dataesr/scientific-paragraphs-categorization huggingface dataset:
000 units) to investigate the acoustic matching when interacting with different speakers. Although the reconstruction error was important in comparison to the results in the first experiment, the network was still able to generalize correctly to this larger temporal sequence. This underlies the capabilities of inference of the architecture despite the large variability found in the database. These attributes for generalization and inference appear in line with what happens during development when facing the corresponding problem. For instance, infants appear to learn a dictionary of prototypical sounds and to know how to adjust different voices and, different contexts in their mother tongue  [1]. One difficulty is to know how speech is decomposed into distinct units to be analyzed. At the end of the developmental stage, a large number of sounds will seem similar to infants although they are different; e.
g. , "r" and "l" in Japanese. This phenomenon, occurring from 6 months to 18 months, is known as perceptual categorization in which discriminating capabilities are narrowing. During this period, infants appear to organize a repertoire of prototypical sounds with which they can compare and infer any sound they think to be the closest as a sort of 'perceptual magnet'  [78, 79]. This repertoire is either perceptual, motor, or sensorimotor and the decision-making seems to correspond Bayesian inference in speech  [80] [81] [82]. The Inferno network present such attributes. In our present research, the sound repertoire encoded is only perceivable as audio primitives as encoded in the GP layer in the form of MFCC vectors. In future research, wewill use audio datasets found in the litterature to compare our results with other models, for instance audio datasets designed for unsupervised learning and development modeling from the Zero Resource Speech Challenge  [77].
(motor) repertoire to the ground truth MFCC vectors? Differently said, how well what the network pronounces match what it listens? We present in Fig 10A-10F different analysis carried out after the learning stage, resp. in a) the correspondence matrix between the retrieved indices of the STR units that match those of the ground truth MFCC vectors present within the audio database, in b) the Euclidean distance between the ground truth MFCC vectors present within the audio database and the retrieved MFCC vectors of the STR units, in c) the correspondence matrix between the ground truth MFCC vectors and the nearest MFCC vector also within the audio dataset which matches the closest the one predicted by the STR unit in a). A zoom in this MFCC correspondence matrix is plotted in Fig 10D and a histogram of the ABX distance computed from the correspondence matrix is presented in Fig 10E.
and plotted in Fig 10E. The graphes in Fig 10C and in Fig 10D are constructed as follows. At first, we select the most probable STR unit X found in Fig 10A for each ground truth MFCC vector A. In second, we compare the MFCC vector generated with all the MFCC vectors in the audio database and select the nearest index B. We plot in c) a small portion of the database of the first 10. 000 MFCC units out of 140. 000 MFCCs units and in Fig 10D between the interval [120. 000; 125. 000] for a better visualization. A one-to-one correspondence between vectors A and B -, which means that they have the same index,-indicates a good generalization and a good matching by the Inferno network. Conversely, items of different index indicate the redundancy within the audio dataset and a mismatch with the Inferno network's prediction, which is something expected within the correspondence problem task.
C. MultiPath TCP (MPTCP) IETF's working group created MultiPath TCP (MPTCP)  [64]  to use multiple interfaces simultaneously. MultiPath TCP goal is to increase the throughput by using multiple interfaces simultaneously and improve path failure by switching to other paths while used path is down. Also, the multipath approach should remove the burden on congested paths. The working group wanted the MPTCP to be compatible with the application layer and transparent to middle boxes. MPTCP solution uses multiple IP addresses in a single TCP session by using multiple paths which enables the use of resource pooling efficiently. A stable implementation of multipath TCP in the Linux kernel at  [14]  claiming to be the fastest TCP connection with multipath TCP by achieving 51. 8Gbit/second with multipath TCP.
The results show that this approach reduces cost (mobile data usage and energy consumption) without any side effects to QoE. However, no comparison results were provided for the MP-DASH player compared with other adaptive video players in the same category. Recently, much interest directed to Virtual Reality (VR) services, particularly 360 VR videos. 360 VR video is video captured in all direction from a specific point, known as spherical video where the viewer can see the panorama in 360 degrees during playback. One of the challenges for streaming VR videos is high bandwidth requirements because most of m360-degree videos have been encoded at 4K resolution. In  [24] , an adaptive bandwidth-efficient 360 VR video streaming system based on DASH protocol. It consists of using Spatial Relationship Description (SRD) to realize dynamic viewport-aware adaptation technique based on the user's viewing viewport where the highest quality is assigned for tiles in the viewport, and lower qualities are assigned to the other tiles.
[45]  tries to answer the question if the current mobile network is providing the right QoE for watching YouTube videos. They indicate the received video degradation level based on the ratio between the video's total stall length and the total length of the video playbacks, which can provide approximate QoE. They validate their prosed model based on lab and field measurements. A machine learning approach proposed by Petrangeli et al. [46]  called YouQ to estimate QoE-relevant metrics for YouTube by extracting features from the stream of encrypted packets, using simple features such as throughput, packet sizes, and packet times. The machine learning models for QoE classification are calculated by collecting data of all the traffic features per video session. However, the accuracy of the predicted QoE values is rather coarse due to frequent pattern adaptation made by the YouTube service.
The architecture of IoT consists of three layers, starting by the perception layer which is responsible for connecting the sensors to their Virtual Objects (VOs) to measure the QoS network condition and evaluate the QoE for cameras based on video sequences will be used based on the quality of the videos such as bitrate, resolution, and codec. After transmitting the processed information into upper layer via layer interfaces, the network layer is used to receive the processed information provided by the perception layer and determine the routes to transmit the data and information to the IoT applications. The application layer receives the data transmitted and provide required services. The basic idea is to correlate all the cameras and sensors to improve the detection process of the surveillance application.
We show in the Appendix that using OLS to estimate (2. 3), b 1j can be expressed by b 1j = n 1 n X i=1 h i Ỹij (2. 6) where h i is a function of the design matrix W with ith row W i = (1, X i , Z i ). These estimates may be plugged into (2. 5) to obtain the estimated test statistic b D n = n p X j=1 s X k=1 b 2 1jk. Because of (2. 6), we furthermore show in the Appendix that the asymptotic distribution of the test statistic may be approximated by a mixture of 2 1 random variables: , where b a j is an estimate of a j based on a consistent estimator for ⌃ (see Appendix for details). Therefore, this allows us to derive a p-value for the significance of a gene with regards to the variable(s) to be tested. In practice, we make use of  Davies (1980)  approximation to compute p-values for the mixture of 2 s, implemented in the CompQuadForm R package  (Duchesne and De Micheaux, 2010).
Note that we obtain a simple limiting distribution without relying on any distributional assumptions on the gene expression. In fact, based on the results in  Li and Duan (1989) , our test will be asymptotically valid as long as there exist any g(•) and any 0j , 1j , 2j such that (2. 3) holds. Lastly, the great number of tests requires the Benjamini-Hochberg (Benjamini and Hochberg, 1995) correction afterwards, which is automatically applied to the raw p-values in the R package of ccdf. b D = ũ| Aũ + o p (1) = ps X j=1 a j ũ2 j + o p (1) (2. 2. 1. 4 Permutation test Permutation tests are a simple way to obtain the sampling distribution for any test statistic, under the null hypothesis that there is no link between the outcome Y and the variable X. The observations of X can then be shu✏ed. Permutation tests are recommended when the number of observations is too small, so that the asymptotic distribution can not be assumed to hold.
Gauthier and others serving as much as possible the count nature of the data (i. e. negative binomial assumption). To do so, we added a small Gaussian noise with mean equal to 0 and variance equal to 0. 01 to the simulated counts. 500 simulated datasets were generated including 10,000 genes each, of which 1,000 are di↵erentially expressed under a two conditions setting for 7 di↵erent sample sizes n  (20, 40, 60, 80, 100, 160, 200). The observations were equally divided into the two groups. Korthauer and others (2016) classified four di↵erent patterns of unimodal or multi-modal distributions: • di↵erential expression (DE): two unimodal distributions with a di↵erent mean in each condition. • di↵erential proportion (DP): two bimodal distributions with equal component means across conditions; the proportion in the low mode is 0.
Multiple comparisons This second scenario deals with a multiple comparisons design where cell observations were split into 4 di↵erent groups. Count data were generated from negative binomial distributions and mixtures of negative binomial distributions and transformed into continuous values as in section 3. 1. 500 simulated datasets were generated including 10,000 genes each, of which 1,000 are di↵erentially expressed under a four conditions setting for 7 di↵erent sample sizes n (40, 80, 120, 160, 200, 320, 400). The observations were then equally divided into four groups. Instead of generating two distributions for each gene as in section 3. 1, we created four distributions and therefore new DD scenarios for this specific DEA simulation: multiple DE, multiple DP, multiple DM and multiple DB (more details in the Supplementary Materials).
Positive control dataset Islam and others (2011) dataset includes 22,928 genes measured across 48 mouse embryonic stem cells and 44 mouse embryonic fibroblasts. We considered 1,000 genes validated through qRT-PCR experiments as a gold standard gene set in the same fashion as Wang and others (2019) in order to compute true positive rate. A gene is defined as a true positive if it is found as DE by the method and belongs to the gold standard set  (Moliner and others, 2008). For ccdf, SigEMD, scDD and MAST, the number of DE genes for an adjusted p-value of 0. 05 and the number of true positive over the 1,000 gold standard genes are given in Table  2. ccdf leads to the highest true positive rate (0. 706) and enables to identify 21. 8% more genes in common with the top 1,000 genes compared to SigEMD (0.
Negative control dataset To get false positive rate, we used the dataset from Grün and others (2014). We selected 80 samples under the same condition. To create 10 datasets, we randomly divided these 80 cells into 2 groups of 40 cells. As there is no di↵erence between the two groups, not a single gene is to be found in each dataset. Performance evaluations are compared across methods in Table  2. ccdf and MAST do not detect any genes which was expected. Although all the cells are under the same condition, scDD and SigEMD identified respectively 5 and 50 DE genes.
Nevertheless, easy parallelization of the permutation test can alleviate this problem. Furthermore, an adaptive procedure, for both the number of permutations and the number of thresholds, is implemented in order to accelerate ccdf while preserving a su cient statistical power along with numerical precision for the lowest p-values. Per-gene asymptotic tests can also be computed in parallel to speed up computations. The proposed approach has been fully implemented in the user-friendly R package ccdf available on Github and soon to be on CRAN. While ccdf can be applied to many types of data thanks to its flexibility, it has been specifically tailored for the need of scRNA-seq data DEA. In the simulation study, ccdf exhibits great results and versatility in complex designs such as multiple conditions comparison, and also allows to analyze data when the experiment design includes confounding variables while most of the competing methods cannot.
Acknowledgments MG is supported within the  Digital Public Health Graduate 's school, fundedby the PIA 3 (Investments for the Future -Project reference:  17-EURE-0019 ). The project is supported through  SWAGR Inria Associate-Team from the Inria@SiliconValley program. Computer time for this study was provided by the computing facilities  MCIA (Mésocentre de Calcul Intensif Aquitain) of the Université de Bordeaux  and of the  Université de Pau et des Pays de l'Adour. BH & MG thank  Franck Picard  for helpful discussion about this work.
Indeed, proteases are responsible for activating viral proteins for particle assembly. Due to their importance within the replication cycle of the virus, they have been proven to be successful targets for antiviral agents and are used to treat many diseases including HIV and hepatitis. 4  In the case of SARS-CoV-2, the main protease is called M pro or 3CL pro. Many efforts have been made to rene the crystallographic structure of M pro as the number of experimental structures available in the Protein Data Bank is increasing. While more than one hundred M pro structures exist and massive efforts to discover a successful inhibitor are underway, computational approaches involving virtual screening and Molecular Dynamics (MD) simulations are needed to help experimentalists to in silico optimize their millions of test molecules.
[23] [24] [25] [26]  For this family of approaches, multiple iterations of independent molecular dynamics simulations are performed, basing the initial conditions at each iteration on the results of previous iteration steps. We propose here a new unsupervised (i. e. fully automated) adaptive sampling strategy dedicated to our specic use of PFFs within large supercomputer systems allowing for the simultaneous use of hundreds or thousands of GPU cards. This characteristic is important as it allows us to benet from the full potential of pre-exascale supercomputers, and will naturally transfer to future exascale machines. The results presented here benet from a GPU acceleration in the newly developed Tinker-HP GPU code  22  that was rst used here for COVID-19 simulations. However the procedure is completely general and can be applied to any homogeneous or heterogeneous computational platforms compatible with Tinker-HP  21, 27  or any MD soware.
At the beginning of iteration k, rst, the protein is aligned in all congurations, using the backbone atoms of the 6LU7 crystal structure from the Protein Data Bank. 3  A principal component analysis (PCA)  28  is then performed, using the scikit-learn  29  and MDTraj 30 packages, on the protein atoms (q i ) 1#i#M k , from which the n ¼ 4 principal modes are considered. This choice was made aer a global analysis of the rst 20 PCA modes of the rst AMOEBA 0. 14 ms which showed that n > 4 modes had variance contributions below 4% (Fig. 1 , ESI †). This has also been corroborated by an analysis of RIKEN and DESRES trajectories, for which, respectively, 3 and 4 PCA modes are above 4% (Fig. 2 , ESI †). We denote by x k : ℝ 3N /ℝ n the orthogonal projection on these n principal modes and we write x i ¼ x k (q i ).
Simulation protocol The presented all-atom simulation was performed using the newly developed GPU module  22  within the Tinker-HP package,  21  which is part of the Tinker 8 platform. 39  This newly developed module is able to efficiently exploit mixed precision  22  offering a strong acceleration of simulations using GPUs. The 98 694 atom initial structure of the fully solvated M pro dimer was extracted from the Protein Data Bank (PDB: 6LU7) and the AMOEBA PFF  33, 34, 36  was used to describe all atoms (protein and water). Periodic boundary conditions using a cubic box with side lengths of 100 Å were used. Langevin molecular dynamics simulations were performed using the BAOAB-RESPA1 integrator 40 using a 10 fs outer timestep, a preconditioned conjugate gradient polarization solver (with a 10 À5 convergence threshold), hydrogen-mass repartitioning (HMR) and random initial velocities.
Performance of the adaptive sampling exploration: comparisons with other available simulations As we mentioned in the method section, we use the PCA  28  as an intermediate quantity to orient the consecutive sampling iteration. However, it is also a good quantity to quickly assess the performance of the adaptive sampling scheme for the exploration of the conformational space. Indeed, the analysis of MD trajectories with PCA is a well-known strategy known in the community as the "essential dynamics". [42] [43] [44]  PCA, being a dimensionality reduction algorithm that evaluates directions maximizing the variance of the dataset, is thus a revealer of a system conformational diversity. Therefore, it can be seen as a way to assess the amount of sampling and can also detect explicit "essential motions" otherwise not discernible using predened collective variables.
However, it has been shown to be useful since the initial studies on the SARS-CoV-1 main protease. 51  In practice, the absence of a well-structured oxyanion hole leads to the inhibition of the enzyme's activity. Experimentally, it is known that the M pro monomeric form is inactive while the active form is a homodimer containing two protomers. 53  In the holo state of SARS-CoV-1, the rst protomer is active while the second one is found inactive. 54  For SARS-CoV-2, a pH ¼ 6 crystal structure (PDB: 1UJ1)  53  predicted a strong asymmetry of the protomers with an inactive conformation for one of the protomers linked to a broken Glu166 and His172 interaction. However, the inactivity of one of the protomers is still a hypothesis as crystallographic studies of the dimer in the space group C2 encounter difficulties in capturing the details of each individual protomer.
Evaluation of the volumes of the enzyme cavities One way to measure some potential global differences between the different simulations is to measure the active site volume in each cluster and to depict the observed trend similarly to the pp stacking previously. Besides the main active site cavity, the main protease exhibits 2 other cavities: the distal site and the dimerization site. Represented in Fig. 5 , these cavities are considered as potential targets for drug inhibition. 60, 61  An accurate description of each of these cavities is essential to the estimation of efficient inhibitors. For each cluster of each dataset, we thus estimated those 3 cavity volumes. Volumes were calculated for each isolated cluster using POVME 3. 0 soware. 62  For each cavity, a 1. 0 Å grid spacing was chosen.
Agreement is also found with volumes obtained by Sztain et al. using a Gaussian accelerated MD (GaMD) enhanced sampling strategy coupled with AMBER ff14SB 8 which also match these results conrming the importance of simulating long enough in conventional MD. Overall, while Tinker-HP clusters 1 and 2 are in good agreement with RIKEN and DESRES clusters, our clusters 3, 4 and 5 appear to be different and specically highlight the importance of the PFF choice, i. e. these data are not obtained using enhanced sampling coupled with non-PFFs. 8  As we pointed out earlier, differences indeed occur between clusters and between different datasets, going in the same direction of the previous analysis of the p-p stacking between residues Phe140 and His163 in chains A and B. For Tinker-HP, we observed a contraction for the three cavities in cluster 3 while in cluster 4 and especially cluster 5, we observed a strong difference with a non-negligible increase of the cavity volumes.
Acknowledgements This work has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement no. 810367 ), project  EMC2  (JPP). FC acknowledges funding from the  French  state funds managed by the  CalSimLab LABEX  and the  ANR  within the  Investissements d'Avenir program  (reference  ANR11-IDEX-0004-02 ) and support from the  Direction Génerale de l'Armement (DGA) Maîtrise NRBC of the French Ministry of Defense. DEA acknowledges funding from the  Lebanese National Council for Scientic Research ,  CNRS -L. Adaptive sampling computations were performed at GENCI thanks to a COVID19 emergency allocation on the  Jean Zay  machine ( IDRIS, Orsay, France ) under grant no. A0070707671  and on the  Irene Joliot Curie  machine thanks to a  PRACE COVID- 19 emergency grant (project  COVID- HP ).
1%, 30 out of 41 strains), prevalent (sample coverage >50%, 22 out of 41), and enriched (root vs. soil, log2FC, Mann-Whitney U test, FDR < 0. 05, 26 out of 41) in A. thaliana root endosphere samples at a continental scale (Fig. 1a ). Quantitatively similar results were obtained using sequence data from the independent rDNA ITS2 locus (Spearman; Sample coverage: rho = 0. 65, P < 0. 01; RA: rho = 0. 59, P < 0. 01; Fig. 1b ). The cumulative RA of the sequence tags corresponding to these 41 fungi accounted for 35% of the total RA measured in root endosphere samples across European sites  18  , despite the underrepresentation of abundant Agaricomycetes and Dothideomycetes taxa (Fig. 1c ). We next assessed the worldwide distribution and prevalence of these fungal taxa across 3,582 root samples from diverse plants retrieved from the GlobalFungi database  38.
Continent-wide analysis revealed that the proportion of samples with positive hits was greater in Europe (sample coverage: up to 30%, median = 4%) than in North America (sample coverage: up to 10%, median = 0. 5%), and largely insignificant in samples from other continents (Fig. 1a ). Interestingly, only a few of these 41 isolates were detected in leaves of A. thaliana at two locations in Germany (data re-analyzed from ref. 39  , n = 51 samples), as well as in 2 647 leaf samples retrieved from the GlobalFungi database  38  (Supplementary Fig. 1 ). Results indicate that most of the cultured A. thaliana root colonizing fungi reproducibly and predominantly colonize plant roots across geographically distant sites irrespective of differences in soil conditions and climates. Root mycobiota members evolved from ancestors with diverse lifestyles.
ITS1 tags from natural site samples were directly mapped against the reference ITS1 sequences of the selected fungi. Sample coverage in roots was computed based on n = 169 root samples and estimated RA were calculated for root samples having a positive hit only. On the RA boxplot, boxes are delimited by first and third quartiles and whiskers extend to show the rest of the distribution. Log 2 Fold-Change (log2FC) in RA between root (n = 169) and soil samples (n = 223) is shown based on the mean RA measured across samples and significant differences are indicated by circle sizes (two-sided Mann-Whitney U test, FDR < 0. 05, see detailed values in Supplementary Data 1). ITS1 sequence coverage measured across 3 582 root samples retrieved from the GlobalFungi database  38. Note that samples were analyzed separately by continent.