dataesr/scientific-paragraphs-categorization huggingface dataset:
The value for d002 decreases from about 4. 0 Å for the 800 °C sample to 3. 6 Å for the 2000 °C sample, while D002 increase from about 0. 8 nm to 1. 3 nm. The diffraction curves of the samples based on the three other lignins were evaluate accordingly. Data for lignosulfonate-based samples can be found in Table  1 , the results o soda and kraft lignin-based samples can be found in the Supplementary Information (Ta ble S1). The values of D002 and d002 are slightly different at the lowest heat treatment tem perature (800 °C), ranging from D002 = 0. 74 nm and d002 = 3. 91 Å for K800 to D002 = 0. 86 nm for L800 and d002 = 3. 98 Å for S800. After heat treatment at 2000 °C, the d002 is quite comparabl for all samples, with a value around 3. 5 Å, while the apparent crystallite size D002 varie in the range of 1.
21 nm to 1. 45 nm. The average number of graphite layers S in the graphit stacking can be estimated by dividing the apparent crystallite size by the lattice distanc  [31] : S = D002/d002. The values of S can be found in Table  1  and Table S1. For the sample heat-treated at 800 °C, the values of S are quite comparable with S ≅ 2. 0. After heat treat ment at 2000 °C, the lignins exhibit comparable values of about 3. 5-3. 7, only S2000 exhibit a higher value of S = 4. 2. The diffraction curves of the samples based on the three other lignins were evaluated accordingly. Data for lignosulfonate-based samples can be found in Table  1 , the results of soda and kraft lignin-based samples can be found in the Supplementary Information (Table  S1 ). The values of D 002 and d 002 are slightly different at the lowest heat treatment temperature (800 • C), ranging from D 002 = 0.
Small-Angle X-ray Scattering (SAXS) SAXS data were recorded in the laboratory for all samples. The curves of organosolv, kraft and soda lignin-based samples were similar in display, i. e. , only one pore population in the nm range was visible, exhibiting increasing pore diameter with increasing heat treatment temperature. Only the lignosulfonate-based sample exhibits a more complex scattering signal with pores of different sizes evolving with higher heat treatment temperatures. To get insight into the pore structure towards the length scale of several hundreds of nanometers, it was decided to perform ultra-small-angle X-ray scattering (USAXS) on a selection of samples. All samples from lignosulfonate and from organosolv were chosen for USAXS. Results of the scattering experiments over the full q range of USAXS and SAXS are depicted in Figure  5a  (lignosulfonate) and Figure  5b (organosolv).
Small-Angle X-ray Scattering (SAXS) SAXS data were recorded in the laboratory for all samples. The curves of organoso kraft and soda lignin-based samples were similar in display, i. e. , only one pore populati in the nm range was visible, exhibiting increasing pore diameter with increasing h treatment temperature. Only the lignosulfonate-based sample exhibits a more comp scattering signal with pores of different sizes evolving with higher heat treatment temp atures. To get insight into the pore structure towards the length scale of several hundre of nanometers, it was decided to perform ultra-small-angle X-ray scattering (USAXS) a selection of samples. All samples from lignosulfonate and from organosolv were chos for USAXS. Results of the scattering experiments over the full q range of USAXS and SA are depicted in Figure  5a  (lignosulfonate) and Figure  5b  (organosolv).
Nanopore Size Evaluation in Real Space: Radial Distribution Function Since the size of nanopores and mesopores could not be evaluated properly for all the lignosulfonate samples, a second approach besides the Guinier approximation was chosen. A fit with the GNOM program [33] of the ATSAS package  [34]  was applied to the corrected data in the nanopore range in order to evaluate the changes in pore size due to heat treatment. With GNOM, the size distribution function p(r) = 4π/3•r 3 •N(r) for polydisperse spheres with radius r and relative number of spheres with this radius N(r) was determined, where p(r) relates to the corrected scattering intensity I'(q) as follows  [35] : ′ 4 sin (7) and p(r) is related to the correlation function γ(r) of the particles as follows: p(r) = r 2 γ(r). For L 800 , the fraction of the mesopore volume is about 9% and the fraction of the nanopore volume is about 30% of the volume measured at 2000 • C.
Nanopore Size Evaluation in Real Space: Radial Distribution Function Since the size of nanopores and mesopores could not be evaluated properly for all the lignosulfonate samples, a second approach besides the Guinier approximation was chosen. A fit with the GNOM program [33] of the ATSAS package  [34]  was applied to the corrected data in the nanopore range in order to evaluate the changes in pore size due to heat treatment. With GNOM, the size distribution function p(r) = 4π/3•r 3 •N(r) for polydisperse spheres with radius r and relative number of spheres with this radius N(r) was determined, where p(r) relates to the corrected scattering intensity I'(q) as follows  [35] : I (q) = 4π D 0 γ(r) sin(qr) qr r 2 dr  (7)  and p(r) is related to the correlation function γ(r) of the particles as follows: p(r) = r 2 γ(r).
Characterization of Samples Scanning electron microscopy (SEM) was conducted using a QuantaTM 250 fieldemission environmental scanning electron microscope equipped with a Schottky field emission gun which operated at 10 kV in high vacuum of 1 × 10 -6 mbar. The samples were sputter-coated with gold and images were taken at different magnifications with the secondary electron detector (ETD). Wide-angle X-ray diffraction (WAXD) was carried out with a 5-circle X-ray diffractometer equipped with Cu-Kα source with a parabolic multilayer mirror in the primary beam and a secondary graphite monochromator and a wavelength of λ = 0. 15418 nm. Intensity data were documented in a step of 0. 02 • between 2θ = 5 • and 50 • with 4 s dwell time. Small-angle X-ray scattering (SAXS) was performed with a Rigaku S-Max3000 3-pinhole SAXS camera with an MM002+ source (Cu-Kα, λ = 1.
54 Å) and a Triton 200 multiwire detector. Scattering images were recorded in the q range 0. 08-8 nm -1 , with q being the magnitude of the scattering vector related to the scattering angle 2θ and the wavelength λ by the Bragg equation: q = 4π/λ sin θ. All 2D data were azimuthally integrated to obtain 1D scattering profiles, i. e. , scattering intensity vs. q, I(q) and later background subtracted for further analysis  [26]. For select samples, additional ultra-small-angle X-ray scattering (USAXS) measurements were performed on ID02 beamline at the ESRF, Grenoble, France. Scattering images were taken for a 10-m and a 31-m sample-to-detector distance with a maximum exposure time of 0. 01 s using a Rayonix MX 170 HS detector. The settings result in a q range from 0. 002 to 0. 765 nm -1. The background-subtracted synchrotron data could be combined with the SAXS data from the laboratory allowing for a full q range of 0.
Funding: This research was funded by the  Austrian Science Promotion Agency (FFG)  ( Bridge  Project No. 846543 ). Part of this work was carried out within the project BioCarb-K cofinanced by the  European Regional Development Fund (EFRE)  and the province of  Upper Austria  through the  IWB 2014-2020 program. Open-access funding was provided by  BOKU Vienna Open Access Publication Fund. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
In addition, the related shape of the clusters appears also to be different. Except for c = 0:098 wt. % (LC3) and, to a much lesser extent for c = 0:082 wt. % (LC2), the pðrÞ functions demonstrate an inverted parabolic profile, typical for a flattened shape of the clusters. The pðrÞ for LC3 points to an elongated shape of presented clusters. In order to obtain tentative shape models of the clusters, ab initio modeling from the SAXS curves at small angles has been performed using the program DAMMIN  (Svergun, 1999)  of the ATSAS package. Figure  7  depicts the resulting ab initio shape models. As size of the dummy beads used to model the SAXS curves, a radius of 3 nm was chosen, which roughly corresponds to the MNP size. For all five samples, the models exhibit an overall elongated shape for the magnetic clusters.
Afterward, samples were cooled down back to 20 C. For the samples within the isotropic phase, there is no preferred orientation and the SAXS patterns were azimuthally averaged and normalized by the P12 beamline SASFLOW pipeline  (Franke et al. , 2012). From these 1D-SAXS curves the background signal from water was subtracted and the difference profiles were further analyzed using the ATSAS software package  (Franke et al. , 2017).
ACKNOWLEDGMENTS The authors thank the support by the  Slovak Academy of Sciences , in the framework of projects  VEGA 2/ 0043/21  and  VEGA 1/0105/20 , the  Slovak Research and Development Agency  under the contract No. APVV-015-0453 , MODEX (ITMS2014+: 313011T548 ) supported by the  Operational Programme Integrated Infrastructure (OPII)  funded by the  ERDF  and COST action CA15119 Nanouptake. We gratefully acknowledge the financial support and provision of beam time at the instrument D33 at the  Institut Laue-Langevin ,  Grenoble ,  France  project No. 9-11-1883. M. A. S. and D. I. S. acknowledge the  Roentgen-Angstroem cluster project  '' TT-SAS '' ( BMBF  project number  05K16YEA ) for financial support. S. B. and M. H. thank the  German Science Foundation (DFG)  for financial support within the  Priority Program  ( SPP1681 ) ''Field controlled particle matrix interactions.
Further, mathematical models are based on the time-fractional Caputo derivative, which involves a power-law of the memory kernel (See Appendix A, Equation (A6)). The fractional partial differential equations of the mathematical models are: ∂ α u(r, t) ∂t α = D 1 r ∂ ∂r r ∂u(r, t) ∂r -Ku(r, t), t ≥ 0, r ∈ (0, 1) (3) and ∂ α u(r, θ, t) ∂t α = D 1 r ∂ ∂r r ∂u(r, θ, t) ∂r + ∂ 2 u(r, θ, t) ∂θ 2 -Ku(r, θ, t), t ≥ 0, r ∈ (0, 1), θ ∈ (0, 2π) (4) In the considered models, the diffusion parameter, D, and the killing rate of cells, K, are assumed to be constant. The solutions to the problems are obtained using the Laplace transform and the finite Hankel transform under appropriate initial and boundary conditions. The effects of the memory parameter, as well as time, on the tumor cell concentration, u, are studied by numerical calculations and graphical illustrations obtained with the Mathcad software.
A clear trend was observed, in which higher temperature and higher H2O loading improved conversion. In the second of these experiment sets, jacket temperature was varied in the same manner, as well as varying the pressure from 10 to 30 bar (see Supporting Information section 2. 2. 8. ), where pressure showed only a minor effect. From these experiments (25 in total, including 2 repetitions) it was possible to build a model (using Sartorius Modde v12. 1 software) representing the most significant reaction variables. As anticipated by qualitative observations, the temperature was by far the most significant parameter, followed by H2O loading. Conversely, reaction pressure had a relatively minimal effect. A contour plot representing predictions of conversion at different conditions could be plotted from this data (Figure  4b ).
Following completion of each audio-recorded interview, the digital file was promptly transcribed by external transcription services: REV. com for English language interviews and Datagain Services for interviews conducted in Spanish. The PI made sure interview files did not contain any identifying data before submitting them for transcription. Coding was conducted using MAXQDA qualitative analysis software. Our team met weekly to conduct preliminary analysis of the interview transcripts to identify broad thematic categories addressed in each interview and to discuss these identified thematic categories which consisted of both a priori constructs (based on the aims of the study and the interview guide) and emerging themes (that were related to the study aims but not specifically anticipated).
To characterize the range of reasons for declining a vaccination and the most salient ones, we calculated the frequency with which themes were endorsed by study participants. This is not to imply that our qualitative data can be generalized to larger populations of people who inject drugs or to vaccine decliners, but to better characterize the range of themes that emerged among a population of people who inject drugs in New York City. As other qualitative studies have suggested, infrequently endorsed themes can be as important for designing public health interventions as frequently endorsed ones  [30]. Once we reached a point where no new thematic areas were identified in newly conducted interviews, we determined by consensus that saturation had been reached; at this point, we completed scheduled interviews and stopped recruiting new interviewees.
Layout analysis and optical character recognition have become traditional tasks for processing historical prints, but are now insufficient. Additional information is found in typographic emphasis, such as bold and italic letters. They carry semantic meaning (titles, emphasis. ) and also outline the structure of the page (entries, sub-parts. ). Retrieving such data is therefore crucial for information extraction and automatic document structuring. In this paper, we introduce the Bold-Italic-Regular (BIR) database, which contains 285 pages of scanned, list-like historical prints that have been annotated at word level with bold and italic emphasis. Baseline results are provided for word detection and style classification using state-of-the-art deep neural network models, highlighting promising possibilities, such as near-human performance for isolated word classification, but also demonstrating limitations for the task at hand.
The BIR database 1  introduced in this paper provides the research community with a benchmark dataset for developing and comparing methods for identifying bold and italic emphasis on scanned page images of list-like historical prints. In an experimental evaluation, we evaluate and compare stateof-the-art deep neural network models, including YOLOv5  [8]  for word detection and MobileNetV2  [17]  and Xception  [1]  for style classification. The obtained baseline results are promising but also demonstrate the difficulty of the task and the current limitations of the state of the art.
Further studies have associated typographic emphasis with a texture. On the one hand, a simple weighted Euclidian distance has been used to classify fonts of synthetic data, after extracting features such as spatial frequency and orientation with multi-channel Gabor filters  [25]. On the other hand, local binary patterns have been used with a synthetically generated database of arabic text images  [12]. Most recent approaches obviously use neural networks, may they be LSTM  [20]  or CNN  [19]. In the past few years, in parallel to OFR, script recognition has benefited from numerous studies  [22] , some of which may well be useful for the identification of typographic emphasis. It is particularly the case of experiments using the neural network implementation of OCR engines to identify multiple scripts at text-line level  [23] , a strategy used successfully for recognizing semantico-typographic classes (dictionary entry, antiqua, fraktur, author's name and letter-spacing) in a real historical use case: Daniel Sander's Wörterbuch der Deutschen Sprache  [15].
Document Collection The BIR database contains a subset of 285 pages come from exhibition  [21]  and sale catalogues  [5] , for which we have strong evidences that using bold and italic information increase the precision of the extraction and the structuring  [6]. Carefully selected excerpts have been taken in the documentation of the Artl@s and the Katabase projects, i. e. mainly 19 th French catalogues, which remains our primary target. Other types of documents (e. g. a 19 th Latin lexicon) or in other languages than French (e. g. the São Art Paulo Biennale) have been added to diversify the data, as well as more recent catalogues (20 th and 21 th c. ). Tab. 1 lists the number of pages considered for the BIR database, according to different categories.
Ground Truth Annotation The words of the original database were segmented and classified according their style with the ABBYY FineReader software  [18]. The word segmentation was excellent but the style classification was far less successful. Each sentence was manually annotated with HTML tags to identify bold and italic words and the transcription was improved. To conduct our experiments it was necessary to align the style information contained in the sentences with the locations of each word. Due to the manual correction of the transcription at the sentence level, the number of words contained in the sentence regularly differed from the number of words in the ABBYY FineReader output. To solve this problem, a string edit distance algorithm was used to find the closest matching words, in order to correctly transfer the typographic emphasis.
Experiments are performed with the PyTorch-based YOLOv5  [8]  version using the medium-sized YOLOv5m model with 21. 4 million parameters pre-trained on the COCO  [11]  database. Image preprocessing includes downscaling to a height of 1024 pixels, keeping the same aspect ratio, in order to fit the input images into the GPU memory. Furthermore, random scaling, translation, and rotation operations are applied during training to augment the number of training samples and improve the generalizability of the model.
Style Classification Style classification aims at determining the style of an individual word image that has already been localized on the scanned page. The BIR database distinguishes bold, italic, and regular words. Two fully-convolutional network architectures are used for establishing baseline results, namely MobileNetV2  [17]  and Xception  [1]. They were chosen with respect to their excellent classification performance on ImageNet  [2]  and to include both a smaller (MobileNetV2: 3. 5 million parameters) and a larger (Xception: 22. 9 million parameters) architecture. Experiments are performed with a Keras-based implementation and ImageNet pre-trained parameters. We drop the top layer, perform global average pooling to reduce the spatial dimensions, add a dense layer with 100 neurons, dropout, and rectified linear unit (ReLu) activation, and finish with a softmax classification layer.
Experimental Setup Two datasets splits are defined for experimental evaluation. • TVT. In this standard setup, the 285 pages are randomly distributed into three distinct sets for training the neural networks (50%), validation of metaparameters (25%), and testing of the final system performance (25%). • CV5. In this cross-validation setup, the 35 documents are split into five distinct parts, each containing 7 documents. Five cross-validations are performed using three parts for training, one part for validation, and one part for testing, thus allowing to test the generalization capability of a trained network to an unseen document. For word detection, the TVT setup is considered, training the YOLOv5m network with its default fine-tuning hyperparameters 100 epochs on the training set until convergence.
This base model is then further fine-tuned on an augmented dataset, randomly scaling, translating, and rotating each page hundred times. The augmented model is trained 20 epochs until convergence, which takes about 1 hour on two Titan RTX cards. The detection performance is evaluated with respect to precision (#correct detections / #detections), recall (#correct detections / #words), and F1-score (harmonic mean of precision and recall), considering a detection as correct if its intersection over union (IoU) with the ground truth box is larger than 50%. For style classification, an initial experiment is conducted with the MobileNetV2 for the TVT setup. Afterwards, more detailed experiments are conducted for the CV5 setup, comparing MobileNetV2 with the larger Xception architecture and the two strategies for coping with class imbalance, class balancing and class weights respectively.
CONCLUSION The BIR database and its baseline results for word detection and style classification demonstrate the difficulty of identifying typewritten emphasis in historical prints. Given the importance of style emphasis for document analysis and understanding, we hope that our research database can contribute to the development of novel methods in this field. A promising line of research is related to building combined word localization, recognition, and style classification models. Furthermore, data augmentation, synthesis, and balancing strategies are expected to play a central role when developing more robust systems that generalize well to unseen document collections. Going beyond isolated word classification by including more contextual information is expected to be of fundamental importance for improving the performance.
Data Collection and AC Regimen All data were retrospectively collected by local investigators in an electronic case-report form via the REDCap R software (Research Electronic Data Capture © , Vanderbilt University, USA) hosted by a secured server from the French Institute of Health and Medical Research at the Paris Cardiovascular Research Center. Baseline information of the patient included demographic characteristics, coexisting medical conditions, cardiovascular comorbidities, and chronic medications. Clinical parameters and biological findings were recorded at admission. On the chest CT scan, the degree of pulmonary lesions with ground-glass opacities and areas of consolidation were categorized as low/moderate (<50% involvement) or severe (≥50% involvement). Data on pharmacological therapies, mode of respiratory support, complications, and final vital status were also gathered throughout the hospitalization.
Patients who had an AC treatment (whatever the regimen) before hospitalization were excluded from the analysis. The AC regimens analyzed during hospitalization were categorized into two groups: standard-dose prophylactic (once daily LMWH; subcutaneous heparin injection or intravenous heparin infusions) or intermediate-dose prophylactic (twice daily LMWH, subcutaneous heparin injection or intravenous heparin infusions). The exact dosing of AC was not reported in the database and was reported according to the treating clinician. During the study period, the dose of the AC regimen was based on the International Society on Thrombosis and Haemostasis  (11)  and the French guidelines  (12). The time from hospitalization (Day 0) to death was used as an outcome. Symptomatic venous and ischemic thrombotic events and outcomes (in-hospital death) were assessed using electronic medical records.
Continuous data were expressed as median (interquartile range, IQR) and categorical data as n (%). Patients were compared according to the AC regimen during hospitalization (intermediate-dose or standarddose prophylactic AC). In the multivariable analysis, we used conditional logistic regression to assess the association between the AC regimen and outcomes. The interactions between the AC regimen and specific subgroups were assessed via the Cochran-Mantel-Haenszel X 2 test. For the survival analysis, the start of the study was triggered by the diagnosis of SARS-CoV-2 infection and hospitalization in a medical ward. The end of the study was defined either by the death of the patient during the hospitalization or by discharge alive from the hospital. We used the Cox proportional hazard (PH) model to investigate the relationships between AC regimen and outcomes.