dataesr/scientific-paragraphs-categorization huggingface dataset:
The validation data set was acquired at man's height, so at least those facets will be appropriately reconstructed. A set of 124 photos were shot with a Nikon D7000 (4928x3264=16Mpix, APS-C 23. 6x15. 7mm, pixel pitch 4. 79µm) equipped with Sigma 20mm f/1. 8 fixed focal length. Georeferencing was performed with Solmeta Geotagger Pro-2, but outcrop scaling used quadrants targets distances among them were surveyed by laser range finder. Vertical of the site was established at both ends with vertically aligned quadrant targets, 2. 1m and 2. 6m apart. Vertical alignment was performed with LEICA Lino2 laser level. Equipments brands and types are referred to for technical specification purposes, not as commercial endorsement. Outcrop azimuth trend is related to a reference direction materialized by two targets on the ground, 5.
This paper presents a test where field structural geology data was collected on a scan line and compared with digitally processed 3D point clouds. A scan line turns out to reduce the amount of geological information drastically (too drastically). Structural analysis of 3D point cloud on the other hand will be overwhelmed by the planar facets occupying the largest surface area. Implementation of the interactive stereogram proved very practical to explore the dataset and segment it. Such segmentation is amenable to further computation such as fracture spacing.
r. t. basic set operations, i. e. , Cartesian product, disjoint union and subsets. This work is an extended version of  [1] ; it represents about 17,560 lines of code (as computed by coqwc: 5k lines of specifications, 10k lines of proofs) written in Coq 8. 6 compiled with OCaml 4. 04. 1. 1. 2. Related Work. Many formal approaches have been used in the context of distributed computing. There exist tools to validate a given distributed algorithm, such as the tools embedded with TLA+ (a model checker and a proof assistant)  [34]. Constructive approaches aim at synthesizing algorithms based on a given specification and a fixed topology; many of them are now based on SMT-solvers, see  [6, 27]. Note that model-checking as well as synthesis are fully automated, but require to fix the topology and sometime the scheduling (e.
We present tools for proving termination in Section 4 and tools for proving quantitative properties in Section 5. In Section 6, we present an algorithm called C(k) (the case study), its specification, and the assumptions under which it will be proven. In Section 7, we present the termination proof of C(k). Section 8 deals with the partial correctness of C(k). Along this paper, we present our work together with few pieces of Coq code that we simplify in order to make them readable. In particular, we intend to use notations as defined in the model or in the algorithm. The Coq definitions, lemmas, theorems, and documentation related to this paper are available as an online browsing at http: //www-verimag. imag. fr/ ~altisen/PADEC/. All source codes are also available at this URL. We encourage the reader to visit this web page for a deeper understanding of our work.
Tools for Quantitative Properties To handle some quantitative properties of an algorithm, we have to set up a library dealing with cardinality of sets in general and also cardinals of finite sets. The need for a new library arises from the absence of setoid-compatible formalization of set cardinality. For example, the Ensembles and Finite_Sets module from the Coq standard library relates subset (predicates) over a fixed universe type U, and elements are considered up to Leibniz equality (see definition of Singleton). Instead, we build a theory allowing to compare the cardinality of arbitrary setoids (i. e. of sets of equivalence classes) built on top of distinct types instead of subsets over the same type. The library contains basic properties about set operations such as Cartesian product, disjoint union, and subset.
Par and Id are declared as read-only variables. Note that to be able to compute a path from each node to its clusterhead, the algorithm requires that channels are totally ordered (to be able to compute the minimum value on a set of channels). Hence we assume a strict total order < C on the type Channel. Furthermore, we chose to encode every number in the algorithm as integers in Z, as α is, since some of them may be negative (see MaxAShort) and computations use minus (see Alpha). Now, every predicate and macro of Algorithm 1 can be directly encoded in Coq: for a node p and a current configuration g, mainly all of them depend on N p , ρ p , (g p), and (local_env g p); then the translation is quasi-syntactic (see Library KClustering_algo in the online browsing) and provides a definition of run.
1 Triplenegative breast cancer is the subtype with the worst prognosis among all breast cancer subtypes, with currently no known molecular targets. 7 We used SynTarget to search cell surface genes with the synergistic potential on survival, whose high expression leads to significantly negative prognosis. For example, ADAM9 is a membrane-anchored protein and has been implicated in a variety of biological processes, as well as being involved in cancer metastasis. RC3H2 is a membraneassociated nucleic acid-binding protein. High expression of both genes individually was slightly negatively associated (P-values ~0. 07 and 0. 04) with survival in triple-negative patients from the METABRIC data set. 3 The subgroup of triplenegative patients where both genes are highly expressed has a significantly negative shift (P-valueo6e -05) in survival, in comparison with other patients (see Supplementary Materials for details).
The PHENOARCH phenotyping platform The  (maize (Zea mays L. ), wheat, rice, sorghum)  (Sciara et al. , 2015) , grapevine  (Coupel-Ledru et al. , 2014)  and apple trees  (Lopez et al. , 2015). The plant density can be adapted for each North was referenced. Hemispherical images were analysed using the Ilastik 1. 1. 8 software  (Sommer et al. , 2011)  (Supporting Information Fig. S1 , Table  S1 ). Calculation of sun paths, transmitted direct and diffuse radiation was then performed using standard astronomical formulae using R scripts  (R_Core_Team, 2015)  available as an open application (Supporting Information Methods S1). The refraction in the glass of the greenhouse changed the angle of sun beams by 0 to 36° for incident angles up to 60°. It returned to its original value in air so refraction resulted in a translation of sunbeams by 0 to 2 cm, and was therefore considered as negligible.
Skeletons were further processed with the 'Analyse Skeleton' ImageJ plugin  (Arganda-Carreras et al. , 2010)  to obtain crossings and the endpoints of the different branches of the skeleton. These points were used to navigate through the skeleton image and segment it into 50-pixels-long elementary lines. The angle of each elementary segment with the vertical was computed as the absolute value of the arctangent between z-and xcoordinates of segments endpoints. All data, namely raw and processed images together with metadata were stored in the PHIS database (http://web. supagro. inra. fr/phis/).
Plant growth The leaf area and the fresh plant weight of individual plants were estimated from images taken in 13 directions. Briefly, pixels extracted from RGB images were converted into fresh plant weight and leaf area using linear models derived from regression of data from multiple side view images and destructive measurements performed at different phenological stages, from 5 to 14 appeared leaves (i. e. from 15 to 50 days at 20°C after emergence). The resulting conversion was accurate and unbiased (Supporting Information Fig. S2 ). The time courses of leaf area or fresh plant weight were then fitted individually to the three-parameter Gompertz function, # = $ × % & '()* (3) using appropriate R scripts  (R_Core_Team, 2015). Time courses were expressed as a function of equivalent days at 20°C  (Parent et al.
Acknowledgements This work was supported by the  European Union   Framework Program 7 'Drought-tolerant yielding plants' (DROPS) project  ( FP7-KBBE-244374 ) and the " Infrastructure Biologie Santé" Phenome  supported by the  National Research Agency  and the " Programme d'Investissements d'Avenir" (PIA)  ( ANR-11-INBS-0012 ). Authors are grateful to  Stéphane Berthézène ,  Antonin Grau ,  Jonathan Mineau ,  Vincent Nègre  and  Carine Palaffre , for their help in conducting the experiments. Simon Artzet  is acknowledged for his help in constructing Figure 5. We thank  Christophe Pradal  and  Marc Saudreau  for the wrapping of RATP model on OpenAlea platform. We thank  Tsu-Wei Chen  for his critical comments on the manuscript.
Spatial variability of incident light in the greenhouse difference (VPD) is estimated at each time step as the difference in water vapour pressure between saturation at air temperature and the current vapour pressure in the air. All data of air/meristem temperature, PPFD and relative humidity are averaged and recorded every 15 min (NI CompactRio, National Instruments, Austin, TX, USA) and stored in the PHIS database (http://web. supagro. inra. fr/phis). Daily incident PPFD over each plant of the platform (PPFD (xy) ) was estimated by combining a 2D map of light transmission and the outside PPFD (PPFD ext ) measured every 15 min with a sensor placed on the greenhouse roof (SKS 1110, Skye Instruments, Powys, UK). Maps of the fraction of transmitted direct (T dir ) and diffuse (T dif ) light were calculated every hour of each day of the year by using 169 hemispherical images of the greenhouse using a digital camera (Nikon Coolpix 4500, Nikon, Melville, NY, USA) fitted with a fisheye lens with a 183° field of view (Nikon FC-E8 Fisheye Converter, Nikon).
Moreover, applications of T 1 -T 2 experiments have focused principally on samples with a small or no solid fraction. We propose here an adaptation of the existing 2D reconstruction algorithm  [29]  in order to process T 1 -weighted FID-CPMG datasets. We have therefore introduced a direct model based on a combination of the standard CPMG decreasing exponentials and Abragam's FID function in the reconstruction method  [35] [36] [37]. We show that these developments lead to an accurate reconstruction of the IR-FID-CPMG relaxation time distributions using the Maximum Entropy Method in two dimensions (MEM2D) and provide more relevant information about the overall composition of complex samples as compared to the distribution obtained from CPMG data alone.
e. temperature and receiver gain (specified in the Result section)  [38]. The 45% hydrated wheat starch sample (SW45) was prepared as in Rondeau-Mouro et al. [38]. The mixture was gently homogenized in a glass vial by stirring for 5 min using a small spatula. The dry matter and the water content of the SW sample were determined by measuring variations in their mass after drying in an oven at 103 °C for 24 h. 1D FID-CPMG relaxation curves were first analyzed by the Marquardt method based on least-squares nonlinear regression  [39] , implemented on Scilab (Scilab Enterprises, Versailles, France). The data were also fitted using the Maximum Entropy Method (MEM) both for 1D and 2D processing. This was performed on Matlab Ò (The MathWorks, Inc. , Version R2006b 7. 3. 0. 267) using the adequate algorithm (see the next section).
Based   2 ) and (  12 ) using u = 172°in Eq. (  2 ). Number of iterations 1000; x = 0 rad; khi 2 0. 3267 at 293 K and 0. 3769 at 323 K; RMSE 0. 0114 at 293 K and 0. 0083 at 323 K. on the T 2 measurements, the water content was 20. 75 ± 0. 12%, compared to 19. 42 ± 0. 08% using gravimetry (Table  2 ). The FID-CPMG signal was also acquired and processed for two starch-water model samples (SWM1 and SWM2) at 293 K. In order to validate the MEM fitting, a step-by-step change of the x value was performed. Fig. 3  shows variations in the criterion values khi 2 and RMSE as a function of the FID value x in Eq. (  11 ) when processing the FID-CPMG signal of SWM1 and SWM2. The two criteria decreased to an optimum value around 2. 5-3 rad, before increasing for higher x values to 7 rad. The average values resulting from the MEM fitting of 1D FID-CPMG data measured on SWM samples are shown in Table  1.
In practice, for a blind search, we need to take into account the number of frequencies tested N trials = T span f Nyq , when T span is the data span and f Nyq = 250 Hz, the effective NuSTAR Nyquist frequency. In computing f p we must allow for the reduced sensitivity of the search due to background contamination in the source aperture (N b ); the minimum detectable pulse fraction f p,min is then increased by (N s + N b )/N s where N s is the source counts. We computed the pulsar signal detectability in individ-ual observations for each source in our sample and find that nearly all sources proved undetectable even if their flux were 100% pulsed. However, we have identified four sources for which we can potentially place a meaningful limit of f p,min < 50% on the pulsed flux, at the 3σ confidence level.
9-2853 (AX J1745. 0-2855), has produced at least a dozen Type I outbursts, typical of LMXBs binaries  (Cocchi et al. 1999) , recorded by several X-ray telescopes over the years (see  Degenaar et al. 2014 , and references therein for a review). NuSTAR observed GRS 1741. 9-2853 four times, during one of which a Type I burst was fully recorded. A comprehensive paper on these data sets is presented in  Barrière et al. (2015). These authors were able to place a lower limit of 6. 3 kpc on the distance to the NS based on the peak flux from the burst assuming the photospheric radius expansion model. They argue that spectral variation during outburst suggests disturbances in the inner accretion disk resulting from the burst. In the work herein we exclude a 352 s burst interval and report our analysis results in Table  4.
ACKNOWLEDGMENT This work was supported under NASA Contract No. NNG08FD60C, and made use of data from the NuSTAR mission, a project led by the California Institute of Technology, managed by the Jet Propulsion Laboratory, and funded by the National Aeronautics and Space Administration. We thank the NuSTAR Operations, Software and Calibration teams for support with the execution and analysis of these observations. We thank G. Ponti for careful reading and suggestions of the manuscript. This research has made use of the NuSTAR Data Analysis Software (NuSTARDAS) jointly developed by the ASI Science Data Center (ASDC, Italy) and the California Institute of Technology (USA). J. In NuSTAR, the threecolor trial maps in the same region show two bright spots (NGPs 32 and 54) with distinct X-ray colors, which are also separated by about 20 (top panel in Fig.
21 Their results revealed that improvements in scientific literacy were related to participation in the social components of the program but not to the amount of data contributed. This highlights the important role of wider communities in learning, a finding that has also been highlighted in the context of informal learning through involvement in digital gaming practices  [Iacovides et al. , 2014]. But again it is unknown whether these findings are applicable to other citizen cyberscience projects. Therefore our second goal was to investigate learning across citizen cyberscience projects.
This resulted in 39 interviews: 28 with volunteers and 11 with professional researchers. See Table  1  for more information. As can be seen in Table  1 , the majority of interviews were conducted in three projects: BOINC, volunteer computing; Old Weather, a volunteer thinking project where participants transcribe weather data and historical information from the handwritten logbooks of 19 th century naval ships; and Eyewire, a volunteer thinking project with a gaming dimension where participants color puzzles and reconstruct neurons of the retina of a rat. For Old Weather, we interviewed 10 volunteers that were still actively involved, and also 7 volunteers that were no longer active in the project but still on the project mailing list. This allowed us to gain an insight into the reasons why volunteers may choose to no longer participate.
Other projects included: Transcribe Bentham, a volunteer thinking project where participants transcribe the unpublished manuscripts of the London philosopher Jeremy Bentham (1748-1832); Bat Detective, a volunteer thinking project where participants listen to audio recordings and classify bat calls; EveryAware, a project with several case studies involving volunteer sensing, where participants use mobile apps to investigate sound pollution and air quality in their local area; and KRAG, a volunteer sensing project where participants record sightings of herpetofauna (reptiles and amphibians), promoting conservation of herpetofauna in Kent. All interviews were semi-structured, based on a general list of pre-defined concepts and probes. The majority of interviews took place virtually over Skype (28 volunteers, 3 researchers).
Four interviews took place in person at UCL (7 researchers, where 3 were interviewed individually and 4 were interviewed in a group). Interviews were audio recorded and varied in length, from 30 minutes to 1 hour. Interview transcripts were analysed using thematic analysis  [Braun and Clarke, 2006]. This involves coding relevant sections of the transcript in a consistent way, and subsequently grouping those codes into themes. Themes help to explain what the data means and relate the data to research questions. Four researchers coded the data separately to uncover themes for motivation, learning and creativity. During a two-day workshop, the researchers discussed and compared their analyses, building a model of volunteers' motivations, learning and creativity. Note that we have presented parts of our interview dataset at workshops and conferences  [Jennett et al.
Another learning outcome is the development of scientific literacy. Volunteers gain a better understanding of what science does and how scientists work, which research questions they are trying to answer and how they try to answer them. Direct experience of scientific data and analyses also allow volunteers to understand that science involved the use of rigorous procedures and protocols, that science takes times, and that failures are considered normal risks and contribute to exploration. These effects can be appreciated for example, by the increased accessibility of popular scientific publications to volunteers: JCOM 15(03)(2016)A05 11 "It transforms the short term vision. Experimenting with Citizen Science projects, people understand that science takes more time and may have different goals.
In Eyewire, one of the vo described how she created a bot (a software application that runs automated tasks) to commonly asked questions. Our thematic analysis revealed many different kinds of personal creations that resulted from participation. Several volunteers described how they suggested improvements for the task, which they communicated by posting on the project forum or sending the researchers a private email. Researchers viewed volunteers' feedback and suggestions as valuable for helping them to improve the task interface: "I sent them from mails from time to time with things I believe might be bugs or things I don't understand  [. ]   Eyewire, one of the volunteers described how she created a bot (a software application that runs automated tasks) to answer commonly asked questions.
Acknowledgments We would like to thank the citizen cyberscience projects for their collaboration: BOINC,  Old Weather , Eyewire,  Transcribe Betham ,  Bat Detective ,  EveryAware , and KRAG. We would especially like to thank all of the volunteers and researchers that took part in our interviews. This research was funded by the  EU project Citizen Cyberlab  (Grant No  317705 ). Alexandra Eveleigh was supported by a  UCL Graduate Research Scholarship for Cross Disciplinary Training , while Ioanna Iacovides is supported by the  EPSRC  funded  CHI+MED  project ( EP/G059063/1 ).
they are really kind to me, they respond! I think they are glad that I am trying to help them. " Eyewire volunteer "The volunteers are our experts really in updating the transcription interface. It's been suggested by them if we could eliminate the mark-up, if we could introduce an automatic save feature [. ], improvements to the image viewer [. ] They've certainly offered a lot of input in that regard. And when we update the transcription interface and test it, they will be the key audience for doing so. " Transcribe Bentham researcher Sometimes volunteers developed help tools and resources in order to make the research task easier. In Old Weather, some of the volunteers created glossaries and naval-related lists to make the task of interpreting the ships' logs easier. In BOINC, there were examples of volunteers translating instructions in other languages.
Section 3 shows how a relaxation of this model is considered in order to perform inference through an EM algorithm. Section 4 explains the model selection procedure of our approach and gives details about Occam's razor automatic selection as well as a link with classical frequentist penalized estimators. In Section 5, a new algorithm, called "spinyReg", for variable selection in high-dimensional regression is introduced. Section 6 presents a benchmark comparison between spinyReg and classical frequentist and Bayesian variable selection procedures, real and simulated data sets are considered. In Section 7, an original high-dimensional regression database, called "OrsayVelib", is introduced and is used to demonstrate the efficiency of our approach.
Lasso and Stability selection were trained using the R package quadrupen  (Grandvalet et al. , 2012). We used the package parcor  (Kraemer et al. , 2009)  to train the adaptive lasso and the package clere  (Yengo et al. , 2014b)  to train CLERE. The spike-and-slab approach of Hernández-Lobato et al. (  2013 ), which uses expectation propagation, will be subsequently denoted SSEP and was trained using the code available on the authors' web n = p/2 n = p n = 2p F-score q q q q q q q q q 0. 00 0. 25 0. 50 0. 75 1. 00 lasso adalasso stabsel clere ssep spiny q q q q q q q 0. 00 0. 25 0. 50 0. 75 1. 00 lasso adalasso stabsel clere ssep spiny q q q q q q q q q q q q q q 0. 00 0. 25 0. 50 0. 75 1. 00 lasso adalasso stabsel clere ssep spiny.
Predicting a touristic index using open data The emergence of open data systems has brought about a surge of complex data illustrating various social behaviors. In this challenging context, the analysis of bike-sharing systems (BSSs) provides a new insight into the touristic patterns of a city. We therefore wanted to see how well, in a city like Paris, bike-sharing data could predict a touristic index, such as the number of visitors of an important museum. With nearly three million annual visitors, the Orsay museum is one of the ten most visited museums in the world  (Skeggs, 2014)   7 ). This data set, thereafter called the "OrsayVelib" database, has several interesting aspects: • While most "large p, small n" regression problems inherit their dimensionality from genomics or signal processing, this data set is purely related to social sciences.
This illustrates the fact that modern social data can also lead to high-dimensional challenging statistical problems. • Since the variables are the Vélib' stations, a sparse solution can be easily interpretable and visualizable. We would expect the relevant predictors to correspond -at least to some extend -to stations used by the visitors of the Orsay museum. In particular, the behavior of the stations closest to the museum are expected to be of important interest. For visualization purposes, one can plot on a map the location of the selected variables, being able to efficiently interpret the selection. • The learning/test segregation of the data harshly punishes overfitting. Indeed, while September 2014 (the learning month) corresponded to exceptionally good weather conditions in Paris, whereas October had some rainy days.