dataesr/scientific-paragraphs-categorization huggingface dataset:
In macromolecular crystallography, mesh (raster) scans are carried out either as part of X-ray-based crystal-centring routines or to identify positions on the sample holder from which diffraction images can be collected. Here, the methods used in MeshBest, software which automatically analyses diffraction images collected during a mesh scan and produces a two-dimensional crystal map showing estimates of the dimensions, centre positions and diffraction qualities of each crystal contained in the mesh area, are presented. Sample regions producing diffraction images resulting from the superposition of more than one crystal are also distinguished from regions with single-crystal diffraction. The applicability of the method is demonstrated using several cases.
, 2009; Bowler et al. ,  Figure  1  Overview of the workflow of the method. Each X-ray mesh scan produces N rows Â N columns diffraction images. These are individually analysed by Dozor, which produces an estimate of the diffraction signal and determines a list of diffraction-spot coordinates and their partial intensities in each image. MeshBest then carries out the analyses described in the main text. resolution range to which they extend. On the ESRF MX beamlines the image analysis is carried out by the program Dozor  (Svensson et al. , 2015; Zander et al. , 2015) , which estimates the diffraction signal based on empirical radial scattering patterns of protein crystals  (Popov & Bourenkov, 2003). The diffraction signal in each particular image is then ranked and the results are displayed as a two-dimensional heat map.
Discriminating between distinct crystals Once MeshBest has identified and discarded all images with unacceptable multi-pattern diffraction, the program searches for the remaining regions of the mesh scan that produce diffraction signal and which are geometrically interconnected. Every pair of images in each region is then analysed to determine how similar their diffraction patterns are. Figure  3  (a) The behaviour of the distance score D as a function of the crystal rotation between frames (Á'). In this experiment several standard 50-frame data collections were carried out from the same crystal of thaumatin using the same data-collection parameters except for the beam transmission, which was changed to imitate different diffraction strengths. D was then calculated between pairs of images and plotted against Á'.
Test results The program MeshBest was implemented as a Python module using standard (NumPy, SciPy) libraries. In order to demonstrate the applicability of MeshBest and its performance, we present four different experiments here. These include MeshBest analyses of a large, homogeneous crystal (x3. 1) and of a disordered crystal with satellites (x3. 2), the analysis of a mesh scan performed prior to subsequent multi-crystal data collection on a sample holder containing membrane-protein crystals buried in opaque lipidic mesophase (x3. 3) and a mesh scan from a sample holder containing a crystal mess with 'dirty  ' diffraction patterns (x3. 4). Table  1  shows the data-collection parameters used in these experiments. Data were collected on ESRF beamlines ID23-1, ID29 and MASSIF-3  (Nurizzo et al.
The measure may be adapted for use with more multi-dimensional objects than strings, objects such as arrays and tensors. To test the measure we demonstrate the power of CTM on low algorithmic-randomness objects that are assigned maximal entropy (e. g. π) but whose numerical approximations are closer to the theoretical low algorithmic-randomness expectation. We also test the measure on larger objects including dual, isomorphic and cospectral graphs for which we know that algorithmic randomness is low. We also release implementations of the methods in most major programming languages-Wolfram Language (Mathematica), Matlab, R, Perl, Python, Pascal, C++, and Haskell -and an online algorithmic complexity calculator.
This is due to the fact that one can always translate a lossless description into any other lossless description simply with a program of a fixed length, hence in effect just adding a constant. Computability theorists are not much concerned with the relatively negligible differences between evaluations of Kolmogorov complexity which are owed to the use of different descriptive frameworks (e. g. different programming languages), yet these differences are fundamental in applications of algorithmic complexity. Here we study a Block Decomposition Method (BDM) that is meant to extend the power of the so-called Coding Theorem Method (CTM). Applications of CTM include image classification  [27]  and visual cognition  [20, 22, 24] , among many ap-plications in cognitive science. In these applications, other complexity measures, including entropy and lossless compressibility, have been outperformed by CTM.
Sections 7. 1. 2 and 7. 2, and Figs. 10, 11, 12 and 15 in [27]  support the agreements in correlation using different rule spaces of Turing machines and different computing models altogether (cellular automata). Section 7. 1. 1 of the same paper provides a first comparison to lossless compression. The sections 'Agreement in probability' and 'Agreement in rank' provide further material comparing rule space (5,2) to the rule space (4,3) previously calculated in  [17]. The section 'Robustness' in  [38]  provides evidence relating to the behaviour of the invariance theorem constant for a standard model of Turing machines  [35]. 4  The Block Decomposition Method (BDM) Because finding the program that reproduces a large object is computationally very expensive and ultimately uncomputable, one can aim at finding short programs that reproduce small fragments of the original object, parts that together compose the larger object.
The parameter m is needed because of the remainder. If |s| is not a multiple of the decomposition length l then the option is to either ignore the remainder in the calculation of BDM or define a sliding window with overlapping m -l. The choice of t and k for CTM in BDM depend only on the available resources for running CTM, which involves running the entire (t, k) space of Turing machines with t symbols and k states. BDM approximates K in the following way: if p i is the minimum program that generates each base string s i , then CT M (s i ) ≈ |p i | and we can define an unique program q that runs each p i , obtaining all the building blocks. How many times each block is present in s can be given in O(log n i ) bits. Therefore, BDM is the sum of the information needed to describe the decomposition of s in base strings.
Recursive BDM In Section 4. 3, we have shown that using smaller partitions for BDM yields more accurate approximations to the algorithmic complexity K. However, the computational costs for calculating CT M are high. We have compiled an exhaustive database for square matrices of size up to 4 × 4. Therefore it is in our best interest to find a method to minimize the partition of a given matrix into squares of size up to d × d = l for a given l. The strategy consists in taking the biggest base matrix multiple of d × d on one corner and dividing it into adjacent square submatrices of the given size. Then we group the remaining cells into 2 submatrices and apply the same procedure, but now for (d -1) × (d -1). We continue dividing into submatrices of size 1 × 1. Let X be a matrix of size m × n with m, n ≥ d.
Error Estimations One can estimate the error in different calculations of BDM, regardless of the error estimations of CTM (quantified in  [17, 38] ), in order to calculate their departure and deviation both from granular entropy and algorithmic complexity, for which we know lower and upper bounds. For example, a maximum upper bound for binary strings is the length of the strings themselves. This is because no string can have an algorithmic complexity greater than its length, simply because the shortest computer program (in bits) to produce the string may be the string itself. In the calculation of BDM, when an object's size is not a multiple of the base object of size d, boundaries of size < d will be produced, and there are various ways of dealing with them to arrive at a more accurate calculation of an object that is not a multiple of the base.
Because M I is symmetric M I(X, Y ) = M I(Y, X); if M I(X, Y ) = 1, then knowing all about Y also implies knowing all about X. In one of its multiple versions MI of X and Y can be defined as: M I(X, Y ) = H(X) -H(X|Y ) (17) where H(X) is the Shannon entropy of X and H(X|Y ) the conditional Shannon entropy of X given Y. In this way, statistically similar base matrices are not counted as requiring 2 completely different computer programs, one for each base matrix, but rather a slightly modified computer program producing 2 similar matrices accounting mostly for one and for the statistical difference of the other. More precisely, BDM can be defined by: BDM (X) = (ri,ni)∈Adj(X) {x i } M IBDM (r i ) + log n i ( 18 ) where MIBDM is defined by: M IBDM (r i ) = min{M I(r i , r j )CT M (r i ) (19) +(1 -M I(r j , r i ))CT M (r j ), M I(r i , r j )CT M (r j ) +(1 -M I(r j , r i ))CT M (r i )} and where M I(r i , r j ) is a weight for each CTM value of each base matrix such that j is the index of the matrix that maximizes M I (or maximizes statistical similarity) over the distribution of all the base matrices such that M I(r i , r j ) ≥ M I(r i , r k ) for all k ∈ {1,.
Because BDM locally estimates algorithmic complexity via algorithmic probability based upon CTM, it is slightly more independent of object description than computable measures such as Shannon entropy, though in the 'worst case' it behaves like Shannon entropy. We have also shown that the various flavours of BDM are extremely robust, both by calculating theoretical errors on tensors and by numerical investigation, establishing that any BDM version is fit for use in most cases. Hence the most basic and efficient one can be used without much concern as to the possible alternative methods that could have been used in its calculation, as we have exhaustively and systematically tested most, if not all, of them. Ready to use online and offline methods and data are being released alongside this paper.
Proof. The strategy is similar: by brute force permutation one can produce all possible adjacency matrices after row and column permutation. Let K(G) be the algorithmic complexity of G and Spec(G) be the spectrum of G. Let p be the program that permutes all rows and columns and tests for cospectrality, and |p| its program length. Let Spec(G) = Spec(G ). Then K(G ) ≤ K(G) + |p| + log(n) where n is the size of G that indicates the index of the right column and row permutation that preserves Spec(G) among all graphs of size n. 12 The Online Algorithmic Complexity Calculator and Language Implementations The Online Algorithmic Complexity Calculator (or OACC) available at http: //www. complexitycalculator. com has now been updated to incorporate 2dimensional data, in preparation for this paper. The OACC implements the most important methods and tools explained here.
Documentation is available at https://cran. r-project. org/web/packages/acss/acss. pdf, as the OACC is an online app based on the acss [21] R package. We are releasing actual implementations of BDM in, C++, Pascal, Perl, Python, Mathematica and Matlab. Table  5  provides an overview of the various implementations. We are also releasing the data generated by CTM which BDM requires to run. This consists in the Kolmogorov complexity evaluations of all strings up to length 11 (12 with the completion of a single one and its complement) as well as for some longer ones, and of all the square arrays of length up to 4×4 as calculated by the canonical 2-dimensional Turing machine model (replacing the unidimensional tape with Rado's 2-dimensional model). The data is also released with a program (a Wolfram Language 'Demonstration') available on-line at the Wolfram's Demonstrations Project website  [15].
× × × × × WL × × R × × × × Matlab × × × Haskell × × × × Perl × × × × × × × Python × × × × × × × Pascal × × × × × × × C++ × × × × × × × Table  5 : Computer programs in different languages implementing various BDM versions. We have shown that all implementations agree with each other in various degrees, with the only differences having to do with under-or over-estimated values and time complexity and scalability properties. They are extremely robust, thereby establishing that the use of the most basic versions (1D n-o, 2D n-o) are justified in most cases. 'WL' stands for Wolfram Language, the language behind e. g. the Mathematica platform, 'Online' for the online calculator, 'Cyc' for Cyclic, 'Norm' stands for normalized, 'Rec' for recursive, 'Smo' for 'Smooth', 'N-o' for Nonoverlapping and 'addcol' for the method that adds rows and columns of lowest complexity to the borders up to the base string/array/tensor size.
Sequencing and Data Analysis Sequencing consisted in paired-end 2 × 300 pb runs, using a MiSeq instrument (Illumina) and the MiSeq Reagent Kit v3 (600 cycles) (Illumina). Sequencing analysis and annotation, estimation of error rate, and normalization by subsampling, as well as validation of our barcoded IgH cDNA sequencing approach, are described in Supplementary Methods. Sequence data were registered in the BioProject ncbi database with the SRA accession number: SRP128087.
Computational Model of IgH VDJ Rearrangements We employed an existing computational tool suite, IGoR  (23) , to construct a generative probabilistic model of the IgH VDJ recombination process and generate corresponding synthetic receptor sequences. The parameters were inferred for each VHC combination sequenced in this work, from the corresponding sets of non-productive sequences. IGoR can then be used to generate synthetic nucleotide sequences, which can readily be translated into amino-acid sequences and compared between synthetic datasets (i. e. , generated by the model). A detailed description of the model is provided in Supplementary Methods.
We define a clonotype as a specific combination of C (µ or τ ), VH, JH, and CDR3 amino acid sequence. We first describe results obtained for unvaccinated controls, which provide a benchmark for the analysis of the changes induced by the vaccination and the boost. Individual fish datasets obtained for each VHC combination were normalized by random subsampling of 7,000 clonotypes without replacement to quantitatively compare all fish together despite varying sequencing depths, while preserving the clonotypes' relative frequencies. Ten subsamplings were performed, and the average results were analyzed (see Supporting methods for details). A comparison of the number of distinct IgM and IgT clonotypes in unvaccinated controls suggested that the repertoires were more diverse for IgT than for IgM for all VH (Table  S2A ).
To this end, we considered the 50 or 100 most abundant clonotypes (called "Top50" and "Top100" clonotypes thereafter). For each group (Ctl, Vac, and Bst), the 4 sets of "Top50" clonotypes from each of the 4 fish in the group were aggregated to obtain lists of non-redundant clonotypes called Top Clonotype Lists (TCL) Ctl , TCL Vac, or TCL Bst. The total number of clonotypes in a TCL was thus between 50 (if all were shared by the 4 fish of the group), and 200 (if none were shared) for the Top50 case. We then analyzed for each clonotype of a TCL in how many fish of a group (Ctl, Vac, or Bst) it was found. The distributions of TCL Ctl clonotypes are represented in the left column in Figure  2 , Figure  S5A. Individual fish datasets, for each VHC combination, were normalized as described above.
Sharing of IgM and IgT Top50 Clonotypes in Unvaccinated Controls Is Determined by Selection Rather Than at the Rearrangement Level These differences between IgM and IgT Top50 clonotypes, i. e. , their contrasted degree of sharing between unvaccinated controls, may already exist in the clonotypes that emerge from the recombination process, before any selection. To assess this possibility, we constructed a model of the recombination machinery using the IGoR software  (23). IGoR can be used to generate "synthetic" nucleotide sequences, which can readily be translated into amino-acid sequences and used for comparisons. It is based on the statistics of IgH VDJ rearrangements computed using the non-productive (either out-of-frame or with an inframe premature stop codon) junctions of our dataset (see Supporting methods), which were not subjected to selection since they were not expressed at the protein level.
Fish Maintain IgM and IgT Components of the Response to Vaccination Over 5 Months in the Spleen The B cell repertoire response signatures persisted in spleen for months after vaccination, involving public clonotypes previously found in fish infected twice at 3 weeks interval  (18). Our previous data suggested that the most frequent clonotypes of the VH5Cµ public response after two infections in quick succession could represent up to 25-30% of the total VH5Cµ transcripts  (18). In this dataset, they reached 10-15% at most, suggesting that the size of public VHSV-specific top clonotypes is smaller 5 months post-vaccination. This is consistent with the maintenance of a lower pool of "memory" clonotypes after the elimination of the attenuated virus. This result differs from immunizations with hapten-carrier conjugates (TNP-KLH), where the high-affinity Abs appeared only 3 months after trout immunization  (35).
ACKNOWLEDGMENTS We acknowledge  L. du Pasquier ,  J. Hansen ,  Yan Jaszczyszyn ,  A. Krasnov ,  J. P. Levraud , and  P. A. Cazenave  for helpful discussions, and the  INRA  fish facilities (IERP). This work has benefited from the facilities and expertise of the  Molecular Biology platform of TEFOR  and  I2BC.
FUNDING This work was supported by the  Institut National de la Recherche Agronomique (INRA) , by the  European Commission  under the  Work Programme 2012 of the 7th Framework Programme for Research and Technological Development  of the  European Union  (Grant Agreements 311993 TARGETFISH, ERCStG n. 306312, and  600391 People Programme ) and by the  ANR-16-CE20-0002-01  (FishRNAVax). SM has been supported by an  INRA-INRIA contract Young scientist. OS has been supported by  NIH  grant#  R01-GM085207-08  and by  NSF  grant#  IOS-1457282.
Python Language Reference, version 2. 7) and its Numpy and Scipy modules  [27]. At the Representative Elementary Volume (REV) scale (microscale) the mechanical stress and WSS distribution identification was performed using finite volume method (ANSYS-Fluent). ANSYS-Fluent code is used because of its advantage in memory usage and solution speed especially when working with common calculus station. Then, the statistical analysis necessary to identify the optimal range of growth stress is developed into a Python code. Finally, velocity and stress into the macroscopic bioceramic material is computed thanks to a finite element analysis with COMSOL, and more specifically the mathematics module that allows users to implement custom model. The mathematics module is so used to solve both equations based on fluid mechanics and on biology in an easier fashion than with a finite volume method.
Statistical analysis -Microscale The statistical analysis is necessary to identify the process of identifying the optimal range of WSS for cell growth which is implemented in Python code using Numpy and Scipy modules. All the data from the CFD computation are stored into a table that contains mesh elements, their volumes and surfaces and the associated Wall Shear Stress. WSS and elements surfaces are extracted from elements that correspond to pores surfaces. For a given element, the Wall Shear Stress contribution is just computed as follows ( Eq. (  2 ) ). P e τ [%] = S e τ S i × 100 ( 2 ) Where e is a volume element, S e τ the value of the surface of the volume element at the interface between solid phase and fluid phase, S i the value of the total surface of the interface and so P e τ the percentage of a computed Wall Shear Stress corresponding to the surface element.
As an example, Fig. 9  shows the distribution of dioxygen concentration for a constant inlet flow rate Q = 0. 69 mL • min -1. A stratification is observed giving us reasons to assume that cell density is more important on the top of the scaffold. At the ending time (seven days of cell culture modelling), the cell density evolution can be calculated. To be accurate, here are the different steps to obtain the optimal flow rate. At first, 6 ranges of Wall Shear Stress are studied (from [10 -5 ; 5 × 10 -5 ] to [5 × 10 -3 ; 10 -2 ] Pa) for 20 flow rates (from 10 -3 to 10 mL • min -1 ). Data analysis is carried out to focus on more specific ranges of WSS and flow rates. Due to the cost of numerical simulations only the two best flow rates (0. 27 and 0. 69 mL • min -1 ) and two most common flow rates (1.
More generally, the common classification pipeline of multi-resolution VHSR images involves three main steps: (1) produce a single resolution dataset by means of a down-sampling/up-sampling or pan-sharpening procedure starting from the multi-resolution sources  [5] , (2) extract spatio-spectral features in a hand-crafted fashion and (3) classify the resulting feature set by means of machine learning techniques  [6]. A notable example is given in  [7] , where the authors propose to extract hand-crafted spatio-spectral features (attribute and morphological profiles) directly from the pansharpened image as a new representation of the input data. Successively, a Random Forest classifier is fed with the extracted features to perform the final classification. With respect to the first step of the aforementioned pipeline, performances can be affected by artifacts or noise introduced upstream by the pan-sharpening process  [5].
The rest of the article is organized as follows: a discussion on current deep learning approaches for land cover classification from VHSR images, as well as the positioning of this paper in this context, is proposed in Section 2, Section 3 introduces the proposed deep learning architecture for the joint classification of PAN and MS imagery. The study sites and the associated data are presented in Section 4 while, the experimental setting and the evaluations are carried out and discussed in Section 5. Finally, Section 6 draws conclusions.
Method In this Section we describe the proposed classification framework (A preliminary version of our work is available at this URL: https://arxiv. org/abs/1806. 11452). Figure  1   Our objective is to provide a per-pixel classification of the source dataset at the PAN spatial resolution. To exploit the spatio-spectral contextual information coming from the two sources, in our workflow each pixel is represented by means of a pair of patches, extracted respectively from PAN and MS images and covering the same geographical area. For this purpose, supposing that the spatial resolution ratio between the PAN and MS image is equal to r, we set the size of the PAN patch equals to (d × d), hence a patch size of d/r × d/r for the MS image. To ensure the best spatial correspondence between PAN and MS patches, d is here chosen as a multiple of r, hence producing even-sized patches at PAN resolution since r is typically an even number (e.
In our case the (random) transformations are sampled from standard data augmentation techniques (90-degree rotation, vertical/horizontal flips, and transpose). For each example, each technique is simultaneously performed on both the PAN and the corresponding MS patch. On average, the final training set has a size around three times the original training set. The field database, constituting the ground truth, was built from various sources: (i) the Registre parcellaire graphique (RPG) 0 reference data of 2016 and (ii) photo interpretation of the VHSR image conducted by an expert, with knowledge of the territory, for distinguishing between natural and urban areas.