dataesr/scientific-paragraphs-categorization huggingface dataset:
For the ENSO, the column ozone signal was comparable in most CCMs but could not be assessed against observations because of the large interannual variability and the weakness of the ozone column signal in observations  (CCMVal, 2010). Regarding the aerosol loading, CCMs showed a considerable spread in their response to volcanic eruptions although most models were forced by a common dataset for the time-varying stratospheric aerosol surface area density (SAD) distribution. The models displayed differing degrees of sensitivity to aerosol levels, leading to different ozone losses. None of the models reproduced the observed hemispheric asymmetry in post-Pinatubo ozone losses, for either full hemispheric means or for mid-latitudes  (CCMVal, 2010). Finally, regarding solar variability, the column ozone signal was reasonably well represented in models.
On the other hand, the NDACC database offers long time series of homogeneous data, a relatively global distribution of sites (with tens of stations covering over the globe) and simultaneous measurements of whole range of chemical species. To quantify the links between forcings (e. g. aerosol loading, solar irradiance, stratospheric halogen loading or a linear trend term, QBO, ENSO) and variations in chemical composition, we perform a multi linear regression (MLR) analysis. Both CCM and observational data records are processed in exactly the same way. The response of a chemical species to an external forcing can involve complex and multi-step mechanisms and might depend on responses of other variables, for instance temperature. However, the aim here is not to explore how well models perform with respect to intermediate steps in the forcing-response links but simply to assess the overall links in model simulations and their consistency with the links derived from observations.
Therefore, the only explanatory variables in the MLR are external forcings and not model variables such as atmospheric temperature, strength of the general circulation or heat flux. Another consequence is that most of the internally generated variability in stratospheric chemical composition is not expected to be accounted for in our MLR. Finally, it is also worth pointing out that, to our best knowledge, this study is the first attempt to analyse simultaneously large NDACC multi-species datasets in the evaluation of CCMs. The paper is divided into 5 sections. Section 2 describes the NDACC observations and the CCMs. The methodology and forcing proxies are presented in Section 3. The results are described and discussed in Section 4. First, seasonal cycles are studied, followed by an analysis of the variance and of the MLR results.
Observations We evaluate CCM responses to external forcings using different ground-based observational datasets without zonal averaging. The NDACC network is composed of monitoring and research stations providing high-quality observations of atmospheric chemical composition which are well distributed over the globe. NDACC instruments are designed to provide consistent, standardized, long-term measurements of atmospheric trace gases, particles, UV radiation reaching the Earth's surface, and physical parameters. NDACC data are also used as reference data in the calibration of satellite data. Here we use O 3 and NO 2 columns measured with Systeme d'Analyse par Observations Zenithales (SAOZ) instruments  (Pommereau et al. , 1988)  and HCl, ClONO 2 , CH 4 , O 3 , N 2 O and HNO 3 columns from Fourier Transform Infra-Red (FT-IR) spectrometers  (Zander et al.
As the periods considered and the length of the time series can have an impact on our results, the modelcalculated and observational data are analysed in the same way over the same periods as the observations (including data gaps) in order to make the results from models and observations as comparable as possible. The comparisons are carried out on monthly means. In contrast to other species, due to a strong diurnal cycle, the SAOZ sunset and sunrise NO 2 data are not averaged together, but treated as two distinct datasets; like the other species, their data are averaged monthly. As no sunset or sunrise NO 2 data over specific sites are provided in the CCMVal model database, adjusted sunset/sunrise CCM values are calculated using the time-varying global daily meanNO 2 fields from CCMs and time series of monthly mean ratios of sunset and sunrise NO 2 over daily mean NO 2 for each site provided by the SLIMCAT chemical transport model SLIMCAT  (Chipperfield, 2006).
e. CFCs, HCFCs and halons) surface mixing ratios follows the 2007 WMO standard scenario  (WMO, 2007). The sea surface temperature (SSTs) are prescribed as monthly means following the global sea ice and sea surface temperature (HADISST1) dataset provided by the UK Met Office Hadley Center  (Rayner et al. , 2006)  except for the CCM LMDz-reprobus that uses AMIP II sea surface data  (Taylor et al. , 2000). The influence of the solar variability on photolysis and heating rates is parameterized according to the intensity of the 10. 7 cm solar radiations  (Lean et al. , 2005). All models except ULAQ have implemented a spectrally resolved solar forcing in the simulation, both for radiation and for photolysis calculations. The three major volcanic eruptions  (Agung in 1963 , El Chichon in 1982 and Mount Pinatubo in 1991)  are taken into account by prescribing observed global distributions of aerosol surface area densities (SADs).
Chapter 2 of  WMO (2011)  summarizes the various ways in which the set-up of some models may deviate from the REF-B1 scenario  (Eyring et al. , 2008). The CCM outputs uploaded from the CCMVal database are monthly mean fields. The analysis is focused on T3M model outputs that correspond to the 3-D monthly mean fields. The modelled values at the different observation sites are obtained by simple linear interpolation of T3M fields. Few results for T2M outputs (zonally averaged monthly mean fields) are also presented for illustrating the importance of zonal deviations. Some modelling groups did not provide T3M outputs for all the species considered here (see Table  2 ). As a result, instead of 7 models, only 4 models are analysed for HCl and 3.
Such manipulations often sacrifice depth, resulting in "flat" imagery (known as the cardboarding effect  [11] ). Since players have full control in a game, it is hard, if not impossible, to accurately guess where a player is looking at any given instant without knowing their current goal. Our key observation is that a player's current goal is highly correlated with the present state of the game, as encoded by game variables. For instance, in a Virtual Reality (VR) shooting game, the player's current goal will be related to the health and ammo of his/her character and of the enemies' movements. Based on this insight, we train a classifier to learn the correlation between game variables and object class the user looks at, using eye-tracking ground-truth data recorded during a training session.
The resulting classifier can predict object category -and thus user gaze -for any subsequent game-play. Our approach is automatic since it uses machine learning to build the classifier, avoiding the need for manual object tagging and/or explicit definition of objects important to a task. Using our online gaze predictor we introduce dynamic and localized disparity manipulation, which provides high-quality depth information in a scene without sacrificing comfort. In summary we make two primary contributions: • We design a machine learning gaze predictor based on game variables and eye tracking data from a video game. • We develop a stereo grading algorithm based on gaze for dynamic disparity management in video games. We evaluate the quality of our gaze predictor using standard machine learning tools and by a confirmatory eye-tracking study.
Data Collection Data collection involves an experimental setup using an HMD, equipped with an eye-tracker. Stimuli. We modified the game level to have 60-90 seconds of game-play for data collection. The players are required to reach a flaming spaceship while avoiding threatening robots, soldiers, zombies and mines (Fig. 4 ). During data collection, the starting position of the player and the spaceship were all similar for every player and trial. An equal number of robots, soldiers, zombies and mines were spawned in random locations in the level during gameplay, ensuring necessary variability in the stimuli, and a dense dataset of possible fixation patterns. Pilot Study. A pilot study indicated that due to differences in individual performance, it was best to fix play time to at least 20 minutes rather than fix the number of trials.
We collected 200 minutes of gameplay data for all participants. During game-play, game state variables were recorded for every sample instance (e. g. , ---→ Robot dx,dy,dz = (x, y, z)). Eye-tracking fixations were used to identify the object via ray-casting and the object together with the game state were inserted into a database. Technical details on eye-tracking data de-projection can be found in the supplemental material. At the end of data collection, our database contains training data T , having N samples of M = 13 features (Table  1 ), T = (X 1 , y 1 ), (X 2 , y 2 ),. , (X N , y N ) that will be used to train the DF. Each record ∈ T includes an input feature vector, X i = x i1 , x i2 ,. , x iM and the object class label y i ∈ Λ (Table  2 ) indicated by the eye tracker at the specific moment that sample X i was taken.
DF Generation, Training and Tuning Following the experiment, data were extracted from the database and imported to the R statistical language  [40]  to optimize parameter selection for a DF classifier. We use standard tree growing and tuning procedures, as described in  [7]. Frequent Classes Under-sampling. When initially processing the data we encountered a class imbalance issue. Since the participants mostly attended moving objects (soldiers, robots, etc. ) in the environment, more samples for these objects were recorded. When a subset of the classes accounts for the majority of the data, the classifier achieves high accuracy by erroneously classifying all the observations into these most frequent classes. This gives high accuracy for frequent classes, but poor predictions for the least frequent ones.
This subset is a set of boostrap datasets which does not contain any record from the original dataset. This is known as the OOB set. There exist N such subsets, one for each data record in the original dataset T. The OOB classifier is the aggregation of votes only over T k such that it does not contain (X i , y i ). The OOB estimate for the generalization error is the error rate of the OOB classifier on the training set, compared to known y i 's. Simply put, the error rate for classification on the OOB portion of the data for each tree is recorded and the same is done after permuting all predictor variables. The difference between the two is then averaged over all trees, and then normalized by the standard deviation of the differences. The OOB error estimate is estimated internally, during forest generation.
The final, balanced set T spanned N = 55151 samples × M = 13 features + Class. We optimized the number of trees to make the OOB error rate converge in terms of a pre-selected error threshold. In the optimized dataset we only kept the object categories for which prediction error rate < 45% was achieved. The prediction error rate for the 8 object categories that fall behind this threshold can be seen in Table  3. The imbalance between sampling rates described previously explains why objects encountered less frequently (e. g. , "Food") have a higher prediction error rate in the DF structure compared to frequently encountered objects (e. g. , "Explosives"). The average DF OOB estimate of error rate was found to converge to 16. 26% for 100 trees. The class imbalance issue for complex games can be amended by increasing the DF training samples.
This is expected to increase the number of successfully predicted object categories. In-game DF Generation and Classification. We employed a custom-made Iterative Dichotomizer 3 (ID3) method in C# to generate a decision tree from the dataset  [39]. As suggested in  [7] , each tree was grown using mtry = log M log 2 random features/game variables for each split of the tree and we have confirmed that this value is the optimal splitting parameter in terms of OOB error.
Robot The procedure yields ntree datasets of the same size as T , grown from a random re-sampling of data in T with-replacement, N times for each dataset. 64% of the data in T were used for the generation of each tree  [7]. This results in T 1 , T 2 ,. , T ntree bootstrap datasets. For each T i bootstrap dataset a tree is grown. To classify any new input data D = x 1 , x 2 ,. , x M we test them against each tree to produce ntree results Y = y 1 , y 2 ,. , y ntree. For classification, the prediction for this data is the majority vote on this set (Fig. 6 ).
We used our HMD's eye tracker to obtain gaze data only during session a. Ten participants not previously involved in any related experiment (2 female, mean age 23. 5) completed the sessions successfully. In each session players played 10 pairwise 10-second game rounds of predetermined gameplay, lasting in total for 200 seconds (10 pairs × 2 conditions × 10 sec). The order in each pair was randomized. Session pairs intentionally imposed large disparity changes: objects moving in the view frustum, camera wildly panning, etc. Example conditions were designed having in mind common disparity events that cause strain and affect depth perception in a game. For example, a moving object (e. g. , an enemy) is about to appear, and will be far away in depth. Depths should be compressed in time to prepare the observer for the moving object to avoid intense convergence motion.
DF-based Predictor Quality We have already presented a validation of the training using OOB estimates in Section 3; here we perform test-time evaluation of our DF predictor using eye-tracking and compare to a state-of-the-art low-level predictor  [53]. During session a, low level (x,y) predicted coordinates, DF predictions and eye tracking data of the view frustum were obtained at a rate of 1Hz, 1Hz and 30Hz respectively. We only considered fixations of a duration greater than 300 milliseconds for our analysis  [42]. Thus for every second we obtain up to 3 possible fixation locations. We also define a baseline estimator R that selects a random object in the same view frustum at 1Hz. We perform a temporal window integration comparison for the 3 predictors (Low-level, DF and baseline). Since the sampling rate of all three compared predictors is 1Hz, if any of the user fixations within this one-second window are predicted correctly by a method, we consider that a hit.
ACKNOWLEDGEMENTS This research has been co-financed by the  European Union (European Social Fund -ESF ) and Greek national funds through the  Operational Program "Education and Lifelong Learning " of the National Strategic Reference Framework (NSRF) -Research Funding Program: Heracleitus II: Investing in knowledge society through the  European Social Fund. The work was partly supported by  EU   FP7  project  ICT-611089-CR-PLAY  www. cr-play. eu.
An evidence for a diphoton resonance at a mass of 750 GeV has been observed in the data collected at the LHC run at a center of mass energy of 13 TeV. We explore several interpretations of this signal in terms of Higgs-like resonances in a two-Higgs doublet model and its supersymmetric incarnation, in which the heavier CP-even and CP-odd states present in the model are produced in gluon fusion and decay into two photons through top quark loops. We show that one cannot accommodate the observed signal in the minimal versions of these models and that an additional particle content is necessary. We then consider the possibility that vector-like quarks or leptons strongly enhance the heavy Higgs couplings to photons and eventually gluons, without altering those of the already observed 125 GeV state.
Introducing vector-like quarks and leptons In order to increase significantly the Higgs couplings to gluons and/or photons, one could consider the contributions of new heavy fermions to the triangular loops 6. These fermions should have vector-like couplings to the electroweak gauge bosons in order to avoid generating their masses through the Higgs mechanism only and then cope with the electroweak precision tests as well as the LHC Higgs data 7. Vector-like fermions appear in many extensions of the SM and recent discussions have been given in Ref. [10]. In our analysis, we will not rely on any specific model (as e. g. Ref. [23] ) and simply adopt an effective approach in which the properties of these fermions are adjusted in order to fit our purpose. In addition to the two Higgs doublets for which we still assume the alignment limit and the mass equality M H ≈ M A , we first consider vector-like quarks (VLQs) with the following minimal Lagrangian describing their Yukawa couplings in the interaction basis -L VLQ = y b L √ 2 0 v d +Φ 0 d +iP 0 d t b L b R + y t L √ 2 v u +Φ 0 u ±iP 0 u 0 t b L t R + {L ↔ R} + m 1 t b L t b R + m 2 t L t R + m 3 b L b R + h.
Acknowledgements: Discussions with  Luciano Maiani  and  François Richard  and correspondence with  Xiao-Fang Han  are gratefully acknowledged. A. D. thanks the  CERN Theory Unit  for its hospitality. This work is supported by the  ERC  advanced grant Higgs@LHC. G. M. is supported by the  "Institut Universitaire de France " and the  European Union   FP7 ITN Invisibles.
Study population This study reports data on 32,438 subjects from 52 study sites that are part of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)  10  consortium and Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA)  11  consortium. Briefly, the CHARGE consortium is a collaboration of predominantly population-based cohort studies that investigate the genetic and molecular underpinnings of age-related complex diseases, including those of the brain. The ENIGMA consortium brings together numerous studies, mainly with a case-control design, which performed neuroimaging in a range of neuropsychiatric or neurodegenerative diseases, as well as healthy normative populations. Studies participated in either the discovery cohort of European ancestry, the replication in European ancestry, or the generalization to other ethnicities.
Genetics Genotyping was performed using a variety of commercial arrays across the contributing sites. Both samples as well as variants underwent similar quality control procedures based on genetic homogeneity, call rate (less than 95%), minor allele frequency (MAF < 0. 01), and Hardy-Weinberg Equilibrium (HWE p-value less than 1 × 10 -6 ). Good quality variants were used as input for imputation to the 1000 Genomes reference panel (phase 1, version 3) using validated software packages (MaCH/minimac, IMPUTE2, BEAGLE, GenABLE). Variants that were poorly imputed (R 2 < 0. 5) or uncommon (MAF < 0. 5%) were removed prior to meta-analysis. Full details on the site-specific genotyping and quality control may be found in Supplementary Table  S2.
Imaging Magnetic resonance imaging (MRI) was obtained from scanners with a diversity of manufacturers, field strengths, and acquisition protocols. Images were used to estimate milliliters of intracranial volume from automated segmentations generated by freely available or in-house methods that have been described and validated earlier. Most sites measured intracranial volume for each participant by multiplying the inverse of the determinant of the transformation matrix required to register the subject's MRI scan to a common template by the template volume (1,948,105 mm3), using the FreeSurfer software. Visual inspections were performed to identify and remove poorly segmented images. Either all scans were visually inspected, or sites generated histogram plots to identify any outliers, which were defined as individuals with a volume more than three standard deviations away from the mean.
Functional annotation All tracks of the regional association plots were taken from the UCSC Genome Browser Human hg19 assembly. SNPs (top 5%) shows the top 5% associated variants within the locus and are colored by their correlation to the top variant. Genes shows the gene models from GENCODE version 19. The tracks give the predicted chromatin states based on computational integration of ChIP-seq data for 12 chromatin marks in various human tissues derived from the Roadmap Epigenomics Consortium  43. Additionally, we used HaploReg version 3 for annotation of the top variants and all variants in LD (> 0. 80) (http:// www. broadinstitute. org/mammals/haploreg/haploreg_v3. php).
Head growth trajectories Head growth trajectory analyses were done within the Generation R study, a longitudinal cohort study situated in Rotterdam, the Netherlands. For this analysis we included 2,824 children of European ancestry followed prenatally until 6 years of age. Head size was measured at the following points: prenatally (using echo) during the first, second, and third trimester, and postnatally (measuring head circumference) at 0-2 months, 2 months, 3 months, 4 months, 5-10 months, 10-13 months, 13-17 months, and 5 years of age. We tested whether a polygenic score of the 7 loci, as well as the 7 loci themselves separately, were related to head growth using linear mixed models and included an interaction term between time and the genetic score/variant (SAS software). Next, the predicted values were calculated for each person and plotted over time, stratified by genotype (0/1/2 risk alleles) using the R software package.
Austrian Stroke Prevention Study Family (ASPS-Fam): The ASPS-Fam is funded by the Austrian Science Fund (FWF) project I904, the Medical University of Graz and the Steiermärkische Krankenanstalten Gesellschaft. BETULA: This sample collection was supported by a Wallenberg Scholar grant from the Knut and Alice Wallenberg (KAW) foundation and a grant from Torsten and Ragnar Söderbergs Foundation to Lars Nyberg. Stephanie le Hellard was supported by a grant from HelseVest RHF (Grant 911554). We are grateful to all study participants and their relatives, general practitioners and neurologists for their contributions and to P. Veraart for her help in genealogy, J. Vergeer for the supervision of the laboratory work and P. Snijders for his help in data collection. Najaf Amin is supported by the Netherlands Brain Foundation (project number F2013(1)-28).
The ERF study genome-wide array data and phenotype data (age and gender) is archived in European Genome-Phenome Database (EGA). The study is archived in the DAC named Erasmus Rucphen Family Study with the accession code: EGAS00001001134. Researchers who wish to use other phenotypic data of the Erasmus Rucphen Family Study must seek approval from the management team of the Erasmus Rucphen Family study. They are advised to contact the study PI, professor Cornelia van Duijn (c. vanduijn@erasmusmc. nl).
GOBS: We acknowledge the ultimate source of our data, the Mexican American community of San Antonio and surrounding areas. Financial support for this study was provided by grants from the National Institute of Mental Health MH0708143 (Principal Investigator [PI]: DC Glahn), MH078111 (PI: J Blangero), and MH083824 (PI: Glahn & Blangero). Theoretical development of SOLAR is supported by MH59490 (PI: Blangero). This investigation was conducted, in part, in facilities constructed with support from Research Facilities Improvement Program Grant Numbers C06 RR13556 and C06 RR017515 from the National Center for Research Resources, NIH. Some of this work was performed at Texas Biomedical Research Institute where Dr. Blangero began this investigator-initiated competitively publicly funded work.