dataesr/scientific-paragraphs-categorization huggingface dataset:
A homogeneous φ field coupled to the electromagnetic field via the Chern-Simons term (i. e. , the Lagrangian contains a term -1 4 g φγ φF µν F µν ), rotates the linear polarization direction uniformly over the sky by an angle β = 1 2 g φγ t0 tL dt φ  [375] [376] [377] [378] , converting E-mode into B-mode polarization. The rotation angle is defined such that 117/156 ORIGINAL UNEDITED MANUSCRIPT β > 0 corresponds to clockwise rotation on the sky in right-handed coordinates with the z-axis taken in the direction of observer's lines of sight. Here, g φγ is the coupling constant of the interaction of φ and photons, F µν and Fµν are the electromagnetic tensor and its dual, and t L and t 0 are the time of last scattering and the present time, respectively. We would therefore observe a B-mode polarization signal even if there were no B-mode polarization initially  [67].
Mapping the Hot Gas in the Universe Electrons in the hot ionized gas transfer their energy to CMB photons by inverse Compton scattering, leading to a characteristic distortion of the blackbody spectrum of the CMB (see Fig. 51 ). This phenomenon is known as the thermal SZ effect  [395, 396]  and has been routinely detected in the directions of galaxy clusters  [397] [398] [399] [400] [401]. The amplitude of the thermal SZ effect is characterized by the so-called "Compton y parameter," which is given by y = τ h k B T e /m e c 2 where τ h is the optical depth of hot gas (which should be distinguished from the optical depth of reionization, τ , discussed in Sect. 6. 1) and T e and m e are the electron temperature and mass, respectively. Using the so-called constrained internal linear combination (cILC) method  [402] [403] [404] [405] , we can reconstruct an all-sky map of the thermal SZ signal and its angular power spectrum 120/156 ORIGINAL UNEDITED MANUSCRIPT Fig.
ORIGINAL UNEDITED MANUSCRIPT The solid line in Fig. 51  shows the SZ spectrum in the non-relativistic limit (where k B T e /m e c 2 ≪ 1) for an example value of y = 5 × 10 -6. The shape is universal and depends only on the mean CMB temperature; however, small relativistic corrections to this shape exist  [418] [419] [420]  and are proportional to k B T e /m e c 2 at leading order  [421] [422] [423]. Detecting this relativistic correction averaged over a full sky SZ map  [424, 425]  can yield the mean gas temperature of the Universe, providing an "integral constraint" on physics of the intergalactic medium  [426]  and stringent and robust constraints on the energy feedback from supernovae and active galactic nuclei (AGN). This complements information that can be obtained about the relationship between the WHIM and halos from correlating the SZ map with gravitational lensing and other tracers of large-scale structure  [427, 428].
ORIGINAL UNEDITED MANUSCRIPT where σ T is the Thomson cross-section, ν ≡ ν/( 8/9cR ∞ ) ≃ ν/(3. 1 × 10 6 GHz), and R ∞ is the Rydberg constant. This additional scattering produces a frequency-dependent shift of the peak of the visibility function to z < 1090, which modifies the Silk damping process and shifts the locations of the acoustic peaks in the temperature and E-mode polarization power spectra  [432, 433]. The Rayleigh-scattering signal can be extracted from the data by cross-correlating the frequency-independent Thomson-scattering component and the frequency-dependent (ν 4 ) Rayleigh-scattering component  [433, 435]. Since the frequency dependence of these two components is known precisely, we can separate them by expliciting rejecting the other component using the cILC method  [402] [403] [404] [405].
LiteBIRD can look for this signal 124/156 ORIGINAL UNEDITED MANUSCRIPT by cross-correlating the measured temperature anisotropies with a map of the chemical potential reconstructed from LiteBIRD's multi-frequency data, since this cross-correlation measures a three-point function (temperature fluctuation on large scales correlated with the squared amplitude of sound waves on small scales). Although the multi-field effect of inflation yields only a small signal-to-noise ratio for LiteBIRD, given the constraints on this type of non-Gaussianity from the Planck data  [57, 58] , non-vacuum effects can yield a large signal-to-noise ratio, offering a powerful test of the physics of inflation at its onset  [447]. Additional sources of anisotropic spectral distortion arise when the background spacetime is itself anisotropic  [448] [449] [450].
ORIGINAL UNEDITED MANUSCRIPT where δ D (k) is the Dirac delta function and δ ij is the Kronecker delta. A power-law power spectrum, P B (k) = A B k nB , is characterized by its spectral index, n B , and amplitude, A B. Spectral indices generated during inflation are usually negative  [465] , e. g. , a nearly scale-invariant spectrum of n B = -2. 9 (the exact scale invariance n B = -3 gives a diverging energy density of the magnetic field), whereas they are positive if generated by causal processes such as the electroweak phase transition  [469] [470] [471] , i. e. , an even integer of n B ≥ 2  [472]. In the literature, the magnetic field strength is usually quoted as the value extrapolated to the present epoch assuming that the fields are frozen to the plasma. Moreover, we often quote the field value smoothed over a length scale λ, calculated from the smoothed energy density of the magnetic field: ρ B,λ = ∞ 0 d 3 k (2π) 3 P B (k)e -k 2 λ 2 = A B Γ[(n B + 3)/2] 4π 2 λ nB+3.
ORIGINAL UNEDITED MANUSCRIPT (l, b) = (209 • , -15 • ), extending over scales to ℓ max ≃ 60 with a significance approaching 3 σ. Interestingly, one such test, based on the anomalous clustering of directions within bands of multipoles, suggests that this asymmetry holds even to relatively small scales. (5) Parity asymmetry. To test whether the CMB is symmetric with respect to reflections about the origin, n → -n, the CMB anisotropy field can be divided into symmetric and anti-symmetric functions with even and odd parity, corresponding to spherical harmonic modes with even and odd ℓ values, respectively. On the largest angular scales, the Universe should be parity neutral, yet an odd-parity preference has been established from analysis of the WMAP and Planck data sets. By computing the ratio between the sum of even and sum of odd modes up to a given ℓ max , it was found that the significance varies with the maximum multipole chosen, with p-values of about 1 % for ℓ max = 20-30  [94].
ORIGINAL UNEDITED MANUSCRIPT to be limited by their presence, as well as the dominance of noise over signal in the largescale Planck polarization data. Indeed, a notable feature of the analyses was the variation in results with the four component-separated maps studied, presumably related to their different responses to noise and systematic residuals, and an incomplete understanding of the noise properties of the data. Such effects should not be significant in the case of LiteBIRD since the E-mode signal in particular is expected to be measured at close to the cosmic variance level, with the actual sensitivity defined by the sky fraction available for analysis. A simple measure of whether LiteBIRD E-mode data are approaching this level of sensitivity can be provided by the inferred error on τ , since in the sample-variance limit, this should be of order 0.
Galactic Astrophysics Observations of Galactic polarization were among the main outcomes of the Planck space mission. Spectacular images combining the intensity of dust emission with the magnetic field orientation derived from polarization data (so called "drapery" patterns, also called "line integral convolution"  [500] ) have received worldwide attention and have become part  130/156  ORIGINAL UNEDITED MANUSCRIPT of the general scientific culture  [501]. Beyond this popular impact, the Planck polarization maps have represented a big step forward for Galactic astrophysics  [54]. We anticipate a comparable breakthrough with LiteBIRD, which will provide full-sky maps of Galactic polarized emission with a sensitivity many times better than that of Planck, both for dust and synchrotron polarization.
Among the various means available to map the structure of interstellar magnetic fields, dust polarization is particularly good for tracing the dynamical interplay between magnetic fields, turbulence, and gravity in the ISM. This interplay helps define the structure of interstellar matter and star formation. The multiphase magnetized ISM is too complex to be described by an analytic theory. Our understanding in this research field progresses through observations, magnetohydrodynamic (MHD) simulations, and phenomenological models. Spectroscopic observations obtained from ground-based telescopes give access to the gas column density and its kinematics. LiteBIRD will provide complementary data about magnetic fields. LiteBIRD observations at its five highest frequencies, from 166 to 448 GHz, will improve on the Planck 353-GHz sensitivity to dust polarization by an order of magnitude (Fig.
Much of our current understanding of the turbulent energy cascade in the ISM derives from MHD simulations that are very far from reproducing its high magnetic Reynolds number  [509]. The LiteBIRD data will be crucial to test how well these simulations match the observations. Ongoing developments  [510] [511] [512]  promise to yield powerful statistical tools for characterizing the LiteBIRD dust data and its comparison with simulations, ultimately leading to improved simulations that fully describe the physical processes in our Galaxy. A specific scientific objective will be to elucidate the origin of parity violation (the TB correlation  [53] ) 131/156 ORIGINAL UNEDITED MANUSCRIPT of polarized dust emission. Today with the Planck data, we cannot decide whether this is a generic feature of interstellar turbulence rather than a random statistical fluctuation  [257].
LiteBIRD will improve on Planck 's sensitivity to polarized synchrotron emission by a factor of 5 at 40 GHz. This gain will extend the range of scales over which the correlation between dust and synchrotron polarization is characterized  [513, 514]. More generally, the analysis of LiteBIRD dust and synchrotron data will contribute to a community effort directed towards modelling of the 3D structure of the Galactic magnetic field  [515] , in particular within the Solar neighbourhood. 6. 7. 2. Interstellar Dust. The analysis of the LiteBIRD data will also yield the spectral characterization of Galactic polarization. The current description of the Galactic contribution to the Planck and WMAP polarization data as arising from two components, namely thermal dust emission and synchrotron  [40, 53] , is likely to prove inadequate for LiteBIRD.
By providing data at 15 frequency bands between 34 and 448 GHz LiteBIRD will challenge our current understanding of Galactic emission. The gain is most significant for studying the nature of dust grains and the origin of the so-called anomalous microwave emission (AME)  [518, 519]. Interstellar dust is often modeled as a mixture of silicate and carbon grains, but polarization observations in emission provide a stringent test challenging existing models  [520, 521]. The analysis of dust polarization at far-IR wavelengths obtained with the balloon-borne experiment BLASTPol  [522]  and at microwave frequencies with Planck  [53]  suggests that dust emission at long wavelengths is dominated by one single type of grain, the same for polarization as for total intensity  [523]. Thanks to its sensitivity in many spectral bands (Fig.
ORIGINAL UNEDITED MANUSCRIPT which in turn is known to be correlated with the magnetic field structure. These couplings break the simple assumption where the spectral frequency dependence of the Galactic polarization and its angular structure on the sky are separable. If a line of sight intercepts multiple dust clouds with different spectral energy distributions and magnetic field orientations, the frequency scaling of each of the Stokes Q and U parameters of the thermal dust emission may be different. Evidence for this effect has been reported using Planck data  [536]. In this context, the interpretation of LiteBIRD data in terms of dust properties in polarization will need to be coupled with the modeling of the 3D structure of the Galactic magnetic field. As discussed in Sect. 3. 2, the current LiteBIRD focal-plane configuration results in a conservative mean Fisher uncertainty of σ(r = 0) = σ r = 6.
ORIGINAL UNEDITED MANUSCRIPT Nevertheless, it is of great interest for the mission as a whole to reduce σ r further in order to increase the margin for other types of systematic errors, both known and unknown. In this respect, we note that one of the dominant sources of uncertainty in the current configuration is a significant degeneracy between CMB and thermal dust emission. With the current focalplane configuration, we find that statistical uncertainties in the thermal dust temperature (or, equivalently, the second derivative of the thermal dust SED) account for as much as 30-40 % of the total error bar on r after component separation. One straightforward approach to decrease this uncertainty is to extend the overall frequency range to higher frequencies. The CMB spectral energy density (as measured in brightness density units) falls faster than exponentially above 300 GHz, while the thermal dust spectral energy density scales as ν 1.
These results are independent of analysis pipelines, and we find that they apply equally to both blind and parametric component-separation methods (e. g. , Commander and GNILC). At the moment, the implications of these issues are being carefully considered by the full LiteBIRD collaboration, from hardware through to parameter estimation. The main 135/156 ORIGINAL UNEDITED MANUSCRIPT advantages of a slightly shifted focal plane are obvious, namely that the tensor-to-scalar uncertainty could be reduced by as much as 30 %, and our ability to reject a potentially false detection due to thermal dust mismodeling is greatly improved. A wider frequency range would also significantly increase the legacy value of the LiteBIRD mission with respect to ground-based B-mode and Galactic science experiments.
Synergy with Other Projects The LiteBIRD space mission has strong synergy with ground-based CMB polarization anisotropy experiments including SO, SPO, and CMB-S4. Given the small amplitude of the inflationary B-mode signal and the obscuring effects of foregounds, lensing, and instrumental systematic uncertainties, having highly sensitive data from both LiteBIRD and ground-based experiments will contribute to building confidence in the robustness of an inflationary signal detection. Furthermore, LiteBIRD provides data on foregrounds at frequencies above the highest frequency that will be observed by above-mentioned ground-based experiments (280 GHz), and the LiteBIRD high-frequency data can be combined with B-mode data from the ground improving foreground separation. As well as LiteBIRD augmenting ground-based experiments, CMB lensing data from the ground can be combined with LiteBIRD data to improve the sensitivity of LiteBIRD to primordial B modes at the angular scales of the recombination peak.
Ground-based experiments generally focus on a relatively small region of sky, which has the benefit of giving high S/N per spatial mode but on a relatively low number of spatial modes. LiteBIRD, in contrast, will measure the entire sky and measure all the spatial modes that are available at moderate S/N. Both deep and wide observations can contribute to our understanding of foreground emission, which is essential in the search for inflationary B modes. Deep measurements give a high S/N characterization of foreground emission, but only on a relatively small region of the sky. LiteBIRD's measurement of the entire sky will probe the variability of the emission in different directions and test the fidelity of the model. There is a similar complementarity in terms of instrumental systematic uncertainties, where the high S/N per mode of ground-based measurements provides a deep probe for discovering weak systematic errors, whereas LiteBIRD has the statistical power to measure many spatial modes, in addition to the advantage of making the observations in the benign and stable 136/156 ORIGINAL UNEDITED MANUSCRIPT environment of space.
Once we carry out the observations successfully, we can use LiteBIRD data to study many additional topics in cosmology, particle physics, and astronomy. Examples include: (1) statistical characterization of large-scale B modes and E modes, including tests of scale-invariance and non-Gaussianity; (2) investigation of possible power-spectrum features in polarization; (3) sample-variance-limited measurements of large-scale (low ℓ) E modes, with implications for the reionization history and the sum of neutrino masses; (4) searches for cosmic birefringence and parity violation; (5) maps of cosmological hot gas through the Sunyaev-Zeldovich effects and relativistic corrections; (6) elucidation of large-angle anomalies in polarization; and (7) Galactic astrophysics. LiteBIRD has very focused mission requirements, and at the same time will provide rich scientific outcomes.
Acknowledgment This work is supported in Japan by  ISAS/JAXA  for Pre-Phase A2 studies, by the acceleration program of JAXA research and development directorate, by the  World Premier International Research Center Initiative (WPI)  of  MEXT , by the  JSPS Core- to-Core Program, and by  JSPS KAKENHI. The Italian LiteBIRD phase A contribution is supported by the  Italian Space Agency (ASI  Grants No. 2020-9-HH. 0and 2016-24-H. 1-2018 ),the  National Institute for Nuclear Physics (INFN) , the  National Institute for Astrophysics (INAF) , and.
us to isolate the effect of beam mismatch only from calibration uncertainties that are studied in Sect. 4. 8. 2. ORIGINAL UNEDITED MANUSCRIPT 90/156 • , 10 • ], [10 • , 15 • ], and [15 • , -] (A sup = 0 for the third window). With the previous parameters, the window is actually effective, i. e. W/W max > 0. 5, in the angle ranges [3. 75 • , 7. 5 • ], [7. 21 • , 11. 69 • ], and [11. 25 • , 90 • ]. The normalization µ is calculated for each value of α such that the beam-convolved maps have the same dipole component (ℓ = 1) amplitude as the unperturbed ones. This procedure allows.
Research Center at KEK, as well as resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U. S. Department of Energy. ORIGINAL UNEDITED MANUSCRIPT ORIGINAL UNEDITED MANUSCRIPT ORIGINAL UNEDITED MANUSCRIPT Glossary 139/156 Λ cold dark matter. 8, 12 Joule-Thomson 1. 75-K cooled stage. 50 Joule-Thomson 4. 8-K cooled stage. 50 analog-to-digital converter. 42 adiabatic demagnetization refrigerator. 32, 37, 55 active galactic nuclei. 123 achromatic half-wave plate. 40 assembly, integration, and testing. 44 assembly, integration, and verification. 44 anomalous microwave emission. 132 attitude orbit-control system. 30 anti-reflection coating. 42, 96, 97 baryonic accoustic oscillation. 116 breadboard model.
github. com/fgbuster/fgbuster 81/156 Downloaded from https://academic. oup. com/ptep/advance-article/doi/10. 1093/ptep/ptac150/6835420 by guest on 29 November 2022 Downloaded from https://academic. oup. com/ptep/advance-article/doi/10. 1093/ptep/ptac150/6835420 by guest on 01 December 2022.
They will therefore be forced to pay for access, and will be forced to set prices determined by the value of the connection to the manufacturer, and not set by competition between Alexa and Nest, and not determined by the cost to produce the service. Automotive manufacturers will be harmed in much the same way, as their cars will require interconnection with an existing platform's LCI in order to deliver full capabilities to their users. While automotive manufacturers could try to develop their own limited Life Control Interfaces for their cars, perhaps based on the cars' internal operating system and entertainment control system, this would never be effective; the car simply does not have access to sufficient data to function as an LCI, nor will it ever have the functionality of Alexa or Google to be a truly empowered digital agent.
Unfortunately, conventional models do not accurately predict readmissions; model c-statistics are rarely seen above 0. 8  [8, 9]. Additionally, most of the existing prediction models rely heavily on manual feature engineering  [5, [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] , which is based on domain knowledge and experience. Those features are often dataset-dependent, thus limiting generalizability between datasets or jurisdictions. Recently, machine learning methods that automatically identify which parts of a given set of data are essential for prediction have gained popularity, and there exists such work applied in the domain of readmission prediction as well. Notably, Rajkomar et al. used electronic health records and deep learning models to predict 30-day readmissions and other outcomes  [19].
Objectives This paper describes models to predict 30-day readmissions, with a focus on testing the predictive performance of input features that are automatically generated using machine learning techniques, as well as manual features. Our study is not limited to a specific patient group -it is instead exploring ways to make accurate predictions for patients of all age groups and with all conditions, except those admitted due to a baby birth or a psychiatric condition. We use detailed longitudinal health data from the province of Alberta, Canada. Alberta has a publicly funded, universally accessible, integrated health system and thus collects high-quality data. Our data set contains very few missing records, in particular, there exist no missing readmissions except for those who moved out of the province during the study period and those who died without being readmitted.
Data and target population Our target population consists of patients who were discharged from any of the acute facilities in the province of Alberta between January 1, 2015 and December 31, 2016, excluding only patients who were hospitalized due to a baby birth or a psychiatric condition. For each patient, we extracted detailed health records from 2011 to 2017 including hospitalizations from Discharge Abstract Database (DAD), ambulatory visits from National Ambulatory Care Reporting System (NACRS), physician office visits from claims data, drug prescriptions from Pharmaceutical Information Network (PIN), and lab test results. From DAD, we extracted institution number, admit and discharge dates, discharge disposition, diagnosis codes, procedure codes, and the role of the providers associated with the patient's care for each hospitalization.
Definition of index hospitalization Patients may have been discharged multiple times in 2015 and 2016. We selected one index hospitalization for each patient from these years, using the following procedure. Among the discharge records (DAD) of the target population in 2015 and 2016, we first excluded from the index hospitalization selection those records whose patient died during the hospitalization or that had an invalid patient identifier, as well as those patients who had at least one record whose postal code is not in Alberta (these criteria excluded 42,900 DAD records). We further removed records whose discharge disposition indicated transfers, which excluded 42,172 DAD records. As previously mentioned, discharge records related to a birth (or with disposition indicating stillbirth or organ/tissue retrieval) were not included in the initial data extraction.