dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We are grateful to  James Ciarlo  ( ICTP ) who made one of the regional climate simulations for the EURO-CORDEX ensemble available on ESGF in advance to publication. The analysis benefitted from the collection of the EURO-CORDEX simulations made within the  Copernicus Climate Change Service. We acknowledge the E-OBS dataset from the  EU-FP6  project  UERRA  (http://www. uerra. eu,last access:  1 October 2021 ), the  Copernicus Climate Change Service  and the data providers in the  ECA&D  project (https://www. ecad. eu,last access:  1 October 2021 ). Our colleague, co-author Geert Jan van Oldenborgh, started the analysis with the authors' team but sadly passed away in October 2021 before the article was submitted. The first author dedicates this study to his mother who passed away the day before the frost event and who gave him her passion for flowers, nature and weather.
ACKNOWLEDGEMENTS L. B. , E. Z. C. , and Y. O. thank the support from  Fundação para a Ciência e a Tecnologia (FCT, Portugal) , namely through projects  UIDB/04540/2020  and  UIDB/50008/2020. L. B. acknowledges the support of  FCT  through scholarship  BD/05268/2021  and of the.
However, there are slight differences between the observed magnetic fields and the model fields  31  , which may be attributed to the influence of induced fields. Upon scrutinizing it further, it appears that the observed fields can be approximated as a linear superposition of the crustal fields and steady Bx-dominated fields (as depicted in Figure  1f ), represented by 𝐵 ⃗ 𝑜𝑏𝑠 = 𝐵 ⃗ 𝑚𝑜𝑑𝑒𝑙 + [14. 8, -0. 93, -4. 15] nT , where 𝐵 ⃗ 𝑜𝑏𝑠 , 𝐵 ⃗ 𝑚𝑜𝑑𝑒𝑙 correspond to the observed magnetic fields and crustal fields model, respectively. The presence of double-sided loss cone distributions of electrons and the magnetic topology analysis  32  (as shown in Supplementary Figure  2 ) further indicates that the field lines primarily consisted of closed crustal field lines with both foot-points located in the nightside ionosphere Confidential manuscript submitted to Nature Communications (MAVEN traversed from about 5 to 3 local time in the early morning sector).
This suggests that the dispersed ions predominantly undergo adiabatic motion, which aligns with our expectations. In addition to the event discussed above, we have identified 16 additional wedge-like dispersion events within the crustal fields from the MAVEN dataset, spanning over a period of 5 years (refer to Supplementary Table  1 ). This indicates that dispersion structures induced by drift motion of particles are rare, which might be related to the setup of electric fields, but it still confirms that the intrinsic magnetospheric ion behavior could also be present in the crustal fields.
Acknowledgments This work is supported by the  National Natural Science Foundation of China  (grants  41922031 ,  41774188 ), the  Strategic Priority Research Program   of Chinese Academy of Sciences  (Grant No. XDA17010201 ,  XDB41000000 ), the  Key Research Program of Chinese Academy of Sciences  (Grant No. ZDBS-SSW-TLC00103 ), and the  Key Research Program   of the Institute of Geology & Geophysics, CAS  (Grant No. IGGCAS-201904 ,  IGGCAS-202102 ). We thank  James P. McFadden ,  Li Li ,  Fei He ,  Zhonghua Yao ,  Wenya Li ,  Markus Fränz , and  Ronan Modolo  for their helpful discussion.
Data Availability All MAVEN data used in this paper are available from NASA's Planetary Data System. STATIC data can be found at https://pds-ppi. igpp. ucla. edu/mission/MAVEN/MAVEN/STATIC. MAG data can be found at https://pds-ppi. igpp. ucla. edu/mission/MAVEN/MAVEN/MAG. SWIA data can be found at https://pds-ppi. igpp. ucla. edu/mission/MAVEN/MAVEN/SWIA. SWEA data can be found at https://pds-ppi. igpp. ucla. edu/mission/MAVEN/MAVEN/SWEA.
Acknowledgments C. D. was funded by a grant from the  Lebanese University. We thank  INRAE, Campus France , and the  INRAE Plant Health and Environment division (SPE)  for their support and funding. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  951444 -PATHOCOM ). This review was performed at the LIPME belonging to the  Laboratoire d'Excellence (LABEX)  entitled  TULIP  ( ANR-10-LABX-41 ). We thank  Claudia Bartoli ,  Joy Bergelson ,  Talia Karasov , and one anonymous reviewer for valuable comments on the manuscript.
At this purpose, the vanilla competitive layer and its dual are presented. The former is representative of a standard competitive layer for deep clustering, while the latter is trained on the transposed matrix. The equivalence of the layers is extensively proven both theoretically and experimentally. The dual competitive layer has better properties. Unlike the vanilla layer, it directly outputs the prototypes of the data inputs, while still allowing learning by backpropagation. More importantly, this paper proves theoretically that the dual layer is better suited for handling high-dimensional data (e. g. , for biological applications), because the estimation of the weights is driven by a constraining subspace which does not depend on the input dimensionality, but only on the dataset cardinality.
A duality theory is proposed and demonstrated, which highlights the relationships between the two layers. The "Methods" section presents the vanilla and dual competitive layers together with the corresponding dual theory and the analysis of the loss function. The "Results" section tests the two layers on three synthetic datasets and confirms the validity of the proposed approach. The "Discussion-Theoretical Analysis" section provides a theoretical justification of the results by means of the analysis of the gradient flows, using both the stochastic approximation theory and the evaluation of their dynamics. Finally, the "Conclusion" section concludes the paper and proposes future directions.
The single-layer neural network with linear activation functions in the lower scheme is here called the dual network of the former one. It has the same number of outputs and n inputs. It is trained on the transpose of the original X database. Its weight matrix is W 2 and the output batch is Y 2. The following theorems state the duality conditions of the two architectures. Figure  2  represents the two networks and their duality. Theorem 2. 1 (Network duality in competitive learning).
(  6 ) Y 1 = W 1 X Y 2 = W 2 X T ⇒ W 1 = Y 1 X T ⇒ W 1 = W 1 XX T ⇒ XX T = I d (7) Y 1 = W 1 X Y 2 = W 2 X T ⇒ W 2 = Y 2 X T ⇒ W 2 = W 2 X T X ⇒ X T X = I n (8) Y 2 = W 1 = W 2 X T (9) Y 1 = W 1 X ⇒ Y 1 = W 2 X T X (10) Y 1 = W 2 = W 1 X (11) Y 2 = W 2 X T ⇒ Y 2 = W 1 XX T = W 1 1 3 Corollary 2. 4. 1 (Self-supervised learning). The assumption of Theorem 2. 4 implies the construction of labels for the base network. Proof. As sketched in Fig. 3 , under the assumption of the equivalence between the training of the dual network (building of prototypes) and the architecture of the base network (output neurons as prototypes), the previous theorem implies the second dual constraint, which means the construction of a self-organized label. Thanks to this corollary, the base network can work in a self-supervised way, by using the results of the dual selforganization, to infer information on the dataset.
Results In order to rigorously assess the main characteristics of the learning process, several metrics are evaluated while training the VCL and DCL networks on three synthetic datasets (  14 ) P2 = � prototype 1 … prototype k � T = Y 2 = W 2 X T = ⎡ ⎢ ⎢ ⎢ ⎣ w T 1 w T 2 ⋯ w T k ⎤ ⎥ ⎥ ⎥ ⎦ � f 1 f 2 … f d � = ⎡ ⎢ ⎢ ⎢ ⎣ w T 1 f 1 w T 1 f 2 … w T 1 f d w T 2 f 1 w T 2 f 2 … w T 2 f d … … ⋱ ⋮ w T k f 1 w T k f 2 … w T k f d ⎤ ⎥ ⎥ ⎥ ⎦ (15) L = L(X, W 1 ) (16) L = L(X T , Y) (17) ∇L(W 1 ) = dL dW 1 (18) ∇L W 2 = dL dW 2 = dL dY 2 ⋅ dY 2 dW 2 containing clusters of different shapes and sizes. Table  1  summarizes the main characteristics of each experiment. While these experiments deal with maximum two clusters, there is no theoretical reason to limit this study to only two clusters. For DCL, the number of output units corresponds to the number of clusters (just like the parameter k in the k-means algorithm), as shown in  [58].
The first dataset is composed of samples drawn from a two-dimensional Archimedean spiral (Spiral). The second dataset consists of samples drawn from two half semicircles (Moons). The last one is composed of two concentric circles (Circles). Each dataset is normalized by removing the mean and scaling to unit variance before fitting neural models. For all the experiments, the number of output units k of the dual network is set to 30. A grid-search optimization is conducted for tuning the hyper-parameters. The learning rate is set to = 0. 008 for VCL and to = 0. 0008 for DCL. Besides, for both networks, the number of epochs is equal to = 400 , while the Lagran- gian multiplier to = 0. 01. For each dataset, both networks are trained 10 times using different initialization seeds in order to statistically compare their performance.
The DCL trajectories are much shorter because of the closeness of the initial prototypes to the input clusters. These considerations reveal the better suitability of DCL to deep learning traditional initializations. The performance of the vanilla competitive layer and its dual network in tackling high-dimensional problems is assessed through numerical experiments. Sure enough, standard distance-based algorithms generally suffer the well-known curse of dimensionality when dealing with high-dimensional data. The MADELON algorithm proposed in  [60]  is used to generate high-dimensional datasets with an increasing number of features and fixed number of samples. This algorithm creates clusters of points, normally distributed about vertices of an n-dimensional hypercube. An equal number of cluster and data is assigned to two different classes.
Both the number of samples (n s ) and the dimensionality of the space (n f ) in which they are placed can be defined programmatically. More precisely, the number of samples is set to n s = 100 while the number of features ranges in n f ∈ [1000, 2000, 3000, 5000, 10000]. The num- ber of required centroids is fixed to one-tenth the number of input samples. Table  2  summarizes the hyper-parameter settings. Three different networks are compared (see Fig. The "Discussion-Theoretical Analysis" section yields a theoretical explanation for the observed results. All the code for the experiments has been implemented in Python 3, relying upon open-source libraries  [61, 62]. All the experiments have been run on the same machine: Intel® Core™ i7-8750H 6-Core Processor at 2. 20 GHz equipped with 8 GiB RAM.
To enable code reuse, the Python code for the mathematical models including parameter values and documentation is freely available under Apache 2. 0 Public License from a GitHub repository1 1  [63]. The whole package can also be downloaded directly from PyPI 2. Unless required by applicable law or agreed to in writing, software is distributed on an "as is" basis, without warranties or conditions of any kind, either express or implied. The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
The Voronoi Set Estimation Consider the matrixX T Y T ∈ ℝ n×j , which contains all the inner products between data and prototypes. From the architecture and notation of the dual layer, it follows = ΩX T , which yields: where the sample autocorrelation data matrix G is the Gram matrix. The Euclidean distance matrixedm(X, Y) ∈ ℝ n×j , which contains the squared distances between the columns of X and Y, i. e. , between data and prototypes, is given by  [64] : where diag(A) is a column vector containing the diagonal entries of A and 1 r is the r-dimensional column vector of all ones. It follows: and considering that YY T = ΩX T ΩX T T = ΩGΩ T , it holds: as a quadratic function of the dual weights. This function allows the straight computation of the edm from the estimated weights, which is necessary in order to evaluate the Voronoi sets of the prototypes for the quantization loss.
Five curves were taken per spot (5 s each). Laser power was set to 0. 1-0. 5% of the total laser power that corresponds to 2-10 mW. Curves were then fitted using the SymphoTime software 2D-diffusion model below  55. G D τ ð Þ ¼ 1 N 1 þ τ τ D À1 where N represents the number of fluorescent species within the beam's focal volume and T D is the diffusion time. Next, the diffusion coefficients were calculated as follows: τ D ¼ ω 2 8ln 2 ð ÞD Where w corresponds to the full-width half maximum of the point spread function; T D is the diffusion time and D is diffusion coefficient. HILO-illumination microscopy. As the specific design of the SLB support may restrict the total internal reflection  23  of the laser beam at the mica/SLB interface, we used an optical configuration similar to HILO 56 (i.
e. , highly inclined thin layer illumination below the angle of total internal reflection). This method provides a high signal-to-background ratio decreasing the illumination of the non-bound lectins in solution. HILO-illumination imaging was performed with a Nikon Eclipse Ti-E fluorescence microscope (with ×100 oil objective, N. A. 1. 49 and an iXon DU-897 EMCCD camera (Andor Technology)). The 488 and 647 nm lasers were used to excite HPC-Bodipy and Cy5, respectively. After lectin application, several areas of each SLB were imaged with exposure times of 30-50 ms and intervals of 10-20 s between frames. Time-lapse data analysis. The resulting time-lapse images of the SLBs were analyzed using a Fiji-based, home-made macro  57, 58. First, bleaching and laserprofile corrections were applied. Then, the total area of Lo domains, the surrounding Ld membrane and furthermore also membrane defects and multilayers were calculated for each frame.
In addition, the sizes of the individual Lo domains were extracted. The macro is available at https://github. com/taras-sych/Tools-for-SLBs. Most of the quantifications presented in the figures were performed based on a single, representative SLB (unless indicated otherwise in the figure caption), even if the number of 'biological' replicates was >5 for all experiments and the general observations were similar. A region of interest was chosen within the SLB and further analyzed. Statistical comparisons between the replicates would be confusing, since the dynamics of the process, i. e. , the time point when membrane reorganization starts, can vary between replicates. In addition, the domain sizes and amount of defects can differ in the initial SLBs, which are all unique. This is the result of different parameters that we cannot fully control during sample preparation (e.
The holder was filled with warm PBS (55 °C) and subsequently washed thoroughly in order to remove unattached lipids. Using the JPK experiment planner module of the Bruker JPK SPM software, the cooling step was programmed in several steps (15 min at 55 °C and gradual temperature decrease to a temperature of 45 °C with 2 °C steps every 6 min and then decrease from 45 °C to the final temperature of 22 °C with 2 °C steps every 3 min). Afterward, the holder was kept at the final temperature during the entire measurement. This stepwise cooling procedure resulted in uniform separation of lipid domains and prevented buffer evaporation. The AFM imaging was performed using sharp nitride cantilevers (Bruker SNL-10 tip A) with a nominal tip radius of 2 nm. With the AC imaging mode, we imaged areas of 10 µm × 10 µm and 20 µm × 20 µm with the resolution of 512 × 512 pixels at a line scan rate of 2 Hz.
After the recording of some areas of the intact bilayer, we added diluted LecA solution to the Biocell holder (to have a final concentration of 100 nM LecA) and started to image the bilayer. Typically the first image was recorded around 2-3 min afterLecA addition. The editing of the images, such as plane subtraction to remove the image tilt and line replacements, was done using JPK data processing software. The line profiles and histograms were extracted from JPK data processing software and plotted using Inkscape. Confocal ratiometric microscopy. The two-color ratiometric imaging was performed on a confocal fluorescence microscope (Nikon Eclipse Ti-E with A1R confocal laser scanner, ×60 oil objective, N. A. 1. 49). For fluorescence excitation, the 488 nm laser was used. The emission of NR12S was simultaneously recorded in two color channels-green (using 525/50 BP filter) and far red (700/75 BP filter).
Lower values correspond to low membrane order whereas higher GP values indicate a higher membrane order. The Fiji macro is available at https://github. com/taras-sych/General-polarization. Statistical data analysis and representation. Data are represented using boxwhiskers plots, where the middle horizontal line represents the mean value, the boxes the 25th to 75th percentiles, and the whiskers the standard deviation. The statistical significance was analyzed with Kruskal-Wallis non-parametric test due to non-normal distributions in all datasets. Reporting summary. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Acknowledgements This study was supported by the  German Research Foundation (DFG)  under Germany's  Excellence Strategy  ( CIBSS -EXC-2189 -Project ID 390939984 ,  BIOSS -EXC 294 ), by  DFG  grants  RO 4341/3-1 ,  Major Research Instrumentation  (project numbers:  438033605  and  290424854 ),  RTG 2202  ( 278002225 ) and  IRTG 1642 , by the  Ministry of Science, Research and the Arts of Baden-Württemberg  (Az:  33-7532. 20 ), by the  German Federal Ministry of Education and Research (BMBF)  in the framework of the  EU ERASynBio project SynGlycTis , by the  Freiburg Institute for Advanced Studies (FRIAS) , and by a starting grant from the  European Research Council  ( Programme Ideas ,  ERC-2011-StG 282105 ). T. Sy. acknowledges support by the  Franco-German University  (programs ' Polymer Sciences'  and ' Cotutelle de thèse')  and the  Collège Doctoral Européen (PDI).
2 ). For Durusdinium-containing colonies, the majority ITS2 sequences were D1 for 15 colonies with D1/D1as, D1/D2d, and D1/D1aa accounting for the 3 remaining colonies (one representative each). These ITS2 profiles are all associated with Durusdinium glynnii. To identify the different Cladocopium lineages, we aligned metatranscriptomic reads of Pocillopora colonies on the predicted genes of the C. goreaui genome  32  and called the SNPs for each Pocillopora colony hosting only Cladocopium. We clustered the frequencies of 3712 biallelic SNPs distributed across 1354 transcripts and obtained an optimal number of five different clusters potentially corresponding to five different lineages hereafter named L1 to L5 (Fig. 1b  and Supplementary Fig. 3a ). We then compared the SNP clustering with the clustering of ITS2 profile distances which also include samples containing both Cladocopium and Durusdinium symbionts (Supplementary Fig.
These results suggest that photosymbiont genotype significantly alters host expression even prior to environmental perturbation. In this study, only 4% of gene expression variation between Eastern Tropical Pacific coral colonies was attributed to differences in the dominant photosymbiont whereas in M. cavernosa colonies up to 14% of expression variation between individuals could be attributed to the switch between dominant symbiont lineages  22. The environment played a much more significant role in determining Pocillopora gene expression in our dataset with 24% of expression variation being explained by the sampling island. Additionally, we observed very little to no overlap in differentially expressed genes between Cladocopiumand Durusdinium-containing Pocillopora colonies across the three islands (Supplementary Fig.
These results provide indirect support for potential thermal acclimatization in that coral colonies containing putatively thermally tolerant photosymbionts (e. g. , Durusdinium) showed reduced expression of ESR. Thus, in our dataset, we observed three photosymbiont lineages (C. pacificum L3, C. latusorum L5, and D. glynnii) which appear to have been in preferential association with Pocillopora hosts inhabiting islands with historically elevated SSTs and/or TSAs. Although nonselective mechanisms could account for this pattern (e. g. , dispersal limitations resulting in the dominance of particular photosymbionts on nearby reefs, particularly if those reefs are geographically isolated), these mechanisms seem unlikely in this study. The fact that we observed the same photosymbiont communities in colonies inhabiting reefs ca.
14,000 km apart across the Pacific Ocean (e. g. , Coiba and Guam) and under very similar environmental conditions (i. e. , mean historical SSTs >26. 8 °C) suggests that the high holobiont fidelity and specificity we observed among multiple lineages of Pocillopora corals is reflective of a process of cospeciation between Pocillopora hosts and their photosymbionts. Given the similar strong patterns of cophylogeny between Pocillopora and Cladocopium we observed and the limited environmental plasticity of photosymbiont expression across the Pacific Ocean, we interpret this strategy as one relying on hostmediated transcriptomic plasticity to buffer the holobiont from environmental change alongside preferential association with specific, thermotolerant, Cladocopium lineages under elevated SSTs. However, at extreme SSTs/TSAs even these heat-tolerant Cladocopium lineages are ultimately replaced by Durusdinium D1 photosymbionts suggesting there may be upper limits to the effectiveness of this strategy under warming.
In contrast, Cladocopium expression profiles were primarily driven by algal genotype and displayed low phenotypic plasticity across environmental gradients. Overall, our data suggest the existence of a two-tiered strategy underpinning thermal acclimatization in Pocillopora holobionts with strong selection for specific photosymbiont lineages (i. e. , host-photosymbiont fidelity) coupled with high host transcriptomic plasticity acting as an environmental buffer. We observed photosymbiont flexibility only in association with elevated SSTs in the far Western and far Eastern Pacific. Although the thermotolerant photosymbiont Durusdinium glynnii was largely absent from Pocillopora colonies in the Central Pacific and rare in the West Pacific (only two colonies) in our dataset, previous studies have shown that D.
8× AMPure XP bead cleanup. RNA libraries were stored at -80 °C until sequencing. Size profiles of DNA and RNA libraries were generated using an Agilent 2100 Bioanalyzer (Agilent Technologies, USA) and libraries were quantified by qPCR with the KAPA Library Quantification Kit for Illumina Libraries (Kapa Biosystems) on an MXPro instrument (Agilent Technologies). DNA (i. e. , metagenomic) and RNA (i. e. , metatranscriptomic) libraries were sequenced using 151 bp paired-end read chemistry on a NovaSeq or HiSeq4000 Illumina sequencer (Illumina, San Diego, CA, USA). Basecalling and calculation of Phred quality scores (Q scores) were performed during sequencing by the Illumina Real Time Analysis (RTA) software. Illumina bcl2fastq Conversion software converted raw BCL files generated by RTA to fastq data.
For both metagenomic and metatranscriptomic reads, we removed short (<30 bp length) and low-quality nucleotides (Q score <20), adapter/ primer sequences with an in-house script based on Fastx-Toolkit software (https://github. com/institut-de-genomique/fastxtend), as well as read pairs that mapped to the Enterobacteria phage PhiX174 genome (GenBank: NC_001422. 1). For metatranscriptomic reads, read pairs that mapped to ribosomal sequences were removed using Sort-MeRNA v2. 1  81. After this filtering, we obtained between 39 M and 104 M metatranscriptomic reads (Supplementary Data 14) and between 81 M and 221 M metagenomic reads (Supplementary Data 15). To identify Symbiodiniaceae lineages, we amplify the ITS2 region of the nuclear ribosomal DNA locus from Coral DNA extractions using SYM-VAR-5.