dataesr/scientific-paragraphs-categorization huggingface dataset:
Online TCC Monitoring software provides more detailed reports of masking status, including the error type information (stuck, swing, noisy). The TCC monitoring is controlled by "ecalView", a web application designed to monitor the status of the ECAL off-detector electronics boards  [4]. Its main purpose is to report the occurrence of link errors, and check for pathological cases, where several neighboring towers are found to be in error. A failure pattern is formed by a minimum of three neighboring towers, including diagonals, and declared as a Single Event Upset (SEU). An auto-recovery procedure is initiated, the run is temporarily paused, the auto-masking suspended, links resynchronized, and link errors cleared. This procedure avoids running with significant parts of the detector being masked for long periods.
Conclusion The implementation of new automatic ECAL trigger tower masking and auto-recovery procedures was crucial for maintaining smooth ECAL operations during LHC Run 2. The operating efficiency was improved despite the much higher LHC beam intensities. The lost luminosity from ECAL trigger issues was reduced by a factor 10 between 2017 and 2018, making the ECAL trigger system one of the lowest sources of lost data in CMS during Run 2.
EEG Data and Processing. EEG recording and amplifying was through a 16-channel biosignal amplifier (g. BSamp, Guger Technologies) of gold electrodes. According to the 10/20 international system, the electrodes were placed at positions Fz, Cz, Pz, Oz, P3, P4, PO7, and PO8. e channels' reference was the right earlobe, and FPz was used as ground. rough the amplifier settings, the signal was bandpass filtered at 0. 5 and 100 Hz, the notch filter (at 50 Hz) was on, and the sensitivity was 500 µV. Next, the EEG data were digitized at a rate of 256 Hz by a 12-bit resolution NI-USB-6210 data acquisition card (National Instruments). Every aspect of EEG data recording and processing was controlled by the BCI2000 software  [20].
As every row and column flashed 10 times (i. e. , 10 sequences), each character was flashed 20 times. e mental task for participants to type a letter or a number was to mentally count every time that his/her desired symbol was flashed before the 6 sec pause. During the calibration task, the participants did not receive any feedback and were asked to focus consecutively on 16 characters to spell three French words and a number, all of them with four characters (four runs). e spelled words were "LUNE," "FEUX," and "KILO" and the number was "2015. " While they were doing the task, their EEG data were recorded in order to analyse them afterwards. e calibration task took approximately 10 minutes. At the end of the calibration task, a stepwise linear discriminant analysis of the last three runs was performed to get the weights of the P300 classifier.
Gaze Direction Control. In order to validate the experiments carried out under the covert attention condition, it was necessary to verify that the subjects fixated his/her gaze on a yellow dot placed at the centre of the screen. To this end, the gaze data were analysed offline during the evaluation phase (i. e. , the online task). e instructions demanded that subjects had to fixate the centre of the screen during the flash period for selecting a letter (30 s) and could gaze the target to locate it during the 6 s pause between flash periods. Due to participants having to spell 12 characters, the trial time period for one experiment was 432 s. Of this period, subjects had to gaze the centre of the screen for 360 s (30 s × 12 characters), enabling them to use the 6 s pause between flash periods to shift attention and gaze at the new character, that is, during 72 s (6 s × 12 period flashes) of the total trial time.
To this end, a novel and unique methodology is adopted: The authors' participation in the H2020 project termed "Training Research and Applications Network to Support the Ultimate Real-Time High-Accuracy EGNSS Solution" (TREASURE. http://www. treasure-gnss. eu accessed on 31 May 2019)  [3]  offers a privileged access to a wide GNSS user community involving big corporates, small and containers where innovative GNSS error mitigation algorithms are deployed as microservices to deliver high-accuracy positioning as final product, ensuring the integrity of the system, since the failure of a single application program interface (API) does not affect the whole system performance. The ICT architecture comprises data acquisition, algorithms for product generation, preprocessing and data consolidation, product distribution for real-time processing and product archiving for post-processing.
However, system components and required performance are assessed through a technical survey submitted to TREASURE's partners and ESRs. An added value of this work is the complementary and multi-disciplinary analysis of the commercial potential of the innovation proposed. As stated in  [13]  in relation to the proposed Ground-Based Scintillation Climatology (GBSC) software tool, the emergence of new user needs implies also business opportunities. Namely, malleability and optimized computational capacity of a cloud-based ICT infrastructure can pave the way for the provision of better-performing and affordable services in new domains of application. The performance of a market assessment highlights the business potential of the proposed ICT cloud infrastructure, on top of its technical appeal.
The design and operation of the ICT infrastructure are handled by an IaaS cloud provider  [9]. According to the technical requirements surveyed through a questionnaire, the users need a minimum of CPU corei7 2. 7 GHz, 8 GB memory and operating system-free, while most of the algorithms are MATLAB compiled software working on Windows OS. As cloud technology is elastic to users' needs, the hardware and software requirements can be customized on the request of users. Since IaaS-based cloud services provide scalable and elastic ICT infrastructures, the hardware capabilities can be tailored to fit user needs.
Data Acquisition, Storage and Distribution Input data are shown in Table  2. In particular, the identifier (INPUT ID) shows the input number and one or more letters indicating the coverage of the considered networks: Global (G), regional (R), and local (L). While some data are available directly from a local network, other data are received from regional and global networks through different communication protocols. Data size is also reported in terms of the size of a single file. Input data can be obtained either from external databases, or directly from local GNSS receivers or local storages. Each microservice receives different types of data, executing its specific algorithm on them. There is a need for GNSS data manipulation to be re-evaluated and re-developed in an optimized ICT model.
Data Distribution A data distribution service provides data to users via dedicated protocols. Concerning real-time output transmission, networked transport of RTCM via internet protocol (NTRIP) and file transfer protocol (FTP) communication protocols are leveraged to meet redundancy requirements. The realtime output is accessible for GNSS high-accuracy users through a web portal for post-processing. The output data is generated in different formats, which require data integration. This can be done through an API gateway. This data is available in local storage in proportion to data sum of individuals' microservices, monitored, and a database management model is given by an administrator. Table  3  shows the output of the proposed ICT system, in terms of the different product types: "Final", i.
e. , no further computation is needed by the end-users or "intermediate", i. e. , additional processing is needed. Data types and sizes (per single file) are shown in  Microservices are composed of data acquisition, an algorithm for product generation, filtering, distribution and finally data assimilation. The functionality of a microservice is designed to fulfill the following benchmarks: Agility, dynamic scalability, and resilience. In our work, we propose the deployment of microservices on a cloud where each microservice has a task to perform, identified according to the emerging user needs collected within TREASURE. The backend of the microservices includes a container and a database. The front-end is an API gateway, to which the microservices are connected through a bus, where all microservices bring a product to the end users.
Data Distribution A data distribution service provides data to users via dedicated protocols. Concerning real-time output transmission, networked transport of RTCM via internet protocol (NTRIP) and file transfer protocol (FTP) communication protocols are leveraged to meet redundancy requirements. The real-time output is accessible for GNSS high-accuracy users through a web portal for post-processing. The output data is generated in different formats, which require data integration. This can be done through an API gateway. This data is available in local storage in proportion to data sum of individuals' microservices, monitored, and a database management model is given by an administrator. Table  3  shows the output of the proposed ICT system, in terms of the different product types: "Final", i.
e. , no further computation is needed by the end-users or "intermediate", i. e. , additional processing is needed. Data types and sizes (per single file) are shown in Table  3  as well, apart from the data format already specified in Table  2 , *. mat are MATLAB proprietary files. Despite the increased complexity, securing a cloud-based microservices architecture is, by now, a highly studied problem, for which well-known solutions and common practices are available. The security objectives in the context of our ICT architecture are: Entity authentication, confidentiality, data integrity, and access control. Entity authentication refers to the process leading to the correct identification of the identity of a user. Confidentiality refers to the need to keep the information secret from all but those who are authorized to see it.
The performance of each microservice is evaluated based on the execution time and the size of the output data derived from the technical questionnaire. Performance of the whole integrated system shall be assessed whenever implemented in terms of overall timing, introduced delays and jitters. Some benchmarks have been introduced by  [10] , in terms of timing performance according to different configurations (RAM, CPUs, etc. ) of the virtual machines and different workloads. A similar approach can be adopted to assess our proposed system as a whole and each microservice whenever in place. However, each microservice is here individually evaluated in terms of the actual execution time in each local infrastructure through a technical questionnaire. The timing of each microservice will be assessed and expected to over-perform the minimum requirements reported in Table  4.
Configuration of the infrastructure on the public cloud is predefined providing easy and cost-effective implementation than the private one. This is the case when the network design is an issue. While Ground-Based Scintillation Climatology (GBSC) software tool, being the GNSS application tested in  [10] , provides ionospheric products for end-users based on a private cloud infrastructure with a preliminary performance result, our approach is architecturally promoting a larger GNSS application which allocates different GNSS products and services for a wide range of customers in near real-time and post-processing methods. To prove the technical feasibility of such a system, a user-driven methodology is developed. The input and output data of each microservice is explained specifically. Our theoretical solution is coming with technical feasibility supported by actual users who need GNSS correction products for improving positioning algorithms and to the end-users who inquire positioning services targeted the GNSS mass market.
When an action is taken, a numerical reward, r t+1 is attributed to this action. Such a reward is a feedback from the environment that depends on the quality of action taken by the agent. An agent that to maximize the total reward 1 3 The capacitated single-allocation p-hub location routing… attributed to it has to be able to exploit the experiences which it has obtained so far (memory) and also be able to explore other parts of the environment and identify better actions  Sutton and Barto (1998). In general, there are two types of reinforcement learning: (a) Online learning heuristic rewards are updated online (i. e. , dynamically) throughout the search according to the performance of every heuristic. (b) Off-line learning heuristic rewards are retrieved from a database, which stores the historical performance of every heuristic (often on similar class of problems) in the past.
Here, learning mechanism, which is presented in Algorithm 3, is inspired from a data-mining technique called Association Rules (AR) guided by a Tabu Search (TS). When looking at the principles of AR, one can observe that AR is actually a kind of reinforcement learning that helps in respecting the dependency order of heuristic applications. The AR learning mechanism is introduced by  Agrawal et al. (1993). This is a technique in data mining that has attracted many attention. Extracting interesting correlation, frequent patterns, associations, or casual structures from group of items in the transaction databases or data repositories are the purpose of AR technique. Traditionally, AR has been widely used in Market Basket Analysis  (Aguinis et al. 2012 ) to find how items purchased by customers are related.
Computational experiments We have generated our instances based on the well-known Australian Post (AP) data set from the OR-Library. The transhipment times are generated randomly for some real values within  [2, 5]  for the given time unit. The existing capacities of the original data (see  Ernst and Krishnamoorthy 1999)  are not used, because they do not always result in feasible solutions as the problem structures are different. We assume that our fleet of vehicles is homogenous and, therefore, has a unique capacity size. Let O i = ∑ j W ij and D i = ∑ j W ij and let p be the number of hubs. The capacity is generated as C = ∑ i O i + ∑ i D i p. All experiments were performed on an Intel 2. 54 GHz core i5 CPU and 4 Gb RAM on a machine with Windows 7 as OS. All instances are named in a format ni_pj_k , where i indicates the number of nodes, j indicates the number of hubs, and k indicates the factor of economies of scale.
Acknowledgements The BENMIP solver development has been supported by the  PGMO , the  Gaspard Monge Programme for Optimization and Operational Research , in the framework of the  BENMIP  project. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creat iveco mmons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Luna et al. [9] , for example, propose WebSpec which is a requirement artifact that can be used in conjunction with mockups to provide UI simulations, allowing some level of requirements validation, but not for out-of-approach UI prototypes like Balsamiq. Buchmann and Karagiannis  [10]  presents a modeling method for the elicitation of requirements for mobile apps that enables semantic traceability for the requirements representation. The method however is not focused on UI prototypes and can only validate requirements modeled within the approach. As far as a common vocabulary for the dialog aspect of a UI is at a concern, SWC  [11]  and SXCML  [12]  offer a language based on the state machine concepts. PANDA  [13]  is a tool which exploits the aforementioned ontology  [4] ,  [5]  to design medium-to-high fidelity executable prototypes allowing the use of interactive behaviors semantically meaningful to the user interface elements.
However, PANDA does not support the design and assessment of low-fidelity wireframes and sketches. Other solutions focused on generating UIs from other software models, which in theory would keep them consistent, is also a topic that has received attention for long time  [14] -  [17]. When considering user requirements specified through BDD to evaluate software models, studies have been conducted to explore its use within user-centered  [18]  and agile  [19]  approaches to support enterprise modeling, when analyzing automated acceptance testing to support BDD traceability  [20] , as well as its compatibility with business modeling  [21] ,  [22]  and BPMN  [23]. There is also an intrinsic relationship between user interface design and task modeling, when considered in a user requirements perspective.
Tool Support The algorithm presented in the previous section has been implemented in Java as an open source project and integrates different frameworks such as JUnit and JDOM. Fig. 6  represents the flow of calls we have designed for running tests on Balsamiq prototypes. The flow starts with the class "MyTest. java" that is a JUnit class in charge of triggering the battery of tests (its content is illustrated in Fig. 7 ). This class indicates which files will be used for testing (flow 1). These files are distributed in two packages. The first one contains the User Story files (". story" files where the scenarios for testing are), and the second one contains the Balsamiq files (which are the BMML source files of Balsamiq prototypes). Both the User Story and the Balsamiq files remain separate files in each package and are tested individually.
In summary, considering the presented architecture, to setup and run a battery of tests, we must: (i) place the set of BMML files that will be tested in the package "Balsamiq UI Prototypes", (ii) place the set of User Stories files (". story") that will be tested in the package "User Stories", (iii) indicate in the "MyTest" class which prototype will be tested with which User Story (only a prototype with a User Story at a time), and (iv) run the "MyTest" class as a JUnit test.
As an integrated approach, User Stories can also be assigned to automatically assess both task models, UI prototypes in different levels of abstraction, and final UIs, ensuring a consistent verification, validation and testing (VV&T) approach for interactive systems with highavailability of tests and immediate feedback about the consistency of artifacts and user requirements since the early stages of development. Next steps on this research include evaluating the impact of maintaining and successively evolving UI prototypes throughout a real software development process. Future studies should also explore the assessment of GUI prototypes using new interaction techniques, which has the potential to bring new challenges. Concerning the tools, the development of an Eclipse plugin to suggest and autocomplete steps of the User Stories scenarios based on the interactive behaviors of the ontology is also envisioned.
Procedures Participants were informed in advance that they had to retain faces for a later recognition task (for a similar use of intentional encoding in face recognition, see  Golby, Gabrieli, Chiao, & Eberhardt, 2001; Rehnman & Herlitz, 2007). However, in order not to interfere with the relationship status variable, no mention of it was made in the instructions. In line with studies on face recognition  (Rehnman & Herlitz, 2006) , our task included 48 colored faces, taken from the FACES database  (Ebner, Riediger, & Lindenberger, 2010) , including 24 male and 24 female faces aged from 18 to 25 years old. This age interval was chosen to control for any potential own age bias, that is, the fact that young participants perform worse in recognizing older than young adult faces  (Rhodes & Anastasi, 2012).
In the FACES database, faces had similar size, lighting conditions, and orientation. All faces depicted neutral expressions, so as to control for any potential effect of emotion on recognition  (El Haj, Antoine, & Nandrino, 2016; El Haj, Fasotti, & Allain, 2015; El Haj, Raffard, Antoine, & Gely-Nargeot, 2015). The task was designed with the Psychopy  (Peirce, 2007)  software package. We used a Dell laptop computer with a 15 in. LCD display. The task included an encoding, an interpolated, and a recognition phase. On the encoding phase, each of 24 pictures (12 males + 12 females) was presented for 1 s (interstimulus interval: 1 s). The encoding phase was followed by the interpolated phase (implemented to prevent reliance on immediate memory) in which participants had to read aloud strings of three-digit numbers for one minute.
GlobalBioIm is an open-source MATLAB ® library for solving inverse problems. The library capitalizes on the strong commonalities between forward models to standardize the resolution of a wide range of imaging inverse problems. Endowed with an operator-algebra mechanism, GlobalBioIm allows one to easily solve inverse problems by combining elementary modules in a lego-like fashion. This user-friendly toolbox gives access to cutting-edge reconstruction algorithms, while its high modularity makes it easily extensible to new modalities and novel reconstruction methods. We expect GlobalBioIm to respond to the needs of imaging scientists looking for reliable and easy-to-use computational tools for solving their inverse problems. In this paper, we present in detail the structure and main features of the library.
Unifying Framework for Solving Inverse Problems The forward models associated with most of the commonly used imaging modalities share important structural properties. This similarity is not surprising since many imaging systems are governed by the same physical principles (e. g. , the wave equation). We express in Table  1  the forward models of a wide range of imaging modalities in terms of a limited number of elementary constituents. By capitalizing on these strong commonalities, the open-source MATLAB ® library GlobalBioIm simplifies, unifies, and standardizes the resolution of inverse problems given by (2). Hence, the GlobalBioIm toolbox gives access to state-of-the-art reconstruction algorithms usable in a wide range of imaging applications. Its design is modular, with three main types of entities: forward models, cost functions, and solvers.
This permits the user to modify each component independently, which is crucial for the handling of a variety of imaging models and solvers within a common framework. This modularity also makes GlobalBioIm easily extensible to new modalities and novel reconstruction methods. GlobalBioIm is distributed as an open-source MATLAB ® software. We expect it to respond to the needs of imaging scientists looking for reliable and easy-touse computational tools for the reconstruction of their images. We also believe that GlobalBioIm will be of interest to developers of algorithms who focus on the mathematical and algorithmic details of the reconstruction methods. The present paper provides a functional description of the structure and the key components of the GlobalBioIm library. It completes and extends our previous brief communication  [38].
Related Work The development of open-source libraries/toolboxes in imaging sciences has received considerable attention during the past two decades. The majority of existing softwares for solving inverse problems are dedicated to specific modalities, with various degrees of sophistication. Moreover, they cover the whole panel of programming languages. There exists a large number of toolboxes dedicated to tomographic reconstruction for x-ray computed tomography, positron-emission tomography, single-photonemission computed tomography, or (scanning) transmission electron microscopy. These include among others ASTRA [39], CASToR  [24] , CONRAD  [22] , RTK  [29] , STIR  [37] , or TIGRE  [6]. For fluorescence microscopy, DeconvolutionLab  [31]  provides a set of deconvolution methods that range from naive inverse filters to more sophisticated iterative approaches.