dataesr/scientific-paragraphs-categorization huggingface dataset:
However, HI observations at Nancay (The98) give a velocity of 3,503 km s -1. Problem: The98, NED and many catalogues give IC4254 the low z, but the optical distance is ≈5 times higher, in a rich cluster within the SSC. Alternative identifications for the Nancay The98 observation: few nearby galaxies are without velocity measurements (the field is that of the A1736 cluster). Possibilities are an Sp 2'20" to  N (at 13 27 44. 11 -27 11 03. 3; R2= 16. 47 , though it looks distant), and more likely, an lsb Irr gal 4'10" to NW  (at 13 27 30. 03 -27 10 27. 5; R2= 16. 8)  ESO 444-082. Data is #5238 in Database (and comments). With 3 consistent positions, a very low HI Nancay velocity of ≈526 km s -1 is given by The98, while 2 independent consistent (QC03, 6dFGS) optical measurements give a value ≈11,300 km s -1.
NED and others chose the low z from The98, which in view of the optical data looks like a wrong identification or bad data. It is unclear if the Nancay position is just that of  NGC 5298, a bright Sp. Data is #6234 in Database (and comments). Nancay The98 HI velocity at 4,083 km s -1 and identification (Ka03). Four optical velocities (LC97, Dr91, Da87 and 6dFGS) and one HI velocity  (Mathewson et al. 1992, Ma92 ) are consistent at ≈4,400 km s -1 , and the position is in A3574 cluster field, at similar z (4,400 km s -1 ). However, NGC 5298 velocity observed by Nancay (The98) has a discrepancy of 350 km s -1 , quite significant for an HI measurement. It seems the galaxy identification could be misplaced, as there are a few surrounding spirals with closer velocities to The98 4,083 km s -1 : closest is 1' to N.
Galaxy L606P Sp. HI Nancay  (Monnier-Ragaine et al. 2003, Mon03)  gives a 541 km s -1 velocity and identification. Data is #6839 in Database (and comments). There is a 6dFGS optical velocity measured of 3,778 km s -1 of this fairly small Sp. Several nearby galaxies have similar z, which is of cluster S0753. The Nancay 541 km s -1 low velocity (it should be a very near galaxy), is discrepant value to 6dFGS. Several surrounding brighter galaxies at 3,800-4,400 km s -1 (in S0753). No likely candidate within 15'. It could be a confused source or an HI cloud. (left)  and Dec (right) of the distribution of galaxies with measured redshifts in the region of the Shapley Supercluster with velocities between 9,000 and 18,000 km s -1. The angle in declination is expanded by a factor 1. 5 relative to its true size for clarity.
Acknowledgements. HQ is grateful to  Las Campanas  and  Cerro Totolo Interamerin Observatories  for generous allocations of telescope time. DP thanks the  Instituto de Astrofísica of Pontificia Universidad Católica de Chile  and  ESO  in the context of the  Visiting Scientists program  for their hospitality in Santiago (Chile). RD and AR acknowledge support from  CONICYT  project  Basal   AFB-170002  (CATA). This research has made use of data obtained from the  Super-COSMOS Science Archive , prepared and hosted by the  Wide Field Astronomy Unit, Institute for Astronomy, University of Edinburgh , which is funded by the  UK Science and Technology Facilities Council.
While COBRA is intended for regression, KernelCobra deals with classification and regression. KernelCobra is included as part of the open source Python package Pycobra (0. 2. 4 and onward), introduced by Guedj and Srinivasa Desikan  [2]. Numerical experiments assess the performance (in terms of pure prediction and computational complexity) of KernelCobra on real-life and synthetic datasets.
We provide a python implementation of KernelCobra in the python package Pycobra, introduced and described by Guedj and Srinivasa Desikan  [2]. We assess on numerical experiments that KernelCobra consistently outperforms the original COBRA algorithm in a variety of situations. The paper is organized as follows. Section 2 discusses related work and Section 3 introduces the ideas leading to KernelCobra. Section 4 presents the actual implementations of KernelCobra in the pycobra Python library. Section 5 illustrates the performance (both in prediction accuracy and computational complexity) on real-life and synthetic datasets, along with comparable aggregation techniques. Section 6 presents avenues for future work.
In a recent work, the original COBRA algorithm (as implemented by the pycobra Python library, see Guedj and Srinivasa Desikan  [2] ) has successfully been adapted by Guedj and Rengot  [10]  to perform image denoising. The authors report that the COBRA-based denoising algorithm outperforms significantly most state-of-the-art denoising algorithms on a benchmark dataset, calling for the broadcasting of non-linear ensemble methods in computer vision and image processing communities.
Our second set of weights (W n,m ) M m=1 used to aggregate the predictions of each machine, can be any sequence of weights summing up to 1, and serves to weight machines. In other words, once the machines have been trained (either in a supervised setting using the outputs in subsample D k , or in an unsupervised setting by discarding all outputs across the dataset D), the estimator defined in  (5)  no longer needs outputs from the second half of the dataset D , therefore extending to semi-supervised and unsupervised settings, further illustrating the flexibility of our approach.
[1, Theorem 2. 1]  proved that the combined estimator with weights chosen as in the initial COBRA algorithm (4) enjoys an oracle guarantee: the average quadratic loss of the estimator is upper bounded by the best (lowest) quadratic loss of the machines up to a remainder term of magnitude O( -2 M+2 ). This result is remarkable as it does not involve the ambient dimension d but rather the (fixed) number of machines M. We focus in the present paper on the introduction of KernelCobra and its variants, and its implementation in Python (detailed in the next section) -we leave for a future work the extension of Biau et al. [1] 's theoretical results.
Implementation All new algorithms described in the present paper are implemented in the Python library pycobra (from version 0. 2. 4 and onward), we refer to Guedj and Srinivasa Desikan  [2]  for more details. The python library pycobra can be installed via pip using the command pip install pycobra. The PyPi page for pycobra is https://pypi. org/project/pycobra/. The code for pycobra is open source and can be found on GitHub at https://github. com/bhargavvader/pycobra. The documentation for pycobra is hosted at https://modal. lille. inria. fr/pycobra/. We describe the general KernelCobra algorithm in Algorithm 1. The pred method implements the algorithm described in Algorithm 1, and the predict method serves as a wrapper for the pred method to ensure it is scikit-learn compatible. It should be noted that the predict method can be customised to pass any user-defined kernel (along with parameters), as suggested by  (3).
We also provide the pseudo-code for the variant of KernelCobra in semi-supervised or unsupervised settings defined by (5) (Algorithm 2), along with the variant for multi-class classification defined by  (7)  (Algorithm 3). # weights-points is a list of size l with each index mapping to information of proximity of a data point; for machine j in basic-machines do pred = basic-machines[j](X) # where basic-machines[j](X) denotes the prediction made by machine j at point X; for index,vector in training-set-responses do weights-points[index] += Kernel(pred, basic-machines[j](vector) ) ; end end # machine-predictions is a list mapping each machine and it's prediction of x i ; # weights-machines is a list mapping each machine and it's weight which must sum to 1 ; weights-points = weights-points / sum(weights-points) ; machine-predictions = weights-machines * machine-predictions ; results = machine-predictions * weights-points ; To conclude this section, let us mention that the complexity of all presented algorithms is O(M ) as we loop over all data points in the subsample D and over all machines.
Numerical Experiments We have conducted numerical experiments to assess the merits of KernelCobra in terms of statistical performance, and computational cost. We compare pythonic implementations of KernelCobra, MixCobra, the original COBRA algorithm as implemented by pycobra, and the default scikit-learn machines used to create our aggregate. We test our method on four synthetic data-sets and two real world data-sets, and report statistical accuracy and CPU-timing. The synthetic datasets are generated using scikit-learn's make-regression, make-friedman1 and make-sparse-uncorrelated functions. The two real world datasets are the Boston Housing dataset, and the Diabetes dataset. Table  1  wraps up our results for statistical accuracy and establishes KernelCobra as a promising new kernel-based ensemble learning algorithm.
Conclusion and future work We have introduced a generalisation of the COBRA algorithm from Biau et al. [1]  which can be used for classification and regression (either supervised, semi-supervised and unsupervised). Our approach, called KernelCobra delivers a kernel-based ensemble learning algorithm which is versatile, computationally cheap and flexible. All variants of KernelCobra ship as part of the pycobra Python library introduced by Guedj and Srinivasa Desikan  [2]  (from version 0. 2. 4), and are designed to be used in a scikit-learn environment. We will conduct in future work a theoretical analysis of the kernelised COBRA algorithm to complete the theory provided by Biau et al. [1]. Author Contributions: Both authors contributed equally to this work.
Simulation of the 14 C dates For the simulation of the 14 C data, the original curve from  Contreras and Meadows (2014)  was used. The data was converted into numerical values by digitizing (using the software Engauge). The corresponding data set is attached as supplementary material or can be accessed in the reproducible analysis. The population numbers were then interpolated by linear approximation on an annual basis and converted into a probability distribution by normalization to the sum of 1. This distribution then served as weighting for a random drawing of calendar dates representing the individual sample. The sample size was defined as a scenario based on the given parameterisation (see above). In the second part of the analysis, this distribution was changed by parameterising the signal strength by linear rescaling in such a way that the drop from the peak before the demographic signal to the minimum of the curve corresponds to the given signal strength.
The random years obtained in this way, whose frequency corresponds to the given population curve of the Black Death, were then processed as a sum calibration using C_Simulate and Sum and calibrated via OxCal (using the package oxcAAR;  Hinz et al. 2018). As in  Contreras and Meadows' study (2014) , the standard deviation was randomly sampled equally distributed in the range of 20-40 years. The smoothing of the resulting calibration result with a moving average, as suggested by  (Williams 2012 ) with a window of 500 years minimum, was considered, but rejected again. The reason for this is that the more turbulent curve of the calibration result produces a more realistic scenario (see Figure  3 ).
2019 ). The null model is a uniform distribution of the data within a specific time window. Thus, no assumption about a possible population development is made in advance, as would be the case with an exponential function in the sense of population growth. With this, I assume a stable population, and those events, which fall out of the hull generated by the simulation, can be considered as significantly different from this null model. A specific helper function is implemented in the package oxcAAR  (Hinz et al. 2018 ) (oxcalSumSim()), which can be used to easily perform such a simulation. It has to be noted that this function is based on R_Simulate of OxCal, and therefore shows rather wider uncertainty ranges than it would be necessary for C_Simulate. In the given context, this rather increases the robustness of the estimation.
Reproducible Research in Simulation Studies Reproducibility has not yet become the standard for archaeological analysis. In many cases, the way archaeological data are collected prevents complete reproducibility of results, as an excavation can only be carried out once. However, in the case of derived, secondary analyses, reproducibility is clearly a preferable design consideration in any research. This is all the more true for simulation studies, which naturally rely on random effects and should therefore be reproducible in their parameterization and which also create the perfect conditions for such a research design regarding their database. Area: where a minimum should be present. Blue Area: Where the signal should be at least 10% higher than in the minimum on average. Unfortunately, especially in the field of summed 14 C analyses, it is often the case that the argumentation relates on single observations or single calibration runs, i.
e. only few results are presented as pars pro toto. At the same time, the source code used to generate these numbers is usually not included in the paper and is also not accessible elsewhere. Therefore the results must be believed as argumentum ab auctoritate. A listing of related papers is deliberately omitted here. If the source code is available or at least reconstructable (as in Contreras and Meadows 2014), a big step towards reproducibility has already been taken. In this article I try to go one step further and choose an Open Science approach in the sense of reproducible research (in the sense of  Marwick 2017). The code underlying the simulations is made available together with the article, based on the package rrtools (https://github. com/benmarwick/ rrtools). It is available as an R package (sensitivity.
CORLI is a consortium of Huma-Num, the French national infrastructure dedicated to the technical support and promotion of digital humanities. The goal of CORLI is to promote and provide tools and information for good and efficient research practices in corpus linguistics and especially spoken language corpora. Because of the time required to collect and transcribe spoken language resources, their number is limited and thus corpora need to be interoperable and reusable in order to improve research on various themes (phonology, prosody, interaction, syntax, textometry…). To help researchers reach this goal, CORLI has designed a set of tools: TEICORPO to assist in the conversion and use of spoken language corpora, and TEIMETA for metadata purposes. TEICORPO is based on the principle of an underlying common format, namely the TEI as described in its specification for spoken language use (ISO/TEI 24624:2016).
This tool enables the conversion of transcriptions created with alignment software such as CLAN, Transcriber, Praat or ELAN as well as common file formats (csv, xlsx, txt or docx) and the TEI format, which plays the role of a pivot format, without losing information. Backward conversion is possible in many cases, with limitations inherent to the destination target format. TEICORPO can run the Treetagger Part of Speech tagger and the Stanford CoreNLP tools on TEI files and can export the resulting files to textometric tools such as TXM, Le Trameur, or Iramuteq, making it a tool dedicated to spoken language corpora editing as well as to various research purposes.
The goal of the TEICORPO project can thus be summarized as: -creating a conversion tool between the different pieces of software used for spoken language corpora creation and editing. The tool should be as lossless as possible; -using the TEI as a conversion pivot format, as the TEI is already widely used especially for written linguistic corpora and is powerful enough for our purposes; -creating complementary conversion tools that make it easier to explore and use the corpora for research.
Similarities and differences with other approaches Many software packages dedicated to editing spoken language transcription contain utilities that can convert many formats. This is the case for example of Exmaralda  (Schmidt 2004 , see https://exmaralda. org), Anvil  (Kipp 2001 , see https://www. anvil-software. org) and ELAN  (Wittenburg et al, 2006 , see https://archive. mpi. nl/tla/elan). However, in all cases, the conversions are limited to the features implemented in the tool itself, for example with a limited set of metadata, and they cannot always be used to prepare data to be used by another tool. While our work is similar to that of  Schmidt (2011) , several differences make our approaches complementary. First, the two main common features are as follows: • the tool can automatically convert data coming from different linguistic tools; • the TEI is used as a destination format.
The list of tools that are considered in the two projects is nearly the same. The only tools missing in the TEICORPO approach are Exmaralda, and Folker  (Schmidt & Schütte, 2010 , see https://exmaralda. org/en/folker-en/), but this was only because the conversion tools from and to Exmaralda, Folker and the TEI already exist. They are available as XLST stylesheets in the open source distribution of Exmaralda (https://github. com/Exmaralda-Org/exmaralda). The other common point is the use of the TEI format, and especially the more recent ISO version of the TEI for spoken language (ISO/TEI see ISO 24624:2016). The TEI format produced by the Exmaralda and Folker software fit within the process chain of TEICORPO. This demonstrates the usefulness of using a well-known and efficient format such as the TEI.
talkbank. org/clan/), ELAN, Praat (Boersma & van Heuven 2001, see http://www. fon. hum. uva. nl/praat/), and Transcriber  (Barras et al. 2000 , see http://trans. sourceforge. net/en/presentation. php and http://perso. enslyon. fr/matthieu. quignard/Transcriber/). These proprietary formats are in XML format or in Unicode text format so that they can be stored for long term conservation. However, they are not all well described or constrained, at least not in the same way as the TEI, which offers moreover a semantically relevant structure as well as an official format for long term conservation in France. Moreover, as the durability of these four pieces of software cannot be guaranteed in the long term, it does not seem safe to keep corpora in a format available only for a given tool that may disappear or fall into disuse.
The second major difference is that the TEICORPO initiative does not specifically target spoken language, but all types of annotation, including media of any type. This covers all spoken languages, vocal as well as sign languages, but also gesture and any type of multimodal coding. The goal of TEICORPO was not to advocate a linguistic mode of coding spoken data as a transcription convention does, but rather to propose a research model for storing and sharing data about language and other modalities. Consequently, the focus of the work was not on how the spoken data were coded (i. e. , the micro-structure), nor on the standard that should be used for transcribing in orthographic format. Instead, the TEICORPO approach focused on how to integrate multiple pieces of information in the TEI (the macrostructure), as this is possible with tools such as ELAN or PRAAT.
The goal was to be able to convert a file produced by these tools so that it can be saved in TEI format for long term conservation. Data in PRAAT and ELAN formats can contain information that is different from what is usually present in an ISO/TEI description, but that remains nonetheless within the structures authorized in the ISO/TEI. For example, the information is stored as described below in spanGrp, a structure available in the ISO/TEI description. This means that whenever information targets the 'classical' approach to spoken language (by 'classical', we mean approaches based on an orthographic transcription represented as a list, as in the script of a play), it will be available for further processing by using the export features of TEICORPO (see part 2. 3 and further below for export functionalities) but other types of information are also available.
Choice of the microstructure representation Processing of the micro-structure, with the exception of information already available in the tools themselves (for example silence in Transcriber), is not done during the conversion to the TEI. In all the tools used by researchers in CORLI, the division into words or other elements such as morphemes or phonemes is not systematically done. When it exists, it is not included in the main transcription line but most often in dependent lines as it represents in fact an annotation with its own rules and guidelines to split the production into words. It is part of the linguistic analysis rather than a simple storage operation. TEICORPO therefore preserves as long-term storage data both the original information that was created in the original softwarethe full unprocessed transcription -, and the other linguistically processed transcriptions and annotations.
Alignment tools Several alignment tools are used by researchers working with spoken language corpora to align the recording, whether an audio or a video file, with its textual transcription and other specific annotations. The choice of tools is usually related to the choice of annotations, or to the common practices in a given research field, or simply to the researcher's preferences, knowledge, and requirements. Some common practices have been identified in our community but other uses of the same software are of course possible: -Transcriber is widely used in sociolinguistics; -CLAN is widely used in language acquisition and especially in the Talkbank project; -Praat is more specialized for phonetic or phonological annotations; -ELAN is recommended for annotating video and particularly multimodality, but is often used for rare languages to describe the organisation of the segments.
Each presentation format has its own pros and cons. Because of the possibilities offered by the presentation formats, and because the same software, even within the same presentation models, rarely provides a solution for all the needs of all users, researchers often have to use two or more pieces of software. The use of multiple tools is quite common. For example, Praat and Transcriber cannot be used when working on video recordings because these programs are limited to audio formats. But if researchers need to conduct spectral analysis for some purpose, they will have to use the Praat software and convert not only the transcription, but also the media. In the field of language acquisition, where the CLAN software is generally used to describe both the child productions and the adult productions, when researchers are interested in gestures, they use the ELAN software, importing the CLAN file to add gesture tiers, as ELAN is more suitable for the fine-grained analysis of visual data.
Basic structures Converting the metadata is straightforward as the four tools (CLAN, ELAN, Praat, Transcriber) do not enable a large amount of metadata to be edited. Most of the metadata available concerns the content of the sequence; some user metadata is also available, especially in CLAN. The insertion of metadata follows the indications of the ISO/TEI 24624:2016 standard. Moreover, some tools, such as Transcriber, include information about silences, pauses, and events in their XML format. This information is also processed within TEICORPO, once again following the recommendations of the ISO/TEI standard. Conversion of the main data, the transcription and the annotations, cannot always be done on the sole basis of the description provided in the ISO/TEI guidelines. These guidelines do however suffice to fully describe the content of the CLAN and Transcriber software.