dataesr/scientific-paragraphs-categorization huggingface dataset:
The sampling ratios of D ref from D are fixed at 0. 3 and 0. 7 for the experiments with GP and MGP, respectively. Each reference function f r is considered a source to provide pieces of evidence for the similarity between (x i , y i ) and (x j , y j ) in D eval. The function f r is not used to provide any information about the similarities between the data instances in D ref or between a data instance in D ref and a data instance in D eval. This is to exclude self-evaluation to ensure the objectivity of the evidence. Regarding a reference function f r , we consider the state of the similarity between (x i , y i ) and (x j , y j ) as To quantitatively evaluate whether (x i , y i ) can be considered to have been generated by the regression function f r , we use the.
The weight assigned to {s, ds} expresses the degree of belief that the evidence provides no information about the similarity (or dissimilarity) between (x i , y i ) and (x j , y j ). Therefore, the mass function m i,j fr , which models a piece of evidence of the similarity between (x i , y i ) and (x j , y j ) collected from f r , is defined as follows: m i,j fr ({s}) ¼ p(O i , O j jf r ) γ i,j , (6) m i,j fr ({ds}) ¼ p(O i , O j jf r ) þ p(O i , O j jf r ) γ i,j , (7) m i,j fr ({s, ds}) ¼ 1 À 1 γ i,j þ p(O i , O j jf r ) γ i,j , (8) where  16, 26  which describes the unreliability of evidence about the similarity between (x i , y i ) and (x j , y j ) collected from a source of evidence f r. Δ y is the variation range of the target variable y in the dataset D. The smaller the σ relative to Δ y , the more reliable the learned regression function f r.
A. Datasets The details of the datasets investigated in this study are as follows. • Binary alloys dataset D binary :  27  A material dataset containing 100 transition rare-earth metal binary alloys, comprising nickel (Ni), manganese (Mn), cobalt (Co), or iron (Fe), and the corresponding Curie temperatures (T C ). This dataset was collected from the Atomwork database of the National Institute for Materials Science. 28, 29 Each binary alloy in D binary is represented using seven descriptors: (1) and (  2 ) the atomic number of transition metal (Z T ) and rare-earth (Z R ) constituents, (3) projection of the spin magnetic moment onto the total angular moment of the 4f elections (J 4f 1 À g j À Á ), (  4 ) and (  5 ) covalent radius (r covT ) and first ionization (IP T ) of the transition metal, and (  6 ) and (  7 ) concentration of the transition metal (C T ) and rare-earth metal (C R ).
The selection of these seven descriptors has been discussed in detail in previous studies. 10, 30  Quaternary high-entropy alloys datasets D quaternary :  27  A material dataset contains 990 equiatomic quaternary high-entropy alloys, which comprise 14 transition metals Ag, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pd, Rh, Ru, Tc, Zn, and the corresponding calculated magnetizations and Curie temperatures in the BCC phase. The dataset was collected from an original dataset of 147 630 equiatomic quaternary high-entropy alloys calculated using the Korringa-Kohn-Rostoker coherent approximation method. 31 Each alloy in D quaternary is represented using 135 compositional descriptors, including the means, standard deviations, and covariance of the atomic representations of their constituent elements  13  and four categorical features indicating the elements comprising the quaternary alloy.
Journal of Applied Physics apply the eRSM to assess the similarities between 100 transition rare earth metal binary alloys comprising nickel (Ni), manganese (Mn), cobalt (Co), or iron (Fe) in the dataset D binary based on their Curie temperatures. We can construct a regression model using a Gaussian process by considering the data instances in D binary. This shows a high prediction accuracy with an R 2 score of 0:963 and a mean absolute error (MAE) of 40 (K) in tenfold cross-validation. However, such a nonparametric regression model does not guarantee the reliability of the model in the subsequent exploratory predictions. This is because the number of observable alloys is relatively small compared to the number of possible alloys. By applying the eRSM on the dataset D binary , we obtain a similarity matrix M binary with moderately high similarity values among the data instances [Fig.
Journal of Applied Physics differences between the mechanisms of the T C of alloys in G 2 , G 3 , and G 4. Moreover, considering the alloys in G 1 , there is a strong linear correlation between T C and the concentration of transition metals in the alloys with a Pearson correlation coefficient of 0:95 (Fig. 4 , triangle scatters). This result is consistent with the observation of the previous research  30  when considering all binary alloys of transition metals and rare-earth metals in D binary ; the range of T C is found to be correlated with the composition ratio of the transition metals. Furthermore, 13 of the 17 alloys in In addition, we examine the behavior of eRSM on toy datasets synthesized with outliers or multiple mechanisms to assess the efficiency of this similarity measure. Detailed results of these experiments are summarized in Sec.
C. Assessment of the similarity between quaternary high-entropy alloys based on mechanisms of magnetization The effectiveness of the eRSM in detecting outliers and identifying mixture mechanisms in the material dataset has been shown in the previous experiment. In the next two experiments, we show the potential of applying the measured similarity to design descriptors for materials. Considering this experiment, we subsequently apply the eRSM to assess the similarities between 990 quaternary high-entropy alloys comprising 14 transition metals in the dataset D Mag quaternary based on their magnetization. To predict the magnetization of these alloys, we attempted to construct an optimal Gaussian process regression model using the designed descriptors. The Gaussian process can poorly regress the magnetization with an R 2 score of 0:75 and an MAE of 0:13 (T) in the tenfold crossvalidation.
The obtained results suggest that the magnetization of these alloys may not be described by a single model in the designed descriptor space. This indicates that the existence of outliers or mixture models of the magnetization properties of these alloys in the descriptor space should be considered in the analysis of this dataset. Applying the eRSM, we obtain a similarity matrix M , and outliers. We monitored the prediction accuracy of the three learned regression models for data in all the groups. The confusion matrix summarizing the correlations between the observed and predicted values of the target variable using the learned regression models is shown in Fig. 5(c ). The diagonal plots illustrate the tenfold cross-validation results of the models learned from these three groups of alloys.
D. Assessment of the similarity between the quaternary high-entropy alloys based on mechanisms of Curie temperature Considering this experiment, the target data are the same as in Sec. III C (D quaternary ); however, the physical property of interest is T C. A regression model can be constructed using a Gaussian process. This shows a rather high prediction accuracy in tenfold cross-validation with an R 2 score of 0:85 and an MAE of 67 (K). We also observe two distinguishable groups of quaternary alloys in the dataset D TC quaternary when applying the eRSM.
Journal of Applied Physics relatively smaller than that of the other alloys. Furthermore, using the data in G TC 1 , we can construct a Gaussian process regression model with a high prediction accuracy with an R 2 score of 0:985 and an MAE of 19 (K) in the tenfold cross-validation. Therefore, we utilize the similarity information to design descriptors for quaternary alloys due to the effectiveness of the data for detecting the mixture of multiple mechanisms in the dataset. We apply similar methods as in the previous experiment to construct a two-dimensional embedding map [Fig. 5(f ) ] and then learn a mixture of experts to predict Curie temperature of quaternary alloys in the dataset D TC quaternary. The proposed mixture of models exhibits higher prediction accuracy than the single model in tenfold cross-validations [Figs.
IV. CONCLUSIONS In this study, we developed a method that can be used to rationally transform material data from multiple sources into evidence of similarities between materials and combine the evidence to conclude the similarities between materials. The extracted similarity-dissimilarity information has significant potential for applications in the subgroup discovery of materials. The effectiveness of the eRSM in detecting homogenous subgroups of materials has been demonstrated by using two experiments on two datasets of magnetic materials. In addition, further analysis of the detected subgroups improves the existing knowledge of problems related to the applied datasets of magnetic materials. For example, we reveal the differences in the mechanisms of the Curie temperature of Co-based binary alloys when using our method to a dataset of 100 transition rare-earth metal binary alloys comprising Ni, Mn, Co, and Fe.
) and6(d)]. The MAE of the proposed mixture of expert reduces approximately 36%, from 67 (K) to 49 (K). E. Discussion of the obtained similarities between materials and the associated physical mechanismsRegarding the experiments with the datasets D Mag quaternary and D TC quaternary focusing on magnetization or T C , the datasets seem to be a self-evident example where magnetization and T C are cases sensitive to finite or zero. As we can see from the results described above (Secs. III C and III D and Sec. VI in the supplementary material), the prediction accuracy is low when considering a single regression model for the entire dataset. In this section, we pay attention to the analysis of the extracted alloys groups G Mag G TC 2 to identify underlying patterns.
ACKNOWLEDGMENTS This work was supported by the  Ministry of Education, Culture, Sports, Science, and Technology of Japan (MEXT)  with the Program for Promoting Research on the Supercomputer Fugaku (DPMSD),  JSPS KAKENHI  grants  20K05301 ,  JP19H05815  ( Grants-in-Aid for Scientific Research on Innovative Areas Interface Ionics ),  21K14396  ( Grant-in-Aid for Early-Career Scientists ), and  20K05068 ,  Japan.
R in the Coulombic interaction term is the distance between donor and acceptor, determined by the crystal structure and lattice constant. Consequently, close DA pairs will emit at higher energy than distant pairs. The last term accounts for LO phonon replicas. As the excitation power increases, acceptors and donors are saturated and the probability of observing a radiative recombination from a close pair increases. The emission shifts toward higher energy. However, DAP recombination generally induces blueshift of the order of a few meV per decade of excitation power. [53] The blueshift observed in the dataset shown in Figure  2c  is significantly larger. The band maximum shifts from 1. 594 to 1. 630 eV within the investigated excitation power range, yielding a blueshift around 20 meV per decade (Figure  2c  and S5, Supporting Information).
Mediterranean climates are characterized by warm, dry summers and mild, rainy winters. Previous studies suggest that over the last 1. 36 Myr, Mediterranean winter rainfalls were in phase with the African monsoon. Here we present a high-resolution terrestrial and marine dataset for the Marine Isotope Stage 17 interglacial (Middle Pleistocene) from Southern Italy, showing that precipitation rates and regimes in the central Mediterranean varied independently of the monsoon system. Specifically, events of extreme summer precipitation were promoted by increased regional insolation rates and/or extratropical cyclones, and their magnitude was further enhanced by the advection of cool and humid North Atlantic air during stadials. Our findings provide new information on the short-to mid-term natural hydroclimatic variability of the Mediterranean basin, and offer new critical insights on land-ocean interactions at the regional scale by complementing previous analyses on the displacement of storm tracks toward southern Europe.
Acknowledgements We thank the three anonymous reviewers for their constructive comments and suggestions. We are grateful to  Mario Marcello Miglietta  for a friendly discussion on summer rainfall mechanisms over the Mediterranean. This research was funded by the  University of Padova ,  SID Geoscienze 2022  to LC. AI acknowledges the support received from the  NBFC , funded by the  Italian Ministry of University  and Research, PNRR,  Missione 4 Componente 2 , ' Dalla ricerca all'impresa ' ,  Investimento 1. 4 , project  CN00000033.
2008)   3. The fastest known field MSP, PSR J0952-0607, was found at the location of a gamma-ray source and subsequently timed with LAT data  (Bassa et al. 2017b; Nieder et al. 2019). A third globular cluster, NGC 6652, was found to have gamma-ray emission dominated by a single MSP, PSR J1835-3259B  (Gautam et al. 2022b; Zhang et al. 2022) , with a fourth recently reported, PSR J1717+4308A in M92  (Zhang et al. 2023). The gamma-ray flux and pulse profile of PSR J2021+4026 in the γ Cygni supernova remnant (SNR) changed during mode transitions in 2011 and 2018  (Allafort et al. 2013; Razzano et al. 2023 ). The LAT sees more than forty 'spider' MSPs and a dozen candidates (see Section 7), compact binary systems where the pulsar wind ablates its companion. Spiders fall into two categories: "black widows" have companion masses 0.
Slope variations due to pulsar flux changes nearly never happen: rare exceptions are the young pulsar PSR J2021+4026  (Allafort et al. 2013; Razzano et al. 2023 ) and transitional MSPs like PSR J1023+0038 (see  Stappers et al. 2014 , and Appendix A) and J1227-4853  (Roy et al. 2015; Johnson et al. 2015) , which lack detectable gamma-ray pulsation during their accretion states. If different analyses were tried, the number of trials must be accounted for in the pulsation significance calculation, requiring a higher H-test value to claim a detection. Before the advent of photon weighting, we sometimes varied the minimum energy and/or the angular extent of our dataset, to explore the pulsar's spectral hardness and local background level. Using a LAT source's measured spectrum to calculate weights allows one single trial for this parameter space.
Unfortunately, it is difficult to time and a phase-connected ephemeris which would yield gamma-ray pulsations is not anticipated soon. Neutron stars can also power X-ray emission either directly from their surfaces or magnetospheres, or via shock emission when the pulsar wind interacts with a companion or the interstellar medium  (Gentile et al. 2014; Marelli et al. 2015; Coti Zelati et al. 2020). The non-thermal shock spectra, sometimes modulated at the orbital period, provide strong signatures. Consequently, X-ray surveys also provide valuable information as demonstrated by varied studies of the error ellipses of unidentified LAT sources. For example, the Swift XRT systematically studied a large number of LAT unassociated source regions 10. This dataset has been mined using machine learning techniques to identify promising pulsar candidates for radio follow up  (Kaur et al.
(2022). They optimize the timing model by maximizing the likelihood, using PINT  (Luo et al. 2021)  to compute the rotational phase for each photon. Parameter uncertainties are derived from the likelihood curvature near the optimum. Particularly useful for MSPs, we have been able to estimate some precise proper motions directly from LAT data, necessary for the Doppler corrections presented in Section 4. 3. When both radio and LAT timing of comparable quality are available, we use the radio ephemeris. LAT timing was used for 142 of the pulsars in this catalog. Twothirds of those were made with the ToA method. The fraction of timing solutions using the unbinned method is increasing, as analysts adopt the tool, as more difficult pulsars are added to the sample, and as the datasets lengthen. The ephemerides we used to fold the 294 pulsars are included in the supplementary on-line data, at https:// fermi.
The lowest is Ė int = 9 +21 -8 × 10 32 erg s -1 for PSR J1946+3417. Improved precision and a larger sample will clarify the gamma-ray "deathline" for MSPs, below which emission in the LAT range ceases. A useful tool to understand the uncertainty on Ė int are plots of µ vs d showing the constraints suggested by the neutron star's transverse velocity v T = µd, minimum Ė int , and so on. A powerful constraint is to require Ė int > L γ (Section 6. 7 addresses luminosity L γ ). Examples of µ vs d plots, or similar, are Figure  11    After preparation according to Section 2, the photon arrival times were converted to rotational phase using timing models obtained from pulsar timing observations conducted in radio or X-rays, or by direct timing of the LAT photons, as described in Section 3. In a number of cases, the timing models' validity interval only partially overlapped with the time range of the LAT dataset.
To avoid contaminating off-pulse phase regions by extrapolating inaccurate models, we restricted the LAT datasets to the validity intervals in the cases of (1) timing solutions containing high-order (≥ 3) frequency derivatives suggestive of strong timing noise; (2) "IFUNC" or "WAVE" terms (also used to mitigate timing noise); or (3) "ORBIFUNC" or high-order (≥ 2) orbital frequency derivatives for pulsars in binary systems, or black widow pulsars that are often subject to timing instabilities. For other objects, generally stable MSPs, all LAT photons were used and we checked that the pulse phases do not drift outside the ephemeris validity intervals (see Figure  4 ). Additionally, we conservatively excluded 10 days of data before and after pulsar glitch epochs, to avoid contamination of offpulse phase intervals caused by imperfect glitch modeling.
Energy-resolved Light Curve Fitting The best-fit profiles above 0. 1 GeV were then used to fit gamma-ray pulse profiles in narrower energy ranges. To this end, we divided the LAT datasets into logarithmically-spaced energy intervals, and fitted the pulsar pulse profiles in each energy interval independently. The number of energy intervals per decade was determined by the H-test TS above 0. 1 GeV: one per decade for pulsars with H-test TS values between 100 and 1000, two per decade when 1000 ≤ H ≤ 10000, and so on. Profile fits are again done only if H-test > 100 in an energy band: 167 pulsars have at least two such intervals. Profile parameters were allowed to vary within the same ranges as for the fits over the entire energy range. By default, the number of components for a given pulsar was the same as for the fit over the entire energy range.
bf A script provided with the Online Material shows how to correctly unpack the parameters and covariance from the electronic catalog. to fit a linear model b = b 0 + m b log 10 ( Ė/10 36 erg s -1 ) to each class and to the full population. We assumed that each measured value follows a Gaussian distribution with the measurement uncertainty, and we also included an intrinsic scatter, σ i , which is added in quadrature to the measurement error. We excluded the Crab pulsar from the fits. In Figures 17 through 21 its wellmeasured parameters generally deviate from those of most other pulsars. With its high Ė, including it changes the fit results substantially, such that they no longer describe most other gamma-ray pulsars well. If the Crab is atypical only in having both high Ė and good statistics, then perhaps our simple linear fit breaks down for high Ė.
Notes on Individual Pulsars Twenty gamma-ray pulsars in Tables  13  and 14  are flagged with † symbols, meaning that no 4FGL-DR3 source with Test Statistic > 25 is colocated with the pulsar. The pulsed signature is detectable in these cases, in spite of a low gamma-ray flux or large local background. Guillemot et al. (2013)  provide spectral parameters for the double pulsar PSR J0737-3039A via a dedicated analysis. PSR J1513-5908 (B1509-58) is brightest in the MeV domain, with a spectral cutoff below 100 MeV, measured by  Kuiper et al. (2018). The colocated extended source 4FGL J1514. 2-5909e corresponds to the pulsar wind nebula MSH 15-52 and its spectral parameters do not apply to the pulsar. Similarly, PSR J1023+0038 is a transitional pulsar, and the 4FGL-DR3 spectral results are dominated by the data obtained after it transitioned to its accreting state, where the (unpulsed) gamma-ray flux is greatly enhanced.
Fewer than 10% of known pulsars have such high spindown power, Ė = 1. 1 × 10 35 erg s -1 , in the Milky Way or in globular clusters. The orbital period is 0. 9 days. In 2020, then-undergraduate Camryn Phillips phase-connected GBT timing observations during PINT code development she was conducting. We phase-folded the gamma-ray data using her ephemeris and obtained the pulsed detection reported here. PSR J1852-1310: The P = 4. 31 ms isolated pulsar PSR J1852-1310 was discovered in a 1900 s pointing on MJD 57793 towards a LAT unidentified source with a pulsar-like spectrum, at 800 MHz using the GUPPI backend on the Green Bank radio telescope. It was initially named PSR J1852-13. Routine timing observations at 1400 MHz with the Lovell telescope at Jodrell Bank Observatory began on MJD 57818. As of MJD 59927, 124 arrival times have been acquired using ROACH, allowing a phase-connected rotation ephemeris, provided with the online material.
79 ms gamma-ray pulsations. F. Camilo then re-analyzed GBT radio data from previous observations of the LAT source position using the LAT rotation ephemeris and discovered radio pulsations and measured the DM. Table  3  lists S 1400 = 2. 50 mJy, extrapolated from the  Frail et al. (2016)  GMRT flux density of 362 ± 15 mJy at 150 MHz and radio spectral index -2. 23. Table  8  lists a 2. 4σ proper motion measurement using the methods of Section 3. 5. Clark and coworkers are studying other LAT unidentified sources co-located with steep-spectrum radio sources and will detail their methods and results in future work. J2256-1024: Crowter et al. (  2020 ) found µ = 9 ± 3 mas yr -1 using GBT timing data. NRT has also monitored this pulsar, but with a substantial gap between the end of the GBT data and the beginning of the NRT observations.
tgz (individual plots) and 3PC AllSEDs. pdf (all plots concatenated into a single file). 3PC SEDPlotter. tgz contains a python script that reads 3PC Catalog+SEDs xxx. fits to generate the SED plots. Most Figures and Tables in this article were created from the FITS and spreadsheet files: the python script Example 3PC CatalogReader. py is a simple example. README 3PC Catalog+SEDs xxx. txt provides more detail. 3PC ProfileData xxx. tgz: Contains two files for each pulsar, one in FITS format and the other in simple text (ascii), containing the weighted gamma-ray phase histograms and, when they exist, the fit to the histogram and/or the radio profile. The python script Example 3PC PlotProfileData. py illustrates how to read the files to reproduce the plots provided in 3PC ProfilePlotsPDF xxx. tgz and 3PC ProfilePlotsPNG xxx.
tgz. The plots of gamma-ray peak evolution with energy, as in Figures  12  and B -5 to B-8, are provided in 3PC LC Eres pdf xxx. tgz, 3PC LC Eres png xxx. tgz. README ProfileData. txt provides details. 3PC TimingModels xxx. tgz: A compressed archive of the 294 rotation ephemerides (". par files") used to calculate the gamma-ray rotational phases using software such as Tempo2 or PINT. README 3PC TimingModels. txt provides details. Phased, weighted FT1 FITS files: available at https://heasarc. gsfc. nasa. gov/ FTP/fermi/data/lat/catalogs/3PC/photon/. Two subdirectories called 3deg 50MeV and 15deg 20MeV each contain one file for each gamma-ray pulsar, in the standard Fermi LAT data format, with additional columns PHASE and MODEL WEIGHT. The 277 files in 3deg 50MeV include photons with energies > 50 MeV, within 3 • of the pulsar position, and were used to create the gamma-ray phase histograms.