dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The database and the analysis tool are available free of charge for the scientific community from the PAN-GAEA website (doi:10. 1594/PANGAEA. 864090). The POLDER/PARASOL Level-1 and Level-2 products were generated by CNES and are distributed by ICARE (http://icare. univ-lille1. fr). CCCC is the column number in the POLDER sinusoidal grid (1 to 6480) The first lines of a BRDF/BPDF data file are given below: Each file starts with 3 header lines. The header provides the pixel location in latitude and longitude, the 20 IGBP class number, the NDVI, the number of valid satellite overpasses, the number of valid observations (each overpass provides up to 16 different directional measurements) and the fraction of the dominant surface type within the POLDER pixel (about 6x6 km 2 ).
, 2015; Olsen et al. , 2016). Improved modelling of these types of grassland depends on the advancement of ecological knowledge, and progress in related topics including multi-species, nutritive value and soil and water modelling (challenges 1, 2, 10). Research priorities: Meta-experiments have been recommended to create international networks of experimental sites which apply the same treatments and recording standards to investigate the responses of swards to environmental change  (Fraser et al. , 2013; White et al. , 2012). Over the long term data from such programmes could facilitate more effective model improvement. Knowledge, data and current model descriptions of the mechanisms underlying grassland plant responses should be reviewed to assess capacity (which species are well characterised, which types of impact and which interactions are incorporated and what are the limitations to the approaches used).
, 2014). Recently, a full-year model (BASGRA), for timothy grass was developed by combining a growing season model with coldhardening and soil physical models for the winter season  (Höglind et al. , Accepted). Research priorities: An important next step for model development in this field will be to test the winter-related functions of grassland models against data from experiments simulating projected future winter conditions. Further model development in this field will depend on the availability of experimental data on cold sensitivity and the state of the sward (such as tiller density and leaf, stem and reserve weight during the growing season and over winter). As well as the collection of new data, the systematic organization of existing datasets on these variables according to temperature, precipitation and photoperiod gradients would be beneficial to the development and applicability of winter modules across geographic regions and climatic conditions.
For example, temperatures that are abnormally low or high often result in lower plant productivity at all subsequent temperatures  (Zaka et al. , Accepted). In climate change impact studies using grassland models, responses to extreme temperatures and prolonged water deficits are still not sufficiently considered  (Reyer et al. , 2013; Ruppert et al. , 2015). They are also scarce in model calibration and validation datasets due to their low frequency in weather data time series (Ben  Touhami and Bellocchi, 2015). The mechanistic relationships between plant processes and the impact of extreme events on these processes have only been fragmentarily documented, and the extent to which plants may be able to respond to extreme weather events remains an open field of research  (Reyer et al. , 2013).
Grassland data relating to previous extreme events can also be examined to better understand resilience. Current projects, such as MODEXTREME (http://modextreme. org/) and MERINOVA (https://merinova. vito. be/Pages/home. aspx) offer collaborative arenas for making progress in overcoming this challenge. The synthesis and sharing of outcomes from these projects in the wider modelling community will be important in the future development of modelling capacity.
jrc. ec. europa. eu) is a good example of a software platform that supports modular model development and evaluation. Research priorities: A key first step to developing more adaptable models is to gain an overview of their current capabilities in relation to different potential uses. Creating a checklist style inventory which clearly compares model applicability in relation to specific tasks would both highlight scales and types of modelling that are missing, and help stakeholders and policy-makers to select the most appropriate modelling tools to support their activities. Model inventories within projects such as MACSUR  (Bellocchi et al. , 2013)  form the basis for the development of such a resource, while online hubs such as Agrimod provide the potential to share this information with wider scientific and stakeholder communities.
Providing data for models The challenge: Models rely on experimental data for their development, evaluation and application to different problems. Data issues vary for different areas of grassland modelling. They can be categorised as 1) The need for data from new experimental work 2) Quality and completeness of available data, 3) Data accessibility, and 4) Variation in data measurement and recording: 1) Datasets which include information about previous management (for example, the age of the grassland, previous fertilisation, cutting or grazing) are often lacking, for example in relation to data on soil carbon and carbon sequestration. In general there have been fewer studies investigating interactions between variables, for example in studies of soil processes (challenge 2) with a focus on single variables more usual.
(2015)  developed a quantitative classification framework to evaluate the quality and consistency of existing agricultural datasets for use in crop models. This framework is likely to be applicable for the identification of data for grassland models, especially for models used to characterise both grassland and cropping systems  (Bellocchi et al. , 2013). New approaches to data collection include the use of remote sensing  (Courault et al. , 2010; Verrelst et al. , 2015)  and the development of virtual weather stations that combine a range of data sources to improve rainfall estimates  (Racca et al. , 2011). These advances can improve data accuracy and provide new data-sources of potential value for grassland modelling. 3) Open access data platforms such as FLUXNET  (Baldocchi et al. , 2001)  provide examples of how standardised collecting, processing and delivery of data can be developed, and that data shared.
Acknowledgements This paper was supported by the  FACCE-JPI  knowledge hub MACSUR with national funding from  BBSRC and Scottish Government (UK) ,  EL&I  (The Netherlands),  INRA (France) ,  MIPAAF (Italy) ,  MMM (Finland) ,  RCN (Norway) ,  SPW (Belgium) ,  The National Centre for Research and Development (Poland) ,  FORMAS (Sweden) ,  JÜLICH  and  BLE (Germany). The authors would like to thank  Dr Panu Korhonen  (Luke) and two anonymous reviewers for their contributions to the revision of this paper.
Recurrent neural network model We developed a recurrent neural network model using reservoir computing (RC) to perform the cognitive task in order to generate predictions that could then be tested with dACC monkey data. According to the RC principle, a fixed, large, random reservoir (recurrent neural network RNN) is excited by input signals, and the desired output is combined from the excited reservoir signals by a trainable readout mechanism (a simple linear regression in the most simple versions). As mentioned, the RC principle has been independently discovered in cognitive neuroscience (temporal recurrent networks,  [8, 25] ), in computational neuroscience (liquid state machine,  [9] ), and in machine learning (echo state networks,  [10] ). Models have been recently developed along the RC principle to reproduce cognitive functions like working memory  [17]  and language comprehension and production  [26] [27] [28].
Neural data Quilodran et al. recorded 546 neurons in the dorsal bank of the cingulate sulcus of two rhesus monkeys and analyzed them along with local field potential for their correlation with the behavioral shift  [21]. The present article reports on a new and separate reanalysis of this dataset to support findings obtained with modeling. All reanalyzes of these data were based on firing rate estimates of the recorded neurons. Subsets of this pool of neurons were selected depending on the requirements of the analyses. The number of neurons per analysis is specified in each case in the related method description.
As illustrated in Table  2. With noise from 1 to 4. 5 SD, the p-value of the correlation is below 0. 05 (see Table  1  for a comparison between dACC and the model with a 1 SD Gaussian noise). However, in this range of noise the model performed poorly. Interestingly, the performances dropped with slight noise, as was the case with the dynamic mixed selectivity. Although this result cannot be accepted as a causal relation between dynamic mixed selectivity and performance, it is reminiscent of the correlation observed by  Rigotti et al. (2013)  between non-linear mixed selectivity and monkey performance. Reservoir networks are by nature dynamic, and representations within single reservoir neurons are themselves highly dynamic. Likewise, it was demonstrated that the presence of mixed selectivity in the activity of single PFC neurons is important for behavior  [7] , and that this For comparison purposes, the dACC ratios are plotted as dashed lines with the same color code (see legend).
Task-relevant mixed selectivity We have seen that certain neurons encode uninterpretable forms of mixed selectivity (e. g. Figs  3  and 4C  and 4D ). Are there forms of dynamic mixed selectivity that are more meaningful? In the problem solving task the first reward is the key transient signal to stop exploring and concentrate on the rewarded target, i. e. to initiate the repeat phase. Quilodran and colleagues found that this transition signal is represented in the activity of a sub-population of dACC neurons. These feedback-related neurons activated only after the feedback of the first rewarded trial (COR1, see Fig 6A ), and thus represented the conjunction of reward and search phase. Their activity was significantly different from incorrect (INC) and correct trials in repeat phase (COR) (12% and 24%, of 146 and 88 feedback related neurons in first and second monkey respectively, see Quilodran et al.
We thus set out to reproduce the continuous decoding results observed with reservoir neurons, now with dACC activity, by training a readout through ridge regression. The readout was trained and then tested on the activity of full search (INC) and repeat (COR2-3) trials to see if it could continuously decode task phase (290/546 neurons having at least 20 trials of each category, ridge parameter obtained through cross-validation, see Methods for details). This method allowed us to assess if a single readout, that can be considered as a downstream neuron connected to the dACC population, can continuously extract phase information from a dynamic population activity. On average, over all time bins of every search and repeat trials tested, the decoder made only 3. 0% ± 2. 1% of errors (mean ± std).
This type of universal coding that is then shaped by learning could be a key feature of primate adaptation. A developmental form of this universal coding and subsequent adaptation has been observed for the vowel space in the auditory system that is initially universal and then focused to the native language in early development  [49]. Reservoir models also provide insights on the dynamics of the population. Since the recurrent network connectivity in RC is fixed and generated according to only a few parameters suited for the task at hand, it elicits generic dynamics that are of interest to understand the inherent dynamics that might be generated in a local cortical network. The autocorrelation results illustrate this point: dynamics in the model tend to closely follow the sequence of inputs fed to the network and appeared to reproduce the major features of the dynamics in the dACC, leading to two conclusions.
The present work was funded by  European research projects IST-231267 (Organic) ,  FP7 270490 (EFAA) ,  FP7 612139  (WYSIWYD), and  CRCNS NSF-ANR   ANR-14-NEUC-0005-1  (Spaquence). EP is funded by the  Agence Nationale de la Recherche   ANR-06-JCJC-0048  and  ANR-11-BSV4-0006 , and by the labex CORTEX  ANR-11-LABX-0042. The authors declare no competing financial interests.
RepeatsDB 2. 0 (URL: http://repeatsdb. bio. unipd. it/) is an update of the database of annotated tandem repeat protein structures. Repeat proteins are a widespread class of non-globular proteins carrying heterogeneous functions involved in several diseases. Here we provide a new version of RepeatsDB with an improved classification schema including high quality annotations for ∼5400 protein structures. RepeatsDB 2. 0 features information on start and end positions for the repeat regions and units for all entries. The extensive growth of repeat unit characterization was possible by applying the novel ReUPred annotation method over the entire Protein Data Bank, with data quality is guaranteed by an extensive manual validation for >60% of the entries. The updated web interface includes a new search engine for complex queries and a fully re-designed entry page for a better overview of structural data.
Therefore, units in tandem repeat proteins represent a fundamental source of information to explain contemporary structural diversity and the physico-chemical properties of highly designable folds  (12). However, identification of sequence periodicity is an extremely hard task, since repetitive proteins evolve quickly, for two main reasons. The process of duplication originating new repeats is error-prone and identical flanking repeat units have an intrinsic tendency to diverge  (13). A number of structure-based methods for the identification of repeats has been developed to fill this gap  (14) (15) (16) (17). RepeatsDB  (18)  was proposed in 2014 as a database of repeat protein structures and a resource for high-quality repeat structure annotation. The data was collected with RAPHAEL  (19) , a state-of-theart method for the detection of Protein Data Bank (PDB)  (20)  structures containing repeat regions.
All subclasses are characterized by a strong structural conservation in repeat units frequently not clearly reflected in sequence. This is the main reason why domain sequence databases such as Pfam  (23)  and SMART (  24 ) fail to detect a large number of repeats  (25). Indeed, as most of the largest clusters of human sequence regions not covered by Pfam were found to be repeated  (25, 26). RepeatsDB was developed to fill this gap and provides the community with a high-quality resource of reliable datasets of repeat structures for various purposes. The first and most obvious goal achieved was to compare the structural classification of repeats with the sequence-based one  (26). Other uses of Re-peatsDB are the extraction of repeat datasets to discuss specific features  (27, 28) , benchmarking both sequence-and structure-based repeat detection methods and discussing the role of proteins with repeats  (17, (29) (30) (31) (32) (33).
DATABASE DESCRIPTION RepeatsDB 2. 0 data have been completely regenerated taking advantage of the new ReUPred predictor  (34)  for automatic detection of tandem repeat units. In the new database version all entries are annotated at the unit level, i. e. providing start and end position for each repeated segment, and classified at the subclass level. Compared to the old version, unit annotations have grown by more than an order of magnitude. A detailed description of the RepeatsDB annotation pipeline follows.
Data curation The initial dataset for RepeatsDB is the entire PDB  (20). Repeat candidates are extracted with RAPHAEL  (19)  and processed with ReUPred  (34)  to confirm the presence of repeat regions and provide detailed unit information. Re-UPred is a predictor able to identify the position of repeated fragments by performing iterative structural alignments against a manually refined library of representative units. ReUPred is also able to assign the class and subclass by transferring this information from the unit library. The final dataset available in RepeatsDB 2. 0 is the result of an iterative process where the ReUPred library has been refined manually multiple times to resolve conflicts, improve its ability to generalize and include newly discovered subclasses. At the end of the process, an extensive validation and refinement of the predictions has been carried out by expert visual inspection.
Implementation RepeatsDB was designed as a multi-tier architecture, with three modules managing data storage, processing and presentation, respectively. Data are stored in a MongoDB database, and processed with Node. js. The server is accessi- ble through a web interface or programmatically exploiting a RESTful architecture. The web interface is designed using the Angular. js and Bootstrap frameworks. Dynamic and interactive elements of the entry page are developed using PV for structure visualization and Bio. js as sequence feature viewer, respectively. Both the database structure and Node. js server have been completely rewritten to improve efficiency and data reliability. Moreover, all data derived from third party resources have been processed and stored locally to prevent broken dependencies.
DATABASE USAGE The user interface presents an intuitive summary table providing direct access to all entries by structural class directly from the home page. For a finer search, the user can visit either the 'Browse' page providing subclass access or the 'Search' page for generating complex queries (Figure  1A  and B ). All entry points redirect to the same result page listing the retrieved proteins in a table (Figure  1C ). The table can be further filtered by providing additional matching strings in the column headers. The 'Browse' page also provides direct access to sequence clusters, where entries are grouped by sequence similarity. The redesigned entry page (Figure  2 ) is much more informative compared to the previous RepeatsDB version, including several cross-links to third party resources.
It also integrates several structural features useful for comparing CATH, SCOP, Pfam and DSSP annotations with RepeatsDB data. Regions, units and insertions are provided for all entries and correctly mapped both to UniProt and PDB reference (SEQRES field in the PDB file) sequences thanks to the SIFTS service. The correct mapping can strongly improve RepeatsDB impact since it is now very easy to link repeat data with other sequence features like mutations or post-translational modifications. Thanks to a RESTful architecture, all RepeatsDB data are accessible from external APIs and third party resources through HTTP URLs. Please refer to the 'Help' page of the website for details on using the RepeatsDB web services. Customized datasets can be downloaded in JSON or text format using the browse function or RESTful web services.
CONCLUSION AND FUTURE WORK RepeatsDB was presented in 2014 with the goal to provide the community with a central resource for high-quality tandem repeat protein structure annotation. It has been cited in a number of different studies regarding repeat proteins, and has been used to extract datasets for repeat proteins analysis and to test algorithms for repeat proteins annotation. The detailed annotation of entries performed by RepeatsDB curators has allowed us to build of a high quality Structure Repeat Unit Library (SRUL). This library was exploited by the ReUPred algorithm  (34)  as a gold standard to define unit position in new entries in an iterative process. The new release of RepeatsDB includes a new annotation pipeline, combining the RAPHAEL algorithm for repeat detection  (19)  and ReUPred for annotation  (34) , producing extensive annotation for all entries.
The pipeline is fully automated and allows the easy regular update of the database. The iterative execution of the pipeline already demonstrated its efficacy both because it identified a large D312 Nucleic Acids Research, 2017, Vol. 45, Database issue number of new entries, and because new subclasses were identified and added to the structural classification scheme. RepeatsDB will benefit from regular updates, which will steadily increase the number of available annotations. Future work will concentrate on exploiting the repeat unit definitions to create profiles for use in detecting repeats from sequence for genome-scale analysis  (37).
A trajectory between two configurations is only validated if the multi-contact non-slipping condition is validated. Algorithm 1 gives the pseudo-code of the motion planning algorithm. It generates a graph, called a roadmap, which can be requested online to compute a sequence of jumps between a start and a goal configuration. validTrunkRandomSample returns a root configuration q 0. q 0 is such that W 0 is collision-free, and ∃k, W k is in collision. findContacts estimates the contact positions on the surfaces resulting of the intersections between the W k shapes and the environment. From the estimated contact locations, the centroidal cone K c is computed with the method computeCone. validSteer is an extension from the parabola-steering-method of  [2]. To generate a trajectory between two configurations q 0 a and q 0 b , the method determines whether there exists a parabola that verifies the non-sliding condition.
Simulations Our framework has been implemented using the open source motion planning software Humanoid Path Planner (HPP)  [26]. The method source code is available online. Final renderings have been generating with Blender 2. 7 using the automatic blender export tool of HPP. We demonstrate the genericity of our approach with four characters in five environments. The characters have different morphologies with from two to six limbs. They also have different jump abilities, expressed as the friction coefficients of the environment, as well as velocity bounds. The Figure  4  illustrates a few simulation results. All the key qualitative results of the method are detailed in the companion video : they include removals of unnatural jumps by the non-sliding constraints, jump transitions with arbitrary numbers of contacts and collision avoidance.
Following agile methodologies such as unit testing or test-first should be supported. As a runtime optimizer is a complex domain, one wants to be able to change the design of the optimizer or an important part of its code base without invalidating the test suite. Therefore it is important to test the optimized code versus its non optimized version but it is not possible to write a proof for each of the increment and design variations. The compiler validation approach presented in this paper was implemented while our team was enhancing a baseline JIT compiler with adaptive optimizations. To support our agile development process, we built a test suite for day to day development as well as a large test suite on our whole system. The first suite is used to validate the code at each commit whereas the latter is used to raise our confidence level in the stability of the product before production.
A baseline x86 JIT compiler has been formally validated  [Myr10] , but baseline JIT compilers do not use dynamic deoptimization. The Maxine VM team took advantage of having a VM written in a high-level language to build an efficient test infrastructure  [DOD12] , but as far as we know their publications do not discuss tests on dynamic deoptimization. Some work has also been done to validate and test other aspects than dynamic deoptimization in a JIT compiler, such as weak mutation testing  [DOD12]  or reduction of the number of false alarms raised by a JIT compiler validator [HLP + 13].