dataesr/scientific-paragraphs-categorization huggingface dataset:
1 mg/ml) and cells were incubated for 2 h at 37 °C, washed in PBS, and then fixed. Specimens were analyzed using a Zeiss LSM880 confocal laser-scanning microscope. The images were deconvolved with Huygens Essential Software (Scientific Volume Imaging). The deconvolved images were used for quantitative mitochondrial network analysis with Huygens Essential Software. Mitochondrial network length was quantified for 48-116 randomly selected individual cells. Generation of Drosophila lines. To generate transgenic Drosophila lines carrying C2C10H, codon-optimized CHCHD10, and their variants, all cDNAs were synthesized with or without a C-terminal FLAG tag and cloned into pUASTattB by GenScript. Transgenic Drosophila lines were generated by BestGene with standard ΦC31 integrase-mediated transgenesis into the attP2 site on chromosome 3 (locus 68A4), the attP40 site on chromosome 2 (locus 25C6), or the VK27 site on chromosome 3 (locus 89E11).
The NMJs at muscle 4 were used for all analyses. Drosophila behavioral assay. Six to seven wandering third-instar larvae from each group were collected, washed, and placed onto a 3% agarose gel in a 10-cm dish. Larval crawling was recorded by a digital camera for 30 s. Moving distances of individual larva were tracked and measured using Tracker software (Open Source Physics, 5. 0. 7). To test flight ability, 20 flies from each group were funneled into a 500-ml glass cylinder. The distribution of flies in the cylinder was recorded by a digital camera, and the average scores from five independent experiments were calculated. Drosophila immunoblotting. Heads of 2-day-old adults were prepared and ground in LDS sample buffer by using a motor-driven plastic pestle homogenizer and centrifuged at 16,000 × g for 10 min.
Image analysis and statistical analysis. Mitochondrial branch length was measured by using the MiNA toolset combined with the ImageJ software  72. Statistical analysis was performed with Prism5 (GraphPad) software. For Drosophila eye quantification, we implemented ilastik, which is an interactively supervised machine learning-based tool for various bioimage analysis to classify ommatidia in fly eye images  73. Then we used Flynotyper 31 , which is a computational tool for assessing the Drosophila eye morphological defects, to calculate phenotypic scores (P-scores), which indicate the irregularity of ommatidial arrangement. Eye images for group A (Fig. 1b , Supplementary Fig. 1d , Fig. 3a , Supplementary Fig. 3d , and Fig. 5a ) were captured with a Leica M205C stereomicroscope equipped with a ring light and a Leica DFC320 digital camera.
Acknowledgements We thank the  Bloomington Drosophila Stock Center , the  VDRC Stock Center for fly lines , the  IRCAN's Molecular and Cellular Core Imaging (PICMI)  facility, and the  Research Instrumentation Lab at the University of Minnesota Duluth  for assistance. We also thank undergraduate volunteers,  Sierra Skudlarek ,  Austin Kurtz ,  Anna Huy , and  Maddie Chalmers , helping in Drosophila maintenance and plasmids preparation. This work was supported by grants from the  Muscular Dystrophy Association , the  Wallin neuroscience discovery fund , the  Engebretson Drug Design and Discovery Grant , and the  NINDS/NIA   1R56NS112296-01  to N. C. K.
3. The pseudo-closure operator is acquired by a supervised multiple instance learning process  [30, 76]. Multiple instance learning is particularly suitable when an observation can be described by multiple instances. This methodology (presented in Section 3) is necessary because we learn a pretopological space from its known elementary closed sets. In pretopology theory, a single closed set (considered as an observation in the learning problem) can be obtained by several propagation models (multiple instances). In fact, the number of valid propagation models, or pseudo-closure operators, is exponential in the size of the elementary closed sets considered. This leads us to a learning problem with a set of input examples whose size is exponential. We propose in Section 4 a method to provide a (low) estimate of the number of positive bags covered (true positives) and the exact number of negative bags covered (false positives) without the need to explicitly generate the dataset.
Bag labels may be assigned differently depending on the target task  [30] , but this simple bag labeling scheme, as proposed by Dietterich et al. [30] , is well suited for the LPS task. The MI task consists in finding a function predicting the label of a new instance knowing only the labels of the bags. A famous example of MI task is the simple jailer problem  [24] : given a locked door and a bunch of key rings, the task is to find which key opens it. But the only information we have is whether a key ring is useful or not, i. e. , whether it contains at least one key capable of unlocking the door. An example of a simple jailer dataset is provided in Table  4. Recall that in real cases, the column "Instance labels" is unknown and is presented here in order to understand why bags 1 and 2 are positive (because they have at least one positive instance).
Building a MI dataset for the LPS task The LPS task consists in learning a pretopological space (E, a Q ) of type V such that its set S Q of elementary closed sets corresponds to a target set S * of elementary closed sets. Let x be an element of E and its target elementary closure be F * ({x}) = {x, y, z}. In general, several steps are necessary to compute an elementary closed set. For example, the valid steps for propagation from {x} to {x, y, z} are {x, y}, {x, z} and {x, y, z}. If the pseudo-closure operator a Q (. ) produces something different during the computation of F Q ({x}), then the target elementary closed set F * ({x}) cannot be retrieved. The set of valid propagation steps for an element x are given by X = {X ∈ P(E) | x ∈ X, X ⊆ F * ({x})}. The set of all propagation steps valid for all elements in E can thus be projected on the Boolean lattice of subsets of E.
Some propagations are impossible, for example {x 2 , x 3 } cannot reach {x 3 , x 4 , x 5 } since the first is not included in the second. In this case, it is useless to insist on the absence of propagation. That is why it is not reported in the set of negative bags. We have just explained how to construct an MI dataset for the LPS task, given a set E of elements and a set S * of target elementary closed sets. But it is not yet possible to learn anything about this dataset due to the missing feature.
The set Q = {q 1 , q 2 , q 3 , q 4 } of four predicates can then be constructed by following the instruction of Equation (  1 ) in Section 2. 3. 2. The Boolean feature vector (q 1 (A, x), q 2 (A, x), q 3 (A, x), q 4 (A, x)) is then constructed for each instance A → x of the dataset. Table  6  shows the bags engendered by x 2 as well as the feature vector associated to each instance. Suppose the DNF Q = (q 1 ∧ q 4 ) ∨ (q 2 ∧ q 3 ) is learned by an unspecified MI learning process. The resulting pretopological space (E, a Q ) of type V would give the set {x 2 , x 3 , x 4 , x 5 } as the elementary closure of x 2. It is obvious that {x 2 } ⊆ {x 2 , x 3 , x 4 , x 5 }, the isotonic property of the pseudo-closure operator can be exploited to deduce that a Q ({x 2 }) ⊆ a Q ({x 2 , x 3 , x 4 , x 5 }) = {x 2 , x 3 , x 4 , x 5 }.
The issue of the exponential MI dataset As illustrated above, the Multiple Instance framework provides an elegant and accurate modeling for the non-trivial problem of learning a pretopological space. However, since pretopology refers to the power-set P(E), the number of positive bags engendered by an element x ∈ E is exponential (proportional to the size of its elementary closure F * ({x})) thus revealing a major issue for the learning process. We have just shown how to construct a dataset based on a target set of elementary closed sets. With such a dataset, one can apply standard MI algorithms to learn a solution from it. A standard MI approach would require enumerating all instances to count the number of bags covered, which will not be a problem with the example given above. But in real-world problems, the number of positive bags generated is overwhelming.
This method relies on a trick based on the properties of V-type pretopological spaces. Counting the number of negative bags covered is not a big problem, so we will not treat it in depth. On the other hand, counting the number of covered positive bags is a problem that requires much more effort. We show that while we are able to count the number of positive bags engendered, it is inefficient to accurately count the number of positive bags covered by a solution. Thus, we estimate this number by subtracting a (high) estimation of the positive bags rejected (i. e. not covered) by the solution from the number of positive bags in the dataset.
Estimation of true/false positives In what follows, we propose a method for estimating the number of positive and negative bags covered by a solution. We begin by describing how to calculate the total number of positive bags in a dataset from the LPS task. This will serve as an introduction to estimating the number of positive bags covered by a solution (i. e. the number of true positives), since the counting mechanism is roughly similar. For completeness, we also show how to compute the total number of negative bags engendered. We then show how to compute the number of negative bags covered by a solution (i. e. the number of false positives).
Multiple Instance LPS LPSMI is a multiple instance variant of Greedy LPS. It takes as input a set S * of target elementary closed sets and a collection V = {V 1 ,. , V k } of k neighborhoods. LPSMI produces a positive DNF Q such that the elementary closed sets computed by the pseudo-closure operator a Q (. ) best match S *. LPSMI builds a positive DNF by adding a new disjunctive clause to the DNF built in the previous iteration. LPSMI is implemented in the same spirit as Greedy LPS, they differ mainly in the quality measure they rely on. LPSMI finds the best clause based by relying on the intrinsic quality measure instead of the extrinsic measure. Thus, we do not show pseudocode of LPSMI because it would be identical to Greedy LPS (Algorithm 2) but with the extrinsic measure replaced by the intrinsic measure.
They propose to compute a clustering of hypernym-hyponym pairs (x, y) according to the offset vector xy. Then, for each cluster i, they train the linear projection φ i projecting the embedding of an hyponym y to the embedding of its hypernym. A term x is considered a hypernym of another term y if the embedding x is close to φ i ( y), where i is the cluster minimizing the euclidean distance between xy and its center. We used k-means to cluster the set of pairs into k groups. It turned out that k = 5 was sufficient because our datasets were small enough. We set the threshold used to determine whether x is close enough to φ i ( y) to 1. This threshold was found empirically and may be inappropriate if applied to other data. Finally, the relation R F u can be defined as below:  2  ), with c i the center of the cluster i.
Experimental data and results We applied the LPSMI algorithm for the reconstruction of four domains extracted from WordNet  [57] : vehicles (108 terms), plants (554 terms), food (1486 terms) and animals (688 terms). Three sub-domains per domain were also extracted, as reported in Table  7. We fed the LPSMI algorithm with a set of five predicates defined by the neighborhood relations described in the previous sections, as explained in Section 2. 3. 2. The three relations R Sand. , R patterns and R strmatch are acquired, each, by an unsupervised data extraction process from raw texts. We consider here the Wikipedia corpus. On the other hand, both R meanH and R F u must be trained. Depending on the objective, they will be trained on different datasets. In the case of the task taxonomy reconstruction (Section 6.
4. 1), whose objective is to train a model to extract a given taxonomy, the train and test datasets are the same. This task is a preliminary to the more interesting taxonomy extraction task (Section 6. 4. 2), whose objective is to extract a new taxonomy by applying previously trained models. R meanH and R F u are trained on a large set of embeddings, which cover a large portion of the terms present in our three datasets (some terms are missing, especially multi-word terms). Since we use the same set of embeddings to train these relations, regardless of the target domain, a relation trained on one domain can be transferred as is to predict relations on another domain. For example, an average hypernym vector learned on crafts can be transferred as is to the domains vehicles or plants. This is not the case with modern embedding-based approaches, such as TransE  [18] , which are not designed to reuse embeddings on a different task.
These results experimentally validate the first hypothesis on the benefits to be expected from a multi-criteria combination. Furthermore, in order to evaluate the relevance of the pretopological combination itself, we seek to compare two approaches for learning structures: on the one hand, (supervised) learning of a structuring model (e. g. LPS) and on the other hand, (supervised) learning of relations (e. g. by traditional classifiers such as Support Vector Machine (SVM) or Decision Tree (DT)). Although recent work tends to rely on neural Language Models to learn semantic relations, the comparison with SVM and DT is actually appropriate to demonstrate the benefits of LPSMI. LPSMI and SVM indeed share some common behaviors since they can both be used to learn non-linearly separable classes.
In fact, the SVM trained on binary data performed slightly better than its counterpart trained on continuous data, while the DT performed better with continuous data. In order to remain consistent with LPSMI, we present only results obtained by the models trained on binary data. Since SVMs and DTs expect tabular data as input instead of a collection of neighborhoods, we transformed our datasets such that the instances are pairs (x, y) and their class labels are 1 if x is a hypernym (direct or not) of y, 0 otherwise. Each instance has five features corresponding to the five neighborhoods. Since, unlike a pretopological model, SVM and DT do not mimic a propagation process and therefore cannot reuse information gathered in the previous steps (since there is only one step), the feature values in the transformed datasets are obtained from the transitive closures of the neighborhoods.
Thus, the SVM and DT models were trained on data pre-propagated. A graph whose nodes are the terms of E and whose edges are relations learned by SVM or DT can then be constructed. The output taxonomies are the transitive closures of these graphs. (F u) ∨ (Sand. ∧ meanH) ∨ (patterns ∧ meanH) ∨ (strmatch) fish (patterns) ∨ (Sand. ∧ meanH) ∨ (strmatch) ∨ (F u) birds (patterns) ∨ (Sand. ∧ meanH) ∨ (F u) ∨ (strmatch) Table  9  shows the performance of each algorithm for the task of capturing the hypernymy relation of a given sub-domain. The implementations used for SVM and DT fail to learn models on the domain food because of the size of the dataset, which contains over 2 millions instances (term pairs). The performance of LPSMI is often better, or at least similar, to that of SVM or DT. Finally, we show the logical formulas learned by LPSMI in Table  10.
Of course, this criticism also applies to our neighborhoods strmatch. JUNLP  [53]. JUNLP is composed of two modules. The first queries BabelNet  [58]  to obtain a list of hyponyms, which is then filtered to reduce noise. BabelNet is a multilingual semantic network built from various sources, including WordNet. Since the evaluation uses gold taxonomies extracted from WordNet, it used only the Wikipedia source of BabelNet. The second module is based on the same assumption as the Baseline: potential hypernyms are extracted based on overlaps between words. For example, the overlap between "biochemistry" and "chemistry" is "chemistry", then "chemistry" is likely to be a hypernym of "biochemistry". It uses this same assumption to enrich the lexical taxonomy with words that are not in the input term list.
USAAR  [70]. The USAAR system assumes that many hypernym-hyponym pairs can be detected by their endocentric grammatical construction. A grammatical construction is endocentric when one of its components performs the same linguistic function as itself. For example, "aircraft" is an endocentric construction since it is a noun, as well as its component "craft". Furthermore, "craft" is indeed a hypernym of "aircraft". The performance of LPSMI models is very heterogeneous depending on the dataset used to train them, as shown by Table  14. 11 Training on plants, or a sub-domain, seems difficult, mainly because our neighborhoods cannot capture the hypernymy relation of these domains. The model trained on the sub-domain wagons also performed poorly, but this is not surprising since wagons has only height terms and seven edges.
Since Kozareva and Hovy initially used the Yahoo!Boss search engine, we replicated their algorithm using Wikipedia data in order to compare the methods on common bases and to understand the influence of the source information corpus (Web source) on the quality of the extracted structures. The evaluation criterion used this time is the Cumulative Fowlkes and Mallows measure (F&M) as defined in  [72] , used in  [15]  and for which executable sources are made available  13  ; this measure compares an extracted taxonomy and the gold standard by measuring the matching between the partitions (of terms) induced at each level of the structures to be compared. Table  15  presents the results of this comparative study. The table is divided into two parts, the first five rows contain the results of the different methods (OntoLearn Reloaded, K&H and LPSMI) based on information extracted from Wikipedia (free) while the last row corresponds to the original K&H method based on Yahoo!Boss (not free).
Each model is then used to reconstruct a complete taxonomy. The model finally retained for a taxonomy to be reconstructed is the one that induces the most relations (without explosion  14  ). Unlike OntoLearn Reloaded and K&H, LPSMI is not a methodology dedicated to the extraction of taxonomies but a generic algorithm for learning pretopological spaces. Nevertheless, we observe that the LPSMI algorithm obtains quite competitive results on this task, outperforming even dedicated methods on the dataset vehicles.
, 2009). A sea level and high latitude 36 Cl production rate for calcium spallation of 42. 2 ± 3. 4 atoms 36 Cl g -1 yr -1  (Schimmelpfennig et al. , 2009; Braucher et al. , 2011)  has been used and scaled after  Stone (2000)  and corrected for topographic shielding (see next paragraph). 36 Cl ages have been determined using the approach of Schimmelpfennig et al. (  2009 ) using a limestone density of 2. 6 g•cm -3. CRE data are displayed in Table  1. Topographic shielding was first determined from field data. Furthermore, we chose to use a high resolution DEM, built from a drone survey of the gorge and its surroundings, in order to be able to precisely recalculate the shielding parameters on each sampled surface.
3  (Gosse and Phillips, 2001). This factor is commonly estimated in the field during surface sampling by measuring, using a compass, the horizon elevation at 360°around the sampled surface  (Gosse and Phillips, 2001). However, such protocol is difficult to implement in gorges while sampling the vertical wall, and is complexified by the 3D geometry of the gorge. Numerical methods exist to estimate TS from Digital Elevation Models (DEM), which have been demonstrated on basin-averaged denudation rate studies (e. g. Codilean, 2006; Balco et al. , 2008). In this paper, we used the tools developed by  Li (2013) , for the GIS software ArcGis, using high-resolution DEMs of the CdB Gorge and its surrounding. The DEMs used are built from the Structure-from-Motion method, using aerial photographs acquired by plane, for the surrounding gorge topography, and UAV, for the mapping of the vertical parts (gorges walls), following an adaptation of the methodology presented in  Vasquez-Tarrio et al.
Acknowledgments This study has been supported and funded by the  French Geological Survey BRGM (Bureau de Recherches Géologiques et Minières)  through the  national program  " Référentiel Géologique de France" (RGF-Alpes) , the  AO7 OSUG@2020  ( ANR10 LABX56 ) and by the  CNRS-INSU SYSTER Program. The 36 Cl measurements were performed at the  ASTER AMS national facility  ( CEREGE, Aix en Provence ) which is supported by the  INSU/CNRS , the  ANR  through the  "Projets thématiques d'excellence" program for the "Equipements d'excellence" ASTER-CEREGE action  and  IRD. Fruitful discussions with  Ludovic Ravanel  benefited the interpretation of the results. The authors are grateful to  Myette Guiomar  from  "Réserve naturelle nationale Géologique de Haute-Provence" (RNGHP)  for sampling autorisation.
Select regions with intensities smaller than some threshold (like 0. 97) as potential glitches; 7. Analyze derivatives in the preselected regions. If the derivative changes the sign from "-" to "+" and has some reasonable amplitude (usually 1-5), declare this as a glitch. In order to process all glitch spectra collected from different samples, we have written a program that takes as an input a text file with glitch spectrum, smoothing radius, and the derivative threshold and produces a text file with the energies of the found glitches. This program can be found at GitHub  [29].
3984 , (2) This means that the change in the energy of the incident X-rays leads to the change in the Ewald sphere radius, and so, the sphere can intersect different RLPs. That explains the appearance of glitches at different energies, when the sphere of corresponding radius K intersects different RLPs. The animated GIFs showing the process of glitches formation were made using the program XViz  [31]  (see at GitHub  [29]  in Supplementary Material).
The process of analysis of the experimental glitch spectra is computationally heavy due to the fact that the glitches are very sensitive to the orientation and cell parameters of the lattice. In order not to miss the correct orientation, one has to do the search with a rather small step (at least 0. 01 Å for UC and 0. 5 • for the angle). Additionally, the range of orientation can be rather large due to the miscut of the crystal slab and due to an error in setting up the optical element. Luckily, the process of the initial search is highly parallelizable, so it is possible to perform the search in just minutes. For this task, a program in C++ was written  [29].
Determining the Exact Orientation and Cell Parameters After the determination of the approximate lens orientation, we can refine it using an analytical approach described in  [30]. In order to do so, we can use the indexed glitch spectra, described in the previous section, and run the analytical solution that will derive the exact orientation and the UC parameters. As we have demonstrated in  [30] , it is enough to have just three indexed glitches to get the orientation and the UC parameter (in the case of the cubic cell). However, having more indexed glitches improves the precision of the determination of the parameters. The refinement program is written in Python and is using NumPy function linalg. lstsq to solve an overdetermined system of equations  [29].
Additionally, for the central part of the lens, this diffracted part of intensity is smaller. The 2D lens was much thinner than the MUL lens; therefore, the drops of intensity, as described above, were also much smaller (Figure  8 ). As can be seen from Figure  8 ,b some glitches overlap, but this is not a problem for our algorithm. The reader can use the program from  [29]  to get the exact energy for each glitch. At the magnified spectra of the diamond plate (Figure  9b ), one can see a not-predicted glitch at E = 16. 95 keV. This glitch is most probably a monochromator glitch that was not fully eliminated during the normalization process. In fact, this glitch is also I, a. u. 7,-3,- 3 7,-3,3 8,4,0 8,0,-4 8,0,4 5,5,-1 5,5,1 9,3,-1 9,3,1 8,-4,0 9,1,-3 9,1,3 9,-1,-3 0. 8 0.