dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequencing and bioinformatic analyses Total RNA from two independent biological replicate of Col-0 and drb7. 2 flowers was extracted with Tri-Reagent (Sigma). A 6 g weight of total RNA was used for preparation of small-RNA libraries. Briefly, a first acrylamide gel purification of small RNA between 18-30 nt was performed. The libraries were then constructed using the TruSeq Small RNA preparation kit from illumina following manufacturer's instructions and the libraries were sequenced using a Illumina Genome Analyser (Fasteris, Switzerland). After removal of the 3 adapters from the reads by Fasteris using an in-house developed script (detailed in the Supplementary Table  S2 ), sequences (18-26 nt in length) were mapped using Bowtie (66) to the TAIR10 assembly. Only unique reads with no mismatch to the genome were kept using an in-house python script (avalailable upon request).
Counts were made with inter-setBed, from bedtools suite (v. 2. 25. 0) (67). Data were normalized with there own conserved miRNA counts  (miR156, 157, 159, 160, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 319, 390, 391, 393, 394, 395, 396, 397, 398, 399, 403, 408, 472)   (68)  under the assumption that they were globally unaffected (Figure  2  and (  51 )). miRNA annotation was downloaded from miRBase v. 21  (69). EndoIR-siRNAs were annotated using the sRNA-producing loci predicted by  (10) , which did not match miRBase miRNAs. Lociproducing siRNAs in a PolIV-dependent manner where retrieved from  (70). Those data have been converted from TAIR6 to TAIR10 coordinates by a perl script provided by the 1001 genomes project (http://1001genomes. org/data/ software/translate tair8/) and manually validated.
2, drb7. 2/dcl2, drb7. 2/dcl3, drb7. 2/dcl4 and dcl2/4 mutant plants was performed by sequential rounds of probing and stripping the same membranes. (B) Accumulation of the same small RNAs depicted in (A) was assessed in wild-type, dcl2, dcl3, dcl4, drb4, drb4/dcl2, drb4/dcl3, drb4/dcl4 and dcl2/4 mutant plants. (C) Similar analysis to the one depicted in (A) was performed in wild-type, dcl2/3, dcl2/4, dcl3/4, drb4/dcl3 and drb7. 2/dcl3 plants. (D) Similar analysis to the one depicted in (A) was performed in wild-type, drb4, drb7. 2 and drb7. 2/drb4 plants. Accumulation of the DCL1-dependent miR159 (@159) and small RNA U6 (@U6) were used as loading control. Figure source data can be found with the Supplementary Data. expressing DRB7. 2:GFP (Figure  3A ). As a positive control for these RIP experiments, we decided to use a dcl4 transgenic line expressing a mutated version of DCL4, DCL4-8, fused in C-terminal to GFP (pDCL4:DCL4-8:GFP/dcl4).
2 plants. Equal loading was verified by Coomassie staining of the membrane after Western blotting. (C) Quantitative real-time PCR of the DRB4 mRNA accumulation in wild type, drb4 and drb7. 2 plants. The mRNA level was normalized to that of Actin2 (At3g18780) and then to the WT plants that was arbitrarily set to 1. Error bars represent standard deviation from two independent experiments in which triplicate PCRs were performed. Figure source data can be found with the Supplementary Data.
2:DRB7. 2:GFP-(two independent transgenic lines, #1 and #12) expressing plants by sequential rounds of probing and stripping the same membranes. (F) Accumulation of GFP or DRB7. 2:GFP protein was verified by Western blot. For RNA blot, miR159 and U6 were used as loading control. For protein blot, Coomassie staining of membranes were used to verify equal loading. Figure source data can be found with the Supplementary Data.
FUNDING Agence Nationale pour la Recherche  [ ANR-14-CE19-0014-01  to P. D. ];  LABEX NetRNA  [ ANR-10-LABX-0036  NETRNA] from the  French National Research Agency  as part of the  Investments for the future program ;  French Ministry of Research  [T. M. ]. Funding for open access charge:  ANR  [ ANR-14-CE19-0014-01 ].
2:GFP-(two independent transgenic lines, #14 and #15) or pDRB7. 2:DRB7. 2:GFP-(two independent transgenic lines, #1 and #12) expressing plants by sequential rounds of probing and stripping the same membranes. (F) Accumulation of GFP or DRB7. 2:GFP protein was verified by Western blot. For RNA blot, miR159 and U6 were used as loading control. For protein blot, Coomassie staining of membranes were used to verify equal loading. Figure source data can be found with the Supplementary Data.
A P P L I C AT I O N T O T W O D I F F E R E N T T E C T O N I C R E G I O N S I N N O RT H A M E R I C A We apply this method to seismic observations recorded at two different locations in North America. First, we invert data from station FFC (Canada), which is a permanent, reliable and well-studied station located at the core of the Slave Craton. Since a large number of studies have already been published about the structure under this station (e. g. Ramesh et al. 2002; Rychert & Shearer 2009; Miller & Eaton 2010; Yuan & Romanowicz 2010b ), we view this as an opportunity to test and validate the proposed scheme. In a second step, we invert seismic data recorded in Arizona at station TA-214A, of the US transportable array, which is a much noisier, recent, and less studied station, located in the southern Basin and Range Province, close to a diffuse plate boundary, where we expect more complex 3-D structure due to recent tectonic activity.
2014; Karato et al. 2015). At 250 km depth, we observe a small negative velocity drop, associated with a strong gradient in the direction of fast axis of anisotropy, going from 15 • to 90 •. When SKS waveforms are added to the data set (lower panels in Fig. 7 ), the fast axis direction below 250 km reduces to 55 • , and becomes aligned with the absolute motion of the North American plate in the hotspot reference frame  (Gripp & Gordon 2002). This has two strong implications: (1) it demonstrates the sensitivity of SKS observations to structure below 250 km, poorly constrained by fun-damental mode surface waves and (2) this allows us to interpret the discontinuity at 250 km as the lithopshere-asthenosphere boundary (LAB), below which the anisotropy would result from present day mantle flow associated with the motion of the North America plate.
oup. com/gji/article/206/1/605/2606499 by guest on 09 September 2021 Figure  9. Body wave observations used for the 1-D inversion under station 214A, located in the Basin and Range province. We use two stacks of P wave seismograms, from Japan (blue) and North Chile (green), as well as five SKKS individual waveforms (black) and one SKS waveform (red). SKKS and SKS waveforms are normalized to unit energy, and there is no amplitude information in the lower right-hand panel. (ii) There is a sharp change of direction of anisotropy at 100 km depth, which confirms the interpretation of the negative discontinuity as the LAB. A distinct layer between 100 and 180 km is clearly visible with a direction of 150 • , that is, parallel to the absolute plate motion of the Pacific Plate, and in agreement with tomographic inversions combining surface waveforms and SKS splitting data  (Yuan & Romanowicz 2010b).
For the first time, azimuthal variations of dispersion curves were jointly inverted with receiver functions and SKS data, for both crust and upper-mantle structure. The procedure was applied to For body waves (left-hand panels), the crossconvolution misfit function is not constructed as a difference between observed and estimated data. Instead, we plot the two vectors H * v(m) and V * h(m), the difference between which we try to minimize. data recorded at two different stations in North America, in two different tectonic regimes. In both cases, results are compatible with previous studies, and allow us to better image anisotropic layering. In both cases, we observed a LAB characterized by both isotropic and anisotropic sharp discontinuities in the mantle, thus implying that the LAB cannot be defined as a simple thermal transition, but also reflects changes in composition and rheology.
Rogue behaviors refer to behavioral anomalies that can occur in human activities and that can thus be retrieved from human generated data. In this paper, we aim at showing that NoSQL graph databases are a useful tool for this purpose. Indeed these database engines exploit property graphs that can easily represent human and object interactions whatever the volume and complexity of the data. These interactions lead to fraud rings in the graphs in the form of sophisticated chains of indirect links between fraudsters representing successive transactions (money, communications, etc. ) from which rogue behaviours are detected. Our work is based on two extensions of such NoSQL graph databases. The first extension allows the handling of time-variant data while the second one is devoted to the management of imprecise queries with a DSL (to define flexible operators and operations with Scala) and the Cypherf declarative flexible query language over NoSQL graph databases.
g. , chemistry). Techniques and algorithms can be distinguished in considering the fact that they are meant to mine relevant patterns or to retrieve them. Detection is achieved through the modelization of fraud rings which are hidden within the graph of interactions. A fraud ring is a set of connections between actors. It can be found in many fraud frameworks  [1]. Although graphs have been studied since the very beginning of computer science in the so-called graph theory field  [3] , their integration within database management systems is more recent. Some of the first systems have been proposed with the emergence of ontologies and RDF triplets queried through SPARQL  [4]. More recently, NoSQL databases have proposed efficient engines devoted to graph databases: GraphDB, Neo4J, etc. [5]  compares some of these engines and points the advantages of the Neo4J system, which is the one we consider.
In this paper, we propose a framework for defining fuzzy temporal pattern matching from NoSQL graph databases. For this purpose, we first recall the basic concepts of NoSQL graph databases, temporal queries and graph pattern matching in Section 2. We then detail the problem we address in Section 3, before presenting a first attempt for addressing the problem using the NoSQL Neo4j graph database in Section 4. The proposition has been implemented. The main contribution of this paper is presented in Section 5. This contribution is mainly based on the use of generalized fuzzy queries. These queries can be userdefined and rely on a a Domain Specific Language (DSL) and on an extension of the declarative query language to better address and describe sophisticated frauds. Section 6 reviews the main contributions from the literature related to rogue detection.
f is an induced subgraph isomorphism if in addition if (u, v) / ∈ E Q , then (f (u), f (v)) / ∈ E. Subgraph matching aims at retrieving the occurrences and does not only focus on the decision problem, as defined above from  [6]. Definition 5 (Subgraph Matching). For a graph G and a subgraph query Q, the goal of subgraph matching is to find every subgraph S = (V S , E S ) in G such that there exists a bijection f : V Q → V S that satisfies ∀V ∈ V Q , T Q (v) = T S (f (v)) and ∀e = (u, v) ∈ E Q , (f (u), f (v)) ∈ E S , where T S (f (v)) represents the label of the vertex f (v) in S. Such pattern matching techniques are implemented in the so-called NoSQL graph databases together with many other features, thus allowing these frameworks to efficiently address rogue behavior detection.
NoSQL Graph Databases NoSQL graph databases  [7]  are based on these concepts, attributes and values over the attributes being stored thanks to the (key, value) paradigm which is very common in NoSQL databases. Fig. 3  shows a graph and its structure in (key, value) pairs. Studies have shown that these technologies present good performances, far better than classical relational databases, for representing and querying such large graph databases. There exist several NoSQL graph database engines (Ori-entDB, Neo4J,HyperGraphDB, etc. )  [8]. Neo4J is recognized as being among the top engines in terms of performance  [5]. It has been recently extended for managing the successive versions of the graph database in the Mnemosyne system described below.
Mnemosyne: an Innovative Temporal Data Management System [9] aims at proposing an innovative model of history management in NoSQL graph databases. There have been several proposals in the literature for managing evolving graphs  [10]. [9]    Rev. 0 stands for the initial version of all the elements, should they be nodes or relations. When several modifications have occurred, the VersionGraph becomes more complex and several revisions appear. These revisions can be traversed with different points of view, depending on the user needs to trace nodes or elements, as illustrated by Fig. 7.
Problem Statement In this paper, we focus on malicious human actions in the form of rogue behaviors. Such activities are known to lead to significant financial losses. These behaviors can be detected as they differ from regular honest ones. Fraud detection is thus often considered as a subtopic of anomaly detection to detect irregular activities. Anomaly detection has been widely studied  [11]. In this work, we claim that fraud data are often fundamentally graphs. Fraudsters act in an indirect manner in order to remain hidden as long as possible. Some of them may rely on vehicles or fake corporations. Most of them operate in groups so as to introduce a complexity and a volume of interactions that prevent the fraud from being visible. However, when focusing on graphs, few works have been produced  [12].
First Attempt As shown above, fraud detection relies on the discovery of graph patterns in the successive states of the data. This section relies on a classic example of bank fraud as depicted in  [14]. Banks and Insurance companies lose billions of dollars every year to fraud. Traditional methods of fraud detection play an important role in minimizing these losses. However increasingly sophisticated fraudsters have developed a variety of ways to elude discovery, both by working together, and by leveraging various other means of constructing false identities. The dataset used in this section is defined in Fig. 10. Green circled nodes represent fraudsters and red circled nodes represent the connection points between these people. In this section, we first introduce a standard pattern matching resolution to detect fraud rings in a graph.
This kind of resolution works well when all the data are available (which means that no valuable information for resolution has been erased or replaced). To handle this problem, there are two main ways: (i) by embedding the history within the operational model. This creates difficulties as the patterns for fraud detection must then be written in an adhoc manner, depending on the specific model; (ii) by considering a generic model for describing the history that is compatible with pattern matching. This allows the user to write generic pattern matching queries for retrieving the fraud rings. MATCH ( c o n t a c t I n f o r m a t i o n )< -[ ] -( a c c o u n t H o l d e r ) , ( a c c o u n t H o l d e r ) -[ r : H A S _ C R E D I T C A R D | H A S _ U N S E C U R E D L O A N ] -> ( ← u n s e c u ( F i n a n c i a l R i s k ) a s F i n a n c i a l R i s k ORDER BY F i n a n c i a l R i s k DESC First, it finds all the account holders that have at least one piece of information in common (line 1 to 5) and then it calculates the ring size (line 3) and the induced financial risk (lines 8 to 12).
The result of the execution of Listing 1 is shown in Fig. 11. Such queries rely on the so called Cypher language which runs as a declarative language over NoSQL graph databases. These queries include the following clauses 1 : 1 See https://neo4j. com/docs/cypher-refcard/current/ for a complete reference card • START: Starting points in the graph, obtained via index lookups or by element IDs; • MATCH: The graph pattern to match, bound to the starting points in START; • WHERE: Filtering criteria; • RETURN: What to return; • CREATE: Creates nodes and relationships; • DELETE: Removes nodes, relationships and properties; • SET: Set values to properties; • FOREACH: Performs updating actions once per element in a list; • WITH: Divides a query into multiple, distinct parts. For instance, the following query returns all the nodes of type accountholder: MATCH ( A : A C C O U N T H O L D E R ) RETURN A The following query returns all the relations between two accountholders, whatever the type of their relation: MATCH ( A : A C C O U N T H O L D E R ) -[ * ] -> ( B : A C C O U N T H O L D E R ) RETURN A , B.
Fraud Ring: Temporal Cheating It should be noted that expressing pattern matching defined in Section 4. 1 can be done on existing insurance/bank data as such applications are meant to store and trace the history. However, this is not always the case. Fig. 12  shows how, if history was not managed in such systems, fraudsters can cheat to stay hidden by creating, updating, and deleting information over time. In this case, the above algorithms will not detect any fraud ring as they cannot manage history. To help organizations to find frauds, it is thus necessary to record historical tracks.
Defining fuzzy clauses Fuzzy queries have been extensively studied in the literature, mainly on relational databases and more recently on NoSQL gaph databases. The propositions can either address classical crisp databases (  [17] ) or fuzzy graphs  [18, 19]. Temporal fuzzy clauses can be defined either by considering the fuzzy Allen intervals or by considering user defined clauses. User can define temporal graduation such as "some days" and "several weeks". Fig. 15  draws a membership function defining "several weeks". This membership function defines a degree of membership of a number of weeks to the term "several weeks". For instance, 3 weeks has a degree of membership of 1 but more than 5 weeks as a lower degree of membership (may be months could be a better term?).
Related Work Fraud detection has been a hot topic for many years as its impact is extensive in many fields and sectors such as industry, finance, bank, insurance, organized and corporate crime, health care, etc. Rogue behaviors can be detected using several methods. The most common ones from the literature are based on statistical methods, pattern recognition, expert systems and machine learning  [25]. These techniques can be applied on several kinds of data such as time series or transactional data, depending on the type of context being considered: computer intrusion, credit card fraud, telecommunication fraud, etc. However there are only few works on graph data.
Frauds and Detection Rogue behaviors and frauds are defined in the literature in various ways. The main characteristics are related to the fact that such behaviors refer to criminal deceptions through intentional human practices and interactions aiming at dishonest advantages. Fraud detection relies on methods for anomaly detection. Such anomalies can be seen as outliers and are thus retrieved by comparing regular and fraudulent situations. This comparison can be achieved using supervised learning  [26]  or cases can be separated using statistical and clustering methods  [27]. Algorithms have been defined for several types of data sources. Transactional and stream data have been extensively addressed, especially for dealing with bank and finance frauds and can be regarded as outlier detection in stream data  [28].
As frauds are generated by humans, their characteristics are often impossible to define in a crisp manner. For this reason, many works have addressed the use of soft computing and formal representations of imprecision and uncertainty. [29] reviews the main methods from intelligent systems that can help, from fuzzy neural networks to genetic algorithms. [30]  proposes the use of fuzzy clustering in the particular context of corporate fraud. [31]  focuses on auction frauds and proposes the use of fuzzy rules that are optimized by genetic algorithms. Regarding graph-based data, outlier detection has been less addressed as it is much more complex. Recent works have addressed the detection of outliers in graph streams  [32]  and temporal data and graphs  [33]. However, no work has addressed fuzzy fraud ring detection from temporal graph data.
Graph Pattern Matching Graph pattern matching is distinguished from graph mining where frequent subgraphs are searched for  [34, 35]. [36] addresses several topics within the framework of the approximate graph matching problem, as for instance the computation of the distance between two graphs. Temporal queries have been studied since the beginning of databases, in relational databases  [37] , data warehouses  [38]  and Web data  [39, 40]. However, very few works have been proposed to represent and query history in graphoriented NoSQL databases  [41]. Many works on temporal queries are based on the Allen interval algebra (for instance bef ore(A 1 , B 1 ) does not hold as M arch2013 > F ebruary2013). Temporal SQL has been extensively studied, and has been fuzzified  [43]  to describe for instance that the event B occurs long before event A, or that event A occurs before or approximately at the same date as the event B.
For all the above-mentioned topics, graphs can be queried through languages. Some of them have been studied in the literature. Some languages have been proposed by scientists and some other ones have been issued by the editors. where subgraph matching appears to be one of the most powerful and necessary query, including approximate matching. [45] proposes a propositional dynamic logic that extends several graph database languages. [46] introduces GraphQL, a graph algebra capable of taking into account nodes, relations, and attributes on both nodes and relations. In this language, the authors define the so-called graph pattern as a pair P = (M, F) where M is a graph motif and F is a predicate on the attributes of the motif. The algebra contains several operations, including selection, cartesian product, join and composition.
Conclusion and Further Work Rogue behaviors often amount to human abnormal interactions also called fraud rings. We thus discuss in this paper how property graphs can help for rogue behavior detection by focusing on interactions. We focus on NoSQL graph databases that offer a useful framework to deal with large graph-based datasets. This paper extends  [47]  which has introduced a first attempt to NoSQL graph queries for rogue detection. It puts the emphasis on the use of fuzzy queries in order to better address the approximate definitions inherent in such human activities. This paper introduces the use of a Domain Specific Language and a declarative fuzzy query language (Cypherf) to better address and resolve sophisticated cases. The use of NoSQL graph databases is a key element of our work.
We especially claim that representing the successive versions of the graph data allows to better retrieve the chains of successive transactions that represent a fraud. For this purpose, we consider using the Mnemosyne system that has been extended for materializing temporal relations between objects. This allows us to directly apply pattern matching on the graph for retrieving the fraud rings. Further works aim at integrating the contribution from this paper to alert systems, especially on stream data. For instance, it would then be possible to build follow-up systems for organized crime or terrorist rings which have particular behaviors such as "gathering-dispersal-reorganization" dynamics. Such chains appear for example when fraudsters assemble for illicit activities and then suddenly disperse to face either an internal or external threat.