dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. PN acknowledges support from the  ESO  Chile visiting scientist programme. RS and PPJ gratefully acknowledge support from the  Indo-French Centre for the Promotion of Advanced Research  ( Centre Franco-Indien pour la Promotion de la Recherche Avancée)  under contract No. 4304-2. The authors of this paper also acknowledge the tremendous effort put forth by the  Sloan Digital Sky Survey team  to produce and release the SDSS survey. Funding for SDSS and SDSS-II has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , the  U. S. Department of Energy , the  National Aeronautics and Space Administration , the  Japanese Monbukagakusho , the  Max Planck Society , and the  Higher Education Funding Council for England.
X-Ray diffraction: The X-Ray diffraction (XRD) patterns were collected using an Empyrean Panalytical diffractometer operating at 45 kV/35 mA, using Cu-K radiation with Ni filter, and working in continuous mode with a step size of 0,013°2θ. Data suitable for Rietveld refinements were collected over a range 5-80° 2θ.
The cell was placed in an isotherm furnace operating up to 750°C. The electrical analyses were carried out in the frequency range ( = 2 ) 1 to 10 7 Hz, with an alternating current associated with a maximum voltage of 1 V. The sample was stabilized for 20 minutes at a fixed temperature and the recording time for the frequency range was of 20 minutes. To ensure thermal stabilization, the sample was subjected to three successive measuring cycles (one temperature rise and drop for each cycle). The final impedance data were chosen during heating mode of the third cycle, as being representative of a stabilized sample.
III. 1. X-Ray Diffraction analyses Figure  1  presents the XRD pattern of the sample thermally treated at 600°C, the identification of this phase was firstly obtained from the comparison with the standard JCPDS files (Joint Committee standards for Powder Diffraction) in which the standard phase ZMO was referenced, This first identification allowed attributing (h,k,l) Miller indices to Bragg peaks. All the diffraction peaks correspond to the ZnMoO 4 P-1 structure according the JCPDS file 70-5387. In order to determine the crystal structure of the as-synthesized ZMO-600 ceramic, Rietveld refinements were performed using General Structure Analysis System (GSAS) Software  [40]  accessed through the EXPGUI  [41]  graphical user interface which allows refinement of atomic coordinates, site occupancies and atomic displacement parameters, as well as profile parameters (instrument parameters, background parameters, lattice constants and peak shape).
Acknowledgments A part of this work was financially supported by materials and environment laboratory (Agadir-Morocco) by the  Regional Council of Provence-Alpes-Côte d'Azur , by the  General Council of Var  Accepted Manuscript and by  Toulon Provence Mediterranean , and was conducted as part of a  CNRS-CNRST  project. The authors gratefully acknowledge the assistance of  Mr. Y. Ben Smida.
Acknowledgements We thank  Aldo Deandrea  for precious discussions and for commenting on the manuscript. This work was supported by the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MEST)  (No. 2012R1A2A2A01045722 ) and also supported by  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  ministry of Education, Science and Technology  (No. 2013R1A1A1062597 ). H. S. acknowledges the  Portuguese FCT  project  PTDC/FIS-NUC/0548/2012. A. P. acknowledges  IBS Korea  under system code IBS-R017-D1-2015-a00. Finally, G. C. and H. C. acknowledge partial support from the  Labex-LIO (Lyon Institute of Origins)  under grant  ANR-10-LABX-66 , and support from the  France-Korea Particle Physics Lab (FKPPL).
CEOS led the creation of a Recovery Observatory Oversight Team, which brings together major recovery stakeholders such as the UNDP and the World Bank/Global Facility for Disaster Reduction and Recovery, value-adding providers and leading space agencies. The principal aims of the Observatory are to: 1. Demonstrate the utility of a wide range of earth observation data to facilitate the recovery and reconstruction phase following a major catastrophic event; 2. Provide a concrete case to focus efforts in identifying and resolving technical and organizational obstacles to facilitating the visibility and access to a relevant set of EO data; and 3. Develop dialogue and establish institutional relationships with the Recovery phase user community to best target data and information requirements; The paper presented here will describe the work conducted in preparing for the triggering of a Recovery Observatory including support to rapid assessments and Post Disaster Needs Assessments by the EO community.
This project, named KalHaiti, has made great progress over the past four years, and has allowed CNES to identify lessons learned that underscore the importance of working before a catastrophic event takes place, and of working collectively, as a community. A single, coordinated approach supported by advance planning will greatly increase the efficiency and impact of actions taken following major events. These lessons have led to the creation of the Recovery Observatory Oversight Team (ROOT), which developed a proposal that was presented to the CEOS Plenary in November 2013, further developed in early 2014, and approved at the CEOS SIT-29 in April 2014. A CEOS-led Recovery Observatory, a one-time demonstration in the 2014-2017 period, would allow the development of specific tools tailored to provide easy access to data over affected areas (pre-event data, response data and coordinated post event acquisitions).
An organised repository, which serves as a discovery platform for data and products, would allow disaster managers to work in a known environment with advanced satellite products and promote their use to key user communities. Currently, while research shows that EO can be a valuable tool for recovery, DRM stakeholders do not have a clear example of how EO can be used to support Recovery efforts in an optimal scenario for data collection. Until now, sharing has not been put forward in a coordinated manner for any major catastrophes. The main objective of the proposed Recovery Observatory is to facilitate access to data and products that support recovery from a catastrophic event. A subsidiary benefit for CEOS and satellite data providers will be the development of specific approaches and protocols for using satellite data to support disaster recovery, and raising awareness with the Disaster Risk Management community of the benefits of a satellite-based approach.
Preparation The goal is to prepare the creation of a Recovery Observatory by working on all the required components in order to have all of them ready before entering "cold storage" and the beginning of the Observatory life-cycle. Numerous aspects have been identified: • Technical components (website, database(s), processing chains) • Data provision flows (data types, providers, licenses) • Agreements with Users communities and partners (intermediary users, science users) This phase should be closed after a "dry-run" formal exercise has been implemented successfully. The on-going support to PDNAs may serve as this dry-run phase. The preparation phase will take place in two-steps, through an iterative process, and will be conducted in close collaboration with CEOS WGISS. Since April 2014, CEOS' WGISS has been conducting a summary review of requirements, and sounded out potential collaborating agencies for contributions in support of the Observatory.
While it is impossible to determine the exact needs of a Recovery Observatory before an event takes place, there are a large number of requirements that are common to all recovery situations after major disasters. Baseline data requirements are required for both EO data and for derived products. These data and products support several different types of activity that change as the recovery phase, and lifetime of the observatory, progresses. These activities might include: • Built area damage assessment (initial and later detailed) • Natural resource and environment assessment • Reconstruction planning support • Reconstruction monitoring; and • Change monitoring. While there is no need for detailed acquisition plans before the triggering of the Observatory, the preparation phase should include the development of detailed EO data requirements for different types of Observatories: one for a major earthquake, one for a major volcanic eruption, one for a major flood and/or hurricane, and a tsunami.
Overview While the long-term goal remains the establishment of a Recovery Observatory, there is much to be gained from smallerscale cooperation to develop institutional linkages between data suppliers, product generators and the user community. In order to develop a better understanding of what is possible and what is most useful, these partners have agreed to collaborate on rapid assessments for major disasters and Post Disaster Needs Assessments (PDNAs) in the first half of 2015, as work continues to prepare for the triggering of the Observatory.
The size of these volumes was chosen to be cylindrical with dimensions comparable to a DNA segment of ten base pairs (one convolution of the DNA double helix). The higher the probability for large cluster sizes-which may produce irreparable double strand breaks-the higher is the probability for the occurrence of radiobiological effects. Statistical parameters of ionization cluster size distributions (de Nardo et al 2002) are promising regarding their potential to link the ionization pattern of the particle tracks to the radiobiological outcome. In order to obtain data for parameterizing the track structure properties, we calculated ICSDs for all simulated proton tracks for all considered energies. The cluster size was determined by scoring the number of ionizations in cylindrical target volumes.
Data points marked with squares corresponds to the 5 MeV series and circles to 25 MeV. The overall track structure was determined in each nucleus, a mean ICSD was determined for all nuclei and the statistical moments presented in section 2. 2 were calculated. The results of these simulations were then compared to the track structure properties calculated with the modified stopping power weighting method: d N i N i i 1 2 1 ( ) ( ) ∑ = = (9) where S is the sum of stopping powers of all tracks, S i is the stopping power of track i and P(t i ) is the track structure property of track i which was obtained from the parameterizations of the original simulation data presented in section 3.
Discussion and conclusion As it is not possible to perform full Monte Carlo track structure simulations with nanometer resolution for all particle species and energies which are present in the clinical situation, a fast assessment of relevant track structure parameters is crucial. For this purpose we calculated ICSDs for protons of various energies and calculated the according statistical moments of the ICSDs in order to characterize them. The presented parameterizations (  4 )-(  6 ) in section 3 enable a fast assessment of the statistical moments of the ICSDs of protons in a clinically relevant energy range between 1 MeV and 100 MeV. The upper limit of 100 MeV was given by the current capabilities of Geant4-DNA. For higher energies, variations in radiation quality are small and the data in figures 2-4 could easily be extrapolated to higher proton energies.
XRF core scanning The split archive halves of Site U1385 were analyzed using three Avaatech X-ray fluorescence (XRF) core scanners to obtain semi-quantitative elemental data at 1-cm spatial resolution. Cores from Holes U1385A and U1385B were measured at the Royal Netherlands Institute for Sea Research (NIOZ) and Holes U1385D and U1385E at the Godwin Laboratory in Cambridge. The core surface was carefully scraped cleaned and covered with a 4-μm thin SPEXCertiPrep Ultralene foil to avoid contamination and reduce desiccation  (Richter et al. , 2006). All XRF scanners irradiated a surface of 10-mm high by 12-mm wide every 1 cm with identical instrument settings used at both NIOZ and Cambridge (Table  1 ). Two additional replicates were measured every 25 measurements to provide confidence limits on the elemental data obtained.
Element intensities were obtained by post-processing of the XRF spectra using the Canberra WinAxil software with standard software settings and spectrum-fit models. The element intensities depend on the element concentration but also on matrix effects, physical properties, the sample geometry, and hardware settings of the scanner  (Tjallingii et al. , 2007). However, log-ratios of element intensities are simple linear functions of log-ratios of concentrations, minimize sample geometry effects, and allow calculation of statistically meaningful confidence limits  (Weltje and Tjallingii, 2008). Therefore, log-ratios provide an objective and convenient way to compare different XRF records measured on different instruments in terms of relative chemical variations. The Ca/Ti data are archived on the PANGAEA and NOAA Paleoclimate data systems and the complete XRF data set will be presented in a companion paper.
For smaller samples (b 80 μg), measurements were performed on a Thermo Finnigan MAT253 mass spectrometer fitted with a Kiel device. Analytical precision is estimated to be ±0. 08‰ for δ 18 O and ±0. 06‰ for δ 13 C, respectively. Results are reported relative to V-PDB. All isotope measurements were made in the Godwin Laboratory, University of Cambridge and are archived on the PANGAEA and NOAA Paleoclimate data systems.
Color scanning Two independent sets of sediment color data were measured. During Expedition 339, color reflectance was measured on the archive halves of sediment cores using an Ocean Optics USB4000 spectrophotometer mounted on the automated "Section Half Multisensor Logger" (Expedition 339  Scientists, Methods, 2013). Freshly split cores were covered with clear plastic wrap and each measurement consisted of sequential 2-nm wide spectral bands from 400 to 900 nm. Color data were also collected using the Avaatech line-scan camera system. Split cores surfaces were prepared by fine scraping with a stainless steel or glass blade. The cores were placed in the Avaatech XRF scanner and the shade over the window closed to prevent interference from external light. Standard image color cards were placed alongside the core for calibration.
The white fluorescent lights were allowed to warm up and stabilize for 30 min before beginning each camera scan. All cores were scanned with the camera aperture set to 8+ with an exposure time of 5 msecs. The monochromatic filters in the camera split the light into RGB channels at 630 nm, 535 nm and 450 nm with good separation among channels. The RGB data were converted to the CIEl-a*-b* color system for comparison with the shipboard measurements. The data were subsequently averaged with a running mean of 10 data points for subsequent data compression. Color data are archived on the PANGAEA and NOAA Paleoclimate data systems.
Acknowledgments We thank the drilling crew, ship's crew, and scientific and technical staff of the drillship JOIDES Resolution without whom recovering Site  U1385  would not have been possible. Jeannie Booth ,  Ian Mather ,  John Nicolson , and  James Rolfe  are thanked for laboratory support. We thank  Larry Peterson  for a thoughtful review that significantly improved the manuscript. This research was supported by the  Natural Environmental Research Council  Grant  NE/K005804/1  to DH and LS and  NE/J017922/1  to DH. Fig. 14 (continued).
Acknowledgements Field surveys were performed during the Master thesis of Patricia Meyer. Data analysis, interpretation and discussion were performed during the PhD thesis of A. C. Authors wish to thank  ECOMERS lab colleagues  who helped during dives for field work. They are grateful to the Editor  Dr. Konstantinos Tsiamis  and three anonymous reviewers whose comments helped improving the manuscript. This work was part of  FOREFISH  and  CIRCE  Project, respectively funded by  Total Foundation  (http:// fondation. total. com/)and the  Agence de l'eau Rhône-Méditerranée-Corse/Pôle Mer PACA  (http://www. eaurmc. fr/),which had no involvement in study design, data collection, analysis or interpretation, writing and submitting decisions. Supporting information: full relevant data set of field observations.
Introduction Representing local image patches in an invariant and discriminative manner is a major research topic in computer vision. While most descriptors, such as SIFT  [16] , rely on hand-crafted features  [1, 13, 16, 22, 27, 28, 32] , there has recently been interest in using machine learning algorithms to learn them from large datasets  [20, 23, 29]. In this paper we draw inspiration from the recent success of Deep CNNs in large-scale image classification prob- We use t-SNE  [30]  to display ∼100 64 × 64 patches of 12 3D points from different images (see Fig. 3  for examples). Corresponding patches are drawn with the same color. (b) We single out the red-circled patch, belonging to the blue point cloud, and consider all of its potential pairings. The line length encodes the closeness between this patch and the rest: positive matches in blue, negative in red.
Most pairs are easy to discriminate and ineffectual for training. (c) We mine the samples to obtain the closest negative (shortest red line) and the most distant positive (longest blue line). This simple strategy allows us to train discriminative networks over large datasets. lems  [14, 26]  to build discriminative descriptors for local patches. In our case discriminative training does not rely on labels of individual patches, but rather on pairs of corresponding, or non-corresponding patches. For this we use a Siamese network architecture  [2]  that employs two CNNs with identical parameters to compare pairs of patches; treating the CNN outputs as patch descriptors, we minimize a loss that enforces the L 2 norm of their difference to be small for corresponding patches and large otherwise.
To train this network we rely on the multi-view stereo dataset (MVS)  [3] , which contains over 1. 5M grayscale 64 × 64 image patches from different views of 500K 3D points. The difficulty with such a large dataset is that it becomes impossible to exhaustively explore all correspond-ing and non-corresponding pairs, so we must resort to some form of random sampling. Based on the observation that after a certain point of learning most pairs are correctly classified, and using them no longer improves the learned embedding, we propose a strategy of aggressive mining of "hard" positives and negatives. During the learning stage we enforce the back-propagation of samples with a large loss, i. e. both corresponding pairs that match poorly and non-corresponding pairs that are hard to discriminate. This proves to be most useful for efficiently learning discriminative descriptors.
CNN-based Descriptors When designing the structure of the CNN we are limited by the size of the input data: in our case 64×64 patches, from the MVS dataset  [3] , while we extract descriptors of the same size as SIFT  [16] , i. e. 128 dimensions. Note that larger patches and/or output spaces would allow us to consider possibly more informative descriptors, but at the same time they would be also more susceptible to occlusions and slower to train and compute. We explored many configurations regarding the number of filters, hidden units, mapping, normalization, and pooling. Such architectures are detailed in the supplemental material, but due to space constraints we use only our top performing model, i. e. 'CNN3', for the following experiments. The architecture of this three-layer network is detailed in Table  1.
Stochastic Sampling Strategy and Mining Our goal is to optimize the network parameters from an arbitrarily large set of training patches. Let us consider a dataset with k patches and m ≤ k unique 3D patch indices, each with c i corresponding image patches. Then, the number of matching image patches, P (positives) and the number of non-matching images patches, N (negatives) is: P = m i=1 c i (c i -1) 2 and N = m i=1 c i (k -c i ). ( 1 ) Since both P and N are intractably large, we resort to Stochastic Gradient Descent, using random subsets of our training set to estimate the gradient of our loss function. For positives we can randomly sample a set of s p 3D point indices from the set {p 1 ,. , p m }, and for each chosen 3D index p i we randomly pick two 2D patches with corresponding 3D point indices.
Results For training we use the Multi-view Stereo Correspondence dataset (MVS)  [3] , which consists of 64×64 grayscale image patches sampled from 3D reconstructions of the Statue of Liberty (LY), Notre Dame (ND) and Half Dome in Yosemite (YO). Patches are extracted using the Difference of Gaussians detector  [16] , and determined as a valid correspondence if they are within 5 pixels in position, 0. 25 octaves in scale and π/8 radians in angle. Fig. 3  shows some samples from each set, which contain significant changes in position, rotation and illumination conditions, and often exhibit very noticeable perspective changes. We join the data from LY and YO to form a training set with over a million patches. Out of these we reserve a subset of 10,000 unique 3D points for validation (∼30,000 patches).
4. 3 for details). We consider four splits: LY+YO (tested on ND), LY+ND (tested on YO), and YO+ND (tested on LY), plus a final split with training data from all three sets. Finally, we apply the models learned over the MVS dataset to different applications. In Sec. 4. 4 we study the robustness of our descriptors to patch rotation. In Sec. 4. 5 we use our models to match wide-baseline images from a different stereo dataset. In Sec. 4. 6 we benchmark our descriptors on a recent dataset with very challenging non-rigid deformations and drastic changes in illumination. Our models outperform state-of-the-art baselines in every case, without fine-tuning over new data, and over considerably different application domains.
e. backpropagating the "hard" positive and negative samples). Note that this is only applicable to the learning stage-once the model is deployed, we discard the Siamese network and do not incur the computational costs related to mining. In order to speed up the learning process we initialize the CNN3 models with positive mining, i. e. 2/2, 4/4, 8/8 and 16/16, with an early iteration of a model trained only with negative mining (1/2). Results are shown in Table  2. We see that for this particular problem, aggressive "hard" mining is fundamental. This is due to the extremely large number of both negatives and positives in the dataset, in combination with models with a relatively low number of parameters. We observe a drastic increase in performance up to 8/8 mining factors.
Generalization & comparison to state of the art In this section we consider the three splits for the MVS dataset of  [3]. We train the top-performing model (i. e. CNN3), with different mining ratios (1/2, 2/2, 4/4 and 8/8), on a combination of two sets, and test it on the remaining set. We select the training iteration that performs best over the corresponding validation set. The test datasets are very large (up to 633K patches) and we use the same procedure as for validation: we consider 10,000 unique points, each with 1,000 random non-corresponding matches. We repeat this process over 10 folds, thus considering 100,000 sets of one corresponding patch vs 1,000 non-corresponding patches. We show results in terms of PR AUC in Table  3 , and the corresponding PR curves are pictured in Fig. 4.