dataesr/scientific-paragraphs-categorization huggingface dataset:
Search for literature We conducted a search for literature on three accessible databases from the Web of Science platform (Clarivate): Web of Science Core Collection (WOSCC), Biological Abstracts, and Zoological Records-using the access rights provided by the French National Museum of Natural History (MNHN). These databases were selected because they cover both biology and ecology and because their functionalities enable an advanced search of literature. The WOSCC search included the following citation indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI and CCR-EXPANDED. A search string was built with English terms as follows: ("fusion frequenc � "OR "flicker threshold$" OR "flicker sensitivit � " OR "flicker detection$" OR "flicker vision$" OR "flicker fusion" OR "flicker frequenc � " OR "temporal sinusoidallymodulated full-field luminance variation$") This search string resulted from an iterative building process achieved by both ecological and physic experts from the MNHN and the National Conservatoire of Arts and Crafts (CNAM) and was used to reach the best comprehensiveness-i.
e. the best recovery from a previously established test list of relevant articles. This test list included 60 articles, of which 56 came from  Inger et al. (2014)   [33]  and 4 were included by the review team (S3 File). Of these 60 articles, 39 were found in the three databases that we used, providing an 85% comprehensiveness (33/39). The final search was conducted on "Topic" (TS) on 1 February 2021.
Other sources of literature To complete our search for literature strategy, we retrieved documents from other sources. First, we extracted references identified when carrying out critical appraisal and where CFF data were cited but were not present in our initial corpus  [28, [42] [43] [44] [45]. Additionally, we included values extracted from the two main previously published reviews on CFF in our corpus  [33, 34]. Other relevant articles found by the review team, but not directly found in the three considered databases, were also added to the final corpus. As these articles all dealt with CFF, they did not go through any screening stage and were directly included in our corpus. A call for literature-and in particular non peer-reviewed articles published in French or in English-was also carried out by sending emails to a group of 40 experts on 12 February 2021.
Data extraction Several experimental methodologies have been used in the literature in order to quantify CFF. Electrophysiological protocols are the most frequently used and often involve electroretinograms (ERG) or, alternatively electroencephalograms, which respectively measure the electrical response of the retina and brain to flickering light. Behavioural protocols have also been used and are mainly of two kinds: either a two-alternative forced choice procedure that requires an animal to choose the preferred light source between the flickering one (usually with increasing frequencies) and the continuous one  [28] ; or optomotor protocols where the nystagmus reflex of the head or eye of an animal is monitored through increasing flicker frequencies  [46]. CFF data were extracted by two reviewers (ML and AL).
AL checked all the extracted data by ML, which means that half of the corpus was double checked. Also, a test was conducted on a subsample of studies by two reviewers (RS and AL). As our goal was to determine the maximum CFF at which animals could be impacted, if an article was to test different light intensities, we extracted the maximum CFF recorded by authors. If data were only presented on graphs, we extracted them using WebPlotDigitizer  [47]. We also extracted metadata from each study: namely methods, either electrophysiological or behavioural, light sources, light intensities at which CFF were measured. We added a measure of the low, variable or high light exposure levels an animal is likely to experience in natural conditions (following the method from Inger et al. (2014) and  Healy et al.
(2013)   [33, 34] ). Briefly, a nocturnal or deep-sea foraging animal is likely to experience a low exposure to light compared to the high exposure of a diurnal or shallow water foraging animal. Cathemeral and crepuscular animals are considered to be exposed to variable quantities of light. Each species was associated with its taxonomic class  [48] , its trophic guild-i. e. herbivore, omnivore, carnivore-and a measure of its body size-i. e. very small, small, medium, large, very large. Categories were build thanks to data mainly provided by two available online trait databases  [49, 50]. Animals with a body mass inferior to 1 g were arbitrarily considered to be very small, between 1 g and 10 3 g small, between 10 3 g and 10 4 g medium, between 10 4 g and 10 5 g large, and superior to 10 5 g very large.
This model also had a species random term as well as a taxonomic class random term to account for the non-independence of CFF across species and taxonomic classes. Due to the low availability of data on body masses and trophic guilds for Insecta and Malacostraca, both classes were discarded and the analysis carried out on a subset of 79 CFF values. Like for the first model, CFF was square root-transformed and model fits were visually checked. The most parsimonious model was selected based on the computation of the relative importance (RI) of each variable proposed by De Kort et al. (2021)  [51]. To assess to what extent variations of CFF were explained by our second model, we computed R GLMM(m) 2 , the marginal R 2 representing the variance explained by the fixed effects and R GLMM (c) 2 , the conditional R 2 representing the variance explained by both fixed and random effects.
Screening process The complete literature screening process is presented on Fig 1. All citations and their inclusion/exclusion on titles, abstracts and full-texts, associated with the reason in case of exclusion, are listed in S4 and S5 Files. At first, 4881 citations were exported from the three databases and 2276 were selected on titles, among which 1041 had an available abstract. Abstracts screening lead to 477 accepted citations. Among them, 445 full-texts PDF were retrieved, of which 106 were kept after the full-text screening step. Over the whole screening process, 45 citations were identified as duplicates and were removed. Sixty-eight additional records came from our call for grey literature but none passed the full-text screening. Lastly, 46 other articles found by the review team that directly dealt with CFF  [28, 33, 34, [42] [43] [44] [45] , but not directly found in the three considered databases, were added to the final corpus.
Knowledge gaps As was first noted by  Inger et al. (2014)   [33] , this review highlights how an overall lack of data on animals' CFF remains and continues to hamper our understanding of the impacts of anthropogenic lighting on biodiversity. We, thence, reiterate the important need for additional primary research in order to improve our knowledge of CFF in as many species as possible. Indeed, on the millions of species that populate the Earth, just over 150 were studied thus far accounting for only 15 taxonomic classes. This scarcity of data is particularly noticeable in key nocturnal taxa, which are likely to be greatly disturbed by ALAN. As such, the flicker perception of many more nocturnal and crepuscular insect species should be studied as well as those of nocturnal birds and bats, two other groups on which no additional data was retrieved in our systematic review compared to previous reviews  [33, 34].
Such values should later be reassessed in order to build a more accurate CFF database. We also advocate the need for a strong and consistent reporting of CFF data. Indeed, accurate values of CFF should absolutely be reported as a mean attached to a measure of variation, which only one third of our studies provided, despite being a basic statistical need. Last but not least, we remind the essential need for replications and controls in order to measure reliable CFF values. Many recent studies have only used one specimen or have failed to report the use of a reference electrode for electroretinograms and had to be excluded after critical appraisal. While we acknowledge the difficulties in collecting and/or training large numbers of individuals, it is nonetheless critical to include measurements of multiple individuals for robust estimates of a species' threshold of flicker perception.
Review limitations Due to time limitation and financing constraints, we sometimes had to reduce our requirements comparing to CEE guidelines. First, we could not include search engines (e. g. Google Scholar) in our search strategy, nor could we request supplementary databases (e. g. Scopus) which would have probably increased the reliability of our search by increasing the number of test list articles indexed in the requested databases. In addition, after title screening, we had to set aside citations that did not possess an appended abstract. Indeed, dealing with these citations would have meant searching for the full-texts of these 1235 citations, which represented an additional workload unfeasible within the scope of our project. However, this additional work remains feasible subsequently thanks to the S7 File which lists these citations without abstract.
We also could not exploit the reviews (i. e. articles that might include some CFF values through their full-texts) identified through citation screening-except  Inger et al. (2014)   [33]  and  Healy et al. (2013)   [34] -and we then collated them in S8 File for further exploitation. We are conscious that these shortcomings may lead to an underestimation of the number of identified CFF, which could potentially have been assessed in more species, and could provide less robust data as more values for a given species might have been found. Nevertheless, our database already provides many additional CFF values compared to previously published reviews  [33, 34]. Lastly, studies were only critically appraised by one reviewer whereas CEE guidelines call for a double independent assessment by two reviewers.
Conclusions This systematic review, based on the method preconized by the CEE, provided a database of 200 critical fusion frequency values for 156 different species. This database complements the existing reviews on CFF by providing a wider range of more confident values. It, then, represents a current and qualitative state of knowledge of flicker perception in the animal kingdom, which could be helpful for scientists and researchers as well as for practitioners, such as lighting managers and designers. Here, we found that the beetle Melanophila acuminata D. could perceive the highest flicker frequency among all the recorded species whereas the snail Lissachatina fulica B. had the lowest temporal resolution. Insects and birds were the two taxa with the highest CFF. Moreover, nocturnal species had lower CFF than diurnal and crepuscular ones.
We observed that some nocturnal and crepuscular insects could potentially perceive the flicker of anthropogenic light sources, which means that they could be subjected to its deleterious adverse effects. We also identified that ensuring the best experimental practices possible when determining CFF is crucial in order to report robust and accurate values of a species threshold of flicker perception. Nevertheless, the number of collected CFF values remains very low in relation to the number of all living animal species. In addition, this lack of data especially concerns key nocturnal taxa like birds and bats that are likely to be exposed to outdoor lighting. Therefore, the results presented here are necessarily provisional. We thus argue that many more species should be pressingly investigated by researchers to improve our knowledge of flicker perception by animals.
Acknowledgments We would like to thank  Simon Potier  who enlightened us about behavioural and optomotor CFF discrimination protocols. We also wish to thank  Francoise Vie  ´not,  Christophe Martinsons ,  Jack Falcon ,  Christian Kerbiriou ,  Lea Mariton  and  Matthieu Iodice  for providing us with literature, as well as  Dakis-Yaoba Oue  ´draogo and  Simon Blanchet  for their fruitful statistical advice which contributed to substantially improve the quality of our manuscript. At last, we sincerely thank  Marie-Pierre Alexandre  for her support and advice all along our study.
| Statistical considerations The quantitative variables were described as medians (interquartile range [IQR]) and the qualitative variables as prevalence. The correlation between the laboratory indicators and the risk of severe complications was determined by the Spearman rank coefficient. The significance of the correlation was assessed using a two-tailed test. The receiver operating characteristics curves for each laboratory indicator significantly associated with a risk of severe complications are reported using 95% confidence intervals (CI) of their areas under the curve. We used XLSTAT software® (Addinsoft) to perform statistical analysis. Results were considered statistically significant when they presented a P value less than 0. 05. All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Yield and yield-contributing characters Harvesting was done three times because around 80% of pods in the high-yielding varieties matured at the same time, whereas pods in the local variety matured at different times (nonsynchronous). At each harvest, the average number of pod plant -1 was estimated by separating and counting all pods from 10 sample plants. The average pod length and number of seed pod -1 were recorded after harvesting from 10 randomly selected pods. One thousand seeds were obtained from the pods of 10 selected plants of each plot and weight was taken. The data on seed yield was converted into kg ha -1 for each plot.
Statistical analysis To know the effects of 11 selected mung bean varieties (i. e. , the independent or fixed variables) on the incidence of the two thrips species, the number and percentage of damage of the thrips on different plant parts, and the yield contributing characteristics were used as dependent variables. The outcome of each dependent variable for each thrips species was analyzed using one-way analysis of variance followed by Duncan's Multiple Range Test (DMRT) to measure the differences between means for the selected varieties, at a 5% level of probability. Using a similar statistical procedure, the differences among the biophysical and biochemical properties of the 11 mung bean varieties were analyzed. All the analyses were completed in statistical software IBM SPSS 26. The correlations of the measured biophysical and biochemical properties with the incidence of the thrips on leaves of the mung bean varieties were also analyzed using Microsoft excel.
CLIP, a vision-language network trained with a multimodal contrastive learning objective on a large dataset of images and captions, has demonstrated impressive zero-shot ability in various tasks. However, recent work showed that in comparison to unimodal (visual) networks, CLIP's multimodal training does not benefit generalization (e. g. few-shot or transfer learning) for standard visual classification tasks such as object, street numbers or animal recognition. Here, we hypothesize that CLIP's improved unimodal generalization abilities may be most prominent in domains that involve human-centric concepts (cultural, social, aesthetic, affective. ); this is because CLIP's training dataset is mainly composed of image annotations made by humans for other humans. To evaluate this, we use 3 tasks that require judging human-centric concepts: sentiment analysis on tweets, genre classification on books or movies.
We introduce and publicly release a new multimodal dataset for movie genre classification. We compare CLIP's visual stream against two visually trained networks and CLIP's textual stream against two linguistically trained networks, as well as multimodal combinations of these networks. We show that CLIP generally outperforms other networks, whether using one or two modalities. We conclude that CLIP's multimodal training is beneficial for both unimodal and multimodal tasks that require classification of human-centric concepts.
Introduction Vision-language pretraining in neural networks is gaining popularity due to the growing interest in multimodal tasks such a Visual Question Answering or Image Captioning  (Anderson et al. , 2017; Lu et al. , 2019; Li et al. , 2019; Singh et al. , 2019) , but also to the availability of online resources that allow to build large-scale training datasets without manual annotations  (Radford et al. , 2021; Jia et al. , 2021). In theory, training a model on multimodal data should help improve its representation of data from each of the modalities. For an image-text model, for instance, the image features could be enriched by the abstraction of the linguistic data -the semantic grounding property, and inversely, the linguistic features could gain informativeness through visual grounding  (Harnad, 1990).
Unfortunately, this does not always happen in practice. Recently,  Devillers et al. (2021)  evaluated the visual generalization abilities of CLIP  (Radford et al. , 2021) , a popular network trained with a contrastive learning objective on more than 400M image-caption pairs scraped from the web, and other multimodal models  (Sariyildiz et al. , 2020; Desai and Johnson, 2020). They showed that for standard object classification tasks (e. g. digit, fashion item or natural image classification), multimodal networks like CLIP underperformed compared to other unimodal (vision-only) models like BiT-M  (Kolesnikov et al. , 2019)  in transfer learning, few-shot learning and unsupervised learning settings. Here, we revisit this question using datasets focusing on more "human-centric" concepts. Human learning generally involves interacting with multimodal data.
Thus, one could expect that CLIP's representations of images and text should be somewhat closer to human representations than those learned by unimodal models. Moreover, given that CLIP was trained on image-caption pairs from a variety of sources from the Internet (including social networks), we can assume that an important part of its training captions was written by humans for other humans. This is different from standard vision datasets, in which labels or annotations are sometimes human-generated (e. g. through Amazon's Mechanical Turk), but always produced for machine-learning purposes. Again, this difference should bring CLIP's representations closer to human ones when compared to unimodal models. Thus, there should exist at least some specific tasks for which CLIP's multimodal training provides advantages over unimodal models.
All tasks can be performed based on visual data (images), text data (tweet, book or movie title, movie plot summary), or both. For the movie genre classification, we introduce a new, large-scale multimodal dataset obtained by a crawling on The Movie Database (TMDb). As detailed below, we find that CLIP outperforms unimodal models in both vision and text-based classification, as well as pairwise combinations of these unimodal models in the case of multimodal (image+text) classification. Consequently, CLIP establishes a new SOTA on these tasks. We provide our code for reproducibility 1.
For BiT-M and RN50, we use the last layer output before the classification head used for their training, which counts 2048 dimensions. For CLIP, we use the latent vector in the multimodal space generated by the visual pipeline, counting 1024 dimensions; for CLIP-T, the one generated by the textual pipeline (1024 dimensions); and for the two Bert models, we use the vectors directly provided by the Sentence Transformer pipelines (1024dimensional for Bert-large and 768-dimensional for Bert-small). Figure  1 : An original cover from the Book Cover dataset (left) and the associated masked cover (right). The title, the name of the author and parts of the text have been blacked out by the EAST algorithm, while the white text was incompletely detected, but subsequently blurred by the second algorithm.
MVSA MVSA or "Multi-View Sentiment Analysis"  (Niu et al. , 2016)  is a dataset of pairs of images and associated text from Twitter, labelled with three possible sentiments (Positive, Neutral or Negative). Each image and each piece of text has three labels given by three different users, adding up to 6 labels for each image-text pair. We assign a score for each label (Positive: 2, Neutral: 1, Negative: 0) and we compute the rounded average score for each pair. By doing so, we get only one label per image-text pair that we can then use for single-label classification across modalities.
Book covers The Book Covers dataset was introduced by Iwana et al. (2017). It consists of 57k images of book covers scraped from the Amazon website, with their title as text information. Each pair of cover+title is labelled with one genre among 30 possibilities. A cleaner version of the dataset, removing one genre and grouping two similar ones, with only 28 classes and 55. 1k images, was later introduced by  Lucieri et al. (2020). This is the dataset we use for our experiments.
Plotster and TMDb We introduce and publicly release the Plotster dataset 2  , obtained by crawling TMDb (www. themoviedb. org) using their provided API. It consists of 207,902 triplets of {poster, title, plot} (split in 189,185 train samples and 18,717 test samples), with each having several potential labels among 19 genres. A representative sample from this dataset is shown in Figure  2. Typically, each movie has between 1 and 6 genres, with an average of 1. 7. Each poster is an RGB image of 900 × 600 pixels (height×width). Plots have an average length of 310. 8 characters, and titles an average length of 18. 6 characters. For text input, in unimodal or multimodal settings, we can choose either plot or title. The results of both configurations were computed and are displayed in this paper.
A previous crawling on TMDb had been made by  Mangolin et al. (2020). It contained only 10,594 movies, as the authors aimed to retrieve other pieces of data such as trailer video clips and subtitles. They had not included titles in their dataset. From these movies, 10,554 (i. e. , 99. 6%) can also be found in Plotster. For comparison, we isolated the posters and plots from this dataset, and verified that our results obtained on the full Plotster were still valid on this subset. In another control experiment, we verified that CLIP's improved performance on the Plotster dataset was not a result of specific movie posters, plots and titles from TMDb having been included in CLIP's training (as the training set is not public, there is no direct way to determine this). For our control experiment, we crawled TMDb again, looking for movies with a release date later than January 5, 2021, date of the OpenAI blog post introducing CLIP.
Masking CLIP has been found to have an ability to "read" text inside images  (Goh et al. , 2021). As most of the images in the Book Cover dataset and in Plotster have text on them, and as this text could be informative about the genre of the book or movie, we worried that this ability could give CLIP an unfair advantage over other vision models. To minimize this possibility, we created alternative versions of these two datasets by applying a masking procedure on the images (see Figure  1 ). We used the EAST algorithm  (Zhou et al. , 2017)  to generate bounding boxes around text; if the score given to a text detection reached a certain threshold, a black rectangle was applied over the corresponding bounding box. On top of that, a second algorithm detects the remaining small white text using a thresholding method, a saturation filter and a size filter, and then does a Telea inpainting  (Telea, 2004)  to remove it.