dataesr/scientific-paragraphs-categorization huggingface dataset:
Otherwise, if combining by sum was preferred, the combination and the pooling would be commutative and combination followed by pooling would be equivalent to combination after pooling. The proposed models appear to perform inconsistently on the 11 databases since there are large (blue vertical) error bars of q s in Figure  4. This is mainly because of their low accuracy on the TID 16;17 and the WIQ database. If excluding them, the worst accuracies (marked by red crosses) are not significantly lower than the best ones. The proposed metric inaccurately measures ''intensity shift'' because the image mean is overlooked by using the ISA bases which are obtained from the whitened data with zero mean. ''Contrast change'' is not measured appropriately here, because contrast change (i. e. , amplitude difference) is simply regarded as distortions no matter if the contrast is enhanced or degraded.
The previously described Zika (800 series set) and chikungunya virus specific primer and probe sets were used  [32, 33]. The DENV-2 oligonucleotide set were designed with the Primer Select software program (DNASTAR) (1085 CCAAACAACCCGCCACTCTAAG, 1244c TTTCCCCATC-CTCTGTCTACCATA, and TaqMan probe 1145 FAM-AACA-GACTCGCGCTGCCCAACACA-BHQ1) and were based on the published GenBank full-length sequences. All real-time assays were performed by using the QuantiTect probe RT-PCR reagent kit (Qiagen). Briefly, a 50ml total reaction volume consisted of kit components, 10 ml of RNA, 400 nM of each primer, and 150 nM of.
Acknowledgments We gratefully acknowledge the people of Yap state for cooperating during the sampling operations in their homes. We would also like to thank the officials of  Ministry of Health of FSM  for inviting us and supporting our operations,  Christina Fillmed  for providing EPA laboratory space, and  Ernie Beyan ,  Peter Fattamag , and  Mathew Thigthen  for assistance in collecting mosquitoes. We thank  Jacob Kool  for assistance with coordination of field efforts and  Myrielle Dupont  for comments on the manuscript. The findings and conclusions in this report are those of the authors only and do not necessarily reflect the views of their respective institutions.
Thus, the belief mass of a meta-class is determined from the distance between the object and the center of meta-class and its corresponding ratio value. An object will be committed to a meta-class with a high belief mass as soon as it is located at (almost) the same distances of several specific classes centers. Because in that case, it means that the object is very difficult to be correctly classified into a specific class. CCR provides credal classification results with low computational burden due to the simple working principle. After a brief presentation of belief functions in Section 2,w e state in Section 3 the principles of CCR and the mathematical computation of bba's for the credal classification. In Section 4,w e present some classification results based on artificial and real data sets, and we compare the performances of the CCR with respect to well-known classification methods.
The different methods in the experiments have been programmed and tested with Matlab TM software. In order to show the ability of CCR to deal with the metaclasses, the class of each object is decided according to the maximal mass of belief criterion. In CCR, the imprecise objects do not count really as misclassifications, but as imprecise classifications in fact. In our simulations, a misclassification is declared (counted) as soon as an object truly originated from w i is classified into A with w i \ A ¼ ∅. I fw i \ A a ∅ and A aw i then it will be considered as an imprecise classification. In our experiments, the error rate and imprecision rate are introduced to evaluate the performance of CCR. The error rate R e is calculated by R e ¼ðN e =TÞÂ100%, where N e is number of misclassification errors, and T is the number of objects under test.
Experiment #4 (with real data sets) Four well-known real data sets obtained from UCI Machine Learning Repository  [31]  (the Iris, Seeds, Wine and Yeast data sets) have been tested in this experiment to evaluate the performances of CCR compared with CART, ANN, EK-NN and BK-NN. For the Yeast data set, three classes named as CYT, NUC and ME3 are selected here, since these three classes are close and difficult to discriminate. The main characteristics of the four data sets are summarized in Table  3  below. All the detailed information can be found on UCI repository archive at http://archive. ics. uci. edu/ml/. The k-fold cross validation is performed on the four data sets by different classification methods, and k generally remains a free parameter  [33]. We use the simplest 2-fold cross validation 9 here, since it has the advantage that the training and test sets are both large, and each sample is used for both training and testing on each fold.
In that case, some other complementary sources of information, or techniques, will be necessary if one wants to precisely discriminate the objects committed to the meta-classes with high belief mass value (if a precise classification is absolutely required). The CCR and BK-NN methods provide similar performances for the Iris, Seeds and Yeast data sets according to the compromise between error rate and imprecision rate. For the Wine data set, CCR yields the lowest error rate due to its inherent working principle which is very different of the other classifiers. It is worth noting that BK-NN requires a very long running time due to the heavy computational load. The proposed CCR method requires less computational time than the other methods. This shows again that CCR working with credal classification can deal efficiently with uncertain data using belief functions with a serious computational complexity advantage over other methods.
). Sequence chromatograms were manually verified and then assembled using Geneious v. 5. 3. 6 (Biomatters Ltd. ). We compared the sequences we found to published sequences in GenBank (http://www. ncbi. nlm. nih. gov/) using the basic local alignment search tool (BLAST) to find the best match (Evalue#10 28 ). Mean genetic distances were estimated using MEGA v. 5. 2. 2  [68]. Then, in order to assign our NS5 sequences to previously identified flavivirus genospecies (see  [69, 70] ), we performed a standard phylogenetic analysis using flavivirus sequences amplified from O. maritimus together with sequences from a selection of strains representative of the Flavivirus genus that are available from GenBank, according to Cook and Holmes  [69]  (see Table  S1 ). Maximum likelihood (ML) trees were constructed using RAxML GUI (https://sites.
google. com/site/raxmlgui/ home/files/oldhelp), a graphical front-end tool for RAxML-VI-HPC (randomized axelerated maximum likelihood;  [71] ); the thorough bootstrap option and 1,000 non-parametric bootstrap replicates were employed. The ML analysis used the general timereversible (GTR) model, with a gamma model of rate heterogeneity and the invariable sites option; the best-fit model was selected using jModelTest  [72]. Trees were visualized using FigTree v. 1. 3. 1 (http://tree. bio. ed. ac. uk/software/figtree).
Molecular detection of flaviviruses in the vectors In the Medes colony, flavivirus RNA was detected in 8 of the 135 tick pools tested (2009: 0/8, 2011: 1/28, and 2012: 7/99). No flavivirus RNA was detected in the mosquitoes. All of the O. maritimus flavivirus sequences reported in this article are available from the GenBank database (Accession No. KJ440085-KJ440090). Two of the eight positive tick amplifications resulted in incomplete sequences that were of insufficient quality, and they were not used in subsequent analyses. The BLAST analysis indicated that the six flavivirus sequences we retrieved significantly matched a NS5 gene fragment found in Meaban virus (. 95% similarity), a flavivirus first isolated in 1981 from O. maritimus ticks (Genbank accession number DQ235144  [70] ). Phylogenetic analyses showed that our O.
Acknowledgments We thank  S. Moutailler  ( ANSES ), P. Despres, and  M. Grandadam  ( National Reference Center for Arboviruses, Institut Pasteur ) for providing the virus strains used in the neutralization tests;  X. de Lamballerie  for providing SW13 cells and the Meaban virus isolated in Brittany, France;  S. Lowenski  for performing the virus neutralization tests. ELISAs were conducted at the  Plateforme d'Analyses Chimiques en Ecologie (CEFE)  or by  A. Pion  within  INRA   UR346. We thank  K. D. McCoy  for discussions on the manuscript. We thank  C. Chastel  for his articles and  S. Barry ,  F. X. Halle ,  N. Rode , and  F. X. Abad  for their help with the manuscript. We are also grateful to our many colleagues at the CEFE and the  University of Barcelona  and the staff of CReSA for help in the field.
However, others (goal-trackers; GTs) learned to approach the food magazine upon CS presentation, and made anticipatory head entries into it. Furthermore, in STs, phasic dopamine release in the nucleus accumbens, measured with fast scan cyclic voltammetry, matched RPE signalling, and dopamine was necessary for the acquisition of a sign-tracking CR. In contrast, despite the fact that GTs acquired a Pavlovian conditioned approach response, this was not accompanied with the expected RPE-like dopamine signal, nor was the acquisition of a goal-tracking CR blocked by administration of a dopamine antagonist (see also  [15] ). Classical dual systems models  [16] [17] [18] [19]  should be able to account for these behavioural and pharmacological data, but the physiological data are not consistent with the classical view of RPE-like dopamine bursts.
Physiological data Not only have Flagel et al. [14]  provided behavioural data but they also provide physiological and pharmacological data. This raises the opportunity to challenge the model at different levels, as developed in the current and next sections. Using Fast Scan Cyclic Voltammetry (FSCV) in the core of the nucleus accumbens they recorded the mean of phasic dopamine (DA) signals upon CS (lever) and US (food) presentation. It was observed that depending on the subgroup of rats, distinct dopamine release patterns emerge (see Figure  7 A, B ) during Pavlovian training. STs display the classical propagation of a phasic dopamine burst from the US to the CS over days of training and the acquisition of conditioned responding (see Figure  7 A ). This pattern of dopamine activity is similar to that seen in the firing of presumed dopamine cells in monkeys reported by Schultz and colleagues  [12]  and interpreted as an RPE corresponding to the reinforcement signal d of Model-Free RL systems  [1].
This work was supported by Grant  ANR-11-BSV4-006 ''LU2 '' ( Learning Under Uncertainty)  from  L'Agence Nationale de la Recherche, France  (FL, OS, MK), by  Grant ''HABOT''  from the  Ville de Paris Emergence(s) Program, France (MK) ,  by Grant ''GoHaL' ' from the  Centre National de la Recherche Scientifique PEPS Program, France (MK) , and by Grant  P01 DA031656  from the  National Institute on Drug Abuse, USA (SBF, TER). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
diclina-S. parasitica complex, both molecular and morphological and physiological data can help solve phylogenetic relationships. Thus, using ITS rRNA gene, five phylogenetic separate clades were identified for the Saprolegnia complex, with all isolates collected from salmonid lesions falling into a single clade (i. e. , clade I). However, within that clade I, parasitic isolates came from a wide range of hosts including, for example, crustaceans, and catfish but also non-pathogenic isolates from soil and water. Molecular analyses have the potential to discriminate at the subspecies or strain level  (Phadee et al. , 2004). The level of sensitivity of the molecular techniques in the clinical context has however been sporadic  (Cunningham, 2002). This is largely due to the relatively low genomic information that is available through public data bases such as Genbank.
Input Data for Applying the MCDA Procedure. We used several datasets to represent the main physical conditions and management practices found in Mato Grosso. First, we acquired MODIS EVI data (MOD13Q1 product, collections 5 and 6) covering all of the Mato Grosso state (image tiles: H11V09, H11V10, H12V09, H12V10, H13V09, The Scientific World Journal and H13V10) for the 2000-2011 study period. The EVI data were chosen for their potential to mitigate cloud cover effects and atmospheric and soil background effects  [9, 21]. The EVI data are a 16-day composite with high radiometric and geometric corrections. The MODIS images and products were preprocessed by the National Aeronautics and Space Administration (NASA) and are available at no charge at https://lpdaac. usgs. gov/data access/data pool.
Validation Data. Two types of datasets were acquired to validate our approach at different scales, that is, stateand municipality-level datasets and pixel-level datasets. First, we used annual soybean agricultural statistics at the state and municipality levels from  [20, 24]  for the entire study area. Second, a field campaign was carried out in 2006 and 2007 to collect validation data at the crop field scale. A total of 76 farms were visited and mapped in 13 municipalities representing the two main agricultural regions (in central and western Mato Grosso, along the BR163 road and on the Chapada dos Parecis, resp. ). A complete map of the visited municipalities is introduced in  [15]. For each farm, information about crop types, yields, sowing, and harvesting dates were collected. In this study, we only considered the crop type information for the 2006/2007 season's harvest.
Municipality Area Estimates. Soybean area was estimated at the municipality level from the crop season 2000/2001 to 2010/2011 and was compared to official estimates provided by IBGE http://www. sidra. ibge. gov. br/ using a regression analysis. Quickly updating the current crop year is challenging because the IBGE municipality data are published approximately one year after the end of the soybean season. Figure  5  presents the linear least squares regression analysis for the municipal soybean estimates from the MCDA procedure and from IBGE for crop years 2000/2001 to 2010/2011 with  2 = 0. 97. Therefore, for the average of the eleven crop years studied, the MCDA explains 97% of the variation of the data estimated by IBGE, which indicates a good agreement between the estimates. The group of points above 500,000 ha represents the crop area of Sorriso municipality, which had 483,000 ha of soybean crop area in 2004  [20].
Members of the family Iridoviridae are animal viruses that infect only invertebrates and poikilothermic vertebrates. The invertebrate iridovirus 31 (IIV31) was originally isolated from adult pill bugs, Armadillidium vulgare (class Crustacea, order Isopoda, suborder Oniscidea), found in southern California on the campus of the University of California, Riverside, USA. IIV31 virions are icosahedral, have a diameter of about 135 nm, and contain a dsDNA genome 220. 222 kbp in length, with 35. 09 mol % G+C content and 203 ORFs. Here, we describe the complete genome sequence of this virus and its annotation. This is the eighth genome sequence of an IIV reported. The GenBank/EMBL/DDBJ accession number for the complete genome sequence of IIV31 is HF920637.
, 2006; Jancovich et al. , 2011) , the only species reported in this genus. The type species for the genus Iridovirus is IIV6, and only two species, IIV1 and IIV6, have been recognized by the International Committee for Virus Taxonomy (ICTV) as representatives of this genus. Ten other related viruses that may be Iridovirus species await biological and genomic data before it can be determined whether they are valid species or variants of existing species. Interestingly, the phylogenetic analyses of proteins encoded by IIV3, IIV6  (Jakob et al. , 2001)  and IIV9  (Wong et al. , 2011)  have revealed that IIV9 is more closely related to IIV3 than to IIV6. This has been confirmed with IIV22, IIV22A, IIV25 and IIV30, four close relatives of IIV9  (Piegu et al. , 2013a, b, c, d) , indicating that some species of the genus Iridovirus are more closely related to members of the genus Chloriridovirus than to other Iridovirus species from insects.
Although first determined through phylogenetic analyses, this classification is strongly supported by data showing that these invertebrate viruses share 26 core genes, of which only 19 are found in vertebrate iridoviruses  (Eaton et al. , 2007; Bigot et al. , 2009). A precise definition of genera among the IIVs therefore remains unresolved at present, and further investigations are required to elucidate whether or not species complexes should be elevated to genus rank. To date, six genomes of IIVs have been sequenced: IIV3, the mosquito iridescent virus  (Delhon et al. , 2006) , IIV6, the Chilo iridescent virus  (Jakob et al. , 2001) , IIV9, the Wiseana iridovirus  (Wong et al. , 2011)   2013d ). Here, we present a summary classification and a set of features for IIV31, the eighth IIV sequenced, together with the description of the sequencing and annotation of its genome.
Here, virions of iridovirus type IIV31 were harvested in April 2010 from isopods collected from a bed of ivy on the campus of the University of California, Riverside, and frozen at 280 u C. IIV31-infected isopod adults are recognizable by a characteristic blue discoloration of the normally grey cuticle. IIV31 virions and their genomic DNA (gDNA) were purified as described elsewhere  (Federici, 1980; Bigot et al. , 2009). The purity of our IIV31 sample was determined by digesting virion gDNA with restriction enzymes using published data as a reference  (Federici, 1980; Williams, 1994). In 2009, the scientific committee of GENOSCOPE selected the IIV31 genome for sequencing. The complete genome sequence and annotation are now available in the EMBL database (accession no. HF920637). A summary of the project results is shown in Table  1.
The genome of IIV31 was sequenced using the 454 FLX pyrosequencing platform (Roche/454). Library construction and sequencing were performed as previously described elsewhere  (Henn et al. , 2010). De novo genome assembly was performed using the Newbler v2. 3 assembly software package as previously described elsewhere  (Henn et al. , 2010). Assembly metrics are described in Table  1. The assembled contig representing the entire IIV30 genome sequence was confirmed by comparing five predicted restriction fragment profiles from the genome, for BamHI, EcoRI, HindIII, PstI and SalI, with the matching fragment profiles produced by actual restriction digestions of the IIV31 genome  (Federici, 1980; Williams, 1994). Genes were identified using the Broad Institute Automated Phage Annotation Protocol as described elsewhere  (Ashburner et al.
A total of 203 genes encoding proteins were predicted. No genes coding for tRNAs were found. Of the 203 coding DNA sequences (CDSs), 125 were in forward orientation and 78 in reverse orientation. Four gene pairs were found to overlap: 103R/104L, 114R/155L, 129L/130R and 162L/ 163R. Eighty-four CDSs (41. 4 %) have been annotated with functional product predictions. The annotation of the 203 genes, described in Table  S1  (available in the online Supplementary Material), revealed that 184 of the 203 CDSs have a related gene in databases, with e values below 10 23. One hundred and seventy-two genes have an orthologue in the IIV6 genome, three genes (004R, 040R, 067L) have an orthologue in the IIV9 genome that did not occur in the IIV6 genome, four genes (015L, 024R, 084R, 119R) have a viral orthologue that did not occur in one of the currently sequenced IIV genomes, and five genes (013L, 022L, 082L, 128L, 136L) have no orthologue in any currently sequenced viral genomes, but have an orthologue in a prokaryotic or a eukaryotic genome.
Finally, 19 genes have no orthologue in databases and putatively correspond to novel genes (003R, 010L, 025L, 034R, 035L, 072L, 074R, 080R, 085R, 091R, 098R, 101R, 102R, 105L, 109R, 114R, 153L, 183R, 192R). Due to its close relatedness to IIV6, the functions of 84 of the proteins, assigned by  Jakob et al. (2001) , are considered the same for this isopod IIV. With regard to repeats, three families of gene paralogues occur in the IIV31 genome. The first contains 10 members that are related to CIV genes 006L, 019R, 029R, 146R, 148R, 211L, 212L, 238R, 313L, 388R, 420R and 468L. The second contains three members related to CIV261R, 396L and 443R. The third contains eight members that belong to the family bro-like genes, a widespread family of repeated genes in NCLDVs  (Bideshi et al. , 2003). Three gene fossils, i.
However, no sequence data for the transposase that mediated the transposition of IIV31-MITE are available to confirm this proposition. IIV31 is, to our knowledge, the first iridovirus genome infecting a crustacean species to be sequenced and reported. This genome revealed 19 new putative proteins with sizes varying from 105 to 435 aa residues. Many (184) of the CDSs identified displayed high conservation with their counterparts in other IIVs, insect and bacterial genomes. Further sequencing of related strains will reveal more about the genetic and functional diversity of these interesting viruses. The discovery of IIV31-MITE, together with that of IIV09-MITE and IIV22-MITE, and of Fanzor1 and 2 DNA transposons  (Bao & Jurka, 2013)  in two ascoviruses, SfAV1a and HvAV3e, suggests that these closely related invertebrate viruses  (Stasiak et al.
Their approach has an overhead equivalent to 3 % loss of memory and cache. In addition, it brings an overhead of 5. 9 to 7. 2 % in terms of computation. Other works around software mechanisms to increase performance isolation exist for other resources: disk  [19] , memory bandwidth  [20] , and network  [21]. The NoHype concept  [22] ,  [23]  consists in removing the virtualization layer while retaining the key features enabled by virtualization. They limit covert-channel by enabling one VM per core but as we have show, other covert-channels exist within the micro-architecture components. Moreover, they are not able to do fair sharing on I/O and memory bus. Thus the performance isolation between VMs with NoHype remains weak.
V. CONCLUSION We have seen that a large range of possible and realworld attacks on Cloud platforms are due to improper isolation. We have shown that the existing method to improve isolation (from a data and performance points of view) are limited to only detecting them or induce a large overhead. Accordingly, we propose to enhance VM placement heuristics with isolation requirements. We have introduce the concept of user's isolation requirements for hierarchical and distributed large scale platform. Then, we have explained how these requirements can be used inside VM placement heuristics. Furthermore, we have introduce 4 new heuristics that use the hierarchical nature of the platform. From experiments, we show that our heuristics perform at least as good as classical ones. Furthermore, on large-scale platforms such as EC2, our heuristics are performing few orders of magnitude better than the classical ones.
Genetic linkage mapping Eight DO samples were excluded from mapping analysis due to missing genotype calls, bringing the total number of mice to 261. QTL mapping was carried out as described by  Svenson et al. (2012). Founder haplotypes were reconstructed using a Hidden Markov Model (HMM) that produced a matrix of 36 genotype probabilities for each sample at each SNP. Genotype probabilities at each SNP were then collapsed to an eight-state allele dosage matrix by summing the probabilities contributed by each founder. Phenotypic data were normalized by natural log transformation prior to linkage mapping analysis. Mapping was performed using QTLRel software (http://www. palmerlab. org/software)  (Cheng 2011). A mixed model was fit with sex and experimental group as additive covariates and a random effect was included to account for kinship.