dataesr/scientific-paragraphs-categorization huggingface dataset:
INTRODUCTION S INCE its inception in 1998, the eXtended Markup Language (XML)  [1]  has emerged as a standard for data representation and exchange over the Internet, as many (mostly scientific, but not only) communities adopted it for various purposes, e. g. , mathematics with MathML  [2] , chemistry with CML  [3] , geography with GML  [4]  and e-learning with SCORM  [5] , just to name a few. As XML became ubiquitous, efficiently querying XML documents quickly appeared primordial and standard XML query languages were developed, namely XPath  [6]  and XQuery  [7]. Research initiatives also complemented these standards, to help fulfill user needs for XML interrogation, e. g. , XML algebras such as TAX  [8]  and XML information retrieval  [9]. Efficiently evaluating path expressions in a treestructured data model such as XML's is crucial for the overall performance of any query engine  [10].
Initial efforts that mapped XML documents into relational databases queried with SQL  [11] ,  [12]  induced costly table joins. Thus, algebraic approaches based on treeshaped patterns became popular for evaluating XML processing natively instead  [13] ,  [14]. Tree algebras indeed provide a formal framework for query expression and optimization, in a way similar to relational algebra with respect to the SQL language  [15]. In this context, a tree pattern (TP), also called pattern tree or tree pattern query (TPQ) in the literature, models a user query over a data tree. Simply put, a tree pattern is a graphic representation that provides an easy and intuitive way of specifying the interesting parts from an input data tree that must appear in • Marouane Hachicha and Jérôme Darmont are from the Université de Lyon (ERIC Lyon 2), 5 avenue Pierre Mendès-France, 69676 Bron Cedex, France.
E-mails: marouane. hachicha@univ-lyon2. fr, jerome. darmont@univ-lyon2. fr query output. More formally, a TP is matched against a tree-structured database to answer a query  [16]. Figure  1  exemplifies this process. The upper left-hand side part of the figure features a simple XML document (a book catalog), and the lower left-hand side a sample XQuery that may be run against this document (and that returns the title and author of each book). The tree representations of the XML document and the associated query are featured on the upper and lower right-hand sides of Figure  1 , respectively. At the tree level, answering the query translates in matching the TP against the data tree. This process can be optimized and outputs a data tree that is eventually translated back as an XML document.
Tree patterns in algebraic frameworks The first XML algebras have appeared in 1999  [24] ,  [25]  in conjunction with efforts aiming to define a powerful XML query language  [26] ,  [27] ,  [28] ,  [29] ,  [30]. Note that they have appeared before the first specification of XQuery, the now standard XML query language, which was issued in 2001  [7]. The aim of an XML tree algebra is to feature a set of operators to manipulate and query data trees. Query results are also data trees.
• Expressiveness: Expressiveness states how a logical TP, i. e. , a TP under its schematic form, translates into the corresponding physical implementation. The physical form of a TP may be either expressed with an XML query language (XQuery, generally), or an implementation within an XML database offering query possibilities. Using a TP for other purposes but querying, e. g. , in optimization algorithms, is not accounted toward expressiveness. Note that a TP that cannot be expressed in physical form is usually considered useless. • Supported optimizations: TPs are an essential element of XML querying  [16] ,  [37]. Hence, many optimization approaches translate XML queries into TPs, optimize them, and then translate them back into optimized queries. Optimizing a TP increases its matching power.
Expressiveness TAX TPs and their derivatives (GTPs and APTs) do not translate into an XML query language, but they are implemented, through the TLC physical algebra  [31] , in the TIMBER XML database management system  [38]. TIMBER permits to store XML in native format and offers a query interface supporting both classical XQuery fragments and TAX operators. Note that TAX operators include a Group by construct that has no equivalent in XQuery. Translating TAX TPs for XML querying follows nine steps: 1) identify all TP elements in the FOR clause;   [39]. The other TPs we survey are used in various algorithms (containment and equivalence testing, TP rewriting, frequent TP mining. ). Hence, their expressiveness is not assessed.
Expressiveness is a complex issue. Translating XML queries into TPs is indeed easier than translating TPs back into an XML query plan. XQuery, although the standard XML query language, suffers from limitations such as the lack of a Group by construct. Thus, it is more efficient to implement TPs and exploit them to enrich XML querying in an ad-hoc environment such as TIMBER's. We think that the richer the pattern, with matching options, ordering specifications, possibility to associate with many operators (and other options if possible), the more efficient querying is, in terms of user need satisfaction. Finally, minimization, relaxation, containment, equivalence and satisfiability issues lead the TP community to optimize these tasks. However, most TPs used in this context are basic, unlike TPs targeted at matching optimization, which allows optimizing XML queries wherein are translated optimized TPs.
Hence, it may output many useless intermediate results for queries with PC relationships. Moreover, it cannot process queries with order predicates. On one hand, in order to reduce the search space, path summaries that exploit schema information may be used  [65]. If a pattern subtree matches a data tree several times, TwigStack loads streams for all distinct paths, whether these streams contribute to the output or not. Path summaries help distinguish whether the occurrences of each pattern subtree are output elements or not. Those that do not are pruned. On the other hand, TwigStackList better controls the size of intermediate results by buffering parent elements in PC relationships in a main-memory list structure  [66]. Thus, only AD relationships in branching edges 1 are handled by TwigStackList, and not in all edges as with TwigStack.
OrderedTJ builds upon TwigStackList by handling order specifications in TPs  [67]. OrderedTJ additionally checks the order conditions of nodes before outputting intermediate paths, with the help of a stack data structure. Since TwigStack and OrderedTJ partition data to streams according to their names alone, two new data streaming techniques are introduced in iTwigJoin  [68] : the tag+level and prefix path schemes. In the Or-deredTJ algorithm, only AD relationships in branching edges are taken into account. The tag+level scheme also takes PC relationships in all edges into account. The prefix path scheme further takes 1branching into account. An eventual enhancement has been brought by Twig 2 Stack, which optimizes TwigStack by further reducing the size of intermediate results  [69]. Twig 2 Stack associates each query node x with a hierarchical stack.
All edges originating from a branching node are called branching edges  [66]. As TwigStackList, TJFast assigns, for each branching node b, a set of nodes S b that are potentially query answers. But with TJFast, the size S b is always bounded by the depth of the data tree. TJFast+L further extends TJFast by including the tag+level streaming scheme  [64]. Eventually, Lu et al. have recently identified a key issue in holistic algorithms, called matching cross  [36]. If a matching cross is encountered, a holistic algorithm either outputs useless intermediate results and becomes suboptimal, or misses useful results and looses its matching power. Based on this observation, the authors designed a set of algorithms, collectively called TreeMatch, which use a concise encoding to present matching results, thus reducing useless intermediate results.
TREE PATTERN USAGES B ESIDE expressing and optimizing queries over tree-structured documents, TPs have also been Fig. 15. Sample tree pattern collection and rooted subtree exploited for various purposes ranging from system optimization (e. g. , query caching  [77] ,  [78] , addressing and routing over a peer-to-peer network  [79] ) to highlevel database operations (e. g. , schema construction  [80] , active XML query satisfiability and relevance  [81] ,  [82] ) and knowledge discovery (e. g. , discovering user communities  [83] ). In this section, we investigate the most prominent of TP usages we found in the literature, which we classify by the means used to achieve the goals we have listed above (e. g. , routing and query satisfiability), i. e. , TP mining (Section 5. 1), TP rewriting (Section 5.
MineFreq further builds upon this principle by mining frequent RST sets  [80]. A frequent RST set must satisfy two requirements: 1) support requirement: sup(r 1 , r 2 ,. , r n ) ≥ minsup; 2) confidence requirement: ∀r i , f req(r1,r2,. ,rn) f req(ri) ≥ minconf , where minconf is a minimum confidence user-specified threshold. The algorithm again proceeds by level, the n-itemset RST candidates being generated by joining (n -1)itemset frequent RSTs. Candidates in which one or more (n -1)-subsets are infrequent are pruned, for they cannot be frequent. Remaining candidates are then filtered with respect to support and confidence. Finally, frequent TPs are built by joining all RSTs in each frequent RST set. Eventually, Stream Tree Miner (STMer) mines frequent labeled ordered subtrees over a tree-structured data stream  [84].
Tree pattern rewriting Query rewriting is casually used when views, whether they are materialized or not, are defined over data. Rewriting a query Q that runs against a database D is formulating a query Q ′ that runs against a view V built from D such that Q and Q ′ output the same result. When data are tree-structured, views are predefined TPs. Then, Q ′ is found among so-called useful embeddings (UEs) of Q in V ; the problem being to prune redundant, useless UEs  [78]. The heuristics that address this issue  [78] ,  [87] ,  [88]  rely on containment testing (Section 4. 1. 1) to output a minimal set of UEs. Query rewriting is also notably used in Peer Data Management Systems (PDMSs). In a PDMS, each peer is associated with a schema that represents the peer's domain of interest, as well as semantic relationships to neighboring peers.
Thus, a query over one peer can obtain relevant data from any reachable peer in the PDMS. Semantic paths are traversed by reformulating queries at a peer into queries on its neighbors, and then to the neighbors' neighbors, recursively. In the Piazza PDMS  [79] , data is modeled in XML, peer schemas in XML Schema, and queries in a subset of XQuery. Thus, query reformulation optimization strongly relates to TP matching optimization. For instance, one query may follow multiple paths in a PDMS, and thus may induce redundant reformulations, i. e. , redundant queries on the peers. Pruning queries help reduce such redundancy. Pruning requires checking query containment (Section 4. 1. 1) between a previously obtained reformulation and a new one. The reformulating process may also introduce redundant subexpressions in a query, leading to query minimization (Section 4.
CONCLUSION W E provide in this paper a comprehensive survey about XML tree patterns, which are nowadays considered crucial in XML querying and its optimization. We first compare TPs from a structural point of view, concluding that the richer a TP is with matching possibilities, the larger the subset of XQuery/XPath it encompasses, and thus the closer to user expectations it is. Secondly, acknowledging that TP querying, i. e. , matching a TP against a data tree, is central in TP usage, we review methods for TP matching optimization. They belong to two main families: TP minimization and holistic matching. We trust we provide a good overview of these approaches' evolution, and highlight the best algorithms in each family as of today. Moreover, we want to emphasize that TP minimization and holistic matching are complementary and should both be used to wholly optimize TP matching.
coli genomes were identified with the CRISPRFinder program (www. crispr. u-psud. fr/Server/). 55 or the identification of E. coli CRISPR2 PAMs associated with A or T CRISPR-leader variants, protospacer regions with at least 30 nt identity to spacers of CRISPR-Cas I-E systems of E. coli strains were searched with the BLASTn program  56  run against the Nucleotide Collection database (www. blast. ncbi. nlm. nih. gov/Blast. cgi) and aligned using the leader-proximal end as a reference. Similarly, the experimental SAMs were determined by the alignment of sequences matching (100% identity) spacers acquired in the integration assays and detected in the replicons of the corresponding host using BLASTn or Genious R6 created by Biomatters Ltd. (available from www. geneious. com/). The DNA strands carrying the protospacer nucleotides complementary to the corresponding spacer sequence in the crRNA were aligned using WebLogo (www.
Acknowledgments This work was funded by the  Ministerio de Economía y Competitividad  ( BIO2011-24417 ). The  University of Alicante (Vicerrectorado de Investigación, Desarrollo e Innovación)  supported the use of its research technical services. We are grateful to  John van der Oost  and  Stan J. J. Brouns  from the  University of Wageningen  for providing the plasmid pWUR399. Manuel Martínez-García  ( University of Alicante ) helped with figures. We thank  Rafael Maldonado  ( University of Alicante ) for critical reading of the manuscript. C. D. -V. and N. M. G. contributed similarly to this work.
To measure mitochondrial membrane potential (MMP), cells were treated with LCA for 1, 4 or 8 h, and tetramethylrhodamine ethyl ester (TMRE) was added to each well at a final concentration of 50 nM. TMRE is a cell permeable, positively charged dye that accumulates in active negatively charged mitochondria. In inactive or depolarized mitochondria, membranes have decreased potential and fail to sequester TMRE. After a 15 min incubation at 37 • C, cells were observed under an inverted fluorescent microscope using a filter cube with excitation wavelengths of 532-587 nm. The photos were then analyzed using ImageJ image processing software  (Schneider, Rasband & Eliceiri, 2012).
Statistical analysis All experiments were performed in at least triplicate using cells after various passages and the data are presented as mean ± SEM. Statistically significant differences (p < 0. 05) between various treatments and untreated cells were determined using a two-tailed Student t-test with Bonferroni correction for multiple comparisons. IC 50 values for inhibition of cell viability were calculated using a sigmoidal curve-fitting model of log-inhibitor concentration versus normalized inhibition response, with variable slope (GraphPad Prism v5. 03, GraphPad Software, San Diego, CA).
Finally, it is worth explaining why some identified risks are "not specifically targeted" in IoT-A, with no relevant technology being developed and no design choice being proposed. These non-targeted risks are of two sorts. Some of them are dependent on highly contextual physical parameters. They depend on the particularities of the communication technology that is put in place and, as such, exhibit highly diverse characteristics in terms of involved stakes. Accordingly, the existing mitigations can only be implemented at the physical layer with variable costs in terms of, for instance, efficiency. The other non-targeted security risks pertain to in-entity security-by-design policies. For example, the protection of a given operating system or the choice to encrypt a user database fit into this category.
When deriving the IoT-A work to a concrete architecture, this modularity and also the loose coupling between those blocks should be kept. This concept is also used in the 'Dispatcher' component  (Hyttinen P ed et al. 2013)  for the standardized processing of incoming requests without exposing the internal methods and functions; • Not all of the systems functionality can be defined in advance. Therefore, some additional spaces and extensions points, e. g. for upcoming functionality, should be reserved. This can for example be done in interface definitions or data models, like the reserved bits in the TCP header definition. This allows the designers and architects to update the system and to adapt it to new requirements. The tactics not considered as relevant are listed in Table  6. 9.
Use Access Policies The tactic of using access policies is a crucial aspect in IoT. Two main functional principles can be distinguished. The policy-based service access uses access control mechanisms to manage to access to information. Therefore the information must be managed accordingly so that it supports the used mechanism. This option can be realised by using the IoT-A Authorisation FC component (Sect. 7. 7. 2). The other possibility is to grant unrestricted access to services. This should be only done in those cases in which data security is not relevant.
Design Choices Addressing Privacy In Sect. 8. 3. 3. 3 the Security Perspective together with a set of tactics is presented. The Design Choices addressing Privacy are presented in Table  6. 16 showing the impact on architectural views by applying tactics relevant for Privacy concerns. 6. 9. 6. 1 Pseudonymisation The tactic "Pseudonymisation" refers to a procedure by which fields that enable identification of a user within a data record or subject are replaced by one or more artificial identifiers. The purpose is to render the subject less identifiable and this way lower IoT user (e. g. customer or patient) objections to its use. This is functionally implemented by the creation of a fictional identity (e. g. root identity, secondary identity, pseudonym, or group identity) and can be realised by integrating the IoT-A Identity Management FC (Sect.
The latter approach is the recommended one for IoT 6. 9. 7. 2 Load Balancing The 'Scale out approach (DC A. 5)' monitors the load of FCs during runtime and triggers offloading tasks to another less busy instance of the respective FC to avoid the FC being overloaded and therefore becoming a performance bottleneck or even out of function. The decision at what limit an FC is considered to be critically busy and to trigger off-loading to another instance is application specific, but the information model needs to provide some metric to specify those parameters for FCs. Logging Transactions 'Circular Logging (DC A. 6)' is a strategy that leads to overwriting old data when designated size of log is reached (IBM 2012). This approach does not support incremental backup strategy. Transactions need to be logged with unique id and status of their completion, indicating which functions need redoing and which need undoing.
ANTARES Neutrino Oscillation Measurement This analysis is based on data taken with the ANTARES detector between March 2007 and December 2010. Until December 2007, ANTARES operated in a 5-line configuration, followed by several months of operation with 10 installed detector lines. The detector construction was completed in May 2008. All physics runs taken under normal conditions have been used. The events selected by two tight trigger conditions are used  [2]. The analysed sample consists of 293 million triggers, dominated by atmospheric muons, corresponding to a detector live time of 863 days. Downgoing atmospheric muons were simulated with the program MUPAGE  [14, 15]  which provides parameterised muon bundles at the detector. Alternatively, also CORSIKA  [16]  was used for cross checks and systematic studies.
Optical properties of South Pole ice are depth dependent. Typical optical absorption lengths are 100-140 m and typical optical effective scattering lengths are 25-35 m. The latter limits the time resolution of the signal pulses from Cherenkov light. Unlike sea water, Antarctic ice has a low concentration in radioactive isotopes. In particular 40 K, abundant in salty sea water, is absent in ice. Furthermore, no light-producing biological activity occurs. Therefore, the observed optical background rate at the single photon level is only 500 Hz per PMT in IceCube. Beginning with the installation of the first string in 2005, IceCube has been operated in approximately year-long data taking seasons. During the Antarctic summer seasons in December and January new strings had been deployed at a pace up to 20 strings per year.
IC79 Zenith Angle Analysis. A first analysis  [25]  exploited two samples of upgoing muon neutrino events which had been collected during a 318. 9-day period with the IC79 configuration, excluding periods of calibration runs, partial detector configurations, and detector downtime. The first sample was obtained from high-energy events using data from the entire IceCube detector. The second sample, selected from events starting in the DeepCore volume, was using the surrounding IceCube array as an active veto to reject atmospheric muon background and high-energy (>100 GeV) neutrinos  [26]. The directions of the neutrino-induced muon tracks in the high-energy sample were determined with the standard maximum likelihood muon track reconstruction of IceCube  [27]. For low-energy events, the same method was applied as an initial step.
Two more studies of Figure  3 : Data and Monte Carlo expectation at world average oscillation parameters  [4]  and at the nonoscillation scenario for the low-energy sample (a) and for the high-energy sample (b). The shaded bands illustrate separately those systematic effects which affect the normalisation (norm) and the shape (shape) of the distributions (figure from  [25] ). IceCube data are under way. They have reported preliminary results recently.
IC79 Two-Dimensional Analysis. As this analysis  [28]  is well based on data in the IC79 configuration, here 312 days are used. The event selection requires that neutrino event candidates should have their interaction vertices inside the instrumented volume of the Deep-Core detector, which reduces background from high-energy neutrino events and downgoing atmospheric muons. When using the surrounding IceCube strings as a veto, their contributions can be partly discarded. Apart from the veto cut, only weak selection criteria are applied, which leads to a final event sample of 8117 events with an estimated purity in ]  events of 70%. The oscillation parameters are fitted on a two-dimensional distribution in zenith angle and energy. The zenith angle is reconstructed using standard IceCube methods  [27].
Euler, Aachen University). For typical densities in the Earth mantle, the resonance is observed at  ] = 6-8 GeV. Neutrino oscillation probabilities are now calculated in a full three-flavour scheme including matter effects in the Earth. The Globes package  [34, 35]  is used for this purpose. Figure  6  shows the survival probabilities for upgoing atmospheric ]  for different zenith angles as function of neutrino energy. The Earth density profile is taken from the PREM model  [36]. The two colors refer to the two MH choices. A striking difference of the oscillation probabilities for the two options can be seen for zenith angles cos Θ > 0. 4 and energies close to the resonance energy of the matter effect in the Earth mantle. Neutrino interactions in this energy range can occur in various channels: quasielastic, via nuclear resonances or as deep-inelastic reactions.
The knowledge of the oscillation parameters Δ 2  32 and sin 2  23 itself can be improved. The octant of the mixing angle can be determined, if its value is sufficiently distinct from maximal mixing and |Δ 2  32 | can be measured with a precision better than 1% based on one year of data with PINGU alone  [41]  under the assumption that systematic effects such as the uncertainty on the ice model do not deteriorate this measurement too strongly. Further, indirect dark matter searches from annihilation processes of gravitationally confined particles can be extended toward lower masses compared to current results from ANTARES and IceCube.