dataesr/scientific-paragraphs-categorization huggingface dataset:
On the right hand side of Figure  2  is the corresponding Horn clauses encoding  3. The predicates IC, GC and T encode the transition relation of the nodes intcounter, greycounter and top respectively. While the predicate IC_Init, GC_Init and T _Init encode the initial states of the three nodes. The predicate M encode the main entry point of the two counters Lustre program; while Error is a predicate used to mark the error states. Using the PDR engine of Z3  [9]  we are able to get the modular invariant of the Horn encoding for the predicate IC, GC, M and T. For example, for the predicates IC_Init, IC and GC_Init, GC we obtain the following invariants: IC_Init(reset, out,time) = (time = 0) ∧ ¬(out) IC(reset, out,time,time ) = (time < 3 → time ≥ 0) ∧ (out → time ≤ 1) ∧ (time ≤ 0 ∨ ¬(time ≤ 3) ∨ ¬(time ≥ 3)) (¬(time ≥ 3) ∨ time ≥ 0) GC_Init(reset, out, a, b) = ¬(out) ∧ ¬(a) ∧ ¬(b) GC(reset, out, a, b, a , b ) = ∧ b ↔ a ∧ ¬a ↔ b ∧ ¬out ↔ ¬a ∨ a For example, for the node intcounter, denoted by the predicates IC_Init and IC, we obtain that the variable time is bound in the interval [0, 3].
Conclusion In this paper, we have described two techniques to synthesis modular invariants for synchronous code encoded as Horn clauses. Modular invariants are very useful for different aspects of analysis, synthesis, testing and program transformation. We have described two techniques to generate modular invariants for code written in the synchronous dataflow language Lustre. The first technique directly encodes the synchronous code in a modular fashion. While in the second technique, we synthesize modular invariants starting from a monolithic invariant. Both techniques take advantage of analysis techniques based on property-directed reachability. Both techniques have been implemented in a tool that targets the verification of safety properties specified in Lustre. We have also sketched a technique for minimizing invariants following a CEGAR-like approach.
* This work was partially supported by the  ANR-INSE-2012   CAFEIN  project, and also by  NASA  Contract No. NNX14AI09G. Copyright 2014  Carnegie Mellon University. This material is based upon work funded and supported by the  Department of Defense  under Contract No. FA8721-05-C-0003  with  Carnegie Mellon University  for the operation of the Software Engineering Institute, a federally funded research and development center. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the  United States Department of Defense. NO WARRANTY. THIS.
We can then use QNOTE, in conjunction with natural language processing, to assess the core elements and note quality. There has been a great deal of interest in the copying and pasting of information from previous notes into the current note. 23, 24 Physicians have been copying from previous notes since long before the introduction of the EHR. It was a time-consuming but necessary activity to carry forward and incorporate relevant clinical information into the daily note. What is different today is that, because little effort is required to cut and paste material in an EHR, indiscriminate copying and pasting is occurring; resulting in bloated and confusing notes that contain redundant, outdated, and even incorrect information. Eliminating the ability of physicians to cut and paste is not a viable option because there are many occasions when important past information needs to be included in the current note.
ACKNOWLEDGEMENTS The authors acknowledge and appreciate the support of  Patrice Waters-Worley, MBA, Michelle Helaire, EdD , and  Karla Herres , RN, CPHQ of  Lockheed Martin, Inc  who traveled nationally to the five medical facilities participating in the study, coordinating the collection, de-identification, and indexing of clinical notes. These contributors were compensated for their efforts. Written permission was obtained from each of these contributors.
Bioinformatics and structure analysis Secondary structure prediction. The secondary structure prediction for the JMR residues was performed using six methods based on the protein primary sequence: (i) GOR4  [33] , an information theory-based method that uses probability parameters derived from empirical studies of crystallographic structures, taking into account not only the propensities of individual amino acids to form particular secondary structures, but also the conditional probability given that its immediate neighbors have already formed that structure. (ii) Jpred  [34]  uses the Jnet  [35]  algorithm to make the prediction of the secondary structure and solvent accessibility by combining BLAST  [36] , to search the protein sequence against sequences in the Protein Data Bank (PDB)  [37]  and Uniref90  [38]  2 in this case, only Uniref90 database was used; PSI-BLAST  [39] , to make an alignment; HMMer  [40] , to construct an hidden Markov model profile based on the alignment; and a Positionspecific scoring matrix (PSSM)  [41] , output from PSI-BLAST.
(iii) SOPMA  [42]  makes the prediction of the secondary structure based on the homolog method of Levin  [43]. The algorithm uses a database of 126 chains of non-homologous proteins to search against the user sequence. (iv) SCRATCH  [44]  combines machine learning methods, evolutionary information in the form of profiles, fragment libraries extracted from the PDB and energy functions to predict protein structural features and also tertiary structures, see the article for further information of the specific methods used by each predictor. (v) NetSurfP  [45]  consists of two neural network ensembles used to predict the secondary structure and the relative surface accessibility of an amino acid. (vi) Psipred  [46]  incorporates two feed-forward neural networks which perform an analysis on output obtained from PSI-BLAST.
Finally, STRIDE, a knowledge-based algorithm that assigns the secondary structure from atomic coordinates based on the combined use of hydrogen bond energy and statistically derived backbone torsional angle information  [47]  was used to predict the secondary structure of JMR in CSF-1R using the crystallographic structure (2OGV)  [22]  as input. VMD  [48]  and PYMOL  [49]  were used for visualization and the analysis graphics were drawn using Grace (http://plasma-gate. weizmann. ac. il/Grace/). Electrostatic potential surface. Electrostatic potential surfaces were calculated on the crystal structures of CSF-1R (PDB code: 20GV) and KIT (PDB code: 1T45) using APBS at PDB2PQR web-based server (http://www. poissonboltzmann. org/pdb2pqr/d/web-servers).
Molecular dynamics simulations Preparation of initial coordinate files. The crystallographic structure of the wild-type (WT) auto-inhibited form of CSF-1R cytoplasmic region (PDB id: 2OGV)  [22]  was retrieved from the Protein Data Bank  [37]. All crystallographic water molecules were removed. MODELLER 9v8  [50, 51]  was used to add missing atoms at some residues (543-545, 606-607, 620-621, 623, 625, 677, 741, 812, 814 and 918). In silico substitution of Asp (D) to Val (V) at position 802 was performed by MODELLER, using the WT structure as template, making them comparable starting models. Generated models of the native CSF-1R and its mutant D802V were referred to as CSF-1R WT and CSF-1R MU respectively. Set up of the systems. The setup of the systems (CSF-1R WT and CSF-1R MU ) was performed using AMBER force field, parameter set 99SB  [52]  inside GROMACS package, version 4.
The hydrogen (H-) bond analyses were done with the program g_hbond available in GROMACS. Time occupancy of H-bonds stabilizing the JMR and the A-loop was recorded every 100 ps of simulation for each model of CSF-1R. H-bonds (DNNNH-A) where defined with a DHA angle cutoff of 120u and a DNNNA distance cutoff of 3. 5 A ˚(D and A are donor and acceptor atoms). Secondary structure prevalence. The secondary structure profile was calculated using the program do_dssp available in GROMACS. The program makes use of DSSP  [62]. The calculation was performed over the merged 90 ns trajectories for both forms of the receptor.
Normal modes analysis Normal modes (NM) analysis was performed using the diagonalization in a mixed basis (DIMB) method  [67]  of the VIBRAN module of CHARMM 35b3  [68, 69]  on MD conformations from (i) CSF-1R WT taken at 1 526, 49 390, 66 530 and 81 680 ps, spanning both replicas contained in the 90-ns merged trajectory, and (ii) CSF-1R MU mutant taken at 5 510, 23 530, 40 670 and 84 680 ps. The selected MD conformations were found to be the most representative of the trajectories, according to the convergence analysis. The first hydration shell (5 A ˚) around the MD conformations was kept to help prevent the solvent-exposed regions of the protein from collapsing during the minimization procedure  [70]. During initial steepest descent energy minimization of the system, mass-weighted harmonic constraints of 250 kcal/mol/A 2 were applied to the starting structure and reduced by a factor of 2 every 1000 minimization steps until they fell below a threshold value of 5 kcal/mol/A 2.
Principal component analysis A Principal Component Analysis (PCA) was applied to each model to identify the main eigenvectors (3N directions) along which the majority of the collective motions are defined. The calculations were performed on the backbone atoms positions recorded every ps along the trajectories for each 45 ns simulation replica. The 100 first modes of each trajectory were extracted. The calculation was performed using the g_covar module of GRO-MACS package. The overlap between the first 10 modes of each trajectory was calculated using the g_anaeig module of GRO-MACS package. Briefly, the method consists in overlapping the subspace spanned by m orthogonal vectors w 1 ,…,w m with a reference subspace spanned by n orthonormal vectors v 1 , …,v n and it can be quantified as follows: overlap(v,w)~1 n X n i~1 X m j~1 (v i :w j ) 2 ð5Þ The overlap will increase with increasing m and will be 1 when set v is a subspace of set w.
Two residues were considered as interacting when they formed at least one non-bonded interaction for 50% of the simulation time. To discriminate between large and short CTs, a threshold CT cut was chosen so that highly connected residues communicate efficiently with about 10% of the total number of residues in the protein  [76]. The threshold values were set to 0. 1 for both models. Statistical analyses were performed with the R software  [77] ; visualization of the structure/interaction/communication characteristics/results are performed with PyMOL  [49]  incorporated in MONETA  [73].
The D802V mutation noticeably enhanced RMSF fluctuations in these regions (Fig. 2D ). A zooming on the A-loop RMSF values indicated the perturbation on the atomic coordinates observed in one of the MD simulations of CSF-1R MU (Fig. 2B, insert ). Systematic analysis of the MD conformations indicated that the structure of CSF-1R cytoplasmic region was globally conserved over the simulation time in CSF-1R WT and CSF-1R MU and shows in general a similarity between these two forms (Fig. 3A ). Nevertheless, a detailed inspection of the secondary structures showed different folding of the A-loop in the two proteins. The crystallographic data of the native receptor (PDB id: 2OGV)  [22]  show that residue D802 is located in a short bend between two small 3 10 -helices formed by residues 798-800 (H1) and 803-805 (H2).
The highly conserved residue F797, appears to serve as a conformational switch in the receptor. The A-loop inactive conformation was also stabilized by interaction of Y809 (A-loop) as a pseudo-substrate with the catalytic loop residue D778 (C-lobe) in CSF-1R WT through the Hbond Y809NNND778, which is decreased by a factor of 2 in CSF-1R MU. This destabilizing effect in mutant is compensated by Hbond R801NNND778, favored by the displacement of the R801 towards D778 (Fig. 6C , Table  1 ). Further, we compared the electrostatic potential surfaces of JMR and kinase domain in both receptors. The calculations were performed by the Adaptive Poisson-Boltzmann Solver (APBS) software using the crystallographic structures describing the inactive auto-inhibited state of the native receptors, CSF-1R (PDB id: 2OGV,  [22]  and KIT 1T45,  [21] ) receptors.
This method identifies clusters of residues named Independent Dynamic Segments (IDSs) that are formed around each seed and display concerted local atomic fluctuations and independent dynamical behavior  [32]. The number of PCA modes retained for LFA was 17 in CSF-1R WT and 19 in CSF-1R MU , the number of IDSs identified by MONETA being 8 in CSF-1R WT and 9 in CSF-1R MU , respectively. The IDSs differences between the two receptors concern their feature, location, and size. To optimize the comparative analysis, the distinct IDSs were referred to as S i , where i = 1,2,…,N. IDSs common to the two forms of receptor are located in JM-B (S1, residues 543-546), in JM-S (S2, residues 553-562 in CSF-1R WT and 554-562 in CSF-1R MU ), in the solvent-exposed loop that connects b2 and b3 (S3, residues 602-611) in the N-lobe, in the pseudo-KID (S4, residues 678-692), in the A-loop (S5, residues 810-817 in CSF-1R WT and 809-817 in CSF-1R MU ), and in the C-terminal tail (S6, residues 914-922) (Fig.
S5 ). Indeed, the P-loop and the Ca-helix in the active state of KIT are shifted respectively to their positions in the inactive autoinhibited state. The relative position of the P-loop and the Ca-helix in the active and inactive forms, which is equivalent in CSF-1R and divergent KIT, may reflect their different implication in the mechanisms regulating the activation of the two receptors. This hypothesis is coherent with the different communication pathways observed in the inactive autoinhibited state of these receptors. Nevertheless, such hypothesis requires an advanced examination of the structural features of both receptors in the active state. The crystallographic structure of CSF-1R active form (PDB id: 3LCD,  [85] ) was stabilized by a co-crystallized kinase inhibitor, while KIT active state structure (PDB id:1PKG,  [86] ) was reported with two phosphorylated tyrosine residues (Y568 and Y570) and with ADP bound in the active site.
6 GHz. This is consistent with the SPT analysis in  Reichardt et al. (2012). These are estimated assuming γ CIB = 0. 8 and r CIB 143 × 217 = 1, and that the CIB emission can be modelled with frequency as a modified blackbody, following  Addison et al. (2012a). The total CIB signal seen by Planck, extrapolated to = 3000 scales, is therefore consistent with the ACT and SPT observations, but given the limited angular range of Planck, the clustered and Poisson part are degenerate. This motivates us to include the ACT and SPT data in many of our cosmological analyses. When combining Planck, ACT and SPT data together, using the same foreground model (except for Poisson power, which  We also consider modifying our model for the clustered part of the CIB. There have been a wealth of CIB models (e.
8 or 0. 6 , or extending the power law model to have a running of the index. The 0. 8 model has been used in  Shirokoff et al. (2011) ;  Reichardt et al. (2012) ;  Addison et al. (2012a) ;  Dunkley et al. (2013) , while the 0. 6 more closely matches the CIB model of  Addison et al. (2013). We find that a simple power law does not fit both the Planck and high-data sufficiently well, but that allowing the additional freedom of a running spectrum opens up the parameter space too much, with little improvement in goodness of fit, motivating our use of the varying γ CIB model. We also test a simple model using just a linear theory dark matter power spectrum, assuming that the non-linear power can be absorbed into the Poisson term. This results in an estimate of the Poisson level at 217 GHz that is inconsistent with ACT and SPT, so we do not use this model.
Consistency of the Planck 70 GHz data Figure  11  shows individual frequency spectra from 70 to 353 GHz. We only use data from the 100 to 217 GHz channels to form the high-likelihoods, but here we compare cosmological parameters derived from the Planck 70 GHz channel alone. Maps at 70 GHz are easier to characterise than the higher frequency channels in terms of instrumental and foreground properties, but the resolution and sensitivity are lower. The 70 GHz noise properties are in general well described by a simple three parameter model involving 1/ f and white noise contributions (Planck Collaboration II 2014). The 70 GHz channel also has the least diffuse foreground emission at large scales (Planck Collaboration XII 2014), and the extragalactic source contribution is dominated by radio galaxies whose emission is well known at these frequencies (Planck Collaboration XXVIII 2014).
Consistency with power spectra of CMB maps obtained by component separation methods The likelihoods we consider in this paper account for component separation by modelling the multi-frequency data at the power spectrum level, to fully exploit the signal at the smallest scales probed by Planck. We can compare the results to those derived from an alternative approach, measuring the power spectrum of CMB maps estimated from component separation techniques. Here we present results obtained with four CMB maps, derived using methods referred to as Commander-Ruler, SMICA, NILC, and SEVEM, described in detailed in the accompanying paper (Planck Collaboration XII 2014). These maps are weighted with the union of the confidence masks of the different component separation methods, covering 72% of the sky (mask U73 of Planck Collaboration XII 2014), with a further cosine apodisation of 30.
8. Figure  30  compares the parameters obtained as a function of max for each method, compared to the values from the high-likelihoods. The results are consistent to 1σ or better for max = 1500 and max = 2000. Despite adopting a simple two-parameter model for the extragalactic foregrounds, the likelihood using a CMB map as input data works reasonably well, and may be further exploited with analysis of simulations, improved extragalactic foreground modelling, and the development of an error model.
Low-likelihood At low multipoles ( < ∼ 50), the distribution of the estimated C s is not well approximated by a Gaussian due to the limited degrees of freedom per (e. g. ,  Efstathiou 2004 ). However, both the CMB signal, s, and instrumental noise, n, are individually nearly Gaussian distributed at the map level, provided that foreground emission and instrumental systematics effects are negligible (e. g. , Planck Collaboration XXIII 2014; Planck Collaboration XXIV 2014), and the actually observed map, m = s + n, is therefore also nearly Gaussian distributed. Under this assumption, the CMB power spectrum likelihood is given by L(C ) = P(m|C ) = 1 2π n/2 |M| 1/2 exp - 1 2 m t M -1 m , ( 21 ) where n is the number of observed pixels, M(C ) = C(C ) + N is the data covariance matrix, and C and N are the CMB and noise covariance matrices, respectively.
Methodology We start by generalizing the above data model to include both multi-frequency observations and a set of foreground signal terms, d ν = s + i f i ν + n ν. ( 26 ) Here d ν denotes the observed sky map at frequency ν, and f i ν denotes a specific foreground signal component. As above, the CMB field is assumed to be a Gaussian random field with power spectrum C , and the noise is assumed Gaussian with covariance matrix N ν. The foreground model can be adjusted as needed for a given data set, and a full description of the model relevant for Planck is presented in Planck Collaboration XII (2014). In short, this consists of a single low-frequency foreground component (i. e. , the sum of synchrotron, anomalous microwave emission, and free-free emission), a carbon monoxide (CO) component, and a thermal dust component, in addition to unknown monopole and dipole components at each frequency.
Given this data model, we map out the full posterior distribution, P(s, f i , C |d), using a Monte Carlo sampling algorithm called Gibbs sampling. Directly drawing samples from P(s, f i , C |d) is computationally prohibitive, but this algorithm achieves the same by iteratively sampling from each corresponding conditional distribution, s ← P(s| f , C , d) f ← P( f |s, C , d) C ← P(C |s, f i , d). It is straightforward to show that P(s| f , C , d) is a multivariate Gaussian distribution, and P(C |s, f i , d) is an inverse Gamma distribution. The foreground distribution, P( f |s, C , d), does not have a closed analytic form, but can easily be mapped out numerically  (Eriksen et al. 2008). Thus, all three distributions are associated with simple textbook sampling algorithms. For CMB power spectrum and likelihood estimation using Gibbs sampling, the crucial intermediate product from the above sampling process is the ensemble of CMB sky samples, s k.
Low-polarisation likelihood The present Planck data release includes only temperature data. In this release, we therefore supplement the Planck likelihood with the 9 year WMAP polarisation likelihood 9  derived from the WMAP polarisation maps at 33, 41, and 61 GHz (Ka, Q, and V bands)  (Page et al. 2007; Bennett et al. 2013 ). However, we introduce one modification to this pixel-based likelihood code, replacing the spherical harmonics coefficients of the temperature field, a T m , in Eqs. (  24 ) and (  25 ) with those derived from the Planck temperature map derived by Commander, for which the Galactic plane has been replaced with a Gaussian constrained realisation. In Fig. 33 , we compare constraints on τ and A s as derived with this split likelihood with those obtained through an exact brute-force evaluation of Eq.
(  21 ), simultaneously including temperature and polarisation measurements at N side = 16. The two methods produce almost indistinguishable results. In Appendix E we assess the robustness of the WMAP polarisation likelihood with respect to dust contamination, by replacing the WMAP polarised dust template with the far more sensitive HFI 353 GHz polarisation map. We find that the optical depth to reionisation, τ, is reduced by about 0. 5-1σ, depending on the template removal method adopted. However, since the Planck polarisation maps are not included from the current data release, we adopt the WMAP polarisation likelihood without further changes for now, and will return to this topic in the next data release.
Acknowledgements. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN, JA  and  RES (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU). A description of the Planck Collaboration and a list of its members with the technical or scientific activities they have been involved into, can be found at http://www. sciops. esa. int/index. php?project= planck&page=Planck_Collaboration. We acknowledge the use of the  CLASS Boltzmann code (Lesgourgues 2011 ) and the  Monte Python package  (Audren et al. 2013) in earlier stages of this work.
For all regional domains, the mean bias has low values at all pressure levels and is generally less than 10 % except for domains 2 (Europe) and 3 (East Asia) at 150 hPa, where it reaches ∼ 13 %. These domains are the regions of intercontinental transport of pollution. Our results regarding higher mean biases compared to other domains are consistent with the findings of  Kopacz et al. (2010) , which state that MO-PITT observational errors are in the 10-30 % range, highest over pollution outflow regions. Consequently, since the errors of TOTCOL_ANALYSES are fixed to be constant into account the averaging kernels as well as the error covariance matrices (blue). Both datasets are averaged over the month of August 2008 and over all the regional domains defined in Fig. 6.