dataesr/scientific-paragraphs-categorization huggingface dataset:
Curvature Exaggeration To emphasize the small scale features of the input 3D face, the curvature exaggeration module performs EDFM on the mesh gradient. In the process, we use PCA as a mean to reduce high frequencies (Figure  3 ). • Offline preprocessing. The edge-based gradient operator E (see Supplementary Material) is used to compute the gradients g of each face mesh s of our custom 3D face dataset (Section 3. 1). Following the results of  Mo et al. (2004)  showing that lowvariance features should be more taken into account, the gradients G are standardized: G std G-g σG. Then, a PCA is performed on the standardized gradients leading to the principal components W and each PCA scores t such that t g std •W. • Runtime curvature exaggeration. The input face mesh s is standardized then projected into the PCA space learnt offline.
DEEP LEARNING BASED AUTOMATIC CARICATURIZATION Rule-based methods allow the use of controllable and interpretable parameters, but are limited to capture information about caricature styles. Supervised learning based methods require a large paired mesh-to-caricature dataset, that are highly consuming in terms of both time and means to build. Instead, we consider the case of an unpaired learning-based approach, taking advantage of our 3D datasets of both neutral and caricatured faces  (Cai et al. , 2021 ) (cf. Section 3. 1). Our network architecture is based on the shared content space assumption of  Liu et al. (2019) , that we adapt to the context of 3D data through the use of 3D convolutions of  Gong et al. (2019) , which define 3D convolution neighborhoods.
We use adaptive instance normalization (AdaIN)  (Huang and Belongie, 2018a)  to inject s into G. We observe that s can represent any style, which removes the necessity of providing y to G and allows G to synthesize meshes of all domains. Style encoder. Given a mesh x, our encoder E extracts the style codes s E(x). Similar to  Liu et al. (2019) , our style encoder benefits from the multi-task learning setup. E can produce diverse style codes using different reference meshes. This allows G to synthesize an output mesh reflecting the style code s of a reference mesh x. (Blanz and Vetter, 1999). Discriminator. Our discriminator D is a multitask discriminator  (Mescheder et al. , 2018; Liu et al. , 2019; Choi et al. , 2020) , which consists of multiple output branches. Each branch D y learns a binary classification determining whether a mesh x is a mesh from the dataset of style y or a fake mesh G (x, s) produced by G.
Results We trained the network for 50k iterations on a Titan X Pascal (4h, 8Go). Results of the approach are visible in Figure  7. The original faces (top row) are encoded using the network illustrated in Figure  6  along with a random caricature of the dataset, producing the caricatured face (bottom row). Facial proportions are hence exaggerated according to the distribution of the neutral and caricatured faces learned during the training stage.
Average Rankings To analyze ranking distributions (Figures 9-Figure  10 ), we first performed a Friedman test with the within-subject factor Method (using the average rank between all 12 scans). We found an effect of the Method on average ranking (χ 2 12. 21; p < 0. 05). The effect is then explored further using a Wilcoxon post-hoc test for pair-wise comparisons. We found significant differences only between EDFM and Deep, Geo. 1, Sela (all p < 0. 05). We found that per method, average rankings vary between 2. 81 (EDFM) and 3. 12 (Deep) 10. In order to determine whether ranking distributions per method differed with identities, we used a Friedman test with within-subject factors Method and Identity. Out of 12 distinct identities, 6  (identities 2, 5, 6, 7, 11, 12)  showed significantly different rankings between methods.
Background: The European Centre for Disease Prevention and Control (ECDC) systematically collates information from sources to rapidly detect early public health threats. The lack of a freely available, customisable and automated early warning tool using data from Twitter prompted the ECDC to develop epitweetr, which collects, geolocates and aggregates tweets generating signals and email alerts. Aim: This study aims to compare the performance of epitweetr to manually monitoring tweets for the purpose of early detecting public health threats. Methods: We calculated the general and specific positive predictive value (PPV) of signals generated by epitweetr between 19 October and 30 November 2020. Sensitivity, specificity, timeliness and www. eurosurveillance. org tool for public health threats using Twitter data.
This showed the relevance and stressed the urgency of having these early warning systems in place to better identify public health threats that may proliferate almost undetected otherwise. Noting the usefulness of having free, customisable and automated early warning tools using social media, the ECDC developed a prototype of an R-based tool in August 2019 for the early detection of public health threats using Twitter data. Twitter was selected from the different social media platforms due to both its widespread use in some regions of the world such as the European Union/European Economic Area (EU/ EEA), and its free and easily accessible data application programming interface (API). The prototype focussed on a Public Health Event of International Concern (PHEIC) that received major attention in social media: the 2019 Ebola virus disease outbreak in the Democratic Republic of the Congo.
The prototype was further extended in October 2019 and January 2020 by the inclusion of two other PHEICs: poliomyelitis and COVID-19. After the favourable results of this prototype, the ECDC developed a free, open-source tool named epitweetr to automatically monitor Twitter data for early warning of public health threats. Using a pre-determined but adaptable selection of keywords, this tool picks up new events of known public health threats, and using a broader set of keywords, it can also pick up new emerging health threats. The first version of this tool was published on the Comprehensive R Archive Network (CRAN) in October 2020  [15]. The main objective of our study is to evaluate epitweetr version 1 published in October 2020, a new automated, open-source, R-based tool for early detection of public health threats using Twitter data.
Methods Epitweetr Epitweetr  [15]  collects Twitter data and metadata using the Twitter Standard Search API 1. 1. It collects these data by sending queries according to a predetermined list of topics with related keywords. Throughout the time of this study period, 11 September to 30 November 2020, a list of 70 unique topics was used, focusing on notifiable or infectious diseases  [16]  (Supplement S1). In parallel with the Twitter data collection, epitweetr processes these data to geolocate tweets, aggregate them, detect signals and send these signals through email alerts (Figure  1 )  [17]. The geolocation process aims to collect tweet and user location, with the tweet location being the primary location used for the signal detection. The tweet location is based on the location found in the tweet, retweet or quote text.
In cases where there is no information in the retweet or quote, the retweeted or quoted text is used. Epitweetr extracts the tweet location in two steps. In the first step, epitweetr looks for words that could be location candidates. It transforms the tweet text into vectors (words) using fasttext  [18] , a well-known natural language processing technique  [16]. Up to 157 languages can be chosen from fasttext; however, for this evaluation the four alphanumeric languages used in the EU/EEA with the highest number of tweets worldwide were selected (English, French, Portuguese and Spanish) due to the EU/EEA focus and to reduce the processing time  [19]. Epitweetr uses a supervised machine learning algorithm which is automatically trained with labelled datasets. This algorithm detects parts of the text referring to geographic locations using locations from the GeoNames database  [20].
The non-location words are obtained by extracting common words in fasttext models not included in the GeoNames database. In the second step, the text of these location candidates is matched against the GeoNames database using the Apache Lucene search engine  [21] , which implements a variant of vector space model (VSM) based on document-query similarity. In each of these steps, a score is allocated. The words with the higher score in the first step are selected as a candidate for the second step, and the highest matching score on the second step is chosen as tweet geolocation if it is higher than the threshold defined by the user. The higher the threshold score is, the lower the false positive rate is expected to be. Having a lower false positive rate, users should expect higher accuracy and specificity; however, a lower sensitivity is expected.
For the user location, the location metadata available from the API is extracted using the same process. The best user location will be selected, with user's location at the time of the tweet a priority, followed by the self-declared user location or location as set in the public profile or the biography of the user. The aggregation process creates the data shown in the three figures in the epitweetr dashboard (Figure  2 ) based on Shiny web application framework  [22] : time series of tweets, map of tweet and/or user location, and the 20 most frequent words in the tweets. Epitweetr detects signals (Supplement S2)  [17] , using the time series of previous tweets for the same topic and country. Epitweetr does not take into account retweets or quoted tweets for signal detection. The length of the time series used for calculating signals can be configured by the user, having a default value of 7 days which was also used in this evaluation.
Each of the univariate time series is processed by a modified version of the Early Aberration Reporting System (EARS) algorithm  [23] , most commonly used for the detection of abnormalities over time, and as implemented in the R package 'surveillance'  [24]. The algorithm calculates a threshold for the expected tweet count for each topic and location (national level or higher) as a given quantile of the predictive distribution. If the threshold is exceeded, a signal is created for that time series. Inspired by Farrington and colleagues  [25] , the estimation of the threshold of an aberration/ signal also downweighs previous values if these are considered outliers. These signals are sent out in email alerts with the following variables for each signal: date, hour, topic, region, top words, number of tweets, percentage of tweets from trusted Twitter users and configuration used to produce these signals (e.
Discussion In this article we present the evaluation of epitweetr, a new automatised, open-source, R-based tool for early detection of public health threats using Twitter data. This tool was developed after finding a lack of such a tool and performing a feasibility study through a nonautomatised prototype. Previous studies have shown the importance of having an appropriate geolocation when using social media data to better understand where the event or threat is happening. We focused our geolocation evaluation on the tweet geolocation since it is more relevant to provide accurate information related to the event rather than to the user. The tweet geolocation evaluation showed an approximate 30% accuracy at national level, which was ca. 4% higher than at subnational level. The majority of the wrongly geolocated tweets were related to a few recurrent errors from the algorithm such as US president and population citizens not being recognised (e.
Epitweetr provides a holistic approach of collecting data from Twitter by collecting and processing all tweets, retweets and quoted tweets publicly available according to the keywords indicated by the user. By default, epitweetr only considers tweets for the signal detection, but it can be easily configured to consider also retweets and quoted tweets. Epitweetr should thus be considered to be one of the many resources supporting the early phases of epidemic intelligence activities. The main limitation of epitweetr relates to the variation in Twitter data dynamics (e. g. new keywords needed if concepts/words used by Twitter users change) and different scopes within early detection of threats that epitweetr users may have. This has been overcome by adding most of the parameters as configurable settings that can be changed not only for the tool itself but also depending on the topic.
The assessment of Tropical Cyclones (TC) statistics requires the direct, objective and automatic detection and tracking of TCs in reanalyses and model simulations. Research groups have independently developed numerous algorithms during recent decades in order to answer that need. Today, there is a large number of algorithms, often referred to as trackers, that aim to detect the positions of tropical cyclones in gridded datasets. This paper compares four trackers with very different formulations in detail. We assess their performances by tracking tropical cyclones in the ERA5 reanalysis and by comparing the outcome to the IBTrACS observations database. The first section of the paper finds typical detection rates of the trackers ranging from 75 to 85%. At the same time, false alarm rates (FAR) greatly vary across the four trackers and can sometimes exceed the number of detected genuine cyclones.
The few high-resolution model results already published clearly demonstrate a dramatic improvement in simulating This paper reports the results of an intercomparison of four different trackers with properties as different as possible from one another in terms of their formulation. The report is based on a comparison between the tracks detected by these trackers on a reanalysis (ERA5,  Hersbach et al. , 2020)  and an observation database (IBTrACS,  Knapp et al. , 2010). This study uses the reanalysis as a bridge between observations and simulation. Our main goal is not to provide an assessment of ERA5 performances in reproducing a given TC climatology but to compare the trackers with one another. Numerous studies have undergone such an assessment on several other reanalyses, including ERA5's predecessor ERA-Interim  (Hodges et al.
, 2017; Schenkel and Hart, 2012; Murakami, 2014; Bell et al. , 2018). Only recently,  Zarzycki et al. (2021)  presented an evaluation of ERA5's TCs against other reanalyses. The study shows that ERA5 performs as well as reanalyses that include specific TC assimilation techniques such as JRA and NCEP and that a significant improvement is brought by the increase in resolution between ERA-Interim and ERA5. A comprehensive assessment of TCs in ERA5 will be presented in a forthcoming paper by Dulac et al. The paper is organized as follows. After a description of the classification and datasets we used, we detail the algorithm of the trackers we used as well as our track matching method (section 2). We then use the four trackers to track TCs in ERA5 and to match the detected tracks with IBTrACS tracks, and we present a detailed analysis of the population of missing and false alarm tracks so obtained (section 3.
1). This knowledge is used to develop two methods common to all trackers that aim at filtering extra-tropical false alarms from the results (section 3. 2 and 3. 3). The filtered datasets are then used to analyze the sensitivity of traditional metrics to the choice of the trackers (section 4). Finally, we use the insight gained from this analysis to consider the complementarity of different trackers and provide some guidelines for applying TC trackers to model results (section 5). The conclusion gives a summary of the trackers' common points and differences (section 6).
Data and methods Our analysis combines resources available on both the database of observed TCs IBTrACS  (Knapp et al. , 2010)  and the ERA5 reanalysis  (Hersbach et al. , 2020). Before describing these two datasets in detail, we first highlight the procedure we used to classify TCs according to their intensities. We next describe the specifics of the four trackers we compare in this paper, and explain our track matching method.
IBTrACS The International Best Track Archive for Climate Stewardship (IBTrACS,  Knapp et al. 2010)  version 4 is the most comprehensive database of observed TCs. We used the "since 1980" subset in the present paper  (Knapp et al. , 2018). It combines data provided by TC centers of the World Meteorological Organization (WMO), namely the Regional Specialized Meteorological Centers (RSMC) and Tropical Cyclone Warning Centers (TCWC), as well as non-WMO centers, such as the China Meteorological Administration, the Hong Kong Observatory, and the Joint Typhoon Warning Center. Because IBTrACS sources are so https://doi. org/10. 5194/egusphere-2022-179 Preprint. Discussion started: 11 April 2022 c Author(s) 2022. CC BY 4. 0 License. diverse, the database is heterogeneous and requires careful treatment before one can safely use it.
The steps we followed are summarized below and detailed in a workflow chart (fig. B1 ). This study considers the cyclonic seasons from 1980 to 2019 in the northern hemisphere (NH, 40 seasons) and from 1981 to 2019 in the southern hemisphere (SH, 39 seasons). We removed seasons after 2019 because they contain provisional tracks. We also filtered out all tracks labeled as "spur" since they correspond to "usually short-lived tracks associated with main track and often represent alternate positions at the beginning of a system [or] actual system interactions" 1. In the remaining tracks, we only kept 6-hourly time steps for consistency with ERA5. Winds and sea-level pressure (SLP) data were retrieved when available, prioritizing the WMO center responsible for the relevant region. Tracks lacking wind data (0.
5% of all tracks) were dropped. Tracks lacking SLP data (7% of tropical-storm intensity tracks) were kept but not be included in those parts of the analysis for which storm intensities are needed. Finally, we removed tracks that do not reach the tropical storm stage (16 m s -1 ) and those that last less than one day. Hereafter, our selection of IBTrACS data will be referred to as IB-TS. We also define IB-TC as the subset of IB-TS tracks that reached the tropical cyclone intensity (u 10 > 29 m s -1 ). IB-TS (resp. IB-TC) contains 3519 (resp. 1938) tracks.
ERA5 We used data from the fifth generation of ECMWF Reanalysis (ERA5,  Hersbach et al. 2020). ERA5 provides hourly estimates of atmospheric variables on a grid with 0. 25°horizontal resolution from 1979 to the present day. For the purpose of this work, we only used 6-hourly data from 1980 to 2019 (as in IBTrACS). Unlike other reanalyses such as JRA-55 or NCEP-CFSR, ERA5 does not perform any specific assimilation for Tropical Cyclones  (Hodges et al. , 2017). Nevertheless, it has recently been assessed as having similar performances for a range of metrics  (Zarzycki et al. , 2021; Roberts et al. , 2020a). These results motivated our choice to use ERA5 as a testbed to benchmark the detection skills of the four different TC trackers we will now describe.
TempestExtremes TempestExtremes (see https://climate. ucdavis. edu/tempestextremes. php) has been developed by  Ullrich and Zarzycki (2017)  as a command-line software enabling a fast and versatile implementation of TC trackers. For the tracking of pointwise features, such as TCs, it provides two functions: DetectNodes that find candidates "nodes" corresponding to local extrema of a given variable, and optionally satisfying a set of additional criteria (closed-contours, thresholds); and StitchNodes that links candidates within a given distance of one another into a track. In this paper, we use TempestExtremes to implement two vastly different TC trackers, UZ and OWZ, respectively described by  Ullrich et al. (2021)  and  Tory et al. (2013d). We describe both algorithms below and provide the associated codes in the appendix C.
TRACK algorithm TRACK derives from an extra-tropical cyclone tracking algorithm  (Hodges, 1994). It is versatile and has since been used to study many types of weather systems, including the detection and tracking of TCs  (Bengtsson et al. , 2007; Hodges et al. , 2017; Roberts et al. , 2020a). The rationale behind TRACK is different from the previously described trackers: Because it aims at tracking all vorticity perturbations, it does not embed any warm-core criterion in its initial fundamental detection. The TC selection, including the warm core test, is only performed in the last step independently of the tracking. In the present paper, we used without any modification the database of trajectories detected by TRACK in ERA5 that was recently published by  Roberts et al. (2020a). For completeness, we detail below the thresholds used in that case.
CNRM algorithm The CNRM algorithm was developed by  Chauvin et al. (2006) , and later used in  Chauvin et al. (2020)  and  Cattiaux et al. (2020). It has been calibrated for ERA5 by  Dulac et al. (in preparation) , and this is the dataset we use here. Candidate points are first tracked with the following criteria: (i) The SLP displays a local minimum which defines the center of the system; (ii) The 850 hPa relative vorticity is larger than 1. 5 × 10 -4 s -1 ; (iii) The 850 hPa wind intensity is larger than 5 m s -1 ; (iv) The sum of the temperature anomalies averaged over the 700, 500, 300 hPa pressure levels is larger than 1 K; (v) The difference between the 850 hPa and the 300 hPa temperature anomalies is smaller than 1 K; (vi) The difference between the 300 hPa and 850 hPa wind intensity is smaller than 5 m s -1.
A common trackers post-treatment We used Eq. (  5 ) and (  6 ) to calculate the POD and FAR of the four trackers with respect to IB-TS. For UZ, we found a POD of 75% and a FAR equal to 18%. These values are almost identical to  Zarzycki et al. (2021) , who report 78% and 14% for their POD and FAR, respectively. Subtle differences in the pre-processing of the IBTrACS data account for this difference  (Zarzycki, private communication)  but the fact that both PODs and FARs are almost identical validates our implementation of that tracker. For TRACK, we found a POD of 85% and a FAR equal to 50%. Both scores are comparable to the values reported by  Hodges et al. (2017)  who applied TRACK to other reanalyses. We note that the POD we report here is on the higher end of the values found by  Hodges et al.
Missing tracks and False Alarms properties These tracks correspond to TCs that reached their maximum intensities after a post-tropical transition. Hits lifetimes (fourth row) also vary with trackers: UZ and the CNRM display the shortest tracks with a distribution that peaks between 5 and 10 days, followed by OWZ with storms durations peaking at 10 days, while TRACK tracks typically last for 15 days. We will come back in more detail to these properties in section 4. 3. Missing tracks (green color in Fig. 1 ) correspond to tropical storms or cyclones that were observed and are reported in the IB-TS database but that a given tracker did not find in ERA5. They typically consist of weak (first row), tropical (second row), and short-lived (fourth row) perturbations for which the amplitude is probably not strong enough to exceed the detection thresholds for long enough time 2.
Results We now analyze the properties of the database of ERA5 tracks we obtained after the post-treatment described above, focusing on the differences between trackers. We first come back to the trackers detection skills (section 4. 1). We then discuss the sensitivity of the metrics introduced by  Table  2  summarizes for completeness the filtered trackers detection skills extensively discussed in the previous section. The PODs are almost unchanged compared to the values discussed in section 3 before post-treatment, and the FARs are as shown in figure  4  for the STJ method. Overall, these numbers illustrate the trade-off between false alarms and missing: improvements of the POD tend to occur at the cost of an increase in the FAR.