dataesr/scientific-paragraphs-categorization huggingface dataset:
Advantages of DoDG filterings 4. 4. 1. Robustness to the issues of DT description Thanks to taking our DoDG into account the filterings, all DoDGfiltered outcomes are complementary and robust to environmental changes, illumination, and noise. This allows that local spatio-temporal features extracted from these outcomes are more insensitive for DT encoding compared to those extracted from a raw video. Indeed, in order to evaluate this advantageous property, we investigate DoDG on noisy datasets to evaluate its ability of noise-resistance. Accordingly, we address the Gaussian zero-mean noise model with different signal-to-noise ratio (SNR) levels, i. e. , SNR  ∈ {1, 2, 3, 4, 5}, to add noise into UCLA  [27]  -the simple dataset, and DynTex  [39]  -the more challenging one (see Table  1  for their attributes in detail).
For each of them, we have achieved 5 noise-datasets corresponding to 5 SNR  levels used for the noise-adding process (see Fig. 7  for noise-instances). We then evaluate our DoDGF 2∕3 , ′ , exploiting the 1st-order DoDG-filtered outcomes with (,  ′ ) = (0. 7, 1) on those noisy datasets. In addition, for a comprehensive evaluation of the noise-resistant ability, several relevant DT descriptors are also addressed on these datasets: DoG-based descriptors, i. e. , DoGF 2∕3 , ′ defined in Section 3. 4, and other LBP-based ones without taking any filters into account their DT encoding, e. g. , VLBP  [62] , LBP-TOP  [62] , CLSP-TOP  [72] , HILOP  [75]. Their specific parameters along with the achieved results of DT recognition are presented in Table  4. It can be seen from Table  4  that taking DoDG into account the DT encoding makes our proposed DoDG-based descriptors more robust against noise compared to the DoG-based ones and other LBP-based variants as well.
Assessments of DoDG-based descriptors Based on the experimental results in Table  7 , it can be stated that our DoDG is the major factor in order to boost the discrimination of DoDGF 2∕3. Hereafter, we discuss their performance thoroughly. • The performance of DoDG-based descriptors is diminished subject to the increasing high-orders of DoDG involved in the filterings. It is due to the weakness of appearances in the larger-orders. Therein, the odd DoDG kernels often handle denoising in more effect (see Table  7 ). • The local patterns extracted from each of the DoDG-filtered outcomes are complementary to enhance the robustness. Indeed, Table  6  shows that DoDGF 2 has higher rates when integrating all those features, as mentioned in Section 3. 4. • It can be seen from Table  7  that the DoDGF 2∕3 descriptors have the nearly same rates on simple datasets (e.
7, 1) often point out the best results on UCLA and Alpha datasets (see Table  7 ). Moreover, the odd-even DoDGF 2∕3 (0. 7,1), descriptors in multi-order analysis (i. e. , {1st, 2nd}, {1st, 4th}, {2nd, 3rd}, {3rd, 4th}) have produced better performances than the others on all datasets (they also obtain the best results on UCLA and Alpha datasets). It means that on the more challenging schemes (Beta, Gamma, and DynTex++), exploiting complementary information by odd and even orders of DoDG allows to enhance the discrimination power. Among of above those, the 1st-order DoDGF 2∕3 (0. 7,1),{1st} descriptors should be addressed for mobile applications due to their small dimension, i. e. , just 2400 bins for the 2D one and 3600 bins for the 3D. For more strict requirement of accuracy, the setting of multi-gradients  = {1st, 2nd} should be addressed for DoDGF 2∕3 (0.
Comprehensive comparison to HoGF descriptors In our prior work  [20] , the 2D/3D Gaussian gradients in multi-scales of standard deviations were directly exploited to filter a video. The obtained HoGF 2∕3 descriptors have good recognition rates compared to state of the art. In this work, thanks to DoDG 2∕3 filterings, our DoDGF 2∕3 descriptors obtain better performance than HoGF 2∕3. Indeed, it can be verified from Table  8  that rates of DoDGF 2∕3 (0. 7,1),{1st,2nd} are generally higher than those of two separate deviation scales of 2-order HoGFs, i. e. , HoGF 2∕3 {=1},{1st,2nd} and HoGF 2∕3 {=0. 7},{1st,2nd} , for DT recognition on challenging datasets. Particularly, on DTDB, rates of our DoDGF 2∕3 (0. 7,1),{1st,2nd} are about 1% to 2% better than those of HoGFs (see Table  8 ). In the case of addressing the best settings, rates of our DoDGFs are just a little lower on Gamma (see Table  9 ) but about 1% higher than those of HoGFs on both DTDB's large-scale schemes (see  Table 11).
Comprehensive comparison to state of the art Generally, it can be seen from Table  9  that our DoDG-based descriptors have obtained the best rates compared to all non-deeplearning methods. Their performances are also better than those of deep-learning-based approaches on UCLA as well as very close to those on DynTex and DynTex++. This is certainly thanks to the leverage contribution of our DoDG. Hereunder, we detail particular discussions of those on each benchmark dataset.
Classification on DTDB dataset Table  10  shows results of our DoDGF 2∕3 on two challenging subsets of DTDB, Dynamics and Appearance. In general, the rates of DoDGF 3 are about 3% better than those of DoDGF 2. This has consolidated the prominence of the filtering kernel DoDG 3 for DT representation by jointly addressing spatial and temporal clues. To validate the prominent performance of DoDGs on DTDB in comparison with several other methods, we also implement and evaluate the Gaussian-gradient-based descriptors (HoGFs  [20] ) using their best settings, i. e. , HoGF 2 {=1},{2nd,3rd} and HoGF 3 {=1},{3rd,4th} with supporting regions {( , )} = {(8, 1), (8, 2)}. To the best of our knowledge, HoGFs currently are the best local descriptors for DT representation (see Table  9 ). In this work, two basic local operators, LBP-TOP  [62]  and CLBP  [16]  are also implemented in the same set of neighbors {( , )} = {(8, 1)} for objective evaluations in recognizing DTs on DTDB.
Conclusions The simple and efficient DoDG kernel has been proposed to deal with the well-known problems of local DT representation. To take our DoDG into account video analysis, an adaptative framework has been presented for local DT representation, which is also available for different LBP-based encodings on robust DoDG-filtered outcomes. Just using a shallow analysis to exploit DoDG-filtered features, we have constructed discriminative DoDGF 2∕3 , ′ , descriptors in slight dimension. Indeed, Tables  9  and 11  show the very good performances of our 2order DoDGF 3 (0. 7,1),{1st,2nd} with 7200 bins as well as those of the single order DoDGF 2 (0. 7,1),{1st} with only 2400 bins on different datasets. Those can be easily applied to edge devices, while maintaining a comparable performance related to deep learning models.
[48,65]son of DT recognition rates (%) on benchmark DT datasets. Note: ''-'' means ''not available''. Superscript ''*'' indicates results using deep learning algorithms. ''N''indicates rates with 1-NN classifier. 50-LOO and 50-4fold denote results on 50-class breakdown using leave-one-out and four cross-fold validation respectively. Dyn35 and Dyn++ are abbreviated for DynTex35 and DynTex++ datasets respectively. Evaluations of VLBP and LBP-TOP operators are referred to the evaluations of implementations in[48,65].
Acknowledgments We would like to express our sincere appreciation for the editors and reviewers, who pointed out the valuable and insightful remarks allowing us to clarify the presentation of this work. Also, we would like to send many thanks to those in Faculty of  IT, HCMC University of Technology and Education, Thu Duc City, Ho Chi Minh City, Vietnam , who gave us enthusiastic support with high-performing computers for the experiments on the large scale datasets.
Cells were washed twice with 50 mM Tris-Cl, pH 8. 0, 5 mM EDTA, resuspended in 2 mg/ml RNase A (Sigma-Aldrich) in 50 mM Tris-Cl, pH 8. 0, 5 mM EDTA, and incubated for 2 h at 37 C. RNase-treated cells were pelleted and resuspended in 5 mg/ml pepsin (Sigma-Aldrich) in 55 mM HCl and incubated for 1 h at 37 C. Cells were washed twice with 50 mM Tris-Cl, pH 7. 5, 5 mM EDTA, and resuspended in 1 lM SYTOX Green Nucleic Acid Stain (Thermo Fisher Scientific) in 50 mM Tris-Cl, pH 7. 5, 5 mM EDTA and stored overnight at 4 C. For ploidy analysis, 10 4 cells were run on a FACS Celesta (BD Biosciences) and data analyzed using FlowJo 10. 7. 1 (Ashland, OR, USA). Histograms of SYTOX fluorescence levels were evaluated in FlowJo and the subpopulation of cells in G1 of the cell cycle was gated for analysis of each sample.
Whole-genome sequencing, heterozygosity levels, and ploidy assays To extract genomic DNA C. albicans isolates were grown overnight in YPD at 30 C and DNA isolated from $10 9 cells using a Qiagen Genomic Buffer Set and a Qiagen Genomic-tip 100/G according to manufacturer's instructions. Libraries were prepared using the Nextera XT DNA Library preparation kit protocol (Illumina) with an input of 2 ng/ll in 10 ll. Each isolate was sequenced using Illumina HiSeq 2000 generating 101 bp paired reads. The nuclear genome sequences and General Feature Files (GFF) for C. albicans SC5314 reference genome (version A22) were downloaded from the Candida Genome Database (http://www. candidagenome. org/ ). Reads were aligned to the SC5314 reference genome assembly 22 (haplotype A chromosomes) using Burrows-Wheeler Aligner (BWA) v0.
Data availability Strains and plasmids are available upon request. Whole-genome sequencing data for D-tagatose-cultured isolates (Tag1-12 and RBY18) is available at NCBI SRA as BioProject PRJNA686496. The whole-genome assembly for SC5314 has been previously published under NCBI BioProject PRJNA193498  (Hirakawa et al. 2015). Supplementary material includes Supplementary Figures  S1-S4  and Supplementary Tables  S1  and S2 , and have been uploaded to figshare: https://doi. org/10. 25387/g3. 13505709.
In total, $20 individual colonies and 2 pooled samples (each pool a combination of 10 individual colonies) were assessed for DNA content for each condition. The most striking observation from this data was that the majority of colonies derived from D-tagatose cultures were closer to diploid than to tetraploid in DNA content, indicative of having undergone a substantial reduction in ploidy, and this occurred at both 30 and 37 C (Figure  2B ). Similar results were obtained whether D-tagatose-cultured cells had been recovered on YPD or 2-DOG media, indicating that ploidy reduction occurred independent of selection for gal1-cells. In contrast, tetraploid cells grown in D-glucose and recovered on YPD showed ploidy levels close to that of the tetraploid (4n) control (Figure  2B ). Selection of D-glucose-cultured cells on 2-DOG medium gave rise to a range of ploidies; experiments performed at 37 C resulted in ploidies close to diploid, while those performed at 30 C produced ploidies that were closer to tetraploid (Figure  2B ).
To guarantee scientific independence, private partners (vaccine manufacturers) are not involved in data collection and analysis. They can and do provide written feedback on the study documents, such as the protocol, statistical analysis plan and results report; their feedback is moderated by an Independent Scientific Committee before being taken into account by the public partners in the consortium  [4]. The study conducted in the 2019/20 season builds upon the framework established in the previous two seasons  [5, 6]. Major improvements include expansion of the DRIVE network by three hospital sites in two countries, simplification of confounderadjustment resulting in less data loss, and easier access to the full set of results through the WebAnnex. In 2019/20, eleven influenza vaccines were marketed in Europe  [7].
Study design The studies were based on the DRIVE generic protocols for TND and population-based cohort studies  [11, 12]. Four primary carebased TND studies were conducted through GP networks in Austria, Italy (two networks), and in the UK. Eight hospital-based TND studies were conducted at five individual hospitals in Finland, Romania and Spain (3 hospitals); and three hospital networks in France, Italy and Spain. One population-based cohort study was conducted by the Finnish Institute for Health and Welfare (THL) among Finnish residents aged 6 months to 6 years and 65 to 100 years. No universal influenza vaccine recommendation is in place in Finland for the age group 7-64y. Also, many of the influenza vaccinations of the working age adults are given within occupational health services, the data from which is not automatically transferred to the National Vaccine Register.
Consequently, influenza vaccine exposure is not reliably captured in the vaccine registry. Primary care and hospital cases could not be differentiated. The reason for this is that the influenza diagnosis in the National Infectious Disease Registry is based on data received directly from laboratories, and this data does not automatically contain the origin of the sample. Definitions for influenza-like illness (ILI), severe acute respiratory infection (SARI) and laboratory-confirmed influenza, along with other study site characteristics, are described in Table  1. Influenza laboratory confirmation for samples was undertaken through molecular tests at the majority of sites, including point of care RT-PCR assays at one site, or otherwise through antigen detection tests. Influenza subtypes were available for all but two sites.
The study population's inclusion and exclusion criteria have been previously described  [5]. Either all or a systematic selection of patients (e. g. , the first 3 ILI patients every week) meeting the ILI/SARI were swabbed or asked to participate in the study. At two TND sites, controls were matched to cases (1:1), based on age and epidemiological week (VHUH) and additionally on gender (GTPUH). For each subject, data on age, sex, date of symptom onset, date of swab, date of vaccination and vaccine brand were collected. Additional covariates (presence of chronic condition, number of healthcare visits or hospitalizations in the past year, pregnancy, influenza vaccination in the previous season) were collected when possible. For studies in the primary care setting, vaccination status, vaccine brand and vaccination data were retrieved from the GP records.
For the 2019/20 season, site-specific estimates were adjusted for age, sex and calendar time (defined as date of symptom onset in TND studies). As recommended by Lane et al  [14] , and supported by a posthoc analysis of the 2018/19 DRIVE data (data not shown), the main analysis was based on a parsimonious model in which age, calendar time, and sex were adjusted for and a sensitivity analysis in which all available potential confounders (age, sex, onset date, presence of at least one chronic condition, pregnancy, number of primary care visits or hospitalizations in the previous year, and influenza vaccination in the previous season) were adjusted for was conducted. To gain insight in potential unmeasured confounding of the IVE estimates for any vaccine against any influenza by age and setting, E-values for the IVE estimates and for the limit of the CI closest to 0 were calculated post-hoc  [15].
Subject and exposure characteristics 1 TND studies Overall, 3,531 cases and 5,546 controls of all ages were included in the main analysis of the TND studies (Table  2 ). The proportion of controls vaccinated ranged from 5. 1% in children in the hospital setting to 61. 3% among adults ! 65y in the primary care setting. Adults ! 65y had the highest proportion of subjects with at least one chronic disease, with ! 5 GP visits in the past year and ! 2 hospitalizations in the past year. Subject characteristics by vaccine brand are available in the WebAnnex. Eight of the eleven vaccines marketed in Europe in 2019/20 were captured in the DRIVE network: Agrippal, Fluad, Fluarix Tetra, Flucelvax Tetra, Fluenz Tetra, Influvac, Influvac Tetra, Vaxigrip and Vaxigrip Tetra. No data was captured on Afluria, Vaxigrip and TIV-HD.
In the 2019/20 TND studies, quadrivalent influenza vaccine (QIV) brands were captured in the pooled DRIVE datasets for most age groups in both settings, whereas more gaps in data availability were observed for TIV, reflective of the gradual phase out of TIV. Age-specific brand distribution among vaccinated subjects by site is shown in Fig. 1. The most commonly reported brands among vaccinated subjects by age were Vaxigrip Tetra in children (58. 1%) and in adults up to 64y (39. 9%), and Fluad in adults ! 65y (47. 7%). The majority of reported vaccine types among vaccinated subjects were quadrivalent egg-based (82. 6%) in children and in adults up to 64y (71. 4%), and adjuvanted trivalent vaccine (47. 7%) and quadrivalent egg-based vaccine (38. 8%) in adults ! 65y. Across all age groups, only 4.
6% of the vaccines were conventional trivalent vaccines. 2 Population-based cohort study In total, 101 thousand person-years for children 6 m-6y and 411 thousand person-years for adults ! 65y were included in the Finnish population-based cohort study, and respectively 1,027 and 1,400 influenza cases were identified. Subject characteristics are shown in Table  3. Two vaccine brands were reported. Among vaccinated children, roughly three quarters received Fluenz Tetra and one quarter received Vaxigrip Tetra. All vaccinated adults ! 65y received Vaxigrip Tetra. Most children and adults < 64y were captured in the primary care setting and the most frequently observed vaccine brand was Vaxigrip Tetra. The estimate against any influenza for Vaxigrip Tetra was 61% (95%CI 38-77) in children. This estimate was obtained by pooling three site-specific estimates (Austria MUV in Austria, and CIRI-IT GP and ISS in Italy), and included data from 2,198 subjects, of whom 50 were vaccinated cases.
Discussion In the 2019/20 season, which represented the third season of DRIVE, the network expanded by three TND hospital sites and included one additional country. In total, the DRIVE network encompassed twelve TND study sites, representing 388 primary care physicians and pediatricians and 19 hospitals, and one population-based cohort. Data from 9077 subjects, of which 3531 were cases, were analyzed in the TND studies, and 511,854 person-years were included in the population-based cohort. Effectiveness estimates were obtained for eight of the eleven influenza vaccine brands marketed in Europe. This included estimates for all quadrivalent vaccines for all approved age indications, whereas no estimates were available for three of the six trivalent vaccines. This difference reflects the current transition to quadrivalent influenza vaccines in Europe.
The corresponding TND point estimate for LAIV in DRIVE is based solely on data from England but is much higher (81%) than the one obtained by PHE. The confidence intervals of these estimates are wide and overlap with the confidence intervals of the respective DRIVE TND primary care estimates. DRIVE point estimates for any vaccine against any influenza among children in primary care (64% from the TND studies and 66% in mixed setting from the population-based cohort study) overlap with the 2019/20 interim estimate from the EU I-MOVE multi-country network (until January 29, 2020), 64% (95% CI 16-85)  [19]. For hospitalized patients ! 65y, the IVE estimate against influenza A from the DRIVE TND studies was 53%, which is comparable to the 62% (95%CI 41-76) reported by the EU I-MOVE multicountry network.
The pandemic and the subsequent lockdown measures interfered with and capped an already mild influenza circulation and impacted data collection within DRIVE study sites. Therefore, the study period for the main analysis was truncated at February 29, 2020, which was two months earlier than originally expected. Other influenza studies in the United States took a similar approach  [20]. Consequently, fewer ILI and SARI subjects were included than expected. In the sensitivity analysis that included data up to April 30th, two additional precise brand-specific IVE estimates were obtained. As compared to previous DRIVE studies, improvements were made to the methods and the reporting. First, the list of confounders considered was simplified. Using a simplified approach to adjust for confounding by defining a minimum set of key confounder variables avoided discarding data due to missing values, permitted participation of sites who have limited data on confounders, and avoided potential over-adjustment.
Strengths An important strength of the DRIVE network, compared to other European study platforms conducting IVE studies, is that information on the specific influenza vaccine brands used is available, allowing the estimation of brand-specific IVE estimates. Moreover, estimates from individual TND sites are pooled, which increases the precision of the IVE estimates and enables DRIVE to capture data on the majority of influenza vaccine brands marketed in Europe. Although brand-specific IVE estimates were obtained from the TND studies, narrower confidence interval estimates were obtained from the THL population-based cohort. Clinical heterogeneity in the estimates was reduced by stratifying by age and, in the TND studies, by setting.
The trade-offs are that the coverage of some vaccine brands is low in the older age groups, that the data will not be representative for the licensed age indication of all influenza vaccines and may not encompass exposure to all vaccine brands available in EU, and that the IVE estimated in the hospital setting is likely to reflect protection against more severe illness. However, it is important to note that national and regional public health institutes in Europe are encouraged to join DRIVE, regardless of the age groups or settings covered as DRIVE will still produce primary care brand-specific IVE estimates using surveillance data from public health institutes. In addition, the DRIVE network aims to develop a transparent open data model to promote research collaboration between sites, which could support advanced knowledge in the field of IVE studies along with the study platform expansion.
Acknowledgments We would like to acknowledge the following people for their contribution to data collection and study implementation: Francesca Centrone ,  Caterina Morcavallo ,  Silvia Campanella  ( University of Bari, Bari, Italy );  Bianca Bruzzone ,  Filippo Ansaldi ,  Beatrice Marina Pennati ,  Stefano Mosca ,  Patrizia Caligiuri ,  Giulia Guarona ,  Emanuela Rapazzo ,  Giovanni Noberasco ,  Matilde Oglias-Medicines Initiative 2 Joint Undertaking  (grant agreement No  777363 ); this Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA. The following sites were co-funded: Austria MUV by  Biomedica ,  Sanofi-Pasteur and Valneva ; Finland HUS by the  Finnish Institute for Health and Welfare and HUS ; Finland THL by the  Finnish Institute for Health and Welfare; Italy BIVE  by the  IRCCS, Bambino Gesù Children's Hospital ; Italy ISS by the  Italian Medicines Agency ;  Romania NIID  by the  National Institute for Infectious Diseases ''Prof.
In this contribution we introduce LoopStructural, a new open-source 3D geological modelling Python package (http://www. github. com/Loop3d/LoopStructural, last access: 15 June 2021). LoopStructural provides a generic API for 3D geological modelling applications harnessing the core Python scientific libraries pandas, numpy and scipy. Six different interpolation algorithms, including three discrete interpolators and 3 polynomial trend interpolators, can be used from the same model design. This means that different interpolation algorithms can be mixed and matched within a geological model allowing for different geological objects, e. g. different conformable foliations, fault surfaces and unconformities to be modelled using different algorithms. Geological features are incorporated into the model using a timeaware approach, where the most recent features are modelled first and used to constrain the geometries of the older features.