dataesr/scientific-paragraphs-categorization huggingface dataset:
While opening new windows or tabs in the browser might help users to run multiple primary searches in parallel, the same actions create an articulatory distance between the main user's focus of interest  [6] , as illustrated in Fig. 1. The latter shows a task model matching two primary searches for finding the Web site of ICWE2016 and WWW2016, respectively, from the Google's search engine. Figure  2 , in turn, shows the recursive pattern of the ancillary search tasks. There, a user visiting the ICWE2016 Web site performs an ancillary search on DBLP for getting a list of related articles for a concrete author, and navigates to Google to search for the online file. Both sites offered complimentary information that allows him to understand better the program of the ICWE2016 Web site and decide to attend or not.
Today, users can type their search queries directly on the browser's interface components, without having to visit the Web site of a search engine. Safari lets users specify the search engine the user wants to use, like Google, Yahoo! or Bing. Mozilla Firefox also allows users to choose a particular search provider when the browser detects that the current Web page provides one. However, such feature works only for well-known Web sites, as IMDB. In all the cases, search results started from the browser's UI are still displayed in a new tab or window. There are generic search engines based on Web content scrapers, as Google, and domain-specific ones, as IMDB. The first ones cover a broad scope and their goal is to provide entries to data sources/Web sites containing the information that users are looking for.
Extensibility As shown in Sect. 5, the Search Service Model is compliant with most of the Web sites' search engines that we analyzed. However, beyond this model and the Search Service specification tool, our approach is extensible in two ways. On the one hand, the model is extensible by means of the creation of services based on the existence of application APIs. On the other hand, the end-user support tool is extensible by the creation of new Visualizers. We next explain both APIBased and Visualizer extension points. -APIBased Search Services: Some Web applications offer APIs for retrieving information from their databases (Twitter, Facebook, etc. ). In these cases, it is very common that APIs expressivity goes much further than what is possible to do with our UI-based Search Service approach.
For instance, the users could be interested in seeing quickly the evolution of the author production in a chart showing the amount of articles per venue, instead of just displaying each article. It is important to note that these two extension points require advance JavaScript programming skills to be developed, but once created, they can be installed and used by end-users, who can configure the parameters of such new visualizers according to specific properties defined for the SearchResultSpec of a given SearchService. In order to support our approach, we implemented our tool as a Firefox extension. It allows both the specification and execution of Search Services. The use of this tool for the first purpose is illustrated in Figs. 6  and 7 , and for the second one in Fig. 8. End users can define UI-based Search Services through our tool.
e. mixing and interspecies interaction. This means that the biochemistry related to interspecies interaction remains convoluted. Recently, we presented Projected Ortogonalized CHemical Encounter MONitoring (POCHEMON) to specifically highlight these metabolic alteration in co-culture  [7]. However, the dynamics of pathogen development in co-culture cannot be directly assessed with POCHEMON or any other of the above mentioned methods. Analysis of Variance can be used to separate the data into contributions related to different factors of variation in the data and their interactions  [20, 21]. Multivariate Analysis of Variance (MANOVA) is the extension of ANOVA to multiple independent variables, which has several disadvantages making it less applicable  [21]. Several of these drawbacks may be overcome by regularization, involving an additional meta-parameter  [22].
3. On the other hand, the Culture-Time interaction, expressed as X Culture þ X CultureÂTime þ X E , can be analysed with POCHEMON (analogous to Eqs. (  12 )e(  16 )) to establish the chemistry of interspecies interaction specific for each separate time point. Summarizing, ANOVA-POCHEMON entails four major parts: 1) Decomposition of the data matrix into effect matrices Culture, Time, and their interaction; following Eq. (  8 ) 2) PCA on the factor Time (defined as X Time þ X E ); following Eq. (  10 ), 3) POCHEMON on the factor Culture (defined as X Culture þ X E ); following Eqs. (  13 )e(  16 ), and 4) POCHEMON on the Culture-Time interaction (defined as X Culture þ X CultureÂTime þ X E ) ; following an adaptation of Eqs. (  13 )e(  16 ) for interaction. In the introduction we mentioned three types of information present in dynamic co-culture data that can be extracted by ANOVA-POCHEMON.
LC-MS data The experimental data used here were obtained from a previously described co-culture experiment  [7]. It contained monocultures of fungal species A. clavatus (Sin141 e isolated from soil) and Fusarium sp. (PS54743 e isolated from a blood sample), stored in the database of Agroscope ACW (Swiss Federal Research Station, W€ adenswil, Route de Duillier, P. O. Box 1012, CH-1260 Nyon, Switzerland, http://mycoscope. bcis. ch/). Both fungal strains were cultivated or co-cultivated in 12-well plates with 2 mL of potato dextrose agar (PDA, Difco). Strains were inoculated by placing 2mm agar plugs of fungal pre-cultures in the centre or on the opposite sides of well for mono-and co-culture respectively. The cultures were incubated at 21 C for 2, 4, 7 or 9 days. Both mono-and the co-cultures were analysed with six replicates (n ¼ 6).
The lyophilized agar with mycelium was extracted by dichloromethaneemethanolewater (64:36:8) under sonication for 20 min, following a previously published protocol  [12]. Finally, the extracts were dried and the extreme non-polar constituents were removed by reversed-phase solid phase extraction. Samples were fingerprinted in randomized order by the UHPLC-TOFMS platform (Acquity UPLC system coupled to a Micromass-LCT Premier Time-of-Flight mass spectrometer by a electrospray interface, Waters, Baden-Daettwil, Switzerland), with a 50 mm Â 1 mm i. d. 1. 7 mm Acquity BEH C 18 UPLC column (Waters, Baden-Daettwil, Switzerland) and a water/acetonitrile gradient that has been detailed in the full protocol  [7]. Data were acquired in positive and negative ionization mode in a m/z of 100e1000. The LC-MS chromatograms were converted to peak lists of features with their corresponding retention time, m/z and peak area in each of the analysed samples using MZmine2  [38].
Data analysis We estimated the significance of the experimental factors in ANOVA using permutation tests to evaluate the summed value from all univariate sums of squares (SSQ) for the variables in each effect matrix  [44]. Significance was defined by a sufficiently low fraction of permuted effect matrices with a total SSQ larger than the total SSQ of the observed effect matrix. In the GC-MS data the same samples were measured at different time points, meaning that a third factor was added to the ANOVAmodel describing the Replicate variation, which is nested within the Culture factor. For POCHEMON and ANOVA-POCHEMON, the dimensionality of the competition model has been established from a scree plot, combined with an evaluation of the interpretability of the competition scores. The monoculture benchmark served as a validation here, as it indicated whether variability in the co-culture scores was also present in the projections of the monocultures on the competition model.
Results and discussion In this section the results will be presented and discussed for the LC-MS and GC-MS data subsequently. For each data set, results of POCHEMON, ANOVA-PCA and ANOVA-POCHEMON will be presented in separate subsections, where the strengths and weaknesses of each method as described in section 2 are pointed out. This will be done more elaborately in the LC-MS section, but the observations translate to the GC-MS example. For all Mixing and Competition models, two PCs were chosen to be optimal.
Computer Engineering This section provides applications of MFTG in computer engineering. It starts with an application of MFTG with number finite state-actions and then focuses on continuous state-action spaces. Application 5 (Virus Spread over Networks). We study a malware propagation over computer networks where the nodes interact through network-based opportunistic meetings (see Figure  12  and Table  4 ). The security level of network is measured as a function of some key control parameters: acceptance/rejection of a meeting, opening/not opening a suspicious e-mail, file or packet. We model the propagation of the virus in network as a sort of epidemic process on a random graph of opportunistic connections  [155]. A computer/node can randomly get online an infected or non infected data from other computers.
Two MFTGs where each user decides its level of participation to the crowdsensing: (i) public good, (ii) information sharing, are presented below. The smartphones are battery-operated mobile devices and sensors suffer from a limited battery lifetime. Hence, there is a need for solutions that will limit the energy consumptions of such mobile Internet-connected objects. Such an involvement is translated into a energy consumption cost. All the data collected from these devices combine both voluntary participator sensing and opportunistic sensing from operators. The data is received by a network of cloud servers. For security and privacy concerns, several information are filtered, anonymized, aggregated and distributions (or mean-field) are computed. The model is a public good game with an extra reward for contributors.
By inverting the above equation one gets u * i [τ 1 ] < u * i [τ 2 ]. Thus, the optimal strategy u * i increases if the time delay τ increases. This proves the following result: Proposition 8. The time delay decreases the prosumer market price. The optimal strategy u * i increases as the time delay τ increases. Numerical methods for delayed stochastic differential equations of mean-field type is not without challenge. Here we implement the Milstein scheme using MATLAB. We choose the following parameters γ = 0, c 1 = c 2 = c 3 = 1 and set the satisfaction function as s(u) = 1 -(1 + µ m2 )e -u where µ > 0 and m2 is the average of all other agents' control actions. A typical shape of the satisfaction function is given in Figure  22. The optimal control is u * (t) = - log p(t) 1 + µ m2 (t) 1l (0,1] (p(t)).
A strategy for coupling ROMs of different kind (i. e. reduced solvers and parametric solutions) is also proposed. The integration of all these models constitutes a Simulation App that allows real-time evaluation of any process conditions. The described methodology can be extended and generalized to other processing technologies. The rest of the paper is organized as follows. "Process description and physics modeling" section presents the manufacturing process as well as the equations describing the physics involved in the manufacturing process of composite outlet guide vanes (OGV). "Simulation based on reduced order modeling" section describes the simulation strategy, including the different simulation modules based on ROM and their coupling. In particular, we recall the main features of the ROM methods that were implemented, emphasizing both the construction of the reduced models in the offline stage and their utilization in the online stage, as a part of the Simulation App.
Simulation based on reduced order modeling In this section, we describe the simulation strategy, including the different simulation modules based on ROM techniques and their coupling. In particular, we recall the main features of the ROM methods that were implemented, emphasizing both the construction of the reduced models in the offline stage and their utilization in the online stage, as a part of the Simulation App. The coupled model described in "Process description and physics modeling" section involves three primary unknown fields, the temperature T (x, t), the pressure P(x, t) and the curing degree α(x, t). The viscosity field, η(x, t), can be obtained as a post-processing of the temperature and curing degree fields. The nonlinearity associated to the coupling can be efficiently addressed by using a semi-implicit incremental time integration, and treating all coupling terms explicitly, therefore allowing for decoupling of the different problems.
The amplitude of the perturbations is in the following ranges: α 0 ∈ [0, 0. 05] (+5%), T 0 ∈ [273, 303] K (±5%), T c ∈ [416, 460] K (±5%), V f 0 ∈ [50, 64]% (±7%). Reduced basis of the following dimension (i. e. number of degrees of freedom of the ROM) were found: N T = 10, N α = 7 and N C = 5, which proves the pertinency of using low dimensional approximations for the thermo-kinetic model. We report hereafter the details for the numerical implementation of the ROM solver: • A variable order (1-5) Numerical Differentiation Formula (MATLAB ode15s) time integrator was used. The simulated time is 2 h and the number of time steps varies adaptively. • The typical running time is in the range 1-10 seconds depending on the process conditions, using the same machine described previously. It is important to emphasize that the simulation took few seconds to completion instead of the many hours needed when solving the full order problem.
Consolidation Module Fig. 11  Schematics showing the operation of both thermo-kinetic and consolidation simulation modules based on ROM, as well as their coupling 1. We evaluate both the specific heat and the curing function at the previous time step, i. e. a semi-implicit scheme is used. The reduced coordinates of both specific heat and curing function, denoted by a C (t j-1 ) and a f (t j-1 ) respectively, are computed using DEIM. 2. With those reduced coordinates at hand, the thermo-kinetic ROM, Eqs. (  24 ) and  (26) , can be assembled as explained in "The thermo-kinetic simulation module" section. 3. By performing a time increment, the reduced coordinates of both temperature and curing degree can be obtained at time t j. 4. Then, the reduced coordinates of the viscosity, a η , can be computed by evaluating the chemo-rheology model and using DEIM.
The user interacts with the Simulation App through a basic graphics user interface (GUI). In order to demonstrate the feasibility and potentiality of this kind of applications, we developed a demonstration version in the MATLAB® environment using GUIDE under MacOS X 10. 10. 5. This allows creating the GUI very easily. The MATLAB Application Compiler TM was used in order to create a standalone executable file that could run outside MATLAB® and be easily transferred to the final user for demonstration purposes. The standalone version of the Simulation App has been tested on Microsoft Windows® 7 OS. Other proper implementations are of course possible although not explored in this paper, as they are outside of its scope. The concept of a Simulation App offers several potentialities. Since the application is process-specific, rather than implementing a general purpose visualisation environment, it is possible to first identify the set of quantities of interest as output and then implement a simple and specific visualization interface.
The Simulation App is composed of three modules: 1. Pre-processing This module performs two basic operations: data loading and parameters conversion. Data loading simply reads all pre-computed data required to run the reduced model. This operation only needs to be done once after launching the application, and it is performed in the GUI via the Load Data button. Parameters con- version gathers data entered by the user and computes the simulation parameters. This operation is performed in the GUI via two different tabs: the Parameters Tab, in which some default values that normally do not change are proposed (e. g. resin and fiber specific weight), and the Data Tab, in which process parameters are defined (e. g. temperature cycle, closure rate, etc. ) (see Fig. 12 ). The conversion operation is launched by the Update button, and the initial degree of curing and the initial fiber volume content, both being simulation parameters, are computed.
This operation is performed by the GUI via two different tabs: the Display Tab and the Quantities of Interest Tab. In the Display Tab, seven scalar fields can be visualized: curing degree, temperature, viscosity, pressure and the three components of the pressure gradient. They are simultaneously displayed on the external boundary of the part as well as in five sections, see Fig. 13. The section view is necessary to appreciate, for instance, the temperature evolu- tion inside the part, which may be higher than in the external boundary due to the exothermic kinetics. Additionally, the location and magnitude of the maximum and minimum of each field can be visualized. In the Quantities of Interest Tab, the time The RAM consumption of the MATLAB® implementation of the Simulation App was measured.
It has also been demonstrated that the Simulation App provides several advantages over general-purpose simulation software, especially if simulation wants to be used directly in the manufacturing facility. In addition to the computational time reduction, the process specificity of the Simulation App makes it possible to conceive simple yet functional graphic interfaces, for both data input and visualization. The process designer is asked to enter only process parameters while the simulation parameters are limited to the bare minimum. Similarly, the visualization module was designed so as to display only the relevant information, mainly the process indicators upon which decisions are made. Note that, by definition, process indicators are specific to a particular process, which is contrary to the spirit of general-purpose software.
METHODS A database of digitally recorded pictures of APLOC was examined by a coordinating group who selected two series of 20 pictures illustrating the various aspects of APLOC. A reading group comprised: eight experts from the Société Nationale Française de Colo Proctologie group of study and research in proctology and one academic dermatologist. All members of the coordinating and reading groups participated in dedicated meetings. The coordinating group initially conducted a literature review to analyse verbatim descriptions used to evaluate APLOC. The study included two phases: establishment of consensual definitions using a formal consensus method and later assessment of interobserver agreement on the diagnosis of APLOC using photos of APLOC, a standardised questionnaire and Fleiss's kappa test or descriptive statistics.
Literature search Medical databases (e. g. , PubMed, MEDLINE, EMBASE) were systematically searched for eligible literature. The eligibility criteria were: studies published in English or in French; studies published between 1955 and 2014 containing the terms: perianal CD, CD, perineal lesions, anal ulceration, anal fissure, anal skin tag, anoperineal fistula, vulva and anal stricture. The literature was searched for definitions of APLOC to be presented to the expert group.
Phase 2: Study of interobserver agreement on the diagnosis of APLOC Each expert evaluated a second set of 20 new APLOC photos, selected by the organising group and completed an online questionnaire on a Formstack © platform. This platform provides the ability to set-up a conditional algorithm and enables the automatic input of the results into a Microsoft Excel © database. The questionnaire required the evaluation of the presence or absence of each of the descriptors selected during the first phase of the study. All items for each picture had to be completed on the platform to allow access to the next photo.
Statistical analysis Quantitative data from the two ratings were expressed as averages. Qualitative data from the annex of the first vote were expressed as proportions. Statistical analysis was performed with IBM SPSS Statistic for Windows© Version 20. 0. The interobserver agreement on the diagnosis of ulceration, anal skin tag, fistula, anoperineal scar and perianal skin lesion was measured using Fleiss's kappa coefficient with 95%CI. The interpretation of the values was performed according to the Landis and Koch scale  [9]  : almost perfect agreement between 0. 811, substantial agreement between 0. 610. 80, moderate agreement between 0. 410. 60, fair agreement between 0. 210. 40, slight agreement between 0. 01-0. 20 and absence of agreement < 0. The interobserver agreement for the other items was estimated using descriptive statistics and a threshold of 80% was arbitrarily chosen to define agreement.
The CFMIP Diagnostic Codes Catalogue assembles cloud metrics, diagnostics and methodologies, together with programs to diagnose them from general circulation model (GCM) outputs written by various members of the CFMIP community. This aims to facilitate use of the diagnostics by the wider community studying climate and climate change. This paper describes the diagnostics and metrics which are currently in the catalogue, together with examples of their application to model evaluation studies and a summary of some of the insights these diagnostics have provided into the main shortcomings in current GCMs. Analysis of outputs from CFMIP and CMIP6 experiments will also be facilitated by the sharing of diagnostic codes via this catalogue. Any code which implements diagnostics relevant to analysing clouds -including cloud-circulation interactions and the contribution of clouds to estimates of climate sensitivity in models -and which is documented in peer-reviewed studies, can be included in the catalogue.
Introduction Cloud feedback remains the largest source of uncertainty associated with estimates of climate sensitivity using current global climate models. Evaluation of clouds is necessary not only for the assessment of model performance, but also for understanding how the representation of the key physical processes contributes to the errors and uncertainties. CFMIP coordinates various experiments and the production of specific output variables to help improve our understanding of cloud-climate feedback mechanisms and processes. To facilitate the evaluation of clouds in models using satellite observations, CFMIP has developed the CFMIP Observation Simulator Package (COSP;  Bodas-Salcedo et al. , 2011). In addition, new satellite data sets have been produced which Published by Copernicus Publications on behalf of the European Geosciences Union.
In order for this understanding to eventually be reflected in better estimates of cloud feedbacks and climate sensitivity, it is vital to continue to develop such tools and to exploit them fully during the model development process. To facilitate the wider use of these tools in the climate modelling community, repositories have been set up to store and document the programs which allow their computation. The CFMIP Diagnostics Code Catalogue lists the up-to-date repositories. Initially, a collection of repositories was set up as a part of the EU Cloud Intercomparison, Process Study & Evaluation Project (EUCLIPSE; http://www. euclipse. eu/ index. html). Subsequent contributions have followed as a result of advertising the catalogue at various meetings and via the CFMIP mailing list. Other such collections of diagnostic codes have also been developed.