dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion IsoSel is a command line software designed for the automatic selection of protein isoforms in the framework of phylogenetic analyses. Based on the SP score, it allows to obtain datasets that are optimized for tree reconstruction. The only other software that can be compared to IsoSel is Guidance but this program presents some limitations. First, it requires the independent installation of a broad range of tools (namely Perl, BioPerl and Ruby) while IsoSel is selfsufficient and is distributed with all the binaries required for its functioning. Then, it can only be run with the JTT substitution model while IsoSel allows the use of all standard sitehomogeneous models. On a practical point of view, Guidance is usually slower than IsoSel when multithreading is enabled (data not shown).
Acknowledgments The authors would like to acknowledge  Dominique Guyot  for his advice and expertise on parallel computing. The computational works have been performed on the LBBE/PRABI cluster. This work was supported by grants from the  CNRS , the  Institut Francais de Bioinformatique  to GP, the  France Genomique consortium for GP , and the  Region Rhone-Alpes  PhD thesis grant  13-012735-01 , which was funding the  Ph. D of HP. CBA is a member of the  Institut Universitaire de France  and is funded by the  "Ancestrome " project through the  Agence Nationale de la Recherche  grant  ANR-10-BINF-01-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This work was supported by grants from the  CNRS , the  Institut Francais de Bioinformatique  to GP, the  France Genomique consortium for GP , and the  Region Rhone-Alpes  PhD thesis grant  13-012735-01 , which was funding the  Ph. D of HP. CBA is a member of the  Institut Universitaire de France  and is funded by the  "Ancestrome " project through the  Agence Nationale de la Recherche  grant  ANR-10-BINF-01-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
certif. com), after which the number of counted photons within that ROI appear in a corresponding counter and are saved in the datafile. However, in many cases the images contain much more information and often the analysis can be significantly improved by processing the saved images. For this purpose, a Python-based open source software (XRStools) has been developed and is described in detail elsewhere  (Sahle et al. , 2015b). The software allows for selection of ROIs in various ways: automatically based on a script for image analysis or the user can define the ROIs manually (rectangular, linear or polygon ROIs). XRStools then integrates over the ROIs and yields final spectra that are normalized to the intensity monitor and averaged over repeated measurements. The results can be saved in an ASCII data file or can be processed further using XRStools.
Performance As the new spectrometer is an upgrade of the previous XRS spectrometer at beamline ID16 of the ESRF  (Verbeni et al. , 2009) , obviously one should directly compare data from the old and new spectrometer. Such a comparison is shown for a spectrum of pure water in Fig. 7. At ID16, the spectrum was measured from a free-flowing liquid water column and scattering was recorded by nine spherically bent Si(660) analyzer crystals at a mean scattering angle of 36 in the horizontal plane. For the data collection with the new spectrometer at ID20, we utilized the miniature liquid-flow cell mentioned earlier and 48 Si(660) analyzer crystals in forward-scattering geometry. The arrangement of the analyzer chambers was with mean scattering angles at 35 for the VD, VU, HR and HL chambers. The recorded intensities are presented in counts s À1 to grant direct comparison; however, the data from ID16 were collected for 120 min whereas the data taken with the new spectrometer were collected within 5 min.
Figs. 8  and 9  show two further examples of data taken with the new spectrometer. In Fig. 8  we show spectra of liquid acetic acid, a simple carboxylic acid, measured over a broad energy-loss regime to cover the characteristic valence electron excitation regime as well as the carbon and oxygen K-edges. The data are shown as raw data averaged over low (q = 3. 8 AE 0. 6 A ˚À1 ), intermediate (q = 6. 8 AE 0. 5 A ˚À1 ) and high (q = 9. 0 AE 0. 5 A ˚À1 ) momentum transfers. For these averages we used individual signals from 36 (low q), 12 (intermediate q) and 24 (high q) Si(660) crystal analyzers, respectively. The overall measurement duration for the shown data was 100 min, where most of the time was spent scanning the carbon and oxygen near-edge regions. The inset of the figure presents a closer view of the oxygen K-edge showing the excellent statistical accuracy of the data.
In Fig. 9 (a) we show raw data from a highly oriented pyrolytic graphite (HOPG) sample. The sample was oriented such that the momentum transfer in the vertical scattering plane is parallel to the sample's ab plane. The data shown are averages over each of the analyzer modules, i. e. averages over scattering signals from 12 analyzer crystals. For the data shown in the figure, 8 min were spent for the collection of data of the large overview scan (spanning energy losses from 10 to 440 eV energy loss) and 30 min for a fine scan around the carbon Kedge. Knowledge of the full loss spectrum greatly facilitates the background removal and normalization to absolute scale. In Fig. 9 (b), the near-edge of the carbon K-edge is shown after post-processing using the XRStools package. The background was subtracted using parameterized Pearson-VII functions and normalization to absolute scale was achieved as described by  Sahle et al.
Conclusions and outlook We have presented a new experimental end-station for X-ray Raman scattering spectroscopy at port ID20 of the ESRF. The design of this new spectrometer was optimized to maximize the covered solid angle of detection while retaining a    Comparison of raw data of liquid water taken with the nine-element spectrometer at ID16 and the new multi-analyzer spectrometer of ID20. For the data from the new spectrometer, we averaged over crystal analyzers with similar momentum transfer as used when collecting data with the old ID16 spectrometer. Intensity is the average per one analyzer. maximum amount of flexibility to account for complicated sample environments. We described the key characteristics of the spectrometer and depicted the use of pixelated area detectors that immensely facilitate the data analysis for experiments involving the mentioned complicated sample environments.
Acknowledgements We kindly thank the  ESRF  for financing the  UPBL6  project as part of the  ESRF Upgrade Phase I. We kindly acknowledge  M. A. Schroer ,  P. Degen  and  C. Sternemann  for making available the ID16 data. This work has been supported by the  Academy of Finland  (grants  1259526  and  1283136 ).
Here I propose to use a system identification framework to optimize the analysis of pupillometric data (see ref. 20  or refs 21-23 for applications of similar methods to neuroimaging and electroencephalographic data, respectively). In particular, the low-frequency noise is modeled with an autoregressive model, in which each data point is predicted by the value of the data points that precede it, within a given time window. In addition, the pupillary response to specific events is modeled by adding exogenous inputs. I apply this approach in a passive task in which participants are presented with fast-paced affective pictures standardized in their arousal content, and in a rest recording session during which respiration is recorded concurrently. It is noteworthy that the objective of the present work is not to provide a biologically plausible model of pupil responses, which has been a very active topic of study in the context of the light reflex  24, 25  , but only to propose a new approach to analyze the pupil signal in psychophysiological experiments.
This signal was recorded at 100 Hz with a 16-bit data acquisition card (National Instruments, Austin, Texas, USA) connected to the computer running Matlab (data acquisition toolbox, The MathWorks, Inc. , Natick, Massachusetts, USA). This recording session lasted 2 minutes. Pupil signal pre-processing. Blinks were tagged by the Eyelink blink detection algorithm. 100 ms of data were additionally tagged as blinks right before and after each automatically detected blink epochs, to ensure that no artifacts were left in the data. Blinks were then linearly interpolated. The pupil size data was downsampled to 10 Hz by taking the mean of the pupil size in successive 100 ms bins. Different downsampling frequencies were tested and higher sampling rate did not improve the results. All analyses were performed with Matlab and the system identification toolbox (The MathWorks, Inc.
Hoeks & Levelt devised a method based on deconvolution  17. First, a parametric, canonical impulse response was determined by optimizing the fit between pupillometric data and an input vector representing events of interest, convolved with the impulse response. A fitting procedure was used in which free parameters controlled the shape of an impulse response, which was then convolved with an input vector representing the events of interest. This fitting procedure allowed Hoeks & Levelt to determine the parameters that maximized the fit between the convolved input vector and the actual data. Once these parameters are determined, they can be used to perform the inverse approach, i. e. to determine the input vector by deconvolving the impulse response from the pupillometric data  17, 18  , or to perform general linear model analysis, similar to the standard univariate analyses used in neuroimaging data  28  , by regressing predicted responses on actual pupillometric data  19.
Acknowledgements This work was supported by grants from  Innoviris (Région Bruxelles-Capitale) , the  ARC  ( Actions de Recherche Concertées ,  Communauté Française de Belgique ), from the  Fondation Médicale Reine Elisabeth (FMRE) , from the  Fonds de la Recherche Scientifique (FNRS -FDP)  and from the  Fondation Louvain. I thank  Andrea Alamia  and  Oleg Solopchuk  for their helpful comments and suggestions.
We introduce a hierarchical architecture for machine reading capable of extracting precise information from long documents. The model divides the document into small, overlapping windows and encodes all windows in parallel with an RNN. It then attends over these window encodings, reducing them to a single encoding, which is decoded into an answer using a sequence decoder. This hierarchical approach allows the model to scale to longer documents without increasing the number of sequential steps. In a supervised setting, our model achieves state of the art accuracy of 76. 8 on the WikiReading dataset. We also evaluate the model in a semi-supervised setting by downsampling the WikiReading training set to create increasingly smaller amounts of supervision, while leaving the full unlabeled document corpus to train a sequence autoencoder on document windows.
(2015) , SWEAR attends over the window representations and reduces them into a single vector for each document. Finally, the answer is decoded from this document vector. Our results show that SWEAR outperforms the previous state-of-the-art on the supervised WikiReading task  (Hewlett et al. , 2016) , improving Mean F1 to 76. 8 from the previous 75. 6  (Choi et al. , 2017). While WikiReading is a large dataset with millions of labeled examples, many applications of machine reading have a much smaller number of labeled examples among a large set of unlabeled documents. To model this situation, we constructed a semi-supervised version of WikiReading by downsampling the labeled corpus into a variety of smaller subsets, while preserving the full unlabeled corpus (i. e. , Wikipedia). To take advantage of the unlabeled data, we evaluated multiple methods of reusing unsupervised recurrent autoencoders in semi-supervised versions of SWEAR.
Importantly, in these models we are able to reuse all the autoencoder parameters without fine-tuning, meaning the supervised phase only has to learn to condition the answer on the document and query. This allows for more efficient training and online operation: Documents can be encoded in a single pass offline and these encodings reused by all models, both during training and when answering queries. Our semisupervised learning models achieve significantly better performance than supervised SWEAR on several subsets with different characteristics. The best-performing model reaches 66. 5 with 1% of the WikiReading dataset, compared to the 2016 state of the art of 71. 8 (with 100% of the dataset).
Given a document D = {d 1 , d 2 , • • • , d N D } and a query Q = {q 1 , q 2 , • • • , q N Q } as sequences of words, our task is to generate a new sequence of words that matches the correct answer A = {a 1 , a 2 , • • • , a N A }. Because we do not assume that A is a subsequence of D, the answer may require blending information from multiple parts of the document, or may be precisely copied from a single location. Our proposed architecture supports both of these use cases. The WikiReading dataset  (Hewlett et al. , 2016) , which includes a mix of categorization and extraction tasks, is the largest dataset matching this problem description. In WikiReading, documents are Wikipedia articles, while queries and answers are Wikidata properties and values, respectively. Example Wikidata property-value pairs are (place of birth, Paris), (genre, Science Fiction).
Semi-Supervised Version We also consider a semi-supervised version of the task, where an additional corpus of documents without labeled (Q, A) pairs is available. Taking advantage of the large size of the WikiReading dataset, we created a series of increasingly challenging semi-supervised problems with the following structure: • Unsupervised: The entire document corpus (about 4M Wikipedia articles), with queries and answers removed. • Supervised: Five smaller training sets created by sampling a random (1%, 0. 5%, 0. 1%) of the WikiReading training set, and taking (200, 100) random samples from each property in the original training set.
Progressive Reviewer (SWEAR-PR) Although the reviewer layer in SWEAR-MLR has global window and question encodings as input, it requires a number of sequential steps equal to the window size, plus any additional reviewer steps. The reviewer layer also has to re-encode windows for each question, which is not ideal for online use. To address these issues, we now present a Progressive Reviewer model (SWEAR-PR) that reviews the outputs of the encoders using a separate RNN that is decoupled from the window size (Figure  4 ). Encoding questions and windows: Similar to SWEAR-MLR, SWEAR-PR first encodes the questions and windows independently using autoencoder layers, hq = Enc(e Q ; θ U ) and hw i = Enc(e D i ; θ U ). To decouple the question and window encoders, however, SWEAR-PR does not have a second layer as a reviewer.
Experimental Evaluation As described in Section 2, we evaluate our models on the WikiReading task. In Section 3. 5 we presented results for the supervised SWEAR on the full WikiReading dataset, establishing it as the highest-scoring method so far developed for WikiReading. We now compare our semisupervised models SWEAR-MLR and SWEAR- PR over various subsets of the WikiReading dataset, using SWEAR as a baseline.
Experimental Setup Following  Hewlett et al. (2016) , we use the Mean F1 metric for WikiReading, which assigns partial credit when there are multiple valid answers. We ran hyperparameter tuning for all models and report the result for the configuration with the highest Mean F1 on the validation set. The supervised SWEAR model was trained on both the full training (results reported in Section 3. 5) and on each subset of training data (results reported below). Unsupervised autoencoders were trained on all documents in the WikiReading training set. We selected the autoencoder with the lowest reconstruction error for use in semi-supervised experiments. After initialization with weights from the best autoencoder, learnable parameters in the semi-supervised models were trained exactly as in the supervised model.
Conclusions We have demonstrated the efficacy of the SWEAR architecture, reaching state of the art performance on supervised WikiReading. The model improves the extraction of precise information from long documents over the baseline seq2seq model. In a semi-supervised setting, our method of reusing (V)RAE encodings in a reading comprehension framework is effective, with SWEAR-PR reaching an accuracy of 66. 5 on 1% of the dataset against last year's state of the art of 71. 8 using the full dataset. However, these methods require careful configuration and tuning to succeed, and making them more robust presents an excellent opportunity for future work.
The two motivating principles above place the search for pair production of top squarks ( t t) among the highest priorities of the LHC program. The most recent searches for direct t t production were carried out by the ATLAS and CMS Collaborations in proton-proton (pp) collisions at center-of-mass energies √ s of 7, 8, and 13 TeV at the LHC. The searches have provided no evidence for sparticle production in models with t masses up to ∼900 GeV and χ 0 1 masses up to ∼400 GeV. This paper presents a search for direct t t production in R-parity conserving SUSY using data collected in pp collisions at √ s = 13 TeV by the CMS experiment at the LHC in 2016, and corresponding to an integrated luminosity of 35. 9 fb -1. The search is based on methods presented in Ref. [44] , and represents an extension of that search to larger sparticle masses by means of a significantly larger dataset and the development of more sensitive search tools.
Signal production cross sections are calculated using NLO with next-to-leading logarithm (NLL) soft-gluon resummations (NLO+NLL)  [65]. The most precise cross section calculations are used to normalize the SM simulated samples, corresponding to next-to-next-to-leading order (NNLO) accuracy  [66] [67] [68] [69]  in most cases. Finally, the transverse momentum ( p T , with magnitude p T ) spectrum of top quarks in tt events is reweighted (referred to as "top quark p T reweighting") to account for effects due to missing higher-order corrections in MC simulation, according to the results presented in Ref. [70]. A full GEANT4-based model  [71]  is used to simulate the response of the CMS detector to SM background samples. The CMS fast simulation package  [72]  is used for signal samples after verifying that it provides results that are consistent with those obtained from the full GEANT4based simulation.
The misidentification rates for these top quark and W boson taggers are measured in data using a sample of multijet events that is dominated by the QCD multijet process, selected with an H T trigger, where H T is defined as the scalar sum of the p T of the primary jets in the event. We require the events to contain at least one large-R jet and H T ≥ 1 TeV. The misidentification rate is measured as a function of the jet p T and η, and then compared to the expected rates in simulation. Data-to-simulation ratios are found to deviate from unity by no more than 20%, and are used to correct results obtained with simulated event samples. The top quark and W boson tagging efficiencies are measured in data using a sample of lep- ton+jets events dominated by the semileptonic tt process and selected using a single-muon trigger.
The muon is required to have p T ≥ 50 GeV and |η| ≤ 2. 1. To suppress other backgrounds, at least one b-tagged jet is required in the same hemisphere as the muon, and the large-R jet is required to be in the opposite hemisphere. Contributions from processes with no quark decays of top quarks or W bosons are corrected through misidentification correction factors applied before obtaining the tagging efficiencies. These observed efficiencies are compared to those estimated in simulation, and simulation-to-data correction factors, typically ranging from 0. 9 to 1. 1, are extracted and applied to simulated events to account for any dependence on p T. Simulated signal events generated in the CMS fast simulation package are corrected in a similar way for the differences in tagging performance relative to the full GEANT4-based simulations.
The performance of the resolved top quark tagger is shown in Fig. 3. The drop in efficiency at very high p T stems from the fact that top quark decay products are kinematically more likely to be merged into single large-R jets. Correspondingly, the efficiency of the merged top quark tagger starts to become significant in this region, as seen in Fig. 2  (left). [GeV] The performance of the resolved top quark tagger is evaluated using the same methodology as that described in Section 4. 1. Simulation-to-data correction factors ranging from 1. 00 to 1. 15 are extracted and applied to simulated events to account for differences with data as a function of p T. Simulated signal events generated in the CMS fast simulation package are corrected in a similar way for differences in tagging performance relative to the full GEANT4based simulations.
The transverse momenta of the tracks associated with an SV are required to sum to p T < 20 GeV, and be separated from any jets (including b-tagged jets) by R > 0. 4. This definition leads to ∼20% efficiency to identify a b hadron in the p T range from 10 to 20 GeV, for a misidentification rate less than one percent. The soft b tagging efficiency in data is measured in a sample dominated by tt events having an eµ pair, p miss T ≥ 50 GeV, a b-tagged jet, and no additional jets. The presence of an additional soft (p T < 20 GeV), nonisolated µ is used to estimate the fraction of soft b quarks in data. The soft b tagging performance in simulation agrees with the performance in data within 16%. Simulated signal events produced in the CMS fast simulation package are corrected for differences in soft b tagging relative to GEANT4-based simulations.
To improve the statistical uncertainty of this background estimation, CR relevant to the high ∆m SR are combined for all top quark and W boson multiplicities in both data and simulation. The top quark and W boson tagger results for the simulated events are corrected by the simulation-to-data correction factors discussed in Section 4. Simulation is used to extrapolate these results to each SR with its particular top quark and W boson multiplicity. The selection efficiency for each of the other search variables is estimated directly from data in the singlelepton sample.
The jet closest in φ to p miss T is chosen for this calculation. Mismeasurement correction factors are extracted by comparing the r pseudo jet distributions in data and simulation. The correction factors are parameterized as functions of r jet and flavor of the most mismeasured jet. The corrections range from 4 to 82%, and are applied to the simulation on an event-by-event basis. Due to the large production cross section of the QCD multijet process, samples of simulated QCD multijet events entering the stringently defined SR have limited statistics. To increase it, we use a method that lets each event from the original sample appear multiple times. To this end, we use event "smearing", whereby a "new" event is created by randomly assigning r jet values to the leading two jets, ranked by their generated jet p T , and then recalculating all search variables based on the "smeared" jets.