dataesr/scientific-paragraphs-categorization huggingface dataset:
The SEBCS was supported by the  BRL (Basic Research Laboratory ) program through the  National Research Foundation of Korea  funded by the  Ministry of Education, Science and Technology  ( 2012-0000347 ). SGBCC is funded by the  NUS start-up Grant ,  NCIS   Centre Grant  and  NMRC Clinician Scientist Award. Additional controls were recruited by the  Singapore Consortium of Cohort Studies-Multi-ethnic cohort (SCCS-MEC ), which was funded by the  Biomedical Research Council , grant number:  05/1/21/19/425. SKKDKFZS is supported by the  DKFZ. The SZBCS was supported by Grant  PBZ_KBN_122/P05/2004. K. J. is a fellow of  International PhD program ,  Postgraduate School of Molecular Medicine, Warsaw Medical University , supported by the  Polish Foundation of Science. The TNBCC was supported by the  NIH  grant ( CA128978 ), the  Breast Cancer Research Foundation ,  Komen Foundation  for the Cure, the  Ohio State University Comprehensive Cancer Center , the  Stefanie Spielman Fund for Breast Cancer Research  and a generous gift from the  David F.
and Margaret T. Grohne Family Foundation  and the  Ting Tsung and Wei Fong Chao Foundation. Part of the TNBCC (DEMOKRITOS) has been co-financed by the  European Union (European Social Fund -ESF ) and  Greek National Funds  through the  Operational Program 'Education and Lifelong Learning ' of the National Strategic Reference Framework (NSRF)- Research Funding Program  of the General Secretariat for Research & Technology: ARISTEIA. The TWBCS is supported by the  Institute of Biomedical Sciences, Academia Sinica  and the  National Science Council, Taiwan. The UKBGS is funded by  Breakthrough Breast Cancer  and the  Institute of Cancer Research (ICR). ICR acknowledges NHS funding to the  NIHR Biomedical Research Centre.
Grant Support The work conducted for this project at  Vanderbilt Epidemiology Center  is supported in part by  NIH  grant  R37CA070867  and endowment funds for the  Ingram Professorship  and  Anne Potter Wilson Chair. BCAC is funded by  Cancer Research UK  ( C1287/A10118 ,  C1287/A12014 ) and by the  European Community 's  Seventh Framework Programme  under grant agreement n°  223175  ( HEALTH-F2-2009-223175 ) (COGS). Meetings of the BCAC have been funded by the  European Union   COST programme  ( BM0606 ). Genotyping of the iCOGS array was funded by the  European Union  ( HEALTH-F2-2009-223175 ),  Cancer Research UK  ( C8197/A16565  and  C1287/A10710 ), the  Canadian Institutes of Health Research for the 'CIHR Team in Familial Risks of Breast Cancer' program  and the  Ministry of Economic Development, Innovation and Export Trade of Quebec  ( PSR-SIIRI-701 ).
The ABCFS was also supported by the  National Health and Medical Research Council of Australia , the  New South Wales Cancer Council , the  Victorian Health Promotion Foundation (Australia)  and the  Victorian Breast Cancer Research Consortium. J. L. H. is a  National Health and Medical Research Council  ( NHMRC) Senior Principal Research Fellow  and M. C. S. is a  NHMRC Senior Research Fellow. The OFBCR work was also supported by the  Canadian Institutes of Health Research 'CIHR Team in Familial Risks of Breast Cancer' program. The ABCS was funded by the  Dutch Cancer Society  Grant no. NKI2007-3839  and  NKI2009-4363. The ACP study is funded by the  Breast Cancer Research Trust, UK. The work of the BBCC was partly funded by  ELAN-Programme of the University Hospital of Erlangen. The BBCS is funded by  Cancer Research UK  and  Breakthrough Breast Cancer and acknowledges NHS  funding to the  NIHR Biomedical Research Centre , and the  National Cancer Research Network (NCRN).
E. S. is supported by  NIHR Comprehensive Biomedical Research Centre, Guy's & St. Thomas' NHS Foundation Trust  in partnership with  King's College London, UK. Core funding to the  Wellcome Trust Centre for Human Genetics  was provided by the  Wellcome Trust  ( 090532/Z/09/Z ). I. T. is supported by the  Oxford Biomedical Research Centre. The BSUCH study was supported by the  Dietmar-Hopp Foundation , the  Helmholtz Society  and the  German Cancer Research Center (DKFZ). The CECILE study was funded by  Fondation de France ,  Institut National du Cancer (INCa) ,  Ligue Nationale contre le Cancer, Agence Nationale de Sécurité Sanitaire (ANSES) ,  Agence Nationale de la Recherche (ANR). The CGPS was supported by the  Chief Physician Johan Boserup and Lise Boserup Fund , the  Danish Medical Research Council and Herlev Hospital.
HAC receives support from the  Lon V Smith Foundation  ( LVS39420 ). The ESTHER study was supported by a grant from the  Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the  German Cancer Aid (Deutsche Krebshilfe ). ). The GENICA was funded by the  Federal Ministry of Education and Research (BMBF)   Germany grants   01KW9975/5 ,  01KW9976/8 ,  01KW9977/0  and  01KW0114 , the  Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ) ,  Heidelberg, Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum  (IPA), as well as the  Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH ,  Johanniter Krankenhaus Bonn ,  Germany.
We say that A j A k (possibly j = k) if ∃y ∈ V ar in (A j ) ∩ V ar out (A k ). In other words an input of A j depends on an output of A k. We say that B has a loop if A j + A j for some A j ( + is the transitive closure of ). Example 3. Q( x, s(y)), R( y, s(x)) (where x, y are variables) has a loop because The language generated by P 1 is L P rog (P 1 ) = {g(t, t sym ) | t ∈ T {c \2 ,a \0 } }, where t sym is the symmetric tree of t (for instance c(c(a, a), a) is the symmetric of c(a, c(a, a))). This language is synchronized, but it is not context-free. Q( x, s(y)) R( y, s(x)) Q( x, Example 6. P rog = {S( c(x, y)) ← P ( x, y, a, b). P ( f (x), g(y), x , y ) ← P ( x, y, h(x ), i(y )). P ( x, y, x, y) ←} is an S-CF program. The language generated by S is L P rog (S) = {c(f n (h n (a)), g n (i n (b))) | n ∈ IN}, P ( c(x), y) ← Q( x, p(y)) is non-copying.
Remark: An S-CF program without input arguments is actually a CS-program (composed of CS-clauses)  [9] , which generates a synchronized language 5. A noncopying normalized CS-program such that every predicate symbol has only one argument is called regular program. It is equivalent to a finite tree automaton. Indeed, clauses are of the form P 0 ( f (x 1 ,. , x n )) ← P 1 ( x 1 ),. , P n ( x n ), which is equivalent to the transition f (P 1 ,. , P n ) → P 0 where P 0 , P 1 ,. , P n are considered as states. Consequently it generates a regular tree language. Conversely, every regular tree language can be generated by a regular program. Given an S-CF program, we focus on two kinds of derivations. Definition 7. Given an S-CF program P rog and a sequence of atoms G, • G derives into G by a resolution step if there exists a clause 6 H ← B in P rog and an atom A ∈ G such that A and H are unifiable by the most general unifier σ (then σ(A) = σ(H)) and G = σ(G)[σ(A) ← σ(B)].
In the remainder of the paper, given an S-CF program P rog and two se- quences of atoms G 1 and G 2 , G 1 Y * P rog G 2 (resp. G 1 → * P rog G 2 ) also denotes that G 2 can be derived (resp. rewritten) from G 1 using clauses of P rog. Note that for any atom A, if A → B then A Y B. On the other hand, A Y σ B implies σ(A) → B. Consequently, if A is ground, A Y B implies A → B. It is well known that resolution is complete. Theorem 1. Let A be a ground atom. A ∈ M od(P rog) iff A Y * P rog ∅.
P rog generates R * (I). Adding critical pairs into the S-CF program is called completion. For technical reasons 10 , we consider only normalized S-CF programs, and P rog is not normalized. However, the critical pair can be normalized using a new predicate symbol, and replaced by the following normalized clauses P 1 ( g(x), y) ← P 2 ( x, y). P 2 ( h(y), y) ←. This is the role of Function norm in the completion algorithm below. In general, adding a critical pair (after normalizing it) into the S-CF program may create new critical pairs, and the completion process may not terminate. To force termination, two bounds predicate-limit and arity-limit are fixed. If predicate-limit is reached, Function norm should re-use existing predicates instead of creating new ones. If a new predicate symbol is created whose arity 11 is greater than arity-limit, then this predicate has to be cut by Function norm into several predicates whose arities do not exceed arity-limit.
On the other hand, for a fixed 12 S-CF program, the number of critical pairs may be infinite. Function removeCycles modifies some clauses so that the number of critical pairs is finite. Strong coherence is technical and will be defined later. It is used to prove the results. However, if the initial program P rog is regular or is a CS-program, i. e. P rog does not have input arguments, then the strong coherence property is automatically satisfied. Definition 9 (comp). Let arity-limit and predicate-limit be positive integers. Let R be a linear rewrite system, and P rog be a finite, normalized and noncopying S-CF program strongly coherent with R. The completion process is defined by: Function comp R (P rog) Prog = removeCycles(P rog) while there exists a non-convergent critical pair H ← B in P rog do 10 Critical pairs are computed only at root positions.
Critical pairs The notion of critical pair is the heart of our technique. Indeed, it allows us to add S-CF clauses into the current S-CF program in order to cover rewriting steps. Definition 10. Let P rog be a non-copying S-CF program and l → r be a leftlinear rewrite rule. Consider distinct variables x 1 ,. , x n such that V ar(l) ∩ {x 1 ,. , x n } = ∅. If there are P and k s. t. the k th argument of P is an output, and P (x 1 ,. , x k-1 , l, x k+1 ,. , x n ) Y + θ G where 13 1. resolution steps are applied only on atoms whose output is not flat, 2. Out(G) is flat and 3. the clause P (t 1 ,. , t n ) ← B used in the first step of this derivation satisfies t k is not a variable 14   then the clause θ(P (x 1 ,. , x k-1 , r, x k+1 ,. , x n )) ← G is called critical pair. if θ does not instantiate the variables of In(P (x 1 ,.
, x k-1 , l, x k+1 ,. , x n )) then the critical pair is said strict. 13 Here, we do not use a hat to indicate output arguments because they may occur anywhere depending on P. 14 In other words, the overlap of l on the clause head P (t 1 ,. , tn) is done at a non-variable position. Example 12. Let P rog be the S-CF program defined by: P rog = {P ( s(x)) ← Q( x, a). Q( f (x), y) ← Q( x, g(y)). Q( x, x) ←. } and con- sider R = {f (f (x)) → h(x)}. Note that L(P ) = {s(f n (g n (a))) | n ∈ IN}. We have Q( f (f (x)), y) Y Q( f (x), g(y)) Y Q( x, g(g(y))). Since Out(Q( x, g(g(y)))) is flat, this generates the strict critical pair Q( h(x), y) ← Q( x, g(g(y))). The following lemma is very important for completion. It shows that when the completion process adds a strict critical pair into the current S-CF program, the resulting program is still S-CF.
Definition 11. A critical pair H ← B is said convergent if H → * P rog B. Definition 12. Let R be a rewrite system. We consider the smallest set of consuming symbols, recursively defined by: f ∈ Σ is consuming if there exists a rewrite rule f (t 1 ,. , t n ) → r in R s. t. some t i is not a variable, or r contains at least one consuming symbol. The S-CF program P rog is strongly coherent with R if: 1) for all l → r ∈ R, the top-symbol of l does not occur in input arguments of P rog, 2) and no consuming symbol occurs in clause-heads having input arguments. Note that a CS-program (no input arguments) is strongly coherent with any rewrite system. In R = {g(s(y)) → h(y)}, g is consuming. Thus P rog = {P ( g(x), x) ←. } is not strongly coherent with R because item 2 is not satisfied. We have P ( g(s(y)), z) Y [x/s(y),z/s(y)] ∅, which generates the critical pair P ( h(y), s(y)) ←.
This critical pair is not a S-CF clause. Lemma 5. If P rog is a normalized S-CF program strongly coherent with R, then every critical pair cp is strict, and P rog ∪ {cp} is strongly coherent with R. Proof. Consider f ( s) → r ∈ R ( s is a tuple of terms), and assume that P ( x 1 , f ( s), x 2 , z) Y [P ( t1, f ( u), t2, v)←B, θ] G Y * σ G such that Out(G ) is flat, x 1 , x 2 , z, u, v P rog = { P ( s(x)) ← Q( x, a). Q( f (x), y) ← Q( x, g(y)). Q( x, x) ←. Q( h(x), y) ← Q( x, g 2 (y)) } and R = {f 2 (x) → h(x)}. So there is only one critical pair, which is convergent thanks to the last clause of P rog. s(f 2 (g 2 (a))) ∈ L(P ) because the atom A = P (s(f 2 (g 2 (a)))) ∈ M od(P rog), moreover A = P (s(f 2 (g 2 (a)))) → R A = P (s(h(g 2 (a)))). Since A ∈ M od(P rog), we have A Y * ∅. More precisely: A = P (s(f 2 (g 2 (a)))) Y A = Q( f 2 (g 2 (a)), a) Y Q( f (g 2 (a)), g(a)) Y Q( g 2 (a), g 2 (a)) Y ∅.
e. in some cases G = ∅. 17 Note that L(P 0 ) = {f (c(a), s n (a)) | n ∈ IN} is a regular language, whereas the S-CF program (which is also a CS-program) is not regular. If it were regular, there would be finitely many critical pairs. needs to compute all critical pairs. This is why we define sufficient conditions to ensure that a given finite S-CF program has finitely many critical pairs. Definition 13. P rog is empty-recursive if there exist a predicate symbol P and two tuples x = (x 1 ,. , x n ), y = (y 1 ,. , y k ) composed of distinct variables s. t. P ( x. y) Y + σ A 1 ,. , P ( x. t ),. , A k where x = (x 1 ,. , x n ) is a tuple of variables and there exist i, j s. t. x i = σ(x i ) and σ(x j ) is not a variable and x j ∈ V ar(σ(x j )). Example 14. Let P rog be the S-CF program defined as follows: P rog = {P ( a, b) ←.
P ( x , s(y )) ← P ( x , y ). }. From P ( x, y), one can obtain the following derivation: P ( x, y) Y [x/x , y/s(y )] P ( x , y ). Consequently, P rog is empty-recursive since σ = [x/x , y/s(y )], x = σ(x) and y is a variable of σ(y) = s(y ). The following lemma shows that the non empty-recursiveness of an S-CF program is sufficient to ensure the finiteness of the number of critical pairs. Lemma 6. Let P rog be a normalized S-CF program. If P rog is not emptyrecursive, then the number of critical pairs is finite. Remark: Note that the S-CF program of Example 13 is normalized and has infinitely many critical pairs. However it is empty-recursive because P 1 ( x, y) Y [x/x , y/s(y )] P 1 ( x , y ). Proof. By contrapositive. Let us suppose there exist infinitely many critical pairs. So there exist P 1 and infinitely many derivations of the form (i): P 1 (x 1 ,.
Note that the atom R( x 1 ) has been removed using simplify. Note also that there is no cycle anymore. removeCycles may enlarge the least Herbrand Model. Lemma 8. Let P rog and P rog be two S-CF programs such that P rog is noncopying and P rog = removeCycles(P rog). Then P rog is a non-copying and non-cyclic S-CF program, and M od(P rog) ⊆ M od(P rog ). Moreover: -if P rog is normalized, then so is P rog , -if P rog is strongly coherent with R, then so is P rog. Consequently, there are finitely many critical pairs in P rog. Proof. removeCycles applies unSync until the program is not cyclic. When applying unSync, one pseudo-empty clause is removed and replaced by a nonpseudo-empty one. Thus, the number of pseudo-empty clauses decreases, and when there are no more pseudo-empty clauses, the program is not cyclic.
If the number of predicate symbols having the same arity as P 1 (including P 1 ) exceeds predicate-limit, an existing predicate symbol (for example Q) must be used instead of the new predicate P 1. This may enlarge M od(P rog) in general and may lead to a strict over-approximation. If the arity of P 1 exceeds arity-limit, P 1 must be replaced in the clause body by several predicate symbols 20  whose arities are less than or equal to arity-limit. This may also enlarge M od(P rog). See  [2]  for more details. In other words norm P rog (H ← B) builds a set of normalized S-CF clauses N such that M od(P rog ∪ {H ← B}) ⊆ M od(P rog ∪ N ). However, when starting from a CS-program (i. e. without input arguments), it could be interesting to normalize by introducing input arguments, in order to profit from the bigger expressiveness of S-CF programs, and consequently to get a better approximation of the set of descendants, or even an exact compu- Proof.
Examples In this section, completion is applied on several examples. I is the initial set of terms and R is the rewrite system. Initially, we define an S-CF program P rog that generates I and that satisfies the assumptions of Definition 9. To make the procedure terminate shortly, we suppose that predicate-limit=1, which means that for all i, there is at most one predicate symbol having i arguments, except for i = 1 we allow two predicate symbols having one argument. When the following example is dealt with synchronized languages, i. e. with CS-programs [2, Example 42], we get a strict over-approximation of the descendants. Now, thanks to the bigger expressivity of S-CF programs, we compute the descendants in an exact way. Using clause (1) we have P f ( f (x, y)) → (1) P a ( x), P a ( y) generating the critical pair: P f ( u(f (v(x), w(y)))) ← P a ( x), P a ( y).
Its normalization gives the clauses: P 3 ( f (x), g(y)) ← P 2 ( x, y, z, h(x ), i(y ), z ), P a ( x ), P a ( y ). (13) and P d ( c(x, y)) ← P 3 ( x, y). (14). Note that the symbols c, f and g have been considered as output parameters. No more critical pairs are detected and the procedure stops. The resulting program P rog is composed of clauses (1) to  (14). Note that the subset of descendants d 2 (f k (h n (a)), g k (i n (a)), s n-k (a)) can be seen (with p = nk) as d 2 (f k (h k+p (a)), g k (i k+p (a )), s p (a)). The reader can check by himself that L P rog (P d ) is exactly R * (I). The run of the completion on this example is also summarized in Fig  4. choice of the predicate symbol to be reused when predicate-limit is reached. On the other hand, the choice of generating function-symbols as output or as input is also crucial.
The resulting observations are mixed continuous and discrete (1-bit decisions) observations, and are combined in the FC to solve the inference problems. We first formulate the problem of spatial field reconstruction, exceedance level estimation and classification in such heterogeneous networks. We show that the resulting posterior predictive distribution, which is key in fusing such disparate observations, involves intractable integrals. To overcome this problem, we develop an algorithm that is based on a multivariate series expansion approach resulting in a Saddle-point type approximation. We then present comprehensive study of the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using real data of insurance storm surge database known as the Extreme Wind Storms Catalogue.
B. Sensor Networks for Insurance: Wind Speed and Insurance Storms In this study we use a publicly available insurance storm surge database known as the Extreme Wind Storms Catalogue 2. The data is available for research as the XWS Datasets: (c) Copyright Met Office, University of Reading and University of Exeter. Licensed under Creative Commons CC BY 4. 0 International License. The data provided is comprehensive and provides features such as the footprint of the observations on a location grid with a rotated pole at degrees, degrees. As discussed in the data description provided with the data-set, this is a standard technique used to ensure that the spacing in km between grid points remains relatively consistent. The footprints are on a regular grid in the rotated coordinate system, with horizontal grid spacing 0.
22 degrees. The data for each of the storms provides a list of grid number and maximum 3-second gust speed in meters per second. The true locations (longitude and latitude) of the grid points are given in grid locations file. We selected two storms to analyze, the first is known as 2 http://www. met. reading. ac. uk/~extws/database/dataDesc Dagmar which took place on 26/12/2011 and affected Finland and Norway; and the second was the storm known as Ulli taking place on 3/1/2012 which affected the UK.
VII. CONCLUSION We developed a new framework for spatial field reconstruction, exceedance level estimation and classification in heterogeneous (mixed analog & digital sensors) wireless sensor networks. We developed novel algorithms that are based on a multivariate series expansion approach resulting in a Saddle-point type approximation. We then studied the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using real data of insurance storm surge database. The results show a significant improvement for both field reconstruction and spatial classification.
and approving it for publication was  Dr. Wenwu Wang. I. Nevat  would like to acknowledge the support of the  National Research Fund (NRF) ,  Singapore. G. W. Peters  would like to acknowledge support of the  Institute of Statistical Mathematics, Tokyo, Japan , and the  Commonwealth Scientific Industrial Research Organisation, Sydney ,  Australia. F. Septier  would like to acknowledge the support of the  BNPSI ANR  project no  ANR-13-BS-03-0006-01. I.
Long-term data sharing Several funding agencies, international regulatory bodies and many major ecological and evolutionary journals now require raw or primary data to be deposited in a permanent open access archive, such as Dryad or TreeBASE, as a condition for funding or publication. The data must be in sufficient detail to allow the analyses in the paper to be replicated. The rationale for open archiving is that archived data are available to posterity when studies are completed, for error checking, for use in new studies, or for future meta-analysis  [1]. In addition it has been argued that the policy would benefit data providers by increasing their citation index through citations by papers with new analyses  [1, 2]. Although it is claimed that over 95% of scientists in evolution and ecology believe data should be publicly archived  [1] , mandatory public data archiving (PDA) is raising many issues in the scientific community as evidenced by debates on websites, in blogs and publications  [2] [3] [4] [5] [6] [7] [8] [9] [10].
The survey To obtain the opinions of scientists with individual-based longitudinal data, a worldwide survey was sent to 146 PIs of long-term research projects. Responses were received from 73 PIs working on 59 bird studies, 13 mammalian studies and 1 plant study. The 92 projects (some PIs have several projects) range in duration from 5 to 68 years (Figure  1 ), with 55 percent collecting data for more than 30 years. Thirtyfive percent of researchers were required to archive data used in a publication by their current funding agency and 19% by their institution. Eight researchers were required to deposit data by both; therefore 59% were not required to archive their data. There was diversity of opinion among PIs about data archiving, but some strong points of consensus emerged. This paper synthesizes the views of all respondents, many of whom have made important contributions to ecology and evolution.
Potential costs to science Flawed science: A major cost would be flawed science resulting from a lack of understanding of the database or the biological system. Open access to long-term data might not allow for a full understanding of all the subtle contexts, nuances and issues involved in the biological system and the structure of the database from which the long-term data are collected. It has been argued that if method sections are sufficiently detailed, misunderstanding the system should not be a major source of error  [54]. However, not all of the complexities of the biological system can be detailed in a method section without making a paper unwieldy. Hence, without the PI's involvement, crucial contextual information is likely to be lost under open access, leading to the potential for erroneous assumptions and interpretations which could add to the growing retraction rate in scientific journals  [55].