dataesr/scientific-paragraphs-categorization huggingface dataset:
| Future climate change projections To analyze the present and future incidence of extreme high temperatures on CTmax, we downloaded bias-corrected raw data of climate layers from the CCAFS (Climate Change, Agriculture and Food Security)-downscaled general circulation model (GCM) data portal (http://www. ccafs-climate. org/), in the form of data for one emissions scenario (RCP 6. 0) and four GCMs: bcc-csm1-1, bcc-csm1-1-m, CSIRO-Mk3-6-0, and MIROC-ESM. In particular, we downloaded maximum temperature data for Raleigh NC (Latitude = 35. 763340, Longitude = -78. 662644) from past periods  and future projections (2045-2070). For both periods, we used the ensemble data for the models.
| RE SULTS A total of 1,837 flies were measured. Means of CTmax across DGRP lines ranged from 40. 05 to 41. 47°C, with a mean value of 40. 98 ± 0. 79 (SD) (Supporting Information Table  S1  shows mean CTmax for each line and sex). In order to analyze genetic variation of CTmax between DGRP lines and sex, we performed a mixed effects model. Significant genetic variation across DGRP lines was found (χ 2 = 28. 73; p = 8 × 10 -8 ), with a broad sense heritability (H 2 ) of 0. 14. The effect of sex on CTmax was not significant (t = -1. 44; p = 0. 16), nor its interaction with the line (χ 2 = 1. 16; p = 0. 38). Thus, data from both sexes were pooled and GWAS performed on the mean CTmax value for each line (Figure  2 ). For the 34 lines measured, 1,288,487 SNPs were analyzed. The analysis showed no effect due to Wolbachia infection (ANOVA, F 1,3 = 0.
Along these lines, Julien-Laferriere et al. (2016)  proposed to extend the gapfilling concept used in metabolic network reconstruction frameworks  (Pan et al. , 2018)  to create synthetic communities among a small number of bacterial candidates. They select species that own enzymes of interest, facultative exogenous genes and model transports between organisms, i. e. exchanged metabolites. The latter can be used either as energy sources or metabolic precursors i. e. building blocks by recipient organisms  (Cavaliere et al. , 2017). The very high combinatorics between all exchanges viewed as variables makes its impossible to scale their method either to the study of massive datasets involving hundreds or thousands of organisms or to the sorting of possibly hundreds of targeted functions. Eng et al.
(2016)  developed CoMiDA for the purpose of scaling to hundreds of species and targeted compounds using a non-compartmentalized, boundary-free level of modeling, called mixed-bag or gene-soup  (Henry et al. , 2016). The price to pay for scalability was to get rid of the number of exchanges metabolites involved in the selected community functioning. Although not applied in the same context in terms of the size of datasets considered or the optimization performed, both methods entail optimizing a parsimonious criterion. In one case, the MultiPus algorithm minimizes the total cost of the synthesis, mostly resulting from exchanges and exogenous reactions (Julien-Laferriere et al. , 2016), whereas the second algorithm minimizes the size of the community  (Eng et al. , 2016)  and provides one single solution that meets the desired objective.
A workflow for the target-based screening of a microbial consortium associated with a host The Miscoto Python tool (MIcrobiome Screening and COmmunity selection using TOpology) encapsulates the previous ASP encodings and takes as input a family of metabolic networks each corresponding to a symbiont, a metabolic network associated with a main species called host (optional), a set of seeds depicting the medium compounds and a set of targets depicting the expected products. For the sake of an exhaustive target study, the family of targets can be set to be equal to all cytosolic compounds of the host, or the whole set of compounds in the microbiome and host. The first step of the analysis of the microbial consortium (Fig. 2 ) is a feasibility analysis. It entails identifying the added-value of the symbionts for the production of metabolic compounds of the system.
Design of the analysis workflow The HMP Consortium stool sample (Human Microbiome Project, 2012) was the first large dataset to describe the abundance and variety of bacterial functions in the gut. It represents a valuable resource for exploring microbial cooperation in the gut metabolism. Eng et al. (2016)  produced metabolic models for the HMP data. Each of the 2051 bacteria has its own metabolic model, the union of all consisting of 3606 unique reactions. The average size of each metabolic model is 1096 reactions. A benchmark was established to validate their flow-inspired algorithm by selecting a minimal community of bacteria to produce a target metabolite from a seed one. Ten thousand random pairs of two metabolites (one seed, one target) were randomly tested. The authors used a ILP algorithm and several formalisms, of which the most constrained was a bipartite graph modeling, equivalent to the mixed-bag framework introduced in Section 2.
2. The goal of the algorithm was to enable the production of the targeted compound, assuming that the seed was considered to be the only available compound for the bacteria in the consortium to activate their reactions. The authors showed that species could be selected to produce the desired target from the unique seed in less than 5% of the cases in the benchmark, thus concluding that in most cases among the ones tested, no path exists between the seed and the target. Miscoto allowed the analysis of  Eng et al. (2016)  to be pursued by designing a pipeline that enables an exhaustive exploration of the previously benchmarked dataset (available on demand to the authors). The pipeline is depicted in Figure  2. We first calculated the metabolic feasibility by a microbial community of all possible 4 948 400 seed/target pairs of metabolites, which we call functions.
Exhaustive feasibility study of the HMP seed/target functions Figure  3  depicts the results of the exhaustive study of feasibility of all of the 4 948 400 functions in the HMP dataset, that is, all possible pairs combining any single seed from the microbiota compounds associated with any single target from the microbiota compounds. We classified functions in three categories: unsolvable functions that can never be reached regardless of the selected community, trivial functions that can be met by a single bacterium and community functions that can only be met through bacterial cooperation (two or more bacteria). Our analysis demonstrated that 76. 8% of functions are unsolvable in the benchmark, which is expected, as we only allow one metabolite to initiate metabolic reactions, i. e. the unique seed.
Identified community functions will require Fig. 5. Feasibility analysis of a family of 89 bacteria (lines) involved in optimal cooperations with Recon according to the producibility of 46 Recon cytosolic compounds (columns, identifiers from the BiGG database). Bacteria enabling the producibility of the corresponding target are depicted by a green spot when the required number of exchanges is minimal among all bacteria associated with the target. A yellow spot describes the case when the number of associated exchanges is not minimal with respect to the considered target. Red spots depict bacteria which do not restore the considered target producibility. The 89 bacteria are partitioned into three clusters such that each of the 381 optimal communities enabling the producibility of the whole set of 46 targets comprises a bacterium in each cluster.
These clusters can be discriminated by the producibility of eight compounds cooperation to occur, either between a host and at least one symbiont, or within a group of two or more bacteria. These functions can be further analyzed to identify all minimal-size communities that can meet them. We showed that the union of symbionts that appear in at least one solution is helpful for determining the redundancy of the function in the microbiome. The following step of exchange minimization within minimal-size communities forms a means for classification of these communities. The application of this workflow to the HMP data constitutes, to the best of our knowledge, the first exhaustive and large scale analysis of cooperation capabilities in a microbiome dataset. A first key feature of the method is to address scalability in large datasets by minimizing the size of the community prior to the exchanges.
However, we consider that not all experimental parameters can reasonably be formalized in such large scale datasets. Therefore, for the sake of genericity, we advocate for the identification of all optimal models without a priori considering experimental knowledge. Furthermore, metabolic transport identification is a challenge in metabolic models that highly depends on genome annotation quality. There usually are few metabolites for which transport between the cell and the extracellular space is assessed in non-models species without an extensive review of the literature  (Sung et al. , 2017)  although efforts are made to compute them automatically  (Dias et al. , 2017). Community solutions that would require unexpected transports can be discarded a posteriori. For others, an additional optimization is an option, by prioritizing exchange of metabolites for which transport is identified in the metabolic models.
Vector space embedding models like word2vec, GloVe, fastText, and ELMo are extremely popular representations in natural language processing (NLP) applications. We present Magnitude, a fast, lightweight tool for utilizing and processing embeddings. Magnitude is an open source Python package with a compact vector storage file format that allows for efficient manipulation of huge numbers of embeddings. Magnitude performs common operations up to 60 to 6,000 times faster than Gensim. Magnitude introduces several novel features for improved robustness like out-of-vocabulary lookups.
Introduction Magnitude is an open source Python package developed by Ajay Patel and Alexander Sands  (Patel and Sands, 2018). It provides a full set of features and a new vector storage file format that make it possible to use vector embeddings in a fast, efficient, and simple manner. It is intended to be a simpler and faster alternative to current utilities for word vectors like Gensim  ( Řehůřek and Sojka, 2010). Magnitude's file format (". magnitude") is an efficient universal vector embedding format. The Magnitude library implements on-demand lazy loading for faster file loading, caching for better performance of repeated queries, and fast processing of bulk key queries. Table  1  gives speed benchmark comparisons between Magnitude and Gensim for various operations on the Google News pre-trained word2vec model  (Mikolov et al.
, 2013). Loading the binary files containing the word vectors takes Gensim 70 seconds, versus 0. 72 seconds to load the corresponding Magnitude file, a 97x speed-up. Gensim uses 5GB of RAM versus 18KB for Magnitude. Magnitude implements functions for looking up vector representations for misspelled or out-ofvocabulary words, quantization of vector models, exact and approximate similarity search, concatenating multiple vector models together, and manipulating models that are larger than a computer's main memory. Magnitude's ease of use and simple interface combined with its speed, efficiency, and novel features make it an excellent tool for cases ranging from applications used in production environments to academic research to students in natural language processing courses.
Motivation Magnitude offers solutions to a number of problems with current utilities. Speed: Existing utilities are prohibitively slow for iterative development. Many projects use Gensim to load the Google News word2vec model directly from a ". bin" or ". txt" file multiple times. It can take between a minute to a minute and a half to load the file. Memory: A production web server will run multiple processes for serving requests. Running Gensim, in the same configuration, will consume >4GB of RAM usage per process. Code duplication: Many developers duplicate effort by writing commonly used routines that are not provided in current utilities. Namely, routines for concatenating embeddings, bulk key lookup, out-of-vocabulary search, and building indexes for approximate k-nearest neighbors. The Magnitude library uses several wellengineered libraries to achieve its performance improvements.
It uses SQLite 1  as its underlying data store, and takes advantage of database indexes for fast key lookups and memory mapping. It uses NumPy 2  to achieve significant performance speedups over native Python code using computations that follow the Single Instruction, Multiple Data (SIMD) paradigm. It uses spatial indexes to perform fast exact similarity search and Annoy 3  to perform approximate k-nearest neighbors in the vector space. To perform feature hashing, it uses xxHash 4  , an extremely fast noncryptographic hash algorithm, working at speeds close to RAM limits. Magnitude's file format uses LZ4 compression 5  for compact storage.
File Format To provide efficiency at runtime, Magnitude uses a custom ". magnitude" file format instead of ". bin", ". txt", ". vec", or ". hdf5" that word2vec, GloVe, fastText, and ELMo use  (Mikolov et al. , 2013; Pennington et al. , 2014; Joulin et al. , 2016; Peters et al. , 2018). The ". magnitude" file is a SQLite database file. There are 3 variants of the file format: Light, Medium, Heavy. Heavy models have the largest file size but support all of the Magnitude library's features. Medium models support all features except approximate similarity search. Light models do not support approximate similarity searches or interpolated OOV lookups, but they still support basic OOV lookups. See Figure  1  for more information about the structure and layout of the ". magnitude" format. Converter The software includes a commandline converter utility for converting word2vec (".
bin", ". txt"), GloVe (". txt"), fastText (". vec"), or ELMo (". hdf5") files to Magnitude files. They can be converted with the command: python -m pymagnitude. converter -i ". / vecs. ( bin | txt | vec | hdf5)" -o ". / vecs. magnitude " The input format will automatically be determined by the extension and the contents of the input file. When the vectors are converted, they will also be unit-length normalized. This conversion process only needs to be completed once per model. After converting, the Magnitude file format is static and it will not be modified or written to in order to make concurrent read access safe. By default, the converter builds a Medium ". magnitude" file. Passing the -s flag will turn off encoding of subword information, and result in a Light flavored file. Passing the -a flag will turn on building the Annoy approximate similarity index, and result in a Heavy flavored file.
Conclusion Magnitude is a new open source Python library and file format for vector embeddings. It makes it easy to integrate embeddings into applications and provides a single interface and configuration that is suitable for both development and production workloads. The library and file format also enable novel features like OOV handling that allow models to be more robust to noisy data. The simple interface, ease of use, and speed of the library, compared to other utilities like Gensim, will enable use by beginners to NLP and individuals in educational environments, such as university NLP and AI courses. Pre-trained word embeddings have been widely adopted in NLP. Researchers in computer vision have started using pre-trained vector embedding models like Deep1B  (Babenko and Lempitsky, 2016)  for images.
Software and Data We release the Magnitude package under the permissive MIT open source license. The full source code and pre-converted ". magnitude" models are on GitHub. The full documentation for all classes, methods, and configurations of the library can be found at https://github. com/ plasticityai/magnitude, along with example usage and tutorials. We have pre-converted several popular embedding models (Google News word2vec, Stanford GloVe, Facebook fastText, AI2 ELMo) to ". magnitude" in all its variants (Light, Medium, and Heavy). You can download them from https://github. com/plasticityai/magnitude#preconverted-magnitude-formats-ofpopular-embeddings-models.
Acknowledgments We would like to thank  Erik Bernhardsson  for the useful feedback on integrating Annoy indexing into Magnitude and thank the numerous contributors who have opened issues, reported bugs, or suggested technical enhancements for Magnitude on GitHub. This material is funded in part by  DARPA  under grant number  HR0011-15-C-0115  (the  LORELEI program ) and by  NSF SBIR  Award # IIP-1820240. The U. S. Government is authorized to reproduce and distribute reprints for Governmental purposes. The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of  DARPA , the  NSF , and the  U. S. Government. This work has also been supported by the  French National Research Agency  under project  ANR-16-CE33-0013.
Hence, the empirical variance is an unbiased estimator of the variance. We then obtain the so-called wavelet spectrum S X,ψ (q -s m ω 0 ) ∆ = E 1 N τ ψ 2 2 w x,s m 2 (19) = 1 ψ 2 2 ∞ 0 S X (ξ)q s m ψ (q s m ξ) 2 dξ , ( 20 ) where ω 0 is the central frequency of | ψ| 2. S X,ψ is a narrowband version of S X centered around frequency ν m = q -s m ω 0. Besides, the bandwidth of the filter is proportional to the frequency ν m. This motivates the introduction of the following estimator SX of S X SX (q -s m ω 0 ) ∆ = 1 N τ ψ 2 2 w x,s m 2. ( 21 ) Finally, the estimate SX is extended to all ξ ∈ [0, F s /2] by linear interpolation. 2) Algorithm: The estimation procedure is implemented in an iterative alternate optimization algorithm. This algorithm whose pseudo-code is given as Algorithm 1 is named Joint Estimation of Frequency, Amplitude, and Spectrum (JEFAS).
A. Synthetic signal We first evaluate the performances of the algorithm on a synthetic signal. This allows us to compare variance and bias with their theoretical values. The simulated signal has length N τ = 2 16 samples, sampled at F s = 8 kHz (meaning the signal duration is t F = (N τ -1)/F s ≈ 8. 2 s). The spectrum S X is written as S X = S 1 + S 2 where S l (ν) = 1 + cos 2π(νν (l) 0 )/∆ (l) ν if |ν -ν (l) 0 | < ∆ (l) ν /2 and vanishes elsewhere (for l ∈ {1, 2}). The amplitude modulation a is a sine wave a(t) = a 0 (1 + a 1 cos(2πt/T 1 )), where a 0 is chosen such that t -1 F t F 0 a 2 (t)dt = 1. The time warping function γ is such that log q (γ ′ (t)) = Γ + cos(2πt/T 2 )e -t/T 3 , where Γ is chosen such that t -1 F t F 0 γ ′ (t)dt = 1. JEFAS is implemented in the MATLAB/Octave scientific environment.
Quite obviously, controlling warping and spectrum opens new perspectives in sound design, for example for cross-synthesis. Future work will apply the aforementioned methods to a task of blind source separation of nonstationary signals. The code and datasets used to produce the numerical results of this paper, and other audio examples (female voice, wind, etc. ) are available at the web site https://github. com/AdMeynard/JEFAS More details on the sharp wavelet and proofs of Theorem 1 and Proposition 1 are given as supplementary material, that also includes another case study (application to wind sound).
Implementation For all images present in the input folder, the "RAPA-macro_InflorescenceSkeleton. txt" macro (Additional file 4) automatically performs image segmentation, (  2 ) GR(t) = rM(t) × 1 - M(t) A (3) RGR(t) = r × 1 - M(t) A skeletonization, and computation of 2D skeleton parameters of inflorescence (Fig. 3a ). 2D skeletons analysis with ImageJ returns nine vectors of variables for each plant (described in Fig. 3 ), which were automatically saved as. xls files by the macro (in an output folder defined by user). The sums of these nine vectors per individual were used as nine predictors of fruit number. Using the same approach as for estimating rosette dry mass, we tested different models and different training population size with cross-validation (R code in Additional file 5). As for rosette dry mass, results showed that the nine skeletons descriptors predict > 90% of fruit number in 100 individuals not used to train the model (Fig.
Discussion Arabidopsis thaliana is the most widely used plant species in molecular biology, ecology and evolution, but we still largely ignore how growth dynamics is related to individual performance and fitness  [37]. This is mainly because traits like RGR and fruit number remain difficult to measure in large scale experiments. Thus, our goal was to develop a set of tools for biologists to analyze these traits with low-cost equipment. Rather than the development of a new methodology or algorithm, we propose an application guide for the implementation of image computerization with free software (R, ImageJ). From simple top-view imaging of rosette and inflorescence, we built robust predictive models of plant dry mass and fruit number. Based on a semi-invasive approach and two computing steps-one to analyze images with ImageJ and one to model data with R -, the method allows a single experimenter to simultaneously measure biomass accumulation, RGR and fruit production over thousands of plants.
After segmentation, inflorescence skeletonization and 2D skeleton analysis were automatically performed with the corresponding functions in ImageJ (see code in Additional file 4). Skeletons were not pruned for loops. Extracted rosette shape and inflorescence skeleton parameters were automatically saved as. xls files. Plants in the training population were harvested at 16 days after vernalization, rosette were dried at 65 °C for three days, and separately weighed with a microbalance (XA52/2X, A. Rauch GmbH, Graz, Austria). All individual rosette parameters extracted after segmentation were saved as. xls files, each row corresponding to a specific date, tray label and pot coordinates. At the end of the life cycle, inflorescence and rosette of the focal population were harvested and separately photographed.
Statistical analyses Different predictive models were evaluated for both the estimation of rosette dry mass and fruit number. We notably compared linear models, quadratic models-where each predictor was fitted as a two-order polynomial function, RIDGE and LASSO regressions (Additional files 2 and 5). Prediction accuracy was tested by cross-validation on 100 individuals not used to train the model, using the Pearson's coefficient of correlation (r 2 ) between observed and predicted trait values. For each model, we tested prediction accuracy according to training size across 100 random permutations of the training dataset (R code in Additional files 2 and 5). Training population size varbetween 10 and 250 for dry mass estimation, and between 10 and 120 for fruit number estimation. Stepwise regression, using step function in R, was used to identify the best model, with minimum predictors, of rosette dry mass and fruit number.
Availability of data and materials The datasets supporting the conclusions of this article are included within the article and its additional files. Codes are available on Github (https ://githu b. com/fvass eur), and phenotypic data are available on the Dryad repository (https. 343bd 84)  [43]. Correspondence and requests for materials should be addressed to weigel@weigelworld. org or franc. vasseur@gmail. com.