dataesr/scientific-paragraphs-categorization huggingface dataset:
This structure, also called the closed-pore conformation (PDB entry 2b6o,  Gonen et al. , 2005) , has the residues forming the pore slightly closer to each other than in the full aquaporin-0. From a cellular point of view, the truncated form results from the maturation process undergone by the fibrillar cells of the eye lens when they migrate from the cortex to the core of the lens  (Bassnett et al. , 2011). As can be seen in Fig. 7 , the fit of the SAXS curve calculated from the 2b6o model of aquaporin-0 associated with the corona previously optimized for the 2b6p model, with parameters a = 29. 6 A ˚, b = 35. 4 A ˚, t = 5. 6 A ˚, e = 1. 12, rapidly deteriorates at medium Q values and leads to a high value of. This is not surprising, as the lack of a substantial fraction of the protein structure should necessarily change the SAXS curve.
The reported excellent agreement between numerical simulations of a generic model, and the observed phenomena in a laser-based experiment, suggests the robustness and the intrinsic character of the underlying dynamical concepts. We anticipate that such delay systems and their related dynamical phenomena will represent a simple but efficient theoretical tools for investigation of complex self-organized motions, as they are naturally developed in living systems, pattern formation, fluid dynamics phenomena, as well as behavior in social and technological networks. This work was supported by the European project PHOCUS (FP7 grant 240763), and the Labex ACTION program (contract ANR-11-LABX-01-01). BP and YM acknowledge the support of the Region Franche-Comté.
A large memory depth (up to 32 million points) digital Lecroy oscilloscope is used for real-time acquisition of long time traces covering up to 10000 time delays. This oscilloscope also provides specific time trace processing capability through short Matlab routines, thus enabling the real-time visualization of the space-time patterns shown in Figs. 2, 3, 4 and 6. The main difficulty for this custom real time Chimera pattern observation, was to design the adequate algorithm capable for the accurate (10 -5 required precision) and fast extraction of the spatial width η = 1 + γ, for which only the Chimera can be clearly observed as a vertical pattern over thousands of time delay duration. This pattern is whether strongly tilted in the space-time plane at 10 -4 precision, or even completely invisible for worth precision.
Increasing f CLK remains to scan the hyperbola from the top left to the down right, i. e. for decreasing maximum number of Chimera heads (the highest possible number being forced by adequate initial conditions, on the top left of the hyperbola). Different hyperbola were scanned through the choice of different θ (i. e. different high pass cut-off frequencies). The observed Chimera pattern in the graphical Matlab window of the oscilloscope allowed for the easy detection of the number of heads N σ , or of the chaotic breather solution (0-head), which number can then reported in Fig. 2. Numerics: Fourth order Runge-Kutta integration scheme is used for all the numerical experiments to calculate x(t), with a fixed time step h = ε/10. The spacetime plots are obtained as in the experiment, through the determination of the duration η revealing "in average" vertical patterns, stacking vertically the n 0 successive waveforms {x(t) = x n (σ) | t = (nη + σ)τ D ) with σ ∈ [0, η], n = 1, 2,.
Many existing open linked datasets include descriptions of real world persons, with the relationships between them. For some traditional and/or emerging relationships, existing ontologies do not provide the adequate links. This paper represents PersonLink, an ontology that defines rigorously and precisely family relationships, and takes into account the differences that may exist between cultures, including new relationships emerging in our societies nowadays. Moreover, the transition from one culture/language to another one cannot be solved with a simple translation of terms, especially when concepts do not intersect in different languages; thus our solution refers to a multicultural meta-ontology of concepts and associated mechanisms. A validation has been performed on two linked datasets DBpedia and Freebase.
Currently, the majority of existing ontologies are in English. We argue that several family relationships cannot be expressed in this language or reversely cannot be translated (samples are given in the motivating examples section). Interesting works have been proposed for translating terms in ontologies  (Hellrich and Hahn, 2014). However, a simple translation remains insufficient, since the meanings of the concepts depend not only on the language but also on the culture defining them. Thus, the definition of the concept may be different between cultures. To deal with this issue, we propose a novel ontology, called PersonLink, that considers the richness of the cultures/languages it represents. For this purpose, we conducted a study to define the interpersonal relationships according to their presence or absence in different cultures/languages.
THE PersonLink ONTOLOGY Our main objective is to represent interpersonal relationships in a precise manner and in different cultures/languages in order to be both generic and adaptable to users. That is why the PersonLink ontology represents and defines the concepts according to the considered culture, and expresses them by using terms of the appropriate language. In order to do so, the first step consists in considering culture in the definition of the concept. For each culture, we look at whether the concept exists or not. If it exists, we describe it using its definition in this culture/language. So, in the ontology, only if the concept exists in the culture, a term is assigned to it using the language related to this culture. We obtain as a result a kind of sparse ontology that we have called "lace ontology" because it contains many null values as we show in Fig.
The Translation Algorithm The main objective of our PersonLink ontology is to express rigorously family relationships by considering the culture/language aspect. Nevertheless, note that to express a source relationship in a different culture, and in order to get the term(s) used to express this relationship in the target related language, we apply an algorithm using our meta-ontology. Given a source language L S and a target language L T , given C S a concept in the source language L S and C L a concept in the target language L T , and given T S a term in the source language L S and T T a term in the target language L T. We proceed by cases: • Case 1: T T = / 0 , which means that in our metaontology, the term corresponding to the concept exists in the target language L T. So we use it; • Case 2: T T = / 0, which means that in our metaontology, the term corresponding to the concept C S does not exist in the target language L T : -We go down in the meta-ontology to find a more specific concept C I defining this relationship in the target language L T (that may imply to get further information about the person, e.
Formal Definition of the Relationships Interpersonal relationships in the PersonLink ontology are represented in a structured way with the OWL2 language that corresponds to the description logic SR OI Q (D). The use of OWL2 is privileged because it provides a high expressiveness and allows us to represent relationships that we were not able to represent in OWL1. In addition, OWL2, with this logic description, allows semantic reasoners to verify the consistency of data, to derive new knowledge or to extract information already present. Besides, the reasoning in OWL2 is complete and decidable. In predicate logic, the hierarchy represented in Fig. 1  means that: 2(?x, ?y) ⇔ 2. 1(?x, ?y) ∨ 2. 2(?x, ?y) and 2. 1(?x, ?y) ⇔ 2. 1. 1(?x, ?y) ∨ 2. 1. 2(?x, ?y) ∨ 2. 1. 3(?x, ?y) ∨ 2. 1. 4(?x, ?y) and 2.
VALIDATION To test the validity of our reasoning and translating mechanism, we have taken as a sample persons described in the context of the Linked Open Data (LOD). We chose as datasets Freebase, which is a large collaborative knowledge base built mainly from data provided by its community members and DB-Pedia, which is a knowledge base built from extracted and structured data from Wikipedia. We show as relationship example, the cousinship relation presented in section 3. First, we searched in the Freebase and DBPedia ontologies properties that could express the "cousinO f " relationship. In the current versions, none of Freebase or DBpedia uses include properties to express this kind of relationship. However, we found other relationships (parental and sibling) that, combined, could be used to express implicitly the cousinship relation.
Hence, we extract from a subset (due to the user queries limitations) of DBpedia and Freebase, entities that are linked to each other by a parent and/or sibling relationships, as well as the relationships themselves. This extraction process is done automatically using scripts running SPARQL (SPARQL Protocol And RDF Query Language) and MQL (Metaweb Query Language) queries. The obtained results are presented in Table  1  and Table 2. From DBpedia, we extracted 1675 entities that, having as type "person" and linked to other ones by the "parent" relationship. We extracted also 5117 persons having children and 4 having siblings. However, as we explained above, no sibling's child or cousin relationships were found. From Freebase, we extracted 2000 entities "person" that have parent relationships.
The "daughterO f " and "sonO f " relationships are inferred by exploiting the "children" and "gender" properties. We note that there are some people with null gender values (this is because data available on these datasets are not always complete). The results we got by reasoning on data are presented in Table  4. Among the 2000 Freebase person, 717 could be represented using the "motherO f " relationship and 1275 the " f atherO f " one. These results show that our PersonLink ontology has the particularity of using properties much more expressive than those present in DBpedia or Freebase. We also proceeded to translate relationships using our translation algorithm based on the lace metaontology showed in Fig. 1. So, for the inferred Freebase "cousinO f " relationships (cf. table 3), the algorithm specifies, in the case of french translation, if this cousin is a "cousineDe" (female cousin) or a "cousinDe" (male cousin).
In addition to the lack of precision, the majority of existing ontologies about family relationships are in English. However, ontologies should be used in different cultures. Some projects aim to solve the issue related to the "syntactic" translation  (Chalupsky, 2000)   (Dou et al. , 2005). But as we saw with motivating examples, a literal translation is not always relevant. Besides that, other important studies targeted the "cultural" translation issue, like the recent European project MONNET which has proposed a model named LEMON to deal with this issue. The authors of this work  (Montiel-Ponsoda et al. , 2011)  developed a translation module which is intended to link between concepts coming from various language/culture ontologies. First, they provide a literary translation of the concept in the target language.
YAGO  (Suchanek et al. , 2007)  is an ontology derived from Wikipedia, WordNet and GeoNames. Its goal is to structure Wikipedia as a linked Database. Thus, an important sub-thematic of YAGO concerns relationships between persons. However, all the relationships are expressed in English and are limited regarding expressiveness. Freebase and DBpedia are two projects similar to YAGO (except that Freebase gathers its information from the community of users), and thus present the same issue.
CONCLUSION AND FUTURE WORKS In this paper we have presented a new ontology called PersonLink, that enables users and applications to represent interpersonal relationships. Person-Link provides a precise definition for each relationship and takes into consideration the culture/language aspect. We have introduced the notion of lace metaontology that facilitates the expression (by refinement of concepts and specification of contraints), in multiple cultures/languages of each relationship and allows to switch between languages and find the right terms expressing the relationships. The particularities of our solution compared to usual ontology translation methods are on the one hand that it merges all concepts and terms in a single ontology rather than providing ontology alignments and on the other hand the translation is perfectly reversible.
This solution is particularly suitable for our domain application where concepts are very closed but not similar in the different languages, and depend on the context (culture, law, gender of the person, etc. ). A set of inference rules allows to deduce new links from the given ones, and to check the consistency of inputs. The current version of PersonLink includes 3 classes (Person, Male and Female), 86 properties, and 582 SWRL rules. We have experimented the reasoning mechanism and the translation algorithm of the PersonLink ontology in the context of Linked Open data. Tests conducted on DBpedia and Freebase datasets show that the use of PersonLink enables inferring much more rigorous relationships than those already present in these datasets. As the PersonLink ontology is available and published in a dereferenceable manner and will be referenced by the Linked Open Vocabulary) it can be used by any Linked Data publishers who need family relationships and should comply with the W3C best practices.
In our laboratory, two on-going applications are using the PersonLink ontology: • The first one is a memory prosthesis called CAP-TAIN MEMO devoted to persons with memory impairments. This application need to store all family relationships of the owner of the prosthesis. Thus, an ontology with a fine-grained relationships definitions is mandatory to allow storing all possible links. Moreover, the reasoning mechanism contributes to check the consistency of the inputs, that is a great help, especially for persons with memory impairments and cognitive discordances. • The second one aims to integrate PersonLink in the SIGIL electronic books editing system. SIGIL allows to edit an electronic book in the Epub format. PersonLink can structure the metadata concerning the genealogy of main characters.
• The state space of the LRG is given by S = i=1,. ,n S i ; hence a state of the model is a vector s = (s i ) i=1,. ,n. • The model behavior is specified in terms of logical rules (or logical functions): the evolution of s i is defined by K i : S → S i with K i (s) specifying the target value of s i when the system is in state s. The software GINsim provides a graphical interface for the LRG definition, including the components (nodes) and their ranges (maximum values), the interactions (signed arcs) and their thresholds, along with the logical rules [using Boolean expressions or logical parameters  (Thomas and D'Ari, 1990) ]. The behavior of an LRG is classically represented in terms of a STG, which encompasses the initial model state(s) together with their direct and indirect successors.
(2013)  for details]. Still, these representations do not address the identification of the attractors in large STGs. In this respect,  Garg et al. (2008)  proposed an efficient algorithm to identify all the attractors (synchronous and asynchronous schemes) of Boolean models. Their method relies on a binary decision diagram representation of the STG and can cope with very large models  (Xie and Beerel, 1998 ). An implementation of this algorithm is available along with the software genYsis 2. To further account for kinetic aspects, several strategies have been proposed. One strategy defines priority classes according to biologically founded time scale separations, e. g. , fast versus slow processes  (Fauré et al. , 2006). Alternatively, time delays and constraints on them can be defined and handled with existing methods to analyze timed automata  (Siebert and Bockmayr, 2006).
Another approach consists in applying continuous time Markov processes on logical state spaces. Based on the delineation of a logical model along with a limited number of kinetic parameters, the software MaBoSS uses Monte-Carlo simulations to compute an estimate of the temporal evolution of probability distributions and of the stationary distributions of the logical states  (Stoll et al. , 2012). Finally, several authors proposed to consider differential models derived from logical models  (Mendoza and Xenarios, 2006; Abou-Jaoudé et al. , 2009; Wittmann et al. , 2009).
, 2007; Grieco et al. , 2013). Beyond the proof of concept, the development of user-friendly tools is required for a wider use of model checking in systems biology. In this respect, we are currently working on improving the interaction between GINsim and NuSMV-ARCTL in two distinct ways, which will be made available in a forthcoming release of GINsim: (1) implementing recurrent temporal logic patterns into our software GINsim to ease the definition of temporal logic formulas; (2) automating the interaction with the model checker and the parsing of the results, as well as the generation of the reprograming graph.
ACKNOWLEDGMENTS Wassim Abou-Jaoudé  has been supported by postdoctoral grants from the  LabEx MemoLife  and from the  Ecole Normale Supérieure. Pedro T. Monteiro  was supported by the  FCT  grants  PEst-OE/EEI/LA0021/2013  and  IF/01333/2013. Maximilien Grandclaudon  was funded by the  Ph. D. program of the  Agence National de Recherche sur Le Sida (ANRS). This work was supported by a  European Research Council   consolidator grant  to  Vassili Soumelis.
As mentioned previously, the general purpose of this project is building a low-cost remote sensing system capable of mapping crops and cultivation precisely. With the purpose of achieving this goal, aerial robot should sample desired crops accurately using aerial photography. A route planner is required to gather complete and precise data from crops. In consequence, software platforms to manage the navigation of UAVs through GPS are essential in this project. These platforms have modules, which select points on a map (usually Google® Maps) and send desired route to the UAV. For instance, QGroundControl is a control station software that connects various autopilots of several UAV types thanks to the MAVLink protocol  (Qgroundcontrol. org, 2009). Mission Planner and APM Planner are two software suites that are capable of controlling UAV flight and actual state remotely, as long as the autopilot type is ArdupilotMega or Pixhawk  (Open Source Autopilot, 2015).
Paparazzy is an open source project known for its control over the AR Drone and Bebop UAV, besides autopilots based on this project. Additionally, there are proper optimized platforms based on application features and developers interests. An interesting case is Naza-M: the main ground control application for the DJI® Phantom quadcopter. In the agricultural field, there is a useful software suite capable of capturing spatial monitoring and diagnosis  (Agribotix, 2015). Route planners are also part of the robot coordination tendencies for UAV use in remote sensing systems. Joao Valente details a three-quadcopter system in a farm located in Spain; platforms used in this study were AR 100 and Ast Tec Hummingbird  (Valente, 2011). The rest of this document is structured as follows: section II describes the methodology for system implementation, section III details results of experimental tests and section IV presents conclusions of presented proposal.
Sin embargo, la estimación de la posición depende de la calidad de la señal y no es recomendable si la aplicación requiere precisión en la altura  (  as 1 meter in altitude and accuracy up to 90% in longitude and latitude  (Chee & Zhong, 2013). With precise positioning of the AR Drone over crops, the next step consists of adopting a quadcopter for topography. For this purpose, we focus on non-linear control techniques based on fuzzy logic, since this tactic models the real world in a qualitative manner and it provides certain answers, just as human beings do. Furthermore, implementation of this technique is highly supported by ROS operating system  (Bayar, Akar, Yayan, Yavuz, & Yazici, 2014). Fuzzy control starts with data reception from sensors in a plant (platform), i. e. interpretation of the real world.
A. Development tool The proposal presented in this document is now at an initial stage, we are updating tools such as the following, in order to progress in desired activities: • Raspberry Pi B+ Model, which is the optional processing unit to handle data from altitude and GPS sensors. We chose this Small Board Computer (SBC) due to its low weight, broad documentation, and acceptable processing capacity. • Robot Operating System (ROS), basis of coverage planner development, driver implementation, and connection with the AR Drone. We use Fuerte/Hydro ROS over Ubuntu 12. 04. • Gazebo and RVIZ visualization tool, free licensed software suites used to support simulation and validation of navigation and height control strategies. claro que para los propósitos del proyecto la elección será el diseño de un planificador global, ya que el uso de sensores para exploración de un cultivo implica un mayor procesamiento y, por tanto, mayor consumo de potencia, lo que reduce el tiempo de vuelo del cuadricóptero.
C. Simulación de algoritmos de planificación global de ruta Actualmente se está trabajando en la simulación del planificador de ruta utilizando paquetes de navegación del proyecto Hector_Quadrotor, debido a que es el paquete base de ROS para el control del AR Drone en el simulador Gazebo (Figura 7). En general los resultados no consideran las condiciones ambientales, ya que el objeto simulado como AR Drone en ROS no está soportado para este tipo de simulación. No obstante se encontraron soportes para enlazar Matlab con Hector_Quadrotor mediante plugins, con los cuales se puede evaluar el voltaje en los motores y crear un vector de viento. Como primer paso hacia el planificador, se simulan algunos algoritmos para obtener la ruta más corta. Los algoritmos tienen como base la búsqueda en grafos para encontrar los nodos correspondientes a la ruta más corta y asumen un entorno conocido (mapa), el cual está divido en celdas cuadradas.
Agradecimientos Los autores agradecen el apoyo recibido de la Vicerrectoría de Investigaciones de: la Universidad del Cauca -proyecto 4194 -Implementación de un sistema de sensado remoto de corto alcance para la obtención de información completa y precisa de un cultivo agrícola-; la Universidad Carlos III de Madrid -proyecto AgroCloud del programa RICCLISA-; y el Ministerio de Economía y Competitividad de España -proyecto TRA2011-29454-C03-03. i-Support: Sistema inteligente basado en agentes de soporte al conductor-.
Acknowledgement The authors would like to express gratitude for the support received from  Research vice-Rectory of the Universidad del Cauca  through project  4194 : "Implementation of a short-range remote sensing system to acquire precise and complete information of rural crops". In addition, support received from  Universidad Carlos III de Madrid , " AgroCloud" project of RICCLISA program , and financial support from  Spain Ministry of Economy and Competitiveness  (Project  TRA2011-29454-C03-03. i-Support: Intelligent System Based on Driver Support Agents) is appreciated.