dataesr/scientific-paragraphs-categorization huggingface dataset:
NMIT is a member of the  Integrative Structural Biology Cluster (ISBUC) at the University of Copenhagen. This work was in part supported by the  Synapsis Foundation Switzerland , the  Heidi-Seiler Stiftung Foundation , and the  Swiss National Science Foundation  (grants  CRSII3_154461 ,  CRSII5_177195. 20020_178792  and  NCCR TransCure) , and the  French Agence Nationale de la Recherche Scientifique  ( ANR-12-BS08-0013-01. ANR-11-LABX-0048  and  ANR-11-IDEX-0007 ). AAA, LB and RM received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 116060  (IMPRiND), the  Swiss State Secretariat for Education, Research and Innovation (SERI)  under contract number  17. 00038 , the  Fondation Bettencourt Schueller , The  Fondation pour la Recherche Medicale  (Contract  DEQ 20160334896 ),The  Fondation Simone et Cino Del Duca of the Institut de France  and the  EC Joint Program on Neurodegenerative Diseases  ( TransPathND ,  ANR-17-JPCD-0002-02  and  Protest-70 ,  ANR-17-JPCD-0005-01 ), by the  French Infrastructure  for Joeri Verasdonck ,  Roland Riek ,  Ronald Melki ,  Beat H Meier ,  Anja Bo ¨ckmann ,  Luc Bousset ,  Henning Stahlberg.
Data availability Raw cryo-EM micrographs are available in EMPIAR, entry numbers EMPIAR-10323. The 3D maps are available in the EMDB, entry numbers EMD-10307 (a-Syn polymorph 2a) and EMD-10305 (a-Synpolymorph 2b). Atomic coordinates are available at the PDB with entry numbers PDB 6SSX (a-Syn polymorph 2a) and PDB 6SST (a-Syn polymorph 2b).
Data availability Raw cryo-EM micrographs are available in EMPIAR, entry numbers EMPIAR-10323. The 3D maps are available in the EMDB, entry numbers EMD-10307 (a-Syn polymorph 2a) and EMD-10305 (a-Synpolymorph 2b). Atomic coordinates are available at the PDB with entry numbers PDB 6SSX (a-Syn polymorph 2a) and PDB 6SST (a-Syn polymorph 2b). The following datasets were generated:.
Then, we provide a detailed description of HMAX, highlighting how it is connected to biological vision. We further present sparse-HMAX, an extension of HMAX that includes a sparse coding scheme. Sparse coding describes the strategy of neural systems to minimize the number of neurons activated simultaneously  (Olshausen & Field 2004). By adding sparse coding to HMAX, we aim both to develop an even more biologically realistic model of perception of colour patterns, and to provide a framework for estimating efficiency in information processing  (Renoult & Mendelson 2019). A fast and easily customizable version of HMAX that can be used to model the perception of colour patterns in most vertebrates and our sparse-HMAX algorithm are available at: https://github. com/EEVCOM-Montpellier/HMAX. In the last part of this article, we apply HMAX to estimate similarities between faces in a nonhuman primate species as an example application.
In this chapter, we provide an overview of different artificial intelligence (AI) and machine learning (ML) techniques and discuss how these techniques have been employed in managing geospatial data sets as they pertain to Digital Earth. We introduce statistical ML methods that are frequently used in spatial problems and their applications. We discuss generative models, one of the hottest topics in ML, to illustrate the possibility of generating new data sets that can be used to train data analysis methods or to create new possibilities for Digital Earth such as virtual reality or augmented reality. We finish the chapter with a discussion of deep learning methods that have high predictive power and have shown great promise in data analysis of geospatial data sets provided by Digital Earth.
We start by discussing some traditional and statistical approaches in machine learning and then present more recent learning techniques employed for geospatial data sets. Traditional methods include predefined models such as linear regression, PCA, SVD, active contour, and SVM, in which the model is fixed and the learning is based on an optimization. We also briefly discuss evolutionary and agent-based methods and autoencoders as traditional methods that can be deep or shallow. We then discuss more recent deep learning techniques, including reinforcement learning, deep convolutional networks and generative models such as variational autoencoders and generative adversarial networks. In this chapter, we describe some applications of these machine learning techniques to handle geospatial data sets that are the main content of Digital Earth.
W(u i , v i ) is a diagonal weighting matrix that contains geographic weights on its diagonal elements for neighbors inside the neighborhood window (red circle in Fig. 10. 3), and Y contains the variable being predicted. Note that the linear system above is similar to the general linear regression system given in Eq. 10. 4. β = X T X -1 X T Y (10. 4) where β is defined globally for the entire dataset. The geographic weights are inversely weighted with respect to the distance. Thus, the weights have large values for neighbors close to the regression location i. Different weighting schemes and neighborhood definitions are possible; the reader is encouraged to explore seminal work on this topic  (Fotheringham et al. 2003). Spatial representation via a weighting scheme can give GWR high predictive power for geospatial datasets in which a strong spatial autocorrelation is observed.
SVM Support vector machine (SVM) is a supervised nonparametric statistical learning method  (Corinna and Vapnik 1995). In its original form, the method comprises a set of labeled data instances and the SVM attempts to find a hyperplane that separates the dataset into a discrete predefined number of classes as consistently as possible for the training data (see Fig. 10. 5)  (Vapnik 1979). It is possible to generalize SVM to nonlinear kernels such as radial basis functions to learn and classify data sets with higher complexity  (Schölkopf and Smola 2002). As studied and discussed by  Mountrakis et al. (2011) , SVMs have been extensively employed in remote sensing and geospatial data analysis due to their ability to use small training data sets, often resulting a higher classification accuracy than the traditional methods  (Mantero et al.
Unsupervised Learning Unsupervised learning aims to infer the distribution of P(X ) in Eq. 10. 1. Unlike supervised learning, P(Y |X ) or P(X, Y ) is not employed  (Hastie et al. 2001). Thus, Fig. 10. 6 Active contours used to extract roads. Image taken form  Laptev (1997)  unsupervised learning does not utilize any training dataset that contains information on P(X, Y ). One of the most common uses of unsupervised learning in geospatial analysis is in defining clusters and regions. These two terms differ, as clustering refers to defining groups based on value similarity in the data whereas regionalization performs clustering under spatial constraints  (Duque et al. 2007 ). Both of these unsupervised learning approaches have wide applications  (Duque et al. 2007; Hastie et al. 2001; Mitchell 1997; von Luxburg 2010).
2001). The SKATER algorithm is a regionalization algorithm that imposes graph-based spatial constraints on the k-means algorithm  (Assunção et al. 2006). Unlike Lloyd's algorithm  (Lloyd 1982) , SKATER only assigns spatially contiguous and similar objects to the same cluster. Regionalization has vast uses in geospatial analysis, including analysis of gerrymandering, healthcare services (Church and Barker 1998) and resource allocation  (Or and Pierskalla 1979). Clustering and regionalization were applied to the same dataset to juxtapose the types of patterns they expose in the data and the resulting understanding gained using these two methods. The average temperature in the United States in June 2012 was used. The resulting clusters and regions are displayed below. The regionalization and clustering results in Fig.
Deep Learning When a large amount of data is involved and/or a complex model for representing the data is used, it is common to employ deep learning methods  (Goodfellow et al. 2016). Digital earth data represents a massive amount of data, for example, high-precision digital elevation models or aerial photography. Because the rules that produce this kind of data are very complex and involve many natural or human processes, it can be difficult to apply standard learning models or algorithms and retain this complexity. Thus, the deep models described in this section are relevant.
Convolutional Networks Deep learning has been popularized by image processing applications. In this context, the processed data is arranged into a regular grid and is adapted to so-called convolutional layers. Data extracted from Digital Earth can be of this nature by construction. For example, raster data such as digital elevation models or aerial photography images are already arranged into regular grids and can be processed out of the box with convolutional layers. Convolutional neural networks rely on the fact that the same processing can be applied to different parts of the image. Traditional Recently, a convolutional network was used to infer the super-resolution of a digital elevation model by using aerial photography  (Argudo et al. 2018 ). Figure  10. 11 shows the architecture of this network.
An RNN is helpful when dealing with a temporal data set. In geospatial data analysis, an RNN has been recently applied in interesting applications such as correction of satellite image classification  (Maggiori et al. 2017 ) and land cover classification  (Ienco et al. 2017). Since many types of geospatial data sets such as weather, satellite images, or seasonal animal behavior have timing attached to them, we expect that RNNs will be widely used in the analysis of geospatial data sets in the near future and that Digital Earth will benefit from such networks.
Conditional GANs (cGANs) are GANs with a particular setup in which the discriminator is trained to recognize the matching between an input image A and an output B whereas a traditional GAN only tests the plausibility of the output without any knowledge of the input. The training principle of a cGAN is explained in Fig. 10. 15. Conditional GANs have recently been used to automatically generate digital elevation models from user sketches  (Guérin et al. 2017). The user sketches the river network, the crests and some altitude cues and obtains a plausible terrain that matches the given constraints, based on a training dataset made of sketch/terrain pairs. The method consists of building such a dataset by extracting the sketch from a real-world terrain. The difficulty of this kind of setup is to automatically build a sketch that is compatible with user sketches, i.
e. , similar to what a user would draw. Building a sketch that is too close to the terrain features will force the user to draw very precisely, which is not relevant in a sketching context but would be useful in a reconstruction process. The digital elevation model must be simplified to produce simpler features. In their work,  Guérin et al. propose initially downsampling the digital elevation model and then smoothing it. This coarse digital elevation model is then processed by a flow simulation, from which the skeleton is extracted. The same process is applied to extract ridges. This feature extraction is illustrated in Fig. 10. 16. The training dataset is formed of pairs that describe the matching between the sketch and the terrain. Figure  10. 17 gives examples of such pairs. To create a more pliable terrain synthesizer, the sketches randomly include one, two or the three features among the river lines, crest lines and altitude cues.
10. 20). Examples of applications that RL can be extremely useful for are games or robot locomotion in which more points and more stable states are the rewards of the game and locomotion environments, respectively. RL has also been used in applications in GIS and geospatial data analysis. For instance, RL has been used to model land cover changes  (Bone and Dragicevic 2009). With recent advances in RL and the growth of computational power, we expect that RL will receive more attention from the GIS and Digital Earth communities. For instance, one application of RL can be to simulate the behavior of endangered species in different simulated environments.
Discussion In the past, machine learning has seen hypes and winter seasons. It started with symbolic AI in the 1960s, which claimed the ability to make machines with intelligence comparable to an average human being in less than a decade. However, people soon realized that they were far from reaching that point. In the 1980s, with the rise of expert systems, similar hype was seen in the area of machine learning, followed by a winter season due to the lack of generality of expert systems and their high maintenance costs  (Chollet 2017). Recently, deep learning methods became popular again and showed great success in different areas of computer science including geospatial analysis, which is an important portion of Digital Earth platforms. Deep learning will likely continue to grow and be applied more in this field, especially because of the availability of computational power and big data sets that help create more powerful models.
However, deep learning cannot solve all problems. For instance, current deep learning models are unable to solve problems that require reasoning or long-term planning  (Chollet 2017). Deep learning models work extremely well in mapping an input to a desired output with very little human-level knowledge about the input or output and their effect on industry and science will probably remain for a very long time. There is plenty of discussion about the future of deep learning and AI, notably by its great pioneers such as  Lecun et al. (2015)  and in the European perspective on AI  (Craglia et al. 2018). Artificial intelligence and particularly machine learning and deep learning have great potential to contribute to the generation, analysis, and management of geospatial data sets. Digital Earth should benefit from such opportunities, as a place holder to represent such data sets and a platform to analyze them.
Conclusion In conclusion, we provided a sampling of artificial intelligence techniques and their applications in geospatial data generation, analysis, and management. We discussed how AI can be beneficial for generating new terrain data sets, identifying roads and analyzing various geospatial data sets such as satellite imagery. AI techniques and deep learning methods appear very promising. Extensive research on these topics will likely make them even more suitable for use in different domains including geospatial analysis and Digital Earth. However, these techniques are unfortunately standalone and have not been integrated into a Digital Earth platform that makes use of such techniques. Appropriate artificial intelligence techniques should be meticulously included in Digital Earth, considering their pros and cons including fairness and bias to provide interactive, comprehensive and meaningful analysis to users.
We provide a detailed description of a serial femtosecond crystallography (SFX) dataset collected at the European X-ray free-electron laser facility (EuXFEL). The EuXFEL is the first high repetition rate XFEL delivering MHz X-ray pulse trains at 10 Hz. The short spacing (<1 µs) between pulses requires fast flowing microjets for sample injection and high frame rate detectors. A data set was recorded of a microcrystalline mixture of at least three different jack bean proteins (urease, concanavalin A, concanavalin B). A one megapixel Adaptive Gain Integrating Pixel Detector (AGIPD) was used which has not only a high frame rate but also a large dynamic range. This dataset is publicly available through the Coherent X-ray Imaging Data Bank (CXIDB) as a resource for algorithm development and for data analysis training for prospective XFEL users.
18. The sample flow rate was 30-40 µl min -1 , and gas pressure 400-500 psi at the inlet of the GDVN's gas supply line, corresponding to a flow rate of 140-250 ml min -1. In order to reproducibly flow enough sample fast enough to close the gap created in the jet by the previous X-ray pulse  19  in time before the next pulse arrives, the jet speed must be measured in situ during data collection both on a regular basis and for each change in flow conditions (e. g. , new sample, crystal concentration, change in liquid flow rate or helium pressure, new GDVN, etc. ). To this end the jet was imaged using a femtosecond laser to prevent blurring of the images as described recently  19. The fs laser pulse and the camera were triggered by the EuXFEL global trigger (10 Hz) that indicates the arrival of an X-ray pulse train, thus the images were recorded at a set delay relative to the arrival of the pulse train.
Data collection. The experiment was performed at the SPB/SFX instrument of the EuXFEL  11, 12. The accelerator was delivering ten pulse trains per second with 60 pulses per train. The first 10 pulses of each train were used for electron orbit feedback and then being sent to the pre-undulator dump, without lasing. The remaining 50 pulses of the train (1. 1. MHz intra-train repetition rate; we measured 886. 15 ± 0. 01 ns spacing between pulses) were used for data collection. The photon energy was tuned to a nominal value of 7. 48 keV. The X-ray focus was ~15 µm, electron bunch length ~50 fs FWHM. For each individual X-ray pulse, the pulse energy was recorded by an X-ray gas monitor detector (XGMD) upstream of the experimental hutch showing that each pulse had 0. 9-1. 5 mJ pulse energy. With a beamline transmission of ~70%, this yields a flux of up to 5.
Subsequently, this information alongside the memory cell index is used to correct the offset/pedestal value for each pixel with the appropriate constant. Offset constants are evaluated as the median pixel value of a set of dark images and adjusted by an additional switching offset for medium and low gain stages, which was derived from pulse capacitor and charge injection data. Pulse capacitor and charge injection data is acquired in special operation modes of the detector, which use ASIC-internal current sources for signal generation without X-rays present. The additional switching offset adjustment is necessary as offsets differ slightly, depending on whether a pixel has automatically switched gain due to integrated charge, or was forced to switch into a particular gain setting (as is the case for dark image data).
Finally, a relative gain correction is performed. The relative gain constants were obtained by first determining the relative slopes of the medium and low gain stages with respect to the high gain stage, using pulse capacitor and charge injection data respectively. The relative high gain of a given pixel with respect to all pixels of the detector was determined from flat field data (Cu-fluorescence), by evaluating the positions of the first five photon peaks. The high-to-medium and high-to-low gain relative slopes are then used to scale this high gain constant, providing constants for medium and low gain. All characterizations further yield bad pixel masks, which are provided on a per-image basis alongside the calibrated data, and are already selected for the appropriate gain and memory cell.
6. 3  26. The sample-detector distance was determined by indexing rate optimization, yielding a value of 121 mm. A nominal value of 7. 47 keV was used for indexing. The position of the sample jet was continuously adjusted to maximize the hit rate. The positions and orientations of individual sensor modules of the AGIPD were refined as described  4. Due to the large number of saturated pixels in the corrected detector images, the top and the bottom row of detector ASICs were excluded from the geometry file to prevent contamination of integrated detector signals with artefacts. In addition, three further ASICs on the right side of the detector were observed to switch off and on during data recording and thus were excluded as well (see Figure  Less_panel_geom  in the Auxiliary File available together with the deposited data on the Coherent X-ray Imaging Data Bank website (CXIDB)  27, 28  ).
Data Records In total, 1,333,750 diffraction images were collected of the microcrystalline jack bean protein mixture. The final number of indexed diffraction patterns is 76,803 for concanavalin A and 23,719 for concanavalin B, with the resolution limit of the Monte-Carlo integrated data being 2. 1 Å in both cases. Data collection statistics for concanavalin A and B are listed in Table  1. We did not follow up on the urease data because of the low resolution of the data. Due to the large size of the raw data we only deposited those images identified as hits on the CXIDB  27  website with the CXIDB ID 87  28. During this experiment the pulse energy determined with the gas monitor detectors was not assigned to the same data location as the diffraction images (the pulse train number metadata, "trainID", did not match).
The pulse energy information per pulse was therefore not used, and was removed from the deposited data. The refined structural models and integrated scaled diffraction data have been deposited in the Protein Data Bank (accession code: 6GW9 (concanavalin A)  31  and 6GWA (concanavalin B)  32. We successfully phased the diffraction data of concanavalin A (using PDB entry 1JBC  33  as the search model after removal of the waters and the metal ions) and concanavalin B (using 1CNV  34  as the search model after removal of the waters) using molecular replacement with PHASER  35. The structures were refined by iterative cycles of rebuilding in COOT  36  and refinement using PHENIX  37  , including simulated annealing. With R work /R free of 0. 186/0. 238 and 0. 161/0. 213 and r. m. s. deviations of 0.
002 Å and 0. 009 Å in bond lengths and 0. 577° and 1. 210° in bond angles for concanavalin A and concanavalin B, respectively, the final models have excellent geometry. Detailed refinement quality indicators can be found in ref. 13. The overall structures are highly similar to those determined using macroscopic crystals, with core RMSDs on Cα atoms against reference structures of 0. 31 Å for concanavalin A (vs. PDB entry 1JBC  33  ) and 0. 24 Å for concanavalin B (vs. PDB entry 1CNV  34  ). Fig. 2  Cumulative intensity distribution of the diffraction data. The plots were calculated using TRUNCATE  38, 39  for (a). Concanavalin A (PDBID: 6gw9) and (b). Concanavalin B (PDBID: 6gwa).
Data collection statistics. The number of indexed crystals are 76,803 for concanavalin A and 23,719 for concanavalin B. a Values in parentheses are for the highest resolution shell. personnel that has enabled this experiment. This research was supported by the Max Planck Society and travel grants from the European XFEL. J. -P. C. , M. W. , M. C. and M. Sl. acknowledge support from ANR Grant (BioXFEL). J. -P. C. acknowledges financial support by CEA, CNRS, Université Grenoble Alpes, and the Agence Nationale de la Recherche (Grants Nr. ANR-15-CE18-0005-02, ANR-17-CE11-0018-01). C. A. S. was supported by startup funds from Rutgers University Newark. We are grateful to Filipe Maia for very helpful discussions and uploading the data to CXIDB. ORG.
Acknowledgements We acknowledge European XFEL in Schenefeld, Germany, for provision of X-ray free-electron laser beam time at the SPB/SFX instrument and thank the instrument group and facility staff for their great assistance. We thank  Oleksandr Yefanov  for helpful discussions on detector geometry optimization, as well as  Harald Sinn  and  Winfried Decking  for helpful discussions on beam and pulse train properties. We thank the  AGIPD consortium  for providing some of the calibration constants for the detector. We also thank  Melanie Müller  for peptide mass fingerprinting. The authors are indebted to the  SFX User Consortium  for the provision of instrumentation and.