dataesr/scientific-paragraphs-categorization huggingface dataset:
Underlying GBD outputs are a range of additional health determinants that contribute to the estimation of morbidity and mortality, for which data are systematically compiled and estimates are produced. For example, GBD comprehensively analyses data from household surveys on vaccine coverage and combines survey estimates with reported administrative data to produce time series of vaccine coverage for all countries from 1990 to 2015. Estimates of vaccine coverage are then included as predictors of vaccine-preventable morbidity and mortality in GBD. Additional health indicators produced as part of GBD and included as health-related SDG indicators in this analysis are: met need with modern contraception among women of reproductive age, adolescent birth rate, skilled birth attendance coverage, and universal health coverage (UHC) tracer interventions.
For natural disasters, data on missing people and people aff ected by disaster are not readily available across countries and over time. Fourth, for occupational health (Indicator 8. 8. 1), we reported on age-standardised all-cause DALY rate attributable to occupational risks rather than the suggested indicator, which is limited to fatal and non-fatal occupational injuries. This revision captures a wider set of occupational risks instead of only those that result in injuries, which supports the stated target of promotion of "safe and secure working environments for all workers" (Target 8. 8). Fifth, for tobacco use prevalence (Indicator 3. a. 1), GBD does not presently assess smokeless tobacco use. Furthermore, smokeless tobacco use has a notably diff erent risk profi le to smoked tobacco use,  63  and thus it might warrant a subindicator akin to childhood malnutrition.
388 October 8, 2016 Health-related SDG indicator Defi nition used in this analysis Further details Inclusion in MDG or non-MDG index Goal 1: End poverty in all its forms everywhere Health-related SDG Defi nition used in this Further details Inclusion in indicator analysis MDG or non-MDG index (Continued from previous page) Target 1. 5: By 2030, build the resilience of the poor and those Target 3. a: Strengthen the implementation of the World Disaster (1. 5. 1; same Smoking (3. a. 1) Age-standardised death rate Age-standardised prevalence Existing datasets do not comprehensively measure We revised this indicator to daily smoking because Non-MDG in vulnerable situations and reduce their exposure and Health Organization Framework Convention on Tobacco as Indicators 11. 5. 1 due to exposure to forces of of daily smoking in missing people and people aff ected by natural of data limitations regarding the systematic vulnerability to climate-related extreme events and other Control in all countries, as appropriate and 13.
Achieve gender equality and empower all women and girls Target 5. 2: Eliminate all forms of violence against all women Intimate partner Age-standardised prevalence Existing datasets do not comprehensively measure Non-MDG and girls in the public and private spheres, including traffi cking violence (5. 2. 1) of women aged 15 years and the status of ever-partnered women relative to and sexual and other types of exploitation older who experienced never-partnered women; therefore, the intimate partner violence, % denominator was revised to all women aged women aged 15 years and 15 years and older. Data on exposure to subtypes of older violence are not systematically available across geographies and over time Goal 6:.
Ensure access to aff ordable, reliable, sustainable, and modern energy for all Target 7. 1: By 2030, ensure universal access to aff ordable, Household air Risk-weighted prevalence of Existing datasets do not comprehensively measure Non-MDG reliable, and modern energy services pollution (7. 1. 2) household air pollution, as population use of clean fuels and technology for measured by the SEV for heating and lighting across geographies; thus, we household air pollution, % revised this indicator to focus on exposure to clean (or unclean) fuels used for cooking Goal 8:.
Make cities and human settlements inclusive, safe, resilient, and sustainable (Continued from previous page) Goal 11: Target 11. 5: By 2030, signifi cantly reduce the number of Disaster (11. 5. 1; same Age-standardised death rate Existing datasets do not comprehensively measure Non-MDG deaths and the number of people aff ected and substantially as Indicators 1. 5. 1 due to exposure to forces missing people and people aff ected by natural decrease the direct economic losses relative to global gross and 13. 1. 2) of nature, per disasters; we revised this indicator to exposure to domestic product caused by disasters, including water-related 100 000 population forces of nature and reported in age-standardised disasters, with a focus on protecting the poor and people in rates vulnerable situations Target 11.
Goal 13: Take urgent action to combat climate change and its impacts Target 13. 1: Strengthen resilience and adaptive capacity to Disaster (13. 1. 2; same Age-standardised death rate Existing datasets do not comprehensively measure Non-MDG climate-related hazards and natural disasters in all countries as Indicators 1. 5. 1 due to exposure to forces missing people and people aff ected by natural and 11. 5. 1) of nature, per disasters; we revised this indicator to exposure to 100 000 population forces of nature and reported in age-standardised rates.
Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all, and build eff ective, accountable and inclusive institutions at all levels Target 16. 1: Signifi cantly reduce all forms of violence and Violence (16. 1. 1) Age-standardised death rate Existing datasets do not comprehensively measure Non-MDG related death rates everywhere due to interpersonal violence, displacement and migratory status of victims of per 100 000 population intentional homicide; we revised this indicator to deaths due to interpersonal violence (ie, homicide) Target 16. 1 (as above) War (16. 1. 2) Age-standardised death rate Existing datasets do not comprehensively measure Non-MDG due to collective violence and the displacement status of deaths due to confl ict; we legal intervention, per revised this indicator to deaths due to collective 100 000 population violence and legal intervention (ie, war) Detailed descriptions of the data sources and methods used to estimate each health-related SDG indicator are in the methods appendix pp 10-311.
Furthermore, the proposed indicator stipulates measurement of not only access to medicines and vaccines, but also access to affordable medicines and vaccines. No comprehensive and comparable datasets on the status of essential medicine and vaccine affordability, in addition to their stocks, presently exist Target 3. b (as above) 3. b. 2: Total net offi cial development assistance to the medical DAH is currently assessed within a comprehensive, comparable research and basic health sectors analytical framework by source, channel, recipient country, and health focus area from 1990 to 2015; however, funding specifi cally for medical research (eg, research and development of vaccines and medicines, as described in Target 3. b) is not systematically available across source and recipient countries.
H1 H2 L1 V1 G1 Interferometers: FIG. 1. On-source windows as defined for the four core-collapse supernovae considered in Section II. The date given for each core-collapse supernova is the published date of discovery. Overplotted in color are the stretches of time covered with sciencequality and Astrowatch-quality data of the various GW interferometers. The percentages given for each core-collapse supernova and interferometer is the fractional coverage of the on-source window with science or astrowatch data by that interferometer. See Table  I  and Sections II and III for details. cate with regions of different color times during which the various interferometers were collecting data. SN 2007gr exploded during the S5/VSR1 joint run between the iLIGO, GEO 600, and Virgo detectors. It has the best coverage of all considered CCSNe: 93% of its onsource window are covered by science-quality data from at least two of H1, H2, L1, and V1.
We search for GWs from SN 2007gr at times when data from the following detector networks are available: H1H2L1V1, H1H2L1, H1H2V1, H1H2, L1V1. The G1 detector was also taking data during SN 2007gr's on-source window, but since its sensitivity was much lower than that of the other detectors, we do not analyze G1 data for SN 2007gr. SNe 2008ax and 2008bk exploded in the A5 astrowatch run between the S5 and S6 iLIGO science runs (cf. Table  II ). Only the G1 and H2 detectors were operating at sensitivities much lower than those of the 4-km L1 and H1 and the 3-km V1 detectors. The coincident duty factor for SN 2008ax is only 8% while that for SN 2008bk is 38%. Preliminary analysis of the available coincident GW data showed that due to a combination of low duty factors and low detector sensitivity, the overall sensitivity to GWs from these CCSNe was much lower than for SNe 2007gr and 2011dh.
Because of this, we exclude SNe 2008ax and 2008bk from the analysis presented in the rest of this paper. SN 2011dh exploded a few days before the start of the S6E/VSR4 run during which the V1 and G1 interferometers were operating (cf. Table  II ). G1 was operating in GEO-HF mode  [127]  that improved its high-frequency (f 1 kHz) sensitivity to within a factor of two of V1's sensitivity. While not officially in a science run during the SN 2011dh on-source window, both G1 and V1 were operating and collecting data that passed the data quality standards necessary for being classified as science-quality data (e. g. ,  [128] [129] [130] ). The coincident G1V1 duty factor is 37% for SN 2011dh. In Figure  2 , we plot the one-side noise amplitude spectral densities of each detector averaged over the on-source windows of SNe 2007gr and 2011dh.
IV. SEARCH METHODOLOGY Two search algorithms are employed in this study: X-Pipeline  [60, 132]  and Coherent WaveBurst (cWB)  [131]. Neither algorithm requires detailed assumptions about the GW morphology and both look for subsecond GW transients in the frequency band 60 Hz to 2000 Hz. This is the most sensitive band of the detector network, where the amplitude of the noise spectrum of the most Noise Amplitude S( f ) [Hz -1/2 ] G1 SN2008ax G1 SN2011dh V1 SN2011dh H1 SN2007gr H2 SN2007gr L1 SN2007gr V1 SN2007gr FIG. 2. Noise amplitude spectral densities of the GW interferometers whose data are analyzed for SNe 2007gr and 2011dh (see Section III). The curves are the results of averaging 1/S(f ) over the on-source windows of the SNe (see Table  I ). We plot the G1 noise spectrum also for SN 2008ax to demonstrate the improvement in high-frequency sensitivity due to GEO-HF  [127]  for SN 2011dh.
sensitive detector is within about an order of magnitude of its minimum. This band also encompasses most models for GW emission from CCSNe (cf. [36, 37, 133] ). The benefit of having two independent algorithms is that they can act as a cross check for outstanding events. Furthermore, sensitivity studies using simulated GWs show some complementarity in the signals detected by each pipeline; this is discussed further in Section V. The two algorithms process the data independently to identify potential GW events for each supernova and network combination. Each algorithm assigns a "loudness" measure to each event; these are described in more detail below. The two algorithms also evaluate measures of signal consistency across different interferometers and apply thresholds on these measures (called coherence tests) to reject background noise events.
We also reject events that occur at times of environmental noise disturbances that are known to be correlated with transients in the GW data via well-established physical mechanisms; these so-called "category 2" data quality cuts are described in  [58]. The most important measure of an event's significance is its false alarm rate (FAR): the rate at which the background noise produces events of equal or higher loudness than events that pass all coherent tests and data quality cuts. Each pipeline estimates the FAR using background events generated by repeating the analysis on time-shifted data -the data from the different detectors are offset in time, in typical increments of ∼ 1 s. The shifts remove the chance of drawing a sub-second GW transient into the background sample since the largest time of flight between the LIGO and Virgo sites is 27 milliseconds (between H1 and V1).
Given a FAR value R, the probability p(R) of noise producing one or more events of FAR less than or equal to R during one or more CCSN on-source windows of total duration T on is p = 1exp (-RT on ). ( ) 1 The smallest such false alarm probability (FAP) that can be measured given an off-source (time-shifted) data duration T off is approximately T on /T off. Several thousand time shifts are therefore sufficient to measure FAP values of O(10 -3 ). We require a FAP below 0. 001, which exceeds 3-σ confidence, in order to consider an event to be a possible GW detection candidate. Figure  3  shows examples of the FAP as a function of event loudness for cWB and X-Pipeline for the H1H2L1V1 network during the SN 2007gr on-source window. If no on-source events have a FAP low enough to be considered GW candidates, then we can set upper limits on the strength of any GW emission by the CCSNe.
B. X-Pipeline In the X-Pipeline  [60, 132, 135]  analysis, the detector data are first whitened, then Fourier transformed. A total energy map is made by summing the spectrogram for each detector, and "hot" pixels are identified as the 1% in each detector with the largest total energy. Hot pixels that share an edge or vertex (nearest neighbors and nextnearest neighbors) are clustered. For each cluster, the raw time-frequency maps are recombined in a number of linear combinations designed to give maximum-likelihood estimates of various GW polarizations given the known sky position of the CCSN. The energy in each combination is recorded for each cluster, along with various time-frequency properties of the cluster. The procedure is repeated using a series of Fourier transform lengths from 1/4 s, 1/8 s,.
C. Simulated Signals and Search Sensitivity An important aspect of the GW search presented in this study is to understand how sensitive the GW detector networks are to GWs emitted by the considered CCSNe. We establish sensitivity via Monte Carlo simulation in the following way: 1. We determine the loudest event in the on-source window that is consistent with the CCSN location (and the angular uncertainty of the search algorithms). 2. We "inject" (add) theoretical waveforms scaled to a specific distance (or emitted GW energy) every 100 s plus a randomly selected time in [-10, 10] s into the time-shifted background data. We compare the loudness of the recovered injections with the loudest on-source event and record the fraction of the injections that passed the coherent tests and data quality cuts and were louder than the loudest on-source event.
V. SEARCH RESULTS As discussed in Section IV, on-source events from each combination of CCSN, detector network, and pipeline are assigned a false alarm rate by comparing to time-slide background events. Table V lists the FAR values of the loudest event found by each pipeline for each network and CCSN. The lowest FAR, 1. 7 ×10 -6 Hz, was reported by cWB for the analysis of SN 2007gr with the H1H2L1V1 network. This rate can be converted to a false alarm probability (FAP) using equation  (1). The total duration of data processed by cWB or X-Pipeline for the two CCSNe was T on = 873461 s. Equation (1) then yields a false-alarm probability of 0. 77 for the loudest event; this is consistent with the event being due to background noise. We conclude that none of the events has a FAP low enough to be considered as a candidate GW detection.
A. Detection efficiency vs. distance Given the loudest event, we can compute detection efficiencies for the search following the procedure detailed in Section IV C. In brief, we measure the fraction of simulated signals that produce events surviving the coherent tests and data quality cuts and which have a FAR (or equivalently FAP) lower than the loudest event. Figures  4  and 5  show the efficiency as a function of distance for the CCSN waveforms from multi-dimensional simulations and the phenomenological waveforms discussed in Section IV C and summarized in Tables III and IV. For SN 2007gr, the maximum distance reach is of order 1 kpc for waveforms from detailed multi-dimensional CCSN simulations, and from ∼100 kpc to ∼1 Mpc for GWs from the phenomenological models (torus fragmentation instability and long-lived rotating bar mode).
ACKNOWLEDGMENTS We thank  L. Dessart  for applying the expanding photosphere method to SN 2008bk to derive an approximate explosion date and  A. Howell  for access to his supernova spectra fit software superfit. 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 GEO 600 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  Council of Scientific and Industrial Research of India , the  Istituto Nazionale di Fisica Nucleare of Italy , the  Spanish Ministerio de Educación y Ciencia , 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 , the  Carnegie Trust , the  Leverhulme Trust , the  David and Lucile Packard Foundation , the  Research Corporation , and the  Alfred P.
Introduction Data with values in the space P m , of m × m symmetric positive definite matrices, play an essential role in many applications, including medical imaging  [1, 2] , computer vision  [3] [4] [5] [6] [7]  and radar signal processing  [8, 9]. In these applications, the location where a dataset is centered has a special interest. While several definitions of this location are possible, its meaning as a representative of the set should be clear. Perhaps, the most known and well-used quantity to represent a center of a dataset is the Fréchet mean. Given a set of points Y 1 , • • • , Y n in P m , their Fréchet mean is defined to be: Mean(Y 1 , • • • , Y n ) = argmin Y∈P m n ∑ i=1 d 2 (Y, Y i ) ( 1 ) where d is Rao's Riemannian distance on P m  [10, 11]. Statistics on general Riemannian manifolds have been powered by the development of different tools for geometric measurements and new probability distributions on manifolds  [12, 13].
When a dataset contains extreme values (or outliers), because of the impact of these values on d 2 , the mean becomes less useful. It is usually replaced with the Riemannian median: Median(Y 1 , • • • , Y n ) = argmin Y∈P m n ∑ i=1 d(Y, Y i ) (3) Definition Equation (3) corresponds to that of the median in statistics based on ordering of the values of a sequence. However, this interpretation does not continue to hold on P m. In fact, the Riemannian distance on P m is not associated with any norm, and it is therefore only possible to compare distances of a set of matrices to a reference matrix. In the presence of outliers, the Gaussian distribution on P m also loses its robustness properties. The main contribution of the present paper is to remedy this problem by introducing the Riemannian Laplace distribution while maintaining the same one-to-one relation between MLE and the Riemannian median.
Mixtures of Laplace Distributions There are several motivations for considering mixtures of distributions in general. The most natural approach is to envisage a dataset as constituted of several subpopulations. Another approach is the fact that there is a support for the argument that mixtures of distributions provide a good approximation to most distributions in a spirit similar to wavelets. The present section introduces the class of probability distributions that are finite mixtures of Riemannian Laplace distributions on P m. These constitute the main theoretical tool, to be used for the EM algorithm is run several times, and the best result is retained, i. e. , the one maximizing the log-likelihood function.
Application to Texture Classification This section presents an application of the mixture of Laplace distributions to the context of texture classification on the MIT Vision Texture (VisTex) database  [38]. The purpose of this experiment is to classify the textures, by taking into consideration the within-class diversity. In addition, the influence of outliers on the classification performances is analyzed. The obtained results for the RLD are compared to those given by the RGD  [15]  and the WD  [17]. The VisTex database contains 40 images, considered as being 40 different texture classes. The database used for the experiment is obtained after several steps. First of all, each texture is decomposed into 169 patches of 128 × 128 pixels, with an overlap of 32 pixels, giving a total number of 6760 textured patches.
Next, some patches are corrupted, in order to introduce abnormal data into the dataset. Therefore, their intensity is modified by applying a gradient of luminosity. For each class, between zero and 60 patches are modified in order to become outliers. An example of a VisTex texture with one of its patches and an outlier patch are shown in Figure  1. Once the database is built, it is 15-times equally and randomly divided in order to obtain the training and the testing sets that are further used in the supervised classification algorithm. Then, for each patch in both databases, a feature vector has to be computed. The luminance channel is first extracted and then normalized in intensity. The grayscale patches are filtered using the stationary wavelet transform Daubechies db4 filter (see  [39] ), with two scales and three orientations.
These estimates are also computed numerically by means of sub-gradient algorithms. The estimation of parameters in mixture models of Laplace distributions are also considered and performed using a new expectation-maximization algorithm. Finally, the main theoretical results are illustrated by an application to texture classification. The proposed experiment consists of introducing abnormal data (outliers) into a set of images from the VisTex database and analyzing their influences on the classification performances. Each image is characterized by a set of 2 × 2 covariance matrices modeled as mixtures of Riemannian Laplace distributions in the space P 2. The number of mixtures is estimated using the BIC criterion. The obtained results are compared to those given by the Riemannian Gaussian distribution, showing the better performance of the proposed method.