dataesr/scientific-paragraphs-categorization huggingface dataset:
Picasso visualizes groups in form of graphs (circles as users and lines as social links) using Java JDK. In Fig. 5 , Picasso shows groups in form of graphs in two consecutive exploration iterations. Community Explorer (C-Explorer). In  [29] , a web-based community analysis tool is proposed. While it offers a natural support for attributed community search as its discovery method  [161]  (i. e. , finding communities as a function of both social links and user actions), it is generic enough to function with different discovery methods. In case more than one discovery method is functioning, a comparison analysis module is proposed to highlight differences in the results of each method. C-Explorer provides a by-facet exploration method. A visualization layer shows communities in form of graphs using JavaServer Pages (JSP).
In Fig. 5 , communities are mined on the DBLP bibliographical dataset. The name of a researcher (e. g. , Jim Gray) is given as a facet. Foresight. A recent trend in the DEV's literature is to tighten the discover-explore-visualize loop, so that analysts can jump to visual insights directly  [83] ,  [162] ,  [163]. In Foresight, visual insights are considered as first class citizens where analysts can instantly explore insights in a visual form  [83]. An insight is defined as one or several groups with outstanding statistical values (e. g. , a high linear correlation between a pair of attributes, the presence of extreme outliers for an attribute, etc. ). Sketches, i. e. , lossy representations of users data, is used to speed up insight discovery. Once insights are discovered, the analyst can either perform a by-distribution exploration to see other insights, or a byexample exploration to navigate other insightful groups.
Foresight employs different visual views to visualize insights, e. g. , scatter plots for linear relationships and boxand-whisker plots for outliers. In Fig. 5 , a visualization view of Foresight depicts the regression of net adjusted disposal income and the personal earnings for a group in OECD dataset. Aware. In  [158] , a statistical approach is proposed which validates whether a visual exploration on a discovered set of groups makes sense. This is extremely important to prevent analysts from making early (possibly incorrect) conclusions. Aware exploits PanoramicData visual interface  [164]  where analysts can perform by-facet explorations. Whenever a new facet is requested, Aware verifies and report False Discovery Rate (FDR) for the exploration. In Fig. 5 , FDRs for a by-facet exploration by age is illustrated.
Lumira is pixel-wise, i. e. , explored groups will be pruned to match available pixels to visualize  [166]. VAS is samplingbased, i. e. , it picks a sampling rate that fits the visualization criteria defined by the analyst  [165]. Ermac. In  [167] , the desiderata for a future data visualization management system (DVMS) is proposed. Ermac suggests to connect different UGA components together using a common declarative language and execute the whole UGA session with traditional database engines. This approach would ideally benefit from both representativity power of visualization and performance power of traditional DBMSs. Full Connectivity. There should be a generic transformation function which facilitates the handshaking between DEV components. Such function translates the output of each component to the input of the consecutive component.
The high confidence associated to a user study with a large population dissolves doubts on bias and incompletion. It is shown in  [177]  that for a dataset with |U| ≥ 100K, at least 1100 participants are needed to achieve results with an error margin of Quantified user study. User studies can be enriched with quantified measures to complement participants' answers. While responding to questions, measures such as time-tothink, mouse actions, eye movements, scrolling actions, dragging speed, number of backtrackings, number of cycles and number of restarts will be recorded for participants  [178]. This enables both qualitative and quantitative evaluation of DEV. Benchmarking. The quality of a DEV approach can be assessed by comparing it against standard tests, i. e. , benchmarks. Benchmarks are a common practice in the database community (e.
Acknowledgement. Kai Huang  and  Sourav S Bhowmick  are supported by the  Singapore-MOE AcRF Tier-2 Grant   MOE2015-T2-1-040. Shuigeng Zhou  is funded by the  Key Projects of Fundamental Research Program of Shanghai Municipal Commission of Science and Technology  under grant No. 14JC1400300. Byron Choi  is supported by  HKRGC   GRF 12201315  and  12232716. Acknowledgement. Kai Huang  and  Sourav S Bhowmick  are supported by the  Singapore-MOE AcRF Tier-2 Grant   MOE2015-T2-1-040. Shuigeng Zhou  is funded by the  Key Projects of Fundamental Research Program of Shanghai Municipal Commission of Science and Technology  under grant No. 14JC1400300. Byron Choi  is supported by  HKRGC   GRF 12201315  and  12232716.
more focused visual exploration. She focuses on group actions with PTC, on group changes with Data Tweening, and on transitions with Scented Widgets. However, in case simplicity has a priority, Vexus, FlashView and ConVis are the methods of choice. While FlashView builds visual explorations very fast, Vexus builds visualizations tailored for user groups. Also ConVis provides a by-facet exploration functionality which is preferred by domain experts. 16. VEGA documentation on signals: https://vega. github. io/vega/docs/ signals/ a.
The current cities' urban challenges go through digitalization and integration of new technologies under the perspective of actual and future ecological, as well as socio-economic commitments. This process is translated into the Open Standardized Urban Data Platform, which plays a pivotal role. Within its main functionalities, data ingestion, analytics and services as vertical domains become necessary to create more environmentally friendly cities. However, there still exist some deficits. Among them, openness and interoperability are outlined. On the one hand, there is a lack of open data initiatives for increasing the smart services stock. On the other hand, interoperability depends upon vendors and integrators, reducing the possibilities of Smart City growth. In this context, under the mySMARTLife project (GA #731297) umbrella, an Open Specifications Framework has been developed in order to address four main issues: (1) data interoperability; (2) services or verticals interoperability; (3) openness; and (4) replicability.
In short, the creation of digital and intelligent services will be (or even nowadays is) essential for the efficient management of the cities' resources. Among them, energy, water or transport could be mentioned. The implementation of Information and Communication Technologies (ICT) that support decision-making tools and services/verticals from a holistic point of view (e. g. , smarter urban mobility, waste disposal and energy management considered together) is, hence, the mechanism to provide more efficient ways. However, still, there is a lack of interoperability and interactivity between verticals that need to be solved whenever creating more sustainable-friendly cities. Under the digitalization paradigm, the implementation of Open Standardized Urban Data Platforms becomes crucial. Its definition is basically "the implemented realization of a logical architecture/design that brings together vertical data flows within and across city systems on a horizontal layer"  [3, 4].
For instance, in  [7] , the connection of co-existing IoT platforms under multiple domains is rendered, trying to integrate them by using software, from a systematic perspective. In contrast, others, like  [8] , are fostering the IoT interoperability via new open frameworks applicable at large-scale. Finally, studies such as the conducted in  [9]  are looking for ensuring interoperability from design, according to a similar conceptual approach. In this sense, mySMARTLife (GA #731297)  [10]  is tackling the interoperability from a cross-domain design perspective. The Smart City and its digital agenda, as well as a holistic point of view, should be considered in the first stage. Then, by establishing an Open Specifications Framework  [11] , the project aims to demonstrate, in the Lighthouse Cities of Nantes (France), Hamburg (Germany) and Helsinki (Finland), that the openness and interoperability of Urban Data Platforms are ensured, integrating the heterogeneous domains and data sources.
• Privacy and security. Besides, this Open Specifications Framework provides not only standard interoperable mechanisms, but also replicability and scalability features. Here, it should be noted that data are essential for the decision-making process. Therefore, the challenge is how to deal with the variety of data in a unified way and into a common operational framework. In order to achieve the aforementioned features, a five-step methodology has been followed. The first step has been the identification of existing initiatives at European level (including on-going projects), as well as research results, concluding in the background that is described in Section 2. Once strengths and weaknesses had been identified, the requirements of the cities (step 2) were collected with the aim of following a bottom-up approach.
(2) Support the devices data exchange by assets management. (3) Storage of data, aggregation and calculation of analytics. (4) Orchestration of processes and services, allowing human-machine interaction. (5) Generic City & Community capabilities, for general purpose services. (6) Specific City & Community capabilities, for specific verticals, e. g. , mobility. (7)  Focused on visualization of information for decision-making. Thanks to these capabilities, a set of specifications are overcome  [2, 3]. There is no direct link between the capabilities and the specifications, but the realization of these capabilities deals with the requirements that are listed below: -Interoperability as main pillar for urban data and infrastructures. With this multi-layered framework proposal, mySMARTLife improves it by including, on one hand, privacy and security mechanism before aggregation, just at the data exchange and ingestion level where it needs to be applied.
Other European Projects Not only are the aforementioned initiatives working on a common definition of Smart Cities Urban Platforms, but also, there exist several projects trying to address the conceptual approach, as well as interoperability matters. Some examples are included within the Synchronicity initiative of the European Commission  [16, 17]  under a common goal to ensure interoperable digital data platforms. Some of them are SmartSantander  [18]  or Organicity  [19]. Concepts for Smart Cities are commonly developed, integrating technologies like IoT, Big-Data, and new concepts like Web APIs and smart objects. The answers keep focus on industrial purposes in order to encourage the technology industry to sit together to reach a consensus for interoperable solutions. SynchroniCity  [16]  mainly aims to the creation of digital solutions where cities and businesses may work together to develop IoT-and AI-enabled services to improve the lives of citizens and to grow local economies.
g. , non-IoT data). Besides, it also presents an architecture for "live Singapore", but it lacks the value and knowledge extraction, being focused on semantic filtering to process data. Having said that, there exist other studies working on this paradigm. The authors in  [26]  present a novel web-based platform to that end, which includes data from heterogeneous sources via semantic integration. However, it is focused on public data and social media, which still is not the single objective of a Smart City, and is missing data from private companies (e. g. , energy generation plants). Something similar is considered in  [27]  with a Resource Description Framework (RDF)-based language for data vocabularies and relational data. As the authors already state, selecting the most suitable ontology is not easy and emerging technologies are important, such as SensorThingsAPI in the case of mySMARTLife project.
Furthermore, next step exposes data  [41]  to overcome with the next level of interoperability (northbound in the interoperability layer) by the publication of the datasets. Finally, services and third parties consume data to provide intelligent services and/or applications (intelligent services layer). In conclusion, mySMARTLife deals with the three levels of interoperability described below: • Foundational interoperability to assure data exchange between two information technologies, without the capability to interpret the data.
Hamburg Following a similar approach like Nantes, Hamburg is currently working with an Urban Platform in charge of data storing and analyzing unit for open and non-open data from different authorities, third parties and few sensor data. Furthermore, one of the main datasets is related to geospatial information classified into several categories  [11]. This approach has been extended following the open conceptual framework defined in mySMARTLife Project into System of Systems approach/architecture; extending an OGC-based Urban Data Platform with a similar one, based on the global oneM2M standard, as illustrated in Figure  7   [11]. This standard also allows heterogeneous systems to be connected. While cities have an interest in applications and standards with, i. e. , geospatial backgrounds like OGC, many industry players need a standard like oneM2M providing additional features like device or access management.
Helsinki Last but not least, Helsinki, which is also part of the SynchroniCity project  [16] , has adapted the reference architecture to the framework. Figure  8  and Table 3  [11]  show the reference architecture and its mapping into the framework, respectively. Like Nantes and Hamburg, Helsinki has extended an existing Urban Platform with the mySMARTLife new features following the Open Specifications Framework. Helsinki has developed the platform under the concept of City-As-a-Platform so as to link together several data-related services, providing a single point of service for developers, city officials and citizens requiring information in a machine-readable format. For the data ingestion, in terms of building energy, information requirements are derived from the Project Haystack terminology lists together with the Unified Code for Units of Measure (UCUM), which is based on ISO 18000 standard.
Context. Dusty high-z galaxies are extreme objects with high star formation rates (SFRs) and luminosities. Characterising the properties of this population and analysing their evolution over cosmic time is key to understanding galaxy evolution in the early Universe. Aims. We select a sample of high-z dusty star-forming galaxies (DSFGs) and evaluate their position on the main sequence (MS) of star-forming galaxies, the well-known correlation between stellar mass and SFR. We aim to understand the causes of their high star formation and quantify the percentage of DSFGs that lie above the MS. Methods. We adopted a multi-wavelength approach with data from optical to submillimetre wavelengths from surveys at the North Ecliptic Pole (NEP) to study a submillimetre sample of high-redshift galaxies.
This survey covered the NEP-Deep area (see Figure  1 ) area reaching a sensitivity of 2mJybeam - 1   (Geach et al. 2017). The SCUBA-2 catalogue at the NEP contains 330 sources which have at least a 3. 5σ detection. In this study, we considered SCUBA-2 de-boosted fluxes, which allow for a proper subtraction of the background and make possible to obtain more precise estimates of the total fluxes (as for the other catalogues used in this paper). These two data sets will form the basis of our selection methods that will be used to select high-z sources over the NEP area.
Mid-and near-infrared data The NEP field has been extensively observed by the AKARI satellite  (Murakami et al. 2007 ) as an AKARI legacy field  (Matsuhara et al. 2006 ). The NEP was in the continuous viewing zone of AKARI and was visible several times per day allowing deep near-to mid-IR imaging of the region. The NEP was observed in all nine bands of the AKARI IRC instrument  (Onaka et al. 2007)   Spitzer-MIPS data at 24 µm over a limited area of the NEP region is available through the Herschel Extragalactic Legacy Project (HELP) 1 , an ongoing program aiming to produce a multi-wavelength database sets for all Herschel extragalactic surveys  (Shirley et al. 2019). HELP uses the XID+  (Hurley et al. 2017 ) algorithm, a deblending prior-based source extraction tool to perform photometry at the positions of known sources in far-infrared confusion dominated maps from shorter wavelength (MIPS 24 µm).
The algorithm based on a probabilistic Bayesian framework which allows for the inclusion of prior information (beyond source positions and fluxes) to obtain the full posterior probability distribution on flux estimates rather than just the maximum likelihood. The availability of data with a higher spatial resolution at shorter wavelengths (optical, radio and midnear-infra-red) enables the de-blending of Herschel maps. We note that, although SPIRE data is also included in the HELP catalogue, the photometric errors in the SPIRE band are large (the relative error is near 40% on average in the SPIRE bands). Moreover, the NEP region the MIPS observations cover about ∼ 2deg 2 for a total of 20 mosaics, which represent approximately a quarter of the area covered by SPIRE in the NEP field. However, the HELP catalogue is extremely valuable and we exploit this catalogue to check the reliability of the techniques that we used to cross-match all the multi-wavelength observations (see Section 3.
Optical data Optical follow-up surveys have been made over both the NEP-Deep and NEP-Wide survey areas. The AKARI catalogue of Murata et al. 2013 accurate cross-matching of the IR detections with their optical counterparts using B, V, R, i, z, NB711 from SUBARU-SuprimeCam, u, g, r, i, z from MegaCam-CFHT and Y, J, Ks from WIRCAM-CFHT data (see  Murata et al. 2013 for details). The NEP-Wide catalogue of  Kim et al. (2012) , besides of the IR data, includes over ∼ 110,000 crossed-matched sources with u, g, r, i, and z band data from MegaCam-CFHT observations to a detection limit down to 26 magnitudes (4σ) covering ∼ 2 deg 2 of the central part of the NEP. Additional optical data from Hyper Suprime camera (HSC) on the SUBARU telescope  (Oi et al. in preparation)  were also included in our multi-wavelength dataset.
This data cover ∼ 6 deg 2 in the g, r, i, z, Y optical and near-IR bands. The final catalogue contains over two million sources with a 5σ limiting magnitudes of  27. 18, 26. 71, 26. 10, 25. 26, and 24. 78 mag [AB]  in the five bands respectively  (Goto et al. 2019). The catalogue includes 89,178 AKARI-IRC infrared sources that are already cross-matched with the observations in the optical bands. In total, there is a broad coverage across the optical and nearmid-IR part of the spectrum, covering 28 bands in the NEP-Deep field and 14 in the NEP-Wide field. The main characteristics of these catalogues are summarised in Table  1.
Spectroscopic redshift catalogues Very limited spectroscopic data is available in the NEP region, mostly located in the NEP-Deep survey area. Some of this data were collected in the HELP catalogue of  Shirley et al. (2019)  and is summarised in  Shim et al. (2013). The quality of the redshifts in the HELP catalogues is defined by a flag assuming a value ranging from 1 (unreliable) to 5 (high quality). There are ∼ 1100 sources with spectroscopic redshifts in the NEP region, however, the quality of these spectroscopic redshifts are all flagged as ≤3. In addition to the HELP spectroscopic sub-sample, we considered the catalogue of  Kim et al. (in preparation). However, the majority (84 %) of the sources reported in this catalogue are located at z < 1. We concluded that the number of sources with an available reliable spectroscopic measurement is not sufficient to complete a robust statistical analysis.
2014). Using these two criteria; SPIRE colours and SCUBA-2 flux, we selected a total of 291 high-z candidates using SPIRE colour criteria (268 with Amblard criteria, 23 risers and 140 with Yuan criteria) and 422 sources using the threshold applied to SCUBA-2 fluxes. We refer them as SPIRE selected sources, and SCUBA-2 selected sources, respectively. We noticed and expected that the number of potential high-z sources is significantly reduced when we cross-matched the submillimetre detections with the rest of the multi-wavelength data after checking the fitted SEDs for possible mismatches (see Sections 3. 2 and 4, respectively).
Multi-wavelength high-z catalogue The high-z candidates selected were cross-matched with the data available in the NEP region (see Section 2) associating every detection in each band to the closest counterpart in the others. The SPIRE maps suffer from lower resolution and photometric confusion leading to the blending of multiples sources due to the relatively large beam areas of Herschel-SPIRE, ranging from 17. 6 -35. 2 arcseconds from 250 -500 µm respectively  (Griffin et al. 2013 ). To minimise these effects and to aid in our multiwavelength cross-matching, the SPIRE catalogue of  Pearson et al. (in preparation)  was created via an iterative extraction procedure to optimise the detection of individual sources. A similar approach has been used to successfully extract sources for the Herschel HeViCS programme  (Davies et al.
2) ACCELEROMETER CORRECTION Assuming that the quaternion 1q acc corrects the prediction q pred to the real quaternion, we can write: q 0 k = 1q acc ⌦ q pred,k (38) In order to find 1q acc , we compute the modified accelerometer vector E g: E g = R(q pred,k ) E a = 0 @ g x g y g z 1 A (39) Then we define: 1q acc = 0 B B B @ +g y p 2(g z + 1) g x p 2(g z + 1) 0 q g z +1 2 1 C C C A (40) We combine the data from both the accelerometer and the gyroscope using linear interpolation by (i) defining the gain ↵ between [0, 1], (ii) updating the correction: 1q acc = ↵ • 1q acc + (1 ↵) • 0 B B @ 0 0 0 1 1 C C A (41) We normalized 1q acc and defined the corrected quaternion as: q 0 k = 1q acc ⌦ q pred,k (42) In our experiments, ↵ = 0. 1 produced the best results.
To achieve the parametric assumptions for ANCOVA we transformed all variables to "logx+1". After transformation, all the variables met the assumptions. We consider a significant result with a p-value <0. 05 but we used the FDA correction to correct all p-values  [39]  and used STATISTICA software for these analyses. We also performed a Non-Metric Multidimensional Scaling (NMDS) fitting the environmental and substrate size statistics as vectors to assess differences in composition of the macroinvertebrate community  [40, 41].
Acknowledgments Raul Acosta ,  University of Barcelona , identified the macroinvertebrate samples. Norlan Mejı ´a Martinez , of the Nicaraguan Company of Sanitary Aqueducts and Sewers (ENACAL), collected samples in mid-April and early May 2014, and conducted laboratory analyses of the periphyton samples. Assistance in field work was graciously provided by  Mario Gutierrez Alarcon of National Agricultural University , staff from the  Nicaraguan Ministry of the Environment and Natural Resources (MARENA) , and ENACAL. Evanor Martinez ,  Universidad de Leon  ( UNAN-Leon ), helped with macroinvertebrate sampling and sorting. Alev Bilginsoy  of the  University of California Berkeley  assisted with manuscript preparation and submission.