are superimposed on various background noises including crowd, cars passing nearby, and traffic jams. From the given dataset, we have extracted three sets of events: CC, TS, and BN, respectively. Each class contains 200 segments with each having a length of 0. 75 s (the average temporal length of events of interest), without any editing or aligning considerations. The segment duration of 0. 75 s was chosen because it is the minimum length corresponding to the shortest events in the database.
In this work, the signals were analyzed using five TFDs: the MBD, the EMBD, the short-time Fourier transform (STFT), the SPEC and the SWVD. The simulations for the MBD and EMBD were carried out with parameters chosen as σ = 0. 9 and β = 0. 01, with a lag window length of 355. Similarly, a Hanning window with a length of 71 samples is utilized for the SWVD, STFT, and SPEC distributions. The simulation results were performed in MATLAB. For the experiments, we have split the data as necessary to apply the N-fold cross-validation technique. Cross-validation is a useful technique used to assess the performance of a classification system on distinct sets of data under different conditions. In this technique, the dataset of interest is separated into training and testing folds whose constituent samples are independent of each other, meaning that the test data are not seen during training.
The data contained in (N-1) folds are used to train the classification model, and the remaining fold is then used to test the system. Finally, the results of the N different test experiments are averaged. In this study, a multi-class SVM classifier with the RBF kernel was trained using a combination of t-, f-and (t, f )-domain features. The MIVIA road dataset was utilized using the same split with k = 4 folds, each containing 200 events per class. Then, each possible set of (k-1) folds was used as a training set, and the remaining fold was given as the test set. The results of all k tests were then averaged to obtain the final result.
Conclusions The ability to detect hazardous events on the road can be a matter of life and death, or it mean the difference between a normal life and a life with a major handicap. In this work, we have proposed a system that combines t-, f-and (t, f) -domain features to simultaneously consider the nonstationary, instantaneous, and long-term properties of audio signals to facilitate automatic detection and classification of audio anomalies. The results of experiments performed on a publicly available benchmark dataset demonstrate the robustness of the proposed method against background noise compared with state-of-the-art approaches for road surveillance applications. Our audio classification system is confirmed to be effective in detecting hazardous events on roads, with a 7% improvement in accuracy rate compared with the state-of-the-art approaches.
Acknowledgments: This publication was made possible by NPRP grant #  NPRP8-140-2-065  from the  Qatar National Research Fund  (a member of the  Qatar Foundation ). The statements made herein are solely the responsibility of the authors. Acknowledgments: This publication was made possible by NPRP grant #  NPRP8-140-2-065  from the  Qatar National Research Fund  (a member of the  Qatar Foundation ). The statements made herein are solely the responsibility of the authors.
Conclusions The ability to detect hazardous events on the road can be a matter of life and death, or it can mean the difference between a normal life and a life with a major handicap. In this work, we have proposed a system that combines t-, f -and (t, f )-domain features to simultaneously consider the non-stationary, instantaneous, and long-term properties of audio signals to facilitate automatic detection and classification of audio anomalies. The results of experiments performed on a publicly available benchmark dataset demonstrate the robustness of the proposed method against background noise compared with state-of-the-art approaches for road surveillance applications. Our audio classification system is confirmed to be effective in detecting hazardous events on roads, with a 7% improvement in accuracy rate compared with the state-of-the-art approaches.