dataesr/scientific-paragraphs-categorization huggingface dataset:
The CLBP S(m 1 ) and WSBP S played a major role in achieving good results, suggesting that the S component encoded more valuable information from each face image. Second, when utilizing both mean (m 1 ) and variance (μ 2 ) moments as the input data for CLBP, the CLBP S(m 1 , μ 2 ) and CLBP M(m 1 , μ 2 ) dramatically increased the classification rate than considering only the first-order (m 1 ) moment, suggesting that fusion of S and M components improved the performance. Indeed, the CLBP S M(m 1 ) produced better results. For instance, when (P, R) = (4, 2), the best results obtained by CLBP S M(m 1 ) with the number of training images (N = 2, 4, 5, and 8) for the ORL dataset were 86. 22%, 96. 2%, 98. 25%, and 99. 45%, respectively. Similarly, the WSBP descriptors reached 87. 91%, 96. 77%, 98.
51%, or 99. 41%, respectively. On the other hand, WSBP and CLBP S M(m 1 , μ 2 ) were similar. For the YALE dataset (Table  2 ), our WSBP outperformed the other methods: 91. 95%, 97. 14%, 98. 72%, and 99. 44% at the scale of (P, R) = (4, 2). Also, our proposed method was compared with other state-of-the-art methods as shown in Table  3 , that summarized techniques and recognition rate corresponding to each method. Ten methods on the top, including ours, were based on several hand-crafted features, and three remaining ones were based on deep features. Our method achieved 98. 51% and 98. 72% recognition rates for ORL and YALE datasets, which were greater than other methods, suggesting that our descriptor was robust against visual challenges, such Fig. 8  The YALE dataset samples which are decomposed into pairs of moment images (mean m 1 , variance μ 2 ) for each two-row pair as illumination variation, facial expressions, head poses (multi-orientation), and occlusion.
Results with Caltech 1999 and KDEF datasets Since the number of images for each class in Caltech 1999 dataset varied, we did not change the number of training images for each class like the previous experiments. Here, we randomly chose half of the images in each class as a training set, and the remaining ones were used as a testing set. Table  4  showed our results for three configurations of (P, R), respectively, suggesting that our WSBP and CLBP S M(m 1 , μ 2 ) achieved the highest recognition rate. Table  5  compared our results with a deep learning approach based on Deep Stack Denoising Sparse Autoencoders (DSDSA)  [13]. As can be seen from these tables, even using one single small scale (P, R) = (4, 2), our descriptor WSBP (Ours 1), CLBP S M(m 1 , μ 2 ) reached the recognition rate of 98.
83%, 98. 96%, respectively, which were greater than the performance of DSDSA. When we considered CLBP S M(m 1 , μ 2 ) at the scale of (P, R) = (6, 3), the performance was 99. 03% (Ours 2). For the KDEF dataset, we conducted the face recognition task by changing the number of training images for each person to verify the accuracy rates of each train/test portion. Indeed, several training images N train (N train = 2, 3, 4, 5) were randomly chosen while the remaining images (7 -N train ) were used for test. Here, the evaluation was repeated 100 times by shuffling data to get the average accuracy. Table  6  showed our results for three configurations of (P, R), respectively. Specifically, at the scale of (P, R) = (4, 2), our descriptor WSBP substantially increased the accuracy up to 94. 11%, 97.
Evaluation with color channel The motivation of this experiment was to check the behaviors of facial descriptors for the color channel. The experiment was conducted with the HSV color images by keeping the other experimental setting was similar to Fig. 11  The AR dataset samples which are decomposed into pairs of moment images (mean m 1 , variance μ 2 ) for each two-row pair Fig. 12  The FERET dataset samples which are decomposed into pairs of moment images (mean m 1 , variance μ 2 ) for each two-row pair that of the gray-scale image. First, an RGB color image was converted into an HSV color image. Second, the Hue channel was extracted from the HSV space, called it H image, and fed it as an input for our experiment. Our descriptors were able to extract the eyes, eye-blows and mouth from H image probably because these areas had the distinctive colors.
Results with the FERET dataset Previous works  [10, 29]  in the literature performed their experiments with a protocol by using only frontal sets (Fa, Fb, Fc, Duplicate I, and Duplicate II) where Fa with 1196 images known as the gallery and others known as probes. Unlike the previous protocol, we did with a subset created from 1400 images (ba, bd, be, bf, bg, bj, bk) in which each person had two facial expression images, two left pose images, two right pose images, and one illumination image. This subset was more challenging than the previous one since it comprised not only frontal faces but also multiple orientations, expressions. Moreover, experimental results   [44]  EL-LBP 97. 12 95. 27 Liang et al. [25]  Wavelet + PC -98. 60 Karczmarek et al. [20]  CCBLBP(7px,3×3,400) 98. 45 -Kas et al.
For most cases, WSBP obtained the best results and reached over 90% accuracy with 2 training images only. Table  11  compared a few recent methods and stated that our descriptors achieved the best performance. In detail, WSBP with 3 training images exceeded MNTCDP  [21]  at 2. 57%, which was not easy to deal with the challenging FERET dataset having images under multiple orientations. As mentioned above, FERET had two different protocols for evaluations. It would not be a fair evaluation if we compared such methods under different protocols. And yet, it is interesting to evaluate the previous reports. For this purpose, we performed the average accuracy result based on recent reports: CLBP  [10] , SLBFLE  [29] , and WPCBP+FLD (HI)  [47]  (see Table  11 ), wherein these methods performed efficiently with the subset of the frontal face cases.
Robustness against degraded images In practical surveillance scenarios, the degradation of images often happened during the acquisition process and could significantly affect the system performance. Therefore, motivation of this experiment was to examine how our facial descriptors dealt with such problems. In the first scenario, the Gaussian noise was added to the original image. For instance, five different levels of Gaussian noise were added by levels = {10%, 20%, 30%, 40%, 50%} using the Matlab function "imnoise". In the second scenario, occlusion was simulated by adding a white rectangle of random positions within the face region. Each rectangle had various sizes, ranging from  [20, 20]  to  [30, 60]  with a Matlab function "insertShape". Figure  14  showed the both scenarios. In each scenario, five images were chosen in each class as training samples, whereas the rest as testing samples, by splitting the data into 100.
The processing time This section describes the computational cost of several descriptors based on LBPs. Experiments of the ORL dataset for 400 images with 92 × 112 pixels were carried out with a machine with 3. 5GHz CPU, 32GB RAM, and Windows 10 64-bit operating system. Table  13  showed the computational cost from two aspects: firstly, the processing time for the feature descriptor extraction phase and; secondly, the processing time for the matching phase (in seconds) of various descriptors with three different configurations of (P, R). The processing time measured here was based on the structuring element B = {(1, 6)}, where the training set had 200 images and the testing sets had 200 images. Table  13  showed that the WSBP required a longer processing time for the feature extraction and matching phase than CLBP S(m 1 ) or WSBP S.
Summary and discussion Based on our experiments, we summarize and discuss several advantages of our proposed descriptors:   using many parameters (P, R) because it could lead to a high dimensional descriptor. -Evaluation using six face datasets suggests that our descriptors outperform state-of-the-art methods, such as EL-LBP  [44] , AECLBP-S (B16)  [22] , Multi-resolution dictionary  [30] , DR-LBP + LDA  [35] , LDENP  [42]. Moreover, our WSBP descriptors achieves better results than some deep facial features such as Deep Belief Net (GDBN)  [8] , Deep Autoencoders (DSDSA)  [13] , Compressive sensing (CS)  [2] , or FDDL + CNN  [39]  (see Tables  3, 5 , and 8). Average recognition rate with the previous protocol CBP  [10]  92. 00 SLBFLE (R=4)  [29]  96. 95 WPCBP+FLD (HI)  [47]  97. 5 -According to an additional experiment with the color channel, it is found that the Magnitude transform captures the relationship of pixel magnitude on grayscale image very well, but is not effective with Hue image (see Fig.
13 ), since a combination of Sign-Magnitude of CLBP α in Hue space performs worse than that of the gray-scale case. Also, fusing statistical moments (m 1 and μ 2 ) in CLBP S and WSBP S achieves the higher accuracy in Hue space, by ignoring texture pixel intensity. This evaluation suggests a new direction in face recognition problems, such as integrating many color channels to enhance face spoofing detection performance  [43]. -Although the YALE dataset contains some facial expression cases, it would be interesting in testing how our descriptor affords systematic variation of facial expressions. In addition, we use the KDEF dataset which has seven facial expressions for each subject to study the effect of facial expressions. Result suggests that our descriptor deals with such cases very well.
-Our facial descriptors using both mean (m 1 ) and variance (μ 2 ) have shown their robustness against degraded images by evaluating the ORL dataset that contains artificial noise. Given that the Gaussian noise level of 50% makes the degraded face more challenging to recognize by human eyes, our WSBP(B 2 ) descriptor still reaches the acceptable accuracy of 93. 05% for noise and 85. 09% for occlusion, which are much higher than those of other LBPs.
Conclusions and future work We present a set of descriptors wherein the local difference distributions in local binary patterns are exploited by  directions, and then a weighting approach for binary patterns is applied to statistical moment images for an efficient and robust facial feature representation. A comprehensive evaluation with several standard face datasets is carried out to validate our proposal. We have analyzed the behaviors of several descriptors with grayscale images and found that our method mostly outperforms state-of-the-arts. Also, an analysis with a set of color images has also been examined using the Hue channel for AR dataset. We have also simulated a few practical scenarios, that can be occurred during the data acquisition stage, by adding various Gaussian noise and random occlusion to the ORL dataset.
Acknowledgements This work was supported by the  Institute of Information & communications Technology Planning & Evaluation (IITP) , grant funded by the  Korea government (MSIT)  (No. 2019-0-00231 ) as well as by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education  ( 2020R1A6A1A03038540 ). We would like to express our deep gratitude to the reviewers and editors, who pointed out the valuable and insightful remarks allowing us to clarify the presentation of this work.
Demographic and clinical baseline data Demographics (sex, age, ethnic group, socioeconomic status) and clinical variables (recruitment centre, inpatient or outpatient status, main diagnosis (Diagnostic and Statistical Manual of Mental Disorders, DSM-5), comorbidities, family metabolic history (eg, obesity, dyslipidaemia, diabetes and gestational diabetes, high BP, cardiovascular disease), reported weight and height of the parents, SGA type and dosage, comedication type and dosage taken by each participant) will be assessed to characterise the patient sample. In order to create an equivalence between the doses of APs received, a conversion to an equivalent dose of chlorpromazine will be carried out according to the published standards. 26 herence to treatment Compliance will be indirectly estimated by the nurse and/or the treating psychiatrist at each measurement time of the CAMESA calendar, according to a voluntary declaration by the participants.
Data collection process In each recruitment centre, nurses monitor the participant's studied variables according to the CAMESA calendar. Data are collected and coded from participant's Open access medical records by a member of the research team. All data are anonymised and preserved in the participant's research file. All of the collected information will be kept confidential unless authorised by the participant or his caregiver or an exception from the law. The computerised data will be kept on a password protected file and the paper questionnaires will be kept in a locked space. The data collected will be kept for 7 years after the end of the study. After this period of time, it will be safely deleted or destroyed by shredding. The parameters collected (table  1 ) for the metabolic monitoring of each SGA-treated participant during follow-up were based on the recommendations of CAMESA guidelines as well as on other, more recent, clinical landmark studies.
0. 3 [46]. First, interaction networks (Figure  1 ) and rarefaction curves (Figure  A1 ) were plotted using, respectively, the "plotweb" function of the "bipartite" package  [47] , and the "specaccum" function of the "vegan" package  [48]. Proportions of individuals belonging to the main families of pollinators (number of individuals > 5) were compared between the two habitats using a chi-squared contingency table test ("chisq. test" function). Species richness and Simpson's diversity index of flower visitors were calculated for each plant species, locality and year with the "diversity" function of the "vegan" package  [48]. Values taken by Simpson's diversity index vary between 0 and 1, with low values recorded for communities dominated by a few, very abundant species. In contrast, functional diversity was estimated only on wild bees, using Rao's quadratic entropy ("dbFD" function of the "FD" package  [49] ) calculated over five traits: lectism, sociality level, nesting habits, brood parasitism status and species average inter-tegular distance (mm).
Rao's quadratic entropy provides a joint assessment of functional richness and functional dispersion, with low values indicating low functional diversity  [50]. To test whether values taken by these diversity indices per plant species, locality and year (n = 20) differed between the two habitats, we used beta-regression generalized linear mixed models (hereafter glmms) (R package "glmmTMB"  [51] ). We defined the plant species ("Lotus corniculatus" vs. "Sinapis alba"), the habitat ("Urban" vs. "Rural") and their interaction, as well as the year (2017 or 2018), as fixed effects, and the sampling site as a random effect. Species richness was modeled with a Poisson error distribution, whereas Simpson's index and Rao's quadratic entropy were modeled with a Gaussian error distribution. In order to complement our data with qualitative information on species rarity, we also compared the amount of specimens from species listed as endangered or notable in the IUCN red list  [52]  and local ZNIEFF lists  [53, 54].
Species richness and Simpson's diversity index of flower visitors were calculated for each plant species, locality and year with the "diversity" function of the "vegan" package  [48]. Values taken by Simpson's diversity index vary between 0 and 1, with low values recorded for communities dominated by a few, very abundant species. In contrast, functional diversity was estimated only on wild bees, using Rao's quadratic entropy ("dbFD" function of the "FD" package  [49] ) calculated over five traits: lectism, sociality level, nesting habits, brood parasitism status and species average inter-tegular distance (mm). Rao's quadratic entropy provides a joint assessment of functional richness and functional dispersion, with low values indicating low functional diversity  [50]. To test whether values taken by these diversity indices per plant species, locality and year (n = 20) differed between the two habitats, we used beta-regression generalized linear mixed models (hereafter glmms) (R package "glmmTMB"  [51] ).
Explanatory variables were the plant species ("Lotus corniculatus" vs. "Sinapis alba"), the habitat ("Urban" vs. "Rural"), the Julian day of the year (scaled) and their interactions. The sampling site and the year were included as random effects. Honey bees were excluded from these analyses. Body size of wild bees was measured using a Zeiss stereomicroscope (Zeiss Discovery V12) with an integrated camera (AxioCam ICc5) to record pictures of each pinned specimen, using the software ZEN 2012 (blue edition) (all distributed by Carl Zeiss Microscopy GmbH, Jena, Germany). Inter-tegular distance (hereafter ITD), which is the standard measurement of body size in bees, was measured with the software imageJ version 1. 52a  [56]. Analyses of ITD variations over time in the two habitats were carried out separately for bumble bees and other wild bee taxa.
Indeed, bumble bees are eusocial species, with important body size differences among different categories of individuals (queens, drones, workers) [57], and whose abundances vary along the course of the active season. In addition, they have an endogenous thermoregulation capacity, which can play a role in the temperature-height relationship  [28, 29, 58]. On the contrary, all other wild bees sampled were either solitary species or more "primitively" eusocial species  [55]  with little to no social status-related variations in body size within the species. Log-transformed ITDs of the specimens were analyzed through glmms in a similar way to trait proportions, with a Gaussian error distribution. As a unimodal trend over time was expected for the ITD variations, we treated the Julian day as an orthogonal degree-2 polynome.
Residuals of all glmms were inspected with the "DHARMa" package  [59]  and no deviations from the specified error distributions were detected (Figure  A2 ). The contribution of interactions to models was tested through type-III tests with the "Anova" function ("car" package  [60] ). Model selection was performed on the basis of the second-order Akaike information criterion (AICc, "MuMIn" package  [61] ), which is used instead of the AIC when the sample size is small in comparison to the number of estimated parameters. Absence of collinearity was verified via the "check_collinearity" function ("performance" package  [62] ).
Conceptualization, V. Z. and I. D. ; data curation, V. Z. ; funding acquisition, I. D. ; investigation, V. Z. , O. B. , A. H. , and C. H. ; methodology, V. Z. ; project administration, I. D. ; resources, A. H. and I. D. ; supervision, I. D. ; validation, A. P. and I. D. ; visualization, V. Z. and A. P. ; writingoriginal draft, V. Z. ; writing-review and editing, A. P. , A. H. , and I. D. All authors have read and agreed to the published version of the manuscript. Funding: This project was partially funded by the CITY OF PARIS through a CIFRE scholarship n 2018/0699; by the PEPS CNRS INEE Adaptation-Adaptabilité program; and by the Institut de la Transition Environnementale of SORBONNE-UNIVERSITE. This work has also benefited from technical and human resources provided by CEREEP-Ecotron IleDeFrance (CNRS/ENS UMS 3194) as well as financial support from the REGIONAL COUNCIL OF ILE-DE-FRANCE under the DIM Program R2DS bearing the reference I-05-098/R.
Acknowledgments: We wish to thank the  Jardin des Plantes of the MNHN (Philippe Barré) , the  Cité Internationale Universitaire de Paris  ( David Otamendi ) and the  Pierre et Marie Curie campus of Sorbonne-Université  ( Guillaume Baloup ) for providing access to the sites; as well as the cultivation services of the  Pierre et Marie Curie campus of Sorbonne-Université (Patrick Dumont ) and of the  MNHN (Alain Karg)  for their help in growing plants. We thank all the specialists who identified the insects at the species level:  David Genoud ,  Rémi Rudelle ,  Jean-Pierre Sarthou  and  Eric Dufrêne. We also thank  Elsa Blareau ,  Ambre Zelela-Bouvard  and  Vincent Leclercq  for their contribution to field work and data collection.
Author Contributions: Conceptualization, V. Z. and I. D. ; data curation, V. Z. ; funding acquisition, I. D. ; investigation, V. Z. , O. B. , A. H. , and C. H. ; methodology, V. Z. ; project administration, I. D. ; resources, A. H. and I. D. ; supervision, I. D. ; validation, A. P. and I. D. ; visualization, V. Z. and A. P. ; writingoriginal draft, V. Z. ; writing-review and editing, A. P. , A. H. , and I. D. All authors have read and agreed to the published version of the manuscript. Funding: This project was partially funded by the CITY OF PARIS through a CIFRE scholarship n 2018/0699; by the PEPS CNRS INEE Adaptation-Adaptabilité program; and by the Institut de la Transition Environnementale of SORBONNE-UNIVERSITE. This work has also benefited from technical and human resources provided by CEREEP-Ecotron IleDeFrance (CNRS/ENS UMS 3194) as well as financial support from the REGIONAL COUNCIL OF ILE-DE-FRANCE under the DIM Program R2DS bearing the reference I-05-098/R.
This article provides a review of tropical cyclone (TC) surface wind estimation from an operational forecasting perspective. First, we provide a summary of operational forecast center practices and historical databases. Next, we discuss current and emerging objective estimates of TC surface winds, including algorithms, archive datasets, and individual algorithm strengths and weaknesses as applied to operational TC surface wind forecast parameters. Our review leads to recommendations about required surface coverage e an area covering at least 1100 km from center of TC at a 2-km resolution in the inner-core, and at a frequency of at least once every 6 h. This is enough coverage to support a complete analysis of the TC surface wind field from center to the extent of the 34-kt (17 m s À1 ) winds at 6-h intervals.
Consequently, the evolutions to and capabilities for wind field estimation require documentation. In this review, we aim to enable readers with information regarding the best use of operational center TC surface wind analyses, historical or climatological datasets, and TC-specific surface wind data currently or soon to be available to operational centers. We define various types of TC surface wind estimates used at the forecast centers, and provide a history of operational practices. Then, we discuss the tools available for assessing TC surface winds, and summarize objective datasets available to researchers for climatological and application development studies. Throughout this review, we use knots (kts, 1 kt ¼ 0. 51444 m s À1 ) as the wind speed unit to be consistent with operational centers.
These estimates form the basis for the subjectively analyzed TC location, intensity, and wind radii over the TC lifetimeda data record commonly referred to as a "best track", discussed in section 2. TC surface winds methods vary considerably among forecast centers and over time. Forecast centers use any of the following data source when available: in situ observations (e. g. , ships, buoys, station reports, aircraft reports), remotely sensed data (e. g. , active and passive sensors on aircraft and satellite platforms) and data scenes, and TC-specific applications (e. g. , algorithms that prescribe circulations based on intensity, forward speed, patterns in satellite images, etc. )d these are discussed in detail in Section 3. Section 4 documents historical records of objective wind structure estimates used for best track datasets, followed by needs in both the operational and research communities.