dataesr/scientific-paragraphs-categorization huggingface dataset:
Positive values for α or β mean a positive effect of the environment on speciation or extinction (and conversely). As environmental variables (Figure  2 ), we used paleotemperature (data retrieved from  Zachos et al. 2008 ) and paleo-elevation of the HTP (a proxy for mountain building), with data compiled from existing data in the literature available on Dryad (Appendix 7). The R-package pspline was used to build environmental vectors from the data as input for the birth-death models. Alternatively, we used RevBayes and the CoMET framework (episodic birth-death models) to implement an analysis in which diversification rates are allowed to co-vary with, or evolve independently from, paleotemperatures and paleo-elevation across discrete time intervals  (Palazzesi al. in review). A correlation coefficient, denoted β, is co-estimated with diversification rates in the model and describes the magnitude and direction of the relationship between the variable and changes in diversification rates: β<0 (negative correlation), β>0 (positive correlation), and β=0 (no correlation, in which case the model collapses to the episodic birth-death models; see Appendix 8 on Dryad).
Divergence time and biogeographic estimates The BEAST analyses (Yule vs. birth-death, molecular vs. total-evidence) yielded almost identical estimates of divergence times with less than one million years of difference for most nodes (Appendix 10 on Dryad). Bayes Factor comparisons were also not conclusive, with little difference in the stepping-stone marginal likelihood estimate among analyses (Appendix 11 on Dryad). We present the results for the analysis with the birth-death prior and the total-evidence dataset (Figure  4 ) because it offered slightly better convergence and incorporating extinction into the tree prior seemed more realistic for such an old lineage (results from the other analyses are available on Dryad, Appendix 10). Based on this analysis and results from the DEC inference, Parnassiinae originated in the late Eocene (38.
Macroevolutionary dynamics A summary of diversification rate analysis results is presented in Table  1. Diversification in a Red Queen model. -Diversity-dependent diversification models estimated an unlimited carrying capacity for the subfamily (Appendix 13 on Dryad), implying that accumulating diversity did not influence diversification rates. The best-fitting DDD model was one in which extinction rate increased with diversity. For Parnassius the best model was a diversity-dependence process with speciation decreasing with increasing diversity. However, none of these models received significant support when compared to other models (∆AICc<2). MuSSE analyses on host-plant preferences selected a model in which speciation rates   showing that only speciation varies according to the geographic trait.
In contrast, the Bayesian paleoenvironment-dependent model implemented in RevBayes indicated that both past temperature and elevation did not impact the diversification of Parnassiinae, since the correlation parameters α and β had credibility intervals that were not significantly different from zero (Appendix 21 on Dryad). Moreover, estimates with this model (Figure  8 ) showed diversification dynamics that were similar to those obtained with CoMET and TreePar, but unlike those from RPANDA. These differences might be attributed to the inference framework (ML in RPANDA vs. BI in RevBayes) or to the different underlying birth-death models (continuous in RPANDA vs. episodic in RevBayes). To test this, we implemented the continuous-paleoenvironmental models  (Condamine et al. 2013)  in TESS. We then compared these models against a constant birth-death model, episodic (piecewise constant) birth-death models (1 to 5 shifts), and time-variable models.
2012). Unfortunately, so far there is no complete phylogeny for any of the large host families for Parnassiinae, including Aristolochiaceae (~500-600 spp. ), Papaveraceae (~700-800 spp. ), Crassulaceae (~1300-1500 spp. ), and Saxifragaceae (~600-700 spp. ). However, we found a positive correlation between species richness of these plant lineages and the speciation rates of their insect feeders (MuSSE: R 2 =0. 96, BAMM: R 2 =0. 87; Appendix 22 on Dryad). This suggests that fast-diversifying clades are associated with families offering a higher number of potential hosts (akin to niche availability). Janz et al. (2006)  found a similar correlation between host-plant diversity and speciation rates in the Nymphalidae; they posited that recurring oscillations between host-plant expansions (i. e.
molecular matrix comprised 4535 nucleotides and 85 extant species of Parnassiinae plus two fossil species, following recent taxonomic reviews (Frankenbach et al. 2012; Bollino and  Racheli 2012) (Appendices 1 and 2 on Dryad). PartitionFinder selected eight partitions as the best scheme for substitution models (Appendix 9 on Dryad). Convergence among runs was supported by the low average standard deviation of split frequencies, PSRF values ≈1. 0, and ESS>>1000 for many parameters. The resulting Bayesian trees were well resolved and robust: 74. 4% of the nodes were recovered with PP>0. 95 with MrBayes (Figure3), and 83% in BEAST (70% with PP=1) when fossil taxa were removed. Phylogenetic relationships agree with previous studies(Nazari et al. 2007; Michel et al. 2008) , showing Luehdorfiini+Zerynthiini as sister-tribes and sister to Parnassiini; within the latter, Hypermnestra is sister to the genus Parnassius.
varied among clades feeding on different host plants; extinction and transition rates were estimated as equal (Appendix 14 on Dryad). Zygophyllaceae feeders (Hypermnestra) had the lowest speciation rate, followed by Aristolochiaceae feeders (Luehdorfiini and Zerynthiini), with the highest rates for clades feeding on Papaveraceae (all Parnassius except subgenus Parnassius) and Crassulaceae+Saxifragaceae (subgenus Parnassius) (Figure 5). MCMC credibility intervals showed significant differences between the speciation rates of Papaveraceae feeders and Crassulaceae+Saxifragaceae feeders and those feeding on other host plants (http://dx. doi. org/10. 5061/dryad. [NNNN], Appendix 15). Transition rates for host-plant switches were often estimated close to zero with strong niche conservatism in the phylogeny; once a lineage shifted to a new host it did not switch back and only rarely colonized another host plant (Appendix 14).
We assessed the robustness of this pattern with a simulation procedure, and Bayesian implementations of HiSSE and MuSSE in RevBayes (http://dx. doi. org/10. 5061/dryad. [NNNN], Appendix 16). All analyses supported the same diversification pattern with speciation rates being different among lineages feeding on different host plants. Our estimates were robust to known biases of SSE models, suggesting a strong effect of host plant on diversification rates of Parnassiinae.
Quantification of COX deficiency A mosaic distribution of affected cells within a given tissue is a characteristic of many mitochondrial diseases. Quantifying this variation would help to decipher the correlation between cellular OXPHOS deficiencies and disease progression. To demonstrate the high sensitivity and reproducibility of the NBTx reaction, we proceeded to evaluate the degree of blue formazan crystals in wild-type heart tissues exposed to increasing amounts of sodium azide. For this purpose,  a simple and accessible workflow was developed using images captured with an upright microscope and subsequently analysed using Fiji, a free image processing software package (supplementary material, Table  S1 ). Concentrations as low as 5 μM induced a small, detectable change in relative optical density (ROD) measurements, followed by a gradual increase which plateaued at about 100 μM.
Acknowledgements We thank the  FACS and Imaging Core Facility  at the  Max Planck Institute for Biology of Ageing  for technical support as well as the  CECAD Imaging Facility (University of Cologne , Cluster of Excellence in Ageing Research). We would like to acknowledge the contribution of  Peter Zentis  from the  CECAD Imaging Facility  for the Fiji macro which was used for quantitative analysis. We thank  Gavin Falkous  and  Anna Smith  ( Wellcome Centre for Mitochondrial Research, Newcastle University ) for technical assistance with obtaining the histochemical images of human tissue sections. We thank  Ana Bratic  and  Luke Tain  ( Max Planck Institute for Biology of Ageing ) for assistance with the Drosophila work. This work was supported by the  Max Planck Society. LCG and RWT are supported by the  Wellcome Centre for Mitochondrial Research  ( 203105/Z/16/Z ).
Related Work 2. 1 Software Side of Interactive Systems Architectures Architectural models for multimodal interactive systems have been presented in research papers  [38]  and  [39]  as a way of explaining the various components of a given system. More generic ones have also been presented, but they are usually bound to one type of modality such as touch interaction  [37]  or speech interaction  [21]. Toolkits and frameworks for supporting the development of prototypes and demonstrations have also been developed such as, for instance, PyMT toolkit  [17]  for interactive applications offering multi-touch interactions. Similarly SensScreen  [34]  is dedicated to interactive applications exploiting multimodal management of sensors distributed in the user environment and presents a very high level architecture dedicated to public displays.
MIODMIT: A Generic Architecture for Interactive Systems The Multiple Input and Output Devices and Multiple Interaction Techniques (MIODMIT) generic interactive system architecture explicitly depicts the various components (both hardware and software) of an information pipe-line in modern interactive systems. In that sense, MIODMIT is compatible with the principles and objectives of the Model Driven Architecture approach at OMG (http://www. omg. org/ mda). Such systems contain multiple input devices each providing an information flow that are usually fused with other ones to offer multiple (and often multimodal) interaction techniques. This architecture presents input and output flows as well as how they can be integrated altogether. The following sections present an overview of MIODMIT and detail functionalities and responsibilities of each component via an illustrative example.
Illustrative Example with a Simple JAVA Application In order to illustrate how MIODMIT is structured, we use a simple example application developed with Java Swing, presented in Fig. 1. This application allows users to add, modify and remove elements in a database. An element is composed of three attributes: a text field (the name), an enumerated field (the number of children) and a Boolean value (married or not). The list of elements in the database are displayed in the listbox (called mother list). Once added to the database, elements can be selected in that listbox to be deleted or modified. In terms of input and output modalities, this application is standard, offering a mouse and a keyboard for input, and a computer screen for display.
Inside the Details of MIODMIT Figure  3  presents the refinement of Fig. 2  making explicit both the content of each component and the information flow between components. Even in its detailed description, MIODMIT remains abstract on purpose which means that each component may be decomposed into several classes. More details about that aspect are given in the case studies section. The MIODMIT architecture, presented in Fig. 3 , uses the AADL notation  [33] , a standard for describing software architecture which been applied in several domains including automotive and aeronautics. Other notations could have been used but the standard nature of AADL eases its understanding. We do not provide here a description of the elements of AADL but a key is provided at the bottom of Fig. 3.
Input Configuration Manager (Input Chains Manager) One of the specificities of MIODMIT is its intrinsic ability to support dynamic reconfiguration of the interaction techniques. It is a functionality of paramount importance for different systems such as: critical systems, systems with a long exploitation life, systems with long start up procedures (such as civil aircrafts), or systems with a high replacement cost. Indeed, if one or several modalities fail at runtime (also called operation time), it may be critical to offer other modalities for allowing operators to perform their tasks even with degraded or less efficient interactions. To this end, MIODMIT includes an "input configuration manager" which is responsible for handling reconfigurations. It is a unique software component, whatever the amount of input devices is.
Tuning the Generic Interactive System Architecture The right-hand side of the diagram (labeled tuning) corresponds to the tuning-ondemand part of the proposed approach while the bottom part focuses on the implementation aspects. These three main phases have been highlighted using gray boxes with dashed lines in Fig. 4. The tuning-on-demand step refines the diagram by concretizing each component of MIODMIT making explicit (in the diagram): • where software parts (e. g. API; libraries…) provided by the input device's manufacturer are distributed in the architecture, • if existing code has already been produced where it has to be distributed in the architecture, • which component have to be coded from scratch. It is important to note that due to an absence of standards, provided software packages often require to split or merge functionalities in order to fit in the structure of the generic architecture.
For instance, the Leap Motion is provided with three software packages: the driver (for a dedicated OS), the library (making it possible to exploit the driver on a dedicated OS and integrating C and C++ API), and a wrapper for high-level programming language (e. g. Java). The driver corresponds to the component "Driver instance" in MIODMIT while the functions in the Leap Motion library cover the "virtual device" component and several interaction transducers (e. g. detection of a circle called CircleGesture) located in the "Global Interaction Techniques" component. The wrapper provides functions for all the other components of the input flow but only covers a very limited set of functionalities. The provided set of interaction techniques is basic and has to be extended according to the expected use of the Leap Motion in the application.
Illustration of the Process with the Java Application Example The result of the application of the tune-on-demand process described above on the Java Application illustrative example is presented in Fig. 5. The Figure can be split in three sections. The left-hand side represent the physical input and output devices used with the Java Application. The center represents the MIODMIT components that are taken care of by the operating system (surrounded by a grey box named "Black Box: OS Windows"). While no access to the Windows source code is given, it remains possible to describe the OS behavior using MIODMIT. Indeed, drivers for a keyboard, mouse and screen as well as part of the rendering system are an integral part of modern OS. The OS merges input and output aspects at hardware level and thus only one component handling both input and output devices drivers is represented.
Overview of the Tuned Generic Interactive System Architecture According to the process in Fig. 4 , the first step, from the prototyping phase, is to refine MIODMIT by specifying all the components. During this refinement, it is important to make explicit where the software provided by the input devices manufacturers is located in the diagram. It is important to note that currently, software packages provided by manufacturers often require splitting or merging in order to fit in the structure of the architecture. As for the description of MIODMIT in previous sections, Fig. 7  presents an overview of the architecture tuned for the weather radar case study. The gray boxes on the left-hand side of the figure correspond to the input and output devices available in the case study. These components will be detailed further in the following sections.
Application to the Case Study: Implementation Following the process in Fig. 4  after MIODMIT tuning, each remaining component has to be implemented. Implementation concerns the definition of the behavior of each component of the tuned architecture. Such implementation can be done using different  programming languages being formal or not. In previous work ICO-based descriptions were used for some of the components (e. g. "Global Interaction Technique" component  [21] , "Dialogue"  [16]  component…). Work such as  [13]  has provided C implementation of all the components of the "Input Chain Kinect". In section "Tune on Demand Process" we have detailed the various implementation steps that are thus not duplicated here as they are generic to every type of application.
Application to the Case Study: Adding a Device One of the most important aspects of MIODMIT is its modularity providing flexibility and modifiability to the applications designed. This section highlights the modifications to be made when an additional input device is added, here a Leap Motion (see Fig. 11 ). Once the device is chosen, two processes can be done in parallel. The first one is to prototype the interaction using the new device (design, evaluation… etc. ). The other is to tune MIODMIT for the chosen device and integrate the tuning within the existing application. In the following, we describe how to add a Leap Motion hand gestures tracker as well as corresponding gesture-based commands to our case study. The Leap Motion is provided with three software packages as explained in Sect.
It addresses the issue of the complexity of engineering current interactive systems integrating non-standard input and output devices and offering multimodal interactions. Both hardware and software aspects are described within a single framework. The generic interactive system architecture makes explicit the relationships between input devices and interaction techniques. It also makes explicit how such elements are related to implementation considerations involving various software entities such as device drivers, transducers, toolkits and APIs. As such, the integration of novel interaction techniques such as gesture interaction is simplified and better structured, and can be tuned depending on the needs of specific applications. The illustrated development process presented helps demonstrating how the MIODMIT generic interactive system architecture can be applied.
Introduction The UniMath 1  language is meant to be a core dependent type theory, making use of as few type constructors as possible. The goal of this restriction to a minimal "practical" type theory is to make a formal proof of (equi-)consistency of the theory feasible. In practice, the UniMath language is (currently) a subset of the language implemented by the proof assistant Coq. Importantly, the UniMath language does not include a primitive for postulating arbitrary inductive types. Concretely, this means that the use of the Coq Inductive vernacular is not part of the subset that constitutes the UniMath language. The purpose of avoiding the Inductive vernacular is to ease the semantic analysis of UniMath, that is, the construction of models of the UniMath language. Another benefit of keeping the language as small as possible is that it will be easier to one day port the library to a potential proof assistant specifically designed for univalent mathematics.
(ii). Instantiating the base category C of the previous section to Set, we construct the data type, as an initial algebra of the endofunctor on endofunctors on the category of sets that is specified by a binding signature, using just the type constructors available in the UniMath language. In particular, we do not use the Coq vernacular Inductive. This work is reported on in Section 3. (iii). In previous work  [22, 3] , a model ("substitution system") of a signature with strength was constructed on a hypothetical initial algebra. This construction was carried out over an arbitrary base category, which, by hypothesis, is sufficiently well-behaved. In particular, right Kan extensions were required to exist. In the present work, we base the needed scheme of generalized iteration in Mendler-style on another theorem in  [10]  that is based on cocontinuity assumptions instead of the existence of right Kan extensions.
About UniMath The UniMath language is a variation of intensional Martin-Löf type theory  [21]. It has dependent function types (also called Π-types), dependent pair types (also called Σ-types), identity types and coproduct types. There are also a few base types: the type of natural numbers, the empty type, the unit type, the type of booleans. Furthermore, we assume that all the types are elements of a universe U-for sake of simplicity, and while waiting for a satisfying universe mechanism (that supports resizing rules besides universe polymorphism), we even assume the inconsistent typing rule U : U. This means that the Coq system does not provide us with a validation of our usage of universes, although we informally claim that we do not exploit that rule in inconsistent ways (to be confirmed in future implementations).
Indeed, while the types above are, for technical reasons, implemented in UniMath using the Inductive vernacular, its use is not permitted outside a "preamble" that introduces those types. In this way we simulate a theory in which the above types are primitive rather than an instance of a general type definition mechanism. It is the purpose of the present work to construct some of the inductive types that could otherwise be defined using the Inductive scheme. 2  Consequently, the experimental Higher Inductive Types (HITs)  [25]  are not part of the UniMath language either. In UniMath, types are stratified according to their homotopy level : we say that a type is contractible if it has exactly one element/inhabitant. A type is a proposition if any two of its inhabitants are identical (there need not be any inhabitant, corresponding to an unprovable proposition).
Conclusions We have formalized some classical category-theoretic results on the construction of initial algebras, as well as on cocontinuity of functors. Maybe surprisingly, the formalization of results yielding ωcocontinuous functors as input to the construction of initial algebras proved to be much more difficult than the construction of colimits in Set as output of that theorem. Our formalization has been integrated into the UniMath library. Statistics related to the contributions of this paper have been summarized in Table  2. 3  The first three columns show lines of code and the last two show the number of vernacular commands. Our datatypes come with a recursion principle, defined via the universal property of the datatype as an initial algebra. This recursion principle allows us to define maps such as foldr for lists.
Acknowledgements: We thank  Dan Grayson  and  Vladimir Voevodsky  for helpful discussion on the subject matter. We particularly thank  Paige North  for pointing to a size problem in an earlier version of one of our categorical constructions during the writing phase of this article. Much of the formalization work on this article was done at the  Institute for Advanced Study. We would like to thank the  IAS  for providing a pleasant and productive work environment.