dataesr/scientific-paragraphs-categorization huggingface dataset:
Multilocus Sequence Typing All strains were cultured on Trypticase Soy Agar with 5% Sheep Blood (bioMérieux, Marcy l'Étoile, France), and one colony was subcultured on the same agar medium for further analysis. DNA extraction was performed as previously described  (Predari et al. , 1991). The amplification of the seven housekeeping genes included in the MLST scheme for S. aureus was performed as previously described  (Enright et al. , 2000; Crisóstomo et al. , 2001). Sequencing was done on a 3500xL Genetic Analyser (Thermo Fisher Scientific Waltham, Massachusetts, United States), and sequence analysis was performed using SeqScape Software v3. 0™ from the same company. The sequence type (ST) was determined for each strain via the PubMLST database  (Jolley et al. , 2018)  1 or after whole-genome sequencing (wgMLST).
DDBJ/ENA/GenBank accession numbers for the 11 WGS are JAGPWS000000000, JAGPWY000000000 and JAGPWZ000000000 (strains 5. 1, 5. 2, and 5. 3 in Patient 5); JAGPWI000000000, JAGPWQ000000000 and JAGPWR000000000 (strains 6. 1, 6. 3, and 6. 4 in Patient 6); JAHXBM000000000, JAHXBL000000000, and JAHXBK000000000  (strains 17. 2, 17. 3, and 17. 6 in Patient 17) ; JAHXBJ000000000 and JAHXBI000000000 (strains 18. 1 and 18. 2 in Patient 18).
However, the brain heart infusion (BHI) medium is recommended by the BRT ® manufacturer and was therefore used with ASM for comparative purposes. As the opacity of the ASM did not provide a compatible image with the BRT ® reading software, 0. 22 μm-filtered ASM was compared to BHI medium on selected isolates with the BRT ®. BHI medium was also compared with ASM in the assays conducted on the BioFlux™ system. According to the medium used for the assay, strains were pre-cultured for 24 h in the same medium, BHI broth, or ASM. Due to the use of these three culture conditions, growth properties of selected strains in BHI broth, ASM, and 0. 22 μm-filtered ASM were compared. Overnight cultures at 37°C were inoculated into the same fresh medium [identical dilution factor for all three conditions to obtain a suspension of optical density (OD) at 600 nm (OD 600 ) of 0.
1], and incubation was carried out at 37°C with agitation for 24 h. Cell growth was monitored over 24 h through colony-forming unit (CFU) counts with eight data points: 0, 0. 5, 1, 2, 3, 4, 6, and 24 h. For CFU counts, cultures were diluted serially in the medium and plated on Trypticase Soy Agar with 5% Sheep Blood plates which were incubated for 24 h at 37°C. All assays were performed in duplicate.
Bacterial Adhesion and Biofilm Formation Assessment Using Biofilm Ring Test ® The BRT ® based on the measurement of the mobility of superparamagnetic microbeads subjected to a magnetic field was used to study the early stages of biofilm formation according to the manufacturer's recommendations  (Chavant et al. , 2007). Briefly, standardized bacterial cultures were incubated at 37°C in 96-well microtiter plates in the presence of magnetic beads. At set time-points, the plates were placed on a magnetic block and put in the reader. The images of each well before and after magnetic attraction were analyzed with the BioFilm Control software (BFC Elements ® 3), which gives a biofilm formation index (BFI). The adhesion ability of each strain was expressed as this BFI that is inversely proportional to the attached cell number.
ACKNOWLEDGMENTS The authors gratefully acknowledge  Teresa Sawyers , English medical writer at  Nîmes University Hospital , for editing this paper, and  Bastien Baud  and  Vincenette Lopez  for their help with clinical and therapeutic data collection. We thank  Nîmes University Hospital  for its structural, human, and financial support through the award obtained by our team during the internal call for tenders "Thématiques phares. " All authors belong to the  FHU InCh (Fédération Hospitalo-Universitaire Infections Chroniques, Aviesan ).
The WHT then acts as a portion of the pupil of the ELT. Figure  1  illustrates the configuration of the experiment with respect to the ELT pupil plane. The main goal of the experiment is to derive an error breakdown of wavefront sensing with an elongated LGS. To achieve this the LGS is superimposed on an NGS from the point of view of the WHT. The wavefront measured with the NGS is used as a reference to which the wavefront measured with the LGS is compared. To build the error breakdown, the difference between the two measurements is decomposed into different known errors attached to wavefront sensing. The residual error (i. e. , the part of the difference between the two wavefronts that remains unaccounted for) then gives clues to the impact of the LGS elongation. The aim of this paper is to present this error breakdown, how it is derived, and the results obtained with the data gathered with this experiment during the last run of observation to this date comprising five nights between September and October 2017.
Experimental setup Figure  2  presents the general layout of the experiment. As stated previously, the experiment relies on simultaneously measuring the on-axis wavefront from two different guide stars: the elongated LGS and a superimposed NGS. The elongation of the LGS is produced by placing the WLGSU generating the LGS approximately 40 m from the WHT (4. 2 m diameter) on which the CANARY system used for this experiment operates. In addition, simultaneous high resolution images of the laser plume are taken using an imager installed on the Isaac Newton Telescope (INT, 2. 54 m in diameter). Finally, photometric data are acquired using a small 356 mm telescope situated next to the Laser Launch Telescope (LLT). In this paper we focus particularly on the AO results, so we do not further discuss the other systems.
Observational strategy In this study we use the data gathered during an observing run of five nights which took place between 27 September and 2 October 2017 (with a break on the night of 29 September). We defined an observing 'sequence', which was repeated throughout the observations. This sequence was divided in 'acquisitions' which consist of blocks of 5000 frames in which raw images from all WFS, WFS slopes, and actuator commands were recorded. The sequence lasts approximately 20 min and uses different configurations of the AO loop, as summarised in Table  2. Each AO configuration is repeated twice before moving on to the next one. The sequence begins with a longer acquisition (10k frames) to provide a large enough statistical sample of the turbulence. The vertical distribution of the refractive index structure constant C 2 n is derived from the slopes of all four NGS WFS using the Learn and Apply algorithm  (Vidal et al.
2010 ). This knowledge is then used to estimate the contribution of the cone effect in the difference between the measurement of the two on-axis WFS (see  Sect. 4. 6). The dithering acquisitions allow measurements of centroid gain for the TS and LS (see  Sect. 4. 4). During these acquisitions, the tip-tilt mirror is used to circularly modulate the image position at 15 Hz with a fixed amplitude of 0. 6 in diameter. To closely monitor the centroid gain these acquisitions are regularly repeated throughout the sequence. Open loop acquisitions were made to provide data in which only the turbulence is recorded on the WFS. However, the steering mirror was still active in order to stabilise the LGS spots on the LS. The LLT was not protected by a dome and the laser jitter was amplified by wind-induced vibrations.
Column 8: FWHM along the LGS spot minor axis. Column 9: flux recorded on the LS camera per subaperture per frame. Column 10: r 0 derived from the TS measurements by fitting the temporal variance of the Zernike coefficients (obtained with open-loop reconstruction of the slopes) with the distribution given by  Noll (1976)  and computed for a wavelength of 500 nm. Columns 6-10 give values averaged over the subapertures and acquisitions. The three highlighted rows correspond to data analysed in greater detail in the rest of the paper. anisoplanatism between the LGS and the NGS. In addition, the NCPA are obviously different for each wavefront sensor. Therefore, the total incoming phase on each wavefront sensor at any given time can be written for the TS, Φ TS = Φ NGS turb + Φ DM + Φ CPA + Φ TS NCPA , (2) and for the LS, Φ LS = Φ LGS turb + Φ DM + Φ CPA + Φ LS NCPA.
2013). Following the reference slopes calibration, we measured a Strehl ratio of 0. 73 in H band on an internal reference source, a value consistent with that obtained in the previous phases of CANARY due to the high frequencies defects on the DM surface. Whether on the bench or on sky, the LS reference slopes are measured while the loop is closed on the TS. For the purpose of off-line data processing, LS reference slopes are recomputed using acquisitions during which the loop was driven by the TS. Thresholds on the LS images used 90-120 brightest pixels during operations, different to the fixed threshold used offline. As the spots are asymmetric, different thresholds on the centre of gravity result in different reference slopes, therefore we cannot use the LS reference slopes measured on-sky.
Acknowledgements. We thank the referee for their useful comments that have improved this paper. This work has been supported by the  OPTICON  project ( EC FP7  grant agreement  312430  and  H2020  grant agreement  730890 ), by  Action Spécifique Haute Résolution Angulaire (CNRS/INSU  and  CNES, France) , by the  European Southern Observatory  and by the  Science and Technology Funding Council (UK)  ( ST/P000541/1 ),  UKRI   Future Leaders Fellowship (UK)  ( MR/S035338/1 ). L. Bardou 's PhD has been funded by  Fondation CFM  pour la recherche. Data for this paper has been obtained under the  International Time Programme of the CCI (International Scientific Committee of the Observatorios de Canarias of the IAC)  with the WHT on the island of  La Palma  in the  Observatorio del Roque de los Muchachos.
Among the wide variety of image generative models, two models stand out: Variational Auto Encoders (VAE) and Generative Adversarial Networks (GAN). GANs can produce realistic images, but they suffer from mode collapse and do not provide simple ways to get the latent representation of an image. On the other hand, VAEs do not have these problems, but they often generate images less realistic than GANs. In this article, we explain that this lack of realism is partially due to a common underestimation of the natural image manifold dimensionality. To solve this issue we introduce a new framework that combines VAE and GAN in a novel and complementary way to produce an auto-encoding model that keeps VAEs properties while generating images of GAN-quality. We evaluate our approach both qualitatively and quantitatively on five image datasets.
In comparison to  [2]  who first introduces the idea of a combination of the two frameworks, we provide theoretical insights to show the pertinence of our approach and we address the problem of the trade-off between realism and reconstruction accuracy. The paper is organized as follows. We begin with a reminder of GAN and VAE frameworks and explain their limitations. Then we investigate how they can be combined effectively. We thus propose an effective approach to do so, named AVAE. At last, we present a qualitative and quantitative evaluation of the performance of our model on a variety of image datasets comparing it with the state of the art. We also show that our method scales well to high resolution images.
Generative Adversarial Networks GAN globally consists in training two neural networks with adversarial objectives to generate samples indistinguishable from the samples taken from the dataset. The generator network parametrized by θ g is trained to map a random vector to the data space. The discriminator or critic network parametrized by θ c is a classifier that is trained to distinguish real samples from generated ones. The key point is that the generator does not have access to real data and can only improve its parameters through its ability to fool the discriminator. The objective of the critic is: O C (θ c ) = E x∼p(x) [log (1 -C θc (x))] + E x∼p θg (x|z) [log C θc (x)] (3) while the generator tries to fool the critic by minimizing: O G (θ g ) = E x∼p θg (x|z) [log (1 -C θc (x))] (4).
Related Works To leverage both the advantages of GANs and VAEs,  [2]  proposed the VAE/GAN architecture which combines them. They propose to add a discriminator to push reconstructions from the VAE toward more realism and replaced the standard reconstruction error by a perceptual similarity metric based on the filters learned by the discriminator. This approach is problematic because the discriminator is trained to predict whether an image is a real one or a fake one. Thus, the features extracted from it may not be adapted to describe image content making them a disputable choice to base a similarity metric on. As an example, we noticed that VAE/GAN sometimes fails to reconstruct precisely skin color on the CelebA dataset (see Figure  7 ) as this information might be useless to some extent for the discriminator.
By combining our reconstruction loss with adversarial training, we are able to produce photo-realistic reconstructions with no compromise on fidelity. Moreover, our framework is theoretically grounded and is not limited to image data as we show on a toy example (Section 5. 1) that it can be used in a more general context. A recent work published at ECCV 2020 proposed an architecture very similar to ours  [20] , but differs from our work on several crucial points. First, the reconstruction loss at the core of our contribution is different, due to the theoretical aspects developed in the following. We were primarily interested in the theoretical aspects behind the realism of generated images while their work focuses more on the disentangled properties of the framework. Our evaluation method also differs on the choice of datasets and metrics.
Hence: L M (θ g ; x, θ e ) = logit C (15) Indeed, logit C ≈ logit C * = log p θg (x) p(x) which is an unbiased estimator of the Kullback-Leibler divergence term. Minimizing the differential entropy H θg of the distribution p θg (x) will push it to Initialize parameters of the models: θ e , θ d , θ g , θ c while training do {Forward pass. } x real ← batch of images sampled from the dataset. z real µ , z real σ ← E θe (x real ) z real ← z real µ + z real σ with ∼ N (0, Id) µ real ← D θ d (z real ) x fake ← G θg (z fake , ξ) with z fake , ξ ∼ N (0, Id) z fake µ , z fake σ ← E θe (x fake ) C real , C fake ← C θc (x real ), C θc (x fake ) {Compute losses gradients and update parameters. } be as peaked as possible and is not data dependent. Moreover, this term is intractable. Hence, as a form of regularization, we remove it.
Datasets: We evaluate the models on six image datasets: LSUN bedroom  [29]  (64x64 images of bedrooms), CelebA  [22]  (64x64 faces cropped images), FFHQ dataset (256x256 faces)  [14] , CIFAR10, CIFAR100  [19]  (32x32 images of 10 and 100 categories) and SVHN  [25]  (32x32 images of house numbers images). Images are resized to the sizes mentioned above and CelebA images are center-cropped at 70%. Implementation details: all the low resolution experiments have been conducted with Tensorflow 2. 0  [1]  on an NVIDIA GTX 1080 Ti GPU with 11Go of memory. Full code will be available on github. All models share similar architecture blocks, inspired by  [27] , to allow a fair comparison. Architecture details are presented in Figure  5. Each model is trained with hyper-parameters recommended in  [27]  for 5e4 iterations with a batch size of 64.
Because the reconstruction loss of the VAE part of VAE/GAN is a perceptual loss which differs from the MSE used in our model and in  The decoder architecture is identical to the generator architecture and the encoder architecture differs from the critic architecture by the number of units in the last layer and by the absence of a Sigmoid activation at the end. Up-block is similar to Down-block but with transposed convolutions instead of convolutions and ReLUs instead of leaky-ReLUs. All convolutions and transposed convolutions share the same filter size (5) and use 'same' padding. σ z is chosen independent of x and is learned directly. w is a width multiplier (we typically use w = 128). For the BiGAN implementation, we use a two-hidden-layer MLP for the latent code inputs and a critic-style architecture for the image inputs.
Toy dataset We begin by testing our approach on a toy dataset to validate the theory. The dataset is composed of 2D points generated from two generative factors z 1 and z 2. The data generation procedure is the following: z 1 , z 2 , ∼ N (0, 1) and x = f (z 1 , z 2 , ) = (3z 1 + 0. 1 , cos(3z 1 ) + tanh(3z 2 ) + 0. 1 ). For the model, we use a latent space of dimension one to simulate the problem of the low dimensionality of the latent space compared to the high dimensionality of the data manifold. Models are two-hiddenlayer perceptrons with 128 units. Models are trained with the method described proposed in this paper. We then draw the manifold of the generated points to see how the model behave compared to a VAE. Results of this experiment can be seen in Figure  6  where we can see that reconstructions from the VAE are in a region of low likelihood of the data distribution while AVAE reconstructions follow the shape of the VAE manifold while covering regions of higher likelihood.
Qualitative results Here, we present some qualitative results on the CelebA SVHN and LSUN bedroom datasets. A comparison of samples reconstruction between our model and other models is presented in Figure  7. We also present a visual comparison of samples generated by our model and other generative models in Figure  8. Additional qualitative results will be available on github. We can see on these figures that generated images are of comparable quality of GAN generated images for both generation and reconstructions. VAE reconstructions and generated samples look blurry, BiGAN generated images are of good quality but reconstructions are not accurate. VAE/GAN produces both good reconstructions and generated samples. However, while our judgment is subjective, we find that reconstructions produced by VAE/GAN are less accurate than ours and images are less realistic than with GAN, BiGAN or our approach.
One may notice that for the LSUN bedroom dataset, reconstructions produced by our model are not convincing. However, we can explain this by the very poor performance of the VAE suggesting that not enough information passes through the latent code to create a reconstruction visually close to the original image. However, even here, our model still produces sharp images close to the target ones in terms of MSE showing that our model follows the latent structure of the VAE trained with the MSE as a reconstruction error. We also conducted an experiment on higher resolution images (256x256 FFHQ face images) to see if our method can be scaled to high resolution images. To conduct this experiment, we made straightforward modifications to the style GAN V2  [15]  using the approach proposed here. Results of this experiments are presented in Figure  9.
Quantitative results To quantitatively evaluate the performance of our method, we selected several metrics. The quality of the reconstructed images is evaluated by the Mean Squared Error or MSE and the LPIPS  [31]. We use the FID  [10]  to measure the realism of generated images. A comparison between VAE, GAN, BiGAN  [8, 7] , VAE/GAN  [2] , and our model is presented in Table  1. Reconstructions errors are computed on validation images not used during training, namely the test or validation splits of TensorFlow datasets. FID is computed over 50000 randomly generated samples and compared to training data samples as FID requires a lot of samples to be calibrated. It must be noted that some metrics are biased toward some architectures: the MSE is favorable to the VAE model because it is the loss used to train it.
Figure5: Generator and critic architectures. The decoder architecture is identical to the generator architecture and the encoder architecture differs from the critic architecture by the number of units in the last layer and by the absence of a Sigmoid activation at the end. Up-block is similar to Down-block but with transposed convolutions instead of convolutions and ReLUs instead of leaky-ReLUs. All convolutions and transposed convolutions share the same filter size (5) and use 'same' padding. σ z is chosen independent of x and is learned directly. w is a width multiplier (we typically use w = 128). For the BiGAN implementation, we use a two-hidden-layer MLP for the latent code inputs and a critic-style architecture for the image inputs. The two outputs representations are then concatenated and used as input of a two-hidden-layers MLP.
23 hing et al. have developed an ANN framework called Cox-nnet to predict patient prognosis from highthroughput transcriptomics data. 24 Bica et al. have introduced a novel neural network architecture for exploring and integrating modalities in omics datasets, especially in cases where a limited number of training examples was available. 25 Also, some examples of application of deep neural networks could be found in using neural networks to learn an embedding that substantially denoises expression data, making replicates of the same compound more similar. 26  Donner et al. used ANNs to identify drugs with shared therapeutic and biological targets, even for compounds with structural dissimilarity, revealing functional relationships between compounds and making a step forward toward the drug repurposing based on expression data.
It is usually possible to infer the enzymatic function of individual proteins, or to reconstruct larger (or even whole) metabolic networks. Techniques such as metabolic flux analysis (MFA) and its improvements (e. g. , isotopically nonstationary MFA), and flux balance analysis have become largely utilized for the predictions of concurrent fluxes of multiple reactions. Recently, computational approaches coupling MFA with mass spectrometry have been also implemented. Single-enzyme function prediction can be carried out by resorting to machine learning, especially when the enzyme does not show significant similarity to existing proteins; or to ''annotation transfer'' approaches, based on the use of reference databases or orthologs to tag specific DNA sequences. Comparative pathway prediction methods use established functional annotations to check for the existence of new reactions, whereas explorative pathway prediction techniques (not using existing annotations) can be graph-theoretic (e.
These maps show whether the expression of individual TFs is related. For example, the KEGG pathways  107  and TRANSFAC database were used for functional enrichment studies. 108 Gene sets containing more than five elements were constructed and tested for enrichment by using the PGSEA package, and the TFs were merged based on their ID irrespective of their binding sites. In this manner, the TF enrichment analyses confirmed an increased unfolded protein response and metabolic decline after depleting one of the genes from cholesterol synthesis in the liver. 109.
165 ntext awareness systems Context awareness systems address complex environments in terms of location, identity, components, and relations. Context refers to the information that describes an entity (person, location, object). 166  The study of such complex environments has been made possible by the availability of Wireless Sensor Networks technologies, which allow heterogeneous sensors, distributed in a physical environment, to share their measurements. Still, these technologies do not protect from problems such as cross-domain sensing and coupling of sensors; to preserve performance and reliability, the data fusion has to be performed with caution. 167  Context awareness systems have an important role in the design of health care monitoring systems, health smart homes, and ambient assisted living, which facilitate the acquisition of both ambient and medical data from sensors.