dataesr/scientific-paragraphs-categorization huggingface dataset:
SIPI database 16 textures images from the SIPI textures  (Weber, 1997)  are used, namely : 1. 1. 01, 1. 1. 02, 1. 1. 03, 1. 1. 04, 1. 1. 05, 1. 1. 06, 1. 1. 07, 1. 1. 08, 1. 1. 09, 1. 1. 10, 1. 1. 12, 1. 1. 13, 1. 5. 02, 1. 5. 03, 1. 5. 04, 1. 5. 06. We also use 12 textures from the SIPI rotated textures, namely : bark, brick, bubbles, grass, leather, pigskin, raffia, sand, straw, water, weave, wood, wool. We use orientations at 0, 30, 60, 90, 120, 150 and 200 degrees. For the 3 previous data bases, following the same procedure as  (Drimbarean & Whelan, 2001)  to enhance the number of training and testing images, each image is randomly divided into 540 overlapped 32 × 32 sub-images. For each class, 30 images are used as a training set for the classifier and the 510 left are used as a testing set to evaluate the performance.
Outex database Textures from 2 sets for classification prepared with the Outex database  (Ojala et al. , 2002)  are used. The first, Outex_TC_00000, contains 480 images with 24 classes. The second, Outex_TC_00013 contains 1360 images with 68 classes. We don't resize the images as previously and use directly the 128 × 128 original images.
Full scale DMD This first experiment evaluates the performance of the modal features calculated on the full scale image. Before evaluating these features on the different datasets, we run an experiment to choose how many coordinates to use from the modal decomposition. The Figure  5  shows the evolution of the performance as a function of the number of modes, up to 100 modes, based on the VisTex dataset. It seems that the more features are added, the more the performance increases, until a stable 91,5% performance level from 90 features and more. The number of features to reach the performance plateau might vary from one dataset to the other, but we decide to use 100 features in the following experiment and make the assumption that they are enough to reach good performances across the different datasets.
Tables  2  and 3  contains the SVM accuracy performances and extraction times for each method across the different datasets. The left parts correspond to the results for the full scale DMD. We notice that DMD features lead to good classification results across the different datasets, comparable to Haralick and LBP features, which are known to be especially adapted for texture classification tasks. Moreover, they always outperform Hog features, which are not specifically designed for texture description. These results demonstrate that the modal features extracted through the full scale DMD approach are relevant to classify image textures. When it comes to extraction times results, the full scale DMD outperforms all of the 3 other techniques for all data bases. This can be explained by the fact that the full scale DMD extraction process consists in a simple dot product between the image and the dual basis.
The results are very especially good for the 3 first datasets because they contain uniform and distinct texture. Indeed, a small size of filter is particularly adapted to describe uniform textures. The textures in Outex00 and Outex13 are also uniform but there are more classes with less distinct differences between textures. This could explain the inferior classification performances on these 2 data bases. Extraction times results in Table  3  (right part) show that the two methods yields very similar performances, with a slit advantage for the filtering DMD. The similar results are logical because the two methods use a similar process with a 3 × 3 moving filtering over the image. The scope of this paper is only focused on a first investigation of the relevance of the modal features, so we only used 3 × 3 filters.
Rotation invariance We evaluate the rotation invariance performance of the full scale DMD features with the SIPI rotated dataset. The classification task includes rotated pictures of the same class at 0, 30, 60, 90, 120, 150 and 200 degrees. We compare the performance to the Haralick features, known to be rotation invariant, to the a non rotation invariant implementation of the LBP and to the Hog features, not considered as rotation invariant. We can see that if the performance of the full scale DMD features is lower to the Haralick one, it drops much less than the non invariant LBP and Hog performance. Future work will focus on investigating the rotation invariance on more datasets.
RESULTS: The WSCC approach guided the creation of a district wellness policy that influenced programming for students with special needs and required Youth Risk Behavior Survey (YRBS) data collection. Using YRBS and School Health Index (SHI) data in planning sexual health education illustrated improvements over time. Formation of the School Health and Wellness Collaborative under WSCC improved family engagement in sexual health education programs and practices. Relationships were established with community partners to impact student's sexual risk behaviors. Finally, the district co-created and implemented an evidenced-based sexual health curriculum, modifying it for special education and ENL students.
Needs Assessment and District Response This section focuses on comprehensive sexual health education for all students with consideration for students with special needs and ENL students. We will outline how the district assessed the need for sexual health education and how the district addressed those needs through the WSCC model. Students, parents, community partners, and district personnel co-created and supported comprehensive sexual health education. We also will review the successes of this district's wellness policy, the data that drove the changes and continual professional development across WSCC domains that supported sexual health education.
The continuous use of the 3Cs, with over 275 community partners, led to data driven wellness policy development coordinated with a variety of partners (Figure  2 ). It took a year to redesign the policy around the WSCC model using the WellSAT 3. 0. 7 District Whole Child Health Committees formed health needs arose and initially reflected the 10 components of the WSCC model. Creating a wellness policy using the WSCC model formalized the development of specific committees. School district departments took leadership roles on these committees. For example, the Department of Food Service became highly engaged to address the obesity issue in our district, the Parent Engagement Office integrated WSCC language into their department policies, and the Department of Health and Physical Education formed the Comprehensive Health Education Committee to implement a stronger health curriculum and address risky sexual health behaviors.
The Whole Child Advisory Board is composed of district and community partner co-leaders from all 16 District Health Committees. The Comprehensive Health Education Committee was formed to assess health education needs and to identify solutions. This committee reviewed data and recognized that comprehensive health education, including age-appropriate sexual health education enables students to master the essential knowledge, attitudes, and skills to adopt and maintain life-long healthy sexual behaviors. The Advisory Board assessed the policy every 3 years using the most current WellSAT tool. Once all WSCC domains have been assessed for clarity, language, and comprehensiveness, District Health Committee leaders then used the assessment results with their committee to revise their designated WSCC domain of the policy.
Assessing Need for Comprehensive Sexual Health Education: YRBS Administration Parents rallied and attended a Board of Education meeting to advocate for the administration of YRBS to assess student health risk behaviors and to evaluate district health programs. Strong parent leadership led to the district administering the US Centers for Disease Control and Prevention's (CDC) YRBS to all students in grades 6-12, starting in 2011. Students complete the YRBS online in a census data collection format. The YRBS results guided the district as well as its WSCC community partners toward addressing identified student sexual health risk behaviors and toward fostering a culture of health and well-being across the district and its schools. The wellness policy mandates the district to collect YRBS every odd numbered year and is led by the YRBS Advisory Board composed of higher education partners and other key research leaders in public health, social work, exercise science, nutrition, and dental medicine.
The YRBS Advisory Board meets to evaluate, plan, lead, implement, and guide YRBS data collection. The WSCC model and its emphasis on coordination, collaboration, and communication around a variety of programs, policies, and practices has reduced the fragmentation related to health and education in this district. As a result, all district high schools and middle schools have shown improvement of risky sexual health behaviors (Tables  1  and 2 ). Comprehensive sexual health education became the district's priority due to alarming data. According to the district 2011, YRBS middle school data, almost 16% of students reported ''ever having sexual intercourse. '' The data has trended down with only 8% of students reporting ''having sexual intercourse'' in 2017; a 49. 4% decrease. Of students reporting that they ''had sexual intercourse before age 11 years,'' results reflect a 43.
11 Additionally, the children of teenage mothers are more likely to have lower school achievement and to drop out of high school, have more health problems, be incarcerated at some time during adolescence, give birth as a teenager, and face unemployment as a young adult. 12 These effects continue for the teen mother and her child even after adjusting for those factors that increased the teenager's risk for pregnancy, such as growing up in poverty, having parents with low levels of education, growing up in a single-parent family, and having poor performance in school. 10 he district used data to determine that there was a need to address specialized sexual health education for several groups of students. School-level YRBS data demonstrated that special education students attending an ungraded school designed for high school students with special needs up to age 21 and students in schools with high populations of ENL learners were determined to be at greater risk for unhealthy and unsafe relationships, dating and sexual violence, and to experience sexual coercion as outlined by their schoollevel YRBS data.
Professional development also provided skill development on how to utilize school YRBS data to prioritize district health education pacing guides for their buildings. Sexual health community partners from Genesee Valley Education Partnership, Planned Parenthood of Western New York, Native American Community Services, Erie County Department of Health, and the Federation of Neighborhood Centers collaborated to supplement sexual health education instruction in seventh and ninth grade health courses. These partners assisted the committee in the creation and implementation of the condom availability policy and provided professional development to all district health teachers on how to incorporate the condom availability policy into the sexual health curriculum. Erie.
Ongoing, annual professional development for new as well as veteran health teachers is imperative to sexual health curriculum success. Sexual health education should be taught by certified health education teachers who desire to teach health education and who are trained to teach to state and/or national standards reflective of the community. 16 We strongly encourage schools to administer the YRBS in their district as data drives policy actions; what gets measured gets done in schools. This case study illustrated how a strong wellness policy, YRBS and SHI data, and ongoing professional development positively altered sexual health behaviors through the WSCC domain of health education and the supporting role of parent and student engagement, community involvement, health services, student support staff (counselor, psychologists and social workers), and community involvement using the 3Ps and 3Cs.
How the mix of land uses affected the ability to attract different types of users (and investors) and examples relating this to the project's resilience; 4. Whether the projects had proper planning, phasing and management; 5. If urban riverfront interventions took into full account the ecological aspects of the landscape and the functions of the river corridor, and what are the opportunities and typical pitfalls in recent projects. For each case study considered, we compiled available data and reports from existing publications and websites, and where necessary, contacted project proponents, designers or funders to obtain additional information. Our inclusion of given sites depended in part on data availability or direct experience. We synthesize our findings in the form of a "check-list" of common pitfalls in urban riverfront interventions, presented in Section 4 of this study.
Acknowledgments: This work has been supported by the  EUR H 2  O' Lyon  ( ANR-17-EURE-0018 ) of  Université de Lyon (UdL) , within the program " Investissements d'Avenir " operated by the  French National Research Agency (ANR). The authors also gratefully acknowledge the  Collegium de Lyon-Institut des Etudes Avancées de l'Université de Lyon , the  EURIAS Fellowship Programme  and the  European Commission  ( Marie-Sklodowska-Curie Actions-COFUND Programme-FP7 ) for support of manuscript preparation by Kondolf and funding for the authors' attendance at the IS River 2018 conference. Pinto benefited from funding by the  Portuguese Foundation for Science and Technology , while working for the project  MetroGov3C  ( FCT-PTDC/GES-URB/30 , 453/2017 ) during preparation of the manuscript.
2020b). The rubric of monophyly, a sequence difference threshold, and morphological diagnostic characters provided guidance for recognizing species given sufficient specimens, for example, Xyleborus glabratus  (Cognato et al. 2019). These DNA based phylogenies and our examination of specimens revealed additional taxonomic problems. 1. Coptodryas  Hopkins, 1915 , Cryptoxyleborus Wood & Bright, 1992 , Microperus Wood, 1980 , Xyleborus and Xylosandrus Reitter, 1913  are likely not monophyletic  (Cognato et al. 2020b). A more robust dataset including more genes and taxa will likely help resolve the monophyly for some genera. In other cases, reexamination of aberrant species may lead to the stabilization of genera (i. e. reassignment of generic placement for certain species) and recognition of new genera as with  Fraudatrix Cognato, Smith & Beaver, 2020 and Tricosa Cognato, Smith & Beaver, 2020 (Cognato et al.
2020b). Stouthamer et al. (2017)  suggest that the synonymy of X. talumalai (Genbank number KU727039) with this species needs reinvestigation given the occurrence of the two species in two different clades with substantially different COI sequences. However, the findings of Stouthamer et al. are incorrect because they are based on a misidentified specimen of E. talumalai which is actually E. velatus. The DNA voucher (Euw. and 1, MSUC) and an additional specimen from the same collecting event in Thailand were examined by SMS and AIC; both specimens exhibited morphology consistent with the E. velatus type. (Wichmann, 1914)  Xyleborus aplanatus  Wichmann, 1914: 412. Euwallacea aplanatus  (Wichmann) :  Wood and Bright 1992: 686. Type material. Syntypes, possibly in Indian Museum, Kolkata, India (M.
Acknowledgements We thank the curators of the following institutions who allowed access to or the loan of specimens from the collections in their care:  Dirk Ahrens  (ZFMK),  Max Barclay (NHMUK) ,  Lutz Behne  (SDEI),  Johannes Bergsten  (SMNH),  James Boone (BPBM) ,  Michel Brancucci  † and  Isabelle Zürcher-Pfander  (NHMB),  Lourdes Chamorro (NMNH) ,  Chris Grinter  (CASC),  Jiři Hájek  (MNHP),  Matthias Hartmann  (NKME),  Martin Husemann  and  Thure Dalsgaard  (UHZM),  Pol Limbourg  (IRSNB),  Katrina Menard  (OMNH),  Otto Merkl  (HNHM),  Hélène Perrin  (MNHN),  Roberto Poggi  and  Maria Tavano  (MCG), Heinrich Schönmann † and  Harald Schillhammer  (NHMW),  Kyle Schnepp  (FSCA),  Sudhir Singh  ( FRI ),  Wichai Srisuka  (QSBG),  David Szymroszczyk  (MIZ),  Joachim Willers  (MFNB),  Hiraku Yoshitake (NIAES).
Arntzen et al. (2011)  went even further, with a VR-based method to asses the impact of aircraft sounds on community well-being. Stevens et al. (2018)  used VR to show how the presence of visual stimuli can alter soundscape perception and categorisation. In their experiments, they first presented participants (N=31) with recordings taken in the North of England (in rural, urban, and suburban environments), each comprising a mixture of natural, human and mechanical sounds. Then they presented the sounds with visual accompanimentforest (natural), rural/suburban & city center(urban) -delivered with 360 • videos through a Head Mounted Display (HMD). When comparing the subjective evaluation data (i. e. valence, arousal and dominance), they found a significant difference in emotion and category rating with and without visuals.
Data source The CCD time-series observations were were obtained under campaigns led by the VSNET Collaboration  (Kato et al. 2004 ). We also used the public data from the AAVSO International Database 1. Outburst detections of many new and known objects relied on the ASAS-SN CV patrol  (Davis et al. 2015)  2 , the MASTER network  (Gorbovskoy et al. 2013 ), and Catalina Real-time Transient Survey (CRTS;  Drake et al. 2009)   3. There were also outburst detections reported to VSNET, AAVSO 4 , BAAVSS alert 5 and cvnet-outburst. 6.
, campus obs. ), Kai (K. Kasai), Kis (S. Kiyota), LCO (C. Littlefield) , Lic (D. Licchelli), Lis (Lisnyky Obs. ), NGW ‡ (G. Myers), MLF (B. Monard), MZK ‡ (K. Menzies), Mas (G. Masi), Mdy (Y. Maeda), NKa (N. Katysheva and S. Shugarov) , Nel (P. Nelson), Njh (K. Nakajima), OYE ‡ (Y. Ögmen), RPc ‡ (R. Pickard), Rui (J. Ruiz), SGE ‡ (G. Stone), SPE ‡ (P. Starr), SRI ‡ (R. Sabo), Shu (S. Shugarov team), Sol (F. Soldán), Ter (Terskol Obs. ), Trt (T. Tordai),  Van (T. Vanmunster) , AAVSO (AAVSO database) † Original identifications, discoverers or data sou rce. ‡ Inclusive of observations from the AAVSO database. vae mainly thanks to the ASAS-SN survey. The major advance since then has been the increase of examples of type-E outbursts. The objects with type-E outbursts have an initial superoutburst corresponding to the 2:1 resonance (high-inclination systems show early superhumps) and the second superoutburst showing the development of ordinary superhumps.
06063(7) d or 0. 05893(7) d. These periods are equally acceptable and phase-averaged profiles are in e-figures 2 and 3. Although there were observations after these two nights, the data quality was not sufficient and we could not determine the superhump period. The object is thus a WZ Sge-type dwarf nova. The rapidly fading light curves resembling those of fast novae were probably caused by viscous decay at the start of the superoutburst (cf. Kato 2015). E-section 3. 2 DH Aquilae DH Aql was discovered as a Mira-type variable (=HV 3899) with a range of 12. 5 to fainter than 16 in photographic range  (Cannon 1925). The SU UMa-type nature of this object was clarified by  Nogami and Kato (1995). Refer to  Kato et al. (2014a)  for more history. The 2017 superoutburst was detected by M. Noriyama at an unfiltered CCD magnitude of 14.
5 while the object was still rising. The object was observed to be at peak magnitude of 12. 4 on the next night. Two superhump maxima were obtained from single-night observations: BJD 2458036. 9216(4) (N=106) and 2458037. 0013(2) (N=152). E-section 3. 3 V1047 Aquilae V1047 Aql was discovered as a dwarf nova (S 8191) by  Hoffmeister (1964). The object was identified to be an SU UMa-type dwarf nova in 2005 by Greg Bolt. Observations of the 2016 superoutburst were reported in  Kato et al. (2017a) , which obtained only two superhump maxima. The 2017 superoutburst was visually detected by R. Stubbings at a magnitude of 15. 2 on 2017 June 26. The ASAS-SN data recorded it at V=16. 2 on 2017 June 20, which further brightened to V=15. 5 on 2017 June 24. The times of superhump maxima are listed in e-table  1.
The superhump stage is unknown. As reported in  Kato et al. (2017a) , this object shows regular superoutbursts with a short supercycle. We extracted superoutbursts in the ASAS-SN data (e-table 2). These superoutbursts can be well expressed by a supercycle of 89. 1(3) d with maximum |O -C| of 4 d. There were also apparently frequent normal outbursts expected for this short supercycle.
E-section 3. 9 GQ Canum Venaticorum This object was discovered as ASASSN-13ao by the ASAS-SN team on 2013 June 8  (Stanek et al. 2013 ). The 2013 superoutburst was studied in  Kato et al. (2014b)  yielding only two superhump maxima. The 2017 superoutburst was detected by the ASAS-SN team at V=14. 7 on 2017 April 8. Superhumps were better observed than in the 2013 superoutburst (vsnet-alert 20899, 20913; e-figure  6 ). The times of superhump maxima are listed in e-table  6. Although there was some tendency of a period decrease, we could not determine the superhump stages. Three superoutbursts have been known in the ASAS-SN data (2013 June 7, V=15. 0; 2016 January 27, V=14. 8 and the present one). The shortest interval between superoutbursts was 437 d.
Acknowledgements This work was also partially supported by Grant  VEGA 2/0008/17  (by  Shugarov, Chochol)  and  APVV-15-0458  (by  Shugarov, Chochol, Dubovsky, Kudzej, Medulka ),  RSF-14-12-00146  (Golysheva for processing observation data from Slovak Observatory). ASAS-SN is supported by the  Gordon and Betty Moore Foundation  through grant  GBMF5490  to the  Ohio State University  and  NSF  grant  AST-1515927. The authors are grateful to observers of VSNET Collaboration and VSOLJ observers who supplied vital data. We acknowledge with thanks the variable star observations from the AAVSO International Database contributed by observers worldwide and used in this research. We are also grateful to the VSOLJ database. This work is helped by outburst detections and announcement by a number of variable star observers worldwide, including participants of CVNET and BAA VSS alert.
The  CCD operation of the Bronberg Observatory  is partly sponsored by the  Center for Backyard Astrophysics. We are grateful to the  Catalina Real-time Transient Survey team  for making their real-time detection of transient objects and the past photometric database available to the public. We are also grateful to the ASAS-3 team for making the past photometric database available to the public. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This research has made use of the  International Variable Star Index  (VSX) database, operated at  AAVSO, Cambridge, Massachusetts, USA.
| Giant pangolin burrowing behaviour The focal male ranges over 38 km 2 of savannah, gallery forest, forest fragments, secondary and mature tropical rain forest. We identified twelve different sleeping sites in gallery and forest fragments that are regularly used by the focal male, nine of which are burrow systems and three are hollow trees. Preliminary telemetry data suggest several more potential burrows exist in mature forest but these have not yet been inspected visually. Seven of the 12 burrows were frequently visited by at least one smaller male. Three of them were used by two different pangolin females with infants that were never observed together. Six of the burrows were frequently visited by a large lone female; with whom the male was observed walking, feeding and interacting during successive nights.