dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Work ongoing in the  Gram Hansen lab  is supported by a  University of Edinburgh Chancellor 's Fellowship as well as by  Worldwide Cancer Research and LifeArc-CSO. Additional funding has been obtained from the  Bone Cancer Research Trust (BCRT) ,  Sarcoma U. K. ( SUK202. 2016 ), the  Wellcome Trust-University of Edinburgh Institutional Strategic Support Fund  ( ISSF3 ) and the  Jonathan Haw Fund/ Kinross Trust. J. P. is funded by a  MRC Precision Medicine DTP Studentship. Figures were made using BioRender.
System and application in precision agriculture K. P. Ferentinos et al. , 2018 present a disease detection technique based on a conventional CNN neural network. This proposed technique is based on the recognition of plant leaves in order to classify the plant. This paper's evaluation of the proposed algorithm is based on a GPU system type Nvidia GTX1080 with a frequency of 1607 Mhz. The programming language used is CUDA, with an accuracy of classification that reaches 99. 53%. The paper's authors were based on several models AlexNet, GoogLeNet, VGG, AlexNetOWTBn, Overfeat. The evaluation of accuracy has shown that the VGG model is more accurate than the other models. The database used in this work contains almost 88,000 images separated into infected sheets and breasts. This database includes 58 classes; each image contains a sheet with their diseases; in the same database, we also have unaffected sheets.
The authors have separated this database into two types, one for the validation of the algorithm and the other for the algorithm's training. The first part of the tests contains 17,600 images and the rest for the training. After evaluating this database, they found that an image's processing takes 2 ms, which implies that the real-time constraint is largely respected. But the weak point of this evaluation is the lack of detail on the implementation part, which makes the optimization of the proposed approach difficult. However, the main objective of developing this type of application is the boarding in embedded systems to have autonomous processing  [16]. W. Yang et al. , 2019 proposed a system for monitoring cold damage in maize fields. The authors used a conventional neural network to extract information from images with visible and near-infrared bands on a range of spectral bands between 450 and 885 mm.
After collecting the images, they used filtering techniques such as the Savitzky-Golay filter to eliminate noise when taking the database. The algorithm proposed in this study is divided into five parts. The first step consisted of collecting the database with a hyperspectral camera type PIKA II, which has a spectral band between 395 and 885 mm, a frequency of 34 Hz, and an integration time of 6. 5 ms. The second part is based on plants' sampling; in this part, they divided the plants into 2 parts, one containing control plants and the other cold. The hyperspectral camera used to collect ten images containing 6 plants divided into 3 × 3. The third step is the calibration of the collected images to eliminate darkness. The fourth step is to apply the deep learning model to train the database collected.
The last step for data processing; this step is based on ENVI version 4. 8 and Matlab 7. 1. first, it takes the collected data and selects the region of interest, and then it takes 3600 pixels in each image to apply the CNN model. The paper's strong point is the application of deep learning techniques to increase the yield of agricultural fields. Still, no embedded implementation has been processed except for the software used, which implies that the proposed algorithm's portability to a real case in an embedded system cannot be achieved  [17]. The algorithm proposed in this study is divided into 4 steps; the first one consists of collecting the database based on a UAV with a time of flight of 15 min. The second part is based on the division of fields with several data depending on the UAV flight alteration, in order to have several patches of agricultural fields.
After evaluating the algorithm, the results showed an F1 score of 0. 91 for two sets of images. The interpretation of the results is made through software developed by the authors (LabelStoma) to facilitate the use of the chosen model. The authors' work is divided into two parts: the biological part and the other for the calculation part. In the biological part, they used images of different species collected in 3 different laboratories. The plants used in this work have been submitted on several environmental conditions. Generally, the data used are divided into a common bean, barley, and sobean with image resolution 1280×960, 1728×1296, 1600×1200 respectively. After the database construction, they used an Nvidia RTX 2080 TI GPU card with a frequency of 1545 MHz and Turing architecture for model training with 86% of the images and the rest for testing.
The paper showed that deep learning techniques could be used without manual diagnostics constraints, and the model used also showed great efficiency. However, the paper was based on only one model, limiting the performance of using this technique on several models to make a comparative study  [19]. The database used in this paper is divided into 4 collections with two cameras, a GoPro HERO5 with a resolution of 2704×1520. the GoPro Hero5 camera has a speed between 30 and 240 fps depending on the resolution used. The second camera used is a Canon EOS 60D with a 5184×3456 resolution; this camera was used to collect the apple flowers. On the other hand, the GoPro camera for peach and Pear. Figure  2  shows the tools used and the images obtained  [20]. Fig. 2. System tools Overview  [20]. The algorithm's evaluation was based on an Intel Xeon E5-2620 CPU system with a frequency of 2.
On the other hand, the processing time problem is summarized on the real-time constraint on the camera's time-lapse; for example, a frequency of 30 frames/s cannot be processed. The work of Y. LI et al. 2019 tried to build a model that would merge the density estimation and convolutional neural network (CNN) based approaches to estimate precisely the seed count from an isolated soybean pod image from a unique viewpoint. The proposed technique is based on a new large-scale seed counting database known as the Soybean-pod. The evaluated database contains 500 labeled pod frames with a total of 32,126 seeds, which is the most extensive annotated data set for soybean counts to date. At the same time, they also used the annotation data to simultaneously produce a field truth density map by convolving a Gaussian kernel.
Y. Pang, et al, 2020 proposed a new MaxArea mask method to segment agricultural fields. The work presents an algorithm that allows the improvisation of crop lines in maize fields based on UAV images. The proposed algorithm was evaluated in a 25-hectare agricultural field with an image resolution of 25 mm. The robustness of the algorithm was tested on a database divided in two different periods of the year with an accuracy of 95. 8%. The model proposed in this study was tested on an Nvidia Titan Xp GPU card; this GPU has a frequency of 1583 Mhz and a Cuda core number 384.
However, for this study, we note that each behavioral phenotype was equally represented by the MAGs (HF: n = 22, LF: n = 23) (Figure  S4 , Supporting Information). The genomic database revealed that two MAGs were affiliated to Archaea, while all remaining MAGs (n = 192) were affiliated to known bacterial orders, including 40% identified at the species level (average nucleotide identity >95%). We identified 15 bacteria phyla; the most frequent MAGs being Firmicutes (n = 94 MAGs), followed by Bacteroidetes (n = 57), Actinobacteriota (n = 14), Proteobacteria (n = 13), Patescibacteria (n = 6), Tenericutes (n = 4), Cyanobacteria (n = 1), Fusobacteria (n = 1), Deferribacteres (n = 1), Spirochaetes (n = 1), Synergistetes (n = 1) and Verrucomicrobia (n = 1) (Figure  3A ). Complete taxonomic assignments using the Genome Taxonomy Database Toolkit (GTDB-Tk)  [27]  are found in Table  S2b  (Supporting Information).
The numbers in parentheses correspond to the frequency of the GBM found in all MAGs and values in bold were considered rare (present in <5% of the MAGs). Pathways and functions were annotated for each GBM along the bottom x-axis. and previously used to validate the GBM framework and B) red junglefowl MAGs (n = 194) identified from fecal samples in the current study. Rare (<5% of genomes) and ubiquitous GBMs (>90% genomes) are highlighted, while others are in gray. GBMs found exclusively in red junglefowl MAGs are in bold. Figure modified from Valles-Colomer et al. [24]  C) Heatmap of the log 10 frequency of 41 GBMs detected in 45 fecal metagenomes of red junglefowl selected for high and low fear toward humans. Clustering of GBMs and metagenomes is based on GBM frequency (Euclidean distance and Ward linkage) and the data were visualized using anvi'o.
Illumina adapter and primer sequences were removed from the 16S metabarcoding sequence data using cutadapt v. 2. 6  [91]  and subsequently analyzed using the program DADA2 v. 1. 12. 1  [92]  and R v. 3. 6. 1  [93]  to infer amplicon sequence variants (ASVs). Complete code was modified from ref. [94]. Briefly, forward and reverse reads were trimmed to 230 bp. The entire dataset was used to define an error rate at each base pair, and all sequences were denoised using the pooled approach to increase the likelihood of resolving rare sequence variants. Forward and reverse reads were merged, and any pair without perfect overlap and <400 bp was removed prior to chimeric sequence filtering. Each ASV was annotated with the RDP Bayesian classifier  [95]  against the SILVA database  [96]  to produce a 16S amplicon taxa table.
[100] For all other samples (n = 45) metagenomic data were prepared using the BEST single-tube library preparation protocol  [101]  as optimized to be BGISEQ-500 compatible. [100] Briefly, genomic DNA was fragmented to 350 bp using an M220 Focused Ultrasonicator (Covaris, Woburn, MA). Sheared DNA was converted into BGISEQ-500 libraries following four steps: blunt end-repair, adapter ligation (2 L of 10 × 10 -6 m BGI 2. 0 adapters), fill-in reaction, and SPRI magnetic bead purification (Sigma-Aldrich). Indexing PCR cycle numbers for all metagenomic libraries (4-14 cycles) were determined through qPCR library quantification. Libraries were pooled equimolar over six lanes in 100bp or 150 bp pairedend mode on the BGISeq-500 platform aiming for a minimum of 50 million reads per sample. Assembly and Genome-Resolved Metagenomics: Prior to sequence assembly, all paired-end reads were demultiplexed and quality filtered.
0. 7. 15 with default parameters. [103] amtools v. 1. 9  [106] was used to sort and index the output SAM files into BAM files. BAM files were used to generate a contigs depth of coverage table with jgi_summarize_bam_contig_depths (MetaBAT2 v. 2. 12. 1). [107] We then applied the automatic binning algorithm in CONCOCT  [108]  on this coverage table to generate 10 large contig clusters to maximize explained patterns while minimizing fragmentation error, as performed elsewhere. [109, 110] Subsequently, a manual binning and curation were performed for each CONCOCT cluster following the contigs workflow implemented in anvi'o v. 6. 2. [25] Briefly, anvi'o was used to generate a contigs database that identified open reading frames using Prodigal v. 2. 6. 3  [111]  and single-copy core genes using HMMER v.
3. 2. 1  [112]  against the collection of built-in HMM profiles for bacteria and archaea. Gene-level taxonomy was classified using Kaiju v. 1. 5. 0,  [113]  with NCBI's nonredundant protein database, including fungi and microbial eukaryotes, and genes were further annotated with functions using the NCBI's Clusters of Orthologous Groups (COG). [114] Anvi'o was then used to profile each metagenomic BAM file to estimate the coverage and detection statistics of contigs in the contigs database and combined mapping profiles into a merged profile database for all individuals. In addition, one imported an anvi'o collection corresponding to the 10 CONCOCT clusters. Finally, each CONCOCT cluster was manually binned and further refined using the anvi'o interactive interface  [115]  taking into account sequence composition, differential coverage, GC content, and taxonomic signal of the considered contigs.
MAGs with completeness >50% and redundancy <10% were retained for downstream analyses  [116]  (Genomic features of the MAGs can be found in Table  S2b  in the Supporting Information). The taxonomy of the final list of MAGs was inferred using the Genome Taxonomy Database Toolkit (GTDB-Tk)  [27]  version 95. However, NCBI taxonomy was used from the GTDB output to describe the phylum of MAGs in the results and discussion sections, in order to be in line with the literature. MAGs were considered to be detected in a given sample when >50% of their length was covered by reads to minimize nonspecific read recruitments. [110] The number of recruited reads below this cutoff was set to 0 before determining vertical coverage, the number of bases covering each genome divided by its length. Gut-Brain Module (GBM) Detection: The red junglefowl shotgun metagenomic data were translated into neuroactive potential using a previously described module-based reconstruction framework.
[24] Briefly, one searched for the presence of 56 gut-brain modules (GBMs), each corresponding to a process of synthesis or degradation of a neuroactive compound by the gut microbiota, in each of the red junglefowl MAGs (n = 192). As module structure follows the Kyoto Encyclopedia of Genes and Genomes (KEGG) database syntax, gene calls for each MAG were exported from the contig database within anvi'o and functionally annotated with KEGG identifiers using GhostKoala. [117] GBM coverage was calculated as the number of pathway steps for which at least one of the orthologous groups is found in a genome, divided by the total number of steps constituting the GBM using Omixer-RPM v. 0. 3. 2 (https://github. com/raeslab/ omixer-rpm). GBM presence in microbial MAGs was defined with a detection threshold of at least 66% coverage, to provide tolerance to miss annotations and missing data in incomplete genomes.
[24] GBM detection was visualized with corrplot v. 0. 84  [118]  in the four differentially abundant or discriminant red junglefowl MAGs to identify over/underrepresented metabolic GBMs between the two behavioral selection lines. Differential Abundance Estimates: Expected relative abundance of microbial taxa was modeled directly from read counts for 16S and shotgun sequence data at different taxonomic levels (phylum, class, order, family, genus, and ASVs) using a beta-binomial model controlling for collection year and controlling for the effect of selection and collection year on dispersion. The model was fit using corncob v. 0. 1. 0,  [119] an rbased package designed specifically for marker gene compositional data, which uses sophisticated models to account for sequencing depth and rare taxa in high-dimensional data and estimates abundance with uncertainties to support hypothesis testing between selection lines.
Acknowledgements The authors thank  Jacob Agerbo Rasmussen  for his assistance in laboratory work and  Antton Alberdi  for his thoughtful guidance in data processing. Photo credit of red junglefowl image for the table of contents to P. J. The research was funded by The  Danish National Research Foundation  (grant no. DNRF143  to M. T. P. G. ) and the  Swedish Research Council (VR)  (grant no  2015-05444  to P. J. ).
Study site and population monitoring The study was conducted on 164 (95 females and 69 males, corresponding to 98 nests) Collared Flycatchers, breeding on the island of Gotland, Sweden (57° 07′ N, 18° 20′ E) between April and July 2015. This patchy population of small, migratory hole-nesting passerine birds has been monitored each year since 1980. Flycatchers readily accept to breed in artificial nest boxes, providing an easy access to recording breeding data and behaviour at the nest  (Doligez et al. 2004 ). Collared Flycatchers display biparental care and feed on average 3 to 5 young from late May to early July during 16 days until fledging. Nests were visited at least every four days to collect data on laying and hatching dates, clutch and brood size (at day 8, 2-3 days after provisioning videos), and final breeding success.
When chicks were 8 to 14 days old, adults were caught and identified using individually numbered rings. They were sexed (based on plumage sexual dimorphism (i. e. males are black and white while females are brownish) and aged (yearling vs. 2 years old or older, based on plumage characteristics, Svensson 1992), and measured (tarsus length, to the nearest 0. 1 mm, and body mass, to the nearest 0. 1 g). Body condition was computed as the body mass divided by tarsus length  (Doligez et al. 2004). Data on daily temperature were obtained from the weather centre 02679 in Hoburgen, Gotland (56° 55′ N, 18° 09′ E; 36 m), which is located around 20 km from our field site. We used the mean daily temperature in Celsius degrees.
Food provisioning Food provisioning behaviour was recorded during 1h20 between 06:00 and 13:00 (previous study in the same population showed no variation in the provisioning rate using the same temporal window;  Sheldon et al. 1997)  when nestlings were 5-6 days old, i. e. during the peak of daily parental activity and nestling growth  (Krist et al. 2004; Rosivall et al. 2009 ). An IR camera was installed in the nest box on the day before the recording to let birds habituate to the device. Videos to be analysed were randomly attributed between two observers (EIM and RL) blind to the individual and environmental variables subsequently used in statistical models. Using the motion-detection software programme Motion Meerkat  (Weinstein 2015) , we recorded for each parental visit the following parameters: (1) entrance and exit time, (2) number of preys delivered, (3) prey categories divided into 4 groups: caterpillar, flying insect, spider or undetermined, (4) prey size on a scale of 1-4, with 1 for preys smaller than beak length, 2 for preys of about beak length, 3 for preys twice longer than beak length and 4 for preys three times longer than beak length or bigger.
We present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.
-To extract more robust descriptors from salient information in statistical moments, we propose the fused histogram of CLBP α , that is constructed by using WSBP α to obtain enriched features. -A comprehensive evaluation of six public datasets suggests that our proposed framework outperforms the state-of-the-art methods. The paper is organized as follows. Section 2 prepares some background on LBP. Section 3 details the proposed framework. In section 4, we analyze the implementations through several parameter settings for evaluations. Experimental results are interpreted in Section 5. A discussion for our proposed framework is analyzed in Section 6, and the last one consists of our conclusion and future works.
For example, Fig. 6e  shows the new variance moment (μ 2 ), built by extracting root 9-th from the original one. With this method, more useful facial features, such as eye, nose, and mouth, can be enhanced as salient regions. Thus, the weighted histogram can enrich the essential areas by exploiting the contribution of every statistical pattern in the variance image. In the next section, we show how μ 2 = 9 √ μ 2 under the B = {(1, 6)} for structuring element makes a huge difference through a series of experiments with six public face datasets.
Experiments This section describes experiments with six face datasets, such as ORL, YALE, AR, Caltech, FERET, and KDEF. Our statistical feature descriptors were processed with the algorithm mentioned above. Below features, that were the concatenation of 4 directions in exploiting CLBP The fusion of different directions and components (S, M, m 1 , μ 2 ) would lead to a very long descriptor as the concatenation of histograms. To handle this problem, the Principal Component Analysis (PCA) with the percentange of cumulative sum of eigenvalues of 95%, was adopted for the dimension reduction purpose. For the classification task, the Linear SVMs were utilized.
Databases and experimental protocols The ORL dataset 1 had 40 subjects and 10 different grayscale images with a size of 92 × 112 were collected from each subject. All ORL images were collected under various conditions such as facial expression, illumination changes, occlusion (sun glasses), see Fig. 7. The YALE Face dataset 2 included 165 images from 15 individuals and 11 different images with the size of 243 × 320 were collected from each subject. The dataset had various expressions and lighting conditions, see Fig. 8. The Caltech 1999 dataset 3 produced by California Institute of Technology had 447 images from 26 persons, yet the number of images for each person was different and collected under the unconstrained background. The dataset had various conditions such as different expression, illuminations and occlusions, see Fig.
9. The KDEF dataset 4 [3] is a set of 4900 photographs of facial expressions. The set had 70 persons (35 males and 35 females) displaying seven facial expressions under five 1 https://cam-orl. co. uk/facedatabase. html 2 http://vision. ucsd. edu/content/yale-face-database 3 http://www. vision. caltech. edu/html-files/archive. html 4 https://www. kdef. se/index. html Fig. 7  The ORL dataset samples which are decomposed into pairs of moment images (mean m 1 , variance μ 2 ) for each two-row pair different viewing angles. For the present evaluation, the frontal view was considered for each facial expression. This subset contained 490 color images for 70 individuals as shown in Fig. 10  where each subject expressed seven different emotions. The AR dataset 5 [31] had 3016 face images for 116 persons (63 men and 53 women), each having 26 color images (768 × 576) under severe illumination conditions (left-light, right-light or all sidelights), 7 basic emotions (happy, sad, neutral, sleepy, anger, surprised and wink), head poses, and occlusion (sun glasses and bangs).
Results with the ORL and YALE datasets For the ORL dataset, N train training images per subject were randomly selected (N train = 2, 4, 5, 8) while the remaining (10 -N train ) images were used for testing. For 5 https://www2. ece. ohio-state. edu/ ∼ aleix/ARdatabase. html 6 http://www. itl. nist. gov/iad/humanid/feret/feret master. html the YALE dataset, N train training images per subject were randomly selected (N train = 2, 4, 6, 8) and the remaining (11 -N train ) images were carried out for testing. The measurements were repeated 100 times by shuffling data process. Results were shown in Tables  1  and 2  for the average classification rate. Tables  1  and 2  summarised our experimental results under the various configurations. First, the CLBP S(m 1 ) and CLBP M(m 1 ), the WSBP S and WSBP M produced similar recognition results.