dataesr/scientific-paragraphs-categorization huggingface dataset:
MAPK Bio-Model 26 The model of the MAPK cascade we are investigating can be found in the BioModels Database  (Li et al. , 2010)  as Model 26 1. This is the first version of the models proposed by  Markevich et al. (2004)  corresponding to the socalled distributive ordered phosphorylation/dephosphorylation mechanism. Hereafter we will refer to it as Model 26. It is given by the following set of differential equations. We have renamed the species names to x 1 ,. , x 11 and the rate constants to k 1 ,. , k 16 to facilitate reading. As usual ẋ means the time derivative of x. ẋ1 = k 2 x 6 + k 15 x 11 -k 1 x 1 x 4 -k 16 x 1 x 5 ẋ2 = k 3 x 6 + k 5 x 7 + k 10 x 9 + k 13 x 10 -x 2 x 5 (k 11 + k 12 ) -k 4 x 2 x 4 ẋ3 = k 6 x 7 + k 8 x 8 -k 7 x 3 x 5 ẋ4 = x 6 (k 2 + k 3 ) + x 7 (k 5 + k 6 ) -k 1 x 1 x 4 -k 4 x 2 x 4 ẋ5 = k 8 x 8 + k 10 x 9 + k 13 x 10 + k 15 x 11 - x 2 x 5 (k 11 + k 12 ) -k 7 x 3 x 5 -k 16 x 1 x 5 ẋ6 = k 1 x 1 x 4 -x 6 (k 2 + k 3 ) ẋ7 = k 4 x 2 x 4 -x 7 (k 5 + k 6 ) ẋ8 = k 7 x 3 x 5 -x 8 (k 8 + k 9 ) ẋ9 = k 9 x 8 -k 10 x 9 + k 11 x 2 x 5 ẋ 10 = k 12 x 2 x 5 -x 10 (k 13 + k 14 ) ẋ 11 = k 14 x 10 -k 15 x 11 + k 16 x 1 x 5.
(1) Later, we will use (1) to refer to (1) with all the left hand sides replaced by 0 in order to find fixed points of the system. The BioModels Database gives us meaningful values for the rate constants: k 1 = 0. 02, k 2 = 1, k 3 = 0. 01, k 4 = 0. 032, k 5 = 1, k 6 = 15, k 7 = 0. 045, k 8 = 1, k 9 = 0. 092, k 10 = 1, k 11 = 0. 01, k 12 = 0. 01, k 13 = 1, k 14 = 0. 5, k 15 = 0. 086, k 16 = 0. 0011. (2) Some of these values are accurately measured and some are well-educated guesses. For the purpose of our study we assume they are all suitable. We may add three linear conservation constraints to this system, which in turn introduce three further constant parameters k 17 , k 18 , k 19 : x 5 + x 8 + x 9 + x 10 + x 11 = k 17 x 4 + x 6 + x 7 = k 18 x 1 + x 2 + x 3 + x 6 + x 7 + x 8 + x 9 + x 10 + x 11 = k 19.
Combing with other symbolic tools We are able to make progress by combining CAD with additional symbolic methods. Two independent investigations were undertaken. The first, described in Section 3, uses the Redlog package in Reduce and combines CAD with virtual substitution. The second, described in Section 4, uses the Regular Chains Library in Maple and combines CAD with real triangularization. In both cases we have combined the corresponding methods by hand, but automation is clearly possible.
Using Real Quantifier Elimination Technology in Redlog In this section we are going to combine Virtual Substitution (VS) with CAD. The former smoothly eliminates the majority of the quantifiers while the latter allows us to count numbers of solutions via decomposition of the remaining low-dimensional spaces. That combination of methods requires the solution of several QE runs with each problem and some combinatorial arguments. Throughout this section we are performing computations using the Redlog Package  (Dolzmann and Sturm, 1997a)  for Reduce revision r3606. Timings are reported for a 2. 4 GHz Intel Core i7 with 3 GB RAM or cores on a compute server with similar speed and memory limitations.
The output of LRT also requires that x 1 be both positive and satisfy: f (x 1 , k 19 ) = 6 i=0 d i x i 1 = 0 (19) where the coefficients d i are univariate polynomials in k 19 of maximum degree 2 as given in Appendix B. Hence there are at most six solutions for x 1 , with the exact number depending on whether solutions of (  19 ) are real and positive. There are four constraints on free parameter k 19 as given below, one of which is the non-vanishing of the polynomial in Appendix Appendix A whose root defined the break point found by Redlog in Section 3. 3. Note that the coefficients break over lines within the final constraint. Software Remark 1. In the authors' ISSAC 2017 paper  (Bradford et al. , 2017)  the description of the evaluated solution component ended here. However, following the publication of that paper a bug was uncovered by one of the authors in the simplifier of the Regular Chains Library when working with a different MAPK model to the one considered presently.
Recall that minimum vertex cover is one of 21 classical NP-complete problems described by  Karp (1972). However, our instances considered here and instances to be expected from other biological models are so small that the use of existing approximation algorithms  (Grandoni et al. , 2008)  appears unnecessary. We have used real quantifier elimination, which did not consume measurable CPU time; alternatively one could use integer linear programming or SAT-solving. It is a most remarkable fact that a significant number of biological models in the databases have that property of loosely connected variables. This phenomenon resembles the well-known community structure of propositional satisfiability problems, which has been identified as one of the key structural reasons for the impressive success of state-of-the-art CDCL-based SAT solvers by  Girvan and Newman (2002).
The idea has been much discussed by  McCallum (1993) ;  Strzeboński (2000) ;  Wilson et al. (2014) , and other names used for it include generic CAD and 1-layered Sub-CAD. It was partly applied by the approach in Redlog in Section 3. It is sufficient to solve problems which are only in strict inequalities, but of course, that is not the case here. By making this restriction we are accepting that our solutions and conclusions are not necessarily valid on cell boundaries: a finite number of curve segments in the (k 17 , k 19 )-plane. However, we have already made such an acceptance, in the use of LRT above. We perform the above steps with the ProjectionCAD package of  England et al. (2014)  in Maple 7  in 17 seconds. The resulting CAD has 533 cells. Step 3: Identify those cells in the upper quadrant of the (k 17 , k 19 )-plane.
MAPK Bio-Model 28 The system with number 28 in the BioModels Database is given by the following set of differential equations. This model is the distributive fully random kinetics version of the models proposed by  Markevich et al. (2004). Hereafter we refer to it as Model 28. Again, we have renamed the species to x 1 ,. , x 16 and the rate constants to k 1 ,. , k 27 to facilitate reading: ẋ1 = k 2 x 9 + k 8 x 10 + k 21 x 15 + k 26 x 16 -k 1 x 1 x 5 -k 7 x 1 x 5 -k 22 x 1 x 6 -k 27 x 1 x 6 ẋ2 = k 3 x 9 + k 5 x 7 + k 24 x 12 -k 4 x 2 x 5 -k 23 x 2 x 6 ẋ3 = k 9 x 10 + k 11 x 8 + k 16 x 13 + k 19 x 14 -k 10 x 3 x 5 -k 17 x 3 x 6 -k 18 x 3 x 6 ẋ4 = k 6 x 7 + k 12 x 8 + k 14 x 11 -k 13 x 4 x 6 ẋ5 = k 2 x 9 + k 3 x 9 + k 5 x 7 + k 6 x 7 + k 8 x 10 + k 9 x 10 + k 11 x 8 + k 12 x 8 - k 1 x 1 x 5 -k 4 x 2 x 5 -k 7 x 1 x 5 -k 10 x 3 x 5 ẋ6 = k 14 x 11 + k 16 x 13 + k 19 x 14 + k 21 x 15 + k 24 x 12 + k 26 x 16 - k 13 x 4 x 6 -k 17 x 3 x 6 -k 18 x 3 x 6 -k 22 x 1 x 6 -k 23 x 2 x 6 -k 27 x 1 x 6 ẋ7 = k 4 x 2 x 5 -k 6 x 7 -k 5 x 7 ẋ8 = k 10 x 3 x 5 -k 12 x 8 -k 11 x 8 ẋ9 = k 1 x 1 x 5 -k 3 x 9 -k 2 x 9 ẋ10 = k 7 x 1 x 5 -k 9 x 10 -k 8 x 10 ẋ11 = k 13 x 4 x 6 -k 15 x 11 -k 14 x 11 ẋ12 = k 23 x 2 x 6 -k 25 x 12 -k 24 x 12 ẋ13 = k 15 x 11 -k 16 x 13 + k 17 x 3 x 6 ẋ14 = k 18 x 3 x 6 -k 20 x 14 -k 19 x 14 ẋ15 = k 20 x 14 -k 21 x 15 + k 22 x 1 x 6 ẋ16 = k 25 x 12 -k 26 x 16 + k 27 x 1 x 6 (26) We denote by (26) the system formed by replacing all left hand sides of (  26 ) by 0.
The estimates of the rate constants given in the BioModels Database are: k 1 = 0. 005, k 2 = 1, k 3 = 1. 08, k 4 = 0. 025, k 5 = 1, k 6 = 0. 007, k 7 = 0. 05, k 8 = 1, k 9 = 0. 008, k 10 = 0. 005, k 11 = 1, k 12 = 0. 45, k 13 = 0. 045, k 14 = 1, k 15 = 0. 092, k 16 = 1, k 17 = 0. 01, k 18 = 0. 01, k 19 = 1, k 20 = 0. 5, k 21 = 0. 086, k 22 = 0. 0011, k 23 = 0. 01, k 24 = 1, k 25 = 0. 47, k 26 = 0. 14, k 27 = 0. 0018. (27) Again, using the left-null space of the stoichiometric matrix under positive conditions as a conservation constraint  (Famili and Palsson, 2003)  we obtain the following three linear conservation constraints: x 6 + x 11 + x 12 + x 13 + x 14 + x 15 + x 16 = k 28 , x 5 + x 7 + x 8 + x 9 + x 10 = k 29 , x 1 + x 2 + x 3 + x 4 + x 7 + x 8 + x 9 + x 10 + x 11 + x 12 + x 13 + x 14 + x 15 + x 16 = k 30 , where k 28 , k 29 , k 30 are new constants.
We will compare this symbolic grid sampling with a fully numerical gird sampling approach using the homotopy solver Bertini developed by  Bates et al. (2013) , in its standard configuration to compute complex roots. Alternatives to Bertini include PHCpack by  Verschelde (2011)  and the Numerical Algebraic Geometry package for Macaulay2 by  Leykin (2011). Reasons for choosing Bertini include that it is the most cited homotopy solver for the past 8 years and that it allows adaptive and very high-precision arithmetic (whereas PHCpack only allows double-double) 9. We parsed the output of Bertini using Python, and determined numerically which of the complex roots are real and positive using a threshold of 10 -6 for positivity. Bertini computations (v1. 5. 1) were carried out on a Linux 64 bit Desktop PC with Intel i7.
Maple computations (v2016 with April 2017 Regular Chains) were carried out on a Windows 7 64 bit Desktop PC with Intel i5. Software Remark 2. For the reduced system of Model 28 Bertini (incorrectly) could not find any roots, not even complex ones, for any of the parameter settings. The situation did not change when going from adaptive precision to a very high fixed precision. However, we have not attempted more sophisticated techniques like providing user homotopies. It seems a bug in Bertini has been triggered by this problem instance. It has been reported to the developers.
Effect of Other Parameters Our work has focussed on understanding the behaviour of the system in the 3-parameter space (k 17 , k 18 , k 19 ) but as described in Section 2 there are many other parameters for which we simply took the values from the BioModels Database. While there is confidence in the accuracy of these values, an important question for future work is the stability of the approaches we present to small perturbations in these values.
Final Thoughts We hope this work will inspire further study on the application of symbolic tools to biological network analysis, from both communities. Indeed, work on developing Mathematica tools for such problems has now been undertaken by  Lichtblau (2017) , inspired by  Bradford et al. (2017)  but based on tools for discriminant varieties not considered there. The study of such real world problems is of great benefit not only to the application domains but also to the software developers: these MAPK studies uncovered bugs in both Regular Chains (see Software Remark 1 in Section 4. 1) and Bertini (see Software Remark 2 in Section 9. 1) which had escaped the numerous other tests and applications of those algorithms. Key areas of future study include the sensitivity of the analysis to variations in the other parameters (Section 10.
Acknowledgements Section 3 uses two great free software tools:  GNU Parallel  for distributing computations on several processors, and yEd for visualization of CAD trees. J. Davenport ,  M. England  and  T. Sturm  are grateful to the  European Union 's  Horizon 2020 Research and Innovation programme , under grant agreement No  712689 (SC 2  ). H. Errami ,  O. Radulescu , and  A. Weber  thanks the  French-German Procope-DAAD program  for partial support of this research. V. Gerdt was partially supported by the  RUDN   University Program 5-100. D. Grigoriev  is grateful to the grant  RSF 16-11-10075  and to MCCME for wonderful working conditions and an inspiring atmosphere. M. Košta has been supported by the  DFG/ANR  Project  STU 483/2-1 SMArT. We thank the anonymous reviewers of the present paper and our earlier conference papers for their useful comments which have improved this work.
It is also possible that unresolved governance disputes removed those protocommunities from possible scholarly scrutiny today. As a result, analysis of governance disputes may be difficult for researchers working with data limited to functioning communities  (Ostrom, 1990) , since by virtue of their existence, these communities have escaped or resolved significant governance disputes and their counterparts that did not may no longer exist. We leverage an unusual research opportunity found in the public discussions on GitHub, an online platform hosting numerous open source software development (OSS) communities, to explore the question of how communities resolve governance disputes. We aim to understand the conditions that increase or decrease the likelihood of resolution, and the processes that underpin differences in outcomes.
Furthermore, they have significant impact on the community's ability to attract not only developers  (Lerner & Tirole, 2005; Subramaniam, Sen, & Nelson, 2009)  but also corporate partners, who then combine OSS products or services with proprietary ones  (Deodhar, Saxena, Gupta, & Ruohonen, 2012; Economides & Katsamakas, 2006). We sample disputes surrounding the initiation or revision of a software license decision, collecting quantitative and qualitative data on these disputes. The dispute instances we discovered involve a large volume of data, making it difficult to rely solely on traditional inductive methods that involve manually analyzing each case separately. Therefore, we combine machine learning analysis with in-depth content analysis to develop a novel theory  (Puranam, Shrestha, He, & von Krogh, 2020; Tidhar & Eisenhardt, 2020).
| Context We chose GitHub as the context to examine governance dispute resolution. GitHub is currently the world's largest OSS development platform  (Gousios, Vasilescu, Serebrenik, & Zaidman, 2014) , hosting 35 million projects and 14 million users. Many GitHub projects comprise communities of individuals (i. e. , owners, collaborators, and other contributors) who jointly develop a software product. 2 Each project has at least one owner who initiates the project and holds the copyright of the project's software repository as long as an open source license is not in place. Collaborators are those who have contributed a significant amount of code to the project. The owner has the power to change the visibility of the repository from private to public or vice versa, and to delete parts or the entire repository when she holds the sole copyright.
, 2014; Raymond, 1999). The GitHub online platform has a discussion page where community members can express their opinions or dissatisfaction (e. g. , with software performance or project direction) and seek solutions  (Bissyande et al. , 2013). These discussions provide an opportunity to examine various topics important to the functioning of online communities, ranging from operational tasks such as bug fixes to major governance decisions such as license choices. Among various discussions, license decisions tend to generate broad participation, conflicting views, and heated debates  (Alspaugh, Scacchi, & Asuncion, 2010; DiBona, Ockman, & Stone, 1999). Because it is almost impossible for a single developer to be familiar with the plethora of licenses available, license decisions are among the most difficult governance decisions to make  (Almeida, Murphy, Wilson, & Hoye, 2017).
Initiator characteristics Whether the initiator of the discussion was a project owner or not, and the amount of source code contributed by the initiator (contribution), could shape the amount of attention and support she receives from other participants. From prior research on operational disputes in online communities, we believe these variables may signify the competence-based status of the initiator  (Dahlander & O'Mahony, 2011; Klapper & Reitzig, 2018). We obtained relevant information on the initiator's characteristics from the GitHub interface.
Recall that our initial • Disputes on attributes Disputing attributes via dialogue "The Ms-Pl is incompatible with many free software licenses, as it imposes a unique restriction: (D). If you distribute any portion of the software in source code form, you may do so only under this license by including a complete copy of this license with your distribution…" "There are many GPL-, LGPL-, and AGPL-licensed libraries, yet we cannot legally combine these with DotNetZip due to the terms of the Ms-Pl. " "Dual-licensing under Apache 2 or MIT would resolve the problem. As GitHub notifies people who are mentioned, re-licensing would involve pasting the list of contributors into this thread and asking for a release of their copyright. Getting approval can take anywhere from a few days to several months, depending upon contributor awareness.
| DISCUSSION Governance disputes and their resolution are critical in the lives of communities, yet they have been under-studied and under-theorized. Exploiting the availability of complete archival records for discussions in GitHub communities, we studied how disputes vis-à-vis the software license-a core governance principle of these communities-are resolved. We found that when governance disputes in communities bear the signature of group problem-solving processes rather than a multi-party bargaining process, they are more likely to be resolved. However, such a resolution is far from inevitable, and our theory reveals two necessary conditions: the emergence of reflective agency and the alignment of preferences over attributes. Reflective agency is manifested when at least one individual in the group is able to reflect on the problem and transform or initiate the discussion along the lines that are attribute focused (rather than alternative focused).
ACKNOWLEDGEMENTS This research received funding from the  Swiss National Science Foundation  (grant  169441 ). All authors contributed equally and are listed in alphabetical order. We are grateful for the excellent editorial guidance of  Will Mitchell  and the insightful comments of the three reviewers. We also thank  Shiko Ben-Menahem ,  Roderick Swaab ,  Ilia Tsetlin , and  Stefan Thau  for their helpful feedback on previous drafts of this manuscript.
High accuracy trajectory tracking with constrained inputs is challenging topic in greenhouse indoor environment control. This is due to nonlinearities and inputs/outputs coupling present in physical model of greenhouse. The objective of this study is about the problem of identification and control of a Multi-Input Multi-Output (MIMO) greenhouse process. Proportional Integral (PI) and sliding mode controllers (SMC) are used in conjunction so that estimated outputs tracks desired trajectories with good performance as near as possible in spite of coupled dynamics and outside disturbances climate. To reflect the practical aspect, the constraints of the process and control elements were taken into account. The results demonstrate the feasibility of PISMC control method. Simulation has been done in the environment of Matlab and Simulink; it shows that combined controllers are capable to manage successfully the microclimate of greenhouse.
SIMULATION RESULTS A genetic algorithm based PISMC controller for the greenhouse system, including the mean performance indices (MSE) is carried out and simulated results are plotted. In order to illustrate the efficiency of the PISMC scheme, a simulation study implemented in the Matlab/Simulink environment, has been carried out using the above nonlinear calibrated equations of greenhouse. The controlled outputs variables (  ,   ) are constrained to track the optimal references (25°C/20 hPa) of daytime and nocturnal optimal references (14°C/13 hPa) during the night. Block diagram of the PI sliding mode control is implemented in the Matlab/Simulink and displayed in Figure  9.
Acknowledgements Open access funding provided by the  Austrian Science Fund (FWF). S. M. , M. F. S. J. M. , and L. G. acknowledge the  Austrian Science Fund (FWF)  for funding through project  I2883  (Denetheor). M. M. and A. M. thank the  ANR  for project  ANR-15-CE29-0027-01  (Denetheor). M. M. is also grateful to  Universidad de Alcalá  for a postdoctoral fellowship. Part of the computational results was obtained at the  Vienna Scientific Cluster  ( VSC3 ). The.
Computer support A software-based solution for research in space ethology is called The Observer XT® software  [7]. It is a professional system that can be used for the collection, analysis, and presentation of observational data. It allows annotation of behavioral descriptors through a traditional encoding process. Its technical specificity is the synchronization of video files with the collection of ethological data as state events or point events, and any other source of information such as psycho-physiological measurements and environmental parameters. Right now, the interface between the human observer and The Observer XT® software is not computerized. Because of the complexity of the behavior to be analyzed, the ethologist's eye remains an essential tool. We can use techniques like the newly developed Facial Action Coding System  [8]  first adopted by psychologists  [9].
The proposed method, called Spectral Decomposition Filter (SpDF), identifies and preserves geometric information along the topology of point clouds and is able to scale to large environments with a non-uniform density. Finally, qualitative and quantitative experiments verify the feasibility of our method and present a large-scale evaluation of SpDF with other seven point cloud sampling algorithms, in the context of the 3D registration problem using the Iterative Closest Point (ICP) algorithm on real-world datasets. Results show that a compression ratio up to 97 % can be achieved when accepting a registration error within the range accuracy of the sensor, here for large scale environments of less than 2 cm.
Given these working hypotheses, contributions of this paper are 2-fold: 1. A novel sampling method, called Spectral Decomposition Filter (SpDF), based on spectral decomposition analysis. This method identifies and preserves geometric information along the topology of point clouds and is scalable to large environments. 2. A large-scale evaluation of current sampling strategies relying on more than 2. 45 million registrations in different types of environments (indoor/structured, outdoor/unstructured), including large-scale outdoors environments from real-world datasets. A visual overview of our sampling strategy is given by the Figure  2. A point cloud is given as input. First, we identify the geometric primitives along with their saliencies using the tensor voting framework. Then, we derive density measures from saliencies: if the density for each geometric primitive is less than the desired density, we stop; else we sub-sample each over-represented geometric primitive, and re-iterate.
RELATED WORKS A point-sampled surface is a good representation for analyzing the properties of 3D shapes  (Alexa et al. , 2001). Unfortunately, most point clouds obtained in robotics context are noisy, sparse, large, and have an uneven density. An important step during the process of analyzing point clouds is to remove noise and outliers. This can be done using filtering algorithms. An extensive review of these algorithms has been realized by  Han et al. (2017). Point cloud simplification is related to the problem addressed by the computer vision field but aims to accelerate graphic rendering. A lot of methods based on meshing are used to address this problem  (Hoppe, 1996; Li and Zhu, 2008). Technically, these methods can be directly extended to point cloud representation, but most of the algorithms perform an expensive dataset meshing pre-step.