dataesr/scientific-paragraphs-categorization huggingface dataset:
SMICA finds highly dipolar and independent source subspaces In this section, we illustrate the ability of SMICA to capture meaningful brain sources. We use the same datasets as in  Delorme et al. (2012). It contains the EEG recording of 15 subjects, with 69 EEG channels. For a target number of independent sources, different ICA procedures described in the article are applied to the datasets. The methods Wiener and Pseudo-Inverse correspond to the combination of SMICA with Infomax, as described in the paragraph 'Combining SMICA with another ICA algorithm', section 2. 3. Wiener corresponds to computing SMICA's sources with Wiener filtering, Pseudo-Inverse corresponds to computing SMICA's sources with pseudo-inversion of the mixing matrix A, as described in the paragraph 'Source estimation by Wiener filtering', Sec.
As such, SMICA can be a useful tool for dimension reduction. Importantly, as illustrated by Figure  2 , SMICA is similar to JDIAG for clean data, since the noise subspace in this case is simple to find. Therefore, SMICA is more likely to be useful for noisy datasets such as clinical recordings. This work is not the first using and highlighting the benefits of spectral ICA for M/EEG recordings. Some works have focused on convolutional mixtures (for which using the Fourier domain turns convolutions into products). In  Dyrholm et al. (2007) , authors use convolutive ICA in time-domain, while  Anemüller et al. (2003)  use a complex Infomax to find travelling waves. In the end, these methods estimate one mixing matrix for each frequency bin. However, independent models in each band might fail to recover brain rhythms with several frequency peaks.
Figure3: Comparison of SMICA and JDIAG on an EEG dataset. Both algorithms return 20 sources. The ordering of the sources of SMICA is made by hand; the ordering of the sources of JDIAG is made by maximizing the correlation with SMICA's sources. Both algorithms accurately recover the eye blinks (source 1). For SMICA, sources 4-20 correspond to brain activity. SMICA finds two dipolar beta-rhythm sources (sources 7 and 10). Each algorithm recovers a source corresponding to line noise, with a large peak at 60 Hz (source 2). For SMICA, there is an additional peak at 50 Hz, which is not an artifact. Spatio-Spectral Decomposition (SSD) tuned to the source of maximal power at 60 Hz yields a similar peak at 50 Hz (c).
Figure5: Distribution of equivalent dipole map residual variance for each source returned by each algorithm on the 15 datasets of 69 sensors, where each algorithm returns 10, 20, 30, 40, 50 or 69 sources. For Infomax, SOBI and JDIAG, PCA is first applied on the data matrix to obtain the desired number of channels. Wiener and Pseudo-Inverse correspond to applying Infomax on the sources recovered by SMICA, either by the Wiener or Pseudo-Inverse method. The figure should be understood in the following way. Looking at the first plot, corresponding to 10 sources, we see that about 80% of components found by the method 'Wiener' have an equivalent dipole map residual variance lower than 10%. About 70% of components found by 'Infomax' have an equivalent dipole map residual variance lower than 10%.
Figure6: Pairwise mutual information (PMI) for different ICA decompositions returned by each algorithm on the 15 datasets of 69 sensors, where each algorithm returns 10, 20, 30, 40, 50 or 69 sources. For Infomax, SOBI and JDIAG, PCA is first applied on the data matrix to obtain the desired number of channels. Wiener and Pseudo-Inverse correspond to applying Infomax on the sources recovered by SMICA, either by the Wiener or Pseudo-Inverse method. PMI is displayed in a Q-Q plot showing the quantiles of the distribution of PMI found by each algorithm against the quantiles of distribution of PMI found by Infomax. Infomax therefore corresponds to the line x = y. Algorithms above the line x = y have less PMI remaining than Infomax, and hence obtain more independent sources than Infomax as quantified by PMI.
Splitting the cache into a tree structure can be used to differentiate the way a cache protects items from eviction. For example, items of one stream may be stored in a different list than items of another stream to avoid cache pollution and optimize hit ratios. G. Casale is with the Department of Computing at Imperial College London, UK; e-mail: (g. casale@imperial. ac. uk). N. Gast is with Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, 38000 Grenoble, France; e-mail: (nicolas. gast@inria. fr). The authors would like to thank Benny van Houdt for helpful comments during preparation of this manuscript. The work of G. C. was supported by the EU H2020 grants DICE (644869) and RADON (825040) and by a UK EPSRC grant (EP/L00738X/1, iBids). The work of N. G. was supported by the ANR project REFINO (ANR-19-CE23-0015).
Research data is available at http://doi. org/10. 5281/zenodo. 1134886 (CC BY 4. 0 license). Our reference model consists of a cache partitioned into h lists, each having a capacity of m l ≥ 1 items, l = 1,. , h. Let the capacity vector be m = (m 1 ,. , m h ) so that the cache has total capacity m = h l=1 m l. The total number of items is n and it is assumed to exceed the cache capacity (n > m) so that a replacement policy needs to decide which items to evict to make room for a new item. The challenge in designing effective replacement policies is that item popularity is not known beforehand, otherwise one could minimize miss rates by keeping the most frequently accessed items in the cache  [16]. The focus is therefore on policies that self-organize item storage so to optimize miss rates. This paper stems from the recent development in  [15]  of a framework to analyze linear list-based caches with the random replacement (RR), first-in first-out (FIFO), or CLIMB policies, under the independent reference model (IRM)  [17] ,  [18].
1) Example: We illustrate the accuracy of the RMF approximation in transient regime by using  (24)  for the original model with N = 1. To compute numerically the values x M F and V , we rely on a fast software implementation  1. The original model consists of n = 50 items, h = 5 lists, each having capacity m l = 3. There is a single stream that issues requests at Poisson rate λ il = r i , where r i is the probability of accessing item i, which is Zipf-like distributed with parameter α = 1. 4. Figure  3  illustrates the accuracy of the RMF approximation in comparison with estimates of π il (t) obtained by averaging 100 cache simulations. In the latter, we use the ratio of miss count to simulation time at t, requiring in the very first samples the value to be upper bounded at the theoretical maximum in this example of 1.
Computation of response function from 3D model with SPECFEM3D Fundamental factors for a successful numerical simulation consist in the availability of a plausible 3D structural model of the spatial domain with an adequate geophysical characterization of the geological formations and in the accurate application of a numerical method for the solution of the elastodynamic wave equation. In the present work, we constructed a digital 3D structural model of the studied area by means of the commercial software Geomodeller3D, which interpolates the available geological data with methods based on the compliance of stratigraphic rules and on the interpretation of geologic surfaces as isopotential surfaces of a scalar potential field  (Calcagno et al. 2008). We considered the geological and geophysical data from the surveys performed in the area during microzonation studies  (ISPRA 2018; Puzzilli et al.
2019). Details on the 3D model we built are discussed in section Models. Considering the need to solve accurately the wave propagation in a spatial domain bounded by an irregularly shaped free surface, we decided to perform the numerical simulations with the Spectral Element Method (SEM). This approach, first introduced in the study of acoustic and elastic waves by  Seriani and Priolo (1994) , combines the geometrical flexibility of Finite Elements Methods (FEM) with the accuracy of the spectral methods. In order to perform the simulations presented in the present work, we relied on the software SPECFEM3D Cartesian from source  (Peter et al. 2011). The code accounts for the intrinsic attenuation properties of the viscoelastic medium by means of a series of standard linear solid elements  (Savage et al.
Conclusions In order to shed light on the three dimensional seismic local effects suffered by the Arquata del Tronto ridge under the 2016 Central Italy seismic sequence we set up a 3D numerical model of the area and computed the linear visco-elastic site response for a vertically incident plane wave by using the well-established software SPECFEM3D. In particular we analyzed the separated and combined effects of irregular topography and subsurface heterogeneity on the seismic ground motion in terms of horizontal component spectral amplification, including the aspects that concern polarization and rotation. To facilitate the discussion, we averaged the results over the frequency (period) bands of engineering interest: 1-2 Hz (0. 5 -1 s or low frequency), 2-4 Hz (0. 25-0. 5 s or intermediate frequency) and 4-8 Hz (0.
Code availability The SPECFEM3D code is an open source software available on the Computational Infrastructure for Geodynamics (http://geodynamics. org). The 3D geological model was computed using the commercial software GeoModeller (https://www. intrepid-geophysics. com/product/geomodeller). Data processing and figures were done using MATLAB (http://www. mathworks. com/products/matlab/) and GMT (https://www. generic-mappingtools. org/).
The main limitations of LC-MS are the limited structural information and the complexity of obtaining absolute quantification, especially in non-targeted approaches. 2. Correctly assign the identity of a metabolite. The vast majority of information collected by metabolomics is "dark matter", i. e. , chemical signatures that remain uncharacterized. Therefore, new computational solutions to illuminating dark matter are needed  [316]. Significant progress has been reported in exploring public data-rich libraries, finding chemicals and associated metadata, and applying molecular networking strategies to accelerate metabolite annotation, especially in the last few years  [317] [318] [319] [320] [321]. Moreover, the development and maintenance of MS-based spectral databases, together with the increasing practice of sharing metabolomics data through these resources, play a key role in translating dark matter into biological knowledge  [322].
Authentic chemical standards should be used to acquire both positive and negative mode MS/MS and MS n spectra to supplement spectral databases such as HMBD  [323] , NIST20  [324] , METLIN  [325] , MZcloud  [326] , MassBank  [327] , ReSpect  [328] , and GNPS  [329]. 3. Establish an extensive reference catalog of microbial genomes present in the human gut, as several big projects are already doing, such as the MetaHIT (METAgenomics of the Human Intestinal Tract) project; the Human Microbiome Project, or others  [2, 3]. 4. Integrate multi-omics datasets to recover microbe-metabolite relationships by using statistical analysis. Linear or neural network methods estimate the conditional probability that each molecule is present, given the presence of a specific microorganism  [330] [331] [332]. The mmmvec tool (https://github.
Funding: D. H. is supported by  GEFLUC Dauphiné-Savoie, Ligue contre le Cancer Comité Isère, Université Grenoble Alpes IDEX Initiatives de Recherche Stratégiques. A. L. G. , and B. T. were supported by  "Vaincre la mucoviscidose" (VLM)  and  "Association Grégory Lemarchal" (AGL)  (Grant number  RF20180502189  and  RF20190502416/1/1/68 ),  ANR-15-IDEX-02 ,  SATT Linksium and Fondation Université Grenoble Alpes.
The total numbers of these sets of outcomes were then tallied and their proportions calculated for each governance type, social research approach, and region. Importantly, none of the reviewed publications reduced the social outcomes to only participation in conservation, or simply assumed that a greater role in governance for IPLCs automatically enhances their wellbeing, which could lead to false conclusions. The examples detailed throughout the results clearly present a more diverse and profound set of social impacts. For the second part of the analysis, the qualitative data extracted from each study were analyzed thematically to describe the dynamics driving those relationships between governance, wellbeing, and conservation effectiveness. The analysis enabled characterization of common pathways, occurring across multiple cases in our sample.
2019). Latin America provides many of the examples to date of socially progressive conservation governance because decentralization processes were prominent across the region beginning in the 1990s and served to increase the recognition of local institutions  (Larson 2003). Locally led efforts may also be supported by large-scale networks to share experiences and best practices, as exemplified by the Indigenous peoples' and Community Conserved Territories and Areas (ICCA) Consortium's work to gain recognition of communityled efforts within the global database of conserved areas  (Tran et al. 2020).
Acknowledgments: We are thankful for funding and support provided by the International Union for the Conservation of Nature Commission on Environmental, Economic and Social Policy (IUCN CEESP), particularly the Chair, Kristen Walker Painemilla, and Deputy Chair, Ameyali Ramos Castillo. We are grateful for advice and voluntary contributions provided by members of the CEESP Theme on Human Wellbeing and Sustainable Livelihoods and especially to Grazia Borrini-Feyerabend for constructive comments. ND and BC were supported through the "Just Conservation" project funded by the Centre for the Synthesis and Analysis of Biodiversity (CESAB) of the French Foundation for Research on Biodiversity (FRB), https://www. fondationbiodiversite. fr/. NGC's time was funded through the Darwin Initiative RESPeCT project.
We are grateful to Samantha Cheng at the American Museum of Natural History for her time and advice on use of Colandr software and to Andy Wright https://www. madebyawdesign. com/ for the illustration of Figure  5. For images in Figure  5  we thank: the MIHARI Network https://mihari-network. org/ who permitted use of images of a fisherwoman speaking and Mangrove reforestation at Belo-sur-Mer, southwestern Madagascar; and Holladay Photo for the image of the Kahana community, Koolauloa, Oahu doing a traditional Hawaiian fishing practice called Hukilau.
Data coding procedure Eleven individuals completed the data extraction between June and November 2019. All are scientists working on social dimensions of conservation or conservation governance, and ten of the eleven are listed among the authors. More than two thirds of publications were reviewed by one of three reviewers, including the lead author. In addition to the protocol, descriptive guidance was provided to all reviewers on inclusion/exclusion decisions, as well as type of and how much detail to provide, and on distinguishing findings supported by empirical evidence from unsubstantiated claims. The lead author and reviewers discussed papers and provided two-way feedback after initial reviews until both were comfortable with decision making and levels of consistency. All inclusion/exclusion decisions made at full review stage were verified by revisiting the original publication, and extracted data were checked by the lead author for completeness and accuracy.
Acknowledgements. The research from  JD  and AP has been supported by the  Federal Ministry of Education and Research (BMBF)  " Make our Planet Great Again -German Research Initiative " (grant no. 57429624 ) implemented by the  German Academic Exchange Service (DAAD). Research by ILM is supported by the  NOAA Climate and Global Change Postdoctoral Fellowship Program , administered by  UCAR's Cooperative Programs for the Advancement of Earth System Science (CPAESS)  under award # NA18NWS4620043B. We thank the  AERIS/ICARE Data and Services Center  for providing access to the data used in this study as well as the CloudSat, CALIPSO and MODIS.
Our procedure is particularly useful in the case where we are given a set of trees or networks, or any object set we want to compare, without the original data. For example, networks of protein-protein interaction or ecological networks are available on databases without any indication of the data they have been built on  [16] [17] [18]. This can also be useful to compare different ways to transform the data, e. g. using different distances or aggregation criteria to build the trees. The rest of the paper is organized as follows: first, we give details about the proposed methodology. Then its performance is evaluated on simulated data and compared to a multiple kernel integration method, and two real-world data sets are analyzed: the first one is a single-cell data set that illustrates the comparison of clusterings for different cell-types in mouse embryos.
Results In this section we describe the results obtained on simulated data, in order to evaluate the performances of the proposed method, as well as on two real data sets. Analyses were performed with R 4. 0. 2 [35]. All code and data are available at https:// github. com/ AudreH/ intTr eeNet. Hierarchical clustering was performed using Euclidean distance and Ward's aggregation criterion as implemented in the "ward. D2" option of the hclust R function  [36]. All trees were transformed using the cophenetic base function. Using cmdscale, the new data coordinates from the MDS approach were obtained. MFA was performed using the MFA function from the factoMineR package  [37]. To assess the differences between clusterings, we used the Adjusted Rand Index (ARI)  [38, 39]  from the aricode R-package  [40] , which measures the agreement between two classifications.
To determine the groups in a hierarchical clustering, we used the DynamicTreeCut method as implemented in the R-package of the same name  [41]. This method identifies groups based on the structure of the tree and the distance matrix used to build the tree. In the graph application, the shortest path distance is computed using the distances function of the igraph R-package  [42]  and default parameters. We compare the results of our process to the ones obtained by combining kernels. When needed, distance or dissimilarity matrices are transformed into similarities using the double-centering formula: B = -1 2 J 2 J. To ensure that matrix B can be considered a kernel, it is reconstructed using only positive eigenvalues. For the inverse transformation, from a similarity (kernel) matrix S to a distance/dissimilarity one, we use the formula: ∀(i, i ′ ), � ii ′ = √ S ii + S i ′ i ′ -2S ii ′.
To compare the kernels between them, we use the similarity coefficient computed as the cosine of the Frobenius norm between kernel matrices, as described in  [2]. The matrix of these coefficients is then transformed into dissimilarity, and hierarchical clustering is performed using complete-linkage. We use the mixKernels R-package  [2]  with the option "full-UMKL" (full Unsupervised Multiple Kernel Learning) and default parameters to find a consensus kernel after we identify the kernel clusters.
B) Fig. 4  Results for the simulation study on hierarchical clustering data for kernel combination. 3 classifications with K = 4, 3 and 5 groups respectively were simulated following Eq. 2 (A) represents the hierarchical clustering obtained with the distance matrix derived from the consensus kernel, performed with complete linkage. B represents the C-coefficient between the cophenetic kernel tables, on which (A) was built and ordered according to the hierarchical clustering of (A) package gave us 3 groups. The kernels corresponding to these groups were combined into 3 consensus kernels, then transformed into dissimilarity matrices. Hierarchical clustering with complete linkage was performed to retrieve the three corresponding consensus trees, as represented in Fig. 5 , with the global consensus tree built on the global consensus kernel.
B) Fig. 8  Results for the simulation study on network data with kernels combination. 3 classifications with K = 4, 3 and 5 groups respectively were simulated following Eq 3. A Represents the hierarchical clustering obtained with the distance matrix derived from the consensus kernel, performed with complete linkage. B Presents the C-coefficients of the network kernels, on which (A) was made and the Xist gene, can be found at https:// github. com/ Mario niLab/ Embry oTime cours e2018/ blob/ master/ analy sis_ scrip ts/ atlas/ core_ funct ions. R. These two steps led to the analysis of 15,086 genes and 3520 samples. Following the procedure explained by  [43] , we selected the most variable genes using the scran R-package and the function modelGeneVar. In total, 318 genes were selected by taking a threshold of 0.
Network integration In this application, we will work at the protein level, this time building networks and considering them as the only source of data. The goal is once again to study how these objects can be grouped. We did not perform here further analyses of the individual coordinates, but it would also be possible. Data are protein expression from 777 patients with breast cancer, divided into 4 subtypes: Basal-like ( n = 151 ), HER2-enriched ( n = 85 ), Luminal A ( n = 283 ), Luminal B ( n = 258 ). In this data set, p = 173 proteins were expressed in at least one sample of any subtype. Using the limma R-package  [45]  to perform a differential analysis, we selected the 5 first proteins by order of adjusted p-value, for each contrast between subtypes, which provided 15 unique proteins.
Networks associated with each subtype were inferred using glasso  [46, 47]  on centered data, and the Bayesian information criterion (BIC)  [48]  was Fig. 10  Consensus clusterings given by MFA for the single-cell data application. Hierarchical clustering obtained by using Euclidean distance and Ward's aggregation criterion on the global MFA individuals (in this context, genes) coordinates and on the sub-groups MFA used to select the adequate level of penalty, as implemented in the huge R-package  [49]. All non-zero coefficients were set to 1 in the adjacency matrices. Using these networks as the set of objects we want to study, and the shortest path distance, we obtained new coordinates by MDS, that were then used in the MFA analysis. The hierarchical clustering based on the objects coordinates provided two groups, consisting in the Luminal A and B subtypes in one group and the HER2-enriched and Basal-like subtypes in the other.
12A. Indeed, the DynamicTreeCut procedure chose two groups: RNA-seq, protein and methylation data tables in the first one, and mirna alone in the second one. We compared each individual tree and the consensus tree with the subtype classification using the Adjusted Rand Index (ARI). The mirna tree was found to have a lower ARI than all the other tables. The consensus tree made from the MFA axes is represented in Fig. 12B , with subtype indicated in the color bars. The consensus has a better ARI than all of the tables, demonstrating that the classification of the patients was improved by using this process. It was also robust to the low ARI value from mirna data table.
Discussion In this paper we proposed a procedure to compare multiple objects built on the same entities, with a focus on trees and networks, in order to define coherent groups of these kind of structures to be further integrated. Because its computation only relies on a singular value decomposition (SVD), and since we may have recourse to a truncated version of SVD, the procedure is very fast and appropriate to analyze a great number of objects. Our procedure was applied to simulated data, both in the context of trees and networks. In both cases, three very different grouping information were generated. The method was able to retrieve these three different structures. Consensus trees and networks were then obtained based on the MFA results and were consistent with the simulated data for both the tree and network examples.