dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, the next time the iCub will interact with this person, it will directly recognize him and not ask for his name. 5) Once this interaction is achieved, the drives continue to decay. Since the iCub has just acquired more information, the decay of the drive for knowledge acquisition is slower and the one for knowledge expression is increased. Still, the drive for knowledge acquisition reaches the threshold first. The behavior for acquiring information is therefore triggered again. This time, the random choice of an unknown entity makes the robot point to an object and ask, "What is this object?". The human replies e. g. "This is the cube". The language reservoir handler extracts the name of the object from the reply and the associations module associates it with the visual representation of the pointed object from object recognition.
Now the cube can later be referred by its name. 6) The drives continue to decay. This time, the drive for knowledge expression reaches the threshold first. This triggers the behavior for expressing the acquired knowledge. A known entity is chosen, in this example it is the cube, which the robot points at while saying "This is a cube". 7) The human asks "Take the cube". A meaning is extracted by the language reservoir handler in perceptions and forms the representation of a goal to achieve in the goal module (here the desired location of the object, i. e. the region of the iCub I for the goal "take", see Figure  4 ). An action plan is built in action plans with the sequence of two actions "Ask the human to push the object" then "Pull the object", together with their pre-and post-conditions in term of states (I, S or H).
The action selection module takes care of the realization of the plan. First, it instantiates the action plan toward the considered object, here the cube, through its connection with associations. Then, it executes each action until its associated post-condition is met (repeating it up to three times before giving up). Since the cube is in the human area H, the iCub first triggers the behavior for interacting verbally with the human, asking "Can you please bring the cube closer to the shared area?". The human pushes the cube to the shared area S and the state transition is noticed by the robot thanks to the real-time object localization performed in the object recognition module. Then the robot triggers a motor action to pull the cube. Once the goal is achieved (i. e. the cube is in I), the drive levels which were frozen during this interaction continue to decay.
These orders trigger action plans combining object manipulation by the iCub with requests to the human to do so, as described above. We note that, due to the distributed implementation of the DAC-h3 systems as interacting YARP modules operating in parallel, deactivating functionalities simply requires to not launch the corresponding modules and does not imply any modification in the code. Three objects are placed on the table: an octopus, a blue cube and a duck. Initially, the names of the objects are unknown to the iCub and they are placed on the table as shown on Figure  7  (left). The task given to the subjects is to achieve a goal configuration of object positions (Figure  7 , right). The experiment terminates when the task is achieved. To do so, the subject is instructed to interact with the iCub in the following way.
V. CONCLUSION AND FUTURE WORKS This paper has introduced DAC-h3, a proactive robot cognitive architecture to acquire and express knowledge about the world and the self. The architecture is based on the Distributed Adaptive Control (DAC) theory of the brain and mind, which provides a biologically grounded framework for organizing various functional modules into a coherent cognitive architecture. Those modules implement state-of-the-art algorithms modeling various cognitive functions for autonomous self-regulation, whole-body motor control, multimodal perception, knowledge representation, natural language processing, and goal-oriented behavior. They are all implemented using the YARP robotic middleware on the iCub robot, ensuring their parallel execution in real time and providing synchronous and asynchronous communication protocols among modules.
The implementation of DAC-h3 is flexible so that existing modules can easily be replaced by more sophisticated ones in the future. Moreover, most modules can be configured according to the user's needs, for example by adding new drives or more complex grammars into the system. This makes DAC-h3 a general framework for designing autonomous robots, especially in HRI setups. The underlying open-source code contains some modules which are specific to the iCub robot (modules related to action execution; which work on both real and simulated iCub's), but all cognitive nodes (autobiographical memory, allostatic controllers, etc. ) can be freely used with other robots (most easily with YARP-driven robots, but thanks to YARP-ROS intercommunication also with ROS driven robots). The main contribution of this paper is not about the modeling of the specific functional modules, which already have been published (see Section III), but rather about the integration of a heterogeneous collection of modules into a coherent and operational cognitive architecture.
A thorough statistical analysis will require collecting much more data to fully demonstrate the utility of some of these principles, for example how proactivity helps to solve the referential indeterminacy problem, as well as the effect of the robot's autonomy on the acceptability of the system by naive users. Third, although DAC-h3 can solve parts of the Symbol Grounding Problem (SGP), it still presupposes a symbolic concept of an entity which is given a priori to the system. Therefore, our contribution is more about the ability to proactively acquire multimodal information about these entities and linking them to linguistic labels that can be reused to express complex goal-oriented behavior later on. We are currently extending the proposed architecture in the following ways. First, we are better integrating some of the available abilities within the interaction scenario as mentioned above.
The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP/2007-2013) / ERC Grant Agreement n. FP7-ICT-612139  ( What You Say Is What You Did  project), as well as the  ERC 's  CDAC  project:  Role of Consciousness in Adaptive Behavior  ( ERC-2013-ADG 341196 ). M. Hoffmann was supported by the  Czech Science Foundation  under Project  GA17-15697Y. P. Nguyen  received funding from  ERC 's  H2020  grant agreement No. 642667  (SECURE). T. Prescott and  D. Camilleri  received support from the  EU   Seventh Framework Programme as part of the Human Brain  ( HBP-SGA1 ,  720270 ) project.
6 To assign functions to proteins and study their evolution, comparative studies have been extensively performed on complete genomes. 7, 8 Among others, OrthoMCL,  9  that uses Markov clustering (MCL) on the results from an all-versus-all BLASTp  10  to infer orthologs and paralogs as well as OMA,  11  which is based on evolutionary distances to infer orthologs among complete genomes, has been used for homology inference. Orthology inference can be used to transfer functional annotation among proteins, aided by the comparison between the query sequences and annotated databases such as RefSeq,  12  TrEMBL,  13  UniProt,  14  the Conserved Domain Database (CDD),  15  and Pfam. 16 Besides that, the inference of distant homologs (homologs with a distant common ancestor and a less conserved sequence) during the past years has been useful for the inference of protein families and superfamilies.
17 Databases such as SUPERFAMILY  18  and Pfam Clans  19  provide the most distantly related domains and so highest level for useful remote homology detection. 18 ince the publication of protozoan complete genome sequences, including Leishmania major,  20  Trypanosoma cruzi,  21  Trypanosoma brucei,  22  and more recently Trypanosoma grayi,  8  all belonging to the trypanosomatid clade, those genomic data helped to increase our understanding on those species evolution, their primary immune evasion strategy, and also the evolution of their cell surface molecules that represent the host-parasite interface. 23, 24 For instance, pan genomics studies of disparate strains of T. brucei, genome-wide studies, allowed the identification of significant host and geographic location associations. Strong purifying selection was detected in genomic regions associated with cytoskeleton structure and regulatory genes associated with antigenic variation, suggesting conservation of these regions in African trypanosomes.
29 n this study, 3 protozoa species belonging to different phyla were used for our reconciliation-based method: Cryptosporidium muris (Apicomplexa), E. invadens (Amoebozoa), and T. grayi (Euglenozoa). As far as we know, those species have not been used for comparative genomics studies before; they look appropriate to test the inference of homologous groups in distant Protozoa species. In this analysis, 2 different software were used to homology detection: (1) OrthoMCL and (2) OMA. Our study aimed to identify homologous groups that could not be inferred separately either using OMA or OrthoMCL, using a reconciliation of those different methodologies and validating our results using (1) conserved domains (CDD) and (  2 ) protein domain families (Pfam-A). These new homologous groups inferred by our reconciliation approach were called homologous Supergroups.
Homologous groups identification using OMA and OrthoMCL Two software were used in this study to identify homologous proteins: OMA and OrthoMCL. OrthoMCL 2. 2 was obtained from orthomcl. org (http://orthomcl. org/common/ downloads/software/) and used together with BLAST version 2. 5. 0+ (ftp://ftp. ncbi. nlm. nih. gov/blast/executables/blast+/ LATEST/). We used 1E-05 as e-value cutoff, according to the protocol described by Coutinho et al. 30  OMA version 1. 0. 1 was obtained from omabrowser. org (http://omabrowser. org/ standalone/) and executed with default parameters.
Reconciliation algorithm-inference of Supergroups Our reconciliation algorithm (Additional file 1; Figure  2 ) divided the homologous groups inferred using OrthoMCL and OMA into 3 categories, according to the degree of agreement between them, namely, "Identical groups" (called Category I), formed by homologous groups where the 2 software agreed, containing exactly the same proteins; "Divergent with intersections" (called Category II), formed by homologous groups that are not identical among the 2 software, but they shared at least 1 protein in common; and "No intersections" (Category III), formed by homologous groups where no protein is shared among the results of the 2 software. In this work, new groups were inferred only by reconciliation of homologous groups of Category II. In this work, only Category II was used for the inference of the Supergroups, joining the homologous groups that have at least 1 protein in common.
Functional categorization The functional categorization was performed using similarity analysis with Hmmer 34 version 3. 0 (http://eddylab. org/software/hmmer3/3. 0/) against the database of orthologous genes-eggNOG  35  version 4 (ftp://eggnog. embl. de/egg-NOG/4. 0/). To infer to which functional category each protein belongs, an e-value cutoff of 1E-05 was used, with the remaining parameters with default values.
KEGG pathways' analysis Pathways were assigned in this study, using similarity analysis with BLASTP (protein-protein BLAST) software version 2. 5. 0+ (ftp://ftp. ncbi. nlm. nih. gov/blast/executables/ blast+/2. 5. 0/) against the database of eukaryotes and prokaryotes genes of the Kyoto Encyclopedia of Genes and Genomes: KEGG version "September 2016" (ftp://ftp. bioinformatics. jp/kegg/genes/fasta/). We also used 1E-05 as BLAST e-value cutoff and the program was executed with default parameters.
Evolutionary distance inference The analysis of results of Belvu program showed that the homologous Supergroups inferred in this study presented greater evolutionary distances when compared with homologous groups inferred using OrthoMCL and OMA (Figure  7 ). Regarding Supergroups, the mean evolutionary distance was 151. 77 PAM, with a minimum evolutionary distance of 80. 37 PAM and maximum evolutionary distance of 292. 28 PAM.
KEGG pathway The result of the analysis performed by BLASTP among the database of eukaryotes and prokaryotes genes of KEGG has shown that 61% (36/59) of the Supergroups have proteins that participate in at least 1 KEGG pathway (Additional file 6). Of these, we found 21. 6% (6/36) Supergroups belonging to "Metabolism" pathway, and we chose these to be used as case study (.
Reconciliation algorithm-inference of Supergroups The inference of the homologous Supergroups (Figure  1 ) was possible due to the different approach used by the 2 software (OrthoMCL and OMA), eg, their cutoff and the algorithms used to infer orthologs. On one hand, in its first steps, the OMA algorithm uses a alignment score >85 to exclude alignments that does not deem significant, which implies that small sequences stay out from its analysis  11  and, in addition, removes from the Table  4. Supergroups belonging to "Metabolism" pathway, chosen to be used as a case study. Evolutionary Bioinformatics initial graph the best bidirectional hits with a high evolutionary distance. 39 On the other hand, OrthoMCL algorithm uses 1E-05 e-value cutoff as its default parameter and recognizes homologous groups by similarity score (best hits).
9 These fundamental differences between the 2 software could explain why some proteins are not included in the homologous inferred groups, and why it was possible to infer the Supergroups in this study, reconciling the homologous groups inferred by the OMA and OrthoMCL. Due to our validation criteria, the Supergroups inferred by our methodology have characteristics that make them homologous Supergroups as their proteins belong to the same protein family or share conserved domains. Evolutionarily conserved sequence fragments (or domains) are good indicators of homology because they can provide functional characterization based on the presence of signature sequence patterns and may serve as a starting point for functional annotation and classification. 14 The same can be said about protein families as they are a set of evolutionarily related sequences.
Conclusions The methodology developed by us was able to reconcile homology inference using homologous groups from OMA and OrthoMCL and then generating homologous Supergroups. These homologous Supergroups could not be inferred separately either using OMA or OrthoMCL. The validation of the homologous Supergroups using conserved domains (CDD) and protein families (Pfam-A), with high stringency criteria, have shown to be useful. In addition, the results presented may be underestimated, since proteins without protein family or conserved domains identified may be multi-domains that have not yet been annotated in CDD and Pfam databases. The homologous Supergroups inferred by us can be compared with SUPERFAMILY and Pfam Clan groups, all 3 representing distant homologous groups. Our methodology can be used as support for the study of any species, regardless of which software for homology inference is used as input in the process.
The TCO has the lowest value. Subsidence over South America brings air masses with high O 3 concentration, resulting in higher TCO than average values. Limb Sounder (MLS) that provides vertical ozone profiles in the upper troposphere and stratosphere. Ziemke et al. (2006)  subtracted the stratospheric column O 3 (of MLS) from the total column O 3 (of OMI) to obtain the tropospheric column O 3 (named hereafter OMI-MLS). They show a large impact of ENSO on tropospheric O 3 in the tropics by analyzing the OMI-MLS data  (Ziemke et al. , 2015). The O 3 sensitivity to ENSO was also studied with the tropospheric emission spectrometer (TES) observations  (Neu et al. , 2014). They studied, during El Niño, the long-range transport of Asian pollution due to the Northern Hemisphere subtropical jet. MLS and TES data were also compared to a chemistry-climate model to study how ENSO can influence the O 3 distribution  (Oman et al.
, 2013). These studies demonstrate that the link between O 3 and ENSO becomes a key element of the chemistry-climate interactions. The combination of OMI and MLS measurements allows insights into the links between tropospheric O 3 and ENSO, but has limitations because the tropospheric partial O 3 columns are obtained as a difference between two large quantities, the total column and the stratospheric column. Hence, possible bias and errors in MLS and OMI data can be amplified when the partial tropospheric column is calculated. The objective of the present study is to obtain direct evaluations of tropospheric ozone using assimilation of ozone profiles from MLS and from IASI. The IASI instrument, launched onboard MetOp-A in 2006, was designed for numerical weather predictions and atmospheric composition observations  (Clerbaux et al.
, 2009). IASI allows a daily global coverage at very high spatial resolution (12 km for nadir observations). Because of its spatial coverage, the day and night retrieval coverage, IASI provides an important added value with respect to other satellites like TES or OMI  (Herbin et al. , 2009; Pittman et al. , 2009; Oetjen et al. , 2016). The IASI mission is meant to last for several decades (MetOp) whereas the instruments OMI, MLS and TES are scientific missions with limited lifespan. Tropospheric O 3 from IASI has been already studied and validated. The IASI ozone data were found to be particularly well suited to the study of O 3 variations in the upper troposphere  (Dufour et al. , 2012; Tocquer et al. , 2015; Barret et al. , 2016). Since we already have about 10 years of data, the IASI mission provides a valuable dataset to study the O 3 variability and trends  (Toihir et al.
IASI and MetOp-A measurements The IASI is one of the instruments onboard the polar-orbiting satellite MetOp-A (Meteorological Operational), which is operated by the European organization for the exploitation of Meteorological Satellites (EUMETSAT). The MetOp-A satellite was launched on 19 October 2006 and has already provided data for about 10 years. Due to its inclination to the equatorial plane and its altitude (817 km), MetOp-A crosses the equatorial plane at 09:30 and 21:30 LT (the equatorial plane at 9:30 and 21:30 local solar time when crossing the Equator). IASI is a nadir-viewing Fourier Transform Spectrometer. The detectors sense in the thermal infrared spectral range between 645 and 2760 cm -1 (15. 5 to 3. 62 µm). IASI provides spectra with a high radiometric quality at a resolution of 0.
We use O 3 retrievals performed with the Software for Fast Retrieval of IASI Data  (Barret et al. , 2011)  developed at Laboratoire of Aerology. The SOFRID (SOftware for a Fast Retrieval of IASI Data) is based on the RTTOV (Radiative Transfer for TOVS,  Saunders et al. , 1999a, b)  fast radiative transfer model coupled to the 1D-Var algorithm developed at UKMO (United Kingdom Met Office,  Pavelin et al. , 2008). SOFRID retrieves the O 3 profiles on 43 levels from 1013. 25 to 0. 1 hPa using a single a priori profile and covariance matrix based on 1 year of in situ observations (see  Barret et al. , 2011 for details). Validation of 6 months of tropospheric O 3 columns from IASI-SOFRID against ozonesondes and airborne data have shown biases of about 5 % and relative standard deviation (RSD) of about 15 % in the tropics.
The OMI-MLS residual method and the Ozone ENSO Index The OMI instrument, is one among a total of four instruments onboard the Aura satellite. It is a nadir-viewing imaging spectrometer that measures the solar radiation reflected by Earth's atmosphere and surface  (Levelt et al. , 2006). It makes spectral measurements in the ultraviolet (270-314 and 306-380 nm) and visible 350-500 nm wavelength regions at 0. 5 nm resolution. OMI provides measurements with a daily global coverage and a very high horizontal spatial resolution of 13 km × 24 km at nadir  (Dobber et al. , 2006). Retrieval errors of the OMI data vary from 6 to 35 % in the troposphere  (Liu et al. , 2010). Total column ozone from OMI have been derived using the TOMS version 8 algorithm  (Ziemke et al. , 2006). To derive TCO with the OMI-MLS residual method,  Ziemke et al.
(2006)  subtracted the stratospheric ozone columns retrieved with MLS from the OMI total column. They selected OMI pixels with near clear-sky conditions (radiative cloud fraction < 30 %). Stratospheric MLS data were spatially interpolated each day on a coarser regular grid. The tropopause height used for the TCO cutoff between OMI and MLS comes from the National Centers for Environmental Prediction (NCEP) using the 2 K km -1 lapse rate tropopause definition  (Craig, 1965)  of the World Meteorological Organization (WMO). We used OMI-MLS data from the NASA GODDARD website for tropospheric ozone (http://acd-ext. gsfc. nasa. gov/Data_services/). All available daily data have been averaged to compute monthly means with a latitudelongitude resolution of 1 • × 1. 25 •. There is no single universal ENSO index reproducing oceanic and atmospheric physical conditions over the tropical Pacific  (Trenberth, 1997).
The OEI is obtained by subtracting the TCO in the region named Pacific Ocean Center (POC, 15 • S-15 • N, 110-180 • W) from the TCO in the region Indonesia with Indian Ocean (IIO, 15 • S-15 • N, 70-140 • E) each month. To compute the TCO, the altitude of the tropopause must be known. Ziemke et al. (2010)  used tropopause heights derived from the NCEP data. The tropopause is defined as the lowest level, with respect to altitude, at which the temperature lapse rate decreases to 2 • C km -1 or less and does not exceed 2 K km -1 for 2 km above. We adopted this tropopause computation to derive the OEI from our analyses. Tropopause pressures, used to compute the Ozone Index with the assimilation of both IASI-MLS and MLS data only, are comprised between 80 hPa at low latitudes and 500 hPa at high latitudes.
Ozonesondes Ozonesondes are launched in many locations over the world on a weekly basis, measuring vertical profiles of O 3 concentration with a high vertical resolution of 150-200 m, from the ground to approximately 10 hPa. Data are collected by the World Ozone and Ultraviolet Radiation Data Center (WOUDC, http://www. woudc. org). During the 6 years considered in this study  (2008 to 2013) , only 270 ozone soundings are available for the Pacific area between 15 • S-15 • N and 70 • E-110 • W (Fig. 2 ). We divide this area into two regions: IIO and POC, which are represented by the two blue rectangles in Fig. 2. WOUDC ozonesonde measurements used for the validation are considered as a reference. Despite their sparse geographical distribution, several studies have used the WOUDC database to validate global models and satellite retrievals  (Geer et al.
Results We have performed three ozone simulations covering the period 2008 to 2013. The first simulation, called Direct Model (DM), has been produced by running the MOCAGE CTM without data assimilation. The model is initialized with a climatology on 1 November 2007 to allow for a spin-up period of 2 months. The second simulation, named MLS-a, started in January 2008 with the assimilation of MLS profiles for the whole period. Finally, the third simulation (IASI-a) was produced with the assimilation of IASI tropospheric O 3 columns and MLS stratospheric O 3 profiles. Both MLS-a and IASI-a are initialized with the direct model output on 1 January 2008. For the three simulations the outputs are recorded every 6 hours. The main results are outlined as follows. Section 3. 1 contains the validation of the simulations against ozonesondes.