dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion In this paper, we have introduced GEM, a living natural language generation benchmark with a focus on evaluation. While GEM does not claim to instantly solve all issues of benchmarks in NLG, we aim to provide an environment in which systems can be tested in a principled manner and which can elevate the prominence of interesting evaluation approaches. By providing a testbed to easily conduct experiments across many datasets and evaluate in a repeatable, consistent, and more interpretable way, we will be able to track progress toward the goals in NLG research much more clearly. Moreover, we will be able to extend and shape GEM in the future to include more multilingual datasets, which will assist in their adoption across the wider research community.
Simplification. Dhruv Kumar, Mounica Maddela, and Wei Xu contributed to the GEM Simpli-fication task. Dhruv Kumar created the data cards for the datasets, added Wiki-Auto and Turk/ASSET datasets to TFDS, and integrated the SARI metric  (Xu et al. , 2016)  into the GEM evaluation framework. Mounica Maddela created baselines for the task and added the Turk benchmark corpus to Hugging Face and TFDS. Wei Xu helped in the organization and planning of the task setup. Human Evaluation. Samira Shaikh was the point of contact for this working group. She led the discussions to make progress on the group goals. She also worked with the group to select the general evaluation criteria as well as the criteria for dialogue and simplification tasks. Khyathi Chandu and Miruna Clinciu worked on selecting evaluation criteria for the summarization task and participated in the group discussions.
Khyathi Chandu worked on identifying where the current datasets fall short to motivate the crowdsourcing of data for a new task. Based on the suggestions from collaborators, she wrote two task proposals in the domains of longform text, conversations, and data-to-text that address an array of challenges in generation and easily scale to multiple languages. Samira Shaikh participated in the discussions and gave feedback on the task proposals in the pilot study phase. Dimitra Gkatzia looked into potential resources for crowdsourcing. Chris Emezue and Rubungo Andre Niyongabo explored potential low-resource African languages for crowdsourcing. We are in the process of piloting the tasks internally. The authors of this paper not named in the groups participated in initial discussions, participated in the surveys, and provided regular feedback and guidance.
Website and Submission System. Aman Madaan, Moin Nadeem, Hendrik Strobelt, and Sebastian Gehrmann are part of this group. Sebastian Gehrmann developed the website. Aman Madaan wrote the initial version of the result presentation. Hendrik Strobelt leads the visualization effort for interactive exploration of results. Model Infrastructure. Yacine Jernite wrote the initial script template for evaluating and fine-tuning Hugging Face models with the CommonGen example. Sebastian Gehrmann generalized the script to work with other datasets. Tosin Adewumi wrote a script for fine-tuning the DialoGPT model for dialogue datasets. Juan Diego Rodriguez worked on the infrastructure to fine-tune mBART on MLSum. Mihir Kale trained all mT5 baselines. Data and Model Sheets and Statements. Salomey Osei, Pawan Sasanka Ammanamanchi, Juan Diego Rodriguez, Sebastian Gehrmann, Yacine Jernite, and Angelina McMillan-Major are part of this group.
The Data Sheet structure was adapted from a combination of designs created for the Hugging Face Datasets library by Angelina McMillan-Major and Yacine Jernite and one written by Sebastian Gehrmann. Juan Diego Rodriguez and Yacine Jernite wrote initial statements for ASSET and Com-monGen respectively. The feedback on those was used to improve the structure of the final template. Everyone contributed to the model card template. Challenge Sets. Simon Mille, Emiel van Miltenburg, Kaustubh Dhole, Varun Prashant Gangal, Saad Mahamood, and Laura Perez-Beltrachini proposed and discussed ideas of interest for the data-to-text and the text-to-text tasks. Simon Mille coordinated the group. Emiel van Miltenburg, Saad Mahamood, and Simon Mille worked on the creation of the data-to-text datasets, while Varun Prashant Gangal, Kaustubh Dhole and Laura Perez-Beltrachini worked on the text-to-text datasets.
Summarization. The summarization group members are Chris Emezue, Esin Durmus, Faisal Ladhak, Jiawei Zhou, Juan Diego Rodriguez, Kaustubh Dhole, Khyathi Chandu, Laura Perez, Pawan Sasanka Ammanamanchi, Pedro Henrique Martins, Rubungo Andre Niyongabo, Shashi Narayan, Vikas Raunak, and Yufang Hou. Pedro Henrique Martins organized the group and wrote the data statement for the MLSum dataset. Pawan Sasanka Ammanamanchi was responsible for the XSum data statement, while Vikas Raunak worked on the Wikilingua statement. Shashi Narayan prepared the GEM version of the XSum dataset and trained its baseline models. Juan Diego Rodriguez was responsible for cleaning the MLSum dataset and trained its baseline models. Faisal Ladhak was responsible for the Wikilingua baseline models. Rubungo Andre Niyongabo participated in the discussions and added related papers to the planning document.
, 2020) Produce an English sentence that de-scribes the highlighted cells in the context of the given table. en 136k Highlighted Table XSum (Narayan et al. , 2018) Highlight relevant points in a news article en *25k Articles WebNLG (Gardent et al. , 2017) Produce a text that verbalises the input triples in a grammatical and natural way. en/ru 50k RDF triple WikiAuto + Turk/ASSET Communicate the same information as (Jiang et al. , 2020) the source sentence using simpler words en 594k Sentence (Alva-Manchego et al. , 2020) and grammar. *ar/cs/de/en WikiLingua (Ladhak et al. , 2020) Produce high quality summaries of an instructional article. es/fr/hi/id/it ja/ko/nl/pt/ru *550k Article th/tr/vi/zh A description of all the datasets included in GEM. The tasks vary in communicative goal, data size, and input type.
For example, while taking care to not spill its contents, remove the gallbladder and its connections with the liver. Wrap the liver in aluminum foil (Figure  2E ). Immediately snap-freeze foil in liquid nitrogen. CRITICAL: When collecting dark-phase time-points, do not expose mice to light at any point. While transporting mice to the dissection room, they can be covered with heavy black trash bags or a blackout curtain. Do not turn on dissection room lights until mice have been decapitated. If collecting brain, remove the eyes before turning on the lights. CRITICAL: Ensure collection is complete within G30 min of the time-point. Consider the number of mice, data gathering (e. g. , blood glucose, body weight), and tissue dissection time. For instance, if there are 10 mice that each take 6-min to process, start the harvest at 30 min prior to the time-point in order to finish at 30 min post-time-point.
FPKM, RPKM, and/or TPM values are compatible with downstream rhythmicity analysis. CRITICAL: High-quality, correctly processed RNA-seq data is indispensable for identification of rhythmic transcripts using this protocol. Users are referred to bioinformaticscore-shared-training. github. io/RNAseq-R/ and bioconductor. org/packages/devel/workflows/ vignettes/rnaseqGene/inst/doc/rnaseqGene. html as references for standard good practice in quality control and processing of RNA-seq data. Note: Apply Principal Component Analysis, Hierarchical Clustering or more specialised tests (see:  Chen et al. , 2020; George et al. , 2015)  to the RNA-seq data, to identify outliers that could potentially compromise rhythmicity analyses.
Recommended: The manuscript and user guide written for JTK_CYCLE by its creators describe in detail the underlying mathematics and give thorough technical advice, respectively. We highly recommended reading both of these informative publications before proceeding. 3. Using the previously-generated circadian RNA-seq data, prepare the annotation and data matrix files that will serve as the input to JTK_CYCLE (Figures  4A  and 4B ). a. The annotation file should comprise a first column listing the IDs of all genes quantified by RNA-seq, followed by columns containing other gene annotations (HUGO symbol, Ensembl ID, etc. ) that the user prefers (Figure  4A ). b. The data matrix file should comprise a first column listing the IDs of all genes quantified by RNA-seq (identical to the first column of the data matrix file), followed by columns chronologically (ZT0, ZT4, ZT8.
) listing their respective expression values for all replicates at all timepoints. Replicates must be placed in adjacent columns (Figure  4B ). CRITICAL: Ensure that the order of gene IDs is identical in the first columns of the data matrix and the annotation files. 4. In the same folder containing the scripts required to run JTK_CYCLE, save both the data matrix and the annotation files as tab-delimited text files (. txt). 5. In a text editor, open the ''Run_JTK_CYCLE (Example1). R'' script file. a. This file can be found inside the folder downloaded from the JTK_CYCLE webpage. 6. Modify the following terms in the script to reflect the dataset being provided to the algorithm: a. On line four, alter the project name to an appropriate description (default: "Example1"). b. On lines seven and eight, change the file names to those of the annotation and the data matrix files, respectively (default: "Example1_annot.
txt" and "Example1_data. txt"). c. For this example, an experiment with six time-points collected over 24 hr and four replicates per time-point, the following lines of the script must also be altered to reflect the dataset structure: i. Line 12, "jtkdist(6,4)" indicates the number of time-points (six) and the number of replicates provided for each time-point (four). Note: If group sizes are unequal due to excluded or missing replicates, an additional line of code must be added to specify the number of replicates at each time-point. For example, if only three replicates are available for the first time-point, the line "group. size <-c(3,4,4,4,4,4) " would be added, and the term "jtkdist" changed to "jtkdist(length(group. sizes),group. sizes)". ii. Line 14, "periods <-6:6" specifies the range of circadian periods that are of interest.
In this instance, only genes with a 24-hr period are of interest; thus, "6:6" is the input (six timepoints per cycle). iii. Line 15, "jtk. init(periods,4)" indicates the separation in hours (four) between time-points. 7. Change the working directory of R to the directory containing the data matrix file, annotation file, and scripts required to run JTK_CYCLE. 8. Paste the modified code into the R terminal and run the script. a. JTK_CYCLE will output a tab-delimited text file containing the results into the same directory (Figure  4C ). This file provides the calculated Benjamini-Hochberg q-value (BH. Q), adjusted pvalue (ADJ. P), period (PER), phase (LAG), and amplitude (AMP) for all genes analyzed.
CRITICAL: The JTK_CYCLE script must be modified correctly to accurately reflect the structure of the RNA-seq data generated. Note: JTK_CYCLE will typically take 10-15 min to run using a standard desktop computer or laptop. 9. To obtain a final list of robustly-rhythmic genes, the output file must be filtered to retain only those genes with clear daily rhythms in expression. Typically, an adjusted p-value of 0. 01 provides a sufficiently stringent cut-off that retains only genes with clear, biologically-relevant rhythmic expression. In addition, selecting genes by expression amplitude can complement a p-value filter, adding an additional level of stringency to the rhythmic transcriptome identified.
CRITICAL: The p-value filter selected must be directed by the level of noise intrinsic to the dataset obtained, which will vary on a case-by-case basis. Visual inspection of core clock genes, such as Bmal1, Per2 and Cry2, can provide a good starting point to assess the technical and biological noise present in a circadian dataset (Figure  3 ). 10. The final set of circadian genes can now be used for further downstream bioinformatics analyses, such as gene ontology and pathway analysis, or to direct future in vivo and in vitro experiments.
Although JTK_CYCLE is one of the most established and frequently cited examples for rhythmicity detection, we recommend validating results using different algorithms  (Guan et al. , 2020; Koronowski et al. , 2019; Welz et al. , 2019). Specifically, users are directed to BIO_CYCLE and MetaCycle as complementary approaches for rhythmicity detection  (Agostinelli et al. , 2016; Wu et al. , 2016). BIO_CYCLE applies machine learning techniques, operates as a web tool, and provides a rapid and effective alternative to JTK. MetaCycle, in contrast, is an R package that enables the user to apply three popular rhythmicity detection algorithms to the same data set (ARSER, JTK_Cycle and Lomb-Scargle), allowing for independent confirmation of rhythmicity and avoidance of false-negatives. Moreover, when comparing the rhythmic transcriptomes of multiple groups (genotypes or conditions) users should consider employing algorithms specifically designed for differential rhythmicity analysis.
Potential solution Typically, an investigator should expect to detect 1000+ genes oscillating in a daily manner when this protocol is applied to the livers of wild-type mice. Nonetheless, this number will vary substantially depending on the whether an animal has been genetically modified or subjected to some environmental/pharmacological perturbation (see:  Welz et al. , 2019 and Koronowski et al. , 2019  for examples of the scale of this variation upon genetic modification). Aside from true variation in the size of the rhythmic transcriptome, the degree of biological and technical variation present in the RNAseq data gathered will determine the sensitivity with which daily rhythms can be identified by JTK_Cycle. Thus, it is essential that investigators: 1) Use a number of biological replicates that reflects the anticipated biological variability between mice, 2) Perform stringent quality control of the RNA-seq data input to JTK_Cycle, 3) Adjust the stringency of the filters used to classify daily rhythms according to the variability present in the data.
ACKNOWLEDGMENTS P. -S. W. was supported by a  Ramon y Cajal Fellowship  from the  Spanish Ministry for Science and Innovation (MICINN). Research in the S. A. B. lab is supported by the  European Research Council  ( ERC Advanced Grant ), the  Government of Catalun ˜a (SGR grant ), the  Government of Spain (MINECO) , the  La Marato/TV3 Foundation , the  Spanish Association for Cancer Research (AECC) , the  Foundation Lilliane Bettencourt , the  Axa/Atresmedia Award , and the  Worldwide Cancer Research Foundation (WCRF). The  IRB Barcelona is a Severo Ochoa Center of Excellence (MINECO  award  SEV-2015-0505 ). K. B. K. was supported by  NIH-NIDDK  (award  F32DK121425 ). T. M. received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No.
Improving Wind Speed Measurements During Vortex Encounters InSight is the first mission to provide high-frequency measurements of wind speed in energetic vortices (pressure drops of up to 9 Pa) with instantaneous wind speed peaks of up to 31. 5 m s -1. Such short-lived peaks in wind speed have been unresolved by other missions. In addition, other missions do not have simultaneous high-frequency meteorological data and observations of surface changes. Since sediment motion at InSight is heavily dependent on the instantaneous forces applied by close vortex encounters, accurately measuring peak wind speeds is critical to infer surface friction wind speeds required for grain detachment. However, limitations in the wind retrieval and the geometry of the encounter during a vortex's passage hamper the estimation of a representative peak wind speed (Section 4.
These twin requirements of high sample rate and high-wind-speed measurement capability may favor a specialized "peak wind sensor. " In contrast to typical wind sensors, which operate at a low sample rate, are designed to accurately measure typical wind speeds, and operate continuously, a peak wind sensor would operate at a very high sample rate, be designed to accurately measure peak wind speeds, and would only operate in short bursts, beginning when its accompanying continuous wind sensor detected the onset of a peak in wind speed. In this way, power and data volume could be kept manageable. In addition, having wind speed sensors at several heights would help to estimate z 0 and its dependence on wind azimuth.
used. The authors also thank the JGR Editor Deanne Rogers, associate editor German Martinez and both the referees, Matt Balme and Simone Silvestro, for their insightful suggestions and constructive review on greatly improving this manuscript. The authors would also like to thank Menghan Wang for constructive criticism of the manuscript. A portion of this study was supported by the InSight Project at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. French co-authors acknowledge support from the Centre National d'Études Spatiales (CNES). Additional funding support was provided by Agence Nationale de la Recherche (ANR-19-CE31-0008-08 MAGIS). I. J. Daubara was supported by NASA Grants 80NM0018F0719 and 80NSSC20K0971.
, 2011). Thus, satellite altimeters continue providing crucial SSH observations for monitoring the spatiotemporal Abstract Satellite altimeters provide global observations of sea surface height (SSH) and present a unique data set for advancing our theoretical understanding of upper-ocean dynamics and monitoring its variability. Considering that mesoscale SSH patterns can evolve on timescales comparable to or shorter than satellite return periods, it is challenging to accurately reconstruct the continuous SSH evolution as currently available altimetry observations are still spatially and temporally sparse. Here we explore the possibility of SSH interpolation via Deep Learning by using synthetic observations from an idealized quasigeostrophic model of baroclinic ocean turbulence. We demonstrate that Convolutional Neural Networks with Residual Learning are superior in SSH reconstruction to linear and recently developed dynamical interpolation techniques.
Also, the deep neural networks can provide a skillful state estimate of unobserved deep ocean currents at mesoscales. These conspicuous results suggest that SSH patterns of eddies might contain substantial information about the underlying deep ocean currents that are necessary for SSH prediction. Our training data are focused on highly idealized physics and diversification of processes needs to be considered to more accurately represent the real ocean. In addition, methodological improvements such as transfer learning and implementation of dynamically aware loss functions might be necessary to consider before its ultimate use with real satellite observations. Nonetheless, by providing a proof of concept based on synthetic data, our results point to deep learning as a viable alternative to existing interpolation and, more generally, state estimation methods for satellite observations of eddying currents.
Plain Language Summary Satellite observations of sea surface height (SSH) are widely used to derive surface ocean currents on a global scale. However, due to gaps in SSH observations, it remains challenging to retrieve the dynamics of rapidly evolving upper-ocean currents. To overcome this limitation, we propose a Deep Learning framework that is based on pattern recognition extracted from SSH observations. Using synthetic data generated from a simplified model of ocean turbulence, we demonstrate that deep learning can accurately estimate both surface and subsurface ocean currents, significantly outperforming the most commonly used techniques. By providing a proof of concept, our study highlights the strong potential of deep learning for estimating ocean currents from satellite observations.
Most commonly used interpolation techniques, such as objective mapping or polynomial interpolation, do not attempt to make use of any potential dynamical constraints present in the data and perform well only for autocorrelated data while failing for sparse data. It is thus crucial to develop frameworks to efficiently extract information about the oceanic eddy dynamics from the spatially and temporally sparse SSH observations. Note, throughout the study, we use the term information qualitatively and do not attempt to quantify it using Shannon's information theory. Below we discuss how the nature of baroclinic ocean turbulence provides dynamical limitations for SSH interpolation and why deep learning might be a viable alternative to other interpolation techniques.
SSH Interpolation and the Associated Dynamical Limitations Spatiotemporal interpolation or gridding of SSH data is inherently linked to ocean eddy physics as the success of a given technique ultimately should rely on the pertinence of its assumed model (either dynamical or statistical) that captures the essence of mesoscale eddy propagation in space and time. To illustrate this point, imagine a coherent eddy moving in a turbulent field and several altimeter tracks passing through it at different times and directions. If there is an accurate model of eddy propagation, it would allow pinpointing the observations taken over this specific eddy and combining this data to better constrain the two-dimensional eddy shape. Thus, to extract the relevant information from various altimetry tracks to the fullest extent, it is necessary to have an accurate model of eddy evolution.
However, due to the stratified nature of geostrophic ocean turbulence, the unobserved deep ocean flows can affect the surface dynamics, and hence the SSH observations on their own may not be self-sufficient to infer its evolution. Given the lack of subsurface data at eddy scales, constructing a closed system of equations for SSH evolution is challenging. Another complication for SSH interpolation arises due to the chaotic nature of geostrophic turbulence that has an increasingly higher sensitivity to initial conditions as time progresses. Upon the dynamical evolution, any ordered set of state-space trajectories will become increasingly stretched and filamented over time, eventually equidistributing its volume over the entire state space, that is, undergoing measure-theoretic and topological mixing.
To avoid prescribing a dynamical model, statistical models like objective interpolation  (Davis, 1985; Ducet et al. , 2000; Le Traon et al. , 1998)  rely on data only. Their premise is to incorporate spatiotemporal correlations and measurement error into a statistical model and provide the most likely estimate of the true continuous field under consideration. However, this method does not rely on any dynamical model of the eddy propagation and hence can lead to an unphysical behavior of the interpolated SSH field. Methods involving dynamical ocean models are typically based on data assimilation, a procedure that minimizes the difference between the observed and modeled fields by adjusting unknown variables like boundary and initial conditions or external forcing (see e. g. , reanalysis product by  Carton & Giese, 2008).