dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , time dimension) in tensor object in C1 layer. It means that, for C2 layer, the patches do not have great size. So, it is better to treat these tensor patches as vector to learn the projection dictionary. [3]  67. 12 PCANet-2  [3]  68. 49 MPCA+LDA  [5]  42. 34 B. Testing on UCF11 database Subsequently, we test the performance of proposed MPCANet on UCF11 for tensor objects classification. UCF11 contains 11 action categories: basketball shooting, biking, diving, golf swinging, horseback riding, soccer juggling, swinging, tennis swinging, trampoline jumping, volleyball spiking, and walking with a dog. They are all 240  320 pixels action videos and manually collected from YouTube. For each category, the videos are grouped into 25 groups with more than 4 action clips in it. We only choose the top-ten group in each category to test the performance of MPCANet, PCANet  [3] , and MPCA+LDA  [5].
The total number of experimental videos is 642. For each group, half videos are randomly selected for training and others for testing. The UCF11 videos have some variation in frame. For time-mode larger than 20, we only choose the first twenty time modes. For a few videos, whose frames are less than 20, we just copy the last frame to fill them. Every videos are resized to 48  64 to be easily calculated. The patch size and overlapping ratio are the same as above experiment shown in UCF sports action database. Three box size sizes 6  8, 12  16, 24  32 are considered here. For MPCA+LDA, the dimensions of feature, which are extracted from MPCA, are changed from 10 to 100. The best performance of MPCANet, PCANet, and MPCA+LDA is listed in Table  II.
V. CONCLUSIONS In this paper, we proposed and implemented a novel deep learning architecture, that is, MPCANet, which involves tensor interactions among stages. MPCANet is composed of projec-tion dictionaries, projected encoder, and pooling layer. We have described an approach to map the tensor objects to a corresponding feature vector. We have evaluated the performance of the MPCANet on UCF sports action dataset and UCF11. The experimental results demonstrate that MPCANet outperforms conventional PCANet and MPCA+LDA in tensor objects classification. It provides us the inspiration to dealing with tensor objects for other convolutional deep architectures. 12 PCANet-1  [3]  58. 68 PCANet-2  [3]  76. 92 MPCA+LDA  [5]  45. 15.
This work was supported by the  National Basic Research Program of China  under Grant  2011CB707904 , by the  NSFC  under Grants  61201344 ,  61271312 ,  11301074 , and by the  SRFDP  under Grants  20110092110023  and  20120092120036 , the Project-sponsored by SRF for ROCS, SEM, and by  Natural Science Foundation of Jiangsu Province  under Grant  BK2012329  and by  Qing Lan Project. This work was supported by  INSERM postdoctoral  fellowship.
Automated intermittent (stop-flow) respirometry cycles were conducted at consecutive 30-min intervals consisting of three phases: A 7-min tunnel flush period followed by tunnel closure to prohibit water/oxygen exchange, a 3-min acclimation period, and a 20-min measurement period. Stop-flow cycles began ca. 2 hr prior to the dark phase of the photoperiod; however, data collection for calculation of SMR was restricted to ten measurement cycles in the absence of light stimulus, following "sunset" and preceding "sunrise (00:00-05:00). " Metabolic rate was estimated based on the decline in oxygen We first tested for allometric differences among groups via analysis of covariance (ANCOVA) of log 10 transformed data (absolute metabolic rate & total wet weight). Critically, results confirmed that (1) U crit = U f + U i × t∕t i , the shape of the size-SMR relationship was similar among morphotypes (i.
e. , no significant difference in scaling exponents; F [2] = 1. 14, p =. 323; Fig. S2a ). Additionally, 95% confidence interval estimates of the common scaling exponent (0. 72 ≤ b ≤ 0. 99) suggested an isometric relationship with absolute SMR over the range of body sizes comprising the current dataset. This was further reinforced by contrasting models fit to morph-specific data, wherein linear model fit was no different from that of a power curve (Fig. S2c, e, g ). The absence of allometric differences allowed us to express data as a simplified mass-specific metabolic rate (MO 2 ; mg O 2 kg -1 hr -1 ), thereby facilitating comparisons with other published estimates, and most critically, permitting subsequent tests for correlated selection between trait pairs. MO 2 was calculated based on the following equation: where [O 2 ] t0 -[O 2 ] t is the amount of oxygen consumed by fish (in mg O 2 /L), t is the time interval of measurement period (in s), V is the volume of the swim tunnel (in L), W is the body weight of the fish (in kg).
Absolute data were analyzed as previously described for SMR: first assuring the absence of allometric differences among morphs (i. e. , no significant difference in scaling exponents; F [2] = 0. 54, p =. 585; Fig. S2b ); secondly, inferring an isometric relationship with absolute AMR over the range of body sizes comprising the current dataset (0. 58 ≤ b ≤ 1. 02; Fig. S2d, f, h ). Simplified mass-specific oxygen consumption (MO 2max ) was also calculated as per Equation  2 , serving as our final estimate of AMR in subsequent analyses.
| Lateral plate coverage Following respirometry, fish were euthanized by cervical dislocation, fixed in 10% buffered formalin for approximately 2 weeks, then transferred to 70% ETOH. Sample preparation involved a modification of  Potthoff's (1984)  protocol in which pigmentation was removed by bleaching in a solution of H 2 O 2 and KOH, and lateral plates were stained with Alizarin red. The left side of every individual was photographed at a standard distance with a common reference scale placed next the fish. Measurements were taken from digital images using the image analysis software ImageJ  (Abràmoff, Magalhães, & Ram, 2004). These included standard length (SL) and the area covered by nonstructural plates (i. e. , the lateral plates not buttressing the dorsal and pelvic spines). Note that only the first 20 nonstructural plates were measured because of difficulties with the identification of small caudal plates, typically associated with the keel region-for a detailed description of lateral plate identification and numbering, see  Bergstrom and Reimchen (2003).
| Comparison among populations/morphs The R statistical computing language/software was used for all analyses (version 3. 0. 1; R Development Core Team, 2013). Replicate measurements for SMR, AMR, and U crit were performed on a subset of individuals (n = 12) one week following initial measurements. To compare the first and the second measurements, we used a paired Welch's t test, which indicated no significant differences between measurements in any of the traits (p >. 3). To test for mean trait differences among populations, we used the Bayesian mixed-effects modeling framework implemented in the "MCMCglmm" package  (Hadfield, 2010). Population/morph was treated as a fixed effect-with effects/coefficients contrasted against the ancestral group (BAR)-and family was included as a random term.
To test specifically for differences between freshwater populations, we ran a series of additional models using the same framework, but with data restricted to the KAR and PUL groups. Models were run with a burn-in of 800,000 iterations, followed by an additional 200,000 iterations from which each 200th point of the Markov Chain was sampled to reduce autocorrelation of estimates. Significance of contrasts was evaluated via p-values profiled from the sampling chain. Population-specific estimates for each trait, conditioned on random effects, were based on the posterior mode (2) MO 2 = O 2 t0 -O 2 t t × V W , and were bounded by 95% posterior density interval estimates (PDIs). We also explored bivariate relationships between all pair-wise trait combinations within each population. We used "MCMCglmm" to model the linear relationship between each pair of traits, incorporating random variation among families.
| Testing for signatures of selection To ascertain whether natural selection or random genetic drift was responsible for trait divergence, we employed a model of multivariate trait differentiation expected under neutrality  (Ovaskainen, Karhunen, Zheng, Arias, & Merilä, 2011) , as implemented in the package "driftsel"  (Karhunen, Merilä, Leinonen, Cano, & Ovaskainen, 2013). In this model, differentiation from a common ancestral population expected under drift is derived from both neutral genetic markers and the pedigrees of experimental animals. To estimate population-level coancestry, we used genotype data (available from Dryad; https:// doi. org/10. 5061/dryad. s6h18) from previously published work that included our study populations  (Leinonen et al. , 2012). First, we used the program "LOSITAN" to identify which markers were putatively under selection  (Antao, Lopes, Lopes, Beja-Pereira, & Luikart, 2008) , retaining 19 putatively neutral loci for subsequent analyses (Table  S1 , n = 30 individuals per population).
Next, we modeled neutral genetic differentiation among populations using an admixture model implemented in "RAFM"  (Karhunen & Ovaskainen, 2012) , a package integrated into the "driftsel" analytical pipeline. RAFM was run for a total of 300,000 iterations which included a burn-in of 200,000 iterations, followed by 100,000 iterations from which each 50th position in the Markov chain was sampled. Estimates of divergence were checked for convergence and autocorrelation using the "CODA" package  (Plummer, Best, Cowles, & Vines, 2006). The next stage in the "driftsel" analytical pipeline involves incorporating the pedigree of experimental animals to estimate genetic covariance among traits and the degree of differentiation among populations in the absence of selection. We began with a fully parameterized model including all five traits (SMR, AMR, lateral plate area, U burst and U crit ):This was run for a burn-in of 100,000 iterations followed by 10,000 iterations from which estimates were sampled from each 10th position in the Markov chain.
ACKNOWLEDGMENTS This work was funded by the  Academy of Finland  (grants  252597  to T. L. , 263722 to J. M. & 259944 to R. J. S. M). Additional funding was provided by the  Finnish Cultural Foundation  (R. J. S. M. ). S. M. was supported by the  Centre for International Mobility , the  Otto A. Malm Foundation , the  Oskar Öflund Foundation , and the  Alfred Kordelin Foundation. Our thanks to  Otso Suominen  and the staff of the  Kevo Subarctic Research Insititute  for their assistance in collecting broodstock.
In this context, the measurement of the 238 U radiative capture cross section is of high priority and is part of the NEA High Priority Request List [6], a compilation of the most relevant nuclear data requirements maintained by the Nuclear Energy Agency of the Organisation for Economic Co-operation and Development (OECD). In fact, 238 U constitutes more than 90% of nuclear fuel in power reactors, being one of the most important isotopes for neutron transport calculations in the active zone. In par-ticular, for ordinary thermal nuclear reactors the uncertainties on fundamental design parameters such as the multiplication factor, the power peak, and the reactivity coefficient significantly depend on the 238 U(n,γ) reaction cross-section for incident neutron energies from 0. 0253 eV to 25 keV.
A. Time-of-flight to energy calibration The neutron velocity and therefore the kinetic energy were derived from the measured time-of-flight and the flight-path length, which is nominally 185 m. The effective flight path length L(E) is not a fixed value but depends on the neutron energy according to the neutron flight time distribution in the spallation target and moderator assembly. This energy dependent distribution is also known as the resolution function (RF). For a given energy E, the effective flight path can be split up as the sum of a fixed flight path length L 0 and an additional component λ(E), which corresponds to the expectation value of the equivalent distance for a neutron of energy E. The value of L 0 , valid in combination with the used RF obtained by simulations, was fitted with the multilevel multichannel R-matrix code SAMMY  [32]  using the first 5 resonances and their corresponding energies.
[38]. The normalization factor N in Eq. 1 has been extracted via the saturated resonance method, evaluating the expected capture yield through a least-squares adjustment of the experimental data with the SAMMY code  [32]. Because an internal normalization is applied, i. e. exploiting a resonance of the nucleus under investigation, the systematic uncertainties due to changes in the experimental conditions are significantly reduced. The two detectors have been treated separately, and for each one the final normalization factors have been chosen as the average of the three resonances. The mean deviation of these factors of 1% has been adopted as the normalization uncertainty.
A. Resolved Resonance Region The energy resolution of the n TOF spectrometer gives an upper energy limit of 20 keV to the RRR, since the experimental broadening becomes comparable with the level spacing. However, due to the decrease of the signalto-background ratio with neutron energy together with the limited counting statistics, resonances could be reliably resolved only up to 3 keV. Within this energy region a resonance shape analysis has been performed by means of the SAMMY code  [32]. The theoretical reaction yield is obtained through the Reich-Moore approximation of the multi-level R-matrix formalism, and convoluted with the experimental resolution. This latter is implemented in SAMMY via a numerical description derived from Monte Carlo FLUKA simulations. Moreover, the code includes the corrections for multiple-scattering effects and uses the free-gas model to take the Doppler broadening due to thermal motion of atoms inside the sample for an effective temperature of 305 K into account, according to the room temperature which was continuously monitored during the measurement  [31].
By trying different variations we found that the difference between a uniform or Gaussian beam profile is negligible. On the other hand, the presence of aluminum has a non-negligible effect as can be seen from the peaks corresponding to Al resonances in Fig. 8. The results were confirmed by simulations with the code GEANT4. The factor C f shown in Fig. 8  was applied to the background-corrected experimental capture yield to obtain the average capture cross section σ γ. Table  IV  summarizes the average capture cross-section together with the total uncertainties derived from the data analyzed in this work. Correlated uncertainties are separated for the different components listed in Table  II. The 238 U capture cross-section from this work has been  tively. The comparison has been also made using a higher threshold (E min dep = 1.
VI. CONCLUSION The 238 U(n,γ) reaction cross-section has been measured at the n TOF facility of CERN using an array of two deuterated-benzene scintillators. The purpose was to provide highly precise results to solve the inconsistencies among data in literature and in evaluated libraries. This cross section is in fact of extreme importance for the calculations of fast and thermal reactor parameters, and in particular the safety of nuclear reactors mainly depends on its uncertainty. Thanks to the low background of the experimental setup, and to the high intensity of the n TOF neutron flux, the cross section could be measured from 1 eV to 700 keV. The measurement has been prepared and the data analyzed with extreme accuracy and precision, in order to provide results with an uncertainty as low as possible.
VII. ACKNOWLEDGMENT This work has been fully supported by the the  European Commission  within the  FP7  project  ANDES  ( FP7-249671 ). We acknowledge the  Joint Research Centre in Geel of the European Commission  ( EC-JRC in Geel, Bel-gium ) to have provided the high-purity sample used for the measurement. GEANT4 simulations of the neutron background have been run at the  Laboratory for Advanced Computing, Faculty of Science, University of Zagreb.
3735 1. 0177 0. 1170 2. 3407 1. 1703 8. 1923 compared to evaluated libraries and previous measure- Fig. 10, this work yields a cross section that is 15 to 25% ments (retrieved from the EXFOR database [45]). From higher than the ENDF/B-VII. 1 evaluation and the cross 3 to 80 keV data from this work are plotted in Fig. 9 section of Carlson et al. , and 21 to 32% higher than the with a resolution of 50 bin/decade (i. e. dE/E = 2%). JEFF-3. 2 evaluation. The measurement from Ullmann As can be seen, up to 20 keV they substantially agree et al. shows a fair agreement with the present work up with the recently published data by Ullmann et al. [46] to 630 keV. and Kim et al. [47], and with the cross section recom- mended by Carlson et al. [12] and by both JEFF-3. 2 and ENDF-B/VII. 1 evaluated libraries.
Data visualization We designed the monitoring system so that both RAW and processed point clouds can be visualized in the field or through a remote connection to the field computer. This was done to avoid large data transfer to a remote server and so results could be directly visualized and interpreted in the field. To support visualization and interpretation, we store point clouds with mapped raw distances, filtered distances and confidence intervals in point cloud libraries binary pcd format. Because all distances are mapped onto the reference point cloud, we also stored all of the measured distances and confidence intervals over time in a database mapped to the index points of the reference point cloud. This allows time series of distances and confidence to be extracted by point picking on the slope.
Acknowledgements. We would like to acknowledge the  Centre  for studies and expertise on Risks, Environment, Mobility, and  Urban and Country (Cerema)  for supporting the research. The first author would like to acknowledge support from the  Natural Sciences and Engineering Research Council of Canada (NSERC)  through the post-graduate scholarship programme. The second author would like to acknowledge the support received from the  H2020 Program  of the  European Commission  under the  Marie Skłodowska-Curie Individual Fellowships  ( MSCA-IF-2015-705215 ). We would also like to acknowledge  Nick Rosser  for helpful advice on atmospheric correction and  Antoine Guerin  for help with field data collection. Edited by: A. Eltner Reviewed by:  R. Salvini  and one anonymous referee.
, 2013). For instance, the relationships between wild and domestic species within the genus Citrus were elucidated based on a phylogenetic analysis of 34 entire chloroplast genomes  (Carbonell-Caballero et al. , 2015). Very recently, the complete chloroplast genome sequences were used to infer phylogenetic relationships in the Quercus genus  (Yang et al. , 2016). The development of NGS technologies has provided highly efficient, low-cost DNA sequencing platforms that produce large volumes of short reads  (Shin et al. , 2013). However, more recently, third generation sequencing technologies producing longer DNA reads have begun to emerge, including the Pacific Biosciences single molecule real-time (SMRT) sequencer that became available in 2011 2. For example, using PacBio sequence data it was possible to generate an entire chloroplast genome assembled into a single large contig with a high degree of accuracy and at a much greater depth of coverage due to longer read lengths  (Ferrarini et al.
5 µg of each individual cpDNA were pooled together with other P. edulis BAC inserts (12 in total) for the construction of an SMRT library using the standard Pacific Biosciences (San Francisco, CA, USA) preparation protocol for 10 kb libraries. The pool was then sequenced in one SMRT Cell using the P4 polymerase in combination with C2 chemistry, following the manufacturer's standard operating procedures and using the Pacific Biosciences PacBio RS II platform. Sequencing was performed by GATC Biotech 4. The reads were assembled following a hierarchical genome assembly process HGAP workflow  (Chin et al. , 2013) , and using the SMRT R Analysis (v2. 2. 0) software suite for HGAP implementation. Reads were first aligned by the PacBio long read aligner or BLASR  (Chaisson and Tesler, 2012)  against the complete genome of Escherichia coli strain K12 substrain DH10B (GenBank: CP000948.
Obtaining the Complete Chloroplast Genome The sequences obtained from both inserts (Pe69Q4G9 and Pe85Q4F4) were aligned using ClustalX software  (Larkin et al. , 2007)  to obtain a single contig. Specific primers were designed at the sequence ends of this contig in order to find out whether the circular chloroplast genome was complete. PCR reactions were then performed using a 9700 thermal cycler (Applied Biosystems, Foster City, CA, USA) in reaction mixtures containing 20 ng template DNA (P. edulis accession 'IAPAR-123'), 1× buffer, 1 mM MgCl 2 , 0. 2 mM of each dNTP, 0. 3 µM of the forward and reverse primers, 1. 2 U Go Taq Flex DNA polymerase (Promega, Madison, WI, USA), and ultra-pure water to bring the final volume up to 20 µL. The thermal profile for amplification was: 95 • C for 5 min, 35 cycles at 95 • C for 40 s, 55 • C for 40 s and 72 • C for 1 min, followed by a final 8 min incubation at 72 • C.
Genome Annotation The cp genome was preliminarily annotated using the DOGMA (Dual Organellar GenoMe Annotator) online program  (Wyman et al. , 2004) , with default settings to identify coding sequences (CDS), rRNAs and tRNAs based on the Plant Plastid Code and BLAST homology searches, followed by manual corrections for start and stop codons, and intron positions. All tRNA genes were further confirmed using the tRNAscan-SE online search server. Pseudogenes were classified based on the loss of parts in their sequences or by the presence of internal stop codons. The circular genome map was designed by the GenomeVx program  (Conant and Wolfe, 2008). Codon usage frequencies and the relative synonymous codon usage (RSCU) were calculated for all exons of the protein-coding genes using DAMBE 5  (Xia, 2013).
221, using the FFT-NS-2 algorithm with default settings. To generate the alignment, inverted sequences detected in the cp genomes of P. edulis and H. brasiliensis were reversed and the respective positions adjusted. The raw alignment was manually corrected in BioEdit  (Hall, 1999)  and further processed in GBLOCKS 0. 91b  (Castresana, 2000)  in order to remove regions containing gap positions, with a minimum block length of five, and maximum number of contiguous non-conserved positions of eight. The resulting alignment was analyzed in jModelTest software version 2. 1. 7 (Darriba et al. , 2012)  to determine the optimal model of molecular evolution and gamma rate heterogeneity using the AIC. Maximum likelihood analysis was performed using PAUP version 4. 0a146  (Swofford, 2002) , based on the transversional (TVM) substitution model, gamma distribution of rate heterogeneity with five discrete categories (+G).
V. vinifera (Vitaceae, Vitales) was chosen to serve as outgroup to produce a rooted tree. A list of the chloroplast gene sequence sources is provided in Supplementary Table  S1. First, each protein-coding gene sequence was aligned using ClustalW with default settings and the raw alignments manually corrected in program BioEdit  (Hall, 1999)  and further processed in GBLOCKS  (Castresana, 2000) , excluding gap positions from the data set, with a minimum block length of five, and a maximum number of contiguous non-conserved positions of eight. Next, all individual filtered alignments were concatenated into a single alignment matrix. Both conserved and variable nucleotide positions in the alignment matrix were analyzed in MEGA6  (Tamura et al. , 2013). jModelTest software version 2. 1. 7 (Darriba et al.