dataesr/scientific-paragraphs-categorization huggingface dataset:
3) using OpenCAD; (b) compute at least one sample point per connected component in the semi-algebraic set S defined by (3. 3) -this is what the command PointsPerComponents from the RAGlib package is designed for; (c) letting α 1 ,. , α r be those sample points, solve the univariate system of polynomial inequalities f t+1 (α i , x n ) > 0,. , f s (α i , x n ) > 0, for each α i. If the system is not consistent over the reals then one can conclude that S is empty (in other words, the system (3. 2) is not consistent over the reals). Launching this computation on system (3. 1) is still not computationally tractable. To further reduce computations, we rely on the following property of the system, which can easily be checked (e. g. , on the Maple worksheet attached with the paper). Lemma 3. 3. In system (3.
We developed the uOPA at the Petawatt High Energy Laser for heavy Ion eXperiments (PHELIX)  [15]  laser facility at GSI-Darmstadt, Germany, but a versatile, selfseeded pump module enables a broad applicability of this uOPA at lasers that work in the spectral range of 1 µm. Aside from a millijoule-level performance, this uOPA features high energy stability, broadband amplification and high beam quality, which makes it a useful high-contrast pre-amplifier without the need for subsequent spatial cleaning amplifiers. The uOPA has already been implemented at the PHELIX facility, providing the highest ASE and pre-pulse contrast levels on the nanosecond scale. In the following, we describe the design of the complete uOPA system and address the system requirements. We also provide insight into the amplifier's performance in terms of output energy, beam and spectral quality and its energy stability.
This limits the choice of available active media for the pump laser to those that have an absorption band that can be pumped by highly stable laser diodes and an emission band that provides sufficient bandwidth to generate short pulses in the 1-2 ps range. Ytterbium-doped YAG (Yb:YAG) combines these properties, is widely available and provides high-energy storage capabilities. To find a suitable pump laser design, the first step is to determine the required pump energy. For this, simulations have been conducted with a self-written 3D, three-wave-mixing code, with a design like SNLO software  [22]  , but with the possibility to add parasitic beams, such as the second harmonic of the idler and the signal. This is especially important when simulating the OPA process close to degeneracy and close to a collinear setup, since the phase mismatch for these processes will be diminished.
Furthermore, the output of this code was crosschecked with outputs of the well-known simulation software SNLO and Miró  [23]  to ensure its functionality. Figures  1(a ) and 1(b) show the simulation results for the simplest configuration: a single-stage uOPA with direct amplification. As input for the simulation, the pump exhibits a Gaussian profile in space and time, with a duration of 1. 5 ps (full width at half maximum (FWHM)), a beam radius of w = 2. 14 mm and a center wavelength of 515 nm. The initial signal parameters are energy of 1 nJ, pulse duration of 66 fs and beam radius of 1. 8 mm. The center wavelength is 1034 nm, which corresponds to the spectrum of the oscillator that is used to set up the uOPA. The simulation was performed up to a maximum crystal length of 1. 5 mm with varying pump energies from 10 to 40 mJ in steps of 7.
In the last step, the system must be approved in terms of energy stability before being implemented in a high-intensity laser system. For this, a measurement of the uOPA output energy over a time span of close to 3 hours was conducted. Figure  13  shows the results of this measurement. Even though the measurement data were taken in a test laboratory whose air conditioning system can only cool, which causes fluctuations in the pump energy, the energy stability was found to be 1. 5% rms including a slope of -7 µJ per hour over the full timespan. This makes the system a useful front-end for high-intensity laser systems, which is why this system has been implemented at the PHELIX facility.
Conclusion We reported on the development of a uOPA module, delivering millijoule-level pulses, to be used as a high-contrast front-end for high-intensity lasers. Thanks to a home-built, highly stable, high beam quality pump laser, an rms stability of 1. 5% of the uOPA output energy could be achieved while keeping an acceptable beam quality (M 2 ) of 1. 45 in the xdirection and 1. 50 in the y-direction. Although we could observe spatial distortions, the beam distribution is free of high-frequency components, is highly symmetric and can still be well approximated by a Gaussian distribution, making it a suitable seed for high-intensity lasers. The output spectra are observed to feature amplitude modulations, which can most likely be attributed to phase errors in the chirped mirrors. We implemented the system at the PHELIX facility, which allowed us to bypass one of the regenerative amplifiers in the front-end, which has been known to create most of the pre-pulses within the nanosecond scale at the PHE-LIX facility.
Acknowledgements This work received funding through the  ATHENA  project of the  Helmholtz Association  and through the  Loewe program  of the state of Hesse. This work was carried out within the framework of the  EUROfusion Consortium , funded by the  European Union  via the  Euratom Research and Training Programme  (grant agreement No. 101052200 -EUROfusion ) and the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 871124   Laserlab-Europe.
For the SMEFT, we discuss the range of validity of the EFT description, the relevance of O(1/Λ 2 ) and O(1/Λ 4 ) truncations, the impact of d = 8 operators and the effects of different quarkflavor alignments. Finally, as a highlight, we extract for several New Physics scenarios the combined limits from high-p T processes, electroweak pole measurements and low-energy flavor data for the b → cτ ν transition, showing the complementarity between these different observables. Our results are compiled in HighPT (https://highpt. github. io), a package in Mathematica which provides a simple way for users to extract the Drell-Yan tails likelihoods for semileptonic effective operators and for leptoquark models.
Furthermore, we will verify the EFT truncation by using a clipping procedure, which imposes a maximal energy cut (M cut ) on the data considered to extract the limits  [47]. As an important by-product of our work, we introduce the Mathematica package HighPT  [48]  that provides the complete SMEFT likelihood for semileptonic operators in Drell-Yan processes at the LHC. 1 This package will complement the ongoing effort to provide tools for the SMEFT phenomenology of low-energy flavor observables  [49] [50] [51] [52] , as well as electroweak and Higgs data  [52] [53] [54]. HighPT also provides the likelihood for specific models of interest such as leptoquarks  [55, 56] , including their propagation effects at the LHC. 2  The comparison of the constraints derived for both EFT and concrete models will allow the users to directly assess the validity of the EFT description for a given high-p T process.
JHEP03(2023)064 For the sake of presentation, we have not explicitly expanded the form-factors in eqs. (2. 15)  and  (2. 25)  in terms of the various regular and pole form-factors defined in (2. 7) and (2. 8). Complete expressions for the hadronic cross-sections in terms of these parameters can be easily extracted for any bin using the Mathematica package HighPT.
Dilepton and monolepton searches at the LHC The experimental searches considered in our analysis are collected in table 3. These correspond to data sets from the full run-II ATLAS and CMS searches for heavy resonances in dilepton and monolepton production at the LHC. In the last two columns we display the tail observables that are measured in each search (x obs ) which serve as proxies for the particle-level observables (x) used to compute the signal cross-sections. Specific details concerning the definition of the measured observables, selection cuts and any other inputs used in these experimental analyses are available in the respective ATLAS and CMS papers listed in table  3. Limits on the SMEFT and on mediator models are extracted with the HighPT package  [48]  where each Drell-Yan search has been repurposed for generic New Physics scenarios.
Only the New Physics signal N A (θ) is computed at tree-level using our form-factor decomposition presented in section 2. The response matrices K AB have been provided in the HighPT package for each LHC search. These were obtained from Monte Carlo simulations using the following pipeline: first all relevant operators in the SMEFT with d ≤ 8 and all mediator Lagrangians in table 2 were implemented with FeynRules  [91]. The resulting UFO model files  [92]  were then imported into MadGraph5  [93]  and used to simulate statistically significant event samples for the dilepton and monolepton processes with all possible initial quark flavors. Samples were then showered and hadronized using Pythia8  [94] , and the final-state object reconstruction and detector simulation were performed using Delphes3  [95]  tuned to match the experimental searches.
Surprisingly, we find that the bounds to the electron couplings turn out to be comparable to the tauonic ones, which is caused by a poor background description of the dielectron data over several high-mass m ee bins  [86]. The complete LHC likelihood for all leptoquarks with all possible flavor couplings are available in HighPT for several benchmark masses. Leptoquarks and LFV at the LHC. Considering a single leptoquark coupling at a time will only lead to lepton flavor conserving Drell-Yan processes. However, if the 18 For example, the S3 ∼ ( 3, 3, 1/3) state being a SU(2)L triplet will contribute to didj → α β via the S (4/3) 3 component and to ūiuj → α β and diuj → ανβ via the S (1/3) 3 component. 19 For the right-handed coupling [x R 2 ]11 of the V2 vector leptoquark, we observe a mild discrepancy with the SM prediction with a low significance of ≈ 2 σ.
JHEP03(2023)064 Furthermore, we derived single-parameter limits on the Wilson coefficients for the dipole and semileptonic four-fermion operators with any possible flavor combination. We also extracted two-parameter limits for specific vector operators, where we analyzed the effects of a non-diagonal CKM matrix with different quark-flavor alignments. These results are intended to be the initial steps towards a global analysis of the semileptonic sector of the SMEFT including all three fermion generations, where the goal is to combine LHC data from high-p T tails with complementary experimental probes such as electroweak precision tests and flavor observables  [98]. With the aim of providing a more complete EFT analysis, we also looked into the limitations of the SMEFT description of the Drell-Yan tails at the LHC.
This last requirement, however, may only be possible in fairly complicated ultraviolet setups. In addition to the SMEFT, we also computed the LHC likelihoods for all possible leptoquark mediators contributing non-resonantly to Drell-Yan production via t/u-channel exchange. These can also be found in HighPT in full generality for each leptoquark state. We derived single-parameter constraints on each leptoquark with arbitrary couplings, and two-parameter limits to illustrate the interplay between bounds from lepton flavor conserving and LFV Drell-Yan production modes. Finally, as a highlight of our results, we considered the example of LFU tests in B-meson decays based on the b → c ν transition, showing that the LHC limits are complementary to low-energy data and to electroweak precision measurements, for the main scenarios aiming to accommodate the observed discrepancies in low-energy data.
Acknowledgments We are grateful to  Gino Isidori  for encouraging us to pursue this project. We also thank  Adam Falkowski  for discussions regarding ref. [71]. This project has received support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  860881-HIDDeN. L. A. , D. A. F. and F. W. received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement  833280  (FLAY), and by the  Swiss National Science Foundation (SNF)  under contract  200020-204428.
Furthermore, each data type was collected by two (or three) independent laboratories to ensure generalization across populations, recording systems, and experimenters. Altogether, we aimed at fostering informativeness, reproducibility, and robustness of the results by (1) dissociating theory from data acquisition/analysis to prevent biases, (2) using a multimodal approach to test theories with an exquisite temporal and spatial precision, (3) acquiring data in a large sample of subjects to increase statistical power, (4) using standardized and preregistered protocols  12  to evaluate theories under the same experimental framework and further minimize confirmatory bias  20  , and finally (5) combining an analysis optimization phase with a final testing phase using independent parts of our dataset to corroborate the robustness of the results (Figure  1b )  27.
90%, SD=12. 29; false alarm rate M=4. 25%, SD=20. 17). We confirmed that subjects were conscious of the stimuli both in the task relevant and irrelevant trials in a separate experiment which included a surprise memory test (see supplementary). As part of our testing framework, after excluding a limited number of subjects due to data quality checks, we conducted an initial optimization phase on 1/3 of the MEG (N=32) and fMRI (N=35) datasets to evaluate data quality across sites and to optimize analysis pipelines. Following the optimization phase, pipelines were preregistered (https://osf. io/92tbg/), and applied to the novel datasets containing twice as much data (MEG, N=65 and fMRI, N=73). In what follows we report results obtained on the novel, unseen datasets (see methods for the strategy used for iEEG and text for numbers of subjects that entered in each analysis).
The stars and arrows on the brain (left) depict the different predictions about the expected synchrony patterns (green: GNWT; blue: IIT). b. The scientific framework under which the Cogitate Consortium (Collaboration On GNW and IIT: Testing Alternative Theories of Experience), tested IIT and GNWT included: adversarial collaboration, (theory-neutral) team science, invasive and non-invasive multimodal data (iEEG, fMRI, MEG), large samples (>250 subjects), standardized protocols across multiple laboratories, built-in preregistered replication, and open methods, data and code. c. Conscious experience is multifaceted in content. Looking at the image of Mona Lisa by Leonardo da Vinci underscores the fact that conscious experiences are rich: The painting is experienced as occupying a location in space, pertaining to a given category (i.
For illustration purposes only, a color line was added to depict the different trial types. Blank intervals between stimuli are not depicted here. f. Distribution of behavioral sensitivity scores (d') separate per data modality and acquisition site. Crossing lines depict average d' per site/modality. Dots depict individual participants d's. Colors depict data modality: MEG N=65 (orange), fMRI N=73 (red), and iEEG N=32 (green), while the hue depicts each site within a modality. g. Distributions of false alarm (FA) rates per site and data modality, separated by task condition: Orange-red depicts task relevant stimuli. Purple depicts task irrelevant stimuli. Dots are individual participants FA rates. Other conventions as in f. h. Top row: Average fixations heatmaps computed over a 0. 5 s window after stimulus onset.
In posterior cortex, face/object decoding showed significant cross-task generalization (>95% accuracy) for the approximate duration of the stimulus (Figure  2b , top row). In PFC, significant cross-task face/object decoding accuracy (~70%) was also evident, but the temporal generalization of this decoding was restricted to ~0. 2-0. 4 s (Figure  2b , bottom row). Training on task relevant and testing on task irrelevant trials showed similar results (Extended Data Figure  1e ; within-task decoding provided in Extended Data Figure  3 ). The sustained (posterior) and phasic (PFC) patterns of cross-task temporal generalization of decoding thus matched both IIT's and GNWT's predictions, respectively. While electrode coverage across our sample of iEEG patients (N=29 for the decoding analyses) was exceptional in the relevant brain regions (Figure  2a , PFC ROIs Nelectrodes=576, Posterior ROIs Nelectrodes=583), we also evaluated these predictions in a larger population of healthy subjects (N=65) in MEG.
Results from the cross-task decoding of stimulus categories using the MEG cortical time series (see methods) combining all parcels within the theoryrelevant ROIs were consistent with the iEEG observations. Cross-task generalization of face/object decoding was significant in both posterior and prefrontal cortex (Figure  2c ) within the theory-predicted time-windows. The extent of cross-temporal generalization of decoding in MEG was sustained in posterior cortex. In PFC, decoding was brief for all three stimulus durations (see supplementary). A limitation of MEG is its spatial imprecision, which can impact source localization. We thus also tested the theories' predictions in a large sample of healthy subjects (N=73) exploiting the high spatial resolution of fMRI. Using a searchlight approach (see methods), we found distributed and robust cross-task generalization (~75%) in striate and extrastriate, ventral temporal, and intraparietal cortex (Figure  2d ; see Extended Data Table  4  for anatomical details).
Generalization in prefrontal cortex had lower accuracy (~60%), and was spatially restricted to middle and inferior frontal cortex regions (Figure  2d ). We obtained similar results with a decoding approach using theory-relevant ROIs defined in the Destrieux atlas (see supplementary). These results also closely matched a theory-relevant ROIs analysis in the iEEG data restricted to the time windows specified by the theories (Figure  2e ). Hence, across recording modalities, we observed that face/object decoding was present both in the posterior and the prefrontal ROI, in line with IIT and GNWT predictions. As the representation of conscious content is rich and multidimensional including features beyond category, we turned to decoding of stimulus orientation (which was always task irrelevant).
Notably though, control analyses could not conclusively rule out that MEG decoding in the PFC stemmed from signal leakage from posterior regions (Extended Data Figure  5b ). Decoding of orientation for the other stimulus categories (letters and false fonts but not for objects) was observed in posterior cortex but not in the prefrontal ROI across the three data modalities (see supplementary). Finally, we tested IIT's prediction that prefrontal regions do not contribute further information beyond that specified by posterior areas (or may even degrade performance as it could introduce noise into the classifiers). The results of this test would challenge IIT if the inclusion of PFC was found to increase decoding accuracy, while a lack of an increase would be consistent with both theories as GNWT holds that workspace neurons in PFC broadcast information from posterior processors rather than adding information.
We compared decoding performance from classifiers exclusively trained on posterior regions with classifiers trained on posterior and prefrontal regions together (see methods). The results across all three data modalities (iEEG, MEG and fMRI) indicate that neither category nor orientation decoding improves, and in some cases even decreases, when adding prefrontal regions to posterior regions (Extended Data Figure  5c ). Considering the primary preregistered tests of both theories, for prediction #1, we found support for IIT: decoding of conscious content (both category and orientation) in posterior cortex was robust, independent of the task manipulation, and consistent across data modalities (iEEG, MEG and fMRI). Also, decoding of category and orientation was found to be the same, or to decrease, when including PFC to posterior regions.
Due to the temporal nature of the predictions, they were tested on the two data modalities with millisecond temporal resolution, iEEG and MEG. First, we tested the theories' predictions investigating neural activation as a function of stimulus duration. In the iEEG data, we used linear mixed models (LMMs, see methods) to model the time course of neural activity in the high gamma (HG) frequency band (70-150 Hz), which correlates with spiking activity  31, 32  , per electrode and theory-relevant ROI as a function of the theories' predicted temporal models (Figure  1a. middle panel) and stimulus duration (LMMs, see methods). To increase sensitivity and to accommodate the (category) selective responses expected in higher-order sensory areas, we included an interaction term with category. Electrode sampling in the posterior cortex and PFC was dense and comparable across the theory-relevant ROIs despite the serendipitous nature of the electrode implantation, enabling us to fairly and exhaustively test theories predictions directly in the human brain.
Conclusion (Cogitate consortium) At this point, the reader might expect the consortium to draw a final conclusion. Instead, we invite the reader to form their own conclusions considering the relative evidence we presented for each of the preregistered predictions, the scope of the evidence with > 250 subjects using the most sophisticated techniques available to human neuroscience, and the challenges in changing people's minds. Science is a social enterprise, and the reader is as much a part of this enterprise as any of the authors from this consortium. the MEG dataset: two due to failure to meet predefined behavioral criteria (i. e. , hits < 80%, and/or FA > 20%), two due to excessive noise from sensors, and one due to incorrect sensor reconstruction. A total of 108 healthy participants were included in the fMRI sample (mean age 23.
28 ± 3. 46 years, 70 females, 105 right-handed), 35 of those datasets were included in the optimization sample (mean age 23. 26 ± 3. 64 years, 21 females, 34 right-handed), and 73 in the replication sample (mean age = 23. 29 ± 3. 37, 49 females, 71 right-handed). Twelve additional subjects were excluded from the fMRI dataset: eight due to motion artifacts, two due to insufficient coverage, and two due to incomplete data.
Stimuli Four stimulus categories were used: faces, objects, letters and false fonts. These stimuli naturally fell into two clearly distinct groups: pictures (faces and objects) and symbols (letters and false fonts). These natural couplings were aimed at creating a clear difference between task relevant and task irrelevant stimuli in each trial block (see Procedure). All stimuli covered a squared aperture at an average visual angle of 6˚ by 6˚. Face stimuli were created with FaceGen Modeler 3. 1; letter and false fonts stimuli were generated with MAXON CINEMA 4D Studio (RC -R20) 20. 059; object stimuli were taken from the Object Databank 2. Stimuli were grayscaled and equated for luminance and size. To facilitate face individuation, faces had different hairstyles and belonged to different ethnicities and genders.