dataesr/scientific-paragraphs-categorization huggingface dataset:
Activated cells were stained for surface markers for 20-25 minutes after the four hours of incubation. Data were acquired on a FACSAria machine (BD Biosciences) with the capacity to detect 18 fluorescent parameters and exported to FlowJo 10. 5. 3 (BD Biosciences) for analysis. The frequency of cells positive for each parameter was compared with the mean and standard deviation for three healthy donors analyzed in parallel with the samples from the patient. IFNAR2 staining was performed on transduced U4C cells surface stained with anti-IFNAR2 antibody (PBL 21385-1) at a 1:100 dilution for 1 hour on ice. Secondary antibody staining was performed with an anti-murine IgG antibody with an Alexa Fluor 647 tag (Thermo Fischer A-21235). Subsequently the cells were stained with a Live/Dead Aqua stain (Thermo L34957) and then fixed in 4% PFA.
Modified inDrops single cell RNA-Seq targeting JAK1 S703I site inDrops and related droplet microfluidics single cell RNA-Seq strategies coencapsulate single ''barcode microbeads'' and individual cells in droplets. Reverse transcription of polyadenylated (polyA) mRNA incorporates a cellular barcode sequence (different for each bead) into nascent cDNA. After downstream high throughput sequencing, reads can be assigned to individual cells by barcode sequences, thereby enabling expression quantification at single cell resolution. As the cellular barcode is introduced downstream of transcript polyA tails, sequence data is typically restricted to transcript regions immediately proximal to 3 0 termini. Because the S703I site is located at position 2402 from 5 0 transcript start and 2690 from the 3 0 transcript polyA tail, it is not accessible by standard droplet microfluidics single cell RNA-Seq platforms.
methods were further analyzed with Seurat (v2. 3. 4)  (Butler et al. , 2018)  in the R statistical framework as follows. For applicable cells in the JAK1-targeted inDrops dataset, JAK1 genotyping information was imported as per cell metadata entries. Genes with detectable expression in fewer than 5 cells per sample were excluded. Cells with detectable expression of fewer than 250 genes or fewer than 1000 UMI counts or greater than 20% UMIs from mitochondrial gene transcripts (measure of cell viability) were removed from further analysis. Gene expression data were log normalized and scaled (regressing out effects based on total UMI counts and mitochondrial gene expression). All genes with detectable expression in both inDrops and 10X datasets were used for CCA dimensionality reduction (20 dimensions).
CCA dimension scores were aligned between datasets using the AlignSubspace function. Aligned data were log-normalized and scaled as above. Graph-based clustering and tSNE visualization was performed with the FindClusters and Run-TSNE functions with default settings (using the first 19 CCA dimensions, based on examination of distinct expression patterns with the DimHeatmap function). Marker genes for individual clusters were identified with the FindMarkers() function in Seurat (paramenters: min. pct = 0. 1, only. pos = TRUE, logfc. threshold = log(1. 5), min. diff. pct = 0. 1, pseudocount. use = 0. 1). Briefly, up to 100 cells were randomly selected from each cluster, and each cluster was contrasted against all others for differential gene expression. Significant genes (p < 0. 05) were ranked by log 2 fold-change; the top 25 genes for each cluster, as well as a select list of additional genes of interest, were selected as marker genes for supervised cluster annotation (Table  S3 ).
Expression patterns were further evaluated on tSNE plots. Select clusters were merged based on shared expression patterns as indicated. Gene expression differences were assessed for cells assigned JAK1 genotype classifications of either MUT+ (S703I) or WT+MUTneg (WT). Plasmablasts and dendritic cells were excluded from analysis due to insufficient cells assigned to both genotype groups. Apparent cellular doublets and cells in the platelet cluster were also excluded. Differential gene expression testing was performed on genes detected in at least 20% of cells in any cell type cluster (of either JAK1 genoytpe group) with edgeR (v. 3. 12. 1)  (Robinson et al. , 2010) , including modifications included for single cell RNA-Seq data as described  (Soneson and Robinson, 2018). A linear model including factors for cellular gene detection rate, cell type, JAK1 genotype group and an interaction term (cell type cluster and JAK1 genotype group), was fit with the glmQLFit function.
Differential gene expression testing (WT versus S703I) was tested across all cell types with the glmQLFtest function, with significance thresholds set at FDR 0. 05. In addition, gene set enrichment testing for Molecular Signatures Database Collections H, C2-CPC, C3-TFT and C7 collections (http://software. broadinstitute. org/ gsea/msigdb), supplemented with two additional interferon stimulated gene sets  (Rosenberg et al. , 2018)  was performed with CAM-ERA  (Wu and Smyth, 2012 ) and the linear model described above. Only gene sets with 5 or more genes in the fitted model were included in the analysis.
ACKNOWLEDGMENTS We thank  Adeeb Rahman ,  Brian Lee ,  Kevin Tuballes , and  Laura Walker  from the  Human Immune Monitoring Center ;  Rachel Brody  from the  Biorepository and Pathology CoRE ; and  Fadi El Salem  in the  Department of Pathology at the Icahn School of Medicine  for their technical assistance and guidance in experimental design. This research was supported by  National Institute of Allergy and Infectious Diseases  grants  R01AI127372 ,  R21 AI134366 , and  R21AI129827 ; the  March of Dimes  (to D. B. ); and the  Genetic Disease Foundation  (to B. D. G. ). This study was also funded by  R01AI137275  to E. M. M. ,  R01AI120989  to J. S. O. , and  R03DK117218  to  D. D. Research  in the Rosenberg.
Robot simulator A simulator of the LettuceThink robot has been developed to ease the testing of different configurations of the learning system. The simulator generates sensorimotor data from requested trajectories of the end-effector camera. Knowing the initial position of the CNC machine and the target position, the simulator linearly interpolates the trajectory and returns the intermediate positions of the camera together with the images captured from each specific position. The sensorimotor data returned by the simulator have been prerecorded by performing a full scan of the (x,y) plane of the CNC machine using a resolution of 5mm. This resulted in 24,964 images, each mapped to an (x,y) position of the CNC machine. The simulator is freely available as as a python script that generates sensorimotor data † from the recorded dataset ‡.
Learning architecture The architecture implementing the curiosity-driven goaldirected exploration behaviours combines one deep and two shallow neural networks, as well as an episodic memory system. A deep convolutional neural network is trained offline using unsupervised methods to encode images using low-dimensional features. The shallow neural networks learn online how to represent the inverse and forward kinematics of the robotics system. In addition, an artificial curiosity system assigns interest values to a set of pre-defined goals and drives the exploration towards goals that are expected to maximise the learning progress. An episodic memory system is included to handle catastrophic forgetting issues. The learning architecture controls the movements of the end-effector camera of the LettuceThink platform using an exploration behaviour driven by artificial curiosity.
1, 0. 01}, for a total of 6 experiments. The values of mem size specify the number of batches (each batch has 16 sensorimotor samples) that are contained in the memory. Each experiment has been run 5 times, for a total of 30 runs. Each run consisted of 5000 iterations of the algorithm described in Section 3. 5. During each run, the mean squared error of the forward and inverse models, calculated on the same test dataset (consisting of 50 images randomly chosen from the simulator dataset) have been monitored. The MSEs have been calculated every 50 iterations of the algorithm (behaviour and model updates). A set of 9 goal images have been chosen from the simulator dataset. Figure  4  shows an example of a run of the exploration behaviour under a specific configuration (mem size = 20, p em = 0.
1) and over different time steps  (50, 1000, 2000, 3000, 4000 and 5000 iterations). The axes specify the x and y motor positions of the CNC machine. Red dots represents the motor projections of the 9 image goals (ground truth data from the simulator dataset). Green dots represent the explored positions of the robot. As it can be seen, the behaviour of the robot successfully tends towards exploring more the regions around the goals. The effect of the -greedy strategy is also evident from the almost uniformly distributed green points within the action space. Figure  5  shows the predictions of the inverse models over time. The inverse model is fed with the encoded goal image as input and returns as output the (x,y) coordinates of the motor. It can be clearly seen that the learning process brings the prediction towards the target motor goals (ground truth data).
We strongly believe that similar mechanisms may represent a bridge between motor and cognitive development in humans, and a promising tool for cognitive robotics. We tested the models on simulated sensorimotor data from a microfarming robot. This paper has contributed also to the widening of the developmental robotics approach towards applications and robotic platforms that are non-conventional in this field. Moreover, our experiments have shown that the intrinsic motivation systems can work also in the presence of a highdimensional sensory space. Adopting an episodic memory system not only prevents the computational models to quickly forget knowledge that has been previously acquired, but also provides new avenues for modulating the balance between plasticity and stability of the system. Several potential research directions are open from this study.
Acknowledgements This work has partially received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  773875  ( EU-H2020 ROMI, Robotics for Microfarms ). Guido Schillaci has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No. 838861  ( Predictive Robots ).
DATA SITES The results presented in this paper come from two airborne data sets. The first have been acquired by the HySpex camera, during a campaign above Lacq industries (France) in June 2017. The hyperspectral camera was a SWIR imager with a spatial resolution of 1. 4 m at flight altitude of 2 km with a swath of 650 m and a spectral resolution of 6 nm. The spectral range of acquisitions extends from 967 nm to 2501 nm with 256 bands. Of all the existing chimneys on the site, one platform was designed as a testing area to release gas in controlled way to simulate accidental gas leaks. In such a way that for each release, the in situ flow rate is recorded. A second set of data were acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) which provides 224 spectral bands with a spectral resolution of 10 nm.
To reduce the size of the initial hyperspectral data, a selection of the 160 spectral bands between 966 and 2495 nm is applied. A spatial resolution of 6. 8 m are obtained with the Twin Otter aircraft from the Aliso Canyon (USA). Aircraft flew over a CH4 superemitter during the explosion of natural gas wells in 2015  (Thompson et al. , 2016). The initial size of image is 320 x 2607 pixels for HySpex and 829 x 3966 pixels for AVIRIS. During the development of the algorithm, only a section around the chimney is taken, reducing the size to 170 x 285 pixels for HySpex and to 650 x 1000 pixels for AVIRIS. A representation of these two data is plotted in the figure  1.
Direct model of measured radiance The total radiance measured by the instrument can be expressed as : Ltot = R E0 • (τ dir + τ dif f ) π(1 -RS) + Latm (1) where Ltot = total measured radiance (in W/m 2 /sr/µm) R = ground reflectance E0 = solar irradiance (in W/m 2 /µm) τ dir = direct transmission τ dif f = effective contribution of scattering effects S = spherical albedo Latm = atmospheric radiance (in W/m 2 /sr/µm) A sensitivity study of the impact of each term of equation 1 with COMANCHE radiative code  (Poutier et al. , 2002)  leads to several simplifications. The (1 -RS) term tends to 1 on the range of reflectance and spherical albedo. At 2. 3 µm, the scattering phenomena are negligible compared to the absorption. Therefore, the atmospheric radiance and the scattering transmission can be deleted of equation (  1 ).
Classification The classification is applied by using the k-means function of Python 3. 6 to cluster each hyperspectral pixel into a class number N (typically 15). The algorithm runs a first classification with N classes. All classes with few pixels are deleted and a second classification is running without taking into account these isolated pixels. The two previous classifications are running without the need to initialize class centers and with 300 iterations. An example of the classification result for HySpex data is shown on the figure 2.
In this HySpex case, the mean of the mass per unit length is equal to 51 g/m. To obtain an estimation of the flow rate, the last step is to multiply the mean mass per unit length by the wind speed. During the Lacq campaign, several sensors measured the speed of the wind at different altitudes. A lidar system ZEPHIR has recorded the wind with a frequency around 15 s at 11 m altitude (and other levels) and a sonic 3D instrument called METEK has measured the wind speed at 10 m of altitude every minute. During the Lacq campaign, the wind was very variable in strength and in direction as shown in the figure  7  for the wind speed. As we can see in the figure  7B , in only 2 minutes, the speed of the wind varies from 0. 78 to 5. 09 m/s for ZEPHIR data at 11 m of altitude and from 1. 52 to 3. 98 m/s for the METEK data at 10 m.
This point can introduce a overestimation of flow rate because the wind is stronger as the altitude is increasing. However, even if the flow rate seems to be overestimated, the in situ value is in the uncertainty range. To conclude the airborne data part, we have developed an algorithm which estimate the flow rate of a source from SWIR hyperspectral data combining with wind data. The obtained results are in good agreement with in situ measurement despite the large uncertainty of the wind speed data.
Top Of Atmosphere level The initial data are recorded during airborne campaigns with 6 km flight altitudes by the AVIRIS imager. The satellite data will be acquired at more than 850 km. We have to take into consideration that the signal will pass through a significant additional atmospheric path. As in the previous part, we assume that the corresponding layer is only composed to water vapor. Using the radiative transfer code COMANCHE  (Poutier et al. , 2002) , we run simulations to determine the direct upward transmission of the atmosphere between flight altitude campaign and the top of atmosphere (TOA). The initial radiance is then affected by this term following : L (ima,T OA) = L (ima,f ly) • τ (f ly-T OA) (10) where L (ima,T OA) = radiance in the TOA level (in W/m 2 /sr/µm) L (ima,f ly) = radiance of airborne campaign (in W/m 2 /sr/µm) τ (f ly-T OA) = direct transmission between the airborne and the top of atmosphere (TOA) As mentioned on the section 3.
However, the mean mass per unit length presents more differences than the previous case. An important point is that the global shape of the curve at 27. 2 m of spatial resolution is similar to the finer spatial resolution. As such, the plume detection and the estimation of its concentration seems still possible with satellite data. But, we can note that between 1000 and 1500 m, an overestimation of the concentration occurs where the plume has a larger width than the other part (the tail plume). This may be due to averaging of pixels square at the initial step that emphasizes the spread of the plume. This will be investigated in the future. The satellite data allow to detect and to quantify the plume in the case of the AVIRIS campaign plume with kilometric extension. The uncertainties of (simulated) satellite results are larger as shown by the figure  8B.
As the HySpex plume is 60 m wide, we focus to the AVIRIS plume for the degradation of the spatial resolution. From 6. 8 m (airborne resolution) to 27. 2 m (near PRISMA or EnMap resolution), the algorithm is able to detect the plume and to estimate its concentration but a 27. 2 m spatial resolution leads to a overestimation of the plume mean mass per unit length in the tail part. In the future, several points have to be investigated. The algorithm can be upgraded by reducing false alarms. The detection and quantification sensitivities of the algorithm should be investigated. Of course they will depend on the spatial and spectral resolution. This study can be conducted using synthetic data. Testing and validating this algorithm with true satellite data will be our final goal.
We show that supervised learning, restricted to the new groups, performs better than the same techniques applied to the whole collection. We also present optimalFlowClassification, which uses a database of gated cytometries and optimalFlowTemplates to assign cell types to a new cytometry. We show that this procedure can outperform state of the art techniques in the proposed datasets. Our code is freely available as optimalFlow a Bioconductor R package at https://bioconductor. org/packages/optimalFlow. optimalFlowTemplates+optimalFlowClassification addresses the problem of using supervised learning while accounting for biological and technical variability. Our methodology provides a robust automated gating workflow that handles the intrinsic variability of flow cytometry data well. Our main innovation is the methodology itself and the optimal-transport techniques that we apply to flow cytometry analysis.
In addition, a procedure able to group similar cytometries could help to detect individuals with a common particular condition, i. e, a particular sickness such as cancer. In our work we show that this indeed happens. optimalFlowTemplates is our procedure for clustering cytometries and obtaining templates. It is based on recent developments in the field of optimal transport such as a similarity distance between clusterings, introduced in  [11] , and a barycenter (Frechet mean, see  [20, 9] ) and k-barycenters (see  [4, 13, 5] ) of probability distributions. Additionally, we introduce a supervised classification tool, optimalFlowClassification, for the case when a database of gated cytometries is available. The procedure uses the prototypes obtained by optimalFlowTemplates on the database. These are used to initialise tclust, a robust extension of k-means that allows for non-spherical shapes, to gate a new cytometry (see  [18] , not to be confused with TCLUST,  [14] ).
Obtaining prototypic cytometries: optimalFlowTemplates Due to the the high variability in flow cytometry data we should expect that learning from different elements in the database should produce significantly different results on the classification of a new cytometry X T = {X T 1 ,. , X T n T } ⊂ R d. Our approach is to search for clusters of existing cytometries in the database. In this way we pursue a notable reduction of variability, thus allowing a good representation of the cytometries in each of these groups through prototypic cytometries. Therefore, using a prototype of a group for learning should produce a similar result for classifying X T to the one obtained using any other cytometry in the same group.
As cell types are known, we take all the black ellipsoids of the left plot, representing the different normal distributions, and obtain the black ellipsoid on the right plot, the barycenter of the group of normal distributions, as a consensus element for Monocytes. Doing this for every cell type gives us the prototype cytometry represented on the right of Figure  2. T i j is empty 7: end if 8: end for 9: T i ← {T i 1 ,. , T i K } Output: T i 2: for j, k ≤ |C i | do 3: W jk ← W 2 (N (m i j , S i j ), N (m i k , S i k )) 4: T i ← {T i 1 ,. , T i |T | } Output: T i However, our templates could be obtained even when we have gated cytometries but without identified cell tpes. This could be the case when unsupervised gating is used to obtain a database of gated cytometries. Density based hierarchical clustering (Algorithm 3) and k-barycenter (Algorithm 4) are based on the idea that clusters that are close in Wasserstein distance should be understood as representing the same, although we may not know which, cell type.
Supervised classification: optimalFlowClassification Now, our goal is to assign cell types to a new cytometry X T , using the information given in a database of gated cytometries {C i } N i=1. The different sources of variability, mainly those of a technical nature and those which are properly due to different cohorts present in the database, advise to search for different cytometric structures. Hence, we should assign X T to the group of cytometries that is more similar to it and then use supervised techniques. Indeed, this is the purpose of optimalFlowClassification, as shown in Algorithm 5. As an input we apply optimalFlowTemplates to the database {C i } N i=1 in order to obtain the partition T and the templates T.
Algorithm 5 optimalFlowClassification Input: X T = {X T 1 ,. , X T n T }, T,T , initialization 1: if initialization = supervized then 2: for i ≤ |T| do 3: C i,u ← tclust on X T initialized with T i 4: end for 5: C u ← arg max of tclust objective function over all C i,u 6: else 7: C u ← apply some clustering procedure on X T 8: end if 9: for i ≤ |T| do 10: S i ← d S (C u , T i ) 11: end for 12: T * ← T arg min S i ; T * ← T arg min S i 13: C T ← labelling of X T using transfer labelling or supervised classification based on T * or T *. Output: C T Lines 1-5 in Algorithm 5 are dedicated to finding an unsupervised partition of the new cytometry X T using as initialization for tclust the prototypes of the database. Initializing with the database entries attempts to use the available information optimally.
Hence, if X T is similar to some of the cytometries in the database, appropriate initialization should be advantageous. However, some other suitable unsupervised initializations can be used, such as the ones proposed in FLOCK, flowPeaks or flowMeans. We need to cluster X T in order to compare it with the template cytometries. Notice that tclust, tclust, introduced in  [18] , is a more sophisticated version of k-means, allowing ellypsoidal clusters with different sizes and shapes. Similarly to k-means, this robust model-based clustering procedure needs an initialization and its behaviour improves notably if that initialization is well suited. Nonetheless, it is possible to use any other unsupervised procedure that allows an initialization with a clustering defined by probability distributions.
, S 0 k ). Subsequently, we only need a set of weights with corresponding means and covariances to initialize tclust. We favour the use of tclust over k-means since it allows for non-spherical clusters and for trimming, making partitions more robust to outliers and even bridge-points. However, our procedure is compatible with any other way of obtaining a partition of the data, which we reflect on lines 6-8. In particular, in our experiments we have used flowMeans as an alternative initialization to tclust. In lines 9-12 we look to assign X T , using the clustering C u , produced in the previous step, to the template that is closest in similarity distance to C u. With this we hope to use only the most relevant information of the database, summarized in T * and T *. The last step in algorithm 5, line 13, is concerned with assigning cell types to X T.