dataesr/scientific-paragraphs-categorization huggingface dataset:
BLASTP-based strategy to predict novel key prokaryotic genes and species involved in mammalian-microbial co-metabolism Next, we used a BLASTP-based strategy to augment the identification of bacterial species involved in indole production reported in KEGG. This approach allowed the identification of 2,626 novel prokaryotic species potentially implicated in indole metabolism (+63. 4 % compared to KEGG) (Figure  2a ). For individual reactions, the increases in the number of prokaryotic species ranged from 7 ("K11816") to 1,380 ("K00128") (Figure  2b ). Similarly, we confirmed that human-specific reactions reported in KEGG were not performed by prokaryotes according to BLASTP searches. We then applied a filtering strategy to select prokaryotic species present in the human gut (Figure  2c-d ). For this purpose, we constructed a database including gut prokaryote species described in HMP and cultivation studies (Rajilic-Stojanovic and de Vos 2014,  Browne, Forster et al.
2016 , Lagier, Khelaifia et al. 2016) , comprising 1,859 organisms from 1,604 species. In the gut-filtered dataset, BLASTP searches predicted a total of 209 novel prokaryotic species (Figure  2c ). While some reactions are performed by a reduced number of species (e. g. "K01593", n = 8 and "K01721", n = 15; corresponding respectively to 0. 8 % and 1. 7 % of the gut prokaryotic dataset), others (e. g. "K00128") are predicted to be performed by 16. 9 % of the gut prokaryotes (n = 254) (Figure  2d  and Table  1 ). No gut prokaryotic species were predicted to be able to catalyse the conversion of indolepyruvate into indole-3-acetate.
Supplementary table 1). The production of indole-3-acetate exhibits considerable metabolic complexity, occurring through three metabolic paths: via indole-3-acetamide (path-1, exclusively bacterial), via indole-3-pyruvate (path-2) or via tryptamine (both microbial and mammalian co-metabolism) Microbial reactions, genes and number of species involved in the production involved in the production of indolic compounds from tryptophan, reported in the KEGG orthology database and newly identified by BLASTP searches (+BLASTP).
The vulnerability of most existing face recognition and authentication systems against face presentation attacks (a. k. a. face spoofing attacks) has been mentioned and studied in many works. This paper introduces a novel parametric approach for face PAD using a statistical model of image noise. In fact, facial images from a presentation attack contain specific textural information caused by the presentation process which makes them different from bona-fide images. The subtle difference between bona-fide and presentation attack images can be interpreted by the difference regarding noise statistics within the skin zone of the face. Our solution is casted in the hypothesis testing framework. A new database for face PAD containing face bona-fide images and images of high-quality presentation attacks has been also introduced.
A. CONTRIBUTIONS OF THE PAPER The significant contributions of the present work are: • We introduce a recent stat-of-the-art of the face PAD problems. • We introduce a statistical noise model for facial images, which can highlight the difference between bona-fide images and images taken from presentation attacks. • The proposed noise model is then exploited by casting in the framework of hypothesis testing to construct a new face presentation attack detector. • We introduce a new database, titled as DF-FPAD (Digital Forensic -Face Presentation Attack Detection), containing high quality images of high quality face presentation attacks, acquired in controlled conditions. Images from this new database contain more information than ones from the other available public databases, such as Replay-Attack Database  [1] , CASIA Face Anti-Spoofing Database  [2] , and MSU Mobile Face Spoofing Database  [3] , NUAA Imposter Face Database  [4] , where the images or videos are generally in low resolution, and the information about the acquisition conditions and the quality of attacks realization is not clear.
The authors in  [4] ,  [42]  proposed to study these differences in the frequency domain. Some authors proposed to analyze in spatial domain by using a local texture descriptors, such as Local Binary Pattern (LBP)  [43] -  [45] , Local Graph Structure (LGS)  [46] ,  [47]. Different from the other authors who study only the gray image,  [7]  proposed to exploit the texture-information in color spaces. These approaches usually employ a non-parametric classification solution such as Support Vector Machines (SVM) to help them classify images. Presentation attacks' images are needed for training the classification system. The classification performance depends therefore significantly on the quality and the size of the training dataset. It may be failed when the trained classifition system is used to predict a presentation attack which does not present in the training database.
B. EXPERIMENTAL DATASET As mentioned in Section I, there are many public datasets for face presentation attack detection such as Replay-Attack Database  [1] , CASIA Face Anti-Spoofing Database  [2] , MSU Mobile Face Spoofing Database  [3] , NUAA Imposter Face Database  [4]. However, images or videos from these databases were taken in variable conditions. In general, the quality of image acquisition process is too low. Actually, for images of these databases, textural noise is often negligible before the acquisition noise. In the proposed method, images have to be captured in the same acquisition conditions, which actually cannot be assured for existing public datasets mentioned above. A new database has been also introduced to validate our proposed approach. The database is constructed by respecting the following conditions: • Each camera model has their own fingerprint  [53].
Images coming from different camera models cannot be compared in the proposed approach. • ISO value characterizes the sensitivity of the image sensor. The higher ISO value is, the more sensitive the camera is to light and the more significant the acquisition noise is. When acquisition noise is too important, impacts caused by textural noise become less significant, the performance of the proposed solution decreases. Therefore, it is proposed to keep ISO unchanged between acquisitions and not too high (400 maximum). • Image-resolution is also an important factor in the proposed approach. When the image resolution is too small, the skin-texture will not be able to modeled correctly. In this work, it is proposed to focus only on high resolution image. In fact, the databased is created by using high sensor-resolution cameras.
Different datasets have been constructed: • S6: contained RAW and 3000 × 5328 JPEG images captured at ISO 200 by a Samsung Galaxy S6, using application OpenCamera. It contains 179 samples of genuine face, 161 samples of Print-Attack and 158 samples of LCD-Attack. • Nikon: contained 4000 × 6000 JPEG images captured at ISO 400 by a Nikon D5200 with the option Noise Reduction deactivated. The detail description of this dataset is given in the table 1.
1) RAW IMAGES Demosaicing by interpolation was applied into RAW images (by DCRAW) before these were converted into RGB image in TIFF format. The NLV features were computed from one of the three channels of the TIFF image. In this work, it is proposed to choose the green channel, which contains in most of the cases more information than the two other ones due to the configuration of the Color Filter Array (CFA). Figure  6  show a typical scatter plot of gamma distribution parameters, (β -1 , α), estimated from images of the S6 dataset. The discrimination between H 0 and H 1 images is visually clear, as well as, the linear relation between β -1 and α can be confirmed graphically. Figure  7  shows the Detection Error Tradeoff (DET) curves of the test with different values of M when we try to classify   RAW images of the S6 dataset.
The BPCER rates obtained for different values of M and for a fixed value of APCER (1%) are given in the table 2. 2) SIMULATED JPEG IMAGES Simulated JPEG image was developed from a natural RAW image by letting it go through different principal steps of the digital image processing pipeline: • Demosaicing • White balancing • Gamma Correction (γ = 2. 2) • JPEG Compression (Quality factor 95%) A simulated JPEG image database was constructed from RAW images of the S6 dataset. The performance of the proposed test for this database is given in figure  8  and the table  2. The performance of the test increase with the value of M. The test performed even better than in the case of RAW images. The discrimination between H 0 and H 1 simulated JPEG images may be amplified when the RAW images pass through the mentioned above operations.
3) NATURAL JPEG IMAGES Eventually, JPEG images, captured by the phone standard camera application, are denoised to obtain a high visual quality (less noise). This denoising operation often happened just after the white balancing and just before the gamma correction operation in the digital image processing pipeline. Each phone's constructor has their private implementation for this denoising. This denoising operation unfortunately destroys the favorable high-frequency information exploited in our proposed method. Consequently, for the classification of JPEG images of the S6 dataset, the proposed approach performed badly. However, this denoising operation can technically desactivated. For recent Android smartphones, including our Samsung Galaxy S6, by using the Camera2 API, it is possible to build an application which gives us the possibility to configure manually all camera settings and apparently including the implementation of the mentioned denoising operation.
For a digital camera, such as Nikon D5200, we have options to deactivate implemented denoising operations. Images from the Nikon dataset were taken with these options deactivated. Figure  9  shows the performance of the proposed test for the classification of these images. Different values of M have been used. The performance of proposed approach has also been compared with the reference methods proposed in  [7] ,  [39] ,  [42] ,  [43]  on the Nikon dataset. In our approach, only bonafide images are needed to estimate the parameters of the test. The first set of bona-fide face images is employed for this estimation. For the reference approaches, which are non-parametric classification solutions, presentation attack images are needed to train the SVM models. In the Nikon dataset, attack images were divided into three subsets basing on the type of attack: Print-Attack, HDPrint-Attack, and Projection-Attack.
By definition, τ EER is the threshold which permits to have an Equal Error Rate (EER), which means APCER=BPCER=EER, when the method is evaluated with the training dataset. The Table  3  shows the results obtained for different methods when different configurations of the training dataset is proposed. Our proposed solution performs steadily and, for some configurations of the training dataset, it performs better than the other ones. Galbally et al. 's solution  [39]  performs in general better than ours. However, by considering the proposed statistics Q H (Equation  30 ) as an Image Quality Assesement, it is proposed to add Q H as an additional mesure into the set of 14 mesures proposed in  [39]. The presence of Q H as descriptor improves considerably the classification performance, see Figures  10  and 11   Once a type of attack is mentioned as known, the SVM models of reference approach have been trained with haft of images from this type of attack.
VI. CONCLUSION In this article, we proposed a new way to approach the problem of face presentation attack detection. Textural noise, which behaves differently between images of bona-fide faces and presentation attacks' ones, was taken into account in an existing heteroscedastic noise model. A model of local noise variance was then introduced, which permitted us to construct a statistical test to detect face presentation attacks. Only bonafide images are needed to estimate the parameters of the test. We introduced also a new database of face presentation attacks to validate our solution. When a lack of knowledge of some types of attack is considered, our proposed solution performed better than some existing solutions mentioned in the reference. The statistic introduced in the proposed solution permits also improve the performance of an existing face PAD solution.
ground in tropical environments. Féret and Asner (2014)  developed a method meeting these requirements and validated its performances for tropical forest diversity mapping based on very high spatial resolution airborne imaging spectroscopy. Sentinel-2 data now provides global coverage of Earth surface with a revisit time of five days and high spatial resolution multispectral imagery. The package biodi-vMapR is an adaptation of the method proposed by  Féret and Asner (2014)  to Sentinel-2 data, and aims at providing operational tools to ecologists and remote-sensing communities in order to accelerate and ease their capacity to assess existing methodologies based on abundant remotely sensed data. Here biodivMapR provides a limited set of functions to process large datasets, from high dimensionality imaging spectroscopy acquired by airborne sensors or satellites, to high spatial coverage multispectral satellites such as Sentinel-2 images.
The method is based on the spectral variation hypothesis  (Palmer, Earls, Hoagland, White, & Wohlgemuth, 2002; Rocchini et al. , 2010; Rocchini, Chiarucci, & Loiselle, 2004) , and takes advantage of high spatial resolution multispectral information to differentiate species or groups of species based on the optical traits corresponding to the reflectance of each pixel  (Homolová, Malenovský, Clevers, García-Santos, & Schaepman, 2013; Ustin & Gamon, 2010). However, it may be applicable for other types of data than optical data, as long as the hypothesis of spatial heterogeneity is relevant to estimate specific biodiversity components. The package includes a sample of Sentinel-2 image, and a script illustrating the workflow of the method: the optical image is transformed with principal component analysis (PCA), and a subset of selected components is used to produce spectral species maps based on k-means clustering.
| DE SCRIP TI ON OF THE PACK AG E The workflow of the core processing of the package is presented in Figure  1  and the full pseudo code is proposed in Appendix S1. Preprocessing functions are available in the package and are introduced in the next section. Front-end user interface is limited to core functions, in order to apply this workflow as straightforwardly as possible, and parameterize the different steps of the process flawlessly, even with limited expertise in image processing. Back-end functions are documented within the code, making it easier for a more experienced person to develop upon biodivMapR. We strongly recommend using bottom of atmosphere reflectance instead of top of atmosphere reflectance or radiance.
| Data checking and conversion The current version of the package requires ENVI HDR format with band-order set to BIL ('Bands Interleaved by Line') for all raster data. This decision was initially motivated by computational efficiency, but next versions of the package will accept more raster formats. Please notice that the current version of the code expects spectral bands to be defined in the field 'wavelength' of the header file associated with the binary image. The raster format (image and mask) is checked internally when running the functions perform _ radiometric _ filtering and perform _ PCA. A message is return, stating if the image format is as expected, or if it should be modified. If the image format should be modified, the process will automatically stop, in order to let the user convert the image.
In this case, the function raster2BIL allows conversion to the expected format. However, raster2BIL is based on the raster package  (Hijmans, 2018)  and converts any GDAL format into ENVI HDR format with BIL interleave. It also includes additional options such as conversion F I G U R E 1 Flowchart of the processing performed in biodivMapR to integers to reduce file volume, and definition of the sensor for automated header formatting.
The importance of CR may not be as strong when the spatial resolution is the same order as or larger than the dimension of tree crowns, as within-crown variability is integrated in the pixel reflectance. Users can choose whether to apply CR or not by setting the Continuum _ Removal argument to TRUE or FALSE when calling perform _ PCA. The illustration in the Example section suggests that the application of CR on Sentinel-2 data leads to relevant results, and more tests should be done. Principal component analysis prepares for feature extraction in order to reduce noise in the image. It is performed on a random subset of the image to ensure computational efficiency for large images. First, a random selection of pixels is performed, and PCA is computed based on this subset after CR. Once PCA is defined for the subset, it is then applied on the entire continuum removed image.
This operation can be considered as analogous to signal averaging, increasing signal to noise ratio by replicating measurements. The function map _ spectral _ species performs this clustering and writes the resulting raster file identifying the cluster corresponding to each pixel and each partition: the raster is named SpectralSpecies, with the same dimensions as the original image and nb _ partitions bands, each band corresponding to a map of centroids for a specific partition. Féret and Asner (2014) set nbClusters to 40 clusters, as a trade-off between performances or the estimation of biodiversity and computational efficiency. Here, we set 50 clusters as the default value in the example provided with the package corresponding to tropical forest. Our experience with biodiversity mapping of tropical forests suggests that this is a reasonable default value.
| β-Diversity mapping β-Diversity maps are derived from the pairwise Bray-Curtis dissimilarity (BC) matrix obtained from the spectral species map. In the initial version of the method, the BC matrix was then processed with an ordination method in order to project the n × n dimensional space of the dissimilarity matrix into an n × 3 dimensional space. However, such procedure rapidly becomes unmanageable when processing large datasets. In order to alleviate computational requirements, β-diversity maps are currently produced in two steps. First, ordination aiming at defining the projected 3D space from a dissimilarity matrix is performed on a limited subset of windows. Then, the dissimilarity between each window in the image and the subset of windows used in ordination is computed. Finally, the coordinates of each window in the resulting 3D space are defined based on the coordinates of the three most similar windows used during ordination, using inverse weighted distance.
| E X AMPLE The package includes a 10 × 10 km Sentinel-2 subset with associated plot network shapefiles. The plots were defined based on visual interpretation of the image, therefore values for α-and β-diversity metrics are not provided. Samples were selected over very diverse forest, moderately diverse forest, monodominant forest, and degraded forest/low vegetation close to the main roads (Figure  2 ). The script used to process the data is provided in Appendix S2 of SI. -PCoA#1 allows differentiating medium and high diversity forests from low diversity forest and low vegetation, but does not discriminate medium and high diversity forests. -PCoA#2 allows differentiating low diversity forest from medium/ high diversity forests and low vegetation -PCoA#3 allows differentiating medium diversity forests from high diversity forests and low vegetation.
ExaStoLog toolbox: calculation of solutions, visualization and parameter sensitivity analysis We implemented the above steps of calculating the stationary solution of a logical model in the ExaS-toLog MATLAB toolbox, available on GitHub. The user first needs to input a logical model in BoolN et  (Müssel et al. , 2010)  format using standard logical notation. The generation and topological sorting of the STG and the identification of its cycles are steps independent of the values of the transition rates, therefore these are performed only once for a given model. This is done by the functions fcn build stg table and fcn scc subgraphs. Because the existence of large cycles in the STG is the main limiting factor in the subsequent matrix calculations, fcn scc subgraphs outputs the size and number of cycles in the STG.
Silicon nitride photonic crystal cavity As described in our previous works  [36, 37] , to design the nanobeam resonators, we first simulated the photonic band structure using the MIT Photonic Bands (MPB) software package and then optimized the characteristics of the optical cavity via finite difference time domain (FDTD) calculations with Lumerical FDTD solutions. The calculations here were carried for a SiN (index, n = 2) nanobeam completely surrounded by a material of n = 1. 45 of effectively infinite thickness above and below the nanobeam. The optimized design of the SiN nanobeam resonators consist of elliptical holes with a period, a = 157 nm, major and minor diameters being 202 nm and 100 nm respectively, with 40 such holes forming the Bragg region on each side of the nanobeam. The cavity is formed by linearly tapering both the period and the major axis of the 10 holes approaching the center from each side, as shown schematically in Fig.
Taking the thickness of the PMMA layer to be the length from the surface of the substrate to the top of the PMMA layer, the thickness is expected to be about 400 nm in accordance to the manufacturer data sheet. Figure  2(c)  shows the scanning electron micrograph (SEM) of the PMMA encapsulated nanobeam. Optical experiments on the samples were carried out in a confocal microscopy system at room temperature. For the time-averaged measurements, a continuous wave He-Cd laser excitation at 442 nm was focused (100 × objective lens, NA = 0. 90) onto the sample. The emission spectra were measured with a Horiba HR Evo spectrometer with a focal length of 800 mm together with a 300 lines/mm grating and a liquid nitrogen cooled Si CCD detector. For time-resolved decay measurements, a Ti:sapphire femtosecond-pulsed laser with ~100 fs pulses at 80 MHz was used.
The extracted Q-factors for the spectra increases from ~900 to ~1500 with decreasing perovskite concentration. The experimental Q-factors are smaller than that obtained in the simulations, which we attribute to the fabrication imperfection due to the small nanobeam feature sizes necessary for operation in the 500 nm wavelength regime. To explain the experimental observations, we carried out further FDTD simulations with MEEP  [39]  software package in which we model a nanobeam being encapsulated by a layer of PMMA with a finite thickness (Fig. 1(b) ) as in the experiments. We simulated the change in the concentration of perovskite nanocrystals in the PMMA layer by varying the index value of the top PMMA layer encapsulating the nanobeam structure. The holes of the nanobeam are taken to be filled with material of the same index as that of the PMMA layer.
, 2016). Other approaches using weakly supervised learning  (Vardazaryan et al. , 2018)  or partly unlabeled datasets  (Ross et al. , 2018)  have shown promising results, but some level of manual annotation made by an expert is still required. In order to use fully unsupervised learning, automatic labeling approaches have been developed. These methods are usually based on object saliency detection  (Yang et al. , 2013; Cheng et al. , 2015; Cho et al. , 2015). Usually, assumptions are made about what makes an object appear salient, e. g. , local contrast differences or a prominent position in the image. Other methods assume that the foreground objects have more complex motions that the   (Stretcu and Leordeanu, 2015). The salient part of the instrument near the tip is well extracted but a large part of the background is also considered as foreground.