dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported in part by the  National Human Genome Research Institute (NHGRI)  ( 2U41HG007234 ),  Wellcome Trust  ( WT108749/Z/15/Z ) and the  European Molecular Biology Laboratory. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The work was partly undertaken at  UCLH/UCL , which received a proportion of funding from the  Department of Health's NIHR Biomedical Research Centres funding scheme. S. M. S. is partly supported by the  Epilepsy Society. J. R. is funded by the  Agency for Innovation by Science and Technology, IWT. H. S. is a PhD fellow of the Fund for Scientific Research Flanders (FWO,  1125416N ). G. C. was funded by  Science Foundation Ireland (SFI)  and the  European Regional Development Fund , grant number  16/RC/3948.
For example, to encode the example given in Equation  3 , we declare an uninterpreted sort Loc and a sort Data as a datatype as follows: VI (declare-sort Loc 0) (declare-datatype Data ((node (d Int) (next Loc)))) (declare-heap (Loc Data)) The last declaration fixes the type of the heap model. The predicate definitions are written into SMT-LIB format using the recursive function definition introduced in version 2. 6. For instance, the definition of the list segment from Equations 4 and 5 is written into SMT-LIB as follows (based on the above declarations of Loc and Data): (define-fun-rec ls ((h Loc) (f Loc)) Bool (or (and emp (= h f)) (exists ((x Loc) (d Int)) (and (distinct h f) (sep (pto h (node d x)) (ls x f)))) ) ) Problem format: Each benchmark file is organized as follows: -Preamble information required by the SMT-LIB format: the sub-logic of SL theory (see Section 3.
Divisions The main difficulty that faces automatic reasoning using SL is that the logic, due to its expressiveness, does not have very nice decidability properties  [1]. For this reason, most program verification tools use incomplete heuristics to solve the satisfiability and entailment problems in SL or restrict the logic employed to decidable fragments. Overviews of decidable results for SL are available in  [26, 8]. Each benchmark instance of SL-COMP refers to one of the sub-logics of the multi-sorted Separation Logic. These sub-logics identify fragments which are handled by at least two participants or have been identified to be of interest during the discussion for the organization of the round. The sub-logics are named using groups of letters, in a way similar to SMT-LIB format. These letters have been chosen to evoke the restrictions used by the sub-logics: -QF for the restriction to quantifier free formulas; -SH for the so-called "symbolic heap fragment" where formulas are restricted to (Boolean and separating) conjunctions of atoms and do not contain magic wand; moreover, pure atoms are only equality or dis-equality atoms; -LS where the only predicate allowed is the acyclic list segment, ls, defined in Equations 4 and 5; -ID for the fragment with user defined predicates; -LID for the fragment of linear user defined predicates, i.
Selection process The benchmark set was built mainly from the contributions of participants. Some of these problems come from academic software analysis or verification tools based on SL (e. g. , SmallFoot  [30] , Hip  [5] ). We did not received any problem issued from industrial tools. The problems were collected in the input format submitted by the participants and then translated into the input format of the competition. With the objective of increasing the size of the benchmark set, we did not limit the number of problems submitted by a participant. In this way, the edition 2018 has seen an increase of 100% in the size of the benchmark set. However in the future we could consider a change in the regulations to find a fair balance between teams. By using the meta-information in the preamble of each file, we are able to track the team which proposed the problem.
ComSPEN The theoretical bases of ComSPEN have been presented in  [11]. The development team is composed of Taolue Chen (University of London, UK), Chong Gao and Zhilin Wu (State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences). The solver deals with both satisfiability and entailment problems in a fragment included in logic QF_SHIDLIA and which extends QF_SHLID with integer linear arithmetics in predicate definitions. The underlaying technique for satisfiability checking of a formula ϕ is to define an abstraction, Abs(ϕ), where Boolean variables are introduced to encode the spatial part of ϕ, together with quantifier-free formulae to represent the transitive closure of the data constraints in the predicate atoms. Checking satisfiability of ϕ is then reduced to checking satisfiability of Abs(ϕ), which can be solved by the state-of-the-art SMT solvers (e.
Sleek Sleek  [5, 28]  participated in all editions of SL-COMP, the submitters at edition 2018 being Benjamin Lee and Wei-Ngan Chin (NUS, Singapore). The solver deals with the satisfiability and entailment checking for the QF_SHID fragment. It is an (incomplete but) automatic prover, that builds a proof tree for the input problem by using the classical inference rules and the frame rule of SL. It also uses a database of lemmas for the inductive definitions in order to discharge the proof obligations on the spatial formulas. The proof obligations on pure formulas are discharged by external provers like CVC4, Mona, or Z3. Sleek was the winner of the division qf_shid_entl in edition 2014, and was in the third position in the same division in edition 2018.
Slide Slide  [14, 29]  was submitted by Adam Rogalewicz (FIT, Brno University of Technology, Czechia), the development team including Michal Cyprian and Tomas Vojnar from the same institute and Radu Iosif (Verimag, University Grenoble Alpes & CNRS, France). The solver deals with the entailment problem in the decidable sub-fragment of QF_SLID defined in  [13]. The main principle of SLIDE is a reduction of entailment problems in SL into inclusion problems of tree automata. For the problems in the fragment identified in  [13] , the decision procedure implemented in Slide is EXPTIME-hard. More precisely, the proof method for checking ϕ ⇒ ψ relies on converting ϕ and ψ into two tree automata A ϕ resp. A ψ , and checking the tree language inclusion of the automaton A ϕ in the automaton A ψ. Slide takes an input in its own input format, which can be generated by the dedicated SL-COMP preprocessor.
The reduction from the system of predicates into tree automata and the join operator is implemented in Python3. The result of the reduction are input files for the VATA tree automata library, which is used as a backend for the inclusion tests. Slide participated in both past editions of SL-COMP. In 2018 edition, Slide solved 61 of 312 problems in division qf_shid_entl, 7 of 60 problems in division qf_shlid_entl, and 15 of 73 problems in division shid_entl. The number of solved problems is related to the fact that Slide a prototype implementation, where our primary goal was to show the advantages of automata techniques. In order to cover more problems, one have to implement a new top-level parser, which would split the input entailment query into a set of subsequent queries, for which the automata-based technique can be used.
This allows users of StarExec to rerun a competition if so desired. More importantly, it allows users to upload a new set of problems of interest in their application domain and to try the various solvers against those problems. This feature was very useful for SL-COMP at edition 2018, because some solvers reused the binaries submitted in 2014. The results of the competition are provided on the competition web page with a link to the CSV files generated by StarExec. We are also archiving the results of previous editions in the GitHub. The SL-COMP initiative fulfilled its goals: an interesting suite of SL problems is publicly available in a common format and the maturity of solvers submitted for this competition has been proven. Moreover, we achieved to propose a common format for SL which is based on a mature and maintained format for first-order theories, SMT-LIB.
As mentioned above, we have to balance the number of problems coming from the same team in each division in order to reach a fair comparison criterium. For each problem, it would be interesting to attach a coefficient which is taken into account in the scoring system and thus obtain a better evaluation of each solver. A classic way to assign a difficulty level is to take into account the size of the formulas and of the inductive definitions used in the problem. Finally, we should intensify the exchanges with related competitions in software verification and automated proving. Such competitions may benefit from SL-COMP results in terms of automation, and may provide interesting benchmark sets. For this, the results of SL-COMP should be made available in forms that allows to understood the state of the art of SL solvers and the contribution of each participating solver to this state of the art.
Carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) are the greenhouse gases largely responsible for anthropogenic climate change. Natural plant and microbial metabolic processes play a major role in the global atmospheric budget of each. We have been studying ecosystem-atmosphere trace gas exchange at a sub-boreal forest in the northeastern United States for over two decades. Historically our emphasis was on turbulent fluxes of CO 2 and water vapor. In 2012 we embarked on an expanded campaign to also measure CH 4 and N 2 O. Here we present continuous tower-based measurements of the ecosystem-atmosphere exchange of CO 2 and CH 4 , recorded over the period 2012-2018 and reported at a 30-minute time step. Additionally, we describe a five-year (2012-2016) dataset of chamber-based measurements of soil fluxes of CO 2 , CH 4 , and N 2 O (2013-2016 only), conducted each year from May to November.
Beyond our tower-based CH 4 measurements at Howland  16  , only a handful of other studies have been published for temperate  [17] [18] [19]  and tropical  20, 21  forests. Much more attention has been paid to wetland systems  [22] [23] [24] [25] [26]  , which are generally strong sources of CH 4. Previous analysis of our data has indicated that at an annual time step, Howland Forest switches from a weak CH 4 source to a weak CH 4 sink depending on hydrologic conditions during late summer  16. We have also conducted measurements of soil-atmosphere greenhouse gas fluxes using automated chamber-based methods  27, 28. Here we describe a complementary five-year (2012-2016) dataset of chamber-based measurements of soil CO 2 , CH 4 , and N 2 O (2013-2016 only) fluxes, conducted along a gradient of upland, transition (not measured in 2013), and wetland sites close to the main research tower.
In 2015-2016, soil fluxes of all three gases were measured in sites representing all three soil drainage classes. A subset of the dataset  29  described here is available through the AmeriFlux data portal  30. We have two goals in describing and distributing a more complete dataset via Figshare. First, we aim to document tower and chamber flux measurements (e. g. , instruments, processing, and QC) that had not yet been fully described in our previous papers  12, 16. Second, we are making data publicly available that cannot otherwise be handled or distributed through the current AmeriFlux data distribution system. This includes a variety of important variables output through the flux processing software (variances and covariances, flux uncertainties, spectral correction factors, and trace gas time lags) as well as all of the chamber data.
, Longmont, CO, USA) and a fast-response CH 4 /CO 2 /H 2 O cavity ring-down spectrometer (model G2311-f, Picarro Inc. , Santa Clara, CA, USA). Sampled air was pulled from the top of the tower through ~46 m of 4. 8 mm (inner diameter) LLDPE (U. S. Plastics Corp. , Lima, OH, USA) tubing (replaced annually), sheathed in flexible PVC pipe to minimize temperature fluctuations, using a vacuum pump (model MD4-NT, Vacuubrand GmbH, Wertheim, Germany) to maintain low cavity pressure and a flow rate ≥7 standard litres per minute. The distance between the air inlet and the sonic anemometer was less than 30 cm. All data, including concentrations of CH 4 and CO 2 reported as dry air mole fractions (mixing ratios), were recorded at 5 Hz on a data logger (model CR1000, Campbell Scientific Inc. , Logan, UT, USA).
Raw, high-frequency data (available on request from D. Y. H. ) were converted to 30 minute fluxes using the open source EddyPro ® Eddy Covariance Processing Software, version 6. 2. 2 (LI-COR Biosciences, Lincoln, NE, USA)  35. Turbulent fluxes calculated include sensible (H) and latent (LE) heat fluxes, as well as fluxes of CO 2 (CO 2 _flux) and CH 4 (CH4_flux). The custom EddyPro settings used are summarized in Table  1. We processed the data in two batches: June 2012-June 2015 and June 2015-June 2018. The resulting files were concatenated in chronological order. The full EddyPro output file, with no filtering, is included here. Filtered tower-based flux measurements. We used the EddyPro output file to generate a filtered data set, also included here, which follows AmeriFux standard formats and which is recommended for most applications.
EddyPro option Setting Axis Environmental measurements. We have for many years conducted measurements, from the main Howland tower, of key environmental and meteorological variables that are relevant to interpretation and modelling of ecosystem-atmosphere flux data. Like the tower fluxes, these data are reported at a 30-minute temporal resolution, which typically represents the mean of higher-frequency instantaneous measurements. For example, solar radiation measurements are taken every 15 s, but only the 30-minute mean is logged. Environmental and meteorological measurements reported here include air temperature (shielded, ventilated platinum resistance thermometer), solar radiation (photosynthetic photon flux density, PPFD; model PAR lite quantum sensor, Kipp & Zonen, Delft, the Netherlands), net radiation (model CNR-4, Kipp & Zonen, Delft, the Netherlands), precipitation (heated tipping bucket rain gage, model TR-525; Texas Electronics, Dallas, TX, USA), and air pressure (model PTB100A analog barometer; Vaisala, Vantaa, Finland), all of which are measured at the top of the tower.
For ease of use, we have divided the tower flux data into four separate files, as follows: (1) Unfiltered EddyPro output. This file contains the processed but unfiltered tower fluxes (calculated using data from the Picarro CRDS), as output by the EddyPro software at a 30 minute time-step, as well as the associated enviro-meteorological data, and is named US-Ho1_HH_201206060000_201806302330_EP. csv. The columns of this data file are described in Online-only Table  1. This file is distributed through Figshare as it contains numerous columns that at present cannot be distributed via AmeriFlux. This includes variances and covariances, flux uncertainties, spectral correction factors, and trace gas time lags that may be of interest to some data users. Additionally, this file has not been filtered using the standard Howland QC flags, providing the data user the opportunity to apply their own filtering methods (e.
g. Mauder and Foken  39   6. This file is distributed through Figshare as the data it contains cannot be distributed via AmeriFlux. (3) Filtered half-hourly AmeriFlux-format dataset. This file contains the filtered tower fluxes, as well as associated enviro-meteorological data, at a 30 minute time step, formatted according to AmeriFlux standards. Following AmeriFlux naming conventions, the AmeriFlux-format tower fluxes dataset is named US-Ho1_HH_201206060000_201806302330. csv. The columns of this file are described in Table  7. This file is distributed through Figshare, and an identical file has been uploaded to the AmeriFlux data archive, where it has undergone the standard AmeriFlux checks for data quality and consistency, and where it is available as part of the larger US-Ho1 data record (since 1996)  30.
(4) Unfiltered EddyPro output for a second gas analyser. This file contains the processed but unfiltered tower fluxes (calculated using data from the LI-COR Li-7200 IRGA; note that this instrument does not measure CH 4 ), as output by the EddyPro software at a 30 minute time-step, and is named US-Ho1_HH_201206060000_201806302330_EP LI-COR. csv. The columns of this data file are described in Online-only Table  1. This dataset is only distributed through Figshare; fluxes calculated from the LI-COR analyser have not been uploaded to AmeriFlux because of concerns about system performance in 2018. Fluxes from this data file were used in the technical validation analyses described below.
Chamber fluxes. The "chamber flux" data file contains measurements of soil fluxes of CO 2 , CH 4 , and N 2 O, with measurements from each chamber reported approximately hourly during the growing season (2012-2016). The chamber fluxes file is named US-Ho1_CMB_201201010000_201701010000. csv, and the columns of the data file are described in Table  8. This file is distributed through Figshare. An identical file has been uploaded to the AmeriFlux archive, but it contains data that cannot be distributed through AmeriFlux. Additional files. The configuration and metadata files used for the Eddy Pro processing described here (files: processing_2018-11-21T083742_adv. eddypro and main2012-2018. metadata), as well as the AmeriFlux-format machine-readable Biological, Ancillary, Disturbance and Metadata (BADM) template (which contains information about the site, including standing biomass, leaf area index, and soil chemistry) (file: AMF_US-Ho1_BIF_ LATEST 04 09 2019.
Technical Validation Site overview. The forest in the vicinity of the main Howland tower is nearly ideal from the perspective of making tower-based flux measurements over tall vegetation; forest cover is extensive and homogeneous, and the topography is generally flat  13. As one of the longest-running AmeriFlux sites, the eddy covariance flux measurements at Howland have been carefully scrutinized over the last two decades. For example, the environmental and flux measurements from the main Howland tower have been regularly evaluated against data recorded by the AmeriFlux Portable Eddy Covariance System, which was most recently deployed adjacent to our own instrumentation for a 10-day period in the summer of 2016. Additionally, since 1998 environmental and flux measurements.
We note also that data from the main and west towers were used to develop a novel method of assessing the random uncertainty in 30-minute CO 2 , H 2 O and energy fluxes  12, 40  , which has then been applied to estimate uncertainties in annual ecosystem C budgets  41, 42. Thus, in general the eddy covariance fluxes measured at Howland are known to be of high quality, with well-characterized uncertainties. Here we conduct three additional analyses to further assess the technical quality of the tower-based measurements. First, we compare LE and CO 2 fluxes calculated using H 2 O and CO 2 concentrations measured with our Picarro CRDS against those calculated using concentrations measured simultaneously with a LI-COR IRGA. Second, we compare the long-term patterns in CH 4 concentration measured with our analyser against independent atmospheric CH 4 concentration measurements from two climate monitoring observatory stations.
From the approximately 18,000 mid-day half-hourly data points recorded between 2012 and 2018, we excluded from the calculation 323 half-hourly measurements where the measured CH 4 concentration was greater than 2600 ppb (61% of these high measurements occurred during a brief period late in 2014), and 8 half-hourly measurements where the measured CH 4 concentration was less than 1500 ppb. There were 1075 missing data points when the CH 4 concentration was not recorded due to power or instrument failure. Within each monthly period, the standard deviation of the mean half-hourly CH 4 concentrations had a mean value of 20 ppb, and with ≈240 measurements averaged each month, the standard error of the mean was in almost all cases less than 2 ppb. The monthly median tended to be somewhat lower (by 4 ± 3 ppb) than the monthly mean, but the temporal patterns were essentially identical.
In Fig. 4 , we compare the Howland (Maine) data with data from Mauna Loa (Hawaii) and Barrow (Alaska), where ongoing long-term atmospheric CH 4 concentration measurements are maintained by researchers from the National Oceanic and Atmospheric Administration (NOAA)  43, 44. For the NOAA data, sub-hourly measurements have been similarly filtered for outliers (<1500 ppb or >2600 ppb), averaged to hourly values, and then screened to distinguish samples of regionally representative air. These are then filtered using a rule-based editing algorithm to exclude measurements obtained when the analytical instrument was not working properly. The NOAA instruments (an automated gas chromatograph using flame ionization detection at Mauna Loa, and since 2013 a laser-based optical analyser at Barrow) are regularly calibrated against reference standards.
There is no evidence of degraded instrument performance over the six years of measurements. Assessment of quality control flags and random uncertainty in tower CH 4 fluxes. Across the more than 100,000 half-hourly periods covered by the eddy covariance dataset, there were missing CH 4 fluxes (due to power or instrument failure) only 8% of the time. A further 15% were assigned a Mauder and Foken 39 (M&F) QC flag of 2, indicating low quality measurements. Therefore, more than 75% of the time the fluxes were considered to be of "usable" quality, with 37% receiving an M&F QC flag of 0 (the highest quality) and 41% an M&F QC flag of 1. Within EddyPro, the method of Finkelstein and Sims  45  was used to estimate random uncertainties in all calculated fluxes. Because the CH 4 fluxes measured at Howland are generally small, an important question is whether we are measuring signal (i.
Usage Notes Recommended filtering criteria. The AmeriFlux-formatted data file included here has been filtered according to standard methods used for over two decades at Howland Forest, and is the data set recommended for most analyses. However, The Mauder and Foken  39  QC flags included in the EddyPro output could alternatively be used for data filtering (avoiding values flagged as "2"). At Howland, we have always adopted the friction velocity ("u* filtering") method of removing night-time data recorded under periods of high atmospheric stability and low turbulence  12. We therefore recommend that data from nocturnal periods (PPFD ≤ 5 μmol m -2 s -1 ) be excluded when u* ≤ 0. 25 m s -1. These periods are indicated by a summary flag value of 4 in the QC and outlier flags file.
Complementary data sets. Long-term data from the Howland AmeriFlux site are available through the AmeriFlux data portal (https://ameriflux. lbl. gov/sites/site-search/#keyword=Howland). This includes CO 2 , H 2 O, and energy fluxes measured via eddy covariance, as well as meteorological and environmental data at a 30 minute time step. Measurements have been conducted at the main Howland tower (AmeriFlux site US-Ho1)  30  since 1996 (the full US-Ho1 dataset available for download from AmeriFlux includes the filtered half-hourly AmeriFlux-format dataset described here); at the west Howland tower (AmeriFlux site US-Ho2)  54  , the site of a low-level N addition experiment, since 1998; and the east Howland tower (AmeriFlux site US-Ho3)  55  , the site of a shelterwood harvest experiment, since 2001.
Acknowledgements This research was supported by  USDA  grant #  2014-67003-22073. Howland Forest is supported by the  Office of Science (BER) ,  US Department of Energy , and the  USDA Forest Service's Northern Research Station. Bob Evans  and  John Lee  assisted with on-site measurements and instrument maintenance. We thank the  Northeast Wilderness Trust  for permission to conduct research activities at  Howland. Ed Dlugokencky  ( NOAA ) provided the Barrow and Mauna Loa CH 4 data.