dataesr/scientific-paragraphs-categorization huggingface dataset:
Preliminaries In this section, we recall the framework of ontology-mediated query answering in the setting of description logics, focusing on the lightweight logic DL-Lite R , which provides the logical underpinnings of the OWL 2 QL profile. Syntax A DL knowledge base (KB) consists of an ABox and a TBox, both constructed from a set N C of concept names (unary predicates), a set of N R of role names (binary predicates), and a set N I of individuals (constants). The ABox (dataset) consists of a finite number of concept assertions of the form A(a) and role assertions of the form R(a, b), where A TBox axiom of the form B 1 B 2 or S 1 S 2 is a positive inclusion, and a TBox axiom of the form B 1 ¬B 2 or S 1 ¬S 2 is a negative inclusion. A ∈ N C , R ∈ N R , Example 1. For our running example, we will use the DL-Lite R KB K * = T * , A * in Figure  1  about the university domain.
• PTime: problems which are solvable in polynomial time in the size of the input. • NP: problems which are solvable in non-deterministic polynomial time. • coNP: problems whose complement is in NP. • BH 2 : problems that are the intersection of a problem in NP and a problem in coNP. • Σ p 2 : problems which are solvable in non-deterministic polynomial time with access to an NP oracle. • Π p 2 : problems whose complement is in Σ p 2. These classes are related as follow: AC 0 ⊆ NL ⊆ PTime, PTime ⊆ NP ⊆ Σ p 2 and PTime ⊆ coNP ⊆ Π p 2. It is known that AC 0 PTime, and it is widely believed that all of the inclusions are proper. We recall that in the database setting, the problem of deciding if a tuple is an answer to an FO query is in AC 0 in data complexity  (Immerman, 1987) , and (the decision version of) CQ evaluation is NP-complete in combined complexity  (Chandra & Merlin, 1977).
Query answering through rewriting We now introduce query rewriting, which is a prominent algorithmic approach to OMQA. The basic idea is to rewrite the query so as to incorporate all relevant information from the TBox, and then evaluate the rewritten query over the ABox, which is treated as a database. Formally, with every ABox A, we associate a corresponding interpretation I A = (∆ I A , • I A ) defined as follows: ∆ I A = Ind(A) A I A = {a | A(a) ∈ A} for every A ∈ N C a I A = a for every a ∈ Ind(A) R I A = {(a, b) | R(a, b) ∈ A} for every R ∈ N R The interpretation treats the ABox like a relational database by making true precisely those assertions appearing in the ABox. A (first-order) rewriting of a query q w. r. t. a TBox T and T -consistent ABoxes is an FO-query q such that cert(q, T , A ) = ans(q , I A ) for all T -consistent ABoxes A.
Observe that the original problem of computing cert(q, T , A ) is reduced to the task of evaluating a first-order query q over the finite interpretation I A , and the latter task can be handled by a relational database system. (We shall see later in the section how to check whether the input ABox is T -consistent, which will allow us to devise rewritings that work for arbitrary ABoxes. ) The DL-Lite family possesses the first-order rewritability property, meaning that for standard DL-Lite dialects like DL-Lite R , there exists an FO-rewriting of every CQ and every TBox (which is not the case for most DLs). In fact, it is sufficient to consider UCQrewritings, which take the more restricted form of unions of conjunctive queries. We note that when working with UCQs, we will sometimes treat them as sets of CQs, using e.
Answering a CQ q over a consistent DL-Lite KB T , A amounts to searching for matches for the CQs in a UCQ-rewriting of q in I A. We introduce the notion of image to capture the parts of the ABox that participate in a match. Formally, an image of a CQ q( x) = ∃ y ψ( x, y) in an ABox A is a set of assertions B ⊆ A such that there is a match π : x ∪ y → Ind(A) for q in I A such that B = atoms(ψ(π( x), π( y))). We can extend the notion of image to UCQs as follows: a set of assertions is an image of a UCQ Q iff it is the image of some CQ q ∈ Q. The following theorem recalls the complexity of query answering in DL-Lite R. Membership in AC 0 for data complexity is an immediate consequence of the reduction to FO-query evaluation, which is known to be in AC 0 for data complexity, and the NP lower bound is likewise inherited from the NP-hardness of CQ evaluation over relational databases.
The AR semantics (ABox Repair semantics)  (Lembo et al. , 2010)  adapts the consistent query answering framework developed in the database arena  (Arenas et al. , 1999; Bertossi, 2006; Chomicki, 2007; Bertossi, 2011)  to DL KBs. Consistent query answering amounts to considering those answers that hold in every repair, defined as consistent subsets of the database that are "as close as possible" to the actual database instance. In the database setting, which adopts the closed world assumption, repairs may be obtained from the actual database by deletion or insertion of tuples (or changes of attributes values), since constraints may be violated by either the presence or absence of certain tuples. By contrast, in the DL setting where open world semantics is used, inconsistency can only stem from the presence of incompatible ABox assertions, leading to a simpler notion of repair based upon removal of assertions: Definition 3.
The CQAPri System We implemented our query answering and explaining framework under AR, IAR and brave semantics over DL-Lite R KBs in Java v1. 7 within our CQAPri ("Consistent Query Answering with Priorities") tool 8. CQAPri is built on top of the relational database server PostgreSQL v9. 3. 2 (www. postgresql. org), the Rapid v1. 0 query rewriting engine for DL-Lite  (Chortaras, Trivela, & Stamou, 2011) , and the SAT4J v2. 3. 4 SAT solver  (Berre & Parrain, 2010). All these building blocks are used with their default settings. Following the framework developed in Section 4, CQAPri utilizes the brave, AR, and IAR together to classify a query answer a into one of 3 classes: • Possible: K |= brave q( a) and K |= AR q( a) • Likely: K |= AR q( a) and K |= IAR q( a) • (Almost) sure: K |= IAR q( a) CQAPri handles ABoxes stored in PostgreSQL, while it keeps the TBox in-memory.
ABoxes are stored as relational databases with one table per concept and role, of one or two columns (named s for concept and s and o for role) that contains the individuals involved in that concept or role. B-Tree indexes have been created for each table, on s for concepts and (s, o) and (o, s) for roles. The concept, role, and individual names are encoded by integers, and a dictionary table relates each name to its identifier. CQAPri computes the set of conflicts for the KB in a preprocessing phase since it is query-independent. Conflicts are computed following the steps in Algorithm 1 and stored as a conflict graph (Definition 4. 4). More precisely, the SQLized rewritings of the queries looking for counter-examples to the negative TBox inclusions are evaluated over the ABox, their images are retrieved and stored as a graph whose vertices are assertions and edges indicate images, and finally the non-minimal ones are discarded by removing edges between any self-inconsistent assertion and the others to obtain the conflict graph.
LUBM ∃ 20 comprises 127 concepts, 27 roles and 202 positive inclusions. To enable contradictions, we added negative inclusions to state the disjointness of pairs of concepts or roles having the same closest super-concept or super-role. Such concepts and roles appear at the same level in the TBox (that is, have the same distance to the top concept Thing). We excluded a small number of such inclusions when they did not seem to reflect the intended meaning of the concepts or roles. Figure  3  illustrates how we added negative inclusions for an example concept AssistantProfessor. We added a total of 875 negative inclusions. This apparently huge number of constraints results from the many pairwise disjoint concepts or roles used in the TBox. Datasets We generated ABoxes of increasing sizes with the Extended University Data Generator (EUGen) provided with LUBM ∃ 20 by setting its data completeness parameter (i.
Experimental Setting All experiments reported in this work were run on an Intel Xeon X5647 at 2. 93 GHz with 16 GB of RAM, running CentOS 6. 8. Reported times are averaged over 5 runs. We did not measure the time that our prototype takes to present the results (i. e. translating the answers back in the original terminology with the dictionary table of the database and printing the results in text files), since the goal of our experiments was to study the properties of the computation of query answers and explanations rather than their presentation (which a full-fledged OMQA system would probably handle in a more refined and efficient way). We use the benchmark presented in Section 6. To test the explanation component of our tool, we explain all answers of the queries over all the ABoxes of our benchmark, except for those which have more than 200,000 answers, because it yields unreasonable experimental times.
Indeed, q9 has 202,579 rewritings, with 4 variables and no constant, that leads to a very costly execution over the database, as illustrated on Figure  7  where almost 90% of the time is spent on executing the rewritten query even in the case of the largest ABox with the highest percentage of conflicts. Query q10 that differs from q9 only by the introduction of a constant, behaves very differently since its answering time stabilizes quickly. As for q4, it has a high number of atoms and variables, which are all free, yielding a huge number of answers (10,362,220 answers on u100c10), which become quickly non-IAR since their causes involve lots of assertions. These two queries are interesting to challenge our system, but are not realistic, especially q4. For the other queries, CQAPri scales up to large ABoxes when the proportion of assertions involved in some conflict is only a few percent, and even a few tens percent for most of the queries.
Moreover, such an approach is not compatible with the AR semantics, which avoids the loss of information that results from discarding all assertions involved in conflicts. We can observe some high-level similarities with Du et al. 's system which also has a preprocessing phase that compiles the KB, then employs SAT solvers and uses a reachability analysis to identify a query-relevant portion of the KB to do query answering. There are also a few systems for querying inconsistent relational databases. Most relevant to our work is EQUIP  (Kolaitis, Pema, & Tan, 2013) , which reduces AR conjunctive query answering in the presence of primary key constraints to binary integer programming (BIP). Similarly to our system, EQUIP first computes the IAR-answers and the causes of the answers with their conflicts.
, 2004a Chomicki et al. , , 2004b)). Other systems handle more general constraints that can lead to dif-ferent kinds of repairs, since for databases it may be necessary to insert or modify tuples to restore consistency. For instance, ConsEx  (Marileo & Bertossi, 2010)  reduces AR query answering to answer set programming (ASP) by building repair programs such that there is a one-to-one correspondence between stable models and repairs. Experiments reported by  Kolaitis et al. (2013)  show that EQUIP outperforms ConsEx on its restricted setting that is closer to our own.
Experimental Settings Involving Inconsistent DL-Lite KBs The QuID benchmark QuID is evaluated using the LUBM TBox containing 43 concepts, 25 roles, 7 attributes and about 200 positive inclusions, approximated in DL-Lite by eliminating the inclusions that go beyond DL-Lite, then enriched with 10 negative inclusions, 5 identifications (that state that some sets of properties identify the instances of some basic concepts), and 3 denial constraints given by Boolean CQs (of the form ∃ y ψ( y), where ψ is a conjunction of atoms using variables from y) which have to be false. Datasets are generated with the UBA Data Generator provided with LUBM, for 1, 5, 10 and 20 universities, leading to a size varying from about 100K to about 2 million assertions. For each of these consistent ABoxes, four inconsistent ABoxes are constructed by adding 1%, 5%, 10% and 20% of assertions that are in conflict.
We could therefore not use these datasets to evaluate CQAPri, since there is no chance obtaining query answer that hold under AR semantics but not under IAR semantics. In Du et al. 's later work (2015)  on abduction over inconsistent DL-Lite KBs, the Semintec and LUBM TBoxes without the non-DL-Lite axioms are used. The authors added negative inclusions to LUBM in a similar fashion to ours.
Du et al. 's benchmark Regarding data, Semintec has a small ABox of about 65K assertions and the number of universities used for LUBM datasets ranges from 1 to 100 as in our experimental setting. Du et al. (2013)  present a tool called Injector to insert conflicts in ABoxes. Given a consistent KB and a number of conflicts to be inserted, Injector randomly selects a functional role or an atomic concept that has disjoint atomic concepts. If the KB already entails assertions that correspond to that role or concept, Injector randomly selects such an assertion and adds an assertion that conflicts it: in the case of a functional role, it relates the corresponding individual of the ABox to a new individual with that role, and in case of concept assertions, it assigns the individual to one of the disjoint atomic concepts.
Du et al. (2015)  added 0 to 400 "conflicts" (i. e. assertions that contradict an original assertion, or inconsistent pairs of assertions) to the consistent ABoxes. We prefer to quantify the degree of inconsistency in terms of assertions involved in some conflicts rather than in terms of assertions added to the consistent ABox, since we can compute the ratio of conflicts (as we define them) of a real dataset, but not its ratio of erroneous facts. However, note that we added many more assertions than Du et al. for original datasets of the same size.
However, the explanations themselves are not computed but result from the interaction between the user and the system. Finally, we note that the problem of explaining query results has been studied in the database community (cf. Cheney, Chiticariu, & Tan, 2009 , for a survey). The lineage of a query answer is the set of tuples of the database that contribute to produce the answer, i. e. the union of the images for the answer. The why-provenance corresponds to the images and the minimal witness basis to the set of causes of the answer. The how-provenance describes how a result was produced from the tuples. The where-provenance provides the location (i. e. relation, tuple and attribute) of the values in the answer tuple.
Query Abduction Since we defined explanations for a negative AR-or IAR-answer using assertions of the ABox that conflict its causes, defining explanations for negative brave-answers (which have no cause) would significantly differ in spirit. The problem of explaining negative answers has been primarily seen as the problem of finding a minimal dataset to be added to the data to get the missing answers, i. e. as query abduction, both in the DL literature (see e. g. Calvanese et al. , 2013; Du et al. , 2014, for DL-Lite and Wang, Chitsaz, Wang, and Du, 2015 , for ELH ⊥ ) and in the database arena (see e. g. Herschel & Hernández, 2010). In both settings, restrictions on the signature of the explanation are allowed. Note that a different approach related to query debugging was proposed in the database context  (Bidoit, Herschel, & Tzompanaki, 2014) , and focuses on finding subqueries responsible for pruning the missing answer from a query result.
However, for EL ⊥ , query answering under AR semantics has the same complexity as in DL-Lite. It would be interesting to see if query answering under these semantics can be done efficiently for EL ⊥ , since the IAR semantics does not provide a tractable approximation of AR in this setting. Regarding the performance of inconsistency-tolerant query answering, we see two possible directions to investigate. First, we could try to lower the cost of query evaluation. Indeed, our approach is based on the computation of the causes of the answers, which are obtained straightforwardly using the UCQ-rewritings of the initial queries. However, database management systems perform poorly on large UCQs, and it is not uncommon for UCQ-rewritings to be (very) large (there were more than 200,000 CQs in the rewritings of some queries of our benchmark!).
Update to the full mechanism of IEPOX-SOA uptake We updated the standard mechanism and code of GEOS-Chem v11-02-rc to include two recent scientific findings influencing IEPOX-SOA uptake rate. First, we considered organic coating effects when we calculated reactive IEPOX uptake by assuming core (inorganic) -shell (organic) mixing state  (Gaston et al. , 2014a). Standard GEOS-Chem assumes no organic coating; only the surface area of sulfate. We updated the model to include suppression of IEPOX reactive uptake by the organic coating, and to use available surface area of the total sulfate-ammonium-nitrate-organic aerosols mixture at a given relative humidity with hygroscopic growth factors. As a result, the inclusion of coating reduces the IEPOX reactive uptake coefficient (γ)  (Anttila et al.
Computational time estimation We estimated computational time related with IEPOX-SOA simulation for the full chemistry and the different parameterizations. The box model was used for estimating the time needed for chemistry calculation using chemical reactions and dry depositions in Table  S1. All the parameterizations showed much faster integration time compared to the full chemistry. For estimation within GEOS-Chem, we used the Gprof function profiling program and categorized the results according to four major processes (chemistry, transport, dry deposition, and wet deposition), as shown in Table  1. One of main advantage using a function profiling program is that all of timings are estimated at once without need for multiple simulations. Because model computational time varies between individual executions even for the same machine and code  (Philip et al.
Acknowledgements. This publication was supported by  US EPA   STAR 83587701-0 ,  NOAA NA18OAR4310113 ,  DOE  ( BER/ASR) DE-SC0016559 ,  NSF   AGS-1822664 , and the  European Research Council  (grant No. 819169 ). It has not been formally reviewed by  EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication. We thank  Prasad Kasibhatla  for useful discussions. Geosci. Model Dev. Discuss. , https://doi. org/10. 5194/gmd-2019-9Manuscript under review for journal  Geosci. Model Dev. Discussion started: 5 February 2019 c Author(s)  2019. CC BY 4. 0 License.
Code and Data Availability. The KinSim box model will be publicly available on the web (http://cires1. colorado. edu/jimenez-group/wiki/index. php/Analysis_Software#KinSim_Software) but it is currently available upon request (jose. jimenez@colorado. edu). GEOS-Chem v11-02-rc and meteorological data can be downloaded from GEOS-Chem website (http://wiki. seas. harvard. edu/geoschem/index. php/Downloading_GEOS-Chem_source_code_and_data). GEOS-Chem code modifications for new parameterizations and global model data are available upon request submitted to the corresponding author (jose. jimenez@colorado. edu).
Subsequently, a pilot test among the intended respondents was undertaken. The survey questionnaire instrument's reliability was ensured in diverse ways, namely, facilitation by clear instructions and wording of questions. The questionnaire contained standardized instructions, namely, "please tick where appropriate. " Also, trait sources of error were minimized through interviewing respondents at their convenience. The validity of the survey data was attained following  Nardi's (2006)  guidelines. The validity of the data was obtained from face-to-face interviews. The administration of the questionnaire took the form of face-to-face interviews, including self-administration. The face-to-face interviews were conducted in both English language and Ghanaian languages, namely Ga, Ewe, and Twi.
Data Analysis Methodological triangulation was deployed to include the combination of methods to understand and explain  (Greenstein, Roberts and Sitas, 2003)  the social care needs of older adults. The answered questionnaire were cleaned and serialized for easy identification. A data entry template for the quantitative data was developed in the Statistical Package for Social Science (SPSS). Subsequently, the responses were entered into SPSS and were analyzed with selected descriptive statistics, namely frequencies and percentages. Analysis of the qualitative data was undertaken along with six distinct steps. The first step involved the transcription of interviews, with the majority done alongside data collection  (Bryman 2008). The second step entailed reading through all the data in order to obtain a general sense of the information; reflect on its overall meaning; ascertain the general ideas emanating from the participants  (Creswell 2009).
It is better to ensure a correct alignment of the inertial sensors with the wheelchair movement direction to obtain the highest acceleration range and so to make the most of the sensor sensitivity. Digital data acquisition is made by a microcontroller card, designed in our lab and based on PIC18F2550 from Microchip. The sampling frequency is 39 Hz. Each sample is composed of eight values : six 3D accelerometer components (a x1 , a y1 , a z1 , a x2 , a y2 and a z2 ) and two angular velocities (ω x , ω y ), as indicated on Fig. 2. All data are sent by USB bus to a laptop and saved in a text file by a data logger software (Sniffer) for an offline analysis.
The resulting vector could be seen as an approximation of the linear accelerations of user's actions on the joystick. After applying a onedimensional median filter (order 20) to remove outlier values, we determine the angular variation for each axis of the bidirectional joystick as follows: θ x = arctan a x a z andθ y = arctan a y a z ( 2 ) where θ x express the angular variation of the joystick in the left/right direction and θ y in the backward/forward direction. To improve the angular variation estimation, we combine the acceleration data from accelerometers and the angular velocity data from the gyrometer with a complementary filter that is described on Fig. 3. The resulting angles are expressed in Laplace form: θx = 1 S (ω x -ω b x ) = 1 S ω x + K p S (θ x -θx )+ K i S 2 (θ x -θx ) (3) θy = 1 S (ω y -ω b y ) = 1 S ω y + K p S (θ y -θy )+ K i S 2 (θ y -θy ) (4) where θx and θy express the estimation of the angular variation of the joystick in the left/right and backward/forward directions respectively.
Protocols Two measurement campaigns have been made with different drivers and wheelchair using the same experimental protocol. A first data logging collection is conducted in a lab environment on a unique EPW equipped with Sense-Joy and with a direct monitoring of the joystick outputs. This first measurement campaign is designed to make a thorough comparison with ground truth recording, with a direct joystick outputs setting, to validate the signal quality and the accuracy of the measurements. A second measurement campaign is conducted in a rehabilitation center on the personal EPW of individuals equipped with Sense-Joy only. This second set of measurements is designed to demonstrate and to validate that our system is pluggable and compatible with any EPW driven by a joystick interface. The EPW used in the first measurement campaign comes from Invacare, it is a "Mistral" model.