dataesr/scientific-paragraphs-categorization huggingface dataset:
In Section 4, we first present the datasets used in the experiments; then we give details about the proposed color texture segmentation method based on FCAMs. Next, we assess the regional SLIC algorithm and discuss its parameter settings. Last, we compare the segmentation results achieved by our supervised segmentation approach with those obtained by fuzzy texture features and by several other state-of-the-art color texture segmentation methods. Concluding remarks about the contribution of this paper are given in Section 5.
Dataset As the experimental dataset, we used the challenging Prague texture segmentation benchmark  [57]. It contains 20 color texture mosaics to be segmented (input test images), some of which are shown in Figure  4  (top) with their corresponding segmentation ground truth images. Each of these images represents from 3 to 12 classes and has been synthetically generated from the same number of original color texture images. The original Prague dataset is composed of 89 images (one image per texture class) grouped into 10 categories of natural and artificial textures. Figure  4  (bottom) shows some of these images that form the training dataset. All images of Prague database are of size 512 × 512 pixels.
This number must be small enough to get superpixels that are large enough to contain homogeneous textures, and small enough for the superpixels to finely fit boundaries between textures. We empirically found that P = 400 is a good trade-off for the Prague dataset. Table  1  shows the average and standard deviation of the four metrics obtained by the basic and modified SLIC algorithms over the 20 images of Prague dataset. From this table, we can see that the results achieved by the regional SLIC are better than those obtained by the basic SLIC according to all metrics. The proposed color texture segmentation requires one to set several parameters. For the training stage, the number of prototypes was set to T = 1000, since CNN-based methods use 1000 training texture mosaic images for each image to segment  [23].
The comparison is based on the segmentation accuracy as the main performance measure and on the computational time required by each feature extraction method. Experiments were carried out using the 1NN classifier instead of the ELM, on a computer with an Intel Core i7 3. 60 GHz CPU and 8 GB of RAM. The results over the 20 images of the Prague dataset are summarized in Table  2. They clearly show that FCAM is more relevant, three times faster to compute, and requires less memory than marginal features (FGLCM and FGLAM). FCAM is also more efficient and slightly faster to compute than FCCM.
Comparison with State-of-the-Art Supervised Segmentation Methods In this section, we compare the results obtained by the proposed segmentation method with the results of several state-of-the-art segmentation methods, on the Prague dataset. The assessment of segmentation performance was based on the conventional measures provided on the Prague texture segmentation website  [57] , which include: (1) regionbased criteria: correct segmentation (CS), over-segmentation (OS), under-segmentation (US), missed error (ME), and noise error (NE); (2) pixel-wise based criteria: omission error (O), commission error (C), class accuracy (CA), recall (CO), precision (CC), type I error (I. ), type II error (II. ), mean class accuracy estimate (EA), mapping score (MS), root mean square proportion estimation error (RM), and comparison index (CI); (3) consistency-error criteria: global consistency error (GCE) and local consistency error (LCE).
All these methods except MRF, COF, and Con-Col, involve a refinement step to improve performance. Regarding the number of prototypes, no information is available for MRF, COF, and Con-Col. In contrast, the CNN-based methods used a training set of 1000 texture mosaic images of size 512 × 512 pixels, specifically created from the original Prague dataset, for each image to segment. From Table  3 , we can see that our proposed method, with and even without refinement, outperforms the classical supervised methods, namely, MRF, COF, and Con-Col. In contrast, EWT-FCNT and other CNN-based methods provided better segmentation results than our method. However, by carefully analyzing these results, we found that-except the EWT-FCNT method, which provided exceptional results-our method provided similar results (to U-Net, FCNT, DA, and PSP-Net).
Unlike other methods, FCAM is not prone to over-segmentation, and classification errors almost only occurred at region boundaries. This is mainly due to our pre-segmentation based on regional SLIC that, despite its superiority over the basic SLIC, lacks the accuracy to correctly determine the boundaries between two or more different texture regions. However, it is important to underline that deep learning-based methods (including EWT-FCNT) need to create a large training set (1000 images for each image to segment) from the original training database. Moreover, they require an expensive learning step to extract features and classify pixels. In order to get an overview on the computational costs of our approach compared to CNN-based methods, the following section gives the computational times measured during both training and segmentation phases.
Processing Time In Section 4. 2, we estimated the FCAM computational time only at superpixels. In order to provide an overview of the computational requirements of the entire FCAM-based color texture segmentation method, we estimated its overall runtime over the Prague dataset on an 3. 60 GHz Intel Core i7 computer with 8 GB RAM. Table  5  displays the average computing time of our proposed method and those of CNN-based methods (EWT-FCNT, FCNT, U-Net, DA, and PSP-Net). These processing times are separated into two parts: the training time and the segmentation time. The segmentation times for CNNs were taken from  [23]. They were measured on a laptop computer with 2. 5 GHz quad-core Intel Core i7 processor and 16 GB memory, equipped with GTX 1080Ti external GPU with 11 GB memory. The training times of these CNNs were unfortunately not provided.
From this table, we can see that the processing time required by our method during the segmentation stage is the highest. It is about 41. 14 s for a 512 × 512 image. FCAM computation for superpixels consumes most of this time (33. 12 s). The remaining time is mainly shared among the regional SLIC computation (8 s) and the classification of superpixels by ELM (0. 02 s). It should be noted that the reported computing time for our method was obtained with MATLAB code without any GPU acceleration, and consequently, our method could be implemented for real-time applications with this enhancement. In contrast, the total duration of the training stage for our method remained lower than 5 min and can be detailed as 260 s to compute the FCAM at the prototypes, and 0. 9 s to train the ELM. The overall training time (260.
Experiments on the Prague texture segmentation benchmark showed that the proposed color texture segmentation based on FCAMs outperforms the classical stateof-the-art segmentation methods and is competitive with recent methods based on deep learning. However, unlike CNN-based approaches that require an expensive learning procedure and a large training set of segmented texture images, whose construction is time-consuming, our method is applied straightforwardly from a much smaller database using ELM-based classification. Despite its respectable performance, our segmentation method remains sensitive to pre-segmentation results. Although the regional SLIC improves segmentation results in comparison with the basic SLIC, the detection of color texture boundaries is still not accurate enough. In future work, we intend to use a texture-aware superpixel procedure that takes into account the properties of the available color spaces.
Matching muons to tracks measured in the silicon tracker results in a relative transverse momentum resolution, for muons with p T up to 100 GeV, of 1% in the barrel and 3% in the endcaps  [25]. The forward hadron (HF) calorimeter uses steel as an absorber and quartz fibers as the sensitive material. The two halves of the HF are located 11. 2 m from the interaction region, one on each end, and together they provide coverage in the range 3. 0 < |η| < 5. 2. Events of interest are selected using a two-tiered trigger system  [26]. The first level (L1), composed of custom hardware processors, uses information from the calorimeters and muon detectors to select events at a rate of around 100 kHz for high luminosity pp collisions and 30 kHz for PbPb collisions. The second level, known as the high-level trigger (HLT), consists of a farm of processors running a version of the full event reconstruction software optimized for fast processing, and reduces the event rate to around 1 kHz before data storage, for both pp and PbPb collisions.
Simulation Monte Carlo simulations are used as a baseline for the J/ψ efficiency and acceptance, as well as the detector response to jets. Correction factors to account for data-to-simulation discrepancies are discussed in the following section. Events are generated with pythia 8 (version 8. 230)  [10] , using the CP5 underlying event tune  [29]. Prompt J/ψ are produced using all color-singlet and color-octet modes with the default matrix elements  [30]. Simulation of PbPb collisions is done by embedding pythia 8 events into PbPb collision events produced with the hydjet generator (version 1. 9)  [31]. The event activity at mid-rapidity is matched to that of minimum bias collision data via an analysis of the energy deposited in randomly distributed cones. The response of the CMS detector is simulated using the Geant4 package  [32].
This smeared mapping is then applied in the unfolding procedure, as described below. The finite p T resolution of jets causes migration of jets across z bins. It also causes migration of jets into and out of the nominal p T selection of 30-40 GeV. In order to capture the full jet response, we also perform the measurement in underflow and overflow bins of 10-30 GeV and 40-50 GeV, respectively. With the current data, a reliable yield cannot be extracted for p T intervals larger than the selected overflow bin, which motivates the choice of the nominal p T interval. To correct for these bin migration effects, we unfold the data simultaneously in these two dimensions (jet p T and z) using the iterative D'Agostini method  [41] , as implemented in the RooUnfold package  [42]. The detector response matrix, which defines the relationship between the true and measured values, is taken from the pythia 8 simulation, embedded into hydjet for the case of PbPb collisions, aside from the aforementioned data-tosimulation scale factors for the jet energy scale and resolution.
The response matrices for pp and PbPb collisions are shown in Fig. 2. The coarse bins show the jet p T dependence, whereas the finer inset bins show the dependence on z. Each column of measured bins is normalized to unity, such that each bin represents the fractional contribution of the given true p T and z values to the measured values for that column. The PbPb data exhibit substantially larger off-diagonal contributions than for pp, as expected from the larger underlying event, which drives the bin migration in PbPb. In this method, the unfolding is initialized with a "prior" distribution in the two variables, which is taken from simulation. To avoid bias from the presumed shape of the z distribution in simulation, which is known to be inaccurate  [8] , the prior distribution is flattened in z.
However, while only one super-iteration is sufficient for pp, 25 super-iterations are required for PbPb collisions. Since the optimal settings depend not only on the statistical uncertainties of the measured z distribution, but also on its shape, we additionally evaluated them in nonprompt J/ψ meson simulation, where the measured distribution is more similar to the data than in the prompt J/ψ meson simulation, by unfolding the nonprompt z distributions with the nonprompt response matrices and using the settings with the best performance as a systematic variation in the regularization procedure.
For PbPb collisions, the uncertainty is evaluated from peripheral data, as well as from pp data recorded in the same yearly running period, and varies from 3% in the barrel to 7% in the endcap and transition regions. In PbPb, there is an additional contribution to the uncertainty because of the modeling of the underlying event in simulation with hydjet. This uncertainty is evaluated by comparing the energy in randomly distributed cones in data and simulation. The difference between the random cone distributions in data and simulation is used to estimate the effect on the jet resolution. Unfolding: The uncertainty in the regularization applied is estimated by varying the number of iterations used in the unfolding, from the settings found to be optimal for prompt J/ψ to those found to be optimal for the nonprompt J/ψ signal, which has a very different signal shape as a function of z.
For nonprompt simulations in pp and PbPb collisions, the use of ten iterations is found to give the best performance, in contrast to three iterations for the prompt simulation. In both cases, the statistical precision of the data is emulated in simulation. The assumption of a prior that is flat in z is also relaxed, by instead initializing the prior to match the shape of the truth z distributions in simulated nonprompt J/ψ signal samples, which are more similar in shape to the measured z distributions. Finally, the statistical uncertainty of the simulated samples used in the unfolding is taken into account, and is generally subdominant. Systematic uncertainties related to the jet energy scale and resolution are propagated by producing variations of the detector response matrix and repeating the unfolding procedure for each variation.
Normalization uncertainties: Cross section measurements in pp collisions have an overall uncertainty from the integrated luminosity of 1. 9%  [44]  that is obtained from an analysis of data from a van der Meer scan  [45]. The PbPb data are normalized by the Fig. 3. The main sources of systematic uncertainty, as a function of z, for the cross section in pp (upper) and the T AA -scaled yield in PbPb (lower) collisions. The normalization uncertainties of 1. 9% for pp and 2. 6% for PbPb are not shown. equivalent number of hadronic nucleon-nucleon interactions in the data sample, which has an uncertainty of 1. 3% coming from the selection of such events, taking into account possible contamination from electromagnetic interactions and beam backgrounds. To compare to data from pp collisions, the PbPb data are normalized by T AA , which is determined from the Monte Carlo implementation of the Glauber model described in Ref.
Summary Jets containing a prompt J/ψ meson were studied in protonproton (pp) and lead-lead (PbPb) collisions at √ s NN = 5. 02 TeV, for jets with transverse momentum 30 < p T < 40 GeV and pseudorapidity |η| < 2. The distribution of the fragmentation variable z, the ratio of the J/ψ p T to that of the jet, was measured in both systems. In pp collisions, prompt J/ψ mesons were found to have more surrounding jet activity, i. e. , to populate lower values of z than predicted by pythia 8 simulations, suggesting that J/ψ production late in the parton shower is underestimated. The pp and PbPb distributions were compared by calculating the nuclear modification factor, R AA , the ratio of PbPb data to the expectation based on pp data. The value of R AA as a function of z shows a rising trend. The suppression at low z is found to be larger in the 20% most central events (i.
Acknowledgements We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MOST , and  NSFC (China) ;  MINCIEN-CIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  NKFIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON ,  ROSATOM ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI , and  FEDER (Spain) ;  MoSTR  ( Sri Lanka );  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Acknowledgments We would like to thank  Pawel Caputa  and  Sandip Trivedi  for discussions. We are grateful to  Antal Jevicki  for comments on an earlier draft which led to improvements,  Satya Majumdar  for correspondences clarifying several points and  Gautam Mandal  for discussions about many aspects of this paper. We thank  William Witczak-Krempa  and  Benoit Estienne  for comments about the first arXiv version. The work of S. R. D. is partially supported by  U. S. National Science Foundation  grants  NSF/PHY-1818878  and  NSF/PHY-2111673  and by the  Jack and Linda Gill Chair Professorship. The work of S. H. is supported by the  ERC  Grant  787320  -QBH Structure. The work of S. L. is partially supported by  NCN Sonata Bis  9 grants.
Analytical techniques Whole-rock analyses of samples, including the pumice starting material, were ground and fused into a lithium tetraborate disk before being analyzed for whole rock major element oxide compositions using X-ray fluorescence (XRF) at the Activation Laboratory in Ancaster, Canada, following the approach outlined in  Norrish and Hutton (1969). Mineral proportions were obtained by counting the phenocryst content, using JMicrovision software on plain polarized images of the entire thin section (for the 1990 samples), and using a Pelcon mechanical point counting stage (for the 2007 and 2014 samples). For each sample, we counted between ca. 1,400-1,600 points to ensure statistically significant results with relative errors of <10%. Results from point counting (Table  2 ) and mineral compositions (Table  3 ) allowed us to determine the composition of the calculated matrix of the 1990, 2007, and 2014 samples (Table  1 ) by subtracting the mineral compositions weighted with their proportions from the whole rock compositions.
Remote sensing of SO 2 emissions To evaluate sulfur dioxide (SO 2 ) emissions from Kelud during the 2007 effusive eruption, we used ultraviolet (UV) satellite data from the Ozone Monitoring Instrument (OMI) on NASA's Aura satellite. Aura/OMI was launched in 2004 and has a spatial resolution at nadir of 13×24 km and high sensitivity to atmospheric SO 2 , permitting detection of small plumes emitted by weak or passive volcanic degassing  (Carn et al. , 2016). OMI measurements since 2004 have provided a longterm record of passive and eruptive volcanic SO 2 emissions that continues to the present  (Carn et al. , 2017 (Carn et al. , , 2016;; Carn, 2022). Column amounts of SO 2 are derived from the UV OMI measurements using a low-noise principal component analysis (PCA) retrieval algorithm  (Li et al.
Due to the small size of many crystals, it was very challenging to obtain a good quantitative chemical analysis, and thus we relied on mineral identification using EDS spectra obtained in SEM or electron probe. We obtained glass compositions from glass-rich experimental charges or for those where glass "pools" larger than about 10 μm, and we found that they are homogeneous in composition (Figures  2A-D ). For charges with low water     b Phase proportions were calculated using grayscale thresholding of BSE, charge maps using ImageJ software and then converted to phase proportions. c See Table  2  for meaning of abbreviations, with the addition of Gl that we refer to glasses only. For charges where only CO 2 was added, we could not calculate the log (fO 2 ). d log (fO 2 ) determined using water saturated Ni-Pd alloy capsules, calculated using the formulation by  Pownceby and O'Neill (1994).
240 Second, the tfd has been an important forum for conducting advocacy for the preparation and dissemination of reports and policy recommendations on hmdcc. 241 For instance, the tfd technical members were recently encouraged by the co-chair of the un ilc Study Group on Sea-level Rise in Relation to International Law, Patricia Galvao-Teles, to provide input to the report on the protection of persons affected by sea-level rise. 242 Another example is how the role of the tfd in disseminating the recently concluded report by the UN Special Rapporteur on the Human Rights of idp s, Cecilia Jimenez-Damary, was affirmed in a recent discussion. 243 Third, according to its new mandate, the tfd is charged with helping Excom wim in guiding the implementation of the recommendations. 244 The 2019-2021 Plan of Action of the tfd, referred to as 'phase 2' , focuses on the themes of national and subnational policy and practice, international and regional policy, data and assessment, and framing and linkages.
Literature review Our systematic database review was conducted by searching published articles on PubMed (https://pubmed. ncbi. nlm. nih. gov/), Web of Science (www. webofknowledge. com), and Google Scholar (https://scho lar. google. com/) that contained the following terms: ("cancer" AND "VOC" OR "volatile organic" OR "odor detection"). Much of the information on the generalities of different cancers was found on the American Cancer Society site (https://www. cancer. org/). We took into account all the studies that have analyzed the chemical composition of odor profiles from human cancerous patients. The literature included articles published in English from 1984 and until August 2020, excluding systematic reviews, meta-analyses, case report studies such as experimental treatments designed to modify odors (e.
g. induced oxidative stress), studies performed on animals, as well as studies that investigated the ability of diverse animals to smell cancer. For each study, we extracted the following data: type of cancer studied, sample type (e. g. , exhaled air, urine. ), year of publication, analytical methods, numbers of compounds detected, and the name of the volatile compounds that were shown to be specific to cancer odors. Each compound found in the literature was verified using the National Institute of Standards and Technology (NIST) (https://www. nist. gov/) and Pubchem databases (https://pubchem. ncbi. nlm. nih. gov/) to ensure that compound annotations were consistent. We categorized publications based on the analytical method used. Gas chromatography-mass spectrometry (GC-MS) included all the analyses conducted with TD-GC-MS (thermal desorption-gas chromatography-mass spectrometry) and/ or GC-TOF-MS (gas chromatography/time-of-flight mass spectrometry).