dataesr/scientific-paragraphs-categorization huggingface dataset:
The resulting PCR products were processed by Eurofins MWG Synthesis GmbH (Ebersberg, Germany). Both strands were sequenced for all specimens to minimize PCR artifacts and ambiguities. Sequences of complementary strands were edited and reconciled using Geneious v5. 1 software (available at: www. geneious. com/). The alignment of all sequences was carried out using MAFFT 7  [112]  with default option settings. No gap was present in the corresponding alignment. We then used Mesquite v3. 0  [113]  to check the coding frame for possible errors or stop codons. Additional sequences generated for the study were deposited to GenBank under numbers KF854152 to KF854234. The final dataset encompasses 679 individuals and 658 aligned characters.
Similarly Tang et al. ,  [110]  highlighted the fact that the branch smoothing procedures are a potential source of biases for the approaches that require ultrametric trees. In summary all these studies underline that molecular species delimitation approaches should not be used blindly. They nonetheless constitute powerful tools that can be effectively used in a concerted way to assess species boundaries (e. g. [137] ). In our study, a high level of consistency was likely achieved because of the size of our dataset and because we carefully followed some of the recommendations of Tang et al. ,  [110]. Because the focus of our study was to reassess Spodoptera species boundaries, and not to explore the impact of tree building procedures on molecular species delimitations, we feel that our analytical pipeline is appropriate and allows us to discuss with some confidence the ambiguous species status of several taxa.
,  [85] ). This result is also statistically supported under GMYC and does not seem artefactual given the marked level of genetic differentiation between the two clades. It is also supported by additional researches on the Barcoding of life database (BOLD; available at http://www. boldsystems. org/); 81 S. exigua specimens from Australia are grouped into a distinct barcode cluster (BOLD:AAA6645) distinct from another barcode cluster (BOLD:AAA6644) grouping all remaining S. exigua individuals (n = 281). Obviously this result requires more investigation in order to determine whether these specimens correspond to a new species with marked morphological differences or to a case of cryptic species complex. For Spodoptera apertura, only one of the four distinct sets of analyses (PTP using the ML tree; S3 The situation is more complex in the case of Spodoptera cosmiodes and Spodoptera descoinsi, two taxa for which marked morphological and biological differences exist  [76, 77].
Both phylogenetic species delimitation procedures suggested the existence of two distinct species clusters. Analyses following the GMYC procedure also included representatives of S. evanida in one of the two clusters (the one with seven specimens S. descoinsi) but this grouping seems artefactual as the two species are morphologically not related  [76] ; in addition a closer examination of the GMYC output indicates that this grouping is not statistically supported. If we consider the remaining specimens, we can distinguish two species clusters, one that groups seven S. descoinsi and another that groups all (n = 35) S. cosmiodes individuals plus two representatives of S. descoinsi. To precise the status of these two individuals, additional studies are likely required; we advocate for the use of species validation procedures (with packages such as BPP  [55]  or Spe-deSTEM  [56] ), using more specimens and a multi locus dataset.
Conclusions This study constitutes another case study that underlines the interest and effectiveness of fast species discovery procedures; the achieved results are congruent and reliable, irrespective of the set of parameters and phylogenetic models applied, even though we only relied on a single locus dataset. Our results unravel at least three potential new species clusters (for S. exigua, S. frugiperda and S. mauritia) and also support the synonymy of S. marima with S. ornithogalli. Though these redefined species boundaries have to be considered cautiously, they nonetheless constitute a new line of evidence that may fuel the debate on Spodoptera species boundaries and inspire future studies on species such as the beet armyworm S. exigua, for which a potential cryptic diversity was not expected.
H. Janzen & W. Hallwachs, USA; RCW: Research Collection of Jeremy deWaard, Guelph, Canada. SI: Smithsonian Institution, Washington, USA; TAU: Tamilnadu Agricultural University, India; UBC: University of British Columbia, Vancouver, Canada; UFP: Universidade Federal do Parana, Curitiba, Brazil; UM: University of Maryland, USA; UR: Université de Rouen, Rouen, France; USDA: USDA ARS, Gainesville, USA; YU: Yangzhou University, China; ZIQ: Zuhai Inspection and Quarantine bureau, China. (DOCX) S2 Table. Results of PartitionFinder analyses for the COI dataset. Three partitions were specified (one per codon position). Selected models and partitions based on the AICc are figured on the left whereas selected models and partitions based on the BIC are figured on the right. (DOCX) S3 Table. Putative species clusters.
Acknowledgments We would to thank  Diego Fontaneto  and two anonymous reviewers for their numerous constructive comments on a previous version of the manuscript. Some of the molecular data used in this work were produced through technical facilities of the  "Laboratoire d'Excellence-Centre Méditerranéen de l'Environnement et de la Biodiversité" (LabEx-CeMEB ), Montpellier, France. The authors also wish to thank  A. Dehne Garcia  for his help on the CBGP HPC computational platform.
Part of the sequencing was supported by a  Genoscope  project  @-Speed-Id (Accurate SPEciEs Delimitation and Identification of eukaryotic biodiversity using DNA  markers) proposed by  F-BoL , the  French Barcode of life initiative  and by the  program  ' Bibliothèque du Vivant' (Project SPODOBAR) supported by a joint "Centre national de la recherche scientifique ", " Institut National de la Recherche Agronomique " and " Muséum national d'Histoire naturelle" consortium. The project was also supported by  INRA (AAP SPE SPODOPTERA)  and a grant from the  French National Research Agency  ( ANR-12-BSV7-0004-01 ADA-SPODO ).
The triclusterings obtained by our method are not constrained to yield identical results on S and D even if S = D (which is the case here). This would be an important limitation as it would constraint an actor to have the same role as a source than as a destination. In the bike share data set, we obtain comparable but not identical clustering structures on the set of source and destination vertices. The main notable difference lies on the segmentation of the financial district of London: one single destination cluster covers the area while it is split into two source clusters (the two types of gray squares on Figure  3  form the source clusters, while most red discs on the right hand side of Figure  4  form the destination cluster).
We consider a heterogeneous activity distribution, so that the obtained networks are characterized by a temporally uncorrelated sequence of snapshots with a heterogeneous aggregated degree distribution. BURSTY is built from the model introduced in  [23]  and accounts for a heterogenous activation pattern describing a sequence of homogeneous networks where the inter-contact time is power-law distributed. Size and period are chosen arbitrarily for all these networks, since the choice of these parameters does not impact the method validation, as discussed more in detail in the following section. As examples of real time-varying networks, we choose datasets describing human contacts of different kind: HT09 is the network of face-to-face proximity during a 2. 5 days scientific conference  [16] ; SEX is a 1-year network of sexual contacts between prostitutes and their clients  [18] ; SCHOOL is a contact network describing one day in a high-school  [49].
Optimal data collection time Available datasets characterizing empirical networks only account for a portion of the real contact process, and the extent of the recording time window may affect the prediction of the epidemic threshold. One may expect that, when the data-collection period is long enough, the data would represent an approximately complete reconstruction of the temporal network properties, thus leading to an accurate estimate of the epidemic threshold. Given the resources needed for the setup of data collection deployments, we explore here the role of the period T aimed at identifying a minimum length of observation of the contact process that is optimal in providing a reliable characterization of the spreading potential. We thus compute the epidemic threshold from Eq. (  8 ) for increasingly larger values of the period T up to the entire data-collection time window, for the three empirical networks under study.
Our framework thus introduces a multilayer formulation of spreading phenomena on time-varying networks that opens the path to new theoretical understandings of the complex interplay between the two temporal processes, disentangling the role of the network dynamical features, such as activation rate, temporal correlations and temporal resolution. Python code for computing the epidemic threshold can be found on https://github. com/eugenio-valdano/threshold. A Proof of Eq. (  8 ) Computing the eigenvalues of M † means solving the equation det x -M † = 0, where the determinant is computed on the R N T space (det N T ). Given that x -M † is composed of T 2 blocks of size N × N , we can use the findings in  [52]  to reduce the dimensionality of the problem, i. e. det N T → det N. Moreover, given that several blocks of x -M † are zero, the general result in  [52]  simplifies to det N T x -M † = (-1) N T det N x T -P.
BURSTY: The network is built from the model introduced by Rocha et al. [23]  where a node becomes active at time t with a probability that depends on the time it was last active, t. If active, it then forms a link with another active node. All links are removed before proceeding to the following snapshots. To enforce a bursty inter-event time distribution, the probability of becoming active is sampled with the distribution (t -t ) -α 1 e -α 2 (t-t ) , with α 1 = 2 and α 2 = 5 • 10 -4. HT09: The dataset was collected by the Sociopatterns group  [16] , and records the interactions among participants at a scientific conference. Links represent face-to-face proximity recorded by wearable RFID (Radio Frequency IDentification) tags. Time resolution of the signal is 20 sec. Each tag emits wave packets that may be recorded by other tags, thus signaling proximity.
Tags were embedded in conference badges, their signal intensity was set to be detectable at a maximum distance of 1m and completely shielded by the human body. With these settings in effect, only close proximity in a face-toface position resulted in interaction. Datasets and additional information are available on www. sociopatterns. org. SEX: It is a network of sexual contacts between female prostitutes and their male clients as inferred from posts on a Brazilian online escort forum where customers could rate their experience with a certain sex worker. The date of the post was taken as a proxy for the time of the sexual intercourse. The (anonymized) dataset can be found in  [18]. SCHOOL: This network represents the face-to-face proximity interactions during a day in a high school  [49]. Students and staff were given wearable RFID sensors, and proximity was recorded in a similar fashion as for HT09.
The ACTIVITY model is a sequence of snapshots built with parameters values: ∆t = 1, m = 2, η = 10, γ = 2. 8, = 3 • 10 -2 , in notation of  [28]. The BURSTY network is built with a power law distributed inter-activation time, with exponent -2, and cutoff equal to the period of the network. For the real networks, the collection time is the total time considered in the dataset.
Acknowledgments We thank  Ciro Cattuto ,  Mario Giacobini ,  Sandro Meloni ,  Yamir Moreno ,  Nicola Perra ,  Mason Porter  and  Alessandro Vespignani  for fruitful discussions. This work has been partially funded by the  EC-Health  contract no. 278433  (PREDEMICS) to VC and  CP ; the  ANR  contract no. ANR-12-MONU-0018 (HARMSFLU)  to V. C. ; the  PHC Programme Galilee  contract no. 28144NH , the  Italian Ministry of Health  contract no. IZS AM 04/11 RC , the  EC-ANIHWA  contract no. ANR-13-ANWA-0007-03  (LIVEepi) to E. V. , C. P. , V. C. ; the  "Pierre Louis" School of Public Health of UPMC, Paris, France  to E. V. ; the  Lagrange Project on Complex Systems -CRT  and  ISI Foundation  to L. F.
ORL: The ORL database includes 400 face images taken from 40 subjects each providing 10 face images  [216]. For some subjects, the images were taken at different times, with varying lighting, facial expressions, and facial details. All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement). Each image was resized to a 56×46 image matrix by using the down-sampling algorithm.
LFW face dataset: The Labeled Faces in the Wild (LFW) face database is designed for the study of unconstrained identity verification and face recognition  [217]. It contains more than 13,000 images of faces collected from the web under the unconstrained conditions. Each face has been labeled with the name of the people pictured. 1680 of the people pictured have two or more distinct photos in the database. In our experiments, we chose 1251 images from 86 peoples and each subject has 10-20 images  [218]. Each image was manually cropped and was resized to 32×32 pixels. Extended YaleB face dataset: The extended YaleB database contains 2432 front face images of 38 individuals and each subject having around 64 near frontal images under different illuminations  [219]. The main challenge of this database is to overcome varying illumination conditions and expressions.
The facial portion of each original image was cropped to a 192×168 image. All images in this data set for our experiments simply resized these face images to 32×32 pixels. COIL20 dataset: Columbia Object Image Library (COIL-20) database consists of 1,440 size normalized gray-scale images of 20 objects  [220]. Different object images are captured at every angle in a 360 rotation. Images of the objects were taken from varying angles at pose intervals of five degrees and each object has 72 images. Fifteen scene dataset: This dataset contains 4485 images under 15 natural scene categories presented in literature  [221]  and each category includes 210 to 410 images. The 15 scenes categories are office, kitchen, living room, bedroom, store, industrial, tall building, inside cite, street, highway, coast, open country, mountain, forest and suburb.
A. Parameter selection Parameter selection, especially selection of the regularization parameter λ in different minimization problems, plays an important role in sparse representation. In order to make fair comparisons with different sparse representation algorithms, performing the optimal parameter selection for different sparse representation algorithms on different datasets is advisable and indispensable. In this subsection, we perform extensive experiments for selecting the best value of the regularization parameter λ with a wide range of options. Specifically, we implement the l 1 l s , FISTA, DALM, homotopy and TPTSR algorithms on different databases to analyze the importance of the regularization parameter. Fig. 5  summarizes the classification accuracies of exploiting different sparse representation based classification methods with varying values of regularization parameter λ on the two face datasets, i.
e. ORL and LFW face datasets, and two object datasets, i. e. COIL20 and Fifteen scene datasets. On the ORL and LFW face datasets, we respectively selected the first five and eight face images of each subject as training samples and the rest of image samples for testing. As for the experiments on the COIL20 and fifteen scene datasets, we respectively treated the first ten images of each subject in both datasets as training samples and used all the remaining images as test samples. Moreover, from Fig. 5 , one can see that the value of regularization parameter λ can significantly dominate the classification results, and the values of λ for achieving the best classification results on different datasets are distinctly different. An interesting scenario is that the performance of the TPTSR algorithm is almost not influenced by the variation of regularization parameter λ in the experiments on fifteen scene dataset, as shown in Fig.
B. Experimental results In order to test the performance of different kinds of sparse representation methods, an empirical study of experimental results is conducted in this subsection and seven typical sparse representation based classification methods are selected for performance evaluation followed with extensive experimental results. For all datasets, following most previous published work, we randomly choose several samples of every class as training samples and used the rest as test samples and the experiments are repeated 10 times with the optimal parameter obtained using the cross validation approach. The gray-level features of all images in these data sets are used to perform classification. For the sake of computational efficiency, principle component analysis algorithm is used as a preprocessing step to preserve 98% energy of all the data sets.
The classification results and computational time have been summarized in Table  I. From the experimental results on different databases, we can conclude that there still does not exist one extraordinary algorithm that can achieve the best classification accuracy on all databases. However, some algorithms are noteworthy to be paid much more attention. For example, the l 1 l s algorithm in most cases can achieve better classification results than the other algorithms on the ORL database, and when the number of training samples of each class is five, the l 1 l s algorithm can obtain the highest classification result of 95. 90%. The TPTSR algorithm is very computationally efficient in comparison with other sparse representation with l 1 -norm minimization algorithms and the classification accuracies obtained by the TPTSR algorithm are very similar and sometimes even better than the other sparse representation based classification algorithms.
Second, although sparse representation algorithms have achieved distinctly promising performance on some real-world databases, many efforts should be made in promoting the accuracy of sparse representation based classification, and the  robustness of sparse representation should be further enhanced. In terms of the recognition accuracy, the algorithms of l 1 l s , homotopy and TPTSR achieve the best overall performance. Considering the experimental results of exploiting the seven algorithms on the five databases, the l 1 l s algorithm has eight highest classification accuracies, followed by homotopy and TPTSR, in comparison with other algorithms. One can see that the sparse representation based classification methods still can not obtain satisfactory results on some challenge databases. For example, all these representative algorithms can achieve relatively inferior experimental results on the LFW dataset shown in Subsection IX-B, because the LFW dataset is designed for studying the problem of unconstrained face recognition  [217]  and most of the face images are captured under complex environments.
One can see that the PALM algorithm has the worst classification accuracy on the LFW dataset and the classification accuracy even decreases mostly with the increase of the number of the training samples. Thus, devising more robust sparse representation algorithm is an urgent issue. Third, enough attention should be paid on the computational inefficiency of sparse representation with l 1 -norm minimization. One can see that high computational complexity is one of the most major drawbacks of the current sparse representation methods and also hampers its applications in real-time processing scenarios. In terms of speed, PALM, FISTA and DALM take much longer time to converge than the other methods. The average computational time of OMP and TPTSR is the two lowest algorithms. Moreover, compared with the l 1 -regularized sparse representation based classification methods, the TPTSR has very competitive classification accuracy but significantly low complexity.
Efficient and effective sparse representation methods are urgently needed by real-time applications. Thus, developing more efficient and effective methods is essential for future study on sparse representation. Finally, the extensive experimental results have demonstrated that there is no absolute winner that can achieve the best performance for all datasets in terms of classification accuracy and computational efficiency. However, l 1 l s , TPTSR and homotopy algorithms as a whole outperform the other algorithms. As a compromising approach, the OMP algorithm can achieve distinct efficiency without sacrificing much recognition rate in comparison with other algorithms and it also has been extensively applied to some complex learning algorithms as a function.
ACKNOWLEDGMENT This work was supported in part by the  National Natural Science Foundation of China  under Grant  61370163 , Grant  61233011 , and Grant  61332011 , and in part by the  Shenzhen Municipal Science and Technology Innovation Council  under Grant  JCYJ20130329151843309 , Grant  JCYJ20130329151843309 , Grant  JCYJ20140417172417174 , Grant  CXZZ20140904154910774 , the  China Postdoctoral Science Foundation  funded project, No. 2014M560264  and the  Shaanxi Key Innovation Team of Science and Technology  (Grant no. :  2012KCT-04 ). We would like to thank  Jian Wu  for many inspiring discussions and he is ultimately responsible for many of the ideas in the algorithm and analysis. We would also like to thank  Dr. Zhihui Lai ,  Dr. Jinxing Liu  and  Xiaozhao Fang  for constructive suggestions.
A central chal-lenge for many early-career groups is building a critical mass of individuals that persists despite organizational turnover. The lack of available best practices for starting and maintaining such groups set new groups at a disadvantage. GCIPs can help: the YES Network offers resources for national chapters, and INNGE's website features a showcase section where early-career groups can highlight their past and ongoing activities. The latter serve to inspire other groups facing similar challenges. In one example, the Ecological Society of America's Student Section worked closely with the ESA's executive board after the 2010 Gulf of Mexico oil spill to organize a database of Gulf Coast ecological data to serve as a baseline for post-spill comparisons  (Ramos et al. 2012).
Any Membership is group individual can based and flexible with contribute and benefit. regard to required group size. INNOVATING Aim to innovate MAHB, Future Earth, INET YSI Working to advance, methods and methods and challenge Open Knowledge inter alia: challenging paradigms, including, Foundation Early-career issues paradigms how science is carried Open science and how it interacts Interdisciplinary with society. ecology Commons There is an effort to Collaborative research rOpenSci Aggregation of free PLATFORM for collect and structure networks. Data resources servicing resources and information for free repositories: NCBI, individual ecologists ideas global use and KNB, Dryad, Figshare (e. g. wiki ) and special contribution. interest groups. Mascarelli 2014ls. orgMascarelli2014).