dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013) on the SPOCA subset, and against the first stage of  (Jarolim et al. , 2019)  (sequentially fine-tuned networks) by adapting their approach to Faster RCNN and testing it on UAD. SPOCA detections were obtained from 171 Å and 195 Å images only, combined as two channels of an RGB image, and SPOCA produces a single detection for both bands. We compare this detection against the ground truth of each of the bands individually. To prove the robustness and versatility of our detector, we also experiment with a combination of chromosphere, transition region, and corona bands on the SPOCA subset in addition to the whole UAD. On the SPOCA subset, over the bands 171 Å and 195 Å for which it is designed, SPOCA gets the poorest performance of all multi-band and single-band experiments. It is worth noting that this method relies on manually tuned parameters according to the developers' own definition and interpretation of AR boundaries, which may differ from the ones we used when annotating the dataset.
SModelS is an automatized tool enabling the fast interpretation of simplified model results from the LHC within any model of new physics respecting a Z 2 symmetry. In this contribution, we report on two important updates of SModelS during 2020: the extension of the SModelS' database with 13 ATLAS and 10 CMS analyses, including 5 ATLAS and 1 CMS analyses at full Run 2 luminosity, and the ability to use full likelihoods now provided by ATLAS in the form of pyhf JSON files. Moreover, we briefly explain how to use SModelS and give an overview of ongoing developments.
Introducing SModelS Most experimental searches for new physics at the LHC interpret their data in the context of simplified models. Such simplified models reduce a full theory to only a few new particles with respect to the Standard Model (SM) and assume a simple decay pattern. For example, the supersymmetry (SUSY) motivated search in the 2 +  miss  final state may typically be interpreted in a simplified model that comprises a stau, τ1 , and a neutralino, χ0 1 , as the only new particles and that considers just stau-pair production and a 100% branching ratio for τ1 →  χ0 1. There is a plethora of ATLAS and CMS analyses from Run 1 and Run 2, looking for new physics in different final states and/or exploiting different analysis techniques, that report their results in the context of such simplified models.
To work out how all these experimental results constrain full models, which may have a large particle content and (much) more complicated decay patterns, is a highly non-trivial task. It may be addressed by reproducing the experimental analyses in Monte Carlo event simulations-this approach, often referred to as "recasting", is however extremely CPU time consuming. Moreover, only a fraction of the experimental searches is implemented in public recasting tools. The alternative is to re-use the published simplified model results -this is the purpose of SModelS  [1] [2] [3] [4] [5] [6]. This approach requires to compute only cross sections times branching ratios,  × BRs, for the tested signals. It is often more conservative but much faster than full recasting. Besides speed, the power of SModelS comes from its large database of experimental results, which allows to check limits from nearly 100 ATLAS and CMS searches for SUSY.
Note that SModelS can conveniently be used beyond SUSY for any Beyond the Standard Model (BSM) scenario with a Z 2 -like symmetry, for which the signal acceptance of the SUSY searches apply  [1]. In the following, we first present in more detail the working principle of SModelS. This is followed by the new developments published in 2020 (SModelS versions 1. 2. 3 and 1. 2. 4), namely a major database update  [5]  and the pyhf support  [6]  for usage of full likelihoods provided by ATLAS. We then briefly explain how to use SModelS. We conclude with a short summary, some remarks on the treatment of long-lived particles, and a brief discussion of ongoing developments for the upcoming SModelS version 2. 0. An overview of other approaches and recasting tools can be found in  [7]  and in the review talk by B.
Working principle SModelS is based on a general procedure to decompose BSM scenarios featuring a Z 2 -like symmetry into simplified-model topologies-also referred to as simplified model spectra (SMS). The decomposed topologies are then matched and constrained with experimental results in the SModelS' database (from currently 48 ATLAS and 47 CMS analyses). The working principle is illustrated in Figure  1. Starting from the BSM particle masses, decay branching ratios and production cross sections provided in the input SLHA or LHE file, SModelS determines the weights in terms of cross sections times branching ratios,  × BR, of all occurring BSM signals. These signal weights are then confronted with the experimental constraints in the database of LHC results as exemplified in Figure  2. This is easier and much faster than reproducing analyses with Monte Carlo event simulation, and it allows for reinterpreting searches which are not just cut and count, e.
g. , analyses which rely on BDT (boosted decision tree) variables. The downside is that the applicability is limited by the simplified model results available in the database (mostly simple, symmetric signal topologies). Moreover, whenever the tested signal splits up into many different channels, as it is often the case in complex models with many new particles, the derived limits tend to be highly conservative. SModelS is thus particularly useful for evaluating constraints and generally characterizing collider signatures in large scans and model surveys. As mentioned, it can be used for any BSM scenario (beyond the minimal supersymmetric SM, MSSM, and beyond SUSY in general) with a Z 2 -like symmetry, as long as the simplified model assumptions apply. This means, the kinematic distributions -and thus the signal acceptance -of the tested signal need to be approximately the same as for the (SUSY) scenario assumed for the simplified model in the experimental analysis; see  [1]  for a discussion of the simplified model assumptions and associated caveats as well as  [8] [9] [10]  for a quantification of their impact.
Database structure The SModelS database makes use of two types of results: upper limit (UL) results and efficiency map (EM) results. UL maps are 95% confidence level (CL) upper limits on  × BR mapped on the parameter space of the given simplified model, typically the BSM masses or slices over the BSM masses. Coming back to the example of the stau-neutralino simplified model (also called TStauStau), the UL map would provide the 95% CL limit on (   → τ1 τ1 ) × [BR( τ1 →  χ0 1 )] 2 in bins of ( χ0 1 ,  τ1 ). Such UL maps allow us to directly set a limit on the corresponding topology appearing in a full model by interpolating the UL value to the given parameter point. However, their statistical interpretation is limited and only allows for a binary decision -excluded or not -on a topology-per-topology basis.
They thus enable a richer and more robust statistical inference. An EM database entry is structured as shown in Figure  3. It has sub-folders for each signal region, here SRhigh and SRlow, which contain the actual EMs, here TStauStau. txt. Moreover, in each signal region sub-folder, there is a dataInfo. txt file with relevant metadata, the number of observed and expected background events and the respective signal upper limits. (We will come back to the globalInfo. txt and. json files in section 3. )  UL database entries are structured analogously, although they do not have specific signal region sub-folders. The database text files are parsed and "pickled", using principal component analysis and Delaunay triangulation, to build a binary database file. The database folder and object structure as well as the binary (pickle) format are explained in detail in the database structure section of the online manual.
Latest database update In the v1. 2. 3 release  [5] , the SModelS database was updated with the results from 13 ATLAS and 10 CMS Run 2 analyses. In total, 76 official UL and EM results have been added in v1. 2. 3 (with 3 more EMs added in v1. 2. 4). This corresponds to all relevant new ATLAS and CMS results available on HEPData or on the analysis' TWiki page as of end of March 2020. An overview of these new results is given in Tables  1  and 2. Moreover, 21 "home-grown" EMs were added for two ATLAS and CMS multi-jet +  miss  searches at 13 TeV. These were produced by us using MadAnalysis 5  [12, 13]  to improve the coverage of topologies with 2-5 jets shown in Figure  4 ; these are important for example when gluino-squark associated production is relevant. Some more comments are in order. Regarding the new ATLAS analyses included in SModelS, half of them have EMs in addition to the UL results, which as mentioned allows for the computation of a likelihood.
It is also interesting to observe that five ATLAS analyses are at full Run 2 luminosity and three have full likelihoods available. On the CMS side, more than half of the official results have expected ULs in addition to the observed ones. However, none of them provides EMs. One CMS results is for full Run 2 luminosity. Figure  5  demonstrates the physics impact of the v1. 2. 3 database update upon the so-called phenomenological MSSM (pMSSM), i. e. the MSSM with 19 free parameters defined at the weak scale. This makes use of the extensive dataset from the ATLAS pMSSM study  [37]  available at  [38] ; concretely we use the "bino LSP" points which are not excluded by the Run 1 searches considered in  [37]. The left plot in Figure  5  compares the marginal gluino mass distributions of Note here that home-grown EMs were included in SModelS before, notably for stable heavy charged particles and R-hadrons  [3, 14].
Analysis L [fb -   points excluded with v1. 2. 3 to those excluded with v1. 2. 2. The plot on the right shows the number of newly excluded points when using only the official ATLAS and CMS results in v1. 2. 3 (dark red) as compared to using the full v1. 2. 3 database including the home-grown EMs (orange). The increase in constraining power due to the database update is quite noticeable. In numbers, SModelS v1. 2. 3 excludes 10% more points than v1. 2. 2, 27% of which are due to the home-grown EMs. Additional plots showing the constraining power can be found in  [5].
This so-called simplified likelihood  [41]  approach assumes that uncertainties can be well approximated by Gaussians. SModelS can make use of these correlation data since its version 1. 2. 0  [3]. ATLAS has recently gone a significant step further by publishing full likelihoods using a JSON serialization  [42] , which provides background estimates, changes under systematic variations, and observed data counts at the same fidelity as used in the experiment. The JSON format describes the HistFactory family of statistical models  [43] , which is used by the majority of ATLAS searches. The pyhf package  [44]  is then used to construct statistical models, and perform statistical inference, within a python environment. The availability of such full likelihoods is a real boon for reinterpretation studies.
From SModelSv1. 2. 4 onwards, we are therefore providing an interface to pyhf, which makes use of the ATLAS JSON files whenever available  [6]. As seen in Figure  3 , the JSON file describing the likelihood is placed in the folder of the associated database entry; in the example shown, the file is called SRcombined. json. Note that this is a background-only JSON file, i. e. it contains only background numbers with their uncertainties as well as experimentally observed numbers. Upon running SModelS with signal region combination turned on, it is "patched" with the BSM contributions in the various signal regions as determined from the EMs. For this to work correctly, the name(s) of the JSON file(s) to use, as well as the names of the relevant signal regions and their order, are specified in the globalInfo.
txt file. (Apart from this, globalInfo. txt contains relevant metadata about the database entry, like the analysis ID, centre-of-mass energy, luminosity, etc. ) In our example this is: datasetOrder: "SRlow", "SRhigh" jsonFiles: {"SRcombined. json": ["SRlow", "SRhigh"]} Note that this fulfills for the first time the Les Houches Recommendations 3b and 3c  [45] ! 1 →  χ0 1 ) result from the ATLAS stau search  [11] , on the left using the best signal region, on the right using the full likelihood. pyhf is then used for the statistical evaluation, returning a CL  that may be varied to infer a cross section upper limit at 95% exclusion CL. To demonstrate the physics impact, we compare in Figure  6  the SModelS exclusion (grey line) with the official exclusion (black line) for the ATLAS stau search  [11] , using the best signal region (left) and using pyhf combination (right).
Installation and usage In this section we provide a short summary of the quick guide for getting started from the tutorial given at this workshop. For more information, the user is kindly referred to the online manual, where a detailed documentation is available. SModelS is a python library that works with python version 2. 6 or later, the default being python3. It is listed in the Python Package Index (PyPI) and can thus be simply installed with pip or pip3 by typing.
pip install [--user] smodels This will install SModelS into the default system or user directory, e. g. , ∼/. local/lib/. It is therefore recommended only for users who are at ease with working with python libraries. Most users may prefer to directly install the package in a local folder using the source file provided on the SModelS releases page on github. To this end, download the source (. tar. gz or. zip) file, extract it at the desired destination and run: make smodels in the top-level directory. This will install the required dependencies (using pip install) and compile Pythia  [46]  and NLL-fast  [47]. For the latter two, a C++ and a Fortran compiler are needed. (If the Fortran compiler isn't found, try make FC=<path-to-gfortran> smodels. ) Details on install options and system-specific installations are given in the installation section of the online manual.
Two particularly important settings in the parameters file are the path to the database and the BSM model definition. For the database, one can provide either the path to the local text database, typically path =. /smodels-database/ in which case the database will be parsed and pickled locally, or an URL to the database pickle file. In the latter case, the binary pickle file will be downloaded from the server. This is often faster than pickling locally. Here, path = official provides a short-hand for the official database of your current SModelS version. A list of public database versions is given on the database release page. The BSM model definition is done in a python file (so-called particle module) that specifies the even and odd particle content of the model. In addition, the quantum numbers of the new particles must be given in a qNumbers dictionary.
model=share. models. idm If no model is given, the MSSM is taken as the default. While writing the particle module for a new model is rather straightforward, producing appropriate input SLHA files (including decay tables and cross sections) may be less so. Here the micrOMEGAs-SModelS interface  [48] , controlled with the switch #define SMODELS from micrOMEGAs 4. 3 onwards, provides a convenient way: for all implemented models, including new ones added by the user, micrOMEGAs automatically produces the needed particle. py and SLHA-style SModelS input files for the parameter point under consideration (decay tables and cross sections are computed at tree level with calcHEP). SModelS is called from micrOMEGAs by the function smodels(Pcm, nf, csMinFb, fileName, wrt) where Pcm is the proton beam energy in GeV and nf is the number of parton flavors used to compute the production cross sections of the BSM particles.
The result is reported in SLHA format. The details of this functionality are described in  [48]  and in the manual shipped with micrOMEGAs. This brings us to the SModelS output. SModelS primarily reports its results in the form of r-values, defined as the ratio of the theory prediction over the observed upper limit, for each experimental constraint that is matched in the database. Generally, all points for which at least one -value equals or exceeds unity ( max ≥ 1) are considered as excluded. For EM-type results, when computeStatistics=True, the likelihood and  2 are also reported. As an example, Figure  7  shows part of the output obtained for the gluino_squarks. slha sample input file located in inputFiles/slha/. Moreover, if testCoverage=True, SModelS prints a list of missing topologies: elements which are not tested by any of the experimental results in the database (independent of the element mass).
Finally, outside grid elements, which could be tested by one or more experimental result, but are not constrained because the mass array is outside the mass grid, are reported. Different output formats are available: summary, stdout, log, python, xml, slha. They are chosen and controlled from the parameters. ini file. A detailed explanation of the information contained in each type of output is given in the SModels Output section of the online manual. Although runSModelS. py provides the main SModelS features with a command line interface, users more familiar with python and the SModelS language may prefer to write their own main program. A simple, commented example code for this purpose is provided as Example. py in the SModelS distribution. The How To's in the online manual provide additional examples for using SModelS and some of the SModelS Tools as a python library.
Summary and outlook to SModelS 2. 0 We gave a short introduction to constraining new physics with SModelS and presented two important updates of the tool published in 2020: (i) a major database update with results from 13 ATLAS and 10 CMS SUSY searches at 13 TeV, and (ii) the interface to pyhf, which enables SModelS to use full likelihoods from ATLAS. The database update significantly increases the constraining power of SModelS and the ability to use full likelihood models leads to a marked gain in accuracy (whenever full likelihoods are available). While the examples discussed in this contribution focus on  miss  final states, the capabilities of SModelS go beyond  miss  searches for new physics. Indeed, long-lived particles (LLPs) constrained by searches for heavy stable charged particles (HSCP) or -hadrons, can be treated in SModelS since version 1.
Accordingly, the experimental results in the database can be width dependent. This will enable to incorporate a wide range of exotic signatures, such as searches for disappearing and kinked tracks, displaced jets and leptons as well as delayed jets and photons. This is especially relevant for LLPs with decay lengths of the order of millimeters to a few meters. The inclusion of the particle quantum numbers in the SMS topologies will provide further opportunities. For instance, it will permit to specify the applicability of certain experimental results to BSM particles with specific spins, allowing to go beyond the assumptions mentioned in footnote 1. Finally, the particle content of full models will be definable through the use of an input SLHA file with QNUMBERS blocks, which will greatly simplify using SModelS for models other than the MSSM.
Figure5: Results for the ATLAS pMSSM points with a bino-like LSP, which were classified as not excluded in [37] , as function of the mass of the gluino. Left: In blue the number of points excluded in version 1. 2. 2, in orange the number of points now excluded in v1. 2. 3 and in grey the total number of point per bin. Right: the number of newly excluded points when using only the official ATLAS and CMS results in v1. 2. 3 (dark red) as compared to using the full v1. 2. 3 database including the home-grown efficiency maps (orange).
Acknowledgements The work of G. A. and S. K. is supported in part by the  IN2P3  project " Théorie -BSMGA ". J. H. acknowledges support from the  F. R. S. -FNRS , of which he is a postdoctoral researcher. Su. K. acknowledges support from the  FWF Elise-Richter fellowship  under project number  V592-N27.
Our method is tested on three datasets against state-of-the-art direct (LSD-SLAM), semi-direct (LCSD) and indirect (ORBSLAM2) algorithms in two different scenarios: a trajectory planning and an AR scenario where a virtual object is displayed on top of the video feed; furthermore, a similar method (LCSD SLAM) is also compared to our proposal. Results show that our fusion algorithm is generally as efficient as the best algorithm both in terms of trajectory (mean errors with respect to ground truth trajectory measurements) as well as in terms of quality of the augmentation (robustness and stability). In short, we can propose a fusion algorithm that, in our tests, takes the best of both the direct and indirect methods.
e. , the ability to relocalize the tracked object in the map after the algorithm becomes lost). ORBSLAM2 is explained in detail in Section 3. 1. Other vSLAM systems are also worth mentioning: OpenVSLAM  [16]  is a framework that offers a highly usable and extensible indirect vSLAM with all the modules needed: keypoint detection and matching, pose optimization, keyframe processing and local and global mapping and optimization. Furthermore OpenVSLAM offers its components as separate and having application programming interfaces (APIs) to make it easier for re-usability and extensibility. OpenVSLAM is also compatible with various camera models and capable of saving and loading maps. RKSLAM  [21]  introduced a multi-homography based keypoint tracking as well as improvements to the local map optimization, which leads to a better performance for AR applications.
In this work, the authors implemented a system where the local mapping and tracking is achieved through the use of a direct vSLAM algorithm (DSO  [4] ). The data resulting from the local mapping and tracking are then exploited by the indirect vSLAM (ORBSLAM2  [5] ) for back-end optimization and loop closure. The system proposes to locally run direct tracking and mapping, and refine the built map and trajectory by running a global indirect vSLAM (notably for loop closing and pose refinement). This is different from the proposed work, in that the indirect module is limited to back-end optimization and therefore cannot locally track nor build a map. Given the similarity of the methods, LCSD has been tested and compared to the proposed system, see Section 4. The main difference between our proposal and the state-of-the-art is: the implementation of a fusion of the initialization and tracking modules, and a rigorous evaluation on multiple datasets.
ORBSLAM2 We chose ORBSLAM2 as the indirect algorithm in our fusion because: (i) it embeds all complementary modules (loop closing, relocalization), and (ii) the algorithm has been considered by the research community as a standard benchmark algorithm against which most other algorithms are compared, see e. g. ,  [3, 21]. In this section, the ORBSLAM2  [5]  algorithm is explained in details based on the additional descriptions from  [3, 34]. The algorithm runs three threads in parallel: tracking, mapping and loop closing. A diagram of the general architecture of the algorithm is presented in Figure  1. A feature extraction module is used by ORBSLAM2 to detect, describe and match feature points. As the name indicates ORBSLAM2 uses ORB  [35]  feature points for both tracking and mapping. The algorithm starts by the detection, description and matching of keypoints.