dataesr/scientific-paragraphs-categorization huggingface dataset:
e. isotopes, adducts, fragments) were discarded based on the conjunction of three criteria 47 : Pearson correlation of sample profiles above 0. 9, difference between retention times below 6 s, and m/z difference matching a reference list corresponding to isotopes, adducts and fragments at a 0. 005 Da tolerance. Finally, intensities were log2 transformed. All steps of this post-processing workflow (Fig. 3 ) are described in the '2_post_processed' vignette from the ProMetIS package (https://github. com/IFB-ElixirFr/ProMetIS; Fig. 4 ), which relies on methods from the phenomis R package (https://github. com/SciDoPhenIA/phenomis). Annotation. Metabolite features were first annotated by using a local spectral database according to accurately measured masses and chromatographic retention times 37,48, containing the reference spectra of more than 2,000 authentic standard compounds analysed in the same analytical conditions, and providing a comprehensive spectral information (i.
e. protonated or deprotonated molecules, adducts and in-source fragment ions). To be identified, ions had to match at least two orthogonal criteria (e. g. , accurately measured mass and retention time) to those of an authentic chemical standard analyzed under the same analytical conditions, as proposed by the Metabolomics Standards Initiative  49. Additionally, metabolite annotations from the Acquity HSS T3 platform were confirmed by running additional LC-MS/MS experiments using a high-resolution Bruker Impact ll UHR-QTOF operated in collision-induced dissociation conditions (CID), conducted on the same QC samples, and with the instrument set in a targeted (Multiple Reaction Monitoring) acquisition mode. Resulting MS/MS spectra were then manually matched to those included in the in-house spectral database and acquired using different collision energies.
Raw data Preclinical data are available on the IMPC database  50, 51. MS proteomics data, including reference files (readme, search database,. dat files), form a complete submission in the ProteomeXchange repository  52. Data were submitted via the PRIDE partner repository under data set identifiers PPXD021664 (liver)  35  and PXD021668 (plasma)  36. MS metabolomics datasets (raw and processed files) are available for both matrices on the MetaboLights repository  53  under the MTBLS1903 identifier  54.
Processed data Each dataset is publicly available in the ProMetIS R package (https://github. com/IFB-ElixirFr/ProMetIS), as 3 tabular files containing the intensities (dataMatrix), the sample metadata (sampleMetadata) and the variable metadata (variableMetadata). These files may be conveniently opened with any spreadsheet editor. They may be imported as an ExpressionSet  55  or MultipleDataSet  56  for further single-or multi-omics analysis in R (see for instance the tutorial vignette from the ProMetIS package). Alternatively, they may be processed by the Galaxy tools from the Workflow4Metabolomics platform  40, 57. Importantly, the post-processing steps used to generate these files are described in the 2_post_processed vignette from the ProMetIS package, to meet the Findable, Accessible, Interoperable and Reusable FAIR guidelines  27.
Those processed datasets (annotated variables only) are also provided in the Supplementary Table (with one sheet per dataset) for the reader's convenience. Fig. 4. Data and code availability in the ProMetIS package (https://github. com/IFB-ElixirFr/ProMetIS). The data are available in the extdata folder, which is organized into subfolders corresponding to the successive steps of the analysis (e. g, 1_processed, 2_post_processed, 5_aggregated, etc. ). Within each subfolder, the 9 datasets are stored as a triplet of tabular files containing the matrix of intensities (dataMatrix. tsv), the sample metadata (sampleMetadata. tsv) and the variable metadata (variableMetadata. tsv). For each step, a vignette describes the methods used to process the data. As an example, the 2_post_processed vignette details how the datasets were post-processed, between the 1_processed and the 2_post_processed folders.
Technical Validation The ProMetIS dataset was generated by the four French Infrastructures for Mouse Phenogenomics (PHENOMIN; http://www. phenomin. fr/en-us), Proteomics (ProFI; http://www. profiproteomics. fr), Metabolomics and Fluxomics (MetaboHUB; https://www. metabohub. fr/home. html) and Bioinformatics (IFB; https://www. francebioinformatique. fr) which meet the international standards for optimal quality, reproducibility and accessibility of the data.
Preclinical data Routine procedures to optimize the quality of phenotyping data at the PHENOMIN infrastructure include i) standardized pipelines and experiments, ii) automated flagging of outlier variables by comparing the WT mice from the project to all other WT mice from the same genetic background previously generated by the platform (as described below), and iii) manager data review and validation. For each quantitative variable, a reference range encompassing 95 % of individual values (rr95) is computed. Method used depends on data distribution normality. We assessed the normality of data distribution for each parameter using the Shapiro-Wilk test. Significant level has been fixed to 10e-4 to take multiple testing into account. For normally distributed parameters, reference ranges are calculated with parametric approach: rr95 reference range: average ± 2 × standard deviation.
We also tried to apply transformation function to make the datafit a normal distribution. We test log, inverse and square root transformations. If transformed data follow a normal distribution, we can use reference range calculation detailed upper on transformed data. Note 66 % of parameters follow a normal distribution. If no transformation allows a normal distribution, reference ranges are calculated with a nonparametric approach. The lower and upper reference limits of rr95 are the 2. 5 and 97. 5 percentiles of data. Clinical and Laboratory Standards Institute (CLSI) recommends testing at least 120 samples for establishment of a statistically significant reference interval to ensure stable lower and upper reference limits (https://clsi. org/media/1421/ep28a3c_sample. pdf ). Data falling out of reference ranges are removed from the dataset.
The rr95 of the quantitative variables was computed from 1,135 WT mice (588 males and 547 females). The 15 WT mice from the ProMetIS project had 99. 5% (respectively, 99. 2%) of their variable means within the rr95 in males (respectively, females), thus confirming the good stability and reproducibility of the measures performed in this study (Fig. 5a ). The average of two (respectively, one) variables were out of the rr95 in males (respectively, in females): the plasma calcium level and the optical coherence tomography of the right anterior chamber depth (respectively, the plasma total bilirubin). The calcium parameter, whose average is slightly above 3 sd (3. 6) was removed from the dataset. Altogether, the minor deviations of these three variables from the other PHENOMIS WT mice, observed in one sex only, are difficult to explain and most likely result from variations in the sampling of the individuals in this project, which represent less than 5% of the total PHENOMIS WT mice in each sex type (2.
S1  of the Supplementary Information). This spiked-in standard is composed of eleven pure synthetic peptides (iRT kit, Biognosys, # Ki-3002-1). Fig. 6. (left) and Fig. S1  illustrate the reproducibility of the features (retention time and raw intensities, respectively) associated with these iRT peptides across the LC-MS runs of the dataset. Second, an external standard of close composition to that of the biological samples themselves was regularly injected during the acquisition sequence. It was prepared by pooling a small aliquot of all initial samples. An LC-MS run of this pool standard was acquired every 4 or 5 analytical runs during the sequence (n=10 pools for liver samples, n=8 pools for plasma samples). These runs were included, together with those of the samples, in the data processing performed with the Proline software.
The identification metrics extracted from the data of this pooled external standard (i. e. number of proteins, peptides, MS/MS scans; Fig. S2 , left, and Fig. S3 , left, as well as the CVs of the raw protein intensities, Fig. 6 , right, and the correlation between the samples, Fig. S4 ) show high repeatability of the results. Third, quality control of the biological datasets was assessed by plotting the same identification and quantitative metrics across all biological samples (Fig. S2 , right, Fig. S3 , right, and Fig. 6 , right). No outlier was identified in the cohorts. The variability of these metrics was close to the one of the pools and was used to assess the global quality of the datasets, excluding any variability related to the composition of the biological samples.
Metabolomics data The quality of the metabolomics datasets was investigated after the post-processing step (including the signal drift correction and the log transformation of intensities but without the final feature filtering on the pooled QC CVs), as a validation of the data before future statistical studies. The stability of the signal detection and quantification along the sequence of injection was first assessed on the metabolite internal and external standards which have been spiked in all crude plasma samples before metabolite extraction (internal standards), or in the final extracts just before injection into the LC-HRMS (external standards)  37, 58, 59. Of note, the mixture of 13 external standards is used as an "universal" standard solution in the laboratory, i. e. whatever the sample to be analysed (e.
The few samples with a z-score absolute value above 3 for one of the metabolite standards were manually validated: no consistent outlier pattern across all spiked standards nor any outlier location on the PCA score plot were observed for any of those samples. We further used a set of five metrics to assess the global quality of all six metabolomics datasets: the first two metrics assess the absence of any analytical drift between the samples by using univariate and multivariate approaches. The remaining three scores focus on the quality of the pooled QC samples: we first define a scoring of the QC spread on the PCA score plot, and then include two metrics corresponding to the coefficient of variation (CV)  44, 60  and the intraclass correlation coefficient (ICC)  61. Each metric, ranging from 0 to 100 is computed as follows (a score of 100 corresponds to the highest quality): 1.
QC CV (coefficient of variation of the pooled QC intensities): percentage of features with a coefficient of variation of the intensities in the QCs inferior to 30% High scores were obtained for all datasets, with an average value of the metrics ranging between 84 and 94% (for the plasma_c18acquity_pos and plasma_hilic_neg datasets, respectively; Table  1  and Fig. 7 ). The normalization based on pooled QCs (respectively, on samples) was necessary to achieve a correction of the signal drift for the plasma_c18hypersil/hilic datasets (respectively, the plasma_c18acquity datasets, Fig. S6 ), as assessed by the Drift Spearman and Drift PCA metrics scores above 90%. Furthermore, the three QC metrics of the datasets were high (QC spread, QC CV, QC ICC), even in the absence of signal drift correction or after a normalization based on the samples, with an average value of 83% (min-max: 58%-99%).
The curves showing the cumulative percentage of compounds as a function of the CVs in the pooled QC samples were similar to those obtained by Zhang et al. 61  with a repeated injection of a single serum sample (Fig. S7 ): in all metabolomics datasets, a minimum of 79% of features had a CV below 30%. The cumulative ICC as a function of the intensity in the pooled QCs was similar to those from Zhang et al. 61  , with an ICC value at the most probable intensity between 71% and 89%, except for the plasma_c18acquity datasets which had slightly lower values (58% and 65%), confirming that although the pooled QCs were less reliable in these latter datasets, the overall quality of all metabolomics datasets was high (Fig. S8 ). Finally, the distribution of CVs in the biological samples were similar for all genotypes (Fig.
Usage Notes The tutorial vignette from the ProMetIS package describes in detail how to load the data and provides examples of single omics and integrative data analysis of the datasets with R packages, such as univariate hypothesis testing (phenomis), orthogonal partial least-squarediscriminant analysis (ropls)  45  , feature selection (biosigner)  63  , sparse multi-block PLS-DA modeling (mixOmics)  64. Alternatively, the proteomics and metabolomics data may also be readily loaded and analyzed online by using the Workflow4Metabolomics41,59 and ProteoRE67 platforms. These resources are based on the Galaxy environment to facilitate workflow management68, and include many modules for statistical analysis and annotation.
61,45: where  0234. 6is the number of features with an intensity CV of less than 30% in the pooled QC; recently,Zhang et al. (2020)proposed to further display the cumulative percentage of compounds as a function of CV for a more detailed overview of the dataset quality61. The c ing graphic is therefore provided as the Fig. S7of the Supplementary Information 5. QC ICC (intraclass correlation coefficient, ICC, between the pooled QC intensities at the most probable abundance 61 ):  +#1( !$#8'8&% '89:)':"% × 100, the use of the ICC between the pooled QC has been recently proposed as a metric to assess the reliability of the measurements61. The cumulative ICCs as a function of the intensities are shown in the Fig. S8of the Supplementary Information, and the ICC value at the intensity bin containing most compounds (most probable abundance) is used as the quality metric61.
Acknowledgements This work was funded by the  four French Infrastructures for Mouse PhenoGenomics (PHENOMIN ,  ANR-10-INBS-0007 ),  Proteomics  ( ProFI ,  ANR-10-INBS-0008 ),  Metabolomics  ( MetaboHUB ,  ANR-11-INBS-0010 ), and  Bioinformatics (IFB ,  ANR-11-INBS-0013 ).
The icc function from the irr package 62 was used with the "twoway" random effect model, "agreement", and "single" parameters Quality metrics for the metabolomics datasets. PCA: principal component analysis; QC: pooled quality control sample; CV: coefficient of variation; ICC: intraclass correlation coefficient. ! *. /0. 2 ! '"'&% × 100, Drift Spearman Drift PCA QC spread QC CV QC ICC liver_c18hypersil_pos 95 90 91 79 71.
The size of the sketch scales with the degrees of freedom of the ToF model (number of objects) and not, fundamentally, with the number of photons or the time-stamp resolution. Moreover, the sketch is highly amenable for on-chip online processing. We show theoretically that the loss of information for compression is controlled and the mean squared error of the inference quickly converges towards the optimal Cramér-Rao bound (i. e. no loss of information) for modest sketch sizes. The proposed compressed single-photon lidar framework is tested and evaluated on real life datasets of complex scenes where it is shown that a compression rate of up-to 150 is achievable in practice without sacrificing the overall resolution of the reconstructed image.
Section 2 details the ToF lidar acquisition systems and the ToF observation model used in single-photon lidar and also presents the idea of summary statistics used for parameter estimation. In Section 3 we detail the construction of the sketch using two different sampling schemes and we further demonstrate how our sketched lidar approach can be implemented in an online processing manner. In Section 4 we detail our proposed compressive single-photon reconstruction algorithm that has computational complexity which scales with the sketch size m as well as quantifying the statistical efficiency of the estimated parameters θ. Results of the compressive lidar framework are analysed on both synthetic and real datasets in Section 5. Section 6 finally summarizes our conclusions and discusses future work.
C. Summary Statistics Our acquisition goal is to obtain parameter estimates θ := (α 0 , α 1 ,. , α K , t 1 ,. , t K ) of the signal model in  (2) , given the time-stamp of photons detected for each pixel in the scene. Parameter estimation involves the inference of a set of parameters θ ∈ Θ ⊂ R 2K+1 associated to a probability model π(• | θ) defined on some space x ∈ R d. In the case of single-photon counting lidar, the dimension d = 1. Typically, we observe a finite dataset X = {x i } n i=1 of n samples which we assume is sampled from the distribution given in  (2). Maximum likelihood estimation (MLE) is a traditional parameter estimation method whereby a likelihood function associated with the finite data is maximized with respect to the model parameters, e. g. θ = arg max θ 1 n n i=1 log π(x i | θ).
In Section IV, we explicitly define the weighting matrix W for compressive single-photon counting lidar. The separable form of g in (  7 ) allows a sketch statistic z n to be formed with a single pass of the data without the need to store X , and it can easily be updated on the fly with minimal computational cost. The sketch statistic has size m, or size 2m if decoupled into its real and imaginary components, which, fundamentally, scales independent of the dimensions of the dataset X , which in the case of single-photon lidar is the photon count n or the binning resolution T. 3) Empirical Characteristic Function: A specific type of GeMM is the empirical characteristic function (ECF) estimation  [21] ,  [22] ,  [32] , and occurs when the generalized moment is chosen to be Φ(x) = [e iω T j x ] m j=1 , where i = √ -1 and ω j is a discrete set of frequencies.
A. Compressing Single Depth Data In the absence of photons originating from background sources and the presence of a single surface or object, the sample mean of all the photon time-stamps (Φ(x) = x) is the simplest summary statistic for estimating the single location parameter t 1. This only holds in the noiseless case as the sample mean estimate is heavily biased toward the centre of the histogram when background photons are detected. Suppose, we instead observe the cosine and sine of each photon count x with angular frequency ω = 2π T , namely Φ(x) = cos 2πx T sin 2πx T , (11) and denote z n the real valued sketch of size 2 (m = 1) computed over the dataset X as in  (8). It is possible to recover an estimate of the single depth location parameter t 1 directly from the sketch, without recourse to the data X , via the trigonometric sample mean t1 = T 2π phase    n j=1 cos 2πx j T + i n j=1 sin 2πx j T    (12) where phase denotes the phasor angle.
A. Statistical Estimation Once the ECF sketch is constructed using either sampling scheme, we must estimate the parameters θ of the observation model π(x | θ) solely from the sketch z n. In general, there is no closed form expression to estimate θ from the sketch of arbitrary size as is the case for the circular mean estimate in  (12). It is well documented in the ECF and GeMM literature, e. g. [20] ,  [21] ,  [42] , that a complex valued ECF sketch z n of size m, computed over a finite dataset X = {x 1 ,. , x n }, satisfies the central limit theorem. Formally, a sketch z n ∈ C m converges asymptotically to a Gaussian random variable z n dist --→ N Ψ π , n -1 Σ θ , (16) where Σ θ ∈ C m×m has entries (Σ θ ) ij = Ψ π (ω i -ω j ) - Ψ π (ω i )Ψ π (-ω j ) for i, j = 1, 2,. , m. The asymptotic normality result in (  16 ) naturally leads to a sketch maximum likelihood estimation (SMLE) algorithm that consists of minimising the following arg min θ m 2 log det(Σ θ ) + n(z n -z θ ) T Σ -1 θ (z n -z θ ), (17) where for convenience we denote z θ = Ψ π (ω j ) m j=1.
1) Processing: Restoration of depth imaging of singlephoton lidar consists of estimating a 3D point cloud from a lidar data cube containing the number of photons n i,j,t in pixel (i, j) at time-stamp t, where i = 1, 2,. , N r , j = 1, 2. , N c and t = 0, 1,. , T -1. We denote the average photon count for each pixel by n and process each pixel (i, j) of the data cube and estimate the true location and intensity parameter, denoted t 1 and α, respectively. The intensity of a point in pixel (i, j) of the point cloud is calculated by the number of photons in the pixel multiplied by the proportion of the signal i. e. α k T -1 t=0 n i,j,t. A data driven impulse response is given for each dataset and we can obtain the characteristic function of the IRF by using  (10). 2) Evaluation Metrics: Two different error metrics are used to evaluate the performance of our proposed sketched lidar framework.
B. Synthetic Data We evaluate the sketched lidar framework on a synthetic dataset simulating a pixel in a scene which consists of a single peak response. We chose the parameters that best replicated a realistic lidar scene and that were akin to the real datasets which will be discussed in V-C. Therefore, we set the binning resolution at T = 250, and impulse response was generated with a true Gaussian function where σ = 5. We ran a Monte-Carlo simulation with 1000 trials to evaluate and compare the performance of our sketched lidar framework for photon counts n ∈ (100, 1000) with varying SBR levels and number of real measurements 2m. For each trial, we uniformly chose t 1 ∼ U(0, 249), and estimated t for the sketched lidar approach, the iFFT method discussed in Section IV as well the alternative compression technique of coarse binning.
C. Real Data In this section we evaluate our sketched lidar framework on two real datasets of increasing complexity. Namely, a polystyrene head imaged at Heriot-Watt University  [5] ,  [30]  which consists mostly of a single peak, and a scene where two humans are standing behind a camouflage net, depicted in  [9] ,  [52] , which contains of 2 objects per pixel with varying intensity. 1) Polystyrene Head: The first scene consists of a polystyrene head placed 40 meters away from the lidar device. The data cube has width and height of 141 pixels, N r = N c = 141 and a total of T = 4613 time-stamps. A total acquisition time of 100 milliseconds was used for each pixel resulting in an average photon count of n = 337 with an SBR of approximately 6. 82. The vast majority of pixels consist of a single peak, although there are a minority of pixels around the borders of the head that consist of two peaks.
Here we omit the small pro-portion of pixels that consist of two peaks from the RMSE calculation for sake of fair comparison with the existing methods that can only estimate a single peak. We observe that our sketched lidar method produces a smaller RMSE as the measurement size increases and achieves a smaller RMSE than the LMF approach for larger measurements. In comparison, the coarse binning method obtain estimates that produce a large RMSE consistently throughout. As such, this suggests that our sketched lidar approach does not compromise reduced resolution in favour of compression which is very apparent in the coarse binning method. Fig. 15:  The RMSE as a function of the number of real measurements (2m) for the polystyrene head dataset.
ACKNOWLEDGEMENTS This work was supported by the  ERC Advanced  grant, project  C-SENSE , ( ERC-ADG-2015-694888 ). Mike. E. Davies  is also supported by a  Royal Society Wolfson   Research Merit Award. The authors would like to thank the singlephoton group at HWU (https://single-photon. com) for the use of the datasets used in Section V-C. The polystyrene head dataset in V-C1 and the camouflage dataset in V-C2 were obtained from [5] and [9], respectively.