dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements: This study was supported by  Human Frontier Science Program  ( RGY0073/2015 ) and the  Basic Science Research Program  through the  National Research Foundation of Korea  funded by the  Ministry of Science, ICT and Future Planning  ( NRF-2013R1A1A2053280 ). Acknowledgements: We acknowledge partial support by the  Helmholtz Alliance through the Initiative and Networking Fund of the Helmholtz Association  and the  Helmholtz Portfolio theme "Supercomputing and Modeling for the Human Brain ". Acknowledgements:  Klinische Forschergruppe  ( KFO219 ,  TP12 ) of the  Deutsche Forschungsgemeinschaft ;  Helmholtz Association, EuroSPIN and Erasmus Mundus Joint Doctorate Programme. Acknowledgements: This work was supported by the  DGIST R&D Program of the Ministry of Science, ICT and Future Planning of Korea  ( 15-RS-02  and  16-RS-02 ).
Acknowledgements: This work was supported by  Institute for Information and communications Technology Promotion (IITP)  grant funded by the  Korea government (MSIP)  ( R0190-15-2054 ,  Development of Personal Identification Technology based on Biomedical Signals to Avoid Identity Theft ). Acknowledgements:: Supported by the  National Research Foundation of Korea  ( NRF-2013R1A1A2011570 ). Acknowledgements: This work was partially supported by  Simons Foundation Collaboration Grants for Mathematicians  (# 317566 ) to CP and  East Carolina University, Department of Biology  fund to FAI. Acknowledgements: This work was supported by the  Grant Agency of the Czech Technical University in Prague , grant No. SGS14/192/ OHK3/3T/13 , the  Czech Science Foundation  Project No. P303-14-02634S , and the  Czech Health Research Council  Project No.
NV15-29835A. Acknowledgements: We acknowledge funding by the  Helmholtz Association : portfolio theme SMHB and Young Investigator's Group  VH-NG-1028 , and  604102  ( Human Brain  Project). All network simulations were carried out with NEST (http://www. nest-initiative. org). Acknowledgements: The study was supported by  NSF  grant  DMS-1226180  awarded to  Yixin Guo. Acknowledgements: This research was supported by the  Australian Research Council (ARC). TK acknowledge support through  ARC Discovery Early Career Researcher Award  ( DE120102210 ). Acknowledgements:  M. D. McDonnell  was supported by an  Australian Research Fellowship  from the  Australian Research Council  (project  DP1093425 ). B. P. Graham's contribution was supported by the  BBSRC  project grant  BB/K01854X/1. Acknowledgements: Supported by  EC Fp7-PEOPLE-2011-ITN PITN-GA-011-290011 (ABC) ,  FP7-ICT-2013-FET-F/604102  (HBP),  FP7-ICT-2013-10/611909  (Koroibot), BMBF, FKZ:  01GQ1002A ,  DFG GI 305/4-1 + KA 1258/15-1.
Acknowledgements: Supported by  EC FP7-ICT-2013-FET-F/604102  (HBP),  Fp7-PEOPLE-2011-ITN PITN-GA-011-290011 (ABC) ,  FP7-ICT-2013-10/611909  (Koroibot), BMBF, FKZ:  01GQ1002A ,  DFG GI 305/4-1 + KA 1258/15-1. Acknowledgements: This research was supported by the  European Research Council  (Grant Agreement No. 268 689 ). Acknowledgements: This study was financially supported by  Advanced Bionics Corporation. Acknowledgements:  EU   FP7  grant  604102  (HBP);  Helmholtz Portfolio Supercomputing  and Modeling for the Human Brain (SMHB). Acknowledgements: VSR computation time Grant  JINB33 ,  Helmholtz Portfolio SMHB ,  EU  Grant  269921  (BrainScaleS),  EU  Grant  604102  ( Human Brain Project, HBP ),  SFB936/A1 ,  Z1  and  TRR 169/A2. Acknowledgements: KG is funded by the  Marie Curie Initial Training Network INDIREA , grant agreement no ITN-2013-606901.
APA was supported by  SEMAINE ERA-Net NEURON Project. MG  acknowledges funding from  FP7 FET ICT Flagship Human Brain Project  ( 604102 ). GD was supported by the  ERC Advanced Human Brain Project  (n. 604102) and the  Plan Estatal de Fomento de la investigación Científica y Técnica de Excelencia  ( PSI2013-42091-P ). Acknowledgements: MG and GD were supported by the  FP7 FET ICT Flagship Human Brain Project  ( 604102 ). GD was supported by the  ERC  Advanced Grant: DYSTRUCTURE (n. 295129), by the  Spanish Research  Project  SAF2010-16085. MLK was supported by the  ERC  Consolidator Grant: CAREGIVING (n. 615539). MLK, MW and PV were supported by the  Center for Music in the Brain , funded by the  Danish National Research Foundation  ( DNRF117 ). Acknowledgements: We thank  Florentin Wörgötter  at the  Department of Computational Neuroscience in Göttingen , where most of this work was conducted.
Acknowledgements: 2 1 2 Supported by the Erasmus Mundus Joint Doctoral program EuroSPIN, the German Federal Ministry of Education and Research, grant 01GQ0830, and the state of Baden-Württemberg through bwHPC. P71 Corticothalamic dynamics: structure, number of solutions and stability of steady-state solutions in the space of synaptic couplings Paula Sanz-Leon 1,2 , Peter A Robinson 1,School of Physics, University of Sydney, New South Wales, Australia; 2 Center for Integrative Brain Function, University of Sydney, New South Wales, Australia Correspondence: Paula Sanz-Leon -paula. sanz-leon@sydney. edu. au BMC Neuroscience 2016, 17(Suppl 1):P71.
This paper reports on our ongoing efforts to collect speech data in under-resourced or endangered languages of Africa. Data collection is carried out using an improved version of the Android application Aikuma developed by Steven Bird and colleagues 1. Features were added to the app in order to facilitate the collection of parallel speech data in line with the requirements of the French-German ANR/DFG BULB (Breaking the Unwritten Language Barrier) project. The resulting app, called Lig-Aikuma, runs on various mobile phones and tablets and proposes a range of different speech collection modes (recording, respeaking, translation and elicitation). Lig-Aikuma's improved features include a smart generation and handling of speaker metadata as well as respeaking and parallel audio data mapping. It was used for field data collections in Congo-Brazzaville resulting in a total of over 80 hours of speech.
Main features of the initial app In Aikuma, both respeaking and oral translation work the same way: the speaker listens to segments of an audio source file, then speak them, either in the same language (respeaking) or in another one (oral translation). The produced segments are concatenated altogether and result in a single respoken (or translated) file. Once concatenated into a file, the segments may not be easy to distinguish, this is why a text file is also produced which contains the alignment of every segment of both audio files. Figure  1  illustrates such an alignment of segments. Additional features were thought for an easy and wide use. The user interface is composed of icons and is text-less, allowing anyone to use it. The application comes with a wireless "sync and share" feature that allows several devices to share the audio files.
Metadata and file naming For every mode, a metadata file is saved with the recording file. Metadata are filled in a form before any recording. Unlike the initial version of Aikuma which proposed four different views for that purpose, the form summarizes all the questions within a single interface. In addition, metadata have been enriched with new details about the languages (language of the recording, mother tongue of the speaker and other spoken languages) and about the speaker (name, age, gender, region of origin). Moreover, in order to save time, a feature saves the latest metadata as a session and uses it to pre-load the form the next time it is necessary. The files are now named using the specific following format: DATE-TIME DEVICE-NAME LANG. As an example 2015 07 22 17 00 00 Samsung tablet fra is the name of a recording made on July, 22nd 2015, at 5pm (17:00:00), in French language, on a Samsung device.
Other updates The interface has been adapted for the large screens of tablets (10 inches), so the app works both on Android powered smartphones and tablets. Apart from the specifications, based on multiple discussions with linguist colleagues, this new version was developped in approximately 3 man-months and generated 5000+ lines of code. All the new code has been put on the LIG forge and is accessible open source 2  for use or development on demand. Following the licence of the initial Aikuma application, the Lig-Aikuma application will be released under the same GNU Affero General Public License  3. The application Lig-Aikuma has been successfully tested on different devices (including Samsung Galaxy SIII, Google Nexus 6, HTC Desire 820 smartphones and a Galaxy Tab 4 tablet).
Objectives and data preparation Successful fieldwork, in particular data collection, requires careful preparation. In our project, we specifically had to consider both linguistic and technological requirements, as the overall aim is to develop NLP tools for linguistic work. Prior to the trip, we discussed the specifications to meet both linguists' and computer scientists' requirements. Beyond standard ethical issues and consent forms, specifications included the collection of "linguistically dense" data e. g. , elicited verb conjugation or small sentences designed to collect most useful data for basic oral language description complying with traditional fieldwork. Figure  3  illustrates the use of the Lig-Aikuma tablet to collect Mboshi verb conjugations (left) or more free conversations including several speakers (right).
Our objective was to collect large volumes of data (at least several tens of hours) from dozens of speakers in different speaking styles. All possible written documents including a 2000-entry Mboshi dictionary 8 , traditional tales and biblical texts in Mboshi were gathered. Furthermore, a large part of the 6000 reference sentences for oral language documentation  9  were translated and written in Mboshi by one of the authors (Guy-Noël Kouarata). These written sources were processed to enter the elicitation recording mode in Lig-Aikuma. A further objective of the fieldtrip was to take tradition and culture related images and videos. These can be used as language-independent speech eliciting material in later collections of purely oral languages. They can also be used to illustrate language resources, such as online dictionaries and education material, which can be seen as a fair payback to the speaker community who accepted to contribute to the study.
Data collected Practically, two 1-month fieldtrips to Congo-Brazzaville were done by one of the authors (Guy-Noël Kouarata) who is a native speaker of Mboshi. The data recording campaign was organised as follows: four Galaxy Tab 4 tablets were given to four main contact persons who were responsible of the material and who were responsible of recruiting volunteer speakers from their relatives, friends and broader acquaintances. The main contact persons were also asked to contribute to the respeaking process. All speakers were paid and the main contact persons will be given the tablets at the end of the collection process as additional reward. So far, within the BULB project, 48 hours of speech data in Mboshi language were collected with Lig-Aikuma in Congo-Brazzaville. The corpus is composed of 33 hours of spontaneous speech (mostly debates and stories), 9.
Conclusion This paper presented Lig-Aikuma, a recording application for language documentation. A first data collection of language resources in Mboshi was conducted and further developments and features are planned in a near future. One of the goals of the BULB project is also to automatically extract knowledge from the parallel speech data collected (like Mboshi / French parallel signals). This could be used, for instance, to automatically learn word units (as well as their pronunciation) in an unknown (and unwritten) language with very little supervision.
0. 2. The best result for each category is highlighted in bold. Nucleic Acids Research quence alignments (MSA) of the binding partners. The In-terEvDock server proposes to fill this gap through an integrated solution to perform rigid-body docking, accounting for co-evolution information. We have previously shown that beyond conservation, incorporation of co-evolution constraints could significantly increase the recognition of correct protein-protein complex decoys  (18). The InterEvScore potential was developed as a residue-based statistical potential, scoring the likelihood of contacts between two to three residues across an interface and for all the sequences in the joint alignments of both partners. The InterEvDock server predicts the structures of protein-protein complexes, using first the FRODOCK rigid-body docking program to generate a large body of potential decoys  (19)  and next running the InterEvScore potential  (18)  combined with other complementary scoring methods provided by the FRODOCK  (19)  and SOAP-PP potentials  (20)  to propose a limited set of 10 most likely models.
The success rates of InterEvDock server was benchmarked on the 85 complexes of the Weng Benchmark 4 (BM4)  (21)  for which a pair of MSAs (with no fewer than 10 sequences) can be generated. 49% of the complexes in the rigid-body category have an acceptable or better solution in the top10 consensus of InterEvDock server. In this category, performances are higher than published benchmarks for the SwarmDock  (10)  or Zdock3. 0. 2  (11)  servers that reach 42% and 28% on the same set, respectively. For the cases in the Weng BM4 difficult category, the InterEvDock success rate is lower than the one of the rigid-body cases, consistent with the sampling strategy which incorporates no flexibility in the docking process. InterEvDock server also predicts a set of 10 residues most likely part of the interface with at least one residue properly predicted in 91% of the 85 test cases.
Web interface Users can either upload their own PDB files or retrieve the chain of interest by typing the PDB code and chain id for automatic retrieval. The sequence used for MSA construction is automatically extracted from the PDB. MSAs can also be uploaded by the user provided their first sequence matches that of the PDB file and that they respect the same order of species as in the automatic mode. Uploading alignments is for instance required when one of the input part-ners is a fusion of more than one protein chain. In case not enough sequences are found by InterEvDock or in case of a protein family with many paralogs, users can also generate the MSAs using the InterEvolAlign server that additionally implements a reciprocal blast search procedure but is more time consuming  (22)  (http://biodev.
Multiple sequence alignments for co-evolved partners The InterEvScore potential is calculated based on MSAs of co-evolved homologs of both input partners. The server runs a fast and automatic protocol as used in  (28)  to retrieve homologs of both partners. For every input protein, homologous sequences are searched using a single BLASTp search  (29)  against the Uniprot-KB database (The UniProt Consortium, 2014) with the threshold sequence identity > 30%, coverage > 75% and E-value < 10-4. In case several sequences were found for one species, only the sequence with the highest sequence identity (and highest coverage if sequence identities are identical) is chosen. Pairs of sequences in every MSA belonging to the same species are collected. Redundant paired sequences with sequence identity higher than 90% are removed and MSAs are re-aligned by MAFFT program  (30).
(9) A 'start analysis. pml' pymol script is distributed in every zip archive which can be opened in a single click to generate pre-processed views of the 3xtop10 models, including color mapping schemes as in  (5). difficult targets. Zdock reached a lower performance with 44% of the cases in which at least one correct residue is predicted on both sides of the interface. Performance was also compared with the EVcomplex server (  16 ) on a subset of 13 bacterial complexes of Weng BM4 (Supplementary Table  S3 ). In most cases, InterEvDock performed better with 6 versus 2 targets out of 13 for which residues could be correctly predicted on both sides of the interface. Altogether, predictions of InterEvDock server can thus be used for several types of applications. For bioinformaticians, these predictions can provide useful priors to help focusing docking simulations and refinement on specific regions.
Step-by-step description of a running case To illustrate how the server runs and how results can be exploited, let us consider an example from the Weng benchmark v4 of the complex 2G77 to be predicted from the unbound proteins the GTPase-activating protein GYP1 (PDB:1FKM chain A) and the Ras-related protein Rab-33B (PDB:1Z06 chain A). The InterEvDock consensus ranks an acceptable solution in the top2 (Figure  2B ). PDB files of the unbound form can be uploaded from the input files distributed in Weng BM4 (http://bioserv. rpbs. univ-parisdiderot. fr/services/InterEvDock/table. html) (or directly retrieved using the PDB codes and the chain label (Figure  3  step 1)). As shown in Figure  3  step 2, users can also upload their own MSAs by toggling on the advanced options. As the server runs, a log of events is provided indexed by the time required for each step to be completed (Figure  3  step 3).
The other important information concerns residues that are predicted as part of the interface (Figure  3 step 7 ). In the case of 2G77, the prediction works very efficiently since all ten residues are part of the correct interface. Decoy structures can be downloaded from the html table or from the zip file attached below (Figure  3  step 8). All these results can also be viewed easily thanks to the pymol script 'start analysis. pml', included in the zip archive (Figure  3  step 9).
(6) List of the 10 best consensus models predicted by InterEvDock server. (7) List of the 10 most likely residues at interface predicted from the consensus analysis. (8) Zip archive containing all the top50 models selected by InterEvScore, SOAP-PP and FRODOCK. (9) A 'start analysis. pml' pymol script is distributed in every zip archive which can be opened in a single click to generate pre-processed views of the 3xtop10 models, including color mapping schemes as in (5).
SIMULATION RESULTS We setup one UAV to explore an area with several forest-fire. A predefined plan is embedded in the UAV. According to the UAV perception, it modifies its plan using its constructed map. Figure  4  is an example of an executed UAV trajectory, using the developed exploration strategy discussed in section 3. 3. Figure  4  represents an horizontal view of the forest-fire. We can see that thirteen forest-fire exists in this example. However, even if the UAV modifies its trajectory it detects four targets. First part of Table  1 , represents a statistical experiment with different environment using one and several UAVs. We used different exploration depths for UAVs to evaluate the decisional and cooperative exploration strategy. Each UAV communicates and  sends its explored data to the other UAVs using predefined communication points (see an example in Figure  8 ).
We apply an optimized method to the adjoint generation of a time-evolving land ice model through algorithmic differentiation (AD). The optimization involves a special treatment of the fixed-point iteration required to solve the nonlinear stress balance, which differs from a straightforward application of AD software, and leads to smaller memory requirements and in some cases shorter computation times of the adjoint. The optimization is done via implementation of the algorithm of Christianson (1994) for reverse accumulation of fixed-point problems, with the AD tool OpenAD. For test problems, the optimized adjoint is shown to have far lower memory requirements, potentially enabling larger problem sizes on memory-limited machines. In the case of the land ice model, implementation of the algorithm allows further optimization by having the adjoint model solve a sequence of linear systems with identical (as opposed to varying) matrices, greatly improving performance.
In this paper, we present an application of the AD software OpenAD  (Utke et al. , 2008)  to the MITgcm time-dependent glacial flow model  (Goldberg and Heimbach, 2013). A different AD tool has previously been applied to this ice model, so here we focus on the implementation of the Christianson algorithm (henceforth called BC94) -an innovation which is observed to yield substantial improvements in performance.
Of key importance is that any storing of variables that takes place within the call to in the INLOOP phase is undone at the end of each iteration. Once convergence is reached, storing takes place as normal in the POSTLOOP phase. The reason for the addition of this layer PHISTAGE is rooted in the nature of OpenAD source transformation. To implement BC94 using this tool, it was found to be sim- FOR n = finalTimeStep DOWNTO initialTimeStep // Constructs δ * H [n] and δ * u from δ * H [n+1] // via the adjoint of the continuity equation : plest to apply the template mechanism provided by OpenAD, that lets the end-user provide a customized differentiation of specific sections of the code by means of a template, handwritten once and for all. Such a template was written for PHISTAGE in order to implement the pseudocode in Pseudocodes 3 and 4.
The subroutine thus serves as a layer which does not affect the diagnostic ice physics represented by the function or the prognostic physics implemented outside of the FFPI loop. Thus, the modularity offered by the AD approach is not lost. Pseudocode 4 shows how the adjoint model is constructed, making use of the OpenAD-generated adjoint code for. In adjoint mode, the calls to PHISTAGE happen in reverse order. The variable w is a placeholder with no real role in the forward computation, but the adjoint of the call to PHISTAGE in the POSTLOOP phase assigns to δ * w the adjoint of velocity resulting from AD_ADVECT_THICKNESS, in other words δ * u *. In the INLOOP phase, δ * w is updated according to the equation CALL AD_ADVECT_THICKNESS() CALL AD_PHISTAGE(POSTLOOP, δ * w, δ * u, δ * â) REPEAT UNTIL CONVERGENCE OF δ * w CALL AD_PHISTAGE(INLOOP, δ * w, δ * u, δ * â) CALL AD_PHISTAGE(PRELOOP, δ * w, δ * u, δ * â) δ * u = 0.
Performance Here, we evaluate the relative performance of the mechanical and fixed-point adjoint models in terms of both timing and memory use. The results are presented in Table  1 , but we must first briefly discuss how the OpenAD-generated adjoint computes sensitivities for a time-dependent model. As mentioned in the introduction, adjoint computation takes place in reverse order relative to forward computation. This presents an issue, because at each time step in this reverse computational mode, the adjoint model requires knowledge of the full model state at the corresponding forward model time step. In Table  1. Timing performance and memory usage of mechanical and fixed-point adjoints. In the "Plain" column, "avg iter" indicates the number of nonlinear iterations per time step. In other columns, "adj iter" indicates the total number of iterations of the adjoint loop described by Pseudocode 4 divided by the number of time steps.
This is followed immediately by a time step computation in adjoint mode. In the model runs we consider, only one level of checkpoints is required. A run of 40 time steps, then, will consist of nearly 40 time steps in plain mode (no taping, but with checkpoint writing), 40 time steps in tape mode, and 40 time steps in adjoint mode. Even if adjoint time steps and writing to disk and to tape are negligible, such a run will still take about twice as long as the forward model. In Table  1  we compare run times for the forward and reverse sweeps for the mechanical and fixed-point adjoints of our test problem, at multiple grid resolutions. We also give run times for the untouched, or plain model, i. e. , code which has not been transformed by OpenAD. The difference between this time and the forward sweep represents writing checkpoints to disk, taping in the final time step, and any other extra steps or changes (e.