dataesr/scientific-paragraphs-categorization huggingface dataset:
σ I and σ Î are the standard deviations used in the estimation of image contrast, and σ I Î corresponds to the covariance between the two images. L is the dynamic range of luminance (usually the maximum gray level). K 1 and K 2 are two constant parameters to adjust the metric variation (the Matlab implementation by the authors in  [37]  used the values of 0. 01 and 0. 03, respectively). Tables  1  and 2  summarize the obtained results in PSNR (dB) and SSIM (%). In Table  3 , we give the average gain of the two various metric comparisons. Several conclusions can be drawn from these experiments: 1. VisuShrink does not have any denoising power or very low performance when the noise level is low (noise variance: 15, 20). 2. The effect of using only the magnitude neighboring thresholding (NeighShrink) for the three images is generally a considerable PSNR and SSIM gain compared to classical VisuShrink thresholding.
The function ƒ  : S 1 × S 2 →ℝ + is a bisimulation function between T 1 and T 2 , if for all (s 1 , s 2 ) ∈ S 1 × S 2 ƒ B (s 1 , s 2 ) ≥ max ⎩ ⎪ ⎪ ⎨ ⎪ ⎪ ⎧ d�O 1 (s 1 ), O 2 (s 2 )�, sup x∈X s 1 ' ∈S 1 (s 1 ,x) inf s 2 ' ∈S 2 (s 2 ,x) ƒ B (s 1 ' ,s 2 ' ) , sup x∈X s 2 ' ∈S 2 (s 2 ,x) inf s 1 ' ∈S 1 (s 1 ,x) ƒ B (s 1 ' ,s 2 ' ) ⎭ ⎪ ⎪ ⎬ ⎪ ⎪ ⎫ (8) where the bisimulation function ƒ  bounds the distance between the observations for a couple (s 1 , s 2 ) by precision ε ≥0 such that ƒ B (s 1 , s 2 ) ≤ ε and non-increasing under operational dynamic conditions. Applications of AB include bisimulation metrics for linear systems, abstractions of hybrid systems and hierarchical control design. A correct 'by design' of embedded control software using bisimulation relations was implemented by tools such as PESSOA  [Mazo, 2010]  and CoSyMa  [Sebti, 2013]  by expressing requirements in temporal logic.
Acknowledgements: This work is the result of a collaboration founded by the  SFB  project  F50  " Algorithmic and Enumerative Combinatorics " and the  Franco-Austrian PHC "Amadeus". Michael Wallner  is supported by the  Austrian Science Fund (FWF)  grant  SFB F50-03  and by  ÖAD , grant  F04/2012. We also thank the two referees for their feedback, and not last but least,  Don Knuth  and  Manuel Kauers  for exchanging on this problem.
In this case, each of local narrow spurious error windows generates two Dirac delta functions with equal values and opposite signs. Based on 1. 2 in Appendix 1, the two delta functions should be close to each other within 1,000 samples. Using this fact, we developed and implemented a recursive filtering procedure called continuity process (CP) to eliminate these spurious windows (see Figure  10 ). The Matlab code is given in Appendix 3. -Figure  10  shows that the impact of the local narrow spurious error windows is completely eliminated. However, our non-stationary indicator still suffers from two-step transition problem, i. e. , the transition between two valid states of the cluster rectangular signals is not immediate (this case can be shown in Figure  8  around the 14,000th and 24,000th samples).
In the first stage, the target metamodel is defined, and then mapped with the source meta-model. Mapping task is implemented using transformation rules. These rules allow producing a target model (conform to the target metamodel) starting from a source model (conform to the source metamodel). The second stage consists of executing the model transformations rules in a particular language (in our case, in SQL (Structured Query Language) script). Indeed, the PLM system configuration and data are stored in a database. Then, the last step of transformation process should make it possible to add, delete and update these data within the database. To sum up, a source model allows producing a target code thanks to the transformation process with Atlas Transformation Language (ATL) As stated previously, providing generic concepts is not of much help when it comes to identifying enterprise's business concepts.
Audros metamodel In order for the model to be exploitable, an SQL script needs to be generated and applied to the PLM system. To achieve this, a metamodel defining Audros PLM's concepts have been created The main goal is to generate an Audros model conform to the Audros metamodel from a business model edited with GMF conform to the business metamodel (introduced in the first part) by means of ATL transformation. The transformation results in a compliant Audros model. The transformation is done by iterating over the input model's content, comparing this content with the source patterns (correspondence rules defined in the. atl file) and producing target patterns in the target model whenever a matching occurs. The following figure (Fig. 3 ) shows a case of ATL correspondence rule defined between the two metamodels.
This rule takes a BusinessObject (defined by Audros metamodel) as an input and transforms it into an Object Class (defined by Database metamodel). The whole transformation process is used to translate and inject the Business Model into the database, to become accessible and usable within the PLM. The obtained specific model is not yet exploitable in Audros PLM. It needs to be transformed into an SQL script to inject into the system database using existent tools in Eclipse (Acceleo and Liquibase). The following figure (Fig. 4 ) illustrates Audros PLM's final state after the transformations' sequences applied to the model introduced earlier in this paper (the objects are usable within the PLM).
Photon, electron and hadron candidates are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic calorimeter and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers  [25]. The analysis is carried out using a sample of data corresponding to an integrated luminosity of 3. 3 pb -1 collected in pp collisions at √ s = 2. 76 TeV. Events of interest are preselected by a trigger consisting of a hardware stage, based on information from the calorimeter and muon systems, followed by a software stage, which applies a full event reconstruction. The presence of two muon candidates with the product of their p T larger than 1. 68 (GeV/c) 2 is required in the hardware trigger.
0% for the Υ(1S), Υ(2S) and Υ(3S) cross-section measurements in the full kinematic region, respectively. To account for differences between the actual tracking efficiency and that estimated with simulation using data-driven techniques  [33, 39] , a systematic uncertainty of 0. 4% is assigned per track. Good agreement between the data and reweighted simulation is observed for all selection variables used in this analysis, in particular for the χ 2 of the dimuon vertex fit and the χ 2 of the global fit  [35]. The discrepancies do not exceed 1. 0%, which is conservatively taken as a systematic uncertainty to account for the disagreement between the data and simulation. The systematic uncertainty associated with the trigger requirements is assessed by studying the performance of the dimuon trigger, described in Sect.
Acknowledgements We thank  G. Bodwin ,  L. S. Kisslinger ,  A. K. Likhoded  and  A. V. Luchinsky  for fruitful discussions about bottomonium production. In addition, we are grateful to  K. -T. Chao ,  H. Han  and  H. -S. Shao  for the next-to-leading order non-relativistic QCD predictions for prompt Υ production at √ s = 2. 76 TeV. We also express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  NSFC (China) ;  CNRS/IN2P3  and  Region Auvergne (France) ;  BMBF ,  DFG ,  HGF  and  MPG (Germany) ;  SFI (Ireland) ;  INFN (Italy) ;  FOM  and  NWO  (The Netherlands); SCSR (Poland);  MEN/IFA (Romania) ;  MinES, Rosatom ,  RFBR  and  NRC "Kurchatov Institute" (Russia) ;  MinECo, XuntaGal and GENCAT (Spain) ;  SNSF  and  SER (Switzerland) ;  NAS Ukraine (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA).
We also acknowledge the support received from the  ERC  under  FP7. The Tier1 computing centres are supported by  IN2P3 (France) ,  KIT  and  BMBF (Germany) ,  INFN (Italy) ,  NWO  and SURF (The Netherlands),  PIC (Spain) ,  GridPP  ( United Kingdom ). We are indebted to the communities behind the multiple open source software packages we depend on. We are also thankful for the computing resources and the access to software R&D tools provided by  Yandex LLC (Russia).
MATERIALS AND METHODS Experiments were run under Windows XP on a Dell PC computer equipped with a mouse device and a high resolution color monitor (EIZO LCD "Color Edge CG275W") with an in-built color calibration device (colorimeter), which uses the Color Navigator 5. 4. 5 interface for Windows. The colors of the stimuli were generated in Photoshop using selective combinations of Adobe RGB increments. The color coordinates (see Table  1 ) for each RGB triple are retrieved from the look-up table of the colorimeter after calibration. All luminance values for calculating the stimulus contrasts (see the Michelson contrasts, with negative and positive contrast polarities, here in Table  2 ) were determined on the basis of standard photometry using an external photometer and adequate interface software (Cambridge Research Instruments).
RESULTS AND DISCUSSION The data from each task were analyzed separately. Response proportions were determined on the basis of the frequency with which a given effect was observed in each of the two tasks. Relative background brightness (task 1) was assessed on the basis of frequencies of contrast effects, assimilation effects, and responses signaling no effect. Relative inducer depth or figure-ground (task 2) was assessed on the basis of frequencies of foreground effects ("in front"), background effects ("behind"), and responses signaling no effect.
Detailed analyses of WMAP and Planck data can have significant implications for noncyclic pre-Big Bang approaches incorporating a new fundamental scale beyond the Planck scale and, potentially, new ultimate constituents of matter with unconventional basic properties as compared to standard particles. Cosmic-ray experiments at the highest energies can also yield relevant information. Hopefully, future studies will be able to deal with alternatives: i) to standard physics for the structure of the physical vacuum, the nature of space-time, the validity of quantum field theory and conventional symmetries, the interpretation of string-like theories. ; ii) to standard cosmology concerning the origin and evolution of our Universe, unconventional solutions to the cosmological constant problem, the validity of inflationary scenarios, the need for dark matter and dark energy.
Supported in part by Grant  ANR-08-BLAN-0311-CSD5  and by the  MISTI MIT-France Seed Fund. 2 Supported in part by Grant  ANR-11-JCJC CHAMU. 3 Supported in part by  NSF  Grants  DMS-06-4558  and  OISE 0730136  and by the  MISTI MIT-France Seed Fund. 4 Supported in part by Grant  ANR-11-JCJC CHAMU. AMS 2000 subject classifications. 60G57 ,  60G15 ,  60D05.
Dataset and trigger The dataset used in this analysis was collected in 2012 with the LHC operating at a centre-of-mass energy of 8 TeV. Application of beam, detector and data-quality requirements resulted in a total integrated luminosity of 20. 3 fb -1. The uncertainty on the integrated luminosity is ±2. 8%, derived by following the same methodology as that detailed in ref. [25]. During the data-taking period, the peak instantaneous luminosity per LHC fill was typically 7 × 10 33 cm -2 s -1 , while the mean number of proton-proton interactions per LHC bunch crossing was 21. The trigger required events to contain a jet with an uncorrected transverse momentum (p T ) above 80 GeV and an uncorrected missing transverse momentum above 100 GeV. The trigger reached its full efficiency for events with a reconstructed jet with p T exceeding 130 GeV and more than 160 GeV of missing transverse momentum, which are requirements of the event selections considered in this analysis.
Three different classes of likelihood fit are employed in this analysis. The first is used to determine the compatibility of the observed event yield in each SR with the corresponding SM background expectation. In this case (the 'background-only fit') the fit is performed using only the observed event yields from the CRs associated with the SR, but not the SR itself, as constraints. It is assumed that signal events from physics beyond standard model (BSM) do not contribute to these yields. The significance of an excess of events observed in the SR above the resulting SM background expectation is quantified by the probability (the one-sided p-value, p 0 ) that the SR event yield obtained in a single hypothetical background-only experiment is greater than that observed in this dataset. The background-only fit is also used to estimate the background event yields in the VRs.
ATLAS -29 -This paper reports a search for squarks and gluinos in final states containing high-p T jets, large missing transverse momentum and no electrons or muons, based on a 20. 3 fb -1 dataset of √ s = 8 TeV proton-proton collisions recorded by the ATLAS experiment at the LHC in 2012. Good agreement is seen between the numbers of events observed in the data and the numbers of events expected from SM processes. Results are interpreted in terms of mSUGRA/CMSSM models with tan β = 30, A 0 = -2m 0 and µ > 0, and in terms of simplified models with only light-flavour squarks, or gluinos, or both, together with a neutralino LSP, with the other SUSY particles decoupled. The results are also interpreted in terms of several other SUSY models. In the mSUGRA/CMSSM models, the 95% confidence level exclusion limit on m 1/2 is greater than 380 GeV for m 0 < 6 TeV and reaches 770 GeV for low m 0.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COL-CIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF ,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZ Š, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
4. 0  [36]. Finally, a third category of background includes all other sources where one or more jets are misidentified as an isolated lepton. The contributions from these fake backgrounds are estimated using a data-driven method as described in Section 6. The contribution from events with only one jet misidentified as an isolated lepton is found to be dominant while those with more than one are found to be negligible. Thus, in this analysis the fake backgrounds are denoted by ℓℓ ′ +jets. An overview of the major MC samples used is presented in Table  1. Monte Carlo (MC) events are processed through the full detector simulation  [37]  using geant4  [38] , and their reconstruction is performed with the same software used to reconstruct data events. Correction factors for lepton reconstruction and identification efficiencies are applied to the simulation to account for differences with respect to data.
It can be interpreted as the probability that a lepton-like jet is instead reconstructed as a nominal lepton. The fake background is dominated by events with one jet misidentified as an isolated lepton, while contributions from other processes with two or three jets misidentified as isolated leptons are found to be negligible. The fake background is thus estimated by applying the fake factor to a data sample (denoted as "tight+loose sample") selected using all signal selection criteria except for a require-ment that one of the three leptons must be a lepton-like jet. Since the electron identification and isolation requirements are different for those coming from a Z or a W candidate decay, the electron fake factor is calculated separately for these two cases. The lepton fake factor is measured in two different data samples: dijet and Z+jets events.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina;  Yer-PhI , Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZ Š, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
Acknowledgments We thank  Michele Papucci  for many precious discussions and for comments on the manuscript. We also thank  Jordy de Vries  and  Emanuele Mereghetti  for useful discussions, and  Martin Gorbahn  and  Ulrich Haisch  for spotting an error (now corrected) in Eq. (2. 9). This work is supported in part by the  European Programme "Unification in the LHC Era ", contract  PITN-GA-2009-237920  (UNILHC), by  MIUR  under the contract  2010YJ2NYW-010 , and by the  European Research Council (ERC)  under the  EU   Seventh Framework Programme  (FP7/2007-2013) /  Erc Starting Grant  (agreement n. 278234 - NewDark  project).
We sampled points from the finite representation of the generalized Cantor set according to the weight assigned to each interval. In general, numerical methods need to deal with finite samples which often gives rise to technical difficulties. No finite sample is a true fractal set, and therefore, the statistical data extracted from a finite sample may not accurately reflect the property of the original set one wishes to study. It is worth noting that simply increasing the number of sample points from an available data set can partially overcome the difficulties associated with numerical methods. While a true mathematical fractal is characterized be an infinite nesting structure, "fractals" found in nature have a limited hierarchal structures and the range where a power law is observed is finite.
Therefore, the function, M γ (n), involves singularities for γ < -D 0. This means that, for the generalized Cantor set, the nearest neighbor is ill-suited for obtaining Correlation Dimension (q = 2) or larger q. The result of D(γ) for four different data sets are obtained using the nearest neighbor method as shown in Fig. 5. In each plot, the numerical results are compared to the corresponding analytical results. The influence of the singularity is observed for a variety of sets. Note that the k-neighbor method does not suffer from this kind of singularity. For the k-neighbor method, the corresponding singularity can be found in Eq. (  16 ). However, this time, the singularity can be avoided by taking a sufficiently large k. Accordingly, the k-neighbor method could generate sensible results in the entire range of γ we have investigated.
1 × 0. 1  [15] , except EM3 (near the EM shower maximum), where it is 0. 05 × 0. 05. The calorimeter allows for a precise measurement of the energy of electrons and photons, providing an energy resolution of approximately 4% (3%) at an energy of 30 (100) GeV. The energy response of the calorimeter to photons is calibrated using electrons from Z boson decays. Because electrons and photons interact differently in the detector material before the calorimeter, additional energy corrections as a function of p γ T are derived using a detailed geant-based  [16]  simulation of the D0 detector response. These corrections are ≈ 2% for photon candidates of p γ T = 30 GeV, and smaller for higher p γ T. The data used in this analysis satisfy D0 experiment data quality requirements and are collected using a combination of triggers requiring a cluster of energy in the EM calorimeter with loose shower shape requirements.
These jet flavor templates are obtained from MC simulations. As an example, the result of one of these maximum likelihood fits to D MJL templates is presented in Fig. 2  (with χ 2 /ndf = 6. 80/5 for data/MC agreement). This shows the one-dimensional projection onto the highest p T jet D MJL axis of the 2D fit, normalized to the number of events in data, for photons with 30 < p γ T < 40 GeV. An independent fit is performed in each p γ T bin, resulting in extracted fractions of 2 b-jet events between 76% and 87%, as shown in Fig. 3. The relative uncertainties of the estimated 2 b-jet fractions range from 5% to 14%, increasing at higher p γ T and are dominated by the limited data statistics. The estimated numbers of signal events in each p γ T bin are corrected for the geometric and kinematic ac- The ratio of the measured γ + 2 bjet production cross sections to the reference NLO predictions.
4) -E γ T < 2. 5 GeV, where E tot T (0. 4) is the total transverse energy of particles within a cone of radius R = (∆η) 2 + (∆φ) 2 = 0. 4 centered on the photon direction and E γ T is the photon transverse energy. The sum of transverse energy in the cone includes all stable particles. [24]. The acceptance is driven by selection requirements in |η det | (applied to avoid edge effects in the calorimeter regions used for the measurement) and |φ det | (to avoid periodic calorimeter module boundaries), photon |η γ | and p γ T , and bin-to-bin migration effects due to the finite energy resolution of the EM calorimeter. The combined photon and jets acceptance with respect to the p T and rapidity selections varies between 66% and 77% in different p γ T bins. Uncertainties on the acceptance due to the jet energy scale  [25] , jet energy resolution, and the difference between results obtained with sherpa and pythia are in the range of (8 -12)%.