dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, experts are able to explore and guide their exploration through a large network by simply analyzing a series of reduced subnetworks instead of the entire network. The M-QuBE 3 process is split into three main phases (Fig. 2 ). The process starts with a keyword search (Panel A). Its results, i. e. the selected nodes, form the initial focus set (A3): a list of reference nodes that allow to define candidate nodes that may potentially be displayed in the extracted sub-network. A score is calculated for these candidate nodes by considering the semantic information of the dataset and the network structure in order to estimate their interest for the user (Panel B). From these scores, a ranking is made to determine a list of the most interesting nodes (chosen list) which are then used to extract the sub-network which is shown to the user (Panel C).
Because experts start with a detail and progress step by step, the M-QuBE 3 process has a similar workflow. The M-QuBE 3 process may be repeated at will in order to explore the data more and more deeply and with greater precision. The user interacts with the resulting sub-network at each iteration by selecting new nodes which they deem relevant. The nodes enrich the focus set and thus improve the next sub-networks. For this purpose, the focus set is kept throughout the series of sub-network extractions and used to compute the next sub-network. We detail below the different steps that make one iteration of the process.
However, a high Figure  2 : Pipeline of the computation of sub-networks of interest for the user from the initial 'hairball' network. This process starts with a selection operation (A) to define a candidate list i. e. a list of nodes potentially shown in the extracted sub-network. Then, a score is computed (B) to represent the interest of the user for these candidates. This score is used to rank the candidate lists. The most interesting nodes are chosen and a sub-network is build by adding every existing edges between these nodes (C). Once the sub-network is extracted and shown to the users, they (un)select one or more nodes in it and repeat all of the previous phases again until they reach satisfaction. degree of diffusion can affect process performance depending on the data if the network is very connected.
Steering functions categorisation Determining an interest measure from a node can be derived either from the semantics of the data (e. g. , text field search, node hand selected by the user), the topology of the network (e. g. , centrality, degree, part of a clique) or, in some cases, both. Steering functions therefore follow this pattern. In addition to this, we define two pairs of categories: the "variation category" ("focus set based" or "constant") and the "layer category" ("single layer" or "layers association based"). The steering functions are determined as a combination of each of these categories which can be also related to the topology of the network, the semantics of the data or both. We then obtained a mathematical model of the user will for each layer or group of layers. We illustrate the following with examples from our DH data set.
Dataset Our historians work with a database containing documents as well as various automatically extracted information on political figures, institutions and places for a total of about 150,000 elements. Documents are heterogeneous in terms of their media types (e. g. text, image, video, audio) as well as in terms of their various forms with, for example, press releases, interviews, articles, etc. Institutions (the French state, the European Council, etc. ), places (city names, country names), persons and social groups (gatherings of persons, associations) are linked to the documents that reference them for generating a network. Thus, a press article on a debate between two politicians is linked to these two politicians in the network. For media such as images or videos, if an element appears or is referenced, it will also be linked to this resource.
We therefore have a graph with these different elements as nodes and the different links between these elements as edges. All these nodes and links can be articulated in a vast multilayer network where the different types of nodes (persons, places, articles, videos, etc. ) determine the layers. This data provide an ideal testing ground for M-QuBE 3 : historians want to find relevant documents and entities to enrich online publications on different aspects of European integration history. Below, we detail two sample use cases using M-QuBE 3 to find new documents and elements in the database starting from a general query. These two use-cases have been suggested and validated by the historians we are collaborating with.
Acknowledgements This work was (partially) funded by the  ANR  grant  BLIZAAR ANR-15-CE23-0002-01  and the  FNR  grant  BLIZAAR INTER/ANR/14/9909176. Thanks to historians of the CVCE and particularly to  Marten DURING  for the constructive working sessions as well as  Fintan Mc Gee  and  Mickaël Stefas  for their implementation work and their support.
The goal of this study is to evaluate the thermal properties of Köyceğiz Lake using in-situ measurements and satellite based thermal infrared imagery. In-situ measurements of surface water temperature and water depth, as well as meteorological data, were used in the analysis. Images of the Landsat 8 TIRS (Thermal Infrared (IR) Sensors) at IR channels were taken from the data archives of United State Geological Survey (USGS), and were validated with surface in-situ measurements. Specific Electrical conductivity (SEC) measurements done at the bottom of the lake were also used for a better understanding of the water thermal stratification and types (cold/hot) that impact on the underwater physical properties of the lake. In-situ measurements at the water surface were interpolated using a new method known as 'Empirical Bayesian Kriging (EBK)'.
There are also several remote sensing studies in the literature that provide surface water temperature monitoring. For example, spatio-temporal information coming from different electromagnetic band spectrums of Landsat 8 can be used in water physical condition monitoring. Landsat 8 satellite was launched in February 2013, which provided remote sensing data at high spatial resolution using the Operational Land Imager (OLI) and the thermal infrared sensors (TIR S). TIRS bands measured reflected radiance at 100 m spatial resolution at the surface using two IR bands (10 and 12 µm). They were resampled by interpolation methods at every 30 meters to match the resolution of OLI multispectral bands  (Lamaro et al. 2013). They stated that Landsat 7 ETM+ satellite data were used to produce a TIR image at the water surface from 8 µm to 15 µm.
In-Situ Measurements Using the floating platform of Muğla Sıtkı Koçman University and YSI 6600 device (Fig. 3 ), SEC, temperature and depth values were measured horizontally along a dense and staggered grids in Köyceğiz Lake  (Fig 4). The water depth temperature and the coordinates were also simultaneously recorded by a sonar-mounted GARMIN 421s GPS device. The coordinates and in-situ measurement data sets was matched by using time data. When the target point was reached, the YSI 6600 device was lowered to the bottom of the lake and the temperature, pH and EC measurements were made along vertical profiles. The details of in-situ measurement are given in  Table 3.
Thermal -infrared image analysis The dataset used in this study is presented in Table  4. The metadata used from Landsat 8 TIR for Band 10 (wavelength: 10. 6-11. 19 µm) is given in Table  5. For Landsat 8, Band 10 is used to estimate the temperature value of each pixel in the image. All Landsat images were further rectified to a common Universal Transverse Mercator (UTM) coordinate system (WGS 84 datum). Table  6  gives the temperature values at the time of the images were recorded. 1 ) and Eq. (2). Table  5  shows the parameters that are needed for satellite based brightness temperature calculations that can be done as: = + , (1) where, M is the radiance multiplier, DN the pixel digital number, B measured reflected radiance, and TOA (top of atmosphere) spectral radiance in units of W m -2 sr -1 µm -1.
Discussion and Conclusion This study provides a different approach for mapping water temperature variations of Köyceğiz Lake, Turkey. This study shows the capability of high spatial resolution of in-situ measurement and satellite images in providing quantitative data for mapping the water temperature variations. This study also demonstrate that the surface water temperature can be correlated with Landsat 8 satellite images. Deep water temperature and SEC measurements evaluated by using different interpolation techniques and they were used to validate several water inputs (hot/cold) to the Lake. The following conclusions can be made according to the obtained results: • The comparison of Landsat 8 thermal images shows a strong correlation (R 2 >0. 68) with the in-situ measurements of the surface water temperature.
In December 2018, the NASA InSight mission deployed a seismometer on the surface of Mars. In preparation for the data analysis, in July 2017 the Mars Quake Service initiated a blind test, in which participants were asked to detect and characterize seismicity embedded in a one Earth year long synthetic dataset of continuous waveforms. Synthetic data were computed for a single station, mimicking the streams that will be available from InSight as well as the expected tectonic and impact seismicity, and noise conditions on Mars  (Clinton et al. 2017). In total, 84 teams from 20 countries registered for the blind test and 11 of them submitted their results in early 2018. The collection of documentations, methods, ideas and codes submitted by the participants exceeds 100 pages. The teams proposed well established as well as novel methods to tackle the challenging target of building a global seismicity catalogue using a single station.
Purpose of the Test The blind test was initiated with the main purpose of improving and extending the set of methods for event location, discrimination and magnitude estimation as well as phase identification and source inversion to be applied in routine analysis of the InSight data set by collecting ideas from outside the InSight science team. It also helped to raise the profile of the InSight mission and to familiarize interested scientists with the data set to be expected from Mars. Beyond this, the test also initiated a major effort to generate a single, consistent, temporal, synthetic data set that collected all best pre-landing estimates of seismicity, impacts, synthetic seismograms, atmospheric pressure variations and related noise, instrument self-noise and 1D structure models. The data set was made available in the same formats, and using similar web services as are now available for the real data from Mars.
For this reason, the data set was also used for various operational readiness tests as well as scientific testing purposes in preparation for data return. Furthermore, the submitted catalogues allow to derive detection and location thresholds as a function of magnitude and distance, that are not based on simple signal to noise ratio assumptions, but include the whole complexity of identifying and locating events in the time series. It is important to note though, that this data set included randomly distributed events over the sphere. Compared to the global fault distribution  (Knapmeyer et al. 2006) , this model may have too many events near the landing site, so the total number of detectable events in this dataset may be higher than predicted by recent seismicity models of similar total activity  (Plesa et al.
Participation and Methods In order to ensure effective communication with participants or anyone who wanted to experiment, registration for the test was mandatory for accessing the dataset. On the other hand, participation was completely voluntary; but we strongly encouraged all registrants to submit their results, particularly with event catalogues. In total, 84 teams registered and 11 of them submitted their analysis. Due to the lack of feedback, we do not have a further overview on how test data was used by other teams that downloaded the data but chose not to participate. The participating teams were composed of researchers both from inside (IPGP, MQS, Max Planck) and outside (Colorado, Geoazur, Houston, Utah) the InSight science team. Participant profiles were rather diverse including senior researchers as well as PhD (Bochum, Oxford), masters (Hamburg) and even high school students (SEISonMars@school).
Comparing seismic and pressure data visually allowed these teams to exclude most non-seismic events. MQS produced daily spectrograms that were visually scanned by different members of the team, which proved a very effective way to maximize event detection. • The green bars represent the number of events for which full location information was provided (origin time, distance and azimuth). • Finally, the red bars represents events that were located within the InSight mission L1 requirements for location accuracy. Figure  6  shows a more detailed view of the 10 submitted catalogues, highlighting false detections (blue vertical lines) as well as detection and location of quakes (circles) impacts (star symbols). The rate of correct detection and location as well as false detections varies significantly over the time span of the dataset.
Distance Estimation Distance estimation (Fig. 8 ) was complicated by the low velocity layers in the upper mantle, which made S-waves very hard to identify in the data with the given noise. An easy estimate based only on the traveltime difference between P and S phase could hence not be applied to most events. On the other hand, Rayleigh wave group arrival times could be used with unrealistically high accuracy in this 1D model, which is one reason for running the current ORT with 3D synthetics. This new test suggests that including estimates of crustal thickness variations from gravity  (Wieczorek and Zuber 2004) , topography from MOLA (Mars Orbiting Laser Altimeter), and ellipticity lateral variations of surface wave arrival times of up to a few hundred seconds should be expected. An additional simplification was employed by most teams by determining the correct model from the 14 candidate models based on the biggest event in the dataset (see table  3 ) and then using that model to locate the smaller events.
In practice, a number of small events are expected to be seen in the data before any event that is big enough to constrain the model. To add this complexity to the problem, the data in the new 3D test was released in weekly chunks. The MQS catalogue included a data quality classification, where reliable locations where classified as quality A, unreliable locations as quality B, and very unreliable/unconstrained locations as quality C. This figure indicates that only class C and a few class B events could not be located correctly  (Clinton et al. 2018).
Impact Discrimination Only one team (MQS) classified the event type as quake/impact in their catalogue. Only a single event was identified as an impact, which was correct, and no other event was mis-labeled as impact. MQS did miss the biggest impact event of the dataset in the detection stage. Hence we cannot evaluate the distinction capability in this test and just document the three strongest impact events together with three quakes for reference in Figure  11 : If the signal is above the noise, the waveforms appear very distinct from quakes due to trapped energy in the high Q shallow layers of the 1D model as well as very short period surface waves excited by the surface source. In contrast, quakes at depth neither excite trapped waves in the shallow layers in this 1D model due to Snel's law nor the very short period surface waves due to their limited penetration depth.
Acknowledgements The co-author list of this paper includes contributors to the evaluation (up to and including  D. Giardini ), contributors to the data set and invitation paper (Table 1) as well as the participants of the blind test (Table 2). This work was jointly funded by (1)  Swiss National Science Foundation  and  French Agence Nationale de la Recherche  ( SNF-ANR  project  157133  " Seismology on Mars "), ( 2) Swiss  State Secretariat for Education, Research and Innovation  (project " MarsQuake Service-Preparatory Phase ") and ( 3) ETH Zurich  (project " Preparatory phase for Mars InSight Ground Segment Support "). Additional support came from the  Swiss National Supercomputing Centre (CSCS)  under project  ID   s682. Some of the research described in this article was supported by the  InSight (Interior exploration using Seismic Investigations, Geodesy and Heat Transport ) project,  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration (NASA).
e. , a ranked list of tuples (restaurants, cuisines, etc. )is possible, i. e. , is obtained for some possible world, and whether it is certain, i. e. , is obtained for every possible world. Here again, note that in a candidate answer a tuple may appear multiple times, and naturally it appears without identifiers (which, as mentioned above, are internal and are unknown to the user). We formally define these two problems for our settings, and then embark on a study of their complexity. Complexity in the General Case (Section 4) We first study the possibility and certainty problems without any restrictions on the input database. As usual in data management, given that queries are typically much smaller than databases, we study the data complexity of the problems, i. e. , the complexity when the query is fixed.
Data Model and Query Language We denote by N the set of nonnegative natural numbers and by N >0 the set of positive natural numbers, i. e. , N >0 := N \ {0}. We fix a countable set of values D that includes N and infinitely many values not in N. A tuple t over D of arity a(t) is an element of D a(t) , denoted v 1 ,. , v a(t) : for 1 i a(t), we write t. i to refer to v i. The simplest notion of ordered relations are then list relations  [17, 18] : a list relation of arity n ∈ N is an ordered list of tuples over D of arity n (where the same tuple may appear multiple times). List relations impose a single order over tuples, but when one combines (e. g. , unions) them, there may be multiple plausible ways to order the results. We thus introduce partially ordered relations (po-relations). A po-relation Γ = (ID , T, <) of arity n ∈ N consists of a finite set of identifiers ID (chosen from some infinite set closed under the Cartesian product, e.
Specifically, let us define the underlying bag relation of a po-relation Γ = (ID , T, <) as (ID , T ). Unlike order, this underlying bag relation is always certain. We extend some classical notions from partial order theory to po-relations. Letting Γ = (ID , T, <) be a po-relation, an order ideal of Γ is a subset S ⊆ ID such that, for all x, y ∈ ID, if x < y and y ∈ S then x ∈ S. An antichain  [47]  of Γ is a set A ⊆ ID of pairwise incomparable tuple identifiers. The width of Γ is the size of its largest antichain, and the width of a po-database is the maximal width of its po-relations. In particular, totally ordered po-relations have width 1, and unordered po-relations have a width equal to their number of tuples; the width of a po-relation can be computed in polynomial time  [24]. A chain partition of Γ is a partition ID = Λ 1 ⊔ • • • ⊔ Λ n such that the restriction of < to each Λ i is a total order: we call each Λ i a chain.
PosRA: Queries Without Accumulation We now define a bag semantics for positive relational algebra operators, to manipulate porelations with queries. The positive relational algebra, written PosRA, is a standard query language for relational data  [1]. We will extend PosRA with accumulation in Section 2. 3, and add further operations in Section 7. Each PosRA operator applies to po-relations and computes a new po-relation; we present them in turn. The selection operator restricts the relation to a subset of its tuples, and the order is the restriction of the input order. The tuple predicates allowed in selections are Boolean combinations of equalities and inequalities, which involve constant values in D and tuple attributes written as. i for i ∈ N >0. For instance, the selection σ. 1 ="a"∧. 2 =.
constant expressions: • for any tuple t, the singleton po-relation [t] has only one tuple with value t; • for any n ∈ N, the po-relation [ n] has arity 1 and has pw ([ n]) = {(1,. , n)}. We have now defined a semantics on po-relations for each PosRA operator. We define a PosRA query in the expected way, as a query built from these operators and from relation names. Calling schema a set S of relation names and arities, with an attribute name for each position of each relation, we define a po-database D as having a po-relation of the correct arity for each relation name R in S. For a po-database D and a PosRA query Q, we denote by |Q| the number of symbols of Q, and we denote by Q(D) the po-relation obtained by evaluating Q over D. Example 2. 1. The po-database D in Figure  1  contains information about restaurants and hotels in Paris: each po-relation has a total order (from top to bottom) according to customer ratings from a given travel website.
kp (Q ′ ), then Q(D) is obtained in time linear in |Q ′ (D)|, and we conclude by the induction hypothesis. • If Q = Q 1 ∪ Q 2 or Q = Q 1 × LEX Q 2 or Q = Q 1 × DIR Q 2 , then Q(D) is obtained in time linear in |Q 1 (D)| × |Q 2 (D) |, and we conclude again by the induction hypothesis. Note that Proposition 2. 2 computes the result of a query as a po-relation Γ. However, we cannot efficiently compute the complete set pw (Γ) of possible worlds of Γ, even if all relations of the input po-database are totally ordered. For instance, consider the query Q := R ∪ S, and a po-database D interpreting R and S as totally ordered relations with disjoint domains and with n tuples each. It is easy to see that the query result Q(D) has 2n n possible worlds, which is exponential in D. This intractability is the reason why will we study the possibility and certainty problems in the sequel.
Incomparability of PosRA Operators Before extending our query language with accumulation, we address the natural question of whether any of our operators is subsumed by the others. We show that this is not the case. Theorem 2. 3. No PosRA operator can be expressed through a combination of the others. We prove Theorem 2. 3 in the rest of this subsection. We consider each operator in turn, and show that it cannot be expressed through a combination of the others. We first consider constant expressions and show differences in expressiveness even when setting the input podatabase to be empty. • For [t], consider the query [ 0 ]. The value 0 is not in the database, and cannot be produced by the [ n] constant expression, and so this query has no equivalent that does not use the [t] constant expression.
union. [ 0 ] ∪  [ 1 ]  (over the empty po-database) cannot be simulated by any combination of operators, as can be simply shown by induction: no other operator will produce a po-relation which has the two elements 0 and 1 in the same attribute. Observe that product operators are the only ones that can increase arity, so taken together they are non-redundant with the other operators. Hence, it only remains to show that each of × DIR and × LEX is not redundant. To do this, let us call PosRA LEX the fragment of PosRA that disallows the × DIR operator, but allows all other operators (including × LEX ). We also define PosRA DIR that disallows × LEX but not × DIR. We will first show that the × DIR product is not redundant, which we will do using the notion of width. Specifically, consider the query Q DIR = R × DIR R and an input po-database D n where R is mapped to [ n] (an input relation of width 1) for an arbitrary R n.