dataesr/scientific-paragraphs-categorization huggingface dataset:
Aggregation of scores Supplementary Figure  21  illustrates the full set of aggregations performed on the raw scores in order to arrive at the final ranking of methods. In order to make the scores produced by the different metrics comparable to one another, across datasets of varying difficulty, the raw scores are percentile rank transformed per metric per dataset to a [0, 1] range. The arithmetic mean is calculated across replicates. In order to ensure a method only obtains a high score if it scores well on all three metrics, the harmonic mean across metrics is calculated. At this point, the aggregated scores of all methods across all datasets is combined. As there can be an overrepresentation of datasets of a certain trajectory type, first an arithmetic mean is calculated per trajectory type, followed by an overall arithmetic mean across all trajectory types, thus obtaining a ranking of the methods.
Benchmark Method execution Each execution of a method on a dataset was performed in a separate task as part of a gridengine job. Each task was allocated one CPU core of an Intel(R) Xeon(R) CPU E5-2665 0 @ 2. 40GHz, and was provided a maximum of 32GB in memory and 6 hours of wall time. One R session was started for each task, with the environment variable R_MAX_NUM_DLLS set to 500. The base::set. seed was overridden in order to prevent stochastic TI methods from pretending to be deterministic and/or robust. Timings of methods were measured for different steps along the executions, including preprocessing, postprocessing, each of the different metrics, and the method itself.
The code assurance category is frequently overlooked, and checks for code testing, continuous integration  99  and an active support system. The documentation category checks the quality of the documentation, both externally (tutorials and function documentation) and internally (inline documentation). The behaviour category assesses the ease by which the method can be run, by looking for unexpected output files and messages, prior information and how easy the trajectory model can be extracted from the output. Finally, we also assessed certain aspects of the study in which the method was proposed, such as publication in a peer-reviewed journal, the number of dataset in which the usefulness of the method was shown, and the scope of method evaluation in the paper. Each aspect was further assigned to one or more applications, based on whether it influenced the user friendliness of the tool (such as code availability, good documentation or contains plotting functions), the developer friendliness of the tool (such as unit testing, inline documentation and a clear licensing), or indications that the tool will be broadly applicable on new datasets (such as being open-source, containing a good tutorial and support system, and being thoroughly evaluated in the study where it was published).
Metrics Supplementary Figure  3  Extended overview of our evaluation pipeline. From literature we extracted a quality control checklist, a list of trajectory inference methods, a set of real datasets containing a trajectory and real regulatory networks used to generate the synthetic data. We created a wrapper of each method, so that its output was transformed into a common probabilistic trajectory model, and used these to infer trajectories on all real and synthetic datasets using their default parameters. Using several similarity metrics, we compared the gold standard of the real and synthetic datasets with the inferred trajectories. We also evaluated the quality of each method using the quality control checklist, and used both the benchmark results and quality control results to produce a final set of guidelines for method's users.
T y p e T o pFigure 2 2 Figure 2Overview of the results on the evaluation of 29 TI methods. a) The methods were characterised according to the most complex trajectory type they can infer, whether or not the inferred topology is constrained by the algorithm or a parameter, and which prior information a method requires or can optionally use. b) The methods are ordered according to the overall score achieved in the benchmark evaluation. Also shown are the aggregated scores per metric, source and trajectory type, as well as the average execution time across all datasets and the percentage of executions in which no output was produced. c) Overall performance in the quality control evaluation is highly variable, even amongst the highest ranked methods according to the benchmark evaluation. Also listed are the quality control scores aggregated according to practicality and the different categories.
Supplementary Figure 20 Edge flip score values for common trajectory topologies, defined in Supplementary Figure17. Dataset 1, trajectory type 1 Raw scores Method 1 Method 2 Method 3. Method M Method 1 Method 2 Method 3. Method M Replicate 1 Trajectory type 1, dataset 1 Replicate 2 Trajectory type 1, dataset 2 Replicate 3 Trajectory type 1, dataset 3 Replicate 4 Trajectory type 2, dataset 4 M e t r ic 1 M e t r ic 2 M e t r ic 3 1. Percentile rank tranform scores per dataset Trajectory type 2, dataset 5. Trajectory type N, dataset X Rank scores Replicate 1 Replicate 2 Replicate 3 Replicate 4 Simple linear Method 1 Method 2 Method 3 Method 1 Method 2 Method 3 2. Calculate arithmetic mean Linear Bifurcating Trifurcating Consecutive bifurcating Method M. Method M. across replicates 3. Add harmonic mean across metrics Method 1 Method 2 Method 3 Method M.
Converging Combine with Cycle Trajectory type N, dataset Y Bifuracting loop other datasets Trajectory type 1 Trajectory type 2 Trajectory type 3. Trajectory type N Bifurcating cycle 4. Calculate arithmetic mean per trajectory type Method 1 Method 2 Method 3 Method M. 5. Calculate arithmetic mean, Method 1 Method 2 Method 3 Method M. create final ranking of methods Simple linear. 8. 67. 57. 8. 8. 67. 5 Overall. 8 Supplementary Figure 21 Aggregation methodology of metric scores. 67. 57. 8. 8. 67 Linear. 5 Bifurcating 8 1. 86. 75 1. 67. 86. 67 Edge flip score 1. 0 Trifurcating. 67. 86 1. 67. 86. 57. 75. 6 0. 9 0. 8 Consecutive bifurcating. 57. 75. 67 1. 75. 5. 67. 73 0. 7 Converging. 8 1. 86. 75 1. 67. 86. 67 0. 6 0. 5 Cycle. 8. 67. 57. 5. 67 1. 86. 44 Bifuracting loop. 67. 86. 75. 67. 86. 86 1.
al. Directed linear timeseries, sample origin 166 Tang et. al. Directed linear timeseries, sample origin 101 GSE64016 human fluidigm c1 Directed cycle clustering: oscope 222 GSE60781 mouse fluidigm c1 Directed linear FACS 238 E-MTAB-3929 human fluidigm c1 Directed linear timeseries 1299 GSE59114 mouse smart-seq smart-seq Directed linear Directed linear FACS FACS 873 493 fluidigm c1 Directed acyclic graph clustering 158 GSE71982 mouse fluidigm c1 fluidigm c1 Directed linear Directed linear clustering clustering 117 85 fluidigm c1 Directed linear clustering 85 GSE74596 mouse fluidigm c1 Bifurcation FACS 197 GSE67310 mouse fluidigm c1 Bifurcation timeseries, clustering: PCA + hierarchical 355 GSE75330 mouse fluidigm c1 fluidigm c1 Directed linear Multifurcation clustering: BackSpinV2 clustering: BackSpinV2 3694 4959 GSE85066 human fluidigm c1 Rooted tree FACS 501 SC3-seq Rooted tree clustering: hierarchical 182 GSE74767 macaque SC3-seq SC3-seq Directed linear Rooted tree clustering: hierarchical clustering: hierarchical 83 272 SC3-seq Disconnected directed graph clustering: hierarchical 351 GSE70240, GSE70243, GSE70244, GSE70236 mouse fluidigm c1 Directed linear FACS 318 GSE70240, GSE70244, GSE70236 mouse fluidigm c1 Rooted tree FACS, clustering: ICGS 376 GSE79363 mouse smart-seq2 Bifurcation FACS 60 fluidigm c1 Directed linear clustering: AP 749 GSE67602 mouse fluidigm c1 Directed linear clustering: AP 699 fluidigm c1 Directed linear clustering: AP 346 GSE90860 mouse fluidigm c1 Bifurcation timeseries, clustering: PCA + hierarchical 213 smart-seq2 Directed linear clustering: DBclust 659 GSE86146 human smart-seq2 smart-seq2 Directed linear Directed linear clustering: DBclust timeseries, sample origin 629 659 smart-seq2 Directed linear timeseries, sample origin 671 GSE87375 mouse smart-seq2 smart-seq2 Directed linear Directed linear timeseries timeseries 322 563 GSE90047 mouse smart-seq2 Bifurcation timeseries,clustering: PCA + hierarchical 503 GSE99951 mouse smart-seq2 smart-seq2 Directed linear Directed linear timeseries timeseries 192 456 smart-seq2 Directed linear timeseries, clustering: ICIM 277 GSE100058 fly smart-seq2 Directed acyclic graph timeseries, clustering: ICIM 169 smart-seq2 Disconnected directed graph timeseries, clustering: ICIM 454 GSE95315 mouse 10X chromium Directed linear clustering: markov 3580 GSE98664 mouse rdRNA-seq Directed linear timeseries 414 36.
System Overview The KINOPTIM system is comprised of multiple modules in order to provide a holistic fall management service. The KINOPTIM project is not a simple collection of technologies but an integration of highly innovative solutions that supports the elderly in a friendly manner. The proposed system uses a depth sensor, a RGB camera, a display, and a workstation. The system is composed of the following modules: 1. a Tele-Monitoring (TM) module; 2. a Rehabilitation and Gaming (RG) module;.
Rehabilitation and Gaming Module This module implements the KINOPTIM exergame protocol. Once the elderly have accessed to their individually customized exergame protocols, they play exergames and at the same time they visualize in real-time the posture performance and the score. The RG module uses depth frames to estimate the body posture of the senior and compares it to the posture required by the exergame. A score (RG_SCORE), which indicates how well a person plays an exergame, is assigned to each user at the end of the exergame  [17]. The Rehabilitation and Gaming module calculates the RG_SCORE using the body posture of the participant (coordinates x, y, z of each skeleton joint) and its matching (each skeleton joint) with the posture required by the exergame in respect of the exergame level chosen by the physiotherapist.
Contributions: Conceptualization, D. K. , E. C. , M. R. and G. P. ; methodology, M. R. , D. K. ; validation, G. P. and E. C. ; formal analysis, G. P. , M. R. and E. C. ; writing-original draft preparation, G. P. ; writing-review and editing, E. C. , M. R. ; supervision, D. K, E. C. , M. R. Funding: This research was partially supported by a Marie Curie IAPP program, FP7-People-IAPP, Grant 324491.
Author Contributions: Conceptualization, D. K. , E. C. , M. R. and G. P. ; methodology, M. R. , D. K. ; validation, G. P. and E. C. ; formal analysis, G. P. , M. R. and E. C. ; writing-original draft preparation, G. P. ; writing-review and editing, E. C. , M. R. ; supervision, D. K, E. C. , M. R. Funding: This research was partially supported by a Marie Curie IAPP program, FP7-People-IAPP, Grant 324491.
Niger Crop and Fallow Data Set The study area is located 60 km east of Niamey in the South West of the Republic of Niger, characterized by a tropical semi-arid climate. It consists of two plots of around 15 ha each in the AMMA-CATCH observatory  [32, 33]. The two data sets used in this study were collected in 2009 over a millet field and a fallow field. Surface temperature data were acquired with 10 • incidence KT15 Heitronics at 2. 9 m above ground in the 8 to 14 µm spectral band. LAI was derived from hemispherical photographs. For a recent description of both the site and data set, see  [34].
Experiment Design Two sets of SPARSE simulations were derived for the layer and the patch versions of the model. In the first set, outputs were not limited by potential flux values (unbounded set) and in the second (bounded set), outputs were bounded by the potential (and fully stressed) flux rates LEs pot and LEv pot , considered as the absolute maximum reachable values for evaporation and transpiration, respectively. Soil sensible and plant sensible heat fluxes were also bounded by the maximum rates in fully stressed conditions. For both versions and both sets, the performances were calculated over the entire data set for each site (from sowing to harvest for the crops; during a calendar year for others). Total water stress estimates were also evaluated to assess the amount of information introduced by the surface temperature.
Overall Performance of the Dual-Source SPARSE Model The performances of the SPARSE model for retrieving LE and its components from remotely-sensed surface temperatures were assessed over multiple crops, including orchards, and multiple climates. Over a large range of LAI values and for contrasting vegetation stress levels, the SPARSE model showed satisfactory retrieval performances of latent and sensible heat fluxes. For most of the data sets, RMSE ranged between 40 and 80 W m -2 for LE (bounded layer version) which is within the range of performances obtained by other models in the literature  [15, 36].
Evaluation of the Capacity of SPARSE to Monitor Water Stress The limit of stress retrieval from noisy TIR data was pointed out by previous studies  [43, 44]. The differences noticed between the observed and retrieved water stress intensities remained reasonable and the dynamics were well reproduced by SPARSE, with most points located within a confidence interval of 0. 2. SPARSE could then be a relevant tool for stress detection, but the hypothesis used to define water stress here did not take into account differences in the crops ability to continue to grow in water stress conditions. Further studies would consist of assimilating the water stress efficiency simulated by SPARSE in a SVAT model in order to evaluate continuous evapotranspiration rates. Soil evaporation efficiency was evaluated amongst sites and compared to a reconstructed time series relying on observed topsoil moisture.
Conclusions SPARSE showed satisfactory retrieval performances of latent and sensible heat fluxes, and the opportunity to bound fluxes by realistic limiting values based on potential conditions improved latent heat flux estimates in many cases, especially, in the layer approach in senescent situations at the end of the season and in semi-arid areas where transpiration and particularly evaporation were retrieved beyond potential levels. The soil evaporation efficiencies estimated by SPARSE should be tested in order to be used to retrieve information on irrigation amount or precipitation inputs from TIR acquisitions. Most models using information in the TIR domain like SPARSE rely on data acquired once a day within the constraints of the time of the satellite overpass, the revisit frequency, and the cloud cover.
This component has the required features to be used with CMOS technologies. The purpose of this paper is to present a ferroelectric memristor model based on measurements allowing to take into account device disparities and switching variabilities. Moreover this model is implemented in Verilog-A language, allowing simulations with usual EDA software. The ferroelectric memristor will be presented in Section II. The model will be described and compared to measurements in Section III. In Section IV, we will present the Verilog-A code allowing a free use of our work. Finally, we will use this model during a training of a small neural network.
B. ROFF: maximal value of resistance The high resistance value follows the same kind of distribution. The Verilog-A code of its implementation is given below. Fig. 3-B  shows the comparison to 33,800 measurements of ROFF. The similar distributions allow us to validate the parameters. test = 0; while (test = = 0) begin rnd = $rdist_uniform(seed,0,1); if (rnd < 0. 6).
C. Switching variabilities Fig. 2  shows a non-symmetrical behavior for the resistance increase and decrease. To model these different behaviors we have introduce a fictional RON that is the minimal value targeted. This fictional value called RON_fict is defined for each step down as long as the memristance value RMEM is higher than 2 MΩ. RON_fict is always higher than RON. The corresponding code is: Ron_fict = 0; if (Rmem < 2e6) Ron_ fict = Ron; while (Ron_fict < Ron) Ron_fict = Ron*(1+Rmem/12e6) + (Rmem/45)*$rdist_ normal(seed,0,1); The update of RMEM is computed at each time step. RMEM is a combination of two parameters z1 and gamma. In this section we present the parameters to compute z1 and gamma depending on the voltage Vr applied to the memristor. The The last computation is dedicated to the Rmem value.
This memristance value is the result of ferroelectric domain changes as described in  [11]. The implementation gives us:  The whole Verilog-A code has been used to reproduce the STDP behavior. In Fig. 4 , we compare two trials with two different seeds with the measurements from Fig. 2. The reproducibility and the global behavior allow us to validate this code for neuromorphic design under Cadence environment. if (Vr < 0) S =.
Multiple colocalization analysis tools integrated in a single web interface To allow the users to explore and use multiple colocalization analysis methods in an easily accessible web interface, we have integrated seven different tools that have the generic aim of determining the significance of colocalization of genomic features. Since each of the integrated tools has specialized functionalities and unique analysis approaches, this integrated system serves as a comprehensive one-stop shop for performing colocalization analysis with wide range of approaches and functionalities. Below is a brief overview of the integrated tools and their unique and specialized analysis approaches. • GenometriCorr determines the correlation of genomewide datasets. The R library implements statistical methods specific to the analysis of spatial correlation of genomic data.
• GIGGLE searches an input file against all annotation files in a database and ranks the results based on a GIG-GLE score. The command line accessible tool combines a Fisher's Exact Test and the odds ratio of a 2×2 contingency table containing the number of intervals that are in (i) both the input and database files, (ii) solely the input file, (iii) solely the database file and (iv) neither the input file nor the database file (estimated by the difference between the union of the two sets and the quotient of the mean interval size of both sets and the genome size) to produce the score. • GoShifter calculates the percentage of loci for which associated variants overlap with tested annotation and compares the observed value to permuted overlap derived by randomly shuffling annotations. The shuffles are independent across the loci and preserve both the linkage disequilibrium (LD) structure between the associated variants and the distance between the annotations.
This ensures that the same number of variants and the annotation density at each locus are maintained with each permutation round. GoShifter is provided as a command line tool. • IntervalStats is a command line tool that computes Pvalues for proximity of genomic elements, avoiding nonbiological variation (like peak width) in the genomic datasets. Evaluation of similarity can be done on a restricted set of genomic regions instead of the whole reference genome. • LOLA counts the number of overlapping regions between a user's input query and various sets of genomic region contained in a reference database. It then does the same counting for a background ('universe') region set. The count numbers for the user and background over- laps of each region-set in the reference database are used to build a contingency table, and a Fisher's exact test is used to assess the significance of the overlap, which is also quantified by the odds ratio.
Assessment of consistency and complementarity of conclusions Assessing the consistency of colocalization analysis conclusions across alternative analytical approaches is a prefer-able way to avoid false-positives  (14, 15). Although individual tools offer some degree of customization, the broadest variability in analytical approach is found between different methods. However, performing an analysis using multiple tools currently requires to install each individual tool, learn the specific functioning of each tool, prepare the data in a format compatible with each tool and find the relevant results in the different forms of output provided by each tool. To fill these gaps, Coloc-stats provides a unified system of parameter selection, data preparation and result collection across multiple tools.
Getting started with Coloc-stats The web page of Coloc-stats contains a variety of material to help users get started with the web tool: • A screencast of how to perform a basic analysis. • A Galaxy history containing an example analysis, where results and parameter selections can be inspected and the analysis redone in its original or in a modified form. • Sample data that allows to quickly try out the tool. • A rich documentation that includes a detailed explanation of parameters and input and output data, as well as a FAQ that covers a variety of potential questions regarding usage of the web system. • The tool itself includes help text for all selections in the GUI, and the results pages include brief guidance on how to interpret the results. There are two main scenarios in which colocalization analysis is applied: (A) to determine whether or not two tracks (a query and a reference track) show a statistically significant degree of colocalization, and (B) to rank a set of reference tracks based on their degree of colocalization with W190 Nucleic Acids Research, 2018, Vol.
Analysis example: colocalization of GATA1 with other chipseq datasets As an example scenario, we here consider the colocalization of different transcription factors (TFs). As input, we use a chip-seq peak dataset of GATA1 binding and assess the degree to which the dataset colocalizes with a collection of 30 other experimental datasets. Along with chip-seq peak datasets of other TFs, we also included chip-seq datasets for RNA polymerase II and a histone modification. We ran Coloc-stats with GATA1 as query track and the chip-seq collection as reference tracks, with each tool being run with its default options in basic mode (Figure  2A ). The consensus rank showed a chip-seq track for a related TF GATA2 as the most highly colocalized, as well as showing high colocalization with other TFs suggested in the literature to have relations to GATA1, such as BRG1/SMARCA4 [  (16, 17) ] (see Figure  2B ).
Input data Coloc-stats can be employed to perform colocalization analysis on any feature that can be represented as a set of coordinates on a reference genome. This encompasses a broad variety of genomic and epigenomic features of the genome that have been annotated in databases or that can be experimentally determined through the application of microarrays or high-throughput sequencing. Examples are datasets on exonic regions, DNA methylation state, histone modifications, experimental data on chromatin accessibility or association of variation at specific loci with traits or disease risk. The BED format has become a de facto standard for representing such data, and is used as input format in the Coloc-stats tool. Note that input files should adhere strictly to the to the file format specifications of the BED format [https://genome.
ucsc. edu/FAQ/ FAQformat. html#format1], where most importantly the first three fields need to be chromosome, start and end. For situations where users have datasets in other formats, the website includes a tool for converting from other formats like GFF or GTrack (tool:'Convert between GTrack /BED/WIG/bedGraph/GFF/FASTA files'), as well as a tool for constructing a track file from nonstandard tabular data (tool:'Create GTrack file from unstructured tabular data'). For analyzing a query track against a collection, the standard Galaxy upload tool allows multiple tracks to be uploaded from local disk or from a list of URLs. Datasets of interest can then be combined in a single GSuite file (tool: 'Create a GSuite from datasets in your history'), where this GSuite can be selected as input in the Coloc-stats tool.