dataesr/scientific-paragraphs-categorization huggingface dataset:
Significance Will different researchers converge on similar findings when analyzing the same data? Seventythree independent research teams used identical crosscountry survey data to test a prominent social science hypothesis: that more immigration will reduce public support for government provision of social policies. Instead of convergence, teams' results varied greatly, ranging from large negative to large positive effects of immigration on social policy support. The choices made by the research teams in designing their statistical tests explain very little of this variation; a hidden universe of uncertainty remains. Considering this variation, scientists, especially those working with the complexities of human societies and behavior, should exercise humility and strive to better account for the uncertainty in their work.
Each team's code was checked and then anonymized for public sharing by the PIs. Some teams failed to report a standardized estimate. Also, different scaling of the two independent immigration variables meant that results were not always distributionally comparable. Therefore, we standardized the teams' results for each coefficient for stock and flow of immigration post hoc. We also transformed the teams' results into average marginal effects (AMEs), which are the standardized average effects of a one-unit change in the respective independent (immigration) variable on the respective dependent (policy support) variable, where this average is based on predictions for each observation in the dataset. The advantage of using AMEs is that they allow for a single marginal estimate in the presence of nonlinearities and present predicted probabilities that reflect the reality of the data sample rather than the mean of each independent variable (Fig.
Next, we examined all 1,261 models and identified 166 distinct research design decisions associated with those models. "Decision" refers to any aspect in the design of a statistical model: for example, the measurement strategy, estimator, hierarchical structure, choice of independent variables, and potential subsetting of the data (SI Appendix, Table  S12 ). For simplicity, decision also refers to variables measuring team characteristics, such as software used, overall familiarity with the subject or methods, and preexisting beliefs as measured in our participant survey (SI Appendix, Table  S1 ). Of the 166 decisions, 107 were taken by at least three teams. We used these 107 as variables that might statistically explain the variation in the results and conclusions because the other 59 were unique to one or two teams and would thus impede statistical identification.
Fourth, our study suggests that if we freed scientists from bias caused by the "perverse incentives" inherent in the institutions of science, their own preexisting biases or beliefs might not matter as much to the outcomes they generate as some may fear. In fairness, the teams indicated what models they intended to run in advance. Although they were free to update and change these at any time, we can assume that this may have reduced potential confirmation bias. Limitations and Outlook. Our study has limitations that warrant discussion. First, we do not know the generalizability of our study to different topics, disciplines, or even datasets. A major segment of social science works with survey data, and our results reflect this type of research. In experimental research, the data-generating model is often clearer or involves fewer decisions.
We, therefore, encourage readers to scrutinize our analytical process by taking advantage of SI Appendix, the reproduction files, and the web-based interactive app that allow for easy exploration of all data underlying this study. ¶ Data, Materials, and Software Availability. Data and code have been deposited in GitHub (https://github. com/nbreznau/CRI) (33) , and Harvard Dataverse (https://doi. org/10. 7910/DVN/UUP8CX) (34). ACKNOWLEDGMENTS. We thank the Mannheim Centre for European Social Research at the University of Mannheim  (Mannheim, Germany)  for providing extra institutional support for this large-scale project and Joachim Gassen at the Humboldt University (Berlin, Germany) for inspiration and feedback for our interactive app. Additionally helpful were comments provided at the Stanford Meta Research Innovation Center at Stanford (METRICS) International Forum, the Ludwig Maximilian University of Munich Sociology Research Colloquium, and the Technical University Chemnitz "Open Science" Conference and in personal communications with Steve Velsko.
† It is available at https://github. com/nbreznau/CRI. ‡ Information is available at https://issp. org/about-issp/. 2 of 8 https://doi. org/10. 1073/pnas. 2203150119 pnas. org Downloaded from https://www. pnas. org by Universiteit van Tilburg on November 22, 2022 from IP address 137. 56. 178. 169.
¶ Information is available at https://nate-breznau. shinyapps. io/shiny/ and https://github. com/nbreznau/CRI. 6 of 8 https://doi. org/10. 1073/pnas. 2203150119 pnas. org Downloaded from https://www. pnas. org by Universiteit van Tilburg on November 22, 2022 from IP address 137. 56. 178. 169.
Implementation In this section, we describe our implementation for formal reasoning about dynamic networks. Our prototype tool, called DyNetiKAT (available at https: //github. com/hcantunc/DyNetiKAT) is based on Maude  [8] , the NetKAT decision procedure  [10] , and Python  [19]  as a glue language. Our modular extension of NetKAT allows for reusing the NetKAT tools in our framework. In our prototype, we focus on checking reachability and waypointing in a dynamic setting. We build upon the methods for checking reachability and waypointing properties in NetKAT  [3]. For a reminder, in NetKAT, reachability and waypointing properties are characterised as follows: for reachability properties, an egress point out is reachable from an ingress point in, in the context of a switch policy p and topology t, whenever the following NetKAT equivalence holds: in•(p•t) * •out ̸ ≡ 0.
Acknowledgements We would like to thank the anonymous reviewers for their insightful feedback. This work was supported in part by  ONR  Grant  N00014-17-1-2787 , by  NSF  awards  CCF-2106845 ,  CCF-2131476 , by  BSF  grant  2016239  and by  ISF  Grant  2507/21. Acknowledgements. We thank the anonymous reviewers for their helpful comments. Acknowledgments We would like to thank  Simon Jantsch  and  Clemens Dubslaff  for their helpful comments and feedback on the topic of causality in MDPs.
Acknowledgements We thank  Nathalie Bertrand  and  Javier Esparza  for many helpful discussions. Acknowledgments. We thank the anonymous referees for their comments, and in particular for suggesting a more intuitive definition of bi-separator. Acknowledgements. The authors would like to thank the various Twitter and Zulip users who contributed to the genesis and development of the theory contained in this paper, notably  Jules Hedges ,  Cole Comfort  and  Reid Barton. Reid Barton  in particular contributed significantly to the proof of completeness. The first author is funded by the  EPSRC  under grant  OUCS/GB/1034913. The second author acknowledges support from  EPSRC  grant  EP/V002376/1.
Acknowledgments We thank  Guillermo A. Pérez  for discussing history-determinism of discountedsum and limit-average automata. Acknowledgements. We thank  Moshe Vardi  and  Orna Kupferman  for suggesting studying the succinctness gap between semantic and syntactic safe formulas, and  Miko laj Bojańczyk  for answering our questions on algebraic automata theory. Acknowledgments. We are grateful to our reviewers for drawing our attention to the translation of threshold counting into standard PA. This work is part of a project that has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (Grant agreement No. 852769 , ARiAT). Acknowledgments. R. Fervari  is supported by  CONICET  project  PIP 11220200100812CO , and by the  LIA SINFIN.
Acknowledgements The author wishes to thank  Bartosz Bednarczyk  for several helpful discussions on interpolation and fragments of first-order logic, and for suggesting the problem Acknowledgements We thank  Niels Voorneveld  for many useful discussions. Richard Garner 's work is an endless source of inspiration. D. M. and T. U. were supported by the  Icelandic Research Fund  project grant no. 196323-053 , T. U. also by the  Estonian Research Council  team grant no. PRG1210. E. R. was supported by the  Estonian Research Council  personal grant no. PSG659. Acknowledgement. The authors thank  Christof Löding  for his pointer to stairparity VPA, and the anonymous reviewers for their constructive feedback.
⋆ This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ERC DuaLL, grant agreement No. 670624), and by the DeLTA ANR project (ANR-16-CE40-0007). c The Author(s) 2022 P. Bouyer and L. Schröder (Eds. ): FoSSaCS 2022, LNCS 13242, pp. 264-284, 2022. https://doi. org/10. 1007/978-3-030-99253-8_14.
Capacitated Vehicle routing problem is NP-hard scheduling problem in which the main concern is to find the best routes with minimum cost for a number of vehicles serving a number of scattered customers under some vehicle capacity constraint. Due to the complex nature of the capacitated vehicle routing problem, metaheuristic optimization algorithms are widely used for tackling this type of challenge. Coronavirus Herd Immunity Optimizer (CHIO) is a recent metaheuristic population-based algorithm that mimics the COVID-19 herd immunity treatment strategy. In this paper, CHIO is modified for capacitated vehicle routing problem. The modifications for CHIO are accomplished by modifying its operators to preserve the solution feasibility for this type of vehicle routing problems. To evaluate the modified CHIO, two sets of data sets are used: the first data set has ten Synthetic CVRP models while the second is an ABEFMP data set which has 27 instances with different models.
2. The feasibility of CVRP is maintained by using repair process during the optimization loop of the modified CHIO. Two sets of dataset are used to evaluate the performance of the modified CHIO: the simple dataset which contains ten models and the complex dataset which contains 27 models. The benchmarks of CVRP used are varied in terms of complexity and problem dimensions. The evaluation results are demonstrated in terms of fitness function values (i. e. , best, worst, and mean costs). As an evaluation step, the performance of the modified CHIO method is also compared with other thirteen metaheuristic algorithms. Interestingly, for the first dataset, the proposed CHIO is able to obtain the same best results as other comparative methods in two out of ten instances and acceptable results for the remaining instances.
For the second and the more complicated dataset, the proposed CHIO is able to achieve very competitive results and ranked the first for 8 instances out of 27. The remaining parts of this paper aare organized as follows: CVRP is defined and discussed in Section 2. The utilization of the modified steps for CHIO optimizer when used for CVRP is presented in Section 3. The VRP dataset descriptions and the results discussions of CHIO against other comparative methods are provided in Section 4. For the last Section 5, we conclude the findings and denote some future works.
Experiments and results In this section, the proposed CHIO algorithm is evaluated using two benchmark data sets. The characteristics of these data sets are provided in Section 4. 1. The experimental scenarios that are designed to fine tune the best parameter settings of CHIO algorithm for solving CVRP are illustrated in Section 4. 2. Finally, the performance of the CHIO algorithm against the other comparative methods is given in Sections 4. 3. 2 and 4. 3. 3. Notably, all comparative methods that are utilized to solve the first data set and the proposed CHIO on the two data sets are programmed using MATLAB R2014b. All experiments have been run on a PC with a core i7 CPU, 8 GB of RAM, and Windows 10 as the operating system.
Synthetic data set This data set includes ten synthetic models of the CVRP. These models are represented as a tuple of two values: number of customers, and number of vehicles. The initiated models are model  8-3, 10-3, 14-4, 20-4, 25-5, 30-5, 40-6, 50-7, 60-7 and 70-8. The data extracted for each CVRP model parameters includes number of customer, number of vehicles, demands boundaries, and x & y boundaries as listed in Table  6. Using the extracted model parameters, the customer demands, customer and depot positions, vehicles capacities, and the network distances will be computed as follows: The customer demands are random integers set to be between the demands boundaries, and stored in r array of 1 Â J size. The positions (x; y) of the customers and the depots are initialized randomly between the value of x 2 ½x min; x max and the value of y 2 ½x min; x max.
ABEFMP dataset For further evaluation, the Set_A instances of the ABEFMP data set are used to evaluate the efficiency of the CHIO on solving CVRP. It should be noted that, Augerate et al. (Uchoa et al. , 2017)  proposed three different classes of capacitated vehicle routing problem which are Set_A, Set_B and Set_P. These classes were also extended to other three more classes; Set_E, Set_F and Set_M to be known as ABEFMP data set. The Set_A includes 27 instances of CVRP given in the following URL. 2 These instances are titled as the substring (A-n) concatenated with number of customers, followed by the substring (-k) concatenated with number of vehicles. The main parts of these instances are the number of customers, the number of vehicles, the demands requested by each customer, constant vehicles capacity (all were set to 100 for all models and all vehicles), the customers coordinates (x & y), and lastly, the depot coordinates (x & y).
Comparison with other algorithms on ABEFMP dataset For more validation, the performance of the proposed CHIO is compared with the other comparative algorithms from the literature using well-known data set in the field of CVRP. The Set_A instances of ABEFMP data set are utilized, while the characteristics of these instances are given in Section 4. 1. It should be noted that the proposed CHIO is run to solve this data set using the best parameter settings obtained in Section 4. 3. 1. The comparative algorithms are genetic algorithm (GA)  (Sbai et al. , 2020) , hybridization of GA with variable neighborhood search (HGA)  (Sbai et al. , 2020) , Adaptive artificial bee colony (AABC)  (Mingprasert and Masuchun, 2017) , simulated annealing (SA)  (Wedyan and Narayanan, 2014) , intelligent water drops algorithm (IWD)  (Wedyan and Narayanan, 2014) , firefly algorithm (FFA)  (Yesodha and Amudha, 2019) , enhanced FFA (EFFA)  (Yesodha and Amudha, 2019) , ant colony optimization (ACO)  (Nategh et al.
In order to measure the performance of the modified CHIO, two CVRP datasets are used: the first dataset has ten Synthetic CVRP models while the second is ABEFMP dataset which has 27 instances with different models. Initially, the sensitivity of modified CHIO to its algorithmic and control parameters (i. e. , PopSize; Max age; BR r , and Max Itr) is studied. This is to show their impact on the modified CHIO performance when CVRP is tackled and also to determine the suitable parameter for modified CHIO-CVRP algorithm. As a result, the selected values of the PopSize; BR r , and Max Itr parameters, and the reasonable value of the Max age lead to the best configuration for modified CHIO tackling the CVRP. The parameter study is accomplished using the first dataset, and consequently, the found optimal parameters are used to find the results for the second more challenging dataset.
The two datasets (Synthetic CVRP and ABEFMP) are used for comparative evaluation. For the first dataset, the proposed CHIO is able to obtain the same best results as other comparative methods in two out of ten instances in addition to acceptable results for the remaining instances. For the second and the more complicated datasets, the proposed CHIO is able to achieve very competitive results and ranked the first for 8 instances out of 27. In conclusion, the modified CHIO is an efficient algorithm for tackling the CVRP. Since CHIO reveals a very successful results for CVRP, other possible future improvement can by accomplished by hybridizing modified CHIO with efficient local search algorithms. Other approaches can be tried for problem formulation. This includes trying different constraint handling mechanisms such as indirect techniques that convert the constrained optimization problem to free optimization problem by means of injecting the constraints as a part of objective function.
paper, we show how the IRT methodology can be combined with the mixed model theory to provide a longitudinal IRT model which exploits the information of a measurement scale provided at the item level while simultaneously handling observation times that may vary across individuals and items. The latent construct is a latent process defined in continuous time that is linked to the observed item responses through a measurement model at each individual-and occasion-specific observation time; we focus here on a Graded Response Model for binary and ordinal items. The Maximum Likelihood Estimation procedure of the model is available in the R package lcmm. The proposed approach is contextualized in a clinical example in end-stage renal disease, the PREDIALA study. The objective is to study the trajectories of depressive symptomatology (as measured by 7 items of the Hospital Anxiety and Depression scale) according to the time fom registration on the renal transplant waiting list and the renal replacement therapy.
In addition to not aligning with the continuous-time nature of health phenomena, it prevents their use in some applications, such as in our study, where measurement times largely vary across subjects. Moreover, neglecting the different lengths of time intervals in longitudinal IRT models may provide biased estimates  [28]. The issue of continuous time in longitudinal psychological studies had already been identified in the literature  [29]  and dealt with by considering continuous-time dynamic systems (with differential equations) to capture the intra-individual serial correlation  [28]  and implemented in ctsem R package  [30]. With the use of a structural mixed model rather than a structural dynamic model, we offer with lcmm R package an alternative approach to flexibly model latent traits in continuous time.
We note that, although commercial Mplus software  [31]  was initially dedicated to discrete-time analyses, it now also offers the posibility to consider time as continuous when defining simple shapes of trajectory (e. g. , linear or quadratic), or time as finely discretized with autoregressive structures  [32]. The definition of the underlying construct as a latent process evolving in continuous time enables the inclusion of items whatever their measurement timing. These timings can differ from one subject to the other (as shown in the application) and they can differ from one item to the other. We recommend however that the span of measurement times is not too dissimilar, at least for a subset of the items, to ensure that the latent process can be correctly anchored during the entire study time window.
Acknowledgements The authors gratefully thank the co-investigators of the study:  Magali Giral ,  Aurélie Meurette ,  Emmanuel Morelon , and  Laetitia Albano. The authors express sincere thanks to  Elodie Faurel-Paul  and  Astrid Fleury  for their assistance for the study, as well as all the participants for their contribution to the study. The authors wish to thank members of the clinical research assistant team and  DI-VAT Consortium Collaborators (Medical Doctors, Surgeons, HLA Biologists ). Nantes:  Gilles Blancho ,  Julien Branchereau ,  Diego Cantarovich ,  Agnès Chapelet ,  Jacques Dantal ,  Clément Deltombe ,  Lucile Figueres ,  Claire Garandeau ,  Magali Giral ,  Caroline Gourraud-Vercel ,  Maryvonne Hourmant ,  Georges Karam ,  Clarisse Kerleau ,  Aurélie Meurette ,  Simon Ville ,  Christine Kandell ,  Anne Moreau ,  Karine Renaudin ,  Anne Cesbron ,  Florent Delbos ,  Alexandre Walencik ,  Anne Devis ;  Lyon E.
Funding This work was funded by the  French National Research Agency  (Project  DyMES  - ANR-18-C36-0004-01 ), the  French Ministry of Health  ( PHRC-13-0224 , 2013) and by the " Appel d offres interne analyse secondaire 2018 " grant from  Nantes University Hospital. Ethical approval The PREDIALA study is part of the PreKit-QoL study which is registered on the ClinicalTrials. govRegistry ( RC14 0078 ,  NCT02154815 ). It has obtained approval from  the ethical Committee for Persons' Protection (CPP, Tours ,  2014-S8 ), and from the advisory committee on research data and information in health (CCTIRS, Paris,  14. 314 ).
Cultural heritage is the legacy of physical artifacts and intangible attributes of a group or society that a re inherited from past generations, maintained in the present and bestowed for the benefit of future generations. What role does software play in it? We claim that software source code is an important product of human creativity, and embodies a growing part of our scientific, organisational and technological knowledge: it is a part of our cultural heritage, and it is our collective responsibility to ensure that it is not lost. Preserving the history of software is also a key enabler for reproducibility of research, and as a means to foster better and more secure software for society. This is the mission of Software Heritage, a nonprofit organization dedicated to building the universal archive of software source code, catering to the needs of science, industry and culture, for the benefit of society as a whole.
A Complex Task Archiving all available source code is a complex task: as detailed in the article  [1] , different strategies are needed depending on whether one seeks to collect open or proprietary source code, and one does not treat source code that is readily available online in the same way as source code that resides on older physical media. For open source code that is readily available online, the most appropriate approach is to build a harvester that automatically collects content from a wide variety of collaborative development platforms, such as GitHub, GitLab. com, or BitBucket, or from software package distribution platforms, such as Debian, NPM. CRAN or Pypi. For the source code of old software, a real process of computer archaeology must be set up, and we have already started this work in a collaboration with the University of Pisa and UNESCO that has resulted in the SWHAP process that has been used to find, document and archive software that is important in the history of computing in Italy (see https://www.
A Universal Mission The founding principles of Software Heritage are  [1] : systematic use of open source software to build the Software Heritage infrastructure, so that its operation can be understood, and replicated if necessary; construction of a global network of independent mirrors of the archive, because a large number of copies is the best protection against loss and attack; choice of a non-profit, international, multi-stakeholder structure, to minimize the risk of having single points of failure, and to ensure that Software Heritage will indeed serve all. For such a mission, institutional legitimacy is required, as well as openness to federate a broad consensus. This is why the framework agreement signed between Inria and UNESCO on April 3, 2017, and renewed in November 2021, is essential.