dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, this control (non-imagery) task was performed first, in order to avoid any contamination from the imagery experience. We also faced a challenge in terms of analysis because we did not provide any material to the subjects prior the experiment in order to neither constrain nor contaminate their mental activity. We aimed at comparing, in both imagery and non-imagery conditions, gaze location at the time of uttering each town and the real location of this town on the map of France according to the Global Positioning System (GPS). We first adapted spatial correspondence methods developed and validated in a previous study without encoding phase  [10]  and applied them to our data set. Secondly, we tested powerful statistical tools which have been developed specifically for comparing bi-dimensional data (like (X, Y) coordinates of eye positions).
Apparatus The eye tracker used was a SensoMotoric Instruments (SMI) iView pupil and corneal reflex imaging system with a sampling frequency of 200 Hz and spatial accuracy of 0. 5u. It consisted of a scene camera and an eye camera mounted on a bicycle helmet. The outputs of the system were two temporally synchronised files: an MPEG video file and a data file providing gaze coordinates for each subject. A fixation was scored if the gaze remained stationary for at least 50 ms (ten consecutive measurement samples)  [35]  with a dispersion threshold of 1u on both X and Y coordinates.
Using BDR Matlab application developed by TJ Pingel (http:// www. geog. ucsb. edu/), we investigated the correlations between the gaze locations at towns' evocation (dependent variables, variant map) and the longitudes and latitudes of these towns, converted to planar (X;Y) coordinates (independent variables, referent map). Like uni-dimensional regression, the BDR provides for each subject a correlation coefficient (R) and a p-value according to the test F for regression. Chronological evolution of the BDR coefficient of determination. The BDR coefficient of determination (R 2 ) reflects the goodness of fit of the regression model. We studied for each subject, the evolution of the value of the R 2 based on the number of cities mentioned in their chronological order, to determine whether the correlation either gained or lost strength and when more towns (data, potential errors) were added.
Graphic Representations of the Mental Maps as Reflected by Gaze Positions Graphical representations of bi-dimensional regressions were realized using DarcyH2. 0 software  [37]. This software extracts graphics after a two-step process: 1) the ''adjustment'' between the two systems of coordinates (gaze and GPS coordinates) according to the BDR parameters (translations, scale adaptation and rotation) is calculated on the observed data points, and 2) the ''interpolation'' extends the adjustment algorithms to the entire studied area (here the map of France) in order to obtain an illustration of the mapping distortion. The interpolation process involves superimposing a grid on the adjusted image in order to obtain values at any point of the map surface.
Each drop would represent a change in one or more of the BDR adjustment parameters, i. e. a translation, a rotation or a scale variation at each new partial image generation rather than a decrease of spatial correlation. Each successive partial mapping can be objectified by illustrations provided by DarcyH 2. 0 Software  [37]. The variations of scales and orientations between successive maps are illustrated on Figure  2c  for a typical subject (see other subjects on Figures  S1-S9 ). The illustration of a unique visual representation produced for the entire sequence of towns is provided for a typical subject on Figure  3b. Even if the spatial correlation is significant for each subject, the graphic does not appear visually consistent with the real map of France. For comparison, Figure  3c  (see other subjects on Figures  S1-S9 ) presents the successive representations separated by the R 2 drops, which clearly show an improved visual consistency with the real map of France.
Obtaining a solution to the problem means that the given physical structure which contains the undesirable or missing interaction must be transformed into a structure in which the desired interaction is achieved. A system of seventy-six inventive standards was proposed by G. S. Altshuller  [1]  to indicate which patterns are to be used to appropriately transform a given Su-Field model. In the survey of "Worldwide status of TRIZ perceptions and uses" implemented by D. Cavallucci in 2009  [4] , two frequencies were obtained, that is, the frequency of TRIZ's main components (most unknown) and the frequency of TRIZ's main components (most often used), as shown in Fig  1 (a)  and  Fig 1 (b). According to these figures, we can observe that the pointers and the database of physical effects ranks highly in the list of the most unknown TRIZ components, and ranks lowly in the list of the most often used components.
Several kinds of invention software and databases were also explored. The CREAX Function database  [9]  organizes a database of effects by function, and uses a web-based application to support the search of effects. As a result, the user obtains a list of compliant effects. Invention Machine's Goldfire  [10] , is a commercial software that links a given function search to a compliant list of sentences extracted in both selected websites and patents that seemingly fit the required search. The Invention Machine Scientific Effects tool helps to stimulate creative problem solving by browsing and searching the Invention Machine Scientific Effects database. However, most of the search options depend directly on the use of the pointers of physical effects, which are chosen manually by users. Our research proposes another direction -to redefine physical effect as the change of two states, which makes it possible to search automatically the heuristic physical effects without the help of the pointers.
We take special care on power consumption measurement, performing an implementation of our scheme with the LTspice IV software  [6] , to better estimate energy requirements. The paper is organized as follows. Section 2 surveys related work. Section 3 describes the design of J3Gen. Section 4 defines the optimal parameters used in the construction of J3Gen. Section 5 evaluates the statistical properties, hardware complexity and power consumption of J3Gen. Section 6 closes the paper.
The three EPC Gen2 statistical requirements tests are presented. To accomplish these tests, 30 million 16-bit pseudorandom sequences are generated using an implementation of the J3Gen design. This dataset size is chosen since it is the minimum necessary for a truly generated random sequence to accomplish the proposed requirement  [5]. First, the probability of occurrence of any given value shall lie between probabilities defined in Equation  2. The results shown in Figure  3  confirm that, after analyzing 30 million 16-bit sequences, the probability of occurrence of any given value lies between the defined boundaries. Furthermore, J3Gen achieves similar statistical results with Random. org true random sequences  [38] , based on its frequency properties. The second property for building an EPC Gen2 compliant PRNG requires that two simultaneous identical sequences must not appear with more than 0.
Indeed, using custom values adapted to the J3Gen design, the average power consumption is estimated in P avg = 178 nW (readers can refer to  [5]  for details). After defining the design of the digital core of J3Gen based on GEs (cf. Section 4), we conduct an electronic circuit simulation of the proposed J3Gen construction, using very-large scale integration (VLSI). The simulation language SPICE  [40]  is used to simulate the circuit, and the LTSpice IV software  [6]  is used to represent the circuit using logical gates. The resulting simulation also allows us to demonstrate the fundamental concepts of the current construction of J3Gen and confirm its validity as a stand-alone device. Power dissipation is one of the most important factors in VLSI design and its technology choice. Therefore, accurate simulation of CMOS power dissipation using languages such as SPICE is highly desirable  [41].
Acknowledgements The authors would like to thank  Josep Domingo-Ferrer ,  Miquel Soriano  and  David Megías  for their helpful comments during the realization of this work. The authors thank as well the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. This work was partly funded by the  Spanish Government  through projects  TSI2007-65406-C03-03 E-AEGIS ,  TIN2011-27076-C03-02 CO-PRIVACY ,  CONSOLIDER INGENIO 2010 CSD2007-0004 ARES , and  TIN2010-15764 N-KHRONOUS.
Max-min is a particular case of α-fairness when the parameter α goes to infinity. The most important TCP variants are FAST TCP (  [15] ), TCP CUBIC (  [16] ) and TCP Compound (  [17] ), which are implemented in Linux and Windows, respectively. The main originality of our approach with respect to previously existing variants is that our index protocol aims at achieving efficiency and fairness on shorter-term time scales, by maximizing the mean fairness of the actual rate instead of the fairness of the mean rates. We have implemented our solution in NS-3  ([18] ) and performed extensive simulations in a benchmark topology to explore and validate the properties of the algorithm, and to assess the improvement with respect to DropTail router (that drops packets when the buffer is full) and RED router (that has implemented the Random Early Detection (RED) AQM mechanism).
In the following paragraphs we discuss several important features not explicitly covered by our model. Short flows. Our model assumes that the source has always data to transmit, so that the connection is permanent. This is a common assumption in the TCP literature, however, in today practice the flows are often short, intertwined by long periods without transmission. It is straightforward to extend our model to such flows: if the exact flow size is known by the source, then the MDP model will have a two-dimensional state (including the remaining number of packets alongside the congestion window); if (more likely) the exact flow size is unknown, e. g. , because of the user impatience or incomplete information, it could be modeled for instance by incorporating discounting. In either case, the indices are computed offline and the implementation would not be altered.
Timeouts and slow-start. Our model can be extended in order to include the slow-start mode of the TCP algorithm. A second Markov chain representing the slow-start mode must be considered so that we can interchange from one chain to the other, when the dynamics of the flow change from slow-start mode to congestion avoidance mode and viceversa. Although we are able to generalize the model to include slow-start, we leave it out of the paper, as we believe it would not affect the index values significantly. Many-flows scalability. The index policy requires packet header inspection, where the index value is read apart from the data (such as destination) currently read. Implemented naively by reading before buffering, this may cause delays and decrease the input capacity of the router. However, it may be perfectly acceptable to read the index once the packet has been buffered, thus avoiding any delays in processing.
Router-Only Implementation The packets may not come with their index in the header, either because the TCP sources are not cooperative, or because modification of TCP headers is not implementable, or because adding the index cell to the packet header of the current TCP variants is not acceptable. Then, our mechanism could be implemented at the routers only. The router must be able to recognize which packet belongs to which flow, the current window and the TCP variant (future behavior) of each flow, and compute (an estimation of) the index for each flow. In this case, a possibility would be that routers focus only on long TCP flows. Traffic measurements on the Internet have consistently showed that even though most TCP flows are small in size, the majority of data is carried by long TCP flows.
Pharmacovigilance plays a key role in the healthcare domain through the assessment, monitoring and discovery of interactions amongst drugs and their effects in the human organism. However, technological advances in this field have been slowing down over the last decade due to miscellaneous legal, ethical and methodological constraints. Pharmaceutical companies started to realize that collaborative and integrative approaches boost current drug research and development processes. Hence, new strategies are required to connect researchers, datasets, biomedical knowledge and analysis algorithms, allowing them to fully exploit the true value behind state-of-the-art pharmacovigilance efforts. This manuscript introduces a new platform directed towards pharmacovigilance knowledge providers. This system, based on a service-oriented architecture, adopts a plugin-based approach to solve fundamental pharmacovigilance software challenges.
Pharmacovigilance research is based on the analysis of "signals". The World Health Organization (WHO) defines signals as undisclosed assertions on direct relationships between adverse events -effects on the human organismand a drug  [8]. To generate comprehensive signal datasets, clinicians and researchers use spontaneous reporting systems (SRS). Electronic SRSs are already in place throughout some European countries and the USA. Likewise, other solutions, such as general practitioners' databases analysis, post market studies or prescription monitoring, among others, are being thoroughly explored. Nevertheless, the majority of data is not publicly available for researchers, which, jointly with other barriers, severely limits signal detection  [9, 10]. Although drug companies are required to track and manage adverse events reported by clinicians, lawyers or patients, the detection process relies mostly on the physician's ability to recognise a given trait as a drug adverse event.
The major difference resides on the partners' data. Whereas in OMOP most data stems from private contractors in the United States of America, in EU-ADR, data are obtained from European nationwide registries. Regarding the statistical analysis, EU-ADR's core longitudinal observation algorithms are LGPS and LEOPARD  [21]. Despite featuring a distributed architecture, Mini-Sentinel is very different from the setup used in OMOP and EU-ADR. Whereas in the latter projects data are summarized and submitted for statistical analysis, in Mini-Sentinel data queries go through a complex network  [22]. Like similar projects, partners in the Mini-Sentinel program translate their dataset to the project's CDM. However, in Mini-Sentinel, data never leaves the original institution  [23]. This addresses FDA's concerns regarding security and privacy.
The EU-ADR Project The foundation for EU-ADR's strategy relies on in-depth semantic data mining of electronic health records from several European countries. This process generates filtered data that can be easily substantiated through distributed computational tools  [26]  -Figure  1. Project partners provide demographics, drug use and clinical data for over 20 million patients from several European countries. These data include clinical history, drug prescriptions, vaccinations, or lab test results  [27, 28]. From a pharmacovigilance perspective and in a European or worldwide scale, mining the amount and type of data collected in these databases is of tremendous importance for an improved postmarketing drug evaluation  [29]. Data mining techniques are used to extract the most relevant information from these data sources  [30, 31] , taking in account the privacy and ethical concerns regarding the collected data  [32].
Next, data are harmonized into a unified dataset containing the list of drug-event pairs identified in the mined records  [33]. This initial detection process generates a raw signal list, as the signal detection techniques highlight all possible relationships discovered in the wealth of collected data  [34]  -Figure  1-1. As mentioned, the creation of these rich signal datasets is a well-established task. However, the actual data analysis and exploration tasks are still missing. Each signal in the raw list provided by the data mining tools must be substantiated for adequate validation. That is, the signal must be analysed by multiple algorithms to identify its real risk, and, if it exists, to provide a scientific explanation behind the cause, the drug, and the effect, a specific adverse reaction.
Web engine Internal Google Web Toolkit The web engine powers the distributed platform user interactions through an innovative web-based workspace. doi: 10. 1371/journal. pone. 0083016. t001 schema covers the domain-specific errors with a set of "status codes" for each of the possible conditions. For example, status code with the value "41" identifies an internal service problem regarding the database connection. • Scoring. Each of the signals in the ranked list has a score that determines its relevant risk within the dataset. When the data are being assessed by the knowledge providers, the scoring attributes will provide each evaluated signal with a numeric value, between 0 (zero) and 1 (one), measuring the relative relevance and impact according to the scientific evidence found to explain the interactions of a given drug-event pair.
A crucial workflow requirement is that the inputs of each activity must match the precedent activity outputs to maintain consistency, a feature already accomplished with the platform interoperability standard. Dealing with workflow execution operations requires the implementation of workflow management applications, whose goal is to abstract the programming side of the application, assisting in the creation of workflows without writing a single line of code  [37]. Taverna emerged as the de facto standard for desktopbased workflow management in the life sciences  [38]. Taverna's success is due to its flexibility, which allows researchers to create complex workflow-based algorithms just by dragging and dropping boxes in its workbench. Alternatives to Taverna, such as Galaxy  [39]  or BioFlow  [40] , are focused on providing workflow management functionality in a webbased interface.
The knowledge base is stored on a cloud environment  [41]. This means that while the underlying data storage layer is distributed through multiple independent data nodes, the access is unified and centralized through a single access point. Common data storage issues such as persistence, security and access are controlled by the cloud-based layer, leaving the relevant data handling tasks to the platform engine  [42]. In the EU-ADR project context, five key datasets are stored, detailed next. • Drugs. Dataset containing the complete list of ATC codes and respective drug names. • Adverse events. Dataset listing the adverse events mined from the project's pharmacovigilance data. • Imported data. Researcher-submitted datasets containing statistical data regarding specific drug-event mapping conditions.
• Results. Datasets with the results from the knowledge providers' algorithms. • Users. Dataset containing the user details and sharing/ collaboration preferences. Collaboration features are implemented according to two distinct methods: project-based and ad hoc sharing. With the project-based collaboration option, new projects with any number of users can be configured. This allows a broad number of users to manage a topic-specific dataset, fostering a deeper collaboration amongst researchers through the sharing of submitted data and obtained results. Ad hoc collaboration is a user-specific approach. Users can share their datasets and results to any other user in the platform through their registration email. This is a more granular approach, where the users can define what data to share and what their collaborators can view or change.
Accessibility. Accessing knowledge and executing available features are key challenges behind pharmacovigilance software  [43] : managed data and knowledge providers' algorithms must be accessible at all times. To accomplish this, the architecture relies on two advanced components: the platform engine and the web engine. The former is the main application controller, coordinating all the others components. The latter manages the presentation layer, providing access to a web-based workspace. The implementation of both is detailed in Figure  4. Taking into account the accessibility and interoperability requirements, the platform engine is implemented as a Java web application. For improved data handling, Hibernate (http:// www. hibernate. org/) was used as a data abstraction layer and object/relational mapper, thus reducing database coupling with the application.
In a sense, the platform engine is an intelligent proxy, coordinating everything that happens with the distributed platform internal components. The web application engine adopts a Model-View-Presenter pattern and is implemented with the Google Web Toolkit (GWT) framework (https://developers. google. com/web-toolkit/). GWT compiles Java code to a browser-targeted JavaScript representation, resulting in an extremely effective web application. In addition, various user interaction components were added to provide a cleaner perspective over the huge datasets and easy access to data analysis and exploration features. To improve on GWT's user interactions library, the Ext GWT package (GXT) (http://www. sencha. com/products/ gxt) was used. This extends the widgets bundled with GWT core distribution to provide a more complete set of user interaction features required by the presentation layer.
The combination of GWT's basic widgets with GXT ones was further improved with Google Gin (http://code. google. com/p/ google-gin/) for dependency injection, achieving a decoupled architecture. Security. Security is a primary concern for any new software, especially considering the rigorous constraints of this field, for both researchers and private pharmaceutical companies  [44, 45]. In the proposed architecture, security Figure  3. From user input to system output the platform engine controls the execution of workflows as follows:. 1) One or more knowledge provider algorithms are selected to evaluate researcher-submitted datasets. The platform engine sends the request to the service execution engine. 2) An XML file with the input data (obeying the platform's interoperability standard) is generated and its path provided to the service execution engine, along with the path for the workflows associated with each knowledge provider algorithm.
Results The pharmacovigilance context opens various opportunities to build new data analysis and exploration ecosystems. With collaboration from partners within the EU-ADR project it was possible to implement a prototype of the proposed distributed platform. The involvement in a large-scale European project allowed for the implementation of real-world algorithms by multiple knowledge providers. This partnership brought access to a comprehensive dataset of drugs, events and statistical data. The resulting EU-ADR Web Platform, is available online at http://bioinformatics. ua. pt/euadr/. This portal is being used within and beyond the EU-ADR project scope, generating successful research results in various areas, such as the identification of drug agents causing acute myocardial infarction  [46].