dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis Data were subjected to thematic analyses  [24, 25] , which included the following: (1) reading or listening to the full dataset to familiarize researchers with content, (2) mapping trajectories of suggestions from inception to rejection or from inception to implementation, (3) parallel coding of full dataset and written summaries by 2 researchers, (4) gradual development of descriptions of thematic content and discrete phases, and (5) corroboration of the description of phases by re-examining data.
Overview The analysis identified 3 temporal phases during the user-involving processes, which were characterized by distinct types of negotiations. The first phase, Suggesting core functions, was characterized by a focus on discussing the potential inclusion of basic app features. The second phase, Refining functions, was characterized by testing and negotiating the design of newly implemented app features. The third phase, Negotiating the finish, was characterized by tests and discussions about the final layout and wording. The 3 data extracts presented in the sections below were selected because they were characteristic of the different phases. Discussions in all 3 phases were characterized by very low levels of displayed disagreement. For instance, when different opinions were voiced, participants would most often resolve disagreement by suggesting that a given app feature should ultimately be optional and adaptable by individual users.
In the following data extract from focus group DK FG #4 (see Textbox 1), which included relatives of MYPLAN users, the participants discussed the potential use of GPS. The prompt for the discussion was a vignette about a young girl who switched on her GPS when she felt suicidal, which would automatically alert parents and clinicians, who would then be able to follow her phone's location. After expressing concerns that MYPLAN users might be bluffing and that there would be a need for sincerity from the users, the relatives started reflecting on the personal costs such alerts would have on themselves as well as issues of intrusions that GPS surveillance might inflict on their sons or daughters.
Phase 3: Negotiating the Finish The third phase primarily took place during workshops AUS WS #4 and 5 when MYPLAN's core functions had been designed, tested, and refined. Fixing glitches continued to take place as it had been done in phase 2, with all issues listed and fed back to the software developers to prioritize and resolve. Unlike the Danish participants, Australian user participants were not introduced to the app by health care professionals who they had met in the clinic. Therefore, the Australian workshop participants relied heavily on the built-in electronic introductions (e-introductions), which they did not find intuitive or helpful. The Australian participants identified a need for better e-introductions and suggested progressive onboarding to MYPLAN's functions. The participants from workshops AUS WS #4 and 5 were engaged in writing and editing the introductory texts for the app, both during the workshops and via email after the workshops.
These texts were sent to the software developers, who implemented them in both the Australian and Danish versions. Later, the text introductions were supplemented by video clips. The Australian participants felt that the text introductions, which were relatively noninteractive, made the app's particular clinical language use unacceptable. The following data extract 3 is taken from AUS WS #4 where a participant, Sarah, highlighted her discomfort concerning the image and text she read after responding to the 5 items of the SIDAS: It says the word suicide a lot and it says a lot of words that probably when you are in distress you don't need to be confronted with. And more importantly, it is talking about and not to you again, which is really kind of demoralizing. I feel like it could be a lot shorter and more pleasant very easily.
[Sarah]  Sarah continued to elaborate on her position that the language use came from a medical and paternalistic position, which, to her, objectified the user and was not helpful for a person in a crisis. This led to a series of discussions about the general coherency: was it a problem that MYPLAN was, on one hand, a personalized self-help tool and, on the other hand, employed medical surveillance and medical psychoeducation? The Australian workshop participants strongly supported the development of a completely nonmedicalized and nonpathologized self-help safety plan, asserting that this would be acceptable for people who felt distressed but who would not identify as being in a crisis (a crisis management plan), let alone being suicidal (a suicide prevention plan). The pushback from the Australian workshops was a genuine surprise for the researchers who were forced to reconsider their own core assumptions about the app's purpose and its users.
Principal Findings The case study identified 3 temporal phases during the user-involving processes, which were characterized by distinct types of negotiations: (1) suggesting core functions, (2) refining functions, and (3) negotiating the finish. The phases most probably reflected that the MYPLAN app was developed gradually over an extended period of time. Researchers controlled most of the concrete user-involving processes, but as stakeholders were presented with increasingly finalized revisions of the app, they were able to grasp new designs and engage more collaboratively with researchers and software developers. Models of user involvement often classify levels of involvement according to the distribution of power and control between user participants and researchers  [26]. For example, Hanley et al  [27]  differentiate among the following: (1) Consultation, where service users' perspectives are explored by researchers and potentially brought in to decision-making processes; (2) Collaboration, where researchers are actively committed to engaging in ongoing partnerships with service users; and (3) User control, where "the locus of power, initiative, and subsequent decision making is with service users rather than with the professional researchers"  [27].
Strengths and Limitations We collected observational data from a range of meetings over a long period of time, which allowed a basic mapping of the introduction and negotiation of ideas over time. However, we did not have observational data of the separate negotiations with the app designers. Such negotiations included discussions among software developers about which of the listed suggestions should be prioritized in light of their complexity and costs and decisions taken by the app programmers. From a methodological perspective, we believe that users' involvement might be perceived as less impactful if these backstage negotiations were part of the analysis. Arguably, users should be formally involved in as many of these crucial design processes as possible to achieve more genuine collaboration.
It is worth noticing that, to be adopted by the designer community, the approach should be compliant with current standardized CMOS-based design techniques and should be implemented in an existing commercial environment. Hence, there is a need for accurate, generic, scalable, and easy-to-use, i. e. comprehensive, compact models. However, to our knowledge, there is no such model in the literature. Indeed, models have been implemented in MatLab to execute transformed conductance matrix  [16]. These approaches are not scalable and cannot be used for complex circuits design. Verilog-A model proposed in  [17]  implements ASL device as a single block, which avoids exploring the design space for its optimization and the hierarchical design. A circuit simulation environment relying on basic electrical circuit elements such as resistors, capacitors and current sources has been defined in  [18]   [19] , and a scalable Verilog-A model is proposed in  [20].
B. Related ASL Compact Modeling Table  I  shows several ASL compact modeling. These ASL modeling is based on the spin-circuit concept, which is proposed in  [16]. In this model, each device is represented as a π-network with the conductance matrices, which allows ASLbased circuits implementation and analysis. However, such approach is not scalable since complex circuits design leads to large-scale matrices that need to be carefully established. Furthermore, the model is implemented in MatLab, which is not a suitable platform for the designer community. In  [17] , the authors proposed a compact model integrating all the devices into a single block using a set of predefined equations. Such model is not suitable for hierarchical circuit design since the bloc is specific to a given cell. A circuit simulation environment relying on electrical circuit elements has been proposed  [18]  and an ASL model has been implemented with Verilog-A  [20].
Authors should highlight important contributions of their model and difference between the two basic approaches, i. e. [25]  and  [26]. The table given seems insufficient. The contribution of the authors seem to be insufficient. Based on the summarization of the contributions in Section Introduction, firstly we present the fundamental equations of different modules and their Verilog-A implementations in Section III, which allows the independent design of different ASL devices. This is illustrated with the examples of a 4-bit adder and a 4-bit multiplier in Section IV. The effects of the spin diffusion delay, the channel breakdown and the scaling are demonstrated in Section V, with simulations of certain circuits. We have complemented and cleared these contributions in the manuscript. In addition, we have checked the references of different models in Table  I.
A nano-magnet injector is sufficient. Yes, you are right. In a simple logic circuit, a nano-magnet injector is sufficient, instead of MTJ, considering the energy consumption and the delay. However, in a large-scale circuit, MTJs are necessary for the interconnection like the latch. Also for the control input, if we want to use ASL in programmable/reconfigurable circuits for memory-in-logic computing, the MTJ is necessary. Furthermore, ASL-based circuits can also be used in neuromorphic circuits, where MTJs are needed. 4. What about the simulation time and convergence of the two approaches. If I did not misunderstand, you mean the two approached in Table  I. We have used MatLab to program these two approaches. We found that the simulation time of the two approaches were basically the same, in the case of a 4bit adder.
However, these tools are generally adapted to the design of simple models with little data (KISS or simple KILT models). We consider that, among the tools mentioned previously, there is none adapted to modelers who are not experts in computer science. Indeed, either they are too complex to be used by non-computer scientists or too limited in terms of implementable models. This article presents the new tools integrated in the GAMA modeling and simulation platform that we developed in order to overcome this shortcoming. GAMA is an open-source modeling and simulation platform for building spatially explicit agent-based simulations. It o ers a complete and comprehensive modeling language (GAML: GAma Modeling Language) and an integrated development environment to support the definition of large-scale models.
The richness of GAML comes from the numerous optimized operators that it integrates. In particular, GAMA provides modelers with a native integration of GIS data  (Taillandier et al. ) , high-level visualization tools  (Grignard & Drogoul ) , cognitive agent architectures (Caillou et al. ; Bourgais et al. ), multilevel models  (Vo et al. ) , and co-modeling (Drogoul et al. ). Like NetLogo, GAMA allows the user to switch very easily between a modeling perspective (writing the model code) and a simulation perspective (running the simulation). Indeed, with few lines of codes, GAMA allows us to define a model and visualize the simulation results. This possibility makes it particularly suitable for rapid prototyping necessary in the KILT approach: rather. The last modeling language that needs to be mentioned is the one proposed by the MAGeo platform.
This language is based on the AOC (Actor -Organization -Behavior) meta-model  (Daudé et al. ). This graphical language is close to UML and respects most of the properties of OOP (Object Oriented Programming). In addition, it allows one to natively define multi-level models. However, this language imposes a lot of constraints due to the limitations of the MAGeo platform. In addition, no distinction is made between what an agent can do and what it is going to do (capabilities versus behaviors).
Our goal for the GAMA platform was thus to propose a modeling language, which is simple enough to manipulate (with a small number of concepts), which allows one to develop KIDS models, and which is as close as possible to implementation in order to avoid a gap between the conceptual model and the implemented one. In order to achieve this objective, we identified several properties that our modeling language had to respect: • Properties of OOP • Di erentiation between capabilities and behaviors • Native handling of multi-level modeling • Possibility of defining elements related to simulation visualization.
Graphical modeling with GAMA Overview. The purpose of our graphical modeling tool is to address the need for participatory tools that would help define KIDS models. It allows GAMA users to graphically define their models and eventually translate them into the GAML language. In addition, this tool enables translation of a GAML model into a graphical one. This feature aims at facilitating the discussion (and communication) pertaining to a model. The tool is based on the Graphiti plugin of Eclipse (Graphiti ) and is integrated in a dedicated GAMA plug-in that can be directly downloaded and installed through GAMA.
Conclusion. To sum up, the graphical modeling tool of GAMA enables graphical definition of a complete and functional GAMA model with a minimal quantity of GAML code. This tool o ers many advantages for a participatory modeling context. The choice made in this tool to use a graphical meta-model that covers the more important concepts of the GAML language, allows modelers to graphically define a whole model. As a consequence, there is no cognitive e ort to switch between the graphical modeling language and the implementation language. On the other hand, the model created graphically can be directly executed in GAMA: this upholds one of the main advantages of the modeling and simulation platforms, i. e. the possibility of executing the model being implemented very quickly and switching between the model and the simulation, thus contributing to the understanding of the model.
Finally, Cormas gives the possibility of going back in time: by just clicking on the backward button, the user can backtrack to the previous step or replay a simulation. While Cormas is particularly well-adapted for use in participatory contexts, developing models, which require using the Smalltlak language and the VisualWorks framework  (Brauer ) , can be complex for non-computer scientists. In addition, like NetLogo, the platform is not adapted to the development of complex KIDS models (no real integration of GIS data, simple representation of the environments, etc. ).
Notes The source code of the Sprite model can be downloaded here: https://goo. gl/n4SRYL. The source code of the  Littosim  project is available on the following GitHub repository: https://github. com/LittoSim. The source code of the  CityScope  project is available on the following GitHub repository: https://github. com/CityScope. This model is available in the GAMA Model Library: Library Models / Features / D Visualization / D Camera and trajectories. gaml. This model is available in the GAMA Model Library:  Plugin Models / Driving Skill / Road Tra ic simple (City ). gaml. The source code of the  MarrakAir  project can be found on the GitHub repository: https://github. com/gnoubi/MarrakAir.
ACKNOWLEDGEMENTS We thank the  Illustris collaboration  for making their simulation data publicly available. MJ was supported by the  Science and Technology Facilities Council  (grant number  ST/L00075X/1 ). CG acknowledges support from the  Italian Ministry of Foreign Affairs and International Cooperation ,  Directorate General for Country Promotion (Project "Crack the lens "); and the financial contribution from the agreement  ASI n. I/023/12/0  "Attività relative alla fase B2/C per la missione Euclid". ML acknowledges  CNRS  and  CNES  for support.
This paper presents a computational program named BINCOR (BINned CORrelation) for estimating the correlation between two unevenly spaced time series. This program is also applicable to the situation of two evenly spaced time series not on the same time grid. BINCOR is based on a novel estimation approach proposed by Mudelsee (2010) for estimating the correlation between two climate time series with different timescales. The idea is that autocorrelation (e. g. an AR1 process) means that memory enables values obtained on different time points to be correlated. Binned correlation is performed by resampling the time series under study into time bins on a regular grid, assigning the mean values of the variable under scrutiny within those bins. We present two examples of our BINCOR package with real data: instrumental and paleoclimatic time series.
The technique of  Mudelsee (2010)  requires the concept of nonzero persistence times, enabling the mixing information (i. e. covariance) to be recovered, even when the two timescales differ. The BINCOR package presented in this paper is based on a method that is not applicable when one or both of the time series under examination have zero persistence. Similarly, this method is not applicable when the time series are sampled with significantly longer spacing than the persistence time, so that the effectively sampled persistence time is zero. A fundamental condition for using this method is that the time spacing should not be much larger than the persistence times. Enough common data points then fall within a time bin, and knowledge can be acquired on the covariance  (Mudelsee, 2010 (Mudelsee, , 2014)).
In this paper we present a computational package named BINCOR (BINned CORrelation), which is based on the approach proposed by  Mudelsee (2010 Mudelsee ( , 2014)). The BINCOR package contains (i) a main function named bin_cor, which is used to convert the irregular time series to a binned time series; (ii) two complementary functions (cor_ts and ccf_ts) for computing the correlation between the two binned climate time series obtained with the bin_cor function; and (iii) an additional function (plot_ts) for plotting the "primary" vs. the binned time series. This package is programmed in R language and is available at the CRAN repository (https://CRAN. R-project. org/package=BINCOR). This paper is divided into four sections. The first outlines the method and the computational program. The second presents a Monte Carlo experiment to study the effect of binning size selection.
The computer program The BINCOR package developed in R version 3. 1. 2 1 to be run from the command line runs on all major operating systems and is available from the CRAN repository (http://CRAN. R-project. org/ package=BINCOR). The BINCOR package contains four functions: 1) bin_cor (the main function for building the binned time series); 2) plot_ts (for plotting and comparing the "primary" and binned time series); 3) cor_ts (for estimating the correlation between the binned time series); and 4) ccf_ts (for estimating the cross-correlation between the binned time series). The graphical outputs can be displayed on the screen or saved as PNG, JPG, or PDF graphics files. BINCOR depends on the dplR  (Bunn et al. , 2015)  and pracma  (Borchers, 2015)  packages. The dplR package is used by the function bin_cor to calculate the persistence for the climate time series under study, whereas the pracma package is used by the functions cor_ts and ccf_ts to remove the linear trend before estimating the correlation.
The third function, cor_ts, calculates three types of correlation coefficient: Pearson's correlation, Spearman's and Kendall's rank correlations. These correlation coefficients are estimated through the native R function cor. test from the R package Stats. The cor_ts function has an option to remove the linear trend of the time series under analysis -other pre-processing methods could be used before the cor_ts function is applied. This function has the following syntax: R> cor_ts(bints1, bints2, varnamets1="", varnamets2="", KoCM, rmltrd="N", device="screen", Hfig, Wfig, Hpdf, Wpdf, resfig, ofilename) where KoCM indicates the correlation estimator: pearson for Pearson (the option by default), spearman for Spearman and kendall for Kendall; rmltrd is the option to remove the linear trend in the time series under study (by default the linear trend is not removed, but the function can be enabled via the option "Y" or "y").
Examples Assessing the link between El Niño-Southern Oscillation and Northern Hemisphere sea surface temperature We first examine two evenly-spaced annually-resolved instrumental climate records that cover the time interval from 1850 to 2006 (N = 157 points) 2. To test our BINCOR package we created irregular time series by randomly removing 20% of the data from the evenly spaced time series. We note that the new "sampling" times are not necessarily the same for both irregular series. The new irregular time series ("primary" hereafter) consist of 125 data points and have an average temporal spacing d of 1. 24 years. Specifically the two time series used were a record of Northern Hemisphere (NH) sea surface temperature (SST) anomalies (HadCRUT3,  Brohan et al. (2006) ) and a record of equatorial Pacific SST anomalies from the El Niño 3 region (2.
, 2012). The D-O cycles are characterised by rather fast atmospheric warming events over Greenland of up to 16 • C that occur within a period of approximately 40 years, followed by gradual cooling leading to the cold stadials  (Sánchez Goñi and Harrison, 2010; Wolff et al. , 2012). The labels indicate the names of the sites where the pollen data were obtained. Figure  6  illustrates the variations in the pollen percentages of the temperate forest, a type of vegetation typical of moderate, warm, wet climates. Figure  6  A shows the primary and binned pollen records from site  MD04-2845 (Sánchez Goñi et al. , 2008; Sánchez Goñi et al. , 2017). Figure  6  B shows the primary and binned pollen records from site MD95-2039  (Roucoux et al. , 2005; Sánchez Goñi et al. , 2017). We use the pollen time series with a harmonised, consistent chronology  (Sánchez Goñi et al.
Finally, Figures 6 C and 6 D show that the two pollen time series, presented as the primary and binned data, may be significantly correlated. This is discussed below. The code used to generate Figure  6  is as follows. # Load the package library(BINCOR) library(pracma) A) C) B) q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q 30000 40000 50000 60000 70000 0 10 20 30 40 50 Years cal B. P. q q q q q q q q q q q q q q q q q q q q q q q q q q q MD04-2845 (Temp. forest %) MD04-2845 (Temp. forest %) (primary). N = 77 elements MD04-2845 (Temp. forest %) (binned). N = 27 elements D) q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q 30000 40000 50000 60000 70000 Years cal B.