dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). Because the distributions of knickpoints were found to share similarities with the distribution of ruptures directly along the fault scarp, this rules out the hypothesis of fully eroded co-seismic knickpoints generated by small-magnitude earthquakes  (Ewiak et al. , 2015). This observation, of knickpoints dominated by large earthquakes and the censoring of smallmagnitude earthquakes, is similar to the results obtained in this paper with the model dominated by aseismic slip at shallow depth (Fig. 3e ). Alternative explanations for the apparent lack of small knickpoints or scarp ruptures in most natural datasets  (Ewiak et al. , 2015; Wei et al. , 2015; He and Ma, 2015; Sun et al. , 2016)  include at least (1) the difficulty to detect the limited displacement induced by earthquakes of magnitude 5 or less and (2) the fault-burial mechanism  (Finnegan and Balco, 2013; Malatesta and Lamb, 2018)  that filters out small co-seismic surface ruptures.
In any case, the depth distribution of earthquakes and of their rupture extent exerts fundamental control on the resulting height distribution of co-seismic knickpoint. In turn, our results suggest that knickpoint datasets, that will become more and more accessible thanks to highresolution topographic data, can be used to assess fault activity. Obviously, the height of knickpoints provides some form of evidence for the earthquakes that have generated them. A negative exponential distribution of knickpoint height points toward a purely seismic fault, while deviations from this trend can suggest aseismic slip or even a slip deficit at shallow depths. The main limitation is yet the poorly known impact of geomorphological processes on evolution of the shape of knickpoints. Some knickpoints along the Atacama Fault System have a reduced height compare to their initial rupture  (Ewiak et al.
Fourth, knickpoint detectability, regarding the horizontal resolution and vertical precision of modern topographic datasets such as lidar or DEMs, directly depends on the river slope that is equal to the ratio between fault slip rate and knickpoint retreat rate. Decreasing the slope increases the horizontal distance between successive knickpoints and enhances knickpoint detectability. On the contrary, the vertical detectability is only limited to the vertical precision of topographic data relatively to the topographic offsets produced during earthquakes. Fifth, when considering several parallel rivers distributed along the strike of the fault, a positive correlation between river profiles is obtained if the rivers are separated by less than half of the maximum rupture length occurring on the fault.
Acknowledgements. We thank  Louise Jeandet ,  Maxime Mouyen ,  Michaël Pons ,  Rodolphe Cattin  and  Philippe Davy  for their helpful comments and for discussions about this work. We also thank  Wolfgang Schwanghart ,  Robert Sare ,  George Hilley  and an anonymous reviewer as well as the editor for their many positive and constructive comments that have contributed to enhance the quality of the manuscript.
Code availability. A simple MATLAB version of the model can be accessed through a GitHub and/or a Zenodo repository: https: //github. com/philippesteer/RiverFault (last access: 3 May 2019) and https://doi. org/10. 5281/zenodo. 2654819 (Steer and Croissant, 2019). www. earth-surf-dynam. net/7/681/2019/ Earth Surf. Dynam. , 7, 681-706, 2019.
• We pointed out that the optimal input for a neuron using rate code has non-zero probability only for a finite number of inputs. However, by showing different input distribution, which nearly achieve the information-metabolic efficiency, we illustrated that the discreteness of the input is not a necessary condition for an effective communication. The core of the simulation code was written in C++ and packaged as a Python module using Cython. This module is available on GitHub (https://github. com/Tom83B/matsim). The analysis of the data was done in Python using the NumPy and SciPy libraries. All necessary code was also uploaded to a GitHub repository (https://github. com/Tom83B/rate-code-eff-2019).
Experimental setup and microscopy A constant flow of carbon-free M9 minimal medium at 20 ml per hour was provided to the microchannels using a high-precision syringe pump (Harvard Apparatus PHD 2000 Programmable) and Hamilton GC-grade glass/PTFE syringes (Gastight 1000 Series). PTFE tubing was used to connect the syringes to the microfluidic chip. The medium was supplemented with 1. 5% (v/v) PEG400 to prevent unspecific adherence of cells to the channels, and PI (5 mg/ml), as a fluorescent indicator of cell viability, was added. Cell viability was monitored using temperature-controlled (37°C) automatic time-lapse microscopy (Zeiss AX10, 63× oil-immersion objective, controlled with MetaMorph software). Focus was maintained by a Z-scanning maximum-contrast procedure using phase-contrast illumination so that dead ends of the microwells are imaged.
Survival analysis The lifespan data of each cohort were fitted parametrically using the family of lifespan distributions described above. Maximum-likelihood (ML) estimators of the model parameters and their CIs were obtained using the R package "flexsurv. " We tested the two-, three-, or fourparameter hazard models mentioned in the previous section, with the four-parameter GGM model being the most general, and chose the best among these candidate models according to the AIC at their ML parameters. Goodness of fit of the ML models were tested using the one-sample version of sup{|Y(t)|}, a Kolmogorov-Smirnov (K-S) type statistic adapted to account for right censorship  (42) , which occurs in our experiments for cells whose PI peaks were observed after alcohol injection. In the one-sample case of goodness-of-fit testing, Y(t) is the normalized K-S distance between the empirical survivorship and the survival function of the fitted model, i.
In addition, we also tested whether the three strains have significant differences in aging rates. See tables S1 to S4 for more details on model selection and hypothesis testing. Fitness models Fitness is defined over a time period from t 1 to t 2 as f(s, t 1 , t 2 ) = Dln (population size)/(t 2t 1 ), where s denotes one of the three strains. We calculated fitness based on time spent in feast or famine episodes. These fitness functions were extrapolated from experimental data. For famine episodes, changes in logarithmic population size were simply the negatives of cumulative hazards H(t 1 ) -H(t 2 ), so that f famine (s, t 1 , t 2 ) = [H s (t 1 ) -H s (t 2 )]/(t 2t 1 ). Extrapolations from experimental data were done using the fitted GGM models. For feast episodes, f feast (s, t 1 , t 2 ) = 1 t2Àt1 ∫ t2 t 1 m s ðtÞdt.
Programming codes and data availability The image analysis procedure described above was implemented in Java as an ImageJ plugin. Fitness modeling, plotting, and time series analyses including noise correction and time-of-death determination were implemented in Python2. We relied on R package "flexsurv" (v1. 1) for the core algorithm of survival analysis and GLM, and Python package "rpy2" (v2. 8. 6) was used for interoperability between the Python and R codes and custom Python codes for other statistical analysis. All source codes and data are made available through GitHub (https://github. com/ y1fanyang/coliTemporalScaling).
Acknowledgments: We thank  X. Song  for contributions on image analysis,  R. Milo  for insightful comments,  D. Misevic  and other members of INSERM  U1001  for continuous advice and discussions. Funding: This study was supported by the  AXA Research Fund Chair on Longevity. Author contributions: Conceived and designed the project: Y. Y. , L. X. , F. T. , and A. B. L. ; conceived and designed the experiments: Y. Y. , L. X. , and A. B. L. ; performed the experiments: Y. Y. , A. L. S. , and C. L. ; data analysis: Y. Y. ; wrote the paper: Y. Y. with contribution from  A. B. L. Competing  interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.
In our context, we define a claim as a statement which has been reviewed by a fact-checking organisation in order to assess its truthfulness. ClaimsKG is generated through a semi-automated pipeline, which periodically harvests data from popular fact-checking websites. The claims and their reviews (articles written by fact-checkers that accompany a claim and explain its context and veracity judgement) are annotated with related entities from DBpedia, and all data are lifted into RDF using a dedicated RDF/S model (dubbed Claims), which is based on established vocabularies such as schema. org and NIF. In order to harmonise data originating from diverse fact-checking sites, we introduce a normalised truth ratings scheme, as well as a simple claim matching strategy. ClaimsKG enables advanced exploration and information discovery, e.
g. , via queries such as "find all false claims by D. Trump in 2017 that also mention the FBI", or "find the top 5 politicians per month involved in false claims", as well as exploitation of data from various sources via federated SPARQL queries, e. g. , "retrieve all claims mentioning journalists". We also provide a Web interface for exploring the graph, enabling users from outside of the computer science community to retrieve information or sample data from our resource. The dataset, as of April 2019, consists of 28,383 claims published since 1996, amounting to 6,606,032 triples in our KG. In summary, we provide (1) the Claims data model for representing factchecked claims and associated information,  (2)  an open-source pipeline for crawling and extracting data from fact-checking websites, and for lifting these data following the Claims model, (3) an openly available dynamic large-scale KG of claims and associated metadata, and (4) a Web interface for search and exploration of the resource.
ClaimsKG in a Nutshell ClaimsKG consists of data extracted from a number of fact-checking websites. To select the fact-checking websites, we relied on the International Fact-Checking Network's (IFCN) signatories list, 1  admitting only sources considered by the fact-checking community as highly reputable. At this stage, we only consider information in English from six sources: africacheck. org, factscan. ca, politifact. com, snopes. com, checkyourfact. com, truthorfiction. com. Note that ClaimsKG is extensible to new websites, however, the information extraction process may vary from one website to another due to structural specificities of the sources (cf. Sect. 3). Each fact-checking article from these sources is parsed for extracting the text of the claim under review as well as useful related (meta)data including the author of the claim, the date the claim was uttered, its veracity label as well as keywords (tags describing topics) and links to related resources.
Moreover, the text of the claim and of its review is annotated with Wikipedia/DBpedia entities mentioned in it. Key links related to ClaimsKG are given in Table  1. The KG is currently accessible from a Virtuoso triplestore with a SPARQL endpoint and downloadable as a Zenodo dump. All represented entities (claims, authors, etc. ) are assigned resolvable identifiers following the W3C best practices (see Sect. 3 for an example). The dataset has a DCAT description and is released for free distribution under a Creative Commons Attribution-NonCommercial-ShareAlike 4. 0 licence. 2  The graph can be also accessed through ClaimsKG's official webpage, which displays detailed up-to-date statistics and a set of example SPARQL queries. All tools developed for the KG's creation are made available as open source on GitHub.
The collected data consist mainly of: (a) the textual statement of the claim; (b) its truth-value or rating (both the normalised and the original one); (c) a link to the claim review from the fact-checking website; (d) the references cited in the claim reviews; (e) the entities extracted from the text of the claim and from the review body; (f) the author of the claim and the author of the review; (g) the date of publication of the claim and that of the review; (h) the title of the review article; (i) a set of keywords extracted from the websites acting as topics (e. g. "abortion"). Note that the extraction process is tailored individually to the structure of each of the different fact-checking websites, resulting in a set of website-specific extractors. The statistics generated at each run of the pipeline (globally and per domain) allow to monitor the "health" of the extracted data by detecting potential issues that may be related to changes of the structures of the respective fact-checking websites that may have occurred between two runs of the pipeline.
Red is Ca chi ng Red is Ca chi ng Entity annotation of the claims. We annotate the entities (e. g. , names of persons, organisations, locations, etc. ) mentioned in the texts of the claims and their reviews using the TagMe tool  [8]. TagMe allows the automatic identification of entities in a text and their linking to a Wikipedia page and a DBpedia URI. It is known to achieve particularly good results when annotating short texts, which is the case for the statements in this domain, although we also annotate the body of the claim reviews. We run a local version of TagMe, allowing us to update the database regularly, using the latest available dump of Wikipedia (October 2018). We performed all the annotations using the optimal parameters described in  [8]. We evaluated our updated TagMe model on the YAGO CONLL-TestB ground truth dataset  [14]  and obtained an accuracy of 75.
5%, which is in line with state of the art performance reported for TagMe. 3 ormalization of ratings. Each of the fact-checking websites has its own labels describing the truthfulness of the claims, with different discrete textual values of ratings. While some sites have a controlled vocabulary of possible truth values, others apply an open-ended rating schema. For example, Truth or Fiction has a large number of non-uniform labels, such as "truth & misleading" or "reported as fiction". In order to harmonize our dataset, alongside the original ratings, we also provide a normalized rating score, applied across all claims contained in the dataset. For each of the sources, we summarised the distribution of rating values and then assigned them to a conservative and coarser-grained set of labels that correspond to the least common denominator between all the classifications of the individual sites.
Statistics SPARQL Endpoint ClaimsKG Explorer Lifting and Serialisation. We created a Python 3. 6 script to read the extracted claims as a CSV file in the extraction step, and then create the corresponding KG following the data model described in Sect. 4. We used the rdflib library to create the model and an abstract RDF graph to then serialize it in one of the many supported formats. All the caching needs of the generation process are met with a Redis server. We generate unique URI identifiers as UUIDs based on an one-way hash of key attributes for each instance. For example, the dereferenceable URI http://data. gesis. org/claimskg/creative_work/ 5f7e8c65-3d8b-57da-bab9-eb3a373bd2ab is created for the claim in https:// www. snopes. com/fact-check/was-megyn-kelly-fired-from-nbc/. The triplification package is made available under an open-source licence on GitHub (along with documentation and usage examples) and will be updated regularly with the latest improvements (link given in Table  1 ).
The Claims Data Model Our data model, depicted in Fig. 2 , exploits terms from established vocabularies, specifically schema. org, NLP Interchange Format (NIF),  5  and Internationalization Tag Set (ITS). 6 The selection of the vocabularies was based on the following objectives: i) avoiding schema violations, ii) enabling data interoperability through term reuse, iii) having stable identifiers, persistent hosting and open license, iv) being supported by a community, v) being extensible (ability to easily extend ClaimsKG with more data). The core elements of our model are the claim and the claim review. To represent them, we make use of schema. org. Following Google's suggestion for Web markup of claims, 7  a claim is of type schema:CreativeWork and a claim review of type schema:ClaimReview.
Use Cases and Exploitation Use-cases and queries. The publication of structured data about a large collection of claims allows the uncovering of explicit and implicit relations between claims, entities, and sources. A number of existing fact-checking applications rely on linking claims to fact-checked statements in a database (e. g. https://fullfact. org/automated/, [32, 35] ). By combining claims and ratings from multiple portals and providing a unified structure, ClaimsKG facilitates these efforts. Moreover, the data can be used to enable supervision of machine learning models to support the advancement of automatic fact-checking algorithms  [13, 20]. In addition to supplying a large number of claims, ClaimsKG enables advanced, entity-centric search, exploration and information discovery, by exploiting data from various sources via federated SPARQL queries.
ClaimsKG Explorer. In order to facilitate data access for researchers from outside of the computer science domain, like journalists and sociologists, we provide ClaimsKG Explorer (link in Table  1 ), a user-friendly, Web-based interface to query and explore ClaimsKG. The application sends HTTP requests to the ClaimsKG SPARQL endpoint and provides information through a Web user interface. The users are given the possibility to filter their search space with respect to a number of facets (Fig. 8a ): entities contained in the text of the claim or its review, keywords (topics related to the claims), truth ratings (the normalised ones), time frame of interest, authors of claims, sources (fact-checking websites), and languages (currently only English; work in progress will incorporate non-English websites).
The latter rely on data in the form of labelled claims in order to train and apply machine learning models. The current section provides an overview of datasets for training and/or evaluation covering these two families of approaches. We categorise these datasets according to the process of their collection. Extracting gold standard from web sources. Alongside ClaimsKG, a number of approaches rely on extracting data from fact-checking websites, allowing the collection of large number of claims together with veracity annotations of high quality that stem from serious journalistic work. One of the first initiatives in that field is introduced in  [28]. The data extraction process is manual, resulting in merely 221 statements collected from Channel 4 and PolitiFact. Since the two sources do not apply the same rating scale, similarly to our approach, the authors map the two respective sets of labels to a common scale consisting of five categories: "true", "mostly true", "half true", "mostly false" and "false".
The Liar benchmark  [33]  collects 12. 8K Politifact claims over 10 years. Additional information is stored regarding the speaker, the context, the label and the justification. The fact-checking approach presented in  [21]  relies on a data set of approximately 10K claims, also crawled from Politifact in 2016, while  [16]  and  [20]  have collected, respectively, 1K and 5K claims from Snopes. Wikipedia's lists of proven hoaxes 13 and fictitious people 14 have been used in  [19, 20]  in order to generate ground truth labels, resulting in 157 claims labelled as "fake". In parallel, the authors also collect around 4. 8K labelled claims from Snopes, published before Faburary 2016. The Clef-2018 challenge includes the fact-checking task Check that! [2]. The benchmark data (150 claims) consists of sentences collected from debates from the 2016 US Presidential Election Campaign, as well as from other political speeches during and after the campaign.
Evaluations are obtained from FactCheck. org articles resulting in labels of the kind "factually true", "half-true", or "false". The Emergent data set 15 results from collecting claims from various web sources, such as Politifact, Snopes and Twitter accounts such as @Hoaxalizer, particularly dedicated to rumors and hoaxes  [9]. The data set contains 300 claims with three types of annotations ("true", "false", "unverified") provided by journalists. A large scale collection of tweet news (126K stories) labelled on the basis of significant degree of agreement among several fact-checking sites (Snopes, Politifact, Factcheck, Truth-or-Fiction, Hoax-slayer and Urbanlegends) is used in  [31]. As a result, a sample of news stories assigned one of three possible labels ("true", "false" or "mixed") is created.
However, the data is only available upon request for the purposes of reproducing the reported experiments. 13 https://en. wikipedia. org/wiki/Listofhoaxes#Provenhoaxes 14 https://en. wikipedia. org/wiki/List_of_fictitious_people 15 http://www. emergent. info Crowdsourced or manually annotated data sets. Crowdsourcing techniques allow for the extraction and labelling of relatively large sets of claims, although some care should be taken to ensure the reliability of the data harvested. The Open Domain Deception Dataset contains "freely contributed truths and lies"  [18]. By using the Amazon Mechanical Turk, each worker has been asked to freely formulate seven one-sentence truths and lies. After cleansing the collection, the final dataset consists of 7. 2K sentences provided by 512 unique contributors, for whom demographic data is also collected and made available.
The SemEval'17 challenge dataset contains 5. 5K crowdsourced annotated claims  [11] , while the FEVER dataset, with 185K entries, extracts claims from Wikipedia. Semanticspreserving sentence altering techniques are then applied and the resulting claims are annotated by the crowd  [25]. Finally, the approach in  [17]  relies on a dataset of 250 manually annotated claims. Automatic annotations. Fact verification methods can be applied in order to construct automatically ground truth datasets for fact-checking. An example is the fake-news dataset, 16 containing text and metadata scraped from 244 websites that have been identified as untrustworthy by the BS Detector Chrome Extension tool. 17 This approach has the risk that the imperfections of the verification system are propagated onto the annotated data.
Knowledge graphs for fact-checking. Finally, we focus on KGs that have been used in a number of fact verification approaches from the reference group. Usually, a statement is modelled as a triple and is verified on the basis of properties of the paths involving elements of that triple in existing KGs, considered as ground truth, such as DBpedia  [4, 10, 23]. The Knowledge Vault  [6]  and the KnowMore  [37]  resources, or the Voldemort KG  [26] , rely on structured markup annotations in order to match them to established KGs or perform graph completion. This process implies the verification of the truthfulness of statements and the production of reliable factual information that can be used as ground truth. Positioning. ClaimsKG is entirely based on data from a number of established fact-checking websites and therefore falls into the first category of datasets presented above.