dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments ODG would like to thank the  National Council of Science and Technology (CONACYT)  from  Mexico  for the Ph. D. scholarship grant. CCA-UNAM  and  DGTIC-UNAM  for computing resources. Bi-national programs JEST (Joint Environmental Study of Términos Lagoon ) and  ECOS-NORD (Evaluation-orientation de la COopération Scientifique) between France and Mexico. This research has been funded by the  Mexican National Council for Science and Technology-Mexican Ministry of Energy-Hydrocarbon Fund , project  201441. This is a contribution of the  Gulf of Mexico Research Consortium (CIGoM). We acknowledge the specific request of PEMEX to the  Hydrocarbon Fund  to.
January to March 2010, they were calibrated performing several test runs with different bottom roughness, and validated with several observed water level datasets that have a wide coverage in space and time in the TL. 19. 0ºN 18. 9ºN 18. 8ºN +SLR75 only wind tide+wind +SLR25 +SLR50 February 25, 2010, at 04:00 GMT 18. 7ºN 18. 6ºN 18. 5ºN 18. 4ºN 18. 3ºN 18. 2ºN a 92. 4ºW 92. 2ºW 92. 0ºW 91. 8ºW 91. 6ºW 91. 4ºW 91. 2ºW 18. 80ºN 18. 78ºN 18. 76ºN 18. 74ºN 18. 72ºN 18. 70ºN 18. 68ºN 18. 66ºN 18. 64ºN 18. 62ºN b 18. 60ºN 91. 85ºW 91. 80ºW 91. 75ºW 91. 70ºW 91. 76ºW 91. 60ºW 91. 56ºW 91. 50ºW from.
(2017b). This work is Paper II of a larger study in which Paper I discusses IRDIS' polarimetric mode, the data reduction and recommendations for observations and instrument upgrades. With our instrument model we aim to achieve in all four broadband filters a total polarimetric accuracy, i. e. the uncertainty in the measured polarization signal, of ∼0. 1% in the degree of linear polarization. In addition, we aim to attain an accuracy of a few degrees in angle of linear polarization in these filters. Reaching these accuracies will enable us to measure the linear polarization of substellar companions (we regard the extremely high degree of linear polarization found by  Ginski et al. (2018)  to be an exception). These accuracies also readily suffice for quantitative polarimetry of circumstellar disks, because the degree of linear polarization of disks is typically much higher than that of substellar companions: on the order of percents to several ten percent (see e.
Calibration measurements and determination of model parameters With the Mueller matrix model of the telescope and instrument defined, we can now determine the model parameters describing the optical path downstream of M4. To this end, we have taken measurements with the internal light source (see Fig. 2 ) using the Y-, J-, H-and K s -band filters. The following data sets were obtained: -On August 15, 2015, a total of 528 exposures were taken with the calibration polarizer inserted, injecting light that is nearly 100% linearly polarized in the vertical direction (in the positive Q-direction in the instrument reference frame). The derotator and HWP were rotated between the exposures with θ der ranging from 0 • to 90 • and θ HWP ranging from 0 • to 101. 25 • (varying step sizes). This data, hereafter called the polarized source measurements, is used to determine for each broadband filter the retardances of the derotator and HWP (∆ der and ∆ HWP ), the offset angles of the derotator and HWP (δ der and δ HWP ) and the diattenuation of the polarizers (d).
-On June 12 and 13, 2016, a total of 400 exposures were taken without the calibration polarizer inserted, so that almost completely unpolarized light was injected. The derotator and HWP were rotated between the exposures with θ der and θ HWP ranging from 0 • to 101. 25 • with a step size of 11. 25 •. This data, hereafter called the unpolarized source measurements, is used to fit for each broadband filter the diattenuations of the derotator and HWP ( der and HWP ). The light injected is actually weakly polarized, because it is reflected off M4 before reaching the HWP. We therefore also fit the injected normalized Stokes parameters q in,unpol and u in,unpol. We pre-process the data by applying dark subtraction, flat fielding and bad-pixel correction according to Paper I. Subsequently, we construct double-difference and double-sum images from Eqs.
We therefore write S HWP = [1, q in,unpol , u in,unpol , 0] T , with q in,unpol and u in,unpol the tobe-determined injected normalized Stokes parameters, assuming that no circularly polarized light will be produced. Note that there are no degeneracies among the model parameters with the above definitions of S HWP , because the derotator, HWP, calibration polarizer and M4 each have their own independent (local) references frames. With the description of the measurements complete, we determine the model parameters by fitting the model function to the data points using non-linear least squares (with sequential least squares programming as implemented in the Python function scipy. optimize. minimize). The HWP and derotator angles required for this are obtained from the headers of the FITS-files of the measurements (see Appendix A).
This computation needs to be performed before determining the diattenuations, because the retardance of M4 affects the measurement of the IP produced by the UT. Because we observed unpolarized (standard) stars, we write S in = [1, 0, 0, 0] T. We determine the diattenuations of the UT and M4 independently for both data sets by fitting the model function to the data points using non-linear least squares. The parallactic, altitude, HWP and derotator angles required for this are obtained from the headers of the FITS-files of the measurements (see Appendix A). We have tested fitting the incident Stokes vectors in addition to the diattenuations (writing S in = [1, q in , u in , 0] T ), and found that the degree of linear polarization of the stars is indeed insignificant (< 0. 1%) in all filters.
Q in -and U in -images � � FITS-headers To construct the Qin -and Ûin -images we use our instrument model. The instrumental polarization effects are different for each measurement, because the parallactic, altitude, HWP and derotator angles change continuously as the telescope tracks the target. To describe these changing instrumental polarization effects, we compute the vector equivalents of the single and double difference (Eqs. 6 and 22) using our instrument model. To this end, we obtain the date, filter and the parallactic, altitude, HWP and derotator angles of each measurement from the headers of the FITS-files of the data (see Appendix A). We then take the model parameters corresponding to the filter from Tables 1 (parameters HWP to d) and 2, taking into account the date of the observations for the latter.
2018). While the outer disk can easily be spatially resolved with SPHERE, the very narrow and close-in inner disk cannot (its extent is only <0. 2 pixel on the IRDIS detector). The data of T Cha was taken on February 20, 2016 under program ID 096. C-0248(C). It consists of a total of 30 HWP We then construct the Q-and U-images from the double difference (Eq. 8) and the I Q -and I U -images from the double sum (Eq. 9). The Q-and U-images show a weak detector artifact comprised of continuous vertical bands. We remove this artifact by subtracting, for every pixel column, the median value of the 60 pixels at the top and bottom of that column (see Paper I). The resulting double-difference Uimages of the first and last (30th) HWP cycle are shown in the left column of Fig. 12. The pronounced differences between the two images are predominantly caused by IP that evolves from negative to positive U during the 78 min total observing time.
Data-reduction pipeline including correction method We have incorporated our correction method in a highlyautomated end-to-end data-reduction pipeline called IRDAP (IRDIS Data reduction for Accurate Polarimetry). IRDAP is publicly available and handles data taken both in field-and pupiltracking mode and using the broadband filters Y, J, H and K s. Data taken with the narrowband filters can be reduced as well, although with a lower accuracy, by using the correction method of the broadband filters. For pupil-tracking observations IRDAP can additionally apply angular differential imaging. Reducing data with IRDAP is very straightforward and does not require the user to do any coding. IRDAP is simply run from a terminal with only a few commands and uses a configuration file with a limited number of input parameters.
The method can be applied to measurements taken both in field-and pupil-tracking mode. We have incorporated our correction method in a highlyautomated end-to-end data-reduction pipeline called IRDAP (IRDIS Data reduction for Accurate Polarimetry). IRDAP is publicly available and the documentation, including the installation and user instructions, can be found at https://irdap. readthedocs. io. To achieve the highest polarimetric accuracy, it is recommended to always use IRDAP for the reduction of IRDIS polarimetric data. Even for observations of nearly face-on circumstellar disks or measurements taken at a high polarimetric efficiency (e. g. when the derotator is kept at a favorable angle or observations are performed in J-band), our correction method makes a significant correction to the angle of linear polarization and increases the signal-to-noise ratio in the final images.
Acknowledgements. A significant part of this work was performed when RGvH, JHG and JdB were affiliated to ESO. RGvH and JdB thank  ESO  for the studentship at  ESO Santiago during  which this project was started. Many thanks go out to the SPHERE team and the instrument scientists and operators of the  ESO Paranal observatory  for their support during the calibration measurements. The research of JdB and FS leading to these results has received funding from the  European Research Council  under  ERC Starting Grant  agreement  678194  (FALCONER). This research has made use of the SIMBAD database, operated at the  CDS, Strasbourg, France. This research has made use of  NASA's Astrophysics Data System Bibliographic Services. SPHERE is an instrument designed and built by a consortium consisting of  IPAG (Grenoble, France ),  MPIA (Heidelberg, Germany) ,  LAM (Marseille, France), LESIA (Paris, France), Laboratoire Lagrange (Nice, France) ,  INAF -Osservatorio di Padova (Italy) ,  Observatoire de Genève (Switzerland) ,  ETH Zurich (Switzerland) ,  NOVA (Netherlands), ON-ERA (France) , and  ASTRON (Netherlands)  in collaboration with  ESO.
SPHERE was funded by  ESO , with additional contributions from the  CNRS (France) ,  MPIA (Germany) ,  INAF (Italy) ,  FINES (Switzerland)  and  NOVA (Netherlands). SPHERE also received funding from the  European Commission Sixth and Seventh Framework Programs as part of the Optical Infrared Coordination Network for Astronomy (OPTICON)  under grant number  RII3-Ct-2004-001566  for FP6 (2004)(2005)(2006)(2007)(2008), grant number  226604  for FP7 (2009)(2010)(2011)(2012), and grant number  312430  for FP7 (2013)(2014)(2015)(2016).
RELATED WORK The Ethereum system has been formalized in the so-called Yellow Paper  (Gavin, 2014)  which has been updated recently  (Gavin, 2019). This update does not impact gas consumption but provides some new instructions which are taken into account in our formal proof. A nice complementary reading is the White Paper  (Buterin, 2013)  which provides useful intuitions about the system. There are several available reference implementations of EVM such as  (pevm, 2017; gevm, 2014). Grishchenko et al. have proposed EtherTrust (ethertrust, 2017)  a verification framework for the static analysis of contracts code  (Grishchenko et al. , 2018a). The static analysis tools focus on proving some security properties on contracts, such as singleentrancy  (Grishchenko et al. , 2018b). EtherTrust comes with a complete small-step semantics for EVM  (Grishchenko et al.
Another mechanized semantics is the one by Everett Hildenbrandt et al. (Hildenbrandt et al. , 2018)  in the K framework. This semantics is fully executable and passes official test suite of EVM  (ETS, 2015). This semantics consumes gas at the call point (see rule <k> callWithCode in https://github. com/kframework/evm-semantics/ blob/master/evm. md). In Section 6, we will discuss termination of contracts in this specific setting. A contract running out of gas stops without completing its task and becomes useless. Thus estimating gas consumption of contracts is an ac-tive research subject. For instance,  (Grech et al. , 2018)  proposes a static analysis of contract's code to detect resumable loops, loops bounded by inputs, etc. that can lead to an execution running out of gas. Our objective here is different since we aim at proving that whatever the contract code, it cannot loop forever while not spending gas.
• System operations This family gathers all the operations that permit to create and destroy a contract (CREATE, SUI-CIDE in  (Gavin, 2014) , or SELFDESTRUCT in  (Gavin, 2019) ) and the call and exit operations on contracts (CALL, CALLCODE, DELEGATECALL, RETURN) and additional (REVERT, CALLSTATIC) in  (Gavin, 2019). The differences between the four types of call (CALL, CALLCODE, DELEGATECALL, CALLSTATIC) are subtle. The differences essentially lies in the way the global state is affected by calling the contract and not about the way gas is consumed. The contract called by CALL changes the state of the callee, like in Example 1. The contract called by CALLCODE changes the state of the caller, like when calling a library code. In Example 1, assume that state of account a j has a field i, then a CALLCODE on a i , would have incremented the value of this field in the state of account a j.
The DELEGATECALL acts as a CALLCODE except that the identity of the contract caller is different. In a contract c 1 , if contract c 2 is called with DELEGATECALL, the call to contract c 2 happens like with a CALLCODE except that identity of the caller is not c 1 but the identity of the caller of c 1. See  (Grishchenko et al. , 2018a)  for details. Finally, CALLSTATIC is similar to CALL except that no modification of the state is permitted. It can be considered as a "pure" function call without side-effects. Since there is no difference between the 4 call instructions w. r. t. to gas consumption, we will abstract them in the same way in Section 5. 2. As explained above, to implement the gas mechanism, EVM's designers have chosen to associate each operation with a cost in gas. All operations, except zero-cost operations (STOP, REVERT and RETURN), have a cost strictly greater than zero.
Halting instructions The execution of a RETURN command requires to read data from the local memory. Consequently the cost for memory consumption is charged. Additionally the read data is recorded in the halting state in order to potentially propagate it to the caller. !µ,◆ = RETURN µ. s = io :: is :: s aw = M (µ. i, io, is) c = Cmem (µ. i, aw) valid (µ. gas, c, |s|) d = µ. m[io, io + is + 1] g = µ. gas c ✏ (µ, ◆, , ⌘) :: S ! HALT( , g, d, ⌘) :: S !µ,◆ = RETURN µ. s = io :: is :: s aw = M (µ. i, io, is) c = Cmem (µ. i, aw) ¬valid (µ. gas, c, |s|) ✏ (µ, ◆, , ⌘) :: S ! EXC :: S !µ,◆ = RETURN |µ. s| < 2 ✏ (µ, ◆, , ⌘) :: S ! EXC :: S The execution of a STOP command halts execution without propagating any data to the caller. 3. The execution of the called code ends with an exception. In this case the remaining arguments are removed from the caller's stack and instead 0 is written to the caller's stack.
The caller does not get the remaining gas refunded As the first two cases can be treated analogously, we just need two rules for returning from a call. !µ,◆ = CALL µ. s = g :: to :: va :: io :: is :: oo :: os :: s toa = to mod 2 160 flag =. toa = ? ? 0 : 1 aw = M (M (µ. i, io, is), oo, os) ccall = Cgascap (va, flag, g, µ. gas) c = Cbase (va, flag) + Cmem (µ. i, aw) + ccall µ 0 = µ[i ! aw][s ! 1 :: s][pc += 1][gas += gas c][m ! µ. m[[oo, oo + s 1] ! d]] ✏ HALT( 0 , ⌘ 0 , gas, d) :: (µ, ◆, , ⌘) :: S ! (µ 0 , ◆, 0 , ⌘ 0 ) :: S !µ,◆ = CALL µ. s = g :: to :: va :: io :: is :: oo :: os :: s toa = to mod 2 160 flag = (toa) = ? ? 0 : 1 aw = M (M (µ. i, io, is), oo, os) ccall = Cgascap (va, flag, g, µ. gas) c = Cbase (va, flag) + Cmem (µ. i, aw) + ccall µ 0 = µ[i ! aw][s ! 0 :: s][pc += 1][gas = c] ✏ EXC :: (µ, ◆, , ⌘) :: S ! (µ 0 , ◆, , ⌘) :: S The two other instructions for calling (CALLCODE and DELEGATECALL) are similar to CALL.
The CALLCODE instruction only differs in the fact that the control flow is not handed over to the called contract, but only its code is executed in the environment of the calling account. This means in particular that the amount of money transferred is only relevant as a guard for the call, but does not need to be actually transferred. In addition, in case that the account whose code should be executed does not exists, this account is not created, but only the empty code is run. However, still the amount of Ether specified on the stack influences the execution cost. this rule, the call stack (µ, ι, σ, η) :: S becomes (µ , ι , σ , η ) :: (µ, ι, σ, η) :: S where (µ , ι , σ , η ) is a new frame stack which has been added on top of the call stack, where µ is a new record, where µ. gas = c call is the gas transferred to the new frame stack by the old one and µ.
Finally an Exception frame represents a contract whose execution has failed because it exhausted the available gas, overflowed the call stack, jumped to an invalid pc or tried to execute an undefined instruction. The call stacks Call stacks will be represented by lists of frames, where the top of the stack is the left-most element of the list. Example 2. (1) Assume that we are running a unique contract c 1 having 18 gas units left, a program counter pc, a program p and an environment e. The corresponding call stack will thus be  [Ok(18, pc, p, e) ]. (2)  Assume that the instruction at position pc in p is a CALL to contract c 2 with a calling gas value of 10, then the call stack becomes [Ok(10, 0, p2, e2), Ok(18, pc, p, e)], where p2 and e2 are the program and environment associated to c 2.
Having CREATE and CALL abstracted by the same Call instruction is coherent with EVM semantics, where the difference between the two is small. In the case of a CALL, the contract name exists in the environment and is associated to a program. In the case of a CRE-ATE the contract name does not exist and the association is added in the environment of the new frame, see  (Genet et al. , 2020)  for details. The last abstract instruction for the family System operations is the Stop instruction which represents STOP, RETURN, REVERT and SELF-DESTRUCT EVM's instructions. Finally, a program p is a list of such instructions and a program counter pc of p is a position between 0 and length(p) -1 in this list. Note that, in EVM, all instructions (except STOP, REVERT and RETURN) have a gas cost which is strictly greater than zero.
These phylogeny-reconciled gene trees allow rigorous, model-based tests to examine the evolution of gene sequences and functions. However, gene trees are known to contain errors related to methodological, technical and biological factors  (Som 2015). A prominent source of errors is low phylogenetic signal in sequences, i. e. insufficient numbers of substitutions to confidently support one gene tree topology over others  (Rasmussen and Kellis 2007). To address this issue, species-treeaware methods use proximity to the structure of the species tree, known as the reconciliation cost, to select among statistically equivalent gene tree topologies  (Durand et al. 2006; Vilella et al. 2009; Rasmussen and Kellis, 2011; Szöllősi et al. 2013; Wu et al. 2013; Scornavacca et al. 2015). Because of computational trade-offs, these methods either rely on heuristic exploration of the gene-tree solution space and result in suboptimal trees, or have an intensive computational cost and are not applicable to large datasets.
SCORPiOs corrects a significant fraction of Neopterygii subtrees in Ensembl We applied SCORPiOs to gene trees from the Ensembl Compara database, version 89  (Vilella et al. 2009) , which includes 70 vertebrate genomes, of which eleven are Neopterygii: ten duplicated teleost genomes, and the spotted gar as an outgroup to the teleost-specific genome duplication (TGD). This set represents a total of 21,431 gene trees reconciled with the vertebrate species tree. About 50% of genes are syntenic between spotted gar and teleost genomes, which makes it a suitable outgroup and proxy for the ancestral gene order (Methods). We ran SCORPiOs in iterative mode, which corrects the gene trees a first time, thus improving the quality of the synteny information, and then again until convergence (window size: 15 genes, with a step of 1 gene).
For this dataset, two iterations were sufficient to reach convergence (see Supplementary Tables S1-2 for detailed, by iteration, results). In brief, in iteration 1, we identified 15,476 Neopterygii loose orthology groups within the 21,431 gene trees. SCORPiOs produced 14,576 orthology graphs, of which 10,172 (70%) were already subdivided into two isolated orthology cliques. The vast majority of families not producing graphs (86 %) are small families (< 3 genes), for which we do not build graphs, as they would result in the same tree topology (Supplementary Table  S1 -2). For 3,391 gene trees (22%), the orthologies and paralogies relationships were in disagreement with the synteny graph. In total, at the end of the two iterations, we were able to find a synteny-aware and statistically equivalent gene tree for 2,387 of these genes families, thus correcting 70% of all syntenyinconsistent gene trees (15% of Ensembl Neopterygii subtrees).
Interestingly, for 672 of the gene trees that we correct (28%), the new tree is better supported by the sequences and results in a significantly higher likelihood (AU test, a = 0. 05). This may reflect difficulty of the Ensembl pipeline to explore the tree topology space due to the high number of species in the database, as previously described  (Wu et al. 2013). Furthermore, we also applied SCORPiOs to version 94 of the Ensembl Compara database, containing 47 teleost genomes and a total of 43,491 gene trees. With this increase in phylogenetic coverage, a higher proportion of gene trees was inconsistent with synteny (7,168 synteny inconsistent gene trees for 15,760 gene families, 45%), and could be improved by SCORPiOs (3,681 corrected gene trees, 23%). Again, this result is in line with the idea that exploration of the topology space becomes limited for larger trees, inducing more errors.
2e-16). Additionally, the SCORPiOs tree is a better fit to the gene sequence information than either the Ensembl or the ParalogyCorrector trees for 9. 5% of the 2,387 test gene families. We then assessed whether the gene trees are concordant with the known species phylogeny. For each software, we identified the species-tree inconsistent nodes, previously referred to as "dubious nodes" or "non-apparent duplication nodes" (  (Vilella et al. 2009; Lafond et al. 2014 ); Methods). We find that SCORPiOs outperforms both RAxML and the original Ensembl trees in terms of species-tree congruence (Wilcoxon signed rank test, p-values < 2. 2e-16, Figure  3B ). Both SCORPiOs and ParalogyCorrector produce trees that are highly congruent to the species phylogeny, with a slight advantage to ParalogyCorrector (Wilcoxon signed rank test, p < 2.
13 Taken together, our results suggest a strong contribution of TGD duplicates to functional novelty in this clade, mediated by the fixation of specialized duplicated genes. Importantly, many of these enrichments were fully obscured by errors in the gene evolutionary histories downloaded from as respected a reference database as Ensembl, which is widely sourced for 415 comparative and evolutionary studies  (Alföldi and Lindblad-Toh 2013; Herrero et al. 2016). SCORPiOs therefore fulfils its purpose in the arsenal of tree building tools and has potential to dramatically further investigations into the evolutionary and functional outcomes of WGD events.