dataesr/scientific-paragraphs-categorization huggingface dataset:
Metrics In order to assess the quality of a submission, we provide several metrics. First, we must take into account the fact that there are some calibration methods that will fail to provide results on certain images, which is why we introduce a "Completeness Ratio" (CR) that is the ratio of the dataset images for which the method provides camera parameters. Then the other metrics are based on the accuracy of the projection of each soccer field element in the image. Using our provided soccer field model, we sample 3D points regularly along each soccer field element, then project each point in the image using the predicted camera parameters for a specific frame. In this way we obtain a set of 2D polylines that we can compare to the annotated polylines. Given a point in the 3D world X that has been sampled along a soccer field element of our 3D soccer field model, we use the predicted camera parameters to derive its projection in the image x.
PLAYER RE-IDENTIFICATION 6. 1 Task description Person re-identification  [29] , or simply ReID, is a person retrieval task which aims at matching an image of a person-of-interest, called the query, with other person images within a large database, called the gallery, captured from various camera viewpoints. ReID has important applications in smart cities, video-surveillance and sport analytics, where it is used to perform person retrieval or tracking. The goal of the SoccerNet ReID task is to re-identify players and referees across multiple camera viewpoints for a given action at a specific time instant during a soccer game. Our SoccerNet reidentification dataset is composed of 340,993 players thumbnails extracted from image frames of broadcast videos from 400 soccer games within 6 major leagues.
R1 -Optimized Strategy for Player Re-identification. Rengang Li, Yaqian Zhao, Hongwei Kan, Zhenhua Guo, Baoyu Fan, Runze Zhang, Xiaochuan Li lirg@inspur. com, zhaoyaqian@inspur. com, kanhongwei@inspur. com, guozhenhua@inspur. com, fanbaoyu@inspur. com, zhangrunze@inspur. com, lixiaochuan@inspur. com We analyzed that the main challenges are the sample imbalance and unrobustness mainly caused by multi-input resolution. We removed the ids whose images are less 3 and employed the focal loss function to solve sample imbalance. We experimented different combination of ReID network module to choose the best representation ability and selected ResNeSt269, combination of Arc-Softmax and Cos-Softmax. We used Auto-Aug, Color Jittering and Random Erase and all of the data augmentation uses the probability of 0.
Results Participants came up with various innovative ideas and have achieved outstanding performances despite the difficulty of the task. We list here some of the keys ideas shared by participants. (i) Apply some pre-processing by removing identities with too few samples in the training set. (ii) Design a handcrafted training batch sampling strategies based on additional SoccerNet ReID dataset labels, such as action id and game id. (iii) Add standard data augmentation strategies: Horizontal Flip, Random Erasing  [31] , Random Cropping, AutoAugment  [8] , AugMix  [15] , Color Jitter,. (iv) Use a strong baseline such as the TransReID-SSL  [21]  baseline with ViT  [11]  backbone and unsupervised pre-training on LUPerson  [13]  dataset. (v) Use specific metric learning loss functions: the Focal Loss  [18] , a custom Centroid loss, the InfoNCE loss  [26] , the Arcface loss  [10] ,.
MULTIPLE PLAYER TRACKING 7. 1 Task description Tracking is a hot topic of research, which is far from being solved. In sports, tracking algorithms enable many interesting applications. They can be used to generate player specific highlights and statistics, or be leveraged for holistic video understanding  [5]. As defined in the SoccerNet-Tracking dataset, the tracking task is split in two steps:  (1)  detecting the objects to track and (2) associating the bounding boxes over time to create the tracklets. For this year's challenge, the participants had access to 150 30-seconds clips recorded only from a single camera, with all ground-truth bounding boxes provided. The goal of the task is therefore to associate these bounding boxes over time to create the final tracklets. The complete tracking task, including both detection and association, will be part of the next edition of the SoccerNet challenges.
S7 -Mixed Spatial and Temporal Attention for Soccer Game Action Spotting (CIHE). Cheuk-Yiu Chan, Chun-Chuen Hui, Wan-Chi Siu, Sin-wai Chan, and H. Anthony Chan Table  8 : Field localization leaderboard. Main metric for the leaderboard and best performance in bold. Team names with a superscript provided a summary that can be found in Appendix A. 2, or in Section 4. 4 for the winner. The baseline description may be found in https://github. com/SoccerNet/sncalibration.
S8 -STE. Abdulrahman Darwish and Tallal El-Shabrawy abdulrahman. darwish@guc. edu. eg, tallal. el-shabrawy@guc. edu. eg STE is a proposed deep model for action spotting in soccer videos. The model reads the pre-extracted Baidu soccer embeddings, generated from SoccerNet-v2 dataset. It encodes the spatial features then the temporal features at different scales. Its architecture consists of 3 blocks: the spatial encoder, the temporal encoder then the prediction block. The first block extracts the spatial semantics through 1D max-pooling, a fully connected layer and 1D convolutional layer. The temporal encoder then extracts features across frames by applying 3 1D convolutional layers with 1D max-pooling after the first layer. The last block predicts two outputs, the first prediction is the class event that occurred in the input window.
Finally, we utilize the action recognition module and overlapped sliding window strategy to extract the features of the video and sent them to model of NetVLAD++ to get the result. S20 -Simultaneous Supervised Contrastive Learning for Spatio-Temporal Action Recognition. Andrei Boiarov and Nikita Kasatkin andrei. boiarov@sit. team, nk@sit. team Our approach is a combination of a spatio-temporal backbone and metric learning for the action recognition problem. The model consists of two networks: backbone (frozen pretrined on Kinetics-400 SlowFast-R101  [12] ) and head. Head network:  2 normalization, two fully connected (FC) blocks (Dropout, FC layer with hidden size 2048, BatchNorm and ELU activation), embedding layer (output size 512) and classification FC layer. Dataset videos have been split as clips of 2 seconds duration.
T9 -Movement Forecasting(MF) in Soccer Player Tracking. Sangrok Lee lsrock1@yonsei. ac. kr We introduce MF, a novel architecture placed on top of Deep-LabV3 to perform movement forecasting of players. In SoccerNet tracking dataset, players move dynamically and cameras also move around to follow a ball. Because of that, it is really hard to apply existing tracking methods like Kalman Filter. To tackle this problem, MF is designed to track dynamically moving players under moving camera conditions. MF predicts 2-dimensional movement vectors of players with only one feed-forwarding step and estimates a future position of objects. Although the MF is precise, it is impossible to recognize the person who disappears and reappears. Therefore, we also adopt a re-identification (re-id) module. Overall tracking process consists of the movement estimation and re-id.
54 Å). The temperature range of the experiment was covered by using a dedicated helium cryofurnace. Structural and magnetic Rietveld refinements of the x-ray synchrotron and neutron patterns were made using the Full-Prof package of programs  [18]. Crystallographic tools from the Bilbao Crystallographic server  [19] [20] [21]  and ISOTROPY Software Suite  [22]  were also used. The illustrations of the crystal structure were obtained using the VESTA program  [23]. The cationic disorder in each sample was determined by SXPD data analysis through the Fe/Cu occupancies at the B sites of the perovskite. It is represented by the "improper occupancy" parameter n d (0 [full order]< n d 0. 5 [full disorder]) described in the next section. A second key parameter for the description of the samples is the modulation q S of the incommensurate spiral magnetic phase, k 2 = (1/2, 1/2, 1/2 ± q S ) being the propagation vector, directly related to n d , which was obtained from the NPD data.
[29]. NPD data collected in our set of YBCFO samples are compatible with an incommensurate spiral phase below T S , described in the form m l j (k) = M R (m)(u j ) cos (2π {k. R l + J }) + M I (m)(v j ) sin (2π {k. R l + J }), (4) where m l j is the magnetic moment of the atom j in the unit cell l, R l is the vector joining the arbitrary origin to the origin of unit cell l, and j is a magnetic phase. u j and v j designate the orientation of the two perpendicular unitary vectors that define the plane of the helix, where M R (real) and M I (imaginary) amplitudes fix the dimensions of the elliptical envelope described by the rotating magnetic moments. In these powder samples (due to the intrinsic limitations mentioned above) it is not possible to independently refine (i) the real and imaginary amplitudes (M R and M I ) or (ii) the two magnetic moments in the bipyramidal units, forcing us to fix their ratios using constraints.
ACKNOWLEDGMENTS We acknowledge financial support from the  Spanish Ministerio de Ciencia, Innovación y Universidades (MINCIU) , through Projects No. MAT2015-68760-C2-2 , No. RTI2018-098537-B-C21 , and No. PID2021-124734OB-C22 , cofunded by  ERDF  from  EU , and " Severo Ochoa" Programme for Centres of Excellence in R&D  [Grant No. SEV-2015-0496  and  FUNFUTURE  (Grant No. CEX2019-000917-S )]. X. Z. was financially supported by  China Scholarship Council (CSC)  with Grant No. 201706080017. A. R. 's work was done as a part of the  Ph. D. program in Materials Science  at  Universitat Autònoma de Barcelona. The granted beam time at ALBA synchrotron (Proposals No. 2017092471  and No. 2017032118) is appreciated. We also acknowledge the  Institut Laue-Langevin (ILL)  for the beamtime allocation under Experiments  5-31-2680 ,  5-31-2549  and  Easy-Access.
We also tested other methods of assignment; for instance, we assigned bActive to 1, if the geometric center of one grid is within a county with reservoirs in categories I, II, or III; otherwise, it is 0. That method did not affect the results of this study. Meanwhile, we have extracted the plague reservoirs known in the United States (Fig. S3 ) by digitizing and merging two maps: one showing positive rodent samples in California  (56) ; the other representing plague-positive counties in Western United States  (57). Note that the map shown in Fig. S1  was just used to show the general spatial distribution of plague reservoirs around the world, rather than to construct the independent variable bActive. Environmental Data. To assess environmental impacts as comprehensively as possible, we began with a large dataset of 44 environmental variables representing soil properties, meteorological data, elevation, and spatial distribution of plague hosts, as listed below.
To be consistent with the above plague reservoir data, all the environmental data were converted to a raster layer at a spatial resolution of approximately 10 × 10 km 2. Consequently, the sample sizes were 139,064, 118,365, and 87,127 for China, the United States, and Europe, respectively. Soil geochemical data. Three different datasets, all collected by authoritative organizations (SI Appendix, Table  S1 ), were used to extract the soil geochemical data of 33 elements, for example Ca, Fe, Mg, and Na. Because of data availability in the soil geochemical dataset, we restricted our study to areas of China, the United States, and Europe (SI Appendix, Fig. S2 ). The soil geochemical data for China were obtained by digitizing the maps of background values of soil elements at 1:25,000,000 scale  (58).
Then, the digitized shapefile was converted to raster data based on the maximum area method. For the United States  (59)  and Europe  (60) , the original sampled data were downloaded and interpolated through the inverse distance weighted method in ArcGIS 10. 3 (see SI Appendix, sections 1. 2. 1 and 1. 2. 3 for more detailed descriptions of the processing of soil geochemical datasets). Note that we have such comprehensive soil geochemical data only for China's mainland (i. e. , no data for Taiwan), the continental United States (i. e. , no data for Alaska, Hawaii, or Puerto Rico), and some European countries (i. e. , no 30°E 20°E 10°E 0°10°W 60°N 50°N 40°N 30°E 20°E 10°E 0°10°W 30°E 20°E 10°E 0°10°W 60°N 50°N 40°N 30°E 20°E 10°E 0°10°W 70°N 60°N 50°N 40°N 30°E 20°E 10°E 0°10°W 30°E 20°E 10°E 0°10°W 70°N 60°N 50°N 40°N.
To be consistent with the sampling depth (SI Appendix, Table  S1 ) of the above geochemical data, the data for CEC, clay, pH, and SOC from that depth were extracted from SoilGrids (61), a widely used global gridded soil dataset. Data with a spatial resolution of 0. 1 arc degree (approximately 10 km) were selected and downloaded. They were then aligned by the "nearest neighbor" method to spatially match the plague reservoir data. See SI Appendix, Table  S3  and SI Appendix for a more detailed description of the data for CEC, clay, pH, and SOC. Bioclimatic variables. Climatic conditions have long been regarded as one of the most important factors in ecological studies. We extracted data for five variables from WorldClim 2. 0 (62) with a spatial resolution of 5 arc minutes: AP, AMT, ISO, temperature seasonality, and precipitation seasonality.
Only three raw variables in WorldClim 2. 0 are used to extract a total of 19 variables: average minimum temperature, average maximum temperature, and total precipitation for each month. Thus, they are highly correlated with each other. The five variables we selected are the ones which best represent annual trends and seasonality for temperature and precipitation and best describe the principal components of all the WorldClim variables. Adding more WorldClim variables will not improve the predictability of the model within the framework of the sufficient dimension reduction method, such as MAVE, which assumes that the directions of the reduced central subspace are orthogonal to each other (see SI Appendix, Table  S3  and SI Appendix for further detail). Elevation. Global Multi-resolution Terrain Elevation Data (GMTED2010) (63)-a suitable data source for information on the elevation for working at the continental scale-was obtained at a spatial resolution of 30 arc second (approximately 1 km) and resampled by the bilinear method to spatially match the plague reservoir data.
(A more detailed description of elevation can be found in SI Appendix. ) Spatial distribution of plague host. A list of plague hosts was kindly provided by Mahmoudi et al. (1)  The list was compared with species in the IUCN Red List of Threatened Species  (64) , resulting in a total of 339 plague hosts. Based on the spatial distribution data of these identified hosts, the number of species of mammalian hosts (denoted as nHost) at every fine grid node was counted; the fine grids were the same as those of the plague reservoir data. (A description of the nHost data obtained can be found in SI Appendix. ) Modeling Approach. A sufficient dimension reduction method, MAVE (20) (conditional MAVE), is used to establish the quantitative relationships between the spatial distribution of active plague reservoirs and potential (and yet to be identified) environmental conditions.
MAVE is applicable to a wide range of models, with fewer restrictions on the distribution of covariates. Since it enables a faster rate of consistency for the parameter estimators, it is possible to estimate the dimension of the space consistently, with fewer observations. As it is a regression-type model, it captures the most relevant information contained in the covariates X for the prediction/explanation of the response variable y. We use the R package "MAVE" and "earth" for our exercise of MAVE method. Because y is a binary variable, MAVE will return an estimate of probability value P(y = 1) directly  (66). In the literature, it is recommended that transforming covariates X closer to normality is optimal  (67). The crossvalidation method is used to find the best choice of dimension of EDR space.
Data, Materials, and Software Availability. The R code used for statistical analyses is listed in Code S1, and all the relevant data are listed in Datasets S1-S7. ACKNOWLEDGMENTS. This work is supported by the National Natural Science Foundation of China (Grant 72091514) and Vanke School of Public Health, Tsinghua University, to L. X. and C. Z. , the University of Oslo and the Research Council of Norway to the Centre for Ecological and Evolutionary Synthesis (CEES) to N. C. S. , the National Natural Science Foundation of China (Grant 71973077) to Y. T. and D. L. , the European Research Council (ERC) MedPlag (Grant AdG #324249) to B. B. , and the SustES project (Grant CZ. 02. 1. 01/0. 0/0. 0/16_019/0000797) and European Research Council (ERC) MONOSTAR (Grant AdG #882727) to U. B. Atle Mysterud is thanked for providing comments on an earlier version of the paper.
Evidence suggests that climatic changes will significantly, and in some cases irreversibly, harm the economy and many ecosystems alike  (Willetts et al. 2010). Widely available maps of current and future climatic conditions and the rapidly increasing number of species observations in scientific and citizen science databases allow for increasingly extensive studies of spatio-temporal changes in species distributions  (Puchałka et al. 2021 (Puchałka et al. , 2022)). Species distribution modeling is a widely used tool for predicting potential changes in the distribution range of plant and animal species and detecting niche shifts with climate change  (Guisan and Thuiller 2005; Hill et al. 2017; Dyderski et al. 2018). Recent studies of the effect of climate change on future species distribution have focused on native and on potentially invasive alien insect pests in agriculture and forest ecosystems  (Hlásny and Turcani 2009; Cudmore et al.
European beewolf occurrence data The occurrences of European beewolf (Supplementary material, Fig. 1 ) were obtained from scientific papers, Global Biodiversity Information Facility (www. gbif. org) and confirmed identifications of georeferenced photographs on the webpage Biodiversidad Virtual (www. biodi versi dadvi rtual. org). The gaps in occurrence data were filled by the authors' unpublished field observations. In total, we obtained 10,574 distribution records with associated geographical coordinates (latitude and longitude), which were loaded into a spatially referenced database. Among all records, 53. 6% were collected after 2015, 84. 4% after 2005, and 92. 9% after 1990. To overcome any disadvantage of citizen science data collection resulting in uneven sampling that could affect analyses, we randomly selected a single record from each 0.
Climatic data -predictors of the potential distribution of the European beewolf We obtained 19 bioclimatic variables (Table  1 ) (2. 5′ resolution)  (Booth 2018 ) from the WorldClim 1. 4 database (www. world clim. org);  (Hijmans et al. 2005). These data on annual, monthly, and/or seasonal temperature and rainfall indices are commonly used in modeling potential geographical ranges  (Booth et al. 2014; Booth 2018)  and are the factors considered to restrict the geographic distribution of species  (Pearson and Dawson 2003; O'Donnell and Ignizio 2012; Sullivan 2014 ). To limit the number of variables and to avoid collinearity, we assessed the correlation matrix of climatic data for European beewolf occurrences. Then, from each pair of variables containing similar information (e. g. , bio5 and bio10, or bio6 and bio11), we removed the one with |r|> 0.
7  (Puchałka et al. 2021). Taking into account information about the studied biology of the species, we can justify the choice of climate variables as follows. Bio2, 3, 7, and 10 are variables that preface the amplitude of daily, seasonal, and annual temperature ranges, which may be important to the survival of populations of this Mediterranean species, which may be affected by temperature decreases  (Braestrup and Nielsen 1941; Leclercq 1944 ). Bio17 may determine population survival because snow cover in colder regions can protect nests from low temperatures. Meanwhile, bio18 and 19 inform on the amount of precipitation during Europe's warmest and wettest period of the year, which falls in summer, the period of imago activity  (Simonthomas and Poorter 1972). This reduced the dataset to seven variables for modeling the potential distribution of European beewolf (Table  1 ).
Potential distribution modeling We used the MaxEnt model to model the potential distribution of European beewolf. This is the presence-only method, which uses point occurrence data and pseudoabsences  (Phillips et al. 2006; Elith et al. 2011). In contrast to parametric models and other classification tools, MaxEnt can work without true absence data in the theoretical assumptions, but instead, it uses background data (so-called pseudoabsences). As MaxEnt analyzes patterns of presences distinct from the background data, the prevalence of background points makes the model more conservative, requiring a stronger signal than would be the case for equal proportions of presences and pseudoabsences  (Elith et al. 2011). Prior to analyses, we split the dataset into a training set (80% of observations) used for model development, and an independent validation set (20% of observations).
We used dismo package  (Hijmans et al. 2020)  for MaxEnt model development and raster  (Hijmans 2020 ) and sf  (Pebesma 2018 ) packages for spatial data processing. We developed a prediction of the species range shifts for two time horizons: 2040-2060 (hereafter 2050) and 2060-2080 (hereafter 2070). Models were created for two scenarios of increasing climate change magnitude, reflected in the 5th Assessment Report of IPCC by the respective representative concentration pathways (RCPs), based on the difference between the pre-industrial level of radiative forcing (in W m -2 ) and that predicted for the year 2100  (van Vuuren et al. 2011; Harris et al. 2014; IPCC 2014). The more optimistic variant (RCP4. 5) predicts an increase in CO 2 concentration in the atmosphere to 650 ppm and an average temperature increase of 1.
Results Our model showed an overall good performance, expressed by a high AUC (0. 864) assessed using a validation dataset. The threshold of occurrence probability, assessed as the point with the highest sum of sensitivity and specificity, was at 0. 533. The most important bioclimatic factors influencing the occurrence of European beewolf were "annual temperature range" (bio7), "mean temperature of the warmest quarter" (bio10), and "precipitation of the warmest quarter" (bio18) with importance > 5%; all other variables had importance < 5% (Fig. 1 ). The probability of European beewolf occurrence decreases with increased annual temperature range (bio7) and drops with increase of precipitation of the warmest quarter (bio18). When it comes to mean temperature of warmest quarter (bio10), it seems that below some threshold value, the probability of European beewolf occurrence is negligible.
Data availability The research was performed based on a data set obtained from the Global Biodiversity Information Facility (GBIF) database (https:// doi. org/ 10. 15468/ dl. 27nf7g) supplemented by occurrence data from the webpage Biodiversidad Virtual (www. biodi versi dadvi rtual. org). The gaps in occurrence data were filed with the author's unpublished data. This dataset is available from the first author (P) on reasonable request.