dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the study of earthquakes faces several limiting factors related to the difficulty of accessing the deep source of the earthquake and of integrating the characteristic timescales of deformation processes that extend from seconds to thousands of years. As a consequence, seismic hazard mitigation is inevitably based on incomplete geological datasets and poorly constrained physical parameters. New technological advances in seismology and geodesy have significantly improved our knowledge of the dynamics of deformation processes associated with earthquakes on all relevant timescales. By substituting space for time, all archetypical phases of the seismic cycle including the coseismic, post-seismic, and inter-seismic phase have been observed in nature (e. g. Klotz et al. , 2001; Wang et al.
4c  and d ). The rigorous implementation of numerical inversion and modelling tools in the approach provides key direct and indirect observables such as surface and volumetric strain and stress as well as the slip distribution and stress changes along the fault plane. Optical measurements are complemented by strain gauges to measure far-field strain evolution and earthquakeinduced stress drops. Acoustic sensor measurements are also being developed to study background seismicity and coseismic event signatures. The analogue models generate a broad variability of earthquake-like slip events constituting large data catalogues that can be used to study earthquake scaling and statistics as well as rupture dynamics. As the model generates tens of successive seismic cycles, these provide the potential to study the short-term and long-term evolution of strain and stress fields.
Additionally, because the characteristic lengths of static and dynamic deformations (e. g. stress shadows, seismic waves) associated with earthquakes are usually regional scale, both small-scale topography and large-scale Earth curvature are often neglected and the surface is modelled as a plane. Analytical solutions to the problem of shear-crack-induced surface and internal deformations in homogenous elastic half-space are given by  Okada (1985 Okada ( , 1992) )  and applied in numerous studies (e. g. King et al. , 1994; Toda and Stein, 2002; Lin and Stein, 2004). A convenient MATLAB ® -based tool ("Coulomb") based on these solutions has been developed by the USGS  (Toda et al. , 2011). Both crack growth as well as elastic dislocation predictions are superb benchmarks for seismotectonic scale models.
Commercial and noncommercial software packages are on the market including LaVision's Strainmaster ® , Correlated Solutions Inc. 's VIC ™ , open-source software MicMac  (Galland et al. , 2016) , MATLAB ® -based open toolboxes MatPIV  (Sveen, 2004) , PIVlab  (Thielicke and Stamhuis, 2014) , and TecPIV (Boutelier, 2016), and COSI-Corr  (Leprince et al. , 2007). The latest developments in strain monitoring using image correlation techniques include a coupling of strain monitoring of the experiment with analytical or numerical elastic dislocation modelling (EDM). For example  Rosenau et al. (2009 Rosenau et al. ( , 2010) )  used elastic EDM to differentiate between elastic and plastic deformation inherent in their elastoplastic models. Rubino et al. (2015)  used EDM to invert strain for stress, applying a linear constitutive behaviour  (Hild and Roux, 2006).
Acknowledgements. Fabio Corbi received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement no. 658034  (AspSync). We thank  Malte Ritter  and  Michael Rudolf  for sharing their rock mechanical data compilations. We thank  Kirsten Elger  and GFZ Data Service for publishing the data. We thank  Michele Cooke  and an anonymous reviewer as well as the editor  Susanne Buiter  for very constructive comments. Edited by:  S. Buiter Reviewed  by:  M. Cooke  and one anonymous referee.
ACKNOWLEDGMENTS This workshop and the resulting report were supported by  NSF   DMR-1619666. The assistance of  Thorston Bayer  ( The Pennsylvania State University ),  Joseph Muth  ( Harvard University ), and  Xiaoting Zhong  ( Carnegie Mellon University ) as scribes during the workshop is greatly appreciated. Joshua C. Agar  ( University of California, Berkeley ) assembled Figure 3. The co-organizers thank  Dr. Lynnette Madsen  for her counsel during the planning and conduct of the workshop.
The growth of the network of smart connected devices requires the application to consider the features of ubiquitous and pervasive computing. These days, many users possess more than one mobile device but with the rise of the Internet of Things (IoT) and smart-* (cities, home, buildings, etc. ), users will be confronted and be expected to handle multiple devices simultaneously (6. 58 devices per person in 2020  4  ). In order to manage the distribution of the required software components in a convenient way, our proposal is built on the Kalimucho 3 middleware. The application starts by collecting contextual information from different devices as input and ends by providing a distributed component-based output (services). In summary, the input components, the output services, and the overall architecture, are all fully modular and mobile.
g. , the primitive Before on the Time axis verifies if the current user's time is before the time of a given situation). Our proposal incorporates this model (described thoroughly in our previous work  9  ) into its workflow and architecture. Situations are injected by the Injector, detected by a module called Event Manager (EM), and the LLA reacts to them using mappings that match situations to deployable services. Every situation can be described with one or multiple projections of primitives on one or multiple axes, with the possibility to specify exceptions (see Figure  1 ). In order to react to these situations, the application needs to find the appropriate services. Therefore, a mapping is associated to each situation. The mapping links the situations to the services. It describes the list of Kalimucho components needed to build the service.
Contextually filtered POIs are filtered and selected according to the user's preferences. The mobile tourism recommendation system (MTRS) developed by  [20]  delivers several personalized recommendation services to mobile users, considering contextual information such as the user's location, current time, weather conditions, and POIs already visited by the users. The iTravel system  [27]  applies a collaborative filtering approach. The similarities between users are evaluated using the user rating lists. Nearby users are detected and rating data are exchanged using mobile peer-to-peer communications. I'm feeling Loco  [13]  is a ubiquitous location-based sites recommendation. It uses the user's preferences derived from the Foursquare location-based social network, the user's location, the used transport modes (e.
This choice is relatively similar to the ones made by related works and that considered between 10 and 20 iteration stages  [56] [57] [58] [59]. The proposed hybrid CBR-ANN approach is used as an interactional tour recommender system by combining content-based recommender system (using ANN) and knowledge-based recommender system (using CBR) (Figure  3 ). As shown in Figure  3 , a supporting database represents the location and type of each POI, streets information, and restaurants' opening times. The system specifies the number of tours that the system recommends to the user at each cycle ( show ), the number of the most similar tours to the selected tour that is used in the retaining phase ( similar ), and the number of iterations. The user defines the starting and ending times of the required tour to the system and assigns a rating to the selected tour.
Of note, BRCA1 and BRCA2 were finally released from patent controls two days before the final selection of SNPs so that common functional variants of these loci could be included in the array. We included additional (non-polymorphic) probes for each exon of BRCA1, BRCA2, MLH1 and MSH2 in order to capture large deletions in these genes. Finally, we included a panel of Y chromosome and mitochondrial markers to provide data on population ancestry. The Division of Cancer Epidemiology and Genetics of the National Cancer Institute accumulated GWAS scan data for other cancer sites including bladder, NHL (Non-Hodgkin's Lymphoma), esophageal, gastric, glioma, kidney, osteosarcoma, pancreas, testis or scan data for non-Caucasian studies including Asian non-smoking female lung cancer and African American lung cancer.
The top 200-400 most significant loci from each scan were selected after ranking by association test p value and LD pruning (r 2 >0. 6). Functional characterization and selection-Risk variants at known susceptibility loci for breast, colorectal, lung, ovarian and prostate cancer were integrated with epigenomic datasets from ENCODE and other published sources, to identify intersections between risk SNPs and tissue-specific regulatory features that define the most likely causal variants and their functional targets. We interrogated associations between SNPs and DNAse Hypersensitivity (DHS) sites generated in the pan-cancer cell line panel from ENCODE, as well the LNCaP cell line (for prostate cancer specific marks), the HMEC line (for breast), the SAEC line (for lung cancer), the HCT116 line (for colorectal cancer) and the CaOV3 line (for ovarian cancer).
Ancestry Analysis Ancestry analysis was performed using a standardized approach in which 2,318 ancestry informative markers (AIMs) with minor allele frequencies of 0. 05 or higher were used on 66,105 samples genotyped at CIDR, Cambridge and Genome Quebec/McGill Innovation Center (the primary contributing centers) and 505 Hapmap 2 samples. We noted that among those individuals not clearly aligning into one of the major continental ancestry groups there are clines connecting ancestral groups along axes connecting the centroids of the ancestral populations. We mapped ancestry to regions of a triangle connecting the three regions, in order to estimate the contribution of European, Asian and African ancestry to each individual. The method is further described in the software package FastPopc(50) distributed to consortium members.
Functional characterization of risk loci Perhaps the greatest challenge facing large collaborative genotyping projects such as the OncoArray is to understand of the functional mechanisms underlying disease development at each susceptibility locus. The pace of discovery of genetic risk associations for cancer and other traits and diseases continues to accelerate, creating an increasing bottleneck between discovery and functional validation. The basic tenets of functional characterization(68)proving causality for risk variants and the genes they regulate -have been described for a tiny fraction of risk associations identified by GWAS  (20, 69). This is partly due to our rudimentary knowledge of the non-coding genome and the effects of genetic variation on gene regulation. Integration of GWAS SNP data with methylome data has identified methylation-quantitative trait loci (meQTLs) showing that inherited genetic variation may affect carcinogenesis by regulating the human methylome  (70, 71).
Acknowledgments FUNDING Sources TRICL ( Transdisciplinary Research for Cancer of Lung ) and  International Lung Cancer Consortium (ILCCO) :  National Institute of Health   U19 CA148127-01  ( C. I. Amos ,  J. Byun ,  Y. Li ,  X. Xiao ,  J. L. Forman ,  A. Risch ,  H. Bickeböller ,  M. Waldenberger ,  I. Brüske ,  J. Manz ,  P. Brennan ,  R. Hung ,  H. Shen ,  Y. Shi ,  A. Kamal ,  C. I. Amos ,  J. K. Field ),  Canadian Cancer Society Research Institute  (no. 020214 ,  R. Hung ). DRIVE ( Discovery, Biology, and Risk of Inherited Variants in Breast Cancer ):  National Institute of Health   U19 CA148065  ( D. J. Hazelett ,  D. F. Easton ,  S. Lindström ,  P. Kraft ,  J. Dennis ,  A. Dunning ,  K. Michailidou ,  L. Fachal ,  S. Benlloch ,  J. Cunningham ,  K. Butterbach ,  K. Offit ,  R. Schmutzler ,  L.
In addition, the goal is to get a thermochemical model and so, the structural effects like expansion/contraction, mechanical delamination and mechanical erosion are not taken into account here. One of the main assumptions is considering the volume of the material as constant. Finally, the developed model considers the heat transfer, the decomposition of the composite, the gas production and their transport into the material linked to the internal pressure. All these phenomena are expressed in the governing equations. A numerical thermochemical model was developed to predict the temperature profile of materials, the weight change, the gas mass flow and the internal pressure of materials. It is actually integrated into the commercial software SAMCEF (V17. 003, Siemens PLM Software, LMS Samtech Company, Liege, Belgium) by the Amaryllis module used for the convenience of the engineering application.
Gas Mass Balance The gas mass balance equation is expressed as a function of the density rate (Equation (  1 )). The accumulation of gases inside the material was neglected since no significant influence appears on the temperature profile of the material  [11]. The gas storage is thus not taken into account in our model. A specific development was done in SAMCEF Amaryllis software in order to obtain gas mass transport vector,. m g in the three directions (x,y,z). To take into account the internal pressure in the governing equation, Darcy's law in three dimensions and the ideal gas law were used to link the pressure to the gas mass balance (Equations (  2 )-(  5 )). - ∂ρ ∂t = ∇•   . m g x. m g y. m g z    (1) where,   . m g x. m g y. m g z    = - PM RT Γ µ •∇P (2) With Γ = Γ v φ φ v , (3) Φ = (1 -α)φ v + αφ e (4) and α = ρ v -ρ ρ v -ρ e ( 5 ) where P is the pressure, Γ, the permeability tensor, µ, the dynamic viscosity, ρ g , the gas density, Γ v , the virgin permeability tensor, M, the gas molecular mass, R is the universal gas constant, Φ v , the virgin porosity, Φ e , the final (end) porosity, and Φ , the porosity of the material, α, the decomposition degree, ρ v , is the virgin density of the material and ρ e , is the final density of the material.
Material and Methods FFPE sections of resected tumours from 36 standard-ofcare treated GBM patients along with overall survival follow-up data were collected retrospectively and subjected to low-complexity signature identification from miRNA microarray data. Based on the expression of the signature miRNAs and cox-proportional hazard coefficients, a risk score was calculated and used for classification into higher-and lower-risk patients. The identified signature was validated in a miRNA array data set of a subset of the TCGA GBM cohort which was matched for sex, age and treatment. In order to assess the functional impact, genes putatively regulated by the signature miRNAs were identified by correlation with global transcriptome data, followed by pathway analysis.
Welsh et al. [8] Gupta et al. [6]  Pierre et al. [3]  Our. Figure  5. Comparison of our approach with state-of-the-art methods on photographies. In contrast to these methods our model is not based on texture comparisons, but on the morphing of the shapes. Therefore it is able to handle faces images, where the background has frequently a similar texture as the skin. b -the patch-based method of Pierre et al. [3] , and -the segmentation approach of Gupta et al. [6]. We implemented our morphing algorithm in Matlab 2016b and used the Matlab intern function for the bilinear interpolation. The parameters are summarized in Table  1. Here λ = µ and K are the parameters for the morphing step. The parameters γ and α appear in the variational model for post-processing and were only applied in three images.
ACKNOWLEDGMENT The research leading to these results received funding from the European Commission H2020 programme under grant agreements 671680 (5G PPP METIS-II project) and 671650 (5G PPP mmMAGIC project). The authors also acknowledge the technical and financial support from Ericsson Research, Wireless Access Network Department, Sweden and from the Ericsson Innovation Center, Brazil, under UFC. 43 Technical Cooperation Contract Ericsson/UFC. Victor F. Monteiro would like to acknowledge FUNCAP for its scholarship support.
In our scenario, we use smartphone sensors and further sensors that are permanently installed in the environment, such as iBeacons, temperature and smoke sensors. Each set of sensor data they emit corresponds to a particular event in the environment. Situational knowledge can be considered as a dynamic knowledge with a high change frequency. In emergencies, streams of events must be evaluated in real-time to achieve situation awareness. A solitary event is usually of low significance since it represents a single incident in the physical world. For instance, it is of low importance if a single person is running, but if a majority of people is doing so, it may indicate mass panic. Complex event processing (CEP) is a software technology that extracts the information value from event streams (see, e.
CEP components Both UA and SB agents analyse the incoming streams of events to deduce the current situation. In this subsection, we discuss the underlying event models and give some examples for appropriate rules for achieving situation awareness. To make the description more comprehensive, we simplify the event model and the corresponding rules. CEP in the user agents User agent exploits sensor data and infers (i) the location and (ii) the behavior of a single user. To explain the CEP component in more detail, we will assume that user agent monitors two types of explicit (or atomic) events to achieve this type of situation awareness: -beaconEvent(beaconID): an iBeacon with a certain ID has been detected. -accelerationEvent(velocity): the smartphone is moving with a certain velocity. The beaconEvents collected by a particular smartphone are used to derive the current position of its owner.
For instance, the data from temperature and smoke sensors can be used to detect a fire situation in a certain space of the building. There are appropriate CEP rules that derive such situations as well. In the following subsection we show how sensors can be used to obtain a safety value for the space in which they are located. Safety calculation Space safety is calculated by combining (aggregating) the values obtained from different sensors. A thorough description of this field can be found in, e. g. ,  [26, 57]. Since this is not a main topic of this paper, here we propose a way of calculating safety values in the case of fire so as to get a feeling of how sensor data and CEP rules can be combined. We assume that a fire is detected by smoke and temperature sensors. The number of sensors of each type in the same physical space usually depends on the size of the space.
I. OVERVIEW AND MAIN RESULTS The present article describes a technique for specification and verification of stimulus-response requirements using a general-purpose programming language (Eiffel) and a program prover (AutoProof  [1] ) based on the principles of Design by Contract  [2]. Real-time, or reactive, systems are often run by a software controller that repeatedly executes one and the same routine and it is specified to take actions at specific time intervals or according to external stimuli  [3]. This architecture is reasonable when the software has to react timely to non-deterministic changes in the environment. In this case the program should react to the external stimuli in small steps, so that in the event of a new change it responds timely. Computation tree logics (CTL)  [4]  represent a frequent choice when it comes to capturing stimulus-response requirements.
In the latter system, which we will refer to as SR-SWAP, a SWAP action pushes the second element of the stack back onto the buffer. Although SR-SWAP is fast and obtained good results, it underperforms  Fernández-González and Martins (2015) 's parser by a large margin. We believe this result does not indicate a fatal problem for the transition-based framework for discontinuous parsing, but emphasizes several limitations inherent to SR-SWAP, in particular the length of derivations (Section 3. 5). Contributions We introduce a novel transition system for discontinuous parsing we call SR-GAP. We evaluate this algorithm on two German treebanks annotated with discontinuous constituents, and show that, in the same experimental settings, it outperforms the previous best transition-based parser of  Maier (2015) , and matches the best published results on these datasets  (Fernández-González and Martins, 2015).
Datasets We evaluated our model on two corpora, namely the Negra corpus  (Skut et al. , 1997)  and the Tiger corpus  (Brants, 1998). To ensure comparability with previous work, we carried out experiments on several instantiations of these corpora. We present results on two instantiations of Negra. NEGRA-30 consists of sentences whose length is smaller than, or equal to, 30 words. We used the same split as  Maier (2015). A second instantiation, NEGRA-ALL, contains all the sentences of the corpus, and uses the standard split  (Dubey and Keller, 2003). For the Tiger corpus, we also use two instantiations. TIGERHN08 is the split used by  Hall and Nivre (2008). TIGERM15 is the split of  Maier (2015) , which corresponds to the SPMRL split  (Seddah et al. , 2013). 2 We refer the reader to Table 8 in Appendix A for further details on the splits used.
Results We report results on test sets in Table  3. All the metrics were computed by DISCO-DOP with the parameters included in this package (proper. prm). The metrics are labelled F1, ignoring roots and punctuation. We also present metrics which consider only the discontinuous constituents (Disc. F1 in Tables  3  and 4 ), as these   80. 44 (41. 13) 80. 34 (43. 42) 79. 79 (43. 56) 83. 57 (50. 91) 82. 43 (48. 81) 79. 42 / 79. 51 (43. 76 ) GAP, +SPANS SR-GAP  81. 64 (42. 94) 81. 70 (47. 17) 81. 28 (46. 85) 84. 40 (51. 98) 83. 16 (49. 76) For experiments on TIGERM15, we additionally report evaluation scores computed with the SPMRL shared task parameters 5 for comparability with previous work. SR-GAP vs SR-CGAP In most experimental settings, SR-CGAP slightly underperformed SR-GAP. This result came as a surprise, as both compound actions for discontinuities  (Maier, 2015)  and ghost reductions  (Crabbé, 2014)  were reported to improve parsing.
Maier (2015)  and  Maier and Lichte (2016)  use a shift-reduce algorithm augmented with a swap transition. Table  3  includes recent results from these various parsers. The most successful approach so far is that of  Fernández-González and Martins (2015) , which outperforms by a large margin transitionbased parsers  (Maier, 2015; Maier and Lichte, 2016). SR-GAP vs SR-SWAP In the same settings (baseline features and beam size of 4), SR-GAP outperforms SR-SWAP by a large margin on all datasets. It is also twice as accurate when we only consider discontinuous constituents. In Section 3. 5, we analyse the properties of both transition systems and give hypotheses for the performance difference. Absolute Scores On all datasets, our model reaches or outperforms the state of the art  (Fernández-González and Martins, 2015).
Feature Locality A second property which explains the performance of SR-GAP is the access to three data structures (vs two for SR-SWAP) for extracting features; SR-GAP has access to an extended domain of locality. Moreover, with SR-SWAP, the semantics of features from the queue does not make a distinction between swapped tokens and tokens that have not been shifted yet. When the parser needs to predict a long sequence of consecutive swaps, it is hardly in a position to have access to the relevant information. The use of three data structures, along with shorter sequences of GAP actions, seems to alleviate this problem.