dataesr/scientific-paragraphs-categorization huggingface dataset:
Slides Preprocessing We first segment the matter on the WSI using a matter-segmentation U-Net  [67]  trained on a private dataset. We then tile the extracted matter using 224x224 tiles at zoom-level 20x. This results in a variable number of tiles for each slide depending on the amount of matter found on the slide. We finally rely on a Imagenetpretrained ResNet-50  [68] , truncated until its penultimate layer, to extract a feature of size 2048 for each tile. Each slide is eventually represented by a bag of such features. The aforementioned pipeline above has proved sufficient to reach good performance, but could be improved in several directions listed below. Normalization Slide normalization in Federated-Learning is a relatively uncharted topic  [30]  because image-level normalization techniques  [69, 70, 71]  are difficult to apply as they require centers to agree on a common template.
Acknowledgements This project is supported by Bpifrance as part of the "Healthchain" project, which resulted from the "Digital Investments Program for the major challenges of the future" RFP. As part of the "Healthchain" project, a consortium coordinated by Owkin (a private company) has been established, including the Substra association, Apricity (a private company), the Assistance Publique des Hôpitaux de Paris, the University Hospital Center of Nantes, the Léon Bérard Center, the French National Center for Scientific Research, the École Polytechnique, the Institut Curie and the University of Paris Descartes.
Methodology To gather data for this study, between November and December 2019, we manually collected a sample of over 200 (n = 213) YouTube videos using keyword search terms "Copyright," "Copyright Strike," "Copyright Claim," and "DMCA. " Videos were collected from a broad range of account sizes, from small creators with less than 100 subscribers to those with over a 100 million subscribers. In our initial data collection, we searched for videos posted on YouTube between 2009 and 2019; however, we ultimately narrowed the scope of the study to videos posted between 2017 and 2019, the time period during which the majority of the videos collected were posted. This narrowed timeframe allowed us to focus on the most current discourses; however, an historical study that analyses changes in copyright discourse over time, in our view, is a topic worthy of future exploration.
From our initial data collection, videos were excluded from analysis if they were non-English language videos and if they were published by a formal institution such as a government agency or educational organization, to ensure we focused on the contributions of digitally situated cultural producers on YouTube. Our final dataset comprised 144 videos and included channels from both professional and self-identified amateur or hobbyist YouTube creators, covering a range of genres including gaming, tech tips, music, personal vlogging, visual arts and crafts, sports, comedy, and religious content (Table  1 ). For each video, we logged the video publication date, URL, title, account, channel, hashtags, and number of views, likes, comments, and subscribers. We conducted a pilot study of 20 (n = 20) randomly selected videos to develop a codebook.
Copyright Sentiment We first coded videos to determine sentiment toward copyright. Sentiment was manually coded according to whether the YouTube creator spoke positively or negatively about copyright law in general and copyright enforcement on YouTube specifically. Sentiment analysis was automatically conducted in NVivo based on notes and quotations from the pilot data to inform the subsequent manual coding of the full dataset. The majority of videos (79. 8%; n = 115) did not express any identifiable sentiment toward copyright law in general and less than half expressed no sentiment toward YouTube's copyright enforcement systems (42. 3%; n = 61). Of the subset of videos that did express sentiment toward copyright (20. 1%, n = 29), approximately half were negative (n = 14). By contrast, of the videos that expressed sentiment toward copyright enforcement on YouTube (57.
One strategy required YouTube creators to create their own original music and distribute it through a digital aggregator to ensure it is included in the Content ID database. The YouTube creator then creates a video that includes something owned by the allegedly abusive copyright owner, for example, a Taylor Swift song that will be claimed by UMG, and includes their own music as an outro. The YouTube creator can then share the revenue stream with UMG once the YouTube creator and UMG both claim ownership of the video. In the video explaining this strategy, this YouTuber suggested that this strategy could be used to abusively upload other copyrighted works, such as a Taylor Swift album, although they also commented that it might be "totally illegal and a bad idea" (2019-103) and might result in a copyright strike or violate the multi-channel networks terms of agreement.
e. , constant rates. Instead, the rewiring rules (17a) have rates proportional to N •-• ; according to (  16 ), we have that N •-• = N •-• x /(N X -1) * (for x ∈ {•, •}) , whence the firing rates of the rewiring transitions of the AVM model are found to be proportional to a state-dependent fraction of their mass-action rates! In contrast, the adoption transitions (17b) as well as the inactive transitions (17c) are found to follow standard mass-action semantics, which might raise the question of whether the overall "mixed" semantics chosen for the AVM model indeed reflect the intended intuitions and empirical findings. Finally, in view of simulation experiments, we would like to advocate the use of dedicated and highperformance Gillespie-style stochastic graph transformation software such as in particular SimSG  [12]  (cf.
Acknowledgment. Uwe Waldmann provided advice and carefully checked the completeness proof. Visa Nummelin led the design of the oSup calculus. Simon Cruanes helped us with the implementation. Martin Desharnais  generated the Sledgehammer benchmarks. Christoph Benzmüller ,  Ahmed Bhayat ,  Mathias Fleury ,  Herman Geuvers ,  Giles Reger ,  Alexander Steen ,  Mark Summerfield ,  Geoff Sutcliffe , and the anonymous reviewers helped us in various ways. We thank them all. Bentkamp, Blanchette, and Vukmirović's research has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 713999 ,  Matryoshka). Blanchette 's research has received funding from the  Netherlands Organization for Scientific Research (NWO)  under the  Vidi program  (project No.
Applying the procedure described by Ulivieri et al. 49  and Ripepe et al. 47  , the occurrence of significant activity at Stromboli is estimated from remote infrasound observations. Infrasound detections at all arrays are filtered according to the true back-azimuth to Stromboli (± 10°), band-pass filtered from 0. 5 to 2 Hz, and the mean amplitude (P) is calculated in a sliding window of 20 min. The time persistency of the signals is quantified by the number of detection per minutes (Ndt), normalized to 60. The mean amplitude (P) is corrected for propagation effect along each source-to-receiver paths in order to infer the source amplitude at a reference distance of 1 km   4  and 5 ), performed on the recorded waveforms band-pass filtered between 0. 5 and 2 Hz. To ease the comparison between data and simulation, picks are reported on the modeled waveforms (right).
Discussion The 2019 paroxysmal eruptions of Stromboli were clearly detected by IMS infrasound stations up to a distance of 3711 km. In addition to the IMS network, signals were also recorded by the national seismological network across Italy. The infrasound signature of the eruptions as measured by infrasound and seismic sensors has been modeled at local and regional distances (direct/tropospheric versus stratospheric returns). The amount and variety of observed infrasound phases represents a unique dataset for statistically evaluating atmospheric models, numerical propagation modeling and localization methods. At distance of about 170 km from Stromboli, transient signals were observed at four seismic stations (MILZ, IFIL, MSRU, USI) with arrival times consistent with T-waves. The timing of the T-waves is not consistent with the paroxysmal explosion but rather to a shallow volcano-tectonic earthquake reported by Giudicepietro et al.
Acknowledgements The authors are grateful to the  CTBT Organization  and IMS station operators for guaranteeing the high-quality infrasound data. The authors are grateful to  Maurizio Ripepe  for useful discussions. The authors also thank  Samuel Kristoffersen  for proofreading of the manuscript. This study was facilitated by previous research performed within the framework of the  ARISE  project  66  , funded by the  European Commission   FP7 and Horizon 2020 programmes  (Grant agreements  284387  and  653980 ).
Autonomous vehicles, such as drones, are gaining great popularity due to their usability and versatility. Nowadays, a significant number of them operate using open source software, such as Robot Operating System (ROS) and the de facto standard MAVLink communication protocol, as they are free and many of them are reusable enough that they can be deployed in various different vehicles. Although these technologies offer a wide variety of resources, using them requires a reasonable background of programming and system engineering. Often, this is not achievable by common drone end-users in the short-term, as they would need to acquire a considerably large amount of know-how before working on specific domains. However, a graphical Domain Specific Modelling Language (DSML) might provide a shortcut to design drone missions using already known concepts to the end-users (or, at least, ones easier to learn).
Thanks to MDE capabilities, it is possible to design a set of Domain Specific Modelling Languages (DSMLs) to ease the creation of a complex mission by an end-user, who would be able to work with a more intuitive graphical language. The desired behaviour can be modelled, and the model can be used to evaluate some of the system's characteristics before its construction, as well as automatically generate https://doi. org/10. 1016/j. sysarc. 2021. 102089 Received 18 January 2020; Received in revised form 11 February 2021; Accepted 5 March 2021 the necessary code that implements this behaviour in a real drone. The resulting program can then be deployed as a Ground Control Station (GCS) or as a companion board, carried by the drone itself. In this perspective, this paper presents RoBMEX: a ROS-based framework composed of a set of DSMLs dedicated to ease the specification of drone missions.
Background Drones work with a control software, also called autopilot: an algorithm that contains control loops to maintain the vehicle's stability. Amongst the available open-source autopilots, Ardupilot  [6]  and PX4  [7]  deserve attention for their wide reusability and modularity. The mentioned autopilots provide a basic set of predetermined behaviours (a. k. a. ''modes'') in which the vehicle can operate. Hence, they can be viewed as state machines, each state having different variables that are stabilised in specific control loops, such as the drone position, or its angular velocity.
g. GPS, GLONASS, BDS, etc. ) as an important constitutive part. Understanding the autopilot architecture, in spite of its modularity, is time-consuming, and manual adjustments are error-prone. If one would like to automatise the process, one should alter the autopilot code so that the drone has a ''Mission-execution'' mode, where the mission instructions would be embedded in the code of the autopilot. The mission would be executed when the instruction to change to this mode is received by the autopilot (from a MAVLink message, for example). This requires a considerable amount of expertise, and it requires the compilation and installation of new embedded software for every new mission that is going to be executed. An alternative to manually changing the autopilot code is conceiving a program that is deployed in another device, such as a companion board or a GCS.
This program communicates with the autopilot through some sort of bridge, like a communication protocol (e. g. MAVLink). For this, one can rely on existing application programming interfaces (APIs) to ease the design of new autonomous behaviours, such as DroneKit  [9]  and FlytOS  [10] , but learning their functions is still time-consuming and exclusively accessible to advanced developers. Hence, there is the need for an approach that is able to face the highlighted limits and facilitate the implementation of new autonomous behaviours. The model-driven engineering (MDE) settings  [5]  can help to achieve this goal by working with models of drone mission concepts as first-class citizens. Models allow the system to become more effective and efficient, since they provide more objective criteria and, therefore, a more appropriate view of the system in development.
RoBMEX methodology In this section, we explain what we propose as a methodology. For this, a certain scenario is considered to be true, and we created metamodels so that they can describe the missions that are instantiated in this scenario. At first, we consider that there exists a system running ROS that is able to link a drone to a ground station in a computer, which is the case for most open source autopilots. RoBMEX must be able to create launchable node codes to work in this environment, translating  a specified mission using a graphical language. The RoBMEX instance will then communicate with the autopilot software through MAVLink messages, as depicted in Fig. 2. Two main kinds of users are considered for the RoBMEX framework: • the end-user, who has a little or no knowledge of programming, but wants to order the vehicle to do something of his/her interest; • the programmer, who can develop functions to be used by the vehicle, but has little experience with ROS.
Both of them would have to use ROBMEX DSMLs to create instances of a ROS system. Hence, this metamodel shall be the first one created, using at least the middleware's basic structures, and being independent from any other metamodel created. With this metamodel, the developer can generate code with a well-defined structure to follow. Then, he/she would complete the missing parts with the required functionalities. In RoBMEX, this metamodel is called ROS Modelling Language (ROSModL). For the programmer, it is interesting to be able to create simple processes, such as variable comparisons or other operations, to save them in a library. Another user, like an end-user with no expertise, would access this library to compose his/her mission. For this, common operations such as mathematical calculations, variable definitions, logical evaluations and block structures (conditionals and loops) have to be modelled.
This metamodel, called ROS Process Modelling Language (ROSProML) in the RoBMEX framework, has to use some of ROS's definitions, which means that it needs to be dependent on ROSModL. At last, composing a mission needs not only the processes built on ROS, but connections defined between their inputs and outputs. The end-user must be able to organise the whole execution of the mission, without deep knowledge regarding the system details. That is why a metamodel of a mission, based on the previous two metamodels, has to be created. It is called ROS Mission Language (ROSMiLan). The three DSMLs interact in dynamic layers, as illustrated in Fig. 3 , composing the RoBMEX framework. ROSProML, represented above the others, defines the behaviour of an execution node, the operations made with the input variables, the called functions, and the values of the outputs.
RoBMEX foundations Let us recall that every DSML is based on two main concepts: abstract syntax (the metamodel structure) and concrete syntax(s) which can be textual, graphic or both of them. Therefore, this section is dedicated to detail the content of each one of the three RoBMEX DSMLs' abstract syntax. The following metamodels are expressed in Ecore language  [11]. It is a MOF (Meta-Object Facility) implementation inside Eclipse IDE (Integrated Development Environment).
RosSystem is the root class of ROSModL. Every instance of RosSystem has an attribute version indicating the ROS version the designer wants to use (e. g. , Indigo, Jade or Kinetic). This is very helpful to generate adequate code. Also, every instance of RosSystem is composed of a set of RosPackage instances. Each instance has a defining name, a path to its location on the current file system, and a boolean value indicating if it already exists (true) or if it requires to be created (false). A RosPackage instance may be composed of instances of Node, Topic, Service, MsgDataStruct, SrvDataStruct, and also other instances of RosPackage as sub-packages. Each instance of Node class has a name, an execution frequency, and may have a reference to existing code in case it is already defined in the file system.
It can provide or request access to any number of instances of Service, and publish or subscribe to any number of instances of Topic. With this defined metamodel, it is now possible to use a code generator (e. g. , Acceleo  [12] ) to automatically generate a compilable C++ code from any ROSModL instance, since almost every information needed to generate the ROS system code is contained in the model. In fact, a transformation code has been written in Acceleo to execute this transformation. After running the transformation code directly on top of a ROSModL instance, the generated C++ code serves as a skeleton for a ROS system, containing all files necessary to compile the system, including package files. The only information this metamodel does not address is that of the core algorithm inside ROS nodes: this is taken into account by the ROSProML metamodel.
ROS process modelling language The ROS Process Modelling Language (ROSProML) can instantiate a general process being executed in a ROS system. A process here is defined by a functionality, a set of instructions executed in order. The metamodel is presented in Fig. 5. Functionality is the root class of this model. Its instances are characterised by a name and a frequency of execution. They are composed of (i) a single instance of InternalBlock class as its main block of code, (ii) group of instances of DataPort as input and output variables, (iii) a group of instances of ServicePort and (iv) their related instances of ServiceBlock class. It is important to notice that the frequency of execution is, in this case, a reference value. In the process of generating the code,   for example, a Functionality instance will be referenced an instance of ROSModL, and this is the one responsible for determining the execution frequency.
As another example, the class Hardcoded represents an instruction that had to be written manually, so the designer can have the possibility to use features that are not described by the metamodel. ServicePort class connects some of its elements with ROSModL entities, in order to allow the connection between the two DSMLs. By using this class, a Functionality instance can be connected to a Service instance from ROSModL. With this definition, it was possible to implement an Acceleo code prototype that transforms the instances of this metamodel into C++ code.
ROS mission language The ROS Mission Language (ROSMiLan) metamodel, presented in Fig. 6 , has its root in the class BlockOfConnectedFunctions. This class is abstract, and its concrete heirs are the classes Mission and Multitask. Its instances have a set of at least one MissionMember ExistingTopic class inherits the characteristics of DataModule, but with an extra reference to a Topic instance from ROSModLtherefore, it is supposed to be used when one wishes to use a ROS topic already instantiated in an external system. MissionMember instances are defined by a name and may be of type Element or Multitask. An instance of Element has an execution frequency and either a core functionality or the path to an existing algorithm in the file system. This functionality is another link to ROSProML. An instance of Multitask, on the other hand, simply connects to at least two instances of MissionMember that will start executing in parallel.
Then, a ROSMiLan instance could be transformed in a ROSModL instance and, from it, have its code generated. This system has to contain at least an execution node, responsible for the first sequence of elements. When these elements are structured only in a sequential manner, there is no need to use more than one node, but every Multitask instance will require more nodes. Considering that each Multitask instance refers to a number  of ''children'' (elements referred to by a launch reference), this would represent the execution of  parallel tasks if all of them are Element instances. In other words, for the considered model, at least  nodes have to be in execution in a given moment. To ease the implementation, the original node, containing the Elements that came before the Multitask instance, will be assigned to simply wait for the execution of all the ''children'' nodes to finish, so it can continue with the sequence of Element instances later.
Tooling We have used the capabilities provided by Eclipse Modelling Framework  [11]  plugin to set up the metamodels of the three DSMLs as well as the tree editors dedicated to the instantiation. The development of another user-friendly graphical concrete syntax is in progress. This latter is based on the Sirius 2  tool. Fig. 9  shows the structure of the RoBMEX tool and the existing relationships between different components of the developed plugin, which expresses the proposed DSMLs. Furthermore, we have developed a C/C++ code generator. The implementation of this latter is based on Java programming language and Acceleo  [12]. The metamodels and transformation tools are available at: https: //forge. lias-lab. fr/projects/robmex.
The Land step will be executed after the reception of the relative command from the pilot, which will finish both the parallel tasks simultaneously. In the first variant of the case, the mission will be composed using modules that communicate directly with the drone's drivers. In a second variant, the modules have to communicate with an autopilot that only supports MAVLink communication and hence will need a kind of translation. For this, an existing ROS system called MAVROS  [13]  will be used. This system is a MAVLink extendable communication node for ROS with proxy for Ground Control Station, and it is responsible of converting data from inside ROS systems (in topics and services) into MAVLink messages and send them to an autopilot softwareand vice-versa. Using the ROS command rqt_graph after initialising MAVROS, it is possible to get a graphical representation of the MAVROS node in execution, as well as the topics and services it relates to.
Direct communication to drivers As a first variant, the mission is instantiated as in Fig. 12  using ROSMiLan. Each Element instance relates to a specific C/C++ file that contains the algorithm for the desired behaviour -either pointing to an already existing one at the location defined by its attribute ''existingFilePath'', or automatically generating the file in case it is non-existent. The instance of DataModule represents the value that is transmitted from ''EvaluateDistance'' to ''DecideMotionDirection''. The latter communicates directly with the autopilot. In this case, the referred C/C++ files can be created as instances of ROSProML. As an example, we instantiate the basic functionalities of the elements of Move, seen in Fig. 13  (reads distance and outputs the value of a comparison of the measured distance and an internal threshold, pointed by ''DecideMotionDirection'') and Fig.