dataesr/scientific-paragraphs-categorization huggingface dataset:
Event topology (RST/WST). -The dataset is subdivided into two statistically independent subsets based on the sign of the product q cos ϕ, where q denotes the charge and ϕ is the azimuthal angle (see Sec. III D). Since neither the beam nor the target is polarized, we expect identical spectra for the two datasets. Differences between the particle spectra FIG. 10. Systematic uncertainties of the single-differential spectra dn=dp for the charged hadrons (top three rows) and V 0 s (bottom row) as a function of momentum for the dataset recorded with p beam ¼ 158 GeV=c (see Fig. 24  in the Appendix C for p beam ¼ 350 GeV=c). derived for the two datasets are small (≲3% in most of the phase space bins), but larger than the statistical uncertainties. We interpret these differences as an evaluation of the residual disagreement between the measured data and the idealized detector simulation originating from, e.
VIII. DISCUSSION For a first assessment of the validity of hadronic interaction models used in air-shower simulations, we compare the ratio of measured particle spectra to the model predictions in Fig. 14. Solid lines denote recent modeltunes  [73] [74] [75] [76] [77]  based on LHC and fixed target data, whereas dashed lines show previous versions of these models  [31, 53]. Most of the models agree reasonably well with the measured pion charge ratio shown in the first row of Fig. 14 , but both versions of the SIBYLL model overpredict the ratio at high particle momenta for the 158 GeV=c dataset. The kaon charge ratio in the second row is best described by EPOS LHC. All models underpredict the antiproton-to-proton ratio at low momenta shown in the third row and the shape of the momentum-dependence of this ratio with two inflection points is best reproduced by EPOS 1.
10  and 24. Finally, the ratio of Λ to Λ baryons is best described by the EPOS 1. 99 model, whereas all recent model retunes slightly overpredict FIG. 16. Comparison of the p T -integrated particle production spectra of π AE , K AE , and pðpÞ at 350 GeV=c with predictions of hadronic interaction models. The data is shown as black circles with statistical error bars. Systematic uncertainties are displayed by gray rectangles. prediction of our data than the current EPOS LHC version. In particular, EPOS 1. 99 provides the best description of the charged pion spectra and a near-spot-on prediction of p production, whereas the newer version of the model gives the best match to our Λ measurements. It will be interesting to see air shower predictions of future versions of these models that describe all aspects of our data.
IX. SUMMARY In this article, we presented a new measurement of particle production in interactions of negatively charged pions with carbon nuclei at beam momenta of 158 and 350 GeV=c. We estimated the production cross section and determined the double-differential p-p T spectra of produced π AE , K AE , p AE , Λ, Λ, and K 0 S. These spectra are of direct relevance to air shower physics, because pions are the most Energy fraction transferred to anti-protons as derived from the measurement presented in this article (data points) and as predicted by hadronic interaction models over the whole range of beam energies relevant for air showers. numerous projectiles in air showers and because the mass of the chosen target is very close to the one of air  [82]. Our measurement thus provides a unique reference dataset with unprecedented precision and large phase-space coverage to enable future tuning of models used for the simulation of particle production in extensive air showers in which pions are the most numerous projectiles.
ACKNOWLEDGMENTS We would like to thank the  CERN EP, BE ,  HSE  and  EN Departments  for the strong support of NA61/SHINE. This work was supported by the  Hungarian Scientific Research Fund  (Grant No. NKFIH 138136/138152 ), the  Polish Ministry of Science and Higher Education  (No. DIR/WK/ 2016/2017/10-1 ,  WUT ID-UB) , the  National Science Centre Poland  (Grants No. 2014/14/E/ST2/00018 , No. 2016/21/D/ ST2/01983 , No. 2017/25/N/ST2/02575 , No. 2018/29/N/ ST2/02595 , No. 2018/30/A/ST2/00226 , No. 2018/31/G/ ST2/03910 , No. 2019/33/B/ST9/03059  and No. 2020/39/ O/ST2/00277 ), the  Norwegian Financial Mechanism   2014-2021  (Grant No. 2019/34/H/ST2/00585 ), the  Polish Minister of Education and Science  (Contract No. 2021/WK/ 10 ), the  Russian Science Foundation  (Grant No. 17-72-20045 ), the  Russian Academy of Science  and the  Russian Foundation for Basic Research  (Grants No.
08-02-00018 , No. 09-02-00664  and No. 12-02-91503-CERN ), the  Russian Foundation for Basic Research (RFBR)  funding within the research Project No. 18-02-40086 , the  Ministry of Science and Higher Education of the Russian Federation , Project "Fundamental properties of elementary particles and cosmology" No. 0723-2020-0041 , the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 871072 , the  Ministry of Education, Culture, Sports, Science and Technology, Japan ,  Grant-in-Aid for Scientific Research  (Grants No. 18071005 , No. 19034011 , No. 19740162 , No. 20740160 , and No. 20039012 ), the  German Research Foundation DFG  (Grants No. GA 1480/8-1  and Project No. 426579465 ), the  Bulgarian Ministry of Education and Science within the National Roadmap for Research Infrastructures   2020-2027 , Contract No.
for π AE , K AE , p AE , Λ, Λ, and FIG. 8. β correction factors for the 158 GeV=c dataset (see Fig. 23 in the Appendix C for p beam ¼ 350 GeV=c). H. ADHIKARY et al. PHYS. REV. D 107, 062004 (2023) 062004-12.
Acknowledgements We acknowledge the GISMO platform for the XRF acquisition and the SOLEIL synchrotron for provision of beamtime and we would like to thank  Sebastian Schöder  ( SOLEIL synchrotron, Paris-Saclay, France ) for assistance at the PUMA beamline. This work was supported by the  ANR  project  AFTER  ( ANR-13-JS06-0001-01 ), the  French  " Investissements d'Avenir" program  (project  ISITE-BFC :  ANR-15-IDEX-03 ), and also received funding from the  programme TelluS  of the  Institut National des Sciences de l'Univers, CNRS. We thank  James F. Jenks  ( West Jordan, Utah, USA ) and  Kevin G. Bylund  ( Spanish Fork, Utah, USA ) for their constant support in our Triassic projects and for their invaluable help in the field. The  Winecup Gamble Ranch (Montello, Nevada, USA)  is also thanked for allowing access to the Montello Canyon site.
Sample management and valorisation benefited from the  ANR  project  RECOLNAT  ( ANR-11-INBS-0004 ). We also acknowledge  Przemysław Gorzelak  ( Insitute of Paleobiology, Polish Academy of Sciences, Warsaw, Poland ) and  Tatsuo Oji  ( University Museum, Nagoya University, Japan ) for their constructive comments on an earlier version of the manuscript.
Examples on DNNs We train DNNs on various datasets to study the interplay between generalization error and the success rate of an attacker that uses the confidence of the target model as a criterion for the attack. We compare the success rate of the attacker to the lower bound provided by Theorem 2, to assess the quality of the bound. The three data sets considered for this experiments are CIFAR10, MNIST and MNIST fashion. In the case of MNIST and MNIST fashion, the training set is picked from a pool of 60k samples. A separate pool of 10k samples is fixed as the test set. For CIFAR10, the pool of training samples is of size 50k, and the pool of test samples is 10k. For a given n, we pick uniform randomly that amount of samples from the training pool to form the training set. The target model in this setup is a Deep Neural Network with 4 convolutional layers and 3 fully connected layers.
We will prospectively collect detailed baseline, diagnostic, treatment, and outcome data from several clinical centers specialized in the treatment of RS. The registry aims to describe different diagnostic techniques and the associated measurements under real-life conditions, as well as the impact of different phenotypic varieties on symptoms and treatment. Furthermore, the systematic collection of longitudinal data on characteristics and outcomes enables the detection of factors affecting outcomes. For example, we are interested in weight gain, method of feeding in affected infants (i. e. , age < 1 year), or inpatient days in the rst year of life. A positive impact of the registry beyond the scienti c work is the strengthening of international cooperation between RS experts and the facilitation of exchanging information on complex cases.
Effectiveness and safety of different treatment approaches Another bene t is that a registry not only makes it possible to compare different treatment approaches but can also be used for research into relevant issues related to the respective therapies. Identifying risk factors and determining rates of complications and adverse events for each treatment approach and the different diagnostic procedures is important to increase the understanding of disease characteristics. In addition to the outlined a priori research questions, it is expected and intended that the availability of registry data will lead to further research questions, unknown at this stage, and provide the opportunity for scienti c evaluation. Since the registry design provides hospitals with a comprehensive database for their own center, researchers will be further motivated to conduct multiple research in this eld.
Design Of The Registry The RS registry is a prospective, observational, noninterventional, multicenter, multinational study. Data are recorded in an online accessible, secure database using Castor Electronic Data Capture System (Castor EDC, Ciwit BV, Amsterdam, The Netherlands) under license from the university medical center of Amsterdam (Amsterdam UMC). This user-friendly database enables data entry from every web-connected device (Fig. 1 ). A user account is required for access, protected by individual passwords and two-factor authentication. Castor applications run on fully managed virtual private servers with providers in the Netherlands. The Castor EDC database complies with the General Data Protection Regulation and is ISO 27001 certi ed (Standards for Information Security Assurance).
Data is encrypted at rest and in transit. The database allows the de nition of individual user rights and data exporting functions, as well as at least partly extracting data directly from clinical data management systems. Data will be entered pseudonymized directly by physicians and academic staff. The principal investigators of the respective study centers will be required to maintain a secure participation list linking the registry ID number with the personal identifying data. The registry ID is a sequential number that is automatically generated by the system and allows identi cation of the reporting center but not the reported patient. The registry will be conducted under routine clinical conditions, according to the treating physicians. Registry participation does not in uence the patients' management or the relationship of the patients with their treating physicians.
No clinical, instrumental, or laboratory assessments will be performed other than those required for disease management according to each site's best practice. Follow-up visits will be scheduled according to the standard practice of each center and the treating physician's best judgment. In addition to clinical data collection, it is planned to assess topics such as quality of life or neurodevelopment (e. g. , developmental milestones) with parental questionnaires. For a long time, de nitions for RS differed between studies and centers. However, at an international consensus meeting in Utrecht in 2014, experts agreed on a valid de nition also forming the basis for inclusion in the registry. Micrognathia, glossoptosis, and airway obstruction are mandatory diagnostic characteristics  [1]. Cleft palate is common in children with RS but not a necessary characteristic of the diagnosis.
Each center regularly can only access its own centerspeci c data. From the other centers, only the number of participating patients is visible. The pooling of center-speci c data only takes place within the framework of a research hypothesis and with the consent of all parties involved. This blinding approach is also intended to minimize possible bias in data entry by preventing comparisons being made only at the time of data entry. The design of the modules will be evaluated regularly. In particular, potential biases in the registry data will be considered. We will reevaluate the modules with regard to generalizability, selection bias, user bias, confounding, and loss to follow-up. Prior to each data analysis, it is up to the principal investigator to check the correctness and plausibility of the data.
However, the general concept, particularly not to collect person-identifying data at any time, remains unaffected. As only anonymized data is available, linking the data with other sources at the registry level is impossible. The central independent data management is located at the study site of the University Hospital in Tübingen and is responsible for implementing and maintaining the database. The study centers must disclose the annual number of cases actually treated and the number of children included in the registry to minimize bias by not including all relevant patients. Due to the low expected annual number of cases, it is reasonable to inform all potential study participants and record refusals to participate or reasons for nonenrolment in writing. The Registry Steering Committee is made up of representatives from the four study centers that launched the registry.
The steering group is open to members from any other actively participating center. In case the number of participating centers exceeds the capacity of the steering committee in the future, centers with a larger number of participants will have priority for a seat on the committee. The steering committee plans to meet virtually at least twice a year. All centers participating in the registry are invited to these committee meetings. Centre-speci c data are permitted to be used for research purposes independent of the other participating centers. Researchers interested in pooling data from the different participating centers to answer a research question are to submit proposals for speci c analysis plans for individual research requests to the Steering Committee for approval before the data are released.
We use data from Italy to study the political and social impact of a refugee reception policy (SPRAR) directly managed by local governments, whose features recall the conditions of the contact theory (Allport,  1954). Instrumental variables estimates indicate that municipalities that opened a refugee center between the 2013 and 2018 national elections experienced a change in the vote shares of extreme-right parties that is approximately 7 percentage points lower compared to municipalities that did not open a refugee center. We document that the positive impact of SPRARs on ''compositional amenities'' (i. e. , local schools) and population growth allows explaining the negative impact on anti-immigrant prejudice. Finally, we provide evidence of spillovers in prejudice reduction in neighboring municipalities without a SPRAR.
We implement the analysis using data from Italy and studying a program to relocate refugees and asylum seekers across Italian municipalities called ''The Protection System for Asylum Seekers and Refugees'' (SPRAR). 2 Although it is not the only system in place, SPRAR is one of the most important refugee reception programs in Italy. 3  We focus on refugee centers 4 related to this relocation policy for two reasons. The first is data availability. We develop the analysis exploiting a new and unique database (see Gamalerio and Negri, 2022; Gamalerio  et al. , 2021 for more information), which contains precise information on the location of these refugee centers. The second and most important reason is the type of contact that SPRARs can potentially produce between natives and migrants. SPRAR centers have the peculiar characteristic of being opened and managed directly by municipal governments.
Second, we document a positive effect of SPRARs on compositional amenities. Precisely, following both the existing literature (Halla et al. ,  2017) and the anecdotal evidence, we look at schools, which represent a local public good for which municipalities share responsibilities with other levels of government (and for which data are available). We collect data on the number of native and international students, classes, and schools at the municipal level. The IV estimates reveal a positive impact of SPRARs on all these outcomes. We also provide suggestive evidence that shows how the positive effect of SPRARs on population and compositional amenities can help to explain the negative effect of refugee centers on the vote shares of anti-immigrant parties. Lastly, we explore the presence of potential spillover effects.
e. , Centers for extraordinary reception). The provincial offices (''Prefetture'') of the Home Office manage CAS centers, and municipalities do not have any decisional powers. Eventually, SPRAR and CAS centers have become the two main reception centers used in Italy in the last few years. Specifically, CAS centers have provided roughly 75%-80% of all places available in reception centers. SPRAR centers approximately 15%-20%. 6  Our analysis is focused on SPRAR centers primarily for two reasons. The first is data-related, as we can exploit a detailed dataset on SPRARs' location and characteristics (See Section 4). The second is because SPRARs can potentially produce interactions between natives and immigrants that fulfill the conditions of the contact theory (Allport,  1954). According to this theory, direct contact between natives and immigrants can reduce prejudice and anti-immigrant attitudes under the following four conditions: equal status between the two groups, common goals, inter-group cooperation, and authority support.
Data In our analysis, we use data coming from different sources. The first important dataset concerns the presence and characteristics of SPRARs. These pieces of information come from a detailed and rare dataset, recently used in the same Italian context (Gamalerio and Negri,  2022; Gamalerio et al. , 2021). Precisely, we have information on the municipalities that bid for a SPRAR center, the winning municipalities (i. e. , those that open the centers), the number of places available, and the fiscal grants received. This dataset covers the period of our analysis (i. e. ,2014-2018) and also reports the few centers opened before 2014. The sources are the Italian Home Office, the official web page of the SPRAR program, and the ''Briguglio archive'', which reports different documents on migration.
The data source for the location of first-level reception centers (CPSA, CDA, CARA) is the Italian Home Office. Data on the presence of CASs at the municipal level comes from the Openpolis Foundation. Source: Gamalerio and Negri (2022) and SPRAR report ''Atlante Sprar'', published on the SPRAR/SAI webpage (https://www. retesai. it/). The graph reports the number of places made available and the number of refugees and asylum seekers hosted every year from 2006 up to 2018. More specifically, the Openpolis Foundation has collected data on the presence of CASs in cooperation with ActionAid Italia and has made this data available on its webpage. As described in the webpage, over the years, Openpolis has collected information on the geographical location of CAS centers merging information from different sources, such as the National database of public contracts (Banca dati nazionale dei contratti pubblici) of the National Anti-Corruption Authority (ANAC), and the web pages of the Italian Home Office's provincial offices.
However, as reported by Openpolis, given the difficulty in obtaining data from the Italian Home Office's provincial offices, it is unclear whether this dataset on CAS centers contains complete information on all these types of centers opened at the municipal level. We use this data to identify municipalities with a first-level reception center or a CAS during the 2014-2018 period and to show that these reception centers do not affect nor drive the effect of SPRAR on electoral outcomes. A caveat on the data on CAS used in the analysis is that Openpolis provides information on the location of CAS at the end of 2018, after the political elections held in the March of 2018. Hence, the analysis below may erroneously codify some municipalities that opened a CAS after the 2018 elections as already having a CAS before the elections.
At the same time, we may not codify as having a CAS during the 2014-2018 period some municipalities that closed the reception centers before the end of 2018. However, as described by Campo et al. (2021), most of the CAS centers were opened between 2014 and 2018, with the maximum number of municipalities hosting CAS reached in 2017. Hence, even though we may capture the presence of CAS centers with some measurement error, we are confident that the Openpolis dataset allows us to identify most of the cases of municipalities with a CAS in the 2014-2018 period. 12  To construct the dependent variables, we download from the Italian Home Office website the electoral outcomes at the municipal level for the 2008, 2013, and 2018 national elections. We use these data to calculate the change between elections in the vote shares of far-right parties, 13 parties in the center of the Italian political spectrum, 14 the Five Stars Movement, 15 far-left parties,  16  and to calculate the change in the electoral turnout.
e. , the average distance in years for the most recent municipal election) for the mayors of all municipalities in the dataset. To deal with the possible presence of different mayors across municipalities over the period studied, we calculate the average of all these variables in 2014-2018. Since these characteristics might be affected by the treatment, in a robustness check presented in Section 5. 4 we run the same analysis using the average mayors' characteristics for the years 1998-2012. Lastly, we introduce macro area fixed effects (𝜙 𝑚 ) to control for the difference in the electoral performance of far-right parties in the different macro-areas of Italy, as documented in Fig. 3. 20  In addition, as described in Section 4, our dataset includes data from the Autonomous Province of Bolzano/South Tyrol.
Also in this respect, the magnitude of our results are comparable to the one found by Steinmayr (2021), who observes a reduction in the vote shares for far-right parties of around 4 percentage points, which is 30 percent of an average change equal to approximately 14 percentage points. Hence, we estimate an effect with a similar magnitude compared to the average growth rate in far-right vote shares. Table  3  indicates the presence of a positive bias in the OLS estimates, which can be due to a twofold explanation. First, even though the dataset on SPRARs is detailed, we cannot exclude measurement errors. As explained by Gamalerio et al. (2021), in some cases, municipalities open SPRAR centers together through municipalities' unions. These unions are local organizations introduced by groups of municipalities that aim to produce public goods together.