dataesr/scientific-paragraphs-categorization huggingface dataset:
Technology development Since 2007 COBRA has provided small scale access to a first generation (G1) InP-based research platform, as described in the present paper. Process capabilities are gradually being improved and presently support design of ASPICs integrating lasers, optical amplifiers, modulators and detectors with 10 Gb s -1 speed, and a variety of passive optical components. The platform is suitable for research and prototyping but not for volume production. In 2009 the EuroPIC project began with the mission of transferring the foundry model from a university environment to industrial and semi-industrial platforms (the wafer fabs of Oclaro and FhG-HHI) and starting development of software design kits and standardized packaging solutions. The capabilities of the EuroPIC platform technologies are listed in table 2, columns 2012.
Acknowledgements Funding is acknowledged by the  EU -projects ePIXnet [38], EuroPIC [39] and PARADIGM [40] and the  Dutch projects NRC Photonics , MEMPHIS [41],  IOP Photonic Devices [42 ] and  STW GTIP [43 ]. Many others have contributed and the authors would like to thank other PARADIGM and EuroPIC partners for their help in discussions, particularly  Michael Robertson  (CIP).
Conclusions: The novel vision of ontologies that we have developed in this paper enables a declarative assembly of different models to obtain composed models guaranteed to be anatomically valid while capturing the complexity of human anatomy. The main interest of this approach is its declarativity that makes possible for domain experts to enrich the knowledge base at any moment through simple editors without having to change the algorithmic machinery. This provides MyCF software environment a flexibility to process and add semantics on purpose for various applications that incorporate not only symbolic information but also 3D geometric models representing anatomical entities as well as other symbolic information like the anatomical functions.
Most of these works rely on ontologies expressed in OWL and use OWL reasoners that are based on Description Logics  [21]. OWL is one the standards recommended by the W3C for the Semantic Web, which enables expressing sophisticated ontological constraints (using Description Logics constructors) but with a high computational complexity in the worst case. RDFS is another W3C standard that is broadly used in particular in Linked Data. Whereas OWL is often seen as an extension of RDF and RDFS, this is not exactly the case, mainly because RDF(S) offers interesting non-first-order features which are not present in the Description Logics at the basis of OWL profiles, like the possibility of treating values both as constants and as classes or properties. In the same spirit, the RDF query language SPARQL makes possible to query at the same time the data and the schema and allows that variables stand for classes and properties.
This goes beyond the first-order conjonctive queries typically considered in DL-based settings. Recently, RDF-based semantic environments such as Jena (http://jena. apache. org/) or Cwm (http://www. w3. org/2000/10/swap/doc/cwm) have included logical rules to perform inferences on top of RDF datasets. Logical rules and Description Logics are two orthogonal decidable fragments of first-order logics that have been extensively studied in knowledge representation and in deductive databases. The interest of logical rules (a la Datalog) is that they are easy to read and write for practitioners and they have a polynomial data complexity while allowing expressing complex interaction between properties and recursivity. When application software provides capabilities to display/select 3D graphic entities, its interactive behavior becomes a mandatory feature to manage the graphic entities attached to the digital objects taking part to this application.
For example, a joint is a part of the articular system (but joint is not a subclass of an articular system), is declared by adding to the ontology data base the RDF triple: mcf:Joint mcf:PartOf mcf:Articular_System. Note that, like for FME (the explorer of FMA), the mcf:PartOf relation can be chosen for defining the tree structure through which the user wants to visualize the anatomical entities in 3D, as an alternative to the tree structure defined by the rdfs:subClassOf relation. • The property mcf:InsertOn is used to specify attach points of anatomical entities. This knowledge is important in anatomy and also for biomechanical simulation purposes. For instance, the distal tendon of right sartorius is inserted on the Medial part of proximal epiphysis of right tibia, is expressed by adding the RDF triple: mcf:Distal_Tendon_Of_Right_Sartorius mcf:InsertOn mcf:Medial_part_of_proximal_epiphysis_of_right_tibia.
Conclusions We have described MyCF with a particular emphasis on its ontology structure, showing how the FMA ontology can be used as basis of the anatomical description of human bodies and empowered with a taxonomy of anatomical functions conforming to the ICF terminology. We have introduced new concepts that are particularly useful for checking the anatomical validity of 3D models containing multiple anatomical entities, also for selecting sets of anatomical entities on the basis of functions rather than being bound to geometric approaches that are not efficient enough to process complex 3D geometric configurations. These high level functionalities can be achieved thanks to the combination of different types of knowledge related to anatomy. The reasoning capabilities brought by the inference rules increase the power of realizing complex tasks by reducing them to querying a knowledge base implemented as a deductive database.
The main interest of this approach is its declarativity that makes possible for domain experts to enrich the knowledge base at any moment through simple editors without having to change the algorithmic machinery. http://www. jbiomedsem. com/content/5/1/20 This provides MyCF software environment a flexibility to process and add semantics on purpose for various applications that incorporate not only symbolic information but also 3D geometric models representing anatomical entities as well as other symbolic information like the anatomical functions. The MyCF ontology is at the heart of the the MyCF Browser: a tool for exploring anatomical 3D models  [32]. Further work will address the use of this environment to feed a bio-simulation engine with the appropriate anatomical entities so that mechanical simulations can be easily set up and extract the required geometric information from the 3D models of anatomical entities.
Semantic technologies used for building and exploiting MyCF In order to make it easy to connect MyCF to the Linked Data cloud  [34] , we have followed the recommendations of W3C and we have chosen the RDF(S) language for expressing MyCF ontology. RDF  [35]  is a standard notation recommended by the W3C for the semantic Web composed of Web data and (simple) ontologies. RDF (Resource Description Framework) provides a simple language for describing annotations about Web resources identified by URIs. An RDF fact consists of a triple made of a subject, a predicate and an object. It expresses a relationship denoted by the predicate between the subject and the object. In a triple, the subject, but also the predicate, are URIs pointing to Web resources, whereas the object may be either a URI or a literal representing a value.
RDFS is the schema language for RDF. It allows specifying a number of useful constraints on the individuals and relationships used in RDF triples. In particular, it allows declaring objects and subjects as instances of certain classes. In addition, inclusion statements between classes and properties make it possible to express semantic relations between classes and between properties. Finally, it is also possible to semantically relate the domain and the range of a property to some classes. The point is that these constraints can be written in triple notation, i. e. , RDFS statements can be written using RDF as a notation. Therefore, a RDF data store can contain in the same format triples expressing that a given acquisition file (identified by a given URL u) is an instance of an anatomical structure (for instance the patella), and triples describing knowledge known in the domain of anatomy about this structure (for instance that the patella is a circular-triangular bone, and that it is part of the knee): Finally, we have chosen to store and process the resulting ontology as a deductive RDF triple-store using a Sesame server.
Sesame  [38]  is a de-facto standard framework for processing RDF data. This includes parsing, storing, inferencing and querying of/over such data. It offers an easy-to-use API that can be connected to all leading RDF storage solutions. Sesame fully supports the SPARQL  [39]  query language for expressive querying and offers transparent access to remote RDF repositories using the exact same API as for local access. However Sesame currently has no built-in support for custom inference rules. Therefore, we had to implement a rule engine on top of it in order to enable sound and complete deductive capabilities. This architecture is of course modular and adjustable. For instance, it is possible to change the triple-store server, or to use an external reasoner supporting Datalog rules for saturating the data.
Structure learning strategies The number of DAGs, G, is super-exponential in the number of random variables in the domain and the problem of learning the most probable a posteriori BN from data is worst-case NP-hard  (Chickering et al. , 2004). One needs to resort to heuristical methods in order to be able to solve very large problems effectively. Both CB and SS heuristic approaches have advantages and disadvantages. CB approaches are relatively quick, deterministic, and have a well defined stopping criterion; however, they rely on an arbitrary significance level to test for independence, and they can be unstable in the sense that an error early on in the search can have a cascading effect that causes many errors to be present in the final graph. SS approaches have the advantage of being able to flexibly incorporate users' background knowledge in the form of prior probabilities over the structures and are also capable of dealing with incomplete records in the database (e.
Experimental validation on synthetic data Before we proceed to the experiments on real-world multi-label data with H2PC, we first conduct an experimental comparison of H2PC against MMHC on synthetic data sets sampled from eight well-known benchmarks with various data sizes in order to gauge the practical relevance of the H2PC. These BNs that have been previously used as benchmarks for BN learning algorithms (see Table  1  for details). Results are reported in terms of various performance indicators to investigate how well the network generalizes to new data and how well the learned dependence structure matches the true structure of the benchmark network. We implemented H2PC in R (R Core Team, 2013) and integrated the code into the bnlearn package from  (Scutari, 2010). MMHC was implemented by Marco Scutari in bnlearn.
The source code of H2PC as well as all data sets used for the empirical tests are publicly available 2. The threshold considered for the type I error of the test is 0. 05. Our experiments were carried out on PC with Intel(R) Core(TM) i5-3470M CPU @3. 20 GHz 4Go RAM running under Linux 64 bits. We do not claim that those data sets resemble realworld problems, however, they make it possible to compare the outputs of the algorithms with the known structure. All BN benchmarks (structure and probability tables) were downloaded from the bnlearn repository 3. Ten sample sizes have been considered:  50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000 and 50000. All experiments are repeated 10 times for each sample size and each BN. We investigate the behavior of both algorithms using the same parametric tests as a reference.
from 1. 2 to 3. 3). This result is in nice agreement with the experiments in the previous section, as H2PC was shown to consistently reduce the rate of false negative edges with respect to MMHC (at the cost of a slightly higher false discovery rate). Second, Table  4  is very instructive as it reveals that on emotions, image, and scene, the MLP approach boils down to the LP scheme. This is easily seen as there is only one minimal label powerset extracted on average. In contrast, on genbase the MLP approach boils down to the simple BR scheme. This is confirmed by inspecting Table  5 : the performances of MMHC and H2PC (without feature selection) are equal. An interesting observation upon looking at the distribution of the label powerset size shows that for the remaining data sets, the MLP mostly decomposes the label set in two parts: one on which it performs BR and the other one on which it performs LP.
Take for instance enron, it can be seen from Table  4  that there are approximately 23 label singletons and a powerset of 30 labels with H2PC for a total of 53 labels. The gain in performance with MLP over BR, our baseline learner, can be ascribed to the quality of the label powerset decomposition as BR is ignoring label dependencies. As expected, the results using MLP clearly dominate those obtained using BR on all data sets except genbase. The DAGs display the minimum label powersets and their relevant features. Third, H2PC compares favorably to MMHC. On scene for instance, the accuracy of MLP+MB has raised from 20% (with MMHC) to 56% (with H2PC). On yeast, it has raised from 7% to 23%. Without feature selection, the difference in global accuracy is less pronounced but still in favor of H2PC.
The difference in accuracy with and without the feature selection was not shown to be statistically significant (p > 0. 20 with a Wilcoxon signed rank paired test). Surprisingly, the feature selection did exceptionally well on genbase. On this data set, the increase in accuracy is the most impressive: it raised from 7% to 98% which is atypical. The dramatic increase in accuracy on genbase is due solely to the restricted feature set as input to the classification model. This is also observed on medical, to a lesser extent though. Interestingly, on large and densely connected networks (e. g. bibtex, slashdot and corel5K), the feature selection performed very well in terms of global accuracy and significantly reduced the input space which is noticeable. On emotions, yeast, image, scene and genbase, the method reduced the feature set down to nearly 1/100 its original size.
Conclusion & avenues for future research We first discussed a hybrid algorithm for global or local (around target nodes) BN structure learning called Hybrid HPC (H2PC). Our extensive experiments showed that H2PC outperforms the state-of-the-art MMHC by a significant margin in terms of edge recall without sacrificing the number of extra edges, which is crucial for the soundness of the super-structure used during the second stage of hybrid methods, like the ones proposed in  (Perrier et al. , 2008; Kojima et al. , 2010). The code of H2PC is open-source and publicly available online at https://github. com/madbix/bnlearn-clone-3. 4. Second, we discussed an application of H2PC to the multi-label learning problem which is a challenging problem in many real-world application domains. We established theoretical results, under the faithfulness condition, in order to characterize graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution and their respective Markov boundaries.
The optimization of the H2PC code is currently being undertaken to lower the computational cost while maintaining its performance. These optimizations will include the use of a cache to store the (in)dependencies and the use of a global structure. Other interesting research avenues to explore are extensions and modifications to the greedy search-and-score procedure which is the most promising part to optimize in terms of computational gains. Regarding the multilabel learning problem, experiments with several thousands of labels are currently been conducted and will be reported in due course. We also intend to work on relaxing the faithfulness assumption and derive a practical multi-label classification algorithm based on H2PC that is correct under milder assumptions underlying the joint distribution (e.
5 ± 1. 9 1 / 1 / 30 (53) 1 / 2 / 334 (753) bibtex 39. 6 ± 4. 3 1 / 1 / 112 (159) 2 / 2 / 1588 (2856) corel5k 97. 7 ± 3. 9 1 / 1 / 263 (374) 1 / 2 / 2749 (3175) MMHC dataset # LPs # labels / LP L(D) min/med/max # classes / LP DL(D) min/med/max emotions 1. 2 ± 0. 4 1 / 6 / 6 (6) 2 / 26 / 27 (27) yeast 2. 0 ± 0. 0 1 / 7 / 13 (14) 2 / 89 / 186 (198) image 1. 0 ± 0. 0 5 / 5 / 5 (5) 19 / 20 / 20 (20) scene 1. 0 ± 0. 0 6 / 6 / 6 (6) 14 / 15 / 15 (15) slashdot 15. 1 ± 0. 6 1 / 1 / 9 (22) 1 / 2 / 46 (156) genbase 27. 0 ± 0. 0 1 / 1 / 1 (27) 1 / 2 / 2 (32) medical 41. 7 ± 1. 4 1 / 1 / 4 (45) 1 / 2 / 7 (94) enron 36. 8 ± 2. 7 1 / 1 / 12 (53) 1 / 2 / 119 (753) bibtex 77. 2 ± 3. 1 1 / 1 / 28 (159) 2 / 2 / 233 (2856) corel5k 165. 3 ± 5. 5 1 / 1 / 177 (374) 1 / 2 / 2294 (3175) Table 5: Global classification accuracies using 4 learning methods (BR, MLP, BR+MB, MLP+MB) based on the DAG obtained with H2PC and MMHC.
Best values between H2PC and MMHC are bold- faced. dataset BR MLP MMHC H2PC MMHC H2PC MMHC H2PC BR+MB MLP+MB emotions 0. 327 0. 371 0. 391 0. 147 0. 266 0. 189 0. 285 yeast 0. 169 0. 273 0. 271 0. 062 0. 155 0. 073 0. 233 image 0. 342 0. 504 0. 504 0. 227 0. 252 0. 308 0. 360 scene 0. 580 0. 733 0. 733 0. 123 0. 361 0. 199 0. 565 slashdot 0. 355 0. 419 0. 474 0. 274 0. 373 0. 278 0. 439 genbase 0. 069 0. 069 0. 069 0. 974 0. 976 0. 974 0. 976 medical 0. 454 0. 499 0. 502 0. 630 0. 651 0. 636 0. 669 enron 0. 134 0. 165 0. 180 0. 024 0. 079 0. 079 0. 157 bibtex 0. 108 0. 115 0. 167 0. 120 0. 133 0. 121 0. 177 corel5k 0. 002 0. 030 0. 043 0. 000 0. 002 0. 010 0. 034.
g. , climate, land use and land cover change (LULCC), atmospheric CO 2 concentrations and nitrogen deposition, on model estimates of carbon fluxes and stocks, a series of sensitivity simulations are prescribed at both spatial scales for a simulation period from 1801 to 2010  (Huntzinger et al. , 2013). Inherent to MsT-MIP's experimental design is the focus on controlling for as many sources of variability in TBM predictions as possible, to isolate and quantify the impact of the model itself (i. e. , structural and parameter uncertainties) on estimates. One source of variability in model estimates is the choice of (and uncertainty associated with) environmental driver and input data sets. Most uncoupled TBMs require, at a minimum, a land-water mask, climate forcing data, soil characteristics and atmospheric CO 2 concentrations to simulate how carbon is exchanged between the land and atmosphere.
Many models also require additional information such as LULCC, phenology, nitrogen deposition rates and disturbance history. Ideally, the temporal resolution of drivers should be fine enough to enable prediction at sub-daily temporal resolution; thus, making it possible to investigate the diurnal cycle of carbon and energy fluxes. To meet the objectives of MsTMIP's experimental design, the goal was to provide modeling teams, to the extent possible, with a complete and consistent set of environmental driver data. In addition to being of high quality, the environmental driving and input data chosen for MsTMIP also needed to meet the following requirements: -data sets must be compatible with over 20 different TBMs; -data sets must provide consistent spatial coverage for the land surface within the two simulation domains: (1) North American: 10-84 -data sets must be physically consistent with one another.
Environmental driver data sets MsTMIP environmental driver and associated data products include data sets describing climatology, time-varying atmospheric CO 2 concentrations, time-varying nitrogen deposition, LULCC, C3 / C4 grasses fractions, major crop distribution, phenology, soil characteristics and a land-water mask, all at 0. 5 • × 0. 5 • for the global domain and 0. 25 • × 0. 25 • for the North American domain (Table  1 ). All MsTMIP model driver data files are stored in Climate and Forecast (CF) 3  1. 4 convention compliant netCDF version 3 format, which is supported by a wide range of programming APIs (e. g. , C, C++, Fortran, Java, Perl) and multiple operating systems (e. g. , Linux, Unix, Mac OS X, Windows). All drivers are saved in Greenwich mean time (GMT) with all sub-monthly drivers (e.
g. , climate) including leap years. For most data categories, the North American data sets are based on the same data sources as the global products. We did, however, choose different climatology and soil data products for the two domains. This decision was driven primarily by the availability of these drivers at the spatial and temporal resolution needed for the regional simulations. However, by holding the source of other drivers constant between the global and North American simulations, we are also creating an opportunity to test the impact of the choice of climate and soil characteristics on model estimates.
The CRU Time Series (TS) 3. 2 product covers the time period from 1901 to present at a 0. 5 • spatial resolution, but only at a monthly temporal resolution. The NCEP/NCAR product, on the other hand, has a finer temporal resolution (6-hourly), but has a coarse spatial resolution (2. 5 • ) and only provides climatology back to 1948. The ECMWF product similarly lacks the temporal coverage required for MsTMIP. Thus, we combined the strengths of the CRU and NCEP/NCAR Reanalysis products, fusing them to produce the CRU-NCEP global climate data set. This new data set provides a globally gridded (0. 5 • × 0. 5 • ) and sub-daily (6hourly) time-varying climatology product that spans the period between 1901 and 2010. Earlier versions of CRU-NCEP data had been used as driver data in past MIP activities, including GCP-TRENDY.
The MsTMIP project updated the CRU-NCEP data by adopting the latest (version 3. 2 at preparation time) CRU TS data. Details of the CRU-NCEP fusion method can be found in Supplement 1. MsTMIP CRU-NCEP contains seven climatology variables, including downward longwave and shortwave radiations, pressure, air specific humidity, precipitation, temperature and wind (Table  1 ). In the process of creating this new climatology product, we also corrected known biases in temperature and shortwave radiation in the NCEP/NCAR Reanalysis product. Zhao et al. (2006)  showed that NCEP/NCAR Reanalysis climatology overestimates downward shortwave radiation, especially in non-tropical regions, and underestimates surface temperature for almost all latitudes. Biases in climatological variables can introduce substantial errors into gross primary productivity (GPP) and net primary productivity (NPP) estimates  (Zhao et al.
North American climate: NARR Several climatology products are available for North America at finer spatial and temporal resolutions than the new CRU-NCEP product. In addition to better addressing the resolution needs of MsTMIP regional simulations (0. 25 • × 0. 25 • spatial and 3-hourly temporal resolution), using a different climate driver data product for the North American simulations (1) makes it possible to test the influence of the choice of climate drivers on model estimates, and (2) provides a closer linkage between model estimates and fine-scale ground-based observations. Both the Daymet  (Thornton et al. , 2012)  and Parameter elevation Regression on Independent Slopes Model (PRISM) 4  products provide temperature and precipitation data at high spatial resolution (e. g. , 1 km) for North America.