dataesr/scientific-paragraphs-categorization huggingface dataset:
Datamon A syntax based on Horn clauses is used to express properties. The syntax is closely related to that of the query language Datalog, described in [?], for deductive databases, however, extended to allow for message variables and temporal relations. Both syntax and semantics are described in the current section. Syntax. Formulas in this logic can be defined with the introduction of terms and atoms, as defined below. Definition 4. A term is either a constant, a variable or a selector variable. In BNF: t ::= c | x | x. l. l. l where c is a constant in some domain (e. g. a message in a trace), x is a variable, l represents a label, and x. l. l. l is called a selector variable, and represents a reference to an element inside a compound value, as defined in Definition 1. Definition 5. An atom is defined as A ::= p k (t,.
Based on these traces and properties extracted from the SIP RFC, tests were performed using our above mentioned methodologies and tools. And all the experiments have been performed on one laptop (2. 5GHz Intel Core i5 with 4GB RAM). Datamon. The testing framework of Datamon 6  is implemented by using Java. It is composed of three main modules: 1) Filtering and conversion of collected traces; 2) Evaluation of tests; and 3) Evaluation of formulas. Figure  1  shows the way the modules interact and the inputs and outputs from each one. The trace processing module takes the raw traces collected from the network exchange, and it converts the messages from the input format. In our particular implementation, the input trace format is PDML, an XML format that can be obtained from Wireshark traces. The purpose of the module is to convert each packet in the raw trace into a data structure (a compound value) conforming to the definition of a message.
This module also performs filtering of the trace in order to only take into account messages of the studied protocol. The test evaluation module receives input of a passive test, as well as a trace from the trace processing module, and produces a verdict from the satisfaction results of the test and conditional formulas. The formula evaluation module receives a trace and a formula, along with the clause definitions and returns a set of satisfaction results for the query in the trace, as well as the messages and variable bindings obtained in the process. Prop-tester. The architecture of Prop-tester 7  is depicted on Figure  2. The property to be tested is translated into an XQuery such that it returns false iff the property is violated, and true iff the property is validated. A parser 8  is constructed to parse the log file captured by SIPp tool in pcap format.
Architecture of SIP The IMS (IP Multimedia Subsystem) is a standardized framework for delivering IP multimedia services to users in mobility. It was originally intended to deliver Internet services over GPRS connectivity. This vision was extended by 3GPP, 3GPP2 and TISPAN standardization bodies to support more access networks, such as Wireless LAN, CDMA2000 and fixed access network. The IMS aims at facilitating the access to voice or multimedia services in an access independent way to develop the fixed-mobile convergence. Further, the IMS makes now part of the LTE core network for the voice and visio over LTE. The core of IMS network consists on the Call Session Control Functions (CSCF) that redirect requests depending on the type of service, the Home Subscriber Server (HSS), a database for the provisioning of users, and the Application Server (AS) where the different services run and interoperate.
Discussions and testers' guidance In this section, we will first point out the drawbacks and advantages of each approach according to different evaluation criteria. Then, we will give some advices to the tester to guide him depending on his testing objectives. -The two approaches are property-based passive testing techniques. The properties are checked on the real execution traces. The Datamon tool is based on definition of Horn clauses which are closely related to the query Datalog language. Such formulas are made of atoms and terms. Formulas with quantifiers and data can be defined. Prop-tester is based on if then clause where the quantifiers are implicit and data can also be expressed. The main difference relies on the distance used by Prop-tester. Prop-tester is clearly an on-line testing tool and it is why such a distance is needed to buffer the traces.
Steady-state imaging and image processing The cells were imaged in 1× PBS using a wide-field inverted microscope (Axiovert 200M) equipped with a 100× objective (oil, numerical aperture [NA] 1. 4, plan Apo), mercury lamp (HBO10; Carl Zeiss, Marly le Roi, France), and an electron-multiplying charge-coupled device (EMCCD) camera (Evolve; Photometrics, Tuscon, AZ). Meta-Morph 7. 7 software (Molecular Devices, Sunnyvale, CA) was used for image acquisition and analysis. Filter sets LF488-B-000  emitter, dichroic] ), LF561-A-000 (FFO2-561/14, FFO1-609/54, Di01-R561), and LF405-A-000 (FFO1-390/40, FFO1-452/45, Di01-R405; Semrock, Rochester, NY) were used to sequentially image cells expressing GFP-Sec4 and labeled with Alexa 546-phalloidin and Alexa 356-concanavalin A, respectively. regulator of formin-mediated actin assembly, explaining the faint actin cable staining and depolarized exocytosis identified by the screen.
Semiautomated quantitative analysis Analysis method. Quantitative analysis of the steady-state multicolor image stacks was performed using Matlab and its Image Processing Toolbox (MathWorks, Natick, MA). Quantification of fluorescence ratios for individual cells. Concanavalin A staining of the cell wall was used for cell segmentation. This facilitated the distinction of mother cells from buds and the estimation of their surface area from a projection of the in-focus planes of the image stack. Cells for which the bud size (area) was <30% of the mother cell size were selected for further analysis. This method excluded larger-budded, nonpolarized cells. Subsequently the total endocytic and exocytic fluorescence intensities in the bud and the mother cell were calculated. The bud-to-mother FR was computed separately for the endocytic and exocytic channels, yielding one endocytic FR and one exocytic FR for each cell.
The multicolor three-dimensional (3D) image stacks were deconvolved using a point spread function (PSF)-based iterative constraint deconvolution algorithm with eight iterations  (Sibarita, 2005)  and displayed as a maximum intensity projection (Figure  1A ). The experimental PSF of each wavelength was extracted using diffraction-limited beads (Molecular Probes, Thermo Fisher, Waltham, MA). Deconvolution was performed separately for the GFP and monomeric RFP channels using their respective PSF. The software package for 3D deconvolution was custom written as a plug-in running within the MetaMorph software environment.
Analysis of the brightness of actin patches and Abp1-marked endocytic vesicles The average intensity of the actin patches was calculated using the Integrated Morphometry Analysis module in MetaMorph as follows. Maximum intensity projections of 10 plane confocal stacks (2 μm) were generated for control cells and mutants. The images were autothresholded to mask all the cells in a frame. The mean average intensity and SD in the intensity were calculated for all the cells. Average intensity + 2×SD was taken as the manual threshold for the untreated intensity projection images of the same cells to calculate the average brightness per pixel corresponding to the patches. This thresholding procedure averaged out any statistical fluctuations or illumination differences between frames. As an additional control, only images with the average fluorescence intensity of the area covered by the cells within 1000-Gy values from that of the control were included for all mutants for this statistics to further overrule any imaging differences.
Construction of the genetic interaction map Interactions among genes selected from the imaging-based screen were analyzed using the open-source software Cytoscape (www. cytoscape. org, version 2. 7. 0; Cline et al. , 2007). Genetic interactions identified from previously published studies were loaded onto Cytoscape from the Saccharomyces Genome Database  (Cherry et al. , 1998; Tong et al. , 2004; Schuldiner et al. , 2005). The interaction map displays genes affecting endocytosis, exocytosis, or both as circles (Figure  4 ). The number of interactions that each gene displayed with other genes of the map was quantified directly from Cytoscape. We also computed the number of interactions displayed by the exocyst complex as a whole. To do this, we compiled all the interactions displayed by all exocyst subunits, removed duplicate interactions, and removed interactions with other exocyst genes.
Analysis of vesicle trajectories Two thousand images were generated for each single-vesicle tracking experiment, which were analyzed to extract single-vesicle localization and dynamics. The single vesicles were localized in each image frame and tracked over time using a combination of wavelet segmentation  (Izeddin et al. , 2012)  and simulated annealing algorithms. The software package used to derive quantitative data on protein localization and dynamics was custom written as a plug-in running within the MetaMorph software environment.
Analysis of exocytic vesicle mobility We observed that all MSDs show a quasilinear dependence at short times, enabling us to calculate an instantaneous diffusion coefficient (D) to characterize mobility. To analyze the global mobility, the software computed the instantaneous diffusion coefficient from the linear regression on the first three points of all trajectories longer than six consecutive frames. The frequency distribution of log D was calculated with a bin width of 0. 2 in GraphPad Prism 5 and plotted in Origin Pro7 to generate the histogram of diffusion coefficients and the cumulative probability distributions  (Figures 3, B and C, and 6, B and C). To study the behavior of vesicle trajectories, MSDs for each trajectory greater than six consecutive frames (and <50 frames to avoid clusters) were fitted with a general model r t k MSD 2 = = + α , laser bench (491, 561 nm; Cobolt).
The time interval between the consecutive selected peaks was displayed as a scatter plot with the mean and SD (GraphPad Prism 5; GraphPad Software, San Diego, CA). An unpaired twotailed test with Welch's correction was used to calculate whether the variance of the distribution of the endocytic intervals of the mutants was significantly different from the control (Figure  2B  and Supplemental Figure  S7A ). This analysis procedure was described previously  (Jose et al. , 2013). The position of the line scan was chosen adjacent to the center of the exocytic pole, where consecutive endocytic events were observed.
ACKNOWLEDGMENTS We thank  David Drubin ,  Charlie Boone ,  Lionel Minvielle-Sebastia , and  Ruth Collins  for yeast strains, anti-Rna15 sera, and plasmids. Work in D. M. 's lab is generously funded by  FP7 Marie Curie Grant   IRG249298 ,  Agence Nationale de la Recherche  Grant  2010 JCJC 1210 01 ,  Fondation pour la Recherche Medicale  Grant  INE20100518678 ,  Agence pour la Recherche sur le Cancer  Grant  PDF20120605172 , and  Centre National de la Recherche Scientifique ,  Université Bordeaux Segalen , and  Conseil Régional d'Aquitaine Volet Recherche Grants   20091301015  and  2012 13 01 012. M. J. and S. T. were funded by the  Fondation pour la Recherche Medicale ,  Agence Nationale de la Recherche , and  Université Bordeaux Segalen. Work  at  CNS , IISC was funded by the  Indian Institute of Science, Bangalore , and the  Department of Biotechnology, India.
This paper presents a comparison of the two approaches to calculating wind risk to forests ("roughness" and "turning moment coefficient" methods) within the hybrid mechanistic-empirical wind risk model ForestGALES, and is the first comprehensive assessment of these models for forests in the Great Britain. On 3rd January 2012 a deep Atlantic depression caused strong winds across much of the UK. Central Scotland was the worst affected area with recorded gusts well over 36 m s À1 , causing substantial wind damage in several forest districts. Cowal and Trossachs Forest District, in the west of central Scotland, reported over 150 ha of wind damage resulting from the storm, with the loss of approximately 180,000 tonnes of timber (over 150,000 m 3 ). The damage was monitored by aerial helicopter flights across the whole district just after the storm, and together with the fact that there was information on the forest prior to the storm within the Great Britain Forestry Commission database, we decided that this storm would be an excellent opportunity for assessing the performance of different versions of the ForestGALES model.
, 2009) , and have been introduced in a number of sheltered valleys. The unit of forest management within the Forestry Commission in Great Britain is a sub-compartment (SC), with information centrally held in the Sub-compartment Database (SCDB)  (Forestry Commission, 2008) , which is kept up-to-date as SCs are planted and felled (or, e. g. , wind blown). The SCDB for Cowal and Trossachs provided the input data for ForestGALES in this study, with a SC as the unit of input for each individual ForestGALES calculation. Of the 12,238 SCs assessed, 6996 SCs were productive forest with species for which ForestGALES has been parameterised (Table  1 ). This subset of the district was prepared as input to ForestGALES, and is referred to hereafter as the validation data set. These SCs ranged from 0. 1 to 68 ha in size, with about 60% being between 1 and 10 ha.
Observed damage In the days following the storm in January 2012 a helicopter survey was done to assess the wind damage across the forest district. A map was produced in GIS format, enabling a shape file of damage to be overlain on a map of the SCs. Damage occurred in patches mainly across the south of the district. Any SC that was overlain in whole or in part by a damage shape was noted as having damage, giving a total of 134 SCs identified as damaged (approximately 2% from the 6996 in the validation data set). Identifying part-damaged SCs as fully damaged exaggerates the actual damage to an extent, but it will be counteracted in part by the fact that the helicopter survey will inevitably have missed some smaller areas of damage. Furthermore, in this paper we are primarily concerned with the number of SCs damaged, because ForestGALES only predicts whether damage is expected in a stand or not and currently gives no estimation of the level of damage in a SC.
Stand characteristics: model input data The current ("roughness") version of ForestGALES requires species, spacing, soil type, rooting depth, mean tree diameter at breast height (dbh, measured at 1. 3 m above the ground), and mean tree height or top height (average height of 100 largest-dbh trees per hectare) as input. The TMC method  (Hale et al. , 2012)  additionally requires spacing before last thinning, and time since thinning (see Section 2. 5 for details). In the forest, a SC can contain more than one species, although within the SCDB the information on component species is not held spatially. For all SCs, the main component species was used as the only input to ForestGALES, in order to generate one row of input data for each SC. In the validation data set the main component species mostly comprised 60e100 % of the SC area, but in some cases it was as low as 20%.
Tree height, diameter and spacing have not historically been recorded in the SCDB. Instead, we used the species, management model, initial spacing and yield class (maximum volume increment in m 3 ha À1 yr À1 ) to identify the yield table associated with each SC, based on the yield models produced by  Edwards and Christie (1981). ForestGALES locates the relevant yield table for each SC and, inferring age from planting year, interpolates within the yield table to extract the estimated dbh, mean height and current spacing. Spacing before thinning, and time since thinning, can also be calculated from these yield tables. Soil type data were available in the SCDB for approximately 60% of the SCs in the validation data set; these were allocated a rooting depth (shallow, medium or deep) based on knowledge of what would be typical for each soil type  (Ray and Rayner, 2002).
For the remaining SCs a default soil type, a gleyed soil with medium rooting depth, was selected as being very typical of the forest district. All SCs were assumed to have a windfirm upwind edge, i. e. assumed not to be adjacent to a recent clear-felling or road construction, which would make the stand particularly vulnerable. The yield class and elevation from the SCDB, and values of dbh, top height and spacing as derived by ForestGALES from the relevant yield tables, were also used to develop the logistic regression model (see Section 2. 7) in order to ensure the analysis data sets were identical. There are clearly limitations to this input data. For example, the derivation of dbh, tree height and spacing from a yield table will only give results that accurately represent the trees in the SC if the specified management model has been adhered to and the yield class is correct; this is probably the most significant source of error in this data set.
Wind speeds during storm To assess whether damage would be predicted by ForestGALES for each SC it is necessary to know the wind speed above each SC during the storm. There was no anemometer within the forested area of the district. The nearest wind measurements were from Bishopton (low-lying ground west of Glasgow, near the southern edge of the forest district) and Glen Ogle (a mountain station 60 km north-east of Bishopton, at the northern edge of the forest district). Hourly wind speed data were obtained for both of these stations covering the period of the storm on 3rd January 2012  (UK Met Office, 2002). Maximum average hourly wind speed during the storm was 32. 5 m s À1 for Glen Ogle, and 21. 1 m s À1 for Bishopton. This corresponded to a return period of 60 years and 380 years at Glen Ogle and Bishopton, respectively.
Two methods were used to estimate the above-canopy wind speed across the forest district during the storm: (i) WAsP, a computational linearised fluid dynamics model and (ii) DAMS, a scoring system based on wind zone and topographic exposure. Mortensen et al. , 1993 ) is a computer package that can extrapolate a wind rose from one location to another, taking into account local roughness, obstacles and topography at both locations, and assuming that both locations are subject to essentially the same weather systems (same wind climate). The wind data used as input for WAsP were based on the measured wind speeds at Bishopton on 3rd January 2012, with wind speed predictions made 10 m above the zero-plane displacement height (d) at a horizontal resolution of 100 m over the whole of Cowal and Trossachs.
The peak hourly wind speed of 21. 1 m s À1 from a direction of 260 was recorded between 9:00e10:00 GMT but during the previous hour the mean hourly wind speed reached 19. 0 m s À1 from a direction of 240. WAsP runs were performed for each of these two periods and the higher of the two wind speeds was selected at each grid point (the change in wind direction during the storm meant that the peak hourly wind speed did not necessarily result in the higher modelled wind speed at all locations). These were output as 100 m grid cells in raster format with the point value at their centre. The raster data were read into ArcGIS and evaluated at SC scale, giving a single value for each SC that represented the average above-canopy wind speed based on the grid points lying within the SC. These values will be referred to as Wind_WAsP in the remainder of the paper.
The digital terrain data providing the elevations used in WAsP were obtained from the NASA ASTER Global Digital Elevation Model  (Hirano et al. , 2003). The data have a vertical accuracy of 17 m at the 95% confidence level, and a horizontal resolution on the order of 75 m. Aerodynamic roughness values (z 0 ) and zero-plane displacement (d) in WAsP were according to Table  4  with d being added to the NASA derived elevations. The values for the forested area were those calculated within ForestGALES for the SCs included in the validation data set and their derivation is discussed in more detail in the online supplement.
Weibull parameters are used within ForestGALES to calculate the annual exceedance probability of the CWS, using a constant value of Weibull_k ¼ 1. 85, and calculating Weibull_A from DAMS as shown below for forested and non-forested areas (Equations (  2 ) and (3), respectively;  Quine, 2000) : Weibull_A ¼ 0:4279$DAMS À 0:9626 ðforested areasÞ (2) Weibull_A ¼ 0:378$DAMS þ 0:5867 ðopen groundÞ (3) The DAMS data set associated with ForestGALES contains gridded values of DAMS at 50 m resolution. In this forest DAMS values ranged from 8 (sheltered: mean wind speed ¼ 2. 2 m s À1 ) to 21 (very exposed: mean wind speed ¼ 7. 1 m s À1 ) with a mean of 13, and the DAMS values at Bishopton and Glen Ogle are 11. 5 and 18. 0, respectively. By overlaying the SC outlines on the DAMS map, an average value of DAMS was found for each SC in the validation data set.
ForestGALES damage prediction The current version of ForestGALES  (Gardiner et al. , 2000)  is designed for uniform, even-aged, single-species stands, in which all trees are assumed equal to the mean tree. It predicts either damage or no damage, i. e. there is no gradation in severity of damage predicted. There are three prerequisites to enable a validation to be done: 1. Stand characteristics at the time of the storm are required, to enable CWS to be calculated. 2. An estimation of the above-canopy wind speed during the storm is needed, to compare against these CWSs, to assess whether or not damage would be predicted by the model. 3. An assessment of actual damage to compare against the predicted damage, to evaluate the accuracy of the model predictions. Based on the validation data set, input files were prepared for the three versions of ForestGALES.
For the remainder of this paper the versions are referred to as FG2. 3 (equivalent to the current release), FG2. 3gfadj¼1 (FG2. 3 with the gust factor adjustment removed), and FG-TMC (the new method, based on the turning moment coefficient). Each of these versions was run on the validation data set to produce a CWS for each SC. In each case this CWS was compared with the above-canopy wind speed that occurred above each SC during the storm, as estimated by WAsP and DAMS (Wind_WAsP and Wind_DAMS, respectively). If the above-canopy wind speed exceeded the CWS, ForestGALES was taken to predict damage for that SC; otherwise damage was not predicted.
For the model based on all species combined, an additional variable defining tree species was included in the candidate set of explanatory variables. These variables were selected as they all potentially influence the resistance of a tree to wind damage and they are all variables used in the ForestGALES model. For the development of the logistic regression model the basic approach was as follows: (i) Using the explanatory variables above, a decision (profit) matrix was created for the target variable, in this case 'damage' (any damage recorded in a SC). Decision weights were allocated using inverse prior probabilities. For example, if 2% of the dataset was damaged and 98% undamaged the profit for estimating a damaged record correctly would be 50 units (1/0. 02), and he profit for correctly identifying an undamaged record would be 1.