dataesr/scientific-paragraphs-categorization huggingface dataset:
Meteodyn parameters The commercial software Meteodyn WT 4. 0 was used as a benchmark for the current experiment. Forest is also treated as a porous media and turbulence is modeled with a one equation closure model based on turbulent length scale. Efforts were made to use approximately the same parameters as the ones used in OpenFOAM when possible. As opposed to OpenFOAM, it is not possible to place the boundaries independently. Based on the dimension from AAV3 to inlet found in the domain sensitivity analysis (135 nodes × 35 m ≈ 5 km) and the distance accounting for smooth introduction of topography (2 km), distance between the center of the domain and lateral boundaries was set to 7 km. To refine a particular location, one must define a result point in the domain, a desired minimum horizontal cell dimension and a horizontal expansion factor.
It was found that Meteodyn uses topographic and roughness data up to 2 km outside the computational domain to define its boundary conditions. Consequently, for directions 290 • to 310 • , Meteodyn would then use a wind profile coming mostly from the water, which would explain the better results. This was further investigated in OpenFOAM by pushing back the inlet of case 320 • so that it falls in the water. Results improved as seen in Figure  1. The variable forest height approach does not seem to significantly improve the results when compared to the constant height approach. The results presented here for a constant forest height (black markers) were obtained using a mean height of 10 m. Note that the distribution of forest on the site of AVV is quite uniform, which may explain the similar performances of both scenarios.
Results First, both terrain separation and LiDAR error were analysed separately in Figure  6. Then, Figure  7  compares the combination of both with experimental data. The latter was recorded between 17/02/2011 and 28/03/2011 where the remote sensor and the anemometer were measuring at ∼80 m. Since there is no major obstacle in the surroundings of the mast and the LiDAR as shown in Figure  5 , the small deviations (0-1. 5%) from a ratio of one were expected. Both softwares show quite good agrement especially for the interval [300 • -320 • ]. Very little deviations from the unity ratio due to LiDAR error are observed. Again, both softwares are within 1% agreement except for [310 • -320 • ] where MeteoDyn is a bit more conservative. Finally, both graphs were combined in order to make a comparison with experimental results.
Results obtained with the WAsP Engineering script developed by  [3]  are also included. The size of the domain is 3 km × 3 km and is centered on LiDAR location. Horizontal resolution was set 20 m, which is the same used by  [3]. OpenFoam and Meteodyn tend to slightly overestimate the error whereas the opposite is observed with WAsP Engineering. Despite a small difference between modeled and experimental error for direction 320 • , all softwares are well within uncertainty. Terrain classification was made using topography up to 340 m away from the LiDAR. When looking at Figure  5 , there is indeed considerable topographic changes up to this radius. However, when considering only the actual area covered by the disc when the sensor scans at 80 m, which is a circle of diameter ≈85 m, terrain shows less than 5 m variation in elevation and less than 5 • slopes.
Conclusion First, an atmospheric flow model capable of handling both complex terrain and variable height forest was implemented in the open source CFD package Open-FOAM. Performances of the model were assessed with the help of MeteoDyn WT 4. 0 and experimental measurements. The effect of variable forest height as opposed to constant forest height was investigated. Since the distribution of forest is quite homogeneous on the site studied, no significant difference was observed between both approaches. A tuning process was used to determine the most appropriate value of drag coefficient to be used as no experimental data were available for AAV. Among 3 tested values, 0. 0015 yielded best results. Results also showed the significant impact of both the location and the nature of the inlet boundary condition.
The objective of this paper is to propose a method of transforming a traditional system into an incremental one at minimal cost. A new module called the Scheduler is inserted between the client and the service so that from the client's point of view, the system behaves incrementally, even though the service works in a traditional way. Section 2 draws a state-of-the-art concerning incremental dialogue systems. The architecture proposed here and the role of the Scheduler are presented in Section 3. In Section 4, two implementations of our method are presented: CFAsT and DictaNum. Then, a discussion is held in Section 5 before concluding the paper and presenting our next objectives in Section 6.
, 2014) , a dialogue system helping the user to find a date and a time for an appointment with a technician (completing the work made during the European project CLASSiC  (Laroche and Putois, 2010) ). Among them, List of Availabilities is an incremental strategy where the system enumerates a list of alternatives for the appointment. The user is supposed to interrupt this enumeration when he hears an option that is convenient for him. An experiment showed that List of Availabilities produced better results than other traditional strategies in terms of task completion and user satisfaction. PARLANCE  (Hastie et al. , 2013)  is an example of a third category system (it was developed in the European project PARLANCE). Its architecture is similar to the traditional ones but it integrates a new module, called MIM (Micro-turn Interaction Manager), which decides when the system should speak, listen to the user and when it should generate back-channels.
The closest approach to the method introduced in this paper is presented in  (Selfridge et al. , 2012)  : the IIM (Incremental Interaction Manager) is an intermediate module between an incremental ASR and a TTS on the one hand and the service on the other hand. Instead of replicating the dialogue context as it is suggested in this paper, different instances of the service are run. Moreover, the IIM is introduced as preliminary work in order to simulate incremental dialogue whereas in this paper, the Scheduler approach is fully studied and placed into the context of the current state-of-the-art concerning incremental dialogue. It is also viewed as a new layer that can be extended later on, into a smart turn-taking manager. The architecture proposed in  (Dohsaka and Shimazu, 1997)  contains eight modules that work in parallel: the Speech Recognizer, the Response Analyzer, the Dialogue Controller, the Problem Solver, the Utterance Planner, the Utterance Controller, the Speech Synthesizer and the Pause Monitor.
The traditional architecture In a traditional architecture, the client receives a stream of data (audio signal, string. ). If it is not the case (a web interface where each button represents a request for example), it does not make sense to transform such a system in an incremental one, so they are out of the scope of this paper. The end of a request is determined by a condition EndT urnCond. It can be a long enough silence  (Raux and Eskenazi, 2008; Wlodarczak and Wag-ner, 2013)  in the case of vocal services or a carriage return for text systems. A dialogue turn is the time interval during which the user sends a request to the system and gets a response. These turns will be called T 1 , T 2 ,. , T k. and each one of them can be split into a user turn T k,U and a system turn T k,S : T k = T k,U ∪ T k,S.
During the user turn, a request Req k is sent and during the system turn, the corresponding response Resp k is received. The instant when a condition goes from f alse to true will be called its activation time. As a consequence, T k,U ends at the activation time of EndT urnCond and T k,S is finished when the system gives the floor to the user. The service is made up of three parts: the internal interface, the internal context and the external interface. The internal interface manages the interactions between the service and the client. The internal context handles the way the client's requests should be acted on and the external interface is in charge of the interactions with the external world (database, remote device. ).
Commit, rollback and double context The request hypothesis fluctuates as long as new increments are taken into account. However, at some point, the system has to take an action that is based on the last hypothesis and visible by the user. For example, a response may be sent to the TTS or a database can be modified. At that point, the system is said to commit to its last hypothesis which means that it engages the dialogue according to its understanding of the request at that moment. It no longer waits for other incremental units to complete the request as it can no longer change it. On the contrary, the system can decide to forget its last hypothesis and come back to the state it was in at the moment of the last commit. This operation is called rollback (both terms are taken from the database terminology).
CFAsT: Content Finder AssitanT CFAsT is an application developed at Orange Labs and which can be used to generate textual dialogue systems and whose objective is to help the user search for some specific content in a database. The client is a web page with a text-box where the user can type a request using natural language (validated by a carriage return or by clicking on the validate button). This page also contains buttons representing keywords or content suggestions. In this implementation, the content base chosen is the list of accepted papers at the NIPS 2013 conference. A list of keywords is maintained through the interaction. It is initially empty and for each new request, if new keywords are detected, they are added to the list. The interaction ends when the user selects a unique content.
On the other hand, systems from group 1 and 2 wait until the end of the user's utterance to send the request to the service, hence, being less reactive. However, systems from the third group work on a restart incremental, reprocessing the whole request at each new increment. On the contrary, systems from the fourth category can process the request increment by increment hence optimizing the processing cost. Sometimes, a new increment can modify the whole request (or a part of it) and those systems are designed to handle this too by canceling some previous processing (revoke mechanism  (Schlangen and Skantze, 2011) ). While integrating incrementality in CFAsT and DictaNum, we noticed that the system responded so quickly that no efforts are necessary to optimise the processing time. However, systems from the fourth group can make the difference if the system needs to process tasks that create a delay (slow access to a remote database for example).
Conclusion and future work This paper describes a method for transforming a traditional dialogue system into an incremental one. The Scheduler is an intermediate module that is inserted between the client and the service. From the client's point of view, the system's behaviour is incremental despite the fact that the service works in a traditional turn-taking manner. Most requests that are sent by the Scheduler to the service are aimed to see what would be the answer if the current request hypothesis is the final one. In this case, the service's context should not be modified. Therefore, two context have to be maintained: the real context and the simulated one. This solution has been implemented in the case of a textual dialogue system generated by the CFAsT application. It helps the user navigate through the NIPS 2013 proceedings titles.
Seed pairing stability is the calculated free energy of the predicted duplex  (Garcia et al. , 2011). Position contribution analyzes the position of the target site within the mRNA  (Grimson et al. , 2007). Machinelearning approaches use training data to develop a model of miRNA targets, and then use the model as part of the miRNAprediction process. Machine-learning techniques are likely to use more features in their predictions because they can be trained to determine the predictive power of each feature on positive and negative datasets. A machine-learning approach used by several of these tools is support vector machines (SVM). Tools that use SVM are noted.
Free energy is calculated by predicting the folding of the miRNA:mRNA hybrid using the Vienna package  (Hofacker et al. , 1994). Although this is a common method, it ignores any additional protein interaction, such as with the RNA-induced silencing complex  (Enright et al. , 2003). miRanda was written in C and provided as source code. It is relatively easy to compile and run. Nevertheless, both this step and the requirement to run miRanda using the command line will present a technical barrier for many users. However, for more advanced users, miRanda provides a number of adjustable parameters that may be helpful in investigating particular miRNA targets. miRANDA-mirSVR miRanda-mirSVR  (Betel et al. , 2010)  is an online tool that combines two approaches (Table  2 ). miRanda is used to identify candidate target sites and mirSVR is used to score them.
Unfortunately, the site is maintained erratically, and does not always use the latest version of miRBase  (Kozomara and Griffiths-Jones, 2011)  for its predictions. This is a particular problem in terms of using the latest nomenclature when searching for a particular miRNA. Nevertheless, the mirSVR score is a unique and useful capability, and the website is easy to navigate. In addition, the site provides analysis of miRNA expression by tissue and links to miRBase and miRo (The miR-Ontology Database)  (Lagana et al. , 2009)  for more information about a miRNA of interest.
MirTarget2 MirTarget2  (Wang, 2008; Wang and El Naqa, 2008)  makes miRNA target predictions using SVM and features extracted from a large microarray training dataset  (Linsley et al. , 2007)  (Table  5 ). This machine-learning approach confirmed the use of several popular prediction features and identified new features significantly correlated with miRNA:mRNA target interactions. The training features used include seed conservation, seed match specifically in positions 2-8, base composition in the regions flanking the seed pairing sites, secondary structure (incorporating site accessibility and free energy), and location of the site within the 3 UTR. MirTarget2 was created in conjunction with miRDB  (Wang, 2008) , and MirTarget2 predictions are available in miRDB. miRDB is a Wikipedia-like functional annotation database for mature miRNA with integration of high throughput automated annotations and manual annotations from individual researchers  (Wang, 2008).
Seed conservation is incorporated and scored by comparing human, mouse, rat, dog, and chicken orthologs, but is not required. One potential limitation of this program is that the training dataset included only 3 UTR sequences with a single seed pairing site, as opposed to multiple target sites. The rationale for this was to minimize complications from determining the contribution of each binding site. While this is an understandable choice, it also presents a theoretical limitation of the training dataset given that target-site abundance can alter the likelihood of miRNA:mRNA interactions  (Garcia et al. , 2011). Overall, miRDB is actively maintained and user friendly. Predicted miRNA:mRNA target interactions can be searched by miRNA or by mRNA. There is also a target mining option with adjustable and default screening options.
rna22-GUI rna22-GUI  (Loher and Rigoutsos, 2012)  is based on rna22  (Hofacker et al. , 1994; Miranda et al. , 2006) , an older target prediction tool (Table  6 ). rna22 used pattern discovery to identify target islands and evaluate the free energy of paired target islands and candidate miRNAs. Unlike rna22, which required a user-provided miRNA and target sequence, rna22-GUI offers the ability to search by miRNA, gene ID, transcript ID, or gene name. Search results can be presented as a cDNA map, which shows the predicted nucleotide interactions of each of the miRNA:mRNA target interaction sites or as a table that lists these predicted duplexes. While some users may gravitate toward the graphical representation of miRNA:mRNA target interactions, novice users may find the map complicated and difficult to navigate.
TargetMiner TargetMiner  (Bandyopadhyay and Mitra, 2009 ) is an SVM-based classifier for identifying potential seed sites between a userprovided miRNA and mRNA of choice (Table  7 ). The user can search as many miRNAs and targets as desired when uploading the input file. The user is provided with the type of seed match, position, and how many of those sites are found within the sequence. The tool is based on machine learning from negative and positive training data in order to provide more accurate seed match predictions between a miRNA and its target. The positive training data was a set of 289 miRNA transcript pairs extracted from the miRecords database  (Xiao et al. , 2009). The negative training data was selected from a pooled dataset of pairs of miRNAs and predicted targets by identification of overlapping false positive pairs generated from multiple target prediction algorithms.
SVMicrO uses these features to predict candidate miRNA:mRNA target pairs. Five features (seed match, conservation, free energy, site accessibility, and target-site abundance) were found to be important in predicting miRNA targets, but the training data allowed these to be defined with a tighter focus. SVMicro's use of numerous granular features in predicting miRNA:mRNA target pairs is powerful. Furthermore, given SVMicrO's relatively large training data set, these features may be useful to other systems. However, usability is currently a limitation. The user needs to build a database containing the UTR to search combined with phastCons conservation scores  (Siepel et al. , 2005). However, there is no documentation for how this should be done. Furthermore, SVMicrO will only install easily on a 32-bit Linux operating system.
, 2006)  and MicroInspector  (Rusinov et al. , 2005) , are also excluded. Some tools, such as Genmir++  (Huang et al. , 2007)  and HuMiTar  (Ruan et al. , 2008) , were excluded on the basis of requiring additional proprietary software and/or expertise for use, which make them inaccessible to the average user. MicroCosm Targets uses the miRanda algorithm and was not separately reviewed  (Griffiths-Jones et al. , 2008). Space also prevents the review of miRNA target prediction programs exclusively for species other than humans. Lastly, while the emerging field of integrated tools is outside of the scope of this review, elucidation of the strengths and limitations of the component tools is highly relevant to the overall user assessment of an integrated tool.
Currently, three of these projects stand out in terms of their wide range of capabilities, ease of use, relatively current input data, and maintenance of the software. These are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. All of these projects have received periodic updates over the last several years and are easy to use. DIANA-microT-CDS uses the most current data out of any of the tools reviewed (miRBase version 18). Furthermore, it is able to make predictions into the CDS in addition to the 3 UTR. Although miRanda-mirSVR uses a somewhat older version of miRBase (miRBase version 15) than DIANA-microT-CDS, its mirSVR score is a unique capability that provides a meaningful indication of the degree of regulation. Most other tools only provide a score of the result's significance (which is also provided by miRanda-mirSVR).
In both DIANA-microT-CDS and miRanda-miRSVR, conservation is a feature not a filter, which increases sensitivity to miRNA targets that are lineage specific. TargetScan is based on only slightly older data (miRBase version 17) than DIANA-microT-CDS. Although it applies a conservation filter, it does allow for poorly conserved targets. Similar to miRanda-mirSVR, it also considers the additional feature of A-U content in the regions flanking the seed region. Among the remaining target prediction tools reviewed, miRanda is still a widely-used tool even though it needs to be downloaded to be used and it lacks the additional mirSVR score available in miRanda-mirSVR, which may be desirable. rna22-GUI offers a graphical representation of miRNA:mRNA target interactions, but it is based on the original rna22 program and therefore does not incorporate recent advancements in the understanding of miRNA:mRNA target interactions.
Tools that allow user-provided data, however, can accommodate some level of tissue specificity by incorporating tissue-specific data such as highly expressed miRNAs or miRNA isoforms, tissue-specific mRNA transcript variants, or lists of highly upregulated or downregulated genes. There is also emerging interest in integrated tools, such as miRmap  (Vejnar et al. , 2013) , that combine multiple miRNA target identification tools to overcome the limitations of individual tools. In addition, some integrated tools, such as MiRonTop  (Le Brigand et al. , 2010)  and CoMiR  (Coronnello and Benos, 2013) , incorporate expression data in the ranking of miRNA target predictions. This review highlights the common features of miRNA target prediction and how they are incorporated into different target prediction tools.
ACKNOWLEDGMENTS This project was supported in part by a  National Institutes of Health COBRE  grant in Quantitative Biology ( J. Moore , PI; Project #3:  Clare Bates Congdon , PI) from the  National Center for Research Resources  ( 5 P20 RR024475-02 ) and the  National Institute of General Medical Sciences  ( 8 P20 GM103534-02 ) to  Clare Bates Congdon  and a  National Science Foundation   CAREER award  (# 953495 ) to  Clare Bates Congdon. This project was also supported in part by a  National Institutes of Health and National Institute of General Medical Sciences COBRE in Stem Cell Biology and Regenerative Medicine  ( 8P20GM103465 , D. Wojchowski, PI; Project #7:  Pradeep Sathyanarayana , PI) to  Pradeep Sathyanarayana. The authors would like to thank  Dr. Christine Duarte , Senior Biostatistician at  Maine Medical Center Research Institute , for critically reviewing this manuscript.
Methodology and Knowledge Management Knowledge management becomes a true stake for the companies. The loss of consciousness due to retirement or turnover and the intrinsic dispersion of knowledge involve high notorious costs. Computer science and in particular artificial intelligence (AI) attempted to provide solutions to help the management of knowledge. AI, while trying in its beginnings to model human intelligence, opened research orientations to set up a decision-making help. The researchers introduce for this purpose the concept of knowledge bases which allow the separation between declarative and procedural statements. The first concrete results appeared with the rules based expert systems, for example, with the software of decision-making medical help MYCIN in 1974  [13]. To reduce production time and production cost, industries are interested in different technologies which depend on research on AI.
g. , trade rules and constraints). Functional design has to be represented by the vocabulary of the domain. This vocabulary is then used in the definition of trade rules, constraints, and scenarios. Thus, we define specific expression called graphonumerical expression (GNE) and specific parameters called graphonumerical parameters (GNP). The particularity of GNE/GNP is to link the vocabulary to knowledge and to geometry. For example, a sentence like "We have to create at the bottom draft of 20 degrees" is transformed in GNE expression to "Create a the bottom draft of 20 ∘. " The GNE expression is saved in a dedicated module in the CAD software (here Rhinoceros3D) which automatically creates functional parameter (GNP). For this example, GNP will be "create (draft)(20 ∘ )(#GeomRef)" (#GeomRef is a keyword to interact with the CAD user to select geometrical entities).