dataesr/scientific-paragraphs-categorization huggingface dataset:
2-Alexa Fluor 700 (BioLegend, 1:50), MR1-BV421 (provided by the NIH Tetramer Core Facility, 1:200), anti-Vα24-Jα18-BV480 (BD Biosciences, 1:50), and anti-Vβ11-APC (Miltenyi Biotec, 1:150) antibodies. The cells were fixed on ice for 30 minutes and permeabilized with the True-Nuclear Transcription Factor Buffer Set (BioLegend). The cells were then stained for intracellular transcription factors overnight in the dark at 4°C with the following panel: anti-Tbet-PE-Cy7 (BioLegend, 1:1350), anti-GATA3-BV711 (BioLegend, 1:50), anti-RORγT-PE (BD Biosciences, 1:50), and anti-EOMES-PerCP-eFluor 710 (eBioscience, 1:50) antibodies. Cells were acquired with an Aurora cytometer (Cytek). Data were inspected using FlowJo, and manually gated live singlets were then exported as FCS files for subsequent iMUBAC analysis.
Public CyTOF datasets CyTOF datasets for PBMCs from patients on PD-1 blockade  20  were downloaded from FlowRepository (https://flowrepository. org/experiments/1124). The study from which these data were taken analyzed PBMCs from patients with melanoma before and about 12 weeks after treatment with either nivolumab or pembrolizumab. Patients were classified as responders or non-responders based on treatment outcomes for the first 15 weeks of treatment. The datasets also contained data for PBMCs from healthy donors at two corresponding time points. The datasets consisted of two batches (i. e. , experiments processed at two separate dates) used as discovery and validation cohorts in the original study. The first batch contained data for five healthy donors, five responders, and five non-responders, whereas the second batch contained data for five healthy donors, six responders, and four non-responders.
Integration of multi-batch cytometry datasets (iMUBAC) The iMUBAC workflow consists of four steps: i) preprocessing, ii) batch correction, iii) unsupervised clustering and cell-type annotation, and iv) batch-specific cell-type prediction. Preprocessing. Batch-specific preprocessing was performed as follows. First, CyTOF data files in the FCS format were imported into R with the ncdfFlow package. The ncdfFlow package can be used for the memory-efficient HDF5-based storage of cytometry data. The truncate_max_range option was disabled. For CyTOF data, the transformation option was also disabled, as the transformation implemented in the underlying read. FCS package is optimized for flow cytometry. Second, channel names were organized. This step resolves batch-to-batch differences in the panel design such that identical markers measured using different channels (i.
e. , fluorochrome-or metal-conjugated antibodies) are aligned. Third, for CyTOF data, doublets and dead cells were excluded in a data-adaptive manner. In this step, all cells from all samples in a single batch of an experiment were pooled, such that identical gates were applied to all samples in a given batch. For DNA-based gating, the dnaGate function in the cydar package was used, and outliers on both the higher and lower sides (considered to be doublets and debris, respectively) were excluded. For event length and dead cell exclusion dye-based gating, the outlierGate function in the cydar package was used, and outliers on the higher side were excluded. In our in-house CyTOF datasets, the intercalator Rh103 was used to exclude dead cells, whereas the intercalators Ir191 and Ir193 were used to exclude doublets and debris.
In the PD-1 blockade CyTOF datasets, the intercalator Pt198 was used to exclude dead cells, whereas the intercalators Ir191 and Ir193 were used to exclude doublets and debris. For our in-house pre-gated spectral flow cytometry datasets, the channel for the Zombie NIR Fixable Viability dye was used to exclude dead cells, and automated gating for doublets and DNA content was disabled. The expression values were then transformed. For CyTOF data, a hyperbolic arcsintransformation was applied, with a cofactor of five. For spectral flow cytometry data, Logicle transformation was applied, with parameters estimated in a data-adaptive manner with the estimateLogicle function implemented in the flowCore package. Finally, any event with zeros for all markers was discarded. After the batch-specific preprocessing, the outputs were concatenated, with only markers common to all batches retained, to form a single SingleCellExperiment object in R.
Batch correction. The goal of this step is to enable the system to learn batch-to-batch deviations due to technical effects but not biological variabilities. We, therefore, used only data from healthy controls for batch correction, excluding data for patients and travel/family controls. Data were first down-sampled to 200,000 cells per batch (unless otherwise stated) by taking approximately equal numbers of cells from each control, to reduce the computational burden. For the in-house spectral flow datasets, we used 500,000 cells per batch, to ensure the robust identification of invariant natural killer T (iNKT) cells, an extremely rare innate-like T-cell subset. For the PD-1 blockade CyTOF Panel 3 (Myeloid Panel) dataset, we used 50,000 cells per batch, due to the low total cell counts in the dataset.
5  Briefly, batch-corrected expression values were subjected to unsupervised clustering with FlowSOM 4 , using the FlowSOM package, followed by metaclustering with the ConsensusClusterPlus package. Euclidean distance was used for metaclustering. For the in-house spectral flow cytometry and CyTOF datasets, we generated 50 and 60 metaclusters, respectively, to improve the resolution of cell-type identification. For the PD-1 blockade CyTOF datasets, we generated 40 metaclusters. In addition to this clustering, we generated a heatmap for the median expression levels of all markers for each of the clusters, to facilitate the manual determination of cell identity. SNN graph method. This approach was inspired by the workflow for single-cell RNA sequencing datasets implemented in the scran package.
Batch-corrected expression values were first dimension-reduced via UMAP into ten dimensions. These dimensions were then used to construct an SNN graph with the buildSNNGraph function in scran, using the default settings. Finally, the graph was divided into communities, or clusters, with the Louvain algorithm, implemented as the cluster_louvain function in the igraph package  24. Batch-specific cell-type prediction. The goal of this step is to allow the system to learn, automatically, the cell-type classification rules, or cell-type boundaries, in a batch-specific manner, and to propagate the boundaries to all the cells in a given batch, including cells from travel/family controls and patients. Importantly, we used non-batch-corrected expression values tied to cluster labels defined in the unsupervised clustering section.
First, cells from the healthy controls used for unsupervised clustering were further downsampled, retaining a maximum of 100 cells per cluster from a given batch. This step reduces the computational burden and also alleviates the class imbalance problem during machine learning, as there are both highly abundant cell subsets (e. g. , CD14 + monocytes) and rare cell subsets (e. g. , plasmacytoid dendritic cells) among human PBMCs. We tested several conditions and found that cell-type classification rules can be learned successfully from 100 events per cell-type. A classifier was then trained with the caret package  25. We selected the extremely randomized trees  26  algorithm implemented in the extraTrees package. After centering and scaling the nonbatch-corrected expression values, we performed five-times repeated 10-fold cross-validations with internal upsampling to maximize the Kappa statistic.
Differential abundance analysis For the PD-1 blockade CyTOF datasets, the raw counts of cell subsets identified from the three panels were tested simultaneously for differential abundance (DA) with the quasi-likelihood F-test (QLF) framework. CC-BY 4. 0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted July 15, 2020. ; https://doi. org/10. 1101/2020. 07. 14. 202432 doi: bioRxiv preprint of the package edgeR  21. When both pre-and post-treatment datasets were used, the DA between groups (i. e. , responders and non-responders) was assessed with adjustment for both treatment and batch effects. When only pretreatment datasets were used, the DA between groups was assessed with adjustment for batch effects only.
The original study compared immunophenotypes between responders and non-responders and reported higher levels of CD14 + CD16 -HLA- Percentages among live single leukocytes before PD-1 blockade are shown. (e) Expansion of the "CD14 Mono 2" cluster, corresponding to CD14 + CD16 -HLA-DR hi CD86 hi PD-L1 hi monocytes, in responders. (f) DA for subsets identified statistically significant when only pretreatment datasets were used for DA testing. Estimated log2 fold-changes between responders and non-responders are also shown. among live single cells were compared for each cell subset. In this analysis, when multiple clusters were identified for a given subset (e. g. , "NK CD16 + CD57 -", "NK CD16 + CD57 + ", "NK CD16 dim ", and "NK CD56 bright " for NK cells), these clusters were merged before the calculation of frequencies.
Representative results are shown for ten subsets. NK, natural killer cells; ILC, innate lymphoid cells; cDC2, type-2 conventional dendritic cells; pDC, plasmacytoid dendritic cells. R, Kendall's correlation coefficient. immunotherapy. The datasets were downloaded from the FlowRepository (FR-FCM-ZY34)  20. The datasets consist of data for PBMCs from 10 healthy controls and 20 patients with stage IV melanoma, before and after PD-1 blockade immunotherapy (N=11 and 9 for responders and non-responders, respectively). The two batches (i. e. , discovery and validation cohorts) were integrated with Harmony 12 , using default parameters. The FlowSOM/ConsensusClusterPlus method initially identified 40 metaclusters. These clusters were then manually inspected and annotated. A heatmap representing scaled median expression levels is shown.
Acknowledgments We would like to thank the patients, their relatives, and their physicians for participating in this study;  Dominick Papandrea ,  Yelena Nemirovskaya ,  Mark Woollett ,  Lazaro Lorenzo-Diaz , and  Cécile Patissier  for administrative assistance;  Tatiana Kochetkov  for technical assistance; the members of the laboratory for helpful discussions. We thank the  Flow Cytometry Resource Center at the Rockefeller University  for technical support for flow cytometry. We also thank the  Human Immune Monitoring Core at the Icahn School of Medicine at Mount Sinai  for technical assistance with mass cytometry. We thank the  National Institutes of Health (NIH) Tetramer Core Facility (NTCF)  for providing the MR1 tetramer, which was developed jointly with  Dr. James McCluskey ,  Dr. Jamie Rossjohn , and  Dr.
Giles Foundation , the  Rockefeller University ,  Howard Hughes Medical Institute ,  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Université de Paris , the  French Foundation for Medical Research (FRM)  ( EQU201903007798 ), the  French National Research Agency  under the  "Investments for the Future" program  (grant number  ANR-10-IAHU-01 ), the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ), the  SCOR Corporate Foundation for Science , and the  GENMSMD  project (grant number  ANR-16-CE17-0005-01  to J. B. ), and  SRC2017  (to J. B. ).
In  [20] , SES is used for meta-modeling of abstract test case generation of the test flight simulation. Selection conditions are used to set the specific configuration for a particular test case. An implementation is carried out in MATLAB/SIMULINK c  [21]  with a specific SES Toolbox implementation. This paper presents an excellent Model-Based Testing approach for flight simulator objective fidelity evaluation. The purpose of this paper can be viewed as a complement of this work by setting the SES variables involved in the pruning process with special metrics related to the fidelity concept. The goal is to show that it is possible to guide the Modeler through the activity model metrics by considering the notion of performance and accuracy of models. The following section introduces the activity concepts and metrics with a special focus on the DEVS M&S aspects.
Activity Metrics Implementation DEVSimPy (Python Simulator for DEVS models)  [30, 31]  is a user-friendly interface (based on wxPython  [32]  graphic library) for collaborative M&S of DEVS systems implemented in the Python  [33]  language and based on the PyDEVS and PyPDEVS  [34]  (the Parallel DEVS implementation of PyDEVS) APIs. DEVSimPy has been set up to facilitate both the coupling and the reusability of the DEVS models that can be stored in a library in order to be reused and shared (Figure  5 ). The DEVSimPy AT plug-in works as follows: • The user enables the plug-in (see Figure  6 ) and chooses the set of DE-VSimPy atomic models for activity-tracking (see Figure  7 ). Before the simulation, the DEVS models are scanned in a recursive way to collect all atomic models selected by the user in the plug-in interface.
Then, when the conditions of melting are met (which can occur several days to several months after the occurrence of precipitation), this water is remobilized. It causes a delayed reaction of the watershed. The behavior of the watershed is obtained by studying the rainfall data (expressed daily) and temperature data (expressed daily and hourly). Different models defined at different levels of abstraction or time granularity are defined and simulated. The simulation results are then compared with the observed streamflow of the watershed (Figure  8 ). Given our objectives, we have analyzed the different aspects of the water cycle based on studies describing this cycle within the watershed  [38, 39]. As depicted in Figure  9 , the distribution of precipitation is as follows: three flows (runoff, hypodermic and deep flow) feed the river and constitute the streamflow.
We evaluate web services QoS, at runtime, using a fuzzy approach; in particular, we propose a fuzzy-consistency system evaluation (FCSE) function, as previously reported in  [1] , we identify useless checkpoint concerning QoS and system consistency. To the best of our knowledge, this is the first mechanism that combines a fuzzy approach for system assessment purposes with CiC mechanisms. We used ChkSim  [16]  and Jfuzzylogic http://jfuzzylogic. sourceforge. net/html/index. html, for simulation as both are compatible with JAVA programming language. We show that our proposal is more efficient than current solutions. To this end, we have compared our approach against three of the most efficient CiC based solutions, reported in State-of-the-Art. We organized the rest of the paper as follows. In Section 2, we give an overview of related work.
To our knowledge, this simple method has been rarely used to embed multilingual sentences  (Vargas et al. , 2019) , and we hypothesize its potential as a strong baseline on other cross-lingual datasets and tasks too. Finally, embedding learning based on retrofitting leads to better multilingual music genre representations than when inferred with pretrained embedding models. This opens the possibility to learn embeddings for rare music genres or languages when aligned music genre ontologies are available. Summing up, our contributions are: 1) a study on how effective language-specific semantic representations of music genres are for modeling crosslingual annotation, without relying on a parallel music item corpus; 2) an extensive evaluation of multilingual pre-trained embedding models to derive representations for multi-word concepts in the music domain.
, 2008; Lee et al. , 2013). To address this subjectivity,  Bogdanov et al. (2019)  proposed a dataset of music items annotated with English music genres by different sources. In this line of work, we address the divergent perception of music genres. Still, we focus on multilingual, unsupervised music genre annotation without relying on content features, i. e. audio or lyrics. We also complement similar studies in other domains (art:  Eleta and Golbeck, 2012)  with another research method. Then, in the literature, there are other works that benchmark pre-trained word and sentence embedding models. van der Heijden et al. (  2019 ) compares multilingual contextual language models for named-entity recognition and part-of-speech tagging. Shwartz and Dagan (2019b)  use multiple static and contextual word embeddings to represent multi-word expressions and assess their capacity to capture meaning shift and implicit meaning in compositionality.
Using DBpedia  (Auer et al. , 2007)  as a proxy to Wikipedia, we collected music items such as artists and albums, annotated with music genres in at least two of the six languages (en, nl, fr, es, cs and ja). We targeted MusicalWork, MusicalArtist and Band DBpedia resource types, and we only kept music items that were annotated with music genres which appeared at least 15 times in the corpus. Our final corpus includes 63246 music items. The number of annotations for each language pair is presented in Table  1. We also show in Table  2  the number of Figure  1 : Wikipedia infoboxes of Puerto Rican artist Milton Cardona, in English (en), Spanish (es) and Japanese (ja) languages. Some music genre annotations are culture-specific, such as World/ワールドミュージック which is present for en and ja but not for es, or 実験音楽 (Experimental Music) for ja only.
Music Genre Ontology Conceptually, music genres are interconnected entities. For example, rap west coast is a sub-genre of hiphop or música electrónica is the origin of synthpunk. Academic and practitioner communities often use ontologies or knowledge graphs to represent music genre relations and enrich the music genre definitions  (Schreiber, 2016; Lisena et al. , 2018). As mentioned in Section 1, we use in this study Wikipedia-based music genre ontologies because the multilingual Wikipedia contributions on the same topic can differ and these differences have been proven aligned with the ones in the physical world  (Pfeil et al. , 2006). We further describe how we crawl the Wikipediabased music genres ontologies for the six languages by relying on DBpedia. For each language, first, we constitute the seed list using two sources: the DBpedia resources of type MusicGenre and their aliases linked through the wikiPageRedirects relation; the music genres discovered when collecting the test corpus (introduced in Section 3.
1) and their aliases. Then, music genres are fetched by visiting the DBpedia resources linked to the seeds through the relations wikiPageRedirects, musicSubgenre, stylisticOrigin, musicFusionGenre and derivative 3. The seed list is updated each time, allowing the crawling to continue until no new resource is found. In DBpedia, resources are sometimes linked to their equivalents in other languages through the relation sameAs. For most experiments, we rely on monolingual music genres ontologies. How-ever, we also collect the cross-lingual links between music genres to include a translation baseline for cross-lingual annotation, i. e. for each music genre in a source language, we predict its equivalent in a target language using DBpedia. Besides, we try to learn aligned embeddings from scratch by relying on these partially aligned music genre ontologies, as will be discussed in Section 4.
We report the mean and standard deviations of the macro AUC scores using 3-fold cross-validation. For each language, we apply an iterative split  (Sechidis et al. , 2011)  of the test corpus that balances the number of samples and the tag distributions across the folds. We pre-process the music genres by either replacing special characters with space ( -/,) or removing them (()':. !$). For Japanese, we introduce spaces between tokens obtained with Mecab 5. Embeddings are then computed from pre-processed tags. We test two translation baselines, one based on Google Translate 6  (GTrans) and one on the DBpedia SameAs relation (DBpSameAs). In this case, a source music genre is mapped on a single or no target music genre, its embedding being in the form {0,1} |T |. For XLM Ctxt , we compute the sentence embedding by averaging the token embeddings obtained with mean pooling across all layers.
Ocean: MOM5 ACCESS-CM2 implemented the 2012 release of MOM model version 5 as the ocean component. Readers are referred to  Griffies (2014)  for a detailed description of the model fundamentals such as equations, physics and dynamics, supported coordinates, and sub-grid scale parameterisations. ACCESS-CM2 shares the same community numerical code as MOM5. 1 (https://mom-ocean. github. io; hereafter MOM5 for simplicity) with a recently developed ocean-sea ice coupled model ACCESS-OM2  (Kiss et al. 2020) , which is an enhanced version of ACCESS-OM  (Bi et al. 2013a ) with configurations for different resolutions. MOM5 in ACCESS-CM2 for CMIP6 uses the same grid and resolution as that of MOM4p1 in ACCESS1. 3 for CMIP5, as described by  Bi et al. (2013a Bi et al. ( , 2013b)). In brief, horizontally the ocean model has 360 longitude by 300 latitude points on a logically rectangular matrix, and vertically the z* coordinate is implemented, with 50 model levels covering 0-6000 m with a resolution ranging from 10 m in the upper layers (0-200 m) to about 333 m for the abyssal ocean.
3, but there are a few notable differences, as detailed below. In ACCESS-CM2, shortwave penetration into the ocean is handled by the simplified CSIRO algorithm (as used for ACCESS1. 3) and a more sophisticated GFDL scheme at different stages of the spin-up period (see Table  1 ). When the CSIRO scheme is used in the early stage of the spin-up, the maximum penetration depth is set to the bottom of the ocean for ACCESS-CM2, compared with the 120 m limitation set in ACCESS1. 3. The associated climatological shortwave attenuation depth (SWAD) data for ACCESS-CM2 has been switched to the diffuse attenuation coefficient (K d , SWAD ¼ 1/K d ) of the downwelling photosynthetically available radiation (K dPAR ) from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project dataset  (Cracknell et al.
2001). In contrast, ACCESS1. 3 inadvertently used K d490 (the default for the CSIRO SW scheme), which is the diffuse attenuation coefficient of the downwelling spectral irradiance at wavelength 490 nm, for the CMIP5 experiments  (Dix et al. 2013; Marsland et al. 2013). The K dPAR data covers a broader, more representative spectrum of light and is considered more appropriate for use in ocean models. Detailed discussions on the impact of shortwave attenuation depth datasets on ocean climate simulation were given in  Bi et al. (2013a)  and  Zhou et al. (2015). For the neutral physics parameterisation, both ACCESS-CM2 and ACCESS1. 3 use isoneutral diffusion  (Redi 1982 ) and a modified  Gent and McWilliams (GM: 1990)  scheme in which skew diffusion relaxes neutral directions towards surfaces of constant geopotential  (Ferrari et al.
The model climatology forcing of iceberg melting is obtained from an early multi-century PI spin-up run using ACCESS-CM2j. Put simply, the NH and the southern hemisphere (SH) land ice increments are diagnosed for each coupling interval in the ice model (and discharged into the ocean) in the ACCESS-CM2j spin-up run, and the data for the first century (years 2-101) is used to get the monthly climatology of the land ice discharge. Then we distribute this climatology of NH and SH land ice amounts onto the distributional map of the iceberg climatology from the GC3. 1 Lagrangian iceberg model (D. Storkey, pers. comm. ). The ACCESS-CM2 climatology of monthly iceberg flux is then the product of the GC3. 1 climatology of iceberg flux multiplied by the ratio of annual total land ice discharge of ACCESS-CM2j to the annual total iceberg amount of GC3.