dataesr/scientific-paragraphs-categorization huggingface dataset:
Performance of the Automatic Detections Regarding the automatic detections, Sleep includes 6 robust algorithms for detecting some of the most prominent features of each sleep stage, including spindles, K-complexes, slow waves, REM, and muscle twitches. Spindle and K-complex detection algorithms were validated on a visually scored dataset including 210 min of N2 sleep from 14 participants and resulted in performances similar to those reported in recent publications. Last but not least, these detections are implemented inside the GUI in an ergonomic and intuitive manner. We think that these detections may represent a valuable help not only in the process of staging sleep, but also for researchers that are interested in the microstructure of sleep. The automatic detection algorithms proposed in Sleep can be used as a starting point for a semi-automatic procedure where users can correct or adjust the output of the detector.
FUTURE DIRECTIONS AND CONCLUSION We are considering to extend the list of the default supported files and we encourage programmers or sleep scientists interested by this project to collaborate on it. Regarding sleep analysis we are working on an automatic scoring function based on machine-learning algorithms, inline with our previous work  (Combrisson and Jerbi, 2015; Lajnef et al. , 2015b; Combrisson et al. , 2017). Finally, as different users have different needs, we are constantly improving the interface and functionalities of the software thanks to the feedback we receive. With the release of Sleep, we offer a portable and cross-platform software, installable and usable on most configuration. While there is still room for improvement, Sleep already provides a complete and intuitive interface designed by and for scientists involved in sleep research.
Snippet 1 | Simplest way to launch Sleep from a Python interpreter. This will open a window asking the user to select the EEG data and corresponding hypnogram. # Load the Sleep module from visbrain: from visbrain import Sleep # Open the default Sleep window: Sleep(). show() Code Snippet 2 | In this example, the paths to the EEG data and hypnogram are entered as inputs arguments of the main Sleep function, resulting in the software opening directly with the dataset and hypnogram loaded. We also show how to change the default display order of the hypnogram by changing the href argument of Sleep main function. The sleep stages will be displayed in the order defined in norder variable, with N3 on top and Art on bottom. # Import the Sleep module from visbrain: from visbrain import Sleep # Define where the data are located: dfile = '/home/perso/myfile.
Computational Implementation and Features SituMan is implemented on the Android platform because of its rich framework and wide acceptance. SituMan is composed of: (1) a shared Android service with an API to provide situation awareness to other applications (including MoodBuster) that relies on an infrastructure for context data gathering and a situation inference engine from gathered data; and (2) a mobile application (SituMan application). SituMan contains an Android service implementing an API to enable situation awareness in other applications. To this end, it defines some basic methods, which can be seen in Figure  4 , used in both SituMan and MoodBuster applications for: (i) maintaining situations in the situations database: addSituation and removeSituation methods; (ii) checking the activated and defined situations, i.
If this is the case, MoodBuster will request the patient to express his/her mental status and experiences using the graphical interfaces illustrated in Figure  1. Methods of this API are accessed via Android Interface Definition Language (AIDL) (http://developer. android. com/guide/components/aidl. html), where clients from different applications can access the service and non-primitive data are encapsulated in JSON format. AIDL provides a language to define a programming interface that both client and service agree to using, and they communicate with each other using interprocess communication (IPC), allowing different applications to access a service concurrently. The SituMan architecture integrated with the MoodBuster application is illustrated in Figure  5. Web services are used to receive information about mental status and experiences of the patients from the MoodBuster mobile application.
This information is sent to the Web services to be consulted by professionals via the Web application. The Web application is also used as a direct patient-professional communication channel, providing a means for professionals to perform remote interventions through the MoodBuster mobile application, such as the continuous adjustment of medicine prescriptions (e. g. , a psychiatrist observing a patient going from a depressed state to a manic state can adjust the medicine accordingly) and behavioural therapeutic feedback to the patient with instructions to patients on how to handle difficult situations (e. g. , a psychologist can send descriptions of actions to take when the patient is feeling depressed). The Situation Inference Engine component contains the fuzzy inference engine, which periodically performs the inference process and saves the result in the Situations Database.
This database is responsible for storing situations defined by users as well as all inferred situations. The Situation Inference Engine component is implemented with the jFuzzyLogic library  [43, 44]. jFuzzyLogic is an open source fuzzy logic library written in Java programming language used to simplify fuzzy systems developments. In this library, fuzzy rules are written using the Fuzzy Control Language (FCL), which is defined in the IEC 61131 Part 7 specification (http://jfuzzylogic. sourceforge. net/html/manual. html#fcl). The Situation Inference Engine component obtains context data from the Context Provider to perform the situation inference process. The Context Provider component is responsible for obtaining, formatting and providing context data. The time is obtained from the local clock of the mobile device.
Proposed Tools for Mental Disorder Treatments SituMan and MoodBuster applications were designed based on the following requirements: be intuitive and simple to use. In this way, they provide easy to use interfaces, requiring little technical expertise from the users. The SituMan Application provides interfaces to: (1) configure situations; (2) check the current user situation; (3) visualize and save a log file of the inferred situations from the Situations Database; and (4) guide the user on how to use the application and provide information about SituMan and E-Compared research objectives. In addition, the SituMan Applicationprovides a summary to search situations experienced by patients in a time period, such as illustrated in Figure  6. All inferred situations are stored in the Situations Database and can be consulted by mental health professionals.
Methodology and Participants We recruited participants from the Institute for Systems Engineering and Computers, Technology and Science (INESC TEC) and the University Institute of Maia (ISMAI) through project meetings and presentations. Volunteers were healthy non-depressed individuals who knew the project objectives and had knowledge of the research areas of intersection: mental health treatments, as well as information and communication technologies (ICT). We chose participants with these characteristics as they are the most likely to identify problems for the use of the proposed system in mental health. All participants were required to have an Android smartphone and to have good knowledge of the English language, because the mobile application and the questionnaire were produced in this language.
All subjects gave verbal consent and were fully committed to participate in the study. We had a total of nine participants (seven male) that answered the questionnaire: five psychologists (they hold a Ph. D. or were Ph. D. candidates in psychology) and four ICT researchers (similarly, they hold a Ph. D. or were Ph. D. candidates in the informatics area). All participants were Portuguese citizens and fluent in the English language. They were aged as follows: two were less than 26 years old, one 26 to 30, two 36 to 40, three 41 to 45, one 51 to 55. Prior to the study, the participants were instructed on use of SituMan, by means of: online documentation on a web-site, oral explanations and a live demonstration of realistic usage scenarios. Any follow-on questions subjects had regarding the operation of the SituMan were responded to by email or direct personal contact.
Results Figure  8  summarizes all results from the nine questions. The first five questions of the questionnaire evaluated the general usability of SituMan. All participants considered SituMan easy to use (Question 1), liked to use the interfaces provided to perform the functionalities (Question 2) and had no problem learning how to use SituMan (Question 3). All subjects answered "very good" or "good" when asked if error and alert messages are expressed in a simple and easy to understand language (Question 4). Moreover, all users also considered that few errors (bugs) caused by the SituMan occurred (Question 5). To evaluate the situation inference engine, the participants were asked if SituMan correctly identified their situations (Question 6) and whether SituMan identified their situations at the right time (Question 7).
Acknowledgments: The authors would like to thank  Fundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do Maranhão (FAPEMA)  and  Brazilian National Council for Scientific and Technological Development (CNPq) , as well as the European Community's  Seventh Framework Programme  ( FP7 ) for the financial support of this work. The authors also would like to sincerely thank the psychologists of ISMAI for their cooperation.
Spot Quantification. Spot quantification was performed as previously described  [11]. Gels were scanned at a GS-800 imaging densitometer (Bio-Rad) in transmission mode and the images were analyzed using the PD Quest 8 software package (Bio-Rad). Normalization of the individual protein spot quantity was performed according to the total density in gel image and was expressed as ppm. Comparison of the expression level of the various proteins spots was performed employing the Mann-Whitney statistical test. Due to the relatively low statistical power of the experiment ( = 4 per cell line), protein spots with fold change >2 were considered as differentially expressed and included in further analysis. However, in all cases Mann-Whitney test was also applied and results with a  value of <0. 05 were considered as statistically significant.
MALDI-TOF-MS (Matrix Assisted Laser Desorption Ionization-Time of Flight-Mass Spectrometry). MALDI-TOF-MS was performed as previously described  [11]. In brief, peptide masses were determined by MALDI-TOF-MS (Ultraflex TOF/TOF, Bruker Daltonics), peak list was created with Flexanalysis v2. 2 software (Bruker Daltonics), smoothing was applied with Savitzky-Golay algorithm (width 0. 2 m/z, cycle number 1), and a signal/noise threshold ratio of 2. 5 was allowed. For peptide matching (Mascot Server 2; Matrix Science), the following settings were used: monoisotopic mass, one miscleavage site allowed, carbamidomethylation of cysteine as fixed, and oxidation of methionine as variable modifications. Stringent criteria were used for protein identification with a maximum allowed mass error of 25 ppm and a minimum of 4 matching peptides.
Ingenuity Pathway Analysis (IPA). The identified differentially expressed proteins were subjected to IPA analysis (IPA, QIAGEN, http://www. qiagen. com/ingenuity) in order to perform pathway analysis and upstream transcription factor analysis. A manually annotated database of protein interactions and metabolic reactions obtained from the scientific literature is included in the IPA. Entry names of differentially expressed proteins were converted to gene names after their entry in the Retrieve/ID mapping of the Uniprot database (http://www. uniprot. org/). The processed gene names were listed in MS Excel and imported into IPA to map the canonical pathways and generate biological networks. Data were submitted as fold change values (ratios) calculated against the control group (HCK1T). Hypothetical networks were built among the experimental proteins and the IPA database proteins.
A total of 67 proteins were differentially expressed in the cancer cell lines versus HCK1T comparison based on the secretome analysis (Table  1 ). To confirm differences to total cell extract, a parallel analysis of the respective cell extracts was performed (Lygirou et al. in preparation). Following use of the SignalP software, 38. 8% of the 67 proteins from the secretome analysis were predicted to be classically secreted, in comparison to 7. 7% in the total cell extract. The (in total) 67 differentially expressed proteins identified in the secretome of cancer cell lines compared to HCK1T were then categorized according to their molecular function, by the Panther Classification System (http:// www. pantherdb. org/). The majority of proteins displayed catalytic activity (41. 6%), while 32.
Ingenuity Pathway Analysis (IPA) and Validation of Bioinformatics Analysis by Western Blot. To further characterize the biological functions and the pathways involved in the regulation of the differentially expressed proteins, we employed the Ingenuity Pathway Analysis (IPA) software. Initially, we compared each cell line with one another and following these comparisons, we proceeded to the comparison of all the cancer cell lines versus the normal HCK1T, as the most representative approach. The differentially expressed proteins of all the cancer cell lines compared to HCK1T, as well as their corresponding entry names and their corresponding fold change, are presented in Table  S8. This analysis was performed for the detection of putative direct or indirect interactions among proteins. Employing this approach, the most relevant associated canonical pathway was glycolysis (Table  2 ).
Previously used by INCOSE  [27] , the guide is the framework followed by Carillo et al. [3]  to assess the features of 38 tools. Even if the method is debatable since tool representatives have evaluated their own product through a questionnaire, the statistical analysis shows that users have an interest in graphical interface and data integration. Moreover, "flexible tracing" obtained a weak rating: tracing between different elements (e. g. text, graphics) is proposed as a tool improvement. Fig. 1. !A screenshot of the DOORS software, used to specify and trace requirements for complex systems Carillo et al. revise the framework with new features (modelling, traceability and collaboration) and report updated tool information  [5]. They find three clusters of RE tools with respect to RE capabilities.
C. !Data collection 1: Contextual Interviews We started with contextual interviews to understand the tasks currently performed by requirement engineers, and identify real life practitioners' problems. Contextual Interviews are one-on-one interviews conducted in the user's workspace that focus on observations of ongoing work  [25]. We asked ques-tions about what was going on in the work and flexibly reacted to the user's answers, investigating issues emerging during the interview. Our goal was to capture the actual RE activities in the workplaces, beyond self-reported practices or official processes (RQ-1). We interviewed ten engineers in their work environment. Each contextual interview lasted two hours. We video recorded them, except in the case of three interviews for confidentiality reasons.
Participants were enthusiastic about the treemap and we analyzed the reasons why the visualization is adequate: it allows them to see the overview of the requirements, focus on a level, and read the system requirement and the refined requirements at the same time for coverage analysis. The available actions on requirements were evaluated as helpful for refinement of requirement while reading. The link with the data model was also addressed through discussions on the right clicking actions. The black hole feature was evaluated as worthwhile for anomaly detection at a glance, in comparison with the tedious matrix reading. Hence, in order to conduct coverage analysis, RE-specific tools shall allow the requirement engineer to: •! Visualize the overview of requirements without any action (efficiency in overview understanding and communication to outsider) (treemap as an example of design) •! Access the requirements of a given level with a minimal number of actions from the overview visualization (efficiency of navigation) (treemap as an example of interaction design) •! Read all the refined requirement statements and the initial requirement statement always displayed in its entirety (efficiency in reading) (treemap as an example of design) •! Access direct actions on a requirement from the overview visualization: validate, reject, comment, modify (efficiency) (treemap with right click actions as an example of interaction design).
The iCub already integrates a mature technology  [87]  covering the upper body, legs and feet soles. Finally, several open-source software libraries have been developed in the last years to support research in whole-body dynamics and contact simulation. Several dynamics simulators have been developed for robotics (see  [37]  for a survey). The most interesting physics engines for our purposes are the ones with Featherstone-like forward dynamics calculation  [23]  [99], built-in collision detection and stable numerical contact forces computations  [64]. Among the kinematic and dynamic libraries it is worth citing HuMAnS, a toolbox for analysis and control of both human and humanoid motion, and iDyn, a generic software library for computation of whole-body dynamics and external contact forces of complex manipulators and humanoids  [36].
The model was devised from measurements on 13 male subjects. Using the kinematical data recorded with frequency of 100Hz, forces that the subjects exerted on the ground, and the forces in the handle, we performed an inverse dynamic procedure and obtained joint torques produced by muscles during the experiment. An illustration of the model is shown in the left panel in Figure  6. Using this modelling approach we are now able to efficiently study the biomechanics of humans in contact with the environment.
While controlling the robot in the proposed human-in-the-loop approach, the human central nervous During the learning and adaptation stage, the human performs the contacts with the environment through the robot (blue section). The acquired data was used to observe and study the human behaviour. When the human learning process and observation is complete, the learnt skill can be directly captured and used in the autonomous robot control (red section). This is the main advantage compared to the conventional biomechanical studies. system had to adapt to a new mechanism through sensorimotor learning to perform the desired contact with the environment. The main goal of studies of human behaviour in contacts with environment is to offer a basis from which we can devise equivalent humanoid robot behaviour.
The most appealing prospect of the proposed approach to study human motion in contacts with the environment is that the data from the study can be used to directly form skills for autonomous robot control. The sensorimotor data was collected while the human was making the desired physical contacts with the environment though robotic mechanism. This data was then used to form the trajectories. The trajectories were encoded with Dynamical Movement Primitives (DMPs)  [35]. The parameters of DMPs were learned by locally weighted regression  [85]. The learned trajectories represented the robot skill for dealing with the contacts with the environment according to the human strategy. The trajectories can be included into the robot control system and used for autonomous execution of the learnt task.
The best one is the overall velocity contrast between underlying bedrock and minimum velocity in the soil column. Because these are the most difficult and expensive parameters to measure, especially for thick deposits, other more convenient parameters are preferred, especially the couple (V s30 , f 0 ) that leads to a variance reduction in at least 60%. From a code perspective, equations and plots are provided describing the dependence of the short-and mid-period amplification factors F a and F v on these two parameters. The robustness of the results is analyzed by performing a similar analysis for two alternative sets of velocity profiles, for which the bedrock velocity is constrained to have the same value for all velocity profiles, which is not the case in the original set.
2008) , NGAWest2  (Gregor et al. 2014; Ancheta et al. 2014) , and GMPEs derived from the RESORCE database  (Douglas et al. 2014 ) still rely on V s30 to describe site conditions. It is sometimes complemented or replaced by other site parameters, such as the fundamental frequency f 0  (Castellaro et al. 2008; Luzi et al. 2011; Cadet et al. 2012; Pitilakis et al. 2012 Pitilakis et al. , 2013) )  or depth to a hard bedrock level defined with a threshold velocity (from 1 to 2. 5 km/s; see  Ancheta et al. 2014). The terms associated to such site proxies provide a mechanism for quantifying the frequency-dependent "amplification factor" with respect to "standard rock" (usually characterized by V s30 = 760 to 800 m/s). The same proxies are also used in regulatory codes to tune the characteristics of design spectra, i.
Seismic input SA(T ) b Fourteen input waveforms (S1-S14), recorded on outcropping rock, are selected from the RESORCE database  (Akkar et al. 2014). Their characteristics are listed in Table  1. The amplification factor defined in Eq. (  1 ) depends on the frequency content of the seismic motion (see, for instance,  Biro and Renault 2012; Renault et al. 2014; Bora et al. 2015 Bora et al. , 2016)). Therefore, it was decided to select real accelerograms, corresponding to near-source rock recordings, with a wide range of spectral contents, and derive the geometrical mean of the amplification factors obtained for each accelerogram. As illustrated in Fig. 1 , the spectral.
However, we developed our own MATLAB ® code and verified its accuracy against DEEPSOIL and EERA. In addition, the damping is estimated in relation to the quality factor Q Si using the well-known equation: (3) u i (z, ω) = A i e iω(z-z i-1) /V i + B i e -iω(z-z i-1) /V i e iωt (4) T f = u 1 (z = 0, ω)/2 = A 1 S1 S2 S3 S4 S5 S6 S7 Sm S8 S9 S10 S11 S12 S13 S14 Fig. 1  Spectral content for the 14 input waveforms considered for the computation of amplification factors. Each thin curve represents the normalized acceleration response spectrum S a,j (T ) bedrock /pga j of the jth acceleration waveform (j = 1-14), the thick solid line represents their geometrical mean Fig. 2  Schematic representation of the 1D site response analysis method for a site consisting of n horizontal layers soils overlying bedrock.
Soil profiles, database, and site parameters Overview of soil profiles a. Set 1: Real Profiles (RP) We consider three sets of soil profiles. The first one, termed RP, is composed of n P = 858 soil profiles. It was originally compiled by C. Cornou  (Salameh 2016; Salameh et al. 2017)  and consists of about 600 Japanese KiK-net sites, more than 200 sites from the USA, made available by D. Boore (http://quake. usgs. gov/~boore), and 22 European sites measured during the NERIES project (Di  Giulio et al. 2012). The main characteristics of this set of site profiles are presented in  Salameh (2016) ,  Almakari et al. (2016)  and  Salameh et al. (2017) : They are primarily usual (i. e. , normally soft, with S-wave velocities generally >200 m/s) and stiff soils, with shallow to intermediate thicknesses, <200 m in most cases, with only few sites-about 50-with fundamental frequency below 1 Hz.