dataesr/scientific-paragraphs-categorization huggingface dataset:
The concept of conditional and joint in-loop probabilities has some parallels to the production rules of Stochastic Context-Free Grammars (SCFG) that can encode base-pair relations differently  [24] , they have also been used to solve the SA&F problem  [25]. The overhead of treating various nucleotides separately during the alignment procedure is similar to the invocation of the full Fig. 5  Family-wise performance analysis on the Bralibase 2. 1 pairwise benchmark set K2. (X-axis) The average difference between the Pankov and SPARSE for the sequence alignment quality metric SPS is computed per RNA family. (Y-axis) Similar to the X-axis but for the structure prediction metric MCC. Families with extreme differences are labeled. Pankov uses the superior estimation of the energy model while SPARSE applies PMcomp's model (see Fig.
Acknowledgements The authors would like to thank the anonymous reviewers. MM and MR are supported by the  German Research Foundation  ( DFG BA2168/14-1  and  BA2168/16-1 ). SW is supported by the  Austrian Science Fund  ( FWF I 2874-N28 ). RB is supported by the  German Research Foundation (DFG)  under  Germany's Excellence Strategy  ( CIBSS -EXC-2189 -Project ID 390939984 ). The article processing charge was funded by the  German Research Foundation (DFG)  and the  University of Freiburg in the Open-Access Publishing funding programme.
Atlantis Ecosystem Model To evaluate the implications of alternative harvest rules on the broader ecosystem, we implement the end-to-end Atlantis C++ code framework  (Fulton et al. , 2004 (Fulton et al. , , 2011;; Audzijonyte et al. , 2019). As an end-to-end model, Atlantis simulates oceanography, nutrient cycling, food web dynamics, fisheries, and other human uses on a three-dimensional domain. In most applications including ours, simulations involve forward projections of differential equations, solved on 12 or 24 h time steps using adaptive-difference techniques. The model tracks species abundance in terms of nitrogen concentrations per model cell (for invertebrates and primary producers), and in terms of numbers-at-age and weights-at-age (in nitrogen units) for vertebrates. Vertebrates typically are driven by fixed seasonal migrations or foraging behavior, while plankton are advected between model cells.
ACKNOWLEDGMENTS We thank  John Field  ( NOAA SWFSC ) and  Cecilie Broms  ( IMR ) for zooplankton time series for the California Current and the  Nordic and Barents Seas. Daniel Howell  (IMR) and  Chris Harvey ,  Jameal Samhouri , and the  Ecosystem Science Program (NOAA NWFSC)  provided feedback on early versions or drafts of this work.
Individual cells are encapsulated with the respective assays, the droplets mapped in two-dimensions and antibody secretion and binding is measured over time. The single-cell and single-antibody data from both assays is combined afterwards, allowing to measure frequencies of total and specific IgG/IgM-SCs, individual secretion rates, and finally to estimate affinities and epitope densities. active surface to perform in-droplet immunoassays to detect bacteria-binding antibodies (Supplementary Fig. 1 ). The direct immobilization using the hydrophobic cholesterol linker minimized epitope masking and allowed to present the antigens in their natural environment, the bacterial membrane. Therefore, all accessible surface epitopes present during immunization were also accessible in the analysis. Additionally, the immobilization via this linker was found to be stable over the time needed to perform the assays.
Shown are six example curves for cells secreting IgG that bound OVA (orange), and six cells that did not secrete IgG (black). In this figure, all droplet data is shown as mean values with SEM (n > 3000 droplets for each point, unless stated otherwise). area (164 ± 30 HK-B per droplet). Since this calculation assumed the bacteria to be only present in a 2-dimensional sheet rather than a 3-dimensional bactoline, this number represented a lower limit of the number of bacteria that are present within a droplet. Indeed, by measuring the 3D-volume of the bactolines by microscopy and assuming a volume of 2 µm 3 per HK-B  37  , the maximum number of bacterial cells in the bactoline volume was calculated to be around 1000 HK-B. This upper limit of HK-B per droplet was confirmed by the measurement of a 50-fold decrease in OD 600 before and after the immobilization step, corresponding to roughly 800 HK-B per droplet.
Once the chamber was filled with the droplets, the chamber was closed and mounted onto a fluorescence microscope for imaging. Imaging and data acquisition and analysis. The droplets in the 2D chamber were imaged using an inverted fluorescence microscope (Eclipse Ti-2, Nikon) equipped with a motorized stage and excitation light (Lumencor Spectra X). The images were recorded using a digital CMOS camera (ORCA-flash4. 0 V2, Hamamatsu, in 2 × 2 binning mode) at an exposure time of 100 ms through a 10× objective (NA 0. 45; Nikon). Each cell experiment was acquired as an array of 10 × 10 images, which covered around 30,000 50 pL droplets. Images were recorded every 15 min for an hour (five time point measurements total) using appropriate FITC, TRITC and Cy5 filters. The images were analyzed by a custom Matlab script as described in refs.
31, 51. Due to the different shapes of bactolines, the script was slightly adapted. To measure bactoline intensity, the algorithm selected the brightest pixels (mean area of bactoline) in the droplet within the FITC channel (auto-fluorescence of HK-B). Smaller isolated fragments, less than half of the total area, were removed from analysis (removal of free-floating HK-B, noise). The output, a binary mask corresponding to the bactoline area, was used to measure fluorescence relocation within the other channels. Droplets containing Ig-SCs were identified by a detectable increase in fluorescence relocation and the presence of a cell. IgG secreting cells were detected by anti-IgG Fc specific secondary antibody conjugated to Alexa647 (red fluorescence), and IgM secreting cells were detected by anti-IgM mu specific secondary antibody conjugated to Alexa555 (yellow fluorescence).
Acknowledgements This work was supported by the  CELLIGO  project funded by the  French government through BPIFrance  under the frame " Programme d'Investissements d'Avenir" (PIA) , the  "Institut Pierre-Gilles de Gennes " through the  laboratoire d'excellence, "Investissements d'avenir"  programs  ANR-10-IDEX-0001-02   PSL ,  ANR-10-EQPX-34  and  ANR-10-LABX-31 , and the  European Research Council (ERC) - Seventh Framework Program.
Acknowledgments This work has been supported by the  French government , through the  UCAJEDI Investments  in the  Future  project managed by the  National Research Agency (ANR) ,  France  with the reference number  ANR-15-IDEX-01 :  UCA   JEDI Idex program under Academy ''Space, Environment, Risk and Resilience'' research program , project name  NR2P2. The authors would like to thank  Academy3 Space Environment , risk and resilience of  Université Côte d'Azur  for support. They also would like to thank all UCA students, firefighters of the SDIS  06 ,  13 ,  14 ,  28 ,  29 ,  37 ,  38 ,  46 ,  50 ,  57 ,  61 ,  64 ,  65 ,  72 ,  77 ,  82  and  84 , and in particular the  SDIS of the departments of Manche, Moselle and Finistere  ( SDIS 50 ,  57 , and  29 ).
The Faster Analysis Software Taskforce (FAST) is a small, European group of HEP researchers that have been investigating and developing modern software approaches to improve HEP analyses. We present here an overview of the key product of this effort: a set of packages that allows a complete implementation of an analysis using almost exclusively YAML files. Serving as an analysis description language (ADL), this toolset builds on top of the evolving technologies from the Scikit-HEP and IRIS-HEP projects as well as industry-standard libraries such as Pandas and Matplotlib. Data processing starts with event-level data (the trees) and can proceed by adding variables, selecting events, performing complex user-defined operations and binning data, as defined in the YAML description. The resulting outputs (the tables) are stored as Pandas dataframes which can be programmatically manipulated and converted to plots or inputs for fitting frameworks.
Introduction Producing high-quality research papers in High-Energy Physics (HEP) involves processing petabytes of data, applying the latest knowledge for the specific experiment and the statistical evaluation of the end-results and their uncertainties. This process often involves the use of experiment specific software frameworks, community packages as well as researcher-written code. All components have to undergo rigorous testing and the result is required to be fully reproducible. The key aims of the Faster Analysis Software Taskforce (FAST) are to: a) reduce the amount of researcher-written code to minimize mistakes, b) lower the entry requirements for new researchers, c) make it easier to share, and d) provide an abstraction between the analysis itself and the processing system that actually runs over the data.
Key design considerations To simplify both use and setup of the FAST analysis tools, the software dependencies are significantly reduced with respect to common HEP analyses. To achieve this we build on Python packages provided by Scikit-HEP (e. g. uproot) and the wider Python community (e. g. pandas). Furthermore, to start analysing data only basic knowledge of YAML is required, while the description of selections and other algorithms is kept close to their mathematical form. The definition of an analysis can then be split into three distinct steps, each addressing a different part of the analysis, and with corresponding FAST tools: 1. What input datasets do you need and what are their analysis-specific metadata? Handled by: fast-curator 2. What do you want to do with this data? Handled by fast-carpenter.
What do you want to show of this data? Handled by fast-plotter To use Our primary interface to these tools right now is through YAML  [3]  configuration files, which are widely used for the description of processing steps for tools like continuous integration (e. g. on gitlab, Travis, Azure), container orchestration (e. g. Ansible, Kubernetes), and within particle physics is used for preservation of data on the HEPData platform. As a superset of JSON, it lacks any flow control structures but makes it easier to read for example by reducing the number of brackets and quotation marks in the document, preferring indentation for item separation and adds options to reuse sections with a reference and anchor system. These features make it natural to use in a declarative approach where the user describes what to do, but lets the system take care of how to achieve this-addressing the separation between processing system and actual analysis decisions.
Describing input data: fast-curator As an analysis evolves, the input data might change too. New datasets can become available as a physics run continues, simulations are extended, or existing data is reprocessed through early stages. Alternatively, metadata of a given data set might be updated, such as various scale factors or cross sections. Input data are described as one or more data sets in YAML files that are generated and interpreted using the fast-curator package. Listing 1 shows such an example: data sets and their meta data can be defined in either a single YAML file (e. g. data and DY definitions, lines 2-12) or imported from other YAML files (WW and WZ data sets, lines 19+). Note that some of the information there is redundant, e. g. the number of events in the files. This information is useful to keep track of changes to the analysis' input datasets as new samples become available.
Describing data processing: fast-carpenter The core of any particle physics analysis is the processing of event-level data. In fast-carpenter the data processing steps are defined as "stages" in a YAML file. The example shown in listing 2 uses three built-in stages: one to define new variables (lines 9 Listing 1: Dataset description for fast-curator. Contains datasets for real data and one simulation (dy) and imports for two more (WW and WZ). Note that some of the redundant information is only there to assist book-keeping and validating changes to the input datasets as new samples become available. They are not all required by fast-carpenter and later stages. to 15), another to remove events based on a series of cuts (lines 30 to 38), and two instances of binned dataframe production i. e. histograms (lines 23 to 28 and 39 to 43).
Beyond these, additional modules produce dataframes with full event-level information and helpers handle systematic weight variables and parameters that define phase-space regions. In addition, the mechanism used to load stages easily extensible; if fast-carpenter does not provide a stage that you need for your analysis, it is easy to write it and include it in your workflow. Such a stage is included in the example Listing 2 on lines 4 and then configured on line 17. Both CutFlow and BinnedDataframe stages write outputs to disk in the form of a Pandas DataFrame, defaulting to CSV format, although any Pandas-supported format can be used. Binned dataframes generalise histograms to multiple dimensions. Each bin captures the raw number of events within its boundaries and, if one ore more are provided, the sum of each weight and square of weights.
Interoperability of the FAST-HEP tools Figure  1  shows the interplay between a user's repository and the various FAST-HEP packages. A user should typically only write their YAML configuration files, and possibly custom fast-carpenter extension stages and additional post-processing code. These will be passed to the corresponding tool to produce the final analysis results. In addition, to the tools     mentioned above, the FAST-HEP toolkit also provides the scikit-validate package to help validate analysis results in a continuous integration (CI) pipeline. All of these tools are available to download from the Python package index (pypi). Documentation is available online for fast-carpenter and fast-plotter and for the overall project at fast-hep. web. cern. ch. The YAML-based interfaces described above are the primary way to use these tools, however we have started investigating other approaches, in particular, using the packages' python APIs directly from Jupyter notebooks.
BasicVars: Define # Define new variables A custom class to form the invariant mass of a two-object system DiMuons: cms_hep_tutorial. DiObjectMass NumberMuons: BinnedDataframe # Filled a binned dataframe EventSelection: CutFlow # Select events by applying cuts DiMuonMass: BinnedDataframe # Fill another binned dataframe Muon_Pt: "sqrt(Muon_Px ** 2 + Muon_Py ** 2)" 12 -IsoMuon_Idx: (Muon_Iso / Muon_Pt) < 0. 10 13 # This next variable will create a single number for each event, 14 # using a set of inputs whose length varies for each event 15 -NIsoMuon: {reduce: count_nonzero, formula: IsoMuon_Idx} 16 # Custom module specific to this analysis uses one an optional parameter 17 DiMuons: {mask: IsoMuon_Idx} 18 # Make a binned dataframe with a column for: 19 # -the dataset name 20 # -the number of muons 21 # -the number of muons considered "isolated" 22 # and weight everything using the EventWeight variable Subsequent stages only see events that pass the following requirements 0, formula: Muon_Pt > 25} 36 # Weight events in the resulting cut flow table 37 weights: {weighted: EventWeight} 38 # Binned dataframe using the dataset and the binned DiMuon_mass DiMuon_Mass, out: dimu_mass, bins: {low: 60, high: 120, nbins: 60}} weights: {weighted: EventWeight} Listing 2: Data processing description example for fast-carpenter.
stages: 2 - 3 # 4 - 5 - 6 - 7 --# 234567 BasicVars: Define # Define new variables A custom class to form the invariant mass of a two-object system DiMuons: cms_hep_tutorial. DiObjectMass NumberMuons: BinnedDataframe # Filled a binned dataframe EventSelection: CutFlow # Select events by applying cuts DiMuonMass: BinnedDataframe # Fill another binned dataframe Muon_Pt: "sqrt(Muon_Px ** 2 + Muon_Py ** 2)" 12 -IsoMuon_Idx: (Muon_Iso / Muon_Pt) < 0. 10 13 # This next variable will create a single number for each event, 14 # using a set of inputs whose length varies for each event 15 -NIsoMuon: {reduce: count_nonzero, formula: IsoMuon_Idx} 16 # Custom module specific to this analysis uses one an optional parameter 17 DiMuons: {mask: IsoMuon_Idx} 18 # Make a binned dataframe with a column for: 19 # -the dataset name 20 # -the number of muons 21 # -the number of muons considered "isolated" 22 # and weight everything using the EventWeight variable Subsequent stages only see events that pass the following requirements 0, formula: Muon_Pt > 25} 36 # Weight events in the resulting cut flow table 37 weights: {weighted: EventWeight} 38 # Binned dataframe using the dataset and the binned DiMuon_mass DiMuon_Mass, out: dimu_mass, bins: {low: 60, high: 120, nbins: 60}} weights: {weighted: EventWeight} Listing 2: Data processing description example for fast-carpenter.
The surrogate data test on unfiltered time series suggests that we cannot reject the null hypothesis for which data can be described via a linear stochastic model (fig. S22 ). Furthermore, for unfiltered time series, we find extremal index values close to 1, with predictability horizons smaller than the data sampling rate, indicating a random system (fig. S26  for an example relative to segment #1). A similar conclusion would be reached by ET-based techniques (figs. S20, S21, and S27). This shows that, if not filtered, the noise in the data is dominating, masking the SSE dynamical structure. A more detailed discussion of the filter effects (and the tested filters) is reported in the "Filter" and "Surrogate data" sections. We stress here that filters applied to pure noise can potentially produce the spurious identification of finite correlation dimension  (34) , and for this reason, we further test the effects of the filters on the predictability of the time series generating (pseudo-)random time series and applying the same filters to them.
The number of generated random time series is equal to the number of subfaults in segment #1, which is the segment with the largest number of subfaults. We then generate surrogate data and calculate the average dimension on both the filtered random time series and the surrogate data. The result shows that we would not be able to reject the null hypothesis according to which the time series were generated by a random process (fig. S28 ). We thus consider unlikely that the filters here adopted are introducing an apparent chaotic dynamics, and we notice that, for the cases that pass the surrogate data test, D is typically between 3 and 4 for all segments, independently of the chosen filter parameters (figs. S22  and S23 ). Not all segments pass the surrogate data test. We conclude that SSEs on the northernmost segments of the Cascadia subduction zone (segments #1 and #2) result from deterministic chaos, while the central part (segments #3 to #5) shows ambiguous results, and for the southernmost portion of the fault (segments #6 to #13), we cannot infer deterministic chaos with the data at our disposal (figs.
For each designed filter, we show the filter response in figs. S34 and S35. The results shown in the main text are those obtained using an EF and the parameters adopted in  (20)  to automatically pick 64 SSEs from the dataset here studied, i. e. , f i passband = 35 -1 and f i stopband = 21 -1. We test also a Hamming Window filter (HWF; which is a weighted moving average filter), commonly used in signal processing, which offers an easier way to tune the filter order (N b ) and its normalized frequency cutoff (f cutoff ). We test both a variable f cutoff fixing N b to 60 (i. e. , we use 2 months of data before the current epoch to smooth the signal; figs. S36 and S37) and a variable N b fixing f cutoff to 28 -1 (figs. S38 and S39). In the first case, we vary { f i cutoff } i=1 10 = (7i) -1 , while in the second case, we vary { N b i } i=1 10 = 7i -1 , i.
e. , we use data of epoch t and the 6 epochs before, 13 epochs before, and so on until using a total of 10 weeks of data. We use the MATLAB built-in function fir1, which adopts a normalized gain of the filter at f cutoff of -6 dB. We observe that reducing the cutoff frequency lets the underlying dynamics emerge, and we consistently observe a finite fractal dimension significantly smaller than what would be calculated from surrogate data (fig. S22 ). We refer the reader to the "Surrogate data" section for more details. All tested filters have a constant group delay (figs. S34 to S39). The information on the group delay is relevant when discussing about the predictability horizon of the system (see Discussion). The effects of the EF filters on the time series are shown in figs. S40 to S44. For readability, the filtered time series have been shifted back in time of the corresponding group delay to be in phase with the original time series.
To test whether there is any difference between causally and noncausally filtered time series, for the filter EF 1/35 1/21 , we also construct a noncausal filter, knowing that noncausal filters cannot be adopted for real-time applications, because they use information from future epochs. We use the function FiltFiltM (https://www. mathworks. com/matlabcentral/fileexchange/32261-filterm) to generate zero-phase shift (i. e. , noncausal) filtered time series, and we find similar results to those obtained with a causal filter (figs. S13, S26, and S28). In all tested cases, we retrieve similar average dimensions, lower than those determined from nonfiltered time series (fig. S45 ). The application of a low-pass filter reduces the dimensionality of the system from values typically >6 to values <4.
The results are overall similar (green dots in figs. S13 and S14). We can now apply the method of surrogate data to each segment and compare the reconstructed attractor dimensions with the one calculated from real data. We refer to the "Surrogate data" section for more details. We notice that using a threshold q = 0. 99 reduces the average dimension to values <3. The threshold quantile q does not affect the conclusion that a chaotic deterministic dynamics can be successfully detected only for the northernmost segments. It affects though the interpretation of the total number of dof needed to explain the system. Following the spring-and-slider interpretation, if 3 < D < 4, then a second RSstate variable may be needed (see Discussion). Longer time series will help to resolve this ambiguity, since the value of q = 0.
Surrogate data The concept behind surrogate data techniques is rooted in statistical hypothesis testing. The method requires to state a null hypothesis, and, using the words in  (24) , "The idea is to test the original time series against the null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. " The necessity to use surrogate data derives from the intrinsic finiteness of the available data: It is always possible to generate the observations with a particular random process. The null hypothesis that we test consists in assuming that the data can be described via a linear stochastic model. Surrogate data should be generated before any filtering  (49) ; thus, we first generate the surrogate data from the original slip potency time series, then filter both the actual and surrogate data, and lastly calculate the slip potency rate.