dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank  Claudio Lagos  and  Antonio Pipino  for predictions from their models and for advice on their use. We acknowledge support for the International Team 181 from the  International Space Science Institute in Berne. This work was supported by the  Swiss National Science Foundation. SCUBA2 observations have been done on the  James Clerk Maxwell Telescope. The James Clerk Maxwell Telescope is operated by the  Joint Astronomy Centre on behalf of the Science and Technology Facilities Council of the United Kingdom , the  National Research Council of Canada , and (until 31 March 2013) the  Netherlands Organisation for Scientific Research.
RIVER DISCHARGES Areas and discharges of the drainage basins were obtained from the Design of Total Runoff Integrating Pathways-TRIP database  (Oki and Sud, 1998). For Brazilian rivers, the data originated from the Brazilian National Water Agency (ANA) and the Brazilian National Agency of Electric Energy (ANEEL). These databases provided information on precipitation, water level and river discharge from Brazil's primary water systems. For the other water systems, the long-term series of the outflow were taken from the Global River Discharge Data Base/SAGE of the University of Wisconsin, USA.
NUTRIENT LOADS The continental fluxes of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) were calculated through regression models that were originally constructed based on the analysis of 165 water systems worldwide and that incorporate information on DIN and DIP flows  (Meybeck and Ragu, 1997; Smith et al. , 2003). The final calculated values were compared with the global estimations  (UNPD, 2004). The monthly data on the discharge of silicon (Si) and iron (Fe) were obtained from different surveys, as indicated in Appendix B (supplementary material). Similar to that of nitrogen and phosphorous, the estimations of the discharge of silicon for the months for which no information was available were based on the methodology used for semi-empirical modeling  (Dürr et al.
LAND-OCEAN-ATMOSPHERE CARBON FLUXES The carbon discharges of the river into the oceans are normally estimated based on models that consider the climatological information and characteristics of the different drainage basins  (Ludwig et al. , 1996a,b; Ludwig and Probst, 1998). DIC is transported by rivers primarily in the dissolved form, which is composed of the following three main fractions: CO 2 , HCO - 3 , and CO - 32. The TA and pH values were used to obtain DIC through the carbonate system equations when data were missing for DIC. We used a simple conservative mixing model to calculate the proportions of estuarine systems and seawater in a given estuary sample  (Cooley and Yager, 2006). For each discrete water sample, we solved the following equations for the estimation of the proportions of the river and seawater concentrations.
The surface seawater endmembers of the DIC (DIC SW ), TA (TA SW ) and salinity (S SW ) were averaged monthly for non-plume sampling regions. Table  1  indicates the DIC SW , TA SW , and S SW values of the seawater of plume endmembers of the major river estuaries flowing into the tropical Atlantic. The partial pressures of CO 2 in seawater (pCO 2 ) and the air-sea exchanges (FCO 2 ) were   et al. , 1998; Bourlès et al. , 1999; Cooley and Yager, 2006. calculated using the CO2calc software  (Robbins et al. , 2010)  once the two parameters of the carbon system, temperature, salinity, phosphate, and silicate concentrations were known.
III. LIGO AND VIRGO DETECTORS Data taken in 2009-2010 with the 4-km-long 'enhanced' LIGO detectors  [1]  and the 3-km-long Virgo detector  [2]  were used in this analysis. The LIGO and Virgo detectors are both power-recycled Michelson interferometers with Fabry-Perot arm cavities. Following the fifth LIGO Science Run (S5), a number of upgrades were made to the 'initial' LIGO 4-kmlong interferometers (H1 in Hanford, Washington, and L1 in Livingston Parish, Louisiana). Most substantially: 1) the initial 10 W laser was upgraded to a new 35 W laser, 2) an 'output mode cleaner' was installed at the output port of the interferometer, 3) the radiofrequency detection scheme (heterodyne) was changed to a DC detection scheme (homodyne), and 4) the detection opto-electronics were moved to an in-vacuum, activelystabilized optical table to reduce seismic motion affecting the read-out optics and electronics.
IV. TWOSPECT ALGORITHM The details of the TwoSpect method have been described previously  [16]. We briefly summarize the algorithm here. Short segments (30 min or less) of gravitational wave detector data are Fourier transformed (socalled Short Fourier Transforms, or SFTs) using the FFTW (Fastest Fourier Transform in the West) algorithm  [34] , and the power of each Fourier coefficient is computed. Next, each SFT is weighted according to the noise present in the SFT, and by the antenna pattern of the detector (the sensitivity) to a given sky location at the particular time that the SFT data were recorded. Time spectrograms of SFTs over a narrow frequency band (∼1 Hz) are created such that the frequency shift caused by Earth's motion is removed by sliding each SFT by an appropriate amount for a specific sky location.
A. Data preparation The S6 and VSR2/3 data sets, each defined here with a length T obs = 40551300 s, are divided into segments of length T SFT = 1800 s. Each sequential segment overlaps the preceding segment by 50%, and each of these segments of data is windowed using the Hann window function, to suppress signal leakage into other frequency bins, before the Fourier transform is computed. The windowed Fourier transform is defined as sk = ∆t C M-1 j=0 w j s j e -2πijk/M , (9) where k = 0, 1, 2,. , (M -1), ∆t = T SFT /M is the sampling interval, the window function is w j = 0. 5[1cos(2πj/M )], and C = ( M-1 j=0 w 2 j /M ) 1/2 = (3/8) 1/2. Physical frequency f k = k/T SFT corresponds to 0 ≤ k ≤ M/2. The 'power' in bin k of SFT n is taken to be P n k = 2 |s n k | 2 T SFT. (10) The SFTs are adjusted for the changing detector velocity with respect to a fixed sky location by shifting SFT bins to correct for this effect in the same manner as other StackSlide-like algorithms  [5, 6].
V. ANALYSIS OF THE DATA Data from the H1 and L1 detectors' sixth Science Run (S6) and V1 detector's second and third Science Runs (VSR2 and VSR3, collectively VSR2/3) were analyzed using the TwoSpect algorithm. Each detector's data set was analyzed separately with T obs = 40551300 s. An outlier from one detector is required to be coincident in parameter space with an outlier in a second detector in order to be considered a candidate signal. Figure  1  shows the period-modulation depth parameter space values covered in this analysis using TwoSpect. ∆ fm a x T w oSp e c t obs e r vable ∆ fm i n T w oSp e c t obs e r vable I nit ial s e ar c h ∆fm a x AT N F c at alog puls ar s in binar ie s w it h ν ≥ 10 H z FIG. 1. Nominal parameter space that is analyzed using the TwoSpect algorithm (shaded region).
A trade-off is thus made in the sensitivity versus parameter space volume to be probed when conducting such a search. LIGO S6 data from H1 and L1 were analyzed from 50 Hz to 520 Hz, covering a range of periods from 2 h to 2,254. 4 h and modulation depths of 0. 277 mHz to 100 mHz. Virgo VSR2/3 data were analyzed from 20 Hz to 100 Hz, over the same range of periods and modulation depths. The range of orbital periods has a lower limit determined by the coherence length of the SFTs, and an upper bound by requiring at least five orbits during the total observation time. The lower limit of modulation depths is determined by the coherence length of the SFTs, and the upper bound is chosen by covering a large region of parameter space without dramatically increasing computational costs. VSR2/3 data are only comparable to or better than LIGO S6 data in the aforementioned range of frequencies.
Analyzing higher frequencies in the Virgo data would add to the total computing cost and add negligibly to the search sensitivity. The TwoSpect program is part of the LALsuite (LIGO Analysis Library suite) software package  [37]. TwoSpect is written in C and is compiled against LAL (LSC Algorithm Library), GSL (GNU Scientific Library)  [38] , and FFTW libraries. On the LIGO computer clusters, the analysis is divided into parallel 'jobs' that are run on many computers simultaneously. Each job is an instance of the TwoSpect program and analyzes a 0. 25 Hz frequency band and a small sky region (typically 200 sky grid locations).
A. Data quality validation Ideally, the noise from a gravitational wave interferometer would be stationary Gaussian noise (in addition to any gravitational wave signal). In practice, data from the LIGO and Virgo detectors are generally stationary and nearly Gaussian on the timescale that one SFT is computed. There are occasions, however, when data must be excluded because: 1) it is known the interferometer data is corrupted (data quality flags are applied); 2) the segment of data passes data quality flags, but the data segment is non-Gaussian (a Kolmogorov-Smirnov test and/or Kuiper's test fails); or 3) sharp, stationary spectral features prevent a full analysis of the selected frequency band. Examples of sharp, stationary spectral features include: power-line harmonics (50/60 Hz), mirror suspension violin modes, and calibration lines injected into the detector by actuating one of the end mirrors.
Science mode and data quality flags Periods of time when the detector was operating in the nominal 'science mode' are first selected. Next, a series of quality checks of the data-known as 'data quality flags'-are applied to remove times when the detector data is known to be of poor quality. Examples include when the calibration of the detector is known to be outside a tolerance range, or when there were periods of very high wind speeds (see table  I )  [39, 40]. After these checks are applied, SFTs are created. The S6 data set contains 18,435 H1 and 16,429 L1 50% overlapping Hann-windowed SFTs with start-times occurring an integer factor of T SFT /2 from the start-time of the first SFT. The resulting duty factors are 0. 409 and 0. 364 for H1 and L1, respectively. The VSR2/3 data set contains 17,879 50% overlapping Hann-windowed SFTs, corresponding to a duty factor of 0.
The same procedure is performed with the detector lists reversed. From this final list of outliers passing coincidence requirements, the outliers are grouped into narrow frequency bands (typically less than ∼30 mHz) for further, manual inspection. The pair-wise combination that has the smallest false alarm probability is considered representative of the outliers in each group  [42]. The sky position of the pair is then averaged and given in table  III. All of the outliers listed in table III are found to be associated with known detector artifacts  [39, 40]. Most of the outliers are caused by a comb of 1 Hz, 2 Hz, or 16 Hz harmonics associated with the LIGO data acquisition system (DAQ). Another outlier is also due to a 392. 2 Hz DAQ line. Two of the outliers are due to fake continuous gravitational wave signals with unrealistically large amplitudes injected into the detectors by modulating the interferometer arm lengths (see, e.
25 Hz because we analyze only full-0. 25 Hz frequency bands. Note that there is good reason, however, to believe that the Scorpius X-1 signal frequency would be higher than this value  [46]. Using Eq. (  8 ), the range of ∆f obs is frequency-dependent and ranges from 1. 663 mHz to 10. 470 mHz. Data from S6 were analyzed from 50 Hz up to 57. 25 Hz, while VSR2/3 data were analyzed from 20 Hz up to 57. 25 Hz. Fr e que nc y ( Hz ) h 0 95% upp e r lim it C ir c ular p olar iz at ion limit s R andom or ie nt at ion limit s FIG. 4. Sco X-1 strain upper limit results of S6/VSR2-3 for continuous gravitational waves assuming the source waves are circularly polarized (blue points) or the source waves are randomly polarized with random pulsar orientations (red points). The black vertical lines indicate 0.
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the  United States National Science Foundation  for the construction and operation of the  LIGO Laboratory , the  Science and Technology Facilities Council of the United Kingdom , the  Max-Planck-Society , and the  State of Niedersachsen/Germany  for support of the construction and operation of the GEO600 detector, and the  Italian Istituto Nazionale di Fisica Nucleare  and the  French Centre National de la Recherche Scientifique  for the construction and operation of the Virgo detector. The authors also gratefully acknowledge the support of the research by these agencies and by the  Australian Research Council , the  International Science Linkages program of the Commonwealth of Australia , the  Council of Scientific and Industrial Research of India , the  Istituto Nazionale di Fisica Nucleare of Italy , the  Spanish Ministerio de Economía y Competitividad , the  Conselleria d'Economia Hisenda i Innovació of the Govern de les Illes Balears , the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , the  Polish Ministry of Science and Higher Education , the  FOCUS Programme of Foundation for Polish Science , the  Royal Society , the  Scottish Funding Council , the  Scottish Universities Physics Alliance , The  National Aeronautics and Space Administration, OTKA of Hungary , the  Lyon Institute of Origins (LIO) , the  National Research Foundation of Korea, Industry Canada  and the  Province of Ontario  through the  Ministry of Economic Development and Innovation , the  National Science and Engineering Research Council Canada , the  Carnegie Trust , the  Leverhulme Trust , the  David and Lucile Packard Foundation , the  Research Corporation , and the  Alfred P.
Attenuation correction is an essential requirement for quantification of positron emission tomography (PET) data. In PET/CT acquisition systems, attenuation maps are derived from computed tomography (CT) images. However, in hybrid PET/MR scanners, magnetic resonance imaging (MRI) images do not directly provide a patient-specific attenuation map. The aim of the proposed work is to improve attenuation correction for PET/MR scanners by generating synthetic CTs and attenuation maps. The synthetic images are generated through a multi-atlas information propagation scheme, locally matching the MRI-derived patient's morphology to a database of MRI/CT pairs, using a local image similarity measure. Results show significant improvements in CT synthesis and PET reconstruction accuracy when compared to a segmentation method using an ultrashort-echo-time MRI sequence and to a simplified atlas-based method.
I. INTRODUCTION P OSITRON emission tomography/magnetic resonance imaging (PET/MR) scanners are expected to offer a new range of applications in neuro-oncology, epilepsy and neurodegenerative diseases such as Alzheimer's disease  [1]. To quantify accurately the radionuclide uptake, PET data need to be corrected for photon attenuation. The attenuation information is usually obtained from a transmission scan in standalone PET or derived from a computed tomography (CT) image in combined PET/CT systems. Regarding PET/MR scanners, the strong magnetic field and the limited bore diameter of the MRI prevent the use of a transmission source. As MRI image intensities do not reflect the electron densities, alternative methods must be developed for PET/MR acquisitions. These methods can be classified into three main categories: emission, segmentation, and atlas-based approaches  [2].
Even though generally robust and accurate, this method encounters difficulties differentiating bone and air in the sinuses. In atlas-based methods, an anatomical model or dataset is deformed to match the patient's anatomy in order to apply the attenuation map from the model to the patient data. Schreibmann et al. [13]  developed a multimodality optical flow deformable model that maps patient MRI images to a CT atlas. The CT atlas is then warped with the deformation field obtained by the registration to generate a simulated CT image that matches the patient anatomy. Johansson et al. [14]  described a Gaussian mixture regression model that links the MRI intensity values to the CT Hounsfield units (HU). Assuming a correspondence between MRI and CT intensities can lead to estimation errors as several materials such as cerebrospinal fluid, air, and bone have similar low intensities in a T1-weighted MRI image but distinct values in a CT image.
Considering spatial information may enhance the results of such techniques, but inaccuracies still remain at boundaries such as air and bone in the nasal sinuses  [15]. In the Hofmann et al. method  [16] , local information derived from a pattern recognition technique and global information obtained by an atlas registration are combined to predict a pseudo-CT image from a given MRI image. Navalpakkam et al. [17]  used a CT image acquired with a PET/CT scanner and images of the same patient derived from 3-D Dixon-volume interpolated breath-hold examination and UTE MRI sequences acquired with a PET/MR scanner to derive a pseudo CT using an epsilon-insensitive support vector regression. These techniques enable the synthesis of continuous valued attenuation maps. Combining the segmentation of an MRI image and the use of a CT image database, Marshall et al.
[18]  proposed a method to correct for attenuation whole-body PET/MR scans. The subject's MRI is compared to a database of CT images using a set of 19 similarity metrics such as gender, age, and body, lung, and bone geometries. The most similar CT image is nonrigidly aligned to the subject's MRI. Bones from the registered CT image are then added to the MRI image previously segmented into four tissue classes (air, lung, fat, and lean tissue). The main weakness of the MRI-based attenuation correction methods is the lack of accuracy of the bone delineation as both bone and air have similar low intensities in MRI images. However, these inaccuracies lead to a strong spatial bias of the PET activity as shown in  [19]  and  [20]. The proposed method follows the principle of multi-atlas propagation to synthesize an attenuation correction map from an MRI image.
A. MRI-CT Database Preprocessing and Inter-Subject Mapping The atlas database consisted of pairs of T1-weighted MRI and CT brain images. T1 images were corrected for intensity nonuniformity following a nonparametric nonuniform intensity normalization method  [24]. CT images were segmented to separate the head from the bed using thresholding and morphological operators. For each subject, the MRI was affinely aligned to the CT using a symmetric approach  [25]  based on Ourselin et al. [26]. Even though this is an intra-subject alignment, a full affine registration was used to compensate for possible gradient drift and calibration in the MRI scans. In order to synthesize the CT from a given MRI, one first needs to register all the MRIs from the atlas database to the target MRI. This inter-subject coordinate mapping was obtained using a symmetric global registration followed by a cubic B-spline parametrized nonrigid registration, using normalized mutual information as a measure of similarity  [27].
B. CT Synthesis The proposed framework uses a local image similarity measure between the target MRI and the set of registered MRIs as a metric of the underlying morphological similarity. Provided the local image intensity is a good approximation of the local morphological similarity between subjects, we assume that if two MRIs are similar at a certain spatial location, the two CTs will also be similar at this location. 1) Image/Morphological Similarity: The similarity measure monitors the local quality of match between the MRI of the target subject and each of the warped MRIs from the atlas database. In this work, two similarity measures were tested: the convolution-based fast local normalized correlation coefficient (LNCC) proposed by Cachier et al. [28]  and the local normalized sum of square differences (NSSD).
The LNCC evaluates the quality of alignment between two images by calculating the correlation between the signals, with a larger focus on high contrast regions, while the NSSD characterises the differences in intensity between the two images, making it more susceptible to contrast problems. Let the target subject's MRI be denoted by and its corresponding unknown CT be denoted by. For each of the atlases in the database, let the mapped MRI and CT images of atlas be denoted by and , respectively. The LNCC between and at voxel is then given by As in  [28] , the means and standard deviations at voxel were calculated using a Gaussian kernel with standard deviation through convolution where denotes the convolution operator. High LNCC values indicate a better local match between the two MRI images.
B. Parameter Optimization The morphological similarity between the target MRI and each of the registered MRIs from the atlas database was assessed using a local image similarity measure. For both the LNCC and NSSD metrics, two parameters were optimized using a leave-one-out cross validation on the 18 subjects from the first dataset: the standard deviation of the Gaussian kernels used in (1) and (2) ( , expressed in voxels) and from (3) whose value influences the repartition of the weights The weighted sum tends to the mean when is small. Using only the MRI image of the subject, a pseudo CT image was generated using the proposed method. This synthetic CT was then compared to the ground truth CT. The metric employed to measure the synthesis error was the mean absolute error, defined as  (6)  where is the number of voxels in the region of interest.
This cost function was estimated between the ground truth CT and the pseudo CT for every subject in the optimization dataset. The average values of MAE over all subjects are shown in Fig. 2. The optimization focused on two regions: the full head and the skull region, obtained setting a threshold at 100 HU. The skull region is important as bones have the highest attenuation coefficients and can induce large errors. Results: For both the full head and the skull region, the LNCC with voxels gives the smallest error. is the skull optimum and guarantees smoother images. With these parameters, the average MAE is 113 HU for the full head and 240 HU for the skull region. A large range of parameters was tested in the optimization, all showing small differences in the results. The existence of a plateau in terms of accuracy in the parameter space means that small changes in parameters result in a very similar pseudo CT and indicates that the method is robust to the choice of parameters.
C. Validation Scheme In order to validate the algorithm with the previously optimized parameters, but on a completely independent dataset, the performance of the proposed synthesis algorithm was ran against ground truth data for 41 subjects. We also compared it to two other methods: a UTE-based method obtained through MRI segmentation and a simplified atlas-based method. The UTE-based method uses the segmentation of a UTE MRI sequence which enables the differentiation of three tissue classes (bone, air, and soft tissue). A prototype version of this method, only made available to early users of the Siemens Biograph mMR hybrid PET/MR scanners, was used in this work. In the following, when the values assigned to the classes are expressed in HU, the image is referred to as UTE CT, and when the values correspond to linear attenuation coefficients in cm , the image is called UTE -map and matches the PET's PSF and voxel size.